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Shorten a couple of names

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- rename "total_orders" columns into "n_orders"
- rename `.make_*_time_series()` methods into `.make_*_ts()`
This commit is contained in:
Alexander Hess 2021-01-31 20:19:12 +01:00
commit 7b824a4a12
Signed by: alexander
GPG key ID: 344EA5AB10D868E0
4 changed files with 71 additions and 71 deletions
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30
src/urban_meal_delivery/forecasts/timify.py
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@ -53,7 +53,7 @@ class OrderHistory:
Returns:
order_totals: a one-column `DataFrame` with a `MultiIndex` of the
"pixel_id"s and "start_at"s (i.e., beginnings of the intervals);
the column with data is "total_orders"
the column with data is "n_orders"
"""
if self._data is None:
self._data = self.aggregate_orders()
@ -69,7 +69,7 @@ class OrderHistory:
SELECT
pixel_id,
start_at,
COUNT(*) AS total_orders
COUNT(*) AS n_orders
FROM (
SELECT
pixel_id,
@ -152,7 +152,7 @@ class OrderHistory:
return data.reindex(index, fill_value=0)
def make_horizontal_time_series( # noqa:WPS210
def make_horizontal_ts( # noqa:WPS210
self, pixel_id: int, predict_at: dt.datetime, train_horizon: int,
) -> Tuple[pd.Series, int, pd.Series]:
"""Slice a horizontal time series out of the `.totals`.
@ -209,19 +209,19 @@ class OrderHistory:
# Take only the counts at the `predict_at` time.
training_ts = intra_pixel.loc[
first_start_at : last_start_at : self._n_daily_time_steps, # type: ignore
'total_orders',
first_start_at : last_start_at : self._n_daily_time_steps, # type:ignore
'n_orders',
]
if len(training_ts) != frequency * train_horizon:
raise RuntimeError('Not enough historic data for `predict_at`')
actuals_ts = intra_pixel.loc[[predict_at], 'total_orders']
actuals_ts = intra_pixel.loc[[predict_at], 'n_orders']
if not len(actuals_ts): # pragma: no cover
raise LookupError('`predict_at` is not in the order history')
return training_ts, frequency, actuals_ts
def make_vertical_time_series( # noqa:WPS210
def make_vertical_ts( # noqa:WPS210
self, pixel_id: int, predict_day: dt.date, train_horizon: int,
) -> Tuple[pd.Series, int, pd.Series]:
"""Slice a vertical time series out of the `.totals`.
@ -277,8 +277,8 @@ class OrderHistory:
# Take all the counts between `first_train_day` and `last_train_day`.
training_ts = intra_pixel.loc[
first_start_at:last_start_at, # type: ignore
'total_orders',
first_start_at:last_start_at, # type:ignore
'n_orders',
]
if len(training_ts) != frequency * train_horizon:
raise RuntimeError('Not enough historic data for `predict_day`')
@ -299,15 +299,15 @@ class OrderHistory:
) - dt.timedelta(minutes=self._time_step)
actuals_ts = intra_pixel.loc[
first_prediction_at:last_prediction_at, # type: ignore
'total_orders',
first_prediction_at:last_prediction_at, # type:ignore
'n_orders',
]
if not len(actuals_ts): # pragma: no cover
raise LookupError('`predict_day` is not in the order history')
return training_ts, frequency, actuals_ts
def make_real_time_time_series( # noqa:WPS210
def make_realtime_ts( # noqa:WPS210
self, pixel_id: int, predict_at: dt.datetime, train_horizon: int,
) -> Tuple[pd.Series, int, pd.Series]:
"""Slice a vertical real-time time series out of the `.totals`.
@ -374,8 +374,8 @@ 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_ts = intra_pixel.loc[
first_start_at:last_start_at, # type: ignore
'total_orders',
first_start_at:last_start_at, # type:ignore
'n_orders',
]
n_time_steps_on_predict_day = (
(
@ -394,7 +394,7 @@ class OrderHistory:
if len(training_ts) != frequency * train_horizon + n_time_steps_on_predict_day:
raise RuntimeError('Not enough historic data for `predict_day`')
actuals_ts = intra_pixel.loc[[predict_at], 'total_orders']
actuals_ts = intra_pixel.loc[[predict_at], 'n_orders']
if not len(actuals_ts): # pragma: no cover
raise LookupError('`predict_at` is not in the order history')