/*
* 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 PipelineStage = require(EclairJS_Globals.NAMESPACE + '/ml/PipelineStage');
var Logger = require(EclairJS_Globals.NAMESPACE + '/Logger');
var Utils = require(EclairJS_Globals.NAMESPACE + '/Utils');
/**
* @classdesc
* Model fitted by {@link VectorIndexer}. Transform categorical features to use 0-based indices
* instead of their original values.
* - Categorical features are mapped to indices.
* - Continuous features (columns) are left unchanged.
* This also appends metadata to the output column, marking features as Numeric (continuous),
* Nominal (categorical), or Binary (either continuous or categorical).
* Non-ML metadata is not carried over from the input to the output column.
*
* This maintains vector sparsity.
*
* numFeatures Number of features, i.e., length of Vectors which this transforms
* categoryMaps Feature value index. Keys are categorical feature indices (column indices).
* Values are maps from original features values to 0-based category indices.
* If a feature is not in this map, it is treated as continuous.
* @class
* @extends module:eclairjs/ml.PipelineStage
* @memberof module:eclairjs/ml/feature
*/
var VectorIndexerModel = function (jvmObject) {
this.logger = Logger.getLogger("ml.feature.VectorIndexerModel_js");
PipelineStage.call(this, jvmObject);
};
VectorIndexerModel.prototype = Object.create(PipelineStage.prototype);
VectorIndexerModel.prototype.constructor = VectorIndexerModel;
/**
* An immutable unique ID for the object and its derivatives.
* @returns {string}
*/
VectorIndexerModel.prototype.uid = function () {
return this.getJavaObject().uid();
};
/**
* @returns {integer}
*/
VectorIndexerModel.prototype.numFeatures = function () {
return this.getJavaObject().numFeatures();
};
/**
* @returns {object} Map object<integer,object<float,integer>>
*/
VectorIndexerModel.prototype.categoryMaps = function () {
var javaObject = this.getJavaObject().javaCategoryMaps();
return Utils.javaToJs(javaObject); // java.util.Map
};
/**
* @param {string} value
* @returns {module:eclairjs/ml/feature.VectorIndexerModel}
*/
VectorIndexerModel.prototype.setInputCol = function (value) {
var javaObject = this.getJavaObject().setInputCol(value);
return new VectorIndexerModel(javaObject);
};
/**
* @param {string} value
* @returns {module:eclairjs/ml/feature.VectorIndexerModel}
*/
VectorIndexerModel.prototype.setOutputCol = function (value) {
var javaObject = this.getJavaObject().setOutputCol(value);
return new VectorIndexerModel(javaObject);
};
/**
* @param {module:eclairjs/sql.Dataset} dataset
* @returns {module:eclairjs/sql.Dataset}
*/
VectorIndexerModel.prototype.transform = function (dataset) {
var dataset_uw = Utils.unwrapObject(dataset);
var javaObject = this.getJavaObject().transform(dataset_uw);
return Utils.javaToJs(javaObject);
};
/**
* @param {module:eclairjs/sql/types.StructType} schema
* @returns {module:eclairjs/sql/types.StructType}
*/
VectorIndexerModel.prototype.transformSchema = function (schema) {
var schema_uw = Utils.unwrapObject(schema);
var javaObject = this.getJavaObject().transformSchema(schema_uw);
return Utils.javaToJs(javaObject);
};
/**
* @param {module:eclairjs/ml/param.ParamMap} extra
* @returns {module:eclairjs/ml/feature.VectorIndexerModel}
*/
VectorIndexerModel.prototype.copy = function (extra) {
var extra_uw = Utils.unwrapObject(extra);
var javaObject = this.getJavaObject().copy(extra_uw);
return new VectorIndexerModel(javaObject);
};
/**
* @returns {module:eclairjs/ml/util.MLWriter}
*/
VectorIndexerModel.prototype.write = function () {
var MLWriter = require(EclairJS_Globals.NAMESPACE + '/ml/util/MLWriter');
var javaObject = this.getJavaObject().write();
/*
the object is an inner class so don't use Utils.javaToJs
to create the MLWriter object.
*/
return new MLWriter(javaObject);
};
/**
* @returns {module:eclairjs/ml/param.IntParam}
*/
VectorIndexerModel.prototype.maxCategories = function () {
var javaObject = this.getJavaObject().maxCategories();
return Utils.javaToJs(javaObject);
};
/**
* @returns {integer}
*/
VectorIndexerModel.prototype.getMaxCategories = function () {
return this.getJavaObject().getMaxCategories();
};
//
// static methods
//
/**
* @returns {module:eclairjs/ml/util.MLReader}
*/
VectorIndexerModel.read = function () {
var MLReader = require(EclairJS_Globals.NAMESPACE + '/ml/util/MLReader');
var javaObject = org.apache.spark.ml.feature.VectorIndexerModel.read();
/*
The object is and inner class so don't user Utils.javaToJs
to create th MLReader.
*/
return new MLReader(javaObject);
};
/**
* @param {string} path
* @returns {module:eclairjs/ml/feature.VectorIndexerModel}
*/
VectorIndexerModel.load = function (path) {
var javaObject = org.apache.spark.ml.feature.VectorIndexerModel.load(path);
return new VectorIndexerModel(javaObject);
};
module.exports = VectorIndexerModel;
})();