From e7078ca96e4aa98ea9af8a782fdc1c378ad802d4 Mon Sep 17 00:00:00 2001
From: sofiane <sziane@quantmetry.com>
Date: Tue, 11 Jul 2023 09:38:54 +0200
Subject: [PATCH] ENH: Change in example to make it work

---
 .../plot_tutorial_multilabel_classification.py        | 11 +++++++----
 1 file changed, 7 insertions(+), 4 deletions(-)

diff --git a/examples/multilabel_classification/1-quickstart/plot_tutorial_multilabel_classification.py b/examples/multilabel_classification/1-quickstart/plot_tutorial_multilabel_classification.py
index ff1cd417a..f7f0e3f29 100644
--- a/examples/multilabel_classification/1-quickstart/plot_tutorial_multilabel_classification.py
+++ b/examples/multilabel_classification/1-quickstart/plot_tutorial_multilabel_classification.py
@@ -118,17 +118,19 @@
 }
 
 clf = MultiOutputClassifier(GaussianNB()).fit(X_train, y_train)
-mapie = MapieMultiLabelClassifier(estimator=clf)
-mapie.fit(X_cal, y_cal)
 
 alpha = np.arange(0.01, 1, 0.01)
 y_pss, recalls, thresholds, r_hats, r_hat_pluss = {}, {}, {}, {}, {}
 y_test_repeat = np.repeat(y_test[:, :, np.newaxis], len(alpha), 2)
 for i, (name, (method, bound)) in enumerate(method_params.items()):
 
+    mapie = MapieMultiLabelClassifier(
+        estimator=clf, method=method, metric_control="recall"
+    )
+    mapie.fit(X_cal, y_cal)
+
     _, y_pss[name] = mapie.predict(
-        X_test, method=method,
-        alpha=alpha, bound=bound, delta=.1
+        X_test, alpha=alpha, bound=bound, delta=.1
     )
     recalls[name] = (
         (y_test_repeat * y_pss[name]).sum(axis=1) /
@@ -138,6 +140,7 @@
     r_hats[name] = mapie.r_hat
     r_hat_pluss[name] = mapie.r_hat_plus
 
+
 ##############################################################################
 # 3. Results
 # ----------