pdstools.infinity.resources.prediction_studio.local_model_utils

logger¶

PEGA_METADATA = 'pegaMetadata'¶

class OutcomeType(*args, **kwds)¶

Bases: enum.Enum

Create a collection of name/value pairs.

Example enumeration:

>>> class Color(Enum):
...     RED = 1
...     BLUE = 2
...     GREEN = 3

Access them by:

attribute access:

>>> Color.RED
<Color.RED: 1>

value lookup:

>>> Color(1)
<Color.RED: 1>

name lookup:

>>> Color['RED']
<Color.RED: 1>

Enumerations can be iterated over, and know how many members they have:

>>> len(Color)
3

>>> list(Color)
[<Color.RED: 1>, <Color.BLUE: 2>, <Color.GREEN: 3>]

Methods can be added to enumerations, and members can have their own attributes – see the documentation for details.

BINARY = 'binary'¶

CATEGORICAL = 'categorical'¶

CONTINUOUS = 'continuous'¶

class Predictor(/, **data: Any)¶

Bases: pydantic.BaseModel

Usage docs: https://docs.pydantic.dev/2.10/concepts/models/

A base class for creating Pydantic models.

Attributes:

__class_vars__: The names of the class variables defined on the model. __private_attributes__: Metadata about the private attributes of the model. __signature__: The synthesized __init__ [Signature][inspect.Signature] of the model.

__pydantic_complete__: Whether model building is completed, or if there are still undefined fields. __pydantic_core_schema__: The core schema of the model. __pydantic_custom_init__: Whether the model has a custom __init__ function. __pydantic_decorators__: Metadata containing the decorators defined on the model.

This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1.

__pydantic_generic_metadata__: Metadata for generic models; contains data used for a similar purpose to: __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.

__pydantic_parent_namespace__: Parent namespace of the model, used for automatic rebuilding of models. __pydantic_post_init__: The name of the post-init method for the model, if defined. __pydantic_root_model__: Whether the model is a [RootModel][pydantic.root_model.RootModel]. __pydantic_serializer__: The pydantic-core SchemaSerializer used to dump instances of the model. __pydantic_validator__: The pydantic-core SchemaValidator used to validate instances of the model.

__pydantic_fields__: A dictionary of field names and their corresponding [FieldInfo][pydantic.fields.FieldInfo] objects. __pydantic_computed_fields__: A dictionary of computed field names and their corresponding [ComputedFieldInfo][pydantic.fields.ComputedFieldInfo] objects.

__pydantic_extra__: A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra]: is set to ‘allow’.

__pydantic_fields_set__: The names of fields explicitly set during instantiation. __pydantic_private__: Values of private attributes set on the model instance.

Parameters:: data (Any)

name: str | None = None¶

index: int | None = None¶

input_name: str | None = None¶

validate_input_name(v)¶

validate_name(v)¶

validate_index(v)¶

class Output(/, **data: Any)¶

Bases: pydantic.BaseModel

Usage docs: https://docs.pydantic.dev/2.10/concepts/models/

A base class for creating Pydantic models.

Attributes:

__class_vars__: The names of the class variables defined on the model. __private_attributes__: Metadata about the private attributes of the model. __signature__: The synthesized __init__ [Signature][inspect.Signature] of the model.

__pydantic_complete__: Whether model building is completed, or if there are still undefined fields. __pydantic_core_schema__: The core schema of the model. __pydantic_custom_init__: Whether the model has a custom __init__ function. __pydantic_decorators__: Metadata containing the decorators defined on the model.

This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1.

__pydantic_generic_metadata__: Metadata for generic models; contains data used for a similar purpose to: __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.

__pydantic_parent_namespace__: Parent namespace of the model, used for automatic rebuilding of models. __pydantic_post_init__: The name of the post-init method for the model, if defined. __pydantic_root_model__: Whether the model is a [RootModel][pydantic.root_model.RootModel]. __pydantic_serializer__: The pydantic-core SchemaSerializer used to dump instances of the model. __pydantic_validator__: The pydantic-core SchemaValidator used to validate instances of the model.

__pydantic_fields__: A dictionary of field names and their corresponding [FieldInfo][pydantic.fields.FieldInfo] objects. __pydantic_computed_fields__: A dictionary of computed field names and their corresponding [ComputedFieldInfo][pydantic.fields.ComputedFieldInfo] objects.

