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pysmac 0.6

python interface to the hyperparameter optimization tool SMAC.

Latest Version: 0.8

Simple python wrapper to SMAC, a versatile tool for optimizing algorithm parameters.

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.



pip install pysmac


python install

Example usage

Let’s take for example the Branin function. (Note that the branin function is not the ideal use case for SMAC, which is designed to be a global optimization tool for costly functions. That said, it’ll serve the purpose of checking that everything is working.)

import numpy as np

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

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

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

>>> branin(**xmin)


SMAC is free for academic & non-commercial usage. Please contact Frank Hutter to discuss obtaining a license for commercial purposes.


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),
                  custom_args={"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),

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,


Let’s for example setup 20 categorical parameters that can either take 1 or 0 as well as the objective function being the number of parameters minus the sum of all the parameter values. This objective function will be minimized if all parameters are set to 1.

ndim = 10
categorical_params = {}
for i in range(ndim):
    categorical_params["%d" % i] = [0, 1]

def sum_binary_params(x_categorical):
    return len(x_categorical.values()) - sum(x_categorical.values())

Now we can go ahead and let SMAC minimize the objective function:

xmin, fval = fmin(minfunc,

Let’s look at the result:

xmin = {'x_categorical': {'0': 1,
  '1': 1,
  '2': 1,
  '3': 1,
  '4': 1,
  '5': 1,
  '6': 1,
  '7': 1,
  '8': 1,
  '9': 1}}
File Type Py Version Uploaded on Size
pysmac-0.6.tar.gz (md5) Source 2014-04-21 7MB
  • Author: Tobias Domhan (python wrapper). Frank Hutter, Holger Hoos, Kevin Leyton-Brown, Kevin Murphy and Steve Ramage (SMAC)
  • Home Page:
  • Keywords: hyperparameter parameter optimization hyperopt bayesian smac global
  • License: SMAC is free for academic & non-commercial usage. Please contact Frank Hutter( to discuss obtaining a license for commercial purposes.
  • Package Index Owner: tdomhan, ML4AAD
  • DOAP record: pysmac-0.6.xml