zoofs is a Python library for performing feature selection using an 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.

Last update: Dec 30, 2022

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Overview

zoofs ( Zoo Feature Selection )

zoofs is a Python library for performing feature selection using an 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.

Installation

Using pip

Use the package manager to install zoofs.

pip install zoofs

Available Algorithms

Algorithm Name	Class Name	Description	References doi
Particle Swarm Algorithm	ParticleSwarmOptimization	Utilizes swarm behaviour	10.1007/978-3-319-13563-2_51
Grey Wolf Algorithm	GreyWolfOptimization	Utilizes wolf hunting behaviour	https://doi.org/10.1016/j.neucom.2015.06.083
Dragon Fly Algorithm	DragonFlyOptimization	Utilizes dragonfly swarm behaviour	10.1016/j.knosys.2020.106131
Genetic Algorithm Algorithm	GeneticOptimization	Utilizes genetic mutation behaviour	10.1109/ICDAR.2001.953980
Gravitational Algorithm	GravitationalOptimization	Utilizes newtons gravitational behaviour	10.1109/ICASSP.2011.5946916

More algos soon, stay tuned !

[Try It Now?]

Usage

Define your own objective function for optimization !

from sklearn.metrics import log_loss
# define your own objective function, make sure the function receives four parameters,
#  fit your model and return the objective value ! 
def objective_function_topass(model,X_train, y_train, X_valid, y_valid):      
    model.fit(X_train,y_train)  
    P=log_loss(y_valid,model.predict_proba(X_valid))
    return P
    
# import an algorithm !  
from zoofs import ParticleSwarmOptimization
# create object of algorithm
algo_object=ParticleSwarmOptimization(objective_function_topass,n_iteration=20,
                                       population_size=20,minimize=True)
import lightgbm as lgb
lgb_model = lgb.LGBMClassifier()                                       
# fit the algorithm
algo_object.fit(lgb_model,X_train, y_train, X_valid, y_valid,verbose=True)
#plot your results
algo_object.plot_history()

Suggestions for Usage

As available algorithms are wrapper algos. It is better to use ml models that build quicker, e.g lightgbm, catboost.
Take sufficient amount for 'population_size' , as this will determine the extent of exploration and exploitation of the algo.
Ensure that your ml model has its hyperparamters optimized before passing it to zoofs algos.

objective score plot

Algorithms

Particle Swarm Algorithm

class zoofs.ParticleSwarmOptimization(objective_function,n_iteration=50,population_size=50,minimize=True,c1=2,c2=2,w=0.9)

Parameters


Parameters	`objective_function` : user made function of the signature 'func(model,X_train,y_train,X_test,y_test)'. The function must return a value, that needs to be minimized/maximized. `n_iteration` : int, default=50 Number of time the algorithm will run `population_size` : int, default=50 Total size of the population `minimize` : bool, default=True Defines if the objective value is to be maximized or minimized `c1` : float, default=2.0 first acceleration coefficient of particle swarm `c2` : float, default=2.0 second acceleration coefficient of particle swarm `w` : float, default=0.9 weight parameter
Attributes	`best_feature_list` : array-like Final best set of features

objective_function : user made function of the signature 'func(model,X_train,y_train,X_test,y_test)'.

The function must return a value, that needs to be minimized/maximized.

n_iteration : int, default=50

Number of time the algorithm will run

population_size : int, default=50

Total size of the population

minimize : bool, default=True

Defines if the objective value is to be maximized or minimized

c1 : float, default=2.0

first acceleration coefficient of particle swarm

c2 : float, default=2.0

second acceleration coefficient of particle swarm

w : float, default=0.9

weight parameter

Attributes

best_feature_list : array-like

Final best set of features

Methods

Methods	Class Name
fit	Run the algorithm
plot_history	Plot results achieved across iteration

fit(model,X_train, y_train, X_test, y_test,verbose=True)

Parameters


Parameters	`model` : machine learning model's object `X_train` : pandas.core.frame.DataFrame of shape (n_samples, n_features) Training input samples to be used for machine learning model `y_train` : pandas.core.frame.DataFrame or pandas.core.series.Series of shape (n_samples) The target values (class labels in classification, real numbers in regression). `X_valid` : pandas.core.frame.DataFrame of shape (n_samples, n_features) Validation input samples `y_valid` : pandas.core.frame.DataFrame or pandas.core.series.Series of shape (n_samples) The Validation target values . `verbose` : bool,default=True Print results for iterations
Returns	`best_feature_list` : array-like Final best set of features

model :

machine learning model's object

X_train : pandas.core.frame.DataFrame of shape (n_samples, n_features)

Training input samples to be used for machine learning model

y_train : pandas.core.frame.DataFrame or pandas.core.series.Series of shape (n_samples)

The target values (class labels in classification, real numbers in regression).

