48 lines
1.8 KiB
Python
48 lines
1.8 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 accuracy_score
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from sacred import Experiment
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from sacred.observers import MongoObserver, FileObserver
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ex = Experiment('464953_evaluation')
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# Dodanie obserwatorów
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ex.observers.append(MongoObserver(url='mongodb://admin:IUM_2021@tzietkiewicz.vm.wmi.amu.edu.pl:27017'))
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ex.observers.append(FileObserver('my_experiment_logs'))
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@ex.config
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def config():
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model_filename = 'model.pkl'
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test_dataset_filename = 'datasets/docker_test_dataset.csv'
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@ex.main
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def run_evaluation(model_filename, test_dataset_filename):
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with open(model_filename, 'rb') as file:
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model = pickle.load(file)
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print("Model został wczytany z pliku:", model_filename)
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test_df = pd.read_csv(test_dataset_filename)
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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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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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accuracy = accuracy_score(Y_test, Y_pred)
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ex.log_scalar('accuracy', accuracy)
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with open('model_predictions.txt', 'w') as f:
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f.write("Real:" + str(Y_test_labels[:20]) + " \nPredicted: " + str(Y_pred_labels[:20]))
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f.write("\nAccuracy:" + str(accuracy))
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ex.add_artifact('model_predictions.txt')
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ex.add_resource(__file__)
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print("Accuracy:", accuracy)
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return accuracy
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if __name__ == '__main__':
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ex.run_commandline()
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