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test_xgboost_converter.py
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test_xgboost_converter.py
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"""
Tests XGBoost converters.
"""
import unittest
import warnings
import numpy as np
from sklearn.datasets import fetch_california_housing
from sklearn.model_selection import train_test_split
import hummingbird.ml
from hummingbird.ml._utils import xgboost_installed, tvm_installed, pandas_installed
from hummingbird.ml import constants
from tree_utils import gbdt_implementation_map
if xgboost_installed():
import xgboost as xgb
if pandas_installed():
import pandas as pd
class TestXGBoostConverter(unittest.TestCase):
# Check tree implementation
@unittest.skipIf(not xgboost_installed(), reason="XGBoost test requires XGBoost installed")
def test_xgb_implementation(self):
warnings.filterwarnings("ignore")
np.random.seed(0)
X = np.random.rand(1, 1)
X = np.array(X, dtype=np.float32)
y = np.array([0], dtype=int)
for model in [xgb.XGBClassifier(n_estimators=1, max_depth=1), xgb.XGBRegressor(n_estimators=1, max_depth=1)]:
for extra_config_param in ["tree_trav", "perf_tree_trav", "gemm"]:
model.fit(X, y)
torch_model = hummingbird.ml.convert(
model, "torch", X[0:1], extra_config={"tree_implementation": extra_config_param}
)
self.assertIsNotNone(torch_model)
self.assertEqual(str(type(list(torch_model.model._operators)[0])), gbdt_implementation_map[extra_config_param])
def _run_xgb_classifier_converter(self, num_classes, extra_config={}):
warnings.filterwarnings("ignore")
for max_depth in [1, 3, 8, 10, 12]:
model = xgb.XGBClassifier(n_estimators=10, max_depth=max_depth)
np.random.seed(0)
X = np.random.rand(100, 200)
X = np.array(X, dtype=np.float32)
y = np.random.randint(num_classes, size=100)
model.fit(X, y)
torch_model = hummingbird.ml.convert(model, "torch", [], extra_config=extra_config)
self.assertIsNotNone(torch_model)
np.testing.assert_allclose(model.predict_proba(X), torch_model.predict_proba(X), rtol=1e-06, atol=1e-06)
# Binary classifier
@unittest.skipIf(not xgboost_installed(), reason="XGBoost test requires XGBoost installed")
def test_xgb_binary_classifier_converter(self):
self._run_xgb_classifier_converter(2)
# Gemm classifier
@unittest.skipIf(not xgboost_installed(), reason="XGBoost test requires XGBoost installed")
def test_xgb_gemm_classifier_converter(self):
self._run_xgb_classifier_converter(2, extra_config={"tree_implementation": "gemm"})
# Tree_trav classifier
@unittest.skipIf(not xgboost_installed(), reason="XGBoost test requires XGBoost installed")
def test_xgb_tree_trav_classifier_converter(self):
self._run_xgb_classifier_converter(2, extra_config={"tree_implementation": "tree_trav"})
# Perf_tree_trav classifier
@unittest.skipIf(not xgboost_installed(), reason="XGBoost test requires XGBoost installed")
def test_xgb_perf_tree_trav_classifier_converter(self):
self._run_xgb_classifier_converter(2, extra_config={"tree_implementation": "perf_tree_trav"})
# Multi classifier
@unittest.skipIf(not xgboost_installed(), reason="XGBoost test requires XGBoost installed")
def test_xgb_multi_classifier_converter(self):
self._run_xgb_classifier_converter(3)
# Gemm multi classifier
@unittest.skipIf(not xgboost_installed(), reason="XGBoost test requires XGBoost installed")
def test_xgb_gemm_multi_classifier_converter(self):
self._run_xgb_classifier_converter(3, extra_config={"tree_implementation": "gemm"})
