[MetraxAudit] Audit fixes for FBetaScore and Dice metrics.

Classification Metrics:
- Fix NameError in FBetaScore.compute() (beta -> self.beta).
- Improve numerical stability in FBetaScore using divide_no_nan.

Image Metrics:
- Improve numerical stability in Dice metric using divide_no_nan.
- Update tests to reflect stability improvements.

PiperOrigin-RevId: 864705338

Author: jshin1394

src/metrax/classification_metrics.py

@@ -454,8 +454,10 @@ def compute(self) -> jax.Array:
 
 @flax.struct.dataclass
 class AUCROC(clu_metrics.Metric):
-  r"""Computes area under the receiver operation characteristic curve for binary classification given `predictions`
-  and `labels`.
+  r"""Computes Area Under the ROC curve (AUC-ROC).
+
+  Computes area under the receiver operation characteristic curve for binary
+  classification given `predictions` and `labels`.
 
   The ROC curve shows the tradeoff between the true positive rate (TPR) and
   false positive
@@ -579,147 +581,120 @@ def compute(self) -> jax.Array:
     # Threshold goes from 0 to 1, so trapezoid is negative.
     return jnp.trapezoid(tp_rate, fp_rate) * -1
 
+
 @flax.struct.dataclass
 class FBetaScore(clu_metrics.Metric):
-    """
-    F-Beta score Metric class
-    Computes the F-Beta score for the binary classification given 'predictions' and 'labels'.
-
-    Formula for F-Beta Score:
-        b2 = beta ** 2
-        f_beta_score = ((1 + b2) * (precision * recall)) / (precision * b2 + recall)
-
-    F-Beta turns into the F1 Score when beta = 1.0
-
-    Attributes:
-        true_positives: The count of true positive instances from the given data,
-            label, and threshold.
-        false_positives: The count of false positive instances from the given data,
-            label, and threshold.
-        false_negatives: The count of false negative instances from the given data,
-            label, and threshold.
-        beta: The beta value used in the F-Score metric
-    """
+  """F-Beta score Metric class.
 
-    true_positives: jax.Array
-    false_positives: jax.Array
-    false_negatives: jax.Array
-    beta: float = 1.0
-
-    # Reset the variables for the class
-    @classmethod
-    def empty(cls) -> 'FBetaScore':
-        return cls(
-            true_positives = jnp.array(0, jnp.float32),
-            false_positives = jnp.array(0, jnp.float32),
-            false_negatives = jnp.array(0, jnp.float32),
-            beta=1.0,
-        )
-
-    @classmethod
-    def from_model_output(
-            cls,
-            predictions: jax.Array,
-            labels: jax.Array,
-            beta = beta,
-            threshold = 0.5,) -> 'FBetaScore':
-        """Updates the metric.
-            Note: When only predictions and labels are given, the score calculated
-            is the F1 score if the FBetaScore beta value has not been previously modified.
-
-            Args:
-                predictions: A floating point 1D vector whose values are in the range [0,
-                  1]. The shape should be (batch_size,).
-                labels: True value. The value is expected to be 0 or 1. The shape should
-                  be (batch_size,).
-                beta: beta value to use in the F-Score metric. A floating number.
-                threshold: threshold value to use in the F-Score metric. A floating number.
-
-            Returns:
-                The updated FBetaScore object.
-
-            Raises:
-                ValueError: If type of `labels` is wrong or the shapes of `predictions`
-                and `labels` are incompatible. If the beta or threshold are invalid values
-                an error is raised as well.
-        """
-
-        # Ensure that beta is a floating number and a valid number
-        if not isinstance(beta, float):
-            raise ValueError('The "Beta" value must be a floating number.')
-        if beta <= 0.0:
-            raise ValueError('The "Beta" value must be larger than 0.0.')
-
-        # Ensure that threshold is a floating number and a valid number
-        if not isinstance(threshold, float):
-            raise ValueError('The "Threshold" value must be a floating number.')
-        if threshold < 0.0 or threshold > 1.0:
-            raise ValueError('The "Threshold" value must be between 0 and 1.')
-
-        # Modify predictions with the given threshold value
-        predictions = jnp.where(predictions >= threshold, 1, 0)
-
-        # Assign the true_positive, false_positive, and false_negative their values
-        """
-        We are calculating these values manually instead of using Metrax's
-        precision and recall classes. This is because the Metrax versions end up
-        outputting a single numerical answer when we need an array of numbers.
-        """
-        true_positives = jnp.sum(predictions * labels, axis = 0)
-        false_positives = jnp.sum(predictions * (1 - labels), axis = 0)
-        false_negatives = jnp.sum((1- predictions) * labels, axis = 0)
-
-        return cls(true_positives = true_positives,
-                   false_positives = false_positives,
-                   false_negatives = false_negatives,
-                   beta = beta)
+  Computes the F-Beta score for the binary classification given 'predictions'
+  and 'labels'.
 
