Adding the threshold to the ROC curve hovertext (#1161)

* Adding the threshold of the roc curve to the hovertext. Adding True, False positive rate as well. * Adding PR 1161 to release notes.
alteryx · Sep 14, 2020 · bab52e2 · bab52e2
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Showing 3 changed files with 8 additions and 3 deletions.
diff --git a/docs/source/release_notes.rst b/docs/source/release_notes.rst
@@ -10,6 +10,7 @@ Release Notes
         * Added label encoder to lightGBM for binary classification :pr:`1152`
         * Added labels for the row index of confusion matrix :pr:`1154`
         * Added AutoMLSearch object as another parameter in search callbacks :pr:`1156`
+        * Added the corresponding probability threshold for each point displayed in `graph_roc_curve` :pr:`1161`
     * Fixes
         * Fixed XGBoost column names for partial dependence methods :pr:`1104`
         * Removed dead code validating column type from `TextFeaturizer` :pr:`1122`

diff --git a/evalml/model_understanding/graphs.py b/evalml/model_understanding/graphs.py
@@ -194,10 +194,12 @@ def graph_roc_curve(y_true, y_pred_proba, custom_class_names=None, title_additio
     data = []
     for i in range(n_classes):
         roc_curve_data = roc_curve(y_one_hot_true[:, i], y_pred_proba[:, i])
+        name = i + 1 if custom_class_names is None else custom_class_names[i]
         data.append(_go.Scatter(x=roc_curve_data['fpr_rates'], y=roc_curve_data['tpr_rates'],
-                                name='Class {name} (AUC {:06f})'
-                                .format(roc_curve_data['auc_score'],
-                                        name=i + 1 if custom_class_names is None else custom_class_names[i]),
+                                hovertemplate="(False Postive Rate: %{x}, True Positive Rate: %{y})<br>" +
+                                              "Threshold: %{text}",
+                                name=f"Class {name} (AUC {roc_curve_data['auc_score']:.06f})",
+                                text=roc_curve_data["thresholds"],
                                 line=dict(width=3)))
     data.append(_go.Scatter(x=[0, 1], y=[0, 1],
                             name='Trivial Model (AUC 0.5)',

diff --git a/evalml/tests/model_understanding_tests/test_graphs.py b/evalml/tests/model_understanding_tests/test_graphs.py
@@ -293,6 +293,7 @@ def test_graph_roc_curve_binary(X_y_binary, data_type):
     roc_curve_data = roc_curve(y_true, y_pred_proba)
     assert np.array_equal(fig_dict['data'][0]['x'], roc_curve_data['fpr_rates'])
     assert np.array_equal(fig_dict['data'][0]['y'], roc_curve_data['tpr_rates'])
+    assert np.array_equal(fig_dict['data'][0]['text'], roc_curve_data['thresholds'])
     assert fig_dict['data'][0]['name'] == 'Class 1 (AUC {:06f})'.format(roc_curve_data['auc_score'])
     assert np.array_equal(fig_dict['data'][1]['x'], np.array([0, 1]))
     assert np.array_equal(fig_dict['data'][1]['y'], np.array([0, 1]))
@@ -325,6 +326,7 @@ def test_graph_roc_curve_multiclass(binarized_ys):
         roc_curve_data = roc_curve(y_tr[:, i], y_pred_proba[:, i])
         assert np.array_equal(fig_dict['data'][i]['x'], roc_curve_data['fpr_rates'])
         assert np.array_equal(fig_dict['data'][i]['y'], roc_curve_data['tpr_rates'])
+        assert np.array_equal(fig_dict['data'][i]['text'], roc_curve_data['thresholds'])
         assert fig_dict['data'][i]['name'] == 'Class {name} (AUC {:06f})'.format(roc_curve_data['auc_score'], name=i + 1)
     assert np.array_equal(fig_dict['data'][3]['x'], np.array([0, 1]))
     assert np.array_equal(fig_dict['data'][3]['y'], np.array([0, 1]))