python: use nose for tests

http://nose.readthedocs.io/en/latest/index.html

.travis.yml

@@ -33,26 +33,24 @@ install:
 - conda install --yes atlas numpy scipy scikit-learn pandas matplotlib
 - conda install --yes -c conda-forge boost=1.63.0
 - pip install pep8
+- pip install -r $TRAVIS_BUILD_DIR/python-package/requirements.txt
 
 script:
 - cd $TRAVIS_BUILD_DIR
 - mkdir build && cd build && cmake .. && make
-- cd $TRAVIS_BUILD_DIR/tests/c_api_test && python test.py
 - cd $TRAVIS_BUILD_DIR/python-package && python setup.py install
-- cd $TRAVIS_BUILD_DIR/tests/python_package_test && python test_basic.py && python test_engine.py && python test_sklearn.py && python test_plotting.py
+- nosetests --rednose tests
 - cd $TRAVIS_BUILD_DIR && pep8 --ignore=E501 --exclude=./compute .
 - rm -rf build && mkdir build && cd build && cmake -DUSE_MPI=ON ..&& make
-- cd $TRAVIS_BUILD_DIR/tests/c_api_test && python test.py
 - cd $TRAVIS_BUILD_DIR/python-package && python setup.py install
-- cd $TRAVIS_BUILD_DIR/tests/python_package_test && python test_basic.py && python test_engine.py && python test_sklearn.py && python test_plotting.py
+- nosetests --rednose tests
 - cd $TRAVIS_BUILD_DIR
 - rm -rf build && mkdir build && cd build && cmake -DUSE_GPU=ON -DBOOST_ROOT="$HOME/miniconda/" -DOpenCL_INCLUDE_DIR=$AMDAPPSDK/include/ ..
 - sed -i 's/std::string device_type = "cpu";/std::string device_type = "gpu";/' ../include/LightGBM/config.h
 - make
 - sed -i 's/std::string device_type = "gpu";/std::string device_type = "cpu";/' ../include/LightGBM/config.h
-- cd $TRAVIS_BUILD_DIR/tests/c_api_test && python test.py
 - cd $TRAVIS_BUILD_DIR/python-package && python setup.py install
-- cd $TRAVIS_BUILD_DIR/tests/python_package_test && python test_basic.py && python test_engine.py && python test_sklearn.py && python test_plotting.py
+- nosetests --rednose tests
 
 notifications:
   email: false

python-package/requirements.txt

@@ -0,0 +1,7 @@
+nose
+rednose
+Cython
+numpy
+scipy
+pandas
+scikit-learn

tests/c_api_test/test.py

@@ -3,15 +3,16 @@
 import ctypes
 import os
 
+import nose.tools
 import numpy as np
 from scipy import sparse
 
 
 def LoadDll():
     if os.name == 'nt':
-        lib_path = '../../windows/x64/DLL/lib_lightgbm.dll'
+        lib_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), '../../windows/x64/DLL/lib_lightgbm.dll')
     else:
-        lib_path = '../../lib_lightgbm.so'
+        lib_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), '../../lib_lightgbm.so')
     lib = ctypes.cdll.LoadLibrary(lib_path)
     return lib
 
@@ -34,6 +35,7 @@ def c_str(string):
     return ctypes.c_char_p(string.encode('ascii'))
 
 
+@nose.tools.nottest
 def test_load_from_file(filename, reference):
     ref = None
     if reference is not None:
@@ -52,10 +54,12 @@ def test_load_from_file(filename, reference):
     return handle
 
 
+@nose.tools.nottest
 def test_save_to_binary(handle, filename):
     LIB.LGBM_DatasetSaveBinary(handle, c_str(filename))
 
 
+@nose.tools.nottest
 def test_load_from_csr(filename, reference):
     data = []
     label = []
@@ -93,6 +97,7 @@ def test_load_from_csr(filename, reference):
     return handle
 
 
+@nose.tools.nottest
 def test_load_from_csc(filename, reference):
     data = []
     label = []
@@ -130,6 +135,7 @@ def test_load_from_csc(filename, reference):
     return handle
 
