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Resume SynthData0523 hyper_parameter_tuning/n11/tvae batch 2

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  1. .gitattributes +218 -0
  2. SynthData0523/hyper_parameter_tuning/n11/tvae/runs/tvae-n11-20260520_173119/staged/public/staged_features.json +3 -0
  3. SynthData0523/hyper_parameter_tuning/n11/tvae/runs/tvae-n11-20260520_173119/staged/public/test.csv +3 -0
  4. SynthData0523/hyper_parameter_tuning/n11/tvae/runs/tvae-n11-20260520_173119/staged/public/train.csv +3 -0
  5. SynthData0523/hyper_parameter_tuning/n11/tvae/runs/tvae-n11-20260520_173119/staged/public/val.csv +3 -0
  6. SynthData0523/hyper_parameter_tuning/n11/tvae/runs/tvae-n11-20260520_173119/staged/tvae/adapter_report.json +3 -0
  7. SynthData0523/hyper_parameter_tuning/n11/tvae/runs/tvae-n11-20260520_173119/staged/tvae/adapter_transforms_applied.json +3 -0
  8. SynthData0523/hyper_parameter_tuning/n11/tvae/runs/tvae-n11-20260520_173119/staged/tvae/model_input_manifest.json +3 -0
  9. SynthData0523/hyper_parameter_tuning/n11/tvae/runs/tvae-n11-20260520_173119/tvae-n11-15215-20260520_173444.csv +3 -0
  10. SynthData0523/hyper_parameter_tuning/n11/tvae/runs/tvae-n11-20260520_173119/tvae_metadata.json +3 -0
  11. SynthData0523/hyper_parameter_tuning/n11/tvae/runs/tvae-n11-20260520_173615/_tvae_generate.py +27 -0
  12. SynthData0523/hyper_parameter_tuning/n11/tvae/runs/tvae-n11-20260520_173615/_tvae_train.py +131 -0
  13. SynthData0523/hyper_parameter_tuning/n11/tvae/runs/tvae-n11-20260520_173615/bo_combo.json +3 -0
  14. SynthData0523/hyper_parameter_tuning/n11/tvae/runs/tvae-n11-20260520_173615/gen_20260520_175306.log +3 -0
  15. SynthData0523/hyper_parameter_tuning/n11/tvae/runs/tvae-n11-20260520_173615/input_snapshot.json +3 -0
  16. SynthData0523/hyper_parameter_tuning/n11/tvae/runs/tvae-n11-20260520_173615/models_400epochs/train_20260520_173615.log +3 -0
  17. SynthData0523/hyper_parameter_tuning/n11/tvae/runs/tvae-n11-20260520_173615/models_400epochs/tvae_400epochs.pt +3 -0
  18. SynthData0523/hyper_parameter_tuning/n11/tvae/runs/tvae-n11-20260520_173615/public_gate/normalized_schema_snapshot.json +3 -0
  19. SynthData0523/hyper_parameter_tuning/n11/tvae/runs/tvae-n11-20260520_173615/public_gate/public_gate_report.json +3 -0
  20. SynthData0523/hyper_parameter_tuning/n11/tvae/runs/tvae-n11-20260520_173615/public_gate/staged_input_manifest.json +3 -0
  21. SynthData0523/hyper_parameter_tuning/n11/tvae/runs/tvae-n11-20260520_173615/run_config.json +3 -0
  22. SynthData0523/hyper_parameter_tuning/n11/tvae/runs/tvae-n11-20260520_173615/runtime_result.json +3 -0
  23. SynthData0523/hyper_parameter_tuning/n11/tvae/runs/tvae-n11-20260520_173615/staged/public/staged_features.json +3 -0
  24. SynthData0523/hyper_parameter_tuning/n11/tvae/runs/tvae-n11-20260520_173615/staged/public/test.csv +3 -0
