def test_many_trees(self):
num_features = 20
num_examples = 1000
X1 = np.random.normal(size=(num_examples, num_features))
X1 = X1.astype(np.float32)
y1 = np.random.normal(size=num_examples)
rf1 = ensemble.RandomForestRegressor(n_estimators=500, max_depth=2)
rf1.fit(X1,y1)
rf1_compiled = CompiledRegressionPredictor(rf1)
assert_array_almost_equal(rf1.predict(X1), rf1_compiled.predict(X1), decimal=10)
def test_predictions_with_invalid_input(self):
num_features = 100
num_examples = 100
X = np.random.normal(size=(num_examples, num_features))
X = X.astype(np.float32)
y = np.random.choice([-1, 1], size=num_examples)
for cls in REGRESSORS:
clf = cls()
clf.fit(X, y)
compiled = CompiledRegressionPredictor(clf)
assert_raises(ValueError, compiled.predict,
np.resize(X, (1, num_features, num_features)))
assert_allclose(compiled.score(X, y), clf.score(X, y))
def test_predictions_with_invalid_input(self):
num_features = 100
num_examples = 100
X = np.random.normal(size=(num_examples, num_features))
X = X.astype(np.float32)
y = np.random.choice([-1, 1], size=num_examples)
for cls in REGRESSORS:
clf = cls()
clf.fit(X, y)
compiled = CompiledRegressionPredictor(clf)
assert_raises(ValueError, compiled.predict,
np.resize(X, (1, num_features, num_features)))
assert_allclose(compiled.score(X, y), clf.score(X, y))
def test_many_trees(self):
num_features = 20
num_examples = 1000
X1 = np.random.normal(size=(num_examples, num_features))
X1 = X1.astype(np.float32)
y1 = np.random.normal(size=num_examples)
rf1 = ensemble.RandomForestRegressor(n_estimators=500, max_depth=2)
rf1.fit(X1, y1)
rf1_compiled = CompiledRegressionPredictor(rf1)
assert_array_almost_equal(rf1.predict(X1),
rf1_compiled.predict(X1),
decimal=10)
def assert_equal_predictions(cls, X, y, kind='regressor'):
clf = cls()
clf.fit(X, y)
if kind == 'regressor':
compiled = CompiledRegressionPredictor(clf)
elif kind == 'classifier':
compiled = CompiledClassifierPredictor(clf)
else:
raise ValueError('Unsupported type "%s"' % kind)
with tempfile.NamedTemporaryFile(delete=False) as tf:
pickle.dump(compiled, tf)
depickled = pickle.load(open(tf.name, 'rb'))
with tempfile.NamedTemporaryFile(delete=False) as tf:
pickle.dump(depickled, tf)
dedepickled = pickle.load(open(tf.name, 'rb'))
with tempfile.NamedTemporaryFile(delete=False) as tf:
cPickle.dump(compiled, tf)
decpickled = cPickle.load(open(tf.name, 'rb'))
predictors = [clf, compiled, depickled, decpickled, dedepickled]
predictions = [p.predict(X) for p in predictors]
# test predictions for single samples
if kind == 'classifier':
single_predictions = []
for p in predictors:
tmp = [p.predict(x.reshape(1, -1)) for x in X]
single_predictions.append(np.vstack(tmp))
for (p1, p2) in pairwise(predictions):
assert_array_almost_equal(p1, p2, decimal=10)
def run(n_estimators):
clf = cls(n_estimators=n_estimators)
clf.fit(X, y)
compiled = CompiledRegressionPredictor(clf)
relative_timing = RelativeTiming(compiled=timing(compiled),
normal=timing(clf))
print(n_estimators, relative_timing)
return relative_timing
def test_few_compiled(self):
num_features = 20
num_examples = 1000
X1 = np.random.normal(size=(num_examples, num_features))
X1 = X1.astype(np.float32)
y1 = np.random.normal(size=num_examples)
X2 = np.random.normal(size=(num_examples, num_features))
X2 = X2.astype(np.float32)
y2 = np.random.normal(size=num_examples)
rf1 = ensemble.RandomForestRegressor()
rf1.fit(X1, y1)
rf2 = ensemble.RandomForestRegressor()
rf2.fit(X2, y2)
rf1_compiled = CompiledRegressionPredictor(rf1)
rf2_compiled = CompiledRegressionPredictor(rf2)
assert_array_almost_equal(rf1.predict(X1),
rf1_compiled.predict(X1),
decimal=10)
assert_array_almost_equal(rf2.predict(X2),
rf2_compiled.predict(X2),
decimal=10)
def test_predictions_with_non_contiguous_input(self):
num_features = 100
num_examples = 100
X_non_contiguous = np.random.normal(size=(num_features, num_examples)).T
X_non_contiguous = X_non_contiguous.astype(np.float32)
self.assertFalse(X_non_contiguous.flags['C_CONTIGUOUS'])
y = np.random.normal(size=num_examples)
rf = ensemble.RandomForestRegressor()
rf.fit(X_non_contiguous, y)
rf_compiled = CompiledRegressionPredictor(rf)
