Metadata-Version: 2.1
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
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.*
Requires-Dist: Click (>=7.0)

============
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.



