# -*- coding: utf-8 -*- import numpy as np import xgboost as xgb import testing as tm import unittest import pytest try: import pandas as pd except ImportError: pass pytestmark = pytest.mark.skipif(**tm.no_pandas()) dpath = 'demo/data/' rng = np.random.RandomState(1994) class TestPandas(unittest.TestCase): def test_pandas(self): 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 np.testing.assert_array_equal(dm.get_label(), np.array([1, 2])) # 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 np.testing.assert_array_equal(dm.get_label(), np.array([1, 2])) 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 df = pd.DataFrame({'A': ['X', 'Y', 'Z'], 'B': [1, 2, 3]}) dummies = pd.get_dummies(df) # B A_X A_Y A_Z # 0 1 1 0 0 # 1 2 0 1 0 # 2 3 0 0 1 result, _, _ = xgb.data._transform_pandas_df(dummies) exp = np.array([[1., 1., 0., 0.], [2., 0., 1., 0.], [3., 0., 0., 1.]]) np.testing.assert_array_equal(result, exp) dm = xgb.DMatrix(dummies) assert dm.feature_names == ['B', 'A_X', 'A_Y', 'A_Z'] assert dm.feature_types == ['int', 'int', 'int', 'int'] assert dm.num_row() == 3 assert dm.num_col() == 4 df = pd.DataFrame({'A=1': [1, 2, 3], 'A=2': [4, 5, 6]}) dm = xgb.DMatrix(df) assert dm.feature_names == ['A=1', 'A=2'] assert dm.feature_types == ['int', 'int'] assert dm.num_row() == 3 assert dm.num_col() == 2 df_int = pd.DataFrame([[1, 1.1], [2, 2.2]], columns=[9, 10]) dm_int = xgb.DMatrix(df_int) df_range = pd.DataFrame([[1, 1.1], [2, 2.2]], columns=range(9, 11, 1)) dm_range = xgb.DMatrix(df_range) assert dm_int.feature_names == ['9', '10'] # assert not "9 " assert dm_int.feature_names == dm_range.feature_names # test MultiIndex as columns df = pd.DataFrame( [ (1, 2, 3, 4, 5, 6), (6, 5, 4, 3, 2, 1) ], columns=pd.MultiIndex.from_tuples(( ('a', 1), ('a', 2), ('a', 3), ('b', 1), ('b', 2), ('b', 3), )) ) dm = xgb.DMatrix(df) assert dm.feature_names == ['a 1', 'a 2', 'a 3', 'b 1', 'b 2', 'b 3'] assert dm.feature_types == ['int', 'int', 'int', 'int', 'int', 'int'] assert dm.num_row() == 2 assert dm.num_col() == 6 def test_pandas_sparse(self): import pandas as pd rows = 100 X = pd.DataFrame( {"A": pd.arrays.SparseArray(np.random.randint(0, 10, size=rows)), "B": pd.arrays.SparseArray(np.random.randn(rows)), "C": pd.arrays.SparseArray(np.random.permutation( [True, False] * (rows // 2)))} ) y = pd.Series(pd.arrays.SparseArray(np.random.randn(rows))) dtrain = xgb.DMatrix(X, y) booster = xgb.train({}, dtrain, num_boost_round=4) predt_sparse = booster.predict(xgb.DMatrix(X)) predt_dense = booster.predict(xgb.DMatrix(X.sparse.to_dense())) np.testing.assert_allclose(predt_sparse, predt_dense) def test_pandas_label(self): # label must be a single column df = pd.DataFrame({'A': ['X', 'Y', 'Z'], 'B': [1, 2, 3]}) self.assertRaises(ValueError, xgb.data._transform_pandas_df, df, None, None, 'label', 'float') # label must be supported dtype df = pd.DataFrame({'A': np.array(['a', 'b', 'c'], dtype=object)}) self.assertRaises(ValueError, xgb.data._transform_pandas_df, df, None, None, 'label', 'float') df = pd.DataFrame({'A': np.array([1, 2, 