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utils.py
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utils.py
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import re
import yfinance as yf
from pandas_datareader import data as pdr
from sklearn.metrics import mean_squared_error, mean_absolute_percentage_error, mean_absolute_error
import math, time
yf.pdr_override()
def to_underscore(name):
s1 = re.sub('(.)([A-Z][a-z]+)', r'\1_\2', name)
return re.sub('([a-z0-9])([A-Z])', r'\1_\2', s1).lower()
def pd_to_underscore(pd_to_rename, columns=[]):
rename_map = {}
if (len(columns) == 0):
columns = list(pd_to_rename)
for column in columns:
rename_map[column] = to_underscore(column)
pd_renamed = pd_to_rename.rename(index=str, columns=rename_map)
return pd_renamed
def load_prediction_dataset(
ticker,
start_date,
end_date
):
fetched_data = pdr.get_data_yahoo(
ticker,
start=start_date,
end=end_date
)
fetched_data['date'] = fetched_data.index
return pd_to_underscore(fetched_data)
def print_metrics(y_true, y_pred):
print('y_true.size: ', y_true.size)
print('y_pred.size: ', y_pred.size)
y_true_trimmed = y_true[-250:]
y_pred_trimmed = y_pred[-250:]
print(y_true_trimmed.size)
print(y_pred_trimmed.size)
# print(y_true_trimmed)
# print(y_pred_trimmed)
print('MAE: ', mean_absolute_error(y_true_trimmed, y_pred_trimmed))
print('MAPE: ', mean_absolute_percentage_error(y_true_trimmed, y_pred_trimmed))
print('RMSE: ', math.sqrt(mean_squared_error(y_true_trimmed, y_pred_trimmed)))
def compare_metrics(y_true, y_pred):
mean_absolute_error_result = mean_absolute_error(y_true, y_pred)
mean_squared_error_result = mean_squared_error(y_true, y_pred)
return {
"mean_absolute_error_result": mean_absolute_error_result,
"mean_squared_error_result": mean_squared_error_result
}