# -*- coding: utf-8 -*- import numpy as np import xgboost as xgb import unittest import matplotlib matplotlib.use('Agg') dpath = 'demo/data/' rng = np.random.RandomState(1994) class TestBasic(unittest.TestCase): def test_basic(self): dtrain = xgb.DMatrix(dpath + 'agaricus.txt.train') dtest = xgb.DMatrix(dpath + 'agaricus.txt.test') param = {'max_depth':2, 'eta':1, 'silent':1, 'objective':'binary:logistic' } # specify validations set to watch performance watchlist = [(dtest,'eval'), (dtrain,'train')] num_round = 2 bst = xgb.train(param, dtrain, num_round, watchlist) # this is prediction preds = bst.predict(dtest) labels = dtest.get_label() err = sum(1 for i in range(len(preds)) if int(preds[i]>0.5)!=labels[i]) / float(len(preds)) # error must be smaller than 10% assert err < 0.1 # save dmatrix into binary buffer dtest.save_binary('dtest.buffer') # save model bst.save_model('xgb.model') # load model and data in bst2 = xgb.Booster(model_file='xgb.model') dtest2 = xgb.DMatrix('dtest.buffer') preds2 = bst2.predict(dtest2) # assert they are the same assert np.sum(np.abs(preds2-preds)) == 0 def test_dmatrix_init(self): data = np.random.randn(5, 5) # different length self.assertRaises(ValueError, xgb.DMatrix, data, feature_names=list('abcdef')) # contains duplicates self.assertRaises(ValueError, xgb.DMatrix, data, feature_names=['a', 'b', 'c', 'd', 'd']) # contains symbol self.assertRaises(ValueError, xgb.DMatrix, data, feature_names=['a', 'b', 'c', 'd', 'e<1']) dm = xgb.DMatrix(data) dm.feature_names = list('abcde') assert dm.feature_names == list('abcde') dm.feature_types = 'q' assert dm.feature_types == list('qqqqq') dm.feature_types = list('qiqiq') assert dm.feature_types == list('qiqiq') def incorrect_type_set(): dm.feature_types = list('abcde') self.assertRaises(ValueError, incorrect_type_set) # reset dm.feature_names = None assert dm.feature_names is None assert dm.feature_types is None def test_feature_names(self): data = np.random.randn(100, 5) target = np.array([0, 1] * 50) cases = [['Feature1', 'Feature2', 'Feature3', 'Feature4', 'Feature5'], [u'要因1', u'要因2', u'要因3', u'要因4', u'要因5']] for features in cases: dm = xgb.DMatrix(data, label=target, feature_names=features) assert dm.feature_names == features assert dm.num_row() == 100 assert dm.num_col() == 5 params={'objective': 'multi:softprob', 'eval_metric': 'mlogloss', 'eta': 0.3, 'num_class': 3} bst = xgb.train(params, dm, num_boost_round=10) scores = bst.get_fscore() assert list(sorted(k for k in scores)) == features dummy = np.random.randn(5, 5) dm = xgb.DMatrix(dummy, feature_names=features) bst.predict(dm) # different feature name must raises error dm = xgb.DMatrix(dummy, feature_names=list('abcde')) self.assertRaises(ValueError, bst.predict, dm) def test_pandas(self): import pandas as pd df = pd.DataFrame([[1, 2., True], [2, 3., False]], columns=['a', 'b', 'c']) dm = xgb.DMatrix(df, label=pd.Series([1, 2])) assert dm.feature_names == ['a', 'b', 'c'] assert dm.feature_types == ['int', 'float', 'i'] assert dm.num_row() == 2 assert dm.num_col() == 3 # overwrite feature_names and feature_types dm = xgb.DMatrix(df, label=pd.Series([1, 2]), feature_names=['x', 'y', 'z'], feature_types=['q', 'q', 'q']) assert dm.feature_names == ['x', 'y', 'z'] assert dm.feature_types == ['q', 'q', 'q'] assert dm.num_row() == 2 assert dm.num_col() == 3 # incorrect dtypes df = pd.DataFrame([[1, 2., 'x'], [2, 3., 'y']], columns=['a', 'b', 'c']) self.assertRaises(ValueError, xgb.DMatrix, df) # numeric columns df = pd.DataFrame([[1, 2., True], [2, 3., False]]) dm = xgb.DMatrix(df, label=pd.Series([1, 2])) assert dm.feature_names == ['0', '1', '2'] assert dm.feature_types == ['int', 'float', 'i'] assert dm.num_row() == 2 assert dm.num_col() == 3 df = pd.DataFrame([[1, 2., 1], [2, 3., 1]], columns=[4, 5, 6]) dm = xgb.DMatrix(df, label=pd.Series([1, 2])) assert dm.feature_names == ['4', '5', '6'] assert dm.feature_types == ['int', 'float', 'int'] assert dm.num_row() == 2 assert dm.num_col() == 3 def test_load_file_invalid(self): self.assertRaises(ValueError, xgb.Booster, model_file='incorrect_path') self.assertRaises(ValueError, xgb.Booster, model_file=u'不正なパス') def test_dmatrix_numpy_init(self): data = np.random.randn(5, 5) dm = xgb.DMatrix(data) assert dm.num_row() == 5 assert