fix

.dvc/.gitignore

@@ -1,3 +0,0 @@
-/config.local
-/tmp
-/cache

.dvc/config

@@ -1,4 +0,0 @@
-[core]
-    remote = ium_ssh_remote
-['remote "ium_ssh_remote"']
-    url = ssh://ium-sftp@tzietkiewicz.vm.wmi.amu.edu.pl

.dvcignore

@@ -1,3 +0,0 @@
-# Add patterns of files dvc should ignore, which could improve
-# the performance. Learn more at
-# https://dvc.org/doc/user-guide/dvcignore

Jenkinsfile

@@ -1,36 +1,37 @@
 pipeline {
     agent any
 
+    triggers {
+        upstream(upstreamProjects: 'z-s464937-create-dataset', threshold: hudson.model.Result.SUCCESS)
+    }
+
     parameters {
-        string(name: 'CUTOFF', defaultValue: '100', description: 'Ilość wierszy do odcięcia')
-	    string(name: 'KAGGLE_USERNAME', defaultValue: '', description: 'Kaggle username')
-        password(name: 'KAGGLE_KEY', defaultValue: '', description: 'Kaggle API key')
+        string(name: 'EPOCHS', defaultValue: '10', description: 'Epochs')
+        buildSelector(defaultSelector: lastSuccessful(), description: 'Build no', name: 'BUILD_SELECTOR')
     }
 
     stages {
-        stage('Clone repo') {
+        stage('Clone Repository') {
             steps {
-                git branch: "main", url: "https://git.wmi.amu.edu.pl/s464937/ium_464937"
+                git branch: 'training', url: "https://git.wmi.amu.edu.pl/s464937/ium_464937.git"
             }
         }
-
-        stage('Download and preprocess') {
-            environment {
-                    KAGGLE_USERNAME = "szymonbartanowicz"
-                    KAGGLE_KEY = "4692239eb65f20ec79f9a59ef30e67eb"
-                }
+        stage('Copy Artifacts') {
             steps {
-                withEnv([
-                    "KAGGLE_USERNAME=${env.KAGGLE_USERNAME}",
-                    "KAGGLE_KEY=${env.KAGGLE_KEY}"
-                ]) {
-                    sh "bash ./script1.sh ${params.CUTOFF}"
-                }
+                 copyArtifacts filter: 'data/dev.csv,data/test.csv,data/train.csv', projectName: 'z-s464937-create-dataset', selector: buildParameter('BUILD_SELECTOR')
             }
         }
-        stage('Archive') {
+        stage("Run") {
+            agent {
+                dockerfile {
+                    filename 'Dockerfile'
+                    reuseNode true
+                }
+            }
             steps {
-                archiveArtifacts artifacts: 'data/*', onlyIfSuccessful: true
+                sh "chmod +x ./model.py"
+                sh "python3 ./model.py ${params.EPOCHS}"
+                archiveArtifacts artifacts: 'powerlifting_model.h5', onlyIfSuccessful: true
             }
         }
     }

dvc.yaml

@@ -1,5 +0,0 @@
-stages:
-  train:
-    cmd: python train.py --epochs=10
-  predict:
-    cmd: python predict.py

environment.yml

@@ -1,192 +0,0 @@
-name: ium
-channels:
-  - anaconda
-  - conda-forge
-  - defaults
-dependencies:
-  - _tflow_select=2.2.0=eigen
-  - abseil-cpp=20211102.0=he9d5cce_0
-  - absl-py=1.4.0=py311hecd8cb5_0
-  - aiohttp=3.9.3=py311h6c40b1e_0
-  - aiosignal=1.2.0=pyhd3eb1b0_0
-  - alembic=1.8.1=py311hecd8cb5_0
-  - aniso8601=9.0.1=pyhd3eb1b0_0
-  - arrow-cpp=11.0.0=h9980dd5_2
-  - astunparse=1.6.3=py_0
-  - attrs=23.1.0=py311hecd8cb5_0
-  - aws-c-common=0.4.57=hb1e8313_1
-  - aws-c-event-stream=0.1.6=h23ab428_5
-  - aws-checksums=0.1.9=hb1e8313_0
-  - aws-sdk-cpp=1.8.185=he271ece_0
-  - bcrypt=3.2.0=py311h6c40b1e_1
-  - blas=1.0=mkl
-  - blinker=1.6.2=py311hecd8cb5_0
-  - boost-cpp=1.77.0=hff03dee_0
-  - bottleneck=1.3.7=py311hb3a5e46_0
-  - brotli=1.0.9=h6c40b1e_8
-  - brotli-bin=1.0.9=h6c40b1e_8
-  - brotli-python=1.0.9=py311hcec6c5f_7
-  - bzip2=1.0.8=h6c40b1e_5
-  - c-ares=1.19.1=h6c40b1e_0
-  - ca-certificates=2024.3.11=hecd8cb5_0
-  - cachetools=4.2.2=pyhd3eb1b0_0
-  - certifi=2024.2.2=py311hecd8cb5_0
-  - cffi=1.16.0=py311h6c40b1e_0
-  - charset-normalizer=2.0.4=pyhd3eb1b0_0
-  - click=8.1.7=py311hecd8cb5_0
-  - cloudpickle=2.2.1=py311hecd8cb5_0
-  - contourpy=1.2.0=py311ha357a0b_0
-  - cryptography=41.0.3=py311ha2381d6_0
-  - cycler=0.11.0=pyhd3eb1b0_0
-  - docker-py=7.0.0=py311hecd8cb5_0
-  - entrypoints=0.4=py311hecd8cb5_0
-  - flask=2.2.5=py311hecd8cb5_0
-  - flatbuffers=2.0.0=h23ab428_0
-  - fonttools=4.51.0=py311h6c40b1e_0
-  - freetype=2.12.1=hd8bbffd_0
-  - frozenlist=1.4.0=py311h6c40b1e_0
-  - gast=0.4.0=pyhd3eb1b0_0
-  - gflags=2.2.2=hcec6c5f_1
-  - giflib=5.2.1=h6c40b1e_3
-  - gitdb=4.0.7=pyhd3eb1b0_0
-  - gitpython=3.1.37=py311hecd8cb5_0
-  - glog=0.5.0=hcec6c5f_1
-  - google-auth=2.22.0=py311hecd8cb5_0
-  - google-auth-oauthlib=0.5.2=py311hecd8cb5_0
-  - google-pasta=0.2.0=pyhd3eb1b0_0
-  - graphene=3.3=py311hecd8cb5_0
-  - graphql-core=3.2.3=py311hecd8cb5_1
-  - graphql-relay=3.2.0=py311hecd8cb5_0
-  - greenlet=3.0.1=py311hcec6c5f_0
-  - grpc-cpp=1.48.2=h3afe56f_0
-  - grpcio=1.48.2=py311h3afe56f_0
-  - gunicorn=21.2.0=py311hecd8cb5_0
-  - h5py=3.9.0=py311hdb7e403_0
-  - hdf5=1.12.1=h2b2ad87_2
-  - icu=68.1=h23ab428_0