+  Formula for F-Beta Score:
+      b2 = beta ** 2
+      f_beta_score = ((1 + b2) * (precision * recall)) / (precision * b2 +
+      recall)
+
+  F-Beta turns into the F1 Score when beta = 1.0
+
+  Attributes:
+    true_positives: The count of true positive instances from the given data,
+      label, and threshold.
+    false_positives: The count of false positive instances from the given data,
+      label, and threshold.
+    false_negatives: The count of false negative instances from the given data,
+      label, and threshold.
+    beta: The beta value used in the F-Score metric.
+  """
+
+  true_positives: jax.Array
+  false_positives: jax.Array
+  false_negatives: jax.Array
+  beta: float = 1.0
+
+  # Reset the variables for the class
+  @classmethod
+  def empty(cls) -> 'FBetaScore':
+    return cls(
+        true_positives=jnp.array(0, jnp.float32),
+        false_positives=jnp.array(0, jnp.float32),
+        false_negatives=jnp.array(0, jnp.float32),
+        beta=1.0,
+    )
+
+  @classmethod
+  def from_model_output(
+      cls,
+      predictions: jax.Array,
+      labels: jax.Array,
+      beta: float = 1.0,
+      threshold: float = 0.5,
+  ) -> 'FBetaScore':
+    """Updates the metric.
+
+    Note: When only predictions and labels are given, the score calculated is
+    the F1 score if the FBetaScore beta value has not been previously modified.
+
+    Args:
+        predictions: A floating point 1D vector whose values are in the range
+          [0, 1]. The shape should be (batch_size,).
+        labels: True value. The value is expected to be 0 or 1. The shape should
+          be (batch_size,).
+        beta: beta value to use in the F-Score metric. A floating number.
+        threshold: threshold value to use in the F-Score metric. A floating
+          number.
+
+    Returns:
+        The updated FBetaScore object.
+
+    Raises:
+        ValueError: If type of `labels` is wrong or the shapes of `predictions`
+        and `labels` are incompatible. If the beta or threshold are invalid
+        values
+        an error is raised as well.
     """
-    This function is currently unused as the 'from_model_output' function can handle the whole
-    dataset without needing to split and merge them. I'm leaving this here for now incase we want to
-    repurpose this or need to change something that requires this function's use again. This function would need
-    to be reworked for it to work with the current implementation of this class.
-    """
-    # # Merge datasets together
-    # def merge(self, other: 'FBetaScore') -> 'FBetaScore':
-    #
-    #     # Check if the incoming beta is the same value as the current beta
-    #     if other.beta == self.beta:
-    #         return type(self)(
-    #             true_positives = self.true_positives + other.true_positives,
-    #             false_positives = self.false_positives + other.false_positives,
-    #             false_negatives = self.false_negatives + other.false_negatives,
-    #             beta=self.beta,
-    #         )
-    #     else:
-    #         raise ValueError('The "Beta" values between the two are not equal.')
-
-    # Compute the F-Beta score metric
-    def compute(self) -> jax.Array:
-
-        # Epsilon fuz factor required to match with the keras version
-        epsilon = 1e-7
-
-        # Manually calculate precision and recall
-        """
-        This is done in this manner since the metrax variants of precision
-        and recall output only single numbers. To match the keras value
-        we need an array of numbers to work with.
-        """
-        precision = jnp.divide(
-            self.true_positives,
-            self.true_positives + self.false_positives + epsilon,
-        )
-        recall = jnp.divide(
-            self.true_positives,
-            self.true_positives + self.false_negatives + epsilon,
-        )
-
-        # Compute the numerator and denominator of the F-Score formula
-        b2 = self.beta ** 2
-        numerator = (1 + b2) * (precision * recall)
-        denominator = (b2 * precision) + recall
-
-        return base.divide_no_nan(
-            numerator, denominator + epsilon,
-        )
+
+    # Ensure that beta is a floating number and a valid number
+    if not isinstance(beta, float):
+      raise ValueError('The "Beta" value must be a floating number.')
+    if beta <= 0.0:
+      raise ValueError('The "Beta" value must be larger than 0.0.')
+
+    # Ensure that threshold is a floating number and a valid number
+    if not isinstance(threshold, float):
+      raise ValueError('The "Threshold" value must be a floating number.')
+    if threshold < 0.0 or threshold > 1.0:
+      raise ValueError('The "Threshold" value must be between 0 and 1.')
+
+    # Modify predictions with the given threshold value
+    predictions = jnp.where(predictions >= threshold, 1, 0)
+
+    # Assign the true_positive, false_positive, and false_negative their values
+    true_positives = jnp.sum(predictions * labels, axis=0)
+    false_positives = jnp.sum(predictions * (1 - labels), axis=0)
+    false_negatives = jnp.sum((1 - predictions) * labels, axis=0)
+
+    return cls(
+        true_positives=true_positives,
+        false_positives=false_positives,
+        false_negatives=false_negatives,
+        beta=beta,
+    )
+
+  # Compute the F-Beta score metric
+  def compute(self) -> jax.Array:
+
+    # Compute the numerator and denominator of the F-Score formula
+    precision = base.divide_no_nan(
+        self.true_positives,
+        self.true_positives + self.false_positives,
+    )
+    recall = base.divide_no_nan(
+        self.true_positives,
+        self.true_positives + self.false_negatives,
+    )
+
+    b2 = self.beta**2
+    numerator = (1 + b2) * (precision * recall)
+    denominator = (b2 * precision) + recall
+
+    return base.divide_no_nan(numerator, denominator)

src/metrax/classification_metrics_test.py

@@ -15,9 +15,10 @@
 """Tests for metrax classification metrics."""
 
