/*
* 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 Param = require('../../ml/param/Param')();
var DoubleParam = require('../param/DoubleParam')();
var StructType = require('../../sql/types/StructType')();
var gKernelP = kernelP;
/**
* @classdesc
* Naive Bayes Classifiers.
* It supports both Multinomial NB
* {@link http://nlp.stanford.edu/IR-book/html/htmledition/naive-bayes-text-classification-1.html}
* which can handle finitely supported discrete data. For example, by converting documents into
* TF-IDF vectors, it can be used for document classification. By making every vector a
* binary (0/1) data, it can also be used as Bernoulli NB
* {@link http://nlp.stanford.edu/IR-book/html/htmledition/the-bernoulli-model-1.html}.
* The input feature values must be nonnegative.
* @class
* @extends module:eclairjs/ml/classification.ProbabilisticClassifier
* @memberof module:eclairjs/ml/classification
* @param {string} [uid]
*/
function NaiveBayes() {
Utils.handleConstructor(this, arguments, gKernelP);
}
/**
* Set the smoothing parameter.
* Default is 1.0.
* @param {number} value
* @returns {module:eclairjs/mllib/classification.NaiveBayes}
*/
NaiveBayes.prototype.setSmoothing = function(value) {
var args = {
target: this,
method: 'setSmoothing',
args: Utils.wrapArguments(arguments),
returnType: NaiveBayes
};
return Utils.generate(args);
};
/**
* Set the model type using a string (case-sensitive).
* Supported options: "multinomial" and "bernoulli".
* Default is "multinomial"
* @param {string} value
* @returns {module:eclairjs/mllib/classification.NaiveBayes}
*/
NaiveBayes.prototype.setModelType = function(value) {
var args = {
target: this,
method: 'setModelType',
args: Utils.wrapArguments(arguments),
returnType: NaiveBayes
};
return Utils.generate(args);
};
/**
* @param {module:eclairjs/ml/param.ParamMap} extra
* @returns {module:eclairjs/mllib/classification.NaiveBayes}
*/
NaiveBayes.prototype.copy = function(extra) {
var args = {
target: this,
method: 'copy',
args: Utils.wrapArguments(arguments),
returnType: NaiveBayes
};
return Utils.generate(args);
};
/**
* The smoothing parameter. (default = 1.0).
* @returns {module:eclairjs/ml/param.DoubleParam}
*/
NaiveBayes.prototype.smoothing = function () {
var args = {
target: this,
method: 'smoothing',
args: Utils.wrapArguments(arguments),
returnType: DoubleParam
};
return Utils.generate(args);
};
/**
*
* @returns {Promise.<Number>}
*/
NaiveBayes.prototype.getSmoothing = function () {
var args = {
target: this,
method: 'getSmoothing',
args: Utils.wrapArguments(arguments),
returnType: Number
};
return Utils.generate(args);};
/**
* The model type which is a string (case-sensitive). Supported options: "multinomial" and "bernoulli". (default = multinomial)
* @returns {module:eclairjs/ml/param.Param}
*/
NaiveBayes.prototype.modelType = function () {
var args = {
target: this,
method: 'modelType',
args: Utils.wrapArguments(arguments),
returnType: Param
};
return Utils.generate(args);
};
/**
*
* @returns {Promise.<string>}
*/
NaiveBayes.prototype.getModelType = function () {
var args = {
target: this,
method: 'getModelType',
args: Utils.wrapArguments(arguments),
returnType: String
};
return Utils.generate(args);
};
/**
*
* @returns {module:eclairjs/ml/util.MLWriter}
*/
NaiveBayes.prototype.write = function () {
throw "not implemented by ElairJS";
};
/**
* Validates and transforms the input schema with the provided param map.
* @param {module:eclairjs/sql/types.StructType} schema
* @param {boolean} fitting whether this is in fitting
* @param {module:eclairjs/sql/types.DataType} featuresDataType SQL DataType for FeaturesType.
* E.g., {@link module:eclairjs/sql/types.VectorUDT}for vector features
* @returns {module:eclairjs/sql/types.StructType}
*/
NaiveBayes.prototype.validateAndTransformSchema = function (schema, fitting, featuresDataType) {
var args = {
target: this,
method: 'validateAndTransformSchema',
args: Utils.wrapArguments(arguments),
returnType: StructType
};
return Utils.generate(args);
};
/**
* Param for label column name.
* @returns {module:eclairjs/ml/param.Param}
*/
NaiveBayes.prototype.labelCol = function () {
var args = {
target: this,
method: 'labelCol',
args: Utils.wrapArguments(arguments),
returnType: Param
};
return Utils.generate(args);
};
/**
* @returns {Promise.<string>}
*/
NaiveBayes.prototype.getLabelCol = function () {
var args = {
target: this,
method: 'getLabelCol',
args: Utils.wrapArguments(arguments),
returnType: String
};
return Utils.generate(args);
};
/**
* Param for features column name.
* @returns {module:eclairjs/ml/param.Param}
*/
NaiveBayes.prototype.featuresCol = function () {
var args = {
target: this,
method: 'featuresCol',
args: Utils.wrapArguments(arguments),
returnType: Param
};
return Utils.generate(args);
};
/**
* @returns {Promise.<string>}
*/
NaiveBayes.prototype.getFeaturesCol = function () {
var args = {
target: this,
method: 'getFeaturesCol',
args: Utils.wrapArguments(arguments),
returnType: String
};
return Utils.generate(args);
};
/**
* Param for prediction column name.
* @returns {module:eclairjs/ml/param.Param}
*/
NaiveBayes.prototype.predictionCol = function () {
var args = {
target: this,
method: 'predictionCol',
args: Utils.wrapArguments(arguments),
returnType: Param
};
return Utils.generate(args);
};
/**
* @returns {Promise.<string>}
*/
NaiveBayes.prototype.getPredictionCol = function () {
return this.getJavaObject().getPredictionCol();
};
/**
* Fits a model to the input data.
* @param {module:eclairjs/sql.DataFrame} dataset
* @param {module:eclairjs/ml/param.ParamMap} [paramMap] Parameter map.
* These values override any specified in this Estimator's embedded ParamMap.
* @returns {module:eclairjs/ml/classification.LogisticRegressionModel} fitted model
*/
NaiveBayes.prototype.fit = function(dataset, paramMap) {
var NaiveBayesModel = require('./NaiveBayesModel')();
var args = {
target: this,
method: 'fit',
args: Utils.wrapArguments(arguments),
returnType: NaiveBayesModel
};
return Utils.generate(args);
};
//
// static methods
//
/**
* @param {string} path
* @returns {NaiveBayes}
*/
NaiveBayes.load = function(path) {
var args = {
target: NaiveBayes,
method: 'load',
kernelP: gKernelP,
static: true,
args: Utils.wrapArguments(arguments),
returnType: NaiveBayes
};
return Utils.generate(args)
};
NaiveBayes.moduleLocation = '/ml/classification/NaiveBayes';
return NaiveBayes;
})();
};