def find_best_single_feature_parameters(self, dataset):
for feature in dataset.suggested_discretize_features:
permutations = self.generate_feature_parameters(feature)
print(permutations)
best_mean_fcs = self.best_fcs[dataset]
best_perm = None
for p, perm in enumerate(permutations):
logging.error("[Parameters Tester][{}][{}][Perm {:03d}] Current permutation: {}".format(dataset, feature, p+1, perm))
dm = DataModel.generate_from_file(dataset, discretize_params=perm)
classes_list = dm.get_classes_list()
f_scores = []
a = 1
for _ in range(self.best_fold[dataset][1]):
for train_set, test_set in dm.generate_k_folds_stratified(self.best_fold[dataset][0]):
model_evaluator = ModelEvaluator(train_set, test_set, classes_list)
model_evaluator.evaluate()
f_scores.append(model_evaluator.get_f_score())
logging.error("[Parameters Tester][{}][{}][Perm {:03d}][{:03d}] FCS: {}".format(dataset, feature, p+1, a, f_scores[-1]))
a += 1
f_score_mean = sum(f_scores) / len(f_scores)
logging.error("[Parameters Tester][{}][{}][Perm {:03d}] Best FCS: {}, Mean FCS {}".format(dataset, feature, p+1, max(f_scores), f_score_mean))
if f_score_mean > best_mean_fcs:
best_perm = perm[0]
best_mean_fcs = f_score_mean
if best_perm is not None:
self.best_discretize_feature_params[dataset].append(best_perm)
logging.error("[Parameters Tester][{}][{}] Best mean FCS: {}, Best parameters: {}".format(dataset, feature, best_mean_fcs, best_perm))
def main():
dm = DataModel.generate_from_file(
PIMA_DIABETES_DATASET,
smooth=True,
discretize_params=[
DiscretizeParam('Age', kbins_discretize, 10),
DiscretizeParam('SkinThickness', kbins_discretize, 10),
DiscretizeParam('Pregnancies', kbins_discretize, 10)
])
print(Discretizer.kmean_models)
def find_best_fold(self, dataset):
dm = DataModel.generate_from_file(dataset)
classes_list = dm.get_classes_list()
for fold in FOLDS:
f_scores = []
a = 1
for _ in range(fold[1]):
for train_set, test_set in dm.generate_k_folds_stratified(fold[0]):
model_evaluator = ModelEvaluator(train_set, test_set, classes_list)
model_evaluator.evaluate()
f_scores.append(model_evaluator.get_f_score())
logging.error("[Parameters Tester][{}][CV{:02d}][{:03d}] FCS: {}".format(dataset, fold[0], a, f_scores[-1]))
a += 1
f_score_mean = sum(f_scores) / len(f_scores)
logging.error("[Parameters Tester][{}][CV{:02d}] Best FCS: {}, Mean FCS {}".format(dataset, fold[0], max(f_scores), f_score_mean))
self.append_result({'dataset':dataset.name, 'fold':fold[0], 'f_score':f_score_mean, 'permutation':-1})
if f_score_mean > self.best_fcs[dataset]:
self.best_fold[dataset] = fold
self.best_fcs[dataset] = f_score_mean
logging.error("[Parameters Tester][{}] Best mean FCS: {}, Best fold: {}".format(dataset, self.best_fcs[dataset], self.best_fold[dataset]))
def find_best_parameters(self, dataset):
permutations = self.generate_permutations(dataset)
for p, perm in enumerate(permutations):
logging.error("[Parameters Tester][{}][Perm {:08d}] Current permutation: {}".format(dataset, p+1, perm))
dm = DataModel.generate_from_file(dataset, discretize_params=perm)
classes_list = dm.get_classes_list()
f_scores = []
a = 1
for _ in range(self.best_fold[dataset][1]):
for train_set, test_set in dm.generate_k_folds_stratified(self.best_fold[dataset][0]):
model_evaluator = ModelEvaluator(train_set, test_set, classes_list)
model_evaluator.evaluate()
f_scores.append(model_evaluator.get_f_score())
logging.error("[Parameters Tester][{}][Perm {:08d}][{:03d}] FCS: {}".format(dataset, p+1, a, f_scores[-1]))
a += 1
f_score_mean = sum(f_scores) / len(f_scores)
logging.error("[Parameters Tester][{}][Perm {:08d}] Best FCS: {}, Mean FCS {}".format(dataset, p+1, max(f_scores), f_score_mean))
for param in perm:
self.append_result({'dataset':dataset.name, 'fold':self.best_fold[dataset][0], 'f_score':f_score_mean, 'permutation':p + 1, 'feature':param.feature_name, 'function':param.discretize_function.__name__, 'bins':param.buckets_amount})
if f_score_mean > self.best_fcs[dataset]:
self.best_discretize_parameters[dataset] = perm
self.best_fcs[dataset] = f_score_mean
logging.error("[Parameters Tester][{}] Best mean FCS: {}, Best parameters: {}".format(dataset, self.best_fcs[dataset], self.best_discretize_parameters[dataset]))
def visualize(dataset):
dm = DataModel.generate_from_file(dataset)
visualize_histograms(dm, 'histograms-{}'.format(str(dataset)))
