def test_overrided_gram_matrix():
X, y, _, _ = build_dataset(n_samples=20, n_features=10)
Gram = X.T.dot(X)
clf = ElasticNet(selection='cyclic',
tol=1e-8,
precompute=Gram,
fit_intercept=True)
assert_warns_message(
UserWarning, "Gram matrix was provided but X was centered"
" to fit intercept, "
"or X was normalized : recomputing Gram matrix.", clf.fit, X, y)
def test_warm_start_convergence_with_regularizer_decrement():
X, y = load_boston(return_X_y=True)
# Train a model to converge on a lightly regularized problem
final_alpha = 1e-5
low_reg_model = ElasticNet(alpha=final_alpha).fit(X, y)
# Fitting a new model on a more regularized version of the same problem.
# Fitting with high regularization is easier it should converge faster
# in general.
high_reg_model = ElasticNet(alpha=final_alpha * 10).fit(X, y)
assert low_reg_model.n_iter_ > high_reg_model.n_iter_
# Fit the solution to the original, less regularized version of the
# problem but from the solution of the highly regularized variant of
# the problem as a better starting point. This should also converge
# faster than the original model that starts from zero.
warm_low_reg_model = deepcopy(high_reg_model)
warm_low_reg_model.set_params(warm_start=True, alpha=final_alpha)
warm_low_reg_model.fit(X, y)
assert low_reg_model.n_iter_ > warm_low_reg_model.n_iter_
def test_sparse_input_convergence_warning():
X, y, _, _ = build_dataset(n_samples=1000, n_features=500)
with pytest.warns(ConvergenceWarning):
ElasticNet(max_iter=1, tol=0).fit(
sparse.csr_matrix(X, dtype=np.float32), y)
# check that the model converges w/o warnings
with pytest.warns(None) as record:
Lasso(max_iter=1000).fit(sparse.csr_matrix(X, dtype=np.float32), y)
assert not record.list
def test_enet_float_precision():
# Generate dataset
X, y, X_test, y_test = build_dataset(n_samples=20, n_features=10)
# Here we have a small number of iterations, and thus the
# ElasticNet might not converge. This is to speed up tests
for normalize in [True, False]:
for fit_intercept in [True, False]:
coef = {}
intercept = {}
for dtype in [np.float64, np.float32]:
clf = ElasticNet(alpha=0.5, max_iter=100, precompute=False,
fit_intercept=fit_intercept,
normalize=normalize)
X = dtype(X)
y = dtype(y)
ignore_warnings(clf.fit)(X, y)
coef[dtype] = clf.coef_
intercept[dtype] = clf.intercept_
assert_equal(clf.coef_.dtype, dtype)
# test precompute Gram array
Gram = X.T.dot(X)
clf_precompute = ElasticNet(alpha=0.5, max_iter=100,
precompute=Gram,
fit_intercept=fit_intercept,
normalize=normalize)
ignore_warnings(clf_precompute.fit)(X, y)
assert_array_almost_equal(clf.coef_, clf_precompute.coef_)
assert_array_almost_equal(clf.intercept_,
clf_precompute.intercept_)
assert_array_almost_equal(coef[np.float32], coef[np.float64],
decimal=4)
assert_array_almost_equal(intercept[np.float32],
intercept[np.float64],
decimal=4)
