From 34c2904a95a707c6e6148480a7e2c86a0f7ad86b Mon Sep 17 00:00:00 2001
From: Andreas Mueller <amueller@ais.uni-bonn.de>
Date: Sun, 6 May 2012 20:20:05 +0200
Subject: [PATCH] FIX testing: random state problem in forest testing.
---
sklearn/ensemble/tests/test_forest.py | 5 +++--
1 file changed, 3 insertions(+), 2 deletions(-)
diff --git a/sklearn/ensemble/tests/test_forest.py b/sklearn/ensemble/tests/test_forest.py
index 0eabf44b48..0259c28927 100644
--- a/sklearn/ensemble/tests/test_forest.py
+++ b/sklearn/ensemble/tests/test_forest.py
@@ -188,7 +188,7 @@ def test_oob_score_classification():
"""Check that oob prediction is as acurate as
usual prediction on the training set.
Not really a good test that prediction is independent."""
- clf = RandomForestClassifier(oob_score=True)
+ clf = RandomForestClassifier(oob_score=True, random_state=rng)
clf.fit(X, y)
training_score = clf.score(X, y)
assert_almost_equal(training_score, clf.oob_score_)
@@ -197,7 +197,8 @@ def test_oob_score_classification():
def test_oob_score_regression():
"""Check that oob prediction is pessimistic estimate.
Not really a good test that prediction is independent."""
- clf = RandomForestRegressor(n_estimators=50, oob_score=True)
+ clf = RandomForestRegressor(n_estimators=50, oob_score=True,
+ random_state=rng)
n_samples = boston.data.shape[0]
clf.fit(boston.data[:n_samples / 2, :], boston.target[:n_samples / 2])
test_score = clf.score(boston.data[n_samples / 2:, :],
--
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