FIX: sections titles in tutorial

Author: vincentblot28

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

@@ -35,9 +35,10 @@
 
 
 ##############################################################################
-# 1. Create the noisy dataset with 10 groups, each of those groups having
-#    a different level of noise.
-# -------------------------------------------------------------------
+# 1. Create the noisy dataset
+# -----------------------------
+# We create a dataset with 10 groups, each of those groups having a different
+# level of noise.
 
 
 n_points = 100000
@@ -86,7 +87,7 @@
 
 ##############################################################################
 # 2. Split the dataset into a training set, a calibration set, and a test set.
-
+# -----------------------------
 
 X_train_temp, X_test, y_train_temp, y_test = train_test_split(
     X, y, test_size=0.2, random_state=0
@@ -118,15 +119,15 @@
 
 ##############################################################################
 # 3. Fit a random forest regressor on the training set.
-
+# -----------------------------
 
 rf = RandomForestRegressor(n_estimators=100)
 rf.fit(X_train, y_train)
 
 
 ##############################################################################
 # 4. Fit a MapieRegressor and a MondrianCP on the calibration set.
-
+# -----------------------------
 
 mapie_regressor = MapieRegressor(rf, cv="prefit")
 mondrian_regressor = MondrianCP(MapieRegressor(rf, cv="prefit"))
@@ -136,7 +137,7 @@
 
 ##############################################################################
 # 5. Predict the prediction intervals on the test set with both methods.
-
+# -----------------------------
 
 _, y_pss_split = mapie_regressor.predict(X_test, alpha=.1)
 _, y_pss_mondrian = mondrian_regressor.predict(
@@ -146,7 +147,7 @@
 
 ##############################################################################
 # 6. Compare the coverage by partition, plot both methods side by side.
-
+# -----------------------------
 
 coverages = {}
 for group in np.unique(partition_test):