eptune 0.1.5

Evolutionary parameter tuning

eptune

eptune (evolutionary parameter tuning) is a python package trying to use evolutionary computation algorithms to do parameter tuning.

Install

pip install eptune

How to use

Using following lines can fine tune MNIST dataset with 5-Fold CV performance using the qtuneSimple function.

from eptune.sample_cases import DigitsCV
from eptune.quick import qtuneSimple
from eptune.parameter import *
from sklearn.svm import SVC

# Prameter space to search
params = [
    LogFloatParameter([0.01, 1e4], 'C'),
    CategoricalParameter(['rbf'], 'kernel'),
    LogFloatParameter([1e-6, 1e4], 'gamma')
]

# Define objective function
cv_svc_digits = DigitsCV(SVC())


def evaluate(params):
    return cv_svc_digits.cv_loss_with_params(cv=5, **params)


# Call `qtuneSimple`
population, logbook, hof = qtuneSimple(params,
                                       evaluate,
                                       n_pop=10,
                                       n_jobs=10,
                                       mutpb=0.6,
                                       cxpb=0.8,
                                       seed=42)

# Plot the logbook if needed
fig = logbook.plot(['min', 'avg'])

gen	nevals	avg          	std        	min          	max          
0  	10    	[-0.28174736]	[0.3288165]	[-0.96772398]	[-0.10072343]
1  	7     	[-0.70684474]	[0.36593114]	[-0.97273233]	[-0.10072343]
2  	4     	[-0.8786867] 	[0.2590384] 	[-0.97273233]	[-0.10183639]
3  	8     	[-0.62526433]	[0.41696083]	[-0.97440178]	[-0.10072343]
4  	8     	[-0.80116861]	[0.34319099]	[-0.97440178]	[-0.10072343]
5  	6     	[-0.96143573]	[0.0257779] 	[-0.97440178]	[-0.89816361]
6  	7     	[-0.9475793] 	[0.06357501]	[-0.97440178]	[-0.75959933]
7  	6     	[-0.97250974]	[0.00531551]	[-0.97440178]	[-0.95659432]
8  	7     	[-0.97445743]	[0.00016694]	[-0.97495826]	[-0.97440178]
9  	8     	[-0.73567056]	[0.36697176]	[-0.97495826]	[-0.10072343]
10 	7     	[-0.79810796]	[0.34639554]	[-0.97495826]	[-0.10072343]

The best parameters are stored in HallofFame object:

hof

[({'C': 197.75053974020003, 'kernel': 'rbf', 'gamma': 0.0005362324820364681}, (-0.9749582637729549,)), ({'C': 197.75053974020003, 'kernel': 'rbf', 'gamma': 0.00044545277111534496}, (-0.9744017807456873,))]

More control

If you want more control, you can check:

1. eptune.sklearn module provides ScikitLearner or ScikitLearnerCV for fine tune parameter of estimators with scikit learn API. Examples are also provided in the documentation.
2. eptune.algorithms module provides algorithms to access the DEAP framework directly.