conda env

# Conflicts:
#	Jenkinsfile

Dockerfile

@@ -0,0 +1,19 @@
+FROM ubuntu:latest
+RUN apt clean
+RUN apt update
+RUN apt install -y python3
+RUN apt install -y python3-pip
+RUN apt install -y unzip
+RUN pip3 install pandas
+RUN pip3 install kaggle
+RUN pip3 install tensorflow
+RUN pip3 install sklearn
+RUN pip3 install pymongo
+RUN pip3 install sacred
+RUN pip3 install GitPython
+COPY ./data_train ./
+COPY ./data_dev ./
+COPY ./neural_network.sh ./
+COPY ./neural_network.py ./
+RUN mkdir /.kaggle
+RUN chmod -R 777 /.kaggle

Jenkinsfile

@@ -0,0 +1,45 @@
+node {
+        stage('Preparation') {
+
+                properties([
+                    parameters([
+                        string(defaultValue: 'karopa',
+                            description: 'Kaggle username',
+                            name: 'KAGGLE_USERNAME',
+                            trim: false),
+                        password(defaultValue: '',
+                            description: 'Kaggle token',
+                            name: 'KAGGLE_KEY'),
+                        string(defaultValue: '5000',
+                            description: 'Data cutoff',
+                            name: 'CUTOFF',
+                            trim: false)
+                            ])
+                ]
+                )
+
+        }
+        stage('Clone repo') {
+                    checkout scm
+                    def testImage = docker.build("karopa/ium:02")
+                    testImage.inside {
+                        withEnv(["KAGGLE_USERNAME=${params.KAGGLE_USERNAME}", "KAGGLE_KEY=${params.KAGGLE_KEY}"]){
+                        checkout([$class: 'GitSCM', branches: [[name: '*/master']], doGenerateSubmoduleConfigurations: false, extensions: [], submoduleCfg: [], userRemoteConfigs: [[url: 'https://git.wmi.amu.edu.pl/s434765/ium_434765']]])
+                        sh '''
+                            #!/usr/bin/env bash
+                            chmod 777 get_data.sh
+                            ./get_data.sh $CUTOFF | tee output.txt
+                        '''
+                        archiveArtifacts "data_dev"
+                        archiveArtifacts "data_shuf"
+                        archiveArtifacts "data_test"
+                        archiveArtifacts "data_train"
+                        archiveArtifacts "output.txt"
+                        }
+                    }
+
+        }
+         stage ("build training") {
+                build 's434765-training/master/'
+        }
+}

JenkinsfileNeural

@@ -0,0 +1,50 @@
+node {
+         stage('Preparation') {
+
+                properties([
+                    copyArtifactPermission('s434765-training'),
+                    parameters([
+                        buildSelector(defaultSelector: lastSuccessful(),
+                            description: 'Which build to use for copying artifacts',
+                            name: 'BUILD_SELECTOR'),
+                        string(defaultValue: '30',
+                            description: 'Amount of epochs',
+                            name: 'EPOCHS',
+                            trim: false)
+                            ])
+                ]
+                )
+
+        }
+        stage('Clone repo') {
+                    /*try {*/ docker.image("karopa/ium:27").inside {
+                        stage('Test') {
+                            checkout([$class: 'GitSCM', branches: [[name: '*/master']], doGenerateSubmoduleConfigurations: false, extensions: [], submoduleCfg: [], userRemoteConfigs: [[url: 'https://git.wmi.amu.edu.pl/s434765/ium_434765']]])
+                            copyArtifacts fingerprintArtifacts: true, projectName: 's434765-create-dataset', selector: buildParameter("BUILD_SELECTOR")
+                            sh '''
+                                #!/usr/bin/env bash
+                                chmod 777 neural_network.sh
+                                ./neural_network.sh $EPOCHS | tee output.txt
+                            '''
+                            archiveArtifacts 'output.txt'
+                            archiveArtifacts 'model/**/*.*'
+                            archiveArtifacts 'my_runs/**/*.*'
+                        }
+                      /*  emailext body: 'Successful build',
+                        subject: "s434765",
+                        to: "26ab8f35.uam.onmicrosoft.com@emea.teams.ms"
+                        }
+
+                    }
+                     catch (e) {
+                            emailext body: 'Failed build',
+                            subject: "s434765",
+                            to: "26ab8f35.uam.onmicrosoft.com@emea.teams.ms"
+                            throw e*/
+                     }
+        }
+     /*    stage ("build evaluation") {
+                build 's434765-evaluation/evaluation/'
+
+        }*/
+}

JenkinsfileStats

@@ -12,13 +12,17 @@ node {
 
         }
         stage('Clone repo') {
-                    checkout([$class: 'GitSCM', branches: [[name: '*/stats']], doGenerateSubmoduleConfigurations: false, extensions: [], submoduleCfg: [], userRemoteConfigs: [[url: 'https://git.wmi.amu.edu.pl/s434765/ium_434765']]])
-                    copyArtifacts filter: 'data_shuf', fingerprintArtifacts: true, projectName: 's434765-create-dataset', selector: buildParameter("BUILD_SELECTOR")
-                    sh '''
-                        #!/usr/bin/env bash
-                        chmod 777 get_stats_simple.sh
-                        ./get_stats_simple.sh | tee output.txt
-                    '''
-                    archiveArtifacts 'output.txt'
-                    }
+                    docker.image("karopa/ium:03").inside {
+                        stage('Test') {
+                            checkout([$class: 'GitSCM', branches: [[name: '*/master']], doGenerateSubmoduleConfigurations: false, extensions: [], submoduleCfg: [], userRemoteConfigs: [[url: 'https://git.wmi.amu.edu.pl/s434765/ium_434765']]])
+                            copyArtifacts fingerprintArtifacts: true, projectName: 's434765-create-dataset', selector: buildParameter("BUILD_SELECTOR")
+                            sh '''
+                                #!/usr/bin/env bash
+                                chmod 777 get_stats.sh
+                                ./get_stats.sh | tee output.txt
+                            '''
+                            archiveArtifacts 'output.txt'
+                        }
+            }
+        }
     }

