37 lines
1.2 KiB
Python
37 lines
1.2 KiB
Python
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 accuracy_score
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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("datasets/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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with open('model_predictions.txt', 'w') as f:
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pass
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with open('model_predictions.txt', 'a') as f:
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labels_dict = {0: 'edm', 1 : 'latin', 2 : 'pop', 3 : 'r&b', 4 : 'rap', 5 :'rock'}
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Y_test_labels = [labels_dict[number] for number in Y_test]
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Y_pred_labels = [labels_dict[number] for number in Y_pred]
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f.write("Real:" + str(Y_test_labels[:20])+ " \nPredicted: "+ str(Y_pred_labels[:20]))
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accuracy = accuracy_score(Y_test, Y_pred)
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f.write("\nAccuracy:" + str(accuracy))
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