update example

yzhao062 · Aug 4, 2019 · c0314c0 · c0314c0
1 parent 557cb0c
commit c0314c0
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Showing 4 changed files with 24 additions and 21 deletions.
diff --git a/examples/compare_selected_classifiers.png b/examples/compare_selected_classifiers.png
diff --git a/examples/compare_selected_classifiers.py b/examples/compare_selected_classifiers.py
@@ -34,6 +34,8 @@
 
 from combo.models.classifier_comb import SimpleClassifierAggregator
 from combo.models.classifier_stacking import Stacking
+from combo.models.classifier_dcs import DCS_LA
+from combo.models.classifier_des import DES_LA
 
 # Define the number of class 0 and class 1
 n_samples = 300
@@ -73,7 +75,9 @@
     'Stacking': Stacking(base_estimators=classifiers, shuffle_data=True),
     'Stacking_RF': Stacking(base_estimators=classifiers, shuffle_data=True,
                             meta_clf=RandomForestClassifier(
-                                random_state=random_state))
+                                random_state=random_state)),
+    'DCS_LA': DCS_LA(base_estimators=classifiers),
+    'DEC_LA': DES_LA(base_estimators=classifiers)
 }
 
 # Show all classifiers
@@ -92,9 +96,8 @@
     X = np.r_[X, np.random.uniform(low=-6, high=6, size=(n_class1, 2))]
 
     # Fit the model
-    plt.figure(figsize=(15, 8))
+    plt.figure(figsize=(15, 12))
     for i, (clf_name, clf) in enumerate(classifiers.items()):
-        print()
         print(i + 1, 'fitting', clf_name)
         # fit the data and tag class 1
 
@@ -107,7 +110,7 @@
         # plot the levels lines and the points
         Z = clf.predict_proba(np.c_[xx.ravel(), yy.ravel()])[:, 1] * -1
         Z = Z.reshape(xx.shape)
-        subplot = plt.subplot(2, 4, i + 1)
+        subplot = plt.subplot(3, 4, i + 1)
         subplot.contourf(xx, yy, Z, levels=np.linspace(Z.min(), threshold, 7),
                          cmap=plt.cm.Blues_r)
         a = subplot.contour(xx, yy, Z, levels=[threshold],

diff --git a/examples/temp_do_not_use.py b/examples/temp_do_not_use.py
@@ -31,7 +31,7 @@
 from sklearn.datasets import load_breast_cancer
 from sklearn.utils.multiclass import check_classification_targets
 
-from combo.models.classifier_dcs import DCS_LA
+from combo.models.classifier_des import DES_LA
 from combo.utils.data import evaluate_print
 
 import warnings
@@ -87,8 +87,9 @@
     evaluate_print('Random Forest        |', y_test, clf.predict(X_test))
 
     print()
-    clf = DCS_LA(classifiers)
+    clf = DES_LA(classifiers, use_weights=True)
     clf.fit(X_train, y_train)
     y_test_predicted = clf.predict(X_test)
     y_test_proba_predicted = clf.predict_proba(X_test)
     evaluate_print('DCS_LA               |', y_test, y_test_predicted)
+    print(roc_auc_score(y_test, y_test_proba_predicted[:, 1]))
diff --git a/notebooks/compare_selected_classifiers.ipynb b/notebooks/compare_selected_classifiers.ipynb