environment.yml

@@ -0,0 +1,145 @@
+name: myenv
+channels:
+  - defaults
+dependencies:
+  - _tflow_select=2.3.0=eigen
+  - absl-py=0.12.0=py38haa95532_0
+  - aiohttp=3.7.4=py38h2bbff1b_1
+  - astor=0.8.1=py38haa95532_0
+  - astunparse=1.6.3=py_0
+  - async-timeout=3.0.1=py38haa95532_0
+  - attrs=21.2.0=pyhd3eb1b0_0
+  - blas=1.0=mkl
+  - blinker=1.4=py38haa95532_0
+  - brotlipy=0.7.0=py38h2bbff1b_1003
+  - ca-certificates=2021.5.25=haa95532_1
+  - cachetools=4.2.2=pyhd3eb1b0_0
+  - certifi=2020.12.5=py38haa95532_0
+  - cffi=1.14.5=py38hcd4344a_0
+  - click=8.0.1=pyhd3eb1b0_0
+  - coverage=5.5=py38h2bbff1b_2
+  - cryptography=2.9.2=py38h7a1dbc1_0
+  - cycler=0.10.0=py38_0
+  - cython=0.29.23=py38hd77b12b_0
+  - freetype=2.10.4=hd328e21_0
+  - gast=0.4.0=py_0
+  - google-auth=1.30.1=pyhd3eb1b0_0
+  - google-auth-oauthlib=0.4.1=py_2
+  - google-pasta=0.2.0=py_0
+  - grpcio=1.36.1=py38hc60d5dd_1
+  - h5py=2.10.0=py38h5e291fa_0
+  - hdf5=1.10.4=h7ebc959_0
+  - icc_rt=2019.0.0=h0cc432a_1
+  - icu=58.2=ha925a31_3
+  - idna=2.10=pyhd3eb1b0_0
+  - importlib-metadata=3.10.0=py38haa95532_0
+  - intel-openmp=2021.2.0=haa95532_616
+  - jpeg=9b=hb83a4c4_2
+  - keras-applications=1.0.8=py_1
+  - keras-preprocessing=1.1.2=pyhd3eb1b0_0
+  - kiwisolver=1.3.1=py38hd77b12b_0
+  - libpng=1.6.37=h2a8f88b_0
+  - libprotobuf=3.14.0=h23ce68f_0
+  - libtiff=4.2.0=hd0e1b90_0
+  - lz4-c=1.9.3=h2bbff1b_0
+  - markdown=3.3.4=py38haa95532_0
+  - matplotlib=3.3.4=py38haa95532_0
+  - matplotlib-base=3.3.4=py38h49ac443_0
+  - mkl=2021.2.0=haa95532_296
+  - mkl-service=2.3.0=py38h2bbff1b_1
+  - mkl_fft=1.3.0=py38h277e83a_2
+  - mkl_random=1.2.1=py38hf11a4ad_2
+  - multidict=5.1.0=py38h2bbff1b_2
+  - numpy=1.20.2=py38ha4e8547_0
+  - numpy-base=1.20.2=py38hc2deb75_0
+  - oauthlib=3.1.0=py_0
+  - olefile=0.46=py_0
+  - openssl=1.1.1k=h2bbff1b_0
+  - opt_einsum=3.3.0=pyhd3eb1b0_1
+  - pandas=1.2.4=py38hd77b12b_0
+  - pillow=8.2.0=py38h4fa10fc_0
+  - pip=21.1.1=py38haa95532_0
+  - protobuf=3.14.0=py38hd77b12b_1
+  - pyasn1=0.4.8=py_0
+  - pyasn1-modules=0.2.8=py_0
+  - pycparser=2.20=py_2
+  - pyjwt=2.1.0=py38haa95532_0
+  - pyopenssl=20.0.1=pyhd3eb1b0_1
+  - pyparsing=2.4.7=pyhd3eb1b0_0
+  - pyqt=5.9.2=py38ha925a31_4
+  - pyreadline=2.1=py38_1
+  - pysocks=1.7.1=py38haa95532_0
+  - python=3.8.10=hdbf39b2_7
+  - python-dateutil=2.8.1=pyhd3eb1b0_0
+  - pytz=2021.1=pyhd3eb1b0_0
+  - qt=5.9.7=vc14h73c81de_0
+  - requests=2.25.1=pyhd3eb1b0_0
+  - requests-oauthlib=1.3.0=py_0
+  - rsa=4.7.2=pyhd3eb1b0_1
+  - scipy=1.6.2=py38h66253e8_1
+  - setuptools=52.0.0=py38haa95532_0
+  - sip=4.19.13=py38ha925a31_0
+  - six=1.15.0=py38haa95532_0
+  - sqlite=3.35.4=h2bbff1b_0
+  - tensorboard=2.5.0=py_0
+  - tensorboard-plugin-wit=1.6.0=py_0
+  - tensorflow=2.3.0=mkl_py38h8c0d9a2_0
+  - tensorflow-base=2.3.0=eigen_py38h75a453f_0
+  - tensorflow-estimator=2.5.0=pyh7b7c402_0
+  - termcolor=1.1.0=py38haa95532_1
+  - tk=8.6.10=he774522_0
+  - tornado=6.1=py38h2bbff1b_0
+  - typing-extensions=3.7.4.3=hd3eb1b0_0
+  - typing_extensions=3.7.4.3=pyh06a4308_0
+  - vc=14.2=h21ff451_1
+  - vs2015_runtime=14.27.29016=h5e58377_2
+  - wheel=0.36.2=pyhd3eb1b0_0
+  - win_inet_pton=1.1.0=py38haa95532_0
+  - wincertstore=0.2=py38_0
+  - wrapt=1.12.1=py38he774522_1
+  - xz=5.2.5=h62dcd97_0
+  - yarl=1.6.3=py38h2bbff1b_0
+  - zipp=3.4.1=pyhd3eb1b0_0
+  - zlib=1.2.11=h62dcd97_4
+  - zstd=1.4.9=h19a0ad4_0
+  - pip:
+    - alembic==1.4.1
+    - chardet==4.0.0
+    - cloudpickle==1.6.0
+    - colorama==0.4.4
+    - databricks-cli==0.14.3
+    - docker==5.0.0
+    - entrypoints==0.3
+    - flask==2.0.1
+    - gitdb==4.0.7
+    - gitpython==3.1.17
+    - greenlet==1.1.0
+    - itsdangerous==2.0.1
+    - jinja2==3.0.1
+    - joblib==1.0.1
+    - kaggle==1.5.12
+    - mako==1.1.4
+    - markupsafe==2.0.1
+    - mlflow==1.17.0
+    - prometheus-client==0.10.1
+    - prometheus-flask-exporter==0.18.2
+    - python-editor==1.0.4
+    - python-slugify==5.0.2
+    - pywin32==227
+    - pyyaml==5.4.1
+    - querystring-parser==1.2.4
+    - scikit-learn==0.24.2
+    - sklearn==0.0
+    - smmap==4.0.0
+    - sqlalchemy==1.4.17
+    - sqlparse==0.4.1
+    - tabulate==0.8.9
+    - tensorboard-data-server==0.6.1
+    - text-unidecode==1.3
+    - threadpoolctl==2.1.0
+    - tqdm==4.61.0
+    - urllib3==1.26.5
+    - waitress==2.0.0
+    - websocket-client==1.0.1
+    - werkzeug==2.0.1
+prefix: C:\Users\karol\anaconda3\envs\myenv

get_data.sh

@@ -1,20 +1,14 @@
 #!/bin/bash
+rm USvideos_modified.csv
 if kaggle datasets download -d sgonkaggle/youtube-trend-with-subscriber && unzip youtube-trend-with-subscriber.zip; then
-    head -n 2 USvideos_modified.csv
     grep -v -e "^$" - USvideos_modified.csv
     COUNT=$(wc -l "USvideos_modified.csv")
     echo "${COUNT}"
     head -n -1 "USvideos_modified.csv" | shuf > "data_shuf"
     head -n 544 "data_shuf" > "data_test"
     head -n 1088 "data_shuf" | tail -n 544 > "data_dev"
-    head -n +1089 "data_shuf" > "data_train"
-    echo "Shuffled dataset"
-    wc -l "data_shuf"
-    echo "Test dataset"
-    wc -l "data_test"
-    echo "Dev dataset"
-    wc -l "data_dev"
-    echo "Train dataset"
-    wc -l "data_train"
-    python main.py USvideos_modified.csv
+    head -n 5441 "data_shuf" | tail -n 4352 > "data_train"
+    tr '\n' '' < "data_dev"
+    sed '/^$/d' "data_dev"
+    python3 get_data.py USvideos_modified.csv
 fi

get_stats.sh

@@ -0,0 +1,9 @@
+#!/bin/bash
+echo "Shuffled dataset"
+wc -l "data_shuf"
+echo "Test dataset"
+wc -l "data_test"
+echo "Dev dataset"
+wc -l "data_dev"
+echo "Train dataset"
+wc -l "data_train"

my_runs/1/config.json

@@ -0,0 +1,4 @@
+{
+  "epochs_amount": 30,
+  "seed": 511320143
+}

my_runs/1/cout.txt

@@ -0,0 +1,79 @@
+views    0
+dtype: int32
+views    488
+dtype: int32
+likes    1
+dtype: int32
+likes    3345
+dtype: int32
+Epoch 1/30
+
 1/19 [>.............................] - ETA: 6s - loss: 0.0834 - mean_absolute_error: 0.0834
19/19 [==============================] - 1s 10ms/step - loss: 0.0679 - mean_absolute_error: 0.0679 - val_loss: 0.0670 - val_mean_absolute_error: 0.0670
+Epoch 2/30
+
 1/19 [>.............................] - ETA: 0s - loss: 0.1142 - mean_absolute_error: 0.1142
19/19 [==============================] - 0s 2ms/step - loss: 0.0657 - mean_absolute_error: 0.0657 - val_loss: 0.0530 - val_mean_absolute_error: 0.0530
+Epoch 3/30
+
 1/19 [>.............................] - ETA: 0s - loss: 0.0940 - mean_absolute_error: 0.0940
19/19 [==============================] - 0s 2ms/step - loss: 0.0608 - mean_absolute_error: 0.0608 - val_loss: 0.0600 - val_mean_absolute_error: 0.0600
+Epoch 4/30
+
 1/19 [>.............................] - ETA: 0s - loss: 0.0524 - mean_absolute_error: 0.0524
19/19 [==============================] - 0s 2ms/step - loss: 0.0521 - mean_absolute_error: 0.0521 - val_loss: 0.0541 - val_mean_absolute_error: 0.0541
+Epoch 5/30
+
 1/19 [>.............................] - ETA: 0s - loss: 0.0440 - mean_absolute_error: 0.0440
19/19 [==============================] - 0s 2ms/step - loss: 0.0518 - mean_absolute_error: 0.0518 - val_loss: 0.0541 - val_mean_absolute_error: 0.0541
+Epoch 6/30
+
 1/19 [>.............................] - ETA: 0s - loss: 0.0576 - mean_absolute_error: 0.0576
19/19 [==============================] - 0s 2ms/step - loss: 0.0579 - mean_absolute_error: 0.0579 - val_loss: 0.0523 - val_mean_absolute_error: 0.0523
+Epoch 7/30
+
 1/19 [>.............................] - ETA: 0s - loss: 0.0310 - mean_absolute_error: 0.0310
19/19 [==============================] - 0s 2ms/step - loss: 0.0497 - mean_absolute_error: 0.0497 - val_loss: 0.0523 - val_mean_absolute_error: 0.0523
+Epoch 8/30
+
 1/19 [>.............................] - ETA: 0s - loss: 0.0628 - mean_absolute_error: 0.0628
19/19 [==============================] - 0s 2ms/step - loss: 0.0531 - mean_absolute_error: 0.0531 - val_loss: 0.0551 - val_mean_absolute_error: 0.0551
+Epoch 9/30
+
 1/19 [>.............................] - ETA: 0s - loss: 0.0425 - mean_absolute_error: 0.0425
19/19 [==============================] - 0s 2ms/step - loss: 0.0543 - mean_absolute_error: 0.0543 - val_loss: 0.0527 - val_mean_absolute_error: 0.0527
+Epoch 10/30
+
 1/19 [>.............................] - ETA: 0s - loss: 0.0560 - mean_absolute_error: 0.0560
19/19 [==============================] - 0s 2ms/step - loss: 0.0549 - mean_absolute_error: 0.0549 - val_loss: 0.0525 - val_mean_absolute_error: 0.0525
+Epoch 11/30
+
 1/19 [>.............................] - ETA: 0s - loss: 0.0391 - mean_absolute_error: 0.0391
19/19 [==============================] - 0s 2ms/step - loss: 0.0520 - mean_absolute_error: 0.0520 - val_loss: 0.0556 - val_mean_absolute_error: 0.0556
+Epoch 12/30
+
 1/19 [>.............................] - ETA: 0s - loss: 0.0417 - mean_absolute_error: 0.0417
19/19 [==============================] - 0s 2ms/step - loss: 0.0578 - mean_absolute_error: 0.0578 - val_loss: 0.0522 - val_mean_absolute_error: 0.0522
+Epoch 13/30
+
 1/19 [>.............................] - ETA: 0s - loss: 0.0834 - mean_absolute_error: 0.0834
19/19 [==============================] - 0s 2ms/step - loss: 0.0605 - mean_absolute_error: 0.0605 - val_loss: 0.0532 - val_mean_absolute_error: 0.0532
+Epoch 14/30
+
 1/19 [>.............................] - ETA: 0s - loss: 0.0430 - mean_absolute_error: 0.0430
19/19 [==============================] - 0s 2ms/step - loss: 0.0582 - mean_absolute_error: 0.0582 - val_loss: 0.0526 - val_mean_absolute_error: 0.0526
+Epoch 15/30
+
 1/19 [>.............................] - ETA: 0s - loss: 0.0506 - mean_absolute_error: 0.0506
19/19 [==============================] - 0s 2ms/step - loss: 0.0512 - mean_absolute_error: 0.0512 - val_loss: 0.0533 - val_mean_absolute_error: 0.0533
+Epoch 16/30
+
 1/19 [>.............................] - ETA: 0s - loss: 0.0402 - mean_absolute_error: 0.0402
19/19 [==============================] - 0s 2ms/step - loss: 0.0514 - mean_absolute_error: 0.0514 - val_loss: 0.0537 - val_mean_absolute_error: 0.0537
+Epoch 17/30
+
 1/19 [>.............................] - ETA: 0s - loss: 0.0247 - mean_absolute_error: 0.0247
19/19 [==============================] - 0s 2ms/step - loss: 0.0463 - mean_absolute_error: 0.0463 - val_loss: 0.0519 - val_mean_absolute_error: 0.0519
+Epoch 18/30
+
 1/19 [>.............................] - ETA: 0s - loss: 0.0401 - mean_absolute_error: 0.0401
19/19 [==============================] - 0s 2ms/step - loss: 0.0537 - mean_absolute_error: 0.0537 - val_loss: 0.0568 - val_mean_absolute_error: 0.0568
+Epoch 19/30
+
 1/19 [>.............................] - ETA: 0s - loss: 0.0930 - mean_absolute_error: 0.0930
19/19 [==============================] - 0s 2ms/step - loss: 0.0534 - mean_absolute_error: 0.0534 - val_loss: 0.0523 - val_mean_absolute_error: 0.0523
+Epoch 20/30
+
 1/19 [>.............................] - ETA: 0s - loss: 0.0631 - mean_absolute_error: 0.0631
19/19 [==============================] - 0s 2ms/step - loss: 0.0577 - mean_absolute_error: 0.0577 - val_loss: 0.0532 - val_mean_absolute_error: 0.0532
+Epoch 21/30
+
 1/19 [>.............................] - ETA: 0s - loss: 0.0524 - mean_absolute_error: 0.0524
19/19 [==============================] - 0s 2ms/step - loss: 0.0538 - mean_absolute_error: 0.0538 - val_loss: 0.0540 - val_mean_absolute_error: 0.0540
+Epoch 22/30
+
 1/19 [>.............................] - ETA: 0s - loss: 0.0435 - mean_absolute_error: 0.0435
19/19 [==============================] - 0s 2ms/step - loss: 0.0510 - mean_absolute_error: 0.0510 - val_loss: 0.0594 - val_mean_absolute_error: 0.0594
+Epoch 23/30
+
 1/19 [>.............................] - ETA: 0s - loss: 0.0324 - mean_absolute_error: 0.0324
19/19 [==============================] - 0s 2ms/step - loss: 0.0573 - mean_absolute_error: 0.0573 - val_loss: 0.0537 - val_mean_absolute_error: 0.0537
+Epoch 24/30
+
 1/19 [>.............................] - ETA: 0s - loss: 0.0354 - mean_absolute_error: 0.0354
19/19 [==============================] - 0s 2ms/step - loss: 0.0510 - mean_absolute_error: 0.0510 - val_loss: 0.0546 - val_mean_absolute_error: 0.0546
+Epoch 25/30
+
 1/19 [>.............................] - ETA: 0s - loss: 0.0474 - mean_absolute_error: 0.0474
19/19 [==============================] - 0s 2ms/step - loss: 0.0539 - mean_absolute_error: 0.0539 - val_loss: 0.0525 - val_mean_absolute_error: 0.0525
+Epoch 26/30
+
 1/19 [>.............................] - ETA: 0s - loss: 0.0928 - mean_absolute_error: 0.0928
19/19 [==============================] - 0s 2ms/step - loss: 0.0612 - mean_absolute_error: 0.0612 - val_loss: 0.0540 - val_mean_absolute_error: 0.0540
+Epoch 27/30
+
 1/19 [>.............................] - ETA: 0s - loss: 0.0582 - mean_absolute_error: 0.0582
19/19 [==============================] - 0s 2ms/step - loss: 0.0535 - mean_absolute_error: 0.0535 - val_loss: 0.0548 - val_mean_absolute_error: 0.0548
+Epoch 28/30
+
 1/19 [>.............................] - ETA: 0s - loss: 0.0415 - mean_absolute_error: 0.0415
19/19 [==============================] - 0s 2ms/step - loss: 0.0511 - mean_absolute_error: 0.0511 - val_loss: 0.0533 - val_mean_absolute_error: 0.0533
+Epoch 29/30
+
 1/19 [>.............................] - ETA: 0s - loss: 0.0491 - mean_absolute_error: 0.0491
19/19 [==============================] - 0s 3ms/step - loss: 0.0532 - mean_absolute_error: 0.0532 - val_loss: 0.0528 - val_mean_absolute_error: 0.0528
+Epoch 30/30
+
 1/19 [>.............................] - ETA: 0s - loss: 0.0475 - mean_absolute_error: 0.0475
19/19 [==============================] - 0s 2ms/step - loss: 0.0529 - mean_absolute_error: 0.0529 - val_loss: 0.0529 - val_mean_absolute_error: 0.0529
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+likes    1
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my_runs/1/info.json

