relevanceai.recipes.model_observability.cluster.report#

Module Contents#

class relevanceai.recipes.model_observability.cluster.report.ClusterReport(name: str, dataset, deployable_id: str = None, **kwargs)#

Adding a pyplot block to Report

start_cluster_evaluator(self, X: Union[list, numpy.ndarray], cluster_labels: Union[List[Union[str, float]], numpy.ndarray], centroids: Union[list, numpy.ndarray, str] = None, cluster_names: Union[list, dict] = None, feature_names: Union[list, dict] = None, model=None, outlier_label: Union[str, int] = - 1, metric: str = 'euclidean', verbose: bool = False)#
start_cluster_evaluator_from_dataset(self, vector_fields: list, alias: str, feature_names: Union[list, dict] = None, metric: str = 'euclidean', verbose: bool = False, show_progress_bar: bool = False)#
section_cluster_report(self, hierarchy_methods=['ward'], color_threshold: float = 1.25, plot_method=None, add=True)#
section_cluster_overview_metrics(self, add=True)#
section_cluster_dendrogram(self, hierarchy_methods=['ward'], color_threshold: float = 1, orientation: str = 'left', plot_method=None, add=True)#
section_cluster_distance_matrix(self, metrics=['cosine', 'euclidean'], decimals: int = 4, add=True)#