-  - idna=3.4=py311hecd8cb5_0
-  - importlib-metadata=7.0.1=py311hecd8cb5_0
-  - intel-openmp=2021.4.0=hecd8cb5_3538
-  - itsdangerous=2.0.1=pyhd3eb1b0_0
-  - jinja2=3.1.3=py311hecd8cb5_0
-  - joblib=1.2.0=py311hecd8cb5_0
-  - jpeg=9e=h6c40b1e_1
-  - keras=2.12.0=py311_0
-  - keras-preprocessing=1.1.2=pyhd3eb1b0_0
-  - kiwisolver=1.4.4=py311hcec6c5f_0
-  - krb5=1.20.1=hdba6334_1
-  - lcms2=2.12=hf1fd2bf_0
-  - lerc=3.0=he9d5cce_0
-  - libbrotlicommon=1.0.9=h6c40b1e_8
-  - libbrotlidec=1.0.9=h6c40b1e_8
-  - libbrotlienc=1.0.9=h6c40b1e_8
-  - libcurl=8.2.1=ha585b31_0
-  - libcxx=14.0.6=h9765a3e_0
-  - libdeflate=1.17=hb664fd8_1
-  - libedit=3.1.20230828=h6c40b1e_0
-  - libev=4.33=h9ed2024_1
-  - libevent=2.1.12=h0a4fc7d_0
-  - libffi=3.4.4=hecd8cb5_0
-  - libgfortran=5.0.0=11_3_0_hecd8cb5_28
-  - libgfortran5=11.3.0=h9dfd629_28
-  - libnghttp2=1.52.0=h1c88b7d_1
-  - libpng=1.6.39=h6c40b1e_0
-  - libprotobuf=3.20.3=hfff2838_0
-  - libsodium=1.0.18=h1de35cc_0
-  - libssh2=1.10.0=hdb2fb19_2
-  - libthrift=0.15.0=h48f73ad_2
-  - libtiff=4.5.1=hcec6c5f_0
-  - libwebp-base=1.3.2=h6c40b1e_0
-  - llvm-openmp=14.0.6=h0dcd299_0
-  - lz4-c=1.9.4=hcec6c5f_1
-  - mako=1.2.3=py311hecd8cb5_0
-  - markdown=3.4.1=py311hecd8cb5_0
-  - markupsafe=2.1.3=py311h6c40b1e_0
-  - matplotlib=3.8.4=py311hecd8cb5_0
-  - matplotlib-base=3.8.4=py311h41a4f6b_0
-  - mkl=2021.4.0=hecd8cb5_637
-  - mkl-service=2.4.0=py311h6c40b1e_0
-  - mkl_fft=1.3.1=py311hbc8bb1e_0
-  - mkl_random=1.2.2=py311hc5848a5_0
-  - mlflow=2.12.2=h6eed73b_0
-  - mlflow-skinny=2.12.2=py311h6eed73b_0
-  - mlflow-ui=2.12.2=py311h6eed73b_0
-  - multidict=6.0.4=py311h6c40b1e_0
-  - ncurses=6.4=hcec6c5f_0
-  - numexpr=2.8.4=py311h72c71eb_0
-  - numpy=1.23.5=py311h72c71eb_0
-  - numpy-base=1.23.5=py311h0e1ec55_0
-  - oauthlib=3.2.2=py311hecd8cb5_0
-  - openjpeg=2.4.0=h66ea3da_0
-  - openssl=1.1.1w=hca72f7f_0
-  - opt_einsum=3.3.0=pyhd3eb1b0_1
-  - orc=1.7.4=h995b336_1
-  - packaging=23.2=py311hecd8cb5_0
-  - pandas=2.2.1=py311hdb55bb0_0
-  - paramiko=2.8.1=pyhd3eb1b0_0
-  - pillow=10.3.0=py311h6c40b1e_0
-  - pip=23.3.1=py311hecd8cb5_0
-  - platformdirs=3.10.0=py311hecd8cb5_0
-  - pooch=1.7.0=py311hecd8cb5_0
