chore: Update plot_cqr_symmetry_difference.py in regression examples

Author: LacombeLouis

examples/regression/1-quickstart/plot_cqr_symmetry_difference.py

@@ -13,7 +13,9 @@
 from mapie.metrics import regression_coverage_score
 from mapie.quantile_regression import MapieQuantileRegressor
 
-# Generate synthetic data
+##############################################################################
+# We generate a synthetic data.
+
 X, y = make_regression(n_samples=500, n_features=1, noise=20, random_state=59)
 
 # Define alpha level
@@ -36,10 +38,10 @@
 
 # Calculate coverage scores
 coverage_score_sym = regression_coverage_score(
-    y, y_pis_sym[:, 0], y_pis_sym[:, 1]
+y, y_pis_sym[:, 0], y_pis_sym[:, 1]
 )
 coverage_score_asym = regression_coverage_score(
-    y, y_pis_asym[:, 0], y_pis_asym[:, 1]
+y, y_pis_asym[:, 0], y_pis_asym[:, 1]
 )
 
 # Sort the values for plotting
@@ -50,7 +52,12 @@
 y_pred_asym_sorted = y_pred_asym[order]
 y_pis_asym_sorted = y_pis_asym[order]
 
-# Plot symmetric prediction intervals
+##############################################################################
+# We will plot the predictions and prediction intervals for both symmetric
+# and asymmetric intervals. The line represents the predicted values, the
+# dashed lines represent the prediction intervals, and the shaded area
+# represents the symmetric and asymmetric prediction intervals.
+
 plt.figure(figsize=(14, 7))
 
 plt.subplot(1, 2, 1)
@@ -61,15 +68,15 @@
 plt.plot(X_sorted, y_pis_sym_sorted[:, 0], color="C1", ls="--")
 plt.plot(X_sorted, y_pis_sym_sorted[:, 1], color="C1", ls="--")
 plt.fill_between(
-    X_sorted.ravel(),
-    y_pis_sym_sorted[:, 0].ravel(),
-    y_pis_sym_sorted[:, 1].ravel(),
-    alpha=0.2,
+X_sorted.ravel(),
+y_pis_sym_sorted[:, 0].ravel(),
+y_pis_sym_sorted[:, 1].ravel(),
+alpha=0.2,
 )
 plt.title(
-    f"Symmetric Intervals\n"
-    f"Target and effective coverages for "
-    f"alpha={alpha:.2f}: ({1-alpha:.3f}, {coverage_score_sym:.3f})"
+f"Symmetric Intervals\n"
+f"Target and effective coverages for "
+f"alpha={alpha:.2f}: ({1-alpha:.3f}, {coverage_score_sym:.3f})"
 )
 
 # Plot asymmetric prediction intervals
@@ -81,24 +88,21 @@
 plt.plot(X_sorted, y_pis_asym_sorted[:, 0], color="C2", ls="--")
 plt.plot(X_sorted, y_pis_asym_sorted[:, 1], color="C2", ls="--")
 plt.fill_between(
-    X_sorted.ravel(),
-    y_pis_asym_sorted[:, 0].ravel(),
-    y_pis_asym_sorted[:, 1].ravel(),
-    alpha=0.2,
+X_sorted.ravel(),
+y_pis_asym_sorted[:, 0].ravel(),
+y_pis_asym_sorted[:, 1].ravel(),
+alpha=0.2,
 )
 plt.title(
-    f"Asymmetric Intervals\n"
-    f"Target and effective coverages for "
-    f"alpha={alpha:.2f}: ({1-alpha:.3f}, {coverage_score_asym:.3f})"
+f"Asymmetric Intervals\n"
+f"Target and effective coverages for "
+f"alpha={alpha:.2f}: ({1-alpha:.3f}, {coverage_score_asym:.3f})"
 )
-
 plt.tight_layout()
 plt.show()
 
-# Explanation of the results
-"""
-The symmetric intervals (`symmetry=True`) are easier to interpret and
-tend to have higher coverage but might not adapt well to varying
-noise levels. The asymmetric intervals (`symmetry=False`) are more
-flexible and better capture heteroscedasticity but can appear more jagged.
-"""
+##############################################################################
+# The symmetric intervals (`symmetry=True`) are easier to interpret and
+# tend to have higher coverage but might not adapt well to varying
+# noise levels. The asymmetric intervals (`symmetry=False`) are more
+# flexible and better capture heteroscedasticity but can appear more jagged.