Zaktualizuj 'Dockerfile'

Dockerfile

@@ -9,5 +9,6 @@ RUN pip3 install numpy
 RUN pip3 install sklearn
 RUN pip3 install tensorflow
 RUN pip3 install matplotlib
-COPY ./steam-200k.csv ./
-COPY ./biblioteki_dl.py ./
+RUN pip3 install sacred
+RUN pip3 install pymongo
+RUN pip3 install mlflow

Jenkinsfile

@@ -1,46 +0,0 @@
-pipeline {
-    parameters {
-		string(
-			defaultValue: 'szymonjadczak',
-			description: 'Kaggle username',
-            name: 'KAGGLE_USERNAME',
-            trim: false
-		)
-		password(
-			defaultValue: '',
-            description: 'Kaggle token taken from kaggle.json file, as described in https://github.com/Kaggle/kaggle-api#api-credentials',
-            name: 'KAGGLE_KEY'
-		)
-		string(
-			defaultValue: '',
-			description: 'Value for head command',
-			name: 'CUTOFF'
-		)
-	}
-	environment {
-		KAGGLE_USERNAME="$params.KAGGLE_USERNAME"
-		KAGGLE_KEY="$params.KAGGLE_KEY"
-		CUTOFF="$params.CUTOFF"
-	}
-	agent { 
-        dockerfile {
-            additionalBuildArgs "-t ium"
-        }
-    }
-	stages {
-		stage('Stage 1') {
-			steps {
-				echo 'Hello world!!!'
-				checkout([$class: 'GitSCM', branches: [[name: '*/master']], extensions: [], userRemoteConfigs: [[url: 'https://git.wmi.amu.edu.pl/s444386/ium_444386']]])
-				sh "chmod u+x ./dataset_download.sh"
-				sh "KAGGLE_USERNAME=${KAGGLE_USERNAME} KAGGLE_KEY=${KAGGLE_KEY} CUTOFF=${CUTOFF} ./dataset_download.sh"
-				archiveArtifacts 'data.csv'
-			}
-		}
-		stage('Stage 2') {
-			steps {
-				build job: 's444386-training/training-evaluation/'
-			}
-		}
-	}    
-}

Jenkinsfile2

@@ -1,21 +0,0 @@
-pipeline{
-	agent {
-        docker { image 'ium' }
-        }
-	parameters {
-		buildSelector(
-			defaultSelector: lastSuccessful(),
-			description: 'Which build to use for copying artifacts',
-			name: 'BUILD_SELECTOR')
-	}
-	stages{
-		stage('copy artefacts') {
-			steps {
-				copyArtifacts filter: 'data.csv', fingerprintArtifacts: true, projectName: 's444386-create-dataset', selector: lastSuccessful()
-				sh 'chmod u+x ./kagle.py'
-                sh 'python3 kagle.py'
-			}
-		}
-	}
-	
-}

Jenkinsfile_evaluation

@@ -0,0 +1,42 @@
+pipeline {
+    agent {
+        dockerfile true
+        }
+	parameters {
+        gitParameter branchFilter: 'origin/(.*)', defaultValue: 'training-evaluation', name: 'BRANCH', type: 'PT_BRANCH'
+        buildSelector(
+			defaultSelector: upstream(),
+			description: 'Which build to use for copying artifacts',
+			name: 'BUILD_SELECTOR'
+		)
+    }
+    stages {
+        stage('Stage 1') {
+            steps {
+                    git branch: "${params.BRANCH}", url: 'https://git.wmi.amu.edu.pl/s444386/ium_444386.git'
+					copyArtifacts filter: 'model.tar.gz', projectName: "s444386-training/${BRANCH}/", selector: buildParameter('BUILD_SELECTOR')
+					copyArtifacts filter: 'xtest.csv', projectName: "s444386-training/${BRANCH}/", selector: buildParameter('BUILD_SELECTOR')
+					copyArtifacts filter: 'ytest.csv', projectName: "s444386-training/${BRANCH}/", selector: buildParameter('BUILD_SELECTOR')  	
+                    copyArtifacts filter: 'eval_results.txt', projectName: 's444386-evaluation/training-evaluation/', optional: true				
+					sh 'tar xvzf model.tar.gz'
+					sh 'python3 evaluation.py'
+                    archiveArtifacts 'evaluation_acuraccy.txt'
+                    archiveArtifacts 'accuraccy.png'
+				}
+            }
+        }
+    post {
+        success {
+            emailext body: 'SUCCESS', subject: 's444386-eval-status', to: 'e19191c5.uam.onmicrosoft.com@emea.teams.ms'
+        }
+        failure {
+            emailext body: 'FAILURE', subject: 's444386-eval-status', to: 'e19191c5.uam.onmicrosoft.com@emea.teams.ms'
+        }
+        unstable {
+            emailext body: 'UNSTABLE', subject: 's444386-eval-status', to: 'e19191c5.uam.onmicrosoft.com@emea.teams.ms'
+        }
+        changed {
+            emailext body: 'CHANGED', subject: 's444386-eval-status', to: 'e19191c5.uam.onmicrosoft.com@emea.teams.ms'
+        }		
+    }
+}

Jenkinsfile_train

@@ -0,0 +1,43 @@
+pipeline {
+    agent {
+        dockerfile true
+        }
+	parameters {
+		string(
+			defaultValue: '5',
+			description: 'learning iterations',
+			name: 'epoch'
+		)
+	}
+    stages {
+        stage('Stage 1') {
+            steps {
+					copyArtifacts filter: 'data.csv', fingerprintArtifacts: true, projectName: 's444386-create-dataset', selector: lastSuccessful()
+				    sh 'chmod u+x ./sacred_training.py'
+					sh 'python3 sacred_training.py $epoch'
+					sh 'tar -czf model.tar.gz model/'
+					archiveArtifacts 'model.tar.gz'
+					archiveArtifacts 'xtest.csv'
+					archiveArtifacts 'ytest.csv'
+                    dir('training') {
+                        archiveArtifacts artifacts: '**/**'
+                    }
+				}
+            }
+        }
+	post {
+        success {
+            emailext body: 'SUCCESS', subject: 's444386-train-status', to: 'e19191c5.uam.onmicrosoft.com@emea.teams.ms'
+            build job: 's444386-evaluation/training-evaluation/'
+        }
+        failure {
+            emailext body: 'FAILURE', subject: 's444386-train-status', to: 'e19191c5.uam.onmicrosoft.com@emea.teams.ms'
+        }
+        unstable {
+            emailext body: 'UNSTABLE', subject: 's444386-train-status', to: 'e19191c5.uam.onmicrosoft.com@emea.teams.ms'
+        }
+        changed {
+            emailext body: 'CHANGED', subject: 's444386-train-status', to: 'e19191c5.uam.onmicrosoft.com@emea.teams.ms'
+        }		
+    }
+}