3], dtype=int)}) result, _, _ = xgb.data._transform_pandas_df(df, None, None, 'label', 'float') np.testing.assert_array_equal(result, np.array([[1.], [2.], [3.]], dtype=float)) dm = xgb.DMatrix(np.random.randn(3, 2), label=df) assert dm.num_row() == 3 assert dm.num_col() == 2 def test_pandas_weight(self): kRows = 32 kCols = 8 X = np.random.randn(kRows, kCols) y = np.random.randn(kRows) w = np.random.randn(kRows).astype(np.float32) w_pd = pd.DataFrame(w) data = xgb.DMatrix(X, y, w_pd) assert data.num_row() == kRows assert data.num_col() == kCols np.testing.assert_array_equal(data.get_weight(), w) def test_cv_as_pandas(self): dm = xgb.DMatrix(dpath + 'agaricus.txt.train') params = {'max_depth': 2, 'eta': 1, 'verbosity': 0, 'objective': 'binary:logistic'} 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 len(cv.columns.intersection(exp)) == 4 # show progress log (result is the same as above) cv = xgb.cv(params, dm, num_boost_round=10, nfold=10, verbose_eval=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 len(cv.columns.intersection(exp)) == 4 cv = xgb.cv(params, dm, num_boost_round=10, nfold=10, verbose_eval=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 len(cv.columns.intersection(exp)) == 4 params = {'max_depth': 2, 'eta': 1, 'verbosity': 0, 'objective': 'binary:logistic', 'eval_metric': 'auc'} cv = xgb.cv(params, dm, num_boost_round=10, nfold=10, as_pandas=True) assert 'eval_metric' in params assert 'auc' in cv.columns[0] params = {'max_depth': 2, 'eta': 1, 'verbosity': 0, 'objective': 'binary:logistic', 'eval_metric': ['auc']} cv = xgb.cv(params, dm, num_boost_round=10, nfold=10, as_pandas=True) assert 'eval_metric' in params assert 'auc' in cv.columns[0] params = {'max_depth': 2, 'eta': 1, 'verbosity': 0, 'objective': 'binary:logistic', 'eval_metric': ['auc']} cv = xgb.cv(params, dm, num_boost_round=10, nfold=10, as_pandas=True, early_stopping_rounds=1) assert 'eval_metric' in params assert 'auc' in cv.columns[0] assert cv.shape[0] < 10 params = {'max_depth': 2, 'eta': 1, 'verbosity': 0, 'objective': 'binary:logistic'} cv = xgb.cv(params, dm, num_boost_round=10, nfold=10, as_pandas=True, metrics='auc') assert 'auc' in cv.columns[0] params = {'max_depth': 2, 'eta': 1, 'verbosity': 0, 'objective': 'binary:logistic'} cv = xgb.cv(params, dm, num_boost_round=10, nfold=10, as_pandas=True, metrics=['auc']) assert 'auc' in cv.columns[0] params = {'max_depth': 2, 'eta': 1, 'verbosity': 0, 'objective': 'binary:logistic', 'eval_metric': ['auc']} cv = xgb.cv(params, dm, num_boost_round=10, nfold=10, as_pandas=True, metrics='error') assert 'eval_metric' in params assert 'auc' not in cv.columns[0] assert 'error' in cv.columns[0] cv = xgb.cv(params, dm, num_boost_round=10, nfold=10, as_pandas=True, metrics=['error']) assert 'eval_metric' in params assert 'auc' not in cv.columns[0] assert 'error' in cv.columns[0] params = list(params.items()) cv = xgb.cv(params, dm, num_boost_round=10, nfold=10, as_pandas=True, metrics=['error']) assert isinstance(params, list) assert 'auc' not in cv.columns[0] assert 'error' in cv.columns[0]