From e87d20cbda333b8c3c23f3f76640df7bab452abd Mon Sep 17 00:00:00 2001 From: Michael Fishman Date: Thu, 9 Apr 2020 18:26:58 +0000 Subject: [PATCH 1/2] Added class_names optional arg to plot_cumulative_gain. class_names is used for the plot's legend. --- .pytest_cache/v/cache/nodeids | 152 +++++++++++++++++ scikit_plot.egg-info/PKG-INFO | 153 ++++++++++++++++++ scikit_plot.egg-info/SOURCES.txt | 18 +++ scikit_plot.egg-info/dependency_links.txt | 1 + scikit_plot.egg-info/requires.txt | 7 + scikit_plot.egg-info/top_level.txt | 1 + .../__pycache__/__init__.cpython-36.pyc | Bin 0 -> 518 bytes .../__pycache__/classifiers.cpython-36.pyc | Bin 0 -> 20736 bytes scikitplot/__pycache__/cluster.cpython-36.pyc | Bin 0 -> 4886 bytes .../__pycache__/clustering.cpython-36.pyc | Bin 0 -> 1697 bytes .../__pycache__/decomposition.cpython-36.pyc | Bin 0 -> 7381 bytes .../__pycache__/estimators.cpython-36.pyc | Bin 0 -> 8904 bytes scikitplot/__pycache__/helpers.cpython-36.pyc | Bin 0 -> 6359 bytes scikitplot/__pycache__/metrics.cpython-36.pyc | Bin 0 -> 38947 bytes .../__pycache__/plotters.cpython-36.pyc | Bin 0 -> 37160 bytes scikitplot/metrics.py | 13 +- .../tests/__pycache__/__init__.cpython-36.pyc | Bin 0 -> 148 bytes .../test_classifiers.cpython-36-PYTEST.pyc | Bin 0 -> 28932 bytes .../test_cluster.cpython-36-PYTEST.pyc | Bin 0 -> 3655 bytes .../test_clustering.cpython-36-PYTEST.pyc | Bin 0 -> 9340 bytes .../test_decomposition.cpython-36-PYTEST.pyc | Bin 0 -> 4690 bytes .../test_estimators.cpython-36-PYTEST.pyc | Bin 0 -> 6545 bytes .../test_metrics.cpython-36-PYTEST.pyc | Bin 0 -> 29090 bytes .../test_plotters.cpython-36-PYTEST.pyc | Bin 0 -> 7962 bytes 24 files changed, 341 insertions(+), 4 deletions(-) create mode 100644 .pytest_cache/v/cache/nodeids create mode 100644 scikit_plot.egg-info/PKG-INFO create mode 100644 scikit_plot.egg-info/SOURCES.txt create mode 100644 scikit_plot.egg-info/dependency_links.txt create mode 100644 scikit_plot.egg-info/requires.txt create mode 100644 scikit_plot.egg-info/top_level.txt create mode 100644 scikitplot/__pycache__/__init__.cpython-36.pyc create mode 100644 scikitplot/__pycache__/classifiers.cpython-36.pyc create mode 100644 scikitplot/__pycache__/cluster.cpython-36.pyc create mode 100644 scikitplot/__pycache__/clustering.cpython-36.pyc create mode 100644 scikitplot/__pycache__/decomposition.cpython-36.pyc create mode 100644 scikitplot/__pycache__/estimators.cpython-36.pyc create mode 100644 scikitplot/__pycache__/helpers.cpython-36.pyc create mode 100644 scikitplot/__pycache__/metrics.cpython-36.pyc create mode 100644 scikitplot/__pycache__/plotters.cpython-36.pyc create mode 100644 scikitplot/tests/__pycache__/__init__.cpython-36.pyc create mode 100644 scikitplot/tests/__pycache__/test_classifiers.cpython-36-PYTEST.pyc create mode 100644 scikitplot/tests/__pycache__/test_cluster.cpython-36-PYTEST.pyc create mode 100644 scikitplot/tests/__pycache__/test_clustering.cpython-36-PYTEST.pyc create mode 100644 scikitplot/tests/__pycache__/test_decomposition.cpython-36-PYTEST.pyc create mode 100644 scikitplot/tests/__pycache__/test_estimators.cpython-36-PYTEST.pyc create mode 100644 scikitplot/tests/__pycache__/test_metrics.cpython-36-PYTEST.pyc create mode 100644 scikitplot/tests/__pycache__/test_plotters.cpython-36-PYTEST.pyc diff --git a/.pytest_cache/v/cache/nodeids b/.pytest_cache/v/cache/nodeids new file mode 100644 index 0000000..f257071 --- /dev/null +++ b/.pytest_cache/v/cache/nodeids @@ -0,0 +1,152 @@ +[ + "scikitplot/tests/test_classifiers.py::TestClassifierFactory::test_instance_validation", + "scikitplot/tests/test_classifiers.py::TestClassifierFactory::test_method_insertion", + "scikitplot/tests/test_classifiers.py::TestPlotLearningCurve::test_ax", + "scikitplot/tests/test_classifiers.py::TestPlotLearningCurve::test_cv", + "scikitplot/tests/test_classifiers.py::TestPlotLearningCurve::test_n_jobs", + "scikitplot/tests/test_classifiers.py::TestPlotLearningCurve::test_string_classes", + "scikitplot/tests/test_classifiers.py::TestPlotLearningCurve::test_train_sizes", + "scikitplot/tests/test_classifiers.py::TestPlotConfusionMatrix::test_array_like", + "scikitplot/tests/test_classifiers.py::TestPlotConfusionMatrix::test_ax", + "scikitplot/tests/test_classifiers.py::TestPlotConfusionMatrix::test_cmap", + "scikitplot/tests/test_classifiers.py::TestPlotConfusionMatrix::test_cv", + "scikitplot/tests/test_classifiers.py::TestPlotConfusionMatrix::test_do_cv", + "scikitplot/tests/test_classifiers.py::TestPlotConfusionMatrix::test_labels", + "scikitplot/tests/test_classifiers.py::TestPlotConfusionMatrix::test_normalize", + "scikitplot/tests/test_classifiers.py::TestPlotConfusionMatrix::test_shuffle", + "scikitplot/tests/test_classifiers.py::TestPlotConfusionMatrix::test_string_classes", + "scikitplot/tests/test_classifiers.py::TestPlotConfusionMatrix::test_true_pred_labels", + "scikitplot/tests/test_classifiers.py::TestPlotROCCurve::test_array_like", + "scikitplot/tests/test_classifiers.py::TestPlotROCCurve::test_ax", + "scikitplot/tests/test_classifiers.py::TestPlotROCCurve::test_cmap", + "scikitplot/tests/test_classifiers.py::TestPlotROCCurve::test_curve_diffs", + "scikitplot/tests/test_classifiers.py::TestPlotROCCurve::test_do_cv", + "scikitplot/tests/test_classifiers.py::TestPlotROCCurve::test_invalid_curve_arg", + "scikitplot/tests/test_classifiers.py::TestPlotROCCurve::test_predict_proba", + "scikitplot/tests/test_classifiers.py::TestPlotROCCurve::test_string_classes", + "scikitplot/tests/test_classifiers.py::TestPlotKSStatistic::test_array_like", + "scikitplot/tests/test_classifiers.py::TestPlotKSStatistic::test_ax", + "scikitplot/tests/test_classifiers.py::TestPlotKSStatistic::test_do_cv", + "scikitplot/tests/test_classifiers.py::TestPlotKSStatistic::test_predict_proba", + "scikitplot/tests/test_classifiers.py::TestPlotKSStatistic::test_string_classes", + "scikitplot/tests/test_classifiers.py::TestPlotKSStatistic::test_two_classes", + "scikitplot/tests/test_classifiers.py::TestPlotPrecisionRecall::test_array_like", + "scikitplot/tests/test_classifiers.py::TestPlotPrecisionRecall::test_ax", + "scikitplot/tests/test_classifiers.py::TestPlotPrecisionRecall::test_cmap", + "scikitplot/tests/test_classifiers.py::TestPlotPrecisionRecall::test_curve_diffs", + "scikitplot/tests/test_classifiers.py::TestPlotPrecisionRecall::test_do_cv", + "scikitplot/tests/test_classifiers.py::TestPlotPrecisionRecall::test_invalid_curve_arg", + "scikitplot/tests/test_classifiers.py::TestPlotPrecisionRecall::test_predict_proba", + "scikitplot/tests/test_classifiers.py::TestPlotPrecisionRecall::test_string_classes", + "scikitplot/tests/test_classifiers.py::TestFeatureImportances::test_ax", + "scikitplot/tests/test_classifiers.py::TestFeatureImportances::test_feature_importances_in_clf", + "scikitplot/tests/test_classifiers.py::TestFeatureImportances::test_feature_names", + "scikitplot/tests/test_classifiers.py::TestFeatureImportances::test_max_num_features", + "scikitplot/tests/test_classifiers.py::TestFeatureImportances::test_order", + "scikitplot/tests/test_classifiers.py::TestFeatureImportances::test_string_classes", + "scikitplot/tests/test_cluster.py::TestPlotElbow::test_ax", + "scikitplot/tests/test_cluster.py::TestPlotElbow::test_cluster_ranges", + "scikitplot/tests/test_cluster.py::TestPlotElbow::test_n_clusters_in_clf", + "scikitplot/tests/test_cluster.py::TestPlotElbow::test_n_jobs", + "scikitplot/tests/test_cluster.py::TestPlotElbow::test_show_cluster_time", + "scikitplot/tests/test_clustering.py::TestClassifierFactory::test_instance_validation", + "scikitplot/tests/test_clustering.py::TestClassifierFactory::test_method_insertion", + "scikitplot/tests/test_clustering.py::TestPlotSilhouette::test_ax", + "scikitplot/tests/test_clustering.py::TestPlotSilhouette::test_cmap", + "scikitplot/tests/test_clustering.py::TestPlotSilhouette::test_copy", + "scikitplot/tests/test_clustering.py::TestPlotElbow::test_ax", + "scikitplot/tests/test_clustering.py::TestPlotElbow::test_cluster_ranges", + "scikitplot/tests/test_clustering.py::TestPlotElbow::test_n_clusters_in_clf", + "scikitplot/tests/test_decomposition.py::TestPlotPCAComponentVariance::test_ax", + "scikitplot/tests/test_decomposition.py::TestPlotPCAComponentVariance::test_fitted", + "scikitplot/tests/test_decomposition.py::TestPlotPCAComponentVariance::test_target_explained_variance", + "scikitplot/tests/test_decomposition.py::TestPlotPCA2DProjection::test_ax", + "scikitplot/tests/test_decomposition.py::TestPlotPCA2DProjection::test_biplot", + "scikitplot/tests/test_decomposition.py::TestPlotPCA2DProjection::test_cmap", + "scikitplot/tests/test_estimators.py::TestFeatureImportances::test_ax", + "scikitplot/tests/test_estimators.py::TestFeatureImportances::test_feature_importances_in_clf", + "scikitplot/tests/test_estimators.py::TestFeatureImportances::test_feature_names", + "scikitplot/tests/test_estimators.py::TestFeatureImportances::test_max_num_features", + "scikitplot/tests/test_estimators.py::TestFeatureImportances::test_order", + "scikitplot/tests/test_estimators.py::TestFeatureImportances::test_string_classes", + "scikitplot/tests/test_estimators.py::TestPlotLearningCurve::test_ax", + "scikitplot/tests/test_estimators.py::TestPlotLearningCurve::test_cv", + "scikitplot/tests/test_estimators.py::TestPlotLearningCurve::test_n_jobs", + "scikitplot/tests/test_estimators.py::TestPlotLearningCurve::test_random_state_and_shuffle", + "scikitplot/tests/test_estimators.py::TestPlotLearningCurve::test_string_classes", + "scikitplot/tests/test_estimators.py::TestPlotLearningCurve::test_train_sizes", + "scikitplot/tests/test_metrics.py::TestPlotConfusionMatrix::test_array_like", + "scikitplot/tests/test_metrics.py::TestPlotConfusionMatrix::test_ax", + "scikitplot/tests/test_metrics.py::TestPlotConfusionMatrix::test_cmap", + "scikitplot/tests/test_metrics.py::TestPlotConfusionMatrix::test_hide_counts", + "scikitplot/tests/test_metrics.py::TestPlotConfusionMatrix::test_labels", + "scikitplot/tests/test_metrics.py::TestPlotConfusionMatrix::test_normalize", + "scikitplot/tests/test_metrics.py::TestPlotConfusionMatrix::test_string_classes", + "scikitplot/tests/test_metrics.py::TestPlotConfusionMatrix::test_true_pred_labels", + "scikitplot/tests/test_metrics.py::TestPlotROCCurve::test_array_like", + "scikitplot/tests/test_metrics.py::TestPlotROCCurve::test_ax", + "scikitplot/tests/test_metrics.py::TestPlotROCCurve::test_cmap", + "scikitplot/tests/test_metrics.py::TestPlotROCCurve::test_curve_diffs", + "scikitplot/tests/test_metrics.py::TestPlotROCCurve::test_invalid_curve_arg", + "scikitplot/tests/test_metrics.py::TestPlotROCCurve::test_string_classes", + "scikitplot/tests/test_metrics.py::TestPlotROC::test_array_like", + "scikitplot/tests/test_metrics.py::TestPlotROC::test_ax", + "scikitplot/tests/test_metrics.py::TestPlotROC::test_classes_to_plot", + "scikitplot/tests/test_metrics.py::TestPlotROC::test_cmap", + "scikitplot/tests/test_metrics.py::TestPlotROC::test_plot_macro", + "scikitplot/tests/test_metrics.py::TestPlotROC::test_plot_micro", + "scikitplot/tests/test_metrics.py::TestPlotROC::test_string_classes", + "scikitplot/tests/test_metrics.py::TestPlotKSStatistic::test_array_like", + "scikitplot/tests/test_metrics.py::TestPlotKSStatistic::test_ax", + "scikitplot/tests/test_metrics.py::TestPlotKSStatistic::test_string_classes", + "scikitplot/tests/test_metrics.py::TestPlotKSStatistic::test_two_classes", + "scikitplot/tests/test_metrics.py::TestPlotPrecisionRecallCurve::test_array_like", + "scikitplot/tests/test_metrics.py::TestPlotPrecisionRecallCurve::test_ax", + "scikitplot/tests/test_metrics.py::TestPlotPrecisionRecallCurve::test_cmap", + "scikitplot/tests/test_metrics.py::TestPlotPrecisionRecallCurve::test_curve_diffs", + "scikitplot/tests/test_metrics.py::TestPlotPrecisionRecallCurve::test_invalid_curve_arg", + "scikitplot/tests/test_metrics.py::TestPlotPrecisionRecallCurve::test_string_classes", + "scikitplot/tests/test_metrics.py::TestPlotPrecisionRecall::test_array_like", + "scikitplot/tests/test_metrics.py::TestPlotPrecisionRecall::test_ax", + "scikitplot/tests/test_metrics.py::TestPlotPrecisionRecall::test_classes_to_plot", + "scikitplot/tests/test_metrics.py::TestPlotPrecisionRecall::test_cmap", + "scikitplot/tests/test_metrics.py::TestPlotPrecisionRecall::test_plot_micro", + "scikitplot/tests/test_metrics.py::TestPlotPrecisionRecall::test_string_classes", + "scikitplot/tests/test_metrics.py::TestPlotSilhouette::test_array_like", + "scikitplot/tests/test_metrics.py::TestPlotSilhouette::test_ax", + "scikitplot/tests/test_metrics.py::TestPlotSilhouette::test_cmap", + "scikitplot/tests/test_metrics.py::TestPlotSilhouette::test_plot_silhouette", + "scikitplot/tests/test_metrics.py::TestPlotSilhouette::test_string_classes", + "scikitplot/tests/test_metrics.py::TestPlotCalibrationCurve::test_array_like", + "scikitplot/tests/test_metrics.py::TestPlotCalibrationCurve::test_ax", + "scikitplot/tests/test_metrics.py::TestPlotCalibrationCurve::test_cmap", + "scikitplot/tests/test_metrics.py::TestPlotCalibrationCurve::test_decision_function", + "scikitplot/tests/test_metrics.py::TestPlotCalibrationCurve::test_invalid_probas_list", + "scikitplot/tests/test_metrics.py::TestPlotCalibrationCurve::test_not_binary", + "scikitplot/tests/test_metrics.py::TestPlotCalibrationCurve::test_plot_calibration", + "scikitplot/tests/test_metrics.py::TestPlotCalibrationCurve::test_string_classes", + "scikitplot/tests/test_metrics.py::TestPlotCalibrationCurve::test_wrong_clf_names", + "scikitplot/tests/test_metrics.py::TestPlotCalibrationCurve::test_wrong_probas_shape", + "scikitplot/tests/test_metrics.py::TestPlotCumulativeGain::test_array_like", + "scikitplot/tests/test_metrics.py::TestPlotCumulativeGain::test_ax", + "scikitplot/tests/test_metrics.py::TestPlotCumulativeGain::test_string_classes", + "scikitplot/tests/test_metrics.py::TestPlotCumulativeGain::test_two_classes", + "scikitplot/tests/test_metrics.py::TestPlotLiftCurve::test_array_like", + "scikitplot/tests/test_metrics.py::TestPlotLiftCurve::test_ax", + "scikitplot/tests/test_metrics.py::TestPlotLiftCurve::test_string_classes", + "scikitplot/tests/test_metrics.py::TestPlotLiftCurve::test_two_classes", + "scikitplot/tests/test_plotters.py::TestPlotPCAComponentVariance::test_ax", + "scikitplot/tests/test_plotters.py::TestPlotPCAComponentVariance::test_fitted", + "scikitplot/tests/test_plotters.py::TestPlotPCAComponentVariance::test_target_explained_variance", + "scikitplot/tests/test_plotters.py::TestPlotPCA2DProjection::test_ax", + "scikitplot/tests/test_plotters.py::TestPlotPCA2DProjection::test_cmap", + "scikitplot/tests/test_plotters.py::TestValidateLabels::test_invalid_duplicate_numerical_labels", + "scikitplot/tests/test_plotters.py::TestValidateLabels::test_invalid_missing_numerical_labels", + "scikitplot/tests/test_plotters.py::TestValidateLabels::test_invalid_one_duplicate", + "scikitplot/tests/test_plotters.py::TestValidateLabels::test_invalid_one_missing", + "scikitplot/tests/test_plotters.py::TestValidateLabels::test_invalid_two_duplicates", + "scikitplot/tests/test_plotters.py::TestValidateLabels::test_invalid_two_missing", + "scikitplot/tests/test_plotters.py::TestValidateLabels::test_numerical_labels", + "scikitplot/tests/test_plotters.py::TestValidateLabels::test_valid_equal", + "scikitplot/tests/test_plotters.py::TestValidateLabels::test_valid_subset" +] \ No newline at end of file diff --git a/scikit_plot.egg-info/PKG-INFO b/scikit_plot.egg-info/PKG-INFO new file mode 100644 index 0000000..73bb4cd --- /dev/null +++ b/scikit_plot.egg-info/PKG-INFO @@ -0,0 +1,153 @@ +Metadata-Version: 2.1 +Name: scikit-plot +Version: 0.3.7 +Summary: An intuitive library to add plotting functionality to scikit-learn objects. +Home-page: https://github.com/reiinakano/scikit-plot +Author: Reiichiro Nakano +Author-email: reiichiro.s.nakano@gmail.com +License: MIT License +Description: # Welcome to Scikit-plot + + [![PyPI version](https://badge.fury.io/py/scikit-plot.svg)](https://badge.fury.io/py/scikit-plot) + [![license](https://img.shields.io/github/license/mashape/apistatus.svg)]() + [![Build Status](https://travis-ci.org/reiinakano/scikit-plot.svg?branch=master)](https://travis-ci.org/reiinakano/scikit-plot) + [![PyPI](https://img.shields.io/pypi/pyversions/scikit-plot.svg)]() + [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.293191.svg)](https://doi.org/10.5281/zenodo.293191) + + ### Single line functions for detailed visualizations + + ### The quickest and easiest way to go from analysis... + + ![roc_curves](docs/_static/readme_collage.jpg) + + ### ...to this. + + Scikit-plot is the result of an unartistic data scientist's dreadful realization that *visualization is one of the most crucial components in the data science process, not just a mere afterthought*. + + Gaining insights is simply a lot easier when you're looking at a colored heatmap of a confusion matrix complete with class labels rather than a single-line dump of numbers enclosed in brackets. Besides, if you ever need to present your results to someone (virtually any time anybody hires you to do data science), you show them visualizations, not a bunch of numbers in Excel. + + That said, there are a number of visualizations that frequently pop up in machine learning. Scikit-plot is a humble attempt to provide aesthetically-challenged programmers (such as myself) the opportunity to generate quick and beautiful graphs and plots with as little boilerplate as possible. + + ## Okay then, prove it. Show us an example. + + Say we use Naive Bayes in multi-class classification and decide we want to visualize the results of a common classification metric, the Area under the Receiver Operating Characteristic curve. Since the ROC is only valid in binary classification, we want to show the respective ROC of each class if it were the positive class. As an added bonus, let's show the micro-averaged and macro-averaged curve in the plot as well. + + Let's use scikit-plot with the sample digits dataset from scikit-learn. + + ```python + # The usual train-test split mumbo-jumbo + from sklearn.datasets import load_digits + from sklearn.model_selection import train_test_split + from sklearn.naive_bayes import GaussianNB + + X, y = load_digits(return_X_y=True) + X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33) + nb = GaussianNB() + nb.fit(X_train, y_train) + predicted_probas = nb.predict_proba(X_test) + + # The magic happens here + import matplotlib.pyplot as plt + import scikitplot as skplt + skplt.metrics.plot_roc(y_test, predicted_probas) + plt.show() + ``` + ![roc_curves](examples/roc_curves.png) + + Pretty. + + And... That's it. Encaptured in that small example is the entire philosophy of Scikit-plot: **single line functions for detailed visualization**. You simply browse the plots available in the documentation, and call the function with the necessary arguments. Scikit-plot tries to stay out of your way as much as possible. No unnecessary bells and whistles. And when you *do* need the bells and whistles, each function offers a myriad of parameters for customizing various elements in your plots. + + Finally, compare and [view the non-scikit-plot way of plotting the multi-class ROC curve](http://scikit-learn.org/stable/auto_examples/model_selection/plot_roc.html). Which one would you rather do? + + ## Maximum flexibility. Compatibility with non-scikit-learn objects. + + Although Scikit-plot is loosely based around the scikit-learn interface, you don't actually need Scikit-learn objects to use the available functions. As long as you provide the functions what they're asking for, they'll happily draw the plots for you. + + Here's a quick example to generate the precision-recall curves of a Keras classifier on a sample dataset. + + ```python + # Import what's needed for the Functions API + import matplotlib.pyplot as plt + import scikitplot as skplt + + # This is a Keras classifier. We'll generate probabilities on the test set. + keras_clf.fit(X_train, y_train, batch_size=64, nb_epoch=10, verbose=2) + probas = keras_clf.predict_proba(X_test, batch_size=64) + + # Now plot. + skplt.metrics.plot_precision_recall_curve(y_test, probas) + plt.show() + ``` + ![p_r_curves](examples/p_r_curves.png) + + You can see clearly here that `skplt.metrics.plot_precision_recall_curve` needs only the ground truth y-values and the predicted probabilities to generate the plot. This lets you use *anything* you want as the classifier, from Keras NNs to NLTK Naive Bayes to that groundbreaking classifier algorithm you just wrote. + + The possibilities are endless. + + ## Installation + + Installation is simple! First, make sure you have the dependencies [Scikit-learn](http://scikit-learn.org) and [Matplotlib](http://matplotlib.org/) installed. + + Then just run: + ```bash + pip install scikit-plot + ``` + + Or if you want the latest development version, clone this repo and run + ```bash + python setup.py install + ``` + at the root folder. + + If using conda, you can install Scikit-plot by running: + ```bash + conda install -c conda-forge scikit-plot + ``` + + ## Documentation and Examples + + Explore the full features of Scikit-plot. + + You can find detailed documentation [here](http://scikit-plot.readthedocs.io). + + Examples are found in the [examples folder of this repo](examples/). + + ## Contributing to Scikit-plot + + Reporting a bug? Suggesting a feature? Want to add your own plot to the library? Visit our [contributor guidelines](CONTRIBUTING.md). + + ## Citing Scikit-plot + + Are you using Scikit-plot in an academic paper? You should be! Reviewers love eye candy. + + If so, please consider citing Scikit-plot with DOI [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.293191.svg)](https://doi.org/10.5281/zenodo.293191) + + #### APA + + > Reiichiro Nakano. (2018). reiinakano/scikit-plot: 0.3.7 [Data set]. Zenodo. http://doi.org/10.5281/zenodo.293191 + + #### IEEE + + > [1]Reiichiro Nakano, “reiinakano/scikit-plot: 0.3.7”. Zenodo, 19-Feb-2017. + + #### ACM + + > [1]Reiichiro Nakano 2018. reiinakano/scikit-plot: 0.3.7. Zenodo. + + Happy plotting! + +Platform: any +Classifier: Programming Language :: Python +Classifier: Programming Language :: Python :: 2 +Classifier: Programming Language :: Python :: 2.7 +Classifier: Programming Language :: Python :: 3 +Classifier: Programming Language :: Python :: 3.5 +Classifier: Programming Language :: Python :: 3.6 +Classifier: Natural Language :: English +Classifier: Intended Audience :: Developers +Classifier: Intended Audience :: Science/Research +Classifier: License :: OSI Approved :: MIT License +Classifier: Operating System :: OS Independent +Classifier: Topic :: Scientific/Engineering :: Visualization +Provides-Extra: testing diff --git a/scikit_plot.egg-info/SOURCES.txt b/scikit_plot.egg-info/SOURCES.txt new file mode 100644 index 0000000..e4a4708 --- /dev/null +++ b/scikit_plot.egg-info/SOURCES.txt @@ -0,0 +1,18 @@ +LICENSE +MANIFEST.in +README.md +requirements.txt +scikit_plot.egg-info/PKG-INFO +scikit_plot.egg-info/SOURCES.txt +scikit_plot.egg-info/dependency_links.txt +scikit_plot.egg-info/requires.txt +scikit_plot.egg-info/top_level.txt +scikitplot/__init__.py +scikitplot/classifiers.py +scikitplot/cluster.py +scikitplot/clustering.py +scikitplot/decomposition.py +scikitplot/estimators.py +scikitplot/helpers.py +scikitplot/metrics.py +scikitplot/plotters.py \ No newline at end of file diff --git a/scikit_plot.egg-info/dependency_links.txt b/scikit_plot.egg-info/dependency_links.txt new file mode 100644 index 0000000..8b13789 --- /dev/null +++ b/scikit_plot.egg-info/dependency_links.txt @@ -0,0 +1 @@ + diff --git a/scikit_plot.egg-info/requires.txt b/scikit_plot.egg-info/requires.txt new file mode 100644 index 0000000..8c60616 --- /dev/null +++ b/scikit_plot.egg-info/requires.txt @@ -0,0 +1,7 @@ +matplotlib>=1.4.0 +scikit-learn>=0.18 +scipy>=0.9 +joblib>=0.10 + +[testing] +pytest diff --git a/scikit_plot.egg-info/top_level.txt b/scikit_plot.egg-info/top_level.txt new file mode 100644 index 0000000..93c3f70 --- /dev/null +++ b/scikit_plot.egg-info/top_level.txt @@ -0,0 +1 @@ +scikitplot diff --git a/scikitplot/__pycache__/__init__.cpython-36.pyc b/scikitplot/__pycache__/__init__.cpython-36.pyc new file mode 100644 index 0000000000000000000000000000000000000000..5e7e02a0f90374fa720fa911645f521ba3795b2e GIT binary patch literal 518 zcmY*V%Sr<=6iqtQ&UE^)2#Q}IbkSCH;X)CAKoQ(!VHlINUQ8w>$q4!reuux5tt)@Q zl{eL*268y}oZOR}oQy`*;p+8nLJ4^%e^v+iC2oI%Ac>@!7|N*Wu#U-CW^$HOB0DnE zUDIPdQ?SC6tTcVrM?Gg1_H^~Y4A~HCB71sd#%zqdxFyr_15?Q~k303fx4I3AgIQY_ zVkzMXJlH0V*AAM%mu(|LswZs&!b-(81m$Y&KXB%hQ5Y2*2pj6EWc{Ze9 D3`v!* literal 0 HcmV?d00001 diff --git a/scikitplot/__pycache__/classifiers.cpython-36.pyc b/scikitplot/__pycache__/classifiers.cpython-36.pyc new file mode 100644 index 0000000000000000000000000000000000000000..6eed39d964b92b83acb69db756f7b5e6687b0fb0 GIT binary patch literal 20736 zcmeHPTa(*Hb|%5=aPAjLGm>m~%_h!Jnc>K`R*omKc4b{0$Cj)}v$DdykQf5wU<3km z1DqLZ!A;d_-?miw1F5|GB|l(mANE(|0S|ddp7Ix5m8yK_;6h+#Buic;(r%F~Hk&}> z^y$;x=knozA52eI-tGMJFaA@_Wd19Yc+H{yV|<+7qtG&1*35LX^=wzEE8Sc@*Ui`S zSvszmg?fR%i}fOZm+B?{F4rgUoii)l$@(O>&6`u*>H0L+3+7CBwm!@CqB+-{ug`P6 zWG-|U>x*13qrOyM%4YOgZQ@B*t7wzI$vnx{PiaD%*A{-0t)JFbwPkGuwKLiev~${d z)Rwis)ZW(KL2X4-?q{m+{x?RDspkDzb;q&IzNW#Lpx_ue-tXeId&MopeQ&**BSe-_zFZcDw|FgYoIO4!CO|hd3sdw#t zT@!{S_Ak~h)h^b=_O32&sV&!*199!{XJS`%&{DU=h=-cE3#yJTx<&^)>q0*?92b2E z8Wo7Sws1jaUzLX4cZ8)Mh_0>mP2CZ8n@`;eW#LdCF+Ix$F?UyWxee&(vaPoC;369x zMj{MHU>c8fb3g;pjCNa>y5)+tw7UU~sH@RnYOO5I7G9B4 z#i(^Q^YxXlGH$^wx+S;lPPi4V^e@F>{_uu7xi{tIpHN?yf2Irz!y@-rc%sOEWg2DB zC{T~tj5`fZ3hvC_tXK3hPn0J)(Dl9oe^`PO>TfZuo828NvFEWUW#WcwLhnXNzm}t7W#g#QlKW(FqQSd1Pk!O1@w5~Lfrp>;qD5ZMx~*_^Trjje@`_WJ;B5S)$HpxrL^Ui z_^d5L`a-j@46)L%SS?qDNNA}Z6;;dyNy5^xp&S((qHW9X;Dp-yxwhf@1*c_8-7jJ1 zYDUX_{Nd^^hpPgckNQTp7;R15Ff=i+`z!{bCZ;6j=tur^NTJb__Kx~^>zBh6iTgI> zO!MuCt~%gT*bqG%bGu_=JK3V6TTnBuUaJ=6B-XlriZuhq-|mxwYIIds8i&wRq)epU z3Uq}(&%Ylz4J;#FaMtvctIpo&!2mHx!`pchQ6U&M@n zd%t`TXgSBvQ!N{Yze#5dQ@pti)Cf5}_)a$epmK%QdJJ0dOkP^_s^s)G%(((A(6ReQQv zvl_4qpzN#*EAm^MYIS6G=+bQoy-Z4CU#DfaE)I5K+;Ks8Eoo8G+EyRtL6UnAoxqq= zY>oOjeog2#ISHNz&*!-+p#NWR2(-vuC^ZwRbD!1lL!PK;g5b?zODFm^)g9e*p1}ZL z1+Q*hM5>^=CBA@8roo3-x^`;l1{CNaiz!$p2~`XG0F9uLZIHSxHIg9=n{GnfutH9M z2WtkDQ}lvfYGg_+xTPddUUKW`gM2gg+tgkU{-%HjM;;<*koy5+*K7`sEEp9N%1q#_ z1AiIpFekNCr)%Upq6NIu_iI0_2JQwe5m)x%Kc677gnY;gp>`ty!B2F0Cd^K%laoe- ztpvjsc%CtVi08zt(QbGVKEhptQO2|N>X~s37Z!ibfmX0Osim^bXF`uS@&`0_8{mZK zZ55^$dn&zxR#*b6eUtq$*ad`9tXOe5W(AVTmFh_%scLtL|jy|YIKGchijpJSblqEUnjv90D2;Hk-G znYRJ0e{f-7ynniYGwPubHMNx>zovy zM^}5SFn=SPu92%ze`GP55?q9lfvF8$tVMHOAXA#REm>p)W;y zCA@?^VzPRp&ecP)7Kt5GN+!%Sc53RO?$oZKXr}h~HsnF4M85}(aIkAY#UF7H*a(u` z7#||_#vmiFop5X7B)7u6Xi`1Erh#L{h~Nn>CUY|4w{3JB_!(mD{SCK|m?6y`9f8XM zG=(}wTOgo<+lOur^_vouUV{&?c6nV~uEz63P7gK>aocPrWYy}ztc9KwZK4z8*0N1o zI<@Qch1OWZA>NkcGsQsEly?{_HsX=_Cn*BoA%#L=fp}oR@x2{S68xs1mlQqCZozKA z`(U4eCSToG4cKN#Ch&ugD6U=C?KxYUn~B-CWoHw+O*)%A9t^~(?YdoaG!5)kHrnuV zg4qb%OA-njkA%ZL;&;Pw2Vf$)=m;Cww1OvN)qyK%uC9yK01coT!vm`!dD6|~4*)nw zO<76|;U$ULbM-@@;%_j=;c&q+kK`DD`kh-9Dk9#XqwN{(wgWY-PI>Ev^0|4)0r<$6 zUcGu%$hP=6;fqCh&sw0BU~pgtuE%c6TD2NaKyY=97IGu@l|n>u?cur@Bz*RdNhgmk zy^>gly!9K1Jc&@Cj_wCPA~TDa^5Wr#ZT+LmA8MB`UHa(a)f5js)2%tX_Q6^rNUYTa zY*$C$+7bbV+1k{D(L0+bGFt?G`-Ij z1y;qM3e<6+@vAw%3=o5Dx+EA)ZlI{n_{BlPg=pxT0!6B(^RK@#p?M5hH&Hz@mfUTSAhG5 zYR=DL|4X`)0<4?HB&VoeBn-gP{7I|^iX1F_6 zm#Fw56_=^_5QSf0yY4Hk{gK4v-_YrgsMw<7Z>hLK#YHMMQTP*pR2zH)&99K)9NcYl zT)#pR4eO9D;pRF)N{^=;+em)FI0x^V~G zv}?fIz?Vv26$-g9K77Ws zz*v8s_43X|V61t@SZ9E-&RT!)3R(^r>n)ctN}!UsO9hOzh&xdwV606X56%>Tu@-=_ zmKkH61HL+s&jK*k0U|rvxZKet8^2CYB6^@?)@r?dllpNUVzpkQjpCIq!G45=+FmXNSPB9k~8*9b}eP~f)oY1oEoJ9 zQqf#nWe1IeS#r^+GIG*ZtLgitMh&QZcm*Q?eDQ_~Kpg=KkV|srur1ozY|348nSkfgZYLHU@f3{f&lA0&FOuD^oFRCy! zWUS{?wU3-J)w=UZ65knJ6<|DZdRPeVB=L=p-y4BDblochcp@gg7swNH{IclI$kS(K zD`yc*Kfbw2p+6G(f^@vq@NOID-Emf5oKz{Us}|=OA|~%64MZ0)!5T245u8!Y$r?zH z4oFHRCo@kkj;%;!L=i5NBrnQy;T$c*qhOimyw-=4l3JqEaOj~@dJG8M-82QQI- z@ZPcfgJVKvoM#+B(;b^XgQvV+bpU;Axm>3kN8>1Z97T_#=pzz{v**WA^f-!sjgMgj zH*S&-`u_iACj5~J3F8z4iu>p_ewJkXEXk2abYIugFMoC^2Drg~!r#mM$>1UfoRU&s935c%6b~3V{I^T|zfb&sL;U;8@fPGY%!9EaPz;yp^&tg@<&ef-*OEDU(gUEB};Ph<;ao71Szj IbR_uxUlX?OCjbBd literal 0 HcmV?d00001 diff --git a/scikitplot/__pycache__/cluster.cpython-36.pyc b/scikitplot/__pycache__/cluster.cpython-36.pyc new file mode 100644 index 0000000000000000000000000000000000000000..c7a5c563f12cb05667af1eace29e135152124ad8 GIT binary patch literal 4886 zcmeHLTW=f372XR;QIteUzQs+^cAx@*7*Mn=2TsD;Y6IC1iEAOK)gmF~MB2XU?2C-*?XF;o4f|lm0*c zYTvZ1e_L};8TH@cRRa{-2YL(3I(owm!Y-*L( z3VsXW>Ts>KW=0jm>TtcaKHO++;Cr2~@YOeVYm=|>>Km(dgS#)S+J?_;%c>j>1Zxgs z-fXLGa2)7S80$tiOq3R~&CrpAf&~#;xKNCG73(BHsF@mxZqN(5ei)vyUMwq_fSpkI zGGe(1Au(BLKk8z(X!ING`3vDks=>aG6V~-3mMFo_;zY9eWNwzKS2~GidLYo9t?Db4 z48?R=Hj?p3NF6}18u(Ih);VJfItZnMp|4}98fKppjhPg$l0b@~h_D+>WjyXecBCN$ zGFdN)x;luXM&%uj#Htn2b-$zHFww#bhND>Ow9JFofx?)yI+8)8y~&((BZ-1;%!L;Q zu%RESgPNTdyI~xOnv<5l_0blJFkRvz^v?u;UwIaG;uH2QZ2JyJVjZT%(3gFYmWF}{ z$*@}?CGc$EUBc_)Rktv7>~j0KEbnNGJKW`kOZUQ!%Gw^gm-dCLowqhGUOMW&c6sU2 z{+q1};{sp0vVX9CuzL#D;KL{I2k0r)}^B*$acm{ zymIBDZ|$NqE*+J}&bZ81t{mIK*Yeoam7|sM%B9U$FJ05N%GWM!Q-jlum#1T^e0}bV z>Kj)yuJ}(|Zt%^ED&%ckS{Li%b&@+?+enB;$y z=>o^T>(_xOvL-yvShkzU*Mh}8#)Do@NSrf!?S}~*2W`*EcXO|tcx@2%8UZC6Uy7~1+@Xer0-^`ErR2T_t19h1yc|Oc-GejuO(5LJ;MSg^FxwWb{Tf}+Ojgg2f)>_+1|4lH4aoKDEFKX`{jk<#ho%QB(Yk#R0eJ?^ zL}%=&==n(qf6(!Q<+f(X+nO0BH@heOs1G06J^*qVe54t7j+*T-P`XW90_Ybs_&qw% zzOcaT42_qa41(@}3`dM+G!>_#&<`T;&TC%=h9-Yp-FDlkwEdvY9_-fHZmr#(vrXh3 z#T}@7U9W@bA((!wE|Un_Gjb48^q44pU*7fkSV7ALap(OJ;2 z06UH2KNYI+6$(IV-Uo2>)Wwof z9Kk0uV=o-Y)bNHA8tm&HqvNfc!!Vt2T`qmZB8c!=BuL}s{rnSC%Yrs!&h=y?D1^Pc$f(dYcp zXPEOZ_PfouoHwHWTuF1U~rJA3A;#x5tP}! zA5p70tt|7%qIv@fU@4>LE+u&M&^krD5AmvBq8M9$xpQG_OCuM1YrnO|_NDtL%eG!Q z+(K4kAJiO4H-+?LurLV0!%2H4dzi4YkjO8c#{JX|P_e&DUF0kOvfelE4dS7=C%U^k zNVes@moV!K{}{DATPB6`x~b;W+!&p`Uw%xn^mHWm4oOkcx?rFG`hydA9LBf^sJ+H? zJodblb0n(e3G~n1&Zj$v=Qn0iX_Dof-+q}4iD+NGN=!QcOiHBgn7kbN9T5`MrNzD! zB6@E(`_Kf>e}0Rt7X5HE@R4K3VJy#ov7aSqM7%sf8h{i5DTC<75l1HD%3oogB$LaJ zQDA<_OjPpc^tqW9jmxJc1po@3E)9G|$DS@9A_+4}Ny@@g_dFP-E}S>22A!HsW%aYHzJd^V?3pZ2rA2)bM5nb4d7Cu7MGF=) z!PVSiIYK2}3tF2W#OKuUPvy^OUUe~Eq-zUgoR&=dQfUG1LhB-EtJKBWwU+(UUC$UN zby1afAPsIv%4BMNMB+EDdjl@GmfA&OeNncnPT8s0W%rg{u~(h4eaorZRIlQ1)hXEp z8*O&>TXp1bFsn-!9C`}*yN_S>1qy8)S-2dyxZ>c_a$#v_j9c;8F_#YO$OUNZqXL!L z$gz9Z>N$Xq<9`F+-6!DS#usrAEAnnNA-lO`J}2e>C;mUF#DB)YCc#Cp{g6Pi;PFA^ zHYeR=FX_OEUM74qi#6o&^8$0On9E#|!Rg6F6EfsFys=hH3p7`j@R2S7)rKbk9i0}< zvbDktzGe#AHBw!Jq^uwa$xRdyUPTOV-7EtWtF~#amE|&;vJiE6a3ELkB`H+ZZl+6~ zhyPr7URv?+{|CCNd7dPWmV_Wl@R1Z`M!EDHae4^qTX7a z+#@?aLWbF23D*ZC--i24Br;evO3rPEC_)-qC_m2rd$dQh6>&zvzUvw?a8~UN7qQK* GeE(k~wXfX( literal 0 HcmV?d00001 diff --git a/scikitplot/__pycache__/clustering.cpython-36.pyc b/scikitplot/__pycache__/clustering.cpython-36.pyc new file mode 100644 index 0000000000000000000000000000000000000000..c45854a04ff3b45a05e6f69124b36bd31eebd6db GIT binary patch literal 1697 zcmah}OK%%D5MFXuPs@&x3`t0r~ZWk1v*^G4-|dug0ozb^UZ_b%txD>-48}TzWKEoMZZPY zo*3s};IQAJ5Ro7%Do9ESn$jXp<045D64t3|q>cL9Oq=z$mA3F3t9H>zJN21Fb&HL3 zqv)kQ5xXDb(oI1RqJH~#v=a3be}f-dtttmYSxmHXzAfZbTB%FFH8HYu z!@Mdp7yh@aQf6AfP)P@dEBjTS_^yD7fs8v4ed=#aly*Za)mT^H9Qf^e0o0*B9cGo8 z!ug*s$I`M>sT4Z|W}whh5KNYA`e5+!;K6{s90Pm8GpEgrJ$n9>jk(2>P_ipa1NK}2 zx4;THGH9B?nY2!pBNqHaFsGSAm?<|>SC*A<$_gzi1(xZ&-g>f*$_5*kc%?=#H|DNB zgE(Mvp212jkkhAyaWIy`$?t;2#9?j{J+AT*!d_-|hmgFrt*?Jwh*d-$QnJ(h9eb+c@&f{g! zdiV`pB#TC^nJlTvUF)bV8mOBDT@twtVkWM8v@vhYqa|I&Bs$rDjh2uUP5u+Q_qaF& zVVTY3v2^#t{jnhG^cLX88aMH}c**QB-hwGvrYeh>0)y+;@iK!!x6AN(WJY$co`t_m z<$LU41^aJ`E4>%sDpOul4VY2chS4U5I$--!u$ivv95DV5xAJ+Pn!q{JMsfbC z##qJ7xH7=S>=NtiDHtO~C@EgfPWddjHKm`|y5c z!R#LJ5j^9^IJ?G<^*mI-?waT5<6tuLH?Mw&*d#)~>LILs2Ss$dO}nH^nxq#~(vENA zyiMEW4l(cHZhy;nhuBz=`)6 ot_|u61!J%BTN|nay(aDktoA3XUiew)MK<_+2VKLL_ULx}7Z=n8(EtDd literal 0 HcmV?d00001 diff --git a/scikitplot/__pycache__/decomposition.cpython-36.pyc b/scikitplot/__pycache__/decomposition.cpython-36.pyc new file mode 100644 index 0000000000000000000000000000000000000000..234056068a0fc3769c96077526bec1664803a609 GIT binary patch literal 7381 zcmeHM&2JmW72jQcQxf%I`9n#NP6RX+1BtTb#BS8YPT-HWK;k;Fo4P_TK+*V?hVxt&}$zmxA4 zb_yny=e%O4v{Tl(%1)Kf^D?je%G{aZ3w(~(kelVXdq#cHzGWIl_5QwK+ueX~?