def test_make_iteration():
our_map = Map(
number_of_clusters=4,
data=data.loc[data["Ожидаемая урожайность / приплод"] == "КРДР"],
)
clusters = our_map._create_clusters(
num_batch=(7, 15, 21, 42),
clusternames=our_map.clusternames,
number_of_clusters=4,
graph=our_map.graph,
)
clusters = our_map._make_iteration(clusters=clusters, graph=our_map.graph)
# for x in clusters:
# print(x.name)
# for x in clusters:
# print(x.square)
print([x.square for x in clusters])
print([x.name for x in clusters])
print([x.adjacent_list for x in clusters])
names = [x.name.split() for x in clusters]
names_2 = [set(x.name.split()) for x in clusters]
for x, y in zip(names, names_2):
if len(x) != len(y):
print("PANIc")
print(x)
print(y)
print(names)
for i in range(len(names)):
for j in range(i, len(names)):
if i != j:
print("shit", i, j, set(names[i]).intersection(set(names[j])))
assert 1 == 0
def test_launch_algo():
our_map = Map(
number_of_clusters=4,
data=data.loc[data["Ожидаемая урожайность / приплод"] == "КРДР"],
)
our_map.launch_algo()
assert 0 == 1
def test_clusters_chose_cluster_with_maxsquare_neighbor():
column_names = ["№", "neighbors", "S"]
data = [
["1", "2, 3", 1],
["2", "1", 1],
["3", "1, 5", 1],
["5", "3", 10],
]
dataframe = pd.DataFrame(data=data, columns=column_names)
our_map = Map(number_of_clusters=4, data=dataframe)
clusters = our_map._create_clusters(
num_batch=(0, 1, 2),
clusternames=our_map.clusternames,
number_of_clusters=4,
graph=our_map.graph,
)
print(
"111",
[(y.name, y.adjacent_list, x.serial_num) for x in clusters
for y in x.nodes_belong],
)
print([x.adjacent_list for x in clusters])
cluster_with_biggest_neithbor = (
our_map._clusters_chose_cluster_with_maxsquare_neighbor(
clusters, our_map.graph))
print(cluster_with_biggest_neithbor)
print([x for x in cluster_with_biggest_neithbor.adjacent_list])
assert {
x.name
for x in cluster_with_biggest_neithbor.nodes_belong
} == {"3"
}, f"{[x.name for x in cluster_with_biggest_neithbor.nodes_belong]=}"
def test_try_chose_smallest_cluster():
column_names = ["№", "neighbors", "S"]
data = [
["1", "", 1],
["2", "", 7],
["3", "1", 10],
["3", "4", 20],
]
dataframe = pd.DataFrame(data=data, columns=column_names)
our_map = Map(number_of_clusters=3, data=dataframe)
clusters = our_map._create_clusters(
num_batch=(0, 1, 2),
clusternames=our_map.clusternames,
number_of_clusters=4,
graph=our_map.graph,
)
for cluster in clusters:
for node in cluster.nodes_belong:
our_map.restricted_nodes.add(node.name)
print(our_map.restricted_nodes)
# TODO сделать реальную проверку
cluster_with_smallest_square = our_map._try_chose_smallest_cluster(
clusters, our_map.restricted_nodes)
print(our_map.restricted_nodes, [x.name for x in clusters])
assert (cluster_with_smallest_square.name == "3"
), f"{cluster_with_smallest_square.name=}"
def test_cut_graph_for_cluster():
column_names = ["№", "neighbors", "S"]
data = [
["1", "2, 3", 1],
["2", "1", 1],
["3", "1, 5, 6", 1],
["5", "3", 1],
["6", "3, 4", 1],
["4", "6, 2", 1],
]
dataframe = pd.DataFrame(data=data, columns=column_names)
our_map = Map(number_of_clusters=4, data=dataframe)
clusters = our_map._create_clusters(
num_batch=(0, 1, 2, 3),
clusternames=our_map.clusternames,
number_of_clusters=4,
graph=our_map.graph,
)
print([(y.name, y.adjacent_list, x.serial_num) for x in clusters
for y in x.nodes_belong])
target_serial_num = 0
cut_graph = our_map._cut_graph_for_cluster(graph=our_map.graph,
clusternum=target_serial_num)
print([(x, y.belongs_to_cluster) for x, y in our_map.graph.items()])
print([(x, y.belongs_to_cluster, y.adjacent_list)
for x, y in cut_graph.items()])
print(cut_graph)
for y in cut_graph.values():
assert (y.belongs_to_cluster == target_serial_num
or not y.belongs_to_cluster), f"{y.belongs_to_cluster=}"
def __init__(self, data: pd.DataFrame, given_sum: int,
number_of_clusters: int):
self.crop_map = Map(data=data, number_of_clusters=number_of_clusters)
self.given_sum = given_sum
self.number_of_clusters = number_of_clusters
self.graph_len = len(self.crop_map.graph)
self.leaders = []
def test_create_batches():
our_map = Map(number_of_clusters=4, data=data)
combinations = our_map._create_batches(graph_len=5, number_of_clusters=3)
result = list(combinations)
assert len(result) == 10, f"{len(result)=}"
def test_cluster_join_field_default():
serial_num = 1
nodes = [Node("1", ["2", "3"], 1)]
test = Cluster(
serial_num=serial_num,
nodes_belong=nodes,
)
column_names = ["№", "neighbors", "S"]
data = [
["1", "2, 3", 1],
["2", "1", 1],
["3", "1, 5, 6", 3],
["5", "3", 1],
["6", "3, 4", 1],
["4", "6", 2],
]
dataframe = pd.DataFrame(data=data, columns=column_names)
graph = Map._build_graph(data=dataframe)
nodes = [graph[x] for x in test.adjacent_list]
func_to_chose_node = test._chose_max_square_node
node_chosen = test.cluster_join_node(func_field_chose=func_to_chose_node,
nodes=nodes)
assert node_chosen == "3", f"{node_chosen=}"
assert test.adjacent_list == {"2", "5", "6"}, f"{test.adjacent_list=}"
def test_cluster_join_specific_node():
serial_num = 1
nodes = [Node("1", ["2", "3"], 1)]
test = Cluster(
serial_num=serial_num,
nodes_belong=nodes,
)
column_names = ["№", "neighbors", "S"]
data = [
["1", "2, 3", 1],
["2", "1", 1],
["3", "1, 5, 6", 3],
["5", "3", 1],
["6", "3, 4", 1],
["4", "6", 2],
]
dataframe = pd.DataFrame(data=data, columns=column_names)
graph = Map._build_graph(data=dataframe)
nodes = [graph[x] for x in test.adjacent_list]
print(test.adjacent_list)
node_chosen = test.cluster_join_specific_node(graph["3"])
print(test.adjacent_list)
assert node_chosen == "3", f"{node_chosen=}"
assert test.adjacent_list == {"2", "5", "6"}, f"{test.adjacent_list=}"
def test_create_clusters():
data = [
["1", "2, 3", 1],
["2", "1, 3", 1],
["3", "1, 2", 1],
]
column_names = ["№", "neighbors", "S"]
dataframe = pd.DataFrame(data=data, columns=column_names)
our_map = Map(number_of_clusters=4, data=dataframe)
result = our_map._create_clusters(
num_batch=(0, 1, 2),
clusternames=our_map.clusternames,
number_of_clusters=4,
graph=our_map.graph,
)
assert len(result) == 3, f"{len(result)=}"
for x in result:
assert not x.adjacent_list, f"{x.adjacent_list=}"
def test_clusters_chose_cluster_with_smallest_square():
column_names = ["№", "neighbors", "S"]
data = [
["1", "2, 3", 10],
["2", "1", 5],
["3", "1, 5", 4],
["5", "3", 3],
]
dataframe = pd.DataFrame(data=data, columns=column_names)
our_map = Map(number_of_clusters=4, data=dataframe)
clusters = our_map._create_clusters(
num_batch=(0, 1, 2, 3),
clusternames=our_map.clusternames,
number_of_clusters=4,
graph=our_map.graph,
)
print([(y.name, y.adjacent_list, x.serial_num, x.square) for x in clusters
for y in x.nodes_belong])
cluster_with_smallest_square = our_map._clusters_chose_cluster_with_smallest_square(
clusters)
print(cluster_with_smallest_square.serial_num)
assert cluster_with_smallest_square.square == 3.0
f"{cluster_with_smallest_square.serial_num=}, {cluster_with_smallest_square.square=}"
def validate_double_edges(data: pd.DataFrame) -> List:
"""
валидирует входную таблицу на наличие неориентированных ребер у соседних нод
:param data:
:return:
"""
# column_names = ["№", "neighbors", "S"]
graph = Map._build_graph(data)
res = []
for key in graph.keys():
for node_name in graph[key].adjacent_list:
if not key in graph[node_name].adjacent_list:
print("PANIC", key, node_name)
res.append((key, node_name))
return res
def validate_node_name_not_in_adj_list(data: pd.DataFrame) -> List:
"""
Валидирует входную таблицу
Args:
data:
Returns:
"""