MLproject

@@ -1,11 +0,0 @@
-name: MLflow
-
-
-docker_env:
-  image: szymonjadczak/mlflow:latest
-
-entry_points:
-  main:
-    parameters:
-      epochs: {type: float, default: 3}
-    command: "python ./biblioteki_dl.py {epochs}"

biblioteki_dl.py

@@ -4,11 +4,14 @@ import pandas as pd
 import numpy as np
 import csv
 from sklearn.model_selection import train_test_split
+import sys
 
-os.system("kaggle datasets download -d tamber/steam-video-games")
-os.system("unzip -o steam-video-games.zip")
+# os.system("kaggle datasets download -d tamber/steam-video-games")
+# os.system("unzip -o steam-video-games.zip")
 
-steam=pd.read_csv('steam-200k.csv',usecols=[0,1,2,3],names=['userId','game','behavior','hoursPlayed'])
+epoch = int(sys.argv[1])
+
+steam=pd.read_csv('data.csv',usecols=[0,1,2,3],names=['userId','game','behavior','hoursPlayed'])
 steam.isnull().values.any()
 steam['userId'] = steam.userId.astype(str)
 purchaseCount = steam[steam["behavior"] != "play"]["game"].value_counts()
@@ -71,17 +74,24 @@ y_train = steam_train['game']
 x_test = steam_test[['hoursPlayed','purchaseCount','playCount','playerPlayCount','playerPurchaseCount']]
 y_test = steam_test['game']
 
+
 x_train = np.array(x_train)
 y_train = np.array(y_train)
 x_test = np.array(x_test)
 y_test = np.array(y_test)
 
+with open('xtest.csv','w',encoding='UTF-8',newline='') as xtest:
+    writer = csv.writer(xtest)
+    for i in x_test:
+        writer.writerow(i)
+    
 for i,j in enumerate(y_train):
     y_train[i] = games[j]
     
 for i,j in enumerate(y_test):
     y_test[i] = games[j]
 
+np.savetxt("ytest.csv",y_test,delimiter=",",fmt='%d')
 
 
 model = tf.keras.models.Sequential([
@@ -102,7 +112,7 @@ y_test = np.array(y_test).astype(np.float32)
 
 
 
-model.fit(x_train, y_train, epochs=100)
+model.fit(x_train, y_train, epochs=epoch)
 model.evaluate(x_test, y_test)
 prediction = model.predict(x_test)
 classes_x=np.argmax(prediction,axis=1)
@@ -118,4 +128,4 @@ with open('results.csv','w',encoding='UTF-8',newline='') as f:
         for row in rows:
             writer.writerow(row)
 
-    
+model.save('./model')    

data.txt

@@ -1,23 +0,0 @@
-151603712,"The Elder Scrolls V Skyrim",purchase,1.0,0
-151603712,"The Elder Scrolls V Skyrim",play,273.0,0
-151603712,"Fallout 4",purchase,1.0,0
-151603712,"Fallout 4",play,87.0,0
-151603712,"Spore",purchase,1.0,0
-151603712,"Spore",play,14.9,0
-151603712,"Fallout New Vegas",purchase,1.0,0
-151603712,"Fallout New Vegas",play,12.1,0
-151603712,"Left 4 Dead 2",purchase,1.0,0
-151603712,"Left 4 Dead 2",play,8.9,0
-151603712,"HuniePop",purchase,1.0,0
-151603712,"HuniePop",play,8.5,0
-151603712,"Path of Exile",purchase,1.0,0
-151603712,"Path of Exile",play,8.1,0
-151603712,"Poly Bridge",purchase,1.0,0
-151603712,"Poly Bridge",play,7.5,0
-151603712,"Left 4 Dead",purchase,1.0,0
-151603712,"Left 4 Dead",play,3.3,0
-151603712,"Team Fortress 2",purchase,1.0,0
-151603712,"Team Fortress 2",play,2.8,0
-151603712,"Tomb Raider",purchase,1.0,0
-151603712,"Tomb Raider",play,2.5,0
-151603712,"The Banner Saga",purchase,1.0,0

dataset_download.sh

@@ -1,6 +0,0 @@
-
-kaggle datasets download -d tamber/steam-video-games
-unzip -o steam-video-games.zip
-> data.csv
-head -n $CUTOFF steam-200k.csv >> data.csv
-

dataset_stats.sh

@@ -1 +0,0 @@
-wc -l data.csv >> number_of_lines.txt

environment.yml

@@ -1,415 +0,0 @@
-name: null
-channels:
-  - defaults
-dependencies:
-  - _anaconda_depends=2021.11=py39_0
-  - alabaster=0.7.12=pyhd3eb1b0_0
-  - anaconda=custom=py39_1
-  - anaconda-client=1.9.0=py39haa95532_0
-  - anaconda-project=0.10.2=pyhd3eb1b0_0
-  - anyio=3.5.0=py39haa95532_0
-  - appdirs=1.4.4=pyhd3eb1b0_0
-  - argh=0.26.2=py39haa95532_0
-  - argon2-cffi=21.3.0=pyhd3eb1b0_0
-  - argon2-cffi-bindings=21.2.0=py39h2bbff1b_0
-  - arrow=1.2.2=pyhd3eb1b0_0
-  - asn1crypto=1.5.1=py39haa95532_0
-  - astroid=2.6.6=py39haa95532_0
-  - astropy=5.0.4=py39h080aedc_0
-  - asttokens=2.0.5=pyhd3eb1b0_0
-  - async_generator=1.10=pyhd3eb1b0_0
-  - atomicwrites=1.4.0=py_0
-  - attrs=21.4.0=pyhd3eb1b0_0
-  - autopep8=1.6.0=pyhd3eb1b0_0
-  - babel=2.9.1=pyhd3eb1b0_0
-  - backcall=0.2.0=pyhd3eb1b0_0
-  - backports=1.1=pyhd3eb1b0_0
-  - backports.shutil_get_terminal_size=1.0.0=pyhd3eb1b0_3
-  - bcrypt=3.2.0=py39h196d8e1_0
-  - beautifulsoup4=4.11.1=py39haa95532_0
-  - binaryornot=0.4.4=pyhd3eb1b0_1
-  - bitarray=2.5.0=py39h2bbff1b_0
-  - bkcharts=0.2=py39haa95532_0
-  - black=19.10b0=py_0
-  - blas=1.0=mkl
-  - bleach=4.1.0=pyhd3eb1b0_0
-  - blosc=1.21.0=h19a0ad4_0
-  - bokeh=2.4.2=py39haa95532_1
-  - boto=2.49.0=py39haa95532_0
-  - bottleneck=1.3.4=py39h080aedc_0
-  - brotli=1.0.9=ha925a31_2
-  - brotlipy=0.7.0=py39h2bbff1b_1003
-  - bzip2=1.0.8=he774522_0
-  - ca-certificates=2022.4.26=haa95532_0
-  - cached-property=1.5.2=py_0
-  - certifi=2022.5.18.1=py39haa95532_0
-  - cffi=1.15.0=py39h2bbff1b_1
-  - cfitsio=3.470=he774522_6
-  - chardet=4.0.0=py39haa95532_1003
-  - charls=2.2.0=h6c2663c_0
-  - charset-normalizer=2.0.4=pyhd3eb1b0_0
-  - click=8.0.4=py39haa95532_0
-  - cloudpickle=2.0.0=pyhd3eb1b0_0
-  - clyent=1.2.2=py39haa95532_1
-  - colorama=0.4.4=pyhd3eb1b0_0
-  - comtypes=1.1.10=py39haa95532_1002
-  - conda=4.12.0=py39haa95532_0
-  - conda-content-trust=0.1.1=pyhd3eb1b0_0
-  - conda-pack=0.6.0=pyhd3eb1b0_0
-  - conda-package-handling=1.8.1=py39h8cc25b3_0
-  - conda-token=0.3.0=pyhd3eb1b0_0
-  - console_shortcut=0.1.1=4
-  - contextlib2=0.6.0.post1=pyhd3eb1b0_0
-  - cookiecutter=1.7.3=pyhd3eb1b0_0
-  - cryptography=37.0.1=py39h21b164f_0
-  - curl=7.82.0=h2bbff1b_0
-  - cycler=0.11.0=pyhd3eb1b0_0
-  - cython=0.29.28=py39hd77b12b_0
-  - cytoolz=0.11.0=py39h2bbff1b_0
-  - daal4py=2021.5.0=py39h8cb3d55_0
-  - dal=2021.5.0=haa95532_796
-  - dask=2022.2.1=pyhd3eb1b0_0
-  - dask-core=2022.2.1=pyhd3eb1b0_0
-  - dataclasses=0.8=pyh6d0b6a4_7
-  - debugpy=1.5.1=py39hd77b12b_0
-  - decorator=5.1.1=pyhd3eb1b0_0
-  - defusedxml=0.7.1=pyhd3eb1b0_0
-  - diff-match-patch=20200713=pyhd3eb1b0_0
-  - distributed=2022.2.1=pyhd3eb1b0_0
-  - docutils=0.17.1=py39haa95532_1
-  - entrypoints=0.4=py39haa95532_0
-  - et_xmlfile=1.1.0=py39haa95532_0
-  - executing=0.8.3=pyhd3eb1b0_0
-  - fastcache=1.1.0=py39h196d8e1_0
-  - filelock=3.6.0=pyhd3eb1b0_0
-  - flake8=3.9.2=pyhd3eb1b0_0
-  - flask=2.0.3=pyhd3eb1b0_0
-  - fonttools=4.25.0=pyhd3eb1b0_0
-  - freetype=2.10.4=hd328e21_0
-  - fsspec=2022.3.0=py39haa95532_0
-  - get_terminal_size=1.0.0=h38e98db_0
-  - gevent=21.8.0=py39h2bbff1b_1
-  - giflib=5.2.1=h62dcd97_0
-  - glob2=0.7=pyhd3eb1b0_0
-  - greenlet=1.1.1=py39hd77b12b_0
-  - h5py=3.6.0=py39h3de5c98_0
-  - hdf5=1.10.6=h7ebc959_0
-  - heapdict=1.0.1=pyhd3eb1b0_0
-  - html5lib=1.1=pyhd3eb1b0_0
-  - icc_rt=2019.0.0=h0cc432a_1
-  - icu=58.2=ha925a31_3
-  - idna=3.3=pyhd3eb1b0_0
-  - imagecodecs=2021.8.26=py39ha1f97ea_0
-  - imageio=2.9.0=pyhd3eb1b0_0
-  - imagesize=1.3.0=pyhd3eb1b0_0
-  - importlib-metadata=4.11.3=py39haa95532_0
-  - importlib_metadata=4.11.3=hd3eb1b0_0
-  - inflection=0.5.1=py39haa95532_0
-  - iniconfig=1.1.1=pyhd3eb1b0_0
-  - intel-openmp=2021.4.0=haa95532_3556
-  - intervaltree=3.1.0=pyhd3eb1b0_0
-  - ipykernel=6.9.1=py39haa95532_0
-  - ipython=8.3.0=py39haa95532_0
-  - ipython_genutils=0.2.0=pyhd3eb1b0_1
-  - ipywidgets=7.6.5=pyhd3eb1b0_1
-  - isort=5.9.3=pyhd3eb1b0_0
-  - itsdangerous=2.0.1=pyhd3eb1b0_0
-  - jdcal=1.4.1=pyhd3eb1b0_0
-  - jedi=0.18.1=py39haa95532_1
-  - jinja2=3.0.3=pyhd3eb1b0_0
-  - jinja2-time=0.2.0=pyhd3eb1b0_3
-  - joblib=1.1.0=pyhd3eb1b0_0
-  - jpeg=9e=h2bbff1b_0
-  - json5=0.9.6=pyhd3eb1b0_0
-  - jsonschema=4.4.0=py39haa95532_0
-  - jupyter=1.0.0=py39haa95532_7
-  - jupyter_client=6.1.12=pyhd3eb1b0_0
-  - jupyter_console=6.4.0=pyhd3eb1b0_0
-  - jupyter_core=4.10.0=py39haa95532_0
-  - jupyter_server=1.13.5=pyhd3eb1b0_0
-  - jupyterlab=3.3.2=pyhd3eb1b0_0
-  - jupyterlab_pygments=0.1.2=py_0
-  - jupyterlab_server=2.12.0=py39haa95532_0
-  - jupyterlab_widgets=1.0.0=pyhd3eb1b0_1
-  - keyring=23.4.0=py39haa95532_0
-  - kiwisolver=1.3.2=py39hd77b12b_0
-  - krb5=1.19.2=h5b6d351_0
-  - lazy-object-proxy=1.6.0=py39h2bbff1b_0
-  - lcms2=2.12=h83e58a3_0
-  - lerc=3.0=hd77b12b_0
-  - libaec=1.0.4=h33f27b4_1
-  - libarchive=3.4.2=h5e25573_0
-  - libcurl=7.82.0=h86230a5_0
-  - libdeflate=1.8=h2bbff1b_5
-  - libiconv=1.16=h2bbff1b_2
-  - liblief=0.11.5=hd77b12b_1
-  - libpng=1.6.37=h2a8f88b_0
-  - libspatialindex=1.9.3=h6c2663c_0
-  - libssh2=1.10.0=hcd4344a_0
-  - libtiff=4.2.0=hd0e1b90_0
-  - libwebp=1.2.2=h2bbff1b_0
-  - libxml2=2.9.12=h0ad7f3c_2
-  - libxslt=1.1.34=he774522_0
-  - libzopfli=1.0.3=ha925a31_0
-  - llvmlite=0.38.0=py39h23ce68f_0
-  - locket=1.0.0=py39haa95532_0
-  - lxml=4.8.0=py39h1985fb9_0
-  - lz4-c=1.9.3=h2bbff1b_1
-  - lzo=2.10=he774522_2
-  - m2w64-gcc-libgfortran=5.3.0=6
-  - m2w64-gcc-libs=5.3.0=7
-  - m2w64-gcc-libs-core=5.3.0=7
-  - m2w64-gmp=6.1.0=2
-  - m2w64-libwinpthread-git=5.0.0.4634.697f757=2
-  - markupsafe=2.0.1=py39h2bbff1b_0
-  - matplotlib=3.5.1=py39haa95532_1
-  - matplotlib-base=3.5.1=py39hd77b12b_1
-  - matplotlib-inline=0.1.2=pyhd3eb1b0_2
-  - mccabe=0.6.1=py39haa95532_1
-  - menuinst=1.4.18=py39h59b6b97_0
-  - mistune=0.8.4=py39h2bbff1b_1000
-  - mkl=2021.4.0=haa95532_640
-  - mkl-service=2.4.0=py39h2bbff1b_0
-  - mkl_fft=1.3.1=py39h277e83a_0
-  - mkl_random=1.2.2=py39hf11a4ad_0
-  - mock=4.0.3=pyhd3eb1b0_0
-  - more-itertools=8.12.0=pyhd3eb1b0_0
-  - mpmath=1.2.1=py39haa95532_0
-  - msgpack-python=1.0.3=py39h59b6b97_0
-  - msys2-conda-epoch=20160418=1
-  - multipledispatch=0.6.0=py39haa95532_0
-  - munkres=1.1.4=py_0
-  - mypy_extensions=0.4.3=py39haa95532_1
-  - nbclassic=0.3.5=pyhd3eb1b0_0
-  - nbclient=0.5.13=py39haa95532_0
-  - nbconvert=6.4.4=py39haa95532_0
-  - nbformat=5.3.0=py39haa95532_0
-  - nest-asyncio=1.5.5=py39haa95532_0
-  - networkx=2.7.1=pyhd3eb1b0_0
-  - nltk=3.7=pyhd3eb1b0_0
-  - nose=1.3.7=pyhd3eb1b0_1008
-  - notebook=6.4.11=py39haa95532_0
-  - numba=0.55.1=py39hf11a4ad_0
-  - numexpr=2.8.1=py39hb80d3ca_0
-  - numpy=1.21.5=py39h7a0a035_2
-  - numpy-base=1.21.5=py39hca35cd5_2
-  - numpydoc=1.2=pyhd3eb1b0_0
-  - olefile=0.46=pyhd3eb1b0_0
-  - openjpeg=2.4.0=h4fc8c34_0
-  - openpyxl=3.0.9=pyhd3eb1b0_0
-  - openssl=1.1.1o=h2bbff1b_0
-  - packaging=21.3=pyhd3eb1b0_0
