diff --git a/samples/prediction/client_secrets.json b/samples/prediction/client_secrets.json new file mode 100644 index 0000000..a232f37 --- /dev/null +++ b/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" + } +} diff --git a/samples/prediction/number.csv b/samples/prediction/number.csv new file mode 100644 index 0000000..5f63b61 --- /dev/null +++ b/samples/prediction/number.csv @@ -0,0 +1,4 @@ +4, 1 +9, 2 +16, 3 + diff --git a/samples/prediction/number.pmml b/samples/prediction/number.pmml new file mode 100644 index 0000000..d81e317 --- /dev/null +++ b/samples/prediction/number.pmml @@ -0,0 +1,23 @@ + +
+ +
+ + + + + + 1.0 + + + + + + + + 2.0 + + + + + diff --git a/samples/prediction/prediction_language_id.py b/samples/prediction/prediction_language_id.py new file mode 100644 index 0000000..9657b3c --- /dev/null +++ b/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 +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 . + +""" % 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) + diff --git a/samples/prediction/prediction_number.py b/samples/prediction/prediction_number.py new file mode 100644 index 0000000..536d6c8 --- /dev/null +++ b/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 +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 . + +""" % 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) + diff --git a/samples/prediction/setup.sh b/samples/prediction/setup.sh index 6df40ed..54bc4e6 100755 --- a/samples/prediction/setup.sh +++ b/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=$1 -gsutil cp language_id.txt gs://$OBJECT_NAME +FILE_NAME=$1 +OBJECT_NAME=$2 +gsutil cp $FILE_NAME gs://$OBJECT_NAME