o xig@sjdZddlmZddlZddlZddlmZmZddl m Z ede ddd d Z dd d Z dddZdS)z;Define plots for regression models built with scikit-learn.) simplefilterN) calculateutils)sharedignore)actioncategory RegressorcCsvtd|dt|||||tdt|||tdt|||tdt|||tddS)a+Generates all sklearn regressor plots supported by W&B. The following plots are generated: learning curve, summary metrics, residuals plot, outlier candidates. Should only be called with a fitted regressor (otherwise an error is thrown). Args: model: (regressor) Takes in a fitted regressor. X_train: (arr) Training set features. y_train: (arr) Training set labels. X_test: (arr) Test set features. y_test: (arr) Test set labels. model_name: (str) Model name. Defaults to 'Regressor' Returns: None: To see plots, go to your W&B run page then expand the 'media' tab under 'auto visualizations'. Example: ```python wandb.sklearn.plot_regressor(reg, X_train, X_test, y_train, y_test, "Ridge") ``` z Plotting .zLogged summary metrics.zLogged learning curve.zLogged outlier candidates.zLogged residuals.N)wandbtermlogrsummary_metricslearning_curveoutlier_candidates residuals)modelX_trainX_testy_trainy_test model_namer\/home/ubuntu/.local/lib/python3.10/site-packages/wandb/integration/sklearn/plot/regressor.py regressors     rcCltj|||d}tj|||d}t|}|r0|r2|r4t|}t|||}t d|idSdSdSdS)aMeasures a datapoint's influence on regression model via cook's distance. Instances with high influences could potentially be outliers. Should only be called with a fitted regressor (otherwise an error is thrown). Please note this function fits the model on the training set when called. Args: model: (regressor) Takes in a fitted regressor. X: (arr) Training set features. y: (arr) Training set labels. Returns: None: To see plots, go to your W&B run page then expand the 'media' tab under 'auto visualizations'. Example: ```python wandb.sklearn.plot_outlier_candidates(model, X, y) ``` rXyrN) r test_missing test_types test_fittednpasarrayrrr log)rrr is_missing correct_types is_fittedoutliers_chartrrrr8    rcCr)aMeasures and plots the regressor's predicted value against the residual. The marginal distribution of residuals is also calculated and plotted. Should only be called with a fitted regressor (otherwise an error is thrown). Please note this function fits variations of the model on the training set when called. Args: regressor: (regressor) Takes in a fitted regressor. X: (arr) Training set features. y: (arr) Training set labels. Returns: None: To see plots, go to your W&B run page then expand the 'media' tab under 'auto visualizations'. Example: ```python wandb.sklearn.plot_residuals(model, X, y) ``` rrN) rrr r!r"r#rrr r$)rrr not_missingr&r'residuals_chartrrrrZr)r)r )NNN)__doc__warningsrnumpyr"r wandb.integration.sklearnrrr FutureWarningrrrrrrrs     ("