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+{
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+}

my_runs/1/metrics.json

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+{}

my_runs/1/run.json

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+{
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+        "dirty": true,
+        "url": "https://git.wmi.amu.edu.pl/s434765/ium_434765.git"
+      }
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+      [
+        "evaluate_network.py",
+        "_sources\\evaluate_network_6bc39a6cabbc78720ddbbd5b23f51cc3.py"
+      ],
+      [
+        "neural_network.py",
+        "_sources\\neural_network_cdaa9eab635a60c87899a6eaac9e398e.py"
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+}

my_runs/2/config.json

@@ -0,0 +1,4 @@
+{
+  "epochs_amount": 30,
+  "seed": 535480662
+}

my_runs/2/cout.txt

@@ -0,0 +1,78 @@
+views    0
+dtype: int32
+views    488
+dtype: int32
+likes    1
+dtype: int32
+likes    3345
+dtype: int32
+Epoch 1/30
+
 1/19 [>.............................] - ETA: 6s - loss: 0.1168 - mean_absolute_error: 0.1168
19/19 [==============================] - 1s 10ms/step - loss: 0.0788 - mean_absolute_error: 0.0788 - val_loss: 0.0639 - val_mean_absolute_error: 0.0639
+Epoch 2/30
+
 1/19 [>.............................] - ETA: 0s - loss: 0.0699 - mean_absolute_error: 0.0699
19/19 [==============================] - 0s 2ms/step - loss: 0.0622 - mean_absolute_error: 0.0622 - val_loss: 0.0589 - val_mean_absolute_error: 0.0589
+Epoch 3/30
+
 1/19 [>.............................] - ETA: 0s - loss: 0.0547 - mean_absolute_error: 0.0547
19/19 [==============================] - 0s 2ms/step - loss: 0.0566 - mean_absolute_error: 0.0566 - val_loss: 0.0542 - val_mean_absolute_error: 0.0542
+Epoch 4/30
+
 1/19 [>.............................] - ETA: 0s - loss: 0.0351 - mean_absolute_error: 0.0351
19/19 [==============================] - 0s 2ms/step - loss: 0.0534 - mean_absolute_error: 0.0534 - val_loss: 0.0524 - val_mean_absolute_error: 0.0524
+Epoch 5/30
+
 1/19 [>.............................] - ETA: 0s - loss: 0.0436 - mean_absolute_error: 0.0436
19/19 [==============================] - 0s 2ms/step - loss: 0.0562 - mean_absolute_error: 0.0562 - val_loss: 0.0560 - val_mean_absolute_error: 0.0560
+Epoch 6/30
+
 1/19 [>.............................] - ETA: 0s - loss: 0.0474 - mean_absolute_error: 0.0474
19/19 [==============================] - 0s 2ms/step - loss: 0.0513 - mean_absolute_error: 0.0513 - val_loss: 0.0533 - val_mean_absolute_error: 0.0533
+Epoch 7/30
+
 1/19 [>.............................] - ETA: 0s - loss: 0.0714 - mean_absolute_error: 0.0714
19/19 [==============================] - 0s 2ms/step - loss: 0.0562 - mean_absolute_error: 0.0562 - val_loss: 0.0519 - val_mean_absolute_error: 0.0519
+Epoch 8/30
+
 1/19 [>.............................] - ETA: 0s - loss: 0.0567 - mean_absolute_error: 0.0567
19/19 [==============================] - 0s 2ms/step - loss: 0.0535 - mean_absolute_error: 0.0535 - val_loss: 0.0526 - val_mean_absolute_error: 0.0526
+Epoch 9/30
+
 1/19 [>.............................] - ETA: 0s - loss: 0.0472 - mean_absolute_error: 0.0472
19/19 [==============================] - 0s 2ms/step - loss: 0.0571 - mean_absolute_error: 0.0571 - val_loss: 0.0559 - val_mean_absolute_error: 0.0559
+Epoch 10/30
+
 1/19 [>.............................] - ETA: 0s - loss: 0.0634 - mean_absolute_error: 0.0634
19/19 [==============================] - 0s 2ms/step - loss: 0.0528 - mean_absolute_error: 0.0528 - val_loss: 0.0527 - val_mean_absolute_error: 0.0527
+Epoch 11/30
+
 1/19 [>.............................] - ETA: 0s - loss: 0.0412 - mean_absolute_error: 0.0412
19/19 [==============================] - 0s 2ms/step - loss: 0.0529 - mean_absolute_error: 0.0529 - val_loss: 0.0542 - val_mean_absolute_error: 0.0542
+Epoch 12/30
+
 1/19 [>.............................] - ETA: 0s - loss: 0.0390 - mean_absolute_error: 0.0390
19/19 [==============================] - 0s 2ms/step - loss: 0.0496 - mean_absolute_error: 0.0496 - val_loss: 0.0596 - val_mean_absolute_error: 0.0596
+Epoch 13/30
+
 1/19 [>.............................] - ETA: 0s - loss: 0.0625 - mean_absolute_error: 0.0625
19/19 [==============================] - 0s 2ms/step - loss: 0.0545 - mean_absolute_error: 0.0545 - val_loss: 0.0560 - val_mean_absolute_error: 0.0560
+Epoch 14/30
+
 1/19 [>.............................] - ETA: 0s - loss: 0.0206 - mean_absolute_error: 0.0206
19/19 [==============================] - 0s 2ms/step - loss: 0.0542 - mean_absolute_error: 0.0542 - val_loss: 0.0542 - val_mean_absolute_error: 0.0542
+Epoch 15/30
+
 1/19 [>.............................] - ETA: 0s - loss: 0.0311 - mean_absolute_error: 0.0311
19/19 [==============================] - 0s 3ms/step - loss: 0.0486 - mean_absolute_error: 0.0486 - val_loss: 0.0527 - val_mean_absolute_error: 0.0527
+Epoch 16/30
+
 1/19 [>.............................] - ETA: 0s - loss: 0.0270 - mean_absolute_error: 0.0270
19/19 [==============================] - 0s 2ms/step - loss: 0.0477 - mean_absolute_error: 0.0477 - val_loss: 0.0558 - val_mean_absolute_error: 0.0558
+Epoch 17/30
+
 1/19 [>.............................] - ETA: 0s - loss: 0.0808 - mean_absolute_error: 0.0808
19/19 [==============================] - 0s 2ms/step - loss: 0.0563 - mean_absolute_error: 0.0563 - val_loss: 0.0546 - val_mean_absolute_error: 0.0546
+Epoch 18/30
+
 1/19 [>.............................] - ETA: 0s - loss: 0.0433 - mean_absolute_error: 0.0433
19/19 [==============================] - 0s 2ms/step - loss: 0.0499 - mean_absolute_error: 0.0499 - val_loss: 0.0551 - val_mean_absolute_error: 0.0551
+Epoch 19/30
+
 1/19 [>.............................] - ETA: 0s - loss: 0.0431 - mean_absolute_error: 0.0431
19/19 [==============================] - 0s 2ms/step - loss: 0.0524 - mean_absolute_error: 0.0524 - val_loss: 0.0530 - val_mean_absolute_error: 0.0530
+Epoch 20/30
+
 1/19 [>.............................] - ETA: 0s - loss: 0.0298 - mean_absolute_error: 0.0298
19/19 [==============================] - 0s 2ms/step - loss: 0.0490 - mean_absolute_error: 0.0490 - val_loss: 0.0517 - val_mean_absolute_error: 0.0517
+Epoch 21/30
+
 1/19 [>.............................] - ETA: 0s - loss: 0.0499 - mean_absolute_error: 0.0499
19/19 [==============================] - 0s 2ms/step - loss: 0.0555 - mean_absolute_error: 0.0555 - val_loss: 0.0557 - val_mean_absolute_error: 0.0557
+Epoch 22/30
+
 1/19 [>.............................] - ETA: 0s - loss: 0.0401 - mean_absolute_error: 0.0401
19/19 [==============================] - 0s 2ms/step - loss: 0.0524 - mean_absolute_error: 0.0524 - val_loss: 0.0602 - val_mean_absolute_error: 0.0602
+Epoch 23/30
+
 1/19 [>.............................] - ETA: 0s - loss: 0.0652 - mean_absolute_error: 0.0652
19/19 [==============================] - 0s 2ms/step - loss: 0.0596 - mean_absolute_error: 0.0596 - val_loss: 0.0567 - val_mean_absolute_error: 0.0567
+Epoch 24/30
+
 1/19 [>.............................] - ETA: 0s - loss: 0.0275 - mean_absolute_error: 0.0275
19/19 [==============================] - 0s 2ms/step - loss: 0.0562 - mean_absolute_error: 0.0562 - val_loss: 0.0531 - val_mean_absolute_error: 0.0531
+Epoch 25/30
+
 1/19 [>.............................] - ETA: 0s - loss: 0.0602 - mean_absolute_error: 0.0602
19/19 [==============================] - 0s 2ms/step - loss: 0.0576 - mean_absolute_error: 0.0576 - val_loss: 0.0540 - val_mean_absolute_error: 0.0540
+Epoch 26/30
+
 1/19 [>.............................] - ETA: 0s - loss: 0.0388 - mean_absolute_error: 0.0388
19/19 [==============================] - 0s 2ms/step - loss: 0.0542 - mean_absolute_error: 0.0542 - val_loss: 0.0555 - val_mean_absolute_error: 0.0555
+Epoch 27/30
+
 1/19 [>.............................] - ETA: 0s - loss: 0.0711 - mean_absolute_error: 0.0711
19/19 [==============================] - 0s 2ms/step - loss: 0.0560 - mean_absolute_error: 0.0560 - val_loss: 0.0538 - val_mean_absolute_error: 0.0538
+Epoch 28/30
+
 1/19 [>.............................] - ETA: 0s - loss: 0.0875 - mean_absolute_error: 0.0875
19/19 [==============================] - 0s 2ms/step - loss: 0.0614 - mean_absolute_error: 0.0614 - val_loss: 0.0535 - val_mean_absolute_error: 0.0535
+Epoch 29/30
+
 1/19 [>.............................] - ETA: 0s - loss: 0.0462 - mean_absolute_error: 0.0462
19/19 [==============================] - 0s 2ms/step - loss: 0.0544 - mean_absolute_error: 0.0544 - val_loss: 0.0562 - val_mean_absolute_error: 0.0562
+Epoch 30/30
+
 1/19 [>.............................] - ETA: 0s - loss: 0.0588 - mean_absolute_error: 0.0588
19/19 [==============================] - 0s 2ms/step - loss: 0.0582 - mean_absolute_error: 0.0582 - val_loss: 0.0593 - val_mean_absolute_error: 0.0593
+views    1
+dtype: int32
+views    488
+dtype: int32
+likes    1
+dtype: int32
+likes    3345
+dtype: int32
+129787.96004765884
+129787.96004765884

my_runs/2/info.json

@@ -0,0 +1,3 @@
+{
+  "prepare_message_ts": "2021-05-20 22:01:49.105722"
+}

my_runs/2/metrics.json

@@ -0,0 +1,13 @@
+{
+  "training.metrics": {
+    "steps": [
+      0
+    ],
+    "timestamps": [
+      "2021-05-20T20:01:53.071700"
+    ],
+    "values": [
+      129787.96004765884
+    ]
+  }
+}

my_runs/2/run.json

@@ -0,0 +1,87 @@
+{
+  "artifacts": [],
+  "command": "my_main",
+  "experiment": {
+    "base_dir": "C:\\Users\\karol\\PycharmProjects\\ium_434765",
+    "dependencies": [
+      "numpy==1.19.5",
+      "pandas==1.2.4",
+      "sacred==0.8.2",
+      "tensorflow==2.5.0rc1"
+    ],
+    "mainfile": "neural_network.py",
+    "name": "sacred_scopes",
+    "repositories": [
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+        "commit": "07479089e2d0bd86c8b0dd3bb005f7178078cc34",
+        "dirty": true,
+        "url": "https://git.wmi.amu.edu.pl/s434765/ium_434765.git"
+      },
+      {
+        "commit": "07479089e2d0bd86c8b0dd3bb005f7178078cc34",
+        "dirty": true,
+        "url": "https://git.wmi.amu.edu.pl/s434765/ium_434765.git"
+      }
+    ],
+    "sources": [
+      [
+        "evaluate_network.py",
+        "_sources\\evaluate_network_6bc39a6cabbc78720ddbbd5b23f51cc3.py"
+      ],
+      [
+        "neural_network.py",
+        "_sources\\neural_network_eca667942d0304c50d970a67f9012302.py"
+      ]
+    ]
+  },
+  "heartbeat": "2021-05-20T20:01:53.071700",
+  "host": {
+    "ENV": {},
+    "cpu": "Unknown",
+    "gpus": {
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+      "gpus": [
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+          "persistence_mode": false,
+          "total_memory": 4096
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+      ]
+    },
+    "hostname": "DESKTOP-5PRPHO6",
+    "os": [
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+      "Windows-10-10.0.19041-SP0"
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+      "--tiny_db": null,
+      "--unobserved": false
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+  "resources": [],
+  "result": null,
+  "start_time": "2021-05-20T20:01:49.099728",
+  "status": "COMPLETED",
+  "stop_time": "2021-05-20T20:01:53.071700"
+}

