Separate dependencies and lightweight test env for Python
This commit is contained in:
terrytangyuan
parent
5f70b4df7a
commit
803a6fe474
@ -15,6 +15,7 @@ env:
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- TASK=r_test
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# python package test
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- TASK=python_test
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- TASK=python_lightweight_test
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# java package test
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- TASK=java_test
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@ -42,7 +42,7 @@ def plot_importance(booster, ax=None, height=0.2,
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-------
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ax : matplotlib Axes
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"""
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# TODO: move this to compat.py
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try:
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import matplotlib.pyplot as plt
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except ImportError:
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@ -3,10 +3,6 @@ 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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@ -102,86 +98,6 @@ class TestBasic(unittest.TestCase):
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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(xgb.core.XGBoostError, xgb.Booster,
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model_file='incorrect_path')
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@ -215,168 +131,8 @@ class TestBasic(unittest.TestCase):
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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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verbose_eval=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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verbose_eval=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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@ -1,37 +0,0 @@
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import xgboost as xgb
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import numpy as np
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from sklearn.datasets import load_digits
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from sklearn.cross_validation import KFold, StratifiedKFold, train_test_split
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from sklearn.metrics import mean_squared_error
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import unittest
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rng = np.random.RandomState(1994)
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class TestCrossValidation(unittest.TestCase):
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def test_cv(self):
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digits = load_digits(3)
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X = digits['data']
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y = digits['target']
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dm = xgb.DMatrix(X, label=y)
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params = {
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'max_depth': 2,
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'eta': 1,
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'silent': 1,
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'objective':
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'multi:softprob',
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'num_class': 3
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}
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seed = 2016
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nfolds = 5
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skf = StratifiedKFold(y, n_folds=nfolds, shuffle=True, random_state=seed)
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import pandas as pd
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cv1 = xgb.cv(params, dm, num_boost_round=10, nfold=nfolds, seed=seed)
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cv2 = xgb.cv(params, dm, num_boost_round=10, folds=skf, seed=seed)
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cv3 = xgb.cv(params, dm, num_boost_round=10, nfold=nfolds, stratified=True, seed=seed)
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assert cv1.shape[0] == cv2.shape[0] and cv2.shape[0] == cv3.shape[0]
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assert cv2.iloc[-1,0] == cv3.iloc[-1,0]
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@ -29,9 +29,6 @@ class TestEarlyStopping(unittest.TestCase):
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eval_set=[(X_test, y_test)])
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assert clf3.best_score == 1
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# TODO: parallel test for early stopping
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# TODO: comment out for now. Will re-visit later
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def evalerror(self, preds, dtrain):
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labels = dtrain.get_label()
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return 'rmse', mean_squared_error(labels, preds)
