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import os |
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import cython |
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from joblib import load |
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from numpy import append, expand_dims |
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from pandas import read_json, to_datetime, Timedelta |
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from tensorflow.keras.models import load_model |
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cdef class Utilities: |
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async def forecasting_utils(self, int sequence_length, |
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int days, str model_name, str algorithm, bint with_pred) -> tuple: |
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cdef str model_path = os.path.join(f'./resources/algorithms/{algorithm}/models', |
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f'{model_name}.keras') |
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model = load_model(model_path) |
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cdef str dataframe_path = os.path.join(f'./resources/algorithms/{algorithm}/posttrained', |
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f'{model_name}-posttrained.json') |
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dataframe = read_json(dataframe_path) |
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dataframe.set_index('Date', inplace=True) |
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minmax_scaler = load(os.path.join(f'./resources/algorithms/{algorithm}/pickles', |
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f'{model_name}_minmax_scaler.pickle')) |
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standard_scaler = load(os.path.join(f'./resources/algorithms/{algorithm}/pickles', |
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f'{model_name}_standard_scaler.pickle')) |
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if with_pred: |
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lst_seq = dataframe[-sequence_length:].values |
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lst_seq = expand_dims(lst_seq, axis=0) |
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predicted_prices = {} |
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last_date = to_datetime(dataframe.index[-1]) |
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for _ in range(days): |
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predicted_price = model.predict(lst_seq) |
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last_date = last_date + Timedelta(days=1) |
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predicted_prices[last_date] = minmax_scaler.inverse_transform(predicted_price) |
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predicted_prices[last_date] = standard_scaler.inverse_transform(predicted_prices[last_date]) |
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lst_seq = append(lst_seq[:, 1:, :], [predicted_price], axis=1) |
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predictions = [ |
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{'date': date.strftime('%Y-%m-%d'), 'price': float(price)} \ |
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for date, price in predicted_prices.items() |
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] |
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else: predictions = [] |
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df_date = dataframe.index[-sequence_length:].values |
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df_date = [to_datetime(date) for date in df_date] |
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dataframe[['Close']] = minmax_scaler.inverse_transform(dataframe) |
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dataframe[['Close']] = standard_scaler.inverse_transform(dataframe) |
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df_close = dataframe.iloc[-sequence_length:]['Close'].values |
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actuals = [ |
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{'date': date.strftime('%Y-%m-%d'), 'price': close} \ |
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for date, close in zip(df_date, df_close) |
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] |
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return actuals, predictions |
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