Add Python examples on how to load models (#371)

* Update Python-API.md

* for a better jump in page

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* fixed something mentioned by @wxchan

* Update Python-API.md

* Add examples on how to use the saved model

* move 'load model' examples to advanced_example

examples/python-guide/README.md

@@ -30,6 +30,8 @@ Examples including:
 - [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
+    - Load model to predict
+    - Dump and load model with pickle
     - Load model file to continue training
     - Change learning rates during training
     - Self-defined objective function

examples/python-guide/advanced_example.py

@@ -3,6 +3,12 @@
 import lightgbm as lgb
 import pandas as pd
 import numpy as np
+from sklearn.metrics import mean_squared_error
+
+try:
+    import cPickle as pickle
+except:
+    import pickle
 
 # load or create your dataset
 print('Load data...')
@@ -57,6 +63,25 @@ print('7th feature name is:', repr(lgb_train.feature_name[6]))
 # save model to file
 gbm.save_model('model.txt')
 
+# load model to predict
+print('Load model to predict')
+bst = lgb.Booster(model_file='model.txt')
+# can only predict with the best iteration (or the saving iteration)
+y_pred = bst.predict(X_test)
+# eval with loaded model
+print('The rmse of loaded model\'s prediction is:', mean_squared_error(y_test, y_pred) ** 0.5)
+
+# dump model with pickle
+with open('model.pkl', 'wb') as fout:
+    pickle.dump(gbm, fout)
+# load model with pickle to predict
+with open('model.pkl', 'rb') as fin:
+    pkl_bst = pickle.load(fin)
+# can predict with any iteration when loaded in pickle way
+y_pred = pkl_bst.predict(X_test, num_iteration=7)
+# eval with loaded model
+print('The rmse of pickled model\'s prediction is:', mean_squared_error(y_test, y_pred) ** 0.5)
+
 # continue training
 # init_model accepts:
 # 1. model file name

examples/python-guide/simple_example.py

@@ -5,6 +5,7 @@ import lightgbm as lgb
 import pandas as pd
 from sklearn.metrics import mean_squared_error
 
+
 # load or create your dataset
 print('Load data...')
 df_train = pd.read_csv('../regression/regression.train', header=None, sep='\t')
@@ -58,6 +59,7 @@ model_json = gbm.dump_model()
 with open('model.json', 'w+') as f:
     json.dump(model_json, f, indent=4)
 
+
 print('Feature names:', gbm.feature_name())
 
 print('Calculate feature importances...')