Python sklearn.linear_model.ARDRegression() Examples

The following are 9 code examples of sklearn.linear_model.ARDRegression(). 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. You may also want to check out all available functions/classes of the module sklearn.linear_model , or try the search function .
Example #1
Source File: scikitlearn.py    From sia-cog with MIT License 6 votes vote down vote up 
def getModels():
    result = []
    result.append("LinearRegression")
    result.append("BayesianRidge")
    result.append("ARDRegression")
    result.append("ElasticNet")
    result.append("HuberRegressor")
    result.append("Lasso")
    result.append("LassoLars")
    result.append("Rigid")
    result.append("SGDRegressor")
    result.append("SVR")
    result.append("MLPClassifier")
    result.append("KNeighborsClassifier")
    result.append("SVC")
    result.append("GaussianProcessClassifier")
    result.append("DecisionTreeClassifier")
    result.append("RandomForestClassifier")
    result.append("AdaBoostClassifier")
    result.append("GaussianNB")
    result.append("LogisticRegression")
    result.append("QuadraticDiscriminantAnalysis")
    return result
Example #2
Source File: main.py    From nni with MIT License 6 votes vote down vote up 
def get_model(PARAMS):
    '''Get model according to parameters'''
    model_dict = {
        'LinearRegression': LinearRegression(),
        'Ridge': Ridge(),
        'Lars': Lars(),
        'ARDRegression': ARDRegression()

    }
    if not model_dict.get(PARAMS['model_name']):
        LOG.exception('Not supported model!')
        exit(1)

    model = model_dict[PARAMS['model_name']]
    model.normalize = bool(PARAMS['normalize'])

    return model
Example #3
Source File: FSRegression.py    From CausalDiscoveryToolbox with MIT License 6 votes vote down vote up 
def predict_features(self, df_features, df_target, idx=0, **kwargs):
        """For one variable, predict its neighbouring nodes.

        Args:
            df_features (pandas.DataFrame):
            df_target (pandas.Series):
            idx (int): (optional) for printing purposes
            kwargs (dict): additional options for algorithms

        Returns:
            list: scores of each feature relatively to the target
        """
        X = df_features.values
        y = df_target.values
        clf = ard(compute_score=True)
        clf.fit(X, y.ravel())

        return np.abs(clf.coef_)
Example #4
Source File: test_sklearn_glm_regressor_converter.py    From sklearn-onnx with MIT License 6 votes vote down vote up 
def test_model_ard_regression(self):
        model, X = fit_regression_model(
            linear_model.ARDRegression(), factor=0.001)
        model_onnx = convert_sklearn(
            model, "ard regression",
            [("input", FloatTensorType([None, X.shape[1]]))])
        self.assertIsNotNone(model_onnx)
        dump_data_and_model(
            X,
            model,
            model_onnx,
            basename="SklearnARDRegression-Dec4",
            allow_failure="StrictVersion("
            "onnxruntime.__version__)"
            "<= StrictVersion('0.2.1')",
        )
Example #5
Source File: test_validation.py    From Mastering-Elasticsearch-7.0 with MIT License 5 votes vote down vote up 
def test_check_is_fitted():
    # Check is ValueError raised when non estimator instance passed
    assert_raises(ValueError, check_is_fitted, ARDRegression, "coef_")
    assert_raises(TypeError, check_is_fitted, "SVR", "support_")

    ard = ARDRegression()
    svr = SVR(gamma='scale')

    try:
        assert_raises(NotFittedError, check_is_fitted, ard, "coef_")
        assert_raises(NotFittedError, check_is_fitted, svr, "support_")
    except ValueError:
        assert False, "check_is_fitted failed with ValueError"

