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            "Development Status :: 3 - Alpha",
            "Intended Audience :: Developers",
            "License :: OSI Approved :: MIT License",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.5",
            "Programming Language :: Python :: 3.6",
            "Topic :: Scientific/Engineering :: Visualization"
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        "description": "[![CircleCI](https://circleci.com/gh/sbarton272/mlplot.svg?style=svg)](https://circleci.com/gh/sbarton272/mlplot)\n\n# mlplot\n\nMachine learning evaluation plots using [matplotlib](https://matplotlib.org/) and [sklearn](http://scikit-learn.org/).\n\n## Install\n\n```\npip install mlplot\n```\n\nML Plot runs with python 3.5 and above! (using format strings and type annotations)\n\n## Contributing\n\nCreate a PR!\n\n# Plots\n\nWork was inspired by [sklearn model evaluation](http://scikit-learn.org/stable/modules/evaluation.html).\n\n## Classification\n\n### ROC with AUC number\n\n```\nfrom mlplot.evaluation import ClassificationEvaluation\neval = ClassificationEvaluation(y_true, y_pred, class_names, model_name)\neval.roc_curve()\n```\n\nhttps://github.com/sbarton272/mlplot/blob/master/tests/output/tests.evaluation.test_classification.test_calibration.png?raw=true\n![ROC plot](https://raw.githubusercontent.com/sbarton272/mlplot/master/tests/output/tests.evaluation.test_classification.test_roc_curve.png)\n\n### Calibration\n\n```\nfrom mlplot.evaluation import ClassificationEvaluation\neval = ClassificationEvaluation(y_true, y_pred, class_names, model_name)\neval.calibration()\n```\n\n![calibration plot](https://raw.githubusercontent.com/sbarton272/mlplot/master/tests/output/tests.evaluation.test_classification.test_calibration.png)\n\n### Precision-Recall\n\n```\nfrom mlplot.evaluation import ClassificationEvaluation\neval = ClassificationEvaluation(y_true, y_pred, class_names, model_name)\neval.precision_recall(x_axis='recall')\neval.precision_recall(x_axis='thresold')\n```\n\n![precision recall curve plot](https://raw.githubusercontent.com/sbarton272/mlplot/master/tests/output/tests.evaluation.test_classification.test_precision_recall_regular.png)\n\n![precision recall threshold plot](https://raw.githubusercontent.com/sbarton272/mlplot/master/tests/output/tests.evaluation.test_classification.test_precision_recall_threshold.png)\n\n### Distribution\n\n```\nfrom mlplot.evaluation import ClassificationEvaluation\neval = ClassificationEvaluation(y_true, y_pred, class_names, model_name)\neval.distribution()\n```\n\n![distribution plot](https://raw.githubusercontent.com/sbarton272/mlplot/master/tests/output/tests.evaluation.test_classification.test_distribution.png)\n\n### Confusion Matrix\n\n```\nfrom mlplot.evaluation import ClassificationEvaluation\neval = ClassificationEvaluation(y_true, y_pred, class_names, model_name)\neval.confusion_matrix(threshold=0.5)\n```\n\n![confusion matrix](https://raw.githubusercontent.com/sbarton272/mlplot/master/tests/output/tests.evaluation.test_classification.test_confusion_matrix.png)\n\n### Classification Report\n\n```\nfrom mlplot.evaluation import ClassificationEvaluation\neval = ClassificationEvaluation(y_true, y_pred, class_names, model_name)\neval.report_table()\n```\n\n![classification report](https://raw.githubusercontent.com/sbarton272/mlplot/master/tests/output/tests.evaluation.test_classification.test_report_table.png)\n\n## Regression\n\n### Scatter Plot\n\n```\nfrom mlplot.evaluation import RegressionEvaluation\neval = RegressionEvaluation(y_true, y_pred, class_names, model_name)\neval.scatter()\n```\n\n![scatter plot](https://raw.githubusercontent.com/sbarton272/mlplot/master/tests/output/tests.evaluation.test_regression.test_scatter.png)\n\n### Residuals Plot\n\n```\nfrom mlplot.evaluation import RegressionEvaluation\neval = RegressionEvaluation(y_true, y_pred, class_names, model_name)\neval.residuals()\n```\n\n![scatter plot](https://raw.githubusercontent.com/sbarton272/mlplot/master/tests/output/tests.evaluation.test_regression.test_residuals.png)\n\n### Residuals Histogram\n\n```\nfrom mlplot.evaluation import RegressionEvaluation\neval = RegressionEvaluation(y_true, y_pred, class_names, model_name)\neval.residuals_histogram()\n```\n\n![scatter plot](https://raw.githubusercontent.com/sbarton272/mlplot/master/tests/output/tests.evaluation.test_regression.test_residuals_histogram.png)\n\n### Regression Report\n\n```\nfrom mlplot.evaluation import RegressionEvaluation\neval = RegressionEvaluation(y_true, y_pred, class_names, model_name)\neval.report_table()\n```\n\n![report table](https://raw.githubusercontent.com/sbarton272/mlplot/master/tests/output/tests.evaluation.test_regression.test_report_table.png)\n\n## Forecasts\n\n- TBD\n\n## Rankings\n\n- TBD\n\n# Development\n\n## Publish to pypi\n\n```\npython setup.py sdist bdist_wheel\ntwine upload --repository-url https://upload.pypi.org/legacy/ dist/*\n```\n\n## Design\n\nBasic interface thoughts\n```\nfrom mlplot.evaluation import ClassificationEvaluation\nfrom mlplot.evaluation import RegressorEvaluation\nfrom mlplot.evaluation import MultiClassificationEvaluation\nfrom mlplot.evaluation import MultiRegressorEvaluation\nfrom mlplot.evaluation import ModelComparison\nfrom mlplot.feature_evaluation import *\n\neval = ClassificationEvaluation(y_true, y_pred)\nax = eval.roc_curve()\nauc = eval.auc_score()\nf1_score = eval.f1_score()\nax = eval.confusion_matrix(threshold=0.7)\n```\n\n- ModelEvaluation base class\n- ClassificationEvaluation class\n    - take in y_true, y_pred, class names, model_name\n- RegressorEvaluation class\n- MultiClassificationEvaluation class\n- ModelComparison\n    - takes in two evaluations of the same type\n\n# TODO\n\n- Fix distribution plot, make lines\n- Add legend with R2 to regression plots\n- Add tests for regression comparison\n- Split apart files for comparison classes\n- Add comparisons to README\n\n\n",
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