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- """Define plots for regression models built with scikit-learn."""
- from warnings import simplefilter
- import numpy as np
- import wandb
- from wandb.integration.sklearn import calculate, utils
- from . import shared
- # ignore all future warnings
- simplefilter(action="ignore", category=FutureWarning)
- def regressor(model, X_train, X_test, y_train, y_test, model_name="Regressor"): # noqa: N803
- """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")
- ```
- """
- wandb.termlog(f"\nPlotting {model_name}.")
- shared.summary_metrics(model, X_train, y_train, X_test, y_test)
- wandb.termlog("Logged summary metrics.")
- shared.learning_curve(model, X_train, y_train)
- wandb.termlog("Logged learning curve.")
- outlier_candidates(model, X_train, y_train)
- wandb.termlog("Logged outlier candidates.")
- residuals(model, X_train, y_train)
- wandb.termlog("Logged residuals.")
- def outlier_candidates(regressor=None, X=None, y=None): # noqa: N803
- """Measures 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)
- ```
- """
- is_missing = utils.test_missing(regressor=regressor, X=X, y=y)
- correct_types = utils.test_types(regressor=regressor, X=X, y=y)
- is_fitted = utils.test_fitted(regressor)
- if is_missing and correct_types and is_fitted:
- y = np.asarray(y)
- outliers_chart = calculate.outlier_candidates(regressor, X, y)
- wandb.log({"outlier_candidates": outliers_chart})
- def residuals(regressor=None, X=None, y=None): # noqa: N803
- """Measures 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)
- ```
- """
- not_missing = utils.test_missing(regressor=regressor, X=X, y=y)
- correct_types = utils.test_types(regressor=regressor, X=X, y=y)
- is_fitted = utils.test_fitted(regressor)
- if not_missing and correct_types and is_fitted:
- y = np.asarray(y)
- residuals_chart = calculate.residuals(regressor, X, y)
- wandb.log({"residuals": residuals_chart})
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