my_runs/3/config.json

@@ -0,0 +1,4 @@
+{
+  "epochs_amount": 30,
+  "seed": 981983024
+}

my_runs/3/cout.txt

@@ -0,0 +1,78 @@
+views    0
+dtype: int32
+views    488
+dtype: int32
+likes    1
+dtype: int32
+likes    3345
+dtype: int32
+Epoch 1/30
+
 1/19 [>.............................] - ETA: 7s - loss: 0.1234 - mean_absolute_error: 0.1234
19/19 [==============================] - 1s 10ms/step - loss: 0.0687 - mean_absolute_error: 0.0687 - val_loss: 0.0587 - val_mean_absolute_error: 0.0587
+Epoch 2/30
+
 1/19 [>.............................] - ETA: 0s - loss: 0.0764 - mean_absolute_error: 0.0764
19/19 [==============================] - 0s 2ms/step - loss: 0.0583 - mean_absolute_error: 0.0583 - val_loss: 0.0541 - val_mean_absolute_error: 0.0541
+Epoch 3/30
+
 1/19 [>.............................] - ETA: 0s - loss: 0.0781 - mean_absolute_error: 0.0781
19/19 [==============================] - 0s 2ms/step - loss: 0.0595 - mean_absolute_error: 0.0595 - val_loss: 0.0572 - val_mean_absolute_error: 0.0572
+Epoch 4/30
+
 1/19 [>.............................] - ETA: 0s - loss: 0.0564 - mean_absolute_error: 0.0564
19/19 [==============================] - 0s 2ms/step - loss: 0.0592 - mean_absolute_error: 0.0592 - val_loss: 0.0541 - val_mean_absolute_error: 0.0541
+Epoch 5/30
+
 1/19 [>.............................] - ETA: 0s - loss: 0.0608 - mean_absolute_error: 0.0608
19/19 [==============================] - 0s 2ms/step - loss: 0.0552 - mean_absolute_error: 0.0552 - val_loss: 0.0524 - val_mean_absolute_error: 0.0524
+Epoch 6/30
+
 1/19 [>.............................] - ETA: 0s - loss: 0.0346 - mean_absolute_error: 0.0346
19/19 [==============================] - 0s 2ms/step - loss: 0.0510 - mean_absolute_error: 0.0510 - val_loss: 0.0544 - val_mean_absolute_error: 0.0544
+Epoch 7/30
+
 1/19 [>.............................] - ETA: 0s - loss: 0.0569 - mean_absolute_error: 0.0569
19/19 [==============================] - 0s 2ms/step - loss: 0.0570 - mean_absolute_error: 0.0570 - val_loss: 0.0521 - val_mean_absolute_error: 0.0521
+Epoch 8/30
+
 1/19 [>.............................] - ETA: 0s - loss: 0.0565 - mean_absolute_error: 0.0565
19/19 [==============================] - 0s 2ms/step - loss: 0.0558 - mean_absolute_error: 0.0558 - val_loss: 0.0542 - val_mean_absolute_error: 0.0542
+Epoch 9/30
+
 1/19 [>.............................] - ETA: 0s - loss: 0.0829 - mean_absolute_error: 0.0829
19/19 [==============================] - 0s 2ms/step - loss: 0.0563 - mean_absolute_error: 0.0563 - val_loss: 0.0535 - val_mean_absolute_error: 0.0535
+Epoch 10/30
+
 1/19 [>.............................] - ETA: 0s - loss: 0.0298 - mean_absolute_error: 0.0298
19/19 [==============================] - 0s 2ms/step - loss: 0.0509 - mean_absolute_error: 0.0509 - val_loss: 0.0520 - val_mean_absolute_error: 0.0520
+Epoch 11/30
+
 1/19 [>.............................] - ETA: 0s - loss: 0.0376 - mean_absolute_error: 0.0376
19/19 [==============================] - 0s 2ms/step - loss: 0.0546 - mean_absolute_error: 0.0546 - val_loss: 0.0557 - val_mean_absolute_error: 0.0557
+Epoch 12/30
+
 1/19 [>.............................] - ETA: 0s - loss: 0.0577 - mean_absolute_error: 0.0577
19/19 [==============================] - 0s 2ms/step - loss: 0.0567 - mean_absolute_error: 0.0567 - val_loss: 0.0521 - val_mean_absolute_error: 0.0521
+Epoch 13/30
+
 1/19 [>.............................] - ETA: 0s - loss: 0.0537 - mean_absolute_error: 0.0537
19/19 [==============================] - 0s 2ms/step - loss: 0.0552 - mean_absolute_error: 0.0552 - val_loss: 0.0556 - val_mean_absolute_error: 0.0556
+Epoch 14/30
+
 1/19 [>.............................] - ETA: 0s - loss: 0.0696 - mean_absolute_error: 0.0696
19/19 [==============================] - 0s 2ms/step - loss: 0.0616 - mean_absolute_error: 0.0616 - val_loss: 0.0571 - val_mean_absolute_error: 0.0571
+Epoch 15/30
+
 1/19 [>.............................] - ETA: 0s - loss: 0.0726 - mean_absolute_error: 0.0726
19/19 [==============================] - 0s 2ms/step - loss: 0.0556 - mean_absolute_error: 0.0556 - val_loss: 0.0531 - val_mean_absolute_error: 0.0531
+Epoch 16/30
+
 1/19 [>.............................] - ETA: 0s - loss: 0.0448 - mean_absolute_error: 0.0448
19/19 [==============================] - 0s 2ms/step - loss: 0.0533 - mean_absolute_error: 0.0533 - val_loss: 0.0562 - val_mean_absolute_error: 0.0562
+Epoch 17/30
+
 1/19 [>.............................] - ETA: 0s - loss: 0.0458 - mean_absolute_error: 0.0458
19/19 [==============================] - 0s 2ms/step - loss: 0.0553 - mean_absolute_error: 0.0553 - val_loss: 0.0558 - val_mean_absolute_error: 0.0558
+Epoch 18/30
+
 1/19 [>.............................] - ETA: 0s - loss: 0.0547 - mean_absolute_error: 0.0547
19/19 [==============================] - 0s 2ms/step - loss: 0.0590 - mean_absolute_error: 0.0590 - val_loss: 0.0561 - val_mean_absolute_error: 0.0561
+Epoch 19/30
+
 1/19 [>.............................] - ETA: 0s - loss: 0.0402 - mean_absolute_error: 0.0402
19/19 [==============================] - 0s 2ms/step - loss: 0.0579 - mean_absolute_error: 0.0579 - val_loss: 0.0554 - val_mean_absolute_error: 0.0554
+Epoch 20/30
+
 1/19 [>.............................] - ETA: 0s - loss: 0.0614 - mean_absolute_error: 0.0614
19/19 [==============================] - 0s 2ms/step - loss: 0.0558 - mean_absolute_error: 0.0558 - val_loss: 0.0539 - val_mean_absolute_error: 0.0539
+Epoch 21/30
+
 1/19 [>.............................] - ETA: 0s - loss: 0.0492 - mean_absolute_error: 0.0492
19/19 [==============================] - 0s 2ms/step - loss: 0.0525 - mean_absolute_error: 0.0525 - val_loss: 0.0540 - val_mean_absolute_error: 0.0540
+Epoch 22/30
+
 1/19 [>.............................] - ETA: 0s - loss: 0.0554 - mean_absolute_error: 0.0554
19/19 [==============================] - 0s 2ms/step - loss: 0.0595 - mean_absolute_error: 0.0595 - val_loss: 0.0533 - val_mean_absolute_error: 0.0533
+Epoch 23/30
+
 1/19 [>.............................] - ETA: 0s - loss: 0.0664 - mean_absolute_error: 0.0664
19/19 [==============================] - 0s 2ms/step - loss: 0.0533 - mean_absolute_error: 0.0533 - val_loss: 0.0518 - val_mean_absolute_error: 0.0518
+Epoch 24/30
+
 1/19 [>.............................] - ETA: 0s - loss: 0.0282 - mean_absolute_error: 0.0282
19/19 [==============================] - 0s 2ms/step - loss: 0.0471 - mean_absolute_error: 0.0471 - val_loss: 0.0517 - val_mean_absolute_error: 0.0517
+Epoch 25/30
+
 1/19 [>.............................] - ETA: 0s - loss: 0.0456 - mean_absolute_error: 0.0456
19/19 [==============================] - 0s 2ms/step - loss: 0.0473 - mean_absolute_error: 0.0473 - val_loss: 0.0536 - val_mean_absolute_error: 0.0536
+Epoch 26/30
+
 1/19 [>.............................] - ETA: 0s - loss: 0.0668 - mean_absolute_error: 0.0668
19/19 [==============================] - 0s 2ms/step - loss: 0.0571 - mean_absolute_error: 0.0571 - val_loss: 0.0532 - val_mean_absolute_error: 0.0532
+Epoch 27/30
+
 1/19 [>.............................] - ETA: 0s - loss: 0.0602 - mean_absolute_error: 0.0602
19/19 [==============================] - 0s 2ms/step - loss: 0.0558 - mean_absolute_error: 0.0558 - val_loss: 0.0520 - val_mean_absolute_error: 0.0520
+Epoch 28/30
+
 1/19 [>.............................] - ETA: 0s - loss: 0.0631 - mean_absolute_error: 0.0631
19/19 [==============================] - 0s 2ms/step - loss: 0.0557 - mean_absolute_error: 0.0557 - val_loss: 0.0528 - val_mean_absolute_error: 0.0528
+Epoch 29/30
+
 1/19 [>.............................] - ETA: 0s - loss: 0.0601 - mean_absolute_error: 0.0601
19/19 [==============================] - 0s 2ms/step - loss: 0.0526 - mean_absolute_error: 0.0526 - val_loss: 0.0521 - val_mean_absolute_error: 0.0521
+Epoch 30/30
+
 1/19 [>.............................] - ETA: 0s - loss: 0.0508 - mean_absolute_error: 0.0508
19/19 [==============================] - 0s 2ms/step - loss: 0.0542 - mean_absolute_error: 0.0542 - val_loss: 0.0534 - val_mean_absolute_error: 0.0534
+views    1
+dtype: int32
+views    488
+dtype: int32
+likes    1
+dtype: int32
+likes    3345
+dtype: int32
+114831.63920784603
+114831.63920784603

my_runs/3/info.json

@@ -0,0 +1,3 @@
+{
+  "prepare_message_ts": "2021-05-20 22:06:00.289863"
+}

my_runs/3/metrics.json

@@ -0,0 +1,13 @@
+{
+  "training.metrics": {
+    "steps": [
+      0
+    ],
+    "timestamps": [
+      "2021-05-20T20:06:03.338305"
+    ],
+    "values": [
+      114831.63920784603
+    ]
+  }
+}

my_runs/3/run.json

@@ -0,0 +1,79 @@
+{
+  "artifacts": [],
+  "command": "my_main",
+  "experiment": {
+    "base_dir": "C:\\Users\\karol\\PycharmProjects\\ium_434765",
+    "dependencies": [
+      "numpy==1.19.5",
+      "pandas==1.2.4",
+      "sacred==0.8.2",
+      "scikit-learn==0.24.1",
+      "tensorflow==2.5.0rc1"
+    ],
+    "mainfile": "neural_network.py",
+    "name": "sacred_scopes",
+    "repositories": [
+      {
+        "commit": "b0346d0b62846839e512344b20a566135e07a4b2",
+        "dirty": true,
+        "url": "https://git.wmi.amu.edu.pl/s434765/ium_434765.git"
+      }
+    ],
+    "sources": [
+      [
+        "neural_network.py",
+        "_sources\\neural_network_33e5177d0655bf5fef22fcd226db36b1.py"
+      ]
+    ]
+  },
+  "heartbeat": "2021-05-20T20:06:03.339305",
+  "host": {
+    "ENV": {},
+    "cpu": "Unknown",
+    "gpus": {
+      "driver_version": "452.06",
+      "gpus": [
+        {
+          "model": "GeForce GTX 1650 Ti",
+          "persistence_mode": false,
+          "total_memory": 4096
+        }
+      ]
+    },
+    "hostname": "DESKTOP-5PRPHO6",
+    "os": [
+      "Windows",
+      "Windows-10-10.0.19041-SP0"
+    ],
+    "python_version": "3.9.2"
+  },
+  "meta": {
+    "command": "my_main",
+    "options": {
+      "--beat-interval": null,
+      "--capture": null,
+      "--comment": null,
+      "--debug": false,
+      "--enforce_clean": false,
+      "--file_storage": null,
+      "--force": false,
+      "--help": false,
+      "--loglevel": null,
+      "--mongo_db": null,
+      "--name": null,
+      "--pdb": false,
+      "--print-config": false,
+      "--priority": null,
+      "--queue": false,
+      "--s3": null,
+      "--sql": null,
+      "--tiny_db": null,
+      "--unobserved": false
+    }
+  },
+  "resources": [],
+  "result": null,
+  "start_time": "2021-05-20T20:06:00.285864",
+  "status": "COMPLETED",
+  "stop_time": "2021-05-20T20:06:03.339305"
+}

