Meta Model

systematica.api.reports.meta_model

MetaModelCVReport

MetaModelCVReport(
    data: vectorbtpro.data.base.Data,
    s1: str,
    s2: str,
    model: ~BaseStatArb,
    use_close: bool = True,
    freq: str = 'auto',
)

Breakdown analysis for meta model, extending BaseModelBreakdownCV.

Method generated by attrs for class BaseReportCV.

Ancestors

• systematica.api.base.BaseReportCV
• systematica.api.base.BaseReportMixin
• abc.ABC

Methods

score

score(
    self,
    estimator: sklearn.base.BaseEstimator,
    X: pandas.core.frame.DataFrame | numpy.ndarray,
    y: numpy.ndarray | pandas.core.series.Series,
    scoring: str = 'accuracy',
    n_jobs: int = None,
    verbose: int = 0,
) ‑> pandas.core.series.Series

Evaluate a score by cross-validation.

This method uses scikit-learn’s cross_val_score to evaluate the performance of an estimator on a given dataset using cross-validation.

See Also:

• cross_validate: To run cross-validation on multiple metrics and also to return train scores, fit times and score times.
• cross_val_predict: Get predictions from each split of cross-validation for diagnostic purposes.
• sklearn.metrics.make_scorer: Make a scorer from a performance metric or loss function.

Parameters:

Name	Type	Default	Description
estimator	BaseEstimator	--	The object to use to fit the data.
X	pd.DataFrame	--	The data to fit.
y	pd.Series	--	The target variable.
scoring	str	accuracy	The scoring method to use for evaluation. It can be one of the predefined strings in scikit-learn, such as ‘accuracy’, ‘f1’, ‘roc_auc’, etc., or a custom scoring function that follows the scikit-learn scorer interface. Defaults to accuracy.
n_jobs	int	None	Number of jobs to run in parallel. Training the estimator and computing the score are parallelized over the cross-validation splits. None means 1 unless in a joblib.parallel_backend context. -1 means using all processors. Default is None.
verbose	int	0	Verbosity level. Controls the verbosity of the output during cross-validation. 0 means no output, 1 means progress messages, and higher values increase verbosity. Default is 0.

Returns:

Type	Description
pd.Series	Scores of the estimator for each run of the cross validation.

RollingMetaModelReport

RollingMetaModelReport(
    data: vectorbtpro.data.base.Data,
    s1: str,
    s2: str,
    model: ~BaseStatArb,
    use_close: bool = True,
    freq: str = 'auto',
)

Breakdown analysis for meta model, extending BaseRollingReport.

Method generated by attrs for class BaseRollingReport.

Ancestors

• systematica.api.base.BaseRollingReport
• systematica.api.base.BaseReportMixin
• abc.ABC