How is it possible to run script with sklearn's GridsearchCV() from command line and not to print all the warnings, when the parameter n_jobs is set to -1? With warnings.simplefilter('ignore') it is not working.
When n_jobs = 1 it is working correctly and no warnings are printed
When I run the script from spyder even with n_jobs = -1 it is working correctly
I tried multiple approaches described here Eliminating warnings from scikit-learn but none of them work when running the script from command line and Gridsearch with n_jobs = -1
I tried also to put the with statement to ignore the warnings after the
if __name__ == '__main__'statement, but this is also not working
I have scikit-learn 0.21.3.
Here is a small reproducible example, where it prints multiple times the FutureWarning and DepreciationWarning from IsolationForest.
from sklearn import datasets
from sklearn.cluster import KMeans
from sklearn.pipeline import Pipeline
from sklearn.model_selection import GridSearchCV
from sklearn.preprocessing import StandardScaler, MinMaxScaler
from sklearn.ensemble import IsolationForest
import warnings
import numpy as np
from sklearn.base import BaseEstimator, TransformerMixin
class OutlierRemover(BaseEstimator, TransformerMixin):
def __init__(self, contamination = 0.1):
self.contamination = contamination
def fit(self, x, y = None):
return self
def transform(self, x, y = None):
predicted_values = IsolationForest(n_jobs = -1, contamination = self.contamination).fit_predict(x)
x = x[np.where(predicted_values == 1)]
return x
def pipe():
iris = datasets.load_iris()
x = iris.data
pipe = Pipeline([('scalers', MinMaxScaler()),
('outlierRemovers', OutlierRemover()),
('cluster', KMeans(n_jobs = -1))])
params = [{'scalers':[MinMaxScaler(), StandardScaler()]}]
gridsearch = GridSearchCV(pipe, params, verbose = 100, cv = 2, n_jobs = -1)
with warnings.catch_warnings():
warnings.simplefilter('ignore')
gridsearch.fit(x)
if __name__ == '__main__':
pipe()
