update Dockerfile, jenkinsfile_train and add jenkinsfile_eval, evaluate.py

Biblioteka_DL/evaluate.py

@@ -0,0 +1,212 @@
+import numpy as np
+import sys
+import os
+import torch
+import pandas as pd
+from torch import nn
+from torch.autograd import Variable
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import accuracy_score
+import torch.nn.functional as F
+from datetime import datetime
+from sklearn.metrics import precision_recall_fscore_support
+from sklearn.metrics import accuracy_score
+import matplotlib.pyplot as plt
+
+class Model(nn.Module):
+    def __init__(self, input_dim):
+        super(Model, self).__init__()
+        self.layer1 = nn.Linear(input_dim, 50)
+        self.layer2 = nn.Linear(50, 40)
+        self.layer3 = nn.Linear(40, 3)
+
+    def forward(self, x):
+        x = F.relu(self.layer1(x))
+        x = F.relu(self.layer2(x))
+        x = F.softmax(self.layer3(x))  # To check with the loss function
+        return x
+
+# funkcja usuwająca wiersze zawierające platformę "Stadia"
+def delete_stadia(games):
+    index_list = []
+    for i in range(0, len(games["platform"])):
+        try:
+            if games["platform"][i] == " Stadia":
+                index_list.append(i)
+        except:
+            continue
+    games.drop(index_list, inplace=True)
+    return games.reset_index()
+
+# funkcja usuwająca wiersze zawierające "tbd" w kolumnie "user_review"
+def delete_tbd(games):
+    index_list = []
+    for i in range(0, len(games["platform"])):
+        try:
+            if games["user_review"][i] == 'tbd':
+                index_list.append(i)
+        except:
+            continue
+    games.drop(index_list, inplace=True)
+    return games.reset_index()
+
+def delete_PC(games):
+    index_list = []
+    for i in range(0, len(games["platform"])):
+        try:
+            if games["platform"][i] == " PC":
+                index_list.append(i)
+        except:
+            continue
+    games.drop(index_list, inplace=True)
+    return games.reset_index()
+
+# funkcja zmieniająca kolumnę "user_review" ze stringa na numeric
+def user_review_to_numeric(games):
+    games["user_review"] = pd.to_numeric(games["user_review"])
+    return games
+
+# funkcja normalizująca wartości w kolumnie "meta_score" i "user_review"
+def normalization(games):
+    games['meta_score'] = games['meta_score'] / 100.0
+    games['user_review'] = games['user_review'] / 10.0
+    return games
+
+# old - 0
+# mid - 1
+# new - 2
+
+def platform_to_number(games):
+    for i in range(0, len(games["platform"])):
+
+        if games["platform"][i] == " PlayStation":
+            games["platform"][i] = 0
+        elif games["platform"][i] == " PlayStation 2":
+            games["platform"][i] = 0
+        elif games["platform"][i] == " PlayStation 3":
+            games["platform"][i] = 1
+        elif games["platform"][i] == " PlayStation 4":
+            games["platform"][i] = 2
+        elif games["platform"][i] == " PlayStation 5":
+            games["platform"][i] = 2
+        elif games["platform"][i] == " PlayStation Vita":
+            games["platform"][i] = 1
+        elif games["platform"][i] == " Xbox":
+            games["platform"][i] = 0
+        elif games["platform"][i] == " Xbox 360":
+            games["platform"][i] = 1
+        elif games["platform"][i] == " Xbox Series X":
+            games["platform"][i] = 2
+        elif games["platform"][i] == " Nintendo 64":
+            games["platform"][i] = 0
+        elif games["platform"][i] == " GameCube":
+            games["platform"][i] = 0
+        elif games["platform"][i] == " DS":
+            games["platform"][i] = 0
+        elif games["platform"][i] == " 3DS":
+            games["platform"][i] = 1
+        elif games["platform"][i] == " Wii":
+            games["platform"][i] = 0
+        elif games["platform"][i] == " Wii U":
+            games["platform"][i] = 1
+        elif games["platform"][i] == " Switch":
+            games["platform"][i] = 2
+        elif games["platform"][i] == " PC":
+            dt = datetime.strptime(games["release_date"][i], '%B %d, %Y')
+            if (dt.year == 1995 or dt.year == 1996 or dt.year == 1997 or dt.year == 1998
+                or dt.year == 1999 or dt.year == 2000 or dt.year == 2001 or dt.year == 2002
+                    or dt.year == 2003 or dt.year == 2004 or dt.year == 2005):
+                games["platform"][i] = 0
+            if (dt.year == 2006 or dt.year == 2007 or dt.year == 2008 or dt.year == 2009
+                or dt.year == 2010 or dt.year == 2011 or dt.year == 2012 or dt.year == 2013
+                    or dt.year == 2014 or dt.year == 2015 or dt.year == 2016):
+                games["platform"][i] = 1
+            if (dt.year == 2017 or dt.year == 2018 or dt.year == 2019
+                or dt.year == 2020 or dt.year == 2021):
+                games["platform"][i] = 2
+
+            # games["platform"][i] = 0
+        elif games["platform"][i] == " Dreamcast":
+            games["platform"][i] = 0
+        elif games["platform"][i] == " Game Boy Advance":
+            games["platform"][i] = 0
+        elif games["platform"][i] == " PSP":
+            games["platform"][i] = 1
+        elif games["platform"][i] == " Xbox One":
+            games["platform"][i] = 2
+
+    return games
+
+def remove_list(games):
+    for i in range(0, len(games)):
+        games['platform'][i] = games['platform'][i][0]
+        games['release_date'][i] = games['release_date'][i][0]
+        games['meta_score'][i] = games['meta_score'][i][0]
+        games['user_review'][i] = games['user_review'][i][0]
+    return games
+
+platform = pd.read_csv('all_games.train.csv', sep=',', usecols=[1], header=None).values.tolist()
+release_date = pd.read_csv('all_games.train.csv', sep=',', usecols=[2], header=None).values.tolist()
+meta_score = pd.read_csv('all_games.train.csv', sep=',', usecols=[4], header=None).values.tolist()
+user_review = pd.read_csv('all_games.train.csv', sep=',', usecols=[5], header=None).values.tolist()
+
+games_test = {'platform': platform,
+     'release_date': release_date,
+     'meta_score': meta_score,
+     'user_review': user_review}
+games_test = pd.DataFrame(games_test)
+
+games_test = remove_list(games_test)
+games_test = platform_to_number(games_test)
+games_test = delete_stadia(games_test)
+games_test = delete_tbd(games_test)
+games_test = user_review_to_numeric(games_test)
+games_test = normalization(games_test)
+
+labels_test_g = pd.DataFrame(games_test["platform"], dtype=np.int64)
+labels_test_g = labels_test_g.to_numpy()
+features_test_g = {'meta_score': games_test['meta_score'],
+              'user_review': games_test['user_review']}
+features_test_g = pd.DataFrame(features_test_g, dtype=np.float64)
+features_test_g = features_test_g.to_numpy()
+
+# Training
+model = torch.load("games_model.pkl")
+
+# Prediction
+x_test = Variable(torch.from_numpy(features_test_g)).float()
+pred = model(x_test)
+
+pred = pred.detach().numpy()
+
+accuracy = accuracy_score(labels_test_g, np.argmax(pred, axis=1))
+
+pred = pd.DataFrame(pred)
+
+predicted = []
+expected = []
+
+for i in range(0, len(x_test)):
+    predicted.append(np.argmax(model(x_test[i]).detach().numpy(), axis=0))
+    expected.append(labels_test_g[i])
+
+for i in range(0, len(expected)):
+    expected[i] = expected[i][0]
+
+precision, recall, fscore, support = precision_recall_fscore_support(expected, predicted, average="micro")
+
+res = f"Accuracy: {accuracy}, Precision: {precision}, Recall: {recall}, F-score: {fscore}"
+
+with open('metrics.txt', 'a+') as f:
+    f.write(str(accuracy) + '\n')
+
+with open('metrics.txt') as f:
+    accuracy_val = [float(line) for line in f if line]
+    builds = list(range(1, len(accuracy_val) + 1))
+
+plt.xlabel('Build')
+plt.ylabel('Accuracy')
+plt.plot(builds, accuracy_val, label='Accuracy')
+plt.legend()
+plt.show()
+plt.savefig('metrics.png')

