/*
* 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 Estimator = require(EclairJS_Globals.NAMESPACE + '/ml/Estimator');
var Logger = require(EclairJS_Globals.NAMESPACE + '/Logger');
var Utils = require(EclairJS_Globals.NAMESPACE + '/Utils');
/**
* @classdesc
* `QuantileDiscretizer` takes a column with continuous features and outputs a column with binned
* categorical features. The bin ranges are chosen by taking a sample of the data and dividing it
* into roughly equal parts. The lower and upper bin bounds will be -Infinity and +Infinity,
* covering all real values. This attempts to find numBuckets partitions based on a sample of data,
* but it may find fewer depending on the data sample values.
* @class
* @extends module:eclairjs/ml.Estimator
* @memberof module:eclairjs/ml/feature
* @param {string} [uid]
*/
var QuantileDiscretizer = function (uid) {
this.logger = Logger.getLogger("ml_feature_QuantileDiscretizer_js");
var jvmObject;
if (uid) {
if (uid instanceof org.apache.spark.ml.feature.QuantileDiscretizer) {
jvmObject = uid;
} else {
jvmObject = new org.apache.spark.ml.feature.QuantileDiscretizer(uid);
}
} else {
jvmObject = new org.apache.spark.ml.feature.QuantileDiscretizer();
}
Estimator.call(this, jvmObject);
};
QuantileDiscretizer.prototype = Object.create(Estimator.prototype);
QuantileDiscretizer.prototype.constructor = QuantileDiscretizer;
/**
* An immutable unique ID for the object and its derivatives.
* @returns {string}
*/
QuantileDiscretizer.prototype.uid = function () {
return this.getJavaObject().uid();
};
/**
* @param {integer} value
* @returns {module:eclairjs/ml/feature.QuantileDiscretizer}
*/
QuantileDiscretizer.prototype.setNumBuckets = function (value) {
var javaObject = this.getJavaObject().setNumBuckets(value);
return new QuantileDiscretizer(javaObject);
};
/**
* @param {string} value
* @returns {module:eclairjs/ml/feature.QuantileDiscretizer}
*/
QuantileDiscretizer.prototype.setInputCol = function (value) {
var javaObject = this.getJavaObject().setInputCol(value);
return new QuantileDiscretizer(javaObject);
};
/**
* @param {string} value
* @returns {module:eclairjs/ml/feature.QuantileDiscretizer}
*/
QuantileDiscretizer.prototype.setOutputCol = function (value) {
var javaObject = this.getJavaObject().setOutputCol(value);
return new QuantileDiscretizer(javaObject);
};
/**
* @param {module:eclairjs/ml/param.ParamMap} extra
* @returns {module:eclairjs/ml/feature.QuantileDiscretizer}
*/
QuantileDiscretizer.prototype.copy = function (extra) {
var extra_uw = Utils.unwrapObject(extra);
var javaObject = this.getJavaObject().copy(extra_uw);
return new QuantileDiscretizer(javaObject);
};
/**
* Maximum number of buckets (quantiles, or categories) into which data points are grouped. Must be >= 2. default: 2
* @returns {module:eclairjs/ml/param.IntParam}
*/
QuantileDiscretizer.prototype.numBuckets = function () {
var javaObject = this.getJavaObject().numBuckets();
return Utils.javaToJs(javaObject);
};
/**
* @returns {integer}
*/
QuantileDiscretizer.prototype.getNumBuckets = function () {
return this.getJavaObject().getNumBuckets();
};
//
// static methods
//
/**
* @param {string} path
* @returns {module:eclairjs/ml/feature.QuantileDiscretizer}
*/
QuantileDiscretizer.load = function (path) {
var javaObject = org.apache.spark.ml.feature.QuantileDiscretizer.load(path);
return new QuantileDiscretizer(javaObject);
};
module.exports = QuantileDiscretizer;
})();