__pydantic_extra__: A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra]: is set to ‘allow’.

__pydantic_fields_set__: The names of fields explicitly set during instantiation. __pydantic_private__: Values of private attributes set on the model instance.

Parameters:: data (Any)

possible_values: List[str | int | float] = None¶

label_name: str | None = None¶

score_name: str | None = None¶

min_value: float | None = None¶

max_value: float | None = None¶

validate_label_name(v)¶

class Metadata(/, **data: Any)¶

Bases: pydantic.BaseModel

Usage docs: https://docs.pydantic.dev/2.10/concepts/models/

A base class for creating Pydantic models.

Attributes:

__class_vars__: The names of the class variables defined on the model. __private_attributes__: Metadata about the private attributes of the model. __signature__: The synthesized __init__ [Signature][inspect.Signature] of the model.

__pydantic_complete__: Whether model building is completed, or if there are still undefined fields. __pydantic_core_schema__: The core schema of the model. __pydantic_custom_init__: Whether the model has a custom __init__ function. __pydantic_decorators__: Metadata containing the decorators defined on the model.

This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1.

__pydantic_generic_metadata__: Metadata for generic models; contains data used for a similar purpose to: __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.

__pydantic_parent_namespace__: Parent namespace of the model, used for automatic rebuilding of models. __pydantic_post_init__: The name of the post-init method for the model, if defined. __pydantic_root_model__: Whether the model is a [RootModel][pydantic.root_model.RootModel]. __pydantic_serializer__: The pydantic-core SchemaSerializer used to dump instances of the model. __pydantic_validator__: The pydantic-core SchemaValidator used to validate instances of the model.

__pydantic_fields__: A dictionary of field names and their corresponding [FieldInfo][pydantic.fields.FieldInfo] objects. __pydantic_computed_fields__: A dictionary of computed field names and their corresponding [ComputedFieldInfo][pydantic.fields.ComputedFieldInfo] objects.

__pydantic_extra__: A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra]: is set to ‘allow’.

__pydantic_fields_set__: The names of fields explicitly set during instantiation. __pydantic_private__: Values of private attributes set on the model instance.

Parameters:: data (Any)

predictor_list: List[Predictor] = None¶

modeling_technique: str | None = None¶

internal: bool | None = None¶

type: OutcomeType | None = None¶

output: Output | None = None¶

validate_type(v, values)¶

validate_output(v, values)¶

to_json() → str¶

Return type:: str

classmethod from_json(json_str: str) → Metadata¶

Parameters:: json_str (str)
Return type:: Metadata

static _convert_keys(data: dict, conversion_func) → dict¶

Parameters:: data (dict)
Return type:: dict

static _to_snake_case(string: str) → str¶

Parameters:: string (str)
Return type:: str

static _to_camel_case(string: str) → str¶

Parameters:: string (str)
Return type:: str

class _ONNXMetadataEncoder(*, skipkeys=False, ensure_ascii=True, check_circular=True, allow_nan=True, sort_keys=False, indent=None, separators=None, default=None)¶

Bases: json.JSONEncoder

Extensible JSON <https://json.org> encoder for Python data structures.

Supports the following objects and types by default:

Python	JSON
dict	object
list, tuple	array
str	string
int, float	number
True	true
False	false
None	null

To extend this to recognize other objects, subclass and implement a .default() method with another method that returns a serializable object for o if possible, otherwise it should call the superclass implementation (to raise TypeError).

default(o)¶

Implement this method in a subclass such that it returns a serializable object for o, or calls the base implementation (to raise a TypeError).

For example, to support arbitrary iterators, you could implement default like this:

def default(self, o):
    try:
        iterable = iter(o)
    except TypeError:
        pass
    else:
        return list(iterable)
    # Let the base class default method raise the TypeError
    return super().default(o)

exception ONNXModelCreationError¶

Bases: Exception

Exception for errors during ONNX conversion and save.

exception ONNXModelValidationError¶

Bases: pdstools.infinity.resources.prediction_studio.base.ModelValidationError

Exception for errors during ONNX validation.

class PMMLModel(file_path: str)¶

Bases: pdstools.infinity.resources.prediction_studio.base.LocalModel

Usage docs: https://docs.pydantic.dev/2.10/concepts/models/

A base class for creating Pydantic models.