def test_same_multiple_output_sparse_dense():
for normalize in [True, False]:
l = ElasticNet(normalize=normalize)
X = [[0, 1, 2, 3, 4],
[0, 2, 5, 8, 11],
[9, 10, 11, 12, 13],
[10, 11, 12, 13, 14]]
y = [[1, 2, 3, 4, 5],
[1, 3, 6, 9, 12],
[10, 11, 12, 13, 14],
[11, 12, 13, 14, 15]]
ignore_warnings(l.fit)(X, y)
sample = np.array([1, 2, 3, 4, 5]).reshape(1, -1)
predict_dense = l.predict(sample)
l_sp = ElasticNet(normalize=normalize)
X_sp = sp.coo_matrix(X)
ignore_warnings(l_sp.fit)(X_sp, y)
sample_sparse = sp.coo_matrix(sample)
predict_sparse = l_sp.predict(sample_sparse)
assert_array_almost_equal(predict_sparse, predict_dense)
def test_enet_toy_explicit_sparse_input():
"""Test ElasticNet for various values of alpha and l1_ratio with sparse
X"""
f = ignore_warnings
# training samples
X = sp.lil_matrix((3, 1))
X[0, 0] = -1
# X[1, 0] = 0
X[2, 0] = 1
Y = [-1, 0, 1] # just a straight line (the identity function)
# test samples
T = sp.lil_matrix((3, 1))
T[0, 0] = 2
T[1, 0] = 3
T[2, 0] = 4
# this should be the same as lasso
clf = ElasticNet(alpha=0, l1_ratio=1.0)
f(clf.fit)(X, Y)
pred = clf.predict(T)
assert_array_almost_equal(clf.coef_, [1])
assert_array_almost_equal(pred, [2, 3, 4])
assert_almost_equal(clf.dual_gap_, 0)
clf = ElasticNet(alpha=0.5, l1_ratio=0.3, max_iter=1000)
clf.fit(X, Y)
pred = clf.predict(T)
assert_array_almost_equal(clf.coef_, [0.50819], decimal=3)
assert_array_almost_equal(pred, [1.0163, 1.5245, 2.0327], decimal=3)
assert_almost_equal(clf.dual_gap_, 0)
clf = ElasticNet(alpha=0.5, l1_ratio=0.5)
clf.fit(X, Y)
pred = clf.predict(T)
assert_array_almost_equal(clf.coef_, [0.45454], 3)
assert_array_almost_equal(pred, [0.9090, 1.3636, 1.8181], 3)
assert_almost_equal(clf.dual_gap_, 0)
def test_enet_multitarget():
n_targets = 3
X, y, _, _ = build_dataset(n_samples=10, n_features=8,
n_informative_features=10, n_targets=n_targets)
estimator = ElasticNet(alpha=0.01, fit_intercept=True)
estimator.fit(X, y)
coef, intercept, dual_gap = (estimator.coef_, estimator.intercept_,
estimator.dual_gap_)
for k in range(n_targets):
estimator.fit(X, y[:, k])
assert_array_almost_equal(coef[k, :], estimator.coef_)
assert_array_almost_equal(intercept[k], estimator.intercept_)
assert_array_almost_equal(dual_gap[k], estimator.dual_gap_)
def test_precompute_invalid_argument():
X, y, _, _ = build_dataset()
for clf in [
ElasticNetCV(precompute="invalid"),
LassoCV(precompute="invalid")
]:
assert_raises_regex(
ValueError, ".*should be.*True.*False.*auto.*"
"array-like.*Got 'invalid'", clf.fit, X, y)
# Precompute = 'auto' is not supported for ElasticNet
assert_raises_regex(ValueError, ".*should be.*True.*False.*array-like.*"
"Got 'auto'",
ElasticNet(precompute='auto').fit, X, y)
def test_enet_multitarget():
n_targets = 3
X, y = make_sparse_data(n_targets=n_targets)
estimator = ElasticNet(alpha=0.01, fit_intercept=True, precompute=None)
# XXX: There is a bug when precompute is not None!
estimator.fit(X, y)
coef, intercept, dual_gap = (estimator.coef_, estimator.intercept_,
estimator.dual_gap_)
for k in range(n_targets):
estimator.fit(X, y[:, k])
assert_array_almost_equal(coef[k, :], estimator.coef_)
assert_array_almost_equal(intercept[k], estimator.intercept_)
assert_array_almost_equal(dual_gap[k], estimator.dual_gap_)
def test_fit_simple_backupsklearn():
df = pd.read_csv("./open_data/simple.txt", delim_whitespace=True)
X = np.array(df.iloc[:, :df.shape[1] - 1], dtype='float32', order='C')
y = np.array(df.iloc[:, df.shape[1] - 1], dtype='float32', order='C')
Solver = h2o4gpu.ElasticNet
enet = Solver(glm_stop_early=False)
print("h2o4gpu fit()")
enet.fit(X, y)
print("h2o4gpu predict()")
print(enet.predict(X))
print("h2o4gpu score()")
print(enet.score(X, y))
enet_wrapper = Solver(positive=True, random_state=1234)
print("h2o4gpu scikit wrapper fit()")
enet_wrapper.fit(X, y)
print("h2o4gpu scikit wrapper predict()")
print(enet_wrapper.predict(X))
print("h2o4gpu scikit wrapper score()")
print(enet_wrapper.score(X, y))
from sklearn.linear_model.coordinate_descent import ElasticNet
enet_sk = ElasticNet(positive=True, random_state=1234)
print("Scikit fit()")
enet_sk.fit(X, y)
print("Scikit predict()")
print(enet_sk.predict(X))
print("Scikit score()")
print(enet_sk.score(X, y))
enet_sk_coef = csr_matrix(enet_sk.coef_, dtype=np.float32).toarray()
print(enet_sk.coef_)
print(enet_sk_coef)
print(enet_wrapper.coef_)
print(enet_sk.intercept_)
print(enet_wrapper.intercept_)
print(enet_sk.n_iter_)
print(enet_wrapper.n_iter_)
print("Coeffs, intercept, and n_iters should match")
assert np.allclose(enet_wrapper.coef_, enet_sk_coef)
assert np.allclose(enet_wrapper.intercept_, enet_sk.intercept_)
def test_check_input_false():
X, y, _, _ = build_dataset(n_samples=20, n_features=10)
X = check_array(X, order='F', dtype='float64')
y = check_array(X, order='F', dtype='float64')
clf = ElasticNet(selection='cyclic', tol=1e-8)
# Check that no error is raised if data is provided in the right format
clf.fit(X, y, check_input=False)
# With check_input=False, an exhaustive check is not made on y but its
# dtype is still cast in _preprocess_data to X's dtype. So the test should
# pass anyway
X = check_array(X, order='F', dtype='float32')
clf.fit(X, y, check_input=False)
# With no input checking, providing X in C order should result in false
# computation
X = check_array(X, order='C', dtype='float64')
assert_raises(ValueError, clf.fit, X, y, check_input=False)
def test_check_input_false():
X, y, _, _ = build_dataset(n_samples=20, n_features=10)
X = check_array(X, order='F', dtype='float64')
y = check_array(X, order='F', dtype='float64')
clf = ElasticNet(selection='cyclic', tol=1e-8)
# Check that no error is raised if data is provided in the right format
clf.fit(X, y, check_input=False)
X = check_array(X, order='F', dtype='float32')
clf.fit(X, y, check_input=True)
# Check that an error is raised if data is provided in the wrong dtype,
# because of check bypassing
assert_raises(ValueError, clf.fit, X, y, check_input=False)
# With no input checking, providing X in C order should result in false
# computation
X = check_array(X, order='C', dtype='float64')
assert_raises(ValueError, clf.fit, X, y, check_input=False)
def test_random_descent():
"""Test that both random and cyclic selection give the same results.
Ensure that the test models fully converge and check a wide
range of conditions.
