Update Python Sample for Prediction v1.4
Reviewed in http://codereview.appspot.com/5252054/. Index: samples/prediction/client_secrets.json =================================================================== new file mode 100644
This commit is contained in:
Joe Gregorio
9
samples/prediction/client_secrets.json
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9
samples/prediction/client_secrets.json
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{
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"web": {
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"client_id": "[[INSERT CLIENT ID HERE]]",
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"client_secret": "[[INSERT CLIENT SECRET HERE]]",
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"redirect_uris": [],
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"auth_uri": "https://accounts.google.com/o/oauth2/auth",
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"token_uri": "https://accounts.google.com/o/oauth2/token"
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}
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}
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4
samples/prediction/number.csv
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4
samples/prediction/number.csv
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4, 1
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9, 2
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16, 3
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23
samples/prediction/number.pmml
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23
samples/prediction/number.pmml
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<PMML version="4.0" xsi:schemaLocation="http://www.dmg.org/PMML-4_0 http://www.dmg.org/v4-0/pmml-4-0.xsd" xmlns="http://www.dmg.org/PMML-4_0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">
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<Header copyright="Copyright (c) 2011, Google Inc. All rights reserved.">
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<Application name="Google Prediction API Sample" version="1.4"/>
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</Header>
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<DataDictionary numberOfFields="1">
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<DataField name="X" optype="continuous" dataType="double"/>
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</DataDictionary>
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<TransformationDictionary>
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<DerivedField name="Y1" dataType="double" optype="continuous">
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<Constant>1.0</Constant>
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</DerivedField>
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<DerivedField name="Y2" dataType="double" optype="continuous">
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<FieldRef field="X"/>
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</DerivedField>
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<DerivedField name="Y3" dataType="double" optype="continuous">
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<Apply function="pow">
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<FieldRef field="X"/>
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<Constant>2.0</Constant>
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</Apply>
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</DerivedField>
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</TransformationDictionary>
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</PMML>
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167
samples/prediction/prediction_language_id.py
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167
samples/prediction/prediction_language_id.py
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@@ -0,0 +1,167 @@
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#!/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 your input data. This sample does
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the same thing as the Hello Prediction! example. You might want to run
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the setup.sh script to load the sample data to Google Storage.
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Usage:
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$ python prediction_language_id.py --model_id="foo"
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--data_file="bucket/object"
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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_language_id.py --help
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To get detailed log output run:
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$ python prediction_language_id.py --logging_level=DEBUG
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"""
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__author__ = 'jcgregorio@google.com (Joe Gregorio)'
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from apiclient.discovery import build_from_document
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import apiclient.errors
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import gflags
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import httplib2
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import logging
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import os
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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 flow_from_clientsecrets
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from oauth2client.tools import run
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FLAGS = gflags.FLAGS
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# CLIENT_SECRETS, name of a file containing the OAuth 2.0 information for this
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# application, including client_id and client_secret, which are found
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# on the API Access tab on the Google APIs
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# Console <http://code.google.com/apis/console>
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CLIENT_SECRETS = 'client_secrets.json'
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# Helpful message to display in the browser if the CLIENT_SECRETS file
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# is missing.
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MISSING_CLIENT_SECRETS_MESSAGE = """
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WARNING: Please configure OAuth 2.0
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To make this sample run you will need to populate the client_secrets.json file
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found at:
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%s
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with information from the APIs Console <https://code.google.com/apis/console>.
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""" % os.path.join(os.path.dirname(__file__), CLIENT_SECRETS)
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# Set up a Flow object to be used if we need to authenticate.
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FLOW = flow_from_clientsecrets(CLIENT_SECRETS,
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scope='https://www.googleapis.com/auth/prediction',
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message=MISSING_CLIENT_SECRETS_MESSAGE)
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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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gflags.DEFINE_string('model_id',
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None,
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'The unique name for the predictive model (ex foo)')
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gflags.DEFINE_string('data_file',
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None,
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'Full Google Storage path of csv data (ex bucket/object)')
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gflags.MarkFlagAsRequired('model_id')
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gflags.MarkFlagAsRequired('data_file')
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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", "v1.4", http=http)
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try:
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# Start training on a data set
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train = service.trainedmodels()
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body = {'id': FLAGS.model_id, 'storageDataLocation': FLAGS.data_file}
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start = train.insert(body=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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try:
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# We check the training job is completed. If it is not it will return
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# an error code.
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status = train.get(id=FLAGS.model_id).execute()
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# Job has completed.
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pprint.pprint(status)
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break
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except apiclient.errors.HttpError as error:
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# Training job not yet completed.
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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 = train.predict(body=body, id=FLAGS.model_id).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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175
samples/prediction/prediction_number.py
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175
samples/prediction/prediction_number.py
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@@ -0,0 +1,175 @@
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#!/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 your input data. This sample does
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the same thing as the Hello Prediction! example. You might want to run
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the setup.sh script to load both the sample data and the pmml file to
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Google Storage.
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Usage:
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$ python prediction_number.py --model_id="foo"
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--data_file="data_bucket/data_object" --pmml_file="pmml_bucket/pmml_object"
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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_number.py --help
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To get detailed log output run:
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$ python prediction_number.py --logging_level=DEBUG
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"""
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__author__ = 'jcgregorio@google.com (Joe Gregorio)'
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from apiclient.discovery import build_from_document
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import apiclient.errors
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import gflags
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import httplib2
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import logging
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import os
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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 flow_from_clientsecrets
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from oauth2client.tools import run
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FLAGS = gflags.FLAGS
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# CLIENT_SECRETS, name of a file containing the OAuth 2.0 information for this
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# application, including client_id and client_secret, which are found
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# on the API Access tab on the Google APIs
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# Console <http://code.google.com/apis/console>
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CLIENT_SECRETS = 'client_secrets.json'
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# Helpful message to display in the browser if the CLIENT_SECRETS file
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# is missing.
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MISSING_CLIENT_SECRETS_MESSAGE = """
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WARNING: Please configure OAuth 2.0
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To make this sample run you will need to populate the client_secrets.json file
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found at:
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%s
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with information from the APIs Console <https://code.google.com/apis/console>.
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""" % os.path.join(os.path.dirname(__file__), CLIENT_SECRETS)
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# Set up a Flow object to be used if we need to authenticate.
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FLOW = flow_from_clientsecrets(CLIENT_SECRETS,
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scope='https://www.googleapis.com/auth/prediction',
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message=MISSING_CLIENT_SECRETS_MESSAGE)
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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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gflags.DEFINE_string('model_id',
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None,
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'The unique name for the predictive model (ex foo)')
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gflags.DEFINE_string('data_file',
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None,
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'Full Google Storage path of csv data (ex bucket/object)')
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gflags.DEFINE_string('pmml_file',
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None,
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'Full Google Storage path of pmml for '
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'preprocessing (ex bucket/object)')
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gflags.MarkFlagAsRequired('model_id')
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gflags.MarkFlagAsRequired('data_file')
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gflags.MarkFlagAsRequired('pmml_file')
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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", "v1.4", http=http)
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try:
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# Start training on a data set
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train = service.trainedmodels()
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body = {'id': FLAGS.model_id, 'storageDataLocation': FLAGS.data_file,
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'storagePMMLLocation': FLAGS.pmml_file}
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start = train.insert(body=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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try:
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# We check the training job is completed. If it is not it will return
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# an error code.
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status = train.get(id=FLAGS.model_id).execute()
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# Job has completed.
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pprint.pprint(status)
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break
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except apiclient.errors.HttpError as error:
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# Training job not yet completed.
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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': [ 5 ]}}
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prediction = train.predict(body=body, id=FLAGS.model_id).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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@@ -7,10 +7,11 @@
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# application.
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#
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# Usage:
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# setup.sh bucket/object
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# setup.sh file_name bucket/object
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#
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# Requirements:
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# gsutil - a client application for interacting with Google Storage. It
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# can be downloaded from https://code.google.com/apis/storage/docs/gsutil.html
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OBJECT_NAME=$1
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gsutil cp language_id.txt gs://$OBJECT_NAME
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FILE_NAME=$1
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OBJECT_NAME=$2
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gsutil cp $FILE_NAME gs://$OBJECT_NAME
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Reference in New Issue
Block a user