added model evaluation

.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

@@ -1,26 +1,17 @@
 FROM ubuntu:latest
 
-ENV KAGGLE_USERNAME=gulczas
-ENV KAGGLE_KEY=default_key
-
 RUN apt-get update && \
     apt-get install -y \
-	python3 \
+    python3 \
     python3-pip \
     wget \
     unzip \
     && rm -rf /var/lib/apt/lists/*
 
-RUN pip3 install pandas scikit-learn requests kaggle numpy
+RUN pip3 install pandas scikit-learn requests numpy matplotlib
 
 WORKDIR /app
 
-COPY model_creator.py /app/
 COPY use_model.py /app/
-COPY run_py_scripts.sh /app/
 
-
-RUN chmod +x model_creator.py
 RUN chmod +x use_model.py
-
-CMD ["bash", "run_py_scripts.sh"]

Jenkinsfile

@@ -1,11 +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')
-        password(name: 'KAGGLE_KEY', defaultValue: '', description: 'Kaggle API key')
-        string(name: 'CUTOFF', defaultValue: '90', description: 'Number of rows to cut')
-    }
+        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') {
@@ -13,28 +18,25 @@ pipeline {
                 git 'https://git.wmi.amu.edu.pl/s464953/ium_464953.git'
             }
         }
-        stage('Cleanup Artifacts') {
-            steps {
-                script {
-                    sh 'rm -rf artifacts'
-                }
-            }
+       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-evaluation/evaluation', selector: buildParameter('BUILD_SELECTOR'), optional: true
+          }
+        }
+        
         stage('Run Script') {
             steps {
-                script {
-                    withEnv([
-                    "KAGGLE_USERNAME=${env.KAGGLE_USERNAME}", 
-                    "KAGGLE_KEY=${env.KAGGLE_KEY}"])  
-                    {
-                        sh "bash ./download_dataset.sh ${params.CUTOFF}"
-                    }
-                }
+                sh "python3 /app/use_model.py ${currentBuild.number}"
             }
         }
         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
-            }
-        }
-    }
-}

data_preparation_script.py

@@ -1,113 +0,0 @@
-#!/usr/bin/env python
-
-# Import bibliotek 
-
-import os
-import shutil
-import pandas as pd
-from sklearn.model_selection import train_test_split
-import requests
-from sklearn.preprocessing import MinMaxScaler
-from kaggle.api.kaggle_api_extended import KaggleApi
-
-
-#funkcja pobierająca plik 
-
-def download_file(url, filename, destination_folder):
-    # Wersja dla datasetów kaggle
-    
-    api = KaggleApi()
-    api.authenticate()
-
-    api.dataset_download_files('gulczas/spotify-dataset', path=destination_folder, unzip=True)
-
-
-# funkcja dzieląca zbiór
-
-def split_dataset(data, test_size=0.2, val_size=0.1, random_state=42):
-    #Podział na test i trening
-    train_data, test_data = train_test_split(data, test_size=test_size, random_state=random_state)
-    #Podział na walidacje i trening
-    train_data, val_data = train_test_split(train_data, test_size=val_size/(1-test_size), random_state=random_state)
-    
-    return train_data, val_data, test_data
-
-
-# Wyświetlanie statystyk zbioru 
-
-def print_dataset_stats(data, subset_name):
-    with open('stats.txt', 'a') as stats_file:
-        print(f"Statystyki dla zbioru {subset_name}:", file=stats_file)
-        print(f"Wielkość zbioru {subset_name}: {len(data)}", file=stats_file)
-
-        print("\nStatystyki wartości poszczególnych parametrów:", file=stats_file)
-        print(data.describe(), file=stats_file)
-
-        for column in data.columns:
-            print(f"Rozkład częstości dla kolumny '{column}':", file=stats_file)
-            print(data[column].value_counts(), file=stats_file)
-            print("\n", file=stats_file)
-
-# Normalizacja danych 
-
-def normalize_data(data):
-    scaler = MinMaxScaler()
-    numeric_columns = data.select_dtypes(include=['int', 'float']).columns
-    scaler.fit(data[numeric_columns])
-    df_normalized = data.copy()
-    df_normalized[numeric_columns] = scaler.transform(df_normalized[numeric_columns])
-    return df_normalized
-
-#Czyszczenie danych 
-
-def clean_dataset(data):
-    data.dropna(inplace=True)
-    data.drop_duplicates(inplace=True)
-    return data
-
-
-# main 
-
-url = "https://www.kaggle.com/datasets/gulczas/spotify-dataset?select=Spotify_Dataset.csv"
-filename = "Spotify_Dataset.csv"
-destination_folder = "datasets"
-
-# Pobieranie jeśli nie ma już pobranego pliku
-if not os.path.exists(destination_folder):
-    os.makedirs(destination_folder)
-    print(f"Utworzono folder: {destination_folder}")
-else:
-    print(f"Folder {destination_folder} już istnieje.")
-
-if 'Spotify_Dataset.csv' not in os.listdir(destination_folder):
-    # Pobranie pliku
-    filepath = download_file(url, filename, destination_folder)
-
-# Wczytanie danych z pliku CSV
-data = pd.read_csv("datasets/Spotify_Dataset.csv", sep=";")
-
-# Podział datasetu na zbiory treningowy, walidacyjny i testowy
-train_data, val_data, test_data = split_dataset(data)
-
-# Zapisanie podzielonych zbiorów danych do osobnych plików CSV
-train_data.to_csv("datasets/train.csv", index=False)
-val_data.to_csv("datasets/val.csv", index=False)
-test_data.to_csv("datasets/test.csv", index=False)
-
-# Wydrukowanie statystyk dla zbiorów
-print_dataset_stats(train_data, "treningowego")
-print("\n")
-print_dataset_stats(val_data, "walidacyjnego")
-print("\n")
-print_dataset_stats(test_data, "testowego")
-
-# Normalizacja i czyszczenie zbirów
-train_data = normalize_data(train_data)
-train_data = clean_dataset(train_data)
-val_data = normalize_data(train_data)
-val_data = clean_dataset(train_data)
-test_data = normalize_data(train_data)
-test_data = clean_dataset(train_data)
-
-
-

dataset_stats.sh

@@ -1,13 +0,0 @@
-#!/bin/bash
-
-echo "------------------ Train dataset stats ------------------"
-wc -l artifacts/train.csv > stats_train.txt
-
-echo "------------------ Validation dataset stats ------------------"
-wc -l artifacts/validation.csv > stats_validation.txt
-
-echo "------------------ Test dataset stats ------------------"
-wc -l artifacts/test.csv > stats_test.txt
-
-mkdir -p data
-mv stats_train.txt stats_validation.txt stats_test.txt artifacts/

download_dataset.sh

@@ -1,26 +0,0 @@
-#!/bin/bash
-
-pip install kaggle --upgrade
-
-kaggle datasets download -d gulczas/spotify-dataset
-
-unzip -o spotify-dataset.zip
-
-echo "------------------ Shufle ------------------"
-shuf Spotify_Dataset.csv -o shuffled_spotify.csv
-
-echo "------------------ Cut off to top $1 rows ------------------"
-head -n $1 shuffled_spotify.csv > cutoff_spotify.csv
-
-echo "------------------ Split ------------------"
-total_lines=$(wc -l < cutoff_spotify.csv)
-num_test=$((total_lines / 10))
-num_train=$((total_lines - (num_test * 2)))
-num_validation=$num_test
-
-head -n $num_train cutoff_spotify.csv > train.csv
-tail -n $((num_test+num_validation)) cutoff_spotify.csv | head -n $num_test > test.csv
-tail -n $num_validation cutoff_spotify.csv > validation.csv
-
-mkdir -p artifacts
-mv Spotify_Dataset.csv cutoff_spotify.csv train.csv validation.csv test.csv artifacts/

model_creator.py

@@ -1,127 +0,0 @@
-import pandas as pd
-import os
-import numpy as np
-from kaggle.api.kaggle_api_extended import KaggleApi
-from sklearn.model_selection import train_test_split
-from sklearn.linear_model import LogisticRegression
-from sklearn.metrics import accuracy_score
-from sklearn.preprocessing import StandardScaler
-from sklearn. preprocessing import LabelEncoder
-import pickle
-
-
-def download_dataset(dataset_address, destination_folder):
-    
-    api = KaggleApi()
-    api.authenticate()
-
-    api.dataset_download_files(dataset_address, path=destination_folder, unzip=True)
-
-
-def check_datasets_presence():
-    
-    dataset_1 = "Spotify_Dataset.csv"
-    dataset_2 = "spotify_songs.csv"
-    destination_folder = "datasets"
-
-    if not os.path.exists(destination_folder):
-        os.makedirs(destination_folder)
-        print(f"Utworzono folder: {destination_folder}")
-    else:
-        print(f"Folder {destination_folder} już istnieje.")
-
-    if dataset_1 not in os.listdir(destination_folder):
-        download_dataset('gulczas/spotify-dataset', destination_folder)
-
-    if dataset_2 not in os.listdir(destination_folder):
-        download_dataset('joebeachcapital/30000-spotify-songs', destination_folder)
-
-
-def datasets_preparation():
-       df_1 = pd.read_csv("datasets/spotify_songs.csv")
-       df_2 = pd.read_csv("datasets/Spotify_Dataset.csv", sep=";")
-
-       df_1 = df_1.dropna()
-       df_2 = df_2.dropna()
-       df_2 = df_2.rename(columns={'Title': 'track_name'})
-
-       columns_to_remove_df_1 = ['track_id', 'track_album_id', 'track_album_name', 'track_album_release_date',
-                            'playlist_id', 'playlist_subgenre']
-       columns_to_remove_df_2 = ['Date','# of Artist', 'Artist (Ind.)', '# of Nationality',
-              'Nationality', 'Continent', 'Points (Total)',
-              'Points (Ind for each Artist/Nat)', 'id', 'Song URL']
-
-       df_1 = df_1.drop(columns=columns_to_remove_df_1)
-       df_2 = df_2.drop(columns=columns_to_remove_df_2)
-       df_1 = df_1.drop_duplicates(subset=['track_name'])
-       df_2 = df_2.drop_duplicates(subset=['track_name'])
-
-       le = LabelEncoder()
-
-       unique_names_df2 = df_2['track_name'].unique()
-       diff_df = df_1[~df_1['track_name'].isin(unique_names_df2)]
-       diff_df = diff_df.iloc[:10000]
-
-       #diff_df = pd.concat([diff_df, df_1.iloc[:20]], ignore_index=True)
-       diff_df['track_artist'] = le.fit_transform(diff_df.track_artist)
-       diff_df['playlist_name'] = le.fit_transform(diff_df.playlist_name)
-       diff_df['playlist_genre'] = le.fit_transform(diff_df.playlist_genre)
-
-       #df_1 = df_1.iloc[20:]
-       
-       if "docker_test_dataset.csv" not in os.listdir("datasets"):
-              diff_df.to_csv("datasets/docker_test_dataset.csv", index=False)
-
-       result_df = pd.merge(df_1, df_2, on='track_name', how='inner') 
-       result_df = result_df.drop_duplicates(subset=['track_name'])
-       columns_to_remove_result_df = ['Rank', 'Artists', 'Danceability', 'Energy', 'Loudness',
-       'Speechiness', 'Acousticness', 'Instrumentalness', 'Valence']
-       result_df = result_df.drop(columns=columns_to_remove_result_df)
-
-       result_df['track_artist'] = le.fit_transform(result_df.track_artist)
-       result_df['playlist_name'] = le.fit_transform(result_df.playlist_name)
-       result_df['playlist_genre'] = le.fit_transform(result_df.playlist_genre)
-       
-       return result_df
-
-
-check_datasets_presence()
-result_df = datasets_preparation()
-Y = result_df[['playlist_genre']]
-X = result_df.drop(columns='playlist_genre')
-X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.10, random_state=42)
-
-
-Y_train = np.ravel(Y_train)
-Y_test = np.ravel(Y_test)
-
-scaler = StandardScaler()
-numeric_columns = X_train.select_dtypes(include=['int', 'float']).columns
-X_train_scaled = scaler.fit_transform(X_train[numeric_columns])
-X_test_scaled = scaler.transform(X_test[numeric_columns])
-
-model = LogisticRegression(max_iter=1000)
-model.fit(X_train_scaled, Y_train)
-
-
-Y_pred = model.predict(X_test_scaled)
-
-accuracy = accuracy_score(Y_test, Y_pred)
-print("Accuracy:", accuracy)
-
-file_path = 'model.pkl'
-
-if os.path.exists(file_path):
-    os.remove(file_path)
-
-if file_path not in os.listdir("./"):
-    with open(file_path, 'wb') as file:
-        pickle.dump(model, file)
-
-print("Model został zapisany do pliku:", file_path)
-
-
-
-
-
-

model_predictions.txt

@@ -1,3 +0,0 @@
-Real:['edm', 'edm', 'edm', 'edm', 'edm', 'edm', 'edm', 'edm', 'edm', 'edm', 'edm', 'edm', 'edm', 'edm', 'edm', 'edm', 'edm', 'edm', 'edm', 'edm'] 
-Predicted: ['pop', 'pop', 'pop', 'pop', 'pop', 'pop', 'pop', 'pop', 'pop', 'pop', 'pop', 'pop', 'pop', 'pop', 'pop', 'pop', 'pop', 'pop', 'pop', 'pop']
-Accuracy:0.1521

run_py_scripts.sh

@@ -1,3 +0,0 @@
-#!/bin/bash
-
-python3 model_creator.py

use_model.py

@@ -2,7 +2,57 @@ import pickle
 import pandas as pd
 import numpy as np
 from sklearn.preprocessing import StandardScaler
-from sklearn.metrics import accuracy_score
+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 = pd.concat([metrics_df, 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.close()
+    
+    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.close()
+    
+    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")
+    plt.close()
 
 np.set_printoptions(threshold=20)
 
@@ -11,7 +61,7 @@ with open(file_path, 'rb') as file:
     model = pickle.load(file)
 print("Model został wczytany z pliku:", file_path)
 
-test_df = pd.read_csv("datasets/docker_test_dataset.csv")
+test_df = pd.read_csv("artifacts/docker_test_dataset.csv")
 
 Y_test = test_df[['playlist_genre']]
 X_test = test_df.drop(columns='playlist_genre')
@@ -23,14 +73,8 @@ X_test_scaled = scaler.fit_transform(X_test[numeric_columns])
 
 Y_pred = model.predict(X_test_scaled)
 
-with open('model_predictions.txt', 'w') as f:
-    pass
-
-with open('model_predictions.txt', 'a') as f:
-    labels_dict = {0: 'edm', 1 : 'latin', 2 : 'pop',  3 : 'r&b', 4 : 'rap', 5 :'rock'}
-    Y_test_labels = [labels_dict[number] for number in Y_test]
-    Y_pred_labels = [labels_dict[number] for number in Y_pred]
-    f.write("Real:" + str(Y_test_labels[:20])+ " \nPredicted: "+ str(Y_pred_labels[:20]))
-    accuracy = accuracy_score(Y_test, Y_pred)
-    f.write("\nAccuracy:" + str(accuracy))
+result = pd.DataFrame({'Predictions': Y_pred, "Real": Y_test})
+result.to_csv("spotify_genre_predictions.csv", index=False)
 
+calculate_metrics(result)
+create_plots()