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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)
# ------------------------------------------------------------------------------