spacr.classify

One Classify entry point over both classifier families.

Classify (CV) trains a Torch model on object crops. Classify (ML) fits a gradient-boosted model on measured features. They answer the same question – which class is this object – and until now they were two modules that shared six setting names out of 78 and 37, two of which disagreed on their default.

spacr.training_basis already unified what defines a CLASS. This module unifies what runs: one settings dict, one classifier_family switch, one call. The two original modules stay exactly as they are – a merged screen that removed them would strand every saved settings CSV and every notebook that imports their entry points.

Nothing here reimplements either pipeline. deep_spacr and generate_ml_scores are called unchanged, which is what makes the merged module honest: a run through it and a run through the module it dispatches to produce the same result, because they are the same code.

Exceptions

ClassifierFamilyError

A classifier family spaCR does not have.

Functions

classify(→ Any)	Run whichever classifier family settings asks for.
inapplicable_settings(→ Tuple[str, ...])	Settings belonging to the OTHER family -- what the panel greys out.
resolve_family(→ str)	Return the classifier family a settings dict asks for.
resolve_ml_model_type(→ str)	Return the classical-ML estimator selected by settings.

Module Contents

exception spacr.classify.ClassifierFamilyError

Bases: ValueError

A classifier family spaCR does not have.

Initialize self. See help(type(self)) for accurate signature.

spacr.classify.classify(settings: Mapping[str, Any]) → Any

Run whichever classifier family settings asks for.

The merged module’s pipeline entry point. It normalises the shared vocabulary, resolves the family, and calls the existing entry point unchanged – so a run here and a run through Classify (CV) or Classify (ML) are the same run, not two implementations that have to be kept in step.

Parameters:

settings – the run settings.

Returns:

whatever the dispatched pipeline returns.

Raises:

• ClassifierFamilyError – an unrecognised family.

• ValueError – when pre-dispatch validation rejects the selected ML estimator or CV crop source.

spacr.classify.inapplicable_settings(family: str) → Tuple[str, ...]

Settings belonging to the OTHER family – what the panel greys out.

Greyed, never removed: INVARIANTS §6. A key absent from the dict makes the pipeline fall back to its own default, which can differ from the value the module needs and says nothing when it does.

Parameters:

family – the chosen family.

Returns:

setting keys the other family owns.

Raises:

ClassifierFamilyError – unknown family.

spacr.classify.resolve_family(settings: Mapping[str, Any]) → str

Return the classifier family a settings dict asks for.

Defaults to 'cv', because the merged module’s own default settings are the CV ones and a dict with no family is most likely a Classify (CV) CSV opened in the merged screen.

Parameters:

settings – the run settings.

Returns:

'cv' or 'ml'.

Raises:

ClassifierFamilyError – an unrecognised family. Guessing would train a different kind of model than the user asked for and report success.

spacr.classify.resolve_ml_model_type(settings: Mapping[str, Any]) → str

Return the classical-ML estimator selected by settings.

model_type_ml is authoritative in a merged payload. model_type is accepted only when the ML-specific key is absent, which migrates the short-lived shared-vocabulary settings files written before the two model controls were separated. The default matches spacr.settings.set_default_analyze_screen().

Parameters:

settings – settings for an ML-family run.

Returns:

a member of ML_MODEL_TYPES.

Raises:

ValueError – when the selected value is not an ML estimator.