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Commit c035e468 authored by Pierre Donat-Bouillud's avatar Pierre Donat-Bouillud
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Collect more data on runs

parent 1cd7b4a8
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in 3 minutes and 31 seconds
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Cargo.lock
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......@@ -24,7 +24,7 @@ name = "atty"
version = "0.2.11"
source = "registry+https://github.com/rust-lang/crates.io-index"
dependencies = [
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......@@ -37,8 +37,8 @@ dependencies = [
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......@@ -94,7 +94,7 @@ source = "registry+https://github.com/rust-lang/crates.io-index"
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[[package]]
......@@ -130,15 +130,15 @@ dependencies = [
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......@@ -177,9 +177,9 @@ version = "0.3.1"
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......@@ -190,7 +190,7 @@ name = "crossbeam-utils"
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[[package]]
......@@ -198,8 +198,8 @@ name = "crossbeam-utils"
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......@@ -208,7 +208,7 @@ version = "1.0.5"
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......@@ -279,12 +279,12 @@ source = "registry+https://github.com/rust-lang/crates.io-index"
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source = "registry+https://github.com/rust-lang/crates.io-index"
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......@@ -297,7 +297,7 @@ name = "memchr"
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source = "registry+https://github.com/rust-lang/crates.io-index"
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......@@ -355,7 +355,7 @@ name = "num_cpus"
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source = "registry+https://github.com/rust-lang/crates.io-index"
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......@@ -389,7 +389,7 @@ dependencies = [
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......@@ -422,7 +422,7 @@ version = "0.7.0"
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......@@ -449,13 +449,13 @@ version = "0.6.5"
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......@@ -629,27 +629,27 @@ source = "registry+https://github.com/rust-lang/crates.io-index"
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......
pipeline.py
View file @ c035e468
......@@ -27,7 +27,6 @@ from operator import itemgetter
from scipy import stats
import bisect
parser = argparse.ArgumentParser(description="Generate graphs, execute them, and then evaluate their quality", \
epilog="Please indicate in a pipeline.json file where the process thqt executes graphs is located.")
group = parser.add_mutually_exclusive_group(required=True)
......@@ -36,6 +35,8 @@ group.add_argument("-n", "--nodes", help="Explore all graphs of size nodes", typ
parser.add_argument("-a", "--all", help="Explore all sizes up to the one precised by --nodes", action="store_true")
parser.add_argument("-d", "--draw", help="Draw graph of quality and cost.", action="store_true")
parser.add_argument("--only-draw", help="Only draws graph", action="store_true")
parser.add_argument("-r", "--random", help="Randomly generates the graphs", action="store_true")
parser.add_argument("--no-error", help="Continue in spite of errors", action="store_true")
parser.add_argument("--dir", help="Directory where to process")
args = parser.parse_args()
......@@ -140,12 +141,17 @@ class GraphResults:
def __repr__(self):
return "{}: {}, {}".format(self.name, self.costs, self.quality)
def process_all_graphs(nb_nodes, dirname):
def process_all_graphs(nb_nodes, dirname, random=False):
"""Process on all weakly connected Dags up to nb_nodes"""
tqdm.write("Enumerating weakily DAGs up to " + str(nb_nodes) + " nodes with result in " + dirname)
#./main.native -dewx -n 5 --node-file ../nodes.ag
subprocess.run([graph_enum, "-dewxr", "-n", str(nb_nodes), "--node-file="+nodes_dic], check=True)
command = [graph_enum]
if random:
command.extend(["-l", "--edge-prob", "0.3"])
command.extend(["-dewxr", "-n", str(nb_nodes), "--node-file="+nodes_dic])
subprocess.run(command, check=True)
nb_errors=0
results={}
tqdm.write("Executing graphs")
#Group them by non-degraded graphs
......@@ -154,89 +160,97 @@ def process_all_graphs(nb_nodes, dirname):
prefix = non_degraded_graph.rsplit("-", maxsplit=1)[0]
tqdm.write(prefix)
result_graph=[]
for graph in tqdm(sorted(glob.iglob(prefix+"*.ag"))):
basename,_ = os.path.splitext(graph)
result= GraphResults(basename)
costs = execute_graph(graph)
result.costs = costs
result_graph.append(result)
result_dict = {}
nb_degraded_graphs = len(result_graph)
result_dict["NbDegradedGraphs"] = nb_degraded_graphs
# We also want to get the following measures:
# - are the worst/best graphs in terms of costs and quality the same in
# the theoretical models and in the experiments. How close are they in both vectors? (in inversions? In position distances?)
# - are costs and qualities correlated? In the experimental model first. And in the theoretical one? (We could even prove it)
# - are all the degraded graphs faster than the non-degraded one? And at least one? How many? Which percentage?
# Shape questions:
# - DONE how many degraded graphs in average for one graph?
# - how many resamplers have been inserted? Downsamplers? Upsamplers?
# TODO later: try to degrade in same order as heuristics and see if it correlates with the order in quality and in cost
# TODO: case of a source => use a real audio file? Or generate a sin wave? Or just noise? Or don't generate sources here?
# Because for now, sources just output a 0 signal, so we get the same quality for each version and
# it does not give an useful ranking for the measured quality.
# TODO: apply merge operation for resampler (the one that inserts a mixer and then a resampler instead of several resamplers)
# Meaningless to compute rank correlation on a vector of size 1
if nb_degraded_graphs > 0:
# Get audio files
audiofiles = glob.glob(prefix+"*.wav")
qualities = compare_audio_files(audiofiles)
# For the correlation, we want the graphs in increasing rank
result_graph.sort(key=lambda res: int(res.name.rsplit("-", maxsplit=1)[1]))
# Update results
for result in result_graph:
#If we've not computed a quality for it, it is the non-degraded graph
result.quality = qualities.get(result.name, 1.0)
costs_mes=[]
qualities_mes=[]
for result in result_graph:
cost,_ = result.costs
costs_mes.append(cost)
qualities_mes.append(result.quality)
# We should get them in the same graph order as in the measured one (non-degraded first)
csvname = prefix.rsplit("-", maxsplit=1)[0] + "-theo.csv"
qualities_th, costs_th = load_csv(csvname)
# Correlations
kendalltau = GraphResults(prefix)
kendalltau.costs = stats.kendalltau(costs_mes, costs_th, nan_policy='raise')
kendalltau.quality = stats.kendalltau(qualities_mes, qualities_th, nan_policy='raise')
spearmanr = GraphResults(prefix)
spearmanr.costs = stats.spearmanr(costs_mes, costs_th, nan_policy='raise')
spearmanr.quality = stats.spearmanr(qualities_mes, qualities_th, nan_policy='raise')
print(kendalltau.name, " Kendal's tau: cost=", kendalltau.costs, " and quality=", kendalltau.quality)
print(spearmanr.name, " Spearman's r: cost=", spearmanr.costs, " and quality=", spearmanr.quality)
result_dict["SpearmanR"] = spearmanr
result_dict["KendallTau"] = kendalltau
# Speed increase? How many graphs are quicker than the non-degraded one
increasing_costs_mes = list(sorted(result_graph, key=lambda res: res.costs))
quicker = 0
while quicker < len(increasing_costs_mes):
if int(increasing_costs_mes[quicker].name.rsplit("-", maxsplit=1)[1]) == 0:
break
quicker = quicker + 1
result_dict["QuickerMes"] = quicker
results[prefix] = result_dict
try:
for graph in tqdm(sorted(glob.iglob(prefix+"*.ag"))):
basename,_ = os.path.splitext(graph)
result= GraphResults(basename)
costs = execute_graph(graph)
result.costs = costs
result_graph.append(result)
except subprocess.CalledProcessError as err:
if args.no_error:
print("Error executing graph: {0}".format(err))
nb_errors = nb_errors + 1
else:
raise err
else:
result_dict = {}
nb_degraded_graphs = len(result_graph)
result_dict["NbDegradedGraphs"] = nb_degraded_graphs
# We also want to get the following measures:
# - are the worst/best graphs in terms of costs and quality the same in
# the theoretical models and in the experiments. How close are they in both vectors? (in inversions? In position distances?)
# - are costs and qualities correlated? In the experimental model first. And in the theoretical one? (We could even prove it)
# - are all the degraded graphs faster than the non-degraded one? And at least one? How many? Which percentage?
# Shape questions:
# - DONE how many degraded graphs in average for one graph?
# - how many resamplers have been inserted? Downsamplers? Upsamplers?
# TODO later: try to degrade in same order as heuristics and see if it correlates with the order in quality and in cost
# TODO: case of a source => use a real audio file? Or generate a sin wave? Or just noise? Or don't generate sources here?
# Because for now, sources just output a 0 signal, so we get the same quality for each version and
# it does not give an useful ranking for the measured quality.
# TODO: apply merge operation for resampler (the one that inserts a mixer and then a resampler instead of several resamplers)
# Meaningless to compute rank correlation on a vector of size 1
if nb_degraded_graphs > 0:
# Get audio files
audiofiles = glob.glob(prefix+"*.wav")
qualities = compare_audio_files(audiofiles)
# For the correlation, we want the graphs in increasing rank
result_graph.sort(key=lambda res: int(res.name.rsplit("-", maxsplit=1)[1]))
# Update results
for result in result_graph:
#If we've not computed a quality for it, it is the non-degraded graph
result.quality = qualities.get(result.name, 1.0)
costs_mes=[]
qualities_mes=[]
for result in result_graph:
cost,_ = result.costs
costs_mes.append(cost)
qualities_mes.append(result.quality)
# We should get them in the same graph order as in the measured one (non-degraded first)
csvname = prefix.rsplit("-", maxsplit=1)[0] + "-theo.csv"
qualities_th, costs_th = load_csv(csvname)
# Correlations
kendalltau = GraphResults(prefix)
kendalltau.costs = stats.kendalltau(costs_mes, costs_th, nan_policy='raise')
kendalltau.quality = stats.kendalltau(qualities_mes, qualities_th, nan_policy='raise')
spearmanr = GraphResults(prefix)
spearmanr.costs = stats.spearmanr(costs_mes, costs_th, nan_policy='raise')
spearmanr.quality = stats.spearmanr(qualities_mes, qualities_th, nan_policy='raise')
print(kendalltau.name, " Kendal's tau: cost=", kendalltau.costs, " and quality=", kendalltau.quality)
print(spearmanr.name, " Spearman's r: cost=", spearmanr.costs, " and quality=", spearmanr.quality)
result_dict["SpearmanR"] = spearmanr
result_dict["KendallTau"] = kendalltau
# Speed increase? How many graphs are quicker than the non-degraded one
increasing_costs_mes = list(sorted(result_graph, key=lambda res: res.costs))
quicker = 0
while quicker < len(increasing_costs_mes):
if int(increasing_costs_mes[quicker].name.rsplit("-", maxsplit=1)[1]) == 0:
break
quicker = quicker + 1
result_dict["QuickerMes"] = quicker
results[prefix] = result_dict
# We remove the audio files here as they can take a log of space
audiofiles = glob.glob(prefix+"*.wav")
for audiofile in audiofiles:
os.remove(audiofile)
print(nb_errors, " graphs were discarded due to execution errors")
return results
......@@ -436,7 +450,7 @@ elif args.nodes:
spearmanr_costs_rhos = []
spearmanr_qualities_rhos = []
if not args.only_draw:
results = process_all_graphs(args.nodes, dirname)
results = process_all_graphs(args.nodes, dirname, args.random)
for result in tqdm(results.values()):
kendaltau = result["KendallTau"]
spearmanr = result["SpearmanR"]
......@@ -466,11 +480,11 @@ elif args.nodes:
# f_kd_cost = fisher_mean(kendalltau_costs_rhos)
# f_kd_quality = fisher_mean(kendalltau_qualities_rhos)
# f_sr_cost = fisher_mean(spearmanr_costs_rhos)
# f_sr_quality = fisher_mean(spearmanr_qualities_rhos)
# print("Correlations: ")
# print("Cost: ", f_kd_cost, " (kendall's tau) ; ", f_sr_cost, " (spearman's r)")
# print("Quality: ", f_kd_quality, " (kendall's tau) ; ", f_sr_quality, " (spearman's r)")
f_sr_cost = fisher_mean(spearmanr_costs_rhos)
f_sr_quality = fisher_mean(spearmanr_qualities_rhos)
print("Correlations (Fisher's transform on Spearman's R): ")
print("Cost: ", f_sr_cost, " (spearman's r)")
print("Quality: ", f_sr_quality, " (spearman's r)")
if args.draw or args.only_draw:
fig, axes = plt.subplots(2,2)
......
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