Update plot_sadinle2019_example.py to v1 API (#674)

Author: FaustinPulveric

examples/classification/3-scientific-articles/plot_sadinle2019_example.py

@@ -3,7 +3,7 @@
 Reproducing Example 7 from Sadinle et al. (2019)
 ================================================
 
-We use :class:`~mapie.classification._MapieClassifier` to reproduce
+We use :class:`~mapie_v1.classification.SplitConformalClassifier` to reproduce
 Example 7 from Sadinle et al. (2019).
 
 We consider a two-dimensional dataset with three labels. The distribution
@@ -12,9 +12,10 @@
 We model the data with Gaussian Naive Bayes classifier
 :class:`~sklearn.naive_bayes.GaussianNB` as a base model.
 
-Prediction sets are estimated by :class:`~mapie.classification._MapieClassifier`
-from the distribution of the softmax scores of the true labels for three
-alpha values (0.2, 0.1, and 0.05) giving different class coverage levels.
+Prediction sets are estimated by
+:class:`~mapie_v1.classification.SplitConformalClassifier` from the distribution of the
+softmax scores of the true labels for three confidence level values (0.8, 0.9, and 0.95)
+giving different class coverage levels.
 
 When the class coverage level is not large enough, the prediction sets can be
 empty.
@@ -25,8 +26,7 @@
 import numpy as np
 from sklearn.naive_bayes import GaussianNB
 
-from mapie.classification import _MapieClassifier
-from mapie.conformity_scores import LACConformityScore
+from mapie.classification import SplitConformalClassifier
 
 # Create training set from multivariate normal distribution
 centers = [(0, 3.5), (-2, 0), (2, 0)]
@@ -35,7 +35,7 @@
 x_min, x_max, y_min, y_max, step = -6, 8, -6, 8, 0.1
 n_samples = 500
 n_classes = 3
-alpha = [0.2, 0.1, 0.05]
+confidence_level = [0.8, 0.9, 0.95]
 np.random.seed(42)
 X_train = np.vstack(
     [
@@ -52,18 +52,20 @@
 )
 X_test = np.stack([xx.ravel(), yy.ravel()], axis=1)
 
-# Apply _MapieClassifier on the dataset to get prediction sets
-clf = GaussianNB().fit(X_train, y_train)
+# Apply SplitConformalClassifier on the dataset to get prediction sets
+clf = GaussianNB()
+clf.fit(X_train, y_train)
 y_pred = clf.predict(X_test)
 y_pred_proba = clf.predict_proba(X_test)
 y_pred_proba_max = np.max(y_pred_proba, axis=1)
-mapie = _MapieClassifier(
+mapie = SplitConformalClassifier(
     estimator=clf,
-    cv="prefit",
-    conformity_score=LACConformityScore()
+    confidence_level=confidence_level,
+    prefit=True,
+    conformity_score="lac"
 )
-mapie.fit(X_train, y_train)
-y_pred_mapie, y_ps_mapie = mapie.predict(X_test, alpha=alpha)
+mapie.conformalize(X_train, y_train)
+y_pred_mapie, y_ps_mapie = mapie.predict_set(X_test)
 
 # Plot the results
 tab10 = plt.cm.get_cmap("Purples", 4)
@@ -84,7 +86,7 @@
     edgecolor="k",
 )
 axs[0].set_title("Predicted labels")
-for i, alpha_ in enumerate(alpha):
+for i, confidence_level_ in enumerate(confidence_level):
     y_ps_sums = y_ps_mapie[:, :, i].sum(axis=1)
     num_labels = axs[i + 1].scatter(
         X_test[:, 0],
@@ -98,5 +100,5 @@
         vmax=3,
     )
     cbar = plt.colorbar(num_labels, ax=axs[i + 1])
-    axs[i + 1].set_title(f"Number of labels for alpha={alpha_}")
+    axs[i + 1].set_title(f"Number of labels for confidence_level={confidence_level_}")
 plt.show()