def test_cora(self):
(trn, val, preproc, df_holdout, G_complete) = gr.graph_nodes_from_csv(
"graph_data/cora/cora.content",
"graph_data/cora/cora.cites",
sample_size=20,
holdout_pct=0.1,
holdout_for_inductive=True,
train_pct=0.1,
sep="\t",
)
learner = ktrain.get_learner(
model=gr.graph_node_classifier(
"graphsage",
trn,
),
train_data=trn,
# val_data=val,
batch_size=64,
)
lr = 0.01
hist = learner.autofit(lr, 10)
# test training results
self.assertAlmostEqual(max(hist.history["lr"]), lr)
self.assertGreater(max(hist.history[ACC_NAME]), 0.9)
# test top losses
obs = learner.top_losses(n=1, val_data=val)
self.assertIn(obs[0][0], list(range(val.targets.shape[0])))
learner.view_top_losses(preproc=preproc, n=1, val_data=val)
# test weight decay
self.assertEqual(learner.get_weight_decay(), None)
learner.set_weight_decay(1e-2)
self.assertAlmostEqual(learner.get_weight_decay(), 1e-2)
# test load and save model
learner.save_model("/tmp/test_model")
learner.load_model("/tmp/test_model")
# test validate
learner.validate(val_data=val)
cm = learner.validate(val_data=val)
print(cm)
for i, row in enumerate(cm):
if i == 5:
continue # many 5s are classified as 6s
self.assertEqual(np.argmax(row), i)
# test predictor
p = ktrain.get_predictor(learner.model, preproc)
self.assertIn(p.predict_transductive(val.ids[0:1])[0], preproc.get_classes())
p.predict_transductive(val.ids[0:1])
p.save("/tmp/test_predictor")
p = ktrain.load_predictor("/tmp/test_predictor")
self.assertIn(p.predict_transductive(val.ids[0:1])[0], preproc.get_classes())
def test_cora(self):
(trn, val, preproc, df_holdout,
G_complete) = gr.graph_nodes_from_csv('graph_data/cora/cora.content',
'graph_data/cora/cora.cites',
sample_size=20,
holdout_pct=0.1,
holdout_for_inductive=True,
train_pct=0.1,
sep='\t')
learner = ktrain.get_learner(
model=gr.graph_node_classifier(
'graphsage',
trn,
),
train_data=trn,
#val_data=val,
batch_size=64)
lr = 0.01
hist = learner.autofit(lr, 10)
# test training results
self.assertAlmostEqual(max(hist.history['lr']), lr)
self.assertGreater(max(hist.history['acc']), 0.9)
# test top losses
obs = learner.top_losses(n=1, val_data=val)
self.assertIn(obs[0][0], list(range(val.targets.shape[0])))
learner.view_top_losses(preproc=preproc, n=1, val_data=val)
# test weight decay
self.assertEqual(len(learner.get_weight_decay()), 1)
self.assertEqual(learner.get_weight_decay()[0], None)
learner.set_weight_decay(1e-4)
self.assertAlmostEqual(learner.get_weight_decay()[0], 1e-4)
# test load and save model
learner.save_model('/tmp/test_model')
learner.load_model('/tmp/test_model')
# test validate
learner.validate(val_data=val)
cm = learner.validate(val_data=val)
print(cm)
for i, row in enumerate(cm):
self.assertEqual(np.argmax(row), i)
# test predictor
p = ktrain.get_predictor(learner.model, preproc)
self.assertIn(
p.predict_transductive(val.ids[0:1])[0], preproc.get_classes())
p.predict_transductive(val.ids[0:1])
p.save('/tmp/test_predictor')
p = ktrain.load_predictor('/tmp/test_predictor')
self.assertIn(
p.predict_transductive(val.ids[0:1])[0], preproc.get_classes())