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import os
import json
import joblib
import argparse
import numpy as np
import pandas as pd
from sklearn.preprocessing import StandardScaler, MinMaxScaler
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import GRU, LSTM, Dense, Dropout
from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint
from warnings import filterwarnings
filterwarnings('ignore')
class DataProcessor:
def __init__(self, datasets_path):
self.datasets_path = datasets_path
self.datasets = self._get_datasets()
def _get_datasets(self):
return sorted([
item for item in os.listdir(self.datasets_path)
if os.path.isfile(os.path.join(self.datasets_path, item)) and item.endswith('.csv')
])
@staticmethod
def create_sequences(df, sequence_length):
labels, sequences = [], []
for i in range(len(df) - sequence_length):
seq = df.iloc[i:i + sequence_length].values
label = df.iloc[i + sequence_length].values[0]
sequences.append(seq)
labels.append(label)
return np.array(sequences), np.array(labels)
@staticmethod
def preprocess_data(dataframe):
for col in dataframe.columns:
if dataframe[col].isnull().any():
if dataframe[col].dtype == 'object':
dataframe[col].fillna(dataframe[col].mode()[0], inplace = True)
else:
dataframe[col].fillna(dataframe[col].mean(), inplace = True)
return dataframe
@staticmethod
def scale_data(dataframe, scaler_cls):
scaler = scaler_cls()
dataframe['Close'] = scaler.fit_transform(dataframe[['Close']])
return scaler, dataframe
class ModelBuilder:
"""
GRU (Gated Recurrent Units) Model
"""
@staticmethod
def gru_model(input_shape):
model = Sequential([
GRU(50, return_sequences = True, input_shape = input_shape),
Dropout(0.2),
GRU(50, return_sequences = True),
Dropout(0.2),
GRU(50, return_sequences = True),
Dropout(0.2),
GRU(50, return_sequences = False),
Dropout(0.2),
Dense(units = 1)
])
model.compile(optimizer = 'nadam', loss = 'mean_squared_error')
return model
"""
LSTM (Long Short-Term Memory) Model
"""
@staticmethod
def lstm_model(input_shape):
model = Sequential([
LSTM(50, return_sequences = True, input_shape = input_shape),
Dropout(0.2),
LSTM(50, return_sequences = True),
Dropout(0.2),
LSTM(50, return_sequences = True),
Dropout(0.2),
LSTM(50, return_sequences = False),
Dropout(0.2),
Dense(units = 1)
])
model.compile(optimizer = 'nadam', loss = 'mean_squared_error')
return model
"""
LSTM (Long Short-Term Memory) and
GRU (Gated Recurrent Units) Model
"""
@staticmethod
def lstm_gru_model(input_shape):
model = Sequential([
LSTM(50, return_sequences = True, input_shape = input_shape),
Dropout(0.2),
GRU(50, return_sequences = True),
Dropout(0.2),
LSTM(50, return_sequences = True),
Dropout(0.2),
GRU(50, return_sequences = False),
Dropout(0.2),
Dense(units = 1)
])
model.compile(optimizer = 'nadam', loss = 'mean_squared_error')
return model
class Trainer:
def __init__(self, model, model_file, sequence_length, epochs, batch_size):
self.model = model
self.model_file = model_file
self.sequence_length = sequence_length
self.epochs = epochs
self.batch_size = batch_size
def train(self, X_train, y_train, X_test, y_test):
early_stopping = EarlyStopping(monitor = 'val_loss', patience = 5, mode = 'min')
model_checkpoint = ModelCheckpoint(
filepath = self.model_file,
save_best_only = True,
monitor = 'val_loss',
mode = 'min'
)
history = self.model.fit(
X_train, y_train,
epochs = self.epochs,
batch_size = self.batch_size,
validation_data = (X_test, y_test),
callbacks = [early_stopping, model_checkpoint]
)
return history
class PostProcessor:
@staticmethod
def inverse_transform(scaler, data):
return scaler.inverse_transform(data)
@staticmethod
def save_json(filename, data):
with open(filename, 'w') as f:
json.dump(data, f)
def main(algorithm: str, sequence_length: int, epochs: int, batch_size: int):
datasets_path = './datasets'
models_path = './models'
posttrained = './posttrained'
pickle_file = './pickles'
data_processor = DataProcessor(datasets_path)
for dataset in data_processor.datasets:
print(f"[TRAINING] {dataset.replace('.csv', '')} ")
dataframe = pd.read_csv(os.path.join(datasets_path, dataset), index_col='Date')[['Close']]
model_file = os.path.join(models_path, f"{dataset.replace('.csv', '')}.keras")
# dataframe = data_processor.preprocess_data(dataframe)
dataframe.dropna(inplace = True)
standard_scaler, dataframe = data_processor.scale_data(dataframe, StandardScaler)
minmax_scaler, dataframe = data_processor.scale_data(dataframe, MinMaxScaler)
sequences, labels = data_processor.create_sequences(dataframe, sequence_length)
input_shape = (sequences.shape[1], sequences.shape[2])
if algorithm == "GRU":
model = ModelBuilder.gru_model(input_shape)
elif algorithm == "LSTM":
model = ModelBuilder.lstm_model(input_shape)
elif algorithm == "LSTM_GRU":
model = ModelBuilder.lstm_gru_model(input_shape)
else: model = ModelBuilder.lstm_model(input_shape)
train_size = int(len(sequences) * 0.8)
X_train, X_test = sequences[:train_size], sequences[train_size:]
y_train, y_test = labels[:train_size], labels[train_size:]
trainer = Trainer(model, model_file, sequence_length, epochs, batch_size)
trainer.train(X_train, y_train, X_test, y_test)
dataframe_json = {'Date': dataframe.index.tolist(), 'Close': dataframe['Close'].tolist()}
PostProcessor.save_json(
os.path.join(posttrained, f'{dataset.replace(".csv", "")}-posttrained.json'),
dataframe_json
)
joblib.dump(minmax_scaler, os.path.join(pickle_file, f'{dataset.replace(".csv", "")}_minmax_scaler.pickle'))
joblib.dump(standard_scaler, os.path.join(pickle_file, f'{dataset.replace(".csv", "")}_standard_scaler.pickle'))
model.load_weights(model_file)
model.save(model_file)
print("\n\n")
if __name__ == "__main__":
parser = argparse.ArgumentParser(description = "Tebakaja Model Trainer")
parser.add_argument('-a', '--algorithm', type = str, required = True,
help = 'select the algorithm to be trained (LSTM, GRU, LSTM_GRU)')
parser.add_argument('-e', '--epochs', type = int, required = True, help = 'epochs')
parser.add_argument('-b', '--batchs', type = int, required = True, help = 'batch length')
parser.add_argument('-s', '--sequences', type = int, required = True, help = 'sequences length')
args = parser.parse_args()
main(
epochs = args.epochs,
batch_size = args.batchs,
algorithm = args.algorithm,
sequence_length = args.sequences
)
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