diff --git a/docs/sphinx-builddir/doctrees/environment.pickle b/docs/sphinx-builddir/doctrees/environment.pickle index 037c01d..0da869b 100644 Binary files a/docs/sphinx-builddir/doctrees/environment.pickle and b/docs/sphinx-builddir/doctrees/environment.pickle differ diff --git a/docs/sphinx-builddir/doctrees/nbsphinx/notebooks/QSARtuna_Tutorial.ipynb b/docs/sphinx-builddir/doctrees/nbsphinx/notebooks/QSARtuna_Tutorial.ipynb index ba84fe3..2a46650 100644 --- a/docs/sphinx-builddir/doctrees/nbsphinx/notebooks/QSARtuna_Tutorial.ipynb +++ b/docs/sphinx-builddir/doctrees/nbsphinx/notebooks/QSARtuna_Tutorial.ipynb @@ -103,7 +103,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 103, "metadata": { "pycharm": { "is_executing": true @@ -147,7 +147,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 104, "metadata": {}, "outputs": [], "source": [ @@ -157,18 +157,9 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 105, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", - " from .autonotebook import tqdm as notebook_tqdm\n" - ] - } - ], + "outputs": [], "source": [ "# Start with the imports.\n", "import sklearn\n", @@ -194,7 +185,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 106, "metadata": {}, "outputs": [], "source": [ @@ -240,7 +231,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 107, "metadata": {}, "outputs": [], "source": [ @@ -270,7 +261,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 108, "metadata": { "scrolled": true }, @@ -279,46 +270,46 @@ "name": "stderr", "output_type": "stream", "text": [ - "[I 2024-07-02 13:17:26,561] A new study created in memory with name: my_study\n", - "[I 2024-07-02 13:17:26,714] A new study created in memory with name: study_name_0\n", - "[I 2024-07-02 13:17:27,022] Trial 0 finished with value: -3594.2228073972638 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 3, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 10, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"PathFP\", \"parameters\": {\"maxPath\": 3, \"fpSize\": 2048}}'}. Best is trial 0 with value: -3594.2228073972638.\n", - "[I 2024-07-02 13:17:27,171] Trial 1 finished with value: -5029.734616310275 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.039054412752107935, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 3.1242780840717016e-07, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 0 with value: -3594.2228073972638.\n", - "[I 2024-07-02 13:17:27,429] Trial 2 finished with value: -4242.092751193529 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 20, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 6, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 0 with value: -3594.2228073972638.\n", - "[I 2024-07-02 13:17:27,579] Trial 3 finished with value: -3393.577488426015 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.06877704223043679, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 3 with value: -3393.577488426015.\n", - "[I 2024-07-02 13:17:27,644] Trial 4 finished with value: -427.45250420148204 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.7896547008552977, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 4 with value: -427.45250420148204.\n", - "[I 2024-07-02 13:17:27,698] Trial 5 finished with value: -3387.245629616474 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 3, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 4 with value: -427.45250420148204.\n", - "[I 2024-07-02 13:17:27,853] Trial 6 finished with value: -5029.734620250011 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 2.3661540064603184, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 0.1799882524170321, 'descriptor': '{\"name\": \"PathFP\", \"parameters\": {\"maxPath\": 3, \"fpSize\": 2048}}'}. Best is trial 4 with value: -427.45250420148204.\n", - "[I 2024-07-02 13:17:28,029] Trial 7 finished with value: -9650.026568221794 and parameters: {'algorithm_name': 'KNeighborsRegressor', 'KNeighborsRegressor_algorithm_hash': '1709d2c39117ae29f6c9debe7241287b', 'metric__1709d2c39117ae29f6c9debe7241287b': , 'n_neighbors__1709d2c39117ae29f6c9debe7241287b': 7, 'weights__1709d2c39117ae29f6c9debe7241287b': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 4 with value: -427.45250420148204.\n", - "[I 2024-07-02 13:17:28,070] Trial 8 finished with value: -5437.151635569594 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.05083825348819038, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 4 with value: -427.45250420148204.\n", - "[I 2024-07-02 13:17:28,336] Trial 9 finished with value: -2669.8534551928174 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 4, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 6, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 4 with value: -427.45250420148204.\n", - "[I 2024-07-02 13:17:28,373] Trial 10 finished with value: -4341.586120152291 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.7921825998469865, 'descriptor': '{\"name\": \"PathFP\", \"parameters\": {\"maxPath\": 3, \"fpSize\": 2048}}'}. Best is trial 4 with value: -427.45250420148204.\n", - "[I 2024-07-02 13:17:28,532] Trial 11 finished with value: -5514.404088878843 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 5, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 7, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 4 with value: -427.45250420148204.\n", - "[I 2024-07-02 13:17:28,680] Trial 12 finished with value: -5431.634989239215 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 10, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 5, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 4 with value: -427.45250420148204.\n", - "[I 2024-07-02 13:17:28,722] Trial 13 finished with value: -3530.5496618991288 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 4, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 4 with value: -427.45250420148204.\n", - "[I 2024-07-02 13:17:28,765] Trial 14 finished with value: -3497.6833185436312 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 2, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 4 with value: -427.45250420148204.\n", - "[I 2024-07-02 13:17:28,793] Trial 15 finished with value: -4382.16208862162 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 5, 'descriptor': '{\"name\": \"PathFP\", \"parameters\": {\"maxPath\": 3, \"fpSize\": 2048}}'}. Best is trial 4 with value: -427.45250420148204.\n", - "[I 2024-07-02 13:17:28,831] Trial 16 finished with value: -5029.734620031822 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.002825619931800395, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 1.309885135051862e-09, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 4 with value: -427.45250420148204.\n", - "[I 2024-07-02 13:17:28,870] Trial 17 finished with value: -679.3109044887755 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.16827992999009767, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 4 with value: -427.45250420148204.\n" + "[I 2024-07-03 09:16:29,300] A new study created in memory with name: my_study\n", + "[I 2024-07-03 09:16:29,343] A new study created in memory with name: study_name_0\n", + "[I 2024-07-03 09:16:30,247] Trial 0 finished with value: -3594.2228073972638 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 3, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 10, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"PathFP\", \"parameters\": {\"maxPath\": 3, \"fpSize\": 2048}}'}. Best is trial 0 with value: -3594.2228073972638.\n", + "[I 2024-07-03 09:16:30,398] Trial 1 finished with value: -5029.734616310275 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.039054412752107935, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 3.1242780840717016e-07, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 0 with value: -3594.2228073972638.\n", + "[I 2024-07-03 09:16:30,673] Trial 2 finished with value: -4242.092751193529 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 20, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 6, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 0 with value: -3594.2228073972638.\n", + "[I 2024-07-03 09:16:30,835] Trial 3 finished with value: -3393.577488426015 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.06877704223043679, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 3 with value: -3393.577488426015.\n", + "[I 2024-07-03 09:16:30,984] Trial 4 finished with value: -427.45250420148204 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.7896547008552977, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 4 with value: -427.45250420148204.\n", + "[I 2024-07-03 09:16:31,098] Trial 5 finished with value: -3387.245629616474 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 3, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 4 with value: -427.45250420148204.\n", + "[I 2024-07-03 09:16:31,154] Trial 6 finished with value: -5029.734620250011 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 2.3661540064603184, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 0.1799882524170321, 'descriptor': '{\"name\": \"PathFP\", \"parameters\": {\"maxPath\": 3, \"fpSize\": 2048}}'}. Best is trial 4 with value: -427.45250420148204.\n", + "[I 2024-07-03 09:16:31,208] Trial 7 finished with value: -9650.026568221794 and parameters: {'algorithm_name': 'KNeighborsRegressor', 'KNeighborsRegressor_algorithm_hash': '1709d2c39117ae29f6c9debe7241287b', 'metric__1709d2c39117ae29f6c9debe7241287b': , 'n_neighbors__1709d2c39117ae29f6c9debe7241287b': 7, 'weights__1709d2c39117ae29f6c9debe7241287b': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 4 with value: -427.45250420148204.\n", + "[I 2024-07-03 09:16:31,250] Trial 8 finished with value: -5437.151635569594 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.05083825348819038, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 4 with value: -427.45250420148204.\n", + "[I 2024-07-03 09:16:31,544] Trial 9 finished with value: -2669.8534551928174 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 4, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 6, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 4 with value: -427.45250420148204.\n", + "[I 2024-07-03 09:16:31,671] Trial 10 finished with value: -4341.586120152291 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.7921825998469865, 'descriptor': '{\"name\": \"PathFP\", \"parameters\": {\"maxPath\": 3, \"fpSize\": 2048}}'}. Best is trial 4 with value: -427.45250420148204.\n", + "[I 2024-07-03 09:16:31,824] Trial 11 finished with value: -5514.404088878843 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 5, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 7, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 4 with value: -427.45250420148204.\n", + "[I 2024-07-03 09:16:31,960] Trial 12 finished with value: -5431.634989239215 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 10, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 5, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 4 with value: -427.45250420148204.\n", + "[I 2024-07-03 09:16:32,015] Trial 13 finished with value: -3530.5496618991288 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 4, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 4 with value: -427.45250420148204.\n", + "[I 2024-07-03 09:16:32,058] Trial 14 finished with value: -3497.6833185436312 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 2, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 4 with value: -427.45250420148204.\n", + "[I 2024-07-03 09:16:32,100] Trial 15 finished with value: -4382.16208862162 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 5, 'descriptor': '{\"name\": \"PathFP\", \"parameters\": {\"maxPath\": 3, \"fpSize\": 2048}}'}. Best is trial 4 with value: -427.45250420148204.\n", + "[I 2024-07-03 09:16:32,142] Trial 16 finished with value: -5029.734620031822 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.002825619931800395, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 1.309885135051862e-09, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 4 with value: -427.45250420148204.\n", + "[I 2024-07-03 09:16:32,172] Trial 17 finished with value: -679.3109044887755 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.16827992999009767, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 4 with value: -427.45250420148204.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "[I 2024-07-02 13:17:28,932] Trial 18 finished with value: -2550.114129318373 and parameters: {'algorithm_name': 'KNeighborsRegressor', 'KNeighborsRegressor_algorithm_hash': '1709d2c39117ae29f6c9debe7241287b', 'metric__1709d2c39117ae29f6c9debe7241287b': , 'n_neighbors__1709d2c39117ae29f6c9debe7241287b': 7, 'weights__1709d2c39117ae29f6c9debe7241287b': , 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 4 with value: -427.45250420148204.\n", - "[I 2024-07-02 13:17:28,974] Trial 19 finished with value: -4847.085792360169 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.735431606118867, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 4 with value: -427.45250420148204.\n", - "[I 2024-07-02 13:17:29,016] Trial 20 finished with value: -5029.268760278916 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.0014840820994557746, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 0.04671166881768783, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 4 with value: -427.45250420148204.\n", - "[I 2024-07-02 13:17:29,096] Trial 21 finished with value: -4783.0470154796785 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 15, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 10, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 4 with value: -427.45250420148204.\n", - "[I 2024-07-02 13:17:29,135] Trial 22 finished with value: -3905.0064899852296 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 4, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 4 with value: -427.45250420148204.\n", - "[I 2024-07-02 13:17:29,259] Trial 23 finished with value: -4030.45773791647 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 11, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 9, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 4 with value: -427.45250420148204.\n", - "[I 2024-07-02 13:17:29,340] Trial 24 finished with value: -4681.602145939593 and parameters: {'algorithm_name': 'KNeighborsRegressor', 'KNeighborsRegressor_algorithm_hash': '1709d2c39117ae29f6c9debe7241287b', 'metric__1709d2c39117ae29f6c9debe7241287b': , 'n_neighbors__1709d2c39117ae29f6c9debe7241287b': 4, 'weights__1709d2c39117ae29f6c9debe7241287b': , 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 4 with value: -427.45250420148204.\n", - "[I 2024-07-02 13:17:29,381] Trial 25 finished with value: -4398.544034028325 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.6452011213193165, 'descriptor': '{\"name\": \"PathFP\", \"parameters\": {\"maxPath\": 3, \"fpSize\": 2048}}'}. Best is trial 4 with value: -427.45250420148204.\n", - "[I 2024-07-02 13:17:29,474] Trial 26 finished with value: -4454.143979828408 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 21, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 9, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 4 with value: -427.45250420148204.\n", - "[I 2024-07-02 13:17:29,503] Trial 27 pruned. Duplicate parameter set\n", - "[I 2024-07-02 13:17:29,533] Trial 28 pruned. Duplicate parameter set\n", - "[I 2024-07-02 13:17:29,600] Trial 29 finished with value: -4397.330360587512 and parameters: {'algorithm_name': 'KNeighborsRegressor', 'KNeighborsRegressor_algorithm_hash': '1709d2c39117ae29f6c9debe7241287b', 'metric__1709d2c39117ae29f6c9debe7241287b': , 'n_neighbors__1709d2c39117ae29f6c9debe7241287b': 8, 'weights__1709d2c39117ae29f6c9debe7241287b': , 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 4 with value: -427.45250420148204.\n", - "[I 2024-07-02 13:17:29,617] Trial 30 pruned. Duplicate parameter set\n", - "[I 2024-07-02 13:17:29,682] Trial 31 finished with value: -2602.7561184287083 and parameters: {'algorithm_name': 'KNeighborsRegressor', 'KNeighborsRegressor_algorithm_hash': '1709d2c39117ae29f6c9debe7241287b', 'metric__1709d2c39117ae29f6c9debe7241287b': , 'n_neighbors__1709d2c39117ae29f6c9debe7241287b': 6, 'weights__1709d2c39117ae29f6c9debe7241287b': , 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 4 with value: -427.45250420148204.\n" + "[I 2024-07-03 09:16:32,320] Trial 18 finished with value: -2550.114129318373 and parameters: {'algorithm_name': 'KNeighborsRegressor', 'KNeighborsRegressor_algorithm_hash': '1709d2c39117ae29f6c9debe7241287b', 'metric__1709d2c39117ae29f6c9debe7241287b': , 'n_neighbors__1709d2c39117ae29f6c9debe7241287b': 7, 'weights__1709d2c39117ae29f6c9debe7241287b': , 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 4 with value: -427.45250420148204.\n", + "[I 2024-07-03 09:16:32,362] Trial 19 finished with value: -4847.085792360169 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.735431606118867, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 4 with value: -427.45250420148204.\n", + "[I 2024-07-03 09:16:32,403] Trial 20 finished with value: -5029.268760278916 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.0014840820994557746, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 0.04671166881768783, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 4 with value: -427.45250420148204.\n", + "[I 2024-07-03 09:16:32,495] Trial 21 finished with value: -4783.0470154796785 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 15, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 10, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 4 with value: -427.45250420148204.\n", + "[I 2024-07-03 09:16:32,538] Trial 22 finished with value: -3905.0064899852296 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 4, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 4 with value: -427.45250420148204.\n", + "[I 2024-07-03 09:16:32,617] Trial 23 finished with value: -4030.4577379164707 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 11, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 9, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 4 with value: -427.45250420148204.\n", + "[I 2024-07-03 09:16:32,684] Trial 24 finished with value: -4681.602145939593 and parameters: {'algorithm_name': 'KNeighborsRegressor', 'KNeighborsRegressor_algorithm_hash': '1709d2c39117ae29f6c9debe7241287b', 'metric__1709d2c39117ae29f6c9debe7241287b': , 'n_neighbors__1709d2c39117ae29f6c9debe7241287b': 4, 'weights__1709d2c39117ae29f6c9debe7241287b': , 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 4 with value: -427.45250420148204.\n", + "[I 2024-07-03 09:16:32,736] Trial 25 finished with value: -4398.544034028325 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.6452011213193165, 'descriptor': '{\"name\": \"PathFP\", \"parameters\": {\"maxPath\": 3, \"fpSize\": 2048}}'}. Best is trial 4 with value: -427.45250420148204.\n", + "[I 2024-07-03 09:16:32,814] Trial 26 finished with value: -4454.143979828407 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 21, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 9, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 4 with value: -427.45250420148204.\n", + "[I 2024-07-03 09:16:32,845] Trial 27 pruned. Duplicate parameter set\n", + "[I 2024-07-03 09:16:32,863] Trial 28 pruned. Duplicate parameter set\n", + "[I 2024-07-03 09:16:32,927] Trial 29 finished with value: -4397.330360587512 and parameters: {'algorithm_name': 'KNeighborsRegressor', 'KNeighborsRegressor_algorithm_hash': '1709d2c39117ae29f6c9debe7241287b', 'metric__1709d2c39117ae29f6c9debe7241287b': , 'n_neighbors__1709d2c39117ae29f6c9debe7241287b': 8, 'weights__1709d2c39117ae29f6c9debe7241287b': , 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 4 with value: -427.45250420148204.\n", + "[I 2024-07-03 09:16:32,954] Trial 30 pruned. Duplicate parameter set\n", + "[I 2024-07-03 09:16:33,029] Trial 31 finished with value: -2602.7561184287083 and parameters: {'algorithm_name': 'KNeighborsRegressor', 'KNeighborsRegressor_algorithm_hash': '1709d2c39117ae29f6c9debe7241287b', 'metric__1709d2c39117ae29f6c9debe7241287b': , 'n_neighbors__1709d2c39117ae29f6c9debe7241287b': 6, 'weights__1709d2c39117ae29f6c9debe7241287b': , 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 4 with value: -427.45250420148204.\n" ] }, { @@ -334,14 +325,15 @@ "name": "stderr", "output_type": "stream", "text": [ - "[I 2024-07-02 13:17:29,715] Trial 32 finished with value: -5267.388279961089 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.2015560027548533, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 4 with value: -427.45250420148204.\n", - "[I 2024-07-02 13:17:29,794] Trial 33 finished with value: -4863.581760751052 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 23, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 8, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 4 with value: -427.45250420148204.\n", - "[I 2024-07-02 13:17:29,836] Trial 34 finished with value: -388.96473594016675 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.5528259214839937, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 34 with value: -388.96473594016675.\n", - "[I 2024-07-02 13:17:29,906] Trial 35 finished with value: -5539.698232987626 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.6400992020612235, 'descriptor': '{\"name\": \"PathFP\", \"parameters\": {\"maxPath\": 3, \"fpSize\": 2048}}'}. Best is trial 34 with value: -388.96473594016675.\n", - "[I 2024-07-02 13:17:29,962] Trial 36 finished with value: -5180.5533034102455 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.8968910439566395, 'descriptor': '{\"name\": \"PathFP\", \"parameters\": {\"maxPath\": 3, \"fpSize\": 2048}}'}. Best is trial 34 with value: -388.96473594016675.\n", - "[I 2024-07-02 13:17:30,005] Trial 37 finished with value: -4989.929984864281 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.04458440839692226, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 4.492108041427977, 'descriptor': '{\"name\": \"PathFP\", \"parameters\": {\"maxPath\": 3, \"fpSize\": 2048}}'}. Best is trial 34 with value: -388.96473594016675.\n", - "[I 2024-07-02 13:17:30,034] Trial 38 pruned. Duplicate parameter set\n", - "[I 2024-07-02 13:17:30,103] Trial 39 finished with value: -6528.215066535042 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.16700143339733753, 'descriptor': '{\"name\": \"PathFP\", \"parameters\": {\"maxPath\": 3, \"fpSize\": 2048}}'}. Best is trial 34 with value: -388.96473594016675.\n" + "[I 2024-07-03 09:16:33,061] Trial 32 finished with value: -5267.388279961089 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.2015560027548533, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 4 with value: -427.45250420148204.\n", + "[I 2024-07-03 09:16:33,200] Trial 33 finished with value: -4863.5817607510535 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 23, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 8, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 4 with value: -427.45250420148204.\n", + "[I 2024-07-03 09:16:33,244] Trial 34 finished with value: -388.96473594016675 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.5528259214839937, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 34 with value: -388.96473594016675.\n", + "[I 2024-07-03 09:16:33,298] Trial 35 finished with value: -5539.698232987626 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.6400992020612235, 'descriptor': '{\"name\": \"PathFP\", \"parameters\": {\"maxPath\": 3, \"fpSize\": 2048}}'}. Best is trial 34 with value: -388.96473594016675.\n", + "[I 2024-07-03 09:16:33,343] Trial 36 finished with value: -5180.5533034102455 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.8968910439566395, 'descriptor': '{\"name\": \"PathFP\", \"parameters\": {\"maxPath\": 3, \"fpSize\": 2048}}'}. Best is trial 34 with value: -388.96473594016675.\n", + "[I 2024-07-03 09:16:33,390] Trial 37 finished with value: -4989.929984864281 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.04458440839692226, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 4.492108041427977, 'descriptor': '{\"name\": \"PathFP\", \"parameters\": {\"maxPath\": 3, \"fpSize\": 2048}}'}. Best is trial 34 with value: -388.96473594016675.\n", + "[I 2024-07-03 09:16:33,418] Trial 38 pruned. Duplicate parameter set\n", + "[I 2024-07-03 09:16:33,489] Trial 39 finished with value: -6528.215066535042 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.16700143339733753, 'descriptor': '{\"name\": \"PathFP\", \"parameters\": {\"maxPath\": 3, \"fpSize\": 2048}}'}. Best is trial 34 with value: -388.96473594016675.\n", + "[I 2024-07-03 09:16:33,582] Trial 40 finished with value: -4168.7955967552625 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 5, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 8, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 34 with value: -388.96473594016675.\n" ] }, { @@ -355,16 +347,16 @@ "name": "stderr", "output_type": "stream", "text": [ - "[I 2024-07-02 13:17:30,240] Trial 40 finished with value: -4168.7955967552625 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 5, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 8, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 34 with value: -388.96473594016675.\n", - "[I 2024-07-02 13:17:30,311] Trial 41 finished with value: -6177.060727800014 and parameters: {'algorithm_name': 'KNeighborsRegressor', 'KNeighborsRegressor_algorithm_hash': '1709d2c39117ae29f6c9debe7241287b', 'metric__1709d2c39117ae29f6c9debe7241287b': , 'n_neighbors__1709d2c39117ae29f6c9debe7241287b': 1, 'weights__1709d2c39117ae29f6c9debe7241287b': , 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 34 with value: -388.96473594016675.\n", - "[I 2024-07-02 13:17:30,401] Trial 42 finished with value: -3963.906954658343 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 21, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 8, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"PathFP\", \"parameters\": {\"maxPath\": 3, \"fpSize\": 2048}}'}. Best is trial 34 with value: -388.96473594016675.\n", - "[I 2024-07-02 13:17:30,435] Trial 43 finished with value: -5029.6805334166565 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.013186009009851564, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 0.001008958590140135, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 34 with value: -388.96473594016675.\n", - "[I 2024-07-02 13:17:30,501] Trial 44 finished with value: -9300.86840721566 and parameters: {'algorithm_name': 'KNeighborsRegressor', 'KNeighborsRegressor_algorithm_hash': '1709d2c39117ae29f6c9debe7241287b', 'metric__1709d2c39117ae29f6c9debe7241287b': , 'n_neighbors__1709d2c39117ae29f6c9debe7241287b': 9, 'weights__1709d2c39117ae29f6c9debe7241287b': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 34 with value: -388.96473594016675.\n", - "[I 2024-07-02 13:17:30,547] Trial 45 finished with value: -5029.734620250011 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 83.87968210939489, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 6.382674443425525e-09, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 34 with value: -388.96473594016675.\n", - "[I 2024-07-02 13:17:30,565] Trial 46 pruned. Duplicate parameter set\n", - "[I 2024-07-02 13:17:30,594] Trial 47 pruned. Duplicate parameter set\n", - "[I 2024-07-02 13:17:30,626] Trial 48 pruned. Duplicate parameter set\n", - "[I 2024-07-02 13:17:30,717] Trial 49 finished with value: -3660.9359502556 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 2, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 7, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"PathFP\", \"parameters\": {\"maxPath\": 3, \"fpSize\": 2048}}'}. Best is trial 34 with value: -388.96473594016675.\n" + "[I 2024-07-03 09:16:33,664] Trial 41 finished with value: -6177.060727800014 and parameters: {'algorithm_name': 'KNeighborsRegressor', 'KNeighborsRegressor_algorithm_hash': '1709d2c39117ae29f6c9debe7241287b', 'metric__1709d2c39117ae29f6c9debe7241287b': , 'n_neighbors__1709d2c39117ae29f6c9debe7241287b': 1, 'weights__1709d2c39117ae29f6c9debe7241287b': , 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 34 with value: -388.96473594016675.\n", + "[I 2024-07-03 09:16:33,758] Trial 42 finished with value: -3963.906954658341 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 21, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 8, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"PathFP\", \"parameters\": {\"maxPath\": 3, \"fpSize\": 2048}}'}. Best is trial 34 with value: -388.96473594016675.\n", + "[I 2024-07-03 09:16:33,799] Trial 43 finished with value: -5029.6805334166565 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.013186009009851564, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 0.001008958590140135, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 34 with value: -388.96473594016675.\n", + "[I 2024-07-03 09:16:33,883] Trial 44 finished with value: -9300.86840721566 and parameters: {'algorithm_name': 'KNeighborsRegressor', 'KNeighborsRegressor_algorithm_hash': '1709d2c39117ae29f6c9debe7241287b', 'metric__1709d2c39117ae29f6c9debe7241287b': , 'n_neighbors__1709d2c39117ae29f6c9debe7241287b': 9, 'weights__1709d2c39117ae29f6c9debe7241287b': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 34 with value: -388.96473594016675.\n", + "[I 2024-07-03 09:16:33,927] Trial 45 finished with value: -5029.734620250011 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 83.87968210939489, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 6.382674443425525e-09, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 34 with value: -388.96473594016675.\n", + "[I 2024-07-03 09:16:33,958] Trial 46 pruned. Duplicate parameter set\n", + "[I 2024-07-03 09:16:33,989] Trial 47 pruned. Duplicate parameter set\n", + "[I 2024-07-03 09:16:34,009] Trial 48 pruned. Duplicate parameter set\n", + "[I 2024-07-03 09:16:34,104] Trial 49 finished with value: -3660.9359502556 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 2, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 7, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"PathFP\", \"parameters\": {\"maxPath\": 3, \"fpSize\": 2048}}'}. Best is trial 34 with value: -388.96473594016675.\n", + "[I 2024-07-03 09:16:34,155] Trial 50 finished with value: -688.5244070398325 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.5267860995545326, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 34 with value: -388.96473594016675.\n" ] }, { @@ -380,68 +372,67 @@ "name": "stderr", "output_type": "stream", "text": [ - "[I 2024-07-02 13:17:30,767] Trial 50 finished with value: -688.5244070398325 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.5267860995545326, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 34 with value: -388.96473594016675.\n", - "[I 2024-07-02 13:17:30,813] Trial 51 finished with value: -690.6494438072099 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.8458809314722497, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 34 with value: -388.96473594016675.\n", - "[I 2024-07-02 13:17:30,848] Trial 52 finished with value: -691.1197058420935 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.9167866889210807, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 34 with value: -388.96473594016675.\n", - "[I 2024-07-02 13:17:30,898] Trial 53 finished with value: -691.3111710449325 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.945685900574672, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 34 with value: -388.96473594016675.\n", - "[I 2024-07-02 13:17:30,934] Trial 54 finished with value: -690.9665592812149 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.8936837761725833, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 34 with value: -388.96473594016675.\n", - "[I 2024-07-02 13:17:30,970] Trial 55 finished with value: -688.4682747008223 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.5183865279530455, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 34 with value: -388.96473594016675.\n", - "[I 2024-07-02 13:17:31,030] Trial 56 finished with value: -687.5230947231512 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.3771771681361766, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 34 with value: -388.96473594016675.\n", - "[I 2024-07-02 13:17:31,078] Trial 57 finished with value: -687.4503442069594 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.3663259819415374, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 34 with value: -388.96473594016675.\n", - "[I 2024-07-02 13:17:31,127] Trial 58 finished with value: -686.9553733616618 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.2925652230875628, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 34 with value: -388.96473594016675.\n", - "[I 2024-07-02 13:17:31,174] Trial 59 finished with value: -370.2038330506566 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.3962903248948568, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 59 with value: -370.2038330506566.\n", - "[I 2024-07-02 13:17:31,222] Trial 60 finished with value: -377.25988028857313 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.45237513161879, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 59 with value: -370.2038330506566.\n", - "[I 2024-07-02 13:17:31,270] Trial 61 finished with value: -379.8933285317637 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.4741161933311207, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 59 with value: -370.2038330506566.\n", - "[I 2024-07-02 13:17:31,319] Trial 62 finished with value: -374.50897467366013 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.4290962207409417, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 59 with value: -370.2038330506566.\n", - "[I 2024-07-02 13:17:31,356] Trial 63 finished with value: -376.5588572940058 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.4464295711264585, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 59 with value: -370.2038330506566.\n", - "[I 2024-07-02 13:17:31,416] Trial 64 finished with value: -379.237448916406 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.4687500034684213, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 59 with value: -370.2038330506566.\n", - "[I 2024-07-02 13:17:31,454] Trial 65 finished with value: -375.7474776359051 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.4395650011783436, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 59 with value: -370.2038330506566.\n", - "[I 2024-07-02 13:17:31,504] Trial 66 finished with value: -362.2834906299732 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.3326755354190032, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 66 with value: -362.2834906299732.\n", - "[I 2024-07-02 13:17:31,542] Trial 67 finished with value: -357.3474880122588 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.2887212943233457, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 67 with value: -357.3474880122588.\n", - "[I 2024-07-02 13:17:31,591] Trial 68 finished with value: -354.279045046449 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.2577677164664005, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 68 with value: -354.279045046449.\n", - "[I 2024-07-02 13:17:31,642] Trial 69 finished with value: -347.36894395697703 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.1672928587680225, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 69 with value: -347.36894395697703.\n", - "[I 2024-07-02 13:17:31,706] Trial 70 finished with value: -345.17697390093394 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.1242367255308854, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 70 with value: -345.17697390093394.\n" + "[I 2024-07-03 09:16:34,192] Trial 51 finished with value: -690.6494438072099 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.8458809314722497, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 34 with value: -388.96473594016675.\n", + "[I 2024-07-03 09:16:34,241] Trial 52 finished with value: -691.1197058420935 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.9167866889210807, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 34 with value: -388.96473594016675.\n", + "[I 2024-07-03 09:16:34,290] Trial 53 finished with value: -691.3111710449325 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.945685900574672, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 34 with value: -388.96473594016675.\n", + "[I 2024-07-03 09:16:34,330] Trial 54 finished with value: -690.9665592812149 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.8936837761725833, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 34 with value: -388.96473594016675.\n", + "[I 2024-07-03 09:16:34,377] Trial 55 finished with value: -688.4682747008223 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.5183865279530455, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 34 with value: -388.96473594016675.\n", + "[I 2024-07-03 09:16:34,428] Trial 56 finished with value: -687.5230947231512 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.3771771681361766, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 34 with value: -388.96473594016675.\n", + "[I 2024-07-03 09:16:34,468] Trial 57 finished with value: -687.4503442069594 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.3663259819415374, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 34 with value: -388.96473594016675.\n", + "[I 2024-07-03 09:16:34,522] Trial 58 finished with value: -686.9553733616618 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.2925652230875628, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 34 with value: -388.96473594016675.\n", + "[I 2024-07-03 09:16:34,573] Trial 59 finished with value: -370.2038330506566 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.3962903248948568, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 59 with value: -370.2038330506566.\n", + "[I 2024-07-03 09:16:34,611] Trial 60 finished with value: -377.25988028857313 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.45237513161879, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 59 with value: -370.2038330506566.\n", + "[I 2024-07-03 09:16:34,657] Trial 61 finished with value: -379.8933285317637 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.4741161933311207, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 59 with value: -370.2038330506566.\n", + "[I 2024-07-03 09:16:34,695] Trial 62 finished with value: -374.50897467366013 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.4290962207409417, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 59 with value: -370.2038330506566.\n", + "[I 2024-07-03 09:16:34,733] Trial 63 finished with value: -376.5588572940058 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.4464295711264585, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 59 with value: -370.2038330506566.\n", + "[I 2024-07-03 09:16:34,772] Trial 64 finished with value: -379.237448916406 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.4687500034684213, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 59 with value: -370.2038330506566.\n", + "[I 2024-07-03 09:16:34,811] Trial 65 finished with value: -375.7474776359051 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.4395650011783436, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 59 with value: -370.2038330506566.\n", + "[I 2024-07-03 09:16:34,849] Trial 66 finished with value: -362.2834906299732 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.3326755354190032, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 66 with value: -362.2834906299732.\n", + "[I 2024-07-03 09:16:34,900] Trial 67 finished with value: -357.3474880122588 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.2887212943233457, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 67 with value: -357.3474880122588.\n", + "[I 2024-07-03 09:16:34,958] Trial 68 finished with value: -354.279045046449 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.2577677164664005, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 68 with value: -354.279045046449.\n", + "[I 2024-07-03 09:16:34,997] Trial 69 finished with value: -347.36894395697703 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.1672928587680225, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 69 with value: -347.36894395697703.\n", + "[I 2024-07-03 09:16:35,059] Trial 70 finished with value: -345.17697390093394 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.1242367255308854, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 70 with value: -345.17697390093394.\n", + "[I 2024-07-03 09:16:35,124] Trial 71 finished with value: -347.74610809299037 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.1728352983905301, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 70 with value: -345.17697390093394.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "[I 2024-07-02 13:17:31,757] Trial 71 finished with value: -347.74610809299037 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.1728352983905301, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 70 with value: -345.17697390093394.\n", - "[I 2024-07-02 13:17:31,807] Trial 72 finished with value: -345.23464281634324 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.1265380781508565, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 70 with value: -345.17697390093394.\n", - "[I 2024-07-02 13:17:31,856] Trial 73 finished with value: -344.6848312222365 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.0829896313820404, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 73 with value: -344.6848312222365.\n", - "[I 2024-07-02 13:17:31,902] Trial 74 finished with value: -344.9111966504334 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.1070414661080543, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 73 with value: -344.6848312222365.\n", - "[I 2024-07-02 13:17:31,966] Trial 75 finished with value: -344.70116419828565 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.0875643695329498, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 73 with value: -344.6848312222365.\n", - "[I 2024-07-02 13:17:32,026] Trial 76 finished with value: -344.62647974688133 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.0716281620790837, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 76 with value: -344.62647974688133.\n", - "[I 2024-07-02 13:17:32,089] Trial 77 finished with value: -344.6759429204596 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.0456289319914898, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 76 with value: -344.62647974688133.\n", - "[I 2024-07-02 13:17:32,141] Trial 78 finished with value: -343.58131497761616 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.0010195360522613, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 78 with value: -343.58131497761616.\n", - "[I 2024-07-02 13:17:32,193] Trial 79 finished with value: -342.7290581014813 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.9073210715005748, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 79 with value: -342.7290581014813.\n", - "[I 2024-07-02 13:17:32,254] Trial 80 finished with value: -342.67866114080107 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.9166305667100072, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 80 with value: -342.67866114080107.\n", - "[I 2024-07-02 13:17:32,317] Trial 81 finished with value: -342.6440308445311 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.9248722692093634, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 81 with value: -342.6440308445311.\n", - "[I 2024-07-02 13:17:32,367] Trial 82 finished with value: -343.02085648448934 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.8776928646870886, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 81 with value: -342.6440308445311.\n", - "[I 2024-07-02 13:17:32,416] Trial 83 finished with value: -343.1662266300702 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.867592364677856, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 81 with value: -342.6440308445311.\n", - "[I 2024-07-02 13:17:32,457] Trial 84 finished with value: -343.30158716569775 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.8599491178327108, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 81 with value: -342.6440308445311.\n", - "[I 2024-07-02 13:17:32,497] Trial 85 finished with value: -344.2803074848341 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.8396948389352923, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 81 with value: -342.6440308445311.\n", - "[I 2024-07-02 13:17:32,547] Trial 86 finished with value: -344.28301101884045 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.8396651775801683, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 81 with value: -342.6440308445311.\n", - "[I 2024-07-02 13:17:32,587] Trial 87 finished with value: -344.6781906268143 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.8356021935129933, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 81 with value: -342.6440308445311.\n", - "[I 2024-07-02 13:17:32,639] Trial 88 finished with value: -354.0405418264898 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.7430046191126949, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 81 with value: -342.6440308445311.\n", - "[I 2024-07-02 13:17:32,677] Trial 89 finished with value: -342.77203208258476 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.9015965341429055, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 81 with value: -342.6440308445311.\n", - "[I 2024-07-02 13:17:32,717] Trial 90 finished with value: -363.1622720320929 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.6746575663752555, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 81 with value: -342.6440308445311.\n", - "[I 2024-07-02 13:17:32,757] Trial 91 finished with value: -342.7403796626193 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.9057564666836629, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 81 with value: -342.6440308445311.\n" + "[I 2024-07-03 09:16:35,185] Trial 72 finished with value: -345.23464281634324 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.1265380781508565, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 70 with value: -345.17697390093394.\n", + "[I 2024-07-03 09:16:35,235] Trial 73 finished with value: -344.6848312222365 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.0829896313820404, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 73 with value: -344.6848312222365.\n", + "[I 2024-07-03 09:16:35,284] Trial 74 finished with value: -344.9111966504334 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.1070414661080543, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 73 with value: -344.6848312222365.\n", + "[I 2024-07-03 09:16:35,336] Trial 75 finished with value: -344.70116419828565 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.0875643695329498, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 73 with value: -344.6848312222365.\n", + "[I 2024-07-03 09:16:35,373] Trial 76 finished with value: -344.62647974688133 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.0716281620790837, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 76 with value: -344.62647974688133.\n", + "[I 2024-07-03 09:16:35,411] Trial 77 finished with value: -344.6759429204596 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.0456289319914898, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 76 with value: -344.62647974688133.\n", + "[I 2024-07-03 09:16:35,461] Trial 78 finished with value: -343.58131497761616 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.0010195360522613, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 78 with value: -343.58131497761616.\n", + "[I 2024-07-03 09:16:35,515] Trial 79 finished with value: -342.7290581014813 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.9073210715005748, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 79 with value: -342.7290581014813.\n", + "[I 2024-07-03 09:16:35,577] Trial 80 finished with value: -342.67866114080107 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.9166305667100072, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 80 with value: -342.67866114080107.\n", + "[I 2024-07-03 09:16:35,616] Trial 81 finished with value: -342.6440308445311 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.9248722692093634, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 81 with value: -342.6440308445311.\n", + "[I 2024-07-03 09:16:35,656] Trial 82 finished with value: -343.02085648448934 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.8776928646870886, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 81 with value: -342.6440308445311.\n", + "[I 2024-07-03 09:16:35,696] Trial 83 finished with value: -343.1662266300702 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.867592364677856, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 81 with value: -342.6440308445311.\n", + "[I 2024-07-03 09:16:35,748] Trial 84 finished with value: -343.30158716569775 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.8599491178327108, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 81 with value: -342.6440308445311.\n", + "[I 2024-07-03 09:16:35,789] Trial 85 finished with value: -344.2803074848341 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.8396948389352923, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 81 with value: -342.6440308445311.\n", + "[I 2024-07-03 09:16:35,832] Trial 86 finished with value: -344.28301101884045 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.8396651775801683, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 81 with value: -342.6440308445311.\n", + "[I 2024-07-03 09:16:35,873] Trial 87 finished with value: -344.6781906268143 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.8356021935129933, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 81 with value: -342.6440308445311.\n", + "[I 2024-07-03 09:16:35,926] Trial 88 finished with value: -354.0405418264898 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.7430046191126949, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 81 with value: -342.6440308445311.\n", + "[I 2024-07-03 09:16:35,965] Trial 89 finished with value: -342.77203208258476 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.9015965341429055, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 81 with value: -342.6440308445311.\n", + "[I 2024-07-03 09:16:36,004] Trial 90 finished with value: -363.1622720320929 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.6746575663752555, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 81 with value: -342.6440308445311.\n", + "[I 2024-07-03 09:16:36,046] Trial 91 finished with value: -342.7403796626193 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.9057564666836629, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 81 with value: -342.6440308445311.\n", + "[I 2024-07-03 09:16:36,099] Trial 92 finished with value: -342.63579667712696 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.9332275205203372, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 92 with value: -342.63579667712696.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "[I 2024-07-02 13:17:32,797] Trial 92 finished with value: -342.63579667712696 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.9332275205203372, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 92 with value: -342.63579667712696.\n", - "[I 2024-07-02 13:17:32,848] Trial 93 finished with value: -342.6886425884964 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.9433063264508291, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 92 with value: -342.63579667712696.\n", - "[I 2024-07-02 13:17:32,898] Trial 94 finished with value: -342.9341048659705 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.884739221967487, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 92 with value: -342.63579667712696.\n", - "[I 2024-07-02 13:17:32,935] Trial 95 finished with value: -342.63507445779743 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.9381000493689634, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 95 with value: -342.63507445779743.\n", - "[I 2024-07-02 13:17:32,986] Trial 96 finished with value: -343.06021011302374 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.963138023068903, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 95 with value: -342.63507445779743.\n", - "[I 2024-07-02 13:17:33,026] Trial 97 finished with value: -342.9990546212019 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.9601651093867907, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 95 with value: -342.63507445779743.\n", - "[I 2024-07-02 13:17:33,066] Trial 98 finished with value: -3821.2267845437514 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 2, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 95 with value: -342.63507445779743.\n", - "[I 2024-07-02 13:17:33,117] Trial 99 finished with value: -356.6786067133016 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.721603508336166, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 95 with value: -342.63507445779743.\n" + "[I 2024-07-03 09:16:36,137] Trial 93 finished with value: -342.6886425884964 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.9433063264508291, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 92 with value: -342.63579667712696.\n", + "[I 2024-07-03 09:16:36,176] Trial 94 finished with value: -342.9341048659705 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.884739221967487, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 92 with value: -342.63579667712696.\n", + "[I 2024-07-03 09:16:36,216] Trial 95 finished with value: -342.63507445779743 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.9381000493689634, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 95 with value: -342.63507445779743.\n", + "[I 2024-07-03 09:16:36,269] Trial 96 finished with value: -343.06021011302374 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.963138023068903, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 95 with value: -342.63507445779743.\n", + "[I 2024-07-03 09:16:36,312] Trial 97 finished with value: -342.9990546212019 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.9601651093867907, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 95 with value: -342.63507445779743.\n", + "[I 2024-07-03 09:16:36,367] Trial 98 finished with value: -3821.2267845437514 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 2, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 95 with value: -342.63507445779743.\n", + "[I 2024-07-03 09:16:36,420] Trial 99 finished with value: -356.6786067133016 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.721603508336166, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 95 with value: -342.63507445779743.\n" ] } ], @@ -461,7 +452,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 109, "metadata": { "scrolled": false }, @@ -494,7 +485,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 110, "metadata": {}, "outputs": [ { @@ -538,7 +529,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 111, "metadata": {}, "outputs": [], "source": [ @@ -555,7 +546,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 112, "metadata": {}, "outputs": [ { @@ -636,7 +627,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 113, "metadata": {}, "outputs": [], "source": [ @@ -652,7 +643,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 114, "metadata": {}, "outputs": [ { @@ -661,7 +652,7 @@ "array([ 67.43103985, 177.99850936])" ] }, - "execution_count": 12, + "execution_count": 114, "metadata": {}, "output_type": "execute_result" } @@ -682,7 +673,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 115, "metadata": {}, "outputs": [], "source": [ @@ -696,7 +687,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 116, "metadata": {}, "outputs": [ { @@ -729,7 +720,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 117, "metadata": {}, "outputs": [ { @@ -779,7 +770,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 118, "metadata": {}, "outputs": [], "source": [ @@ -842,7 +833,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 119, "metadata": {}, "outputs": [], "source": [ @@ -882,7 +873,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 120, "metadata": { "scrolled": true }, @@ -891,162 +882,154 @@ "name": "stderr", "output_type": "stream", "text": [ - "[I 2024-07-02 13:17:36,922] A new study created in memory with name: my_study_stratified_split\n", - "[I 2024-07-02 13:17:36,963] A new study created in memory with name: study_name_0\n", - "[I 2024-07-02 13:17:37,046] Trial 0 finished with value: -1856.4459752935309 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 4, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 0 with value: -1856.4459752935309.\n", - "[I 2024-07-02 13:17:37,123] Trial 1 finished with value: -1692.0451328577294 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.2918844591266672, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 1 with value: -1692.0451328577294.\n", - "[I 2024-07-02 13:17:37,592] Trial 2 finished with value: -1378.9731014410709 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.471164936778079, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 2 with value: -1378.9731014410709.\n", - "[I 2024-07-02 13:17:37,688] Trial 3 finished with value: -2658.13214897931 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 4, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 2 with value: -1378.9731014410709.\n", - "[I 2024-07-02 13:17:37,804] Trial 4 finished with value: -2059.3079659969176 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 27, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 9, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 2 with value: -1378.9731014410709.\n", - "[I 2024-07-02 13:17:38,330] Trial 5 finished with value: -280.17777558722315 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.7001901522391756, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 5 with value: -280.17777558722315.\n", - "[I 2024-07-02 13:17:38,422] Trial 6 finished with value: -3551.475476217507 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 31, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 5, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 5 with value: -280.17777558722315.\n", - "[I 2024-07-02 13:17:38,466] Trial 7 finished with value: -2124.9660426577593 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 2, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 5 with value: -280.17777558722315.\n", - "[I 2024-07-02 13:17:38,509] Trial 8 finished with value: -1686.5737716985532 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.9841058851292832, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 5 with value: -280.17777558722315.\n", - "[I 2024-07-02 13:17:38,552] Trial 9 finished with value: -1702.174704715547 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.861494545249233, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 5 with value: -280.17777558722315.\n", - "[I 2024-07-02 13:17:38,578] Trial 10 pruned. Duplicate parameter set\n", - "[I 2024-07-02 13:17:38,621] Trial 11 finished with value: -1204.636967895143 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.5238298142840006, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 5 with value: -280.17777558722315.\n", - "[I 2024-07-02 13:17:38,676] Trial 12 finished with value: -228.44505332657158 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.9836853549192415, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 12 with value: -228.44505332657158.\n", - "[I 2024-07-02 13:17:38,729] Trial 13 finished with value: -3949.499774068696 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.04535826280986047, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 2.012999584021838e-09, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 12 with value: -228.44505332657158.\n" + "[I 2024-07-03 09:16:38,118] A new study created in memory with name: my_study_stratified_split\n", + "[I 2024-07-03 09:16:38,159] A new study created in memory with name: study_name_0\n", + "[I 2024-07-03 09:16:38,245] Trial 0 finished with value: -1422.6893033211163 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.3506885259152708, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 0 with value: -1422.6893033211163.\n", + "[I 2024-07-03 09:16:38,768] Trial 1 finished with value: -3949.4997737240788 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 1.114418824594481, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 0.002226268245894711, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 0 with value: -1422.6893033211163.\n", + "[I 2024-07-03 09:16:38,854] Trial 2 finished with value: -228.21268605718763 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.9792392258490488, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 2 with value: -228.21268605718763.\n", + "[I 2024-07-03 09:16:38,921] Trial 3 finished with value: -1702.9050979147669 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.9025687106861437, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 2 with value: -228.21268605718763.\n", + "[I 2024-07-03 09:16:39,021] Trial 4 finished with value: -3247.6912883239856 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 26, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 7, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 2 with value: -228.21268605718763.\n", + "[I 2024-07-03 09:16:39,084] Trial 5 finished with value: -3949.4997740833423 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 2.705951912360815, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 0.02209358064818234, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 2 with value: -228.21268605718763.\n", + "[I 2024-07-03 09:16:39,187] Trial 6 finished with value: -2983.9453142752113 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 31, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 9, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 2 with value: -228.21268605718763.\n", + "[I 2024-07-03 09:16:39,277] Trial 7 finished with value: -2198.950805957436 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.1928709077332853, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 2 with value: -228.21268605718763.\n", + "[I 2024-07-03 09:16:39,332] Trial 8 finished with value: -3949.2319858272 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.00010401866577354432, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 0.016852011028048477, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 2 with value: -228.21268605718763.\n", + "[I 2024-07-03 09:16:39,371] Trial 9 finished with value: -282.24592651550216 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.494633149075681, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 2 with value: -228.21268605718763.\n", + "[I 2024-07-03 09:16:39,411] Trial 10 finished with value: -3949.4997740833423 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 17.301741221650847, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 4.47427593386346e-09, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 2 with value: -228.21268605718763.\n", + "[I 2024-07-03 09:16:39,453] Trial 11 finished with value: -1227.6702954948303 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.8716291123223312, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 2 with value: -228.21268605718763.\n", + "[I 2024-07-03 09:16:39,494] Trial 12 finished with value: -3949.499098730656 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.0026930781846823872, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 2.9422417677400336e-06, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 2 with value: -228.21268605718763.\n", + "[I 2024-07-03 09:16:39,587] Trial 13 finished with value: -1931.955535244796 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 15, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 10, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 2 with value: -228.21268605718763.\n", + "[I 2024-07-03 09:16:39,631] Trial 14 finished with value: -2124.9660426577593 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 2, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 2 with value: -228.21268605718763.\n", + "[I 2024-07-03 09:16:39,721] Trial 15 finished with value: -3286.345885718371 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 20, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 9, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 2 with value: -228.21268605718763.\n", + "[I 2024-07-03 09:16:39,764] Trial 16 finished with value: -280.7162909122767 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.3549486376243653, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 2 with value: -228.21268605718763.\n", + "[I 2024-07-03 09:16:39,818] Trial 17 finished with value: -244.84820223527166 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.2758926310849665, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 2 with value: -228.21268605718763.\n", + "[I 2024-07-03 09:16:39,860] Trial 18 finished with value: -1228.103944004991 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.8460298330938263, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 2 with value: -228.21268605718763.\n" ] }, { - "name": "stdout", + "name": "stderr", "output_type": "stream", "text": [ - "Duplicated trial: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 2, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}, return [-2124.9660426577593]\n" + "[I 2024-07-03 09:16:39,902] Trial 19 finished with value: -1194.7031625463042 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.5492512678428934, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 2 with value: -228.21268605718763.\n", + "[I 2024-07-03 09:16:39,955] Trial 20 finished with value: -1724.557551910545 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.892106924021418, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 2 with value: -228.21268605718763.\n", + "[I 2024-07-03 09:16:39,998] Trial 21 finished with value: -261.13000775604115 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.572116315247876, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 2 with value: -228.21268605718763.\n", + "[I 2024-07-03 09:16:40,067] Trial 22 finished with value: -2001.3060405990927 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.47902806935499087, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 2 with value: -228.21268605718763.\n", + "[I 2024-07-03 09:16:40,112] Trial 23 finished with value: -281.1146424526534 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.1153402525180756, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 2 with value: -228.21268605718763.\n", + "[I 2024-07-03 09:16:40,166] Trial 24 finished with value: -1890.76109666487 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.7599101082108883, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 2 with value: -228.21268605718763.\n", + "[I 2024-07-03 09:16:40,243] Trial 25 finished with value: -2175.126598549413 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.23455690290740283, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 2 with value: -228.21268605718763.\n", + "[I 2024-07-03 09:16:40,300] Trial 26 finished with value: -3946.8342189209093 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.011086475398399418, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 0.013887466467005373, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 2 with value: -228.21268605718763.\n", + "[I 2024-07-03 09:16:40,351] Trial 27 finished with value: -221.4006022524013 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.8445709244824666, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 27 with value: -221.4006022524013.\n", + "[I 2024-07-03 09:16:40,404] Trial 28 finished with value: -3949.499483217993 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.026247815852988552, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 9.15702072587365e-06, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 27 with value: -221.4006022524013.\n", + "[I 2024-07-03 09:16:40,458] Trial 29 finished with value: -3949.031574503982 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.004448995249821876, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 0.019893469499202194, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 27 with value: -221.4006022524013.\n", + "[I 2024-07-03 09:16:40,501] Trial 30 finished with value: -3949.4995119480113 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.03676077353551019, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 1.984117431783922e-06, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 27 with value: -221.4006022524013.\n", + "[I 2024-07-03 09:16:40,540] Trial 31 finished with value: -281.1485116995759 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.0955265235124376, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 27 with value: -221.4006022524013.\n", + "[I 2024-07-03 09:16:40,586] Trial 32 finished with value: -1328.6100724258592 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.6432156192415805, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 27 with value: -221.4006022524013.\n", + "/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.529e+01, tolerance: 3.820e+01\n", + " model = cd_fast.enet_coordinate_descent(\n", + "[I 2024-07-03 09:16:40,688] Trial 33 finished with value: -359.62121933507666 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.05966299879541892, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 27 with value: -221.4006022524013.\n", + "[I 2024-07-03 09:16:40,732] Trial 34 finished with value: -1230.2160884709206 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.9805503701482912, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 27 with value: -221.4006022524013.\n", + "[I 2024-07-03 09:16:40,778] Trial 35 finished with value: -282.64701779602615 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.29245080833279413, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 27 with value: -221.4006022524013.\n", + "[I 2024-07-03 09:16:40,867] Trial 36 finished with value: -3551.475476217507 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 21, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 5, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 27 with value: -221.4006022524013.\n", + "[I 2024-07-03 09:16:40,958] Trial 37 finished with value: -2906.3484169581297 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 23, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 10, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 27 with value: -221.4006022524013.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "[I 2024-07-02 13:17:38,829] Trial 14 finished with value: -2856.917927507731 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 13, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 10, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 12 with value: -228.44505332657158.\n", - "/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.306e+01, tolerance: 3.824e+01\n", - " model = cd_fast.enet_coordinate_descent(\n", - "[I 2024-07-02 13:17:38,882] Trial 15 finished with value: -2554.2079198900733 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.10588223712643852, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 12 with value: -228.44505332657158.\n", - "[I 2024-07-02 13:17:38,922] Trial 16 finished with value: -1261.484274761188 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.0950442632698256, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 12 with value: -228.44505332657158.\n", - "[I 2024-07-02 13:17:38,965] Trial 17 finished with value: -282.6478019258886 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.2920636100136971, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 12 with value: -228.44505332657158.\n", - "[I 2024-07-02 13:17:39,004] Trial 18 finished with value: -1814.6019641143478 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 3, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 12 with value: -228.44505332657158.\n", - "[I 2024-07-02 13:17:39,048] Trial 19 finished with value: -1284.7430070920798 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.1729012287538991, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 12 with value: -228.44505332657158.\n", - "[I 2024-07-02 13:17:39,103] Trial 20 finished with value: -237.98783693000647 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.1721667984096773, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 12 with value: -228.44505332657158.\n", - "[I 2024-07-02 13:17:39,192] Trial 21 finished with value: -2129.55317061882 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 12, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 8, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 12 with value: -228.44505332657158.\n", - "[I 2024-07-02 13:17:39,235] Trial 22 finished with value: -3949.4997740833423 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 3.779895470793612, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 4.260941957410989e-09, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 12 with value: -228.44505332657158.\n", - "[I 2024-07-02 13:17:39,279] Trial 23 finished with value: -1740.8894369939983 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.02841448247455669, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 12 with value: -228.44505332657158.\n", - "/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.698e+02, tolerance: 3.824e+01\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.280e+02, tolerance: 3.820e+01\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.352e+02, tolerance: 3.770e+01\n", - " model = cd_fast.enet_coordinate_descent(\n", - "[I 2024-07-02 13:17:39,373] Trial 24 finished with value: -3317.417858905051 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.003050380617617421, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 12 with value: -228.44505332657158.\n", - "[I 2024-07-02 13:17:39,404] Trial 25 pruned. Duplicate parameter set\n", - "[I 2024-07-02 13:17:39,448] Trial 26 finished with value: -1256.7270466276807 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.1594144041655936, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 12 with value: -228.44505332657158.\n", - "[I 2024-07-02 13:17:39,491] Trial 27 finished with value: -1245.1399766270456 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.336730512398918, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 12 with value: -228.44505332657158.\n", - "[I 2024-07-02 13:17:39,583] Trial 28 finished with value: -2908.3563960057677 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 14, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 10, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 12 with value: -228.44505332657158.\n" + "[I 2024-07-03 09:16:41,016] Trial 38 finished with value: -3949.4995127562875 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.00046194410462432797, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 4.029411427548193e-06, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 27 with value: -221.4006022524013.\n", + "[I 2024-07-03 09:16:41,108] Trial 39 finished with value: -2181.820041426174 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 8, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 8, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 27 with value: -221.4006022524013.\n", + "[I 2024-07-03 09:16:41,154] Trial 40 finished with value: -1691.4178445876241 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.25660511317441, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 27 with value: -221.4006022524013.\n", + "[I 2024-07-03 09:16:41,199] Trial 41 finished with value: -2720.793752592223 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 3, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 27 with value: -221.4006022524013.\n", + "[I 2024-07-03 09:16:41,243] Trial 42 finished with value: -3949.499773431924 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.00014288445814174256, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 2.6632131996063853e-07, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 27 with value: -221.4006022524013.\n", + "[I 2024-07-03 09:16:41,285] Trial 43 finished with value: -2756.046839500092 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 2, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 27 with value: -221.4006022524013.\n", + "[I 2024-07-03 09:16:41,340] Trial 44 finished with value: -3973.451158876369 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 42.628521164232666, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 7.902365014811196, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 27 with value: -221.4006022524013.\n", + "[I 2024-07-03 09:16:41,385] Trial 45 finished with value: -1243.123067137538 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.3751930712764295, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 27 with value: -221.4006022524013.\n", + "[I 2024-07-03 09:16:41,428] Trial 46 finished with value: -1693.6468746309602 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.3819621852265203, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 27 with value: -221.4006022524013.\n", + "[I 2024-07-03 09:16:41,460] Trial 47 pruned. Duplicate parameter set\n", + "[I 2024-07-03 09:16:41,505] Trial 48 finished with value: -1693.2195874041652 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.357933416798716, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 27 with value: -221.4006022524013.\n", + "[I 2024-07-03 09:16:41,596] Trial 49 finished with value: -3551.4754762175066 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 18, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 5, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 27 with value: -221.4006022524013.\n", + "[I 2024-07-03 09:16:41,655] Trial 50 finished with value: -252.22880044604048 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.4074905424870299, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 27 with value: -221.4006022524013.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "Duplicated trial: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 4, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}, return [-2658.13214897931]\n" + "Duplicated trial: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 2, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}, return [-2124.9660426577593]\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "[I 2024-07-02 13:17:39,628] Trial 29 finished with value: -1775.55204856041 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 5, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 12 with value: -228.44505332657158.\n", - "[I 2024-07-02 13:17:39,721] Trial 30 finished with value: -2059.3079659969176 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 19, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 9, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 12 with value: -228.44505332657158.\n", - "[I 2024-07-02 13:17:39,765] Trial 31 finished with value: -1257.9288888831513 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.1441514794000534, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 12 with value: -228.44505332657158.\n", - "[I 2024-07-02 13:17:39,808] Trial 32 finished with value: -280.98174313112844 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.1939105579414777, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 12 with value: -228.44505332657158.\n", - "[I 2024-07-02 13:17:39,900] Trial 33 finished with value: -3054.7066202193805 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 23, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 10, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 12 with value: -228.44505332657158.\n", - "[I 2024-07-02 13:17:39,944] Trial 34 finished with value: -1227.082986184029 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.909508127148669, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 12 with value: -228.44505332657158.\n", - "[I 2024-07-02 13:17:39,988] Trial 35 finished with value: -1676.7481962719485 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.4307837873914335, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 12 with value: -228.44505332657158.\n", - "[I 2024-07-02 13:17:40,079] Trial 36 finished with value: -2059.307965996918 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 26, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 9, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 12 with value: -228.44505332657158.\n", - "[I 2024-07-02 13:17:40,168] Trial 37 finished with value: -3441.9109103644514 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 12, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 5, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 12 with value: -228.44505332657158.\n", - "[I 2024-07-02 13:17:40,211] Trial 38 finished with value: -1670.5213862925175 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.07945856808433427, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 12 with value: -228.44505332657158.\n", - "[I 2024-07-02 13:17:40,264] Trial 39 finished with value: -2756.046839500092 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 2, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 12 with value: -228.44505332657158.\n", - "[I 2024-07-02 13:17:40,320] Trial 40 finished with value: -3949.4997735530674 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.022099719935614482, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 1.4657380646234507e-08, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 12 with value: -228.44505332657158.\n", - "[I 2024-07-02 13:17:40,376] Trial 41 finished with value: -3949.4997740833423 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 1.0862402902634642, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 0.12519632281925502, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 12 with value: -228.44505332657158.\n", - "[I 2024-07-02 13:17:40,467] Trial 42 finished with value: -3438.566583971217 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 5, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 7, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 12 with value: -228.44505332657158.\n", - "[I 2024-07-02 13:17:40,524] Trial 43 finished with value: -254.4422556954731 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.19967589906728334, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 12 with value: -228.44505332657158.\n", - "/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.016e+01, tolerance: 3.820e+01\n", - " model = cd_fast.enet_coordinate_descent(\n", - "[I 2024-07-02 13:17:40,618] Trial 44 finished with value: -359.7639743940817 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.059252880514551576, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 12 with value: -228.44505332657158.\n", - "[I 2024-07-02 13:17:40,662] Trial 45 finished with value: -1246.7813032646238 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.3074782262329858, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 12 with value: -228.44505332657158.\n", - "[I 2024-07-02 13:17:40,755] Trial 46 finished with value: -2224.3845873049813 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 17, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 6, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 12 with value: -228.44505332657158.\n" + "[I 2024-07-03 09:16:41,716] Trial 51 finished with value: -255.31635034669202 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.4589283601339789, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 27 with value: -221.4006022524013.\n", + "[I 2024-07-03 09:16:41,778] Trial 52 finished with value: -256.2177495190004 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.4731058898850313, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 27 with value: -221.4006022524013.\n", + "[I 2024-07-03 09:16:41,836] Trial 53 finished with value: -250.8048373346144 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.3833942364290719, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 27 with value: -221.4006022524013.\n", + "[I 2024-07-03 09:16:41,895] Trial 54 finished with value: -247.44361793349344 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.3204936132139333, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 27 with value: -221.4006022524013.\n", + "[I 2024-07-03 09:16:41,953] Trial 55 finished with value: -237.67743592416863 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.1672668791669807, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 27 with value: -221.4006022524013.\n", + "[I 2024-07-03 09:16:42,013] Trial 56 finished with value: -237.41469942991935 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.163017148435057, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 27 with value: -221.4006022524013.\n", + "[I 2024-07-03 09:16:42,075] Trial 57 finished with value: -236.77997190244454 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.1526366249808588, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 27 with value: -221.4006022524013.\n", + "[I 2024-07-03 09:16:42,133] Trial 58 finished with value: -232.71036684152705 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.0734555939518273, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 27 with value: -221.4006022524013.\n", + "[I 2024-07-03 09:16:42,196] Trial 59 finished with value: -227.49921956230682 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.9650384727405148, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 27 with value: -221.4006022524013.\n", + "[I 2024-07-03 09:16:42,260] Trial 60 finished with value: -228.050800345174 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.9760887954474786, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 27 with value: -221.4006022524013.\n", + "[I 2024-07-03 09:16:42,322] Trial 61 finished with value: -226.5411165412594 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.944315662351203, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 27 with value: -221.4006022524013.\n", + "[I 2024-07-03 09:16:42,383] Trial 62 finished with value: -223.6882024574651 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.8904553119984893, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 27 with value: -221.4006022524013.\n", + "[I 2024-07-03 09:16:42,447] Trial 63 finished with value: -221.90351644528945 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.8544623841490979, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 27 with value: -221.4006022524013.\n", + "[I 2024-07-03 09:16:42,512] Trial 64 finished with value: -220.8253198336247 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.8345668974354062, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 64 with value: -220.8253198336247.\n", + "[I 2024-07-03 09:16:42,573] Trial 65 finished with value: -217.53232342915194 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.776773300543389, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 65 with value: -217.53232342915194.\n", + "[I 2024-07-03 09:16:42,633] Trial 66 finished with value: -218.7389829217058 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.801400475195701, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 65 with value: -217.53232342915194.\n", + "[I 2024-07-03 09:16:42,696] Trial 67 finished with value: -217.69056535924156 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.7804054136584848, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 65 with value: -217.53232342915194.\n", + "[I 2024-07-03 09:16:42,758] Trial 68 finished with value: -216.91097015882943 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.7602085351484837, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 68 with value: -216.91097015882943.\n", + "[I 2024-07-03 09:16:42,822] Trial 69 finished with value: -215.85510851960055 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.7039388910745159, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 69 with value: -215.85510851960055.\n", + "[I 2024-07-03 09:16:42,885] Trial 70 finished with value: -215.668920424489 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.6832925660429156, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 70 with value: -215.668920424489.\n", + "[I 2024-07-03 09:16:42,961] Trial 71 finished with value: -215.7329787021866 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.6973711773317055, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 70 with value: -215.668920424489.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "[I 2024-07-02 13:17:40,810] Trial 47 finished with value: -1673.9639799911165 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.2737740844660712, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 12 with value: -228.44505332657158.\n", - "[I 2024-07-02 13:17:40,896] Trial 48 finished with value: -3163.129883232068 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 32, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 6, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 12 with value: -228.44505332657158.\n", - "[I 2024-07-02 13:17:40,987] Trial 49 finished with value: -2753.414173913392 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 10, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 10, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 12 with value: -228.44505332657158.\n", - "[I 2024-07-02 13:17:41,057] Trial 50 finished with value: -263.1352845182604 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.627030918721665, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 12 with value: -228.44505332657158.\n", - "[I 2024-07-02 13:17:41,105] Trial 51 finished with value: -271.2979718788249 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.8548903728617034, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 12 with value: -228.44505332657158.\n", - "[I 2024-07-02 13:17:41,165] Trial 52 finished with value: -277.86441431259567 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.9605867591283856, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 12 with value: -228.44505332657158.\n", - "[I 2024-07-02 13:17:41,227] Trial 53 finished with value: -277.4329099850367 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.9537398361705693, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 12 with value: -228.44505332657158.\n", - "[I 2024-07-02 13:17:41,274] Trial 54 finished with value: -274.3838070241422 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.9045589309769144, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 12 with value: -228.44505332657158.\n", - "[I 2024-07-02 13:17:41,334] Trial 55 finished with value: -260.4460398258507 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.5589021326002044, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 12 with value: -228.44505332657158.\n", - "[I 2024-07-02 13:17:41,383] Trial 56 finished with value: -257.95032410206767 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.5053759377103249, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 12 with value: -228.44505332657158.\n", - "[I 2024-07-02 13:17:41,444] Trial 57 finished with value: -256.5958038666581 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.4789082433356577, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 12 with value: -228.44505332657158.\n", - "[I 2024-07-02 13:17:41,495] Trial 58 finished with value: -253.4269973575198 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.4281024602273042, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 12 with value: -228.44505332657158.\n", - "[I 2024-07-02 13:17:41,560] Trial 59 finished with value: -249.40822811603962 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.3546313579812586, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 12 with value: -228.44505332657158.\n", - "[I 2024-07-02 13:17:41,620] Trial 60 finished with value: -245.71101688809983 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.2913960369109012, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 12 with value: -228.44505332657158.\n", - "[I 2024-07-02 13:17:41,675] Trial 61 finished with value: -247.88538215472033 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.3274897484709072, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 12 with value: -228.44505332657158.\n", - "[I 2024-07-02 13:17:41,737] Trial 62 finished with value: -244.23847775159297 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.2647865635312279, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 12 with value: -228.44505332657158.\n", - "[I 2024-07-02 13:17:41,803] Trial 63 finished with value: -247.59033004585282 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.3228443521984092, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 12 with value: -228.44505332657158.\n", - "[I 2024-07-02 13:17:41,863] Trial 64 finished with value: -243.40694430653753 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.2489205103047292, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 12 with value: -228.44505332657158.\n", - "[I 2024-07-02 13:17:41,928] Trial 65 finished with value: -223.85145692792733 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.8934822741396387, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 65 with value: -223.85145692792733.\n", - "[I 2024-07-02 13:17:41,990] Trial 66 finished with value: -221.94026043724057 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.8552798675517863, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 66 with value: -221.94026043724057.\n" + "[I 2024-07-03 09:16:43,026] Trial 72 finished with value: -215.67102006571292 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.6844742580756044, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 70 with value: -215.668920424489.\n", + "[I 2024-07-03 09:16:43,091] Trial 73 finished with value: -215.81327642163203 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.6524345509991505, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 70 with value: -215.668920424489.\n", + "[I 2024-07-03 09:16:43,152] Trial 74 finished with value: -216.23233489315405 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.6397208322054673, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 70 with value: -215.668920424489.\n", + "[I 2024-07-03 09:16:43,202] Trial 75 finished with value: -216.9059768853624 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.6240525627120483, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 70 with value: -215.668920424489.\n", + "[I 2024-07-03 09:16:43,262] Trial 76 finished with value: -216.96916882508208 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.6217835876523938, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 70 with value: -215.668920424489.\n", + "[I 2024-07-03 09:16:43,321] Trial 77 finished with value: -216.93975408338443 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.622943081420681, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 70 with value: -215.668920424489.\n", + "[I 2024-07-03 09:16:43,383] Trial 78 finished with value: -218.81062565770313 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.4843223898274973, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 70 with value: -215.668920424489.\n", + "[I 2024-07-03 09:16:43,446] Trial 79 finished with value: -215.73445853349577 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.6608907827069467, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 70 with value: -215.668920424489.\n", + "[I 2024-07-03 09:16:43,509] Trial 80 finished with value: -216.98523880564207 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.6212698211320781, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 70 with value: -215.668920424489.\n", + "[I 2024-07-03 09:16:43,574] Trial 81 finished with value: -215.67775335107788 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.6868039524504028, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 70 with value: -215.668920424489.\n", + "[I 2024-07-03 09:16:43,638] Trial 82 finished with value: -215.6684451623601 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.675099077419251, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 82 with value: -215.6684451623601.\n", + "[I 2024-07-03 09:16:43,699] Trial 83 finished with value: -221.50779683862856 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.4128411420851856, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 82 with value: -215.6684451623601.\n", + "[I 2024-07-03 09:16:43,762] Trial 84 finished with value: -215.88335246660642 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.7053781179584059, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 82 with value: -215.6684451623601.\n", + "[I 2024-07-03 09:16:43,826] Trial 85 finished with value: -215.72603469919923 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.696467737188581, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 82 with value: -215.6684451623601.\n", + "[I 2024-07-03 09:16:43,887] Trial 86 finished with value: -218.4875213911104 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.49262152423320377, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 82 with value: -215.6684451623601.\n", + "[I 2024-07-03 09:16:43,940] Trial 87 finished with value: -2733.5772576431627 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 5, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 82 with value: -215.6684451623601.\n", + "[I 2024-07-03 09:16:44,003] Trial 88 finished with value: -223.3316330463421 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.3583011871980122, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 82 with value: -215.6684451623601.\n", + "[I 2024-07-03 09:16:44,055] Trial 89 finished with value: -217.6145666264675 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.5336056878471176, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 82 with value: -215.6684451623601.\n", + "[I 2024-07-03 09:16:44,120] Trial 90 finished with value: -215.901895553071 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.7063667764245258, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 82 with value: -215.6684451623601.\n", + "[I 2024-07-03 09:16:44,184] Trial 91 finished with value: -215.70489918927308 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.6655648428214861, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 82 with value: -215.6684451623601.\n", + "[I 2024-07-03 09:16:44,249] Trial 92 finished with value: -215.75938837926353 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.6991246060107018, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 82 with value: -215.6684451623601.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "[I 2024-07-02 13:17:42,048] Trial 67 finished with value: -219.60947928367543 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.8149866573467666, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 67 with value: -219.60947928367543.\n", - "[I 2024-07-02 13:17:42,108] Trial 68 finished with value: -221.84441955310717 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.8531301788095305, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 67 with value: -219.60947928367543.\n", - "[I 2024-07-02 13:17:42,170] Trial 69 finished with value: -221.24134912135943 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.8418420411160932, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 67 with value: -219.60947928367543.\n", - "[I 2024-07-02 13:17:42,232] Trial 70 finished with value: -223.34805357903284 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.883998932301903, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 67 with value: -219.60947928367543.\n", - "[I 2024-07-02 13:17:42,293] Trial 71 finished with value: -221.99342925522842 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.8564564664338091, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 67 with value: -219.60947928367543.\n", - "[I 2024-07-02 13:17:42,353] Trial 72 finished with value: -222.50886633416462 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.8672069097403997, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 67 with value: -219.60947928367543.\n", - "[I 2024-07-02 13:17:42,415] Trial 73 finished with value: -221.61235541906441 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.8482856353268698, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 67 with value: -219.60947928367543.\n", - "[I 2024-07-02 13:17:42,479] Trial 74 finished with value: -217.7749814513912 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.7823980442129331, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 74 with value: -217.7749814513912.\n", - "[I 2024-07-02 13:17:42,538] Trial 75 finished with value: -216.00225784039503 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.7113129125761161, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 75 with value: -216.00225784039503.\n", - "[I 2024-07-02 13:17:42,601] Trial 76 finished with value: -216.8736767409489 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.6250904023479531, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 75 with value: -216.00225784039503.\n", - "[I 2024-07-02 13:17:42,666] Trial 77 finished with value: -216.94414119442342 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.6227757503715069, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 75 with value: -216.00225784039503.\n", - "[I 2024-07-02 13:17:42,731] Trial 78 finished with value: -216.45936690929625 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.6343056785694773, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 75 with value: -216.00225784039503.\n", - "[I 2024-07-02 13:17:42,797] Trial 79 finished with value: -216.63861804615567 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.6302707941523814, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 75 with value: -216.00225784039503.\n", - "[I 2024-07-02 13:17:42,860] Trial 80 finished with value: -1969.3749442111905 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.00019861806798724335, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 89.586529041453, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 75 with value: -216.00225784039503.\n", - "[I 2024-07-02 13:17:42,923] Trial 81 finished with value: -215.82051598778696 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.6518244359516081, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 81 with value: -215.82051598778696.\n", - "[I 2024-07-02 13:17:42,987] Trial 82 finished with value: -216.06387687700067 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.6440087841656821, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 81 with value: -215.82051598778696.\n", - "[I 2024-07-02 13:17:43,041] Trial 83 finished with value: -216.24994687849525 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.6393212787552464, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 81 with value: -215.82051598778696.\n", - "[I 2024-07-02 13:17:43,106] Trial 84 finished with value: -216.92984604804667 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.6232144947646524, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 81 with value: -215.82051598778696.\n", - "[I 2024-07-02 13:17:43,170] Trial 85 finished with value: -217.25506613319246 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.603388647930941, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 81 with value: -215.82051598778696.\n", - "[I 2024-07-02 13:17:43,223] Trial 86 finished with value: -2733.5772576431627 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 5, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 81 with value: -215.82051598778696.\n", - "[I 2024-07-02 13:17:43,287] Trial 87 finished with value: -217.29854648789728 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.5873312673596333, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 81 with value: -215.82051598778696.\n" + "[I 2024-07-03 09:16:44,312] Trial 93 finished with value: -217.37803497618862 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.561405815846269, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 82 with value: -215.6684451623601.\n", + "[I 2024-07-03 09:16:44,378] Trial 94 finished with value: -215.6889579245111 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.689830092202269, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 82 with value: -215.6684451623601.\n", + "[I 2024-07-03 09:16:44,442] Trial 95 finished with value: -215.86864957230281 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.7047077164804413, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 82 with value: -215.6684451623601.\n", + "[I 2024-07-03 09:16:44,508] Trial 96 finished with value: -221.35631440135322 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.42672301990746925, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 82 with value: -215.6684451623601.\n", + "[I 2024-07-03 09:16:44,547] Trial 97 pruned. Duplicate parameter set\n", + "[I 2024-07-03 09:16:44,613] Trial 98 finished with value: -217.35658017290132 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.5640798240863641, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 82 with value: -215.6684451623601.\n", + "[I 2024-07-03 09:16:44,717] Trial 99 finished with value: -2295.758226990139 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 2, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 7, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 82 with value: -215.6684451623601.\n" ] }, { - "name": "stderr", + "name": "stdout", "output_type": "stream", "text": [ - "[I 2024-07-02 13:17:43,347] Trial 88 finished with value: -221.16592450348784 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.4337907998582289, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 81 with value: -215.82051598778696.\n", - "[I 2024-07-02 13:17:43,410] Trial 89 finished with value: -215.68514116107337 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.6695836226711808, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 89 with value: -215.68514116107337.\n", - "[I 2024-07-02 13:17:43,475] Trial 90 finished with value: -220.8939514172608 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.4420925048614356, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 89 with value: -215.68514116107337.\n", - "[I 2024-07-02 13:17:43,535] Trial 91 finished with value: -215.72299797702155 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.6960582933068138, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 89 with value: -215.68514116107337.\n", - "[I 2024-07-02 13:17:43,601] Trial 92 finished with value: -215.69285146262294 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.69078828949453, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 89 with value: -215.68514116107337.\n", - "[I 2024-07-02 13:17:43,665] Trial 93 finished with value: -216.0538787714827 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.7144357045239296, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 89 with value: -215.68514116107337.\n", - "[I 2024-07-02 13:17:43,728] Trial 94 finished with value: -216.4213281391621 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.7353090312302926, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 89 with value: -215.68514116107337.\n", - "[I 2024-07-02 13:17:43,794] Trial 95 finished with value: -3949.4997740833423 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 50.74724725664498, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 8.92653950485437e-05, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 89 with value: -215.68514116107337.\n", - "[I 2024-07-02 13:17:43,858] Trial 96 finished with value: -216.12287184152592 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.7183304951103088, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 89 with value: -215.68514116107337.\n", - "[I 2024-07-02 13:17:43,922] Trial 97 finished with value: -216.22186485689846 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.7234233661662641, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 89 with value: -215.68514116107337.\n", - "[I 2024-07-02 13:17:43,977] Trial 98 finished with value: -2720.793752592223 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 3, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 89 with value: -215.68514116107337.\n", - "[I 2024-07-02 13:17:44,042] Trial 99 finished with value: -219.3855763846717 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.4726201914486088, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 89 with value: -215.68514116107337.\n" + "Duplicated trial: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 5, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}, return [-2733.5772576431627]\n" ] } ], @@ -1070,7 +1053,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 121, "metadata": {}, "outputs": [ { @@ -1108,7 +1091,7 @@ " 'concordance_index']" ] }, - "execution_count": 19, + "execution_count": 121, "metadata": {}, "output_type": "execute_result" } @@ -1127,7 +1110,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 122, "metadata": {}, "outputs": [], "source": [ @@ -1164,7 +1147,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 123, "metadata": { "scrolled": true }, @@ -1173,40 +1156,39 @@ "name": "stderr", "output_type": "stream", "text": [ - "[I 2024-07-02 13:17:44,945] A new study created in memory with name: my_study_r2\n", - "[I 2024-07-02 13:17:44,947] A new study created in memory with name: study_name_0\n", - "[I 2024-07-02 13:17:45,072] Trial 0 finished with value: -0.011171868665159623 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 6, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 5, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 0 with value: -0.011171868665159623.\n", - "[I 2024-07-02 13:17:45,197] Trial 1 finished with value: -0.08689402230378174 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 7, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 6, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 0 with value: -0.011171868665159623.\n", - "[I 2024-07-02 13:17:45,283] Trial 2 finished with value: -0.12553701248394863 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 5.141096648805748, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 2.4893466963980463e-08, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 0 with value: -0.011171868665159623.\n", - "[I 2024-07-02 13:17:45,358] Trial 3 finished with value: 0.3039309544203818 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 5, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 3 with value: 0.3039309544203818.\n", - "[I 2024-07-02 13:17:45,410] Trial 4 finished with value: 0.20182749628697164 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 3, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 3 with value: 0.3039309544203818.\n", - "[I 2024-07-02 13:17:45,485] Trial 5 finished with value: 0.8187194367176578 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.7896547008552977, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 5 with value: 0.8187194367176578.\n", - "[I 2024-07-02 13:17:45,558] Trial 6 finished with value: 0.4647239019719945 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.6574750183038587, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 5 with value: 0.8187194367176578.\n", - "[I 2024-07-02 13:17:45,611] Trial 7 finished with value: 0.8614818478547979 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.3974313630683448, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 7 with value: 0.8614818478547979.\n", - "[I 2024-07-02 13:17:45,705] Trial 8 finished with value: -0.12769795082909816 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 28, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 8, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 7 with value: 0.8614818478547979.\n", - "[I 2024-07-02 13:17:45,773] Trial 9 finished with value: 0.8639946428338224 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.2391884918766034, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 9 with value: 0.8639946428338224.\n", - "[I 2024-07-02 13:17:45,838] Trial 10 finished with value: -0.12553701248377633 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.00044396482429275296, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 2.3831436879125245e-10, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 9 with value: 0.8639946428338224.\n", - "[I 2024-07-02 13:17:45,892] Trial 11 finished with value: -0.12553700871203702 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.00028965395242758657, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 2.99928292425642e-07, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 9 with value: 0.8639946428338224.\n", - "[I 2024-07-02 13:17:45,934] Trial 12 finished with value: 0.2935582042429075 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 4, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 9 with value: 0.8639946428338224.\n", - "[I 2024-07-02 13:17:45,976] Trial 13 finished with value: 0.18476333152695587 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 2, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 9 with value: 0.8639946428338224.\n", - "[I 2024-07-02 13:17:46,029] Trial 14 finished with value: 0.8190707459213998 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.4060379177903557, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 9 with value: 0.8639946428338224.\n", - "[I 2024-07-02 13:17:46,118] Trial 15 finished with value: 0.12206148974315871 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 20, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 8, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 9 with value: 0.8639946428338224.\n", - "[I 2024-07-02 13:17:46,174] Trial 16 finished with value: 0.3105263811279067 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.344271094811757, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 9 with value: 0.8639946428338224.\n", - "[I 2024-07-02 13:17:46,215] Trial 17 finished with value: 0.3562469062424869 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.670604991178476, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 9 with value: 0.8639946428338224.\n", - "[I 2024-07-02 13:17:46,316] Trial 18 finished with value: 0.045959695906983344 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 22, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 6, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 9 with value: 0.8639946428338224.\n" + "[I 2024-07-03 09:16:45,785] A new study created in memory with name: my_study_r2\n", + "[I 2024-07-03 09:16:45,786] A new study created in memory with name: study_name_0\n", + "[I 2024-07-03 09:16:45,908] Trial 0 finished with value: -0.01117186866515977 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 6, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 5, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 0 with value: -0.01117186866515977.\n", + "[I 2024-07-03 09:16:46,487] Trial 1 finished with value: -0.08689402230378156 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 7, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 6, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 0 with value: -0.01117186866515977.\n", + "[I 2024-07-03 09:16:46,586] Trial 2 finished with value: -0.12553701248394863 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 5.141096648805748, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 2.4893466963980463e-08, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 0 with value: -0.01117186866515977.\n", + "[I 2024-07-03 09:16:46,670] Trial 3 finished with value: 0.3039309544203818 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 5, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 3 with value: 0.3039309544203818.\n", + "[I 2024-07-03 09:16:46,711] Trial 4 finished with value: 0.20182749628697164 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 3, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 3 with value: 0.3039309544203818.\n", + "[I 2024-07-03 09:16:46,790] Trial 5 finished with value: 0.8187194367176578 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.7896547008552977, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 5 with value: 0.8187194367176578.\n", + "[I 2024-07-03 09:16:46,867] Trial 6 finished with value: 0.4647239019719945 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.6574750183038587, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 5 with value: 0.8187194367176578.\n", + "[I 2024-07-03 09:16:46,921] Trial 7 finished with value: 0.8614818478547979 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.3974313630683448, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 7 with value: 0.8614818478547979.\n", + "[I 2024-07-03 09:16:47,025] Trial 8 finished with value: -0.1276979508290982 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 28, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 8, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 7 with value: 0.8614818478547979.\n", + "[I 2024-07-03 09:16:47,119] Trial 9 finished with value: 0.8639946428338224 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.2391884918766034, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 9 with value: 0.8639946428338224.\n", + "[I 2024-07-03 09:16:47,185] Trial 10 finished with value: -0.12553701248377633 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.00044396482429275296, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 2.3831436879125245e-10, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 9 with value: 0.8639946428338224.\n", + "[I 2024-07-03 09:16:47,250] Trial 11 finished with value: -0.12553700871203702 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.00028965395242758657, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 2.99928292425642e-07, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 9 with value: 0.8639946428338224.\n", + "[I 2024-07-03 09:16:47,290] Trial 12 finished with value: 0.2935582042429075 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 4, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 9 with value: 0.8639946428338224.\n", + "[I 2024-07-03 09:16:47,343] Trial 13 finished with value: 0.18476333152695587 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 2, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 9 with value: 0.8639946428338224.\n", + "[I 2024-07-03 09:16:47,396] Trial 14 finished with value: 0.8190707459213998 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.4060379177903557, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 9 with value: 0.8639946428338224.\n", + "[I 2024-07-03 09:16:47,483] Trial 15 finished with value: 0.12206148974315867 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 20, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 8, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 9 with value: 0.8639946428338224.\n", + "[I 2024-07-03 09:16:47,546] Trial 16 finished with value: 0.3105263811279067 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.344271094811757, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 9 with value: 0.8639946428338224.\n", + "[I 2024-07-03 09:16:47,590] Trial 17 finished with value: 0.3562469062424869 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.670604991178476, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 9 with value: 0.8639946428338224.\n", + "[I 2024-07-03 09:16:47,681] Trial 18 finished with value: 0.045959695906983344 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 22, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 6, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 9 with value: 0.8639946428338224.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "[I 2024-07-02 13:17:46,381] Trial 19 finished with value: 0.8583939656024446 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.5158832554303112, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 9 with value: 0.8639946428338224.\n", - "[I 2024-07-02 13:17:46,433] Trial 20 finished with value: 0.3062574078515544 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 4, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 9 with value: 0.8639946428338224.\n", - "[I 2024-07-02 13:17:46,487] Trial 21 finished with value: -0.11657354998283716 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.0009327650919528738, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 6.062479210472502, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 9 with value: 0.8639946428338224.\n", - "[I 2024-07-02 13:17:46,586] Trial 22 pruned. Duplicate parameter set\n", - "[I 2024-07-02 13:17:46,629] Trial 23 finished with value: 0.8498478905829554 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.1366172066709432, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 9 with value: 0.8639946428338224.\n", - "[I 2024-07-02 13:17:46,733] Trial 24 finished with value: -0.1276979508290982 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 26, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 8, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 9 with value: 0.8639946428338224.\n", - "[I 2024-07-02 13:17:46,786] Trial 25 finished with value: -0.13519830637607919 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 43.92901911959232, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 27.999026012594694, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 9 with value: 0.8639946428338224.\n" + "[I 2024-07-03 09:16:47,749] Trial 19 finished with value: 0.8583939656024446 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.5158832554303112, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 9 with value: 0.8639946428338224.\n", + "[I 2024-07-03 09:16:47,802] Trial 20 finished with value: 0.3062574078515544 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 4, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 9 with value: 0.8639946428338224.\n", + "[I 2024-07-03 09:16:47,868] Trial 21 finished with value: -0.11657354998283716 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.0009327650919528738, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 6.062479210472502, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 9 with value: 0.8639946428338224.\n", + "[I 2024-07-03 09:16:47,897] Trial 22 pruned. Duplicate parameter set\n", + "[I 2024-07-03 09:16:47,937] Trial 23 finished with value: 0.8498478905829554 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.1366172066709432, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 9 with value: 0.8639946428338224.\n", + "[I 2024-07-03 09:16:48,052] Trial 24 finished with value: -0.12769795082909816 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 26, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 8, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 9 with value: 0.8639946428338224.\n" ] }, { @@ -1220,19 +1202,20 @@ "name": "stderr", "output_type": "stream", "text": [ - "[I 2024-07-02 13:17:46,839] Trial 26 finished with value: 0.8198078293055633 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.5888977841391714, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 9 with value: 0.8639946428338224.\n", - "[I 2024-07-02 13:17:46,878] Trial 27 finished with value: 0.8201573964824842 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.19435298754153707, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 9 with value: 0.8639946428338224.\n", - "[I 2024-07-02 13:17:46,958] Trial 28 finished with value: 0.045959695906983344 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 13, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 6, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 9 with value: 0.8639946428338224.\n", - "[I 2024-07-02 13:17:47,013] Trial 29 finished with value: -0.12553701248394863 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 1.6285506249643193, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 0.35441495011256785, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 9 with value: 0.8639946428338224.\n", - "[I 2024-07-02 13:17:47,103] Trial 30 finished with value: 0.11934070343348298 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 10, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 8, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 9 with value: 0.8639946428338224.\n", - "[I 2024-07-02 13:17:47,145] Trial 31 finished with value: 0.4374125584543907 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.2457809516380005, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 9 with value: 0.8639946428338224.\n", - "[I 2024-07-02 13:17:47,213] Trial 32 finished with value: 0.3625576518621392 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.6459129458824919, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 9 with value: 0.8639946428338224.\n", - "[I 2024-07-02 13:17:47,254] Trial 33 finished with value: 0.36175556871883746 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.8179058888285398, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 9 with value: 0.8639946428338224.\n", - "[I 2024-07-02 13:17:47,285] Trial 34 pruned. Duplicate parameter set\n", - "[I 2024-07-02 13:17:47,330] Trial 35 finished with value: 0.8202473217121523 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.0920052840435055, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 9 with value: 0.8639946428338224.\n", - "[I 2024-07-02 13:17:47,372] Trial 36 finished with value: 0.3672927879319306 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.8677032984759461, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 9 with value: 0.8639946428338224.\n", - "[I 2024-07-02 13:17:47,402] Trial 37 pruned. Duplicate parameter set\n", - "[I 2024-07-02 13:17:47,445] Trial 38 finished with value: 0.40076792599874356 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.2865764368847064, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 9 with value: 0.8639946428338224.\n" + "[I 2024-07-03 09:16:48,109] Trial 25 finished with value: -0.13519830637607919 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 43.92901911959232, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 27.999026012594694, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 9 with value: 0.8639946428338224.\n", + "[I 2024-07-03 09:16:48,162] Trial 26 finished with value: 0.8198078293055633 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.5888977841391714, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 9 with value: 0.8639946428338224.\n", + "[I 2024-07-03 09:16:48,207] Trial 27 finished with value: 0.8201573964824842 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.19435298754153707, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 9 with value: 0.8639946428338224.\n", + "[I 2024-07-03 09:16:48,295] Trial 28 finished with value: 0.04595969590698327 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 13, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 6, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 9 with value: 0.8639946428338224.\n", + "[I 2024-07-03 09:16:48,361] Trial 29 finished with value: -0.12553701248394863 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 1.6285506249643193, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 0.35441495011256785, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 9 with value: 0.8639946428338224.\n", + "[I 2024-07-03 09:16:48,441] Trial 30 finished with value: 0.1193407034334832 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 10, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 8, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 9 with value: 0.8639946428338224.\n", + "[I 2024-07-03 09:16:48,487] Trial 31 finished with value: 0.4374125584543907 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.2457809516380005, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 9 with value: 0.8639946428338224.\n", + "[I 2024-07-03 09:16:48,542] Trial 32 finished with value: 0.3625576518621392 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.6459129458824919, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 9 with value: 0.8639946428338224.\n", + "[I 2024-07-03 09:16:48,587] Trial 33 finished with value: 0.36175556871883746 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.8179058888285398, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 9 with value: 0.8639946428338224.\n", + "[I 2024-07-03 09:16:48,618] Trial 34 pruned. Duplicate parameter set\n", + "[I 2024-07-03 09:16:48,661] Trial 35 finished with value: 0.8202473217121523 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.0920052840435055, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 9 with value: 0.8639946428338224.\n", + "[I 2024-07-03 09:16:48,705] Trial 36 finished with value: 0.3672927879319306 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.8677032984759461, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 9 with value: 0.8639946428338224.\n", + "[I 2024-07-03 09:16:48,735] Trial 37 pruned. Duplicate parameter set\n", + "[I 2024-07-03 09:16:48,781] Trial 38 finished with value: 0.40076792599874356 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.2865764368847064, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 9 with value: 0.8639946428338224.\n" ] }, { @@ -1247,11 +1230,11 @@ "name": "stderr", "output_type": "stream", "text": [ - "[I 2024-07-02 13:17:47,535] Trial 39 finished with value: 0.26560316846701765 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 5, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 5, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 9 with value: 0.8639946428338224.\n", - "[I 2024-07-02 13:17:47,632] Trial 40 finished with value: 0.41215254857081174 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 5, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 9, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 9 with value: 0.8639946428338224.\n", - "[I 2024-07-02 13:17:47,671] Trial 41 pruned. Duplicate parameter set\n", - "[I 2024-07-02 13:17:47,763] Trial 42 finished with value: -0.00461414372160085 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 25, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 5, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 9 with value: 0.8639946428338224.\n", - "[I 2024-07-02 13:17:47,808] Trial 43 finished with value: 0.27282533524183633 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 2, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 9 with value: 0.8639946428338224.\n" + "[I 2024-07-03 09:16:48,883] Trial 39 finished with value: 0.26560316846701765 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 5, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 5, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 9 with value: 0.8639946428338224.\n", + "[I 2024-07-03 09:16:48,975] Trial 40 finished with value: 0.41215254857081174 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 5, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 9, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 9 with value: 0.8639946428338224.\n", + "[I 2024-07-03 09:16:49,016] Trial 41 pruned. Duplicate parameter set\n", + "[I 2024-07-03 09:16:49,121] Trial 42 finished with value: -0.004614143721600923 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 25, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 5, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 9 with value: 0.8639946428338224.\n", + "[I 2024-07-03 09:16:49,167] Trial 43 finished with value: 0.27282533524183633 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 2, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 9 with value: 0.8639946428338224.\n" ] }, { @@ -1265,38 +1248,38 @@ "name": "stderr", "output_type": "stream", "text": [ - "[I 2024-07-02 13:17:47,919] Trial 44 finished with value: -0.10220127407364991 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 22, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 9, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 9 with value: 0.8639946428338224.\n", - "[I 2024-07-02 13:17:47,975] Trial 45 finished with value: 0.30323404130582854 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 3, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 9 with value: 0.8639946428338224.\n", - "[I 2024-07-02 13:17:48,030] Trial 46 finished with value: 0.3044553805553568 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.6437201185807124, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 9 with value: 0.8639946428338224.\n", - "[I 2024-07-02 13:17:48,076] Trial 47 finished with value: -0.12553701248394863 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 82.41502276709562, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 0.10978379088847677, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 9 with value: 0.8639946428338224.\n", - "[I 2024-07-02 13:17:48,120] Trial 48 finished with value: 0.36160209098547913 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.022707289534838138, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 9 with value: 0.8639946428338224.\n", - "[I 2024-07-02 13:17:48,175] Trial 49 finished with value: 0.2916101445983833 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 3, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 9 with value: 0.8639946428338224.\n", + "[I 2024-07-03 09:16:49,273] Trial 44 finished with value: -0.10220127407364969 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 22, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 9, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 9 with value: 0.8639946428338224.\n", + "[I 2024-07-03 09:16:49,328] Trial 45 finished with value: 0.30323404130582854 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 3, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 9 with value: 0.8639946428338224.\n", + "[I 2024-07-03 09:16:49,384] Trial 46 finished with value: 0.3044553805553568 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.6437201185807124, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 9 with value: 0.8639946428338224.\n", + "[I 2024-07-03 09:16:49,438] Trial 47 finished with value: -0.12553701248394863 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 82.41502276709562, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 0.10978379088847677, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 9 with value: 0.8639946428338224.\n", + "[I 2024-07-03 09:16:49,497] Trial 48 finished with value: 0.36160209098547913 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.022707289534838138, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 9 with value: 0.8639946428338224.\n", + "[I 2024-07-03 09:16:49,551] Trial 49 finished with value: 0.2916101445983833 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 3, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 9 with value: 0.8639946428338224.\n", "/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.936e+02, tolerance: 4.782e+01\n", " model = cd_fast.enet_coordinate_descent(\n", "/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.434e+02, tolerance: 4.977e+01\n", " model = cd_fast.enet_coordinate_descent(\n", - "[I 2024-07-02 13:17:48,276] Trial 50 finished with value: 0.8609413020928532 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.04987590926279814, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 9 with value: 0.8639946428338224.\n", + "[I 2024-07-03 09:16:49,645] Trial 50 finished with value: 0.8609413020928532 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.04987590926279814, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 9 with value: 0.8639946428338224.\n", "/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.794e+02, tolerance: 4.977e+01\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.830e+02, tolerance: 4.906e+01\n", - " model = cd_fast.enet_coordinate_descent(\n", "/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.578e+02, tolerance: 4.782e+01\n", " model = cd_fast.enet_coordinate_descent(\n", - "[I 2024-07-02 13:17:48,387] Trial 51 finished with value: 0.8610289662757457 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.019211413400468974, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 9 with value: 0.8639946428338224.\n", + "/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.830e+02, tolerance: 4.906e+01\n", + " model = cd_fast.enet_coordinate_descent(\n", + "[I 2024-07-03 09:16:49,746] Trial 51 finished with value: 0.8610289662757457 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.019211413400468974, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 9 with value: 0.8639946428338224.\n", "/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.754e+02, tolerance: 4.977e+01\n", " model = cd_fast.enet_coordinate_descent(\n", "/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.843e+02, tolerance: 4.906e+01\n", " model = cd_fast.enet_coordinate_descent(\n", "/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.507e+02, tolerance: 4.782e+01\n", " model = cd_fast.enet_coordinate_descent(\n", - "[I 2024-07-02 13:17:48,493] Trial 52 finished with value: 0.8610070549049179 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.018492644772509947, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 9 with value: 0.8639946428338224.\n", + "[I 2024-07-03 09:16:49,844] Trial 52 finished with value: 0.8610070549049179 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.018492644772509947, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 9 with value: 0.8639946428338224.\n", "/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.840e+02, tolerance: 4.977e+01\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.513e+02, tolerance: 4.782e+01\n", - " model = cd_fast.enet_coordinate_descent(\n", "/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.924e+02, tolerance: 4.906e+01\n", " model = cd_fast.enet_coordinate_descent(\n", - "[I 2024-07-02 13:17:48,600] Trial 53 finished with value: 0.8569771623635769 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.008783442408928633, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 9 with value: 0.8639946428338224.\n" + "/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.513e+02, tolerance: 4.782e+01\n", + " model = cd_fast.enet_coordinate_descent(\n", + "[I 2024-07-03 09:16:49,950] Trial 53 finished with value: 0.8569771623635769 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.008783442408928633, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 9 with value: 0.8639946428338224.\n" ] }, { @@ -1307,62 +1290,62 @@ " model = cd_fast.enet_coordinate_descent(\n", "/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.014e+02, tolerance: 4.977e+01\n", " model = cd_fast.enet_coordinate_descent(\n", - "[I 2024-07-02 13:17:48,700] Trial 54 finished with value: 0.8624781673814641 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.05782221001517797, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 9 with value: 0.8639946428338224.\n", + "[I 2024-07-03 09:16:50,050] Trial 54 finished with value: 0.8624781673814641 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.05782221001517797, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 9 with value: 0.8639946428338224.\n", "/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.113e+02, tolerance: 4.977e+01\n", " model = cd_fast.enet_coordinate_descent(\n", "/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.935e+02, tolerance: 4.782e+01\n", " model = cd_fast.enet_coordinate_descent(\n", "/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.122e+02, tolerance: 4.906e+01\n", " model = cd_fast.enet_coordinate_descent(\n", - "[I 2024-07-02 13:17:48,798] Trial 55 finished with value: 0.8618589507037001 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.02487072255316275, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 9 with value: 0.8639946428338224.\n", - "[I 2024-07-02 13:17:48,886] Trial 56 finished with value: 0.864754359721037 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.2079910754941946, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 56 with value: 0.864754359721037.\n", - "[I 2024-07-02 13:17:48,946] Trial 57 finished with value: 0.8622236413326235 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.333215560931422, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 56 with value: 0.864754359721037.\n", - "[I 2024-07-02 13:17:49,009] Trial 58 finished with value: 0.861832165638517 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.3628098560209365, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 56 with value: 0.864754359721037.\n", - "[I 2024-07-02 13:17:49,068] Trial 59 finished with value: 0.8620108533993581 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.34240779695521706, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 56 with value: 0.864754359721037.\n", - "[I 2024-07-02 13:17:49,142] Trial 60 finished with value: 0.8638540565650902 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.26493714991266293, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 56 with value: 0.864754359721037.\n", - "[I 2024-07-02 13:17:49,217] Trial 61 finished with value: 0.8629799500771645 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.30596394512914815, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 56 with value: 0.864754359721037.\n", - "[I 2024-07-02 13:17:49,276] Trial 62 finished with value: 0.8621408609583922 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.33648829357762355, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 56 with value: 0.864754359721037.\n", - "[I 2024-07-02 13:17:49,351] Trial 63 finished with value: 0.8638132124078156 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.2679814646317183, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 56 with value: 0.864754359721037.\n", - "[I 2024-07-02 13:17:49,424] Trial 64 finished with value: 0.863983758876634 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.24062119162159595, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 56 with value: 0.864754359721037.\n", - "[I 2024-07-02 13:17:49,500] Trial 65 finished with value: 0.8627356047945115 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.3141728910335158, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 56 with value: 0.864754359721037.\n", - "[I 2024-07-02 13:17:49,583] Trial 66 finished with value: 0.8639203054085788 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.23391390640786494, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 56 with value: 0.864754359721037.\n", - "[I 2024-07-02 13:17:49,644] Trial 67 finished with value: 0.8570103863991635 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.6124885145996103, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 56 with value: 0.864754359721037.\n", - "[I 2024-07-02 13:17:49,742] Trial 68 finished with value: 0.8647961976727571 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.2059976546070975, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 68 with value: 0.8647961976727571.\n", - "[I 2024-07-02 13:17:49,830] Trial 69 finished with value: 0.8648312544921793 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.20266060662750784, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 69 with value: 0.8648312544921793.\n" + "[I 2024-07-03 09:16:50,145] Trial 55 finished with value: 0.8618589507037001 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.02487072255316275, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 9 with value: 0.8639946428338224.\n", + "[I 2024-07-03 09:16:50,235] Trial 56 finished with value: 0.864754359721037 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.2079910754941946, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 56 with value: 0.864754359721037.\n", + "[I 2024-07-03 09:16:50,297] Trial 57 finished with value: 0.8622236413326235 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.333215560931422, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 56 with value: 0.864754359721037.\n", + "[I 2024-07-03 09:16:50,360] Trial 58 finished with value: 0.861832165638517 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.3628098560209365, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 56 with value: 0.864754359721037.\n", + "[I 2024-07-03 09:16:50,424] Trial 59 finished with value: 0.8620108533993581 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.34240779695521706, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 56 with value: 0.864754359721037.\n", + "[I 2024-07-03 09:16:50,499] Trial 60 finished with value: 0.8638540565650902 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.26493714991266293, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 56 with value: 0.864754359721037.\n", + "[I 2024-07-03 09:16:50,572] Trial 61 finished with value: 0.8629799500771645 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.30596394512914815, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 56 with value: 0.864754359721037.\n", + "[I 2024-07-03 09:16:50,634] Trial 62 finished with value: 0.8621408609583922 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.33648829357762355, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 56 with value: 0.864754359721037.\n", + "[I 2024-07-03 09:16:50,709] Trial 63 finished with value: 0.8638132124078156 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.2679814646317183, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 56 with value: 0.864754359721037.\n", + "[I 2024-07-03 09:16:50,784] Trial 64 finished with value: 0.863983758876634 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.24062119162159595, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 56 with value: 0.864754359721037.\n", + "[I 2024-07-03 09:16:50,858] Trial 65 finished with value: 0.8627356047945115 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.3141728910335158, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 56 with value: 0.864754359721037.\n", + "[I 2024-07-03 09:16:50,935] Trial 66 finished with value: 0.8639203054085788 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.23391390640786494, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 56 with value: 0.864754359721037.\n", + "[I 2024-07-03 09:16:50,996] Trial 67 finished with value: 0.8570103863991635 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.6124885145996103, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 56 with value: 0.864754359721037.\n", + "[I 2024-07-03 09:16:51,085] Trial 68 finished with value: 0.8647961976727571 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.2059976546070975, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 68 with value: 0.8647961976727571.\n", + "[I 2024-07-03 09:16:51,175] Trial 69 finished with value: 0.8648312544921793 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.20266060662750784, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 69 with value: 0.8648312544921793.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "[I 2024-07-02 13:17:49,926] Trial 70 finished with value: 0.8648431452862716 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.20027647978240445, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 70 with value: 0.8648431452862716.\n", - "[I 2024-07-02 13:17:50,010] Trial 71 finished with value: 0.8648491459660418 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.1968919999787333, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: 0.8648491459660418.\n", - "[I 2024-07-02 13:17:50,106] Trial 72 finished with value: 0.8650873115156988 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.174598921162764, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 72 with value: 0.8650873115156988.\n", - "[I 2024-07-02 13:17:50,204] Trial 73 finished with value: 0.8650350577921149 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.16468002989641095, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 72 with value: 0.8650873115156988.\n", - "[I 2024-07-02 13:17:50,300] Trial 74 finished with value: 0.8649412283687147 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.1606717091615047, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 72 with value: 0.8650873115156988.\n", + "[I 2024-07-03 09:16:51,263] Trial 70 finished with value: 0.8648431452862716 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.20027647978240445, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 70 with value: 0.8648431452862716.\n", + "[I 2024-07-03 09:16:51,351] Trial 71 finished with value: 0.8648491459660418 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.1968919999787333, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: 0.8648491459660418.\n", + "[I 2024-07-03 09:16:51,441] Trial 72 finished with value: 0.8650873115156988 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.174598921162764, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 72 with value: 0.8650873115156988.\n", + "[I 2024-07-03 09:16:51,544] Trial 73 finished with value: 0.8650350577921149 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.16468002989641095, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 72 with value: 0.8650873115156988.\n", + "[I 2024-07-03 09:16:51,647] Trial 74 finished with value: 0.8649412283687147 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.1606717091615047, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 72 with value: 0.8650873115156988.\n", "/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.986e+01, tolerance: 4.782e+01\n", " model = cd_fast.enet_coordinate_descent(\n", - "[I 2024-07-02 13:17:50,396] Trial 75 finished with value: 0.8649537211609554 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.14694925097689848, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 72 with value: 0.8650873115156988.\n", - "[I 2024-07-02 13:17:50,506] Trial 76 finished with value: 0.8649734575435447 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.147612713300643, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 72 with value: 0.8650873115156988.\n", + "[I 2024-07-03 09:16:51,758] Trial 75 finished with value: 0.8649537211609554 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.14694925097689848, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 72 with value: 0.8650873115156988.\n", + "[I 2024-07-03 09:16:51,872] Trial 76 finished with value: 0.8649734575435447 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.147612713300643, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 72 with value: 0.8650873115156988.\n", "/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.446e+01, tolerance: 4.782e+01\n", " model = cd_fast.enet_coordinate_descent(\n", - "[I 2024-07-02 13:17:50,620] Trial 77 finished with value: 0.8648761002838515 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.14440434705706803, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 72 with value: 0.8650873115156988.\n", + "[I 2024-07-03 09:16:51,972] Trial 77 finished with value: 0.8648761002838515 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.14440434705706803, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 72 with value: 0.8650873115156988.\n", "/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.398e+02, tolerance: 4.782e+01\n", " model = cd_fast.enet_coordinate_descent(\n", - "[I 2024-07-02 13:17:50,775] Trial 78 finished with value: 0.8639826593122782 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.1265357179513065, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 72 with value: 0.8650873115156988.\n", + "[I 2024-07-03 09:16:52,074] Trial 78 finished with value: 0.8639826593122782 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.1265357179513065, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 72 with value: 0.8650873115156988.\n", "/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.690e+01, tolerance: 4.782e+01\n", " model = cd_fast.enet_coordinate_descent(\n", - "[I 2024-07-02 13:17:50,875] Trial 79 finished with value: 0.864435565531768 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.1374245525868926, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 72 with value: 0.8650873115156988.\n", - "[I 2024-07-02 13:17:50,938] Trial 80 finished with value: 0.8590221951825531 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.49890830155012533, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 72 with value: 0.8650873115156988.\n", - "[I 2024-07-02 13:17:51,042] Trial 81 finished with value: 0.8649098880804443 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.1573428812070292, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 72 with value: 0.8650873115156988.\n", + "[I 2024-07-03 09:16:52,186] Trial 79 finished with value: 0.864435565531768 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.1374245525868926, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 72 with value: 0.8650873115156988.\n", + "[I 2024-07-03 09:16:52,252] Trial 80 finished with value: 0.8590221951825531 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.49890830155012533, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 72 with value: 0.8650873115156988.\n", + "[I 2024-07-03 09:16:52,348] Trial 81 finished with value: 0.8649098880804443 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.1573428812070292, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 72 with value: 0.8650873115156988.\n", "/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.405e+01, tolerance: 4.782e+01\n", " model = cd_fast.enet_coordinate_descent(\n", - "[I 2024-07-02 13:17:51,142] Trial 82 finished with value: 0.864536410656637 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.13886104722511608, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 72 with value: 0.8650873115156988.\n", - "[I 2024-07-02 13:17:51,208] Trial 83 finished with value: 0.8597401050431873 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.47746341180045787, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 72 with value: 0.8650873115156988.\n", - "[I 2024-07-02 13:17:51,259] Trial 84 finished with value: 0.8537465461603838 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.8599491178327108, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 72 with value: 0.8650873115156988.\n", + "[I 2024-07-03 09:16:52,446] Trial 82 finished with value: 0.864536410656637 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.13886104722511608, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 72 with value: 0.8650873115156988.\n", + "[I 2024-07-03 09:16:52,510] Trial 83 finished with value: 0.8597401050431873 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.47746341180045787, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 72 with value: 0.8650873115156988.\n", + "[I 2024-07-03 09:16:52,575] Trial 84 finished with value: 0.8537465461603838 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.8599491178327108, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 72 with value: 0.8650873115156988.\n", "/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.050e+01, tolerance: 4.782e+01\n", " model = cd_fast.enet_coordinate_descent(\n", - "[I 2024-07-02 13:17:51,388] Trial 85 finished with value: 0.8642643827090003 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.13446778921611002, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 72 with value: 0.8650873115156988.\n" + "[I 2024-07-03 09:16:52,684] Trial 85 finished with value: 0.8642643827090003 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.13446778921611002, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 72 with value: 0.8650873115156988.\n" ] }, { @@ -1371,33 +1354,33 @@ "text": [ "/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.175e+02, tolerance: 4.782e+01\n", " model = cd_fast.enet_coordinate_descent(\n", - "[I 2024-07-02 13:17:51,524] Trial 86 finished with value: 0.8641621818665252 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.1286796719653316, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 72 with value: 0.8650873115156988.\n", + "[I 2024-07-03 09:16:52,784] Trial 86 finished with value: 0.8641621818665252 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.1286796719653316, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 72 with value: 0.8650873115156988.\n", "/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.446e+01, tolerance: 4.782e+01\n", " model = cd_fast.enet_coordinate_descent(\n", - "[I 2024-07-02 13:17:51,625] Trial 87 finished with value: 0.864182755916388 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.13303218726548235, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 72 with value: 0.8650873115156988.\n", - "[I 2024-07-02 13:17:51,693] Trial 88 finished with value: -0.1255357440899417 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.021711452917433944, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 5.559714273835951e-05, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 72 with value: 0.8650873115156988.\n", - "[I 2024-07-02 13:17:51,758] Trial 89 finished with value: 0.8604596648091501 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.43644874418279245, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 72 with value: 0.8650873115156988.\n", + "[I 2024-07-03 09:16:52,882] Trial 87 finished with value: 0.864182755916388 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.13303218726548235, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 72 with value: 0.8650873115156988.\n", + "[I 2024-07-03 09:16:52,951] Trial 88 finished with value: -0.1255357440899417 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.021711452917433944, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 5.559714273835951e-05, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 72 with value: 0.8650873115156988.\n", + "[I 2024-07-03 09:16:53,014] Trial 89 finished with value: 0.8604596648091501 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.43644874418279245, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 72 with value: 0.8650873115156988.\n", "/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.463e+02, tolerance: 4.782e+01\n", " model = cd_fast.enet_coordinate_descent(\n", - "[I 2024-07-02 13:17:51,861] Trial 90 finished with value: 0.8635689909135862 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.10940922083495383, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 72 with value: 0.8650873115156988.\n", - "[I 2024-07-02 13:17:51,951] Trial 91 finished with value: 0.8648544336551733 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.1912756875742137, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 72 with value: 0.8650873115156988.\n", - "[I 2024-07-02 13:17:52,042] Trial 92 finished with value: 0.8648496595672595 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.19628449928540487, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 72 with value: 0.8650873115156988.\n", - "[I 2024-07-02 13:17:52,096] Trial 93 finished with value: 0.8452625121122099 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.4324661283995224, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 72 with value: 0.8650873115156988.\n", - "[I 2024-07-02 13:17:52,149] Trial 94 finished with value: 0.8378670635846416 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.839206620815206, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 72 with value: 0.8650873115156988.\n", + "[I 2024-07-03 09:16:53,109] Trial 90 finished with value: 0.8635689909135862 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.10940922083495383, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 72 with value: 0.8650873115156988.\n", + "[I 2024-07-03 09:16:53,196] Trial 91 finished with value: 0.8648544336551733 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.1912756875742137, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 72 with value: 0.8650873115156988.\n", + "[I 2024-07-03 09:16:53,286] Trial 92 finished with value: 0.8648496595672595 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.19628449928540487, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 72 with value: 0.8650873115156988.\n", + "[I 2024-07-03 09:16:53,336] Trial 93 finished with value: 0.8452625121122099 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.4324661283995224, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 72 with value: 0.8650873115156988.\n", + "[I 2024-07-03 09:16:53,388] Trial 94 finished with value: 0.8378670635846416 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.839206620815206, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 72 with value: 0.8650873115156988.\n", "/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.002e+01, tolerance: 4.977e+01\n", " model = cd_fast.enet_coordinate_descent(\n", "/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.082e+02, tolerance: 4.782e+01\n", " model = cd_fast.enet_coordinate_descent(\n", - "[I 2024-07-02 13:17:52,249] Trial 95 finished with value: 0.8649365368153895 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.07270781179126021, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 72 with value: 0.8650873115156988.\n", - "[I 2024-07-02 13:17:52,374] Trial 96 finished with value: 0.8875676754699953 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.0006995169897945908, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 96 with value: 0.8875676754699953.\n", - "/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.586e+01, tolerance: 4.977e+01\n", - " model = cd_fast.enet_coordinate_descent(\n", + "[I 2024-07-03 09:16:53,524] Trial 95 finished with value: 0.8649365368153895 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.07270781179126021, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 72 with value: 0.8650873115156988.\n", + "[I 2024-07-03 09:16:53,707] Trial 96 finished with value: 0.8875676754699953 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.0006995169897945908, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 96 with value: 0.8875676754699953.\n", "/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.618e+01, tolerance: 4.782e+01\n", " model = cd_fast.enet_coordinate_descent(\n", + "/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.586e+01, tolerance: 4.977e+01\n", + " model = cd_fast.enet_coordinate_descent(\n", "/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.234e+01, tolerance: 4.906e+01\n", " model = cd_fast.enet_coordinate_descent(\n", - "[I 2024-07-02 13:17:52,484] Trial 97 finished with value: 0.8730555131061773 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.0018186269840273495, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 96 with value: 0.8875676754699953.\n", - "[I 2024-07-02 13:17:52,552] Trial 98 finished with value: -0.12553508835019533 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.04867556317570456, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 0.0011658455138452, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 96 with value: 0.8875676754699953.\n" + "[I 2024-07-03 09:16:53,830] Trial 97 finished with value: 0.8730555131061773 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.0018186269840273495, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 96 with value: 0.8875676754699953.\n", + "[I 2024-07-03 09:16:53,901] Trial 98 finished with value: -0.12553508835019533 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.04867556317570456, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 0.0011658455138452, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 96 with value: 0.8875676754699953.\n" ] }, { @@ -1406,11 +1389,11 @@ "text": [ "/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.284e+02, tolerance: 4.782e+01\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.016e+02, tolerance: 4.906e+01\n", - " model = cd_fast.enet_coordinate_descent(\n", "/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.177e+02, tolerance: 4.977e+01\n", " model = cd_fast.enet_coordinate_descent(\n", - "[I 2024-07-02 13:17:52,664] Trial 99 finished with value: 0.8586292788613132 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.005078762921098462, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 96 with value: 0.8875676754699953.\n" + "/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.016e+02, tolerance: 4.906e+01\n", + " model = cd_fast.enet_coordinate_descent(\n", + "[I 2024-07-03 09:16:54,006] Trial 99 finished with value: 0.8586292788613132 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.005078762921098462, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 96 with value: 0.8875676754699953.\n" ] } ], @@ -1420,7 +1403,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 124, "metadata": {}, "outputs": [ { @@ -1455,7 +1438,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 125, "metadata": {}, "outputs": [ { @@ -1483,7 +1466,7 @@ " optunaz.config.optconfig.Mapie)" ] }, - "execution_count": 23, + "execution_count": 125, "metadata": {}, "output_type": "execute_result" } @@ -1580,46 +1563,36 @@ "name": "stderr", "output_type": "stream", "text": [ - "[I 2024-07-02 13:17:53,733] A new study created in memory with name: my_study\n", - "[I 2024-07-02 13:17:53,734] A new study created in memory with name: study_name_0\n", - "/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/joblib/memory.py:577: JobLibCollisionWarning: Possible name collisions between functions 'calculate_from_smi' (/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/optunaz/descriptors.py:-1) and 'calculate_from_smi' (/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/optunaz/descriptors.py:180)\n", - " return self._cached_call(args, kwargs, shelving=False)[0]\n", - "/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/joblib/memory.py:577: JobLibCollisionWarning: Possible name collisions between functions 'calculate_from_smi' (/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/optunaz/descriptors.py:-1) and 'calculate_from_smi' (/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/optunaz/descriptors.py:180)\n", - " return self._cached_call(args, kwargs, shelving=False)[0]\n", - "/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/joblib/memory.py:577: JobLibCollisionWarning: Possible name collisions between functions 'calculate_from_smi' (/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/optunaz/descriptors.py:-1) and 'calculate_from_smi' (/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/optunaz/descriptors.py:180)\n", - " return self._cached_call(args, kwargs, shelving=False)[0]\n", - "/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/joblib/memory.py:577: JobLibCollisionWarning: Possible name collisions between functions 'calculate_from_smi' (/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/optunaz/descriptors.py:-1) and 'calculate_from_smi' (/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/optunaz/descriptors.py:180)\n", - " return self._cached_call(args, kwargs, shelving=False)[0]\n", - "/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/joblib/memory.py:577: JobLibCollisionWarning: Possible name collisions between functions 'calculate_from_smi' (/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/optunaz/descriptors.py:-1) and 'calculate_from_smi' (/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/optunaz/descriptors.py:180)\n", - " return self._cached_call(args, kwargs, shelving=False)[0]\n", - "[I 2024-07-02 13:18:00,764] Trial 0 finished with value: -0.08099580623289632 and parameters: {'algorithm_name': 'PRFClassifier', 'PRFClassifier_algorithm_hash': 'efe0ba9870529a6cde0dd3ad22447cbb', 'max_depth__efe0ba9870529a6cde0dd3ad22447cbb': 13, 'n_estimators__efe0ba9870529a6cde0dd3ad22447cbb': 20, 'max_features__efe0ba9870529a6cde0dd3ad22447cbb': , 'min_py_sum_leaf__efe0ba9870529a6cde0dd3ad22447cbb': 5, 'use_py_gini__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'use_py_leafs__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 0 with value: -0.08099580623289632.\n", - "[I 2024-07-02 13:18:05,408] Trial 1 finished with value: -0.07261454017489567 and parameters: {'algorithm_name': 'PRFClassifier', 'PRFClassifier_algorithm_hash': 'efe0ba9870529a6cde0dd3ad22447cbb', 'max_depth__efe0ba9870529a6cde0dd3ad22447cbb': 6, 'n_estimators__efe0ba9870529a6cde0dd3ad22447cbb': 20, 'max_features__efe0ba9870529a6cde0dd3ad22447cbb': , 'min_py_sum_leaf__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'use_py_gini__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'use_py_leafs__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 1 with value: -0.07261454017489567.\n", - "[I 2024-07-02 13:18:07,780] Trial 2 finished with value: -0.08791063872794351 and parameters: {'algorithm_name': 'PRFClassifier', 'PRFClassifier_algorithm_hash': 'efe0ba9870529a6cde0dd3ad22447cbb', 'max_depth__efe0ba9870529a6cde0dd3ad22447cbb': 2, 'n_estimators__efe0ba9870529a6cde0dd3ad22447cbb': 20, 'max_features__efe0ba9870529a6cde0dd3ad22447cbb': , 'min_py_sum_leaf__efe0ba9870529a6cde0dd3ad22447cbb': 5, 'use_py_gini__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'use_py_leafs__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 1 with value: -0.07261454017489567.\n", - "[I 2024-07-02 13:18:11,911] Trial 3 finished with value: -0.07114663955819509 and parameters: {'algorithm_name': 'PRFClassifier', 'PRFClassifier_algorithm_hash': 'efe0ba9870529a6cde0dd3ad22447cbb', 'max_depth__efe0ba9870529a6cde0dd3ad22447cbb': 7, 'n_estimators__efe0ba9870529a6cde0dd3ad22447cbb': 20, 'max_features__efe0ba9870529a6cde0dd3ad22447cbb': , 'min_py_sum_leaf__efe0ba9870529a6cde0dd3ad22447cbb': 2, 'use_py_gini__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'use_py_leafs__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 3 with value: -0.07114663955819509.\n", - "[I 2024-07-02 13:18:15,879] Trial 4 finished with value: -0.06537440628140882 and parameters: {'algorithm_name': 'PRFClassifier', 'PRFClassifier_algorithm_hash': 'efe0ba9870529a6cde0dd3ad22447cbb', 'max_depth__efe0ba9870529a6cde0dd3ad22447cbb': 20, 'n_estimators__efe0ba9870529a6cde0dd3ad22447cbb': 20, 'max_features__efe0ba9870529a6cde0dd3ad22447cbb': , 'min_py_sum_leaf__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'use_py_gini__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'use_py_leafs__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 4 with value: -0.06537440628140882.\n", - "[I 2024-07-02 13:18:28,446] Trial 5 finished with value: -0.05680450487193368 and parameters: {'algorithm_name': 'PRFClassifier', 'PRFClassifier_algorithm_hash': 'efe0ba9870529a6cde0dd3ad22447cbb', 'max_depth__efe0ba9870529a6cde0dd3ad22447cbb': 26, 'n_estimators__efe0ba9870529a6cde0dd3ad22447cbb': 20, 'max_features__efe0ba9870529a6cde0dd3ad22447cbb': , 'min_py_sum_leaf__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'use_py_gini__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'use_py_leafs__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 5 with value: -0.05680450487193368.\n", - "[I 2024-07-02 13:18:29,968] Trial 6 pruned. Duplicate parameter set\n" + "[I 2024-07-02 16:03:21,869] A new study created in memory with name: my_study\n", + "[I 2024-07-02 16:03:21,870] A new study created in memory with name: study_name_0\n", + "[I 2024-07-02 16:03:24,369] Trial 0 finished with value: -0.08130897134023123 and parameters: {'algorithm_name': 'PRFClassifier', 'PRFClassifier_algorithm_hash': 'efe0ba9870529a6cde0dd3ad22447cbb', 'max_depth__efe0ba9870529a6cde0dd3ad22447cbb': 13, 'n_estimators__efe0ba9870529a6cde0dd3ad22447cbb': 20, 'max_features__efe0ba9870529a6cde0dd3ad22447cbb': , 'min_py_sum_leaf__efe0ba9870529a6cde0dd3ad22447cbb': 5, 'use_py_gini__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'use_py_leafs__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 0 with value: -0.08130897134023123.\n", + "[I 2024-07-02 16:03:29,080] Trial 1 finished with value: -0.07343360276296992 and parameters: {'algorithm_name': 'PRFClassifier', 'PRFClassifier_algorithm_hash': 'efe0ba9870529a6cde0dd3ad22447cbb', 'max_depth__efe0ba9870529a6cde0dd3ad22447cbb': 6, 'n_estimators__efe0ba9870529a6cde0dd3ad22447cbb': 20, 'max_features__efe0ba9870529a6cde0dd3ad22447cbb': , 'min_py_sum_leaf__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'use_py_gini__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'use_py_leafs__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 1 with value: -0.07343360276296992.\n", + "[I 2024-07-02 16:03:32,469] Trial 2 finished with value: -0.08824787163512451 and parameters: {'algorithm_name': 'PRFClassifier', 'PRFClassifier_algorithm_hash': 'efe0ba9870529a6cde0dd3ad22447cbb', 'max_depth__efe0ba9870529a6cde0dd3ad22447cbb': 2, 'n_estimators__efe0ba9870529a6cde0dd3ad22447cbb': 20, 'max_features__efe0ba9870529a6cde0dd3ad22447cbb': , 'min_py_sum_leaf__efe0ba9870529a6cde0dd3ad22447cbb': 5, 'use_py_gini__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'use_py_leafs__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 1 with value: -0.07343360276296992.\n", + "[I 2024-07-02 16:03:36,303] Trial 3 finished with value: -0.06946431925905228 and parameters: {'algorithm_name': 'PRFClassifier', 'PRFClassifier_algorithm_hash': 'efe0ba9870529a6cde0dd3ad22447cbb', 'max_depth__efe0ba9870529a6cde0dd3ad22447cbb': 7, 'n_estimators__efe0ba9870529a6cde0dd3ad22447cbb': 20, 'max_features__efe0ba9870529a6cde0dd3ad22447cbb': , 'min_py_sum_leaf__efe0ba9870529a6cde0dd3ad22447cbb': 2, 'use_py_gini__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'use_py_leafs__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 3 with value: -0.06946431925905228.\n", + "[I 2024-07-02 16:03:40,978] Trial 4 finished with value: -0.06871810141928683 and parameters: {'algorithm_name': 'PRFClassifier', 'PRFClassifier_algorithm_hash': 'efe0ba9870529a6cde0dd3ad22447cbb', 'max_depth__efe0ba9870529a6cde0dd3ad22447cbb': 20, 'n_estimators__efe0ba9870529a6cde0dd3ad22447cbb': 20, 'max_features__efe0ba9870529a6cde0dd3ad22447cbb': , 'min_py_sum_leaf__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'use_py_gini__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'use_py_leafs__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 4 with value: -0.06871810141928683.\n", + "[I 2024-07-02 16:03:52,341] Trial 5 finished with value: -0.05194238309203199 and parameters: {'algorithm_name': 'PRFClassifier', 'PRFClassifier_algorithm_hash': 'efe0ba9870529a6cde0dd3ad22447cbb', 'max_depth__efe0ba9870529a6cde0dd3ad22447cbb': 26, 'n_estimators__efe0ba9870529a6cde0dd3ad22447cbb': 20, 'max_features__efe0ba9870529a6cde0dd3ad22447cbb': , 'min_py_sum_leaf__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'use_py_gini__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'use_py_leafs__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 5 with value: -0.05194238309203199.\n", + "[I 2024-07-02 16:03:53,835] Trial 6 pruned. Duplicate parameter set\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "Duplicated trial: {'algorithm_name': 'PRFClassifier', 'PRFClassifier_algorithm_hash': 'efe0ba9870529a6cde0dd3ad22447cbb', 'max_depth__efe0ba9870529a6cde0dd3ad22447cbb': 20, 'n_estimators__efe0ba9870529a6cde0dd3ad22447cbb': 20, 'max_features__efe0ba9870529a6cde0dd3ad22447cbb': , 'min_py_sum_leaf__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'use_py_gini__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'use_py_leafs__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}, return [-0.06537440628140882]\n" + "Duplicated trial: {'algorithm_name': 'PRFClassifier', 'PRFClassifier_algorithm_hash': 'efe0ba9870529a6cde0dd3ad22447cbb', 'max_depth__efe0ba9870529a6cde0dd3ad22447cbb': 20, 'n_estimators__efe0ba9870529a6cde0dd3ad22447cbb': 20, 'max_features__efe0ba9870529a6cde0dd3ad22447cbb': , 'min_py_sum_leaf__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'use_py_gini__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'use_py_leafs__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}, return [-0.06871810141928683]\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "[I 2024-07-02 13:18:33,543] Trial 7 finished with value: -0.0656836821774901 and parameters: {'algorithm_name': 'PRFClassifier', 'PRFClassifier_algorithm_hash': 'efe0ba9870529a6cde0dd3ad22447cbb', 'max_depth__efe0ba9870529a6cde0dd3ad22447cbb': 27, 'n_estimators__efe0ba9870529a6cde0dd3ad22447cbb': 20, 'max_features__efe0ba9870529a6cde0dd3ad22447cbb': , 'min_py_sum_leaf__efe0ba9870529a6cde0dd3ad22447cbb': 2, 'use_py_gini__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'use_py_leafs__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 5 with value: -0.05680450487193368.\n", - "[I 2024-07-02 13:18:37,333] Trial 8 finished with value: -0.07863564862376404 and parameters: {'algorithm_name': 'PRFClassifier', 'PRFClassifier_algorithm_hash': 'efe0ba9870529a6cde0dd3ad22447cbb', 'max_depth__efe0ba9870529a6cde0dd3ad22447cbb': 5, 'n_estimators__efe0ba9870529a6cde0dd3ad22447cbb': 20, 'max_features__efe0ba9870529a6cde0dd3ad22447cbb': , 'min_py_sum_leaf__efe0ba9870529a6cde0dd3ad22447cbb': 3, 'use_py_gini__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'use_py_leafs__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 5 with value: -0.05680450487193368.\n", - "[I 2024-07-02 13:18:42,329] Trial 9 finished with value: -0.0648840199215795 and parameters: {'algorithm_name': 'PRFClassifier', 'PRFClassifier_algorithm_hash': 'efe0ba9870529a6cde0dd3ad22447cbb', 'max_depth__efe0ba9870529a6cde0dd3ad22447cbb': 22, 'n_estimators__efe0ba9870529a6cde0dd3ad22447cbb': 20, 'max_features__efe0ba9870529a6cde0dd3ad22447cbb': , 'min_py_sum_leaf__efe0ba9870529a6cde0dd3ad22447cbb': 2, 'use_py_gini__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'use_py_leafs__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 5 with value: -0.05680450487193368.\n", - "[I 2024-07-02 13:18:46,014] Trial 10 finished with value: -0.07861037073288182 and parameters: {'algorithm_name': 'PRFClassifier', 'PRFClassifier_algorithm_hash': 'efe0ba9870529a6cde0dd3ad22447cbb', 'max_depth__efe0ba9870529a6cde0dd3ad22447cbb': 32, 'n_estimators__efe0ba9870529a6cde0dd3ad22447cbb': 20, 'max_features__efe0ba9870529a6cde0dd3ad22447cbb': , 'min_py_sum_leaf__efe0ba9870529a6cde0dd3ad22447cbb': 4, 'use_py_gini__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'use_py_leafs__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 5 with value: -0.05680450487193368.\n", - "[I 2024-07-02 13:18:50,608] Trial 11 finished with value: -0.06669924317660021 and parameters: {'algorithm_name': 'PRFClassifier', 'PRFClassifier_algorithm_hash': 'efe0ba9870529a6cde0dd3ad22447cbb', 'max_depth__efe0ba9870529a6cde0dd3ad22447cbb': 30, 'n_estimators__efe0ba9870529a6cde0dd3ad22447cbb': 20, 'max_features__efe0ba9870529a6cde0dd3ad22447cbb': , 'min_py_sum_leaf__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'use_py_gini__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'use_py_leafs__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 5 with value: -0.05680450487193368.\n", - "[I 2024-07-02 13:18:54,997] Trial 12 finished with value: -0.06734611679947522 and parameters: {'algorithm_name': 'PRFClassifier', 'PRFClassifier_algorithm_hash': 'efe0ba9870529a6cde0dd3ad22447cbb', 'max_depth__efe0ba9870529a6cde0dd3ad22447cbb': 14, 'n_estimators__efe0ba9870529a6cde0dd3ad22447cbb': 20, 'max_features__efe0ba9870529a6cde0dd3ad22447cbb': , 'min_py_sum_leaf__efe0ba9870529a6cde0dd3ad22447cbb': 2, 'use_py_gini__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'use_py_leafs__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 5 with value: -0.05680450487193368.\n", - "[I 2024-07-02 13:18:59,526] Trial 13 finished with value: -0.06810559387741143 and parameters: {'algorithm_name': 'PRFClassifier', 'PRFClassifier_algorithm_hash': 'efe0ba9870529a6cde0dd3ad22447cbb', 'max_depth__efe0ba9870529a6cde0dd3ad22447cbb': 18, 'n_estimators__efe0ba9870529a6cde0dd3ad22447cbb': 20, 'max_features__efe0ba9870529a6cde0dd3ad22447cbb': , 'min_py_sum_leaf__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'use_py_gini__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'use_py_leafs__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 5 with value: -0.05680450487193368.\n", - "[I 2024-07-02 13:19:11,856] Trial 14 finished with value: -0.0528189695245453 and parameters: {'algorithm_name': 'PRFClassifier', 'PRFClassifier_algorithm_hash': 'efe0ba9870529a6cde0dd3ad22447cbb', 'max_depth__efe0ba9870529a6cde0dd3ad22447cbb': 25, 'n_estimators__efe0ba9870529a6cde0dd3ad22447cbb': 20, 'max_features__efe0ba9870529a6cde0dd3ad22447cbb': , 'min_py_sum_leaf__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'use_py_gini__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'use_py_leafs__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 14 with value: -0.0528189695245453.\n" + "[I 2024-07-02 16:03:58,626] Trial 7 finished with value: -0.06827558910154698 and parameters: {'algorithm_name': 'PRFClassifier', 'PRFClassifier_algorithm_hash': 'efe0ba9870529a6cde0dd3ad22447cbb', 'max_depth__efe0ba9870529a6cde0dd3ad22447cbb': 27, 'n_estimators__efe0ba9870529a6cde0dd3ad22447cbb': 20, 'max_features__efe0ba9870529a6cde0dd3ad22447cbb': , 'min_py_sum_leaf__efe0ba9870529a6cde0dd3ad22447cbb': 2, 'use_py_gini__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'use_py_leafs__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 5 with value: -0.05194238309203199.\n", + "[I 2024-07-02 16:04:01,350] Trial 8 finished with value: -0.0753121162867017 and parameters: {'algorithm_name': 'PRFClassifier', 'PRFClassifier_algorithm_hash': 'efe0ba9870529a6cde0dd3ad22447cbb', 'max_depth__efe0ba9870529a6cde0dd3ad22447cbb': 5, 'n_estimators__efe0ba9870529a6cde0dd3ad22447cbb': 20, 'max_features__efe0ba9870529a6cde0dd3ad22447cbb': , 'min_py_sum_leaf__efe0ba9870529a6cde0dd3ad22447cbb': 3, 'use_py_gini__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'use_py_leafs__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 5 with value: -0.05194238309203199.\n", + "[I 2024-07-02 16:04:06,268] Trial 9 finished with value: -0.06780438903573768 and parameters: {'algorithm_name': 'PRFClassifier', 'PRFClassifier_algorithm_hash': 'efe0ba9870529a6cde0dd3ad22447cbb', 'max_depth__efe0ba9870529a6cde0dd3ad22447cbb': 22, 'n_estimators__efe0ba9870529a6cde0dd3ad22447cbb': 20, 'max_features__efe0ba9870529a6cde0dd3ad22447cbb': , 'min_py_sum_leaf__efe0ba9870529a6cde0dd3ad22447cbb': 2, 'use_py_gini__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'use_py_leafs__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 5 with value: -0.05194238309203199.\n", + "[I 2024-07-02 16:04:09,960] Trial 10 finished with value: -0.07776700667235492 and parameters: {'algorithm_name': 'PRFClassifier', 'PRFClassifier_algorithm_hash': 'efe0ba9870529a6cde0dd3ad22447cbb', 'max_depth__efe0ba9870529a6cde0dd3ad22447cbb': 32, 'n_estimators__efe0ba9870529a6cde0dd3ad22447cbb': 20, 'max_features__efe0ba9870529a6cde0dd3ad22447cbb': , 'min_py_sum_leaf__efe0ba9870529a6cde0dd3ad22447cbb': 4, 'use_py_gini__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'use_py_leafs__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 5 with value: -0.05194238309203199.\n", + "[I 2024-07-02 16:04:14,207] Trial 11 finished with value: -0.0669981340211799 and parameters: {'algorithm_name': 'PRFClassifier', 'PRFClassifier_algorithm_hash': 'efe0ba9870529a6cde0dd3ad22447cbb', 'max_depth__efe0ba9870529a6cde0dd3ad22447cbb': 30, 'n_estimators__efe0ba9870529a6cde0dd3ad22447cbb': 20, 'max_features__efe0ba9870529a6cde0dd3ad22447cbb': , 'min_py_sum_leaf__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'use_py_gini__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'use_py_leafs__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 5 with value: -0.05194238309203199.\n", + "[I 2024-07-02 16:04:18,747] Trial 12 finished with value: -0.0631730323000491 and parameters: {'algorithm_name': 'PRFClassifier', 'PRFClassifier_algorithm_hash': 'efe0ba9870529a6cde0dd3ad22447cbb', 'max_depth__efe0ba9870529a6cde0dd3ad22447cbb': 14, 'n_estimators__efe0ba9870529a6cde0dd3ad22447cbb': 20, 'max_features__efe0ba9870529a6cde0dd3ad22447cbb': , 'min_py_sum_leaf__efe0ba9870529a6cde0dd3ad22447cbb': 2, 'use_py_gini__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'use_py_leafs__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 5 with value: -0.05194238309203199.\n", + "[I 2024-07-02 16:04:23,231] Trial 13 finished with value: -0.07284403955164376 and parameters: {'algorithm_name': 'PRFClassifier', 'PRFClassifier_algorithm_hash': 'efe0ba9870529a6cde0dd3ad22447cbb', 'max_depth__efe0ba9870529a6cde0dd3ad22447cbb': 18, 'n_estimators__efe0ba9870529a6cde0dd3ad22447cbb': 20, 'max_features__efe0ba9870529a6cde0dd3ad22447cbb': , 'min_py_sum_leaf__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'use_py_gini__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'use_py_leafs__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 5 with value: -0.05194238309203199.\n", + "[I 2024-07-02 16:04:35,164] Trial 14 finished with value: -0.056585161860849706 and parameters: {'algorithm_name': 'PRFClassifier', 'PRFClassifier_algorithm_hash': 'efe0ba9870529a6cde0dd3ad22447cbb', 'max_depth__efe0ba9870529a6cde0dd3ad22447cbb': 25, 'n_estimators__efe0ba9870529a6cde0dd3ad22447cbb': 20, 'max_features__efe0ba9870529a6cde0dd3ad22447cbb': , 'min_py_sum_leaf__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'use_py_gini__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'use_py_leafs__efe0ba9870529a6cde0dd3ad22447cbb': 1, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 5 with value: -0.05194238309203199.\n" ] } ], @@ -1642,7 +1615,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", 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", 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", 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" ] @@ -1738,9 +1711,9 @@ "name": "stderr", "output_type": "stream", "text": [ - "[I 2024-07-02 13:19:17,760] A new study created in memory with name: my_study\n", - "[I 2024-07-02 13:19:17,800] A new study created in memory with name: study_name_0\n", - "[W 2024-07-02 13:19:17,801] Trial 0 failed with parameters: {} because of the following error: ValueError('PRFClassifier supplied but response column outside [0.0-1.0] acceptable range. Response max: 9.7, response min: 5.3 ').\n", + "[I 2024-07-02 16:04:43,555] A new study created in memory with name: my_study\n", + "[I 2024-07-02 16:04:43,595] A new study created in memory with name: study_name_0\n", + "[W 2024-07-02 16:04:43,596] Trial 0 failed with parameters: {} because of the following error: ValueError('PRFClassifier supplied but response column outside [0.0-1.0] acceptable range. Response max: 9.7, response min: 5.3 ').\n", "Traceback (most recent call last):\n", " File \"/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/optuna/study/_optimize.py\", line 196, in _run_trial\n", " value_or_values = func(trial)\n", @@ -1749,7 +1722,7 @@ " File \"/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/optunaz/objective.py\", line 264, in _validate_algos\n", " raise ValueError(\n", "ValueError: PRFClassifier supplied but response column outside [0.0-1.0] acceptable range. Response max: 9.7, response min: 5.3 \n", - "[W 2024-07-02 13:19:17,807] Trial 0 failed with value None.\n" + "[W 2024-07-02 16:04:43,603] Trial 0 failed with value None.\n" ] }, { @@ -1868,11 +1841,11 @@ "name": "stderr", "output_type": "stream", "text": [ - "[I 2024-07-02 13:19:17,867] A new study created in memory with name: my_study\n", - "[I 2024-07-02 13:19:17,868] A new study created in memory with name: study_name_0\n", + "[I 2024-07-02 16:04:43,669] A new study created in memory with name: my_study\n", + "[I 2024-07-02 16:04:43,670] A new study created in memory with name: study_name_0\n", "INFO:root:Enqueued ChemProp manual trial with sensible defaults: {'activation__668a7428ff5cdb271b01c0925e8fea45': 'ReLU', 'aggregation__668a7428ff5cdb271b01c0925e8fea45': 'mean', 'aggregation_norm__668a7428ff5cdb271b01c0925e8fea45': 100, 'batch_size__668a7428ff5cdb271b01c0925e8fea45': 50, 'depth__668a7428ff5cdb271b01c0925e8fea45': 3, 'dropout__668a7428ff5cdb271b01c0925e8fea45': 0.0, 'features_generator__668a7428ff5cdb271b01c0925e8fea45': 'none', 'ffn_hidden_size__668a7428ff5cdb271b01c0925e8fea45': 300, 'ffn_num_layers__668a7428ff5cdb271b01c0925e8fea45': 2, 'final_lr_ratio_exp__668a7428ff5cdb271b01c0925e8fea45': -4, 'hidden_size__668a7428ff5cdb271b01c0925e8fea45': 300, 'init_lr_ratio_exp__668a7428ff5cdb271b01c0925e8fea45': -4, 'max_lr_exp__668a7428ff5cdb271b01c0925e8fea45': -3, 'warmup_epochs_ratio__668a7428ff5cdb271b01c0925e8fea45': 0.1, 'algorithm_name': 'ChemPropRegressor', 'ChemPropRegressor_algorithm_hash': '668a7428ff5cdb271b01c0925e8fea45'}\n", - "[I 2024-07-02 13:20:11,301] Trial 0 finished with value: -6833.034983241957 and parameters: {'algorithm_name': 'ChemPropRegressor', 'ChemPropRegressor_algorithm_hash': '668a7428ff5cdb271b01c0925e8fea45', 'activation__668a7428ff5cdb271b01c0925e8fea45': , 'aggregation__668a7428ff5cdb271b01c0925e8fea45': , 'aggregation_norm__668a7428ff5cdb271b01c0925e8fea45': 100.0, 'batch_size__668a7428ff5cdb271b01c0925e8fea45': 50.0, 'depth__668a7428ff5cdb271b01c0925e8fea45': 3.0, 'dropout__668a7428ff5cdb271b01c0925e8fea45': 0.0, 'ensemble_size__668a7428ff5cdb271b01c0925e8fea45': 1, 'epochs__668a7428ff5cdb271b01c0925e8fea45': 5, 'features_generator__668a7428ff5cdb271b01c0925e8fea45': , 'ffn_hidden_size__668a7428ff5cdb271b01c0925e8fea45': 300.0, 'ffn_num_layers__668a7428ff5cdb271b01c0925e8fea45': 2.0, 'final_lr_ratio_exp__668a7428ff5cdb271b01c0925e8fea45': -4, 'hidden_size__668a7428ff5cdb271b01c0925e8fea45': 300.0, 'init_lr_ratio_exp__668a7428ff5cdb271b01c0925e8fea45': -4, 'max_lr_exp__668a7428ff5cdb271b01c0925e8fea45': -3, 'warmup_epochs_ratio__668a7428ff5cdb271b01c0925e8fea45': 0.1, 'descriptor': '{\"name\": \"SmilesFromFile\", \"parameters\": {}}'}. Best is trial 0 with value: -6833.034983241957.\n", - "[I 2024-07-02 13:21:02,026] Trial 1 finished with value: -6445.608102397302 and parameters: {'algorithm_name': 'ChemPropRegressor', 'ChemPropRegressor_algorithm_hash': '668a7428ff5cdb271b01c0925e8fea45', 'activation__668a7428ff5cdb271b01c0925e8fea45': , 'aggregation__668a7428ff5cdb271b01c0925e8fea45': , 'aggregation_norm__668a7428ff5cdb271b01c0925e8fea45': 78.0, 'batch_size__668a7428ff5cdb271b01c0925e8fea45': 105.0, 'depth__668a7428ff5cdb271b01c0925e8fea45': 5.0, 'dropout__668a7428ff5cdb271b01c0925e8fea45': 0.16, 'ensemble_size__668a7428ff5cdb271b01c0925e8fea45': 1, 'epochs__668a7428ff5cdb271b01c0925e8fea45': 5, 'features_generator__668a7428ff5cdb271b01c0925e8fea45': , 'ffn_hidden_size__668a7428ff5cdb271b01c0925e8fea45': 1700.0, 'ffn_num_layers__668a7428ff5cdb271b01c0925e8fea45': 3.0, 'final_lr_ratio_exp__668a7428ff5cdb271b01c0925e8fea45': -2, 'hidden_size__668a7428ff5cdb271b01c0925e8fea45': 2300.0, 'init_lr_ratio_exp__668a7428ff5cdb271b01c0925e8fea45': -2, 'max_lr_exp__668a7428ff5cdb271b01c0925e8fea45': -3, 'warmup_epochs_ratio__668a7428ff5cdb271b01c0925e8fea45': 0.1, 'descriptor': '{\"name\": \"SmilesFromFile\", \"parameters\": {}}'}. Best is trial 1 with value: -6445.608102397302.\n" + "[I 2024-07-02 16:05:32,125] Trial 0 finished with value: -6833.034983241957 and parameters: {'algorithm_name': 'ChemPropRegressor', 'ChemPropRegressor_algorithm_hash': '668a7428ff5cdb271b01c0925e8fea45', 'activation__668a7428ff5cdb271b01c0925e8fea45': , 'aggregation__668a7428ff5cdb271b01c0925e8fea45': , 'aggregation_norm__668a7428ff5cdb271b01c0925e8fea45': 100.0, 'batch_size__668a7428ff5cdb271b01c0925e8fea45': 50.0, 'depth__668a7428ff5cdb271b01c0925e8fea45': 3.0, 'dropout__668a7428ff5cdb271b01c0925e8fea45': 0.0, 'ensemble_size__668a7428ff5cdb271b01c0925e8fea45': 1, 'epochs__668a7428ff5cdb271b01c0925e8fea45': 5, 'features_generator__668a7428ff5cdb271b01c0925e8fea45': , 'ffn_hidden_size__668a7428ff5cdb271b01c0925e8fea45': 300.0, 'ffn_num_layers__668a7428ff5cdb271b01c0925e8fea45': 2.0, 'final_lr_ratio_exp__668a7428ff5cdb271b01c0925e8fea45': -4, 'hidden_size__668a7428ff5cdb271b01c0925e8fea45': 300.0, 'init_lr_ratio_exp__668a7428ff5cdb271b01c0925e8fea45': -4, 'max_lr_exp__668a7428ff5cdb271b01c0925e8fea45': -3, 'warmup_epochs_ratio__668a7428ff5cdb271b01c0925e8fea45': 0.1, 'descriptor': '{\"name\": \"SmilesFromFile\", \"parameters\": {}}'}. Best is trial 0 with value: -6833.034983241957.\n", + "[I 2024-07-02 16:06:17,937] Trial 1 finished with value: -6793.886041846807 and parameters: {'algorithm_name': 'ChemPropRegressor', 'ChemPropRegressor_algorithm_hash': '668a7428ff5cdb271b01c0925e8fea45', 'activation__668a7428ff5cdb271b01c0925e8fea45': , 'aggregation__668a7428ff5cdb271b01c0925e8fea45': , 'aggregation_norm__668a7428ff5cdb271b01c0925e8fea45': 61.0, 'batch_size__668a7428ff5cdb271b01c0925e8fea45': 100.0, 'depth__668a7428ff5cdb271b01c0925e8fea45': 3.0, 'dropout__668a7428ff5cdb271b01c0925e8fea45': 0.12, 'ensemble_size__668a7428ff5cdb271b01c0925e8fea45': 1, 'epochs__668a7428ff5cdb271b01c0925e8fea45': 5, 'features_generator__668a7428ff5cdb271b01c0925e8fea45': , 'ffn_hidden_size__668a7428ff5cdb271b01c0925e8fea45': 900.0, 'ffn_num_layers__668a7428ff5cdb271b01c0925e8fea45': 3.0, 'final_lr_ratio_exp__668a7428ff5cdb271b01c0925e8fea45': 0, 'hidden_size__668a7428ff5cdb271b01c0925e8fea45': 800.0, 'init_lr_ratio_exp__668a7428ff5cdb271b01c0925e8fea45': -3, 'max_lr_exp__668a7428ff5cdb271b01c0925e8fea45': -6, 'warmup_epochs_ratio__668a7428ff5cdb271b01c0925e8fea45': 0.1, 'descriptor': '{\"name\": \"SmilesFromFile\", \"parameters\": {}}'}. Best is trial 1 with value: -6793.886041846807.\n" ] } ], @@ -2026,7 +1999,7 @@ "\n", "`ChemProp` models trained using Hyperopt use the [original implementation](https://chemprop.readthedocs.io/en/latest/hyperopt.html), but one key difference is the `search_parameter_level` setting created for `QSARtuna`; Instead of using pre-defined search spaces as in the original package, `QSARtuna` can (and will by the default since `search_parameter_level`=`auto` unless changed) alter the space depending on the characteristics of user input data. For example, no. training set compounds, hyperparameter trials (`num_iters`) & epochs (`epochs`) are used by the `auto` setting to ensure search spaces are not too large for limited data/epochs, and _vice-versa_, an extensive search space is trailed when applicable.\n", "\n", - "N.B: Users can also manually define `Hyperopt` search spaces by altering `search_parameter_level` from `auto` to a different level between `[0-8]`, representing the increasing search space size (see the [QSARtuna documentation](https://pages.scp.astrazeneca.net/mai/qsartuna/optunaz.config.html#optunaz.config.optconfig.ChemPropSearch_Parameter_Level) for details)." + "N.B: Users can also manually define `Hyperopt` search spaces by altering `search_parameter_level` from `auto` to a different level between `[0-8]`, representing the increasing search space size (see the [QSARtuna documentation](https://molecularai.github.io/QSARtuna/optunaz.config.html#optunaz.config.optconfig.ChemPropSearch_Parameter_Level) for details)." ] }, { @@ -2168,26 +2141,26 @@ "name": "stderr", "output_type": "stream", "text": [ - "[I 2024-07-02 13:21:03,347] A new study created in memory with name: my_study\n", - "[I 2024-07-02 13:21:03,350] A new study created in memory with name: study_name_0\n", - "[I 2024-07-02 13:21:05,443] Trial 0 finished with value: -5817.944008002311 and parameters: {'algorithm_name': 'ChemPropHyperoptRegressor', 'ChemPropHyperoptRegressor_algorithm_hash': 'db9e60f9b8f0a43eff4b41917b6293d9', 'ensemble_size__db9e60f9b8f0a43eff4b41917b6293d9': 1, 'epochs__db9e60f9b8f0a43eff4b41917b6293d9': 4, 'features_generator__db9e60f9b8f0a43eff4b41917b6293d9': , 'num_iters__db9e60f9b8f0a43eff4b41917b6293d9': 1, 'search_parameter_level__db9e60f9b8f0a43eff4b41917b6293d9': , 'descriptor': '{\"name\": \"SmilesAndSideInfoFromFile\", \"parameters\": {\"file\": \"../tests/data/DRD2/subset-50/train_side_info.csv\", \"input_column\": \"canonical\", \"aux_weight_pc\": {\"low\": 0, \"high\": 100, \"q\": 10}}}', 'aux_weight_pc__db9e60f9b8f0a43eff4b41917b6293d9': 50}. Best is trial 0 with value: -5817.944008002311.\n", - "[I 2024-07-02 13:21:05,495] Trial 1 pruned. Duplicate parameter set\n" + "[I 2024-07-02 16:06:19,217] A new study created in memory with name: my_study\n", + "[I 2024-07-02 16:06:19,219] A new study created in memory with name: study_name_0\n", + "[I 2024-07-02 16:06:21,170] Trial 0 finished with value: -5817.944430632202 and parameters: {'algorithm_name': 'ChemPropHyperoptRegressor', 'ChemPropHyperoptRegressor_algorithm_hash': 'db9e60f9b8f0a43eff4b41917b6293d9', 'ensemble_size__db9e60f9b8f0a43eff4b41917b6293d9': 1, 'epochs__db9e60f9b8f0a43eff4b41917b6293d9': 4, 'features_generator__db9e60f9b8f0a43eff4b41917b6293d9': , 'num_iters__db9e60f9b8f0a43eff4b41917b6293d9': 1, 'search_parameter_level__db9e60f9b8f0a43eff4b41917b6293d9': , 'descriptor': '{\"name\": \"SmilesAndSideInfoFromFile\", \"parameters\": {\"file\": \"../tests/data/DRD2/subset-50/train_side_info.csv\", \"input_column\": \"canonical\", \"aux_weight_pc\": {\"low\": 0, \"high\": 100, \"q\": 10}}}', 'aux_weight_pc__db9e60f9b8f0a43eff4b41917b6293d9': 50}. Best is trial 0 with value: -5817.944430632202.\n", + "[I 2024-07-02 16:06:21,224] Trial 1 pruned. Duplicate parameter set\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "Duplicated trial: {'algorithm_name': 'ChemPropHyperoptRegressor', 'ChemPropHyperoptRegressor_algorithm_hash': 'db9e60f9b8f0a43eff4b41917b6293d9', 'ensemble_size__db9e60f9b8f0a43eff4b41917b6293d9': 1, 'epochs__db9e60f9b8f0a43eff4b41917b6293d9': 4, 'features_generator__db9e60f9b8f0a43eff4b41917b6293d9': , 'num_iters__db9e60f9b8f0a43eff4b41917b6293d9': 1, 'search_parameter_level__db9e60f9b8f0a43eff4b41917b6293d9': , 'descriptor': '{\"name\": \"SmilesAndSideInfoFromFile\", \"parameters\": {\"file\": \"../tests/data/DRD2/subset-50/train_side_info.csv\", \"input_column\": \"canonical\", \"aux_weight_pc\": {\"low\": 0, \"high\": 100, \"q\": 10}}}', 'aux_weight_pc__db9e60f9b8f0a43eff4b41917b6293d9': 50}, return [-5817.944008002311]\n" + "Duplicated trial: {'algorithm_name': 'ChemPropHyperoptRegressor', 'ChemPropHyperoptRegressor_algorithm_hash': 'db9e60f9b8f0a43eff4b41917b6293d9', 'ensemble_size__db9e60f9b8f0a43eff4b41917b6293d9': 1, 'epochs__db9e60f9b8f0a43eff4b41917b6293d9': 4, 'features_generator__db9e60f9b8f0a43eff4b41917b6293d9': , 'num_iters__db9e60f9b8f0a43eff4b41917b6293d9': 1, 'search_parameter_level__db9e60f9b8f0a43eff4b41917b6293d9': , 'descriptor': '{\"name\": \"SmilesAndSideInfoFromFile\", \"parameters\": {\"file\": \"../tests/data/DRD2/subset-50/train_side_info.csv\", \"input_column\": \"canonical\", \"aux_weight_pc\": {\"low\": 0, \"high\": 100, \"q\": 10}}}', 'aux_weight_pc__db9e60f9b8f0a43eff4b41917b6293d9': 50}, return [-5817.944430632202]\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "[I 2024-07-02 13:21:07,433] Trial 2 finished with value: -5796.34392897437 and parameters: {'algorithm_name': 'ChemPropHyperoptRegressor', 'ChemPropHyperoptRegressor_algorithm_hash': 'db9e60f9b8f0a43eff4b41917b6293d9', 'ensemble_size__db9e60f9b8f0a43eff4b41917b6293d9': 1, 'epochs__db9e60f9b8f0a43eff4b41917b6293d9': 4, 'features_generator__db9e60f9b8f0a43eff4b41917b6293d9': , 'num_iters__db9e60f9b8f0a43eff4b41917b6293d9': 1, 'search_parameter_level__db9e60f9b8f0a43eff4b41917b6293d9': , 'descriptor': '{\"name\": \"SmilesAndSideInfoFromFile\", \"parameters\": {\"file\": \"../tests/data/DRD2/subset-50/train_side_info.csv\", \"input_column\": \"canonical\", \"aux_weight_pc\": {\"low\": 0, \"high\": 100, \"q\": 10}}}', 'aux_weight_pc__db9e60f9b8f0a43eff4b41917b6293d9': 80}. Best is trial 2 with value: -5796.34392897437.\n", - "[I 2024-07-02 13:21:09,439] Trial 3 finished with value: -5795.086720713623 and parameters: {'algorithm_name': 'ChemPropHyperoptRegressor', 'ChemPropHyperoptRegressor_algorithm_hash': 'db9e60f9b8f0a43eff4b41917b6293d9', 'ensemble_size__db9e60f9b8f0a43eff4b41917b6293d9': 1, 'epochs__db9e60f9b8f0a43eff4b41917b6293d9': 4, 'features_generator__db9e60f9b8f0a43eff4b41917b6293d9': , 'num_iters__db9e60f9b8f0a43eff4b41917b6293d9': 1, 'search_parameter_level__db9e60f9b8f0a43eff4b41917b6293d9': , 'descriptor': '{\"name\": \"SmilesAndSideInfoFromFile\", \"parameters\": {\"file\": \"../tests/data/DRD2/subset-50/train_side_info.csv\", \"input_column\": \"canonical\", \"aux_weight_pc\": {\"low\": 0, \"high\": 100, \"q\": 10}}}', 'aux_weight_pc__db9e60f9b8f0a43eff4b41917b6293d9': 100}. Best is trial 3 with value: -5795.086720713623.\n", - "[I 2024-07-02 13:21:09,470] Trial 4 pruned. Duplicate parameter set\n" + "[I 2024-07-02 16:06:23,195] Trial 2 finished with value: -5796.343328806865 and parameters: {'algorithm_name': 'ChemPropHyperoptRegressor', 'ChemPropHyperoptRegressor_algorithm_hash': 'db9e60f9b8f0a43eff4b41917b6293d9', 'ensemble_size__db9e60f9b8f0a43eff4b41917b6293d9': 1, 'epochs__db9e60f9b8f0a43eff4b41917b6293d9': 4, 'features_generator__db9e60f9b8f0a43eff4b41917b6293d9': , 'num_iters__db9e60f9b8f0a43eff4b41917b6293d9': 1, 'search_parameter_level__db9e60f9b8f0a43eff4b41917b6293d9': , 'descriptor': '{\"name\": \"SmilesAndSideInfoFromFile\", \"parameters\": {\"file\": \"../tests/data/DRD2/subset-50/train_side_info.csv\", \"input_column\": \"canonical\", \"aux_weight_pc\": {\"low\": 0, \"high\": 100, \"q\": 10}}}', 'aux_weight_pc__db9e60f9b8f0a43eff4b41917b6293d9': 80}. Best is trial 2 with value: -5796.343328806865.\n", + "[I 2024-07-02 16:06:25,119] Trial 3 finished with value: -5795.086720713623 and parameters: {'algorithm_name': 'ChemPropHyperoptRegressor', 'ChemPropHyperoptRegressor_algorithm_hash': 'db9e60f9b8f0a43eff4b41917b6293d9', 'ensemble_size__db9e60f9b8f0a43eff4b41917b6293d9': 1, 'epochs__db9e60f9b8f0a43eff4b41917b6293d9': 4, 'features_generator__db9e60f9b8f0a43eff4b41917b6293d9': , 'num_iters__db9e60f9b8f0a43eff4b41917b6293d9': 1, 'search_parameter_level__db9e60f9b8f0a43eff4b41917b6293d9': , 'descriptor': '{\"name\": \"SmilesAndSideInfoFromFile\", \"parameters\": {\"file\": \"../tests/data/DRD2/subset-50/train_side_info.csv\", \"input_column\": \"canonical\", \"aux_weight_pc\": {\"low\": 0, \"high\": 100, \"q\": 10}}}', 'aux_weight_pc__db9e60f9b8f0a43eff4b41917b6293d9': 100}. Best is trial 3 with value: -5795.086720713623.