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from collections import OrderedDict, namedtuple
from functools import cmp_to_key, reduce
from itertools import combinations
from .pairs_storage import PairsStorage, key
class Item:
@property
def id(self):
return self.__item_id
@property
def value(self):
return self.__value
@property
def weights(self):
return self.__weights
def __init__(self, item_id, value):
self.__item_id = item_id
self.__value = value
self.set_weights([])
def __str__(self):
return str(self.__dict__)
def set_weights(self, weights):
self.__weights = weights
def get_max_combination_number(prameter_matrix, n):
param_len_list = [len(value_list) for value_list in prameter_matrix]
return sum([reduce(lambda x, y: x * y, z) for z in combinations(param_len_list, n)])
def cmp_item(lhs, rhs):
if lhs.weights == rhs.weights:
return 0
return -1 if lhs.weights < rhs.weights else 1
class AllPairs:
def __init__(self, parameters, filter_func=lambda x: True, previously_tested=None, n=2):
"""
TODO: check that input arrays are:
- (optional) has no duplicated values inside single array / or compress such values
"""
if not previously_tested:
previously_tested = [[]]
self.__validate_parameter(parameters)
self.__is_ordered_dict_param = isinstance(parameters, OrderedDict)
self.__param_name_list = self.__extract_param_name_list(parameters)
self.__pairs_class = namedtuple("Pairs", self.__param_name_list)
self.__filter_func = filter_func
self.__n = n
self.__pairs = PairsStorage(n)
value_matrix = self.__extract_value_matrix(parameters)
self.__max_unique_pairs_expected = get_max_combination_number(value_matrix, n)
self.__working_item_matrix = self.__get_working_item_matrix(value_matrix)
for arr in previously_tested:
if not arr:
continue
if len(arr) != len(self.__working_item_matrix):
raise RuntimeError("previously tested combination is not complete")
if not self.__filter_func(arr):
raise ValueError("invalid tested combination is provided")
tested = []
for i, val in enumerate(arr):
idxs = [
Item(item.id, 0) for item in self.__working_item_matrix[i] if item.value == val
]
if len(idxs) != 1:
raise ValueError(
"value from previously tested combination is not "
"found in the parameters or found more than "
"once"
)
tested.append(idxs[0])
self.__pairs.add_sequence(tested)
def __iter__(self):
return self
def next(self):
return self.__next__()
def __next__(self):
assert len(self.__pairs) <= self.__max_unique_pairs_expected
if len(self.__pairs) == self.__max_unique_pairs_expected:
# no reasons to search further - all pairs are found
raise StopIteration()
previous_unique_pairs_count = len(self.__pairs)
chosen_item_list = [None] * len(self.__working_item_matrix)
indexes = [None] * len(self.__working_item_matrix)
direction = 1
i = 0
while -1 < i < len(self.__working_item_matrix):
if direction == 1:
# move forward
self.__resort_working_array(chosen_item_list[:i], i)
indexes[i] = 0
elif direction == 0 or direction == -1:
# scan current array or go back
indexes[i] += 1
if indexes[i] >= len(self.__working_item_matrix[i]):
direction = -1
if i == 0:
raise StopIteration()
i += direction
continue
direction = 0
else:
raise ValueError(f"next(): unknown 'direction' code '{direction}'")
chosen_item_list[i] = self.__working_item_matrix[i][indexes[i]]
if self.__filter_func(self.__get_values(chosen_item_list[: i + 1])):
assert direction > -1
direction = 1
else:
direction = 0
i += direction
if len(self.__working_item_matrix) != len(chosen_item_list):
raise StopIteration()
self.__pairs.add_sequence(chosen_item_list)
if len(self.__pairs) == previous_unique_pairs_count:
# could not find new unique pairs - stop
raise StopIteration()
# replace returned array elements with real values and return it
return self.__get_iteration_value(chosen_item_list)
def __validate_parameter(self, value):
if isinstance(value, OrderedDict):
for parameter_list in value.values():
if not parameter_list:
raise ValueError("each parameter arrays must have at least one item")
return
if len(value) < 2:
raise ValueError("must provide more than one option")
for parameter_list in value:
if not parameter_list:
raise ValueError("each parameter arrays must have at least one item")
def __resort_working_array(self, chosen_item_list, num):
for item in self.__working_item_matrix[num]:
data_node = self.__pairs.get_node_info(item)
new_combs = [
# numbers of new combinations to be created if this item is
# appended to array
{key(z) for z in combinations(chosen_item_list + [item], i + 1)}
- self.__pairs.get_combs()[i]
for i in range(0, self.__n)
]
# weighting the node node that creates most of new pairs is the best
weights = [-len(new_combs[-1])]
# less used outbound connections most likely to produce more new
# pairs while search continues
weights.extend(
[len(data_node.out)]
+ [len(x) for x in reversed(new_combs[:-1])]
+ [-data_node.counter] # less used node is better
)
# otherwise we will prefer node with most of free inbound
# connections; somehow it works out better ;)
weights.append(-len(data_node.in_))
item.set_weights(weights)
self.__working_item_matrix[num].sort(key=cmp_to_key(cmp_item))
def __get_working_item_matrix(self, parameter_matrix):
return [
[
Item(f"a{param_idx:d}v{value_idx:d}", value)
for value_idx, value in enumerate(value_list)
]
for param_idx, value_list in enumerate(parameter_matrix)
]
@staticmethod
def __get_values(item_list):
return [item.value for item in item_list]
def __get_iteration_value(self, item_list):
if not self.__param_name_list:
return [item.value for item in item_list]
return self.__pairs_class(*[item.value for item in item_list])
def __extract_param_name_list(self, parameters):
if not self.__is_ordered_dict_param:
return []
return list(parameters)
def __extract_value_matrix(self, parameters):
if not self.__is_ordered_dict_param:
return parameters
return [v for v in parameters.values()]
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