mirror of
https://github.com/webclinic017/drift.git
synced 2026-08-08 00:27:50 +00:00
Mark Aron Szulyovszky
106 lines
2.9 KiB
Python
106 lines
2.9 KiB
Python
#%% Import all the stuff, load data, define constants
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from load_data import load_files
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import pandas as pd
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from tensorflow import keras
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from utils.normalize import normalize
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import tensorflow as tf
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data = load_files('data/', False)
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data.reset_index(drop=True, inplace=True)
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data = data[[column for column in data.columns if not column.endswith('volume')]]
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ticker_to_predict = 'ETH_returns'
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learning_rate = 0.001
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batch_size = 128
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epochs = 30
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split_fraction = 0.715
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train_split = int(split_fraction * int(data.shape[0]))
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past = 10
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future = 1
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start = past + future
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end = start + train_split
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#%% split data into training - validation sets
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train_data = data.loc[0 : train_split - 1]
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val_data = data.loc[train_split:]
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#%% create features and target for training set & keras dataset
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x_train = normalize(train_data).values
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# x_train = train_data.drop(ticker_to_predict, axis=1).values
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y_train = normalize(data).iloc[start:end][ticker_to_predict].values
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dataset_train = keras.preprocessing.timeseries_dataset_from_array(
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x_train,
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y_train,
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sequence_length=past,
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batch_size=batch_size,
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)
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#%% create features and target for validation set & keras dataset
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x_end = len(val_data) - past - future
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label_start = train_split + past + future
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x_val = normalize(val_data).iloc[:x_end].values
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# x_val = val_data.iloc[:x_end].drop(ticker_to_predict, axis=1).values
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y_val = normalize(data).iloc[label_start:][ticker_to_predict].values
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dataset_val = keras.utils.timeseries_dataset_from_array(
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x_val,
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y_val,
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sequence_length=past,
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batch_size=batch_size,
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)
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#%%
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for batch in dataset_train.take(1):
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batch_inputs, batch_targets = batch
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print("Input shape:", batch_inputs.shape)
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print("Target shape:", batch_targets.shape)
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# %%
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# model = keras.Sequential()
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# model.add(keras.layers.LSTM(units = 50, return_sequences = True, input_shape=(batch_inputs.shape[1], batch_inputs.shape[2])))
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# model.add(keras.layers.Dropout(0.2))
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# model.add(keras.layers.Dense(units = 1))
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inputs = keras.layers.Input(shape=(batch_inputs.shape[1], batch_inputs.shape[2]))
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lstm_out = keras.layers.LSTM(32)(inputs)
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outputs = keras.layers.Dense(1)(lstm_out)
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model = keras.Model(inputs=inputs, outputs=outputs)
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optimizer = keras.optimizers.Adam(learning_rate=learning_rate, clipnorm = 1.)
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model.compile(optimizer=optimizer, loss="mean_squared_error")
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model.summary()
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# %%
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path_checkpoint = "model_checkpoint.h5"
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# es_callback = keras.callbacks.EarlyStopping(monitor="val_loss", min_delta=0, patience=5)
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# modelckpt_callback = keras.callbacks.ModelCheckpoint(
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# monitor="val_loss",
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# filepath=path_checkpoint,
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# verbose=1,
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# save_weights_only=True,
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# save_best_only=True,
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# )
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tf.debugging.enable_check_numerics(
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stack_height_limit=30, path_length_limit=50
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)
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history = model.fit(
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dataset_train,
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epochs=epochs,
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validation_data=dataset_val,
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# callbacks=[modelckpt_callback],
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)
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# %%
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