def check_regressors_pickle(name, Regressor):
X, y = _boston_subset()
y = StandardScaler().fit_transform(y) # X is already scaled
y = multioutput_estimator_convert_y_2d(name, y)
if name == 'OrthogonalMatchingPursuitCV':
# FIXME: This test is unstable on Travis, see issue #3190.
check_skip_travis()
rnd = np.random.RandomState(0)
# catch deprecation warnings
with warnings.catch_warnings(record=True):
regressor = Regressor()
set_fast_parameters(regressor)
if not hasattr(regressor, 'alphas') and hasattr(regressor, 'alpha'):
# linear regressors need to set alpha, but not generalized CV ones
regressor.alpha = 0.01
if name in CROSS_DECOMPOSITION:
y_ = np.vstack([y, 2 * y + rnd.randint(2, size=len(y))])
y_ = y_.T
else:
y_ = y
regressor.fit(X, y_)
y_pred = regressor.predict(X)
# store old predictions
pickled_regressor = pickle.dumps(regressor)
unpickled_regressor = pickle.loads(pickled_regressor)
pickled_y_pred = unpickled_regressor.predict(X)
assert_array_almost_equal(pickled_y_pred, y_pred)
def check_regressors_pickle(name, Regressor):
X, y = _boston_subset()
y = StandardScaler().fit_transform(y) # X is already scaled
y = multioutput_estimator_convert_y_2d(name, y)
if name == 'OrthogonalMatchingPursuitCV':
# FIXME: This test is unstable on Travis, see issue #3190.
check_skip_travis()
rnd = np.random.RandomState(0)
# catch deprecation warnings
with warnings.catch_warnings(record=True):
regressor = Regressor()
set_fast_parameters(regressor)
if not hasattr(regressor, 'alphas') and hasattr(regressor, 'alpha'):
# linear regressors need to set alpha, but not generalized CV ones
regressor.alpha = 0.01
if name in CROSS_DECOMPOSITION:
y_ = np.vstack([y, 2 * y + rnd.randint(2, size=len(y))])
y_ = y_.T
else:
y_ = y
regressor.fit(X, y_)
y_pred = regressor.predict(X)
# store old predictions
pickled_regressor = pickle.dumps(regressor)
unpickled_regressor = pickle.loads(pickled_regressor)
pickled_y_pred = unpickled_regressor.predict(X)
assert_array_almost_equal(pickled_y_pred, y_pred)
def check_regressors_int(name, Regressor, X, y):
if name == 'OrthogonalMatchingPursuitCV':
# FIXME: This test is unstable on Travis, see issue #3190.
check_skip_travis()
rnd = np.random.RandomState(0)
# catch deprecation warnings
with warnings.catch_warnings(record=True):
# separate estimators to control random seeds
regressor_1 = Regressor()
regressor_2 = Regressor()
set_random_state(regressor_1)
set_random_state(regressor_2)
if name in ('_PLS', 'PLSCanonical', 'PLSRegression'):
y_ = np.vstack([y, 2 * y + rnd.randint(2, size=len(y))])
y_ = y_.T
else:
y_ = y
# fit
regressor_1.fit(X, y_)
pred1 = regressor_1.predict(X)
regressor_2.fit(X, y_.astype(np.float))
pred2 = regressor_2.predict(X)
assert_array_almost_equal(pred1, pred2, 2, name)
def check_regressors_train(name, Regressor, X, y):
if name == 'OrthogonalMatchingPursuitCV':
# FIXME: This test is unstable on Travis, see issue #3190.
check_skip_travis()
rnd = np.random.RandomState(0)
# catch deprecation warnings
with warnings.catch_warnings(record=True):
regressor = Regressor()
if not hasattr(regressor, 'alphas') and hasattr(regressor, 'alpha'):
# linear regressors need to set alpha, but not generalized CV ones
regressor.alpha = 0.01
# raises error on malformed input for fit
assert_raises(ValueError, regressor.fit, X, y[:-1])
# fit
if name in ('PLSCanonical', 'PLSRegression', 'CCA'):
y_ = np.vstack([y, 2 * y + rnd.randint(2, size=len(y))])
y_ = y_.T
else:
y_ = y
set_random_state(regressor)
regressor.fit(X, y_)
regressor.predict(X)
# TODO: find out why PLS and CCA fail. RANSAC is random
# and furthermore assumes the presence of outliers, hence
# skipped
if name not in ('PLSCanonical', 'CCA', 'RANSACRegressor'):
assert_greater(regressor.score(X, y_), 0.5)
def check_regressors_train(name, Regressor, X, y):
if name == 'OrthogonalMatchingPursuitCV':
