module `onnxrt.ops_cpu.op_dict_vectorizer`#

Short summary#

module mlprodict.onnxrt.ops_cpu.op_dict_vectorizer

Runtime operator.

Classes#

class	truncated documentation
`DictVectorizer`	DictVectorizer (ai.onnx.ml) =========================== Uses an index mapping to convert a dictionary to an array. Given …

Properties#

property	truncated documentation
`args_default`	Returns the list of arguments as well as the list of parameters with the default values (close to the signature). …
`args_default_modified`	Returns the list of modified parameters.
`args_mandatory`	Returns the list of optional arguments.
`args_optional`	Returns the list of optional arguments.
`atts_value`	Returns all parameters in a dictionary.

Methods#

method	truncated documentation
`__init__`
`_run`

Documentation#

Runtime operator.

source on GitHub

class mlprodict.onnxrt.ops_cpu.op_dict_vectorizer.DictVectorizer(ai.onnx.ml)#

Bases: OpRun

Uses an index mapping to convert a dictionary to an array.

Given a dictionary, each key is looked up in the vocabulary attribute corresponding to the key type. The index into the vocabulary array at which the key is found is then used to index the output 1-D tensor ‘Y’ and insert into it the value found in the dictionary ‘X’.

The key type of the input map must correspond to the element type of the defined vocabulary attribute. Therefore, the output array will be equal in length to the index mapping vector parameter. All keys in the input dictionary must be present in the index mapping vector. For each item in the input dictionary, insert its value in the output array. Any keys not present in the input dictionary, will be zero in the output array.

For example: if the string_vocabulary parameter is set to ["a", "c", "b", "z"], then an input of {"a": 4, "c": 8} will produce an output of [4, 8, 0, 0].

Attributes

int64_vocabulary: An integer vocabulary array. One and only one of the vocabularies must be defined. default value cannot be automatically retrieved (INTS)
string_vocabulary: A string vocabulary array. One and only one of the vocabularies must be defined. default value cannot be automatically retrieved (STRINGS)

Inputs

X (heterogeneous)T1: A dictionary.

Outputs

Y (heterogeneous)T2: A 1-D tensor holding values from the input dictionary.

Type Constraints

T1 map(string, int64), map(int64, string), map(int64, float), map(int64, double), map(string, float), map(string, double): The input must be a map from strings or integers to either strings or a numeric type. The key and value types cannot be the same.
T2 tensor(int64), tensor(float), tensor(double), tensor(string): The output will be a tensor of the value type of the input map. It’s shape will be [1,C], where C is the length of the input dictionary.

Version

Onnx name: DictVectorizer

This version of the operator has been available since version 1 of domain ai.onnx.ml.

Runtime implementation: DictVectorizer

__init__(onnx_node, desc=None, **options)#

_run(x, attributes=None, verbose=0, fLOG=None)#

Should be overwritten.