Skip to content

GitLab

Commit 678f0477 authored by Yoann Dufresne's avatar Yoann Dufresne
Browse files

path search refactoring

parent 05916aa3
Hide whitespace changes
Inline Side-by-side
Showing with 54 additions and 176 deletions
deconvolution/d2graph/d2_graph.py
View file @ 678f0477
......@@ -91,7 +91,7 @@ class D2Graph(nx.Graph):
self.bidict_nodes = self.create_graph_from_node_neighborhoods(neighbor_threshold)
def _lcp_from_obj(self, obj):
def get_lcp(self, obj):
if type(obj) == str:
obj = int(obj)
if type(obj) == int:
......@@ -99,7 +99,7 @@ class D2Graph(nx.Graph):
return obj
def get_covering_variables(self, obj):
lcp = self._lcp_from_obj(obj)
lcp = self.get_lcp(obj)
if lcp not in self.variables_per_lcp:
variables = []
for e in lcp.edges:
......
deconvolution/d2graph/d2_path.py
View file @ 678f0477
......@@ -12,13 +12,7 @@ class Path(list):
self.covering_value = 0
def append(self, obj) -> None:
# Transform the input into a lcp
if type(obj) == str:
obj = self.d2g.node_by_idx(int(obj))
elif type(obj) == int:
obj = self.d2g.node_by_idx(obj)
lcp = obj
lcp = self.d2g.get_lcp(obj)
# Update the covering variables
for barcode_edge in lcp.edges:
......@@ -29,28 +23,40 @@ class Path(list):
super(Path, self).append(lcp)
def pop(self, index=-1):
lcp = super(Path, self).pop(index)
# Update the covering variables
for barcode_edge in lcp.edges:
edge_idx = self.d2g.barcode_edge_idxs[barcode_edge]
self.covering_variables[edge_idx] -= 1
if self.covering_variables[edge_idx] == 0:
self.covering_value -= 1
return lcp
def copy(self):
copy = Path(self.d2g)
# Copy the list
for x in self:
super(Path, copy).append(x)
# Copy the variables
for key, val in self.covering_variables.items():
copy.covering_variables[key] = val
copy.covering_value = self.covering_value
return copy
def add_path(self, path):
for lcp in path:
self.append(lcp)
def covering_score(self):
return self.covering_value / len(self.covering_variables)
""" Count the number of variable covered by path that are not self covered.
"""
def covering_difference(self, path):
self_covered = [var for var, num in self.covering_variables.items() if num > 0]
self_covered = frozenset(self_covered)
num_covered = 0
for var, val in path.covering_variables.items():
if val > 0 and var not in self_covered:
num_covered += 1
return num_covered
def save_path(self, filename):
def save_gexf(self, filename):
d2p = nx.Graph()
# Add the nodes
for udg in self:
......@@ -70,32 +76,3 @@ class Path(list):
nx.write_gexf(d2p, filename)
def save_path_in_graph(self, filename):
d2c = self.d2g.clone()
for idx, udg in enumerate(self):
d2c.nodes[str(udg.idx)]["path"] = idx
nx.write_gexf(d2c, filename)
class Unitig(Path):
def __init__(self, d2g):
super(Unitig, self).__init__(d2g)
def add_right(self, udg):
self.add_path([udg])
def d2_path_to_barcode_path(path):
barcode_per_idx = [set(udg.to_list()) for udg in path]
diff_barcode_per_idx = []
rev_diff_barcode_per_idx = []
for idx in range(len(barcode_per_idx)-1):
diff_barcode_per_idx.append(barcode_per_idx[idx] - barcode_per_idx[idx+1])
rev_diff_barcode_per_idx.append(barcode_per_idx[idx+1] - barcode_per_idx[idx])
diff_barcode_per_idx.append(barcode_per_idx[-1] - diff_barcode_per_idx[-1])
rev_diff_barcode_per_idx.insert(0, barcode_per_idx[0] - rev_diff_barcode_per_idx[0])
for diff, rev_diff in zip(diff_barcode_per_idx, rev_diff_barcode_per_idx):
print(diff, rev_diff)
deconvolution/d2graph/path_optimization.py
View file @ 678f0477
import random
import networkx as nx
from collections import Counter
from deconvolution.d2graph.d2_path import Path
......@@ -9,38 +8,26 @@ class Optimizer:
def __init__(self, d2g):
self.d2g = d2g
self.solutions = []
def init_random_solutions(self, nb_solutions):
for _ in range(nb_solutions):
rnd_sol = Solution(self.d2g)
rnd_sol.random_init_best_quality()
self.solutions.append(rnd_sol)
def bb_solution(self, verbose=False):
def bb_solution(self, start_node=None, verbose=False):
nb_steps_since_last_score_up = 0
total_nb_steps = 0
first_pass = True
max_cov = set()
for node in self.d2g.nodes():
v = self.d2g.get_covering_variables(node)
max_cov.update(v)
max_cov = len(max_cov)
used_nodes = {n:False for n in self.d2g.nodes()}
coverage = Counter()
used_nodes = {n: False for n in self.d2g.nodes()}
# Init solution for
init = list(used_nodes.keys())
init = init[random.randint(0, len(init)-1)]
if start_node == None:
start_node = list(used_nodes.keys())
start_node = start_node[random.randint(0, len(start_node)-1)]
best_path = []
best_score = 0
stack = [[init]]
current_path = []
best_path = Path(self.d2g)
stack = [[start_node]]
current_path = Path(self.d2g)
last_increase_node = None
max_cov = len(current_path.covering_variables)
while len(stack) > 0:
total_nb_steps += 1
# 1 - Get next node to extend
......@@ -50,19 +37,15 @@ class Optimizer:
# 2.1 - Add the node
used_nodes[current_node] = True
current_path.append(current_node)
