.. _op_ai_onnx_ml_LabelEncoder:

LabelEncoder
============

- **Domain**: ``ai.onnx.ml``
- **Since version**: 4

Maps each element in the input tensor to another value. 
The mapping is determined by the two parallel attributes, 'keys\*' and
'values\*' attribute. The i-th value in the specified 'keys\*' attribute
would be mapped to the i-th value in the specified 'values\*' attribute. It
implies that input's element type and the element type of the specified
'keys\*' should be identical while the output type is identical to the
specified 'values\*' attribute. Note that the 'keys\*' and 'values\*' attributes
must have the same length. If an input element can not be found in the
specified 'keys\*' attribute, the 'default\*' that matches the specified
'values\*' attribute may be used as its output value. The type of the 'default\*'
attribute must match the 'values\*' attribute chosen.  
Let's consider an example which maps a string tensor to an integer tensor.
Assume and 'keys_strings' is ["Amy", "Sally"], 'values_int64s' is [5, 6],
and 'default_int64' is '-1'.  The input ["Dori", "Amy", "Amy", "Sally",
"Sally"] would be mapped to [-1, 5, 5, 6, 6]. 
Since this operator is an one-to-one mapping, its input and output shapes
are the same. Notice that only one of 'keys\*'/'values\*' can be set. 
Float keys with value 'NaN' match any input 'NaN' value regardless of bit
value. If a key is repeated, the last key takes precedence.

**Inputs**

- **X** (*T1*): Input data. It must have the same element type as the keys\* attribute set.

**Outputs**

- **Y** (*T2*): Output data. This tensor's element type is based on the values\* attribute set.

**Type Constraints**

- **T1**: The input type is a tensor of any shape.
  Allowed types: tensor(double), tensor(float), tensor(int16), tensor(int32), tensor(int64), tensor(string).
- **T2**: Output type is determined by the specified 'values\*' attribute.
  Allowed types: tensor(double), tensor(float), tensor(int16), tensor(int32), tensor(int64), tensor(string).

Examples
--------

**test_cc_label_encoder_float_to_int64**

.. code-block:: text

    Attributes:
      keys_floats = [1.0, 2.0, 3.0]
      values_int64s = [10, 20, 30]
      default_int64 = -1

.. code-block:: text

    Inputs:
      x: shape=(2, 2), dtype=float32
        [[1., 2.],
         [3., 9.]]

    Outputs:
      y: shape=(2, 2), dtype=int64
        [[10, 20],
         [30, -1]]

**test_cc_label_encoder_int64_to_float**

.. code-block:: text

    Attributes:
      keys_int64s = [0, 1, 2]
      values_floats = [0.5, 1.5, 2.5]
      default_float = -1.0

.. code-block:: text

    Inputs:
      x: shape=(4,), dtype=int64
        [0, 1, 2, 7]

    Outputs:
      y: shape=(4,), dtype=float32
        [ 0.5,  1.5,  2.5, -1. ]