Why does classifier chain ask for at least 2 classes, when I have it

Question

I'm using Classifier Chain with logistic regression and when i try to use fit, i get

This solver needs samples of at least 2 classes in the data, but the data contains only one class: 1

but I'm pretty sure I have two classes in my data, this is my X_test

<5825x2000 sparse matrix of type '<class 'numpy.float64'>'
    with 219990 stored elements in Compressed Sparse Row format>

and I have this as my y_train:

array([[1, 0, 0, ..., 0, 1, 0],
       [1, 0, 0, ..., 1, 1, 0],
       [1, 0, 0, ..., 0, 1, 0],
       ...,
       [1, 0, 0, ..., 1, 1, 0],
       [1, 0, 0, ..., 0, 1, 0],
       [1, 0, 0, ..., 0, 1, 0]])

and this is the part I'm trying to run

cc_clf = ClassifierChain(LogisticRegression(penalty='l1', C=1, dual=False, solver='liblinear'))
cc_clf.fit(X_train, y_train)
cc_y_pred = cc_clf.predict(X_test)

Am i using the classifier chain incorrectly? When I pass in the same data into one vs rest, it works.

clf = OneVsRestClassifier(classifier1)
clf.fit(X_train, y_train)

Answer 1

To use ClassifierChain every column of y_train has both 0 and 1. Here is a valid and invalid example:

import numpy as np
from sklearn.multioutput  import ClassifierChain
from sklearn.linear_model import LogisticRegression

X_train = np.array([[1], [2]])

# Runs 
y_train = np.array([[0, 1], [1, 0]]) 
cc_clf = ClassifierChain(LogisticRegression(penalty='l1', C=1, dual=False, solver='liblinear'))
cc_clf.fit(X_train, y_train)

# ValueError
y_train = np.array([[0, 1], [0, 0]])   
cc_clf = ClassifierChain(LogisticRegression(penalty='l1', C=1, dual=False, solver='liblinear'))
cc_clf.fit(X_train, y_train)