class_docstr: Factor producing the slope of a regression line between each asset's daily
  returns to the daily returns of a single "target" asset.
  
  Parameters
  ----------
  target : zipline.Asset
      Asset against which other assets should be regressed.
  regression_length : int
      Number of days of daily returns to use for the regression.
  allowed_missing_percentage : float, optional
      Percentage of returns observations (between 0 and 1) that are allowed
      to be missing when calculating betas. Assets with more than this
      percentage of returns observations missing will produce values of
      NaN. Default behavior is that 25% of inputs can be missing.
class_docstr_tok: ["Factor","producing","the","slope","of","a","regression","line","between","each","asset","'s","daily","returns","to","the","daily","returns","of","a","single","``","target","''","asset",".","Parameters","--","--","--","--","--","target",":","zipline.Asset","Asset","against","which","other","assets","should","be","regressed",".","regression_length",":","int","Number","of","days","of","daily","returns","to","use","for","the","regression",".","allowed_missing_percentage",":","float",",","optional","Percentage","of","returns","observations","(","between","0","and","1",")","that","are","allowed","to","be","missing","when","calculating","betas",".","Assets","with","more","than","this","percentage","of","returns","observations","missing","will","produce","values","of","NaN",".","Default","behavior","is","that","25","%","of","inputs","can","be","missing","."]
code: def compute(
      self,
      today,
      assets,
      out,
      all_returns,
      target_returns,
      allowed_missing_count,
  ):
      vectorized_beta(
          dependents=all_returns,
          independent=target_returns,
          allowed_missing=allowed_missing_count,
          out=out,
      )
  
imports: from numexpr import evaluate
  import numpy
  from numpy import broadcast_arrays
  from scipy.stats import linregress, spearmanr
  from zipline.assets import Asset
  from zipline.errors import IncompatibleTerms
  from zipline.pipeline.factors import CustomFactor
  from zipline.pipeline.filters import SingleAsset
  from zipline.pipeline.mixins import StandardOutputs
  from zipline.pipeline.sentinels import NotSpecified
  from zipline.pipeline.term import AssetExists
  from zipline.utils.input_validation import expect_bounded, expect_dtypes, expect_types
  from zipline.utils.math_utils import nanmean
  from zipline.utils.numpy_utils import float64_dtype, int64_dtype
  from .basic import Returns
label_desc: Use image node_id 2 for calling the SimpleBeta obj's underlying member method code with example usage: obj.compute(today, assets, out, all_returns, target_returns, allowed_missing_count) without return types