| #------------------------------------------------------------- |
| # |
| # 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. |
| # |
| #------------------------------------------------------------- |
| |
| # INPUT PARAMETERS: |
| # --------------------------------------------------------------------------------------------- |
| # NAME TYPE DEFAULT MEANING |
| # --------------------------------------------------------------------------------------------- |
| # P Double --- vector of Predictions |
| # Y Double --- vector of Golden standard One Hot Encoded |
| # --------------------------------------------------------------------------------------------- |
| # OUTPUT: |
| # --------------------------------------------------------------------------------------------- |
| # NAME TYPE DEFAULT MEANING |
| # --------------------------------------------------------------------------------------------- |
| # ConfusionSum Double --- The Confusion Matrix Sums of classifications |
| # ConfusionAvg Double --- The Confusion Matrix averages of each true class |
| |
| # Output is like: |
| # True Labels |
| # 1 2 |
| # 1 TP | FP |
| # Predictions ----+---- |
| # 2 FN | TN |
| # |
| # TP = True Positives |
| # FP = False Positives |
| # FN = False Negatives |
| # TN = True Negatives |
| |
| m_confusionMatrix = function(Matrix[Double] P, Matrix[Double] Y) |
| return(Matrix[Double] confusionSum, Matrix[Double] confusionAvg) |
| { |
| if(ncol(P) > 1 | ncol(Y) > 1) |
| stop("CONFUSION MATRIX: Invalid input number of cols should be 1 in both P ["+ncol(P)+"] and Y ["+ncol(Y)+"]") |
| if(nrow(P) != nrow(Y)) |
| stop("CONFUSION MATRIX: The number of rows have to be equal in both P ["+nrow(P)+"] and Y ["+nrow(Y)+"]") |
| if(min(P) < 1 | min(Y) < 1) |
| stop("CONFUSION MATRIX: All Values in P and Y should be abore or equal to 1, min(P):" + min(P) + " min(Y):" + min(Y) ) |
| |
| dim = max(max(Y),max(P)) |
| confusionSum = table(P, Y, dim, dim) |
| # max to avoid devision by 0, in case a colum contain no entries. |
| confusionAvg = confusionSum / max(1,colSums(confusionSum)) |
| } |