bootstrap

Module containing methods to calculate confidence intervals using bootstrapping.

human_protocol_sdk.agreement.bootstrap.confidence_intervals(data, statistic_fn, n_iterations=1000, n_sample=None, confidence_level=0.95, algorithm='bca', seed=None)

Returns a tuple, containing the confidence interval for the boostrap estimates of the given statistic and statistics of the bootstrap samples.

• Parameters:

  • data (Sequence) – Data to estimate the statistic.

  • statistic_fn (Callable) – Function to calculate the statistic. statistic_fn(data) must return a number.

  • n_iterations (int) – Number of bootstrap samples to use for the estimate.

  • n_sample (Optional[int]) – If provided, determines the size of each bootstrap sample drawn from the data. If omitted, is equal to the length of the data.

  • confidence_level – Size of the confidence interval.

  • algorithm – Which algorithm to use for the confidence interval estimation. “bca” uses the “Bias Corrected Bootstrap with Acceleration”, “percentile” simply takes the appropriate percentiles from the bootstrap distribution.

  • seed – Random seed to use.

• Return type: Tuple[Tuple[float, float], ndarray]

• Returns: Confidence interval and bootstrap distribution.

• Example:

  from human_protocol_sdk.agreement.bootstrap import confidence_interval
  import numpy as np
  
  np.random.seed(42)
  data = np.random.randn(10_000)
  fn = np.mean
  sample_mean = fn(data)
  print(f"Sample mean is {sample_mean:.3f}")
  # Sample mean is -0.002
  
  cl = 0.99
  ci, _ = confidence_interval(data, fn, confidence_level=cl)
  print(f"Population mean is between {ci[0]:.2f} and {ci[1]:.2f} with a probablity of {cl}")
  # Population mean is between -0.02 and 0.02 with a probablity of 0.99