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import os |
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import joblib |
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import argparse |
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import pandas as pd |
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from sklearn.preprocessing import StandardScaler, MinMaxScaler |
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from training.trainer import train |
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from training.post_processor import save_json, inverse_transform |
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from training.data_processor import ( |
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scale_data, |
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get_datasets, |
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preprocess_data, |
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create_sequences |
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) |
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from training.model_builder import ( |
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gru_model, |
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lstm_model, |
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lstm_gru_model |
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) |
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from warnings import filterwarnings |
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filterwarnings('ignore') |
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def main(algorithm: str, sequence_length: int, epochs: int, batch_size: int): |
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datasets_path = './datasets' |
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models_path = './models' |
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posttrained = './posttrained' |
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pickle_file = './pickles' |
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for dataset in get_datasets(datasets_path): |
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print(f"[TRAINING] {dataset.replace('.csv', '')} ") |
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dataframe = pd.read_csv(os.path.join(datasets_path, dataset), index_col='Date')[['Close']] |
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model_file = os.path.join(models_path, f"{dataset.replace('.csv', '')}.keras") |
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dataframe.dropna(inplace = True) |
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standard_scaler, dataframe = scale_data(dataframe, StandardScaler) |
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minmax_scaler, dataframe = scale_data(dataframe, MinMaxScaler) |
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sequences, labels = create_sequences(dataframe, sequence_length) |
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input_shape = (sequences.shape[1], sequences.shape[2]) |
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if algorithm == "GRU": |
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model = gru_model(input_shape) |
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elif algorithm == "LSTM": |
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model = lstm_model(input_shape) |
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elif algorithm == "LSTM_GRU": |
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model = lstm_gru_model(input_shape) |
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else: model = lstm_model(input_shape) |
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train_size = int(len(sequences) * 0.8) |
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X_train, X_test = sequences[:train_size], sequences[train_size:] |
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y_train, y_test = labels[:train_size], labels[train_size:] |
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train({ |
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'model': model, |
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'model_file': model_file, |
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'sequence_length': sequence_length, |
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'epochs': epochs, |
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'batch_size': batch_size |
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}, X_train, y_train, X_test, y_test) |
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dataframe_json = {'Date': dataframe.index.tolist(), 'Close': dataframe['Close'].tolist()} |
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save_json( |
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os.path.join(posttrained, f'{dataset.replace(".csv", "")}-posttrained.json'), |
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dataframe_json |
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) |
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joblib.dump(minmax_scaler, os.path.join(pickle_file, f'{dataset.replace(".csv", "")}_minmax_scaler.pickle')) |
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joblib.dump(standard_scaler, os.path.join(pickle_file, f'{dataset.replace(".csv", "")}_standard_scaler.pickle')) |
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model.load_weights(model_file) |
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model.save(model_file) |
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print("\n\n") |
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