Metadata-Version: 1.2
Name: lazypredict
Version: 0.2.7
Summary: Lazy Predict help build a lot of basic models without much code and helps understand which models works better without any parameter tuning
Home-page: https://github.com/shankarpandala/lazypredict
Author: Shankar Rao Pandala
Author-email: shankar.pandala@live.com
License: MIT license
Description: ============
        Lazy Predict
        ============
        
        
        .. image:: https://img.shields.io/pypi/v/lazypredict.svg
                :target: https://pypi.python.org/pypi/lazypredict
        
        .. image:: https://img.shields.io/travis/shankarpandala/lazypredict.svg
                :target: https://travis-ci.org/shankarpandala/lazypredict
        
        .. image:: https://readthedocs.org/projects/lazypredict/badge/?version=latest
                :target: https://lazypredict.readthedocs.io/en/latest/?badge=latest
                :alt: Documentation Status
        
        .. image:: https://pepy.tech/badge/lazypredict
             :target: https://pepy.tech/project/lazypredict
             :alt: Downloads
        
        
        Lazy Predict help build a lot of basic models without much code and helps understand which models works better without any parameter tuning
        
        
        * Free software: MIT license
        * Documentation: https://lazypredict.readthedocs.io.
        
        =====
        Usage
        =====
        
        To use Lazy Predict in a project::
        
            import lazypredict
        
        ==============
        Classification
        ==============
        
        Example ::
        
            from lazypredict.Supervised import LazyClassifier
            from sklearn.datasets import load_breast_cancer
            from sklearn.model_selection import train_test_split
            data = load_breast_cancer()
            X = data.data
            y= data.target
            X_train, X_test, y_train, y_test = train_test_split(X, y,test_size=.5,random_state =123)
            clf = LazyClassifier(verbose=0,ignore_warnings=True, custom_metric=None)
            models,predictions = clf.fit(X_train, X_test, y_train, y_test)
            models
        
        
            | Model                          |   Accuracy |   Balanced Accuracy |   ROC AUC |   F1 Score |   Time Taken |
            |:-------------------------------|-----------:|--------------------:|----------:|-----------:|-------------:|
            | LinearSVC                      |   0.989474 |            0.987544 |  0.987544 |   0.989462 |    0.0150008 |
            | SGDClassifier                  |   0.989474 |            0.987544 |  0.987544 |   0.989462 |    0.0109992 |
            | MLPClassifier                  |   0.985965 |            0.986904 |  0.986904 |   0.985994 |    0.426     |
            | Perceptron                     |   0.985965 |            0.984797 |  0.984797 |   0.985965 |    0.0120046 |
            | LogisticRegression             |   0.985965 |            0.98269  |  0.98269  |   0.985934 |    0.0200036 |
            | LogisticRegressionCV           |   0.985965 |            0.98269  |  0.98269  |   0.985934 |    0.262997  |
            | SVC                            |   0.982456 |            0.979942 |  0.979942 |   0.982437 |    0.0140011 |
            | CalibratedClassifierCV         |   0.982456 |            0.975728 |  0.975728 |   0.982357 |    0.0350015 |
            | PassiveAggressiveClassifier    |   0.975439 |            0.974448 |  0.974448 |   0.975464 |    0.0130005 |
            | LabelPropagation               |   0.975439 |            0.974448 |  0.974448 |   0.975464 |    0.0429988 |
            | LabelSpreading                 |   0.975439 |            0.974448 |  0.974448 |   0.975464 |    0.0310006 |
            | RandomForestClassifier         |   0.97193  |            0.969594 |  0.969594 |   0.97193  |    0.033     |
            | GradientBoostingClassifier     |   0.97193  |            0.967486 |  0.967486 |   0.971869 |    0.166998  |
            | QuadraticDiscriminantAnalysis  |   0.964912 |            0.966206 |  0.966206 |   0.965052 |    0.0119994 |
            | HistGradientBoostingClassifier |   0.968421 |            0.964739 |  0.964739 |   0.968387 |    0.682003  |
            | RidgeClassifierCV              |   0.97193  |            0.963272 |  0.963272 |   0.971736 |    0.0130029 |
            | RidgeClassifier                |   0.968421 |            0.960525 |  0.960525 |   0.968242 |    0.0119977 |
            | AdaBoostClassifier             |   0.961404 |            0.959245 |  0.959245 |   0.961444 |    0.204998  |
            | ExtraTreesClassifier           |   0.961404 |            0.957138 |  0.957138 |   0.961362 |    0.0270066 |
            | KNeighborsClassifier           |   0.961404 |            0.95503  |  0.95503  |   0.961276 |    0.0560005 |
            | BaggingClassifier              |   0.947368 |            0.954577 |  0.954577 |   0.947882 |    0.0559971 |
            | BernoulliNB                    |   0.950877 |            0.951003 |  0.951003 |   0.951072 |    0.0169988 |
            | LinearDiscriminantAnalysis     |   0.961404 |            0.950816 |  0.950816 |   0.961089 |    0.0199995 |
            | GaussianNB                     |   0.954386 |            0.949536 |  0.949536 |   0.954337 |    0.0139935 |
            | NuSVC                          |   0.954386 |            0.943215 |  0.943215 |   0.954014 |    0.019989  |
            | DecisionTreeClassifier         |   0.936842 |            0.933693 |  0.933693 |   0.936971 |    0.0170023 |
            | NearestCentroid                |   0.947368 |            0.933506 |  0.933506 |   0.946801 |    0.0160074 |
            | ExtraTreeClassifier            |   0.922807 |            0.912168 |  0.912168 |   0.922462 |    0.0109999 |
            | CheckingClassifier             |   0.361404 |            0.5      |  0.5      |   0.191879 |    0.0170043 |
            | DummyClassifier                |   0.512281 |            0.489598 |  0.489598 |   0.518924 |    0.0119965 |
            
