Merge pull request #440 from scikit-learn-contrib/439-specifying-cond…

…a-version chore: update miniconda version to latest stable
scikit-learn-contrib · May 13, 2024 · 18cb1c5 · 18cb1c5
2 parents 3813eae + c666194
commit 18cb1c5
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diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml
@@ -48,6 +48,7 @@ jobs:
           python-version: ${{ matrix.python-version }}
           environment-file: environment.ci.yml
           channels: defaults, conda-forge
+          miniconda-version: "latest"
       - name: Install numpy
         run: conda install numpy=${{ matrix.numpy-version }}
       - name: Check linting

diff --git a/HISTORY.rst b/HISTORY.rst
@@ -2,6 +2,12 @@
 History
 =======
 
+0.8.3 (2024-**-**)
+------------------
+
+* Fix conda versionning.
+* Reduce precision for test in `MapieCalibrator`.
+
 0.8.3 (2024-03-01)
 ------------------
 
@@ -13,7 +19,7 @@ History
 0.8.2 (2024-01-11)
 ------------------
 
-* * Resolve issue still present in 0.8.1 by updating pandas.
+* Resolve issue still present in 0.8.1 by updating pandas.
 
 0.8.1 (2024-01-11)
 ------------------

diff --git a/examples/regression/2-advanced-analysis/plot_timeseries_enbpi.py b/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: "

diff --git a/mapie/calibration.py b/mapie/calibration.py
@@ -97,15 +97,15 @@ class MapieCalibrator(BaseEstimator, ClassifierMixin):
     >>> 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]]
+    [[0.84......        nan        nan]
+     [0.75......        nan        nan]
+     [0.62......        nan        nan]
+     [       nan 0.33......        nan]
+     [       nan 0.33......        nan]
+     [       nan 0.33......        nan]
+     [       nan        nan 0.33......]
+     [       nan        nan 0.54......]
+     [       nan        nan 0.66......]]
     """
 
     fit_attributes = [

diff --git a/mapie/tests/test_calibration.py b/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"],