ium_487187/evaluate.py

from sacred import Experiment
from sacred.observers import MongoObserver, FileStorageObserver

ex = Experiment('s487187_experiment', interactive=True)
ex.observers.append(MongoObserver(url='mongodb://admin:IUM_2021@172.17.0.1:27017', db_name='sacred'))
ex.observers.append(FileStorageObserver('results'))  

@ex.config
def my_config():
    model_path = 'model.h5'
    test_data_path = 'data.csv'
    metrics_file_path = 'metrics.txt'
    plot_path = 'plot.png'

@ex.capture
def evaluate_model(model_path, test_data_path, metrics_file_path, plot_path):
    import tensorflow as tf
    import pandas as pd
    import numpy as np
    from sklearn.preprocessing import MinMaxScaler
    import matplotlib.pyplot as plt
    import os

    model = tf.keras.models.load_model(model_path)

    test_data = pd.read_csv(test_data_path, sep=';')
    test_data = pd.get_dummies(test_data, columns=['Sex', 'Medal'])
    test_data = test_data.drop(columns=['Name', 'Team', 'NOC', 'Games', 'Year', 'Season', 'City', 'Sport', 'Event'])

    scaler = MinMaxScaler()
    test_data = pd.DataFrame(scaler.fit_transform(test_data), columns=test_data.columns)

    X_test = test_data.filter(regex='Sex|Age')
    y_test = test_data.filter(regex='Medal')
    y_test = pd.get_dummies(y_test)

    X_test = X_test.fillna(0)
    y_test = y_test.fillna(0)

    y_pred = model.predict(X_test)

    top_1_accuracy = tf.keras.metrics.categorical_accuracy(y_test, y_pred)
    top_5_accuracy = tf.keras.metrics.top_k_categorical_accuracy(y_test, y_pred, k=5)

    if os.path.exists(metrics_file_path):
        metrics_df = pd.read_csv(metrics_file_path)
    else:
        metrics_df = pd.DataFrame(columns=['top_1_accuracy', 'top_5_accuracy'])

    new_row = pd.DataFrame([{'top_1_accuracy': np.mean(top_1_accuracy.numpy()), 'top_5_accuracy': np.mean(top_5_accuracy.numpy())}])
    metrics_df = pd.concat([metrics_df, new_row], ignore_index=True)
    metrics_df.to_csv(metrics_file_path, index=False)

    plt.figure(figsize=(10, 6))
    plt.plot(metrics_df['top_1_accuracy'], label='Top-1 Accuracy')
    plt.plot(metrics_df['top_5_accuracy'], label='Top-5 Accuracy')
    plt.legend()
    plt.savefig(plot_path)

    ex.log_scalar('top_1_accuracy', np.mean(top_1_accuracy.numpy()))
    ex.log_scalar('top_5_accuracy', np.mean(top_5_accuracy.numpy()))
    ex.add_artifact(model_path)
    ex.add_artifact(metrics_file_path)
    ex.add_artifact(plot_path)

@ex.automain
def main():
    evaluate_model()