Merge pull request #457 from scikit-learn-contrib/447-mapieregressor-sets-method-to-base-from-aci

Solve conflict between ACI and base methods in MapieTimeSeriesRegressor

Author: thibaultcordier

AUTHORS.rst

@@ -40,4 +40,5 @@ Contributors
 * Pierre de Fréminville <pidefrem>
 * Ambros Marzetta <ambrosm>
 * Carl McBride Ellis <Carl-McBride-Ellis>
+* Baptiste Calot <[email protected]>
 To be continued ...

HISTORY.rst

@@ -6,6 +6,7 @@ History
 ------------------
 
 * Fix the quantile formula to ensure valid coverage for any number of calibration data in `ConformityScore`.
+* Fix overloading of the value of the `method` attribute when using `MapieRegressor` and `MapieTimeSeriesRegressor`.
 * Fix conda versionning.
 * Reduce precision for test in `MapieCalibrator`.
 * Fix invalid certificate when downloading data.

mapie/regression/regression.py

@@ -425,7 +425,8 @@ def _check_fit_parameters(
         cv = check_cv(
             self.cv, test_size=self.test_size, random_state=self.random_state
         )
-        if self.cv in ["split", "prefit"] and self.method != "base":
+        if self.cv in ["split", "prefit"] and \
+                self.method in ["naive", "plus", "minmax"]:
             self.method = "base"
         estimator = self._check_estimator(self.estimator)
         agg_function = self._check_agg_function(self.agg_function)

mapie/tests/test_regression.py

@@ -793,3 +793,19 @@ def test_predict_infinite_intervals() -> None:
     _, y_pis = mapie_reg.predict(X, alpha=0., allow_infinite_bounds=True)
     np.testing.assert_allclose(y_pis[:, 0, 0], -np.inf)
     np.testing.assert_allclose(y_pis[:, 1, 0], np.inf)
+
+
+@pytest.mark.parametrize("method", ["minmax", "naive", "plus", "base"])
+@pytest.mark.parametrize("cv", ["split", "prefit"])
+def test_check_change_method_to_base(method: str, cv: str) -> None:
+    """Test of the overloading of method attribute to `base` method in fit"""
+
+    X_train, X_val, y_train, y_val = train_test_split(
+        X, y, test_size=0.5, random_state=random_state
+    )
+    estimator = LinearRegression().fit(X_train, y_train)
+    mapie_reg = MapieRegressor(
+        cv=cv, method=method, estimator=estimator
+    )
+    mapie_reg.fit(X_val, y_val)
+    assert mapie_reg.method == "base"

mapie/tests/test_time_series_regression.py

@@ -518,3 +518,24 @@ def test_method_error_in_update(monkeypatch: Any, method: str) -> None:
     with pytest.raises(ValueError, match=r".*Invalid method.*"):
         mapie_ts_reg.fit(X_toy, y_toy)
         mapie_ts_reg.update(X_toy, y_toy)
+
+
+@pytest.mark.parametrize("method", ["enbpi", "aci"])
+@pytest.mark.parametrize("cv", ["split", "prefit"])
+def test_methods_preservation_in_fit(method: str, cv: str) -> None:
+    """Test of enbpi and aci method preservation in the fit MapieRegressor"""
+
+    X_train_val, X_test, y_train_val, y_test = train_test_split(
+        X, y, test_size=0.33, random_state=random_state
+    )
+    X_train, X_val, y_train, y_val = train_test_split(
+        X_train_val, y_train_val, test_size=0.5, random_state=random_state
+    )
+    estimator = LinearRegression().fit(X_train, y_train)
+    mapie_ts_reg = MapieTimeSeriesRegressor(
+        estimator=estimator,
+        cv=cv, method=method
+    )
+    mapie_ts_reg.fit(X_val, y_val)
+    mapie_ts_reg.update(X_test, y_test, gamma=0.1, alpha=0.1)
+    assert mapie_ts_reg.method == method