docstr: Tests for the built-in factor `RollingLinearRegressionOfReturns`.
docstr_tok: ["Tests","for","the","built-in","factor","`","RollingLinearRegressionOfReturns","`","."]
code: def test_regression_of_returns_factor(
      self, returns_length, regression_length
  ):
      """
      Tests for the built-in factor `RollingLinearRegressionOfReturns`.
      """
      assets = self.assets
      my_asset = self.my_asset
      my_asset_column = self.my_asset_column
      dates = self.dates
      start_date = self.pipeline_start_date
      end_date = self.pipeline_end_date
      start_date_index = self.start_date_index
      end_date_index = self.end_date_index
      num_days = self.num_days
      run_pipeline = self.run_pipeline
  
      # The order of these is meant to align with the output of `linregress`.
      outputs = ["beta", "alpha", "r_value", "p_value", "stderr"]
  
      returns = Returns(window_length=returns_length)
      masks = self.cascading_mask, self.alternating_mask, NotSpecified
      expected_mask_results = (
          self.expected_cascading_mask_result,
          self.expected_alternating_mask_result,
          self.expected_no_mask_result,
      )
  
      for mask, expected_mask in zip(masks, expected_mask_results):
          regression_factor = RollingLinearRegressionOfReturns(
              target=my_asset,
              returns_length=returns_length,
              regression_length=regression_length,
              mask=mask,
          )
  
          columns = {
              output: getattr(regression_factor, output)
              for output in outputs
          }
          pipeline = Pipeline(columns=columns)
          if mask is not NotSpecified:
              pipeline.add(mask, "mask")
  
          results = run_pipeline(pipeline, start_date, end_date)
          if mask is not NotSpecified:
              mask_results = results["mask"].unstack()
              check_arrays(mask_results.values, expected_mask)
  
          output_results = {}
          expected_output_results = {}
          for output in outputs:
              output_results[output] = results[output].unstack()
              expected_output_results[output] = full_like(
                  output_results[output],
                  nan,
              )
  
          # Run a separate pipeline that calculates returns starting
          # (regression_length - 1) days prior to our start date. This is
          # because we need (regression_length - 1) extra days of returns to
          # compute our expected regressions.
          results = run_pipeline(
              Pipeline(columns={"returns": returns}),
              dates[start_date_index - (regression_length - 1)],
              dates[end_date_index],
          )
          returns_results = results["returns"].unstack()
  
          # On each day, calculate the expected regression results for Y ~ X
          # where Y is the asset we are interested in and X is each other
          # asset. Each regression is calculated over `regression_length`
          # days of data.
          for day in range(num_days):
              todays_returns = returns_results.iloc[
                  day : day + regression_length
              ]
              my_asset_returns = todays_returns.iloc[:, my_asset_column]
              for (
                  asset,
                  other_asset_returns,
              ) in todays_returns.iteritems():
                  asset_column = int(asset) - 1
                  expected_regression_results = linregress(
                      y=other_asset_returns,
                      x=my_asset_returns,
                  )
                  for i, output in enumerate(outputs):
                      expected_output_results[output][
                          day, asset_column
                      ] = expected_regression_results[i]
  
          for output in outputs:
              output_result = output_results[output]
              expected_output_result = DataFrame(
                  where(
                      expected_mask,
                      expected_output_results[output],
                      nan,
                  ),
                  index=dates[start_date_index : end_date_index + 1],
                  columns=assets,
              )
              assert_frame_equal(output_result, expected_output_result)
  
imports: import numpy
  from numpy import arange, full, full_like, nan, where
  from pandas import DataFrame, date_range, Int64Index, Timestamp
  from pandas.util.testing import assert_frame_equal
  from scipy.stats import linregress, pearsonr, spearmanr
  from empyrical.stats import beta_aligned
  from zipline.assets import Equity, ExchangeInfo
  from zipline.errors import IncompatibleTerms, NonExistentAssetInTimeFrame
  from zipline.pipeline import CustomFactor, Pipeline
  from zipline.pipeline.data import USEquityPricing
  from zipline.pipeline.data.testing import TestingDataSet
  from zipline.pipeline.domain import US_EQUITIES
  from zipline.pipeline.engine import SimplePipelineEngine
  from zipline.pipeline.factors import Returns, RollingLinearRegressionOfReturns, RollingPearsonOfReturns, RollingSpearmanOfReturns, SimpleBeta
  from zipline.pipeline.factors.statistical import vectorized_beta, vectorized_pearson_r
  from zipline.pipeline.loaders.frame import DataFrameLoader
  from zipline.pipeline.sentinels import NotSpecified
  from zipline.testing import AssetID, AssetIDPlusDay, check_arrays, make_alternating_boolean_array, make_cascading_boolean_array, parameter_space
  import zipline.testing.fixtures
  from zipline.testing.predicates import assert_equal
  from zipline.utils.numpy_utils import as_column, bool_dtype, datetime64ns_dtype, float64_dtype
label_desc: Use image node_id 3 for calling the StatisticalBuiltInsTestCase obj's underlying member method code with example usage: obj.test_regression_of_returns_factor(returns_length, regression_length) without return types