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Pulling examples of baselines #42

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Jun 13, 2023
Merged

Pulling examples of baselines #42

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2b3de28
small modifications
May 27, 2023
707eea4
minor changes
May 28, 2023
f799db1
small changes
May 31, 2023
9cc53a3
new dataset stats added
Jun 1, 2023
69ea725
improved log info
Jun 1, 2023
b8fc09d
small changes
Jun 1, 2023
3219adc
small changeserge branch 'farimah_lpp' of https://github.com/shenyang…
Jun 1, 2023
a9deaa4
small changes
Jun 2, 2023
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7 changes: 5 additions & 2 deletions examples/linkproppred/amazonreview/dyrep_early_stop.py
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Original file line number Diff line number Diff line change
Expand Up @@ -284,14 +284,15 @@ def test_one_vs_many(loader, neg_sampler, split_mode):

# define an early stopper
save_model_dir = f'{osp.dirname(osp.abspath(__file__))}/saved_models/'
save_model_id = f'{MODEL_NAME}_{DATA}_{run_idx}'
save_model_id = f'{MODEL_NAME}_{DATA}_{SEED}_{run_idx}'
early_stopper = EarlyStopMonitor(save_model_dir=save_model_dir, save_model_id=save_model_id,
tolerance=TOLERANCE, patience=PATIENCE)

# ==================================================== Train & Validation
# loading the validation negative samples
dataset.load_val_ns()

val_perf_list = []
start_train_val = timeit.default_timer()
for epoch in range(1, NUM_EPOCH + 1):
# training
Expand All @@ -306,6 +307,7 @@ def test_one_vs_many(loader, neg_sampler, split_mode):
perf_metric_val = test_one_vs_many(val_loader, neg_sampler, split_mode="val")
print(f"\tValidation {metric}: {perf_metric_val: .4f}")
print(f"\tValidation: Elapsed time (s): {timeit.default_timer() - start_val: .4f}")
val_perf_list.append(perf_metric_val)

# check for early stopping
if early_stopper.step_check(perf_metric_val, model):
Expand Down Expand Up @@ -334,7 +336,8 @@ def test_one_vs_many(loader, neg_sampler, split_mode):
'data': DATA,
'run': run_idx,
'seed': SEED,
metric: perf_metric_test,
f'val {metric}': val_perf_list,
f'test {metric}': perf_metric_test,
'test_time': test_time,
'tot_train_val_time': train_val_time
},
Expand Down
9 changes: 6 additions & 3 deletions examples/linkproppred/amazonreview/tgn_early_stop.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -272,14 +272,15 @@ def test_one_vs_many(loader, neg_sampler, split_mode):

# define an early stopper
save_model_dir = f'{osp.dirname(osp.abspath(__file__))}/saved_models/'
save_model_id = f'{MODEL_NAME}_{DATA}_{run_idx}'
save_model_id = f'{MODEL_NAME}_{DATA}_{SEED}_{run_idx}'
early_stopper = EarlyStopMonitor(save_model_dir=save_model_dir, save_model_id=save_model_id,
tolerance=TOLERANCE, patience=PATIENCE)

# ==================================================== Train & Validation
# loading the validation negative samples
dataset.load_val_ns()


val_perf_list = []
start_train_val = timeit.default_timer()
for epoch in range(1, NUM_EPOCH + 1):
# training
Expand All @@ -294,6 +295,7 @@ def test_one_vs_many(loader, neg_sampler, split_mode):
perf_metric_val = test_one_vs_many(val_loader, neg_sampler, split_mode="val")
print(f"\tValidation {metric}: {perf_metric_val: .4f}")
print(f"\tValidation: Elapsed time (s): {timeit.default_timer() - start_val: .4f}")
val_perf_list.append(perf_metric_val)

# check for early stopping
if early_stopper.step_check(perf_metric_val, model):
Expand Down Expand Up @@ -322,7 +324,8 @@ def test_one_vs_many(loader, neg_sampler, split_mode):
'data': DATA,
'run': run_idx,
'seed': SEED,
metric: perf_metric_test,
f'val {metric}': val_perf_list,
f'test {metric}': perf_metric_test,
'test_time': test_time,
'tot_train_val_time': train_val_time
},
Expand Down
7 changes: 5 additions & 2 deletions examples/linkproppred/opensky/dyrep_early_stop.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -284,14 +284,15 @@ def test_one_vs_many(loader, neg_sampler, split_mode):

