# 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

# ------------------------------------------------------------------------------

[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]

Raises:
TypeError: If any of the input arguments does not match its
expected type.

"""

# ----------------------------------------------------------------

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()

Raises:
KeyError: If an unsupported instance variable is
encountered.

TypeError: If any instance variable is of wrong type.

ValueError: If any instance variable does not match its
possible choices (string) or is out of the expected
bounds (numerical).

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)

# ------------------------------------------------------------------------------