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Declarative Machine Learning
#-------------------------------------------------------------
#
# 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 SystemML.jar -f naive-bayes.dml -nvargs X= Y= laplace= prior= conditionals= accuracy= 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);