XGBOOST feature name error - Python

Question

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))

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.

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)

After this I get the below error

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

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

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

Thank you so much for any help and feedback.

PS: The version of XGBOOST is 0.7

xgboost.__version__
Out[37]: '0.7'

Can you please post stack trace so that we know where is code facing error. — Upasana Mittal
– Upasana Mittal, Commented Aug 16, 2018 at 14:06
@UpasanaMittal - Sure. I'll edit my question to show where the error occurs. It is right after model.predict(prod_df) line. Although, I might have found the answer which I have posted earlier today. I am just waiting for more feedback. Thanks — Infinite_Loop
– Infinite_Loop, Commented Aug 16, 2018 at 14:16

Infinite_Loop · Accepted Answer · 2018-08-16 13:56:21Z

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))

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]

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.

Thanks

So I applied this technique to a data-set at work and it worked.

Collectives™ on Stack Overflow

XGBOOST feature name error - Python

1 Answer 1

1 Comment

Your Answer

Linked

Hot Network Questions

Collectives™ on Stack Overflow

1 Answer 1

1 Comment

Your Answer

Sign up or log in

Post as a guest

Linked

Related