fix(integrations): remove wandb.sklearn.plot_decision_boundaries that contains dead logic

wandb/sklearn/__init__.py

@@ -5,7 +5,6 @@
     plot_classifier,
     plot_clusterer,
     plot_confusion_matrix,
-    plot_decision_boundaries,
     plot_elbow_curve,
     plot_feature_importances,
     plot_learning_curve,
@@ -30,7 +29,6 @@
     "plot_roc",
     "plot_precision_recall",
     "plot_confusion_matrix",
-    "plot_decision_boundaries",
     "plot_elbow_curve",
     "plot_silhouette",
     "plot_residuals",

wandb/sklearn/plot/__init__.py

@@ -3,7 +3,6 @@
 from .classifier import class_proportions as plot_class_proportions
 from .classifier import classifier as plot_classifier
 from .classifier import confusion_matrix as plot_confusion_matrix
-from .classifier import decision_boundaries as plot_decision_boundaries
 from .classifier import feature_importances as plot_feature_importances
 from .classifier import precision_recall as plot_precision_recall
 from .classifier import roc as plot_roc
@@ -28,7 +27,6 @@
     "plot_roc",
     "plot_precision_recall",
     "plot_confusion_matrix",
-    "plot_decision_boundaries",
     "plot_elbow_curve",
     "plot_silhouette",
     "plot_residuals",

wandb/sklearn/plot/classifier.py

@@ -319,68 +319,3 @@ def calibration_curve(clf=None, X=None, y=None, clf_name="Classifier"):
         calibration_curve_chart = calculate.calibration_curves(clf, X, y, clf_name)
 
         wandb.log({"calibration_curve": calibration_curve_chart})
-
-
-def decision_boundaries(binary_clf=None, X=None, y=None):
-    """Visualizes decision boundaries of a binary classifier.
-
-    Works by sampling from the feature space where the classifier's uncertainty
-    if greater than > 0.5 and projecting these point to 2D space.
-
-    Useful for measuring model (decision boundary) complexity, visualizing
-    regions where the model falters, and to determine whether any over or
-    underfitting occured.
-
-    Should only be called with a fitted **binary** classifer (otherwise an error is
-    thrown). Please note this function fits variations of the model on the
-    training set when called.
-
-    Arguments:
-        model: (clf) Takes in a fitted binary classifier.
-        X_train: (arr) Training set features.
-        y_train: (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_decision_boundaries(binary_classifier, X, y)
-    ```
-    """
-    if utils.test_missing(binary_clf=binary_clf, X=X, y=y) and utils.test_types(
-        binary_clf=binary_clf, X=X, y=y
-    ):
-        y = np.asarray(y)
-        # plot high-dimensional decision boundary
-        db = DBPlot(binary_clf)
-        db = None
-        db.fit(X, y)
-        (
-            decision_boundary_x,
-            decision_boundary_y,
-            decision_boundary_color,
-            train_x,
-            train_y,
-            train_color,
-            test_x,
-            test_y,
-            test_color,
-        ) = db.plot()
-
-        wandb.log(
-            {
-                "decision_boundaries": calculate.decision_boundaries(
-                    decision_boundary_x,
-                    decision_boundary_y,
-                    decision_boundary_color,
-                    train_x,
-                    train_y,
-                    train_color,
-                    test_x,
-                    test_y,
-                    test_color,
-                )
-            }
-        )