bonus dvc

.dvc/.gitignore

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

.dvc/config

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

.dvcignore

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

Jenkinsfile

@@ -1,62 +1,37 @@
 pipeline {
-   agent {
+    agent any
-    dockerfile true
+
+    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')
     }
 
-   triggers {
-     upstream(upstreamProjects: 's464937-training/training', threshold: hudson.model.Result.SUCCESS)
-   }
-
-   parameters {
-     buildSelector(defaultSelector: lastSuccessful(), description: 'Which build to use for copying artifacts', name: 'BUILD_SELECTOR')
-     gitParameter branchFilter: 'origin/(.*)', defaultValue: 'training', name: 'BRANCH', type: 'PT_BRANCH'
-   }
-
     stages {
-        stage('Clone Repository') {
+        stage('Clone repo') {
             steps {
-                git branch: 'evaluation', url: "https://git.wmi.amu.edu.pl/s464937/ium_464937"
+                git branch: "main", url: "https://git.wmi.amu.edu.pl/s464937/ium_464937"
             }
         }
-        stage('Copy Dataset Artifacts') {
-            steps {
-                 copyArtifacts filter: 'data/dev.csv,data/test.csv,data/train.csv', projectName: 'z-s464937-create-dataset', selector: buildParameter('BUILD_SELECTOR')
-            }
-        }
-        stage('Copy Training Artifacts') {
-          steps {
-            copyArtifacts filter: 'powerlifting_model.h5', projectName: 's464937-training/' + params.BRANCH, selector: buildParameter('BUILD_SELECTOR')
-          }
-        }
-        stage('Copy Evaluation Artifacts') {
-          steps {
-            copyArtifacts filter: 'metrics.txt', projectName: 's464937-evaluation/evaluation', selector: buildParameter('BUILD_SELECTOR'), optional: true
-          }
-        }
-        stage("Run predictions") {
-            steps {
-                sh "chmod +x ./predict.py"
-                sh "python3 ./predict.py"
-                archiveArtifacts artifacts: 'powerlifting_test_predictions.csv', onlyIfSuccessful: true
-            }
-        }
-        stage('Run metrics') {
-          steps {
-            sh 'chmod +x ./metrics.py'
-            sh "python3 ./metrics.py ${currentBuild.number}"
-          }
-        }
 
-        stage('Run plot') {
+        stage('Download and preprocess') {
-          steps {
+            environment {
-            sh 'chmod +x ./plot.py'
+                    KAGGLE_USERNAME = "szymonbartanowicz"
-            sh 'python3 ./plot.py'
+                    KAGGLE_KEY = "4692239eb65f20ec79f9a59ef30e67eb"
-          }
+                }
+            steps {
+                withEnv([
+                    "KAGGLE_USERNAME=${env.KAGGLE_USERNAME}",
+                    "KAGGLE_KEY=${env.KAGGLE_KEY}"
+                ]) {
+                    sh "bash ./script1.sh ${params.CUTOFF}"
+                }
+            }
         }
-         stage('Archive Artifacts') {
+        stage('Archive') {
-          steps {
+            steps {
-            archiveArtifacts artifacts: '*', onlyIfSuccessful: true
+                archiveArtifacts artifacts: 'data/*', onlyIfSuccessful: true
-          }
+            }
         }
     }
-}
+}

dvc.yaml

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

environment.yml

@@ -0,0 +1,192 @@
+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

metrics.py

@@ -1,14 +0,0 @@
-import pandas as pd
-from sklearn.metrics import accuracy_score, precision_recall_fscore_support, mean_squared_error
-from math import sqrt
-import sys
-
-data = pd.read_csv('powerlifting_test_predictions.csv')
-y_pred = data['predicted_TotalKg']
-y_test = data['actual_TotalKg']
-
-build_number = sys.argv[1]
-rmse = sqrt(mean_squared_error(y_test, y_pred))
-
-with open(r"metrics.txt", "a") as f:
-    f.write(f"{build_number},{rmse}\n")

mlflow/MLproject

@@ -0,0 +1,7 @@
+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

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

mlflow/mlflow_model.py

@@ -0,0 +1,79 @@
+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

@@ -0,0 +1,6 @@
+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

@@ -0,0 +1,15 @@
+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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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