/*
* Copyright 2016 IBM Corp.
*
* 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.
*/
module.exports = function(kernelP) {
return (function() {
var Utils = require('../../utils.js');
var ClassificationModel = require('./ClassificationModel');
/**
* @classdesc
*
* Model produced by a {@link ProbabilisticClassifier}.
* Classes are indexed {0, 1, ..., numClasses - 1}.
*
* @class
* @memberof module:eclairjs/ml/classification
* @extends module:eclairjs/ml/classification.ClassificationModel
*/
function ProbabilisticClassificationModel(kernelP, refIdP) {
Utils.handleAbstractConstructor(this, arguments);
}
ProbabilisticClassificationModel.prototype = Object.create(ClassificationModel.prototype);
ProbabilisticClassificationModel.prototype.constructor = ProbabilisticClassificationModel;
/**
* @param {string} value
* @returns {module:eclairjs/ml/classification.ProbabilisticClassificationModel}
*/
ProbabilisticClassificationModel.prototype.setProbabilityCol = function(value) {
var args = {
target: this,
method: 'setProbabilityCol',
args: Utils.wrapArguments(arguments),
returnType: ProbabilisticClassificationModel
};
return Utils.generate(args);
};
/**
* @param {number[]} value
* @returns {module:eclairjs/ml/classification.ProbabilisticClassificationModel}
*/
ProbabilisticClassificationModel.prototype.setThresholds = function(value) {
var args = {
target: this,
method: 'setThresholds',
args: Utils.wrapArguments(arguments),
returnType: ProbabilisticClassificationModel
};
return Utils.generate(args);
};
/**
* Transforms dataset by reading from {@link featuresCol}, and appending new columns as specified by
* parameters:
* - predicted labels as [[predictionCol]] of type {@link Double}
* - raw predictions (confidences) as [[rawPredictionCol]] of type {@link Vector}
* - probability of each class as [[probabilityCol]] of type {@link Vector}.
*
* @param {module:eclairjs/sql.Dataset} dataset input dataset
* @returns {module:eclairjs/sql.Dataset} transformed dataset
*/
ProbabilisticClassificationModel.prototype.transform = function(dataset) {
var DataFrane = require('../../sql/Dataset');
var args = {
target: this,
method: 'transform',
args: Utils.wrapArguments(arguments),
returnType: DataFrane
};
return Utils.generate(args);
};
return ProbabilisticClassificationModel;
})();
};