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#
# 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.
#
library(SparkR)
args <- commandArgs(trailing = TRUE)
if (length(args) != 3) {
print("Usage: logistic_regression <file> <iters> <dimension>")
q("no")
}
# Initialize Spark context
sc <- sparkR.init(appName = "LogisticRegressionR")
iterations <- as.integer(args[[2]])
D <- as.integer(args[[3]])
readPartition <- function(part){
part = strsplit(part, " ", fixed = T)
list(matrix(as.numeric(unlist(part)), ncol = length(part[[1]])))
}
# Read data points and convert each partition to a matrix
points <- cache(lapplyPartition(textFile(sc, args[[1]]), readPartition))
# Initialize w to a random value
w <- runif(n=D, min = -1, max = 1)
cat("Initial w: ", w, "\n")
# Compute logistic regression gradient for a matrix of data points
gradient <- function(partition) {
partition = partition[[1]]
Y <- partition[, 1] # point labels (first column of input file)
X <- partition[, -1] # point coordinates
# For each point (x, y), compute gradient function
dot <- X %*% w
logit <- 1 / (1 + exp(-Y * dot))
grad <- t(X) %*% ((logit - 1) * Y)
list(grad)
}
for (i in 1:iterations) {
cat("On iteration ", i, "\n")
w <- w - reduce(lapplyPartition(points, gradient), "+")
}
cat("Final w: ", w, "\n")
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