| #------------------------------------------------------------- |
| # |
| # 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. |
| # |
| #------------------------------------------------------------- |
| |
| # impute the data by mean value and if the feature is categorical then by mode value |
| # Related to [SYSTEMDS-2662] dependency function for cleaning pipelines |
| # |
| # INPUT: |
| # ------------------------------------------------------------------------------------- |
| # X Data Matrix (Recoded Matrix for categorical features) |
| # mask A 0/1 row vector for identifying numeric (0) and categorical features (1) |
| # ------------------------------------------------------------------------------------- |
| # |
| # OUTPUT: |
| # ----------------------------------------------------------------------------------- |
| # X imputed dataset |
| # ----------------------------------------------------------------------------------- |
| |
| m_imputeByMean = function(Matrix[Double] X, Matrix[Double] mask) |
| return(Matrix[Double] X, Matrix[Double] imputedVec) |
| { |
| |
| # mean imputation |
| colMean = matrix(0, rows=1, cols=ncol(X)) |
| parfor(i in 1:ncol(X)) |
| { |
| if(as.scalar(mask[1, i]) == 0) |
| { |
| nX = removeEmpty(target=X[, i], margin="rows", select = (is.na(X[, i]) == 0)) |
| colMean[1, i] = mean(nX) |
| } |
| } |
| |
| if(sum(mask) > 0) |
| { |
| # mode imputation |
| cX = X*mask |
| [X_c, colMode] = imputeByMode(cX) |
| imputedVec = colMean + colMode |
| } |
| else |
| { |
| imputedVec = colMean |
| } |
| X = imputeByMeanApply(X, imputedVec) |
| } |