\n", + "[I 2024-07-02 16:06:25,151] Trial 4 pruned. Duplicate parameter set\n" ] }, { @@ -2201,8 +2174,8 @@ "name": "stderr", "output_type": "stream", "text": [ - "[I 2024-07-02 13:21:11,241] Trial 5 finished with value: -5820.227555999914 and parameters: {'algorithm_name': 'ChemPropHyperoptRegressor', 'ChemPropHyperoptRegressor_algorithm_hash': 'db9e60f9b8f0a43eff4b41917b6293d9', 'ensemble_size__db9e60f9b8f0a43eff4b41917b6293d9': 1, 'epochs__db9e60f9b8f0a43eff4b41917b6293d9': 4, 'features_generator__db9e60f9b8f0a43eff4b41917b6293d9': , 'num_iters__db9e60f9b8f0a43eff4b41917b6293d9': 1, 'search_parameter_level__db9e60f9b8f0a43eff4b41917b6293d9': , 'descriptor': '{\"name\": \"SmilesAndSideInfoFromFile\", \"parameters\": {\"file\": \"../tests/data/DRD2/subset-50/train_side_info.csv\", \"input_column\": \"canonical\", \"aux_weight_pc\": {\"low\": 0, \"high\": 100, \"q\": 10}}}', 'aux_weight_pc__db9e60f9b8f0a43eff4b41917b6293d9': 0}. Best is trial 3 with value: -5795.086720713623.\n", - "[I 2024-07-02 13:21:11,283] Trial 6 pruned. Duplicate parameter set\n" + "[I 2024-07-02 16:06:27,065] Trial 5 finished with value: -5820.227555999914 and parameters: {'algorithm_name': 'ChemPropHyperoptRegressor', 'ChemPropHyperoptRegressor_algorithm_hash': 'db9e60f9b8f0a43eff4b41917b6293d9', 'ensemble_size__db9e60f9b8f0a43eff4b41917b6293d9': 1, 'epochs__db9e60f9b8f0a43eff4b41917b6293d9': 4, 'features_generator__db9e60f9b8f0a43eff4b41917b6293d9': , 'num_iters__db9e60f9b8f0a43eff4b41917b6293d9': 1, 'search_parameter_level__db9e60f9b8f0a43eff4b41917b6293d9': , 'descriptor': '{\"name\": \"SmilesAndSideInfoFromFile\", \"parameters\": {\"file\": \"../tests/data/DRD2/subset-50/train_side_info.csv\", \"input_column\": \"canonical\", \"aux_weight_pc\": {\"low\": 0, \"high\": 100, \"q\": 10}}}', 'aux_weight_pc__db9e60f9b8f0a43eff4b41917b6293d9': 0}. Best is trial 3 with value: -5795.086720713623.\n", + "[I 2024-07-02 16:06:27,094] Trial 6 pruned. Duplicate parameter set\n" ] }, { @@ -2216,7 +2189,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "[I 2024-07-02 13:21:13,322] Trial 7 finished with value: -5852.160071204277 and parameters: {'algorithm_name': 'ChemPropHyperoptRegressor', 'ChemPropHyperoptRegressor_algorithm_hash': 'db9e60f9b8f0a43eff4b41917b6293d9', 'ensemble_size__db9e60f9b8f0a43eff4b41917b6293d9': 1, 'epochs__db9e60f9b8f0a43eff4b41917b6293d9': 4, 'features_generator__db9e60f9b8f0a43eff4b41917b6293d9': , 'num_iters__db9e60f9b8f0a43eff4b41917b6293d9': 1, 'search_parameter_level__db9e60f9b8f0a43eff4b41917b6293d9': , 'descriptor': '{\"name\": \"SmilesAndSideInfoFromFile\", \"parameters\": {\"file\": \"../tests/data/DRD2/subset-50/train_side_info.csv\", \"input_column\": \"canonical\", \"aux_weight_pc\": {\"low\": 0, \"high\": 100, \"q\": 10}}}', 'aux_weight_pc__db9e60f9b8f0a43eff4b41917b6293d9': 10}. Best is trial 3 with value: -5795.086720713623.\n" + "[I 2024-07-02 16:06:29,109] Trial 7 finished with value: -5852.16017644995 and parameters: {'algorithm_name': 'ChemPropHyperoptRegressor', 'ChemPropHyperoptRegressor_algorithm_hash': 'db9e60f9b8f0a43eff4b41917b6293d9', 'ensemble_size__db9e60f9b8f0a43eff4b41917b6293d9': 1, 'epochs__db9e60f9b8f0a43eff4b41917b6293d9': 4, 'features_generator__db9e60f9b8f0a43eff4b41917b6293d9': , 'num_iters__db9e60f9b8f0a43eff4b41917b6293d9': 1, 'search_parameter_level__db9e60f9b8f0a43eff4b41917b6293d9': , 'descriptor': '{\"name\": \"SmilesAndSideInfoFromFile\", \"parameters\": {\"file\": \"../tests/data/DRD2/subset-50/train_side_info.csv\", \"input_column\": \"canonical\", \"aux_weight_pc\": {\"low\": 0, \"high\": 100, \"q\": 10}}}', 'aux_weight_pc__db9e60f9b8f0a43eff4b41917b6293d9': 10}. Best is trial 3 with value: -5795.086720713623.\n" ] } ], @@ -2373,42 +2346,48 @@ "name": "stderr", "output_type": "stream", "text": [ - "[I 2024-07-02 13:21:13,577] A new study created in memory with name: my_study\n", - "[I 2024-07-02 13:21:13,629] Trial 0 finished with value: -inf and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.9525489095524835, 'descriptor': '{\"name\": \"SmilesAndSideInfoFromFile\", \"parameters\": {\"file\": \"../tests/data/DRD2/subset-50/train_side_info.csv\", \"input_column\": \"canonical\", \"aux_weight_pc\": {\"low\": 0, \"high\": 40, \"q\": 10}}}', 'aux_weight_pc__cfa1990d5153c8812982f034d788d7ee': 30}. Best is trial 0 with value: -inf.\n", - "[I 2024-07-02 13:21:13,777] Trial 1 finished with value: -4824.686269039228 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.7731425652872588, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 1 with value: -4824.686269039228.\n", - "[I 2024-07-02 13:21:13,819] Trial 2 pruned. Incompatible subspace\n", - "[I 2024-07-02 13:21:13,849] Trial 3 pruned. Incompatible subspace\n", - "[I 2024-07-02 13:21:13,997] Trial 4 finished with value: -4409.946844928445 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.791002332112292, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 4 with value: -4409.946844928445.\n", - "[I 2024-07-02 13:21:14,021] Trial 5 pruned. Incompatible subspace\n", - "[I 2024-07-02 13:21:14,167] Trial 6 finished with value: -5029.734620250011 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 23.329624779366306, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 0.00015024763718638216, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 4 with value: -4409.946844928445.\n", - "[I 2024-07-02 13:21:14,196] Trial 7 pruned. Incompatible subspace\n", - "[I 2024-07-02 13:21:14,228] Trial 8 pruned. Incompatible subspace\n", - "[I 2024-07-02 13:21:14,269] Trial 9 pruned. Incompatible subspace\n", - "[I 2024-07-02 13:21:14,523] Trial 10 finished with value: -4396.722635068717 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 17, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 7, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 10 with value: -4396.722635068717.\n", - "[I 2024-07-02 13:21:14,559] Trial 11 pruned. Duplicate parameter set\n", - "[I 2024-07-02 13:21:14,753] Trial 12 finished with value: -4030.4577379164707 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 30, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 9, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 12 with value: -4030.4577379164707.\n" + "[I 2024-07-02 16:06:29,404] A new study created in memory with name: my_study\n", + "/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/joblib/memory.py:577: JobLibCollisionWarning: Possible name collisions between functions 'calculate_from_smi' (/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/optunaz/descriptors.py:-1) and 'calculate_from_smi' (/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/optunaz/descriptors.py:736)\n", + " return self._cached_call(args, kwargs, shelving=False)[0]\n", + "/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/joblib/_store_backends.py:215: CacheWarning: Unable to cache to disk. Possibly a race condition in the creation of the directory. Exception: [Errno 2] No such file or directory: '/var/folders/1v/9y_z128d7gvcp8mf8q0pz3ch0000gq/T/tmp036btnoj/joblib/optunaz/descriptors/SmilesAndSideInfoFromFile/calculate_from_smi/5c35a12d8bcea8acbc6d5682447dac4e/output.pkl.thread-140297255347648-pid-10944' -> '/var/folders/1v/9y_z128d7gvcp8mf8q0pz3ch0000gq/T/tmp036btnoj/joblib/optunaz/descriptors/SmilesAndSideInfoFromFile/calculate_from_smi/5c35a12d8bcea8acbc6d5682447dac4e/output.pkl'.\n", + " warnings.warn(\n", + "/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/joblib/memory.py:577: JobLibCollisionWarning: Possible name collisions between functions 'calculate_from_smi' (/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/optunaz/descriptors.py:-1) and 'calculate_from_smi' (/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/optunaz/descriptors.py:736)\n", + " return self._cached_call(args, kwargs, shelving=False)[0]\n", + "/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/joblib/memory.py:577: JobLibCollisionWarning: Possible name collisions between functions 'calculate_from_smi' (/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/optunaz/descriptors.py:-1) and 'calculate_from_smi' (/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/optunaz/descriptors.py:736)\n", + " return self._cached_call(args, kwargs, shelving=False)[0]\n", + "/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/joblib/memory.py:577: JobLibCollisionWarning: Possible name collisions between functions 'calculate_from_smi' (/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/optunaz/descriptors.py:-1) and 'calculate_from_smi' (/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/optunaz/descriptors.py:736)\n", + " return self._cached_call(args, kwargs, shelving=False)[0]\n", + "/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/joblib/memory.py:577: JobLibCollisionWarning: Possible name collisions between functions 'calculate_from_smi' (/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/optunaz/descriptors.py:-1) and 'calculate_from_smi' (/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/optunaz/descriptors.py:736)\n", + " return self._cached_call(args, kwargs, shelving=False)[0]\n", + "[I 2024-07-02 16:06:29,481] Trial 0 finished with value: -inf and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.9525489095524835, 'descriptor': '{\"name\": \"SmilesAndSideInfoFromFile\", \"parameters\": {\"file\": \"../tests/data/DRD2/subset-50/train_side_info.csv\", \"input_column\": \"canonical\", \"aux_weight_pc\": {\"low\": 0, \"high\": 40, \"q\": 10}}}', 'aux_weight_pc__cfa1990d5153c8812982f034d788d7ee': 30}. Best is trial 0 with value: -inf.\n", + "[I 2024-07-02 16:06:29,643] Trial 1 finished with value: -4824.686269039228 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.7731425652872588, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 1 with value: -4824.686269039228.\n", + "[I 2024-07-02 16:06:29,696] Trial 2 pruned. Incompatible subspace\n", + "[I 2024-07-02 16:06:29,725] Trial 3 pruned. Incompatible subspace\n", + "[I 2024-07-02 16:06:30,207] Trial 4 finished with value: -4409.946844928445 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.791002332112292, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 4 with value: -4409.946844928445.\n", + "[I 2024-07-02 16:06:30,254] Trial 5 pruned. Incompatible subspace\n", + "[I 2024-07-02 16:06:30,399] Trial 6 finished with value: -5029.734620250011 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 23.329624779366306, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 0.00015024763718638216, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 4 with value: -4409.946844928445.\n", + "[I 2024-07-02 16:06:30,428] Trial 7 pruned. Incompatible subspace\n", + "[I 2024-07-02 16:06:30,457] Trial 8 pruned. Incompatible subspace\n", + "[I 2024-07-02 16:06:30,500] Trial 9 pruned. Incompatible subspace\n", + "[I 2024-07-02 16:06:30,781] Trial 10 finished with value: -4396.722635068717 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 17, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 7, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 10 with value: -4396.722635068717.\n", + "[I 2024-07-02 16:06:30,815] Trial 11 pruned. Duplicate parameter set\n", + "[I 2024-07-02 16:06:30,980] Trial 12 finished with value: -4030.4577379164707 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 30, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 9, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 12 with value: -4030.4577379164707.\n", + "[I 2024-07-02 16:06:31,013] Trial 13 pruned. Duplicate parameter set\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "Duplicated trial: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 17, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 7, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}, return [-4396.722635068717]\n" + "Duplicated trial: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 17, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 7, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}, return [-4396.722635068717]\n", + "Duplicated trial: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 30, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 9, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}, return [-4030.4577379164707]\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "[I 2024-07-02 13:21:14,790] Trial 13 pruned. Duplicate parameter set\n", - "[I 2024-07-02 13:21:14,960] Trial 14 finished with value: -4030.4577379164707 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 26, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 9, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 12 with value: -4030.4577379164707.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Duplicated trial: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 30, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 9, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}, return [-4030.4577379164707]\n" + "[I 2024-07-02 16:06:31,229] Trial 14 finished with value: -4030.4577379164707 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 26, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 9, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 12 with value: -4030.4577379164707.\n" ] } ], @@ -2508,7 +2487,7 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 154, "metadata": { "scrolled": true }, @@ -2517,10 +2496,10 @@ "name": "stderr", "output_type": "stream", "text": [ - "[I 2024-07-02 13:21:15,255] A new study created in memory with name: my_study\n", - "[I 2024-07-02 13:21:15,256] A new study created in memory with name: study_name_0\n", + "[I 2024-07-03 12:07:35,764] A new study created in memory with name: my_study\n", + "[I 2024-07-03 12:07:35,766] A new study created in memory with name: study_name_0\n", "INFO:root:Enqueued ChemProp manual trial with sensible defaults: {'activation__e0d3a442222d4b38f3aa1434851320db': 'ReLU', 'aggregation__e0d3a442222d4b38f3aa1434851320db': 'mean', 'aggregation_norm__e0d3a442222d4b38f3aa1434851320db': 100, 'batch_size__e0d3a442222d4b38f3aa1434851320db': 50, 'depth__e0d3a442222d4b38f3aa1434851320db': 3, 'dropout__e0d3a442222d4b38f3aa1434851320db': 0.0, 'features_generator__e0d3a442222d4b38f3aa1434851320db': 'none', 'ffn_hidden_size__e0d3a442222d4b38f3aa1434851320db': 300, 'ffn_num_layers__e0d3a442222d4b38f3aa1434851320db': 2, 'final_lr_ratio_exp__e0d3a442222d4b38f3aa1434851320db': -4, 'hidden_size__e0d3a442222d4b38f3aa1434851320db': 300, 'init_lr_ratio_exp__e0d3a442222d4b38f3aa1434851320db': -4, 'max_lr_exp__e0d3a442222d4b38f3aa1434851320db': -3, 'warmup_epochs_ratio__e0d3a442222d4b38f3aa1434851320db': 0.1, 'algorithm_name': 'ChemPropRegressor', 'ChemPropRegressor_algorithm_hash': 'e0d3a442222d4b38f3aa1434851320db'}\n", - "[I 2024-07-02 13:21:58,856] Trial 0 finished with value: -4937.540075659691 and parameters: {'algorithm_name': 'ChemPropRegressor', 'ChemPropRegressor_algorithm_hash': 'e0d3a442222d4b38f3aa1434851320db', 'activation__e0d3a442222d4b38f3aa1434851320db': , 'aggregation__e0d3a442222d4b38f3aa1434851320db': , 'aggregation_norm__e0d3a442222d4b38f3aa1434851320db': 100.0, 'batch_size__e0d3a442222d4b38f3aa1434851320db': 50.0, 'depth__e0d3a442222d4b38f3aa1434851320db': 3.0, 'dropout__e0d3a442222d4b38f3aa1434851320db': 0.0, 'ensemble_size__e0d3a442222d4b38f3aa1434851320db': 1, 'epochs__e0d3a442222d4b38f3aa1434851320db': 4, 'features_generator__e0d3a442222d4b38f3aa1434851320db': , 'ffn_hidden_size__e0d3a442222d4b38f3aa1434851320db': 300.0, 'ffn_num_layers__e0d3a442222d4b38f3aa1434851320db': 2.0, 'final_lr_ratio_exp__e0d3a442222d4b38f3aa1434851320db': -4, 'hidden_size__e0d3a442222d4b38f3aa1434851320db': 300.0, 'init_lr_ratio_exp__e0d3a442222d4b38f3aa1434851320db': -4, 'max_lr_exp__e0d3a442222d4b38f3aa1434851320db': -3, 'warmup_epochs_ratio__e0d3a442222d4b38f3aa1434851320db': 0.1, 'descriptor': '{\"name\": \"SmilesFromFile\", \"parameters\": {}}'}. Best is trial 0 with value: -4937.540075659691.\n", + "[I 2024-07-03 12:08:30,402] Trial 0 finished with value: -4937.540075659691 and parameters: {'algorithm_name': 'ChemPropRegressor', 'ChemPropRegressor_algorithm_hash': 'e0d3a442222d4b38f3aa1434851320db', 'activation__e0d3a442222d4b38f3aa1434851320db': , 'aggregation__e0d3a442222d4b38f3aa1434851320db': , 'aggregation_norm__e0d3a442222d4b38f3aa1434851320db': 100.0, 'batch_size__e0d3a442222d4b38f3aa1434851320db': 50.0, 'depth__e0d3a442222d4b38f3aa1434851320db': 3.0, 'dropout__e0d3a442222d4b38f3aa1434851320db': 0.0, 'ensemble_size__e0d3a442222d4b38f3aa1434851320db': 1, 'epochs__e0d3a442222d4b38f3aa1434851320db': 4, 'features_generator__e0d3a442222d4b38f3aa1434851320db': , 'ffn_hidden_size__e0d3a442222d4b38f3aa1434851320db': 300.0, 'ffn_num_layers__e0d3a442222d4b38f3aa1434851320db': 2.0, 'final_lr_ratio_exp__e0d3a442222d4b38f3aa1434851320db': -4, 'hidden_size__e0d3a442222d4b38f3aa1434851320db': 300.0, 'init_lr_ratio_exp__e0d3a442222d4b38f3aa1434851320db': -4, 'max_lr_exp__e0d3a442222d4b38f3aa1434851320db': -3, 'warmup_epochs_ratio__e0d3a442222d4b38f3aa1434851320db': 0.1, 'descriptor': '{\"name\": \"SmilesFromFile\", \"parameters\": {}}'}. Best is trial 0 with value: -4937.540075659691.\n", " \r" ] } @@ -2559,7 +2538,7 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 155, "metadata": { "scrolled": true }, @@ -2568,9 +2547,9 @@ "name": "stderr", "output_type": "stream", "text": [ - "[I 2024-07-02 13:23:02,954] A new study created in memory with name: my_study\n", - "[I 2024-07-02 13:23:02,997] A new study created in memory with name: study_name_0\n", - "[I 2024-07-02 13:23:47,043] Trial 0 finished with value: -5114.7131239123555 and parameters: {'algorithm_name': 'ChemPropRegressorPretrained', 'ChemPropRegressorPretrained_algorithm_hash': 'dfc518a76317f23d95e5aa5a3eac77f0', 'frzn__dfc518a76317f23d95e5aa5a3eac77f0': , 'epochs__dfc518a76317f23d95e5aa5a3eac77f0': 4, 'descriptor': '{\"name\": \"SmilesFromFile\", \"parameters\": {}}'}. Best is trial 0 with value: -5114.7131239123555.\n" + "[I 2024-07-03 12:14:50,109] A new study created in memory with name: my_study\n", + "[I 2024-07-03 12:14:50,157] A new study created in memory with name: study_name_0\n", + "[I 2024-07-03 12:15:34,865] Trial 0 finished with value: -5114.7131239123555 and parameters: {'algorithm_name': 'ChemPropRegressorPretrained', 'ChemPropRegressorPretrained_algorithm_hash': 'dfc518a76317f23d95e5aa5a3eac77f0', 'frzn__dfc518a76317f23d95e5aa5a3eac77f0': , 'epochs__dfc518a76317f23d95e5aa5a3eac77f0': 4, 'descriptor': '{\"name\": \"SmilesFromFile\", \"parameters\": {}}'}. Best is trial 0 with value: -5114.7131239123555.\n" ] } ], @@ -2609,29 +2588,23 @@ }, { "cell_type": "code", - "execution_count": 44, - "metadata": {}, + "execution_count": 156, + "metadata": { + "scrolled": true + }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "[I 2024-07-02 13:23:47,172] A new study created in memory with name: my_study\n", - "[I 2024-07-02 13:23:47,174] A new study created in memory with name: study_name_0\n", + "[I 2024-07-03 12:15:34,986] A new study created in memory with name: my_study\n", + "[I 2024-07-03 12:15:34,988] A new study created in memory with name: study_name_0\n", "INFO:root:Enqueued ChemProp manual trial with sensible defaults: {'activation__e0d3a442222d4b38f3aa1434851320db': 'ReLU', 'aggregation__e0d3a442222d4b38f3aa1434851320db': 'mean', 'aggregation_norm__e0d3a442222d4b38f3aa1434851320db': 100, 'batch_size__e0d3a442222d4b38f3aa1434851320db': 50, 'depth__e0d3a442222d4b38f3aa1434851320db': 3, 'dropout__e0d3a442222d4b38f3aa1434851320db': 0.0, 'features_generator__e0d3a442222d4b38f3aa1434851320db': 'none', 'ffn_hidden_size__e0d3a442222d4b38f3aa1434851320db': 300, 'ffn_num_layers__e0d3a442222d4b38f3aa1434851320db': 2, 'final_lr_ratio_exp__e0d3a442222d4b38f3aa1434851320db': -4, 'hidden_size__e0d3a442222d4b38f3aa1434851320db': 300, 'init_lr_ratio_exp__e0d3a442222d4b38f3aa1434851320db': -4, 'max_lr_exp__e0d3a442222d4b38f3aa1434851320db': -3, 'warmup_epochs_ratio__e0d3a442222d4b38f3aa1434851320db': 0.1, 'algorithm_name': 'ChemPropRegressor', 'ChemPropRegressor_algorithm_hash': 'e0d3a442222d4b38f3aa1434851320db'}\n", - "[I 2024-07-02 13:24:09,495] Trial 0 finished with value: -5891.7552821093905 and parameters: {'algorithm_name': 'ChemPropRegressor', 'ChemPropRegressor_algorithm_hash': 'e0d3a442222d4b38f3aa1434851320db', 'activation__e0d3a442222d4b38f3aa1434851320db': , 'aggregation__e0d3a442222d4b38f3aa1434851320db': , 'aggregation_norm__e0d3a442222d4b38f3aa1434851320db': 100.0, 'batch_size__e0d3a442222d4b38f3aa1434851320db': 50.0, 'depth__e0d3a442222d4b38f3aa1434851320db': 3.0, 'dropout__e0d3a442222d4b38f3aa1434851320db': 0.0, 'ensemble_size__e0d3a442222d4b38f3aa1434851320db': 1, 'epochs__e0d3a442222d4b38f3aa1434851320db': 4, 'features_generator__e0d3a442222d4b38f3aa1434851320db': , 'ffn_hidden_size__e0d3a442222d4b38f3aa1434851320db': 300.0, 'ffn_num_layers__e0d3a442222d4b38f3aa1434851320db': 2.0, 'final_lr_ratio_exp__e0d3a442222d4b38f3aa1434851320db': -4, 'hidden_size__e0d3a442222d4b38f3aa1434851320db': 300.0, 'init_lr_ratio_exp__e0d3a442222d4b38f3aa1434851320db': -4, 'max_lr_exp__e0d3a442222d4b38f3aa1434851320db': -3, 'warmup_epochs_ratio__e0d3a442222d4b38f3aa1434851320db': 0.1, 'descriptor': '{\"name\": \"SmilesFromFile\", \"parameters\": {}}'}. Best is trial 0 with value: -5891.7552821093905.\n", - "[I 2024-07-02 13:24:31,625] Trial 1 finished with value: -5891.7552821093905 and parameters: {'algorithm_name': 'ChemPropRegressor', 'ChemPropRegressor_algorithm_hash': 'e0d3a442222d4b38f3aa1434851320db', 'activation__e0d3a442222d4b38f3aa1434851320db': , 'aggregation__e0d3a442222d4b38f3aa1434851320db': , 'aggregation_norm__e0d3a442222d4b38f3aa1434851320db': 98.0, 'batch_size__e0d3a442222d4b38f3aa1434851320db': 40.0, 'depth__e0d3a442222d4b38f3aa1434851320db': 3.0, 'dropout__e0d3a442222d4b38f3aa1434851320db': 0.0, 'ensemble_size__e0d3a442222d4b38f3aa1434851320db': 1, 'epochs__e0d3a442222d4b38f3aa1434851320db': 4, 'features_generator__e0d3a442222d4b38f3aa1434851320db': , 'ffn_hidden_size__e0d3a442222d4b38f3aa1434851320db': 300.0, 'ffn_num_layers__e0d3a442222d4b38f3aa1434851320db': 2.0, 'final_lr_ratio_exp__e0d3a442222d4b38f3aa1434851320db': -4, 'hidden_size__e0d3a442222d4b38f3aa1434851320db': 300.0, 'init_lr_ratio_exp__e0d3a442222d4b38f3aa1434851320db': -4, 'max_lr_exp__e0d3a442222d4b38f3aa1434851320db': -3, 'warmup_epochs_ratio__e0d3a442222d4b38f3aa1434851320db': 0.1, 'descriptor': '{\"name\": \"SmilesFromFile\", \"parameters\": {}}'}. Best is trial 0 with value: -5891.7552821093905.\n", - "[I 2024-07-02 13:24:53,140] Trial 2 finished with value: -5890.94653501547 and parameters: {'algorithm_name': 'ChemPropRegressorPretrained', 'ChemPropRegressorPretrained_algorithm_hash': '77dfc8230317e08504ed5e643243fbc2', 'frzn__77dfc8230317e08504ed5e643243fbc2': , 'epochs__77dfc8230317e08504ed5e643243fbc2': 0, 'descriptor': '{\"name\": \"SmilesFromFile\", \"parameters\": {}}'}. Best is trial 2 with value: -5890.94653501547.\n", - "[I 2024-07-02 13:24:53,181] Trial 3 pruned. Duplicate parameter set\n", - "[I 2024-07-02 13:24:53,211] Trial 4 pruned. Duplicate parameter set\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Duplicated trial: {'algorithm_name': 'ChemPropRegressorPretrained', 'ChemPropRegressorPretrained_algorithm_hash': '77dfc8230317e08504ed5e643243fbc2', 'frzn__77dfc8230317e08504ed5e643243fbc2': , 'epochs__77dfc8230317e08504ed5e643243fbc2': 0, 'descriptor': '{\"name\": \"SmilesFromFile\", \"parameters\": {}}'}, return [-5890.94653501547]\n", - "Duplicated trial: {'algorithm_name': 'ChemPropRegressorPretrained', 'ChemPropRegressorPretrained_algorithm_hash': '77dfc8230317e08504ed5e643243fbc2', 'frzn__77dfc8230317e08504ed5e643243fbc2': , 'epochs__77dfc8230317e08504ed5e643243fbc2': 0, 'descriptor': '{\"name\": \"SmilesFromFile\", \"parameters\": {}}'}, return [-5890.94653501547]\n" + "[I 2024-07-03 12:15:56,468] Trial 0 finished with value: -5891.7552821093905 and parameters: {'algorithm_name': 'ChemPropRegressor', 'ChemPropRegressor_algorithm_hash': 'e0d3a442222d4b38f3aa1434851320db', 'activation__e0d3a442222d4b38f3aa1434851320db': , 'aggregation__e0d3a442222d4b38f3aa1434851320db': , 'aggregation_norm__e0d3a442222d4b38f3aa1434851320db': 100.0, 'batch_size__e0d3a442222d4b38f3aa1434851320db': 50.0, 'depth__e0d3a442222d4b38f3aa1434851320db': 3.0, 'dropout__e0d3a442222d4b38f3aa1434851320db': 0.0, 'ensemble_size__e0d3a442222d4b38f3aa1434851320db': 1, 'epochs__e0d3a442222d4b38f3aa1434851320db': 4, 'features_generator__e0d3a442222d4b38f3aa1434851320db': , 'ffn_hidden_size__e0d3a442222d4b38f3aa1434851320db': 300.0, 'ffn_num_layers__e0d3a442222d4b38f3aa1434851320db': 2.0, 'final_lr_ratio_exp__e0d3a442222d4b38f3aa1434851320db': -4, 'hidden_size__e0d3a442222d4b38f3aa1434851320db': 300.0, 'init_lr_ratio_exp__e0d3a442222d4b38f3aa1434851320db': -4, 'max_lr_exp__e0d3a442222d4b38f3aa1434851320db': -3, 'warmup_epochs_ratio__e0d3a442222d4b38f3aa1434851320db': 0.1, 'descriptor': '{\"name\": \"SmilesFromFile\", \"parameters\": {}}'}. Best is trial 0 with value: -5891.7552821093905.\n", + "[I 2024-07-03 12:16:18,120] Trial 1 finished with value: -5891.7552821093905 and parameters: {'algorithm_name': 'ChemPropRegressor', 'ChemPropRegressor_algorithm_hash': 'e0d3a442222d4b38f3aa1434851320db', 'activation__e0d3a442222d4b38f3aa1434851320db': , 'aggregation__e0d3a442222d4b38f3aa1434851320db': , 'aggregation_norm__e0d3a442222d4b38f3aa1434851320db': 105.0, 'batch_size__e0d3a442222d4b38f3aa1434851320db': 60.0, 'depth__e0d3a442222d4b38f3aa1434851320db': 3.0, 'dropout__e0d3a442222d4b38f3aa1434851320db': 0.0, 'ensemble_size__e0d3a442222d4b38f3aa1434851320db': 1, 'epochs__e0d3a442222d4b38f3aa1434851320db': 4, 'features_generator__e0d3a442222d4b38f3aa1434851320db': , 'ffn_hidden_size__e0d3a442222d4b38f3aa1434851320db': 300.0, 'ffn_num_layers__e0d3a442222d4b38f3aa1434851320db': 2.0, 'final_lr_ratio_exp__e0d3a442222d4b38f3aa1434851320db': -4, 'hidden_size__e0d3a442222d4b38f3aa1434851320db': 300.0, 'init_lr_ratio_exp__e0d3a442222d4b38f3aa1434851320db': -4, 'max_lr_exp__e0d3a442222d4b38f3aa1434851320db': -3, 'warmup_epochs_ratio__e0d3a442222d4b38f3aa1434851320db': 0.1, 'descriptor': '{\"name\": \"SmilesFromFile\", \"parameters\": {}}'}. Best is trial 0 with value: -5891.7552821093905.\n", + "[I 2024-07-03 12:16:40,381] Trial 2 finished with value: -5846.868596513772 and parameters: {'algorithm_name': 'ChemPropRegressor', 'ChemPropRegressor_algorithm_hash': 'e0d3a442222d4b38f3aa1434851320db', 'activation__e0d3a442222d4b38f3aa1434851320db': , 'aggregation__e0d3a442222d4b38f3aa1434851320db': , 'aggregation_norm__e0d3a442222d4b38f3aa1434851320db': 14.0, 'batch_size__e0d3a442222d4b38f3aa1434851320db': 10.0, 'depth__e0d3a442222d4b38f3aa1434851320db': 2.0, 'dropout__e0d3a442222d4b38f3aa1434851320db': 0.24, 'ensemble_size__e0d3a442222d4b38f3aa1434851320db': 1, 'epochs__e0d3a442222d4b38f3aa1434851320db': 4, 'features_generator__e0d3a442222d4b38f3aa1434851320db': , 'ffn_hidden_size__e0d3a442222d4b38f3aa1434851320db': 1600.0, 'ffn_num_layers__e0d3a442222d4b38f3aa1434851320db': 2.0, 'final_lr_ratio_exp__e0d3a442222d4b38f3aa1434851320db': -4, 'hidden_size__e0d3a442222d4b38f3aa1434851320db': 900.0, 'init_lr_ratio_exp__e0d3a442222d4b38f3aa1434851320db': -1, 'max_lr_exp__e0d3a442222d4b38f3aa1434851320db': -2, 'warmup_epochs_ratio__e0d3a442222d4b38f3aa1434851320db': 0.1, 'descriptor': '{\"name\": \"SmilesFromFile\", \"parameters\": {}}'}. Best is trial 2 with value: -5846.868596513772.\n", + "[I 2024-07-03 12:17:02,053] Trial 3 finished with value: -5890.94653501547 and parameters: {'algorithm_name': 'ChemPropRegressorPretrained', 'ChemPropRegressorPretrained_algorithm_hash': '77dfc8230317e08504ed5e643243fbc2', 'frzn__77dfc8230317e08504ed5e643243fbc2': , 'epochs__77dfc8230317e08504ed5e643243fbc2': 0, 'descriptor': '{\"name\": \"SmilesFromFile\", \"parameters\": {}}'}. Best is trial 2 with value: -5846.868596513772.\n", + "[I 2024-07-03 12:17:24,942] Trial 4 finished with value: -5890.881210303758 and parameters: {'algorithm_name': 'ChemPropRegressorPretrained', 'ChemPropRegressorPretrained_algorithm_hash': 'dfc518a76317f23d95e5aa5a3eac77f0', 'frzn__dfc518a76317f23d95e5aa5a3eac77f0': , 'epochs__dfc518a76317f23d95e5aa5a3eac77f0': 4, 'descriptor': '{\"name\": \"SmilesFromFile\", \"parameters\": {}}'}. Best is trial 2 with value: -5846.868596513772.\n" ] } ], @@ -2659,7 +2632,7 @@ " cross_validation=1,\n", " n_trials=5,\n", " n_startup_trials=0,\n", - " random_seed=1545,\n", + " random_seed=0, # ensure all model types trialed\n", " direction=OptimizationDirection.MAXIMIZATION,\n", " ),\n", ")\n", @@ -2682,12 +2655,12 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 157, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -2699,8 +2672,7 @@ "source": [ "sns.set_theme(style=\"darkgrid\")\n", "default_reg_scoring= config.settings.scoring\n", - "ax = sns.scatterplot(data=tl_study, x=\"number\", \n", - " y=\"value\",hue='Model type')\n", + "ax = sns.scatterplot(data=tl_study, x=\"number\", y=\"value\",hue='Model type')\n", "ax.set(xlabel=\"Trial number\",ylabel=f\"Ojbective value\\n({default_reg_scoring})\")\n", "sns.move_legend(ax, \"upper right\", bbox_to_anchor=(1.6, 1), ncol=1, title=\"\")" ] @@ -2709,7 +2681,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "For this toy example we do not observe a large difference between the three model types, but in a real world setting a user can build the best model from the three model types evaluated." + "For this toy example we do not observe a large difference between the adapted and global model types, but in a real world setting a user can build the best model from the three model types evaluated." ] }, { @@ -2843,9 +2815,9 @@ "name": "stderr", "output_type": "stream", "text": [ - "[I 2024-07-02 13:25:15,173] A new study created in memory with name: calibrated_rf\n", - "[I 2024-07-02 13:25:15,175] A new study created in memory with name: study_name_0\n", - "[I 2024-07-02 13:25:16,110] Trial 0 finished with value: 0.8353535353535354 and parameters: {'algorithm_name': 'CalibratedClassifierCVWithVA', 'CalibratedClassifierCVWithVA_algorithm_hash': 'e788dfbfc5075967acb5ddf9d971ea20', 'n_folds__e788dfbfc5075967acb5ddf9d971ea20': 5, 'max_depth__e788dfbfc5075967acb5ddf9d971ea20': 16, 'n_estimators__e788dfbfc5075967acb5ddf9d971ea20': 100, 'max_features__e788dfbfc5075967acb5ddf9d971ea20': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 0 with value: 0.8353535353535354.\n" + "[I 2024-07-02 16:10:27,089] A new study created in memory with name: calibrated_rf\n", + "[I 2024-07-02 16:10:27,092] A new study created in memory with name: study_name_0\n", + "[I 2024-07-02 16:10:28,093] Trial 0 finished with value: 0.8353535353535354 and parameters: {'algorithm_name': 'CalibratedClassifierCVWithVA', 'CalibratedClassifierCVWithVA_algorithm_hash': 'e788dfbfc5075967acb5ddf9d971ea20', 'n_folds__e788dfbfc5075967acb5ddf9d971ea20': 5, 'max_depth__e788dfbfc5075967acb5ddf9d971ea20': 16, 'n_estimators__e788dfbfc5075967acb5ddf9d971ea20': 100, 'max_features__e788dfbfc5075967acb5ddf9d971ea20': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 0 with value: 0.8353535353535354.\n" ] } ], @@ -2920,9 +2892,9 @@ "name": "stderr", "output_type": "stream", "text": [ - "[I 2024-07-02 13:25:18,566] A new study created in memory with name: uncalibrated_rf\n", - "[I 2024-07-02 13:25:18,608] A new study created in memory with name: study_name_0\n", - "[I 2024-07-02 13:25:18,915] Trial 0 finished with value: 0.8185858585858585 and parameters: {'algorithm_name': 'RandomForestClassifier', 'RandomForestClassifier_algorithm_hash': '167e1e88dd2a80133e317c78f009bdc9', 'max_depth__167e1e88dd2a80133e317c78f009bdc9': 16, 'n_estimators__167e1e88dd2a80133e317c78f009bdc9': 100, 'max_features__167e1e88dd2a80133e317c78f009bdc9': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 0 with value: 0.8185858585858585.\n" + "[I 2024-07-02 16:10:30,400] A new study created in memory with name: uncalibrated_rf\n", + "[I 2024-07-02 16:10:30,442] A new study created in memory with name: study_name_0\n", + "[I 2024-07-02 16:10:30,739] Trial 0 finished with value: 0.8185858585858585 and parameters: {'algorithm_name': 'RandomForestClassifier', 'RandomForestClassifier_algorithm_hash': '167e1e88dd2a80133e317c78f009bdc9', 'max_depth__167e1e88dd2a80133e317c78f009bdc9': 16, 'n_estimators__167e1e88dd2a80133e317c78f009bdc9': 100, 'max_features__167e1e88dd2a80133e317c78f009bdc9': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 0 with value: 0.8185858585858585.\n" ] } ], @@ -3247,9 +3219,9 @@ "name": "stderr", "output_type": "stream", "text": [ - "[I 2024-07-02 13:25:20,500] A new study created in memory with name: calibrated_rf\n", - "[I 2024-07-02 13:25:20,548] A new study created in memory with name: study_name_0\n", - "[I 2024-07-02 13:25:21,537] Trial 0 finished with value: 0.8213131313131313 and parameters: {'algorithm_name': 'CalibratedClassifierCVWithVA', 'CalibratedClassifierCVWithVA_algorithm_hash': '79765fbec1586f3c917ff30de274fdb4', 'n_folds__79765fbec1586f3c917ff30de274fdb4': 5, 'max_depth__79765fbec1586f3c917ff30de274fdb4': 16, 'n_estimators__79765fbec1586f3c917ff30de274fdb4': 100, 'max_features__79765fbec1586f3c917ff30de274fdb4': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 0 with value: 0.8213131313131313.\n" + "[I 2024-07-02 16:10:32,303] A new study created in memory with name: calibrated_rf\n", + "[I 2024-07-02 16:10:32,345] A new study created in memory with name: study_name_0\n", + "[I 2024-07-02 16:10:33,181] Trial 0 finished with value: 0.8213131313131313 and parameters: {'algorithm_name': 'CalibratedClassifierCVWithVA', 'CalibratedClassifierCVWithVA_algorithm_hash': '79765fbec1586f3c917ff30de274fdb4', 'n_folds__79765fbec1586f3c917ff30de274fdb4': 5, 'max_depth__79765fbec1586f3c917ff30de274fdb4': 16, 'n_estimators__79765fbec1586f3c917ff30de274fdb4': 100, 'max_features__79765fbec1586f3c917ff30de274fdb4': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 0 with value: 0.8213131313131313.\n" ] } ], @@ -3355,7 +3327,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -3404,10 +3376,10 @@ "name": "stderr", "output_type": "stream", "text": [ - "[I 2024-07-02 13:25:25,916] A new study created in memory with name: my_study\n", - "[I 2024-07-02 13:25:25,959] A new study created in memory with name: study_name_0\n", + "[I 2024-07-02 16:10:37,199] A new study created in memory with name: my_study\n", + "[I 2024-07-02 16:10:37,240] A new study created in memory with name: study_name_0\n", "INFO:root:Enqueued ChemProp manual trial with sensible defaults: {'activation__fd833c2dde0b7147e6516ea5eebb2657': 'ReLU', 'aggregation__fd833c2dde0b7147e6516ea5eebb2657': 'mean', 'aggregation_norm__fd833c2dde0b7147e6516ea5eebb2657': 100, 'batch_size__fd833c2dde0b7147e6516ea5eebb2657': 50, 'depth__fd833c2dde0b7147e6516ea5eebb2657': 3, 'dropout__fd833c2dde0b7147e6516ea5eebb2657': 0.0, 'features_generator__fd833c2dde0b7147e6516ea5eebb2657': 'none', 'ffn_hidden_size__fd833c2dde0b7147e6516ea5eebb2657': 300, 'ffn_num_layers__fd833c2dde0b7147e6516ea5eebb2657': 2, 'final_lr_ratio_exp__fd833c2dde0b7147e6516ea5eebb2657': -4, 'hidden_size__fd833c2dde0b7147e6516ea5eebb2657': 300, 'init_lr_ratio_exp__fd833c2dde0b7147e6516ea5eebb2657': -4, 'max_lr_exp__fd833c2dde0b7147e6516ea5eebb2657': -3, 'warmup_epochs_ratio__fd833c2dde0b7147e6516ea5eebb2657': 0.1, 'algorithm_name': 'ChemPropClassifier', 'ChemPropClassifier_algorithm_hash': 'fd833c2dde0b7147e6516ea5eebb2657'}\n", - "[I 2024-07-02 13:32:26,200] Trial 0 finished with value: 0.65625 and parameters: {'algorithm_name': 'ChemPropClassifier', 'ChemPropClassifier_algorithm_hash': 'fd833c2dde0b7147e6516ea5eebb2657', 'activation__fd833c2dde0b7147e6516ea5eebb2657': , 'aggregation__fd833c2dde0b7147e6516ea5eebb2657': , 'aggregation_norm__fd833c2dde0b7147e6516ea5eebb2657': 100.0, 'batch_size__fd833c2dde0b7147e6516ea5eebb2657': 50.0, 'depth__fd833c2dde0b7147e6516ea5eebb2657': 3.0, 'dropout__fd833c2dde0b7147e6516ea5eebb2657': 0.0, 'ensemble_size__fd833c2dde0b7147e6516ea5eebb2657': 5, 'epochs__fd833c2dde0b7147e6516ea5eebb2657': 4, 'features_generator__fd833c2dde0b7147e6516ea5eebb2657': , 'ffn_hidden_size__fd833c2dde0b7147e6516ea5eebb2657': 300.0, 'ffn_num_layers__fd833c2dde0b7147e6516ea5eebb2657': 2.0, 'final_lr_ratio_exp__fd833c2dde0b7147e6516ea5eebb2657': -4, 'hidden_size__fd833c2dde0b7147e6516ea5eebb2657': 300.0, 'init_lr_ratio_exp__fd833c2dde0b7147e6516ea5eebb2657': -4, 'max_lr_exp__fd833c2dde0b7147e6516ea5eebb2657': -3, 'warmup_epochs_ratio__fd833c2dde0b7147e6516ea5eebb2657': 0.1, 'descriptor': '{\"name\": \"SmilesFromFile\", \"parameters\": {}}'}. Best is trial 0 with value: 0.65625.\n", + "[I 2024-07-02 16:17:22,733] Trial 0 finished with value: 0.65625 and parameters: {'algorithm_name': 'ChemPropClassifier', 'ChemPropClassifier_algorithm_hash': 'fd833c2dde0b7147e6516ea5eebb2657', 'activation__fd833c2dde0b7147e6516ea5eebb2657': , 'aggregation__fd833c2dde0b7147e6516ea5eebb2657': , 'aggregation_norm__fd833c2dde0b7147e6516ea5eebb2657': 100.0, 'batch_size__fd833c2dde0b7147e6516ea5eebb2657': 50.0, 'depth__fd833c2dde0b7147e6516ea5eebb2657': 3.0, 'dropout__fd833c2dde0b7147e6516ea5eebb2657': 0.0, 'ensemble_size__fd833c2dde0b7147e6516ea5eebb2657': 5, 'epochs__fd833c2dde0b7147e6516ea5eebb2657': 4, 'features_generator__fd833c2dde0b7147e6516ea5eebb2657': , 'ffn_hidden_size__fd833c2dde0b7147e6516ea5eebb2657': 300.0, 'ffn_num_layers__fd833c2dde0b7147e6516ea5eebb2657': 2.0, 'final_lr_ratio_exp__fd833c2dde0b7147e6516ea5eebb2657': -4, 'hidden_size__fd833c2dde0b7147e6516ea5eebb2657': 300.0, 'init_lr_ratio_exp__fd833c2dde0b7147e6516ea5eebb2657': -4, 'max_lr_exp__fd833c2dde0b7147e6516ea5eebb2657': -3, 'warmup_epochs_ratio__fd833c2dde0b7147e6516ea5eebb2657': 0.1, 'descriptor': '{\"name\": \"SmilesFromFile\", \"parameters\": {}}'}. Best is trial 0 with value: 0.65625.\n", " \r" ] } @@ -3466,7 +3438,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -3519,10 +3491,10 @@ "name": "stderr", "output_type": "stream", "text": [ - "[I 2024-07-02 14:01:03,422] A new study created in memory with name: my_study\n", - "[I 2024-07-02 14:01:03,468] A new study created in memory with name: study_name_0\n", + "[I 2024-07-02 16:45:35,973] A new study created in memory with name: my_study\n", + "[I 2024-07-02 16:45:36,018] A new study created in memory with name: study_name_0\n", "INFO:root:Enqueued ChemProp manual trial with sensible defaults: {'activation__c73885c5d5a4182168b8b002d321965a': 'ReLU', 'aggregation__c73885c5d5a4182168b8b002d321965a': 'mean', 'aggregation_norm__c73885c5d5a4182168b8b002d321965a': 100, 'batch_size__c73885c5d5a4182168b8b002d321965a': 50, 'depth__c73885c5d5a4182168b8b002d321965a': 3, 'dropout__c73885c5d5a4182168b8b002d321965a': 0.0, 'features_generator__c73885c5d5a4182168b8b002d321965a': 'none', 'ffn_hidden_size__c73885c5d5a4182168b8b002d321965a': 300, 'ffn_num_layers__c73885c5d5a4182168b8b002d321965a': 2, 'final_lr_ratio_exp__c73885c5d5a4182168b8b002d321965a': -4, 'hidden_size__c73885c5d5a4182168b8b002d321965a': 300, 'init_lr_ratio_exp__c73885c5d5a4182168b8b002d321965a': -4, 'max_lr_exp__c73885c5d5a4182168b8b002d321965a': -3, 'warmup_epochs_ratio__c73885c5d5a4182168b8b002d321965a': 0.1, 'algorithm_name': 'ChemPropClassifier', 'ChemPropClassifier_algorithm_hash': 'c73885c5d5a4182168b8b002d321965a'}\n", - "[I 2024-07-02 14:02:28,149] Trial 0 finished with value: 0.46875 and parameters: {'algorithm_name': 'ChemPropClassifier', 'ChemPropClassifier_algorithm_hash': 'c73885c5d5a4182168b8b002d321965a', 'activation__c73885c5d5a4182168b8b002d321965a': , 'aggregation__c73885c5d5a4182168b8b002d321965a': , 'aggregation_norm__c73885c5d5a4182168b8b002d321965a': 100.0, 'batch_size__c73885c5d5a4182168b8b002d321965a': 50.0, 'depth__c73885c5d5a4182168b8b002d321965a': 3.0, 'dropout__c73885c5d5a4182168b8b002d321965a': 0.0, 'ensemble_size__c73885c5d5a4182168b8b002d321965a': 1, 'epochs__c73885c5d5a4182168b8b002d321965a': 5, 'features_generator__c73885c5d5a4182168b8b002d321965a': , 'ffn_hidden_size__c73885c5d5a4182168b8b002d321965a': 300.0, 'ffn_num_layers__c73885c5d5a4182168b8b002d321965a': 2.0, 'final_lr_ratio_exp__c73885c5d5a4182168b8b002d321965a': -4, 'hidden_size__c73885c5d5a4182168b8b002d321965a': 300.0, 'init_lr_ratio_exp__c73885c5d5a4182168b8b002d321965a': -4, 'max_lr_exp__c73885c5d5a4182168b8b002d321965a': -3, 'warmup_epochs_ratio__c73885c5d5a4182168b8b002d321965a': 0.1, 'descriptor': '{\"name\": \"SmilesFromFile\", \"parameters\": {}}'}. Best is trial 0 with value: 0.46875.\n", + "[I 2024-07-02 16:47:00,208] Trial 0 finished with value: 0.46875 and parameters: {'algorithm_name': 'ChemPropClassifier', 'ChemPropClassifier_algorithm_hash': 'c73885c5d5a4182168b8b002d321965a', 'activation__c73885c5d5a4182168b8b002d321965a': , 'aggregation__c73885c5d5a4182168b8b002d321965a': , 'aggregation_norm__c73885c5d5a4182168b8b002d321965a': 100.0, 'batch_size__c73885c5d5a4182168b8b002d321965a': 50.0, 'depth__c73885c5d5a4182168b8b002d321965a': 3.0, 'dropout__c73885c5d5a4182168b8b002d321965a': 0.0, 'ensemble_size__c73885c5d5a4182168b8b002d321965a': 1, 'epochs__c73885c5d5a4182168b8b002d321965a': 5, 'features_generator__c73885c5d5a4182168b8b002d321965a': , 'ffn_hidden_size__c73885c5d5a4182168b8b002d321965a': 300.0, 'ffn_num_layers__c73885c5d5a4182168b8b002d321965a': 2.0, 'final_lr_ratio_exp__c73885c5d5a4182168b8b002d321965a': -4, 'hidden_size__c73885c5d5a4182168b8b002d321965a': 300.0, 'init_lr_ratio_exp__c73885c5d5a4182168b8b002d321965a': -4, 'max_lr_exp__c73885c5d5a4182168b8b002d321965a': -3, 'warmup_epochs_ratio__c73885c5d5a4182168b8b002d321965a': 0.1, 'descriptor': '{\"name\": \"SmilesFromFile\", \"parameters\": {}}'}. Best is trial 0 with value: 0.46875.\n", " \r" ] } @@ -3589,7 +3561,7 @@ "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", 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", 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", "text/plain": [ "
" ] @@ -3686,9 +3658,9 @@ "name": "stderr", "output_type": "stream", "text": [ - "[I 2024-07-02 14:22:47,822] A new study created in memory with name: my_study\n", - "[I 2024-07-02 14:22:47,862] A new study created in memory with name: study_name_0\n", - "[I 2024-07-02 14:22:49,237] Trial 0 finished with value: -4430.271946796234 and parameters: {'algorithm_name': 'Mapie', 'Mapie_algorithm_hash': '976d211e4ac64e5568d369bcddd3aeb1', 'mapie_alpha__976d211e4ac64e5568d369bcddd3aeb1': 0.05, 'max_depth__976d211e4ac64e5568d369bcddd3aeb1': 9, 'n_estimators__976d211e4ac64e5568d369bcddd3aeb1': 50, 'max_features__976d211e4ac64e5568d369bcddd3aeb1': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 0 with value: -4430.271946796234.\n" + "[I 2024-07-02 17:07:15,971] A new study created in memory with name: my_study\n", + "[I 2024-07-02 17:07:16,012] A new study created in memory with name: study_name_0\n", + "[I 2024-07-02 17:07:17,576] Trial 0 finished with value: -4342.479127043105 and parameters: {'algorithm_name': 'Mapie', 'Mapie_algorithm_hash': '976d211e4ac64e5568d369bcddd3aeb1', 'mapie_alpha__976d211e4ac64e5568d369bcddd3aeb1': 0.05, 'max_depth__976d211e4ac64e5568d369bcddd3aeb1': 12, 'n_estimators__976d211e4ac64e5568d369bcddd3aeb1': 50, 'max_features__976d211e4ac64e5568d369bcddd3aeb1': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 0 with value: -4342.479127043105.\n" ] } ], @@ -3766,7 +3738,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -3798,7 +3770,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -3831,7 +3803,7 @@ "outputs": [ { "data": { - "image/png": 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kGa2tHRDF9oiWhXWkLj3fG6wfdeteP+G8rtV0z4g2vIbUjfWjbj3rJ1ruRUajwe/eHREN4t988000NTV5jYMHgPvvvx/vvvsuXnzxRTQ2NuKaa67BN998g5deegkzZ8702tZoNHr9LggCJk2ahKNHjw6qbC5XbFyQkiTHzLnEItaPurF+vEmSAkXp+qmWvwvrSB36em+wftRNkuSwXtdqvGdEG15D6sb6UTd3/cTivSiiQXxpaalXzncAWLBgAYqLi/HjH/8Yra2tuOyyy2C1WrF27VpMmTLFa9uPPvoIixcvxj//+U+MGzcOQFe3+3379mHBggVDdh5EREREREREQyGiQfyoUaN8Lk9PT8eoUaNw55134tChQ3jxxReRlpaGb7/91rNNWloaTjrpJJhMJtxxxx24++67ERcXh9WrV8NsNuPyyy8forMgIiKKXTabDXa7rd9tLBYzXC4XLBYzgK7JgyTJ3VVbCej19HoDDIahGWfq69wcDjscDnsfewxOfLweer3ea9lQni8R0XAjyzL27KlEVVUVMjLGorCwqFf2s2gU8Ynt+iJJEt599104nU5cdtllvdb/97//xdixY7FmzRqUlpbiqquugsPhwMknn4zXXnsNI0aMiECpiYiIYsuBA3WoqqrsdxtJcqGtrRUVFeUQRS0URYbT2Ym4uHhoNIF9WcrNzUde3uAyzPjL17nZbB39PrRQFEBRZGg0AjSavpf50hWwJ3gtG8rzJSIaTj766EM8/vgK1NbWoL3divXr30R29hQUFy9DUdG8SBdvUFQXxNfU1Hj+v3v37gG3Hz9+PMrKysJZJCIiomFr4sTJyMwc2+82FosZ27aVY/bsIhiNqbBaW7F9+xbMmnU6kpJSAno9vX7oWqV9ndtALfFWaxs+++xTFBScjKSk5D6X+dJXSzwREYXWpk2bsGzZYrS1tcFoTIEoCtDpDKiu3ouSksUoLV0V1YG86oJ4IiIiUg+Dwb/u3lqtFkZjKkymtO9y8WqRkpIKo9E0BKUMjr/n1l1LSzOqqvYgM3McTKa0PpdFg3B2M43VLqxEpH6yLOOxxx6D1dqG0aMzIUkSnE4H9Ho9EhMT0dBQj7KylVF9X2IQT0RERDTMbNq0CQ899AhqavaFvJtpeflmlJWtjMkurESkfrt370JNTQ1MpjRoeoxx0mg0SE1Nw/79dais3IUZMwoiVMrBic5HD0REREQUlI8++hDXXXcdqqr2ICEhEUajEQkJiZ5upuXlm4M+dnn5ZpSULEZV1d6QH5uIyB9NTU3o7OyETqfzuV6n08HpdKKpqWmISxY6DOKJiIiIhglZlvHEEyvR1tbVzVSv10MQBOj1emRkZKK93YqyspWQ5cBzKcuyjLKylbBarSE/NhGRv9LT0xEfHw+Hw+FzvcPhQFxcHNLT04e4ZKHDIJ6IiIhomKis3IX9+2uRnp4+YDfT4I5d51cXViKicJk+fQamTJmClpYWKIp3mlNFUWA2NyMrazLy82dEqISDxyCeiIiIaJjo6mbqDEs306amJjid4Tk2EZG/BEHAnXfeiaSkJDQ01MNut0GWZdjtNjQ01CMpKRnFxcuidlI7gEE8ERER0bDR1c00LizdTNPT0xEXF55jExEFYv78+Vi5chVycvLQ0dEBi8WCjo4O5OTkYfnyJ6J+kk3OTk9EREQ0TOTnz0BWVjaqq/ciI2O01zp3N9OcnLygupl2HXsyqqv3Qq/PDOmxiYgCNXfuGZg163Rs3VqOTZs2YP78BVGdVq676D8DIiIiipju+cD37KnkpGUqJwgClixZhuTkZBw5ciSk3UwFQUBx8TIkJsZuF1Yiii6CIGDatHzk5uZi2rT8mLn/xMZZEBER0ZArL9+MSy65EDfffB1effVPuPnm63DJJReiomJrpItG/Zg79ww8//zzyM0NfTfToqJ5KC2N3S6sRERqwO70REREFDB3PnCr1QqjMQWiKECnM6C6ei/uueduXHTRhfjBD86OdDGpD/Pnz8eMGTOxefPmkHczLSqah8LCopjswkpEpAYM4omIIsRms8Futw3Z6+n1BhgMhiF7PYpdPfOBS5IEp9MBvV6PxMRE1Nd/g/feew833FAc6aJSP9zdTBsaDoe8m2k4j01ENNwxiCciipADB+pQVVUZ0D6yLMPhsEOn0wf8pTg3Nx95edMD2ofIl4Hzgafg6NGjqKraizlz5kaolERERLGJQTwRUYRMnDgZmZljA9rHYjFj27ZyzJpVCKMxNaB99Xq2wlNoDJQPPD5eB5fLhebm5iEuGRERUexjEE9EFCEGQ3Dd27VaLYzGVJhMaWEoFdHAuucD9/Ue7ux0QKvVIi2N71EiIqJQ4wAlIiIiCog7H7jZ3AxFUbzWdeUDb8WoUaOQm5sXoRISERHFLrbEExFRv9wT8FksZrhcLlgs5rC+HifgUz93PvCSksVoaKhHcrLRkw+8rc2CxMREnH322ZzMjIiIKAwYxBMRUb/cE/BJkgttba2oqCiHKPb/8cEJ+GKfOx94WdlK1NbWoL3disREGTk5ebj66mvQ0dEW6SISERHFJAbxRMMYU5yRP9wT8Lkn1Zs9u2jASfU4Ad/w0Fc+cKu1Ff/973uRLh4REVFMYhBPNIwxxRn5o/sEfIFMqscJ+IaHWMsHPtDDTV/DSgYz1IQPN4mIKFAM4omGMaY4IyLyNtDDTV/DSpzOTlgsrdi6dTPi4uIDej0+3CQiokAxiCcaxpjijIjI20APN30NK6mvP4QtWz7EjBknITNzXECvx4ebREQUKAbxRERERN/x5+FmzweZFosZGo0GSUnJfLhJRERhF90D14iIiIiGiCzL2LOnElVVVdizpxKyLEe6SERENAyxJZ6IiIhoAOXlm73S6a1f/yays6eguHgZxo8PrAs9ERHRYLAlnoiIiKgf5eWbUVKyGFVVe5GQkAij0YiEhERUV+9FScli7NjxSaSLSEREwwiDeCIiIqI+yLKMsrKVsFqtGD06E3p9V3pNvV6PjIxMtLdb8ac/vcyu9URENGQYxBMRERH1obJyF/bvr4PJlAaNRuO1TqPRIDU1DV9++SWOHDkSoRISEdFwwzHxREQUE2w2G+x225C9nl4fXIpGii5NTU1wOp3Q6XQ+1+t0OrhcLnR0dAxxyYiIaLhiEE9ERDHhwIE6VFVVBrSPLMtwOOzQ6bq6SAciNzcfeXnTA9qHok96ejri4uLgcDh8PrRxOBzQarVISEiIQOmIiGg4YhBPREQxYeLEycjMHBvQPhaLGdu2lWPWrEIYjakB7avXsxV+OMjPn4GsrMmort4LvT7Ta52iKDCbm3HCCRMxevToCJWQiIiGGwbxREQxLJRdzC0WM1wuFywWc5/bRLKLucEQ3GtrtVoYjakwmdLCUCqKdoIgoLh4GUpKFqOhoR7JyUbIsgy73Ya2NguSkpJx2WVXorm5MdJFJSKiYYJBPBFRDAtlF3NJcqGtrRUVFeUQRd8fH+xiTrGoqGgeSktXeeWJT0yUkZOT58kT//77/xfpYhIR0TDBIJ6IKIaFsou5e/ns2UV9dj1nF3OKVUVF81BYWIStW8uxadMGzJ+/AIWFRRAEAV99dSDSxSMiomGEQTwRUQwLdRdzdj2n4UwQBEyblo+GhsOYNi0/4MkQiYhocPwZJiiKGkiSDa2tHZAkxa/hgH1RayYaBvFERERERESkev4ME9RoAFEUIEkyFKVrmKDT2Ynt27fGTCYaBvFERERERESkev4MExRFDVJSEjwt8YOh1mGCDOKJiIiIiIhI9fwZJqjVCjCZEiGK7XC55CEq2dBSVRB/8OBBXHTRRbj33ntx0UUXAQCqq6vxyCOPYM+ePUhLS8Pll1+O3/zmN559ZFnGU089hb/97W9oa2vDzJkzcd9992HcuHGROg0iIiKKsFCmV+zO19hKq7UNijK41h4iIiJ/qSaIdzqdKCkpQUdHh2dZS0sLrrjiCsyfPx8PPvggPv/8czz44INITEzExRdfDAB45plnsG7dOjz22GPIyMjA8uXLcfXVV+Odd95BfHx8pE6HiIiIIiiU6RW785VqsbPTwSCeiIiGjGqC+CeffBJJSUley9544w3ExcXhd7/7HbRaLSZNmoSvvvoKq1evxsUXX4zOzk68/PLLKCkpwRlnnAEAePzxx1FUVIQNGzbgvPPOi8CZEBERUaSFMr2ir226p1qsrz+ELVs+HFyBiYiI/KSKIH7Hjh3461//irfeessTjAPAzp07ceqpp0Kr/b6Ys2bNwvPPP49jx46hvr4e7e3tmD17tme90WhEbm4uduzYwSCeiIhomAp1esX+trFYzNBoNEGXlYiIKBARD+ItFgtuv/123HPPPRg9erTXuoaGBmRnZ3stGzlyJADgyJEjaGhoAIBe+40cOdKzLlhabXTnfhVFwesnqUs0148oar5L3aGJ+uukL2qun6H4+/f1GoG8drjLGao6Gg7v56HQ8+/YvX6i6e/qz/vB1zaCoPH8jIbz7V4/4bwGeH0FT82fQ8T6UbvhUD8RD+IfeOABFBQU4Pzzz++1zm639xrXrtPpAAAOhwM2W9eENb62aW1tDbpMgqCByZQY9P5qYjSqMy0CdYnG+pEkG0RRQEpKQsxcJ31RY/0Mxd+/r9cI5LWH6n0y2DoaTu/ncOr5d5Skrs/npCR9VP1d/Xk/+Nrm2DEDNBoNkpMNUXW+RqMBnZ0JYbsGeH0Nnho/h+h7rB91i+X6iWgQ/9Zbb2Hnzp145513fK7X6/Xo7Oz0WuZwOAAACQkJ0Ov1AIDOzk7P/93bBNOFzk2WFVgsHQNvqGKiKMBoNMBisUGSYjO1QjSL5vrpyrkpo7W1A6LYHunihIWa62co/v59vUYgrx3ucoaqjobD+3ko9Pw7Wq12AIDVao+qv6s/7wdf27S12aAoCtrabGhpUf/5dr9+wnkN8PoKnpo/h4j1o3bRWj9Go8Hv3gMRDeLffPNNNDU1eY2DB4D7778f7777LjIyMtDY2Oi1zv37qFGj4HK5PMvGjx/vtc2UKVMGVbZYySkoSXLMnEssisb6kSQFitL1M5JlD1f6KKCr62dnZ8J3X0C7ZpzW64MbXxtqQ/H37+s1AnntoXqfDPYaUsv7Odr1/Du6vzRF2z3On/eDr21kWfH8jK7zlcN6DfD6Grxou4aGG9aPusVy/UQ0iC8tLYXdbvdatmDBAhQXF+PHP/4x3n77bbz++uuQJAmiKAIAtm/fjhNOOAHp6elITk5GUlISPv74Y08Qb7FYUFVVhUWLFg35+RDR0AlX+igA343hFCBJMtxZo3Jz85GXN30wRSaKSv48MOuZO91qbYXL5UJrq9nzIMxfanlgRkREpFYRDeJHjRrlc3l6ejpGjRqFiy++GC+++CJ++9vf4uqrr8bu3buxZs0aPPjggwC6xsIvWrQIpaWlSEtLw5gxY7B8+XJkZGRgwYIFQ3kqRDTEwpU+CuhqiU9J6d0STzQc+fPArGfudJfLCYulFVu2bIZWGxfQ6/GBGRERUf8iPrFdf9LT0/Hiiy/ikUcewYUXXojjjjsOt99+Oy688ELPNsXFxXC5XLjnnntgt9sxc+ZMvPTSS4iLC+xLAxFFl/CmjxJgMiVCFNtjthsWkb/8eWDWM3f6kSOHsWXLh5gx4ySMHh3Yw7ZgHpiFanhNzx4FfW0jy7wvEBFR5KguiK+pqfH6ffr06fjrX//a5/aiKOK2227DbbfdFu6iERERDTv+PjDr/oDMam39brb25H4fmIVKz94CsixDUfoPtGVZRmdnJ+Lj4z3DayRJgtXahi1bPvQM4+tJkiQ4nU6vQL+z0zH4kyAiIvKT6oJ4IiIiokD07C2wf38N9u+v7XcfRVHgcNgRFxcHRQEURYbD4fDMg6H0M5Tf4bB7hg4AQEbG6EGfAxERkb8YxKtEqGfaFkUNJMnmNaa3O04cRMONLMvYs6cSVVVVyMgYi8LCon4ntyOi6NGzt8C0aSciK6v/LDXuIQBz5nQNAbBYzNiy5UPEx+tw+uln9DlvRs+hAwBw7NhRVFfvCdXpEBER9YtBvEqEeqZtX7Nrd8eJg2g4KS/fjLKylaitrUF7uxXr17+J7OwpKC5ehqKieZEuHlFMkWUZ1dVVqKurw/HHV2HMmOOH/IFZMEMAgK4heooCP+bN8N6vvzH0REREocYgXiVCPdO2r9m1u+NM2zRclJdvRknJYlitVhiNKRBFATqdAdXVe1FSshilpasYyBOFSEXFNrzyyguoqamG1WrFe++9hylTVvOBGRERUQgxiFeJUM+0zdm1ibpaBMvKVsJqtWL06MzvJqRyQK/XIzExEQ0N9SgrWxk1XettNptfs2cPlvs1HA572F6DYk9NzT68/fbb6OjoQFJSEgDAYEjgAzMiIqIQCziIv/rqq3HmmWfizDPPRGZmZjjKREQUEpWVu7B/fx1MpjRoNBqvdRqNBqmpadi/vw6VlbswY0ZBhErpvwMH6lBZ+ZlXPu6B9Dfspi/unN+HDn2FjAze52lgsixjw4YNsFqtGDNmLJzOTjgcduj1eiQnG6PugRn1FszcPYN56Mi5e4iI+hZwEB8fH48VK1bg4YcfRlZWFubPn48zzjgDBQXq/wJMRMNLU1MTnE4ndDqdz/U6nQ5mcwuampqGuGTBmThxMpKSknpNqtWf/obdDLTPuHHHD67ANGxUVe3F0aMNSEsbEVUPzGRZ9gSYFosZkiRBUfof4+4rMLVa26AoCqzWNrS0NPe5bzQHpsHO3eN0dmL79q0BP7zh3D1ERH0LOIh/5pln4HK5sHPnTnz00Uf44IMP8PzzzyMtLQ1z587FmWeeiYULF4ajrEREAUlPT0dcXBwcDofPL84OhwNxcXFIT0+PQOkCZzAYYDSm9jmMpi+Bbu/eR6fTB1tUGmZaWpohSRLi4+N9rlfrA7PuqeIkyQWrtQ0A+u3p4isw7ex0QJIkfP75p6iq6nuW+mgOTIOZu2cwOHcPEVHfghoTr9VqMWvWLMyaNQu33347Pv/8cyxfvhxvvfUW3n77bVRXV4e6nEREAcvPn4GsrMmort4Lvd67W7iiKDCbm5GTk4f8/BkRKiFRbDCZ0iCKIjo7O30G8mp9YKbT6T29VNwp5hQFfvd0AbqC+s2bN+Gzz/6HOXPmYt68+X22OkdzYBrs3D1ERBR6QQXx33zzDXbs2IFPPvkEO3bswOHDh2EwGDB37lycdtppoS4jEVFQBEFAcfEylJQsRkNDPZKTjZBlGXa7DW1tFiQlJaO4eBnH6BINUm5uHkaNysCxY98iMTHRa52aH5gJghBUijm38vLNWLVqBfbsqURHRzv+7//+hWnT8rF48a2cxI+IiMIm4CB+/vz5OHLkCJKSkjB9+nT89Kc/xWmnnYb8/HyIohiOMhIRBa2oaB5KS1d55YlPTJSRk5PHtFdEISIIAhYsWIC3334bDQ31SExM+u6BmR1NTcdi8oFZeflm3HTTtWhqOgZZlqEoCpxOJyoqtqK2tgZPP72a9xciIgqLgIN4QRCgKArS0tIwbtw4zz8G8ESkVkVF81BYWIStW8uxadMGzJ+/gLNkE4XYlClT8eCDp3nliQcQkw/MZFnGgw/ei8bGRmg0GoiiCFmWIQgCJEnGt9824sEH78WGDR/yPkNERCEXcBC/ceNGfPPNN6ioqEBFRQV+//vfo6mpCSeccAJOPfVUnHbaaTjnnHPCUVYioqAJgoBp0/LR0HAY06blD/sv1v2li+orLVQg6aJ6bhvNs3KT/2bPnoOzzz4XGzf+Gxs3vo+zzlqIs846J+aut127PkNNTTU0GiAuruurlKIoEAQBgiDA6XSipqYau3Z9hoKCkyNcWiIiijVBjYkfM2YMfvrTn+KnP/0pAKCyshLPP/88Xn/9dfz1r39lEE9EpHL9pYty54nvOUO309kJi6UVW7duRlyc71nI+zpGNM/KTYERBAE5Obn46qsDyMnJjbkAHgA+/XQnnE4n4uLioNFooCiKZ51Go4FWq4XT6cSnn+5kEE9ERCEXVBCvKAp2796Nbdu2Ydu2bdi1axdkWcbMmTMxb17sdJcjIopV/aWLcueJ7zlDd339IWzZ8iFmzDgJmZnj+j1+z2NE86zcNDT66x0SDg6HfdDH6Ba7+7WciIgoFAIO4m+66SZ88sknsFqtSE1Nxdy5c/HLX/4SRUVFSE5ODkcZiYgoxPpKFyXLMvbsqURtbS3Gjp2AwsIJnpZUi8UMjUaDpKRkv2buDiY/PQ1f/fUO6Yssy3A47NDp9AG3+E+YMDGg7bs7+eSZiIuLg8vlgij27pUiSS7ExcXh5JNnBv0aREREfQk4iD9y5AgWLVqEM844A9OnT4dGowlHuYiI+hVMq52/Y7pFUQNJsqG1tQOS1NWkNhzGdJeXb/aaxX/9+jeRnT0l5iYlI3Xqr3dIX9w9Pty53gPhcNjx5ZcHAtrHbcaMEzF1ag727KlEZ2fnd6npFMiyDEmSAABTp+ZgxowTgzo+ERFRfwIO4tevXx+OchARBSTYVjunsxPbt2/tt9VOowFEsWuWaXe32Fgf011evhklJYthtVphNKZAFAXodAZUV+9FSclilJauwvjx/XehJxqMvnqHDCTYHh8tLc0Bv5abIAi4776HPCnmJEnypJkTBAEjRhyH++57KCbnAyAiosjzK4j/zW9+4/cBNRoN/vSnPwVdICIifwTTaucvUdQgJSWhV0t8rJJlGWVlK2G1WjF6dCYkSYLT6YBer0diYiIaGupRVrYSf/zjikgXlUg1iorm4emnV6OsbCX27NmNjo4OJCQkYNq06ey9QkREYeVXEP/JJ59Ao9Fg8uTJSElJ6XdbhbO5ENEQCLbVzh9arQCTKRGi2A6XSw7La6hJZeUu7N9fB5MprdcQKY1Gg9TUNOzfX4eamn0RKiGROhUVzUNhYRE2bHgX//3v+/jBDxZiwYJz2QJPRERh5VcQf/XVV+Pf//43Dhw4gMLCQvzoRz/CWWedhYSEhHCXj4iIwqypqQlOpxM6nc7nep1OB7O5BWazeWgLRhQF3Cn1vv46dlPqERGRuvj1SVNSUoL//ve/WLt2LSZMmIAVK1Zgzpw5WLJkCTZu3IjOzs5wl5OIiMIkPT0dcXFxcDgcPtc7HA7ExcUhNTV1aAtGRERERL0E9Lh4xowZuOuuu/Dhhx9i9erVSE1NxX333Yc5c+bgrrvuwpYtWyDLsd/1lIgoluTnz0BW1mSYzc29hkQpigKzuRlZWZMhyzLq6upQXV3Fez0RERFRhAQ8Oz3QNUby1FNPxamnnor77rsP27dvx7vvvosbbrgBRqMRW7duDXU5iYhinj9p8/xNk+fP9u60eYIgoLh4GUpKFqOhoR7JyUbIsgy73Ya2NgtEUYuWlhbccUcJ2tuteO+99/HSSy9w8i4iIiKiCAgqiO9u165d2Lx5M7Zs2QKn04m0tMBSvBARURd/0uZJkgttba2oqCiHKGohyzIcDjt0Or3Psbg9t++ue9q8oqJ5KC1d5ZUnPjFRxujRmTh69CiOHKlHUlISNBrAYEjwSj3HQJ6IiIho6AQVxH/22Wf497//jQ0bNqChoQEnnHACLr74Ypx77rmYNGlSqMtIRDQs+JM2z2IxY9u2csyeXQSjMdXz+6xZhTAaUwfcvrueafPcM21v3VqOTZs24Mwzz0JZ2eOor6/H6NGZcDo74XDYodfrkZxs9KSeKyws4mReREREREPE7yC+Z+A+btw4XHDBBTjnnHMwderUcJaRiGhY8DdtnlarhdGYCpMpzefvA23fH0EQMG1aPhoaDkOjEfDFF/sHTD1XWbkLM2YU+HGG5C9/hlaEkntoBREREamfX0H8GWecgaNHj2L06NH40Y9+hHPPPRd5eXnhLhsREUVQS0uzX6nnmpqahrhksc+foRU9DTS0oj/dh1YQRQofXhER+cevIL6hocHzheC9997De++91+e2Go0GGzduDE3piIgoYkymNE/qOV9fdN2p59LT0yNQutjmz9CKngYaWtGfnkMriCKBD6+IiPzjVxB/4YUXhrscRESkMrm5ecjKmozq6r3Q6zO91rlTz+Xk5CE/f0afx3A47GhpaQ552URRA0myobW1A5L0fVq8WGlZ83doBRCa1ku73f9jxMrfmNSHD6+IiPzjVxD/6KOPhrscRESkMj1TzyUmJn2Xes6OpqZjSEpKRnHxsn5bvw4d+gpffnkgoNf1p2VNowFEUYAkyeie2n44tqy5Wy/7y0TQE1svSY0CeXjVXSDzfhARxYJBp5gjIqLY1T313L591Whvt0JRgJycPL/yxI8bdzyysqYE9Jr+tKyJogYpKQk+W+KHG3frZX+ZCHpi62Vs8rdXRveeLBaLGS6XCxaLOeDXY68MIqLIYBBPRET9cqee27DhXfz3v+/jBz9YiAULzvWrBVen0wfVOjbwjPsCTKZEiGI7XC454OPHku6tl4G0SLL1Mvb4O6a8e08WSZLhdHZi+/at7JVBRBQlGMQTEdGABEFATk4uvv76AHJycpkXnkiF/B1T3ldPlkCxVwYRUWQwiCciogHJsozq6irU1dVh/PgqjBs3gYE8xRRZlvH111/Dam3H2LETUFhYFHXvcX/HlLMnCxFRdGMQT0RE/Sov3+w1Jv69997HSy+94NeYeKJoUF6+GStW/BHV1Xvgcrnw9ttvITt7Ct/jRESkSn4F8Tt27AjooDNnzgyqMEREpC7l5ZtRUrIYVqsVSUlJ0GgAgyEB1dV7UVKyGKWlqxjkRLFQpQDsOTlaW1sbFEVBW1ub1/FDORGaLMvYs6cSVVVVyMgYG3TLeUXFNjzwwG/R0tICjQbQ6/V8jxMRkar5FcRfeuml0Gg0Ptcp3+X26b6+uro6BEUjIqJIkmUZZWUrYbVaMXp0JpzOTjgcduj1eiQnG9HQUI+yspVBB099zaTtz2zZfeWJ7w9n0u4tVCkAe6a36+x0QJIkfPbZTuzd+/1Ea6GaCM3dO6S2tgbt7VasX/9mUC3nsixj+fJH8c03h6EogCxL0Gg06OiwYdSoUbBa2wb1HiciIgoHv4L4P//5z57/19fX495778XFF1+Mc845B8cddxzMZjM2bdqE119/Hb/73e8CKkBTUxMee+wxlJeXw+FwYObMmbjjjjswadIkXHrppfjkk0987veHP/wBP/nJTyBJEgoKCuBwOLzW33zzzbjlllsCKgsREX2vqmov9u+vg8mU1utBrkajQWpqGvbvr0Nl5S7MmFEQ8PH7mkm7v3zn7gBSr9cjLk7bK098f2J5Ju1AWqW7b5uefhzmz18YUIDqKz1dz/R2R44cxpYtH6Kg4BSMHv39RGuhmAite+8QozEFoihApzME1XK+dWs59u2rAoDv3mtdbya73YbDhw9j5MiRg3qPExERhYNfQfypp57q+f+ll16Kyy+/HLfeeqvXNieddBL0ej1eeeUVnHvuuX4X4KabboIsy1i9ejUSExOxatUqXH755diwYQOefPJJOJ1Oz7aKomDp0qVobW3FD3/4QwDAl19+CYfDgbfffhvp6emebRMSEvwuAxHRcNNfPuljx47C4XBg//4a2O02JCUlobPT4bkfu39qNIDdbkd19V6kpqZ4HcNqbYPD4UB9/SFYLGbEx+uh1+u9tklPH4FTT53da11/+c6/X3c6xo/PDLglvrtQdceOtEBapUPVgu0rPV33ZVZrKzQaDZKTk0Oawq5n7xBJkuB0OqDX65GYmOh37xBZllFZuQsbN26EJEnQ6XTQaAQoStckb6KohSS5YDa3wGBIQFNTU8jOgYiIaLACnthu9+7duOGGG3yuKygowAsvvOD3sVpbWzFmzBhcd911yM7OBgDceOONuOCCC1BXV4fp071bTF577TXs3r0bb7/9NhITEwEANTU1SEpKwtSpUwM9FSKiYau/fNLt7VZ0dLSjoaEBLpcLzc1NiIuLA9D1MLWjox1AVzDvcjlRXV2J1tZmKIoMjUaARtO1nSRJ2LLlQ2g0mu+6svt+uOqrhby/HOZarRYpKakYMWJE0LNrhyqYjbRAWqVD2YIdKZWVuwbdO8Rd93v37vEE552dTmi1Wq9jiaIIu90OgyHBq5GAiIgo0gIO4jMyMlBeXo45c+b0Wvfee+9h/Pjxfh8rJSUFK1as8Pze3NyMNWvWICMjA1lZWV7bNjc344knnsANN9yAiRMnepbX1NRg0qRJgZ7GgLRa9bfGiKIGGk3Xz57lFUXB6yepSzTXT3/vu1ih5vrp+fcfqD76Wp+dnY1x48b5fI3GxqP4+ONtmDt3Pj777HPs318HozEFsiyjo6MDCQkJEAQB337biKlTc3Dlldeivb0dn322EwUFpyA5ORltbW1ev+v1euh0ep+vZzAYvMrW/73NvS74Ovroow9x221LYLW2eYJZvd6A6uoq3HbbEqxcuQpz554R8HGHmizLePLJx9HebkVm5vet0gaDHklJiThy5AiefPJxzJvXFZj7u63DYYfN5ruXBgBYra2QJBes1laIosbnso4O63cPfKywWFr6PY+utGj+9Z4zm1vgdDqh1+ug0XT1BgHg+b9er0NrawvM5haf10P3unc/mNJoNFAUGS6XE4IgeFrwNRoNZFnG6NGjUVBQ0G/LviBoPD+j4b6o5ntcoGL1MymW6igWsX7UbTjUT8BB/BVXXIEHHngAjY2NOPPMM2EymXDs2DG89957+PDDD7Fy5cqgCnLvvffijTfeQHx8PJ599tle3eFfeOEF6PV6XHXVVV7La2tr4XK5cNVVV2Hfvn0YNWoULrvsMlxwwQVBlQPo+hA2mRKD3n+oSJINoiggJSWhz/IajZzESc2isX78ed/FCjXWT8+//0D10df6/uouJSUBlZWfIT8/B7///SO47rrr0NLSjJSUFHSNGVbQ0tKMtLQ0/P73j+CUU07EsWPHUFOzF3l5UzBixIhevw/mHH2tS0rqeiAQaB3Jsoynn16F9nYrxo4dC0mS0NnpQGJiAozGZHzzzTd4+ulVOP/8c1Tftf7TTz/FgQP7MWLECGi1ItzjuUVRgFYrIj09DQcO7MeXX9YCQEDbfvrpp32+rsvlQlubBdu3b/G0XsuyDEly4ZNPtkEQBDgc7ontPkVV1Z5+z+Pkk0/GySef7Nc5n3DCWOj1OrhcThgMBijK91/URFH4bsI9HU44YWyv907PurdarWhs7AranU6np/fI90G9C4Ig4IYbrkd6enK/5Tp2zPDd8AFDVN0X1XiPC1SsfybFQh3FMtaPusVy/QQcxP/iF7+Ay+XCs88+i3/961+e5aNHj0ZpaSnOOeecoApy2WWX4ZJLLsHatWtx0003Yd26dcjLywMAWK1WvPHGG7j55puh0+m89qurq4MsyyguLkZGRgY2b96Mu+66C06nEz/96U+DKossK7BYOoLadyh1jQWV0draAVFs91onigKMRgMsFhskKfCuphRe0Vw//b3vYoWa66fn33+g+gimvrrvU1BwGkpLn8ATT6xETc0+tLdbIUkycnLysGTJMhQUnIaWlt7lGMz7pL993eusVjtGjEDAdfT555+huroaqakmyLLi2VeSuoYCpKaaUF1djc2bt+HEE9U9kdnBg4dhtzuQmmqCJMk9zkVGXFw8HI5mHDx4GAD83nbOnDn4wQ+O6/N1W1vN2Lr1I8yadTpSUlJ9bnPkyGFs3vwBCgpO9prYzheDwYCWFv/eIxMmZGPixCxUVe3F6NGjvc4DkNDU1Izc3DxMmJDd65g96z4+Xoe4uDi4XC7Ex+vgdHZ6AnkAEAQReXm5+NnPfj1g+drabN+l1LP5fS6RpOZ7XKBi9TMpluooFrF+1C1a68doNPjdeyDgIB4AFi1ahEWLFuHAgQNobW2FyWTChAkTgjmUh7v7/COPPIJdu3bhtddew6OPPgoA2LhxIzo7O3HxxRf32u///u//IEmSZ4z81KlTUV9fj5deeinoIB5AUGMsh5Isy9i1azf27q3CqFF9T8gkSbLqz2U4i8b6kSQFitL1M9rKHqhw1k9/E8v1x2Ixw+l0oaWlBZKk9Pq9r+07OmwwGv07l551PGfOXMyadTq2bi3Hpk0bMH/+As89x/336bnPYN4n/e37/brvg7dAjt/Y+C06O50wmXRQFHhmtnf/Pz5eh5aWFjQ2fqv693dqqglxcXGw2x3ftUp3LXefi93ugFYbh9RUEwD4vW1cnB5xcb6HPgBddSCKWiQlpcBoNPncprXVDI1Gg4SEpD636S6Qv/UttyxFScliHDlSj+RkI2RZhs1mQ1ubBUlJybjllqWQ5a7Pye581b3RaITZbIYsS56APiEhAVqtFiZTGu699yGfx+pJlhXPT7W/b7qLxs+gnmL9MykW6iiWsX7ULZbrJ6ggHuialO7gwYNobGzEwoULceDAAZxwwgl95pP3pbm5GRUVFVi4cKGnS54gCMjKykJjY6Nnu40bN2LevHkwGo29jtFztmOga6znP//5zyDOKjrEyoRMRMNZfxPL9aUrWOmAw+HwpF/rLx0b8H26tkOHvkJGRmbQ5RUEAdOm5aOh4TCmTctXfVfzvqSnpyMuLg4Oh8NnzniHw4G4uLiomMgsP38GsrImo7p6L/R677pVFAVmczNycvKQnz8DAALaVs2KiuahtHSV1+dgYmJX75D+Pgd91b1er0dm5hgcO3YMdntXa7pGo0Fubj5uvfV2fqYSEZEqBRXEP/vss3j++edht9uh0Wgwffp0PPHEE2hpacHLL7/sM9j25dixY1i2bBlefPFFFBUVAeia7biqqgrz58/3bLdz506fOd8tFgvOOuss3Hnnnbjooos8yysrKzF58uRgTk31YmF2YSICJk6cjMzM/rsZ92SxmLFly4fQauM86df6S8fm3mfbtnKMG3d8iEoe3QINfNVMEAQUFy9DScliNDR83yptt3/fKl1cvMzzwCWQbdWuqGgeCguLfPYO6UtfdZ+YmITkZCMOHfoKSUlJuOSSX+Dqq29EenpgczkQERENlYA/rV977TU8+eSTuOKKK/DGG29A+a5P3qJFi3Do0CGsWrXK72NlZ2dj7ty5ePjhh7Fjxw7U1tbizjvvhMViweWXXw4AOHLkCFpaWnymkDMajZg1axYef/xxbN68GV9++SVWr16Nf/7znz6D/mjXMz+uXq+HIAjQ6/XIyMhEe7sVZWUrB+z2R0SRZzAYYDKlBfTPaEyFKIoQRdGTfs1oTPVKx+ZrH61W2+fM8MONO/BNTExCQ0M97HabJ5htaKiPymC2tHQVcnLy0NHRAYvFgo6ODuTk5GH58ie8HuoGsm00cPcOyc3N9at3SH91f/ToEaSkpOInP/kJxo8/Pmrqn4iIhqeAW+JfffVVXHvttVi8eLFn8hcAmDdvHpYsWYLVq1fj3nvv9ft4K1euxIoVK7B06VK0tbXhlFNOwdq1a5GZ2fWU/NtvvwUApKam+tz/97//PZ588kncf//9aGpqwqRJk1BWVuZp2Y8lgeTH9XemXyKi4SbY7thqFUirdDAt2LGkv7q/4oprYLE0eeYLICIiUquAg/j6+nqceuqpPtdNnDgRx44dC+h4ycnJeOCBB/DAAw/4XD99+nTU1NT0uX9SUhLuuusu3HXXXQG9bjRqamqC0+nsNUO/m06ng9ncgqampiEuGRFRdIm1YDaQOQtiZX6DYPVV962tZrz//juRLh4REdGAAv7kHj16ND777DOf6/bs2YPRo0cPulDkW/dJeXyJpgmZiCh2ybKMPXsqUVVVhT17KgMa4jOYfQMVaHdsih2seyIiimYBt8T/9Kc/xZNPPgm9Xo8zzjgDANDR0YH3338fzz//PK644opQl5G+E0sTMhFR4GRZxtdffw2rtR1jx05AYaH6hg1VVGzDK6+8EFT2DGbeICIiIhpYwEH8Nddcg8OHD6O0tBSlpaUAgN/85jcAgPPPPx/XXXddaEtIHoHORExE6hVonviKim149tky1NTUQJJc+Mc/1mPSpMlYtOhSuFwuWCxmn/tZLGa4XC44HPYQlbxvNTX78Pbbb6OjoyPg7BnMvEFE0STQe3igRFEDSbKhtbUDkqRArzf4TItJRMNTwEG8RqPB7373O1x55ZXYvn07zGYzkpOTMXPmTGRnZ4ejjNRNrE3IRDRcBZInvqZmH/7yl3Ww2+3Q6/XQ63VQFAWVlbtw//37cN5550GjQVjzxA9ElmVs2LABVqsVY8aMhSRJcDod0Ov1SExMRENDPcrKVvocd94z80Z/+1JkdR/ukJExts/6rK6uQl1dHY4/vgpjxnC2d4o9gdzD3WRZhsNhh06nH/Ca6LqnC5AkGYoC5ObmIy9v+mCKTEQxJOAg/qmnnsLPfvYzTJgwARMmTPBad/jwYbz88su47777QlU+8iHWJmQiGo78zRMvyzLWrl0HSZKRmmpCe7sVGo0GaWnpSE8XcfToEWzbVoHrr78FqalpvfYfqjzxVVV7cfRoA9LSRgyYPWPGjAKv9YFk3hg/nvnuI8Wf4Q7ubWpqqmG1WvHee+9hypTVUfuQ2d/WVqu1DYqiwGptQ0tLc9Cvx9bW6OHvPbw79/141qxCGI2p/W4rihqkpCR4tcQTEbkFHMQ//fTTmDt3LkaNGtVr3a5du/C3v/2NQfwQGO6zCxNFO4PBvy/ru3Z9htraatjtNlgsrZBlGRqNBu3tHRg1ahQMhgR8881hbN68Gb/5zRU+7wVDkSe+paUZkiQhPj7e5/r+smcEknmDQXxv7kDTPXSir6EV3QWyLdA1nOP++++G1WpFSorv4Q4APEMikpKSAAAGQ0JUD4nwt7W1s9MBSZLw+eefYs+e3X63tvbE1tbo4e89vCetVgujMRUmU++Hrt7bCTCZEiGK7XC5wjfBJxFFJ7+C+F/84hfYtWsXgK4J1C655JI+t83Pzw9NyYiICJs3f4iWlhYAgCiKnuU2mw0HDx6ARqOBoih47LGH8K9//bNXi6fDYQ8oWAP6DvD6C/zi4+MgiiIcDrvPQL6/7BndM2/4+lLMzBv9cwea7qETFRXlPodWdCfLMpzOTmzfvnXAQFOWZTz99FNobm5CamoqBEGAIAhewx1WrVoBjUbjGRLhdHbC4ega/pGcbOxzOMVgxxUH+jDCn5ZuWZY9x0tPH4FTT5094HEbGxvw6aefIDt7KhISEvHZZ59i2rTpSEpKHnDf+Hg99Hq9p3xEREQD8SuIf/jhh/Hee+9BURQ8/fTTuPjii5GRkeG1jSAIMBqNWLBgQVgKSkQ03MiyjHfffQeKokCrjYMgdAXsiqJAliXPdhqNBgkJiT5bPA8d+srvwK776/oK8PoLEp3OTqSlpaGpqblX4DJQ9oxAMm+0tpr9OodoF0hwm5bWFWharW347LNPkZ9/ol/BY3c6nb7P3hp79lSitbUVI0dmQK/XQ1FkdHZ2pTp1D3eorq6CRgO/hkR0H04x2HHFiiIH9P72p6Xb6ewM6Hpx7wMAdXU1EAQBHR1WVFZ+HrIyERERdefXJ1RWVhZuvvlmAF0fxj/72c+8utO7XC5otQH3zCcion5UVu7C0aMN0On0cDo7IQhd91lJ+j6A7wrwtTAaU6DVanu1eI4bdzz276/F7NlFA47BHIh7PKevY1ksZtTV1eGdd94JOHuGv5k3AHgmVRszZhxOOCGw8ajRJNjgVpYl7NmzO6TduJ1OJyRJQkJCAgRBgMvl8lqv0+nQ2dnp+b8vfQ2nGOy4YgB9vid98aelOy4uPuDrxV2mOXOKwlImIiKi7gKOvG+++WasXr0aO3fuxOrVqwEAn376KW699VZcf/31WLRoUcgLSUQ0HLnHimdkZOCbbw7D6XQC6Arc3d3oASAhIQEajcZni6dOp/d7DKY/+jtWbm4eZs6c45Un3t/sGQNl3gCASy650GtStaefzsNNNy3GnDlzB31eahNMcDsY/QWS/gx3iI+Ph0aDgIdEDHZccff/h+L9Dbh7FgZ+vHCWiYiIqLuAg/iXX34ZTzzxhFewPn78eJx99tl47LHHoNPp8LOf/SykhSQidfAnvRSFjjt4iouLx7hx49HQcAQ2W1cXa0Xp6hklCIJXENTfBHJDYfbsOTj77HODyp7RV+aNrVvLe+WQ1+sN2L17N5YtW4zly5+IugnTBhJscBsO/g13yIVGo/FrSAQRERENTsDfvl9//XUsWbIEd999t2fZ6NGjcc899+Dmm2/GmjVrQlk+IlKJ8vLNuOSSC3Hzzdfh1Vf/hJtvvg6XXHIhyss3R7poMcsdPJnNzUhMTMK4ceNhNBohCAK0WhGCIEKvNyAuLs6zjxomgXNnz8jNzQ04e0bPfQF45ZDX6/WeSdXGjBkDq9WKsrKVkGXO3hwu7uEOiYlJaGioh91u8wx3aGioR1JSMhYvvrXHNvbvtrF7tulrOAUREREFJuBP06NHj/Y5A/2MGTNw+PDhQReKiNSlvHwzSkoWo6pqLxISEmE0Gr0mUmMgHx49gyeHwwG9Xg9RFCFJEkRRwIgRx3kmEnO3eGZlTY6ZFs+BcsibTCbP8AEKH/dwh5ycPHR0dMBisaCjowM5OXmenhDdt7HZOmC1WmGzeW9DREREgxdwd/oxY8agoqICs2f3TrmyY8eOXrPWE1F0k2XZqyVUkiQ4nQ6v9FK+UkdRaHQfK75vXzXa263Q6XQQRRF6vR5arQiHw+nXBHKD1V+6up6pvgJN/eXrWA6H3a8c8i0tkRs+MJz0Ndyh+3vNvc3Gjf/Gxo3v46yzFuKss87hvYGIiCiEAg7if/7zn2P58uVwOp0466yzkJ6ejubmZnzwwQd45ZVXcOutt4ajnEQUIQO1hPaVOor6F0gKsWnT8vHccy9h8+ZN2Lp1M9LS0jF58lSsW7cWX3xRh46ODiQkuJCVNQXXXnsDpk3LR0tLMwDvgHiw+ktX1zP9nPv3rVs3w+VyQafT+x3Iufc9dOgr5pBXGfdwh4aGw5g8eXKfKf/Gjh2HrKwsjB07LmRpAf3J8U5ERDQcBBzEX3755Th69CheffVVr/HvoijisssuwxVXXBHK8hFRhPnTEhrJidSiVTApxJzOTkyePBkajQay7MQvf/kLfPXVl/j220Ycd9xIHH/8BLS3t2Ljxn979ukeEGdkZPZz9IH1l66uZ/o59+/Tp5+I3bs/x6xZhX6n7HLvO27c8Rg5MqPfSdVaWlqQk5MbM8MH1KjnAyf3g6G6un345ptDPvfp7OyEy+XCzp0fIz7+M89yWZbR2dmJ+Pj4Ph/qaDSCz3XMp05ERNQlqOTud9xxB2688UZ8/vnnMJvNMBqNmD59OkwmU6jLR0QRxpbQ8Ag2P/aWLR9CUdArWJ4zx3dO6u4B8WANlK6u5zqtVoukpOSg0m1ptVpP631fOeS//bYRSUlJnDAtzHo+cPr+wZAMjcb3310QBM+/7zIhAuh68OJw2BEXF+e1vLtJk7KQlTWl13LmUyciIuoSVBAPAImJiTjuuOOgKApOOukkuFyuUJaLSLUC6QbdF1HUQJJsaG3tgCT18U32O5HuQupfeimmjgpUsCnERFGEoqBXsNxfkOwOiKOVrxzykiRj+vTpMZsnXk16PnAa6MERAFitrdi+fQtmzTodSUkpfu3rcNjhcNgRH+/7vWq3d917e863EOzcC6EYYkLkL6ZoJaJQCiqIf/vtt7FixQp8++230Gg0+Nvf/oYnn3wScXFxWLFiBeLj40NdTiLVCKYbtCzLcDjsnpZFjQYQRQGSJPfZGuUW6S6k/bWEhnsiNSK3npOq/fCHC3H++eegtdUGl4vp5cLJ1wOngR4ciaIGWq0WKSmpMBpNfu27d+9uv+6t3edf0GgEOJ2d2L59a8D3oAkTJga0PVGwyss3ez2EXL/+TWRnT0Fx8TJmbSCioAQcxL/77ru444478OMf/xhnnnkmli5dCgD44Q9/iAcffBDPPPMMlixZEupyEqlGsN2gt20r94wLFkUNUlIS/G6JjzRfLaGJiTJycvL4JYSGTPdJ1QLNP0/q5++9tef8C8FyOOz48ssDQe/viyzLqKray9ZW8nCnaLVarTAaUyCKAnQ6gydFa2npKn6GElHAAg7in3vuOfziF7/AAw88AEmSPMsvvvhiNDc344033mAQTzEt2G7Q3VuftFoBJlMiRLE9aloR/UkvRUQUrEDurcHMs9CTO4NDqNTU7MPatetw8OAXbG0lAEzRSkThE/Ad4+DBg/jhD3/oc92MGTNw9OjRQReKiNTJ3RKam5vLllAiou9UVGzDX/6yDjU11UhISITRaERCQqKntbW8fHOki0gREEiKViKiQATcEp+eno4vvvgChYWFvdZ98cUXnKGaiIhIRUIxGWd3PSeW60kNQ4CGkizLWL36Wdjtdowbdzw0Gg1bWwkAU7QSUfgEHMSfe+65KCsrw8iRIzFvXlf3MI1Ggz179uCZZ57BeeedF/JCEhERUXBCMRlnd90nlhPF3l8jcnPzMW7cuEGVOZpUVu7CwYNfIDExccDW1hkzCiJUSooEpmglonAJOIhfsmQJamtrsWTJEs8H+6WXXoqOjg6ccsopWLx4ccgLSURE5K9AWp4HalX2R6TTQA4kFJNx+lrX18Ryer0BTufwSd/mbm3V69naOtTUnraNKVqJKFwCDuLj4+Px4osvYuvWraioqEBrayuSk5Nx6qmnYt68eb2eQhMREQ2lQFqe3a3KW7duhsvl8tnyPJBIp4EcSCgm4wxkHYBhFcS7W1tdLpfP9WxtDY9oSNvGFK1EFC5B5YkHgMLCQsycORMWiwUpKSmIi4sLZbmIiKgbd4tTdXU1EhMTIcvRkdUgEgJpeXa3Kk+ffiJ27/7cZ8vzQIbbGHDylp8/AyecMAmVlbuQlqZ4NWawtTU8oiltG1O0ElE4BBXEf/TRR3jmmWewe/duKIoCURRx8sknY/HixTjppJNCXUYiomGte4uT1doGrVaLbdu249Zbb8e0afmRLp7qBNryrNVqkZSUHJK0ZTT8CIKAa6+9AbfdthiNjQ1ISUlla2sYRWPaNqZoJaJQCziIf//997FkyRJMnToVN998M9LT0/Htt9/iP//5D37zm99gzZo1OOWUU8JRViKiYadni5MgaOByuVBbW42SksW4//6HI11EimJ9zR/Q31wB/swjYLW2wuVyobXVDElSPMsdjtjsZj979hz88pe/ws6d//PkiWdra3gEkrZNTRMJulO0NjQcZopWIhq0gIP4p59+GgsXLsQTTzzhtfzmm2/GLbfcghUrVuAvf/lLqMpHRDRs+Wpx6uy0Iz4+HsnJKTh2rBGrVz+LX/7yF5EuKkWpvuYP6G8GelmW4XR2Yvv2rX0GIl37W7B160de+0+YMDG0JxAGsiwHPNGhxWLGpElZ+PWvL0NNzT5s27YFc+acjpkzZ0EQBLS0NPe5r9onRlQbpm0jIgoiiP/qq69w++23+1z385//HLfccsugC0VERP61OB08+AUOHz4UoRIOrLPTEfDs7/60Are2mnHsWAJaWzu8WnoZEAWmr/kDBpqBfiBWayu2b9+CWbNOR1JSime5w2HHl18eGEyR/eJPhoKe7zOLxQxJkuBwOLBly4cQBDGg13Q6O/HJJxVQFBljx2bCbG7Cpk3vD7if2idGVBumbSMiCiKInzRpEiorK3H66af3Wnfw4EGMHRtYGhsiIvLNnxanlhYnrNb2IS6Z/xoajvSbU9yX/lp6v59N/iPs3BkPSZKhfB/DMyAKUH/zBwxmjgBR1ECr1SIlJRVGo8mzvL8W6VDyJ0NBz94GXe87J5zOTuh0OgSSbGfSpGxkZU0BEPgDEE6MGBimbSMiCiKIf+CBB3D99ddDo9HgJz/5CUaOHAmz2YyNGzeirKwMDzzwAOrr6z3bZ2Zm9nM0IiLqiz8tTlptHJKSEiNQOv9kZIzG4cOHgm7R7ckdIBUWzsX48Zk+W+KJ/MlQ4CvYdi+bMyew92vPHiCcJDF8mLaNiCiIIP7nP/85AOCJJ57AqlWrPMuV75pCbrvtNq/tq6urB1M+IqJhy58Wp6ysKRg7dpzP/d1p6aqqqpCRMTYksyH3NV7YV9dkl8uFzs7OQb1e9+DIZrMN2C3fbh+4G3V37nJarW29uvCza3708jdDga9gmwG4+jFtGxENdwEH8b///e97jc0kIqLQ66vFyeVyobGxAUZjCq699ga0t7f22rd7Wrr2divWr38T2dlTBv0F1+Gw++wa37Nrsvv3zz//FFarFR988B8YDAkBP0To3j3+wIE6VFZ+1m93+p5kWYbDYYdOp/f52t3L2d5u9To3ds0nUi+mbSOi4SzgIP6iiy7qd73FYoHRaAy6QERE9D1fLU6iKCInZ5onT/zGjf/22qdnWjpRFKDTGVBdvRclJYtRWroq6EBep9Nj1qzCXl2Ne3ZNdv8+ffqJ+OyzT6HRwOd+A+nePX7ixMlISkrqtzt9T+5y9PXa3cu5e/fnXl2r2TWfSN2Yto2IhquAg/irrroKjz32GI477rhe6z788EPcd999+Oijj0JSOCIi8m5x2rjx30hMTMRVV92A9PQRvSYK85WWzul0QK/XIzExEQ0N9SgrWxl0i5UgCH12Ne7ZDVmr1SIpKRmiKEKjwaC7KBsMBhiNqZ4J00aMGAFRbIfLJfe730Ddo93lZDdqIiIiigYBB/FVVVU4//zz8dBDD+GHP/whAMBqteKRRx7BP/7xD+Tn5wd0vKamJjz22GMoLy+Hw+HAzJkzcccdd2DSpEkAgHvuuQd/+9vfvPYZM2YMNm3aBKDrC+tTTz2Fv/3tb2hra8PMmTNx3333Ydw432NEiYiikbvF6ZtvvoSioM8A3J+0dPv316GychdmzCgYiqL3yZ80YD0NlGKur+0tFjPHuBMREVFMCDiI/9e//oV7770Xt9xyCy666CKceeaZePjhh9HW1oa7774bl156aUDHu+mmmyDLMlavXo3ExESsWrUKl19+OTZs2ACDwYCamhpcf/31WLRokWcfUfw+d+szzzyDdevW4bHHHkNGRgaWL1+Oq6++Gu+88w7i4+MDPT0ioqjmT1o6s7kFTU1NQ1yy3vxJA9aTO/1cRcUW7NypHXBMfPex+vn5BRzjTkRERFEv4CA+LS0NTz/9NP7xj3/gt7/9Lf7xj39g6tSpeOONNzBq1KiAjtXa2ooxY8bguuuuQ3Z2NgDgxhtvxAUXXIC6ujrk5+dj//79uPbaa3123+/s7MTLL7+MkpISnHHGGQCAxx9/HEVFRdiwYQPOO++8QE+PiCiq+ZOWLi4uDunp6REonTd/0oD1RRQ1SEnxryXePVZ/5MjRwRaVVM5Xrw673Y7OTnuf+1itbXA4HKivP+TJSuBrmS86nR46nd5rGXt6EBHRUAk4iAeAjz/+GC+88AIEQcDUqVOxZ88ePP3007jtttuQnJzs93FSUlKwYsUKz+/Nzc1Ys2YNMjIykJWVha+//hodHR2YOHGiz/337duH9vZ2zJ4927PMaDQiNzcXO3bsYBBPRMOOOy1dVdUeaLWjoCgyFEWBLEtwOhU0Nx9DdnYOxo4d12s8/UB6ppHrzuHoO1jqi79pwHzRagWYTIn9jol3p9irra3F2LETMG7chKBei9TPV68Om62j3+EaigIoiowtWzbDPfLE1zJfugL2BK9lzGYwvAxmONBAqTKBrgeVkmTzPKjkQyIi6i7gIP6uu+7CW2+9hezsbPz973/H1KlT8de//hV//OMfsWnTJtx3331YsGBBwAW599578cYbbyA+Ph7PPvssEhISUFtbCwB49dVX8dFHH0EQBMydOxdLly5FcnIyGhoaAACjR3u3rowcOdKzLlharfpnOBVFDTSarp89yyuKgtdPiqyedRXN9dPf+y5WqLV+RLErqvB+L/WsDwFLl96Km2++HocPfw2DwQCNRoPm5ibYbDbodDrMnHkSPvjg/V7HHygdm8v1fdd0rdb74+OEEyb5LJcgaDzBUCjfMwPV0UcffYgnnliJmpp9nhR7U6ZMxZIlyzB37hndjuNdzlh+XwdisNd59/rpvn8o7h++jpGdnd1rLhyHww67PfCHS/7Q6fTQ671b4g0GQx/XpPqo9R4XjEj9vb/6aj/27Nkd0D5dKUI78fHHW/2aWFQUBUhS10PKadOmY9q0GUGVlUIvlq6hWDQc6ifgIP6dd97B9ddfj5tuusnzJe6SSy7B6aefjt/+9rdYvHgxqqurAy7IZZddhksuuQRr167FTTfdhHXr1qG2thaCIGDkyJF47rnn8PXXX+OPf/wj6urq8Kc//Qk2W9cT0J5j33U6HVpbe+dN9pcgaGAyJQa9/1CRJBtEUUBKSkKf5TUa+dQ20mRZxv/+V4d9+6qRnT0ZJ5ww1vPhHY3148/7LlaorX7cf3sAnr+/r/q44IIfIS7ueSxfvhw1NTVob29HYmIiTjrpJBQXF6OoqMjn8VtaWvCf//wHP/zhD2EymQJab7PZ8PXXB3uVKznZ0KvMoeSrjjZt2oSSkiVoa2tDSkpXir2EhITvUuwtwfPPP4/58+cDQK9yDof3tT8Ge51LUtfnc1KS3mv/UNw/fB1DTXUWTfdItd3jghGpv/fJJ5+InJzsIXu9hIQEJCQkDLwhDalYuIZiWSzXT8BB/F//+lfk5eX1Wj5mzBisWbMG69atC6ogWVlZAIBHHnkEu3btwmuvvYZHHnkEv/rVrzxfFrOzs3Hcccfh5z//OSorKz1PwTs7O72eiPc1FtRfsqzAYukIev+h0tXFSkZrawdEsd1rnSgKMBoNsFhsnqe4NPR6tgb+7W9/x5QpU7FsWQnOP/+cqKyf/t53sUKt14/7b+/+vyi291kfs2fPw9//XoQtWz7Cxo3v46yzFuL00+f22/ojCB3QaAQIgg6i2Pse2t/6zk7vcrjL1dZm61XmUOirjmRZxkMPPQKLxeJJsWe32xEfr0NGxmgcOXIEDz30CGbMmAlBEHqVM5bf14EY7HVutdo9P7vvH4r7x1Deg2y2Dk+Dgb9aW81wODrx9df1aG0N7LtE1xCT8Adqar3HBSOSn0m+7pOhO7Z3HTkcChwO3pvUIpauoVgUrfVjNH7f8DGQgIN4XwG8m8PhwEknneT3sZqbm1FRUYGFCxd6WvUFQUBWVhYaGxshCEKv1p7JkycDABoaGjzd6BsbGzF+/HjPNo2NjZgyZYrf5fBloLzDoRbs2Cqn04WWlpZeEzuJogadnX1P+sSxVeFXXr4ZJSWLYbVaYTR2tQbqdAZUVe3BkiW3IDFxNQoKThvy99pgSZICRen6GW1lD5QkyUN6jgPdBywWMyRJgqLAc933dx8AgDFjxmLy5GyMGTMWTU3eY+B73gcGqtv+1vdc5/5dlrt+ajThec/0rKNduz5DXV0tUlPTAGg8M9d3/dQgNdWEurpafPbZZ5gxo6BXOYfD+9ofg73O3V+aetZPKO4fQ3kPqq2tDSqDQmdnJ7Zt2+JXl+nuhnpc/VDf48Ih1j+TYqGOYhnrR91iuX78CuJPP/10vPDCC8jJyfEse+WVV3DBBRcgLS3Ns2zfvn34xS9+4Xd3+mPHjmHZsmV48cUXPd07nU4nqqqqMH/+fNx+++1obGzEmjVrPPtUVnZ9mGZlZWHcuHFISkrCxx9/7AniLRYLqqqqvFLSRYPBpFravr332Kqu8WFCn+mXOAFPeMmyjLKylbBarZ7WQKfTAb1ej8TERDQ0HMFjjz2Gv/zlzUgXlVRkoPuAJLlgtbYBACoqyiGK2n7vA+593OPYRdH7lh+L94FoSrFH6jeYDArB0Ov5cJ2IiAbmVxB/7NgxOJ1Oz++SJOGPf/wjTj31VK8gPlDZ2dmYO3cuHn74YTz88MNISUnB888/D4vFgssvvxzV1dW48cYb8dRTT+HHP/4xDh48iN/97nc477zzMGnSJADAokWLUFpairS0NIwZMwbLly9HRkZGUJPrRVKovygMlH6JXxTCq7JyF/bvr4PJlAZNjymONRoNTCYTampqsHv3Lk5UQx4D3QcsFjO2bPkQigLMnl0EozF1wGN2T7HWc/uhuA/IsoxDh75Ge3s7xoyZgMLCooBbJwMRTSn2SP0Gk0GBiIgoXIJKMQcAiq/m3SCsXLkSK1aswNKlS9HW1oZTTjkFa9euRWZmJjIzM/HEE09g9erVeOGFF5CcnIzzzz8fS5Ys8exfXFwMl8uFe+65B3a7HTNnzsRLL72EuLi4kJRvqIT6i4I/6ZcofPxpDWxtNbM1kLz4cx8QRRGKAhiNqTCZ/HuIqtVqA9o+VGpq9uHPf/4Tamtr4HK5vstsMgXFxctQVDQvLK/pTrFXXb0Xen2m1zpFUWA2NyMnJw/5+Xx4RkRERNEp6CA+VJKTk/HAAw/ggQce8Ln+nHPOwTnnnNPn/qIo4rbbbsNtt90WphISBc6f1sD4+Hi2BlLMqqjYhr/8ZR0kSUZ8fDwSEhKg17tniF+M0tJVYQnkBUFAcfEylJQsRkNDPZKTjZBlGXa7DW1tFiQlJaO4eFlYewMQERERhRO/xRCFgbs10Gxu7tVrRVEUtLS0YMqUKZg+na2BFHtkWcbq1c/Cbrdj5MiRiI+PhyAI0Ov1yMjIRHu7FWVlKyHL4eklVFQ0D6Wlq5CTk4eOjg5YLBZ0dHQgJycPy5c/0evhgSzLqK6uQlVVFfbsqQxbuYiIiIhCIeIt8USxyJ/WwDvvvBOCIDBgoJhTWbkLBw9+gcTERJ9zQqSmpmH//jpUVu7CjBkFYSlDUdE8FBYWYevWcmzatAHz5y/wOR6/omIbnn76KbS0tMBm68D69W+Gvcv/cCXLMvbsqURVVRUyMsaGfX4EIiKiWDWoT8+eX86I6Hv9tQauXLkK8+fPj3QRicLCPSeEO3VoTzqdDk6nM+xzQgiCgGnT8pGbm4tp0/J7BYzl5Ztx//1345tvDiMhIQFGoxEJCYmeLv/l5ZvDWr7hpLx8My655ELcfPN1ePXVP+Hmm6/DJZdcyL8xERFREPxuib/pppsQHx/vtez666/3mkCus7MzdCUjigF9tQbGx7MTDMUu95wQLpfL53o1zBDfPQ1kamoq9Ho9JMnVLQ1kPcrKVrK1OATKyzejpGQxrFYrjMYUiKIAnc4Q9vkRiIiIYpVfkcSFF14Y7nIQxSx3a2BDw2GfrYFEsSY/fwZOOGESKit3ITXV5LVOLTPEu9NApqaa4HQ6vNYNVZf/4aD7w5LRozMhSRKcTgcflhAREQ2CX0H8o48+Gu5yEBFRjBAEAddeewNuu20xvv22EVqtFlqtVlUzxLu7/BuNKb2CeKCry7/Z3MI0kINUVbUX+/fXwWRKi9j8CERERLGGfXqJiCjkZs+eg1/+8lf4+OOPUVtbA5vNBllWkJOTF/SkcTabDXa7DQAgihpIkg2trR2QJKXPfSwWM1wuFywWs9fyuLg4iKIIu90GRVEgSRIURYEsS3C5ALvdBq1WyzSQg9Tc3Ayn0wmdTudzPR+WEBERBY5BPBERhcWUKVPx619fhr/97S9ob2/HWWedM6hu0wcO1KGqqhIAoNEAoihAkmQofcfwkGUZTmcntm/f6vW6siwjJSUF33xzGElJSejo6IAsS7Ba2wAAZnMrsrOnRLTLfyxIS0tDXFwcHA4HDAZDr/VqmB+BiIgo2jCIJyKisBEEAePGjYdGg0HPCTFx4mRkZo4F0NUSn5KSMGBLfH+Sk0249947YTa3IC0tDYoiQ6uNQ1ubBWlpabj11js4TnuQcnPzkJU1GdXVe6HXZ3qtU8v8CERERNGG306IiMgrh/eePZWQZTkkx6yurkJ1dTW+/vrrQR/TYDDAZEqDyZSGtLR0jBgxAmlp6Z5len3vlt7+zJ49B3fddQ8yM8fAZrPDYmlDR0cHsrNz8OCDv8fJJ5+ClpbmPv/ZbLZBnc9wIAgCiouXITExCQ0N9bDbbZBlGXa7DQ0N9aqYH4GIiCjasCWeiGiYKy/fjLKylaitrUF7uxXr17+J7OwpQY9dB4CKim14+umn0NLSgo6Odmi1Wmzbth233np72NKJde9u7y9ZlnHVVVfh6NGjOHbsWxx33Egcf/wEtLe3YuPGf/e7b25uPvLypg+myMNCUdE8lJau8nqPJSbKfs2P0H0eBLe+5jkIBb3e4LPbPxERkZowiCciGsbCkcO7vHwz7r//bjQ3N2HEiOOg1YpwuVyora0Oa17w7t3tA2WxmLFtWznmzCmC0Zjq1z6BtvwPZ0VF81BYWIStW8uxadMGzJ+/wK/5EXw9mJEkF9raWlFRUQ5R7P01RpZlOBx26HT6gFv4+WCGiIiiAYN4IqIQ89V6GAoWi/m7WdTh1QoZbOthOHJ4dz9mamoq9Ho9XC4n4uPjkZycgmPHGsOWF9xgGFwrqlarhdGYCpMpLYSlIjdBEDBtWj4aGg77PT+Crwcz7gcus2f7fuDiXj9rVqHfD2Tc+GCGiIiiAYN4lQnVl39/0y+x6yBR6AXbrXug1kNJcnlmT+/eChls62Fl5a6Q5/B25wVPTTX1yr/OvOAUqL4ezAz0wIUPZIiIKJYxiFeZUH359zf9ErsOEoVeMN26/Wk9tFjM2LLlQygKvFohg209bGpqCnkO75aWrrzgRmNKryA+2GMSuXWfgDEjY2xYenQQERGpHYN4lQnVl39/0y+x6yBR6AXbrduf1kNRFKEoCEkrY3p6eshzeJtMXXnBOzs7fa5nXnAKVjgmYCQiIopGDOJVJlRf/rVaASZTIkSxHS7X4FNFEVHsyc+fEfIc3u684Hv3Vva6lzEvOAUrHBMwEhERRSsG8UREEdBz/gt/0mb1NbFdf9zHdTjsvda5c3iXlCxGQ0M9kpONnhzebW2WoHJ4u4+5bNktaG5uhlYbB1mW4XK50NjYAKMxhXnBKSDhmICRiIgomjGIJyKKgJ7zXwyUNgvoCmacTiccDke/23XnPu6hQ18hIyOz13pfObwTEiRkZU3BtdfegGnT8tHS0gyg64GA09mJ+vpDvR4iWK1tcDgcqK8/hPHjx6G4eAmef/5ZtLQ0w263QxRFTJo0GVdddQ3Gjx+Hr7464NlXp9NDp9P7LD8n36RwTMBIREQUzRjEExFFQM/5LwZKmxXodj23Hzfu+D636ZnDe/LkKRBFDdrbW7Fx478920mSCxZLK7Zs+bBXMKUoCiRJ8qxTFAW/+MUlaGxshNVqRVJSEkaPzkRzcyPef///vPbtCtQTfJaNk29GXjBZU6zWVrhcLrS2mr3mZfGnx0nPBzfhmICRiIgomjGIJyKKAF/zX/ibFivQ9FlarbbPlm637jm8i4rO9Lm9xWLG1q2bMWPGSUhKSvZa19jYgE8//QQnnTQTI0dmwGptw2effYrTTpuDmpp9KCg4udc+bgO1xFNkBZM1RVFkOJ2dqKjYAo3m+y7u/vQ46fngJhwTMBIREUUzBvFERORFp9P3+YAgLi4emZnjfK4XBAEjR2bg+OMnoqWlGVVVezByZAa+/PJgn/uQ+gWTNaWvDCn+9CTp+eAmHBMwEhERRTMG8URENCiyLKO6ugp1dXUYP74K48ZNiHSRKISCyZrSX4aUQHuShGMCRiIiomjGIJ6IiILmzt29b1812tuteO+99/HSSy/giiuuiXTRKIb4moAxMVFGTk4e88QTEdGwwyCeiIiC0j13d1JSEjQawGBIQHX1Xtx//9244IILcOqpsyNdTIoRPSdgnD9/AdPKERHRsMRPPiIiCljP3N16vR6CIECv1yMjIxPt7VZs2LABsiwPfDAiP7knYMzNzcW0afkM4ImIaFjipx8REQVsoNzdKSmpOHq0ATU1+yJUQiIiIqLYxO70RAEKJmdyz9zIoqiBJNl6zdzsS8+cyURqMFDu7vh4HSRJgtlshihqfG5DRERERIFjEE8UoGByJstyV87k7du3QhAEaDSAKAqQJBlK/zF8r5zJRGrQPXe3Xq//7uGWHfHxNsTFxaOz0wFRFJGamoq2ttZIF5eIiIgoZjCIJwpQMDmTe+orh7IvPXMmE6mBO3f3rl2fweWSYLfbIMsyLBYL9PpjEAQNMjPHYMqUqdi58+NIF5eIokAoeroFgj3diChaMYgnClAwOZN76i+HMlE0EAQBZ575A2zdWg5JkiCKomdsfHu7FaIoIicnhxOPEZHfQtHTLRDs6UZE0YpBPBERBUyWZXzwwX+RmJgEl8sFh8MORVGg0WiQkJAIrVZEdXU1Z6enISXLMvbsqURVVRUyMsYyBV2UCUVPt0CwpxsRRSsG8UREFDD37PSjRmVAr9fDYmlFW5sFyclGGI0paG+3Rmx2enbJHZ7KyzejrGwlamtr0N5uxfr1byI7ewqKi5ehqGhepItHfghFTzciouGAQTwRUYRFY+th99npNRoNDAYDOjsdMBgM0Gg0EZ2dnl1yh5/y8s0oKVkMq9UKozEFoihApzOgunovSkoWo7R0FQN5IiKKGQziiYgiKFpbD7vPTu+r5SySs9OzS+7wIssyyspWwmq1YvToTEiSBKezK2tCYmIiGhrqUVa2MioejhEREfmDQTwRUYSEs/XQ3aU8kG7iA23bff3YseNw/PEnoLa2GiNHZniNfVcUBa2tZowalRGR2enZJXd4cQ/tMJnSPJMrumk0GqSmpmH//jpUVu7CjBkFESolERFR6DCIJyKKgHC3Hrq7lEuSC21traioKIco9n/LH6hLec9jnXLKSair24dDh75GSooRsizDbrejqekYEhOTsGDBArZ8Uth1H9rhi06ng9ncgqampiEuGRERUXgwiCciioBwtx66u5RbLGZs21aO2bOLYDSmDqrMPY911lnn4KSTTsPq1c/iiy9q0d7eDkUBcnLycMUV16C9fWi70dPwNNDQDofDgbi4OKSnp0egdERERKGniiC+qakJjz32GMrLy+FwODBz5kzccccdmDRpEgBg06ZNePrpp3HgwAGYTCYsXLgQixcvhl6vBwB8+umn+NWvftXruH/+859x2mmnDem5DLVonBCLiMLfeti9S7lWq4XRmAqTKS3o8rr1PNa5556Hs88+Fxs2vIv//vd9/OAHC7FgwblobTVj48Z/D/r1iAaSnz8DWVmTUV29F3p9ptc6RVFgNjcjJycP+fkzIlRCIiKi0FJFEH/TTTdBlmWsXr0aiYmJWLVqFS6//HJs2LABe/fuxc0334zi4mKcffbZ+Oqrr3DffffBbDbj0UcfBQDU1NRg/PjxWLdunddxU1JSInE6Q6a/CbHOPPPMSBePiPoRS62HgiAgJycXX399ADk5uXyQSENKEAQUFy9DScliNDTUIznZPbTDhrY2C5KSklFcvIzvSyIiihkR/0RrbW3FmDFj8PDDD2P69OmYNGkSbrzxRjQ2NqKurg6vv/46TjvtNFx//fWYMGEC5s2bh6VLl+Kdd95BZ2cnAKC2thZZWVk47rjjvP7Fx8dH+OzCxz0hVlXVXiQkJMJoNCIhIdEzIdZHH30Y6SISUT/crYdmczMURfFa5249zMqazNZDIj8UFc1Daekq5OTkoaOjAxaLBR0dHcjJycPy5U+oOtMDERFRoCLeEp+SkoIVK1Z4fm9ubsaaNWuQkZGBrKwsXHnllb2enguCAKfTCavVirS0NNTU1ODkk08Oabm02og/3+iTLMt48snH0d5uRWbm9xNiGQx6JCUl4siRI1i1aiXOP/8ciKJ6z2M4c9dLNNaPKGqg0XT9VPN1MhhDUz8Cli69FcuWLUZDwxEYjcmQZRkOhw0WSxuSkpKxdOmtiI/3vk0H+vcPZX31dyxB0Hh+arWCZ1tBCM/7JZqvoWgw2PdNX/UTivdjX8c488wzMW/ePGzZ8hE2bnwfZ521EKefPpct8D7w+lE/1pG6sX7UbTjUT8SD+O7uvfdevPHGG4iPj8ezzz6LhIQE5Obmem3jdDqxZs0aTJs2DWlpXWMy6+rqYDKZcNFFF+Ho0aPIzs7G0qVLMX369KDKIQgamEyJgz6fcPn0009x4MB+jBgxAlqtCKCrFU8UBWi1ItLT0/DFF/vx2WefhfzhBoWW0Rh9abAkyQZRFJCSkqDq6yQUwl0/F1zwIyQnG/DYY4+hqqoKbW1tUBQFM2ZMx5133on58+f32ifQv38o66u/Yx07ZoBGo0FysgEmU6Jn2+RkQ1jfL9F4DUWDUL1vetZPKI470DEKC0/D0aPfoLDwNKSnJwf1GsMFrx/1Yx2pG+tH3WK5flQVxF922WW45JJLsHbtWtx0001Yt24d8vLyPOtdLhduv/121NXVYe3atQCAI0eOoK2tDR0dHbjnnnsgiiJee+01LFq0COvXr0dWVlbA5ZBlBRZLR8jOK9QOHjwMu92B1FQTJEmGJHXlZ5YkGRqNjLi4eDgczTh27BgsFptnPamHKAowGg1RWT+trR2QJBmtrR0QxfZIFycshrJ+CgpOw1/+8qbP1sOWlt5/30D//qGoL5utAzabDa2tZjgcnfj663q0tnrfI+vrGyHLMurrGyEIOs+29fWNfe7Tl65J+RL63Saar6FoMNj3TV/1E4r340DHGA73qMHi9aN+rCN1Y/2oW7TWj9Fo8Lv3gKqCeHfA/cgjj2DXrl147bXXPJPXWa1WLFmyBJ988gmeeuopTyv76NGjsWPHDhgMBsTFxQEA8vPzUVVVhVdffRUPPvhgUGVxudRb4ampJsTFxcFu75oQyz2cVlG6/tntXRNijRgxApIkq/pchrtorB9JUqAoXT+jreyBGsr6ycmZhm++OYScnGmQ5a5hM77LFNjfPxT1VVtb68k5b7G0YuvWj3rlnO/sdECSJHz22U7s3VsJWZbR2dmJzz//FFZrm899+pKbm4+8PP96UkXjNRQNQnWd96yfUBx3oGMMp3vUYPH6UT/WkbqxftQtlusn4kF8c3MzKioqsHDhQmi1XcURBAFZWVlobGwEADQ2NuKaa67BN998g5deegkzZ870OobRaPT6XRAETJo0CUePHh2akxhi/qTTyc2dhoKCArS22iJUSiKKFf7knK+vP4QtWz7EiSeejMzMcZ7lweSp1+tjt/sbERER0WBFPIg/duwYli1bhhdffBFFRUUAusa9V1VVYf78+WhtbcVll10Gq9WKtWvXYsqUKV77f/TRR1i8eDH++c9/Yty4ri+OLpcL+/btw4IFC4b8fIaCP+l0lixhOh0iCo7NZoPdPnQPAPV6g880e6Ru/b1PRFEDSbJ917X9++wLFosZLpcLFos54Nfj+4SIiKhLxIP47OxszJ07Fw8//DAefvhhpKSk4Pnnn4fFYsHll1+ORx99FIcOHcKLL76ItLQ0fPvtt55909LScNJJJ8FkMuGOO+7A3Xffjbi4OKxevRpmsxmXX3555E4szNzpdLrniU9MlJGTk4fi4mWYO/eMSBeRiKLUgQN1qKqq7LVcklxoa2tFRUV5r67xTmcnFEXBrl3/Q1XVHr/2cQuk+zyFnyzL2LOnElVVVcjIGIvCwiKfD4X7ep8A+G72eAGSJKN7BkVZluF0dmL79q0BP2jm+4SIiKhLxIN4AFi5ciVWrFiBpUuXoq2tDaeccgrWrl2LUaNG4d1334XT6cRll13Wa7///ve/GDt2LNasWYPS0lJcddVVcDgcOPnkk/Haa69hxIgRETiboVNUNA+FhUXYurUcmzZtwPz5C/r8skVE5C939/me+usa7143Z473On+607P7vHqUl2/2eji8fv2byM6eguLiZb1yrff1PgG6WuJTUhJ6tcQPBt8nREREXVQRxCcnJ+OBBx7AAw880Gvd7t27B9x//PjxKCsrC0PJ1E8QBEyblo+GhsOYNi2fATwRDVrX7PC+AyatVgujMRUmU5rf6/rbJ1Q4BGDwyss3o6RkMaxWK4zGFIiiAJ3OgOrqvSgpWYzS0lVegXz/7xMBJlMiRLE9ZicVIiIiihRVBPFERESD0V/X7r7IsgyHww6dTj/su3bLsoyyspWwWq0YPToTkiTB6XRAr9cjMTERDQ31KCtbyd5eREREKsAgnogoQrq3Hvs74VegE4N13z4WW4/d+uva3Rd3V/9Zswr9njnfLda6dldW7sL+/XUwmdKg0Wi81mk0GqSmpmH//jpUVu7CjBkFESolERERAQziiYgipnvrsT8TwAGBTwzW/bj5+QUx1XrcXX9du/szFF39o0FTUxOcTid0Op3P9TqdDmZzC5qamoa4ZERERNQTg3giogjp3nocTD51f3Q/7siRo0N2XIot6enpiIuLg8Ph8PkwxOFwIC4uDunp6REoHREREXXHIJ6IKEJ6th6Hq1XYfdxY7UpPg5efPwNZWZNRXb0Xen2m1zpFUWA2NyMnJw/5+TMiVEIiIiJy4+w0REREw5wgCCguXobExCQ0NNTDbrdBlmXY7TY0NNQjKSkZxcXLOKkdERGRCvDTmIiIiFBUNA+lpauQk5OHjo4OWCwWdHR0ICcnD8uXP9ErTzwRERFFBrvTExEREYCuQL6wsAhbt5Zj06YNmD9/AdPKERERqQyDeCIiIvIQBAHTpuWjoeEwpk3LZwBPRESkMvxkJiIiIiIiIooSbIknIiIiVbLZbLDbbV7LLBYzXC4XLBazz30GWt8fvd7ALA5ERKR6DOKJiIhIlQ4cqENVVaXXMklyoa2tFRUV5RDF3l9jZFmG09mJ7du3BjwUIDc3H3l50wdVZiIionBjEE80jPlq5RoIW7mIaKhMnDgZmZljvZZZLGZs21aO2bOLYDSmhvT19Hren4iISP0YxBMNY75auQbCVq6BxerDEYfD3mcZ+yp/NJwXqZfB4Ps9oNVqYTSmwmRKi0CpiIiIIotBPNEw5quVK5yGSytXrD4cOXToqz67MffVxTkazouIiIgomjCIJxrG+mrlosGJ1Ycj48Ydj/37a312Yw5HF+fh8tCHiIiIKBAM4omIQixWH47odPp+uzGzizMRERFR+DFPPBERDTuyLGPPnkpUVVVhz55KyLIc6SIRERER+YUt8URENKyUl29GWdlK1NbWoL3divXr30R29hQUFy9DUdG8SBePiIiIqF9siSciomGjvHwzSkoWo6pqLxISEmE0GpGQkIjq6r0oKVmM8vLNkS4iERERUb8YxBMR0bAgyzLKylbCarVi9OhM6PV6CIIAvV6PjIxMtLdbUVa2kl3riYiISNXYnZ6IKErEav75oVJZuQv799fBZEqDRqPxWqfRaJCamob9++tQWbkLM2YURKiURERERP1jEK8yofqSLooaSJINra0dkCSlz31j7Us6USyL1fzzQ6WpqQlOpxM6nc7nep1OB7O5BU1NTUNcMiIiIiL/MYhXmVB9SddoAFEUIEkylL5j+Jj7kk4Uy2I1//xQSU9PR1xcHBwOh8+Hlw6HA3FxcUhPT49A6YiIiIj8wyBeZUL1JV0UNUhJSfCrJZ6IokOs5p8fKvn5M5CVNRnV1Xuh12d6rVMUBWZzM3Jy8pCfPyNCJSQiIiIaGIN4lQnVl3StVoDJlAhRbIfLxUmaiIgEQUBx8TKUlCxGQ0M9kpONkGUZdrsNbW0WJCUlo7h4WcDDDoiIiIiGEr+pEBHRsFFUNA+lpauQk5OHjo4OWCwWdHR0ICcnD8uXP8E88URERKR6bIknIqJhpahoHgoLi7B1azk2bdqA+fMXoLCwiC3wREREFBUYxBMR0bAjCAKmTctHQ8NhTJuWzwCeiIiIogaDeCIiIoqIUKVV9RfTqhIRUSxgEE9EREQREaq0qv5iWlUiIooFDOKJiIgoIkKVVtVfTKtKRESxgEE8ERERRUSo0qoSERENJ5zJh4iIiIiIiChKMIgnIiIiIiIiihIM4omIiIiIiIiiBIN4IiIiIiIioigR8SC+qakJt912G2bNmoWCggJce+21+OKLLzzrq6ursWjRIpx44omYP38+/vznP3vtL8syysrKUFRUhBNPPBHXXHMNDh06NNSnQURERERERBR2EQ/ib7rpJnz11VdYvXo1/v73v0Ov1+Pyyy+HzWZDS0sLrrjiCowfPx5vvvkmbrrpJpSWluLNN9/07P/MM89g3bp1eOihh/D6669DlmVcffXV6OzsjOBZEREREREREYVeRFPMtba2YsyYMbjuuuuQnZ0NALjxxhtxwQUXoK6uDhUVFYiLi8Pvfvc7aLVaTJo0yRPwX3zxxejs7MTLL7+MkpISnHHGGQCAxx9/HEVFRdiwYQPOO++8CJ4dERERERERUWhFtCU+JSUFK1as8ATwzc3NWLNmDTIyMpCVlYWdO3fi1FNPhVb7/bOGWbNm4csvv8SxY8ewb98+tLe3Y/bs2Z71RqMRubm52LFjx5CfDxEREREREVE4RbQlvrt7770Xb7zxBuLj4/Hss88iISEBDQ0NngDfbeTIkQCAI0eOoKGhAQAwevToXtu41wVLq434SINBEUXB6yepC+tH3Vg/vomiBhpN18+e98j+1oWnLIOvo6EuczQZ7N+G15C6sX7Uj3WkbqwfdRsO9aOaIP6yyy7DJZdcgrVr1+Kmm27CunXrYLfbER8f77WdTqcDADgcDthsNgDwuU1ra2vQZREEDUymxKD3VxOj0RDpIlA/WD/qxvrxJkk2iKKAlJSEXvfI/taF02DqKFJljgah+tvwGlI31o/6sY7UjfWjbrFcP6oJ4rOysgAAjzzyCHbt2oXXXnsNer2+1wR1DocDAJCQkAC9Xg8A6Ozs9PzfvY3BEHylybICi6Uj6P3VQBQFGI0GWCw2SJIc6eJQD6wfdWP9+Nba2gFJktHa2gFRbPd7XTj0rCObrcPzYNdfra1mOByd+PrrerS2BnbPNxgMMBgSAtonmgy2PnkNqRvrR/1YR+rG+lG3aK0fo9Hgd++BiAbxzc3NqKiowMKFCz3j3gVBQFZWFhobG5GRkYHGxkavfdy/jxo1Ci6Xy7Ns/PjxXttMmTJlUGVzuaKnwvsjSXLMnEssYv2oG+vHmyQpUJSunz3/Lv2tC2+ZuuqotrYWVVWVAe0ryzI6OzuxbdsWCEJgXe5yc/ORlzc9oH2iSajqk9eQurF+1I91pG6sH3WL5fqJaBB/7NgxLFu2DC+++CKKiooAAE6nE1VVVZg/fz5GjBiB119/HZIkQRRFAMD27dtxwgknID09HcnJyUhKSsLHH3/sCeItFguqqqqwaNGiiJ0XERENrYkTJyMzc+yQvZ5eH7td9IiIiEjdIhrEZ2dnY+7cuXj44Yfx8MMPIyUlBc8//zwsFgsuv/xy6HQ6vPjii/jtb3+Lq6++Grt378aaNWvw4IMPAugaC79o0SKUlpYiLS0NY8aMwfLly5GRkYEFCxZE8tSIiGgIdXVvZ2BNREREsS/iY+JXrlyJFStWYOnSpWhra8Mpp5yCtWvXIjMzEwDw4osv4pFHHsGFF16I4447DrfffjsuvPBCz/7FxcVwuVy45557YLfbMXPmTLz00kuIi4uL1CkREamezWaD3R7YGHKLxQyXywWLxRzQOqCr5ZpBNhEREdHgaRRFUSJdCLWRJBnNzeGfmCmctFoBJlMiWlraY3YsSDRj/ajbcKifvXt3BzWG3OGwQ6fT9xpDLkkuWCytMBpTIIq9nw+Hegz5cKijSGppacbGjf/GWWedA5MpLeD9WT/qxvpRP9aRurF+1C1a6yctLTE6JrYjIqLICPUYcovFjG3byjF7dhGMxtRe6zmGnIiIiCg0GMQTEQ1D4RhDrtVqYTSmBtVyS0RERET+CSynDhERERERERFFDIN4IiIiIiIioijBIJ6IiIiIiIgoSjCIJyIiIiIiIooSDOKJiIiIiIiIogSDeCIiIiIiIqIowSCeiIiIiIiIKEowiCciIiIiIiKKEgziiYiIiIiIiKIEg3giIiIiIiKiKMEgnoiIiIiIiChKaCNdACIiUhebzQa73RbQPhaLGS6XCxaLOeDX0+sNMBgMAe9HRERENBwxiCciIi8HDtShqqoyoH1kWYbT2Ynt27dCEALr5JWbm4+8vOkB7UNEREQ0XDGIJyIiLxMnTkZm5tghez29nq3wRERERP5iEE9ERF4MBnZvJyIiIlIrTmxHREREREREFCUYxBMRERERERFFCQbxRERERERERFGCQTwRERERERFRlGAQT0RERERERBQlGMQTERERERERRQkG8URERERERERRgkE8ERERERERUZRgEE9EREREREQUJRjEExEREREREUUJBvFEREREREREUYJBPBEREREREVGUYBBPREREREREFCW0kS4AERERhY/NZoPdbgtoH4vFDJfLBYvFHPDr6fUGJCcnBrwfERER+YdBPBERUQw7cKAOVVWVAe0jyzKczk5s374VghBYp73c3HzMmHFiQPsQERGR/xjEExERxbCJEycjM3PskL2eXm8YstciIiIajhjEExERxTCDwQCDgYE1ERFRrODEdkRERERERERRgkE8ERERERERUZRgEE9EREREREQUJRjEExEREREREUWJiE9sZzabsXLlSnz44YewWq2YMmUKbr31VpxyyimYP38+vvnmG5/7vfbaa5g5cyaOHj2KuXPn9lr/6KOP4qKLLgp38YmIiIiIiIiGTMSD+GXLluHbb7/FypUrkZ6ejldffRVXXXUV/vGPf+Dvf/87JEnybNvZ2Ykrr7wSGRkZKCgoAADs27cPOp0OGzduhEaj8WybnJw85OdCREREREREFE4RDeK/+uorbN26FevWrcPJJ58MALj33ntRXl6Od955B4sXL/ba/g9/+AMsFgv+8pe/QKvtKnptbS0mTJiAkSNHDnn5iYiIiIiIiIZSRMfEm0wmrF69Gvn5+Z5lGo0GGo0GFovFa9v9+/fjz3/+M+68806kpaV5ltfU1GDSpElDVmYiIiIiIiKiSIloS7zRaMS8efO8lr3//vv46quvcPfdd3stLysrQ3Z2Ni644AKv5bW1tTCZTPj1r3+NgwcP4vjjj8cNN9zgc5x8ILTa6J7zTxQFr5+kLqwfdWP9qB/rSN1YP+rG+lE/1pG6sX7UbTjUj0ZRFCXShXD73//+h6uvvhqFhYV48sknPcsPHTqEBQsWYNWqVViwYIFnucvlwoknnoisrCzceeedSEpKwr/+9S+88soreOWVVzB79uygyqEoitf4eiIiIiIiIiI1iPjEdm4bN25ESUkJTjrpJJSWlnqt++c//4n09HScddZZXsu1Wi0+/vhjiKIIvV4PAJg2bRrq6urw0ksvBR3Ey7ICi6UjuBNRCVEUYDQaYLHYIElypItDPbB+1I31o36sI3Vj/agb60f9WEfqxvpRt2itH6PR4HfvAVUE8a+99hoeeeQRnH322fjDH/6A+Ph4r/UbN27Ej370IwhC75NKTEzstWzy5MnYsmXLoMrkckVPhfdHkuSYOZdYxPpRN9aP+rGO1I31o26sH/VjHakb60fdYrl+Ij5QYN26dXjooYfw61//GitXruwVwFutVlRXV2POnDm99q2rq8NJJ52Ejz/+2Gv5nj17kJWVFdZyExEREREREQ21iLbEHzx4EL///e/xwx/+ENdddx2OHTvmWafX65GcnIx9+/ZBURRMnTq11/6TJk3CxIkT8bvf/Q4PPvggTCYT3njjDXz++ed48803gy6XIGiQlta7hT8aGY2GSBeB+sH6UTfWj/qxjtSN9aNurB/1Yx2pG+tH3aKtfgTB/znZIhrEv//++3A6nfjPf/6D//znP17rLrzwQjz22GNobGwEAKSmpvbaXxAEPPfcc1ixYgWWLFkCi8WC3NxcvPLKK8jOzg66XBqNBqIYGxPbxfKsjLGA9aNurB/1Yx2pG+tH3Vg/6sc6UjfWj7rFcv2oanZ6IiIiIiIiIupb7D6eICIiIiIiIooxDOKJiIiIiIiIogSDeCIiIiIiIqIowSCeiIiIiIiIKEowiCciIiIiIiKKEgziiYiIiIiIiKIEg3giIiIiIiKiKMEgnoiIiIiIiChKMIgnIiIiIiIiihIM4omIiIiIiIiiBIN4IiIiIiIioijBIJ6IiIiIiIgoSjCIjwKyLKOsrAxFRUU48cQTcc011+DQoUN9bl9XV4drr70Wp512GmbPno3i4mLU19d7bbN27Vr84Ac/wPTp0/GrX/0KVVVV4T6NmBbqOpIkCdOnT8eUKVO8/j355JNDcToxJ9D62bt3Ly677DIUFBRg1qxZuO+++9DW1ua1zb///W+ce+65mD59On7yk5+goqIi3KcRs8JRPwsWLOh1/dx5553hPpWYFWgddffPf/4TU6ZMweHDh72W8xoKnXDUD6+h0Aq0jtz10vNf93riNRQ64agfXkOhE2j9OJ1OrFixwrP9okWLUF1d7bVNRUUFLrroIsyYMQNnn302/vWvf4X7NEJLIdV78sknldNOO0354IMPlOrqauXKK69UFixYoDgcjl7bNjc3K4WFhcott9yi1NTUKJWVlcqvf/1r5ZxzzlHsdruiKIqyfv16Zfr06crbb7+t1NXVKbfddpty6qmnKk1NTUN9ajEj1HW0f/9+JTs7W6murlYaGxs9/6xW61CfWkwIpH6+/fZbZebMmcpdd92lHDhwQPn000+Vc889V7nxxhs921RUVCh5eXnKn/70J2X//v3KY489pkybNk3Zv3//UJ5WzAh1/bS3tytTp05VPvjgA6/rx2KxDOVpxZRA6qi7w4cPKyeffLKSnZ2tHDp0yLOc11Bohbp+eA2FXqB19Mc//lFZtGiR19+/sbFRcblciqLwGgq1UNcPr6HQCrR+7r77bmXOnDnKRx99pOzfv1+55ZZblMLCQs/ff//+/Up+fr6ycuVKZf/+/cqLL76o5ObmKtu2bRvK0xoUBvEq53A4lIKCAmXt2rWeZa2trcr06dOVd955p9f2b7zxhlJQUKDYbDbPsvr6eiU7O9vzxlywYIHyxz/+0bPe6XQq8+bNU5577rkwnknsCkcd/etf/1JOOumk8Bd+GAi0fj7//HNl6dKlitPp9Cxbs2aNMmPGDM/vV155pbJ48WKv/S655BLl3nvvDXn5Y1046mfXrl1Kdna2Yjabw1r24SLQOnKTJEn55S9/qfzmN7/pFSTyGgqdcNQPr6HQCqaOrr76auWhhx7q85i8hkInHPXDayh0Aq2fr7/+WpkyZYrywQcfeG1/5plner5n33vvvcpPf/pTr/2WLVumXHnlleE5iTBgd3qV27dvH9rb2zF79mzPMqPRiNzcXOzYsaPX9rNnz8YzzzwDvV7vWSYIXdVssVjQ1NSEL7/80ut4Wq0Wp5xyis/j0cBCXUcAUFNTg0mTJoW55MNDoPUzY8YMrFy5ElqtFgDwxRdf4O2330ZhYSGAri5d//vf/7yOBwCnnXYar6EghLp+gK7rZ8SIEUhJSQn/CQwDgdaR23PPPQen04nrrrvOazmvodAKdf0AvIZCLZg66u97AK+h0Ap1/bjX8xoKjUDrZ+vWrUhOTsbcuXO9tt+0aZPnGDt37ux1/cyaNQuffvopFEUJ05mEFoN4lWtoaAAAjB492mv5yJEjPeu6Gzt2LGbNmuW1bPXq1dDr9Zg5c2bAx6OBhbqOAKC2thYulwtXXXUVCgsLcdFFF+Htt98O0xnEtsG85xcuXIhzzz0XZrMZv/3tbwF0PWjp6OhARkZGwMej3kJdP0DXl6eEhAQUFxfj9NNPx/nnn481a9ZAluXQn8AwEEwd7d69Gy+//DKWL18OURS91vEaCq1Q1w/AayjUAq2j1tZWHD16FDt37sT555+P008/HTfeeCMOHjwIgNdQqIW6fgBeQ6EUaP0cPHgQ48aNw4YNG3DRRRehsLAQ11xzDb744guvY/q6fmw2G1paWsJwFqHHIF7lbDYbACA+Pt5ruU6ng8PhGHD/V199Fa+99hpKSkqQlpY26ONRb6GuI6Br4juz2YxLL70UL730EhYuXIi77roLf//730N/AjFuMPVTWlqKV199Fenp6fjNb36D9vZ22O32oI9HvYW6foCu68disWDhwoV46aWX8Mtf/hKrVq3ixJBBCrSOOjo6UFJSgpKSEkyYMKHXel5DoRXq+gF4DYVaoHVUV1cHAFAUBY8++iieeOIJOBwO/OpXv8KxY8d4DYVYqOvHvQ2vodAItH6sViu++uorPPPMM1i2bBmeffZZaLVa/OpXv0JTUxOArs+hnsdz/97Z2RmO0wg5baQLQP1zd7nu7Oz06n7tcDhgMBj63E9RFKxatQrPPvssbrjhBlx66aW9jtfdQMejvoW6jgDg//7v/yBJEhITEwEAU6dORX19PV566SX89Kc/DdOZxKZg6wcA8vPzAQBPPfUU5s2bh//85z+YN2+e53jd8RoKTqjr5yc/+QleeOEFOBwOJCcnAwCmTJkCq9WKZ599Frfccotn+Ar5J9A6evjhh3HCCSfgF7/4hc/j6XQ6z/G64zUUnFDXDwBeQyEWaB2dcsopqKiogMlkgkajAdB1nzvjjDOwfv16/OxnP/McrzteQ8EJdf1ce+21vIZCKND60Wq1sFqtePzxxz1DHh5//HHMmzcP//jHP3D11VdDp9P1un7cv0fLNcR3kMq5u440NjZ6LW9sbMSoUaN87uN0OnHbbbfhueeew1133YUlS5YM6njUv1DXEdB1w3IH8G7Z2dnsJheEQOvnwIED+PDDD72WjRo1CqmpqTh69ChSU1ORkJDAayhEQl0/QNfTdPcXJ7fs7Gx0dHSgtbU1hKUfHgKtozfffBPbtm1DQUEBCgoKcM011wD4//buPSiqI/0b+JcIlgqCiEa84AVFBGS4KaMBiSICicYLoqFQEtgAioSsF1YhKqKySBQxCqIIsmYNlRCjQpSFCG4U1gvjJV6ItYWUboTCkN2ElYuyBOj3D4rzMoDKyCiQ3/dTRZVzuqfPc6bnKI/dpxuYN28eDh48yHtIzdTdPwDvIXV7kd8TBg8eLCWIQHNiMWrUKP479BKou38A3kPqpGr/GBoaQlNTU2nNgn79+sHIyEjaAnD48OEdtjdgwIB2/dZTMYnv4SZNmgQdHR0UFhZKx6qqqnDnzh3p+em21q9fj5ycHOzevRu+vr5KZQYGBhg3bpxSew0NDbh69epT26NnU3cfVVVVwd7eHidOnFA6fvv2bZiYmKg9/t87Vfvn4sWL+Oijj6RFBgHgwYMHqKysxPjx46GhoQFbW1soFAql9xUWFmLKlCkv70J+p9TdP0IIuLi4ICEhQel9t2/fxtChQ6Gvr//yLuZ3StU+OnPmDE6fPo2MjAxkZGQgKioKQPPaH15eXryH1Ezd/cN7SP1U7aP09HTI5XI8fvxYOlZTU4N//etfmDBhAu8hNVN3//AeUi9V+2fq1KloaGjA7du3pWN1dXUoLS3FmDFjADTPpmh7/1y+fBm2tra9Z5ZE9y2MT50VFxcn7O3tRV5entLeiPX19aKhoUH8/PPP0nZlx48fFxMnThQpKSnt9q5sqZOeni5kMpk4ceKEtE+8XC7nPvFdoO4+CgkJEY6OjuLcuXPi/v37IikpSZiZmYn8/PzuvMxeS5X+qaysFDNmzBCBgYGiuLhYXLlyRSxYsEB4enpK+78WFBQIMzMzkZqaKkpKSsQnn3wiZDIZ9+d9Qerun5iYGGFtbS2ysrLEjz/+KL788kshk8lEenp6d15mr6ZKH7V1+fLldluY8R5SL3X3D+8h9VOlj8rLy8WUKVNEcHCwKC4uFrdu3RK+vr7CxcVF1NXVCSF4D6mbuvuH95B6qfp3nK+vr3jrrbfElStXxN27d0VISIiYPn26lOsUFxcLCwsLsWvXLlFSUiIOHz7MfeJJ/RoaGsTOnTvFtGnThLW1tQgICJD+sS0tLRUTJ04Ux48fF0II4efnJyZOnNjhT0sdIYRISUkRTk5OQiaTCW9vb3Hnzp1uubbfC3X3UXV1tYiOjhZvvvmmmDx5sliwYIHIzc3ttuvr7VTpHyGEuHfvnggMDBR2dnbC3t5ehIeHi0ePHim1efLkSTFnzhxhaWkpFi1a1Kv+4u9p1N0/v/32m0hISBCzZ88WFhYWws3Njb84dZGqfdRaR0miELyH1End/cN7SP1U7aOioiLh5+cn7OzshK2trQgJCRHl5eVKbfIeUh919w/vIfVStX+qq6vFli1bhFwuF1ZWVsLPz0/cvXtXqc3z58+LefPmicmTJwt3d3eRlZX1Sq+pqzSE6CWb4RERERERERH9H9dLJv0TEREREREREZN4IiIiIiIiol6CSTwRERERERFRL8EknoiIiIiIiKiXYBJPRERERERE1EswiSciIiIiIiLqJZjEExEREREREfUSTOKJiIheAiFEd4dA9Fz8nhIR9T5M4omIqNv4+PjA3Nwct2/f7rDc2dkZYWFhrziqrqmvr0d0dDROnTr1Ss4XHx8PU1NTtb+nsLAQpqamKCws7Ep4L6SsrAyzZs3Cr7/+CqD5e2Bra4vy8vIO65uamiI+Pl56rUr9+vp6uLu748aNG12OOywsDM7Ozl1up/X3Xp390Da+s2fPYsOGDV1ul4iIXi0m8URE1K0aGxsRHh6O+vr67g5FLX7++Wd89tlnaGhoeCXnW7JkCdLT01/JuV4FIQTCw8Px/vvvY/DgwdLx2tpabNq0qdPtdLZ+3759ERoaig0bNqCuru6FYm6xatUqJCQkdKkNAEhISMCqVau63E5bbeM7cuQIHj58qPbzEBHRy8UknoiIutXAgQNx9+5d7N+/v7tD6ZUMDQ1hbW3d3WGoTW5uLoqLi+Ht7a10XFdXFxcuXMBXX33VqXZUqe/i4gItLS188cUXLxRzi9GjR8Pc3LxLbQCAubk5Ro8e3eV22lJXfERE1L2YxBMRUbcyMzPDwoULkZKSgqKioufWP3bsGObOnYvJkydj5syZiI+PR2NjY7s6Hh4esLa2hkwmw4IFC5CdnS2VnzhxAubm5jh27BgcHBxgb2+PkpISAEBeXh48PDxgaWkJBwcHREVF4fHjx9J76+rqEBkZCScnJ0yePBnu7u44fPgwgOZp4LNnzwYAhIeHP3NqtampKdLS0rBx40bY29vDxsYGf/zjH/Gf//xHqd7z4uloavzhw4cxe/ZsyGQyeHl54e9//3uHU7LPnTuH+fPnw9LSEm5ubsjIyGgXZ0lJCby9vWFpaYk5c+bg6NGjSuX/+9//sH//fri7u8PS0hKurq44dOgQmpqapDo+Pj4IDQ3FRx99BGtra/j5+T31c0lKSoKbmxv69u2rdNzZ2Rn29vb45JNPOjV6rGr9d955B3/5y1+eOSOkqKgI77//Puzs7GBjYwNfX1+lafhtp6s7OzsjISEB0dHRkMvlsLGxwbp161BbW4tDhw7ByckJdnZ2CAkJQWVlpdL7nvUYSV5eHry9vWFjYyN9B9PS0qTylin4X375JWbNmgVbW1tcuHBBKT4fHx8oFAooFAqYmpri4sWLcHR0xLp169qdz9XVVaVZEERE9HIxiSciom738ccfQ19f/7nT6pOSkrB582ZMnz4dBw8exLJly5CcnIzNmzdLddLS0hAREQEXFxckJSUhNjZWmjL9008/SfUaGxuRmpqKP//5zwgPD8f48eNx6tQpBAcHw9jYGPv378eHH36Ib775BqtWrZIWAIuOjkZ+fj42bNggJcs7d+7E8ePH8frrr0vTlYOCgp47tXrPnj1oampCXFwc1q9fj++++w7R0dFSeWfiaSshIQGxsbF46623kJiYCCsrK6xevbrDuhEREfD19cWBAwdgaGiIsLAw/POf/1Sqs2PHDlhbW+PAgQOYMWMGoqKi8NlnnwFonvq+cuVKpKSkYMmSJTh48CDc3d3x6aefYsuWLUrtZGdnQ1tbGwcOHIC/v3+H8dy7dw9FRUVwdXVtV6ahoYHo6Gg0NTV1KqFUtb67uzsqKiqgUCg6LK+pqYG/vz/09fURHx+PPXv24MmTJ/jggw9QXV391HZTU1Px8OFD7NmzB0FBQTh9+jQWL16Mf/zjH9i+fTvWrl2Ls2fPYt++fc+NEWj+j5fg4GBYWFggMTER8fHxMDIywrZt23Dz5k2lugkJCdiwYQMiIiJgY2OjVLZlyxaYm5vD3Nwc6enpkMlkWLhwIfLy8lBTUyPVu3btGn788Ud4eHh0Kj4iInr5NLs7ACIiIj09PWzbtg1BQUHYv38/1qxZ065OdXU1EhMT8e6770pJmaOjIwYNGoRNmzbBz88PJiYmKC0txQcffKD0TPHIkSPh4eGBa9euYe7cudLxlStXYubMmQCaE9LY2FjMmDEDsbGxUp2xY8fC19cX58+fx8yZM6FQKODg4CC1I5fLMWDAABgYGKBv374wMzMD0LmpyxMnTsSOHTuk17du3UJOTo5K8bT2+PFjJCcnY9myZQgNDZU+oydPnnT43HxUVBScnJykeOfMmQOFQoFJkyZJdZYuXYr169dLbVVUVCApKQk+Pj4oKCjAxYsXERcXJ30eDg4O6NevH/bu3Yv33nsPJiYmAAAtLS1s3bq13Qh7a5cvXwYAyGSyDsuNjIywdu1aREVF4dixY1iyZMlT21K1/pgxY6Cnp4dLly7B0dGxXXlJSQkqKyvx3nvvwdbWFgBgbGyM9PR01NbWYuDAgR22q6Ojgz179kBTUxNvvPEGTp48iYqKChw7dkx6T0FBAa5fv/7Ma2kdx6JFi7Bx40bpmI2NDeRyOQoLC2FlZSUd9/b2hru7e4ftTJgwATo6OgAgPY6xePFiJCcn49tvv8XixYsBABkZGRg7dqx0zURE1P04Ek9ERD2Cs7Mz5s+fj5SUFPzwww/tyr///nvU1dXB2dkZDQ0N0k/L9OALFy4AaJ7SHBoaiqqqKty4cQOZmZnSVOO2o/wtCTfQPAr8008/tWt/6tSp0NHRkdqXy+X46quvEBAQgM8//xylpaUIDg5ul1B3Rttn2Q0NDfHkyROV4mntxo0bqKura5e4zZs3r8PzT5kyRfrzqFGjAABVVVVKdd5++22l13PmzMEvv/yCe/fuQaFQQFNTs9355s+fDwBKo9rGxsbPTOABoLS0FLq6utDV1X1qneXLl2Pq1KmIiYlRmlmhjvojRoxAWVlZh2UmJiYYPHgwVq5ciYiICOTm5mLIkCH405/+BENDw6e2KZPJoKn5/8dMhgwZgnHjxikl/YMGDXrmaH5r/v7+iImJQW1tLYqKivC3v/0NSUlJAJ79/e6McePGwc7ODpmZmQCaHx3Jzs7mKDwRUQ/DJJ6IiHqMTZs2SdPqf/vtN6Wy//73vwCAwMBAWFhYSD9vvPEGgOZV4QHgwYMH8PX1xdSpU7F8+XIcPnxYWim+7RT0AQMGtGt/69atSu1bWFigpqZGan/jxo1YvXo1ysrKsH37dri4uMDLy6vdNPTO6N+/v9Lr1157TYqxs/G01rIlW+tV3QHAwMCgw/O3vv7XXmv+laDtZzRkyJAO23r06BEePXoEfX199OnTR6nO0KFDAUApMdXW1u4whtZqamrafSZttUyTb2xsVGlafWfq9+/fX2kqeWva2tpIS0vDm2++iezsbHz44YeYPn06IiIinvkISMtod2utP3dV/frrrwgJCcGUKVOwdOlSxMfHSzE/6/vdWZ6enlAoFHj48CHy8vJQW1uLhQsXvnC8RESkfpxOT0REPYaenh4iIyMRHByMxMREpbKW0dnY2FiMHTu23XuHDBmCpqYmBAYGQktLC19//TXMzMygqamJkpISaXTxaVraX79+Pezt7TuMDWjekiwoKAhBQUEoLy/Hd999h8TERKxbtw5ZWVkvctldiqe1lhHhX375BcbGxtLxluT+RTx69EjpdcvCewYGBtDT00NlZSUaGxuVEvmW/2DQ19dX6Vz6+vqdGpEePXo01qxZg+joaHz99ddqq19VVYURI0Y8tdzY2Bi7du1CY2Mjbt26hczMTHzxxRcYPXr0U5/zV7fQ0FDcu3cPR44cgY2NDfr27YsnT550etX+53F3d0dUVBRycnJw9epVODg4YNiwYWppm4iI1IMj8URE1KO4uLhg3rx5OHTokFLyaWVlBS0tLVRUVMDS0lL60dTURFxcHMrKylBZWYn79+/D09NTKgOA/Px8AFBaMb0tY2NjGBgYoKysTKn9YcOGYffu3bhz5w7q6urg5uaG1NRUAM3Tr5ctW4a5c+eivLwcANqNSr+ozsTT1qRJkzBw4EDk5uYqHT9z5swLx3Hu3Dml11lZWRg+fDjGjBkDe3t7NDQ0SM/xt/jmm28AAHZ2diqda8SIEXj8+HG7/zjoiI+PD+zs7BATE9Optp9XXwiBiooKjBw5ssPynJwcTJs2Df/+97/Rp08f2NjYIDIyErq6ulLfvwrXrl2Dq6sr5HK59HhCZ77fHWmZfdHagAED8Pbbb+P06dO4cOECp9ITEfVAHIknIqIeZ/Pmzbh8+bLSdmv6+vrw9/fH3r17UVNTA7lcjoqKCuzduxcaGhpSAjty5EikpaXB0NAQurq6KCgowF//+lcAkJ4370ifPn2wZs0aREREoE+fPpg1axaqqqqQmJiIiooKWFhYoF+/frCwsEBCQgK0tLRgamqK+/fv4+TJk3BzcwMA6VnnS5cuYfz48UoLjamiM/G0paOjA39/f+zbtw/9+/eHvb09FAqFtP95R0nb8xw9ehTa2towNzdHVlYWCgoKsHPnTmhoaMDJyQlyuRybNm1CRUUFJk2aBIVCgeTkZCxatAgTJkxQ6VwODg4AmhPVZ23P13ItO3bskJ6/f57n1S8uLkZ1dTVmzJjRYbmtrS2ampoQHByMwMBAaGtrIzs7G9XV1R2upv+yyGQynDp1ChYWFjA0NMT169dx6NAhaGhoPPP73RFdXV18//33uHTpEszNzaXZHZ6ennj33Xehp6cHFxeXl3EZRETUBRyJJyKiHmfQoEGIjIxsd3z16tUICwtDbm4uAgICsGvXLtjZ2eHzzz+XkufExEQMGzYMYWFhWL16NW7evIkDBw7A2NgYV69efeZ5lyxZgt27d+P69etYuXIlIiMjMWrUKBw9ehRGRkYAgG3btsHDwwOpqan4wx/+gMTERHh6ekrx6ujowM/PD3l5eQgICGj3bL8qOhNPWytWrEBISAgyMzOxYsUKXL16VVqp/kWekW6ZWh0YGIjr168jLi4OCxYsAND8vHlSUhK8vLxw5MgRBAYGIicnB2vXrlXaKq+zjIyMYGFhgfPnz3eq/pgxYzrcyeBF6ufn52Po0KFPXYX99ddfR0pKCgYOHIiNGzdixYoV+OGHHxAfH49p06Z1OoauiomJgZWVFbZv347g4GCcPXsWW7duhaOj43O/320tW7YMWlpaCAgIkEbzgeYFFwcNGoS5c+c+dzFCIiJ69TTE0zaaJSIiol6loaEBp0+fhlwux/Dhw6XjaWlpiIqKQmFh4TNXfu8Jvv32W3z88cfIz8/v1GJ46iCEgJubG7y9veHr6/tKztmT3bx5E0uXLkVmZqbSdoNERNQzcCSeiIjod0JTUxPJyclYtWoVzpw5gytXriAtLQ2ffvopFi5c2OMTeABwdXWFiYmJ9AjAq3DmzBk0NjbCy8vrlZ2zJyosLMS+ffuwZs0aODo6MoEnIuqhOBJPRET0O1JaWoq4uDgUFhZKq63Pnz8fK1asgJaWVneH1ykPHjzA8uXLkZGR0W67PHWrr6/HO++8g+joaJUX4vu9ycnJQXh4OExMTLB3716l2RxERNRzMIknIiIiIiIi6iU4nZ6IiIiIiIiol2AST0RERERERNRLMIknIiIiIiIi6iWYxBMRERERERH1EkziiYiIiIiIiHoJJvFEREREREREvQSTeCIiIiIiIqJegkk8ERERERERUS/x/wDZSGn/jk2K1gAAAABJRU5ErkJggg==", + "image/png": 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", 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" ] @@ -3864,7 +3836,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -3936,7 +3908,7 @@ }, { "cell_type": "code", - "execution_count": 70, + "execution_count": 69, "metadata": { "scrolled": true }, @@ -3945,13 +3917,13 @@ "name": "stderr", "output_type": "stream", "text": [ - "[I 2024-07-02 14:22:54,504] A new study created in memory with name: my_study\n", - "[I 2024-07-02 14:22:54,540] A new study created in memory with name: study_name_0\n", + "[I 2024-07-02 17:07:22,799] A new study created in memory with name: my_study\n", + "[I 2024-07-02 17:07:22,837] A new study created in memory with name: study_name_0\n", "/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/sklearn/linear_model/_ridge.py:243: UserWarning: Singular matrix in solving dual problem. Using least-squares solution instead.\n", " warnings.warn(\n", "/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/sklearn/linear_model/_ridge.py:243: UserWarning: Singular matrix in solving dual problem. Using least-squares solution instead.\n", " warnings.warn(\n", - "[I 2024-07-02 14:22:55,559] Trial 0 finished with value: -0.34035600917066766 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.676421027478709, 'descriptor': '{\"parameters\": {\"descriptors\": [{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}, {\"name\": \"MACCS_keys\", \"parameters\": {}}, {\"name\": \"UnscaledJazzyDescriptors\", \"parameters\": {\"jazzy_names\": [\"dga\", \"dgp\", \"dgtot\", \"sa\", \"sdc\", \"sdx\"], \"jazzy_filters\": {\"NumHAcceptors\": 25, \"NumHDonors\": 25, \"MolWt\": 1000}}}, {\"name\": \"UnscaledPhyschemDescriptors\", \"parameters\": {\"rdkit_names\": [\"MaxAbsEStateIndex\", \"MaxEStateIndex\", \"MinAbsEStateIndex\", \"MinEStateIndex\", \"qed\", \"SPS\", \"MolWt\", \"HeavyAtomMolWt\", \"ExactMolWt\", \"NumValenceElectrons\", \"NumRadicalElectrons\", \"MaxPartialCharge\", \"MinPartialCharge\", \"MaxAbsPartialCharge\", \"MinAbsPartialCharge\", \"FpDensityMorgan1\", \"FpDensityMorgan2\", \"FpDensityMorgan3\", \"BCUT2D_MWHI\", \"BCUT2D_MWLOW\", \"BCUT2D_CHGHI\", \"BCUT2D_CHGLO\", \"BCUT2D_LOGPHI\", \"BCUT2D_LOGPLOW\", \"BCUT2D_MRHI\", \"BCUT2D_MRLOW\", \"AvgIpc\", \"BalabanJ\", \"BertzCT\", \"Chi0\", \"Chi0n\", \"Chi0v\", \"Chi1\", \"Chi1n\", \"Chi1v\", \"Chi2n\", \"Chi2v\", \"Chi3n\", \"Chi3v\", \"Chi4n\", \"Chi4v\", \"HallKierAlpha\", \"Ipc\", \"Kappa1\", \"Kappa2\", \"Kappa3\", \"LabuteASA\", \"PEOE_VSA1\", \"PEOE_VSA10\", \"PEOE_VSA11\", \"PEOE_VSA12\", \"PEOE_VSA13\", \"PEOE_VSA14\", \"PEOE_VSA2\", \"PEOE_VSA3\", \"PEOE_VSA4\", \"PEOE_VSA5\", \"PEOE_VSA6\", \"PEOE_VSA7\", \"PEOE_VSA8\", \"PEOE_VSA9\", \"SMR_VSA1\", \"SMR_VSA10\", \"SMR_VSA2\", \"SMR_VSA3\", \"SMR_VSA4\", \"SMR_VSA5\", \"SMR_VSA6\", \"SMR_VSA7\", \"SMR_VSA8\", \"SMR_VSA9\", \"SlogP_VSA1\", \"SlogP_VSA10\", \"SlogP_VSA11\", \"SlogP_VSA12\", \"SlogP_VSA2\", \"SlogP_VSA3\", \"SlogP_VSA4\", \"SlogP_VSA5\", \"SlogP_VSA6\", \"SlogP_VSA7\", \"SlogP_VSA8\", \"SlogP_VSA9\", \"TPSA\", \"EState_VSA1\", \"EState_VSA10\", \"EState_VSA11\", \"EState_VSA2\", \"EState_VSA3\", \"EState_VSA4\", \"EState_VSA5\", \"EState_VSA6\", \"EState_VSA7\", \"EState_VSA8\", \"EState_VSA9\", \"VSA_EState1\", \"VSA_EState10\", \"VSA_EState2\", \"VSA_EState3\", \"VSA_EState4\", \"VSA_EState5\", \"VSA_EState6\", \"VSA_EState7\", \"VSA_EState8\", \"VSA_EState9\", \"FractionCSP3\", \"HeavyAtomCount\", \"NHOHCount\", \"NOCount\", \"NumAliphaticCarbocycles\", \"NumAliphaticHeterocycles\", \"NumAliphaticRings\", \"NumAromaticCarbocycles\", \"NumAromaticHeterocycles\", \"NumAromaticRings\", \"NumHAcceptors\", \"NumHDonors\", \"NumHeteroatoms\", \"NumRotatableBonds\", \"NumSaturatedCarbocycles\", \"NumSaturatedHeterocycles\", \"NumSaturatedRings\", \"RingCount\", \"MolLogP\", \"MolMR\", \"fr_Al_COO\", \"fr_Al_OH\", \"fr_Al_OH_noTert\", \"fr_ArN\", \"fr_Ar_COO\", \"fr_Ar_N\", \"fr_Ar_NH\", \"fr_Ar_OH\", \"fr_COO\", \"fr_COO2\", \"fr_C_O\", \"fr_C_O_noCOO\", \"fr_C_S\", \"fr_HOCCN\", \"fr_Imine\", \"fr_NH0\", \"fr_NH1\", \"fr_NH2\", \"fr_N_O\", \"fr_Ndealkylation1\", \"fr_Ndealkylation2\", \"fr_Nhpyrrole\", \"fr_SH\", \"fr_aldehyde\", \"fr_alkyl_carbamate\", \"fr_alkyl_halide\", \"fr_allylic_oxid\", \"fr_amide\", \"fr_amidine\", \"fr_aniline\", \"fr_aryl_methyl\", \"fr_azide\", \"fr_azo\", \"fr_barbitur\", \"fr_benzene\", \"fr_benzodiazepine\", \"fr_bicyclic\", \"fr_diazo\", \"fr_dihydropyridine\", \"fr_epoxide\", \"fr_ester\", \"fr_ether\", \"fr_furan\", \"fr_guanido\", \"fr_halogen\", \"fr_hdrzine\", \"fr_hdrzone\", \"fr_imidazole\", \"fr_imide\", \"fr_isocyan\", \"fr_isothiocyan\", \"fr_ketone\", \"fr_ketone_Topliss\", \"fr_lactam\", \"fr_lactone\", \"fr_methoxy\", \"fr_morpholine\", \"fr_nitrile\", \"fr_nitro\", \"fr_nitro_arom\", \"fr_nitro_arom_nonortho\", \"fr_nitroso\", \"fr_oxazole\", \"fr_oxime\", \"fr_para_hydroxylation\", \"fr_phenol\", \"fr_phenol_noOrthoHbond\", \"fr_phos_acid\", \"fr_phos_ester\", \"fr_piperdine\", \"fr_piperzine\", \"fr_priamide\", \"fr_prisulfonamd\", \"fr_pyridine\", \"fr_quatN\", \"fr_sulfide\", \"fr_sulfonamd\", \"fr_sulfone\", \"fr_term_acetylene\", \"fr_tetrazole\", \"fr_thiazole\", \"fr_thiocyan\", \"fr_thiophene\", \"fr_unbrch_alkane\", \"fr_urea\"]}}]}, \"name\": \"CompositeDescriptor\"}'}. Best is trial 0 with value: -0.34035600917066766.\n" + "[I 2024-07-02 17:07:23,954] Trial 0 finished with value: -0.33771030427395493 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.0051470093492099, 'descriptor': '{\"parameters\": {\"descriptors\": [{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}, {\"name\": \"MACCS_keys\", \"parameters\": {}}, {\"name\": \"UnscaledJazzyDescriptors\", \"parameters\": {\"jazzy_names\": [\"dga\", \"dgp\", \"dgtot\", \"sa\", \"sdc\", \"sdx\"], \"jazzy_filters\": {\"NumHAcceptors\": 25, \"NumHDonors\": 25, \"MolWt\": 1000}}}, {\"name\": \"UnscaledPhyschemDescriptors\", \"parameters\": {\"rdkit_names\": [\"MaxAbsEStateIndex\", \"MaxEStateIndex\", \"MinAbsEStateIndex\", \"MinEStateIndex\", \"qed\", \"SPS\", \"MolWt\", \"HeavyAtomMolWt\", \"ExactMolWt\", \"NumValenceElectrons\", \"NumRadicalElectrons\", \"MaxPartialCharge\", \"MinPartialCharge\", \"MaxAbsPartialCharge\", \"MinAbsPartialCharge\", \"FpDensityMorgan1\", \"FpDensityMorgan2\", \"FpDensityMorgan3\", \"BCUT2D_MWHI\", \"BCUT2D_MWLOW\", \"BCUT2D_CHGHI\", \"BCUT2D_CHGLO\", \"BCUT2D_LOGPHI\", \"BCUT2D_LOGPLOW\", \"BCUT2D_MRHI\", \"BCUT2D_MRLOW\", \"AvgIpc\", \"BalabanJ\", \"BertzCT\", \"Chi0\", \"Chi0n\", \"Chi0v\", \"Chi1\", \"Chi1n\", \"Chi1v\", \"Chi2n\", \"Chi2v\", \"Chi3n\", \"Chi3v\", \"Chi4n\", \"Chi4v\", \"HallKierAlpha\", \"Ipc\", \"Kappa1\", \"Kappa2\", \"Kappa3\", \"LabuteASA\", \"PEOE_VSA1\", \"PEOE_VSA10\", \"PEOE_VSA11\", \"PEOE_VSA12\", \"PEOE_VSA13\", \"PEOE_VSA14\", \"PEOE_VSA2\", \"PEOE_VSA3\", \"PEOE_VSA4\", \"PEOE_VSA5\", \"PEOE_VSA6\", \"PEOE_VSA7\", \"PEOE_VSA8\", \"PEOE_VSA9\", \"SMR_VSA1\", \"SMR_VSA10\", \"SMR_VSA2\", \"SMR_VSA3\", \"SMR_VSA4\", \"SMR_VSA5\", \"SMR_VSA6\", \"SMR_VSA7\", \"SMR_VSA8\", \"SMR_VSA9\", \"SlogP_VSA1\", \"SlogP_VSA10\", \"SlogP_VSA11\", \"SlogP_VSA12\", \"SlogP_VSA2\", \"SlogP_VSA3\", \"SlogP_VSA4\", \"SlogP_VSA5\", \"SlogP_VSA6\", \"SlogP_VSA7\", \"SlogP_VSA8\", \"SlogP_VSA9\", \"TPSA\", \"EState_VSA1\", \"EState_VSA10\", \"EState_VSA11\", \"EState_VSA2\", \"EState_VSA3\", \"EState_VSA4\", \"EState_VSA5\", \"EState_VSA6\", \"EState_VSA7\", \"EState_VSA8\", \"EState_VSA9\", \"VSA_EState1\", \"VSA_EState10\", \"VSA_EState2\", \"VSA_EState3\", \"VSA_EState4\", \"VSA_EState5\", \"VSA_EState6\", \"VSA_EState7\", \"VSA_EState8\", \"VSA_EState9\", \"FractionCSP3\", \"HeavyAtomCount\", \"NHOHCount\", \"NOCount\", \"NumAliphaticCarbocycles\", \"NumAliphaticHeterocycles\", \"NumAliphaticRings\", \"NumAromaticCarbocycles\", \"NumAromaticHeterocycles\", \"NumAromaticRings\", \"NumHAcceptors\", \"NumHDonors\", \"NumHeteroatoms\", \"NumRotatableBonds\", \"NumSaturatedCarbocycles\", \"NumSaturatedHeterocycles\", \"NumSaturatedRings\", \"RingCount\", \"MolLogP\", \"MolMR\", \"fr_Al_COO\", \"fr_Al_OH\", \"fr_Al_OH_noTert\", \"fr_ArN\", \"fr_Ar_COO\", \"fr_Ar_N\", \"fr_Ar_NH\", \"fr_Ar_OH\", \"fr_COO\", \"fr_COO2\", \"fr_C_O\", \"fr_C_O_noCOO\", \"fr_C_S\", \"fr_HOCCN\", \"fr_Imine\", \"fr_NH0\", \"fr_NH1\", \"fr_NH2\", \"fr_N_O\", \"fr_Ndealkylation1\", \"fr_Ndealkylation2\", \"fr_Nhpyrrole\", \"fr_SH\", \"fr_aldehyde\", \"fr_alkyl_carbamate\", \"fr_alkyl_halide\", \"fr_allylic_oxid\", \"fr_amide\", \"fr_amidine\", \"fr_aniline\", \"fr_aryl_methyl\", \"fr_azide\", \"fr_azo\", \"fr_barbitur\", \"fr_benzene\", \"fr_benzodiazepine\", \"fr_bicyclic\", \"fr_diazo\", \"fr_dihydropyridine\", \"fr_epoxide\", \"fr_ester\", \"fr_ether\", \"fr_furan\", \"fr_guanido\", \"fr_halogen\", \"fr_hdrzine\", \"fr_hdrzone\", \"fr_imidazole\", \"fr_imide\", \"fr_isocyan\", \"fr_isothiocyan\", \"fr_ketone\", \"fr_ketone_Topliss\", \"fr_lactam\", \"fr_lactone\", \"fr_methoxy\", \"fr_morpholine\", \"fr_nitrile\", \"fr_nitro\", \"fr_nitro_arom\", \"fr_nitro_arom_nonortho\", \"fr_nitroso\", \"fr_oxazole\", \"fr_oxime\", \"fr_para_hydroxylation\", \"fr_phenol\", \"fr_phenol_noOrthoHbond\", \"fr_phos_acid\", \"fr_phos_ester\", \"fr_piperdine\", \"fr_piperzine\", \"fr_priamide\", \"fr_prisulfonamd\", \"fr_pyridine\", \"fr_quatN\", \"fr_sulfide\", \"fr_sulfonamd\", \"fr_sulfone\", \"fr_term_acetylene\", \"fr_tetrazole\", \"fr_thiazole\", \"fr_thiocyan\", \"fr_thiophene\", \"fr_unbrch_alkane\", \"fr_urea\"]}}]}, \"name\": \"CompositeDescriptor\"}'}. Best is trial 0 with value: -0.33771030427395493.\n" ] } ], @@ -4000,7 +3972,7 @@ }, { "cell_type": "code", - "execution_count": 71, + "execution_count": 70, "metadata": { "scrolled": true }, @@ -4035,35 +4007,35 @@ " \n", " \n", " 2227\n", - " 2.042023e+01\n", + " 2.042239e+01\n", " UnscaledPhyschemDescriptors\n", " 7.0\n", " MolWt\n", " \n", " \n", " 2229\n", - " 2.025199e+01\n", + " 2.025405e+01\n", " UnscaledPhyschemDescriptors\n", " 9.0\n", " ExactMolWt\n", " \n", " \n", " 2228\n", - " 1.802158e+01\n", + " 1.802308e+01\n", " UnscaledPhyschemDescriptors\n", " 8.0\n", " HeavyAtomMolWt\n", " \n", " \n", " 2267\n", - " 2.387276e+00\n", + " 2.386346e+00\n", " UnscaledPhyschemDescriptors\n", " 47.0\n", " LabuteASA\n", " \n", " \n", " 2230\n", - " 2.106653e+00\n", + " 2.106611e+00\n", " UnscaledPhyschemDescriptors\n", " 10.0\n", " NumValenceElectrons\n", @@ -4076,39 +4048,39 @@ " ...\n", " \n", " \n", - " 1784\n", - " 4.598471e-07\n", + " 1189\n", + " 3.564139e-07\n", " ECFP\n", - " 1785.0\n", - " c1(OC)c(OC)ccc(C)c1\n", - " \n", - " \n", - " 583\n", - " 4.598471e-07\n", - " ECFP\n", - " 584.0\n", - " C1(c(cc)cc)=NS(=O)(=O)NC(C)=C1\n", + " 1190.0\n", + " C1=C(C(=O)N)N(C)SN=C1c(c)c\n", " \n", " \n", " 995\n", - " 4.598471e-07\n", + " 3.564139e-07\n", " ECFP\n", " 996.0\n", " C(C(N)=C)(=O)N(C)C\n", " \n", " \n", " 845\n", - " 4.598471e-07\n", + " 3.564139e-07\n", " ECFP\n", " 846.0\n", " c(c(c)C)c(O)c\n", " \n", " \n", - " 1375\n", - " 4.598471e-07\n", + " 583\n", + " 3.564139e-07\n", " ECFP\n", - " 1376.0\n", - " S1(=O)(=O)N=C(c)C=C(C)N1C\n", + " 584.0\n", + " C1(c(cc)cc)=NS(=O)(=O)NC(C)=C1\n", + " \n", + " \n", + " 334\n", + " 3.564139e-07\n", + " ECFP\n", + " 335.0\n", + " N(C)(C)C\n", " \n", " \n", "\n", @@ -4117,17 +4089,17 @@ ], "text/plain": [ " shap_value descriptor bit \\\n", - "2227 2.042023e+01 UnscaledPhyschemDescriptors 7.0 \n", - "2229 2.025199e+01 UnscaledPhyschemDescriptors 9.0 \n", - "2228 1.802158e+01 UnscaledPhyschemDescriptors 8.0 \n", - "2267 2.387276e+00 UnscaledPhyschemDescriptors 47.0 \n", - "2230 2.106653e+00 UnscaledPhyschemDescriptors 10.0 \n", + "2227 2.042239e+01 UnscaledPhyschemDescriptors 7.0 \n", + "2229 2.025405e+01 UnscaledPhyschemDescriptors 9.0 \n", + "2228 1.802308e+01 UnscaledPhyschemDescriptors 8.0 \n", + "2267 2.386346e+00 UnscaledPhyschemDescriptors 47.0 \n", + "2230 2.106611e+00 UnscaledPhyschemDescriptors 10.0 \n", "... ... ... ... \n", - "1784 4.598471e-07 ECFP 1785.0 \n", - "583 4.598471e-07 ECFP 584.0 \n", - "995 4.598471e-07 ECFP 996.0 \n", - "845 4.598471e-07 ECFP 846.0 \n", - "1375 4.598471e-07 ECFP 1376.0 \n", + "1189 3.564139e-07 ECFP 1190.0 \n", + "995 3.564139e-07 ECFP 996.0 \n", + "845 3.564139e-07 ECFP 846.0 \n", + "583 3.564139e-07 ECFP 584.0 \n", + "334 3.564139e-07 ECFP 335.0 \n", "\n", " info \n", "2227 MolWt \n", @@ -4136,16 +4108,16 @@ "2267 LabuteASA \n", "2230 NumValenceElectrons \n", "... ... \n", - "1784 c1(OC)c(OC)ccc(C)c1 \n", - "583 C1(c(cc)cc)=NS(=O)(=O)NC(C)=C1 \n", + "1189 C1=C(C(=O)N)N(C)SN=C1c(c)c \n", "995 C(C(N)=C)(=O)N(C)C \n", "845 c(c(c)C)c(O)c \n", - "1375 S1(=O)(=O)N=C(c)C=C(C)N1C \n", + "583 C1(c(cc)cc)=NS(=O)(=O)NC(C)=C1 \n", + "334 N(C)(C)C \n", "\n", "[1570 rows x 4 columns]" ] }, - "execution_count": 71, + "execution_count": 70, "metadata": {}, "output_type": "execute_result" } @@ -4183,7 +4155,7 @@ }, { "cell_type": "code", - "execution_count": 72, + "execution_count": 71, "metadata": { "scrolled": false }, @@ -4192,10 +4164,16 @@ "name": "stderr", "output_type": "stream", "text": [ - "[I 2024-07-02 14:22:59,978] A new study created in memory with name: my_study\n", - "[I 2024-07-02 14:23:00,032] A new study created in memory with name: study_name_0\n", + "[I 2024-07-02 17:07:28,219] A new study created in memory with name: my_study\n", + "[I 2024-07-02 17:07:28,415] A new study created in memory with name: study_name_0\n", "INFO:root:Enqueued ChemProp manual trial with sensible defaults: {'activation__e0d3a442222d4b38f3aa1434851320db': 'ReLU', 'aggregation__e0d3a442222d4b38f3aa1434851320db': 'mean', 'aggregation_norm__e0d3a442222d4b38f3aa1434851320db': 100, 'batch_size__e0d3a442222d4b38f3aa1434851320db': 50, 'depth__e0d3a442222d4b38f3aa1434851320db': 3, 'dropout__e0d3a442222d4b38f3aa1434851320db': 0.0, 'features_generator__e0d3a442222d4b38f3aa1434851320db': 'none', 'ffn_hidden_size__e0d3a442222d4b38f3aa1434851320db': 300, 'ffn_num_layers__e0d3a442222d4b38f3aa1434851320db': 2, 'final_lr_ratio_exp__e0d3a442222d4b38f3aa1434851320db': -4, 'hidden_size__e0d3a442222d4b38f3aa1434851320db': 300, 'init_lr_ratio_exp__e0d3a442222d4b38f3aa1434851320db': -4, 'max_lr_exp__e0d3a442222d4b38f3aa1434851320db': -3, 'warmup_epochs_ratio__e0d3a442222d4b38f3aa1434851320db': 0.1, 'algorithm_name': 'ChemPropRegressor', 'ChemPropRegressor_algorithm_hash': 'e0d3a442222d4b38f3aa1434851320db'}\n", - "[I 2024-07-02 14:23:43,818] Trial 0 finished with value: -4937.540075659691 and parameters: {'algorithm_name': 'ChemPropRegressor', 'ChemPropRegressor_algorithm_hash': 'e0d3a442222d4b38f3aa1434851320db', 'activation__e0d3a442222d4b38f3aa1434851320db': , 'aggregation__e0d3a442222d4b38f3aa1434851320db': , 'aggregation_norm__e0d3a442222d4b38f3aa1434851320db': 100.0, 'batch_size__e0d3a442222d4b38f3aa1434851320db': 50.0, 'depth__e0d3a442222d4b38f3aa1434851320db': 3.0, 'dropout__e0d3a442222d4b38f3aa1434851320db': 0.0, 'ensemble_size__e0d3a442222d4b38f3aa1434851320db': 1, 'epochs__e0d3a442222d4b38f3aa1434851320db': 4, 'features_generator__e0d3a442222d4b38f3aa1434851320db': , 'ffn_hidden_size__e0d3a442222d4b38f3aa1434851320db': 300.0, 'ffn_num_layers__e0d3a442222d4b38f3aa1434851320db': 2.0, 'final_lr_ratio_exp__e0d3a442222d4b38f3aa1434851320db': -4, 'hidden_size__e0d3a442222d4b38f3aa1434851320db': 300.0, 'init_lr_ratio_exp__e0d3a442222d4b38f3aa1434851320db': -4, 'max_lr_exp__e0d3a442222d4b38f3aa1434851320db': -3, 'warmup_epochs_ratio__e0d3a442222d4b38f3aa1434851320db': 0.1, 'descriptor': '{\"name\": \"SmilesFromFile\", \"parameters\": {}}'}. Best is trial 0 with value: -4937.540075659691.\n", + "/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/joblib/memory.py:577: JobLibCollisionWarning: Possible name collisions between functions 'calculate_from_smi' (/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/optunaz/descriptors.py:-1) and 'calculate_from_smi' (/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/optunaz/descriptors.py:669)\n", + " return self._cached_call(args, kwargs, shelving=False)[0]\n", + "/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/joblib/memory.py:577: JobLibCollisionWarning: Possible name collisions between functions 'calculate_from_smi' (/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/optunaz/descriptors.py:-1) and 'calculate_from_smi' (/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/optunaz/descriptors.py:669)\n", + " return self._cached_call(args, kwargs, shelving=False)[0]\n", + "/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/joblib/memory.py:577: JobLibCollisionWarning: Possible name collisions between functions 'calculate_from_smi' (/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/optunaz/descriptors.py:-1) and 'calculate_from_smi' (/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/optunaz/descriptors.py:669)\n", + " return self._cached_call(args, kwargs, shelving=False)[0]\n", + "[I 2024-07-02 17:08:13,378] Trial 0 finished with value: -4937.540075659691 and parameters: {'algorithm_name': 'ChemPropRegressor', 'ChemPropRegressor_algorithm_hash': 'e0d3a442222d4b38f3aa1434851320db', 'activation__e0d3a442222d4b38f3aa1434851320db': , 'aggregation__e0d3a442222d4b38f3aa1434851320db': , 'aggregation_norm__e0d3a442222d4b38f3aa1434851320db': 100.0, 'batch_size__e0d3a442222d4b38f3aa1434851320db': 50.0, 'depth__e0d3a442222d4b38f3aa1434851320db': 3.0, 'dropout__e0d3a442222d4b38f3aa1434851320db': 0.0, 'ensemble_size__e0d3a442222d4b38f3aa1434851320db': 1, 'epochs__e0d3a442222d4b38f3aa1434851320db': 4, 'features_generator__e0d3a442222d4b38f3aa1434851320db': , 'ffn_hidden_size__e0d3a442222d4b38f3aa1434851320db': 300.0, 'ffn_num_layers__e0d3a442222d4b38f3aa1434851320db': 2.0, 'final_lr_ratio_exp__e0d3a442222d4b38f3aa1434851320db': -4, 'hidden_size__e0d3a442222d4b38f3aa1434851320db': 300.0, 'init_lr_ratio_exp__e0d3a442222d4b38f3aa1434851320db': -4, 'max_lr_exp__e0d3a442222d4b38f3aa1434851320db': -3, 'warmup_epochs_ratio__e0d3a442222d4b38f3aa1434851320db': 0.1, 'descriptor': '{\"name\": \"SmilesFromFile\", \"parameters\": {}}'}. Best is trial 0 with value: -4937.540075659691.\n", " \r" ] } @@ -4227,7 +4205,7 @@ }, { "cell_type": "code", - "execution_count": 73, + "execution_count": 72, "metadata": {}, "outputs": [ { @@ -4253,185 +4231,185 @@ }, { "cell_type": "code", - "execution_count": 74, + "execution_count": 73, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "[14:25:51] Can't kekulize mol. Unkekulized atoms: 8 9 18 19 20 21 22 23 24\n", - "[14:25:51] Can't kekulize mol. Unkekulized atoms: 6 7 16 17 18 19 20 21 22\n", - "[14:25:51] Can't kekulize mol. Unkekulized atoms: 6 7 16 17 18 19 20 21 22\n", - "[14:25:51] Can't kekulize mol. Unkekulized atoms: 5 6 15 16 17 18 19 20 21\n", - "[14:25:51] Can't kekulize mol. Unkekulized atoms: 4 5 14 15 16 17 18 19 20\n", - "[14:25:51] Can't kekulize mol. Unkekulized atoms: 2 3 12 13 14 15 16 17 18\n", - "[14:25:51] Can't kekulize mol. Unkekulized atoms: 2 3 12 13 14 15 16 17 18\n", - "[14:25:51] Can't kekulize mol. Unkekulized atoms: 6 7 9 10 11 12 13 14 15\n", - "[14:25:51] Can't kekulize mol. Unkekulized atoms: 6 7 8 9 10 11 12 13 14\n", - "[14:25:51] Can't kekulize mol. Unkekulized atoms: 0 1 2 3 4 5 6 7 11\n", - "[14:25:51] Can't kekulize mol. Unkekulized atoms: 0 1 2 3 4 5 6 7 10\n", - "[14:25:51] Can't kekulize mol. Unkekulized atoms: 2 3 4 5 6\n", - "[14:25:51] Can't kekulize mol. Unkekulized atoms: 7 8 17 18 19 20 21 22 23\n", - "[14:25:51] Can't kekulize mol. Unkekulized atoms: 5 6 15 16 17 18 19 20 21\n", - "[14:25:51] Can't kekulize mol. Unkekulized atoms: 5 6 15 16 17 18 19 20 21\n", - "[14:25:51] Can't kekulize mol. Unkekulized atoms: 6 7 12 13 14 15 16 17 18\n", - "[14:25:51] Can't kekulize mol. Unkekulized atoms: 6 7 11 12 13 14 15 16 17\n", - "[14:25:51] Can't kekulize mol. Unkekulized atoms: 6 7 10 11 12 13 14 15 16\n", - "[14:25:51] Can't kekulize mol. Unkekulized atoms: 6 7 10 11 12\n", - "[14:25:51] Can't kekulize mol. Unkekulized atoms: 6 7 9 10 11\n", - "[14:25:51] Can't kekulize mol. Unkekulized atoms: 6 7 8 9 10\n", - "[14:25:51] Can't kekulize mol. Unkekulized atoms: 2 3 4 5 6\n", - "[14:25:51] Can't kekulize mol. Unkekulized atoms: 6 7 16 17 18 19 20 21 22\n", - "[14:25:51] Can't kekulize mol. Unkekulized atoms: 6 7 16 17 18 19 20 21 22\n", - "[14:25:51] Can't kekulize mol. Unkekulized atoms: 6 7 13 14 15 16 17 18 19\n", - "[14:25:51] Can't kekulize mol. Unkekulized atoms: 6 7 13 14 15\n", - "[14:25:51] Can't kekulize mol. Unkekulized atoms: 6 7 12 13 14\n", - "[14:25:51] Can't kekulize mol. Unkekulized atoms: 6 7 11 12 13\n", - "[14:25:51] Can't kekulize mol. Unkekulized atoms: 2 3 7 8 9\n", - "[14:25:51] Can't kekulize mol. Unkekulized atoms: 2 3 6 7 8\n", - "[14:25:51] Can't kekulize mol. Unkekulized atoms: 2 3 5 6 7\n", - "[14:25:51] Can't kekulize mol. Unkekulized atoms: 2 3 4 6 7\n", - "[14:25:51] Can't kekulize mol. Unkekulized atoms: 0 1 3 4 5\n", - "[14:25:51] Can't kekulize mol. Unkekulized atoms: 8 9 18 19 20\n", - "[14:25:51] Can't kekulize mol. Unkekulized atoms: 6 7 16 17 18\n", - "[14:25:51] Can't kekulize mol. Unkekulized atoms: 6 7 16 17 18\n", - "[14:25:51] Can't kekulize mol. Unkekulized atoms: 5 6 15 16 17\n", - "[14:25:51] Can't kekulize mol. Unkekulized atoms: 4 5 14 15 16\n", - "[14:25:51] Can't kekulize mol. Unkekulized atoms: 2 3 12 13 14\n", - "[14:25:51] Can't kekulize mol. Unkekulized atoms: 2 3 12 13 14\n", - "[14:25:51] Can't kekulize mol. Unkekulized atoms: 2 3 7 8 9\n", - "[14:25:51] Can't kekulize mol. Unkekulized atoms: 2 3 4 7 8\n", - "[14:25:51] Can't kekulize mol. Unkekulized atoms: 2 3 6 7 8\n", - "[14:25:51] Can't kekulize mol. Unkekulized atoms: 8 9 13 14 15 16 17 18 19\n", - "[14:25:51] Can't kekulize mol. Unkekulized atoms: 6 7 11 12 13 14 15 16 17\n", - "[14:25:51] Can't kekulize mol. Unkekulized atoms: 6 7 11 12 13 14 15 16 17\n", - "[14:25:51] Can't kekulize mol. Unkekulized atoms: 5 6 10 11 12 13 14 15 16\n", - "[14:25:51] Can't kekulize mol. Unkekulized atoms: 4 5 9 10 11 12 13 14 15\n", - "[14:25:51] Can't kekulize mol. Unkekulized atoms: 2 3 7 8 9 10 11 12 13\n", - "[14:25:51] Can't kekulize mol. Unkekulized atoms: 2 3 7 8 9 10 11 12 13\n", - "[14:25:51] Can't kekulize mol. Unkekulized atoms: 0 1 2 3 4 5 6 8 12\n", - "[14:25:51] Can't kekulize mol. Unkekulized atoms: 0 1 2 3 4 5 6 8 11\n", - "[14:25:51] Can't kekulize mol. Unkekulized atoms: 2 3 4 6 7\n", - "[14:25:51] Can't kekulize mol. Unkekulized atoms: 5 6 15 16 17 18 19 20 21\n", - "[14:25:51] Can't kekulize mol. Unkekulized atoms: 5 6 15 16 17\n", - "[14:25:51] Can't kekulize mol. Unkekulized atoms: 5 6 15 16 17\n", - "[14:25:51] Can't kekulize mol. Unkekulized atoms: 5 6 10 11 12\n", - "[14:25:51] Can't kekulize mol. Unkekulized atoms: 2 3 8 9 10\n", - "[14:25:51] Can't kekulize mol. Unkekulized atoms: 2 3 4 9 10\n", - "[14:25:51] Can't kekulize mol. Unkekulized atoms: 0 1 6 7 8\n", - "[14:25:51] Can't kekulize mol. Unkekulized atoms: 2 3 4 8 9\n", - "[14:25:51] Can't kekulize mol. Unkekulized atoms: 0 1 5 6 7\n", - "[14:25:51] Can't kekulize mol. Unkekulized atoms: 2 3 4 7 8\n", - "[14:25:51] Can't kekulize mol. Unkekulized atoms: 0 1 4 5 6\n", - "[14:25:51] Can't kekulize mol. Unkekulized atoms: 5 6 15 16 17 18 19 20 21\n", - "[14:25:51] Can't kekulize mol. Unkekulized atoms: 5 6 10 11 12 13 14 15 16\n", - "[14:25:51] Can't kekulize mol. Unkekulized atoms: 5 6 10 11 12 13 14 15 16\n", - "[14:25:51] Can't kekulize mol. Unkekulized atoms: 5 6 9 10 11 12 13 14 15\n", - "[14:25:51] Can't kekulize mol. Unkekulized atoms: 0 1 2 3 4 5 6 11 14\n", - "[14:25:51] Can't kekulize mol. Unkekulized atoms: 0 1 2 3 4 5 6 10 13\n", - "[14:25:51] Can't kekulize mol. Unkekulized atoms: 7 8 9 11 12\n", - "[14:25:51] Can't kekulize mol. Unkekulized atoms: 0 1 2 3 4 5 6 9 12\n", - "[14:25:51] Can't kekulize mol. Unkekulized atoms: 0 1 3 4 5\n", - "[14:25:51] Can't kekulize mol. Unkekulized atoms: 2 3 4 7 8\n", - "[14:25:51] Can't kekulize mol. Unkekulized atoms: 0 1 3 4 5\n", - "[14:25:51] Can't kekulize mol. Unkekulized atoms: 6 7 16 17 18\n", - "[14:25:51] Can't kekulize mol. Unkekulized atoms: 6 7 16 17 18\n", - "[14:25:51] Can't kekulize mol. Unkekulized atoms: 6 7 11 12 13\n", - "[14:25:51] Can't kekulize mol. Unkekulized atoms: 5 6 10 11 12\n", - "[14:25:51] Can't kekulize mol. Unkekulized atoms: 4 5 9 10 11\n", - "[14:25:51] Can't kekulize mol. Unkekulized atoms: 2 3 7 8 9\n", - "[14:25:51] Can't kekulize mol. Unkekulized atoms: 2 3 4 7 8\n", - "[14:25:51] Can't kekulize mol. Unkekulized atoms: 2 3 6 7 8\n", - "[14:25:51] Can't kekulize mol. Unkekulized atoms: 7 8 17 18 19\n", - "[14:25:51] Can't kekulize mol. Unkekulized atoms: 7 8 12 13 14\n", - "[14:25:51] Can't kekulize mol. Unkekulized atoms: 5 6 10 11 12\n", - "[14:25:51] Can't kekulize mol. Unkekulized atoms: 2 3 9 10 11\n", - "[14:25:51] Can't kekulize mol. Unkekulized atoms: 2 3 4 10 11\n", - "[14:25:51] Can't kekulize mol. Unkekulized atoms: 0 1 7 8 9\n", - "[14:25:51] Can't kekulize mol. Unkekulized atoms: 8 9 12 13 14 15 16 17 18\n", - "[14:25:51] Can't kekulize mol. Unkekulized atoms: 6 7 10 11 12 13 14 15 16\n", - "[14:25:51] Can't kekulize mol. Unkekulized atoms: 6 7 10 11 12 13 14 15 16\n", - "[14:25:51] Can't kekulize mol. Unkekulized atoms: 6 7 8 11 12\n", - "[14:25:51] Can't kekulize mol. Unkekulized atoms: 5 6 7 10 11\n", - "[14:25:51] Can't kekulize mol. Unkekulized atoms: 4 5 6 9 10\n", - "[14:25:51] Can't kekulize mol. Unkekulized atoms: 2 3 4 7 8\n", - "[14:25:51] Can't kekulize mol. Unkekulized atoms: 7 8 17 18 19\n", - "[14:25:51] Can't kekulize mol. Unkekulized atoms: 8 9 10 12 13\n", - "[14:25:51] Can't kekulize mol. Unkekulized atoms: 5 6 10 11 12\n", - "[14:25:51] Can't kekulize mol. Unkekulized atoms: 5 6 7 10 11\n", - "[14:25:51] Can't kekulize mol. Unkekulized atoms: 5 6 9 10 11\n", - "[14:25:51] Can't kekulize mol. Unkekulized atoms: 5 6 7 10 11\n", - "[14:25:51] Can't kekulize mol. Unkekulized atoms: 5 6 9 10 11\n", - "[14:25:51] Can't kekulize mol. Unkekulized atoms: 6 7 16 17 18 19 20 21 22\n", - "[14:25:51] Can't kekulize mol. Unkekulized atoms: 6 7 11 12 13 14 15 16 17\n", - "[14:25:51] Can't kekulize mol. Unkekulized atoms: 0 1 2 3 4 5 6 12 16\n", - "[14:25:51] Can't kekulize mol. Unkekulized atoms: 2 3 4 13 14\n", - "[14:25:51] Can't kekulize mol. Unkekulized atoms: 0 1 2 3 4 5 6 11 15\n", - "[14:25:51] Can't kekulize mol. Unkekulized atoms: 2 3 4 8 9\n", - "[14:25:51] Can't kekulize mol. Unkekulized atoms: 0 1 2 3 4 5 6 10 14\n", - "[14:25:51] Can't kekulize mol. Unkekulized atoms: 2 3 4 7 8\n", - "[14:25:51] Can't kekulize mol. Unkekulized atoms: 0 1 2 3 4 5 6 9 13\n", - "[14:25:51] Can't kekulize mol. Unkekulized atoms: 2 3 4 6 7\n", - "[14:25:51] Can't kekulize mol. Unkekulized atoms: 6 7 11 12 13 14 15 16 17\n", - "[14:25:51] Can't kekulize mol. Unkekulized atoms: 6 7 11 12 13\n", - "[14:25:51] Can't kekulize mol. Unkekulized atoms: 6 7 8 11 12\n", - "[14:25:51] Can't kekulize mol. Unkekulized atoms: 6 7 10 11 12\n", - "[14:25:51] Can't kekulize mol. Unkekulized atoms: 6 7 8 11 12\n", - "[14:25:51] Can't kekulize mol. Unkekulized atoms: 6 7 10 11 12\n", - "[14:25:51] Can't kekulize mol. Unkekulized atoms: 5 6 7 10 11\n", - "[14:25:51] Can't kekulize mol. Unkekulized atoms: 5 6 9 10 11\n", - "[14:25:51] Can't kekulize mol. Unkekulized atoms: 4 5 6 9 10\n", - "[14:25:51] Can't kekulize mol. Unkekulized atoms: 4 5 8 9 10\n", - "[14:25:51] Can't kekulize mol. Unkekulized atoms: 10 11 12 14 15\n", - "[14:25:51] Can't kekulize mol. Unkekulized atoms: 6 7 16 17 18\n", - "[14:25:51] Can't kekulize mol. Unkekulized atoms: 5 6 15 16 17\n" + "[17:10:22] Can't kekulize mol. Unkekulized atoms: 8 9 18 19 20 21 22 23 24\n", + "[17:10:22] Can't kekulize mol. Unkekulized atoms: 6 7 16 17 18 19 20 21 22\n", + "[17:10:22] Can't kekulize mol. Unkekulized atoms: 6 7 16 17 18 19 20 21 22\n", + "[17:10:22] Can't kekulize mol. Unkekulized atoms: 5 6 15 16 17 18 19 20 21\n", + "[17:10:22] Can't kekulize mol. Unkekulized atoms: 4 5 14 15 16 17 18 19 20\n", + "[17:10:23] Can't kekulize mol. Unkekulized atoms: 2 3 12 13 14 15 16 17 18\n", + "[17:10:23] Can't kekulize mol. Unkekulized atoms: 2 3 12 13 14 15 16 17 18\n", + "[17:10:23] Can't kekulize mol. Unkekulized atoms: 6 7 9 10 11 12 13 14 15\n", + "[17:10:23] Can't kekulize mol. Unkekulized atoms: 6 7 8 9 10 11 12 13 14\n", + "[17:10:23] Can't kekulize mol. Unkekulized atoms: 0 1 2 3 4 5 6 7 11\n", + "[17:10:23] Can't kekulize mol. Unkekulized atoms: 0 1 2 3 4 5 6 7 10\n", + "[17:10:23] Can't kekulize mol. Unkekulized atoms: 2 3 4 5 6\n", + "[17:10:23] Can't kekulize mol. Unkekulized atoms: 7 8 17 18 19 20 21 22 23\n", + "[17:10:23] Can't kekulize mol. Unkekulized atoms: 5 6 15 16 17 18 19 20 21\n", + "[17:10:23] Can't kekulize mol. Unkekulized atoms: 5 6 15 16 17 18 19 20 21\n", + "[17:10:23] Can't kekulize mol. Unkekulized atoms: 6 7 12 13 14 15 16 17 18\n", + "[17:10:23] Can't kekulize mol. Unkekulized atoms: 6 7 11 12 13 14 15 16 17\n", + "[17:10:23] Can't kekulize mol. Unkekulized atoms: 6 7 10 11 12 13 14 15 16\n", + "[17:10:23] Can't kekulize mol. Unkekulized atoms: 6 7 10 11 12\n", + "[17:10:23] Can't kekulize mol. Unkekulized atoms: 6 7 9 10 11\n", + "[17:10:23] Can't kekulize mol. Unkekulized atoms: 6 7 8 9 10\n", + "[17:10:23] Can't kekulize mol. Unkekulized atoms: 2 3 4 5 6\n", + "[17:10:23] Can't kekulize mol. Unkekulized atoms: 6 7 16 17 18 19 20 21 22\n", + "[17:10:23] Can't kekulize mol. Unkekulized atoms: 6 7 16 17 18 19 20 21 22\n", + "[17:10:23] Can't kekulize mol. Unkekulized atoms: 6 7 13 14 15 16 17 18 19\n", + "[17:10:23] Can't kekulize mol. Unkekulized atoms: 6 7 13 14 15\n", + "[17:10:23] Can't kekulize mol. Unkekulized atoms: 6 7 12 13 14\n", + "[17:10:23] Can't kekulize mol. Unkekulized atoms: 6 7 11 12 13\n", + "[17:10:23] Can't kekulize mol. Unkekulized atoms: 2 3 7 8 9\n", + "[17:10:23] Can't kekulize mol. Unkekulized atoms: 2 3 6 7 8\n", + "[17:10:23] Can't kekulize mol. Unkekulized atoms: 2 3 5 6 7\n", + "[17:10:23] Can't kekulize mol. Unkekulized atoms: 2 3 4 6 7\n", + "[17:10:23] Can't kekulize mol. Unkekulized atoms: 0 1 3 4 5\n", + "[17:10:23] Can't kekulize mol. Unkekulized atoms: 8 9 18 19 20\n", + "[17:10:23] Can't kekulize mol. Unkekulized atoms: 6 7 16 17 18\n", + "[17:10:23] Can't kekulize mol. Unkekulized atoms: 6 7 16 17 18\n", + "[17:10:23] Can't kekulize mol. Unkekulized atoms: 5 6 15 16 17\n", + "[17:10:23] Can't kekulize mol. Unkekulized atoms: 4 5 14 15 16\n", + "[17:10:23] Can't kekulize mol. Unkekulized atoms: 2 3 12 13 14\n", + "[17:10:23] Can't kekulize mol. Unkekulized atoms: 2 3 12 13 14\n", + "[17:10:23] Can't kekulize mol. Unkekulized atoms: 2 3 7 8 9\n", + "[17:10:23] Can't kekulize mol. Unkekulized atoms: 2 3 4 7 8\n", + "[17:10:23] Can't kekulize mol. Unkekulized atoms: 2 3 6 7 8\n", + "[17:10:23] Can't kekulize mol. Unkekulized atoms: 8 9 13 14 15 16 17 18 19\n", + "[17:10:23] Can't kekulize mol. Unkekulized atoms: 6 7 11 12 13 14 15 16 17\n", + "[17:10:23] Can't kekulize mol. Unkekulized atoms: 6 7 11 12 13 14 15 16 17\n", + "[17:10:23] Can't kekulize mol. Unkekulized atoms: 5 6 10 11 12 13 14 15 16\n", + "[17:10:23] Can't kekulize mol. Unkekulized atoms: 4 5 9 10 11 12 13 14 15\n", + "[17:10:23] Can't kekulize mol. Unkekulized atoms: 2 3 7 8 9 10 11 12 13\n", + "[17:10:23] Can't kekulize mol. Unkekulized atoms: 2 3 7 8 9 10 11 12 13\n", + "[17:10:23] Can't kekulize mol. Unkekulized atoms: 0 1 2 3 4 5 6 8 12\n", + "[17:10:23] Can't kekulize mol. Unkekulized atoms: 0 1 2 3 4 5 6 8 11\n", + "[17:10:23] Can't kekulize mol. Unkekulized atoms: 2 3 4 6 7\n", + "[17:10:23] Can't kekulize mol. Unkekulized atoms: 5 6 15 16 17 18 19 20 21\n", + "[17:10:23] Can't kekulize mol. Unkekulized atoms: 5 6 15 16 17\n", + "[17:10:23] Can't kekulize mol. Unkekulized atoms: 5 6 15 16 17\n", + "[17:10:23] Can't kekulize mol. Unkekulized atoms: 5 6 10 11 12\n", + "[17:10:23] Can't kekulize mol. Unkekulized atoms: 2 3 8 9 10\n", + "[17:10:23] Can't kekulize mol. Unkekulized atoms: 2 3 4 9 10\n", + "[17:10:23] Can't kekulize mol. Unkekulized atoms: 0 1 6 7 8\n", + "[17:10:23] Can't kekulize mol. Unkekulized atoms: 2 3 4 8 9\n", + "[17:10:23] Can't kekulize mol. Unkekulized atoms: 0 1 5 6 7\n", + "[17:10:23] Can't kekulize mol. Unkekulized atoms: 2 3 4 7 8\n", + "[17:10:23] Can't kekulize mol. Unkekulized atoms: 0 1 4 5 6\n", + "[17:10:23] Can't kekulize mol. Unkekulized atoms: 5 6 15 16 17 18 19 20 21\n", + "[17:10:23] Can't kekulize mol. Unkekulized atoms: 5 6 10 11 12 13 14 15 16\n", + "[17:10:23] Can't kekulize mol. Unkekulized atoms: 7 8 9 11 12\n", + "[17:10:23] Can't kekulize mol. Unkekulized atoms: 5 6 10 11 12 13 14 15 16\n", + "[17:10:23] Can't kekulize mol. Unkekulized atoms: 0 1 3 4 5\n", + "[17:10:23] Can't kekulize mol. Unkekulized atoms: 5 6 9 10 11 12 13 14 15\n", + "[17:10:23] Can't kekulize mol. Unkekulized atoms: 0 1 3 4 5\n", + "[17:10:23] Can't kekulize mol. Unkekulized atoms: 0 1 2 3 4 5 6 11 14\n", + "[17:10:23] Can't kekulize mol. Unkekulized atoms: 0 1 2 3 4 5 6 10 13\n", + "[17:10:23] Can't kekulize mol. Unkekulized atoms: 0 1 2 3 4 5 6 9 12\n", + "[17:10:23] Can't kekulize mol. Unkekulized atoms: 2 3 4 7 8\n", + "[17:10:23] Can't kekulize mol. Unkekulized atoms: 6 7 16 17 18\n", + "[17:10:23] Can't kekulize mol. Unkekulized atoms: 6 7 16 17 18\n", + "[17:10:23] Can't kekulize mol. Unkekulized atoms: 6 7 11 12 13\n", + "[17:10:23] Can't kekulize mol. Unkekulized atoms: 5 6 10 11 12\n", + "[17:10:23] Can't kekulize mol. Unkekulized atoms: 4 5 9 10 11\n", + "[17:10:23] Can't kekulize mol. Unkekulized atoms: 2 3 7 8 9\n", + "[17:10:23] Can't kekulize mol. Unkekulized atoms: 2 3 4 7 8\n", + "[17:10:23] Can't kekulize mol. Unkekulized atoms: 2 3 6 7 8\n", + "[17:10:23] Can't kekulize mol. Unkekulized atoms: 7 8 17 18 19\n", + "[17:10:23] Can't kekulize mol. Unkekulized atoms: 7 8 12 13 14\n", + "[17:10:23] Can't kekulize mol. Unkekulized atoms: 5 6 10 11 12\n", + "[17:10:23] Can't kekulize mol. Unkekulized atoms: 2 3 9 10 11\n", + "[17:10:23] Can't kekulize mol. Unkekulized atoms: 2 3 4 10 11\n", + "[17:10:23] Can't kekulize mol. Unkekulized atoms: 0 1 7 8 9\n", + "[17:10:23] Can't kekulize mol. Unkekulized atoms: 8 9 12 13 14 15 16 17 18\n", + "[17:10:23] Can't kekulize mol. Unkekulized atoms: 6 7 10 11 12 13 14 15 16\n", + "[17:10:23] Can't kekulize mol. Unkekulized atoms: 6 7 10 11 12 13 14 15 16\n", + "[17:10:23] Can't kekulize mol. Unkekulized atoms: 6 7 8 11 12\n", + "[17:10:23] Can't kekulize mol. Unkekulized atoms: 5 6 7 10 11\n", + "[17:10:23] Can't kekulize mol. Unkekulized atoms: 4 5 6 9 10\n", + "[17:10:23] Can't kekulize mol. Unkekulized atoms: 8 9 10 12 13\n", + "[17:10:23] Can't kekulize mol. Unkekulized atoms: 2 3 4 7 8\n", + "[17:10:23] Can't kekulize mol. Unkekulized atoms: 7 8 17 18 19\n", + "[17:10:23] Can't kekulize mol. Unkekulized atoms: 5 6 10 11 12\n", + "[17:10:23] Can't kekulize mol. Unkekulized atoms: 5 6 7 10 11\n", + "[17:10:23] Can't kekulize mol. Unkekulized atoms: 5 6 9 10 11\n", + "[17:10:23] Can't kekulize mol. Unkekulized atoms: 5 6 7 10 11\n", + "[17:10:23] Can't kekulize mol. Unkekulized atoms: 5 6 9 10 11\n", + "[17:10:23] Can't kekulize mol. Unkekulized atoms: 6 7 16 17 18 19 20 21 22\n", + "[17:10:23] Can't kekulize mol. Unkekulized atoms: 2 3 4 13 14\n", + "[17:10:23] Can't kekulize mol. Unkekulized atoms: 2 3 4 8 9\n", + "[17:10:23] Can't kekulize mol. Unkekulized atoms: 6 7 11 12 13 14 15 16 17\n", + "[17:10:23] Can't kekulize mol. Unkekulized atoms: 2 3 4 7 8\n", + "[17:10:23] Can't kekulize mol. Unkekulized atoms: 2 3 4 6 7\n", + "[17:10:23] Can't kekulize mol. Unkekulized atoms: 0 1 2 3 4 5 6 12 16\n", + "[17:10:23] Can't kekulize mol. Unkekulized atoms: 0 1 2 3 4 5 6 11 15\n", + "[17:10:23] Can't kekulize mol. Unkekulized atoms: 0 1 2 3 4 5 6 10 14\n", + "[17:10:23] Can't kekulize mol. Unkekulized atoms: 0 1 2 3 4 5 6 9 13\n", + "[17:10:23] Can't kekulize mol. Unkekulized atoms: 6 7 11 12 13 14 15 16 17\n", + "[17:10:23] Can't kekulize mol. Unkekulized atoms: 6 7 11 12 13\n", + "[17:10:23] Can't kekulize mol. Unkekulized atoms: 10 11 12 14 15\n", + "[17:10:23] Can't kekulize mol. Unkekulized atoms: 6 7 8 11 12\n", + "[17:10:23] Can't kekulize mol. Unkekulized atoms: 6 7 10 11 12\n", + "[17:10:23] Can't kekulize mol. Unkekulized atoms: 6 7 8 11 12\n", + "[17:10:23] Can't kekulize mol. Unkekulized atoms: 6 7 10 11 12\n", + "[17:10:23] Can't kekulize mol. Unkekulized atoms: 5 6 7 10 11\n", + "[17:10:23] Can't kekulize mol. Unkekulized atoms: 5 6 9 10 11\n", + "[17:10:23] Can't kekulize mol. Unkekulized atoms: 4 5 6 9 10\n", + "[17:10:23] Can't kekulize mol. Unkekulized atoms: 4 5 8 9 10\n", + "[17:10:23] Can't kekulize mol. Unkekulized atoms: 6 7 16 17 18\n", + "[17:10:23] Can't kekulize mol. Unkekulized atoms: 5 6 15 16 17\n", + "[17:10:23] Can't kekulize mol. Unkekulized atoms: 2 3 8 9 10\n", + "[17:10:23] Can't kekulize mol. Unkekulized atoms: 2 3 4 8 9\n", + "[17:10:23] Can't kekulize mol. Unkekulized atoms: 0 1 5 6 7\n", + "[17:10:23] Can't kekulize mol. Unkekulized atoms: 7 8 12 13 14 15 16 17 18\n", + "[17:10:23] Can't kekulize mol. Unkekulized atoms: 7 8 11 12 13 14 15 16 17\n", + "[17:10:23] Can't kekulize mol. Unkekulized atoms: 5 6 9 10 11 12 13 14 15\n", + "[17:10:23] Can't kekulize mol. Unkekulized atoms: 5 6 7 10 11\n", + "[17:10:23] Can't kekulize mol. Unkekulized atoms: 5 6 7 10 11\n", + "[17:10:23] Can't kekulize mol. Unkekulized atoms: 2 3 4 9 10\n", + "[17:10:23] Can't kekulize mol. Unkekulized atoms: 2 3 4 8 9\n", + "[17:10:23] Can't kekulize mol. Unkekulized atoms: 7 8 12 13 14 15 16 17 18\n", + "[17:10:23] Can't kekulize mol. Unkekulized atoms: 5 6 9 10 11 12 13 14 15\n", + "[17:10:23] Can't kekulize mol. Unkekulized atoms: 0 1 2 3 4 5 6 12 15\n", + "[17:10:23] Can't kekulize mol. Unkekulized atoms: 2 3 4 10 11\n", + "[17:10:23] Can't kekulize mol. Unkekulized atoms: 6 7 16 17 18\n", + "[17:10:23] Can't kekulize mol. Unkekulized atoms: 6 7 12 13 14\n", + "[17:10:23] Can't kekulize mol. Unkekulized atoms: 9 10 11 13 14\n", + "[17:10:23] Can't kekulize mol. Unkekulized atoms: 2 3 6 7 8\n", + "[17:10:23] Can't kekulize mol. Unkekulized atoms: 6 7 13 14 15 16 17 18 19\n", + "[17:10:23] Can't kekulize mol. Unkekulized atoms: 6 7 12 13 14\n", + "[17:10:23] Can't kekulize mol. Unkekulized atoms: 2 3 4 7 8\n", + "[17:10:23] Can't kekulize mol. Unkekulized atoms: 0 1 4 5 6\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "[14:25:51] Can't kekulize mol. Unkekulized atoms: 2 3 8 9 10\n", - "[14:25:51] Can't kekulize mol. Unkekulized atoms: 2 3 4 8 9\n", - "[14:25:51] Can't kekulize mol. Unkekulized atoms: 0 1 5 6 7\n", - "[14:25:51] Can't kekulize mol. Unkekulized atoms: 7 8 12 13 14 15 16 17 18\n", - "[14:25:51] Can't kekulize mol. Unkekulized atoms: 7 8 11 12 13 14 15 16 17\n", - "[14:25:51] Can't kekulize mol. Unkekulized atoms: 5 6 9 10 11 12 13 14 15\n", - "[14:25:51] Can't kekulize mol. Unkekulized atoms: 5 6 7 10 11\n", - "[14:25:51] Can't kekulize mol. Unkekulized atoms: 5 6 7 10 11\n", - "[14:25:51] Can't kekulize mol. Unkekulized atoms: 2 3 4 9 10\n", - "[14:25:51] Can't kekulize mol. Unkekulized atoms: 2 3 4 8 9\n", - "[14:25:51] Can't kekulize mol. Unkekulized atoms: 7 8 12 13 14 15 16 17 18\n", - "[14:25:51] Can't kekulize mol. Unkekulized atoms: 5 6 9 10 11 12 13 14 15\n", - "[14:25:51] Can't kekulize mol. Unkekulized atoms: 0 1 2 3 4 5 6 12 15\n", - "[14:25:51] Can't kekulize mol. Unkekulized atoms: 2 3 4 10 11\n", - "[14:25:51] Can't kekulize mol. Unkekulized atoms: 6 7 16 17 18\n", - "[14:25:52] Can't kekulize mol. Unkekulized atoms: 6 7 12 13 14\n", - "[14:25:52] Can't kekulize mol. Unkekulized atoms: 2 3 6 7 8\n", - "[14:25:52] Can't kekulize mol. Unkekulized atoms: 6 7 13 14 15 16 17 18 19\n", - "[14:25:52] Can't kekulize mol. Unkekulized atoms: 6 7 12 13 14\n", - "[14:25:52] Can't kekulize mol. Unkekulized atoms: 2 3 4 7 8\n", - "[14:25:52] Can't kekulize mol. Unkekulized atoms: 0 1 4 5 6\n", - "[14:25:52] Can't kekulize mol. Unkekulized atoms: 8 9 13 14 15\n", - "[14:25:52] Can't kekulize mol. Unkekulized atoms: 9 10 11 13 14\n", - "[14:25:52] Can't kekulize mol. Unkekulized atoms: 8 9 10 13 14\n", - "[14:25:52] Can't kekulize mol. Unkekulized atoms: 8 9 12 13 14\n", - "[14:25:52] Can't kekulize mol. Unkekulized atoms: 7 8 9 12 13\n", - "[14:25:52] Can't kekulize mol. Unkekulized atoms: 7 8 11 12 13\n", - "[14:25:52] Can't kekulize mol. Unkekulized atoms: 6 7 11 12 13 14 15 16 17\n", - "[14:25:52] Can't kekulize mol. Unkekulized atoms: 5 6 9 10 11 12 13 14 15\n", - "[14:25:52] Can't kekulize mol. Unkekulized atoms: 5 6 7 10 11\n", - "[14:25:52] Can't kekulize mol. Unkekulized atoms: 10 11 12 15 16\n", - "[14:25:52] Can't kekulize mol. Unkekulized atoms: 9 10 11 14 15\n", - "[14:25:52] Can't kekulize mol. Unkekulized atoms: 2 3 4 13 14\n", - "[14:25:52] Can't kekulize mol. Unkekulized atoms: 2 3 4 8 9\n", - "[14:25:52] Can't kekulize mol. Unkekulized atoms: 2 3 4 7 8\n", - "[14:25:52] Can't kekulize mol. Unkekulized atoms: 2 3 4 6 7\n", - "[14:25:52] Can't kekulize mol. Unkekulized atoms: 10 11 12 14 15\n", - "[14:25:52] Can't kekulize mol. Unkekulized atoms: 2 3 4 15 16\n", - "[14:25:52] Can't kekulize mol. Unkekulized atoms: 2 3 4 14 15\n", - "[14:25:52] Can't kekulize mol. Unkekulized atoms: 11 12 13 15 16\n" + "[17:10:23] Can't kekulize mol. Unkekulized atoms: 8 9 13 14 15\n", + "[17:10:23] Can't kekulize mol. Unkekulized atoms: 8 9 10 13 14\n", + "[17:10:23] Can't kekulize mol. Unkekulized atoms: 8 9 12 13 14\n", + "[17:10:23] Can't kekulize mol. Unkekulized atoms: 7 8 9 12 13\n", + "[17:10:23] Can't kekulize mol. Unkekulized atoms: 7 8 11 12 13\n", + "[17:10:23] Can't kekulize mol. Unkekulized atoms: 10 11 12 15 16\n", + "[17:10:23] Can't kekulize mol. Unkekulized atoms: 6 7 11 12 13 14 15 16 17\n", + "[17:10:23] Can't kekulize mol. Unkekulized atoms: 9 10 11 14 15\n", + "[17:10:23] Can't kekulize mol. Unkekulized atoms: 2 3 4 13 14\n", + "[17:10:23] Can't kekulize mol. Unkekulized atoms: 5 6 9 10 11 12 13 14 15\n", + "[17:10:23] Can't kekulize mol. Unkekulized atoms: 2 3 4 8 9\n", + "[17:10:23] Can't kekulize mol. Unkekulized atoms: 5 6 7 10 11\n", + "[17:10:23] Can't kekulize mol. Unkekulized atoms: 2 3 4 7 8\n", + "[17:10:24] Can't kekulize mol. Unkekulized atoms: 2 3 4 6 7\n", + "[17:10:24] Can't kekulize mol. Unkekulized atoms: 10 11 12 14 15\n", + "[17:10:24] Can't kekulize mol. Unkekulized atoms: 2 3 4 15 16\n", + "[17:10:24] Can't kekulize mol. Unkekulized atoms: 2 3 4 14 15\n", + "[17:10:24] Can't kekulize mol. Unkekulized atoms: 11 12 13 15 16\n" ] }, { @@ -4517,7 +4495,7 @@ "0 n1c([CH2:1]N[CH3:1])[cH:1][cH:1][cH:1][n:1]1 387.854 " ] }, - "execution_count": 74, + "execution_count": 73, "metadata": {}, "output_type": "execute_result" } @@ -4555,7 +4533,7 @@ }, { "cell_type": "code", - "execution_count": 75, + "execution_count": 158, "metadata": { "scrolled": true }, @@ -4564,42 +4542,41 @@ "name": "stderr", "output_type": "stream", "text": [ - "[I 2024-07-02 14:25:53,892] A new study created in memory with name: transform_example\n", - "[I 2024-07-02 14:25:53,932] A new study created in memory with name: study_name_0\n", - "[I 2024-07-02 14:25:54,028] Trial 0 finished with value: -0.5959493772536109 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 6, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 5, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 0 with value: -0.5959493772536109.\n", - "[I 2024-07-02 14:25:54,127] Trial 1 finished with value: -0.6571993250300608 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 7, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 6, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 0 with value: -0.5959493772536109.\n", - "[I 2024-07-02 14:25:54,169] Trial 2 finished with value: -4.1511102853256885 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 5.141096648805748, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 2.4893466963980463e-08, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 0 with value: -0.5959493772536109.\n", - "[I 2024-07-02 14:25:54,259] Trial 3 finished with value: -1.2487063317112765 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 5, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 0 with value: -0.5959493772536109.\n", - "[I 2024-07-02 14:25:54,288] Trial 4 finished with value: -0.6714912461080983 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 3, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 0 with value: -0.5959493772536109.\n", - "[I 2024-07-02 14:25:54,329] Trial 5 finished with value: -0.2725944467796781 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.7896547008552977, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 5 with value: -0.2725944467796781.\n", - "[I 2024-07-02 14:25:54,369] Trial 6 finished with value: -2.194926264155893 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.6574750183038587, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 5 with value: -0.2725944467796781.\n", - "[I 2024-07-02 14:25:54,395] Trial 7 finished with value: -0.7520919188596032 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.3974313630683448, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 5 with value: -0.2725944467796781.\n", - "[I 2024-07-02 14:25:54,469] Trial 8 finished with value: -0.7803723847416691 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 28, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 8, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 5 with value: -0.2725944467796781.\n", - "[I 2024-07-02 14:25:54,499] Trial 9 finished with value: -0.6397753979196248 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.2391884918766034, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 5 with value: -0.2725944467796781.\n", - "[I 2024-07-02 14:25:54,528] Trial 10 finished with value: -4.151110299986041 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.00044396482429275296, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 2.3831436879125245e-10, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 5 with value: -0.2725944467796781.\n", - "[I 2024-07-02 14:25:54,558] Trial 11 finished with value: -4.151110111437006 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.00028965395242758657, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 2.99928292425642e-07, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 5 with value: -0.2725944467796781.\n", - "[I 2024-07-02 14:25:54,584] Trial 12 finished with value: -0.5410418750776741 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 4, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 5 with value: -0.2725944467796781.\n", - "[I 2024-07-02 14:25:54,612] Trial 13 finished with value: -0.7183231137124538 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 2, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 5 with value: -0.2725944467796781.\n", - "[I 2024-07-02 14:25:54,640] Trial 14 finished with value: -0.2721824844856162 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.4060379177903557, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 14 with value: -0.2721824844856162.\n", - "[I 2024-07-02 14:25:54,716] Trial 15 finished with value: -1.1900929470222508 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 20, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 8, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 14 with value: -0.2721824844856162.\n", - "[I 2024-07-02 14:25:54,745] Trial 16 finished with value: -2.194926264155893 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.344271094811757, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 14 with value: -0.2721824844856162.\n", - "[I 2024-07-02 14:25:54,774] Trial 17 finished with value: -0.5585323973564646 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.670604991178476, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 14 with value: -0.2721824844856162.\n", - "[I 2024-07-02 14:25:54,951] Trial 18 finished with value: -1.3169218304262786 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 22, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 6, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 14 with value: -0.2721824844856162.\n" + "[I 2024-07-03 14:48:19,102] A new study created in memory with name: transform_example\n", + "[I 2024-07-03 14:48:19,106] A new study created in memory with name: study_name_0\n", + "/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/joblib/memory.py:577: JobLibCollisionWarning: Possible name collisions between functions 'calculate_from_smi' (/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/optunaz/descriptors.py:-1) and 'calculate_from_smi' (/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/optunaz/descriptors.py:180)\n", + " return self._cached_call(args, kwargs, shelving=False)[0]\n", + "[I 2024-07-03 14:48:19,801] Trial 0 finished with value: -0.5959493772536109 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 6, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 5, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 0 with value: -0.5959493772536109.\n", + "[I 2024-07-03 14:48:20,050] Trial 1 finished with value: -0.6571993250300608 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 7, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 6, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 0 with value: -0.5959493772536109.\n", + "[I 2024-07-03 14:48:20,174] Trial 2 finished with value: -4.1511102853256885 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 5.141096648805748, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 2.4893466963980463e-08, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 0 with value: -0.5959493772536109.\n", + "[I 2024-07-03 14:48:20,264] Trial 3 finished with value: -1.2487063317112765 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 5, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 0 with value: -0.5959493772536109.\n", + "[I 2024-07-03 14:48:20,353] Trial 4 finished with value: -0.6714912461080983 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 3, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 0 with value: -0.5959493772536109.\n", + "[I 2024-07-03 14:48:20,430] Trial 5 finished with value: -0.2725944467796781 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.7896547008552977, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 5 with value: -0.2725944467796781.\n", + "[I 2024-07-03 14:48:20,516] Trial 6 finished with value: -2.194926264155893 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.6574750183038587, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 5 with value: -0.2725944467796781.\n", + "[I 2024-07-03 14:48:20,632] Trial 7 finished with value: -0.7520919188596032 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.3974313630683448, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 5 with value: -0.2725944467796781.\n", + "[I 2024-07-03 14:48:20,770] Trial 8 finished with value: -0.7803723847416691 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 28, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 8, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 5 with value: -0.2725944467796781.\n", + "[I 2024-07-03 14:48:20,800] Trial 9 finished with value: -0.6397753979196248 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.2391884918766034, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 5 with value: -0.2725944467796781.\n", + "[I 2024-07-03 14:48:20,830] Trial 10 finished with value: -4.151110299986041 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.00044396482429275296, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 2.3831436879125245e-10, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 5 with value: -0.2725944467796781.\n", + "[I 2024-07-03 14:48:20,857] Trial 11 finished with value: -4.151110111437006 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.00028965395242758657, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 2.99928292425642e-07, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 5 with value: -0.2725944467796781.\n", + "[I 2024-07-03 14:48:20,886] Trial 12 finished with value: -0.5410418750776741 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 4, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 5 with value: -0.2725944467796781.\n", + "[I 2024-07-03 14:48:20,914] Trial 13 finished with value: -0.7183231137124538 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 2, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 5 with value: -0.2725944467796781.\n", + "[I 2024-07-03 14:48:21,018] Trial 14 finished with value: -0.2721824844856162 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.4060379177903557, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 14 with value: -0.2721824844856162.\n", + "[I 2024-07-03 14:48:21,154] Trial 15 finished with value: -1.1900929470222508 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 20, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 8, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 14 with value: -0.2721824844856162.\n", + "[I 2024-07-03 14:48:21,220] Trial 16 finished with value: -2.194926264155893 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.344271094811757, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 14 with value: -0.2721824844856162.\n", + "[I 2024-07-03 14:48:21,251] Trial 17 finished with value: -0.5585323973564646 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.670604991178476, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 14 with value: -0.2721824844856162.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "[I 2024-07-02 14:25:54,980] Trial 19 finished with value: -0.7974925066137679 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.5158832554303112, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 14 with value: -0.2721824844856162.\n", - "[I 2024-07-02 14:25:55,008] Trial 20 finished with value: -1.218395226466336 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 4, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 14 with value: -0.2721824844856162.\n", - "[I 2024-07-02 14:25:55,039] Trial 21 finished with value: -1.1474226942497083 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.0009327650919528738, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 6.062479210472502, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 14 with value: -0.2721824844856162.\n", - "[I 2024-07-02 14:25:55,054] Trial 22 pruned. Duplicate parameter set\n", - "[I 2024-07-02 14:25:55,083] Trial 23 finished with value: -1.0239005731675412 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.1366172066709432, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 14 with value: -0.2721824844856162.\n", - "[I 2024-07-02 14:25:55,160] Trial 24 finished with value: -0.7803723847416691 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 26, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 8, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 14 with value: -0.2721824844856162.\n", - "[I 2024-07-02 14:25:55,191] Trial 25 finished with value: -2.178901060853144 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 43.92901911959232, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 27.999026012594694, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 14 with value: -0.2721824844856162.\n", - "[I 2024-07-02 14:25:55,222] Trial 26 finished with value: -0.27137790098830755 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.5888977841391714, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 26 with value: -0.27137790098830755.\n", - "[I 2024-07-02 14:25:55,250] Trial 27 finished with value: -0.2710284516876423 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.19435298754153707, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 27 with value: -0.2710284516876423.\n" + "[I 2024-07-03 14:48:21,314] Trial 18 finished with value: -1.3169218304262786 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 22, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 6, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 14 with value: -0.2721824844856162.\n", + "[I 2024-07-03 14:48:21,343] Trial 19 finished with value: -0.7974925066137679 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.5158832554303112, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 14 with value: -0.2721824844856162.\n", + "[I 2024-07-03 14:48:21,371] Trial 20 finished with value: -1.218395226466336 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 4, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 14 with value: -0.2721824844856162.\n", + "[I 2024-07-03 14:48:21,403] Trial 21 finished with value: -1.1474226942497083 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.0009327650919528738, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 6.062479210472502, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 14 with value: -0.2721824844856162.\n", + "[I 2024-07-03 14:48:21,419] Trial 22 pruned. Duplicate parameter set\n", + "[I 2024-07-03 14:48:21,448] Trial 23 finished with value: -1.0239005731675412 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.1366172066709432, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 14 with value: -0.2721824844856162.\n", + "[I 2024-07-03 14:48:21,589] Trial 24 finished with value: -0.7803723847416691 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 26, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 8, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 14 with value: -0.2721824844856162.\n" ] }, { @@ -4613,18 +4590,21 @@ "name": "stderr", "output_type": "stream", "text": [ - "[I 2024-07-02 14:25:55,329] Trial 28 finished with value: -1.3169218304262786 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 13, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 6, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 27 with value: -0.2710284516876423.\n", - "[I 2024-07-02 14:25:55,361] Trial 29 finished with value: -3.6273152492418945 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 1.6285506249643193, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 0.35441495011256785, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 27 with value: -0.2710284516876423.\n", - "[I 2024-07-02 14:25:55,438] Trial 30 finished with value: -1.1900929470222508 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 10, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 8, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 27 with value: -0.2710284516876423.\n", - "[I 2024-07-02 14:25:55,469] Trial 31 finished with value: -2.194926264155893 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.2457809516380005, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 27 with value: -0.2710284516876423.\n", - "[I 2024-07-02 14:25:55,496] Trial 32 finished with value: -2.1907041717628215 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.6459129458824919, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 27 with value: -0.2710284516876423.\n", - "[I 2024-07-02 14:25:55,528] Trial 33 finished with value: -1.3209075619139279 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.8179058888285398, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 27 with value: -0.2710284516876423.\n", - "[I 2024-07-02 14:25:55,545] Trial 34 pruned. Duplicate parameter set\n", - "[I 2024-07-02 14:25:55,577] Trial 35 finished with value: -0.2709423025014604 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.0920052840435055, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 35 with value: -0.2709423025014604.\n", - "[I 2024-07-02 14:25:55,609] Trial 36 finished with value: -1.3133943310851415 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.8677032984759461, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 35 with value: -0.2709423025014604.\n", - "[I 2024-07-02 14:25:55,626] Trial 37 pruned. Duplicate parameter set\n", - "[I 2024-07-02 14:25:55,657] Trial 38 finished with value: -1.257769959239938 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.2865764368847064, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 35 with value: -0.2709423025014604.\n", - "[I 2024-07-02 14:25:55,735] Trial 39 finished with value: -0.40359637945134746 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 5, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 5, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 35 with value: -0.2709423025014604.\n" + "[I 2024-07-03 14:48:21,621] Trial 25 finished with value: -2.178901060853144 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 43.92901911959232, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 27.999026012594694, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 14 with value: -0.2721824844856162.\n", + "[I 2024-07-03 14:48:21,650] Trial 26 finished with value: -0.27137790098830755 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.5888977841391714, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 26 with value: -0.27137790098830755.\n", + "[I 2024-07-03 14:48:21,679] Trial 27 finished with value: -0.2710284516876423 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.19435298754153707, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 27 with value: -0.2710284516876423.\n", + "[I 2024-07-03 14:48:21,815] Trial 28 finished with value: -1.3169218304262786 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 13, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 6, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 27 with value: -0.2710284516876423.\n", + "[I 2024-07-03 14:48:21,847] Trial 29 finished with value: -3.6273152492418945 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 1.6285506249643193, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 0.35441495011256785, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 27 with value: -0.2710284516876423.\n", + "[I 2024-07-03 14:48:21,915] Trial 30 finished with value: -1.1900929470222508 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 10, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 8, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 27 with value: -0.2710284516876423.\n", + "[I 2024-07-03 14:48:21,947] Trial 31 finished with value: -2.194926264155893 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.2457809516380005, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 27 with value: -0.2710284516876423.\n", + "[I 2024-07-03 14:48:21,979] Trial 32 finished with value: -2.1907041717628215 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.6459129458824919, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 27 with value: -0.2710284516876423.\n", + "[I 2024-07-03 14:48:22,011] Trial 33 finished with value: -1.3209075619139279 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.8179058888285398, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 27 with value: -0.2710284516876423.\n", + "[I 2024-07-03 14:48:22,027] Trial 34 pruned. Duplicate parameter set\n", + "[I 2024-07-03 14:48:22,057] Trial 35 finished with value: -0.2709423025014604 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.0920052840435055, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 35 with value: -0.2709423025014604.\n", + "[I 2024-07-03 14:48:22,088] Trial 36 finished with value: -1.3133943310851415 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.8677032984759461, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 35 with value: -0.2709423025014604.\n", + "[I 2024-07-03 14:48:22,107] Trial 37 pruned. Duplicate parameter set\n", + "[I 2024-07-03 14:48:22,137] Trial 38 finished with value: -1.257769959239938 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.2865764368847064, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 35 with value: -0.2709423025014604.\n", + "[I 2024-07-03 14:48:22,203] Trial 39 finished with value: -0.40359637945134746 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 5, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 5, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 35 with value: -0.2709423025014604.\n" ] }, { @@ -4639,12 +4619,10 @@ "name": "stderr", "output_type": "stream", "text": [ - "[I 2024-07-02 14:25:55,817] Trial 40 finished with value: -0.4127882135896648 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 5, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 9, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 35 with value: -0.2709423025014604.\n", - "[I 2024-07-02 14:25:55,836] Trial 41 pruned. Duplicate parameter set\n", - "[I 2024-07-02 14:25:55,905] Trial 42 finished with value: -0.5959493772536109 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 25, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 5, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 35 with value: -0.2709423025014604.\n", - "[I 2024-07-02 14:25:55,935] Trial 43 finished with value: -0.9246005133276612 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 2, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 35 with value: -0.2709423025014604.\n", - "[I 2024-07-02 14:25:56,003] Trial 44 finished with value: -0.8908739215746116 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 22, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 9, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 35 with value: -0.2709423025014604.\n", - "[I 2024-07-02 14:25:56,035] Trial 45 finished with value: -1.107536316777608 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 3, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 35 with value: -0.2709423025014604.\n" + "[I 2024-07-03 14:48:22,341] Trial 40 finished with value: -0.4127882135896648 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 5, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 9, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 35 with value: -0.2709423025014604.\n", + "[I 2024-07-03 14:48:22,360] Trial 41 pruned. Duplicate parameter set\n", + "[I 2024-07-03 14:48:22,447] Trial 42 finished with value: -0.5959493772536109 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 25, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 5, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 35 with value: -0.2709423025014604.\n", + "[I 2024-07-03 14:48:22,479] Trial 43 finished with value: -0.9246005133276612 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 2, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 35 with value: -0.2709423025014604.\n" ] }, { @@ -4658,72 +4636,74 @@ "name": "stderr", "output_type": "stream", "text": [ - "[I 2024-07-02 14:25:56,067] Trial 46 finished with value: -2.194926264155893 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.6437201185807124, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 35 with value: -0.2709423025014604.\n", - "[I 2024-07-02 14:25:56,098] Trial 47 finished with value: -4.054360360588395 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 82.41502276709562, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 0.10978379088847677, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 35 with value: -0.2709423025014604.\n", - "[I 2024-07-02 14:25:56,129] Trial 48 finished with value: -0.5428179904345867 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.022707289534838138, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 35 with value: -0.2709423025014604.\n", - "[I 2024-07-02 14:25:56,160] Trial 49 finished with value: -0.5696273642213351 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 3, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 35 with value: -0.2709423025014604.\n", - "[I 2024-07-02 14:25:56,194] Trial 50 finished with value: -0.27099769667470536 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.1580741708125475, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 35 with value: -0.2709423025014604.\n", - "[I 2024-07-02 14:25:56,232] Trial 51 finished with value: -0.2709564785634315 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.10900413894771653, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 35 with value: -0.2709423025014604.\n", - "[I 2024-07-02 14:25:56,268] Trial 52 finished with value: -0.2709799905898163 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.13705914456987853, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 35 with value: -0.2709423025014604.\n", - "[I 2024-07-02 14:25:56,302] Trial 53 finished with value: -0.27097230608092054 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.12790870116376127, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 35 with value: -0.2709423025014604.\n", - "[I 2024-07-02 14:25:56,337] Trial 54 finished with value: -0.2709499903064464 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.10123180962907431, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 35 with value: -0.2709423025014604.\n", - "[I 2024-07-02 14:25:56,374] Trial 55 finished with value: -0.2710895886052581 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.26565663774320425, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 35 with value: -0.2709423025014604.\n", - "[I 2024-07-02 14:25:56,411] Trial 56 finished with value: -0.2708711012023424 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.005637048678674678, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 56 with value: -0.2708711012023424.\n", - "[I 2024-07-02 14:25:56,446] Trial 57 finished with value: -0.27092322402109364 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.06902647427781451, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 56 with value: -0.2708711012023424.\n", - "[I 2024-07-02 14:25:56,482] Trial 58 finished with value: -0.2712140349882 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.4076704953178294, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 56 with value: -0.2708711012023424.\n", - "[I 2024-07-02 14:25:56,515] Trial 59 finished with value: -0.27090080367174 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.04187106800188596, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 56 with value: -0.2708711012023424.\n", - "[I 2024-07-02 14:25:56,550] Trial 60 finished with value: -0.27086925247190047 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.003371853599610078, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 60 with value: -0.27086925247190047.\n", - "[I 2024-07-02 14:25:56,586] Trial 61 finished with value: -0.2708933298483799 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.032781796328385376, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 60 with value: -0.27086925247190047.\n", - "[I 2024-07-02 14:25:56,623] Trial 62 finished with value: -0.27087205624489635 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.006806773659187283, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 60 with value: -0.27086925247190047.\n", - "[I 2024-07-02 14:25:56,658] Trial 63 finished with value: -0.2708869511176179 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.025009489814943348, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 60 with value: -0.27086925247190047.\n", - "[I 2024-07-02 14:25:56,695] Trial 64 finished with value: -0.2711465077924297 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.3311125627707556, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 60 with value: -0.27086925247190047.\n", - "[I 2024-07-02 14:25:56,729] Trial 65 finished with value: -0.2708756855936628 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.011249102380159387, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 60 with value: -0.27086925247190047.\n", - "[I 2024-07-02 14:25:56,766] Trial 66 finished with value: -0.27087301924224993 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.007985924302396141, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 60 with value: -0.27086925247190047.\n" + "[I 2024-07-03 14:48:22,615] Trial 44 finished with value: -0.8908739215746116 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 22, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 9, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 35 with value: -0.2709423025014604.\n", + "[I 2024-07-03 14:48:22,648] Trial 45 finished with value: -1.107536316777608 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 3, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 35 with value: -0.2709423025014604.\n", + "[I 2024-07-03 14:48:22,680] Trial 46 finished with value: -2.194926264155893 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.6437201185807124, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 35 with value: -0.2709423025014604.\n", + "[I 2024-07-03 14:48:22,713] Trial 47 finished with value: -4.054360360588395 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 82.41502276709562, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 0.10978379088847677, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 35 with value: -0.2709423025014604.\n", + "[I 2024-07-03 14:48:22,743] Trial 48 finished with value: -0.5428179904345867 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.022707289534838138, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 35 with value: -0.2709423025014604.\n", + "[I 2024-07-03 14:48:22,774] Trial 49 finished with value: -0.5696273642213351 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 3, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 35 with value: -0.2709423025014604.\n", + "[I 2024-07-03 14:48:22,811] Trial 50 finished with value: -0.27099769667470536 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.1580741708125475, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 35 with value: -0.2709423025014604.\n", + "[I 2024-07-03 14:48:22,848] Trial 51 finished with value: -0.2709564785634315 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.10900413894771653, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 35 with value: -0.2709423025014604.\n", + "[I 2024-07-03 14:48:22,884] Trial 52 finished with value: -0.2709799905898163 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.13705914456987853, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 35 with value: -0.2709423025014604.\n", + "[I 2024-07-03 14:48:22,919] Trial 53 finished with value: -0.27097230608092054 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.12790870116376127, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 35 with value: -0.2709423025014604.\n", + "[I 2024-07-03 14:48:22,953] Trial 54 finished with value: -0.2709499903064464 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.10123180962907431, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 35 with value: -0.2709423025014604.\n", + "[I 2024-07-03 14:48:22,989] Trial 55 finished with value: -0.2710895886052581 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.26565663774320425, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 35 with value: -0.2709423025014604.\n", + "[I 2024-07-03 14:48:23,025] Trial 56 finished with value: -0.2708711012023424 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.005637048678674678, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 56 with value: -0.2708711012023424.\n", + "[I 2024-07-03 14:48:23,060] Trial 57 finished with value: -0.27092322402109364 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.06902647427781451, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 56 with value: -0.2708711012023424.\n", + "[I 2024-07-03 14:48:23,096] Trial 58 finished with value: -0.2712140349882 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.4076704953178294, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 56 with value: -0.2708711012023424.\n", + "[I 2024-07-03 14:48:23,133] Trial 59 finished with value: -0.27090080367174 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.04187106800188596, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 56 with value: -0.2708711012023424.\n", + "[I 2024-07-03 14:48:23,171] Trial 60 finished with value: -0.27086925247190047 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.003371853599610078, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 60 with value: -0.27086925247190047.\n", + "[I 2024-07-03 14:48:23,209] Trial 61 finished with value: -0.2708933298483799 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.032781796328385376, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 60 with value: -0.27086925247190047.\n", + "[I 2024-07-03 14:48:23,245] Trial 62 finished with value: -0.27087205624489635 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.006806773659187283, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 60 with value: -0.27086925247190047.\n", + "[I 2024-07-03 14:48:23,283] Trial 63 finished with value: -0.2708869511176179 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.025009489814943348, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 60 with value: -0.27086925247190047.\n", + "[I 2024-07-03 14:48:23,321] Trial 64 finished with value: -0.2711465077924297 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.3311125627707556, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 60 with value: -0.27086925247190047.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "[I 2024-07-02 14:25:56,802] Trial 67 finished with value: -0.2708685399954944 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.00249856291483601, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 67 with value: -0.2708685399954944.\n", - "[I 2024-07-02 14:25:56,839] Trial 68 finished with value: -0.27121879554836553 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.4130244908975993, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 67 with value: -0.2708685399954944.\n", - "[I 2024-07-02 14:25:56,880] Trial 69 finished with value: -0.2708693196600531 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.0034541978803366022, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 67 with value: -0.2708685399954944.\n", - "[I 2024-07-02 14:25:56,918] Trial 70 finished with value: -0.27110195265802334 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.27994943662091765, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 67 with value: -0.2708685399954944.\n", - "[I 2024-07-02 14:25:56,956] Trial 71 finished with value: -0.2708682582859318 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.0021532199144365088, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.2708682582859318.\n", - "[I 2024-07-02 14:25:56,995] Trial 72 finished with value: -0.27087024523986086 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.0045884092728113585, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.2708682582859318.\n", - "[I 2024-07-02 14:25:57,030] Trial 73 finished with value: -0.27087351807632193 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.008596600952859433, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.2708682582859318.\n", - "[I 2024-07-02 14:25:57,070] Trial 74 finished with value: -0.2710818633795896 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.2567049271070902, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.2708682582859318.\n", - "[I 2024-07-02 14:25:57,109] Trial 75 finished with value: -0.27103241786565463 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.1990111983307052, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.2708682582859318.\n", - "[I 2024-07-02 14:25:57,146] Trial 76 finished with value: -0.2710350879598171 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.20214459724424078, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.2708682582859318.\n", - "[I 2024-07-02 14:25:57,183] Trial 77 finished with value: -0.2708688328221868 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.00285750520671645, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.2708682582859318.\n", - "[I 2024-07-02 14:25:57,221] Trial 78 finished with value: -0.27100832234449684 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.17064008990759916, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.2708682582859318.\n", - "[I 2024-07-02 14:25:57,258] Trial 79 finished with value: -0.27268613236193845 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.8725420109733135, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.2708682582859318.\n", - "[I 2024-07-02 14:25:57,296] Trial 80 finished with value: -0.27119617446689237 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.387533542012365, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.2708682582859318.\n", - "[I 2024-07-02 14:25:57,333] Trial 81 finished with value: -0.2708691110831552 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.0031985656730512953, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.2708682582859318.\n", - "[I 2024-07-02 14:25:57,370] Trial 82 finished with value: -0.27086852174155146 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.002476186542950981, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.2708682582859318.\n", - "[I 2024-07-02 14:25:57,410] Trial 83 finished with value: -0.27135383618835024 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.5626643670396761, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.2708682582859318.\n", - "[I 2024-07-02 14:25:57,449] Trial 84 finished with value: -0.2709819654433871 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.1394077979875128, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.2708682582859318.\n", - "[I 2024-07-02 14:25:57,488] Trial 85 finished with value: -0.2718548944510965 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.0858347526799794, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.2708682582859318.\n", - "[I 2024-07-02 14:25:57,528] Trial 86 finished with value: -4.1508084699212935 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.03329943145150872, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 0.00025672309762227527, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.2708682582859318.\n", - "[I 2024-07-02 14:25:57,566] Trial 87 finished with value: -0.27249853374634975 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.702026434077893, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.2708682582859318.\n" + "[I 2024-07-03 14:48:23,357] Trial 65 finished with value: -0.2708756855936628 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.011249102380159387, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 60 with value: -0.27086925247190047.\n", + "[I 2024-07-03 14:48:23,392] Trial 66 finished with value: -0.27087301924224993 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.007985924302396141, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 60 with value: -0.27086925247190047.\n", + "[I 2024-07-03 14:48:23,427] Trial 67 finished with value: -0.2708685399954944 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.00249856291483601, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 67 with value: -0.2708685399954944.\n", + "[I 2024-07-03 14:48:23,466] Trial 68 finished with value: -0.27121879554836553 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.4130244908975993, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 67 with value: -0.2708685399954944.\n", + "[I 2024-07-03 14:48:23,502] Trial 69 finished with value: -0.2708693196600531 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.0034541978803366022, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 67 with value: -0.2708685399954944.\n", + "[I 2024-07-03 14:48:23,538] Trial 70 finished with value: -0.27110195265802334 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.27994943662091765, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 67 with value: -0.2708685399954944.\n", + "[I 2024-07-03 14:48:23,575] Trial 71 finished with value: -0.2708682582859318 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.0021532199144365088, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.2708682582859318.\n", + "[I 2024-07-03 14:48:23,612] Trial 72 finished with value: -0.27087024523986086 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.0045884092728113585, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.2708682582859318.\n", + "[I 2024-07-03 14:48:23,650] Trial 73 finished with value: -0.27087351807632193 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.008596600952859433, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.2708682582859318.\n", + "[I 2024-07-03 14:48:23,685] Trial 74 finished with value: -0.2710818633795896 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.2567049271070902, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.2708682582859318.\n", + "[I 2024-07-03 14:48:23,722] Trial 75 finished with value: -0.27103241786565463 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.1990111983307052, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.2708682582859318.\n", + "[I 2024-07-03 14:48:23,760] Trial 76 finished with value: -0.2710350879598171 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.20214459724424078, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.2708682582859318.\n", + "[I 2024-07-03 14:48:23,797] Trial 77 finished with value: -0.2708688328221868 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.00285750520671645, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.2708682582859318.\n", + "[I 2024-07-03 14:48:23,834] Trial 78 finished with value: -0.27100832234449684 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.17064008990759916, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.2708682582859318.\n", + "[I 2024-07-03 14:48:23,873] Trial 79 finished with value: -0.27268613236193845 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.8725420109733135, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.2708682582859318.\n", + "[I 2024-07-03 14:48:23,908] Trial 80 finished with value: -0.27119617446689237 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.387533542012365, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.2708682582859318.\n", + "[I 2024-07-03 14:48:23,946] Trial 81 finished with value: -0.2708691110831552 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.0031985656730512953, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.2708682582859318.\n", + "[I 2024-07-03 14:48:23,984] Trial 82 finished with value: -0.27086852174155146 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.002476186542950981, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.2708682582859318.\n", + "[I 2024-07-03 14:48:24,021] Trial 83 finished with value: -0.27135383618835024 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.5626643670396761, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.2708682582859318.\n", + "[I 2024-07-03 14:48:24,059] Trial 84 finished with value: -0.2709819654433871 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.1394077979875128, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.2708682582859318.\n", + "[I 2024-07-03 14:48:24,098] Trial 85 finished with value: -0.2718548944510965 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.0858347526799794, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.2708682582859318.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "[I 2024-07-02 14:25:57,604] Trial 88 finished with value: -0.27095660957755363 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.10916094511173127, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.2708682582859318.\n", - "[I 2024-07-02 14:25:57,643] Trial 89 finished with value: -0.27102160995407715 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.18630665884100353, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.2708682582859318.\n", - "[I 2024-07-02 14:25:57,681] Trial 90 finished with value: -0.27095708822582026 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.10973377642487026, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.2708682582859318.\n", - "[I 2024-07-02 14:25:57,721] Trial 91 finished with value: -0.27088222008661084 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.019235980282946118, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.2708682582859318.\n", - "[I 2024-07-02 14:25:57,762] Trial 92 finished with value: -0.2708703086029017 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.004666043957133775, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.2708682582859318.\n", - "[I 2024-07-02 14:25:57,799] Trial 93 finished with value: -0.27095279044622245 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.1045877457096882, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.2708682582859318.\n", - "[I 2024-07-02 14:25:57,840] Trial 94 finished with value: -0.2709408288690431 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.09023455456986404, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.2708682582859318.\n", - "[I 2024-07-02 14:25:57,879] Trial 95 finished with value: -0.9289218260898663 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.8200088368788958, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.2708682582859318.\n", - "[I 2024-07-02 14:25:57,917] Trial 96 finished with value: -0.27086675101898655 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.00030502148265565063, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 96 with value: -0.27086675101898655.\n", - "[I 2024-07-02 14:25:57,957] Trial 97 finished with value: -0.2710491243757999 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.21858260742423916, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 96 with value: -0.27086675101898655.\n", - "[I 2024-07-02 14:25:58,001] Trial 98 finished with value: -4.1491615840508995 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.024725853754515203, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 0.0011658455138452, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 96 with value: -0.27086675101898655.\n", - "[I 2024-07-02 14:25:58,040] Trial 99 finished with value: -0.2709462479577586 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.0967427718847167, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 96 with value: -0.27086675101898655.\n" + "[I 2024-07-03 14:48:24,136] Trial 86 finished with value: -4.1508084699212935 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.03329943145150872, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 0.00025672309762227527, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.2708682582859318.\n", + "[I 2024-07-03 14:48:24,174] Trial 87 finished with value: -0.27249853374634975 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.702026434077893, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.2708682582859318.\n", + "[I 2024-07-03 14:48:24,214] Trial 88 finished with value: -0.27095660957755363 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.10916094511173127, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.2708682582859318.\n", + "[I 2024-07-03 14:48:24,254] Trial 89 finished with value: -0.27102160995407715 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.18630665884100353, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.2708682582859318.\n", + "[I 2024-07-03 14:48:24,291] Trial 90 finished with value: -0.27095708822582026 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.10973377642487026, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.2708682582859318.\n", + "[I 2024-07-03 14:48:24,329] Trial 91 finished with value: -0.27088222008661084 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.019235980282946118, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.2708682582859318.\n", + "[I 2024-07-03 14:48:24,582] Trial 92 finished with value: -0.2708703086029017 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.004666043957133775, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.2708682582859318.\n", + "[I 2024-07-03 14:48:24,620] Trial 93 finished with value: -0.27095279044622245 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.1045877457096882, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.2708682582859318.\n", + "[I 2024-07-03 14:48:24,662] Trial 94 finished with value: -0.2709408288690431 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.09023455456986404, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.2708682582859318.\n", + "[I 2024-07-03 14:48:24,701] Trial 95 finished with value: -0.9289218260898663 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.8200088368788958, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.2708682582859318.\n", + "[I 2024-07-03 14:48:24,741] Trial 96 finished with value: -0.27086675101898655 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.00030502148265565063, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 96 with value: -0.27086675101898655.\n", + "[I 2024-07-03 14:48:24,779] Trial 97 finished with value: -0.2710491243757999 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.21858260742423916, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 96 with value: -0.27086675101898655.\n", + "[I 2024-07-03 14:48:24,821] Trial 98 finished with value: -4.1491615840508995 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.024725853754515203, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 0.0011658455138452, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 96 with value: -0.27086675101898655.\n", + "[I 2024-07-03 14:48:24,861] Trial 99 finished with value: -0.2709462479577586 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.0967427718847167, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 96 with value: -0.27086675101898655.\n" ] } ], @@ -4782,50 +4762,52 @@ }, { "cell_type": "code", - "execution_count": 76, - "metadata": {}, + "execution_count": 159, + "metadata": { + "collapsed": true + }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "[I 2024-07-02 14:26:00,252] A new study created in memory with name: non-transform_example\n", - "[I 2024-07-02 14:26:00,254] A new study created in memory with name: study_name_0\n", - "[I 2024-07-02 14:26:00,332] Trial 0 finished with value: -3501.942111261296 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 6, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 5, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 0 with value: -3501.942111261296.\n", - "[I 2024-07-02 14:26:00,422] Trial 1 finished with value: -5451.207265576796 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 7, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 6, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 0 with value: -3501.942111261296.\n", - "[I 2024-07-02 14:26:00,459] Trial 2 finished with value: -208.1049201007814 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 5.141096648805748, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 2.4893466963980463e-08, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 2 with value: -208.1049201007814.\n", - "[I 2024-07-02 14:26:00,500] Trial 3 finished with value: -9964.541364058234 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 5, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 2 with value: -208.1049201007814.\n", - "[I 2024-07-02 14:26:00,528] Trial 4 finished with value: -3543.953608539901 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 3, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 2 with value: -208.1049201007814.\n", - "[I 2024-07-02 14:26:00,570] Trial 5 finished with value: -6837.057544630979 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.7896547008552977, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 2 with value: -208.1049201007814.\n", - "[I 2024-07-02 14:26:00,613] Trial 6 finished with value: -2507.1794330606067 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.6574750183038587, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 2 with value: -208.1049201007814.\n", - "[I 2024-07-02 14:26:00,650] Trial 7 finished with value: -21534.719219668405 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.3974313630683448, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 2 with value: -208.1049201007814.\n", - "[I 2024-07-02 14:26:00,726] Trial 8 finished with value: -2899.736555614694 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 28, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 8, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 2 with value: -208.1049201007814.\n", + "[I 2024-07-03 14:48:27,098] A new study created in memory with name: non-transform_example\n", + "[I 2024-07-03 14:48:27,100] A new study created in memory with name: study_name_0\n", + "[I 2024-07-03 14:48:27,189] Trial 0 finished with value: -3501.942111261296 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 6, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 5, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 0 with value: -3501.942111261296.\n", + "[I 2024-07-03 14:48:27,278] Trial 1 finished with value: -5451.207265576796 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 7, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 6, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 0 with value: -3501.942111261296.\n", + "[I 2024-07-03 14:48:27,320] Trial 2 finished with value: -208.1049201007814 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 5.141096648805748, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 2.4893466963980463e-08, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 2 with value: -208.1049201007814.\n", + "[I 2024-07-03 14:48:27,361] Trial 3 finished with value: -9964.541364058234 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 5, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 2 with value: -208.1049201007814.\n", + "[I 2024-07-03 14:48:27,392] Trial 4 finished with value: -3543.953608539901 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 3, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 2 with value: -208.1049201007814.\n", + "[I 2024-07-03 14:48:27,447] Trial 5 finished with value: -6837.057544630979 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.7896547008552977, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 2 with value: -208.1049201007814.\n", + "[I 2024-07-03 14:48:27,488] Trial 6 finished with value: -2507.1794330606067 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.6574750183038587, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 2 with value: -208.1049201007814.\n", + "[I 2024-07-03 14:48:27,528] Trial 7 finished with value: -21534.719219668405 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.3974313630683448, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 2 with value: -208.1049201007814.\n", + "[I 2024-07-03 14:48:27,604] Trial 8 finished with value: -2899.736555614694 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 28, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 8, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 2 with value: -208.1049201007814.\n", "/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.294e+02, tolerance: 2.760e+01\n", " model = cd_fast.enet_coordinate_descent(\n", - "[I 2024-07-02 14:26:00,790] Trial 9 finished with value: -21674.445000284228 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.2391884918766034, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 2 with value: -208.1049201007814.\n", - "[I 2024-07-02 14:26:00,819] Trial 10 finished with value: -208.1049203123567 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.00044396482429275296, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 2.3831436879125245e-10, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 2 with value: -208.1049201007814.\n", - "[I 2024-07-02 14:26:00,849] Trial 11 finished with value: -208.1049192609138 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.00028965395242758657, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 2.99928292425642e-07, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 11 with value: -208.1049192609138.\n", - "[I 2024-07-02 14:26:00,877] Trial 12 finished with value: -3630.72768093756 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 4, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 11 with value: -208.1049192609138.\n", - "[I 2024-07-02 14:26:00,907] Trial 13 finished with value: -3431.942816967268 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 2, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 11 with value: -208.1049192609138.\n", - "[I 2024-07-02 14:26:00,934] Trial 14 finished with value: -6908.462045154488 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.4060379177903557, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 11 with value: -208.1049192609138.\n", - "[I 2024-07-02 14:26:01,008] Trial 15 finished with value: -5964.65935954044 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 20, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 8, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 11 with value: -208.1049192609138.\n", - "[I 2024-07-02 14:26:01,036] Trial 16 finished with value: -21070.107195348774 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.344271094811757, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 11 with value: -208.1049192609138.\n", - "[I 2024-07-02 14:26:01,065] Trial 17 finished with value: -4977.068508997133 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.670604991178476, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 11 with value: -208.1049192609138.\n" + "[I 2024-07-03 14:48:27,656] Trial 9 finished with value: -21674.445000284228 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.2391884918766034, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 2 with value: -208.1049201007814.\n", + "[I 2024-07-03 14:48:27,685] Trial 10 finished with value: -208.1049203123567 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.00044396482429275296, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 2.3831436879125245e-10, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 2 with value: -208.1049201007814.\n", + "[I 2024-07-03 14:48:27,716] Trial 11 finished with value: -208.1049192609138 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.00028965395242758657, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 2.99928292425642e-07, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 11 with value: -208.1049192609138.\n", + "[I 2024-07-03 14:48:27,745] Trial 12 finished with value: -3630.72768093756 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 4, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 11 with value: -208.1049192609138.\n", + "[I 2024-07-03 14:48:27,775] Trial 13 finished with value: -3431.942816967268 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 2, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 11 with value: -208.1049192609138.\n", + "[I 2024-07-03 14:48:27,807] Trial 14 finished with value: -6908.462045154488 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.4060379177903557, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 11 with value: -208.1049192609138.\n", + "[I 2024-07-03 14:48:27,885] Trial 15 finished with value: -5964.65935954044 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 20, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 8, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 11 with value: -208.1049192609138.\n", + "[I 2024-07-03 14:48:27,915] Trial 16 finished with value: -21070.107195348774 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.344271094811757, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 11 with value: -208.1049192609138.\n", + "[I 2024-07-03 14:48:27,945] Trial 17 finished with value: -4977.068508997133 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.670604991178476, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 11 with value: -208.1049192609138.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "[I 2024-07-02 14:26:01,133] Trial 18 finished with value: -8873.669262669626 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 22, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 6, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 11 with value: -208.1049192609138.\n", - "[I 2024-07-02 14:26:01,173] Trial 19 finished with value: -21387.63697424318 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.5158832554303112, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 11 with value: -208.1049192609138.\n", - "[I 2024-07-02 14:26:01,202] Trial 20 finished with value: -9958.573006910125 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 4, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 11 with value: -208.1049192609138.\n", - "[I 2024-07-02 14:26:01,370] Trial 21 finished with value: -180.5182695600183 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.0009327650919528738, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 6.062479210472502, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 21 with value: -180.5182695600183.\n", - "[I 2024-07-02 14:26:01,387] Trial 22 pruned. Duplicate parameter set\n", - "[I 2024-07-02 14:26:01,428] Trial 23 finished with value: -20684.56412138056 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.1366172066709432, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 21 with value: -180.5182695600183.\n", - "[I 2024-07-02 14:26:01,515] Trial 24 finished with value: -2899.736555614694 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 26, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 8, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 21 with value: -180.5182695600183.\n", - "[I 2024-07-02 14:26:01,544] Trial 25 finished with value: -150.3435882510586 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 43.92901911959232, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 27.999026012594694, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 25 with value: -150.3435882510586.\n", - "[I 2024-07-02 14:26:01,571] Trial 26 finished with value: -7068.705383113378 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.5888977841391714, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 25 with value: -150.3435882510586.\n" + "[I 2024-07-03 14:48:28,021] Trial 18 finished with value: -8873.66926266963 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 22, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 6, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 11 with value: -208.1049192609138.\n", + "[I 2024-07-03 14:48:28,063] Trial 19 finished with value: -21387.63697424318 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.5158832554303112, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 11 with value: -208.1049192609138.\n", + "[I 2024-07-03 14:48:28,094] Trial 20 finished with value: -9958.573006910125 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 4, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 11 with value: -208.1049192609138.\n", + "[I 2024-07-03 14:48:28,126] Trial 21 finished with value: -180.5182695600183 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.0009327650919528738, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 6.062479210472502, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 21 with value: -180.5182695600183.\n", + "[I 2024-07-03 14:48:28,142] Trial 22 pruned. Duplicate parameter set\n", + "[I 2024-07-03 14:48:28,186] Trial 23 finished with value: -20684.56412138056 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.1366172066709432, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 21 with value: -180.5182695600183.\n", + "[I 2024-07-03 14:48:28,262] Trial 24 finished with value: -2899.736555614694 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 26, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 8, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 21 with value: -180.5182695600183.\n", + "[I 2024-07-03 14:48:28,291] Trial 25 finished with value: -150.3435882510586 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 43.92901911959232, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 27.999026012594694, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 25 with value: -150.3435882510586.\n", + "[I 2024-07-03 14:48:28,319] Trial 26 finished with value: -7068.705383113378 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.5888977841391714, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 25 with value: -150.3435882510586.\n" ] }, { @@ -4839,17 +4821,19 @@ "name": "stderr", "output_type": "stream", "text": [ - "[I 2024-07-02 14:26:01,599] Trial 27 finished with value: -7150.482090052133 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.19435298754153707, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 25 with value: -150.3435882510586.\n", - "[I 2024-07-02 14:26:01,976] Trial 28 finished with value: -8873.669262669626 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 13, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 6, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 25 with value: -150.3435882510586.\n", - "[I 2024-07-02 14:26:02,077] Trial 29 finished with value: -203.93637462922368 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 1.6285506249643193, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 0.35441495011256785, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 25 with value: -150.3435882510586.\n", - "[I 2024-07-02 14:26:02,160] Trial 30 finished with value: -5964.65935954044 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 10, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 8, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 25 with value: -150.3435882510586.\n", - "[I 2024-07-02 14:26:02,193] Trial 31 finished with value: -2570.5111262532305 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.2457809516380005, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 25 with value: -150.3435882510586.