# 2.2 - Compute the coverage score
vars = self.d2g.get_covering_variables(current_node)
coverage += Counter(vars)
# 2.3 - If best, save
if len(coverage) > best_score:
if current_path.covering_value > best_path.covering_value:
nb_steps_since_last_score_up = 0
best_path = current_path.copy()
best_score = len(coverage)
last_increase_node = current_node
if verbose:
print(f"New best: {len(best_path)} {best_score} / {max_cov}")
print(f"New best: {len(best_path)} {best_path.covering_value} / {max_cov}")
if len(coverage) == max_cov:
if best_path.covering_value == max_cov:
print("Max coverage")
return best_path
else:
......@@ -74,33 +57,31 @@ class Optimizer:
def node_sorting_value(node):
u = opt.d2g.node_by_idx[int(node)]
return (0 if len(Counter(opt.d2g.get_covering_variables(node)) - coverage) == 0 else 1,
return (0 if len(opt.d2g.get_covering_variables(node)) - current_path.covering_value == 0 else 1,
- u.get_link_divergence(),
- self.d2g[str(u.idx)][str(current_node)]["distance"])
- opt.d2g[str(u.idx)][str(current_node)]["distance"])
neighbors.sort(key=node_sorting_value)
stack.append(neighbors)
# 4 - No more neighbors here - back up
while len(stack[-1]) == 0:
stack.pop()
if len(stack) == 0:
break
previous = current_path.pop()
used_nodes[previous] = False
prev_vars = self.d2g.get_covering_variables(previous)
coverage -= Counter(prev_vars)
# 5 - Reinit the search from a side and stop when done
if total_nb_steps > 10000 and nb_steps_since_last_score_up >= total_nb_steps / 2:
if len(stack) == 0 or (total_nb_steps > 10000 and nb_steps_since_last_score_up >= total_nb_steps / 2):
if first_pass:
used_nodes = {n: False for n in self.d2g.nodes()}
coverage = Counter()
best_path = []
best_score = 0
best_path = Path(self.d2g)
stack = [[last_increase_node]]
current_path = []
current_path = Path(self.d2g)
if verbose:
print("Start again !")
import time
time.sleep(3)
time.sleep(1)
first_pass = False
total_nb_steps = 0
nb_steps_since_last_score_up = 0
......@@ -108,71 +89,3 @@ class Optimizer:
return best_path
return best_path
def extends_until_end(self, solution):
while self.extends(solution):
continue
solution.reverse()
while self.extends(solution):
continue
def extends(self, solution):
# Get all the neighbors
cur_id = str(solution[-1].idx)
neighbors = [self.d2g.node_by_idx[int(x)] for x in self.d2g.neighbors(cur_id) if
self.d2g.node_by_idx[int(x)] not in solution]
current_vars = frozenset([x for x, y in solution.covering_variables.items() if y > 0])
if len(neighbors) == 0:
return False
# Choose using the multiple optimization directions
next_udg = min(neighbors,
key=lambda x: (1 if len(self.d2g.get_covering_variables(x) - current_vars) == 0 else 0,
x.get_link_divergence(),
self.d2g[str(x.idx)][cur_id]["distance"]))
solution.add_path([next_udg])
return True
class Solution(Path):
def __init__(self, d2g):
super(Solution, self).__init__(d2g)
def random_init_best_quality(self):
nodes = [self.d2g.node_by_idx[int(x)] for x in list(self.d2g.nodes())]
min_div = (min(nodes, key=lambda x: x.get_link_divergence())).get_link_divergence()
nodes = [x for x in nodes if x.get_link_divergence() == min_div]
random_udg = random.choice(nodes)
self.clear()
self.add_path([random_udg])
""" Only respect counts for now
"""
def to_barcode_path(self):
barcode_per_position = [set(udg.to_sorted_list()) for udg in self]
compressed_barcodes = []
for idx, barcodes in enumerate(barcode_per_position):
for barcode in barcodes:
offset = 1
while idx + offset < len(barcode_per_position) and barcode in barcode_per_position[idx + offset]:
barcode_per_position[idx + offset].remove(barcode)
offset += 1
compressed_barcodes.append(barcode)
return compressed_barcodes
def save_barcode_path(self, filename):
barcodes = self.to_barcode_path()
G = nx.Graph()
for idx, barcode in enumerate(barcodes):
G.add_node(idx)
G.nodes[idx]["center"] = barcode
nx.write_gexf(G, filename)
deconvolution/main/d2_to_path.py
View file @ 678f0477
......@@ -43,24 +43,12 @@ def main():
# Start optimization
optimizer = po.Optimizer(largest_component)
nodes = optimizer.bb_solution(verbose=args.verbose)
solution = po.Solution(largest_component)
solution.add_path([largest_component.node_by_idx[int(n)] for n in nodes])
# optimizer.init_random_solutions(1)
path = optimizer.bb_solution(verbose=args.verbose)
# solution = optimizer.solutions[0]
# print("Solution creation...")
# optimizer.extends_until_end(solution)
print(f"covering score: {solution.covering_score()}")
#
# solution.save_path_in_graph(f"{args.out_prefix}_d2_path.gexf")
solution.save_path(f"{args.out_prefix}_path.gexf")
# solution.save_barcode_path(f"{args.out_prefix}_barcode_count.gexf")
print(f"covering score: {path.covering_score()}")
path.save_gexf(f"{args.out_prefix}_path.gexf")
print("Solution saved")
# from d2_path import d2_path_to_barcode_path
# d2_path_to_barcode_path(solution)
if __name__ == "__main__":
main()
Markdown is supported
0% or .
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment