Add sample_generator.py and update samples to via generator.
Reviewed in: http://codereview.appspot.com/4449062/
diff --git a/samples/prediction/prediction.py b/samples/prediction/prediction.py
new file mode 100644
index 0000000..24fdf19
--- /dev/null
+++ b/samples/prediction/prediction.py
@@ -0,0 +1,130 @@
+#!/usr/bin/python2.4
+# -*- coding: utf-8 -*-
+#
+# Copyright (C) 2010 Google Inc.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""Simple command-line sample for the Google Prediction API
+
+Command-line application that trains on some data. This sample does
+the same thing as the Hello Prediction! example.
+
+Usage:
+ $ python prediction.py
+
+You can also get help on all the command-line flags the program understands
+by running:
+
+ $ python prediction.py --help
+
+To get detailed log output run:
+
+ $ python prediction.py --logging_level=DEBUG
+"""
+
+__author__ = 'jcgregorio@google.com (Joe Gregorio)'
+
+import gflags
+import httplib2
+import logging
+import pprint
+import sys
+
+from apiclient.discovery import build
+from oauth2client.file import Storage
+from oauth2client.client import OAuth2WebServerFlow
+from oauth2client.tools import run
+
+FLAGS = gflags.FLAGS
+
+# Set up a Flow object to be used if we need to authenticate. This
+# sample uses OAuth 2.0, and we set up the OAuth2WebServerFlow with
+# the information it needs to authenticate. Note that it is called
+# the Web Server Flow, but it can also handle the flow for native
+# applications <http://code.google.com/apis/accounts/docs/OAuth2.html#IA>
+# The client_id client_secret are copied from the API Access tab on
+# the Google APIs Console <http://code.google.com/apis/console>. When
+# creating credentials for this application be sure to choose an Application
+# type of "Installed application".
+FLOW = OAuth2WebServerFlow(
+ client_id='433807057907.apps.googleusercontent.com',
+ client_secret='jigtZpMApkRxncxikFpR+SFg',
+ scope='https://www.googleapis.com/auth/prediction',
+ user_agent='prediction-cmdline-sample/1.0')
+
+# The gflags module makes defining command-line options easy for
+# applications. Run this program with the '--help' argument to see
+# all the flags that it understands.
+gflags.DEFINE_enum('logging_level', 'ERROR',
+ ['DEBUG', 'INFO', 'WARNING', 'ERROR', 'CRITICAL'],
+ 'Set the level of logging detail.')
+
+
+def main(argv):
+ # Let the gflags module process the command-line arguments
+ try:
+ argv = FLAGS(argv)
+ except gflags.FlagsError, e:
+ print '%s\\nUsage: %s ARGS\\n%s' % (e, argv[0], FLAGS)
+ sys.exit(1)
+
+ # Set the logging according to the command-line flag
+ logging.getLogger().setLevel(getattr(logging, FLAGS.logging_level))
+
+ # If the Credentials don't exist or are invalid run through the native client
+ # flow. The Storage object will ensure that if successful the good
+ # Credentials will get written back to a file.
+ storage = Storage('prediction.dat')
+ credentials = storage.get()
+ if credentials is None or credentials.invalid:
+ credentials = run(FLOW, storage)
+
+ # Create an httplib2.Http object to handle our HTTP requests and authorize it
+ # with our good Credentials.
+ http = httplib2.Http()
+ http = credentials.authorize(http)
+
+ service = build("prediction", "v1.2", http=http)
+
+ # Name of Google Storage bucket/object that contains the training data
+ OBJECT_NAME = "apiclient-prediction-sample/prediction_models/languages"
+
+ # Start training on a data set
+ train = service.training()
+ start = train.insert(data=OBJECT_NAME, body={}).execute()
+
+ print 'Started training'
+ pprint.pprint(start)
+
+ import time
+ # Wait for the training to complete
+ while True:
+ status = train.get(data=OBJECT_NAME).execute()
+ pprint.pprint(status)
+ if 'RUNNING' != status['trainingStatus']:
+ break
+ print 'Waiting for training to complete.'
+ time.sleep(10)
+ print 'Training is complete'
+
+ # Now make a prediction using that training
+ body = {'input': {'csvInstance': ["mucho bueno"]}}
+ prediction = service.predict(body=body, data=OBJECT_NAME).execute()
+ print 'The prediction is:'
+ pprint.pprint(prediction)
+
+
+
+if __name__ == '__main__':
+ main(sys.argv)