from abc import ABC, abstractmethod
class ObjectiveBase(ABC):
"""Base class for all objectives."""
@property
@classmethod
@abstractmethod
def name(cls):
"""Returns a name describing the objective."""
raise NotImplementedError("This objective must have a `name` attribute as a class variable")
@property
@classmethod
@abstractmethod
def greater_is_better(cls):
"""Returns a boolean determining if a greater score indicates better model performance."""
raise NotImplementedError("This objective must have a `greater_is_better` boolean attribute as a class variable")
@property
@classmethod
@abstractmethod
def score_needs_proba(cls):
"""Returns a boolean determining if the score() method needs probability estimates. This should be true for objectives which work with predicted probabilities, like log loss or AUC, and false for objectives which compare predicted class labels to the actual labels, like F1 or correlation.
"""
raise NotImplementedError("This objective must have a `score_needs_proba` boolean attribute as a class variable")
@classmethod
@abstractmethod
def objective_function(cls, y_true, y_predicted, X=None):
"""Computes the relative value of the provided predictions compared to the actual labels, according a specified metric
Arguments:
y_predicted (pd.Series) : predicted values of length [n_samples]
y_true (pd.Series) : actual class labels of length [n_samples]
X (pd.DataFrame or np.array) : extra data of shape [n_samples, n_features] necessary to calculate score
Returns:
numerical value used to calculate score
"""
raise NotImplementedError("`objective_function` must be implemented.")
def score(self, y_true, y_predicted, X=None):
"""Returns a numerical score indicating performance based on the differences between the predicted and actual values.
Arguments:
y_predicted (pd.Series) : predicted values of length [n_samples]
y_true (pd.Series) : actual class labels of length [n_samples]
X (pd.DataFrame or np.array) : extra data of shape [n_samples, n_features] necessary to calculate score
Returns:
score
"""
return self.objective_function(y_true, y_predicted, X=X)