X_valid : pandas.core.frame.DataFrame of shape (n_samples, n_features)

Validation input samples

y_valid : pandas.core.frame.DataFrame or pandas.core.series.Series of shape (n_samples)

The Validation target values .

verbose : bool,default=True

Print results for iterations

Returns

best_feature_list : array-like

Final best set of features

plot_history()

Plot results across iterations

Example

from sklearn.metrics import log_loss
# define your own objective function, make sure the function receives four parameters,
#  fit your model and return the objective value ! 
def objective_function_topass(model,X_train, y_train, X_valid, y_valid):      
    model.fit(X_train,y_train)  
    P=log_loss(y_valid,model.predict_proba(X_valid))
    return P
    
# import an algorithm !  
from zoofs import ParticleSwarmOptimization
# create object of algorithm
algo_object=ParticleSwarmOptimization(objective_function_topass,n_iteration=20,
                                       population_size=20,minimize=True,c1=2,c2=2,w=0.9)
import lightgbm as lgb
lgb_model = lgb.LGBMClassifier()                      
# fit the algorithm
algo_object.fit(lgb_model,X_train, y_train, X_valid, y_valid,verbose=True)
#plot your results
algo_object.plot_history()

Grey Wolf Algorithm

class zoofs.GreyWolfOptimization(objective_function,n_iteration=50,population_size=50,minimize=True)

Parameters


Parameters	`objective_function` : user made function of the signature 'func(model,X_train,y_train,X_test,y_test)'. The function must return a value, that needs to be minimized/maximized. `n_iteration` : int, default=50 Number of time the algorithm will run `population_size` : int, default=50 Total size of the population `minimize` : bool, default=True Defines if the objective value is to be maximized or minimized
Attributes	`best_feature_list` : array-like Final best set of features

objective_function : user made function of the signature 'func(model,X_train,y_train,X_test,y_test)'.

The function must return a value, that needs to be minimized/maximized.

n_iteration : int, default=50

Number of time the algorithm will run

population_size : int, default=50

Total size of the population

minimize : bool, default=True

Defines if the objective value is to be maximized or minimized

Attributes

best_feature_list : array-like

Final best set of features

Methods

Methods	Class Name
fit	Run the algorithm
plot_history	Plot results achieved across iteration

fit(model,X_train,y_train,X_valid,y_valid,method=1,verbose=True)

Parameters


Parameters	`model` : machine learning model's object `X_train` : pandas.core.frame.DataFrame of shape (n_samples, n_features) Training input samples to be used for machine learning model `y_train` : pandas.core.frame.DataFrame or pandas.core.series.Series of shape (n_samples) The target values (class labels in classification, real numbers in regression). `X_valid` : pandas.core.frame.DataFrame of shape (n_samples, n_features) Validation input samples `y_valid` : pandas.core.frame.DataFrame or pandas.core.series.Series of shape (n_samples) The Validation target values . `method` : {1, 2}, default=1 Choose the between the two methods of grey wolf optimization `verbose` : bool,default=True Print results for iterations
Returns	`best_feature_list` : array-like Final best set of features

model :

machine learning model's object

X_train : pandas.core.frame.DataFrame of shape (n_samples, n_features)

Training input samples to be used for machine learning model

y_train : pandas.core.frame.DataFrame or pandas.core.series.Series of shape (n_samples)

The target values (class labels in classification, real numbers in regression).

X_valid : pandas.core.frame.DataFrame of shape (n_samples, n_features)

Validation input samples

y_valid : pandas.core.frame.DataFrame or pandas.core.series.Series of shape (n_samples)

The Validation target values .

method : {1, 2}, default=1

Choose the between the two methods of grey wolf optimization

verbose : bool,default=True

Print results for iterations

Returns

best_feature_list : array-like

Final best set of features

plot_history()

Plot results across iterations

Example

from sklearn.metrics import log_loss
# define your own objective function, make sure the function receives four parameters,
#  fit your model and return the objective value ! 
def objective_function_topass(model,X_train, y_train, X_valid, y_valid):      
    model.fit(X_train,y_train)  
    P=log_loss(y_valid,model.predict_proba(X_valid))
    return P
    
# import an algorithm !  
from zoofs import GreyWolfOptimization
# create object of algorithm
algo_object=GreyWolfOptimization(objective_function_topass,n_iteration=20,
                                    population_size=20,minimize=True)
import lightgbm as lgb
lgb_model = lgb.LGBMClassifier()                                       
# fit the algorithm
algo_object.fit(lgb_model,X_train, y_train, X_valid, y_valid,method=1,verbose=True)
#plot your results
algo_object.plot_history()

Dragon Fly Algorithm

class zoofs.DragonFlyOptimization(objective_function,n_iteration=50,population_size=50,minimize=True)

Parameters


Parameters	`objective_function` : user made function of the signature 'func(model,X_train,y_train,X_test,y_test)'. The function must return a value, that needs to be minimized/maximized. `n_iteration` : int, default=50 Number of time the algorithm will run `population_size` : int, default=50 Total size of the population `minimize` : bool, default=True Defines if the objective value is to be maximized or minimized
Attributes	`best_feature_list` : array-like Final best set of features

objective_function : user made function of the signature 'func(model,X_train,y_train,X_test,y_test)'.

The function must return a value, that needs to be minimized/maximized.

n_iteration : int, default=50

Number of time the algorithm will run

population_size : int, default=50

Total size of the population

minimize : bool, default=True

Defines if the objective value is to be maximized or minimized

Attributes

best_feature_list : array-like

Final best set of features

Methods

Methods	Class Name
fit	Run the algorithm
plot_history	Plot results achieved across iteration

fit(model,X_train,y_train,X_valid,y_valid,method='sinusoidal',verbose=True)

Parameters


Parameters	`model` : machine learning model's object `X_train` : pandas.core.frame.DataFrame of shape (n_samples, n_features) Training input samples to be used for machine learning model `y_train` : pandas.core.frame.DataFrame or pandas.core.series.Series of shape (n_samples) The target values (class labels in classification, real numbers in regression). `X_valid` : pandas.core.frame.DataFrame of shape (n_samples, n_features) Validation input samples `y_valid` : pandas.core.frame.DataFrame or pandas.core.series.Series of shape (n_samples) The Validation target values . `method` : {'linear','random','quadraic','sinusoidal'}, default='sinusoidal' Choose the between the three methods of Dragon Fly optimization `verbose` : bool,default=True Print results for iterations
Returns	`best_feature_list` : array-like Final best set of features

model :

machine learning model's object

X_train : pandas.core.frame.DataFrame of shape (n_samples, n_features)

Training input samples to be used for machine learning model

y_train : pandas.core.frame.DataFrame or pandas.core.series.Series of shape (n_samples)

The target values (class labels in classification, real numbers in regression).

X_valid : pandas.core.frame.DataFrame of shape (n_samples, n_features)

Validation input samples

y_valid : pandas.core.frame.DataFrame or pandas.core.series.Series of shape (n_samples)

The Validation target values .

method : {'linear','random','quadraic','sinusoidal'}, default='sinusoidal'

Choose the between the three methods of Dragon Fly optimization

verbose : bool,default=True

Print results for iterations

Returns

best_feature_list : array-like

Final best set of features

plot_history()

Plot results across iterations

Example

from sklearn.metrics import log_loss
# define your own objective function, make sure the function receives four parameters,
#  fit your model and return the objective value ! 
def objective_function_topass(model,X_train, y_train, X_valid, y_valid):      
    model.fit(X_train,y_train)  
    P=log_loss(y_valid,model.predict_proba(X_valid))
    return P
    
# import an algorithm !  
from zoofs import DragonFlyOptimization
# create object of algorithm
algo_object=DragonFlyOptimization(objective_function_topass,n_iteration=20,
                                    population_size=20,minimize=True)
import lightgbm as lgb
lgb_model = lgb.LGBMClassifier()                                     
# fit the algorithm
algo_object.fit(lgb_model,X_train, y_train, X_valid, y_valid, method='sinusoidal', verbose=True)
#plot your results
algo_object.plot_history()

Genetic Algorithm

class zoofs.GeneticOptimization(objective_function,n_iteration=20,population_size=20,selective_pressure=2,elitism=2,mutation_rate=0.05,minimize=True)

Parameters


Parameters	`objective_function` : user made function of the signature 'func(model,X_train,y_train,X_test,y_test)'. The function must return a value, that needs to be minimized/maximized. `n_iteration`: int, default=50 Number of time the algorithm will run `population_size` : int, default=50 Total size of the population `selective_pressure`: int, default=2 measure of reproductive opportunities for each organism in the population `elitism`: int, default=2 number of top individuals to be considered as elites `mutation_rate`: float, default=0.05 rate of mutation in the population's gene `minimize`: bool, default=True Defines if the objective value is to be maximized or minimized
Attributes	`best_feature_list` : array-like Final best set of features

objective_function : user made function of the signature 'func(model,X_train,y_train,X_test,y_test)'.

