added model evaluation

.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

.gitignore

@@ -1,2 +0,0 @@
-/Spotify_Dataset.csv
-/spotify_songs.csv

.ipynb_checkpoints/Dockerfile-checkpoint

@@ -0,0 +1,17 @@
+FROM ubuntu:latest
+
+RUN apt-get update && \
+    apt-get install -y \
+    python3 \
+    python3-pip \
+    wget \
+    unzip \
+    && rm -rf /var/lib/apt/lists/*
+
+RUN pip3 install pandas scikit-learn requests numpy
+
+WORKDIR /app
+
+COPY use_model.py /app/
+
+RUN chmod +x use_model.py

.ipynb_checkpoints/Jenkinsfile-checkpoint

@@ -0,0 +1,43 @@
+pipeline {
+    agent {
+        dockerfile true
+    }
+    
+    triggers {
+        upstream(upstreamProjects: 's464953-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') {
+            steps {
+                git 'https://git.wmi.amu.edu.pl/s464953/ium_464953.git'
+            }
+        }
+       stage('Copy Training Artifacts') {
+          steps {
+            copyArtifacts filter: 'artifacts/*', projectName: 's464953-training/' + params.BRANCH, selector: buildParameter('BUILD_SELECTOR')
+          }
+        }
+        stage('Copy Evaluation Artifacts') {
+          steps {
+            copyArtifacts filter: 'metrics_df.csv', projectName: 's464953-training/' + params.BRANCH, selector: buildParameter('BUILD_SELECTOR')
+          }
+        }
+        
+        stage('Run Script') {
+            steps {
+                sh "python3 /app/use_model.py ${currentBuild.number}"
+            }
+        }
+        stage('Archive Artifacts') {
+            steps {
+                archiveArtifacts artifacts: '*', onlyIfSuccessful: true
+            }
+        }
+    }
+}

.ipynb_checkpoints/use_model-checkpoint.py

@@ -0,0 +1,77 @@
+import pickle
+import pandas as pd
+import numpy as np
+from sklearn.preprocessing import StandardScaler
+from sklearn.metrics import mean_squared_error, f1_score, accuracy_score
+import sys
+import os
+import matplotlib.pyplot as plt
+
+def calculate_metrics(result):        
+    rmse = np.sqrt(mean_squared_error(result["Real"], result["Predictions"]))
+    f1 = f1_score(result["Real"], result["Predictions"], average='macro')
+    accuracy = accuracy_score(result["Real"], result["Predictions"])
+    
+    filename = 'metrics_df.csv'
+    if os.path.exists(filename):
+        metrics_df = pd.read_csv(filename)
+        new_row = pd.DataFrame({'Build number': sys.argv[1], 'RMSE': [rmse], 'F1 Score': [f1], 'Accuracy': [accuracy]})
+        metrics_df = metrics_df.append(new_row, ignore_index=True)
+    else:
+        metrics_df = pd.DataFrame({'Build number': sys.argv[1], 'RMSE': [rmse], 'F1 Score': [f1], 'Accuracy': [accuracy]})
+
+    
+    metrics_df.to_csv(filename, index=False)
+
+def create_plots():
+    
+    metrics_df = pd.read_csv("metrics_df.csv")
+    
+    plt.plot(metrics_df["Build number"], metrics_df["Accuracy"])
+    plt.xlabel("Build Number")
+    plt.ylabel("Accuracy")
+    plt.title("Accuracy of the model over time")
+    plt.xticks(range(min(metrics_df["Build number"]), max(metrics_df["Build number"]) + 1))
+    plt.show()
+    plt.savefig("Accuracy_plot.png")
+    
+    plt.plot(metrics_df["Build number"], metrics_df["F1 Score"])
+    plt.xlabel("Build Number")
+    plt.ylabel("F1 Score")
+    plt.title("F1 Score of the model over time")
+    plt.xticks(range(min(metrics_df["Build number"]), max(metrics_df["Build number"]) + 1))
+    plt.show()
+    plt.savefig("F1_score_plot.png")
+    
+    plt.plot(metrics_df["Build number"], metrics_df["RMSE"])
+    plt.xlabel("Build Number")
+    plt.ylabel("RMSE")
+    plt.title("RMSE of the model over time")
+    plt.xticks(range(min(metrics_df["Build number"]), max(metrics_df["Build number"]) + 1))
+    plt.show()
+    plt.savefig("RMSE_plot.png")
+
+np.set_printoptions(threshold=20)
+
+file_path = 'model.pkl'
+with open(file_path, 'rb') as file:
+    model = pickle.load(file)
+print("Model został wczytany z pliku:", file_path)
+
+test_df = pd.read_csv("artifacts/docker_test_dataset.csv")
+
+Y_test = test_df[['playlist_genre']]
+X_test = test_df.drop(columns='playlist_genre')
+Y_test = np.ravel(Y_test)
+
+scaler = StandardScaler()
+numeric_columns = X_test.select_dtypes(include=['int', 'float']).columns
+X_test_scaled = scaler.fit_transform(X_test[numeric_columns])
+
+Y_pred = model.predict(X_test_scaled)
+
+result = pd.DataFrame({'Predictions': Y_pred, "Real": Y_test})
+result.to_csv("spotify_genre_predictions.csv", index=False)
+
+calculate_metrics(result)
+create_plots()

Dockerfile

@@ -2,11 +2,16 @@ FROM ubuntu:latest
 
 RUN apt-get update && \
     apt-get install -y \
-	python3 \
+    python3 \
     python3-pip \
-    git \
     wget \
     unzip \
     && rm -rf /var/lib/apt/lists/*
 
-RUN pip3 install pandas scikit-learn requests kaggle numpy sacred pymongo --break-system-package
+RUN pip3 install pandas scikit-learn requests numpy matplotlib
+
+WORKDIR /app
+
+COPY use_model.py /app/
+
+RUN chmod +x use_model.py

Jenkinsfile

@@ -1,10 +1,16 @@
 pipeline {
-    agent any
+    agent {
+        dockerfile true
+    }
+    
+    triggers {
+        upstream(upstreamProjects: 's464953-training/training', threshold: hudson.model.Result.SUCCESS)
+   }
     
     parameters {
-        string(name: 'KAGGLE_USERNAME', defaultValue: 'gulczas', description: 'Kaggle username')
+        buildSelector(defaultSelector: lastSuccessful(), description: 'Which build to use for copying artifacts', name: 'BUILD_SELECTOR')
-        password(name: 'KAGGLE_KEY', defaultValue: '', description: 'Kaggle API key')
+        gitParameter branchFilter: 'origin/(.*)', defaultValue: 'training', name: 'BRANCH', type: 'PT_BRANCH'
-    }
+   }
     
     stages {
         stage('Clone Repository') {
@@ -12,28 +18,25 @@ pipeline {
                 git 'https://git.wmi.amu.edu.pl/s464953/ium_464953.git'
             }
         }
-        stage('Cleanup Artifacts') {
+       stage('Copy Training Artifacts') {
-            steps {
+          steps {
-                script {
+            copyArtifacts filter: 'artifacts/*', projectName: 's464953-training/' + params.BRANCH, selector: buildParameter('BUILD_SELECTOR')
-                    sh 'rm -rf artifacts'
+          }
-                }
-            }
         }
+        stage('Copy Evaluation Artifacts') {
+          steps {
+            copyArtifacts filter: 'metrics_df.csv', projectName: '_s464953-evaluation/evaluation', selector: buildParameter('BUILD_SELECTOR'), optional: true
+          }
+        }
+        
         stage('Run Script') {
             steps {
-                script {
+                sh "python3 /app/use_model.py ${currentBuild.number}"
-                    withEnv([
-                    "KAGGLE_USERNAME=${env.KAGGLE_USERNAME}", 
-                    "KAGGLE_KEY=${env.KAGGLE_KEY}"])  
-                    {
-                        sh "bash ./download_dataset.sh"
-                    }
-                }
             }
         }
         stage('Archive Artifacts') {
             steps {
-                archiveArtifacts artifacts: 'artifacts/*', onlyIfSuccessful: true
+                archiveArtifacts artifacts: 'metrics_df.csv, spotify_genre_predictions.csv, F1_score_plot.png, RMSE_plot.png, Accuracy_plot.png', onlyIfSuccessful: true
             }
         }
     }