try:
rf_compiled.predict(X_non_contiguous)
except ValueError as e:
self.fail("predict(X) raised ValueError")
X_contiguous = np.ascontiguousarray(X_non_contiguous)
self.assertTrue(X_contiguous.flags['C_CONTIGUOUS'])
assert_array_equal(rf_compiled.predict(X_non_contiguous), rf_compiled.predict(X_contiguous))
def test_float32_and_float_64_predictions_are_equal(self):
num_features = 100
num_examples = 100
X = np.random.normal(size=(num_features, num_examples))
X_32 = X.astype(np.float32)
X_64 = X.astype(np.float64)
y = np.random.normal(size=num_examples)
# fit on X_32
rf = ensemble.RandomForestRegressor()
rf.fit(X_32, y)
rf = CompiledRegressionPredictor(rf)
assert_array_equal(rf.predict(X_32), rf.predict(X_64))
# fit on X_64
rf = ensemble.RandomForestRegressor()
rf.fit(X_64, y)
rf = CompiledRegressionPredictor(rf)
assert_array_equal(rf.predict(X_32), rf.predict(X_64))
def test_few_compiled(self):
num_features = 20
num_examples = 1000
X1 = np.random.normal(size=(num_examples, num_features))
X1 = X1.astype(np.float32)
y1 = np.random.normal(size=num_examples)
X2 = np.random.normal(size=(num_examples, num_features))
X2 = X2.astype(np.float32)
y2 = np.random.normal(size=num_examples)
rf1 = ensemble.RandomForestRegressor()
rf1.fit(X1,y1)
rf2 = ensemble.RandomForestRegressor()
rf2.fit(X2,y2)
rf1_compiled = CompiledRegressionPredictor(rf1)
rf2_compiled = CompiledRegressionPredictor(rf2)
assert_array_almost_equal(rf1.predict(X1), rf1_compiled.predict(X1), decimal=10)
assert_array_almost_equal(rf2.predict(X2), rf2_compiled.predict(X2), decimal=10)
def test_float32_and_float_64_predictions_are_equal(self):
num_features = 100
num_examples = 100
X = np.random.normal(size=(num_features, num_examples))
X_32 = X.astype(np.float32)
X_64 = X.astype(np.float64)
y = np.random.normal(size=num_examples)
# fit on X_32
rf = ensemble.RandomForestRegressor()
rf.fit(X_32, y)
rf = CompiledRegressionPredictor(rf)
assert_array_equal(rf.predict(X_32), rf.predict(X_64))
# fit on X_64
rf = ensemble.RandomForestRegressor()
rf.fit(X_64, y)
rf = CompiledRegressionPredictor(rf)
assert_array_equal(rf.predict(X_32), rf.predict(X_64))
def assert_equal_predictions(cls, X, y):
clf = cls()
clf.fit(X, y)
compiled = CompiledRegressionPredictor(clf)
with tempfile.NamedTemporaryFile(delete=False) as tf:
pickle.dump(compiled, tf)
depickled = pickle.load(open(tf.name, 'rb'))
with tempfile.NamedTemporaryFile(delete=False) as tf:
pickle.dump(depickled, tf)
dedepickled = pickle.load(open(tf.name, 'rb'))
with tempfile.NamedTemporaryFile(delete=False) as tf:
cPickle.dump(compiled, tf)
decpickled = cPickle.load(open(tf.name, 'rb'))
predictors = [clf, compiled, depickled, decpickled, dedepickled]
predictions = [p.predict(X) for p in predictors]
for (p1, p2) in pairwise(predictions):
assert_array_almost_equal(p1, p2, decimal=10)
def test_predictions_with_non_contiguous_input(self):
num_features = 100
num_examples = 100
X_non_contiguous = np.random.normal(size=(num_features,
num_examples)).T
X_non_contiguous = X_non_contiguous.astype(np.float32)
self.assertFalse(X_non_contiguous.flags['C_CONTIGUOUS'])
y = np.random.normal(size=num_examples)
rf = ensemble.RandomForestRegressor()
rf.fit(X_non_contiguous, y)
rf_compiled = CompiledRegressionPredictor(rf)
try:
rf_compiled.predict(X_non_contiguous)
except ValueError as e:
self.fail("predict(X) raised ValueError")
X_contiguous = np.ascontiguousarray(X_non_contiguous)
self.assertTrue(X_contiguous.flags['C_CONTIGUOUS'])
assert_array_equal(rf_compiled.predict(X_non_contiguous),
rf_compiled.predict(X_contiguous))
def test_rejects_classifiers_as_compilable():
for cls in CLASSIFIERS:
assert_equal(CompiledRegressionPredictor.compilable(cls()), False)
assert_raises(ValueError, CompiledRegressionPredictor, cls())
def test_rejects_unfitted_regressors_as_compilable():
for cls in REGRESSORS:
assert_equal(CompiledRegressionPredictor.compilable(cls()), False)
assert_raises(ValueError, CompiledRegressionPredictor, cls())
def assert_equal_predictions(cls, X, y):
clf = cls()
clf.fit(X, y)
compiled = CompiledRegressionPredictor(clf)
assert_array_almost_equal(clf.predict(X), compiled.predict(X))
