s

train.py

@@ -1,69 +1,69 @@
 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.use_git = False
+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.use_git = False
 
-# @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.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
+@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=random_state)
-data = pd.read_csv(data_file, sep=';')
+    smote = SMOTE(random_state=random_state)
+    data = pd.read_csv(data_file, sep=';')
 
-print('Total rows:', len(data))
-print('Rows with medal:', len(data.dropna(subset=['Medal'])))
+    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'])
+    data = pd.get_dummies(data, columns=['Sex', 'Medal'])
+    data = data.drop(columns=['Name', 'Team', 'NOC', 'Games', 'Year', 'Season', 'City', 'Sport', 'Event'])
 
-scaler = MinMaxScaler()
-data = pd.DataFrame(scaler.fit_transform(data), columns=data.columns)
+    scaler = MinMaxScaler()
+    data = pd.DataFrame(scaler.fit_transform(data), columns=data.columns)
 
-X = data.filter(regex='Sex|Age')
-y = data.filter(regex='Medal')
-y = pd.get_dummies(y)
+    X = data.filter(regex='Sex|Age')
+    y = data.filter(regex='Medal')
+    y = pd.get_dummies(y)
 
-X = X.fillna(0)
-y = y.fillna(0)
+    X = X.fillna(0)
+    y = y.fillna(0)
 
-y = y.values
+    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=test_size, random_state=random_state)
+    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=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')) 
-model.add(tf.keras.layers.Dense(32, activation='relu'))
-model.add(tf.keras.layers.Dense(y.shape[1], activation='softmax')) 
+    model = tf.keras.models.Sequential()
+    model.add(tf.keras.layers.Dense(64, input_dim=X_train.shape[1], activation='relu')) 
+    model.add(tf.keras.layers.Dense(32, activation='relu'))
+    model.add(tf.keras.layers.Dense(y.shape[1], activation='softmax')) 
 
-model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
+    model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
 
-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.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_file)
+    model.save(model_file)
 
-# return accuracy  
+    return accuracy  
 
-# @ex.main
-# def run_experiment():
-#     accuracy = train_model()
-#     ex.log_scalar('accuracy', accuracy)  
-#     ex.add_artifact('model.h5')  
+@ex.main
+def run_experiment():
+    accuracy = train_model()
+    ex.log_scalar('accuracy', accuracy)  
+    ex.add_artifact('model.h5')