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Model

This section documents the model components of the neural_network module.

model

Defines sklearn.neural_network models interoperability.

This module provides functions for creating and configuring scikit-learn neural network models using Nextmv options.

FUNCTION DESCRIPTION
MLPRegressor

Create a Multi-layer Perceptron regressor with Nextmv options.

MLPRegressor 

MLPRegressor(options: Options) -> MLPRegressor

Creates a sklearn.neural_network.MLPRegressor from the provided options.

You can import the MLPRegressor function directly from neural_network:

from nextmv_sklearn.neural_network import MLPRegressor

This function takes Nextmv options and creates a scikit-learn MLPRegressor model with the specified parameters. The options must be compatible with the MLPRegressor parameters as defined in the options module.

PARAMETER DESCRIPTION

options

Options for the MLPRegressor. These can include: - hidden_layer_sizes : str The ith element represents the number of neurons in the ith hidden layer. (e.g. "1,2,3") - activation : {'identity', 'logistic', 'tanh', 'relu'} Activation function for the hidden layer. - solver : {'lbfgs', 'sgd', 'adam'} The solver for weight optimization. - alpha : float Strength of the L2 regularization term. - And other parameters as defined in MLP_REGRESSOR_PARAMETERS.

TYPE: Options

RETURNS DESCRIPTION
MLPRegressor

A sklearn.neural_network.MLPRegressor instance configured with the provided options.

Examples:

>>> import nextmv
>>> from nextmv_sklearn.neural_network import MLPRegressor
>>> from nextmv_sklearn.neural_network.options import MLPRegressorOptions
>>>
>>> # Create options
>>> options = MLPRegressorOptions().to_nextmv()
>>> options.set("hidden_layer_sizes", "100,50")
>>> options.set("activation", "relu")
>>>
>>> # Create regressor
>>> regressor = MLPRegressor(options)
>>> regressor.fit(X_train, y_train)
>>> predictions = regressor.predict(X_test)
Source code in nextmv-scikit-learn/nextmv_sklearn/neural_network/model.py 
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def MLPRegressor(options: nextmv.Options) -> neural_network.MLPRegressor:
    """
    Creates a `sklearn.neural_network.MLPRegressor` from the provided options.

    You can import the `MLPRegressor` function directly from `neural_network`:

    ```python
    from nextmv_sklearn.neural_network import MLPRegressor
    ```

    This function takes Nextmv options and creates a scikit-learn MLPRegressor model with
    the specified parameters. The options must be compatible with the MLPRegressor parameters
    as defined in the options module.

    Parameters
    ----------
    options : nextmv.Options
        Options for the MLPRegressor. These can include:
        - hidden_layer_sizes : str
            The ith element represents the number of neurons in the ith hidden layer. (e.g. "1,2,3")
        - activation : {'identity', 'logistic', 'tanh', 'relu'}
            Activation function for the hidden layer.
        - solver : {'lbfgs', 'sgd', 'adam'}
            The solver for weight optimization.
        - alpha : float
            Strength of the L2 regularization term.
        - And other parameters as defined in MLP_REGRESSOR_PARAMETERS.

    Returns
    -------
    neural_network.MLPRegressor
        A sklearn.neural_network.MLPRegressor instance configured with the provided options.

    Examples
    --------
    >>> import nextmv
    >>> from nextmv_sklearn.neural_network import MLPRegressor
    >>> from nextmv_sklearn.neural_network.options import MLPRegressorOptions
    >>>
    >>> # Create options
    >>> options = MLPRegressorOptions().to_nextmv()
    >>> options.set("hidden_layer_sizes", "100,50")
    >>> options.set("activation", "relu")
    >>>
    >>> # Create regressor
    >>> regressor = MLPRegressor(options)
    >>> regressor.fit(X_train, y_train)
    >>> predictions = regressor.predict(X_test)
    """

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

    return neural_network.MLPRegressor(**opt_dict)