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import os
from joblib import load
from numpy import append, expand_dims
from pandas import read_json, to_datetime, Timedelta
from tensorflow.keras.models import load_model
class Utilities:
def __init__(self) -> None:
self.model_path = './models'
self.posttrained_path = './posttrained'
self.scaler_path = './pickles'
async def cryptocurrency_prediction_utils(self,
days: int, sequence_length: int, model_name: str) -> tuple:
model_path = os.path.join(self.model_path, f'{model_name}.keras')
model = load_model(model_path)
dataframe_path = os.path.join(self.posttrained_path, f'{model_name}-posttrained.json')
dataframe = read_json(dataframe_path)
dataframe.set_index('Date', inplace = True)
minmax_scaler = load(os.path.join(self.scaler_path, f'{model_name}_minmax_scaler.pickle'))
standard_scaler = load(os.path.join(self.scaler_path, f'{model_name}_standard_scaler.pickle'))
lst_seq = dataframe[-sequence_length:].values
lst_seq = expand_dims(lst_seq, axis = 0)
# Predicted
predicted_prices = {}
last_date = to_datetime(dataframe.index[-1])
for _ in range(days):
predicted_price = model.predict(lst_seq)
last_date = last_date + Timedelta(days = 1)
predicted_prices[last_date] = minmax_scaler.inverse_transform(predicted_price)
predicted_prices[last_date] = standard_scaler.inverse_transform(predicted_prices[last_date])
lst_seq = append(lst_seq[:, 1:, :], [predicted_price], axis = 1)
predictions = [
{'date': date.strftime('%Y-%m-%d'), 'price': float(price)} \
for date, price in predicted_prices.items()
]
# Actual
df_date = dataframe.index[-sequence_length:].values
df_date = [to_datetime(date) for date in df_date]
dataframe[['Close']] = minmax_scaler.inverse_transform(dataframe)
dataframe[['Close']] = standard_scaler.inverse_transform(dataframe)
df_close = dataframe.iloc[-sequence_length:]['Close'].values
actuals = [
{'date': date.strftime('%Y-%m-%d'), 'price': close} \
for date, close in zip(df_date, df_close)
]
return actuals, predictions
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