The function must return a value, that needs to be minimized/maximized.

n_iteration: int, default=50

Number of time the algorithm will run

population_size : int, default=50

Total size of the population

selective_pressure: int, default=2

measure of reproductive opportunities for each organism in the population

elitism: int, default=2

number of top individuals to be considered as elites

mutation_rate: float, default=0.05

rate of mutation in the population's gene

minimize: bool, default=True

Defines if the objective value is to be maximized or minimized

Attributes

best_feature_list : array-like

Final best set of features

Methods

Methods	Class Name
fit	Run the algorithm
plot_history	Plot results achieved across iteration

fit(model,X_train,y_train,X_valid,y_valid,verbose=True)

Parameters


Parameters	`model` : machine learning model's object `X_train` : pandas.core.frame.DataFrame of shape (n_samples, n_features) Training input samples to be used for machine learning model `y_train` : pandas.core.frame.DataFrame or pandas.core.series.Series of shape (n_samples) The target values (class labels in classification, real numbers in regression). `X_valid` : pandas.core.frame.DataFrame of shape (n_samples, n_features) Validation input samples `y_valid` : pandas.core.frame.DataFrame or pandas.core.series.Series of shape (n_samples) The Validation target values . `verbose` : bool,default=True Print results for iterations
Returns	`best_feature_list` : array-like Final best set of features

model :

machine learning model's object

X_train : pandas.core.frame.DataFrame of shape (n_samples, n_features)

Training input samples to be used for machine learning model

y_train : pandas.core.frame.DataFrame or pandas.core.series.Series of shape (n_samples)

The target values (class labels in classification, real numbers in regression).

X_valid : pandas.core.frame.DataFrame of shape (n_samples, n_features)

Validation input samples

y_valid : pandas.core.frame.DataFrame or pandas.core.series.Series of shape (n_samples)

The Validation target values .

verbose : bool,default=True

Print results for iterations

Returns

best_feature_list : array-like

Final best set of features

plot_history()

Plot results across iterations

Example

from sklearn.metrics import log_loss
# define your own objective function, make sure the function receives four parameters,
#  fit your model and return the objective value ! 
def objective_function_topass(model,X_train, y_train, X_valid, y_valid):      
    model.fit(X_train,y_train)  
    P=log_loss(y_valid,model.predict_proba(X_valid))
    return P
    
# import an algorithm !  
from zoofs import GeneticOptimization
# create object of algorithm
algo_object=GeneticOptimization(objective_function_topass,n_iteration=20,
                            population_size=20,selective_pressure=2,elitism=2,
                            mutation_rate=0.05,minimize=True)
import lightgbm as lgb
lgb_model = lgb.LGBMClassifier()                            
# fit the algorithm
algo_object.fit(lgb_model,X_train, y_train,X_valid, y_valid, verbose=True)
#plot your results
algo_object.plot_history()

Gravitational Algorithm

class zoofs.GravitationalOptimization(self,objective_function,n_iteration=50,population_size=50,g0=100,eps=0.5,minimize=True)

Parameters


Parameters	`objective_function` : user made function of the signature 'func(model,X_train,y_train,X_test,y_test)'. The function must return a value, that needs to be minimized/maximized. `n_iteration`: int, default=50 Number of time the algorithm will run `population_size` : int, default=50 Total size of the population `g0`: float, default=100 gravitational strength constant `eps`: float, default=0.5 distance constant `minimize`: bool, default=True Defines if the objective value is to be maximized or minimized
Attributes	`best_feature_list` : array-like Final best set of features

objective_function : user made function of the signature 'func(model,X_train,y_train,X_test,y_test)'.

The function must return a value, that needs to be minimized/maximized.

n_iteration: int, default=50

Number of time the algorithm will run

population_size : int, default=50

Total size of the population

g0: float, default=100

gravitational strength constant

eps: float, default=0.5

distance constant

minimize: bool, default=True

Defines if the objective value is to be maximized or minimized

Attributes

best_feature_list : array-like

Final best set of features

Methods

Methods	Class Name
fit	Run the algorithm
plot_history	Plot results achieved across iteration

fit(model,X_train,y_train,X_valid,y_valid,verbose=True)

Parameters


Parameters	`model` : machine learning model's object `X_train` : pandas.core.frame.DataFrame of shape (n_samples, n_features) Training input samples to be used for machine learning model `y_train` : pandas.core.frame.DataFrame or pandas.core.series.Series of shape (n_samples) The target values (class labels in classification, real numbers in regression). `X_valid` : pandas.core.frame.DataFrame of shape (n_samples, n_features) Validation input samples `y_valid` : pandas.core.frame.DataFrame or pandas.core.series.Series of shape (n_samples) The Validation target values . `verbose` : bool,default=True Print results for iterations
Returns	`best_feature_list` : array-like Final best set of features

model :

machine learning model's object

X_train : pandas.core.frame.DataFrame of shape (n_samples, n_features)

Training input samples to be used for machine learning model

y_train : pandas.core.frame.DataFrame or pandas.core.series.Series of shape (n_samples)

The target values (class labels in classification, real numbers in regression).

