Repository: deepchecks/deepchecks

Issue:
[BUG] Receiving FutureWarning for each label on Calibration Score check
**Describe the bug**
Receiving FutureWarning for each label on Calibration Score

**To Reproduce**
Run a categorical Dataset on Calibration Score check

**Expected behavior**
No warnings

**Screenshots**
![Screen Shot 2022-01-09 at 16 28 07](https://user-images.githubusercontent.com/26321553/148686605-d509df45-37be-4bd7-9e00-2201ca8431b9.png)


**Environment (please complete the following information):**
 - OS: mac
 - Python Version: 3.8
 - Deepchecks Version: 0.2.1



Discussion:

Resolving patch:
diff --git a/deepchecks/checks/performance/calibration_score.py b/deepchecks/checks/performance/calibration_score.py
--- a/deepchecks/checks/performance/calibration_score.py
+++ b/deepchecks/checks/performance/calibration_score.py
@@ -17,7 +17,6 @@
 from deepchecks import Dataset, CheckResult, SingleDatasetBaseCheck
 from deepchecks.utils.metrics import ModelType, task_type_validation
 
-
 __all__ = ['CalibrationScore']
 
 
@@ -52,11 +51,11 @@ def _calibration_score(self, dataset: Dataset, model):
         briers_scores = {}
 
         if len(dataset.classes) == 2:
-            briers_scores[0] = brier_score_loss(ds_y, y_pred[:, 1], pos_label=dataset.classes[1])
+            briers_scores[0] = brier_score_loss(ds_y == dataset.classes[1], y_pred[:, 1])
         else:
             for class_index, class_name in enumerate(dataset.classes):
                 prob_pos = y_pred[:, class_index]
-                clf_score = brier_score_loss(ds_y == class_name, prob_pos, pos_label=class_name)
+                clf_score = brier_score_loss(ds_y == class_name, prob_pos)
                 briers_scores[class_name] = clf_score
 
         fig = go.Figure()


Test patch:
diff --git a/tests/checks/performance/calibration_score_test.py b/tests/checks/performance/calibration_score_test.py
--- a/tests/checks/performance/calibration_score_test.py
+++ b/tests/checks/performance/calibration_score_test.py
@@ -35,22 +35,6 @@ def test_regresion_model(diabetes_split_dataset_and_model):
                 raises(DeepchecksValueError, r'Expected model to be a type from'
                                            r' \[\'multiclass\', \'binary\'\], but received model of type: regression'))
 
-
-def test_model_info_object(iris_labeled_dataset, iris_adaboost):
-    # Arrange
-    check = CalibrationScore()
-    # Act X
-    result = check.run(iris_labeled_dataset, iris_adaboost).value
-    # Assert
-    assert len(result) == 3  # iris has 3 targets
-
-    assert_that(result, has_entries({
-        0: close_to(0.99, 0.05),
-        1: close_to(0.002, 0.05),
-        2: close_to(0.28, 0.05)
-    }))
-
-
 def test_model_info_object(iris_labeled_dataset, iris_adaboost):
     # Arrange
     check = CalibrationScore()
@@ -60,9 +44,9 @@ def test_model_info_object(iris_labeled_dataset, iris_adaboost):
     assert len(result) == 3  # iris has 3 targets
 
     assert_that(result, has_entries({
-        0: close_to(0.99, 0.05),
-        1: close_to(0.002, 0.05),
-        2: close_to(0.28, 0.05)
+        0: close_to(0.0, 0.0001),
+        1: close_to(0.026, 0.001),
+        2: close_to(0.026, 0.001)
     }))
 
 def test_binary_model_info_object(iris_dataset_single_class_labeled, iris_random_forest_single_class):