ritam-guha / py_fs Goto Github PK

from Py_FS.evaluation import evaluate

gives the following error

"cannot import name 'plot_confusion_matrix' from 'sklearn.metrics' (/Desktop/Aurora/tesi/41.tesi_2023/venv3/lib/python3.8/site-packages/sklearn/metrics/init.py)"

Hi Guha
I want to use wrapper feature selection methods. My data consists of 200 features and I want to select the best 20 features. Is it available to select the max feature number in your code?

Hello There,
I am a bit new to all these python things but thanks to your wonderful code I am getting a bit of confidence.
I tried to use Filter method Pearson Correlation Coefficient. Got feature ranks and all.
But I couldn't understand how to prepare my dataset with the selected best features.
If you could please guide.

Hi there,

Many thanks to your great work :-) I am wondering about the potential use of the Py_FS for any regression problems?

We work in the main theme of ecology remote sensing and most of the contents are for regression problems. So, it would be great if we have a chance to use your codes with our data.

Thang

The default weights for evaluation is set at micro if this can be customised according to user inputs by adding an argument, it would have been much better

Hi, I want to contribute a dataset to run for Wrapper based PSO algorithm. How to do it?
Link to Kaggle dataset: https://www.kaggle.com/datasets/vunguyenthanhtam/spectra-nirs-mango-dataset?select=MSC+BLC+Spectra+data.csv
thanks!

In many of the algorithms, something similar to the below line has been used to split the data into two partitions.

train_test_split(train_data, train_label, stratify=train_label, test_size=0.2)

I am suggesting, instead of keeping the test_size parameter fixed to 0.2, a keyword argument can be added with a default value of 0.2. This would allow users to pass in custom values if they want while keeping a suitable default value.

Note: Please close this issue if I am mistaken.

from Py_FS.filter import SCC
fs=SCC(x.values,target)
print(fs.scores)

Above code shows 'None'. Is there anything that needs to change? How to calculate the score for either SCC or PCC?

It seems that copyright notice is not correct. Please check your license text.

"Copyright (c) 2010-2020 Google, Inc. http://angularjs.org"

ImportError: cannot import name 'MF' from 'Py_FS.wrapper.nature_inspired' (/usr/local/lib/python3.7/dist-packages/Py_FS/wrapper/nature_inspired/init.py)

If this is meant to be a comprehensive list of feature selection techniques, might be worth mentioning in the README if not incorporating pysisso https://github.com/Matgenix/pysisso