FIX typos

Author: gmartinonQM

HISTORY.rst

@@ -10,8 +10,7 @@ History
 * Add MNIST example for classification
 * Add cross-conformal for classification
 * Add `notebooks` folder containing notebooks used for generating documentation tutorials
-* Uniformize the use of matrix k_ and add an argument "ensemble" to method 
-"predict" in regression.py
+* Uniformize the use of matrix k_ and add an argument "ensemble" to method "predict" in regression.py
 * Add replication of the Chen Xu's tutorial testing Jackknife+aB vs Jackknife+
 * Add Jackknife+-after-Bootstrap documentation
 * Improve scikit-learn pipelines compatibility

examples/classification/2-advanced-analysis/plot_digits_classification.py

@@ -209,20 +209,20 @@ def get_datasets(dataset: Any) -> Tuple[
 # the "real" coverage obtained on the test set.
 
 coverages1 = [
-    classification_coverage_score(y_test1, y_ps1[:, :, alpha_])
-    for alpha_, _ in enumerate(alpha)
+    classification_coverage_score(y_test1, y_ps1[:, :, i])
+    for i, _ in enumerate(alpha)
 ]
 coverages2 = [
-    classification_coverage_score(y_test2, y_ps2[:, :, alpha_])
-    for alpha_, _ in enumerate(alpha)
+    classification_coverage_score(y_test2, y_ps2[:, :, i])
+    for i, _ in enumerate(alpha)
 ]
 widths1 = [
-    classification_mean_width_score(y_ps1[:, :, alpha_])
-    for alpha_, _ in enumerate(alpha)
+    classification_mean_width_score(y_ps1[:, :, i])
+    for i, _ in enumerate(alpha)
 ]
 widths2 = [
-    classification_mean_width_score(y_ps2[:, :, alpha_])
-    for alpha_, _ in enumerate(alpha)
+    classification_mean_width_score(y_ps2[:, :, i])
+    for i, _ in enumerate(alpha)
 ]
 
 _, axes = plt.subplots(nrows=1, ncols=2, figsize=(12, 5))