Precision.md

TFSimilarity.classification_metrics.Precision

Calculates the precision of the query classification.

Inherits From: ClassificationMetric, ABC

TFSimilarity.classification_metrics.Precision(
    name: str = precision
) -> None

Computes the precision given the query classification counts.

$$ Precision = rac extrm{true_positives}}{ extrm{true_positives} + extrm{false_positives} $$

args: name: Name associated with a specific metric object, e.g., [email protected]

Usage with tf.similarity.models.SimilarityModel():

model.calibrate(x=query_examples,
                y=query_labels,
                calibration_metric='precision')

Methods

compute

View source

compute(
    tp: <a href="../../TFSimilarity/callbacks/FloatTensor.md">TFSimilarity.callbacks.FloatTensor```
</a>,
    fp: <a href="../../TFSimilarity/callbacks/FloatTensor.md">TFSimilarity.callbacks.FloatTensor```
</a>,
    tn: <a href="../../TFSimilarity/callbacks/FloatTensor.md">TFSimilarity.callbacks.FloatTensor```
</a>,
    fn: <a href="../../TFSimilarity/callbacks/FloatTensor.md">TFSimilarity.callbacks.FloatTensor```
</a>,
    count: int
) -> <a href="../../TFSimilarity/callbacks/FloatTensor.md">TFSimilarity.callbacks.FloatTensor```
</a>

Compute the classification metric.

The compute() method supports computing the metric for a set of values, where each value represents the counts at a specific distance threshold.

Args
tp	A 1D FloatTensor containing the count of True Positives at each distance threshold.
fp	A 1D FloatTensor containing the count of False Positives at each distance threshold.
tn	A 1D FloatTensor containing the count of True Negatives at each distance threshold.
fn	A 1D FloatTensor containing the count of False Negatives at each distance threshold.
count	The total number of queries

Returns
A 1D FloatTensor containing the metric at each distance threshold.

get_config

View source

get_config()