57 lines
2.7 KiB
Plaintext
57 lines
2.7 KiB
Plaintext
Data Setup:
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----------
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Before you can run the prediction sample prediction.rb, you must load some csv
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formatted data into Google Storage. You can do this by running setup.sh with a
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bucket/object name of your choice. You must first create the bucket you want to
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use. This can be done with the gsutil function or via the web UI (Storage
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Access) in the Google APIs Console. i.e.:
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# chmod 744 setup.sh
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# ./setup.sh BUCKET/OBJECT
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Note you need gsutil in your path for this to work.
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In the script, you must then modify the datafile string. This must correspond with the
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bucket/object of your dataset (if you are using your own dataset). We have
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provided a setup.sh which will upload some basic sample data. The section is
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near the bottom of the script, under 'FILL IN DATAFILE'
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API setup:
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---------
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We need to allow the application to use your API access. Go to APIs Console
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https://code.google.com/apis/console, and select the project you want, go to API
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Access, and create an OAuth2 client if you have not yet already. You should
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generate a client ID and secret.
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This example will run through the server-side example, where the application
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gets authorization ahead of time, which is the normal use case for Prediction
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API. You can also set it up so the user can grant access.
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First, run the google-api script to generate access and refresh tokens. Ex.
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# cd google-api-ruby-client
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# ruby-1.9.2-p290 bin/google-api oauth-2-login --scope=https://www.googleapis.com/auth/prediction --client-id=NUMBER.apps.googleusercontent.com --client-secret=CLIENT_SECRET
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Fill in your client-id and client-secret from the API Access page. You will
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probably have to set a redirect URI in your client ID
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(ex. http://localhost:12736/). You can do this by hitting 'Edit settings' in the
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API Access / Client ID section, and adding it to Authorized Redirect URIs. Not
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that this has to be exactly the same URI, http://localhost:12736 and
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http://localhost:12736/ are not the same in this case.
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This should pop up a browser window, where you grant access. This will then
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generate a ~/.google-api.yaml file. You have two options here, you can either
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copy the the information directly in your code, or you can store this as a file
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and load it in the sample as a yaml. In this example we do the latter. NOTE: if
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you are loading it as a yaml, ensure you rename/move the file, as the
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~/.google-api.yaml file can get overwritten. The script will work as is if you
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move the .google-api.yaml file to the sample directory.
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This sample currently does not cover some newer features of Prediction API such
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as streaming training, hosted models or class weights. If there are any
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questions or suggestions to improve the script please email us at
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prediction-api-discuss@googlegroups.com.
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