tensorflow_probability/python/glm/fisher_scoring.py
def prepare_args(model_matrix,
                 response,
                 model_coefficients,
                 predicted_linear_response,
                 offset,
                 name=None):
  """Helper to `fit` which sanitizes input args.

  Args:
    model_matrix: (Batch of) `float`-like, matrix-shaped `Tensor` where each row
      represents a sample's features.
    response: (Batch of) vector-shaped `Tensor` where each element represents a
      sample's observed response (to the corresponding row of features). Must
      have same `dtype` as `model_matrix`.
    model_coefficients: Optional (batch of) vector-shaped `Tensor` representing
      the model coefficients, one for each column in `model_matrix`. Must have
      same `dtype` as `model_matrix`.
      Default value: `tf.zeros(tf.shape(model_matrix)[-1], model_matrix.dtype)`.
    predicted_linear_response: Optional `Tensor` with `shape`, `dtype` matching
      `response`; represents `offset` shifted initial linear predictions based
      on current `model_coefficients`.
      Default value: `offset` if `model_coefficients is None`, and
      `tf.linalg.matvec(model_matrix, model_coefficients_start) + offset`
      otherwise.
    offset: Optional `Tensor` with `shape`, `dtype` matching `response`;
      represents constant shift applied to `predicted_linear_response`.
      Default value: `None` (i.e., `tf.zeros_like(response)`).
    name: Python `str` used as name prefix to ops created by this function.
      Default value: `"prepare_args"`.

  Returns:
    model_matrix: A `Tensor` with `shape`, `dtype` and values of the
      `model_matrix` argument.
    response: A `Tensor` with `shape`, `dtype` and values of the
      `response` argument.
    model_coefficients_start: A `Tensor` with `shape`, `dtype` and
      values of the `model_coefficients_start` argument if specified.
      A (batch of) vector-shaped `Tensors` with `dtype` matching `model_matrix`
      containing the default starting point otherwise.
    predicted_linear_response:  A `Tensor` with `shape`, `dtype` and
      values of the `predicted_linear_response` argument if specified.
      A `Tensor` with `shape`, `dtype` matching `response` containing the
      default value otherwise.
    offset: A `Tensor` with `shape`, `dtype` and values of the `offset` argument
      if specified or `None` otherwise.
  """
  graph_deps = [model_matrix, response, model_coefficients,
                predicted_linear_response, offset]
  with tf.compat.v1.name_scope(name, 'prepare_args', graph_deps):
    dtype = dtype_util.common_dtype(graph_deps, np.float32)

    model_matrix = tf.convert_to_tensor(
        value=model_matrix, dtype=dtype, name='model_matrix')

    if offset is not None:
      offset = tf.convert_to_tensor(value=offset, dtype=dtype, name='offset')

    response = tf.convert_to_tensor(
        value=response, dtype=dtype, name='response')

    use_default_model_coefficients = model_coefficients is None
    if use_default_model_coefficients:
      # User did not supply model coefficients; assume they're all zero.
      batch_shape = tf.shape(input=model_matrix)[:-2]
      num_columns = tf.shape(input=model_matrix)[-1]
      model_coefficients = tf.zeros(
          shape=tf.concat([batch_shape, [num_columns]], axis=0),
          dtype=dtype, name='model_coefficients')
    else:
      # User did supply model coefficients; convert to Tensor in case it's
      # numpy or literal.
      model_coefficients = tf.convert_to_tensor(
          value=model_coefficients, dtype=dtype, name='model_coefficients')

    if predicted_linear_response is None:
      if use_default_model_coefficients:
        # Since we're using zeros for model_coefficients, we know the predicted
        # linear response will also be all zeros.
        if offset is None:
          predicted_linear_response = tf.zeros_like(
              response, dtype, name='predicted_linear_response')
        else:
          predicted_linear_response = tf.broadcast_to(
              offset,
              tf.shape(input=response),
              name='predicted_linear_response')
      else:
        # We were given model_coefficients but not the predicted linear
        # response.
        predicted_linear_response = calculate_linear_predictor(
            model_matrix, model_coefficients, offset)
    else:
      predicted_linear_response = tf.convert_to_tensor(
          value=predicted_linear_response,
          dtype=dtype,
          name='predicted_linear_response')

  return [
      model_matrix,
      response,
      model_coefficients,
      predicted_linear_response,
      offset,
  ]
def prepare_args(model_matrix,
                 response,
                 model_coefficients,
                 predicted_linear_response,
                 offset,
                 name=None):
  graph_deps = [model_matrix, response, model_coefficients,
                predicted_linear_response, offset]
  with tf.compat.v1.name_scope(name, 'prepare_args', graph_deps):
    dtype = dtype_util.common_dtype(graph_deps, np.float32)

    model_matrix = tf.convert_to_tensor(
        value=model_matrix, dtype=dtype, name='model_matrix')

    if offset is not None:
      offset = tf.convert_to_tensor(value=offset, dtype=dtype, name='offset')

    response = tf.convert_to_tensor(
        value=response, dtype=dtype, name='response')

    use_default_model_coefficients = model_coefficients is None
    if use_default_model_coefficients:
      # User did not supply model coefficients; assume they're all zero.
      batch_shape = tf.shape(input=model_matrix)[:-2]
      num_columns = tf.shape(input=model_matrix)[-1]
      model_coefficients = tf.zeros(
          shape=tf.concat([batch_shape, [num_columns]], axis=0),
          dtype=dtype, name='model_coefficients')
    else:
      # User did supply model coefficients; convert to Tensor in case it's
      # numpy or literal.
      model_coefficients = tf.convert_to_tensor(
          value=model_coefficients, dtype=dtype, name='model_coefficients')

