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