Python SynapticPartners Exemples

Exemple #1

Fichier : partnerreport_luigi.py Projet : lxmwust/CNNectome

 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)

Exemple #2

Fichier : evaluate_synaptic_partners.py Projet : lxmwust/CNNectome

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

Exemple #3

 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)

Exemple #4

Fichier : example_evaluation.py Projet : cremi/cremi_python

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)

Exemple #5

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)