Ejemplo n.º 1
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def test_invalid_k():
    X = [[0, 1, 0], [0, -1, -1], [0, .5, .5]]
    y = [1, 0, 1]

    with pytest.raises(ValueError):
        SelectKBest(k=-1).fit(X, y)
    with pytest.raises(ValueError):
        SelectKBest(k=4).fit(X, y)
    with pytest.raises(ValueError):
        GenericUnivariateSelect(mode='k_best', param=-1).fit(X, y)
    with pytest.raises(ValueError):
        GenericUnivariateSelect(mode='k_best', param=4).fit(X, y)
Ejemplo n.º 2
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def test_classes_property():
    iris = load_iris()
    X = iris.data
    y = iris.target

    reg = make_pipeline(SelectKBest(k=1), LinearRegression())
    reg.fit(X, y)
    assert_raises(AttributeError, getattr, reg, "classes_")

    clf = make_pipeline(SelectKBest(k=1), LogisticRegression(random_state=0))
    assert_raises(AttributeError, getattr, clf, "classes_")
    clf.fit(X, y)
    assert_array_equal(clf.classes_, np.unique(y))
Ejemplo n.º 3
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def test_selectkbest_tiebreaking():
    # Test whether SelectKBest actually selects k features in case of ties.
    # Prior to 0.11, SelectKBest would return more features than requested.
    Xs = [[0, 1, 1], [0, 0, 1], [1, 0, 0], [1, 1, 0]]
    y = [1]
    dummy_score = lambda X, y: (X[0], X[0])
    for X in Xs:
        sel = SelectKBest(dummy_score, k=1)
        X1 = ignore_warnings(sel.fit_transform)([X], y)
        assert X1.shape[1] == 1
        assert_best_scores_kept(sel)

        sel = SelectKBest(dummy_score, k=2)
        X2 = ignore_warnings(sel.fit_transform)([X], y)
        assert X2.shape[1] == 2
        assert_best_scores_kept(sel)
Ejemplo n.º 4
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def test_select_kbest_classif():
    # Test whether the relative univariate feature selection
    # gets the correct items in a simple classification problem
    # with the k best heuristic
    X, y = make_classification(n_samples=200,
                               n_features=20,
                               n_informative=3,
                               n_redundant=2,
                               n_repeated=0,
                               n_classes=8,
                               n_clusters_per_class=1,
                               flip_y=0.0,
                               class_sep=10,
                               shuffle=False,
                               random_state=0)

    univariate_filter = SelectKBest(f_classif, k=5)
    X_r = univariate_filter.fit(X, y).transform(X)
    X_r2 = GenericUnivariateSelect(f_classif, mode='k_best',
                                   param=5).fit(X, y).transform(X)
    assert_array_equal(X_r, X_r2)
    support = univariate_filter.get_support()
    gtruth = np.zeros(20)
    gtruth[:5] = 1
    assert_array_equal(support, gtruth)
Ejemplo n.º 5
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def test_mutual_info_classif():
    X, y = make_classification(n_samples=100,
                               n_features=5,
                               n_informative=1,
                               n_redundant=1,
                               n_repeated=0,
                               n_classes=2,
                               n_clusters_per_class=1,
                               flip_y=0.0,
                               class_sep=10,
                               shuffle=False,
                               random_state=0)

    # Test in KBest mode.
    univariate_filter = SelectKBest(mutual_info_classif, k=2)
    X_r = univariate_filter.fit(X, y).transform(X)
    X_r2 = GenericUnivariateSelect(mutual_info_classif, mode='k_best',
                                   param=2).fit(X, y).transform(X)
    assert_array_equal(X_r, X_r2)
    support = univariate_filter.get_support()
    gtruth = np.zeros(5)
    gtruth[:2] = 1
    assert_array_equal(support, gtruth)

    # Test in Percentile mode.
    univariate_filter = SelectPercentile(mutual_info_classif, percentile=40)
    X_r = univariate_filter.fit(X, y).transform(X)
    X_r2 = GenericUnivariateSelect(mutual_info_classif,
                                   mode='percentile',
                                   param=40).fit(X, y).transform(X)
    assert_array_equal(X_r, X_r2)
    support = univariate_filter.get_support()
    gtruth = np.zeros(5)
    gtruth[:2] = 1
    assert_array_equal(support, gtruth)
Ejemplo n.º 6
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def test_mutual_info_regression():
    X, y = make_regression(n_samples=100,
                           n_features=10,
                           n_informative=2,
                           shuffle=False,
                           random_state=0,
                           noise=10)