        ==========
        Regression
        ==========
        
        Example ::
        
            from lazypredict.Supervised import LazyRegressor
            from sklearn import datasets
            from sklearn.utils import shuffle
            import numpy as np
            boston = datasets.load_boston()
            X, y = shuffle(boston.data, boston.target, random_state=13)
            X = X.astype(np.float32)
            offset = int(X.shape[0] * 0.9)
            X_train, y_train = X[:offset], y[:offset]
            X_test, y_test = X[offset:], y[offset:]
            reg = LazyRegressor(verbose=0,ignore_warnings=False, custom_metric=None )
            models,predictions = reg.fit(X_train, X_test, y_train, y_test)
        
        
            | Model                         |   R-Squared |     RMSE |   Time Taken |
            |:------------------------------|------------:|---------:|-------------:|
            | SVR                           |   0.877199  |  2.62054 |    0.0330021 |
            | RandomForestRegressor         |   0.874429  |  2.64993 |    0.0659981 |
            | ExtraTreesRegressor           |   0.867566  |  2.72138 |    0.0570002 |
            | AdaBoostRegressor             |   0.865851  |  2.73895 |    0.144999  |
            | NuSVR                         |   0.863712  |  2.7607  |    0.0340044 |
            | GradientBoostingRegressor     |   0.858693  |  2.81107 |    0.13      |
            | KNeighborsRegressor           |   0.826307  |  3.1166  |    0.0179954 |
            | HistGradientBoostingRegressor |   0.810479  |  3.25551 |    0.820995  |
            | BaggingRegressor              |   0.800056  |  3.34383 |    0.0579946 |
            | MLPRegressor                  |   0.750536  |  3.73503 |    0.725997  |
            | HuberRegressor                |   0.736973  |  3.83522 |    0.0370018 |
            | LinearSVR                     |   0.71914   |  3.9631  |    0.0179989 |
            | RidgeCV                       |   0.718402  |  3.9683  |    0.018003  |
            | BayesianRidge                 |   0.718102  |  3.97041 |    0.0159984 |
            | Ridge                         |   0.71765   |  3.9736  |    0.0149941 |
            | LinearRegression              |   0.71753   |  3.97444 |    0.0190051 |
            | TransformedTargetRegressor    |   0.71753   |  3.97444 |    0.012001  |
            | LassoCV                       |   0.717337  |  3.9758  |    0.0960066 |
            | ElasticNetCV                  |   0.717104  |  3.97744 |    0.0860076 |
            | LassoLarsCV                   |   0.717045  |  3.97786 |    0.0490005 |
            | LassoLarsIC                   |   0.716636  |  3.98073 |    0.0210001 |
            | LarsCV                        |   0.715031  |  3.99199 |    0.0450008 |
            | Lars                          |   0.715031  |  3.99199 |    0.0269964 |
            | SGDRegressor                  |   0.714362  |  3.99667 |    0.0210009 |
            | RANSACRegressor               |   0.707849  |  4.04198 |    0.111998  |
            | ElasticNet                    |   0.690408  |  4.16088 |    0.0190012 |
            | Lasso                         |   0.662141  |  4.34668 |    0.0180018 |
            | OrthogonalMatchingPursuitCV   |   0.591632  |  4.77877 |    0.0180008 |
            | ExtraTreeRegressor            |   0.583314  |  4.82719 |    0.0129974 |
            | PassiveAggressiveRegressor    |   0.556668  |  4.97914 |    0.0150032 |
            | GaussianProcessRegressor      |   0.428298  |  5.65425 |    0.0580051 |
            | OrthogonalMatchingPursuit     |   0.379295  |  5.89159 |    0.0180039 |
            | DecisionTreeRegressor         |   0.318767  |  6.17217 |    0.0230272 |
            | DummyRegressor                |  -0.0215752 |  7.55832 |    0.0140116 |
            | LassoLars                     |  -0.0215752 |  7.55832 |    0.0180008 |
            | KernelRidge                   |  -8.24669   | 22.7396  |    0.0309792 |
        
        
        .. warning::
            Regression and Classification are replaced with LazyRegressor and LazyClassifier.
            Regression and Classification classes will be removed in next release
        
        
        
        
        =======
        History
        =======
        
        0.2.7 (2020-07-09)
        ------------------
        
        * Removed catboost regressor and classifier
        
        0.2.6 (2020-01-22)
        ------------------
        
        * Added xgboost, lightgbm, catboost regressors and classifiers
        
        0.2.5 (2020-01-20)
        ------------------
        
        * Removed troublesome regressors from list of CLASSIFIERS
        
        0.2.4 (2020-01-19)
        ------------------
        
        * Removed troublesome regressors from list of REGRESSORS
        * Added feature to input custom metric for evaluation
        * Added feature to return predictions as dataframe
        * Added model training time for each model
        
        0.2.3 (2019-11-22)
        ------------------
        
        * Removed TheilSenRegressor from list of REGRESSORS
        * Removed GaussianProcessClassifier from list of CLASSIFIERS
        
        
        0.2.2 (2019-11-18)
        ------------------
        
        * Fixed automatic deployment issue.
        
        0.2.1 (2019-11-18)
        ------------------
        
        * Release of Regression feature.
        
        0.2.0 (2019-11-17)
        ------------------
        
        * Release of Classification feature.
        
        0.1.0 (2019-11-16)
        ------------------
        
        * First release on PyPI.
        
        
Keywords: lazypredict
Platform: UNKNOWN
Classifier: Development Status :: 2 - Pre-Alpha
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Requires-Python: >=3.6, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*
