def calculate_uplift_interp()

in lib/report.py [0:0]

• 20 lines of code
• 2 McCabe index (conditional complexity)

  def calculate_uplift_interp(self, quantiles, branch, segment, metric_type, metric):
    control = self.data["branches"][0]

    quantiles_control = self.data[control][segment][metric_type][metric]["quantiles"]
    values_control = self.data[control][segment][metric_type][metric]["quantile_vals"]
    [quantiles_control_n, values_control_n] = cubic_spline_prep(quantiles_control, values_control)
    tck = interpolate.splrep(quantiles_control_n, values_control_n, k=1)
    values_control_n = interpolate.splev(quantiles, tck, der=0)

    quantiles_branch = self.data[branch][segment][metric_type][metric]["quantiles"]
    values_branch = self.data[branch][segment][metric_type][metric]["quantile_vals"]
    [quantiles_branch_n, values_branch_n] = cubic_spline_prep(quantiles_branch, values_branch)
    tck = interpolate.splrep(quantiles_branch_n, values_branch_n, k=1)
    values_branch_n = interpolate.splev(quantiles, tck, der=0)

    uplifts = []
    diffs = []
    for i in range(len(quantiles)):
      diff = values_branch_n[i] - values_control_n[i]
      uplift = diff/values_control_n[i]*100
      diffs.append(diff)
      uplifts.append(uplift)

    return [diffs, uplifts]