docstr: Tests for `Factor.linear_regression` when passed another 2D factor
  instead of a Slice.
docstr_tok: ["Tests","for","`","Factor.linear_regression","`","when","passed","another","2D","factor","instead","of","a","Slice","."]
code: def test_factor_regression_method_two_factors(
      self, regression_length
  ):
      """
      Tests for `Factor.linear_regression` when passed another 2D factor
      instead of a Slice.
      """
      assets = self.assets
      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"]
  
      # Ensure that the `linear_regression` method cannot be called with two
      # 2D factors which have different masks.
      returns_masked_1 = Returns(
          window_length=5,
          inputs=[self.col],
          mask=AssetID().eq(1),
      )
      returns_masked_2 = Returns(
          window_length=5,
          inputs=[self.col],
          mask=AssetID().eq(2),
      )
      with self.assertRaises(IncompatibleTerms):
          returns_masked_1.linear_regression(
              target=returns_masked_2,
              regression_length=regression_length,
          )
  
      returns_5 = Returns(window_length=5, inputs=[self.col])
      returns_10 = Returns(window_length=10, inputs=[self.col])
  
      regression_factor = returns_5.linear_regression(
          target=returns_10,
          regression_length=regression_length,
      )
  
      columns = {
          output: getattr(regression_factor, output)
          for output in outputs
      }
      pipeline = Pipeline(columns=columns)
  
      results = run_pipeline(pipeline, start_date, end_date)
  
      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.
      columns = {"returns_5": returns_5, "returns_10": returns_10}
      results = run_pipeline(
          Pipeline(columns=columns),
          dates[start_date_index - (regression_length - 1)],
          dates[end_date_index],
      )
      returns_5_results = results["returns_5"].unstack()
      returns_10_results = results["returns_10"].unstack()
  
      # On each day, for each asset, calculate the expected regression
      # results of Y ~ X where Y is the asset's rolling 5 day returns and X
      # is the asset's rolling 10 day returns. Each regression is calculated
      # over `regression_length` days of data.
      for day in range(num_days):
          todays_returns_5 = returns_5_results.iloc[
              day : day + regression_length
          ]
          todays_returns_10 = returns_10_results.iloc[
              day : day + regression_length
          ]
          for asset, asset_returns_5 in todays_returns_5.iteritems():
              asset_column = int(asset) - 1
              asset_returns_10 = todays_returns_10[asset]
              expected_regression_results = linregress(
                  y=asset_returns_5,
                  x=asset_returns_10,
              )
              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(
              expected_output_results[output],
              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 7 for calling the StatisticalMethodsTestCase obj's underlying member method code with example usage: obj.test_factor_regression_method_two_factors(regression_length) without return types