通过sklearn 实现LabelEnconder 编码，之后进行xgboost预测。

from sklearn.preprocessing import LabelEncoder
from sklearn.model_selection import train_test_split
import xgboost as xgb
import pandas as pd

def GitdataCate():
    df=pd.read_csv("Training.csv")
    one_hot_feature=["prognosis"]
    lbc = LabelEncoder()
    for feature in one_hot_feature:
        try:
            df[feature] = lbc.fit_transform(df[feature].apply(int))
        except:
            df[feature] = lbc.fit_transform(df[feature])
    # X=df.as_matrix()，此方法后面回去删除，可以使用df.values
    X=df.values
    print(X)
    #返回映射后的classes_的编码
    terminal_type1 = {index: label for index, label in enumerate( lbc.classes_)}
    print(terminal_type1)

 for feature in one_hot_feature:
        try:
            df[feature] = lbc.fit_transform(df[feature].apply(int))
        except:
            df[feature] = lbc.fit_transform(df[feature])
    # X=df.as_matrix()，此方法后面回去删除，可以使用df.values
        terminal_type1 = {index: label for index, label in enumerate( lbc.classes_)}

        print(terminal_type1)

with open('terminal_type', 'w') as json_file:
        json_file.write(json.dumps(terminal_type1))

from sklearn.preprocessing import LabelEncoder 
包初始化 
gle = LabelEncoder() 
建立映射 
terminal_type= gle.fit_transform(data1[‘terminal_type’]) 
映射后的对应值 
terminal_type1 = {index: label for index, label in enumerate(gle.classes_)} 
添加映射后的列 
data1[‘terminal_type1’] = terminal_type 
删除映射前对的列 
data1 = data1.drop([‘terminal_type’],axis=1) 
data1.head()

In [1]: from sklearn import preprocessing
   ...: le = preprocessing.LabelEncoder()
   ...: le.fit([1,2,2,6,3])
   ...:
Out[1]: LabelEncoder()

In [2]: le.classes_
Out[2]: array([1, 2, 3, 6])

In [3]: le.transform([1,1,3,6,2])
Out[3]: array([0, 0, 2, 3, 1], dtype=int64)

In [4]: le.inverse_transform([0, 0, 2, 3, 1])
Out[4]: array([1, 1, 3, 6, 2])

In [5]: from sklearn import preprocessing
   ...: le =preprocessing.LabelEncoder()
   ...: le.fit(["paris", "paris", "tokyo", "amsterdam"])
   ...: print('标签个数:%s'% le.classes_)
   ...: print('标签值标准化:%s' % le.transform(["tokyo", "tokyo", "paris"]))
   ...: print('标准化标签值反转:%s' % le.inverse_transform([2, 2, 1]))
   ...: