Update prediction sample to use sample_tools.
Reviewed in https://codereview.appspot.com/9325044/.
diff --git a/samples/prediction/prediction.py b/samples/prediction/prediction.py
index 76c2e55..9f33dfb 100644
--- a/samples/prediction/prediction.py
+++ b/samples/prediction/prediction.py
@@ -37,104 +37,45 @@
__author__ = ('jcgregorio@google.com (Joe Gregorio), '
'marccohen@google.com (Marc Cohen)')
-import apiclient.errors
-import gflags
-import httplib2
-import logging
+import argparse
import os
import pprint
import sys
import time
-from apiclient.discovery import build
-from oauth2client.file import Storage
-from oauth2client.client import AccessTokenRefreshError
-from oauth2client.client import flow_from_clientsecrets
-from oauth2client.tools import run
+from apiclient import discovery
+from apiclient import sample_tools
+from oauth2client import client
-FLAGS = gflags.FLAGS
-
-# CLIENT_SECRETS, name of a file containing the OAuth 2.0 information for this
-# application, including client_id and client_secret, which are found
-# on the API Access tab on the Google APIs
-# Console <http://code.google.com/apis/console>
-CLIENT_SECRETS = 'samples/prediction/client_secrets.json'
-
-# Helpful message to display in the browser if the CLIENT_SECRETS file
-# is missing.
-MISSING_CLIENT_SECRETS_MESSAGE = """
-WARNING: Please configure OAuth 2.0
-
-To make this sample run you will need to populate the client_secrets.json file
-found at:
-
- %s
-
-with information from the APIs Console <https://code.google.com/apis/console>.
-
-""" % os.path.join(os.path.dirname(__file__), CLIENT_SECRETS)
-
-# Set up a Flow object to be used if we need to authenticate.
-FLOW = flow_from_clientsecrets(CLIENT_SECRETS,
- scope='https://www.googleapis.com/auth/prediction',
- message=MISSING_CLIENT_SECRETS_MESSAGE)
-
-# 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.')
-
-gflags.DEFINE_string('object_name',
- None,
- 'Full Google Storage path of csv data (ex bucket/object)')
-gflags.MarkFlagAsRequired('object_name')
-
-gflags.DEFINE_string('id',
- None,
- 'Model Id of your choosing to name trained model')
-gflags.MarkFlagAsRequired('id')
# Time to wait (in seconds) between successive checks of training status.
SLEEP_TIME = 10
+
+# Declare command-line flags.
+argparser = argparse.ArgumentParser(add_help=False)
+argparser.add_argument('object_name',
+ help='Full Google Storage path of csv data (ex bucket/object)')
+argparser.add_argument('id',
+ help='Model Id of your choosing to name trained model')
+
+
def print_header(line):
'''Format and print header block sized to length of line'''
header_str = '='
header_line = header_str * len(line)
print '\n' + header_line
print line
- print header_line
-
+ print header_line
+
+
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, flags = sample_tools.init(
+ argv, 'prediction', 'v1.5', __doc__, __file__, parents=[argparser],
+ scope='https://www.googleapis.com/auth/prediction')
try:
-
# Get access to the Prediction API.
- service = build("prediction", "v1.5", http=http)
papi = service.trainedmodels()
# List models.
@@ -145,15 +86,15 @@
# Start training request on a data set.
print_header('Submitting model training request')
- body = {'id': FLAGS.id, 'storageDataLocation': FLAGS.object_name}
+ body = {'id': flags.id, 'storageDataLocation': flags.object_name}
start = papi.insert(body=body).execute()
print 'Training results:'
pprint.pprint(start)
-
+
# Wait for the training to complete.
print_header('Waiting for training to complete')
while True:
- status = papi.get(id=FLAGS.id).execute()
+ status = papi.get(id=flags.id).execute()
state = status['trainingStatus']
print 'Training state: ' + state
if state == 'DONE':
@@ -163,7 +104,7 @@
continue
else:
raise Exception('Training Error: ' + state)
-
+
# Job has completed.
print 'Training completed:'
pprint.pprint(status)
@@ -171,25 +112,26 @@
# Describe model.
print_header('Fetching model description')
- result = papi.analyze(id=FLAGS.id).execute()
+ result = papi.analyze(id=flags.id).execute()
print 'Analyze results:'
pprint.pprint(result)
# Make a prediction using the newly trained model.
print_header('Making a prediction')
body = {'input': {'csvInstance': ["mucho bueno"]}}
- result = papi.predict(body=body, id=FLAGS.id).execute()
+ result = papi.predict(body=body, id=flags.id).execute()
print 'Prediction results...'
pprint.pprint(result)
# Delete model.
print_header('Deleting model')
- result = papi.delete(id=FLAGS.id).execute()
+ result = papi.delete(id=flags.id).execute()
print 'Model deleted.'
- except AccessTokenRefreshError:
+ except client.AccessTokenRefreshError:
print ("The credentials have been revoked or expired, please re-run"
"the application to re-authorize")
+
if __name__ == '__main__':
main(sys.argv)