    # Test in KBest mode.
    univariate_filter = SelectKBest(mutual_info_regression, k=2)
    X_r = univariate_filter.fit(X, y).transform(X)
    assert_best_scores_kept(univariate_filter)
    X_r2 = GenericUnivariateSelect(mutual_info_regression,
                                   mode='k_best',
                                   param=2).fit(X, y).transform(X)
    assert_array_equal(X_r, X_r2)
    support = univariate_filter.get_support()
    gtruth = np.zeros(10)
    gtruth[:2] = 1
    assert_array_equal(support, gtruth)

    # Test in Percentile mode.
    univariate_filter = SelectPercentile(mutual_info_regression, percentile=20)
    X_r = univariate_filter.fit(X, y).transform(X)
    X_r2 = GenericUnivariateSelect(mutual_info_regression,
                                   mode='percentile',
                                   param=20).fit(X, y).transform(X)
    assert_array_equal(X_r, X_r2)
    support = univariate_filter.get_support()
    gtruth = np.zeros(10)
    gtruth[:2] = 1
    assert_array_equal(support, gtruth)
Ejemplo n.º 7
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def test_boundary_case_ch2():
    # Test boundary case, and always aim to select 1 feature.
    X = np.array([[10, 20], [20, 20], [20, 30]])
    y = np.array([[1], [0], [0]])
    scores, pvalues = chi2(X, y)
    assert_array_almost_equal(scores, np.array([4., 0.71428571]))
    assert_array_almost_equal(pvalues, np.array([0.04550026, 0.39802472]))

    filter_fdr = SelectFdr(chi2, alpha=0.1)
    filter_fdr.fit(X, y)
    support_fdr = filter_fdr.get_support()
    assert_array_equal(support_fdr, np.array([True, False]))

    filter_kbest = SelectKBest(chi2, k=1)
    filter_kbest.fit(X, y)
    support_kbest = filter_kbest.get_support()
    assert_array_equal(support_kbest, np.array([True, False]))

    filter_percentile = SelectPercentile(chi2, percentile=50)
    filter_percentile.fit(X, y)
    support_percentile = filter_percentile.get_support()
    assert_array_equal(support_percentile, np.array([True, False]))

    filter_fpr = SelectFpr(chi2, alpha=0.1)
    filter_fpr.fit(X, y)
    support_fpr = filter_fpr.get_support()
    assert_array_equal(support_fpr, np.array([True, False]))

    filter_fwe = SelectFwe(chi2, alpha=0.1)
    filter_fwe.fit(X, y)
    support_fwe = filter_fwe.get_support()
    assert_array_equal(support_fwe, np.array([True, False]))
Ejemplo n.º 8
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def test_feature_union_weights():
    # test feature union with transformer weights
    iris = load_iris()
    X = iris.data
    y = iris.target
    pca = PCA(n_components=2, svd_solver='randomized', random_state=0)
    select = SelectKBest(k=1)
    # test using fit followed by transform
    fs = FeatureUnion([("pca", pca), ("select", select)],
                      transformer_weights={"pca": 10})
    fs.fit(X, y)
    X_transformed = fs.transform(X)
    # test using fit_transform
    fs = FeatureUnion([("pca", pca), ("select", select)],
                      transformer_weights={"pca": 10})
    X_fit_transformed = fs.fit_transform(X, y)
    # test it works with transformers missing fit_transform
    fs = FeatureUnion([("mock", Transf()), ("pca", pca), ("select", select)],
                      transformer_weights={"mock": 10})
    X_fit_transformed_wo_method = fs.fit_transform(X, y)
    # check against expected result

    # We use a different pca object to control the random_state stream
    assert_array_almost_equal(X_transformed[:, :-1], 10 * pca.fit_transform(X))
    assert_array_equal(X_transformed[:, -1],
                       select.fit_transform(X, y).ravel())
    assert_array_almost_equal(X_fit_transformed[:, :-1],
                              10 * pca.fit_transform(X))
    assert_array_equal(X_fit_transformed[:, -1],
                       select.fit_transform(X, y).ravel())
    assert X_fit_transformed_wo_method.shape == (X.shape[0], 7)
Ejemplo n.º 9
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def test_tied_scores():
    # Test for stable sorting in k-best with tied scores.
    X_train = np.array([[0, 0, 0], [1, 1, 1]])
    y_train = [0, 1]

    for n_features in [1, 2, 3]:
        sel = SelectKBest(chi2, k=n_features).fit(X_train, y_train)
        X_test = sel.transform([[0, 1, 2]])
        assert_array_equal(X_test[0], np.arange(3)[-n_features:])
Ejemplo n.º 10
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def test_select_kbest_all():
    # Test whether k="all" correctly returns all features.
    X, y = make_classification(n_samples=20,
                               n_features=10,
                               shuffle=False,
                               random_state=0)

    univariate_filter = SelectKBest(f_classif, k='all')
    X_r = univariate_filter.fit(X, y).transform(X)
    assert_array_equal(X, X_r)
Ejemplo n.º 11
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def test_nans():
    # Assert that SelectKBest and SelectPercentile can handle NaNs.
    # First feature has zero variance to confuse f_classif (ANOVA) and
    # make it return a NaN.
    X = [[0, 1, 0], [0, -1, -1], [0, .5, .5]]
    y = [1, 0, 1]

    for select in (SelectKBest(f_classif,
                               2), SelectPercentile(f_classif, percentile=67)):
        ignore_warnings(select.fit)(X, y)
        assert_array_equal(select.get_support(indices=True), np.array([1, 2]))
Ejemplo n.º 12
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def test_scorefunc_multilabel():
    # Test whether k-best and percentiles works with multilabels with chi2.

    X = np.array([[10000, 9999, 0], [100, 9999, 0], [1000, 99, 0]])
    y = [[1, 1], [0, 1], [1, 0]]

    Xt = SelectKBest(chi2, k=2).fit_transform(X, y)
    assert Xt.shape == (3, 2)
    assert 0 not in Xt

    Xt = SelectPercentile(chi2, percentile=67).fit_transform(X, y)
    assert Xt.shape == (3, 2)
    assert 0 not in Xt
Ejemplo n.º 13
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def test_pipeline_methods_anova():
    # Test the various methods of the pipeline (anova).
    iris = load_iris()
    X = iris.data
    y = iris.target
    # Test with Anova + LogisticRegression
    clf = LogisticRegression()
    filter1 = SelectKBest(f_classif, k=2)
    pipe = Pipeline([('anova', filter1), ('logistic', clf)])
    pipe.fit(X, y)
    pipe.predict(X)
    pipe.predict_proba(X)
    pipe.predict_log_proba(X)
    pipe.score(X, y)
Ejemplo n.º 14
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def test_select_kbest_zero():
    # Test whether k=0 correctly returns no features.
    X, y = make_classification(n_samples=20,
                               n_features=10,
                               shuffle=False,
                               random_state=0)

    univariate_filter = SelectKBest(f_classif, k=0)
    univariate_filter.fit(X, y)
    support = univariate_filter.get_support()
    gtruth = np.zeros(10, dtype=bool)
    assert_array_equal(support, gtruth)
    X_selected = assert_warns_message(UserWarning, 'No features were selected',
                                      univariate_filter.transform, X)
    assert X_selected.shape == (20, 0)
Ejemplo n.º 15
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def test_feature_union():
    # basic sanity check for feature union
    iris = load_iris()
    X = iris.data
    X -= X.mean(axis=0)
    y = iris.target
    svd = TruncatedSVD(n_components=2, random_state=0)
    select = SelectKBest(k=1)
    fs = FeatureUnion([("svd", svd), ("select", select)])
    fs.fit(X, y)
    X_transformed = fs.transform(X)
    assert X_transformed.shape == (X.shape[0], 3)

    # check if it does the expected thing
    assert_array_almost_equal(X_transformed[:, :-1], svd.fit_transform(X))
    assert_array_equal(X_transformed[:, -1],
                       select.fit_transform(X, y).ravel())

    # test if it also works for sparse input
    # We use a different svd object to control the random_state stream
    fs = FeatureUnion([("svd", svd), ("select", select)])
    X_sp = sparse.csr_matrix(X)
    X_sp_transformed = fs.fit_transform(X_sp, y)
    assert_array_almost_equal(X_transformed, X_sp_transformed.toarray())

