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b/SynthData0523/main/n15/tabbyflow/tabbyflow-n15-20260510_211308/_efvfm_runtime/tests/test_flow_model.py @@ -0,0 +1,219 @@ +import torch +import numpy as np +from unittest.mock import patch + +from ef_vfm.models.flow_model import ExpVFM, Velocity +from ef_vfm.modules.main_modules import UniModMLP + + +# ---- mixed_loss tests ---- + +def test_mixed_loss_returns_two_scalars(make_flow_model, make_dummy_inputs, dims): + d = dims + flow = make_flow_model(d["d_numerical"], d["categories"]) + _, _, x_cat_int, _ = make_dummy_inputs(d["d_numerical"], d["categories"], d["batch_size"]) + x_num = torch.randn(d["batch_size"], d["d_numerical"]) + x = torch.cat([x_num, x_cat_int.float()], dim=1) + d_loss, c_loss = flow.mixed_loss(x) + assert d_loss.dim() == 0 or d_loss.numel() == 1 + assert c_loss.dim() == 0 or c_loss.numel() == 1 + + +def test_mixed_loss_finite(make_flow_model, make_dummy_inputs, dims): + d = dims + flow = make_flow_model(d["d_numerical"], d["categories"]) + _, _, x_cat_int, _ = make_dummy_inputs(d["d_numerical"], d["categories"], d["batch_size"]) + x_num = torch.randn(d["batch_size"], d["d_numerical"]) + x = torch.cat([x_num, x_cat_int.float()], dim=1) + d_loss, c_loss = flow.mixed_loss(x) + assert torch.isfinite(d_loss).all() + assert torch.isfinite(c_loss).all() + + +def test_mixed_loss_gradients_flow(make_flow_model, make_dummy_inputs, dims): + d = dims + flow = make_flow_model(d["d_numerical"], d["categories"]) + _, _, x_cat_int, _ = make_dummy_inputs(d["d_numerical"], d["categories"], d["batch_size"]) + x_num = torch.randn(d["batch_size"], d["d_numerical"]) + x = torch.cat([x_num, x_cat_int.float()], dim=1) + d_loss, c_loss = flow.mixed_loss(x) + total = d_loss + c_loss + total.backward() + grads = [p.grad for p in flow.parameters() if p.grad is not None] + assert len(grads) > 0 + + +def test_mixed_loss_numerical_only(make_flow_model, make_dummy_inputs, dims_numerical_only): + d = dims_numerical_only + flow = make_flow_model(d["d_numerical"], d["categories"]) + x = torch.randn(d["batch_size"], d["d_numerical"]) + d_loss, c_loss = flow.mixed_loss(x) + assert d_loss.item() == 0.0 # no discrete features + assert c_loss.item() > 0.0 + + +# ---- sample tests (with mocked odeint) ---- + +def _make_flow(d_numerical, categories): + cats_list = list(categories) if categories is not None else [] + cats_np = np.array(cats_list) + model = UniModMLP(d_numerical, cats_list, 1, 16, n_head=1, factor=4, dim_t=64, activation='gelu') + return ExpVFM(cats_np, d_numerical, model, device=torch.device('cpu')) + + +def test_sample_output_shape(dims): + d = dims + flow = _make_flow(d["d_numerical"], d["categories"]) + d_in = d["d_numerical"] + sum(d["categories"]) + n = 5 + fake_trajectory = torch.randn(2, n, d_in) + with patch("ef_vfm.models.flow_model.odeint", return_value=fake_trajectory): + result = flow.sample(n) + d_out = d["d_numerical"] + len(d["categories"]) + assert result.shape == (n, d_out) + + +def test_sample_categorical_in_range(dims): + d = dims + flow = _make_flow(d["d_numerical"], d["categories"]) + d_in = d["d_numerical"] + sum(d["categories"]) + n = 16 + fake_trajectory = torch.randn(2, n, d_in) + with patch("ef_vfm.models.flow_model.odeint", return_value=fake_trajectory): + result = flow.sample(n) + for i, k in enumerate(d["categories"]): + col = d["d_numerical"] + i + assert (result[:, col] >= 0).all() + assert (result[:, col] < k).all() + + +def test_sample_returns_cpu(dims): + d = dims + flow = _make_flow(d["d_numerical"], d["categories"]) + d_in = d["d_numerical"] + sum(d["categories"]) + fake_trajectory = torch.randn(2, 4, d_in) + with patch("ef_vfm.models.flow_model.odeint", return_value=fake_trajectory): + result = flow.sample(4) + assert result.device == torch.device('cpu') + + +def test_sample_single_sample(dims): + d = dims + flow = _make_flow(d["d_numerical"], d["categories"]) + d_in = d["d_numerical"] + sum(d["categories"]) + fake_trajectory = torch.randn(2, 1, d_in) + with patch("ef_vfm.models.flow_model.odeint", return_value=fake_trajectory): + result = flow.sample(1) + d_out = d["d_numerical"] + len(d["categories"]) + assert result.shape == (1, d_out) + + +# ---- to_one_hot tests ---- + +def test_to_one_hot_shape(dims): + d = dims + flow = _make_flow(d["d_numerical"], d["categories"]) + cats = d["categories"] + x_cat = torch.stack([torch.randint(0, k, (8,)) for k in cats], dim=1) + oh = flow.to_one_hot(x_cat) + assert oh.shape == (8, sum(cats)) + + +def test_to_one_hot_roundtrip(dims): + d = dims + flow = _make_flow(d["d_numerical"], d["categories"]) + cats = d["categories"] + x_cat = torch.stack([torch.randint(0, k, (8,)) for k in cats], dim=1) + oh = flow.to_one_hot(x_cat) + # Recover indices via argmax per category slice + idx = 0 + for i, k in enumerate(cats): + recovered = oh[:, idx:idx + k].argmax(dim=1) + assert torch.equal(recovered, x_cat[:, i]) + idx += k + + +def test_to_one_hot_binary_values(dims): + d = dims + flow = _make_flow(d["d_numerical"], d["categories"]) + cats = d["categories"] + x_cat = torch.stack([torch.randint(0, k, (8,)) for k in cats], dim=1) + oh = flow.to_one_hot(x_cat) + assert set(oh.unique().tolist()).issubset({0, 1}) + + +# ---- Regression tests ---- + +def test_regression_d_in_no_extra_len(): + """d_in must be num_numerical + sum(num_classes), NOT + len(num_classes).""" + d_numerical = 4 + categories = np.array([3, 5, 2]) + flow = _make_flow(d_numerical, categories) + expected_d_in = d_numerical + sum(categories) # 14, not 17 + assert flow.num_numerical_features + sum(flow.num_classes) == expected_d_in + + +def test_regression_sampling_indices_correct(): + """Categorical argmax must go to columns [d_num, d_num+1, ...], not [0, 1, ...].""" + d_numerical = 4 + categories = np.array([3, 5, 2]) + n = 10 + d_in = d_numerical + sum(categories) + d_out = d_numerical + len(categories) + + # Simulate the post-processing from sample() + out = torch.randn(n, d_in) + sample = torch.zeros(n, d_out) + sample[:, :d_numerical] = out[:, :d_numerical] + + idx = d_numerical # correct starting index + for i, val in enumerate(categories): + col = d_numerical + i # correct column + sample[:, col] = torch.argmax(out[:, idx:idx + val], dim=1) + idx += val + + # Numerical columns must be untouched + assert torch.allclose(sample[:, :d_numerical], out[:, :d_numerical]) + # Categorical columns at correct positions + for i, val in enumerate(categories): + col = d_numerical + i + assert (sample[:, col] >= 0).all() + assert (sample[:, col] < val).all() + + +def test_regression_d_out_correct(): + """d_out must be d_num + len(categories).""" + d_numerical = 4 + categories = np.array([3, 5, 2]) + flow = _make_flow(d_numerical, categories) + expected_d_out = d_numerical + len(categories) # 7 + assert expected_d_out == 7 + + +# ---- Velocity tests ---- + +def test_velocity_output_shape(dims): + d = dims + cats_list = list(d["categories"]) + model = UniModMLP(d["d_numerical"], cats_list, 1, d["d_token"], + n_head=1, factor=4, dim_t=64, activation='gelu') + vel = Velocity(model) + d_in = d["d_numerical"] + sum(d["categories"]) + x = torch.randn(d["batch_size"], d_in) + t = torch.tensor(0.5) + out = vel(t, x) + assert out.shape == (d["batch_size"], d_in) + + +def test_velocity_scalar_t_broadcast(dims): + d = dims + cats_list = list(d["categories"]) + model = UniModMLP(d["d_numerical"], cats_list, 1, d["d_token"], + n_head=1, factor=4, dim_t=64, activation='gelu') + vel = Velocity(model) + d_in = d["d_numerical"] + sum(d["categories"]) + x = torch.randn(d["batch_size"], d_in) + # Scalar t should work (gets broadcast internally) + t = torch.tensor(0.3) + out = vel(t, x) + assert out.shape == x.shape diff --git a/SynthData0523/main/n15/tabbyflow/tabbyflow-n15-20260510_211308/_efvfm_runtime/tests/test_mlp.py b/SynthData0523/main/n15/tabbyflow/tabbyflow-n15-20260510_211308/_efvfm_runtime/tests/test_mlp.py new file mode 100644 index 0000000000000000000000000000000000000000..0cf9ad4d6832d792cb65a1bb01bdc784385f9fcd --- /dev/null +++ b/SynthData0523/main/n15/tabbyflow/tabbyflow-n15-20260510_211308/_efvfm_runtime/tests/test_mlp.py @@ -0,0 +1,85 @@ +import torch +import torch.nn as nn +from ef_vfm.modules.main_modules import MLP, PositionalEmbedding + + +# ---- PositionalEmbedding tests ---- + +def test_positional_embedding_shape(): + pe = PositionalEmbedding(num_channels=64) + x = torch.rand(8) + out = pe(x) + assert out.shape == (8, 64) + + +def test_positional_embedding_bounded(): + pe = PositionalEmbedding(num_channels=64) + x = torch.rand(8) + out = pe(x) + assert out.min() >= -1.0 + assert out.max() <= 1.0 + + +def test_positional_embedding_deterministic(): + pe = PositionalEmbedding(num_channels=64) + x = torch.tensor([0.1, 0.5, 0.9]) + out1 = pe(x) + out2 = pe(x) + assert torch.equal(out1, out2) + + +def test_positional_embedding_different_timesteps(): + pe = PositionalEmbedding(num_channels=64) + t1 = torch.tensor([0.1]) + t2 = torch.tensor([0.9]) + assert not torch.allclose(pe(t1), pe(t2)) + + +# ---- MLP tests ---- + +def test_mlp_output_shape(make_mlp): + mlp = make_mlp(d_in=32, dim_t=64) + x = torch.randn(8, 32) + t = torch.rand(8) + out = mlp(x, t) + assert out.shape == (8, 32) + + +def test_mlp_use_mlp_true(make_mlp): + mlp = make_mlp(d_in=32, dim_t=64, use_mlp=True) + assert isinstance(mlp.mlp, nn.Sequential) + + +def test_mlp_use_mlp_false(make_mlp): + mlp = make_mlp(d_in=32, dim_t=64, use_mlp=False) + assert isinstance(mlp.mlp, nn.Linear) + + +def test_mlp_time_conditioning(make_mlp): + mlp = make_mlp(d_in=32, dim_t=64) + mlp.eval() + x = torch.randn(4, 32) + t1 = torch.zeros(4) + t2 = torch.ones(4) + out1 = mlp(x, t1) + out2 = mlp(x, t2) + assert not torch.allclose(out1, out2) + + +def test_mlp_gradient_flows(make_mlp): + mlp = make_mlp(d_in=32, dim_t=64) + x = torch.randn(4, 32) + t = torch.rand(4) + out = mlp(x, t) + out.sum().backward() + assert mlp.proj.weight.grad is not None and mlp.proj.weight.grad.abs().sum() > 0 + assert mlp.map_noise.num_channels == 64 # sanity check on PE config + + +def test_mlp_different_dim_t(make_mlp): + for dim_t in [32, 128, 256]: + mlp = make_mlp(d_in=16, dim_t=dim_t) + x = torch.randn(4, 16) + t = torch.rand(4) + out = mlp(x, t) + assert out.shape == (4, 16) diff --git a/SynthData0523/main/n15/tabbyflow/tabbyflow-n15-20260510_211308/_efvfm_runtime/tests/test_reconstructor.py b/SynthData0523/main/n15/tabbyflow/tabbyflow-n15-20260510_211308/_efvfm_runtime/tests/test_reconstructor.py new file mode 100644 index 0000000000000000000000000000000000000000..cdc39880d19f644fb8ac6b457af6a8cb3d83cbfa --- /dev/null +++ b/SynthData0523/main/n15/tabbyflow/tabbyflow-n15-20260510_211308/_efvfm_runtime/tests/test_reconstructor.py @@ -0,0 +1,51 @@ +import torch +import numpy as np +from ef_vfm.modules.transformer import Reconstructor + + +def test_output_shapes_mixed(make_reconstructor, dims): + d = dims + r = make_reconstructor(d["d_numerical"], d["categories"], d["d_token"]) + seq_len = d["d_numerical"] + len(d["categories"]) + h = torch.randn(d["batch_size"], seq_len, d["d_token"]) + x_num, x_cat = r(h) + assert x_num.shape == (d["batch_size"], d["d_numerical"]) + assert len(x_cat) == len(d["categories"]) + for i, k in enumerate(d["categories"]): + assert x_cat[i].shape == (d["batch_size"], k) + + +def test_categorical_count(make_reconstructor, dims): + d = dims + r = make_reconstructor(d["d_numerical"], d["categories"], d["d_token"]) + seq_len = d["d_numerical"] + len(d["categories"]) + h = torch.randn(d["batch_size"], seq_len, d["d_token"]) + _, x_cat = r(h) + assert len(x_cat) == len(d["categories"]) + + +def test_empty_categories(make_reconstructor): + r = make_reconstructor(4, np.array([]), 16) + h = torch.randn(8, 4, 16) + x_num, x_cat = r(h) + assert x_num.shape == (8, 4) + assert len(x_cat) == 0 + + +def test_weight_shape(make_reconstructor, dims): + d = dims + r = make_reconstructor(d["d_numerical"], d["categories"], d["d_token"]) + assert r.weight.shape == (d["d_numerical"], d["d_token"]) + + +def test_gradient_flows(make_reconstructor, dims): + d = dims + r = make_reconstructor(d["d_numerical"], d["categories"], d["d_token"]) + seq_len = d["d_numerical"] + len(d["categories"]) + h = torch.randn(d["batch_size"], seq_len, d["d_token"]) + x_num, x_cat = r(h) + loss = x_num.sum() + sum(c.sum() for c in x_cat) + loss.backward() + assert r.weight.grad is not None and r.weight.grad.abs().sum() > 0 + for recon in r.cat_recons: + assert recon.weight.grad is not None diff --git a/SynthData0523/main/n15/tabbyflow/tabbyflow-n15-20260510_211308/_efvfm_runtime/tests/test_tokenizer.py b/SynthData0523/main/n15/tabbyflow/tabbyflow-n15-20260510_211308/_efvfm_runtime/tests/test_tokenizer.py new file mode 100644 index 0000000000000000000000000000000000000000..ea8c55737d473605c2bb1c87f0394fad64baeb18 --- /dev/null +++ b/SynthData0523/main/n15/tabbyflow/tabbyflow-n15-20260510_211308/_efvfm_runtime/tests/test_tokenizer.py @@ -0,0 +1,85 @@ +import torch +import numpy as np + + +def test_forward_shape_mixed(make_tokenizer, make_dummy_inputs, dims): + tok = make_tokenizer(dims["d_numerical"], dims["categories"], dims["d_token"]) + x_num, x_cat_oh, _, _ = make_dummy_inputs(dims["d_numerical"], dims["categories"], dims["batch_size"]) + out = tok(x_num, x_cat_oh) + expected_seq = 1 + dims["d_numerical"] + len(dims["categories"]) + assert out.shape == (dims["batch_size"], expected_seq, dims["d_token"]) + + +def test_forward_shape_numerical_only(make_tokenizer, make_dummy_inputs, dims_numerical_only): + d = dims_numerical_only + tok = make_tokenizer(d["d_numerical"], d["categories"], d["d_token"]) + x_num, _, _, _ = make_dummy_inputs(d["d_numerical"], d["categories"], d["batch_size"]) + out = tok(x_num, None) + expected_seq = 1 + d["d_numerical"] + assert out.shape == (d["batch_size"], expected_seq, d["d_token"]) + + +def test_forward_shape_single_feature(make_tokenizer, make_dummy_inputs, dims_single): + d = dims_single + tok = make_tokenizer(d["d_numerical"], d["categories"], d["d_token"]) + x_num, x_cat_oh, _, _ = make_dummy_inputs(d["d_numerical"], d["categories"], d["batch_size"]) + out = tok(x_num, x_cat_oh) + expected_seq = 1 + d["d_numerical"] + len(d["categories"]) + assert out.shape == (d["batch_size"], expected_seq, d["d_token"]) + + +def test_n_tokens_property(make_tokenizer, dims): + tok = make_tokenizer(dims["d_numerical"], dims["categories"], dims["d_token"]) + expected = dims["d_numerical"] + 1 + len(dims["categories"]) + assert tok.n_tokens == expected + + +def test_n_tokens_numerical_only(make_tokenizer, dims_numerical_only): + d = dims_numerical_only + tok = make_tokenizer(d["d_numerical"], d["categories"], d["d_token"]) + assert tok.n_tokens == d["d_numerical"] + 1 + + +def test_cls_token_position(make_tokenizer, make_dummy_inputs, dims): + tok = make_tokenizer(dims["d_numerical"], dims["categories"], dims["d_token"], bias=False) + x_num, x_cat_oh, _, _ = make_dummy_inputs(dims["d_numerical"], dims["categories"], dims["batch_size"]) + out = tok(x_num, x_cat_oh) + # CLS token: ones * weight[0], so all batch rows should have the same CLS token + cls_tokens = out[:, 0, :] + assert torch.allclose(cls_tokens[0], cls_tokens[1]) + assert torch.allclose(cls_tokens[0], tok.weight[0]) + + +def test_bias_vs_no_bias(make_tokenizer, make_dummy_inputs, dims): + d = dims + tok_bias = make_tokenizer(d["d_numerical"], d["categories"], d["d_token"], bias=True) + tok_no_bias = make_tokenizer(d["d_numerical"], d["categories"], d["d_token"], bias=False) + assert tok_bias.bias is not None + assert tok_no_bias.bias is None + + +def test_category_offsets_values(make_tokenizer): + cats = np.array([3, 5, 2]) + tok = make_tokenizer(4, cats, 16) + assert torch.equal(tok.category_offsets, torch.tensor([0, 3, 8])) + assert torch.equal(tok.category_ends, torch.tensor([3, 8, 10])) + + +def test_cat_weight_shape(make_tokenizer, dims): + tok = make_tokenizer(dims["d_numerical"], dims["categories"], dims["d_token"]) + assert tok.cat_weight.shape == (sum(dims["categories"]), dims["d_token"]) + + +def test_weight_shape(make_tokenizer, dims): + tok = make_tokenizer(dims["d_numerical"], dims["categories"], dims["d_token"]) + assert tok.weight.shape == (dims["d_numerical"] + 1, dims["d_token"]) + + +def test_gradient_flows(make_tokenizer, make_dummy_inputs, dims): + tok = make_tokenizer(dims["d_numerical"], dims["categories"], dims["d_token"]) + x_num, x_cat_oh, _, _ = make_dummy_inputs(dims["d_numerical"], dims["categories"], dims["batch_size"]) + out = tok(x_num, x_cat_oh) + out.sum().backward() + assert tok.weight.grad is not None and tok.weight.grad.abs().sum() > 0 + assert tok.cat_weight.grad is not None and tok.cat_weight.grad.abs().sum() > 0 + assert tok.bias.grad is not None and tok.bias.grad.abs().sum() > 0 diff --git a/SynthData0523/main/n15/tabbyflow/tabbyflow-n15-20260510_211308/_efvfm_runtime/tests/test_trainer.py b/SynthData0523/main/n15/tabbyflow/tabbyflow-n15-20260510_211308/_efvfm_runtime/tests/test_trainer.py new file mode 100644 index 0000000000000000000000000000000000000000..592c538d2aa1f8f34098a0d7c6c4fc0b1f0c5ddf --- /dev/null +++ b/SynthData0523/main/n15/tabbyflow/tabbyflow-n15-20260510_211308/_efvfm_runtime/tests/test_trainer.py @@ -0,0 +1,98 @@ +import torch +import numpy as np + + +# ---- Gradient clipping tests ---- + +def test_grad_clipping_applied(make_trainer, tmp_path): + trainer = make_trainer(max_grad_norm=0.5, tmp_path=tmp_path) + batch = next(iter(trainer.train_iter)) + trainer._run_step(batch, closs_weight=1.0, dloss_weight=1.0) + # After clipping, total gradient norm should be <= max_grad_norm (with tolerance) + total_norm = torch.nn.utils.clip_grad_norm_(trainer.flow.parameters(), float('inf')) + # Gradients were already clipped in _run_step, then optimizer.step() zeroed them. + # So we re-run to check: do a fresh forward-backward without step + trainer.optimizer.zero_grad() + dloss, closs = trainer.flow.mixed_loss(batch.to(trainer.device)) + (dloss + closs).backward() + torch.nn.utils.clip_grad_norm_(trainer.flow.parameters(), 0.5) + total_norm = 0.0 + for p in trainer.flow.parameters(): + if p.grad is not None: + total_norm += p.grad.data.norm(2).item() ** 2 + total_norm = total_norm ** 0.5 + assert total_norm <= 0.5 + 1e-6 + + +def test_grad_clipping_disabled(make_trainer, tmp_path): + trainer = make_trainer(max_grad_norm=0, tmp_path=tmp_path) + assert trainer.max_grad_norm == 0 + + +def test_run_step_returns_losses(make_trainer, tmp_path): + trainer = make_trainer(tmp_path=tmp_path) + batch = next(iter(trainer.train_iter)) + dloss, closs = trainer._run_step(batch, closs_weight=1.0, dloss_weight=1.0) + assert isinstance(dloss, torch.Tensor) + assert isinstance(closs, torch.Tensor) + assert torch.isfinite(dloss) + assert torch.isfinite(closs) + + +# ---- LR warmup tests ---- + +def test_warmup_lr_linear_ramp(make_trainer, tmp_path): + init_lr = 0.01 + warmup = 5 + trainer = make_trainer(lr=init_lr, warmup_epochs=warmup, tmp_path=tmp_path) + # Simulate warmup epochs + for epoch in range(warmup): + expected_lr = init_lr * (epoch + 1) / warmup + if trainer.warmup_epochs > 0 and (epoch + 1) <= trainer.warmup_epochs: + warmup_lr = trainer.init_lr * (epoch + 1) / trainer.warmup_epochs + for pg in trainer.optimizer.param_groups: + pg["lr"] = warmup_lr + actual_lr = trainer.optimizer.param_groups[0]["lr"] + assert abs(actual_lr - expected_lr) < 1e-8, f"Epoch {epoch}: expected {expected_lr}, got {actual_lr}" + + +def test_warmup_overrides_scheduler(make_trainer, tmp_path): + trainer = make_trainer(warmup_epochs=10, lr_scheduler='reduce_lr_on_plateau', tmp_path=tmp_path) + initial_lr = trainer.optimizer.param_groups[0]["lr"] + # During warmup, scheduler.step should NOT be called (we just set LR directly) + # Simulate epoch 1 warmup + warmup_lr = trainer.init_lr * 1 / trainer.warmup_epochs + for pg in trainer.optimizer.param_groups: + pg["lr"] = warmup_lr + assert trainer.optimizer.param_groups[0]["lr"] == warmup_lr + assert warmup_lr < initial_lr # warmup starts lower + + +def test_no_warmup_when_zero(make_trainer, tmp_path): + trainer = make_trainer(warmup_epochs=0, tmp_path=tmp_path) + assert trainer.warmup_epochs == 0 + # LR should be the init_lr from the start + assert trainer.optimizer.param_groups[0]["lr"] == trainer.init_lr + + +# ---- LR scheduler tests ---- + +def test_anneal_lr(make_trainer, tmp_path): + trainer = make_trainer(lr=0.01, steps=100, lr_scheduler='anneal', tmp_path=tmp_path) + trainer._anneal_lr(50) + expected = 0.01 * (1 - 50 / 100) + assert abs(trainer.optimizer.param_groups[0]["lr"] - expected) < 1e-8 + + +# ---- EMA tests ---- + +def test_ema_model_created(make_trainer, tmp_path): + trainer = make_trainer(tmp_path=tmp_path) + # EMA model should exist and have same structure as flow._vf_fn + assert trainer.ema_model is not None + ema_params = list(trainer.ema_model.parameters()) + model_params = list(trainer.flow._vf_fn.parameters()) + assert len(ema_params) == len(model_params) + # EMA params should be detached (requires_grad=False) + for p in ema_params: + assert not p.requires_grad diff --git a/SynthData0523/main/n15/tabbyflow/tabbyflow-n15-20260510_211308/_efvfm_runtime/tests/test_transformer.py b/SynthData0523/main/n15/tabbyflow/tabbyflow-n15-20260510_211308/_efvfm_runtime/tests/test_transformer.py new file mode 100644 index 0000000000000000000000000000000000000000..ff56e884615e818841fbb912f8ef4e9961729197 --- /dev/null +++ b/SynthData0523/main/n15/tabbyflow/tabbyflow-n15-20260510_211308/_efvfm_runtime/tests/test_transformer.py @@ -0,0 +1,73 @@ +import pytest +import torch +from ef_vfm.modules.transformer import Transformer + + +def test_output_shape_preserved(make_transformer): + t = make_transformer(d_token=16, n_layers=2) + x = torch.randn(4, 5, 16) + out = t(x) + assert out.shape == x.shape + + +def test_activation_gelu(make_transformer): + t = make_transformer(d_token=16, activation='gelu') + x = torch.randn(4, 5, 16) + out = t(x) + assert out.shape == x.shape + + +def test_activation_silu(make_transformer): + t = make_transformer(d_token=16, activation='silu') + x = torch.randn(4, 5, 16) + out = t(x) + assert out.shape == x.shape + + +def test_activation_relu(make_transformer): + t = make_transformer(d_token=16, activation='relu') + x = torch.randn(4, 5, 16) + out = t(x) + assert out.shape == x.shape + + +def test_invalid_activation_raises(): + with pytest.raises(ValueError, match="Unknown activation"): + Transformer(2, 16, 1, 16, 4, activation='bad') + + +def test_prenorm_first_layer_no_norm0(): + t = Transformer(2, 16, 1, 16, 4, prenormalization=True) + assert 'norm0' not in t.layers[0] + # Second layer should have norm0 + assert 'norm0' in t.layers[1] + + +def test_no_prenorm_all_layers_have_norm0(): + t = Transformer(2, 16, 1, 16, 4, prenormalization=False) + for layer in t.layers: + assert 'norm0' in layer + + +def test_single_layer(): + t = Transformer(1, 16, 1, 16, 4) + x = torch.randn(4, 5, 16) + out = t(x) + assert out.shape == x.shape + + +def test_multi_layer(): + t = Transformer(4, 16, 1, 16, 4) + x = torch.randn(4, 5, 16) + out = t(x) + assert out.shape == x.shape + + +def test_gradient_flows(make_transformer): + t = make_transformer(d_token=16, n_layers=2) + x = torch.randn(4, 5, 16, requires_grad=True) + out = t(x) + out.sum().backward() + assert x.grad is not None and x.grad.abs().sum() > 0 + # Check gradients through at least the first layer's linear0 + assert t.layers[0]['linear0'].weight.grad is not None diff --git a/SynthData0523/main/n15/tabbyflow/tabbyflow-n15-20260510_211308/_efvfm_runtime/tests/test_unimodmlp.py b/SynthData0523/main/n15/tabbyflow/tabbyflow-n15-20260510_211308/_efvfm_runtime/tests/test_unimodmlp.py new file mode 100644 index 0000000000000000000000000000000000000000..d935e7c48dba78fd7e4821287628aa861aa6d1b4 --- /dev/null +++ b/SynthData0523/main/n15/tabbyflow/tabbyflow-n15-20260510_211308/_efvfm_runtime/tests/test_unimodmlp.py @@ -0,0 +1,72 @@ +import torch +import numpy as np + + +def test_forward_shapes_mixed(make_unimodmlp, make_dummy_inputs, dims): + d = dims + model = make_unimodmlp(d["d_numerical"], d["categories"], d_token=d["d_token"]) + x_num, x_cat_oh, _, t = make_dummy_inputs(d["d_numerical"], d["categories"], d["batch_size"]) + x_num_pred, x_cat_pred = model(x_num, x_cat_oh, t) + assert x_num_pred.shape == (d["batch_size"], d["d_numerical"]) + assert x_cat_pred.shape == (d["batch_size"], sum(d["categories"])) + + +def test_forward_shapes_numerical_only(make_unimodmlp, make_dummy_inputs, dims_numerical_only): + d = dims_numerical_only + model = make_unimodmlp(d["d_numerical"], d["categories"], d_token=d["d_token"]) + x_num, _, _, t = make_dummy_inputs(d["d_numerical"], d["categories"], d["batch_size"]) + x_cat = torch.zeros(d["batch_size"], 0) + x_num_pred, x_cat_pred = model(x_num, x_cat, t) + assert x_num_pred.shape == (d["batch_size"], d["d_numerical"]) + # When no categories, cat_pred should be zeros with shape matching x_cat + assert x_cat_pred.shape[0] == d["batch_size"] + assert torch.all(x_cat_pred == 0) + + +def test_forward_shapes_single_feature(make_unimodmlp, make_dummy_inputs, dims_single): + d = dims_single + model = make_unimodmlp(d["d_numerical"], d["categories"], d_token=d["d_token"]) + x_num, x_cat_oh, _, t = make_dummy_inputs(d["d_numerical"], d["categories"], d["batch_size"]) + x_num_pred, x_cat_pred = model(x_num, x_cat_oh, t) + assert x_num_pred.shape == (d["batch_size"], d["d_numerical"]) + assert x_cat_pred.shape == (d["batch_size"], sum(d["categories"])) + + +def test_d_in_computation(make_unimodmlp, dims): + d = dims + model = make_unimodmlp(d["d_numerical"], d["categories"], d_token=d["d_token"]) + expected = d["d_token"] * (d["d_numerical"] + len(d["categories"])) + assert model.mlp.proj.in_features == expected + + +def test_output_dtypes(make_unimodmlp, make_dummy_inputs, dims): + d = dims + model = make_unimodmlp(d["d_numerical"], d["categories"], d_token=d["d_token"]) + x_num, x_cat_oh, _, t = make_dummy_inputs(d["d_numerical"], d["categories"], d["batch_size"]) + x_num_pred, x_cat_pred = model(x_num, x_cat_oh, t) + assert x_num_pred.dtype == torch.float32 + assert x_cat_pred.dtype == torch.float32 + + +def test_gradient_flows_end_to_end(make_unimodmlp, make_dummy_inputs, dims): + d = dims + model = make_unimodmlp(d["d_numerical"], d["categories"], d_token=d["d_token"]) + x_num, x_cat_oh, _, t = make_dummy_inputs(d["d_numerical"], d["categories"], d["batch_size"]) + x_num_pred, x_cat_pred = model(x_num, x_cat_oh, t) + loss = x_num_pred.sum() + x_cat_pred.sum() + loss.backward() + params_with_grad = sum(1 for p in model.parameters() if p.grad is not None and p.grad.abs().sum() > 0) + total_params = sum(1 for _ in model.parameters()) + # Transformer.head is defined but unused in forward(), so not all params get gradients + assert params_with_grad > total_params * 0.8, f"Only {params_with_grad}/{total_params} params got gradients" + + +def test_different_activations(make_unimodmlp, make_dummy_inputs, dims): + d = dims + x_num, x_cat_oh, _, t = make_dummy_inputs(d["d_numerical"], d["categories"], d["batch_size"]) + for act in ['relu', 'gelu', 'silu']: + model = make_unimodmlp(d["d_numerical"], d["categories"], d_token=d["d_token"], activation=act) + x_num_pred, x_cat_pred = model(x_num, x_cat_oh, t) + assert x_num_pred.shape == (d["batch_size"], d["d_numerical"]) + assert torch.isfinite(x_num_pred).all() + assert torch.isfinite(x_cat_pred).all() diff --git a/SynthData0523/main/n15/tabbyflow/tabbyflow-n15-20260510_211308/_efvfm_runtime/tests/test_utils.py b/SynthData0523/main/n15/tabbyflow/tabbyflow-n15-20260510_211308/_efvfm_runtime/tests/test_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..fbce0db0ac74052a4038dee89fd753b9d97717aa --- /dev/null +++ b/SynthData0523/main/n15/tabbyflow/tabbyflow-n15-20260510_211308/_efvfm_runtime/tests/test_utils.py @@ -0,0 +1,49 @@ +import torch +import numpy as np + +from utils_train import update_ema, concat_y_to_X + + +# ---- update_ema tests ---- + +def test_update_ema_basic(): + target = [torch.tensor([1.0, 2.0])] + source = [torch.tensor([3.0, 4.0])] + target[0].requires_grad_(False) + rate = 0.9 + update_ema(target, source, rate=rate) + expected = 0.9 * torch.tensor([1.0, 2.0]) + 0.1 * torch.tensor([3.0, 4.0]) + assert torch.allclose(target[0], expected) + + +def test_update_ema_rate_zero(): + target = [torch.tensor([1.0, 2.0])] + source = [torch.tensor([3.0, 4.0])] + target[0].requires_grad_(False) + update_ema(target, source, rate=0.0) + assert torch.allclose(target[0], torch.tensor([3.0, 4.0])) + + +def test_update_ema_rate_one(): + target = [torch.tensor([1.0, 2.0])] + source = [torch.tensor([3.0, 4.0])] + target[0].requires_grad_(False) + update_ema(target, source, rate=1.0) + assert torch.allclose(target[0], torch.tensor([1.0, 2.0])) + + +# ---- concat_y_to_X tests ---- + +def test_concat_y_to_X_with_X(): + X = np.array([[1, 2], [3, 4]]) + y = np.array([10, 20]) + result = concat_y_to_X(X, y) + expected = np.array([[10, 1, 2], [20, 3, 4]]) + np.testing.assert_array_equal(result, expected) + + +def test_concat_y_to_X_without_X(): + y = np.array([10, 20, 30]) + result = concat_y_to_X(None, y) + expected = np.array([[10], [20], [30]]) + np.testing.assert_array_equal(result, expected) diff --git a/SynthData0523/main/n15/tabbyflow/tabbyflow-n15-20260510_211308/_efvfm_runtime/utils_train.py b/SynthData0523/main/n15/tabbyflow/tabbyflow-n15-20260510_211308/_efvfm_runtime/utils_train.py new file mode 100644 index 0000000000000000000000000000000000000000..f00c40d190a763f011526ef4aa11e5e960ce2b7b --- /dev/null +++ b/SynthData0523/main/n15/tabbyflow/tabbyflow-n15-20260510_211308/_efvfm_runtime/utils_train.py @@ -0,0 +1,183 @@ +import numpy as np +import os + +import src +from torch.utils.data import Dataset + +import torch + + +class TabularDataset(Dataset): + def __init__(self, X_num, X_cat): + self.X_num = X_num + self.X_cat = X_cat + + def __getitem__(self, index): + this_num = self.X_num[index] + this_cat = self.X_cat[index] + + sample = (this_num, this_cat) + + return sample + + def __len__(self): + return self.X_num.shape[0] + +class EFVFMDataset(Dataset): + def __init__(self, dataname, data_dir, info, isTrain=True, dequant_dist='none', int_dequant_factor=0.0): + self.dataname = dataname + self.data_dir = data_dir + self.info = info + self.isTrain = isTrain + + X_num, X_cat, categories, d_numerical, num_inverse, int_inverse, cat_inverse = preprocess(data_dir, dequant_dist, int_dequant_factor, task_type = info['task_type'], inverse=True) + categories = np.array(categories) + + X_train_num, _ = X_num + X_train_cat, _ = X_cat + + X_train_num, X_test_num = X_num + X_train_cat, X_test_cat = X_cat + + X_train_num, X_test_num = torch.tensor(X_train_num).float(), torch.tensor(X_test_num).float() + X_train_cat, X_test_cat = torch.tensor(X_train_cat), torch.tensor(X_test_cat) + + self.X = torch.cat((X_train_num, X_train_cat), dim=1) if isTrain else torch.cat((X_test_num, X_test_cat), dim=1) + self.num_inverse = num_inverse + self.int_inverse = int_inverse + self.cat_inverse = cat_inverse + self.d_numerical = d_numerical + self.categories = categories + + def __getitem__(self, index): + return self.X[index] + + def __len__(self): + return self.X.shape[0] + +def preprocess(dataset_path, dequant_dist='none', int_dequant_factor=0.0, task_type = 'binclass', inverse = False, cat_encoding = None, concat = True): + + T_dict = {} + + T_dict['normalization'] = "quantile" + T_dict['num_nan_policy'] = 'mean' + T_dict['cat_nan_policy'] = None + T_dict['cat_min_frequency'] = None + T_dict['cat_encoding'] = cat_encoding + T_dict['y_policy'] = "default" + T_dict['dequant_dist'] = dequant_dist + T_dict['int_dequant_factor'] = int_dequant_factor + + T = src.Transformations(**T_dict) + + dataset = make_dataset( + data_path = dataset_path, + T = T, + task_type = task_type, + change_val = False, + concat = concat, + ) + + if cat_encoding is None: + X_num = dataset.X_num + X_cat = dataset.X_cat + + X_train_num, X_test_num = X_num['train'], X_num['test'] + X_train_cat, X_test_cat = X_cat['train'], X_cat['test'] + + categories = src.get_categories(X_train_cat) + d_numerical = X_train_num.shape[1] + + X_num = (X_train_num, X_test_num) + X_cat = (X_train_cat, X_test_cat) + + + if inverse: + num_inverse = dataset.num_transform.inverse_transform if dataset.num_transform is not None else lambda x: x + int_inverse = dataset.int_transform.inverse_transform if dataset.int_transform is not None else lambda x: x + cat_inverse = dataset.cat_transform.inverse_transform if dataset.cat_transform is not None else lambda x: x + + return X_num, X_cat, categories, d_numerical, num_inverse, int_inverse, cat_inverse + else: + return X_num, X_cat, categories, d_numerical + else: + return dataset + + +def update_ema(target_params, source_params, rate=0.999): + """ + Update target parameters to be closer to those of source parameters using + an exponential moving average. + :param target_params: the target parameter sequence. + :param source_params: the source parameter sequence. + :param rate: the EMA rate (closer to 1 means slower). + """ + for target, source in zip(target_params, source_params): + target.detach().mul_(rate).add_(source.detach(), alpha=1 - rate) + + + +def concat_y_to_X(X, y): + if X is None: + return y.reshape(-1, 1) + return np.concatenate([y.reshape(-1, 1), X], axis=1) + + +def make_dataset( + data_path: str, + T: src.Transformations, + task_type, + change_val: bool, + concat = True, +): + + # classification + if task_type == 'binclass' or task_type == 'multiclass': + X_cat = {} if os.path.exists(os.path.join(data_path, 'X_cat_train.npy')) else None + X_num = {} if os.path.exists(os.path.join(data_path, 'X_num_train.npy')) else None + y = {} if os.path.exists(os.path.join(data_path, 'y_train.npy')) else None + + for split in ['train', 'test']: + X_num_t, X_cat_t, y_t = src.read_pure_data(data_path, split) + if X_num is not None: + X_num[split] = X_num_t + if X_cat is not None: + if concat: + X_cat_t = concat_y_to_X(X_cat_t, y_t) + X_cat[split] = X_cat_t + if y is not None: + y[split] = y_t + else: + # regression + X_cat = {} if os.path.exists(os.path.join(data_path, 'X_cat_train.npy')) else None + X_num = {} if os.path.exists(os.path.join(data_path, 'X_num_train.npy')) else None + y = {} if os.path.exists(os.path.join(data_path, 'y_train.npy')) else None + + for split in ['train', 'test']: + X_num_t, X_cat_t, y_t = src.read_pure_data(data_path, split) + if X_num is not None: + if concat: + X_num_t = concat_y_to_X(X_num_t, y_t) + X_num[split] = X_num_t + if X_cat is not None: + X_cat[split] = X_cat_t + if y is not None: + y[split] = y_t + + info = src.load_json(os.path.join(data_path, 'info.json')) + int_col_idx_wrt_num = info['int_col_idx_wrt_num'] + + D = src.Dataset( + X_num, + X_cat, + y, + int_col_idx_wrt_num, + y_info={}, + task_type=src.TaskType(info['task_type']), + n_classes=info.get('n_classes') + ) + + if change_val: + D = src.change_val(D) + + return src.transform_dataset(D, T, None) \ No newline at end of file diff --git a/SynthData0523/main/n15/tabbyflow/tabbyflow-n15-20260510_211308/_tabbyflow_gen.py b/SynthData0523/main/n15/tabbyflow/tabbyflow-n15-20260510_211308/_tabbyflow_gen.py new file mode 100644 index 0000000000000000000000000000000000000000..22d84d53873db74f2111f5f98fdc596c7fcb1970 --- /dev/null +++ b/SynthData0523/main/n15/tabbyflow/tabbyflow-n15-20260510_211308/_tabbyflow_gen.py @@ -0,0 +1,43 @@ + +import os, shutil, subprocess, sys +root = r"/workspace/ef-vfm" +rt = r"/work/output-Benchmark-trainonly-v1/n15/tabbyflow/tabbyflow-n15-20260510_211308/_efvfm_runtime" +name = r"pipeline_n15" +src = r"/work/output-Benchmark-trainonly-v1/n15/tabbyflow/tabbyflow-n15-20260510_211308/tabular_bundle/pipeline_n15" + +if not os.path.exists(rt): + def _ignore(_, names): + skip = {"__pycache__", "data", "synthetic", "result", "results", "ckpt"} + return [n for n in names if n in skip or n.endswith(".pyc")] + shutil.copytree(root, rt, ignore=_ignore) + +dst_data = os.path.join(rt, "data", name) +shutil.rmtree(dst_data, ignore_errors=True) +os.makedirs(os.path.dirname(dst_data), exist_ok=True) +shutil.copytree(src, dst_data) +dst_syn = os.path.join(rt, "synthetic", name) +os.makedirs(dst_syn, exist_ok=True) +for fn in ("real.csv", "test.csv", "val.csv"): + shutil.copy(os.path.join(src, fn), os.path.join(dst_syn, fn)) +os.chdir(rt) +os.environ["PYTHONPATH"] = rt + os.pathsep + os.environ.get("PYTHONPATH", "") +os.environ.setdefault("EFVFM_SAMPLE_BATCH_SIZE", "128") +subprocess.check_call([ + sys.executable, os.path.join(rt, "main.py"), + "--dataname", name, "--mode", "test", "--gpu", "0", + "--no_wandb", "--exp_name", r"adapter_efvfm", + "--ckpt_path", r"/work/output-Benchmark-trainonly-v1/n15/tabbyflow/tabbyflow-n15-20260510_211308/_efvfm_runtime/ef_vfm/ckpt/pipeline_n15/adapter_efvfm/model_100.pt", + "--num_samples_to_generate", str(int(7857)), +]) +base = os.path.join(rt, "ef_vfm", "result", name, r"adapter_efvfm") +best = None +best_t = -1.0 +for r, _, files in os.walk(base): + if "samples.csv" in files: + p = os.path.join(r, "samples.csv") + t = os.path.getmtime(p) + if t > best_t: + best_t, best = t, p +if not best: + raise SystemExit("tabbyflow: no samples.csv in " + base) +shutil.copy(best, r"/work/output-Benchmark-trainonly-v1/n15/tabbyflow/tabbyflow-n15-20260510_211308/tabbyflow-n15-7857-20260510_211454.csv") diff --git a/SynthData0523/main/n15/tabbyflow/tabbyflow-n15-20260510_211308/_tabbyflow_train.py b/SynthData0523/main/n15/tabbyflow/tabbyflow-n15-20260510_211308/_tabbyflow_train.py new file mode 100644 index 0000000000000000000000000000000000000000..f550da5e8f252dc0518e22e279baefe17d834bcd --- /dev/null +++ b/SynthData0523/main/n15/tabbyflow/tabbyflow-n15-20260510_211308/_tabbyflow_train.py @@ -0,0 +1,33 @@ + +import os, shutil, subprocess, sys +root = r"/workspace/ef-vfm" +rt = r"/work/output-Benchmark-trainonly-v1/n15/tabbyflow/tabbyflow-n15-20260510_211308/_efvfm_runtime" +name = r"pipeline_n15" +src = r"/work/output-Benchmark-trainonly-v1/n15/tabbyflow/tabbyflow-n15-20260510_211308/tabular_bundle/pipeline_n15" + +shutil.rmtree(rt, ignore_errors=True) + +def _ignore(_, names): + skip = {"__pycache__", "data", "synthetic", "result", "results", "ckpt"} + return [n for n in names if n in skip or n.endswith(".pyc")] + +shutil.copytree(root, rt, ignore=_ignore) +dst_data = os.path.join(rt, "data", name) +dst_syn = os.path.join(rt, "synthetic", name) +shutil.rmtree(dst_data, ignore_errors=True) +os.makedirs(os.path.dirname(dst_data), exist_ok=True) +shutil.copytree(src, dst_data) +os.makedirs(dst_syn, exist_ok=True) +for fn in ("real.csv", "test.csv", "val.csv"): + shutil.copy(os.path.join(src, fn), os.path.join(dst_syn, fn)) +os.chdir(rt) +os.environ["PYTHONPATH"] = rt + os.pathsep + os.environ.get("PYTHONPATH", "") +os.environ["EFVFM_SMOKE_STEPS"] = "100" +os.environ["EFVFM_ADAPTER_TRAIN"] = "1" +os.environ.setdefault("EFVFM_SAMPLE_BATCH_SIZE", "128") +os.environ.setdefault("EFVFM_EVAL_NUM_SAMPLES", "512") +subprocess.check_call([ + sys.executable, os.path.join(rt, "main.py"), + "--dataname", name, "--mode", "train", "--gpu", "0", + "--no_wandb", "--exp_name", r"adapter_efvfm", +]) diff --git a/SynthData0523/main/n15/tabbyflow/tabbyflow-n15-20260510_211308/gen_20260510_211454.log b/SynthData0523/main/n15/tabbyflow/tabbyflow-n15-20260510_211308/gen_20260510_211454.log new file 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b/SynthData0523/main/n15/tabbyflow/tabbyflow-n15-20260510_211308/tabular_bundle/pipeline_n15/y_val.npy new file mode 100644 index 0000000000000000000000000000000000000000..488cfc0c84b890c5bd339c60afea1516fd0c957d --- /dev/null +++ b/SynthData0523/main/n15/tabbyflow/tabbyflow-n15-20260510_211308/tabular_bundle/pipeline_n15/y_val.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e88aebc24d81840aeb928a5c7a9d684c21976d8791950cf665c21ee941caf14a +size 7984 diff --git a/SynthData0523/main/n15/tabbyflow/tabbyflow-n15-20260510_211308/train_20260510_211309.log b/SynthData0523/main/n15/tabbyflow/tabbyflow-n15-20260510_211308/train_20260510_211309.log new file mode 100644 index 0000000000000000000000000000000000000000..31d0001d2ca06a13e697ff2124647290d50120fa --- /dev/null +++ b/SynthData0523/main/n15/tabbyflow/tabbyflow-n15-20260510_211308/train_20260510_211309.