I am getting warnings in my Python code running in a Jupyter Notebook, even though I have tried to suppress them using warnings.filterwarnings('ignore'). Specifically, I am getting the warning "FutureWarning: The default value of n_init will change from 10 to 'auto' in 1.4. Set the value of n_init explicitly to suppress the warning" and UserWarning.
How can I suppress these warnings in Jupyter Notebook other than set the value of n_init?
Here is the code I am using:
import warnings
warnings.filterwarnings('ignore')
#parameter selection
bow_mx_copy = bow_mx.toarray().copy()
bow_mx_copy[bow_mx_copy==0] = 1e-6 #set a very small value to avoid zero vector, because Agglomerative Clustering doens't support when use Cosine
dbs_params = {
"n_clusters": range(2,10),
}
print("K-means, Euclidean Distance")
metric = "euclidean"
gridsearch = GridSearchCV(KMeans(), dbs_params, scoring = 'adjusted_rand_score',\
n_jobs=-1).fit(bow_mx_copy[:500,]) #use the first 500 samples for parameter selection
best_estimator = gridsearch.best_estimator_
clustering = best_estimator.fit(bow_mx_copy)
ed_labels = clustering.labels_
score = silhouette_score(bow_mx, clustering.labels_, metric=metric)
print(f"metric={metric}, \n best params={gridsearch.best_params_}, \n best silhouette_score={score}")
I have also tried the following codes which is not working as well.
import warnings
warnings.simplefilter(action='ignore', category=FutureWarning)
There is the little part of warnings I received (too long to paste, I removed dumplicates):
warnings.warn(
/Users/haoren/.pyenv/versions/3.10.0/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning
warnings.warn(
/Users/haoren/.pyenv/versions/3.10.0/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning
warnings.warn(
/Users/haoren/.pyenv/versions/3.10.0/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning
warnings.warn(
/Users/haoren/.pyenv/versions/3.10.0/lib/python3.10/site-packages/sklearn/model_selection/_validation.py:778: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "/Users/haoren/.pyenv/versions/3.10.0/lib/python3.10/site-packages/sklearn/model_selection/_validation.py", line 765, in _score
scores = scorer(estimator, X_test)
TypeError: _BaseScorer.__call__() missing 1 required positional argument: 'y_true'
warnings.warn(
/Users/haoren/.pyenv/versions/3.10.0/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning
warnings.warn(
/Users/haoren/.pyenv/versions/3.10.0/lib/python3.10/site-packages/sklearn/model_selection/_validation.py:778: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "/Users/haoren/.pyenv/versions/3.10.0/lib/python3.10/site-packages/sklearn/model_selection/_validation.py", line 765, in _score
scores = scorer(estimator, X_test)
TypeError: _BaseScorer.__call__() missing 1 required positional argument: 'y_true'
I have also tried the following codes which is not working as well.
import warnings
warnings.simplefilter(action='ignore', category=FutureWarning)
What I don't understand is why python still returns the warning even after I set it to ignore warnings. And why is the warning here so long?
I have checked the similar question "Cannot supress Python Warnings with warnings.filterwarnings("ignore")" but it seems that not working for me. I am sure I run the ignore part before running the cell.
I would be very grateful for any answers and help from you.