dm.num_col() == 5 data = np.matrix([[1, 2], [3, 4]]) dm = xgb.DMatrix(data) assert dm.num_row() == 2 assert dm.num_col() == 2 # 0d array self.assertRaises(ValueError, xgb.DMatrix, np.array(1)) # 1d array self.assertRaises(ValueError, xgb.DMatrix, np.array([1, 2, 3])) # 3d array data = np.random.randn(5, 5, 5) self.assertRaises(ValueError, xgb.DMatrix, data) # object dtype data = np.array([['a', 'b'], ['c', 'd']]) self.assertRaises(ValueError, xgb.DMatrix, data) def test_cv(self): dm = xgb.DMatrix(dpath + 'agaricus.txt.train') params = {'max_depth':2, 'eta':1, 'silent':1, 'objective':'binary:logistic' } import pandas as pd cv = xgb.cv(params, dm, num_boost_round=10, nfold=10) assert isinstance(cv, pd.DataFrame) exp = pd.Index([u'test-error-mean', u'test-error-std', u'train-error-mean', u'train-error-std']) assert cv.columns.equals(exp) # show progress log (result is the same as above) cv = xgb.cv(params, dm, num_boost_round=10, nfold=10, show_progress=True) assert isinstance(cv, pd.DataFrame) exp = pd.Index([u'test-error-mean', u'test-error-std', u'train-error-mean', u'train-error-std']) assert cv.columns.equals(exp) cv = xgb.cv(params, dm, num_boost_round=10, nfold=10, show_progress=True, show_stdv=False) assert isinstance(cv, pd.DataFrame) exp = pd.Index([u'test-error-mean', u'test-error-std', u'train-error-mean', u'train-error-std']) assert cv.columns.equals(exp) # return np.ndarray cv = xgb.cv(params, dm, num_boost_round=10, nfold=10, as_pandas=False) assert isinstance(cv, np.ndarray) assert cv.shape == (10, 4) def test_plotting(self): bst2 = xgb.Booster(model_file='xgb.model') # plotting from matplotlib.axes import Axes from graphviz import Digraph ax = xgb.plot_importance(bst2) assert isinstance(ax, Axes) assert ax.get_title() == 'Feature importance' assert ax.get_xlabel() == 'F score' assert ax.get_ylabel() == 'Features' assert len(ax.patches) == 4 ax = xgb.plot_importance(bst2, color='r', title='t', xlabel='x', ylabel='y') assert isinstance(ax, Axes) assert ax.get_title() == 't' assert ax.get_xlabel() == 'x' assert ax.get_ylabel() == 'y' assert len(ax.patches) == 4 for p in ax.patches: assert p.get_facecolor() == (1.0, 0, 0, 1.0) # red ax = xgb.plot_importance(bst2, color=['r', 'r', 'b', 'b'], title=None, xlabel=None, ylabel=None) assert isinstance(ax, Axes) assert ax.get_title() == '' assert ax.get_xlabel() == '' assert ax.get_ylabel() == '' assert len(ax.patches) == 4 assert ax.patches[0].get_facecolor() == (1.0, 0, 0, 1.0) # red assert ax.patches[1].get_facecolor() == (1.0, 0, 0, 1.0) # red assert ax.patches[2].get_facecolor() == (0, 0, 1.0, 1.0) # blue assert ax.patches[3].get_facecolor() == (0, 0, 1.0, 1.0) # blue g = xgb.to_graphviz(bst2, num_trees=0) assert isinstance(g, Digraph) ax = xgb.plot_tree(bst2, num_trees=0) assert isinstance(ax, Axes) def test_importance_plot_lim(self): np.random.seed(1) dm = xgb.DMatrix(np.random.randn(100, 100), label=[0, 1]*50) bst = xgb.train({}, dm) assert len(bst.get_fscore()) == 71 ax = xgb.plot_importance(bst) assert ax.get_xlim() == (0., 11.) assert ax.get_ylim() == (-1., 71.) ax = xgb.plot_importance(bst, xlim=(0, 5), ylim=(10, 71)) assert ax.get_xlim() == (0., 5.) assert ax.get_ylim() == (10., 71.) def test_sklearn_api(self): from sklearn import datasets from sklearn.cross_validation import train_test_split np.random.seed(1) iris = datasets.load_iris() tr_d, te_d, tr_l, te_l = train_test_split(iris.data, iris.target, train_size=120) classifier = xgb.XGBClassifier() classifier.fit(tr_d, tr_l) preds = classifier.predict(te_d) labels = te_l err = sum([1 for p, l in zip(preds, labels) if p != l]) / len(te_l) # error must be smaller than 10% assert err < 0.1 def test_sklearn_plotting(self): from sklearn import datasets iris = datasets.load_iris() classifier = xgb.XGBClassifier() classifier.fit(iris.data, iris.target) import matplotlib matplotlib.use('Agg') from matplotlib.axes import Axes from graphviz import Digraph ax = xgb.plot_importance(classifier) assert isinstance(ax, Axes) assert ax.get_title() == 'Feature importance' assert ax.get_xlabel() == 'F score' assert ax.get_ylabel() == 'Features' assert len(ax.patches) == 4 g = xgb.to_graphviz(classifier, num_trees=0) assert isinstance(g, Digraph) ax = xgb.plot_tree(classifier, num_trees=0) assert isinstance(ax, Axes)