# column_names = ["№", "neighbors", "S"]
graph = Map._build_graph(data)
res = []
for node in graph.values():
if node.name in node.adjacent_list:
res.append((node.name, node.adjacent_list))
return res
def test_build_graph_default():
column_names = ["№", "neighbors", "S"]
data = [
["1", "2, 3", 1],
["2", "1", 1],
["3", "1, 5, 6", 1],
["5", "3", 1],
["6", "3, 4", 1],
["4", "6", 1],
]
dataframe = pd.DataFrame(data=data, columns=column_names)
graph = Map._build_graph(data=dataframe)
assert (graph["1"] == Node(name="1", adjacent_list={"2", "3"}, square=1)
and graph["2"] == Node(name="2", adjacent_list={"1"}, square=1)
and graph["3"] == Node(
name="3", adjacent_list={"1", "5", "6"}, square=1)
and graph["5"] == Node(name="5", adjacent_list={"3"}, square=1) and
graph["6"] == Node(name="6", adjacent_list={"3", "4"}, square=1)
and graph["4"] == Node(name="4", adjacent_list={"6"},
square=1)), f"{graph['1'].adjacent_list=}"
def test_clusters_bring_closer():
column_names = ["№", "neighbors", "S"]
data = [
["1", "2, 3", 1],
["2", "1, 3, 4", 7],
["3", "1, 2, 5, 6", 10],
["4", "2, 5, 8", 6],
["5", "3, 4, 6, 7, 8", 4],
["6", "3, 5, 7", 6],
["7", "5, 6, 8", 1],
["8", "4, 5, 7", 5],
]
dataframe = pd.DataFrame(data=data, columns=column_names)
our_map = Map(number_of_clusters=3, data=dataframe)
clusters = our_map._create_clusters(
num_batch=(0, 5, 7),
clusternames=our_map.clusternames,
number_of_clusters=4,
graph=our_map.graph,
)
cluster_with_smallest_square = our_map._clusters_chose_cluster_with_smallest_square(
clusters)
# def _chose_max_square_node(nodes: List[Node]) -> Node:
node = cluster_with_smallest_square._chose_max_square_node(
[our_map.graph[x] for x in cluster_with_smallest_square.adjacent_list])
clusters.pop(clusters.index(cluster_with_smallest_square))
cluster_with_smallest_square.cluster_join_specific_node(node)
# TODO сделать настоящий сет
our_map._clusters_bring_closer(clusters,
cluster_with_smallest_square.square,
set(["a", "b"]))
clusters.append(cluster_with_smallest_square)
for cluster in clusters:
assert cluster.square == 11.0, f"{cluster.name=}, {cluster.square=}"
class Solver:
"""
решатель на основе перебора, с предварительным склеиванием вершин до значения
given_sum-количество вершин, до которого нужно уменьшить количество вершин в графе
"""
def __init__(self, data: pd.DataFrame, given_sum: int,
number_of_clusters: int):
self.crop_map = Map(data=data, number_of_clusters=number_of_clusters)
self.given_sum = given_sum
self.number_of_clusters = number_of_clusters
self.graph_len = len(self.crop_map.graph)
self.leaders = []
@staticmethod
def _make_combinations_with_given_sum(given_sum: int,
number_of_clusters: int,
graph_len: int) -> Iterator[Tuple]:
"""
выдает всевозможные комбинации заданной длины с заданной суммой
:param given_sum: заданная сумма
:param number_of_clusters: количество кластеров(размерность тупла)
:param graph_len: размер графа
:return: комбинация
"""
for x in itertools.product(range(1, given_sum),
repeat=number_of_clusters):
if not sum(x) == given_sum:
continue
yield x
@staticmethod
def _make_combinations_with_given_sum_new(
given_sum: int, number_of_clusters: int,
graph_len: int) -> Iterator[Tuple]:
"""
выдает всевозможные комбинации заданной длины с заданной суммой
:param given_sum: заданная сумма
:param number_of_clusters: количество кластеров(размерность тупла)
:param graph_len: размер графа
:return: комбинация
"""
iterset = set()
for x in itertools.product(range(1, given_sum),
repeat=number_of_clusters):
if not sum(x) == given_sum:
continue
xx = tuple(sorted([i for i in x]))
if xx in iterset:
continue
iterset.add(xx)
yield xx
@staticmethod
def _make_combinations_with_given_sum_extra(
given_sum: int, number_of_clusters: int, graph_len: int,
cut_bound_max: int) -> Iterator[Tuple]:
"""
выдает всевозможные комбинации заданной длины с заданной суммой
:param given_sum: заданная сумма
:param number_of_clusters: количество кластеров(размерность тупла)
:param graph_len: размер графа
:return: комбинация
"""
iterset = set()
for x in itertools.product(range(1, given_sum),
repeat=number_of_clusters):
if not sum(x) == given_sum:
continue
xx = tuple(sorted([i for i in x]))
if xx in iterset:
continue
min_xx = min(xx)
max_xx = max(xx)
if abs(min_xx - max_xx) > cut_bound_max:
continue
iterset.add(xx)
yield xx
@staticmethod
def _find_pair_with_min_square(graph: Dict):
"""
находит пару нод, которые являются соседними
и сумма площадей которых является наименьшей
в рамках актуального графа
:param graph: граф с нодами
:return:
"""
nodes = [x for x in graph.values()]
ans_max = []
ans_min = []
for node in nodes:
adj_list = [(graph[x].name, graph[x].square)
for x in node.adjacent_list]
if not adj_list:
continue
min_elem = adj_list[adj_list.index(
min(adj_list, key=lambda x: x[1]))]
ans_min.append((node.name, node.square, min_elem[0], min_elem[1]))
res = ans_min[ans_min.index(min(ans_min, key=lambda x: x[1] + x[3]))]
return (res[0], res[2])
@staticmethod
def _first_node_absorb_second(first: str, second: str, graph: Dict):
"""
поглощение первой нодой второй ноды.
проход по графу с внесением соответствующих изменений в списки смежности нод
:param first: имя поглощающей ноды
:param second: имя поглощаемой ноды
:param graph: граф с нодами
:return:
"""
if first == second:
return
graph[first].absorbed.append(graph[second])
graph[first].absorbed_names.add(second)
graph[first].absorbed_names = graph[first].absorbed_names.union(
graph[second].absorbed_names)
graph[first].square += graph[second].square
graph[first].adjacent_list.remove(second)
graph[first].adjacent_list = graph[first].adjacent_list.union(
graph[second].adjacent_list)
graph[first].adjacent_list.discard(second)
graph[first].adjacent_list.discard(first)
graph.pop(second)
for node in graph.values():
if node == graph[first]:
continue
if second in node.adjacent_list:
node.adjacent_list.remove(second)
if first in node.adjacent_list:
continue
node.adjacent_list.add(first)
# input()
@staticmethod
def _reduce_nodes_quantity(graph: Dict, nodes_quantity: int):
"""
уменьшает количество нод в графе до уровня nodes_quantity
:param graph: граф с нодами-полями
:param nodes_quantity:количество точек до которого уменьшится граф
:return:
"""
while len(graph) > nodes_quantity:
first, second = Solver._find_pair_with_min_square(graph=graph)
Solver._first_node_absorb_second(first=first,
second=second,
graph=graph)
@staticmethod
def _check_cluster_for_connectivity(graph: Dict, cluster: Cluster) -> bool:
"""
метод осуществляет проверку кластера на связность, проверяя,
находится ли в всписках смежности каждой из вершины, принадлежащей кластеру,
какая нибудь вершина из этого же кластера
:param graph: граф
:param cluster: кластер
:return: True-кластер 'связный'
"""
# def BFS(self, s):
# Mark all the vertices as not visited
if not cluster.nodes_belong or len(cluster.nodes_belong) == 1:
return True
node_name = cluster.nodes_belong[0].name
gr = {
x: y
for (x, y) in graph.items()
if x in set([n.name for n in cluster.nodes_belong])
}
for x in gr.values():
tmp = x.adjacent_list.copy()
x.adjacent_list = set([
n for n in x.adjacent_list
if n in set([nn.name for nn in cluster.nodes_belong])
])
x.cut_for_check = tmp.difference(x.adjacent_list)
visited = {x: False for x in gr.keys()}
# Create a queue for BFS
queue = []
# Mark the source node as
# visited and enqueue it
queue.append(node_name)
visited[node_name] = True
while queue:
# Dequeue a vertex from
# queue and print it
s = queue.pop(0)
