def plot(self, X=None, palette='Set2'):
if X is None:
X = self.X
labels = self.labels
_scatter_clusters_outliers(_store_X(X, True), labels, palette)
def plot(self, X='None', palette='Set2'):
if X is None:
X = self.X
labels = self.labels
factor = self.negative_outlier_factor
_scatter_clusters_outliers_local(_store_X(X, True), labels, factor,
False, palette)
def __init__(self, obj, X, labels_true=None, store_X=False, digits=3):
self.model = obj
self.labels = obj.predict(X)
self.n_clusters = np.unique(self.labels)
self.variables = obj.n_features_in_
self.SIL, self.DB, self.CH = _clustering_metrics(
self.labels, X, digits)
self.centers = _clust_centers_X(X, self.labels)
self.labels_names = np.unique(self.labels)
self.cluster_size, self.cluster_weights = _clust_weight(self.labels)
self.ARI, self.FM = _clustering_evaluation(self.labels, labels_true,
digits)
self.X = _store_X(X, store_X)
self.n_estimators = obj.n_estimators
self.base_estimator = obj.base_estimator_
self.max_features = obj.max_features
self.bootstrap = obj.bootstrap
self.contamination = obj.offset_
self.max_samples = obj.max_samples
def __init__(self, obj, X, labels_true=None, store_X=False, digits=3):
self.model = obj
self.labels = obj.predict(X)
self.n_clusters = np.unique(self.labels)
self.variables = obj.n_features_in_
self.SIL, self.DB, self.CH = _clustering_metrics(
self.labels, X, digits)
self.centers = _clust_centers_X(X, self.labels)
self.labels_names = np.unique(self.labels)
self.cluster_size, self.cluster_weights = _clust_weight(self.labels)
self.ARI, self.FM = _clustering_evaluation(self.labels, labels_true,
digits)
self.X = _store_X(X, store_X)
self.C = obj.C
self.kernel = obj.kernel
self.degree = obj.degree
self.gamma = obj.gamma
self.shrinking = obj.shrinking
self.support_vectors_ = pd.DataFrame(obj.support_vectors_)
self.n_support = pd.DataFrame(obj.n_support_)
def __init__(self, obj, X, labels_true=None, store_X=False, digits=3):
self.model = obj
self.labels = obj.fit_predict(X)
self.n_clusters = np.unique(self.labels)
self.variables = obj.n_features_in_
self.SIL, self.DB, self.CH = _clustering_metrics(
self.labels, X, digits)
self.centers = _clust_centers_X(X, self.labels)
self.labels_names = np.unique(self.labels)
self.cluster_size, self.cluster_weights = _clust_weight(self.labels)
self.ARI, self.FM = _clustering_evaluation(self.labels, labels_true,
digits)
self.X = _store_X(X, store_X)
self.labels_true = labels_true
self.n_neighbors = obj.n_neighbors_
self.radius = obj.radius
self.metric = obj.effective_metric_
self.p = obj.p
self.fit_method = obj._fit_method
self.leaf_size = obj.leaf_size
self.contamination = obj.offset_
self.novelty = obj.novelty
self.negative_outlier_factor = pd.Series(obj.negative_outlier_factor_)