Invalid classes inferred from unique values of `y`. Expected: [0 1 2 3 4 5], got [1 2 3 4 5 6]

Question

I've trained dataset using XGB Classifier, but I got this error in local. It worked on Colab and also my friends don't have any problem with same code. I don't know what that error means...

Invalid classes inferred from unique values of y. Expected: [0 1 2 3 4 5], got [1 2 3 4 5 6]

this is my code, but I guess it's not the reason.

start_time = time.time()
xgb = XGBClassifier(n_estimators = 400, learning_rate = 0.1, max_depth = 3)
xgb.fit(X_train.values, y_train)
print('Fit time : ', time.time() - start_time)

Enyo

how are you creating the y_train and y_test vectors? it looks like one of them is starting numeration at 1 and the other t 0. – Krimmer

You can also, transform your variables into a one-hot encoded representation. – Aldin

You must use the same LabelEncoder to encode the target for training and evaluation datasets: xgboosting.com/… – Trireme

Answer 1

That happens because the class column has to start from 0 (as required since version 1.3.2). An easy way to solve that is using LabelEncoder from sklearn.preprocssing library.

Solution (works for version 1.6):

from sklearn.preprocessing import LabelEncoder
le = LabelEncoder()
y_train = le.fit_transform(y_train)

And then you try/run your code again:

start_time = time.time()
xgb = XGBClassifier(n_estimators = 400, learning_rate = 0.1, max_depth = 3)
xgb.fit(X_train.values, y_train)
print('Fit time : ', time.time() - start_time)

Lyingin

Answer 2

It's because the y_train must be encoded in a newer update XGBoost model before training it, i.e., you must use some categorical transformation like label encoders:

from sklearn.preprocessing import LabelEncoder
le = LabelEncoder()
y_train = le.fit_transform(y_train)

Then apply it to XGBoost model for training:

from xgboost import XGBClassifier
classifier = XGBClassifier()
classifier.fit(X = X_train,y =  y_train)

After training to find out its confusion matrix you must inverse transform the predicted y values, as shown:

from sklearn.metrics import confusion_matrix, accuracy_score
y_pred = classifier.predict(X_test)
y_pred = le.inverse_transform(y_pred)
cm = confusion_matrix(y_test, y_pred)
print(cm)
accuracy_score(y_test, y_pred)

Usance

Answer 3

Try to adding stratify to the train_test_split code:

X_train, X_test, y_train, y_test = train_test_split(data, labels, test_size=test_size, stratify = labels)

Clift

It's not the case. Using LabelEncoder works fine. – Dissyllable

Answer 4

The erros comes with the new version of xgboost, Uninstall current Xgboost and install xgboost 0.90

pip uninstall xgboost 

pip install xgboost==0.90

Lawman

Answer 5

Downgrading to 1.5.0 worked for me

Also got this warning message during execution

UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release.

using the Label encoder in 1.6 returns this error for me:

MultiClassEvaluation: label must be in [0, num_class), num_class=6 but found 6 in label

Striptease

Answer 6

If it helps, i just rolled back to version 1.2.1

Duggan

As it’s currently written, your answer is unclear. Please edit to add additional details that will help others understand how this addresses the question asked. You can find more information on how to write good answers in the help center. – Yorke

Answer 7

Use python version 3.7 as used in colab

Farquhar

Answer 8

it happens because the version of ur xgboost , so :

try this :

y_train_xgb = y_train.map({"1": 0, "2": 1, "3": 2}

Entresol

Answer 9

I verified in the source code of xgboost that LabelEncoder() was deprecated in version 1.3 with this PR:

https://github.com/dmlc/xgboost/pull/6269/files

And then LabelEncoder() was removed in version 1.6.0 with this PR: https://github.com/dmlc/xgboost/pull/7357

which was then merged here: https://github.com/dmlc/xgboost/commit/3c4aa9b2ead21d11ef1589059db2ea50208c55ea

The approach mentioned by @jefferson-santos to explicitly use LabelEncoder() is correct, and worked for me.

Throb