X_valid : pandas.core.frame.DataFrame of shape (n_samples, n_features)

Validation input samples

y_valid : pandas.core.frame.DataFrame or pandas.core.series.Series of shape (n_samples)

The Validation target values .

verbose : bool,default=True

Print results for iterations

Returns

best_feature_list : array-like

Final best set of features

plot_history()

Plot results across iterations

Example

from sklearn.metrics import log_loss
# define your own objective function, make sure the function receives four parameters,
#  fit your model and return the objective value ! 
def objective_function_topass(model,X_train, y_train, X_valid, y_valid):      
    model.fit(X_train,y_train)  
    P=log_loss(y_valid,model.predict_proba(X_valid))
    return P
    
# import an algorithm !  
from zoofs import GravitationalOptimization
# create object of algorithm
algo_object=GravitationalOptimization(objective_function,n_iteration=50,
                                population_size=50,g0=100,eps=0.5,minimize=True) 
import lightgbm as lgb
lgb_model = lgb.LGBMClassifier()                                
# fit the algorithm
algo_object.fit(lgb_model,X_train, y_train, X_valid, y_valid, verbose=True)
#plot your results
algo_object.plot_history()

Support `zoofs`

The development of zoofs relies completely on contributions.

Contributing

Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.

Please make sure to update tests as appropriate.

First roll out

18,08,2021

License

apache-2.0

Comments

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.

enhancement

opened by hanamthang 9
[Snyk] Security upgrade mistune from 0.8.4 to 2.0.1
Snyk has created this PR to fix one or more vulnerable packages in the `pip` dependencies of this project.

Changes included in this PR

Changes to the following files to upgrade the vulnerable dependencies to a fixed version:

docs/requirement.txt

⚠️ Warning

notebook 5.7.13 requires terminado, which is not installed. nbformat 4.4.0 requires jsonschema, which is not installed. nbconvert 5.6.1 has requirement mistune<2,>=0.8.1, but you have mistune 2.0.2. mkdocs-material 8.0.1 requires mkdocs, which is not installed. mkdocs-material 8.0.1 requires pymdown-extensions, which is not installed. mkdocs-material 8.0.1 requires mkdocs-material-extensions, which is not installed. mkdocs-material 8.0.1 requires markdown, which is not installed.

Vulnerabilities that will be fixed

By pinning:

Severity | Issue | Upgrade | Breaking Change | Exploit Maturity :-------------------------:|:-------------------------|:-------------------------|:-------------------------|:------------------------- | Cross-site Scripting (XSS)
SNYK-PYTHON-MISTUNE-2328096 | mistune:
0.8.4 -> 2.0.1
| No | No Known Exploit

Some vulnerabilities couldn't be fully fixed and so Snyk will still find them when the project is tested again. This may be because the vulnerability existed within more than one direct dependency, but not all of the effected dependencies could be upgraded.

Check the changes in this PR to ensure they won't cause issues with your project.

Note: You are seeing this because you or someone else with access to this repository has authorized Snyk to open fix PRs.

For more information: 🧐 View latest project report

🛠 Adjust project settings

📚 Read more about Snyk's upgrade and patch logic
opened by snyk-bot 2
[Snyk] Fix for 3 vulnerabilities
Snyk has created this PR to fix one or more vulnerable packages in the `pip` dependencies of this project.

Changes included in this PR

Changes to the following files to upgrade the vulnerable dependencies to a fixed version:

docs/requirement.txt

⚠️ Warning

notebook 5.7.13 requires terminado, which is not installed. nbformat 4.4.0 requires jsonschema, which is not installed. mkdocs-material 8.0.1 requires mkdocs, which is not installed. mkdocs-material 8.0.1 requires pymdown-extensions, which is not installed. mkdocs-material 8.0.1 requires mkdocs-material-extensions, which is not installed. mkdocs-material 8.0.1 requires markdown, which is not installed.

Vulnerabilities that will be fixed

By pinning:

Severity | Priority Score (*) | Issue | Upgrade | Breaking Change | Exploit Maturity :-------------------------:|-------------------------|:-------------------------|:-------------------------|:-------------------------|:------------------------- | 624/1000
Why? Has a fix available, CVSS 8.2 | Arbitrary Code Execution
SNYK-PYTHON-IPYTHON-2348630 | ipython:
5.10.0 -> 7.16.3
| No | No Known Exploit | 696/1000
Why? Proof of Concept exploit, Has a fix available, CVSS 7.5 | Regular Expression Denial of Service (ReDoS)
SNYK-PYTHON-PYGMENTS-1086606 | pygments:
2.5.2 -> 2.7.4
| No | Proof of Concept | 589/1000
Why? Has a fix available, CVSS 7.5 | Denial of Service (DoS)
SNYK-PYTHON-PYGMENTS-1088505 | pygments:
2.5.2 -> 2.7.4
| No | No Known Exploit

(*) Note that the real score may have changed since the PR was raised.

Some vulnerabilities couldn't be fully fixed and so Snyk will still find them when the project is tested again. This may be because the vulnerability existed within more than one direct dependency, but not all of the effected dependencies could be upgraded.

Check the changes in this PR to ensure they won't cause issues with your project.

Note: You are seeing this because you or someone else with access to this repository has authorized Snyk to open fix PRs.

For more information: 🧐 View latest project report

🛠 Adjust project settings

📚 Read more about Snyk's upgrade and patch logic
opened by snyk-bot 2
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)

opened by veeresh-dammur 2
Add a Gitter chat badge to README.md

jaswinder9051998/zoofs now has a Chat Room on Gitter

@jaswinder9051998 has just created a chat room. You can visit it here: https://gitter.im/zooFeatureSelection/general.

This pull-request adds this badge to your README.md:

If my aim is a little off, please let me know.

Happy chatting.

PS: Click here if you would prefer not to receive automatic pull-requests from Gitter in future.

opened by gitter-badger 1
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

opened by hanamthang 1
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.

opened by aigarspetresevics 0
[Snyk] Fix for 2 vulnerabilities
This PR was automatically created by Snyk using the credentials of a real user.

Snyk has created this PR to fix one or more vulnerable packages in the `pip` dependencies of this project.

Changes included in this PR

Changes to the following files to upgrade the vulnerable dependencies to a fixed version:

docs/requirement.txt

⚠️ Warning

mkdocs-material 8.0.1 requires pygments, which is not installed. mkdocs-material 8.0.1 requires mkdocs-material-extensions, which is not installed. mkdocs-material 8.0.1 requires markdown, which is not installed. mkdocs-material 8.0.1 requires pymdown-extensions, which is not installed. mkdocs-material 8.0.1 requires mkdocs, which is not installed. jupyter-nbextensions-configurator 0.6.1 requires notebook, which is not installed. jupyter-contrib-nbextensions 0.7.0 requires nbconvert, which is not installed. jupyter-contrib-nbextensions 0.7.0 requires notebook, which is not installed. jupyter-contrib-core 0.4.2 requires notebook, which is not installed.

Vulnerabilities that will be fixed

By pinning:

Severity | Priority Score (*) | Issue | Upgrade | Breaking Change | Exploit Maturity :-------------------------:|-------------------------|:-------------------------|:-------------------------|:-------------------------|:------------------------- | 551/1000
Why? Recently disclosed, Has a fix available, CVSS 5.3 | Regular Expression Denial of Service (ReDoS)
SNYK-PYTHON-SETUPTOOLS-3180412 | setuptools:
39.0.1 -> 65.5.1
| No | No Known Exploit | 551/1000
Why? Recently disclosed, Has a fix available, CVSS 5.3 | Regular Expression Denial of Service (ReDoS)
SNYK-PYTHON-WHEEL-3180413 | wheel:
0.30.0 -> 0.38.0
| No | No Known Exploit

(*) Note that the real score may have changed since the PR was raised.

Some vulnerabilities couldn't be fully fixed and so Snyk will still find them when the project is tested again. This may be because the vulnerability existed within more than one direct dependency, but not all of the affected dependencies could be upgraded.

Check the changes in this PR to ensure they won't cause issues with your project.

Note: You are seeing this because you or someone else with access to this repository has authorized Snyk to open fix PRs.

For more information: 🧐 View latest project report

🛠 Adjust project settings

📚 Read more about Snyk's upgrade and patch logic

Learn how to fix vulnerabilities with free interactive lessons:

🦉 Regular Expression Denial of Service (ReDoS) 🦉 Regular Expression Denial of Service (ReDoS)
opened by jaswinder9051998 2
[Snyk] Security upgrade setuptools from 39.0.1 to 65.5.1
This PR was automatically created by Snyk using the credentials of a real user.

Snyk has created this PR to fix one or more vulnerable packages in the `pip` dependencies of this project.

