d3ec116322
The old (before fix) best_ntree_limit ignores the num_class parameters, which is incorrect. In before we workarounded it in c++ layer to avoid possible breaking changes on other language bindings. But the Python interpretation stayed incorrect. The PR fixed that in Python to consider num_class, but didn't remove the old workaround, so tree calculation in predictor is incorrect, see PredictBatch in CPUPredictor.
167 lines
6.4 KiB
Python
167 lines
6.4 KiB
Python
import xgboost as xgb
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import testing as tm
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import numpy as np
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import pytest
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rng = np.random.RandomState(1337)
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class TestTrainingContinuation:
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num_parallel_tree = 3
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def generate_parameters(self, use_json):
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xgb_params_01_binary = {
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'nthread': 1,
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}
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xgb_params_02_binary = {
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'nthread': 1,
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'num_parallel_tree': self.num_parallel_tree
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}
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xgb_params_03_binary = {
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'nthread': 1,
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'num_class': 5,
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'num_parallel_tree': self.num_parallel_tree
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}
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if use_json:
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xgb_params_01_binary[
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'enable_experimental_json_serialization'] = True
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xgb_params_02_binary[
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'enable_experimental_json_serialization'] = True
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xgb_params_03_binary[
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'enable_experimental_json_serialization'] = True
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return [
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xgb_params_01_binary, xgb_params_02_binary, xgb_params_03_binary
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]
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def run_training_continuation(self, xgb_params_01, xgb_params_02,
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xgb_params_03):
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from sklearn.datasets import load_digits
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from sklearn.metrics import mean_squared_error
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digits_2class = load_digits(2)
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digits_5class = load_digits(5)
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X_2class = digits_2class['data']
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y_2class = digits_2class['target']
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X_5class = digits_5class['data']
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y_5class = digits_5class['target']
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dtrain_2class = xgb.DMatrix(X_2class, label=y_2class)
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dtrain_5class = xgb.DMatrix(X_5class, label=y_5class)
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gbdt_01 = xgb.train(xgb_params_01, dtrain_2class,
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num_boost_round=10)
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ntrees_01 = len(gbdt_01.get_dump())
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assert ntrees_01 == 10
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gbdt_02 = xgb.train(xgb_params_01, dtrain_2class,
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num_boost_round=0)
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gbdt_02.save_model('xgb_tc.model')
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gbdt_02a = xgb.train(xgb_params_01, dtrain_2class,
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num_boost_round=10, xgb_model=gbdt_02)
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gbdt_02b = xgb.train(xgb_params_01, dtrain_2class,
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num_boost_round=10, xgb_model="xgb_tc.model")
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ntrees_02a = len(gbdt_02a.get_dump())
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ntrees_02b = len(gbdt_02b.get_dump())
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assert ntrees_02a == 10
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assert ntrees_02b == 10
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res1 = mean_squared_error(y_2class, gbdt_01.predict(dtrain_2class))
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res2 = mean_squared_error(y_2class, gbdt_02a.predict(dtrain_2class))
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assert res1 == res2
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res1 = mean_squared_error(y_2class, gbdt_01.predict(dtrain_2class))
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res2 = mean_squared_error(y_2class, gbdt_02b.predict(dtrain_2class))
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assert res1 == res2
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gbdt_03 = xgb.train(xgb_params_01, dtrain_2class,
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num_boost_round=3)
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gbdt_03.save_model('xgb_tc.model')
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gbdt_03a = xgb.train(xgb_params_01, dtrain_2class,
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num_boost_round=7, xgb_model=gbdt_03)
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gbdt_03b = xgb.train(xgb_params_01, dtrain_2class,
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num_boost_round=7, xgb_model="xgb_tc.model")
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ntrees_03a = len(gbdt_03a.get_dump())
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ntrees_03b = len(gbdt_03b.get_dump())
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assert ntrees_03a == 10
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assert ntrees_03b == 10
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res1 = mean_squared_error(y_2class, gbdt_03a.predict(dtrain_2class))
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res2 = mean_squared_error(y_2class, gbdt_03b.predict(dtrain_2class))
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assert res1 == res2
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gbdt_04 = xgb.train(xgb_params_02, dtrain_2class,
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num_boost_round=3)
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assert gbdt_04.best_ntree_limit == (gbdt_04.best_iteration +
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1) * self.num_parallel_tree
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res1 = mean_squared_error(y_2class, gbdt_04.predict(dtrain_2class))
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res2 = mean_squared_error(y_2class,
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gbdt_04.predict(
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dtrain_2class,
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ntree_limit=gbdt_04.best_ntree_limit))
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assert res1 == res2
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gbdt_04 = xgb.train(xgb_params_02, dtrain_2class,
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num_boost_round=7, xgb_model=gbdt_04)
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assert gbdt_04.best_ntree_limit == (
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gbdt_04.best_iteration + 1) * self.num_parallel_tree
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res1 = mean_squared_error(y_2class, gbdt_04.predict(dtrain_2class))
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res2 = mean_squared_error(y_2class,
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gbdt_04.predict(
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dtrain_2class,
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ntree_limit=gbdt_04.best_ntree_limit))
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assert res1 == res2
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gbdt_05 = xgb.train(xgb_params_03, dtrain_5class,
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num_boost_round=7)
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assert gbdt_05.best_ntree_limit == (
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gbdt_05.best_iteration + 1) * self.num_parallel_tree
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gbdt_05 = xgb.train(xgb_params_03,
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dtrain_5class,
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num_boost_round=3,
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xgb_model=gbdt_05)
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assert gbdt_05.best_ntree_limit == (
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gbdt_05.best_iteration + 1) * self.num_parallel_tree
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res1 = gbdt_05.predict(dtrain_5class)
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res2 = gbdt_05.predict(dtrain_5class,
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ntree_limit=gbdt_05.best_ntree_limit)
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np.testing.assert_almost_equal(res1, res2)
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@pytest.mark.skipif(**tm.no_sklearn())
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def test_training_continuation_binary(self):
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params = self.generate_parameters(False)
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self.run_training_continuation(params[0], params[1], params[2])
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@pytest.mark.skipif(**tm.no_sklearn())
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def test_training_continuation_json(self):
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params = self.generate_parameters(True)
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for p in params:
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p['enable_experimental_json_serialization'] = True
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self.run_training_continuation(params[0], params[1], params[2])
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@pytest.mark.skipif(**tm.no_sklearn())
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def test_training_continuation_updaters_binary(self):
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updaters = 'grow_colmaker,prune,refresh'
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params = self.generate_parameters(False)
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for p in params:
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p['updater'] = updaters
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self.run_training_continuation(params[0], params[1], params[2])
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@pytest.mark.skipif(**tm.no_sklearn())
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def test_training_continuation_updaters_json(self):
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# Picked up from R tests.
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updaters = 'grow_colmaker,prune,refresh'
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params = self.generate_parameters(True)
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for p in params:
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p['updater'] = updaters
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self.run_training_continuation(params[0], params[1], params[2])
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