\n", - "[I 2024-07-02 14:26:02,237] Trial 32 finished with value: -21987.659957192194 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.6459129458824919, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 25 with value: -150.3435882510586.\n", - "[I 2024-07-02 14:26:02,269] Trial 33 finished with value: -9889.493204596083 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.8179058888285398, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 25 with value: -150.3435882510586.\n", - "[I 2024-07-02 14:26:03,369] Trial 34 pruned. Duplicate parameter set\n", - "[I 2024-07-02 14:26:03,413] Trial 35 finished with value: -7172.208490771303 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.0920052840435055, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 25 with value: -150.3435882510586.\n", - "[I 2024-07-02 14:26:03,537] Trial 36 finished with value: -9804.512701665093 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.8677032984759461, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 25 with value: -150.3435882510586.\n", - "[I 2024-07-02 14:26:03,555] Trial 37 pruned. Duplicate parameter set\n" + "[I 2024-07-03 14:48:28,350] Trial 27 finished with value: -7150.482090052133 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.19435298754153707, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 25 with value: -150.3435882510586.\n", + "[I 2024-07-03 14:48:28,426] Trial 28 finished with value: -8873.669262669626 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 13, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 6, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 25 with value: -150.3435882510586.\n", + "[I 2024-07-03 14:48:28,456] Trial 29 finished with value: -203.93637462922368 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 1.6285506249643193, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 0.35441495011256785, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 25 with value: -150.3435882510586.\n", + "[I 2024-07-03 14:48:28,536] Trial 30 finished with value: -5964.65935954044 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 10, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 8, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 25 with value: -150.3435882510586.\n", + "[I 2024-07-03 14:48:28,566] Trial 31 finished with value: -2570.5111262532305 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.2457809516380005, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 25 with value: -150.3435882510586.\n", + "[I 2024-07-03 14:48:28,610] Trial 32 finished with value: -21987.659957192194 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.6459129458824919, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 25 with value: -150.3435882510586.\n", + "[I 2024-07-03 14:48:28,639] Trial 33 finished with value: -9889.493204596083 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.8179058888285398, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 25 with value: -150.3435882510586.\n", + "[I 2024-07-03 14:48:28,656] Trial 34 pruned. Duplicate parameter set\n", + "[I 2024-07-03 14:48:28,686] Trial 35 finished with value: -7172.208490771303 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.0920052840435055, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 25 with value: -150.3435882510586.\n", + "[I 2024-07-03 14:48:28,718] Trial 36 finished with value: -9804.512701665093 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.8677032984759461, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 25 with value: -150.3435882510586.\n", + "[I 2024-07-03 14:48:28,737] Trial 37 pruned. Duplicate parameter set\n", + "[I 2024-07-03 14:48:28,770] Trial 38 finished with value: -9165.74081120673 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.2865764368847064, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 25 with value: -150.3435882510586.\n", + "[I 2024-07-03 14:48:28,850] Trial 39 finished with value: -543.0280270800017 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 5, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 5, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 25 with value: -150.3435882510586.\n" ] }, { @@ -4864,13 +4848,11 @@ "name": "stderr", "output_type": "stream", "text": [ - "[I 2024-07-02 14:26:03,585] Trial 38 finished with value: -9165.74081120673 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.2865764368847064, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 25 with value: -150.3435882510586.\n", - "[I 2024-07-02 14:26:03,665] Trial 39 finished with value: -543.0280270800017 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 5, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 5, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 25 with value: -150.3435882510586.\n", - "[I 2024-07-02 14:26:03,745] Trial 40 finished with value: -161.1602933782954 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 5, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 9, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 25 with value: -150.3435882510586.\n", - "[I 2024-07-02 14:26:03,763] Trial 41 pruned. Duplicate parameter set\n", - "[I 2024-07-02 14:26:03,831] Trial 42 finished with value: -3501.888460860864 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 25, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 5, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 25 with value: -150.3435882510586.\n", - "[I 2024-07-02 14:26:03,864] Trial 43 finished with value: -8414.932694243476 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 2, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 25 with value: -150.3435882510586.\n", - "[I 2024-07-02 14:26:03,944] Trial 44 finished with value: -2270.540799189147 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 22, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 9, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 25 with value: -150.3435882510586.\n" + "[I 2024-07-03 14:48:28,931] Trial 40 finished with value: -161.1602933782954 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 5, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 9, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 25 with value: -150.3435882510586.\n", + "[I 2024-07-03 14:48:28,949] Trial 41 pruned. Duplicate parameter set\n", + "[I 2024-07-03 14:48:29,027] Trial 42 finished with value: -3501.888460860864 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 25, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 5, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 25 with value: -150.3435882510586.\n", + "[I 2024-07-03 14:48:29,060] Trial 43 finished with value: -8414.932694243476 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 2, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 25 with value: -150.3435882510586.\n", + "[I 2024-07-03 14:48:29,138] Trial 44 finished with value: -2270.5407991891466 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 22, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 9, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 25 with value: -150.3435882510586.\n" ] }, { @@ -4884,73 +4866,73 @@ "name": "stderr", "output_type": "stream", "text": [ - "[I 2024-07-02 14:26:03,977] Trial 45 finished with value: -10383.79559309305 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 3, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 25 with value: -150.3435882510586.\n", - "[I 2024-07-02 14:26:04,008] Trial 46 finished with value: -20815.025469865475 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.6437201185807124, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 25 with value: -150.3435882510586.\n", - "[I 2024-07-02 14:26:04,040] Trial 47 finished with value: -206.7560385808573 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 82.41502276709562, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 0.10978379088847677, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 25 with value: -150.3435882510586.\n", - "[I 2024-07-02 14:26:04,070] Trial 48 finished with value: -5264.4700789389035 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.022707289534838138, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 25 with value: -150.3435882510586.\n", - "[I 2024-07-02 14:26:04,103] Trial 49 finished with value: -3668.255064135424 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 3, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 25 with value: -150.3435882510586.\n", - "[I 2024-07-02 14:26:04,140] Trial 50 finished with value: -156.12174877890536 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 56.793408178086295, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 9.99902820845678, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 25 with value: -150.3435882510586.\n", - "[I 2024-07-02 14:26:04,177] Trial 51 finished with value: -157.371632749506 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 57.88307313087517, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 8.140915461519354, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 25 with value: -150.3435882510586.\n", - "[I 2024-07-02 14:26:04,218] Trial 52 finished with value: -153.66773675231477 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 46.177324126813716, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 40.77906017834145, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 25 with value: -150.3435882510586.\n", - "[I 2024-07-02 14:26:04,256] Trial 53 finished with value: -186.52056745848623 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 89.4565714180547, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 93.6710444346508, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 25 with value: -150.3435882510586.\n", - "[I 2024-07-02 14:26:04,294] Trial 54 finished with value: -153.30976119334312 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 35.62916671166313, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 40.023639423189294, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 25 with value: -150.3435882510586.\n", - "[I 2024-07-02 14:26:04,334] Trial 55 finished with value: -181.053696900694 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 23.914617418880486, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 86.31140591484044, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 25 with value: -150.3435882510586.\n", - "[I 2024-07-02 14:26:04,374] Trial 56 finished with value: -201.33573874994386 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 12.569769302718845, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 0.5781354926491789, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 25 with value: -150.3435882510586.\n", - "[I 2024-07-02 14:26:04,412] Trial 57 finished with value: -190.1384885119049 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 95.87666716965626, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 98.2537791489618, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 25 with value: -150.3435882510586.\n", - "[I 2024-07-02 14:26:04,451] Trial 58 finished with value: -208.076949848299 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.9559574710535281, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 0.0032830967319653665, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 25 with value: -150.3435882510586.\n", - "[I 2024-07-02 14:26:04,491] Trial 59 finished with value: -170.764974036324 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 15.03910427457823, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 3.406811480459925, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 25 with value: -150.3435882510586.\n", - "[I 2024-07-02 14:26:04,528] Trial 60 finished with value: -164.4477304958181 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 17.701690847791482, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 4.819274780536123, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 25 with value: -150.3435882510586.\n", - "[I 2024-07-02 14:26:04,567] Trial 61 finished with value: -157.87939164358104 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 28.32187661108304, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 7.660320437878754, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 25 with value: -150.3435882510586.\n", - "[I 2024-07-02 14:26:04,607] Trial 62 finished with value: -157.01705178481896 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 38.61397716361812, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 8.603665957830847, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 25 with value: -150.3435882510586.\n", - "[I 2024-07-02 14:26:04,645] Trial 63 finished with value: -155.73257312230092 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 40.759645965959294, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 11.503212714246787, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 25 with value: -150.3435882510586.\n" + "[I 2024-07-03 14:48:29,171] Trial 45 finished with value: -10383.79559309305 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 3, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 25 with value: -150.3435882510586.\n", + "[I 2024-07-03 14:48:29,204] Trial 46 finished with value: -20815.025469865475 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.6437201185807124, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 25 with value: -150.3435882510586.\n", + "[I 2024-07-03 14:48:29,238] Trial 47 finished with value: -206.7560385808573 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 82.41502276709562, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 0.10978379088847677, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 25 with value: -150.3435882510586.\n", + "[I 2024-07-03 14:48:29,272] Trial 48 finished with value: -5264.4700789389035 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.022707289534838138, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 25 with value: -150.3435882510586.\n", + "[I 2024-07-03 14:48:29,304] Trial 49 finished with value: -3668.255064135424 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 3, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 25 with value: -150.3435882510586.\n", + "[I 2024-07-03 14:48:29,342] Trial 50 finished with value: -156.12174877890536 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 56.793408178086295, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 9.99902820845678, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 25 with value: -150.3435882510586.\n", + "[I 2024-07-03 14:48:29,382] Trial 51 finished with value: -157.371632749506 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 57.88307313087517, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 8.140915461519354, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 25 with value: -150.3435882510586.\n", + "[I 2024-07-03 14:48:29,432] Trial 52 finished with value: -153.66773675231477 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 46.177324126813716, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 40.77906017834145, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 25 with value: -150.3435882510586.\n", + "[I 2024-07-03 14:48:29,471] Trial 53 finished with value: -186.52056745848623 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 89.4565714180547, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 93.6710444346508, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 25 with value: -150.3435882510586.\n", + "[I 2024-07-03 14:48:29,512] Trial 54 finished with value: -153.30976119334312 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 35.62916671166313, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 40.023639423189294, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 25 with value: -150.3435882510586.\n", + "[I 2024-07-03 14:48:29,551] Trial 55 finished with value: -181.053696900694 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 23.914617418880486, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 86.31140591484044, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 25 with value: -150.3435882510586.\n", + "[I 2024-07-03 14:48:29,588] Trial 56 finished with value: -201.33573874994386 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 12.569769302718845, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 0.5781354926491789, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 25 with value: -150.3435882510586.\n", + "[I 2024-07-03 14:48:29,627] Trial 57 finished with value: -190.1384885119049 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 95.87666716965626, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 98.2537791489618, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 25 with value: -150.3435882510586.\n", + "[I 2024-07-03 14:48:29,664] Trial 58 finished with value: -208.076949848299 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.9559574710535281, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 0.0032830967319653665, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 25 with value: -150.3435882510586.\n", + "[I 2024-07-03 14:48:29,703] Trial 59 finished with value: -170.764974036324 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 15.03910427457823, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 3.406811480459925, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 25 with value: -150.3435882510586.\n", + "[I 2024-07-03 14:48:29,744] Trial 60 finished with value: -164.4477304958181 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 17.701690847791482, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 4.819274780536123, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 25 with value: -150.3435882510586.\n", + "[I 2024-07-03 14:48:29,784] Trial 61 finished with value: -157.87939164358104 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 28.32187661108304, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 7.660320437878754, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 25 with value: -150.3435882510586.\n", + "[I 2024-07-03 14:48:29,824] Trial 62 finished with value: -157.01705178481896 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 38.61397716361812, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 8.603665957830847, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 25 with value: -150.3435882510586.\n", + "[I 2024-07-03 14:48:29,863] Trial 63 finished with value: -155.73257312230092 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 40.759645965959294, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 11.503212714246787, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 25 with value: -150.3435882510586.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "[I 2024-07-02 14:26:04,684] Trial 64 finished with value: -154.46848394144124 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 93.8546740801317, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 15.35327336610912, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 25 with value: -150.3435882510586.\n", - "[I 2024-07-02 14:26:04,724] Trial 65 finished with value: -161.20421802817864 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 93.57596974747163, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 51.84756262407801, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 25 with value: -150.3435882510586.\n", - "[I 2024-07-02 14:26:04,763] Trial 66 finished with value: -190.51233215278089 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 6.3564642040401464, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 1.5034542273159819, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 25 with value: -150.3435882510586.\n", - "[I 2024-07-02 14:26:04,802] Trial 67 finished with value: -207.68667089892196 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 24.034895878929095, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 0.03653571911285094, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 25 with value: -150.3435882510586.\n", - "[I 2024-07-02 14:26:04,842] Trial 68 finished with value: -102.52277054278186 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.01961499216484045, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 17.670937191883546, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 68 with value: -102.52277054278186.\n", - "[I 2024-07-02 14:26:04,881] Trial 69 finished with value: -97.28722475694815 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.012434370509176538, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 19.34222704431493, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 69 with value: -97.28722475694815.\n", - "[I 2024-07-02 14:26:04,921] Trial 70 finished with value: -93.87402050281146 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.008452015347522093, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 24.914863578437455, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 70 with value: -93.87402050281146.\n", - "[I 2024-07-02 14:26:04,960] Trial 71 finished with value: -89.38847505937936 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.01573542234868893, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 27.99307522974174, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 71 with value: -89.38847505937936.\n", - "[I 2024-07-02 14:26:04,999] Trial 72 finished with value: -81.96336195786391 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.009845516063879428, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 80.59422914099683, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 72 with value: -81.96336195786391.\n", - "[I 2024-07-02 14:26:05,039] Trial 73 finished with value: -89.19345618324213 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.009382525091504246, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 98.35573659237662, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 72 with value: -81.96336195786391.\n", - "[I 2024-07-02 14:26:05,080] Trial 74 finished with value: -86.30772721342525 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.010579672066291478, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 84.35550323165882, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 72 with value: -81.96336195786391.\n", - "[I 2024-07-02 14:26:05,117] Trial 75 finished with value: -90.23970902543148 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.013369359066405863, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 87.4744102498801, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 72 with value: -81.96336195786391.\n", - "[I 2024-07-02 14:26:05,155] Trial 76 finished with value: -81.34331248758777 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.011398351701814368, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 72.54146340620301, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 76 with value: -81.34331248758777.\n", - "[I 2024-07-02 14:26:05,195] Trial 77 finished with value: -208.104535853341 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.011708779850509646, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 1.682286191624579e-05, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 76 with value: -81.34331248758777.\n", - "[I 2024-07-02 14:26:05,235] Trial 78 finished with value: -80.0653774146952 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.009806826677473646, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 76.90274406278985, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 78 with value: -80.0653774146952.\n", - "[I 2024-07-02 14:26:05,276] Trial 79 finished with value: -81.64646042813787 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.0038598153381434685, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 73.20918134828555, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 78 with value: -80.0653774146952.\n", - "[I 2024-07-02 14:26:05,316] Trial 80 finished with value: -78.68420472011734 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.0032474576673554513, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 98.35551178979624, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 80 with value: -78.68420472011734.\n", - "[I 2024-07-02 14:26:05,357] Trial 81 finished with value: -80.85985201823172 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.003187930738019005, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 89.29431603544847, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 80 with value: -78.68420472011734.\n", - "[I 2024-07-02 14:26:05,399] Trial 82 finished with value: -80.21583898009355 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.003122319313153475, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 93.83526418992966, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 80 with value: -78.68420472011734.\n" + "[I 2024-07-03 14:48:29,902] Trial 64 finished with value: -154.46848394144124 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 93.8546740801317, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 15.35327336610912, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 25 with value: -150.3435882510586.\n", + "[I 2024-07-03 14:48:29,941] Trial 65 finished with value: -161.20421802817864 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 93.57596974747163, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 51.84756262407801, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 25 with value: -150.3435882510586.\n", + "[I 2024-07-03 14:48:29,981] Trial 66 finished with value: -190.51233215278089 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 6.3564642040401464, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 1.5034542273159819, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 25 with value: -150.3435882510586.\n", + "[I 2024-07-03 14:48:30,019] Trial 67 finished with value: -207.68667089892196 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 24.034895878929095, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 0.03653571911285094, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 25 with value: -150.3435882510586.\n", + "[I 2024-07-03 14:48:30,058] Trial 68 finished with value: -102.52277054278186 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.01961499216484045, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 17.670937191883546, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 68 with value: -102.52277054278186.\n", + "[I 2024-07-03 14:48:30,099] Trial 69 finished with value: -97.28722475694815 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.012434370509176538, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 19.34222704431493, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 69 with value: -97.28722475694815.\n", + "[I 2024-07-03 14:48:30,152] Trial 70 finished with value: -93.87402050281146 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.008452015347522093, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 24.914863578437455, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 70 with value: -93.87402050281146.\n", + "[I 2024-07-03 14:48:30,193] Trial 71 finished with value: -89.38847505937936 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.01573542234868893, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 27.99307522974174, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 71 with value: -89.38847505937936.\n", + "[I 2024-07-03 14:48:30,236] Trial 72 finished with value: -81.96336195786391 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.009845516063879428, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 80.59422914099683, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 72 with value: -81.96336195786391.\n", + "[I 2024-07-03 14:48:30,276] Trial 73 finished with value: -89.19345618324213 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.009382525091504246, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 98.35573659237662, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 72 with value: -81.96336195786391.\n", + "[I 2024-07-03 14:48:30,318] Trial 74 finished with value: -86.30772721342525 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.010579672066291478, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 84.35550323165882, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 72 with value: -81.96336195786391.\n", + "[I 2024-07-03 14:48:30,357] Trial 75 finished with value: -90.23970902543148 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.013369359066405863, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 87.4744102498801, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 72 with value: -81.96336195786391.\n", + "[I 2024-07-03 14:48:30,399] Trial 76 finished with value: -81.34331248758777 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.011398351701814368, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 72.54146340620301, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 76 with value: -81.34331248758777.\n", + "[I 2024-07-03 14:48:30,442] Trial 77 finished with value: -208.104535853341 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.011708779850509646, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 1.682286191624579e-05, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 76 with value: -81.34331248758777.\n", + "[I 2024-07-03 14:48:30,481] Trial 78 finished with value: -80.0653774146952 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.009806826677473646, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 76.90274406278985, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 78 with value: -80.0653774146952.\n", + "[I 2024-07-03 14:48:30,524] Trial 79 finished with value: -81.64646042813787 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.0038598153381434685, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 73.20918134828555, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 78 with value: -80.0653774146952.\n", + "[I 2024-07-03 14:48:30,565] Trial 80 finished with value: -78.68420472011734 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.0032474576673554513, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 98.35551178979624, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 80 with value: -78.68420472011734.\n", + "[I 2024-07-03 14:48:30,606] Trial 81 finished with value: -80.85985201823172 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.003187930738019005, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 89.29431603544847, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 80 with value: -78.68420472011734.\n", + "[I 2024-07-03 14:48:30,647] Trial 82 finished with value: -80.21583898009355 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.003122319313153475, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 93.83526418992966, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 80 with value: -78.68420472011734.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "[I 2024-07-02 14:26:05,437] Trial 83 finished with value: -83.34787242859676 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.002781955938462633, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 89.76228981520067, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 80 with value: -78.68420472011734.\n", - "[I 2024-07-02 14:26:05,478] Trial 84 finished with value: -194.70914272129673 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.0023173546614751305, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 1.3000082904498813, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 80 with value: -78.68420472011734.\n", - "[I 2024-07-02 14:26:05,519] Trial 85 finished with value: -208.10492031097328 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.002606064524407, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 1.7861330234653922e-10, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 80 with value: -78.68420472011734.\n", - "[I 2024-07-02 14:26:05,558] Trial 86 finished with value: -208.1049154281806 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.0029210589377408366, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 4.200933937391094e-07, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 80 with value: -78.68420472011734.\n", - "[I 2024-07-02 14:26:05,599] Trial 87 finished with value: -208.10492028002287 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.06431564840324226, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 3.2981641934644904e-09, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 80 with value: -78.68420472011734.\n", - "[I 2024-07-02 14:26:05,640] Trial 88 finished with value: -196.56066541774658 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.0010848843623839548, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 2.151493073951163, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 80 with value: -78.68420472011734.\n", - "[I 2024-07-02 14:26:05,682] Trial 89 finished with value: -76.76337597039308 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.004134805589645341, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 90.88115336652716, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 89 with value: -76.76337597039308.\n", - "[I 2024-07-02 14:26:05,724] Trial 90 finished with value: -108.58009587759925 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.004763418454688096, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 22.02920758025023, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 89 with value: -76.76337597039308.\n", - "[I 2024-07-02 14:26:05,766] Trial 91 finished with value: -113.35230417583477 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.0009098023238189749, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 79.57100980886017, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 89 with value: -76.76337597039308.\n", - "[I 2024-07-02 14:26:05,809] Trial 92 finished with value: -113.30807467406214 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.03739791555156691, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 27.12818940557025, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 89 with value: -76.76337597039308.\n", - "[I 2024-07-02 14:26:05,850] Trial 93 finished with value: -76.44100655116532 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.006380481141720477, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 88.4882351186755, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 93 with value: -76.44100655116532.\n", - "[I 2024-07-02 14:26:05,891] Trial 94 finished with value: -150.35181001564942 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.0036244007454981787, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 5.608797806921866, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 93 with value: -76.44100655116532.\n", - "[I 2024-07-02 14:26:05,935] Trial 95 finished with value: -124.3719027482892 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.0014198536004321608, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 35.05588994284273, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 93 with value: -76.44100655116532.\n", - "[I 2024-07-02 14:26:05,978] Trial 96 finished with value: -95.28568052794907 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.005434972462746285, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 30.215759789700954, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 93 with value: -76.44100655116532.\n", - "[I 2024-07-02 14:26:06,018] Trial 97 finished with value: -20325.66479442037 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.9696417046589247, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 93 with value: -76.44100655116532.\n", - "[I 2024-07-02 14:26:06,057] Trial 98 finished with value: -132.21507621375022 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.0004528978867024753, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 84.80386923876023, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 93 with value: -76.44100655116532.\n", - "[I 2024-07-02 14:26:06,097] Trial 99 finished with value: -166.85570350846885 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.0016948043699497222, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 5.455627755557016, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 93 with value: -76.44100655116532.\n" + "[I 2024-07-03 14:48:30,686] Trial 83 finished with value: -83.34787242859676 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.002781955938462633, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 89.76228981520067, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 80 with value: -78.68420472011734.\n", + "[I 2024-07-03 14:48:30,726] Trial 84 finished with value: -194.70914272129673 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.0023173546614751305, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 1.3000082904498813, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 80 with value: -78.68420472011734.\n", + "[I 2024-07-03 14:48:30,767] Trial 85 finished with value: -208.10492031097328 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.002606064524407, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 1.7861330234653922e-10, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 80 with value: -78.68420472011734.\n", + "[I 2024-07-03 14:48:30,809] Trial 86 finished with value: -208.1049154281806 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.0029210589377408366, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 4.200933937391094e-07, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 80 with value: -78.68420472011734.\n", + "[I 2024-07-03 14:48:30,851] Trial 87 finished with value: -208.10492028002287 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.06431564840324226, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 3.2981641934644904e-09, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 80 with value: -78.68420472011734.\n", + "[I 2024-07-03 14:48:30,895] Trial 88 finished with value: -196.56066541774658 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.0010848843623839548, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 2.151493073951163, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 80 with value: -78.68420472011734.\n", + "[I 2024-07-03 14:48:30,939] Trial 89 finished with value: -76.76337597039308 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.004134805589645341, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 90.88115336652716, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 89 with value: -76.76337597039308.\n", + "[I 2024-07-03 14:48:30,984] Trial 90 finished with value: -108.58009587759925 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.004763418454688096, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 22.02920758025023, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 89 with value: -76.76337597039308.\n", + "[I 2024-07-03 14:48:31,029] Trial 91 finished with value: -113.35230417583477 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.0009098023238189749, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 79.57100980886017, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 89 with value: -76.76337597039308.\n", + "[I 2024-07-03 14:48:31,075] Trial 92 finished with value: -113.30807467406214 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.03739791555156691, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 27.12818940557025, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 89 with value: -76.76337597039308.\n", + "[I 2024-07-03 14:48:31,121] Trial 93 finished with value: -76.44100655116532 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.006380481141720477, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 88.4882351186755, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 93 with value: -76.44100655116532.\n", + "[I 2024-07-03 14:48:31,163] Trial 94 finished with value: -150.35181001564942 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.0036244007454981787, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 5.608797806921866, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 93 with value: -76.44100655116532.\n", + "[I 2024-07-03 14:48:31,209] Trial 95 finished with value: -124.3719027482892 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.0014198536004321608, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 35.05588994284273, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 93 with value: -76.44100655116532.\n", + "[I 2024-07-03 14:48:31,252] Trial 96 finished with value: -95.28568052794907 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.005434972462746285, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 30.215759789700954, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 93 with value: -76.44100655116532.\n", + "[I 2024-07-03 14:48:31,291] Trial 97 finished with value: -20325.66479442037 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.9696417046589247, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 93 with value: -76.44100655116532.\n", + "[I 2024-07-03 14:48:31,334] Trial 98 finished with value: -132.21507621375022 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.0004528978867024753, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 84.80386923876023, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 93 with value: -76.44100655116532.\n", + "[I 2024-07-03 14:48:31,379] Trial 99 finished with value: -166.85570350846885 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.0016948043699497222, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 5.455627755557016, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 93 with value: -76.44100655116532.\n" ] } ], @@ -5003,22 +4985,22 @@ }, { "cell_type": "code", - "execution_count": 77, + "execution_count": 160, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 77, + "execution_count": 160, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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"text/plain": [ "
" ] @@ -5052,7 +5034,7 @@ }, { "cell_type": "code", - "execution_count": 78, + "execution_count": 162, "metadata": {}, "outputs": [ { @@ -5061,7 +5043,7 @@ "array([1126.56968721, 120.20237903])" ] }, - "execution_count": 78, + "execution_count": 162, "metadata": {}, "output_type": "execute_result" } @@ -5089,7 +5071,7 @@ }, { "cell_type": "code", - "execution_count": 79, + "execution_count": 163, "metadata": {}, "outputs": [ { @@ -5098,7 +5080,7 @@ "array([2.94824194, 3.92008694])" ] }, - "execution_count": 79, + "execution_count": 163, "metadata": {}, "output_type": "execute_result" } @@ -5118,7 +5100,7 @@ }, { "cell_type": "code", - "execution_count": 80, + "execution_count": 164, "metadata": { "scrolled": true }, @@ -5127,42 +5109,42 @@ "name": "stderr", "output_type": "stream", "text": [ - "[I 2024-07-02 14:26:10,518] A new study created in memory with name: ptr_and_transform_example\n", - "[I 2024-07-02 14:26:10,558] A new study created in memory with name: study_name_0\n", - "[I 2024-07-02 14:26:10,728] Trial 0 finished with value: -0.002341918451736245 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 6, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 5, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 0 with value: -0.002341918451736245.\n", - "[I 2024-07-02 14:26:10,805] Trial 1 finished with value: -0.0024908979029632677 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 7, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 6, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 0 with value: -0.002341918451736245.\n", - "[I 2024-07-02 14:26:10,847] Trial 2 finished with value: -0.007901407671048116 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 5.141096648805748, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 2.4893466963980463e-08, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 0 with value: -0.002341918451736245.\n", - "[I 2024-07-02 14:26:10,888] Trial 3 finished with value: -0.00496231674623194 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 5, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 0 with value: -0.002341918451736245.\n", - "[I 2024-07-02 14:26:10,917] Trial 4 finished with value: -0.0026848278110363512 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 3, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 0 with value: -0.002341918451736245.\n", - "[I 2024-07-02 14:26:10,959] Trial 5 finished with value: -0.0010872728889471893 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.7896547008552977, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 5 with value: -0.0010872728889471893.\n", - "[I 2024-07-02 14:26:11,000] Trial 6 finished with value: -0.008706109201510277 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.6574750183038587, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 5 with value: -0.0010872728889471893.\n", - "[I 2024-07-02 14:26:11,027] Trial 7 finished with value: -0.008706109201510277 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.3974313630683448, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 5 with value: -0.0010872728889471893.\n", - "[I 2024-07-02 14:26:11,093] Trial 8 finished with value: -0.002999462459688867 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 28, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 8, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 5 with value: -0.0010872728889471893.\n", - "[I 2024-07-02 14:26:11,120] Trial 9 finished with value: -0.00825680029907454 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.2391884918766034, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 5 with value: -0.0010872728889471893.\n", - "[I 2024-07-02 14:26:11,148] Trial 10 finished with value: -0.007901407993550248 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.00044396482429275296, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 2.3831436879125245e-10, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 5 with value: -0.0010872728889471893.\n", - "[I 2024-07-02 14:26:11,174] Trial 11 finished with value: -0.007901405163828307 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.00028965395242758657, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 2.99928292425642e-07, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 5 with value: -0.0010872728889471893.\n", - "[I 2024-07-02 14:26:11,201] Trial 12 finished with value: -0.0021653695362066753 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 4, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 5 with value: -0.0010872728889471893.\n", - "[I 2024-07-02 14:26:11,227] Trial 13 finished with value: -0.002869169486971014 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 2, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 5 with value: -0.0010872728889471893.\n", - "[I 2024-07-02 14:26:11,255] Trial 14 finished with value: -0.0010855652626111146 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.4060379177903557, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 14 with value: -0.0010855652626111146.\n", - "[I 2024-07-02 14:26:11,329] Trial 15 finished with value: -0.00550533804299308 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 20, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 8, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 14 with value: -0.0010855652626111146.\n", - "[I 2024-07-02 14:26:11,359] Trial 16 finished with value: -0.008706109201510277 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.344271094811757, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 14 with value: -0.0010855652626111146.\n", - "[I 2024-07-02 14:26:11,386] Trial 17 finished with value: -0.002236800860454562 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.670604991178476, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 14 with value: -0.0010855652626111146.\n", - "[I 2024-07-02 14:26:11,466] Trial 18 finished with value: -0.006105985607235417 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 22, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 6, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 14 with value: -0.0010855652626111146.\n" + "[I 2024-07-03 14:48:56,090] A new study created in memory with name: ptr_and_transform_example\n", + "[I 2024-07-03 14:48:56,129] A new study created in memory with name: study_name_0\n", + "[I 2024-07-03 14:48:56,233] Trial 0 finished with value: -0.002341918451736245 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 6, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 5, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 0 with value: -0.002341918451736245.\n", + "[I 2024-07-03 14:48:56,312] Trial 1 finished with value: -0.002490897902963267 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 7, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 6, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 0 with value: -0.002341918451736245.\n", + "[I 2024-07-03 14:48:56,353] Trial 2 finished with value: -0.007901407671048116 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 5.141096648805748, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 2.4893466963980463e-08, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 0 with value: -0.002341918451736245.\n", + "[I 2024-07-03 14:48:56,395] Trial 3 finished with value: -0.00496231674623194 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 5, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 0 with value: -0.002341918451736245.\n", + "[I 2024-07-03 14:48:56,424] Trial 4 finished with value: -0.0026848278110363512 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 3, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 0 with value: -0.002341918451736245.\n", + "[I 2024-07-03 14:48:56,466] Trial 5 finished with value: -0.0010872728889471893 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.7896547008552977, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 5 with value: -0.0010872728889471893.\n", + "[I 2024-07-03 14:48:56,509] Trial 6 finished with value: -0.008706109201510277 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.6574750183038587, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 5 with value: -0.0010872728889471893.\n", + "[I 2024-07-03 14:48:56,540] Trial 7 finished with value: -0.008706109201510277 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.3974313630683448, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 5 with value: -0.0010872728889471893.\n", + "[I 2024-07-03 14:48:56,616] Trial 8 finished with value: -0.0029994624596888677 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 28, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 8, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 5 with value: -0.0010872728889471893.\n", + "[I 2024-07-03 14:48:56,646] Trial 9 finished with value: -0.00825680029907454 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.2391884918766034, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 5 with value: -0.0010872728889471893.\n", + "[I 2024-07-03 14:48:56,674] Trial 10 finished with value: -0.007901407993550248 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.00044396482429275296, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 2.3831436879125245e-10, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 5 with value: -0.0010872728889471893.\n", + "[I 2024-07-03 14:48:56,704] Trial 11 finished with value: -0.007901405163828307 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.00028965395242758657, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 2.99928292425642e-07, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 5 with value: -0.0010872728889471893.\n", + "[I 2024-07-03 14:48:56,733] Trial 12 finished with value: -0.0021653695362066753 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 4, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 5 with value: -0.0010872728889471893.\n", + "[I 2024-07-03 14:48:56,760] Trial 13 finished with value: -0.002869169486971014 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 2, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 5 with value: -0.0010872728889471893.\n", + "[I 2024-07-03 14:48:56,789] Trial 14 finished with value: -0.0010855652626111146 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.4060379177903557, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 14 with value: -0.0010855652626111146.\n", + "[I 2024-07-03 14:48:56,864] Trial 15 finished with value: -0.005505338042993082 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 20, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 8, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 14 with value: -0.0010855652626111146.\n", + "[I 2024-07-03 14:48:56,895] Trial 16 finished with value: -0.008706109201510277 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.344271094811757, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 14 with value: -0.0010855652626111146.\n", + "[I 2024-07-03 14:48:56,923] Trial 17 finished with value: -0.002236800860454562 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.670604991178476, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 14 with value: -0.0010855652626111146.\n", + "[I 2024-07-03 14:48:56,999] Trial 18 finished with value: -0.006105985607235417 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 22, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 6, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 14 with value: -0.0010855652626111146.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "[I 2024-07-02 14:26:11,495] Trial 19 finished with value: -0.008706109201510277 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.5158832554303112, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 14 with value: -0.0010855652626111146.\n", - "[I 2024-07-02 14:26:11,523] Trial 20 finished with value: -0.004846526544994462 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 4, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 14 with value: -0.0010855652626111146.\n", - "[I 2024-07-02 14:26:11,550] Trial 21 finished with value: -0.006964668794465202 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.0009327650919528738, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 6.062479210472502, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 14 with value: -0.0010855652626111146.\n", - "[I 2024-07-02 14:26:11,565] Trial 22 pruned. Duplicate parameter set\n", - "[I 2024-07-02 14:26:11,594] Trial 23 finished with value: -0.008706109201510277 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.1366172066709432, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 14 with value: -0.0010855652626111146.\n", - "[I 2024-07-02 14:26:11,670] Trial 24 finished with value: -0.002999462459688867 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 26, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 8, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 14 with value: -0.0010855652626111146.\n", - "[I 2024-07-02 14:26:11,699] Trial 25 finished with value: -0.008384326901042542 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 43.92901911959232, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 27.999026012594694, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 14 with value: -0.0010855652626111146.\n", - "[I 2024-07-02 14:26:11,730] Trial 26 finished with value: -0.001082194093844804 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.5888977841391714, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 26 with value: -0.001082194093844804.\n", - "[I 2024-07-02 14:26:11,761] Trial 27 finished with value: -0.0010807084256204563 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.19435298754153707, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 27 with value: -0.0010807084256204563.\n" + "[I 2024-07-03 14:48:57,030] Trial 19 finished with value: -0.008706109201510277 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.5158832554303112, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 14 with value: -0.0010855652626111146.\n", + "[I 2024-07-03 14:48:57,061] Trial 20 finished with value: -0.004846526544994462 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 4, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 14 with value: -0.0010855652626111146.\n", + "[I 2024-07-03 14:48:57,088] Trial 21 finished with value: -0.006964668794465202 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.0009327650919528738, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 6.062479210472502, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 14 with value: -0.0010855652626111146.\n", + "[I 2024-07-03 14:48:57,103] Trial 22 pruned. Duplicate parameter set\n", + "[I 2024-07-03 14:48:57,131] Trial 23 finished with value: -0.008706109201510277 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.1366172066709432, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 14 with value: -0.0010855652626111146.\n", + "[I 2024-07-03 14:48:57,211] Trial 24 finished with value: -0.0029994624596888664 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 26, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 8, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 14 with value: -0.0010855652626111146.\n", + "[I 2024-07-03 14:48:57,240] Trial 25 finished with value: -0.008384326901042542 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 43.92901911959232, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 27.999026012594694, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 14 with value: -0.0010855652626111146.\n", + "[I 2024-07-03 14:48:57,271] Trial 26 finished with value: -0.001082194093844804 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.5888977841391714, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 26 with value: -0.001082194093844804.\n", + "[I 2024-07-03 14:48:57,299] Trial 27 finished with value: -0.0010807084256204563 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.19435298754153707, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 27 with value: -0.0010807084256204563.\n" ] }, { @@ -5176,18 +5158,18 @@ "name": "stderr", "output_type": "stream", "text": [ - "[I 2024-07-02 14:26:11,839] Trial 28 finished with value: -0.006105985607235417 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 13, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 6, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 27 with value: -0.0010807084256204563.\n", - "[I 2024-07-02 14:26:11,868] Trial 29 finished with value: -0.008384326901042542 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 1.6285506249643193, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 0.35441495011256785, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 27 with value: -0.0010807084256204563.\n", - "[I 2024-07-02 14:26:11,948] Trial 30 finished with value: -0.005505338042993082 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 10, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 8, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 27 with value: -0.0010807084256204563.\n", - "[I 2024-07-02 14:26:11,979] Trial 31 finished with value: -0.008706109201510277 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.2457809516380005, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 27 with value: -0.0010807084256204563.\n", - "[I 2024-07-02 14:26:12,008] Trial 32 finished with value: -0.008706109201510277 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.6459129458824919, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 27 with value: -0.0010807084256204563.\n", - "[I 2024-07-02 14:26:12,039] Trial 33 finished with value: -0.005247934991526694 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.8179058888285398, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 27 with value: -0.0010807084256204563.\n", - "[I 2024-07-02 14:26:12,057] Trial 34 pruned. Duplicate parameter set\n", - "[I 2024-07-02 14:26:12,089] Trial 35 finished with value: -0.0010803393728928605 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.0920052840435055, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 35 with value: -0.0010803393728928605.\n", - "[I 2024-07-02 14:26:12,120] Trial 36 finished with value: -0.005218354425190125 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.8677032984759461, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 35 with value: -0.0010803393728928605.\n", - "[I 2024-07-02 14:26:12,138] Trial 37 pruned. Duplicate parameter set\n", - "[I 2024-07-02 14:26:12,169] Trial 38 finished with value: -0.004999207507691546 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.2865764368847064, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 35 with value: -0.0010803393728928605.\n", - "[I 2024-07-02 14:26:12,245] Trial 39 finished with value: -0.0015694919308122948 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 5, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 5, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 35 with value: -0.0010803393728928605.\n" + "[I 2024-07-03 14:48:57,367] Trial 28 finished with value: -0.006105985607235417 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 13, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 6, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 27 with value: -0.0010807084256204563.\n", + "[I 2024-07-03 14:48:57,398] Trial 29 finished with value: -0.008384326901042542 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 1.6285506249643193, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 0.35441495011256785, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 27 with value: -0.0010807084256204563.\n", + "[I 2024-07-03 14:48:57,476] Trial 30 finished with value: -0.005505338042993084 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 10, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 8, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 27 with value: -0.0010807084256204563.\n", + "[I 2024-07-03 14:48:57,507] Trial 31 finished with value: -0.008706109201510277 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.2457809516380005, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 27 with value: -0.0010807084256204563.\n", + "[I 2024-07-03 14:48:57,537] Trial 32 finished with value: -0.008706109201510277 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.6459129458824919, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 27 with value: -0.0010807084256204563.\n", + "[I 2024-07-03 14:48:57,569] Trial 33 finished with value: -0.005247934991526694 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.8179058888285398, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 27 with value: -0.0010807084256204563.\n", + "[I 2024-07-03 14:48:57,585] Trial 34 pruned. Duplicate parameter set\n", + "[I 2024-07-03 14:48:57,617] Trial 35 finished with value: -0.0010803393728928605 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.0920052840435055, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 35 with value: -0.0010803393728928605.\n", + "[I 2024-07-03 14:48:57,648] Trial 36 finished with value: -0.005218354425190125 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.8677032984759461, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 35 with value: -0.0010803393728928605.\n", + "[I 2024-07-03 14:48:57,668] Trial 37 pruned. Duplicate parameter set\n", + "[I 2024-07-03 14:48:57,700] Trial 38 finished with value: -0.004999207507691546 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.2865764368847064, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 35 with value: -0.0010803393728928605.\n", + "[I 2024-07-03 14:48:57,780] Trial 39 finished with value: -0.0015694919308122952 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 5, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 5, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 35 with value: -0.0010803393728928605.\n" ] }, { @@ -5202,11 +5184,11 @@ "name": "stderr", "output_type": "stream", "text": [ - "[I 2024-07-02 14:26:12,326] Trial 40 finished with value: -0.0019757694194001384 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 5, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 9, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 35 with value: -0.0010803393728928605.\n", - "[I 2024-07-02 14:26:12,343] Trial 41 pruned. Duplicate parameter set\n", - "[I 2024-07-02 14:26:12,421] Trial 42 finished with value: -0.002341918451736244 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 25, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 5, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 35 with value: -0.0010803393728928605.\n", - "[I 2024-07-02 14:26:12,453] Trial 43 finished with value: -0.00368328296527152 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 2, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 35 with value: -0.0010803393728928605.\n", - "[I 2024-07-02 14:26:12,521] Trial 44 finished with value: -0.003412828259848677 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 22, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 9, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 35 with value: -0.0010803393728928605.\n" + "[I 2024-07-03 14:48:57,859] Trial 40 finished with value: -0.0019757694194001397 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 5, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 9, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 35 with value: -0.0010803393728928605.\n", + "[I 2024-07-03 14:48:57,876] Trial 41 pruned. Duplicate parameter set\n", + "[I 2024-07-03 14:48:57,954] Trial 42 finished with value: -0.002341918451736245 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 25, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 5, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 35 with value: -0.0010803393728928605.\n", + "[I 2024-07-03 14:48:57,986] Trial 43 finished with value: -0.00368328296527152 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 2, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 35 with value: -0.0010803393728928605.\n", + "[I 2024-07-03 14:48:58,066] Trial 44 finished with value: -0.003412828259848677 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 22, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 9, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 35 with value: -0.0010803393728928605.\n" ] }, { @@ -5220,73 +5202,73 @@ "name": "stderr", "output_type": "stream", "text": [ - "[I 2024-07-02 14:26:12,551] Trial 45 finished with value: -0.004412110711416997 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 3, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 35 with value: -0.0010803393728928605.\n", - "[I 2024-07-02 14:26:12,583] Trial 46 finished with value: -0.008706109201510277 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.6437201185807124, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 35 with value: -0.0010803393728928605.\n", - "[I 2024-07-02 14:26:12,616] Trial 47 finished with value: -0.008384326901042542 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 82.41502276709562, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 0.10978379088847677, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 35 with value: -0.0010803393728928605.\n", - "[I 2024-07-02 14:26:12,647] Trial 48 finished with value: -0.0021743798524909573 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.022707289534838138, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 35 with value: -0.0010803393728928605.\n", - "[I 2024-07-02 14:26:12,679] Trial 49 finished with value: -0.0022761245849848527 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 3, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 35 with value: -0.0010803393728928605.\n", - "[I 2024-07-02 14:26:12,715] Trial 50 finished with value: -0.0010805768178458735 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.1580741708125475, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 35 with value: -0.0010803393728928605.