-  - pandas=1.4.2=py39hd77b12b_0
-  - pandocfilters=1.5.0=pyhd3eb1b0_0
-  - paramiko=2.8.1=pyhd3eb1b0_0
-  - parso=0.8.3=pyhd3eb1b0_0
-  - partd=1.2.0=pyhd3eb1b0_1
-  - path=16.2.0=pyhd3eb1b0_0
-  - path.py=12.5.0=hd3eb1b0_0
-  - pathlib2=2.3.6=py39haa95532_2
-  - pathspec=0.7.0=py_0
-  - patsy=0.5.2=py39haa95532_1
-  - pep8=1.7.1=py39haa95532_0
-  - pexpect=4.8.0=pyhd3eb1b0_3
-  - pickleshare=0.7.5=pyhd3eb1b0_1003
-  - pillow=9.0.1=py39hdc2b20a_0
-  - pip=21.2.4=py39haa95532_0
-  - pkginfo=1.8.2=pyhd3eb1b0_0
-  - pluggy=1.0.0=py39haa95532_1
-  - ply=3.11=py39haa95532_0
-  - powershell_shortcut=0.0.1=3
-  - poyo=0.5.0=pyhd3eb1b0_0
-  - prometheus_client=0.13.1=pyhd3eb1b0_0
-  - prompt-toolkit=3.0.20=pyhd3eb1b0_0
-  - prompt_toolkit=3.0.20=hd3eb1b0_0
-  - psutil=5.8.0=py39h2bbff1b_1
-  - ptyprocess=0.7.0=pyhd3eb1b0_2
-  - pure_eval=0.2.2=pyhd3eb1b0_0
-  - py=1.11.0=pyhd3eb1b0_0
-  - py-lief=0.11.5=py39hd77b12b_1
-  - pycodestyle=2.7.0=pyhd3eb1b0_0
-  - pycosat=0.6.3=py39h2bbff1b_0
-  - pycparser=2.21=pyhd3eb1b0_0
-  - pycurl=7.44.1=py39hcd4344a_1
-  - pydocstyle=6.1.1=pyhd3eb1b0_0
-  - pyerfa=2.0.0=py39h2bbff1b_0
-  - pyflakes=2.3.1=pyhd3eb1b0_0
-  - pygments=2.11.2=pyhd3eb1b0_0
-  - pylint=2.9.6=py39haa95532_1
-  - pyls-spyder=0.4.0=pyhd3eb1b0_0
-  - pynacl=1.4.0=py39hbd8134f_1
-  - pyodbc=4.0.32=py39hd77b12b_1
-  - pyopenssl=22.0.0=pyhd3eb1b0_0
-  - pyparsing=3.0.4=pyhd3eb1b0_0
-  - pyqt=5.9.2=py39hd77b12b_6
-  - pyreadline=2.1=py39haa95532_1
-  - pyrsistent=0.18.0=py39h196d8e1_0
-  - pysocks=1.7.1=py39haa95532_0
-  - pytables=3.6.1=py39h56d22b6_1
-  - pytest=7.1.1=py39haa95532_0
-  - python=3.9.2=h6244533_0
-  - python-dateutil=2.8.2=pyhd3eb1b0_0
-  - python-fastjsonschema=2.15.1=pyhd3eb1b0_0
-  - python-libarchive-c=2.9=pyhd3eb1b0_1
-  - python-lsp-black=1.0.0=pyhd3eb1b0_0
-  - python-lsp-jsonrpc=1.0.0=pyhd3eb1b0_0
-  - python-lsp-server=1.2.4=pyhd3eb1b0_0
-  - python-slugify=5.0.2=pyhd3eb1b0_0
-  - pytz=2021.3=pyhd3eb1b0_0
-  - pywavelets=1.3.0=py39h2bbff1b_0
-  - pywin32-ctypes=0.2.0=py39haa95532_1000
-  - pywinpty=2.0.2=py39h5da7b33_0
-  - pyyaml=6.0=py39h2bbff1b_1
-  - pyzmq=22.3.0=py39hd77b12b_2
-  - qdarkstyle=3.0.2=pyhd3eb1b0_0
-  - qstylizer=0.1.10=pyhd3eb1b0_0
-  - qt=5.9.7=vc14h73c81de_0
-  - qtawesome=1.0.3=pyhd3eb1b0_0
-  - qtconsole=5.3.0=pyhd3eb1b0_0
-  - qtpy=2.0.1=pyhd3eb1b0_0
-  - regex=2022.3.15=py39h2bbff1b_0
-  - requests=2.27.1=pyhd3eb1b0_0
-  - rope=0.22.0=pyhd3eb1b0_0
-  - rtree=0.9.7=py39h2eaa2aa_1
-  - ruamel_yaml=0.15.100=py39h2bbff1b_0
-  - scikit-image=0.19.2=py39hf11a4ad_0
-  - scikit-learn=1.0.2=py39hf11a4ad_1
-  - scikit-learn-intelex=2021.5.0=py39haa95532_0
-  - scipy=1.7.3=py39h0a974cb_0
-  - seaborn=0.11.2=pyhd3eb1b0_0
-  - send2trash=1.8.0=pyhd3eb1b0_1
-  - setuptools=61.2.0=py39haa95532_0
-  - simplegeneric=0.8.1=py39haa95532_2
-  - singledispatch=3.7.0=pyhd3eb1b0_1001
-  - sip=4.19.13=py39hd77b12b_0
-  - six=1.16.0=pyhd3eb1b0_1
-  - snappy=1.1.9=h6c2663c_0
-  - sniffio=1.2.0=py39haa95532_1
-  - snowballstemmer=2.2.0=pyhd3eb1b0_0
-  - sortedcollections=2.1.0=pyhd3eb1b0_0
-  - sortedcontainers=2.4.0=pyhd3eb1b0_0
-  - soupsieve=2.3.1=pyhd3eb1b0_0
-  - sphinx=4.4.0=pyhd3eb1b0_0
-  - sphinxcontrib=1.0=py39haa95532_1
-  - sphinxcontrib-applehelp=1.0.2=pyhd3eb1b0_0
-  - sphinxcontrib-devhelp=1.0.2=pyhd3eb1b0_0
-  - sphinxcontrib-htmlhelp=2.0.0=pyhd3eb1b0_0
-  - sphinxcontrib-jsmath=1.0.1=pyhd3eb1b0_0
-  - sphinxcontrib-qthelp=1.0.3=pyhd3eb1b0_0
-  - sphinxcontrib-serializinghtml=1.1.5=pyhd3eb1b0_0
