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test_swa.py
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189 lines (173 loc) · 6.22 KB
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from dicee.executer import Execute
import pytest
from dicee.config import Namespace
class TestSWA:
@pytest.mark.filterwarnings('ignore::UserWarning')
def test_k_vs_all_swa(self):
"""Test SWA with Keci model using PL trainer."""
args = Namespace()
args.model = 'Keci'
args.p = 0
args.q = 1
args.scoring_technique = "KvsAll"
args.dataset_dir = "KGs/UMLS"
args.trainer = "PL"
args.num_epochs = 100
args.lr = 0.1
args.embedding_dim = 32
args.batch_size = 1024
args.swa = True
swa_report = Execute(args).start()
args = Namespace()
args.model = 'Keci'
args.p = 0
args.q = 1
args.scoring_technique = "KvsAll"
args.dataset_dir = "KGs/UMLS"
args.trainer = "PL"
args.num_epochs = 100
args.lr = 0.1
args.embedding_dim = 32
args.batch_size = 1024
args.swa = True
args.swa_start_epoch = 50
deferred_swa_report = Execute(args).start()
assert deferred_swa_report["Val"]["MRR"] > swa_report["Val"]["MRR"]
assert deferred_swa_report["Val"]["H@1"] > swa_report["Val"]["H@1"]
assert deferred_swa_report["Test"]["MRR"] > swa_report["Test"]["MRR"]
assert deferred_swa_report["Test"]["H@1"] > swa_report["Test"]["H@1"]
@pytest.mark.filterwarnings('ignore::UserWarning')
def test_k_vs_all_swa_cpu_trainer(self):
"""Test SWA with Keci model using CPU trainer."""
args = Namespace()
args.model = 'Keci'
args.p = 0
args.q = 1
args.scoring_technique = "KvsAll"
args.dataset_dir = "KGs/UMLS"
args.trainer = "TorchCPUTrainer"
args.num_epochs = 100
args.lr = 0.1
args.embedding_dim = 32
args.batch_size = 1024
args.swa = True
swa_report = Execute(args).start()
args = Namespace()
args.model = 'Keci'
args.p = 0
args.q = 1
args.scoring_technique = "KvsAll"
args.dataset_dir = "KGs/UMLS"
args.trainer = "TorchCPUTrainer"
args.num_epochs = 100
args.lr = 0.1
args.embedding_dim = 32
args.batch_size = 1024
args.swa = True
args.swa_start_epoch = 50
deferred_swa_report = Execute(args).start()
assert deferred_swa_report["Val"]["MRR"] > swa_report["Val"]["MRR"]
assert deferred_swa_report["Val"]["H@1"] > swa_report["Val"]["H@1"]
assert deferred_swa_report["Test"]["MRR"] > swa_report["Test"]["MRR"]
assert deferred_swa_report["Test"]["H@1"] > swa_report["Test"]["H@1"]
@pytest.mark.filterwarnings('ignore::UserWarning')
def test_k_vs_all_swag(self):
"""Test SWAG with Keci and PL trainer."""
args = Namespace()
args.model = 'Keci'
args.p = 0
args.q = 1
args.scoring_technique = "KvsAll"
args.dataset_dir = "KGs/UMLS"
args.trainer = "PL"
args.num_epochs = 200
args.lr = 0.1
args.embedding_dim = 32
args.batch_size = 1024
report = Execute(args).start()
args = Namespace()
args.model = 'Keci'
args.p = 0
args.q = 1
args.scoring_technique = "KvsAll"
args.dataset_dir = "KGs/UMLS"
args.trainer = "PL"
args.num_epochs = 200
args.lr = 0.1
args.embedding_dim = 32
args.batch_size = 1024
args.swag = True
swag_report = Execute(args).start()
assert swag_report["Val"]["MRR"] > report["Val"]["MRR"]
assert swag_report["Val"]["H@1"] > report["Val"]["H@1"]
assert swag_report["Test"]["MRR"] > report["Test"]["MRR"]
assert swag_report["Test"]["H@1"] > report["Test"]["H@1"]
@pytest.mark.filterwarnings('ignore::UserWarning')
def test_k_vs_all_ema(self):
"""Test EMA with Keci model using PL trainer."""
args = Namespace()
args.model = 'Keci'
args.p = 0
args.q = 1
args.scoring_technique = "KvsAll"
args.dataset_dir = "KGs/UMLS"
args.trainer = "PL"
args.num_epochs = 100
args.lr = 0.1
args.embedding_dim = 32
args.batch_size = 1024
args.ema = True
ema_report = Execute(args).start()
args = Namespace()
args.model = 'Keci'
args.p = 0
args.q = 1
args.scoring_technique = "KvsAll"
args.dataset_dir = "KGs/UMLS"
args.trainer = "PL"
args.num_epochs = 100
args.lr = 0.1
args.embedding_dim = 32
args.batch_size = 1024
args.ema = True
args.swa_start_epoch = 50
deferred_ema_report = Execute(args).start()
assert deferred_ema_report["Val"]["MRR"] > ema_report["Val"]["MRR"]
assert deferred_ema_report["Val"]["H@1"] > ema_report["Val"]["H@1"]
assert deferred_ema_report["Test"]["MRR"] > ema_report["Test"]["MRR"]
assert deferred_ema_report["Test"]["H@1"] > ema_report["Test"]["H@1"]
@pytest.mark.filterwarnings('ignore::UserWarning')
def test_k_vs_all_twa(self):
"""Test TWA with Keci model using PL trainer."""
args = Namespace()
args.model = 'Keci'
args.p = 0
args.q = 1
args.scoring_technique = "KvsAll"
args.dataset_dir = "KGs/UMLS"
args.trainer = "PL"
args.num_epochs = 200
args.lr = 0.1
args.embedding_dim = 32
args.batch_size = 1024
args.twa = True
args.swa_start_epoch = 10
twa_report = Execute(args).start()
args = Namespace()
args.model = 'Keci'
args.p = 0
args.q = 1
args.scoring_technique = "KvsAll"
args.dataset_dir = "KGs/UMLS"
args.trainer = "PL"
args.num_epochs = 100
args.lr = 0.1
args.embedding_dim = 32
args.batch_size = 1024
args.twa = True
args.swa_start_epoch = 50
deferred_twa_report = Execute(args).start()
assert deferred_twa_report["Val"]["MRR"] > twa_report["Val"]["MRR"]
assert deferred_twa_report["Val"]["H@1"] > twa_report["Val"]["H@1"]
assert deferred_twa_report["Test"]["MRR"] > twa_report["Test"]["MRR"]
assert deferred_twa_report["Test"]["H@1"] > twa_report["Test"]["H@1"]