/* * 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 RDD = require('../../rdd/RDD.js'); var gKernelP = kernelP; /** * A class which implements a decision tree learning algorithm for classification and regression. * It supports both continuous and categorical features. * @param strategy The configuration parameters for the tree algorithm which specify the type * of algorithm (classification, regression, etc.), feature type (continuous, * categorical), depth of the tree, quantile calculation strategy, etc. * @classdesc */ /** * @param {module:eclairjs/mllib/tree/configuration.Strategy} strategy * @class * @memberof module:eclairjs/mllib/tree */ function DecisionTree() { Utils.handleConstructor(this, arguments, gKernelP); } /** * Method to train a decision tree model over an RDD * @param {module:eclairjs/rdd.RDD} input Training data: RDD of {@link LabeledPoint} * @returns {module:eclairjs/mllib/tree/model.DecisionTreeModel} DecisionTreeModel that can be used for prediction */ DecisionTree.prototype.run = function(input) { throw "not implemented by ElairJS"; // var args ={ // target: this, // method: 'run', // args: [ // { value: input, type: 'RDD' } // ], // returnType: DecisionTreeModel // // }; // // return Utils.generate(args); }; // // static methods // /** * Method to train a decision tree model. * The method supports binary and multiclass classification and regression. * * Note: Using [[org.apache.spark.mllib.tree.DecisionTree$#trainClassifier]] * and [[org.apache.spark.mllib.tree.DecisionTree$#trainRegressor]] * is recommended to clearly separate classification and regression. * * @param {module:eclairjs/rdd.RDD} input Training dataset: RDD of {@link LabeledPoint}. * For classification, labels should take values {0, 1, ..., numClasses-1}. * For regression, labels are real numbers. * @param {module:eclairjs/mllib/tree/configuration.Strategy} strategy The configuration parameters for the tree algorithm which specify the type * of algorithm (classification, regression, etc.), feature type (continuous, * categorical), depth of the tree, quantile calculation strategy, etc. * @returns {module:eclairjs/mllib/tree/model.DecisionTreeModel} DecisionTreeModel that can be used for prediction */ DecisionTree.train0 = function(input,strategy) { throw "not implemented by ElairJS"; // var args ={ // target: DecisionTree, // method: 'train', // args: [ // { value: input, type: 'RDD' }, // { value: strategy, type: 'Strategy' } // ], // returnType: DecisionTreeModel // // }; // // return Utils.generate(args); }; /** * Method to train a decision tree model. * The method supports binary and multiclass classification and regression. * * Note: Using [[org.apache.spark.mllib.tree.DecisionTree$#trainClassifier]] * and [[org.apache.spark.mllib.tree.DecisionTree$#trainRegressor]] * is recommended to clearly separate classification and regression. * * @param {module:eclairjs/rdd.RDD} input Training dataset: RDD of {@link LabeledPoint}. * For classification, labels should take values {0, 1, ..., numClasses-1}. * For regression, labels are real numbers. * @param {Algo} algo algorithm, classification or regression * @param {Impurity} impurity impurity criterion used for information gain calculation * @param {number} maxDepth Maximum depth of the tree. * E.g., depth 0 means 1 leaf node; depth 1 means 1 internal node + 2 leaf nodes. * @returns {module:eclairjs/mllib/tree/model.DecisionTreeModel} DecisionTreeModel that can be used for prediction */ DecisionTree.train1 = function(input,algo,impurity,maxDepth) { throw "not implemented by ElairJS"; // var args ={ // target: DecisionTree, // method: 'train', // args: [ // { value: input, type: 'RDD' }, // { value: algo, type: 'Algo' }, // { value: impurity, type: 'Impurity' }, // { value: maxDepth, type: 'number' } // ], // returnType: DecisionTreeModel // // }; // // return Utils.generate(args); }; /** * Method to train a decision tree model. * The method supports binary and multiclass classification and regression. * * Note: Using [[org.apache.spark.mllib.tree.DecisionTree$#trainClassifier]] * and [[org.apache.spark.mllib.tree.DecisionTree$#trainRegressor]] * is recommended to clearly separate classification and regression. * * @param {module:eclairjs/rdd.RDD} input Training dataset: RDD of {@link LabeledPoint}. * For classification, labels should take values {0, 1, ..., numClasses-1}. * For regression, labels are real numbers. * @param {Algo} algo algorithm, classification or regression * @param {Impurity} impurity impurity criterion used for information gain calculation * @param {number} maxDepth Maximum depth of the tree. * E.g., depth 0 means 1 leaf node; depth 1 means 1 internal node + 2 leaf nodes. * @param {number} numClasses number of classes for classification. Default value of 2. * @returns {module:eclairjs/mllib/tree/model.DecisionTreeModel} DecisionTreeModel that can be used for prediction */ DecisionTree.train2 = function(input,algo,impurity,maxDepth,numClasses) { throw "not implemented by ElairJS"; // var args ={ // target: DecisionTree, // method: 'train', // args: [ // { value: input, type: 'RDD' }, // { value: algo, type: 'Algo' }, // { value: impurity, type: 'Impurity' }, // { value: maxDepth, type: 'number' }, // { value: numClasses, type: 'number' } // ], // returnType: DecisionTreeModel // // }; // // return Utils.generate(args); }; /** * Method to train a decision tree model. * The method supports binary and multiclass classification and regression. * * Note: Using [[org.apache.spark.mllib.tree.DecisionTree$#trainClassifier]] * and [[org.apache.spark.mllib.tree.DecisionTree$#trainRegressor]] * is recommended to clearly separate classification and regression. * * @param {module:eclairjs/rdd.RDD} input Training dataset: RDD of {@link LabeledPoint}. * For classification, labels should take values {0, 1, ..., numClasses-1}. * For regression, labels are real numbers. * @param {Algo} algo classification or regression * @param {Impurity} impurity criterion used for information gain calculation * @param {number} maxDepth Maximum depth of the tree. * E.g., depth 0 means 1 leaf node; depth 1 means 1 internal node + 2 leaf nodes. * @param {number} numClasses number of classes for classification. Default value of 2. * @param {number} maxBins maximum number of bins used for splitting features * @param {QuantileStrategy} quantileCalculationStrategy algorithm for calculating quantiles * @param {Map} categoricalFeaturesInfo Map storing arity of categorical features. * E.g., an entry (n -> k) indicates that feature n is categorical * with k categories indexed from 0: {0, 1, ..., k-1}. * @returns {module:eclairjs/mllib/tree/model.DecisionTreeModel} DecisionTreeModel that can be used for prediction */ DecisionTree.train3 = function(input,algo,impurity,maxDepth,numClasses,maxBins,quantileCalculationStrategy,categoricalFeaturesInfo) { throw "not implemented by ElairJS"; // var args ={ // target: DecisionTree, // method: 'train', // args: [ // { value: input, type: 'RDD' }, // { value: algo, type: 'Algo' }, // { value: impurity, type: 'Impurity' }, // { value: maxDepth, type: 'number' }, // { value: numClasses, type: 'number' }, // { value: maxBins, type: 'number' }, // { value: quantileCalculationStrategy, type: 'QuantileStrategy' }, // { value: categoricalFeaturesInfo, type: 'Map' } // ], // returnType: DecisionTreeModel // // }; // // return Utils.generate(args); }; /** * Method to train a decision tree model for binary or multiclass classification. * * @param {module:eclairjs/rdd.RDD} input Training dataset: RDD of {@link LabeledPoint}. * Labels should take values {0, 1, ..., numClasses-1}. * @param {number} numClasses number of classes for classification. * @param {object} categoricalFeaturesInfo object name key pair map storing arity of categorical features. * E.g., an entry (n -> k) indicates that feature n is categorical * with k categories indexed from 0: {0, 1, ..., k-1}. * @param {string} impurity Criterion used for information gain calculation. * Supported values: "gini" (recommended) or "entropy". * @param {number} maxDepth Maximum depth of the tree. * E.g., depth 0 means 1 leaf node; depth 1 means 1 internal node + 2 leaf nodes. * (suggested value: 5) * @param {number} maxBins maximum number of bins used for splitting features * (suggested value: 32) * @returns {module:eclairjs/mllib/tree/model.DecisionTreeModel} DecisionTreeModel that can be used for prediction */ DecisionTree.trainClassifier = function(input,numClasses,categoricalFeaturesInfo,impurity,maxDepth,maxBins) { var DecisionTreeModel = require('./model/DecisionTreeModel.js')(this.kernelP); var args = { target: this, method: 'trainClassifier', args: Utils.wrapArguments(arguments), returnType: DecisionTreeModel, kernelP: gKernelP, static: true }; return Utils.generate(args); }; /** * Method to train a decision tree model for regression. * * @param {module:eclairjs/rdd.RDD} input Training dataset: RDD of {@link LabeledPoint}. * Labels are real numbers. * @param {Map} categoricalFeaturesInfo Map storing arity of categorical features. * E.g., an entry (n -> k) indicates that feature n is categorical * with k categories indexed from 0: {0, 1, ..., k-1}. * @param {string} impurity Criterion used for information gain calculation. * Supported values: "variance". * @param {number} maxDepth Maximum depth of the tree. * E.g., depth 0 means 1 leaf node; depth 1 means 1 internal node + 2 leaf nodes. * (suggested value: 5) * @param {number} maxBins maximum number of bins used for splitting features * (suggested value: 32) * @returns {module:eclairjs/mllib/tree/model.DecisionTreeModel} DecisionTreeModel that can be used for prediction */ DecisionTree.trainRegressorwithnumber = function(input,categoricalFeaturesInfo,impurity,maxDepth,maxBins) { throw "not implemented by ElairJS"; // var args ={ // target: DecisionTree, // method: 'trainRegressor', // args: [ // { value: input, type: 'RDD' }, // { value: categoricalFeaturesInfo, type: 'Map' }, // { value: impurity, type: 'string' }, // { value: maxDepth, type: 'number' }, // { value: maxBins, type: 'number' } // ], // returnType: DecisionTreeModel // // }; // // return Utils.generate(args); }; /** * Java-friendly API for [[org.apache.spark.mllib.tree.DecisionTree$#trainRegressor]] * @param {JavaRDD} input * @param {Map} categoricalFeaturesInfo * @param {string} impurity * @param {number} maxDepth * @param {number} maxBins * @returns {module:eclairjs/mllib/tree/model.DecisionTreeModel} */ DecisionTree.trainRegressorwithnumber = function(input,categoricalFeaturesInfo,impurity,maxDepth,maxBins) { throw "not implemented by ElairJS"; // var args ={ // target: DecisionTree, // method: 'trainRegressor', // args: [ // { value: input, type: 'JavaRDD' }, // { value: categoricalFeaturesInfo, type: 'Map' }, // { value: impurity, type: 'string' }, // { value: maxDepth, type: 'number' }, // { value: maxBins, type: 'number' } // ], // returnType: DecisionTreeModel // // }; // // return Utils.generate(args); }; DecisionTree.moduleLocation = '/mllib/tree/DecisionTree'; return DecisionTree; })();};