dae.pheno.prepare package

Submodules

dae.pheno.prepare.measure_classifier module

class dae.pheno.prepare.measure_classifier.Convertible(value, names=None, *, module=None, qualname=None, type=None, start=1, boundary=None)

Bases: Enum

nan = 0

non_numeric = 2

numeric = 1

class dae.pheno.prepare.measure_classifier.InferenceReport(*, value_type: type, histogram_type: type[NullHistogram | CategoricalHistogram | NumberHistogram], min_individuals: int, count_total: int, count_with_values: int, count_without_values: int, count_unique_values: int, min_value: float | int | None, max_value: float | int | None, values_domain: str)

Bases: BaseModel

Inference results report.

count_total: int

count_unique_values: int

count_with_values: int

count_without_values: int

histogram_type: type[NullHistogram | CategoricalHistogram | NumberHistogram]

max_value: float | int | None

min_individuals: int

min_value: float | int | None

model_computed_fields: ClassVar[dict[str, ComputedFieldInfo]] = {}

A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

model_fields: ClassVar[dict[str, FieldInfo]] = {'count_total': FieldInfo(annotation=int, required=True), 'count_unique_values': FieldInfo(annotation=int, required=True), 'count_with_values': FieldInfo(annotation=int, required=True), 'count_without_values': FieldInfo(annotation=int, required=True), 'histogram_type': FieldInfo(annotation=type[Union[NullHistogram, CategoricalHistogram, NumberHistogram]], required=True), 'max_value': FieldInfo(annotation=Union[float, int, NoneType], required=True), 'min_individuals': FieldInfo(annotation=int, required=True), 'min_value': FieldInfo(annotation=Union[float, int, NoneType], required=True), 'value_type': FieldInfo(annotation=type, required=True), 'values_domain': FieldInfo(annotation=str, required=True)}

Metadata about the fields defined on the model, mapping of field names to [FieldInfo][pydantic.fields.FieldInfo].

This replaces Model.__fields__ from Pydantic V1.

value_type: type

values_domain: str

dae.pheno.prepare.measure_classifier.convert_to_float(value: int | float | str | None) → float | None

dae.pheno.prepare.measure_classifier.convert_to_int(value: int | float | str | None) → int | None

dae.pheno.prepare.measure_classifier.convert_to_numeric(val: Any) → float | float64

Convert passed value to float.

dae.pheno.prepare.measure_classifier.convert_to_string(val: Any) → str | None

Convert passed value to string.

dae.pheno.prepare.measure_classifier.determine_histogram_type(report: InferenceReport, config: InferenceConfig) → type[NullHistogram | CategoricalHistogram | NumberHistogram]

Given an inference report and a configuration, return histogram type.

dae.pheno.prepare.measure_classifier.force_type_inference(values: list[str | None], config: InferenceConfig) → tuple[list[float | None] | list[int | None] | list[str | None], InferenceReport]

Perform type inference when a type is forced.

dae.pheno.prepare.measure_classifier.inference_reference_impl(values: list[str | None], config: InferenceConfig) → tuple[list[float | None] | list[int | None] | list[str | None], InferenceReport]

Infer value and histogram type for a list of values.

dae.pheno.prepare.measure_classifier.is_convertible_to_numeric(val: Any) → Convertible

Check if the passed string is convertible to number.

dae.pheno.prepare.measure_classifier.is_nan(val: Any) → bool

Check if the passed value is a NaN.

Module contents