def init_class_fixtures(cls):
super(StatisticalMethodsTestCase, cls).init_class_fixtures()
# Using these start and end dates because they are a contigous span of
# 5 days (Monday - Friday) and they allow for plenty of days to look
# back on when computing correlations and regressions.
cls.dates = dates = cls.trading_days
cls.start_date_index = start_date_index = 14
cls.end_date_index = end_date_index = 18
cls.pipeline_start_date = cls.trading_days[start_date_index]
cls.pipeline_end_date = cls.trading_days[end_date_index]
sids = cls.sids
cls.assets = assets = cls.asset_finder.retrieve_all(sids)
cls.my_asset_column = my_asset_column = 0
cls.my_asset = assets[my_asset_column]
cls.num_days = num_days = end_date_index - start_date_index + 1
cls.num_assets = num_assets = len(assets)
cls.cascading_mask = \
AssetIDPlusDay() < (sids[-1] + dates[start_date_index].day)
cls.expected_cascading_mask_result = make_cascading_boolean_array(
shape=(num_days, num_assets),
)
cls.alternating_mask = (AssetIDPlusDay() % 2).eq(0)
cls.expected_alternating_mask_result = make_alternating_boolean_array(
shape=(num_days, num_assets),
)
cls.expected_no_mask_result = full(
shape=(num_days, num_assets), fill_value=True, dtype=bool_dtype,
)
# Random input for factors.
cls.col = TestingDataSet.float_col
def init_class_fixtures(cls):
super(StatisticalBuiltInsTestCase, cls).init_class_fixtures()
day = cls.trading_calendar.day
cls.dates = dates = date_range(
'2015-02-01', '2015-02-28', freq=day, tz='UTC',
)
# Using these start and end dates because they are a contigous span of
# 5 days (Monday - Friday) and they allow for plenty of days to look
# back on when computing correlations and regressions.
cls.start_date_index = start_date_index = 14
cls.end_date_index = end_date_index = 18
cls.pipeline_start_date = dates[start_date_index]
cls.pipeline_end_date = dates[end_date_index]
cls.num_days = num_days = end_date_index - start_date_index + 1
sids = cls.sids
cls.assets = assets = cls.asset_finder.retrieve_all(sids)
cls.my_asset_column = my_asset_column = 0
cls.my_asset = assets[my_asset_column]
cls.num_assets = num_assets = len(assets)
cls.raw_data = raw_data = DataFrame(
data=arange(len(dates) * len(sids), dtype=float64_dtype).reshape(
len(dates), len(sids),
),
index=dates,
columns=assets,
)
# Using mock 'close' data here because the correlation and regression
# built-ins use USEquityPricing.close as the input to their `Returns`
# factors. Since there is no way to change that when constructing an
# instance of these built-ins, we need to test with mock 'close' data
# to most accurately reflect their true behavior and results.
close_loader = DataFrameLoader(USEquityPricing.close, raw_data)
cls.run_pipeline = SimplePipelineEngine(
{USEquityPricing.close: close_loader}.__getitem__,
dates,
cls.asset_finder,
).run_pipeline
cls.cascading_mask = \
AssetIDPlusDay() < (sids[-1] + dates[start_date_index].day)
cls.expected_cascading_mask_result = make_cascading_boolean_array(
shape=(num_days, num_assets),
)
cls.alternating_mask = (AssetIDPlusDay() % 2).eq(0)
cls.expected_alternating_mask_result = make_alternating_boolean_array(
shape=(num_days, num_assets),
)
cls.expected_no_mask_result = full(
shape=(num_days, num_assets), fill_value=True, dtype=bool_dtype,
)
def _test_masked_single_column_output(self):
"""
Tests for masking custom factors that compute a 1D out.
"""
start_date = self.pipeline_start_date
end_date = self.pipeline_end_date
alternating_mask = (AssetIDPlusDay() % 2).eq(0)
cascading_mask = AssetIDPlusDay() < (self.sids[-1] + start_date.day)
alternating_mask.window_safe = True
cascading_mask.window_safe = True
for mask in (alternating_mask, cascading_mask):
class SingleColumnOutput(CustomFactor):
window_length = 1
inputs = [self.col, mask]
window_safe = True
ndim = 1
def compute(self, today, assets, out, col, mask):
# Because we specified ndim as 1, `out` should always be a
# singleton array but `close` should be a sized based on
# the mask we passed.
assert out.shape == (1,)
assert col.shape == (1, mask.sum())
out[:] = col.sum()
# Since we cannot add single column output factors as pipeline
# columns, we have to test its output through another factor.
class UsesSingleColumnInput(CustomFactor):
window_length = 1
inputs = [self.col, mask, SingleColumnOutput(mask=mask)]
def compute(self,
today,
assets,
out,
col,
mask,
single_column_output):
# Make sure that `single_column` has the correct value
# based on the masked it used.
assert single_column_output.shape == (1, 1)
single_column_output_value = single_column_output[0][0]
expected_value = where(mask, col, 0).sum()
assert single_column_output_value == expected_value
columns = {'uses_single_column_input': UsesSingleColumnInput()}
# Assertions about the expected shapes of our data are made in the
# `compute` function of our custom factors above.
self.run_pipeline(Pipeline(columns=columns), start_date, end_date)
def test_single_column_output(self):
"""
Tests for custom factors that compute a 1D out.
"""
start_date = self.pipeline_start_date
end_date = self.pipeline_end_date
alternating_mask = (AssetIDPlusDay() % 2).eq(0)
cascading_mask = AssetIDPlusDay() < (self.sids[-1] + start_date.day)
class SingleColumnOutput(CustomFactor):
window_length = 1
inputs = [self.col]
window_safe = True
ndim = 1
def compute(self, today, assets, out, col):
# Because we specified ndim as 1, `out` should be a singleton
# array but `close` should be a regular sized input.
assert out.shape == (1, )
assert col.shape == (1, 3)
out[:] = col.sum()
# Since we cannot add single column output factors as pipeline
# columns, we have to test its output through another factor.
class UsesSingleColumnOutput(CustomFactor):
window_length = 1
inputs = [SingleColumnOutput()]
def compute(self, today, assets, out, single_column_output):
# Make sure that `single_column` has the correct shape. That
# is, it should always have one column regardless of any mask
# passed to `UsesSingleColumnInput`.
assert single_column_output.shape == (1, 1)
for mask in (alternating_mask, cascading_mask):
columns = {
'uses_single_column_output':
UsesSingleColumnOutput(),
'uses_single_column_output_masked':
UsesSingleColumnOutput(mask=mask, ),
}
# Assertions about the expected shapes of our data are made in the
# `compute` function of our custom factors above.
self.run_pipeline(Pipeline(columns=columns), start_date, end_date)