6bd1869026
* Add prediction of feature contributions This implements the idea described at http://blog.datadive.net/interpreting-random-forests/ which tries to give insight in how a prediction is composed of its feature contributions and a bias. * Support multi-class models * Calculate learning_rate per-tree instead of using the one from the first tree * Do not rely on node.base_weight * learning_rate having the same value as the node mean value (aka leaf value, if it were a leaf); instead calculate them (lazily) on-the-fly * Add simple test for contributions feature * Check against param.num_nodes instead of checking for non-zero length * Loop over all roots instead of only the first
277 lines
10 KiB
Python
277 lines
10 KiB
Python
# -*- coding: utf-8 -*-
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import numpy as np
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import xgboost as xgb
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import unittest
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import itertools
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import json
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dpath = 'demo/data/'
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rng = np.random.RandomState(1994)
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class TestBasic(unittest.TestCase):
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def test_basic(self):
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dtrain = xgb.DMatrix(dpath + 'agaricus.txt.train')
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dtest = xgb.DMatrix(dpath + 'agaricus.txt.test')
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param = {'max_depth': 2, 'eta': 1, 'silent': 1, 'objective': 'binary:logistic'}
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# specify validations set to watch performance
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watchlist = [(dtest, 'eval'), (dtrain, 'train')]
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num_round = 2
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bst = xgb.train(param, dtrain, num_round, watchlist)
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# this is prediction
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preds = bst.predict(dtest)
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labels = dtest.get_label()
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err = sum(1 for i in range(len(preds)) if int(preds[i] > 0.5) != labels[i]) / float(len(preds))
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# error must be smaller than 10%
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assert err < 0.1
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# save dmatrix into binary buffer
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dtest.save_binary('dtest.buffer')
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# save model
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bst.save_model('xgb.model')
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# load model and data in
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bst2 = xgb.Booster(model_file='xgb.model')
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dtest2 = xgb.DMatrix('dtest.buffer')
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preds2 = bst2.predict(dtest2)
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# assert they are the same
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assert np.sum(np.abs(preds2 - preds)) == 0
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def test_record_results(self):
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dtrain = xgb.DMatrix(dpath + 'agaricus.txt.train')
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dtest = xgb.DMatrix(dpath + 'agaricus.txt.test')
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param = {'max_depth': 2, 'eta': 1, 'silent': 1, 'objective': 'binary:logistic'}
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# specify validations set to watch performance
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watchlist = [(dtest, 'eval'), (dtrain, 'train')]
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num_round = 2
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result = {}
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res2 = {}
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xgb.train(param, dtrain, num_round, watchlist,
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callbacks=[xgb.callback.record_evaluation(result)])
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xgb.train(param, dtrain, num_round, watchlist,
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evals_result=res2)
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assert result['train']['error'][0] < 0.1
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assert res2 == result
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def test_multiclass(self):
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dtrain = xgb.DMatrix(dpath + 'agaricus.txt.train')
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dtest = xgb.DMatrix(dpath + 'agaricus.txt.test')
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param = {'max_depth': 2, 'eta': 1, 'silent': 1, 'num_class': 2}
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# specify validations set to watch performance
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watchlist = [(dtest, 'eval'), (dtrain, 'train')]
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num_round = 2
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bst = xgb.train(param, dtrain, num_round, watchlist)
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# this is prediction
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preds = bst.predict(dtest)
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labels = dtest.get_label()
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err = sum(1 for i in range(len(preds)) if preds[i] != labels[i]) / float(len(preds))
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# error must be smaller than 10%
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assert err < 0.1
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# save dmatrix into binary buffer
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dtest.save_binary('dtest.buffer')
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# save model
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bst.save_model('xgb.model')
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# load model and data in
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bst2 = xgb.Booster(model_file='xgb.model')
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dtest2 = xgb.DMatrix('dtest.buffer')
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preds2 = bst2.predict(dtest2)
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# assert they are the same
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assert np.sum(np.abs(preds2 - preds)) == 0
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def test_dmatrix_init(self):
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data = np.random.randn(5, 5)
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# different length
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self.assertRaises(ValueError, xgb.DMatrix, data,
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feature_names=list('abcdef'))
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# contains duplicates
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self.assertRaises(ValueError, xgb.DMatrix, data,
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feature_names=['a', 'b', 'c', 'd', 'd'])
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# contains symbol
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self.assertRaises(ValueError, xgb.DMatrix, data,
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feature_names=['a', 'b', 'c', 'd', 'e<1'])
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dm = xgb.DMatrix(data)
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dm.feature_names = list('abcde')
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assert dm.feature_names == list('abcde')
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dm.feature_types = 'q'
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assert dm.feature_types == list('qqqqq')
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dm.feature_types = list('qiqiq')
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assert dm.feature_types == list('qiqiq')
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def incorrect_type_set():
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dm.feature_types = list('abcde')
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self.assertRaises(ValueError, incorrect_type_set)
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# reset
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dm.feature_names = None
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self.assertEqual(dm.feature_names, ['f0', 'f1', 'f2', 'f3', 'f4'])
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assert dm.feature_types is None
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def test_feature_names(self):
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data = np.random.randn(100, 5)
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target = np.array([0, 1] * 50)
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cases = [['Feature1', 'Feature2', 'Feature3', 'Feature4', 'Feature5'],
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[u'要因1', u'要因2', u'要因3', u'要因4', u'要因5']]
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for features in cases:
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dm = xgb.DMatrix(data, label=target,
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feature_names=features)
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assert dm.feature_names == features
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assert dm.num_row() == 100
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assert dm.num_col() == 5
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params = {'objective': 'multi:softprob',
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'eval_metric': 'mlogloss',
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'eta': 0.3,
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'num_class': 3}
