Skip to content

Model

This section documents the model components of the ensemble module.

model

Defines sklearn.ensemble models interoperability.

This module provides wrappers around scikit-learn ensemble models, allowing them to be created using Nextmv option objects.

FUNCTION DESCRIPTION
GradientBoostingRegressor

Creates a sklearn.ensemble.GradientBoostingRegressor from options.
RandomForestRegressor

Creates a sklearn.ensemble.RandomForestRegressor from options.

GradientBoostingRegressor 

GradientBoostingRegressor(
    options: Options,
) -> GradientBoostingRegressor

Creates a sklearn.ensemble.GradientBoostingRegressor from the provided options.

You can import the GradientBoostingRegressor function directly from ensemble:

from nextmv_sklearn.ensemble import GradientBoostingRegressor

This function takes a Nextmv Options object and converts it to the appropriate parameters for the scikit-learn GradientBoostingRegressor.

PARAMETER DESCRIPTION

options

Options for the GradientBoostingRegressor. Should contain parameters defined in GRADIENT_BOOSTING_REGRESSOR_PARAMETERS.

TYPE: Options

RETURNS DESCRIPTION
GradientBoostingRegressor

A sklearn.ensemble.GradientBoostingRegressor instance.

Examples:

>>> from nextmv_sklearn.ensemble.options import GradientBoostingRegressorOptions
>>> options = GradientBoostingRegressorOptions().to_nextmv()
>>> options.set("n_estimators", 100)
>>> options.set("learning_rate", 0.1)
>>> gbr = GradientBoostingRegressor(options)
Source code in nextmv-scikit-learn/nextmv_sklearn/ensemble/model.py 
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
def GradientBoostingRegressor(options: nextmv.Options) -> ensemble.GradientBoostingRegressor:
    """
    Creates a `sklearn.ensemble.GradientBoostingRegressor` from the provided
    options.

    You can import the `GradientBoostingRegressor` function directly from `ensemble`:

    ```python
    from nextmv_sklearn.ensemble import GradientBoostingRegressor
    ```

    This function takes a Nextmv Options object and converts it to the appropriate
    parameters for the scikit-learn GradientBoostingRegressor.

    Parameters
    ----------
    options : nextmv.Options
        Options for the GradientBoostingRegressor. Should contain parameters
        defined in GRADIENT_BOOSTING_REGRESSOR_PARAMETERS.

    Returns
    -------
    ensemble.GradientBoostingRegressor
        A sklearn.ensemble.GradientBoostingRegressor instance.

    Examples
    --------
    >>> from nextmv_sklearn.ensemble.options import GradientBoostingRegressorOptions
    >>> options = GradientBoostingRegressorOptions().to_nextmv()
    >>> options.set("n_estimators", 100)
    >>> options.set("learning_rate", 0.1)
    >>> gbr = GradientBoostingRegressor(options)
    """

    names = {p.name for p in GRADIENT_BOOSTING_REGRESSOR_PARAMETERS}
    opt_dict = {k: v for k, v in options.to_dict().items() if k in names if v is not None}

    return ensemble.GradientBoostingRegressor(**opt_dict)

RandomForestRegressor 

RandomForestRegressor(
    options: Options,
) -> RandomForestRegressor

Creates a sklearn.ensemble.RandomForestRegressor from the provided options.

You can import the RandomForestRegressor function directly from ensemble:

from nextmv_sklearn.ensemble import RandomForestRegressor

This function takes a Nextmv Options object and converts it to the appropriate parameters for the scikit-learn RandomForestRegressor.

PARAMETER DESCRIPTION

options

Options for the RandomForestRegressor. Should contain parameters defined in RANDOM_FOREST_REGRESSOR_PARAMETERS.

TYPE: Options

RETURNS DESCRIPTION
RandomForestRegressor

A sklearn.ensemble.RandomForestRegressor instance.

Examples:

>>> from nextmv_sklearn.ensemble.options import RandomForestRegressorOptions
>>> options = RandomForestRegressorOptions().to_nextmv()
>>> options.set("n_estimators", 100)
>>> options.set("max_depth", 10)
>>> rfr = RandomForestRegressor(options)
Source code in nextmv-scikit-learn/nextmv_sklearn/ensemble/model.py 
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
def RandomForestRegressor(options: nextmv.Options) -> ensemble.RandomForestRegressor:
    """
    Creates a `sklearn.ensemble.RandomForestRegressor` from the provided options.

    You can import the `RandomForestRegressor` function directly from `ensemble`:

    ```python
    from nextmv_sklearn.ensemble import RandomForestRegressor
    ```

    This function takes a Nextmv Options object and converts it to the appropriate
    parameters for the scikit-learn RandomForestRegressor.

    Parameters
    ----------
    options : nextmv.Options
        Options for the RandomForestRegressor. Should contain parameters
        defined in RANDOM_FOREST_REGRESSOR_PARAMETERS.

    Returns
    -------
    ensemble.RandomForestRegressor
        A sklearn.ensemble.RandomForestRegressor instance.

    Examples
    --------
    >>> from nextmv_sklearn.ensemble.options import RandomForestRegressorOptions
    >>> options = RandomForestRegressorOptions().to_nextmv()
    >>> options.set("n_estimators", 100)
    >>> options.set("max_depth", 10)
    >>> rfr = RandomForestRegressor(options)
    """

    names = {p.name for p in RANDOM_FOREST_REGRESSOR_PARAMETERS}
    opt_dict = {k: v for k, v in options.to_dict().items() if k in names if v is not None}

    return ensemble.RandomForestRegressor(**opt_dict)