我一直在尝试运行一些机器学习代码，但在运行我的pipeline后总是卡在拟合阶段。我查看了各种论坛但收效甚微。我发现有人说不能在pipeline中使用LabelEncoder。我不确定这是不是真的。如果有人对此有任何见解，我会非常高兴听到他们的意见。
我一直得到这个错误：
TypeError: fit_transform() takes 2 positional arguments but 3 were given因此我不知道问题出在我还是Python上。这是我的代码：
data = pd.read_csv("ks-projects-201801.csv", index_col="ID", parse_dates=["deadline","launched"], infer_datetime_format=True)var = list(data)data = data.drop(labels=[1014746686,1245461087, 1384087152, 1480763647, 330942060, 462917959, 69489148])missing = [i for i in var if data[i].isnull().any()]data = data.dropna(subset=missing,axis=0)le = LabelEncoder()oe = OrdinalEncoder()oh = OneHotEncoder()y = [i for i in var if i=="state"]y = data[var.pop(8)]p,p.index = pd.Series(le.fit_transform(y)),y.indexq = pd.read_csv("y.csv",index_col="ID")["0"]label_y = le.fit_transform(y)x = data[var]obj_feat = x.select_dtypes(include="object")dat_feat = x.select_dtypes(include="datetime64[ns]")dat_feat = dat_feat.assign(dmonth=dat_feat.deadline.dt.month.astype("int64"), dyear = dat_feat.deadline.dt.year.astype("int64"), lmonth=dat_feat.launched.dt.month.astype("int64"), lyear=dat_feat.launched.dt.year.astype("int64"))dat_feat = dat_feat.drop(labels=["deadline","launched"],axis=1)num_feat = x.select_dtypes(include=["int64","float64"])u = dict(zip(list(obj_feat),[len(obj_feat[i].unique()) for i in obj_feat]))le_obj = [i for i in u if u[i]<10]oh_obj = [i for i in u if u[i]<20 and u[i]>10]te_obj = [i for i in u if u[i]>20 and u[i]<25]cb_obj = [i for i in u if u[i]>100]# Pipeline time#Impute and encodestrat = ["constant","most_frequent","mean","median"]sc = StandardScaler()oh_unk = "ignore"encoders = [LabelEncoder(), OneHotEncoder(handle_unknown=oh_unk), TargetEncoder(), CatBoostEncoder()]#num_trans = Pipeline(steps=[("imp",SimpleImputer(strategy=strat[2])),num_trans = Pipeline(steps=[("sc",sc)])#obj_imp = Pipeline(steps=[("imp",SimpleImputer(strategy=strat[1]))])oh_enc = Pipeline(steps=[("oh_enc",encoders[1])])te_enc = Pipeline(steps=[("te_enc",encoders[2])])cb_enc = Pipeline(steps=[("cb_enc",encoders[0])])trans = ColumnTransformer(transformers=[ ("num",num_trans,list(num_feat)+list(dat_feat)), #("obj",obj_imp,list(obj_feat)), ("onehot",oh_enc,oh_obj), ("target",te_enc,te_obj), ("catboost",cb_enc,cb_obj) ])models = [RandomForestClassifier(random_state=0), KNeighborsClassifier(), DecisionTreeClassifier(random_state=0)]model = models[2]print("Check 4")# Chaining it all togetherrun = Pipeline(steps=[("Transformation",trans),("Model",model)])x = pd.concat([obj_feat,dat_feat,num_feat],axis=1)print("Check 5")run.fit(x,p)代码运行到run.fit时一切正常，但随后就抛出了错误。我很乐意听到任何人的建议，并且任何可能的解决方案也将不胜感激！谢谢你。
回答：
问题与此回答中提到的一样，但在你的情况下是LabelEncoder。LabelEncoder的fit_transform方法接受的是：
def fit_transform(self, y): """Fit label encoder and return encoded labels ...而Pipeline期望它的所有转换器都接受三个位置参数fit_transform(self, X, y)。
你可以按照前述回答中的方法创建一个自定义转换器，然而，LabelEncoder不应被用作特征转换器。关于为什么不应该这样做的详细解释，可以查看LabelEncoder用于分类特征？。因此，我建议不要使用LabelEncoder，如果特征数量过多，可以使用其他贝叶斯编码器，比如你编码器列表中的TargetEncoder。