# FIXME: This test is unstable on Travis, see issue #3190.
check_skip_travis()
rnd = np.random.RandomState(0)
# catch deprecation warnings
with warnings.catch_warnings(record=True):
regressor = Regressor()
if not hasattr(regressor, 'alphas') and hasattr(regressor, 'alpha'):
# linear regressors need to set alpha, but not generalized CV ones
regressor.alpha = 0.01
# raises error on malformed input for fit
assert_raises(ValueError, regressor.fit, X, y[:-1])
# fit
if name in ('PLSCanonical', 'PLSRegression', 'CCA'):
y_ = np.vstack([y, 2 * y + rnd.randint(2, size=len(y))])
y_ = y_.T
else:
y_ = y
set_random_state(regressor)
regressor.fit(X, y_)
regressor.predict(X)
# TODO: find out why PLS and CCA fail. RANSAC is random
# and furthermore assumes the presence of outliers, hence
# skipped
if name not in ('PLSCanonical', 'CCA', 'RANSACRegressor'):
assert_greater(regressor.score(X, y_), 0.5)
def check_regressors_int(name, Regressor):
X, _ = _boston_subset()
X = X[:50]
rnd = np.random.RandomState(0)
y = rnd.randint(3, size=X.shape[0])
y = multioutput_estimator_convert_y_2d(name, y)
if name == 'OrthogonalMatchingPursuitCV':
# FIXME: This test is unstable on Travis, see issue #3190.
check_skip_travis()
rnd = np.random.RandomState(0)
# catch deprecation warnings
with warnings.catch_warnings(record=True):
# separate estimators to control random seeds
regressor_1 = Regressor()
regressor_2 = Regressor()
set_fast_parameters(regressor_1)
set_fast_parameters(regressor_2)
set_random_state(regressor_1)
set_random_state(regressor_2)
if name in CROSS_DECOMPOSITION:
y_ = np.vstack([y, 2 * y + rnd.randint(2, size=len(y))])
y_ = y_.T
else:
y_ = y
# fit
regressor_1.fit(X, y_)
pred1 = regressor_1.predict(X)
regressor_2.fit(X, y_.astype(np.float))
pred2 = regressor_2.predict(X)
assert_array_almost_equal(pred1, pred2, 2, name)
def check_regressors_train(name, Regressor):
X, y = _boston_subset()
y = StandardScaler().fit_transform(y) # X is already scaled
y = multioutput_estimator_convert_y_2d(name, y)
if name == 'OrthogonalMatchingPursuitCV':
# FIXME: This test is unstable on Travis, see issue #3190.
check_skip_travis()
rnd = np.random.RandomState(0)
# catch deprecation warnings
with warnings.catch_warnings(record=True):
regressor = Regressor()
set_fast_parameters(regressor)
if not hasattr(regressor, 'alphas') and hasattr(regressor, 'alpha'):
# linear regressors need to set alpha, but not generalized CV ones
regressor.alpha = 0.01
# raises error on malformed input for fit
assert_raises(ValueError, regressor.fit, X, y[:-1])
# fit
if name in CROSS_DECOMPOSITION:
y_ = np.vstack([y, 2 * y + rnd.randint(2, size=len(y))])
y_ = y_.T
else:
y_ = y
set_random_state(regressor)
regressor.fit(X, y_)
regressor.fit(X.tolist(), y_.tolist())
regressor.predict(X)
# TODO: find out why PLS and CCA fail. RANSAC is random
# and furthermore assumes the presence of outliers, hence
# skipped
if name not in ('PLSCanonical', 'CCA', 'RANSACRegressor'):
assert_greater(regressor.score(X, y_), 0.5)
def check_regressors_int(name, Regressor):
X, _ = _boston_subset()
X = X[:50]
rnd = np.random.RandomState(0)
y = rnd.randint(3, size=X.shape[0])
y = multioutput_estimator_convert_y_2d(name, y)
if name == 'OrthogonalMatchingPursuitCV':
# FIXME: This test is unstable on Travis, see issue #3190.
check_skip_travis()
rnd = np.random.RandomState(0)
# catch deprecation warnings
with warnings.catch_warnings(record=True):
# separate estimators to control random seeds
regressor_1 = Regressor()
regressor_2 = Regressor()
set_fast_parameters(regressor_1)
set_fast_parameters(regressor_2)
set_random_state(regressor_1)
set_random_state(regressor_2)
if name in CROSS_DECOMPOSITION:
y_ = np.vstack([y, 2 * y + rnd.randint(2, size=len(y))])
y_ = y_.T
else:
y_ = y
# fit
regressor_1.fit(X, y_)
pred1 = regressor_1.predict(X)
regressor_2.fit(X, y_.astype(np.float))
pred2 = regressor_2.predict(X)
assert_array_almost_equal(pred1, pred2, 2, name)
def check_regressors_train(name, Regressor):
X, y = _boston_subset()
y = StandardScaler().fit_transform(y) # X is already scaled
y = multioutput_estimator_convert_y_2d(name, y)
if name == 'OrthogonalMatchingPursuitCV':
# FIXME: This test is unstable on Travis, see issue #3190.
check_skip_travis()
rnd = np.random.RandomState(0)
# catch deprecation warnings
with warnings.catch_warnings(record=True):
regressor = Regressor()
set_fast_parameters(regressor)
if not hasattr(regressor, 'alphas') and hasattr(regressor, 'alpha'):
# linear regressors need to set alpha, but not generalized CV ones
regressor.alpha = 0.01
# raises error on malformed input for fit
assert_raises(ValueError, regressor.fit, X, y[:-1])
# fit
if name in CROSS_DECOMPOSITION:
y_ = np.vstack([y, 2 * y + rnd.randint(2, size=len(y))])
y_ = y_.T
else:
y_ = y
set_random_state(regressor)
regressor.fit(X, y_)
regressor.fit(X.tolist(), y_.tolist())
regressor.predict(X)
# TODO: find out why PLS and CCA fail. RANSAC is random
# and furthermore assumes the presence of outliers, hence
# skipped
if name not in ('PLSCanonical', 'CCA', 'RANSACRegressor'):
assert_greater(regressor.score(X, y_), 0.5)
def test_omp_cv():
check_skip_travis()
y_ = y[:, 0]
gamma_ = gamma[:, 0]
ompcv = OrthogonalMatchingPursuitCV(normalize=True, fit_intercept=False,
max_iter=10, cv=5)
ompcv.fit(X, y_)
assert_equal(ompcv.n_nonzero_coefs_, n_nonzero_coefs)
assert_array_almost_equal(ompcv.coef_, gamma_)
omp = OrthogonalMatchingPursuit(normalize=True, fit_intercept=False,
n_nonzero_coefs=ompcv.n_nonzero_coefs_)
omp.fit(X, y_)
assert_array_almost_equal(ompcv.coef_, omp.coef_)
def test_omp_cv():
# FIXME: This test is unstable on Travis, see issue #3190 for more detail.
check_skip_travis()
y_ = y[:, 0]
gamma_ = gamma[:, 0]
ompcv = OrthogonalMatchingPursuitCV(normalize=True, fit_intercept=False,
max_iter=10, cv=5)
ompcv.fit(X, y_)
assert_equal(ompcv.n_nonzero_coefs_, n_nonzero_coefs)
assert_array_almost_equal(ompcv.coef_, gamma_)
omp = OrthogonalMatchingPursuit(normalize=True, fit_intercept=False,
n_nonzero_coefs=ompcv.n_nonzero_coefs_)
omp.fit(X, y_)
assert_array_almost_equal(ompcv.coef_, omp.coef_)
def test_regressors_int():
# test if regressors can cope with integer labels (by converting them to
# float)
regressors = all_estimators(type_filter='regressor')
X, _ = _boston_subset()
X = X[:50]
rnd = np.random.RandomState(0)
y = rnd.randint(3, size=X.shape[0])
for name, Regressor in regressors:
if name in dont_test or name in ('CCA'):
continue
elif name in ('OrthogonalMatchingPursuitCV'):