-  - prometheus_client=0.14.1=py311hecd8cb5_0
-  - prometheus_flask_exporter=0.22.4=py311hecd8cb5_0
-  - protobuf=3.20.3=py311hcec6c5f_0
-  - pyarrow=11.0.0=py311hf41f4e6_1
-  - pyasn1=0.4.8=pyhd3eb1b0_0
-  - pyasn1-modules=0.2.8=py_0
-  - pycparser=2.21=pyhd3eb1b0_0
-  - pyjwt=2.4.0=py311hecd8cb5_0
-  - pynacl=1.5.0=py311h6c40b1e_0
-  - pyopenssl=23.2.0=py311hecd8cb5_0
-  - pyparsing=3.0.9=py311hecd8cb5_0
-  - pysocks=1.7.1=py311hecd8cb5_0
-  - python=3.11.5=h1fd4e5f_0
-  - python-dateutil=2.8.2=pyhd3eb1b0_0
-  - python-flatbuffers=2.0=pyhd3eb1b0_0
-  - python-tzdata=2023.3=pyhd3eb1b0_0
-  - python_abi=3.11=2_cp311
-  - pytz=2023.3.post1=py311hecd8cb5_0
-  - pyyaml=6.0.1=py311h6c40b1e_0
-  - querystring_parser=1.2.4=py311hecd8cb5_0
-  - re2=2022.04.01=he9d5cce_0
-  - readline=8.2=hca72f7f_0
-  - requests=2.31.0=py311hecd8cb5_1
-  - requests-oauthlib=1.3.0=py_0
-  - rsa=4.7.2=pyhd3eb1b0_1
-  - scikit-learn=1.2.2=py311hcec6c5f_1
-  - scipy=1.10.1=py311hb23b6d4_0
-  - setuptools=68.2.2=py311hecd8cb5_0
-  - six=1.16.0=pyhd3eb1b0_1
-  - smmap=4.0.0=pyhd3eb1b0_0
-  - snappy=1.1.10=hcec6c5f_1
-  - sqlalchemy=2.0.25=py311h6c40b1e_0
-  - sqlite=3.41.2=h6c40b1e_0
-  - sqlparse=0.4.4=py311hecd8cb5_0
-  - tbb=2021.8.0=ha357a0b_0
-  - tensorboard=2.12.1=py311_0
-  - tensorboard-data-server=0.7.0=py311h7242b5c_0
-  - tensorboard-plugin-wit=1.6.0=py_0
-  - tensorflow=2.12.0=eigen_py311h4c7017d_0
-  - tensorflow-base=2.12.0=eigen_py311hbf87084_0
-  - tensorflow-estimator=2.12.0=py311_0
-  - termcolor=2.1.0=py311hecd8cb5_0
-  - threadpoolctl=2.2.0=pyh0d69192_0
-  - tk=8.6.12=h5d9f67b_0
-  - tornado=6.3.3=py311h6c40b1e_0
-  - typing-extensions=4.9.0=py311hecd8cb5_1
-  - typing_extensions=4.9.0=py311hecd8cb5_1
-  - tzdata=2024a=h04d1e81_0
-  - unicodedata2=15.1.0=py311h6c40b1e_0
-  - urllib3=1.26.18=py311hecd8cb5_0
-  - utf8proc=2.6.1=h6c40b1e_1
-  - websocket-client=1.8.0=py311hecd8cb5_0
-  - werkzeug=2.3.8=py311hecd8cb5_0
-  - wheel=0.35.1=pyhd3eb1b0_0
-  - wrapt=1.14.1=py311h6c40b1e_0
-  - xz=5.4.6=h6c40b1e_0
-  - yaml=0.2.5=haf1e3a3_0
-  - yarl=1.9.3=py311h6c40b1e_0
-  - zipp=3.17.0=py311hecd8cb5_0
-  - zlib=1.2.13=h4dc903c_0
-  - zstd=1.5.5=hc035e20_2
-prefix: /opt/anaconda3/envs/ium

