2023-05-10 17:30:54 +02:00
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import tensorflow as tf
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import pandas as pd
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import numpy as np
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from sklearn.preprocessing import MinMaxScaler
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from sklearn.metrics import accuracy_score, f1_score, mean_squared_error
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import matplotlib.pyplot as plt
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import os
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model = tf.keras.models.load_model('model.h5')
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2023-05-10 18:58:18 +02:00
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test_data = pd.read_csv('data.csv', sep=';')
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2023-05-10 17:30:54 +02:00
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test_data = pd.get_dummies(test_data, columns=['Sex', 'Medal'])
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test_data = test_data.drop(columns=['Name', 'Team', 'NOC', 'Games', 'Year', 'Season', 'City', 'Sport', 'Event'])
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scaler = MinMaxScaler()
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test_data = pd.DataFrame(scaler.fit_transform(test_data), columns=test_data.columns)
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X_test = test_data.filter(regex='Sex|Age')
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y_test = test_data.filter(regex='Medal')
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y_test = pd.get_dummies(y_test)
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X_test = X_test.fillna(0)
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y_test = y_test.fillna(0)
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y_pred = model.predict(X_test)
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top_1_accuracy = tf.keras.metrics.categorical_accuracy(y_test, y_pred)
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top_5_accuracy = tf.keras.metrics.top_k_categorical_accuracy(y_test, y_pred, k=5)
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metrics_file = 'metrics.txt'
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if os.path.exists(metrics_file):
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metrics_df = pd.read_csv(metrics_file)
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else:
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metrics_df = pd.DataFrame(columns=['top_1_accuracy', 'top_5_accuracy'])
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metrics_df = metrics_df.append({'top_1_accuracy': np.mean(top_1_accuracy), 'top_5_accuracy': np.mean(top_5_accuracy)}, ignore_index=True)
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metrics_df.to_csv(metrics_file, index=False)
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plt.figure(figsize=(10, 6))
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plt.plot(metrics_df['top_1_accuracy'], label='Top-1 Accuracy')
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plt.plot(metrics_df['top_5_accuracy'], label='Top-5 Accuracy')
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plt.legend()
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plt.savefig('plot.png')
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