getml.predictors.LogisticRegression dataclass

LogisticRegression(
    learning_rate: float = 0.9, reg_lambda: float = 1e-10
)

Bases: _Predictor

Simple predictor for classification problems.

Learns a simple linear relationship using the sigmoid function:

\[ \hat{y} = \sigma(w_0 + w_1 * feature_1 + w_2 * feature_2 + ...) \]

\(\sigma\) denotes the sigmoid function:

\[ \sigma(z) = \frac{1}{1 + exp(-z)} \]

The weights are optimized by minimizing the cross entropy loss of the predictions \(\hat{y}\) w.r.t. the targets \(y\).

\[ L(\hat{y},y) = - y*\log \hat{y} - (1 - y)*\log(1 - \hat{y}) \]

Logistic regressions are always trained numerically.

If you decide to pass categorical features: annotating_roles_categorical to the 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.

PARAMETER	DESCRIPTION
learning_rate	The learning rate used for the Adaptive Moments algorithm (only relevant when categorical features are included). Range: (0, ∞] TYPE: float DEFAULT: 0.9
reg_lambda	L2 regularization parameter. Range: [0, ∞] TYPE: float DEFAULT: 1e-10

validate

validate(params: Optional[dict] = None)

Checks both the types and the values of all instance variables and raises an exception if something is off.

PARAMETER	DESCRIPTION
params	A dictionary containing the parameters to validate. If not is passed, the own parameters will be validated. TYPE: Optional[dict] DEFAULT: None

Examples:

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 sent to the getML Engine.

Source code in getml/predictors/logistic_regression.py

def validate(self, params: Optional[dict] = None):
    """Checks both the types and the values of all instance
    variables and raises an exception if something is off.

    Args:
        params: A dictionary containing
            the parameters to validate. If not is passed,
            the own parameters will be validated.

    Examples:
        ```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 sent 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)