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
    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

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