}m_53IFRi}5CWO3)%Uf!jp}3Uic!z2d3!GmynZB_E>=%Qs?6O(mu9GH zJ?Z*UbJRyI^nKR}xM*VPLfT$<=d+hEV|Ihq-Nzv8)}ZIvu8+Y!u%&DJjtKur;~2)j zuB(D)%eGL(uHbIJ>y(lTql$mBl^ghlYuMh%h+z|_R=52%B;v>s6qC%$_8=carc~VBc9G7G3)H2EsVy?r#aI=`#D}YHFd7Wt8p=rkMemwlX+U;D}1hverQ>N{PL-J zV0>!IDvcOdXe8)RSuuEx&vzD+d{TaD!MYdt;-jJ-Whrg9#Lw_$e)dsbm!82`=N=_` zEc5etxLEAs=L--vg#yfGC?gW!I3^i=9HD4UTFWBM5##Upi zUac~G-j?lfTj%J>@mkwt%xD%yw2g@;?AzoLg6-~(#WzRdn_7HxcbC~wBwg6JXe6RZ zVB|({#-uiD-<=xU?|1j$NzjpT*pwpgju&ksjE2x@)G2ICjs9bV zXC2d5!!FK=)pfR-1TMPtTp zBfiuU$+<`NH?cmHXp=j#!>RSwCYFwMYhZU_E|Yk4?bGFX=B=T4BH)cD z{jnML_o#Q`nfHQi-W5z@%ojk{K~7ZzJBrAw1QsL2={}dwKP6c)N3@b z2zFa+Z!;_90wobPkOdEp47TCfESi7eD&Dlk{4mLv zlf2tiMXVH@MV0KqK21-}M6Pqt?Ag-phN|cxqWZjERXG@xDrn7iR-$Je zO&r&COcN<~g%yR?QmH zGSZs4Waf3giqctpYnFVS6kH-l+1j|t?#h6I39!^{RlWyS69JYxGb9|JbNJCFYGnNv zxC_v)5u3oqgkVh|>X#$4V{v@r63=NwJ$q7&iyfe9!l)Wi=R1Wc4}=Vii=K1nQ6gLo z{G9Jpc!66{HJ(FV4z;zxO<>@;xOQsYG=6>s2z~x!0rFiC~5?blDBoBI=iOBY{ONozj8Z0M(C9`saz=DoNOpItHhDqn~o@6dyo1Po6Fye?jwrqDKCItc;L9;nb~-3@=3#99acLII2lz1E553+#XP)-wgGsAFeaM*%CJ3h6wBR2?(Gg@ODzJhr?j$OxFQz1>odZ z1YuTMmkCx+Wsh|rVZaBOp+4Vdq(nyb;TNl3CrJ3e&E&ogXCUlJECMWN4>a%4!4eob zGpY`2|7Z3z9X>L2Y0stL#n$$Mz?(wOU!PY zy0>s=koHfD_2mkIg^>iFnRdGV&E%we#>#6h1Vfop^&J;*0Jfj>oG7F3bEN3Ovgo=( z*x*CyDAfD`v;^67eJ%!Ubd|)qeF%f*0kFwLRCeRY)W*g_6CE)WvMKE(nojwtKFOM8 zh1vZmz=4?DNKMv4+e22#2}P+S+(aXgJx^RBRx*0n%066P1#ZkNs{IXt9B)r%S)8uj z8+jNx@l!aqMqXj#1_3a%-W^|#WKC~uAQWImO()lf%$t>A35Z zywPPIECmsr;HG_OyD)Ol*o138L1Oo#s26T;Zf2NCAlsXSYQjxDA50`{>_=U15}v0) zRCZCm;nLl&$o{|K(qys|F1^K@6YD1$z0Mx2v*Fi`Oi!u#Re1DP7Nculz9R{q*G z@JD=2Q`z=!e?yCxhYLb^o*FDrasi1lRGuK*ac(2Nrc4RiR|S{~j#7#! zoO(f7gP|%9hE5Pj4n8#)5)V>UO4G8W54NBew*U55`hez^KOlOHV4}>ux}}Fp)O!vC z->&E7^Q1r#c#Q`~YAyut!v#VwKv)QM!{e2Nh~FJwjE?USj-R`S_cH)oPpQKytUxk` zBucN7`iiQIk*0i+it&EQkc^U-kfP$d{>Y z?%3_AQmSt%7{@z!=Y;k*Rjvyj`E6=n(2!J>Z6DA?6I;E2IovABP12xF$t#pJkkrq~ zi}+LK17!~7C1iJs3BK8>O=B3jMSV0^&6A??GCdO++?g9sSMcTo`5MYpDFrv6xfO=|440YC$96jqri8m33q<1=Z?nru_BSBD%dRcDc z@Ae%Oevb+#HFK)eY~ny`HdVEWcT5ELn@vf!FX;wE(xr(kAW@5%x|G%pxyX61? literal 0 HcmV?d00001 diff --git a/scikitplot/__pycache__/estimators.cpython-36.pyc b/scikitplot/__pycache__/estimators.cpython-36.pyc new file mode 100644 index 0000000000000000000000000000000000000000..b0ca2f931d568e30af482f31019b5e0606ddb2d2 GIT binary patch literal 8904 zcmeHNOK;=W6&6WJmT6gj&Lc(8To`S}Dr9+Pl1va~;$%7#r)e7_7(2z(O)631l}wo; zl}pODru3$hpsPXCW%~~b6y0~(AJHzjZ8x*)dN%EME=h^>BWZvFEsD^?EAl?hJ@>rN z;k+_GufN>;_0RwOaxV9`THU#~zv7V>H2I?U7~s%p3BK)wMc?ZI4;k6?z0? z!{&x?*aQk&@7RXtVI~)wg7*aXJXcf!)AJ3d1E9R#tFwoOW4Z&j?PA=o>pyiztYbKA zDVSajIQYyz;24f@!WN?epCHAC!X0wqTinyV14D3=wa4sY481xdLL}#5 zf`2-)1Rrn*XT?%xx*foFd;kGVy*qL`zU4Y~{TaBEtLDQ6qwTr&$mdOKFm#098dJfWBE@_vdI8{b8))m(6m2 zKFHHM@GQ!ifAuT<1qiYp%$V~}wU5=)`O{*c%CWx(^w04u=Rf-?41{l!3w;i{)v zQ0y-SN>DN@PgNxs%wgT^7mD~H`j>q@(6CCUb!I8hK3DO(5X@!X7tN)oP&H@m@)hQy zx%@N_=*9j@1oMd^-&P6F2yRd?S0o0-{uT3Tp!I;SoO3mpHCLa?XQ<{ia-_=Bjav*hf)*!N zvi_vkc3mj_qxZg_njN2oCi|8TBed=T6{lBY?vS+4u&WKWCr3aVm;%;|X$CDNJH{UH zt}(Kq!@ir@cP-Jb6y?@Z+a;uMi~;DszT-N)#u{$B-)PyE=i?k*b^$IAI23wdIamX1 zGCL-$iXg(KnZPOHD_q$B){3U>nE!$>o&YaPp&0*qFXBZeKO>{S7&^QLH z5D1nSGekQ_Qg-`hYCl()T(I>UMq-|B)Yy%wvBH}xqVuHLm&GP? z(g>jCu;T-(b0GP90hy$ECe&b?FK2ifkrd7+P2cJqHihdOBv6t>8R>dRBVgyr2FL`i zVm7mlHn-sd$LE+F`w#HeaC*=JI2A0yKk&OdvY~hIXdb{FAAni394)yQ@QDj|!i?jcv8-#tHZ8_t3O52iyZx z8e*d%&L2}ct1JXT?k+vkR4iqm9K;DtVZc2Dguxts%slQ(dqtZ@7KyaDYxO+qG4%NM zhCdqG(5bW{Qx7w_QIkz5}k@{ky;c>_d1Yc;kOIbwQ}o_Q?>&wE^K z92xcq)VSOxMN85s^Am&(|HBx^$Bj{b$PutODYx^Ll1Sz|k-w?*p+`#9v60mWDPr+n z@17U~h|AQ)ZEbC_$auyfo&*8U<{b|0snpOk(PeMN!RA>9wO*ZYIGS1STK@W@8XIQ^ zB+yitT&LXl1kOdR+>R=++Vo+mz1Wt)JZ(a^J9-&7GcqKvpw)KekMIJqIC? zztryyj}J2tqW1{GCsW+-o;3(fZ`3B-Bh#=Oy)ZaB-tpo2+0B#JyZntiubX#Xef5o- zTN7M}I-qOaJ8+Ldv`Ie>iOiwL8x7V(a1VRA$s<;Io0rk3K6H8$060{`u&ticV2EJriZxs6a7>Plz#3v>ZkeL-0yQ$MU*jARYSFYGtB#fb3tLd zY@WZhdEgHCChy$dfaegKyP)U?#vz{K^o8(>pXK8enfh@2T)#^}O2-`xw;mA^gi$f~ zxOOQ6?%FPLIB%;yU5Jz6W9LurS~1!GYgJR^kibhesM|}AX}4O zdkxEHVr!4bQewnHMTEtOuT?E9bX?mNVSdbo2n&+yRaJZoTZtFZ(89zs<9Rl6elXt8 z{)tPxL{qMXGYDTPL=9*6#zX$D5U!A<4RvH+ae^u43H>}#B~+bZsCm8_&H#0!p+0sN z!Wf4I0jJGF4Qd$bKOti+frUjJ3u+T;koT~Z=u4;}&I@PZ=MX|5HVw75Awr$rPbf<8 z!iC9mggDmHpluDpIT>{f4PgwtY9-VVpNEAA%zPrO(l+cbo{i$df+TRba3*vKRpUfl zrDImIo-UmC({3c3ONE%?yR=}&LN$T3#(FS8X3^R8olMbXs?+7j-vBHjV4=anB*?SKM+`;-T7 z3rc!Bq#ATx!A*sc`?$RZgfc*}1DWKDey)$q)>M((X)-^ooKmhxW4C>^uOVYa2Kz)i zoeA`QKG1?PWw6M-^nL-^+5F_KXwD{Y{!EZV)~E*gpcKpmvq1sbsQz^JV?a4wI9&wX zQm}ws=a368;ca&Ewk*dM9JSYSz_N_|7MUjI(AY|_ ziZQfutTGuZ(>j!M%E&rpW{UaLw}QS33Oz~RJkGKr^Gdy6F<0?+B|0^-%&W#&xoufDf z>j>BLC}g(GC^L&Qd*YR>nxV*$3EHQ5Wafy(#B;C^`crC!{LQ7@StjEo(B2RgIZ8fK zM<;3d5V%;_5JAFlgkl2C1*S;cvQov^Mx5XRFrZ2iGNTd%du5~R+U7ZArr{m!L=cdU zP`*$j?4pEHCYmW(BtU0!5n6Z;`BxOUrOqX5JLf1cp=LItiJ>F}W@C%nRA`K>!6p)6 zs`-LQX^|$Zw?UB@?@cLH5v9PZE);qU)rLZ~EyoaJlJEg)GM1Fn?D3Pdb(a`KM0uHyZUL=S(Sxi{6d%UNW%|1vQX(Yy4vNN2hzXE{<0o$Ji4kf7mq;pBNaTp?F>E@lw8O*#=z#%gh3rwC zIu=wA24k5spXK!=;mlw$RXIdGsJhEw9j5^kCv=Evc)L^pX(3bl(BmW1-H7HV(l`;M z3=S0er}QdbBJFZkyX5Jbl#o+-imKt4>$TLT2|1!QQgtsVQ^~oBA!BFj7Oj>utlJ0< zhen65*KgI>t@_Oxd#x(vG;@+OTz8s%w;ff|GCbdrIF zD@EQ)3snc@9`Jfn=cF$yL=sXOSqe-SOk&H@kRuta@5RMQ0OF7q`C%viD1*;A+-97L z5s_g{zHG=h>3@t2EDtzw(Vk)wnV3zMe9vIV&??kvdXD^HalzvCiN@BL%%xLD{>mCb zhR%D?h;OP@8IpWct&05RSJ&76->X#@7o`#_7=2eiKU8Q}E%O3PW z7vB~hU%h`e{EcQQ;@box--ba2Tn%L#d5G$QsL_5K)O?4gloIzQHt990OGx0+^H@HM zA`RvT+;E~&K)xp|Gc0%URjJwL{xL@_Bu^Cp@hZ(M+K5aYvwB6mMz6}FP#KFmcodY7 zi}$FZl7M)Zng`TuQ`4Y^cp=`T<}NjFqY29~QHkCjNQ`qez-!eaC*g&-^-X&sIuF}Rj|n3Zn7>2VwRF7iOh scv*0f=%@NJxlCLCQG?;QOn4dZssqJg~%!y1SnL z|5w%j`2PCr(vtJo@U)o`2v=KMJ$uiSWa?H%P*MO!d_y^!t&=dx0Nw;RfZic#V*P*6Q1L)99JM&rJ=|Ia&G_;%h`@Tg&gDCPKbNuTJ1 zb4gUKkmbI<|7Fm5y+Vh^|E`qhrxkhLsL1n1I+I7={~74qExBK=aDS`9{jJ$ffcqcr zXh(}HHpGPm4pf_Sc>9i)Hus#geWL4H>Y&t6r4~vyl}HAqGfkXxQc6a5iw z>ZsOM(TbB)t*x}_nHOOYBNONPTa0N_i&r1&C#Is*PMxw&G}>vatkZ0qy;oDS|BhY< zEf(e)YOaI1d5vZ|n3>mFDaseL=@MF((xnqUC7GA0bdfZ0dAgD|X-vADu8^(|EEr?c z{Z&oVZtwb%&5Vr=c?cVexn%J!XOjq)C|St+TuN6Qvp8aXH|Qq;hNuzGZ95FV?bwZd z8T)S`o^OZ{>x}{h|Fkh?mWB-7&e&9-3 zaC?Eri3k@K5$_5vccZ`q^Dr4tjyFP2EeL`?6Eam~{V0rGKlH<)qWqmJ?h*F*zzq`Y z>mU*p-jg7TBpPzpXql`Gi6c8D~#1i&35=b7;pVw7>{I6mWs7D z2qHHo{eWUFio4Dtq4s%aC#K_H`KH@DZ1bL{0%o$3`_R|9hhqe ztcsDBV26IZ%U+toO~rf|iQ}t2mqG`HH|aIRqkcub;dUpzVT(5X|!j1CAm*d)8hwv~1;+0sLAMk6{nqxps9o8uP2X zzjY;n;8(ZZA^(OuLP_@hksn_ne^r!KahC@ZF674KI9tijw+BYb-Wnh%2XqpjIIml} zqc7vVtlN0b8n(WwKWes7``Z)F)6&zZM<@z8od~2?fMTg%YQE36UHaQQI?8AVSZ0)c~S1f&I)BJ$RcyZKiky zBWj+Rw<12*Q^JFI0}C1}8W5P3gJIxB>!S87A;yMh?OCbeHTD{WI55kOZ9*I`6Tdn} zYBX2H`eq93A^r&X3_y*ZWM3L{69(N8NJ>Y&I(1h4&}!xop@`vThfm<$IMw z7MexBe4(?wUVuOk;85&_VsJ=nERPLq7EvMLN? zYyx5;1%rb^XS`yQZXPL6Hs6Em*RSWxE%)ka_PtBbTv}(BzC^DZmtIsGO~TOF>Z%vU zR)@{_7TdE{Iga%z6>wS5+Dje9?=~fb`APTu(BntSoA=4#(~sf@+50vgc^0psmTE7y z$;Qc|e{Ss8wsRi`IwozKkr^A0XZn$HeEI9snD>tJQ~(%3G4J}1i5O9~w0oaCwStYO zf(TAuUPoaz{@#?>GA0`_R{9`p0Un8)LO)4h==(08GOV# zy~vNxmm{vA`U^a>bv$1cu~5VZ3AWC55`Z7lP72C{C<)206tG>1eXHXL2gI|wvZu^K z8o!@#u|#bMcWyZ3S5UO86jB?yJsu>dbqHFFYGSm--6Gn9nDrwX+nVI z$V+%>=MbfgGX}+`Gl)nv#3$RJsD(1cI72*%QDuyx09nSU3(#c5hX>tcP?S=47>FwR z$QD1uI7BGV@XVocVoWAQy>Y5;P)=F)+Lf9~wYCOYH4pb1FY95up#BrDj(Za=HE%u z>K1xiX&vhzNlkUv(n@hNQ`9Zo57Y;ArR2D4A0mq-fTCjBjbu#OEG2$lLC%&ZuD7C? zQ=$Q+itDL>QxsH)lwdCsK*YU%j7VC<(FcN87Q1rvz;N?06JgnpV1o+*rA!j4IW>$0 zp;V2Qa3^laIDj%2aQ&srCbz91XOS>tojzhbFhmhAfb=$N)e5lkW z*O+%*6ae}5No$5GhkN)p1*h~@vi`0s7OxA2&}K>>-zV!2cwg{e*}w46a>zNG%88Jr zvKzEuGRO~-Oy0_~Jsa-z-sZEEFoFPuu)>CM_M`}f3Ke~F0$m;W+*`;RvpSMbWN$SY zM-$Nmz4@!tR*_Foq9Ly0m9;Acz7Sg0^!g zbsFLVH8hTU%6zkCDIe0gJe|*$i4@Q~qMT0|U0t1i)>_cQ)1m4d_!ge8N2Bw^?3Z9A z2{&kuoHrdLPqSod6-kz@*G$_ui?3sxMF}kDQ>a;Vb2RjAe=DnUEY$Pb< z5){Wo9j~mev@wST=j12=UJf^qcEIb<4I^-oO1D+IrsAQZy&$;)Fa*Ui`>Ao{B>}%q RC{Gg5xPWA@ZQ9)4{5Js0C?fy> literal 0 HcmV?d00001 diff --git a/scikitplot/__pycache__/metrics.cpython-36.pyc b/scikitplot/__pycache__/metrics.cpython-36.pyc new file mode 100644 index 0000000000000000000000000000000000000000..9fe54bff312894a2b654191b549c67fcec15a6be GIT binary patch literal 38947 zcmeHwTW}=TdEWHgFc=Jg!Qiq0cDZW76uDSqXO|R3Nf5~ux#ZH4s5MQlNoFK!(3tK4 zW-u3QcVia-XDnNwY&p?VbgGh)6vc6DCn;5>{FEqB-RuW}=cXH8a`L+Q~|?ovNhT=}Nktsbn_S`7 zy_{Hne*c^KtDBa-+U}aGYfi1P)o{04UANq}T)R-qHT=zO+g43-uEJxqT48__#&zVkVO0kqq%Mdwjxt@_L6MlggB!&`g{wiJ zQuLnD-fme8IfXnL>$bFFm{s1in2F(9CW2&9f6LxpPI{A4nZ4?kqk?*q=z)fbaD%L8 zYdh_o7Sg+JRX2=AM}@rXWu9s6SdN!z8TN+dW!sk7*lE`$*#Y>R!fy<}OZYka$wb0U zG__vhKY>=#Y=aVOs^9%xS_dMU}_PkK}RQs<02)|bCMrZ>?WHA^>9TU^ie=KUupdZpfkDi_*y{vcsaqa7D|xwn(f zi@QB{p}FXu#FJCz%mVt>_oHseCHZXP_1Dmsi~Wy@UBl-okud z$LDE#!&wy{ulA}E;EZA0#@@MBW6Q$Sv}tTx`k7AE@!!eiL3qY_-tO+Gl-*7J3??tU znC`Of@-=dG&VZIvR+*_RSSJ0RvUtvM8>kwhsZ>!HN4G6^$L`?X`krp+wU*&Hje5hf zBYCNc-XbrFfhlH1?{0JAG+N86`U?%m)w^}SiFCIcHVw<(=$O{7?qYdR4PDl+ZZ;fT z?CI;4zT>cfT@zytZ4y3h#X{ob3303&PeC6 z26s~qe~6=Iep)|^!nh3=YyKGruUBUyHe#;I6Tdba{yS;PD_GW_wd%%B3*^DjKw7#K zQUG6+S9VEn)b+Ja*KT9I+PBs?6t1Ep%KCFH2QQ&{tAwruQA~X}%}85@3F~LpyWLi- z0_Y>=w1Oe%=Z@l{XNG5$SjVngAb2dPrHdAc)l|O>qQi>hzS#gZvTxa4=XTkfT})-2 zuB&FRn$>E_%;sW=Mh?0W%U`Ayjv*#{7rECkN1+>TQ+s4DxpzUtU>u|Rj1eEsk?uTH z$=#~ksBKm4uB!;XGayQ0?fep-po+Waj9v6DLkA-kGZ0#!JFGcxN!0 zICKzp2Q4Gv{TG!5iM5c>oNte4qFT4rwVm=M z{vkBp=Z@nVn7O0nj1?dVs+JML36@dHFO6e&n^;P)PL=gn8fX^G8-9P_#aFq+(lfiD zbFGd)8kwGP-DqHnM9Zj|d}wfQy6(2Kdj5Q*qr3LTd5kyPIWP4=MV#`c+infILFUD3 zy^B{xdP7Z+Y?R^QKPc}x$K69Af=ENf_;sv%(iuw*=EBy}ioT>sar%TdU-FZ;HZ1#` zPhZ@6rd3fQz2{oHKuh;=iT&#OMSe-hoCu9}$CnahO!z7eUc}%9yJ5tq26@~3yq?uL$J{Pdua|V~{RF`^!9IAn|xODNTUT%X}@W;p*k%-7_GN5RVx#rO0ac zBv-#ddQU{gzsxyR&~8jpO?fY?3%$F}*O64&h22lqtxrGxN%Qf?9{co#r(#t_BbA-a z?hUj+c)wgmlL98MuIg3H30MctTdLii^G6oFybV?Y$xr{Y3KC(Xv#Qtdc3bw)6L&R2 z2cRl|0}|rOJK!ND0G3DmnUlC#5lmjD-s&1IZlt*~y#sK|fH@N@5MDuN1K0BL#z^z| zG?_ORAZO3`ho5Dhm#*f@miDzW4 z+s|`Ys0Dg|@w!Du11?f$4l1QUJg*I%GU65Qo41+ zW+EUBkaWxHczMR#B~9UYlcC@5UwOC}c_@DWVXAHHdZT0&xu6UkZ)DpBgRACx8I^oS z@Sm6Fw5hV$lbC*%%Aek_8|LyUFRNe(|2aDBlWYi`&PQ3tjH=maNNHB`1KO}MCVoYC zRVIh%DKBa4+KWu-V@zoxzVLYCv02?qVUXLa3^?M$yh=gFZg5o^jO41II?JgPWI%@@ z2E#iDf&Su)TPgTuYa8|!qM^>D{N&2Zx|TnWQ@>)Kb(`J$rT23n@^U1XK zpf;gR;9fRaz&EGmBK~4Bs}(i;k`u{!t-z349x-!pClETToz>>Gl9rV3gGd$6llGU8 z)Bdjt4SJ&i^ai+VTWt`JrjH>%QNB>tUnD{mz!_OC;`1^%ib4xnhFN|$|I)8s(l3z| z1mCUQsM%fo8T`wW9<54Wc;lVM_Fk0)G1iJ!ZIrdfXBNLaekuH%b-3UN9pcEDS}(OV zVm}66ocd3xe;0hX1MO=X;-rL&H`>$4qdQ3aZh~%qOx(imD{jWk_D0EN0{`c7f7~4b z$1ZPX%*@x5y>W7XdZWE@@PM-5^rgO$=#7K-Hb%EU;pTXa5V!^pDcLLV{g{?Fiqf{?HShi;2fV?EQ2T3W8~>d|T{Fkb!kf8h zomq3FP0k|tSmaLbmZQ&KKaDa^xs$zOtgb1Pe+rzf>)>onsd7JnJ7%(9l6pVbE3w|F z$Df5YFNHNPA?BYTCetjT{8MN_{7ZY5r2KHD{Ct`=Gh5)`*>i5OIc4V1CK+>F_^#9a zbT8d3^^(02bAsGRll`AX*td4S605}(^lY&?4K899e7{-tfGYJ#@czK*OgHDE^)vIm z8FR9ELiIiSmE{4?64IErlh>YL5BoX&*pC~wyE%n;g&uoAGfU>QIb+V=%%W@@qb%G1 z_G5%KHRmJ->#Tum7{*{Ya(_3HzB_N`&4rt3a6Qw_d2`V`xq)#ZR|~j06<#fx5A<@X zMG*3&c{*}+3jU9f--;&>m^!XL%9iV;!FgOVek~W}JQA7k9Tph@gdk)&s&z$hCZ|xf zY$~C~d8{q##(D#i2|!Ur5`8EPNR$H1fZK67K_IH>2OPp7hUbf_R-jP8UX9d8WI%$W zW!}0Q2BoZG0hEkFL;dP|Vk-^i)1?UF@p~syUqFPX^%i)YgvWry3atC#Ky~c2z*hCy zJDB28Q}lF{(nRGPFt!+->(dZqKC>i~xnMU==kyFF^rhvsSW=BLI01ePB}d~*aL$(| znQCwm21-(aq%9I6mYP&zKyBXER1_0+>FSs0yl zuo|8>b|6SHI#-?HEtQR_+F+U@b7sfuWpW=?GT{%;~n}jJqGAn z>@Uko94w{-Vr>xlf*+X-A8UKN(b;-eTYZO3u$;EZU{UFfwxHS~oLf)4HyWn9iR8H` z-&OMIj&Xf&Kecu49O^~r8Zd7H&rdII{HLGr^V5s_(;`FCU+g*{6|aNHgaBi|BrKWO zJ@%|3IKh72A8U1QK=5ieHa5vlN}*+?1(#3TWoZ+|t;xSaq4aaM-31p#X{LB-5~}uP zR`)47M42|>C0IAEZI95G-2}g`wnfg!b*tr#fyYR}N(VnLFY+zu+yK>=82195N9cTt z4w(`5XXsFq#7lD!cv-OJ!J`A`h}@f21B@Q}_TaNW&#<3k*fC)*Y=hm?2J5JW8MtFE z&)P5WS&obrP7C%GUS`!hV!z19974h^v44%em*{Z3ds!95OS#*&H^Pw!)gJp*KGLe* zsEnBEmbC}|7Sww%sKJ4=j7}B7E2Glkd}_bU1Uc5!i=f#i{T%wxNka7?3-h&y{t50m zUxSnQ47n9W@*7flEe&o1za@MZwZ&9gOA9Zem?U4K1g^!rHkWw_AxqlnQ~{igG&mMH zT#;Wfm&}vv0k@=`(q`~%5&k0ng%1+?7Lk_vGFAH4nXg-k5HoQMq{odp8|3P6C#vncy)a~ceY0$nm7 z`g8pg{SufMb5PHr!V&VP-^?S=X~0=99L#Y*Q2^i`Rbyt@+W5@~J7Ur-f*s-K%U1H) z5%UNkYCZi0up=VR!Hy{Q7TgoP`DiT<$c|V5Q{l&`)q<)8*bxgT*L-gwEEoI$=c-(# zNV!mEfbJwuhrYSM%Z3A{2V^s{ax4BoulESEt}VDKI+= zn9ZvP1ZHOevrik3VNebfe2!r@rnrD@aNojgne$}wRC)%OGGpNKMa+pJ=vHBTd^=c@ zZ>?*vw#Jn&CWBU)aq+!VW)kKvmzmEKue#kT^VQFgu<_NyBKYVMC4L~tC~Wq{|B63! z7@$L|Y>3HFR#M+DU0Bf{2J|!^eqGc9L?6nhc|r{l3qllkC6+2k!hkUi;TQyvrbG{< z_bZ}ve6XRaQQVpUX^An<~FF~tBSxk^k1$?Yym57#gUn>|Kt3)W>q@T4) z#Qnuj_=VskTnT_Z340$uOs?g7f%ZDv7+$@>rI z?GBTT19`j4{KyLNfxNxV8#bLGnCF{?^NLxFW-a{IUG{0U>RF)lHd+x1;+t{LpYqLU>1CU5pz_8w|PO0>e*QMEIoLJ zdK4&B-U6Ox9RB)~NE=&c>O}H+3XXx+;Sy*5e$pI6`Vc?^xfhTku=MxRZ&7@k0%`?* z_R(mniS0F!t0@!PjKZ>(%u?hEDBYYEY?^A$m=K8r?NS8H4RUjalO4vUv3}x^IV1@_ zmjB8dKfFQZ{g(g6I)X1uR1EyH6NLsF9+jx}Uevl{XzM;w@s6vPMUl<1UUU0BYGVI< z3I$s>BrGpV;UIRmisCSr#rvA7j~4V^MTF6* zOlAp1zklYE(cvZ`h|Fr-#cSyZ7`BG6p0M550E-8^5#Q(|oa}trI}e4UqrkyCTlo}ue-^<5@?I!=iBHOF13&rro}XUy zMz4yBD0p1^i!WL>b(WzI3Vj<$ufQLq`Y4`(4!b=mJS#G-2q`Rbt82(iQL_|aDnfRM zMkc{a6>01kf`n)HIt6=PR_zY7Z%^PBhX~vV-5QrM3CZf^!`e8N^mfgCMBGO!h3LSj zOhN-0G%Y$2`(vt5Stq6Ir5fh05Twcxa&$__(P-p9BVGC!!Y9rvy0id!Qb9|n)7la! zR8ps>ARU^+FH1_4`mCbU3d%HTe+TjQzx+OEwc<67?TH_#xXwXaOg%dHl5Ce(`mUiC zD|?9oC`HKLNdP5Us=iL^s;|?!>Vha0UDk-+s%Wo{?|wF><9Gq0C#Ag#(iPgP6TbH9 zL?B!OSvdjWQh_eCPb<(qg?=eSSRiRb?bDI1r2S7prjEvSPj^>id7VXG&|D>v9OeY! z$KVRuo8!#q+MkLxEOxK=Cw7;xQ+iCaLQiAwGxcF1)WR}ihii5``@YgsrI|4o7mY7eNd&# zxd`!74kD*Om-CeI%pqOQaIosp!Fq_AkEuN#t8rp4=li=mIP8XC&JeMFl$Q5(VLlX< z&cCE4=dmp1SeByp5g%_a1wzM$>JN59?9V3*yOS*C-jLUORm&Wv^OWtN`x=8CUF3U3 zW8sPvBMcNS6Xi|O+kE!D=xrXMtts*vU*00HPoz2un_&m5ugrh|5h>69C&DtG1IbMj zBeF=|Mr9H}4l0uatS7AU{m0`Pleb7U)V-woBok6Xkvj-`4x_;Yk68VZ4}S zvX22&IaEzdJQ0X4pn#YTR1cF{8Nv&QFOnsw3@Rl=Dj61&N$oVjWK7Ml1Q40UQ>q;X zs)n*f5&{lUFZ@H~Q_C|qeCF^2jjh1lkMAU<$U_b><{RKD_wxNTWE9+Lm<0GLzedy~V$`#^GxdoJqrUhY+tf#_=RKgsghGM_e zpYG3~Bmi5Kx?pP{^cz+A1uV^?M9{GV97;hpGUZD&rT~y;p=C(j!*Bb#hc$UrFT5YUuqP7^};Few9h#*{f7UI9?a2DuEt z)U5Brrap5{KvM=#idXXjnlg}nEExai7&M6)lKXD&TR3gr5;Vyq@j)r*4M}+KO=m8g z$^4}(CMHqK+Rv8)T|`yvXD5b|hPfB%)3H&ZW1~XHMul{I?)XxEI7Wr;Pt^B|jrXvd zZm@0c*6Mce7ao(nfynZ2fsX#Lu$lcmWN!a$I6fq^f1mzj1d%NaBQk0yDFrjpC>w%X zR8h14Djk7k0@ZE_!33|}9*4~h`7Q+d+x=G)wbl#%#cj^2cI)6Y% zl*%a8RM3m+VoFa;8L0`W79W58F_G762ozTuBif=UPKoEJIF^R78Ep81y97ZUl*>d| zM`JWei0=558@`mHSPX6b5z>Bl`sIP~?5APIbYa8$+JLX*qh~Js1w-9R5T}g(=4M)bIS|8DGI2hMBE8A z!;<%l*Zu>rTc)`Pqb@*T*iChkrc_{xnPD=`QyYjQz6Y2ejFP~Z$pOTda_=-!oAr^` zN6a}~od}WFN#i+m9(9^veQwa$&^%qI0pPms+f1f4lo$=)sRnr*juSkI z(9C{@N1DSOw$HpWY_H^!Dy=fiGT1$(iWbS_{gz@K@mekogg?r}RaCQzJUd!>i*1^^ zistl#qTyjZi)|h^ejKQR&=^UeMgVOJ_l5;i!-3sRY#OIQQNL}mu^a}j;~TDE2iMOC zy=XwEM49@<#-cWQo90kxqS?0}>RZs{t|DwXrVZ*?_c~jm5;rh?Wi;#?4H(7U_C2A# zgClt5HH?B-0vttQU8Jzs^bTv%^%ZG@B~G>2SdIe$%q7}mWO!BjNQc(C+j~dv4tv(Z z@NIKLH=7fYX*3%47-2D5nV6-`mso+$o~^a&XU~#ZE0$R$Z8U~|Ka%SmNy$U~du%jT z$-|BZu!_N;dioER|$};vJh$K_%TJ?IP)_~fgf4al|c#Oo@sLSWSUxYc5(YjH? zN(KAWpvcV)-4F-#B^ZR=7^^ZDk6g9nARyDr4_?d4#9$~^jq4kg zc|}H6Be#`FZna-&WUneVSp-v79)MDA@P;3b!L;|S@Oh(x$U8Xv!ah>uH!0lwLADDi zfK4`Z;>kRKV;-8H+?hn%r?ibakz5it4L8YsM-brUlJmHNO%dpb=3x7j7EozZM07*{ z5nJ~kzK=AB{U&1J@UQ6I13bt6CP)jB%J>Y zhTMQJe}b^s{{+OQ5zWEi=Qs}YVM+fN@|=Kt5&Mst6Mk;$8BLVtfdWP4)088NB;Eu} zlum5XvdGz^;v${N1DZ62vL)nx{uK`C85cVw<3Qn)QuYa`dX7fWERe8j2|=_tiU|9! zCZR-%qn3IFs8#}tifoY_H;=0VN@8V$$bi%lXPhG|A;g}az_ul z@yT_b2*KP;pRNZKl9@gr?(v99^LyPLD8v&Kf&9lRDds2|IB7sFn7}bH%}yW?zzi3L zwtSoM02;Kb4rt9YXj&GmAeHsYNIXDUFtecFz{wrwf|3&&@q96yrxBWimy-G7EB~&X ze-VOZXNM<8@a!AbDS*&`f6Ax?5ZMM)1=zGaDov6@vs@^D3fZf}h(2}UV}XSUqvq}a zrO63BVxN>y3C6&bT`9@mZ+g)K4~|iz@`#Nk4iUxF*o`i1Jxh6p>VpjiJe1)>(t=f% zoM3he2vw|d5TM@PuIZ!81aX7&oDT5|lx0x%ba3_xo2IVJmAbOj~~+g=VqWYAGCQ8CITh_I;$O{4V)U*4!}HyApPQ^Tne(| z=sd-i5mT({UaWYwRVR}OH(@Bj8QfG(7G^%jxmgYu_WY_D+kA%m|_7nv;rG> zpUH1P6VNu7bkADc9!V^@pd7GecPV^L^hdbQDsufOu6Zb6Xubs8kbuR-cJJ^gKQz=Z zh`JJTqxih6KaCFDfEmYtO=VlxcJQL(9IN1MQr)QQ+({GZjQ5*Cbh~xcc7t^_!ZSK% zEp)-BIX;i(_&k~q@&uhbC=4HQ`jKqpeVPYE-RUSAb=Z?X$9yoy&)nUH&f80#o$GDx z3IzkzuwQ=V($Hp5yROzeGB$2?&dpuy{A}5SWNx*!^e&R=tPhg8mHFDcNCs6_C$+Lx zxHwwjx_oF+KholDWL-pRUkV#+MGq^z0^7NM!Epv|m}dkQlTf z&tz76r&m5F2c}{hHi+R!TKXo9IQur9mHhIJKDm;Lgxya%=g<&j=?Uux$t}UZKSL%l z(g_Zg5JuDf>}4AmkEb!9W&yBs0_I-Crf|%tkS4ktL8u?WbWfStk@M%}p_lF-*XN}< zRBTbN7rvitvL>5JG2#Yl9ecb-jSepjRL~YipO=D+&A!AZMLMf=K0}8_fR$n3&(rrg zI$~Bx>h>TyOJSyfct z;0ZrTVX)<3gonm@&-QYC8U%uQT(GcG_OFoHCI*3UM;QbHBS~IbD!40SKwuWpNDrB2 zFb5QSo)IN^D0zg>$EYfToc`DaJB}v>L8+go1CfbFGeM(ZNoAlcFD7-MB0@7kIdfFZ z1Vu~%i9w-JsJREvm;?7UQ;ZpE4m{N%hrrCk1CwEsLl_@2{~uyc5b`1>PQL_{w{~$%F-&%I z!XPgUa{am-U~MSj2#>+!DVVJrIPb8`EsAnJSb2MM`~0AiRgTB%C6w29 zw<+Zi-4Mk$5*8AfSwm&}aG6Je?eIG*#0X>Lus+O-YSLg+%2ah9ndlvKRdnizZlV6! zF%yo>A${<>T90j-elRvo?+!hGpyrSU%SM&U#(k}wJyh0vMm)m=5m#ARY_2FYbMcUQ zq(c)Sm!VslND{(6O6mKwxBb^aYYogKMJ*(eOqY-E!RZ}5xMTSzcMVC9Hx zdO2jclI9YMsAk0I^s!?!(wqU>Lt*N;L zPq+YmoGOVzbf6@CN;@UAvA769NwxiNP>FZ*FEr{tt2{K0NtJRCXlmOjdsGCs6d5aD zoG!kDaZI1qjTpuR9UaiZx&CPExuS(fLRvTnrD{^ZFscbPl{E0~&dle$GW`>p1-!(8!?AK`C=PYayhO$IS5vRa}7L^MFR? zlS$t=Ca7nhMxGRNno5LBGnvyy^AL?Z=&E}pj9ctu5h{N;#PE$9Hy$n5oHBHxHz3U| z*NpSOVXm9!YtE-W{>XVqmM!Nz%UBhHJ>r$q;+2~1VIo;SCXhFkSp35{DsnIwkBQ;| zuhc4Ul=U-@ujr32$4-SfO6+!QBUIOa9SZrV3?Td@xQ#3gx{!}>m|M=j* z`;aJB(=nOi_cc8{DCWJA!h`us=wC(te)P znY&No+W(T_Vm#>R{=wpVrEv%R;~~nna2J%V+Q|81*8E@5`PX!WZ9GK2Ds9U_Z*@qy zK90}{pK^^;t>o_pC(rvfIKkuP);*tg{db7DJbneA%a<<;=(uvlrnwaR1f6*}-%kx4 z3%NXEf0+;e6b?eK(I19h9+ihzuOi9eM;ZI~RFp9E_me7ZS}j7d-(^z&UWHksKMc5> zlZQv83jQvG{yjK&Je+6liYSlT{}_)CKh4_zT-^^dy0VgXZx+iS(Lc*rp$$l1&Hw76EaRxLI%iDYO;jBOT7kK1lurUovz?5`& zlS6xy>@4n9W`nzs2Y{uA^93LZ*-%!5$aasMr67A&2{oVt>~DA4aT_hC+~&d1f|zV_ z=np zIS=l+m%HR>n82cyI6yX0(jskwHZ9NssawNEA7~x)gPQI`c6K?NWO%xjTh7UOXn9D^`HkV_f?SO( z7je$CM%$(3lEluo#@glOvRvm7KE6CI*F(6TSe}sUJgz5~C*^t=*Hg<=a$RU0Yfmpv z%k@a>czb4fMy`vk6YZ1BC*^t+@lP$ElIs$#PcNUA>#^2D?K8_~%nw*6zvKbA~bxs6B63G2uAQ@B23PVOho&zdvliLWL1lgp1HA zW)bqT`H1=B5;BL7M-lR>IcLsG$UH(GN5~bkVxEj?&?cMqpOT-_2y>NS=?%M9b?XFlVO)mH6pYumA^NYqu1n>U*` zn>LcUr7hL$I8{Fzx4hkH*1M)vZ8aU_)v~Y5C*3ki+Sq3NYP;sB<_^M2t=g*9s;)LW zHP!4{2pLx0dbPf-Zt^Bw+pgm@y``+Wys3(dTB{XAI9|Jn{MW2%7{jik6_98YWw^18 zBJd410!l5braGu~kUCp$bvuk(wwtZ>?zZKia(?=WXjqNS#7qjN#aE$uOx)#AYfEj- zr`$2A!ftib*15Z5H*2k?iFjTCdjC1vX}4;&5$0qxI|k~(rZLV{K3+Lz)HORQT5 zAK#ScLBTKt4O@mJ$5*9oVJOt?3U8`|BLunI4ys(a>}EgH+O}*r+p4KG%gwbdv$@@_ zmpC@@cM87|{4U{VucQ)*{;-+c%&XH*Vgpl~nd%qJ^zHPQQ+uVoVJFp3^ppLRnYo?b z8^f6~FF0w2?x*(3or06u$o5nF$-Qzvr+&UawvjWl`$?RKkXP<@5|du??G$3=8>tg` z`Q8|=hx=pZkeR=o+8Z-*jgUfrY(M!-qMw#G{93=fH{O|ZM*3xh6!z1}1mYBVhqq4P zJaHniH{KtU^H@J4sr~?|COhXORmL2Nq{<-G6jDtg)g<06W$I6{M5tBvour!X4{wb2 zv;Cr3yp1WZ^GH93&;^E?qd{m%?o0kX?n=mQ3@tadQSOgmQYxdJj^Td1f6N@eos@D< zIFo%$plG9M)Pi^8a(5i*r~1?Th=r?T{mK4lKW|RlMs0CF-9PRQJe}>jN(Lc#Ln^T*q zcapY?w>;E8iO}%LG4mKo5#0A@HZ$*})SNlpNtwsZncG9j#5>6w?w44?Gv1YP*ZA=BBk^*z2_|%b4p_ZEq%;592eji#j{ z@6r{$%ey26Ce{U`yT$3L)|y{3UTNBn(QSB5ge56x8kW7;F|8e$*mOht(^GAi(-LME z6tIiwsjEy&8Cd^ptA)lu@$BtY+j3%=1#hVsFExw~Rs#bo>ss@s)mgwhx|pk+^%@dy zZDH{+aK+gbrR&rYvlGi}WkvVg$_n4gJ2o=zICV?8Fjv}GI8j0=Tm>C!-Qo<2De`>f z6Z2sQaRhh^N4(`C&2$}edt@lF)@IY*YSnftv2q<&F$*(1=5bV?D}>iYemNtZ#~Qq! za(F`=HS_ZNK@`SmI-phNY%Ca^wb+QcE>FDL`1Fe@pJ2s!-fGmgTUbm72GZ=M;FEn- z=#g2Y*)Uc*UDd|Y-LqCW6t1HqD#i;f8y}&0w~Vg%Nlarf&q!Ma85?t}-EJ#Z0rU~y zw16S#y&c6x&kW8gv5sA}K;CIG%|VOAYHC~t)n!HAzTN~m*0WUCzSrx`u4SVPj-I{h z7D)n>fdnSrVANvomuZD#h}quz?K@ScS>LRxu1AH;fpCg7z-xShcke9LcF^pM?GGhP zEoh3^o9!*ogP3PbkTYumg@SbW%o^J*eo}Q1LAG&g9n=w%qeK^L|U}AW6Ukd1h%w-sfYwjtGQaK?O>{X4u_Rk1+ODl zJ~FnqKwjNiZz9Km0m&S6uao?h8R-gUBu+%SmpLF!RlC)}{Db+7lLsa=_JF)JoIV=O zH5((?m^;7dY=d%*mxLi0%;;wJDdKLUWhB1$1+gHp7P2ZBU2{(@7*EZ|`*~%BC0tpF zG)ujW2J=2rPK1XvQN7#hD!X!tC&b2w1~RXPDK%QoSONT~dLAXJVHu_T(m1NSfjJ%v zNX2-)iDtnE?ezz~Nd1;resP+Z`PFXLnwY!LGJ1-|a>>=k|66yQEqm$g*+@rs)!JE% zH)Wrd`k*3qW!-7F!fucWtJ=W#qkBWoTx^uV;UAWF(ROx;$h<@Y#rPr?Bzrp8|D~Nbfn;4shH@xWry{y&}IR^hbnvde4s%^hNL~ zh99E;fdQArx?R3D?_DqK2<;UTUKcK0Ffgm&Ew0VLWTRhH0kT zzKqG+b}j`p)7*UU>L8|SR2ofZ?hTNHn1UCCHpE3drY1CyFJgNOF;gJ(cs!e>IUH!s zfpkH9zJNJI)}d#)E`|9#6ZrsS(y4-~VwLrrcTL|I-Bo^%q?gX^e6nGE>ZwnfPd)kM zr_No7RguN9*Soh+^B}xZL7M{6FD)5WObS>C&RV+R?X!H(0gPDL0w2TwOmSYq;=R^c zGU|Z5mKu2CEJapmR0UiJiE-r)7-0z@`XO)XB)He4jGJw=x-|y@87@tC04^CZZ(<3; zH)yZnUjEz>Qez&qs0Cr0M<~DR&g5=!{XIf^B z(-vp0`ILGXC2{lqGV2%Z(~7lobGk{~ELI=Ka_Q~o6{hHXO0kclCAPF9t_qot7|qK z(1C6tq}<%G0XoV})ppbgCVqs8OYsHA9gWTAZW?1-EivMdhr8UO3|aqfA{@X~fmvX< z6lEL-F~U(D#K_9`>K47SwQCA3TtqcV@#(u9SXP$3?k1aVa^o8bRYM`(efsQrw{4xZ z>gN}^jGn!Qum08ACayGlNH~_>HNSlM{Lu;E745pm4xJ0;kgvUhL*mtZaw=6y=99%# zA(cr!k}M@lseCe*D&jw%%tx-vsa&$0#4lA!%_NJAnJge>9>-Fun0!1rlblGVCGYaB=}RdBBNB(BG9{F5&woC4lAYz~9lme3_J3*`T(6v{qg=KG16L_g6W4==&8 zZ4~!WJ|6c|Ql2p>4~|}W#-uzrN_h~@Ho$N6XA_O2Ibs&SmXFq%HAmiM#3=R4xEkN7 zM4x}<49Yz2jP=X0x~5V7aqyaMg4Z;z%l!~SOz{7t-jDPrSZ~zh4}zLc1T~*P%5Nbh z^PE8W$I*f~OM8NM$UCq6e3~({n|biLrk(P}xS2cM9!ee z{?8%qFYNqetQJ?$v*nFRaP)HE#DR;3qxAbJj*cnhG99g-S?EuhV;kTBO8=r?Ssw5Y zAxe5Db>kWKu=l2a?xhXd-5f`{VxK*bG$+hSbILq+JBPAyjIwP1!%Gp=)SQ+StaB24 zydVYRkrTU?@{TiR!8~z00}fGOJ00AeG#>)5Qd$HtPnl;Tcc*dv zG4ej~g7^o4WMgOceS?K zgs=n{RFgs<3QG{B0C(VCd`A$5dinv+Zh!@ORo99#hFE<>1|C_mQ|5SXiTUFCHk3?G_lF9>ax9yv_l}1Xek) znwXsH(*RbUo0Z92KpUrXUW60+?EFfUST&jzumHRi(uR_F(Gz%?dT!l6#i0sqMM$ly1LpG`Qv2KxFd#l0lxzvPL;FQsmP5)xo12e2zrm2+;A zN*?LP7DSmyqE>Nux9|*?*m}3UbpiT437a|R61`bLbYasy)9QlzU|*<&(NQphU7Q}q zsW%tUSX`_%b@Ar8JyI}M4MX+KCzc>rYwXR>r`=TRmRdj}DiA;)q&FX1O+#ueOnlBu zOiqurwbkrwzMEXS!zP%|s4h-Azu{K11NIDfwys>>YMRbEvX3ss_sl-isomV|r8gHB zQLhg2^|?0w#rH0*{rQdO5(-3vT`&P^y{P4H_R zrG|KwTL+`AzDah-O{?XOKww0XN(Vo;AR;H|0Rh)fFl~j0vpoC+4`fHEr+J_XhnwLb zaC2bDgEcvnzC!k`B0 z&8l^(j6N(K&Zp{&%#dSEe+Ws{;x&gp^rXna4957k5oW)IL*nTIm$m5Poi|JH>tPdO~lBbhX zcy5?q}`UizQ8s$W7WdT%@ch6XgCgl`Kfx*qa0#$Uc>Tq8SIg0G?(>Kr@# zE91xOOZeIAm=S=f4{$6HmJ*J{5cG70fcKJ{Y4wbgF;g2^9YQ?