Dockerfile

@@ -29,6 +29,7 @@ WORKDIR /app
 COPY download.sh .
 COPY Zajecia_2/main.py .
 COPY Biblioteka_DL/dllib.py .
+COPY Biblioteka_DL/evaluate.py .
 COPY Biblioteka_DL/all_games.csv .
 
 RUN ./download.sh $CUTOFF

Jenkinsfile_eval

@@ -0,0 +1,46 @@
+pipeline {
+  	agent {
+		docker {
+			image 'maciejczajka'
+		}
+	}
+	parameters {
+	    gitParameter branchFilter: 'origin/(.*)', defaultValue: 'master', name: 'BRANCH', type: 'PT_BRANCH'
+		buildSelector(
+			defaultSelector: lastSuccessful(),
+			description: 'Which build to use for copying artifacts',
+			name: 'BUILD_SELECTOR'
+		)
+	}
+   stages {
+	stage('Script'){
+		steps {
+                        copyArtifacts filter: '*', projectName: 's444356-create-dataset', selector: buildParameter('BUILD_SELECTOR')
+                copyArtifacts filter: '*', projectName: 's444356-training/${BRANCH}', selector: buildParameter('BUILD_SELECTOR')
+                copyArtifacts filter: '*', projectName: 's444356-evaluation/master', selector: buildParameter('BUILD_SELECTOR'), optional: true
+			sh 'python3 ./Biblioteka_DL/evaluate.py'
+			archiveArtifacts artifacts: 'metrics.txt, metrics.png', followSymlinks: false
+			script {
+                       	    ACC = sh (
+                        	script: 'tail -1 metrics.txt',
+                        	returnStdout: true
+                            ).trim()
+                    	}
+		}
+	}
+    }
+    post {
+        success {
+            emailext body: 'SUCCESS, ACC = ${ACC}', subject: 's444356-evaluation', to: 'e19191c5.uam.onmicrosoft.com@emea.teams.ms'
+        }
+        failure {
+            emailext body: 'FAILURE, ACC = ${ACC}', subject: 's444356-evaluation', to: 'e19191c5.uam.onmicrosoft.com@emea.teams.ms'
+        }
+        unstable {
+            emailext body: 'UNSTABLE, ACC = ${ACC}', subject: 's444356-evaluation', to: 'e19191c5.uam.onmicrosoft.com@emea.teams.ms'
+        }
+        changed {
+            emailext body: 'CHANGED, ACC = ${ACC}', subject: 's444356-evaluation', to: 'e19191c5.uam.onmicrosoft.com@emea.teams.ms'
+        }		
+    }
+}

Jenkinsfile_train

@@ -18,6 +18,7 @@ pipeline {
                         copyArtifacts filter: '*', projectName: 's444356-create-dataset'
 			sh 'python3 ./Biblioteka_DL/dllib.py $EPOCHS'
 			archiveArtifacts artifacts: 'games_model.pkl', followSymlinks: false
+			build job: 's444356-evaluation/master/'
 			
 		}
 	}