879 lines
31 KiB
Python
879 lines
31 KiB
Python
import numpy as np
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import xgboost as xgb
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from xgboost.sklearn import XGBoostLabelEncoder
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import testing as tm
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import tempfile
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import os
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import shutil
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import pytest
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import unittest
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import json
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from test_basic import captured_output
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rng = np.random.RandomState(1994)
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pytestmark = pytest.mark.skipif(**tm.no_sklearn())
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class TemporaryDirectory(object):
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"""Context manager for tempfile.mkdtemp()"""
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def __enter__(self):
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self.name = tempfile.mkdtemp()
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return self.name
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def __exit__(self, exc_type, exc_value, traceback):
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shutil.rmtree(self.name)
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def test_binary_classification():
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from sklearn.datasets import load_digits
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from sklearn.model_selection import KFold
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digits = load_digits(2)
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y = digits['target']
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X = digits['data']
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kf = KFold(n_splits=2, shuffle=True, random_state=rng)
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for cls in (xgb.XGBClassifier, xgb.XGBRFClassifier):
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for train_index, test_index in kf.split(X, y):
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clf = cls(random_state=42)
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xgb_model = clf.fit(X[train_index], y[train_index], eval_metric=['auc', 'logloss'])
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preds = xgb_model.predict(X[test_index])
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labels = y[test_index]
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err = sum(1 for i in range(len(preds))
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if int(preds[i] > 0.5) != labels[i]) / float(len(preds))
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assert err < 0.1
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def test_multiclass_classification():
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from sklearn.datasets import load_iris
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from sklearn.model_selection import KFold
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def check_pred(preds, labels, output_margin):
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if output_margin:
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err = sum(1 for i in range(len(preds))
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if preds[i].argmax() != labels[i]) / float(len(preds))
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else:
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err = sum(1 for i in range(len(preds))
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if preds[i] != labels[i]) / float(len(preds))
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assert err < 0.4
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iris = load_iris()
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y = iris['target']
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X = iris['data']
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kf = KFold(n_splits=2, shuffle=True, random_state=rng)
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for train_index, test_index in kf.split(X, y):
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xgb_model = xgb.XGBClassifier().fit(X[train_index], y[train_index])
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preds = xgb_model.predict(X[test_index])
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# test other params in XGBClassifier().fit
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preds2 = xgb_model.predict(X[test_index], output_margin=True,
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ntree_limit=3)
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preds3 = xgb_model.predict(X[test_index], output_margin=True,
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ntree_limit=0)
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preds4 = xgb_model.predict(X[test_index], output_margin=False,
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ntree_limit=3)
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labels = y[test_index]
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check_pred(preds, labels, output_margin=False)
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check_pred(preds2, labels, output_margin=True)
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check_pred(preds3, labels, output_margin=True)
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check_pred(preds4, labels, output_margin=False)
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def test_ranking():
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# generate random data
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x_train = np.random.rand(1000, 10)
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y_train = np.random.randint(5, size=1000)
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train_group = np.repeat(50, 20)
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x_valid = np.random.rand(200, 10)
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y_valid = np.random.randint(5, size=200)
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valid_group = np.repeat(50, 4)
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x_test = np.random.rand(100, 10)
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params = {'tree_method': 'exact', 'objective': 'rank:pairwise',
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'learning_rate': 0.1, 'gamma': 1.0, 'min_child_weight': 0.1,
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'max_depth': 6, 'n_estimators': 4}
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model = xgb.sklearn.XGBRanker(**params)
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model.fit(x_train, y_train, train_group,
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eval_set=[(x_valid, y_valid)], eval_group=[valid_group])
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pred = model.predict(x_test)
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train_data = xgb.DMatrix(x_train, y_train)
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valid_data = xgb.DMatrix(x_valid, y_valid)
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test_data = xgb.DMatrix(x_test)
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train_data.set_group(train_group)
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assert train_data.get_label().shape[0] == x_train.shape[0]
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valid_data.set_group(valid_group)
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params_orig = {'tree_method': 'exact', 'objective': 'rank:pairwise',
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'eta': 0.1, 'gamma': 1.0,
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'min_child_weight': 0.1, 'max_depth': 6}
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xgb_model_orig = xgb.train(params_orig, train_data, num_boost_round=4,
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evals=[(valid_data, 'validation')])
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pred_orig = xgb_model_orig.predict(test_data)
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np.testing.assert_almost_equal(pred, pred_orig)
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@pytest.mark.skipif(**tm.no_pandas())
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def test_feature_importances_weight():
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from sklearn.datasets import load_digits
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digits = load_digits(2)
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y = digits['target']
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X = digits['data']
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xgb_model = xgb.XGBClassifier(random_state=0,
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tree_method="exact",
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learning_rate=0.1,
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importance_type="weight").fit(X, y)
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exp = np.array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.00833333, 0.,
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0., 0., 0., 0., 0., 0., 0., 0.025, 0.14166667, 0., 0., 0.,
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0., 0., 0., 0.00833333, 0.25833333, 0., 0., 0., 0.,
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0.03333334, 0.03333334, 0., 0.32499999, 0., 0., 0., 0.,
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0.05, 0.06666667, 0., 0., 0., 0., 0., 0., 0., 0.04166667,
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0., 0., 0., 0., 0., 0., 0., 0.00833333, 0., 0., 0., 0.,
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0.], dtype=np.float32)
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np.testing.assert_almost_equal(xgb_model.feature_importances_, exp)
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# numeric columns
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import pandas as pd
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y = pd.Series(digits['target'])
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X = pd.DataFrame(digits['data'])
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xgb_model = xgb.XGBClassifier(random_state=0,
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tree_method="exact",
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learning_rate=0.1,
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importance_type="weight").fit(X, y)
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np.testing.assert_almost_equal(xgb_model.feature_importances_, exp)
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xgb_model = xgb.XGBClassifier(random_state=0,
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tree_method="exact",
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learning_rate=0.1,
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importance_type="weight").fit(X, y)
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np.testing.assert_almost_equal(xgb_model.feature_importances_, exp)
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@pytest.mark.skipif(**tm.no_pandas())
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def test_feature_importances_gain():
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from sklearn.datasets import load_digits
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digits = load_digits(2)
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y = digits['target']
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X = digits['data']
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xgb_model = xgb.XGBClassifier(
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random_state=0, tree_method="exact",
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learning_rate=0.1,
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importance_type="gain").fit(X, y)
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exp = np.array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
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0.00326159, 0., 0., 0., 0., 0., 0., 0., 0.,
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0.00297238, 0.00988034, 0., 0., 0., 0., 0., 0.,
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0.03512521, 0.41123885, 0., 0., 0., 0.,
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0.01326332, 0.00160674, 0., 0.4206952, 0., 0., 0.,
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0., 0.00616747, 0.01237546, 0., 0., 0., 0., 0.,
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0., 0., 0.08240705, 0., 0., 0., 0., 0., 0., 0.,
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0.00100649, 0., 0., 0., 0., 0.], dtype=np.float32)
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np.testing.assert_almost_equal(xgb_model.feature_importances_, exp)
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# numeric columns
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import pandas as pd
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y = pd.Series(digits['target'])
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X = pd.DataFrame(digits['data'])
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xgb_model = xgb.XGBClassifier(
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random_state=0, tree_method="exact",
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learning_rate=0.1,
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importance_type="gain").fit(X, y)
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np.testing.assert_almost_equal(xgb_model.feature_importances_, exp)
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xgb_model = xgb.XGBClassifier(
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random_state=0, tree_method="exact",
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learning_rate=0.1,
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importance_type="gain").fit(X, y)
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np.testing.assert_almost_equal(xgb_model.feature_importances_, exp)
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def test_select_feature():
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from sklearn.datasets import load_digits
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from sklearn.feature_selection import SelectFromModel
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digits = load_digits(2)
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y = digits['target']
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X = digits['data']
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cls = xgb.XGBClassifier()
