simple_spearmint

A thin wrapper class around spearmint, which allows you to use it without setting up MongoDB, writing a config file, creating a separate experiment script, etc. etc.

Example

import simple_spearmint
import numpy as np

# Define a parameter space
# Supported parameter types are 'int', 'float', and 'enum'
parameter_space = {'x': {'type': 'float', 'min': -2, 'max': 2},
                   'y': {'type': 'int', 'min': 0, 'max': 3},
                   'function': {'type': 'enum', 'options': ['sin', 'cos']}}
# Create an optimizer
ss = simple_spearmint.SimpleSpearmint(parameter_space)

# Define an objective function, must return a scalar value
def objective(x, y, function):
    if function == 'sin':
        return np.sin(x)**y - y
    elif function == 'cos':
        return np.cos(x)**y - y

# Seed with 5 randomly chosen parameter settings
# (this step is optional, but can be beneficial)
for n in xrange(5):
    # Get random parameter settings
    suggestion = ss.suggest_random()
    # Retrieve an objective value for these parameters
    value = objective(suggestion['x'],
                      suggestion['y'],
                      suggestion['function'])
    print "Random trial {}: {} -> {}".format(n + 1, suggestion, value)
    # Update the optimizer on the result
    ss.update(suggestion, value)

# Run for 100 hyperparameter optimization trials
for n in xrange(100):
    # Get a suggestion from the optimizer
    suggestion = ss.suggest()
    # Get an objective value; the ** syntax is equivalent to
    # the call to objective above
    value = objective(**suggestion)
    print "GP trial {}: {} -> {}".format(n + 1, suggestion, value)
    # Update the optimizer on the result
    ss.update(suggestion, value)
best_parameters, best_objective = ss.get_best_parameters()
print "Best parameters {} for objective {}".format(
    best_parameters, best_objective)