# FIXME: This test is unstable on Travis, see issue #3190.
check_skip_travis()
yield (check_regressors_int, name, Regressor, X,
multioutput_estimator_convert_y_2d(name, y))
def test_omp_cv():
check_skip_travis()
y_ = y[:, 0]
gamma_ = gamma[:, 0]
ompcv = OrthogonalMatchingPursuitCV(normalize=True,
fit_intercept=False,
max_iter=10,
cv=5)
ompcv.fit(X, y_)
assert_equal(ompcv.n_nonzero_coefs_, n_nonzero_coefs)
assert_array_almost_equal(ompcv.coef_, gamma_)
omp = OrthogonalMatchingPursuit(normalize=True,
fit_intercept=False,
n_nonzero_coefs=ompcv.n_nonzero_coefs_)
omp.fit(X, y_)
assert_array_almost_equal(ompcv.coef_, omp.coef_)
def test_regressors_int():
# test if regressors can cope with integer labels (by converting them to
# float)
regressors = all_estimators(type_filter='regressor')
X, _ = _boston_subset()
X = X[:50]
rnd = np.random.RandomState(0)
y = rnd.randint(3, size=X.shape[0])
for name, Regressor in regressors:
if name in dont_test or name in ('CCA'):
continue
elif name in ('OrthogonalMatchingPursuitCV'):
# FIXME: This test is unstable on Travis, see issue #3190.
check_skip_travis()
yield (check_regressors_int, name, Regressor, X,
multioutput_estimator_convert_y_2d(name, y))
def set_fast_parameters(estimator):
# speed up some estimators
params = estimator.get_params()
if estimator.__class__.__name__ == 'OrthogonalMatchingPursuitCV':
# FIXME: This test is unstable on Travis, see issue #3190.
check_skip_travis()
if ("n_iter" in params
and estimator.__class__.__name__ != "TSNE"):
estimator.set_params(n_iter=5)
if "max_iter" in params:
# NMF
if estimator.max_iter is not None:
estimator.set_params(max_iter=min(5, estimator.max_iter))
# LinearSVR
if estimator.__class__.__name__ == 'LinearSVR':
estimator.set_params(max_iter=20)
if "n_resampling" in params:
# randomized lasso
estimator.set_params(n_resampling=5)
if "n_estimators" in params:
# especially gradient boosting with default 100
estimator.set_params(n_estimators=min(5, estimator.n_estimators))
if "max_trials" in params:
# RANSAC
estimator.set_params(max_trials=10)
if "n_init" in params:
# K-Means
estimator.set_params(n_init=2)
if estimator.__class__.__name__ == "SelectFdr":
# be tolerant of noisy datasets (not actually speed)
estimator.set_params(alpha=.5)
if estimator.__class__.__name__ == "TheilSenRegressor":
estimator.max_subpopulation = 100
if isinstance(estimator, BaseRandomProjection):
# Due to the jl lemma and often very few samples, the number
# of components of the random matrix projection will be probably
# greater than the number of features.
# So we impose a smaller number (avoid "auto" mode)
estimator.set_params(n_components=1)
if isinstance(estimator, SelectKBest):
# SelectKBest has a default of k=10
# which is more feature than we have in most case.
estimator.set_params(k=1)
def test_omp_cv():
