def test_predict(self):
row = [1, 1, 1, 1, 'label']
data = pandas.DataFrame([row])
solution = knn.kNN(123, data)
result = solution.predict(data)
self.assertIsInstance(result, list)
self.assertEqual(result, ['label'])
def test_score_bad_label(self):
row = [1, 1, 1, 1, 'label']
data = pandas.DataFrame([row])
solution = knn.kNN(123, data)
result = solution.score(data, ['bad_label'])
self.assertIsInstance(result, float)
self.assertEqual(result, 0)
print "Parameters:"
print "k ",options.k
print "weight",options.weight
print "distance ", options.dist
print "fast", options.fast
print "radius ",options.radius
print "Computing on", options.pu
train_reader = csv.reader(open(options.training), delimiter='\t')
train = [row for row in train_reader]
labels_reader = csv.reader(open(options.labels), delimiter='\t')
labels = [row[0] for row in labels_reader]
ts_reader = csv.reader(open(options.testset), delimiter='\t')
ts = [row for row in ts_reader]
nn = knn.kNN(ts,
train,
labels,
options.weight,
options.dist,
options.fast,
options.radius,
options.pu)
res = nn.compute(options.k)
w = csv.writer(open(options.foutp, 'w'), delimiter='\t')
for line in res:
w.writerow([line])
def test_constructor_accepts_arguments(self):
knn.kNN(123, [1, 2, 3])
print "radius ",options.radius
print "Computing on", options.pu
train_reader = csv.reader(open(options.training), delimiter='\t')
train = [row for row in train_reader]
labels_reader = csv.reader(open(options.labels), delimiter='\t')
labels = [row[0] for row in labels_reader]
ts_reader = csv.reader(open(options.testset), delimiter='\t')
ts = [row for row in ts_reader]
nn = knn.kNN(ts,
train,
labels,
options.weight,
options.dist,
options.fast,
options.radius,
options.pu)
res = nn.compute(options.k)
w = csv.writer(open(options.foutp, 'w'), delimiter='\t')
for line in res:
w.writerow([line])
if options.time is True:
end = time.time()
elapsed = end - start
if elapsed > 60:
msg = "%f min" % (elapsed / 60.0)
else: