def test_sk_OrthogonalMatchingPursuitCV():
print("Testing sklearn, OrthogonalMatchingPursuitCV...")
mod = linear_model.OrthogonalMatchingPursuitCV()
X, y = iris_data
mod.fit(X, y)
docs = {'name': "OrthogonalMatchingPursuitCV test"}
fv = X[0, :]
upload(mod, fv, docs)
def test_sk_PassiveAggressiveRegressor():
print("Testing sklearn, PassiveAggressiveRegressor...")
mod = linear_model.PassiveAggressiveRegressor()
X, y = iris_data
mod.fit(X, y)
docs = {'name': "PassiveAggressiveRegressor test"}
fv = X[0, :]
upload(mod, fv, docs)
def test_sk_LinearSVC():
print("Testing sklearn, LinearSVC...")
mod = svm.LinearSVC(max_iter=10000) # Needs more iterations to converge
X, y = iris_data
mod.fit(X, y)
docs = {'name': "LinearSVC test"}
fv = X[0, :]
upload(mod, fv, docs)
def test_sk_NuSVR():
print("Testing sklearn, NuSVR...")
mod = svm.NuSVR()
X, y = iris_data
mod.fit(X, y)
docs = {'name': "NuSVR test"}
fv = X[0, :]
upload(mod, fv, docs)
def test_sk_LassoLarsIC():
print("Testing sklearn, LassoLarsIC...")
mod = linear_model.LassoLarsIC()
X, y = iris_data
mod.fit(X, y)
docs = {'name': "LassoLarsIC test"}
fv = X[0, :]
upload(mod, fv, docs)
def test_sk_LinearRegression():
print("Testing sklearn, LinearRegression...")
mod = linear_model.LinearRegression()
X, y = iris_data
mod.fit(X, y)
docs = {'name': "LinearRegression test"}
fv = X[0, :]
upload(mod, fv, docs)
def test_sk_ExtraTreesRegressor():
print("Testing sklearn, Ensemble ExtraTreeRegressor...")
mod = ensemble.ExtraTreesRegressor()
X, y = iris_data
mod.fit(X, y)
docs = {'name': "ExtraTreeRegressor test"}
fv = X[0, :]
upload(mod, fv, docs)
def test_sk_ExtraTreeClassifier():
print("Testing sklearn, ExtraTreeClassifier...")
mod = tree.ExtraTreeClassifier()
X, y = iris_data
mod.fit(X, y)
docs = {'name': "ExtraTreeClassifier test"}
fv = X[0, :]
upload(mod, fv, docs)
def test_sk_ElasticNetCV():
print("Testing sklearn, ElasticNetCV...")
mod = linear_model.ElasticNetCV()
X, y = iris_data
mod.fit(X, y)
docs = {'name': "ElasticNetCV test"}
fv = X[0, :]
upload(mod, fv, docs)
def test_sk_Perceptron():
print("Testing sklearn, Perceptron...")
mod = linear_model.Perceptron()
X, y = iris_data
mod.fit(X, y)
docs = {'name': "Perceptron test"}
fv = X[0, :]
upload(mod, fv, docs)
def test_sk_RidgeClassifier():
print("Testing sklearn, RidgeClassifier...")
mod = linear_model.RidgeClassifier()
X, y = iris_data
mod.fit(X, y)
docs = {'name': "RidgeClassifier test"}
fv = X[0, :]
upload(mod, fv, docs)
def test_sm_QuantReg():
print("Testing SM, QuantReg...")
X, y = iris_data
est = sm.QuantReg(y, X)
mod = est.fit()
docs = {'name': "QuantReg test"}
fv = X[0, :]
upload(mod, fv, docs=docs)
def test_lgb_SAGARegressor():
print("Testing lightgbm, SAGARegressor...")
mod = lgb.SAGARegressor()
X, y = iris_data
mod.fit(X, y)
docs = {'name': "SAGARegressor test"}
fv = X[0, :]
upload(mod, fv, docs)
def test_lgb_FistaRegressor():
print("Testing lightning, FistaRegressor...")
mod = lgb.FistaRegressor()
X, y = iris_data
mod.fit(X, y)
docs = {'name': "FistaRegressor test"}
fv = X[0, :]
upload(mod, fv, docs)
def test_lgb_KernelSVC():
print("Testing lightning, KernelSVC...")
mod = ln.KernelSVC()
X, y = iris_data
mod.fit(X, y)
docs = {'name': "KernelSVC test"}
fv = X[0, :]
upload(mod, fv, docs)
def test_lgb_CDClassifier():
print("Testing lightning, CDClassifier...")
mod = lgb.CDClassifier()
X, y = iris_data
mod.fit(X, y)
docs = {'name': "CDClassifier test"}
fv = X[0, :]
upload(mod, fv, docs)
def test_sm_OLS():
print("Testing SM, OLS...")
