def test_multinomial_binary():
"""Test multinomial LR on a binary problem."""
target = (iris.target > 0).astype(np.intp)
target = np.array(["setosa", "not-setosa"])[target]
clf = LogisticRegression(solver='lbfgs', multi_class='multinomial')
clf.fit(iris.data, target)
assert_equal(clf.coef_.shape, (1, iris.data.shape[1]))
assert_equal(clf.intercept_.shape, (1,))
assert_array_equal(clf.predict(iris.data), target)
mlr = LogisticRegression(solver='lbfgs', multi_class='multinomial',
fit_intercept=False)
mlr.fit(iris.data, target)
pred = clf.classes_[np.argmax(clf.predict_log_proba(iris.data), axis=1)]
assert_greater(np.mean(pred == target), .9)
def test_multinomial_binary():
"""Test multinomial LR on a binary problem."""
target = (iris.target > 0).astype(np.intp)
target = np.array(["setosa", "not-setosa"])[target]
clf = LogisticRegression(solver='lbfgs', multi_class='multinomial')
clf.fit(iris.data, target)
assert_equal(clf.coef_.shape, (1, iris.data.shape[1]))
assert_equal(clf.intercept_.shape, (1,))
assert_array_equal(clf.predict(iris.data), target)
mlr = LogisticRegression(solver='lbfgs', multi_class='multinomial',
fit_intercept=False)
mlr.fit(iris.data, target)
pred = clf.classes_[np.argmax(clf.predict_log_proba(iris.data), axis=1)]
assert_greater(np.mean(pred == target), .9)
def test_multinomial_binary():
# Test multinomial LR on a binary problem.
target = (iris.target > 0).astype(np.intp)
target = np.array(["setosa", "not-setosa"])[target]
for solver in ['lbfgs', 'newton-cg', 'sag']:
clf = LogisticRegression(solver=solver, multi_class='multinomial',
random_state=42, max_iter=2000)
clf.fit(iris.data, target)
assert_equal(clf.coef_.shape, (1, iris.data.shape[1]))
assert_equal(clf.intercept_.shape, (1,))
assert_array_equal(clf.predict(iris.data), target)
mlr = LogisticRegression(solver=solver, multi_class='multinomial',
random_state=42, fit_intercept=False)
mlr.fit(iris.data, target)
pred = clf.classes_[np.argmax(clf.predict_log_proba(iris.data),
axis=1)]
assert_greater(np.mean(pred == target), .9)
def test_multinomial_binary():
# Test multinomial LR on a binary problem.
target = (iris.target > 0).astype(np.intp)
target = np.array(["setosa", "not-setosa"])[target]
for solver in ['lbfgs', 'newton-cg', 'sag']:
clf = LogisticRegression(solver=solver, multi_class='multinomial',
random_state=42, max_iter=2000)
clf.fit(iris.data, target)
assert_equal(clf.coef_.shape, (1, iris.data.shape[1]))
assert_equal(clf.intercept_.shape, (1,))
assert_array_equal(clf.predict(iris.data), target)
mlr = LogisticRegression(solver=solver, multi_class='multinomial',
random_state=42, fit_intercept=False)
mlr.fit(iris.data, target)
pred = clf.classes_[np.argmax(clf.predict_log_proba(iris.data),
axis=1)]
assert_greater(np.mean(pred == target), .9)
def test_fit_credit_backupsklearn():
df = pd.read_csv("./open_data/creditcard.csv")
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.LogisticRegression
enet_h2o4gpu = Solver(glm_stop_early=False)
print("h2o4gpu fit()")
enet_h2o4gpu.fit(X, y)
print("h2o4gpu predict()")
print(enet_h2o4gpu.predict(X))
print("h2o4gpu score()")
print(enet_h2o4gpu.score(X, y))
enet = Solver(dual=True, max_iter=100, tol=1E-4, random_state=1234)
print("h2o4gpu scikit wrapper fit()")
enet.fit(X, y)
print("h2o4gpu scikit wrapper predict()")
print(enet.predict(X))
print("h2o4gpu scikit wrapper predict_proba()")
print(enet.predict_proba(X))
print("h2o4gpu scikit wrapper predict_log_proba()")
print(enet.predict_log_proba(X))
print("h2o4gpu scikit wrapper score()")
print(enet.score(X, y))
print("h2o4gpu scikit wrapper decision_function()")
