pysmac 0.3

python interface to SMAC.

Simple python wrapper to SMAC

fmin(objective, x0, xmin, xmax, x0_int, xmin_int, xmax_int, xcategorical, params)
   min_x f(x) s.t. xmin < x < xmax

 objective: The objective function that should be optimized.

Installation

Pip

pip install pysmac

Manual

python setup.py install

Example usage

Let’s take for example the Branin function:

import numpy as np

def branin(x):
    x1 = x[0]
    x2 = x[1]
    a = 1.
    b = 5.1 / (4.*np.pi**2)
    c = 5. / np.pi
    r = 6.
    s = 10.
    t = 1. / (8.*np.pi)
    ret  = a*(x2-b*x1**2+c*x1-r)**2+s*(1-t)*np.cos(x1)+s
    print ret
    return ret

For x1 ∈ [-5, 10], x2 ∈ [0, 15] the function reaches a minimum value of: 0.397887.

Note: fmin accepts any function that has a parameter called x (the input array) and returns an objective value.

from pysmac.optimize import fmin

xmin, fval = fmin(branin, x0=(0,0),xmin=(-5, 0), xmax=(10, 15), max_evaluations=5000)

As soon as the evaluations are finished, we can check the output:

>>> xmin
{'x': array([ 3.14305644,  2.27827543])}

>>> fval
0.397917

Let’s run the objective function with the found parameters:

>>> branin(**xmin)
0.397917

Advanced

Custom arguments to the objective function:

Note: make sure there is no naming collission with the parameter names and the custom arguments.

def minfunc(x, custom_arg1, custom_arg2):
    print "custom_arg1:", custom_arg1
    print "custom_arg2:", custom_arg2
    return 1


xmin, fval = fmin(minfunc, x0=(0,0),xmin=(-5, 0), xmax=(10, 15),
                  max_evaluations=5000,
                  custom_arg1="test",
                  custom_arg2=123)

Integer parameters

Integer parameters can be encoded as follows:

def minfunc(x, x_int):
    print "x: ", x
    print "x_int: ", x_int
    return 1.

xmin, fval = fmin(minfunc,
                  x0=(0,0), xmin=(-5, 0), xmax=(10, 15),
                  x0_int=(0,0), xmin_int=(-5, 0), xmax_int=(10, 15),
                  max_evaluations=5000)

Categorical parameters

Categorical parameters can be specified as a dictionary of lists of values they can take on, e.g.:

categorical_params = {"param1": [1,2,3,4,5,6,7],
                      "param2": ["string1", "string2", "string3"]}

def minfunc(x_categorical):
    print "param1: ", x_categorical["param1"]
    print "param2: ", x_categorical["param2"]
    return 1.

xmin, fval = fmin(minfunc,
                  x_categorical=categorical_params,
                  max_evaluations=5000)