-  - sphinxcontrib-websupport=1.2.4=py_0
-  - spyder=5.1.5=py39haa95532_1
-  - spyder-kernels=2.1.3=py39haa95532_0
-  - sqlalchemy=1.4.32=py39h2bbff1b_0
-  - sqlite=3.38.3=h2bbff1b_0
-  - stack_data=0.2.0=pyhd3eb1b0_0
-  - statsmodels=0.13.2=py39h2bbff1b_0
-  - sympy=1.10.1=py39haa95532_0
-  - tbb=2021.5.0=h59b6b97_0
-  - tbb4py=2021.5.0=py39h59b6b97_0
-  - tblib=1.7.0=pyhd3eb1b0_0
-  - terminado=0.13.1=py39haa95532_0
-  - testpath=0.5.0=pyhd3eb1b0_0
-  - text-unidecode=1.3=pyhd3eb1b0_0
-  - textdistance=4.2.1=pyhd3eb1b0_0
-  - threadpoolctl=2.2.0=pyh0d69192_0
-  - three-merge=0.1.1=pyhd3eb1b0_0
-  - tifffile=2021.7.2=pyhd3eb1b0_2
-  - tinycss=0.4=pyhd3eb1b0_1002
-  - tk=8.6.11=h2bbff1b_1
-  - toml=0.10.2=pyhd3eb1b0_0
-  - tomli=1.2.2=pyhd3eb1b0_0
-  - toolz=0.11.2=pyhd3eb1b0_0
-  - tornado=6.1=py39h2bbff1b_0
-  - tqdm=4.64.0=py39haa95532_0
-  - traitlets=5.1.1=pyhd3eb1b0_0
-  - typed-ast=1.4.3=py39h2bbff1b_1
-  - typing-extensions=4.1.1=hd3eb1b0_0
-  - typing_extensions=4.1.1=pyh06a4308_0
-  - tzdata=2022a=hda174b7_0
-  - ujson=5.1.0=py39hd77b12b_0
-  - unicodecsv=0.14.1=py39haa95532_0
-  - unidecode=1.2.0=pyhd3eb1b0_0
-  - urllib3=1.26.9=py39haa95532_0
-  - vc=14.2=h21ff451_1
-  - vs2015_runtime=14.27.29016=h5e58377_2
-  - watchdog=2.1.6=py39haa95532_0
-  - wcwidth=0.2.5=pyhd3eb1b0_0
-  - webencodings=0.5.1=py39haa95532_1
-  - websocket-client=0.58.0=py39haa95532_4
-  - werkzeug=2.0.3=pyhd3eb1b0_0
-  - wheel=0.37.1=pyhd3eb1b0_0
-  - whichcraft=0.6.1=pyhd3eb1b0_0
-  - widgetsnbextension=3.5.2=py39haa95532_0
-  - win_inet_pton=1.1.0=py39haa95532_0
-  - win_unicode_console=0.5=py39haa95532_0
-  - wincertstore=0.2=py39haa95532_2
-  - winpty=0.4.3=4
-  - wrapt=1.12.1=py39h196d8e1_1
-  - xlrd=2.0.1=pyhd3eb1b0_0
-  - xlsxwriter=3.0.3=pyhd3eb1b0_0
-  - xlwings=0.24.9=py39haa95532_0
-  - xlwt=1.3.0=py39haa95532_0
-  - xz=5.2.5=h8cc25b3_1
-  - yaml=0.2.5=he774522_0
-  - yapf=0.31.0=pyhd3eb1b0_0
-  - zfp=0.5.5=hd77b12b_6
-  - zict=2.0.0=pyhd3eb1b0_0
-  - zipp=3.8.0=py39haa95532_0
-  - zlib=1.2.12=h8cc25b3_2
-  - zope=1.0=py39haa95532_1
-  - zope.event=4.5.0=py39haa95532_0
-  - zope.interface=5.4.0=py39h2bbff1b_0
-  - zstd=1.4.9=h19a0ad4_0
-  - pip:
-    - absl-py==1.0.0
-    - alembic==1.7.7
-    - astunparse==1.6.3
-    - cachetools==5.1.0
-    - databricks-cli==0.16.6
-    - docker==5.0.3
-    - docopt==0.6.2
-    - flatbuffers==1.12
-    - gast==0.4.0
-    - gitdb==4.0.9
-    - gitpython==3.1.27
-    - google-auth==2.6.6
-    - google-auth-oauthlib==0.4.6
-    - google-pasta==0.2.0
-    - grpcio==1.46.3
-    - jsonpickle==1.5.2
-    - kaggle==1.5.12
-    - keras==2.9.0
-    - keras-preprocessing==1.1.2
-    - libclang==14.0.1
-    - mako==1.2.0
-    - markdown==3.3.7
-    - mlflow==1.26.0
-    - munch==2.5.0
-    - oauthlib==3.2.0
-    - opt-einsum==3.3.0
-    - prometheus-flask-exporter==0.20.1
-    - protobuf==3.20.1
-    - py-cpuinfo==8.0.0
-    - pyasn1==0.4.8
-    - pyasn1-modules==0.2.8
-    - pyjwt==2.4.0
-    - pymongo==4.1.1
-    - pywin32==227
-    - querystring-parser==1.2.4
-    - requests-oauthlib==1.3.1
-    - rsa==4.8
-    - sacred==0.8.2
-    - sklearn==0.0
-    - smmap==5.0.0
-    - sqlparse==0.4.2
-    - tabulate==0.8.9
-    - tensorboard==2.9.0
-    - tensorboard-data-server==0.6.1
-    - tensorboard-plugin-wit==1.8.1
-    - tensorflow==2.9.0
-    - tensorflow-estimator==2.9.0
-    - tensorflow-io-gcs-filesystem==0.26.0
-    - termcolor==1.1.0
-    - waitress==2.1.1
-prefix: D:\anaconda\envs\s444386

