pep8 fix.

sklearn/ensemble/tests/test_forest.py

@@ -678,8 +678,9 @@ def check_warm_start(name, X, y):
     clf_ws = None
     for n_estimators in [1, 10, 20]:
         if clf_ws is None:
-            clf_ws = ForestEstimator(n_estimators=n_estimators, random_state=seed,
-                                     n_jobs=1, warm_start=True)
+            clf_ws = ForestEstimator(n_estimators=n_estimators,
+                                     random_state=seed, n_jobs=1,
+                                     warm_start=True)
         else:
             clf_ws.set_params(n_estimators=n_estimators)
         clf_ws.fit(X, y)
@@ -699,6 +700,47 @@ def test_warm_start():
         yield check_warm_start, name, X, y
 
 
+def check_warm_start_zero_n_estimators(name, X, y):
+    """Test if warm start with zero n_estimators raises error """
+    ForestEstimator = FOREST_ESTIMATORS[name]
+    clf = ForestEstimator(n_estimators=100, max_depth=1, warm_start=True)
+    clf.fit(X, y)
+    clf.set_params(n_estimators=0)
+    assert_raises(ValueError, clf.fit, X, y)
+
+
+def test_warm_start_zero_n_estimators():
+    X, y = datasets.make_hastie_10_2(n_samples=100, random_state=1)
+    X = X.astype(np.float32)
+    for name in FOREST_ESTIMATORS:
+        yield check_warm_start_zero_n_estimators, name, X, y
+
+
+def check_warm_start_clear(name, X, y):
+    """Test if fit clears state and grows a new forest when warm_start==False.
+    """
+    ForestEstimator = FOREST_ESTIMATORS[name]
+    clf = ForestEstimator(n_estimators=20, max_depth=1, warm_start=False,
+                          random_state=1)
+    clf.fit(X, y)
+
+    clf_2 = ForestEstimator(n_estimators=20, max_depth=1, warm_start=True,
+                            random_state=None)
+    clf_2.fit(X, y)  # inits state
+    # assert_array_almost_equal(clf_2.predict(X), clf.predict(X))
+    clf_2.set_params(warm_start=False, random_state=1)
+    clf_2.fit(X, y)  # clears old state and equals clf
+
+    assert_array_almost_equal(clf_2.apply(X), clf.apply(X))
+
+
+def test_warm_start_clear():
+    X, y = datasets.make_hastie_10_2(n_samples=60, random_state=1)
+    X = X.astype(np.float32)
+    for name in FOREST_ESTIMATORS:
+        yield check_warm_start_zero_n_estimators, name, X, y
+
+
 if __name__ == "__main__":
     import nose
     nose.runmodule()