  25. SynthData0523/hyper_parameter_tuning/n11/tvae/runs/tvae-n11-20260520_173615/staged/public/train.csv +3 -0
  26. SynthData0523/hyper_parameter_tuning/n11/tvae/runs/tvae-n11-20260520_173615/staged/public/val.csv +3 -0
  27. SynthData0523/hyper_parameter_tuning/n11/tvae/runs/tvae-n11-20260520_173615/staged/tvae/adapter_report.json +3 -0
  28. SynthData0523/hyper_parameter_tuning/n11/tvae/runs/tvae-n11-20260520_173615/staged/tvae/adapter_transforms_applied.json +3 -0
  29. SynthData0523/hyper_parameter_tuning/n11/tvae/runs/tvae-n11-20260520_173615/staged/tvae/model_input_manifest.json +3 -0
  30. SynthData0523/hyper_parameter_tuning/n11/tvae/runs/tvae-n11-20260520_173615/tvae-n11-15215-20260520_175306.csv +3 -0
  31. SynthData0523/hyper_parameter_tuning/n11/tvae/runs/tvae-n11-20260520_173615/tvae_metadata.json +3 -0
  32. SynthData0523/hyper_parameter_tuning/n11/tvae/runs/tvae-n11-20260520_174051/_tvae_generate.py +27 -0
  33. SynthData0523/hyper_parameter_tuning/n11/tvae/runs/tvae-n11-20260520_174051/_tvae_train.py +131 -0
  34. SynthData0523/hyper_parameter_tuning/n11/tvae/runs/tvae-n11-20260520_174051/bo_combo.json +3 -0
  35. SynthData0523/hyper_parameter_tuning/n11/tvae/runs/tvae-n11-20260520_174051/gen_20260520_180159.log +3 -0
  36. SynthData0523/hyper_parameter_tuning/n11/tvae/runs/tvae-n11-20260520_174051/input_snapshot.json +3 -0
  37. SynthData0523/hyper_parameter_tuning/n11/tvae/runs/tvae-n11-20260520_174051/models_125epochs/train_20260520_174052.log +3 -0
  38. SynthData0523/hyper_parameter_tuning/n11/tvae/runs/tvae-n11-20260520_174051/models_125epochs/tvae_125epochs.pt +3 -0
  39. SynthData0523/hyper_parameter_tuning/n11/tvae/runs/tvae-n11-20260520_174051/public_gate/normalized_schema_snapshot.json +3 -0
  40. SynthData0523/hyper_parameter_tuning/n11/tvae/runs/tvae-n11-20260520_174051/public_gate/public_gate_report.json +3 -0
  41. SynthData0523/hyper_parameter_tuning/n11/tvae/runs/tvae-n11-20260520_174051/public_gate/staged_input_manifest.json +3 -0
  42. SynthData0523/hyper_parameter_tuning/n11/tvae/runs/tvae-n11-20260520_174051/run_config.json +3 -0
  43. SynthData0523/hyper_parameter_tuning/n11/tvae/runs/tvae-n11-20260520_174051/runtime_result.json +3 -0
  44. SynthData0523/hyper_parameter_tuning/n11/tvae/runs/tvae-n11-20260520_174051/staged/public/staged_features.json +3 -0
  45. SynthData0523/hyper_parameter_tuning/n11/tvae/runs/tvae-n11-20260520_174051/staged/public/test.csv +3 -0
  46. SynthData0523/hyper_parameter_tuning/n11/tvae/runs/tvae-n11-20260520_174051/staged/public/train.csv +3 -0
  47. SynthData0523/hyper_parameter_tuning/n11/tvae/runs/tvae-n11-20260520_174051/staged/public/val.csv +3 -0
  48. SynthData0523/hyper_parameter_tuning/n11/tvae/runs/tvae-n11-20260520_174051/staged/tvae/adapter_report.json +3 -0
  49. SynthData0523/hyper_parameter_tuning/n11/tvae/runs/tvae-n11-20260520_174051/staged/tvae/adapter_transforms_applied.json +3 -0
  50. SynthData0523/hyper_parameter_tuning/n11/tvae/runs/tvae-n11-20260520_174051/staged/tvae/model_input_manifest.json +3 -0
.gitattributes CHANGED
@@ -19250,3 +19250,221 @@ SynthData0523/hyper_parameter_tuning/n11/tvae/runs/tvae-n11-20260520_173119/publ
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SynthData0523/hyper_parameter_tuning/n11/tvae/runs/tvae-n11-20260520_173615/_tvae_generate.py ADDED
@@ -0,0 +1,27 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import os, sys
2
+ sys.path.insert(0, "/work")
3
+ from src.SpecificModels.ctgan_joblib_parallel_cap import apply_parallel_cap_from_env
4
+ apply_parallel_cap_from_env()
5
+ from src.SpecificModels.ctgan_rdt_inverse_fix import apply_ctgan_inverse_fix
6
+ apply_ctgan_inverse_fix()
7
+ import pandas as pd
8
+ from ctgan.synthesizers.tvae import TVAE
9
+ def _fit_cpu_staged_compat(self, *args, **kwargs):
10
+ raise RuntimeError("_fit_cpu_staged_compat should never be called during generate-only load")
11
+ if not hasattr(TVAE, "_fit_cpu_staged"):
12
+ TVAE._fit_cpu_staged = _fit_cpu_staged_compat
13
+ os.environ.setdefault("LOKY_MAX_CPU_COUNT", "8")
14
+ os.environ.setdefault("OPENBLAS_NUM_THREADS", "8")
15
+ os.environ.setdefault("MKL_NUM_THREADS", "8")
16
+ model = TVAE.load("/work/output-Benchmark-trainonly-v1/n11/tvae/tvae-n11-20260520_173615/models_400epochs/tvae_400epochs.pt")
17
+ total = 15215
18
+ chunk = min(50000, total) if total > 50000 else total
19
+ parts = []
20
+ left = total
21
+ while left > 0:
22
+ take = min(chunk, left)
23
+ parts.append(model.sample(take))
24
+ left -= take
25
+ samples = pd.concat(parts, ignore_index=True) if len(parts) > 1 else parts[0]
26
+ samples.to_csv("/work/output-Benchmark-trainonly-v1/n11/tvae/tvae-n11-20260520_173615/tvae-n11-15215-20260520_175306.csv", index=False)
27
+ print(f"[TVAE] Generated {total} rows (chunks={len(parts)}) -> /work/output-Benchmark-trainonly-v1/n11/tvae/tvae-n11-20260520_173615/tvae-n11-15215-20260520_175306.csv")
SynthData0523/hyper_parameter_tuning/n11/tvae/runs/tvae-n11-20260520_173615/_tvae_train.py ADDED
@@ -0,0 +1,131 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import json, os, sys
2
+ sys.path.insert(0, "/work")
3
+ from src.SpecificModels.ctgan_joblib_parallel_cap import apply_parallel_cap_from_env
4
+ apply_parallel_cap_from_env()
5
+ import pandas as pd
6
+ import torch
7
+ from torch.optim import Adam
8
+ from torch.utils.data import DataLoader, TensorDataset
9
+ from tqdm import tqdm
10
+ from ctgan.data import read_csv
11
+ from ctgan.data_transformer import DataTransformer
12
+ from ctgan.synthesizers.tvae import TVAE, Encoder, Decoder, _loss_function
13
+
14
+ # Keep transform stage parallelism bounded for stability on shared host.