evaluation.py

@@ -0,0 +1,27 @@
+import tensorflow as tf
+import os
+import pandas as pd
+import numpy as np
+import csv
+from sklearn.model_selection import train_test_split
+from matplotlib import pyplot as plt
+
+x_test = pd.read_csv('xtest.csv')
+y_test = pd.read_csv('ytest.csv')
+
+model = tf.keras.models.load_model('./model')
+
+res = model.evaluate(x_test, y_test,verbose=0)
+
+with open('evaluation_acuraccy.txt', 'a+') as f:
+    f.write(str(res[1])+'\n')
+with open('evaluation_acuraccy.txt') as f:  
+    scores = [float(line) for line in f if line]
+    print(scores)
+    builds = list(range(1, len(scores) + 1))
+    plot = plt.plot(builds, scores)
+    plt.xlabel('Build')
+    plt.xticks(range(1, len(scores) + 1))
+    plt.ylabel('Accuraccy')
+    plt.show()
+    plt.savefig('accuraccy.png')

kagle.py

@@ -1,79 +0,0 @@
-import os
-import pandas as pd
-from sklearn.model_selection import train_test_split
-
-#os.system("kaggle datasets download -d tamber/steam-video-games")
-#os.system("unzip -o steam-video-games.zip")
-
-steam=pd.read_csv('data.csv',usecols=[0,1,2,3],names=['userId','game','behavior','hoursPlayed'])
-steam.isnull().values.any()
-steam['userId'] = steam.userId.astype(str)
-
-print("Zbior danych:")
-print(steam)
-
-print("Describe:")
-print(steam.describe(include='all'),"\n\n")
-
-print("Gracze z najwieksza aktywnoscia:")
-print(steam["userId"].value_counts(),"\n\n")
-
-print("Gracze z najwieksza liczba kupionych gier:")
-print(steam[steam["behavior"] != "play"]["userId"].value_counts())
-print("Mediana:")
-print(steam[steam["behavior"] != "play"]["userId"].value_counts().median(),"\n\n")
-
-print("Gracze ktorzy zagrali w najwieksza liczbe gier:")
-print(steam[steam["behavior"] != "purchase"]["userId"].value_counts())
-print("Mediana:")
-print(steam[steam["behavior"] != "purchase"]["userId"].value_counts().median(),"\n\n")
-
-
-print("Gry:")
-print(steam["game"].value_counts(),"\n\n")
-
-print("Sredni czas grania w grania w dana gre")
-print(steam[steam["behavior"] != "purchase"].groupby("game").mean().sort_values(by="hoursPlayed",ascending=False))
-print("Mediana:")
-print(steam[steam["behavior"] != "purchase"].groupby("game").mean().sort_values(by="hoursPlayed",ascending=False).median(),"\n\n")
-
-print("Najczesciej kupowana gra")
-print(steam[steam["behavior"] != "play"]["game"].value_counts())
-print("Mediana:")
-print(steam[steam["behavior"] != "play"]["game"].value_counts().median(),"\n\n")
-
-print("Gra w ktora zagralo najwiecej graczy")
-print(steam[steam["behavior"] != "purchase"]["game"].value_counts())
-print("Mediana:")
-print(steam[steam["behavior"] != "purchase"]["game"].value_counts().median(),"\n\n")
-
-print("Liczba kupionych gier i liczba gier w ktore gracze zagrali")
-print(steam["behavior"].value_counts(),"\n\n")
-
-
-print("Gra z najwieksza liczba godzin dla jednego gracza")
-print(steam[steam["behavior"] != "purchase"][["userId","hoursPlayed","game"]].sort_values(by="hoursPlayed",ascending=False))
-print("Mediana:")
-print(steam[steam["behavior"] != "purchase"]["hoursPlayed"].sort_values(ascending=False).median(),"\n\n")
-
-print("Suma rozegranych godzin dla danej gry")
-print(steam[steam["behavior"] != "purchase"].groupby("game").sum().sort_values(by="hoursPlayed",ascending=False))
-print("Mediana:")
-print(steam[steam["behavior"] != "purchase"].groupby("game").sum().sort_values(by="hoursPlayed",ascending=False).median(),"\n\n")
-
-#odrzucenie gier dla których jest mniej niż 10 wierszy
-steam = steam.groupby("game").filter(lambda x: len(x)>10)
-#rozmiar zbioru testowego i dev proporcje 8:1:1
-size=int(len(steam)/10)
-
-steam_train, steam_test = train_test_split(steam, test_size=size, random_state=1, stratify=steam["game"])
-steam_train, steam_dev = train_test_split(steam_train, test_size=size, random_state=1, stratify=steam_train["game"])
-
-print("Zbior trenujacy")
-print(steam_train["game"].value_counts(),"\n")
-
-print("Zbior testujacy")
-print(steam_test["game"].value_counts(),"\n")
-
-print("Zbior dev")
-print(steam_dev["game"].value_counts(),"\n")

