googleapis
Version:
Google APIs Client Library for Node.js
232 lines (231 loc) • 8.45 kB
TypeScript
/**
* Copyright 2015 Google Inc. All Rights Reserved.
*
* 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.
*/
import { AxiosPromise } from 'axios';
import { GoogleApis } from '../..';
import { BodyResponseCallback, GlobalOptions, MethodOptions } from '../../lib/api';
/**
* Prediction API
*
* Lets you access a cloud hosted machine learning service that makes it easy to
* build smart apps
*
* @example
* const google = require('googleapis');
* const prediction = google.prediction('v1.4');
*
* @namespace prediction
* @type {Function}
* @version v1.4
* @variation v1.4
* @param {object=} options Options for Prediction
*/
export declare class Prediction {
_options: GlobalOptions;
google: GoogleApis;
root: this;
hostedmodels: Resource$Hostedmodels;
trainedmodels: Resource$Trainedmodels;
constructor(options: GlobalOptions, google: GoogleApis);
getRoot(): this;
}
export interface Schema$Input {
/**
* Input to the model for a prediction
*/
input: any;
}
export interface Schema$Output {
/**
* The unique name for the predictive model.
*/
id: string;
/**
* What kind of resource this is.
*/
kind: string;
/**
* The most likely class label [Categorical models only].
*/
outputLabel: string;
/**
* A list of class labels with their estimated probabilities [Categorical
* models only].
*/
outputMulti: any[];
/**
* The estimated regression value [Regression models only].
*/
outputValue: number;
/**
* A URL to re-request this resource.
*/
selfLink: string;
}
export interface Schema$Training {
/**
* Data Analysis.
*/
dataAnalysis: any;
/**
* The unique name for the predictive model.
*/
id: string;
/**
* What kind of resource this is.
*/
kind: string;
/**
* Model metadata.
*/
modelInfo: any;
/**
* A URL to re-request this resource.
*/
selfLink: string;
/**
* Google storage location of the training data file.
*/
storageDataLocation: string;
/**
* Google storage location of the preprocessing pmml file.
*/
storagePMMLLocation: string;
/**
* Google storage location of the pmml model file.
*/
storagePMMLModelLocation: string;
/**
* The current status of the training job. This can be one of following:
* RUNNING; DONE; ERROR; ERROR: TRAINING JOB NOT FOUND
*/
trainingStatus: string;
/**
* A class weighting function, which allows the importance weights for class
* labels to be specified [Categorical models only].
*/
utility: any[];
}
export interface Schema$Update {
/**
* The input features for this instance
*/
csvInstance: any[];
/**
* The class label of this instance
*/
label: string;
/**
* The generic output value - could be regression value or class label
*/
output: string;
}
export declare class Resource$Hostedmodels {
root: Prediction;
constructor(root: Prediction);
getRoot(): Prediction;
/**
* prediction.hostedmodels.predict
* @desc Submit input and request an output against a hosted model.
* @alias prediction.hostedmodels.predict
* @memberOf! ()
*
* @param {object} params Parameters for request
* @param {string} params.hostedModelName The name of a hosted model.
* @param {().Input} params.resource Request body data
* @param {object} [options] Optionally override request options, such as `url`, `method`, and `encoding`.
* @param {callback} callback The callback that handles the response.
* @return {object} Request object
*/
predict(params?: any, options?: MethodOptions): AxiosPromise<Schema$Output>;
predict(params?: any, options?: MethodOptions | BodyResponseCallback<Schema$Output>, callback?: BodyResponseCallback<Schema$Output>): void;
}
export declare class Resource$Trainedmodels {
root: Prediction;
constructor(root: Prediction);
getRoot(): Prediction;
/**
* prediction.trainedmodels.delete
* @desc Delete a trained model.
* @alias prediction.trainedmodels.delete
* @memberOf! ()
*
* @param {object} params Parameters for request
* @param {string} params.id The unique name for the predictive model.
* @param {object} [options] Optionally override request options, such as `url`, `method`, and `encoding`.
* @param {callback} callback The callback that handles the response.
* @return {object} Request object
*/
delete(params?: any, options?: MethodOptions): AxiosPromise<void>;
delete(params?: any, options?: MethodOptions | BodyResponseCallback<void>, callback?: BodyResponseCallback<void>): void;
/**
* prediction.trainedmodels.get
* @desc Check training status of your model.
* @alias prediction.trainedmodels.get
* @memberOf! ()
*
* @param {object} params Parameters for request
* @param {string} params.id The unique name for the predictive model.
* @param {object} [options] Optionally override request options, such as `url`, `method`, and `encoding`.
* @param {callback} callback The callback that handles the response.
* @return {object} Request object
*/
get(params?: any, options?: MethodOptions): AxiosPromise<Schema$Training>;
get(params?: any, options?: MethodOptions | BodyResponseCallback<Schema$Training>, callback?: BodyResponseCallback<Schema$Training>): void;
/**
* prediction.trainedmodels.insert
* @desc Begin training your model.
* @alias prediction.trainedmodels.insert
* @memberOf! ()
*
* @param {object} params Parameters for request
* @param {().Training} params.resource Request body data
* @param {object} [options] Optionally override request options, such as `url`, `method`, and `encoding`.
* @param {callback} callback The callback that handles the response.
* @return {object} Request object
*/
insert(params?: any, options?: MethodOptions): AxiosPromise<Schema$Training>;
insert(params?: any, options?: MethodOptions | BodyResponseCallback<Schema$Training>, callback?: BodyResponseCallback<Schema$Training>): void;
/**
* prediction.trainedmodels.predict
* @desc Submit model id and request a prediction
* @alias prediction.trainedmodels.predict
* @memberOf! ()
*
* @param {object} params Parameters for request
* @param {string} params.id The unique name for the predictive model.
* @param {().Input} params.resource Request body data
* @param {object} [options] Optionally override request options, such as `url`, `method`, and `encoding`.
* @param {callback} callback The callback that handles the response.
* @return {object} Request object
*/
predict(params?: any, options?: MethodOptions): AxiosPromise<Schema$Output>;
predict(params?: any, options?: MethodOptions | BodyResponseCallback<Schema$Output>, callback?: BodyResponseCallback<Schema$Output>): void;
/**
* prediction.trainedmodels.update
* @desc Add new data to a trained model.
* @alias prediction.trainedmodels.update
* @memberOf! ()
*
* @param {object} params Parameters for request
* @param {string} params.id The unique name for the predictive model.
* @param {().Update} params.resource Request body data
* @param {object} [options] Optionally override request options, such as `url`, `method`, and `encoding`.
* @param {callback} callback The callback that handles the response.
* @return {object} Request object
*/
update(params?: any, options?: MethodOptions): AxiosPromise<Schema$Training>;
update(params?: any, options?: MethodOptions | BodyResponseCallback<Schema$Training>, callback?: BodyResponseCallback<Schema$Training>): void;
}