def dump(self):
table = []
metrics = [
'generality', 'accuracy', 'precision', 'recall', 'fallout',
'f_score'
]
# header
table.append([''] + metrics)
# per class metrics
for c in self.classes():
row = [c]
for name in metrics:
row.append(getattr(self, name)(c))
table.append(row)
# averaging metrics
for prefix in 'macro', 'micro':
row = [prefix + ' average']
for name in metrics:
row.append(getattr(self, '%s_%s' % (prefix, name))())
table.append(row)
# format numeric cells
for row in table:
for j, cell in enumerate(row):
if isinstance(cell, float):
row[j] = '%.2f%%' % (cell * 100)
return _pretty_print(table)
def drawConfusionMatrix(self):
''' Draw the Confusion Matrix '''
table = []
#header
table.append([''] + list(self._labels))
#class metrics
for c in range(len(self._matrix)):
table.append([self._labels[c]] + self._matrix[c])
#averaging metrics
for prefix in ['TNR/TPR']:
table.append([prefix] + [self.sensitivity(), self.specificity()])
return _pretty_print(table)
def drawConfusionMatrix(self): ''' Draw the Confusion Matrix ''' table = [] #header table.append([''] + list(self._labels)) #class metrics for c in range(len(self._matrix)): table.append([self._labels[c]] + self._matrix[c]) #averaging metrics for prefix in ['TNR/TPR']: table.append([prefix] + [self.sensitivity(), self.specificity()]) return _pretty_print(table)
def dump(self):
table = []
metrics = ["generality", "accuracy", "precision", "recall", "fallout", "f_score"]
# header
table.append([""] + metrics)
# per class metrics
for c in self.classes():
row = [c]
for name in metrics:
row.append(getattr(self, name)(c))
table.append(row)
# averaging metrics
for prefix in "macro", "micro":
row = [prefix + " average"]
for name in metrics:
row.append(getattr(self, "%s_%s" % (prefix, name))())
table.append(row)
# format numeric cells
for row in table:
for j, cell in enumerate(row):
if isinstance(cell, float):
row[j] = "%.2f%%" % (cell * 100)
return _pretty_print(table)