15
+ os.environ.setdefault("LOKY_MAX_CPU_COUNT", "8")
16
+ os.environ.setdefault("OPENBLAS_NUM_THREADS", "8")
17
+ os.environ.setdefault("MKL_NUM_THREADS", "8")
18
+ _nj = (os.environ.get("TVAE_CTGAN_JOBTRANS_N_JOBS") or "").strip()
19
+ if _nj:
20
+ print("[TVAE] joblib Parallel cap ON, TVAE_CTGAN_JOBTRANS_N_JOBS=" + _nj)
21
+ else:
22
+ print("[TVAE] joblib Parallel cap OFF (unset TVAE_CTGAN_JOBTRANS_N_JOBS)")
23
+ print("[TVAE] LOKY_MAX_CPU_COUNT=" + str(os.environ.get("LOKY_MAX_CPU_COUNT", "")))
24
+
25
+ csv_path = "/work/output-Benchmark-trainonly-v1/n11/tvae/tvae-n11-20260520_173615/staged/public/train.csv"
26
+ meta_path = "/work/output-Benchmark-trainonly-v1/n11/tvae/tvae-n11-20260520_173615/tvae_metadata.json"
27
+ save_path = "/work/output-Benchmark-trainonly-v1/n11/tvae/tvae-n11-20260520_173615/models_400epochs/tvae_400epochs.pt"
28
+ epochs = int(os.environ.get("TVAE_EPOCHS", 400))
29
+ batch_size = int(os.environ.get("TVAE_BATCH_SIZE", "500"))
30
+ embedding_dim = int(os.environ.get("TVAE_EMBEDDING_DIM", "128"))
31
+ l2scale = float(os.environ.get("TVAE_L2SCALE", "1e-5"))
32
+ loss_factor = float(os.environ.get("TVAE_LOSS_FACTOR", "2"))
33
+ def _parse_dims(name, default):
34
+ raw = (os.environ.get(name) or "").strip()
35
+ if not raw:
36
+ return default
37
+ return tuple(int(x.strip()) for x in raw.split(",") if x.strip())
38
+ compress_dims = _parse_dims("TVAE_COMPRESS_DIMS", (128, 128))
39
+ decompress_dims = _parse_dims("TVAE_DECOMPRESS_DIMS", (128, 128))
40
+
41
+ def _fit_cpu_staged(self, train_data, discrete_columns=()):
42
+ self.transformer = DataTransformer()
43
+ self.transformer.fit(train_data, discrete_columns)
44
+ train_data = self.transformer.transform(train_data)
45
+ pin_memory = getattr(self._device, "type", str(self._device)) == "cuda"
46
+ dataset = TensorDataset(torch.from_numpy(train_data.astype("float32")))
47
+ loader = DataLoader(
48
+ dataset,
49
+ batch_size=self.batch_size,
50
+ shuffle=True,
51
+ drop_last=False,
52
+ pin_memory=pin_memory,
53
+ )
54
+
55
+ data_dim = self.transformer.output_dimensions
56
+ encoder = Encoder(data_dim, self.compress_dims, self.embedding_dim).to(self._device)
57
+ self.decoder = Decoder(self.embedding_dim, self.decompress_dims, data_dim).to(self._device)
58
+ optimizerAE = Adam(
59
+ list(encoder.parameters()) + list(self.decoder.parameters()),
60
+ weight_decay=self.l2scale,
61
+ )
62
+
63
+ self.loss_values = pd.DataFrame(columns=["Epoch", "Batch", "Loss"])
64
+ iterator = tqdm(range(self.epochs), disable=(not self.verbose))
65
+ if self.verbose:
66
+ iterator_description = "Loss: {loss:.3f}"
67
+ iterator.set_description(iterator_description.format(loss=0))
68
+
69
+ for i in iterator:
70
+ loss_values = []
71
+ batch = []
72
+ for id_, data in enumerate(loader):
73
+ optimizerAE.zero_grad()
74
+ real = data[0].to(self._device, non_blocking=pin_memory)
75
+ mu, std, logvar = encoder(real)
76
+ eps = torch.randn_like(std)
77
+ emb = eps * std + mu