"""
# This uses the coordinate descent algo using the gram trick.
X, y, _, _ = build_dataset(n_samples=50, n_features=20)
clf_cyclic = ElasticNet(selection='cyclic', tol=1e-8)
clf_cyclic.fit(X, y)
clf_random = ElasticNet(selection='random', tol=1e-8, random_state=42)
clf_random.fit(X, y)
assert_array_almost_equal(clf_cyclic.coef_, clf_random.coef_)
assert_almost_equal(clf_cyclic.intercept_, clf_random.intercept_)
# This uses the descent algo without the gram trick
clf_cyclic = ElasticNet(selection='cyclic', tol=1e-8)
clf_cyclic.fit(X.T, y[:20])
clf_random = ElasticNet(selection='random', tol=1e-8, random_state=42)
clf_random.fit(X.T, y[:20])
assert_array_almost_equal(clf_cyclic.coef_, clf_random.coef_)
assert_almost_equal(clf_cyclic.intercept_, clf_random.intercept_)
# Sparse Case
clf_cyclic = ElasticNet(selection='cyclic', tol=1e-8)
clf_cyclic.fit(sparse.csr_matrix(X), y)
clf_random = ElasticNet(selection='random', tol=1e-8, random_state=42)
clf_random.fit(sparse.csr_matrix(X), y)
assert_array_almost_equal(clf_cyclic.coef_, clf_random.coef_)
assert_almost_equal(clf_cyclic.intercept_, clf_random.intercept_)
# Multioutput case.
new_y = np.hstack((y[:, np.newaxis], y[:, np.newaxis]))
clf_cyclic = MultiTaskElasticNet(selection='cyclic', tol=1e-8)
clf_cyclic.fit(X, new_y)
clf_random = MultiTaskElasticNet(selection='random',
tol=1e-8,
random_state=42)
clf_random.fit(X, new_y)
assert_array_almost_equal(clf_cyclic.coef_, clf_random.coef_)
assert_almost_equal(clf_cyclic.intercept_, clf_random.intercept_)
# Raise error when selection is not in cyclic or random.
clf_random = ElasticNet(selection='invalid')
assert_raises(ValueError, clf_random.fit, X, y)
def test_warm_start_convergence():
X, y, _, _ = build_dataset()
model = ElasticNet(alpha=1e-3, tol=1e-3).fit(X, y)
n_iter_reference = model.n_iter_
# This dataset is not trivial enough for the model to converge in one pass.
assert_greater(n_iter_reference, 2)
# Check that n_iter_ is invariant to multiple calls to fit
# when warm_start=False, all else being equal.
model.fit(X, y)
n_iter_cold_start = model.n_iter_
assert_equal(n_iter_cold_start, n_iter_reference)
# Fit the same model again, using a warm start: the optimizer just performs
# a single pass before checking that it has already converged
model.set_params(warm_start=True)
model.fit(X, y)
n_iter_warm_start = model.n_iter_
assert_equal(n_iter_warm_start, 1)
Pipeline([('earth', Earth()),
('logistic', LogisticRegression())]))],
'hard',
weights=[1.01, 1.01]), ['predict'],
create_weird_classification_problem_1()),
(GradientBoostingClassifier(max_depth=10,
n_estimators=10), ['predict_proba', 'predict'],
create_weird_classification_problem_1()),
(LogisticRegression(), ['predict_proba', 'predict'],
create_weird_classification_problem_1()),
(IsotonicRegression(out_of_bounds='clip'), ['predict'],
create_isotonic_regression_problem_1()),
(Earth(), ['predict', 'transform'], create_regression_problem_1()),
(Earth(allow_missing=True), ['predict', 'transform'],
create_regression_problem_with_missingness_1()),
(ElasticNet(), ['predict'], create_regression_problem_1()),
(ElasticNetCV(), ['predict'], create_regression_problem_1()),
(LassoCV(), ['predict'], create_regression_problem_1()),
(Ridge(), ['predict'], create_regression_problem_1()),
(RidgeCV(), ['predict'], create_regression_problem_1()),