    # Test clone
    fs2 = assert_no_warnings(clone, fs)
    assert fs.transformer_list[0][1] is not fs2.transformer_list[0][1]

    # test setting parameters
    fs.set_params(select__k=2)
    assert fs.fit_transform(X, y).shape == (X.shape[0], 4)

    # test it works with transformers missing fit_transform
    fs = FeatureUnion([("mock", Transf()), ("svd", svd), ("select", select)])
    X_transformed = fs.fit_transform(X, y)
    assert X_transformed.shape == (X.shape[0], 8)

    # test error if some elements do not support transform
    assert_raises_regex(TypeError,
                        'All estimators should implement fit and '
                        'transform.*\\bNoTrans\\b',
                        FeatureUnion,
                        [("transform", Transf()), ("no_transform", NoTrans())])

    # test that init accepts tuples
    fs = FeatureUnion((("svd", svd), ("select", select)))
    fs.fit(X, y)
Ejemplo n.º 16
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def test_tied_pvalues():
    # Test whether k-best and percentiles work with tied pvalues from chi2.
    # chi2 will return the same p-values for the following features, but it
    # will return different scores.
    X0 = np.array([[10000, 9999, 9998], [1, 1, 1]])
    y = [0, 1]

    for perm in itertools.permutations((0, 1, 2)):
        X = X0[:, perm]
        Xt = SelectKBest(chi2, k=2).fit_transform(X, y)
        assert Xt.shape == (2, 2)
        assert 9998 not in Xt

        Xt = SelectPercentile(chi2, percentile=67).fit_transform(X, y)
        assert Xt.shape == (2, 2)
        assert 9998 not in Xt
Ejemplo n.º 17
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def test_no_feature_selected():
    rng = np.random.RandomState(0)

    # Generate random uncorrelated data: a strict univariate test should
    # rejects all the features
    X = rng.rand(40, 10)
    y = rng.randint(0, 4, size=40)
    strict_selectors = [
        SelectFwe(alpha=0.01).fit(X, y),
        SelectFdr(alpha=0.01).fit(X, y),
        SelectFpr(alpha=0.01).fit(X, y),
        SelectPercentile(percentile=0).fit(X, y),
        SelectKBest(k=0).fit(X, y),
    ]
    for selector in strict_selectors:
        assert_array_equal(selector.get_support(), np.zeros(10))
        X_selected = assert_warns_message(UserWarning,
                                          'No features were selected',
                                          selector.transform, X)
        assert X_selected.shape == (40, 0)
Ejemplo n.º 18
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def test_select_kbest_regression():
    # Test whether the relative univariate feature selection
    # gets the correct items in a simple regression problem
    # with the k best heuristic
    X, y = make_regression(n_samples=200,
                           n_features=20,
                           n_informative=5,
                           shuffle=False,
                           random_state=0,
                           noise=10)

    univariate_filter = SelectKBest(f_regression, k=5)
    X_r = univariate_filter.fit(X, y).transform(X)
    assert_best_scores_kept(univariate_filter)
    X_r2 = GenericUnivariateSelect(f_regression, mode='k_best',
                                   param=5).fit(X, y).transform(X)
    assert_array_equal(X_r, X_r2)
    support = univariate_filter.get_support()
    gtruth = np.zeros(20)
    gtruth[:5] = 1
    assert_array_equal(support, gtruth)
Ejemplo n.º 19
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def test_pipeline_init():
    # Test the various init parameters of the pipeline.
    assert_raises(TypeError, Pipeline)
    # Check that we can't instantiate pipelines with objects without fit
    # method
    assert_raises_regex(TypeError,
                        'Last step of Pipeline should implement fit '
                        'or be the string \'passthrough\''
                        '.*NoFit.*',
                        Pipeline, [('clf', NoFit())])
    # Smoke test with only an estimator
    clf = NoTrans()
    pipe = Pipeline([('svc', clf)])
    assert (pipe.get_params(deep=True) ==
                 dict(svc__a=None, svc__b=None, svc=clf,
                      **pipe.get_params(deep=False)))

    # Check that params are set
    pipe.set_params(svc__a=0.1)
    assert clf.a == 0.1
    assert clf.b is None
    # Smoke test the repr:
    repr(pipe)

    # Test with two objects
    clf = SVC()
    filter1 = SelectKBest(f_classif)
    pipe = Pipeline([('anova', filter1), ('svc', clf)])

    # Check that estimators are not cloned on pipeline construction
    assert pipe.named_steps['anova'] is filter1
    assert pipe.named_steps['svc'] is clf

    # Check that we can't instantiate with non-transformers on the way
    # Note that NoTrans implements fit, but not transform
    assert_raises_regex(TypeError,
                        'All intermediate steps should be transformers'
                        '.*\\bNoTrans\\b.*',
                        Pipeline, [('t', NoTrans()), ('svc', clf)])

    # Check that params are set
    pipe.set_params(svc__C=0.1)
    assert clf.C == 0.1
    # Smoke test the repr:
    repr(pipe)

    # Check that params are not set when naming them wrong
    assert_raises(ValueError, pipe.set_params, anova__C=0.1)

    # Test clone
    pipe2 = assert_no_warnings(clone, pipe)
    assert not pipe.named_steps['svc'] is pipe2.named_steps['svc']

    # Check that apart from estimators, the parameters are the same
    params = pipe.get_params(deep=True)
    params2 = pipe2.get_params(deep=True)

    for x in pipe.get_params(deep=False):
        params.pop(x)

    for x in pipe2.get_params(deep=False):
        params2.pop(x)

    # Remove estimators that where copied
    params.pop('svc')
    params.pop('anova')
    params2.pop('svc')
    params2.pop('anova')
    assert params == params2
Ejemplo n.º 20
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pipe = Pipeline([
    # the reduce_dim stage is populated by the param_grid
    ('reduce_dim', 'passthrough'),
    ('classify', LinearSVC(dual=False, max_iter=10000))
])

N_FEATURES_OPTIONS = [2, 4, 8]
C_OPTIONS = [1, 10, 100, 1000]
param_grid = [
    {
        'reduce_dim': [PCA(iterated_power=7), NMF()],
        'reduce_dim__n_components': N_FEATURES_OPTIONS,
        'classify__C': C_OPTIONS
    },
    {
        'reduce_dim': [SelectKBest(chi2)],
        'reduce_dim__k': N_FEATURES_OPTIONS,
        'classify__C': C_OPTIONS
    },
]
reducer_labels = ['PCA', 'NMF', 'KBest(chi2)']

grid = GridSearchCV(pipe, n_jobs=1, param_grid=param_grid)
X, y = load_digits(return_X_y=True)
grid.fit(X, y)

mean_scores = np.array(grid.cv_results_['mean_test_score'])
# scores are in the order of param_grid iteration, which is alphabetical
mean_scores = mean_scores.reshape(len(C_OPTIONS), -1, len(N_FEATURES_OPTIONS))
# select score for best C
mean_scores = mean_scores.max(axis=0)
Ejemplo n.º 21
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from mrex.datasets import samples_generator
from mrex.feature_selection import SelectKBest, f_regression
from mrex.pipeline import make_pipeline
from mrex.model_selection import train_test_split
from mrex.metrics import classification_report

print(__doc__)

# import some data to play with
X, y = samples_generator.make_classification(n_features=20,
                                             n_informative=3,
                                             n_redundant=0,
                                             n_classes=4,
                                             n_clusters_per_class=2)

X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)

# ANOVA SVM-C
# 1) anova filter, take 3 best ranked features
anova_filter = SelectKBest(f_regression, k=3)
# 2) svm
clf = svm.LinearSVC()

anova_svm = make_pipeline(anova_filter, clf)
anova_svm.fit(X_train, y_train)
y_pred = anova_svm.predict(X_test)
print(classification_report(y_test, y_pred))

coef = anova_svm[:-1].inverse_transform(anova_svm['linearsvc'].coef_)
print(coef)
Ejemplo n.º 22
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from mrex.pipeline import Pipeline, FeatureUnion
from mrex.model_selection import GridSearchCV
from mrex.svm import SVC
from mrex.datasets import load_iris
from mrex.decomposition import PCA
from mrex.feature_selection import SelectKBest

iris = load_iris()

X, y = iris.data, iris.target

# This dataset is way too high-dimensional. Better do PCA:
pca = PCA(n_components=2)

# Maybe some original features where good, too?
selection = SelectKBest(k=1)

# Build estimator from PCA and Univariate selection:

combined_features = FeatureUnion([("pca", pca), ("univ_select", selection)])