Changes included in this PR

Changes to the following files to upgrade the vulnerable dependencies to a fixed version:

docs/requirement.txt

⚠️ Warning

notebook 5.7.16 requires terminado, which is not installed. nbformat 4.4.0 requires jsonschema, which is not installed. nbconvert 5.6.1 has requirement mistune<2,>=0.8.1, but you have mistune 2.0.4. mkdocs-material 8.0.1 requires mkdocs, which is not installed. mkdocs-material 8.0.1 requires pymdown-extensions, which is not installed. mkdocs-material 8.0.1 requires mkdocs-material-extensions, which is not installed. mkdocs-material 8.0.1 requires markdown, which is not installed. jupyter-nbextensions-configurator 0.5.0 has requirement notebook>=6.0, but you have notebook 5.7.16. ipython 5.10.0 requires simplegeneric, which is not installed.

Vulnerabilities that will be fixed

By pinning:

Severity | Priority Score (*) | Issue | Upgrade | Breaking Change | Exploit Maturity :-------------------------:|-------------------------|:-------------------------|:-------------------------|:-------------------------|:------------------------- | 441/1000
Why? Recently disclosed, Has a fix available, CVSS 3.1 | Regular Expression Denial of Service (ReDoS)
SNYK-PYTHON-SETUPTOOLS-3113904 | setuptools:
39.0.1 -> 65.5.1
| No | No Known Exploit

(*) Note that the real score may have changed since the PR was raised.

Some vulnerabilities couldn't be fully fixed and so Snyk will still find them when the project is tested again. This may be because the vulnerability existed within more than one direct dependency, but not all of the affected dependencies could be upgraded.

Check the changes in this PR to ensure they won't cause issues with your project.

Note: You are seeing this because you or someone else with access to this repository has authorized Snyk to open fix PRs.

For more information: 🧐 View latest project report

🛠 Adjust project settings

📚 Read more about Snyk's upgrade and patch logic

Learn how to fix vulnerabilities with free interactive lessons:

🦉 Regular Expression Denial of Service (ReDoS)
opened by jaswinder9051998 1
[Snyk] Security upgrade wheel from 0.30.0 to 0.38.0
Snyk has created this PR to fix one or more vulnerable packages in the `pip` dependencies of this project.

Changes included in this PR

Changes to the following files to upgrade the vulnerable dependencies to a fixed version:

docs/requirement.txt

⚠️ Warning

notebook 5.7.16 requires pyzmq, which is not installed. notebook 5.7.16 requires terminado, which is not installed. nbformat 4.4.0 requires jsonschema, which is not installed. nbconvert 5.6.1 has requirement mistune<2,>=0.8.1, but you have mistune 2.0.4. mkdocs-material 8.0.1 requires mkdocs, which is not installed. mkdocs-material 8.0.1 requires markdown, which is not installed. mkdocs-material 8.0.1 requires mkdocs-material-extensions, which is not installed. mkdocs-material 8.0.1 requires pymdown-extensions, which is not installed. jupyter-nbextensions-configurator 0.5.0 has requirement notebook>=6.0, but you have notebook 5.7.16. jupyter-client 5.3.5 requires pyzmq, which is not installed. ipython 5.10.0 requires simplegeneric, which is not installed.

Vulnerabilities that will be fixed

By pinning:

Severity | Priority Score (*) | Issue | Upgrade | Breaking Change | Exploit Maturity :-------------------------:|-------------------------|:-------------------------|:-------------------------|:-------------------------|:------------------------- | 551/1000
Why? Recently disclosed, Has a fix available, CVSS 5.3 | Regular Expression Denial of Service (ReDoS)
SNYK-PYTHON-WHEEL-3092128 | wheel:
0.30.0 -> 0.38.0
| No | No Known Exploit

(*) Note that the real score may have changed since the PR was raised.

Some vulnerabilities couldn't be fully fixed and so Snyk will still find them when the project is tested again. This may be because the vulnerability existed within more than one direct dependency, but not all of the affected dependencies could be upgraded.

Check the changes in this PR to ensure they won't cause issues with your project.

Note: You are seeing this because you or someone else with access to this repository has authorized Snyk to open fix PRs.

For more information: 🧐 View latest project report

🛠 Adjust project settings

📚 Read more about Snyk's upgrade and patch logic

Learn how to fix vulnerabilities with free interactive lessons:

🦉 Regular Expression Denial of Service (ReDoS)
opened by snyk-bot 1
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

opened by aigarspetresevics 0
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 ??

opened by klil21 2

Releases(v0.1.24)

v0.1.24(Mar 5, 2022)

Source code(tar.gz)
Source code(zip)
v0.1.20(Mar 1, 2022)

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Source code(zip)
v0.1.8(Feb 22, 2022)

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v0.1.7(Feb 4, 2022)

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v0.1.4(Nov 2, 2021)

Source code(tar.gz)
Source code(zip)
v0.1.2(Sep 16, 2021)

Source code(tar.gz)
Source code(zip)
v0.1.1_zenodo(Sep 15, 2021)

Source code(tar.gz)
Source code(zip)
v0.1.1(Sep 12, 2021)

0.1.1 new release
Source code(tar.gz)
Source code(zip)

Owner

Jaswinder Singh

Associate Software Engineer - Data Science

GitHub Repository

This repository provides some codes to demonstrate several variants of Markov-Chain-Monte-Carlo (MCMC) Algorithms.

