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

Jump to bottom

Update dependencies, add python 3.10, 3.11 tests, ensure code compatibility #106

Open
wants to merge 7 commits into
base: develop
Choose a base branch
from
Open

Update dependencies, add python 3.10, 3.11 tests, ensure code compatibility #106

Changes from 1 commit
Commits
Show all changes
7 commits
Select commit Hold shift + click to select a range
d9e8ea4
move click to seperate section, remove req file
mateusz-wozny Nov 20, 2023
ed5b204
improve readme
mateusz-wozny Nov 20, 2023
9d872b6
loose dependencies, update notebooks, update code to use newest featu…
mateusz-wozny May 17, 2024
fa25221
scikit poshocs~=0.7
mateusz-wozny May 17, 2024
4f5d47c
fix flake8
mateusz-wozny May 17, 2024
658aa67
Update readme, add py310 and py311
mateusz-wozny May 17, 2024
2ae819a
Update readme
mateusz-wozny May 17, 2024
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
Prev Previous commit
Next Next commit
improve readme
  • Loading branch information
@mateusz-wozny
mateusz-wozny committed Nov 20, 2023
commit ed5b204ca7f12f6baf5b193f988953f646d3c0b6
7 changes: 5 additions & 2 deletions README.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -46,15 +46,18 @@ Our package includes implementation of such algorithms, as:
## Example usage
```python
from multi_imbalance.resampling.mdo import MDO
import numpy as np
from sklearn.tree import DecisionTreeClassifier
from sklearn.model_selection import train_test_split

# Mahalanbois Distance Oversampling
mdo = MDO(k=9, k1_frac=0, seed=0)

# read the data
X_train, y_train, X_test, y_test = ...
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)

# preprocess
X_train_resampled, y_train_resampled = mdo.fit_transform(np.copy(X_train), np.copy(y_train))
X_train_resampled, y_train_resampled = mdo.fit_resample(np.copy(X_train), np.copy(y_train))

# train the classifier on preprocessed data
clf_tree = DecisionTreeClassifier(random_state=0)
Expand Down