# Get all adjacent vertices of the
# dequeued vertex s. If a adjacent
# has not been visited, then mark it
# visited and enqueue it
for name in gr[s].adjacent_list:
if not visited[name]:
queue.append(name)
visited[name] = True
for x in gr.values():
x.adjacent_list = x.adjacent_list.union(x.cut_for_check)
x.cut_for_check = set()
return not (False in visited.values())
def solve_new(self, cut_bound_max: int) -> List:
"""
алгоритм кластеризации на основе перебора
:return:
"""
start = time.time()
self._reduce_nodes_quantity(self.crop_map.graph, self.given_sum)
self.crop_map.clusternames = {
x: y
for (x, y) in zip(range(len(self.crop_map.graph.keys())),
self.crop_map.graph.keys())
}
cluster_for_checking_map_full_connecticity = Cluster(
serial_num=-1, nodes_belong=list(self.crop_map.graph.values()))
ic([
(
self.crop_map.graph[x].name,
# self.crop_map.graph[x].absorbed_names,
self.crop_map.graph[x].adjacent_list,
) for x in self.crop_map.graph.keys()
])
new_dots = []
for index, x in enumerate(self.crop_map.graph.values()):
names = [x.name]
names.extend(list(name for name in x.absorbed_names))
for name in names:
new_dots.append([name, index])
new_dots_data = pd.DataFrame(data=new_dots)
new_dots_data.to_excel(excel_writer="new_dots.xlsx", index=False)
iterable = set([x for x in self.crop_map.clusternames.keys()])
leader = math.inf
best_clusters = []
counter = 0
oh_shit = set()
start = time.time()
for node_quantity_combination in self._make_combinations_with_given_sum_extra(
given_sum=self.given_sum,
number_of_clusters=self.number_of_clusters,
graph_len=self.graph_len,
cut_bound_max=cut_bound_max,
):
ic(node_quantity_combination)
start = time.time()
for first_nums in itertools.combinations(
iterable=iterable, r=node_quantity_combination[0]):
first_cluster = Cluster(
serial_num=0,
nodes_belong=[
self.crop_map.graph[self.crop_map.clusternames[x]]
for x in first_nums
],
)
if not self._check_cluster_for_connectivity(
self.crop_map.graph, first_cluster):
# return
continue
new_it = iterable.difference(set(first_nums))
for second_nums in itertools.combinations(
iterable=new_it, r=node_quantity_combination[1]):
second_cluster = Cluster(
serial_num=1,
nodes_belong=[
self.crop_map.graph[self.crop_map.clusternames[x]]
for x in second_nums
],
)
if not self._check_cluster_for_connectivity(
self.crop_map.graph, second_cluster):
continue
newest_it = new_it.difference(set(second_nums))
for third_nums in itertools.combinations(
iterable=newest_it,
r=node_quantity_combination[2]):
new_newest_it = tuple(
newest_it.difference(set(third_nums)))
first = str(sorted([str(x) for x in first_nums]))
second = str(sorted([str(x) for x in second_nums]))
third = str(sorted([str(x) for x in third_nums]))
fourth = str(sorted([str(x) for x in new_newest_it]))
guess = tuple(sorted([first, second, third, fourth]))
if guess in oh_shit:
continue
oh_shit.add(guess)
third_cluster = Cluster(
serial_num=2,
nodes_belong=[
self.crop_map.graph[
self.crop_map.clusternames[x]]
for x in third_nums
],
)
if not self._check_cluster_for_connectivity(
self.crop_map.graph, third_cluster):
continue
fourth_cluster = Cluster(
serial_num=3,
nodes_belong=[
self.crop_map.graph[
self.crop_map.clusternames[x]]
for x in new_newest_it
],
)
if not self._check_cluster_for_connectivity(
self.crop_map.graph, fourth_cluster):
continue
clusters = [
first_cluster,
second_cluster,
third_cluster,
fourth_cluster,
]
counter += 1
res = self.crop_map.count_cluster_metrics_mean_deviation(
clusters=clusters)
if res < leader:
leader = res
best_clusters = clusters
self.leaders = best_clusters
iteration_time = time.time() - start
ic(iteration_time, leader, [x.square for x in best_clusters])
ic(iteration_time)
oh_shit = set()
return (time.time() - start, best_clusters, leader)
