Merge pull request scikit-learn#25 from glouppe/adaboost

Fix issues raised by reviewers
glouppe · Jan 23, 2013 · 7ed0063 · 7ed0063
2 parents f973c27 + 2497856
commit 7ed0063
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Showing 5 changed files with 96 additions and 86 deletions.
diff --git a/examples/ensemble/plot_adaboost_hastie_10_2.py b/examples/ensemble/plot_adaboost_hastie_10_2.py
@@ -50,14 +50,14 @@
     base_estimator=dt_stump,
     learning_rate=learning_rate,
     n_estimators=n_estimators,
-    real=False)
+    algorithm="SAMME")
 ada_discrete.fit(X_train, y_train)
 
 ada_real = AdaBoostClassifier(
     base_estimator=dt_stump,
     learning_rate=learning_rate,
     n_estimators=n_estimators,
-    real=True)
+    algorithm="SAMME.R")
 ada_real.fit(X_train, y_train)
 
 fig = plt.figure()

diff --git a/examples/ensemble/plot_adaboost_multiclass.py b/examples/ensemble/plot_adaboost_multiclass.py
@@ -51,7 +51,7 @@
     DecisionTreeClassifier(max_depth=2),
     n_estimators=600,
     learning_rate=1.5,
-    real=False)
+    algorithm="SAMME")
 
 bdt_real.fit(X_train, y_train)
 bdt_discrete.fit(X_train, y_train)
@@ -89,11 +89,11 @@
 pl.xlim((-20, len(bdt_discrete) + 20))
 
 pl.subplot(133)
-pl.plot(n_trees, bdt_discrete.weights_, "b", label='SAMME')
+pl.plot(n_trees, bdt_discrete.estimator_weights_, "b", label='SAMME')
 pl.legend()
 pl.ylabel('Weight')
 pl.xlabel('Tree')
-pl.ylim((0, bdt_discrete.weights_.max() * 1.2))
+pl.ylim((0, bdt_discrete.estimator_weights_.max() * 1.2))
 pl.xlim((-20, len(bdt_discrete) + 20))
 
 # prevent overlapping y-axis labels

diff --git a/examples/ensemble/plot_adaboost_twoclass.py b/examples/ensemble/plot_adaboost_twoclass.py
@@ -26,7 +26,7 @@
 
 bdt = AdaBoostClassifier(
     DecisionTreeClassifier(max_depth=1),
-    real=False,
+    algorithm="SAMME",
     n_estimators=50)
 
 bdt.fit(X, y)

diff --git a/sklearn/ensemble/tests/test_weight_boosting.py b/sklearn/ensemble/tests/test_weight_boosting.py
@@ -180,6 +180,10 @@ def test_error():
                   AdaBoostClassifier(learning_rate=-1).fit,
                   X, y)
 
+    assert_raises(ValueError,
+                  AdaBoostClassifier(algorithm="foo").fit,
+                  X, y)
+
     assert_raises(TypeError,
                   AdaBoostClassifier(base_estimator=DummyRegressor()).fit,
                   X, y)
@@ -197,7 +201,7 @@ def test_base_estimator():
     clf = AdaBoostClassifier(RandomForestClassifier())
     clf.fit(X, y)
 
-    clf = AdaBoostClassifier(SVC(), real=False)
+    clf = AdaBoostClassifier(SVC(), algorithm="SAMME")
     clf.fit(X, y)
 
     from sklearn.ensemble import RandomForestRegressor