Python roc Examples

Example #1

def TestClassifierThreshold(classifier, labelled_data):
    """Returns a sorted list by the threshold (threshold, (label, guess))."""
    roc = []
    for (label, sample) in labelled_data:
        (threshold, guess) = classifier.ClassifyThreshold(sample)
        roc.append((threshold, (label, guess)))
    roc = sorted(roc, reverse=True)
    tf_roc = [label == guess for (t, (label, guess)) in roc]
    graph.roc(tf_roc)
    return roc

Example #2

File: Test.py Project: andresusanopinto/novelty-detection-thesis

def TestClassifierThreshold(classifier, labelled_data):
  """Returns a sorted list by the threshold (threshold, (label, guess))."""
  roc = []
  for (label, sample) in labelled_data:
    (threshold, guess) = classifier.ClassifyThreshold(sample)
    roc.append((threshold, (label, guess)))
  roc = sorted(roc, reverse=True)
  tf_roc = [label == guess for (t,(label,guess)) in roc]
  graph.roc(tf_roc)
  return roc

Example #3

File: seminovelty.py Project: andresusanopinto/novelty-detection-thesis

def plot_roc(threshold, samples, known_labels = known_labels, title=''):
  def ThresholdAndLabel(sample):
    label, sample = dataset.ExtractLabel(sample)
    return threshold(sample), label

  roc_curve = []
  #for threshold, label in sorted(map(ThresholdAndLabel, samples), key = lambda x: x[0], reverse=True):
  for threshold, label in sorted(map(ThresholdAndLabel, samples), key = lambda x: x, reverse=True):
    roc_curve.append( label in known_labels )
  graph.roc(roc_curve, label = title)
  pass

Example #4

File: run.py Project: andresusanopinto/novelty-detection-thesis

def p_roc(threshold, title, style, samples = test_samples, known_labels = KNOWN_LABELS):
  def ThresholdAndLabel(sample):
    label, sample = dataset.ExtractLabel(sample)
    return threshold(sample), label
  
  ct = collections.defaultdict(lambda: [0, 0])
  for threshold, label in map(ThresholdAndLabel, samples):
    ct[threshold][label in KNOWN_LABELS] += 1
  
  roc_curve = []
  for threshold, score in sorted(ct.items(), key = lambda x: x[0], reverse=True):
    roc_curve.append(tuple(score))
  graph.roc(roc_curve, style, label = title)

Example #5

def plot_roc(threshold, samples, known_labels=known_labels, title=''):
    def ThresholdAndLabel(sample):
        label, sample = dataset.ExtractLabel(sample)
        return threshold(sample), label

    roc_curve = []
    #for threshold, label in sorted(map(ThresholdAndLabel, samples), key = lambda x: x[0], reverse=True):
    for threshold, label in sorted(map(ThresholdAndLabel, samples),
                                   key=lambda x: x,
                                   reverse=True):
        roc_curve.append(label in known_labels)
    graph.roc(roc_curve, label=title)
    pass

Example #6

def p_roc(threshold,
          title,
          style,
          samples=test_samples,
          known_labels=KNOWN_LABELS):
    def ThresholdAndLabel(sample):
        label, sample = dataset.ExtractLabel(sample)
        return threshold(sample), label

    ct = collections.defaultdict(lambda: [0, 0])
    for threshold, label in map(ThresholdAndLabel, samples):
        ct[threshold][label in KNOWN_LABELS] += 1

    roc_curve = []
    for threshold, score in sorted(ct.items(),
                                   key=lambda x: x[0],
                                   reverse=True):
        roc_curve.append(tuple(score))
    graph.roc(roc_curve, style, label=title)

Example #7

    roc_curve = []
    for threshold, score in sorted(ct.items(),
                                   key=lambda x: x[0],
                                   reverse=True):
        roc_curve.append(tuple(score))
    print roc_curve[0:10]
    return roc_curve


for output, nfeatures in [('synthetic-all.pdf', [5, 10, 15, 20, 35, 50]),
                          ('synthetic-3features.pdf', [3]),
                          ('synthetic-5features.pdf', [5]),
                          ('synthetic-10features.pdf', [10]),
                          ('synthetic-50features.pdf', [50])]:
    print "Generating ", output
    test_samples = list(
        generate_samples(classes=ALL_CLASSES,
                         samples=5000,
                         labelled=True,
                         nfeatures=nfeatures))
    R1 = roc_performance(exact_novelty_detector, test_samples, KNOWN_CLASSES)
    R2 = roc_performance(uniform_novelty_detector, test_samples, KNOWN_CLASSES)
    R3 = roc_performance(independent_novelty_detector, test_samples,
                         KNOWN_CLASSES)

    graph.newfig()
    graph.roc(data=R1, style='g^-', label='exact')
    graph.roc(data=R2, style='b*-', label='uniform')
    graph.roc(data=R3, style='ro-', label='independent')
    graph.savefig(output)

Example #8

File: synthetic.py Project: andresusanopinto/novelty-detection-thesis

    ct[threshold][label in known_labels] += 1
  
  roc_curve = []
  for threshold, score in sorted(ct.items(), key = lambda x: x[0], reverse=True):
    roc_curve.append(tuple(score))
  print roc_curve[0:10]
  return roc_curve
    


for output, nfeatures in [('synthetic-all.pdf', [5, 10, 15, 20, 35, 50]),
                          ('synthetic-3features.pdf',  [3]),
                          ('synthetic-5features.pdf',  [5]),
                          ('synthetic-10features.pdf', [10]),
                          ('synthetic-50features.pdf', [50])]:
  print "Generating ", output
  test_samples = list(generate_samples(classes = ALL_CLASSES,
                                  samples = 5000,
                                  labelled = True,
                                  nfeatures = nfeatures))
  R1 = roc_performance(exact_novelty_detector,       test_samples, KNOWN_CLASSES)
  R2 = roc_performance(uniform_novelty_detector,     test_samples, KNOWN_CLASSES)
  R3 = roc_performance(independent_novelty_detector, test_samples, KNOWN_CLASSES)
  
  graph.newfig()
  graph.roc(data = R1, style = 'g^-', label = 'exact')
  graph.roc(data = R2, style = 'b*-', label = 'uniform')
  graph.roc(data = R3, style = 'ro-', label = 'independent')
  graph.savefig(output)