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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
parent d45c60b764
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
View file

@ -53,7 +53,7 @@ class OrderHistory:
Returns: Returns:
order_totals: a one-column `DataFrame` with a `MultiIndex` of the order_totals: a one-column `DataFrame` with a `MultiIndex` of the
"pixel_id"s and "start_at"s (i.e., beginnings of the intervals); "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: if self._data is None:
self._data = self.aggregate_orders() self._data = self.aggregate_orders()
@ -69,7 +69,7 @@ class OrderHistory:
SELECT SELECT
pixel_id, pixel_id,
start_at, start_at,
COUNT(*) AS total_orders COUNT(*) AS n_orders
FROM ( FROM (
SELECT SELECT
pixel_id, pixel_id,
@ -152,7 +152,7 @@ class OrderHistory:
return data.reindex(index, fill_value=0) 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, self, pixel_id: int, predict_at: dt.datetime, train_horizon: int,
) -> Tuple[pd.Series, int, pd.Series]: ) -> Tuple[pd.Series, int, pd.Series]:
"""Slice a horizontal time series out of the `.totals`. """Slice a horizontal time series out of the `.totals`.
@ -209,19 +209,19 @@ class OrderHistory:
# Take only the counts at the `predict_at` time. # Take only the counts at the `predict_at` time.
training_ts = intra_pixel.loc[ training_ts = intra_pixel.loc[
first_start_at : last_start_at : self._n_daily_time_steps, # type: ignore first_start_at : last_start_at : self._n_daily_time_steps, # type:ignore
'total_orders', 'n_orders',
] ]
if len(training_ts) != frequency * train_horizon: if len(training_ts) != frequency * train_horizon:
raise RuntimeError('Not enough historic data for `predict_at`') 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 if not len(actuals_ts): # pragma: no cover
raise LookupError('`predict_at` is not in the order history') raise LookupError('`predict_at` is not in the order history')
return training_ts, frequency, actuals_ts 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, self, pixel_id: int, predict_day: dt.date, train_horizon: int,
) -> Tuple[pd.Series, int, pd.Series]: ) -> Tuple[pd.Series, int, pd.Series]:
"""Slice a vertical time series out of the `.totals`. """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`. # Take all the counts between `first_train_day` and `last_train_day`.
training_ts = intra_pixel.loc[ training_ts = intra_pixel.loc[
first_start_at:last_start_at, # type: ignore first_start_at:last_start_at, # type:ignore
'total_orders', 'n_orders',
] ]
if len(training_ts) != frequency * train_horizon: if len(training_ts) != frequency * train_horizon:
raise RuntimeError('Not enough historic data for `predict_day`') raise RuntimeError('Not enough historic data for `predict_day`')
@ -299,15 +299,15 @@ class OrderHistory:
) - dt.timedelta(minutes=self._time_step) ) - dt.timedelta(minutes=self._time_step)
actuals_ts = intra_pixel.loc[ actuals_ts = intra_pixel.loc[
first_prediction_at:last_prediction_at, # type: ignore first_prediction_at:last_prediction_at, # type:ignore
'total_orders', 'n_orders',
] ]
if not len(actuals_ts): # pragma: no cover if not len(actuals_ts): # pragma: no cover
raise LookupError('`predict_day` is not in the order history') raise LookupError('`predict_day` is not in the order history')
return training_ts, frequency, actuals_ts 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, self, pixel_id: int, predict_at: dt.datetime, train_horizon: int,
) -> Tuple[pd.Series, int, pd.Series]: ) -> Tuple[pd.Series, int, pd.Series]:
"""Slice a vertical real-time time series out of the `.totals`. """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`, # Take all the counts between `first_train_day` and `last_train_day`,
# including the ones on the `predict_at` day prior to `predict_at`. # including the ones on the `predict_at` day prior to `predict_at`.
training_ts = intra_pixel.loc[ training_ts = intra_pixel.loc[
first_start_at:last_start_at, # type: ignore first_start_at:last_start_at, # type:ignore
'total_orders', 'n_orders',
] ]
n_time_steps_on_predict_day = ( 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: if len(training_ts) != frequency * train_horizon + n_time_steps_on_predict_day:
raise RuntimeError('Not enough historic data for `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 if not len(actuals_ts): # pragma: no cover
raise LookupError('`predict_at` is not in the order history') raise LookupError('`predict_at` is not in the order history')

8
tests/forecasts/conftest.py
View file

@ -42,8 +42,8 @@ def horizontal_datetime_index():
@pytest.fixture @pytest.fixture
def horizontal_no_demand(horizontal_datetime_index): def horizontal_no_demand(horizontal_datetime_index):
"""A horizontal time series of order totals when there was no demand.""" """A horizontal time series with order totals: no demand."""