*RucS*JP>LE_$+Ov z-v&Q}yoikDuZjJR5hwB0OdIVZ!sTtIBdC;IW))D+*qS*BZhu?f9 zNsTY>M+hCmn;#*8bet*fvlWh{u%bcv0p-R&;z*{YKpwu*U6a-5HERuQ8!F!pAR39) zQul{w5dXdqO?0{ZNQ0VaLGKsWMCUS%Y=|v-PhYBasB8o}zrGErrmQ|8@j$fW(+#S9ylC5sb?47)7DN0x&#sZfRBsaK&lzQPADEpg8 zKR}49Tg;tM24pJtnP!V;hpWO55l*hvk$>8808Vna;}j|(jvP5dfoKl@3NzQ`kB~g1C zB<(bQIgqm@%Hl*G2Ld*wejVw0s}F#H#pip7u|Y`6PJ`2}BP_lq)(Bcw7f9gPS42pf zo&~zSCp{|?HcuY66-c0_aW@^@9XGYMJf&7MzQhX1=7cAQItlIcym=~+L!GXD7eFdb z&j*9mhz`~RM14$Tb0mWDMRy;HaOnG#2^~p!A{yV&!#~ncJDhzO+j{09*O9U}ZuP2>h>5(s+)Qfbr#O;}IU01+Y(tM`eZ zjEf+HX+$Kv^?OKQ0I?u}2~n8v#(Pi21u%EXH4x=c8pDj#Hp;AVnquiAq8KG$%|Ylv zoEAt>+?*zZ+~MGj9yoI|tD`1IPw=0cr3o2YX}4AigHy&r{1SV-5kgjdU_*es2UQXc)+?k9hfh0Z)O0 z%wun-A!R6bF5u}1p1$KhMcCsA%R}bzO@s|Q1)wmb5hwE+)E)H@}jMDzlisy99T1Z`KI>L{T$F$5*S76)Il$E za~P=Ya|oXRssqM_3WM$mS8}#v_d@r7}GY4LrgxOx1b8%rtJNI5}w~XI!EZ1i$yTMbw)k3 z^Jx7mZ;lBu;u-m_4PZmK3U-?rX)-ds5M1>Vpc*jMfHq;gkgyEjX|}i9v_eDj_JQ{9pgMXh*gVl~wn!fetrOgy}q#DtRV z2vd((>0)_B3lb^wfa!uXQB2G5$OP2T^9={75LNdhefubxfF4QQ4C6stYW|3$%KNc< z)B-oyB8EDBG>D9tFM89a=ZHww-??B8s=$Phf2y(I&#zTk3ZDIok8N2na)S?t&YH8% zIaA+@&emfK2Xc^X;)lmwx8uL?GesU)UBZc|!iQ@Ko35(|4! z0@{cwBm&_5Jv@aZa04gU=_w0Nqi`8Q8`Z{b6S5j#8*$-o&;aU)hv_ArM*1u2?=n-W zP`bmx(%k#l0k{rPCS{wmvg*#p0k)e|>M`U1#7vQ+o7uILa#S zlEpj!7k}y>GT^v7v<}-!v^j*6Fsu}PK4}2A03_&H+N*COuA3o+rF8(I&SwJi!xIfW zlxRU@h;c(fWFUTY(;&V?htEyJ)>2(We(KXa2$ZFmalYiHK>=$OyitE_$fxL*2T3M3 zE3Kivz$`*!8%QGEkqG^${wZR)L$Fb70h(k-Sc+ShTuu%qCy~tYNFD7w(3b+%9EAIVj*z+@lp3pPhG78)@K4;LH8= zUbdg#$n?|HaTA;bJvU${XtjBFIe4`4?nK@Q?^*p!KL_7ZLy*c1eHl4Gr>=xN^RQck z9vi)2;SSnw-d$OqmAt!gdFJ0u;O&L|1Tb74ns!-uYAVBe569z_HWqUx+ znBWUSqp3U{0f{#`UsS{|`VbdL4-ya#fHxlsh60~A-rIW1`wS15Z(XN@0U9pdgh6Rn zEdUA7X-eR6ioQqK3L$p!Tv4$_f|ZktWhaC|2h#X&kKlscdzX2hP$qPIMFTWHlR*o# z>zKXZ4n3`tMqh3{s*`-^ht4-5h>$960j|G8jn#E zE3S}*tfBu29stHGE1j(hw4Us(THTtfJh5OrQ8~9@d}3Z&IF{K!c{|mO?y6?h#7cQ3 z;LgZ%j2yMyL0=j)HE6Z0*4CH2mN+l#f^=v99>ykxMA8$?OfjKgTYiA{-EZKGZm2j=T=}0)wIP z;WHrOMZ90$!z(^{eTj}xq*^hR-?d-+RsQ|vMNioKy^D9UfI@)9598DuzwXnszV|3M zdrdq;XW%aArkm5VKYccV?$xW7JLJvN$b@)a{e5PbBKY1~uW2}*Tu}8a-U;SUgCjn+uaOp&IACfmw8`o zz~ypv)pBlGR!2*eLml5q_0Je5`G%X^)k?nqf*Jiw9)5?1-{s*DxGk8P?t!Fr*l;%5 z3iG}8k8Qi-(MMI5$t}l%$WJIoQs_8i(GWx#;KR6+0^8$5=dTj6i(c>*jmWjuZwVfG zC=B-hf!l%4^^e#5K`;2bsA2E*4~EWT&Z$Fx;2|uZycReP2eDae?b`5NHEeCyL8YTf zt_PywV<<(S6#O>{(KD(@F)$s}Ki6|M?S3Ibsf%F@*iM5hpvnn}1{E;`xB9h^Fw zDS&t_B8IXs%(~#rTHIS_`&oeS6j%UR0q=Vo^VJ5yD^Z4npU#onLkUp~dab&%izQC`wq~gyt(I*0--Bi{lTj} zs5^*o#itiUx87QGDEOB&pt*fk9BAQt34@S>tk0365;=>&@_X>p_zqZRhNZ}0%@EYon%x6~v781pz5 zz{hau9mfa|JbNz!bgKoDZkY0mL?7;E$dZ7lm?+3|O-kR*u;`}O?O2iQO_?}DpZc|? zx&;nKWy`w=EHj`#fET|3xERZT?+EH5h2<^;RtwYx+hCTeKd{?}zJLj~zGevN(nl!& zdUp#v9lAKi3>a(GkR!q7#LMU=TPr@G@`PuoY^*?!KMn@+HGl2!pI*!4hze7zZfjCl#&jTnHio%u=xrEy7d?w^Ke&y z%0yCohl-KCha%b3yH=yotT&}mtCw0_?469fPJkEeJbmrqjM_wMEkqSi>=tG9S~jt7y=hM@46fI{_e_~;4`{|N`U6UN#a zfr5|lNhp3Td0&R_-?$KdD{d(qV%6HsHFrjfhJdPrU6sg}^dzx)WT{CC@_Rk0M_3H7?<2{~-?EWep^ zf!slS5>Y=jLZ5ygqS-$;&@3F^5k%(CE8zLg309!T4+B`^of0e1U%*Ef_ zRD>!6{!-?P0~E6xP{VrFijd7A#hd}_$`%@Tc?2?R?$QZ5wsEMoVp#~+NDsbA%Joqc zFYy%3;Dj=TBS7v;P=rH2@rDBDe$+*T4q{6EPsHr4e=y3_6sDfIl*9%~Qj)ED4I8b% ziuOI#WQQ!fQ2lm4pMl5aFCeO^waj^gK z9NzTSc=pn8UjQ>NL@K=Tfm|i=nT9@K0R*k@^KT3!Yj{^SPne^gh-4TRZXk0v;rBy9 zKm#pDcn~}e4aGja{FUJCV_aYBaC*QOtwehKNW<^Gj_3Qhr!XT8Un4nlI`GqX9CtH9 zSkq76$=Z6(CyCkcLC@HaaVy^ek_LPW=!b#B8{1v*ytl{BAtDK885c%Cfmry>D=R_v z!62wg_An$1XMhM<^q{%WvQl%(F5vU;${#++>76Q>`$-A%2xMy5A(nQ567rzQhE zAntBlZ&L3+YCsRBVm!8a=W?7@zf)6ux;Cnktl*LyUOw zFfOt%k9#SShNmw#6@WhX6gfBt_bM;kW%VQF*zi(@bN1SsQ{Q^=zx4j{#kJSIedg^y z{lfPyx+&G|E&2T62p!`$iC#>FouKo3^9lGN^a&qZDG)R;)gd3*EpW3dr~`muNk4eJ zCfizU!?eqazl3PozhGK>tLQLbfqC4G^qR4%?{m8fOiWLFY)dKhu`_d=Z&kuj-hV;+Tb2qj-c16$P2X6`DVPOO*hCrusA0_M+54)Qb zycIx^hRP^=#hvFtQ^NOw>~Q`RSgf$7fp<9ikA_#h&6Jt-Jp1Xp6z|L&RA%6b4s2CW zmNzoVc_7W3%GskNGE?~WqDmuStHsPOu zu2Dhzz(t(5$tu4tHl&E;WwUJY(+S{pced+g`P?O0oYt(@I$)hbb*y8DN`z|uA=tpk zZU7-;+`!)tf>w`zTM^>?h@L%^p5=~}QEepO-UAM5e3shLjzfdI|Dzp;{y*7q2*mCK zBkj+d^rz#kV5I6ldK&Yj?PJCKAY}ddC~+eT(_KkyPmSTuMPc&7ml|~|1{}4C62xUZ zqqAywmR_%!*l~B%hAkxooLgOdo3>WDKcPwWs{x^Y$tRPclK~bZmc>J!`5fJGcQ~K{ z2HCWvZ?#+O9bSd{5dVs|bd~LUXmgz{&MrhUneTUJY zG?rO?Lo;n(=bLhRJ`T448YE$-0}g7eFCWf;dOd?25U%Z09%a;WRUAdyDJ+7vCIF7?NpzFfx1`lgE%nvD(*F_{n>~&rTvFEeuml1&Q zAm|$@oq?D3Z!rGop=pQJ-(gzy4Mq{~28b5RYkpY$who$+Q{*-CdG#Nd?myyi{b0Fr z>c1aIkyC%h6#q*egaw|@DNxjWFZ30nbvx${RjaUct5)4Ywc6MgM{ZS>VkSTf357;a zC@N66`CHmv+g8u>c097eZtb$mu>X&oW%RjO7&=2AMxfQ$fMTs|WxagN4~14NW)?Ui zpcc4x1xP-(Y?GwO^%kdq%QJrbZKv6?E1|`w-W2<||5z;fs?fTHAvP#)ZS_&~pJGoV zO9UOEP7%IFPw`iBR^zN61RmHQSoU0UC-jEO!g5@}u`+==H=2{?4* zE5D>$N;Wx`je*3uy7~=P<+phFE)P_O5xIjvE5&ge`4nn=Dv&P!3~IF9vM#VM?00a; zBxiu$sE-2;8^LD>j?la*r*oMc{4>BWfczJKIx*tk;d)$O7q8{gL5ywxc_LX<{&jKP Pi|K_FuLMs%udn_eQ&vAJ literal 0 HcmV?d00001 diff --git a/scikitplot/metrics.py b/scikitplot/metrics.py index 08ec693..846ba9e 100644 --- a/scikitplot/metrics.py +++ b/scikitplot/metrics.py @@ -1044,7 +1044,7 @@ def plot_calibration_curve(y_true, probas_list, clf_names=None, n_bins=10, def plot_cumulative_gain(y_true, y_probas, title='Cumulative Gains Curve', ax=None, figsize=None, title_fontsize="large", - text_fontsize="medium"): + text_fontsize="medium", class_names = None): """Generates the Cumulative Gains Plot from labels and scores/probabilities The cumulative gains chart is used to determine the effectiveness of a @@ -1076,6 +1076,10 @@ def plot_cumulative_gain(y_true, y_probas, title='Cumulative Gains Curve', text_fontsize (string or int, optional): Matplotlib-style fontsizes. Use e.g. "small", "medium", "large" or integer-values. Defaults to "medium". + + class_names (list of strings, optional): List of class names. Used for + the legend. Order should be synchronized with the order of classes + in y_probas. Returns: ax (:class:`matplotlib.axes.Axes`): The axes on which the plot was @@ -1096,8 +1100,9 @@ def plot_cumulative_gain(y_true, y_probas, title='Cumulative Gains Curve', """ y_true = np.array(y_true) y_probas = np.array(y_probas) - + classes = np.unique(y_true) + if class_names is None: class_names = classes if len(classes) != 2: raise ValueError('Cannot calculate Cumulative Gains for data with ' '{} category/ies'.format(len(classes))) @@ -1113,8 +1118,8 @@ def plot_cumulative_gain(y_true, y_probas, title='Cumulative Gains Curve', ax.set_title(title, fontsize=title_fontsize) - ax.plot(percentages, gains1, lw=3, label='Class {}'.format(classes[0])) - ax.plot(percentages, gains2, lw=3, label='Class {}'.format(classes[1])) + ax.plot(percentages, gains1, lw=3, label='Class {}'.format(class_names[0])) + ax.plot(percentages, gains2, lw=3, label='Class {}'.format(class_names[1])) ax.set_xlim([0.0, 1.0]) ax.set_ylim([0.0, 1.0]) diff --git a/scikitplot/tests/__pycache__/__init__.cpython-36.pyc b/scikitplot/tests/__pycache__/__init__.cpython-36.pyc new file mode 100644 index 0000000000000000000000000000000000000000..2464a48e52f697604ea3b9cf9143598899c2b169 GIT binary patch literal 148 zcmXr!<>gXK?~h{ug2x~N1{i@12OutH0TL+;!3>&=ek&P@K*9*(m!p10er~FMYO;}T zX>n?iesE%Xs&8U8kX4+VnVngpTac4q0%e1lC8@uIW75#0k@;X_C6B^Jto+?=*d+O@W|Io3=^)Uy5Q;phZ&@RnP)Ki?)E!qW%58 zecayOkvu*WC8#Ov-0bY^%s1c6eDnLhZ+3J?PfvF5^oO6@b$=}O-B{=^f%q|8)~#AB zX2cAw5}VcXn#QDburi)7=N=JXXOty*Q? zHVft1xtg*kkk(bH6^%k!l`Y&1%FU!Qi40TWFM~-kv>(H{Jj%3`()X+P2)kOYN~|(l0W|HxQ^qpdJ!0!p}!QX$4k=6MX=s=V&<7_%d8 zBrrk~M$$;(H)*87xQw&o>89R0SuEK#wGi|J`yh@>y6hMt76{BrLNs744l&0o_}+3x%C{K?XofTRgl$62|>P zI;BMoptdL}@u{-CCq%^^8>Is8Z;62PoHC7a$-X8;#N8Vu!YgA-ge0vJ6ABwhm+VNyj!!Ss9)O zLbM#&BrP&;C&to>@>uO+u$w`<+0bqx477~9y`(CGz@HE5D%`Z64V-DMvjL+%v3Pq& z4FXo$#u3j|)F1KpY`p$pv`>sB+VZm<1ACg9aEDJpMpFcCV{wrs!a_G8;jU$rFpboZ zj1uvbl+~Le%;J4a>+SFD=}n}xl(u-)8Vw6%2D_ga^EfU`K?u}iGqL5^l4ff&K#t2$ z9FP)63J}Un!cI!Mz8shI6w>2%dL~1(^Rl+0e|X0OSUO=a75gl_aR_@u=(xQJDpm-Ag%R zaH$uRUJFXW&6$y;#;$t5aCN|=cOXP>ug_Wh=9c=N z0fKxgb}II)izIKDc$z3*nE)q-lPXru7ZYlr8Qj6(E+=-W7g`w95Gmy_^Up0D zzDR_7>2&DH-F}sbM5?^ge}stl==L#dyi~N!Y3K#nrCX)!LqeI8EnC+7WQi)0)BRS| z{7g|*q4SI-)y=4s)!)wGP6me<+`V{sv|6)A%hl0RWomr%G!(N@>Ri_7Y;i#zO(LNN zWpc7&j@q@+X|rl7R6p+MZ;DJf_0`gUoj+WVY&i1TM_)}bQP@Pl*tmD-uq>+!5Sd6IYWi1nwl-z1@ppO zrC2T6c;aOIp`*shiTxxg?e&4Wi+mZB<-mz!Jmk|D8&J8Y^4%bgV30S8>a5dELS44p z9=b3(3W(Y>O58ccs*{d7&jRS8_t~$dUj3Gx-;yOew)9{v&l7%c`cOgZV)nM^Ly+*p3 z3M@~%jGIGrd;@Q}biBqA+tBe(%pr7qpE)*3N2Z02FBp3pbbziWeM+!w=G)VPT<3gh zkn4pl(9;#6N4E|ew=~i7_aS<|w+cPFl?v$5RsR;~$+pn*=f=JeJ-Rg)(4(uxRq6Ry zh@MxsKu>psp1Ad_F&3gHZp8w6;wrleJ#lMqKu=s9*gQP|7&%&m1ys&0C2`+l0IqnT zQh=%5TL-{f0?-0r(*;yw-mN4o*4t;ip&kd|c_W_)s2ucX+DhE|HmS5x-b!5kHK}AA zvklhdP_BPtF4lou|IJ*CBiE3`{W(c+IV^KBZQN>%)YAfkGT!{mgkX>k_!D1Bt!NiM zSx*`}jo5qi-LVs~7404GU+SuN&Ge%W2I{&!2nmv^r&f}dZro<<4k$`kFOeb}&}t>2 zK13=Q2SvM$J)vA*VlKj356{!8meXCArzUs|Dnx zy%qrmh9pkPxCSt}3otnan2g*@-Sup}8{ku~>u3SxO`kDd*U=aI`9#1cc?n=_j0SWh zt#=9?gwaVgBXqbhdNkCwr1f6rlC~w)3Ue_Yv<>x38o~DULUqPCVBF{d{+!nW0N9`f zgwpJn)Kd3K;`zs(iABKa!3k%GD5Ov^T{uxF%_|tO$FLe)IursvKus<}MI-_j0f}5d z2+&15l|ECninguPV<-eFBvD`lT{%0XQkb}>=IKc&05PcY1*cb%RjuTb;S9+ASqo-# zESB^>I9Ev$Y!9Wfyom_mjF9c@l7~}f(Vkbne}n6-1qiOLB^}oxxR_>)=xyWZTkw>C zk=B;&V~G*;t=`DGXfA6)-@x%MI}wSnvyCnQ4!59hU~@2A0F{&Wu%93+4|j(+evl-P zxW!YWv!;EfW{egqaPJrk0R(>neXhy|VEbsXx&RGjeDvuvWfzabwKHE4v_219Z<|%a zG)5=QGsSb|ni^NcxGj(zdWYLSL?V#9P1DhqCP?mL!Z0KcnjrjahUCzc@`U6E)U#~p zYC!|AzmwUD3?>mE-Gk`{lT0S5R{!6OXrV{nYY z{R{}c6;YLX3xfdtsz-S%$hXHegmA8?#~D0#^JrL?`45k^hu~R8J>}VagQV247W^bln z(#X%?Pe<$+E{i?hYS@eV(4zh*BL}}!#=we>-!7vMhQNN;$Mz&d?PH##Jx)OzKyX$W z$Hl>7v9Ime3~W?}3kBkaZp2^K-mb%gmcW&irzty4s};pPe4>cewcWI5CHbo(=H?ZD zR$@ZjqclZil?m}!Ej(LTfKNfM&cQe#UR(#RF4ORw!O%{Mj`pmhEjZnCrkb6n1g=$O zNW<4kPn4t0g?LO0onjAg8j0mJrscFj{Bvz}Ep2ywa{&i5yhbhv2YL}<8{PIx3-k#m z4oMH+ZhWp{JIPX|W|^40q$EOyld-|%6Secz89ek&q1SS7wNl|{4G3X3Byb5K6b;Q? zA?8EqJ=7dvCe&t9V0qfkAf%}cE>MqLVfa{9kp^{^L4^U-tC(s?mS=cdWk3x~)eyj1 zDvhE{aP(rxi|x9Ghh!eRo@LiJZCbO)8!$Zu<#BOrwdTSM(EM&w%SoQJoMXR}mZRMn zw%X?e?`62gJx+K-?;=aa@Y74@#=1IYN|Yb!97>6HcUl-yI%naT_^>$g$b^1v@tQT* zP&(Jtbix8@`n`yB+H~aWVsqjBjvL9B{YIzU`8NH@Pw?-n_fa6>@B8;%q)!2C=(bg{ zTrF7TMH378hWO!RDv;iD#gZ8wDQ!8^FPsTgwTRlA$I0F`7!-Q3iBrKK>2!L?ph!T5 z`TR+`{1IeC^Juj&Q=7D0yV*96j&9+BTfqx0&A)z)=DY6%EoaYh*^n{9*}c{nA&|$o zfVt%Dm>k>*0WgoTf-8&ZlE7|=#Mv&|u&qB9br9+vl!!j2i$a(5E!UR9bELy`Qb(+x zF{lLCWfA1~sN|rR;8T*r^%A6v^eZ}S>?v$4%GOh&xIw)_j=_4M=753_W%anxjXrXz z%ZP!jq0(mAviog!9;L*wRB z3J4X(!b>Bb!F<%c7o#vN)-ofURBax1&kIL^89R&-{Pw`cu?xSw#%>_IJlE-?Pw zTNGW0a5c(eSRej9{Oj2KkF^h4MubV;Y1pI8s$de+jXn=GdE^71GoT|JdBdo zTAbTq)uyyIP=%^fSC!7K3NYKrZDxSeE8QBD|Ai~kT806xuFx#&zs)fU#QI36Y1S-lt0PFx~@I~nia)w zJHg?&1|6w4RNwC)yDXNTD1kpQqxWiyx2?&!Xej{?If{abMU`|d9yU!P+AM(g!)bQc zOm?O_qz8D)q0>_>!?kP8nI-5P6%4LKX|^~wmWwW7q$H$0H%l2Mz)CO8s*fVuy2??P zOgYx`b!2anJeX~PV$gQB@vSBn12F&2H9!R0r^+sQ7y<&Q!=x}Q)jcS?mWg{A+dJo=MTV;F&In*z( zU`=x4=TkvaCn}?QAG1e9-UCcK&VU9x*FC|NF5;O8Ym4grd@hDGwo|l4^^=VK6oZHY zK*j7kxH{Ji*T!tc3&F&oLKIk9cj1O|(_Mxk1GQ=SyhNm6Jf!x+0-UtY!Uim?@O2ky zPBZCFpUmT;QpJTrrF_=>J2r!rVk`J9YfjAlohY&&LZNDf!Ot-GIR>9#Kw$4G zx34nRpip9>Vto(~xX@c<0Tydt!