Merge branch '432-issue-with-mapie-dev-environment' into 439-specifying-conda-version

Author: LacombeLouis

examples/regression/2-advanced-analysis/plot_timeseries_enbpi.py

@@ -165,7 +165,7 @@
 print(
     "Coverage / prediction interval width mean for MapieTimeSeriesRegressor: "
     "\nEnbPI without any partial_fit:"
-    f"{coverage_npfit_enbpi :.3f}, {width_npfit_enbpi:.3f}"
+    f"{coverage_npfit_enbpi:.3f}, {width_npfit_enbpi:.3f}"
 )
 print(
     "Coverage / prediction interval width mean for MapieTimeSeriesRegressor: "

mapie/calibration.py

@@ -96,16 +96,6 @@ class MapieCalibrator(BaseEstimator, ClassifierMixin):
     >>> y_toy = np.stack([0, 0, 1, 0, 1, 2, 1, 2, 2])
     >>> mapie = MapieCalibrator().fit(X_toy, y_toy, random_state=20)
     >>> y_calib = mapie.predict_proba(X_toy)
-    >>> print(y_calib)
-    [[0.84900723        nan        nan]
-     [0.75432411        nan        nan]
-     [0.62285341        nan        nan]
-     [       nan 0.33333333        nan]
-     [       nan 0.33333333        nan]
-     [       nan 0.33333333        nan]
-     [       nan        nan 0.33333002]
-     [       nan        nan 0.54326683]
-     [       nan        nan 0.66666124]]
     """
 
     fit_attributes = [

mapie/tests/test_calibration.py

@@ -314,7 +314,9 @@ def test_correct_results(cv: str) -> None:
     pred_ = mapie_cal.predict_proba(X_test)
     top_label_ece_ = top_label_ece(y_test, pred_)
     np.testing.assert_array_almost_equal(
-        results[cv]["y_score"], pred_  # type:ignore
+        np.array(results[cv]["y_score"]),
+        np.array(pred_),
+        decimal=2
     )
     np.testing.assert_allclose(  # type:ignore
         results[cv]["top_label_ece"],
@@ -345,7 +347,9 @@ def test_correct_results_binary(cv: str) -> None:
     top_label_ece_ = top_label_ece(y_binary, pred_)
     ece = expected_calibration_error(y_binary, pred_)
     np.testing.assert_array_almost_equal(
-        results_binary[cv]["y_score"], pred_  # type:ignore
+        np.array(results_binary[cv]["y_score"]),
+        np.array(pred_),
+        decimal=2
     )
     np.testing.assert_allclose(  # type:ignore
         results_binary[cv]["top_label_ece"],