Python ClassifierTask Examples

Example #1

File: xgboost_classifier.py Project: NajibMAHJOUBI/costa_rican_household_poverty

 def __init__(self,
              max_depth=defaults_parameters["max_depth"],
              n_estimators=defaults_parameters["n_estimators"],
              learning_rate=defaults_parameters["learning_rate"]):
     ClassifierTask.__init__(self)
     self.max_depth = max_depth
     self.n_estimators = n_estimators
     self.learning_rate = learning_rate

Example #2

File: svc.py Project: NajibMAHJOUBI/costa_rican_household_poverty

 def __init__(self,
              kernel=defaults_parameters["kernel"],
              C=defaults_parameters["C"],
              degree=defaults_parameters["degree"],
              gamma=defaults_parameters["gamma"]):
     ClassifierTask.__init__(self)
     self.__kernel__ = kernel
     self.__C__ = C
     self.__degree__ = degree
     self.__gamma__ = gamma

Example #3

 def __init__(self,
              penalty=defaults_parameters["penalty"],
              dual=defaults_parameters["dual"],
              C=defaults_parameters["C"],
              fit_intercept=defaults_parameters["fit_intercept"]):
     ClassifierTask.__init__(self)
     self.__penalty__ = penalty
     self.__dual__ = dual
     self.__C__ = C
     self.__fit_intercept__ = fit_intercept

Example #4

File: decision_tree_classifier.py Project: NajibMAHJOUBI/costa_rican_household_poverty

 def __init__(self,
              criterion=defaults_parameters["criterion"],
              max_depth=defaults_parameters["max_depth"],
              min_samples_split=defaults_parameters["min_samples_split"],
              min_samples_leaf=defaults_parameters["min_samples_leaf"]):
     ClassifierTask.__init__(self)
     self.__criterion__ = criterion
     self.__max_depth__ = max_depth
     self.__min_samples_split__ = min_samples_split
     self.__min_samples_leaf__ = min_samples_leaf

Example #5

 def __init__(self,
              n_neighbors=defaults_parameters["n_neighbors"],
              algorithm=defaults_parameters["algorithm"],
              weights=defaults_parameters["weights"],
              leaf_size=defaults_parameters["leaf_size"],
              metric=defaults_parameters["metric"]):
     ClassifierTask.__init__(self)
     self.__n_neighbors__ = n_neighbors
     self.__algorithm__ = algorithm
     self.__weights__ = weights
     self.__leaf_size__ = leaf_size
     self.__metric__ = metric

Example #6

 def __init__(self,
              n_estimators=defaults_parameters["n_estimators"],
              criterion=defaults_parameters["criterion"],
              max_depth=defaults_parameters["max_depth"],
              min_samples_split=defaults_parameters["min_samples_split"],
              min_samples_leaf=defaults_parameters["min_samples_leaf"]):
     ClassifierTask.__init__(self)
     self.n_estimators = n_estimators
     self.criterion = criterion
     self.min_samples_split = min_samples_split
     self.min_samples_leaf = min_samples_leaf
     self.max_depth = max_depth

Example #7

File: one_vs_rest.py Project: NajibMAHJOUBI/costa_rican_household_poverty

 def __init__(self):
     ClassifierTask.__init__(self)
     self.__base_estimator__ = None

Example #8

 def __init__(self):
     ClassifierTask.__init__(self)

Example #9

File: bagging_classifier.py Project: NajibMAHJOUBI/costa_rican_household_poverty

 def __init__(self,
              base_estimator=defaults_parameters["base_estimator"],
              n_estimators=defaults_parameters["n_estimators"]):
     ClassifierTask.__init__(self)
     self.__base_estimator__ = base_estimator
     self.__n_estimators__ = n_estimators