From 97681d967debaa688e73e0360c0ebef01d76eaa0 Mon Sep 17 00:00:00 2001
From: Adrian Charkiewicz <riraasaa@gmail.com>
Date: Sat, 7 May 2022 04:46:30 +0200
Subject: [PATCH] evaluation appends to csv

---
 Dockerfile             |  1 +
 Jenkinsfile_evaluation |  4 ++--
 evaluation.py          | 14 +++++++++++---
 3 files changed, 14 insertions(+), 5 deletions(-)

diff --git a/Dockerfile b/Dockerfile
index e1d4373..504dd11 100644
--- a/Dockerfile
+++ b/Dockerfile
@@ -13,6 +13,7 @@ RUN pip3 install torch
 RUN pip3 install seaborn
 RUN pip3 install torchvision
 RUN pip3 install sklearn
+RUN pip3 install csv
 # RUN python3 -m pip install kaggle
 RUN python3 -m pip install pandas
 RUN pip3 install matplotlib
diff --git a/Jenkinsfile_evaluation b/Jenkinsfile_evaluation
index 85ef402..b917043 100644
--- a/Jenkinsfile_evaluation
+++ b/Jenkinsfile_evaluation
@@ -37,8 +37,8 @@ pipeline {
                 copyArtifacts projectName: 's444354-create-dataset'
                 copyArtifacts projectName: 's444354-training/$TRAINING_BRANCH'
                 sh 'python3 ./evaluation.py'
-                archiveArtifacts 'TRAIN_winequality-red.csv'
-                sh "ls -la"
+                archiveArtifacts 'eval.csv'
+               
             }
         }
     }
diff --git a/evaluation.py b/evaluation.py
index 75101a5..c9a0009 100644
--- a/evaluation.py
+++ b/evaluation.py
@@ -2,6 +2,7 @@ import pandas as pd
 import re
 from sklearn import metrics
 import numpy as np
+import csv 
 
 f = open("result.txt", "r")
 
@@ -16,6 +17,13 @@ for x in f:
 
     list_result.append(result)
     list_predicted.append(predicted)
-print("MAE: ", metrics.mean_absolute_error(list_result, list_predicted))
-print("MSE: ",metrics.mean_squared_error(list_result, list_predicted))
-print("RMSE: ",np.sqrt(metrics.mean_absolute_error(list_result, list_predicted)))
\ No newline at end of file
+metrics = metrics.mean_absolute_error(list_result, list_predicted),  metrics.mean_squared_error(list_result, list_predicted),np.sqrt(metrics.mean_absolute_error(list_result, list_predicted))
+print("MAE: ", metrics[0])
+print("MSE: ",metrics[1])
+print("RMSE: ",metrics[2])
+
+
+
+with open('eval.csv', 'a', newline='') as f:
+    writer = csv.writer(f)
+    writer.writerow((metrics[0],metrics[1], metrics[2]))