tf.keras.metrics.PearsonCorrelation

Calculates the Pearson Correlation Coefficient (PCC).

Inherits From: MeanMetricWrapper, Mean, Metric

tf.keras.metrics.PearsonCorrelation(
    name='pearson_correlation', dtype=None, axis=-1
)

PCC measures the linear relationship between the true values (y_true) and the predicted values (y_pred). The coefficient ranges from -1 to 1, where a value of 1 implies a perfect positive linear correlation, 0 indicates no linear correlation, and -1 indicates a perfect negative linear correlation.

This metric is widely used in regression tasks where the strength of the linear relationship between predictions and true labels is an important evaluation criterion.

Args
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:

pcc = keras.metrics.PearsonCorrelation(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]]
pcc.update_state(y_true, y_pred)
pcc.result()
0.9966996338993913

Usage with compile() API:

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

Attributes
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.