36 lines
1.4 KiB
Python
36 lines
1.4 KiB
Python
import mlflow.sklearn
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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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def load_model_and_predict():
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with mlflow.start_run():
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model = mlflow.sklearn.load_model("model")
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test_df = pd.read_csv("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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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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mlflow.log_metric("accuracy", accuracy)
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mlflow.log_artifact("model_predictions.txt")
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mlflow.end_run()
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if __name__ == "__main__":
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mlflow.set_tracking_uri("http://localhost:5000")
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load_model_and_predict()
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