ENH use score functions in some examples for classification

Author: gmartinonQM

examples/regression/1-quickstart/plot_timeseries_example.py

@@ -30,13 +30,17 @@
 Jackknife+ and CV+ methods.
 """
 import pandas as pd
-from mapie.metrics import regression_coverage_score
-from mapie.regression import MapieRegressor
 from matplotlib import pylab as plt
 from scipy.stats import randint
 from sklearn.ensemble import RandomForestRegressor
 from sklearn.model_selection import RandomizedSearchCV, TimeSeriesSplit
 
+from mapie.metrics import (
+    regression_coverage_score,
+    regression_mean_width_score
+)
+from mapie.regression import MapieRegressor
+
 # Load input data and feature engineering
 demand_df = pd.read_csv(
     "../../data/demand_temperature.csv", parse_dates=True, index_col=0
@@ -86,7 +90,7 @@
 mapie.fit(X_train, y_train)
 y_pred, y_pis = mapie.predict(X_test, alpha=alpha)
 coverage = regression_coverage_score(y_test, y_pis[:, 0, 0], y_pis[:, 1, 0])
-width = (y_pis[:, 1, 0] - y_pis[:, 0, 0]).mean()
+width = regression_mean_width_score(y_pis[:, 0, 0], y_pis[:, 1, 0])
 
 # Print results
 print(

examples/regression/3-scientific-articles/plot_barber2020_simulations.py

@@ -34,7 +34,10 @@
 from sklearn.linear_model import LinearRegression
 from matplotlib import pyplot as plt
 
-from mapie.metrics import regression_coverage_score
+from mapie.metrics import (
+    regression_coverage_score,
+    regression_mean_width_score
+)
 from mapie.regression import MapieRegressor
 from mapie._typing import ArrayLike
 
@@ -116,12 +119,14 @@ def PIs_vs_dimensions(
                     **params
                 )
                 mapie.fit(X_train, y_train)
-                y_pred, y_pis = mapie.predict(X_test, alpha=alpha)
+                _, y_pis = mapie.predict(X_test, alpha=alpha)
                 coverage = regression_coverage_score(
                     y_test, y_pis[:, 0, 0], y_pis[:, 1, 0]
                 )
                 results[strategy][dimension]["coverage"][trial] = coverage
-                width_mean = (y_pis[:, 1, 0] - y_pis[:, 0, 0]).mean()
+                width_mean = regression_mean_width_score(
+                    y_pis[:, 0, 0], y_pis[:, 1, 0]
+                )
                 results[strategy][dimension]["width_mean"][trial] = width_mean
     return results