my_runs/_sources/evaluate_network_6bc39a6cabbc78720ddbbd5b23f51cc3.py

@@ -0,0 +1,53 @@
+import pandas as pd
+import numpy as np
+from sklearn.metrics import mean_squared_error
+from tensorflow import keras
+import matplotlib.pyplot as plt
+
+
+def evaluate_model():
+    model = keras.models.load_model('model')
+    data = pd.read_csv("data_dev", sep=',', error_bad_lines=False,
+                       skip_blank_lines=True, nrows=527, names=["video_id", "last_trending_date",
+                                                                "publish_date", "publish_hour", "category_id",
+                                                                "channel_title", "views", "likes", "dislikes",
+                                                                "comment_count"]).dropna()
+    X_test = data.loc[:, data.columns == "views"].astype(int)
+    y_test = data.loc[:, data.columns == "likes"].astype(int)
+
+    min_val_sub = np.min(X_test)
+    max_val_sub = np.max(X_test)
+    X_test = (X_test - min_val_sub) / (max_val_sub - min_val_sub)
+    print(min_val_sub)
+    print(max_val_sub)
+
+    min_val_like = np.min(y_test)
+    max_val_like = np.max(y_test)
+    print(min_val_like)
+    print(max_val_like)
+
+    prediction = model.predict(X_test)
+
+    prediction_denormalized = []
+    for pred in prediction:
+        denorm = pred[0] * (max_val_like[0] - min_val_like[0]) + min_val_like[0]
+        prediction_denormalized.append(denorm)
+
+    f = open("predictions.txt", "w")
+    for (pred, test) in zip(prediction_denormalized, y_test.values):
+        f.write("predicted: %s expected: %s\n" % (str(pred), str(test[0])))
+
+    error = mean_squared_error(y_test, prediction_denormalized)
+    print(error)
+
+    with open("rmse.txt", "a") as file:
+        file.write(str(error) + "\n")
+
+    with open("rmse.txt", "r") as file:
+        lines = file.readlines()
+        plt.plot(range(len(lines)), [line[:-2] for line in lines])
+        plt.tight_layout()
+        plt.ylabel('RMSE')
+        plt.xlabel('evaluation no')
+        plt.savefig('evaluation.png')
+    return error

my_runs/_sources/neural_network_33e5177d0655bf5fef22fcd226db36b1.py

@@ -0,0 +1,108 @@
+from datetime import datetime
+
+import pandas as pd
+import numpy as np
+from sacred.observers import FileStorageObserver, MongoObserver
+from sacred import Experiment
+from sklearn.metrics import mean_squared_error
+
+from tensorflow import keras
+
+ex = Experiment("sacred_scopes", interactive=True)
+# ex.observers.append(MongoObserver(url='mongodb://mongo_user:mongo_password_IUM_2021@172.17.0.1:27017',
+#                                   db_name='sacred'))
+ex.observers.append(FileStorageObserver('my_runs'))
+
+
+@ex.config
+def my_config():
+    epochs_amount = 30
+
+
+def normalize_data(data):
+    return (data - np.min(data)) / (np.max(data) - np.min(data))
+
+
+@ex.capture
+def prepare_model(epochs_amount, _run):
+    _run.info["prepare_message_ts"] = str(datetime.now())
+
+    data = pd.read_csv("data_train", sep=',', skip_blank_lines=True, nrows=1087, error_bad_lines=False,
+                       names=["vipip install sacreddeo_id", "last_trending_date", "publish_date", "publish_hour",
+                              "category_id",
+                              "channel_title", "views", "likes", "dislikes", "comment_count"]).dropna()
+
+    X = data.loc[:, data.columns == "views"].astype(int)
+    y = data.loc[:, data.columns == "likes"].astype(int)
+
+    min_val_sub = np.min(X)
+    max_val_sub = np.max(X)
+    X = (X - min_val_sub) / (max_val_sub - min_val_sub)
+    print(min_val_sub)
+    print(max_val_sub)
+
+    min_val_like = np.min(y)
+    max_val_like = np.max(y)
+    y = (y - min_val_like) / (max_val_like - min_val_like)
+
+    print(min_val_like)
+    print(max_val_like)
+
+    model = keras.Sequential([
+        keras.layers.Dense(512, input_dim=X.shape[1], activation='relu'),
+        keras.layers.Dense(256, activation='relu'),
+        keras.layers.Dense(256, activation='relu'),
+        keras.layers.Dense(128, activation='relu'),
+        keras.layers.Dense(1, activation='linear'),
+    ])
+
+    model.compile(loss='mean_absolute_error', optimizer="Adam", metrics=['mean_absolute_error'])
+
+    model.fit(X, y, epochs=int(epochs_amount), validation_split=0.3)
+
+    data = pd.read_csv("data_dev", sep=',', error_bad_lines=False,
+                       skip_blank_lines=True, nrows=527, names=["video_id", "last_trending_date",
+                                                                "publish_date", "publish_hour", "category_id",
+                                                                "channel_title", "views", "likes", "dislikes",
+                                                                "comment_count"]).dropna()
+    X_test = data.loc[:, data.columns == "views"].astype(int)
+    y_test = data.loc[:, data.columns == "likes"].astype(int)
+
+    min_val_sub = np.min(X_test)
+    max_val_sub = np.max(X_test)
+    X_test = (X_test - min_val_sub) / (max_val_sub - min_val_sub)
+    print(min_val_sub)
+    print(max_val_sub)
+
+    min_val_like = np.min(y_test)
+    max_val_like = np.max(y_test)
+    print(min_val_like)
+    print(max_val_like)
+
+    prediction = model.predict(X_test)
+
+    prediction_denormalized = []
+    for pred in prediction:
+        denorm = pred[0] * (max_val_like[0] - min_val_like[0]) + min_val_like[0]
+        prediction_denormalized.append(denorm)
+
+    f = open("predictions.txt", "w")
+    for (pred, test) in zip(prediction_denormalized, y_test.values):
+        f.write("predicted: %s expected: %s\n" % (str(pred), str(test[0])))
+
+    error = mean_squared_error(y_test, prediction_denormalized)
+    print(error)
+
+    model.save('model')
+
+    _run.log_scalar("training.metrics", error)
+    return error
+
+
+@ex.main
+def my_main(epochs_amount):
+    print(prepare_model())
+
+
+ex.run()
+ex.add_artifact("model.pb")

my_runs/_sources/neural_network_cdaa9eab635a60c87899a6eaac9e398e.py

@@ -0,0 +1,79 @@
+from datetime import datetime
+
+import pandas as pd
+import numpy as np
+from sacred.observers import FileStorageObserver, MongoObserver
+from sacred import Experiment
+
+from tensorflow import keras
+import sys
+
+from evaluate_network import evaluate_model
+
+ex = Experiment("sacred_scopes", interactive=True)
+# ex.observers.append(MongoObserver(url='mongodb://mongo_user:mongo_password_IUM_2021@172.17.0.1:27017',
+#                                   db_name='sacred'))
+ex.observers.append(FileStorageObserver('my_runs'))
+
+
+@ex.config
+def my_config():
+    epochs_amount = 30
+
+
+def normalize_data(data):
+    return (data - np.min(data)) / (np.max(data) - np.min(data))
+
+
+@ex.capture
+def prepare_model(epochs_amount, _run):
+    _run.info["prepare_message_ts"] = str(datetime.now())
+
+    data = pd.read_csv("data_train", sep=',', skip_blank_lines=True, nrows=1087, error_bad_lines=False,
+                       names=["vipip install sacreddeo_id", "last_trending_date", "publish_date", "publish_hour",
+                              "category_id",
+                              "channel_title", "views", "likes", "dislikes", "comment_count"]).dropna()
+
+    X = data.loc[:, data.columns == "views"].astype(int)
+    y = data.loc[:, data.columns == "likes"].astype(int)
+
+    min_val_sub = np.min(X)
+    max_val_sub = np.max(X)
+    X = (X - min_val_sub) / (max_val_sub - min_val_sub)
+    print(min_val_sub)
+    print(max_val_sub)
+
+    min_val_like = np.min(y)
+    max_val_like = np.max(y)
+    y = (y - min_val_like) / (max_val_like - min_val_like)
+
+    print(min_val_like)
+    print(max_val_like)
+
+    model = keras.Sequential([
+        keras.layers.Dense(512, input_dim=X.shape[1], activation='relu'),
+        keras.layers.Dense(256, activation='relu'),
+        keras.layers.Dense(256, activation='relu'),
+        keras.layers.Dense(128, activation='relu'),
+        keras.layers.Dense(1, activation='linear'),
+    ])
+
+    model.compile(loss='mean_absolute_error', optimizer="Adam", metrics=['mean_absolute_error'])
+
+    model.fit(X, y, epochs=int(epochs_amount), validation_split=0.3)
+
+    model.save('model')
+
+    metrics = evaluate_model()
+    print(metrics)
+
+    return metrics
+
+
+@ex.main
+def my_main(epochs_amount):
+    print(prepare_model())
+
+
+ex.run()
+ex.add_artifact("model.pb")

