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feat(Modles): lstm model now using StandardScaler

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Mark Aron Szulyovszky
2021-11-12 23:25:38 +01:00
parent 58bef94769
commit 1429051c4c
1 changed files with 12 additions and 9 deletions
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model_lstm.py
+12 -9
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@@ -5,13 +5,14 @@ from tensorflow import keras
from utils.normalize import normalize from utils.normalize import normalize
import tensorflow as tf import tensorflow as tf
from utils.visualize import visualize_loss from utils.visualize import visualize_loss
from sklearn.preprocessing import StandardScaler
data = load_files('data/', False) data = load_files('data/', True)
data.reset_index(drop=True, inplace=True) data.reset_index(drop=True, inplace=True)
data = data[[column for column in data.columns if not column.endswith('volume')]] data = data[[column for column in data.columns if not column.endswith('volume')]]
# data = data[["ETH_returns", "BTC_returns"]] data = data[["BTC_returns", "BTC_mom_10", "BTC_mom_20", "BTC_mom_30", "BTC_vol_10", "BTC_mom_20", "BTC_vol_20"]]
ticker_to_predict = 'ETH_returns' ticker_to_predict = 'BTC_returns'
learning_rate = 0.002 learning_rate = 0.002
batch_size = 64 batch_size = 64
@@ -31,10 +32,12 @@ train_data = data.loc[0 : train_split - 1]
val_data = data.loc[train_split:] val_data = data.loc[train_split:]
#%% create features and target for training set & keras dataset #%% create features and target for training set & keras dataset
feature_scaler = StandardScaler()
target_scaler = StandardScaler()
x_train = normalize(train_data).values x_train = feature_scaler.fit_transform(train_data.values) # you get the mean and std
# x_train = normalize(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 y_train = target_scaler.fit_transform(data.iloc[start:end][ticker_to_predict].values.reshape(-1, 1))
dataset_train = keras.preprocessing.timeseries_dataset_from_array( dataset_train = keras.preprocessing.timeseries_dataset_from_array(
x_train, x_train,
@@ -48,9 +51,9 @@ dataset_train = keras.preprocessing.timeseries_dataset_from_array(
x_end = len(val_data) - past - future x_end = len(val_data) - past - future
label_start = train_split + past + future label_start = train_split + past + future
x_val = normalize(val_data).iloc[:x_end].values x_val = feature_scaler.transform(val_data.iloc[:x_end].values) # you use the training data's mean and std
# x_val = normalize(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 y_val = target_scaler.transform(data.iloc[label_start:][ticker_to_predict].values.reshape(-1, 1))
dataset_val = keras.utils.timeseries_dataset_from_array( dataset_val = keras.utils.timeseries_dataset_from_array(
x_val, x_val,
@@ -73,9 +76,9 @@ print(batch_targets)
# %% # %%
model = keras.Sequential() 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.LSTM(units = 32, return_sequences = True, activation = 'sigmoid', input_shape=(batch_inputs.shape[1], batch_inputs.shape[2])))
model.add(keras.layers.Dropout(0.4)) model.add(keras.layers.Dropout(0.4))
model.add(keras.layers.Dense(units = 10, activation = 'relu')) model.add(keras.layers.Dense(units = 10, activation = 'sigmoid'))
model.add(keras.layers.Dropout(0.4)) model.add(keras.layers.Dropout(0.4))
model.add(keras.layers.Dense(units = 1)) model.add(keras.layers.Dense(units = 1))