def check(model, model_filename, test, lessthan, msg_prefix=""):
lines, labels = read_labels(test["data"])
predictions = []
for line in lines:
pred_label, _ = model.predict(line)
predictions.append(pred_label)
p1_local_out, r1_local_out = util.test(predictions, labels)
self.assertEqual(
len(predictions), test["n"], msg_prefix + "N: Want: " +
str(test["n"]) + " Is: " + str(len(predictions))
)
self.assertTrue(
p1_local_out >= test["p1"], msg_prefix + "p1: Want: " +
str(test["p1"]) + " Is: " + str(p1_local_out)
)
self.assertTrue(
r1_local_out >= test["r1"], msg_prefix + "r1: Want: " +
str(test["r1"]) + " Is: " + str(r1_local_out)
)
path_size = get_path_size(model_filename)
size_msg = str(test["size"]) + " Is: " + str(path_size)
if lessthan:
self.assertTrue(
path_size <= test["size"],
msg_prefix + "Size: Want at most: " + size_msg
)
else:
self.assertTrue(
path_size == test["size"],
msg_prefix + "Size: Want: " + size_msg
)
def check(data):
third = int(len(data) / 3)
train_data = data[:2 * third]
valid_data = data[third:]
with tempfile.NamedTemporaryFile(
delete=False
) as tmpf, tempfile.NamedTemporaryFile(delete=False) as tmpf2:
for line in train_data:
tmpf.write(
("__label__" + line.strip() + "\n").encode("UTF-8")
)
tmpf.flush()
for line in valid_data:
tmpf2.write(
("__label__" + line.strip() + "\n").encode("UTF-8")
)
tmpf2.flush()
model = train_supervised(input=tmpf.name, **kwargs)
true_labels = []
all_words = []
with open(tmpf2.name, 'r') as fid:
for line in fid:
if sys.version_info < (3, 0):
line = line.decode("UTF-8")
if len(line.strip()) == 0:
continue
words, labels = model.get_line(line.strip())
if len(labels) == 0:
continue
all_words.append(" ".join(words))
true_labels += [labels]
predictions, _ = model.predict(all_words)
p, r = util.test(predictions, true_labels)
N = len(predictions)
Nt, pt, rt = model.test(tmpf2.name)
self.assertEqual(N, Nt)
self.assertEqual(p, pt)
self.assertEqual(r, rt)
def check(model, model_filename, test, lessthan, msg_prefix=""):
lines, labels = read_labels(test["data"])
predictions = []
for line in lines:
pred_label, _ = model.predict(line)
predictions.append(pred_label)
p1_local_out, r1_local_out = util.test(predictions, labels)
self.assertEqual(
len(predictions), test["n"], msg_prefix + "N: Want: " +
str(test["n"]) + " Is: " + str(len(predictions)))
self.assertTrue(
p1_local_out >= test["p1"], msg_prefix + "p1: Want: " +
str(test["p1"]) + " Is: " + str(p1_local_out))
self.assertTrue(
r1_local_out >= test["r1"], msg_prefix + "r1: Want: " +
str(test["r1"]) + " Is: " + str(r1_local_out))
path_size = get_path_size(model_filename)
size_msg = str(test["size"]) + " Is: " + str(path_size)
if lessthan:
self.assertTrue(path_size <= test["size"],
msg_prefix + "Size: Want at most: " + size_msg)
else:
self.assertTrue(path_size == test["size"],
msg_prefix + "Size: Want: " + size_msg)
labels = []
with open(filename) as f:
for line in f:
line_labels = []
tokens = line.split()
for token in tokens:
if token.startswith(prefix):
line_labels.append(token)
labels.append(line_labels)
return labels
if __name__ == "__main__":
train_data = os.path.join(os.getenv("DATADIR", ''), 'cooking.train')
valid_data = os.path.join(os.getenv("DATADIR", ''), 'cooking.valid')
# train_supervised uses the same arguments and defaults as the fastText cli
model = train_supervised(input=train_data,
epoch=25,
lr=1.0,
wordNgrams=2,
verbose=2,
minCount=1)
k = 1
predictions, _ = get_predictions(valid_data, model, k=k)
valid_labels = get_labels_from_file(valid_data)
p, r = test(predictions, valid_labels, k=k)
print("N\t" + str(len(valid_labels)))
print("P@{}\t{:.3f}".format(k, p))
print("R@{}\t{:.3f}".format(k, r))
model.save_model(train_data + '.bin')