# Tree_trav multi classifier
@unittest.skipIf(not xgboost_installed(), reason="XGBoost test requires XGBoost installed")
def test_xgb_tree_trav_multi_classifier_converter(self):
self._run_xgb_classifier_converter(3, extra_config={"tree_implementation": "tree_trav"})
# Perf_tree_trav multi classifier
@unittest.skipIf(not xgboost_installed(), reason="XGBoost test requires XGBoost installed")
def test_xgb_perf_tree_trav_multi_classifier_converter(self):
self._run_xgb_classifier_converter(3, extra_config={"tree_implementation": "perf_tree_trav"})
def _run_xgb_ranker_converter(self, num_classes, extra_config={}):
warnings.filterwarnings("ignore")
for max_depth in [1, 3, 8, 10, 12]:
model = xgb.XGBRanker(n_estimators=10, max_depth=max_depth)
np.random.seed(0)
X = np.random.rand(100, 200)
X = np.array(X, dtype=np.float32)
y = np.random.randint(num_classes, size=100)
model.fit(X, y, group=[X.shape[0]])
torch_model = hummingbird.ml.convert(model, "torch", X, extra_config=extra_config)
self.assertIsNotNone(torch_model)
np.testing.assert_allclose(model.predict(X), torch_model.predict(X), rtol=1e-06, atol=1e-06)
# Ranker
@unittest.skipIf(not xgboost_installed(), reason="XGBoost test requires XGBoost installed")
def test_xgb_binary_ranker_converter(self):
self._run_xgb_ranker_converter(1000)
# Gemm ranker
@unittest.skipIf(not xgboost_installed(), reason="XGBoost test requires XGBoost installed")
def test_xgb_gemm_ranker_converter(self):
self._run_xgb_ranker_converter(1000, extra_config={"tree_implementation": "gemm"})
# Tree_trav ranker
@unittest.skipIf(not xgboost_installed(), reason="XGBoost test requires XGBoost installed")
def test_xgb_tree_trav_ranker_converter(self):
self._run_xgb_ranker_converter(1000, extra_config={"tree_implementation": "tree_trav"})
# Perf_tree_trav ranker
@unittest.skipIf(not xgboost_installed(), reason="XGBoost test requires XGBoost installed")
def test_xgb_perf_tree_trav_ranker_converter(self):
self._run_xgb_ranker_converter(1000, extra_config={"tree_implementation": "perf_tree_trav"})
def _run_xgb_regressor_converter(self, num_classes, extra_config={}):
warnings.filterwarnings("ignore")
for max_depth in [1, 3, 8, 10, 12]:
model = xgb.XGBRegressor(n_estimators=10, max_depth=max_depth)
np.random.seed(0)
X = np.random.rand(100, 200)
X = np.array(X, dtype=np.float32)
y = np.random.randint(num_classes, size=100)
model.fit(X, y)
torch_model = hummingbird.ml.convert(model, "torch", X, extra_config=extra_config)
self.assertIsNotNone(torch_model)
np.testing.assert_allclose(model.predict(X), torch_model.predict(X), rtol=1e-06, atol=1e-06)
# Regressor
@unittest.skipIf(not xgboost_installed(), reason="XGBoost test requires XGBoost installed")
def test_xgb_binary_regressor_converter(self):
self._run_xgb_regressor_converter(1000)
# Gemm regressor
@unittest.skipIf(not xgboost_installed(), reason="XGBoost test requires XGBoost installed")
def test_xgb_gemm_regressor_converter(self):
self._run_xgb_regressor_converter(1000, extra_config={"tree_implementation": "gemm"})
# Tree_trav regressor
@unittest.skipIf(not xgboost_installed(), reason="XGBoost test requires XGBoost installed")
def test_xgb_tree_trav_regressor_converter(self):
self._run_xgb_regressor_converter(1000, extra_config={"tree_implementation": "tree_trav"})
# Perf_tree_trav regressor
@unittest.skipIf(not xgboost_installed(), reason="XGBoost test requires XGBoost installed")