 
+@nose.tools.nottest
 def test_load_from_mat(filename, reference):
     data = []
     label = []
@@ -164,17 +170,18 @@ def test_load_from_mat(filename, reference):
     return handle
 
 
+@nose.tools.nottest
 def test_free_dataset(handle):
     LIB.LGBM_DatasetFree(handle)
 
 
 def test_dataset():
-    train = test_load_from_file('../../examples/binary_classification/binary.train', None)
-    test = test_load_from_mat('../../examples/binary_classification/binary.test', train)
+    train = test_load_from_file(os.path.join(os.path.dirname(os.path.realpath(__file__)), '../../examples/binary_classification/binary.train'), None)
+    test = test_load_from_mat(os.path.join(os.path.dirname(os.path.realpath(__file__)), '../../examples/binary_classification/binary.test'), train)
     test_free_dataset(test)
-    test = test_load_from_csr('../../examples/binary_classification/binary.test', train)
+    test = test_load_from_csr(os.path.join(os.path.dirname(os.path.realpath(__file__)), '../../examples/binary_classification/binary.test'), train)
     test_free_dataset(test)
-    test = test_load_from_csc('../../examples/binary_classification/binary.test', train)
+    test = test_load_from_csc(os.path.join(os.path.dirname(os.path.realpath(__file__)), '../../examples/binary_classification/binary.test'), train)
     test_free_dataset(test)
     test_save_to_binary(train, 'train.binary.bin')
     test_free_dataset(train)
@@ -183,8 +190,8 @@ def test_dataset():
 
 
 def test_booster():
-    train = test_load_from_mat('../../examples/binary_classification/binary.train', None)
-    test = test_load_from_mat('../../examples/binary_classification/binary.test', train)
+    train = test_load_from_mat(os.path.join(os.path.dirname(os.path.realpath(__file__)), '../../examples/binary_classification/binary.train'), None)
+    test = test_load_from_mat(os.path.join(os.path.dirname(os.path.realpath(__file__)), '../../examples/binary_classification/binary.test'), train)
     booster = ctypes.c_void_p()
     LIB.LGBM_BoosterCreate(train, c_str("app=binary metric=auc num_leaves=31 verbose=0"), ctypes.byref(booster))
     LIB.LGBM_BoosterAddValidData(booster, test)
@@ -204,7 +211,7 @@ def test_booster():
     num_total_model = ctypes.c_long()
     LIB.LGBM_BoosterCreateFromModelfile(c_str('model.txt'), ctypes.byref(num_total_model), ctypes.byref(booster2))
     data = []
-    inp = open('../../examples/binary_classification/binary.test', 'r')
+    inp = open(os.path.join(os.path.dirname(os.path.realpath(__file__)), '../../examples/binary_classification/binary.test'), 'r')
     for line in inp.readlines():
         data.append([float(x) for x in line.split('\t')[1:]])
     inp.close()
@@ -223,9 +230,5 @@ def test_booster():
         50,
         ctypes.byref(num_preb),
         preb.ctypes.data_as(ctypes.POINTER(ctypes.c_double)))
-    LIB.LGBM_BoosterPredictForFile(booster2, c_str('../../examples/binary_classification/binary.test'), 0, 0, 50, c_str('preb.txt'))
+    LIB.LGBM_BoosterPredictForFile(booster2, c_str(os.path.join(os.path.dirname(os.path.realpath(__file__)), '../../examples/binary_classification/binary.test')), 0, 0, 50, c_str('preb.txt'))
     LIB.LGBM_BoosterFree(booster2)
-
-
-test_dataset()
-test_booster()

tests/python_package_test/test_basic.py

@@ -1,6 +1,7 @@
 # coding: utf-8
 # pylint: skip-file
 import os
+import subprocess
 import tempfile
 import unittest
 
