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)