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