Add tests that compare the output of the time adjustment implemetnati…

…ons.

metricflow-semantics/tests_metricflow_semantics/time/test_time_adjuster.py

@@ -0,0 +1,159 @@
+from __future__ import annotations
+
+import datetime
+import logging
+from typing import Dict, List, Sequence, Tuple
+
+import pytest
+import tabulate
+from _pytest.fixtures import FixtureRequest
+from dbt_semantic_interfaces.test_utils import as_datetime
+from dbt_semantic_interfaces.type_enums import TimeGranularity
+from metricflow_semantics.filters.time_constraint import TimeRangeConstraint
+from metricflow_semantics.test_helpers.config_helpers import MetricFlowTestConfiguration
+from metricflow_semantics.test_helpers.snapshot_helpers import assert_str_snapshot_equal
+from metricflow_semantics.time.dateutil_adjuster import DateutilTimePeriodAdjuster
+from metricflow_semantics.time.pandas_adjuster import PandasTimePeriodAdjuster
+
+logger = logging.getLogger(__name__)
+
+
+@pytest.fixture(scope="session")
+def date_times_to_check() -> Sequence[datetime.datetime]:  # noqa: D103
+    date_times = []
+    # Cover leap years, non-leap years
+    # 1900 was tested to work, though that requires a change to `TimeRangeConstraint.ALL_TIME_BEGIN`
+    for year in (2000, 2000, 2021):
+        start_date_time = datetime.datetime(year=year, month=1, day=1)
+        end_date_time = datetime.datetime(year=year, month=12, day=31)
+        current_date_time = start_date_time
+        while True:
+            date_times.append(current_date_time)
+            current_date_time += datetime.timedelta(days=1)
+            if current_date_time == end_date_time:
+                break
+    return date_times
+
+
+@pytest.fixture(scope="session")
+def grain_to_count_in_year() -> Dict[TimeGranularity, int]:
+    """Returns the maximum number of times the given item occurs in a year."""
+    return {
+        TimeGranularity.DAY: 366,
+        TimeGranularity.WEEK: 53,
+        TimeGranularity.MONTH: 31,
+        TimeGranularity.QUARTER: 4,
+        TimeGranularity.YEAR: 1,
+    }
+
+
+def test_start_and_end_periods(  # noqa: D103
+    request: FixtureRequest,
+    mf_test_configuration: MetricFlowTestConfiguration,
+    date_times_to_check: Sequence[datetime.datetime],
+) -> None:
+    pandas_adjuster = PandasTimePeriodAdjuster()
+    dateutil_adjuster = DateutilTimePeriodAdjuster()
+
+    rows: List[Tuple[str, ...]] = []
+    for date_time in date_times_to_check:
+        for time_granularity in TimeGranularity:
+            # Pandas implementation of `adjust_to_start_of_period` doesn't support DAY.
+            if time_granularity == TimeGranularity.DAY:
+                pandas_start_of_period = None
+                pandas_end_of_period = None
+            else:
+                pandas_start_of_period = pandas_adjuster.adjust_to_start_of_period(time_granularity, date_time)
+                pandas_end_of_period = pandas_adjuster.adjust_to_end_of_period(time_granularity, date_time)
+            dateutil_start_of_period = dateutil_adjuster.adjust_to_start_of_period(time_granularity, date_time)
+            dateutil_end_of_period = dateutil_adjuster.adjust_to_end_of_period(time_granularity, date_time)
+            assert (
+                pandas_start_of_period or dateutil_start_of_period
+            ) == dateutil_start_of_period, f"start-of-period mismatch: {date_time.isoformat()} {time_granularity}"
+            assert (
+                pandas_end_of_period or dateutil_end_of_period
+            ) == dateutil_end_of_period, f"end-of-period mismatch: {date_time.isoformat()} {time_granularity}"
+            rows.append(
+                (
+                    date_time.isoformat(),
+                    time_granularity.name,
+                    dateutil_start_of_period.isoformat(),
+                    dateutil_end_of_period.isoformat(),
+                )
+            )
+    assert_str_snapshot_equal(
+        request=request,
+        mf_test_configuration=mf_test_configuration,
+        snapshot_id="results",
+        snapshot_str=tabulate.tabulate(rows, headers=["Date", "Grain", "Period Start", "Period End"]),
+    )
+
+
+def test_expand_time_constraint_to_fill_granularity(  # noqa: D103
+    date_times_to_check: Sequence[datetime.datetime], grain_to_count_in_year: Dict[TimeGranularity, int]
+) -> None:
+    pandas_adjuster = PandasTimePeriodAdjuster()
+    dateutil_adjuster = DateutilTimePeriodAdjuster()
+
+    test_cases = tuple(
+        (start_time, end_time, time_granularity)
+        for start_time in date_times_to_check
+        for time_granularity in TimeGranularity
+        for end_time in (
+            start_time + datetime.timedelta(days=day_offset)
+            # Add 2 to cross time grain boundaries.
+            for day_offset in range(grain_to_count_in_year[time_granularity] + 2)
+        )
+    )
+
+    test_case_count = len(test_cases)
+    logger.info(f"There are {test_case_count} test cases")
+
+    finished_count = 0
+
+    for start_time, end_time, time_granularity in test_cases:
+        time_constraint = TimeRangeConstraint(start_time=start_time, end_time=end_time)
+        pandas_adjuster_result = pandas_adjuster.expand_time_constraint_to_fill_granularity(
+            time_constraint, time_granularity
+        )
+
+        dateutil_adjuster_result = dateutil_adjuster.expand_time_constraint_to_fill_granularity(
+            time_constraint, time_granularity
+        )
+
+        assert (
+            pandas_adjuster_result == dateutil_adjuster_result
+        ), f"Expansion mismatch: {pandas_adjuster_result=} {dateutil_adjuster_result=} {time_granularity=}"
+        finished_count += 1
+        if finished_count % 100000 == 0 or finished_count == test_case_count:
+            logger.info(f"Progress {finished_count / test_case_count * 100:.0f}%")
+
+
+def test_expand_time_constraint_for_cumulative_metric(  # noqa: D103
+    grain_to_count_in_year: Dict[TimeGranularity, int]
+) -> None:
+    pandas_adjuster = PandasTimePeriodAdjuster()
+    dateutil_adjuster = DateutilTimePeriodAdjuster()
+
+    test_cases = tuple(
+        (as_datetime("2020-01-01"), time_granularity, count)
+        for time_granularity in TimeGranularity
+        for count in (range(grain_to_count_in_year[time_granularity] + 2))
+    )
+
+    test_case_count = len(test_cases)
+    logger.info(f"There are {test_case_count} test cases")
+
+    for start_time, time_granularity, count in test_cases:
+        time_constraint = TimeRangeConstraint(start_time=start_time, end_time=start_time)
+        pandas_adjuster_result = pandas_adjuster.expand_time_constraint_for_cumulative_metric(
+            time_constraint, time_granularity, count
+        )
+
+        dateutil_adjuster_result = dateutil_adjuster.expand_time_constraint_for_cumulative_metric(
+            time_constraint, time_granularity, count
+        )
+
+        assert (
+            pandas_adjuster_result == dateutil_adjuster_result
+        ), f"Expansion mismatch: {start_time=}, {time_granularity=}, {count=}"