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python interface to the hyperparameter optimization tool SMAC.

## Project Description

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.
```

## Installation

### Pip

```pip install pysmac
```

### Manual

```python setup.py 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
0.397917
```

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

```>>> branin(**xmin)
0.397917
```

### 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_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),
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)
```

#### Example

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,
x_categorical=categorical_params,
max_evaluations=500)
```

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}}
```

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