[python] add python examples to test && fix dump model examples (#924)

* add python examples to test [test should fail]

* fix example && move some to advanced_example

* fix last line

* refine test python version

.travis.yml

@@ -17,16 +17,13 @@ env:
     - PYTHON_VERSION=3.6
   matrix:
     - TASK=regular
-    - TASK=mpi
+    - TASK=mpi PYTHON_VERSION=2.7
     - TASK=pylint
     - TASK=if-else
-    - TASK=sdist
-    - TASK=bdist
+    - TASK=sdist PYTHON_VERSION=3.4
+    - TASK=bdist PYTHON_VERSION=3.5
     - TASK=gpu METHOD=source
     - TASK=gpu METHOD=pip
-    - TASK=regular PYTHON_VERSION=2.7
-    - TASK=regular PYTHON_VERSION=3.4
-    - TASK=regular PYTHON_VERSION=3.5
 
 matrix:
   exclude:
@@ -34,6 +31,8 @@ matrix:
       env: TASK=gpu METHOD=source
     - os: osx
       env: TASK=gpu METHOD=pip
+    - os: osx
+      env: TASK=if-else
     - os: osx
       env: TASK=pylint
 

.travis/test.sh

@@ -92,3 +92,7 @@ make _lightgbm || exit -1
 
 cd $TRAVIS_BUILD_DIR/python-package && python setup.py install --precompile || exit -1
 cd $TRAVIS_BUILD_DIR && pytest . || exit -1
+
+if [[ ${TASK} == "regular" ]]; then
+    cd $TRAVIS_BUILD_DIR/examples/python-guide && python simple_example.py && python sklearn_example.py && python advanced_example.py || exit -1
+fi

examples/python-guide/README.md

@@ -27,14 +27,15 @@ Examples include:
     - Eval during training 
     - Early stopping
     - Save model to file
-    - Dump model to json format
-    - Feature importances
 - [sklearn_example.py](https://github.com/Microsoft/LightGBM/blob/master/examples/python-guide/sklearn_example.py)
     - Basic train and predict with sklearn interface
     - Feature importances with sklearn interface
 - [advanced_example.py](https://github.com/Microsoft/LightGBM/blob/master/examples/python-guide/advanced_example.py)
     - Set feature names
     - Directly use categorical features without one-hot encoding
+    - Dump model to json format
+    - Get feature importances
+    - Get feature names
     - Load model to predict
     - Dump and load model with pickle
     - Load model file to continue training

examples/python-guide/advanced_example.py

 # coding: utf-8
 # pylint: disable = invalid-name, C0111
+import json
 import lightgbm as lgb
 import pandas as pd
 import numpy as np
@@ -63,6 +64,19 @@ print('7th feature name is:', repr(lgb_train.feature_name[6]))
 # save model to file
 gbm.save_model('model.txt')
 
+# dump model to json (and save to file)
+print('Dump model to JSON...')
+model_json = gbm.dump_model()
+
+with open('model.json', 'w+') as f:
+    json.dump(model_json, f, indent=4)
+
+# feature names
+print('Feature names:', gbm.feature_name())
+
+# feature importances
+print('Feature importances:', list(gbm.feature_importance()))
+
 # load model to predict
 print('Load model to predict')
 bst = lgb.Booster(model_file='model.txt')

examples/python-guide/simple_example.py

@@ -46,17 +46,8 @@ print('Save model...')
 # save model to file
 gbm.save_model('model.txt')
 
-# dump model to json format
-with open('model.json', 'w') as model:
-    model.write(gbm.dump_model(num_iteration=gbm.best_iteration))
-
 print('Start predicting...')
 # predict
 y_pred = gbm.predict(X_test, num_iteration=gbm.best_iteration)
 # eval
 print('The rmse of prediction is:', mean_squared_error(y_test, y_pred) ** 0.5)
-
-print('Feature names:', gbm.feature_name())
-
-# feature importances
-print('Feature importances:', list(gbm.feature_importance()))