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test_e2e.py
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import sys
import lightgbm
import pytest
import numpy as np
import xgboost
import statsmodels.api as sm
from statsmodels.regression.process_regression import ProcessMLE
import lightning.classification as light_clf
import lightning.regression as light_reg
from sklearn import linear_model, svm
from sklearn import tree
from sklearn import ensemble
from tests import utils
from tests.e2e import executors
RECURSION_LIMIT = 5000
# pytest marks
PYTHON = pytest.mark.python
JAVA = pytest.mark.java
C = pytest.mark.c_lang
GO = pytest.mark.go_lang
JAVASCRIPT = pytest.mark.javascript
VISUAL_BASIC = pytest.mark.visual_basic
C_SHARP = pytest.mark.c_sharp
POWERSHELL = pytest.mark.powershell
R = pytest.mark.r_lang
PHP = pytest.mark.php
DART = pytest.mark.dart
REGRESSION = pytest.mark.regr
CLASSIFICATION = pytest.mark.clf
# Set of helper functions to make parametrization less verbose.
def regression(model):
return (
model,
utils.get_regression_model_trainer(),
REGRESSION,
)
def classification(model):
return (
model,
utils.get_classification_model_trainer(),
CLASSIFICATION,
)
def classification_binary(model):
return (
model,
utils.get_binary_classification_model_trainer(),
CLASSIFICATION,
)
def regression_random(model):
return (
model,
utils.get_regression_random_data_model_trainer(0.01),
REGRESSION,
)
def classification_random(model):
return (
model,
utils.get_classification_random_data_model_trainer(0.01),
CLASSIFICATION,
)
def classification_binary_random(model):
return (
model,
utils.get_classification_binary_random_data_model_trainer(0.01),
CLASSIFICATION,
)
# Absolute tolerance. Used in np.isclose to compare 2 values.
# We compare 6 decimal digits.
ATOL = 1.e-6
RANDOM_SEED = 1234
TREE_PARAMS = dict(random_state=RANDOM_SEED)
FOREST_PARAMS = dict(n_estimators=10, random_state=RANDOM_SEED)
XGBOOST_PARAMS = dict(base_score=0.6, n_estimators=10,
random_state=RANDOM_SEED)
XGBOOST_PARAMS_LINEAR = dict(base_score=0.6, n_estimators=10,
feature_selector="shuffle", booster="gblinear",
random_state=RANDOM_SEED)
XGBOOST_PARAMS_RF = dict(base_score=0.6, n_estimators=10,
random_state=RANDOM_SEED)
XGBOOST_PARAMS_LARGE = dict(base_score=0.6, n_estimators=100, max_depth=12,
random_state=RANDOM_SEED)
LIGHTGBM_PARAMS = dict(n_estimators=10, random_state=RANDOM_SEED)
LIGHTGBM_PARAMS_DART = dict(n_estimators=10, boosting_type='dart',
max_drop=30, random_state=RANDOM_SEED)
LIGHTGBM_PARAMS_GOSS = dict(n_estimators=10, boosting_type='goss',
top_rate=0.3, other_rate=0.2,
random_state=RANDOM_SEED)
LIGHTGBM_PARAMS_RF = dict(n_estimators=10, boosting_type='rf',
subsample=0.7, subsample_freq=1,
random_state=RANDOM_SEED)
LIGHTGBM_PARAMS_LARGE = dict(n_estimators=100, num_leaves=100, max_depth=64,
random_state=RANDOM_SEED)
SVC_PARAMS = dict(random_state=RANDOM_SEED, decision_function_shape="ovo")
STATSMODELS_LINEAR_REGULARIZED_PARAMS = dict(method="elastic_net",
alpha=7, L1_wt=0.2)
@utils.cartesian_e2e_params(
# These are the languages which support all models specified in the
# next list.
[
(executors.PythonExecutor, PYTHON),
(executors.JavaExecutor, JAVA),
(executors.CExecutor, C),
(executors.GoExecutor, GO),
(executors.JavascriptExecutor, JAVASCRIPT),
(executors.VisualBasicExecutor, VISUAL_BASIC),
(executors.CSharpExecutor, C_SHARP),
(executors.PowershellExecutor, POWERSHELL),
(executors.RExecutor, R),
(executors.PhpExecutor, PHP),
(executors.DartExecutor, DART),
],
# These models will be tested against each language specified in the
# previous list.
[
# LightGBM
regression(lightgbm.LGBMRegressor(**LIGHTGBM_PARAMS)),
classification(lightgbm.LGBMClassifier(**LIGHTGBM_PARAMS)),
classification_binary(lightgbm.LGBMClassifier(**LIGHTGBM_PARAMS)),
# LightGBM (DART)
regression(lightgbm.LGBMRegressor(**LIGHTGBM_PARAMS_DART)),
classification(lightgbm.LGBMClassifier(**LIGHTGBM_PARAMS_DART)),
classification_binary(lightgbm.LGBMClassifier(
**LIGHTGBM_PARAMS_DART)),
# LightGBM (GOSS)
regression(lightgbm.LGBMRegressor(**LIGHTGBM_PARAMS_GOSS)),
classification(lightgbm.LGBMClassifier(**LIGHTGBM_PARAMS_GOSS)),
classification_binary(lightgbm.LGBMClassifier(
**LIGHTGBM_PARAMS_GOSS)),
# LightGBM (RF)
regression(lightgbm.LGBMRegressor(**LIGHTGBM_PARAMS_RF)),
classification(lightgbm.LGBMClassifier(**LIGHTGBM_PARAMS_RF)),
classification_binary(lightgbm.LGBMClassifier(**LIGHTGBM_PARAMS_RF)),
# LightGBM (Large Trees)
regression_random(
lightgbm.LGBMRegressor(**LIGHTGBM_PARAMS_LARGE)),
classification_random(
lightgbm.LGBMClassifier(**LIGHTGBM_PARAMS_LARGE)),
classification_binary_random(
lightgbm.LGBMClassifier(**LIGHTGBM_PARAMS_LARGE)),
# XGBoost
regression(xgboost.XGBRegressor(**XGBOOST_PARAMS)),
classification(xgboost.XGBClassifier(**XGBOOST_PARAMS)),
classification_binary(xgboost.XGBClassifier(**XGBOOST_PARAMS)),
# XGBoost (LINEAR)
regression(xgboost.XGBRegressor(**XGBOOST_PARAMS_LINEAR)),
classification(xgboost.XGBClassifier(**XGBOOST_PARAMS_LINEAR)),
classification_binary(xgboost.XGBClassifier(**XGBOOST_PARAMS_LINEAR)),
# XGBoost (RF)
regression(xgboost.XGBRFRegressor(**XGBOOST_PARAMS_RF)),
classification_binary(xgboost.XGBRFClassifier(**XGBOOST_PARAMS_RF)),
# XGBoost (Large Trees)
regression_random(
xgboost.XGBRegressor(**XGBOOST_PARAMS_LARGE)),
classification_random(
xgboost.XGBClassifier(**XGBOOST_PARAMS_LARGE)),
classification_binary_random(
xgboost.XGBClassifier(**XGBOOST_PARAMS_LARGE)),
# Sklearn Linear SVM
regression(svm.LinearSVR(random_state=RANDOM_SEED)),
classification(svm.LinearSVC(random_state=RANDOM_SEED)),
classification_binary(svm.LinearSVC(random_state=RANDOM_SEED)),
# Lightning Linear SVM
regression(light_reg.LinearSVR(random_state=RANDOM_SEED)),
classification(light_clf.LinearSVC(
criterion="accuracy", random_state=RANDOM_SEED)),
classification(light_clf.LinearSVC(
criterion="auc", random_state=RANDOM_SEED)),
classification_binary(light_clf.LinearSVC(
criterion="accuracy", random_state=RANDOM_SEED)),
classification_binary(light_clf.LinearSVC(
criterion="auc", random_state=RANDOM_SEED)),
# Sklearn SVM
regression(svm.NuSVR(kernel="rbf")),
regression(svm.SVR(kernel="rbf")),
classification(svm.NuSVC(kernel="rbf", **SVC_PARAMS)),
classification(svm.SVC(kernel="rbf", **SVC_PARAMS)),
classification_binary(svm.NuSVC(kernel="rbf", **SVC_PARAMS)),
classification_binary(svm.SVC(kernel="linear", **SVC_PARAMS)),
classification_binary(svm.SVC(
kernel="poly",
C=1.5, degree=2, gamma=0.1, coef0=2.0, **SVC_PARAMS)),
classification_binary(svm.SVC(kernel="rbf", **SVC_PARAMS)),
classification_binary(svm.SVC(kernel="sigmoid", **SVC_PARAMS)),
# Lightning SVM
classification_binary(light_clf.KernelSVC(
kernel="rbf", random_state=RANDOM_SEED)),
classification_binary(light_clf.KernelSVC(
kernel="linear", random_state=RANDOM_SEED)),
