feat(WalkForward): added regression/classification switch, archived old experiments, wrapped the process into run_whole_pipeline() (#10)

* refactor(WalkForward): cleaned up training & evaluation code * refactor: added run_whole_pipeline(), moved all previous models to archive
2026-08-26 01:08:04 +00:00 · 2021-12-14 18:16:17 +01:00
parent 7aedb91069
commit d047b7417e
89 changed files with 10763 additions and 6477 deletions
@@ -0,0 +1,102 @@
+import keras
+
+def create_basic_lstm_model(input_shape, num_classes):
+    model = keras.Sequential()
+    model.add(keras.layers.LSTM(units = 10, return_sequences = True, activation = 'sigmoid', input_shape=input_shape))
+    model.add(keras.layers.Flatten())
+    model.add(keras.layers.Dropout(0.3))
+    model.add(keras.layers.Dense(units = 64, activation = 'sigmoid'))
+    model.add(keras.layers.Dropout(0.3))
+    model.add(keras.layers.Dense(units = 32, activation = 'sigmoid'))
+    model.add(keras.layers.Dropout(0.3))
+    model.add(keras.layers.Dense(units = num_classes, activation = 'linear'))
+    return model
+
+def create_basic_cnn_model(input_shape, num_classes):
+    model = keras.Sequential()
+    model.add(keras.layers.Conv1D(filters=32, kernel_size=3, padding="same", input_shape=input_shape))
+    model.add(keras.layers.BatchNormalization())
+    model.add(keras.layers.ReLU())
+    model.add(keras.layers.Conv1D(filters=32, kernel_size=3, padding="same"))
+    model.add(keras.layers.BatchNormalization())
+    model.add(keras.layers.ReLU())
+    model.add(keras.layers.Conv1D(filters=32, kernel_size=3, padding="same"))
+    model.add(keras.layers.BatchNormalization())
+    model.add(keras.layers.ReLU())
+    model.add(keras.layers.GlobalAveragePooling1D())
+    model.add(keras.layers.Dense(num_classes, activation="linear"))
+    return model
+
+
+def create_resnet_cnn_model(input_shape, num_classes):
+    n_feature_maps = 24
+
+    input_layer = keras.layers.Input(input_shape)
+
+
+    conv_x = keras.layers.Conv1D(filters=n_feature_maps, kernel_size=8, padding='same')(input_layer)
+    conv_x = keras.layers.BatchNormalization()(conv_x)
+    conv_x = keras.layers.Activation('relu')(conv_x)
+
+    conv_y = keras.layers.Conv1D(filters=n_feature_maps, kernel_size=5, padding='same')(conv_x)
+    conv_y = keras.layers.BatchNormalization()(conv_y)
+    conv_y = keras.layers.Activation('relu')(conv_y)
+
+    conv_z = keras.layers.Conv1D(filters=n_feature_maps, kernel_size=3, padding='same')(conv_y)
+    conv_z = keras.layers.BatchNormalization()(conv_z)
+
+    # expand channels for the sum
+    shortcut_y = keras.layers.Conv1D(filters=n_feature_maps, kernel_size=1, padding='same')(input_layer)
+    shortcut_y = keras.layers.BatchNormalization()(shortcut_y)
+
+    output_block_1 = keras.layers.add([shortcut_y, conv_z])
+    output_block_1 = keras.layers.Activation('relu')(output_block_1)
+
+    # BLOCK 2
+
+    conv_x = keras.layers.Conv1D(filters=n_feature_maps * 2, kernel_size=8, padding='same')(output_block_1)
+    conv_x = keras.layers.BatchNormalization()(conv_x)
+    conv_x = keras.layers.Activation('relu')(conv_x)
+
+    conv_y = keras.layers.Conv1D(filters=n_feature_maps * 2, kernel_size=5, padding='same')(conv_x)
+    conv_y = keras.layers.BatchNormalization()(conv_y)
+    conv_y = keras.layers.Activation('relu')(conv_y)
+
+    conv_z = keras.layers.Conv1D(filters=n_feature_maps * 2, kernel_size=3, padding='same')(conv_y)
+    conv_z = keras.layers.BatchNormalization()(conv_z)
+
+    # expand channels for the sum
+    shortcut_y = keras.layers.Conv1D(filters=n_feature_maps * 2, kernel_size=1, padding='same')(output_block_1)
+    shortcut_y = keras.layers.BatchNormalization()(shortcut_y)
+
+    output_block_2 = keras.layers.add([shortcut_y, conv_z])
+    output_block_2 = keras.layers.Activation('relu')(output_block_2)
+
+    # BLOCK 3
+
+    conv_x = keras.layers.Conv1D(filters=n_feature_maps * 2, kernel_size=8, padding='same')(output_block_2)
+    conv_x = keras.layers.BatchNormalization()(conv_x)
+    conv_x = keras.layers.Activation('relu')(conv_x)
+
+    conv_y = keras.layers.Conv1D(filters=n_feature_maps * 2, kernel_size=5, padding='same')(conv_x)
+    conv_y = keras.layers.BatchNormalization()(conv_y)
+    conv_y = keras.layers.Activation('relu')(conv_y)
+
+    conv_z = keras.layers.Conv1D(filters=n_feature_maps * 2, kernel_size=3, padding='same')(conv_y)
+    conv_z = keras.layers.BatchNormalization()(conv_z)
+
+    # no need to expand channels because they are equal
+    shortcut_y = keras.layers.BatchNormalization()(output_block_2)
+
+    output_block_3 = keras.layers.add([shortcut_y, conv_z])
+    output_block_3 = keras.layers.Activation('relu')(output_block_3)
+
+    # FINAL
+
+    gap_layer = keras.layers.GlobalAveragePooling1D()(output_block_3)
+
+    output_layer = keras.layers.Dense(num_classes, activation='linear')(gap_layer)
+    model = keras.models.Model(inputs=input_layer, outputs=output_layer)
+
+
+    return model
@@ -0,0 +1,41 @@
+from keras import layers
+import keras
+
+def transformer_encoder(inputs, head_size, num_heads, ff_dim, dropout=0):
+    # Normalization and Attention
+    x = layers.LayerNormalization(epsilon=1e-6)(inputs)
+    x = layers.MultiHeadAttention(
+        key_dim=head_size, num_heads=num_heads, dropout=dropout
+    )(x, x)
+    x = layers.Dropout(dropout)(x)
+    res = x + inputs
+
+    # Feed Forward Part
+    x = layers.LayerNormalization(epsilon=1e-6)(res)
+    x = layers.Conv1D(filters=ff_dim, kernel_size=1, activation="relu")(x)
+    x = layers.Dropout(dropout)(x)
+    x = layers.Conv1D(filters=inputs.shape[-1], kernel_size=1)(x)
+    return x + res
+
+def create_basic_transformer_model(
+    input_shape,
+    n_classes,
+    head_size,
+    num_heads,
+    ff_dim,
+    num_transformer_blocks,
+    mlp_units,
+    dropout=0,
+    mlp_dropout=0,
+):
+    inputs = keras.Input(shape=input_shape)
+    x = inputs
+    for _ in range(num_transformer_blocks):
+        x = transformer_encoder(x, head_size, num_heads, ff_dim, dropout)
+
+    x = layers.GlobalAveragePooling1D(data_format="channels_first")(x)
+    for dim in mlp_units:
+        x = layers.Dense(dim, activation="relu")(x)
+        x = layers.Dropout(mlp_dropout)(x)
+    outputs = layers.Dense(n_classes, activation="softmax")(x)
+    return keras.Model(inputs, outputs)