update Dockerfile, add dllib-mlflow, jenkinsfile_train-mlflow

Biblioteka_DL/dllib-mlflow.py

@@ -0,0 +1,328 @@
+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 sacred import Experiment
+from sacred.observers import FileStorageObserver
+from sacred.observers import MongoObserver
+
+
+# EPOCHS = int(sys.argv[1])
+
+mlflow.set_experiment("s444356")
+
+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
+
+# PlayStation - 0
+# PlayStation 2 - 1
+# PlayStation 3 - 2
+# PlayStation 4 - 3
+# PlayStation 5 - 4
+# PlayStation Vita - 5
+# Xbox - 6
+# Xbox 360 - 7
+# Xbox Series X - 8
+# Nintendo 64 - 9
+# GameCube - 10
+# DS - 11
+# 3DS - 12
+# Wii - 13
+# Wii U - 14
+# Switch - 15
+# PC - 16
+# Dreamcast - 17
+# Game Boy Advance - 18
+# PSP - 19
+# Xbox One - 20
+
+# 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] = 1
+#         elif games["platform"][i] == " PlayStation 3":
+#             games["platform"][i] = 2
+#         elif games["platform"][i] == " PlayStation 4":
+#             games["platform"][i] = 3
+#         elif games["platform"][i] == " PlayStation 5":
+#             games["platform"][i] = 4
+#         elif games["platform"][i] == " PlayStation Vita":
+#             games["platform"][i] = 5
+#         elif games["platform"][i] == " Xbox":
+#             games["platform"][i] = 6
+#         elif games["platform"][i] == " Xbox 360":
+#             games["platform"][i] = 7
+#         elif games["platform"][i] == " Xbox Series X":
+#             games["platform"][i] = 8
+#         elif games["platform"][i] == " Nintendo 64":
+#             games["platform"][i] = 9
+#         elif games["platform"][i] == " GameCube":
+#             games["platform"][i] = 10
+#         elif games["platform"][i] == " DS":
+#             games["platform"][i] = 11
+#         elif games["platform"][i] == " 3DS":
+#             games["platform"][i] = 12
+#         elif games["platform"][i] == " Wii":
+#             games["platform"][i] = 13
+#         elif games["platform"][i] == " Wii U":
+#             games["platform"][i] = 14
+#         elif games["platform"][i] == " Switch":
+#             games["platform"][i] = 15
+#         elif games["platform"][i] == " PC":
+#             games["platform"][i] = 16
+#         elif games["platform"][i] == " Dreamcast":
+#             games["platform"][i] = 17
+#         elif games["platform"][i] == " Game Boy Advance":
+#             games["platform"][i] = 18
+#         elif games["platform"][i] == " PSP":
+#             games["platform"][i] = 19
+#         elif games["platform"][i] == " Xbox One":
+#             games["platform"][i] = 20
+#
+#     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
+
+        # games = pd.read_csv('all_games.csv', sep=',')
+# games = platform_to_number(games)
+# games = delete_stadia(games)
+# games = delete_tbd(games)
+# games = user_review_to_numeric(games)
+# games = normalization(games)
+# games.drop(['level_0', 'index'], axis='columns', inplace=True)
+
+# labels_g = pd.DataFrame(games["platform"], dtype=np.int64)
+# labels_g = labels_g.to_numpy()
+# features_g = {'meta_score': games['meta_score'],
+#               'user_review': games['user_review']}
+# features_g = pd.DataFrame(features_g, dtype=np.float64)
+# features_g = features_g.to_numpy()
+
+def my_main(epochs):
+    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_train = {'platform': platform,
+         'release_date': release_date,
+         'meta_score': meta_score,
+         'user_review': user_review}
+    games_train = pd.DataFrame(games_train)
+
+    games_test = {'platform': platform,
+         'release_date': release_date,
+         'meta_score': meta_score,
+         'user_review': user_review}
+    games_test = pd.DataFrame(games_test)
+
+    games_train = remove_list(games_train)
+    games_train = platform_to_number(games_train)
+    games_train = delete_stadia(games_train)
+    games_train = delete_tbd(games_train)
+    games_train = user_review_to_numeric(games_train)
+    games_train = normalization(games_train)
+
+    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_train_g = pd.DataFrame(games_train["platform"], dtype=np.int64)
+    labels_train_g = labels_train_g.to_numpy()
+    features_train_g = {'meta_score': games_train['meta_score'],
+                  'user_review': games_train['user_review']}
+    features_train_g = pd.DataFrame(features_train_g, dtype=np.float64)
+    features_train_g = features_train_g.to_numpy()
+
+    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 = Model(features_train_g.shape[1])
+    optimizer = torch.optim.Adam(model.parameters(), lr=0.01)
+    loss_fn = nn.CrossEntropyLoss()
+    # epochs = 1000
+    # epochs = epochs
+    alpha = int(sys.argv[1]) if len(sys.argv) > 1 else 20
+    mlflow.log_param("epochs", epochs)
+    
+    def print_(loss):
+        print ("The loss calculated: ", loss)
+
+
+    # Not using dataloader
+    x_train, y_train = Variable(torch.from_numpy(features_train_g)).float(), Variable(torch.from_numpy(labels_train_g)).long()
+    for epoch in range(1, epochs + 1):
+        print("Epoch #", epoch)
+        y_pred = model(x_train)
+
+        loss = loss_fn(y_pred, y_train.squeeze(-1))
+        print_(loss.item())
+
+        # Zero gradients
+        optimizer.zero_grad()
+        loss.backward()  # Gradients
+        optimizer.step()  # Update
+
+    # Prediction
+    x_test = Variable(torch.from_numpy(features_test_g)).float()
+    pred = model(x_test)
+
+    pred = pred.detach().numpy()
+
+    print("The accuracy is", accuracy_score(labels_test_g, np.argmax(pred, axis=1)))
+    mlflow.log_metric("accuracy", accuracy_score(labels_test_g, np.argmax(pred, axis=1)))
+
+    pred = pd.DataFrame(pred)
+
+    pred.to_csv('result.csv')
+
+    # save model
+    torch.save(model, "games_model.pkl")
+
+

