def is_correctly_generated(graph: DiGraph, min_args, max_args) -> bool:
"""
Check if a graph is correctly generated
"""
# no empty graphs
if graph.number_of_nodes() == 0 or graph.number_of_edges() == 0:
return False
# no incorrectly generated graphs
if not min_args <= len(graph.nodes) <= max_args:
return False
# no graphs with isolated subgraphs
if nx.number_connected_components(graph.to_undirected()) > 1:
return False
return True
def get_dependency(cls,
workflow: Workflow,
processor_id: str,
graph: DiGraph,
spark: SparkSession) -> Union[Dependency, None]:
processor_config = workflow.get_processor(processor_id)
predecessors = []
if graph.number_of_edges() > 0:
predecessors = list(graph.predecessors(processor_id))
dependencies = []
if not bool(predecessors):
predecessors = []
for predecessor in predecessors:
dependencies.append(
cls.get_dependency(workflow, predecessor, graph, spark))
processor_context = ProcessorContext(
spark_session=spark,
property_groups=processor_config.property_groups,
dependencies=dependencies)
processor = SparkProcessor.get_spark_processor(processor_config.type)
return processor.run(processor_context)
def tree0(weight_value, startwindow, term):
print 'start window:', startwindow
# windowGraph = {}
cliqueGraph = DiGraph()
dic_term = {}
dic_last_time = {}
dic_temp = {}
dic_term_num = {}
dic_intersect_level = {}
# term = 183
root = 0
cliqueGraph.add_node(root, annotation='root', windowsize='root', weight_value='root')
w = data.shape[1]
i = 0
q = 0
for window in range(startwindow, w):
dic_intersect_level.clear()
#print window ## mine
if window == startwindow:
for clique in find_cliques(windowGraph[window]):
if len(clique) >size_clique:
cliqueGraph.add_node(term, annotation=list(clique), windowsize=[window],
weight=weight_value) # generate a term
cliqueGraph.add_edge(root, term)
dic_term[frozenset(clique)] = [window] # dic_term 记录 window和clique or Dic_term records window and clique
dic_term_num[frozenset(clique)] = term # dic_term_num 记录 term 序号和clique or Dic_term_num record term number and clique
dic_last_time[frozenset(clique)] = [window] # dic_last_time 记录上一时刻生成的交集 用于下一时刻的比较 or Dic_last_time records the intersection generated at the last moment for comparison at the next moment
term = term + 1
print 'for start window '
else:
continue
# print len(dic_last_time), len(dic_term), cliqueGraph.number_of_nodes()
else:
for clique in find_cliques(windowGraph[window]):
if len(clique) > size_clique:
#print window, 'clique:', clique ## mine
for key, value in dic_last_time.items(): # key 是clique ,value是 [window] or Key is clique, value is [window]
intersect = sorted(set(key).intersection(set(clique)))
q = 0
# if len(intersect) >= size_clique:
if len(intersect) >= size_clique:
#print 'intersect', intersect
# 同一层判断交集之间是否有重复的父子关系。 每生成一个交集， 判断当前层的其他term和交集的关系。or The same layer determines whether there are
#duplicate parent-child relationships between intersections. Each generation of an intersection determines the relationship
#between other terms and intersections of the current layer.
for ik, iv in dic_intersect_level.items():
if set(intersect) == (set(ik)): # 生成一模一样的交集 or Generate exactly the same intersection
# 判断两个的编号是否一样？or Is the two numbers the same?
if dic_term_num[frozenset(key)] != dic_term_num[frozenset(ik)]:
cliqueGraph.add_edge(dic_term_num[frozenset(key)], dic_term_num[frozenset(ik)])
q = 1
break
elif set(intersect).issuperset(set(ik)): # 生成了超集 or Superset generated
cliqueGraph.remove_node(dic_term_num[frozenset(ik)])
dic_term.pop(frozenset(ik)) # 从四个字典中都删除该节点的信息 or Delete the node's information from all four dictionaries
dic_term_num.pop(frozenset(ik))
dic_intersect_level.pop(frozenset(ik))
dic_temp.pop(frozenset(ik))
elif set(intersect).issubset(set(ik)): # 生成了子集 or Generated subset
q = 1
break
if q == 1:
continue
dic_intersect_level[frozenset(intersect)] = 1
if dic_term.has_key(frozenset(intersect)):
# 交集已经出现过 or Intersection has appeared
parent = cliqueGraph.predecessors(dic_term_num[frozenset(intersect)])
children = cliqueGraph.successors(dic_term_num[frozenset(intersect)])
#print 'parent',len(parent)
if len(parent) > 0:
# 是交集生成的term，则重定向 or Is the intersection of generated term, then redirect
cliqueGraph.add_node(term, annotation=list(intersect),
windowsize=value + [window],
weight=weight_value)
for p in parent:
cliqueGraph.add_edge(p, term) # 连边 // Edge
for c in children:
cliqueGraph.add_edge(term, c) # 连边 // edge
cliqueGraph.remove_node(dic_term_num[frozenset(intersect)]) # 从图中删除冗余结点 or Remove redundant nodes from the figure
# print 'deleted intersect nodes:',dic_term_num[frozenset(intersect)]
i = i + 1
dic_term.pop(frozenset(intersect)) # 字典中删除 // Delete in dictionary
dic_term_num.pop(frozenset(intersect))
dic_term[frozenset(intersect)] = value + [window] # 新节点插入字典 // New node insert dictionary
dic_term_num[frozenset(intersect)] = term
dic_temp[frozenset(intersect)] = value + [window] # 记录到dic_temp里 // Record to dic_temp
term = term + 1
continue
else:
# 是window生成的term // Is the term generated by the window
continue
else:
# 交集没有出现过， 则生成新的term // No intersection occurs, then a new term is generated
# print 'new term intersect never appear:', term
cliqueGraph.add_node(term, annotation=list(intersect), windowsize=value + [window],
weight=weight_value) # generate a term
cliqueGraph.add_edge(dic_term_num[frozenset(key)], term) # 连边，变化：只连接交集作为父亲。// Edge, change: Only connect intersections as fathers.
dic_term[frozenset(intersect)] = value + [window] # 新节点插入字典 // New node insert dictionary
dic_term_num[frozenset(intersect)] = term
dic_temp[frozenset(intersect)] = value + [window] # 记录到dic_temp里 // Record to dic_temp
term = term + 1
else:
continue
else:
continue
dic_last_time.clear()
for key, value in dic_temp.items():
dic_last_time[key] = value
dic_temp.clear()
print 'window', startwindow, 'size is', cliqueGraph.number_of_nodes(), cliqueGraph.number_of_edges()## mine
# print 'deleted nodes:', i
# fw = open('0904edges_remove.txt', 'w')
# fw2 = open('0904terms_remove.txt', 'w')
# fw.write('parent' + '\t' + 'child' + '\n')
# for edge in cliqueGraph.edges():
# fw.write(str(edge[0]) + '\t' + str(edge[1]) + '\n')
# fw.close()
# fw2.write('term_id' + '\t' + 'anno_genes' + '\t' + 'window' + '\t' + 'gene_size' + '\t' + 'window_size' + '\n')
# for key, value in dic_term.items():
# fw2.write(str(dic_term_num[key]) + '\t' + str(key) + '\t' + str(value) + '\t' + str(len(key)) + '\t' + str(len(value)) + '\n')
# fw2.close()
# for nodes in cliqueGraph.nodes():
# if cliqueGraph.degree(nodes) == 0:
# print nodes
return cliqueGraph, dic_term, dic_term_num, term
