from scipy.optimize import minimize_scalar
from evalml.problem_types import handle_problem_types
class ObjectiveBase:
needs_fitting = False
greater_is_better = True
score_needs_proba = False
uses_extra_columns = False
problem_types = []
def __init__(self, verbose=False):
self.verbose = verbose
@classmethod
def supports_problem_type(cls, problem_type):
""" Checks if objective supports given ProblemType
Arguments:
problem_type(str or ProblemType): problem type to check
Returns:
bool: whether objective supports ProblemType
"""
problem_type = handle_problem_types(problem_type)
if problem_type in cls.problem_types:
return True
return False
def fit(self, y_predicted, y_true, extra_cols=None):
"""Learn the objective function based on the predictions from a model.
If needs_fitting is false, this method won't be called
Arguments:
y_predicted (list): the predictions from the model. If needs_proba is True,
it is the probability estimates
y_true (list): the ground truth for the predictions.
extra_cols (pd.DataFrame): any extra columns that are needed from training
data to fit. Only provided if uses_extra_columns is True.
Returns:
self
"""
def cost(threshold):
if extra_cols is not None:
predictions = self.decision_function(y_predicted, extra_cols, threshold)
cost = self.objective_function(predictions, y_true, extra_cols)
else:
predictions = self.decision_function(y_predicted, threshold)
cost = self.objective_function(predictions, y_true)
if self.greater_is_better:
return -cost
return cost
self.optimal = minimize_scalar(cost, method='Golden', options={"maxiter": 100})
self.threshold = self.optimal.x
if self.verbose:
print("Best threshold found at: ", self.threshold)
return self
def predict(self, y_predicted, extra_cols=None):
"""Apply the learned objective function to the output of a model.
If needs_fitting is false, this method won't be called
Arguments:
y_predicted: the prediction to transform to final prediction
Returns:
predictions
"""
if extra_cols is not None:
predictions = self.decision_function(y_predicted, extra_cols, self.threshold)
else:
predictions = self.decision_function(y_predicted, self.threshold)
return predictions
def score(self, y_predicted, y_true, extra_cols=None):
"""Calculate score from applying fitted objective to predicted values
If a higher score is better than a lower score, set greater_is_better attribute to True
Arguments:
y_predicted (list): the predictions from the model. If needs_proba is True,
it is the probability estimates
y_true (list): the ground truth for the predictions.
extra_cols (pd.DataFrame): any extra columns that are needed from training
data to fit. Only provided if uses_extra_columns is True.
Returns:
score
"""
if extra_cols is not None:
return self.objective_function(y_predicted, y_true, extra_cols)
else:
return self.objective_function(y_predicted, y_true)