pykoop.score_trajectory

score_trajectory(X_predicted, X_expected, n_steps=None, discount_factor=1, regression_metric='neg_mean_squared_error', regression_metric_kw=None, error_score=nan, min_samples=1, episode_feature=False)

Score a predicted data matrix compared to an expected data matrix.

Parameters:

  • X_predicted (np.ndarray) – Predicted state data matrix.

  • X_expected (np.ndarray) – Expected state data matrix.

  • n_steps (Optional[int]) – Number of steps ahead to predict. If None or longer than the episode, will score the entire episode.

  • discount_factor (float) – Discount factor used to weight the error timeseries. Should be positive, with magnitude 1 or slightly less. The error at each timestep is weighted by discount_factor**k, where k is the timestep.

  • regression_metric (Union[str, Callable]) –

    Regression metric to use. One of

    • 'explained_variance',

    • 'neg_mean_absolute_error',

    • 'neg_mean_squared_error',

    • 'neg_mean_squared_log_error',

    • 'neg_median_absolute_error',

    • 'r2', or

    • 'neg_mean_absolute_percentage_error',

    which are existing scikit-learn regression metrics [1]. Can also directly specify a function with the same keyword arguments as the scikit-learn ones. That is, at least y_true, y_pred, sample_weight, and multioutput.

  • regression_metric_kw (Optional[Dict[str, Any]]) – Keyword arguments for regression_method. If sample_weight keyword argument is specified, discount_factor is ignored.

  • error_score (Union[str, float]) – Value to assign to the score if X_predicted has diverged or if an error has occured in estimator fitting. If set to 'raise', a ValueError is raised. If a numerical value is given, a sklearn.exceptions.FitFailedWarning warning is raised and the specified score is returned. The error score defines the worst possible score. If a score is finite but lower than the error score, the error score will be returned instead.

  • min_samples (int) – Number of samples in initial condition.

  • episode_feature (bool) – True if first feature indicates which episode a timestep is from.

Returns:

Score (greater is better).

Return type:

float

Raises:

ValueError – If error_score='raise' and an error occurs in scoring.

References