(precision, recall, beta=1, epsilon=1e-10)
return (1 + beta**2) * (precision * recall) / (
      (beta**2 * precision) + recall + epsilon)
Computes F_beta scores for the given precision/recall values.

  The F_beta scores for all precision/recall pairs will be computed and
  returned.

  For precision p and recall r, the F_beta score is defined as:
  F_beta = (1 + beta^2) * (p * r) / ((beta^2 * p) + r)

  Args:
    precision: 1D NumPy array of precision values in [0, 1].
    recall: 1D NumPy array of precision values in [0, 1].
    beta: Beta parameter. Must be positive. The default value is 1.
    epsilon: Small constant to avoid numerical instability caused by division
             by 0 when precision and recall are close to zero.

  Returns:
    NumPy array of same shape as precision and recall with the F_beta scores for
    each pair of precision/recall.

  Raises:
    ValueError: If any value in precision or recall is outside of [0, 1].
    ValueError: If beta is not positive.
Computes F_beta scores for the given precision/recall values.
Computes F_beta scores for the given precision / recall values .
def _prd_to_f_beta(precision, recall, beta=1, epsilon=1e-10):
  """Computes F_beta scores for the given precision/recall values.

  The F_beta scores for all precision/recall pairs will be computed and
  returned.

  For precision p and recall r, the F_beta score is defined as:
  F_beta = (1 + beta^2) * (p * r) / ((beta^2 * p) + r)

  Args:
    precision: 1D NumPy array of precision values in [0, 1].
    recall: 1D NumPy array of precision values in [0, 1].
    beta: Beta parameter. Must be positive. The default value is 1.
    epsilon: Small constant to avoid numerical instability caused by division
             by 0 when precision and recall are close to zero.

  Returns:
    NumPy array of same shape as precision and recall with the F_beta scores for
    each pair of precision/recall.

  Raises:
    ValueError: If any value in precision or recall is outside of [0, 1].
    ValueError: If beta is not positive.
  """

  if not ((precision >= 0).all() and (precision <= 1).all()):
    raise ValueError('All values in precision must be in [0, 1].')
  if not ((recall >= 0).all() and (recall <= 1).all()):
    raise ValueError('All values in recall must be in [0, 1].')
  if beta <= 0:
    raise ValueError('Given parameter beta %s must be positive.' % str(beta))

  return (1 + beta**2) * (precision * recall) / (
      (beta**2 * precision) + recall + epsilon)
def _prd_to_f_beta ( precision , recall , beta = 1 , epsilon = 1e-10 ) : if not ( ( precision >= 0 ) . all ( ) and ( precision <= 1 ) . all ( ) ) : raise ValueError ( 'All values in precision must be in [0, 1].' ) if not ( ( recall >= 0 ) . all ( ) and ( recall <= 1 ) . all ( ) ) : raise ValueError ( 'All values in recall must be in [0, 1].' ) if beta <= 0 : raise ValueError ( 'Given parameter beta %s must be positive.' % str ( beta ) ) return ( 1 + beta ** 2 ) * ( precision * recall ) / ( ( beta ** 2 * precision ) + recall + epsilon )