def test_factor_regression_method(self, returns_length, regression_length):
"""
Ensure that `Factor.linear_regression` is consistent with the built-in
factor `RollingLinearRegressionOfReturns`.
"""
my_asset = self.my_asset
start_date = self.pipeline_start_date
end_date = self.pipeline_end_date
run_pipeline = self.run_pipeline
returns = Returns(window_length=returns_length, inputs=[self.col])
returns_slice = returns[my_asset]
regression = returns.linear_regression(
target=returns_slice, regression_length=regression_length,
)
expected_regression = RollingLinearRegressionOfReturns(
target=my_asset,
returns_length=returns_length,
regression_length=regression_length,
)
# This built-in constructs its own Returns factor to use as an input,
# so the only way to set our own input is to do so after the fact. This
# should not be done in practice. It is necessary here because we want
# Returns to use our random data as an input, but by default it is
# using USEquityPricing.close.
expected_regression.inputs = [returns, returns_slice]
columns = {
'regression': regression,
'expected_regression': expected_regression,
}
results = run_pipeline(Pipeline(columns=columns), start_date, end_date)
regression_results = results['regression'].unstack()
expected_regression_results = results['expected_regression'].unstack()
assert_frame_equal(regression_results, expected_regression_results)
def test_factor_regression_method(self, returns_length, regression_length):
"""
Ensure that `Factor.linear_regression` is consistent with the built-in
factor `RollingLinearRegressionOfReturns`.
"""
my_asset = self.asset_finder.retrieve_asset(self.sids[0])
returns = Returns(window_length=returns_length, inputs=[self.col])
returns_slice = returns[my_asset]
regression = returns.linear_regression(
target=returns_slice,
regression_length=regression_length,
)
expected_regression = RollingLinearRegressionOfReturns(
target=my_asset,
returns_length=returns_length,
regression_length=regression_length,
)
# These built-ins construct their own Returns factor to use as inputs,
# so the only way to set our own inputs is to do so after the fact.
# This should not be done in practice. It is necessary here because we
# want Returns to use our random data as an input, but by default it is
# using USEquityPricing.close.
expected_regression.inputs = [returns, returns_slice]
class MyFactor(CustomFactor):
inputs = ()
window_length = 1
def compute(self, today, assets, out):
out[:] = 0
columns = {
'regression': regression,
'expected_regression': expected_regression,
}
results = self.run_pipeline(
Pipeline(columns=columns),
self.pipeline_start_date,
self.pipeline_end_date,
)
regression_results = results['regression'].unstack()
expected_regression_results = results['expected_regression'].unstack()
assert_frame_equal(regression_results, expected_regression_results)
# Make sure we cannot call the linear regression method on factors or
# slices of dtype `datetime64[ns]`.
class DateFactor(CustomFactor):
window_length = 1
inputs = []
dtype = datetime64ns_dtype
window_safe = True
def compute(self, today, assets, out):
pass
date_factor = DateFactor()
date_factor_slice = date_factor[my_asset]
with self.assertRaises(TypeError):
date_factor.linear_regression(
target=returns_slice,
regression_length=regression_length,
)
with self.assertRaises(TypeError):
returns.linear_regression(
target=date_factor_slice,
regression_length=regression_length,
)
def test_factor_regression_method(self, returns_length, regression_length):
"""
Ensure that `Factor.linear_regression` is consistent with the built-in
factor `RollingLinearRegressionOfReturns`.
"""
my_asset = self.asset_finder.retrieve_asset(self.sids[0])
returns = Returns(window_length=returns_length, inputs=[self.col])
returns_slice = returns[my_asset]
regression = returns.linear_regression(
target=returns_slice, regression_length=regression_length,
)
expected_regression = RollingLinearRegressionOfReturns(
target=my_asset,
returns_length=returns_length,
regression_length=regression_length,
)
# These built-ins construct their own Returns factor to use as inputs,
# so the only way to set our own inputs is to do so after the fact.
# This should not be done in practice. It is necessary here because we
# want Returns to use our random data as an input, but by default it is
# using USEquityPricing.close.
expected_regression.inputs = [returns, returns_slice]
columns = {
'regression': regression,
'expected_regression': expected_regression,
}
results = self.run_pipeline(
Pipeline(columns=columns),
self.pipeline_start_date,
self.pipeline_end_date,
)
regression_results = results['regression'].unstack()
expected_regression_results = results['expected_regression'].unstack()
assert_frame_equal(regression_results, expected_regression_results)
# Make sure we cannot call the linear regression method on factors or
# slices of dtype `datetime64[ns]`.
class DateFactor(CustomFactor):
window_length = 1
inputs = []
dtype = datetime64ns_dtype
window_safe = True
def compute(self, today, assets, out):
pass
date_factor = DateFactor()
date_factor_slice = date_factor[my_asset]
with self.assertRaises(TypeError):
date_factor.linear_regression(
target=returns_slice, regression_length=regression_length,
)
with self.assertRaises(TypeError):
returns.linear_regression(
target=date_factor_slice, regression_length=regression_length,
)