    # NotFittedError is a subclass of both ValueError and AttributeError
    try:
        check_is_fitted(ard, "coef_", "Random message %(name)s, %(name)s")
    except ValueError as e:
        assert_equal(str(e), "Random message ARDRegression, ARDRegression")

    try:
        check_is_fitted(svr, "support_", "Another message %(name)s, %(name)s")
    except AttributeError as e:
        assert_equal(str(e), "Another message SVR, SVR")

    ard.fit(*make_blobs())
    svr.fit(*make_blobs())

    assert_equal(None, check_is_fitted(ard, "coef_"))
    assert_equal(None, check_is_fitted(svr, "support_"))
Example #6
Source File: regression.py    From Azimuth with BSD 3-Clause "New" or "Revised" License 5 votes vote down vote up 
def ARDRegression_on_fold(feature_sets, train, test, y, y_all, X, dim, dimsum, learn_options):
    '''
    '''
    clf = ARDRegression()
    clf.fit(X[train], y[train][:, 0])
    y_pred = clf.predict(X[test])[:, None]
    return y_pred, clf
Example #7
Source File: test_linear_model.py    From pandas-ml with 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 #8
Source File: test_validation.py    From twitter-stock-recommendation with MIT License 5 votes vote down vote up 
def test_check_is_fitted():
    # Check is ValueError raised when non estimator instance passed
    assert_raises(ValueError, check_is_fitted, ARDRegression, "coef_")
    assert_raises(TypeError, check_is_fitted, "SVR", "support_")

    ard = ARDRegression()
    svr = SVR()

    try:
        assert_raises(NotFittedError, check_is_fitted, ard, "coef_")
        assert_raises(NotFittedError, check_is_fitted, svr, "support_")
    except ValueError:
        assert False, "check_is_fitted failed with ValueError"

    # NotFittedError is a subclass of both ValueError and AttributeError
    try:
        check_is_fitted(ard, "coef_", "Random message %(name)s, %(name)s")
    except ValueError as e:
        assert_equal(str(e), "Random message ARDRegression, ARDRegression")

    try:
        check_is_fitted(svr, "support_", "Another message %(name)s, %(name)s")
    except AttributeError as e:
        assert_equal(str(e), "Another message SVR, SVR")

    ard.fit(*make_blobs())
    svr.fit(*make_blobs())

    assert_equal(None, check_is_fitted(ard, "coef_"))
    assert_equal(None, check_is_fitted(svr, "support_"))
Example #9
Source File: scikitlearn.py    From sia-cog with MIT License 4 votes vote down vote up 
def getSKLearnModel(modelName):
    if modelName == 'LinearRegression':
        model = linear_model.LinearRegression()
    elif modelName == 'BayesianRidge':
        model = linear_model.BayesianRidge()
    elif modelName == 'ARDRegression':
        model = linear_model.ARDRegression()
    elif modelName == 'ElasticNet':
        model = linear_model.ElasticNet()
    elif modelName == 'HuberRegressor':
        model = linear_model.HuberRegressor()
    elif modelName == 'Lasso':
        model = linear_model.Lasso()
    elif modelName == 'LassoLars':
        model = linear_model.LassoLars()
    elif modelName == 'Rigid':
        model = linear_model.Ridge()
    elif modelName == 'SGDRegressor':
        model = linear_model.SGDRegressor()
    elif modelName == 'SVR':
        model = SVR()
    elif modelName=='MLPClassifier':
        model = MLPClassifier()
    elif modelName=='KNeighborsClassifier':
        model = KNeighborsClassifier()
    elif modelName=='SVC':
        model = SVC()
    elif modelName=='GaussianProcessClassifier':
        model = GaussianProcessClassifier()
    elif modelName=='DecisionTreeClassifier':
        model = DecisionTreeClassifier()
    elif modelName=='RandomForestClassifier':
        model = RandomForestClassifier()
    elif modelName=='AdaBoostClassifier':
        model = AdaBoostClassifier()
    elif modelName=='GaussianNB':
        model = GaussianNB()
    elif modelName=='LogisticRegression':
        model = linear_model.LogisticRegression()
    elif modelName=='QuadraticDiscriminantAnalysis':
        model = QuadraticDiscriminantAnalysis()

    return model