Add OrderHistory.make_horizontal_time_series()

- the method slices out a horizontal time series from the data within
  an `OrderHistory` object

tests/forecasts/timify/test_make_time_series.py

@@ -0,0 +1,173 @@
+"""Test the time series related code."""
+# pylint:disable=no-self-use,unused-argument
+
+import datetime
+
+import pandas as pd
+import pytest
+
+from tests import config as test_config
+from urban_meal_delivery import config
+from urban_meal_delivery.forecasts import timify
+
+
+@pytest.fixture
+def good_pixel_id():
+    """A `pixel_id` that is on the `grid`."""
+    return 1
+
+
+@pytest.fixture
+def order_totals(good_pixel_id):
+    """A mock for `OrderHistory.totals`.
+
+    To be a bit more realistic, we sample two pixels on the `grid`.
+    """
+    pixel_ids = [good_pixel_id, good_pixel_id + 1]
+
+    gen = (
+        (pixel_id, start_at)
+        for pixel_id in pixel_ids
+        for start_at in pd.date_range(
+            test_config.START, test_config.END, freq=f'{test_config.LONG_TIME_STEP}T',
+        )
+        if config.SERVICE_START <= start_at.hour < config.SERVICE_END
+    )
+
+    # Re-index `data` filling in `0`s where there is no demand.
+    index = pd.MultiIndex.from_tuples(gen)
+    index.names = ['pixel_id', 'start_at']
+
+    df = pd.DataFrame(data={'total_orders': 0}, index=index)
+
+    # Sanity check: n_pixels * n_time_steps_per_day * n_weekdays * n_weeks.
+    assert len(df) == 2 * 12 * (7 * 2 + 1)
+
+    return df
+
+
+@pytest.fixture
+def order_history(order_totals, grid):
+    """An `OrderHistory` object that does not need the database."""
+    oh = timify.OrderHistory(grid=grid, time_step=test_config.LONG_TIME_STEP)
+    oh._data = order_totals  # pylint:disable=protected-access
+
+    return oh
+
+
+@pytest.fixture
+def good_predict_at():
+    """A `predict_at` within `START`-`END` and ...
+
+    ... a long enough history so that either `train_horizon=1`
+    or `train_horizon=2` works.
+    """
+    return datetime.datetime(
+        test_config.END.year, test_config.END.month, test_config.END.day, 12, 0,
+    )
+
+
+@pytest.fixture
+def bad_predict_at():
+    """A `predict_at` within `START`-`END` but ...
+
+    ... not a long enough history so that both `train_horizon=1`
+    and `train_horizon=2` do not work.
+    """
+    predict_day = test_config.END - datetime.timedelta(weeks=1, days=1)
+    return datetime.datetime(
+        predict_day.year, predict_day.month, predict_day.day, 12, 0,
+    )
+
+
+class TestMakeHorizontalTimeSeries:
+    """Test the `OrderHistory.make_horizontal_time_series()` method."""
+
+    @pytest.mark.parametrize('train_horizon', test_config.TRAIN_HORIZONS)
+    def test_wrong_pixel(self, order_history, good_predict_at, train_horizon):
+        """A `pixel_id` that is not in the `grid`."""
+        with pytest.raises(LookupError):
+            order_history.make_horizontal_time_series(
+                pixel_id=999_999,
+                predict_at=good_predict_at,
+                train_horizon=train_horizon,
+            )
+
+    @pytest.mark.parametrize('train_horizon', test_config.TRAIN_HORIZONS)
+    def test_time_series_are_dataframes(
+        self, order_history, good_pixel_id, good_predict_at, train_horizon,
+    ):
+        """The time series come in a one-column `pd.DataFrame`."""
+        result = order_history.make_horizontal_time_series(
+            pixel_id=good_pixel_id,
+            predict_at=good_predict_at,
+            train_horizon=train_horizon,
+        )
+
+        training_df, _, actual_df = result
+
+        assert isinstance(training_df, pd.DataFrame)
+        assert training_df.columns == ['total_orders']
+        assert isinstance(actual_df, pd.DataFrame)
+        assert actual_df.columns == ['total_orders']
+
+    @pytest.mark.parametrize('train_horizon', test_config.TRAIN_HORIZONS)
+    def test_time_series_have_correct_length(
+        self, order_history, good_pixel_id, good_predict_at, train_horizon,
+    ):
+        """The length of a training time series must be a multiple of `7` ...
+
+        whereas the time series with the actual order counts always holds `1` value.
+        """
+        result = order_history.make_horizontal_time_series(
+            pixel_id=good_pixel_id,
+            predict_at=good_predict_at,
+            train_horizon=train_horizon,
+        )
+
+        training_df, _, actual_df = result
+
+        assert len(training_df) == 7 * train_horizon
+        assert len(actual_df) == 1
+
+    @pytest.mark.parametrize('train_horizon', test_config.TRAIN_HORIZONS)
+    def test_frequency_is_number_of_weekdays(
+        self, order_history, good_pixel_id, good_predict_at, train_horizon,
+    ):
+        """The `frequency` must be `7`."""
+        result = order_history.make_horizontal_time_series(
+            pixel_id=good_pixel_id,
+            predict_at=good_predict_at,
+            train_horizon=train_horizon,
+        )
+
+        _, frequency, _ = result  # noqa:WPS434
+
+        assert frequency == 7
+
+    @pytest.mark.parametrize('train_horizon', test_config.TRAIN_HORIZONS)
+    def test_no_long_enough_history1(
+        self, order_history, good_pixel_id, bad_predict_at, train_horizon,
+    ):
+        """If the `predict_at` day is too early in the `START`-`END` horizon ...
+
+        ... the history of order totals is not long enough.
+        """
+        with pytest.raises(RuntimeError):
+            order_history.make_horizontal_time_series(
+                pixel_id=good_pixel_id,
+                predict_at=bad_predict_at,
+                train_horizon=train_horizon,
+            )
+
+    def test_no_long_enough_history2(
+        self, order_history, good_pixel_id, good_predict_at,
+    ):
+        """If the `train_horizon` is longer than the `START`-`END` horizon ...
+
+        ... the history of order totals can never be long enough.
+        """
+        with pytest.raises(RuntimeError):
+            order_history.make_horizontal_time_series(
+                pixel_id=good_pixel_id, predict_at=good_predict_at, train_horizon=999,
+            )