Python sklearn.linear_model.LassoLarsCV() Examples

The following are 14 code examples for showing how to use sklearn.linear_model.LassoLarsCV(). 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.

You may check out the related API usage on the sidebar.

You may also want to check out all available functions/classes of the module sklearn.linear_model , or try the search function .

Example 1
Project: few   Author: lacava   File: test_few.py    License: GNU General Public License v3.0 6 votes vote down vote up
def test_few_fit_shapes():
    """test_few.py: fit and predict return correct shapes """
    np.random.seed(202)
    # load example data
    boston = load_boston()
    d = pd.DataFrame(data=boston.data)
    print("feature shape:",boston.data.shape)

    learner = FEW(generations=1, population_size=5,
                mutation_rate=0.2, crossover_rate=0.8,
                ml = LassoLarsCV(), min_depth = 1, max_depth = 3,
                sel = 'epsilon_lexicase', tourn_size = 2,
                random_state=0, verbosity=0,
                disable_update_check=False, fit_choice = 'mse')

    score = learner.fit(boston.data[:300], boston.target[:300])
    print("learner:",learner._best_estimator)
    yhat_test = learner.predict(boston.data[300:])
    test_score = learner.score(boston.data[300:],boston.target[300:])
    print("train score:",score,"test score:",test_score,
    "test r2:",r2_score(boston.target[300:],yhat_test))
    assert yhat_test.shape == boston.target[300:].shape 
Example 2
Project: few   Author: lacava   File: test_few.py    License: GNU General Public License v3.0 6 votes vote down vote up
def test_few_with_parents_weight():
    """test_few.py: few performs without error with parent pressure for selection"""
    np.random.seed(1006987)
    boston = load_boston()
    d = np.column_stack((boston.data,boston.target))
    np.random.shuffle(d)
    features = d[:,0:-1]
    target = d[:,-1]

    print("feature shape:",boston.data.shape)

    learner = FEW(generations=1, population_size=5,
                mutation_rate=1, crossover_rate=1,
                ml = LassoLarsCV(), min_depth = 1, max_depth = 3,
                sel = 'tournament', fit_choice = 'r2',tourn_size = 2, random_state=0, verbosity=0,
                disable_update_check=False, weight_parents=True)

    learner.fit(features[:300], target[:300])
    few_score = learner.score(features[:300], target[:300])
    test_score = learner.score(features[300:],target[300:])

    print("few score:",few_score)
    print("few test score:",test_score) 
Example 3
Project: Mastering-Elasticsearch-7.0   Author: PacktPublishing   File: test_least_angle.py    License: MIT License 6 votes vote down vote up
def test_lars_cv_max_iter(recwarn):
    warnings.simplefilter('always')
    with np.errstate(divide='raise', invalid='raise'):
        X = diabetes.data
        y = diabetes.target
        rng = np.random.RandomState(42)
        x = rng.randn(len(y))
        X = diabetes.data
        X = np.c_[X, x, x]  # add correlated features
        lars_cv = linear_model.LassoLarsCV(max_iter=5, cv=5)
        lars_cv.fit(X, y)
    # Check that there is no warning in general and no ConvergenceWarning
    # in particular.
    # Materialize the string representation of the warning to get a more
    # informative error message in case of AssertionError.
    recorded_warnings = [str(w) for w in recwarn]
    assert recorded_warnings == [] 
Example 4
Project: Mastering-Elasticsearch-7.0   Author: PacktPublishing   File: test_least_angle.py    License: MIT License 6 votes vote down vote up
def test_estimatorclasses_positive_constraint():
    # testing the transmissibility for the positive option of all estimator
    # classes in this same function here
    default_parameter = {'fit_intercept': False}

    estimator_parameter_map = {'LassoLars': {'alpha': 0.1},
                               'LassoLarsCV': {},
                               'LassoLarsIC': {}}
    for estname in estimator_parameter_map:
        params = default_parameter.copy()
        params.update(estimator_parameter_map[estname])
        estimator = getattr(linear_model, estname)(positive=False, **params)
        estimator.fit(X, y)
        assert estimator.coef_.min() < 0
        estimator = getattr(linear_model, estname)(positive=True, **params)
        estimator.fit(X, y)
        assert min(estimator.coef_) >= 0 
Example 5
Project: Mastering-Elasticsearch-7.0   Author: PacktPublishing   File: test_coordinate_descent.py    License: MIT License 6 votes vote down vote up
def test_lasso_cv():
    X, y, X_test, y_test = build_dataset()
    max_iter = 150
    clf = LassoCV(n_alphas=10, eps=1e-3, max_iter=max_iter).fit(X, y)
    assert_almost_equal(clf.alpha_, 0.056, 2)

