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
Normal file
9
samples/prediction/client_secrets.json
Normal file
@@ -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"
|
||||
}
|
||||
}
|
||||
4
samples/prediction/number.csv
Normal file
4
samples/prediction/number.csv
Normal file
@@ -0,0 +1,4 @@
|
||||
4, 1
|
||||
9, 2
|
||||
16, 3
|
||||
|
||||
|
23
samples/prediction/number.pmml
Normal file
23
samples/prediction/number.pmml
Normal file
@@ -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>
|
||||
|
||||
167
samples/prediction/prediction_language_id.py
Normal file
167
samples/prediction/prediction_language_id.py
Normal file
@@ -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)
|
||||
|
||||
175
samples/prediction/prediction_number.py
Normal file
175
samples/prediction/prediction_number.py
Normal file
@@ -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)
|
||||
|
||||
@@ -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
|
||||
|
||||
Reference in New Issue
Block a user