jaswinder9051998 / zoofs Goto Github PK

zoofs is a python library for performing feature selection using a variety of nature-inspired wrapper algorithms. The algorithms range from swarm-intelligence to physics-based to Evolutionary. It's easy to use , flexible and powerful tool to reduce your feature size.

Home Page: https://jaswinder9051998.github.io/zoofs/

License: Apache License 2.0

Python 100.00%

evolutionary-algorithms feature-selection genetic-algorithm grey-wolf grey-wolf-optimizer machine-learning machine-learning-algorithms machinelearning optimization optimization-algorithms optimization-methods optimization-tools particle-swarm particle-swarm-optimization python subset-selection supervised-learning

zoofs's Introduction

Hi , I am Jaswinder

Data scientist

I have experience working in Fintech and Banking domain.
Author of Zoofs and NitroFE

EXPERIENCE ....

✔ Data Science

Sep 2020 - Present
Experience in ML development for integrating various B2B applications, leveraging machine learning,
data regression, rule-based models, robust algorithms and techniques. Responsible for measuring and optimizing the quality of algorithms and models.

✔ Product I have worked on

B2B Credit Limit Suggestion
Created the product for predicting Credit Limit actions (upgrade/downgrade/extension) to be taken for B2B customers.
Historical action, Sales forecast and industry specific credit information is utilized to predict credit action to mitigate risk for the companies.
Model has been adopted by two companies to perform proactive credit management and manage defaulting customers.
Customer Segmentation
Created the product for periodic customer segmentation of B2B customers to facilitate better collection strategies,
using clustering algorithms and a unique ELO based-customer rating system.
Also Responsible for addition of explainable k-means algorithms for cluster explanation to boost customer trust in the solution.
The final delivery helped the companies to curate collection strategies using customer segments.
Payment date prediction
Created the product for predicting the payment date of invoices for B2B customers.
Model has been adopted by companies to proactively identify delayed invoices and act on them to improve their order to cash lifecycle.

✔ My open source work

✔ My publications

COLLECTIVE STATE IMPLEMENTATION ON PARTICLE SWARM FOR FEATURE SELECTION
ICDICI-2020, ISBN : 978-981-15-8529-6
Binary particle search optimization has limitations of premature and slow convergence. Hence a new collective
state implementation for particle swarm optimization is proposed in this work. The proposed method is
validated with five benchmark datasets. To measure the impact of the algorithm, our proposed solution was
compared against two popular feature selection algorithms Binary Particle Swarm Optimization (BPSO) and
Genetic Algorithm (GA). Our results show that our proposed solution Collective state implementation- Particle
Swarm optimization (CSI-BPSO) achieves competitive scores in feature selection

My working tools...

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zoofs's Issues

Disabling verbose still prints logs

Setting verbose=False still produces output at every iteration. This is problematic since the JSON file can get very large when the fit function runs for prolonged period of time.

Hyperparameter optimization for algorithms in zoofs

Hi Jaswinder,

Would you consider to add the function like GridSearch for hyper-parameter optimization of the algorithm, such as GWO, in the zoofs?
This library, PySwarm (https://github.com/tisimst/pyswarm) for instance, they provide a GridSearch to find the best combination of the parameters c, w1, w2.

For now, I have to do the trial and error to test which ranges of parameters in the GWO (population, iteration, method) deliver the best result for my dataset.

Many thanks,
Thang

Use zoofs for regression problems?

Hi there,

Is there any chance to use zoofs for regression problems?

Many thanks,
Thang

Is Zoofs support GPU rather than using CPU?

We all know optimization a very time consuming task. So, do Zoofs support a GPU implementation recently? Or may be in a near future?
Thanks for your great work, which help us improve the current works a lot.
Thang

Cross validating

Would you please be able to help in how to run this using cross validation?

Are you planning to support sklearn's API?

I think that it will be very convenient if pipeline can be used as follows

from sklearn.pipeline import Pipeline
from sklearn.ensemble import RandomForestRegressor

pipe = Pipeline(steps=[("selector", ParticleSwarmOptimization()), 
                　　　　　　　　　　　　　　("Regressor", RandomForestRegressor())])
pipe.fit(X,y)

Speed-up suggestions

It doesn't accept numpy arrays and so numba is out of question.
Any suggestions to improve speed? When you have 100+ feature columns it takes atleast 2 weeks running 24/7

Number of features

First of all i want to thank you for this amazing library , just i want to ask can the size of best_feature_list can be declared before starting the algorithm ??

Whale optimization algorithm

Is your feature request related to a problem? Please describe.
Look into the feasibility of Whale optimization algorithm

prediction

While implementing prediction in classification of ZOOFS PSO model im getting error, kindly add the prediction part in your code

Looking for integrated Harris Haw Optimization in the zoofs

Additional context
Harris Haw Optimization (HHO) is a novel meta-heuristic optimization algorithm released in 2019 with an increasing of applied research papers. It would be great if the team can add the HHO to the zoofs which will be potential for further testing and make the zoofs more popular.

How to run GreyWolf optimizer for multiple objective?

Feature importance

Hi,
Thanks for the great repo. I would like to know whether we can get the ranking of the selected features after using one of your algorithm (ex: particle swarm optimization)

jaswinder9051998 / zoofs Goto Github PK

zoofs's Introduction

Hi , I am Jaswinder

Data scientist

zoofs's People

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Stargazers

Watchers

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zoofs's Issues

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