def metrics():
_value = main.get_metrics(
application.config['CB_DATABASE'],
application.config['CB_USERNAME'],
application.config['CB_PASSWORD'])
if application.config['CB_STREAMING']:
def generate():
for row in _value:
yield(row + "\n")
return Response(stream_with_context(generate()), mimetype='text/plain')
else:
metrics_str = "\n"
metrics_str = metrics_str.join(_value)
return Response(metrics_str, mimetype='text/plain')
def test_get_metrics():
dataset = sklearn.datasets.load_breast_cancer()
var2 = sklearn.cluster.KMeans(4)
var2.fit(dataset['data'])
true = dataset['target']
pred = var2.predict(dataset['data'])
print("H(reel)", sklearn.metrics.cluster.entropy(true))
print("H(pred)", sklearn.metrics.cluster.entropy(pred))
MI = sklearn.metrics.cluster.mutual_info_score(true, pred)
print("MI", MI)
cont = utility.compute_contingency(true, pred)
rep = main.get_metrics(true, pred)
for a in rep:
print(a, "\t", rep[a])
print(cont)
def metrics():
num_samples = 60
if request.args.get('num_samples'):
num_samples = int(request.args.get('num_samples'))
result_set = 60
if application.config['CB_RESULTSET']:
result_set = application.config['CB_RESULTSET']
_value = main.get_metrics(
application.config['CB_DATABASE'],
application.config['CB_USERNAME'],
application.config['CB_PASSWORD'],
num_samples,
result_set)
if application.config['CB_STREAMING']:
def generate():
for row in _value:
yield(row + '\n')
return Response(stream_with_context(generate()), mimetype='text/plain')
else:
metrics_str = '\n'
metrics_str = metrics_str.join(_value)
return Response(metrics_str, mimetype='text/plain')
def test_get_metrics(self):
print(main.get_metrics(good_event, context))