301540f1d9
* fix DeprecationWarning on sklearn.cross_validation * fix syntax * fix kfold n_split issue * fix mistype * fix n_splits multiple value issue * split should pass a iterable * use np.arange instead of xrange, py3 compatibility
111 lines
4.0 KiB
Python
111 lines
4.0 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 unittest
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rng = np.random.RandomState(1994)
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class TestFastHist(unittest.TestCase):
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def test_fast_hist(self):
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tm._skip_if_no_sklearn()
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from sklearn.datasets import load_digits
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try:
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from sklearn.model_selection import train_test_split
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except:
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from sklearn.cross_validation import train_test_split
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digits = load_digits(2)
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X = digits['data']
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y = digits['target']
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X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)
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dtrain = xgb.DMatrix(X_train, y_train)
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dtest = xgb.DMatrix(X_test, y_test)
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param = {'objective': 'binary:logistic',
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'tree_method': 'hist',
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'grow_policy': 'depthwise',
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'max_depth': 3,
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'eval_metric': 'auc'}
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res = {}
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xgb.train(param, dtrain, 10, [(dtrain, 'train'), (dtest, 'test')],
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evals_result=res)
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assert self.non_decreasing(res['train']['auc'])
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assert self.non_decreasing(res['test']['auc'])
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param2 = {'objective': 'binary:logistic',
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'tree_method': 'hist',
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'grow_policy': 'lossguide',
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'max_depth': 0,
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'max_leaves': 8,
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'eval_metric': 'auc'}
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res = {}
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xgb.train(param2, dtrain, 10, [(dtrain, 'train'), (dtest, 'test')],
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evals_result=res)
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assert self.non_decreasing(res['train']['auc'])
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assert self.non_decreasing(res['test']['auc'])
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param3 = {'objective': 'binary:logistic',
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'tree_method': 'hist',
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'grow_policy': 'lossguide',
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'max_depth': 0,
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'max_leaves': 8,
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'max_bin': 16,
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'eval_metric': 'auc'}
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res = {}
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xgb.train(param3, dtrain, 10, [(dtrain, 'train'), (dtest, 'test')],
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evals_result=res)
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assert self.non_decreasing(res['train']['auc'])
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# fail-safe test for dense data
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from sklearn.datasets import load_svmlight_file
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dpath = 'demo/data/'
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X2, y2 = load_svmlight_file(dpath + 'agaricus.txt.train')
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X2 = X2.toarray()
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dtrain2 = xgb.DMatrix(X2, label=y2)
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param = {'objective': 'binary:logistic',
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'tree_method': 'hist',
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'grow_policy': 'depthwise',
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'max_depth': 2,
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'eval_metric': 'auc'}
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res = {}
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xgb.train(param, dtrain2, 10, [(dtrain2, 'train')], evals_result=res)
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assert self.non_decreasing(res['train']['auc'])
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assert res['train']['auc'][0] >= 0.85
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for j in range(X2.shape[1]):
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for i in np.random.choice(X2.shape[0], size=10, replace=False):
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X2[i, j] = 2
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dtrain3 = xgb.DMatrix(X2, label=y2)
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res = {}
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xgb.train(param, dtrain3, 10, [(dtrain3, 'train')], evals_result=res)
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assert self.non_decreasing(res['train']['auc'])
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assert res['train']['auc'][0] >= 0.85
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for j in range(X2.shape[1]):
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for i in np.random.choice(X2.shape[0], size=10, replace=False):
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X2[i, j] = 3
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dtrain4 = xgb.DMatrix(X2, label=y2)
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res = {}
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xgb.train(param, dtrain4, 10, [(dtrain4, 'train')], evals_result=res)
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assert self.non_decreasing(res['train']['auc'])
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assert res['train']['auc'][0] >= 0.85
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# fail-safe test for max_bin=2
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param = {'objective': 'binary:logistic',
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'tree_method': 'hist',
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'grow_policy': 'depthwise',
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'max_depth': 2,
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'eval_metric': 'auc',
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'max_bin': 2}
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res = {}
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xgb.train(param, dtrain2, 10, [(dtrain2, 'train')], evals_result=res)
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assert self.non_decreasing(res['train']['auc'])
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assert res['train']['auc'][0] >= 0.85
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def non_decreasing(self, L):
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return all(x <= y for x, y in zip(L, L[1:]))
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