print(enet.decision_function(X))
print("h2o4gpu scikit wrapper densify()")
print(enet.densify())
print("h2o4gpu scikit wrapper sparsify")
print(enet.sparsify())
from sklearn.linear_model.logistic import LogisticRegression
enet_sk = LogisticRegression(dual=True,
max_iter=100,
tol=1E-4,
random_state=1234)
print("Scikit fit()")
enet_sk.fit(X, y)
print("Scikit predict()")
print(enet_sk.predict(X))
print("Scikit predict_proba()")
print(enet_sk.predict_proba(X))
print("Scikit predict_log_proba()")
print(enet_sk.predict_log_proba(X))
print("Scikit score()")
print(enet_sk.score(X, y))
print("Scikit decision_function()")
print(enet_sk.decision_function(X))
print("Scikit densify()")
print(enet_sk.densify())
print("Sciki sparsify")
print(enet_sk.sparsify())
enet_sk_coef = csr_matrix(enet_sk.coef_, dtype=np.float32).toarray()
print(enet_sk.coef_)
print(enet_sk_coef)
print(enet.coef_)
print(enet_sk.intercept_)
print("Coeffs, intercept, and n_iters should match")
assert np.allclose(enet.coef_, enet_sk_coef)
assert np.allclose(enet.intercept_, enet_sk.intercept_)
assert np.allclose(enet.n_iter_, enet_sk.n_iter_)
print("Preds should match")
assert np.allclose(enet.predict_proba(X), enet_sk.predict_proba(X))
assert np.allclose(enet.predict(X), enet_sk.predict(X))
assert np.allclose(enet.predict_log_proba(X), enet_sk.predict_log_proba(X))
def test_fit_credit_backupsklearn():
df = pd.read_csv("./open_data/creditcard.csv")
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.LogisticRegression
enet_h2o4gpu = Solver(glm_stop_early=False)
print("h2o4gpu fit()")
enet_h2o4gpu.fit(X, y)
print("h2o4gpu predict()")
print(enet_h2o4gpu.predict(X))
print("h2o4gpu score()")
print(enet_h2o4gpu.score(X,y))
enet = Solver(dual=True, max_iter=100, tol=1E-4, intercept_scaling=0.99, random_state=1234)
print("h2o4gpu scikit wrapper fit()")
enet.fit(X, y)
print("h2o4gpu scikit wrapper predict()")
print(enet.predict(X))
print("h2o4gpu scikit wrapper predict_proba()")
print(enet.predict_proba(X))
print("h2o4gpu scikit wrapper predict_log_proba()")
print(enet.predict_log_proba(X))
print("h2o4gpu scikit wrapper score()")
print(enet.score(X,y))
print("h2o4gpu scikit wrapper decision_function()")
print(enet.decision_function(X))
print("h2o4gpu scikit wrapper densify()")
print(enet.densify())
print("h2o4gpu scikit wrapper sparsify")
print(enet.sparsify())
from sklearn.linear_model.logistic import LogisticRegression
enet_sk = LogisticRegression(dual=True, max_iter=100, tol=1E-4, intercept_scaling=0.99, random_state=1234)
print("Scikit fit()")
enet_sk.fit(X, y)
print("Scikit predict()")
print(enet_sk.predict(X))
print("Scikit predict_proba()")
print(enet_sk.predict_proba(X))
print("Scikit predict_log_proba()")
print(enet_sk.predict_log_proba(X))
print("Scikit score()")
print(enet_sk.score(X,y))
print("Scikit decision_function()")
print(enet_sk.decision_function(X))
print("Scikit densify()")
print(enet_sk.densify())
print("Sciki sparsify")
print(enet_sk.sparsify())
enet_sk_coef = csr_matrix(enet_sk.coef_, dtype=np.float32).toarray()
print(enet_sk.coef_)
print(enet_sk_coef)
print(enet.coef_)
print(enet_sk.intercept_)
print("Coeffs, intercept, and n_iters should match")
assert np.allclose(enet.coef_, enet_sk_coef)
assert np.allclose(enet.intercept_, enet_sk.intercept_)
assert np.allclose(enet.n_iter_, enet_sk.n_iter_)
print("Preds should match")
assert np.allclose(enet.predict_proba(X), enet_sk.predict_proba(X))
assert np.allclose(enet.predict(X), enet_sk.predict(X))
assert np.allclose(enet.predict_log_proba(X), enet_sk.predict_log_proba(X))