(SGDRegressor(), ['predict'], create_regression_problem_1()),
(Lasso(), ['predict'], create_regression_problem_1()),
(Pipeline([('earth', Earth()), ('logistic', LogisticRegression())]),
['predict', 'predict_proba'], create_weird_classification_problem_1()),
(FeatureUnion([('earth', Earth()), ('earth2', Earth(max_degree=2))],
transformer_weights={
'earth': 1,
'earth2': 2
}), ['transform'], create_weird_classification_problem_1()),
(RandomForestRegressor(), ['predict'], create_regression_problem_1()),
(CalibratedClassifierCV(LogisticRegression(),
'BaggingClassifier':BaggingClassifier(), 'BaggingRegressor':BaggingRegressor(), 'BayesianGaussianMixture':BayesianGaussianMixture(), 'BayesianRidge':BayesianRidge(), 'BernoulliNB':BernoulliNB(), 'BernoulliRBM':BernoulliRBM(), 'Binarizer':Binarizer(), 'Birch':Birch(), 'CCA':CCA(), 'CalibratedClassifierCV':CalibratedClassifierCV(), 'DBSCAN':DBSCAN(), 'DPGMM':DPGMM(), 'DecisionTreeClassifier':DecisionTreeClassifier(), 'DecisionTreeRegressor':DecisionTreeRegressor(), 'DictionaryLearning':DictionaryLearning(), 'ElasticNet':ElasticNet(), 'ElasticNetCV':ElasticNetCV(), 'EmpiricalCovariance':EmpiricalCovariance(), 'ExtraTreeClassifier':ExtraTreeClassifier(), 'ExtraTreeRegressor':ExtraTreeRegressor(), 'ExtraTreesClassifier':ExtraTreesClassifier(), 'ExtraTreesRegressor':ExtraTreesRegressor(), 'FactorAnalysis':FactorAnalysis(), 'FastICA':FastICA(), 'FeatureAgglomeration':FeatureAgglomeration(), 'FunctionTransformer':FunctionTransformer(), 'GMM':GMM(), 'GaussianMixture':GaussianMixture(), 'GaussianNB':GaussianNB(), 'GaussianProcess':GaussianProcess(), 'GaussianProcessClassifier':GaussianProcessClassifier(),
print X_train[123, :]
'''
norm1 = np.linalg.norm(y_train)
if norm1 != 0:
y_train, y_test = y_train/norm1, y_test/norm1
print norm1
'''
print y_train.shape
model = SVR(C=1.0, gamma=1.0)
model = LinearRegression()
lasso = Lasso(alpha=0.1).fit(X_train, y_train)
enet = ElasticNet(alpha=0.1, l1_ratio=0.7).fit(X_train, y_train)
y_pred = lasso.predict(X_test)
print "MSE", mean_squared_error(y_test, y_pred)
m = np.mean(y_test)
print "MSE (Mean)", mean_squared_error(y_test, m * np.ones(len(y_test)))
print "r^2 on test data", r2_score(y_test, y_pred)
plt.plot(enet.coef_, label='Elastic net coefficients')
plt.plot(lasso.coef_, label='Lasso coefficients')
plt.legend(loc='best')
plt.title("Lasso R^2: %f, Elastic Net R^2: %f" % (r2_score(
y_test, lasso.predict(X_test)), r2_score(y_test, enet.predict(X_test))))
plt.show()
boston = datasets.load_boston()
# print(type(boston), boston.keys())
# print(boston.feature_names, boston.DESCR, boston.filename)
X, y = boston.data, boston.target
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_size=.2,
random_state=22)
print(X.shape, y.shape)
# plot the price
# plt.plot(y)
# plt.show()
model = SVR(gamma='scale')
model.fit(X_train, y_train)
preds = model.predict(X_test)
plt.plot(y_test, label='y_test')
plt.plot(preds, label='y_predict')
plt.legend()
plt.show()
print(model.score(X_test, y_test))
# compare with ElasticNet linear model
from sklearn.linear_model.coordinate_descent import ElasticNet
model = ElasticNet()
model.fit(X_train, y_train)
preds = model.predict(X_test)
plt.plot(y_test, label='y_test')
plt.plot(preds, label='y_predict')
plt.legend()
plt.show()
print(model.score(X_test, y_test))
