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TEST: Test that the mask is what we expect.

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This commit is contained in:
Scott Sanderson
2016-04-07 17:04:14 -04:00
committed by dmichalowicz
parent 8db59b387b
commit 4449f289c2
1 changed files with 24 additions and 15 deletions
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tests/pipeline/test_engine.py
+24 -15
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@@ -397,30 +397,36 @@ class ConstantInputTestCase(TestCase):
expected_values = where(mask, expected_value, nan)
return DataFrame(expected_values, index=dates, columns=assets)
# Produce a mask that looks like:
#
# Equity(0 [A]) Equity(1 [B]) Equity(2 [C]) Equity(3 [D])
# Day 1 True True True False
# Day 2 True True False False
# Day 3 True False False False
#
cascading_mask = AssetIDPlusDay() < (asset_ids[-1] + dates[0].day)
expected_cascading_mask_result = array(
[[True, True, True, False],
[True, True, False, False],
[True, False, False, False]],
dtype=bool,
)
# And another one that looks like:
#
# Equity(0 [A]) Equity(1 [B]) Equity(2 [C]) Equity(3 [D])
# Day 1 False True False True
# Day 2 True False True False
# Day 3 False True False True
#
alternating_mask = (AssetIDPlusDay() % 2).eq(0)
expected_alternating_mask_result = array(
[[False, True, False, True],
[True, False, True, False],
[False, True, False, True]],
dtype=bool,
)
for mask in (cascading_mask, alternating_mask):
masks = cascading_mask, alternating_mask
expected_mask_results = (
expected_cascading_mask_result,
expected_alternating_mask_result,
)
for mask, expected_mask in zip(masks, expected_mask_results):
# Test running a pipeline with a single masked factor.
columns = {'factor1': OpenPrice(mask=mask), 'mask': mask}
pipeline = Pipeline(columns=columns)
results = engine.run_pipeline(pipeline, dates[0], dates[-1])
mask_results = results['mask'].unstack()
check_arrays(mask_results.values, expected_mask)
factor1_results = results['factor1'].unstack()
factor1_expected = create_expected_results(factor1_value,
mask_results)
@@ -433,7 +439,10 @@ class ConstantInputTestCase(TestCase):
columns['factor2'] = RollingSumDifference(mask=mask)
pipeline = Pipeline(columns=columns)
results = engine.run_pipeline(pipeline, dates[0], dates[-1])
mask_results = results['mask'].unstack()
check_arrays(mask_results.values, expected_mask)
factor1_results = results['factor1'].unstack()
factor2_results = results['factor2'].unstack()
factor1_expected = create_expected_results(factor1_value,