384 lines
14 KiB
Python
384 lines
14 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 matplotlib
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matplotlib.use('Agg')
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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_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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assert dm.feature_names is None
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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_pandas(self):
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import pandas as pd
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df = pd.DataFrame([[1, 2., True], [2, 3., False]], columns=['a', 'b', 'c'])
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dm = xgb.DMatrix(df, label=pd.Series([1, 2]))
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assert dm.feature_names == ['a', 'b', 'c']
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assert dm.feature_types == ['int', 'float', 'i']
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assert dm.num_row() == 2
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assert dm.num_col() == 3
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# overwrite feature_names and feature_types
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dm = xgb.DMatrix(df, label=pd.Series([1, 2]),
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feature_names=['x', 'y', 'z'], feature_types=['q', 'q', 'q'])
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assert dm.feature_names == ['x', 'y', 'z']
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assert dm.feature_types == ['q', 'q', 'q']
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assert dm.num_row() == 2
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assert dm.num_col() == 3
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# incorrect dtypes
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df = pd.DataFrame([[1, 2., 'x'], [2, 3., 'y']], columns=['a', 'b', 'c'])
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self.assertRaises(ValueError, xgb.DMatrix, df)
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# numeric columns
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df = pd.DataFrame([[1, 2., True], [2, 3., False]])
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dm = xgb.DMatrix(df, label=pd.Series([1, 2]))
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assert dm.feature_names == ['0', '1', '2']
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assert dm.feature_types == ['int', 'float', 'i']
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assert dm.num_row() == 2
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assert dm.num_col() == 3
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df = pd.DataFrame([[1, 2., 1], [2, 3., 1]], columns=[4, 5, 6])
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dm = xgb.DMatrix(df, label=pd.Series([1, 2]))
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assert dm.feature_names == ['4', '5', '6']
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assert dm.feature_types == ['int', 'float', 'int']
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assert dm.num_row() == 2
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assert dm.num_col() == 3
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df = pd.DataFrame({'A': ['X', 'Y', 'Z'], 'B': [1, 2, 3]})
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dummies = pd.get_dummies(df)
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# B A_X A_Y A_Z
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# 0 1 1 0 0
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# 1 2 0 1 0
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# 2 3 0 0 1
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result, _, _ = xgb.core._maybe_pandas_data(dummies, None, None)
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exp = np.array([[1., 1., 0., 0.],
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[2., 0., 1., 0.],
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[3., 0., 0., 1.]])
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np.testing.assert_array_equal(result, exp)
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dm = xgb.DMatrix(dummies)
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assert dm.feature_names == ['B', 'A_X', 'A_Y', 'A_Z']
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assert dm.feature_types == ['int', 'float', 'float', 'float']
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assert dm.num_row() == 3
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assert dm.num_col() == 4
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df = pd.DataFrame({'A=1': [1, 2, 3], 'A=2': [4, 5, 6]})
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dm = xgb.DMatrix(df)
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assert dm.feature_names == ['A=1', 'A=2']
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assert dm.feature_types == ['int', 'int']
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assert dm.num_row() == 3
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assert dm.num_col() == 2
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def test_pandas_label(self):
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import pandas as pd
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# label must be a single column
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df = pd.DataFrame({'A': ['X', 'Y', 'Z'], 'B': [1, 2, 3]})
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self.assertRaises(ValueError, xgb.core._maybe_pandas_label, df)
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# label must be supported dtype
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df = pd.DataFrame({'A': np.array(['a', 'b', 'c'], dtype=object)})
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self.assertRaises(ValueError, xgb.core._maybe_pandas_label, df)
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df = pd.DataFrame({'A': np.array([1, 2, 3], dtype=int)})
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result = xgb.core._maybe_pandas_label(df)
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np.testing.assert_array_equal(result, np.array([[1.], [2.], [3.]], dtype=float))
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dm = xgb.DMatrix(np.random.randn(3, 2), label=df)
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assert dm.num_row() == 3
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assert dm.num_col() == 2
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def test_load_file_invalid(self):
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self.assertRaises(ValueError, xgb.Booster,
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model_file='incorrect_path')
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self.assertRaises(ValueError, 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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import pandas as pd
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cv = xgb.cv(params, dm, num_boost_round=10, nfold=10)
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assert isinstance(cv, pd.DataFrame)
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exp = pd.Index([u'test-error-mean', u'test-error-std',
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u'train-error-mean', u'train-error-std'])
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assert cv.columns.equals(exp)
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# show progress log (result is the same as above)
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cv = xgb.cv(params, dm, num_boost_round=10, nfold=10,
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show_progress=True)
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assert isinstance(cv, pd.DataFrame)
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exp = pd.Index([u'test-error-mean', u'test-error-std',
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u'train-error-mean', u'train-error-std'])
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assert cv.columns.equals(exp)
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cv = xgb.cv(params, dm, num_boost_round=10, nfold=10,
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show_progress=True, show_stdv=False)
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assert isinstance(cv, pd.DataFrame)
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exp = pd.Index([u'test-error-mean', u'test-error-std',
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u'train-error-mean', u'train-error-std'])
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assert cv.columns.equals(exp)
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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, np.ndarray)
