Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Fix error with numpy version #129

Open
wants to merge 1 commit into
base: master
Choose a base branch
from
Open

Fix error with numpy version #129

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
14 changes: 7 additions & 7 deletions boruta/boruta_py.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -316,12 +316,12 @@ def _fit(self, X, y):
# 0 - default state = tentative in original code
# 1 - accepted in original code
# -1 - rejected in original code
dec_reg = np.zeros(n_feat, dtype=int)
dec_reg = np.zeros(n_feat, dtype=np.int)
# counts how many times a given feature was more important than
# the best of the shadow features
hit_reg = np.zeros(n_feat, dtype=int)
hit_reg = np.zeros(n_feat, dtype=np.int)
# these record the history of the iterations
imp_history = np.zeros(n_feat, dtype=float)
imp_history = np.zeros(n_feat, dtype=np.float)
sha_max_history = []

# set n_estimators
Expand Down Expand Up @@ -393,13 +393,13 @@ def _fit(self, X, y):

# basic result variables
self.n_features_ = confirmed.shape[0]
self.support_ = np.zeros(n_feat, dtype=bool)
self.support_ = np.zeros(n_feat, dtype=np.bool)
self.support_[confirmed] = 1
self.support_weak_ = np.zeros(n_feat, dtype=bool)
self.support_weak_ = np.zeros(n_feat, dtype=np.bool)
self.support_weak_[tentative] = 1

# ranking, confirmed variables are rank 1
self.ranking_ = np.ones(n_feat, dtype=int)
self.ranking_ = np.ones(n_feat, dtype=np.int)
# tentative variables are rank 2
self.ranking_[tentative] = 2
# selected = confirmed and tentative
Expand All @@ -425,7 +425,7 @@ def _fit(self, X, y):
self.ranking_[not_selected] = ranks
else:
# all are selected, thus we set feature supports to True
self.support_ = np.ones(n_feat, dtype=bool)
self.support_ = np.ones(n_feat, dtype=np.bool)

self.importance_history_ = imp_history

Expand Down