def sample_reducer(self, length, max_value):
'''Sample Reducer (Helper)
NOTE: The Scan() object is in self.main_self because
the object being passed here is ParamGrid() object where
the Scan() object is attached as self.main_self.
Utilize 'grid_downsample', 'shuffle', and 'random_method'
to reduce the param_grid before starting the experiment.
This is the simplest method in Talos for dealing with curse
of dimensionality.
Options are uniform random, stratified random, latin hypercube
sampling, and latin hypercube with sudoku style constraint.
Returns the reduced param_grid as numpy array.
'''
random_method = self.main_self.random_method
# calculate the size of the downsample
n = int(max_value * self.main_self.grid_downsample)
# throw an error if
if n < 1:
raise TalosDataError(
"No permutations in grid. Incease grid_downsample")
# Initialize Randomizer()
r = chances.Randomizer(max_value, length)
# use the user selected method
if random_method == 'sobol':
out = r.sobol()
elif random_method == 'quantum':
out = r.quantum()
elif random_method == 'halton':
out = r.halton()
elif random_method == 'korobov_matrix':
out = r.korobov_matrix()
elif random_method == 'latin_sudoku':
out = r.latin_sudoku()
elif random_method == 'latin_matrix':
out = r.latin_matrix()
elif random_method == 'latin_improved':
out = r.latin_improved()
elif random_method == 'uniform_mersenne':
out = r.uniform_mersenne()
elif random_method == 'uniform_crypto':
out = r.uniform_crypto()
elif random_method == 'ambience':
out = r.ambience()
else:
print(
'check random_method, no eligble method found. Using uniform mersenne.'
)
out = r.uniform_mersenne()
return out
def sample_reducer(self):
'''Sample Reducer (Helper)
NOTE: The Scan() object is in self.main_self because
the object being passed here is ParamGrid() object where
the Scan() object is attached as self.main_self.
Utilize 'grid_downsample', 'shuffle', and 'random_method'
to reduce the param_grid before starting the experiment.
This is the simplest method in Talos for dealing with curse
of dimensionality.
Options are uniform random, stratified random, latin hypercube
sampling, and latin hypercube with sudoku style constraint.
Returns the reduced param_grid as numpy array.
'''
random_method = self.main_self.random_method
# calculate the size of the downsample
n = int(len(self.param_grid) * self.main_self.grid_downsample)
# initialize with random shuffle if needed
if self.main_self.shuffle is True:
random.shuffle(self.param_grid)
# creates a stratified sample
if random_method == 'stratified':
size = self.main_self.grid_downsample / 2
train, test = train_test_split(self.param_grid,
train_size=size,
test_size=size,
stratify=None)
return vstack((train, test))
# Initialize Randomizer()
r = chances.Randomizer(len(self.param_grid), n)
# use the user selected method
if random_method == 'sobol':
out = r.sobol()
elif random_method == 'quantum':
out = r.quantum()
elif random_method == 'halton':
out = r.halton()
elif random_method == 'korobov_matrix':
out = r.korobov_matrix()
elif random_method == 'latin_sudoku':
out = r.latin_sudoku()
elif random_method == 'latin_matrix':
out = r.latin_matrix()
elif random_method == 'latin_improved':
out = r.latin_improved()
elif random_method == 'uniform_mersenne':
out = r.uniform_mersenne()
elif random_method == 'uniform_crypto':
out = r.uniform_crypto()
elif random_method == 'ambience':
out = r.ambience()
return self.param_grid.take(out, axis=0)
def sample_reducer(limit, max_value, random_method):
'''Sample Reducer (Helper)
NOTE: The Scan() object is in self.main_self because
the object being passed here is ParamGrid() object where
the Scan() object is attached as self.main_self.
Utilize 'grid_downsample', 'shuffle', and 'random_method'
to reduce the param_grid before starting the experiment.
This is the simplest method in Talos for dealing with curse
of dimensionality.
Options are uniform random, stratified random, latin hypercube
sampling, and latin hypercube with sudoku style constraint.
Returns the reduced param_grid as numpy array.
'''
import chances as ch
# calculate the size of the downsample
if isinstance(limit, float):
n = int(max_value * limit)
if isinstance(limit, int):
n = limit
max_value = int(max_value)
# throw an error if
from ..utils.exceptions import TalosDataError
if n < 1:
raise TalosDataError("Limiters lead to < 1 permutations.")
# Initialize Randomizer()
r = ch.Randomizer(max_value, n)
# use the user selected method
if random_method == 'sobol':
out = r.sobol()
elif random_method == 'quantum':
out = r.quantum()
elif random_method == 'halton':
out = r.halton()
elif random_method == 'korobov_matrix':
out = r.korobov_matrix()
elif random_method == 'latin_sudoku':
out = r.latin_sudoku()
elif random_method == 'latin_matrix':
out = r.latin_matrix()
elif random_method == 'latin_improved':
out = r.latin_improved()
elif random_method == 'uniform_mersenne':
out = r.uniform_mersenne()
elif random_method == 'uniform_crypto':
out = r.uniform_crypto()
elif random_method == 'ambience':
out = r.ambience()
else:
print('No eligble random_method found. Using uniform_mersenne.')
out = r.uniform_mersenne()
return out
import chances
r = chances.Randomizer(100, 10)
nums = r.ambience()
print('Ambience passed')
nums = r.halton()
print('Halton passed')
nums = r.korobov_matrix()
print('Korobov passed')
nums = r.latin_improved()
print('Latin improved passed')
nums = r.latin_matrix()
print('Latin passed')
nums = r.latin_sudoku()
print('Latin sudoku passed')
nums = r.quantum()
print('Quantum passed')
nums = r.sobol()
print('Sobol passed')
nums = r.uniform_crypto()
print('Crypto passed')
nums = r.uniform_mersenne()
print('Mersenne passed')