Dockerfile

@@ -36,6 +36,7 @@ COPY Zajecia_2/main.py .
 COPY Biblioteka_DL/dllib.py .
 COPY Biblioteka_DL/dllib-sacred.py .
 COPY Biblioteka_DL/evaluate.py .
+COPY Biblioteka_DL/dllib-mlflow.py .
 COPY Biblioteka_DL/all_games.csv .
 
 RUN ./download.sh $CUTOFF

Jenkinsfile_train-mlflow

@@ -0,0 +1,38 @@
+pipeline {
+  	agent {
+		docker {
+            		image 'maciejczajka'
+        	}
+	}
+	parameters {
+        string(
+            defaultValue: '100',
+            description: 'Number of epochs',
+            name: 'EPOCHS',
+            trim: false
+        )
+    }
+   stages {
+	stage('Script'){
+		steps {
+                        copyArtifacts filter: '*', projectName: 's444356-create-dataset'
+			sh 'python Biblioteka_DL/dllib-sacred.py  -e ${params.EPOCHS}'
+			archiveArtifacts artifacts: 'games_model.pkl'
+		}
+	}
+    }
+    post {
+        success {
+            emailext body: 'SUCCESS', subject: 's444356-training', to: 'e19191c5.uam.onmicrosoft.com@emea.teams.ms'
+        }
+        failure {
+            emailext body: 'FAILURE', subject: 's444356-training', to: 'e19191c5.uam.onmicrosoft.com@emea.teams.ms'
+        }
+        unstable {
+            emailext body: 'UNSTABLE', subject: 's444356-training', to: 'e19191c5.uam.onmicrosoft.com@emea.teams.ms'
+        }
+        changed {
+            emailext body: 'CHANGED', subject: 's444356-training', to: 'e19191c5.uam.onmicrosoft.com@emea.teams.ms'
+        }		
+    }
+}