Update Python Sample for Prediction v1.4

Reviewed in http://codereview.appspot.com/5252054/.

Index: samples/prediction/client_secrets.json
===================================================================
new file mode 100644

samples/prediction/client_secrets.json

@@ -0,0 +1,9 @@
+{
+  "web": {
+    "client_id": "[[INSERT CLIENT ID HERE]]",
+    "client_secret": "[[INSERT CLIENT SECRET HERE]]",
+    "redirect_uris": [],
+    "auth_uri": "https://accounts.google.com/o/oauth2/auth",
+    "token_uri": "https://accounts.google.com/o/oauth2/token"
+  }
+}

samples/prediction/number.csv

@@ -0,0 +1,4 @@
+4, 1
+9, 2
+16, 3
+

samples/prediction/number.pmml

@@ -0,0 +1,23 @@
+<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">
+  <Header copyright="Copyright (c) 2011, Google Inc. All rights reserved.">
+    <Application name="Google Prediction API Sample" version="1.4"/>
+  </Header>
+  <DataDictionary numberOfFields="1">
+    <DataField name="X" optype="continuous" dataType="double"/>
+  </DataDictionary>
+  <TransformationDictionary>
+    <DerivedField name="Y1" dataType="double" optype="continuous">
+      <Constant>1.0</Constant>
+    </DerivedField>
+    <DerivedField name="Y2" dataType="double" optype="continuous">
+      <FieldRef field="X"/>
+    </DerivedField>
+    <DerivedField name="Y3" dataType="double" optype="continuous">
+      <Apply function="pow">
+        <FieldRef field="X"/>
+        <Constant>2.0</Constant>
+      </Apply>
+    </DerivedField>
+  </TransformationDictionary>
+</PMML>
+

samples/prediction/prediction_language_id.py

@@ -0,0 +1,167 @@
+#!/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 your input data. This sample does
+the same thing as the Hello Prediction! example. You might want to run
+the setup.sh script to load the sample data to Google Storage.
+
+Usage:
+  $ python prediction_language_id.py --model_id="foo" 
+    --data_file="bucket/object"
+
+You can also get help on all the command-line flags the program understands
+by running:
+
+  $ python prediction_language_id.py --help
+
+To get detailed log output run:
+
+  $ python prediction_language_id.py --logging_level=DEBUG
+"""
+
+__author__ = 'jcgregorio@google.com (Joe Gregorio)'
+
+from apiclient.discovery import build_from_document
+
+import apiclient.errors
+import gflags
+import httplib2
+import logging
+import os
+import pprint
+import sys
+
+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
+
+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 = '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('model_id',
+                     None,
+                     'The unique name for the predictive model (ex foo)')
+
+gflags.DEFINE_string('data_file',
+                     None,
+                     'Full Google Storage path of csv data (ex bucket/object)')
+
+gflags.MarkFlagAsRequired('model_id')
+gflags.MarkFlagAsRequired('data_file')
+
+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", "v1.4", http=http)
+
+  try:
+
+    # Start training on a data set
+    train = service.trainedmodels()
+    body = {'id': FLAGS.model_id, 'storageDataLocation': FLAGS.data_file}
+    start = train.insert(body=body).execute()
+
+    print 'Started training'
+    pprint.pprint(start)
+
+    import time
+    # Wait for the training to complete
+    while True:
+      try:
+        # We check the training job is completed. If it is not it will return
+        # an error code.
+        status = train.get(id=FLAGS.model_id).execute()
+        # Job has completed.
+        pprint.pprint(status)
+        break
+      except apiclient.errors.HttpError as error:
+        # Training job not yet completed.
+        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 = train.predict(body=body, id=FLAGS.model_id).execute()
+    print 'The prediction is:'
+    pprint.pprint(prediction)
+
+
+  except AccessTokenRefreshError:
+    print ("The credentials have been revoked or expired, please re-run"
+      "the application to re-authorize")
+
+if __name__ == '__main__':
+  main(sys.argv)
+

samples/prediction/prediction_number.py

@@ -0,0 +1,175 @@
+#!/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 your input data. This sample does
+the same thing as the Hello Prediction! example. You might want to run
+the setup.sh script to load both the sample data and the pmml file to 
+Google Storage.
+
+Usage:
+  $ python prediction_number.py --model_id="foo" 
+    --data_file="data_bucket/data_object" --pmml_file="pmml_bucket/pmml_object"
+
+You can also get help on all the command-line flags the program understands
+by running:
+
+  $ python prediction_number.py --help
+
+To get detailed log output run:
+
+  $ python prediction_number.py --logging_level=DEBUG
+"""
+
+__author__ = 'jcgregorio@google.com (Joe Gregorio)'
+
+from apiclient.discovery import build_from_document
+
+import apiclient.errors
+import gflags
+import httplib2
+import logging
+import os
+import pprint
+import sys
+
+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
+
+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 = '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('model_id',
+                     None,
+                     'The unique name for the predictive model (ex foo)')
+
+gflags.DEFINE_string('data_file',
+                     None,
+                     'Full Google Storage path of csv data (ex bucket/object)')
+
+gflags.DEFINE_string('pmml_file',
+                     None,
+                     'Full Google Storage path of pmml for '
+                         'preprocessing (ex bucket/object)')
+
+gflags.MarkFlagAsRequired('model_id')
+gflags.MarkFlagAsRequired('data_file')
+gflags.MarkFlagAsRequired('pmml_file')
+
+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", "v1.4", http=http)
+
+  try:
+
+    # Start training on a data set
+    train = service.trainedmodels()
+    body = {'id': FLAGS.model_id, 'storageDataLocation': FLAGS.data_file,
+        'storagePMMLLocation': FLAGS.pmml_file}
+    start = train.insert(body=body).execute()
+
+    print 'Started training'
+    pprint.pprint(start)
+
+    import time
+    # Wait for the training to complete
+    while True:
+      try:
+        # We check the training job is completed. If it is not it will return
+        # an error code.
+        status = train.get(id=FLAGS.model_id).execute()
+        # Job has completed.
+        pprint.pprint(status)
+        break
+      except apiclient.errors.HttpError as error:
+        # Training job not yet completed.
+        print 'Waiting for training to complete.'
+        time.sleep(10)
+
+    print 'Training is complete'
+
+    # Now make a prediction using that training
+    body = {'input': {'csvInstance': [ 5 ]}}
+    prediction = train.predict(body=body, id=FLAGS.model_id).execute()
+    print 'The prediction is:'
+    pprint.pprint(prediction)
+
+
+  except AccessTokenRefreshError:
+    print ("The credentials have been revoked or expired, please re-run"
+      "the application to re-authorize")
+
+if __name__ == '__main__':
+  main(sys.argv)
+

samples/prediction/setup.sh

@@ -4,13 +4,14 @@
 # Author: jcgregorio@google.com (Joe Gregorio)
 #
 # Uploads a training data set to Google Storage to be used by this sample
-# application. 
+# application.
 #
 # Usage:
-# setup.sh bucket/object 
+# setup.sh file_name bucket/object
 #
 # Requirements:
 #   gsutil - a client application for interacting with Google Storage. It
 #     can be downloaded from https://code.google.com/apis/storage/docs/gsutil.html
-OBJECT_NAME=$1
+FILE_NAME=$1
-gsutil cp language_id.txt gs://$OBJECT_NAME
+OBJECT_NAME=$2
+gsutil cp $FILE_NAME gs://$OBJECT_NAME