czbenchmarks.metrics.types
Classes
Enumeration of all supported metric types. |
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Stores metadata about a metric. |
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Central registry for all available metrics. |
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!!! abstract "Usage Documentation" |
Module Contents
- class czbenchmarks.metrics.types.MetricType(*args, **kwds)[source]
Bases:
enum.Enum
Enumeration of all supported metric types.
Each metric type corresponds to a specific evaluation metric that can be computed. The value is the string identifier used in results dictionaries.
- ADJUSTED_RAND_INDEX = 'adjusted_rand_index'
- NORMALIZED_MUTUAL_INFO = 'normalized_mutual_info'
- SILHOUETTE_SCORE = 'silhouette_score'
- ENTROPY_PER_CELL = 'entropy_per_cell'
- BATCH_SILHOUETTE = 'batch_silhouette'
- MEAN_FOLD_ACCURACY = 'mean_fold_accuracy'
- MEAN_FOLD_F1_SCORE = 'mean_fold_f1'
- MEAN_FOLD_PRECISION = 'mean_fold_precision'
- MEAN_FOLD_RECALL = 'mean_fold_recall'
- MEAN_FOLD_AUROC = 'mean_fold_auroc'
- MEAN_SQUARED_ERROR = 'mean_squared_error'
- PEARSON_CORRELATION = 'PEARSON_CORRELATION'
- JACCARD = 'jaccard'
- class czbenchmarks.metrics.types.MetricInfo(/, **data: Any)[source]
Bases:
pydantic.BaseModel
Stores metadata about a metric.
- func: Callable
The function that computes the metric
- class czbenchmarks.metrics.types.MetricRegistry[source]
Central registry for all available metrics.
Handles registration and computation of metrics with proper validation.
- register(metric_type: MetricType, func: Callable, required_args: Set[str] | None = None, default_params: Dict[str, Any] | None = None, description: str = '', tags: Set[str] | None = None) None [source]
Register a new metric.
- Parameters:
metric_type – Type of metric to register
func – Function that computes the metric
required_args – Set of required argument names
default_params – Default parameters for the metric function
description – Documentation string
tags – Set of tags for grouping metrics
- compute(metric_type: MetricType, **kwargs) float [source]
Compute a metric with the given parameters.
- Parameters:
metric_type – Type of metric to compute
**kwargs – Arguments to pass to metric function
- Returns:
Computed metric value
- Raises:
ValueError – If metric type unknown or missing required args
- get_info(metric_type: MetricType) MetricInfo [source]
Get metadata about a metric.
- Parameters:
metric_type – Type of metric
- Returns:
MetricInfo object with metric metadata
- Raises:
ValueError – If metric type unknown
- list_metrics(tags: Set[str] | None = None) Set[MetricType] [source]
List available metrics, optionally filtered by tags.
- Parameters:
tags – If provided, only return metrics with all these tags
- Returns:
Set of matching MetricType values
- class czbenchmarks.metrics.types.MetricResult(/, **data: Any)[source]
Bases:
pydantic.BaseModel
- !!! abstract “Usage Documentation”
[Models](../concepts/models.md)
A base class for creating Pydantic models.
- __class_vars__
The names of the class variables defined on the model.
- __private_attributes__
Metadata about the private attributes of the model.
- __signature__
The synthesized __init__ [Signature][inspect.Signature] of the model.
- __pydantic_complete__
Whether model building is completed, or if there are still undefined fields.
- __pydantic_core_schema__
The core schema of the model.
- __pydantic_custom_init__
Whether the model has a custom __init__ function.
- __pydantic_decorators__
Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1.
- __pydantic_generic_metadata__
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- __pydantic_parent_namespace__
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__
The name of the post-init method for the model, if defined.
- __pydantic_root_model__
Whether the model is a [RootModel][pydantic.root_model.RootModel].
- __pydantic_serializer__
The pydantic-core SchemaSerializer used to dump instances of the model.
- __pydantic_validator__
The pydantic-core SchemaValidator used to validate instances of the model.
- __pydantic_fields__
A dictionary of field names and their corresponding [FieldInfo][pydantic.fields.FieldInfo] objects.
- __pydantic_computed_fields__
A dictionary of computed field names and their corresponding [ComputedFieldInfo][pydantic.fields.ComputedFieldInfo] objects.
- __pydantic_extra__
A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] is set to ‘allow’.
- __pydantic_fields_set__
The names of fields explicitly set during instantiation.
- __pydantic_private__
Values of private attributes set on the model instance.
- metric_type: MetricType