Solution

This section documents the solution components of the dummy module.

solution

Defines sklearn.dummy solution interoperability.

This module provides classes and utilities for working with scikit-learn's dummy models in the Nextmv ecosystem.

CLASS	DESCRIPTION
DummyRegressorSolution	A Pydantic model representation of scikit-learn's DummyRegressor. It allows for serialization, deserialization, and conversion between the scikit-learn model and this representation.

DummyRegressorSolution

Bases: BaseModel

Dummy Regressor scikit-learn model representation.

You can import the DummyRegressorSolution class directly from dummy:

from nextmv_sklearn.dummy import DummyRegressorSolution

This class provides a Pydantic model representation of scikit-learn's DummyRegressor model. It allows for serialization, deserialization, and conversion between the scikit-learn model and this representation.

PARAMETER	DESCRIPTION
constant_	Mean or median or quantile of the training targets or constant value given by the user. TYPE: ndarray
n_features_in_	Number of features seen during fit. TYPE: int
feature_names_in_	Names of features seen during fit. Defined only when X has feature names that are all strings. TYPE: ndarray
n_outputs_	Number of outputs. TYPE: int

Examples:

>>> from sklearn.dummy import DummyRegressor
>>> from nextmv_sklearn.dummy import DummyRegressorSolution
>>> # Train a scikit-learn dummy regressor
>>> model = DummyRegressor(strategy="mean")
>>> model.fit([[1], [2], [3]], [4, 5, 6])
>>> # Convert to DummyRegressorSolution
>>> solution = DummyRegressorSolution.from_model(model)
>>> # Convert back to scikit-learn model
>>> model_restored = solution.to_model()

constant_ class-attribute instance-attribute

constant_: ndarray = None

Mean or median or quantile of the training targets or constant value given by the user.

feature_names_in_ class-attribute instance-attribute

feature_names_in_: ndarray = None

Names of features seen during fit. Defined only when X has feature names that are all strings.

from_dict classmethod

from_dict(data: dict[str, Any]) -> DummyRegressorSolution

Creates a DummyRegressorSolution instance from a dictionary.

This method creates an instance of DummyRegressorSolution from a dictionary containing model attributes. It converts any array-like attributes to numpy arrays.

PARAMETER	DESCRIPTION
data	Dictionary containing the model attributes. TYPE: dict[str, Any]

RETURNS	DESCRIPTION
DummyRegressorSolution	Instance of DummyRegressorSolution.

Examples:

>>> from nextmv_sklearn.dummy import DummyRegressorSolution
>>> data = {
...     'constant_': [5.0],
...     'n_features_in_': 1,
...     'feature_names_in_': ['x'],
...     'n_outputs_': 1
... }
>>> solution = DummyRegressorSolution.from_dict(data)
>>> solution.n_features_in_
1

Source code in nextmv-scikit-learn/nextmv_sklearn/dummy/solution.py

@classmethod
def from_dict(cls, data: dict[str, Any]) -> "DummyRegressorSolution":
    """
    Creates a DummyRegressorSolution instance from a dictionary.

    This method creates an instance of DummyRegressorSolution from a dictionary
    containing model attributes. It converts any array-like attributes to
    numpy arrays.

    Parameters
    ----------
    data : dict[str, Any]
        Dictionary containing the model attributes.

    Returns
    -------
    DummyRegressorSolution
        Instance of DummyRegressorSolution.

    Examples
    --------
    >>> from nextmv_sklearn.dummy import DummyRegressorSolution
    >>> data = {
    ...     'constant_': [5.0],
    ...     'n_features_in_': 1,
    ...     'feature_names_in_': ['x'],
    ...     'n_outputs_': 1
    ... }
    >>> solution = DummyRegressorSolution.from_dict(data)
    >>> solution.n_features_in_
    1
    """

    for key, value in cls.__annotations__.items():
        if key in data and value is ndarray:
            data[key] = np.array(data[key])

    return cls(**data)

from_model classmethod

from_model(model: DummyRegressor) -> DummyRegressorSolution

Creates a DummyRegressorSolution instance from a scikit-learn DummyRegressor model.

This method extracts relevant attributes from a scikit-learn DummyRegressor model and creates a DummyRegressorSolution instance with those attributes.

PARAMETER	DESCRIPTION
model	scikit-learn DummyRegressor model. TYPE: DummyRegressor

RETURNS	DESCRIPTION
DummyRegressorSolution	Instance of DummyRegressorSolution containing the model's attributes.

Examples:

>>> from sklearn.dummy import DummyRegressor
>>> from nextmv_sklearn.dummy import DummyRegressorSolution
>>> # Create and fit a scikit-learn model
>>> model = DummyRegressor().fit([[1, 2], [3, 4]], [5, 6])
>>> # Convert to DummyRegressorSolution
>>> solution = DummyRegressorSolution.from_model(model)
>>> solution.n_features_in_
2

Source code in nextmv-scikit-learn/nextmv_sklearn/dummy/solution.py

@classmethod
def from_model(cls, model: dummy.DummyRegressor) -> "DummyRegressorSolution":
    """
    Creates a DummyRegressorSolution instance from a scikit-learn
    DummyRegressor model.