78
+ rec, sigmas = self.decoder(emb)
79
+ loss_1, loss_2 = _loss_function(
80
+ rec,
81
+ real,
82
+ sigmas,
83
+ mu,
84
+ logvar,
85
+ self.transformer.output_info_list,
86
+ self.loss_factor,
87
+ )
88
+ loss = loss_1 + loss_2
89
+ loss.backward()
90
+ optimizerAE.step()
91
+ self.decoder.sigma.data.clamp_(0.01, 1.0)
92
+
93
+ batch.append(id_)
94
+ loss_values.append(loss.detach().cpu().item())
95
+
96
+ epoch_loss_df = pd.DataFrame({
97
+ "Epoch": [i] * len(batch),
98
+ "Batch": batch,
99
+ "Loss": loss_values,
100
+ })
101
+ if not self.loss_values.empty:
102
+ self.loss_values = pd.concat(
103
+ [self.loss_values, epoch_loss_df]
104
+ ).reset_index(drop=True)
105
+ else:
106
+ self.loss_values = epoch_loss_df
107
+
108
+ if self.verbose:
109
+ iterator.set_description(
110
+ iterator_description.format(loss=loss.detach().cpu().item())
111
+ )
112
+
113
+ data, discrete_columns = read_csv(csv_path, meta_path, header=True, discrete=None)
114
+ print(f"[TVAE] Training on {len(data)} rows, {len(data.columns)} cols, epochs={epochs}, batch_size={batch_size}, embedding_dim={embedding_dim}, compress_dims={compress_dims}, decompress_dims={decompress_dims}, l2scale={l2scale}, loss_factor={loss_factor}")
115
+ _orig_fit = TVAE.fit
116
+ TVAE.fit = _fit_cpu_staged
117
+ try:
118
+ model = TVAE(
119
+ epochs=epochs,
120
+ batch_size=batch_size,
121
+ embedding_dim=embedding_dim,
122
+ compress_dims=compress_dims,
123
+ decompress_dims=decompress_dims,
124
+ l2scale=l2scale,
125
+ loss_factor=loss_factor,
126
+ )
127
+ model.fit(data, discrete_columns)
128
+ finally:
129
+ TVAE.fit = _orig_fit
130
+ model.save(save_path)
131
+ print(f"[TVAE] Model saved -> {save_path}")
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SynthData0523/hyper_parameter_tuning/n11/tvae/runs/tvae-n11-20260520_174051/_tvae_generate.py ADDED
@@ -0,0 +1,27 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import os, sys
2
+ sys.path.insert(0, "/work")
3
+ from src.SpecificModels.ctgan_joblib_parallel_cap import apply_parallel_cap_from_env
4
+ apply_parallel_cap_from_env()
5
+ from src.SpecificModels.ctgan_rdt_inverse_fix import apply_ctgan_inverse_fix
6
+ apply_ctgan_inverse_fix()
7
+ import pandas as pd
8
+ from ctgan.synthesizers.tvae import TVAE
9
+ def _fit_cpu_staged_compat(self, *args, **kwargs):
10
+ raise RuntimeError("_fit_cpu_staged_compat should never be called during generate-only load")
11
+ if not hasattr(TVAE, "_fit_cpu_staged"):
12
+ TVAE._fit_cpu_staged = _fit_cpu_staged_compat
13
+ os.environ.setdefault("LOKY_MAX_CPU_COUNT", "8")
14
+ os.environ.setdefault("OPENBLAS_NUM_THREADS", "8")
15
+ os.environ.setdefault("MKL_NUM_THREADS", "8")
16
+ model = TVAE.load("/work/output-Benchmark-trainonly-v1/n11/tvae/tvae-n11-20260520_174051/models_125epochs/tvae_125epochs.pt")
17
+ total = 15215
18
+ chunk = min(50000, total) if total > 50000 else total
19
+ parts = []
20
+ left = total
21
+ while left > 0:
22
+ take = min(chunk, left)
23
+ parts.append(model.sample(take))
24
+ left -= take
25
+ samples = pd.concat(parts, ignore_index=True) if len(parts) > 1 else parts[0]
26
+ samples.to_csv("/work/output-Benchmark-trainonly-v1/n11/tvae/tvae-n11-20260520_174051/tvae-n11-15215-20260520_180159.csv", index=False)
27
+ print(f"[TVAE] Generated {total} rows (chunks={len(parts)}) -> /work/output-Benchmark-trainonly-v1/n11/tvae/tvae-n11-20260520_174051/tvae-n11-15215-20260520_180159.csv")
SynthData0523/hyper_parameter_tuning/n11/tvae/runs/tvae-n11-20260520_174051/_tvae_train.py ADDED
@@ -0,0 +1,131 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import json, os, sys
2
+ sys.path.insert(0, "/work")
3
+ from src.SpecificModels.ctgan_joblib_parallel_cap import apply_parallel_cap_from_env
4
+ apply_parallel_cap_from_env()
5
+ import pandas as pd
6
+ import torch
7
+ from torch.optim import Adam
8
+ from torch.utils.data import DataLoader, TensorDataset
9
+ from tqdm import tqdm
10
+ from ctgan.data import read_csv
11
+ from ctgan.data_transformer import DataTransformer
12
+ from ctgan.synthesizers.tvae import TVAE, Encoder, Decoder, _loss_function
13
+
14
+ # Keep transform stage parallelism bounded for stability on shared host.
15
+ os.environ.setdefault("LOKY_MAX_CPU_COUNT", "8")
16
+ os.environ.setdefault("OPENBLAS_NUM_THREADS", "8")
17
+ os.environ.setdefault("MKL_NUM_THREADS", "8")
18
+ _nj = (os.environ.get("TVAE_CTGAN_JOBTRANS_N_JOBS") or "").strip()
19
+ if _nj:
20
+ print("[TVAE] joblib Parallel cap ON, TVAE_CTGAN_JOBTRANS_N_JOBS=" + _nj)
21
+ else:
22
+ print("[TVAE] joblib Parallel cap OFF (unset TVAE_CTGAN_JOBTRANS_N_JOBS)")
23
+ print("[TVAE] LOKY_MAX_CPU_COUNT=" + str(os.environ.get("LOKY_MAX_CPU_COUNT", "")))
24
+
25
+ csv_path = "/work/output-Benchmark-trainonly-v1/n11/tvae/tvae-n11-20260520_174051/staged/public/train.csv"
26
+ meta_path = "/work/output-Benchmark-trainonly-v1/n11/tvae/tvae-n11-20260520_174051/tvae_metadata.json"
27
+ save_path = "/work/output-Benchmark-trainonly-v1/n11/tvae/tvae-n11-20260520_174051/models_125epochs/tvae_125epochs.pt"
28
+ epochs = int(os.environ.get("TVAE_EPOCHS", 125))
29
+ batch_size = int(os.environ.get("TVAE_BATCH_SIZE", "500"))
30
+ embedding_dim = int(os.environ.get("TVAE_EMBEDDING_DIM", "128"))
31
+ l2scale = float(os.environ.get("TVAE_L2SCALE", "1e-5"))
32
+ loss_factor = float(os.environ.get("TVAE_LOSS_FACTOR", "2"))
33
+ def _parse_dims(name, default):
34
+ raw = (os.environ.get(name) or "").strip()
35
+ if not raw:
36
+ return default
37
+ return tuple(int(x.strip()) for x in raw.split(",") if x.strip())
38
+ compress_dims = _parse_dims("TVAE_COMPRESS_DIMS", (128, 128))
39
+ decompress_dims = _parse_dims("TVAE_DECOMPRESS_DIMS", (128, 128))
40
+
41
+ def _fit_cpu_staged(self, train_data, discrete_columns=()):
42
+ self.transformer = DataTransformer()
43
+ self.transformer.fit(train_data, discrete_columns)
44
+ train_data = self.transformer.transform(train_data)
45
+ pin_memory = getattr(self._device, "type", str(self._device)) == "cuda"
46
+ dataset = TensorDataset(torch.from_numpy(train_data.astype("float32")))
47
+ loader = DataLoader(
48
+ dataset,
49
+ batch_size=self.batch_size,
50
+ shuffle=True,
51
+ drop_last=False,
52
+ pin_memory=pin_memory,
53
+ )
54
+
55
+ data_dim = self.transformer.output_dimensions
56
+ encoder = Encoder(data_dim, self.compress_dims, self.embedding_dim).to(self._device)
57
+ self.decoder = Decoder(self.embedding_dim, self.decompress_dims, data_dim).to(self._device)
58
+ optimizerAE = Adam(
59
+ list(encoder.parameters()) + list(self.decoder.parameters()),
60
+ weight_decay=self.l2scale,
61
+ )
62
+
63
+ self.loss_values = pd.DataFrame(columns=["Epoch", "Batch", "Loss"])
64
+ iterator = tqdm(range(self.epochs), disable=(not self.verbose))
65
+ if self.verbose:
66
+ iterator_description = "Loss: {loss:.3f}"
67
+ iterator.set_description(iterator_description.format(loss=0))
68
+
69
+ for i in iterator:
70
+ loss_values = []
71
+ batch = []
72
+ for id_, data in enumerate(loader):
73
+ optimizerAE.zero_grad()
74
+ real = data[0].to(self._device, non_blocking=pin_memory)
75
+ mu, std, logvar = encoder(real)
76
+ eps = torch.randn_like(std)
77
+ emb = eps * std + mu
78
+ rec, sigmas = self.decoder(emb)
79
+ loss_1, loss_2 = _loss_function(
80
+ rec,
81
+ real,
82
+ sigmas,
83
+ mu,
84
+ logvar,
85
+ self.transformer.output_info_list,
86
+ self.loss_factor,
87
+ )
88
+ loss = loss_1 + loss_2
89
+ loss.backward()
90
+ optimizerAE.step()
91
+ self.decoder.sigma.data.clamp_(0.01, 1.0)
92
+
93
+ batch.append(id_)
94
+ loss_values.append(loss.detach().cpu().item())
95
+
96
+ epoch_loss_df = pd.DataFrame({
97
+ "Epoch": [i] * len(batch),
98
+ "Batch": batch,
99
+ "Loss": loss_values,
100
+ })
101
+ if not self.loss_values.empty:
102
+ self.loss_values = pd.concat(
103
+ [self.loss_values, epoch_loss_df]
104
+ ).reset_index(drop=True)
105
+ else:
106
+ self.loss_values = epoch_loss_df
107
+
108
+ if self.verbose:
109
+ iterator.set_description(
110
+ iterator_description.format(loss=loss.detach().cpu().item())
111
+ )
112
+
113
+ data, discrete_columns = read_csv(csv_path, meta_path, header=True, discrete=None)
114
+ print(f"[TVAE] Training on {len(data)} rows, {len(data.columns)} cols, epochs={epochs}, batch_size={batch_size}, embedding_dim={embedding_dim}, compress_dims={compress_dims}, decompress_dims={decompress_dims}, l2scale={l2scale}, loss_factor={loss_factor}")
115
+ _orig_fit = TVAE.fit
116
+ TVAE.fit = _fit_cpu_staged
117
+ try:
118
+ model = TVAE(
119
+ epochs=epochs,
120
+ batch_size=batch_size,
121
+ embedding_dim=embedding_dim,
122
+ compress_dims=compress_dims,
123
+ decompress_dims=decompress_dims,
124
+ l2scale=l2scale,
125
+ loss_factor=loss_factor,
126
+ )
127
+ model.fit(data, discrete_columns)
128
+ finally:
129
+ TVAE.fit = _orig_fit
130
+ model.save(save_path)
131
+ print(f"[TVAE] Model saved -> {save_path}")
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