Are test samples used in training for early stopping and reporting the best performance? #4

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Open
Are test samples used in training for early stopping and reporting the best performance?#4

Description

@liupei101
liupei101
opened on Dec 14, 2025

Thank the authors for sharing the source code.

I wonder why test samples are used in training for early stopping and reporting the best performance.

Your code: sample_id_paths comes from test_sample_ids (which derives from the test set of current split). Is test data taken as validation data?

# using the same train sample ids for validationsample_id_paths= [
HESTDatasetPath(
name=sample_id,
h5_path=os.path.join(args.embed_dataroot, args.dataset, args.feature_encoder, f"fp32/{sample_id}.h5"),
h5ad_path=os.path.join(args.source_dataroot, args.dataset, f"adata/{sample_id}.h5ad"),
gene_list_path=os.path.join(args.source_dataroot, args.dataset, args.gene_list
),
) forsample_idintest_sample_ids
]
val_loaders= [
torch.utils.data.DataLoader(
HESTDataset(
sample_id_path, distribution="constant_1.0", normalize_method=normalize_method,
sample_times=1
),
batch_size=1, collate_fn=padding_batcher()
) forsample_id_pathinsample_id_paths
]

Your code: The val_loaders (it is actually test data) is evaluated in training for early stopping and reporting the best performance. Is this the right way to evaluate the model?

val_perf_dict, pred_dump=test(args, diffusier, model, val_loaders, return_all=True)
ifval_perf_dict["all"]['pearson_mean'] >best_pearson:
best_pearson=val_perf_dict["all"]['pearson_mean']
best_val_dict=val_perf_dictforpatch_name, dataset_resinval_perf_dict.items():
withopen(os.path.join(val_save_dir, f'{patch_name}_results.json'), 'w') asf:
json.dump(dataset_res, f, sort_keys=True, indent=4)
# save_pkl(os.path.join(val_save_dir, 'inference_dump.pkl'), pred_dump)early_stop_step=0else:
early_stop_step+=1ifearly_stop_step>=20:
print("Early stopping")
break

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      Skip to content
      Are test samples used in training for early stopping and reporting the best performance? #4
      Open
      Open
      Are test samples used in training for early stopping and reporting the best performance?#4
      Description
      @liupei101
      liupei101
      opened on Dec 14, 2025
      Thank the authors for sharing the source code.
      I wonder why test samples are used in training for early stopping and reporting the best performance.
      Your code: sample_id_paths comes from test_sample_ids (which derives from the test set of current split). Is test data taken as validation data?
      # using the same train sample ids for validationsample_id_paths= [
HESTDatasetPath(
name=sample_id,
h5_path=os.path.join(args.embed_dataroot, args.dataset, args.feature_encoder, f"fp32/{sample_id}.h5"),
h5ad_path=os.path.join(args.source_dataroot, args.dataset, f"adata/{sample_id}.h5ad"),
gene_list_path=os.path.join(args.source_dataroot, args.dataset, args.gene_list
),
) forsample_idintest_sample_ids
]
val_loaders= [
torch.utils.data.DataLoader(
HESTDataset(
sample_id_path, distribution="constant_1.0", normalize_method=normalize_method,
sample_times=1
),
batch_size=1, collate_fn=padding_batcher()
) forsample_id_pathinsample_id_paths
]
      Your code: The val_loaders (it is actually test data) is evaluated in training for early stopping and reporting the best performance. Is this the right way to evaluate the model?
      val_perf_dict, pred_dump=test(args, diffusier, model, val_loaders, return_all=True)
ifval_perf_dict["all"]['pearson_mean'] >best_pearson:
best_pearson=val_perf_dict["all"]['pearson_mean']
best_val_dict=val_perf_dictforpatch_name, dataset_resinval_perf_dict.items():
withopen(os.path.join(val_save_dir, f'{patch_name}_results.json'), 'w') asf:
json.dump(dataset_res, f, sort_keys=True, indent=4)
# save_pkl(os.path.join(val_save_dir, 'inference_dump.pkl'), pred_dump)early_stop_step=0else:
early_stop_step+=1ifearly_stop_step>=20:
print("Early stopping")
break
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          Skip to content
          Are test samples used in training for early stopping and reporting the best performance? #4
          Open
          Open
          Are test samples used in training for early stopping and reporting the best performance?#4
          Description
          @liupei101
          liupei101
          opened on Dec 14, 2025
          Thank the authors for sharing the source code.
          I wonder why test samples are used in training for early stopping and reporting the best performance.
          Your code: sample_id_paths comes from test_sample_ids (which derives from the test set of current split). Is test data taken as validation data?
          # using the same train sample ids for validationsample_id_paths= [
HESTDatasetPath(
name=sample_id,
h5_path=os.path.join(args.embed_dataroot, args.dataset, args.feature_encoder, f"fp32/{sample_id}.h5"),
h5ad_path=os.path.join(args.source_dataroot, args.dataset, f"adata/{sample_id}.h5ad"),
gene_list_path=os.path.join(args.source_dataroot, args.dataset, args.gene_list
),
) forsample_idintest_sample_ids
]
val_loaders= [
torch.utils.data.DataLoader(
HESTDataset(
sample_id_path, distribution="constant_1.0", normalize_method=normalize_method,
sample_times=1
),
batch_size=1, collate_fn=padding_batcher()
) forsample_id_pathinsample_id_paths
]
          Your code: The val_loaders (it is actually test data) is evaluated in training for early stopping and reporting the best performance. Is this the right way to evaluate the model?
          val_perf_dict, pred_dump=test(args, diffusier, model, val_loaders, return_all=True)
ifval_perf_dict["all"]['pearson_mean'] >best_pearson:
best_pearson=val_perf_dict["all"]['pearson_mean']
best_val_dict=val_perf_dictforpatch_name, dataset_resinval_perf_dict.items():
withopen(os.path.join(val_save_dir, f'{patch_name}_results.json'), 'w') asf:
json.dump(dataset_res, f, sort_keys=True, indent=4)
# save_pkl(os.path.join(val_save_dir, 'inference_dump.pkl'), pred_dump)early_stop_step=0else:
early_stop_step+=1ifearly_stop_step>=20:
print("Early stopping")
break
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              Skip to content
              Are test samples used in training for early stopping and reporting the best performance? #4