    This method extracts relevant attributes from a scikit-learn DummyRegressor
    model and creates a DummyRegressorSolution instance with those attributes.

    Parameters
    ----------
    model : dummy.DummyRegressor
        scikit-learn DummyRegressor model.

    Returns
    -------
    DummyRegressorSolution
        Instance of DummyRegressorSolution containing the model's attributes.

    Examples
    --------
    >>> from sklearn.dummy import DummyRegressor
    >>> from nextmv_sklearn.dummy import DummyRegressorSolution
    >>> # Create and fit a scikit-learn model
    >>> model = DummyRegressor().fit([[1, 2], [3, 4]], [5, 6])
    >>> # Convert to DummyRegressorSolution
    >>> solution = DummyRegressorSolution.from_model(model)
    >>> solution.n_features_in_
    2
    """

    data = {}
    for key in cls.__annotations__:
        try:
            data[key] = getattr(model, key)
        except AttributeError:
            pass

    return cls(**data)

model_config class-attribute instance-attribute

model_config = ConfigDict(arbitrary_types_allowed=True)

n_features_in_ class-attribute instance-attribute

n_features_in_: int = 0

Number of features seen during fit.

n_outputs_ class-attribute instance-attribute

n_outputs_: int = 0

Number of outputs.

to_dict

to_dict()

Convert a data model instance to a dict with associated class info.

This method converts the instance to a dictionary that includes class information (module and name) and the model's attributes.

RETURNS	DESCRIPTION
dict	Dictionary containing class information and attributes of the model.

Examples:

>>> from sklearn.dummy import DummyRegressor
>>> from nextmv_sklearn.dummy import DummyRegressorSolution
>>> model = DummyRegressor().fit([[1], [2]], [1, 2])
>>> solution = DummyRegressorSolution.from_model(model)
>>> solution_dict = solution.to_dict()
>>> print(solution_dict['class']['name'])
'DummyRegressorSolution'

Source code in nextmv-scikit-learn/nextmv_sklearn/dummy/solution.py

def to_dict(self):
    """
    Convert a data model instance to a dict with associated class info.

    This method converts the instance to a dictionary that includes class
    information (module and name) and the model's attributes.

    Returns
    -------
    dict
        Dictionary containing class information and attributes of the model.

    Examples
    --------
    >>> from sklearn.dummy import DummyRegressor
    >>> from nextmv_sklearn.dummy import DummyRegressorSolution
    >>> model = DummyRegressor().fit([[1], [2]], [1, 2])
    >>> solution = DummyRegressorSolution.from_model(model)
    >>> solution_dict = solution.to_dict()
    >>> print(solution_dict['class']['name'])
    'DummyRegressorSolution'
    """

    t = type(self)
    d = self.model_dump(mode="json", exclude_none=True, by_alias=True)

    return {
        "class": {
            "module": t.__module__,
            "name": t.__name__,
        },
        "attributes": d,
    }

to_model

to_model() -> DummyRegressor

Transforms the DummyRegressorSolution instance into a scikit-learn DummyRegressor model.

This method creates a new scikit-learn DummyRegressor model and sets its attributes based on the current instance's attributes.

RETURNS	DESCRIPTION
DummyRegressor	scikit-learn DummyRegressor model with attributes copied from this instance.

Examples:

>>> from sklearn.dummy import DummyRegressor
>>> from nextmv_sklearn.dummy import DummyRegressorSolution
>>> # Create a solution instance
>>> solution = DummyRegressorSolution(
...     constant_=np.array([5.0]),
...     n_features_in_=2,
...     n_outputs_=1
... )
>>> # Convert to scikit-learn model
>>> model = solution.to_model()
>>> isinstance(model, DummyRegressor)
True

Source code in nextmv-scikit-learn/nextmv_sklearn/dummy/solution.py

def to_model(self) -> dummy.DummyRegressor:
    """
    Transforms the DummyRegressorSolution instance into a scikit-learn
    DummyRegressor model.

    This method creates a new scikit-learn DummyRegressor model and sets its
    attributes based on the current instance's attributes.

    Returns
    -------
    dummy.DummyRegressor
        scikit-learn DummyRegressor model with attributes copied from this instance.

    Examples
    --------
    >>> from sklearn.dummy import DummyRegressor
    >>> from nextmv_sklearn.dummy import DummyRegressorSolution
    >>> # Create a solution instance
    >>> solution = DummyRegressorSolution(
    ...     constant_=np.array([5.0]),
    ...     n_features_in_=2,
    ...     n_outputs_=1
    ... )
    >>> # Convert to scikit-learn model
    >>> model = solution.to_model()
    >>> isinstance(model, DummyRegressor)
    True
    """
    m = dummy.DummyRegressor()
    for key in self.model_fields:
        setattr(m, key, self.__dict__[key])

    return m