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evolearn-1.0.8
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مشاهده بیشتر
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مشاهده بیشتر
تبلیغات ما
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مشاهده بیشتر
تبلیغات ما
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مشاهده بیشترتوضیحات
Machine learning related GA tools.
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | evolearn-1.0.8 |
| نام | evolearn |
| نسخه کتابخانه | 1.0.8 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Hindy Yuen |
| ایمیل نویسنده | hindy888@hotmail.com |
| آدرس صفحه اصلی | https://github.com/HindyDS/evo-learn |
| آدرس اینترنتی | https://pypi.org/project/evolearn/ |
| مجوز | MIT |
Evolutionary Algorithm For Machine Learning
Installation
------------
To use evolearn, first install it using pip:
pip install evolearn
Genetic Optimization CV
----------------
To perform hyperparameter tuning using genetic algoritm,
you need to first import other modules from
1) ``evolearn.hyperparameter_tuning.initialization``
2) ``evolearn.hyperparameter_tuning.evaluation``
3) ``evolearn.hyperparameter_tuning.selection``
4) ``evolearn.hyperparameter_tuning.mating``
5) ``evolearn.hyperparameter_tuning.reproduction``
6) ``evolearn.hyperparameter_tuning.mutation``
7) ``evolearn.hyperparameter_tuning.environment`` (optional)
8) ``evolearn.hyperparameter_tuning.genetic_hyperparameter_tuning``
Although the modules from ``environment`` are optional for you to determine to
use them in your search or not, the searching might end up stopping early or not
finding the ideal results. These modules can help to prevent pre-mature convergence
and also control other hyperparameters for GA.
For example:
from evolearn.hyperparameter_tuning.initialization import Genes
from evolearn.hyperparameter_tuning.evaluation import FitnessFunction
from evolearn.hyperparameter_tuning.selection import (RankSelection,
RouletteWheelSelection,
SteadyStateSelection,
TournamentSelection,
StochasticUniversalSampling,
BoltzmannSelection
)
from evolearn.hyperparameter_tuning.mating import MatingFunction
from evolearn.hyperparameter_tuning.reproduction import (KPointCrossover,
LinearCombinationCrossover,
FitnessProportionateAverage
)
from evolearn.hyperparameter_tuning.mutation import (Boundary,
Shrink
)
from evolearn.hyperparameter_tuning.environment import (AdaptiveReproduction,
AdaptiveMutation,
Elitism
)
from evolearn.hyperparameter_tuning.genetic_hyperparameter_tuning import GenesSearchCV
from sklearn.ensemble import RandomForestRegressor
search_space_rf = {
'max_depth':(1, 16, 'uniform'),
'n_estimators':(100, 1000, 'uniform'),
'criterion':('squared_error', 'absolute_error', 'poisson')
}
opt = GenesSearchCV(
n_gen=10,
initialization_fn=Genes(search_space=search_space_rf, pop_size=30),
fitness_fn=FitnessFunction(
estimator=RandomForestRegressor(n_jobs=-1),
cv=3,
scoring='neg_mean_absolute_error',
),
selection_fn=StochasticUniversalSampling(.7),
mating_fn=MatingFunction(increst_prevention=False),
reproduction_fn=KPointCrossover(1),
mutation_fn=Shrink(),
adaptive_population=AdaptiveReproduction(10),
elitism=Elitism(),
adaptive_mutation=AdaptiveMutation()
)
opt.fit(X_train, y_train)
Max Fitness: -2023.200579609583
{'max_depth': 5, 'n_estimators': 561, 'criterion': 'absolute_error'}
The choices of ``selection_fn``, ``reproduction_fn``, ``mutation_fn`` are
actually up to your personal preference. One can pick what they believe
are most benefit to their searching preocess.
Genetic Feature Selection
-------------------------
To perform feature selection using genetic algoritm,
you need to first import other modules from
1) ``evolearn.feature_selection.initialization``
2) ``evolearn.feature_selection.evaluation``
3) ``evolearn.feature_selection.selection``
4) ``evolearn.feature_selection.mating``
5) ``evolearn.feature_selection.reproduction``
6) ``evolearn.feature_selection.mutation``
7) ``evolearn.feature_selection.environment`` (optional)
8) ``evolearn.feature_selection.genetic_feature_selection``
The modules looks similar to those modules from the
``GenesSearchCV`` section, but in fact their internal mechanisim
work slightly differently. You need to be ware of importing the
wrong modules when using genetic feature selection.
For example:
from evolearn.feature_selection.initialization import Genes
from evolearn.feature_selection.evaluation import FitnessFunction
from evolearn.feature_selection.selection import (RankSelection,
RouletteWheelSelection,
SteadyStateSelection,
TournamentSelection,
StochasticUniversalSampling,
BoltzmannSelection
)
from evolearn.feature_selection.mating import MatingFunction
from evolearn.feature_selection.reproduction import KPointCrossover
from evolearn.feature_selection.mutation import (BitStringMutation,
ExchangeMutation,
ShiftMutation
)
from evolearn.feature_selection.environment import (AdaptiveReproduction,
AdaptiveMutation,
Elitism
)
from evolearn.feature_selection.genetic_feature_selection import GeneticFeatureSelectionCV
from sklearn.ensemble import RandomForestRegressor
opt = GeneticFeatureSelectionCV(
n_gen=10,
initialization_fn=Genes(pop_size=50),
fitness_fn=FitnessFunction(
estimator=RandomForestRegressor(n_jobs=-1),
cv=3,
scoring='neg_mean_absolute_error'
),
selection_fn=RouletteWheelSelection(.7),
mating_fn=MatingFunction(),
reproduction_fn=KPointCrossover(k=4),
mutation_fn=BitStringMutation(),
adaptive_population=None,
elitism=None,
adaptive_mutation=None
)
opt.fit(X_train, y_train)
print(opt.best_fitness_)
print(opt.best_params_)
-2797.7245589631652
{'age': True, 'sex': False, 'bmi': True, 'children': True, 'smoker': True, 'region': False}
زبان مورد نیاز
| مقدار | نام |
|---|---|
| >=3.6 | Python |
نحوه نصب
نصب پکیج whl evolearn-1.0.8:
pip install evolearn-1.0.8.whl
نصب پکیج tar.gz evolearn-1.0.8:
pip install evolearn-1.0.8.tar.gz