# define an early stopper
save_model_dir = f'{osp.dirname(osp.abspath(__file__))}/saved_models/'
save_model_id = f'{MODEL_NAME}_{DATA}_{run_idx}'
save_model_id = f'{MODEL_NAME}_{DATA}_{SEED}_{run_idx}'
early_stopper = EarlyStopMonitor(save_model_dir=save_model_dir, save_model_id=save_model_id,
tolerance=TOLERANCE, patience=PATIENCE)

# ==================================================== Train & Validation
# loading the validation negative samples
dataset.load_val_ns()

val_perf_list = []
start_train_val = timeit.default_timer()
for epoch in range(1, NUM_EPOCH + 1):
# training
Expand All @@ -306,6 +307,7 @@ def test_one_vs_many(loader, neg_sampler, split_mode):
perf_metric_val = test_one_vs_many(val_loader, neg_sampler, split_mode="val")
print(f"\tValidation {metric}: {perf_metric_val: .4f}")
print(f"\tValidation: Elapsed time (s): {timeit.default_timer() - start_val: .4f}")
val_perf_list.append(perf_metric_val)

# check for early stopping
if early_stopper.step_check(perf_metric_val, model):
Expand Down Expand Up @@ -334,7 +336,8 @@ def test_one_vs_many(loader, neg_sampler, split_mode):
'data': DATA,
'run': run_idx,
'seed': SEED,
metric: perf_metric_test,
f'val {metric}': val_perf_list,
f'test {metric}': perf_metric_test,
'test_time': test_time,
'tot_train_val_time': train_val_time
},
Expand Down
7 changes: 5 additions & 2 deletions examples/linkproppred/opensky/tgn_early_stop.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -272,14 +272,15 @@ def test_one_vs_many(loader, neg_sampler, split_mode):

# define an early stopper
save_model_dir = f'{osp.dirname(osp.abspath(__file__))}/saved_models/'
save_model_id = f'{MODEL_NAME}_{DATA}_{run_idx}'
save_model_id = f'{MODEL_NAME}_{DATA}_{SEED}_{run_idx}'
early_stopper = EarlyStopMonitor(save_model_dir=save_model_dir, save_model_id=save_model_id,
tolerance=TOLERANCE, patience=PATIENCE)

# ==================================================== Train & Validation
# loading the validation negative samples
dataset.load_val_ns()

val_perf_list = []
start_train_val = timeit.default_timer()
for epoch in range(1, NUM_EPOCH + 1):
# training
Expand All @@ -294,6 +295,7 @@ def test_one_vs_many(loader, neg_sampler, split_mode):
perf_metric_val = test_one_vs_many(val_loader, neg_sampler, split_mode="val")
print(f"\tValidation {metric}: {perf_metric_val: .4f}")
print(f"\tValidation: Elapsed time (s): {timeit.default_timer() - start_val: .4f}")
val_perf_list.append(perf_metric_val)

# check for early stopping
if early_stopper.step_check(perf_metric_val, model):
Expand Down Expand Up @@ -322,7 +324,8 @@ def test_one_vs_many(loader, neg_sampler, split_mode):
'data': DATA,
'run': run_idx,
'seed': SEED,
metric: perf_metric_test,
f'val {metric}': val_perf_list,
f'test {metric}': perf_metric_test,
'test_time': test_time,
'tot_train_val_time': train_val_time
},
Expand Down
7 changes: 5 additions & 2 deletions examples/linkproppred/redditcomments/dyrep_early_stop.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -284,14 +284,15 @@ def test_one_vs_many(loader, neg_sampler, split_mode):

# define an early stopper
save_model_dir = f'{osp.dirname(osp.abspath(__file__))}/saved_models/'
save_model_id = f'{MODEL_NAME}_{DATA}_{run_idx}'
save_model_id = f'{MODEL_NAME}_{DATA}_{SEED}_{run_idx}'
early_stopper = EarlyStopMonitor(save_model_dir=save_model_dir, save_model_id=save_model_id,
tolerance=TOLERANCE, patience=PATIENCE)

# ==================================================== Train & Validation
# loading the validation negative samples
dataset.load_val_ns()

val_perf_list = []
start_train_val = timeit.default_timer()
for epoch in range(1, NUM_EPOCH + 1):
# training
Expand All @@ -306,6 +307,7 @@ def test_one_vs_many(loader, neg_sampler, split_mode):
perf_metric_val = test_one_vs_many(val_loader, neg_sampler, split_mode="val")
print(f"\tValidation {metric}: {perf_metric_val: .4f}")
print(f"\tValidation: Elapsed time (s): {timeit.default_timer() - start_val: .4f}")
val_perf_list.append(perf_metric_val)

