def test_train_save_load_hybrid_self_attention(datasets):
model_save_path = "self_att_hybrid_model.pth"
model = MatchingModel(attr_summarizer=attr_summarizers.Hybrid(
word_contextualizer="self-attention"))
model.run_train(
datasets.train,
datasets.valid,
epochs=1,
batch_size=8,
best_save_path=model_save_path,
pos_neg_ratio=3,
)
s1 = model.run_eval(datasets.test)
model2 = MatchingModel(attr_summarizer=attr_summarizers.Hybrid(
word_contextualizer="self-attention"))
model2.load_state(model_save_path)
s2 = model2.run_eval(datasets.test)
assert s1 == s2
if os.path.exists(model_save_path):
os.remove(model_save_path)
def test_predict_unlabeled_hybrid(datasets):
model_save_path = "hybrid_model.pth"
model = MatchingModel(attr_summarizer="hybrid")
model.run_train(
datasets.train,
datasets.valid,
epochs=1,
batch_size=8,
best_save_path=model_save_path,
pos_neg_ratio=3,
)
unlabeled = process_unlabeled(
path=os.path.join(test_dir_path, "test_datasets",
"test_unlabeled.csv"),
trained_model=model,
ignore_columns=("left_id", "right_id"),
)
pred_test = model.run_eval(datasets.test, return_predictions=True)
pred_unlabeled = model.run_prediction(unlabeled)
assert sorted(tup[1] for tup in pred_test) == sorted(
list(pred_unlabeled["match_score"]))
if os.path.exists(model_save_path):
os.remove(model_save_path)
def test_rnn(self):
model_save_path = 'rnn_model.pth'
model = MatchingModel(attr_summarizer='rnn')
model.run_train(self.train,
self.valid,
epochs=1,
batch_size=8,
best_save_path=model_save_path,
pos_neg_ratio=3)
s1 = model.run_eval(self.test)
model2 = MatchingModel(attr_summarizer='rnn')
model2.load_state(model_save_path)
s2 = model2.run_eval(self.test)
self.assertEqual(s1, s2)
if os.path.exists(model_save_path):
os.remove(model_save_path)
def test_train_save_load_rnn(datasets):
model_save_path = "rnn_model.pth"
model = MatchingModel(attr_summarizer="rnn")
model.run_train(
datasets.train,
datasets.valid,
epochs=1,
batch_size=8,
best_save_path=model_save_path,
pos_neg_ratio=3,
)
s1 = model.run_eval(datasets.test)
model2 = MatchingModel(attr_summarizer="rnn")
model2.load_state(model_save_path)
s2 = model2.run_eval(datasets.test)
assert s1 == s2
if os.path.exists(model_save_path):
os.remove(model_save_path)
def test_hybrid_self_attention(self):
model_save_path = 'self_att_hybrid_model.pth'
model = MatchingModel(attr_summarizer=attr_summarizers.Hybrid(
word_contextualizer='self-attention'))
model.run_train(self.train,
self.valid,
epochs=1,
batch_size=8,
best_save_path=model_save_path,
pos_neg_ratio=3)
s1 = model.run_eval(self.test)
model2 = MatchingModel(attr_summarizer=attr_summarizers.Hybrid(
word_contextualizer='self-attention'))
model2.load_state(model_save_path)
s2 = model2.run_eval(self.test)
self.assertEqual(s1, s2)
if os.path.exists(model_save_path):
os.remove(model_save_path)
def test_hybrid(self):
model_save_path = 'hybrid_model.pth'
model = MatchingModel(attr_summarizer='hybrid')
model.run_train(self.train,
self.valid,
epochs=1,
batch_size=8,
best_save_path=model_save_path,
pos_neg_ratio=3)
unlabeled = process_unlabeled(path=os.path.join(
test_dir_path, 'test_datasets', 'test_unlabeled.csv'),
trained_model=model,
ignore_columns=('left_id', 'right_id'))
pred_test = model.run_eval(self.test, return_predictions=True)
pred_unlabeled = model.run_prediction(unlabeled)
self.assertEqual(sorted([tup[1] for tup in pred_test]),
sorted(list(pred_unlabeled['match_score'])))
if os.path.exists(model_save_path):
os.remove(model_save_path)
