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I/O

glide.io

to_json 

to_json(result)

Convert a MeanInferenceResult to a JSON string representation.

Parameters:

Name Type Description Default
result MeanInferenceResult

The inference result object containing mean, standard deviation, and confidence interval.

 required

Returns:

Type Description
str

A JSON-formatted string representation of the inference result with 2-space indentation.

Examples:

>>> from glide.io import to_json
>>> from glide.mean_inference_results import MeanInferenceResult
>>> from glide.confidence_intervals import CLTConfidenceInterval
>>> confidence_interval = CLTConfidenceInterval(mean=0, std=1)
>>> inference_result = MeanInferenceResult(confidence_interval=confidence_interval,     metric_name="metric", estimator_name="none")
>>> print(to_json(inference_result))
{
  "confidence_interval": {
    "confidence_level": 0.95,
    "lower_bound": -1.95...,
    "upper_bound": 1.95...,
    "width": 3.91...
  },
  "metric_name": "metric",
  "estimator_name": "none",
  "mean": 0,
  "std": 1
}
Source code in glide/io/export.py 
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def to_json(result: MeanInferenceResult) -> str:
    """Convert a MeanInferenceResult to a JSON string representation.

    Parameters
    ----------
    result : MeanInferenceResult
        The inference result object containing mean, standard deviation, and confidence interval.

    Returns
    -------
    str
        A JSON-formatted string representation of the inference result with 2-space indentation.

    Examples
    --------
    >>> from glide.io import to_json
    >>> from glide.mean_inference_results import MeanInferenceResult
    >>> from glide.confidence_intervals import CLTConfidenceInterval
    >>> confidence_interval = CLTConfidenceInterval(mean=0, std=1)
    >>> inference_result = MeanInferenceResult(confidence_interval=confidence_interval, \
    metric_name="metric", estimator_name="none")
    >>> print(to_json(inference_result))  # doctest: +ELLIPSIS
    {
      "confidence_interval": {
        "confidence_level": 0.95,
        "lower_bound": -1.95...,
        "upper_bound": 1.95...,
        "width": 3.91...
      },
      "metric_name": "metric",
      "estimator_name": "none",
      "mean": 0,
      "std": 1
    }
    """
    data = asdict(result)
    data["mean"] = result.mean
    data["std"] = result.std
    # Reconstruct confidence_interval dict in desired field order
    ci_dict = {
        "confidence_level": result.confidence_interval.confidence_level,
        "lower_bound": result.confidence_interval.lower_bound,
        "upper_bound": result.confidence_interval.upper_bound,
        "width": result.confidence_interval.width,
    }
    data["confidence_interval"] = ci_dict
    json_str = json.dumps(data, indent=2)
    return json_str