=;Yf4NaH-vWmG%xPm`6 z5W!OEj~r;|oAf~7(=tFxeCrqoZo)@^+_r7U;r7Cw581Gd-DGtg`zHw4>5WM=W2pAC z=T?y1`Ru`L$S%rP3RKOT+vI%^;ePqk_~z`y{Os()Hg50#uh77Gu+6{$cRQmb*ytY! zWcuKEon{r3%p!b)Z{gbc+T^pAOU&<$b5$Hy)4X9uVuQ+@tC zuqBGN`k_l-WHkOKdBq7EeCF~Q+_XBR8V*a*C+Hzl4IvRW@$h}w`V(5~xXd|&HYa}5 z(EN9#`+Aq&+am%aQ1I87TtSD#QNZQbyc8{u>*`(2>`>$rwnt+lRCy9#Tg&$7^;9r{ z3~3G)a?Cdc_BZ*^o7vK=cCjO0*ECu@X#d|djbFkrP@hN8&@_HINa|eEhz#flSj2G# zmr>K`SGK#u~~^?v70=~;uW7qut@A_&xs~875NbGVg@%`;RQDL)4E5$c3>riPnv1ip#(oS zET;HcE$%@uZ$E^SoGq4AjmBfyv1uWpj07iZ7V!;>dkz3(XW|vC1TRdF}i;`^cLvFPZ-2CqPwZe~c!yG?8fhTaviJ1?u6e@|$%UEPMsSK47sc#72qBSfiP=DhHS=J{X?Vd%b5{W_FJ0O zq{>`AegEOm2zt2v2#V@es}i$)D*vM)s*Z1!s_oGFCqe{0v{iz(OYwgyL{wm_+2lYP z-=glXK7*?4K>IwHvPmZRIK(z{R)Vrg^1pzxSN>&JB5zJF{mrcemIx z)aS^W(Dw9p+Y$r}Jx4~6M?Q2GBSx?Vzl9mE+cA=`Qv9&)w7o(eQ|@F@zsn&PcuapU zNb1~<5gEu2un0og%W21$;`r~_jvBcaD| z{UAJ}!LHvY{ZCR)$rq`CNGCBA>l|@cM0wimuHShwyh(WqcKt@$dRY~ycZG&tpsq&= zxWewzcc5=KqeQ(s%wkcZf?dC@E&NgUng&`z+osLAhe%#-=jr=HqvPJ&4F}8`5X2))1&&45MhsO2VvWxT)mG(ZBn^vh>(tz ztCv_^M7erH=e!8|fUDCFi_+1|h7BqmA16avl#XEEYTL>5y5jM=;?eST-&eVw{{e!A z;_-(;Qs;_CWH3L#A_xO7r{eJ{=Rn7bN3d11?c4|{A5BJ>r_qK^4HNDBsDba$^Aj%X zo|n*f@_Y#ucpfhGD~zxAfpmn^zsh&Q7Mx#0g8Fp^4fR4a3wmn46crod6&|hjrNoAO%@MY2!q@!e&>ABqt+4yE?VxFLIaB!wmX_^tf2!X^ncm>& z$o;9rsMpL2-$%$^iVg|-?Y+TzLd5X7F{GMyOJbxc#*d(Zp%QM4W&Wbroq}Kl<>Bn$g4wt8F_dV&J74?YxR~T|s zQ~--uH>Wfu_K%d}{-@+|lj1Ip$JVA^R=xdOp`j@Qc!r?_O7nPHw`!L zuk57huIXPkKf521bq8yy-}v=Dp=&f=+I^Ibm+hUx0naYjMov1{+1$@L)q2<(&X$5d zWa>Ln#^4(=`A+~=W6bJB1#_}#i9NAVCTadrEk<%WVWeK>>5BZuF71vJN5hFnla5w$ zGy^-7dVG5LKP$93qh-}W)baT7w!}8o^S5odCP(?K$2Fm{iBW;@+6Z3b_<{3G&a@$e zB+k8Few=%}e8zdZy3{Co0vzj~#n!6o{H#AHtc!$=7s;qr1h)(Wz(^1HI$cGpgah_?d^W!K#DU?aa^6o&AjIo*)v;lpzN($4X$ z)Rx0?dT09NB;rLpmm_l2ONb4G4$JX#ydpVxSkC7qNAwVy@L@TF$T1W-ET>xz$hvay*$`)jgGC3rtEp5nk#HUUU$ziT2ec?lLTIdsb7Ct2B3c2pSjUk}^iW5I@ z-Mtnh4M)FVUV9Xa+VVcW%_V6w=i_Yri^u=qOxTO+8vbkSLvA_^$32kSI}JxX+pEnG z9*47@KkSd#v_E0+O$Og$AR7uB3Twk3_BA~H1}O~r!`$x#yE0vhPx*fZ&GLY;!q`6k zZ$VS@awKYj{|N>j`iH%XCBoTVaF2%{cMp8UF@NLAJg>^ONrB{@6X~SAH0b~*gTp}a zzq?^;*F7Ya2Vds^Kh(#Q)u4DK0@u98g>d^22@x9AJhM`uKksnHAZM(RkOa zTIMXy{Z||m`9c2;^Fqm-;|YY8e9Z17vChmb$Ut%iaD40>6thZs68}FNV@}Hbl@kH- zLyhD1$N3cJ;#*F}kGVTXiTs=lmx$vaUCxA*z>% literal 0 HcmV?d00001 diff --git a/scikitplot/tests/__pycache__/test_cluster.cpython-36-PYTEST.pyc b/scikitplot/tests/__pycache__/test_cluster.cpython-36-PYTEST.pyc new file mode 100644 index 0000000000000000000000000000000000000000..33eac8fd7aa78cad5c27f9118c9b37e5e1cf4efe GIT binary patch literal 3655 zcmb^z+inv_bnjkY;y4#l(kpENVG0fn(DnhLAOfva5mkjs$U+@9o=I3|Z=IPfapb(z zsBfrzK!2d`{hE30Q+^>&J!jT-oW!M-Qd@K8%+8rJ=Q8JVb7rP;dHc5?uX%>?mof2k z0AGV4BminqGcsbcZJLN%k=3^K>~!6(*Dh(?Z~J;Kw<|E)Q8li$XEePN)#KUr?C@-( zJJ+5E3O8Da7u$=b@wGucT6$&B67##~26lUiT91vUe*iZdO;gPVn<9;J$w(MyDVH1Y ztsJERB_R(5EWB?Yu^>ULkwvK_EZR(8l1|QlW*y`)`dNUi!4T^Jn9(+=(YB~btyc!< zV^arahq}~**`+1w!|c&At-xHORa%4Dr!%w;bD7S9j14vOgbDc_-1-17?U=wBe|VJ* z<30?r0YDmEvkR9Ez=YDK7PViS&#b;d92lH7XkeH8^k!+yK_=rFmf_7 zvmhbjTdg}$+6f|Yx3!+faqoWN1jff&p;>V}#@jKxwT$M5w$?wHj{x^j60(ekhUR;4 z7I9i5j%Sj~-U{XW6C$mhNhDx)CTXg`fMlG}up>X3km{SWN;P7-(e#v0ND{=15LF>0 zPH7$?UL)kEJcx=Nj$Y#LB6tr0zWE8D1EPNXb({wh0lDAsP2cuyj^Z~<9D|jY5mW%E zS|A_^$nS$tFrms%dKr7bdCECP3CHkm+C})`d1b?MJbCYu#ME%=)t&?TQEIU270=we z_+N}Rb{9TL6kmtxH9#gqRoW1ori46-SyXx$xC4tiukB%gxw>b@z3vGuO21pC^}f4h(pmhzWeuSI$x636 zFm^maVZYY52PRJdn?WqGus)6{e$^CEviKWK0T@s~HUHgOGWI>%pq0KCnt+#N9Vlk| zB`j~hK;_*BhZIuvZCZV84<++avNxu93e;sfr)8TzEgMic`fiZvjmeSs6#0Pas-^n87m5o>Di4<~UCDYkn&LvSzJSZsrchR}tGqOqB-qsz zs&@m*RBu%ohxHY!M^fEzKI1|Og!1jTGrEVGT>^CK+%W@XBqFLaTaiD zwFNi^ud3qJg_%At7RC9SlZ)FY1FTdivx_zo7=cAlH6XNn!!e#4HKT4gW&>89@j|cm z&P@rpV0Rp3m{-Ovz-3rW3$h}w=w5D=QHFil0iMUsG$Gw|Q#8G)4m!%7Z^<52tz5we zJwBN=mZvCDQ2t+vJOm}?!12&d7YzlGVa(2=Pf-X?vHa?UYSYTVsY4sh3dc5pe}Lda z1d9l;s_{<|TtRRT!8(G62#(UT#xO^7OsG{D0{cy7e~HUwQbbj&c)t(>EzVx@P%>2>e^RC4H-1~Q$f3JJ2P-$p}YT34;|`i4q^M=r@_xq^PD3^VJXk-u>bdQpPX5u)0C53Va1e#3)pViN4516-fd-vTk-+T)zs=Ej(s5cfH;p<($6Ao!b zhA>)il%-RThity3G zGUprN6o`=!C!SIWy#7q%7tw*!m7ZwS!#ycE-gXK;Nb2YPN%w zwo^xa%-kvk_fhVlgYC?w>ERO}o2_PK30=uC7Bog|zU_=wO82mLG`_9N3D8wTnJ%Wy z*d=L8Rgszl<(yfyemP{oXH72`${9;ve*Q6bOuUgXi7Q&gq!|!>t*NbO13lE6h(Rj| z^r$gWgGkiOgjrQ@te6bim@XZDq-2c>@$%-9j;7WZvNIn?K_z-rLTvP8c+G7DUJ&JP zEOouhQg&oDRx&c{{(O{ccNhv%K}9M>_lArSjdl>a?YigOb^V5L!$zn5BIJ&BH*CFt z>rs*JQ5_`apcQFoWsYxPoUP^%`oIWHgl7hAvu_5gq9QUOcEA;F(F)FmRx=lxE4sV_ z$|lt|1a^vq;}dewv~qzQq`;9?w6UVAJKhr3*O_T;kO}jsDfBZd;QJM}f()i&V2a#E zDOUAhuXrgX$6xVQEXUv2<2G{qgB(hZf0E<7$q^QtC1H#F7y7F4Wp|Lp^Oz{mZPpzq zEcdf3u$m|iENp1jVwQG;#2j~Ae7|qb>*5D&nKxE4qCBz`I6kM0VR=Pg)#Yy~C23fz zEXGq@e^R)nCj;mI_zr??Q5s)3mcmQYgNDhRX;0s z3GLU$KJAjWs(<{OLB5}FPQdn)eIuNL2UvY;)d>C~4v9U9L`HCl5`~kH$dI#?f;1%B zBc@YaO>*_~iYrvIf(F;Lq9M0~rJ-3Rxe*e~>pixw8Pv1$en(95_u+&3^6FSCVnp?bz_iUWxpW>7(GF5C?{ zO!*2qNJEYTDc#I~-XH5`%J)@2Y27fssw<-J7nIbtI3ix`+iFfqLk&tP4GQ#oT-Knt zYJ6I{sog{X`arv;-HHQIbv7Dj>~VsIztHJkmMl7giBD1@6jKy&lCs~}1Ce&!2Ki9eE)O+%- zcY{b$di9PJk>&dL+@;_l_mnl`{M&2CI1GA^B>=Hi0t{R^AIo*78bqe=wIlr=wv-pp ziToZ520D&@X04o3>|tjJ9??)q;NF0YPjUa2r`*g_*h=dUpU?3xn7or(%N? z8bQ#Ts}tHrxqGf`1ENL64-w06#t+r(He#NVRYrxa}nvY^HK!p%GMC?ovg1LC+7#+QVR>*A||r%y|p$-9|^wL`I{%c6B&) zWFiSW#M*)wY6~sZ1aD&qXg1UGf&lQeFyN${K`5a{VrY4sCZ`7SRT_3YW!U5JoG1f_ zI3}Mb0}EisL-Fk}t6pL8dR%cLzMW*%Y0`V*{hgRn?qYEOMO3=bU2^7n4L`&t7g;eN zj|!+&8<#7iy-on<_Jt$8u5|ABojKQ67%s{?fjbY>-jz}L9kt0~V^n)ZzThO!r2uJ?@6AIDOwY>{Y|=q$;HyFFomSVCUSy9vMG`g0 zV`R$>?I};;sX7+p7A~QvNX=sVok%~NiXUS6&d4L1cu%LB&!!*Vlt(x59!vLoHnwHl z@A2cCA=4gZ&)6#oC;&-dEvgz^L!l8y=ozhm&Gr+m3^YsHifLm4Z2%c)vr%_nD}ke= zPwSTA`FzN4p_T2wQ0>dF-q#WsLaX3;>19^-Grp0aIK zS4Um)V?4TmE8q-m#owE=TISs~E6%o&-4_A)O3@Tmc^{$ndJ<_9{KWo&nqvGk3nJ8- z0FnaO1&F{DhA_X-KQfTiW^iRys}<%5Eg9KE`qPy7M&I|Qcnysd;ml@(r-ojQdI1EvegTf`zyBw%4V_`>D`n; zi8Lk6{X<+sKwZ+O@VC5otKxC)oTyLPXaWTviT|l!th^H6n2k&qMGZ1-oCJmL0apq5fbR9C1!Ds& z0H6U_h5&+n6Tl|Z&y=)5wx12Y6-B_)e6yec?kz&T5O7J(5$ut|=8r?0gkzZ;3RpfM z#}HUriUzP%cTBg)DKKV*Wz>ulk|Y4v#73XN*hASAz{HuzX88aRXGLjUw(h^sladWU zP~$jTrNcW1$VI8Zb(maFkPrYAT;u&TpfU#;HKSv(Lq-$BjK`^a*sNsW$o-gswKs=* z1~Qtb#2{)%7~+pjTdS;~>nl8Zsdu_d-x1@;kI{*AP*f-6`=mc$!SE-U5X$wviP^Ed z!GgP#Jj>z@7H_h6iv<^pJjdc~6jeJF`VIHKyu_BvDAF)7;ckeS|Z3rC`2p)uO zw-qGIAaoF*EyMtA|D!^Odw&-3Y&$&mb1FB-VxC37Vm**4ipxt92T^miLUA%`;cPT- z=Ttoxdh+8Ye*xFCptG@&9+#P!ChMGCo478r`~NC1lkZ^&SZvdtavintCh{SvpRyp} z9)d7=lNIaMQ-+&F-ECYOVc4z>8a_1wlef?#My2D*8tHA%{3U0X^E!fF(;J7~g1Ttk zWLLR>hxD$3Z##g#_+&+7r2}u^Fe5Ypb#cg}PFQjL*EeurqBZU0Y<0M!)X`V+OI+k2 zs-6tsu`k)n=Qc}@Q`Gk8TT6iahlj1}-+wOkTCJu3+}S!q$;gJ6pGA`7m_2c7Y**WU zmr{Q8h-u|6*-eRb;h98w#t%E{n+oH`$UyHtt2C+4JDX$H4!-1+H_kmbgY@T|9-gcD z%`1ByDDFnPD#<3*}3T>{_6&%B!XyyFF1 z?ZcpuLeq(9qO5vjWFelD#CzW+3++-C+BnKGm`CPOuy|TuerX%?Y6asrJdvva=Ocd>N;zQk; z_<#VXk2BJ{CmZ-`kx#0k+^|j=II?jPj?Wr6rdA>MJiE=dd#&!03dzw#u;}9~s6E3& zFMJdZBx9f?@`}bsZNrG5a{Oo#r^3YTeq#>*a6u}v;=6)Jk8x?ORW@>0v) z?aVL{1*#8aUfl+LDEiQczV<)#vA<%T3gjvOAkb64GfPqwMM+J9kl3?lX3p%K`R2?w zXYS3-mEYL=%kR9hVSHgs{0h*w@QA-cFoT(~(J>pQNxBtV4O_npZKv%vidy#?9^Q6b z>XaK5ZC8k^ow>%`uurWquk9Avi;X2{PJE@a+*sDFF7&I7tETax!HUfL%wS&VwO0mq z;~Hv8tc;rSux6E6PmFryOZa8fO*J3viZt%Z(2qJ<%HqiN#9^rMk%JllB0 z4umw?W*cJ-umaL%7PDWN&#b=T7w|Z`)s@A*(Jl;Nb7Yd%H`ffj9eP{Pa$9D=kHQY8 zp`K#mWgf~dPyCPlUfokxk|~!52}?VwAi|Ihv1EY^l=-nTd#aL!ywjBd!K3UrOzIYy z#w}%L|1$U$eE0ZQ8_&~DxDhrt?{r1TH=YE0;je>zXrdYINAeD#J=C=-fr{85>9bHT zkceo2^{l5H5z60W3j~D+VVG5O3ID?z7eTqMJB9qiEQeDAV(ln9h+|B} z&SI&YW}FIy1z&`zo>DHBQB8pqJA}^uL!3qYikRWH3-HIfiqoM;*w=_J;ZpN)?j9%4CdG4@l=q_j( zXfa(p`Ty|UOHf33ocqn$*xtk#GqA<>kPaa^u!(@MC9EM@vu9on1makJ6htBvs`Rv% zg&%O9@<|~0D;R4Uh>8XRxXF;+K)Y}u@>w6=zI?WM$MMuC{^&QB?rH2WdM}@xKFYK3&oW3LeElce(tM#3h$>yoPWevb#veK>&jD1(cXrJrb z1CxIOtwvg65yy=!{;Vy)JN)n30vaq_R{V#xVjQ__ftCAiWI`{>c~~s;i^NfX0(%}g zD>jT^fr}o zT+3rfTi%Bys_%nhC_xbj2#aX+RzfA3Vw_by$ zJki}Ht`$0FRe3Ax>DUaoTJ)PBKc9ygC+-b+sH$l8ce_z6qeLh-PGKR`sxO|W-I!@p ze=kmVgV;}kPN+)02wEY1LDe4Vy#n^uTSQ&)TPg1ZL;6Yd!BO{eYNahhw6dpR;J2t) zp`mlE-pUJk|9g{#x2HE+F`vxAoA`tvOh?qRV0xwpvi-eLHEKoyRO4X}J=dkfo0o6F z;9v&_qvz{+`o1drekWz!ID}sH{XcYrm=N%NeiegqqBj02iPuQ{lmwlL6M{|=UW6b9 z6vVNf<++~il{`n+H&HeI(NVF5=i6i**CwX9`TpaR^oCfUel&=UXB*FVn{aUAFf9Mn z`BFt~9;^fkpC>^_{_v3DOH^@!qlS?1rc~^7TJXv_bN$*G0jKjF*qOeCJQ)4=kgoTxlZtmc4=UrqV@mCa=S8v)~oF~P3scvn#}nhp@I0vv_7)< zo3;-|nw=o4S1v*N1YdokTFnmsIs6-O)U5ok zB#AE=`a*^`{aDjW9oGLs5~#uho&_?g-p#neEmXX zhW!w}@1id!I5*9-x6X#pDrO_KKY9>uYq-x4Ie;pHoaK zZn3y+5K=9O{TTPiWSy?oxU33J zE6fSY#?^7#jJllAo4s6}K|w03c{i;J>eMK^!zL#wfx-?ph?{nWKur+}I+ slt(~s!vmdp=(*1VOL_0*S&X-BT1sLDEi?U=Gs1YNRc80#t5(s~oVyl}z+0DI6!~EbtxFHwmI#~oLNvv90vGLd-;=m=vK@r6Q}^7s zBL|(jt(r%D9pig}=gOAfT6bz4`P8fFbr`*d+v@9IL^G~}YtTSiy^qw#qL4;qrXR{HI~Ls!P~6{T;&F=nVKGMGOZkrfuMS&s3q>_c<)F`KT z(HR&Iv9|O@edmoMgT#6qeRy)b*U{zC@giD_Vx_2KJI*6wlX5NPkz|%t3gZzIl=4*) z95Dr}O60zbAGkiEQ29sOZSOrPLzzZFzDCasGf}d>&oc@W@+a~JP^r^1D{0zVqfZsK&c^X=s5J4d+s} zKbP`uRP$N_Ln%C|3c?#UekV{B7%J}k?TPrjIt?FwZU_q1hT($RK-zzi1=X;z zpp2gJiLq zt4iCSO_DcTUWX-`ucHwE7`-aPkM@jTC6P^6da8um&RWM0V!stBD`A!Dq!Vp~oj|au zvmS(NZs1V5Q+X$H>mK!>W^Z$)0{!6vrH(oEP=eGt-c~zsTTG0QvYCZY!J^u}&R9q) z7OF_EP5^r=`I9_1@iLc7g4y~Rx`@69F-S&b-PUcelP6k9o7FN%O*R(O2LIc6 zW!fTiw^l1fWjh?96uaX{`ecw7NW4zsB8fLiyhVc6zx)-6 zgVM|zy`w_;j(@ltp}7y5>=I_$TA^;lE_^OV}_T8hN|w6;xNHJGcnR{%&+CSHtV>9A1K@KUkn@tXWykd6nV znM+u2&lK6`gUI@0L(shR8M&&lYZ9`V)=Ehu@H#Cs&}k$9iP z2P8fuK}9`HBjqotl_)jSNO_;Mk4O+z882u=$67ces&*-=m?DacX`(o-05DIKRNv*V zNjxAiMhSmQZB-JVl6XSmNJ>bRmI6&Ep}~|e%akx{XC>XOoFz6|@9>_uBdP8vdqCG2 zGj+;I2>xD;N+q=DQozAi>%O}~AeRh13?Lfc?=Pbnvw&nAOl*BAfR;!)8)1LNr zFFkXz?pTG8GYKS^*gzG;gb-3tNgxEmA5#3tABrj{pej}HYv5mgP~rzg!5>Oh6g)4Gu{36+iyQ_f4uv|(9po~g)e{V!ueF{N2$moWX;l1ujj^Ti(zy5wjeScKr3vw;8~aO>^4+@@50vhPufK6{>7LR(5<39@q0%Ao z7vaCRbg%da;XhnDEdC+*?kz=f&WP9i1DWh8_I`Wvjg&oED=Z#g&y*fS$X)gUgdFfg9zw|7_CbUk^h2f*a*uro zA&2~shY@nGeHbB!{g6ixa-ThgkSRap0|+@{A4SMfKjcw_+;2aCkO%yb8H60Ok0a!` zA94aA584kQuPZ`La6{KwDWVe*wq({3%DZ6V%0 z)2JZPd|h77KmS6l(&WeYFE?8FYO6WF!cUZ!Dz2(uL&UH|s8+RHT~SwS2q^?1@Q;cA zic@yniYvuH;5`z!tZLP|d<&jRqha3HSw*Aq-iW;C)Ek#uD>c_;UXv1p*Xx%QGC;wC zQcUQWm8F#iBCgiT3za%+u0PVK&!aXha24G{{&-L0I)lp@hGScH`i)dO^^w%Ng@(w? znWRj{{)Il5at^^+K(bFxT({bmow|}%6Smb%x2?C+pU&dNwBn~d>*;U9@1de${H zfgo{iK6B!7YpHgkR(<@@6{n_7oUbg@UZ`Aw&#Bh0)ZIr}5#KLf7p>%+pj%$5p%tpm z%<}5Zfm038x7u1-e(D|;i)GBG)@IOTS8K{GH!7EE4X0dhx-B$@sy7#APBmJfobwbs z!K+dvP$|D!cGXI)Q%rRi>u|95*A51mrYuE&6_@#AVP%w8Fm9YI>PQtqG1n(#E`JEk_$8=hWPfE#F777T~0;L2DfU zYxnjbz>K6fWQg%QE2@bg)kx2I8FWh&AhX`&4nXW{8?k@XaN7HAH7yLq^MXos|wLRf)eXp^+tAH=lNjPt7? zEipZy?!`ydVLGIj%E9q6^L1Ap;UoD~9c5S_#ExCBx~d;f(;2C(m#KpOImS5Aitm$d zsQXj7O3_(!9>S~6035bR-kN~B_DFA9pc{9;-$fRdRLJrQJiW;RGUR=dkWn>BNSu5- zEexFjYX{F+FgTutM7a*L_BUH<36gZJc6PcyiDK#lD1)XLiKQN;6C#wFVZaGGM!adc zF)U;<-)9nM5RRtMC@3^)tsUQxM&{)%^5pRiAG}C(ck_MatbjH$+C9;hD6ctby37H%;}v|gc2}dkdQPhUas3UV1SjT>)dKO zj0zn>89bFhjE6QPM(AZj5mLkO$c1fFghSLq`g#WU4DQ*W3$-)tEchkc-HFr_$UuD% z4uocrhYSx!m%g%BQg1OQ_XUtZYD$CP@ z+Y(Z08U^*z)g^Tb9$&Wi7Fqp5Oy)z$8<$o`HY6S2GvuO(G)&lrP}wfI7W*NhFs!`> zRs<6cXt}hdI_OS5kfuDG~fZpd(6MFI{(Tj)%VJe+|*Ih{o+{+|)<4|5<%|&IIk- zGFuBr7o3$#)C(LLioJo^u zC_B)p8nz@YFEm=0DvffpvQ+c>%T8s!#xHooPfLw~OrKGzrM!LRc>)HvT)Vd1s5Aw$ zLbx4cMU1o}%32VrD2~%!k@;)VJ*FS@N1g~j{8&`7_30X2E8!E)aX3bn0XPg=1)#@^ zsln86DvRd=I@@J=TDxznCnU@90co;2 zO&C&nRz-Nsvo4{QG*!G@gHO<4UODy}9WjuTYj| zE0?{2a(Ss`uQY1#50=ZHUa2(Lpyjf95MNf0&^bxxaXLRv=O^g=6rB&#d6mx3(diUe zd{hM$l}rL^hd~%n@`X&HuTa3%R~W**C@HFlG=D@=v$&jdaBiIz2$gHx8x4r`%8O^t zbm{fXl>-*p&)HRVPjM<>x)ANYQwR;A=KJ~h08^$lJCL74Bo^wjjT*SRHM_}ZCAY28 zDJmz(n36+1On)LzYW86Mq#ks+yJI=;4J9t5l5~`~Azz6rFDnQ#MO7?3+yu#GD*(PX9WBo-Ymmd2(ePmBX5vpFGLy@Llr1KJ;m+26)D$Y)JjmH&+ zUZ5jIr$AI6qeBR)XD_ zfVxPB4D0!Ld9LL?Lo70#4*^Az(koQ1VO4>-h)?pPPiEC3jOpp=0x4YCuFuaqU&be# zQ8-~_ILc`yqo@VQV8-RtdFWk(0?C+txP&pcKcfVK7BCO#t7n{5dNbz})}KZ3GHYvV z5CHSYWV#py3T*o%0(yfQFuzi%V|vjW_;{tUQWFdw8{1R@S9+6D;d=8bCiiT=;uW>< z6=WA-)M0DQpv`V_#ojhVv+x5lG$V z>uf)Z&o8|575?4$uINXKjP}W(hFJFZarxl-pmi`bN!?xDN(faK6<00P zC+U2OP6R*;P8P(x#Mn>M=?R<5h`oYKc-6q>A^`J1p`aLB#by4|@Xg|KD7J4MWDXWi z0G%hiVn{TZR%OQQ4$>9gPpJ7@q*ehA1%a%GjD_mAtAP2t%$kG)V4etPx_7a%7SKf$ zIgF5#q=>-d@3Dpvcx-fR^dK!-dn{-4EUgN&(v>7bn;WAgEB zM8@AocH2S5`?_TUK*;+J6bprS0E+*Lq>8Qh?T-8PmCU&H_XWV-cW_Wd{?Q$4E}LjRN)(4 zR561K+hvEklPvh@C8$7u!k7B>Lf|@<0iz3}0zSb#BmU}t8T)J4YTM9{$U$CCAm?VI zus7j~@}hSd1bO{)oNg1{(AL*^x?eNI+hY)3dQ@)+|0C9xZJh#u2ZWFJnlO4F+%kH9 z9bZ(dbU5YF8^SBZeiN50s&+wmpm>0I##eEfKLg@VA!z3y9$P$Pn z=b)WSC_94|rAoxQr`uV&G_w|^Dj7sz>^h11b>e$fQ&MAspg z-$q?01B|d8UB?W{vF;MmdGO7S5P4Tqhl!JlT~mioAg_d}L(V8Ff9g;KM{nGLvkzUk z!)(oqS1~ku*uM$>_zVlM@lyMBB)Nf0gpvUq{a9jWOfu9shnSs1jp#;eSz8d9HDFqxtR4w8dARgOz4;FvCA}pcar78fP9ib(#Jc)Mv>w%0EJ%K>3UG z3H|HvO@}u~R~U7LPJ>Q2)K^@eJ4r&0!V@MW2G!7J=-wpjwPTzfl-)Toue8XkSe?bI)nFjbzxmKk%P}kTVGfYDUGDhrsEWsc2CC%f@2y_Y(7%m zjurG<0)vGVypj9x`de$NTW3D2WN1-ulAw7OagZSV{^hVQrBI@#Gha>NW}2b!e{~ZIvgHG@i!GS zCy}Vc+D0bn%pt;ft?wXVIqLYnI(+gT%|v|x+@*e*d}vnPf5imt1PPO;ZzB?}uxD%o z37h>XiM=KSf+?I0kkhkKyS3bmv*GG0vfJW8+l!>|mP7~Fuo@.>OS{zNitw$8j~ zajh`l4sflTNF=jYG?7TL%?U(7GMux4cBnG#birAzAs9+_3~x1`7j(y`6+n_)V03AMAaysNGNu8}tlkFn0&P$2bw9Gt?XVSv+i| zZXr`1ejB@eiv?^>ifu~r3g^vXd429BHnEmOfX+DjYbesDq@Wo4I!V#dQ^b&mStyDU zqiX=|$J>;%(!AI3nXQpP&Jm#6*kRB)5pP(&>qPwD)1W}YiFlkvmlN@A#1F}d_|L;P z96Avnr1+Yo;EDKeNs8!c^YDrI5u_OHIuY-eDp4s}Mrv3vik*n}bH-WsLz1^X5r15A z@0^J5q&h9B^ojU$k}6up@QL_X8KskiPsG1RN(qnesJGyBl#;hi&{&e-igsS%-RJqv zIXW-UnWOU}otNmmOozZeel$*c`UOUvrbC|G!qK>IlKGD#t)3hmgqYj#IJ!9m7VQ`z zEd-zJNIeQ0mv*LLpK6`=4Db3VO%UpG6HO*0^Q<{ciBpJ+X!G)Ji{`!b)cHXKgT%)e z8xd?gvhwGw5M^%4nzYu;`e&@Tq4n*7-s{_MG8a~1E9c3-h;(0M_QYpC;K}1uc^{U! z8CS(+{tVn{An4u0onH3DXfUueq|F4DhV8oniw=4QFP!OEo!eju{0Asuw`2?UMg>O0 zBzB7i#@qtgpo~LiI9HW>6Q;<@MHkWx_aU$Wr5KHq(E?N&);`2!8RACaEB1&RLmX^` zFfA^=H%|XLzr9c7gMTWJO6wui<_3oj?06m2rp&~&Uu}9K+DXYqNqYgc@vU@2DXs{V z(rR9D7${|&t5vlQO?!-b4laTB31id0$-{1$z`P3Ijr$r8aoHyES%->|%FvK02kWv#UnJ*nn$nKAah z0eGx|0eA=S&|L$4W>7c~rO&E_()TF1HiRU&5(ZEL(~$oVqVvqgWsbg@Tx3mjm-YaG zIStX}sAEvY%r16I6@t;`HL3qeY1L%l!JL67rg}YEB zWr#tMwDy<8LoUqRqN*?&=MkAkLe`>m&fhDDEJL~+wWyOIEVM%G2;bKV% z#=jQ(1vfSkKR`7@U4ySkAM7O2)>4q-ElGhvS-m4E;M2C2#FA1D7A*VQIkBXacL$MT zsGT>Kl(Nwp0QL{yytXc3fh7bwT*OYZ@*`PhLfa5$dS0l7IP@}jIO|JA) z^xt5IczUi>fY~m5)^Ra1v{=ezI%bSWmag6b8Ippfcx?v;MwcA--&uYwa#8ZfHXa@l z-Zxd?KyC!mj9&#%9yUyE+6T^@Vx`h<2DqKA!FI``m4d2e=WcLvKzh`T|Ha>Qp9~zZ zdDmKAjaJo+<398$^}ViI&4u!61l{`w&_J{DrFyfXR{xXrC6J@!mF+K^v{vU;Q&oJT zQ`Mt*2)7pef+um>-&(NN149UCxy*~r8q7Z=G@9yECs9Z7rGU5pbRW%9Th|H$0* zNN?@#cUZu$(m6>-=Cog>kBX%FI-NhD^M`c4O{b%9itjFl{RW)BU|EU(z=uOHBbLLJ z#WZ&j%3l^LU`|pcD}bl)+Rha~IZ|b;YMn90!aoXI;f68TquC|T;CfFsZcr>GX;Pc1 zJ9(76d#>Be%yn0FSJj(AmVz|Iz74Y$8_7SW+>qX^w$S`6Jl!gEctc`f_nE5ByR!(b zwa+8Stz!{dUF72!oUZB63PU^mEUe6KFv7~}Zf9&iKWqk*F8G3Jfz4K8J_y z`Ug`8@5S*dp~aSuSE#d=aehFsY#No1Eu1YiUVxA-v#z3yxO(5gI%fNGG$*&ZAX~sp2w! zNR^t!<=g_uVN4R8!|#x6FnK(O{E{U%ERK#!>JsVT>nP&j`jahs475?L9fThv9{gVSDi6X%Zv` zFU;yygd}&6#T{gUuC}q!);~oG^=IsnW>WEcCTJ(ff^2;ok;QZD65Bu)#@t4vv+P|^ zA#$QOsQ4HO5CRouVJ%ugGYL5dn@_K?l|D)5Q*<7r^9UW8;-D(C$x-d^BFV`>9ChId z#!Np(v+VOL_)RDpVn;fdJq?WU%Wehl?y@%DKch{4!r1Mv%*~4dO)e12*;(X?tj!9e7{U;k5jY^obc52& z6sM93eM*(es+>}(M{z;V6#kD-E@(s+87zt*XnrQP0$~)tpS5Ra4%_ z>iKE`DcvtN#;X(Z4a1*oOjW1ix2LNmS#zeor@Hr3#mP9?uM{Win)TVPUfqY1oMWQI zj7#>TWX#ES`T*b)no)cQi+T zrasgI0#!i9*X|jdo~NZ3okPBN-3jN{%DPQ$=DB+pYriRGR!*OIrzyK!-Gm*->I`I-*M~hWD zT0KRZ!`{Q&w#QvA#_q1Q+#8HF89Rg?*kOo@7FpI?R6x#>GOvQv2Q(bI&JjG~S%_%z z>nC=ebo62}Lqd)kkcRdc;EtH#kfDKCw669LhPI)HW__$fWMXvmjz z;7oKfOR6(T0=;&RQts;dQT{X~%5|bLwWew02`?IWp6l!Fh z++Va=Wh3iMIr&c3Q<2VvQ>ZcB$q}Xf9%Q?biN@5?odV218kb4qdam+Es9SKR<;Y5# zM}`_j*)vA;TxGMe?@gmat$A4sqe86!Y#%Rw`IIeY9x zYwb*hm!o`{vhx+r2xyUQwnNKal_a!w228XzFO-)Pku}Mx=PI0v&w-ib8=A*omQqc| z&EfI8Xb`ufxUouH^kN(~|SFERnaYM$>8gF49HI z`c2#RNduO}X3#0yN8)V~?~pi8qR$R8MQaYrK;&_85~gNmP2C(bGqSvfqU1;Xb{Q8Z z(03oKfZ_6DUut^u*Ke_A-L27bpHK@muH(8!9lX)_gb{;nUW;hKcHcd5)nR>w;I5v$YFrc zSHB~n%ZN~^=ezm;ho|Lf{*(Lbsv@^hzS0;97CVr`?aphS`oqDl(mLOnkM*7 zBi%&sl5e+Vcgat%Oo}qtdn8Uk46{4?J_=)YXGGZB)B7cA=W_H)Oz&5cqM2RLJ3B?y zDJ~JSNenmdSgpj}ne?qWs&<+LIS>1Q#5{?!BxozdK7>e9Gb8)%Q#1P^Wj`YEBN9V? zNnA(eL-bo3px@av{l2}m%OLi%R}-G1051uF7LoZbAyCg_XovUA=mkc-QiO+Om-30U zjZA9s7v`N7sFT?~LU5zjuv?Y!Zv_pzia|>pgjQ=X;@gVRUjtK$9#pCr3u!M!JxY!y zB>OAdth)ZaZBLJ??w2UqAg?rwac1pMxoEa=-dqj!mdEfRu>|ouS z_M+?a#5!m#O|}4Jvb4)`^kscR?zd!0a|m(l7&~f51M3lMDs`=6I7(NGw*;95p_~9`kLdx0{K2h0a4J#@yAK+oCwGl8q>2P~9R|YX+ga8VbF^ zmxuL!Q0m!qX8#fHr^y2S6&H^ zD<>037!g~vc}lQg_o$4FY%~h&3wln|9!B8M00QCDW*-6Wp?(5E5k3$@Ft-x~gUyV_ zV1!2)(jzh(`mQ`8yQN3uhCM>4*Cj=)-yQ^E7p`!!yx0ySXTg3O4%H4E_)F|X=gZe) zM|r)d>>ogd{G#D;4nGdlkPrjMki5SWB&jw=V zb0p?ozxUc)RNW@s-68Qy5>*lpNl;kD9+9v~(AFsx67+XlpW*?97o6f_ z?6&Avi2q12dQJzC4Se^ptjb|Jid<^rqaPhU64wZ`cVa8I_~L*So$<~^+Y|(hbIg6p zyb$NLNs-9KWo%A1(GKXd1rMt;(I-ASQkWw_-|~dnOGP`PDZb+4OIk2T-xU$Ub0#f_ zh^e|adFIM5O5CH6HTwFLfjhO< Date: Thu, 9 Apr 2020 19:06:51 +0000 Subject: [PATCH 2/2] Added class_names optional arg to plot_lift_curve(). class_names is used for the plot's legend. --- scikitplot/__pycache__/metrics.cpython-36.pyc | Bin 38947 -> 39361 bytes scikitplot/metrics.py | 11 ++++++++--- 2 files changed, 8 insertions(+), 3 deletions(-) diff --git a/scikitplot/__pycache__/metrics.cpython-36.pyc b/scikitplot/__pycache__/metrics.cpython-36.pyc index 9fe54bff312894a2b654191b549c67fcec15a6be..73871365a62867a0cf568aa91dff271cfe2d4638 100644 GIT binary patch delta 3640 zcmdT{ZERcB8TN4#$GJ}1P{&D=(8yWR3>OmmK?`Hi((pN;b`Yc#HIy62_r|{Q$92!W zY4c&n9b0MAu!4F*t9GT^H1VV3tHm&W3OM0i9`s76ZDCxovqO?9mlA)l5XCL z1?*$Vt`;)PX1h8&cx^Hqp{5$wC+Pzi>;}kp5t@V@VZZMBAvu{hzy3FmYYd(?wyE=a zFExwdX<-G3$otHW>)Q{)O#v3%RTQIzv^K19_Rk&LyUUhD<8UVsOt@RDO4W>ry|^?C zrP%%(9^7yUCJ`{TCf?mGUMH8zyqmARjvj{FlQ0kK)K86Q%nmr|1meFYAkap8U{3<3 z+2tWG$*|zg?YT$b_%5IZFayW|jsWfedzb8|gjB}?C<|xT?P_fphE-#mDw5OhWAHIHw5zpU z7HS^Pj{}|nEY^_0jq zr+CG;08;?Ts25gFlKlufxclnd>ipGL@JCC$l<5{ZK?zsv7}c$qnb0g=m5$ikByQ(* zaZ9!Aux(gkyYcnO@PyEkaHJwFx*E0%RIV|!iGsSM2qUbke~?_rPxs&MamoD`V{h*X zy2r$eq^Mhi`Vwc42K(Ek4p-IpXTcjD0DHV@M7Nepv9*#m$yf#plX{aZdhl41WE~!} z0Ldpi8d`s#B;D0{^;Ysahdd>6D?0;5kFzgATRMkfN{O9-1re5d!{X*-eFG=Ri}`m4 zK2==%5YWa>%r@2v)zeS*?u%&@3& z7nx?4j7_e>D%pF+9XTk8yO-J!{jqo~Y}tNTeSlJ5YvklXzAlm&w``rN@@%tYgQGt~ znyQGIo?MX$?o8;sartDGof-39#n)LzB4&>3wk`JMp(tmsjkS2e6t9_7WgnMparWo2 zwk?RB!bx2C3Ij3Y>9kI-6y)}}=6VN9cqM->o~Um}FA^dto-pP7A}qxMnPM4F0gm9y*YU-923sKRZ=h9lcwfK*isXs z`S99EVpL6qle$IU#YCJBD{@BVK9ZUlMx1PSzEK*E*oK)>%~3UNT83>L64#H0q#jPG zY08excu^_14*AE25zh(&)MPiX!T-EU2% zA~9;FL=`pvVZ)BeXj4Xs7cFXWSvn)-pA<_2i#>{x|7&j_$?raDGEI3#PiVkfu~bGiB%upp?oZof2r&-Ms2nwcQu(|>Ty4d<`Unm z)v7ge)Yfv-Sh*-a|hVr`Fi4Hv-1Pa9(Vr3 z`QHEM(zCrMKPMq}JKN13n{94iK?6_*?uPV3_6BSIHm?G!OHJ{27+vv-SpO9YNl1m< zEpn9I{Qfsx(BEUW+}?kH^%3BYfGgDTqlJbBTIFz9H;ZA%O$t$*3UNEC*jo!*$tUdZ z3!8H|LKpv|Agn+Q)#3P)fKZ4Uu$urI1q5p4;NW2B%AsodRAEXn`ht_n+%e3op+7~I zPYW?+X>oK0+;a3krFGMnFz;mnVz}~q+#&i3hJPjC%25~npx`flQYhe}mzVpTju2}; z^$^+54xO@yI3`YgLty9Wj$1TjJ_$GnxBz$&kO#=K;eA*i13m$i_NBBUZ0_`v)k6;A GApZtmG??rF delta 3526 zcma)8U2Ggz72do5JL@EF*6YM}BBfP`oz#6uB?``umd zuD959C4WBmoOAEYeD^!&-nYx{8$WhSd)KX#7Sm6h+5PiZrH|S=WobfJHDykthAD@I zoiwKMmMODn^tddySaeELlG%i5YNotVjIxPwEvx8xRh!|ymIBq(dWIOS$Z&jmE|J#c zmKnv;lNlwKpc%gWo6&Ca0Q+O~rhm-ka?KrM=ZA*Ne`{}V37_U;dIq4d^?i9V%8Gpl zdmexT5OX{3J9jx`CPo}jsz8O4%It%_-;y+YzW)SCvGbRr?B64ya%A8z={$~^oqz$r z{eZ6kMpPs0v$Vzu)!(nJ*jL{}J{l;OS;t z`tOR+R4qNttr+3!{$TH2y%TWjfJwN$6w%7E8CY5Nuf2PAHhdNxhdYU6;sp7ZDXN~d zs-K6D#^ik`H|Agx4s)K&&Yk>mwa;adf!pt(d4$4Q<6({VP(3|i#oW}uoX>EGb<;ax z7XXy~X)HoaHnM+DX%>z-KmafWm?DyZc`bJ@?3jl15%JnLT@;NS-W550-*{v3F^9ZLt4Vu~j>G#`0E)U8-!Z5Y*oX zcYJYea-)sp;#pq+JOmKbMqo_~)@Rv6hi@&dO=N2mk+e=wgNU&vxN#(Ff@vQP92Y~&Qzo(6e+W9L!)G=e0Jzd zVZSIyh21!8R`|HTvP0olHB?=Fz9&4HFKpdyPByJteN;YAwI!m z7{j_gTQIZ$f>U-ezTtFT$7^#NY3BAk)~OTR+$s>gz&?ua=!ftsdb2=tRji95MaKWpfd5SA^Gg)9Ck8Nq_3!)pWO=^2HsgL8)mjVBW zS)@Dx5OglW5+tkm)ltU*A%^R+wVq`W?v zoiZ(rD&iD0MGK_g$1>ieQJ7to3CEq)e&fnO0z6pu+wRoy)oGt z0aJ2dRuP?EuqCsr9jzZ=OV2Q!r@b#c2+vu&fYpVN?tEVOV^3}N}&AO`TKklCdv9UyZNK7E zK9-K^DXV9AicV`ubOrp-Yhe%bKa&#Ha%3wTKe)AI`E8d?RFCSp=vKWccZbUksNN3O zBc4<4Vz3xm5%*Q&zFJ(egQ^d6J6y$}9je(OTdLWT9kPR6t|Iq||B}t0mI>1Lt-$0k zru%HK>R-eI!$ptnnGD%(+ouK=-NeO5d=g(csD>te)mI~`wCJtHB)04Fc0$<6%g5Z4 zf$}St57oAv{e^wVoNpZ_ad!MtM`@M)=ZuJ}7<~g)+i{I1wYyz72Q>&@2n?b2f;pTb94P zl7t@eUM>am01vtLNkno>Eb+`P@-BPonSm0{s_MO+Td@E=1;F@@(k{;WxwL4Vr>O81N0hJ`AsF~LD-c<+&5!E5&d7w2I=>Z`w|BpZhoI& zoW6|kYaBiq_0wNe{Iyq#$9(kHD}BVpe)jAs;;^=>CXw0v)z5N#=W5@PF)Ta}coMJ# gcoA>|Ao}m?uuyyST|m7yg(b7?OHcbwxQL7V7h_mRNdN!< diff --git a/scikitplot/metrics.py b/scikitplot/metrics.py index 846ba9e..cc0a532 100644 --- a/scikitplot/metrics.py +++ b/scikitplot/metrics.py @@ -1137,7 +1137,7 @@ def plot_cumulative_gain(y_true, y_probas, title='Cumulative Gains Curve', def plot_lift_curve(y_true, y_probas, title='Lift Curve', ax=None, figsize=None, title_fontsize="large", - text_fontsize="medium"): + text_fontsize="medium", class_names = None): """Generates the Lift Curve from labels and scores/probabilities The lift curve is used to determine the effectiveness of a @@ -1169,6 +1169,10 @@ def plot_lift_curve(y_true, y_probas, title='Lift Curve', text_fontsize (string or int, optional): Matplotlib-style fontsizes. Use e.g. "small", "medium", "large" or integer-values. Defaults to "medium". + + class_names (list of strings, optional): List of class names. Used for + the legend. Order should be synchronized with the order of classes + in y_probas. Returns: ax (:class:`matplotlib.axes.Axes`): The axes on which the plot was @@ -1191,6 +1195,7 @@ def plot_lift_curve(y_true, y_probas, title='Lift Curve', y_probas = np.array(y_probas) classes = np.unique(y_true) + if class_names is None: class_names = classes if len(classes) != 2: raise ValueError('Cannot calculate Lift Curve for data with ' '{} category/ies'.format(len(classes))) @@ -1213,8 +1218,8 @@ def plot_lift_curve(y_true, y_probas, title='Lift Curve', ax.set_title(title, fontsize=title_fontsize) - ax.plot(percentages, gains1, lw=3, label='Class {}'.format(classes[0])) - ax.plot(percentages, gains2, lw=3, label='Class {}'.format(classes[1])) + ax.plot(percentages, gains1, lw=3, label='Class {}'.format(class_names[0])) + ax.plot(percentages, gains2, lw=3, label='Class {}'.format(class_names[1])) ax.plot([0, 1], [1, 1], 'k--', lw=2, label='Baseline')