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assert cv.shape == (10, 4)
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params = {'max_depth': 2, 'eta': 1, 'silent': 1, 'objective': 'binary:logistic', 'eval_metric': 'auc'}
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cv = xgb.cv(params, dm, num_boost_round=10, nfold=10, as_pandas=True)
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assert 'eval_metric' in params
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assert 'auc' in cv.columns[0]
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params = {'max_depth': 2, 'eta': 1, 'silent': 1, 'objective': 'binary:logistic', 'eval_metric': ['auc']}
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cv = xgb.cv(params, dm, num_boost_round=10, nfold=10, as_pandas=True)
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assert 'eval_metric' in params
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assert 'auc' in cv.columns[0]
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params = {'max_depth': 2, 'eta': 1, 'silent': 1, 'objective': 'binary:logistic', 'eval_metric': ['auc']}
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cv = xgb.cv(params, dm, num_boost_round=10, nfold=10, as_pandas=True, early_stopping_rounds=1)
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assert 'eval_metric' in params
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assert 'auc' in cv.columns[0]
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assert cv.shape[0] < 10
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params = {'max_depth': 2, 'eta': 1, 'silent': 1, 'objective': 'binary:logistic'}
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cv = xgb.cv(params, dm, num_boost_round=10, nfold=10, as_pandas=True, metrics='auc')
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assert 'auc' in cv.columns[0]
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params = {'max_depth': 2, 'eta': 1, 'silent': 1, 'objective': 'binary:logistic'}
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cv = xgb.cv(params, dm, num_boost_round=10, nfold=10, as_pandas=True, metrics=['auc'])
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assert 'auc' in cv.columns[0]
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params = {'max_depth': 2, 'eta': 1, 'silent': 1, 'objective': 'binary:logistic', 'eval_metric': ['auc']}
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cv = xgb.cv(params, dm, num_boost_round=10, nfold=10, as_pandas=True, metrics='error')
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assert 'eval_metric' in params
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assert 'auc' not in cv.columns[0]
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assert 'error' in cv.columns[0]
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cv = xgb.cv(params, dm, num_boost_round=10, nfold=10, as_pandas=True, metrics=['error'])
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assert 'eval_metric' in params
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assert 'auc' not in cv.columns[0]
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assert 'error' in cv.columns[0]
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params = list(params.items())
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cv = xgb.cv(params, dm, num_boost_round=10, nfold=10, as_pandas=True, metrics=['error'])
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assert isinstance(params, list)
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assert 'auc' not in cv.columns[0]
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assert 'error' in cv.columns[0]
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def test_plotting(self):
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bst2 = xgb.Booster(model_file='xgb.model')
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# plotting
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from matplotlib.axes import Axes
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from graphviz import Digraph
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ax = xgb.plot_importance(bst2)
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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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ax = xgb.plot_importance(bst2, color='r',
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title='t', xlabel='x', ylabel='y')
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assert isinstance(ax, Axes)
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assert ax.get_title() == 't'
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assert ax.get_xlabel() == 'x'
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assert ax.get_ylabel() == 'y'
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assert len(ax.patches) == 4
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for p in ax.patches:
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assert p.get_facecolor() == (1.0, 0, 0, 1.0) # red
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ax = xgb.plot_importance(bst2, color=['r', 'r', 'b', 'b'],
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title=None, xlabel=None, ylabel=None)
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assert isinstance(ax, Axes)
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assert ax.get_title() == ''
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assert ax.get_xlabel() == ''
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assert ax.get_ylabel() == ''
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assert len(ax.patches) == 4
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assert ax.patches[0].get_facecolor() == (1.0, 0, 0, 1.0) # red
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assert ax.patches[1].get_facecolor() == (1.0, 0, 0, 1.0) # red
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assert ax.patches[2].get_facecolor() == (0, 0, 1.0, 1.0) # blue
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assert ax.patches[3].get_facecolor() == (0, 0, 1.0, 1.0) # blue
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g = xgb.to_graphviz(bst2, num_trees=0)
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assert isinstance(g, Digraph)
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ax = xgb.plot_tree(bst2, num_trees=0)
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assert isinstance(ax, Axes)
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def test_importance_plot_lim(self):
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np.random.seed(1)
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dm = xgb.DMatrix(np.random.randn(100, 100), label=[0, 1] * 50)
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bst = xgb.train({}, dm)
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assert len(bst.get_fscore()) == 71
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ax = xgb.plot_importance(bst)
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assert ax.get_xlim() == (0., 11.)
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assert ax.get_ylim() == (-1., 71.)
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ax = xgb.plot_importance(bst, xlim=(0, 5), ylim=(10, 71))
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assert ax.get_xlim() == (0., 5.)
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assert ax.get_ylim() == (10., 71.)
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def test_sklearn_api(self):
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from sklearn import datasets
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from sklearn.cross_validation import train_test_split
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np.random.seed(1)
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iris = datasets.load_iris()
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tr_d, te_d, tr_l, te_l = train_test_split(iris.data, iris.target, train_size=120)
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classifier = xgb.XGBClassifier()
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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]) / len(te_l)
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# error must be smaller than 10%
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assert err < 0.1
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def test_sklearn_plotting(self):
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from sklearn import datasets
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iris = datasets.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 Digraph
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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, Digraph)
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ax = xgb.plot_tree(classifier, num_trees=0)
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assert isinstance(ax, Axes)
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