ENH: rename regression_coverage_score_v2 to regression_coverage_score (#640)

* ENH: rename regression_coverage_score_v2 to regression_coverage_score

* FIX: fix lint error in mapie/metrics/regression.py (line 206)

Author: FaustinPulveric

doc/api.rst

@@ -92,7 +92,7 @@ Regression Metrics
    :toctree: generated/
    :template: function.rst
 
-   mapie.metrics.regression.regression_coverage_score_v2
+   mapie.metrics.regression.regression_coverage_score
    mapie.metrics.regression.regression_mean_width_score
    mapie.metrics.regression.regression_ssc
    mapie.metrics.regression.regression_ssc_score

doc/quick_start.rst

@@ -69,9 +69,9 @@ Here, we generate one-dimensional noisy data that we fit with a linear model.
 
     # You can compute the coverage of your prediction intervals.
 
-    from mapie.metrics.regression import regression_coverage_score_v2
+    from mapie.metrics.regression import regression_coverage_score
 
-    coverage_scores = regression_coverage_score_v2(y_test, y_pred_intervals)
+    coverage_scores = regression_coverage_score(y_test, y_pred_intervals)
 
     #  The estimated prediction intervals can then be plotted as follows.
 

examples/mondrian/1-quickstart/plot_main-tutorial-mondrian-regression.py

@@ -26,7 +26,7 @@
 from sklearn.model_selection import train_test_split
 from sklearn.ensemble import RandomForestRegressor
 
-from mapie.metrics.regression import regression_coverage_score_v2
+from mapie.metrics.regression import regression_coverage_score
 from mapie.mondrian import MondrianCP
 from mapie.regression import MapieRegressor
 
@@ -152,10 +152,10 @@
 coverages = {}
 for group in np.unique(partition_test):
     coverages[group] = {}
-    coverages[group]["split"] = regression_coverage_score_v2(
+    coverages[group]["split"] = regression_coverage_score(
         y_test[partition_test == group], y_pss_split[partition_test == group]
     )
-    coverages[group]["mondrian"] = regression_coverage_score_v2(
+    coverages[group]["mondrian"] = regression_coverage_score(
         y_test[partition_test == group],
         y_pss_mondrian[partition_test == group]
     )

examples/regression/1-quickstart/plot_compare_conformity_scores.py

@@ -42,7 +42,7 @@
 from sklearn.ensemble import RandomForestRegressor
 from sklearn.model_selection import train_test_split
 
-from mapie.metrics.regression import regression_coverage_score_v2
+from mapie.metrics.regression import regression_coverage_score
 from mapie_v1.regression import CrossConformalRegressor
 
 RANDOM_STATE = 42
@@ -111,7 +111,7 @@
     X_test
 )
 
-coverage_absconfscore = regression_coverage_score_v2(
+coverage_absconfscore = regression_coverage_score(
     y_test, y_pis_absconfscore
 )[0]
 
@@ -146,7 +146,7 @@ def get_yerr(y_pred, y_pis):
     X_test
 )
 
-coverage_gammaconfscore = regression_coverage_score_v2(
+coverage_gammaconfscore = regression_coverage_score(
     y_test, y_pis_gammaconfscore
 )[0]
 

examples/regression/1-quickstart/plot_prefit.py

@@ -27,7 +27,7 @@
 from sklearn.neural_network import MLPRegressor
 
 from numpy.typing import NDArray
-from mapie.metrics.regression import regression_coverage_score_v2
+from mapie.metrics.regression import regression_coverage_score
 from mapie_v1.regression import SplitConformalRegressor, ConformalizedQuantileRegressor
 from mapie_v1.utils import train_conformalize_test_split
 
@@ -99,7 +99,7 @@ def f(x: NDArray) -> NDArray:
 
 # Evaluate prediction and coverage level on testing set
 y_pred, y_pis = mapie.predict_interval(X_test.reshape(-1, 1))
-coverage = regression_coverage_score_v2(y_test, y_pis)[0]
+coverage = regression_coverage_score(y_test, y_pis)[0]
 
 
 ##############################################################################
@@ -206,7 +206,7 @@ def f(x: NDArray) -> NDArray:
 
 # Evaluate prediction and coverage level on testing set
 y_pred_cqr, y_pis_cqr = mapie_cqr.predict_interval(X_test.reshape(-1, 1))
-coverage_cqr = regression_coverage_score_v2(
+coverage_cqr = regression_coverage_score(
     y_test,
     y_pis_cqr
 )[0]

examples/regression/1-quickstart/plot_toy_model.py

@@ -9,7 +9,7 @@
 from matplotlib import pyplot as plt
 from sklearn.datasets import make_regression
 
-from mapie.metrics.regression import regression_coverage_score_v2
+from mapie.metrics.regression import regression_coverage_score
 from mapie_v1.regression import SplitConformalRegressor
 from mapie_v1.utils import train_conformalize_test_split
 
@@ -33,7 +33,7 @@
 mapie_regressor.conformalize(X_conformalize, y_conformalize)
 y_pred, y_pred_interval = mapie_regressor.predict_interval(X_test)
 