def test_xgb_perf_tree_trav_regressor_converter(self):
self._run_xgb_regressor_converter(1000, extra_config={"tree_implementation": "perf_tree_trav"})
# Float 64 data tests
@unittest.skipIf(not xgboost_installed(), reason="XGBoost test requires XGBoost installed")
def test_float64_xgb_classifier_converter(self):
warnings.filterwarnings("ignore")
num_classes = 3
for max_depth in [1, 3, 8, 10, 12]:
model = xgb.XGBClassifier(n_estimators=10, max_depth=max_depth)
np.random.seed(0)
X = np.random.rand(100, 200)
y = np.random.randint(num_classes, size=100)
model.fit(X, y)
torch_model = hummingbird.ml.convert(model, "torch", [])
self.assertIsNotNone(torch_model)
np.testing.assert_allclose(model.predict_proba(X), torch_model.predict_proba(X), rtol=1e-06, atol=1e-06)
@unittest.skipIf(not xgboost_installed(), reason="XGBoost test requires XGBoost installed")
def test_float64_xgb_ranker_converter(self):
warnings.filterwarnings("ignore")
num_classes = 3
for max_depth in [1, 3, 8, 10, 12]:
model = xgb.XGBRanker(n_estimators=10, max_depth=max_depth)
np.random.seed(0)
X = np.random.rand(100, 200)
y = np.random.randint(num_classes, size=100)
model.fit(X, y, group=[X.shape[0]])
torch_model = hummingbird.ml.convert(model, "torch", X[0:1])
self.assertIsNotNone(torch_model)
np.testing.assert_allclose(model.predict(X), torch_model.predict(X), rtol=1e-06, atol=1e-06)
@unittest.skipIf(not xgboost_installed(), reason="XGBoost test requires XGBoost installed")
def test_float64_xgb_regressor_converter(self):
warnings.filterwarnings("ignore")
num_classes = 3
for max_depth in [1, 3, 8, 10, 12]:
model = xgb.XGBRegressor(n_estimators=10, max_depth=max_depth)
np.random.seed(0)
X = np.random.rand(100, 200)
y = np.random.randint(num_classes, size=100)
model.fit(X, y)
torch_model = hummingbird.ml.convert(model, "torch", X[0:1])
self.assertIsNotNone(torch_model)
np.testing.assert_allclose(model.predict(X), torch_model.predict(X), rtol=1e-06, atol=1e-06)
# Small tree.
@unittest.skipIf(not xgboost_installed(), reason="XGBoost test requires XGBoost installed")
def test_run_xgb_classifier_converter(self):
warnings.filterwarnings("ignore")
for extra_config_param in ["tree_trav", "perf_tree_trav", "gemm"]:
model = xgb.XGBClassifier(n_estimators=1, max_depth=1)
np.random.seed(0)
X = np.random.rand(1, 1)
X = np.array(X, dtype=np.float32)
y = np.array([0], dtype=int)
model.fit(X, y)
torch_model = hummingbird.ml.convert(model, "torch", [], extra_config={"tree_implementation": extra_config_param})
self.assertIsNotNone(torch_model)
np.testing.assert_allclose(model.predict_proba(X), torch_model.predict_proba(X), rtol=1e-06, atol=1e-06)
# Test xgboost with pandas.
@unittest.skipIf(not xgboost_installed() or not pandas_installed(), reason="test requires XGBoost and Pandas installed")
def test_run_xgb_pandas(self):
cali = fetch_california_housing()
data = pd.DataFrame(cali.data)
data.columns = cali.feature_names
X, y = data.iloc[:, :-1], data.iloc[:, -1]
# Split the data into training and testing dataset by taking train_size as 75%
X_train, X_test, y_train, y_test = train_test_split(X, y, train_size=0.75, random_state=42)
model = xgb.XGBRegressor(colsample_bytree=0.3, learning_rate=0.1, max_depth=5, alpha=10, n_estimators=10)
model.fit(X_train, y_train)
torch_model = hummingbird.ml.convert(model, "torch")
self.assertIsNotNone(torch_model)
np.testing.assert_allclose(model.predict(X_test), torch_model.predict(X_test), rtol=1e-06, atol=1e-06)
