https://github.com/rsteca/sklearn-deap

Use evolutionary algorithms instead of gridsearch in scikit-learn
https://github.com/rsteca/sklearn-deap

Last synced: 3 months ago
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Use evolutionary algorithms instead of gridsearch in scikit-learn

• Host: GitHub
• URL: https://github.com/rsteca/sklearn-deap
• Owner: rsteca
• License: mit
• Created: 2015-10-28T22:52:34.000Z (about 9 years ago)
• Default Branch: master
• Last Pushed: 2024-02-10T07:16:54.000Z (12 months ago)
• Last Synced: 2024-10-01T15:07:36.122Z (4 months ago)
• Language: Jupyter Notebook
• Size: 108 KB
• Stars: 771
• Watchers: 30
• Forks: 131
• Open Issues: 27
• Metadata Files:
  • Readme: README.md
  • License: LICENSE.txt

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README

        
# sklearn-deap

Use evolutionary algorithms instead of gridsearch in scikit-learn. This allows you to reduce the time required to find the best parameters for your estimator. Instead of trying out every possible combination of parameters, evolve only the combinations that give the best results.

[Here](https://github.com/rsteca/sklearn-deap/blob/master/test.ipynb) is an ipython notebook comparing EvolutionaryAlgorithmSearchCV against GridSearchCV and RandomizedSearchCV.

It's implemented using deap library: https://github.com/deap/deap

Install

-------

To install the library use pip:

    pip install sklearn-deap

or clone the repo and just type the following on your shell:

    python setup.py install

Usage examples

--------------

Example of usage:

```python

import sklearn.datasets

import numpy as np

import random

data = sklearn.datasets.load_digits()

X = data["data"]

y = data["target"]

from sklearn.svm import SVC

from sklearn.model_selection import StratifiedKFold

paramgrid = {"kernel": ["rbf"],

             "C"     : np.logspace(-9, 9, num=25, base=10),

             "gamma" : np.logspace(-9, 9, num=25, base=10)}

random.seed(1)

from evolutionary_search import EvolutionaryAlgorithmSearchCV

cv = EvolutionaryAlgorithmSearchCV(estimator=SVC(),

                                   params=paramgrid,

                                   scoring="accuracy",

                                   cv=StratifiedKFold(n_splits=4),

                                   verbose=1,

                                   population_size=50,

                                   gene_mutation_prob=0.10,

                                   gene_crossover_prob=0.5,

                                   tournament_size=3,

                                   generations_number=5,

                                   n_jobs=4)

cv.fit(X, y)

```

Output:

        Types [1, 2, 2] and maxint [0, 24, 24] detected

        --- Evolve in 625 possible combinations ---

        gen	nevals	avg     	min    	max

        0  	50    	0.202404	0.10128	0.962716

        1  	26    	0.383083	0.10128	0.962716

        2  	31    	0.575214	0.155259	0.962716

        3  	29    	0.758308	0.105732	0.976071

        4  	22    	0.938086	0.158041	0.976071

        5  	26    	0.934201	0.155259	0.976071

        Best individual is: {'kernel': 'rbf', 'C': 31622.776601683792, 'gamma': 0.001}

        with fitness: 0.976071229827

Example for maximizing just some function:

```python

from evolutionary_search import maximize

def func(x, y, m=1., z=False):

    return m * (np.exp(-(x**2 + y**2)) + float(z))

param_grid = {'x': [-1., 0., 1.], 'y': [-1., 0., 1.], 'z': [True, False]}

args = {'m': 1.}

best_params, best_score, score_results, _, _ = maximize(func, param_grid, args, verbose=False)

```

Output:

```python

best_params = {'x': 0.0, 'y': 0.0, 'z': True}

best_score  = 2.0

score_results = (({'x': 1.0, 'y': -1.0, 'z': True}, 1.1353352832366128),

 ({'x': -1.0, 'y': 1.0, 'z': True}, 1.3678794411714423),

 ({'x': 0.0, 'y': 1.0, 'z': True}, 1.3678794411714423),

 ({'x': -1.0, 'y': 0.0, 'z': True}, 1.3678794411714423),

 ({'x': 1.0, 'y': 1.0, 'z': True}, 1.1353352832366128),

 ({'x': 0.0, 'y': 0.0, 'z': False}, 2.0),

 ({'x': -1.0, 'y': -1.0, 'z': False}, 0.36787944117144233),

 ({'x': 1.0, 'y': 0.0, 'z': True}, 1.3678794411714423),

 ({'x': -1.0, 'y': -1.0, 'z': True}, 1.3678794411714423),

 ({'x': 0.0, 'y': -1.0, 'z': False}, 1.3678794411714423),

 ({'x': 1.0, 'y': -1.0, 'z': False}, 1.1353352832366128),

 ({'x': 0.0, 'y': 0.0, 'z': True}, 2.0),

 ({'x': 0.0, 'y': -1.0, 'z': True}, 2.0))

```