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:5181ca6377e360ac0215cdf5bf263e800342989c7f4a1533e7ba1224db09b95c +size 94428 diff --git a/SynthData0523/main/n15/tabddpm/tabddpm-n15-20260321_161922/_tabddpm_sample.py b/SynthData0523/main/n15/tabddpm/tabddpm-n15-20260321_161922/_tabddpm_sample.py new file mode 100644 index 0000000000000000000000000000000000000000..fa8cd60477973b86510cf3fe7ba7f18f0c538508 --- /dev/null +++ b/SynthData0523/main/n15/tabddpm/tabddpm-n15-20260321_161922/_tabddpm_sample.py @@ -0,0 +1,66 @@ +import os, sys, subprocess, json +import numpy as np +import pandas as pd + +tabddpm_root = "/workspace/tabddpm/code" +assert os.path.isdir(tabddpm_root), f"TabDDPM source not mounted: {tabddpm_root}" +env = os.environ.copy() +env["PYTHONPATH"] = tabddpm_root + (os.pathsep + env.get("PYTHONPATH", "")) + +# Reuse the compat wrapper (patches collections.Sequence for skorch) +wrapper = os.path.join(tabddpm_root, "_compat_run.py") +if not os.path.exists(wrapper): + with open(wrapper, "w") as f: + f.write( + "import collections, collections.abc\n" + "for _a in ('Sequence','MutableSequence','MutableMapping','Mapping'," + "'MutableSet','Set','Callable','Iterable','Iterator'):\n" + " if not hasattr(collections, _a): setattr(collections, _a, getattr(collections.abc, _a, None))\n" + "import sys, runpy\n" + "sys.argv = sys.argv[1:]\n" + "runpy.run_path(sys.argv[0], run_name='__main__')\n" + ) + +print(f"[TabDDPM] Sampling 7857 rows") +ret = subprocess.run( + [sys.executable, wrapper, "scripts/pipeline.py", + "--config", "/work/output-SpecializedModels/n15/tabddpm/tabddpm-n15-20260321_161922/config_sample_20260425_075524.toml", + "--sample"], + cwd=tabddpm_root, + env=env +) +if ret.returncode != 0: + sys.exit(ret.returncode) + +# 将 .npy 输出转为 CSV（npy 在 TabDDPM 的 parent_dir，即 npy_dir） +info_path = "/work/output-SpecializedModels/n15/tabddpm/tabddpm-n15-20260321_161922/data/info.json" +with open(info_path) as f: + info = json.load(f) + +output_dir = "/work/output-SpecializedModels/n15/tabddpm/tabddpm-n15-20260321_161922/output" +col_names = info.get("column_names", []) + +parts = [] +x_num_path = os.path.join(output_dir, "X_num_train.npy") +x_cat_path = os.path.join(output_dir, "X_cat_train.npy") +y_path = os.path.join(output_dir, "y_train.npy") + +if os.path.exists(x_num_path): + parts.append(np.load(x_num_path, allow_pickle=True)) +if os.path.exists(x_cat_path): + parts.append(np.load(x_cat_path, allow_pickle=True).astype(float)) +if os.path.exists(y_path): + y = np.load(y_path, allow_pickle=True) + parts.append(y.reshape(-1, 1) if y.ndim == 1 else y) + +if parts: + combined = np.concatenate(parts, axis=1) + if col_names and len(col_names) == combined.shape[1]: + df = pd.DataFrame(combined, columns=col_names) + else: + df = pd.DataFrame(combined) + df.to_csv("/work/output-SpecializedModels/n15/tabddpm/tabddpm-n15-20260321_161922/tabddpm-n15-7857-20260425_075524.csv", index=False) + print(f"[TabDDPM] Saved {len(df)} rows -> /work/output-SpecializedModels/n15/tabddpm/tabddpm-n15-20260321_161922/tabddpm-n15-7857-20260425_075524.csv") +else: + print("[TabDDPM] WARNING: No output .npy files found") + sys.exit(1) diff --git a/SynthData0523/main/n15/tabddpm/tabddpm-n15-20260321_161922/_tabddpm_train.py b/SynthData0523/main/n15/tabddpm/tabddpm-n15-20260321_161922/_tabddpm_train.py new file mode 100644 index 0000000000000000000000000000000000000000..c3e9c5b07506a67a6080eb6163cd78251a32d3aa --- /dev/null +++ b/SynthData0523/main/n15/tabddpm/tabddpm-n15-20260321_161922/_tabddpm_train.py @@ -0,0 +1,32 @@ +import os, sys, subprocess + +tabddpm_root = "/workspace/tabddpm/code" +assert os.path.isdir(tabddpm_root), f"TabDDPM source not mounted: {tabddpm_root}" +env = os.environ.copy() +env["PYTHONPATH"] = tabddpm_root + (os.pathsep + env.get("PYTHONPATH", "")) + +# Write a wrapper that patches collections.Sequence (removed in Python 3.10+) +# before running pipeline.py - needed because skorch uses old API +wrapper = os.path.join(tabddpm_root, "_compat_run.py") +with open(wrapper, "w") as f: + f.write( + "import collections, collections.abc\n" + "for _a in ('Sequence','MutableSequence','MutableMapping','Mapping'," + "'MutableSet','Set','Callable','Iterable','Iterator'):\n" + " if not hasattr(collections, _a): setattr(collections, _a, getattr(collections.abc, _a, None))\n" + "import sys, runpy\n" + "sys.argv = sys.argv[1:]\n" + "runpy.run_path(sys.argv[0], run_name='__main__')\n" + ) + +print(f"[TabDDPM] Training, config=/work/output-SpecializedModels/n15/tabddpm/tabddpm-n15-20260321_161922/config.toml") +ret = subprocess.run( + [sys.executable, wrapper, "scripts/pipeline.py", + "--config", "/work/output-SpecializedModels/n15/tabddpm/tabddpm-n15-20260321_161922/config.toml", + "--train"], + cwd=tabddpm_root, + env=env +) +if ret.returncode != 0: + sys.exit(ret.returncode) +print("[TabDDPM] Training complete") diff --git a/SynthData0523/main/n15/tabddpm/tabddpm-n15-20260321_161922/config.toml b/SynthData0523/main/n15/tabddpm/tabddpm-n15-20260321_161922/config.toml new file mode 100644 index 0000000000000000000000000000000000000000..e632960a401498205d631bc4f41b5f17349bc78c --- /dev/null +++ b/SynthData0523/main/n15/tabddpm/tabddpm-n15-20260321_161922/config.toml @@ -0,0 +1,39 @@ +seed = 0 +parent_dir = "/work/output-SpecializedModels/n15/tabddpm/tabddpm-n15-20260321_161922/output" +real_data_path = "/work/output-SpecializedModels/n15/tabddpm/tabddpm-n15-20260321_161922/data" +model_type = "mlp" +num_numerical_features = 54 +device = "cuda:0" + +[model_params] +d_in = 85 +num_classes = 3 +is_y_cond = true + +[model_params.rtdl_params] +d_layers = [256, 256] +dropout = 0.0 + +[diffusion_params] +num_timesteps = 1000 +gaussian_loss_type = "mse" + +[train.main] +steps = 5000 +lr = 0.001 +weight_decay = 0.0 +batch_size = 256 + +[train.T] +seed = 0 +normalization = "quantile" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" + +[sample] +num_samples = 1000 +batch_size = 1000 +seed = 0 diff --git a/SynthData0523/main/n15/tabddpm/tabddpm-n15-20260321_161922/config_sample_20260424_211539.toml b/SynthData0523/main/n15/tabddpm/tabddpm-n15-20260321_161922/config_sample_20260424_211539.toml new file mode 100644 index 0000000000000000000000000000000000000000..ba6fb8d977b5f7732a27bacc05c92cae3c5cfb0d --- /dev/null +++ b/SynthData0523/main/n15/tabddpm/tabddpm-n15-20260321_161922/config_sample_20260424_211539.toml @@ -0,0 +1,39 @@ +seed = 0 +parent_dir = "/work/output-SpecializedModels/n15/tabddpm/tabddpm-n15-20260321_161922/output" +real_data_path = "/work/output-SpecializedModels/n15/tabddpm/tabddpm-n15-20260321_161922/data" +model_type = "mlp" +num_numerical_features = 54 +device = "cuda:0" + +[model_params] +d_in = 85 +num_classes = 3 +is_y_cond = true + +[model_params.rtdl_params] +d_layers = [256, 256] +dropout = 0.0 + +[diffusion_params] +num_timesteps = 1000 +gaussian_loss_type = "mse" + +[train.main] +steps = 5000 +lr = 0.001 +weight_decay = 0.0 +batch_size = 256 + +[train.T] +seed = 0 +normalization = "quantile" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" + +[sample] +num_samples = 7857 +batch_size = 1000 +seed = 0 diff --git a/SynthData0523/main/n15/tabddpm/tabddpm-n15-20260321_161922/config_sample_20260425_033100.toml b/SynthData0523/main/n15/tabddpm/tabddpm-n15-20260321_161922/config_sample_20260425_033100.toml new file mode 100644 index 0000000000000000000000000000000000000000..ba6fb8d977b5f7732a27bacc05c92cae3c5cfb0d --- /dev/null +++ b/SynthData0523/main/n15/tabddpm/tabddpm-n15-20260321_161922/config_sample_20260425_033100.toml @@ -0,0 +1,39 @@ +seed = 0 +parent_dir = "/work/output-SpecializedModels/n15/tabddpm/tabddpm-n15-20260321_161922/output" +real_data_path = "/work/output-SpecializedModels/n15/tabddpm/tabddpm-n15-20260321_161922/data" +model_type = "mlp" +num_numerical_features = 54 +device = "cuda:0" + +[model_params] +d_in = 85 +num_classes = 3 +is_y_cond = true + +[model_params.rtdl_params] +d_layers = [256, 256] +dropout = 0.0 + +[diffusion_params] +num_timesteps = 1000 +gaussian_loss_type = "mse" + +[train.main] +steps = 5000 +lr = 0.001 +weight_decay = 0.0 +batch_size = 256 + +[train.T] +seed = 0 +normalization = "quantile" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" + +[sample] +num_samples = 7857 +batch_size = 1000 +seed = 0 diff --git a/SynthData0523/main/n15/tabddpm/tabddpm-n15-20260321_161922/config_sample_20260425_075524.toml b/SynthData0523/main/n15/tabddpm/tabddpm-n15-20260321_161922/config_sample_20260425_075524.toml new file mode 100644 index 0000000000000000000000000000000000000000..ba6fb8d977b5f7732a27bacc05c92cae3c5cfb0d --- /dev/null +++ b/SynthData0523/main/n15/tabddpm/tabddpm-n15-20260321_161922/config_sample_20260425_075524.toml @@ -0,0 +1,39 @@ +seed = 0 +parent_dir = "/work/output-SpecializedModels/n15/tabddpm/tabddpm-n15-20260321_161922/output" +real_data_path = "/work/output-SpecializedModels/n15/tabddpm/tabddpm-n15-20260321_161922/data" +model_type = "mlp" +num_numerical_features = 54 +device = "cuda:0" + +[model_params] +d_in = 85 +num_classes = 3 +is_y_cond = true + +[model_params.rtdl_params] +d_layers = [256, 256] +dropout = 0.0 + +[diffusion_params] +num_timesteps = 1000 +gaussian_loss_type = "mse" + +[train.main] +steps = 5000 +lr = 0.001 +weight_decay = 0.0 +batch_size = 256 + +[train.T] +seed = 0 +normalization = "quantile" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" + +[sample] +num_samples = 7857 +batch_size = 1000 +seed = 0 diff --git a/SynthData0523/main/n15/tabddpm/tabddpm-n15-20260321_161922/data/X_cat_test.npy b/SynthData0523/main/n15/tabddpm/tabddpm-n15-20260321_161922/data/X_cat_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..90c4952db70d0bf30260ca9343b0fa5d8cc98dcf --- /dev/null +++ b/SynthData0523/main/n15/tabddpm/tabddpm-n15-20260321_161922/data/X_cat_test.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e71ac4ca2c52934779fc57e6e63e996f30737a5ec4d7a6fe1a0cd69ccb749181 +size 243912 diff --git a/SynthData0523/main/n15/tabddpm/tabddpm-n15-20260321_161922/data/X_cat_train.npy b/SynthData0523/main/n15/tabddpm/tabddpm-n15-20260321_161922/data/X_cat_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..3caea3071878e8e38ca12d67bebf2d563f002643 --- /dev/null +++ b/SynthData0523/main/n15/tabddpm/tabddpm-n15-20260321_161922/data/X_cat_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid 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0000000000000000000000000000000000000000..62cb4846622482bfdf196fb461e22d5d300b27b8 --- /dev/null +++ b/SynthData0523/main/n15/tabdiff/tabdiff-n15-20260501_191117/_tabdiff_gen.py @@ -0,0 +1,36 @@ + +import os, shutil, subprocess, sys +td = r"/workspace/TabDiff" +name = r"pipeline_n15" +src = r"/work/output-Benchmark-trainonly-v1/n15/tabdiff/tabdiff-n15-20260501_191117/tabular_bundle/pipeline_n15" +dst_data = os.path.join(td, "data", name) +dst_syn = os.path.join(td, "synthetic", name) +shutil.rmtree(dst_data, ignore_errors=True) +shutil.copytree(src, dst_data) +os.makedirs(dst_syn, exist_ok=True) +for fn in ("real.csv", "test.csv", "val.csv"): + shutil.copy(os.path.join(src, fn), os.path.join(dst_syn, fn)) +os.chdir(td) +os.environ["PYTHONPATH"] = td + os.pathsep + os.environ.get("PYTHONPATH", "") +subprocess.check_call([ + sys.executable, "-m", "tabdiff.main", + "--dataname", name, "--mode", "test", "--gpu", "0", + "--no_wandb", "--exp_name", r"adapter_learnable", + "--ckpt_path", r"/workspace/TabDiff/tabdiff/ckpt/pipeline_n15/adapter_learnable/model_500.pt", + "--num_samples_to_generate", str(int(7857)), +]) +# test() 写入 tabdiff/result////samples.csv +import glob as g +base = os.path.join(td, "tabdiff", "result", name, r"adapter_learnable") +best = None +best_t = -1.0 +for root, _, files in os.walk(base): + if "samples.csv" in files: + p = os.path.join(root, "samples.csv") + t = os.path.getmtime(p) + if t > best_t: + best_t = t + best = p +if not best: + raise SystemExit("tabdiff: no samples.csv under " + base) +shutil.copy(best, r"/work/output-Benchmark-trainonly-v1/n15/tabdiff/tabdiff-n15-20260501_191117/tabdiff-n15-7857-20260501_191635.csv") diff --git a/SynthData0523/main/n15/tabdiff/tabdiff-n15-20260501_191117/_tabdiff_train.py b/SynthData0523/main/n15/tabdiff/tabdiff-n15-20260501_191117/_tabdiff_train.py new file mode 100644 index 0000000000000000000000000000000000000000..26223b456cfe2211264051ab1fc9439d66299d45 --- /dev/null +++ b/SynthData0523/main/n15/tabdiff/tabdiff-n15-20260501_191117/_tabdiff_train.py @@ -0,0 +1,21 @@ + +import os, shutil, subprocess, sys +td = r"/workspace/TabDiff" +name = r"pipeline_n15" +src = r"/work/output-Benchmark-trainonly-v1/n15/tabdiff/tabdiff-n15-20260501_191117/tabular_bundle/pipeline_n15" +dst_data = os.path.join(td, "data", name) +dst_syn = os.path.join(td, "synthetic", name) +shutil.rmtree(dst_data, ignore_errors=True) +shutil.copytree(src, dst_data) +os.makedirs(dst_syn, exist_ok=True) +for fn in ("real.csv", "test.csv", "val.csv"): + shutil.copy(os.path.join(src, fn), os.path.join(dst_syn, fn)) +os.chdir(td) +os.environ["PYTHONPATH"] = td + os.pathsep + os.environ.get("PYTHONPATH", "") +os.environ["TABDIFF_SMOKE_STEPS"] = "500" +os.environ["TABDIFF_ADAPTER_TRAIN"] = "1" +subprocess.check_call([ + sys.executable, "-m", "tabdiff.main", + "--dataname", name, "--mode", "train", "--gpu", "0", + "--no_wandb", "--exp_name", r"adapter_learnable", +]) diff --git 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file mode 100644 index 0000000000000000000000000000000000000000..527acc2a474b9175bc3a64a582d431fddce82c5f --- /dev/null +++ b/SynthData0523/main/n15/tabpfgen/n15-migrated-20260422_183752/_tabpfgen_generate.py @@ -0,0 +1,68 @@ +import numpy as np +import pandas as pd +import json +from tabpfgen import TabPFGen + +df = pd.read_csv("/work/temp/tabpfgen_regen_parallel_deadline/20260422_070318/n15/staged/public/train.csv") +target_col = "ABYSTAND" + +feature_cols = [c for c in df.columns if c != target_col] + +# --- Label-encode categorical / object columns --- +cat_encodings = {} # col -> list of unique values (index = code) +for col in feature_cols: + if df[col].dtype == object or str(df[col].dtype) == 'category': + cats = sorted(df[col].dropna().unique().tolist(), key=str) + cat_map = {v: i for i, v in enumerate(cats)} + df[col] = df[col].map(cat_map).astype(float) + cat_encodings[col] = cats + print(f"[TabPFGen] Label-encoded '{col}' ({len(cats)} categories)") + +# Encode target if categorical +target_cats = None +if df[target_col].dtype == object or str(df[target_col].dtype) == 'category': + cats = sorted(df[target_col].dropna().unique().tolist(), key=str) + t_map = {v: i for i, v in enumerate(cats)} + df[target_col] = df[target_col].map(t_map).astype(float) + target_cats = cats + print(f"[TabPFGen] Label-encoded target '{target_col}' ({len(cats)} categories)") + +X = df[feature_cols].values.astype(np.float32) +y = df[target_col].values + +# Handle NaN +for i in range(X.shape[1]): + col_vals = X[:, i] + mask = np.isnan(col_vals) + if mask.any(): + mean_val = np.nanmean(col_vals) + X[mask, i] = mean_val if not np.isnan(mean_val) else 0.0 + +gen = TabPFGen( + n_sgld_steps=1000, + sgld_step_size=0.01, + sgld_noise_scale=0.01, + device="auto", +) + +print(f"[TabPFGen] Generating 7857 rows via generate_classification") +X_syn, y_syn = gen.generate_classification(X, y, n_samples=7857) + +syn_df = pd.DataFrame(X_syn, columns=feature_cols) +syn_df[target_col] = y_syn + +# --- Inverse label-encoding for categorical columns --- +for col, cats in cat_encodings.items(): + # Round to nearest integer index, clamp to valid range + codes = np.round(syn_df[col].values).astype(int) + codes = np.clip(codes, 0, len(cats) - 1) + syn_df[col] = [cats[c] for c in codes] + +if target_cats is not None: + codes = np.round(syn_df[target_col].values).astype(int) + codes = np.clip(codes, 0, len(target_cats) - 1) + syn_df[target_col] = [target_cats[c] for c in codes] + +syn_df = syn_df[list(df.columns)] +syn_df.to_csv("/work/temp/tabpfgen_regen_parallel_deadline/20260422_070318/n15/tabpfgen-n15-7857-20260422_070321.csv", index=False) +print(f"[TabPFGen] Saved {len(syn_df)} rows -> /work/temp/tabpfgen_regen_parallel_deadline/20260422_070318/n15/tabpfgen-n15-7857-20260422_070321.csv") diff --git a/SynthData0523/main/n15/tabpfgen/n15-migrated-20260422_183752/gen_20260422_070321.log b/SynthData0523/main/n15/tabpfgen/n15-migrated-20260422_183752/gen_20260422_070321.log new file 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"/data/jialinzhang/SynthesizePipeline-server/temp/tabpfgen_regen_parallel_deadline/20260422_070318/n15/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/temp/tabpfgen_regen_parallel_deadline/20260422_070318/n15/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/SynthData0523/main/n15/tabpfgen/n15-migrated-20260422_183752/tabpfgen-n15-7857-20260422_070321.csv b/SynthData0523/main/n15/tabpfgen/n15-migrated-20260422_183752/tabpfgen-n15-7857-20260422_070321.csv new file mode 100644 index 0000000000000000000000000000000000000000..b7ac51809a328f394ad2434a9959e2e7b76d23a1 --- /dev/null +++ b/SynthData0523/main/n15/tabpfgen/n15-migrated-20260422_183752/tabpfgen-n15-7857-20260422_070321.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:1cbb642d1f0a225e79b998c700e77ba5dccc1e55c6d752e5470495d0bf08b85d +size 1411870 diff --git a/SynthData0523/main/n15/tabsyn/tabsyn-n15-20260426_220916/_tabsyn_sample.py b/SynthData0523/main/n15/tabsyn/tabsyn-n15-20260426_220916/_tabsyn_sample.py new file mode 100644 index 0000000000000000000000000000000000000000..c6343b43581fc0d8f86b3ca39edfebac74884019 --- /dev/null +++ b/SynthData0523/main/n15/tabsyn/tabsyn-n15-20260426_220916/_tabsyn_sample.py @@ -0,0 +1,39 @@ +import os, sys, subprocess + +work_dir = "/work/output-SpecializedModels/n15/tabsyn/tabsyn-n15-20260426_220916" +dataname = "tabsyn_n15" +output_csv = "/work/output-SpecializedModels/n15/tabsyn/tabsyn-n15-20260426_220916/tabsyn-n15-7857-20260426_224314.csv" +tabsyn_root = "/workspace/tabsyn" + +assert os.path.exists(tabsyn_root), f"TabSyn source not mounted: {tabsyn_root}" + +old = os.environ.get("PYTHONPATH", "") +os.environ["PYTHONPATH"] = tabsyn_root + (os.pathsep + old if old else "") +sys.path.insert(0, tabsyn_root) + +os.chdir(tabsyn_root) + +# Ensure data symlink exists +data_link = os.path.join(tabsyn_root, "data", dataname) +data_src = os.path.join(work_dir, "data", dataname) +os.makedirs(os.path.join(tabsyn_root, "data"), exist_ok=True) +if os.path.exists(data_link): + os.remove(data_link) +os.symlink(data_src, data_link) + +print(f"[TabSyn] Sampling 7857 rows") +env = os.environ.copy() +env.setdefault("TABSYN_RESUME", "1") +ret = subprocess.run( + [sys.executable, "main.py", + "--dataname", dataname, + "--mode", "sample", + "--method", "tabsyn", + "--gpu", "0", + "--save_path", output_csv], + cwd=tabsyn_root, + env=env +) +if ret.returncode != 0: + sys.exit(ret.returncode) +print(f"[TabSyn] Saved -> {output_csv}") diff --git a/SynthData0523/main/n15/tabsyn/tabsyn-n15-20260426_220916/_tabsyn_train.py b/SynthData0523/main/n15/tabsyn/tabsyn-n15-20260426_220916/_tabsyn_train.py new file mode 100644 index 0000000000000000000000000000000000000000..70663281b099c19b06ffee7aab55f11aa091491f --- /dev/null +++ b/SynthData0523/main/n15/tabsyn/tabsyn-n15-20260426_220916/_tabsyn_train.py @@ -0,0 +1,63 @@ +import os, sys, subprocess + +work_dir = "/work/output-SpecializedModels/n15/tabsyn/tabsyn-n15-20260426_220916" +dataname = "tabsyn_n15" +tabsyn_root = "/workspace/tabsyn" + +assert os.path.exists(tabsyn_root), f"TabSyn source not mounted: {tabsyn_root}" + +old = os.environ.get("PYTHONPATH", "") +os.environ["PYTHONPATH"] = tabsyn_root + (os.pathsep + old if old else "") +sys.path.insert(0, tabsyn_root) + +os.chdir(tabsyn_root) + +# Symlink data dir into TabSyn data/ +data_link = os.path.join(tabsyn_root, "data", dataname) +data_src = os.path.join(work_dir, "data", dataname) +os.makedirs(os.path.join(tabsyn_root, "data"), exist_ok=True) +if os.path.exists(data_link): + os.remove(data_link) +os.symlink(data_src, data_link) + +env = os.environ.copy() +env.setdefault("TABSYN_RESUME", "1") +env.setdefault("TABSYN_VAE_BATCH_SIZE", "1024") +_te = 1000 +if _te is not None: + env["TABSYN_VAE_EPOCHS"] = str(_te) + env["TABSYN_DIFFUSION_MAX_EPOCHS"] = str(max(_te + 1, 2)) + +# Data preprocessing is done on the host side (_prepare_data_dir) +# which creates .npy files, train/test CSVs, and info.json + +# Step 1: Train VAE (produces latent embeddings) +print(f"[TabSyn] Step 1/2: Training VAE in {tabsyn_root}, dataname={dataname}") +ret = subprocess.run( + [sys.executable, "main.py", + "--dataname", dataname, + "--mode", "train", + "--method", "vae", + "--gpu", "0"], + cwd=tabsyn_root, + env=env +) +if ret.returncode != 0: + print("[TabSyn] VAE training failed") + sys.exit(ret.returncode) + +# Step 2: Train diffusion model on latent space +print(f"[TabSyn] Step 2/2: Training diffusion model") +ret = subprocess.run( + [sys.executable, "main.py", + "--dataname", dataname, + "--mode", "train", + "--method", "tabsyn", + "--gpu", "0"], + cwd=tabsyn_root, + env=env +) +if ret.returncode != 0: + print("[TabSyn] Diffusion training failed") + sys.exit(ret.returncode) +print("[TabSyn] Training complete (VAE + Diffusion)") diff --git 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a/SynthData0523/main/n15/tabsyn/tabsyn-n15-20260426_220916/train_20260426_220917.log b/SynthData0523/main/n15/tabsyn/tabsyn-n15-20260426_220916/train_20260426_220917.log new file mode 100644 index 0000000000000000000000000000000000000000..fc7ee633f18cd4255735b0f20e97645cd5998212 --- /dev/null +++ b/SynthData0523/main/n15/tabsyn/tabsyn-n15-20260426_220916/train_20260426_220917.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:47a3e405c0f2c01c2cc0dbd9615fed45a3e1d0798654321c93689cab88ef4a3c +size 1057314 diff --git a/SynthData0523/main/n15/tvae/tvae-n15-20260320_080449/_tvae_generate.py b/SynthData0523/main/n15/tvae/tvae-n15-20260320_080449/_tvae_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..05577231c3cc7cd5cc7a251b66aa9059849917e7 --- /dev/null +++ b/SynthData0523/main/n15/tvae/tvae-n15-20260320_080449/_tvae_generate.py @@ -0,0 +1,5 @@ +from ctgan.synthesizers.tvae import TVAE +model = TVAE.load("/work/output-SpecializedModels/n15/tvae/tvae-n15-20260320_080449/models_300epochs/tvae_300epochs.pt") +samples = model.sample(7857) +samples.to_csv("/work/output-SpecializedModels/n15/tvae/tvae-n15-20260320_080449/tvae-n15-7857-20260330_070643.csv", index=False) +print(f"[TVAE] Generated 7857 rows -> /work/output-SpecializedModels/n15/tvae/tvae-n15-20260320_080449/tvae-n15-7857-20260330_070643.csv") diff --git a/SynthData0523/main/n15/tvae/tvae-n15-20260320_080449/_tvae_train.py b/SynthData0523/main/n15/tvae/tvae-n15-20260320_080449/_tvae_train.py new file mode 100644 index 0000000000000000000000000000000000000000..998d29b50b5569c1fe9712139c90e2450dc7d5dc --- /dev/null +++ b/SynthData0523/main/n15/tvae/tvae-n15-20260320_080449/_tvae_train.py @@ -0,0 +1,16 @@ +import json, sys +import pandas as pd +from ctgan.data import read_csv +from ctgan.synthesizers.tvae import TVAE + +csv_path = "/work/DatasetNew/n15/n15-train.csv" +meta_path = "/work/output-SpecializedModels/n15/tvae/tvae-n15-20260320_080449/tvae_metadata.json" +save_path = "/work/output-SpecializedModels/n15/tvae/tvae-n15-20260320_080449/models_300epochs/tvae_300epochs.pt" +epochs = 300 + +data, discrete_columns = read_csv(csv_path, meta_path, header=True, discrete=None) +print(f"[TVAE] Training on {len(data)} rows, {len(data.columns)} cols, epochs={epochs}") +model = TVAE(epochs=epochs, batch_size=500) +model.fit(data, discrete_columns) +model.save(save_path) +print(f"[TVAE] Model saved -> {save_path}") diff --git a/SynthData0523/main/n15/tvae/tvae-n15-20260320_080449/gen_20260320_081401.log b/SynthData0523/main/n15/tvae/tvae-n15-20260320_080449/gen_20260320_081401.log new file mode 100644 index 0000000000000000000000000000000000000000..9ad84c6f29d0e40758ee30c1734904962b825002 --- /dev/null +++ b/SynthData0523/main/n15/tvae/tvae-n15-20260320_080449/gen_20260320_081401.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid 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