XGBOOST 功能名称错误 - Python [英] XGBOOST feature name error - Python

问题描述

可能这个问题已经以不同的形式被问过很多次了.但是，我的问题是，当我将 XGBClassifier() 与生产之类的数据一起使用时，出现功能名称不匹配错误.我希望有人能告诉我我做错了什么.这是我的代码.顺便说一句，数据完全是编的:

Probably this question has been asked many times in different forms. However, my problem is when I use XGBClassifier() with a production like data, I get a feature name mismatch error. I am hoping someone could please tell me what I am doing wrong. Here is my code. BTW, the data is completely made up:

import pandas as pd
from sklearn.preprocessing import LabelEncoder, OneHotEncoder
from sklearn.model_selection import train_test_split, KFold, cross_val_score
from sklearn.metrics import accuracy_score
import xgboost as xgb

data = {"Age":[44,27,30,38,40,35,70,48,50,37],
        "BMI":["25-29","35-39","30-35","40-45","45-49","20-25","<19",">70","50-55","55-59"],
        "BP":["<140/90",">140/90",">140/90",">140/90","<140/90","<140/90","<140/90",">140/90",">140/90","<140/90"],
        "Risk":["No","Yes","Yes","Yes","No","No","No","Yes","Yes","No"]}

df = pd.DataFrame(data)

X = df.iloc[:, :-1]
y = df.iloc[:, -1]

labelencoder = LabelEncoder()

def encoder_X(columns):
    for i in columns:
        X.iloc[:, i] = labelencoder.fit_transform(X.iloc[:, i])

encoder_X([1,2])

y = labelencoder.fit_transform(y)

onehotencdoer = OneHotEncoder(categorical_features = [[1,2]])
X = onehotencdoer.fit_transform(X).toarray()

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 13)

model = xgb.XGBClassifier()
model.fit(X_train, y_train, verbose = True)

y_pred = model.predict(X_test)
predictions = [round(value) for value in y_pred]

accuracy = accuracy_score(y_test, predictions)
print("Accuracy: {0}%".format(accuracy*100))

到目前为止一切顺利，没有错误.准确率是 100%，但那是因为它是一个虚构的数据集，所以我并不担心.

So far so good, no error. The accuracy score is 100%, but that's because it is a made up data set so I am not worried about that.

当我尝试根据模型对新数据集进行分类时，出现特征名称不匹配错误":

When I try to classify a new dataset based on the model, I get "feature name mismatch error":

proddata = {"Age":[65,50,37],
        "BMI":["25-29","35-39","30-35"],
        "BP":["<140/90",">140/90",">140/90"]}

prod_df = pd.DataFrame(proddata)

def encoder_prod(columns):
    for i in columns:
        prod_df.iloc[:, i] = labelencoder.fit_transform(prod_df.iloc[:, i])

encoder_prod([1,2])

onehotencdoer = OneHotEncoder(categorical_features = [[1,2]])
prod_df = onehotencdoer.fit_transform(prod_df).toarray()

predictions = model.predict(prod_df)

此后我收到以下错误

predictions = model.predict(prod_df)
Traceback (most recent call last):

  File "<ipython-input-24-456b5626e711>", line 1, in <module>
    predictions = model.predict(prod_df)

  File "c:\users\sozdemir\appdata\local\programs\python\python35\lib\site-packages\xgboost\sklearn.py", line 526, in predict
    ntree_limit=ntree_limit)

  File "c:\users\sozdemir\appdata\local\programs\python\python35\lib\site-packages\xgboost\core.py", line 1044, in predict
    self._validate_features(data)

  File "c:\users\sozdemir\appdata\local\programs\python\python35\lib\site-packages\xgboost\core.py", line 1288, in _validate_features
    data.feature_names))

ValueError: feature_names mismatch: ['f0', 'f1', 'f2', 'f3', 'f4', 'f5', 'f6', 'f7', 'f8', 'f9', 'f10', 'f11', 'f12'] ['f0', 'f1', 'f2', 'f3', 'f4', 'f5']
expected f6, f11, f12, f9, f7, f8, f10 in input data

我知道这是由于 OneHotEncoding 在适合并转换为数组时发生的.不过我可能错了.

I know this is happening as a result of OneHotEncoding when fit and transform to an array. I might be wrong though.

如果这是 OneHotEncoding 的结果，我是否可以不使用 OneHotEncoding，因为 LabelEncoder() 已经对分类值进行了编码?

If this is as a result of OneHotEncoding, can I just not use OneHotEncoding since LabelEncoder() already codes the categorical values?

非常感谢您的任何帮助和反馈.

Thank you so much for any help and feedback.

PS:XGBOOST的版本是0.7

PS: The version of XGBOOST is 0.7

xgboost.__version__
Out[37]: '0.7'

推荐答案

看来编码器安装后需要保存.我使用了 sklearn 中的 joblib.https://machinelearningmastery.com/ 的 Jason 给了我保存编码器的想法.以下为修改后的版本:

It seems like the encoder needs to be saved after it is being fitted. I used joblib from sklearn. Jason from https://machinelearningmastery.com/ gave me the idea of saving the encoder. The below is an edited version:

import pandas as pd
from sklearn.preprocessing import LabelEncoder, OneHotEncoder
from sklearn.model_selection import train_test_split, KFold, cross_val_score
from sklearn.metrics import accuracy_score
from sklearn.externals import joblib
import xgboost as xgb

data = {"Age":[44,27,30,38,40,35,70,48,50,37],
        "BMI":["25-29","35-39","30-35","40-45","45-49","20-25","<19",">70","50-55","55-59"],
        "BP":["<140/90",">140/90",">140/90",">140/90","<140/90","<140/90","<140/90",">140/90",">140/90","<140/90"],
        "Risk":["No","Yes","Yes","Yes","No","No","No","Yes","Yes","No"]}

df = pd.DataFrame(data)

X = df.iloc[:, :-1]
y = df.iloc[:, -1]

labelencoder = LabelEncoder()

def encoder_X(columns):
    for i in columns:
        X.iloc[:, i] = labelencoder.fit_transform(X.iloc[:, i])

encoder_X([1,2])

y = labelencoder.fit_transform(y)

onehotencdoer = OneHotEncoder(categorical_features = [[1,2]])
onehotencdoer.fit(X)
enc = joblib.dump(onehotencdoer, "encoder.pkl") # save the fitted encoder
X = onehotencdoer.transform(X).toarray()

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 13)

model = xgb.XGBClassifier()
model.fit(X_train, y_train, verbose = True)

y_pred = model.predict(X_test)
predictions = [round(value) for value in y_pred]

accuracy = accuracy_score(y_test, predictions)
print("Accuracy: {0}%".format(accuracy*100))

现在，我们可以使用拟合编码器来转换 prod 数据:

And now, we can use the fitted encoder to transform the prod data:

proddata = {"Age":[65,50,37],
        "BMI":["25-29","35-39","30-35"],
        "BP":["<140/90",">140/90",">140/90"]}

prod_df = pd.DataFrame(proddata)

def encoder_prod(columns):
    for i in columns:
        prod_df.iloc[:, i] = labelencoder.fit_transform(prod_df.iloc[:, i])

encoder_prod([1,2])
enc = joblib.load("encoder.pkl")
prod_df = enc.transform(prod_df).toarray()

predictions = model.predict(prod_df)
results = [round(val) for val in predictions]

它似乎适用于这个例子，我将在更大的数据集上尝试这种方法.请告诉我你的想法.

It seems to be working for this example and I'll try this method at work for a larger data-set. Please, let me know what you think.

谢谢

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