feat: sacred

evaluate.py

@@ -1,14 +1,29 @@
+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
-from sklearn.metrics import accuracy_score, f1_score, mean_squared_error
     import matplotlib.pyplot as plt
     import os
 
-model = tf.keras.models.load_model('model.h5')
+    model = tf.keras.models.load_model(model_path)
 
-test_data = pd.read_csv('data.csv', sep=';')
+    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'])
 
@@ -27,18 +42,27 @@ 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)
 
-metrics_file = 'metrics.txt'
+    if os.path.exists(metrics_file_path):
-if os.path.exists(metrics_file):
+        metrics_df = pd.read_csv(metrics_file_path)
-    metrics_df = pd.read_csv(metrics_file)
     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, index=False)
+    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.png')
+    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()

train.py

@@ -1,18 +1,34 @@
+from sacred import Experiment
+from sacred.observers import MongoObserver, FileStorageObserver
+
+ex = Experiment('s487187-training') 
+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():
+    data_file = 'data.csv'
+    model_file = 'model.h5'
+    epochs = 10
+    batch_size = 32
+    test_size = 0.2
+    random_state = 42
+
+@ex.capture
+def train_model(data_file, model_file, epochs, batch_size, test_size, random_state):
     import pandas as pd
     from sklearn.model_selection import train_test_split
     from sklearn.preprocessing import MinMaxScaler
     import tensorflow as tf
     from imblearn.over_sampling import SMOTE
 
-smote = SMOTE(random_state=42)
+    smote = SMOTE(random_state=random_state)
-data = pd.read_csv('data.csv', sep=';')
+    data = pd.read_csv(data_file, sep=';')
 
     print('Total rows:', len(data))
     print('Rows with medal:', len(data.dropna(subset=['Medal'])))
 
-
     data = pd.get_dummies(data, columns=['Sex', 'Medal'])
-
     data = data.drop(columns=['Name', 'Team', 'NOC', 'Games', 'Year', 'Season', 'City', 'Sport', 'Event'])
 
     scaler = MinMaxScaler()
@@ -28,7 +44,7 @@ y = y.fillna(0)
     y = y.values
 
     X_resampled, y_resampled = smote.fit_resample(X, y)
-X_train, X_test, y_train, y_test = train_test_split(X_resampled, y_resampled, test_size=0.2, random_state=42)
+    X_train, X_test, y_train, y_test = train_test_split(X_resampled, y_resampled, test_size=test_size, random_state=random_state)
 
     model = tf.keras.models.Sequential()
     model.add(tf.keras.layers.Dense(64, input_dim=X_train.shape[1], activation='relu')) 
@@ -37,10 +53,16 @@ model.add(tf.keras.layers.Dense(y.shape[1], activation='softmax'))
 
     model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
 
-model.fit(X_train, y_train, epochs=10, batch_size=32)
+    model.fit(X_train, y_train, epochs=epochs, batch_size=batch_size)
     loss, accuracy = model.evaluate(X_test, y_test)
     print('Test accuracy:', accuracy)
 
-model.save('model.h5')
+    model.save(model_file)
 
+    return accuracy  
 
+@ex.main
+def run_experiment():
+    accuracy = train_model()
+    ex.log_scalar('accuracy', accuracy)  
+    ex.add_artifact('model.h5')