import itertools
from skopt.space import Integer, Real
from .tuner import Tuner
from .tuner_exceptions import NoParamsException
[docs]class GridSearchTuner(Tuner):
"""Grid Search Optimizer
Example:
>>> tuner = GridSearchTuner([(1,10), ['A', 'B']], n_points=5)
>>> print(tuner.propose())
(1.0, 'A')
>>> print(tuner.propose())
(1.0, 'B')
>>> print(tuner.propose())
(3.25, 'A')
"""
[docs] def __init__(self, space, n_points=10, random_state=0):
""" Generate all of the possible points to search for in the grid
Arguments:
space: A list of all dimensions available to tune
n_points: The number of points to sample from along each dimension
defined in the ``space`` argument
random_state: Unused in this class
"""
raw_dimensions = list()
for dimension in space:
# Categorical dimension
if isinstance(dimension, list):
range_values = dimension
elif isinstance(dimension, (Real, Integer, tuple)):
if isinstance(dimension, (tuple)) and isinstance(dimension[0], (int, float)) and isinstance(dimension[1], (int, float)):
if dimension[1] > dimension[0]:
low = dimension[0]
high = dimension[1]
else:
error_text = "Upper bound must be greater than lower bound. Parameter lower bound is {0} and upper bound is {1}"
error_text = error_text.format(dimension[0], dimension[1])
raise ValueError(error_text)
else:
low = dimension.low
high = dimension.high
delta = (high - low) / (n_points - 1)
if isinstance(dimension, Integer):
range_values = [int((x * delta) + low) for x in range(n_points)]
else:
range_values = [(x * delta) + low for x in range(n_points)]
else:
raise TypeError("Invalid dimension type in tuner")
raw_dimensions.append(range_values)
self._grid_points = itertools.product(*raw_dimensions)
self.curr_params = None
[docs] def add(self, parameters, score):
"""Not applicable to grid search tuner as generated parameters are
not dependent on scores of previous parameters.
Arguments:
parameters: Hyperparameters used
score: Associated score
"""
pass
[docs] def propose(self):
""" Returns hyperparameters from _grid_points iterations
If all possible combinations of parameters have been scored, then ``NoParamsException`` is raised.
Returns:
dict: proposed hyperparameters
"""
if not self.curr_params:
self.is_search_space_exhausted()
params = self.curr_params
self.curr_params = None
return params
[docs] def is_search_space_exhausted(self):
"""Checks if it is possible to generate a set of valid parameters. Stores generated parameters in
``self.curr_params`` to be returned by ``propose()``.
Raises:
NoParamsException: If a search space is exhausted, then this exception is thrown.
Returns:
bool: If no more valid parameters exists in the search space, return false.
"""
try:
self.curr_params = next(self._grid_points)
return False
except StopIteration:
raise NoParamsException("Grid search has exhausted all possible parameters.")
return True