Attributes:

__class_vars__: The names of the class variables defined on the model. __private_attributes__: Metadata about the private attributes of the model. __signature__: The synthesized __init__ [Signature][inspect.Signature] of the model.

__pydantic_complete__: Whether model building is completed, or if there are still undefined fields. __pydantic_core_schema__: The core schema of the model. __pydantic_custom_init__: Whether the model has a custom __init__ function. __pydantic_decorators__: Metadata containing the decorators defined on the model.

This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1.

__pydantic_generic_metadata__: Metadata for generic models; contains data used for a similar purpose to: __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.

__pydantic_parent_namespace__: Parent namespace of the model, used for automatic rebuilding of models. __pydantic_post_init__: The name of the post-init method for the model, if defined. __pydantic_root_model__: Whether the model is a [RootModel][pydantic.root_model.RootModel]. __pydantic_serializer__: The pydantic-core SchemaSerializer used to dump instances of the model. __pydantic_validator__: The pydantic-core SchemaValidator used to validate instances of the model.

__pydantic_fields__: A dictionary of field names and their corresponding [FieldInfo][pydantic.fields.FieldInfo] objects. __pydantic_computed_fields__: A dictionary of computed field names and their corresponding [ComputedFieldInfo][pydantic.fields.ComputedFieldInfo] objects.

__pydantic_extra__: A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra]: is set to ‘allow’.

__pydantic_fields_set__: The names of fields explicitly set during instantiation. __pydantic_private__: Values of private attributes set on the model instance.

Parameters:: file_path (str)

file_path: str¶

get_file_path() → str¶

Returns the file path of the model.

Returns:: str
Return type:: The file path of the model.

class H2OModel(file_path: str)¶

Bases: pdstools.infinity.resources.prediction_studio.base.LocalModel

Usage docs: https://docs.pydantic.dev/2.10/concepts/models/

A base class for creating Pydantic models.

Attributes:

__class_vars__: The names of the class variables defined on the model. __private_attributes__: Metadata about the private attributes of the model. __signature__: The synthesized __init__ [Signature][inspect.Signature] of the model.

__pydantic_complete__: Whether model building is completed, or if there are still undefined fields. __pydantic_core_schema__: The core schema of the model. __pydantic_custom_init__: Whether the model has a custom __init__ function. __pydantic_decorators__: Metadata containing the decorators defined on the model.

This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1.

__pydantic_generic_metadata__: Metadata for generic models; contains data used for a similar purpose to: __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.

__pydantic_parent_namespace__: Parent namespace of the model, used for automatic rebuilding of models. __pydantic_post_init__: The name of the post-init method for the model, if defined. __pydantic_root_model__: Whether the model is a [RootModel][pydantic.root_model.RootModel]. __pydantic_serializer__: The pydantic-core SchemaSerializer used to dump instances of the model. __pydantic_validator__: The pydantic-core SchemaValidator used to validate instances of the model.

__pydantic_fields__: A dictionary of field names and their corresponding [FieldInfo][pydantic.fields.FieldInfo] objects. __pydantic_computed_fields__: A dictionary of computed field names and their corresponding [ComputedFieldInfo][pydantic.fields.ComputedFieldInfo] objects.

__pydantic_extra__: A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra]: is set to ‘allow’.

__pydantic_fields_set__: The names of fields explicitly set during instantiation. __pydantic_private__: Values of private attributes set on the model instance.

Parameters:: file_path (str)

file_path: str¶

get_file_path() → str¶

Returns the file path of the model.

Returns:: str
Return type:: The file path of the model.

class ONNXModel(model: onnx.ModelProto)¶

Bases: pdstools.infinity.resources.prediction_studio.base.LocalModel

Usage docs: https://docs.pydantic.dev/2.10/concepts/models/

A base class for creating Pydantic models.

Attributes:

__class_vars__: The names of the class variables defined on the model. __private_attributes__: Metadata about the private attributes of the model. __signature__: The synthesized __init__ [Signature][inspect.Signature] of the model.

__pydantic_complete__: Whether model building is completed, or if there are still undefined fields. __pydantic_core_schema__: The core schema of the model. __pydantic_custom_init__: Whether the model has a custom __init__ function. __pydantic_decorators__: Metadata containing the decorators defined on the model.

This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1.

__pydantic_generic_metadata__: Metadata for generic models; contains data used for a similar purpose to: __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.