    if predicted_linear_response is None:
      if use_default_model_coefficients:
        # Since we're using zeros for model_coefficients, we know the predicted
        # linear response will also be all zeros.
        if offset is None:
          predicted_linear_response = tf.zeros_like(
              response, dtype, name='predicted_linear_response')
        else:
          predicted_linear_response = tf.broadcast_to(
              offset,
              tf.shape(input=response),
              name='predicted_linear_response')
      else:
        # We were given model_coefficients but not the predicted linear
        # response.
        predicted_linear_response = calculate_linear_predictor(
            model_matrix, model_coefficients, offset)
    else:
      predicted_linear_response = tf.convert_to_tensor(
          value=predicted_linear_response,
          dtype=dtype,
          name='predicted_linear_response')

  return [
      model_matrix,
      response,
      model_coefficients,
      predicted_linear_response,
      offset,
  ]
def prepare_args ( model_matrix , response , model_coefficients , predicted_linear_response , offset , name = None ) : graph_deps = [ model_matrix , response , model_coefficients , predicted_linear_response , offset ] with tf . compat . v1 . name_scope ( name , 'prepare_args' , graph_deps ) : dtype = dtype_util . common_dtype ( graph_deps , np . float32 ) model_matrix = tf . convert_to_tensor ( value = model_matrix , dtype = dtype , name = 'model_matrix' ) if offset is not None : offset = tf . convert_to_tensor ( value = offset , dtype = dtype , name = 'offset' ) response = tf . convert_to_tensor ( value = response , dtype = dtype , name = 'response' ) use_default_model_coefficients = model_coefficients is None if use_default_model_coefficients : # User did not supply model coefficients; assume they're all zero. batch_shape = tf . shape ( input = model_matrix ) [ : - 2 ] num_columns = tf . shape ( input = model_matrix ) [ - 1 ] model_coefficients = tf . zeros ( shape = tf . concat ( [ batch_shape , [ num_columns ] ] , axis = 0 ) , dtype = dtype , name = 'model_coefficients' ) else : # User did supply model coefficients; convert to Tensor in case it's # numpy or literal. model_coefficients = tf . convert_to_tensor ( value = model_coefficients , dtype = dtype , name = 'model_coefficients' ) if predicted_linear_response is None : if use_default_model_coefficients : # Since we're using zeros for model_coefficients, we know the predicted # linear response will also be all zeros. if offset is None : predicted_linear_response = tf . zeros_like ( response , dtype , name = 'predicted_linear_response' ) else : predicted_linear_response = tf . broadcast_to ( offset , tf . shape ( input = response ) , name = 'predicted_linear_response' ) else : # We were given model_coefficients but not the predicted linear # response. predicted_linear_response = calculate_linear_predictor ( model_matrix , model_coefficients , offset ) else : predicted_linear_response = tf . convert_to_tensor ( value = predicted_linear_response , dtype = dtype , name = 'predicted_linear_response' ) return [ model_matrix , response , model_coefficients , predicted_linear_response , offset , ]
Helper to `fit` which sanitizes input args.

  Args:
    model_matrix: (Batch of) `float`-like, matrix-shaped `Tensor` where each row
      represents a sample's features.
    response: (Batch of) vector-shaped `Tensor` where each element represents a
      sample's observed response (to the corresponding row of features). Must
      have same `dtype` as `model_matrix`.
    model_coefficients: Optional (batch of) vector-shaped `Tensor` representing
      the model coefficients, one for each column in `model_matrix`. Must have
      same `dtype` as `model_matrix`.
      Default value: `tf.zeros(tf.shape(model_matrix)[-1], model_matrix.dtype)`.
    predicted_linear_response: Optional `Tensor` with `shape`, `dtype` matching
      `response`; represents `offset` shifted initial linear predictions based
      on current `model_coefficients`.
      Default value: `offset` if `model_coefficients is None`, and
      `tf.linalg.matvec(model_matrix, model_coefficients_start) + offset`
      otherwise.
    offset: Optional `Tensor` with `shape`, `dtype` matching `response`;
      represents constant shift applied to `predicted_linear_response`.
      Default value: `None` (i.e., `tf.zeros_like(response)`).
    name: Python `str` used as name prefix to ops created by this function.
      Default value: `"prepare_args"`.

  Returns:
    model_matrix: A `Tensor` with `shape`, `dtype` and values of the
      `model_matrix` argument.
    response: A `Tensor` with `shape`, `dtype` and values of the
      `response` argument.
    model_coefficients_start: A `Tensor` with `shape`, `dtype` and
      values of the `model_coefficients_start` argument if specified.
      A (batch of) vector-shaped `Tensors` with `dtype` matching `model_matrix`
      containing the default starting point otherwise.
    predicted_linear_response:  A `Tensor` with `shape`, `dtype` and
      values of the `predicted_linear_response` argument if specified.
      A `Tensor` with `shape`, `dtype` matching `response` containing the
      default value otherwise.
    offset: A `Tensor` with `shape`, `dtype` and values of the `offset` argument
      if specified or `None` otherwise.
Helper to fit which sanitizes input args .