return pd.Series(0, index=horizontal_datetime_index, name='order_totals') return pd.Series(0, index=horizontal_datetime_index, name='n_orders')
@pytest.fixture @pytest.fixture
@ -72,5 +72,5 @@ def vertical_datetime_index():
@pytest.fixture @pytest.fixture
def vertical_no_demand(vertical_datetime_index): def vertical_no_demand(vertical_datetime_index):
"""A vertical time series of order totals when there was no demand.""" """A vertical time series with order totals: no demand."""
return pd.Series(0, index=vertical_datetime_index, name='order_totals') return pd.Series(0, index=vertical_datetime_index, name='n_orders')

46
tests/forecasts/timify/test_aggregate_orders.py
View file

@ -91,9 +91,9 @@ class TestAggregateOrders:
# The resulting `DataFrame` has 12 rows holding `1`s. # The resulting `DataFrame` has 12 rows holding `1`s.
assert len(result) == 12 assert len(result) == 12
assert result['total_orders'].min() == 1 assert result['n_orders'].min() == 1
assert result['total_orders'].max() == 1 assert result['n_orders'].max() == 1
assert result['total_orders'].sum() == 12 assert result['n_orders'].sum() == 12
def test_evenly_distributed_ad_hoc_orders_with_no_demand_late( # noqa:WPS218 def test_evenly_distributed_ad_hoc_orders_with_no_demand_late( # noqa:WPS218
self, db_session, one_pixel_grid, restaurant, make_order, self, db_session, one_pixel_grid, restaurant, make_order,
@ -123,10 +123,10 @@ class TestAggregateOrders:
# Even though there are only 10 orders, there are 12 rows in the `DataFrame`. # Even though there are only 10 orders, there are 12 rows in the `DataFrame`.
# That is so as `0`s are filled in for hours without any demand at the end. # That is so as `0`s are filled in for hours without any demand at the end.
assert len(result) == 12 assert len(result) == 12
assert result['total_orders'].min() == 0 assert result['n_orders'].min() == 0
assert result['total_orders'].max() == 1 assert result['n_orders'].max() == 1
assert result.iloc[:10]['total_orders'].sum() == 10 assert result.iloc[:10]['n_orders'].sum() == 10
assert result.iloc[10:]['total_orders'].sum() == 0 assert result.iloc[10:]['n_orders'].sum() == 0
def test_one_ad_hoc_order_every_other_hour( def test_one_ad_hoc_order_every_other_hour(
self, db_session, one_pixel_grid, restaurant, make_order, self, db_session, one_pixel_grid, restaurant, make_order,
@ -155,9 +155,9 @@ class TestAggregateOrders:
# The resulting `DataFrame` has 12 rows, 6 holding `0`s, and 6 holding `1`s. # The resulting `DataFrame` has 12 rows, 6 holding `0`s, and 6 holding `1`s.
assert len(result) == 12 assert len(result) == 12
assert result['total_orders'].min() == 0 assert result['n_orders'].min() == 0
assert result['total_orders'].max() == 1 assert result['n_orders'].max() == 1
assert result['total_orders'].sum() == 6 assert result['n_orders'].sum() == 6
def test_one_ad_hoc_and_one_pre_order( def test_one_ad_hoc_and_one_pre_order(
self, db_session, one_pixel_grid, restaurant, make_order, self, db_session, one_pixel_grid, restaurant, make_order,
@ -199,9 +199,9 @@ class TestAggregateOrders:
# The resulting `DataFrame` has 12 rows, 11 holding `0`s, and one holding a `1`. # The resulting `DataFrame` has 12 rows, 11 holding `0`s, and one holding a `1`.
assert len(result) == 12 assert len(result) == 12
assert result['total_orders'].min() == 0 assert result['n_orders'].min() == 0
assert result['total_orders'].max() == 1 assert result['n_orders'].max() == 1
assert result['total_orders'].sum() == 1 assert result['n_orders'].sum() == 1
def test_evenly_distributed_ad_hoc_orders_with_half_hour_time_steps( # noqa:WPS218 def test_evenly_distributed_ad_hoc_orders_with_half_hour_time_steps( # noqa:WPS218
self, db_session, one_pixel_grid, restaurant, make_order, self, db_session, one_pixel_grid, restaurant, make_order,
@ -234,10 +234,10 @@ class TestAggregateOrders:
# The resulting `DataFrame` has 24 rows for the 24 30-minute time steps. # The resulting `DataFrame` has 24 rows for the 24 30-minute time steps.