X, y = iris_data
est = sm.OLS(y, X)
mod = est.fit()
docs = {'name': "OLS test"}
fv = X[0, :]
upload(mod, fv, docs=docs)
def test_sk_RandomForestRegressor():
print("Testing sklearn, RandomForestRegressor...")
mod = ensemble.RandomForestRegressor()
X, y = iris_data
mod.fit(X, y)
docs = {'name': "RandomForestRegressor test"}
fv = X[0, :]
upload(mod, fv, docs)
def test_lgb_LGBMClassifier():
print("Testing lightgbm, LGBMClassifier...")
mod = lgb.LGBMClassifier()
X, y = iris_data
mod.fit(X, y)
docs = {'name': "LGBMClassifier test"}
fv = X[0, :]
upload(mod, fv, docs)
def test_sk_TheilSenRegressor():
mod = linear_model.TheilSenRegressor()
X, y = iris_data
mod.fit(X, y)
docs = {'name': "TheilSenRegressor test"}
fv = X[0, :]
upload(mod, fv, docs)
print("Tested sklearn, TheilSenRegressor...")
def test_sm_GLSAR():
print("Testing SM, GLSAR...")
X, y = iris_data
est = sm.GLS(y, X, rho=2)
mod = est.fit()
docs = {'name': "GLSAR test"}
fv = X[0, :]
upload(mod, fv, docs)
def test_sk_SGDClassifier():
mod = linear_model.SGDClassifier()
X, y = iris_data
mod.fit(X, y)
docs = {'name': "SGDClassifier test"}
fv = X[0, :]
upload(mod, fv, docs)
print("Tested sklearn, SGDClassifier...")
def test_sk_SVR():
mod = svm.SVR()
X, y = iris_data
mod.fit(X, y)
docs = {'name': "SVR test"}
fv = X[0, :]
upload(mod, fv, docs)
print("Tested sklearn, SVR...")
def test_sk_RidgeCV():
mod = linear_model.RidgeCV()
X, y = iris_data
mod.fit(X, y)
docs = {'name': "RidgeCV test"}
fv = X[0, :]
upload(mod, fv, docs)
print("Tested sklearn, RidgeCV...")
def test_sk_DecisionTreeRegressor():
print("Testing sklearn, DecisionTreeRegressor...")
mod = tree.DecisionTreeRegressor()
X, y = iris_data
mod.fit(X, y)
docs = {'name': "DecisionTreeClassifier test"}
fv = X[0, :]
upload(mod, fv, docs)
def test_sk_RANSACRegressor():
print("Testing sklearn, RANSACRegressor...")
mod = linear_model.RANSACRegressor()
X, y = iris_data
mod.fit(X, y)
docs = {'name': "RANSACRegressor test"}
fv = X[0, :]
upload(mod, fv, docs)
def test_lgb_SDCARegressor():
print("Testing lightning, SDCARegressor...")
mod = lgb.SDCAClassifier()
X, y = iris_data
mod.fit(X, y)
docs = {'name': "SDCARegressor test"}
fv = X[0, :]
upload(mod, fv, docs)
def test_xg_XGBRFRegressor():
print("Testing xgboost, XGBRFRegressor...")
mod = XGBRFRegressor()
X, y = iris_data
mod.fit(X, y)
docs = {'name': "XGBRFRegressor test"}
fv = X[0, :]
upload(mod, fv, docs)
def test_sk_BayesianRidge():
print("Testing sklearn, BayesianRidge...")
mod = linear_model.BayesianRidge()
X, y = iris_data
mod.fit(X, y)
docs = {'name': "BayesianRidge test"}
fv = X[0, :]
upload(mod, fv, docs)
def test_sm_GLS():
print("Testing SM, GLS...")
data = sm.datasets.longley.load(as_pandas=False)
X = sm.add_constant(data.exog)
est = sm.GLS(data.endog, X, sigma=1)
mod = est.fit()
docs = {'name': "GLS test"}
fv = X[0, :]
upload(mod, fv, docs)