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bst = xgb.train(params, dm, num_boost_round=10)
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scores = bst.get_fscore()
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assert list(sorted(k for k in scores)) == features
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dummy = np.random.randn(5, 5)
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dm = xgb.DMatrix(dummy, feature_names=features)
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bst.predict(dm)
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# different feature name must raises error
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dm = xgb.DMatrix(dummy, feature_names=list('abcde'))
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self.assertRaises(ValueError, bst.predict, dm)
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def test_feature_importances(self):
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data = np.random.randn(100, 5)
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target = np.array([0, 1] * 50)
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features = ['Feature1', 'Feature2', 'Feature3', 'Feature4', 'Feature5']
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dm = xgb.DMatrix(data, label=target,
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feature_names=features)
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params = {'objective': 'multi:softprob',
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'eval_metric': 'mlogloss',
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'eta': 0.3,
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'num_class': 3}
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bst = xgb.train(params, dm, num_boost_round=10)
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# number of feature importances should == number of features
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scores1 = bst.get_score()
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scores2 = bst.get_score(importance_type='weight')
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scores3 = bst.get_score(importance_type='cover')
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scores4 = bst.get_score(importance_type='gain')
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assert len(scores1) == len(features)
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assert len(scores2) == len(features)
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assert len(scores3) == len(features)
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assert len(scores4) == len(features)
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# check backwards compatibility of get_fscore
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fscores = bst.get_fscore()
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assert scores1 == fscores
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def test_dump(self):
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data = np.random.randn(100, 2)
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target = np.array([0, 1] * 50)
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features = ['Feature1', 'Feature2']
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dm = xgb.DMatrix(data, label=target, feature_names=features)
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params = {'objective': 'binary:logistic',
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'eval_metric': 'logloss',
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'eta': 0.3,
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'max_depth': 1}
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bst = xgb.train(params, dm, num_boost_round=1)
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# number of feature importances should == number of features
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dump1 = bst.get_dump()
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self.assertEqual(len(dump1), 1, "Expected only 1 tree to be dumped.")
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self.assertEqual(len(dump1[0].splitlines()), 3,
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"Expected 1 root and 2 leaves - 3 lines in dump.")
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dump2 = bst.get_dump(with_stats=True)
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self.assertEqual(dump2[0].count('\n'), 3,
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"Expected 1 root and 2 leaves - 3 lines in dump.")
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self.assertGreater(dump2[0].find('\n'), dump1[0].find('\n'),
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"Expected more info when with_stats=True is given.")
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dump3 = bst.get_dump(dump_format="json")
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dump3j = json.loads(dump3[0])
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self.assertEqual(dump3j["nodeid"], 0, "Expected the root node on top.")
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dump4 = bst.get_dump(dump_format="json", with_stats=True)
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dump4j = json.loads(dump4[0])
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self.assertIn("gain", dump4j, "Expected 'gain' to be dumped in JSON.")
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def test_load_file_invalid(self):
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self.assertRaises(xgb.core.XGBoostError, xgb.Booster,
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model_file='incorrect_path')
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self.assertRaises(xgb.core.XGBoostError, xgb.Booster,
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model_file=u'不正なパス')
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def test_dmatrix_numpy_init(self):
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data = np.random.randn(5, 5)
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dm = xgb.DMatrix(data)
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assert dm.num_row() == 5
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assert dm.num_col() == 5
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data = np.matrix([[1, 2], [3, 4]])
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dm = xgb.DMatrix(data)
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assert dm.num_row() == 2
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assert dm.num_col() == 2
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# 0d array
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self.assertRaises(ValueError, xgb.DMatrix, np.array(1))
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# 1d array
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self.assertRaises(ValueError, xgb.DMatrix, np.array([1, 2, 3]))
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# 3d array
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data = np.random.randn(5, 5, 5)
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self.assertRaises(ValueError, xgb.DMatrix, data)
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# object dtype
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data = np.array([['a', 'b'], ['c', 'd']])
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self.assertRaises(ValueError, xgb.DMatrix, data)
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def test_cv(self):
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dm = xgb.DMatrix(dpath + 'agaricus.txt.train')
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params = {'max_depth': 2, 'eta': 1, 'silent': 1, 'objective': 'binary:logistic'}
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# return np.ndarray
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cv = xgb.cv(params, dm, num_boost_round=10, nfold=10, as_pandas=False)
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assert isinstance(cv, dict)
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assert len(cv) == (4)
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def test_cv_no_shuffle(self):
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dm = xgb.DMatrix(dpath + 'agaricus.txt.train')
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params = {'max_depth': 2, 'eta': 1, 'silent': 1, 'objective': 'binary:logistic'}
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# return np.ndarray
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cv = xgb.cv(params, dm, num_boost_round=10, shuffle=False, nfold=10, as_pandas=False)
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assert isinstance(cv, dict)
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assert len(cv) == (4)
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def test_contributions():
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dtrain = xgb.DMatrix(dpath + 'agaricus.txt.train')
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dtest = xgb.DMatrix(dpath + 'agaricus.txt.test')
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def test_fn(max_depth, num_rounds):
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# train
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params = {'max_depth': max_depth, 'eta': 1, 'silent': 1}
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bst = xgb.train(params, dtrain, num_boost_round=num_rounds)
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# predict
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preds = bst.predict(dtest)
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contribs = bst.predict(dtest, pred_contribs=True)
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# result should be (number of features + BIAS) * number of rows
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assert contribs.shape == (dtest.num_row(), dtest.num_col() + 1)
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# sum of contributions should be same as predictions
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np.testing.assert_array_almost_equal(np.sum(contribs, axis=1), preds)
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for max_depth, num_rounds in itertools.product(range(0, 3), range(1, 5)):
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yield test_fn, max_depth, num_rounds
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