def main():
root = os.path.join(os.path.dirname(__file__), "../../")
dataset = MultiNLIDataset(root)
classifier = TfidfClassifier()
train_data = dataset.train_data()
scores = classifier.fit(train_data["text"], train_data["label"])
test_data = dataset.test_data()
y_pred = classifier.predict(test_data["text"])
print(classification_report(test_data["label"], y_pred,
target_names=dataset.labels()))
def test_download(self):
root = os.path.join(os.path.dirname(__file__), "../../")
dataset = MultiNLIDataset(root, prefix="test")
dataset.download()
train_data = dataset.train_data()
test_data = dataset.test_data()
for d in [train_data, test_data]:
self.assertTrue(len(d) > 0)
counts = d["label"].value_counts().values.tolist()
c = counts[0]
for _c in counts:
self.assertEqual(c, _c)
for k in ["train", "test"]:
self.assertTrue(os.path.exists(dataset.interim_file(k)))
os.remove(dataset.interim_file(k))
self.assertTrue(os.path.exists(dataset.processed_file(k)))
os.remove(dataset.processed_file(k))