From efd942063455f1c148c3c691d8100d726b09ac90 Mon Sep 17 00:00:00 2001 From: Benjamin Bengfort Date: Sat, 19 Feb 2022 16:27:18 -0600 Subject: [PATCH 01/27] version bump v1.4 --- docs/changelog.rst | 51 ++++++++++++++++++++++++++ docs/governance/minutes/2021-01-13.rst | 42 ++++++--------------- tests/__init__.py | 2 +- yellowbrick/version.py | 6 +-- 4 files changed, 67 insertions(+), 34 deletions(-) diff --git a/docs/changelog.rst b/docs/changelog.rst index f706c00ad..501851e99 100644 --- a/docs/changelog.rst +++ b/docs/changelog.rst @@ -3,6 +3,57 @@ Changelog ========= +Version 1.4 +----------- + +* Tag: v1.4_ +* Deployed Saturday, February 19, 2022 +* Current Contributors: Benjamin Bengfort, Larry Gray, Rebecca Bilbro, @pkaf, Antonio Carlos Falcão Petri, Aarni Koskela, Prema Roman, Nathan Danielsen, Eleni Markou, Patrick Deziel, Adam Morris, Hung-Tien Huang, @charlesincharge + +Major + - Upgrade dependencies to support sklearn v1.0, Numpy 1.20+, Scipy 1.6, nltk 3.6.7, and Matplotlib 3.4.1 + - Implement new ``set_params`` and ``get_params`` on ModelVisualizers to ensure wrapped estimator is being correctly accessed via the new ``Estimator`` methods. + - Fix the test dependencies to prevent variability in CI (must periodically review dependencies to ensure we're testing what our users are experiencing). + - Change ``model`` param to ``estimator`` param to ensure that Visualizer arguments match their property names so that inspect works with get and set params and other scikit-learn utility functions. + +Minor + - Improved argmax handling in ``DiscriminationThreshold`` Visualizer + - Improved error handling in ``FeatureImportances`` Visualizer + - Gave option to remove colorer from ``ClassificationReport`` Visualizer + - Allowed for more flexible ``KElbow`` colors that use default palette by default + - Import scikit-learn private API _safe_indexing without error. + - Remove any calls to ``set_params`` in Visualizer ``__init__`` methods. + - Modify test fixtures and baseline images to accommodate new sklearn implementation + - Temporarily set the numpy dependency to be less than 1.20 because this is causing Pickle issues with joblib and umap + - Add ``shuffle=True`` argument to any CV class that uses a random seed. + - Set our CI matrix to Python and Miniconda 3.7 and 3.8 + +Bugs + - Fixed score label display in ``PredictionError`` Visualizer + - Fixed axes limit in ``PredictionError`` Visualizer + - Fixed ``KElbowVisualizer`` to handle null cluster encounters + - Fixed broken url to pytest fixtures + - Fixed ``random_state`` to be in sync with ``PCA`` transformer + - Fixed the inability to place ``FeatureCorrelations`` into subplots + - Fixed hanging printing impacting model visualizers + - Fixed error handling when decision function models encounter binary data + - Fixed missing code in README.md + +Infrastructure/Housekeeping/documentation + - Updated status badges for build result and code coverage + - Removed deprecated pytest-runner from testing + - Replaced Travis with Github Actions + - Changed our master branch to the main branch + - Created a release issue template + - Updated our CI to test Python 3.8 and 3.9 + - Managed test warnings + - Adds .gitattributes to fix handle white space changes + - Updated to use ``add_css_file`` for documentation because of deprecation of ``add_stylesheet`` + - Added a Sphinx build to GitHub Actions for ensuring that the docs build correctly + - Switched to a YB-specific data lake for datasets storage + +.. _v1.4: https://github.com/DistrictDataLabs/yellowbrick/releases/tag/v1.4 + Version 1.3.post1 ----------------- diff --git a/docs/governance/minutes/2021-01-13.rst b/docs/governance/minutes/2021-01-13.rst index 72516fe21..6111475f3 100644 --- a/docs/governance/minutes/2021-01-13.rst +++ b/docs/governance/minutes/2021-01-13.rst @@ -33,32 +33,22 @@ A broad overview of the topics for discussion in the order they were presented: Fall 2021 Semester Retrospective -------------------------------- -- Rebecca gave a talk "Thrifty Machine Learning" and was highlighted by PyLadies Berlin (and they highlighted Yellowbrick!) -on the 19th day of their 2020 [Advent Calendar Tweet Series](https://twitter.com/PyLadiesBer/status/1340321653839040513?s=20) - +- Rebecca gave a talk "Thrifty Machine Learning" and was highlighted by PyLadies Berlin (and they highlighted Yellowbrick!) on the 19th day of their 2020 [Advent Calendar Tweet Series](https://twitter.com/PyLadiesBer/status/1340321653839040513?s=20) - The entire team showed resiliency in the face of COVID-19 and being unable to meet in person by moving the project forward. - - We closed 19 issues and had 13 open issues. We had 3 open PRs. - -- Approved PRs (Contributors): - - Rebecca and Ben approved 3, Larry approved 1, Michael Garod and @arkvei approved 1 each. - +- Approved PRs (Contributors): Rebecca and Ben approved 3, Larry approved 1, Michael Garod and @arkvei approved 1 each. - Summary of Fall PR Topics- Complete changelog since v1.2 can be found [here:](https://github.com/DistrictDataLabs/yellowbrick/pull/1110) Main PR Topic Areas included: Yellowbrick1.2 release, Dependence Management issue [PR 1111](https://github.com/DistrictDataLabs/yellowbrick/pull/1111), update to Dispersion plot color and title, update to kneed algorithm, added FAQ on wrapper, third party estimator wrapper, adjustment to top_n param for feature importances. We also addressed a public/private API bug in [PR 1124](https://github.com/DistrictDataLabs/yellowbrick/pull/1124) Board Shout-outs ------------------------- -- Ben for his constant contributions to the project +- Ben for his constant contributions to the project - Rebecca for her rapid response to issues all Semester long. - - Adam for meeting the 1000 mark for Social Media Followers (@scikit-yb) - - Strong user interactions with the library: 2300 downloads per day and 60,000 per month! - - Robust package evidenced by low number of issues being opened over the Semester. - - Kristen, Larry, Edwin for "surviving the 2nd worst year" 2021 Advisory Board @@ -90,26 +80,18 @@ Since we have 9 advisors for this year, the dues totaled $30.17 per advisor alth Thank you to everyone for paying your dues on time! - In our January meeting, it was noted that if someone had something they’d like to add to the budget, we could put it to a vote the next semester. - - We will likely have a little extra since a large portion of the stickers cost was intended for PyCon stickers. - - The Treasurer, Edwin, provided an update that we needed to approve the budget. - - The group discussed and decided removing stickers from the 2021 budget due to COVID-19 and the lack of in person events (previously cost $133.50 and paid for by Rebecca. Thank you Rebecca!) - - The board decided to reallocate this sticker money towards buying small thank you gifts for developers who make significant contributions to Yellowbrick. - -- *Ben suggested that we add an additional item to the budget*Add cost for gifts to Reviews and Contributors* - such as coffee and a YB branded T-Shirt. This is to show the YB spirit of Gratitude. A budget of $750. We have two potential sponsors (detailed below) +- *Ben suggested that we add an additional item to the budget* Add cost for gifts to Reviews and Contributors* - such as coffee and a YB branded T-Shirt. This is to show the YB spirit of Gratitude. A budget of $750. We have two potential sponsors (detailed below) - Proposal 2 lines of budget: Board gifts 8 of us - Create budget $320 External Funding, External Gifts paid for partly by board dues/external funding: - 1. We voted to split these 2 lines items into separate voted - Unanimously Support - - 2. Vote only external funding for Board gifts - Unanimously supported - - 3. Vote to remove Sticker budget and put back into budget for external gifts - Unanimously supported - - 4. Vote to remove Nathan from Board Roster + 1. We voted to split these 2 lines items into separate voted - Unanimously Support + 2. Vote only external funding for Board gifts - Unanimously supported + 3. Vote to remove Sticker budget and put back into budget for external gifts - Unanimously supported + 4. Vote to remove Nathan from Board Roster *2021 Annual Budget* ------------------------- @@ -149,17 +131,17 @@ Milestone planning: - We need to research PEP517 and how to implement “pip install -e .” See how python is now dealing with python packaging. Ideas for next Administrative Projects: - + 1.) Release a User Survey on Twitter - + 2.) Content Marketing through Twitter - + 3.) Prema to review backlog Member Topics -------------------- -- Kristen suggested exploring incorporating pip dependency resolver:In its January release (21.0), pip will use the new dependency resolver by default. The +- Kristen suggested exploring incorporating pip dependency resolver:In its January release (21.0), pip will use the new dependency resolver by default. The documentation gives a good overview of the new changes and guidance on how to respond to the new ResolutionImpossible error message. - Kristen recommended replacing the iris dataset with other datasets in ours documentation. - Changes to sklearn.utils for Sklearn Private/Public addressed in API [PR 1138] (https://github.com/DistrictDataLabs/yellowbrick/pull/1138) diff --git a/tests/__init__.py b/tests/__init__.py index f9b674e3f..f8f859ca3 100644 --- a/tests/__init__.py +++ b/tests/__init__.py @@ -27,7 +27,7 @@ ## Test Constants ########################################################################## -EXPECTED_VERSION = "1.3.post1" +EXPECTED_VERSION = "1.4" ########################################################################## diff --git a/yellowbrick/version.py b/yellowbrick/version.py index 9ee1ff671..114f9a0e2 100644 --- a/yellowbrick/version.py +++ b/yellowbrick/version.py @@ -19,11 +19,11 @@ __version_info__ = { "major": 1, - "minor": 3, + "minor": 4, "micro": 0, "releaselevel": "final", - "post": 1, - "serial": 21, + "post": 0, + "serial": 22, } ########################################################################## From 4be320d7e01a461f02fbd291a0b1fee4f3bfe6ee Mon Sep 17 00:00:00 2001 From: Larry Gray Date: Fri, 25 Feb 2022 15:56:01 -0700 Subject: [PATCH 02/27] Fixed is_fitted parameter not setting to given value (#1221) Set `super().init()` for the visualizer to include `is_fitted` --- yellowbrick/regressor/prediction_error.py | 8 +++++--- yellowbrick/regressor/residuals.py | 8 +++++--- 2 files changed, 10 insertions(+), 6 deletions(-) diff --git a/yellowbrick/regressor/prediction_error.py b/yellowbrick/regressor/prediction_error.py index 2922d0b70..0eae3e31e 100644 --- a/yellowbrick/regressor/prediction_error.py +++ b/yellowbrick/regressor/prediction_error.py @@ -121,11 +121,13 @@ def __init__( is_fitted="auto", **kwargs ): - # Whether or not to check if the model is already fitted - self.is_fitted = is_fitted # Initialize the visualizer - super(PredictionError, self).__init__(estimator, ax=ax, **kwargs) + super(PredictionError, self).__init__( + estimator, + is_fitted=is_fitted, + ax=ax, + **kwargs) # Visual arguments self.colors = { diff --git a/yellowbrick/regressor/residuals.py b/yellowbrick/regressor/residuals.py index d9336788f..58a8a7f1a 100644 --- a/yellowbrick/regressor/residuals.py +++ b/yellowbrick/regressor/residuals.py @@ -154,11 +154,13 @@ def __init__( is_fitted="auto", **kwargs ): - # Whether or not to check if the model is already fitted - self.is_fitted = is_fitted # Initialize the visualizer base - super(ResidualsPlot, self).__init__(estimator, ax=ax, **kwargs) + super(ResidualsPlot, self).__init__( + estimator, + ax=ax, + is_fitted=is_fitted, + **kwargs) # TODO: allow more scatter plot arguments for train and test points # See #475 (RE: ScatterPlotMixin) From 62423f4e57eab21f704cef08ae9a11deab45b9cc Mon Sep 17 00:00:00 2001 From: Larry Gray Date: Fri, 25 Feb 2022 19:20:49 -0700 Subject: [PATCH 03/27] Create tests for `is_fitted` parameter for ResidualsPlot and PredictionError (#1223) This PR adds tests to #1221. These tests assure us that the is_fitted param's state is maintained when the visualizer is instantiated. I have made the following changes: Added test for ResidualPlots Added test for PredictionError Removed unnecessary import so that flake8 passed --- tests/test_regressor/test_prediction_error.py | 12 ++++++++++++ tests/test_regressor/test_residuals.py | 10 ++++++++++ 2 files changed, 22 insertions(+) diff --git a/tests/test_regressor/test_prediction_error.py b/tests/test_regressor/test_prediction_error.py index 4c7c0a6e8..28e089c07 100644 --- a/tests/test_regressor/test_prediction_error.py +++ b/tests/test_regressor/test_prediction_error.py @@ -215,6 +215,18 @@ def test_alpha_param(self): assert "alpha" in scatter_kwargs assert scatter_kwargs["alpha"] == 0.7 + def test_is_fitted_param(self): + """ + Test that the user can supply an is_fitted param and it's state is maintained + """ + # Instantiate a sklearn regressor + model = Lasso(random_state=23, alpha=10) + # Instantiate a prediction error plot, provide custom alpha + visualizer = PredictionError(model, bestfit=False, identity=False, is_fitted=False) + + # Test param gets set correctly + assert visualizer.is_fitted == False + @pytest.mark.xfail( reason="""third test fails with AssertionError: Expected fit to be called once. Called 0 times.""" diff --git a/tests/test_regressor/test_residuals.py b/tests/test_regressor/test_residuals.py index 999cfd45d..d9c5b5bf9 100644 --- a/tests/test_regressor/test_residuals.py +++ b/tests/test_regressor/test_residuals.py @@ -314,6 +314,16 @@ def test_alpha_param(self, mock_sca): assert "alpha" in scatter_kwargs assert scatter_kwargs["alpha"] == 0.75 + def test_is_fitted_param(self): + """ + Test that the user can supply an is_fitted param and it's state is maintained + """ + # Instantiate a prediction error plot, provide custom is_fitted + visualizer = ResidualsPlot(Ridge(random_state=8893), is_fitted=False) + + # Test param gets set correctly + assert visualizer.is_fitted == False + @pytest.mark.xfail( reason="""third test fails with AssertionError: Expected fit to be called once. Called 0 times.""" From 6fb2e9b7e5b2998c6faa4dcca81a4b0f91bf29b4 Mon Sep 17 00:00:00 2001 From: Larry Gray Date: Sat, 26 Feb 2022 15:02:41 -0700 Subject: [PATCH 04/27] Remove try/except clauses from tests (#1224) This PR fixes issue #1218 which reported technical debt in which there were several try/except clauses within tests that needed to be removed. This was first identified by @rebeccabilbro in PR #1197 --- tests/test_cluster/test_icdm.py | 7 +- tests/test_cluster/test_silhouette.py | 107 +++++++++++-------------- tests/test_regressor/test_residuals.py | 5 +- 3 files changed, 52 insertions(+), 67 deletions(-) diff --git a/tests/test_cluster/test_icdm.py b/tests/test_cluster/test_icdm.py index fe5c5d2f6..9d94714d0 100644 --- a/tests/test_cluster/test_icdm.py +++ b/tests/test_cluster/test_icdm.py @@ -290,10 +290,9 @@ def test_no_legend_matplotlib_version(self, mock_toolkit): assert not inset_locator - try: - InterclusterDistance(KMeans(), legend=False) - except YellowbrickValueError as e: - self.fail(e) + + InterclusterDistance(KMeans(), legend=False) + @pytest.mark.xfail( reason="""third test fails with AssertionError: Expected fit diff --git a/tests/test_cluster/test_silhouette.py b/tests/test_cluster/test_silhouette.py index b47d84043..6f6615857 100644 --- a/tests/test_cluster/test_silhouette.py +++ b/tests/test_cluster/test_silhouette.py @@ -53,17 +53,16 @@ def test_integrated_kmeans_silhouette(self): n_samples=1000, n_features=12, centers=8, shuffle=False, random_state=0 ) - try: - fig = plt.figure() - ax = fig.add_subplot() + + fig = plt.figure() + ax = fig.add_subplot() - visualizer = SilhouetteVisualizer(KMeans(random_state=0), ax=ax) - visualizer.fit(X) - visualizer.finalize() + visualizer = SilhouetteVisualizer(KMeans(random_state=0), ax=ax) + visualizer.fit(X) + visualizer.finalize() - self.assert_images_similar(visualizer, remove_legend=True) - except Exception as e: - self.fail("error during silhouette: {}".format(e)) + self.assert_images_similar(visualizer, remove_legend=True) + @pytest.mark.xfail(sys.platform == "win32", reason="images not close on windows") def test_integrated_mini_batch_kmeans_silhouette(self): @@ -77,17 +76,15 @@ def test_integrated_mini_batch_kmeans_silhouette(self): n_samples=1000, n_features=12, centers=8, shuffle=False, random_state=0 ) - try: - fig = plt.figure() - ax = fig.add_subplot() + fig = plt.figure() + ax = fig.add_subplot() - visualizer = SilhouetteVisualizer(MiniBatchKMeans(random_state=0), ax=ax) - visualizer.fit(X) - visualizer.finalize() + visualizer = SilhouetteVisualizer(MiniBatchKMeans(random_state=0), ax=ax) + visualizer.fit(X) + visualizer.finalize() - self.assert_images_similar(visualizer, remove_legend=True) - except Exception as e: - self.fail("error during silhouette: {}".format(e)) + self.assert_images_similar(visualizer, remove_legend=True) + @pytest.mark.skip(reason="no negative silhouette example available yet") def test_negative_silhouette_score(self): @@ -106,19 +103,17 @@ def test_colormap_silhouette(self): n_samples=1000, n_features=12, centers=8, shuffle=False, random_state=0 ) - try: - fig = plt.figure() - ax = fig.add_subplot() + + fig = plt.figure() + ax = fig.add_subplot() - visualizer = SilhouetteVisualizer( - MiniBatchKMeans(random_state=0), ax=ax, colormap="gnuplot" - ) - visualizer.fit(X) - visualizer.finalize() + visualizer = SilhouetteVisualizer( + MiniBatchKMeans(random_state=0), ax=ax, colormap="gnuplot" + ) + visualizer.fit(X) + visualizer.finalize() - self.assert_images_similar(visualizer, remove_legend=True) - except Exception as e: - self.fail("error during silhouette: {}".format(e)) + self.assert_images_similar(visualizer, remove_legend=True) @pytest.mark.xfail(sys.platform == "win32", reason="images not close on windows") def test_colors_silhouette(self): @@ -131,22 +126,19 @@ def test_colors_silhouette(self): n_samples=1000, n_features=12, centers=8, shuffle=False, random_state=0 ) - try: - fig = plt.figure() - ax = fig.add_subplot() - - visualizer = SilhouetteVisualizer( - MiniBatchKMeans(random_state=0), - ax=ax, - colors=["red", "green", "blue", "indigo", "cyan", "lavender"], - ) - visualizer.fit(X) - visualizer.finalize() + fig = plt.figure() + ax = fig.add_subplot() - self.assert_images_similar(visualizer, remove_legend=True) - except Exception as e: - self.fail("error during silhouette: {}".format(e)) + visualizer = SilhouetteVisualizer( + MiniBatchKMeans(random_state=0), + ax=ax, + colors=["red", "green", "blue", "indigo", "cyan", "lavender"], + ) + visualizer.fit(X) + visualizer.finalize() + self.assert_images_similar(visualizer, remove_legend=True) + def test_colormap_as_colors_silhouette(self): """ Test no exceptions for modifying the colors in a silhouette visualizer @@ -157,23 +149,20 @@ def test_colormap_as_colors_silhouette(self): n_samples=1000, n_features=12, centers=8, shuffle=False, random_state=0 ) - try: - fig = plt.figure() - ax = fig.add_subplot() - - visualizer = SilhouetteVisualizer( - MiniBatchKMeans(random_state=0), ax=ax, colors="cool" - ) - visualizer.fit(X) - visualizer.finalize() - - tol = ( - 3.2 if sys.platform == "win32" else 0.01 - ) # Fails on AppVeyor with RMS 3.143 - self.assert_images_similar(visualizer, remove_legend=True, tol=tol) - except Exception as e: - self.fail("error during silhouette: {}".format(e)) + fig = plt.figure() + ax = fig.add_subplot() + visualizer = SilhouetteVisualizer( + MiniBatchKMeans(random_state=0), ax=ax, colors="cool" + ) + visualizer.fit(X) + visualizer.finalize() + + tol = ( + 3.2 if sys.platform == "win32" else 0.01 + ) # Fails on AppVeyor with RMS 3.143 + self.assert_images_similar(visualizer, remove_legend=True, tol=tol) + def test_quick_method(self): """ Test the quick method producing a valid visualization diff --git a/tests/test_regressor/test_residuals.py b/tests/test_regressor/test_residuals.py index d9c5b5bf9..16aea6fe7 100644 --- a/tests/test_regressor/test_residuals.py +++ b/tests/test_regressor/test_residuals.py @@ -173,10 +173,7 @@ def test_no_hist_matplotlib_version(self, mock_toolkit): assert not make_axes_locatable - try: - ResidualsPlot(LinearRegression(), hist=False) - except YellowbrickValueError as e: - self.fail(e) + ResidualsPlot(LinearRegression(), hist=False) @pytest.mark.xfail( IS_WINDOWS_OR_CONDA, From 092c0ca25187b3cde9f608a1f7bc6d8c2b998f96 Mon Sep 17 00:00:00 2001 From: charles Date: Sat, 16 Apr 2022 10:06:14 -0700 Subject: [PATCH 05/27] Random input feature dropping curve, model selection visualization [issue #1024] (#1206) * Create dropping_curve.py * Add imports * Add a stub DroppingCurve class Only has API documentation for now * Initialize the class with the __init__ variables * Rename from test set to validation set * Outline for the fit() method - need to figure out how to wrap the feature subsetting though * Adapt draw() method from learning_curve.py * Add finalize method * Add helper quick method for dropping_curve * Add random scoring method and also the start of a feature-dropping curve. Need to solidify the feature_sizes_ variable though Perhaps could use Percentile best, too * Convert feature_sizes_ to integers that we can use for plotting later * Fix some typos * Quick-script example for dropping_curve Seems to create a buch of empty figures - not sure why? * 2022 Maintenance (#1207) This PR switches to a YB-specific data lake for datasets storage and updates the prior nltk dependency that has a CVE. * FIXES 1086: Corrects legend issues other than R2 in PredictionError (#1212) * BUG: Corrects legend issues other than R2 in PredictionError This PR fixes issue #1086 Unfortunately, the generic .score() method in scikit-learn does not return the name of the scoring metrics used. The fix I have patched will return the correct label if and when the estimator has an attribute providing the name of the scoring metric otherwise it will fall back to the default value i.e. R2 Co-authored-by: Pkaf Co-authored-by: Gray * BUG: Fixes axes limit for PredictionError plot #1193 (#1208) Co-authored-by: Pkaf * Updates sklearn to v1.0.0 (#1217) * PrePredict Estimator (#1189) * BUG: Adds missing X and Y axes labels in ClassificationReport (#1210) Co-authored-by: Pkaf Co-authored-by: Larry Gray * version bump v1.4 * Fixed is_fitted parameter not setting to given value (#1221) Set `super().init()` for the visualizer to include `is_fitted` * Create tests for `is_fitted` parameter for ResidualsPlot and PredictionError (#1223) This PR adds tests to #1221. These tests assure us that the is_fitted param's state is maintained when the visualizer is instantiated. I have made the following changes: Added test for ResidualPlots Added test for PredictionError Removed unnecessary import so that flake8 passed * Remove try/except clauses from tests (#1224) This PR fixes issue #1218 which reported technical debt in which there were several try/except clauses within tests that needed to be removed. This was first identified by @rebeccabilbro in PR #1197 * Create tests * Modify dropping_curve.py to be compliant with Sklearn version bump The switch to Sklearn 1.0 lead to a deep copy error of ax within the sk_validation_curve call. @bbengfort and I fixed this by removing the input of self into the the score_func of SelectKBest and replaced it with a lambda function * Add Dropping Curve Test * Fix quick_method and classifier test * Address review comments to turn asserts to YellowbrickValueError, remove clustering from test, and rearrange parameters so ax=none is in the second position * Fix YellowbrickValueError Errors Co-authored-by: Benjamin Bengfort Co-authored-by: pkaf Co-authored-by: Pkaf Co-authored-by: Gray Co-authored-by: Lawrence Gray --- examples/cguan/dropping-curve.py | 48 +++ .../test_dropping_curve/test_classifier.png | Bin 0 -> 46048 bytes .../test_numpy_integration.png | Bin 0 -> 34994 bytes .../test_pandas_integration.png | Bin 0 -> 37786 bytes .../test_dropping_curve/test_quick_method.png | Bin 0 -> 33545 bytes .../test_dropping_curve/test_regression.png | Bin 0 -> 18136 bytes .../test_dropping_curve.py | 191 +++++++++ yellowbrick/model_selection/__init__.py | 1 + yellowbrick/model_selection/dropping_curve.py | 379 ++++++++++++++++++ 9 files changed, 619 insertions(+) create mode 100644 examples/cguan/dropping-curve.py create mode 100644 tests/baseline_images/test_model_selection/test_dropping_curve/test_classifier.png create mode 100644 tests/baseline_images/test_model_selection/test_dropping_curve/test_numpy_integration.png create mode 100644 tests/baseline_images/test_model_selection/test_dropping_curve/test_pandas_integration.png create mode 100644 tests/baseline_images/test_model_selection/test_dropping_curve/test_quick_method.png create mode 100644 tests/baseline_images/test_model_selection/test_dropping_curve/test_regression.png create mode 100644 tests/test_model_selection/test_dropping_curve.py create mode 100644 yellowbrick/model_selection/dropping_curve.py diff --git a/examples/cguan/dropping-curve.py b/examples/cguan/dropping-curve.py new file mode 100644 index 000000000..1d7982755 --- /dev/null +++ b/examples/cguan/dropping-curve.py @@ -0,0 +1,48 @@ +#!/usr/bin/env python +# coding: utf-8 + +# # Random feature dropping curve +# +# This notebook demonstrates the random feature dropping curve (also called a neuron dropping curve in neural decoding research). + +# In[1]: + + +import matplotlib.pyplot as plt +import numpy as np + +# Import scikit-learn utilities +from sklearn.pipeline import make_pipeline +from sklearn.ensemble import RandomForestClassifier +from sklearn.linear_model import LogisticRegression +from sklearn.naive_bayes import MultinomialNB +from sklearn.preprocessing import OneHotEncoder, LabelEncoder + +# Import all of the Yellowbrick classifiers +from yellowbrick.datasets import load_game +from yellowbrick.model_selection.dropping_curve import dropping_curve + + +def main(): + # Load Connect-4 game data + X, y = load_game() + + print(f'X.shape={X.shape}') + print(f'y.shape={y.shape}') + + X_enc = OneHotEncoder().fit_transform(X) + le = LabelEncoder() + y_enc = le.fit_transform(y) + + fig, ax = plt.subplots() + dropping_curve( + MultinomialNB(), + X_enc, + y_enc, + feature_sizes=np.linspace(0.05, 1, 20), + ax=ax, + ) + + +if __name__ == '__main__': + main() diff --git a/tests/baseline_images/test_model_selection/test_dropping_curve/test_classifier.png b/tests/baseline_images/test_model_selection/test_dropping_curve/test_classifier.png new file mode 100644 index 0000000000000000000000000000000000000000..e07f547ce8942ebf85ba5aa6ff9d1213051f9f7c GIT binary patch literal 46048 zcmeFYWmJ@5+cr9gf=Gj;lqj9j4HBZXG($-VICOW2v<%YHC0!0F-5@O>-Q6*C4Dj6` z&-1=}t^I%RAKwqxxJGB@y07bu<2;Vz3RY5(#(YBh1Ox(MzI!XF3<4ojfj|#vAEN;8 z6zJ_X126mzQko7b)*l_54eU%n@&*nz7S;|H=7v;GCU*Ab)>fRX+^nydsmvT4Z0rTt z*ew71A6TvJOxeZ_4(fp~L9=|Nd@x^9@A(?|n03G^)FQ7Y_=E_`h#H 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zCs}wwbnVGW&F{whw*= zgXjUR|8rXu$V7MO{$<^niy%2C|HHZ-rwNEb`^qs>(``@?hnX7G_7p5N`Tt zp+{U$Yjk>h43}hCFM`AwE~M{t$&zWGmq1PTFyih$5pCAsvVKdi!^Ko5SCQ4v!u{F{9E6M!4338}N= z=ruS+{#b*KkC;mG5?~U2e+v=*1PYKGs00K%j{}hO$9IT8ofOe*t|wo1lIEuTpS?w( z2nGC0BmiywWNiG?VkyCEf2VbrmRbam=rNC>P!8~3jmA1 z4&(lJ0I*pu7TM|E^Q0=6`OhEwct;9O5B=8>^Zz6M7|Pla5gSX4NUZSM4PY|}aa&CR JbIUC7zX9JABkuqJ literal 0 HcmV?d00001 diff --git a/tests/test_model_selection/test_dropping_curve.py b/tests/test_model_selection/test_dropping_curve.py new file mode 100644 index 000000000..0b2763f35 --- /dev/null +++ b/tests/test_model_selection/test_dropping_curve.py @@ -0,0 +1,191 @@ +# tests.test_model_selection.test_dropping_curve +# Tests for the DroppingCurve visualizer +# +# Author: Larry Gray +# Created: Fri Apr 15 06:25:05 2022 -0400 +# +# Copyright (C) 2018 The scikit-yb developers +# For license information, see LICENSE.txt +# +# ID: test_dropping_curve.py [c5355ee] lwgray@gmail.com $ + +""" +Tests for the DroppingCurve visualizer +""" + +########################################################################## +# Imports +########################################################################## + +import sys +import pytest +import numpy as np + +from unittest.mock import patch +from tests.base import VisualTestCase + +from sklearn.svm import SVC +from sklearn.naive_bayes import BernoulliNB, MultinomialNB +from sklearn.tree import DecisionTreeRegressor +from sklearn.preprocessing import OneHotEncoder +from sklearn.neighbors import KNeighborsClassifier +from sklearn.model_selection import ShuffleSplit, StratifiedKFold + +from yellowbrick.datasets import load_mushroom +from yellowbrick.exceptions import YellowbrickValueError +from yellowbrick.model_selection import DroppingCurve, dropping_curve + + +try: + import pandas as pd +except ImportError: + pd = None + + +########################################################################## +# Test Cases +########################################################################## + + +@pytest.mark.usefixtures("classification", "regression") +class TestDroppingCurve(VisualTestCase): + """ + Test the DroppingCurve visualizer + """ + + @patch.object(DroppingCurve, "draw") + def test_fit(self, mock_draw): + """ + Assert that fit returns self and creates expected properties + """ + X, y = self.classification + params = ( + "train_scores_", + "train_scores_mean_", + "train_scores_std_", + "valid_scores_", + "valid_scores_mean_", + "valid_scores_std_", + ) + + oz = DroppingCurve( + MultinomialNB(), + feature_sizes=np.linspace(0.05, 1, 20) + ) + + for param in params: + assert not hasattr(oz, param) + + assert oz.fit(X, y) is oz + mock_draw.assert_called_once() + + for param in params: + assert hasattr(oz, param) + + @pytest.mark.xfail(sys.platform == "win32", reason="images not close on windows") + def test_classifier(self): + """ + Test image closeness on a classification dataset with MultinomialNB + """ + X, y = self.classification + + cv = ShuffleSplit(3, random_state=288) + + oz = DroppingCurve( + KNeighborsClassifier(), + cv=cv, + feature_sizes=np.linspace(0.05, 1, 20), + random_state=42 + ) + + oz.fit(X, y) + oz.finalize() + + self.assert_images_similar(oz) + + def test_regression(self): + """ + Test image closeness on a regression dataset with a DecisionTree + """ + X, y = self.regression + + cv = ShuffleSplit(3, random_state=938) + param_range = np.arange(3, 10) + + oz = DroppingCurve( + DecisionTreeRegressor(random_state=23), + param_name="max_depth", + param_range=param_range, + cv=cv, + scoring="r2", + random_state=42 + ) + + oz.fit(X, y) + oz.finalize() + + self.assert_images_similar(oz, tol=12.0) + + @pytest.mark.xfail(sys.platform == "win32", reason="images not close on windows") + def test_quick_method(self): + """ + Test validation curve quick method with image closeness on SVC + """ + X, y = self.classification + + pr = np.logspace(-6, -1, 3) + cv = ShuffleSplit(n_splits=5, test_size=0.2, random_state=321) + viz = dropping_curve( + SVC(), X, y, logx=True, param_name="gamma", + param_range=pr, cv=cv, show=False, random_state=42 + ) + + self.assert_images_similar(viz) + + @pytest.mark.xfail(sys.platform == "win32", reason="images not close on windows") + @pytest.mark.skipif(pd is None, reason="test requires pandas") + def test_pandas_integration(self): + """ + Test on mushroom dataset with pandas DataFrame and Series and NB + """ + data = load_mushroom(return_dataset=True) + X, y = data.to_pandas() + + X = pd.get_dummies(X) + + assert isinstance(X, pd.DataFrame) + assert isinstance(y, pd.Series) + + cv = StratifiedKFold(n_splits=2, shuffle=True, random_state=11) + oz = DroppingCurve(MultinomialNB(), cv=cv, random_state=42) + oz.fit(X, y) + oz.finalize() + + self.assert_images_similar(oz) + + @pytest.mark.xfail(sys.platform == "win32", reason="images not close on windows") + def test_numpy_integration(self): + """ + Test on mushroom dataset with NumPy arrays + """ + data = load_mushroom(return_dataset=True) + X, y = data.to_numpy() + + X = OneHotEncoder().fit_transform(X).toarray() + + cv = StratifiedKFold(n_splits=2, shuffle=True, random_state=11) + pr = np.linspace(0.1, 3.0, 6) + oz = DroppingCurve(BernoulliNB(), cv=cv, + param_range=pr, param_name="alpha", + random_state=42) + oz.fit(X, y) + oz.finalize() + + self.assert_images_similar(oz) + + def test_bad_train_sizes(self): + """ + Test learning curve with bad input for feature size. + """ + with pytest.raises(YellowbrickValueError): + DroppingCurve(SVC(), param_name="gamma", feature_sizes=100) \ No newline at end of file diff --git a/yellowbrick/model_selection/__init__.py b/yellowbrick/model_selection/__init__.py index 06892aa4f..acf6dee88 100644 --- a/yellowbrick/model_selection/__init__.py +++ b/yellowbrick/model_selection/__init__.py @@ -17,6 +17,7 @@ from .learning_curve import LearningCurve, learning_curve from .validation_curve import ValidationCurve, validation_curve from .cross_validation import CVScores, cv_scores +from .dropping_curve import DroppingCurve, dropping_curve # RFECV and Feature Importances moved here as of YB v1.0 from .importances import FeatureImportances, feature_importances diff --git a/yellowbrick/model_selection/dropping_curve.py b/yellowbrick/model_selection/dropping_curve.py new file mode 100644 index 000000000..0fb441472 --- /dev/null +++ b/yellowbrick/model_selection/dropping_curve.py @@ -0,0 +1,379 @@ +# yellowbrick.model_selection.dropping_curve +# Implements a feature dropping curve visualization for model selection. +# +# Author: Charles Guan +# Created: Wed Dec 8 15:03:00 2021 -0800 + +""" +Implements a random-input-dropout curve visualization for model selection. +Another common name: neuron dropping curve (NDC), in neural decoding research +""" + +########################################################################## +## Imports +########################################################################## + +import numpy as np + +from yellowbrick.base import ModelVisualizer +from yellowbrick.style import resolve_colors +from yellowbrick.exceptions import YellowbrickValueError + +from sklearn.model_selection import validation_curve as sk_validation_curve +from sklearn.pipeline import make_pipeline +from sklearn.feature_selection import SelectKBest + + +# Default ticks for the model selection curve, relative number of features +DEFAULT_FEATURE_SIZES = np.linspace(0.1, 1.0, 5) + + +########################################################################## +# DroppingCurve visualizer +########################################################################## + + +class DroppingCurve(ModelVisualizer): + """ + Selects random subsets of features and estimates the training and + crossvalidation performance. Subset sizes are swept to visualize a + feature-dropping curve. + The visualization plots the score relative to each subset and shows + the number of (randomly selected) features needed to achieve a score. + The curve is often shaped like log(1+x). For example, see: + https://www.frontiersin.org/articles/10.3389/fnsys.2014.00102/full + Parameters + ---------- + estimator : a scikit-learn estimator + An object that implements ``fit`` and ``predict``, can be a + classifier, regressor, or clusterer so long as there is also a valid + associated scoring metric. + Note that the object is cloned for each validation. + feature_sizes: array-like, shape (n_values,) + default: ``np.linspace(0.1,1.0,5)`` + Relative or absolute numbers of input features that will be used to + generate the learning curve. If the dtype is float, it is regarded as + a fraction of the maximum number of features, otherwise it is + interpreted as absolute numbers of features. + groups : array-like, with shape (n_samples,) + Optional group labels for the samples used while splitting the dataset + into train/test sets. + ax : matplotlib.Axes object, optional + The axes object to plot the figure on. + logx : boolean, optional + If True, plots the x-axis with a logarithmic scale. + cv : int, cross-validation generator or an iterable, optional + Determines the cross-validation splitting strategy. + Possible inputs for cv are: + - None, to use the default 3-fold cross-validation, + - integer, to specify the number of folds. + - An object to be used as a cross-validation generator. + - An iterable yielding train/test splits. + see the scikit-learn + `cross-validation guide `_ + for more information on the possible strategies that can be used here. + scoring : string, callable or None, optional, default: None + A string or scorer callable object / function with signature + ``scorer(estimator, X, y)``. See scikit-learn model evaluation + documentation for names of possible metrics. + n_jobs : integer, optional + Number of jobs to run in parallel (default 1). + pre_dispatch : integer or string, optional + Number of predispatched jobs for parallel execution (default is + all). The option can reduce the allocated memory. The string can + be an expression like '2*n_jobs'. + random_state : int, RandomState instance or None, optional (default=None) + If int, random_state is the seed used by the random number generator; + If RandomState instance, random_state is the random number generator; + If None, the random number generator is the RandomState instance used + by `np.random`. Used to generate feature subsets. + kwargs : dict + Keyword arguments that are passed to the base class and may influence + the visualization as defined in other Visualizers. + Attributes + ---------- + feature_sizes_ : array, shape = (n_unique_ticks,), dtype int + Numbers of features that have been used to generate the + dropping curve. Note that the number of ticks might be less + than n_ticks because duplicate entries will be removed. + train_scores_ : array, shape (n_ticks, n_cv_folds) + Scores on training sets. + train_scores_mean_ : array, shape (n_ticks,) + Mean training data scores for each training split + train_scores_std_ : array, shape (n_ticks,) + Standard deviation of training data scores for each training split + valid_scores_ : array, shape (n_ticks, n_cv_folds) + Scores on validation set. + valid_scores_mean_ : array, shape (n_ticks,) + Mean scores for each validation split + valid_scores_std_ : array, shape (n_ticks,) + Standard deviation of scores for each validation split + Examples + -------- + >>> from yellowbrick.model_selection import DroppingCurve + >>> from sklearn.naive_bayes import GaussianNB + >>> model = DroppingCurve(GaussianNB()) + >>> model.fit(X, y) + >>> model.show() + Notes + ----- + This visualizer is based on sklearn.model_selection.validation_curve + """ + + def __init__( + self, + estimator, + ax=None, + feature_sizes=DEFAULT_FEATURE_SIZES, + groups=None, + logx=False, + cv=None, + scoring=None, + n_jobs=None, + pre_dispatch='all', + random_state=None, + **kwargs + ): + + # Initialize the model visualizer + super(DroppingCurve, self).__init__(estimator, ax=ax, **kwargs) + + # Validate the feature sizes + feature_sizes = np.asarray(feature_sizes) + if feature_sizes.ndim != 1: + raise YellowbrickValueError( + "must specify 1-D array of feature sizes, '{}' is not valid".format( + repr(feature_sizes) + ) + ) + + # Set the metric parameters to be used later + self.feature_sizes = feature_sizes + self.groups = groups + self.logx = logx + self.cv = cv + self.scoring = scoring + self.n_jobs = n_jobs + self.pre_dispatch = pre_dispatch + self.random_state = random_state + + def fit(self, X, y=None): + """ + Fits the feature dropping curve with the wrapped model to the specified data. + Draws training and cross-validation score curves and saves the scores to the + estimator. + Parameters + ---------- + X : array-like, shape (n_samples, n_features) + Input vector, where n_samples is the number of samples and + n_features is the number of features. + y : array-like, shape (n_samples) or (n_samples, n_features), optional + Target relative to X for classification or regression; + None for unsupervised learning. + """ + # Get feature_sizes in whole numbers + n_features = X.shape[-1] + if np.issubdtype(self.feature_sizes.dtype, np.integer): + if (self.feature_sizes <= 0).all() or (self.feature_sizes >= n_features).all(): + raise YellowbrickValueError('Expected feature sizes in [0, n_features]') + self.feature_sizes_ = self.feature_sizes + else: + if (self.feature_sizes <= 0.0).all() or (self.feature_sizes >= 1.0).all(): + raise YellowbrickValueError('Expected feature ratio in [0,1]') + self.feature_sizes_ = np.ceil(n_features * self.feature_sizes).astype(int) + + # The easiest way to prepend a random-dropout layer is to use + # SelectKBest with a random scoring function. + feature_dropping_pipeline = make_pipeline( + SelectKBest( + score_func=lambda X,y: np.random.default_rng(self.random_state).standard_normal(size=X.shape[-1]) + ), + self.estimator, + ) + + # arguments to pass to sk_validation_curve + skvc_kwargs = { + key: self.get_params()[key] + for key in ( + "groups", + "cv", + "scoring", + "n_jobs", + "pre_dispatch", + ) + } + + self.train_scores_, self.valid_scores_ = sk_validation_curve( + feature_dropping_pipeline, + X, + y, + param_name="selectkbest__k", + param_range=self.feature_sizes_, + **skvc_kwargs + ) + + # compute the mean and standard deviation of the training data + self.train_scores_mean_ = np.mean(self.train_scores_, axis=1) + self.train_scores_std_ = np.std(self.train_scores_, axis=1) + + # compute the mean and standard deviation of the validation data + self.valid_scores_mean_ = np.mean(self.valid_scores_, axis=1) + self.valid_scores_std_ = np.std(self.valid_scores_, axis=1) + + # draw the curves on the current axes + self.draw() + return self + + def draw(self, **kwargs): + """ + Renders the training and validation learning curves. + """ + # Specify the curves to draw and their labels + labels = ("Training Score", "Cross Validation Score") + curves = ( + (self.train_scores_mean_, self.train_scores_std_), + (self.valid_scores_mean_, self.valid_scores_std_), + ) + + # Get the colors for the train and test curves + colors = resolve_colors(n_colors=2) + + # Plot the fill betweens first so they are behind the curves. + for idx, (mean, std) in enumerate(curves): + # Plot one standard deviation above and below the mean + self.ax.fill_between( + self.feature_sizes_, mean - std, mean + std, alpha=0.25, color=colors[idx] + ) + + # Plot the mean curves so they are in front of the variance fill + for idx, (mean, _) in enumerate(curves): + self.ax.plot( + self.feature_sizes_, mean, "o-", color=colors[idx], label=labels[idx] + ) + + if self.logx: + self.ax.set_xscale("log") + + return self.ax + + def finalize(self, **kwargs): + """ + Add the title, legend, and other visual final touches to the plot. + """ + # Set the title of the figure + self.set_title("Random-feature dropping curve for {}".format(self.name)) + + # Add the legend + self.ax.legend(frameon=True, loc="best") + + # Set the axis labels + self.ax.set_xlabel("number of features") + self.ax.set_ylabel("score") + + +########################################################################## +# Quick Method +########################################################################## + + +def dropping_curve( + estimator, + X, + y, + feature_sizes=DEFAULT_FEATURE_SIZES, + groups=None, + ax=None, + logx=False, + cv=None, + scoring=None, + n_jobs=None, + pre_dispatch='all', + random_state=None, + show=True, + **kwargs +) -> DroppingCurve: + """ + Displays a random-feature dropping curve, comparing feature size to training + and cross validation scores. The dropping curve aims to show how a model + improves with more information. + This helper function wraps the DroppingCurve class for one-off analysis. + Parameters + ---------- + estimator : a scikit-learn estimator + An object that implements ``fit`` and ``predict``, can be a + classifier, regressor, or clusterer so long as there is also a valid + associated scoring metric. + Note that the object is cloned for each validation. + X : array-like, shape (n_samples, n_features) + Input vector, where n_samples is the number of samples and + n_features is the number of features. + y : array-like, shape (n_samples) or (n_samples, n_features), optional + Target relative to X for classification or regression; + None for unsupervised learning. + feature_sizes: array-like, shape (n_values,) + default: ``np.linspace(0.1,1.0,5)`` + Relative or absolute numbers of input features that will be used to + generate the learning curve. If the dtype is float, it is regarded as + a fraction of the maximum number of features, otherwise it is + interpreted as absolute numbers of features. + groups : array-like, with shape (n_samples,) + Optional group labels for the samples used while splitting the dataset + into train/test sets. + ax : matplotlib.Axes object, optional + The axes object to plot the figure on. + logx : boolean, optional + If True, plots the x-axis with a logarithmic scale. + cv : int, cross-validation generator or an iterable, optional + Determines the cross-validation splitting strategy. + Possible inputs for cv are: + - None, to use the default 3-fold cross-validation, + - integer, to specify the number of folds. + - An object to be used as a cross-validation generator. + - An iterable yielding train/test splits. + see the scikit-learn + `cross-validation guide `_ + for more information on the possible strategies that can be used here. + scoring : string, callable or None, optional, default: None + A string or scorer callable object / function with signature + ``scorer(estimator, X, y)``. See scikit-learn model evaluation + documentation for names of possible metrics. + n_jobs : integer, optional + Number of jobs to run in parallel (default 1). + pre_dispatch : integer or string, optional + Number of predispatched jobs for parallel execution (default is + all). The option can reduce the allocated memory. The string can + be an expression like '2*n_jobs'. + random_state : int, RandomState instance or None, optional (default=None) + If int, random_state is the seed used by the random number generator; + If RandomState instance, random_state is the random number generator; + If None, the random number generator is the RandomState instance used + by `np.random`. Used to generate feature subsets. + kwargs : dict + Keyword arguments that are passed to the base class and may influence + the visualization as defined in other Visualizers. + Returns + ------- + dc : DroppingCurve + Returns the fitted visualizer. + """ + dc = DroppingCurve( + estimator, + feature_sizes=feature_sizes, + groups=groups, + ax=ax, + logx=logx, + cv=cv, + scoring=scoring, + n_jobs=n_jobs, + pre_dispatch=pre_dispatch, + random_state=random_state, + **kwargs + ) + + # Fit and show the visualizer + dc.fit(X, y) + if show: + dc.show() + else: + dc.finalize() + return dc \ No newline at end of file From ff8e3d247265b59721fbeeb074455c24979c5ef5 Mon Sep 17 00:00:00 2001 From: Larry Gray Date: Tue, 19 Apr 2022 10:02:10 -0600 Subject: [PATCH 06/27] Add pairwise distance metrics to scoring metrics (#1238) Add pairwise distance metrics to scoring metrics in KElbowVisualizer --- .../test_elbow/test_distance_metric.png | Bin 0 -> 17087 bytes tests/test_cluster/test_elbow.py | 30 +++++++++++++++++ yellowbrick/cluster/elbow.py | 31 +++++++++++++++++- 3 files changed, 60 insertions(+), 1 deletion(-) create mode 100644 tests/baseline_images/test_cluster/test_elbow/test_distance_metric.png diff --git a/tests/baseline_images/test_cluster/test_elbow/test_distance_metric.png b/tests/baseline_images/test_cluster/test_elbow/test_distance_metric.png new file mode 100644 index 0000000000000000000000000000000000000000..38b325f11129fccbf3f4d86d166ab8261f44d7be GIT binary patch literal 17087 zcmeIaXHb;e)-L)Y1_S{W!6gzzL6U$05J7?gwLya9ARsDPf@GR#02CIWO;X8{GfI@~ zLPS9_4U$nLHIiv^IAgYZ@4M^V^XFE5e{R+Jv8qenFvlEoq~{sqUC&jOuhY}A(qb4! 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z&C-D%Y#Y8;N(ddDfYy&i_W)Ct*^h`h16H`-t9GzgJ)|f-*%%0q2Qzz>3_hsK+DK=4 zlo`k-rJz=lSdNZbqGU(wP#)|2!cR{Ahr^ToFQ~J9Voa%Qgpc$98 zBzb+!g~AV|Gx;M5ccmMwDQC`SU+Y=Ec(qKjXKjN$fPFx-f}|P2Ju=!$o*I3EIuF@{ z!L6xBmjnctKX>Jy{6i_oi;2TfD5M;$9meGG9qCDN96>Ejhu16|{Yz?L;^SJ9R7bK|K1L!2dISM;Mim=6Vml z^J{}Px#u4ZqN{Zd5T(AIY!}H<+x`C@lJegC*N{{}_rpnwrgQ}U&)}ykiXjaKiVNMO zt|6u|tFL_U)4x8ZRj#HG92k}kcqGHd1VJGbh~YzpHP=l;_>zkVv&xGwq`CNO*~*b; zJ;eLkud&l^0Oob?4WC3~NRSPbp<$023+^&3dHki zFEK>_K-3oW304znxMZR!$sbEy_`?=_@(&$4j6Myn=C5GwzIx%p1xY7T7>qs@P98^5AR#I8>$o4{ir>+yD3fBJe+~ge}JVf=5&RKHgtN%EiemU(LCE=kfmn DggR+M literal 0 HcmV?d00001 diff --git a/tests/test_cluster/test_elbow.py b/tests/test_cluster/test_elbow.py index dc63107e8..be7470db9 100644 --- a/tests/test_cluster/test_elbow.py +++ b/tests/test_cluster/test_elbow.py @@ -296,6 +296,29 @@ def test_calinski_harabasz_metric(self): self.assert_images_similar(visualizer) assert_array_almost_equal(visualizer.k_scores_, expected) + @pytest.mark.xfail(sys.platform == "win32", reason="images not close on windows") + def test_distance_metric(self): + """ + Test the manhattan distance metric of the distortion metric of the k-elbow visualizer + """ + visualizer = KElbowVisualizer( + KMeans(random_state=0), + k=5, + metric="distortion", + distance_metric='manhattan', + timings=False, + locate_elbow=False, + ) + visualizer.fit(self.clusters.X) + assert len(visualizer.k_scores_) == 4 + assert visualizer.elbow_value_ is None + + expected = np.array([189.060129, 154.096223, 124.271208, 107.087566]) + + visualizer.finalize() + self.assert_images_similar(visualizer) + assert_array_almost_equal(visualizer.k_scores_, expected) + @pytest.mark.xfail( IS_WINDOWS_OR_CONDA, reason="computation of k_scores_ varies by 2.867 max absolute difference", @@ -347,6 +370,13 @@ def test_bad_metric(self): with pytest.raises(YellowbrickValueError): KElbowVisualizer(KMeans(), k=5, metric="foo") + def test_bad_distance_metric(self): + """ + Assert KElbow raises an exception when a bad distance metric is supplied + """ + with pytest.raises(YellowbrickValueError): + KElbowVisualizer(KMeans(), k=5, distance_metric="foo") + @pytest.mark.xfail( IS_WINDOWS_OR_CONDA, reason="font rendering different in OS and/or Python; see #892", diff --git a/yellowbrick/cluster/elbow.py b/yellowbrick/cluster/elbow.py index 33d535a3a..951b1fd26 100644 --- a/yellowbrick/cluster/elbow.py +++ b/yellowbrick/cluster/elbow.py @@ -130,6 +130,9 @@ def distortion_score(X, labels, metric="euclidean"): "calinski_harabasz": chs, } +DISTANCE_METRICS = ['cityblock', 'cosine', 'euclidean', 'haversine', + 'l1', 'l2', 'manhattan', 'nan_euclidean', 'precomputed'] + class KElbowVisualizer(ClusteringScoreVisualizer): """ @@ -182,6 +185,12 @@ class KElbowVisualizer(ClusteringScoreVisualizer): - **silhouette**: mean ratio of intra-cluster and nearest-cluster distance - **calinski_harabasz**: ratio of within to between cluster dispersion + distance_metric : str or callable, default='euclidean' + The metric to use when calculating distance between instances in a + feature array. If metric is a string, it must be one of the options allowed + by sklearn's metrics.pairwise.pairwise_distances. If X is the distance array itself, + use metric="precomputed". + timings : bool, default: True Display the fitting time per k to evaluate the amount of time required to train the clustering model. @@ -250,6 +259,7 @@ def __init__( ax=None, k=10, metric="distortion", + distance_metric='euclidean', timings=True, locate_elbow=True, **kwargs @@ -263,11 +273,18 @@ def __init__( "use one of distortion, silhouette, or calinski_harabasz" ) + if distance_metric not in DISTANCE_METRICS: + raise YellowbrickValueError( + "'{} is not a defined distance metric " + "use one of the sklearn metric.pairwise.pairwise_distances" + ) + # Store the arguments self.scoring_metric = KELBOW_SCOREMAP[metric] self.metric = metric self.timings = timings self.locate_elbow = locate_elbow + self.distance_metric = distance_metric # Set the values of the colors self.colors = { @@ -331,7 +348,11 @@ def fit(self, X, y=None, **kwargs): # Append the time and score to our plottable metrics self.k_timers_.append(time.time() - start) - self.k_scores_.append(self.scoring_metric(X, self.estimator.labels_)) + if self.metric != 'calinski_harabasz': + self.k_scores_.append(self.scoring_metric(X, self.estimator.labels_, + metric=self.distance_metric)) + else: + self.k_scores_.append(self.scoring_metric(X, self.estimator.labels_)) if self.locate_elbow: locator_kwargs = { @@ -465,6 +486,7 @@ def kelbow_visualizer( ax=None, k=10, metric="distortion", + distance_metric='euclidean', timings=True, locate_elbow=True, show=True, @@ -504,6 +526,12 @@ def kelbow_visualizer( distance - **calinski_harabasz**: ratio of within to between cluster dispersion + distance_metric : str or callable, default='euclidean' + The metric to use when calculating distance between instances in a + feature array. If metric is a string, it must be one of the options allowed + by sklearn's metrics.pairwise.pairwise_distances. If X is the distance array itself, + use metric="precomputed". + timings : bool, default: True Display the fitting time per k to evaluate the amount of time required to train the clustering model. @@ -542,6 +570,7 @@ def kelbow_visualizer( ax=ax, k=k, metric=metric, + distance_metric='euclidean', timings=timings, locate_elbow=locate_elbow, **kwargs From e339ce25235fd1538ca5a7625ef378d80606d6c5 Mon Sep 17 00:00:00 2001 From: pdamodaran Date: Sat, 7 May 2022 17:00:55 -0400 Subject: [PATCH 07/27] Missing values (#1242) * Add pairwise distance metrics to scoring metrics (#1238) Add pairwise distance metrics to scoring metrics in KElbowVisualizer * fixed issue with pandas dataframes Signed-off-by: Larry Gray * fixed missing values visualizer issue with handling float dtypes and added tests for missing values visualizer Signed-off-by: Larry Gray * fixed tests for missing values visualizer Signed-off-by: Larry Gray * Set function specific random states for Test with out changing global seed Signed-off-by: Larry Gray * fixed issue with missing values visualizer when a user does not pass in a list of features and added relevant test cases Signed-off-by: Larry Gray * fixed merge conflicts Signed-off-by: Larry Gray * fixed issue with missing values visualizer when a user does not pass in a list of features and added relevant test cases Signed-off-by: Larry Gray Co-authored-by: Larry Gray Co-authored-by: Prema Roman --- ...singvaluesbar_numpy_no_features_passed.png | Bin 0 -> 6493 bytes ...ssingvaluesbar_numpy_with_mixed_dtypes.png | Bin 0 -> 5241 bytes ...uesbar_numpy_with_string_and_bool_cols.png | Bin 0 -> 5234 bytes ...ingvaluesbar_pandas_no_features_passed.png | Bin 0 -> 6499 bytes ...singvaluesbar_pandas_with_mixed_dtypes.png | Bin 0 -> 5241 bytes ...esbar_pandas_with_string_and_bool_cols.png | Bin 0 -> 5234 bytes tests/test_contrib/test_missing/test_bar.py | 181 ++++++++++++++++++ yellowbrick/contrib/missing/bar.py | 14 +- yellowbrick/contrib/missing/base.py | 3 + 9 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self.assert_images_similar(viz, tol=self.tol) @@ -97,6 +122,29 @@ def test_missingvaluesbar_numpy(self): self.assert_images_similar(viz, tol=self.tol) + def test_missingvaluesbar_numpy_no_features_passed(self): + """ + Integration test of visualizer with numpy without target y passed in + """ + X, y = make_classification( + n_samples=400, + n_features=20, + n_informative=8, + n_redundant=8, + n_classes=2, + n_clusters_per_class=4, + random_state=856, + ) + + # add nan values to a range of values in the matrix + X[X > 1.5] = np.nan + + viz = MissingValuesBar() + viz.fit(X) + viz.finalize() + + self.assert_images_similar(viz, tol=self.tol) + def test_missingvaluesbar_numpy_with_y_target(self): """ Integration test of visualizer with numpy without target y passed in @@ -146,3 +194,136 @@ def test_missingvaluesbar_numpy_with_y_target_with_labels(self): viz.finalize() self.assert_images_similar(viz, tol=self.tol) + + def test_missingvaluesbar_numpy_with_string_and_bool_cols(self): + """ + Integration test of visualizer with numpy array with string and boolean columns + """ + X, y = make_classification( + n_samples=400, + n_features=10, + n_informative=2, + n_redundant=3, + n_classes=2, + n_clusters_per_class=2, + random_state=854 + ) + + # add nan values to a range of values in the matrix + X[X > 1.5] = np.nan + + rng = np.random.default_rng(2021) + fruit_choices = np.array(['apples', 'pears', 'peaches', "", np.nan, 'bananas']) + fruits = rng.choice(fruit_choices, (400, 1)) + + bool_choices = np.array([np.nan, False, True]) + booleans = rng.choice(bool_choices, (400, 1)) + + X = np.append(X, fruits, axis=1) + X = np.append(X, booleans, axis=1) + + features = [str(n) for n in range(12)] + viz = MissingValuesBar(features=features) + viz.fit(X, y) + viz.finalize() + + self.assert_images_similar(viz, tol=5) + + @pytest.mark.skipif(pd is None, reason="pandas is required") + def test_missingvaluesbar_pandas_with_string_and_bool_cols(self): + """ + Integration test of visualizer with pandas dataframe with string and boolean columns + """ + X, y = make_classification( + n_samples=400, + n_features=10, + n_informative=2, + n_redundant=3, + n_classes=2, + n_clusters_per_class=2, + random_state=854 + ) + + # add nan values to a range of values in the matrix + X[X > 1.5] = np.nan + + rng = np.random.default_rng(2021) + fruit_choices = np.array(['apples', 'pears', 'peaches', "", np.nan, 'bananas']) + fruits = rng.choice(fruit_choices, (400, 1)) + + bool_choices = np.array([np.nan, False, True]) + booleans = rng.choice(bool_choices, (400, 1)) + + X = np.append(X, fruits, axis=1) + X = np.append(X, booleans, axis=1) + + X_ = pd.DataFrame(X) + + features = [str(n) for n in range(12)] + viz = MissingValuesBar(features=features) + viz.fit(X_, y) + viz.finalize() + + self.assert_images_similar(viz, tol=5) + + def test_missingvaluesbar_numpy_with_mixed_dtypes(self): + """ + Integration test of visualizer with numpy array with mixed dtypes in a single column + """ + X, y = make_classification( + n_samples=400, + n_features=10, + n_informative=2, + n_redundant=3, + n_classes=2, + n_clusters_per_class=2, + random_state=854 + ) + + # add nan values to a range of values in the matrix + X[X > 1.5] = np.nan + + rng = np.random.default_rng(2021) + mixed_dtype_choices = np.array(['apples', 'pears', 'peaches', "", np.nan, 'bananas', 1, 2.4, 5.6, False, True]) + mixed_dtypes = rng.choice(mixed_dtype_choices, (400, 1)) + + X_with_mixed_dtypes = np.append(X, mixed_dtypes, axis=1) + + features = [str(n) for n in range(11)] + viz = MissingValuesBar(features=features) + viz.fit(X_with_mixed_dtypes, y) + viz.finalize() + + self.assert_images_similar(viz, tol=5) + + @pytest.mark.skipif(pd is None, reason="pandas is required") + def test_missingvaluesbar_pandas_with_mixed_dtypes(self): + """ + Integration test of visualizer with pandas dataframe with mixed dtypes in a single column + """ + X, y = make_classification( + n_samples=400, + n_features=10, + n_informative=2, + n_redundant=3, + n_classes=2, + n_clusters_per_class=2, + random_state=854 + ) + + # add nan values to a range of values in the matrix + X[X > 1.5] = np.nan + + rng = np.random.default_rng(2021) + mixed_dtype_choices = np.array(['apples', 'pears', 'peaches', "", np.nan, 'bananas', 1, 2.4, 5.6, False, True]) + mixed_dtypes = rng.choice(mixed_dtype_choices, (400, 1)) + + X_with_mixed_dtypes = np.append(X, mixed_dtypes, axis=1) + X_ = pd.DataFrame(X_with_mixed_dtypes) + + features = [str(n) for n in range(11)] + viz = MissingValuesBar(features=features) + viz.fit(X_, y) + viz.finalize() + + self.assert_images_similar(viz, tol=5) diff --git a/yellowbrick/contrib/missing/bar.py b/yellowbrick/contrib/missing/bar.py index 3350555c8..8b3410fb3 100644 --- a/yellowbrick/contrib/missing/bar.py +++ b/yellowbrick/contrib/missing/bar.py @@ -100,17 +100,16 @@ def __init__(self, width=0.5, color=None, colors=None, classes=None, **kwargs): self.colors = color_palette(kwargs.pop("colors", None), n_colors) def get_nan_col_counts(self, **kwargs): - # where matrix contains strings, handle them - if np.issubdtype(self.X.dtype, np.string_) or np.issubdtype( - self.X.dtype, np.unicode_ + if np.issubdtype(self.X.dtype, np.floating) or np.issubdtype( + self.X.dtype, np.integer ): - mask = np.where(self.X == "") + nan_matrix = self.X.astype(np.float64) + else: + # where matrix contains strings, handle them + mask = np.where((self.X == "") | (self.X == 'nan')) nan_matrix = np.zeros(self.X.shape) nan_matrix[mask] = np.nan - else: - nan_matrix = self.X.astype(np.float64) - if self.y is None: nan_col_counts = [np.count_nonzero(np.isnan(col)) for col in nan_matrix.T] return nan_col_counts @@ -126,7 +125,6 @@ def get_nan_col_counts(self, **kwargs): [np.count_nonzero(np.isnan(col)) for col in target_matrix.T] ) nan_counts.append((target_value, nan_col_counts)) - return nan_counts def draw(self, X, y, **kwargs): diff --git a/yellowbrick/contrib/missing/base.py b/yellowbrick/contrib/missing/base.py index 5f6512538..ba5601967 100644 --- a/yellowbrick/contrib/missing/base.py +++ b/yellowbrick/contrib/missing/base.py @@ -66,6 +66,9 @@ def fit(self, X, y=None, **kwargs): self.features_ = X.columns else: self.X = X + if self.features_ is None: + n_columns = X.shape[1] + self.features_ = np.arange(0, n_columns) self.y = y From b7990df2bd0e974d5bf7e76714b4bc036203f0e2 Mon Sep 17 00:00:00 2001 From: Uri <50647681+uricod@users.noreply.github.com> Date: Sat, 21 May 2022 12:32:24 -0400 Subject: [PATCH 08/27] Full example of Regression Model Visuals (#1234) --- examples/uricod/ShoeSizeToHeight.ipynb | 698 +++++++++++++++++++++++++ 1 file changed, 698 insertions(+) create mode 100644 examples/uricod/ShoeSizeToHeight.ipynb diff --git a/examples/uricod/ShoeSizeToHeight.ipynb b/examples/uricod/ShoeSizeToHeight.ipynb new file mode 100644 index 000000000..b0344fa9c --- /dev/null +++ b/examples/uricod/ShoeSizeToHeight.ipynb @@ -0,0 +1,698 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### **GOAL IS TO SHOW IF THERE IS LINEAR RELATIONSHIP BETWEEN HEIGHT AND SHOE SIZE - USING YELLOWBRICK**" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.model_selection import train_test_split, KFold\n", + "from sklearn.linear_model import LinearRegression, Ridge, SGDRegressor, ElasticNet\n", + "from sklearn.kernel_ridge import KernelRidge\n", + "from sklearn.svm import SVR\n", + "from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor\n", + "from yellowbrick.features import Rank2D, JointPlotVisualizer\n", + "from yellowbrick.regressor import ResidualsPlot, PredictionError, ManualAlphaSelection, CooksDistance\n", + "from yellowbrick.model_selection import cv_scores, LearningCurve, FeatureImportances, ValidationCurve\n", + "import pandas as pd\n", + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'c:\\\\Users\\\\uri\\\\Documents\\\\Char Cap\\\\yellowbrick\\\\examples\\\\regressionVisuals'" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# GET THE CURRENT WORKING DIRECTORY SO YOU CAN LOAD THE PATH TO THE WO_MEN.XLSX FILE\n", + "import os\n", + "os.getcwd()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### **DATASET WAS TAKEN FROM https://osf.io/ja9dw/ AND CONVERTED TO AMERICAN SHOE SIZES MANUALLY. SEE OTHER TABS IN WO_MEN.XLSX" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "# YOU WILL HAVE TO INSTALL OPENPYXL - pip install openpyxl - TO BE ABLE TO OPEN EXCEL FILES WITH PANDAS\n", + "df = pd.read_excel('data/wo_men.xlsx', sheet_name='wo_men')\n" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "

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timesexheightshoe_size - Germanheight in feet - Stringheight in inchesHeight in FeetBinarySexshoe_size-american
004.10.2016 17:58:51woman160.040.05'3\"62.9921265.24934418.0
104.10.2016 17:58:59woman171.039.05'7\"67.3228355.61023617.0
\n", + "
" + ], + "text/plain": [ + " time sex height shoe_size - German \\\n", + "0 04.10.2016 17:58:51 woman 160.0 40.0 \n", + "1 04.10.2016 17:58:59 woman 171.0 39.0 \n", + "\n", + " height in feet - String height in inches Height in Feet BinarySex \\\n", + "0 5'3\" 62.992126 5.249344 1 \n", + "1 5'7\" 67.322835 5.610236 1 \n", + "\n", + " shoe_size-american \n", + "0 8.0 \n", + "1 7.0 " + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.head(2)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(99, 3)" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ds = df.drop(['time', 'sex', 'height', 'shoe_size - German', 'height in feet - String', 'height in inches'], axis=1)\n", + "ds.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['Height in Feet', 'BinarySex', 'shoe_size-american'], dtype='object')" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ds.columns" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "X = ds.drop(['shoe_size-american'], axis=1)\n", + "y = ds['shoe_size-american']\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### Women is coded as 1 vs Man being 0 so that's why there is negative correlation between sex and shoe size" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "viz = Rank2D(algorithm='pearson')\n", + "viz.fit_transform(ds)\n", + "viz.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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UlBYoigJJkhxVhYmIiLxNc1+nIyIiYDQaAy6ZAeAonqc1Op0ORqOxSeE+IiIib9Lk1+lAPgvg7iPpRERE9CvNnaEhIiIiuhATGiIiItI8JjRERESkeUxoiIiISPOY0BARUdCy2CWcqqmHxS75OhTykKoJTX5+PjIymr5ca+vWrZgwYYKa3RIRETklyTJezTmIaZty8JfNezBtUw5ezTkISZZ9HRq1k2rPP69btw4ff/wxQkN/fYfSTz/9hM2bN7NEPhER+dT63GJkF5VCFHQIMYiwSjKyi0oBAFNT+/k4OmoP1c7QxMbGYs2aNY6fKysr8cwzz+Cxxx5Tq0siIiKXLHYJO0vKIApNC7SKgg67Ssp8fvkpYfkWiLM3+jQGLVLtDM2YMWNw7NgxAOfe8bNgwQLMnz+/za9ELygoUCM8v5WXl+frELwmmMYKBNd4g2msAMerNWfq7Th6shxGsXnFeauk4Ouc79Et9Nzh0Zdj1fo8e5tXSu4WFhbiyJEjWLJkCSwWCw4dOoTly5djwYIFLtsmJSW1OQnSqry8PCQnJ/s6DK8IprECwTXeYBorwPFqkcUu4Z3/5cAqNb9fxiQKGJY6GCa9qMpYLRaL21/UtT7PHc3V3Hklobn88svxySefAACOHTuGWbNmuZXMEBERdTSTXsTQuBjHPTSNJFnBkL4xMOlFH0ZH7RW4L0UiIiJqxZSUBADArpIyVFtsiDQZMKRvjONzXypekBY0VyY6kqoJTe/evZGVleXyMyIiIm8SBQFTU/shY3A8quqtiAo18syMxvEMDRERBS2TXkT3iFDXv0h+j5WCiYiISPOY0BAREZHmMaEhIiIizWNCQ0RERJrHhIaIiIg0jwkNERERaR4TGiIiItI8JjRERESkeUxoiIiISPOY0BAREZHmMaEhIiIizWNCQ0RERJrHhIaIiIg0jwkNERERaR4TGiIiItI8JjRERESkeUxoiIiISPOY0BAREZHmMaEhIiIizWNCQ0QdyirJOFVTD4td8nUoQcVilzjvFNT0vg6AiAKDJMtYn1uMj78vhaHQjMgQA4bGxWBKSgJEgd+d1NI47ztLylDdYOO8U9BiQkNEHWJ9bjGyi0phkxVEGERYJRnZRaUAgKmp/XwcXeBqnHdR0CGE8x4QEpZvQanZ1uQzaXWGj6LRDqbvROQxi13CzpIyiIKuyeeioMOukjJeBlEJ553oV0xoiMhjVfVWVDfYWlxWbbGhqt7q5YiCA+ed6FdMaIjIY1GhRkSGGFpcFmkyICrU6OWIggPnnehXTGiIyGMmvYihcTGQZKXJ55KsYEhcDEx60UeRBTbOO9GveFMwEXWIKSkJAICteVWw2CVEmgwY0jfG8Tmpo3F+d5WUodpi47wHgOIFaTCZTL4OQ3OY0BBRhxAFAVNT++EyoQrx/ZMQFWrkGQIvaJz3jMHxqKq3ct4paDGhIaIOZRQFdI8I9XUYQcekFznvFNR4Dw0RERFpnqoJTX5+PjIyzhUD2r9/P9LT05GRkYF7770Xp0+fVrNrIiIiCiKqJTTr1q3DwoULYbFYAADLly/H448/jo0bN2L06NFYt26dWl0TERFRkFEtoYmNjcWaNWscPz/zzDPo378/AECSJN7BTURERB1GpyiK4vrX2ufYsWOYNWsWsrKyHJ/t3bsXCxYswFtvvYWuXbu22tZisaCgoECt0IiIiHwmKSmp2Rd7Hvfc09LcAV5+yunTTz/FSy+9hFdeecVpMnO+1gIPRHl5eUhOTvZ1GF4RTGMFgmu8wTRWgOMNZGqM1Z2kJZiOe23hau68ltB89NFHeO+997Bx40ZERUV5q1siIiIKAl5JaCRJwvLly9GzZ0/89a9/BQBcddVVmDFjhje6JyIiogCnakLTu3dvx/0zubm5anZFREREQYyF9YiIiEjzmNAQERGR5jGhISIiIs1jQkNERESax4SGiIiINI8JDREREWkeExoiIiLSPCY0REREpHluFdb7z3/+g2effRbV1dVQFAWKokCn02Hbtm1qx0dERETkklsJTWZmJubNm4d+/fpBp9OpHRMRERFRm7iV0HTp0gUjRoxQOxYiIiKidnEroUlOTsaKFStw7bXXNnml+VVXXaVaYETUOotdQlW9FVGhRpj0oq/DaRMtx05E/suthObHH38EAPz000+Oz3Q6HTZs2KBOVETUIkmWsT63GDtLylDdYENkiAFD42IwJSUBouDf9/hrOXYib0pYvgWlZluTz6TVGT6KRjvcSmg2btyodhxE5Ib1ucXILiqFKOgQYhBhlWRkF5UCAKam9vNxdM5pOXYi8n9uJTTff/89/vnPf6Kurg6KokCWZZw4cQLbt29XOz4i+j8Wu4SdJWUQhaY35ouCDrtKypAxON5vL+FoOXYi0ga3zvMuXLgQo0aNgiRJ+POf/4w+ffpg1KhRasdGROepqreiusHW4rJqiw1V9VYvR+Q+LcdORNrg1hmakJAQ3HbbbTh+/DgiIyORmZmJW2+9Ve3YiOg8UaFGRIYYYJXkZssiTQZEhRp9EJV7tBw7kbcVL0hr8gAOucetMzQmkwlVVVWIi4tDfn4+dDod6urq1I6NiM5j0osYGhcDSVaafC7JCobExfj1JRstx05E2uBWQnP33Xdj5syZGDFiBD788EPcdNNNSEpKUjs2IrrAlJQEjErsCZMowGKXYBIFjErsiSkpCb4OzSUtx05E/s+tS05jx47F6NGjodfrsXnzZhw4cABXXnml2rER0QVEQcDU1H7IGByvuVouWo6diPyfW2doPv30U6SlpQEAKioqMGvWLD7hRORDJr2I7hGhmkwItBw7Efkvt87QvPTSS1i/fj0AIDY2Flu2bME999zDJ52InDi/Iq6a625PYqBmtd6Keiu+PnQSA3p0RnR4aIeu2x2sRExa11JhvfYItmJ8biU0NpsNF110kePnbt26QVEUJy2IgldLFXF7C/UYdIXscUVcT6vtqlmtt95qxciXsvHjiTOQlWIYRBGX9eiMbQ+OQqhR/aeYWImYKLi59b88OTkZs2bNwo4dO7Bjxw78v//3/zBo0CCVQyPSpsaKuFZJdlTEzT1Zi/W5xaqsO7uo1O11e9remZEvZSP/RCUURQdRECArCvJPVGLkS9ker9sdao6NiPyfWwnN4sWLcdlll+G9997D+++/jwEDBmDhwoVqx0akOa1VxBV05yriWuxSh6+7sdquq3V72t6Z8tp6FJ48C0HXfNyFJ8+ivLa+3et2h5pjIyJtcHrJqby8HNHR0Th9+jTGjh2LsWPHOpadPn0avXr1Uj1AIi1prIgbYmh+70ZjRdzuEe27r8TTdasZ208nz8ImSS1e2rHLEn46eRbD+qp3P42aYyMibXCa0CxcuBBr167FXXfdBd1537wURYFOp8O2bdtUD5BIS9SsiOvputWMbUCPzjCIIuQW7q3TCyIG9Ojc7nW7g5WIKZCwUnD7OE1o1q5dCwB4/PHHMWLECK8ERKRljRVxG98q3UhWPK+I29q6JVnBkL6u1+1pe2eiw0NxWY/OyD9R2eRzWVHwu55Rqj/tpObYiEgb3LqH5umnn1Y7DqKA0VJF3JQe4R1SEdfTartqVuvd9uAoDOzVBYIOkBUZgk6Hgb26YNuD3invwErERMHNrce2f/vb32L+/PkYOHAgQkJCHJ/fcsstasVFpFktVcQtyN/XIY8Oe1ptV81qvaFGI3Y9/Af8+9vdMPaI83odGlYiJgpubiU0Xbp0AQDk5+c3+ZwJDVHrGivi+uO61Yyta6gRyX17qLJud6g5NiLyX24lNCtWrAAAnD17Fp07q3tzHxGpj9V0ifxXY6XgYKv06ym3zoEfOHAAN954I8aNG4dTp05h9OjRKCwsdNkuPz8fGRnnNsiRI0cwceJEpKenY/HixZDl5k8jEJG6JFnGqzkHMW1TDv6yeQ+mbcrBqzkHIfH/IxFpnFsJzbJly/DCCy8gKioK3bt3x5IlS7B48WKnbdatW4eFCxfCYrEAOHeW55FHHsHbb78NRVH4yDeRD7CaLhEFKrcSmvr6eiQk/PqkwNChQ2G1Wp22iY2NxZo1axw/FxYWIiUlBQBw3XXXYdeuXe2Jl4jaidV0iSiQuXUPTVRUFA4cOOAorvfxxx+7vJdmzJgxOHbsmOPnxmJ8ABAWFoaamhq3AiwoKHDr9wJFXl6er0PwmmAaK+D78Z6pt+PoyXIYRV2zZVZJwdc536NbqFt/Elzy9Vi9jeMNXL4Y60fj+vmsby1z66/XkiVLMHfuXBw8eBCDBw9Gnz598NRTT7WpI+G8R1bNZjMiIyPdapeUlBQ0FRPz8vKQnJzs6zC8IpjGCvjHeC12Ce/8L6fFaromUcCw1MEdcoOwP4zVmzjewKXGWC0Wi8sv6sF03GsLV3PnVkITGxuLd955B3V1dZBlGeHh4W0OZMCAAdizZw+uvvpqfPPNN0hNTW3zOoio/VhNl4gCmVsJzffff4833ngDZ8+ebfL5hg0b3O5o7ty5ePzxx/HMM88gPj4eY8aMaVukROSxxqq5u0rKUG2xIdJkwJC+MaymS0Sa51ZCM2/ePEyfPr3Nb9fu3bs3srKyAABxcXF488032x4hEXUYVtMlokDlVkLTvXt3VgUmCiCspktEgcathCYjIwOPPvooUlNTodf/2oRJDhEREfkDtxKat99+G0DzR8iY0BAREZE/cCuhKS8vx2effaZ2LERERETt4lal4MGDB2PHjh2w2+1qx0NERETUZm6dodmxYwc2bdrU5DOdTof9+/erEhQRERFRW7iV0Hz77bdqx0FERETUbm4lNGfOnMHWrVthNpuhKApkWcaxY8ewatUqteMjIiIicsmte2imT5+O/fv34+OPP0Z9fT22b9/e5N1MRERERL7kVlZSWVmJlStX4vrrr8cNN9yAjRs34uDBg2rHRkREROQWty45de7cGcC51xccOHAAAwcO5BNPGmKxS07L3Hu63Jd9VzdYcbTSjNguYYgMMTpte+HPztp2hPP762hqx+4JqyTjVE09X6tA1E4Jy7eg1GzzdRhOSaszfB1CM24lNKmpqZgxYwbmzp2Le+65B4WFhXy1uQZIsoz1ucXYWVKG6gYbIkMMGBp37kWEoiB4vNyzvhW8mnOw3X1b7Xbcl5WDXYfLUWe1o5NRjyEXR2Pd+FSIgtCkbbjp3G6ugw41lnM/HyyvRlltA+ptUpO2Rr1b/yXaPPbeQj0GXSG7nDdXnI27I2L3ROO4P/6+FIZCc5v2FyIiT7n1V2bmzJl49NFH8Zvf/Mbxtuw1a9aoHRt5aH1uMbKLSmGVZIQYRFglGdlFpVifW9whyz3pe+svVR71fV9WDr46dBJ2WYFRL8IuK/jq0Encl5XTrO3PZWfx1aGTOFB2FiEGEd/+Uob8E5U422Br1latec89WevWvLnibNy+1jhum6y0eX8hIvKU21+bYmNjAQDvvvsu7r77bnTv3l21oMhzFruEnSVlEAVdk89FQYddJWWobrB6tNxilzzqO7+8rt19l9fWY9fh8mY3pguCgF0l5dhx8KSjrSQrKK+1QBAEnDY3wGKXUG62QNDpYLHJUBTF0Xb34XJUN1hbHZc7Whu7oHM9b65UN1hbHXdHxO4JV9vck3ETEbmjzeeoCwoK1IiDOlhVvRXVDTaEGFq478Riw9FKs0fLq+qtrb6t2Z2+zTYZES20dafvn06eRZ3VDmML92eYrXacqq13xGaTZNjkc5d6bJKCs/U2SLIMQaeDBBmyAoj/dwyus9lxtNKMpJ7tvyfF1didzZsrRyvNrY67I2L3hJrjJgo2xQvSeFtHO7T5wnbjN1ryb1GhRkSGGFpcFmky/N/NpO1f7uxGV3f6DjO0vOu50/eAHp3RydhyLh5m1KN7+K8HToMowPB/ZzQMog6dQw2O+zlECDj/hEIngx6xXcJaHZc7XI3dkxuEY7uEtTrujojdE2qOm4jIHW1OaDIzM9WIgzqYSS9iaFwMJLlpAirJCobExSAyxOjRcmdPr7jT98DoTu3uOzo8FEMujoYsy02Wy7KMIXHRGNGvh6OtKOgQHW6CLMu4KCwEJr2I6DATZEWBySBAp9M52l5zcbTHTwy1NnZZcT1vrkSGGFsdd0fE7glX25xPOxGR2txKaM6ePYuFCxdi0qRJ6NWrF+bPn4+zZ8+qHRt5aEpKAkYl9oRJFGCxSzCJAkYl9sSUlIQOWe5J3zfHR3nU97rxqRjetwcMgg42SYJB0GF43x5YNz61WdtLYzpjeN8e6N+9Myx2CdfGx2Bgry6ICjE0a6vWvKf0CHdr3lxxNm5faxy3UdS1eX8hIvKUTnHjGtKMGTMwdOhQvPXWW9i8eTNeeOEF7N+/H6+88opqgVksFhQUFCApKSloriXm5eUhOTm5w9frj3VoGscaLHVoCvL3dei29ec6NLtzv0N8/6SgqUOj1v9bfxVM41VjrM6ObcF43GsLV/Pj1hmaY8eOYcKECRAEAUajETNnzsTJkyc7PFhSh0kvontEaKsHF0+X+7LvyBAjknp2afGgfmHbC3921rYjeDJvrqgduyeMoqDauImIWuNWQiOKImpqahz3Gxw+fJjvciJyobrBioLSSp8+Tt0af47NYpdwqqaej3oTUZu49dj2X//6V2RkZKC0tBR/+ctfsG/fPjzxxBNqx0akSS1V870kQsAHAwf6vJqvFioNt6cyNRGRW3/BrrvuOiQlJeHHH3+EJElYunQpIiMj1Y6NSJMaq/kKguCo5pt3yoz7snLwRvrv/S62xkrDvo6tsdKwKOiaVBoGgKmp/XwaGxH5P7e+9tx88804evQohg8fjpEjR+Kiiy7ChAkT1I6NSHNarear0/m8mi8rDRNRIHPrDM3Zs2exYMECTJ48GePHjwfAAntELfHnar7+HBsrDRP9qq1v2/bHN1/7gltnaLp164a33noLn3zyCRYtWgS73e64QZiIfuXP1Xz9OTZWGiYiT7mV0CiKgqioKLz22mswGAzIyMiAxWJROzYizWm1mq+i+LyaLysNE1EgcyuhueSSSwCce3z78ccfR1paGo4ePapqYERa1VI13+TuYX5RzVcLlYbbU5maiMite2hWrlzZ5Ofx48c77qUhoqaMej3eSP99k2q+Bwv/6/PHoluLzV+K84mCgKmp/ZAxOL7dlamJKHg5/QublpaGLVu24NJLL3XcM9N4M7BOp8P+/fvVj5BIo85V8/WPZOFC/hxbY4VlomBVvCCNrz5oB6cJzZYtWwAABw4c8Eowwarx23Kt1e7rUPyOqzMJzt71dKyqFt8dPYOrYruhd1R4m9q607en/PEsCRGRVrl1Dvzo0aPYt28fbr75ZixevBiFhYWYP38+Bg8e3KbObDYb5s2bh+PHj0MQBCxbtgwJCcF7ffzCqq2iYseIEptfVG31NVcVbZ1Vla23WjFw9Sc4VlkHSQFEHdC7Syfkz74J4SEhLivSql1N15+r9RIRaZVbNwXPnz8fBoMB27ZtQ0lJCebPn49Vq1a1ubOvv/4adrsd7777Lh566CE899xzbV5HIGms2mqXFRj1IiQZjqqtwe7CuTm/oi3wa1VZqyQ3qSq7PrcYA1d/giMVdVCgg6DTQYEORyrqMHD1Jy7butO32mMjIqK2c+vroMViwdixY7FgwQLcfPPNGDx4MOz2tl8eiYuLgyRJkGUZtbW10Afxt1F3qrYG62UIV3NTXlvfalXZfx8oxbHKOggX1EkSdDocq6xD8emzTivSjkvqrep24XYnIlfaWlhPbVop3OdWRiGKIr744gt89dVXePjhh5Gdnd2ut2136tQJx48fx9ixY1FZWYmXX37ZZZuCgoI296MFhyobcNZcD8MFB1arxYI6WcFn336Hvl1CfBSdd+Tl5bX4eWtzAwB1soItX+fi6MlKGMXmy4/VWGFXAEHXvJK1rAAbtuXi6Elzi22t0rl1O+vbk+2Sl5fncmyBst1b27aBiuMNXME01tZoZQ7cSmiWLl2K119/HYsWLUJMTAw++eQTZGZmtrmz119/Hb///e8xe/ZslJaWYvLkydi6davTu7mTkpIC8m7vfg1WdM4tg/28QmJWiwVGkwlhgg5jf39VQH9Tz8vLQ3JycovLWpqbRmGCDmnDUrC7ei+sktxseWSkgqLKI1DQPGEQdAomjUzB0X8XtNjWJApIG3Ylnsr/vNW+27tdGsframyBsN2dbdtAxPEGLjXGarFYNPdF3V+2t6u5c7uw3ooVKzBmzBgAwLPPPotLL70UwLlHu90VGRmJiIgIAEDnzp1ht9shScH50jl/rtrqa67mJjo8tNWqsqMv7YneXTpBvuBdY7KioHeXTki4qLPTirTR4aGqbhdudyIidbT9utEF2vKSyrvvvhuFhYVIT0/H5MmTMXPmTHTq1MnTEDTrwqqtej+q2uprriraOqsqmz/7JvTp2gk6KJAVBToo6NP13FNOrtq607faYyMiorbz+K7ctrykMiwsDH//+9897TJgXFi19czhIgy75mpfh+UXXFW0dVZVNjwkBMULbmu1Do2rirRqV9P152q9ROR7LKzXPsH7mJEfaazamneCm+NCriraOqsq2zsqvMWCeu60dadvT/lztV4iIq3x+JITERERka959R4aIiIiIjW4ndDU1dXhwIEDUBQFdXV1js/vv/9+VQIjIiIicpdbCc3u3bsxbtw4/OUvf0F5eTmuv/56fPvttwCAP/zhD6oGSEREROSKWwnNM888g7fffhuRkZGIiYnBm2++2a53ORERERGpwa2ERpZlREdHO37u27evagERERERtZVbzwn36NEDO3bsgE6nQ3V1Nd566y306tVL7diIiIiI3OLWGZqlS5di69atKC0txejRo7F//34sXbpU7diIiIiI3OLWGZpu3bph5cqV+OWXXyBJEhITE6HXswgcERER+Qe3spL//ve/ePjhhxEVFQVZlnH69Gm88MILGDhwoNrxEREREbnkVkKzfPlyPPvss44EZt++fVi2bBk2b96sanBEnrDYpRbf1aSV9RMRkfvcSmjq6uqanI0ZNGgQLBaLakEReUKSZazPLcbOkjJUN9gQGWLA0LgYTElJgCh4/rYPtddPRERt51ZC07lzZ2RnZ2PUqFEAgOzsbERFRakZF1G7rc8tRnZRKURBhxCDCKskI7uoFAAwNbWf36+fiIJbwvItKDXbmnwmrc7wUTTa4dbXyWXLlmHt2rW4+uqrkZKSgpdffhl/+9vf1I6NqM0sdgk7S8ogCromn4uCDrtKymCxS369fiIiah+3ztBcfPHF2LRpE+rq6iDLMsLDw9WOi6hdquqtqG6wIcTQ/J6WaosNVfVWdI8I9dv1ExFR+7iV0Pz444947bXXUFlZ2eTt2hs2bFAtMKL2iAo1IjLEAKskN1sWaTIgKtTo1+snIqL2cSuhmTt3Lu666y707dsXOp3OdQMiHzHpRQyNi3Hc49JIkhUM6Rvj8dNIaq+fiIjax62EJiQkBH/+85/VjoWoQ0xJSQAA7CopQ7XFhkiTAUP6xjg+9/f1E1FwK16QBpPJ5OswNMdpQnPixAkAQP/+/fH6669j5MiREMVfv4HyfU7kj0RBwNTUfsgYHK9KnRi1109ERG3nNKG56667oNPpoCgKcnJysGHDhiaXnLZt26Z6gETtZdKLqt6gq/b6iYjIfU4Tmu3btwMA8vPzkZeXh7vuugvTpk1DYWEhH9smIiIiv+FWHZrly5fjd7/7Hb788kuEhITgww8/xLp169SOjYiIiMgtbiU0sizjqquuwo4dO3DDDTegZ8+ekCQWECMiIiL/4FZCExoaitdeew179uzBiBEj8MYbbyAsLEzt2IiIiIjc4lZC8/TTT6Ourg7PP/88OnfujLKyMqxevVrt2IiIiIjc4lYdmu7du2P69OmOn+fMmaNaQERERERt5dYZGiIiIiJ/xoSGiIiINI8JDREREWkeExoiIiLSPCY0XlLdYEVBaSWqG6zNllnsEk7V1MMqyS22bVxusbev9s/+kxV47utC7D9Z0a72zvr3NLadxaWYvnk3dhaXtrlvwPm8ekrNdRMRUcdy6ymnjrR27Vps374dNpsNEydOxB133OHtELzKarfjvqwc7DpcjjqrHZ2Megy5OBrrxqdCFASszy3GzpIyVDfYYDNX40/yQUxJSYAoCJBkucnyyBADhsbFOJa7UlFbhz6ZW1BnO5cozQbQySDgyMI0dA3v5LK9s/4BeBTb8dp6pMze6Pj5pd2HAABHF9yC33SNcDl2Z/Nq1Hu2W6u5biIiVxKWb0Gp2ebrMPyGtDrDrd/z6hmaPXv24IcffsA777yDjRs34uTJk97s3ifuy8rBV4dOwi4rMOpF2GUFXx06ifuycrA+txjZRaWwSjJCDCJssoLsolKszy0GgGbLrZLcZLkr5yczjepsMvpkbnGrvbP+PY0t7eOSFj+PXf6hy74B5/PqKTXXTURE6vBqQvPtt98iMTERDz30EKZNm4bhw4d7s3uvq26wYtfhcggXnLEQBAG7Ssqx4+BJiIKuyTJR0GFXSRmqG6zYWVLW6nJXl3j2n6xolsw0qrPJLi8/WexSq/3/p/gU/vPLqXbH1trlpUY7io47HXt5bX2r87r7cLlHl4icbTNP101EROrx6vnzyspKnDhxAi+//DKOHTuGBx98EJ9//jl0Ol2rbQoKCrwYYcc6VNmAs+Z6GITm46uVZRw6UYauoU03QUVFBaySgs++/Q5HT5bDKDZva5UUfJ3zPbqFtr753t5f7jS2V7Nzkd4/utXlZ+rtrfZfa5WgAIgwiu2K7bnc405je+aL71BnR6tj3/J1bqvzWiefm7u+XUKc9tEaZ9vM03Xn5eW1q50WBdNYAY43kAXTWP2Vu9vAqwlNVFQU4uPjYTQaER8fD5PJhIqKCnTr1q3VNklJSTCZTF6MsuP0a7Cic24Z7LLSbFknnQ59e10EnHfcrKioQNeuXWESBYz9/VX4vPy7Fm8UNokChqUOhknfPKFwrP83FXjuh09aXT51VAr69+ja6nKLXcI7/8tpsf8YnQ7QocVxuRPbI1G98MGh7FaXzxpzFTbkHW517GnDrsRT+Z+32H+YoMPY31+FyBBjq+t3xtk282TdeXl5SE5ObldMWhNMYwU43kCmxlgtFoumv6j7QuM2cDV3Xk1okpOTsWHDBkyZMgVlZWWor69HVFSUN0PwqsgQI4ZcHI2vDp1scglDlmUM6dsD18Z3R3ZRaZNLK5KsYEjfGESGGDE0LqbV5c4SBgDo36MrOhmEFi87dTIITpMZADDpxVb7H5HYAwDaHdvQhJ5Ol49I/A2KK+paXX90eGir83pNfI92JzOA823m6bqJiNxRvCBNs1/kfcmr99CMGDEC/fv3x+23344HH3wQixYtgig6P/hp3brxqRjetwcMgg42SYJB0GF43x5YNz4VU1ISMCqxJ0yiAItdglHUYVRiT8dTRBcuN4lCk+WuHFmYhk6Gppu48Skndzjr39PYtvwprsXPjy64xWXfgPN59ZSa6yYiInXoFEVpfm7dDzSeWtLyJafzVTdYcbTSjNguYc2+5VvsEqrqrfhlfwGuSbmqWdvG5VGhRpdnP1qy/2QFvvi5FGMu6enyzExLnPXf3tgaT+XuLC7FOz8cxsQrLm7xzI2r9TubV0915Lp5mj5wcbyBS81LTi0d2wLtuNfRXM0Pi2p4SWSIEUk9Wz4omvQiukeE4pjY8gmzxuXt1b9H13YlMu7072lsQxN6Or0E5Wr9zubVU2qum4iIOhYrBRMREZHmMaEhIiIizWNCQ0RERJrHhIaIiIg0jwkNERERaR4TGiIiItI8JjRERESkeUxoiIiISPOY0BAREZHmBU1CY7FLOFVTD4tdUqV9dYMVBaWVqG6wtnndjW1rrfZ29e3u+luLTc2x+TO19wkiIvKegH/1gSTLWJ9bjJ0lZahusCEyxIChcTGYkpIAUXCdz7lqb7XbcV9WDnYdLked1Y5ORj2GXByNdeNTYdQ7n94L24qKHSNKbI62nsbuKjY1x+bP1N4niIg8kbB8C0rNNl+H4SCtzvB1CG4J+L++63OLkV1UCqskI8QgwirJyC4qxfrc4g5pf19WDr46dBJ2WYFRL8IuK/jq0Encl5Xjct0XtpVkNGnraeyuYlNzbP5M7X2CiIi8L6ATGotdws6SMoiCrsnnoqDDrpIyl5cKXLUvr63HrsPlEC74Vi4IAnYfLnd6iaa6weq0bXltvUexe7p+T8bmz9TeJ3j5iYjINwI6oamqt6K6oeXTdtUWG6rqnR+UXbX/6eRZ1LVy30udzY6jleZW13200uy07U8nz3oUu6fr92Rs/kztfcJVeyIiUkdAJzRRoUZEhhhaXBZpMiAq1OhR+wE9OqOTseV7SToZ9IjtEtbqumO7hDltO6BHZ49i93T9nozNn6m9T7hqT0RE6tDunZ1uMOlFDI2LQXZRaZNLBJKsYEjfGJj0okfto8NDMeTiaHx16GSTSzOyLOOa+B6IDGn94BYZYnTaNjo81KPYPV2/J2PzZ2rvE67aExG5UrwgDSaTyddhaE5An6EBgCkpCRiV2BMmUYDFLsEkChiV2BNTUhI6pP268akY3rcHDIIONkmCQdBheN8eWDc+1eW6L2yrv6Ctp7G7ik3NsfkztfcJIiLyPp2iKIqvg2iJxWJBQUEBkpKSOiRTtdglVNVbERVqbNe3aFftqxusOFppRmyXsDafvWhse+ZwEYZdc3WHx+4qNjXH1pq8vDwkJyd3yLraS+194nz+MF5vCaaxAhxvIFNjrM6ObR193As0ruYnoC85nc+kF9E9IlS19pEhRiT1bN/BvrFt3omWN4ensbuKTc2x+TO19wkiIvKegL/kRERERIGPCQ0RERFpHhMaIiIi0jwmNERERKR5TGiIiIhI85jQEBERkeYxoSEiIiLNY0JDREREmseEhoiIiDSPCQ0RERFpXtC8+sAVT9/r48n6G5dZJbnD190R7dWcG7XnnYhIaxKWb0Gp2ebxeqTVGR0QjXb4JKE5c+YMbr31Vrz22mtISPDtG4olWcb63GLsLClDdYMNkSEGDI2LwZSUBIiC5yewnK0fQJNlNnM1/iQfdLtvT2N31V7NuZFkBa/mHFRt3omIKLh4PaGx2WxYtGgRQkJCvN11i9bnFiO7qBSioEOIQYRVkpFdVAoAmJraT9X1A2iyrE5W2tS3p7G7aq/m3Gz9pQoHLXWqzTsREQUXr38VXrlyJe68807ExMR4u+tmLHYJO0vKIAq6Jp+Lgg67SspgsUuqrf8/xafwn19OtbtvT2N31b66wara3FjsEvLL61SbdyIiCj5ePUPzwQcfoGvXrrj22mvxyiuvuNWmoKBAtXjO1Ntx9GQ5jKKu2TKrpODrnO/RLbT9U+Rs/bVWCQqACGPT+0YqKirc6tvT2F21/+zb71SbmzP1dphtMmwVFR2+bn+Wl5fn6xC8JpjGCnC8gUzLY9Vy7O3h1aPG+++/D51Oh927d2P//v2YO3cuXnrpJURHR7faJikpCSaTSZV4LHYJ7/wvp8WbcU2igGGpgz26UdXZ+mN0OkAH2GXF8VlFRQW6du3qVt+exu6q/djfX4XPy79TZW4sdgn/2PcJIqK6dPi6/VVeXh6Sk5N9HYZXBNNYAY43kKkxVovF4vKLevGCNNWOe1rmau68esnprbfewptvvomNGzeif//+WLlypdNkRm0mvYihcTGQzksqgHM3rA6Ji/H4oOps/dcmdMe18d3b3bensbtqHxliVG1uTHoRA6M7qTbvREQUfALvvH4bNT5ttKukDNUWGyJNBgzp++tTSN5Yf+Myo6jDqMSebvftaeyu2qs5NzfHR+FHqbNq805ERMHFZwnNxo0bfdV1E6IgYGpqP2QMjlelHoqr9Z+/7Jf9Bbgmxf0nfDyN3VV7NedGFHSYepV6805ERMEl6M/QNDLpRXSPCPXJ+huXHRPbdwXQ09hdtVdzbtSedyIiCg6sYEZERESax4SGiIiINI8JDREREWkeExoiIiLSPCY0REREpHlMaIiIiEjzmNAQERGR5jGhISIiIs1jQkNERESax4SGiIjIjyQs3wJxtn+8HkhLmNB0EItdwqmaeljskq9DISIiCjp8l5OHJFnG+txi7CwpQ3WDDZEhBgyNO/fWaFFgvkhEROQNPOJ6aH1uMbKLSmGVZIQYRFglGdlFpVifW+zr0IiIiIIGExoPWOwSdpaUQRR0TT4XBR12lZTx8hMREZGX8JKTB6rqrahusCHEIDZbVm2xoareiu4RoT6IjIiItKp4QRpMJpOvw9AcnqHxQFSoEZEhhhaXRZoMiAo1ejkiIiKi4MSExgMmvYihcTGQZKXJ55KsYEhcDEz65mduiIiIqOPxkpOHpqQkAAB2lZSh2mJDpMmAIX1jHJ8TERGR+pjQeEgUBExN7YeMwfGoqrciKtTIMzNERERexoSmg5j0Im8AJiIi8hHeQ0NERESax4SGiIiINI8JDREREWkeExoiIiLSPCY0REREpHl++5STopwrVme1Wn0ciXdZLBZfh+A1wTRWILjGG0xjBTjeQNbRY208pjUe484XrMc9dzmbOwDQKa0t8bGamhoUFRX5OgwiIqIOl5iYiIiIiCaf8bjnnpbmDvDjhEaWZZjNZhgMBuh0OtcNiIiI/JyiKLDZbAgLC4MgNL3rg8c955zNHeDHCQ0RERGRu3hTMBEREWkeExoiIiLSPCY0REREpHlMaIiIiEjz/LYOTaBJS0tDeHg4AKB3795YsWKFY9nrr7+OTz75BAAwbNgwTJ8+HYqi4LrrrsPFF18MABg0aBBmz57t9bjby9l4MzMzsXfvXoSFhQEAXnzxRdhsNjz66KNoaGhATEwMVqxYgdBQ7by9vLXx7t+/H0888YTj9/bt24cXXngBl19+OcaMGYPExEQAwKhRozB58mTvB94Oa9euxfbt22Gz2TBx4kTccccdjmXbt2/HCy+8AL1ej9tuuw3jx49HQ0MD5syZgzNnziAsLAwrV65E165dfTiCtnE23n/961944403IIoiEhMTsWTJEgiC4HT/92fOxvr6669j06ZNjm33t7/9Db169QrIbVteXo5Zs2Y5fm///v2YPXs27rzzTtX+LsuyjCVLluDnn3+G0WhEZmYm+vTp0yHr9iabzYbHHnsMx48fh9VqxYMPPoi+ffti3rx50Ol06NevHxYvXtziU0oeU0h1DQ0Nyrhx41pcdvToUSUtLU2x2+2KLMvKhAkTlP379yuHDx9WHnjgAe8G2kGcjVdRFOXOO+9Uzpw50+SzZcuWKe+//76iKIqydu1aZf369SpG2LFcjbfRp59+qsyaNUtRFEXZuXOnsnTpUpUj63g5OTnKAw88oEiSpNTW1irPP/+8Y5nValVGjRqlVFVVKRaLRbn11luV8vJy5bXXXnP83r/+9S9l2bJlvgq/zZyNt76+Xhk5cqRSV1enKIqizJw5U8nOznZ7f/A3zsaqKIoye/Zs5b///W+TzwJ1255v7969SkZGhmK321X9u/zFF18oc+fOVRRFUX744Qdl2rRpqvSjts2bNyuZmZmKoihKZWWlMmzYMOWBBx5QcnJyFEVRlMcff1z58ssvVembl5y84MCBA6ivr8c999yDSZMmYd++fY5lPXr0wKuvvgpRFKHT6WC322EymVBYWIhTp04hIyMD9913H3755RffDaCNnI1XlmUcOXIEixYtwp133onNmzcDAPLy8nDttdcCAK677jrs2rXLF6G3i7PxNqqrq8OaNWuwYMECAEBBQQEKCwtx1113YcaMGSgrK/Ny1O3z7bffIjExEQ899BCmTZuG4cOHO5YVFxcjNjYWnTt3htFoRHJyMr777rtm23b37t0+ir7tnI3XaDTi3XffdZxJbPy/687+4I+cjRUACgsL8corr2DixIlYu3YtgOb/bwNl2zZSFAXLli3DkiVLIIqiqn+Xz5/LQYMGoaCgoMPW7U033ngjHn74YQDn5q9x3lJSUgCo+/edl5y8ICQkBPfeey/uuOMOHD58GPfddx8+//xz6PV6GAwGdO3aFYqiYNWqVRgwYADi4uJw+vRp3H///Rg7diy+//57zJkzB++//76vh+IWZ+Otq6vDXXfdhSlTpkCSJEyaNAlJSUmora11VH4MCwtDTU2Nj0fhPmfjbbR582bceOONjtPx8fHxSEpKwpAhQ/Dxxx8jMzMTzz//vK+G4LbKykqcOHECL7/8Mo4dO4YHH3wQn3/+OXQ6XZNtCJzbjrW1tZrets7GKwgCLrroIgDAxo0bUVdXh6FDh6KoqMjl/uCPnI0VAG666Sakp6cjPDwc06dPx44dOwJ22zbavn07+vXrh/j4eABAdHS0an+Xa2trHZcpAUAURdjtdr/fby7UeCtBbW0tZsyYgUceeQQrV650zKua+4m2Zkqj4uLi0KdPH+h0OsTFxSEqKgrl5eXo2bMngHPvCnnssccQFhaGxYsXAwCSkpIgiiIAYPDgwSgrK4OiKJqoHulsvKGhoZg0aZLjW21qaioOHDiA8PBwmM1mhISEwGw2IzIy0sejcJ+r7QsAW7dubZKwpKamOuZg9OjRmkhmACAqKgrx8fEwGo2Ij4+HyWRCRUUFunXr5tiGjcxmMyIiIpp8rrVt62y8wLkzjk899RRKSkqwZs0axz7gan/wR87GqigKJk+e7Ehehg0bhp9++imgty0AfPzxx5g0aZLjZzX/Ll/4/0eWZc0lM41KS0vx0EMPIT09HTfffDOeeuopxzI19xNecvKCzZs348knnwQAnDp1CrW1tYiOjgZw7pTcX/7yF1xyySVYunSp4z/LP/7xD7zxxhsAzl3S6NmzpyaSGcD5eA8fPoyJEydCkiTYbDbs3bsXl112Ga688kp8/fXXAIBvvvkGycnJPou/rZyNFzj3fhar1drkgLZw4UJ88cUXAIDdu3fjsssu827Q7ZScnIz//Oc/UBQFp06dQn19PaKiogAACQkJOHLkCKqqqmC1WvH999/jiiuu0PS2dTZeAFi0aBEsFgtefPFFR4Lqan/wV87GWltbiz/+8Y8wm81QFAV79uxBUlJSQG9b4Nyl4SuvvNLxs5p/l6+88kp88803AM49PND4wIDWnD59Gvfccw/mzJmD22+/HQAwYMAA7NmzB8C5/WTw4MGq9M1XH3iB1WrF/PnzceLECeh0Ojz66KPIz89HbGwsZFnGrFmzMGjQIMfvz5o1C/Hx8ZgzZw7q6uogiiIWLVqEhIQE3w2iDZyNd+TIkXj11Vfx2WefwWAwYNy4cZg4cSJOnz6NuXPnwmw2o0uXLli9ejU6derk66G4xdV4f/zxR7z88st48cUXHW3+97//4bHHHgMAhIaGIjMzEzExMb4aQpusWrUKe/bsgaIomDlzJqqqqlBXV4cJEyY4nnJSFAW33XYb/vznP6O+vh5z585FeXk5DAYDVq9erYkDfKPWxpuUlITbbrsNgwcPdhzUJk2ahGHDhjXbH84/KPozZ9v2ww8/xMaNG2E0GnHNNddgxowZAbttJ0yYgIqKCkyZMgUfffSR4/fPnj2r2t/lxqecioqKoCgKnnjiCc38zT9fZmYmPvvsM8dlOgBYsGABMjMzYbPZEB8fj8zMTMeX947EhIaIiIg0j5eciIiISPOY0BAREZHmMaEhIiIizWNCQ0RERJrHhIaIiIg0jwkNkY8dO3YM119/fbPPL7nkEqft/vvf/zpepdCaefPm4YMPPmj2+Y8//tik2FWjbdu24e9//7uLiJuuf/jw4Rg3bpzj37PPPut2+/M9//zz+P7779vVlohIm2UIiQi/+93v8Lvf/a5dbQ8dOoQzZ840+3zkyJEYOXJkm9Y1Y8YM3Hrrre2K43zfffcdrr76ao/XQ0TBiWdoiPycJElYsWIF0tLS8Kc//Qmvv/46AGDPnj3IyMgAABQVFeHWW2/FuHHjsGzZMowePdrR/quvvsLtt9+OESNG4L333kN1dTWef/55bN++HS+99FKTvj744APMmzcPAHD99dfjueeew+23346bbrqpzS/L+/DDD5GWloZx48bhscceg8ViAXCuUujtt9+OW265BdOnT0dlZSU+/PBDFBQUYOHChfj555/bO1VEFMR4hobID5SVlWHcuHEtLsvKygIAbNmyBVarFffeey+SkpKa/M68efPw8MMPY9iwYXj99dchSZJjmdVqxaZNm3Dw4EFMmjQJEyZMwIwZM5Cbm4sHH3zQaVxRUVHYvHkzNm7ciLVr12LNmjXNfuf55593lIMHgLfeegulpaXIysrCu+++C5PJhNWrV+Of//wn7rzzTqxevRobNmxA586d8e677+Lpp5/G8uXL8f7772P69OkuL7UREbWECQ2RH4iJiWlSYh349R6a3bt3Y//+/cjJyQEA1NXV4eeff0bfvn0BAFVVVTh+/DiGDRsGALjtttuwYcMGx3pGjhwJnU6Hfv36obKysk1xXXvttQCAfv364csvv2zxd1q65LRnzx4cOXIE48ePBwDYbDYMGDAA+fn5KC0tdbzwT5ZldO7cuU0xERG1hAkNkZ+TJAlz5szBDTfcAACoqKhAp06dkJ+fDwAQRRHO3mDS+M6U9rxEz2QytautJEkYO3YsFi5cCODcG3YlSUJubi6uvPJKvPzyywDOvWn+/DcMExG1F++hIfJzqampyMrKgs1mg9lsRnp6uiOZAYCIiAjExsY63nq8detWl+sURRF2u121mK+++mr8+9//xpkzZ6AoCpYsWYI33ngDAwcOxL59+1BSUgIAePHFF7Fq1SpHTOdfKiMiagsmNER+7s4778TFF1+MtLQ03Hbbbbj11lubPQ20cuVKvPjii0hLS8OPP/6IkJAQp+u8/PLLkZ+fj6efflqVmC+99FJMnz4dkydPxk033QRZlnH//fcjOjoaTzzxBB555BHcfPPNKCwsxNy5cwGcu7y1ePFi7N27V5WYiCiw8W3bRAHgH//4B8aPH4+YmBh8+eWX2Lp1a4s38BIRBSreQ0MUAHr16oV77rkHer0ekZGRWL58ua9DIiLyKp6hISIiIs3jPTRERESkeUxoiIiISPOY0BAREZHmMaEhIiIizWNCQ0RERJrHhIaIiIg07/8DtMXELf+pq44AAAAASUVORK5CYII=", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "viz = JointPlotVisualizer(columns=['Height in Feet', 'shoe_size-american'])\n", + "viz.fit_transform(ds)\n", + "viz.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.4, random_state=42)" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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", 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ardY6Na+qbZYpjSaTCSaTiZX9XoTJ3wcFaOshKKAlcorOlhv6lyAhKKAlq/7Jp+3atQs7d+6EKIr4+9//jn//+9/Iy8vD1atXER8fj9jYWOsJQ5s2bXDo0CHo9XpcvHgRCQkJiIuLK7e/jIwMtG7dGpIk4YMPPsDLL79s3bZ582YcPXoUeXl5uHDhAkaNGmXdFh8fj4KCApv4pk6dip49e1p/zsrKQvPmza0/h4aG4uTJk+Vek5mZicaNG2P69Ok4ffo0OnbsiJkzZyIgIKDKbWazGXFxcbh48SLi4+MRERFR6+NLysDk76MSIuNKq/0FPUSptNpfDpxySEoSGBiINWvW4NSpU3jsscfwyCOPICsrC2PHjkVsbGy55+bn52P9+vU4f/48nnvuuXLJ//LlyygoKMCECROQlZWFdu3a4cUXX7Ruf+qpp/DUU0/ZjWHLli0u+31MJhN++eUXzJo1CxEREZg/fz7Wrl2LpKSkKrep1WqkpKQgNzcXEydOREZGBhuxeQkmfx+lUfvhhT4jZE+6nHJIStS6dWsAQEhICDZt2oTPP/8cOp0OJpPJ5rnt27cHANx22202DX8yMjLQvXt3vP/++8jJycHgwYPxww8/oGvXrtXG4OiVf2hoKK5cuWL9OSsrC6Gh5Wt2mjdvjubNm1uv3AcOHGgtDKxqm0VgYCDuv/9+fPPNN0z+XoLfrj4uQFtP1uK+0imHAgA1VCidcvhCnxGyxUW+zbJa2r/+9S906dIF8fHx+O9//4tDhw7ZPLeq++BnzpxBhw4dAACNGjXC4MGDcejQIYeSv6NX/vfccw/Onz+PS5cuITQ0FHv37sXy5cvLPadp06Zo3ry59VbF0aNHrQ2KKtt248YN+Pn5ITAwEHq9HkeOHEFCQoJDMZHyMfmTbCxTDlVC5VMOeQuA5NS3b1/Mnz8f+/btQ8OGDaFWq2vUzvfMmTPligOjoqKwYMGCcvf9a8vPzw+zZ8/G+PHjYTabMWzYMNx1110AgISEBMyfPx+hoaGYNWsWJk+eDKPRiJYtW2LRokXWfdjbdvnyZUybNg1msxmSJGHgwIHo27evy+Imecna2Kc6vtAAwpeb05zLzsLHx98DBNsph4AZI7qPr3JUwpePXW0pqbFPxrVcWar9ncXGPuXZa9TkSGOf0/q9nOpnh7vyHq/8STacckhAyZz8X6fFVv/EGu6TiCrH5E+y4ZRDAgC1SlVnV+lEZB+X9yVZJUTGoVH9cIiSBoAZoqRBo/rhsk05JCLyBbzyJ1kpZcohEbnX8O5TvbaWyxMw+ZMiyD3lkIjIl3DYn4iIyMcw+RMREfkYJn8iIiIfw+RfjUJDMc5lZ6HQUCx3KERERC7Bgr9KeErDGSVXySs5NiI5HDhwAF9//TXy8/MxfPhwREZGyh2SbHZ8v8TuCn/O8vWVAWtKOVlMYZTecEbJJydKjo2UR5RE5Omvu3SfDf2bQCXIN7C5detWrFq1Ck2aNEFhYSESExMxdOhQ9OvXD/369UNOTg6WLFnidPI/fPgwFixYAFEU8cQTT2DChAl2n5ebm4vk5GRkZGRAEAQsXLgQ9957b6WPFxcXY/To0TAYDDCbzRgwYAAmTZpUm0NBCsVvYjs8oeGMkk9OlBwbKU+e/jo+SVte/RNr4PFur6BR/aZVPmfx4sU4deoUrl27Br1ej5YtWyI4OBgrV6506D2Ki4uxZ88ePPHEEzbbMjIykJiYiFGjRuHkyZNISEjA0KFDrdvXrFmD0aNH1+h3sjCbzZg7dy42bNiA0NBQDB8+HFFRUbjzzjttnrtgwQL06tULK1euhMFggF6vr/JxrVaLTZs2oUGDBjAajYiPj0fv3r3RpUsXp2Il5eI9fzuu5N6CCnq721SCHldyb7k3oAosJydll8QFyp+cyEXJsRGVNW3aNGzevBkTJkzA4MGDsXnzZocTPwBcu3YN27dvt7vtzJkzaN26NQAgLCwMGo0GACBJEpYuXYrevXujY8eOTsV98uRJ3HHHHWjZsiW0Wi0ee+wxHDx40OZ5eXl5OH78OIYPHw6gJLEHBgZW+jhQ0p7Y0rTIZDLBZDJV2bKYPBev/O1QesOZ0pMT2254lpMTuRbMUXJsRI4wGo2YM2cOLly4AFEUkZSUhGbNmmH69Onw8/OD0WjEihUr8M477+B///sfVq9ejcTExHL7yMjIQOvWrSFJEj744ANrC9/Nmzfj6NGjyMvLw4ULFzBq1Khyr4uPj0dBQYFNTFOnTkXPnj0BAFlZWWjevLl1W2hoKE6ePGnzmszMTDRu3BjTp0/H6dOn0bFjR8ycObPSxwMCAgCUjCzExcXh4sWLiI+PR0RERO0OKCkSk78dSm84o+STEyXHRuSI7du3Izg4GAsXLsTNmzcxZswYxMfHo3PnzpgyZQpSU1ORl5eH5557zjq8X9bly5dRUFCACRMmICsrC+3atcOLL74IAHjqqafw1FNPVfreW7ZscdnvYTKZ8Msvv2DWrFmIiIjA/PnzsXbtWkRHR9t9PCkpCQCgVquRkpKC3NxcTJw4ERkZGWjbtq3L4qoOC/fcg8P+lVBaw5myUw4tJycSpHLPUcLJiZJjI3JERkYGDh8+jLFjx2LSpEkwmUyIjo5GYGAgxo8fj23btkGtth3ZKvv67t27IyUlBfv378e5c+fwww8/OPTe8fHxiI2NtfnfkSNHrM8JDQ3FlStXrD9nZWUhNNR2NK158+Zo3ry59cp94MCB+OWXXyp9vKLAwEDcf//9+OabbxyKnTwLr/wroZSGM5VVzo97MAYbj+75qzBRD1EqraiXW0JkXGnMCouNqDpt2rRB8+bN8dxzz0Gv12PNmjU4ceIEunXrhsTEROzcuRPvvfceXnzxRYiiaPP6M2fOoEOHDgCARo0aYfDgwTh06BC6du1a7Xs7cuV/zz334Pz587h06RJCQ0Oxd+9eLF9uWzDZtGlTNG/eHOfOnUObNm1w9OhRhIeHV/o4ANy4cQN+fn4IDAyEXq/HkSNHkJCQUG1M5HmY/Kshd8OZyirnNx7do4iTE3uUcuJE5IyRI0ciOTkZY8aMQX5+PuLj49GpUydMnToVa9asgdFoRHJyMpo0aQKj0YilS5diypQp1tefOXMGvXv3tv4cFRWFBQsWWO/715afnx9mz56N8ePHw2w2Y9iwYbjrrrus2xMSEjB//nyEhoZi1qxZmDx5MoxGI1q2bIlFixYBQKWPX716FdOmTYPZbIYkSRg4cCD69u3rkrhJWQRJkqTqnyaP4uJipKeno1OnTl7b+jEtLQ3dunWzu63QUIwVB1dDJdi7f65BUnSiTyfVqo4dVU3OY2cwGACUVJkDnjfPv6CgwFoRT7b/nkDV392Wbaf1e+0u8uPr9/zdlfd45a9grJwnX6ASVNXOySci12LyVzBWzhORtxrefarXjuh6Alb7Kxgr54lI6RR855iqwOSvcEqbckhUWyqVCiaTSe4wyEXMZjNUKqYST8Nhf4Vj5Tx5Gz8/PxQVFaGwsBBqtdrjlo81Go3WIjdfJkkSzGYzzGYz/PyYSjwNT9c8hGXKIRM/eYOGDRtCq9V6XOIHgLNnz8odgiIIggCtVouGDRvKHQo5gadrRCQLT75aLDutjcgT8cqfiIjIxzD5ExER+RgmfyIiIh/D5C+jQkMxrhTmotBQLHcoRETkQ9xacWM0GjFjxgz88ccfMBgMeP755xEdHe3OEBShbKc+SSrETwdPWLveadSeWwRFvoXTT6k2dny/xO7a/lXx9XX/AdflUbdmmj179iAoKAhLly7FrVu3MHToUJ9M/mU79ZlFFVSqkk5961J34YU+I+QOj6hKlbWZ5skrUd1zVR5167D/wIED8dJLLwEoWSBCrbZtWOPtCg3FuFV4CQLKz28WIOBW4SXeAiDFKz15NQKCGiqh9OSViOqWq/KoW0/TLW0w8/PzMWnSJCQlJTn0uvT09DqMyr2uFOZCkgphFkvPu8xm81//ZcRXx46geUCgPMF5oLS0NLlD8FjOHDu9yYjsnHNQq0Wbbdk55/Dtsf/C30/jivAUjX938uBxdz6PVuT2MbrLly9j4sSJiI+Px5AhQxx6TV33NXanQkMxfjp4AipVSac+s9lsPXMTJX/0vb8n7586SM6e9J7O2WN3LjsL2hsHAcH2akOtNuO28NZe32aaf3fVs/SkdzVfOO6OHDtn8mhFbk3+2dnZePbZZzF79mw8+OCD7nxrxbB06sspOltu6J+d+sgTsM00kbxclUfdmvzfeecd5Obm4p///Cf++c9/AgDWrVsHf39/d4Yhu4TIOGvBFGCEKJUWTBEpGU9eyVWGd5/qNSO67uSqPOrW5J+cnIzk5GR3vqUile3U99WxIxzqJ49S9uRVJeh58krkRq7Ko5yXI6MAbT00Dwhk4iePwjbTRJ6PyZ+InGJpM01EnofL+xIREfkYXvkTEZHbVba8L5fwdQ9e+RMREfkYJn8XKjQU41x2FpfoJSIiReOwvwuw0QkREXkSXvm7ABudEBGRJ2HyryV26SMiIk/DMelaupJ7CyroAdg2OlEJelzJvcW50EREFXB5X3nxyr+WLI1O7GGjEyIiUiImfydZKvsBICigJSRI5baz0QkRESkVh/1ryF5lf2BAGBrWD0cuG51UKzs/F6ezMtE+NAwhukC5wyEi8klM/jVUWtkvAFBDBSPyis6hUf1wJEUnstFJJQoNeqw4uBo6zU1o1SJO/K5CvjEYSdGJCND6VktnIiK5MfnXgKWyvyTxl7JU9gNgcV8lVhxcjSD/6wBUEKGCnxoIUl/HioOrMWPQZLnDIyI3q2x5X2dxWeCa4T3/Giit7LdlqewnW9n5udBpbsL2z00FneYmsvNz5QiLiMhnMfnXACv7nXM6KxNatWh3m0Yt4nRWppsjIiLybUz+NRCgrcfKfie0Dw2DwWz/T81oVqF9aJibIyIi8m1M/jWUEBmHRvXDIUoaAGaIkgaN6ofLVtmfnZ+L1LO/KHroPEQXiHxjMICKV/8i8o3BrPonInIzFvzVkEbthxf6jEChoVjWyn5Pq55Pik60xqtRizCaVcg3NkFSdKLcoRER+RwmfycFaOvJWtnvadXzAVp/zBg0mfP8ichprOh3HSZ/D+RI9bxSE2uILhCRug5yh0FE5NN4z98DsXqeiIhqg8nfA7F6noiIaoPJ3wOxep6IiGqD9/w9RMXZBayeJyJfszF1WqXbWAxYM0z+Cmevi6ClYyCr56ks/i0QkaOY/BXOXhfBnKKzWJe6Cy/0GcHqefK4NR+ISH68569gli6CAux3ESw0FMsUGSmJZc0HPzVK13zwL1nzgYjIHiZ/BWMXQaoOOyYSkTOY/BWMXQSpOlzzgYicweSvYOwiSNXhmg9E5AyfTP6FhmKcy85S5D3zil362EWQqsI1H4jIGT5V7V/VtDmNWt5DUVXFNrsIUlW45gMR1ZRPXfmXTpszAoIaKqF02pzcqqvYtnQRlGuonxXlymXpmBj/wFTc02os4h+YihmDJvOkjIgq5TPJX8nT5pResa30+KhEiC4QkeEdONRPRNXymWH/0mlzapttlmlzbUJC3R8YSiu2RTvnYpaKbTkX8lF6fETkG7iEr+v4zJW/kqfNKb1iW+nxERFRzfhM8lfytDmlV2wrPT4iIqoZWZL/Tz/9hLFjx7r9fZU2ba6spOhE3NI3gckMCBBhMgO39PJVbFecDqm0+IiIfFlt86jb7/mvW7cOe/bsQf369d391tCo/RQxbc4eS8W23J3Z2EWQiEjZXJFH3Z78W7VqhVWrVuHVV19191tbWabNKZHcXfrYRZCI5MSivuq5Io+6PfkPGDAAmZk1W288PT29jqJRhrS0NLlDAADoTUZk55yD2s5a8dk55/Dtsf/C308jQ2SVU8qx80Q8ds7jsas7PLbVcyaPVuQRU/06deqEevWUMTzvamlpaejWrZvcYQAAzmVnQXvjICDYTodUq824Lby1okZMlHTsPA2PnfN47KpXXFzs9EWbrx/b2hy7mvCZan+qnpKnQxIRkesw+buBkhsJlaXk6ZBEROQ6sgz7h4WF4eOPP5bjrd1KyY2ELL5IP47vLhzFfXc8iP6deiAhMq40ZkEPUSqNmYiIlKG2eVQZGchLVVc5L6f//XEBh/63BioV0CIQyLy5E+sP7USfO59X7HRIIvJ+G1OnyR2CLNw9y4HD/nVEyY2EAFgTf1kqVcnjgPxdBImIqO4w+deR0kZCtiyNhOTyRfpxm8RvoVKVbCciIu/F5F9HlFw5/92Fo7XaTkREno3Jv44ouXL+vjserNV2IiLybCz4c6GK694rtXK+f6ceWH9op92hf1Es2U5E5Em4LHDNMPm7QKFBjxUHV0OnuQmtWsSJ31XINwYjKTpRsZXzfe583qboTxRLHpeDEo8REZG3YvJ3gRUHVyPI/zoAFUSo4KcGgtTXseLgaswYNFmRjYTuvP0O3Hn7Ypt5/u7mCWshEBF5G3671lJ2fi50mpuwLZ9QQae5iez8XEW3vu3fqYesw/xKXguBiMhbseCvlk5nZUJrpwseAGjUIk5n1a7zkjdT8loInrIkMxGRM3jlX0vtQ8Nw4veSof6KjGYV2oeGuT8oD1G6FoLtwbOsheDu2yW8DUFEvoDfZrUUogtEvjEYQeqSe/6lROQbmyh6yF9ulrUQVDDabJNrLQTehiBSLlb0uw6H/V0gKToRt/RNYDIDAkSYzMAtfRMkRSfKEo+nDFkrbS0EJd+GICJyJV75u0CA1h8zBk22mefvbp44ZK2ktRBcfRtC7r8HIqLKKDMjeKgQXSAidR1ke39PHLLWqP0UsxaCq25DVLXuQ4DW/pLPRETuxGF/L+HpQ9ZK6CLoqtsQlnUf/NQoXffBv2TdByIiJeCVv5dQYuW8pyg76lDb2xCevu4DkZJtTJ1W6TYWA9YMk7+XUGLlvNJVVSNhNJudug1hWfdBtDOoZln3Qc5bQ0REAIf9vYbSKuc9QWmNhBEQ1FAJpTUSzt6GaB8aBoPZ/seK6z4QkVIw+VcjOz8XqWd/QXZ+rtyh2FV2Wl9CZBwa1Q+HKGkAmCFKGjSqHy57F0ElqqsaCcu6D0DFVR9F5BuDOeRPRIrAYf9KKL1iuy6GrH1JXdZIJEUnWv92NGoRRrMK+Ub51n0gIqqIyb8S1XXqk1t10/pY3Fe1uqyRUMq6D0RElXFo2P/ixYvYs2cPJEnCrFmzMGzYMHz//fd1HZtsHKnYlpOnT+tTAnfUSIToAhEZ3oGJn4gUx6HkP336dGg0Ghw8eBDnz5/H9OnT8cYbb9R1bLJReqe+0iFrW5Yha6oeaySIyFc5NOxfXFyMQYMGYebMmRgyZAi6d+8Ok8lU17HJRumd+jitzzWUtLogEZE7OXTlr1arsX//fnz99dd4+OGHceDAAahU3jdRwFI5H6Ctp+iKbU7rcy0lrC5IRORODl35z507Fxs3bsTs2bPRrFkz7N27F/Pnz6/r2NzGXuV8YP0w3CwCGiq0YltJDXF45UxE5FmqTP5//vknAKBhw4Z48cUXrY9NmTKl7iNzI3uV83n63xEcEI4R3SYosmJbCUPWnthFkOTDk0Qi5ajyG3rMmDEQBAGSJNlsEwQBBw8erLPA3MVSOV+S+EtZKucDtPUQGa7c5VgtQ9Zy8MQuguR+PEkkUp4qP3lffvmlu+KQDRviOKe6k6ZCQzGv7ggATxKJlMih0+5z585hy5YtKCwshCRJEEURmZmZ+PDDD+s6vjrHynnn8KSJHMGTRCJlcqhk/+WXX0ZgYCB+/fVX3H333bh+/Truuuuuuo7NLVg57xzLSZM9PGkiC65JQaRMDl35i6KISZMmwWQyoUOHDhg5ciRGjhxZ17G5jZIq549fPINvzxzHQ+16oEerdm5/f0dZTppyis6WW2mQJ01UFkfWyF02pk5z6f7GRS526f6UxqHkX79+fRgMBvztb3/DqVOn0L17dxQXe88SskqonL9y4yr2nPwHNGqgkT+QfiEdP5wDYjr/H5o3bubWWBylpJMmUiaeJBIpk0PJPyYmBs899xyWLVuGJ598Et988w1CQ73vfq6clfOWxF+WRl3y+ISHlXkGqoSTJlI+niQSKY9DyX/MmDEYOnQodDodNm/ejJ9//hmRkZF1HZvPOH7xjE3it9CoS7Yr/RYAi/uoMjxJJFIeh5L/6tWrbR47c+YMEhOVsdqdp/v2zHE0sl87Z92u5ORP5AieJBIpR40X6Dcajfjyyy9x/fr1uojHJz3UrketthMREdWEQ1f+Fa/wJ06ciGeffbZOAvJFPVq1ww/nYHfo32gGr/qJyGd4e5W9UjjVmq+goMC67r+SWbr0FRqUPzMhpvP/wWgu/5jRXPI4ERGRKzl05R8VFQXhrxW6JElCbm6u01f+oijitddew5kzZ6DVajF//nzccccdTu2rMp64lnjzxs0w4eHFHjPPX07Z+bk4nZUJo75Q7lDsYmEbEdUVV+VQhzLh5s2brf8tCAICAwOh0+lq/GYAcODAARgMBmzbtg0//vgjFi9ejDVr1ji1r8p48lriPVq1Y9KvRKFBjxUHV0OnuQmtWoTBrMLRT48hKToRAdoqKibdxBNPOonIs7gqh1b5jbR79+4qXzx06NAav2FaWhp69eoFAOjSpQvS09Orfc2gQYMcLjCUJAm5+jwAtp0IAQHv+M+3jmIogcFggFarlTsMj5BTlAuVYAasi8WU/Buvld5Go/ryt1vO0xdAlEw2jy8TZqChfwMZIqoc/+6cx2NXvSZNmmDp0qVVPmfV1BRcy75m8/ib9T6tq7A8QnXHzpkcak+Vyf/YsWMAgIsXL+LChQvo06cP1Go1UlNTceeddzqV/PPz88uNGqjVaphMJvj5VR6K0WiEwWBwaP9mSYT9xA8AEvSGYqgFp0od6oyjv5svEyUJgiD+9VP5f19BEKEvLrZpHuNOkiTBLJpgLwSzaEJxcbGiTjoB/t3VBo9d1YxG2+WcHZVffNOp12kFZZ1gO6u6Y+dMDrWnymcvWrQIADB27Fjs2bMHjRs3BgDk5ORg4sSJNXojC51Oh4KCAuvPoihWG/SBAwdQr55j904LDcVYcXA1VIK9tcQ1fw0RK+c+bFpaGrp16yZ3GIqXevYXpF/cDLFsjaokAYIAASLuaTUWkeEdZIvvXHYWPj7+HiDYW63JjBHdxytqjjv/7pzHY1e94uLiaq9IX1wSC6Pkurodb5klUN2xcyaH2uPQJfDVq1cRFBRk/bl+/fq4ds12uMYRXbt2xeHDhwEAP/74I9q2bevUfirDLn3eqX1oGAxm+3+uRrMK7UPD3BxReexySETu4Koc6tDpwsMPP4xnnnkGjzzyCERRxGeffYZBgwY59Yb9+/fHt99+i5EjR0KSJCxcuNCp/VRFaWuJs/q79kJ0gcg3BiNIfR3lz1lF5BubIEQn7z1/NrAhIndwVQ51KPlPnz4d+/fvx3fffQdBEPDss88iOjraqTdUqVSYO3euU691lFLWEmf1t2slRSdaq/01ahFGUYV8Y2MkRStjmWmlnXQSkfdxVQ6tMgOdOnUKHTt2xPHjx9G4cWMMHDjQuu348ePo0UPZy87KvZa4J085VKIArT9mDJpcOs//ynX07ddL7rCslHLSSURUnSqT/9atWzFv3jysXLnSZpsgCHj//ffrLDBPV2go/usKsHyFtwABtwovodBQzMTgpBBdICJ1HZB2K03uUOyS+6STyJN5S+Ge0lWZ/OfNmweg/CI/gO1UA7J1JfcWVNADsK3+Vgl6XMm9xQRBRESycKja/6uvvsLSpUtRUFCAQYMGITo6Gh9++GFdx+bRWP1NRERK5VDyX716NeLi4rBv3z507twZX375JXbu3FnXsXk0TjkkIiKlcnipu/DwcHz99deIiopCgwYNarWCk7eq2EUwITIOjeqHQ5Q0AMwQJQ0a1Q9n9TcREcnKoflmISEhmDdvHtLT07F06VIsXrwYLVq0qOvYPEZVU/pY/U2eJPPGNaRd+g3dWt6FsMZN5Q6HfNDG1Gku3yeLCG05lPyXL1+OAwcO4Omnn0ZAQABatmyJxERlzK1Wguqm9LH6m5Qurygf7xxahiB/PVQq4NObwC29P57rMxkN67O4l8jbODTsr9PpoFKpsHPnThQVFaFBgwas9v+LZUpf2VXdgPJT+oiU7p1Dy9A4oCTxA4BKBTQO0OOdQ8vkDYyI6oRDyX/ZsmU4fPgwPv/8c5jNZuzcuROLF3MYBSg7pc+WZUofkZJl3riGIH/7f8NB/npk3nCujwcRKZdDyT81NRVLly5FvXr1oNPpsGHDBmtjAV/HKX3k6dIu/Wa94q9IpSrZTkTexaHkr/rrm8HSj9xgMFgf83Wc0keerlvLuyCK9reJYsl2IvIuDmXwgQMHIikpCTk5Odi4cSNGjx6NwYMH13Vsipadn4vUs78gOz+XU/pqoOxxI2UIa9wUt/T2R69u6f1Z9U/khaqt9j937hxiY2Nx9913o0WLFrhy5QrGjRuH77//3h3xKU6hQW/tLKdVizjxuwr5xuC/OssJnNJXiaqOW4DWfuIh93muz+Ry1f6iWFrtT0Tep8or/1WrVmHYsGEYOHAgVCoVJk+ejMaNG+P111/Hn3/+6a4YFWXFwdUI8r8OPzUgQgU/NRDkfx0rDq62Tulj4rdV1XEj+TWsr8OUga9hUOdXENIoBoM6v4IpA1/jND8iL1Xllf/u3buxf/9+XL16FStXrsR7772H7OxsvPXWW+jVSzmtVN0lOz8XOs1N2J4zqaDT3ER2fi5CdIFyhKZoPG6eI6xxUw7zE/mAKq/8GzRogGbNmqFTp044efIk2rVrh927d/tk4geA01mZ0KrtV0Zp1CJOZ2W6OSLPwONGRKQsVV75l63oDw4OxrRprl920ZO0Dw3Did9LhqwrMppVaB8a5v6gPACPGxFVNLz7VNSrx1ukcqnyyt8ytQ8A/P1ZlBWiC0S+MRhAxatYEfnGYA5dV4LHjYhIWaq88v/tt98QHR0NAMjKyrL+tyRJEAQBBw8erPsIFSYpOtFata5RizCaVcg3Nvmr2p8qw+NGRKQcVSb//fv3uysOjxGg9ceMQZORnZ+L01mZaB8axitXB/C4kadhN07yZlUm/9tvv91dcXicEF0gInUd5A7D4/C4kdJV1aJbo3aoESqR4vEvmYiojOpadJNr7Ph+CYxSYY1eMy6SDeVchQv0ExH9hS26yVcw+RMR/cXXWnQXGopxLjuLJzU+iMP+VGuZN64h7dJv6NbyLq4ORx7N0qJbBaPNNkuL7pvw/EWpWNdA/Fcmp+UV5ZdrBvPpzdJmMEpZE96XZhewOr32LC26c4rOlhv697YW3axrICZ/cto7h5ahcUDpEKlKBTQO0OOdQ8swZeBr8gUG3+oiyKs410qIjCs9noIeolR6PL2Bpa5BJVRe1+AtJzlUOX4zkFMyb1xDkL/9e6NB/npk3rgm6y0ASxdBQFXaRVBd0kVwxiDvalPLqzjX0qj98EKfEV47klJa12C73ralrqFNSKj7A3PAxlT3LTHv7TMLWPBHTkm79BtUlfz1qFQl2+XiSBdBb8Hq9LrjrS26LXUN9ljqGsj7MfmTU7q1vAui/UZ9EMWS7XLxpS6CvladTrVnqWuQIJV73NvqGqhqTP7klLDGTXFLb//q4ZbeX9Yh//ahYTCY7f9pe1sXQV7FkTMSIuPQqH44REkDwAxR0qBR/XCvqWug6vGeP9VI2fugz/WZXK7aXxRLq/3lZOkiGKQuuedfSkS+sYlXVf37SnU6uZa31zVQ9Zj8ySGVVZQn9U9GVs5Nxc3z96Uugt5enU51x1LXoCTeXminFEz+5JDqKsqVkvQtfKmLIK/iiKimmPypWp48L9iXuggq8SqOiJSJBX9ULVaUExF5FyZ/qhYryomIvAuTP1WL84KJiLyLLPf8v/jiC3z22WdYvny5HG9PDqhYKMeKciJyB2eX8OUsgRKO5le3J//58+cjNTUVd999t7vfmhxQVUMcVpS7H483ETmqJvnV7cm/a9eu6NevH7Zt2+butyYHVNcQhxXl7sFOfURUUzXJr3X2LbJ9+3Zs2rSp3GMLFy7Eo48+imPHjtVoX+np6a4MTXHS0tLkDgEAkKsvLGmIIwlAufv7AnSam/jq228Q6B8gV3h2KeXYudpnf5yApMqCIAgwAwD0uJGXgTf2bsDA27u65D289di5A4+d8vjSv4kr8mudJf8nnngCTzzxhEv21alTJ9Sr551DnmlpaejWrZvcYQAAUs/+Au1FEaKdOlCNWoSmeRN0C1fOnHklHTtXKjQUY//Vr6EWbD+eopSLu+/pVOtbAN567NyBx656xcXFbr9o85Z/E0eOnSvyK8cPyap9aBhO/F4y1F+RtzXEUTJP7rdO5Kjh3ad67UWdJ+BUP7KyNMQBKrbDFZFvDPba5XGVhusqEFFdkyX533///XjzzTfleGuqRlJ0Im7pm8BkBgSIMJmBW3r5GuIUGopxLjsLhYZiWd5fDlxXgYic5Wh+5bA/laOUhji+Xu3OdRWIqC55/7coOUXuhjjVdRH0duzUR0R1iff8SXEsXQQFVN5F0FdY1lVg4iciV+KVPykOq92JvN+O75fAKBXaPM5let2DV/6kOKx2JyKqW0z+pDisdiciqltM/qRICZFxaFQ/HKKkAWCGKGnQqH44q92JiFyA9/xJkVjtTkRUd5j8SdHYRZDIO3F5X3lx2J+IiMjHMPkTERH5GCZ/IiIiH8PkT0RE5GOY/InKcGUXQV/sSEhEnoHV/kRwbRdBX+9ISOSIypb3rQqX/nUdXvkToWwXQSMgqKESSrsIyrkvIqK6wORPPs+VXQTZkZCIPAGTP/m80i6CtixdBOXYF5EcWKviG3gDknyepYugCkabbTXtIujKfRG5E2tVfAv/RanOZOfn4nRWJtqHhiFEFyh3OJWydBHMKTpbbrjemS6CrtwXkTuV1qoIANRQobRW5YU+I+QODwCwMXVapdtYDFgzTP7kcoUGPVYcXA2d5ia0ahEnflch3xiMpOhEBGj95Q7ProTIuNKrHkEPUSq96pFzX0TuYKlVKUn8pcrWqvDE1bsw+ZPLrTi4GkH+1wGoIEIFPzUQpL6OFQdXY8agyXKHZ5cruwiyIyF5mtJaFbXNNkutChtseRcW/JFLZefnQqe5Cds/LRV0mpvIzs+VIyyHWboIuiJZu3JfRHXJUqtiD2tVvBOTP7nUyT/OQaM2AxBttmnUIk5nZbo/KCKqkqVWRYJU7nHWqngvDvuTS5RWCp9HswAAkCBKZpgkAZZzTKNZhfahYXKGSUSVUEqtCgv33IPJn1yibKWw3qxGfT8z1AIAUYJJAgAR+cYmiq76J/JlrFXxLUz+VGvZ+bnIKTwPS6FwvkEHIB/+ajNUAmAyScg3NkFSdKKscRJR9Sy1KuTdmPzJaaVD/b+jsf8tmCUVis1+MJjrI9/QEPmQoIYR/TqOxr0tw+UOl4iI/sLkT06zDPULAmCWVBAEwN+vZGU7g7k+AAFGqQHa8T4/EZGisNqfnFK2gY0AAcVmNUoq/AXUU5tQUifMSmEiIiXilT85peKiICVX+kA9tRkqQYQkqdEo4G9c1Y6ISIGY/Mkptg1sBBjMASg2S5AkFSb0eo6V/URECsVhf3JKZYuCAEBQQGsmfiIiBWPypxrJzs9F6tlfkJ2fi4TIODSqHw5R0gAwQ5Q0aFQ/nEP9drBHOhEpCYf9ySFVdeoDBC4KUgn2SCciJeK3Dzmkuk59XBTEPk/okU6kJBtTpzn1Oi4LXDMc9qdqeXqnPrmUnQ5ZVtke6UREcmDyp2qdzsqEVm3bpQ9gp76qlE6HtGXpkU5EJAcmf6pW+9AwGMz2/1TYqa9y7JFORErF5E/VCtEFIt8YjJIV/MoSkW8M5rS+SrBHOhEplVuTf15eHp577jmMGTMGTz75JH744Qd3vj3VQMWpaUnRibilbwKTGRAgwmQGbunZqa86nA5JRO5Q0/zq1mr/DRs24IEHHsC4ceNw7tw5vPLKK/jkk0/cGQJVo6qpaTMGTUZ2fi5OZ2WifWgYr/gdwB7pRDXDqn3n1DS/ujX5jxs3DlqtFgBgNptRrx6/BJWmuqlpIbpAROo6yB2mx2GPdCKqSzXNr3WW/Ldv345NmzaVe2zhwoXo3Lkzrl27hilTpmDGjBkO7Ss9Pb0uQlSMtLQ0uUMAAOhNRmTnnIPaTmV/ds45fHvsv/D308gQWeWUcuw8EY+d83js6g6PbfVckV/rLPk/8cQTeOKJJ2weP3PmDP7v//4Pr776Ku677z6H9tWpUyevHSVIS0tDt27d5A4DAHAuOwvaGwcBQW2zTa0247bw1oq6elXSsfM0PHbO47GrXnFxsdMXbb5+bB05dq7Ir24d9v/f//6Hl156CStWrED79u3d+dbkANtOfaU4NY2ISLlqml/dmvyXL18Og8GABQsWAAB0Oh3WrFnjzhCogoqFaEEBLZFTdLbcqnScmkZEpGw1za9uTf5M9MpRWVX/uAdjsPHonpLHBT1EqbTan4iIlKmm+ZWNfXxUZVX9G4/u4dQ0IiIvx+TvgywNZ0oSf6myDWc4NY2IyHtxeV8fxIYzRES+jcnfB7HhDBGRb+Owvw9iVT8RKdXG1GmVbuPSv67DK38Xys7PRerZX5Cdnyt3KHaVbdbDhjNERL6LV/4uUGjQY8XB1dBpbkKrFnHidxXyjcFIik5EgNb+8Lo7VdWsx2g2s6qfrDjLg8g3MPm7wIqDqxHkfx2ACiJU8FMDQerrWHFwNWYMmix3eNU262FVP1V1gqhR82uCyNtw2L+WsvNzodPchO2hVEGnuSn7LQDLtL6y9/aB8tP6iEpPEI2AoIZKKD1BJCLvw+RfS6ezMqG10wUPADRqEaezMt0cUXmc1kfV4Qkike9h8q+l9qFhMJjtH0ajWYX2oWFujqg8Tuuj6vAEkcj3MPnXUoguEPnGYAAVr/5F5BuDEaILlCMsK8u0PglSucc5rY8seIJI5HuY/F0gKToRt/RNYDIDAkSYzMAtfRMkRSfKHRoAcFofVYkniES+h2W8LhCg9ceMQZORnZ+L01mZaB8aJvsVf1katR+b9VCVEiLjSqv92c2RyOsx+btQiC4QkboOcodRKTbrocrwBJHcbXj3qahXj39jcmHyJyIrniAS+Qbe8yciIvIxTP5EREQ+hsmfiIjIxzD5e7iynfqIiIgcwYI/D8VGLETkyXZ8vwRGqdDm8XGRi2WIxvfwyt9DsRELERE5i8nfA7ERCxER1QaTvwdiIxYiIqoNJn8PxEYsRERUG6wM8xAVl10NCmiJnKKz5Yb+2YiFiDzdxtRplW5jMaDrMPkrXGVV/eMejMHGo3vYiKUaXKueiMgWk7/ClVb1CwDUUKGkqn/j0T1sxFIFToUkIqoc7/krmCNV/ZZGLEz85XEqJBFR5Zj8FYxV/c7hVEgioqox+SsYq/qdw5MmIqKq8eangrGq3zmWkyYVjDbbeNJEpGys6HcPXvkrTMVGPQmRcWhUPxyipAFghihp0Kh+OKv6q2A5aZIglXucJ01ERCV45a8QVVWns6q/5hIi40qPJ6dCEhGVw+SvEJVN6VuXugsv9Blhreonx2jUfjxpIiKqBIf9FYDV6XWHUyGJiGzxyl8BSqvT1TbbLNXpvOonIm8yvPtU1KvHk3K58MpfATilj4iI3InJXwFYnU5ERO7k1uRfWFiI559/HqNHj8a4ceOQlZXlzrdXNE7pIyIiZ9U0v7r1nv/HH3+Mjh07IjExEbt27cK6deuQnJzszhAUi9XpRETkrJrmV7cm/3HjxsFsNgMA/vzzTwQGBrrz7T0Cp/QREVFN1TS/1lny3759OzZt2lTusYULF6Jz58546qmnkJGRgQ0bNtTV2xMREXklV+RXQZIkqcpn1JGzZ8/i//2//4cDBw5U+pzi4mKkp6e7MSoiInKVTp062Uzn4/e6Y+wdO0c5kl/dOuz/7rvvIjQ0FEOHDkWDBg2gVtvOa7enNgdB6dLS0tCtWze5w/BIPHbO47FzHo9d9RxJ8N78vV4bzp4c1TS/ujX5Dxs2DFOnTsXOnTthNpuxcOFCd749ERGRV6ppfnVr8g8JCcH69evd+ZZERERer6b5lYv8EBER+RgmfyIiIh+j6MY+lokIBoNB5kjqVnExu/Y5i8fOeTx2zuOxq5rlO9veZDJf+V53VlXHzpVkm+rniLy8PGRkZMgdBhEROaFt27Zo2LBhucf4ve4Ye8fOlRSd/EVRREFBATQaDQRBqP4FREQkO0mSYDQa0aBBA6hU5e8u83u9alUdO1dSdPInIiIi12PBHxERkY9h8iciIvIxTP5EREQ+hsmfiIjIxzD5y+jdd9/Fk08+ibi4OGzfvl3ucDyG0WjEK6+8gpEjRyI+Ph5nz56VOyTF++mnnzB27FgAwIULFzBq1CjEx8djzpw5EEVR5uiUreyx+/XXXxEfH4+xY8fi73//O7Kzs2WOzrOIoojZs2fjySefxNixY3HhwgW5Q5KN3J9JJn+ZHDt2DD/88AM++ugjbN68GVeuXJE7JI9x6NAhmEwmbN26FRMnTsSKFSvkDknR1q1bh+TkZOvCNIsWLUJSUhK2bNkCSZJw8OBBmSNUrorHbsGCBZg1axY2b96M/v37Y926dTJH6FkOHDgAg8GAbdu24ZVXXsHixYvlDkkWSvhMMvnLJDU1FW3btsXEiRPx3HPP4eGHH5Y7JI/RunVrmM1miKKI/Px8+PkpeqFK2bVq1QqrVq2y/nzq1Cncd999AIDevXvjyJEjcoWmeBWP3T/+8Q/cfffdAACz2cyWtDWUlpaGXr16AQC6dOniVOtab6CEzyS/NWVy8+ZN/Pnnn3jnnXeQmZmJ559/Hp999hkXvXBAQEAA/vjjDwwaNAg3b97EO++8I3dIijZgwABkZmZaf5Ykyfp31qBBA+Tl5ckVmuJVPHbNmjUDAJw4cQIffPABPvzwQ7lC80j5+fnQ6XTWn9VqNUwmk8+dwCvhM8krf5kEBQUhMjISWq0Wbdq0Qb169XDjxg25w/IIGzduRGRkJPbv34+UlBRMmzaNa63XQNlVwwoKChAYGChjNJ5n3759mDNnDtauXYvGjRvLHY5H0el0KCgosP4siqLPJX575PhMMvnLpFu3bvjmm28gSRKysrJQVFSEoKAgucPyCIGBgdY1rxs1agSTyQSz2SxzVJ6jQ4cOOHbsGADg8OHD6N69u8wReY6UlBR88MEH2Lx5M1q2bCl3OB6na9euOHz4MADgxx9/RNu2bWWOSBnk+EzylEsmffv2xfHjxzF8+HBIkoTZs2dDrVbLHZZHGDduHGbMmIH4+HgYjUa8/PLLCAgIkDssjzF16lTMmjUL//jHP9CmTRsMGDBA7pA8gtlsxoIFC3DbbbfhxRdfBAD06NEDkyZNkjkyz9G/f398++23GDlyJCRJwsKFC+UOSRHk+ExybX8iIiIfw2F/IiIiH8PkT0RE5GOY/ImIiHwMkz8REZGPYfInIiLyMUz+5PUyMzPRqVMnxMbGYujQoXjsscfwzDPP1Kqfwq5duzBt2jQAQEJCArKysip97sqVK/H999/XaP/t2rUr93N+fj7uvfdem/f57rvv8Pjjj1e6n6ioqHIriRF5irKf29jYWAwYMACTJk1CdnY2fv75Z8ycObPS1166dAkzZsywu+2jjz7CRx99BMD2c1adL7/8Ehs2bLDZjyfiPH/yCc2aNUNKSor15+XLl2PevHl4++23a73v6pq7HD9+HPfff3+t3kOn06F///7Yu3cvnn32Wevju3fvxrBhw2q1byKlKvu5lSQJ//jHPzBp0iRs2bIF99xzT6Wv+/PPP3Hp0iW720aNGuV0PKdOnXLJfpSAyZ98Uvfu3fHll18CKLk67ty5M3799Vds2bIF33zzDTZt2gRRFNGxY0fMmTMH9erVw+7du7FmzRrodDrcfvvt1oWFoqKi8P7776Np06Z4/fXXkZaWBo1GgxdeeAEGgwHp6elITk7G6tWr4e/vj9deew23bt2Cv78/Zs2ahQ4dOiAzMxNTpkxBYWEhIiIi7MY8bNgwLFmyxJr8i4uL8fXXX2Pq1Kn44IMPkJKSgqKiIgiCgBUrViA8PNz62l27duG7776zdlEbO3YsEhMTcf/992Pt2rX49NNPYTabERkZiSlTprDHBCmOIAh48cUX8dBDD+H999/HF198gc2bN2PDhg345JNPoFKp0LlzZ8ydOxfz589HZmYmXn/9dQwcOBBLly6FKIq46667EBYWBgDWhZpmzZqFkydPIjg4GAsXLkSLFi3KfT4yMzPx1FNPYe3atdi6dSsAoEWLFvjzzz+t+/nqq6+wYsUKiKKIli1bYu7cuQgJCUFUVBRiYmKQmpqKoqIiLFmyBJ06dZLnAFbAYX/yOUajEZ9++im6du1qfax3797Yv38/bty4gY8//hhbt25FSkoKmjRpgvXr1yMrKwvLli3Dhx9+iG3btpVbn9xi8+bNKCwsxKeffooNGzbg7bffxqOPPopOnTph/vz5aNeuHaZOnYopU6bgk08+wbx58/Dyyy8DAObNm4e4uDikpKSUi6us++67D7m5uTh37hyAkvaoDzzwANRqNQ4cOIDNmzfjP//5D/r164ctW7Y4dCwOHz6M9PR07NixA7t370ZWVhb27NlT00NK5BZarRZ33HEHQkJCAAAmkwnvvvsudu7ciV27dkEQBGRlZSE5ORmdOnXCnDlzAADnz5/Hpk2bsGTJEpt99ujRAykpKejfvz8WLFhQ6XvfeeedGDlyJEaOHFlutO369euYPXs23n77bfz73/9G165dMXfuXOv2oKAg7NixAyNHjsS7777rqkNRa7zyJ59w9epVxMbGAgAMBgM6d+6MV155xbrdcrV97NgxXLhwASNGjABQcqLQoUMH/PDDD7j33nutXzpDhgzBf//733Lvcfz4cYwYMQIqlQpNmzbF3r17y20vKChAeno6pk+fbn2ssLAQN2/exHfffYfly5cDAGJiYpCcnGzzOwiCgMcffxz/+c9/MGnSJKSkpGDcuHHQ6XRYvnw59u7di/Pnz+Obb76xtp2tztGjR3Hy5EnExcUBAPR6PVq0aOHQa4nkIAgC/P39AQB+fn649957MXz4cERHR2P06NEIDQ3F+fPny72mdevW1n4gZfn7+yMmJgYAEBsbixUrVtQ4npMnT6Jz587WEYUnn3wSa9eutW63tDC+66678Pnnn9d4/3WFyZ98QsV7/hVZ+rKbzWYMGjTImnwLCgpgNptx9OhRiKJofb69TmQVH7tw4QJuu+0268+iKEKr1ZaL48qVK9aGTpaVtgVBqHTY/fHHH8ezzz6L+Ph4/P7773jwwQdx+fJljB07FmPGjEHv3r0REhKCX3/9tdzrBEFA2ZW8jUaj9fd9+umn8cwzzwAAcnNz2WOCFMtgMOD333/H9evXrY/985//xI8//ojDhw9j/PjxWLZsmc3rLCcLFZXtpidJUrnPsOXzYjKZqoyp7PeC5XVlX2P5blHarTQO+xOVcf/99+OLL77A9evXIUkSXnvtNWzatAndunXDTz/9hKysLIiiiH379tm8tkePHvj0008hSRKuX7+OMWPGwGAwQK1Ww2w2o2HDhvjb3/5mTf7ffvstRo8eDQDo2bOndbj9888/h8FgsBtfixYt0KJFC6xcuRKxsbEQBAE///wz7rjjDowbNw4RERE4fPiwTZfD4OBgnD17FpIk4dKlSzhz5gwA4IEHHkBKSgoKCgpgMpkwceJE7N+/32XHk8hVRFHEqlWrEBERgVatWgEAbty4gUGDBqFt27Z46aWX8NBDD+HMmTNQq9XVJm2gZOTt4MGDAICdO3eiZ8+eAEo+L//73/8AlNxes7C334iICPz000/WWTXbtm2rdYGvO/DKn6iM9u3bIzExEU8//TREUcTdd9+NCRMmoF69ekhOTsa4ceNQv3593HnnnTavjY+Px/z5863DiLNmzYJOp0OvXr0wZ84cLFmyBEuXLsVrr72G9957DxqNBm+++SYEQcDs2bMxZcoUbN26Fffccw8aNGhQaYxxcXF49dVX8cUXXwAAHnroIXz00Ud49NFHodVq0blzZ/z222/lXtOzZ0/s3LkTAwcOROvWrdGtWzcAJcWKp0+fxogRI2A2m9GrV68qpw4SuVPZ23WWz+Py5cutJ6+NGzfGyJEjMXz4cNSvXx+33XYbHn/8cRiNRuTl5WHKlCkYPnx4pfsPDAzEgQMH8NZbbyE0NBSLFi0CAIwfPx7Tpk3Dzp07ER0dbX1+jx49MHXqVOvtPwAICQnB3LlzkZiYCKPRiBYtWlRZO6AU7OpHRETkYzjsT0RE5GOY/ImIiHwMkz8REZGPYfInIiLyMUz+REREPobJn4iIyMcw+RMREfkYJn8iIiIf8/8Bf0lsfFjmVL8AAAAASUVORK5CYII=", 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", 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", 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", 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", 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", 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", 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hhIaGEhYWVikz3wJVNoB1Wi3Terbkma7NbGVo5b3wJoRwbZ6enowcObJcLSUrokquARfmqddRJ8BbwleIKsZoNPL1119XuKVkRagWwEePHiUqKuqWx7Zu3coTTzyh0oiEEFXFuXPn2LRpE7/99hv//e9/VRuHKksQMTExbNmyBS8vL9tjJ06cYN26dbjgNUEhhAsxGo3s2rWLkJAQIiIiKtTPt6JUmQHXr1+fefPm2T6+fv06c+fO5dVXX1VjOEKIKqKg1ExRlEorNbsbVWbA/fr1Iykp/7Zfi8XCa6+9xowZM+5YpnE3x48fv+Xj8PBwzGaz3capljvdAeeMzGYzCQkJZXrNoUOHHDQadcl5Ob9z586xa9cuFEWhZcuW+Pn5cfjwYVXHpHoVRFxcHImJibz11luYTCbi4+OZPXs2r732WomvrUgdsCNs2LCB33//nZdeeqncx1i0aBETJ0685bHz588THR1NmzZt8Pf3Z/z48Xh7e7Nnzx4GDhxY0WGXW25uLvfdd5/UAct5OT1FUTAajbZlBz8/Pzp27Ojw9y2oAy6O6gHcunVrvv76awCSkpJ44YUXShW+7upOAXzo0CF69uzJK6+8Ynts//79fP/996oGsBCuQqPRMHToUOLi4mjXrp3qM98Cbl2GFhgYWOx/S5YssT1vyZIld31uWfz666+MHTuWYcOGsXPnTgAOHDjAqFGjePLJJ5kxYwZms5kzZ84wcuRInnzySSIjI7l06RKff/45aWlpvPXWW7bjXbx4kfnz5/Ptt9+yYsUKoqKiSEhIYP78+fz888+sXr3aDl8pIdzThQsXbGVmBS0lnam3tWoBHBYWxpo1a0p8zNV4eXmxZMkSFixYwKxZs7BYLMycOZNPPvmE5cuXExwczMaNG9m7dy+tW7fmX//6F8899xzp6ek8/fTTBAQE3BLAderUYfLkyTz66KNERkbaHv/zn/9M586dpWxPiGIYjUa+/PJLtm7dagthZ6P6EoQjpaamlup548aNY9y4cXZ5zw4dOqDRaAgKCsLPz4/r169z5coVnn/+eQBycnLo0qULzzzzDDExMUyaNAk/Pz9b03YhRMUVbqzj7+/vVLPewtw6gNXw22+/AXD16lWysrKoUaMGISEhfPbZZ/j5+bFjxw68vb3ZsWMHHTp04Nlnn+Wrr75i4cKFzJkzp9R10Fqt1ml/qwuhJjW6mpWXBLCd5eTkMGbMGLKyspg1axY6nY7XXnuNyZMnoygKPj4+/OMf/yAzM5Pp06fz+eefY7VamTFjBgCNGjXipZde4oMPPrjr+9SvXx+j0ciSJUvsNnsXwtW5UvhCFW5H6aykHaXrkfNyDmVpKVlZ5ybtKIUQVUJoaCh16tShbt26Tj/zLSABLIRwCwaDgZEjR6LT6VwifMHN64CFEO7NaDTeUmZW2f18K0pmwEIIl1T4glt4eLhtM1xXIjNgIYTLKVrtoGZLyYqQABZCuBRXKzW7GwlgO9q9e/dtvRkef/xxW+vN0jKZTKxduxbI77C2Y8cOAJYvX26fgQrhotwpfEEC2K66d+9ul94MV69etQXw0KFDeeihhwD4/PPPK3xsIVyVoigcOXLEbcIX3Pwi3Lvvvlvs5x5++GHatm0L5Hcw+/bbb4t9buE2kHdT0A9Yp9OxZ88eQkJCuH79OgDp6em89tprto9ff/11mjZtSt++fWnfvj1nzpwhKCiId999l/nz5xMfH88nn3yCoijUrFmTGzdu2DqlpaenM3DgQHr27ElCQgLvvfceCxYsKOVXRQjXpNFoGDJkCCdOnKBNmzYuH74gM2C7O378OAcPHmTdunW2W44B5s+fT+fOnYmNjeXtt9+2dTw7f/48f/nLX1i9ejWpqanExcXx5z//mcaNG/Pss8/ajlu4U9qIESPYuHEjAOvWrWP48OGVfp5CVJYLFy5gsViA/Frftm3bukX4gpvPgEs7c23btq1tNlxRly5dokePHmi1Wnx9fWnSpAmQv3b1888/88033wCQlpYGQI0aNQgNDQXy7+QpuL33biIiInjnnXdITU3lp59+4oUXXrDL2IVwNgVrvk2aNGHQoEFote41Z3TrAFZDWFgYx44dw2q1kpOTQ3x8PJDfZGfQoEEMHDiQlJQU2xrvnX6TF9fprKBth0ajYdCgQbzzzjs88MADeHh4OPCMhFBH4QtuAQEBbjPrLUwC2M6aN2+Ot7c3w4cPp3bt2gQFBQH5DdRfe+011qxZQ0ZGxi3LC0UFBQVhNpt5//33qVatmu3x8PBwW6e0oUOH0rNnTzZv3uzwcxKisrlbtUNxJIDtaOjQobZ/P/PMM7d9/rPPPrvtsZ9++sn2748++ojMzEw8PT3vGKyxsbG2f1ssFjp06EB4eHhFhy2EU6kq4QtyEc4lbd++nUmTJjF16lS1hyKEXZ07d67KhC/IDNgl9e3bl759+6o9DCHsLiQkhHr16hESEuL24QsSwKICFEVx+/9BROUyGAyMGDHCpVpKVoTbLEFotVry8vLUHkaVYrFY3K4sSFS+U6dOsXnzZlutr6u1lKwIt5kB6/V6srOzycrKcunfnmazuVS1wGpSFAWLxYLFYkGvd5sfIaGCgvAtaCnZqlUrtYdUqdxq+uLn54fBYHDZ8AVISEhQewgl0mg0GAwG/Pz81B6KcGGFwzciIoKWLVuqPaRK53bTF3eYkbn6xqJClKRo+FaFC2534lYzYCGE85Pw/R8JYCFEpVEUhaNHj0r4/sH1/14XQriMgpaScXFxbtNSsiJkBiyEcLikpCRbmZmHh4dbtZSsCAlgIYRDGY1GVqxYYbvFWPyPBLAQwmEKN9YJDAyUWW8REsBCCIeoSl3NyksCWAhhdxK+pSMBLISwq6rWUrIipAxNCGFXoaGh1K9fn+DgYAnfEkgACyHsysPDgxEjRqDVaiV8SyBLEEKICjMajWzcuNFW6+vKHQkrkwSwEKJCCi64nTp1ihMnTqg9HJciASyEKLei1Q5VrZ9vRUkACyHKRUrNKk4CWAhRZhK+9iEBLIQoE0VROHbsmISvHUgZmhCiTDQaDYMHD+bEiRO0bt1awrcCZAYshCiVpKQk287jHh4e0s/XDiSAhRAlKmgpWXj7eFFxEsBCiLsq2lJSq5XYsBfVvpJHjx4lKioKgJMnTxIZGUlUVBQTJ07k2rVrag1LCFGIVDs4lioBHBMTw+uvv47JZAJg9uzZzJw5k9jYWPr06UNMTIwawxJCFCJdzRxPlQCuX78+8+bNs308d+5cmjdvDoDFYsHT01ONYQkh/nD+/Hl27dol4etgqpSh9evXj6SkJNvHtWvXBuDw4cMsX76cL7/8slTHOX78uEPGp7ZDhw6pPQSHcddzc7fzslgshIaGUqNGDfz8/Dh8+LDaQ7I7Z/ieOU0d8LZt2/j8889ZsGABgYGBpXpNq1at3G62fOjQITp06KD2MBzCXc/Nnc5LUZRbZrqdOnVyy5lvZX3PTCbTXSeKTnE5c/PmzSxfvpzY2Fjq1aun9nCEqJIKWkoW1PpKS0nHU30GbLFYmD17NqGhoTz33HNA/m/dqVOnqjwyIaqOwtUOJ0+e5L777lN7SFWCagEcFhbGmjVrADhw4IBawxCiypOWkupxiiUIIYQ6pM5XXRLAQlRREr7qkwAWogpSFIXffvtNwldlql+EE0JUvsItJe+77z4JX5XIDFiIKuT8+fO2MjO9Xi/9fFUmASxEFWE0Glm5cuUt28cLdUkAC1EFFL7gVrNmTWkp6STkuyCEm5NqB+clASyEG5PwdW4SwEK4qaSkJAlfJydlaEK4qZCQEBo1akRQUJCEr5OSABbCzRS0lNTr9QwZMgStVivh66RkCUIIN2I0Glm/fr20lHQREsBCuImCC27x8fHExcWpPRxRChLAQriBotUOrVu3VntIohQkgIVwcVJq5rokgIVwYRK+rk0CWAgXpSgKx48fl/B1YVKGJoSL0mg0DBo0iJMnT9KqVSsJXxckM2AhXMz58+cxm81AfktJ6efruiSAhXAhBS0lN2zYIC0l3YAEsBAuovAFt9q1a0tLSTcg30EhXIBUO7gnCWAhnJyEr/uSABbCiUlLSfcmZWhCOLHQ0FDCw8MJDAyU8HVDEsBCOKGClpI6nY4hQ4ag0WgkfN2QLEEI4WROnTrFunXrbLW+0s/XfUkAC+FETp06xebNm0lISODEiRNqD0c4mASwEE6iIHylpWTVIQEshBMoGr5ywa1qkAAWQmUSvlWXBLAQKjt58qSEbxUlZWhCqGzgwIE0btyYli1bSvhWMTIDFkIF586ds5WZ6XQ66edbRUkAC1HJjEYjq1atYt26dbbt40XVJAEsRCUq3FgnJCQEnU6n9pCEiiSAhagk0tWs6sktoWm+BLAQlUDCt2qxWK18tDOOKev33/V5EsBCOJi0lHQupjwLV7PMmPIct6XTx7tPsiUuiSzT3d9DytCEcLDQ0FDuvfdeqlevLuGrIovVyse7T7IzIZnEKyk0MGbRMzyYqd2bo7Pj9k6mPAs7E5LRluL7LAEshIMUbik5ePBgaSmpsoJZqVajwaDVkmHKY0tcEgDTera02/ukZJpIyTThqS/5AqssQQjhAEajkTVr1pCbmwtIS0m1FTcr1Wo07ExItutyRJCPJ0E+nqV6rgSwEHZWcMHtzJkz0lLSSRTMSu8kNav4z5WHp15Hz/BgrIpS4nMlgIWwo6LVDm3atFF7SIK7z0oDvUs/Yy2tqd2bM6BJMNnxR+76PAlgIexESs2cV3GzUqui0DM8uFTrtWVx2mhk0dRR/L558V2fJwEshB1I+Dq/qd2bM6hlGL6eesxWBV9PPYNahjG1e/MKH1tRFBITE20fh4eHo9FoCA8Pv+vrVAvgo0ePEhUVBUBiYiKjRo0iMjKSN998E6vVqtawhCiX//73vxK+Tk6n1TKtZ0vWjO3BnK5hrBnbg2k9W1aoBC0nJ4eVK1fy0EMP8ac//YnU1FQAPDw82L59O+vWrbvr61UJ4JiYGF5//XVMpvyF7zlz5vD888+zYsUKFEVhx44dagxLiHIbMGAAAwcOlPB1AZ56HbW8PSq07JCUlMTbb7/Nfffdx5QpU/j111/x8vLi5MmTtueEhYWV+LOgSgDXr1+fefPm2T6Oi4vj/vvvB6B79+7s3btXjWEJUSaJiYm2MjOdTif9fKsAs9nMuHHjaNeuHR999BEpKSm0bt2aefPmcfz4cR544IEyHU+VGzH69etHUlKS7eOCgnUAHx8f0tPTS3Wc48ePO2R8ajt06JDaQ3AYdzm3c+fOsWvXLmrVqkWfPn3c5ryKctfzgtKfm8lkwmAw2DLq/PnzAPTs2ZPBgwfbfvGWp+TQKe6E0xZag8nMzMTf379Ur2vVqhWenvYtH1HboUOH6NChg9rDcAh3OTej0cjp06cJCQkhIiICrVbrFudVlLt8v+6kNOd29uxZFi1axPLly1mzZg0dO3YE4NNPPyUgIIDQ0NAS38dkMt11ougUVRAtWrRg//78rkG7d++2nagQzkaqHdyboij88MMPREZG0qFDBz799FPS0tL47rvvbM9p1qxZqcK3NJxiBjx9+nRmzpzJ3LlzadSoEf369VN7SELcRsLXva1YsYJ//vOfnD59GgCDwcCQIUOIjo6mffv2DnlP1QI4LCyMNWvWANCwYUOWL1+u1lCEKNGFCxckfEvBlGchJdNEkI+n3W9ucITC159OnDjB6dOnCQ0NZfz48YwdO5ZatWo59P2dYgYshLMLDQ2lSZMmBAQESPjeQeFWjwUB7IhWj/ZgtVrZv38/c+bMYdCgQYwZMwaAp556io4dOzJgwAA8PDwqZSwSwELcRcEMSavVMmjQIGkpWYzCrR499TqHtXqsiLS0NL788ksWLVrEmTNnALhx44YtgOvVq0e9evUqdUwSwEIUw2g0cujQIYYNG4bBYLilWkf8T0mtHp/p2kzV5YjTp0/z+eefs2bNGrKysgAIDg7m6aeftt2NqxYJYCHuoPAFt7i4ONq1a6f2kJzW3RqQF7R6rBPgrcLI8v3yyy8sWbIEgB49ehAdHU3NmjVtN3+pSQJYiCKKVju0bdtW7SE5tYJWjxmmvNs+54hWj3eTkpJCbGwsZrOZl19+GYAhQ4YQFxfHk08+SbNmzQDnucFEAliIQqTUrOwKWj0WrAEXcFSrxzs5duwYCxYsYP369ZhMJnx8fHjqqafw9/enWrVqvPPOOw4fQ3mUOYC3b99O3759HTEWIVQl4Vt+BS0ddyYkk5plItD7f1UQjmI2m9m6dSsxMTG2G7kAevfuTXR0NL6+vg57b3spMYDT09N5//33mTVrFgBr165l/fr1vPnmm9SpU8fhAxSishiNRgnfcipo9fhM12aVVgd86tQpJk2aBICfnx+RkZFMmjSpxB68zqTEAB41ahQffPCB7eOYmBi2bdvGuHHjGD58OBMnTkSnc/6CayFK0r9/fxo1akTz5s0lfMvJU69z2AW3X375he+//56//vWvQH4vmNGjR9OuXTsef/xxl5jxFlViXU3//v1ZunTpbY9t2LCBK1euMGTIEH755ReHDVAIR0pMTLT1pdZqtbRo0ULC14mYTCZWr15N79696du3L++++y7Hjh2zfX7evHlMmDDBJcMXSjEDfuaZZ0hISLjlMaPRyJEjR8jIyCA5OZnJkyczYMAAXn31Vby8vBw2WCHs6dSpU2zevJk6deowcuRI9Hq5Ju0sLly4wJIlS1i6dCnXrl0DoEaNGkRFRTn89uDKVKqfuMJrKh07dqRWrVp06NCBzp07M2XKFOrWrcvSpUuZOnUqMTExDhusEPZSEL5Wq5W6devKMpoTMZlMdOvWjRs3bgD5Sw3R0dEMGzYMb2/16okdoVxVEIGBgbc9Pn78eNauXWuXQQnhSIXDVy64qS87O5sNGzYwbNgwqlWrhqenJyNHjuTSpUtMnjyZzp07u+33p8wBfKfwLfDJJ59UaDBCOJqEr/M4d+4cixcvJjY2luvXr6PVahk1ahQAs2fPrhLfF7suejVq1MiehxPCri5cuCDhqzJFUdi9ezcxMTF8++23th3Q27dvT82aNW3PqyrfF7nqIKqM0NBQmjVrhp+fn4SvSsaPH8+WLVuA/K3bhw8fzqRJk6rsLjgSwMJp2au5d+GWko8++qi0lKxECQkJeHt727bw6dWrFwcPHmTcuHGMHTuW2rVrqzxCdUkAC6djz+beRqORX375hWHDhuHp6SktJSuB1Wplx44dxMTE8J///Ienn36a2bNnA/k3dkVGRlZaw3NnJwEsnI69mnsXbSnpqH29RL6bN2+yYsUKFi5cyO+//w6Ap6cniqLYnmMwGNQanlOSABZOxV7NvYs21nGmfr43c3KJv5ZO45p++Fdzj0BauXIlf/3rX8nMzASgTt26TJo4kaioKIKCglQenfOSABZOxR7NvZ21q1luXh6PL93N/nPXyMq14G3QEVG/JmvGdsfgYnfhWSwWUlJSbGu44Y0bk5mZSUB4K/w69uaedn9CaVKH6jVqqDxS5yYLYsKpFDT3vpPSNPd21vAFeHzpbnYlJJObZ0Wv1ZCbZ2VXQjKPL92t9tBKLTU1lY8//pj27dszefJk2+M/ZXrT7Pm5NBr/OrXu60xmnsKWuCQ+3n1SxdE6P9f6tSvcXkWbe8fHxztl+N7MyWX/uWt3XFrZf+4aN3NynXo54vjx4yxYsIB169aRk5MDgF6v5+bNm3h6+7AzIRmvmre2p3WWPeGcmQSwcDoVae798MMPc8899zhdS8n4a+lk5VrQa28fU3auhfhr6bQPc761UqPRyLRp09i3b5/tsQcffJDJkyfTu3dvtFotF9OynHpPOGcmASycTlmbe589e5bQ0FBbmVmLFi0qcbSl07imH94GHbl51ts+52XQ0bimnwqjurPc3FxbtUKtWrX49ddf8fX1JTIykokTJ3Lvvffe8nxn2hPO1cgasHBpRqORNWvWsHr1asxms9rDKZZ/NQMR9WtiLVSSBflLKxH1azrF8sPhw4d5+umnuf/++8nNzQXyW0CuXLmSuLg43n333dvCF/63bHSnc6usPeFclcyAhdMp7Y0YhS+41atXz+n7+a4Z291WBZGda8GrUBWEWnJzc9m8eTMLFiyw7RSs0Wg4ePAg1apVA6B795LHp8aecO7AuX9iRZVUmhsxnLnaoTgGvZ5NEx90ijrgnJwcPvroI5YuXcqVK1cAqF69Ok8++SQTJ06kQYMGZdq6XY094dyBBLBwKqW5ESPx9wSXC9/C/KsZVL/gZjAY2LRpE1euXKFFixZER0czYsSICjc8d+SecO5IAlg4lZJuxDiRcJbvNrtu+KohJyeH9evX869//YslS5YQFhaGVqvlnXfewdvbmy5dusjXUCUSwMKplHRFvVnD+lxs2RJvb28J3xKcP3/e1vA8NTUVgNjYWGbMmAFAnz591ByeQAJYOJnibsSwWK30DA/Gy+BB//79garTtLusfvrpJ7744gu2bdtma3jetm1boqOjGTJkiMqjE4VJAAunU/SKerWMa4RlXWLyhPyr8RK8dxcTE8NXX32Fh4cHQ4cOJTo6mo4dO8rXzQlJAAunU/iK+sFjcezZfgyNj8J/T56kQ4cOag/PqZw5c4aFCxfSt29fevToAcCUKVNo3rw5Y8eOJSQkROURiruRABZOK/H3BPb+51s0KEREREg/3z9YrVa+//57Fi5cyHfffYeiKJw+fdoWwJ06daJTp04qj1KUhgSwcEpGo5HV69ZzLSOHvj26utUFt/JutXTz5k1WrlzJokWLiI+PB/IbnhcsMwjXIwEsnE7cyZM8+bePSbqRQW6thqz+KZXOST+4ZN/cwiq61dLChQt55513AKhTpw4T/2h4Xng3YeFaXPenWbitPy/cytmUdDSh4ejCmmG2KLa+uZsmPqj28MqtLFstWSwWvvvuO3JycnjssccAiIqKYs+ePYwfP57+/fs7/a3XomTyHRRO5WZOLqd9GqBprIcaobZlB1fpm1uc0m61dOPGDWJjY1m8eDGJiYmEhYXx6KOPotfrqVWrFhs3blTpDIQjSAALp3D27FlCQkKIv5ZJttmKPrDObc9x5r65JSnpDr99h35l08pY1q5dS3Z2NgANGjRg4sSJ5OXlyWzXTcl3VaiuoLFO7dq1GThshMv0zS2Lu93hp7ucwNBHRts+7tWrF9HR0fTp0wedThrauDMJYKGqwl3NGjRoQKCvNxH1a7KryJ/rztQ3tzwK3+FnzUonI/EU1Vt0wqooDHywO5bdLenSpQsTJ06kSZMmag9XVBIJYFEpTHkWrmaZMeVZbH+GF9dS0hn75tpDD38Tm79fyvE9O1CsViJem0/fds2Y2r0503rtQluKSgjhXiSAhUMVLr1KvJJCA2MWPcODeSRUz5bNm+/Y1cyZ+uZWVG5uLlu3bmXBggUcPHgQyL+Vukevh5jd/z5aNJXZblUmASwcqnDplUGrJcOUx/p9x9h9/hAR9WvetaWkM/TNrYjMzEzuv/9+Ll26BEBAQACjR49m4sSJNGzYUOXRCWcgASwcprjSK4N/IFc9a9K+Yye3usNNURR+/fVX2rZti0ajwcfHhzZt2lC9enVbw3MfHx+1hymciNMEsNls5pVXXuHChQtotVrefvttwsPD1R6WqICipVfKH5s2ajQaaNialh07u0X45ubmsnLlSmJiYvj111/ZtGmTbR+1+fPn4+fn5xbnKezPaQJ4165d5OXlsWrVKn766Sf+7//+j3nz5qk9LFEBhUuvcq5eIP3UUXwjeqP1MBDkU42avtXUHmKFJCUl8a9//YvFixeTlpYGQGBgIMnJybbn+Pv7qzU84QKcJoAbNmyIxWLBarWSkZEhheduoKD0as3O/aQd34/ZlEP25US8whq7/HblL774IkuXLrU1PG/Tpo2t4bmXl5fKoxOuwmlSztvbmwsXLvDII49w/fp15s+fX+Jrjh8/Xgkjq3xl2Y3W2dVPScTr9H6um0141AnHEBRKuwAND/hku9R5Zmdno9FobFu15+bmotFo6NmzJ4899hgtWrRAo9Fw4sQJlUdqX670PSorZzg3jVKwMKeyOXPmYDAYePHFF7l06RJjx45l69ateHp63vZck8nE8ePHadWq1R0/78oOHTrkNk3HC9f5tu/YCYvBmwe73O9SM9/ExEQWLlzI8uXLefnll3nmmWcASE1NxWQyERoa6lbfs8Lc9byg8s6tpKxymhmwv78/Hh4eQH65Tl5eHhaLReVRifK6000Whw8fdonwVRSFH374gZiYGLZv3267eHjkyBHbcwIDA9UannAjThPA48aN49VXXyUyMhKz2cy0adPw9vZWe1iinM6ePeuSW8dv27aNv/3tb5w+fRoAg8Fga3jerl07lUcn3I3TBLCPjw///Oc/1R6GsJM+ffpQv359mjZt6vTha7FYbE1vcnNzOX36NKGhoUyYMIExY8ZQq1YtlUco3JXTBLBwfWfOnCEkJAQvLy80Gg3NmjVTe0jFslqtfPfdd8TExBAaGmoreXz00UdZunQpDz/8sG1JTAhHkQAWdlG4pWRkZCQGg3P2bkhLS2P58uUsWrSIs2fPAlC9enX+8Y9/4OXlhV6vZ+DAgeoOshjl3UtOOC8JYFFhRVtKOuPM8fz583z00UesWbOGrKwsAOrVq2fbV82Za3crupeccF4SwKJCimspqaY7zRRzcnJYsmQJAD169CA6Opp+/fq5RMPzsuwlJ1yLBLAoN2cL34KZ4nfHTvP7j9uxXPqdp2d9wF96tODee+/l3XffpUePHjRt2lS1MZZVafeSE65JAliUy+XLl50qfAFmLNnM+hXLuHFsL0qeGYBV/96FRqNhWs+WTJ48WdXxlUdJe8mlZJqoEyDlmq5KAliUS3BwMG3atMFgMKgavmazma1bt/LFggUcPHDA9rh/k7bU7NwP37Bwl54p3m0vuUDv/M8J1yUBLMpEURQ0Gg0ajYa+ffsCqDrzNZvNvPTSS9y4cQOtpxdBHXpSK6IfnkEhtue48kyx8F5yRffIc/WGRkICWJSB0Whk3759PP7447Za38r2yy+/sHTpUubMmYOvry/e3t5Mnz4djVbHFnMo2dxegeHqM8Wp3ZsDsDMhmdQsE4He/6uCEK5NAliUSuELbnFxcXTs2LHS3ttkMrFx40YWLlzI4cOHAWjXrh0TJkwA4KmnngIgc2ecW84UdVot03q25JmuzaQO2M1IAIsSFa12qKwOWRcuXGDJkiUsXbqUa9euAVCjRg2ioqLo06fPbc9395mip17nkssoongSwOKu1Cw1mzhxIgf+uLDWqlUroqOjGTZsWLFNmmSmKFyNBLAoVmWGb1ZWFuvWraNz5840aZK/VXt0dDShoaE89dRTRERElPq9ZaYoXIUEsChWYmKiw8P33LlzLFq0iNjYWG7cuMG4ceOYO3cuAMOGDWPYsGFlPqb0TBCuQgJYFKt37940aNCAe++9167hqygKu3fvJiYmhm+//da2r1r79u3p1q1buY8rPROEq5EAFrc4c+YMwcHBeHt7o9FobMsB9vT3v/+dDz/8EAAPDw+GDx9OdHR0hS/uSc8E4WpkWiBsjEYja9euZdWqVeTm5trtuAkJCbds5zNkyBBCQ0OZMWMGv/32G/Pnz69w+JbUM8GUJ9tbCecjM2AB3HrBrWHDhhVuKWm1WtmxYwcxMTH85z//oUOHjrz4xixMeRZatGjB0aNH0evt9+MnPROEK5IAFnatdrh58yYrVqxg0aJFJCQkAKA3GEjS+jN95+/cY8xySG2u9EwQrkgCuIqzZ/j+9NNPjBw5kszMTADCwsJo0vNRkuu1x+DjT3p6usPWZQv3TAAwWxQ8dPnn4ep3wgn3JWvAVVhycnKFwtdisXDq1Cnbx61bt0ar1dKtWzeWLVvGvgMHMbfpjcHH/5bXOWpddkrXpgRU8+C/V9I4fuk6/72SRkA1D6Z0dZ3+v6JqkRlwFVa7dm3atWuHXq8vU/impqayfPlyFi9eTHp6OsePH8fLyws/Pz8OHTpEzZo1AbiYllWp67Kf/niKtBwzzWoHYLZY8dBpScsx8+mPp6QKQjglCeAqyGq1otVq0Wg09O7dGyhdS8nffvuNmJgY1q1bR05ODgANGzbk7NmzNG+ev6ZbEL5QueuyRasgCoe+K/cDFu5NliCciCnPwtUss0NLpoxGI8uWLbOt0xb09r2btLQ0+vfvT48ePVi+fDk5OTk8+OCDrFq1ioMHD9rCt6iCdVmrotzyuCM6lBVUQdxJwWxbCGcjM2AnUPgOrsQrKTQoVClgzzu4Cl9wO3HiBJ06dSr2uTdv3sTfP3/t1t/fn8zMTHx9fYmMjGTixInce++9pXrPWzqUpSn4euqlCkKIP0gAO4HCd3AZtFqHVAoUrXYorp/v4cOHiYmJYfPmzezevZvGjRuj0WhYsGABderUwc/Pr0zvW7hD2Q/7DtLrT50cshQgVRDCFckShMoq4w6ukkrNTCYTa9asoU+fPvTu3ZvVq1djMpn48ccfbc9p2rRpmcO3ME+9jlreHg4NQqmCEK5GZsAqc/QdXCWF79y5c/niiy+4evUqANWrV+fJJ59kwoQJ3HPPPeV+XzVIFYRwNRLAKnP02uX58+dvCV/438aaAPHx8Vy9epWWLVsSHR3N8OHDi2147sykCkK4IlmCUFnhSgGropBrsdr+bY+1ywcffJBhw4YRERHBihUr6NWrF1u3brV9/vnnn+err75i9+7djBkzxiXDF26tgsizWknPMZP3R5tLqYIQzkpmwE5gStem7EpIZv+5a2Tk5OJrSiOifs1yr13+/vvvBAcH4+Pjw4ULF1ixYgWxsbGkpqYCsGbNGgYNGgRAkyZNHNJysrIF+XhSw8vAz+eukp6Th8WqoNNq8Kump3P9WlIFIZySBLATKLx2ef1mOjX8/cq9dlmw5puens7vv//Ov//9b1vD87Zt2xIdHc2QIUMccRp3VbjG2VFVEGevZ3Ajy4xWq0Gr1aAAN7LMnL2eIcsPwilJAKus6NqlQaux/busa5eFL7hlZGTwzTff4OHhwdChQ4mOjqZjx46VtqFmgcqqcb6Zk8vVDBMGvY48qxVFAY0GDHodVzNM3MzJxb+awW7vJ4Q9SACrrHAVRK7FSprJgqe3FYNOW+oqiDNnzvD++++TnJxMp06diIiIoF27doSGhjJ27FiCg4Mr6WxuVxk1zgDx19LJNluopteioLUFsAbIMVuIv5ZO+7Agu72fEPYgAayyIB9Pqnt5sCvhCtlmCwqQkHYVLw8dPcJrF7t2abVa+f7771m4cCHbt28HwNvbm6efftpWavbXv/61Es/kdiXVONuzMqFxTT+8DTpy86xoyA/fAl4GHY1rlr+GWQhHkQBWmadex+GkVLLMFgrHVJbZwuGk1NsC6ubNm6xatYqFCxcSHx8PgE6no3nz5owZM4aHHnqo0pcZilOZu1T4VzMQUb8mu4oEvlVRiKhfU5YfhFOSAFbZ1YxsUrNMaICCljUK+X86p2aZuJqRTS1fL9vzDx8+zCuvvAJA3bp1GT9+PPfccw81atRw2Nbx5VXZ/RnWjO3O40t3s//cNbJzLXgZdETUr8masd3t+j5C2IsEsMoOJ6VisYJOq8FqVWzhq9VqsORZWLByPR7XL9qWE3r06EFkZCR9+vRhwIAB6PV6lD+6jTlT+MKt/RmKzkod0Z/BoNezaeKD3MzJJf5aOo1r+snMVzg1CWCVtQ8LRKeFvD/CF0AxZeFh/BmvUz/y4dIUtFotI0eOpH79+mg0GqZOncpPP/2EyWRCr9c7XfAWVlnd0Arzr2aQC27CJUgAq6yWrxeB3p4kZ5jQpV7AcHIPHgm/oLGYAWjQoAETJ06kevXqwK2lZnFxcdx///0qjr5kldUNrTBTnoWUTBNBPp5S/yucmgSwykx5FtqHBbIr7nf0Wz5Ao+TfNGENa0aL3oP5z5xpeHvm/xldtLHO3fr5OpvK6IZWuOa4IIAdUXMshL24dADnWiy46g2m165dY926dQx4fDQ3ss3c3+QezrTrQY5FIazbAPxC6pFrsXAjJw9vT4Nddy92VwU1x/k03MwxO6TmWAh7cekAnrJ+P+3q1XapGc6vv/5KTEwMGzZswGQyUa9BA1ulQMOhT5Genm7ru1tQKSDhWzJTnoUf4pO5kJbFjexcW0P26l4GfoiXbmjCObl0AGeZLC4xw8nNzWXLli0sWLCAX375BcivWOjXrx+hwcH09PJkS1wSeVYrN0wWPL0t6LVaW6XAxYsXJXxLkJJp4vjlG9zIzs2vItGAxapwLdNE3OUbdt+BWQh7cOkAtqI45K4qe+vfvz+HDx8G8vdXe/LJJ5k4cSINGzYEoElODu//EMeVDBMK8Huaidq+nkRP7AHkl56FhYURHh4u4VsMX0892eY8in51NECWOQ9fT5f+URduyqV/Ko1Xb5Jr1VLbz9NpZjiKonDw4EHCw8MJCsovhRowYADZ2dlER0czYsQIfHx8bnnNnz7+N1czTPkdvP5oln7tYhIRH2wm7vUn0Gg0NG7cWI3TcRkZpjy89DpMZsstv6QURcHbQ0eGKU9qgoXTcaqF0y+++IInnniCoUOHsnbt2hKfb7UqXMvM4UaO2WH9Xk15Fi6mZZW4N1tOTg4rVqzgwQcf5OGHH2bp0qW2z02ZMoUff/yRcePG3Ra+VzOyib+Wjlarye9hAGhvJGM4c4jEn3dwIeWG/U/KDQX5eNIqtDo1farl38Si5N/MUtOnGi1Dqks/YOGUnGYGvH//fo4cOcLKlSvJzs5m8eLFpXuhRoNGKflpZVXakqakpCT+9a9/sWzZMlJSUgAIDAzE0/N//8MbDMXPvA4npZJnUdBq82dtmhvJ6M8eAUUhz7cWv13JoG5QdfufoJvx1Ovo1TiEdFMedfG27QkH0KtxiNMuT4mqzWkC+Mcff6RJkyZMmTKFjIyMUnXyyp/heBLgZbD7EkRpSpo++ugj/v73v2Ox5M+O27RpY2t47uXldafD3qZ9WCB6nQarkh++ujNHAAVLcCM0dZvSoZ573NHl6IbsUOSuuywT/tU8HH7XnRAV4TQBfP36dS5evMj8+fNJSkri6aef5ttvv73rRae63nqu5+ShM+eQeCqOSzr7rKjkWqxs/OU8iem5pOdayVMU9BoNvhoza65coLN3Fgad1jbL7dmzJ4899hgtWrRAo9Fw4sSJMr1fXR89584loT97BI2iYAluiDmkCff4enDu1AnO2eWs1GGxKqw6lcqh5EzScvMIOHCJDsE+jGwaiE5r/wuK3f2g831+pJm8CfDUYdDl8OuRI3Z/n6IOHTrk8PdQg7ueFzjHuTlNAFevXp1GjRphMBho1KgRnp6epKam2i5k3Ymfjw+5eguDWobxp/vtV4Z2MS2LpO3nSc8DjVaL/uY1OL6LjJN7+f2eljR4YSR1Arxp164dQ4cOJSQkpELv90NICF2fnkkqCrm1G6INa0bzWv4ceP4RvO6yfOEKPtoZx5E0Bb2XD4a8dPRePhxJU6iX6eXUpYNlcejQITp06KD2MOzOXc8LKu/cTCYTx48fL/bzTnMRrkOHDuzZswdFUUhOTiY7O9vW/6A43p46BrUMs/ufmL6eerJyzXD+BMq2z+DLN+HoDsjNJi/9BtX++Ataq9VWOHwBQkNCGPRgd1q27UDdpq3o3iiECfc3xqB3mt+P5VJSQ/aSLmwK4e6c5v/wXr16cfDgQYYPH46iKLzxxhvodHdfK/x0WAR+DthGfc/en8lb8TdIvZz/gFYPjdtDq55Uv+decuyUG1arFa1Wy7w9/+U3fQjejUKpmZFBnqK4xA0mJanMhuxCuCKnCWCgzFvoGEoI6LLIysrC+48wb9qwPtbryWh9a6C07Ial2QN4+PpTo5qBZsH+dilpMhqN/PjjjwweOoydCcm33UrtCjeYlKSyG7IL4WqcKoArm9Vq5bvvvmPBggVcvHiRvXv35t/00PAexr7zCUfM/ihaHdm5eXgZ9Gg1GruUNBXu7fDz4aNuO0us7IbsQrgap1kDrkxpaWl89tlndOrUiVGjRvHDDz9w7tw5Tp8+bXvOe9EjqO7jhfHqTYxX0zFevUlANQ+mdG1aofcu2ljnoW5dip0JusMscWr35gxqGYavpx6zNb8huyPW7YVwRVVqBnzjxg1mzZrFmjVryMrKAqBevXpMnDiRqKgoatSoYXvupz+eIi3HTLPaAbbOWmk5Zj798VS512WL62rmzrNENRqyC+Eq3D6AC3orQP627d988w1ZWVn06NGD6Oho+vXrd9vFvsJX762KQsE2mRVZl71bS0k1tu2pbJXRkF0IV+O2AZySkkJsbCwrV67k3//+N9WrV8dgMPDxxx9Tr149mjVrVvxrM01cy8jhaoaJ6zn/6y1bo5qh3I1/Ll26VGxLSZklClE1uV0AHzt2jAULFrB+/XpMJhMAGzduZPz48QD06dOnxGME+XiSZjJzLTMHjUaDTvO/xj8GD2251mW7d+9OWFgYjRo1KvbuPpklClG1uEUAK4rCxo0bWbBgAQcOHLA93qdPH6Kjo3nwwQfLfEyNAhQNyjI2/klISKB27dr4+fmh0WgIDw8v8ziEEO7LLQJYo9GwdOlSDhw4gJ+fH6NHj2bSpEk0atSoXMdLyTTh72XAZLHmb29jVfDQ5m9vU9rGPwVrvtWrV2fMmDFUq1atXGMRQrgvlw7g48eP2+7nfuGFFxg8eDCPP/44vr6+FTpukI8nNX08qabXUTfA27YGrNVo8PXUl7gEUfiC27333ntLa0ohhCjg0nXAq1atsv27R48eTJgwocLhC/+7gcCq5G955KnX2ioiSioNkw00hRCl5dIz4GeeecZhxy7aWzbQ27PE0jAJXyFEWbh0ANepU8dhxy5cGlawI8bdZr5Xr16V8BVClIlLB3Bl8NTrSlXzW7NmTSIiIrBarRK+QohSkQCuoIKWkhqNhu7duwNI+AohSsWlL8KpzWg0snjxYm7evAnkB6+ErxCitCSAy6nggtu1a9fKvAecEEKABHC5FK12iIiIUHtIQggXJAFcRlJqJoSwFwngMpDwFULYkwRwGSQnJ0v4CiHsRsrQyqBr167UrVuXhg0bSvgKISpMZsAlSEhIuKXM7G79fIUQoiwkgO/CaDSyfv16Vq5cSU5OjtrDEUK4GQngYhS+4NakSRNpKSmEsDsJ4DuQagchRGWQAC5CwlcIUVkkgAtJSUmR8BVCVBopQyskKCiILl26YDabJXyFEA4nAQxYLBZ0uvxm6127dkVRFAlfIYTDVfkliKItJUH6+QohKkeVDuCCC24pKSnSUlIIUemqbABLS0khhNqqZABLqZkQwhlUuQCW8BVCOIsqF8DXrl2T8BVCOIUqV4bWpUsX6tSpQ4MGDSR8hRCqqhIz4ISEBNLS0mwf33PPPRK+QgjVuX0AF24pmZ2drfZwhBDCxq0DuPAFt6ZNm1KtWjW1hySEEDZuG8BS7SCEcHZuGcASvkIIV+B2ASwtJYUQrsLtytCCgoJ44IEHyM3NlfAVQjg1twngwi0lH3jgAWkpKYRwem6xBGE0Glm0aNEttb4SvkIIZ+fyAVxwwS01NZWTJ0+qPRwhhCg1l16COHPmDFu2bJGWkkIIl+RUM+CUlBR69OhBQkJCqZ6/fft2qXYQQrgspwlgs9nMG2+8Uaa71SR8hRCuzGmWIN577z1GjhzJggULSnyuoigAtGvXjoiICHJzcx09vEplMpnUHoLDuOu5yXm5nso4t4JsKsisopwigDds2EBgYCDdunUrVQCbzWYAfH19iYuLc/TwKt3x48fVHoLDuOu5yXm5nso8N7PZfMe/7jVKcdFciUaPHo1Go0Gj0XDy5EnuuecePv/8c2rVqnXH51utVjIzM/Hw8JClByGE01IUBbPZjI+PD1rt7Su+ThHAhUVFRfHWW28RHh6u9lCEEMKhnOYinBBCVDVONwMWQoiqQmbAQgihEglgIYRQiUsGcFnvmHMVX3zxBU888QRDhw5l7dq1ag/HLsxmMy+++CIjR44kMjLSLb5nR48eJSoqCoDExERGjRpFZGQkb775JlarVeXRVUzhczt58iSRkZFERUUxceJErl27pvLoyq/weRXYunUrTzzxhEojyudyAVyeO+Zcwf79+zly5AgrV64kNjaWy5cvqz0ku9i1axd5eXmsWrWKKVOm8H//939qD6lCYmJieP31121F/HPmzOH5559nxYoVKIrCjh07VB5h+RU9t9mzZzNz5kxiY2Pp06cPMTExKo+wfIqeF8CJEydYt25dsTdIVBaXC+CCO+Zq166t9lDs6scff6RJkyZMmTKFP//5z/Ts2VPtIdlFw4YNsVgsWK1WMjIy0Oud4t6fcqtfvz7z5s2zfRwXF8f9998PQPfu3dm7d69aQ6uwouc2d+5cmjdvDuT32/b09FRraBVS9LyuX7/O3LlzefXVV1UcVT6XCuDCd8y5m+vXr3P8+HH++c9/8re//Y2XXnpJ9d/O9uDt7c2FCxd45JFHmDlz5m1/Brqafv363fJLpHDjfx8fH9LT09UaWoUVPbeCSc7hw4dZvnw548aNU2lkFVP4vCwWC6+99hozZszAx8dH5ZG5WACvX7+evXv3EhUVxcmTJ5k+fTpXr15Ve1h2Ub16dbp27YrBYKBRo0Z4enqSmpqq9rAqbMmSJXTt2pV///vfbN68mVdeecWt+gsUvrspMzMTf39/FUdjf9u2bePNN99kwYIFBAYGqj2cCouLiyMxMZG33nqLF154gfj4eGbPnq3aeFzq78Evv/zS9u+CO+aKu13Z1XTo0IFly5Yxfvx4rly5QnZ2NtWrV1d7WBXm7++Ph4cHAAEBAeTl5WGxWFQelf20aNGC/fv3ExERwe7du+ncubPaQ7KbzZs3s3r1amJjY93iZxGgdevWfP311wAkJSXxwgsv8Nprr6k2HpcKYHfWq1cvDh48yPDhw1EUhTfeeMO2x50rGzduHK+++iqRkZGYzWamTZuGt7e32sOym+nTpzNz5kzmzp1Lo0aN6Nevn9pDsguLxcLs2bMJDQ3lueeeA6BTp05MnTpV5ZG5F7kTTgghVOJSa8BCCOFOJICFEEIlEsBCCKESCWAhhFCJBLAQQqhEAlgIIVQiASyEECqRABZVWkpKCh06dLiljeSkSZP49ttvVRyVqCokgEWVFhQURM2aNTEajUB+7wONRsPDDz+s8shEVSC3Iosqr2PHjhw5coSwsDA++ugjFi9erPaQRBUhASyqvI4dO/Lzzz8THx/PsGHDqFevntpDElWE9IIQVd758+cZPnw4tWvXZsOGDbbubUI4mqwBiyqvTp065ObmMnPmTAlfUakkgEWVt2zZMvr372/bWkiIyiJrwKLKSkhI4Nlnn6VOnTp8/PHHag9HVEGyBiyEECqRJQghhFCJBLAQQqhEAlgIIVQiASyEECqRABZCCJVIAAshhEokgIUQQiUSwEIIoZL/B1CvBuRUxQ1DAAAAAElFTkSuQmCC", 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", 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", 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "models = [\n", + " LinearRegression(),\n", + " Ridge(alpha=2),\n", + " SGDRegressor(max_iter=100),\n", + " KernelRidge(alpha=2),\n", + " SVR(),\n", + " RandomForestRegressor(n_estimators=5),\n", + " GradientBoostingRegressor(n_estimators=5)\n", + "]\n", + "\n", + "\n", + "def visualize_model(X, y, estimator, **kwargs):\n", + " viz = ResidualsPlot(estimator, **kwargs)\n", + " viz.fit(X.values, y)\n", + " viz.score(X.values, y)\n", + " viz.show()\n", + "\n", + " viz = PredictionError(model)\n", + " viz.fit(X_train.values, y_train)\n", + " viz.score(X_test.values, y_test)\n", + " viz.show()\n", + "\n", + "for model in models:\n", + " visualize_model(X, y, model)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### **SPECIFIC MODEL TUNING**" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [], + "source": [ + "model = RandomForestRegressor(n_estimators=5)" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "cv = KFold(n_splits=5, shuffle=True, random_state=42)\n", + "viz = cv_scores(model, X, y, cv=cv, scoring='r2')" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "viz = LearningCurve(model, cv=cv, scoring='r2', )\n", + "viz.fit(X, y)\n", + "viz.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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8R506dVLHjh1Vs2ZNtWzZUt9//32O20/fp67dfvovCjntG+ljuFb6/f6qP1v/9c/x4cOHq3HjxpKuntCblJQkSQoPD1d0dLS2bNmiDz74QCtWrND777+f47YDAwO1d+9eDRw4UCtXrpS7u7scDoemT5+uChUqSJIuXLggFxcX7dixI9NYr/3F4PpxZrdPd+7cWU2bNlVkZKQ2b96siIgIffHFF9len9X3l/S5uX67yIw/20auKFeunLy8vJxBc+bMGQUEBCgqKkqRkZFq2rSpunTpoqpVq+q7775TWlqapKvfyNOfsMWKFdPp06d17tw5GWP03XffZbu9Bg0aaMWKFbp48aIkafr06c7zTnJDXFycNm3aJElav369PDw85OPjo4YNG2rp0qVKSUmRw+HQokWL5O/vn+U6rn1sP/zwg7p27ap27dqpePHi2rJli3MOslOnTh1t3bpVsbGxkqSlS5fqnXfeUd26dRUZGamjR49KkjZu3Ki2bdsqKSlJb7zxhtasWaPWrVtrzJgx8vb21pkzZzKsN/3+J0+elCTnuTvXHjHKyoIFC7Ro0SJ9+umnNxUzkuTv76+vvvrK+ZiWLFmirl27Sro6R3379tWzzz4rFxcX7d27N8v9pGjRos4jBnFxcdq5c2eW2/L29la1atW0YMECSVd/WL3wwgtat26dvv/+e4WEhKh69ep67bXX1K5dOx06dCjTOq49OnHixAn9/PPPkqRDhw4pICBAFSpUUK9evRQSEuJc9me8vb1Vo0YNrVq1SpJ08uRJbd269S8dHfP09NSYMWO0bNkyHThwQFFRUSpWrJheffVVNWzY0BkzOe1bRYoU0eOPP67ly5dLkg4cOKDo6GhJN79v/FUNGjTQmjVrFBcXJ+nqUZMiRYrokUceyfK2ixYtUnJyshwOh0aNGqUpU6YoLi5OjRs3VpEiRRQSEqL+/fs75/7a/SQrb7zxhmJjY51Hxxo0aKAPP/xQxhglJyerT58+WrhwoWrUqOE84iVJ3377rTN2rpfTPt25c2cdPHhQHTp00Pjx43XhwgXFx8dne336Y04fz6081+5FHKFBrvD09NR7772nsLAwzZs3T6mpqfrHP/6hmjVrqkiRIho0aJDatGkjNzc31apVy3kyb/Xq1TVt2jT17dtXs2bNUufOnRUYGKj7779fTZo0yXZ7QUFBOnv2rDp27CgXFxeVKlVKkyZNyrXHkx5n4eHhypcvn2bNmiU3Nzf16dNHkydPVrt27ZSamio/Pz+NGjUqy3U0atRI48ePlyT17dtXb7/9tt577z25ubmpRo0aOnHiRI5jqFSpkgYPHqzu3btLku6//3699dZbKlmypMaNG6eBAwfKGCN3d3e9//77KlCggF599VWNGDFCy5Ytk5ubm55++mk9+eSTGdZbsWJFjRkzRv369VNaWpry5cun2bNnO19KyEpycrKmT5+uQoUKqV+/fs7rW7ZsqT59+vylOZWkhg0bqkePHurWrZtcXFzk7e2tiIgIubi4aMCAAerbt68KFy6s/Pnzq3bt2s45atq0qSZPnqyUlBQFBwdr0KBBatGihcqUKZPp8V0rPDxc48ePV5s2bZScnKyAgAC1bdtWaWlp2rRpkwICAlSgQAEVLlzY+bW6Vp8+fRQaGqqNGzeqfPnyzkP+lStXVqtWrRQYGKgCBQooX758mY7O5GTy5MkaMWKEFi9erJIlS6pMmTIZjqbkpFatWmrTpo3Gjx+v+fPna8WKFWrZsqXy588vPz8/FStWTMePH89xHVOmTNGwYcOcR+/Kly8v6eb2jRvh7++vkJAQde3aVQ6HQ8WKFdOcOXMyvSwqSa+++qomT56s9u3bKy0tTb6+vgoNDZW3t7f69OmjkJAQ5cuXT25ubs6Xg659zlWrVi3TOgsXLqxBgwZp4sSJCggI0IgRIxQWFqY2bdooJSVF9evXV/fu3eXh4aEpU6Zo6NChcnV1VZUqVeTu7u480netnPbpQYMG6a233tK0adPk6uqqfv36qUyZMtleP3LkSE2YMME5noYNG6p37965Mvf3AheT1TFA4B4WExOjNm3aaPfu3Xk9FNyl3n//fT3zzDOqUKGCEhIS1LZtW33wwQfW/Gn63e7ixYt677339Nprryl//vw6cOCAevXqpc2bN+fae/8g93GEBgDusL/97W8aMGCAXF1dlZaWph49ehAz/0O8vb3l4eGh559/Xu7u7nJ3d9e0adOImf9xHKEBAADW46RgAABgPYIGAABYj3NoboDD4VBiYmKm9zIBAAC3lzFGKSkpKliwYJZ/GUfQ3IDExETn+zUAAIA7z8fHJ8u3EiBobkD6u6j6+PhkesdM3D5RUVGqUqVKXg/jnsKc33nM+Z3HnN95tzLnycnJio6OzvYdzQmaG5D+MpOnp2eGt6rH7cd833nM+Z3HnN95zPmdd6tznt0pH5wUDAAArEfQAAAA6xE0AADAegQNAACwHkEDAACsR9AAAADrETQAAMB6BA0AALAeQQMAAKxH0AAAAOsRNAAAwHoEDQAAsJ6LMcbk9SBskZSUpKioKB268pVSzKW8Hg4AAP9zQhpMynbZrl27VLNmzZtab/rP4CpVqmT5AZccoQEAANYjaAAAgPUIGgAAYD2CBgAAWI+gAQAA1iNoAACA9QgaAABgPYIGAABYj6ABAADWI2gAAID1CBoAAGA9ggYAAFiPoAEAANYjaAAAgPUIGgAAYD2CBgAAWI+gAQAA1iNoAACA9QgaAABgPYIGAABYj6ABAADWI2gAAID1CBoAAGA9ggYAAFiPoAEAANYjaAAAgPUIGgAAYD2CBgAAWI+gAQAA1iNoAACA9QgaAABgPYIGAABYj6ABAADWI2gAAID1CBoAAGA9ggYAAFiPoAEAANYjaAAAgPUIGgAAYD2CBgAAWI+gAQAA1iNoAACA9QgaAABgPYIGAABYj6ABAADWI2gAAID1CBoAAGA9ggYAAFiPoAEAANYjaAAAgPUIGgAAYD2CBgAAWI+gAQAA1iNoAACA9QgaAABgPYIGAABYj6ABAADWI2gAAID1CBoAAGA9ggYAAFiPoAEAANbLMWi2b9+uAQMGZLguPDxcq1atyvY+YWFhOn36dLbLn3rqKSUlJWW4LikpScuXL7/hdV2/3hdffFHBwcEKDg5Wv379/tL9rrV27VqdPXv2hu8HAADylntur3DEiBE3fJ/ffvtNy5cvV1BQ0C2ta/78+fLy8rrh7af7+OOPNXbsWJUsWfKm1wEAAO68Wwqad999Vzt37pTD4VBISIhatWql4OBgjR07VkWLFtWgQYOUnJyscuXKadu2bVq7dq0kaezYsYqJiZEkRUREaPbs2Tpy5IgiIiIyHFlJX9eaNWsUExOjc+fO6fTp0xo2bJgaNmz4l8b49ddf68MPP5Srq6tq1qypQYMGKSEhQSNGjNAff/whSRo5cqTOnDmjgwcPaujQoVq8eLE8PT1vZWoAAMAd9KdBs23bNgUHBzsvnzx5Uq+//ro2btyomJgYLVmyRElJSerYsaP8/f2dt5s9e7aaNWumF198UZGRkYqMjHQuCwwMVK1atRQaGqrIyEj17t1b0dHROb5M5OnpqXnz5ikyMlLz58/PMmi6desmV9err6K98sorqlatmmbOnKmVK1cqf/78Gjx4sCIjI7VlyxbVrVtXXbp00bFjxzRs2DAtWbJEvr6+Gjt2LDEDAIBl/jRo6tatq6lTpzovh4eHS5Kio6N14MABZ+ykpqbq1KlTztsdPXpU7du3lyTVqlUrwzqrVKkiSSpRooSuXLnylwbq6+srSXrwwQeVnJyc5W2uf8lp3759iouLU8+ePSVJiYmJOnHihKKjo7Vt2zZ9/fXXkqT4+Pi/NAYAAPC/6aZfcipfvrzq1Kmj8ePHy+Fw6L333tPDDz/sXO7j46Pdu3fL19dXe/bsyXBfFxeXDJddXV3lcDhy3N719/krypQpo1KlSmn+/Pny8PDQqlWr5Ovrq2PHjqlt27Zq06aNzp075zwh2cXFRcaYG94OAADIWzf9Z9tPPfWUChQooC5duqhDhw6SJG9vb+fyHj16aP369QoODtann34qd/fs26l48eJKSUnRO++8c7PDyVKxYsUUEhKi4OBgBQUFadOmTfrb3/6m3r176+uvv1ZwcLC6d++uRx99VJJUvXp1DRkyROfPn8/VcQAAgNvLxdymQxIbN25U0aJF5efnpy1btmj27Nn6+OOPb8em7pikpCRFRUXp0JWvlGIu5fVwAAD4nxPSYFK2y3bt2qWaNWve1HrTfwZXqVIly79ozvU/205XpkwZDR8+XG5ubnI4HDf159wAAAB/xW0LmgoVKmjZsmW3a/UAAABOfPQBAACwHkEDAACsR9AAAADrETQAAMB6BA0AALAeQQMAAKxH0AAAAOsRNAAAwHoEDQAAsB5BAwAArEfQAAAA6xE0AADAegQNAACwHkEDAACsR9AAAADrETQAAMB6BA0AALAeQQMAAKxH0AAAAOsRNAAAwHoEDQAAsB5BAwAArEfQAAAA6xE0AADAegQNAACwHkEDAACsR9AAAADrETQAAMB6BA0AALAeQQMAAKxH0AAAAOsRNAAAwHoEDQAAsB5BAwAArEfQAAAA6xE0AADAegQNAACwHkEDAACsR9AAAADrETQAAMB6BA0AALAeQQMAAKxH0AAAAOsRNAAAwHoEDQAAsB5BAwAArEfQAAAA6xE0AADAegQNAACwHkEDAACsR9AAAADrETQAAMB6BA0AALAeQQMAAKxH0AAAAOsRNAAAwHoEDQAAsB5BAwAArEfQAAAA67nn9QBs9HytofLy8srrYdwzdu3apZo1a+b1MO4pzPmdx5zfecz53YUjNAAAwHoEDQAAsB5BAwAArEfQAAAA6xE0AADAegQNAACwHkEDAACsR9AAAADrETQAAMB6BA0AALAeQQMAAKxH0AAAAOsRNAAAwHoEDQAAsB5BAwAArEfQAAAA6xE0AADAegQNAACwHkEDAACsR9AAAADrETQAAMB6BA0AALAeQQMAAKxH0AAAAOsRNAAAwHoEDQAAsB5BAwAArEfQAAAA6xE0AADAegQNAACwHkEDAACsR9AAAADruef1AGxUIWy1ziSmZLo+7d3gPBgNAADgCA0AALAeQQMAAKxH0AAAAOsRNAAAwHoEDQAAsB5BAwAArEfQAAAA6xE0AADAegQNAACwHkEDAACsR9AAAADrETQAAMB6BA0AALAeQQMAAKxH0AAAAOsRNAAAwHoEDQAAsB5BAwAArEfQAAAA6xE0AADAegQNAACwHkEDAACsR9AAAADrETQAAMB6BA0AALAeQQMAAKxH0AAAAOsRNAAAwHoEDQAAsB5BAwAArEfQAAAA6xE0AADAegQNAACwHkEDAACsR9AAAADrETQAAMB6BA0AALAeQQMAAKxH0AAAAOsRNAAAwHoEDQAAsB5BAwAArEfQAAAA6xE0AADAegQNAACwHkEDAACsR9AAAADrETQAAMB6BA0AALAeQQMAAKxH0AAAAOsRNAAAwHoEDQAAsB5BAwAArEfQAAAA6xE0AADAegQNAACwHkEDAACsR9AAAADrETQAAMB6BA0AALAeQQMAAKznfic2sn37dvXv318VK1aUMUbJyckaO3asVq9erb///e8qXbp0rm7v+PHjCgsLU2pqqi5evKjatWvrjTfekKsr/QYAwN3ojgSNJNWtW1dTp06VJP3www+aPn265syZc1u2NWXKFL300ktq1KiRjDHq16+f1q1bp+bNm9+W7QEAgLx1x4LmWhcuXFCxYsUUHByssWPHas2aNYqJidG5c+d0+vRpDRs2TA0bNtQ333yjRYsWKTU1VS4uLoqIiNDhw4cVHh4uDw8P1a9fXxs2bNCKFSskSf3791e3bt1UokQJrV69WgULFpSfn5+mTZsmd/erD/Xdd9/Vzp075XA4FBISoubNm+ull15S37595evrq65du2revHkqVapUXkwNAAC4CXcsaLZt26bg4GAlJyfr0KFDmjVrVoYjNJ6enpo3b54iIyM1f/58NWzYUMeOHdPcuXOVP39+jR49Wj/88INKliyppKQkLV++3LneI0eOqESJEoqJiZGfn58qV66sxYsXa8qUKYqOjlbjxo01evRo7d69WzExMVqyZImSkpLUsWNH+fv7Kzw8XL1799b999+vIUOGEDMAAFgmT15y+uWXX9S5c2c98sgjzuW+vr6SpAcffFDJycmSpOLFi2vo0KEqWLCgfvnlF1WrVk2SVK5cOef9goKCtGrVKpUuXVpt27aVdDVyQkJCFBISosTERE2ePFnvvfeeihcvrgMHDig4OFiSlJqaqlOnTsnX11c1atTQnj171KhRo9s+FwAAIHflyVmyJUqUyHSdi4tLhssJCQmaMWOGpk6dqgkTJsjLy0vGGEnKcHJvy5YtFRkZqbVr1zqD5p133tGPP/4oSSpYsKDKlSsnT09PlS9fXnXq1NEnn3yijz76SK1atdLDDz+sPXv26PDhw6pdu7bmz59/ux42AAC4Te74S06urq5KTExUaGioVq9ene3tvb29VaNGDXXq1Enu7u667777FBsbqzJlymS4nZeXl2rXrq24uDgVKVJEkjRt2jRNmDBBkyZNkqenp8qUKaOxY8eqYMGC+vHHH9WlSxddunRJTz/9tIwxGjFihCIiIlS6dGkFBQXpySefVNWqVW/ndAAAgFzkYtIPe1jszTff1DPPPKN69erd1u0kJSUpKipKz31+WGcSUzItT3s3+LZu/161a9cu1axZM6+HcU9hzu885vzOY87vvFuZ8/SfwVWqVJGXl1em5da/MUu3bt104cKF2x4zAADgf1ee/Nl2buKcFwAAYP0RGgAAAIIGAABYj6ABAADWI2gAAID1CBoAAGA9ggYAAFiPoAEAANYjaAAAgPUIGgAAYD2CBgAAWI+gAQAA1iNoAACA9QgaAABgPYIGAABYj6ABAADWI2gAAID1CBoAAGA9ggYAAFiPoAEAANYjaAAAgPUIGgAAYD2CBgAAWI+gAQAA1iNoAACA9QgaAABgPYIGAABYj6ABAADWI2gAAID1CBoAAGA9ggYAAFiPoAEAANYjaAAAgPUIGgAAYD2CBgAAWI+gAQAA1iNoAACA9QgaAABgPYIGAABYj6ABAADWI2gAAID1CBoAAGA9ggYAAFiPoAEAANYjaAAAgPUIGgAAYD2CBgAAWI+gAQAA1iNoAACA9QgaAABgPYIGAABYj6ABAADWI2gAAID1CBoAAGA9ggYAAFiPoAEAANYjaAAAgPUIGgAAYD2CBgAAWI+gAQAA1nPP6wHY6OiI9vLy8srrYQAAgP+PIzQAAMB6BA0AALAeQQMAAKxH0AAAAOsRNAAAwHoEDQAAsB5BAwAArEfQAAAA6xE0AADAegQNAACwHkEDAACsR9AAAADrETQAAMB6fNr2DTDGSJKSk5PzeCT3nqSkpLwewj2HOb/zmPM7jzm/8252ztN/9qb/LL6ei8luCTJJSEhQdHR0Xg8DAIB7lo+PjwoVKpTpeoLmBjgcDiUmJsrDw0MuLi55PRwAAO4ZxhilpKSoYMGCcnXNfMYMQQMAAKzHScEAAMB6BA0AALAeQQMAAKxH0AAAAOsRNNlwOBwaPXq0OnXqpODgYB0/fjzD8k8//VQdOnRQx44d9f333+fRKO8ufzbnH374oYKCghQUFKSIiIg8GuXd5c/mPP023bt315IlS/JghHeXP5vvjRs3qmPHjgoKCtLYsWOzfb8N/HV/Nufz589Xhw4dFBgYqLVr1+bRKO9Oe/fuVXBwcKbr169fr8DAQHXq1Emffvpp7m3QIEvffvutGTp0qDHGmN27d5vevXs7l8XGxpqAgACTlJRkLly44Pw/bk1Oc37ixAnTvn17k5qaahwOh+nUqZM5ePBgXg31rpHTnKd79913TVBQkFm8ePGdHt5dJ6f5TkhIMK1btzbnzp0zxhgzd+5c5/9x83Ka8/j4eNO4cWOTlJRkzp8/b5o0aZJXw7zrzJ071wQEBJigoKAM1ycnJ5unn37anD9/3iQlJZkOHTqY3377LVe2yRGabOzatUsNGzaUJFWrVk1RUVHOZfv27VP16tXl6empQoUKqWzZsjp06FBeDfWukdOcP/jgg5o3b57c3Nzk4uKi1NRUeXl55dVQ7xo5zbkkffPNN3JxcXHeBrcmp/nevXu3fHx8NHnyZHXp0kUlSpRQsWLF8mqod42c5jx//vwqXbq0Ll++rMuXL/P+YrmobNmymjlzZqbrjx49qrJly6pw4cLy9PRUzZo1tWPHjlzZJh99kI2LFy/K29vbednNzU2pqalyd3fXxYsXM7xLYcGCBXXx4sW8GOZdJac59/DwULFixWSM0dtvv63HHntM5cqVy8PR3h1ymvPo6Gh9+eWXmjFjhmbNmpWHo7x75DTff/zxh7Zv367PPvtMBQoU0Isvvqhq1aqxn9+inOZckkqVKqXWrVsrLS1NvXr1yqth3nVatGihmJiYTNffzp+fBE02vL29lZiY6LzscDicT4DrlyUmJmb5Nsy4MTnNuXT18z+GDx+uggULasyYMXkxxLtOTnP+2Wef6ezZs+ratatOnTolDw8PPfTQQ2rUqFFeDdd6Oc13kSJFVLVqVd1///2SpFq1aungwYMEzS3Kac43bdqk2NhYrVu3TpL0yiuvqEaNGvLz88uTsd4LbufPT15yykaNGjW0adMmSdKePXvk4+PjXObn56ddu3YpKSlJCQkJOnr0aIbluDk5zbkxRq+++qoqVaqkcePGyc3NLa+GeVfJac6HDBmi5cuX65NPPlH79u0VEhJCzNyinOb78ccfV3R0tOLi4pSamqq9e/eqYsWKeTXUu0ZOc164cGHly5dPnp6e8vLyUqFChXThwoW8Guo9oUKFCjp+/LjOnz+v5ORk7dy5U9WrV8+VdXOEJhvNmzdXZGSkOnfuLGOM3nrrLS1YsEBly5ZVs2bNFBwcrC5dusgYowEDBnA+Ry7Iac4dDod+/PFHJScna/PmzZKkgQMH5toT4V71Z/s5ctefzfcbb7yh7t27S5JatmzJL0q54M/mfMuWLerYsaNcXV1Vo0YN+fv75/WQ70r/+te/dOnSJXXq1EmhoaF65ZVXZIxRYGCgSpYsmSvb4LOcAACA9XjJCQAAWI+gAQAA1iNoAACA9QgaAABgPYIGAABYj6ABcNOGDRumFi1a6Msvv7zh+86YMUM7d+7M9TGNGDFC+/fvz/X1ZmfYsGE6derUHdsegKwRNABu2urVq/Wvf/1LAQEBN3zfHTt2KC0tLdfHFBYWpqpVq+b6erOzfft2PhUb+B/AG+sBuCm9e/eWMUZBQUGaP3++Nm/erI8++kgOh0OPP/64xowZIy8vLy1cuFCff/6588P/pk2bpv379ysqKkojR45URESEJkyYoH79+qlOnTqKiYnRyy+/rPXr1ys0NFTnz5/X8ePHNXjwYJUoUUITJ07UlStXVLRoUb355pt6+OGHM4wrODhY/fr1kyTNnj1bxhidOHFCLVq0UKFChfTdd99JkubOnasSJUqobt26atq0qaKiolSwYEGFh4erTJky2rNnj8LCwpSUlKSiRYtq3LhxeuSRRxQcHKzChQvr8OHDCgwMVGxsrHr27KlFixZp27ZtWrBgga5cuaKkpCRNmDBBtWvXVnBwsKpWrapdu3YpLi5OI0eOVOPGjXXq1CkNGzZMcXFxypcvnyZMmKDKlSvrs88+y3IuAeQgVz6zG8A9ycfHxxhjTHR0tHnhhRfMlStXjDHGhIeHm1mzZpmEhATTtWtXc/nyZWOMMdOmTTPjxo0zxhjz0ksvmW3btmX6/8mTJ03Tpk2NMcYMHTrUDB061BhjTFJSkmnTpo05deqUMcaYTZs2ma5du2YaU/q6tm3bZqpXr25Onz5tLl26ZKpVq2aWLFlijDEmNDTUfPjhh87HsGrVKmOMMR9//LHp1auXSUpKMk2bNjV79+41xhizZs0a06FDB+f6Z8yY4dxe06ZNzcmTJ01aWpp5+eWXzblz54wxxixfvtz06tXLeZ8JEyYYY4xZt26dad++vTHGmB49epiFCxcaY4zZsGGDef3117OdSwA54wgNgFu2fft2HT9+XB07dpQkpaSk6LHHHpO3t7feffddffXVVzp27Jg2b94sX1/fG1p3+gcFHjt2TCdPnlSfPn2cy/7sU3p9fHxUqlQpSVLRokVVr149SVLp0qWdn9nj5eWldu3aSZLat2+vKVOm6NixY7rvvvuc227VqpVGjx6thISEDGO6lqurq2bNmqX169fr119/1Y8//ihX1/++qt+wYUNJ0qOPPqrz589Luvqy25QpUyRJjRs3VuPGjbVw4cIs5xJAzggaALcsLS1NrVq10siRIyVd/QTdtLQ0nTlzRsHBwXrppZfUqFEjlShRQgcPHsxyHeb/n4eSmpqa4fp8+fJJuvopyWXKlNHnn3/u3Obvv/+e47g8PDwyXM7qQ01dXV3l4uLi3Iabm5scDkeW40s/5yd9TNdKTExUYGCgnnvuOdWuXVuVKlXSokWLnMvTXzJK35akDJ8mb4zR0aNHs51LADnjpGAAt6xOnTpau3atzp07J2OMxo4dq48++kj79+/XI488opCQED3xxBPatGmT84ezm5ub8/9FixbVkSNHJMl5jsv1ypcvr/j4eOdfRq1cuVKDBg265bFfvnxZ69evlyStWrVKjRo1Uvny5XX+/Hnt27dPkrRmzRqVLl1aRYoUyXT/9Mdx7Ngxubq6qnfv3qpbt26Gx5qdWrVq6auvvpIkbdmyRaNGjcp2LgHkjCM0AG5Z5cqV1a9fP3Xt2lUOh0O+vr7q2bOnUlNTtWTJEj377LPy9PSUn5+fDh8+LOnqSzBjxozR5MmT1b17d4WGhmrlypXZfsq3p6enpk+f7jxR19vbW5MnT86V8X/zzTeaOnWqHnjgAU2ePFmenp6aOnWqxo8fr8uXL6tw4cKaOnVqlvdt0qSJevbsqQ8++EC+vr5q1aqV8uXLp9q1a+v06dM5bnf06NEaOXKkFi9erPz582vChAmqWLFilnMJIGd82jaAe1qlSpX0888/5/UwANwiXnICAADW4wgNAACwHkdoAACA9QgaAABgPYIGAABYj6ABAADWI2gAAID1CBoAAGC9/weOHJs7mcsZ1AAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "viz = FeatureImportances(model, stack=True, relative=False)\n", + "viz.fit(X, y)\n", + "viz.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### **RANDOM FOREST REGRESSOR HYPERPARAMETER TUNING**" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "viz = ValidationCurve(RandomForestRegressor(), param_name='n_estimators', param_range=range(1, 10), cv=cv, scoring='r2')\n", + "viz.fit(X, y)\n", + "viz.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### **USE MODEL THAT HAS ALPHA AS PARAMETERS - RIDGE**" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "alphas = np.logspace(1, 2, 20)\n", + "viz = ManualAlphaSelection(Ridge(), alphas=alphas, cv=cv, scoring='r2')\n", + "viz.fit(X, y)\n", + "viz.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "viz = CooksDistance()\n", + "viz.fit(X, y)\n", + "viz.show()" + ] + } + ], + "metadata": { + "interpreter": { + "hash": "394904829c10590c5c5bf5fb432c1ee107b97fab16312b5a34e725f056653d89" + }, + "kernelspec": { + "display_name": "Python 3.8.0 ('venv': venv)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.0" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 233d9d1686dd34cfdc013b5396af585dad83d565 Mon Sep 17 00:00:00 2001 From: charles Date: Sat, 21 May 2022 10:14:17 -0700 Subject: [PATCH 09/27] Addressing issue #1235: Dropping curve documentation (#1241) --- docs/api/model_selection/dropping_curve.rst | 79 +++++++++++++++++++ docs/api/model_selection/index.rst | 2 + yellowbrick/model_selection/dropping_curve.py | 47 +++++++++++ 3 files changed, 128 insertions(+) create mode 100644 docs/api/model_selection/dropping_curve.rst diff --git a/docs/api/model_selection/dropping_curve.rst b/docs/api/model_selection/dropping_curve.rst new file mode 100644 index 000000000..d87cb2f09 --- /dev/null +++ b/docs/api/model_selection/dropping_curve.rst @@ -0,0 +1,79 @@ +.. -*- mode: rst -*- + +Feature Dropping Curve +============================= + + ================= ===================== + Visualizer :class:`~yellowbrick.model_selection.dropping_curve.DroppingCurve` + Quick Method :func:`~yellowbrick.model_selection.dropping_curve.dropping_curve` + Models Classification, Regression, Clustering + Workflow Model Selection + ================= ===================== + +A feature dropping curve (FDC) shows the relationship between the score and the number of features used. +This visualizer randomly drops input features, showing how the estimator benefits from additional features of the same type. +For example, how many air quality sensors are needed across a city to accurately predict city-wide pollution levels? + +Feature dropping curves helpfully complement :doc:`rfecv` (RFECV). +In the air quality sensor example, RFECV finds which sensors to keep in the specific city. +Feature dropping curves estimate how many sensors a similar-sized city might need to track pollution levels. + +Feature dropping curves are common in the field of neural decoding, where they are called `neuron dropping curves `_ (`example `_, panels C and H). +Neural decoding research often quantifies how performance scales with neuron (or electrode) count. +Because neurons do not correspond directly between participants, we use random neuron subsets to simulate what performance to expect when recording from other participants. + +To show how this works in practice, consider an image classification example using `handwritten digits `_. + +.. plot:: + :context: close-figs + :alt: Dropping Curve on the digits dataset + + from sklearn.svm import SVC + from sklearn.datasets import load_digits + + from yellowbrick.model_selection import DroppingCurve + + # Load dataset + X, y = load_digits(return_X_y=True) + + # Initialize visualizer with estimator + visualizer = DroppingCurve(SVC()) + + # Fit the data to the visualizer + visualizer.fit(X, y) + # Finalize and render the figure + visualizer.show() + +This figure shows an input feature dropping curve. +Since the features are informative, the accuracy increases with more larger feature subsets. +The shaded area represents the variability of cross-validation, one standard deviation above and below the mean accuracy score drawn by the curve. + +The visualization can be interpreted as the performance if we knew some image pixels were corrupted. +As an alternative interpretation, the dropping curve roughly estimates the accuracy if the image resolution was downsampled. + +Quick Method +------------ +The same functionality can be achieved with the associated quick method ``dropping_curve``. This method will build the ``DroppingCurve`` with the associated arguments, fit it, then (optionally) immediately show the visualization. + +.. plot:: + :context: close-figs + :alt: Dropping Curve Quick Method on the digits dataset + + from sklearn.svm import SVC + from sklearn.datasets import load_digits + + from yellowbrick.model_selection import dropping_curve + + # Load dataset + X, y = load_digits(return_X_y=True) + + dropping_curve(SVC(), X, y) + + +API Reference +------------- + +.. automodule:: yellowbrick.model_selection.dropping_curve + :members: DroppingCurve, dropping_curve + :undoc-members: + :show-inheritance: diff --git a/docs/api/model_selection/index.rst b/docs/api/model_selection/index.rst index 126490862..a1796250c 100644 --- a/docs/api/model_selection/index.rst +++ b/docs/api/model_selection/index.rst @@ -14,6 +14,7 @@ The currently implemented model selection visualizers are as follows: - :doc:`cross_validation`: displays cross-validated scores as a bar chart with average as a horizontal line. - :doc:`importances`: rank features by relative importance in a model - :doc:`rfecv`: select a subset of features by importance +- :doc:`dropping_curve`: select subsets of features randomly Model selection makes heavy use of cross validation to measure the performance of an estimator. Cross validation splits a dataset into a training data set and a test data set; the model is fit on the training data and evaluated on the test data. This helps avoid a common pitfall, overfitting, where the model simply memorizes the training data and does not generalize well to new or unknown input. @@ -27,3 +28,4 @@ There are many ways to define how to split a dataset for cross validation. For m cross_validation importances rfecv + dropping_curve diff --git a/yellowbrick/model_selection/dropping_curve.py b/yellowbrick/model_selection/dropping_curve.py index 0fb441472..fc7201d3a 100644 --- a/yellowbrick/model_selection/dropping_curve.py +++ b/yellowbrick/model_selection/dropping_curve.py @@ -38,83 +38,109 @@ class DroppingCurve(ModelVisualizer): Selects random subsets of features and estimates the training and crossvalidation performance. Subset sizes are swept to visualize a feature-dropping curve. + The visualization plots the score relative to each subset and shows the number of (randomly selected) features needed to achieve a score. The curve is often shaped like log(1+x). For example, see: https://www.frontiersin.org/articles/10.3389/fnsys.2014.00102/full + Parameters ---------- estimator : a scikit-learn estimator An object that implements ``fit`` and ``predict``, can be a classifier, regressor, or clusterer so long as there is also a valid associated scoring metric. + Note that the object is cloned for each validation. + feature_sizes: array-like, shape (n_values,) default: ``np.linspace(0.1,1.0,5)`` + Relative or absolute numbers of input features that will be used to generate the learning curve. If the dtype is float, it is regarded as a fraction of the maximum number of features, otherwise it is interpreted as absolute numbers of features. + groups : array-like, with shape (n_samples,) Optional group labels for the samples used while splitting the dataset into train/test sets. + ax : matplotlib.Axes object, optional The axes object to plot the figure on. + logx : boolean, optional If True, plots the x-axis with a logarithmic scale. + cv : int, cross-validation generator or an iterable, optional Determines the cross-validation splitting strategy. Possible inputs for cv are: + - None, to use the default 3-fold cross-validation, - integer, to specify the number of folds. - An object to be used as a cross-validation generator. - An iterable yielding train/test splits. + see the scikit-learn `cross-validation guide `_ for more information on the possible strategies that can be used here. + scoring : string, callable or None, optional, default: None A string or scorer callable object / function with signature ``scorer(estimator, X, y)``. See scikit-learn model evaluation documentation for names of possible metrics. + n_jobs : integer, optional Number of jobs to run in parallel (default 1). + pre_dispatch : integer or string, optional Number of predispatched jobs for parallel execution (default is all). The option can reduce the allocated memory. The string can be an expression like '2*n_jobs'. + random_state : int, RandomState instance or None, optional (default=None) If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by `np.random`. Used to generate feature subsets. + kwargs : dict Keyword arguments that are passed to the base class and may influence the visualization as defined in other Visualizers. + Attributes ---------- feature_sizes_ : array, shape = (n_unique_ticks,), dtype int Numbers of features that have been used to generate the dropping curve. Note that the number of ticks might be less than n_ticks because duplicate entries will be removed. + train_scores_ : array, shape (n_ticks, n_cv_folds) Scores on training sets. + train_scores_mean_ : array, shape (n_ticks,) Mean training data scores for each training split + train_scores_std_ : array, shape (n_ticks,) Standard deviation of training data scores for each training split + valid_scores_ : array, shape (n_ticks, n_cv_folds) Scores on validation set. + valid_scores_mean_ : array, shape (n_ticks,) Mean scores for each validation split + valid_scores_std_ : array, shape (n_ticks,) Standard deviation of scores for each validation split + Examples -------- + >>> from yellowbrick.model_selection import DroppingCurve >>> from sklearn.naive_bayes import GaussianNB >>> model = DroppingCurve(GaussianNB()) >>> model.fit(X, y) >>> model.show() + Notes ----- This visualizer is based on sklearn.model_selection.validation_curve @@ -162,11 +188,13 @@ def fit(self, X, y=None): Fits the feature dropping curve with the wrapped model to the specified data. Draws training and cross-validation score curves and saves the scores to the estimator. + Parameters ---------- X : array-like, shape (n_samples, n_features) Input vector, where n_samples is the number of samples and n_features is the number of features. + y : array-like, shape (n_samples) or (n_samples, n_features), optional Target relative to X for classification or regression; None for unsupervised learning. @@ -296,61 +324,80 @@ def dropping_curve( Displays a random-feature dropping curve, comparing feature size to training and cross validation scores. The dropping curve aims to show how a model improves with more information. + This helper function wraps the DroppingCurve class for one-off analysis. + Parameters ---------- estimator : a scikit-learn estimator An object that implements ``fit`` and ``predict``, can be a classifier, regressor, or clusterer so long as there is also a valid associated scoring metric. + Note that the object is cloned for each validation. + X : array-like, shape (n_samples, n_features) Input vector, where n_samples is the number of samples and n_features is the number of features. + y : array-like, shape (n_samples) or (n_samples, n_features), optional Target relative to X for classification or regression; None for unsupervised learning. + feature_sizes: array-like, shape (n_values,) default: ``np.linspace(0.1,1.0,5)`` + Relative or absolute numbers of input features that will be used to generate the learning curve. If the dtype is float, it is regarded as a fraction of the maximum number of features, otherwise it is interpreted as absolute numbers of features. + groups : array-like, with shape (n_samples,) Optional group labels for the samples used while splitting the dataset into train/test sets. + ax : matplotlib.Axes object, optional The axes object to plot the figure on. + logx : boolean, optional If True, plots the x-axis with a logarithmic scale. + cv : int, cross-validation generator or an iterable, optional Determines the cross-validation splitting strategy. Possible inputs for cv are: + - None, to use the default 3-fold cross-validation, - integer, to specify the number of folds. - An object to be used as a cross-validation generator. - An iterable yielding train/test splits. + see the scikit-learn `cross-validation guide `_ for more information on the possible strategies that can be used here. + scoring : string, callable or None, optional, default: None A string or scorer callable object / function with signature ``scorer(estimator, X, y)``. See scikit-learn model evaluation documentation for names of possible metrics. + n_jobs : integer, optional Number of jobs to run in parallel (default 1). + pre_dispatch : integer or string, optional Number of predispatched jobs for parallel execution (default is all). The option can reduce the allocated memory. The string can be an expression like '2*n_jobs'. + random_state : int, RandomState instance or None, optional (default=None) If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by `np.random`. Used to generate feature subsets. + kwargs : dict Keyword arguments that are passed to the base class and may influence the visualization as defined in other Visualizers. + Returns ------- dc : DroppingCurve From ad0d13348cccee4ce4a1f35b6ceac93a856edf75 Mon Sep 17 00:00:00 2001 From: Benjamin Bengfort Date: Sun, 22 May 2022 07:25:13 -0500 Subject: [PATCH 10/27] Fix KElbow get_params (#1251) * Fix KElbow get_params * fix pylint check * add dropping curve get params test --- examples/bbengfort/corpus.ipynb | 520 ++++++++++++++ examples/bbengfort/testing.ipynb | 649 +++++++----------- tests/test_cluster/test_elbow.py | 33 +- .../test_dropping_curve.py | 10 +- tests/test_utils/test_wrapper.py | 21 + yellowbrick/cluster/elbow.py | 56 +- yellowbrick/model_selection/dropping_curve.py | 2 +- yellowbrick/utils/wrapper.py | 10 +- 8 files changed, 858 insertions(+), 443 deletions(-) create mode 100644 examples/bbengfort/corpus.ipynb diff --git a/examples/bbengfort/corpus.ipynb b/examples/bbengfort/corpus.ipynb new file mode 100644 index 000000000..1be26ddb4 --- /dev/null +++ b/examples/bbengfort/corpus.ipynb @@ -0,0 +1,520 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Visual Diagnosis of Text Analysis with Baleen \n", + "\n", + "This notebook has been created as part of the [Yellowbrick user study](http://www.scikit-yb.org/en/latest/evaluation.html). I hope to explore how visual methods might improve the workflow of text classification on a small to medium sized corpus. \n", + "\n", + "## Dataset \n", + "\n", + "The dataset used in this study is a sample of the [Baleen Corpus](http://baleen.districtdatalabs.com/). The Baleen corpus has been ingesting RSS feeds on the hour from a variety of topical feeds since March 2016, including news, hobbies, and political documents and currently has over 1.2M posts from 373 feeds. [Baleen](https://github.com/bbengfort/baleen) (an open source system) has a sister library called [Minke](https://github.com/bbengfort/minke) that provides multiprocessing support for dealing with Gigabytes worth of text. \n", + "\n", + "The dataset I'll use in this study is a sample of the larger data set that contains 68,052 or roughly 6% of the total corpus. For this test, I've chosen to use the preprocessed corpus, which means I won't have to do any tokenization, but can still apply normalization techniques. The corpus is described as follows:\n", + "\n", + "Baleen corpus contains 68,052 files in 12 categories.\n", + "Structured as:\n", + "\n", + "- 1,200,378 paragraphs (17.639 mean paragraphs per file)\n", + "- 2,058,635 sentences (1.715 mean sentences per paragraph).\n", + "\n", + "Word count of 44,821,870 with a vocabulary of 303,034 (147.910 lexical diversity).\n", + "\n", + "Category Counts: \n", + "\n", + "- books: 1,700 docs\n", + "- business: 9,248 docs\n", + "- cinema: 2,072 docs\n", + "- cooking: 733 docs\n", + "- data science: 692 docs\n", + "- design: 1,259 docs\n", + "- do it yourself: 2,620 docs\n", + "- gaming: 2,884 docs\n", + "- news: 33,253 docs\n", + "- politics: 3,793 docs\n", + "- sports: 4,710 docs\n", + "- tech: 5,088 docs\n", + "\n", + "This is quite a lot of data, so for now we'll simply create a classifier for the \"hobbies\" categories: e.g. books, cinema, cooking, diy, gaming, and sports. \n", + "\n", + "Note: this data set is not currently publically available, but I am happy to provide it on request. " + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "%matplotlib inline " + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import os \n", + "import sys \n", + "import nltk\n", + "import pickle\n", + "\n", + "# To import yellowbrick \n", + "sys.path.append(\"../..\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Loading Data \n", + "\n", + "In order to load data, I'd typically use a `CorpusReader`. However, for the sake of simplicity, I'll load data using some simple Python generator functions. I need to create two primary methods, the first loads the documents using pickle, and the second returns the vector of targets for supervised learning. " + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "CORPUS_ROOT = os.path.join(os.getcwd(), \"data\") \n", + "CATEGORIES = [\"books\", \"cinema\", \"cooking\", \"diy\", \"gaming\", \"sports\"]\n", + "\n", + "def fileids(root=CORPUS_ROOT, categories=CATEGORIES): \n", + " \"\"\"\n", + " Fetch the paths, filtering on categories (pass None for all). \n", + " \"\"\"\n", + " for name in os.listdir(root):\n", + " dpath = os.path.join(root, name)\n", + " if not os.path.isdir(dpath):\n", + " continue \n", + " \n", + " if categories and name in categories: \n", + " for fname in os.listdir(dpath):\n", + " yield os.path.join(dpath, fname)\n", + "\n", + "\n", + "def documents(root=CORPUS_ROOT, categories=CATEGORIES):\n", + " \"\"\"\n", + " Load the pickled documents and yield one at a time. \n", + " \"\"\"\n", + " for path in fileids(root, categories):\n", + " with open(path, 'rb') as f:\n", + " yield pickle.load(f)\n", + "\n", + "\n", + "def labels(root=CORPUS_ROOT, categories=CATEGORIES):\n", + " \"\"\"\n", + " Return a list of the labels associated with each document. \n", + " \"\"\" \n", + " for path in fileids(root, categories):\n", + " dpath = os.path.dirname(path) \n", + " yield dpath.split(os.path.sep)[-1]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Feature Extraction and Normalization \n", + "\n", + "In order to conduct analyses with Scikit-Learn, I'll need some helper transformers to modify the loaded data into a form that can be used by the `sklearn.feature_extraction` text transformers. I'll be mostly using the `CountVectorizer` and `TfidfVectorizer`, so these normalizer transformers and identity functions help a lot. " + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "from nltk.corpus import wordnet as wn\n", + "from nltk.stem import WordNetLemmatizer \n", + "from unicodedata import category as ucat\n", + "from nltk.corpus import stopwords as swcorpus\n", + "from sklearn.base import BaseEstimator, TransformerMixin \n", + "\n", + "\n", + "def identity(args):\n", + " \"\"\"\n", + " The identity function is used as the \"tokenizer\" for \n", + " pre-tokenized text. It just passes back it's arguments. \n", + " \"\"\"\n", + " return args \n", + "\n", + "\n", + "def is_punctuation(token):\n", + " \"\"\"\n", + " Returns true if all characters in the token are\n", + " unicode punctuation (works for most punct). \n", + " \"\"\"\n", + " return all(\n", + " ucat(c).startswith('P')\n", + " for c in token \n", + " )\n", + "\n", + "\n", + "def wnpos(tag):\n", + " \"\"\"\n", + " Returns the wn part of speech tag from the penn treebank tag. \n", + " \"\"\"\n", + " return {\n", + " \"N\": wn.NOUN,\n", + " \"V\": wn.VERB,\n", + " \"J\": wn.ADJ, \n", + " \"R\": wn.ADV, \n", + " }.get(tag[0], wn.NOUN)\n", + "\n", + "\n", + "class TextNormalizer(BaseEstimator, TransformerMixin):\n", + " \n", + " def __init__(self, stopwords='english', lowercase=True, lemmatize=True, depunct=True):\n", + " self.stopwords = frozenset(swcorpus.words(stopwords)) if stopwords else frozenset()\n", + " self.lowercase = lowercase \n", + " self.depunct = depunct \n", + " self.lemmatizer = WordNetLemmatizer() if lemmatize else None \n", + " \n", + " def fit(self, docs, labels=None):\n", + " return self\n", + "\n", + " def transform(self, docs): \n", + " for doc in docs: \n", + " yield list(self.normalize(doc)) \n", + " \n", + " def normalize(self, doc):\n", + " for paragraph in doc:\n", + " for sentence in paragraph:\n", + " for token, tag in sentence: \n", + " if token.lower() in self.stopwords:\n", + " continue \n", + " \n", + " if self.depunct and is_punctuation(token):\n", + " continue \n", + " \n", + " if self.lowercase:\n", + " token = token.lower() \n", + " \n", + " if self.lemmatizer:\n", + " token = self.lemmatizer.lemmatize(token, wnpos(tag))\n", + " \n", + " yield token " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Corpus Analysis \n", + "\n", + "At this stage, I'd like to get a feel for what was in my corpus, so that I can start thinking about how to best vectorize the text and do different types of counting. With the Yellowbrick 0.3.3 release, support has been added for two text visualizers, which I think I will test out at scale using this corpus. " + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.5/site-packages/sklearn/cross_validation.py:44: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n", + " \"This module will be removed in 0.20.\", DeprecationWarning)\n" + ] + }, + { + "ename": "AttributeError", + "evalue": "'NoneType' object has no attribute 'transform'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 9\u001b[0m ])\n\u001b[1;32m 10\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 11\u001b[0;31m \u001b[0mvisualizer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit_transform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdocuments\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabels\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 12\u001b[0m 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\u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 496\u001b[0m \u001b[0;31m# fit method of arity 2 (supervised transformation)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 497\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mfit_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtransform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 498\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 499\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mAttributeError\u001b[0m: 'NoneType' object has no attribute 'transform'" + ] + }, + { + "data": { + "image/png": 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fiBEjkiRvvfXWoNUBAACUqur3watUKknS7cVXTqipqRm0OgAAgFJV/b4Bo0ePTpK0trZ2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\noVAoFAqFQqG8I/wf27lDuEaJvt4AAAAASUVORK5CYII=\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from sklearn.pipeline import Pipeline \n", + "from sklearn.feature_extraction.text import CountVectorizer \n", + "from yellowbrick.text import FreqDistVisualizer\n", + "\n", + "visualizer = Pipeline([\n", + " ('norm', TextNormalizer()),\n", + " ('count', CountVectorizer(tokenizer=lambda x: x, preprocessor=None, lowercase=False)),\n", + " ('viz', FreqDistVisualizer())\n", + "])\n", + "\n", + "visualizer.fit_transform(documents(), labels())\n", + "visualizer.named_steps['viz'].show()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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Aq6OsV/mU/hcMCZMAcF6pB7zatWtLko4fP+62Lz09XVWq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x9/4NKlSzhy5Ah+++037N+/H61atcKYMWNga2vLqWtZWlpyOzHJyclwcHCQOz8+NzcXixcvliu4\nA4D//e9/8PPzk5iO1aZNG0yePFksdYVPbRyf1Lhz585h3rx5GDBgALy9vblU2Y4dO0JfXx8hISFo\n3bq12M4u33oLPvCZrEkyS5aF+fPn482bN0hKSkLbtm3x448/onHjxrh37x727NkDBwcHToClNvLW\nP/IRgpB1oaY2fJUjk5KSMGjQIGzbtk3mHWs+tWd8ZeMVEZB5W1CjjMUuPv3+9OlTfP311yoJ7l68\neIFLly5xdiJ///03gOq+mTFjBoYNGyZ2zhdffIHKykq0b98eXl5eGDt2rEzpnHyDGj73WJXjk5OT\nExwcHHDjxg1kZ2dDIBCgU6dOIhk+teF7j/n0BR97Hz5qvXz/F58+fYq2bdtK/cw6Ojoi6qlt2rRB\ncXGxShcZpalolpWV4c6dO3jw4AGmTJmCGTNmAOA/x1CI+rHXI4iPixEjRjAPDw9WWVnJ8vLymJ6e\nHrtz5w5jjLHTp08zIyMjdv36dYnnymvGWl5ezubNm8cMDAzY69evWXl5OXNycuKMmPv06cNOnTpV\nL5/zfcLDw0PENLw2J0+eZGPGjKnzPSoqKtjp06fZ999/z5lNjxo1ioWGhrIHDx4o3EZbW1sWHh4u\n93nm5uZs48aNUo9HRUWxoUOHijwnrT+ys7PZzZs32YkTJ8T6Y8KECWzRokXc4wULFjA3NzfusSQj\n9kmTJjFnZ2dWWVkpZt5eUVHB3NzcmKOjo1g7FDHPlpcpU6Ywa2trVlRUJHbs5cuXzNramnl7eyvl\nWtIoKyur05B+y5YtTE9PjxkaGrLBgwczfX19ZmlpyRkDjxgxgoWEhNRrG2Xh/v37bO/evey///0v\ns7S05MYZOzs7ia83NDRkiYmJvK5VXl7OmZZnZmayy5cvs+PHj0vtx2vXrjFnZ2cxM+bx48ezjIwM\nqdc5ceIEMzc3l9nE+fnz5+zXX39ly5cvZzY2NpzxuYODA1u/fj1LTU3l9XnrQp5+d3JyYmvXrlV6\nG2rj4ODADAwMmJ6eHjMxMWF+fn5s//79LD8/v87z/P392ZUrVxhjjD158oRdvXqVFRUVsdLS0joN\n4w0NDdnevXt5tbXmPRber7rusSrHJz4oco/l7Yv09HTWp08f5ubmxrZv38709fVZVFQU27lzJ7Oy\nsmK9e/dmFy5cEDnHysqKLVmyhHs8bdo0ZmJiws1lwsLC2BdffCF2rZMnT4r8L76tbYxV/245Ojqy\n0tJSsWOlpaXM0dGRjRs3jnvO39+f2djYqPR3ofbYIvwzMDBgw4YNY8HBwaykpEQp1+IL7eARhAzw\nVTAEqlff+/XrJ+bLIo0GDRpg3bp1WLhwITQ0NABU7wQcOXIEz58/xxdffCGz99eHRElJiYhk/+XL\nlzFq1Ch07txZ7LV1qR7WRFb/MSsrK15t9vHxQVBQEL788ksxf7K6kMWvx9XVVaQgu67+aNq0Kc6e\nPSvWH3xS427duoU5c+ZIrAlSV1eHra0tp7woD3WZZ8uLIiILyqJhw4bcDmzt+ltAsfrH+kYR5cie\nPXtyuzrycOHCBaxbt04spdrExAQdOnSQuJvNRzYeqPbssrS0xI0bN0QEZPr27StWt+fo6Ig7d+6g\nqqoKTZs2xZAhQ+Dt7Y1hw4ZxynvKgm+/z507F35+fujfv79MflqKMH36dAwbNgz9+/eXucbxxx9/\nRFpaGpycnKTK7kuyBejZsydX3yovwnuckZGBixcvQl1dHWZmZjA0NORldq7M8YkPitzjmt/3S5cu\ngTEGMzMz9O7dW2JfyGrvUxM+ar18xZz4+Pp+++23iIqKUtnvgiQVzfcNCvAIQgb4KBgqwsGDB3H+\n/HkUFBSI5cKfOnVKKSqV7xuvX7+Gg4ODwqqHNVHUf+xtpKWlQVNTE+PGjUPXrl3RsmVLsR9mSfdK\nFr+emzdvYtOmTSKTDnn7g09qXMOGDcUEL2pSWFgose5KEfNsZaPMyRqf+luAf/2jKlBEOfK7777D\nnDlzYGpqytU3vo1z585hxowZ0NLSgru7Oz7//HMwxvD333/j8OHDcHV1xe7du0WEEGSVSs/Pz4eW\nlhbatWsnUz2g8Nya/5N8gho+yNrvktK4ysvL4evri8aNG6NFixYSxxlFVTRl8faShCyy+5qammLf\nF0WCGkkLBhs3boSJiQkWL14s5sX4Po1PgHLvsbyKzIBs9j414aPWy1fMiY+v77Rp0xAVFSX1PYX9\np8wgvq55mvB673KeJmDCbwNBEFLx8PCAtrY2IiMjAQBLlizBrVu3cODAAQDV+elxcXG4fPmywtfa\nsGEDoqOjuR0CaQPhu5YErg/OnDkjt+ph7dU6af5jdnZ2Yv5jADBx4kT8/fffSE1Nlbu9shZs175X\nsp5XUlLCSee/fPkS1tbWcvcHUC3MU3O1VCjjLkmWfdasWfjrr79w8OBBvH79GoMHD0ZsbCwGDx6M\n/Px8ODk5wdDQEJs3bxY578qVK5gyZQp0dXWl1lvExMTU6a8mK1999RXy8vJw4MABiZO18ePHo23b\ntoiLi1P4WmvXrpVaf/vPP/+gW7du8PT0FPO+GjBgABYtWsQpR06cOBEDBgzgPCwTExMREhKCS5cu\nKdxGedm9ezenHPnixQvo6OjILIfv7e2Nu3fvoqCgQOaJ6MSJE/Hy5Uvs3btXLKD5999/4eLigo4d\nO4pMhpYsWSKTVPrVq1dRUVEBNTU1tGnTRuYA7V2Mn7L2u4eHh9zvLRAIlPJ954OXlxdyc3M52f0h\nQ4ZwY8arV6/g6uoKTU1NkbopIUL1THmCmpoLBvb29mILBpWVlWILBqocn2SBzz0GxC2baioy29vb\niykyq6mpiSkyA/Lb+yxcuBBnzpyBj48P4uPj8fTpU5w5cwYAsH//fmzYsAGTJ08Wq02rKSr0559/\n4tmzZzKJOQmpy9e3qqoKlZWV3MKOKn8XPoR5GgV4BCEDR48exZw5czBgwABs2bIF169fx7Rp0+Do\n6Ihu3bohOjoaxsbG2LZtm8LXMjc3R69evTihlU+VRYsW8VI91NfXB1Cd+mRnZ/dW/zE/Pz88fPgQ\nBw8eVKi99Q3f/pCX+/fvY9KkSdDR0YG5uTl2794NNzc3qKurIzk5GaWlpdi7d6/YCjlQ/WMmVDAD\nRM2zly5dii+//FIpbVTlZG3kyJHo0KEDduzYgYKCAlhYWODQoUPo1asXzpw5g9mzZ2PXrl1iq//O\nzs7o2bMnt+P6/fffIzs7m/Ox27p1K6KiopCWlqZwGxWhpnJkWlraW+XwZZmY1g42+vXrh7lz5+Kr\nr76S+PqYmBiEh4fj6tWr3HPx8fFYvXo1QkNDpUqlCwNooVT60KFDuZSz9x15+t3DwwMzZ84U8xkT\ncurUKYSEhCAlJUUVTRdjwIABmDlzJry9vfHs2TORRSGgOrANCwvjxmZ5kBS48lkwAICTJ08iICAA\neXl5YqJnyhyflEVZWRn+/PNPqKmpYciQIRJTGj08PJCXlydRkTk/Px8uLi4wMDAQUWSW1d6nZnBS\nVFSE2bNn48KFC2jSpAkCAgJga2uL9PR0uLq6wszMDOHh4RIXGIWwWqJCjx8/lirmxAdV/i58EPM0\nlVf9EcQHSmJiIrOxsWEVFRWMMcZWrFjBFdYOHz6c3bt3TynX6d+/P28RA4KxkJAQTgBHFoT3k/h/\n7ty5w9zd3SWKW1y9elXiOT4+PmzPnj3s4cOHcokK8eXEiRPMwsJCZmEBvvTp04ft2rWLezxkyBAR\nYYhly5ax6dOni523Z88epqenx+bNm8devXrFjh07xvT09Fh4eDhLSUlhQ4cOZZMnT1ZaOxUlPz+f\nJScnswkTJnB9Kom0tLQ63+fRo0di/TFy5Ei2fv16qedER0czS0tLsXPqEp1Yv349s7Ky4h5HRESI\nCRN9CEjq9zdv3rDs7GzuT09Pj8XFxYk8J/x79OgR8/f3Z/369Xtnn6F///5s586djDEmJszEmGQx\nJ8YYc3d35yWkZWhoyGJjY6Wet23bNmZkZCT2fHZ2NgsODhYZn6Kjo9mqVavqFExSBaWlpWzZsmVs\n2rRp3GMHBwdubLO1tZXYRmNjY7Z9+3ap7xsdHc1MTExEnrO2tmYODg6soKBA7nY+ffpURPzkzZs3\nLDMz863nySvmxBd5BWf48iHM06gGjyBkxNnZmUu3AoBly5bBy8sLL168QI8ePWSu/3gbw4YNw8WL\nF0WuRciOLP5jNanPmpsPkSlTpuCbb77Brl278Pz5c5H6jNatW+PUqVOwtbUV2y1Q1DxbXgwMDGBj\nYwMbGxuueP/x48coLCyEiYmJ0q7Dt/6WT/2jKuEjhw9Up2hGRUVh0KBBIs9XVlYiJiYGmzdvRmlp\nqcixr7/+GkFBQTAxMRF731u3bmHHjh345ptvRJ7nK5X+viNLv9dHPXJ9Iqvsfm0hrdTUVNy/f19u\nIa02bdpwIjWSqKysFBOruXv3Ljw8PFBcXIxx48Zxoi/r1q3Dnj17kJKSgj179qBTp04yf25lwseG\nAKi2Sanre88YE/MX5GvvA8hnq6SImBNfpAks1SXqwocPYZ5GKZoEIQOyFvzr6OigX79+mDp1Kq/B\nE6hOMZk6dSp69eoFKysr6OjoSLz2wIEDeb0/QdTkzZs3IpOlESNGYMmSJRJFAKqqqrBt2zYkJyfj\n2rVrYscVrbeQlZqTtf3793OpX+vWrUNcXByaNWumtMmaovW3tesfU1NT8eLFC4n1j6pCknKkhYWF\nTMqRzs7OuHfvHsLDw7lgLSMjAz/88APu3bsHfX19qKmpQUtLS+S8zMxMlJSUoGfPnujatSsEAgGy\ns7Nx48YNNGvWDObm5iLplc7OzqisrMTevXvFUsjKysowadIkVFVVcanVP/74Iy5fvqxUsStlI0+/\nK6MeWVVkZGTAw8MDxsbGGDlyJNasWYPvvvsOGhoa2LVrF3JychATE4NevXph9OjRXOD6NoSBa0xM\njMjzSUlJCAoKwsaNGyUuGHh5eeGbb74RSSeeMWMG7t27h+3bt3Ope0IePXoET09PGBoaIiwsjF8n\nKMioUaNgamrKpU56eXnh+vXruHDhAho0aICNGzdi3759OHv2rMh527dvR2RkJGJjYyUqMru6usLT\n0xPTp0/nnrezs8Po0aPh6+tbr59JX1+fExXy9PSUS8xJUfLz85Gbm8vV7jZo0ECpC40fwjyNAjyC\nkAFZC/6Liopw//596OjoIDExEe3bt5f7WtevX4efn59InUBNGGMQCARiqn0EwYfCwkKlTLokva6+\n6i1UOVlTZf2tqnB0dIS5uTkv5cjXr19j1qxZSEtLw4oVK5Ceno6kpCRoamrCz88Pbm5uvGxHBAIB\nTp48yT0+c+YMZs6cic8//7xOqfQvv/xSRCpdaCz8PsK331VVf6sI58+fh7+/v9iOW+36Nj6BqySF\nY3kXDExNTTFr1iyJu4xAdaC0bds2/Pnnn4p0A28MDQ3h7++PCRMm4M2bNzA1NYWlpSVXj7lv3z4E\nBgaKLayFhobi0KFDyM3NlarIXHux7vHjx8jKysLu3bvlsveRF0XEnPiSlpaGoKAguew6+PAhzNMo\nRZMgZKB37944fPgwNm3aJLXg38HBQaTgPywsjFfB/4oVK1BUVAQvLy906dJFqWkFBFGbli1bYu3a\ntXJPuuriwYMHuHLlCveXm5sLgUCAbt26KaXNV69exaxZs8SCOwDo1KkT3N3dlRZw2djYoLi4GLGx\nsdDQ0MCQIUPg5uaG+Ph4AED79u2xaNEiqRPHunhXMtp85fABoEmTJtiyZQvmzZuHhQsXQiAQwN7e\nHgsWLOB2JJWhHMdXKv19hm+/r1q1SsktUT7SZPf79u0r8hsm9CUFgJycHJkCV0lpmsLUvuLiYmRm\nZnLP6+rqAqgW3KhJVVUVSkpKpF6DMVbn8fqGjw0BABw6dAhAtQ9dVlYWsrKyuGPCXeGMjAyRc54/\nf47mzZvLbe8jL+7u7nB3dwcgKioUFhb2VjEnPvC16+DDhzBPox08gpABKysrjDMxJIQAAAfhSURB\nVB49WmrNzIYNG3DkyBEcP34cABAZGYmEhAScO3dO7msZGRnB19dXJKWCIFQF390CafUWAwcOVHq9\nxcCBAzF9+nT4+PhIPB4TE4PIyEikp6cr5XqSyMnJwYsXL9C9e3c0atRIZuuL2nyodieMMfj7+yMp\nKQnLly+v11oUeaTSCUIS06dPx19//YXk5GRoa2uLHHv16hXGjx+Pdu3aITY29p20j68NAR/42vso\ng4KCApw/fx7x8fHIzMxU6i6XrHYde/bsUfhaH8I87f0LOQniPUSVBf+6urr1IkpBELLAd7dAEfNs\neTE2NsZPP/2ESZMmSZys7du3T+npbBUVFcjMzERubi4GDRoEbW1taGpqcvVhdU2GCgsLkZOTgwYN\nGqBjx45i9WnvM5JqMWtSVVUFf39/EZNhZZhu10RPT0/ijjJQvatM4yXxNnx9feHu7s5Z53Tu3BkC\ngQAPHz5ESkoKCgoK3ulO6eLFi/HkyROsXr0aTZo0QWBgILS1tZGeno7Vq1fDzMxMaTVzqlxU4ivm\nxIeMjAzMnDkTjRs3xps3b0SOaWpqwtnZWWk1lh/CPI0CPIKQgR49eiA5ORkuLi4SC/4PHjwokn52\n48YNXvV3QLVKXXh4OCwsLOolR50g6oOlS5dy9RZr1qxBTExMvdVbqHqydvToUQQFBeHp06cAqus6\nysvL4efnB19fX3h7e0s8Lz09HWvWrMH169chTJZRV1fH0KFDsWDBAnTv3l1pbawv3jaO8R3nCEKV\nGBkZITY2FqtXrxarH9bX18eqVavQv3//d9S6ajXM2NhYFBYWQktLi5tn9O7dG0lJSVKVKt9nJIkK\neXt7yyTmxBdpnn5AtaBYVVWVUq7zIczTKEWTIGRAlQX/AQEBOHHiBAoKCtCpUye0atVKrBj/XdXu\nEIQsyGuezYcrV65g9erVIvU3QPVkbdGiRTA1NVXKdc6dOwcfHx8MGDAAVlZWCA4ORmxsLNq2bYvF\nixfj2rVrCA4Oxrhx40TOS0tLw1dffYXGjRvD3t4eXbp0QWVlJbKysnD48GGoqakhISHhvZ0cEMTH\nSmFhIbKzs1FVVYV27dpJrG0jFEcRMSc+TJ06Fa9evUJiYiKePXuGwYMHcymar1+/hqOjI9q1a8fV\n5inChzBPowCPIGTk999/x8qVK/Ho0SOxgv+FCxdyBf/m5uYYO3YsVqxYwasu5F3mxxOEMqnPegsh\n9T1Zmzx5MifX/+LFC5FJQ2VlJTw9PfH69WvONkGIh4cH8vLysHfvXjE7hPz8fLi4uMDAwACbNm1S\nansJgiA+RSTZdcyePRtNmjQRseswMzNT+FofwjyNAjyCkBMq+CcIydRVbyFcyVWmCbkqMDY2xpw5\nc+Dp6Sm2KgwACQkJWLNmjZhSXf/+/eHn54epU6dKfN8tW7Zg69atSE1NrffPQBAE8Skgq13HpwDV\n4BGEnFDBP0GI8y7qLVRBw4YNUVFRIfV4YWGhxAUdbW3tOoWWGGP47LPPlNJGgiAI4v/tOjIzM3Hp\n0iUwxmBqaoo+ffq8l1YG9cmn9WkJgiCIemP69Okqq7dQFYMGDUJSUhLn51ST/Px8JCQk4D//+Y/Y\nMU9PT0RGRsLCwgL9+vUTOfbw4UPs2rULnp6e9dZugiCIT42PQdhKWVCKJkEQBEFI4cGDB3BxcYGO\njg7Mzc2xe/duuLm5QV1dHcnJySgrK0NCQgIMDAxEzgsNDcWhQ4eQm5uLwYMHo0ePHmjQoAEePXqE\n06dPQ11dXaIFQUhIiKo+GkEQxEcDCVuJQgEeQRAEQdTBnTt3EBgYKFYv17dvXyxduhTGxsZi5/Ax\nPhcIBDh58iTvdhIEQXyqkLCVKBTgEQRBEIQMPH/+HA8fPkRpaSlycnLQokULDBky5JOr7SAIgnjf\nIGErUehXiSAIgiCkUFZWhsDAQGRnZyMmJgZNmjSBi4sLbt++DQDo3r07du7cKbZiTBAEQagOErYS\nheT+CIIgCEIKERERSExMhK6uLgDg4MGDuHXrFjw8PLBy5UoUFBQgLCzsHbeSIAji08bT0xM7duzA\n9evXxY59isJWtINHEARBEFI4evQoJkyYgMDAQADAr7/+iqZNm2LBggWcaMq+ffvecSsJgiA+bYqK\nitCsWTO4uLhIFba6ffs25s2bJ3LexypsRQEeQRAEQUghLy+PE1F58+YNUlNTYWlpydXdtWvXDkVF\nRe+yiQRBEJ88hw4dAlA9JmdlZSErK4s7JvRizcjIEDlHIBCorH2qhgI8giAIgpBCq1at8O+//wIA\nzp49i7KyMlhaWnLH79y5gzZt2ryj1hEEQRAAcOrUqXfdhPcKCvAIgiAIQgqmpqbYuXMnPvvsM8TH\nx0NDQwNWVlYoKirC/v37kZiYiMmTJ7/rZhIEQRAEB9kkEARBEIQUioqKMHv2bFy4cAFNmjRBQEAA\nbG1tkZ6eDldXV5iZmSE8PBxNmzZ9100lCIIgCAAU4BEEQRDEWyksLISWlhYaNWoEACgpKcH9+/fR\nt2/fd9wygiAIghCFAjyCIAiCIAiCIIiPBPLBIwiCIAiCIAiC+EigAI8gCIIgCIIgCOIjgQI8giAI\ngiAIgiCIjwQK8AiCIAiCIAiCID4SKMAjCIIgCIIgCIL4SKAAjyAIgiAIgiAI4iOBAjyCIAiCIAiC\nIIiPBArwCIIgCIIgCIIgPhL+D4YIpwprbr3zAAAAAElFTkSuQmCC\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "vect = Pipeline([\n", + " ('norm', TextNormalizer()),\n", + " ('count', CountVectorizer(tokenizer=lambda x: x, preprocessor=None, lowercase=False)),\n", + "])\n", + "\n", + "docs = vect.fit_transform(documents(), labels())\n", + "viz = FreqDistVisualizer() \n", + "viz.fit(docs, vect.named_steps['count'].get_feature_names())\n", + "viz.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from sklearn.pipeline import Pipeline \n", + "from sklearn.feature_extraction.text import TfidfVectorizer \n", + "from yellowbrick.text import TSNEVisualizer\n", + "\n", + "vect = Pipeline([\n", + " ('norm', TextNormalizer()),\n", + " ('tfidf', TfidfVectorizer(tokenizer=lambda x: x, preprocessor=None, lowercase=False)),\n", + "])\n", + "\n", + "docs = vect.fit_transform(documents(), labels())\n", + "\n", + "viz = TSNEVisualizer() \n", + "viz.fit(docs, labels())\n", + "viz.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Classification \n", + "\n", + "The primary task for this kind of corpus is classification - sentiment analysis, etc. " + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.model_selection import train_test_split as tts \n", + "\n", + "docs_train, docs_test, labels_train, labels_test = tts(docs, list(labels()), test_size=0.2)" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n", + " intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n", + " penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n", + " verbose=0, warm_start=False)" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.linear_model import LogisticRegression \n", + "from yellowbrick.classifier import ClassBalance, ClassificationReport, ROCAUC\n", + "\n", + "logit = LogisticRegression()\n", + "logit.fit(docs_train, labels_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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C+SvhwSTAnwcjbgCKVGRkpL799lv16dNHlSpVUmJiouLj43X27Fm1aNFCkydP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\nvy9JCgkJ0SeffKLGjRtLksq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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "logit_balance = ClassBalance(logit, classes=set(labels_test))\n", + "logit_balance.score(docs_test, labels_test)\n", + "logit_balance.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "ename": "IndexError", + "evalue": "list index out of range", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mIndexError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mlogit_balance\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mClassificationReport\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlogit\u001b[0m\u001b[0;34m,\u001b[0m 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\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 136\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 137\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/Users/benjamin/Repos/tmp/yellowbrick/yellowbrick/classifier.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, y, y_pred)\u001b[0m\n\u001b[1;32m 158\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mcolumn\u001b[0m \u001b[0;32min\u001b[0m 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "logit_balance = ClassificationReport(logit, classes=set(labels_test))\n", + "logit_balance.score(docs_test, labels_test)\n", + "logit_balance.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "ename": "IndexError", + "evalue": "list index out of range", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mIndexError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mlogit_balance\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mClassificationReport\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mLogisticRegression\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m 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\u001b[0mmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mlambda\u001b[0m \u001b[0ms\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mdict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclasses_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ms\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscores\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 134\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscores\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkeys\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscores\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 135\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 136\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 137\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/Users/benjamin/Repos/tmp/yellowbrick/yellowbrick/classifier.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, y, y_pred)\u001b[0m\n\u001b[1;32m 158\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mcolumn\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmatrix\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 159\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mrow\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclasses_\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 160\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcolumn\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mrow\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmatrix\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mrow\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcolumn\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mva\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'center'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mha\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'center'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 161\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 162\u001b[0m \u001b[0mfig\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmatrix\u001b[0m\u001b[0;34m,\u001b[0m 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "logit_balance = ClassificationReport(LogisticRegression())\n", + "logit_balance.fit(docs_train, labels_train)\n", + "logit_balance.score(docs_test, labels_test)\n", + "logit_balance.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "ename": "ValueError", + "evalue": "Data is not binary and pos_label is not specified", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mlogit_balance\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mROCAUC\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlogit\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m 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"\u001b[0;32m/usr/local/lib/python3.5/site-packages/sklearn/metrics/ranking.py\u001b[0m in \u001b[0;36mroc_curve\u001b[0;34m(y_true, y_score, pos_label, sample_weight, drop_intermediate)\u001b[0m\n\u001b[1;32m 503\u001b[0m \"\"\"\n\u001b[1;32m 504\u001b[0m fps, tps, thresholds = _binary_clf_curve(\n\u001b[0;32m--> 505\u001b[0;31m y_true, y_score, pos_label=pos_label, sample_weight=sample_weight)\n\u001b[0m\u001b[1;32m 506\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 507\u001b[0m \u001b[0;31m# Attempt to drop thresholds corresponding to points in between and\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/lib/python3.5/site-packages/sklearn/metrics/ranking.py\u001b[0m in \u001b[0;36m_binary_clf_curve\u001b[0;34m(y_true, y_score, pos_label, sample_weight)\u001b[0m\n\u001b[1;32m 312\u001b[0m \u001b[0marray_equal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mclasses\u001b[0m\u001b[0;34m,\u001b[0m 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"logit_balance.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.2" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/examples/bbengfort/testing.ipynb b/examples/bbengfort/testing.ipynb index d9eb483e3..9ec594950 100644 --- a/examples/bbengfort/testing.ipynb +++ b/examples/bbengfort/testing.ipynb @@ -1,550 +1,397 @@ { "cells": [ - { - "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, - "source": [ - "# Visual Diagnosis of Text Analysis with Baleen \n", - "\n", - "This notebook has been created as part of the [Yellowbrick user study](http://www.scikit-yb.org/en/latest/evaluation.html). I hope to explore how visual methods might improve the workflow of text classification on a small to medium sized corpus. \n", - "\n", - "## Dataset \n", - "\n", - "The dataset used in this study is a sample of the [Baleen Corpus](http://baleen.districtdatalabs.com/). The Baleen corpus has been ingesting RSS feeds on the hour from a variety of topical feeds since March 2016, including news, hobbies, and political documents and currently has over 1.2M posts from 373 feeds. [Baleen](https://github.com/bbengfort/baleen) (an open source system) has a sister library called [Minke](https://github.com/bbengfort/minke) that provides multiprocessing support for dealing with Gigabytes worth of text. \n", - "\n", - "The dataset I'll use in this study is a sample of the larger data set that contains 68,052 or roughly 6% of the total corpus. For this test, I've chosen to use the preprocessed corpus, which means I won't have to do any tokenization, but can still apply normalization techniques. The corpus is described as follows:\n", - "\n", - "Baleen corpus contains 68,052 files in 12 categories.\n", - "Structured as:\n", - "\n", - "- 1,200,378 paragraphs (17.639 mean paragraphs per file)\n", - "- 2,058,635 sentences (1.715 mean sentences per paragraph).\n", - "\n", - "Word count of 44,821,870 with a vocabulary of 303,034 (147.910 lexical diversity).\n", - "\n", - "Category Counts: \n", - "\n", - "- books: 1,700 docs\n", - "- business: 9,248 docs\n", - "- cinema: 2,072 docs\n", - "- cooking: 733 docs\n", - "- data science: 692 docs\n", - "- design: 1,259 docs\n", - "- do it yourself: 2,620 docs\n", - "- gaming: 2,884 docs\n", - "- news: 33,253 docs\n", - "- politics: 3,793 docs\n", - "- sports: 4,710 docs\n", - "- tech: 5,088 docs\n", - "\n", - "This is quite a lot of data, so for now we'll simply create a classifier for the \"hobbies\" categories: e.g. books, cinema, cooking, diy, gaming, and sports. \n", - "\n", - "Note: this data set is not currently publically available, but I am happy to provide it on request. " - ] - }, { "cell_type": "code", "execution_count": 1, - "metadata": { - "collapsed": true, - "deletable": true, - "editable": true - }, - "outputs": [], - "source": [ - "%matplotlib inline " - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": true, - "deletable": true, - "editable": true - }, + "id": "3e15c26c", + "metadata": {}, "outputs": [], "source": [ - "import os \n", "import sys \n", - "import nltk\n", - "import pickle\n", + "sys.path.append(\"../..\")\n", + "\n", + "import numpy as np\n", + "import yellowbrick as yb \n", + "import matplotlib.pyplot as plt \n", "\n", - "# To import yellowbrick \n", - "sys.path.append(\"../..\")" + "from sklearn.cluster import KMeans\n", + "from yellowbrick.cluster import KElbowVisualizer" ] }, { - "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "cell_type": "code", + "execution_count": 2, + "id": "6f04357a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ - "### Loading Data \n", - "\n", - "In order to load data, I'd typically use a `CorpusReader`. However, for the sake of simplicity, I'll load data using some simple Python generator functions. I need to create two primary methods, the first loads the documents using pickle, and the second returns the vector of targets for supervised learning. " + "visualizer = KElbowVisualizer(KMeans(), k=(2,12))\n", + "visualizer" ] }, { "cell_type": "code", "execution_count": 3, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "id": "a8eb2417", + "metadata": {}, "outputs": [], "source": [ - "CORPUS_ROOT = os.path.join(os.getcwd(), \"data\") \n", - "CATEGORIES = [\"books\", \"cinema\", \"cooking\", \"diy\", \"gaming\", \"sports\"]\n", - "\n", - "def fileids(root=CORPUS_ROOT, categories=CATEGORIES): \n", - " \"\"\"\n", - " Fetch the paths, filtering on categories (pass None for all). \n", - " \"\"\"\n", - " for name in os.listdir(root):\n", - " dpath = os.path.join(root, name)\n", - " if not os.path.isdir(dpath):\n", - " continue \n", - " \n", - " if categories and name in categories: \n", - " for fname in os.listdir(dpath):\n", - " yield os.path.join(dpath, fname)\n", - "\n", - "\n", - "def documents(root=CORPUS_ROOT, categories=CATEGORIES):\n", - " \"\"\"\n", - " Load the pickled documents and yield one at a time. \n", - " \"\"\"\n", - " for path in fileids(root, categories):\n", - " with open(path, 'rb') as f:\n", - " yield pickle.load(f)\n", - "\n", - "\n", - "def labels(root=CORPUS_ROOT, categories=CATEGORIES):\n", - " \"\"\"\n", - " Return a list of the labels associated with each document. \n", - " \"\"\" \n", - " for path in fileids(root, categories):\n", - " dpath = os.path.dirname(path) \n", - " yield dpath.split(os.path.sep)[-1]" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, - "source": [ - "### Feature Extraction and Normalization \n", - "\n", - "In order to conduct analyses with Scikit-Learn, I'll need some helper transformers to modify the loaded data into a form that can be used by the `sklearn.feature_extraction` text transformers. I'll be mostly using the `CountVectorizer` and `TfidfVectorizer`, so these normalizer transformers and identity functions help a lot. " + "from yellowbrick.utils.wrapper import Wrapper\n", + "from yellowbrick.base import Visualizer" ] }, { "cell_type": "code", "execution_count": 4, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "id": "b1664801", + "metadata": {}, "outputs": [], "source": [ - "from nltk.corpus import wordnet as wn\n", - "from nltk.stem import WordNetLemmatizer \n", - "from unicodedata import category as ucat\n", - "from nltk.corpus import stopwords as swcorpus\n", - "from sklearn.base import BaseEstimator, TransformerMixin \n", - "\n", - "\n", - "def identity(args):\n", - " \"\"\"\n", - " The identity function is used as the \"tokenizer\" for \n", - " pre-tokenized text. It just passes back it's arguments. \n", - " \"\"\"\n", - " return args \n", - "\n", - "\n", - "def is_punctuation(token):\n", - " \"\"\"\n", - " Returns true if all characters in the token are\n", - " unicode punctuation (works for most punct). \n", - " \"\"\"\n", - " return all(\n", - " ucat(c).startswith('P')\n", - " for c in token \n", - " )\n", - "\n", - "\n", - "def wnpos(tag):\n", - " \"\"\"\n", - " Returns the wn part of speech tag from the penn treebank tag. \n", - " \"\"\"\n", - " return {\n", - " \"N\": wn.NOUN,\n", - " \"V\": wn.VERB,\n", - " \"J\": wn.ADJ, \n", - " \"R\": wn.ADV, \n", - " }.get(tag[0], wn.NOUN)\n", - "\n", - "\n", - "class TextNormalizer(BaseEstimator, TransformerMixin):\n", - " \n", - " def __init__(self, stopwords='english', lowercase=True, lemmatize=True, depunct=True):\n", - " self.stopwords = frozenset(swcorpus.words(stopwords)) if stopwords else frozenset()\n", - " self.lowercase = lowercase \n", - " self.depunct = depunct \n", - " self.lemmatizer = WordNetLemmatizer() if lemmatize else None \n", + "class Outer(Visualizer, Wrapper):\n", " \n", - " def fit(self, docs, labels=None):\n", - " return self\n", - "\n", - " def transform(self, docs): \n", - " for doc in docs: \n", - " yield list(self.normalize(doc)) \n", + " def __init__(self, estimator):\n", + " self.estimator = estimator\n", + " Wrapper.__init__(self, self.estimator)\n", + " Visualizer.__init__(self, ax=None, fig=None)\n", " \n", - " def normalize(self, doc):\n", - " for paragraph in doc:\n", - " for sentence in paragraph:\n", - " for token, tag in sentence: \n", - " if token.lower() in self.stopwords:\n", - " continue \n", - " \n", - " if self.depunct and is_punctuation(token):\n", - " continue \n", - " \n", - " if self.lowercase:\n", - " token = token.lower() \n", - " \n", - " if self.lemmatizer:\n", - " token = self.lemmatizer.lemmatize(token, wnpos(tag))\n", - " \n", - " yield token " + " def get_params(self, deep=True):\n", + " \"\"\"\n", + " After v0.24 - scikit-learn is able to determine that ``self.estimator`` is\n", + " nested and fetches its params using ``estimator__param``. This functionality is\n", + " pretty cool but it's a pretty big overhaul to change our \"wrapped\" estimator API\n", + " to a \"nested\" estimator API, therefore we override ``get_params`` to flatten out\n", + " the estimator params.\n", + " \"\"\"\n", + " params = super(Outer, self).get_params(deep=deep)\n", + " for param in list(params.keys()):\n", + " if param.startswith(\"estimator__\"):\n", + " params[param[len(\"estimator__\"):]] = params.pop(param)\n", + " return params" ] }, { - "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "cell_type": "code", + "execution_count": 5, + "id": "73ff50c2", + "metadata": {}, + "outputs": [], "source": [ - "### Corpus Analysis \n", - "\n", - "At this stage, I'd like to get a feel for what was in my corpus, so that I can start thinking about how to best vectorize the text and do different types of counting. With the Yellowbrick 0.3.3 release, support has been added for two text visualizers, which I think I will test out at scale using this corpus. " + "class Subouter(Outer):\n", + " \n", + " def __init__(self, estimator, k=4):\n", + " super(Subouter, self).__init__(estimator)\n", + " self.k = k\n", + " " ] }, { "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/usr/local/lib/python3.5/site-packages/sklearn/cross_validation.py:44: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n", - " \"This module will be removed in 0.20.\", DeprecationWarning)\n" - ] - }, - { - "ename": "AttributeError", - "evalue": "'NoneType' object has no attribute 'transform'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 9\u001b[0m ])\n\u001b[1;32m 10\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 11\u001b[0;31m \u001b[0mvisualizer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit_transform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdocuments\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabels\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 12\u001b[0m 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\u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 496\u001b[0m \u001b[0;31m# fit method of arity 2 (supervised transformation)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 497\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mfit_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtransform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 498\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 499\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mAttributeError\u001b[0m: 'NoneType' object has no attribute 'transform'" - ] - }, - { - "data": { - "image/png": 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fiBEjkiRvvfXWoNUBAACUqur3watUKknS7cVXTqipqRm0OgAAgFJV/b4Bo0ePTpK0trZ2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\noVAoFAqFQqG8I/wf27lDuEaJvt4AAAAASUVORK5CYII=\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": 6, + "id": "44c63ce3", + "metadata": {}, + "outputs": [], "source": [ - "from sklearn.pipeline import Pipeline \n", - "from sklearn.feature_extraction.text import CountVectorizer \n", - "from yellowbrick.text import FreqDistVisualizer\n", - "\n", - "visualizer = Pipeline([\n", - " ('norm', TextNormalizer()),\n", - " ('count', CountVectorizer(tokenizer=lambda x: x, preprocessor=None, lowercase=False)),\n", - " ('viz', FreqDistVisualizer())\n", - "])\n", - "\n", - "visualizer.fit_transform(documents(), labels())\n", - "visualizer.named_steps['viz'].show()" + "o = Outer(KMeans())" ] }, { "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "execution_count": 7, + "id": "79ea1d05", + "metadata": {}, "outputs": [ { - "data": { - "image/png": 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Aq6OsV/mU/hcMCZMAcF6pB7zatWtLko4fP+62Lz09XVWq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GvWDObm5iLplc7OzqisrMTevXvFUsjKysowadIkVFVVcanVP/74Iy5fvqxUsStlI0+/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\nIIiPBArwCIIgCIIgCIIgPhL+D4YIpwprbr3zAAAAAElFTkSuQmCC\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" + "name": "stdout", + "output_type": "stream", + "text": [ + "KMeans()\n", + "8\n" + ] } ], "source": [ - "vect = Pipeline([\n", - " ('norm', TextNormalizer()),\n", - " ('count', CountVectorizer(tokenizer=lambda x: x, preprocessor=None, lowercase=False)),\n", - "])\n", - "\n", - "docs = vect.fit_transform(documents(), labels())\n", - "viz = FreqDistVisualizer() \n", - "viz.fit(docs, vect.named_steps['count'].get_feature_names())\n", - "viz.show()" + "print(o.estimator)\n", + "print(o.n_clusters)\n", + "# print(o.foo)" ] }, { "cell_type": "code", "execution_count": 8, - "metadata": { - "collapsed": false - }, + "id": "2ef6c843", + "metadata": {}, "outputs": [ { "data": { - "image/png": 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+ "text/html": [ + "
Subouter(estimator=KMeans())
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" + ], "text/plain": [ - "" + "Subouter(estimator=KMeans())" ] }, + "execution_count": 8, "metadata": {}, - "output_type": "display_data" + "output_type": "execute_result" } ], "source": [ - "from sklearn.pipeline import Pipeline \n", - "from sklearn.feature_extraction.text import TfidfVectorizer \n", - "from yellowbrick.text import TSNEVisualizer\n", - "\n", - "vect = Pipeline([\n", - " ('norm', TextNormalizer()),\n", - " ('tfidf', TfidfVectorizer(tokenizer=lambda x: x, preprocessor=None, lowercase=False)),\n", - "])\n", - "\n", - "docs = vect.fit_transform(documents(), labels())\n", - "\n", - "viz = TSNEVisualizer() \n", - "viz.fit(docs, labels())\n", - "viz.show()" + "Subouter(KMeans())" ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": 9, + "id": "15b7b68a", "metadata": {}, + "outputs": [], "source": [ - "## Classification \n", - "\n", - "The primary task for this kind of corpus is classification - sentiment analysis, etc. " + "s = Subouter(KMeans(), k=9)" ] }, { "cell_type": "code", - "execution_count": 19, - "metadata": { - "collapsed": false - }, - "outputs": [], + "execution_count": 10, + "id": "a92f539f", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "KMeans()\n", + "8\n", + "9\n" + ] + } + ], "source": [ - "from sklearn.model_selection import train_test_split as tts \n", - "\n", - "docs_train, docs_test, labels_train, labels_test = tts(docs, list(labels()), test_size=0.2)" + "print(s.estimator)\n", + "print(s.n_clusters)\n", + "print(s.k)\n", + "# print(s.foo)" ] }, { "cell_type": "code", - "execution_count": 21, - "metadata": { - "collapsed": false - }, + "execution_count": 11, + "id": "0b23c03e", + "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n", - " intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n", - " penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n", - " verbose=0, warm_start=False)" + "{'estimator': KMeans(),\n", + " 'k': 9,\n", + " 'algorithm': 'lloyd',\n", + " 'copy_x': True,\n", + " 'init': 'k-means++',\n", + " 'max_iter': 300,\n", + " 'n_clusters': 8,\n", + " 'n_init': 10,\n", + " 'random_state': None,\n", + " 'tol': 0.0001,\n", + " 'verbose': 0}" ] }, - "execution_count": 21, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "from sklearn.linear_model import LogisticRegression \n", - "from yellowbrick.classifier import ClassBalance, ClassificationReport, ROCAUC\n", - "\n", - "logit = LogisticRegression()\n", - "logit.fit(docs_train, labels_train)" + "s.get_params()" ] }, { "cell_type": "code", - "execution_count": 23, - "metadata": { - "collapsed": false - }, + "execution_count": 12, + "id": "7d73dd40", + "metadata": {}, "outputs": [ { "data": { - "image/png": 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C+SvhwSTAnwcjbgCKVGRkpL799lv16dNHlSpVUmJiouLj43X27Fm1aNFCkydP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\nvy9JCgkJ0SeffKLGjRtLksq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Subouter(estimator=KMeans(), k=9)
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" + ], "text/plain": [ - "" + "Subouter(estimator=KMeans(), k=9)" ] }, + "execution_count": 12, "metadata": {}, - "output_type": "display_data" + "output_type": "execute_result" } ], "source": [ - "logit_balance = ClassBalance(logit, classes=set(labels_test))\n", - "logit_balance.score(docs_test, labels_test)\n", - "logit_balance.show()" + "s" ] }, { "cell_type": "code", - "execution_count": 27, - "metadata": { - "collapsed": false - }, + "execution_count": 13, + "id": "65d018fd", + "metadata": {}, "outputs": [ { - "ename": "IndexError", - "evalue": "list index out of range", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mIndexError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mlogit_balance\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mClassificationReport\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlogit\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mclasses\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabels_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mlogit_balance\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscore\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdocs_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabels_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0mlogit_balance\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/Users/benjamin/Repos/tmp/yellowbrick/yellowbrick/classifier.py\u001b[0m in \u001b[0;36mscore\u001b[0;34m(self, X, y, **kwargs)\u001b[0m\n\u001b[1;32m 133\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscores\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mlambda\u001b[0m \u001b[0ms\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mdict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclasses_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ms\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscores\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 134\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscores\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkeys\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscores\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 135\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 136\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 137\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/Users/benjamin/Repos/tmp/yellowbrick/yellowbrick/classifier.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, y, y_pred)\u001b[0m\n\u001b[1;32m 158\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mcolumn\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmatrix\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 159\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mrow\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclasses_\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 160\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcolumn\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mrow\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmatrix\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mrow\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcolumn\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mva\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'center'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mha\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'center'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 161\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 162\u001b[0m \u001b[0mfig\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmatrix\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minterpolation\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'nearest'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcmap\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcmap\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvmin\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvmax\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mIndexError\u001b[0m: list index out of range" - ] + "data": { + "text/html": [ + "
ModelVisualizer(ax=<AxesSubplot:>, estimator=KMeans(),\n",
+       "                fig=<Figure size 432x288 with 1 Axes>)
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" + ], + "text/plain": [ + "ModelVisualizer(ax=, estimator=KMeans(),\n", + " fig=
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"text/plain": [ - "" + "
" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], "source": [ - "logit_balance = ClassificationReport(logit, classes=set(labels_test))\n", - "logit_balance.score(docs_test, labels_test)\n", - "logit_balance.show()" + "from yellowbrick.base import ModelVisualizer \n", + "\n", + "v = ModelVisualizer(KMeans())\n", + "v" ] }, { "cell_type": "code", - "execution_count": 29, - "metadata": { - "collapsed": false - }, + "execution_count": 14, + "id": "6a55c544", + "metadata": {}, + "outputs": [], + "source": [ + "from yellowbrick.model_selection import CVScores \n", + "from sklearn.naive_bayes import GaussianNB" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "78392b14", + "metadata": {}, "outputs": [ { - "ename": "IndexError", - "evalue": "list index out of range", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mIndexError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mlogit_balance\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mClassificationReport\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mLogisticRegression\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0mlogit_balance\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdocs_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabels_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mlogit_balance\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscore\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdocs_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabels_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 4\u001b[0m \u001b[0mlogit_balance\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/Users/benjamin/Repos/tmp/yellowbrick/yellowbrick/classifier.py\u001b[0m in \u001b[0;36mscore\u001b[0;34m(self, X, y, **kwargs)\u001b[0m\n\u001b[1;32m 133\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscores\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mlambda\u001b[0m \u001b[0ms\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mdict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclasses_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ms\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscores\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 134\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscores\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkeys\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscores\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 135\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 136\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 137\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/Users/benjamin/Repos/tmp/yellowbrick/yellowbrick/classifier.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, y, y_pred)\u001b[0m\n\u001b[1;32m 158\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mcolumn\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmatrix\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 159\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mrow\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclasses_\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 160\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcolumn\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mrow\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmatrix\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mrow\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcolumn\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mva\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'center'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mha\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'center'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 161\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 162\u001b[0m \u001b[0mfig\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmatrix\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minterpolation\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'nearest'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcmap\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcmap\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvmin\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvmax\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mIndexError\u001b[0m: list index out of range" + "name": "stdout", + "output_type": "stream", + "text": [ + "None\n" ] - }, + } + ], + "source": [ + "c = CVScores(GaussianNB())\n", + "print(c.color)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "6b561595", + "metadata": {}, + "outputs": [ { "data": { - "image/png": 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"text/plain": [ - "" + "{'ax': ,\n", + " 'color': None,\n", + " 'cv': None,\n", + " 'estimator': GaussianNB(),\n", + " 'scoring': None,\n", + " 'priors': None,\n", + " 'var_smoothing': 1e-09}" ] }, + "execution_count": 16, "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], "source": [ - "logit_balance = ClassificationReport(LogisticRegression())\n", - "logit_balance.fit(docs_train, labels_train)\n", - "logit_balance.score(docs_test, labels_test)\n", - "logit_balance.show()" + "c.get_params()" ] }, { "cell_type": "code", - "execution_count": 28, - "metadata": { - "collapsed": false - }, + "execution_count": 17, + "id": "2be7cf21", + "metadata": {}, "outputs": [ { - "ename": "ValueError", - "evalue": "Data is not binary and pos_label is not specified", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mlogit_balance\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mROCAUC\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlogit\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mlogit_balance\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscore\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdocs_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabels_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0mlogit_balance\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/Users/benjamin/Repos/tmp/yellowbrick/yellowbrick/classifier.py\u001b[0m in \u001b[0;36mscore\u001b[0;34m(self, X, y, **kwargs)\u001b[0m\n\u001b[1;32m 311\u001b[0m \"\"\"\n\u001b[1;32m 312\u001b[0m \u001b[0my_pred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 313\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfpr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtpr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mthresholds\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mroc_curve\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 314\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mroc_auc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mauc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfpr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtpr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 315\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/usr/local/lib/python3.5/site-packages/sklearn/metrics/ranking.py\u001b[0m in \u001b[0;36mroc_curve\u001b[0;34m(y_true, y_score, pos_label, sample_weight, drop_intermediate)\u001b[0m\n\u001b[1;32m 503\u001b[0m \"\"\"\n\u001b[1;32m 504\u001b[0m fps, tps, thresholds = _binary_clf_curve(\n\u001b[0;32m--> 505\u001b[0;31m y_true, y_score, pos_label=pos_label, sample_weight=sample_weight)\n\u001b[0m\u001b[1;32m 506\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 507\u001b[0m \u001b[0;31m# Attempt to drop thresholds corresponding to points in between and\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/usr/local/lib/python3.5/site-packages/sklearn/metrics/ranking.py\u001b[0m in \u001b[0;36m_binary_clf_curve\u001b[0;34m(y_true, y_score, pos_label, sample_weight)\u001b[0m\n\u001b[1;32m 312\u001b[0m \u001b[0marray_equal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mclasses\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mor\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 313\u001b[0m array_equal(classes, [1]))):\n\u001b[0;32m--> 314\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Data is not binary and pos_label is not specified\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 315\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0mpos_label\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 316\u001b[0m \u001b[0mpos_label\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m1.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mValueError\u001b[0m: Data is not binary and pos_label is not specified" - ] + "data": { + "text/html": [ + "
CVScores(ax=<AxesSubplot:>, estimator=GaussianNB())
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" + ], + "text/plain": [ + "CVScores(ax=, estimator=GaussianNB())" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ - "logit_balance = ROCAUC(logit)\n", - "logit_balance.score(docs_test, labels_test)\n", - "logit_balance.show()" + "c" ] } ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -558,9 +405,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.2" + "version": "3.10.2" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 5 } diff --git a/tests/test_cluster/test_elbow.py b/tests/test_cluster/test_elbow.py index be7470db9..ce3ad4dde 100644 --- a/tests/test_cluster/test_elbow.py +++ b/tests/test_cluster/test_elbow.py @@ -205,12 +205,16 @@ def test_invalid_k(self): """ Assert that invalid values of K raise exceptions """ + # Generate a blobs data set + X, y = make_blobs( + n_samples=1000, n_features=12, centers=6, shuffle=True, random_state=42 + ) with pytest.raises(YellowbrickValueError): - KElbowVisualizer(KMeans(), k=(1, 2, 3, "foo", 5)) + KElbowVisualizer(KMeans(), k=(1, 2, 3, "foo", 5)).fit(X) with pytest.raises(YellowbrickValueError): - KElbowVisualizer(KMeans(), k="foo") + KElbowVisualizer(KMeans(), k="foo").fit(X) def test_valid_k(self): """ @@ -220,16 +224,21 @@ def test_valid_k(self): # if k is a tuple of 2 ints, k_values = range(k[0], k[1]) # if k is an iterable, k_values_ = list(k) - visualizer = KElbowVisualizer(KMeans(), k=8) + # Generate a blobs data set + X, y = make_blobs( + n_samples=1000, n_features=12, centers=6, shuffle=True, random_state=42 + ) + + visualizer = KElbowVisualizer(KMeans(), k=8).fit(X) assert visualizer.k_values_ == list(np.arange(2, 8 + 1)) - visualizer = KElbowVisualizer(KMeans(), k=(4, 12)) + visualizer = KElbowVisualizer(KMeans(), k=(4, 12)).fit(X) assert visualizer.k_values_ == list(np.arange(4, 12)) - visualizer = KElbowVisualizer(KMeans(), k=np.arange(10, 100, 10)) + visualizer = KElbowVisualizer(KMeans(), k=np.arange(10, 100, 10)).fit(X) assert visualizer.k_values_ == list(np.arange(10, 100, 10)) - visualizer = KElbowVisualizer(KMeans(), k=[10, 20, 30, 40, 50, 60, 70, 80, 90]) + visualizer = KElbowVisualizer(KMeans(), k=[10, 20, 30, 40, 50, 60, 70, 80, 90]).fit(X) assert visualizer.k_values_ == list(np.arange(10, 100, 10)) @pytest.mark.xfail(sys.platform == "win32", reason="images not close on windows") @@ -491,4 +500,14 @@ def test_set_colors_manually(self): # Execute drawing oz.draw() oz.finalize() - self.assert_images_similar(oz, tol=3.2) \ No newline at end of file + self.assert_images_similar(oz, tol=3.2) + + def test_get_params(self): + """ + Ensure the get params works for sklearn-compatibility + """ + oz = KElbowVisualizer( + KMeans(random_state=0), k=5, + ) + params = oz.get_params() + assert len(params) > 0 \ No newline at end of file diff --git a/tests/test_model_selection/test_dropping_curve.py b/tests/test_model_selection/test_dropping_curve.py index 0b2763f35..8e72047d0 100644 --- a/tests/test_model_selection/test_dropping_curve.py +++ b/tests/test_model_selection/test_dropping_curve.py @@ -188,4 +188,12 @@ def test_bad_train_sizes(self): Test learning curve with bad input for feature size. """ with pytest.raises(YellowbrickValueError): - DroppingCurve(SVC(), param_name="gamma", feature_sizes=100) \ No newline at end of file + DroppingCurve(SVC(), param_name="gamma", feature_sizes=100) + + def test_get_params(self): + """ + Ensure dropping curve get params works correctly + """ + oz = DroppingCurve(MultinomialNB()) + params = oz.get_params() + assert len(params) > 0 \ No newline at end of file diff --git a/tests/test_utils/test_wrapper.py b/tests/test_utils/test_wrapper.py index f6cbf9162..9be2a4d07 100644 --- a/tests/test_utils/test_wrapper.py +++ b/tests/test_utils/test_wrapper.py @@ -17,10 +17,13 @@ ## Imports ########################################################################## +import pytest + from unittest import mock from yellowbrick.base import Visualizer from yellowbrick.utils.wrapper import * +from yellowbrick.exceptions import YellowbrickAttributeError, YellowbrickTypeError from sklearn.naive_bayes import MultinomialNB from sklearn.naive_bayes import GaussianNB @@ -133,3 +136,21 @@ def test_rewrap_object(self): obj.predict() old.predict.assert_called_once() new.predict.assert_called_once() + + def test_wrapper_recursion(self): + """ + Ensure wrapper recursion isn't possible + """ + obj = Wrapper("") + obj._wrapped = obj + with pytest.raises(YellowbrickTypeError): + obj.foo + + def test_attribute_error(self): + """ + Attribute errors should return a YellowbrickAttributeError + """ + obj = WrappedEstimator() + pat = r"neither visualizer 'WrappedEstimator' nor wrapped estimator 'MagicMock' have attribute 'notaproperty'" + with pytest.raises(YellowbrickAttributeError, match=pat): + obj.notaproperty \ No newline at end of file diff --git a/yellowbrick/cluster/elbow.py b/yellowbrick/cluster/elbow.py index 951b1fd26..a20f5aff1 100644 --- a/yellowbrick/cluster/elbow.py +++ b/yellowbrick/cluster/elbow.py @@ -186,7 +186,7 @@ class KElbowVisualizer(ClusteringScoreVisualizer): - **calinski_harabasz**: ratio of within to between cluster dispersion distance_metric : str or callable, default='euclidean' - The metric to use when calculating distance between instances in a + The metric to use when calculating distance between instances in a feature array. If metric is a string, it must be one of the options allowed by sklearn's metrics.pairwise.pairwise_distances. If X is the distance array itself, use metric="precomputed". @@ -280,6 +280,7 @@ def __init__( ) # Store the arguments + self.k = k self.scoring_metric = KELBOW_SCOREMAP[metric] self.metric = metric self.timings = timings @@ -293,50 +294,41 @@ def __init__( CVLINE: LINE_COLOR, } - # Convert K into a tuple argument if an integer - if isinstance(k, int): - self.k_values_ = list(range(2, k + 1)) + def fit(self, X, y=None, **kwargs): + """ + Fits n KMeans models where n is the length of ``self.k_values_``, + storing the silhouette scores in the ``self.k_scores_`` attribute. + The "elbow" and silhouette score corresponding to it are stored in + ``self.elbow_value`` and ``self.elbow_score`` respectively. + This method finishes up by calling draw to create the plot. + """ + # Convert K into a tuple argument if an integer + if isinstance(self.k, int): + self.k_values_ = list(range(2, self.k + 1)) elif ( - isinstance(k, tuple) - and len(k) == 2 - and all(isinstance(x, (int, np.integer)) for x in k) + isinstance(self.k, tuple) + and len(self.k) == 2 + and all(isinstance(x, (int, np.integer)) for x in self.k) ): - self.k_values_ = list(range(*k)) - elif isinstance(k, Iterable) and all( - isinstance(x, (int, np.integer)) for x in k + self.k_values_ = list(range(*self.k)) + elif isinstance(self.k, Iterable) and all( + isinstance(x, (int, np.integer)) for x in self.k ): - self.k_values_ = list(k) + self.k_values_ = list(self.k) else: raise YellowbrickValueError( ( "Specify an iterable of integers, a range, or maximal K value," - " the value '{}' is not a valid argument for K.".format(k) + " the value '{}' is not a valid argument for K.".format(self.k) ) ) - # Holds the values of the silhoutte scores - self.k_scores_ = None - - # Set Default Elbow Value - self.elbow_value_ = None - - def fit(self, X, y=None, **kwargs): - """ - Fits n KMeans models where n is the length of ``self.k_values_``, - storing the silhouette scores in the ``self.k_scores_`` attribute. - The "elbow" and silhouette score corresponding to it are stored in - ``self.elbow_value`` and ``self.elbow_score`` respectively. - This method finishes up by calling draw to create the plot. - """ - self.k_scores_ = [] self.k_timers_ = [] self.kneedle = None self.knee_value = None - - if self.locate_elbow: - self.elbow_value_ = None - self.elbow_score_ = None + self.elbow_value_ = None + self.elbow_score_ = None for k in self.k_values_: # Compute the start time for each model @@ -527,7 +519,7 @@ def kelbow_visualizer( - **calinski_harabasz**: ratio of within to between cluster dispersion distance_metric : str or callable, default='euclidean' - The metric to use when calculating distance between instances in a + The metric to use when calculating distance between instances in a feature array. If metric is a string, it must be one of the options allowed by sklearn's metrics.pairwise.pairwise_distances. If X is the distance array itself, use metric="precomputed". diff --git a/yellowbrick/model_selection/dropping_curve.py b/yellowbrick/model_selection/dropping_curve.py index fc7201d3a..e335d3705 100644 --- a/yellowbrick/model_selection/dropping_curve.py +++ b/yellowbrick/model_selection/dropping_curve.py @@ -243,7 +243,7 @@ def fit(self, X, y=None): # compute the mean and standard deviation of the training data self.train_scores_mean_ = np.mean(self.train_scores_, axis=1) self.train_scores_std_ = np.std(self.train_scores_, axis=1) - + # compute the mean and standard deviation of the validation data self.valid_scores_mean_ = np.mean(self.valid_scores_, axis=1) self.valid_scores_std_ = np.std(self.valid_scores_, axis=1) diff --git a/yellowbrick/utils/wrapper.py b/yellowbrick/utils/wrapper.py index f5a586c0a..2fa523097 100644 --- a/yellowbrick/utils/wrapper.py +++ b/yellowbrick/utils/wrapper.py @@ -17,6 +17,8 @@ ## Wrapper Class ########################################################################## +from yellowbrick.exceptions import YellowbrickAttributeError, YellowbrickTypeError + class Wrapper(object): """ @@ -38,5 +40,11 @@ def __init__(self, obj): self._wrapped = obj def __getattr__(self, attr): + if self is self._wrapped: + raise YellowbrickTypeError("wrapper cannot wrap itself or recursion will occur") + # proxy to the wrapped object - return getattr(self._wrapped, attr) + try: + return getattr(self._wrapped, attr) + except AttributeError as e: + raise YellowbrickAttributeError(f"neither visualizer '{self.__class__.__name__}' nor wrapped estimator '{type(self._wrapped).__name__}' have attribute '{attr}'") from e From b9626fc4d556fc0125fc5e66c9ff1ffd42710580 Mon Sep 17 00:00:00 2001 From: Larry Gray Date: Sat, 28 May 2022 13:04:20 -0600 Subject: [PATCH 11/27] Fixes #1248 Adds Sklearn Pipeline tests to ModelVisualizers - Part 1 (#1249) Tests for: CPE, ClsRpt, ConfustionMatrix, PRC, 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