Jenkinsfile-docker-image-build

@@ -1,57 +0,0 @@
-pipeline {
-    agent any
-
-    parameters {
-        string(name: 'KAGGLE_USERNAME', defaultValue: 'gulczas', description: 'Kaggle username')
-        password(name: 'KAGGLE_KEY', defaultValue: '', description: 'Kaggle API key')
-    }
-
-    stages {
-        stage('Clone Repository') {
-            steps {
-               git 'https://git.wmi.amu.edu.pl/s464953/ium_464953.git'
-            }
-        }
-        
-        stage('Stop and remove existing container') {
-            steps {
-                script {
-                    sh "docker stop s464953 || true"
-                    sh "docker rm s464953 || true"
-                }
-            }
-        }
-
-        stage('Build Docker image') {
-            steps {
-                script {
-                    withEnv([
-                        "KAGGLE_USERNAME=${env.KAGGLE_USERNAME}", 
-                        "KAGGLE_KEY=${env.KAGGLE_KEY}"
-                    ]) {
-                        sh "docker build --build-arg KAGGLE_USERNAME=$KAGGLE_USERNAME --build-arg KAGGLE_KEY=$KAGGLE_KEY -t s464953 ."
-                    }
-                }
-            }
-        }
-
-        stage('Run Docker container') {
-            steps {
-                script {
-                    withEnv([
-                        "KAGGLE_USERNAME=${env.KAGGLE_USERNAME}", 
-                        "KAGGLE_KEY=${env.KAGGLE_KEY}"
-                    ]) {
-                        sh "docker run --name s464953 -e KAGGLE_USERNAME=$KAGGLE_USERNAME -e KAGGLE_KEY=$KAGGLE_KEY -v ${WORKSPACE}:/app s464953"
-                    }
-                }
-            }
-        }
-
-        stage('Archive stats.txt artifact') {
-            steps {
-                archiveArtifacts artifacts: 'stats.txt', allowEmptyArchive: true
-            }
-        }
-    }
-}

Jenkinsfile-docker-image-pull

@@ -1,44 +0,0 @@
-pipeline {
-    agent any
-
-    parameters {
-        string(name: 'KAGGLE_USERNAME', defaultValue: 'gulczas', description: 'Kaggle username')
-        password(name: 'KAGGLE_KEY', defaultValue: '', description: 'Kaggle API key')
-    }
-
-    stages {
-        stage('Clone Repository') {
-            steps {
-               git 'https://git.wmi.amu.edu.pl/s464953/ium_464953.git'
-            }
-        }
-
-        stage('Stop and remove existing container') {
-            steps {
-                script {
-                    sh "docker stop s464953 || true"
-                    sh "docker rm s464953 || true"
-                }
-            }
-        }
-
-        stage('Run Docker container') {
-            steps {
-                script {
-                    withEnv([
-                        "KAGGLE_USERNAME=${env.KAGGLE_USERNAME}", 
-                        "KAGGLE_KEY=${env.KAGGLE_KEY}"
-                    ]) {
-                        sh "docker run --name s464953 -e KAGGLE_USERNAME=$KAGGLE_USERNAME -e KAGGLE_KEY=$KAGGLE_KEY -v ${WORKSPACE}:/app michalgulczynski/ium_s464953:1.0"
-                    }
-                }
-            }
-        }
-
-        stage('Archive stats.txt artifact') {
-            steps {
-                archiveArtifacts artifacts: 'stats.txt', allowEmptyArchive: true
-            }
-        }
-    }
-}

Jenkinsfile-ium-6

@@ -1,57 +0,0 @@
-pipeline {
-    agent any
-
-    parameters {
-        string(name: 'KAGGLE_USERNAME', defaultValue: 'gulczas', description: 'Kaggle username')
-        password(name: 'KAGGLE_KEY', defaultValue: '', description: 'Kaggle API key')
-    }
-
-    stages {
-        stage('Clone Repository') {
-            steps {
-               git 'https://git.wmi.amu.edu.pl/s464953/ium_464953.git'
-            }
-        }
-        
-        stage('Stop and remove existing container') {
-            steps {
-                script {
-                    sh "docker stop s464953 || true"
-                    sh "docker rm s464953 || true"
-                }
-            }
-        }
-
-        stage('Build Docker image') {
-            steps {
-                script {
-                    withEnv([
-                        "KAGGLE_USERNAME=${env.KAGGLE_USERNAME}", 
-                        "KAGGLE_KEY=${env.KAGGLE_KEY}"
-                    ]) {
-                        sh "docker build --build-arg KAGGLE_USERNAME=$KAGGLE_USERNAME --build-arg KAGGLE_KEY=$KAGGLE_KEY -t s464953 ."
-                    }
-                }
-            }
-        }
-
-        stage('Run Docker container') {
-            steps {
-                script {
-                    withEnv([
-                        "KAGGLE_USERNAME=${env.KAGGLE_USERNAME}", 
-                        "KAGGLE_KEY=${env.KAGGLE_KEY}"
-                    ]) {
-                        sh "docker run --name s464953 -e KAGGLE_USERNAME=$KAGGLE_USERNAME -e KAGGLE_KEY=$KAGGLE_KEY -v ${WORKSPACE}:/app s464953"
-                    }
-                }
-            }
-        }
-
-        stage('Archive stats.txt artifact') {
-            steps {
-                archiveArtifacts artifacts: 'model.pkl', allowEmptyArchive: true
-            }
-        }
-    }
-}

Jenkinsfile-stats

@@ -1,42 +0,0 @@
-pipeline {
-    agent any
-
-    parameters {
-        buildSelector( defaultSelector: lastSuccessful(), description: 'Build for copying artifacts', name: 'BUILD_SELECTOR')
-    }
-
-    stages {
-        stage('Clone Repository') {
-            steps {
-                git 'https://git.wmi.amu.edu.pl/s464953/ium_464953.git'
-            }
-        }
-        stage('Cleanup Artifacts') {
-            steps {
-                script {
-                    sh 'rm -rf artifacts'
-                }
-            }
-        }
-        stage('Copy Artifact') {
-           steps {
-                withEnv([
-                "BUILD_SELECTOR=${params.BUILD_SELECTOR}" 
-                ]) {
-                copyArtifacts fingerprintArtifacts: true, projectName: 'z-s464953-create-dataset', selector: buildParameter('$BUILD_SELECTOR')}
-            }
-        }
-         stage('Execute Shell Script') {
-            steps {
-                script {
-                    sh "bash ./dataset_stats.sh"
-                }
-            }
-        }
-        stage('Archive Results') {
-            steps {
-                archiveArtifacts artifacts: 'artifacts/*', onlyIfSuccessful: true
-            }
-        }
-    }
-}

Jenkinsfile_sacred

@@ -1,50 +0,0 @@
-pipeline {
-  agent any
-  
-  parameters {
-    string(name: 'KAGGLE_USERNAME', defaultValue: 'gulczas', description: 'Kaggle username')
-    password(name: 'KAGGLE_KEY', defaultValue: '', description: 'Kaggle API key')
-  }
-
-  stages {
-    stage('Clone Repository') {
-      steps {
-        git 'https://git.wmi.amu.edu.pl/s464953/ium_464953.git'
-      }
-    }
-
-    stage('Download datasets') {
-      steps {
-        withEnv(["KAGGLE_USERNAME=${params.KAGGLE_USERNAME}", "KAGGLE_KEY=${params.KAGGLE_KEY}"]) {
-          sh "bash ./download_dataset.sh"
-        }
-      }
-    }
-
-    stage('Build and Run Experiments') {
-      agent {
-        dockerfile {
-          reuseNode true
-        }
-      } 
-  
-      environment {
-        KAGGLE_USERNAME = "${params.KAGGLE_USERNAME}"
-        KAGGLE_KEY = "${params.KAGGLE_KEY}"
-      }
-
-      steps {
-        sh 'chmod +x sacred/sacred_model_creator.py'
-        sh 'python3 sacred/sacred_model_creator.py'
-        sh 'chmod +x sacred/sacred_use_model.py'
-        sh 'python3 sacred/sacred_use_model.py'
-      }
-    }
-
-    stage('Archive Artifacts from Experiments') {
-      steps {
-        archiveArtifacts artifacts: 'my_experiment_logs/**', allowEmptyArchive: true
-      }
-    }
-  }
-}

MLProject/MLProject

@@ -1,11 +0,0 @@
-name: MLflow Example
-
-conda_env: conda.yaml
-
-entry_points:
-  main:
-    command: "python model_creator.py {max_iter}"
-    parameters:
-      max_iter: {type: int, default: 1000}
-  test:
-    command: "python use_model.py"

MLProject/conda.yaml

@@ -1,11 +0,0 @@
-name: Spotify genre recognition - s464953
-channels:
-  - defaults
-dependencies:
-  - python=3.9
-  - pip
-  - pip:
-      - mlflow
-      - pandas
-      - scikit-learn
-      - numpy

MLProject/mlruns/0/04eba1c93f6a4510b4487ad0789fa76f/artifacts/model/MLmodel

@@ -1,20 +0,0 @@
-artifact_path: model
-flavors:
-  python_function:
-    env:
-      conda: conda.yaml
-      virtualenv: python_env.yaml
-    loader_module: mlflow.sklearn
-    model_path: model.pkl
-    predict_fn: predict
-    python_version: 3.9.19
-  sklearn:
-    code: null
-    pickled_model: model.pkl
-    serialization_format: cloudpickle
-    sklearn_version: 1.4.2
-mlflow_version: 2.12.2
-model_size_bytes: 1446
-model_uuid: 9026270861774aad82aee9fc231054b4
-run_id: 04eba1c93f6a4510b4487ad0789fa76f
-utc_time_created: '2024-05-13 21:25:05.523657'

MLProject/mlruns/0/04eba1c93f6a4510b4487ad0789fa76f/artifacts/model/conda.yaml

@@ -1,15 +0,0 @@
-channels:
-- conda-forge
-dependencies:
-- python=3.9.19
-- pip<=24.0
-- pip:
-  - mlflow==2.12.2
-  - cloudpickle==3.0.0
-  - numpy==1.26.4
-  - packaging==23.1
-  - psutil==5.9.5
-  - pyyaml==6.0.1
-  - scikit-learn==1.4.2
-  - scipy==1.13.0
-name: mlflow-env

MLProject/mlruns/0/04eba1c93f6a4510b4487ad0789fa76f/artifacts/model/metadata/MLmodel

@@ -1,20 +0,0 @@
-artifact_path: model
-flavors:
-  python_function:
-    env:
-      conda: conda.yaml
-      virtualenv: python_env.yaml
-    loader_module: mlflow.sklearn
-    model_path: model.pkl
-    predict_fn: predict
-    python_version: 3.9.19
-  sklearn:
-    code: null
-    pickled_model: model.pkl
-    serialization_format: cloudpickle
-    sklearn_version: 1.4.2
-mlflow_version: 2.12.2
-model_size_bytes: 1446
-model_uuid: 9026270861774aad82aee9fc231054b4
-run_id: 04eba1c93f6a4510b4487ad0789fa76f
-utc_time_created: '2024-05-13 21:25:05.523657'

MLProject/mlruns/0/04eba1c93f6a4510b4487ad0789fa76f/artifacts/model/metadata/conda.yaml

@@ -1,15 +0,0 @@
-channels:
-- conda-forge
-dependencies:
-- python=3.9.19
-- pip<=24.0
-- pip:
-  - mlflow==2.12.2
-  - cloudpickle==3.0.0
-  - numpy==1.26.4
-  - packaging==23.1
-  - psutil==5.9.5
-  - pyyaml==6.0.1
-  - scikit-learn==1.4.2
-  - scipy==1.13.0
-name: mlflow-env

MLProject/mlruns/0/04eba1c93f6a4510b4487ad0789fa76f/artifacts/model/metadata/python_env.yaml

@@ -1,7 +0,0 @@
-python: 3.9.19
-build_dependencies:
-- pip==24.0
-- setuptools
-- wheel==0.43.0
-dependencies:
-- -r requirements.txt

MLProject/mlruns/0/04eba1c93f6a4510b4487ad0789fa76f/artifacts/model/metadata/requirements.txt

@@ -1,8 +0,0 @@
-mlflow==2.12.2
-cloudpickle==3.0.0
-numpy==1.26.4
-packaging==23.1
-psutil==5.9.5
-pyyaml==6.0.1
-scikit-learn==1.4.2
-scipy==1.13.0

MLProject/mlruns/0/04eba1c93f6a4510b4487ad0789fa76f/artifacts/model/python_env.yaml

@@ -1,7 +0,0 @@
-python: 3.9.19
-build_dependencies:
-- pip==24.0
-- setuptools
-- wheel==0.43.0
-dependencies:
-- -r requirements.txt

MLProject/mlruns/0/04eba1c93f6a4510b4487ad0789fa76f/artifacts/model/requirements.txt

@@ -1,8 +0,0 @@
-mlflow==2.12.2
-cloudpickle==3.0.0
-numpy==1.26.4
-packaging==23.1
-psutil==5.9.5
-pyyaml==6.0.1
-scikit-learn==1.4.2
-scipy==1.13.0

MLProject/mlruns/0/04eba1c93f6a4510b4487ad0789fa76f/meta.yaml

@@ -1,15 +0,0 @@
-artifact_uri: file:///D:/studia/inzynieria%20uczenia%20maszynowego/ium_464953/MLProject/mlruns/0/04eba1c93f6a4510b4487ad0789fa76f/artifacts
-end_time: 1715635510283
-entry_point_name: ''
-experiment_id: '0'
-lifecycle_stage: active
-run_id: 04eba1c93f6a4510b4487ad0789fa76f
-run_name: valuable-goat-689
-run_uuid: 04eba1c93f6a4510b4487ad0789fa76f
-source_name: ''
-source_type: 4
-source_version: ''
-start_time: 1715635487472
-status: 3
-tags: []
-user_id: Michał

MLProject/mlruns/0/04eba1c93f6a4510b4487ad0789fa76f/metrics/accuracy

@@ -1 +0,0 @@
-1715635505497 0.4782608695652174 0

MLProject/mlruns/0/04eba1c93f6a4510b4487ad0789fa76f/params/max_iter

@@ -1 +0,0 @@
-1000

MLProject/mlruns/0/04eba1c93f6a4510b4487ad0789fa76f/params/model_type

@@ -1 +0,0 @@
-LogisticRegression

MLProject/mlruns/0/04eba1c93f6a4510b4487ad0789fa76f/params/test_size

@@ -1 +0,0 @@
-0.1

MLProject/mlruns/0/04eba1c93f6a4510b4487ad0789fa76f/tags/mlflow.gitRepoURL

@@ -1 +0,0 @@
-https://git.wmi.amu.edu.pl/s464953/ium_464953.git

MLProject/mlruns/0/04eba1c93f6a4510b4487ad0789fa76f/tags/mlflow.log-model.history

@@ -1 +0,0 @@
-[{"run_id": "04eba1c93f6a4510b4487ad0789fa76f", "artifact_path": "model", "utc_time_created": "2024-05-13 21:25:05.523657", "flavors": {"python_function": {"model_path": "model.pkl", "predict_fn": "predict", "loader_module": "mlflow.sklearn", "python_version": "3.9.19", "env": {"conda": "conda.yaml", "virtualenv": "python_env.yaml"}}, "sklearn": {"pickled_model": "model.pkl", "sklearn_version": "1.4.2", "serialization_format": "cloudpickle", "code": null}}, "model_uuid": "9026270861774aad82aee9fc231054b4", "mlflow_version": "2.12.2", "model_size_bytes": 1446}]

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