Python covariance.EllipticEnvelope方法代碼示例

# 需要導入模塊: from sklearn import covariance [as 別名]
# 或者: from sklearn.covariance import EllipticEnvelope [as 別名]
def test_elliptic_envelope():
    rnd = np.random.RandomState(0)
    X = rnd.randn(100, 10)
    clf = EllipticEnvelope(contamination=0.1)
    assert_raises(NotFittedError, clf.predict, X)
    assert_raises(NotFittedError, clf.decision_function, X)
    clf.fit(X)
    y_pred = clf.predict(X)
    scores = clf.score_samples(X)
    decisions = clf.decision_function(X)

    assert_array_almost_equal(
        scores, -clf.mahalanobis(X))
    assert_array_almost_equal(clf.mahalanobis(X), clf.dist_)
    assert_almost_equal(clf.score(X, np.ones(100)),
                        (100 - y_pred[y_pred == -1].size) / 100.)
    assert(sum(y_pred == -1) == sum(decisions < 0)) 

# 需要導入模塊: from sklearn import covariance [as 別名]
# 或者: from sklearn.covariance import EllipticEnvelope [as 別名]
def initialize_fact_model(self):
        # Extract all RASA training sentences for all facts
        rasa_fact_paths = glob.glob(self.RASA_FACT_DIR + '*.json')
        all_sentences = []
        for fact_path in rasa_fact_paths:
            with open(fact_path, 'r') as f:
                file_json = json.loads(f.read().encode('utf-8'))
            for example in file_json['rasa_nlu_data']['common_examples']:
                all_sentences.append(example['text'].lower())

        # TF-IDF model
        tfidf_vectorizer = TfidfVectorizer(ngram_range=self.NGRAM_RANGE, strip_accents='ascii')
        X_tfidf = tfidf_vectorizer.fit_transform(all_sentences)

        # Fit to robust covariance estimation
        outlier_estimator = EllipticEnvelope(contamination=self.CONTAMINATION)
        outlier_estimator.fit(X_tfidf.toarray())

        # Binarize for future use
        with open(self.TFIFD_PICKLE_FILE, 'wb') as f:
            joblib.dump(tfidf_vectorizer, f, compress=True)
        with open(self.OUTLIER_PICKLE_FILE, 'wb') as f:
            joblib.dump(outlier_estimator, f, compress=True) 

# 需要導入模塊: from sklearn import covariance [as 別名]
# 或者: from sklearn.covariance import EllipticEnvelope [as 別名]
def fit_pipe(self, X, y=None):
        self.elliptic_envelope_ = EllipticEnvelope(**self.get_params())
        self.elliptic_envelope_.fit(X)
        return self.transform_pipe(X, y) 

# 需要導入模塊: from sklearn import covariance [as 別名]
# 或者: from sklearn.covariance import EllipticEnvelope [as 別名]
def __init__(self, _id, _config):
        super(EllipticEnvelope, self).__init__(_id, _config)
        self._nb_samples = int(_config['nb_samples']) 

# 需要導入模塊: from sklearn import covariance [as 別名]
# 或者: from sklearn.covariance import EllipticEnvelope [as 別名]
def get_default_config():
        return {
            'module': EllipticEnvelope.__name__,
            'nb_samples': N_SAMPLES
        } 

# 需要導入模塊: from sklearn import covariance [as 別名]
# 或者: from sklearn.covariance import EllipticEnvelope [as 別名]
def _get_best_detector(self, train):
        detector = covariance.EllipticEnvelope()
        detector.fit(train)
        return detector 

# 需要導入模塊: from sklearn import covariance [as 別名]
# 或者: from sklearn.covariance import EllipticEnvelope [as 別名]
def setUp(self):
        super(TestEllipticEnvelope, self).setUp()
        self.ee_sml = elliptic_envelope.EllipticEnvelope(
            "fakeid", {"module": "fake", "nb_samples": 1000}) 

# 需要導入模塊: from sklearn import covariance [as 別名]
# 或者: from sklearn.covariance import EllipticEnvelope [as 別名]
def test_learn_structure(self):
        data = self.get_testing_data()
        clf = self.ee_sml.learn_structure(data)
        self.assertIsInstance(clf, covariance.EllipticEnvelope) 

# 需要導入模塊: from sklearn import covariance [as 別名]
# 或者: from sklearn.covariance import EllipticEnvelope [as 別名]
def test_score_samples():
    X_train = [[1, 1], [1, 2], [2, 1]]
    clf1 = EllipticEnvelope(contamination=0.2).fit(X_train)
    clf2 = EllipticEnvelope().fit(X_train)
    assert_array_equal(clf1.score_samples([[2., 2.]]),
                       clf1.decision_function([[2., 2.]]) + clf1.offset_)
    assert_array_equal(clf2.score_samples([[2., 2.]]),
                       clf2.decision_function([[2., 2.]]) + clf2.offset_)
    assert_array_equal(clf1.score_samples([[2., 2.]]),
                       clf2.score_samples([[2., 2.]])) 

# 需要導入模塊: from sklearn import covariance [as 別名]
# 或者: from sklearn.covariance import EllipticEnvelope [as 別名]
def test_raw_values_deprecation():
    X = [[0.0], [1.0]]
    clf = EllipticEnvelope().fit(X)
    assert_warns_message(DeprecationWarning,
                         "raw_values parameter is deprecated in 0.20 and will"
                         " be removed in 0.22.",
                         clf.decision_function, X, raw_values=True) 

# 需要導入模塊: from sklearn import covariance [as 別名]
# 或者: from sklearn.covariance import EllipticEnvelope [as 別名]
def test_threshold_deprecation():
    X = [[0.0], [1.0]]
    clf = EllipticEnvelope().fit(X)
    assert_warns_message(DeprecationWarning,
                         "threshold_ attribute is deprecated in 0.20 and will"
                         " be removed in 0.22.",
                         getattr, clf, "threshold_") 

# 需要導入模塊: from sklearn import covariance [as 別名]
# 或者: from sklearn.covariance import EllipticEnvelope [as 別名]
def test_objectmapper(self):
        df = pdml.ModelFrame([])
        self.assertIs(df.covariance.EmpiricalCovariance, covariance.EmpiricalCovariance)
        self.assertIs(df.covariance.EllipticEnvelope, covariance.EllipticEnvelope)
        self.assertIs(df.covariance.GraphLasso, covariance.GraphLasso)
        self.assertIs(df.covariance.GraphLassoCV, covariance.GraphLassoCV)
        self.assertIs(df.covariance.LedoitWolf, covariance.LedoitWolf)
        self.assertIs(df.covariance.MinCovDet, covariance.MinCovDet)
        self.assertIs(df.covariance.OAS, covariance.OAS)
        self.assertIs(df.covariance.ShrunkCovariance, covariance.ShrunkCovariance)

        self.assertIs(df.covariance.shrunk_covariance, covariance.shrunk_covariance)
        self.assertIs(df.covariance.graph_lasso, covariance.graph_lasso)