Metadata-Version: 2.1
Name: tiresia
Version: 0.0.11
Summary: Tiny sklearn-based AutoML tool.
Home-page: https://github.com/nicomignoni/tiresia
Author: Nicola Mignoni
Author-email: nicola.mignoni@gmail.com
License: UNKNOWN
Description: <!---
        <div align="center">
            <img src="docs/tiresia.png" width=150 height=180>
        </div>
        --->
        
        # tiresia
        
        ## Installation
        ```
        pip install tiresia
        ```
        
        ## Description
        [tiresia](https://en.wikipedia.org/wiki/Tiresias) is just a wrapper around [scikit-learn](https://scikit-learn.org/stable/) ```GridSearchCV```. The idea is to simplify the model testing workflow. With tiresia you can choose which models to test and to exclude and provide a ```param_grid``` for the ones you want to explore deeper, while keeping the parameters of the less interesting ones on default. 
        
        ## Example
        ```python
        from tiresia.predictor import AutoPredictor
        
        from sklearn.datasets import make_regression, make_classification
        from sklearn.model_selection import train_test_split
        from sklearn.metrics import roc_auc_score, r2_score
        
        test_type = "regressor"
        
        if test_type == "classifier":
            train, target = make_classification(5000, 20)
        elif test_type == "regressor":
            train, target = make_regression(5000, 20)
            
        x_train, x_test, y_train, y_test = train_test_split(train, target, test_size=0.3)
        
        autopred = AutoPredictor(estimator_type=test_type,
                                 scoring=r2_score,
                                 greater_is_better=True)
        autopred.fit(x_train, y_train, x_test, y_test)
        
        predictions = autopred.predictions
        results     = autopred.results
        
        print(results)
        
                                REGRESSOR r2_score
        18                        LassoCV    1.000
        12                 HuberRegressor    1.000
        23                      LinearSVR    1.000
        22               LinearRegression    1.000
        21                    LassoLarsIC    1.000
        20                    LassoLarsCV    1.000
        29                RANSACRegressor    1.000
        17                          Lasso    1.000
        16                         LarsCV    1.000
        15                           Lars    1.000
        14                    KernelRidge    1.000
        28     PassiveAggressiveRegressor    1.000
        31                          Ridge    1.000
        32                        RidgeCV    1.000
        33                   SGDRegressor    1.000
        35     TransformedTargetRegressor    1.000
        2                   BayesianRidge    1.000
        24                   MLPRegressor    0.998
        6                    ElasticNetCV    0.992
        11  HistGradientBoostingRegressor    0.957
        10      GradientBoostingRegressor    0.925
        5                      ElasticNet    0.881
        8             ExtraTreesRegressor    0.853
        27    OrthogonalMatchingPursuitCV    0.827
        30          RandomForestRegressor    0.824
        1                BaggingRegressor    0.794
        0               AdaBoostRegressor    0.751
        36               TweedieRegressor    0.743
        13            KNeighborsRegressor    0.686
        3           DecisionTreeRegressor    0.486
        26      OrthogonalMatchingPursuit    0.387
        7              ExtraTreeRegressor    0.386
        34                            SVR    0.357
        19                      LassoLars    0.353
        25                          NuSVR    0.285
        9        GaussianProcessRegressor    0.038
        4                  DummyRegressor   -0.003
        ```
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
