multilabel version

models2.py

+from tensorflow.keras.models import Sequential
+from tensorflow.keras.layers import Dense, Dropout, Flatten
+from tensorflow.keras.layers import Conv1D, MaxPooling1D, LeakyReLU
+
+# models
+num_classes = 2
+
+
+def model_architecture(c):
+    """
+    (c,b,e) corresponds to the combinations of the specific hyperparameters to build the model
+    c belongs to {0,1,2,3} and represents the layer architecture
+    b belongs to {0,1} and represents the batch size
+    e belongs to {0,1} and represents the number of epochs
+    """
+    #defining the keras model
+    model = Sequential()
+
+    if c == 0:
+        model.add(Conv1D(64, kernel_size = 3, activation = 'linear', input_shape = (30, 1)))
+        model.add(Conv1D(64,3, activation = 'relu'))
+        model.add(MaxPooling1D(pool_size = 2))
+        model.add(Flatten())
+        model.add(Dense(100, activation = 'relu'))
+        model.add(Dense(num_classes, activation = 'sigmoid'))
+
+    if c == 1:
+        model.add(Conv1D(16, kernel_size = 3, activation = 'linear', input_shape = (30, 1), padding = 'same'))
+        model.add(LeakyReLU(alpha = 0.1))
+        model.add(MaxPooling1D(2, padding = 'same'))
+        model.add(Conv1D(64,3, activation = 'linear', padding = 'same'))
+        model.add(LeakyReLU(alpha = 0.1))
+        model.add(MaxPooling1D(pool_size = 2, padding = 'same'))
+        model.add(Flatten())
+        model.add(Dense(128, activation = 'linear'))
+        model.add(LeakyReLU(alpha = 0.1))
+        model.add(Dense(num_classes, activation = 'sigmoid'))
+
+    if c == 2:
+        model.add(Conv1D(filters=64, kernel_size=3, activation='relu', input_shape=(30,1)))
+        model.add(Conv1D(filters=64, kernel_size=3, activation='relu'))
+        model.add(Dropout(0.5))
+        model.add(MaxPooling1D(pool_size=2))
+        model.add(Flatten())
+        model.add(Dense(100, activation='relu'))
+        model.add(Dense(num_classes, activation='sigmoid'))
+
+    if c == 3:
+        model.add(Conv1D(32, kernel_size = 3, activation='relu', input_shape=(30,1)))
+        model.add(Conv1D(32, 3, activation='relu'))
+        model.add(MaxPooling1D(pool_size=2))
+        model.add(Dropout(0.25))
+
+        model.add(Conv1D(64, 3, activation='relu'))
+        model.add(Conv1D(64, 3, activation='relu'))
+        model.add(MaxPooling1D(pool_size=2))
+        model.add(Dropout(0.25))
+
+        model.add(Flatten())
+        model.add(Dense(256, activation='relu'))
+        model.add(Dropout(0.5))
+        model.add(Dense(num_classes, activation='sigmoid'))
+
+
+    return model
+
+def choose_batch_epochs(b,e):
+    if b == 0 and e == 0:
+        return 16, 12
+    if b == 0 and e == 1:
+        return 16, 512
+    if b == 1 and e == 0:
+        return 64, 12
+    if b == 1 and e == 1:
+        return 64, 512
+
+def choose_balancing_method(o):
+    if o == 0:
+        return 'smote'
+    elif o == 1:
+        return 'adasyn'
+    elif o == 2:
+        return 'class_weight'
+    elif o == 3:
+        return 'undersampling'