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Added maximize parameter to CV init #51

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Added maximize parameter to CV init #51

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f676ecb
Added maximize parameter to CV init
craymichael May 9, 2018
cd5fcd4
Resolve issues with minimization functionality
craymichael May 10, 2018
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1 change: 1 addition & 0 deletions evolutionary_search/__init__.py
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Original file line number Diff line number Diff line change
@@ -1,2 +1,3 @@
from .cv import *
from .optimize import maximize, compile

27 changes: 22 additions & 5 deletions evolutionary_search/cv.py
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Original file line number Diff line number Diff line change
Expand Up @@ -216,6 +216,10 @@ class EvolutionaryAlgorithmSearchCV(BaseSearchCV):
FitFailedWarning is raised. This parameter does not affect the refit
step, which will always raise the error.

maximize : boolean, default=True
If True, maximize the scoring function result. Otherwise, minimize
the scoring function result.


Examples
--------
Expand Down Expand Up @@ -283,7 +287,7 @@ def __init__(self, estimator, params, scoring=None, cv=4,
gene_mutation_prob=0.1, gene_crossover_prob=0.5,
tournament_size=3, generations_number=10, gene_type=None,
n_jobs=1, iid=True, error_score='raise',
fit_params={}):
fit_params={}, maximize=True):
super(EvolutionaryAlgorithmSearchCV, self).__init__(
estimator=estimator, scoring=scoring, fit_params=fit_params,
iid=iid, refit=refit, cv=cv, verbose=verbose,
Expand All @@ -302,8 +306,16 @@ def __init__(self, estimator, params, scoring=None, cv=4,
self.best_params_ = None
self.score_cache = {}
self.n_jobs = n_jobs
creator.create("FitnessMax", base.Fitness, weights=(1.0,))
creator.create("Individual", list, est=clone(self.estimator), fitness=creator.FitnessMax)
self.maximize = maximize
if maximize:
weights = (1.0,)
fitness_name = "FitnessMax"
else:
weights = (-1.0,)
fitness_name = "FitnessMin"
creator.create(fitness_name, base.Fitness, weights=weights)
creator.create("Individual", list, est=clone(self.estimator),
fitness=getattr(creator, fitness_name))

@property
def possible_params(self):
Expand Down Expand Up @@ -349,7 +361,10 @@ def best_index_(self):

def fit(self, X, y=None):
self.best_estimator_ = None
self.best_mem_score_ = float("-inf")
if self.maximize:
self.best_mem_score_ = float("-inf")
else:
self.best_mem_score_ = float("inf")
self.best_mem_params_ = None
for possible_params in self.possible_params:
_check_param_grid(possible_params)
Expand Down Expand Up @@ -454,7 +469,8 @@ def _fit(self, X, y, parameter_dict):
print("Best individual is: %s\nwith fitness: %s" % (
current_best_params_, current_best_score_))

if current_best_score_ > self.best_mem_score_:
if ((self.maximize and (current_best_score_ > self.best_mem_score_)) or
(not self.maximize and (current_best_score_ < self.best_mem_score_))):
self.best_mem_score_ = current_best_score_
self.best_mem_params_ = current_best_params_

Expand All @@ -468,3 +484,4 @@ def _fit(self, X, y, parameter_dict):

self.best_score_ = current_best_score_
self.best_params_ = current_best_params_