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Yoann DUFRESNE
linked reads molecule ordering
Commits
31a82afc
Commit
31a82afc
authored
May 04, 2020
by
Rayan Chikhi
Browse files
fixes
parent
99fef3be
Changes
1
Hide whitespace changes
Inline
Side-by-side
deconvolution/main/d2_path_evaluation.py
View file @
31a82afc
...
...
@@ -28,24 +28,23 @@ def load_graph(filename):
print
(
"Wrong file format. Require graphml or gefx format"
,
file
=
sys
.
stderr
)
exit
()
""" return a random path in G starting in u and having n nodes """
import
random
def
findRandomPath
(
G
,
u
,
n
):
def
findRandomPath
(
G
,
u
,
n
,
previous_path_nodes
=
set
()
):
if
n
==
0
:
return
[
u
]
path
=
[
u
]
poss_neigh
=
list
(
G
.
neighbors
(
u
))
while
u
in
path
:
if
len
(
poss_neigh
)
==
0
:
return
None
neighbor
=
random
.
choice
(
poss_neigh
)
poss_neigh
.
remove
(
neighbor
)
path
=
findRandomPath
(
G
,
neighbor
,
n
-
1
)
if
path
is
None
:
return
None
poss_neigh
=
list
(
set
(
G
.
neighbors
(
u
))
-
previous_path_nodes
)
if
len
(
poss_neigh
)
==
0
:
return
None
neighbor
=
random
.
choice
(
poss_neigh
)
new_previous_path_nodes
=
previous_path_nodes
|
set
([
u
])
path
=
findRandomPath
(
G
,
neighbor
,
n
-
1
,
new_previous_path_nodes
)
if
path
is
None
:
return
None
return
[
u
]
+
path
import
itertools
def
is_there_path
(
central_nodes
,
overlap_length
):
def
is_there_path
_acc
(
central_nodes
,
overlap_length
):
for
mols
in
itertools
.
product
(
*
central_nodes
):
#print(mols)
last_end
=
None
...
...
@@ -78,7 +77,7 @@ def is_coherent_path(central_nodes, overlap_length):
for
node
in
central_nodes
:
cur_node_mols
=
central_node_to_molecules
(
node
)
mols
+=
[
cur_node_mols
]
return
is_there_path
(
mols
,
overlap_length
)
return
is_there_path
_acc
(
mols
,
overlap_length
)
graph
=
None
def
evaluate_accuracy_paths
(
path_len
,
overlap_length
=
7000
,
max_paths_per_node
=
100
):
...
...
@@ -87,9 +86,12 @@ def evaluate_accuracy_paths(path_len,overlap_length=7000,max_paths_per_node=100)
nb_good_paths
=
0
for
node
in
graph
.
nodes
():
nb_paths
=
0
seen_paths
=
set
()
for
_
in
range
(
max_paths_per_node
):
path
=
findRandomPath
(
graph
,
node
,
path_len
)
if
path
is
None
:
continue
if
tuple
(
sorted
(
path
))
in
seen_paths
:
continue
# avoids looking at the same path twice
seen_paths
.
add
(
tuple
(
sorted
(
path
)))
#print("path",path)
central_nodes
=
[
graph
.
nodes
[
x
][
'udg'
].
split
()[
0
]
for
x
in
path
]
#print(path,central_nodes)
...
...
@@ -99,6 +101,8 @@ def evaluate_accuracy_paths(path_len,overlap_length=7000,max_paths_per_node=100)
nb_bad_paths
+=
1
print
(
"accuracy for l=%d:"
%
path_len
,
nb_good_paths
/
(
nb_good_paths
+
nb_bad_paths
))
# ---- sensitivity evaluation
def
is_there_path
(
graph
,
molecules_to_nodes
,
sought_path
):
possible_central_nodes
=
[]
for
mol
in
sought_path
:
...
...
@@ -108,7 +112,7 @@ def is_there_path(graph,molecules_to_nodes,sought_path):
if
nx
.
is_connected
(
graph
.
subgraph
(
mols
)):
#print("found connected path",mols)
return
True
print
(
"found no connected paths"
,
sought_path
)
#
print("found no connected paths",sought_path)
return
False
def
evaluate_sensitivity_paths
(
path_len
,
overlap_length
=
7000
):
...
...
@@ -140,8 +144,7 @@ def main():
graph
=
load_graph
(
args
.
filename
)
p
=
Pool
(
4
)
#p.map(evaluate_accuracy_paths, [1,2,3,4])
p
.
map
(
evaluate_accuracy_paths
,
[
1
,
2
,
3
,
4
])
p
.
map
(
evaluate_sensitivity_paths
,
[
1
,
2
,
3
,
4
])
if
__name__
==
"__main__"
:
...
...
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