Python sklearn.linear_model.PassiveAggressiveRegressor() Examples

The following are 13 code examples for showing how to use sklearn.linear_model.PassiveAggressiveRegressor(). These examples are extracted from open source projects. You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example.

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Example 1
Project: Mastering-Elasticsearch-7.0   Author: PacktPublishing   File: test_passive_aggressive.py    License: MIT License 6 votes vote down vote up
def test_regressor_mse():
    y_bin = y.copy()
    y_bin[y != 1] = -1

    for data in (X, X_csr):
        for fit_intercept in (True, False):
            for average in (False, True):
                reg = PassiveAggressiveRegressor(
                    C=1.0, fit_intercept=fit_intercept,
                    random_state=0, average=average, max_iter=5)
                reg.fit(data, y_bin)
                pred = reg.predict(data)
                assert_less(np.mean((pred - y_bin) ** 2), 1.7)
                if average:
                    assert hasattr(reg, 'average_coef_')
                    assert hasattr(reg, 'average_intercept_')
                    assert hasattr(reg, 'standard_intercept_')
                    assert hasattr(reg, 'standard_coef_')


# 0.23. warning about tol not having its correct default value. 
Example 2
Project: Mastering-Elasticsearch-7.0   Author: PacktPublishing   File: test_passive_aggressive.py    License: MIT License 6 votes vote down vote up
def test_regressor_partial_fit():
    y_bin = y.copy()
    y_bin[y != 1] = -1

    for data in (X, X_csr):
        for average in (False, True):
            reg = PassiveAggressiveRegressor(
                C=1.0, fit_intercept=True, random_state=0,
                average=average, max_iter=100)
            for t in range(50):
                reg.partial_fit(data, y_bin)
            pred = reg.predict(data)
            assert_less(np.mean((pred - y_bin) ** 2), 1.7)
            if average:
                assert hasattr(reg, 'average_coef_')
                assert hasattr(reg, 'average_intercept_')
                assert hasattr(reg, 'standard_intercept_')
                assert hasattr(reg, 'standard_coef_')


# 0.23. warning about tol not having its correct default value. 
Example 3
Project: causallib   Author: IBM   File: test_standardization.py    License: Apache License 2.0 6 votes vote down vote up
def ensure_many_models(self):
        from sklearn.ensemble import GradientBoostingRegressor, RandomForestRegressor
        from sklearn.neural_network import MLPRegressor
        from sklearn.linear_model import ElasticNet, RANSACRegressor, HuberRegressor, PassiveAggressiveRegressor
        from sklearn.neighbors import KNeighborsRegressor
        from sklearn.svm import SVR, LinearSVR

        import warnings
        from sklearn.exceptions import ConvergenceWarning
        warnings.filterwarnings('ignore', category=ConvergenceWarning)

        for learner in [GradientBoostingRegressor, RandomForestRegressor, MLPRegressor,
                        ElasticNet, RANSACRegressor, HuberRegressor, PassiveAggressiveRegressor,
                        KNeighborsRegressor, SVR, LinearSVR]:
            learner = learner()
            learner_name = str(learner).split("(", maxsplit=1)[0]
            with self.subTest("Test fit using {learner}".format(learner=learner_name)):
                model = self.estimator.__class__(learner)
                model.fit(self.data_lin["X"], self.data_lin["a"], self.data_lin["y"])
                self.assertTrue(True)  # Fit did not crash 
Example 4
Project: sklearn-onnx   Author: onnx   File: test_sklearn_glm_regressor_converter.py    License: MIT License 6 votes vote down vote up
def test_model_passive_aggressive_regressor(self):
        model, X = fit_regression_model(
            linear_model.PassiveAggressiveRegressor())
        model_onnx = convert_sklearn(
            model, "passive aggressive regressor",
            [("input", FloatTensorType([None, X.shape[1]]))])
        self.assertIsNotNone(model_onnx)
        dump_data_and_model(
            X,
            model,
            model_onnx,
            verbose=False,
            basename="SklearnPassiveAggressiveRegressor-Dec4",
            allow_failure="StrictVersion("
            "onnxruntime.__version__)"
            "<= StrictVersion('0.2.1')",
        ) 
Example 5
Project: twitter-stock-recommendation   Author: alvarobartt   File: test_passive_aggressive.py    License: MIT License 6 votes vote down vote up
def test_regressor_mse():
    y_bin = y.copy()
    y_bin[y != 1] = -1

    for data in (X, X_csr):
        for fit_intercept in (True, False):
            for average in (False, True):
                reg = PassiveAggressiveRegressor(
                    C=1.0, fit_intercept=fit_intercept,
                    random_state=0, average=average, max_iter=5)
                reg.fit(data, y_bin)
                pred = reg.predict(data)
                assert_less(np.mean((pred - y_bin) ** 2), 1.7)
                if average:
                    assert_true(hasattr(reg, 'average_coef_'))
                    assert_true(hasattr(reg, 'average_intercept_'))
                    assert_true(hasattr(reg, 'standard_intercept_'))
                    assert_true(hasattr(reg, 'standard_coef_')) 
Example 6
Project: twitter-stock-recommendation   Author: alvarobartt   File: test_passive_aggressive.py    License: MIT License 6 votes vote down vote up
def test_regressor_partial_fit():
    y_bin = y.copy()
    y_bin[y != 1] = -1

    for data in (X, X_csr):
        for average in (False, True):
            reg = PassiveAggressiveRegressor(
                C=1.0, fit_intercept=True, random_state=0,
                average=average, max_iter=100)
            for t in range(50):
                reg.partial_fit(data, y_bin)
            pred = reg.predict(data)
            assert_less(np.mean((pred - y_bin) ** 2), 1.7)
            if average:
                assert_true(hasattr(reg, 'average_coef_'))
                assert_true(hasattr(reg, 'average_intercept_'))
                assert_true(hasattr(reg, 'standard_intercept_'))
                assert_true(hasattr(reg, 'standard_coef_')) 
Example 7
Project: twitter-stock-recommendation   Author: alvarobartt   File: test_passive_aggressive.py    License: MIT License 6 votes vote down vote up
def test_regressor_correctness():
    y_bin = y.copy()
    y_bin[y != 1] = -1

    for loss in ("epsilon_insensitive", "squared_epsilon_insensitive"):
        reg1 = MyPassiveAggressive(
            C=1.0, loss=loss, fit_intercept=True, n_iter=2)
        reg1.fit(X, y_bin)

        for data in (X, X_csr):
            reg2 = PassiveAggressiveRegressor(
                C=1.0, tol=None, loss=loss, fit_intercept=True, max_iter=2,
                shuffle=False)
            reg2.fit(data, y_bin)

            assert_array_almost_equal(reg1.w, reg2.coef_.ravel(), decimal=2) 
Example 8
Project: Mastering-Elasticsearch-7.0   Author: PacktPublishing   File: test_passive_aggressive.py    License: MIT License 5 votes vote down vote up
def test_regressor_correctness(loss):
    y_bin = y.copy()
    y_bin[y != 1] = -1

    reg1 = MyPassiveAggressive(
        C=1.0, loss=loss, fit_intercept=True, n_iter=2)
    reg1.fit(X, y_bin)

    for data in (X, X_csr):
        reg2 = PassiveAggressiveRegressor(
            C=1.0, tol=None, loss=loss, fit_intercept=True, max_iter=2,
            shuffle=False)
        reg2.fit(data, y_bin)

        assert_array_almost_equal(reg1.w, reg2.coef_.ravel(), decimal=2) 
Example 9
Project: Mastering-Elasticsearch-7.0   Author: PacktPublishing   File: test_passive_aggressive.py    License: MIT License 5 votes vote down vote up
def test_regressor_undefined_methods():
    reg = PassiveAggressiveRegressor(max_iter=100)
    for meth in ("transform",):
        assert_raises(AttributeError, lambda x: getattr(reg, x), meth) 
Example 10
Project: causallib   Author: IBM   File: test_doublyrobust.py    License: Apache License 2.0 5 votes vote down vote up
def ensure_many_models(self):
        from sklearn.ensemble import GradientBoostingRegressor, RandomForestRegressor
        from sklearn.neural_network import MLPRegressor
        from sklearn.linear_model import ElasticNet, RANSACRegressor, HuberRegressor, PassiveAggressiveRegressor
        from sklearn.neighbors import KNeighborsRegressor
        from sklearn.svm import SVR, LinearSVR

        from sklearn.ensemble import GradientBoostingClassifier, RandomForestClassifier
        from sklearn.neural_network import MLPClassifier
        from sklearn.neighbors import KNeighborsClassifier

        from sklearn.exceptions import ConvergenceWarning
        warnings.filterwarnings('ignore', category=ConvergenceWarning)

        data = self.create_uninformative_ox_dataset()
        for propensity_learner in [GradientBoostingClassifier(n_estimators=10),
                                   RandomForestClassifier(n_estimators=100),
                                   MLPClassifier(hidden_layer_sizes=(5,)),
                                   KNeighborsClassifier(n_neighbors=20)]:
            weight_model = IPW(propensity_learner)
            propensity_learner_name = str(propensity_learner).split("(", maxsplit=1)[0]
            for outcome_learner in [GradientBoostingRegressor(n_estimators=10), RandomForestRegressor(n_estimators=10),
                                    MLPRegressor(hidden_layer_sizes=(5,)),
                                    ElasticNet(), RANSACRegressor(), HuberRegressor(), PassiveAggressiveRegressor(),
                                    KNeighborsRegressor(), SVR(), LinearSVR()]:
                outcome_learner_name = str(outcome_learner).split("(", maxsplit=1)[0]
                outcome_model = Standardization(outcome_learner)

                with self.subTest("Test fit & predict using {} & {}".format(propensity_learner_name,
                                                                            outcome_learner_name)):
                    model = self.estimator.__class__(outcome_model, weight_model)
                    model.fit(data["X"], data["a"], data["y"], refit_weight_model=False)
                    model.estimate_individual_outcome(data["X"], data["a"])
                    self.assertTrue(True)  # Fit did not crash 
Example 11
Project: pandas-ml   Author: pandas-ml   File: test_linear_model.py    License: BSD 3-Clause "New" or "Revised" License 5 votes vote down vote up
def test_objectmapper(self):
        df = pdml.ModelFrame([])
        self.assertIs(df.linear_model.ARDRegression, lm.ARDRegression)
        self.assertIs(df.linear_model.BayesianRidge, lm.BayesianRidge)
        self.assertIs(df.linear_model.ElasticNet, lm.ElasticNet)
        self.assertIs(df.linear_model.ElasticNetCV, lm.ElasticNetCV)

        self.assertIs(df.linear_model.HuberRegressor, lm.HuberRegressor)

        self.assertIs(df.linear_model.Lars, lm.Lars)
        self.assertIs(df.linear_model.LarsCV, lm.LarsCV)
        self.assertIs(df.linear_model.Lasso, lm.Lasso)
        self.assertIs(df.linear_model.LassoCV, lm.LassoCV)
        self.assertIs(df.linear_model.LassoLars, lm.LassoLars)
        self.assertIs(df.linear_model.LassoLarsCV, lm.LassoLarsCV)
        self.assertIs(df.linear_model.LassoLarsIC, lm.LassoLarsIC)

        self.assertIs(df.linear_model.LinearRegression, lm.LinearRegression)
        self.assertIs(df.linear_model.LogisticRegression, lm.LogisticRegression)
        self.assertIs(df.linear_model.LogisticRegressionCV, lm.LogisticRegressionCV)
        self.assertIs(df.linear_model.MultiTaskLasso, lm.MultiTaskLasso)
        self.assertIs(df.linear_model.MultiTaskElasticNet, lm.MultiTaskElasticNet)
        self.assertIs(df.linear_model.MultiTaskLassoCV, lm.MultiTaskLassoCV)
        self.assertIs(df.linear_model.MultiTaskElasticNetCV, lm.MultiTaskElasticNetCV)

        self.assertIs(df.linear_model.OrthogonalMatchingPursuit, lm.OrthogonalMatchingPursuit)
        self.assertIs(df.linear_model.OrthogonalMatchingPursuitCV, lm.OrthogonalMatchingPursuitCV)
        self.assertIs(df.linear_model.PassiveAggressiveClassifier, lm.PassiveAggressiveClassifier)
        self.assertIs(df.linear_model.PassiveAggressiveRegressor, lm.PassiveAggressiveRegressor)

        self.assertIs(df.linear_model.Perceptron, lm.Perceptron)
        self.assertIs(df.linear_model.RandomizedLasso, lm.RandomizedLasso)
        self.assertIs(df.linear_model.RandomizedLogisticRegression, lm.RandomizedLogisticRegression)
        self.assertIs(df.linear_model.RANSACRegressor, lm.RANSACRegressor)
        self.assertIs(df.linear_model.Ridge, lm.Ridge)
        self.assertIs(df.linear_model.RidgeClassifier, lm.RidgeClassifier)
        self.assertIs(df.linear_model.RidgeClassifierCV, lm.RidgeClassifierCV)
        self.assertIs(df.linear_model.RidgeCV, lm.RidgeCV)
        self.assertIs(df.linear_model.SGDClassifier, lm.SGDClassifier)
        self.assertIs(df.linear_model.SGDRegressor, lm.SGDRegressor)
        self.assertIs(df.linear_model.TheilSenRegressor, lm.TheilSenRegressor) 
Example 12
Project: twitter-stock-recommendation   Author: alvarobartt   File: test_passive_aggressive.py    License: MIT License 5 votes vote down vote up
def test_regressor_undefined_methods():
    reg = PassiveAggressiveRegressor(max_iter=100)
    for meth in ("transform",):
        assert_raises(AttributeError, lambda x: getattr(reg, x), meth) 
Example 13
Project: causallib   Author: IBM   File: test_doublyrobust.py    License: Apache License 2.0 4 votes vote down vote up
def test_many_models(self):
        from sklearn.ensemble import GradientBoostingRegressor, RandomForestRegressor
        from sklearn.neural_network import MLPRegressor
        from sklearn.linear_model import ElasticNet, RANSACRegressor, HuberRegressor, PassiveAggressiveRegressor
        from sklearn.neighbors import KNeighborsRegressor
        from sklearn.svm import SVR, LinearSVR

        from sklearn.ensemble import GradientBoostingClassifier, RandomForestClassifier
        from sklearn.neural_network import MLPClassifier
        from sklearn.neighbors import KNeighborsClassifier

        from sklearn.exceptions import ConvergenceWarning
        warnings.filterwarnings('ignore', category=ConvergenceWarning)

        data = self.create_uninformative_ox_dataset()

        for propensity_learner in [GradientBoostingClassifier(n_estimators=10),
                                   RandomForestClassifier(n_estimators=100),
                                   MLPClassifier(hidden_layer_sizes=(5,)),
                                   KNeighborsClassifier(n_neighbors=20)]:
            weight_model = IPW(propensity_learner)
            propensity_learner_name = str(propensity_learner).split("(", maxsplit=1)[0]
            for outcome_learner in [GradientBoostingRegressor(n_estimators=10),
                                    RandomForestRegressor(n_estimators=10),
                                    RANSACRegressor(), HuberRegressor(), SVR(), LinearSVR()]:
                outcome_learner_name = str(outcome_learner).split("(", maxsplit=1)[0]
                outcome_model = Standardization(outcome_learner)

                with self.subTest("Test fit using {} & {}".format(propensity_learner_name, outcome_learner_name)):
                    model = self.estimator.__class__(outcome_model, weight_model)
                    model.fit(data["X"], data["a"], data["y"], refit_weight_model=False)
                    self.assertTrue(True)  # Fit did not crash

            for outcome_learner in [MLPRegressor(hidden_layer_sizes=(5,)), ElasticNet(),
                                    PassiveAggressiveRegressor(), KNeighborsRegressor()]:
                outcome_learner_name = str(outcome_learner).split("(", maxsplit=1)[0]
                outcome_model = Standardization(outcome_learner)

                with self.subTest("Test fit using {} & {}".format(propensity_learner_name, outcome_learner_name)):
                    model = self.estimator.__class__(outcome_model, weight_model)
                    with self.assertRaises(TypeError):
                        # Joffe forces learning with sample_weights,
                        # not all ML models support that and so calling should fail
                        model.fit(data["X"], data["a"], data["y"], refit_weight_model=False)