def run(self):
progress = 0.
results = dict()
self.set_progress_percentage(progress)
for s in self.samples:
truth = os.path.join('/groups/saalfeld/saalfeldlab/larissa/data/cremieval/', self.de,
s + '.' + self.m + '.h5')
test = os.path.join(os.path.dirname(self.input()[0].fn), s+'.'+self.m+'.h5')
truth = CremiFile(truth, 'a')
test = CremiFile(test, 'a')
synaptic_partners_eval = SynapticPartners()
print(test.read_annotations())
fscore, precision, recall, fp, fn, filtered_matches = synaptic_partners_eval.fscore(
test.read_annotations(), truth.read_annotations(), truth.read_neuron_ids(), all_stats=True)
results[s] = dict()
results[s]['fscore'] = fscore
results[s]['precision'] = precision
results[s]['recall'] = recall
results[s]['fp'] = fp
results[s]['fn'] = fn
results[s]['filtered_matches'] = filtered_matches
progress += 100. / len(self.samples)
try:
self.set_progress_percentage(progress)
except:
pass
with self.output().open('w') as done:
json.dump(results, done)
def evaluate(test, truth):
synaptic_partners_eval = SynapticPartners()
fscore, precision, recall, fp, fn, filtered_matches = synaptic_partners_eval.fscore(test.read_annotations(),
truth.read_annotations(), truth.read_neuron_ids(),
all_stats=True)
logging.info("\tfscore: " + str(fscore))
logging.info("\tprecision: " + str(precision))
logging.info("\trecall: " + str(recall))
logging.info("\tfp: " + str(fp))
logging.info("\tfn: " + str(fn))
return fscore
def run(self):
progress = 0.0
results = dict()
self.set_progress_percentage(progress)
for s in self.samples:
truth = os.path.join(
"/groups/saalfeld/saalfeldlab/larissa/data/cremieval/",
self.de,
s + "." + self.m + ".h5",
)
test = os.path.join(
os.path.dirname(self.input()[0].fn), s + "." + self.m + ".h5"
)
truth = CremiFile(truth, "a")
test = CremiFile(test, "a")
synaptic_partners_eval = SynapticPartners()
print(test.read_annotations())
fscore, precision, recall, fp, fn, filtered_matches = synaptic_partners_eval.fscore(
test.read_annotations(),
truth.read_annotations(),
truth.read_neuron_ids(),
all_stats=True,
)
results[s] = dict()
results[s]["fscore"] = fscore
results[s]["precision"] = precision
results[s]["recall"] = recall
results[s]["fp"] = fp
results[s]["fn"] = fn
results[s]["filtered_matches"] = filtered_matches
progress += 100.0 / len(self.samples)
try:
self.set_progress_percentage(progress)
except:
pass
with self.output().open("w") as done:
json.dump(results, done)
print "Neuron IDs" print "==========" print "\tvoi split : " + str(voi_split) print "\tvoi merge : " + str(voi_merge) print "\tadapted RAND: " + str(adapted_rand) clefts_evaluation = Clefts(test.read_clefts(), truth.read_clefts()) false_positive_count = clefts_evaluation.count_false_positives() false_negative_count = clefts_evaluation.count_false_negatives() false_positive_stats = clefts_evaluation.acc_false_positives() false_negative_stats = clefts_evaluation.acc_false_negatives() print "Clefts" print "======" print "\tfalse positives: " + str(false_positive_count) print "\tfalse negatives: " + str(false_negative_count) print "\tdistance to ground truth: " + str(false_positive_stats) print "\tdistance to proposal : " + str(false_negative_stats) synaptic_partners_evaluation = SynapticPartners() fscore = synaptic_partners_evaluation.fscore(test.read_annotations(), truth.read_annotations(), truth.read_neuron_ids()) print "Synaptic partners" print "=================" print "\tfscore: " + str(fscore)
print "=========="
print "\tvoi split : " + str(voi_split)
print "\tvoi merge : " + str(voi_merge)
print "\tadapted RAND: " + str(adapted_rand)
clefts_evaluation = Clefts(test.read_clefts(), truth.read_clefts())
false_positive_count = clefts_evaluation.count_false_positives()
false_negative_count = clefts_evaluation.count_false_negatives()
false_positive_stats = clefts_evaluation.acc_false_positives()
false_negative_stats = clefts_evaluation.acc_false_negatives()
print "Clefts"
print "======"
print "\tfalse positives: " + str(false_positive_count)
print "\tfalse negatives: " + str(false_negative_count)
print "\tdistance to ground truth: " + str(false_positive_stats)
print "\tdistance to proposal : " + str(false_negative_stats)
synaptic_partners_evaluation = SynapticPartners()
fscore = synaptic_partners_evaluation.fscore(test.read_annotations(),
truth.read_annotations(),
truth.read_neuron_ids())
print "Synaptic partners"
print "================="
print "\tfscore: " + str(fscore)