# The rows' values are `0` and `1` alternating. # The rows' values are `0` and `1` alternating.
assert len(result) == 24 assert len(result) == 24
assert result['total_orders'].min() == 0 assert result['n_orders'].min() == 0
assert result['total_orders'].max() == 1 assert result['n_orders'].max() == 1
assert result.iloc[::2]['total_orders'].sum() == 12 assert result.iloc[::2]['n_orders'].sum() == 12
assert result.iloc[1::2]['total_orders'].sum() == 0 assert result.iloc[1::2]['n_orders'].sum() == 0
def test_ad_hoc_orders_over_two_days( def test_ad_hoc_orders_over_two_days(
self, db_session, one_pixel_grid, restaurant, make_order, self, db_session, one_pixel_grid, restaurant, make_order,
@ -285,9 +285,9 @@ class TestAggregateOrders:
# The resulting `DataFrame` has 24 rows, 12 for each day. # The resulting `DataFrame` has 24 rows, 12 for each day.
assert len(result) == 24 assert len(result) == 24
assert result['total_orders'].min() == 0 assert result['n_orders'].min() == 0
assert result['total_orders'].max() == 1 assert result['n_orders'].max() == 1
assert result['total_orders'].sum() == 18 assert result['n_orders'].sum() == 18
@pytest.fixture @pytest.fixture
def two_pixel_grid( # noqa:WPS211 def two_pixel_grid( # noqa:WPS211
@ -381,6 +381,6 @@ class TestAggregateOrders:
# The resulting `DataFrame` has 24 rows, 12 for each pixel. # The resulting `DataFrame` has 24 rows, 12 for each pixel.
assert len(result) == 24 assert len(result) == 24
assert result['total_orders'].min() == 0 assert result['n_orders'].min() == 0
assert result['total_orders'].max() == 2 assert result['n_orders'].max() == 2
assert result['total_orders'].sum() == 30 assert result['n_orders'].sum() == 30

58
tests/forecasts/timify/test_make_time_series.py
View file

@ -41,7 +41,7 @@ def order_totals(good_pixel_id):
index = pd.MultiIndex.from_tuples(gen) index = pd.MultiIndex.from_tuples(gen)
index.names = ['pixel_id', 'start_at'] index.names = ['pixel_id', 'start_at']
df = pd.DataFrame(data={'total_orders': 0}, index=index) df = pd.DataFrame(data={'n_orders': 0}, index=index)
# Sanity check: n_pixels * n_time_steps_per_day * n_weekdays * n_weeks. # Sanity check: n_pixels * n_time_steps_per_day * n_weekdays * n_weeks.
assert len(df) == 2 * 12 * (7 * 2 + 1) assert len(df) == 2 * 12 * (7 * 2 + 1)
@ -88,13 +88,13 @@ def bad_predict_at():
class TestMakeHorizontalTimeSeries: class TestMakeHorizontalTimeSeries:
"""Test the `OrderHistory.make_horizontal_time_series()` method.""" """Test the `OrderHistory.make_horizontal_ts()` method."""
@pytest.mark.parametrize('train_horizon', test_config.TRAIN_HORIZONS) @pytest.mark.parametrize('train_horizon', test_config.TRAIN_HORIZONS)
def test_wrong_pixel(self, order_history, good_predict_at, train_horizon): def test_wrong_pixel(self, order_history, good_predict_at, train_horizon):
"""A `pixel_id` that is not in the `grid`.""" """A `pixel_id` that is not in the `grid`."""
with pytest.raises(LookupError): with pytest.raises(LookupError):
order_history.make_horizontal_time_series( order_history.make_horizontal_ts(
pixel_id=999_999, pixel_id=999_999,
predict_at=good_predict_at, predict_at=good_predict_at,
train_horizon=train_horizon, train_horizon=train_horizon,
@ -105,7 +105,7 @@ class TestMakeHorizontalTimeSeries:
self, order_history, good_pixel_id, good_predict_at, train_horizon, self, order_history, good_pixel_id, good_predict_at, train_horizon,
): ):
"""The time series come as a `pd.Series`.""" """The time series come as a `pd.Series`."""
result = order_history.make_horizontal_time_series( result = order_history.make_horizontal_ts(
pixel_id=good_pixel_id, pixel_id=good_pixel_id,
predict_at=good_predict_at, predict_at=good_predict_at,
train_horizon=train_horizon, train_horizon=train_horizon,
@ -114,9 +114,9 @@ class TestMakeHorizontalTimeSeries:
training_ts, _, actuals_ts = result training_ts, _, actuals_ts = result
assert isinstance(training_ts, pd.Series) assert isinstance(training_ts, pd.Series)
assert training_ts.name == 'total_orders' assert training_ts.name == 'n_orders'
assert isinstance(actuals_ts, pd.Series) assert isinstance(actuals_ts, pd.Series)
assert actuals_ts.name == 'total_orders' assert actuals_ts.name == 'n_orders'
@pytest.mark.parametrize('train_horizon', test_config.TRAIN_HORIZONS) @pytest.mark.parametrize('train_horizon', test_config.TRAIN_HORIZONS)
def test_time_series_have_correct_length( def test_time_series_have_correct_length(
@ -126,7 +126,7 @@ class TestMakeHorizontalTimeSeries:
... whereas the time series with the actual order counts has only `1` value. ... whereas the time series with the actual order counts has only `1` value.
""" """
result = order_history.make_horizontal_time_series( result = order_history.make_horizontal_ts(
pixel_id=good_pixel_id, pixel_id=good_pixel_id,
predict_at=good_predict_at, predict_at=good_predict_at,
train_horizon=train_horizon, train_horizon=train_horizon,
@ -142,7 +142,7 @@ class TestMakeHorizontalTimeSeries:
self, order_history, good_pixel_id, good_predict_at, train_horizon, self, order_history, good_pixel_id, good_predict_at, train_horizon,
): ):
"""The `frequency` must be `7`.""" """The `frequency` must be `7`."""
result = order_history.make_horizontal_time_series( result = order_history.make_horizontal_ts(
pixel_id=good_pixel_id, pixel_id=good_pixel_id,
predict_at=good_predict_at, predict_at=good_predict_at,
train_horizon=train_horizon, train_horizon=train_horizon,
@ -161,7 +161,7 @@ class TestMakeHorizontalTimeSeries:
... the history of order totals is not long enough. ... the history of order totals is not long enough.
""" """
with pytest.raises(RuntimeError): with pytest.raises(RuntimeError):
order_history.make_horizontal_time_series( order_history.make_horizontal_ts(
pixel_id=good_pixel_id, pixel_id=good_pixel_id,
predict_at=bad_predict_at, predict_at=bad_predict_at,
train_horizon=train_horizon, train_horizon=train_horizon,
@ -175,19 +175,19 @@ class TestMakeHorizontalTimeSeries:
... the history of order totals can never be long enough. ... the history of order totals can never be long enough.
""" """
with pytest.raises(RuntimeError): with pytest.raises(RuntimeError):
order_history.make_horizontal_time_series( order_history.make_horizontal_ts(
pixel_id=good_pixel_id, predict_at=good_predict_at, train_horizon=999, pixel_id=good_pixel_id, predict_at=good_predict_at, train_horizon=999,
) )
class TestMakeVerticalTimeSeries: class TestMakeVerticalTimeSeries:
"""Test the `OrderHistory.make_vertical_time_series()` method.""" """Test the `OrderHistory.make_vertical_ts()` method."""
