class ArmClassifier(BaseEstimator, ClassifierMixin):
def __init__(self,n_estimators=100,min_samples_split=2):
print('ARM: {},{}'.format(n_estimators,min_samples_split))
self.m_nestimators = n_estimators
self.m_minsamplessplit = min_samples_split
self.classy = MultiOutputClassifier(RandomForestClassifier(n_estimators=n_estimators,min_samples_split=min_samples_split))
def fit(self,X,y=None):
return self.classy.fit(X,y)
def set_params(self, **params):
print('ARM Setparams: {}'.format(params))
self.classy.set_params(**params)
def predict(self,X,y=None):
return self.classy.predict(X)
def build_classifiers_build_params(classifiers_configs):
'''
INPUT
classifiers_configs - dict, a dictionary containing the configuration for each classifier
OUTPUT
classifiers_params_dict - dict, a dictionary containing the grid params to be used for
each classifier in the training process
This function builds a dictionary with grid params to be used in training process for each
classifier whose configurations were given as input.
It can handle a single classifier or a list of classifiers.
'''
if len(classifiers_configs) > 1:
classifiers_params_list = []
classifiers_params_dict = {}
for classifier in classifiers_configs:
classifier_estimator = classifier['estimator']
classifier_obj = build_estimator_obj(classifier_estimator)
classifier_obj = MultiOutputClassifier(classifier_obj.set_params(**classifier['params']))
classifiers_params_list.append(classifier_obj)
classifiers_params_dict['clf'] = classifiers_params_list
elif len(classifiers_configs) == 1:
classifier = classifiers_configs[0]
classifier_estimator = classifier['estimator']
classifier_obj = build_estimator_obj(classifier_estimator)
classifier_obj = MultiOutputClassifier(classifier_obj)
classifiers_params_dict = {'clf' : [classifier_obj]}
classifiers_params_dict.update(classifier['params'])
print(classifiers_params_dict)
return classifiers_params_dict
