| #------------------------------------------------------------- |
| # |
| # 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. |
| # |
| #------------------------------------------------------------- |
| |
| args<-commandArgs(TRUE) |
| library(mice) |
| library("Matrix") |
| library(dplyr) |
| |
| d <- read.csv(args[1], header=FALSE ) |
| mass <- as.matrix(readMM(paste(args[2], "M.mtx", sep=""))); |
| if(sum(mass) == ncol(d)) |
| { |
| d = d[,3:4] |
| d[] <- lapply(d, factor) |
| d |
| mass = mass[1,3:4] |
| meth = meth= rep("polyreg", ncol(d)) |
| #impute |
| imputeD <- mice(d,where = is.na(d), method = meth, m=3) |
| imputeD |
| R = as.matrix(complete(imputeD,3)) |
| writeMM(as(R, "CsparseMatrix"), paste(args[3], "C", sep="")); |
| } else if (sum(mass) == 0) |
| { |
| imputeD <- mice(d,where = is.na(d), method = "norm.predict", m=3) |
| R = as.matrix(complete(imputeD,3)) |
| writeMM(as(as.matrix(R), "CsparseMatrix"), paste(args[3], "N", sep="")); |
| } else { |
| meth="" |
| for(i in 1: ncol(mass)) { |
| if(as.integer(mass[1,i]) == 1) { |
| d[[names(d)[i]]] = as.factor(d[[names(d)[i]]]); |
| meth = c(meth, "polyreg") |
| } else meth = c(meth, "norm.predict") |
| } |
| |
| meth=meth[-1] |
| # set the prediction matrix |
| pred <- make.predictorMatrix(d) |
| pred = pred * diag(1, ncol(mass)) |
| |
| pred[names(d)[1], names(d)[2]] = 1 |
| pred[names(d)[2], names(d)[1]] = 1 |
| |
| pred[names(d)[1], names(d)[4]] = 1 |
| pred[names(d)[4], names(d)[1]] = 1 |
| |
| pred[names(d)[2], names(d)[4]] = 1 |
| pred[names(d)[4], names(d)[2]] = 1 |
| |
| pred[names(d)[3], names(d)[4]] = 1 |
| pred[names(d)[4], names(d)[3]] = 1 |
| |
| |
| #impute |
| imputeD <- mice(d,where = is.na(d), method = meth, m=3, pred = pred) |
| R = data.frame(complete(imputeD,3)) |
| c = select_if(R, is.factor) |
| # convert factor into numeric before casting to matrix |
| c = sapply(c, function(x) as.numeric(as.character(x))) |
| n = select_if(R, is.numeric) |
| writeMM(as(as.matrix(c), "CsparseMatrix"), paste(args[3], "C", sep="")); |
| writeMM(as(as.matrix(n), "CsparseMatrix"), paste(args[3], "N", sep="")); |
| } |
