simpleml.metrics.base_metric module

class simpleml.metrics.base_metric.AbstractMetric(name=None, has_external_files=False, author=None, project=None, version_description=None, save_method='disk_pickled', **kwargs)

Bases: simpleml.persistables.base_persistable.Persistable

Abstract Base class for all Metric objects

name: the metric name values: JSON object with key: value pairs for performance on test dataset

(ex: FPR: TPR to create ROC Curve) Singular value metrics take the form - {‘agg’: value}

add_model(model)

Setter method for model used

load(**kwargs)

Extend main load routine to load relationship class

object_type = 'METRIC'

save(**kwargs)

Extend parent function with a few additional save routines

score(**kwargs)

Abstract method for each metric to define

Should set self.values

values = Column(None, JSON(), table=None, nullable=False)

class simpleml.metrics.base_metric.Metric(name=None, has_external_files=False, author=None, project=None, version_description=None, save_method='disk_pickled', **kwargs)

Bases: simpleml.metrics.base_metric.AbstractMetric

Base class for all Metric objects

model_id: foreign key to the model that was used to generate predictions

TODO: Should join criteria be composite of model and dataset for multiple

duplicate metric objects computed over different test datasets?

author

created_timestamp

filepaths

has_external_files

hash_

id

metadata_

model

model_id

modified_timestamp

name

project

registered_name

values

version

version_description