predict_maxfit

Performs a relevance-based maxfit prediction using the CSA Prediction Engine API.

This method determines the optimal relevance-based prediction by evaluating adjusted fit across various thresholds for the input data.

This function supports three types of prediction tasks:

1. Single prediction task: A single dependent variable and a single set of circumstances.

2. Multi-y prediction task: Multiple dependent variables (y) with a single set of circumstances (theta).

3. Multi-theta prediction task: A single dependent variable with multiple sets of circumstances (theta).

Multi-y and multi-theta prediction tasks cannot be performed simultaneously. Ensure that your input dimensions are structured accordingly, i.e., you can loop through multiple calls to handle these cases separately.

Parameters

y : ndarray

Dependent variable(s) represented as either:

• Single task: Column vector [N-by-1]

• Multi-y task: Matrix [N-by-Q], where Q is the number of dependent variables.

X : ndarray

Independent variables matrix of shape [N-by-K], where K is the number of features.

theta : ndarray

Circumstances represented as either

• Single task: Row vector [1-by-K].

• Multi-theta task: Matrix [Q-by-K], where Q is the number of different sets of circumstances.

options : MaxFitOptions

Configuration object containing key-value parameters required for the maxfit prediction task.

Returns

yhat : ndarray

Predicted outcome(s) based on the input data and circumstances.

yhat_details : dict

Dictionary containing additional details about the prediction model and results.

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Raises

Raises a ValueError if both multi-y and multi-theta are specified simultaneously, or if the dimensions of y, X, and theta are not compatible.