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cls.fit(X, y)
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selector = SelectFromModel(cls, prefit=True, max_features=1)
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X_selected = selector.transform(X)
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assert X_selected.shape[1] == 1
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def test_num_parallel_tree():
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from sklearn.datasets import load_boston
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reg = xgb.XGBRegressor(n_estimators=4, num_parallel_tree=4,
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tree_method='hist')
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boston = load_boston()
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bst = reg.fit(X=boston['data'], y=boston['target'])
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dump = bst.get_booster().get_dump(dump_format='json')
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assert len(dump) == 16
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reg = xgb.XGBRFRegressor(n_estimators=4)
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bst = reg.fit(X=boston['data'], y=boston['target'])
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dump = bst.get_booster().get_dump(dump_format='json')
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assert len(dump) == 4
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config = json.loads(bst.get_booster().save_config())
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assert int(config['learner']['gradient_booster']['gbtree_train_param'][
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'num_parallel_tree']) == 4
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def test_boston_housing_regression():
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from sklearn.metrics import mean_squared_error
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from sklearn.datasets import load_boston
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from sklearn.model_selection import KFold
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boston = load_boston()
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y = boston['target']
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X = boston['data']
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kf = KFold(n_splits=2, shuffle=True, random_state=rng)
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for train_index, test_index in kf.split(X, y):
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xgb_model = xgb.XGBRegressor().fit(X[train_index], y[train_index])
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preds = xgb_model.predict(X[test_index])
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# test other params in XGBRegressor().fit
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preds2 = xgb_model.predict(X[test_index], output_margin=True,
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ntree_limit=3)
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preds3 = xgb_model.predict(X[test_index], output_margin=True,
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ntree_limit=0)
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preds4 = xgb_model.predict(X[test_index], output_margin=False,
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ntree_limit=3)
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labels = y[test_index]
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assert mean_squared_error(preds, labels) < 25
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assert mean_squared_error(preds2, labels) < 350
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assert mean_squared_error(preds3, labels) < 25
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assert mean_squared_error(preds4, labels) < 350
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def test_boston_housing_rf_regression():
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from sklearn.metrics import mean_squared_error
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from sklearn.datasets import load_boston
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from sklearn.model_selection import KFold
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boston = load_boston()
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y = boston['target']
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X = boston['data']
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kf = KFold(n_splits=2, shuffle=True, random_state=rng)
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for train_index, test_index in kf.split(X, y):
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xgb_model = xgb.XGBRFRegressor(random_state=42).fit(
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X[train_index], y[train_index])
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preds = xgb_model.predict(X[test_index])
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labels = y[test_index]
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assert mean_squared_error(preds, labels) < 35
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def test_parameter_tuning():
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from sklearn.model_selection import GridSearchCV
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from sklearn.datasets import load_boston
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boston = load_boston()
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y = boston['target']
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X = boston['data']
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xgb_model = xgb.XGBRegressor(learning_rate=0.1)
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clf = GridSearchCV(xgb_model, {'max_depth': [2, 4, 6],
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'n_estimators': [50, 100, 200]},
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cv=3, verbose=1)
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clf.fit(X, y)
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assert clf.best_score_ < 0.7
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assert clf.best_params_ == {'n_estimators': 100, 'max_depth': 4}
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def test_regression_with_custom_objective():
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from sklearn.metrics import mean_squared_error
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from sklearn.datasets import load_boston
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from sklearn.model_selection import KFold
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def objective_ls(y_true, y_pred):
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grad = (y_pred - y_true)
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hess = np.ones(len(y_true))
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return grad, hess
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boston = load_boston()
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y = boston['target']
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X = boston['data']
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kf = KFold(n_splits=2, shuffle=True, random_state=rng)
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for train_index, test_index in kf.split(X, y):
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xgb_model = xgb.XGBRegressor(objective=objective_ls).fit(
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X[train_index], y[train_index]
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)
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preds = xgb_model.predict(X[test_index])
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labels = y[test_index]
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assert mean_squared_error(preds, labels) < 25
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# Test that the custom objective function is actually used
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class XGBCustomObjectiveException(Exception):
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pass
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def dummy_objective(y_true, y_pred):
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raise XGBCustomObjectiveException()
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xgb_model = xgb.XGBRegressor(objective=dummy_objective)
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np.testing.assert_raises(XGBCustomObjectiveException, xgb_model.fit, X, y)
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def test_classification_with_custom_objective():
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from sklearn.datasets import load_digits
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from sklearn.model_selection import KFold
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def logregobj(y_true, y_pred):
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y_pred = 1.0 / (1.0 + np.exp(-y_pred))
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grad = y_pred - y_true
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hess = y_pred * (1.0 - y_pred)
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return grad, hess
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digits = load_digits(2)
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y = digits['target']
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X = digits['data']
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kf = KFold(n_splits=2, shuffle=True, random_state=rng)
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for train_index, test_index in kf.split(X, y):
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xgb_model = xgb.XGBClassifier(objective=logregobj)
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xgb_model.fit(X[train_index], y[train_index])
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preds = xgb_model.predict(X[test_index])
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labels = y[test_index]
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err = sum(1 for i in range(len(preds))
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if int(preds[i] > 0.5) != labels[i]) / float(len(preds))
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assert err < 0.1
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# Test that the custom objective function is actually used
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class XGBCustomObjectiveException(Exception):
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pass
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def dummy_objective(y_true, y_preds):
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raise XGBCustomObjectiveException()
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xgb_model = xgb.XGBClassifier(objective=dummy_objective)
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np.testing.assert_raises(
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XGBCustomObjectiveException,
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xgb_model.fit,
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X, y
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)
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def test_sklearn_api():
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from sklearn.datasets import load_iris
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from sklearn.model_selection import train_test_split
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iris = load_iris()
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tr_d, te_d, tr_l, te_l = train_test_split(iris.data, iris.target,
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train_size=120, test_size=0.2)
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classifier = xgb.XGBClassifier(booster='gbtree', n_estimators=10)
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classifier.fit(tr_d, tr_l)
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preds = classifier.predict(te_d)
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labels = te_l
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err = sum([1 for p, l in zip(preds, labels) if p != l]) * 1.0 / len(te_l)
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assert err < 0.2
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def test_sklearn_api_gblinear():
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from sklearn.datasets import load_iris
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from sklearn.model_selection import train_test_split
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iris = load_iris()
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tr_d, te_d, tr_l, te_l = train_test_split(iris.data, iris.target,
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train_size=120)
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classifier = xgb.XGBClassifier(booster='gblinear', n_estimators=100)
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classifier.fit(tr_d, tr_l)
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preds = classifier.predict(te_d)
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labels = te_l
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err = sum([1 for p, l in zip(preds, labels) if p != l]) * 1.0 / len(te_l)
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assert err < 0.5
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@pytest.mark.skipif(**tm.no_matplotlib())
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def test_sklearn_plotting():
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from sklearn.datasets import load_iris
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iris = load_iris()
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classifier = xgb.XGBClassifier()
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classifier.fit(iris.data, iris.target)
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import matplotlib
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matplotlib.use('Agg')
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from matplotlib.axes import Axes
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from graphviz import Source
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ax = xgb.plot_importance(classifier)
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assert isinstance(ax, Axes)
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assert ax.get_title() == 'Feature importance'
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assert ax.get_xlabel() == 'F score'
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assert ax.get_ylabel() == 'Features'
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assert len(ax.patches) == 4
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g = xgb.to_graphviz(classifier, num_trees=0)
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assert isinstance(g, Source)
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ax = xgb.plot_tree(classifier, num_trees=0)
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assert isinstance(ax, Axes)
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@pytest.mark.skipif(**tm.no_pandas())
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def test_sklearn_nfolds_cv():
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from sklearn.datasets import load_digits
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from sklearn.model_selection import StratifiedKFold
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digits = load_digits(3)
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X = digits['data']
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y = digits['target']
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dm = xgb.DMatrix(X, label=y)
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params = {
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'max_depth': 2,
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'eta': 1,
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'verbosity': 0,
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'objective':
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'multi:softprob',
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'num_class': 3
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}
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seed = 2016
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nfolds = 5
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skf = StratifiedKFold(n_splits=nfolds, shuffle=True, random_state=seed)
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cv1 = xgb.cv(params, dm, num_boost_round=10, nfold=nfolds,
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seed=seed, as_pandas=True)
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cv2 = xgb.cv(params, dm, num_boost_round=10, nfold=nfolds,
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folds=skf, seed=seed, as_pandas=True)
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cv3 = xgb.cv(params, dm, num_boost_round=10, nfold=nfolds,
|
|
stratified=True, seed=seed, as_pandas=True)
|
|
assert cv1.shape[0] == cv2.shape[0] and cv2.shape[0] == cv3.shape[0]
|
|
assert cv2.iloc[-1, 0] == cv3.iloc[-1, 0]
|
|
|
|
|
|
@pytest.mark.skipif(**tm.no_pandas())
|
|
def test_split_value_histograms():
|
|
from sklearn.datasets import load_digits
|
|
|
|
digits_2class = load_digits(2)
|
|
|
|
X = digits_2class['data']
|
|
y = digits_2class['target']
|
|
|
|
dm = xgb.DMatrix(X, label=y)
|
|
params = {'max_depth': 6, 'eta': 0.01, 'verbosity': 0,
|
|
'objective': 'binary:logistic'}
|
|
|
|
gbdt = xgb.train(params, dm, num_boost_round=10)
|
|
assert gbdt.get_split_value_histogram("not_there",
|
|
as_pandas=True).shape[0] == 0
|
|
assert gbdt.get_split_value_histogram("not_there",
|
|
as_pandas=False).shape[0] == 0
|
|
assert gbdt.get_split_value_histogram("f28", bins=0).shape[0] == 1
|
|
assert gbdt.get_split_value_histogram("f28", bins=1).shape[0] == 1
|
|
assert gbdt.get_split_value_histogram("f28", bins=2).shape[0] == 2
|
|
assert gbdt.get_split_value_histogram("f28", bins=5).shape[0] == 2
|
|
assert gbdt.get_split_value_histogram("f28", bins=None).shape[0] == 2
|
|
|
|
|
|
def test_sklearn_random_state():
|
|
clf = xgb.XGBClassifier(random_state=402)
|
|
assert clf.get_xgb_params()['random_state'] == 402
|
|
|
|
clf = xgb.XGBClassifier(random_state=401)
|
|
assert clf.get_xgb_params()['random_state'] == 401
|
|
|
|
random_state = np.random.RandomState(seed=403)
|
|
clf = xgb.XGBClassifier(random_state=random_state)
|
|
assert isinstance(clf.get_xgb_params()['random_state'], int)
|
|
|
|
|
|
def test_sklearn_n_jobs():
|
|
clf = xgb.XGBClassifier(n_jobs=1)
|
|
assert clf.get_xgb_params()['n_jobs'] == 1
|
|
|
|
clf = xgb.XGBClassifier(n_jobs=2)
|
|
assert clf.get_xgb_params()['n_jobs'] == 2
|
|
|
|
|
|
def test_kwargs():
|
|
params = {'updater': 'grow_gpu_hist', 'subsample': .5, 'n_jobs': -1}
|
|
clf = xgb.XGBClassifier(n_estimators=1000, **params)
|
|
assert clf.get_params()['updater'] == 'grow_gpu_hist'
|
|
assert clf.get_params()['subsample'] == .5
|
|
assert clf.get_params()['n_estimators'] == 1000
|
|
|
|
|
|
def test_kwargs_error():
|
|
params = {'updater': 'grow_gpu_hist', 'subsample': .5, 'n_jobs': -1}
|
|
with pytest.raises(TypeError):
|
|
clf = xgb.XGBClassifier(n_jobs=1000, **params)
|
|
assert isinstance(clf, xgb.XGBClassifier)
|
|
|
|
|
|
def test_kwargs_grid_search():
|
|
from sklearn.model_selection import GridSearchCV
|
|
from sklearn import datasets
|
|
|
|
params = {'tree_method': 'hist'}
|
|
clf = xgb.XGBClassifier(n_estimators=1, learning_rate=1.0, **params)
|
|
assert clf.get_params()['tree_method'] == 'hist'
|
|
# 'max_leaves' is not a default argument of XGBClassifier
|
|
# Check we can still do grid search over this parameter
|
|
search_params = {'max_leaves': range(2, 5)}
|
|
grid_cv = GridSearchCV(clf, search_params, cv=5)
|
|
iris = datasets.load_iris()
|
|
grid_cv.fit(iris.data, iris.target)
|
|
|
|
# Expect unique results for each parameter value
|
|
# This confirms sklearn is able to successfully update the parameter
|
|
means = grid_cv.cv_results_['mean_test_score']
|
|
assert len(means) == len(set(means))
|
|
|
|
|
|
def test_sklearn_clone():
|
|
from sklearn.base import clone
|
|
|
|
clf = xgb.XGBClassifier(n_jobs=2)
|
|
clf.n_jobs = -1
|
|
clone(clf)
|
|
|
|
|
|
def test_sklearn_get_default_params():
|
|
from sklearn.datasets import load_digits
|
|
digits_2class = load_digits(2)
|
|
X = digits_2class['data']
|
|
y = digits_2class['target']
|
|
cls = xgb.XGBClassifier()
|
|
assert cls.get_params()['base_score'] is None
|
|
cls.fit(X[:4, ...], y[:4, ...])
|
|
assert cls.get_params()['base_score'] is not None
|
|
|
|
|
|
def test_validation_weights_xgbmodel():
|
|
from sklearn.datasets import make_hastie_10_2
|
|
|
|
# prepare training and test data
|
|
X, y = make_hastie_10_2(n_samples=2000, random_state=42)
|
|
labels, y = np.unique(y, return_inverse=True)
|
|
X_train, X_test = X[:1600], X[1600:]
|
|
y_train, y_test = y[:1600], y[1600:]
|
|
|
|
# instantiate model
|
|
param_dist = {'objective': 'binary:logistic', 'n_estimators': 2,
|
|
'random_state': 123}
|
|
clf = xgb.sklearn.XGBModel(**param_dist)
|
|
|
|
# train it using instance weights only in the training set
|
|
weights_train = np.random.choice([1, 2], len(X_train))
|
|
clf.fit(X_train, y_train,
|
|
sample_weight=weights_train,
|
|
eval_set=[(X_test, y_test)],
|
|
eval_metric='logloss',
|
|
verbose=False)
|
|
|
|
# evaluate logloss metric on test set *without* using weights
|
|
evals_result_without_weights = clf.evals_result()
|
|
logloss_without_weights = evals_result_without_weights[
|
|
"validation_0"]["logloss"]
|
|
|
|
# now use weights for the test set
|
|
np.random.seed(0)
|
|
weights_test = np.random.choice([1, 2], len(X_test))
|
|
clf.fit(X_train, y_train,
|
|
sample_weight=weights_train,
|
|
eval_set=[(X_test, y_test)],
|
|
sample_weight_eval_set=[weights_test],
|
|
eval_metric='logloss',
|
|
verbose=False)
|
|
evals_result_with_weights = clf.evals_result()
|
|
logloss_with_weights = evals_result_with_weights["validation_0"]["logloss"]
|
|
|
|
# check that the logloss in the test set is actually different when using
|
|
# weights than when not using them
|
|
assert all((logloss_with_weights[i] != logloss_without_weights[i]
|
|
for i in [0, 1]))
|
|
|
|
with pytest.raises(AssertionError):
|
|
# length of eval set and sample weight doesn't match.
|
|
clf.fit(X_train, y_train, sample_weight=weights_train,
|
|
eval_set=[(X_train, y_train), (X_test, y_test)],
|
|
sample_weight_eval_set=[weights_train])
|
|
|
|
with pytest.raises(AssertionError):
|
|
cls = xgb.XGBClassifier()
|
|
cls.fit(X_train, y_train, sample_weight=weights_train,
|
|
eval_set=[(X_train, y_train), (X_test, y_test)],
|
|
sample_weight_eval_set=[weights_train])
|
|
|
|
def test_validation_weights_xgbclassifier():
|
|
from sklearn.datasets import make_hastie_10_2
|
|
|
|
# prepare training and test data
|
|
X, y = make_hastie_10_2(n_samples=2000, random_state=42)
|
|
labels, y = np.unique(y, return_inverse=True)
|
|
X_train, X_test = X[:1600], X[1600:]
|
|
y_train, y_test = y[:1600], y[1600:]
|
|
|
|
# instantiate model
|
|
param_dist = {'objective': 'binary:logistic', 'n_estimators': 2,
|
|
'random_state': 123}
|
|
clf = xgb.sklearn.XGBClassifier(**param_dist)
|
|
|
|
# train it using instance weights only in the training set
|
|
weights_train = np.random.choice([1, 2], len(X_train))
|
|
clf.fit(X_train, y_train,
|
|
sample_weight=weights_train,
|
|
eval_set=[(X_test, y_test)],
|
|
eval_metric='logloss',
|
|
verbose=False)
|
|
|
|
# evaluate logloss metric on test set *without* using weights
|
|
evals_result_without_weights = clf.evals_result()
|
|
logloss_without_weights = evals_result_without_weights[
|
|
"validation_0"]["logloss"]
|
|
|
|
# now use weights for the test set
|
|
np.random.seed(0)
|
|
weights_test = np.random.choice([1, 2], len(X_test))
|
|
clf.fit(X_train, y_train,
|
|
sample_weight=weights_train,
|
|
eval_set=[(X_test, y_test)],
|
|
sample_weight_eval_set=[weights_test],
|
|
eval_metric='logloss',
|
|
verbose=False)
|
|
evals_result_with_weights = clf.evals_result()
|
|
logloss_with_weights = evals_result_with_weights["validation_0"]["logloss"]
|
|
|
|
# check that the logloss in the test set is actually different
|
|
# when using weights than when not using them
|
|
assert all((logloss_with_weights[i] != logloss_without_weights[i]
|
|
for i in [0, 1]))
|
|
|
|
|
|
def save_load_model(model_path):
|
|
from sklearn.datasets import load_digits
|
|
from sklearn.model_selection import KFold
|
|
|
|
digits = load_digits(2)
|
|
y = digits['target']
|
|
X = digits['data']
|
|
kf = KFold(n_splits=2, shuffle=True, random_state=rng)
|
|
for train_index, test_index in kf.split(X, y):
|
|
xgb_model = xgb.XGBClassifier().fit(X[train_index], y[train_index])
|
|
xgb_model.save_model(model_path)
|
|
xgb_model = xgb.XGBClassifier()
|
|
xgb_model.load_model(model_path)
|
|
assert isinstance(xgb_model.classes_, np.ndarray)
|
|
assert isinstance(xgb_model._Booster, xgb.Booster)
|
|
assert isinstance(xgb_model._le, XGBoostLabelEncoder)
|
|
assert isinstance(xgb_model._le.classes_, np.ndarray)
|
|
preds = xgb_model.predict(X[test_index])
|
|
labels = y[test_index]
|
|
err = sum(1 for i in range(len(preds))
|
|
if int(preds[i] > 0.5) != labels[i]) / float(len(preds))
|
|
assert err < 0.1
|
|
assert xgb_model.get_booster().attr('scikit_learn') is None
|
|
|
|
# test native booster
|
|
preds = xgb_model.predict(X[test_index], output_margin=True)
|
|
booster = xgb.Booster(model_file=model_path)
|
|
predt_1 = booster.predict(xgb.DMatrix(X[test_index]),
|
|
output_margin=True)
|
|
assert np.allclose(preds, predt_1)
|
|
|
|
with pytest.raises(TypeError):
|
|
xgb_model = xgb.XGBModel()
|
|
xgb_model.load_model(model_path)
|
|
|
|
|
|
def test_save_load_model():
|
|
with TemporaryDirectory() as tempdir:
|
|
model_path = os.path.join(tempdir, 'digits.model')
|
|
save_load_model(model_path)
|
|
|
|
with TemporaryDirectory() as tempdir:
|
|
model_path = os.path.join(tempdir, 'digits.model.json')
|
|
save_load_model(model_path)
|
|
|
|
from sklearn.datasets import load_digits
|
|
with TemporaryDirectory() as tempdir:
|
|
model_path = os.path.join(tempdir, 'digits.model.json')
|
|
digits = load_digits(2)
|
|
y = digits['target']
|
|
X = digits['data']
|
|
booster = xgb.train({'tree_method': 'hist',
|
|
'objective': 'binary:logistic'},
|
|
dtrain=xgb.DMatrix(X, y),
|
|
num_boost_round=4)
|
|
predt_0 = booster.predict(xgb.DMatrix(X))
|
|
booster.save_model(model_path)
|
|
cls = xgb.XGBClassifier()
|
|
cls.load_model(model_path)
|
|
predt_1 = cls.predict(X)
|
|
assert np.allclose(predt_0, predt_1)
|
|
|
|
cls = xgb.XGBModel()
|
|
cls.load_model(model_path)
|
|
predt_1 = cls.predict(X)
|
|
assert np.allclose(predt_0, predt_1)
|
|
|
|
|
|
def test_RFECV():
|
|
from sklearn.datasets import load_boston
|
|
from sklearn.datasets import load_breast_cancer
|
|
from sklearn.datasets import load_iris
|
|
from sklearn.feature_selection import RFECV
|
|
|
|
# Regression
|
|
X, y = load_boston(return_X_y=True)
|
|
bst = xgb.XGBClassifier(booster='gblinear', learning_rate=0.1,
|
|
n_estimators=10,
|
|
objective='reg:squarederror',
|
|
random_state=0, verbosity=0)
|
|
rfecv = RFECV(
|
|
estimator=bst, step=1, cv=3, scoring='neg_mean_squared_error')
|
|
rfecv.fit(X, y)
|
|
|
|
# Binary classification
|
|
X, y = load_breast_cancer(return_X_y=True)
|
|
bst = xgb.XGBClassifier(booster='gblinear', learning_rate=0.1,
|
|
n_estimators=10,
|
|
objective='binary:logistic',
|
|
random_state=0, verbosity=0)
|
|
rfecv = RFECV(estimator=bst, step=1, cv=3, scoring='roc_auc')
|
|
rfecv.fit(X, y)
|
|
|
|
# Multi-class classification
|
|
X, y = load_iris(return_X_y=True)
|
|
bst = xgb.XGBClassifier(base_score=0.4, booster='gblinear',
|
|
learning_rate=0.1,
|
|
n_estimators=10,
|
|
objective='multi:softprob',
|
|
random_state=0, reg_alpha=0.001, reg_lambda=0.01,
|
|
scale_pos_weight=0.5, verbosity=0)
|
|
rfecv = RFECV(estimator=bst, step=1, cv=3, scoring='neg_log_loss')
|
|
rfecv.fit(X, y)
|
|
|
|
X[0:4, :] = np.nan # verify scikit_learn doesn't throw with nan
|
|
reg = xgb.XGBRegressor()
|
|
rfecv = RFECV(estimator=reg)
|
|
rfecv.fit(X, y)
|
|
|
|
cls = xgb.XGBClassifier()
|
|
rfecv = RFECV(estimator=cls, step=1, cv=3,
|
|
scoring='neg_mean_squared_error')
|
|
rfecv.fit(X, y)
|
|
|
|
|
|
def test_XGBClassifier_resume():
|
|
from sklearn.datasets import load_breast_cancer
|
|
from sklearn.metrics import log_loss
|
|
|
|
with TemporaryDirectory() as tempdir:
|
|
model1_path = os.path.join(tempdir, 'test_XGBClassifier.model')
|
|
model1_booster_path = os.path.join(tempdir, 'test_XGBClassifier.booster')
|
|
|
|
X, Y = load_breast_cancer(return_X_y=True)