mlflow/MLproject

@@ -1,7 +0,0 @@
-name: MLflow_s464937
-conda_env: conda.yaml
-entry_points:
-  main:
-    parameters:
-      epochs: { type: int, default: 20 }
-    command: 'python mlflow_model.py {epochs}'

mlflow/conda.yaml

@@ -1,12 +0,0 @@
-name: MLflow_s464937
-channels:
-  - defaults
-dependencies:
-  - python=3.11
-  - pip
-  - pip:
-      - wheel
-      - mlflow
-      - tensorflow
-      - pandas
-      - scikit-learn

mlflow/mlflow_model.py

@@ -1,79 +0,0 @@
-import sys
-import pandas as pd
-from sklearn.model_selection import train_test_split
-from sklearn.preprocessing import StandardScaler, OneHotEncoder
-from sklearn.compose import ColumnTransformer
-from sklearn.pipeline import Pipeline
-from tensorflow.keras.models import Sequential
-from tensorflow.keras.layers import Dense
-import tensorflow as tf
-from sklearn.metrics import accuracy_score, precision_recall_fscore_support, mean_squared_error
-from math import sqrt
-import mlflow
-
-
-mlflow.set_tracking_uri("http://localhost:5000")
-
-
-def main():
-    data = pd.read_csv('../openpowerlifting.csv')
-    data = data[['Sex', 'Age', 'BodyweightKg', 'TotalKg']].dropna()
-    data['Age'] = pd.to_numeric(data['Age'], errors='coerce')
-    data['BodyweightKg'] = pd.to_numeric(data['BodyweightKg'], errors='coerce')
-    data['TotalKg'] = pd.to_numeric(data['TotalKg'], errors='coerce')
-    features = data[['Sex', 'Age', 'BodyweightKg']]
-    target = data['TotalKg']
-    X_train, X_test, y_train, y_test = train_test_split(features, target, test_size=0.2, random_state=42)
-
-    with mlflow.start_run() as run:
-        preprocessor = ColumnTransformer(
-            transformers=[
-                ('num', StandardScaler(), ['Age', 'BodyweightKg']),
-                ('cat', OneHotEncoder(), ['Sex'])
-            ],
-        )
-
-        model = Sequential([
-            Dense(64, activation='relu', input_dim=5),
-            Dense(64, activation='relu'),
-            Dense(1)
-        ])
-
-        model.compile(optimizer='adam', loss='mse', metrics=['mae'])
-        pipeline = Pipeline(steps=[
-            ('preprocessor', preprocessor),
-            ('model', model)
-        ])
-
-        X_train_excluded = X_train.iloc[1:]
-        y_train_excluded = y_train.iloc[1:]
-        pipeline.fit(X_train_excluded, y_train_excluded, model__epochs=int(sys.argv[1]), model__validation_split=0.1)
-        pipeline['model'].save('powerlifting_model.h5')
-        loaded_model = tf.keras.models.load_model('powerlifting_model.h5')
-
-        test_data = pd.read_csv('openpowerlifting.csv')
-        test_data = test_data[['Sex', 'Age', 'BodyweightKg', 'TotalKg']].dropna()
-        test_data['Age'] = pd.to_numeric(test_data['Age'], errors='coerce')
-        test_data['BodyweightKg'] = pd.to_numeric(test_data['BodyweightKg'], errors='coerce')
-        test_data['TotalKg'] = pd.to_numeric(test_data['TotalKg'], errors='coerce')
-        test_features = test_data[['Sex', 'Age', 'BodyweightKg']]
-        test_target = test_data['TotalKg']
-
-        X_test_transformed = preprocessor.transform(test_features)
-        predictions = loaded_model.predict(X_test_transformed)
-        predictions_df = pd.DataFrame(predictions, columns=['predicted_TotalKg'])
-        predictions_df['actual_TotalKg'] = test_target.reset_index(drop=True)
-        predictions_df.to_csv('powerlifting_test_predictions.csv', index=False)
-
-        data = pd.read_csv('powerlifting_test_predictions.csv')
-        y_pred = data['predicted_TotalKg']
-        y_test = data['actual_TotalKg']
-        rmse = sqrt(mean_squared_error(y_test, y_pred))
-
-
-        mlflow.log_param("epochs", int(sys.argv[1]))
-        mlflow.log_metric("rmse", rmse)
-
-
-if __name__ == '__main__':
-    main()

mlflow/mlruns/0/meta.yaml

@@ -1,6 +0,0 @@
-artifact_location: mlflow-artifacts:/0
-creation_time: 1716052613528
-experiment_id: '0'
-last_update_time: 1716052613528
-lifecycle_stage: active
-name: Default

mlruns/0/31cdeea18bf145dfbd195bc6b8d5b080/meta.yaml

@@ -1,15 +0,0 @@
-artifact_uri: mlflow-artifacts:/0/31cdeea18bf145dfbd195bc6b8d5b080/artifacts
-end_time: 1716232849257
-entry_point_name: ''
-experiment_id: '0'
-lifecycle_stage: active
-run_id: 31cdeea18bf145dfbd195bc6b8d5b080
-run_name: thundering-ant-548
-run_uuid: 31cdeea18bf145dfbd195bc6b8d5b080
-source_name: ''
-source_type: 4
-source_version: ''
-start_time: 1716232828540
-status: 4
-tags: []
-user_id: szymonbartanowicz

mlruns/0/31cdeea18bf145dfbd195bc6b8d5b080/params/epochs

@@ -1 +0,0 @@
-12

mlruns/0/31cdeea18bf145dfbd195bc6b8d5b080/tags/mlflow.gitRepoURL

@@ -1 +0,0 @@
-https://git.wmi.amu.edu.pl/s464937/ium_464937.git

mlruns/0/31cdeea18bf145dfbd195bc6b8d5b080/tags/mlflow.project.backend

@@ -1 +0,0 @@
-local

mlruns/0/31cdeea18bf145dfbd195bc6b8d5b080/tags/mlflow.project.entryPoint

@@ -1 +0,0 @@
-main

mlruns/0/31cdeea18bf145dfbd195bc6b8d5b080/tags/mlflow.project.env

@@ -1 +0,0 @@
-conda

mlruns/0/31cdeea18bf145dfbd195bc6b8d5b080/tags/mlflow.runName

@@ -1 +0,0 @@
-thundering-ant-548

mlruns/0/31cdeea18bf145dfbd195bc6b8d5b080/tags/mlflow.source.git.commit

@@ -1 +0,0 @@
-248ef29471dcdc13631ea1693f701335a05c4801

mlruns/0/31cdeea18bf145dfbd195bc6b8d5b080/tags/mlflow.source.git.repoURL

@@ -1 +0,0 @@
-https://git.wmi.amu.edu.pl/s464937/ium_464937.git

mlruns/0/31cdeea18bf145dfbd195bc6b8d5b080/tags/mlflow.source.name

@@ -1 +0,0 @@
-file:///Users/szymonbartanowicz/studia/mag_1/inzynieria_uczenia_maszynowego/ium_464937#mlflow

mlruns/0/31cdeea18bf145dfbd195bc6b8d5b080/tags/mlflow.source.type

@@ -1 +0,0 @@
-PROJECT

mlruns/0/31cdeea18bf145dfbd195bc6b8d5b080/tags/mlflow.user

@@ -1 +0,0 @@
-szymonbartanowicz

mlruns/0/4937350a1f5a4dfb94d8e959ebb7baff/meta.yaml

@@ -1,15 +0,0 @@
-artifact_uri: mlflow-artifacts:/0/4937350a1f5a4dfb94d8e959ebb7baff/artifacts
-end_time: 1716232283577
-entry_point_name: ''
-experiment_id: '0'
-lifecycle_stage: active
-run_id: 4937350a1f5a4dfb94d8e959ebb7baff
-run_name: luminous-gnat-829
-run_uuid: 4937350a1f5a4dfb94d8e959ebb7baff
-source_name: ''
-source_type: 4
-source_version: ''
-start_time: 1716232281564
-status: 4
-tags: []
-user_id: szymonbartanowicz

mlruns/0/4937350a1f5a4dfb94d8e959ebb7baff/params/epochs

@@ -1 +0,0 @@
-12

mlruns/0/4937350a1f5a4dfb94d8e959ebb7baff/tags/mlflow.gitRepoURL

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