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65
tests/python/test_plotting.py
Normal file
65
tests/python/test_plotting.py
Normal file
@ -0,0 +1,65 @@
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# -*- 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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from matplotlib.axes import Axes
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from graphviz import Digraph
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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 TestPlotting(unittest.TestCase):
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def test_plotting(self):
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bst2 = xgb.Booster(model_file='xgb.model')
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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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153
tests/python/test_with_pandas.py
Normal file
153
tests/python/test_with_pandas.py
Normal file
@ -0,0 +1,153 @@
|
||||
# -*- coding: utf-8 -*-
|
||||
import numpy as np
|
||||
import xgboost as xgb
|
||||
import unittest
|
||||
import pandas as pd
|
||||
|
||||
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
|
||||
|
||||
# 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
|
||||
|
||||
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.core._maybe_pandas_data(dummies, None, None)
|
||||
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', 'float', 'float', 'float']
|
||||
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
|
||||
|
||||
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.core._maybe_pandas_label, df)
|
||||
|
||||
# label must be supported dtype
|
||||
df = pd.DataFrame({'A': np.array(['a', 'b', 'c'], dtype=object)})
|
||||
self.assertRaises(ValueError, xgb.core._maybe_pandas_label, df)
|
||||
|
||||
df = pd.DataFrame({'A': np.array([1, 2, 3], dtype=int)})
|
||||
result = xgb.core._maybe_pandas_label(df)
|
||||
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_cv_as_pandas(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,
|
||||
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 cv.columns.equals(exp)
|
||||
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 cv.columns.equals(exp)
|
||||
|
||||
params = {'max_depth': 2, 'eta': 1, 'silent': 1, '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, 'silent': 1, '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, 'silent': 1, '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, 'silent': 1, '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, 'silent': 1, '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, 'silent': 1, '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]
|
||||
@ -4,6 +4,7 @@ from sklearn.cross_validation import KFold
|
||||
from sklearn.metrics import mean_squared_error
|
||||
from sklearn.grid_search import GridSearchCV
|
||||
from sklearn.datasets import load_iris, load_digits, load_boston
|
||||
from sklearn.cross_validation import KFold, StratifiedKFold, train_test_split
|
||||
|
||||
rng = np.random.RandomState(1994)
|
||||
|
||||
@ -130,3 +131,65 @@ def test_classification_with_custom_objective():
|
||||
X, y
|
||||
)
|
||||
|
||||
def test_sklearn_api():
|
||||
iris = 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)
|
||||
assert err < 0.2
|
||||
|
||||
def test_sklearn_plotting():
|
||||
iris = 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)
|
||||
|
||||
def test_sklearn_nfolds_cv():
|
||||
digits = load_digits(3)
|
||||
X = digits['data']
|
||||
y = digits['target']
|
||||
dm = xgb.DMatrix(X, label=y)
|
||||
|
||||
params = {
|
||||
'max_depth': 2,
|
||||
'eta': 1,
|
||||
'silent': 1,
|
||||
'objective':
|
||||
'multi:softprob',
|
||||
'num_class': 3
|
||||
}
|
||||
|
||||
seed = 2016
|
||||
nfolds = 5
|
||||
skf = StratifiedKFold(y, n_folds=nfolds, shuffle=True, random_state=seed)
|
||||
|
||||
import pandas as pd
|
||||
cv1 = xgb.cv(params, dm, num_boost_round=10, nfold=nfolds, seed=seed)
|
||||
cv2 = xgb.cv(params, dm, num_boost_round=10, folds=skf, seed=seed)
|
||||
cv3 = xgb.cv(params, dm, num_boost_round=10, nfold=nfolds, stratified=True, seed=seed)
|
||||
assert cv1.shape[0] == cv2.shape[0] and cv2.shape[0] == cv3.shape[0]
|
||||
assert cv2.iloc[-1,0] == cv3.iloc[-1,0]
|
||||
@ -38,6 +38,23 @@ if [ ${TASK} == "python_test" ]; then
|
||||
exit 0
|
||||
fi
|
||||
|
||||
if [ ${TASK} == "python_lightweight_test" ]; then
|
||||
make all || exit -1
|
||||
echo "-------------------------------"
|
||||
source activate python3
|
||||
python --version
|
||||
conda install numpy scipy nose
|
||||
python -m pip install graphviz
|
||||
python -m nose tests/python/test_basic*.py || exit -1
|
||||
source activate python2
|
||||
echo "-------------------------------"
|
||||
python --version
|
||||
conda install numpy scipy nose
|
||||
python -m pip install graphviz
|
||||
python -m nose tests/python/test_basic*.py || exit -1
|
||||
exit 0
|
||||
fi
|
||||
|
||||
if [ ${TASK} == "r_test" ]; then
|
||||
set -e
|
||||
export _R_CHECK_TIMINGS_=0
|
||||
|
||||
@ -10,7 +10,7 @@ if [ ${TASK} == "lint" ]; then
|
||||
fi
|
||||
|
||||
|
||||
if [ ${TASK} == "python_test" ]; then
|
||||
if [ ${TASK} == "python_test" ] || [ ${TASK} == "python_lightweight_test" ]; then
|
||||
# python2
|
||||
if [ ${TRAVIS_OS_NAME} == "osx" ]; then
|
||||
wget -O conda.sh https://repo.continuum.io/miniconda/Miniconda3-latest-MacOSX-x86_64.sh
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user