feat(Model): continued with the keras model scaffolding

model_lstm.py

@@ -1,5 +1,72 @@
+#%% Import all the stuff, load data, define constants
 from load_data import load_files
 import pandas as pd
+import keras
+from utils.normalize import normalize
 
-data = load_files()
+data = load_files('data/', False)
 
+ticker_to_predict = 'ETH_returns'
+
+learning_rate = 0.001
+batch_size = 256
+epochs = 10
+
+split_fraction = 0.715
+train_split = int(split_fraction * int(data.shape[0]))
+
+past = 720
+future = 72
+
+start = past + future
+end = start + 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]
+
+
+dataset_train = keras.preprocessing.timeseries_dataset_from_array(
+    x_train,
+    y_train,
+    sequence_length=past,
+    sampling_rate=1,
+    batch_size=32,
+)
+
+for batch in dataset_train.take(1):
+    inputs, targets = batch
+
+print("Input shape:", inputs.numpy().shape)
+print("Target shape:", targets.numpy().shape)
+inputs
+
+# %%
+inputs = keras.layers.Input(shape=(inputs.shape[1], 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")
+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,
+)
+
+history = model.fit(
+    dataset_train,
+    epochs=epochs,
+    validation_data=dataset_val,
+    callbacks=[es_callback, modelckpt_callback],
+)
+# %%