# ClassifierMetric¶

Inheritance Diagram

class ashpy.metrics.classifier.ClassifierMetric(metric, model_selection_operator=None, logdir='/home/docs/checkouts/readthedocs.org/user_builds/ashpy/checkouts/latest/docs/source/log', processing_predictions={'fn': <function argmax_v2>, 'kwargs': {'axis': -1}})[source]

Wrap a metric using argmax to extract predictions out of a classifier’s output.

Methods

 __init__(metric[, model_selection_operator, …]) Initialize the Metric. update_state(context) Update the internal state of the metric, using the information from the context object.

Attributes

 best_folder Retrieve the folder used to save the best model when doing model selection. best_model_sel_file Retrieve the path to JSON file containing the measured performance of the best model. logdir Retrieve the log directory. metric Retrieve the tf.keras.metrics.Metric object. model_selection_operator Retrieve the operator used for model selection. name Retrieve the metric name.
__init__(metric, model_selection_operator=None, logdir='/home/docs/checkouts/readthedocs.org/user_builds/ashpy/checkouts/latest/docs/source/log', processing_predictions={'fn': <function argmax_v2>, 'kwargs': {'axis': -1}})[source]

Initialize the Metric.

Parameters
• metric (tf.keras.metrics.Metric) – The Keras Metric to use with the classifier (e.g.: Accuracy()).

• model_selection_operator (typing.Callable) –

The operation that will be used when model_selection is triggered to compare the metrics, used by the update_state. Any typing.Callable behaving like an operator is accepted.

Note

Model selection is done ONLY if an model_selection_operator is specified here.

• logdir (str) – Path to the log dir, defaults to a log folder in the current directory.

• processing_predictions (typing.Dict) – A dict in the form of {“fn”: tf.argmax, “kwargs”: {“axis”: -1}} with a function “fn” to be used for predictions processing purposes and its “kwargs” as its keyword-arguments.

Return type

None

update_state(context)[source]

Update the internal state of the metric, using the information from the context object.

Parameters

context (ashpy.contexts.ClassifierContext) – An AshPy Context holding all the information the Metric needs.

Return type

None