    clf = LassoCV(n_alphas=10, eps=1e-3, max_iter=max_iter, precompute=True)
    clf.fit(X, y)
    assert_almost_equal(clf.alpha_, 0.056, 2)

    # Check that the lars and the coordinate descent implementation
    # select a similar alpha
    lars = LassoLarsCV(normalize=False, max_iter=30).fit(X, y)
    # for this we check that they don't fall in the grid of
    # clf.alphas further than 1
    assert np.abs(np.searchsorted(clf.alphas_[::-1], lars.alpha_) -
                  np.searchsorted(clf.alphas_[::-1], clf.alpha_)) <= 1
    # check that they also give a similar MSE
    mse_lars = interpolate.interp1d(lars.cv_alphas_, lars.mse_path_.T)
    np.testing.assert_approx_equal(mse_lars(clf.alphas_[5]).mean(),
                                   clf.mse_path_[5].mean(), significant=2)

    # test set
    assert_greater(clf.score(X_test, y_test), 0.99) 
Example 6
Project: jh-kaggle-util   Author: jeffheaton   File: ensemble_glm.py    License: Apache License 2.0 6 votes vote down vote up
def fit_ensemble(x,y):
    fit_type = jhkaggle.jhkaggle_config['FIT_TYPE']
    if 1:
        if fit_type == jhkaggle.const.FIT_TYPE_BINARY_CLASSIFICATION:
            blend = SGDClassifier(loss="log", penalty="elasticnet")  # LogisticRegression()
        else:
            # blend = SGDRegressor()
            #blend = LinearRegression()
            #blend = RandomForestRegressor(n_estimators=10, n_jobs=-1, max_depth=5, criterion='mae')
            blend = LassoLarsCV(normalize=True)
            #blend = ElasticNetCV(normalize=True)
            #blend = LinearRegression(normalize=True)
        blend.fit(x, y)
    else:
        blend = LogisticRegression()
        blend.fit(x, y)


    return blend 
Example 7
Project: twitter-stock-recommendation   Author: alvarobartt   File: test_coordinate_descent.py    License: MIT License 6 votes vote down vote up
def test_lasso_cv():
    X, y, X_test, y_test = build_dataset()
    max_iter = 150
    clf = LassoCV(n_alphas=10, eps=1e-3, max_iter=max_iter).fit(X, y)
    assert_almost_equal(clf.alpha_, 0.056, 2)

    clf = LassoCV(n_alphas=10, eps=1e-3, max_iter=max_iter, precompute=True)
    clf.fit(X, y)
    assert_almost_equal(clf.alpha_, 0.056, 2)

    # Check that the lars and the coordinate descent implementation
    # select a similar alpha
    lars = LassoLarsCV(normalize=False, max_iter=30).fit(X, y)
    # for this we check that they don't fall in the grid of
    # clf.alphas further than 1
    assert_true(np.abs(
        np.searchsorted(clf.alphas_[::-1], lars.alpha_) -
        np.searchsorted(clf.alphas_[::-1], clf.alpha_)) <= 1)
    # check that they also give a similar MSE
    mse_lars = interpolate.interp1d(lars.cv_alphas_, lars.mse_path_.T)
    np.testing.assert_approx_equal(mse_lars(clf.alphas_[5]).mean(),
                                   clf.mse_path_[5].mean(), significant=2)

