sklearn_quantile

class RandomForestQuantileRegressorVectorRegressionModel(confidence: float, random_state=42, **kwargs)

Bases: sensai.sklearn.sklearn_base.AbstractSkLearnMultipleOneDimVectorRegressionModel

__init__(confidence: float, random_state=42, **kwargs)

Parameters

• q – the default quantile that is used for predictions

• kwargs – keyword arguments to pass on to RandomForestQuantileRegressor

predict_confidence_intervals(x: pandas.core.frame.DataFrame, var_name: Optional[str] = None)

Parameters

• x – the input data

• var_name – the predicted variable name; may be None if there is only one predicted variable

Returns

an array of shape [2, N], where the first dimension contains the confidence interval’s lower bounds and the second its upper bounds

class QuantileRegressionMetric(name: Optional[str] = None, bounds: Optional[Tuple[float, float]] = None)

Bases: sensai.evaluation.eval_stats.eval_stats_regression.RegressionMetric, abc.ABC

static compute_confidence_intervals(model: sensai.vector_model.VectorRegressionModel, io_data: sensai.data.InputOutputData = None) → numpy.ndarray

name: str

class QuantileRegressionMetricAccuracyInConfidenceInterval(name: Optional[str] = None, bounds: Optional[Tuple[float, float]] = None)

Bases: sensai.sklearn_quantile.QuantileRegressionMetric

Metric reflecting the accuracy of the confidence interval, i.e. the relative frequency of predictions where the confidence interval contains the ground true value

name: str = 'AccuracyInCI'

classmethod compute_value(y_true: numpy.ndarray, y_predicted: numpy.ndarray, model: Optional[sensai.vector_model.VectorRegressionModel] = None, io_data: Optional[sensai.data.InputOutputData] = None)

class QuantileRegressionMetricConfidenceIntervalMeanSize(name: Optional[str] = None, bounds: Optional[Tuple[float, float]] = None)

Bases: sensai.sklearn_quantile.QuantileRegressionMetric

Metric for the mean size of the confidence interval

name: str = 'MeanSizeCI'

classmethod compute_value(y_true: numpy.ndarray, y_predicted: numpy.ndarray, model: Optional[sensai.vector_model.VectorRegressionModel] = None, io_data: Optional[sensai.data.InputOutputData] = None)

class QuantileRegressionMetricConfidenceIntervalMedianSize(name: Optional[str] = None, bounds: Optional[Tuple[float, float]] = None)

Bases: sensai.sklearn_quantile.QuantileRegressionMetric

Metric for the median size of the confidence interval

name: str = 'MedianSizeCI'

classmethod compute_value(y_true: numpy.ndarray, y_predicted: numpy.ndarray, model: Optional[sensai.vector_model.VectorRegressionModel] = None, io_data: Optional[sensai.data.InputOutputData] = None)

class QuantileRegressionMetricRelFreqMaxSizeConfidenceInterval(max_size: float)

Bases: sensai.sklearn_quantile.QuantileRegressionMetric

Relative frequency of confidence interval having the given maximum size

__init__(max_size: float)

Parameters

• name – the name of the metric; if None use the class’ name attribute

• bounds – the minimum and maximum values the metric can take on (or None if the bounds are not specified)

compute_value(y_true: numpy.ndarray, y_predicted: numpy.ndarray, model: Optional[sensai.vector_model.VectorRegressionModel] = None, io_data: Optional[sensai.data.InputOutputData] = None)

name: str