@pytest.mark.parametrize('train_horizon', test_config.TRAIN_HORIZONS) @pytest.mark.parametrize('train_horizon', test_config.TRAIN_HORIZONS)
def test_wrong_pixel(self, order_history, good_predict_at, train_horizon): def test_wrong_pixel(self, order_history, good_predict_at, train_horizon):
"""A `pixel_id` that is not in the `grid`.""" """A `pixel_id` that is not in the `grid`."""
with pytest.raises(LookupError): with pytest.raises(LookupError):
order_history.make_vertical_time_series( order_history.make_vertical_ts(
pixel_id=999_999, pixel_id=999_999,
predict_day=good_predict_at.date(), predict_day=good_predict_at.date(),
train_horizon=train_horizon, train_horizon=train_horizon,
@ -198,7 +198,7 @@ class TestMakeVerticalTimeSeries:
self, order_history, good_pixel_id, good_predict_at, train_horizon, self, order_history, good_pixel_id, good_predict_at, train_horizon,
): ):
"""The time series come as `pd.Series`.""" """The time series come as `pd.Series`."""
result = order_history.make_vertical_time_series( result = order_history.make_vertical_ts(
pixel_id=good_pixel_id, pixel_id=good_pixel_id,
predict_day=good_predict_at.date(), predict_day=good_predict_at.date(),
train_horizon=train_horizon, train_horizon=train_horizon,
@ -207,9 +207,9 @@ class TestMakeVerticalTimeSeries:
training_ts, _, actuals_ts = result training_ts, _, actuals_ts = result
assert isinstance(training_ts, pd.Series) assert isinstance(training_ts, pd.Series)
assert training_ts.name == 'total_orders' assert training_ts.name == 'n_orders'
assert isinstance(actuals_ts, pd.Series) assert isinstance(actuals_ts, pd.Series)
assert actuals_ts.name == 'total_orders' assert actuals_ts.name == 'n_orders'
@pytest.mark.parametrize('train_horizon', test_config.TRAIN_HORIZONS) @pytest.mark.parametrize('train_horizon', test_config.TRAIN_HORIZONS)
def test_time_series_have_correct_length( def test_time_series_have_correct_length(
@ -223,7 +223,7 @@ class TestMakeVerticalTimeSeries:
The time series with the actual order counts always holds one observation The time series with the actual order counts always holds one observation
per time step of a day. per time step of a day.
""" """
result = order_history.make_vertical_time_series( result = order_history.make_vertical_ts(
pixel_id=good_pixel_id, pixel_id=good_pixel_id,
predict_day=good_predict_at.date(), predict_day=good_predict_at.date(),
train_horizon=train_horizon, train_horizon=train_horizon,
@ -245,7 +245,7 @@ class TestMakeVerticalTimeSeries:
self, order_history, good_pixel_id, good_predict_at, train_horizon, self, order_history, good_pixel_id, good_predict_at, train_horizon,
): ):
"""The `frequency` is the number of weekly time steps.""" """The `frequency` is the number of weekly time steps."""
result = order_history.make_vertical_time_series( result = order_history.make_vertical_ts(
pixel_id=good_pixel_id, pixel_id=good_pixel_id,
predict_day=good_predict_at.date(), predict_day=good_predict_at.date(),
train_horizon=train_horizon, train_horizon=train_horizon,
@ -270,7 +270,7 @@ class TestMakeVerticalTimeSeries:
... the history of order totals is not long enough. ... the history of order totals is not long enough.
""" """
with pytest.raises(RuntimeError): with pytest.raises(RuntimeError):
order_history.make_vertical_time_series( order_history.make_vertical_ts(
pixel_id=good_pixel_id, pixel_id=good_pixel_id,
predict_day=bad_predict_at.date(), predict_day=bad_predict_at.date(),
train_horizon=train_horizon, train_horizon=train_horizon,
@ -284,7 +284,7 @@ class TestMakeVerticalTimeSeries:
... the history of order totals can never be long enough. ... the history of order totals can never be long enough.
""" """
with pytest.raises(RuntimeError): with pytest.raises(RuntimeError):
order_history.make_vertical_time_series( order_history.make_vertical_ts(
pixel_id=good_pixel_id, pixel_id=good_pixel_id,
predict_day=good_predict_at.date(), predict_day=good_predict_at.date(),
train_horizon=999, train_horizon=999,
@ -292,13 +292,13 @@ class TestMakeVerticalTimeSeries:
class TestMakeRealTimeTimeSeries: class TestMakeRealTimeTimeSeries:
"""Test the `OrderHistory.make_real_time_time_series()` method.""" """Test the `OrderHistory.make_realtime_ts()` method."""
@pytest.mark.parametrize('train_horizon', test_config.TRAIN_HORIZONS) @pytest.mark.parametrize('train_horizon', test_config.TRAIN_HORIZONS)
def test_wrong_pixel(self, order_history, good_predict_at, train_horizon): def test_wrong_pixel(self, order_history, good_predict_at, train_horizon):
"""A `pixel_id` that is not in the `grid`.""" """A `pixel_id` that is not in the `grid`."""
with pytest.raises(LookupError): with pytest.raises(LookupError):
order_history.make_real_time_time_series( order_history.make_realtime_ts(
pixel_id=999_999, pixel_id=999_999,
predict_at=good_predict_at, predict_at=good_predict_at,
train_horizon=train_horizon, train_horizon=train_horizon,
@ -309,7 +309,7 @@ class TestMakeRealTimeTimeSeries:
self, order_history, good_pixel_id, good_predict_at, train_horizon, self, order_history, good_pixel_id, good_predict_at, train_horizon,
): ):
"""The time series come as `pd.Series`.""" """The time series come as `pd.Series`."""
result = order_history.make_real_time_time_series( result = order_history.make_realtime_ts(
pixel_id=good_pixel_id, pixel_id=good_pixel_id,
predict_at=good_predict_at, predict_at=good_predict_at,
train_horizon=train_horizon, train_horizon=train_horizon,
@ -318,9 +318,9 @@ class TestMakeRealTimeTimeSeries:
training_ts, _, actuals_ts = result training_ts, _, actuals_ts = result
assert isinstance(training_ts, pd.Series) assert isinstance(training_ts, pd.Series)
assert training_ts.name == 'total_orders' assert training_ts.name == 'n_orders'
assert isinstance(actuals_ts, pd.Series) assert isinstance(actuals_ts, pd.Series)
assert actuals_ts.name == 'total_orders' assert actuals_ts.name == 'n_orders'
@pytest.mark.parametrize('train_horizon', test_config.TRAIN_HORIZONS) @pytest.mark.parametrize('train_horizon', test_config.TRAIN_HORIZONS)
def test_time_series_have_correct_length1( def test_time_series_have_correct_length1(
@ -341,7 +341,7 @@ class TestMakeRealTimeTimeSeries:
config.SERVICE_START, config.SERVICE_START,
0, 0,
) )
result = order_history.make_real_time_time_series( result = order_history.make_realtime_ts(
pixel_id=good_pixel_id, predict_at=predict_at, train_horizon=train_horizon, pixel_id=good_pixel_id, predict_at=predict_at, train_horizon=train_horizon,
) )
@ -372,7 +372,7 @@ class TestMakeRealTimeTimeSeries:
""" """
assert good_predict_at.hour == test_config.NOON assert good_predict_at.hour == test_config.NOON
result = order_history.make_real_time_time_series( result = order_history.make_realtime_ts(
pixel_id=good_pixel_id, pixel_id=good_pixel_id,
predict_at=good_predict_at, predict_at=good_predict_at,
train_horizon=train_horizon, train_horizon=train_horizon,
@ -400,7 +400,7 @@ class TestMakeRealTimeTimeSeries:
self, order_history, good_pixel_id, good_predict_at, train_horizon, self, order_history, good_pixel_id, good_predict_at, train_horizon,
): ):
"""The `frequency` is the number of weekly time steps.""" """The `frequency` is the number of weekly time steps."""
result = order_history.make_real_time_time_series( result = order_history.make_realtime_ts(
pixel_id=good_pixel_id, pixel_id=good_pixel_id,
predict_at=good_predict_at, predict_at=good_predict_at,
train_horizon=train_horizon, train_horizon=train_horizon,
@ -425,7 +425,7 @@ class TestMakeRealTimeTimeSeries:
... the history of order totals is not long enough. ... the history of order totals is not long enough.
""" """
with pytest.raises(RuntimeError): with pytest.raises(RuntimeError):
order_history.make_real_time_time_series( order_history.make_realtime_ts(
pixel_id=good_pixel_id, pixel_id=good_pixel_id,
predict_at=bad_predict_at, predict_at=bad_predict_at,
train_horizon=train_horizon, train_horizon=train_horizon,
@ -439,6 +439,6 @@ class TestMakeRealTimeTimeSeries:
... the history of order totals can never be long enough. ... the history of order totals can never be long enough.
""" """
with pytest.raises(RuntimeError): with pytest.raises(RuntimeError):
order_history.make_real_time_time_series( order_history.make_realtime_ts(
pixel_id=good_pixel_id, predict_at=good_predict_at, train_horizon=999, pixel_id=good_pixel_id, predict_at=good_predict_at, train_horizon=999,
) )