# Torchscript backends.
# Test TorchScript backend regression.
@unittest.skipIf(not xgboost_installed(), reason="XGBoost test requires XGBoost installed")
def test_xgb_regressor_converter_torchscript(self):
warnings.filterwarnings("ignore")
import torch
for max_depth in [1, 3, 8, 10, 12]:
model = xgb.XGBRegressor(n_estimators=10, max_depth=max_depth)
np.random.seed(0)
X = np.random.rand(100, 200)
X = np.array(X, dtype=np.float32)
y = np.random.randint(1000, size=100)
model.fit(X, y)
torch_model = hummingbird.ml.convert(model, "torchscript", X)
self.assertIsNotNone(torch_model)
np.testing.assert_allclose(model.predict(X), torch_model.predict(X), rtol=1e-06, atol=1e-06)
# Test TorchScript backend classification.
@unittest.skipIf(not xgboost_installed(), reason="XGBoost test requires XGBoost installed")
def test_xgb_classifier_converter_torchscript(self):
warnings.filterwarnings("ignore")
import torch
for max_depth in [1, 3, 8, 10, 12]:
model = xgb.XGBClassifier(n_estimators=10, max_depth=max_depth)
np.random.seed(0)
X = np.random.rand(100, 200)
X = np.array(X, dtype=np.float32)
y = np.random.randint(2, size=100)
model.fit(X, y)
torch_model = hummingbird.ml.convert(model, "torchscript", X)
self.assertIsNotNone(torch_model)
np.testing.assert_allclose(model.predict_proba(X), torch_model.predict_proba(X), rtol=1e-06, atol=1e-06)
# TVM backend tests.
# TVM backend regression.
@unittest.skipIf(not xgboost_installed(), reason="XGBoost test requires XGBoost installed")
@unittest.skipIf(not tvm_installed(), reason="TVM test requires TVM installed")
def test_xgb_regressor_converter_tvm(self):
warnings.filterwarnings("ignore")
import torch
for max_depth in [1, 3, 8, 10, 12]:
model = xgb.XGBRegressor(n_estimators=10, max_depth=max_depth)
np.random.seed(0)
X = np.random.rand(100, 200)
X = np.array(X, dtype=np.float32)
y = np.random.randint(1000, size=100)
model.fit(X, y)
tvm_model = hummingbird.ml.convert(model, "tvm", X, extra_config={constants.TVM_MAX_FUSE_DEPTH: 30})
self.assertIsNotNone(tvm_model)
np.testing.assert_allclose(model.predict(X), tvm_model.predict(X), rtol=1e-06, atol=1e-06)
# Test TVM backend classification.
@unittest.skipIf(not xgboost_installed(), reason="XGBoost test requires XGBoost installed")
@unittest.skipIf(not tvm_installed(), reason="TVM test requires TVM installed")
def test_xgb_classifier_converter_tvm(self):
warnings.filterwarnings("ignore")
import torch
for max_depth in [1, 3, 8, 10, 12]:
model = xgb.XGBClassifier(n_estimators=10, max_depth=max_depth)
np.random.seed(0)
X = np.random.rand(100, 200)
X = np.array(X, dtype=np.float32)
y = np.random.randint(2, size=100)
model.fit(X, y)
tvm_model = hummingbird.ml.convert(model, "tvm", X, extra_config={constants.TVM_MAX_FUSE_DEPTH: 30})
self.assertIsNotNone(tvm_model)
np.testing.assert_allclose(model.predict_proba(X), tvm_model.predict_proba(X), rtol=1e-06, atol=1e-06)
if __name__ == "__main__":
unittest.main()