Update tests

Author: Bougeant

ml_inspector/_metrics_curves.py

@@ -118,8 +118,6 @@ class as well as for the micro-average.
         """
         curve_data = {}
         for i, c in enumerate(classes):
-            if i == 0:
-                print(y_true == c, y_prob[:, i])
             curve_data[c] = self.curve_function(y_true == c, y_prob[:, i])
         curve_data["Average"] = self.curve_function(
             label_binarize(y_true, classes=classes).ravel(), y_prob.ravel()

ml_inspector/gain_curves.py

@@ -27,13 +27,11 @@ def calculate_gain_curve(y_true: np.ndarray, y_prob: np.ndarray) -> tuple:
         * the corresponding thresholds
     """
     y_prob = pd.Series(np.array(y_prob)).sort_values(ascending=False)
-    y_true = pd.Series(np.array(y_true).astype(int)).reindex_like(y_prob)
-    print("y_prob", y_prob)
-    print("y_true", y_true)
+    y_true = pd.Series(np.array(y_true)).reindex_like(y_prob)
     recalls = y_true.cumsum() / y_true.sum()
     fractions = [i / len(y_true) for i in range(len(y_true))]
     thresholds = y_prob
-    return np.array(fractions).round(3), np.array(recalls), np.array(thresholds)
+    return np.array(fractions), np.array(recalls), np.array(thresholds)
 
 
 class GainCurves(MetricsCurves):

tests/fixtures.py

@@ -31,7 +31,7 @@ def multiclass_predictions():
             [0.2, 0.6, 0.1, 0.1],
             [0.4, 0.1, 0.2, 0.3],
             [0.2, 0.8, 0.0, 0.0],
-            [0.3, 0.4, 0.2, 0.1],
+            [0.2, 0.4, 0.3, 0.1],
             [0.6, 0.1, 0.1, 0.2],
         ]
     )

tests/test_gain_curves.py

@@ -32,7 +32,7 @@ def test_plot_gain_curves_multi_class(self, multiclass_predictions):
         assert fig.data[3]["name"] == "Class 3 (Training): AUC=0.83"
         assert fig.data[4]["name"] == "Micro-average Gain curve (Training): AUC=0.84"
         print(fig.data[5])
-        assert fig.data[5]["name"] == "Class 0 (Test): AUC=0.72"
+        assert fig.data[5]["name"] == "Class 0 (Test): AUC=0.71"
         assert fig.data[-1]["name"] == "Random decision: AUC=0.50"
 
     def test_plot_gain_curves_error_single_class(self, binary_predictions):

tests/test_precision_recall_curves.py

@@ -33,7 +33,7 @@ def test_plot_pr_curves_multi_class(self, multiclass_predictions):
         assert fig.data[4]["name"] == (
             "Micro-average Precision-Recall curve (Training): AUC=0.90"
         )
-        assert fig.data[5]["name"] == "Class 0 (Test): AUC=0.75"
+        assert fig.data[5]["name"] == "Class 0 (Test): AUC=0.79"
         assert fig.data[-1]["name"] == "Random decision: AUC=0.25"
 
     def test_plot_pr_curves_error_single_class(self, binary_predictions):

tests/test_roc_curves.py

@@ -31,7 +31,7 @@ def test_plot_roc_curves_multi_class(self, multiclass_predictions):
         assert fig.data[2]["name"] == "Class 2 (Training): AUC=1.00"
         assert fig.data[3]["name"] == "Class 3 (Training): AUC=1.00"
         assert fig.data[4]["name"] == "Micro-average ROC curve (Training): AUC=0.96"
-        assert fig.data[5]["name"] == "Class 0 (Test): AUC=0.81"
+        assert fig.data[5]["name"] == "Class 0 (Test): AUC=0.88"
         assert fig.data[-1]["name"] == "Random decision: AUC=0.50"
 
     def test_plot_roc_curves_error_single_class(self, binary_predictions):