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"""
A simple linear regression model for predicting regression problems.
"""
import numbers
from dataclasses import dataclass
import numpy as np
from getml.helpers import _check_parameter_bounds
from .predictor import _Predictor
# --------------------------------------------------------------------
[docs]@dataclass(repr=False)
class LinearRegression(_Predictor):
"""Simple predictor for regression problems.
Learns a simple linear relationship using ordinary least squares (OLS)
regression:
.. math::
\\hat{y} = w_0 + w_1 * feature_1 + w_2 * feature_2 + ...
The weights are optimized by minimizing the squared loss of the
predictions :math:`\\hat{y}` w.r.t. the :ref:`targets
<annotating_roles_target>` :math:`y`.
.. math::
L(y,\\hat{y}) = \\frac{1}{n} \\sum_{i=1}^{n} (y_i -\\hat{y}_i)^2
Linear regressions can be trained arithmetically or numerically.
Training arithmetically is more accurate, but suffers worse
scalability.
If you decide to pass :ref:`categorical
features<annotating_roles_categorical>` to the
:class:`~getml.predictors.LinearRegression`, it will be trained
numerically. Otherwise, it will be trained arithmetically.
Args:
learning_rate (float, optional):
The learning rate used for training numerically (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.LinearRegression()
l.learning_rate = 8.1
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)
# ------------------------------------------------------------------------------
def _validate_linear_model_parameters(parameters):
"""Checks both the types and values of the `parameters` and raises an
exception is something is off.
Examples:
.. code-block:: python
getml.helpers.validation._validate_linear_model_parameters(
{'learning_rate': 0.1})
Args:
parameters (dict): Dictionary containing some of all
parameters supported in
:class:`~getml.predictors.LinearRegression` and
:class:`~getml.predictors.LogisticRegression`.
Raises:
KeyError: If an unsupported parameter is encountered.
TypeError: If any parameter is of wrong type.
ValueError: If any parameter does not match its possible
choices (string) or is out of the expected bounds (numerical).
Note:
Both :class:`~getml.predictors.LinearRegression` and
:class:`~getml.predictors.LogisticRegression` have an instance
variable called ``type``, which is not checked in this
function but in the corresponding
:meth:`~getml.predictors.LinearRegression.validate` method. If
it is supplied to this function, it won't cause harm but will
be ignored instead of checked.
"""
allowed_parameters = {"learning_rate", "reg_lambda", "type"}
# ----------------------------------------------------------------
for kkey in parameters:
if kkey not in allowed_parameters:
raise KeyError("'unknown parameter: " + kkey)
if kkey == "learning_rate":
if not isinstance(parameters["learning_rate"], numbers.Real):
raise TypeError("'learning_rate' must be a real number")
_check_parameter_bounds(
parameters["learning_rate"],
"learning_rate",
[np.finfo(np.float64).resolution, np.finfo(np.float64).max],
)
if kkey == "reg_lambda":
if not isinstance(parameters["reg_lambda"], numbers.Real):
raise TypeError("'reg_lambda' must be a real number")
_check_parameter_bounds(
parameters["reg_lambda"], "reg_lambda", [0.0, np.finfo(np.float64).max]
)
# --------------------------------------------------------------------