sacred_training.py

@@ -1,139 +1,147 @@
-import tensorflow as tf
-import os
-import pandas as pd
-import numpy as np
-import csv
-from sklearn.model_selection import train_test_split
-import sys
-import mlflow
-
-# os.system("kaggle datasets download -d tamber/steam-video-games")
-# os.system("unzip -o steam-video-games.zip")
-
-with mlflow.start_run():
-    epoch = int(sys.argv[1])
-
-    steam=pd.read_csv('data.csv',usecols=[0,1,2,3],names=['userId','game','behavior','hoursPlayed'])
-    steam.isnull().values.any()
-    steam['userId'] = steam.userId.astype(str)
-    purchaseCount = steam[steam["behavior"] != "play"]["game"].value_counts()
-    playCount = steam[steam["behavior"] != "purchase"]["game"].value_counts()
-
-    playerPurchaseCount = steam[steam["behavior"] != "play"]["userId"].value_counts()
-    playerPlayCount = steam[steam["behavior"] != "purchase"]["userId"].value_counts()
-
-    steam = steam[steam['behavior'] != 'purchase']
-    steam = steam.groupby("game").filter(lambda x: len(x)>10)
-    size=int(len(steam)/10)
-
-    meanGame = steam[steam["behavior"] != "purchase"].groupby("game").mean()
-    meanGame = meanGame.to_dict()
-    meanGame = meanGame['hoursPlayed']
-
-    purchaseCount = purchaseCount.to_dict()
-    playCount = playCount.to_dict()
-    playerPurchaseCount = playerPurchaseCount.to_dict()
-    playerPlayCount = playerPlayCount.to_dict()
-
-    steam['meanTime'] = 0;
-    steam['purchaseCount'] = 0;
-    steam['playCount'] = 0;
-    steam['playerPurchaseCount'] =0;
-    steam['playerPlayCount'] =0;
-    steam['playPercent'] =0;
-
-    for i in steam.index:
-        steam.at[i,'meanTime'] = meanGame[steam.at[i,'game']]
-        steam.at[i,'purchaseCount'] = purchaseCount[steam.at[i,'game']]
-        steam.at[i,'playCount'] = playCount[steam.at[i,'game']]
-        steam.at[i,'playerPurchaseCount'] = playerPurchaseCount[steam.at[i,'userId']]
-        steam.at[i,'playerPlayCount'] = playerPlayCount[steam.at[i,'userId']]
-        steam.at[i,'playPercent'] = playerPlayCount[steam.at[i,'userId']]/playerPurchaseCount[steam.at[i,'userId']]
-
-
-    steam_train, steam_test = train_test_split(steam, test_size=size, random_state=1, stratify=steam["game"])
-    steam_train, steam_dev = train_test_split(steam_train, test_size=size, random_state=1, stratify=steam_train["game"])
-
-    print(steam)
-
-    games = {}
-    for i in steam['game']:
-        games[i] = 0
-
-    j=0
-    for key,game in games.items():
-        games[key]=j
-        j=j+1
-
-    for i in steam['game']:
-        i = games[i]
-        
-    invGames = {v: k for k, v in games.items()}
-
-    x_train = steam_train[['hoursPlayed','purchaseCount','playCount','playerPlayCount','playerPurchaseCount']]
-    y_train = steam_train['game']
-
-    x_test = steam_test[['hoursPlayed','purchaseCount','playCount','playerPlayCount','playerPurchaseCount']]
-    y_test = steam_test['game']
-
-
-    x_train = np.array(x_train)
-    y_train = np.array(y_train)
-    x_test = np.array(x_test)
-    y_test = np.array(y_test)
-
-    with open('xtest.csv','w',encoding='UTF-8',newline='') as xtest:
-        writer = csv.writer(xtest)
-        for i in x_test:
-            writer.writerow(i)
-        
-    for i,j in enumerate(y_train):
-        y_train[i] = games[j]
-        
-    for i,j in enumerate(y_test):
-        y_test[i] = games[j]
-
-    np.savetxt("ytest.csv",y_test,delimiter=",",fmt='%d')
-
-
-    model = tf.keras.models.Sequential([
-      tf.keras.layers.Flatten(input_shape=(5,1)),
-      tf.keras.layers.Dense(256, activation='relu'),
-      tf.keras.layers.Dropout(0.01),
-      tf.keras.layers.Dense(1000, activation='softmax')
-    ])
-    mlflow.log_param("layers dense relu",256)
-    mlflow.log_param("layers droout",0.01)
-    mlflow.log_param("layers dense softmax",1000)
-    mlflow.log_param("iterations",epoch)
-
-
-    model.compile(optimizer='adam',
-                  loss='sparse_categorical_crossentropy',
-                  metrics=['accuracy'])
-
-    y_train = np.array(y_train).astype(np.float32)
-    y_test = np.array(y_test).astype(np.float32)
-
-
-
-    model.fit(x_train, y_train, epochs=epoch)
-    eval = model.evaluate(x_test, y_test)
-    
-    mlflow.log_metric("accuraccy", eval[1])
-    
-    prediction = model.predict(x_test)
-    classes_x=np.argmax(prediction,axis=1)
-
-    rows = []
-
-    for j,i in enumerate(classes_x):
-        row = [invGames[i],invGames[y_test[j]]]
-        rows.append(row)
-    with open('results.csv','w',encoding='UTF-8',newline='') as f:
-            writer = csv.writer(f)
-            writer.writerow(["predicted", "expected"])
-            for row in rows:
-                writer.writerow(row)
-    
-    model.save('./model')    
+import tensorflow as tf
+import os
+import pandas as pd
+import numpy as np
+import csv
+from sklearn.model_selection import train_test_split
+import sys
+from sacred.observers import MongoObserver
+from sacred.observers import FileStorageObserver
+from sacred import Experiment
+
+ex = Experiment("444386 sacred_scopes", interactive=True, save_git_info=False)
+ex.observers.append(MongoObserver(url='mongodb://admin:IUM_2021@172.17.0.1:27017',db_name='sacred'))
+ex.observers.append(FileStorageObserver('training'))
+epochs = int(sys.argv[1])
+
+@ex.config
+def my_config():
+    epoch = epochs
+    layerDenseRelu = 256
+    layerDropout = 0.01
+    layerDenseSoftMax = 1000.0
+
+#ex.add_config("config.json")
+
+@ex.capture
+def prepare_data():
+    steam=pd.read_csv('data.csv',usecols=[0,1,2,3],names=['userId','game','behavior','hoursPlayed'])
+    steam.isnull().values.any()
+    steam['userId'] = steam.userId.astype(str)
+    purchaseCount = steam[steam["behavior"] != "play"]["game"].value_counts()
+    playCount = steam[steam["behavior"] != "purchase"]["game"].value_counts()
+
+    playerPurchaseCount = steam[steam["behavior"] != "play"]["userId"].value_counts()
+    playerPlayCount = steam[steam["behavior"] != "purchase"]["userId"].value_counts()
+
+    steam = steam[steam['behavior'] != 'purchase']
+    steam = steam.groupby("game").filter(lambda x: len(x)>10)
+    size=int(len(steam)/10)
+
+    meanGame = steam[steam["behavior"] != "purchase"].groupby("game").mean()
+    meanGame = meanGame.to_dict()
+    meanGame = meanGame['hoursPlayed']
+
+    purchaseCount = purchaseCount.to_dict()
+    playCount = playCount.to_dict()
+    playerPurchaseCount = playerPurchaseCount.to_dict()
+    playerPlayCount = playerPlayCount.to_dict()
+
+    steam['meanTime'] = 0;
+    steam['purchaseCount'] = 0;
+    steam['playCount'] = 0;
+    steam['playerPurchaseCount'] =0;
+    steam['playerPlayCount'] =0;
+    steam['playPercent'] =0;
+
+    for i in steam.index:
+        steam.at[i,'meanTime'] = meanGame[steam.at[i,'game']]
+        steam.at[i,'purchaseCount'] = purchaseCount[steam.at[i,'game']]
+        steam.at[i,'playCount'] = playCount[steam.at[i,'game']]
+        steam.at[i,'playerPurchaseCount'] = playerPurchaseCount[steam.at[i,'userId']]
+        steam.at[i,'playerPlayCount'] = playerPlayCount[steam.at[i,'userId']]
+        steam.at[i,'playPercent'] = playerPlayCount[steam.at[i,'userId']]/playerPurchaseCount[steam.at[i,'userId']]
+
+    steam_train, steam_test = train_test_split(steam, test_size=size, random_state=1, stratify=steam["game"])
+    steam_train, steam_dev = train_test_split(steam_train, test_size=size, random_state=1, stratify=steam_train["game"])
+    
+    games = {}
+    for i in steam['game']:
+        games[i] = 0
+
+    j=0
+    for key,game in games.items():
+        games[key]=j
+        j=j+1
+
+    for i in steam['game']:
+        i = games[i]
+        
+    invGames = {v: k for k, v in games.items()}
+
+    x_train = steam_train[['hoursPlayed','purchaseCount','playCount','playerPlayCount','playerPurchaseCount']]
+    y_train = steam_train['game']
+
+    x_test = steam_test[['hoursPlayed','purchaseCount','playCount','playerPlayCount','playerPurchaseCount']]
+    y_test = steam_test['game']
+
+
+    x_train = np.array(x_train)
+    y_train = np.array(y_train)
+    x_test = np.array(x_test)
+    y_test = np.array(y_test)
+
+    with open('xtest.csv','w',encoding='UTF-8',newline='') as xtest:
+        writer = csv.writer(xtest)
+        for i in x_test:
+            writer.writerow(i)
+        
+    for i,j in enumerate(y_train):
+        y_train[i] = games[j]
+        
+    for i,j in enumerate(y_test):
+        y_test[i] = games[j]
+    y_train = np.array(y_train).astype(np.float32)
+    y_test = np.array(y_test).astype(np.float32)
+    np.savetxt("ytest.csv",y_test,delimiter=",",fmt='%d')
+    return x_train, y_train, x_test, y_test, invGames
+
+@ex.main
+def my_main(epoch,layerDenseRelu,layerDropout,layerDenseSoftMax,_run):
+    x_train, y_train, x_test, y_test, invGames = prepare_data()
+    model = tf.keras.models.Sequential([
+        tf.keras.layers.Flatten(input_shape=(5,1)),
+        tf.keras.layers.Dense(layerDenseRelu, activation='relu'),
+        tf.keras.layers.Dropout(layerDropout),
+        tf.keras.layers.Dense(layerDenseSoftMax, activation='softmax')
+    ])
+
+    model.compile(optimizer='adam',
+                  loss='sparse_categorical_crossentropy',
+                  metrics=['accuracy'])
+
+
+    model.fit(x_train, y_train, epochs=epoch)
+    evaluation = model.evaluate(x_test, y_test)
+    _run.log_scalar("training.loss", evaluation[0])
+    _run.log_scalar("training.accuracy", evaluation[1])
+    
+    prediction = model.predict(x_test)
+    classes_x=np.argmax(prediction,axis=1)
+
+    rows = []
+
+    for j,i in enumerate(classes_x):
+        row = [invGames[i],invGames[y_test[j]]]
+        rows.append(row)
+    with open('results.csv','w',encoding='UTF-8',newline='') as f:
+            writer = csv.writer(f)
+            writer.writerow(["predicted", "expected"])
+            for row in rows:
+                writer.writerow(row)
+    
+    model.save('./model')
+    ex.add_artifact('./model/saved_model.pb')
+    
+
+ex.run()