diff --git a/cases/credit_scoring/credit_scoring_problem.py b/cases/credit_scoring/credit_scoring_problem.py index 028029bd72..21111e3059 100644 --- a/cases/credit_scoring/credit_scoring_problem.py +++ b/cases/credit_scoring/credit_scoring_problem.py @@ -1,6 +1,7 @@ import logging import os import random +from pathlib import Path import numpy as np from sklearn.metrics import roc_auc_score as roc_auc @@ -39,7 +40,8 @@ def run_credit_scoring_problem(train_file_path, test_file_path, metrics = automl.get_metrics() if automl.history: - print(automl.history.get_leaderboard()) + lb = automl.history.get_leaderboard() + Path(os.path.join('D:/', "leaderboard.csv")).write_text(lb) if visualization: automl.current_pipeline.show() @@ -68,6 +70,6 @@ def get_scoring_data(): full_path_train, full_path_test = get_scoring_data() run_credit_scoring_problem(full_path_train, full_path_test, - timeout=5, + timeout=2, visualization=True, with_tuning=True) diff --git a/cases/metocean_forecasting_problem.py b/cases/metocean_forecasting_problem.py index b1832d489c..f2e768d8db 100644 --- a/cases/metocean_forecasting_problem.py +++ b/cases/metocean_forecasting_problem.py @@ -45,6 +45,7 @@ def run_metocean_forecasting_problem(train_file_path, test_file_path, fedot = Fedot(problem='ts_forecasting', task_params=TsForecastingParams(forecast_length=forecast_length), timeout=timeout, logging_level=logging.DEBUG) + fedot.current_pipeline pipeline = fedot.fit(features=historical_data, target=ssh_history) fedot.forecast(historical_data) diff --git a/docs/source/benchmarks/amlb_res.csv b/docs/source/benchmarks/amlb_res.csv index 07fb820c62..569f98431d 100644 --- a/docs/source/benchmarks/amlb_res.csv +++ b/docs/source/benchmarks/amlb_res.csv @@ -1,32 +1,32 @@ Dataset name,Metric name,AutoGluon,FEDOT,H2O,LAMA -APSFailure,auc,0.99,0.991,,0.992 -Amazon_employee_access,auc,0.857,0.865,,0.879 +APSFailure,auc,0.99,0.991,0.992,0.992 +Amazon_employee_access,auc,0.857,0.865,0.873,0.879 Australian,auc,0.94,0.939,0.939,0.945 -Covertype,neg_logloss,-0.071,-0.117,, -Fashion-MNIST,neg_logloss,-0.329,-0.373,,-0.248 -Jannis,neg_logloss,-0.728,-0.737,,-0.664 -KDDCup09_appetency,auc,0.804,0.822,,0.85 +Covertype,neg_logloss,-0.071,-0.117,-0.265, +Fashion-MNIST,neg_logloss,-0.329,-0.373,-0.38,-0.248 +Jannis,neg_logloss,-0.728,-0.737,-0.691,-0.664 +KDDCup09_appetency,auc,0.804,0.822,0.829,0.85 MiniBooNE,auc,0.982,0.981,,0.988 -Shuttle,neg_logloss,-0.001,-0.001,,-0.001 -Volkert,neg_logloss,-0.917,-1.097,,-0.806 -adult,auc,0.91,0.925,,0.932 -bank-marketing,auc,0.931,0.935,,0.94 +Shuttle,neg_logloss,-0.001,-0.001,-0.0,-0.001 +Volkert,neg_logloss,-0.917,-1.097,-0.976,-0.806 +adult,auc,0.91,0.925,0.931,0.932 +bank-marketing,auc,0.931,0.935,0.939,0.94 blood-transfusion,auc,0.69,0.759,0.765,0.75 car,neg_logloss,-0.117,-0.011,-0.004,-0.002 christine,auc,0.804,0.812,0.823,0.83 cnae-9,neg_logloss,-0.332,-0.211,-0.175,-0.156 -connect-4,neg_logloss,-0.502,-0.456,,-0.337 +connect-4,neg_logloss,-0.502,-0.456,-0.338,-0.337 credit-g,auc,0.795,0.778,0.789,0.796 dilbert,neg_logloss,-0.148,-0.159,-0.05,-0.033 fabert,neg_logloss,-0.788,-0.895,-0.752,-0.766 guillermo,auc,0.9,0.891,,0.926 jasmine,auc,0.883,0.888,0.887,0.88 -jungle chess,neg_logloss,-0.431,-0.193,,-0.149 +jungle chess,neg_logloss,-0.431,-0.193,-0.24,-0.149 kc1,auc,0.822,0.843,,0.831 kr-vs-kp,auc,0.999,1.0,,1.0 mfeat-factors,neg_logloss,-0.161,-0.094,,-0.082 -nomao,auc,0.995,0.994,,0.997 -numerai28_6,auc,0.517,0.529,,0.531 +nomao,auc,0.995,0.994,0.996,0.997 +numerai28_6,auc,0.517,0.529,0.531,0.531 phoneme,auc,0.965,0.965,,0.965 segment,neg_logloss,-0.094,-0.062,,-0.061 sylvine,auc,0.985,0.988,,0.988 diff --git a/docs/source/benchmarks/amlb_res.html b/docs/source/benchmarks/amlb_res.html new file mode 100644 index 0000000000..d0c976963d --- /dev/null +++ b/docs/source/benchmarks/amlb_res.html @@ -0,0 +1,719 @@ + +
+ | ++ framework + | ++ AutoGluon + | ++ FEDOT + | ++ H2O + | ++ LAMA + | +
---|---|---|---|---|---|
+ Dataset name + | ++ Metric name + | ++ | ++ | ++ | ++ | +
+ APSFailure + | ++ auc + | ++ 0.990 + | ++ 0.991 + | ++ 0.992 + | ++ 0.992 + | +
+ Amazon_employee_access + | ++ auc + | ++ 0.857 + | ++ 0.865 + | ++ 0.873 + | ++ 0.879 + | +
+ Australian + | ++ auc + | ++ 0.940 + | ++ 0.939 + | ++ 0.938 + | ++ 0.945 + | +
+ Covertype + | ++ neg_logloss + | ++ -0.071 + | ++ -0.117 + | ++ -0.265 + | ++ nan + | +
+ Fashion-MNIST + | ++ neg_logloss + | ++ -0.329 + | ++ -0.373 + | ++ -0.380 + | ++ -0.248 + | +
+ Jannis + | ++ neg_logloss + | ++ -0.728 + | ++ -0.737 + | ++ -0.691 + | ++ -0.664 + | +
+ KDDCup09_appetency + | ++ auc + | ++ 0.804 + | ++ 0.822 + | ++ 0.829 + | ++ 0.850 + | +
+ MiniBooNE + | ++ auc + | ++ 0.982 + | ++ 0.981 + | ++ nan + | ++ 0.988 + | +
+ Shuttle + | ++ neg_logloss + | ++ -0.001 + | ++ -0.001 + | ++ -0.000 + | ++ -0.001 + | +
+ Volkert + | ++ neg_logloss + | ++ -0.917 + | ++ -1.097 + | ++ -0.976 + | ++ -0.806 + | +
+ adult + | ++ auc + | ++ 0.910 + | ++ 0.925 + | ++ 0.931 + | ++ 0.932 + | +
+ bank-marketing + | ++ auc + | ++ 0.931 + | ++ 0.935 + | ++ 0.939 + | ++ 0.940 + | +
+ blood-transfusion + | ++ auc + | ++ 0.690 + | ++ 0.759 + | ++ 0.754 + | ++ 0.750 + | +
+ car + | ++ neg_logloss + | ++ -0.117 + | ++ -0.011 + | ++ -0.003 + | ++ -0.002 + | +
+ christine + | ++ auc + | ++ 0.804 + | ++ 0.812 + | ++ 0.815 + | ++ 0.830 + | +
+ cnae-9 + | ++ neg_logloss + | ++ -0.332 + | ++ -0.211 + | ++ -0.262 + | ++ -0.156 + | +
+ connect-4 + | ++ neg_logloss + | ++ -0.502 + | ++ -0.456 + | ++ -0.338 + | ++ -0.337 + | +
+ credit-g + | ++ auc + | ++ 0.795 + | ++ 0.778 + | ++ 0.798 + | ++ 0.796 + | +
+ dilbert + | ++ neg_logloss + | ++ -0.148 + | ++ -0.159 + | ++ -0.103 + | ++ -0.033 + | +
+ fabert + | ++ neg_logloss + | ++ -0.788 + | ++ -0.895 + | ++ -0.792 + | ++ -0.766 + | +
+ guillermo + | ++ auc + | ++ 0.900 + | ++ 0.891 + | ++ nan + | ++ 0.926 + | +
+ jasmine + | ++ auc + | ++ 0.883 + | ++ 0.888 + | ++ 0.888 + | ++ 0.880 + | +
+ jungle chess + | ++ neg_logloss + | ++ -0.431 + | ++ -0.193 + | ++ -0.240 + | ++ -0.149 + | +
+ kc1 + | ++ auc + | ++ 0.822 + | ++ 0.843 + | ++ nan + | ++ 0.831 + | +
+ kr-vs-kp + | ++ auc + | ++ 0.999 + | ++ 1.000 + | ++ 1.000 + | ++ 1.000 + | +
+ mfeat-factors + | ++ neg_logloss + | ++ -0.161 + | ++ -0.094 + | ++ -0.093 + | ++ -0.082 + | +
+ nomao + | ++ auc + | ++ 0.995 + | ++ 0.994 + | ++ 0.996 + | ++ 0.997 + | +
+ numerai28_6 + | ++ auc + | ++ 0.517 + | ++ 0.529 + | ++ 0.531 + | ++ 0.531 + | +
+ phoneme + | ++ auc + | ++ 0.965 + | ++ 0.965 + | ++ 0.968 + | ++ 0.965 + | +
+ segment + | ++ neg_logloss + | ++ -0.094 + | ++ -0.062 + | ++ -0.060 + | ++ -0.061 + | +
+ sylvine + | ++ auc + | ++ 0.985 + | ++ 0.988 + | ++ 0.989 + | ++ 0.988 + | +
+ vehicle + | ++ neg_logloss + | ++ -0.515 + | ++ -0.354 + | ++ -0.331 + | ++ -0.404 + | +