Source code for getml.predictors.logistic_regression

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"""
A simple logistic regression model for predicting classification problems.
"""

from dataclasses import dataclass

from .linear_regression import _validate_linear_model_parameters
from .predictor import _Predictor

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[docs]@dataclass(repr=False) class LogisticRegression(_Predictor): """Simple predictor for classification problems. Learns a simple linear relationship using the sigmoid function: .. math:: \\hat{y} = \\sigma(w_0 + w_1 * feature_1 + w_2 * feature_2 + ...) :math:`\\sigma` denotes the sigmoid function: .. math:: \\sigma(z) = \\frac{1}{1 + exp(-z)} The weights are optimized by minimizing the cross entropy loss of the predictions :math:`\\hat{y}` w.r.t. the :ref:`targets <annotating_roles_target>` :math:`y`. .. math:: L(\\hat{y},y) = - y*\\log \\hat{y} - (1 - y)*\\log(1 - \\hat{y}) Logistic regressions are always trained numerically. If you decide to pass :ref:`categorical features<annotating_roles_categorical>` to the :class:`~getml.predictors.LogisticRegression`, it will be trained using the Broyden-Fletcher-Goldfarb-Shannon (BFGS) algorithm. Otherwise, it will be trained using adaptive moments (Adam). BFGS is more accurate, but less scalable than Adam. Args: learning_rate (float, optional): The learning rate used for the Adaptive Moments algorithm (only relevant when categorical features are included). Range: (0, :math:`\\infty`] reg_lambda (float, optional): L2 regularization parameter. Range: [0, :math:`\\infty`] """ # ---------------------------------------------------------------- learning_rate: float = 0.9 reg_lambda: float = 1e-10 # ----------------------------------------------------------------
[docs] def validate(self, params=None): """Checks both the types and the values of all instance variables and raises an exception if something is off. Args: params (dict, optional): A dictionary containing the parameters to validate. If not is passed, the own parameters will be validated. Examples: .. code-block:: python l = getml.predictors.LogisticRegression() l.learning_rate = 20 l.validate() Note: This method is called at end of the __init__ constructor and every time before the predictor - or a class holding it as an instance variable - is send to the getML engine. """ if params is None: params = self.__dict__ else: params = {**self.__dict__, **params} if not isinstance(params, dict): raise ValueError("params must be None or a dictionary!") _validate_linear_model_parameters(params)
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