-coverage_scores = regression_coverage_score_v2(y_test, y_pred_interval)
+coverage_scores = regression_coverage_score(y_test, y_pred_interval)
 
 plt.xlabel("x")
 plt.ylabel("y")

examples/regression/1-quickstart/plot_ts-tutorial.py

@@ -57,7 +57,7 @@ class that block bootstraps the training set.
 from sklearn.model_selection import RandomizedSearchCV, TimeSeriesSplit
 
 from mapie.metrics.regression import (
-    regression_coverage_score_v2,
+    regression_coverage_score,
     regression_mean_width_score, coverage_width_based,
 )
 from mapie.regression import MapieTimeSeriesRegressor
@@ -217,7 +217,7 @@ class that block bootstraps the training set.
     allow_infinite_bounds=True
 )
 y_pis_enbpi_npfit = np.clip(y_pis_enbpi_npfit, 1, 10)
-coverage_enbpi_npfit = regression_coverage_score_v2(
+coverage_enbpi_npfit = regression_coverage_score(
     y_test, y_pis_enbpi_npfit
 )[0]
 width_enbpi_npfit = regression_mean_width_score(
@@ -258,7 +258,7 @@ class that block bootstraps the training set.
         y_pis_aci_npfit[step:step + gap, :, :], 1, 10
     )
 
-coverage_aci_npfit = regression_coverage_score_v2(
+coverage_aci_npfit = regression_coverage_score(
     y_test, y_pis_aci_npfit
 )[0]
 width_aci_npfit = regression_mean_width_score(
@@ -307,7 +307,7 @@ class that block bootstraps the training set.
     y_pis_enbpi_pfit[step:step + gap, :, :] = np.clip(
         y_pis_enbpi_pfit[step:step + gap, :, :], 1, 10
     )
-coverage_enbpi_pfit = regression_coverage_score_v2(
+coverage_enbpi_pfit = regression_coverage_score(
     y_test, y_pis_enbpi_pfit
 )[0]
 width_enbpi_pfit = regression_mean_width_score(
@@ -360,7 +360,7 @@ class that block bootstraps the training set.
         y_pis_aci_pfit[step:step + gap, :, :], 1, 10
     )
 
-coverage_aci_pfit = regression_coverage_score_v2(
+coverage_aci_pfit = regression_coverage_score(
     y_test, y_pis_aci_pfit
 )[0]
 width_aci_pfit = regression_mean_width_score(
@@ -462,23 +462,23 @@ class that block bootstraps the training set.
 
 for i in range(window, len(y_test), 1):
     rolling_coverage_aci_npfit.append(
-        regression_coverage_score_v2(
+        regression_coverage_score(
             y_test[i-window:i], y_pis_aci_npfit[i-window:i]
         )[0]
     )
     rolling_coverage_aci_pfit.append(
-        regression_coverage_score_v2(
+        regression_coverage_score(
             y_test[i-window:i], y_pis_aci_pfit[i-window:i]
         )[0]
     )
 
     rolling_coverage_enbpi_npfit.append(
-        regression_coverage_score_v2(
+        regression_coverage_score(
             y_test[i-window:i], y_pis_enbpi_npfit[i-window:i]
         )[0]
     )
     rolling_coverage_enbpi_pfit.append(
-        regression_coverage_score_v2(
+        regression_coverage_score(
             y_test[i-window:i], y_pis_enbpi_pfit[i-window:i]
         )[0]
     )

examples/regression/2-advanced-analysis/plot-coverage-width-based-criterion.py

@@ -25,7 +25,7 @@
 from sklearn.model_selection import train_test_split
 
 from mapie.metrics.regression import (
-    regression_coverage_score_v2,
+    regression_coverage_score,
     regression_mean_width_score, coverage_width_based,
 )
 from mapie_v1.regression import (
@@ -260,7 +260,7 @@ def plot_1d_data(
 cwc_score = {}
 
 for strategy in STRATEGIES:
-    coverage_score[strategy] = regression_coverage_score_v2(
+    coverage_score[strategy] = regression_coverage_score(
         y_test,
         y_pis[strategy]
     )[0]

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

@@ -22,7 +22,7 @@
 
 from mapie.conformity_scores import ResidualNormalisedScore
 from mapie.metrics.regression import (
-    regression_coverage_score_v2,
+    regression_coverage_score,
     regression_ssc_score,
 )
 from mapie_v1.regression import SplitConformalRegressor
@@ -197,7 +197,7 @@ def predict(self, X):
         mapie.fit(X_train, y_train)
         mapie.conformalize(X_conformalize, y_conformalize)
     y_pred[strategy_name], y_pis[strategy_name] = mapie.predict_interval(X_test)
-    coverage[strategy_name] = regression_coverage_score_v2(
+    coverage[strategy_name] = regression_coverage_score(
         y_test, y_pis[strategy_name]
     )
     cond_coverage[strategy_name] = regression_ssc_score(

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

@@ -30,7 +30,7 @@
 from mapie.conformity_scores import (GammaConformityScore,
                                      ResidualNormalisedScore)
 from mapie.metrics.regression import (
-    regression_coverage_score_v2,
+    regression_coverage_score,
     regression_ssc,
     regression_ssc_score, hsic,
 )
@@ -162,7 +162,7 @@ def sin_with_controlled_noise(
         y_pred[strategy_name], y_pis[strategy_name] = mapie.predict_interval(X_test)
 
     # computing metrics
-    coverage[strategy_name] = regression_coverage_score_v2(
+    coverage[strategy_name] = regression_coverage_score(
         y_test, y_pis[strategy_name]
     )
     cond_coverage[strategy_name] = regression_ssc_score(