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    "info": {
        "author": "Andres Arrieche",
        "author_email": "andres.arrieche.7@gmail.com",
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        "classifiers": [
            "License :: OSI Approved :: Apache Software License",
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        "description": "scikit-learn-helper\n============\n\nscikit-learn-helper is a light library with the purpose of providing utility functions that makes working with scikit-learn even easier, by letting us to focus on the solving the probling instead of writting boilerplate code\n\n### Installation\n\n#### Dependencies\nscikit-learn-helper requires:\n\n    scikit-learn (>= 0.20.2) Of course :)\n\n#### User installation\n\n\n    pip install scikit-learn-helper\n\n\n#### Source code\n\n\n    https://github.com/aras7/scikit-learn-helper\n\n### Examples\n\n#### How to use it?\n\n```python\nfrom sklearn_helper.model.evaluator import Evaluator\nmodels = {\n    \"DummyRegressor\": {\n        \"model\": DummyRegressor()\n    }\n}\nevaluator = Evaluator(models)\n\ndataset = datasets.load_boston()\nX, y = dataset.data, dataset.target\nmodel = evaluator.evaluate(X, y)\n```\n\n###### Output\n\n```\nModel: DummyRegressor | cleaner:DummyCleaner | mean_squared_error:111.1508 |Time:0.00 sec\n```\n\n#### How to use other another metric for evaluation?\n```\nfrom sklearn_helper.model.evaluator import Evaluator\nfrom sklearn.metrics import r2_score\nmodels = {\n    \"DummyRegressor\": {\n        \"model\": DummyRegressor()\n    }\n}\nevaluator = Evaluator(models, main_metric=r2_score)\n\ndataset = datasets.load_boston()\nX, y = dataset.data, dataset.target\nmodel = evaluator.evaluate(X, y)\n```\n\n###### Output\n```\nModel: DummyRegressor | cleaner:DummyCleaner | r2_score:-0.6891 |Time:0.00 sec\n```\n\n#### How to compare 2+ models?\n\n```python\nfrom sklearn_helper.model.evaluator import Evaluator\nmodels = {\n    \"DummyRegressor\": {\n        \"model\": DummyRegressor()\n    }, \n    \"LinearRegression\": {\n        \"model\": linear_model.LinearRegression()\n    }\n}\nevaluator = Evaluator(models)\n\ndataset = datasets.load_boston()\nX, y = dataset.data, dataset.target\nmodel = evaluator.evaluate(X, y)\n\n```\n\n###### Output\n\n```\nModel: DummyRegressor   | cleaner:DummyCleaner | mean_squared_error:111.1508 |Time:0.00 sec\nModel: LinearRegression | cleaner:DummyCleaner | mean_squared_error:169.0083 |Time:0.00 sec\n```\n\n#### How to maximize instead of minimize ?\n\n```python\nfrom sklearn.metrics import accuracy_score\nfrom sklearn_helper.model.evaluator import Evaluator\n\nmodels = {\n    \"DummyClassifier\": {\n        \"model\": DummyClassifier()\n    },\n    \"SVC\": {\n        \"model\": svm.SVC()\n    }\n}\nevaluator = Evaluator(models, main_metric=accuracy_score, maximize_metric=True)\n\ndigits = datasets.load_digits()\nX, y = digits.data, digits.target\nmodel = evaluator.evaluate(X, y)\n```\n\n###### Output\n\n```\nModel: SVC             | cleaner:DummyCleaner | accuracy_score:0.4179 |Time:0.89 sec\nModel: DummyClassifier | cleaner:DummyCleaner | accuracy_score:0.0896 |Time:0.00 sec\n```\n\n#### How to compare and tune models?\n```python\nfrom sklearn.metrics import accuracy_score\nfrom sklearn_helper.model.evaluator import Evaluator\n\nmodels = {\n    \"SVC\": {\n        \"model\": svm.SVC()\n    },\n    \"Improved SVC\": {\n        \"model\": svm.SVC(),\n        \"params\": {\n             \"gamma\": np.linspace(0, 0.1, num=10),\n             \"C\": range(1, 10)\n         }\n    }\n}\nevaluator = Evaluator(models, main_metric=accuracy_score, maximize_metric=True)\n\ndigits = datasets.load_digits()\nX, y = digits.data, digits.target\nmodel = evaluator.evaluate(X, y)\nprint(model)\n```\n\n###### Output\n```\nModel: Improved SVC | cleaner:DummyCleaner | accuracy_score:0.6511 |Time:0.86 sec\nModel: SVC          | cleaner:DummyCleaner | accuracy_score:0.4179 |Time:0.89 sec\nSVC(C=2, cache_size=200, class_weight=None, coef0=0.0,\n  decision_function_shape='ovr', degree=3, gamma=0.011111111111111112,\n  kernel='rbf', max_iter=-1, probability=False, random_state=None,\n  shrinking=True, tol=0.001, verbose=False)\n```\n\n\n#### How to get multiple metric?\n```\nfrom sklearn.metrics import roc_auc_score, f1_score, accuracy_score\nfrom sklearn_helper.model.evaluator import Evaluator\nmodels = {\n    \"DummyClassifier\": {\n        \"model\": DummyClassifier()\n    },\n    \"SVC\": {\n        \"model\": svm.SVC()\n    }\n}\nevaluator = Evaluator(models, main_metric=roc_auc_score, \n                             additional_metrics=[f1_score, accuracy_score], \n                             maximize_metric=True)\ndigits = datasets.load_breast_cancer()\nX, y = digits.data, digits.target\nmodel = evaluator.evaluate(X, y)\n```\n\n###### Output\n```\nModel: SVC             | cleaner:DummyCleaner | roc_auc_score:0.5000 |f1_score:0.7650 |accuracy_score:0.6277 |Time:0.11 sec\nModel: DummyClassifier | cleaner:DummyCleaner | roc_auc_score:0.4961 |f1_score:0.6063 |accuracy_score:0.5308 |Time:0.01 sec\n```\n\n#### Can I compare data engineering process? yes :)\n```\nfrom sklearn.metrics import accuracy_score\nfrom sklearn_helper.model.evaluator import Evaluator\nfrom sklearn_helper.data.DataCleaner import DataCleaner\nfrom sklearn_helper.data.DummyCleaner import DummyCleaner # No transformation is applied\n\nclass Thresholding(DataCleaner):\n    THRESHOLD = 3\n    def clean_training_data(self, x, y):\n        return self.clean_x(x), y\n    def clean_testing_data(self, x):\n        _x = np.copy(x)\n        _x[_x <= self.THRESHOLD] = 0\n        _x[_x > self.THRESHOLD] = 1\n        return _x\n\nmodels = {\n    \"DummyClassifier\": {\n        \"model\": DummyClassifier()\n    },\n    \"SVC\": {\n        \"model\": svm.SVC(C=2, gamma=0.0111)\n    }\n}\nevaluator = Evaluator(models, data_cleaners=[Thresholding(), DummyCleaner()], main_metric=accuracy_score)\n\ndigits = datasets.load_digits()\nX, y = digits.data, digits.target\nmodel = evaluator.evaluate(X, y)\n```\n\n###### Output\n```\nModel: DummyClassifier | cleaner:DummyCleaner | accuracy_score:0.0940 |Time:0.00 sec\nModel: DummyClassifier | cleaner:Thresholding | accuracy_score:0.1035 |Time:0.00 sec\nModel: SVC             | cleaner:DummyCleaner | accuracy_score:0.6516 |Time:0.87 sec\nModel: SVC             | cleaner:Thresholding | accuracy_score:0.8848 |Time:0.28 sec\n```\n\n#### For more examples refer to:\n\n    https://github.com/aras7/scikit-learn-helper/tree/master/examples\n\n\n### ToDo:\n\n* Add unit tests\n* Add prediction time to printed results\n* Improve documentation\n * Splitter\n* Add functionality to test different dataset as an alternative to `from sklearn.model_selection import cross_val_score`. It might be useful when resampling inside DataCleaners\n* Add codecov.io suport\n\n\n",
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