Demo-of-MCMC These files are based on the class materials of AEROSP 567 taught by Prof. Alex Gorodetsky at University of Michigan. Author: Hung-Hsiang

1 Feb 05, 2022

Rover. Finding the shortest pass by Dijkstra’s shortest path algorithm

rover Rover. Finding the shortest path by Dijkstra’s shortest path algorithm Задача Вы — инженер, проектирующий роверы-беспилотники. Вам надо спроекти

1 Nov 11, 2021

marching Squares algorithm in python with clean code.

Marching Squares marching Squares algorithm in python with clean code. Tools Python 3 EasyDraw Creators Mohammad Dori Run the Code Installation Requir

3 Jul 15, 2022

A lightweight, pure-Python mobile robot simulator designed for experiments in Artificial Intelligence (AI) and Machine Learning, especially for Jupyter Notebooks

aitk.robots A lightweight Python robot simulator for JupyterLab, Notebooks, and other Python environments. Goals A lightweight mobile robotics simulat

3 Oct 22, 2021

zoofs is a Python library for performing feature selection using an 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.

Related tags

Overview

zoofs ( Zoo Feature Selection )

Installation

Using pip

Available Algorithms

Usage

Suggestions for Usage

objective score plot

Algorithms

Particle Swarm Algorithm

class zoofs.ParticleSwarmOptimization(objective_function,n_iteration=50,population_size=50,minimize=True,c1=2,c2=2,w=0.9)

Methods

fit(model,X_train, y_train, X_test, y_test,verbose=True)

plot_history()

Example

Grey Wolf Algorithm

class zoofs.GreyWolfOptimization(objective_function,n_iteration=50,population_size=50,minimize=True)

Methods

fit(model,X_train,y_train,X_valid,y_valid,method=1,verbose=True)

plot_history()

Example

Dragon Fly Algorithm

class zoofs.DragonFlyOptimization(objective_function,n_iteration=50,population_size=50,minimize=True)

Methods

fit(model,X_train,y_train,X_valid,y_valid,method='sinusoidal',verbose=True)

plot_history()

Example

Genetic Algorithm

class zoofs.GeneticOptimization(objective_function,n_iteration=20,population_size=20,selective_pressure=2,elitism=2,mutation_rate=0.05,minimize=True)

Methods

fit(model,X_train,y_train,X_valid,y_valid,verbose=True)

plot_history()

Example

Gravitational Algorithm

class zoofs.GravitationalOptimization(self,objective_function,n_iteration=50,population_size=50,g0=100,eps=0.5,minimize=True)

Methods

fit(model,X_train,y_train,X_valid,y_valid,verbose=True)

plot_history()

Example

Support zoofs

Contributing

First roll out

License

Comments

Snyk has created this PR to fix one or more vulnerable packages in the `pip` dependencies of this project.

Changes included in this PR

Vulnerabilities that will be fixed

By pinning:

Snyk has created this PR to fix one or more vulnerable packages in the `pip` dependencies of this project.

Changes included in this PR

Vulnerabilities that will be fixed

By pinning:

jaswinder9051998/zoofs now has a Chat Room on Gitter

Snyk has created this PR to fix one or more vulnerable packages in the `pip` dependencies of this project.

Changes included in this PR

Vulnerabilities that will be fixed

By pinning:

Snyk has created this PR to fix one or more vulnerable packages in the `pip` dependencies of this project.

Changes included in this PR

Vulnerabilities that will be fixed

By pinning:

Snyk has created this PR to fix one or more vulnerable packages in the `pip` dependencies of this project.

Changes included in this PR

Vulnerabilities that will be fixed

By pinning:

Releases(v0.1.24)

v0.1.24(Mar 5, 2022)

v0.1.20(Mar 1, 2022)

v0.1.8(Feb 22, 2022)

v0.1.7(Feb 4, 2022)

v0.1.4(Nov 2, 2021)

v0.1.2(Sep 16, 2021)

v0.1.1_zenodo(Sep 15, 2021)

v0.1.1(Sep 12, 2021)

Owner

Jaswinder Singh

This repository provides some codes to demonstrate several variants of Markov-Chain-Monte-Carlo (MCMC) Algorithms.

Rover. Finding the shortest pass by Dijkstra’s shortest path algorithm

Support `zoofs`