samples/prediction/prediction.py

#!/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)