| #------------------------------------------------------------- |
| # |
| # Licensed to the Apache Software Foundation (ASF) under one |
| # or more contributor license agreements. See the NOTICE file |
| # distributed with this work for additional information |
| # regarding copyright ownership. The ASF licenses this file |
| # to you 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. |
| # |
| #------------------------------------------------------------- |
| |
| # Implements multinomial naive Bayes classifier with Laplace correction |
| # |
| # Example Usage: |
| # hadoop jar SystemDS.jar -f naive-bayes.dml -nvargs X=<Data> Y=<labels> laplace=<Laplace Correction> prior=<Model file1> conditionals=<Model file2> accuracy=<accuracy file> fmt="text" |
| # |
| |
| # defaults |
| laplaceCorrection = ifdef($laplace, 1) |
| fmt = ifdef($fmt, "text") |
| |
| # reading input args |
| D = read($X) |
| C = read($Y) |
| numRows = nrow(D) |
| numFeatures = ncol(D) |
| minFeatureVal = min(D) |
| numClasses = max(C) |
| minLabelVal = min(C) |
| |
| # sanity checks of data and arguments |
| if(minFeatureVal < 0) |
| stop("Stopping due to invalid argument: Multinomial naive Bayes is meant for count-based feature values, minimum value in X is negative") |
| if(numRows < 2) |
| stop("Stopping due to invalid inputs: Not possible to learn a classifier without at least 2 rows") |
| if(minLabelVal < 1) |
| stop("Stopping due to invalid argument: Label vector (Y) must be recoded") |
| if(numClasses == 1) |
| stop("Stopping due to invalid argument: Maximum label value is 1, need more than one class to learn a multi-class classifier") |
| if(sum(abs(C%%1 == 0)) != numRows) |
| stop("Stopping due to invalid argument: Please ensure that Y contains (positive) integral labels") |
| if(laplaceCorrection < 0) |
| stop("Stopping due to invalid argument: Laplacian correction (laplace) must be non-negative") |
| |
| # Compute conditionals |
| # Compute the feature counts for each class |
| classFeatureCounts = aggregate(target=D, groups=C, fn="sum", ngroups=as.integer(numClasses)) |
| |
| # Compute the total feature count for each class |
| # and add the number of features to this sum |
| # for subsequent regularization (Laplace's rule) |
| classSums = rowSums(classFeatureCounts) + numFeatures*laplaceCorrection |
| |
| # Compute class conditional probabilities |
| classConditionals = (classFeatureCounts + laplaceCorrection) / classSums |
| |
| # Compute class priors |
| classCounts = aggregate(target=C, groups=C, fn="count", ngroups=as.integer(numClasses)) |
| classPrior = classCounts / numRows; |
| |
| # Compute accuracy on training set |
| logProbs = D %*% t(log(classConditionals)) + t(log(classPrior)); |
| acc = sum(rowIndexMax(logProbs) == C) / numRows * 100 |
| |
| acc_str = "Training Accuracy (%): " + acc |
| print(acc_str) |
| accuracyFile = $accuracy |
| if(accuracyFile != " ") { |
| write(acc, accuracyFile) |
| } |
| |
| extraModelParams = as.matrix(numFeatures) |
| classPrior = rbind(classPrior, extraModelParams) |
| |
| # write out the model |
| write(classPrior, $prior, format=fmt); |
| write(classConditionals, $conditionals, format=fmt); |