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')