@@ -51,9 +52,4 @@ def test(self):
             # we need to check the consistency of model file here, so test for exact equal
             self.assertEqual(*preds)
         # check pmml
-        os.system('python ../../pmml/pmml.py model.txt')
-
-
-print("----------------------------------------------------------------------")
-print("running test_basic.py")
-unittest.main()
+        subprocess.call(['python', os.path.join(os.path.dirname(os.path.realpath(__file__)), '../../pmml/pmml.py'), 'model.txt'])

tests/python_package_test/test_engine.py

@@ -194,8 +194,8 @@ def test_cv(self):
         lgb.cv(params, lgb_train, num_boost_round=10, data_splitter=tss, nfold=5,  # test if wrong nfold is ignored
                metrics='l2', verbose_eval=False)
         # lambdarank
-        X_train, y_train = load_svmlight_file('../../examples/lambdarank/rank.train')
-        q_train = np.loadtxt('../../examples/lambdarank/rank.train.query')
+        X_train, y_train = load_svmlight_file(os.path.join(os.path.dirname(os.path.realpath(__file__)), '../../examples/lambdarank/rank.train'))
+        q_train = np.loadtxt(os.path.join(os.path.dirname(os.path.realpath(__file__)), '../../examples/lambdarank/rank.train.query'))
         params_lambdarank = {'objective': 'lambdarank', 'verbose': -1}
         lgb_train = lgb.Dataset(X_train, y_train, group=q_train)
         lgb.cv(params_lambdarank, lgb_train, num_boost_round=10, nfold=3, metrics='l2', verbose_eval=False)
@@ -286,8 +286,3 @@ def test_pandas_categorical(self):
         np.testing.assert_almost_equal(pred0, pred2)
         np.testing.assert_almost_equal(pred0, pred3)
         np.testing.assert_almost_equal(pred0, pred4)
-
-
-print("----------------------------------------------------------------------")
-print("running test_engine.py")
-unittest.main()

tests/python_package_test/test_plotting.py

@@ -104,8 +104,3 @@ def test_plot_metrics(self):
         self.assertEqual(ax2.get_title(), '')
         self.assertEqual(ax2.get_xlabel(), '')
         self.assertEqual(ax2.get_ylabel(), '')
-
-
-print("----------------------------------------------------------------------")
-print("running test_plotting.py")
-unittest.main()

tests/python_package_test/test_sklearn.py

@@ -1,6 +1,7 @@
 # coding: utf-8
 # pylint: skip-file
 import math
+import os
 import unittest
 
 import lightgbm as lgb
@@ -52,10 +53,10 @@ def test_multiclass(self):
         self.assertAlmostEqual(ret, gbm.evals_result['valid_0']['multi_logloss'][-1], places=5)
 
     def test_lambdarank(self):
-        X_train, y_train = load_svmlight_file('../../examples/lambdarank/rank.train')
-        X_test, y_test = load_svmlight_file('../../examples/lambdarank/rank.test')
-        q_train = np.loadtxt('../../examples/lambdarank/rank.train.query')
-        q_test = np.loadtxt('../../examples/lambdarank/rank.test.query')
+        X_train, y_train = load_svmlight_file(os.path.join(os.path.dirname(os.path.realpath(__file__)), '../../examples/lambdarank/rank.train'))
+        X_test, y_test = load_svmlight_file(os.path.join(os.path.dirname(os.path.realpath(__file__)), '../../examples/lambdarank/rank.test'))
+        q_train = np.loadtxt(os.path.join(os.path.dirname(os.path.realpath(__file__)), '../../examples/lambdarank/rank.train.query'))
+        q_test = np.loadtxt(os.path.join(os.path.dirname(os.path.realpath(__file__)), '../../examples/lambdarank/rank.test.query'))
         gbm = lgb.LGBMRanker()
         gbm.fit(X_train, y_train, group=q_train, eval_set=[(X_test, y_test)],
                 eval_group=[q_test], eval_at=[1, 3], verbose=False,
@@ -150,8 +151,3 @@ def test_joblib(self):
         self.assertEqual(len(pred_origin), len(pred_pickle))
         for preds in zip(pred_origin, pred_pickle):
             self.assertAlmostEqual(*preds, places=5)
-
-
-print("----------------------------------------------------------------------")
-print("running test_sklearn.py")
-unittest.main()