              Open
              Open
              Are test samples used in training for early stopping and reporting the best performance?#4
              Description
              @liupei101
              liupei101
              opened on Dec 14, 2025
              Thank the authors for sharing the source code.
              I wonder why test samples are used in training for early stopping and reporting the best performance.
              Your code: sample_id_paths comes from test_sample_ids (which derives from the test set of current split). Is test data taken as validation data?
              # using the same train sample ids for validationsample_id_paths= [
HESTDatasetPath(
name=sample_id,
h5_path=os.path.join(args.embed_dataroot, args.dataset, args.feature_encoder, f"fp32/{sample_id}.h5"),
h5ad_path=os.path.join(args.source_dataroot, args.dataset, f"adata/{sample_id}.h5ad"),
gene_list_path=os.path.join(args.source_dataroot, args.dataset, args.gene_list
),
) forsample_idintest_sample_ids
]
val_loaders= [
torch.utils.data.DataLoader(
HESTDataset(
sample_id_path, distribution="constant_1.0", normalize_method=normalize_method,
sample_times=1
),
batch_size=1, collate_fn=padding_batcher()
) forsample_id_pathinsample_id_paths
]
              Your code: The val_loaders (it is actually test data) is evaluated in training for early stopping and reporting the best performance. Is this the right way to evaluate the model?
              val_perf_dict, pred_dump=test(args, diffusier, model, val_loaders, return_all=True)
ifval_perf_dict["all"]['pearson_mean'] >best_pearson:
best_pearson=val_perf_dict["all"]['pearson_mean']
best_val_dict=val_perf_dictforpatch_name, dataset_resinval_perf_dict.items():
withopen(os.path.join(val_save_dir, f'{patch_name}_results.json'), 'w') asf:
json.dump(dataset_res, f, sort_keys=True, indent=4)
# save_pkl(os.path.join(val_save_dir, 'inference_dump.pkl'), pred_dump)early_stop_step=0else:
early_stop_step+=1ifearly_stop_step>=20:
print("Early stopping")
break
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                  Skip to content
                  Are test samples used in training for early stopping and reporting the best performance? #4
                  Open
                  Open
                  Are test samples used in training for early stopping and reporting the best performance?#4
                  Description
                  @liupei101
                  liupei101
                  opened on Dec 14, 2025
                  Thank the authors for sharing the source code.
                  I wonder why test samples are used in training for early stopping and reporting the best performance.
                  Your code: sample_id_paths comes from test_sample_ids (which derives from the test set of current split). Is test data taken as validation data?
                  # using the same train sample ids for validationsample_id_paths= [
HESTDatasetPath(
name=sample_id,
h5_path=os.path.join(args.embed_dataroot, args.dataset, args.feature_encoder, f"fp32/{sample_id}.h5"),
h5ad_path=os.path.join(args.source_dataroot, args.dataset, f"adata/{sample_id}.h5ad"),
gene_list_path=os.path.join(args.source_dataroot, args.dataset, args.gene_list
),
) forsample_idintest_sample_ids
]
val_loaders= [
torch.utils.data.DataLoader(
HESTDataset(
sample_id_path, distribution="constant_1.0", normalize_method=normalize_method,
sample_times=1
),
batch_size=1, collate_fn=padding_batcher()
) forsample_id_pathinsample_id_paths
]
                  Your code: The val_loaders (it is actually test data) is evaluated in training for early stopping and reporting the best performance. Is this the right way to evaluate the model?
                  val_perf_dict, pred_dump=test(args, diffusier, model, val_loaders, return_all=True)
ifval_perf_dict["all"]['pearson_mean'] >best_pearson:
best_pearson=val_perf_dict["all"]['pearson_mean']
best_val_dict=val_perf_dictforpatch_name, dataset_resinval_perf_dict.items():
withopen(os.path.join(val_save_dir, f'{patch_name}_results.json'), 'w') asf:
json.dump(dataset_res, f, sort_keys=True, indent=4)
# save_pkl(os.path.join(val_save_dir, 'inference_dump.pkl'), pred_dump)early_stop_step=0else:
early_stop_step+=1ifearly_stop_step>=20:
print("Early stopping")
break
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var __m = "*";
var __re = new RegExp('^' + ".*" + '