|
|
|
|
model1 = xgb.XGBClassifier(
|
|
learning_rate=0.3, random_state=0, n_estimators=8)
|
|
model1.fit(X, Y)
|
|
|
|
pred1 = model1.predict(X)
|
|
log_loss1 = log_loss(pred1, Y)
|
|
|
|
# file name of stored xgb model
|
|
model1.save_model(model1_path)
|
|
model2 = xgb.XGBClassifier(
|
|
learning_rate=0.3, random_state=0, n_estimators=8)
|
|
model2.fit(X, Y, xgb_model=model1_path)
|
|
|
|
pred2 = model2.predict(X)
|
|
log_loss2 = log_loss(pred2, Y)
|
|
|
|
assert np.any(pred1 != pred2)
|
|
assert log_loss1 > log_loss2
|
|
|
|
# file name of 'Booster' instance Xgb model
|
|
model1.get_booster().save_model(model1_booster_path)
|
|
model2 = xgb.XGBClassifier(
|
|
learning_rate=0.3, random_state=0, n_estimators=8)
|
|
model2.fit(X, Y, xgb_model=model1_booster_path)
|
|
|
|
pred2 = model2.predict(X)
|
|
log_loss2 = log_loss(pred2, Y)
|
|
|
|
assert np.any(pred1 != pred2)
|
|
assert log_loss1 > log_loss2
|
|
|
|
|
|
def test_constraint_parameters():
|
|
reg = xgb.XGBRegressor(interaction_constraints='[[0, 1], [2, 3, 4]]')
|
|
X = np.random.randn(10, 10)
|
|
y = np.random.randn(10)
|
|
reg.fit(X, y)
|
|
|
|
config = json.loads(reg.get_booster().save_config())
|
|
assert config['learner']['gradient_booster']['updater']['grow_colmaker'][
|
|
'train_param']['interaction_constraints'] == '[[0, 1], [2, 3, 4]]'
|
|
|
|
|
|
def test_parameter_validation():
|
|
reg = xgb.XGBRegressor(foo='bar', verbosity=1)
|
|
X = np.random.randn(10, 10)
|
|
y = np.random.randn(10)
|
|
with captured_output() as (out, err):
|
|
reg.fit(X, y)
|
|
output = out.getvalue().strip()
|
|
|
|
assert output.find('foo') != -1
|
|
|
|
reg = xgb.XGBRegressor(n_estimators=2, missing=3,
|
|
importance_type='gain', verbosity=1)
|
|
X = np.random.randn(10, 10)
|
|
y = np.random.randn(10)
|
|
with captured_output() as (out, err):
|
|
reg.fit(X, y)
|
|
output = out.getvalue().strip()
|
|
|
|
assert len(output) == 0
|
|
|
|
|
|
class TestBoostFromPrediction(unittest.TestCase):
|
|
def run_boost_from_prediction(self, tree_method):
|
|
from sklearn.datasets import load_breast_cancer
|
|
X, y = load_breast_cancer(return_X_y=True)
|
|
model_0 = xgb.XGBClassifier(
|
|
learning_rate=0.3, random_state=0, n_estimators=4,
|
|
tree_method=tree_method)
|
|
model_0.fit(X=X, y=y)
|
|
margin = model_0.predict(X, output_margin=True)
|
|
|
|
model_1 = xgb.XGBClassifier(
|
|
learning_rate=0.3, random_state=0, n_estimators=4,
|
|
tree_method=tree_method)
|
|
model_1.fit(X=X, y=y, base_margin=margin)
|
|
predictions_1 = model_1.predict(X, base_margin=margin)
|
|
|
|
cls_2 = xgb.XGBClassifier(
|
|
learning_rate=0.3, random_state=0, n_estimators=8,
|
|
tree_method=tree_method)
|
|
cls_2.fit(X=X, y=y)
|
|
predictions_2 = cls_2.predict(X)
|
|
assert np.all(predictions_1 == predictions_2)
|
|
|
|
@pytest.mark.skipif(**tm.no_sklearn())
|
|
def test_boost_from_prediction_hist(self):
|
|
self.run_boost_from_prediction('hist')
|
|
|
|
@pytest.mark.skipif(**tm.no_sklearn())
|
|
def test_boost_from_prediction_approx(self):
|
|
self.run_boost_from_prediction('approx')
|
|
|
|
@pytest.mark.skipif(**tm.no_sklearn())
|
|
def test_boost_from_prediction_exact(self):
|
|
self.run_boost_from_prediction('exact')
|