classification_binary(light_clf.KernelSVC(
kernel="poly", alpha=1.5, solver="cg",
degree=2, gamma=0.1, coef0=2.0, random_state=RANDOM_SEED)),
classification_binary(light_clf.KernelSVC(
kernel="sigmoid", random_state=RANDOM_SEED)),
classification_binary(light_clf.KernelSVC(
kernel="cosine", random_state=RANDOM_SEED)),
# Sklearn Linear Regression
regression(linear_model.ARDRegression()),
regression(linear_model.BayesianRidge()),
regression(linear_model.ElasticNet(random_state=RANDOM_SEED)),
regression(linear_model.ElasticNetCV(random_state=RANDOM_SEED)),
regression(linear_model.HuberRegressor()),
regression(linear_model.Lars()),
regression(linear_model.LarsCV()),
regression(linear_model.Lasso(random_state=RANDOM_SEED)),
regression(linear_model.LassoCV(random_state=RANDOM_SEED)),
regression(linear_model.LassoLars()),
regression(linear_model.LassoLarsCV()),
regression(linear_model.LassoLarsIC()),
regression(linear_model.LinearRegression()),
regression(linear_model.OrthogonalMatchingPursuit()),
regression(linear_model.OrthogonalMatchingPursuitCV()),
regression(linear_model.PassiveAggressiveRegressor(
random_state=RANDOM_SEED)),
regression(linear_model.RANSACRegressor(
base_estimator=tree.ExtraTreeRegressor(**TREE_PARAMS),
random_state=RANDOM_SEED)),
regression(linear_model.Ridge(random_state=RANDOM_SEED)),
regression(linear_model.RidgeCV()),
regression(linear_model.SGDRegressor(random_state=RANDOM_SEED)),
regression(linear_model.TheilSenRegressor(random_state=RANDOM_SEED)),
# Statsmodels Linear Regression
regression(utils.StatsmodelsSklearnLikeWrapper(
sm.GLS,
dict(init=dict(sigma=np.eye(
len(utils.get_regression_model_trainer().y_train)) + 1)))),
regression(utils.StatsmodelsSklearnLikeWrapper(
sm.GLS,
dict(init=dict(sigma=np.eye(
len(utils.get_regression_model_trainer().y_train)) + 1),
fit_regularized=STATSMODELS_LINEAR_REGULARIZED_PARAMS))),
regression(utils.StatsmodelsSklearnLikeWrapper(
sm.GLSAR,
dict(init=dict(fit_intercept=True, rho=3)))),
regression(utils.StatsmodelsSklearnLikeWrapper(
sm.GLSAR,
dict(iterative_fit=dict(maxiter=2)))),
regression(utils.StatsmodelsSklearnLikeWrapper(
sm.GLSAR,
dict(fit_regularized=STATSMODELS_LINEAR_REGULARIZED_PARAMS))),
regression(utils.StatsmodelsSklearnLikeWrapper(
sm.OLS,
dict(init=dict(fit_intercept=True)))),
regression(utils.StatsmodelsSklearnLikeWrapper(
sm.OLS,
dict(fit_regularized=STATSMODELS_LINEAR_REGULARIZED_PARAMS))),
regression(utils.StatsmodelsSklearnLikeWrapper(
ProcessMLE,
dict(init=dict(exog_scale=np.ones(
(len(utils.get_regression_model_trainer().y_train), 2)),
exog_smooth=np.ones(
(len(utils.get_regression_model_trainer().y_train), 2)),
exog_noise=np.ones(
(len(utils.get_regression_model_trainer().y_train), 2)),
time=np.kron(
np.ones(
len(utils.get_regression_model_trainer().y_train) // 3),
np.arange(3)),
groups=np.kron(
np.arange(
len(utils.get_regression_model_trainer().y_train) // 3),
np.ones(3))),
fit=dict(maxiter=2)))),
regression(utils.StatsmodelsSklearnLikeWrapper(
sm.QuantReg,
dict(init=dict(fit_intercept=True)))),
regression(utils.StatsmodelsSklearnLikeWrapper(
sm.WLS,
dict(init=dict(fit_intercept=True, weights=np.arange(
len(utils.get_regression_model_trainer().y_train)))))),
regression(utils.StatsmodelsSklearnLikeWrapper(
sm.WLS,
dict(init=dict(fit_intercept=True, weights=np.arange(
len(utils.get_regression_model_trainer().y_train))),
fit_regularized=STATSMODELS_LINEAR_REGULARIZED_PARAMS))),
# Lightning Linear Regression
regression(light_reg.AdaGradRegressor(random_state=RANDOM_SEED)),
regression(light_reg.CDRegressor(random_state=RANDOM_SEED)),
regression(light_reg.FistaRegressor()),
regression(light_reg.SAGARegressor(random_state=RANDOM_SEED)),
regression(light_reg.SAGRegressor(random_state=RANDOM_SEED)),
regression(light_reg.SDCARegressor(random_state=RANDOM_SEED)),