__pydantic_parent_namespace__: Parent namespace of the model, used for automatic rebuilding of models. __pydantic_post_init__: The name of the post-init method for the model, if defined. __pydantic_root_model__: Whether the model is a [RootModel][pydantic.root_model.RootModel]. __pydantic_serializer__: The pydantic-core SchemaSerializer used to dump instances of the model. __pydantic_validator__: The pydantic-core SchemaValidator used to validate instances of the model.

__pydantic_fields__: A dictionary of field names and their corresponding [FieldInfo][pydantic.fields.FieldInfo] objects. __pydantic_computed_fields__: A dictionary of computed field names and their corresponding [ComputedFieldInfo][pydantic.fields.ComputedFieldInfo] objects.

__pydantic_extra__: A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra]: is set to ‘allow’.

__pydantic_fields_set__: The names of fields explicitly set during instantiation. __pydantic_private__: Values of private attributes set on the model instance.

Parameters:: model (onnx.ModelProto)

_model: onnx.ModelProto¶

classmethod from_onnx_proto(model: onnx.ModelProto) → ONNXModel¶

Creates an ONNXModel object.

Parameters:

model (ModelProto) – An onnx ModelProto object

Returns:

An instance of the ONNXModel class.

Return type:

ONNXModel

Raises:

ImportError – If the optional dependencies for ONNX Conversion are not installed.
ONNXModelCreationError – If the ONNXModel object cannot be created.

classmethod from_sklearn_pipeline(model: sklearn.pipeline.Pipeline, initial_types: list) → ONNXModel¶

Creates an ONNXModel object.

Parameters:

model (Pipeline) – A sklearn Pipeline object
initial_types (list) – A list of initial types for the model’s input variables if the model is a Sklearn Pipeline object.

Returns:

An instance of the ONNXModel class.

Return type:

ONNXModel

Raises:

ImportError – If the optional dependencies for ONNX Conversion are not installed.
ONNXModelCreationError – If the ONNXModel object cannot be created.

add_metadata(metadata: Metadata) → ONNXModel¶

Adds metadata to the ONNX model.

Parameters:: metadata (Meta) – The metadata to be added.
Returns:: The ONNXModel object with the added metadata.
Return type:: ONNXModel
Raises:: ImportError – If the optional dependencies for ONNX Metadata addition are not installed.

validate() → bool¶

Validates an ONNX model.

Raises:

ImportError – If the optional dependencies for ONNX Validation are not installed.:
ONNXModelValidationError – If the model is invalid or if the validation process fails.:

Return type:

bool

run(test_data: dict)¶

Run the prediction using the provided test data.

Parameters:

test_data (dict) –

The test data to be used for prediction. It is a dictionary where each key is a column name from the dataset, and each value is a NumPy array representing the column data as a vector. For example:

{: ‘column1’: array([[value1], [value2], [value3]]), ‘column2’: array([[value4], [value5], [value6]]), ‘column3’: array([[value7], [value8], [value9]])

}

Returns:

The prediction result.

Return type:

Any

save(onnx_file_path: str)¶

Saves the ONNX model to the specified file path.

Parameters:: onnx_file_path (str) – The file path where the ONNX model should be saved.
Raises:: ImportError – If the optional dependencies for ONNX Conversion are not installed.:

__check_for_valid_input_node_structure(error_stream, session, metadata) → bool¶

Checks if the output node structure of the ONNX model is valid.

Parameters:

error_stream (Any) – The stream to which error messages are written.
session (onnxruntime.InferenceSession) – The ONNX runtime session containing the model.
metadata (Meta) – The metadata associated with the model.

Returns:

True if the output node structure is valid, False otherwise.

Return type:

bool

__validate_input_nodes(error_stream, model_input_info, metadata) → bool¶

Validates the input nodes of the ONNX model.

Parameters:

error_stream (Any) – The stream to which error messages are written.
model_input_info (dict) – A dictionary containing information about the model’s input nodes.
metadata (Meta) – The metadata associated with the model.

Returns:

True if the input nodes are valid, False otherwise.

Return type:

bool

__check_for_valid_output_node_structure(error_stream, session, metadata) → bool¶

Checks if the output node structure of the ONNX model is valid.

Parameters:

error_stream (Any) – The stream to which error messages are written.
session (onnxruntime.InferenceSession) – The ONNX runtime session containing the model.
metadata (Meta) – The metadata associated with the model.