# Use combined features to transform dataset:
X_features = combined_features.fit(X, y).transform(X)
print("Combined space has", X_features.shape[1], "features")

svm = SVC(kernel="linear")

# Do grid search over k, n_components and C:

pipeline = Pipeline([("features", combined_features), ("svm", svm)])
Ejemplo n.º 23
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# Split dataset to select feature and evaluate the classifier
X_train, X_test, y_train, y_test = train_test_split(X,
                                                    y,
                                                    stratify=y,
                                                    random_state=0)

plt.figure(1)
plt.clf()

X_indices = np.arange(X.shape[-1])

# #############################################################################
# Univariate feature selection with F-test for feature scoring
# We use the default selection function to select the four
# most significant features
selector = SelectKBest(f_classif, k=4)
selector.fit(X_train, y_train)
scores = -np.log10(selector.pvalues_)
scores /= scores.max()
plt.bar(X_indices - .45,
        scores,
        width=.2,
        label=r'Univariate score ($-Log(p_{value})$)',
        color='darkorange',
        edgecolor='black')

# #############################################################################
# Compare to the weights of an SVM
clf = make_pipeline(MinMaxScaler(), LinearSVC())
clf.fit(X_train, y_train)
print('Classification accuracy without selecting features: {:.3f}'.format(
Ejemplo n.º 24
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def test_gridsearch_pipeline():
    # render a pipeline inside a gridsearch
    pp = _EstimatorPrettyPrinter(compact=True, indent=1, indent_at_name=True)

    pipeline = Pipeline([
        ('reduce_dim', PCA()),
        ('classify', SVC())
    ])
    N_FEATURES_OPTIONS = [2, 4, 8]
    C_OPTIONS = [1, 10, 100, 1000]
    param_grid = [
        {
            'reduce_dim': [PCA(iterated_power=7), NMF()],
            'reduce_dim__n_components': N_FEATURES_OPTIONS,
            'classify__C': C_OPTIONS
        },
        {
            'reduce_dim': [SelectKBest(chi2)],
            'reduce_dim__k': N_FEATURES_OPTIONS,
            'classify__C': C_OPTIONS
        }
    ]
    gspipline = GridSearchCV(pipeline, cv=3, n_jobs=1, param_grid=param_grid)
    expected = """
GridSearchCV(cv=3, error_score='raise-deprecating',
             estimator=Pipeline(memory=None,
                                steps=[('reduce_dim',
                                        PCA(copy=True, iterated_power='auto',
                                            n_components=None,
                                            random_state=None,
                                            svd_solver='auto', tol=0.0,
                                            whiten=False)),
                                       ('classify',
                                        SVC(C=1.0, cache_size=200,
                                            class_weight=None, coef0=0.0,
                                            decision_function_shape='ovr',
                                            degree=3, gamma='auto_deprecated',
                                            kernel='rbf', max_iter=-1,
                                            probability=False,
                                            random_state=None, shrinking=True,
                                            tol=0.001, verbose=False))]),
             iid='warn', n_jobs=1,
             param_grid=[{'classify__C': [1, 10, 100, 1000],
                          'reduce_dim': [PCA(copy=True, iterated_power=7,
                                             n_components=None,
                                             random_state=None,
                                             svd_solver='auto', tol=0.0,
                                             whiten=False),
                                         NMF(alpha=0.0, beta_loss='frobenius',
                                             init=None, l1_ratio=0.0,
                                             max_iter=200, n_components=None,
                                             random_state=None, shuffle=False,
                                             solver='cd', tol=0.0001,
                                             verbose=0)],
                          'reduce_dim__n_components': [2, 4, 8]},
                         {'classify__C': [1, 10, 100, 1000],
                          'reduce_dim': [SelectKBest(k=10,
                                                     score_func=<function chi2 at some_address>)],
                          'reduce_dim__k': [2, 4, 8]}],
             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,
             scoring=None, verbose=0)"""

    expected = expected[1:]  # remove first \n
    repr_ = pp.pformat(gspipline)
    # Remove address of '<function chi2 at 0x.....>' for reproducibility
    repr_ = re.sub('function chi2 at 0x.*>',
                   'function chi2 at some_address>', repr_)
    assert repr_ == expected
Ejemplo n.º 25
0
def mkchi2(k):
    """Make k-best chi2 selector"""
    return SelectKBest(chi2, k=k)