                      Skip to content
                      Are test samples used in training for early stopping and reporting the best performance? #4
                      Open
                      Open
                      Are test samples used in training for early stopping and reporting the best performance?#4
                      Description
                      @liupei101
                      liupei101
                      opened on Dec 14, 2025
                      Thank the authors for sharing the source code.
                      I wonder why test samples are used in training for early stopping and reporting the best performance.
                      Your code: sample_id_paths comes from test_sample_ids (which derives from the test set of current split). Is test data taken as validation data?
                      # using the same train sample ids for validationsample_id_paths= [
HESTDatasetPath(
name=sample_id,
h5_path=os.path.join(args.embed_dataroot, args.dataset, args.feature_encoder, f"fp32/{sample_id}.h5"),
h5ad_path=os.path.join(args.source_dataroot, args.dataset, f"adata/{sample_id}.h5ad"),
gene_list_path=os.path.join(args.source_dataroot, args.dataset, args.gene_list
),
) forsample_idintest_sample_ids
]
val_loaders= [
torch.utils.data.DataLoader(
HESTDataset(
sample_id_path, distribution="constant_1.0", normalize_method=normalize_method,
sample_times=1
),
batch_size=1, collate_fn=padding_batcher()
) forsample_id_pathinsample_id_paths
]
                      Your code: The val_loaders (it is actually test data) is evaluated in training for early stopping and reporting the best performance. Is this the right way to evaluate the model?
                      val_perf_dict, pred_dump=test(args, diffusier, model, val_loaders, return_all=True)
ifval_perf_dict["all"]['pearson_mean'] >best_pearson:
best_pearson=val_perf_dict["all"]['pearson_mean']
best_val_dict=val_perf_dictforpatch_name, dataset_resinval_perf_dict.items():
withopen(os.path.join(val_save_dir, f'{patch_name}_results.json'), 'w') asf:
json.dump(dataset_res, f, sort_keys=True, indent=4)
# save_pkl(os.path.join(val_save_dir, 'inference_dump.pkl'), pred_dump)early_stop_step=0else:
early_stop_step+=1ifearly_stop_step>=20:
print("Early stopping")
break
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                          Skip to content
                          Are test samples used in training for early stopping and reporting the best performance? #4
                          Open
                          Open
                          Are test samples used in training for early stopping and reporting the best performance?#4
                          Description
                          @liupei101
                          liupei101
                          opened on Dec 14, 2025
                          Thank the authors for sharing the source code.
                          I wonder why test samples are used in training for early stopping and reporting the best performance.
                          Your code: sample_id_paths comes from test_sample_ids (which derives from the test set of current split). Is test data taken as validation data?
                          # using the same train sample ids for validationsample_id_paths= [
HESTDatasetPath(
name=sample_id,
h5_path=os.path.join(args.embed_dataroot, args.dataset, args.feature_encoder, f"fp32/{sample_id}.h5"),
h5ad_path=os.path.join(args.source_dataroot, args.dataset, f"adata/{sample_id}.h5ad"),
gene_list_path=os.path.join(args.source_dataroot, args.dataset, args.gene_list
),
) forsample_idintest_sample_ids
]
val_loaders= [
torch.utils.data.DataLoader(
HESTDataset(
sample_id_path, distribution="constant_1.0", normalize_method=normalize_method,
sample_times=1
),
batch_size=1, collate_fn=padding_batcher()
) forsample_id_pathinsample_id_paths
]
                          Your code: The val_loaders (it is actually test data) is evaluated in training for early stopping and reporting the best performance. Is this the right way to evaluate the model?
                          val_perf_dict, pred_dump=test(args, diffusier, model, val_loaders, return_all=True)
ifval_perf_dict["all"]['pearson_mean'] >best_pearson:
best_pearson=val_perf_dict["all"]['pearson_mean']
best_val_dict=val_perf_dictforpatch_name, dataset_resinval_perf_dict.items():
withopen(os.path.join(val_save_dir, f'{patch_name}_results.json'), 'w') asf:
json.dump(dataset_res, f, sort_keys=True, indent=4)
# save_pkl(os.path.join(val_save_dir, 'inference_dump.pkl'), pred_dump)early_stop_step=0else:
early_stop_step+=1ifearly_stop_step>=20:
print("Early stopping")
break
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                              Are test samples used in training for early stopping and reporting the best performance? #4
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                              Are test samples used in training for early stopping and reporting the best performance?#4
                              Description
                              @liupei101
                              liupei101
                              opened on Dec 14, 2025
                              Thank the authors for sharing the source code.
                              I wonder why test samples are used in training for early stopping and reporting the best performance.
                              Your code: sample_id_paths comes from test_sample_ids (which derives from the test set of current split). Is test data taken as validation data?
                              # using the same train sample ids for validationsample_id_paths= [
HESTDatasetPath(
name=sample_id,
h5_path=os.path.join(args.embed_dataroot, args.dataset, args.feature_encoder, f"fp32/{sample_id}.h5"),
h5ad_path=os.path.join(args.source_dataroot, args.dataset, f"adata/{sample_id}.h5ad"),
gene_list_path=os.path.join(args.source_dataroot, args.dataset, args.gene_list
),
) forsample_idintest_sample_ids
]
val_loaders= [
torch.utils.data.DataLoader(
HESTDataset(
sample_id_path, distribution="constant_1.0", normalize_method=normalize_method,
sample_times=1
),
batch_size=1, collate_fn=padding_batcher()
) forsample_id_pathinsample_id_paths
]
                              Your code: The val_loaders (it is actually test data) is evaluated in training for early stopping and reporting the best performance. Is this the right way to evaluate the model?
                              val_perf_dict, pred_dump=test(args, diffusier, model, val_loaders, return_all=True)
ifval_perf_dict["all"]['pearson_mean'] >best_pearson:
best_pearson=val_perf_dict["all"]['pearson_mean']
best_val_dict=val_perf_dictforpatch_name, dataset_resinval_perf_dict.items():
withopen(os.path.join(val_save_dir, f'{patch_name}_results.json'), 'w') asf:
json.dump(dataset_res, f, sort_keys=True, indent=4)
# save_pkl(os.path.join(val_save_dir, 'inference_dump.pkl'), pred_dump)early_stop_step=0else:
early_stop_step+=1ifearly_stop_step>=20:
print("Early stopping")
break
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