my_runs/_sources/neural_network_eca667942d0304c50d970a67f9012302.py

@@ -0,0 +1,78 @@
+from datetime import datetime
+
+import pandas as pd
+import numpy as np
+from sacred.observers import FileStorageObserver, MongoObserver
+from sacred import Experiment
+
+from tensorflow import keras
+import sys
+
+from evaluate_network import evaluate_model
+
+ex = Experiment("sacred_scopes", interactive=True)
+# ex.observers.append(MongoObserver(url='mongodb://mongo_user:mongo_password_IUM_2021@172.17.0.1:27017',
+#                                   db_name='sacred'))
+ex.observers.append(FileStorageObserver('my_runs'))
+
+
+@ex.config
+def my_config():
+    epochs_amount = 30
+
+
+def normalize_data(data):
+    return (data - np.min(data)) / (np.max(data) - np.min(data))
+
+
+@ex.capture
+def prepare_model(epochs_amount, _run):
+    _run.info["prepare_message_ts"] = str(datetime.now())
+
+    data = pd.read_csv("data_train", sep=',', skip_blank_lines=True, nrows=1087, error_bad_lines=False,
+                       names=["vipip install sacreddeo_id", "last_trending_date", "publish_date", "publish_hour",
+                              "category_id",
+                              "channel_title", "views", "likes", "dislikes", "comment_count"]).dropna()
+
+    X = data.loc[:, data.columns == "views"].astype(int)
+    y = data.loc[:, data.columns == "likes"].astype(int)
+
+    min_val_sub = np.min(X)
+    max_val_sub = np.max(X)
+    X = (X - min_val_sub) / (max_val_sub - min_val_sub)
+    print(min_val_sub)
+    print(max_val_sub)
+
+    min_val_like = np.min(y)
+    max_val_like = np.max(y)
+    y = (y - min_val_like) / (max_val_like - min_val_like)
+
+    print(min_val_like)
+    print(max_val_like)
+
+    model = keras.Sequential([
+        keras.layers.Dense(512, input_dim=X.shape[1], activation='relu'),
+        keras.layers.Dense(256, activation='relu'),
+        keras.layers.Dense(256, activation='relu'),
+        keras.layers.Dense(128, activation='relu'),
+        keras.layers.Dense(1, activation='linear'),
+    ])
+
+    model.compile(loss='mean_absolute_error', optimizer="Adam", metrics=['mean_absolute_error'])
+
+    model.fit(X, y, epochs=int(epochs_amount), validation_split=0.3)
+
+    model.save('model')
+
+    metrics = evaluate_model()
+    _run.log_scalar("training.metrics", metrics)
+    return metrics
+
+
+@ex.main
+def my_main(epochs_amount):
+    print(prepare_model())
+
+
+ex.run()
+ex.add_artifact("model.pb")

neural_network.py

@@ -0,0 +1,109 @@
+import sys
+from datetime import datetime
+
+import pandas as pd
+import numpy as np
+from sacred.observers import FileStorageObserver, MongoObserver
+from sacred import Experiment
+from sklearn.metrics import mean_squared_error
+
+from tensorflow import keras
+
+ex = Experiment("s434765", interactive=True, save_git_info=False)
+ex.observers.append(MongoObserver(url='mongodb://mongo_user:mongo_password_IUM_2021@172.17.0.1:27017',
+                                  db_name='sacred'))
+ex.observers.append(FileStorageObserver('my_runs'))
+
+
+@ex.config
+def my_config():
+    epochs_amount = int(sys.argv[1])
+
+
+def normalize_data(data):
+    return (data - np.min(data)) / (np.max(data) - np.min(data))
+
+
+@ex.capture
+def prepare_model(epochs_amount, _run):
+    _run.info["prepare_message_ts"] = str(datetime.now())
+
+    data = pd.read_csv("data_train", sep=',', skip_blank_lines=True, nrows=1087, error_bad_lines=False,
+                       names=["vipip install sacreddeo_id", "last_trending_date", "publish_date", "publish_hour",
+                              "category_id",
+                              "channel_title", "views", "likes", "dislikes", "comment_count"]).dropna()
+
+    X = data.loc[:, data.columns == "views"].astype(int)
+    y = data.loc[:, data.columns == "likes"].astype(int)
+
+    min_val_sub = np.min(X)
+    max_val_sub = np.max(X)
+    X = (X - min_val_sub) / (max_val_sub - min_val_sub)
+    print(min_val_sub)
+    print(max_val_sub)
+
+    min_val_like = np.min(y)
+    max_val_like = np.max(y)
+    y = (y - min_val_like) / (max_val_like - min_val_like)
+
+    print(min_val_like)
+    print(max_val_like)
+
+    model = keras.Sequential([
+        keras.layers.Dense(512, input_dim=X.shape[1], activation='relu'),
+        keras.layers.Dense(256, activation='relu'),
+        keras.layers.Dense(256, activation='relu'),
+        keras.layers.Dense(128, activation='relu'),
+        keras.layers.Dense(1, activation='linear'),
+    ])
+
+    model.compile(loss='mean_absolute_error', optimizer="Adam", metrics=['mean_absolute_error'])
+
+    model.fit(X, y, epochs=int(epochs_amount), validation_split=0.3)
+
+    data = pd.read_csv("data_dev", sep=',', error_bad_lines=False,
+                       skip_blank_lines=True, nrows=527, names=["video_id", "last_trending_date",
+                                                                "publish_date", "publish_hour", "category_id",
+                                                                "channel_title", "views", "likes", "dislikes",
+                                                                "comment_count"]).dropna()
+    X_test = data.loc[:, data.columns == "views"].astype(int)
+    y_test = data.loc[:, data.columns == "likes"].astype(int)
+
+    min_val_sub = np.min(X_test)
+    max_val_sub = np.max(X_test)
+    X_test = (X_test - min_val_sub) / (max_val_sub - min_val_sub)
+    print(min_val_sub)
+    print(max_val_sub)
+
+    min_val_like = np.min(y_test)
+    max_val_like = np.max(y_test)
+    print(min_val_like)
+    print(max_val_like)
+
+    prediction = model.predict(X_test)
+
+    prediction_denormalized = []
+    for pred in prediction:
+        denorm = pred[0] * (max_val_like[0] - min_val_like[0]) + min_val_like[0]
+        prediction_denormalized.append(denorm)
+
+    f = open("predictions.txt", "w")
+    for (pred, test) in zip(prediction_denormalized, y_test.values):
+        f.write("predicted: %s expected: %s\n" % (str(pred), str(test[0])))
+
+    error = mean_squared_error(y_test, prediction_denormalized)
+    print(error)
+
+    model.save('model')
+
+    _run.log_scalar("training.metrics", error)
+    return error
+
+
+@ex.main
+def my_main(epochs_amount):
+    print(prepare_model())
+
+
+ex.run()
+ex.add_artifact("model/saved_model.pb")

neural_network.sh

@@ -0,0 +1,2 @@
+#!/bin/bash
+python3 neural_network.py $1

predictions.txt

@@ -0,0 +1,422 @@
+predicted: 400.330885887146 expected: 617
+predicted: 27.162359654903412 expected: 172
+predicted: 1451.7506175041199 expected: 611
+predicted: 89.5334190428257 expected: 269
+predicted: 1451.7506175041199 expected: 1095
+predicted: 89.5334190428257 expected: 68
+predicted: 26.76018589735031 expected: 5
+predicted: 400.330885887146 expected: 986
+predicted: 179.86350238323212 expected: 262
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