feat(Model): trying to get to a model that can train

model_lstm.py

@@ -1,74 +1,105 @@
 #%% Import all the stuff, load data, define constants
 from load_data import load_files
 import pandas as pd
-import keras
+from tensorflow import keras
 from utils.normalize import normalize
+import tensorflow as tf
 
 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')]]
 
 ticker_to_predict = 'ETH_returns'
 
 learning_rate = 0.001
-batch_size = 256
-epochs = 10
+batch_size = 128
+epochs = 30
 
 split_fraction = 0.715
 train_split = int(split_fraction * int(data.shape[0]))
 
-past = 720
-future = 72
+past = 10
+future = 1
 
 start = past + future
 end = start + train_split
 
-#%%
+#%% split data into training - validation sets
+train_data = data.loc[0 : train_split - 1]
+val_data = data.loc[train_split:]
 
-x_train = data.loc[0 : train_split - 1].drop(ticker_to_predict, axis=1).values
-y_train = data.iloc[start:end][ticker_to_predict]
+#%% 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
+y_train = normalize(data).iloc[start:end][ticker_to_predict].values
 
 dataset_train = keras.preprocessing.timeseries_dataset_from_array(
     x_train,
     y_train,
     sequence_length=past,
-    sampling_rate=1,
-    batch_size=32,
+    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
+y_val = normalize(data).iloc[label_start:][ticker_to_predict].values
+
+dataset_val = keras.utils.timeseries_dataset_from_array(
+    x_val,
+    y_val,
+    sequence_length=past,
+    batch_size=batch_size,
+)
+
+
+#%%
+
 for batch in dataset_train.take(1):
-    inputs, targets = batch
+    batch_inputs, batch_targets = batch
+
+print("Input shape:", batch_inputs.shape)
+print("Target shape:", batch_targets.shape)
 
-print("Input shape:", inputs.numpy().shape)
-print("Target shape:", targets.numpy().shape)
-inputs
 
 # %%
-inputs = keras.layers.Input(shape=(inputs.shape[1], inputs.shape[2]))
+# 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))
+
+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)
-model.compile(optimizer=keras.optimizers.Adam(learning_rate=learning_rate), loss="mse")
+
+optimizer = keras.optimizers.Adam(learning_rate=learning_rate, clipnorm = 1.)
+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)
+# 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,
+# 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=[es_callback, modelckpt_callback],
+    # callbacks=[modelckpt_callback],
 )
 # %%