#constructing daily error dataframe
days = predictions.groupby(
[predictions.index.year, predictions.index.month,
predictions.index.day]).count().index.values
rush_hour_predictions = predictions[(predictions.index.hour > 15)
& (predictions.index.hour < 22)]
daily_error = []
rush_hour_error = []
for day in days:
day_real = predictions[predictions.index.day == day[2]]['Real'].values
day_pred = predictions[predictions.index.day ==
day[2]]['Predictions'].values
daily_error.append(bm.mean_absolute_percentage_error(day_real, day_pred))
rush_real = rush_hour_predictions[rush_hour_predictions.index.day ==
day[2]]['Real'].values
rush_pred = rush_hour_predictions[rush_hour_predictions.index.day ==
day[2]]['Predictions'].values
rush_hour_error.append(
bm.mean_absolute_percentage_error(rush_real, rush_pred))
from datetime import date
daily_error = np.array(daily_error).transpose()
rush_hour_errors = np.array(rush_hour_error).transpose()
print(daily_error.shape)
indexes = [date(day[0], day[1], day[2]).ctime() for day in days]
data = {'Daily Error': daily_error, 'Rush Hour Error': rush_hour_error}
mapes = {}
for file_name in FILES.keys():
time_interval = 20
data = bm.read_data(FILES[file_name])
data['Scaled'], sc = bm.scale_data(data)
data.drop(['Speed'], axis='columns', inplace=True)
data.replace(0, np.nan, inplace=True)
reframed = bm.series_to_supervised(data, time_interval, 7 * 24 * 60, 5)
reframed = reframed[reframed.index.month == 5]
est = np.mean(reframed.values[:, :-1], axis=1)
result = reframed.values[:, -1]
mape = bm.mean_absolute_percentage_error(result, est)
mapes[file_name] = mape
mapes_20 = pd.DataFrame.from_dict(data=mapes,
orient='index',
columns=['mape_20'])
mapes = {}
for file_name in FILES.keys():
time_interval = 30
data = bm.read_data(FILES[file_name])
data['Scaled'], sc = bm.scale_data(data)
data.drop(['Speed'], axis='columns', inplace=True)
data.replace(0, np.nan, inplace=True)
