code: # -------------------------------------------------------------------------
  # Copyright (c) Microsoft Corporation. All rights reserved.
  # Licensed under the MIT License. See License.txt in the project root for
  # license information.
  # --------------------------------------------------------------------------
  """
  Common functions to convert any learner based on trees.
  """
  import numpy as np
  import numbers
  import six
  from ._registration import register_shape_calculator
  from .data_types import Int64TensorType, FloatTensorType, StringTensorType, DictionaryType, SequenceType
  from .utils import check_input_and_output_numbers, check_input_and_output_types, compare_strict_version
  
  
  def calculate_linear_classifier_output_shapes(operator):
      '''
      This operator maps an input feature vector into a scalar label if the number of outputs is one. If two outputs
      appear in this operator's output list, we should further generate a map storing all classes' probabilities.
  
      Allowed input/output patterns are
          1. [N, C] ---> [N, 1], A sequence of map
  
      Note that the second case is not allowed as long as ZipMap only produces dictionary.
      '''
      check_input_and_output_numbers(operator, input_count_range=1, output_count_range=[1, 2])
      check_input_and_output_types(operator, good_input_types=[FloatTensorType, Int64TensorType])
  
      if len(operator.inputs[0].type.shape) != 2:
          raise RuntimeError('Input must be a [N, C]-tensor')
  
      N = operator.inputs[0].type.shape[0]
  
      class_labels = operator.raw_operator.classes_
      if all(isinstance(i, np.ndarray) for i in class_labels):
          class_labels = np.concatenate(class_labels)
      if all(isinstance(i, (six.string_types, six.text_type)) for i in class_labels):
          operator.outputs[0].type = StringTensorType(shape=[N])
          if len(class_labels) > 2 or operator.type != 'SklearnLinearSVC':
              # For multi-class classifier, we produce a map for encoding the probabilities of all classes
              if compare_strict_version(operator.targeted_onnx_version, '1.2') < 0:
                  operator.outputs[1].type = DictionaryType(StringTensorType([1]), FloatTensorType([1]))
              else:
                  operator.outputs[1].type = SequenceType(DictionaryType(StringTensorType([]), FloatTensorType([])), N)
          else:
              # For binary classifier, we produce the probability of the positive class
              operator.outputs[1].type = FloatTensorType(shape=[N, 1])
      elif all(isinstance(i, (numbers.Real, bool, np.bool_)) for i in class_labels):
          operator.outputs[0].type = Int64TensorType(shape=[N])
          if len(class_labels) > 2 or operator.type != 'SklearnLinearSVC':
              # For multi-class classifier, we produce a map for encoding the probabilities of all classes
              if compare_strict_version(operator.targeted_onnx_version, '1.2') < 0:
                  operator.outputs[1].type = DictionaryType(Int64TensorType([1]), FloatTensorType([1]))
              else:
                  operator.outputs[1].type = SequenceType(DictionaryType(Int64TensorType([]), FloatTensorType([])), N)
          else:
              # For binary classifier, we produce the probability of the positive class
              operator.outputs[1].type = FloatTensorType(shape=[N, 1])
      else:
          raise ValueError('Unsupported or mixed label types')
  
  
  def calculate_linear_regressor_output_shapes(operator):
      '''
      Allowed input/output patterns are
          1. [N, C] ---> [N, 1]
  
      This operator produces a scalar prediction for every example in a batch. If the input batch size is N, the output
      shape may be [N, 1].
      '''
      check_input_and_output_numbers(operator, input_count_range=1, output_count_range=1)
  
      N = operator.inputs[0].type.shape[0]
      operator.outputs[0].type = FloatTensorType([N, 1])
  
  
apis: numpy.concatenate