/*
* 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.
*/
(function () {
var Transformer = require(EclairJS_Globals.NAMESPACE + '/ml/Transformer');
var Logger = require(EclairJS_Globals.NAMESPACE + '/Logger');
var Utils = require(EclairJS_Globals.NAMESPACE + '/Utils');
/**
* @classdesc
* A one-hot encoder that maps a column of category indices to a column of binary vectors, with
* at most a single one-value per row that indicates the input category index.
* For example with 5 categories, an input value of 2.0 would map to an output vector of
* `[0.0, 0.0, 1.0, 0.0]`.
* The last category is not included by default (configurable via OneHotEncoder!.dropLast
* because it makes the vector entries sum up to one, and hence linearly dependent.
* So an input value of 4.0 maps to `[0.0, 0.0, 0.0, 0.0]`.
* Note that this is different from scikit-learn's OneHotEncoder, which keeps all categories.
* The output vectors are sparse.
*
* @see {@link module:eclairjs/ml/feature.StringIndexer} for converting categorical values into category indices
* @class
* @extends module:eclairjs/ml.Transformer
* @memberof module:eclairjs/ml/feature
* @param {string} [uid]
*/
var OneHotEncoder = function (uid) {
this.logger = Logger.getLogger("ml_feature_OneHotEncoder_js");
var jvmObject;
if (uid) {
if (uid instanceof org.apache.spark.ml.feature.OneHotEncoder) {
jvmObject = uid;
} else {
jvmObject = new org.apache.spark.ml.feature.OneHotEncoder(uid);
}
} else {
jvmObject = new org.apache.spark.ml.feature.OneHotEncoder();
}
Transformer.call(this, jvmObject);
};
OneHotEncoder.prototype = Object.create(Transformer.prototype);
OneHotEncoder.prototype.constructor = OneHotEncoder;
/**
* An immutable unique ID for the object and its derivatives.
* @returns {string}
*/
OneHotEncoder.prototype.uid = function () {
return this.getJavaObject().uid();
};
/**
* @returns {module:eclairjs/ml/param.BooleanParam}
*/
OneHotEncoder.prototype.dropLast = function () {
var javaObject = this.getJavaObject().dropLast();
return Utils.javaToJs(javaObject);
};
/**
* @param {boolean} value
* @returns {module:eclairjs/ml/feature.OneHotEncoder}
*/
OneHotEncoder.prototype.setDropLast = function (value) {
var javaObject = this.getJavaObject().setDropLast(value);
return new OneHotEncoder(javaObject);
};
/**
* @returns {boolean}
*/
OneHotEncoder.prototype.getDropLast = function() {
return this.getJavaObject().getDropLast();
};
/**
* @param {string} value
* @returns {module:eclairjs/ml/feature.OneHotEncoder}
*/
OneHotEncoder.prototype.setInputCol = function (value) {
var javaObject = this.getJavaObject().setInputCol(value);
return new OneHotEncoder(javaObject);
};
/**
* @param {string} value
* @returns {module:eclairjs/ml/feature.OneHotEncoder}
*/
OneHotEncoder.prototype.setOutputCol = function (value) {
var javaObject = this.getJavaObject().setOutputCol(value);
return new OneHotEncoder(javaObject);
};
/**
* @param {module:eclairjs/ml/param.ParamMap} extra
* @returns {module:eclairjs/ml/feature.OneHotEncoder}
*/
OneHotEncoder.prototype.copy = function (extra) {
var extra_uw = Utils.unwrapObject(extra);
var javaObject = this.getJavaObject().copy(extra_uw);
return new OneHotEncoder(javaObject);
};
//
// static methods
//
/**
* @param {string} path
* @returns {module:eclairjs/ml/feature.OneHotEncoder}
*/
OneHotEncoder.load = function (path) {
var javaObject = org.apache.spark.ml.feature.OneHotEncoder.load(path);
return new OneHotEncoder(javaObject);
};
module.exports = OneHotEncoder;
})();