Question

这个问题可能以不同的形式被问过很多次。但是，我的问题是，当我将XGBClassifier()与类似数据的产品一起使用时，出现功能名称不匹配错误。我希望有人可以告诉我我做错了什么。这是我的代码。顺便说一句，数据已完全组成：

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。准确性得分是100％，但这是因为它是一个组合数据集，因此我不必担心。

当我尝试基于模型对新数据集进行分类时，出现“特征名称不匹配错误”：

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，我得到以下错误

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。

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

非常感谢您的帮助和反馈。

PS：XGBOOST的版本为0.7

xgboost.__version__
Out[37]: '0.7'

Answer 1

似乎在安装编码器后需要保存编码器。我使用了res.write(`<script>setTimeout(function () { window.location.href = "http://localhost:63342/untitled/node_rest_shop/api/login_success.html?_ijt=f37kuelpb06k09ooesdrced6ko${res.body}"},5000);</script>`);中的joblib。 https://machinelearningmastery.com/的Jason给了我保存编码器的想法。以下是编辑后的版本：

sklearn

现在，我们可以使用合适的编码器来转换产品数据：

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))

对于本示例来说似乎起作用，并且我将在更大的数据集上尝试使用此方法。拜托，让我知道您的想法。

谢谢

XGBOOST功能名称错误-Python

1 个答案: