def test_transformers():
# test if transformers do something sensible on training set
# also test all shapes / shape errors
estimators = all_estimators()
transformers = [(name, E) for name, E in estimators if issubclass(E,
TransformerMixin)]
iris = load_iris()
X, y = iris.data, iris.target
X, y = shuffle(X, y, random_state=0)
X, y = X[:10], y[:10]
n_samples, n_features = X.shape
X = Scaler().fit_transform(X)
X -= X.min()
for name, Trans in transformers:
if Trans in dont_test or Trans in meta_estimators:
continue
# these don't actually fit the data:
if Trans in [AdditiveChi2Sampler, Binarizer, Normalizer]:
continue
# catch deprecation warnings
with warnings.catch_warnings(record=True):
trans = Trans()
if hasattr(trans, 'compute_importances'):
trans.compute_importances = True
if Trans is SelectKBest:
# SelectKBest has a default of k=10
# which is more feature than we have.
trans.k = 1
# fit
trans.fit(X, y)
X_pred = trans.fit_transform(X, y=y)
assert_equal(X_pred.shape[0], n_samples)
if hasattr(trans, 'transform'):
X_pred2 = trans.transform(X)
assert_array_almost_equal(X_pred, X_pred2, 2)
# raises error on malformed input for transform
assert_raises(ValueError, trans.transform, X.T)
def test_transformers():
# test if transformers do something sensible on training set
# also test all shapes / shape errors
estimators = all_estimators()
transformers = [(name, E) for name, E in estimators if issubclass(E,
TransformerMixin)]
X, y = make_blobs(n_samples=30, centers=[[0, 0, 0], [1, 1, 1]],
random_state=0, n_features=2, cluster_std=0.1)
n_samples, n_features = X.shape
X = Scaler().fit_transform(X)
X -= X.min()
succeeded = True
for name, Trans in transformers:
if Trans in dont_test or Trans in meta_estimators:
continue
# these don't actually fit the data:
if Trans in [AdditiveChi2Sampler, Binarizer, Normalizer]:
continue
# catch deprecation warnings
with warnings.catch_warnings(record=True):
trans = Trans()
if hasattr(trans, 'compute_importances'):
trans.compute_importances = True
if Trans is SelectKBest:
# SelectKBest has a default of k=10
# which is more feature than we have.
trans.k = 1
# fit
if Trans in (_PLS, PLSCanonical, PLSRegression, CCA, PLSSVD):
y_ = np.vstack([y, 2 * y + np.random.randint(2, size=len(y))])
y_ = y_.T
else:
y_ = y
try:
trans.fit(X, y_)
X_pred = trans.fit_transform(X, y=y_)
if isinstance(X_pred, tuple):
for x_pred in X_pred:
assert_equal(x_pred.shape[0], n_samples)
else:
assert_equal(X_pred.shape[0], n_samples)
except Exception as e:
print trans
print e
print
succeeded = False
if hasattr(trans, 'transform'):
if Trans in (_PLS, PLSCanonical, PLSRegression, CCA, PLSSVD):
X_pred2 = trans.transform(X, y_)
else:
X_pred2 = trans.transform(X)
if isinstance(X_pred, tuple) and isinstance(X_pred2, tuple):
for x_pred, x_pred2 in zip(X_pred, X_pred2):
assert_array_almost_equal(x_pred, x_pred2, 2,
"fit_transform not correct in %s" % Trans)
else:
assert_array_almost_equal(X_pred, X_pred2, 2,
"fit_transform not correct in %s" % Trans)
# raises error on malformed input for transform
assert_raises(ValueError, trans.transform, X.T)
assert_true(succeeded)
def test_transformers():
# test if transformers do something sensible on training set
# also test all shapes / shape errors
estimators = all_estimators()
transformers = [(name, E) for name, E in estimators
if issubclass(E, TransformerMixin)]
X, y = make_blobs(n_samples=30,
centers=[[0, 0, 0], [1, 1, 1]],
random_state=0,
n_features=2,
cluster_std=0.1)
n_samples, n_features = X.shape
X = Scaler().fit_transform(X)
X -= X.min()
succeeded = True
for name, Trans in transformers:
if Trans in dont_test or Trans in meta_estimators:
continue
# these don't actually fit the data:
if Trans in [AdditiveChi2Sampler, Binarizer, Normalizer]:
continue
# catch deprecation warnings
with warnings.catch_warnings(record=True):
trans = Trans()
if hasattr(trans, 'compute_importances'):
trans.compute_importances = True
if Trans is SelectKBest:
# SelectKBest has a default of k=10
# which is more feature than we have.
trans.k = 1
# fit
if Trans in (_PLS, PLSCanonical, PLSRegression, CCA, PLSSVD):
y_ = np.vstack([y, 2 * y + np.random.randint(2, size=len(y))])
y_ = y_.T
else:
y_ = y
try:
trans.fit(X, y_)
X_pred = trans.fit_transform(X, y=y_)
if isinstance(X_pred, tuple):
for x_pred in X_pred:
assert_equal(x_pred.shape[0], n_samples)
else:
assert_equal(X_pred.shape[0], n_samples)
except Exception as e:
print trans
print e
print
succeeded = False
if hasattr(trans, 'transform'):
if Trans in (_PLS, PLSCanonical, PLSRegression, CCA, PLSSVD):
X_pred2 = trans.transform(X, y_)
else:
X_pred2 = trans.transform(X)
if isinstance(X_pred, tuple) and isinstance(X_pred2, tuple):
for x_pred, x_pred2 in zip(X_pred, X_pred2):
assert_array_almost_equal(
x_pred, x_pred2, 2,
"fit_transform not correct in %s" % Trans)
else:
assert_array_almost_equal(
X_pred, X_pred2, 2,
"fit_transform not correct in %s" % Trans)
# raises error on malformed input for transform
assert_raises(ValueError, trans.transform, X.T)
assert_true(succeeded)
