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import os
import tempfile
import numpy as np
import pytest
import xgboost as xgb
from xgboost import testing as tm
from xgboost.testing.continuation import run_training_continuation_model_output
rng = np.random.RandomState(1337)
class TestTrainingContinuation:
num_parallel_tree = 3
def generate_parameters(self):
xgb_params_01_binary = {
"nthread": 1,
}
xgb_params_02_binary = {
"nthread": 1,
"num_parallel_tree": self.num_parallel_tree,
}
xgb_params_03_binary = {
"nthread": 1,
"num_class": 5,
"num_parallel_tree": self.num_parallel_tree,
}
return [xgb_params_01_binary, xgb_params_02_binary, xgb_params_03_binary]
def run_training_continuation(self, xgb_params_01, xgb_params_02, xgb_params_03):
from sklearn.datasets import load_digits
from sklearn.metrics import mean_squared_error
digits_2class = load_digits(n_class=2)
digits_5class = load_digits(n_class=5)
X_2class = digits_2class["data"]
y_2class = digits_2class["target"]
X_5class = digits_5class["data"]
y_5class = digits_5class["target"]
dtrain_2class = xgb.DMatrix(X_2class, label=y_2class)
dtrain_5class = xgb.DMatrix(X_5class, label=y_5class)
gbdt_01 = xgb.train(xgb_params_01, dtrain_2class, num_boost_round=10)
ntrees_01 = len(gbdt_01.get_dump())
assert ntrees_01 == 10
gbdt_02 = xgb.train(xgb_params_01, dtrain_2class, num_boost_round=0)
gbdt_02.save_model("xgb_tc.json")
gbdt_02a = xgb.train(
xgb_params_01, dtrain_2class, num_boost_round=10, xgb_model=gbdt_02
)
gbdt_02b = xgb.train(
xgb_params_01, dtrain_2class, num_boost_round=10, xgb_model="xgb_tc.json"
)
ntrees_02a = len(gbdt_02a.get_dump())
ntrees_02b = len(gbdt_02b.get_dump())
assert ntrees_02a == 10
assert ntrees_02b == 10
res1 = mean_squared_error(y_2class, gbdt_01.predict(dtrain_2class))
res2 = mean_squared_error(y_2class, gbdt_02a.predict(dtrain_2class))
assert res1 == res2
res1 = mean_squared_error(y_2class, gbdt_01.predict(dtrain_2class))
res2 = mean_squared_error(y_2class, gbdt_02b.predict(dtrain_2class))
assert res1 == res2
gbdt_03 = xgb.train(xgb_params_01, dtrain_2class, num_boost_round=3)
gbdt_03.save_model("xgb_tc.json")
gbdt_03a = xgb.train(
xgb_params_01, dtrain_2class, num_boost_round=7, xgb_model=gbdt_03
)
gbdt_03b = xgb.train(
xgb_params_01, dtrain_2class, num_boost_round=7, xgb_model="xgb_tc.json"
)
ntrees_03a = len(gbdt_03a.get_dump())
ntrees_03b = len(gbdt_03b.get_dump())
assert ntrees_03a == 10
assert ntrees_03b == 10
os.remove("xgb_tc.json")
res1 = mean_squared_error(y_2class, gbdt_03a.predict(dtrain_2class))
res2 = mean_squared_error(y_2class, gbdt_03b.predict(dtrain_2class))
assert res1 == res2
gbdt_04 = xgb.train(xgb_params_02, dtrain_2class, num_boost_round=3)
res1 = mean_squared_error(y_2class, gbdt_04.predict(dtrain_2class))
res2 = mean_squared_error(
y_2class,
gbdt_04.predict(
dtrain_2class, iteration_range=(0, gbdt_04.num_boosted_rounds())
),
)
assert res1 == res2
gbdt_04 = xgb.train(
xgb_params_02, dtrain_2class, num_boost_round=7, xgb_model=gbdt_04
)
res1 = mean_squared_error(y_2class, gbdt_04.predict(dtrain_2class))
res2 = mean_squared_error(
y_2class,
gbdt_04.predict(
dtrain_2class, iteration_range=(0, gbdt_04.num_boosted_rounds())
),
)
assert res1 == res2
gbdt_05 = xgb.train(xgb_params_03, dtrain_5class, num_boost_round=7)
gbdt_05 = xgb.train(
xgb_params_03, dtrain_5class, num_boost_round=3, xgb_model=gbdt_05
)
res1 = gbdt_05.predict(dtrain_5class)
res2 = gbdt_05.predict(
dtrain_5class, iteration_range=(0, gbdt_05.num_boosted_rounds())
)
np.testing.assert_almost_equal(res1, res2)
@pytest.mark.skipif(**tm.no_sklearn())
def test_training_continuation_json(self):
params = self.generate_parameters()
self.run_training_continuation(params[0], params[1], params[2])
@pytest.mark.skipif(**tm.no_sklearn())
def test_training_continuation_updaters_json(self):
# Picked up from R tests.
updaters = "grow_colmaker,prune,refresh"
params = self.generate_parameters()
for p in params:
p["updater"] = updaters
self.run_training_continuation(params[0], params[1], params[2])
@pytest.mark.skipif(**tm.no_sklearn())
def test_changed_parameter(self):
from sklearn.datasets import load_breast_cancer
X, y = load_breast_cancer(return_X_y=True)
clf = xgb.XGBClassifier(n_estimators=2, eval_metric="logloss")
clf.fit(X, y, eval_set=[(X, y)])
assert tm.non_increasing(clf.evals_result()["validation_0"]["logloss"])
with tempfile.TemporaryDirectory() as tmpdir:
clf.save_model(os.path.join(tmpdir, "clf.json"))
loaded = xgb.XGBClassifier()
loaded.load_model(os.path.join(tmpdir, "clf.json"))
clf = xgb.XGBClassifier(n_estimators=2)
# change metric to error
clf.set_params(eval_metric="error")
clf.fit(X, y, eval_set=[(X, y)], xgb_model=loaded)
assert tm.non_increasing(clf.evals_result()["validation_0"]["error"])
@pytest.mark.parametrize("tree_method", ["hist", "approx", "exact"])
def test_model_output(self, tree_method: str) -> None:
run_training_continuation_model_output("cpu", tree_method)
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