pitci.base.AbsoluteErrorConformalPredictor¶

class pitci.base.AbsoluteErrorConformalPredictor(model)[source]¶

Bases: pitci.base.ConformalPredictor

Conformal interval predictor for an underlying Any model using non-scaled absolute error as the nonconformity measure.

Class implements inductive conformal intervals where a calibration dataset is used to learn the information that is used when generating intervals for new instances.

The predictor outputs fixed width intervals for every new instance, there is no interval scaling implemented in this class.

Parameters: model (Any) – Underlying Any model to generate prediction intervals with.

__version__¶

The version of the pitci package that generated the object.

Type: str

model¶

The underlying Any model passed in initialising the object.

Type: Any

baseline_interval¶

The default or baseline conformal half interval width. Will be applied without modification to provide an interval for all new instances. Attribute is set when the calibrate() method is run.

Type: float

alpha¶

The confidence level of the conformal intervals that will be produced. Attribute is set when the calibrate() method is run.

Type: int or float

abstract __init__(model)¶: Initialize self. See help(type(self)) for accurate signature.

Methods

`__init__`(model)	Initialize self.
`calibrate`(data, response[, alpha])	Calibrate conformal intervals that will be applied to new instances when calling `predict_with_interval`.
`predict_with_interval`(data)	Generate predictions with conformal intervals using the underlying `model`.

calibrate(data, response, alpha=0.95)¶

Calibrate conformal intervals that will be applied to new instances when calling predict_with_interval.

The value passed in alpha is stored in an attribute of the same name.

Parameters

data (Any) – Dataset to calibrate baselines on.
response (np.ndarray or pd.Series) – The associated response values for every record in data.
alpha (int or float, default = 0.95) – Confidence level for the intervals.

predict_with_interval(data)¶

Generate predictions with conformal intervals using the underlying model.

Parameters: data (Any) – Dataset to generate predictions with intervals on.
Returns: predictions_with_interval – Array of predictions with intervals for each row in data. Output array will have 3 columns where the first is the lower interval, second are the predictions and the third is the upper interval.
Return type: np.ndarray