I'm trying to split my dataset into a training and a test set by using the train_test_split
function from scikit-learn, but I'm getting this error:
In [1]: y.iloc[:,0].value_counts()
Out[1]:
M2 38
M1 35
M4 29
M5 15
M0 15
M3 15
In [2]: xtrain, xtest, ytrain, ytest = train_test_split(X, y, test_size=1/3, random_state=85, stratify=y)
Out[2]:
Traceback (most recent call last):
File "run_ok.py", line 48, in <module>
xtrain,xtest,ytrain,ytest = train_test_split(X,y,test_size=1/3,random_state=85,stratify=y)
File "/home/aurora/.pyenv/versions/3.6.0/lib/python3.6/site-packages/sklearn/model_selection/_split.py", line 1700, in train_test_split
train, test = next(cv.split(X=arrays[0], y=stratify))
File "/home/aurora/.pyenv/versions/3.6.0/lib/python3.6/site-packages/sklearn/model_selection/_split.py", line 953, in split
for train, test in self._iter_indices(X, y, groups):
File "/home/aurora/.pyenv/versions/3.6.0/lib/python3.6/site-packages/sklearn/model_selection/_split.py", line 1259, in _iter_indices
raise ValueError("The least populated class in y has only 1"
ValueError: The least populated class in y has only 1 member, which is too few. The minimum number of groups for any class cannot be less than 2.
However, all classes have at least 15 samples. Why am I getting this error?
X is a pandas DataFrame which represents the data points, y is a pandas DataFrame with one column that contains the target variable.
I cannot post the original data because it's proprietary, but it is fairly reproducible by creating a random pandas DataFrame (X) with 1k rows x 500 columns, and a random pandas DataFrame (y) with the same number of rows (1k) of X, and, for each row the target variable (a categorical label).
The y pandas DataFrame should have different categorical labels (e.g. 'class1', 'class2'...) and each labels should have at least 15 occurrences.
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