lazypredict-nightly

v0.3.2

[Updated] Lazy Predict help build a lot of basic models without much code and helps understand which models works better without any parameter tuning For more information about how to use this package see README

Latest version published 7 months ago

License: MIT

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GitHub Stars: 15
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Contributors: 20

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Release Date: Mar 25, 2024
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License

MIT

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Open Issues: 3
Open PR: 0
Last Release: 7 months ago
Last Commit: 7 months ago

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Package

Python Versions Compatibility: >=3.6

Age: 8 months
Latest Release: 7 months ago
Dependencies: 7 Direct / 17 Total
Versions: 3
Maintainers: 1
Wheels: OS Independent

Readme

Lazy Predict

[Nightly Updated] Lazy Predict 2.0 to help you benchmark models without much code and understand what works better without any hyper-parameter tuning.

Coming soon

[ ] LLM based task benchmarking
- [ ] Text Classification
- [ ] Token Classification
- [ ] Text Summarization
- [ ] Text Similarity
[ ] Stats model benchmarking

Getting started

To install Lazy Predict Nightly:

pip install lazypredict-nightly

To use Lazy Predict in a project:

import lazypredict

Classification

    from lazypredict 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)

    print(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

    from lazypredict 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)

    print(models)


    | Model                         | Adjusted R-Squared | R-Squared |  RMSE | Time Taken |
    |:------------------------------|-------------------:|----------:|------:|-----------:|
    | SVR                           |               0.83 |      0.88 |  2.62 |       0.01 |
    | BaggingRegressor              |               0.83 |      0.88 |  2.63 |       0.03 |
    | NuSVR                         |               0.82 |      0.86 |  2.76 |       0.03 |
    | RandomForestRegressor         |               0.81 |      0.86 |  2.78 |       0.21 |
    | XGBRegressor                  |               0.81 |      0.86 |  2.79 |       0.06 |
    | GradientBoostingRegressor     |               0.81 |      0.86 |  2.84 |       0.11 |
    | ExtraTreesRegressor           |               0.79 |      0.84 |  2.98 |       0.12 |
    | AdaBoostRegressor             |               0.78 |      0.83 |  3.04 |       0.07 |
    | HistGradientBoostingRegressor |               0.77 |      0.83 |  3.06 |       0.17 |
    | PoissonRegressor              |               0.77 |      0.83 |  3.11 |       0.01 |
    | LGBMRegressor                 |               0.77 |      0.83 |  3.11 |       0.07 |
    | KNeighborsRegressor           |               0.77 |      0.83 |  3.12 |       0.01 |
    | DecisionTreeRegressor         |               0.65 |      0.74 |  3.79 |       0.01 |
    | MLPRegressor                  |               0.65 |      0.74 |  3.80 |       1.63 |
    | HuberRegressor                |               0.64 |      0.74 |  3.84 |       0.01 |
    | GammaRegressor                |               0.64 |      0.73 |  3.88 |       0.01 |
    | LinearSVR                     |               0.62 |      0.72 |  3.96 |       0.01 |
    | RidgeCV                       |               0.62 |      0.72 |  3.97 |       0.01 |
    | BayesianRidge                 |               0.62 |      0.72 |  3.97 |       0.01 |
    | Ridge                         |               0.62 |      0.72 |  3.97 |       0.01 |
    | TransformedTargetRegressor    |               0.62 |      0.72 |  3.97 |       0.01 |
    | LinearRegression              |               0.62 |      0.72 |  3.97 |       0.01 |
    | ElasticNetCV                  |               0.62 |      0.72 |  3.98 |       0.04 |
    | LassoCV                       |               0.62 |      0.72 |  3.98 |       0.06 |
    | LassoLarsIC                   |               0.62 |      0.72 |  3.98 |       0.01 |
    | LassoLarsCV                   |               0.62 |      0.72 |  3.98 |       0.02 |
    | Lars                          |               0.61 |      0.72 |  3.99 |       0.01 |
    | LarsCV                        |               0.61 |      0.71 |  4.02 |       0.04 |
    | SGDRegressor                  |               0.60 |      0.70 |  4.07 |       0.01 |
    | TweedieRegressor              |               0.59 |      0.70 |  4.12 |       0.01 |
    | GeneralizedLinearRegressor    |               0.59 |      0.70 |  4.12 |       0.01 |
    | ElasticNet                    |               0.58 |      0.69 |  4.16 |       0.01 |
    | Lasso                         |               0.54 |      0.66 |  4.35 |       0.02 |
    | RANSACRegressor               |               0.53 |      0.65 |  4.41 |       0.04 |
    | OrthogonalMatchingPursuitCV   |               0.45 |      0.59 |  4.78 |       0.02 |
    | PassiveAggressiveRegressor    |               0.37 |      0.54 |  5.09 |       0.01 |
    | GaussianProcessRegressor      |               0.23 |      0.43 |  5.65 |       0.03 |
    | OrthogonalMatchingPursuit     |               0.16 |      0.38 |  5.89 |       0.01 |
    | ExtraTreeRegressor            |               0.08 |      0.32 |  6.17 |       0.01 |
    | DummyRegressor                |              -0.38 |     -0.02 |  7.56 |       0.01 |
    | LassoLars                     |              -0.38 |     -0.02 |  7.56 |       0.01 |
    | KernelRidge                   |             -11.50 |     -8.25 | 22.74 |       0.01 |

History

0.3.2 (2024-03-25)

Major import bug fix
Cleanup

0.3.1 (2024-03-03)

Minor cleanups

0.3.0 (2024-03-03)

Fixed OneHotEncoder Bug

0.2.11 (2022-02-06)

Updated the default version to 3.9

0.2.10 (2022-02-06)

Fixed issue with older version of Scikit-learn
Reduced dependencies sctrictly to few

0.2.8 (2021-02-06)

Removed StackingRegressor and CheckingClassifier.
Added provided_models method.
Added adjusted r-squared metric.
Added cardinality check to split categorical columns into low and high cardinality features.
Added different transformation pipeline for low and high cardinality features.
Included all number dtypes as inputs.
Fixed dependencies.
Improved documentation.

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.

lazypredict-nightly dependencies

click joblib lightgbm pandas scikit-learn tqdm xgboost

FAQs

What is lazypredict-nightly?

[Updated] Lazy Predict help build a lot of basic models without much code and helps understand which models works bette... Visit Snyk Advisor to see a full health score report for lazypredict-nightly, including popularity, security, maintenance & community analysis.

Is lazypredict-nightly popular?

The python package lazypredict-nightly receives a total of 1,584 weekly downloads. As such, lazypredict-nightly popularity was classified as small. Visit the popularity section on Snyk Advisor to see the full health analysis.

Is lazypredict-nightly well maintained?

We found indications that lazypredict-nightly is an Inactive project. See the full package health analysis to learn more about the package maintenance status.

Is lazypredict-nightly safe to use?

While scanning the latest version of lazypredict-nightly, we found that a security review is needed. A total of 0 vulnerabilities or license issues were detected. See the full security scan results.

Last updated on 2 November-2024, at 10:24 (UTC).

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