    # test set
    assert_greater(clf.score(X_test, y_test), 0.99) 
Example 8
Project: few   Author: lacava   File: test_few.py    License: GNU General Public License v3.0 5 votes vote down vote up
def test_few_at_least_as_good_as_default():
    """test_few.py: few performs at least as well as the default ML """
    np.random.seed(1006987)
    boston = load_boston()
    d = np.column_stack((boston.data,boston.target))
    np.random.shuffle(d)
    features = d[:,0:-1]
    target = d[:,-1]

    print("feature shape:",boston.data.shape)

    learner = FEW(generations=1, population_size=5,
                ml = LassoLarsCV(), min_depth = 1, max_depth = 3,
                sel = 'tournament')

    learner.fit(features[:300], target[:300])
    few_score = learner.score(features[:300], target[:300])
    few_test_score = learner.score(features[300:],target[300:])

    lasso = LassoLarsCV()
    lasso.fit(features[:300], target[:300])
    lasso_score = lasso.score(features[:300], target[:300])
    lasso_test_score = lasso.score(features[300:],target[300:])
    print("few score:",few_score,"lasso score:",lasso_score)
    print("few test score:",few_test_score,"lasso test score:",
          lasso_test_score)
    assert round(few_score,8) >= round(lasso_score,8)

    print("lasso coefficients:",lasso.coef_)

    # assert False 
Example 9
Project: Mastering-Elasticsearch-7.0   Author: PacktPublishing   File: test_least_angle.py    License: MIT License 5 votes vote down vote up
def test_lars_cv():
    # Test the LassoLarsCV object by checking that the optimal alpha
    # increases as the number of samples increases.
    # This property is not actually guaranteed in general and is just a
    # property of the given dataset, with the given steps chosen.
    old_alpha = 0
    lars_cv = linear_model.LassoLarsCV()
    for length in (400, 200, 100):
        X = diabetes.data[:length]
        y = diabetes.target[:length]
        lars_cv.fit(X, y)
        np.testing.assert_array_less(old_alpha, lars_cv.alpha_)
        old_alpha = lars_cv.alpha_
    assert not hasattr(lars_cv, 'n_nonzero_coefs') 
Example 10
Project: sparsereg   Author: Ohjeah   File: efs.py    License: MIT License 5 votes vote down vote up
def _fit_model(x, y, names, operators, **kw):
    steps = [("trafo", LibTrafo(names, operators)), ("lasso", LassoLarsCV(**kw))]
    model = Pipeline(steps).fit(x, y)
    return model, model.score(x, y) 
Example 11
Project: sklearn-onnx   Author: onnx   File: test_sklearn_glm_regressor_converter.py    License: MIT License 5 votes vote down vote up
def test_model_lasso_lars_cv(self):
        model, X = fit_regression_model(linear_model.LassoLarsCV())
        model_onnx = convert_sklearn(
            model, "lasso lars cv",
            [("input", FloatTensorType([None, X.shape[1]]))])
        self.assertIsNotNone(model_onnx)
        dump_data_and_model(
            X,
            model,
            model_onnx,
            basename="SklearnLassoLarsCV-Dec4",
            allow_failure="StrictVersion("
            "onnxruntime.__version__)"
            "<= StrictVersion('0.2.1')",
        ) 
Example 12
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 13
Project: twitter-stock-recommendation   Author: alvarobartt   File: test_least_angle.py    License: MIT License 5 votes vote down vote up
def test_lars_cv():
    # Test the LassoLarsCV object by checking that the optimal alpha
    # increases as the number of samples increases.
    # This property is not actually guaranteed in general and is just a
    # property of the given dataset, with the given steps chosen.
    old_alpha = 0
    lars_cv = linear_model.LassoLarsCV()
    for length in (400, 200, 100):
        X = diabetes.data[:length]
        y = diabetes.target[:length]
        lars_cv.fit(X, y)
        np.testing.assert_array_less(old_alpha, lars_cv.alpha_)
        old_alpha = lars_cv.alpha_
    assert_false(hasattr(lars_cv, 'n_nonzero_coefs')) 
Example 14
Project: twitter-stock-recommendation   Author: alvarobartt   File: test_least_angle.py    License: MIT License 5 votes vote down vote up
def test_lars_cv_max_iter():
    with warnings.catch_warnings(record=True) as w:
        X = diabetes.data
        y = diabetes.target
        rng = np.random.RandomState(42)
        x = rng.randn(len(y))
        X = np.c_[X, x, x]  # add correlated features
        lars_cv = linear_model.LassoLarsCV(max_iter=5)
        lars_cv.fit(X, y)
    assert_true(len(w) == 0)