https://github.com/aloncrack7/data-degradation-detector - 1.0.6

API Reference

univariate module

• get_distribution_descriptors(column: pd.Series) -> DistributionDescriptors
  Compute distribution descriptors (mean, std, min, max, quartiles) for a column.
• get_distribution_descriptors_all_columns(df: pd.DataFrame) -> dict
  Compute descriptors for all columns in a DataFrame.
• plot_distribution_descriptors(column: pd.Series, ...)
  Plot distribution and descriptors for a column.
• plot_distribution_descriptors_all_columns(df: pd.DataFrame, ...)
  Plot distributions for all columns.
• compare_distributions(original: pd.Series, new_data: pd.Series, ...) -> DistributionChanges
  Compare two distributions and visualize changes.
• compare_distribbutions_all_columns(original: pd.DataFrame, new_data: pd.DataFrame, ...)
  Compare all columns between two DataFrames.
• descriptor_evolution(dfs: list[pd.Series], ...)
  Plot evolution of descriptors for a column across multiple DataFrames.
• descriptor_evolution_all_columns(dfs: list[pd.DataFrame], ...)
  Plot evolution for all columns.

multivariate module

• get_best_clusters(X: pd.DataFrame, path: str = None, plot: bool = True) -> Cluster_statistics
  Find optimal KMeans clusters and return statistics.
• get_cluster_defined_number(X: pd.DataFrame, num_clusters: int, ...) -> Cluster_statistics
  Run KMeans with a fixed number of clusters.
• compare_clusters(cluster_stats1, cluster_stats2, delta=0.1) -> ClusterChanges
  Compare two clusterings and return changes.
• clustering_evolution(dfs: list[pd.DataFrame], num_clusters: int, ...)
  Visualize clustering evolution across multiple DataFrames.
• correlation_matrix(df: pd.DataFrame, path: str = None)
  Plot and/or save a correlation matrix heatmap.
• get_cluster_info_from_json(json_data)
  Load cluster statistics from JSON.

Classes

• Cluster_statistics
  Holds statistics for a clustering (num_clusters, silhouette, centroids, radius, label percentages).
• ClusterChanges
  Represents and quantifies changes between two clusterings.

report module

• get_number_of_output_classes(y: pd.Series) -> int
  Get the number of unique classes in a target column.
• create_initial_report(df: pd.DataFrame, target: str, base_metrics: dict, path: str, number_of_output_classes: int = None)
  Generate initial visualizations and statistics for a dataset.
• create_report(original_df, original_clusters, degraded_dfs, base_metrics, path, new_metrics=None)
  Generate a full report comparing original and degraded datasets.

- Jupyter Notebook
Published by aloncrack7 6 months ago