def test_lof():
    # Toy sample (the last two samples are outliers):
    X = [[-2, -1], [-1, -1], [-1, -2], [1, 1], [1, 2], [2, 1], [5, 3], [-4, 2]]

    # Test LocalOutlierFactor:
    clf = neighbors.LocalOutlierFactor(n_neighbors=5)
    score = clf.fit(X).negative_outlier_factor_
    assert_array_equal(clf._fit_X, X)

    # Assert largest outlier score is smaller than smallest inlier score:
    assert_greater(np.min(score[:-2]), np.max(score[-2:]))

    # Assert predict() works:
    clf = neighbors.LocalOutlierFactor(contamination=0.25,
                                       n_neighbors=5).fit(X)
    assert_array_equal(clf._predict(), 6 * [1] + 2 * [-1])
    assert_array_equal(clf.fit_predict(X), 6 * [1] + 2 * [-1]) 

def test_lof_values():
    # toy samples:
    X_train = [[1, 1], [1, 2], [2, 1]]
    clf1 = neighbors.LocalOutlierFactor(n_neighbors=2,
                                        contamination=0.1,
                                        novelty=True).fit(X_train)
    clf2 = neighbors.LocalOutlierFactor(n_neighbors=2,
                                        novelty=True).fit(X_train)
    s_0 = 2. * sqrt(2.) / (1. + sqrt(2.))
    s_1 = (1. + sqrt(2)) * (1. / (4. * sqrt(2.)) + 1. / (2. + 2. * sqrt(2)))
    # check predict()
    assert_array_almost_equal(-clf1.negative_outlier_factor_, [s_0, s_1, s_1])
    assert_array_almost_equal(-clf2.negative_outlier_factor_, [s_0, s_1, s_1])
    # check predict(one sample not in train)
    assert_array_almost_equal(-clf1.score_samples([[2., 2.]]), [s_0])
    assert_array_almost_equal(-clf2.score_samples([[2., 2.]]), [s_0])
    # check predict(one sample already in train)
    assert_array_almost_equal(-clf1.score_samples([[1., 1.]]), [s_1])
    assert_array_almost_equal(-clf2.score_samples([[1., 1.]]), [s_1]) 

def test_lof_precomputed(random_state=42):
    """Tests LOF with a distance matrix."""
    # Note: smaller samples may result in spurious test success
    rng = np.random.RandomState(random_state)
    X = rng.random_sample((10, 4))
    Y = rng.random_sample((3, 4))
    DXX = metrics.pairwise_distances(X, metric='euclidean')
    DYX = metrics.pairwise_distances(Y, X, metric='euclidean')
    # As a feature matrix (n_samples by n_features)
    lof_X = neighbors.LocalOutlierFactor(n_neighbors=3, novelty=True)
    lof_X.fit(X)
    pred_X_X = lof_X._predict()
    pred_X_Y = lof_X.predict(Y)

    # As a dense distance matrix (n_samples by n_samples)
    lof_D = neighbors.LocalOutlierFactor(n_neighbors=3, algorithm='brute',
                                         metric='precomputed', novelty=True)
    lof_D.fit(DXX)
    pred_D_X = lof_D._predict()
    pred_D_Y = lof_D.predict(DYX)

    assert_array_almost_equal(pred_X_X, pred_D_X)
    assert_array_almost_equal(pred_X_Y, pred_D_Y) 

def test_novelty_training_scores():
    # check that the scores of the training samples are still accessible
    # when novelty=True through the negative_outlier_factor_ attribute
    X = iris.data

    # fit with novelty=False
    clf_1 = neighbors.LocalOutlierFactor()
    clf_1.fit(X)
    scores_1 = clf_1.negative_outlier_factor_

    # fit with novelty=True
    clf_2 = neighbors.LocalOutlierFactor(novelty=True)
    clf_2.fit(X)
    scores_2 = clf_2.negative_outlier_factor_

    assert_array_almost_equal(scores_1, scores_2) 

def test_hasattr_prediction():
    # check availability of prediction methods depending on novelty value.
    X = [[1, 1], [1, 2], [2, 1]]

    # when novelty=True
    clf = neighbors.LocalOutlierFactor(novelty=True)
    clf.fit(X)
    assert hasattr(clf, 'predict')
    assert hasattr(clf, 'decision_function')
    assert hasattr(clf, 'score_samples')
    assert not hasattr(clf, 'fit_predict')

    # when novelty=False
    clf = neighbors.LocalOutlierFactor(novelty=False)
    clf.fit(X)
    assert hasattr(clf, 'fit_predict')
    assert not hasattr(clf, 'predict')
    assert not hasattr(clf, 'decision_function')
    assert not hasattr(clf, 'score_samples') 

def test_lof():
    # Toy sample (the last two samples are outliers):
    X = [[-2, -1], [-1, -1], [-1, -2], [1, 1], [1, 2], [2, 1], [5, 3], [-4, 2]]

    # Test LocalOutlierFactor:
    clf = neighbors.LocalOutlierFactor(n_neighbors=5)
    score = clf.fit(X).negative_outlier_factor_
    assert_array_equal(clf._fit_X, X)

    # Assert largest outlier score is smaller than smallest inlier score:
    assert_greater(np.min(score[:-2]), np.max(score[-2:]))

    # Assert predict() works:
    clf = neighbors.LocalOutlierFactor(contamination=0.25,
                                       n_neighbors=5).fit(X)
    assert_array_equal(clf._predict(), 6 * [1] + 2 * [-1]) 

def test_lof_performance():
    # Generate train/test data
    rng = check_random_state(2)
    X = 0.3 * rng.randn(120, 2)
    X_train = X[:100]

    # Generate some abnormal novel observations
    X_outliers = rng.uniform(low=-4, high=4, size=(20, 2))
    X_test = np.r_[X[100:], X_outliers]
    y_test = np.array([0] * 20 + [1] * 20)

    # fit the model
    clf = neighbors.LocalOutlierFactor().fit(X_train)

    # predict scores (the lower, the more normal)
    y_pred = -clf._decision_function(X_test)

    # check that roc_auc is good
    assert_greater(roc_auc_score(y_test, y_pred), .99) 

def test_lof_precomputed(random_state=42):
    """Tests LOF with a distance matrix."""
    # Note: smaller samples may result in spurious test success
    rng = np.random.RandomState(random_state)
    X = rng.random_sample((10, 4))
    Y = rng.random_sample((3, 4))
    DXX = metrics.pairwise_distances(X, metric='euclidean')
    DYX = metrics.pairwise_distances(Y, X, metric='euclidean')
    # As a feature matrix (n_samples by n_features)
    lof_X = neighbors.LocalOutlierFactor(n_neighbors=3)
    lof_X.fit(X)
    pred_X_X = lof_X._predict()
    pred_X_Y = lof_X._predict(Y)

    # As a dense distance matrix (n_samples by n_samples)
    lof_D = neighbors.LocalOutlierFactor(n_neighbors=3, algorithm='brute',
                                         metric='precomputed')
    lof_D.fit(DXX)
    pred_D_X = lof_D._predict()
    pred_D_Y = lof_D._predict(DYX)

    assert_array_almost_equal(pred_X_X, pred_D_X)
    assert_array_almost_equal(pred_X_Y, pred_D_Y) 

def fit(self, X, y=None):
        """Fit detector. y is ignored in unsupervised methods.

        Parameters
        ----------
        X : numpy array of shape (n_samples, n_features)
            The input samples.

        y : Ignored
            Not used, present for API consistency by convention.

        Returns
        -------
        self : object
            Fitted estimator.
        """
        # validate inputs X and y (optional)
        X = check_array(X)
        self._set_n_classes(y)

        self.detector_ = LocalOutlierFactor(n_neighbors=self.n_neighbors,
                                            algorithm=self.algorithm,
                                            leaf_size=self.leaf_size,
                                            metric=self.metric,
                                            p=self.p,
                                            metric_params=self.metric_params,
                                            contamination=self.contamination,
                                            n_jobs=self.n_jobs)
        self.detector_.fit(X=X, y=y)

        # Invert decision_scores_. Outliers comes with higher outlier scores
        self.decision_scores_ = invert_order(
            self.detector_.negative_outlier_factor_)
        self._process_decision_scores()
        return self 

def test_lof_performance():
    # Generate train/test data
    rng = check_random_state(2)
    X = 0.3 * rng.randn(120, 2)
    X_train = X[:100]

    # Generate some abnormal novel observations
    X_outliers = rng.uniform(low=-4, high=4, size=(20, 2))
    X_test = np.r_[X[100:], X_outliers]
    y_test = np.array([0] * 20 + [1] * 20)

    # fit the model for novelty detection
    clf = neighbors.LocalOutlierFactor(novelty=True).fit(X_train)

    # predict scores (the lower, the more normal)
    y_pred = -clf.decision_function(X_test)

    # check that roc_auc is good
    assert_greater(roc_auc_score(y_test, y_pred), .99) 

def test_n_neighbors_attribute():
    X = iris.data
    clf = neighbors.LocalOutlierFactor(n_neighbors=500).fit(X)
    assert_equal(clf.n_neighbors_, X.shape[0] - 1)

    clf = neighbors.LocalOutlierFactor(n_neighbors=500)
    assert_warns_message(UserWarning,
                         "n_neighbors will be set to (n_samples - 1)",
                         clf.fit, X)
    assert_equal(clf.n_neighbors_, X.shape[0] - 1) 

def test_contamination():
    X = [[1, 1], [1, 0]]
    clf = neighbors.LocalOutlierFactor(contamination=0.6)
    assert_raises(ValueError, clf.fit, X) 

def test_novelty_errors():
    X = iris.data

    # check errors for novelty=False
    clf = neighbors.LocalOutlierFactor()
    clf.fit(X)
    # predict, decision_function and score_samples raise ValueError
    for method in ['predict', 'decision_function', 'score_samples']:
        msg = ('{} is not available when novelty=False'.format(method))
        assert_raises_regex(AttributeError, msg, getattr, clf, method)

    # check errors for novelty=True
    clf = neighbors.LocalOutlierFactor(novelty=True)
    msg = 'fit_predict is not available when novelty=True'
    assert_raises_regex(AttributeError, msg, getattr, clf, 'fit_predict') 

def test_novelty_true_common_tests():

    # the common tests are run for the default LOF (novelty=False).
    # here we run these common tests for LOF when novelty=True
    check_estimator(neighbors.LocalOutlierFactor(novelty=True)) 

def test_predicted_outlier_number():
    # the number of predicted outliers should be equal to the number of
    # expected outliers unless there are ties in the abnormality scores.
    X = iris.data
    n_samples = X.shape[0]
    expected_outliers = 30
    contamination = float(expected_outliers)/n_samples

    clf = neighbors.LocalOutlierFactor(contamination=contamination)
    y_pred = clf.fit_predict(X)

    num_outliers = np.sum(y_pred != 1)
    if num_outliers != expected_outliers:
        y_dec = clf.negative_outlier_factor_
        check_outlier_corruption(num_outliers, expected_outliers, y_dec) 

def _fit(self, X):
        self.estimator_   = LocalOutlierFactor(
            algorithm     = self.algorithm,
            contamination = self.contamination,
            leaf_size     = self.leaf_size,
            metric        = self.metric,
            novelty       = self.novelty,
            n_jobs        = self.n_jobs,
            n_neighbors   = self.n_neighbors,
            p             = self.p,
            metric_params = self.metric_params
        ).fit(X)

        return self 

def _get_pipeline(self):
        return [('scaler', StandardScaler()),
                ('model', LocalOutlierFactor(n_jobs=self.conf.n_jobs,
                                             novelty=True))] 

def test_lof_values():
    # toy samples:
    X_train = [[1, 1], [1, 2], [2, 1]]
    clf = neighbors.LocalOutlierFactor(n_neighbors=2).fit(X_train)
    s_0 = 2. * sqrt(2.) / (1. + sqrt(2.))
    s_1 = (1. + sqrt(2)) * (1. / (4. * sqrt(2.)) + 1. / (2. + 2. * sqrt(2)))
    # check predict()
    assert_array_almost_equal(-clf.negative_outlier_factor_, [s_0, s_1, s_1])
    # check predict(one sample not in train)
    assert_array_almost_equal(-clf._decision_function([[2., 2.]]), [s_0])
    # # check predict(one sample already in train)
    assert_array_almost_equal(-clf._decision_function([[1., 1.]]), [s_1]) 

def test_n_neighbors_attribute():
    X = iris.data
    clf = neighbors.LocalOutlierFactor(n_neighbors=500).fit(X)
    assert_equal(clf.n_neighbors_, X.shape[0] - 1)

    clf = neighbors.LocalOutlierFactor(n_neighbors=500)
    assert_warns_message(UserWarning,
                         "n_neighbors will be set to (n_samples - 1)",
                         clf.fit, X)
    assert_equal(clf.n_neighbors_, X.shape[0] - 1) 

def remove_outliers(object_points):
    if len(object_points) > 100:
        points_t0 = object_points[:, 0]
        points_t1 = object_points[:, 1]
        mask = np.zeros(len(object_points), dtype=np.bool)
        # fit the model for outlier detection (default)
        for points in [points_t0, points_t1]:
            clf = LocalOutlierFactor(n_neighbors=20)
            clf.fit_predict(points)
            X_scores = clf.negative_outlier_factor_
            X_scores = (X_scores.max() - X_scores) / (X_scores.max() - X_scores.min())
            median_score = np.median(X_scores)
            mask = np.logical_or([X_scores[i] > median_score for i in range(len(points))], mask)

            # print(X_scores)
            # print('median_score ', mean_score)
            # plt.title("Local Outlier Factor (LOF)")
            # plt.scatter(points[:, 0], points[:, 2], color='k', s=3., label='Data points')
            # # plot circles with radius proportional to the outlier scores
            # plt.scatter(points[:, 0], points[:, 2], s=1000 * X_scores, edgecolors='r',
            #             facecolors='none', label='Outlier scores')
            # plt.axis('tight')
            # plt.xlim((-5, 5))
            # plt.ylim((-5, 5))
            # legend = plt.legend(loc='upper left')
            # legend.legendHandles[0]._sizes = [10]
            # legend.legendHandles[1]._sizes = [20]

            # points = points[np.logical_not(mask)]
            # X_scores = X_scores[np.logical_not(mask)]
            # plt.title("Local Outlier Factor (LOF)")
            # plt.scatter(points[:, 0], points[:, 2], color='k', s=3., label='Data points')
            # # plot circles with radius proportional to the outlier scores
            # plt.scatter(points[:, 0], points[:, 2], s=1000 * X_scores, edgecolors='r',
            #             facecolors='none', label='Outlier scores')
            # plt.axis('tight')
            # plt.xlim((-5, 5))
            # plt.ylim((-5, 5))
            # legend = plt.legend(loc='upper left')
            # legend.legendHandles[0]._sizes = [10]
            # legend.legendHandles[1]._sizes = [20]
            # plt.show()
        if len(object_points[np.logical_not(mask)]) > 10:
            object_points = object_points[np.logical_not(mask)]

    return object_points