 import os
-os.environ['KERAS_BACKEND'] = 'jax'
 
-from absl.testing import absltest
+os.environ['KERAS_BACKEND'] = 'jax'  # pylint: disable=g-import-not-at-top
+
+from absl.testing import absltest  # pylint: disable=g-import-not-at-top
 from absl.testing import parameterized
 import jax.numpy as jnp
 import keras
@@ -47,6 +48,7 @@
     (BATCHES, 1),
 ).astype(np.float32)
 
+
 class ClassificationMetricsTest(parameterized.TestCase):
 
   def test_precision_empty(self):
@@ -273,17 +275,53 @@ def test_aucroc(self, inputs, dtype):
         atol=1e-2 if y_true.dtype in (jnp.float16, jnp.bfloat16) else 1e-7,
     )
 
-# Testing function for F-Beta classification
+  # Testing function for F-Beta classification
   # name, output true, output prediction, threshold, beta
   @parameterized.named_parameters(
       ('basic_f16_beta_1.0', OUTPUT_LABELS, OUTPUT_PREDS_F16, 0.5, 1.0),
       ('basic_f32_beta_1.0', OUTPUT_LABELS, OUTPUT_PREDS_F32, 0.5, 1.0),
-      ('low_threshold_f32_beta_1.0', OUTPUT_LABELS, OUTPUT_PREDS_F32, 0.1, 1.0),
-      ('high_threshold_bf16_beta_1.0', OUTPUT_LABELS, OUTPUT_PREDS_BF16, 0.7, 1.0),
-      ('batch_size_one_beta_1.0', OUTPUT_LABELS_BS1, OUTPUT_PREDS_BS1, 0.5, 1.0),
-      ('high_threshold_f16_beta_2.0', OUTPUT_LABELS, OUTPUT_PREDS_F16, 0.7, 2.0),
-      ('high_threshold_f32_beta_2.0', OUTPUT_LABELS, OUTPUT_PREDS_F32, 0.7, 2.0),
-      ('low_threshold_bf16_beta_2.0', OUTPUT_LABELS, OUTPUT_PREDS_BF16, 0.1, 2.0),
+      (
+          'low_threshold_f32_beta_1.0',
+          OUTPUT_LABELS,
+          OUTPUT_PREDS_F32,
+          0.1,
+          1.0,
+      ),
+      (
+          'high_threshold_bf16_beta_1.0',
+          OUTPUT_LABELS,
+          OUTPUT_PREDS_BF16,
+          0.7,
+          1.0,
+      ),
+      (
+          'batch_size_one_beta_1.0',
+          OUTPUT_LABELS_BS1,
+          OUTPUT_PREDS_BS1,
+          0.5,
+          1.0,
+      ),
+      (
+          'high_threshold_f16_beta_2.0',
+          OUTPUT_LABELS,
+          OUTPUT_PREDS_F16,
+          0.7,
+          2.0,
+      ),
+      (
+          'high_threshold_f32_beta_2.0',
+          OUTPUT_LABELS,
+          OUTPUT_PREDS_F32,
+          0.7,
+          2.0,
+      ),
+      (
+          'low_threshold_bf16_beta_2.0',
+          OUTPUT_LABELS,
+          OUTPUT_PREDS_BF16,
+          0.1,
+          2.0,
+      ),
       ('low_threshold_f16_beta_3.0', OUTPUT_LABELS, OUTPUT_PREDS_F16, 0.1, 3.0),
       ('basic_bf16_beta_3.0', OUTPUT_LABELS, OUTPUT_PREDS_BF16, 0.5, 3.0),
   )
@@ -307,6 +345,26 @@ def test_fbetascore(self, y_true, y_pred, threshold, beta):
         atol=atol,
     )
 
+  def test_fbeta_default_init(self):
+    """Test usage of FBetaScore with default beta (should be 1.0)."""
+    y_true = jnp.array([1, 0, 1, 0], dtype=jnp.float32)
+    y_pred = jnp.array([1, 0, 0, 1], dtype=jnp.float32)
+
+    # Init without beta argument
+    metric = metrax.FBetaScore.from_model_output(
+        predictions=y_pred,
+        labels=y_true,
+    )
+
+    # Should default to beta=1.0 (F1 Score)
+    self.assertEqual(metric.beta, 1.0)
+
+    # Calculate expected F1
+    # TP=1 (idx 0), FP=1 (idx 3), FN=1 (idx 2)
+    # Precision = 1/2, Recall = 1/2
+    # F1 = 2 * (0.5 * 0.5) / (0.5 + 0.5) = 0.5
+    self.assertAlmostEqual(metric.compute(), 0.5)
+
 
 if __name__ == '__main__':
   absltest.main()