# Sklearn Linear Classifiers
classification(linear_model.LogisticRegression(
random_state=RANDOM_SEED)),
classification(linear_model.LogisticRegressionCV(
random_state=RANDOM_SEED)),
classification(linear_model.PassiveAggressiveClassifier(
random_state=RANDOM_SEED)),
classification(linear_model.Perceptron(
random_state=RANDOM_SEED)),
classification(linear_model.RidgeClassifier(
random_state=RANDOM_SEED)),
classification(linear_model.RidgeClassifierCV()),
classification(linear_model.SGDClassifier(
random_state=RANDOM_SEED)),
classification_binary(linear_model.LogisticRegression(
random_state=RANDOM_SEED)),
classification_binary(linear_model.LogisticRegressionCV(
random_state=RANDOM_SEED)),
classification_binary(linear_model.PassiveAggressiveClassifier(
random_state=RANDOM_SEED)),
classification_binary(linear_model.Perceptron(
random_state=RANDOM_SEED)),
classification_binary(linear_model.RidgeClassifier(
random_state=RANDOM_SEED)),
classification_binary(linear_model.RidgeClassifierCV()),
classification_binary(linear_model.SGDClassifier(
random_state=RANDOM_SEED)),
# Lightning Linear Classifiers
classification(light_clf.AdaGradClassifier(
random_state=RANDOM_SEED)),
classification(light_clf.CDClassifier(
random_state=RANDOM_SEED)),
classification(light_clf.CDClassifier(
penalty="l1/l2", multiclass=True, random_state=RANDOM_SEED)),
classification(light_clf.FistaClassifier()),
classification(light_clf.FistaClassifier(multiclass=True)),
classification(light_clf.SAGAClassifier(
random_state=RANDOM_SEED)),
classification(light_clf.SAGClassifier(
random_state=RANDOM_SEED)),
classification(light_clf.SDCAClassifier(
random_state=RANDOM_SEED)),
classification(light_clf.SGDClassifier(
random_state=RANDOM_SEED)),
classification(light_clf.SGDClassifier(
multiclass=True, random_state=RANDOM_SEED)),
classification_binary(light_clf.AdaGradClassifier(
random_state=RANDOM_SEED)),
classification_binary(light_clf.CDClassifier(
random_state=RANDOM_SEED)),
classification_binary(light_clf.FistaClassifier()),
classification_binary(light_clf.SAGAClassifier(
random_state=RANDOM_SEED)),
classification_binary(light_clf.SAGClassifier(
random_state=RANDOM_SEED)),
classification_binary(light_clf.SDCAClassifier(
random_state=RANDOM_SEED)),
classification_binary(light_clf.SGDClassifier(
random_state=RANDOM_SEED)),
# Decision trees
regression(tree.DecisionTreeRegressor(**TREE_PARAMS)),
regression(tree.ExtraTreeRegressor(**TREE_PARAMS)),
classification(tree.DecisionTreeClassifier(**TREE_PARAMS)),
classification(tree.ExtraTreeClassifier(**TREE_PARAMS)),
classification_binary(tree.DecisionTreeClassifier(**TREE_PARAMS)),
classification_binary(tree.ExtraTreeClassifier(**TREE_PARAMS)),
# Random forest
regression(ensemble.ExtraTreesRegressor(**FOREST_PARAMS)),
regression(ensemble.RandomForestRegressor(**FOREST_PARAMS)),
classification(ensemble.ExtraTreesClassifier(**FOREST_PARAMS)),
classification(ensemble.RandomForestClassifier(**FOREST_PARAMS)),
classification_binary(ensemble.ExtraTreesClassifier(**FOREST_PARAMS)),
classification_binary(
ensemble.RandomForestClassifier(**FOREST_PARAMS)),
],
# Following is the list of extra tests for languages/models which are
# not fully supported yet.
# <empty>
)
def test_e2e(estimator, executor_cls, model_trainer,
is_fast, global_tmp_dir):
sys.setrecursionlimit(RECURSION_LIMIT)
X_test, y_pred_true, fitted_estimator = model_trainer(estimator)
executor = executor_cls(fitted_estimator)
idxs_to_test = [0] if is_fast else range(len(X_test))
executor.prepare_global(global_tmp_dir=global_tmp_dir)
with executor.prepare_then_cleanup():
for idx in idxs_to_test:
y_pred_executed = executor.predict(X_test[idx])
print("expected={}, actual={}".format(y_pred_true[idx],
y_pred_executed))
res = np.isclose(y_pred_true[idx], y_pred_executed, atol=ATOL)
assert res if isinstance(res, bool) else res.all()