#lang scribble/manual @(require planet/scribble (for-label racket)) @title{Prediction API v1.5} @margin-note{This documentation has been automatically generated using information supplied by the Google API Discovery service.} Lets you access a cloud hosted machine learning service that makes it easy to build smart apps @hyperlink["https://developers.google.com/prediction/docs/developer-guide" "Google documentation."] @table-of-contents{} @defmodule[gapi/macro] @racket[(require-gapi-doc "prediction.v1.5.js")] @section{API Parameters} The following optional keyword arguments may be passed to @italic{all} functions for this web service: @defproc[(_ [#:alt alt string? 'N/A] [#:fields fields string? 'N/A] [#:key key string? (api-key)] [#:oauth_token oauth_token string? 'N/A] [#:prettyPrint prettyPrint string? 'N/A] [#:quotaUser quotaUser string? 'N/A] [#:userIp userIp string? 'N/A] ) jsexpr?]{ @margin-note{This is not actually a function. This is just using Scribble's defproc form to list the optional keyword arguments that may be passed to @italic{all} functions for this service.} @racket[alt]: Data format for the response. @racket[fields]: Selector specifying which fields to include in a partial response. @racket[key]: API key. Your API key identifies your project and provides you with API access, quota, and reports. Required unless you provide an OAuth 2.0 token. @racket[oauth_token]: OAuth 2.0 token for the current user. @racket[prettyPrint]: Returns response with indentations and line breaks. @racket[quotaUser]: Available to use for quota purposes for server-side applications. Can be any arbitrary string assigned to a user, but should not exceed 40 characters. Overrides userIp if both are provided. @racket[userIp]: IP address of the site where the request originates. Use this if you want to enforce per-user limits. } @section{Resources} @subsection{hostedmodels} @defproc[(prediction-hostedmodels-predict [#:hostedModelName hostedModelName string?] [#:input input string? 'N/A] [#:alt alt string? 'N/A] [#:fields fields string? 'N/A] [#:key key string? (api-key)] [#:oauth_token oauth_token string? 'N/A] [#:prettyPrint prettyPrint string? 'N/A] [#:quotaUser quotaUser string? 'N/A] [#:userIp userIp string? 'N/A] ) jsexpr?]{ Submit input and request an output against a hosted model. @racket[hostedModelName]: The name of a hosted model. @racket[input]: Input to the model for a prediction } @subsection{trainedmodels} @defproc[(prediction-trainedmodels-list [#:maxResults maxResults string? 'N/A] [#:pageToken pageToken string? 'N/A] [#:alt alt string? 'N/A] [#:fields fields string? 'N/A] [#:key key string? (api-key)] [#:oauth_token oauth_token string? 'N/A] [#:prettyPrint prettyPrint string? 'N/A] [#:quotaUser quotaUser string? 'N/A] [#:userIp userIp string? 'N/A] ) jsexpr?]{ List available models. @racket[maxResults]: Maximum number of results to return @racket[pageToken]: Pagination token } @defproc[(prediction-trainedmodels-get [#:id id string?] [#:alt alt string? 'N/A] [#:fields fields string? 'N/A] [#:key key string? (api-key)] [#:oauth_token oauth_token string? 'N/A] [#:prettyPrint prettyPrint string? 'N/A] [#:quotaUser quotaUser string? 'N/A] [#:userIp userIp string? 'N/A] ) jsexpr?]{ Check training status of your model. @racket[id]: The unique name for the predictive model. } @defproc[(prediction-trainedmodels-predict [#:id id string?] [#:input input string? 'N/A] [#:alt alt string? 'N/A] [#:fields fields string? 'N/A] [#:key key string? (api-key)] [#:oauth_token oauth_token string? 'N/A] [#:prettyPrint prettyPrint string? 'N/A] [#:quotaUser quotaUser string? 'N/A] [#:userIp userIp string? 'N/A] ) jsexpr?]{ Submit model id and request a prediction. @racket[id]: The unique name for the predictive model. @racket[input]: Input to the model for a prediction } @defproc[(prediction-trainedmodels-analyze [#:id id string?] [#:alt alt string? 'N/A] [#:fields fields string? 'N/A] [#:key key string? (api-key)] [#:oauth_token oauth_token string? 'N/A] [#:prettyPrint prettyPrint string? 'N/A] [#:quotaUser quotaUser string? 'N/A] [#:userIp userIp string? 'N/A] ) jsexpr?]{ Get analysis of the model and the data the model was trained on. @racket[id]: The unique name for the predictive model. } @defproc[(prediction-trainedmodels-insert [#:id id string? 'N/A] [#:modelInfo modelInfo string? 'N/A] [#:storageDataLocation storageDataLocation string? 'N/A] [#:storagePMMLLocation storagePMMLLocation string? 'N/A] [#:storagePMMLModelLocation storagePMMLModelLocation string? 'N/A] [#:trainingComplete trainingComplete string? 'N/A] [#:trainingStatus trainingStatus string? 'N/A] [#:utility utility string? 'N/A] [#:created created string? 'N/A] [#:kind kind string? 'N/A] [#:selfLink selfLink string? 'N/A] [#:alt alt string? 'N/A] [#:fields fields string? 'N/A] [#:key key string? (api-key)] [#:oauth_token oauth_token string? 'N/A] [#:prettyPrint prettyPrint string? 'N/A] [#:quotaUser quotaUser string? 'N/A] [#:userIp userIp string? 'N/A] ) jsexpr?]{ Begin training your model. @racket[id]: The unique name for the predictive model. @racket[modelInfo]: Model metadata. @racket[storageDataLocation]: Google storage location of the training data file. @racket[storagePMMLLocation]: Google storage location of the preprocessing pmml file. @racket[storagePMMLModelLocation]: Google storage location of the pmml model file. @racket[trainingComplete]: Training completion time (as a RFC 3339 timestamp). @racket[trainingStatus]: The current status of the training job. This can be one of following: RUNNING; DONE; ERROR; ERROR: TRAINING JOB NOT FOUND @racket[utility]: A class weighting function, which allows the importance weights for class labels to be specified [Categorical models only]. @racket[created]: Insert time of the model (as a RFC 3339 timestamp). @racket[kind]: What kind of resource this is. @racket[selfLink]: A URL to re-request this resource. } @defproc[(prediction-trainedmodels-update [#:id id string?] [#:csvInstance csvInstance string? 'N/A] [#:label label string? 'N/A] [#:alt alt string? 'N/A] [#:fields fields string? 'N/A] [#:key key string? (api-key)] [#:oauth_token oauth_token string? 'N/A] [#:prettyPrint prettyPrint string? 'N/A] [#:quotaUser quotaUser string? 'N/A] [#:userIp userIp string? 'N/A] ) jsexpr?]{ Add new data to a trained model. @racket[id]: The unique name for the predictive model. @racket[csvInstance]: The input features for this instance @racket[label]: The class label of this instance } @defproc[(prediction-trainedmodels-delete [#:id id string?] [#:alt alt string? 'N/A] [#:fields fields string? 'N/A] [#:key key string? (api-key)] [#:oauth_token oauth_token string? 'N/A] [#:prettyPrint prettyPrint string? 'N/A] [#:quotaUser quotaUser string? 'N/A] [#:userIp userIp string? 'N/A] ) jsexpr?]{ Delete a trained model. @racket[id]: The unique name for the predictive model. }