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Return the resulting time series as pd.Series

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This commit is contained in:
Alexander Hess 2021-01-10 16:11:40 +01:00
commit 84876047c1
Signed by: alexander
GPG key ID: 344EA5AB10D868E0
2 changed files with 56 additions and 52 deletions
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42
src/urban_meal_delivery/forecasts/timify.py
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@ -149,7 +149,7 @@ class OrderHistory:
def make_horizontal_time_series( # noqa:WPS210
self, pixel_id: int, predict_at: dt.datetime, train_horizon: int,
) -> Tuple[pd.DataFrame, int, int]:
) -> Tuple[pd.Series, int, pd.Series]:
"""Slice a horizontal time series out of the `.totals`.
Create a time series covering `train_horizon` weeks that can be used
@ -203,19 +203,20 @@ class OrderHistory:
frequency = 7
# Take only the counts at the `predict_at` time.
training_df = intra_pixel.loc[
first_start_at : last_start_at : self._n_daily_time_steps # type: ignore
training_ts = intra_pixel.loc[
first_start_at : last_start_at : self._n_daily_time_steps, # type: ignore
'total_orders',
]
if len(training_df) != frequency * train_horizon:
if len(training_ts) != frequency * train_horizon:
raise RuntimeError('Not enough historic data for `predict_at`')
actual_df = intra_pixel.loc[[predict_at]]
actuals_ts = intra_pixel.loc[[predict_at], 'total_orders']
return training_df, frequency, actual_df
return training_ts, frequency, actuals_ts
def make_vertical_time_series( # noqa:WPS210
self, pixel_id: int, predict_day: dt.date, train_horizon: int,
) -> Tuple[pd.DataFrame, int, pd.DataFrame]:
) -> Tuple[pd.Series, int, pd.Series]:
"""Slice a vertical time series out of the `.totals`.
Create a time series covering `train_horizon` weeks that can be used
@ -268,10 +269,11 @@ class OrderHistory:
frequency = 7 * self._n_daily_time_steps
# Take all the counts between `first_train_day` and `last_train_day`.
training_df = intra_pixel.loc[
first_start_at:last_start_at # type: ignore
training_ts = intra_pixel.loc[
first_start_at:last_start_at, # type: ignore
'total_orders',
]
if len(training_df) != frequency * train_horizon:
if len(training_ts) != frequency * train_horizon:
raise RuntimeError('Not enough historic data for `predict_day`')
first_prediction_at = dt.datetime(
@ -289,15 +291,16 @@ class OrderHistory:
0,
) - dt.timedelta(minutes=self._time_step)
actuals_df = intra_pixel.loc[
first_prediction_at:last_prediction_at # type: ignore
actuals_ts = intra_pixel.loc[
first_prediction_at:last_prediction_at, # type: ignore
'total_orders',
]
return training_df, frequency, actuals_df
return training_ts, frequency, actuals_ts
def make_real_time_time_series( # noqa:WPS210
self, pixel_id: int, predict_at: dt.datetime, train_horizon: int,
) -> Tuple[pd.DataFrame, int, int]:
) -> Tuple[pd.Series, int, pd.Series]:
"""Slice a vertical real-time time series out of the `.totals`.
Create a time series covering `train_horizon` weeks that can be used
@ -361,8 +364,9 @@ class OrderHistory:
# Take all the counts between `first_train_day` and `last_train_day`,
# including the ones on the `predict_at` day prior to `predict_at`.
training_df = intra_pixel.loc[
first_start_at:last_start_at # type: ignore
training_ts = intra_pixel.loc[
first_start_at:last_start_at, # type: ignore
'total_orders',
]
n_time_steps_on_predict_day = (
(
@ -378,9 +382,9 @@ class OrderHistory:
// 60 # -> minutes
// self._time_step
)
if len(training_df) != frequency * train_horizon + n_time_steps_on_predict_day:
if len(training_ts) != frequency * train_horizon + n_time_steps_on_predict_day:
raise RuntimeError('Not enough historic data for `predict_day`')
actual_df = intra_pixel.loc[[predict_at]]
actuals_ts = intra_pixel.loc[[predict_at], 'total_orders']
return training_df, frequency, actual_df
return training_ts, frequency, actuals_ts