# FIXME: This test is unstable on Travis, see issue #3190 for more detail.
check_skip_travis()
y_ = y[:, 0]
gamma_ = gamma[:, 0]
ompcv = OrthogonalMatchingPursuitCV(normalize=True,
fit_intercept=False,
max_iter=10,
cv=5)
ompcv.fit(X, y_)
assert_equal(ompcv.n_nonzero_coefs_, n_nonzero_coefs)
assert_array_almost_equal(ompcv.coef_, gamma_)
omp = OrthogonalMatchingPursuit(normalize=True,
fit_intercept=False,
n_nonzero_coefs=ompcv.n_nonzero_coefs_)
omp.fit(X, y_)
assert_array_almost_equal(ompcv.coef_, omp.coef_)
def set_fast_parameters(estimator):
# speed up some estimators
params = estimator.get_params()
if estimator.__class__.__name__ == 'OrthogonalMatchingPursuitCV':
# FIXME: This test is unstable on Travis, see issue #3190.
check_skip_travis()
if ("n_iter" in params and estimator.__class__.__name__ != "TSNE"):
estimator.set_params(n_iter=5)
if "max_iter" in params:
# NMF
if estimator.max_iter is not None:
estimator.set_params(max_iter=min(5, estimator.max_iter))
# LinearSVR
if estimator.__class__.__name__ == 'LinearSVR':
estimator.set_params(max_iter=20)
if "n_resampling" in params:
# randomized lasso
estimator.set_params(n_resampling=5)
if "n_estimators" in params:
# especially gradient boosting with default 100
estimator.set_params(n_estimators=min(5, estimator.n_estimators))
if "max_trials" in params:
# RANSAC
estimator.set_params(max_trials=10)
if "n_init" in params:
# K-Means
estimator.set_params(n_init=2)
if estimator.__class__.__name__ == "SelectFdr":
# be tolerant of noisy datasets (not actually speed)
estimator.set_params(alpha=.5)
if estimator.__class__.__name__ == "TheilSenRegressor":
estimator.max_subpopulation = 100
if isinstance(estimator, BaseRandomProjection):
# Due to the jl lemma and often very few samples, the number
# of components of the random matrix projection will be probably
# greater than the number of features.
# So we impose a smaller number (avoid "auto" mode)
estimator.set_params(n_components=1)
if isinstance(estimator, SelectKBest):
# SelectKBest has a default of k=10
# which is more feature than we have in most case.
estimator.set_params(k=1)
def check_regressors_pickle(name, Regressor, X, y):
if name == 'OrthogonalMatchingPursuitCV':
# FIXME: This test is unstable on Travis, see issue #3190.
check_skip_travis()
rnd = np.random.RandomState(0)
# catch deprecation warnings
with warnings.catch_warnings(record=True):
regressor = Regressor()
if not hasattr(regressor, 'alphas') and hasattr(regressor, 'alpha'):
# linear regressors need to set alpha, but not generalized CV ones
regressor.alpha = 0.01
if name in ('PLSCanonical', 'PLSRegression', 'CCA'):
y_ = np.vstack([y, 2 * y + rnd.randint(2, size=len(y))])
y_ = y_.T
else:
y_ = y
regressor.fit(X, y_)
y_pred = regressor.predict(X)
# store old predictions
pickled_regressor = pickle.dumps(regressor)
unpickled_regressor = pickle.loads(pickled_regressor)
pickled_y_pred = unpickled_regressor.predict(X)
assert_array_almost_equal(pickled_y_pred, y_pred)
def check_regressors_pickle(name, Regressor, X, y):
if name == 'OrthogonalMatchingPursuitCV':
# FIXME: This test is unstable on Travis, see issue #3190.
check_skip_travis()
rnd = np.random.RandomState(0)
# catch deprecation warnings
with warnings.catch_warnings(record=True):
regressor = Regressor()
if not hasattr(regressor, 'alphas') and hasattr(regressor, 'alpha'):
# linear regressors need to set alpha, but not generalized CV ones
regressor.alpha = 0.01
if name in ('PLSCanonical', 'PLSRegression', 'CCA'):
y_ = np.vstack([y, 2 * y + rnd.randint(2, size=len(y))])
y_ = y_.T
else:
y_ = y
regressor.fit(X, y_)
y_pred = regressor.predict(X)
# store old predictions
pickled_regressor = pickle.dumps(regressor)
unpickled_regressor = pickle.loads(pickled_regressor)
pickled_y_pred = unpickled_regressor.predict(X)
assert_array_almost_equal(pickled_y_pred, y_pred)
