Parametric family fitting, transform metadata, and the unified distribution selector used by the pipeline.
Package source: tabnetics.distribution
Package overview
Distribution fitting and transform helpers.
Stable exports
class BootstrapGOFSelector(class) - Source. Monte-Carlo goodness-of-fit selector tailored to small samples.def select_best_distribution_bootstrap(data: np.ndarray, distributions: Dict[str, sps.rv_continuous] | None = None, statistic: str = 'cvm', n_boot: int = 2000, random_state: Any = None) -> Tuple[str, List[Dict[str, Any]]](function) - Source. Return (best_dist_name, sorted_result_list).def select_best_distribution_bic(data: np.ndarray, distributions: Dict[str, sps.rv_continuous] | None = None) -> Tuple[str, List[Dict[str, Any]]](function) - Source. Return (best_dist_name, sorted_result_list) using BIC.def auto_select_distribution(data: np.ndarray, *, small_sample_threshold: int = 30, statistic: str = 'cvm', n_boot: int = 2000, random_state: Any = None) -> Tuple[str, List[Dict[str, Any]]](function) - Source. Dispatch to the most appropriate selection strategy.class DistributionFeatures(class) - Source. Represents statistical features extracted from data, used to guide distribution selection. These features help in applying heuristics and bonuses for more accurate fitting.class TransformInfo(class) - Source. Stores information about data transformations (shifting, scaling) applied prior to fitting a distribution. Used to reverse-transform fitted parameters.class LRTResult(class) - Source. Stores results of Likelihood Ratio Test between nested distributions.class CVResult(class) - Source. Stores cross-validation results for a distribution.class FitResult(class) - Source. Holds the results of fitting a single distribution to data, including parameters, goodness-of-fit statistics, and any applied transformations.class UnifiedDistributionSelectorV6(class) - Source. Hybrid distribution selector combining statistical fitting, goodness-of-fit tests, and feature-based heuristics to identify the best-fitting distribution for given data. This version incorporates improvements for robustness and accuracy, including LRT and CV.
Module details
tabnetics.distribution.__init__
Distribution fitting and transform helpers.
No top-level public symbols are exported directly from this module.
tabnetics.distribution.bootstrap
class BootstrapGOFSelector(class) - Source. Monte-Carlo goodness-of-fit selector tailored to small samples.def select_best_distribution_bootstrap(data: np.ndarray, distributions: Dict[str, sps.rv_continuous] | None = None, statistic: str = 'cvm', n_boot: int = 2000, random_state: Any = None) -> Tuple[str, List[Dict[str, Any]]](function) - Source. Return (best_dist_name, sorted_result_list).def select_best_distribution_bic(data: np.ndarray, distributions: Dict[str, sps.rv_continuous] | None = None) -> Tuple[str, List[Dict[str, Any]]](function) - Source. Return (best_dist_name, sorted_result_list) using BIC.def auto_select_distribution(data: np.ndarray, *, small_sample_threshold: int = 30, statistic: str = 'cvm', n_boot: int = 2000, random_state: Any = None) -> Tuple[str, List[Dict[str, Any]]](function) - Source. Dispatch to the most appropriate selection strategy.
tabnetics.distribution.selector
class DistributionFeatures(class) - Source. Represents statistical features extracted from data, used to guide distribution selection. These features help in applying heuristics and bonuses for more accurate fitting.class TransformInfo(class) - Source. Stores information about data transformations (shifting, scaling) applied prior to fitting a distribution. Used to reverse-transform fitted parameters.class LRTResult(class) - Source. Stores results of Likelihood Ratio Test between nested distributions.class CVResult(class) - Source. Stores cross-validation results for a distribution.class FitResult(class) - Source. Holds the results of fitting a single distribution to data, including parameters, goodness-of-fit statistics, and any applied transformations.class UnifiedDistributionSelectorV6(class) - Source. Hybrid distribution selector combining statistical fitting, goodness-of-fit tests, and feature-based heuristics to identify the best-fitting distribution for given data. This version incorporates improvements for robustness and accuracy, including LRT and CV.
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