\n", - "[I 2024-07-02 14:26:12,750] Trial 51 finished with value: -0.001080400188305814 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.10900413894771653, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 35 with value: -0.0010803393728928605.\n", - "[I 2024-07-02 14:26:12,784] Trial 52 finished with value: -0.0010805009783570441 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.13705914456987853, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 35 with value: -0.0010803393728928605.\n", - "[I 2024-07-02 14:26:12,817] Trial 53 finished with value: -0.0010804680472500541 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.12790870116376127, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 35 with value: -0.0010803393728928605.\n", - "[I 2024-07-02 14:26:12,853] Trial 54 finished with value: -0.0010803723579987025 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.10123180962907431, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 35 with value: -0.0010803393728928605.\n", - "[I 2024-07-02 14:26:12,890] Trial 55 finished with value: -0.001080969596032512 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.26565663774320425, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 35 with value: -0.0010803393728928605.\n", - "[I 2024-07-02 14:26:12,925] Trial 56 finished with value: -0.0010800333715082816 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.005637048678674678, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 56 with value: -0.0010800333715082816.\n", - "[I 2024-07-02 14:26:12,962] Trial 57 finished with value: -0.0010802574700236845 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.06902647427781451, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 56 with value: -0.0010800333715082816.\n", - "[I 2024-07-02 14:26:13,000] Trial 58 finished with value: -0.0010814994986419817 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.4076704953178294, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 56 with value: -0.0010800333715082816.\n", - "[I 2024-07-02 14:26:13,037] Trial 59 finished with value: -0.001080161136846237 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.04187106800188596, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 56 with value: -0.0010800333715082816.\n", - "[I 2024-07-02 14:26:13,071] Trial 60 finished with value: -0.0010800254136811547 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.003371853599610078, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 60 with value: -0.0010800254136811547.\n", - "[I 2024-07-02 14:26:13,107] Trial 61 finished with value: -0.0010801290036870739 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.032781796328385376, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 60 with value: -0.0010800254136811547.\n", - "[I 2024-07-02 14:26:13,142] Trial 62 finished with value: -0.001080037482216557 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.006806773659187283, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 60 with value: -0.0010800254136811547.\n", - "[I 2024-07-02 14:26:13,179] Trial 63 finished with value: -0.0010801015705851358 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.025009489814943348, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 60 with value: -0.0010800254136811547.\n", - "[I 2024-07-02 14:26:13,217] Trial 64 finished with value: -0.0010812122378841013 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.3311125627707556, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 60 with value: -0.0010800254136811547.\n", - "[I 2024-07-02 14:26:13,254] Trial 65 finished with value: -0.0010800531021304936 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.011249102380159387, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 60 with value: -0.0010800254136811547.\n" + "[I 2024-07-03 14:48:58,098] Trial 45 finished with value: -0.004412110711416997 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 3, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 35 with value: -0.0010803393728928605.\n", + "[I 2024-07-03 14:48:58,131] Trial 46 finished with value: -0.008706109201510277 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.6437201185807124, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 35 with value: -0.0010803393728928605.\n", + "[I 2024-07-03 14:48:58,160] Trial 47 finished with value: -0.008384326901042542 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 82.41502276709562, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 0.10978379088847677, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 35 with value: -0.0010803393728928605.\n", + "[I 2024-07-03 14:48:58,193] Trial 48 finished with value: -0.0021743798524909573 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.022707289534838138, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 35 with value: -0.0010803393728928605.\n", + "[I 2024-07-03 14:48:58,224] Trial 49 finished with value: -0.0022761245849848527 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 3, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 35 with value: -0.0010803393728928605.\n", + "[I 2024-07-03 14:48:58,262] Trial 50 finished with value: -0.0010805768178458735 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.1580741708125475, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 35 with value: -0.0010803393728928605.\n", + "[I 2024-07-03 14:48:58,307] Trial 51 finished with value: -0.001080400188305814 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.10900413894771653, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 35 with value: -0.0010803393728928605.\n", + "[I 2024-07-03 14:48:58,345] Trial 52 finished with value: -0.0010805009783570441 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.13705914456987853, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 35 with value: -0.0010803393728928605.\n", + "[I 2024-07-03 14:48:58,381] Trial 53 finished with value: -0.0010804680472500541 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.12790870116376127, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 35 with value: -0.0010803393728928605.\n", + "[I 2024-07-03 14:48:58,418] Trial 54 finished with value: -0.0010803723579987025 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.10123180962907431, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 35 with value: -0.0010803393728928605.\n", + "[I 2024-07-03 14:48:58,455] Trial 55 finished with value: -0.001080969596032512 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.26565663774320425, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 35 with value: -0.0010803393728928605.\n", + "[I 2024-07-03 14:48:58,492] Trial 56 finished with value: -0.0010800333715082816 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.005637048678674678, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 56 with value: -0.0010800333715082816.\n", + "[I 2024-07-03 14:48:58,530] Trial 57 finished with value: -0.0010802574700236845 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.06902647427781451, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 56 with value: -0.0010800333715082816.\n", + "[I 2024-07-03 14:48:58,564] Trial 58 finished with value: -0.0010814994986419817 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.4076704953178294, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 56 with value: -0.0010800333715082816.\n", + "[I 2024-07-03 14:48:58,602] Trial 59 finished with value: -0.001080161136846237 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.04187106800188596, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 56 with value: -0.0010800333715082816.\n", + "[I 2024-07-03 14:48:58,639] Trial 60 finished with value: -0.0010800254136811547 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.003371853599610078, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 60 with value: -0.0010800254136811547.\n", + "[I 2024-07-03 14:48:58,675] Trial 61 finished with value: -0.0010801290036870739 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.032781796328385376, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 60 with value: -0.0010800254136811547.\n", + "[I 2024-07-03 14:48:58,710] Trial 62 finished with value: -0.001080037482216557 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.006806773659187283, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 60 with value: -0.0010800254136811547.\n", + "[I 2024-07-03 14:48:58,747] Trial 63 finished with value: -0.0010801015705851358 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.025009489814943348, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 60 with value: -0.0010800254136811547.\n", + "[I 2024-07-03 14:48:58,782] Trial 64 finished with value: -0.0010812122378841013 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.3311125627707556, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 60 with value: -0.0010800254136811547.\n", + "[I 2024-07-03 14:48:58,818] Trial 65 finished with value: -0.0010800531021304936 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.011249102380159387, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 60 with value: -0.0010800254136811547.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "[I 2024-07-02 14:26:13,291] Trial 66 finished with value: -0.00108004162698813 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.007985924302396141, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 60 with value: -0.0010800254136811547.\n", - "[I 2024-07-02 14:26:13,328] Trial 67 finished with value: -0.0010800223466649803 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.00249856291483601, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 67 with value: -0.0010800223466649803.\n", - "[I 2024-07-02 14:26:13,364] Trial 68 finished with value: -0.0010815197263834202 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.4130244908975993, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 67 with value: -0.0010800223466649803.\n", - "[I 2024-07-02 14:26:13,402] Trial 69 finished with value: -0.0010800257029027847 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.0034541978803366022, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 67 with value: -0.0010800223466649803.\n", - "[I 2024-07-02 14:26:13,439] Trial 70 finished with value: -0.0010810223438672223 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.27994943662091765, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 67 with value: -0.0010800223466649803.\n", - "[I 2024-07-02 14:26:13,475] Trial 71 finished with value: -0.0010800211339555509 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.0021532199144365088, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.0010800211339555509.\n", - "[I 2024-07-02 14:26:13,513] Trial 72 finished with value: -0.0010800296871141684 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.0045884092728113585, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.0010800211339555509.\n", - "[I 2024-07-02 14:26:13,548] Trial 73 finished with value: -0.0010800437739166451 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.008596600952859433, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.0010800211339555509.\n", - "[I 2024-07-02 14:26:13,587] Trial 74 finished with value: -0.0010809366267195716 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.2567049271070902, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.0010800211339555509.\n", - "[I 2024-07-02 14:26:13,627] Trial 75 finished with value: -0.001080725386603206 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.1990111983307052, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.0010800211339555509.\n", - "[I 2024-07-02 14:26:13,664] Trial 76 finished with value: -0.0010807368035830652 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.20214459724424078, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.0010800211339555509.\n", - "[I 2024-07-02 14:26:13,704] Trial 77 finished with value: -0.0010800236072155854 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.00285750520671645, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.0010800211339555509.\n", - "[I 2024-07-02 14:26:13,741] Trial 78 finished with value: -0.0010806223050773966 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.17064008990759916, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.0010800211339555509.\n", - "[I 2024-07-02 14:26:13,779] Trial 79 finished with value: -0.0010876516369772728 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.8725420109733135, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.0010800211339555509.\n", - "[I 2024-07-02 14:26:13,819] Trial 80 finished with value: -0.00108142358144501 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.387533542012365, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.0010800211339555509.\n", - "[I 2024-07-02 14:26:13,857] Trial 81 finished with value: -0.0010800248050489667 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.0031985656730512953, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.0010800211339555509.\n", - "[I 2024-07-02 14:26:13,894] Trial 82 finished with value: -0.001080022268085466 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.002476186542950981, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.0010800211339555509.\n", - "[I 2024-07-02 14:26:13,932] Trial 83 finished with value: -0.0010820922958715991 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.5626643670396761, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.0010800211339555509.\n", - "[I 2024-07-02 14:26:13,969] Trial 84 finished with value: -0.0010805094397523254 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.1394077979875128, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.0010800211339555509.\n", - "[I 2024-07-02 14:26:14,007] Trial 85 finished with value: -0.0010841993753324146 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.0858347526799794, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.0010800211339555509.\n", - "[I 2024-07-02 14:26:14,048] Trial 86 finished with value: -0.007899735988203994 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.03329943145150872, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 0.00025672309762227527, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.0010800211339555509.\n" + "[I 2024-07-03 14:48:58,854] Trial 66 finished with value: -0.00108004162698813 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.007985924302396141, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 60 with value: -0.0010800254136811547.\n", + "[I 2024-07-03 14:48:58,892] Trial 67 finished with value: -0.0010800223466649803 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.00249856291483601, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 67 with value: -0.0010800223466649803.\n", + "[I 2024-07-03 14:48:58,928] Trial 68 finished with value: -0.0010815197263834202 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.4130244908975993, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 67 with value: -0.0010800223466649803.\n", + "[I 2024-07-03 14:48:58,964] Trial 69 finished with value: -0.0010800257029027847 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.0034541978803366022, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 67 with value: -0.0010800223466649803.\n", + "[I 2024-07-03 14:48:59,000] Trial 70 finished with value: -0.0010810223438672223 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.27994943662091765, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 67 with value: -0.0010800223466649803.\n", + "[I 2024-07-03 14:48:59,035] Trial 71 finished with value: -0.0010800211339555509 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.0021532199144365088, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.0010800211339555509.\n", + "[I 2024-07-03 14:48:59,072] Trial 72 finished with value: -0.0010800296871141684 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.0045884092728113585, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.0010800211339555509.\n", + "[I 2024-07-03 14:48:59,108] Trial 73 finished with value: -0.0010800437739166451 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.008596600952859433, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.0010800211339555509.\n", + "[I 2024-07-03 14:48:59,147] Trial 74 finished with value: -0.0010809366267195716 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.2567049271070902, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.0010800211339555509.\n", + "[I 2024-07-03 14:48:59,184] Trial 75 finished with value: -0.001080725386603206 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.1990111983307052, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.0010800211339555509.\n", + "[I 2024-07-03 14:48:59,221] Trial 76 finished with value: -0.0010807368035830652 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.20214459724424078, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.0010800211339555509.\n", + "[I 2024-07-03 14:48:59,262] Trial 77 finished with value: -0.0010800236072155854 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.00285750520671645, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.0010800211339555509.\n", + "[I 2024-07-03 14:48:59,300] Trial 78 finished with value: -0.0010806223050773966 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.17064008990759916, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.0010800211339555509.\n", + "[I 2024-07-03 14:48:59,335] Trial 79 finished with value: -0.0010876516369772728 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.8725420109733135, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.0010800211339555509.\n", + "[I 2024-07-03 14:48:59,373] Trial 80 finished with value: -0.00108142358144501 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.387533542012365, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.0010800211339555509.\n", + "[I 2024-07-03 14:48:59,412] Trial 81 finished with value: -0.0010800248050489667 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.0031985656730512953, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.0010800211339555509.\n", + "[I 2024-07-03 14:48:59,449] Trial 82 finished with value: -0.001080022268085466 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.002476186542950981, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.0010800211339555509.\n", + "[I 2024-07-03 14:48:59,486] Trial 83 finished with value: -0.0010820922958715991 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.5626643670396761, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.0010800211339555509.\n", + "[I 2024-07-03 14:48:59,525] Trial 84 finished with value: -0.0010805094397523254 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.1394077979875128, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.0010800211339555509.\n", + "[I 2024-07-03 14:48:59,567] Trial 85 finished with value: -0.0010841993753324146 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.0858347526799794, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.0010800211339555509.\n", + "[I 2024-07-03 14:48:59,609] Trial 86 finished with value: -0.007899735988203994 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.03329943145150872, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 0.00025672309762227527, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.0010800211339555509.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "[I 2024-07-02 14:26:14,086] Trial 87 finished with value: -0.0010868762004637347 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.702026434077893, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.0010800211339555509.\n", - "[I 2024-07-02 14:26:14,124] Trial 88 finished with value: -0.001080400750193767 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.10916094511173127, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.0010800211339555509.\n", - "[I 2024-07-02 14:26:14,163] Trial 89 finished with value: -0.0010806791616300314 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.18630665884100353, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.0010800211339555509.\n", - "[I 2024-07-02 14:26:14,201] Trial 90 finished with value: -0.0010804028029753213 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.10973377642487026, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.0010800211339555509.\n", - "[I 2024-07-02 14:26:14,240] Trial 91 finished with value: -0.0010800812188506515 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.019235980282946118, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.0010800211339555509.\n", - "[I 2024-07-02 14:26:14,277] Trial 92 finished with value: -0.0010800299598580359 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.004666043957133775, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.0010800211339555509.\n", - "[I 2024-07-02 14:26:14,316] Trial 93 finished with value: -0.0010803843696362083 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.1045877457096882, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.0010800211339555509.\n", - "[I 2024-07-02 14:26:14,353] Trial 94 finished with value: -0.001080333048974234 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.09023455456986404, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.0010800211339555509.\n", - "[I 2024-07-02 14:26:14,394] Trial 95 finished with value: -0.008706109201510277 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.8200088368788958, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.0010800211339555509.\n", - "[I 2024-07-02 14:26:14,432] Trial 96 finished with value: -0.001080014645182176 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.00030502148265565063, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 96 with value: -0.001080014645182176.\n", - "[I 2024-07-02 14:26:14,473] Trial 97 finished with value: -0.0010807968027851892 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.21858260742423916, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 96 with value: -0.001080014645182176.\n", - "[I 2024-07-02 14:26:14,516] Trial 98 finished with value: -0.007907028395366658 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.024725853754515203, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 0.0011658455138452, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 96 with value: -0.001080014645182176.\n", - "[I 2024-07-02 14:26:14,553] Trial 99 finished with value: -0.0010803563024666294 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.0967427718847167, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 96 with value: -0.001080014645182176.\n" + "[I 2024-07-03 14:48:59,647] Trial 87 finished with value: -0.0010868762004637347 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.702026434077893, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.0010800211339555509.\n", + "[I 2024-07-03 14:48:59,685] Trial 88 finished with value: -0.001080400750193767 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.10916094511173127, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.0010800211339555509.\n", + "[I 2024-07-03 14:48:59,724] Trial 89 finished with value: -0.0010806791616300314 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.18630665884100353, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.0010800211339555509.\n", + "[I 2024-07-03 14:48:59,766] Trial 90 finished with value: -0.0010804028029753213 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.10973377642487026, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.0010800211339555509.\n", + "[I 2024-07-03 14:48:59,804] Trial 91 finished with value: -0.0010800812188506515 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.019235980282946118, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.0010800211339555509.\n", + "[I 2024-07-03 14:48:59,842] Trial 92 finished with value: -0.0010800299598580359 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.004666043957133775, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.0010800211339555509.\n", + "[I 2024-07-03 14:48:59,883] Trial 93 finished with value: -0.0010803843696362083 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.1045877457096882, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.0010800211339555509.\n", + "[I 2024-07-03 14:48:59,923] Trial 94 finished with value: -0.001080333048974234 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.09023455456986404, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.0010800211339555509.\n", + "[I 2024-07-03 14:48:59,963] Trial 95 finished with value: -0.008706109201510277 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.8200088368788958, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 71 with value: -0.0010800211339555509.\n", + "[I 2024-07-03 14:49:00,002] Trial 96 finished with value: -0.001080014645182176 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.00030502148265565063, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 96 with value: -0.001080014645182176.\n", + "[I 2024-07-03 14:49:00,042] Trial 97 finished with value: -0.0010807968027851892 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.21858260742423916, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 96 with value: -0.001080014645182176.\n", + "[I 2024-07-03 14:49:00,085] Trial 98 finished with value: -0.007907028395366658 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.024725853754515203, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 0.0011658455138452, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 96 with value: -0.001080014645182176.\n", + "[I 2024-07-03 14:49:00,126] Trial 99 finished with value: -0.0010803563024666294 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.0967427718847167, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 96 with value: -0.001080014645182176.\n" ] } ], @@ -5343,7 +5325,7 @@ }, { "cell_type": "code", - "execution_count": 81, + "execution_count": 165, "metadata": {}, "outputs": [ { @@ -5370,21 +5352,21 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "In comparison to log scaled models trained without the PRF transform, log-transformed models trained with PTR functions will always output the probabilistic class membership likelihoods from the PTR function: " + "Similar to log scaled models trained without the PRF transform, log-transformed models trained with PTR functions will reverse the probabilistic class membership likelihoods from the PTR function, and then further log reverse transform the log scaling applied to the original data: " ] }, { "cell_type": "code", - "execution_count": 82, + "execution_count": 175, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([False])" + "array([1219.10660868])" ] }, - "execution_count": 82, + "execution_count": 175, "metadata": {}, "output_type": "execute_result" } @@ -5405,7 +5387,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "This is intended behaviour since 1.) the PTR is only inteded to alter model outputs so that predictions opterate on a probabilstic scale, and 2.) the PTR transform is a lossy transformation anyway (values at the extremes of the probability transformation scale are intentionally clipped and cannot be reversed). Hence, reverse transform the of PTR is not possible at inference time." + "This allows " ] }, { @@ -5433,7 +5415,7 @@ }, { "cell_type": "code", - "execution_count": 83, + "execution_count": 82, "metadata": { "scrolled": true }, @@ -5442,18 +5424,18 @@ "name": "stderr", "output_type": "stream", "text": [ - "[I 2024-07-02 14:26:17,282] A new study created in memory with name: covariate_example\n", - "[I 2024-07-02 14:26:17,323] A new study created in memory with name: study_name_0\n", - "[I 2024-07-02 14:26:17,422] Trial 0 finished with value: -5186.767663956718 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 6, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 5, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 0 with value: -5186.767663956718.\n", - "[I 2024-07-02 14:26:17,522] Trial 1 finished with value: -4679.740824270968 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 7, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 6, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 1 with value: -4679.740824270968.\n", - "[I 2024-07-02 14:26:17,575] Trial 2 finished with value: -4890.6705099499995 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 5.141096648805748, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 2.4893466963980463e-08, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 1 with value: -4679.740824270968.\n", - "[I 2024-07-02 14:26:17,628] Trial 3 finished with value: -3803.9324375833753 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 5, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 3 with value: -3803.9324375833753.\n", - "[I 2024-07-02 14:26:17,667] Trial 4 finished with value: -3135.6497388676926 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 3, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 4 with value: -3135.6497388676926.\n", - "[I 2024-07-02 14:26:17,722] Trial 5 finished with value: -551.2518812859375 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.7896547008552977, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 5 with value: -551.2518812859375.\n", - "[I 2024-07-02 14:26:17,778] Trial 6 finished with value: -4309.124112370974 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.6574750183038587, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 5 with value: -551.2518812859375.\n", - "[I 2024-07-02 14:26:17,818] Trial 7 finished with value: -362.30159424580074 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.3974313630683448, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 7 with value: -362.30159424580074.\n", - "[I 2024-07-02 14:26:17,897] Trial 8 finished with value: -4357.02827013125 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 28, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 8, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 7 with value: -362.30159424580074.\n", - "[I 2024-07-02 14:26:17,963] Trial 9 finished with value: -386.1437929337522 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.2391884918766034, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 7 with value: -362.30159424580074.\n" + "[I 2024-07-02 17:10:49,185] A new study created in memory with name: covariate_example\n", + "[I 2024-07-02 17:10:49,226] A new study created in memory with name: study_name_0\n", + "[I 2024-07-02 17:10:49,349] Trial 0 finished with value: -5186.767663956718 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 6, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 5, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 0 with value: -5186.767663956718.\n", + "[I 2024-07-02 17:10:49,448] Trial 1 finished with value: -4679.740824270968 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 7, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 6, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 1 with value: -4679.740824270968.\n", + "[I 2024-07-02 17:10:49,504] Trial 2 finished with value: -4890.6705099499995 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 5.141096648805748, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 2.4893466963980463e-08, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 1 with value: -4679.740824270968.\n", + "[I 2024-07-02 17:10:49,556] Trial 3 finished with value: -3803.9324375833753 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 5, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 3 with value: -3803.9324375833753.\n", + "[I 2024-07-02 17:10:49,586] Trial 4 finished with value: -3135.6497388676926 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 3, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 4 with value: -3135.6497388676926.\n", + "[I 2024-07-02 17:10:49,640] Trial 5 finished with value: -551.2518812859375 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.7896547008552977, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 5 with value: -551.2518812859375.\n", + "[I 2024-07-02 17:10:49,689] Trial 6 finished with value: -4309.124112370974 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.6574750183038587, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 5 with value: -551.2518812859375.\n", + "[I 2024-07-02 17:10:49,732] Trial 7 finished with value: -362.30159424580074 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.3974313630683448, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 7 with value: -362.30159424580074.\n", + "[I 2024-07-02 17:10:49,822] Trial 8 finished with value: -4357.02827013125 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 28, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 8, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 7 with value: -362.30159424580074.\n", + "[I 2024-07-02 17:10:49,890] Trial 9 finished with value: -386.1437929337522 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.2391884918766034, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 7 with value: -362.30159424580074.\n" ] } ], @@ -5502,7 +5484,7 @@ }, { "cell_type": "code", - "execution_count": 84, + "execution_count": 83, "metadata": {}, "outputs": [ { @@ -5511,7 +5493,7 @@ "array([52.45281013, 52.45281013])" ] }, - "execution_count": 84, + "execution_count": 83, "metadata": {}, "output_type": "execute_result" } @@ -5557,7 +5539,7 @@ }, { "cell_type": "code", - "execution_count": 85, + "execution_count": 84, "metadata": { "scrolled": true }, @@ -5566,22 +5548,22 @@ "name": "stderr", "output_type": "stream", "text": [ - "[I 2024-07-02 14:26:18,237] A new study created in memory with name: vector_aux_example\n", - "[I 2024-07-02 14:26:18,278] A new study created in memory with name: study_name_0\n", - "[I 2024-07-02 14:26:18,353] Trial 0 finished with value: -2200.6817959410578 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.011994365911634164, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 0 with value: -2200.6817959410578.\n", - "[I 2024-07-02 14:26:18,396] Trial 1 finished with value: -2200.95660880078 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.029071783512897825, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 0 with value: -2200.6817959410578.\n", - "[I 2024-07-02 14:26:18,454] Trial 2 finished with value: -5798.564494725643 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.022631709120790048, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 6.2198637677605415, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 0 with value: -2200.6817959410578.\n", - "[I 2024-07-02 14:26:18,499] Trial 3 finished with value: -972.2899178898048 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.8916194399474267, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 3 with value: -972.2899178898048.\n", - "[I 2024-07-02 14:26:18,556] Trial 4 finished with value: -647.3336440433073 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.5914093983615214, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 4 with value: -647.3336440433073.\n", - "[I 2024-07-02 14:26:18,614] Trial 5 finished with value: -653.3036472748931 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.6201811079699818, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 4 with value: -647.3336440433073.\n", - "[I 2024-07-02 14:26:18,657] Trial 6 finished with value: -3807.8035919667395 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 4, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 4 with value: -647.3336440433073.\n", - "/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.901e+01, tolerance: 1.892e+01\n", - " model = cd_fast.enet_coordinate_descent(\n", + "[I 2024-07-02 17:10:50,166] A new study created in memory with name: vector_aux_example\n", + "[I 2024-07-02 17:10:50,207] A new study created in memory with name: study_name_0\n", + "[I 2024-07-02 17:10:50,282] Trial 0 finished with value: -2200.6817959410578 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.011994365911634164, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 0 with value: -2200.6817959410578.\n", + "[I 2024-07-02 17:10:50,337] Trial 1 finished with value: -2200.95660880078 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 0.029071783512897825, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. Best is trial 0 with value: -2200.6817959410578.\n", + "[I 2024-07-02 17:10:50,397] Trial 2 finished with value: -5798.564494725643 and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.022631709120790048, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 6.2198637677605415, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 0 with value: -2200.6817959410578.\n", + "[I 2024-07-02 17:10:50,441] Trial 3 finished with value: -972.2899178898048 and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.8916194399474267, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 3 with value: -972.2899178898048.\n", + "[I 2024-07-02 17:10:50,500] Trial 4 finished with value: -647.3336440433073 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.5914093983615214, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 4 with value: -647.3336440433073.\n", + "[I 2024-07-02 17:10:50,552] Trial 5 finished with value: -653.3036472748931 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.6201811079699818, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 4 with value: -647.3336440433073.\n", + "[I 2024-07-02 17:10:50,584] Trial 6 finished with value: -3807.8035919667395 and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 4, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 4 with value: -647.3336440433073.\n", "/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.986e+01, tolerance: 1.914e+01\n", " model = cd_fast.enet_coordinate_descent(\n", - "[I 2024-07-02 14:26:18,752] Trial 7 finished with value: -5019.459500770764 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.1376436589359351, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 4 with value: -647.3336440433073.\n", - "[I 2024-07-02 14:26:18,836] Trial 8 finished with value: -2756.4017711284796 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 25, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 6, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 4 with value: -647.3336440433073.\n", - "[I 2024-07-02 14:26:18,893] Trial 9 finished with value: -771.797115414836 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.74340620175102, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 4 with value: -647.3336440433073.\n" + "/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.901e+01, tolerance: 1.892e+01\n", + " model = cd_fast.enet_coordinate_descent(\n", + "[I 2024-07-02 17:10:50,664] Trial 7 finished with value: -5019.459500770764 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.1376436589359351, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. Best is trial 4 with value: -647.3336440433073.\n", + "[I 2024-07-02 17:10:50,748] Trial 8 finished with value: -2756.4017711284796 and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 25, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 6, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 4 with value: -647.3336440433073.\n", + "[I 2024-07-02 17:10:50,794] Trial 9 finished with value: -771.797115414836 and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.74340620175102, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. Best is trial 4 with value: -647.3336440433073.\n" ] } ], @@ -5636,7 +5618,7 @@ }, { "cell_type": "code", - "execution_count": 86, + "execution_count": 85, "metadata": {}, "outputs": [ { @@ -5652,7 +5634,7 @@ " (40, 512))" ] }, - "execution_count": 86, + "execution_count": 85, "metadata": {}, "output_type": "execute_result" } @@ -5672,7 +5654,7 @@ }, { "cell_type": "code", - "execution_count": 87, + "execution_count": 86, "metadata": {}, "outputs": [ { @@ -5683,7 +5665,7 @@ " 237.80585907, 346.48565041])" ] }, - "execution_count": 87, + "execution_count": 86, "metadata": {}, "output_type": "execute_result" } @@ -5712,14 +5694,18 @@ }, { "cell_type": "code", - "execution_count": 88, + "execution_count": 87, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "head: ../tests/data/peptide/toxinpred3/train.csv: No such file or directory\r\n" + "Peptide,Class,Smiles\r\n", + "MDLITITWASVMVAFTFSLSLVVWGRSGL,0,N[C@@H](CCSC)C(=O)N[C@@H](CC(=O)O)C(=O)N[C@@H](CC(C)C)C(=O)N[C@@H]([C@H](CC)C)C(=O)N[C@@H]([C@@H](C)O)C(=O)N[C@@H]([C@H](CC)C)C(=O)N[C@@H]([C@@H](C)O)C(=O)N[C@@H](Cc1c[nH]c2ccccc12)C(=O)N[C@@H](C)C(=O)N[C@@H](CO)C(=O)N[C@@H](C(C)C)C(=O)N[C@@H](CCSC)C(=O)N[C@@H](C(C)C)C(=O)N[C@@H](C)C(=O)N[C@@H](Cc1ccccc1)C(=O)N[C@@H]([C@@H](C)O)C(=O)N[C@@H](Cc1ccccc1)C(=O)N[C@@H](CO)C(=O)N[C@@H](CC(C)C)C(=O)N[C@@H](CO)C(=O)N[C@@H](CC(C)C)C(=O)N[C@@H](C(C)C)C(=O)N[C@@H](C(C)C)C(=O)N[C@@H](Cc1c[nH]c2ccccc12)C(=O)NCC(=O)N[C@@H](CCCNC(=N)N)C(=O)N[C@@H](CO)C(=O)NCC(=O)N[C@@H](CC(C)C)C(=O)O\r\n", + "ARRGGVLNFGQFGLQALECGFVTNR,0,N[C@@H](C)C(=O)N[C@@H](CCCNC(=N)N)C(=O)N[C@@H](CCCNC(=N)N)C(=O)NCC(=O)NCC(=O)N[C@@H](C(C)C)C(=O)N[C@@H](CC(C)C)C(=O)N[C@@H](CC(=O)N)C(=O)N[C@@H](Cc1ccccc1)C(=O)NCC(=O)N[C@@H](CCC(=O)N)C(=O)N[C@@H](Cc1ccccc1)C(=O)NCC(=O)N[C@@H](CC(C)C)C(=O)N[C@@H](CCC(=O)N)C(=O)N[C@@H](C)C(=O)N[C@@H](CC(C)C)C(=O)N[C@@H](CCC(=O)O)C(=O)N[C@@H](CS)C(=O)NCC(=O)N[C@@H](Cc1ccccc1)C(=O)N[C@@H](C(C)C)C(=O)N[C@@H]([C@@H](C)O)C(=O)N[C@@H](CC(=O)N)C(=O)N[C@@H](CCCNC(=N)N)C(=O)O\r\n", + "GWCGDPGATCGKLRLYCCSGACDCYTKTCKDKSSA,1,NCC(=O)N[C@@H](Cc1c[nH]c2ccccc12)C(=O)N[C@@H](CS)C(=O)NCC(=O)N[C@@H](CC(=O)O)C(=O)N1[C@@H](CCC1)C(=O)NCC(=O)N[C@@H](C)C(=O)N[C@@H]([C@@H](C)O)C(=O)N[C@@H](CS)C(=O)NCC(=O)N[C@@H](CCCCN)C(=O)N[C@@H](CC(C)C)C(=O)N[C@@H](CCCNC(=N)N)C(=O)N[C@@H](CC(C)C)C(=O)N[C@@H](Cc1ccc(O)cc1)C(=O)N[C@@H](CS)C(=O)N[C@@H](CS)C(=O)N[C@@H](CO)C(=O)NCC(=O)N[C@@H](C)C(=O)N[C@@H](CS)C(=O)N[C@@H](CC(=O)O)C(=O)N[C@@H](CS)C(=O)N[C@@H](Cc1ccc(O)cc1)C(=O)N[C@@H]([C@@H](C)O)C(=O)N[C@@H](CCCCN)C(=O)N[C@@H]([C@@H](C)O)C(=O)N[C@@H](CS)C(=O)N[C@@H](CCCCN)C(=O)N[C@@H](CC(=O)O)C(=O)N[C@@H](CCCCN)C(=O)N[C@@H](CO)C(=O)N[C@@H](CO)C(=O)N[C@@H](C)C(=O)O\r\n", + "NGNLLGGLLRPVLGVVKGLTGGLGKK,1,N[C@@H](CC(=O)N)C(=O)NCC(=O)N[C@@H](CC(=O)N)C(=O)N[C@@H](CC(C)C)C(=O)N[C@@H](CC(C)C)C(=O)NCC(=O)NCC(=O)N[C@@H](CC(C)C)C(=O)N[C@@H](CC(C)C)C(=O)N[C@@H](CCCNC(=N)N)C(=O)N1[C@@H](CCC1)C(=O)N[C@@H](C(C)C)C(=O)N[C@@H](CC(C)C)C(=O)NCC(=O)N[C@@H](C(C)C)C(=O)N[C@@H](C(C)C)C(=O)N[C@@H](CCCCN)C(=O)NCC(=O)N[C@@H](CC(C)C)C(=O)N[C@@H]([C@@H](C)O)C(=O)NCC(=O)NCC(=O)N[C@@H](CC(C)C)C(=O)NCC(=O)N[C@@H](CCCCN)C(=O)N[C@@H](CCCCN)C(=O)O\r\n" ] } ], @@ -5736,16 +5722,16 @@ }, { "cell_type": "code", - "execution_count": 91, + "execution_count": 88, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "[I 2024-07-02 14:31:29,029] A new study created in memory with name: zscale_aux_example\n", - "[I 2024-07-02 14:31:29,089] A new study created in memory with name: study_name_0\n", - "[I 2024-07-02 14:31:54,458] Trial 0 finished with value: 0.8886986575836505 and parameters: {'algorithm_name': 'KNeighborsClassifier', 'KNeighborsClassifier_algorithm_hash': 'e51ca55089f389fc37a736adb2aa0e42', 'metric__e51ca55089f389fc37a736adb2aa0e42': , 'n_neighbors__e51ca55089f389fc37a736adb2aa0e42': 5, 'weights__e51ca55089f389fc37a736adb2aa0e42': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 128, \"returnRdkit\": false}}'}. Best is trial 0 with value: 0.8886986575836505.\n" + "[I 2024-07-02 17:10:55,776] A new study created in memory with name: zscale_aux_example\n", + "[I 2024-07-02 17:10:55,823] A new study created in memory with name: study_name_0\n", + "[I 2024-07-02 17:11:21,299] Trial 0 finished with value: 0.8886986575836505 and parameters: {'algorithm_name': 'KNeighborsClassifier', 'KNeighborsClassifier_algorithm_hash': 'e51ca55089f389fc37a736adb2aa0e42', 'metric__e51ca55089f389fc37a736adb2aa0e42': , 'n_neighbors__e51ca55089f389fc37a736adb2aa0e42': 5, 'weights__e51ca55089f389fc37a736adb2aa0e42': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 128, \"returnRdkit\": false}}'}. Best is trial 0 with value: 0.8886986575836505.\n" ] } ], @@ -5797,7 +5783,7 @@ }, { "cell_type": "code", - "execution_count": 92, + "execution_count": 89, "metadata": {}, "outputs": [ { @@ -5813,7 +5799,7 @@ " (7062, 5))" ] }, - "execution_count": 92, + "execution_count": 89, "metadata": {}, "output_type": "execute_result" } @@ -5833,7 +5819,7 @@ }, { "cell_type": "code", - "execution_count": 93, + "execution_count": 90, "metadata": {}, "outputs": [ { @@ -5842,7 +5828,7 @@ "array([0.2, 0. , 1. , ..., 0.2, 0.8, 0.2])" ] }, - "execution_count": 93, + "execution_count": 90, "metadata": {}, "output_type": "execute_result" } @@ -5860,7 +5846,7 @@ }, { "cell_type": "code", - "execution_count": 94, + "execution_count": 91, "metadata": {}, "outputs": [ { @@ -5912,7 +5898,7 @@ }, { "cell_type": "code", - "execution_count": 95, + "execution_count": 92, "metadata": { "scrolled": true }, @@ -5921,39 +5907,41 @@ "name": "stderr", "output_type": "stream", "text": [ - "[I 2024-07-02 14:32:36,740] A new study created in memory with name: example_multi-parameter_analysis\n", - "[I 2024-07-02 14:32:36,779] A new study created in memory with name: study_name_0\n", - "[I 2024-07-02 14:32:37,080] Trial 0 finished with values: [-1.4008740644240856, 0.9876203329634794] and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 6, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 5, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. \n", - "[I 2024-07-02 14:32:37,331] Trial 1 finished with values: [-1.3561484909673425, 0.9875061220991906] and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 7, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 6, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. \n", - "[I 2024-07-02 14:32:37,472] Trial 2 finished with values: [-2.7856521165563053, 0.21863029956806662] and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 5.141096648805748, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 2.4893466963980463e-08, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. \n", - "[I 2024-07-02 14:32:37,525] Trial 3 finished with values: [-0.9125905675311808, 0.7861693342190089] and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 5, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. \n", - "[I 2024-07-02 14:32:37,603] Trial 4 finished with values: [-0.5238765412750027, 0.2789424384877304] and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 3, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. \n", - "[I 2024-07-02 14:32:37,657] Trial 5 finished with values: [-0.5348363849100434, 0.5741725628917808] and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.7896547008552977, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. \n", - "[I 2024-07-02 14:32:37,746] Trial 6 finished with values: [-2.0072511048320134, 0.2786318125997387] and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.6574750183038587, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. \n", - "[I 2024-07-02 14:32:37,786] Trial 7 finished with values: [-0.9625764609276656, 0.27575381401822424] and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.3974313630683448, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. \n", - "[I 2024-07-02 14:32:38,036] Trial 8 finished with values: [-1.1114006274062536, 0.7647766019001522] and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 28, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 8, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. \n", - "[I 2024-07-02 14:32:38,080] Trial 9 finished with values: [-0.7801680863916906, 0.2725738454485389] and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.2391884918766034, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. \n", - "[I 2024-07-02 14:32:38,121] Trial 10 finished with values: [-2.785652116470164, 0.21863029955530786] and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.00044396482429275296, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 2.3831436879125245e-10, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. \n", - "[I 2024-07-02 14:32:38,152] Trial 11 finished with values: [-2.785651973436432, 0.21863032832257323] and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.00028965395242758657, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 2.99928292425642e-07, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. \n", - "[I 2024-07-02 14:32:38,180] Trial 12 finished with values: [-0.6101359993004856, 0.3011280543457062] and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 4, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. \n", - "[I 2024-07-02 14:32:38,209] Trial 13 finished with values: [-0.5361950698070447, 0.23560786523195643] and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 2, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. \n", - "[I 2024-07-02 14:32:38,274] Trial 14 finished with values: [-0.5356113574175657, 0.5769721187181905] and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.4060379177903557, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. \n", - "[I 2024-07-02 14:32:38,439] Trial 15 finished with values: [-0.543430366921729, 0.514747412346662] and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 20, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 8, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. \n", - "[I 2024-07-02 14:32:38,479] Trial 16 finished with values: [-2.0072511048320134, 0.2786318125997387] and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.344271094811757, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. \n", - "[I 2024-07-02 14:32:38,508] Trial 17 finished with values: [-0.5194661889628072, 0.40146744515282495] and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.670604991178476, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. \n", - "[I 2024-07-02 14:32:38,659] Trial 18 finished with values: [-0.659749443628722, 0.6659085938841998] and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 22, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 6, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. \n", - "[I 2024-07-02 14:32:38,876] Trial 19 finished with values: [-1.1068495306229729, 0.24457822094737378] and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.5158832554303112, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. \n" + "[I 2024-07-02 17:12:03,348] A new study created in memory with name: example_multi-parameter_analysis\n", + "[I 2024-07-02 17:12:03,385] A new study created in memory with name: study_name_0\n", + "[I 2024-07-02 17:12:03,502] Trial 0 finished with values: [-1.4008740644240856, 0.9876203329634794] and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 6, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 5, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. \n", + "[I 2024-07-02 17:12:03,604] Trial 1 finished with values: [-1.3561484909673425, 0.9875061220991905] and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 7, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 6, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. \n", + "[I 2024-07-02 17:12:03,658] Trial 2 finished with values: [-2.7856521165563053, 0.21863029956806662] and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 5.141096648805748, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 2.4893466963980463e-08, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. \n", + "[I 2024-07-02 17:12:03,713] Trial 3 finished with values: [-0.9125905675311808, 0.7861693342190089] and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 5, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. \n", + "[I 2024-07-02 17:12:03,741] Trial 4 finished with values: [-0.5238765412750027, 0.2789424384877304] and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 3, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. \n", + "[I 2024-07-02 17:12:03,793] Trial 5 finished with values: [-0.5348363849100434, 0.5741725628917808] and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.7896547008552977, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. \n", + "[I 2024-07-02 17:12:03,836] Trial 6 finished with values: [-2.0072511048320134, 0.2786318125997387] and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.6574750183038587, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. \n", + "[I 2024-07-02 17:12:03,877] Trial 7 finished with values: [-0.9625764609276656, 0.27575381401822424] and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.3974313630683448, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. \n", + "[I 2024-07-02 17:12:03,957] Trial 8 finished with values: [-1.1114006274062536, 0.7647766019001522] and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 28, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 8, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. \n", + "[I 2024-07-02 17:12:03,987] Trial 9 finished with values: [-0.7801680863916906, 0.2725738454485389] and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.2391884918766034, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. \n", + "[I 2024-07-02 17:12:04,026] Trial 10 finished with values: [-2.785652116470164, 0.21863029955530786] and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.00044396482429275296, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 2.3831436879125245e-10, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. \n", + "[I 2024-07-02 17:12:04,055] Trial 11 finished with values: [-2.785651973436432, 0.21863032832257323] and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.00028965395242758657, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 2.99928292425642e-07, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. \n", + "[I 2024-07-02 17:12:04,084] Trial 12 finished with values: [-0.6101359993004856, 0.3011280543457062] and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 4, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. \n", + "[I 2024-07-02 17:12:04,114] Trial 13 finished with values: [-0.5361950698070447, 0.23560786523195643] and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 2, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. \n", + "[I 2024-07-02 17:12:04,142] Trial 14 finished with values: [-0.5356113574175657, 0.5769721187181905] and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.4060379177903557, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. \n", + "[I 2024-07-02 17:12:04,210] Trial 15 finished with values: [-0.543430366921729, 0.514747412346662] and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 20, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 8, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. \n", + "[I 2024-07-02 17:12:04,249] Trial 16 finished with values: [-2.0072511048320134, 0.2786318125997387] and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.344271094811757, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. \n", + "[I 2024-07-02 17:12:04,291] Trial 17 finished with values: [-0.5194661889628072, 0.40146744515282495] and parameters: {'algorithm_name': 'Ridge', 'Ridge_algorithm_hash': 'cfa1990d5153c8812982f034d788d7ee', 'alpha__cfa1990d5153c8812982f034d788d7ee': 1.670604991178476, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. \n", + "[I 2024-07-02 17:12:04,356] Trial 18 finished with values: [-0.659749443628722, 0.6659085938841998] and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 22, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 6, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. \n", + "[I 2024-07-02 17:12:04,386] Trial 19 finished with values: [-1.1068495306229729, 0.24457822094737378] and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 0.5158832554303112, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. \n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "[I 2024-07-02 14:32:38,918] Trial 20 finished with values: [-0.8604898820838102, 0.7086875504668667] and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 4, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. \n", - "[I 2024-07-02 14:32:38,949] Trial 21 finished with values: [-0.5919869916997383, 0.2367498627927979] and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.0009327650919528738, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 6.062479210472502, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. \n", - "[I 2024-07-02 14:32:38,977] Trial 22 pruned. Duplicate parameter set\n", - "[I 2024-07-02 14:32:39,009] Trial 23 finished with values: [-1.2497762395862362, 0.10124660026536195] and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.1366172066709432, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. \n", - "[I 2024-07-02 14:32:39,151] Trial 24 finished with values: [-1.1114006274062536, 0.7647766019001522] and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 26, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 8, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. \n" + "[I 2024-07-02 17:12:04,414] Trial 20 finished with values: [-0.8604898820838102, 0.7086875504668667] and parameters: {'algorithm_name': 'PLSRegression', 'PLSRegression_algorithm_hash': '9f2f76e479633c0bf18cf2912fed9eda', 'n_components__9f2f76e479633c0bf18cf2912fed9eda': 4, 'descriptor': '{\"name\": \"MACCS_keys\", \"parameters\": {}}'}. \n", + "[I 2024-07-02 17:12:04,444] Trial 21 finished with values: [-0.5919869916997383, 0.2367498627927979] and parameters: {'algorithm_name': 'SVR', 'SVR_algorithm_hash': 'ea7ccc7ef4a9329af0d4e39eb6184933', 'gamma__ea7ccc7ef4a9329af0d4e39eb6184933': 0.0009327650919528738, 'C__ea7ccc7ef4a9329af0d4e39eb6184933': 6.062479210472502, 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. \n", + "[I 2024-07-02 17:12:04,461] Trial 22 pruned. Duplicate parameter set\n", + "[I 2024-07-02 17:12:04,491] Trial 23 finished with values: [-1.2497762395862362, 0.10124660026536195] and parameters: {'algorithm_name': 'Lasso', 'Lasso_algorithm_hash': '5457f609662e44f04dcc9423066d2f58', 'alpha__5457f609662e44f04dcc9423066d2f58': 1.1366172066709432, 'descriptor': '{\"name\": \"ECFP_counts\", \"parameters\": {\"radius\": 3, \"useFeatures\": true, \"nBits\": 2048}}'}. \n", + "[I 2024-07-02 17:12:04,572] Trial 24 finished with values: [-1.1114006274062536, 0.7647766019001522] and parameters: {'algorithm_name': 'RandomForestRegressor', 'RandomForestRegressor_algorithm_hash': 'f1ac01e1bba332215ccbd0c29c9ac3c3', 'max_depth__f1ac01e1bba332215ccbd0c29c9ac3c3': 26, 'n_estimators__f1ac01e1bba332215ccbd0c29c9ac3c3': 8, 'max_features__f1ac01e1bba332215ccbd0c29c9ac3c3': , 'descriptor': '{\"name\": \"ECFP\", \"parameters\": {\"radius\": 3, \"nBits\": 2048, \"returnRdkit\": false}}'}. \n", + "[I 2024-07-02 17:12:04,625] A new study created in memory with name: study_name_1\n", + "INFO:root:Enqueued ChemProp manual trial with sensible defaults: {'activation__668a7428ff5cdb271b01c0925e8fea45': 'ReLU', 'aggregation__668a7428ff5cdb271b01c0925e8fea45': 'mean', 'aggregation_norm__668a7428ff5cdb271b01c0925e8fea45': 100, 'batch_size__668a7428ff5cdb271b01c0925e8fea45': 50, 'depth__668a7428ff5cdb271b01c0925e8fea45': 3, 'dropout__668a7428ff5cdb271b01c0925e8fea45': 0.0, 'features_generator__668a7428ff5cdb271b01c0925e8fea45': 'none', 'ffn_hidden_size__668a7428ff5cdb271b01c0925e8fea45': 300, 'ffn_num_layers__668a7428ff5cdb271b01c0925e8fea45': 2, 'final_lr_ratio_exp__668a7428ff5cdb271b01c0925e8fea45': -4, 'hidden_size__668a7428ff5cdb271b01c0925e8fea45': 300, 'init_lr_ratio_exp__668a7428ff5cdb271b01c0925e8fea45': -4, 'max_lr_exp__668a7428ff5cdb271b01c0925e8fea45': -3, 'warmup_epochs_ratio__668a7428ff5cdb271b01c0925e8fea45': 0.1, 'algorithm_name': 'ChemPropRegressor', 'ChemPropRegressor_algorithm_hash': '668a7428ff5cdb271b01c0925e8fea45'}\n" ] }, { @@ -5967,16 +5955,8 @@ "name": "stderr", "output_type": "stream", "text": [ - "[I 2024-07-02 14:32:39,205] A new study created in memory with name: study_name_1\n", - "INFO:root:Enqueued ChemProp manual trial with sensible defaults: {'activation__668a7428ff5cdb271b01c0925e8fea45': 'ReLU', 'aggregation__668a7428ff5cdb271b01c0925e8fea45': 'mean', 'aggregation_norm__668a7428ff5cdb271b01c0925e8fea45': 100, 'batch_size__668a7428ff5cdb271b01c0925e8fea45': 50, 'depth__668a7428ff5cdb271b01c0925e8fea45': 3, 'dropout__668a7428ff5cdb271b01c0925e8fea45': 0.0, 'features_generator__668a7428ff5cdb271b01c0925e8fea45': 'none', 'ffn_hidden_size__668a7428ff5cdb271b01c0925e8fea45': 300, 'ffn_num_layers__668a7428ff5cdb271b01c0925e8fea45': 2, 'final_lr_ratio_exp__668a7428ff5cdb271b01c0925e8fea45': -4, 'hidden_size__668a7428ff5cdb271b01c0925e8fea45': 300, 'init_lr_ratio_exp__668a7428ff5cdb271b01c0925e8fea45': -4, 'max_lr_exp__668a7428ff5cdb271b01c0925e8fea45': -3, 'warmup_epochs_ratio__668a7428ff5cdb271b01c0925e8fea45': 0.1, 'algorithm_name': 'ChemPropRegressor', 'ChemPropRegressor_algorithm_hash': '668a7428ff5cdb271b01c0925e8fea45'}\n", - "/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/joblib/memory.py:577: JobLibCollisionWarning: Possible name collisions between functions 'calculate_from_smi' (/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/optunaz/descriptors.py:-1) and 'calculate_from_smi' (/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/optunaz/descriptors.py:669)\n", - " return self._cached_call(args, kwargs, shelving=False)[0]\n", - "/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/joblib/memory.py:577: JobLibCollisionWarning: Possible name collisions between functions 'calculate_from_smi' (/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/optunaz/descriptors.py:-1) and 'calculate_from_smi' (/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/optunaz/descriptors.py:669)\n", - " return self._cached_call(args, kwargs, shelving=False)[0]\n", - "/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/joblib/memory.py:577: JobLibCollisionWarning: Possible name collisions between functions 'calculate_from_smi' (/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/optunaz/descriptors.py:-1) and 'calculate_from_smi' (/Users/kljk345/PycharmProjects/Public_Qptuna/D/QSARtuna/venv/lib/python3.10/site-packages/optunaz/descriptors.py:669)\n", - " return self._cached_call(args, kwargs, shelving=False)[0]\n", - "[I 2024-07-02 14:33:47,802] Trial 0 finished with values: [-2.0621601907738047, 0.2749020946925899] and parameters: {'algorithm_name': 'ChemPropRegressor', 'ChemPropRegressor_algorithm_hash': '668a7428ff5cdb271b01c0925e8fea45', 'activation__668a7428ff5cdb271b01c0925e8fea45': , 'aggregation__668a7428ff5cdb271b01c0925e8fea45': , 'aggregation_norm__668a7428ff5cdb271b01c0925e8fea45': 100.0, 'batch_size__668a7428ff5cdb271b01c0925e8fea45': 50.0, 'depth__668a7428ff5cdb271b01c0925e8fea45': 3.0, 'dropout__668a7428ff5cdb271b01c0925e8fea45': 0.0, 'ensemble_size__668a7428ff5cdb271b01c0925e8fea45': 1, 'epochs__668a7428ff5cdb271b01c0925e8fea45': 5, 'features_generator__668a7428ff5cdb271b01c0925e8fea45': , 'ffn_hidden_size__668a7428ff5cdb271b01c0925e8fea45': 300.0, 'ffn_num_layers__668a7428ff5cdb271b01c0925e8fea45': 2.0, 'final_lr_ratio_exp__668a7428ff5cdb271b01c0925e8fea45': -4, 'hidden_size__668a7428ff5cdb271b01c0925e8fea45': 300.0, 'init_lr_ratio_exp__668a7428ff5cdb271b01c0925e8fea45': -4, 'max_lr_exp__668a7428ff5cdb271b01c0925e8fea45': -3, 'warmup_epochs_ratio__668a7428ff5cdb271b01c0925e8fea45': 0.1, 'descriptor': '{\"name\": \"SmilesFromFile\", \"parameters\": {}}'}. \n", - "[I 2024-07-02 14:34:59,830] Trial 1 finished with values: [-2.0621601907738047, 0.2749020946925899] and parameters: {'algorithm_name': 'ChemPropRegressor', 'ChemPropRegressor_algorithm_hash': '668a7428ff5cdb271b01c0925e8fea45', 'activation__668a7428ff5cdb271b01c0925e8fea45': , 'aggregation__668a7428ff5cdb271b01c0925e8fea45': , 'aggregation_norm__668a7428ff5cdb271b01c0925e8fea45': 100.0, 'batch_size__668a7428ff5cdb271b01c0925e8fea45': 45.0, 'depth__668a7428ff5cdb271b01c0925e8fea45': 3.0, 'dropout__668a7428ff5cdb271b01c0925e8fea45': 0.0, 'ensemble_size__668a7428ff5cdb271b01c0925e8fea45': 1, 'epochs__668a7428ff5cdb271b01c0925e8fea45': 5, 'features_generator__668a7428ff5cdb271b01c0925e8fea45': , 'ffn_hidden_size__668a7428ff5cdb271b01c0925e8fea45': 300.0, 'ffn_num_layers__668a7428ff5cdb271b01c0925e8fea45': 2.0, 'final_lr_ratio_exp__668a7428ff5cdb271b01c0925e8fea45': -4, 'hidden_size__668a7428ff5cdb271b01c0925e8fea45': 300.0, 'init_lr_ratio_exp__668a7428ff5cdb271b01c0925e8fea45': -4, 'max_lr_exp__668a7428ff5cdb271b01c0925e8fea45': -3, 'warmup_epochs_ratio__668a7428ff5cdb271b01c0925e8fea45': 0.1, 'descriptor': '{\"name\": \"SmilesFromFile\", \"parameters\": {}}'}. \n" + "[I 2024-07-02 17:13:12,240] Trial 0 finished with values: [-2.0621601907738047, 0.2749020946925899] and parameters: {'algorithm_name': 'ChemPropRegressor', 'ChemPropRegressor_algorithm_hash': '668a7428ff5cdb271b01c0925e8fea45', 'activation__668a7428ff5cdb271b01c0925e8fea45': , 'aggregation__668a7428ff5cdb271b01c0925e8fea45': , 'aggregation_norm__668a7428ff5cdb271b01c0925e8fea45': 100.0, 'batch_size__668a7428ff5cdb271b01c0925e8fea45': 50.0, 'depth__668a7428ff5cdb271b01c0925e8fea45': 3.0, 'dropout__668a7428ff5cdb271b01c0925e8fea45': 0.0, 'ensemble_size__668a7428ff5cdb271b01c0925e8fea45': 1, 'epochs__668a7428ff5cdb271b01c0925e8fea45': 5, 'features_generator__668a7428ff5cdb271b01c0925e8fea45': , 'ffn_hidden_size__668a7428ff5cdb271b01c0925e8fea45': 300.0, 'ffn_num_layers__668a7428ff5cdb271b01c0925e8fea45': 2.0, 'final_lr_ratio_exp__668a7428ff5cdb271b01c0925e8fea45': -4, 'hidden_size__668a7428ff5cdb271b01c0925e8fea45': 300.0, 'init_lr_ratio_exp__668a7428ff5cdb271b01c0925e8fea45': -4, 'max_lr_exp__668a7428ff5cdb271b01c0925e8fea45': -3, 'warmup_epochs_ratio__668a7428ff5cdb271b01c0925e8fea45': 0.1, 'descriptor': '{\"name\": \"SmilesFromFile\", \"parameters\": {}}'}. \n", + "[I 2024-07-02 17:14:17,087] Trial 1 finished with values: [-2.0621601907738047, 0.2749020946925899] and parameters: {'algorithm_name': 'ChemPropRegressor', 'ChemPropRegressor_algorithm_hash': '668a7428ff5cdb271b01c0925e8fea45', 'activation__668a7428ff5cdb271b01c0925e8fea45': , 'aggregation__668a7428ff5cdb271b01c0925e8fea45': , 'aggregation_norm__668a7428ff5cdb271b01c0925e8fea45': 100.0, 'batch_size__668a7428ff5cdb271b01c0925e8fea45': 45.0, 'depth__668a7428ff5cdb271b01c0925e8fea45': 3.0, 'dropout__668a7428ff5cdb271b01c0925e8fea45': 0.0, 'ensemble_size__668a7428ff5cdb271b01c0925e8fea45': 1, 'epochs__668a7428ff5cdb271b01c0925e8fea45': 5, 'features_generator__668a7428ff5cdb271b01c0925e8fea45': , 'ffn_hidden_size__668a7428ff5cdb271b01c0925e8fea45': 300.0, 'ffn_num_layers__668a7428ff5cdb271b01c0925e8fea45': 2.0, 'final_lr_ratio_exp__668a7428ff5cdb271b01c0925e8fea45': -4, 'hidden_size__668a7428ff5cdb271b01c0925e8fea45': 300.0, 'init_lr_ratio_exp__668a7428ff5cdb271b01c0925e8fea45': -4, 'max_lr_exp__668a7428ff5cdb271b01c0925e8fea45': -3, 'warmup_epochs_ratio__668a7428ff5cdb271b01c0925e8fea45': 0.1, 'descriptor': '{\"name\": \"SmilesFromFile\", \"parameters\": {}}'}. \n" ] } ], @@ -6029,7 +6009,7 @@ }, { "cell_type": "code", - "execution_count": 96, + "execution_count": 93, "metadata": { "scrolled": false }, @@ -6040,7 +6020,7 @@ "Text(0, 0.5, 'Standard Deviation across folds')" ] }, - "execution_count": 96, + "execution_count": 93, "metadata": {}, "output_type": "execute_result" }, @@ -6091,7 +6071,7 @@ }, { "cell_type": "code", - "execution_count": 97, + "execution_count": 94, "metadata": {}, "outputs": [ { @@ -6208,26 +6188,26 @@ "mode": "markers", "showlegend": false, "text": [ - "{
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