tf.keras.metrics.ConcordanceCorrelation

Calculates the Concordance Correlation Coefficient (CCC).

Inherits From: MeanMetricWrapper, Mean, Metric

tf.keras.metrics.ConcordanceCorrelation(
    name='concordance_correlation', dtype=None, axis=-1
)

CCC evaluates the agreement between true values (y_true) and predicted values (y_pred) by considering both precision and accuracy. The coefficient ranges from -1 to 1, where a value of 1 indicates perfect agreement.

This metric is useful in regression tasks where it is important to assess how well the predictions match the true values, taking into account both their correlation and proximity to the 45-degree line of perfect concordance.

name (Optional) string name of the metric instance.
dtype (Optional) data type of the metric result.
axis (Optional) integer or tuple of integers of the axis/axes along which to compute the metric. Defaults to -1.

Example:

ccc = keras.metrics.ConcordanceCorrelation(axis=-1)
y_true = [[0, 1, 0.5], [1, 1, 0.2]]
y_pred = [[0.1, 0.9, 0.5], [1, 0.9, 0.2]]
ccc.update_state(y_true, y_pred)
ccc.result()
0.9816320385426076

Usage with compile() API:

model.compile(optimizer='sgd',
              loss='mean_squared_error',
              metrics=[keras.metrics.ConcordanceCorrelation()])

dtype

variables

Methods

add_variable

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add_variable(
    shape, initializer, dtype=None, aggregation='sum', name=None
)

add_weight

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add_weight(
    shape=(), initializer=None, dtype=None, name=None
)

from_config

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@classmethod
from_config(
    config
)

get_config

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get_config()

Return the serializable config of the metric.

reset_state

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reset_state()

Reset all of the metric state variables.

This function is called between epochs/steps, when a metric is evaluated during training.

result

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result()

Compute the current metric value.

Returns
A scalar tensor, or a dictionary of scalar tensors.

stateless_reset_state

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stateless_reset_state()

stateless_result

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stateless_result(
    metric_variables
)

stateless_update_state

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stateless_update_state(
    metric_variables, *args, **kwargs
)

update_state

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update_state(
    y_true, y_pred, sample_weight=None
)

Accumulate statistics for the metric.

__call__

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__call__(
    *args, **kwargs
)

Call self as a function.