feat(Model): now successfully training the basic LSTM model

model_lstm.py

@@ -4,16 +4,18 @@ import pandas as pd
 from tensorflow import keras
 from utils.normalize import normalize
 import tensorflow as tf
+from utils.visualize import visualize_loss
 
 data = load_files('data/', False)
 data.reset_index(drop=True, inplace=True)
 data = data[[column for column in data.columns if not column.endswith('volume')]]
+# data = data[["ETH_returns", "BTC_returns"]]
 
 ticker_to_predict = 'ETH_returns'
 
-learning_rate = 0.001
-batch_size = 128
-epochs = 30
+learning_rate = 0.002
+batch_size = 64
+epochs = 100
 
 split_fraction = 0.715
 train_split = int(split_fraction * int(data.shape[0]))
@@ -31,7 +33,7 @@ val_data = data.loc[train_split:]
 #%% create features and target for training set & keras dataset
 
 x_train = normalize(train_data).values
-# x_train = train_data.drop(ticker_to_predict, axis=1).values
+# x_train = normalize(train_data).drop(ticker_to_predict, axis=1).values
 y_train = normalize(data).iloc[start:end][ticker_to_predict].values
 
 dataset_train = keras.preprocessing.timeseries_dataset_from_array(
@@ -41,12 +43,13 @@ dataset_train = keras.preprocessing.timeseries_dataset_from_array(
     batch_size=batch_size,
 )
 
+
 #%% create features and target for validation set & keras dataset
 x_end = len(val_data) - past - future
 label_start = train_split + past + future
 
 x_val = normalize(val_data).iloc[:x_end].values
-# x_val = val_data.iloc[:x_end].drop(ticker_to_predict, axis=1).values
+# x_val = normalize(val_data).iloc[:x_end].drop(ticker_to_predict, axis=1).values
 y_val = normalize(data).iloc[label_start:][ticker_to_predict].values
 
 dataset_val = keras.utils.timeseries_dataset_from_array(
@@ -59,47 +62,35 @@ dataset_val = keras.utils.timeseries_dataset_from_array(
 
 #%%
 
-for batch in dataset_train.take(1):
+for batch in dataset_train.take(10):
     batch_inputs, batch_targets = batch
 
 print("Input shape:", batch_inputs.shape)
 print("Target shape:", batch_targets.shape)
 
+print(batch_inputs)
+print(batch_targets)
 
 # %%
-# model = keras.Sequential()
-# model.add(keras.layers.LSTM(units = 50, return_sequences = True, input_shape=(batch_inputs.shape[1], batch_inputs.shape[2])))
-# model.add(keras.layers.Dropout(0.2))
-# model.add(keras.layers.Dense(units = 1))
+model = keras.Sequential()
+model.add(keras.layers.LSTM(units = 32, return_sequences = True, activation = 'relu', input_shape=(batch_inputs.shape[1], batch_inputs.shape[2])))
+# model.add(keras.layers.Dropout(0.4))
+# model.add(keras.layers.Dense(units = 10, activation = 'relu'))
+model.add(keras.layers.Dropout(0.4))
+model.add(keras.layers.Dense(units = 1, activation = 'relu'))
 
-inputs = keras.layers.Input(shape=(batch_inputs.shape[1], batch_inputs.shape[2]))
-lstm_out = keras.layers.LSTM(32)(inputs)
-outputs = keras.layers.Dense(1)(lstm_out)
-model = keras.Model(inputs=inputs, outputs=outputs)
-
-optimizer = keras.optimizers.Adam(learning_rate=learning_rate, clipnorm = 1.)
+optimizer = keras.optimizers.Adam(learning_rate=learning_rate, clipnorm=1.0)
 model.compile(optimizer=optimizer, loss="mean_squared_error")
 model.summary()
 
 # %%
 path_checkpoint = "model_checkpoint.h5"
-# es_callback = keras.callbacks.EarlyStopping(monitor="val_loss", min_delta=0, patience=5)
-
-# modelckpt_callback = keras.callbacks.ModelCheckpoint(
-#     monitor="val_loss",
-#     filepath=path_checkpoint,
-#     verbose=1,
-#     save_weights_only=True,
-#     save_best_only=True,
-# )
-tf.debugging.enable_check_numerics(
-    stack_height_limit=30, path_length_limit=50
-)
 
 history = model.fit(
     dataset_train,
     epochs=epochs,
     validation_data=dataset_val,
-    # callbacks=[modelckpt_callback],
 )
 # %%
+
+visualize_loss(history, "Training and Validation Loss")

utils/normalize.py

@@ -3,4 +3,4 @@ import pandas as pd
 def normalize(data: pd.DataFrame) -> pd.DataFrame:
     data_mean = data.mean(axis=0)
     data_std = data.std(axis=0)
-    return (data - data_mean) / data_std
+    return ((data - data_mean) / data_std).fillna(0.)

utils/visualize.py

@@ -0,0 +1,15 @@
+import matplotlib.pyplot as plt
+
+
+def visualize_loss(history, title: str):
+    loss = history.history["loss"]
+    val_loss = history.history["val_loss"]
+    epochs = range(len(loss))
+    plt.figure()
+    plt.plot(epochs, loss, "b", label="Training loss")
+    plt.plot(epochs, val_loss, "r", label="Validation loss")
+    plt.title(title)
+    plt.xlabel("Epochs")
+    plt.ylabel("Loss")
+    plt.legend()
+    plt.show()