Returns:

True if the output node structure is valid, False otherwise.

Return type:

bool

__validate_tensor_output_node_structure(error_stream, node_name, value_info) → bool¶

Validates the tensor output node structure of the ONNX model.

Parameters:

error_stream (Any) – The stream to which error messages are written.
node_name (str) – The name of the output node.
value_info (Any) – The value information of the output node.

Returns:

True if the output node is of type Tensor, False otherwise.

Return type:

bool

__validate_label_output_node_exist(error_stream, model_output_info, metadata) → bool¶

Validates the existence of the label output node in the ONNX model.

Parameters:

error_stream (Any) – The stream to which error messages are written.
model_output_info (dict) – A dictionary containing information about the model’s output nodes.
metadata (Meta) – The metadata associated with the model.

Returns:

True if the label output node exists, False otherwise.

Return type:

bool

__validate_input_node_dimensions(error_stream, model_input_info) → bool¶

Validates the dimensions of the input nodes in the ONNX model.

Parameters:

error_stream (Any) – The stream to which error messages are written.
model_input_info (dict) – A dictionary containing information about the model’s input nodes.

Returns:

True if all input nodes have valid dimensions, False otherwise.

Return type:

bool

__validate_input_node_shapes(error_stream, model_input_info) → bool¶

Validates the dimensions of the input nodes in the ONNX model.

Parameters:

error_stream (Any) – The stream to which error messages are written.
model_input_info (dict) – A dictionary containing information about the model’s input nodes.

Returns:

True if all input nodes have valid dimensions, False otherwise.

Return type:

bool

__validate_predictor_mappings(error_stream, model_input_info, metadata) → bool¶

Validates the predictor mappings in the ONNX model.

Parameters:

error_stream (Any) – The stream to which error messages are written.
model_input_info (dict) – A dictionary containing information about the model’s input nodes.
metadata (Meta) – The metadata associated with the model.

Returns:

True if all predictor mappings are valid, False otherwise.

Return type:

bool

__validate_predictor_index_mappings(error_stream, metadata) → bool¶

Validates the predictor index mappings in the ONNX model’s metadata.

Parameters:

error_stream (Any) – The stream to which error messages are written.
metadata (Meta) – The metadata associated with the model.

Returns:

True if the predictor index mappings are valid, False otherwise.

Return type:

bool

__create_predictor_map(metadata)¶

Creates a mapping of input names to their corresponding predictors.

Parameters:: metadata (Meta) – The metadata associated with the model, containing the predictor list.
Returns:: A dictionary where the keys are input names and the values are lists of predictors.
Return type:: dict

__validate_input_node_sizes(error_stream, model_input_info, metadata) → bool¶

Validates the sizes of the input nodes in the ONNX model.

Parameters:

error_stream (Any) – The stream to which error messages are written.
model_input_info (dict) – A dictionary containing information about the model’s input nodes.
metadata (Meta) – The metadata associated with the model, containing the predictor list.

Returns:

True if all input nodes have valid sizes, False otherwise.

Return type:

bool

__get_missing_predictors(model_input_info, predictor_input_names) → str¶

Identifies the missing predictors in the ONNX model’s input nodes.

Parameters:

model_input_info (dict) – A dictionary containing information about the model’s input nodes.
predictor_input_names (list) – A list of predictor input names.

Returns:

A comma-separated string of missing predictor names.

Return type:

str

pdstools.infinity.resources.prediction_studio.local_model_utils¶

Attributes¶

Exceptions¶

Classes¶

Module Contents¶

`ONNXModelCreationError`	Exception for errors during ONNX conversion and save.
`ONNXModelValidationError`	Exception for errors during ONNX validation.

`OutcomeType`	Create a collection of name/value pairs.
`Predictor`	Usage docs: https://docs.pydantic.dev/2.10/concepts/models/
`Output`	Usage docs: https://docs.pydantic.dev/2.10/concepts/models/
`Metadata`	Usage docs: https://docs.pydantic.dev/2.10/concepts/models/
`PMMLModel`	Usage docs: https://docs.pydantic.dev/2.10/concepts/models/
`H2OModel`	Usage docs: https://docs.pydantic.dev/2.10/concepts/models/
`ONNXModel`	Usage docs: https://docs.pydantic.dev/2.10/concepts/models/