def __init__( self, k, mode = 0, distanceFunction = None) :
self.k = k
Classifier.__init__( self)
self.logger.setDebugLevel( 0 )
self.logger.setFileDebugLevel( 3 )
self.distances = {}
self.mode = mode
self.dist = distanceFunction
if(self.dist == None):
self.dist = self.calculateDistance
def __init__(self, _data, _trans, _cv):
Classifier.__init__(self, _cv)
self.data = _data.copy(deep=True)
self.names = list(OrderedDict.fromkeys(self.data['CATEGORIA'].values))
self.y = self.data['CATEGORIA'].astype("category").cat.codes.values
self.data.drop(['CATEGORIA ESPECIFICA', 'CATEGORIA'],
axis=1,
inplace=True)
self.X = self.data.values
def __init__(self, max_depth, min_group_size):
"""
Args:
max_depth: Maximum Depth of Decision Tree
min_group_size: Smallest number of samples before stopping splitting a node
"""
Classifier.__init__(self)
self.min_group_size = min_group_size
self.max_depth = max_depth
self.root = None
self.nodes = []
def __init__(self, training_data, nclassifiers=2):
Classifier.__init__(self, training_data)
self.nclassifiers = nclassifiers
self.classifiers = []
self.classifier_weight = []
def __init__(self, one_hot=False):
Classifier.__init__(self)
self.one_hot = one_hot
def __init__(self, training_data):
Classifier.__init__(self, training_data)
# dictionary to count the occurrences of attributes
self.attr_counter = defaultdict(lambda: (defaultdict(lambda: defaultdict(int))))
self.label_counter = defaultdict(int)
self.max_feature_per_index = defaultdict(int)
