Closed NimaSarajpoor closed 1 year ago
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I will fix this in the upcoming days.
@rasbt I think it is ready. if there is something that I missed, please let me know.
Was just testing the code and it definitely improved the startup time. When I am trying an example like
import numpy as np
from sklearn.linear_model import LogisticRegression
from mlxtend.feature_selection import ExhaustiveFeatureSelector as EFS
seed = 0
X = np.random.rand(10000, 10) # 10k samples, with 10 features
y = np.random.choice([0, 1], size=10000)
model = LogisticRegression()
efs1 = EFS(model,
min_features=1,
max_features=10,
scoring='accuracy',
print_progress=True,
n_jobs=1,
cv=5)
efs1 = efs1.fit(X, y)
print('Best accuracy score: %.2f' % efs1.best_score_)
print('Best subset (indices):', efs1.best_idx_)
print('Best subset (corresponding names):', efs1.best_feature_names_)
it still seems to be a bit stuck though. I.e., it would not show any output for like 2-3 min and then iterate through the 1k possibilities in like 1 sec.
I wonder if that's an issue with the verbose
display functionality though 🤔
EDIT: No worries, it was a computer issue. It works perfectly now. Actually it solves the problem. Before, a user could not see the progress printed to the command line until all combinations were evaluated. Now, you get the feedback immediately if n_jobs==1
EDIT: No worries, it was a computer issue. It works perfectly now. Actually it solves the problem. Before, a user could not see the progress printed to the command line until all combinations were evaluated. Now, you get the feedback immediately if
n_jobs==1
Thanks for the info :)
This PR fixes issue #971
Performance Code
Computing Time
main
: 103 sec