# check for early stopping
if early_stopper.step_check(perf_metric_val, model):
Expand Down Expand Up @@ -334,7 +336,8 @@ def test_one_vs_many(loader, neg_sampler, split_mode):
'data': DATA,
'run': run_idx,
'seed': SEED,
metric: perf_metric_test,
f'val {metric}': val_perf_list,
f'test {metric}': perf_metric_test,
'test_time': test_time,
'tot_train_val_time': train_val_time
},
Expand Down
7 changes: 5 additions & 2 deletions examples/linkproppred/redditcomments/tgn_early_stop.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -272,14 +272,15 @@ def test_one_vs_many(loader, neg_sampler, split_mode):

# define an early stopper
save_model_dir = f'{osp.dirname(osp.abspath(__file__))}/saved_models/'
save_model_id = f'{MODEL_NAME}_{DATA}_{run_idx}'
save_model_id = f'{MODEL_NAME}_{DATA}_{SEED}_{run_idx}'
early_stopper = EarlyStopMonitor(save_model_dir=save_model_dir, save_model_id=save_model_id,
tolerance=TOLERANCE, patience=PATIENCE)

# ==================================================== Train & Validation
# loading the validation negative samples
dataset.load_val_ns()

val_perf_list = []
start_train_val = timeit.default_timer()
for epoch in range(1, NUM_EPOCH + 1):
# training
Expand All @@ -294,6 +295,7 @@ def test_one_vs_many(loader, neg_sampler, split_mode):
perf_metric_val = test_one_vs_many(val_loader, neg_sampler, split_mode="val")
print(f"\tValidation {metric}: {perf_metric_val: .4f}")
print(f"\tValidation: Elapsed time (s): {timeit.default_timer() - start_val: .4f}")
val_perf_list.append(perf_metric_val)

# check for early stopping
if early_stopper.step_check(perf_metric_val, model):
Expand Down Expand Up @@ -322,7 +324,8 @@ def test_one_vs_many(loader, neg_sampler, split_mode):
'data': DATA,
'run': run_idx,
'seed': SEED,
metric: perf_metric_test,
f'val {metric}': val_perf_list,
f'test {metric}': perf_metric_test,
'test_time': test_time,
'tot_train_val_time': train_val_time
},
Expand Down
8 changes: 5 additions & 3 deletions examples/linkproppred/stablecoin/dyrep_early_stop.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -18,7 +18,6 @@
import torch
from sklearn.metrics import average_precision_score, roc_auc_score
from torch.nn import Linear
from torch_geometric.datasets import JODIEDataset
from torch_geometric.loader import TemporalDataLoader
from torch_geometric.nn import TransformerConv

Expand Down Expand Up @@ -284,14 +283,15 @@ def test_one_vs_many(loader, neg_sampler, split_mode):

# define an early stopper
save_model_dir = f'{osp.dirname(osp.abspath(__file__))}/saved_models/'
save_model_id = f'{MODEL_NAME}_{DATA}_{run_idx}'
save_model_id = f'{MODEL_NAME}_{DATA}_{SEED}_{run_idx}'
early_stopper = EarlyStopMonitor(save_model_dir=save_model_dir, save_model_id=save_model_id,
tolerance=TOLERANCE, patience=PATIENCE)

# ==================================================== Train & Validation
# loading the validation negative samples
dataset.load_val_ns()

val_perf_list = []
start_train_val = timeit.default_timer()
for epoch in range(1, NUM_EPOCH + 1):
# training
Expand All @@ -306,6 +306,7 @@ def test_one_vs_many(loader, neg_sampler, split_mode):
perf_metric_val = test_one_vs_many(val_loader, neg_sampler, split_mode="val")
print(f"\tValidation {metric}: {perf_metric_val: .4f}")
print(f"\tValidation: Elapsed time (s): {timeit.default_timer() - start_val: .4f}")
val_perf_list.append(perf_metric_val)

# check for early stopping
if early_stopper.step_check(perf_metric_val, model):
Expand Down Expand Up @@ -334,7 +335,8 @@ def test_one_vs_many(loader, neg_sampler, split_mode):
'data': DATA,
'run': run_idx,
'seed': SEED,
metric: perf_metric_test,
f'val {metric}': val_perf_list,
f'test {metric}': perf_metric_test,
'test_time': test_time,
'tot_train_val_time': train_val_time
},
Expand Down
7 changes: 5 additions & 2 deletions examples/linkproppred/stablecoin/tgn_early_stop.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -272,14 +272,15 @@ def test_one_vs_many(loader, neg_sampler, split_mode):

# define an early stopper
save_model_dir = f'{osp.dirname(osp.abspath(__file__))}/saved_models/'
save_model_id = f'{MODEL_NAME}_{DATA}_{run_idx}'
save_model_id = f'{MODEL_NAME}_{DATA}_{SEED}_{run_idx}'
early_stopper = EarlyStopMonitor(save_model_dir=save_model_dir, save_model_id=save_model_id,
tolerance=TOLERANCE, patience=PATIENCE)

# ==================================================== Train & Validation
# loading the validation negative samples
dataset.load_val_ns()

val_perf_list = []
start_train_val = timeit.default_timer()
for epoch in range(1, NUM_EPOCH + 1):
# training
Expand All @@ -294,6 +295,7 @@ def test_one_vs_many(loader, neg_sampler, split_mode):
perf_metric_val = test_one_vs_many(val_loader, neg_sampler, split_mode="val")
print(f"\tValidation {metric}: {perf_metric_val: .4f}")
print(f"\tValidation: Elapsed time (s): {timeit.default_timer() - start_val: .4f}")
val_perf_list.append(perf_metric_val)

# check for early stopping
if early_stopper.step_check(perf_metric_val, model):
Expand Down Expand Up @@ -322,7 +324,8 @@ def test_one_vs_many(loader, neg_sampler, split_mode):
'data': DATA,
'run': run_idx,
'seed': SEED,
metric: perf_metric_test,
f'val {metric}': val_perf_list,
f'test {metric}': perf_metric_test,
'test_time': test_time,
'tot_train_val_time': train_val_time
},
Expand Down
6 changes: 4 additions & 2 deletions examples/linkproppred/wikipedia/dyrep_early_stop.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -284,14 +284,15 @@ def test_one_vs_many(loader, neg_sampler, split_mode):

# define an early stopper
save_model_dir = f'{osp.dirname(osp.abspath(__file__))}/saved_models/'
save_model_id = f'{MODEL_NAME}_{DATA}_{run_idx}'
save_model_id = f'{MODEL_NAME}_{DATA}_{SEED}_{run_idx}'
early_stopper = EarlyStopMonitor(save_model_dir=save_model_dir, save_model_id=save_model_id,
tolerance=TOLERANCE, patience=PATIENCE)

# ==================================================== Train & Validation
# loading the validation negative samples
dataset.load_val_ns()

val_perf_list = []
start_train_val = timeit.default_timer()
for epoch in range(1, NUM_EPOCH + 1):
# training
Expand Down Expand Up @@ -334,7 +335,8 @@ def test_one_vs_many(loader, neg_sampler, split_mode):
'data': DATA,
'run': run_idx,
'seed': SEED,
metric: perf_metric_test,
f'val {metric}': val_perf_list,
f'test {metric}': perf_metric_test,
'test_time': test_time,
'tot_train_val_time': train_val_time
},
Expand Down
7 changes: 5 additions & 2 deletions examples/linkproppred/wikipedia/tgn_early_stop.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -271,14 +271,15 @@ def test_one_vs_many(loader, neg_sampler, split_mode):

# define an early stopper
save_model_dir = f'{osp.dirname(osp.abspath(__file__))}/saved_models/'
save_model_id = f'{MODEL_NAME}_{DATA}_{run_idx}'
save_model_id = f'{MODEL_NAME}_{DATA}_{SEED}_{run_idx}'
early_stopper = EarlyStopMonitor(save_model_dir=save_model_dir, save_model_id=save_model_id,
tolerance=TOLERANCE, patience=PATIENCE)

# ==================================================== Train & Validation
# loading the validation negative samples
dataset.load_val_ns()

val_perf_list = []
start_train_val = timeit.default_timer()
for epoch in range(1, NUM_EPOCH + 1):
# training
Expand All @@ -293,6 +294,7 @@ def test_one_vs_many(loader, neg_sampler, split_mode):
perf_metric_val = test_one_vs_many(val_loader, neg_sampler, split_mode="val")
print(f"\tValidation {metric}: {perf_metric_val: .4f}")
print(f"\tValidation: Elapsed time (s): {timeit.default_timer() - start_val: .4f}")
val_perf_list.append(perf_metric_val)

# check for early stopping
if early_stopper.step_check(perf_metric_val, model):
Expand Down Expand Up @@ -321,7 +323,8 @@ def test_one_vs_many(loader, neg_sampler, split_mode):
'data': DATA,
'run': run_idx,
'seed': SEED,
metric: perf_metric_test,
f'val {metric}': val_perf_list,
f'test {metric}': perf_metric_test,
'test_time': test_time,
'tot_train_val_time': train_val_time
},
Expand Down
0 tgb/datasets/lastfmGenre/lastfm.py → tgb/datasets/lastfmgenre/lastfm.py
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