Question

我正在调用这个方法build_feature_category

obt=ObliviousTree(data,"hello",True)#data is pandas.DataFrame
obt.load_tree() 
obt.build_feature_category()

但它给出了这个错误，我认为这与它们在创建之前使用实例变量有关但我不太确定我在哪里做错了：这是我在该类上面调用上述方法后得到的错误

   label  session  week  hour  category       item    dwell
      -1        1     0    10         0  214536502    0.000
      -1        1     0    10         0  214536500  180.591
      -1        1     0    10         0  214536506   37.130                               -1         1     0    10         0  214577561  133.308
      -1        2     0    13         0  214662742    0.000
      -1        2     0    13         0  214662742   41.759
      -1        2     0    13         0  214825110   78.073
      -1        2     0    13         0  214757390   73.264
      -1        2     0    14         0  214757407   47.537
      -1        2     0    14         0  214551617  118.642
['label', 'session', 'week', 'hour', 'category', 'item', 'dwell']
Traceback (most recent call last):
File "dataloader.py", line 39, in <module>
obt.build_feature_category()
File "/sdfs/challenge/odg/ObliviousTree.py",  line 24, in  build_feature_category
self.feature_name_values[feature_name]=np.unique[self.data[feature_name]]
TypeError: 'function' object is not subscriptable



import numpy as np
class ObliviousTree:

'This is an implementation of Oblvious Tree'

def __init__(self,data=[],func="C.45",autoload=False):
    self.data=data
    self.split_funct=func
    self.autoload=autoload
    self.feature_names=self.data.columns.tolist()
    self.feature_name_values={}


def load_tree(self):
    if not self.autoload:
        print("skipped autoloading")
    else:
        print(self.data)
        print(self.feature_names)


def build_feature_category(self):
    for feature_name in self.feature_names:
        self.feature_name_values[feature_name]=np.unique[self.data[feature_name]]
    print(self.feature_name_values)

Answer 1

np.unique是一种方法，但您将其称为dict。它应该是：

... = np.unique(self.data[feature_name])

TypeError：＆＃39;功能＆＃39;对象不可订阅[Python3]

1 个答案: