tensorflow_probability/python/glm/proximal_hessian.py
def _grad_neg_log_likelihood_and_fim(model_matrix, linear_response, response,
                                     model):
  """Computes the neg-log-likelihood gradient and Fisher information for a GLM.

  Note that Fisher information is related to the Hessian of the log-likelihood
  by the equation

  ```none
  FisherInfo = E[Hessian with respect to model_coefficients of -LogLikelihood(
      Y | model_matrix, model_coefficients)]
  ```

  where `LogLikelihood` is the log-likelihood of a generalized linear model
  parameterized by `model_matrix` and `model_coefficients`, and the expectation
  is taken over Y, distributed according to the same GLM with the same parameter
  values.

  Args:
    model_matrix: (Batch of) matrix-shaped, `float` `Tensor` or `SparseTensor`
      where each row represents a sample's features.  Has shape `[N, n]` where
      `N` is the number of data samples and `n` is the number of features per
      sample.
    linear_response: (Batch of) vector-shaped `Tensor` with the same dtype as
      `model_matrix`, equal to `model_matix @ model_coefficients` where
      `model_coefficients` are the coefficients of the linear component of the
      GLM.
    response: (Batch of) vector-shaped `Tensor` with the same dtype as
      `model_matrix` where each element represents a sample's observed response
      (to the corresponding row of features).
    model: `tfp.glm.ExponentialFamily`-like instance, which specifies the link
      function and distribution of the GLM, and thus characterizes the negative
      log-likelihood. Must have sufficient statistic equal to the response, that
      is, `T(y) = y`.

  Returns:
    grad_neg_log_likelihood: (Batch of) vector-shaped `Tensor` with the same
      shape and dtype as a single row of `model_matrix`, representing the
      gradient of the negative log likelihood of `response` given linear
      response `linear_response`.
    fim_middle: (Batch of) vector-shaped `Tensor` with the same shape and dtype
      as a single column of `model_matrix`, satisfying the equation
      `Fisher information =
      Transpose(model_matrix)
      @ diag(fim_middle)
      @ model_matrix`.
  """
  # TODO(b/111926503): Determine whether there are some practical cases where it
  # is computationally favorable to compute the full FIM.
  mean, variance, grad_mean = model(linear_response)

  is_valid = (
      tf.math.is_finite(grad_mean) & tf.not_equal(grad_mean, 0.)
      & tf.math.is_finite(variance) & (variance > 0.))

  def _mask_if_invalid(x, mask):
    mask = tf.fill(
        tf.shape(input=x), value=np.array(mask, x.dtype.as_numpy_dtype))
    return tf.where(is_valid, x, mask)

  # TODO(b/111923449): Link to derivation once it's available.
  v = (response - mean) * _mask_if_invalid(grad_mean, 1) / _mask_if_invalid(
      variance, np.inf)
  grad_log_likelihood = sparse_or_dense_matvecmul(
      model_matrix, v, adjoint_a=True)
  fim_middle = _mask_if_invalid(grad_mean, 0.)**2 / _mask_if_invalid(
      variance, np.inf)
  return -grad_log_likelihood, fim_middle
def _grad_neg_log_likelihood_and_fim(model_matrix, linear_response, response,
                                     model):
  # TODO(b/111926503): Determine whether there are some practical cases where it
  # is computationally favorable to compute the full FIM.
  mean, variance, grad_mean = model(linear_response)

  is_valid = (
      tf.math.is_finite(grad_mean) & tf.not_equal(grad_mean, 0.)
      & tf.math.is_finite(variance) & (variance > 0.))

  def _mask_if_invalid(x, mask):
    mask = tf.fill(
        tf.shape(input=x), value=np.array(mask, x.dtype.as_numpy_dtype))
    return tf.where(is_valid, x, mask)

  # TODO(b/111923449): Link to derivation once it's available.
  v = (response - mean) * _mask_if_invalid(grad_mean, 1) / _mask_if_invalid(
      variance, np.inf)
  grad_log_likelihood = sparse_or_dense_matvecmul(
      model_matrix, v, adjoint_a=True)
  fim_middle = _mask_if_invalid(grad_mean, 0.)**2 / _mask_if_invalid(
      variance, np.inf)
  return -grad_log_likelihood, fim_middle
def _grad_neg_log_likelihood_and_fim ( model_matrix , linear_response , response , model ) : # TODO(b/111926503): Determine whether there are some practical cases where it # is computationally favorable to compute the full FIM. mean , variance , grad_mean = model ( linear_response ) is_valid = ( tf . math . is_finite ( grad_mean ) & tf . not_equal ( grad_mean , 0. ) & tf . math . is_finite ( variance ) & ( variance > 0. ) ) def _mask_if_invalid ( x , mask ) : mask = tf . fill ( tf . shape ( input = x ) , value = np . array ( mask , x . dtype . as_numpy_dtype ) ) return tf . where ( is_valid , x , mask ) # TODO(b/111923449): Link to derivation once it's available. v = ( response - mean ) * _mask_if_invalid ( grad_mean , 1 ) / _mask_if_invalid ( variance , np . inf ) grad_log_likelihood = sparse_or_dense_matvecmul ( model_matrix , v , adjoint_a = True ) fim_middle = _mask_if_invalid ( grad_mean , 0. ) ** 2 / _mask_if_invalid ( variance , np . inf ) return - grad_log_likelihood , fim_middle
Computes the neg-log-likelihood gradient and Fisher information for a GLM.

  Note that Fisher information is related to the Hessian of the log-likelihood
  by the equation

  ```none
  FisherInfo = E[Hessian with respect to model_coefficients of -LogLikelihood(
      Y | model_matrix, model_coefficients)]
  ```

  where `LogLikelihood` is the log-likelihood of a generalized linear model
  parameterized by `model_matrix` and `model_coefficients`, and the expectation
  is taken over Y, distributed according to the same GLM with the same parameter
  values.

  Args:
    model_matrix: (Batch of) matrix-shaped, `float` `Tensor` or `SparseTensor`
      where each row represents a sample's features.  Has shape `[N, n]` where
      `N` is the number of data samples and `n` is the number of features per
      sample.
    linear_response: (Batch of) vector-shaped `Tensor` with the same dtype as
      `model_matrix`, equal to `model_matix @ model_coefficients` where
      `model_coefficients` are the coefficients of the linear component of the
      GLM.
    response: (Batch of) vector-shaped `Tensor` with the same dtype as
      `model_matrix` where each element represents a sample's observed response
      (to the corresponding row of features).
    model: `tfp.glm.ExponentialFamily`-like instance, which specifies the link
      function and distribution of the GLM, and thus characterizes the negative
      log-likelihood. Must have sufficient statistic equal to the response, that
      is, `T(y) = y`.

  Returns:
    grad_neg_log_likelihood: (Batch of) vector-shaped `Tensor` with the same
      shape and dtype as a single row of `model_matrix`, representing the
      gradient of the negative log likelihood of `response` given linear
      response `linear_response`.
    fim_middle: (Batch of) vector-shaped `Tensor` with the same shape and dtype
      as a single column of `model_matrix`, satisfying the equation
      `Fisher information =
      Transpose(model_matrix)
      @ diag(fim_middle)
      @ model_matrix`.
Computes the neg - log - likelihood gradient and Fisher information for a GLM .