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b/SynthData0523/main/m8/tabbyflow/tabbyflow-m8-20260501_012923/_tabbyflow_gen.py @@ -0,0 +1,33 @@ + +import os, shutil, subprocess, sys +root = r"/workspace/ef-vfm" +name = r"pipeline_m8" +src = r"/work/output-Benchmark-trainonly-v1/m8/tabbyflow/tabbyflow-m8-20260501_012923/tabular_bundle/pipeline_m8" +dst_data = os.path.join(root, "data", name) +shutil.rmtree(dst_data, ignore_errors=True) +shutil.copytree(src, dst_data) +dst_syn = os.path.join(root, "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(root) +os.environ["PYTHONPATH"] = root + os.pathsep + os.environ.get("PYTHONPATH", "") +subprocess.check_call([ + sys.executable, "main.py", + "--dataname", name, "--mode", "test", "--gpu", "0", + "--no_wandb", "--exp_name", r"adapter_efvfm", + "--ckpt_path", r"/workspace/ef-vfm/ef_vfm/ckpt/pipeline_m8/adapter_efvfm/model_500.pt", + "--num_samples_to_generate", str(int(36168)), +]) +base = os.path.join(root, "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/m8/tabbyflow/tabbyflow-m8-20260501_012923/tabbyflow-m8-36168-20260501_014108.csv") diff --git a/SynthData0523/main/m8/tabbyflow/tabbyflow-m8-20260501_012923/_tabbyflow_train.py b/SynthData0523/main/m8/tabbyflow/tabbyflow-m8-20260501_012923/_tabbyflow_train.py new file mode 100644 index 0000000000000000000000000000000000000000..53dd60fba1a50953b40ceef15d6023e266f8dc3f --- /dev/null +++ b/SynthData0523/main/m8/tabbyflow/tabbyflow-m8-20260501_012923/_tabbyflow_train.py @@ -0,0 +1,22 @@ + +import os, shutil, subprocess, sys +root = r"/workspace/ef-vfm" +name = r"pipeline_m8" +src = r"/work/output-Benchmark-trainonly-v1/m8/tabbyflow/tabbyflow-m8-20260501_012923/tabular_bundle/pipeline_m8" +os.makedirs(os.path.join(root, "data", name), exist_ok=True) +dst_data = os.path.join(root, "data", name) +dst_syn = os.path.join(root, "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(root) +os.environ["PYTHONPATH"] = root + os.pathsep + os.environ.get("PYTHONPATH", "") +os.environ["EFVFM_SMOKE_STEPS"] = "500" +os.environ["EFVFM_ADAPTER_TRAIN"] = "1" +subprocess.check_call([ + sys.executable, "main.py", + "--dataname", name, "--mode", "train", "--gpu", "0", + "--no_wandb", "--exp_name", r"adapter_efvfm", +]) diff --git a/SynthData0523/main/m8/tabbyflow/tabbyflow-m8-20260501_012923/gen_20260501_014108.log 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sha256:5ec8d3e122ce392c7b91f99d55bdb1fd29b29ac29fed015559f9f7317940f608 +size 1062721 diff --git a/SynthData0523/main/m8/tabbyflow/tabbyflow-m8-20260501_034536/_tabbyflow_gen.py b/SynthData0523/main/m8/tabbyflow/tabbyflow-m8-20260501_034536/_tabbyflow_gen.py new file mode 100644 index 0000000000000000000000000000000000000000..b7666c6ae07cf2a6cade6eb1938ca3d36ea06c79 --- /dev/null +++ b/SynthData0523/main/m8/tabbyflow/tabbyflow-m8-20260501_034536/_tabbyflow_gen.py @@ -0,0 +1,33 @@ + +import os, shutil, subprocess, sys +root = r"/workspace/ef-vfm" +name = r"pipeline_m8" +src = r"/work/output-Benchmark-trainonly-v1/m8/tabbyflow/tabbyflow-m8-20260501_034536/tabular_bundle/pipeline_m8" +dst_data = os.path.join(root, "data", name) +shutil.rmtree(dst_data, ignore_errors=True) +shutil.copytree(src, dst_data) +dst_syn = os.path.join(root, "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(root) +os.environ["PYTHONPATH"] = root + os.pathsep + os.environ.get("PYTHONPATH", "") +subprocess.check_call([ + sys.executable, "main.py", + "--dataname", name, "--mode", "test", "--gpu", "0", + "--no_wandb", "--exp_name", r"adapter_efvfm", + "--ckpt_path", r"/workspace/ef-vfm/ef_vfm/ckpt/pipeline_m8/adapter_efvfm/model_500.pt", + "--num_samples_to_generate", str(int(36168)), +]) +base = os.path.join(root, "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/m8/tabbyflow/tabbyflow-m8-20260501_034536/tabbyflow-m8-36168-20260501_035503.csv") diff --git a/SynthData0523/main/m8/tabbyflow/tabbyflow-m8-20260501_034536/_tabbyflow_train.py b/SynthData0523/main/m8/tabbyflow/tabbyflow-m8-20260501_034536/_tabbyflow_train.py new file mode 100644 index 0000000000000000000000000000000000000000..3433fc95ca11c236c7b7cf5d6ccaed92f627c183 --- /dev/null +++ b/SynthData0523/main/m8/tabbyflow/tabbyflow-m8-20260501_034536/_tabbyflow_train.py @@ -0,0 +1,22 @@ + +import os, shutil, subprocess, sys +root = r"/workspace/ef-vfm" +name = r"pipeline_m8" +src = r"/work/output-Benchmark-trainonly-v1/m8/tabbyflow/tabbyflow-m8-20260501_034536/tabular_bundle/pipeline_m8" +os.makedirs(os.path.join(root, "data", name), exist_ok=True) +dst_data = os.path.join(root, "data", name) +dst_syn = os.path.join(root, "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(root) +os.environ["PYTHONPATH"] = root + os.pathsep + os.environ.get("PYTHONPATH", "") +os.environ["EFVFM_SMOKE_STEPS"] = "500" +os.environ["EFVFM_ADAPTER_TRAIN"] = "1" +subprocess.check_call([ + sys.executable, "main.py", + "--dataname", name, "--mode", "train", "--gpu", "0", + "--no_wandb", "--exp_name", r"adapter_efvfm", +]) diff --git a/SynthData0523/main/m8/tabbyflow/tabbyflow-m8-20260501_034536/gen_20260501_035503.log b/SynthData0523/main/m8/tabbyflow/tabbyflow-m8-20260501_034536/gen_20260501_035503.log new file mode 100644 index 0000000000000000000000000000000000000000..af88885f17db5995175d5e8e6d5010fe2232ce9e --- /dev/null +++ b/SynthData0523/main/m8/tabbyflow/tabbyflow-m8-20260501_034536/gen_20260501_035503.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e88c176fc55b44d975796711371a0f8d70596d2bfc8fc6d3eb960234f15fb18a +size 3261 diff --git a/SynthData0523/main/m8/tabbyflow/tabbyflow-m8-20260501_034536/input_snapshot.json b/SynthData0523/main/m8/tabbyflow/tabbyflow-m8-20260501_034536/input_snapshot.json new file mode 100644 index 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0000000000000000000000000000000000000000..6de42d9b9d2e98ac13fbeb62391fe185ffdd6210 --- /dev/null +++ b/SynthData0523/main/m8/tabbyflow/tabbyflow-m8-20260501_034536/tabular_bundle/pipeline_m8/y_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:3fe448bbe15f46b1714e7d0ebca94f3df952a9ef5364159dda69ccefcdce02c6 +size 289472 diff --git a/SynthData0523/main/m8/tabbyflow/tabbyflow-m8-20260501_034536/tabular_bundle/pipeline_m8/y_val.npy b/SynthData0523/main/m8/tabbyflow/tabbyflow-m8-20260501_034536/tabular_bundle/pipeline_m8/y_val.npy new file mode 100644 index 0000000000000000000000000000000000000000..6de42d9b9d2e98ac13fbeb62391fe185ffdd6210 --- /dev/null +++ b/SynthData0523/main/m8/tabbyflow/tabbyflow-m8-20260501_034536/tabular_bundle/pipeline_m8/y_val.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:3fe448bbe15f46b1714e7d0ebca94f3df952a9ef5364159dda69ccefcdce02c6 +size 289472 diff --git a/SynthData0523/main/m8/tabbyflow/tabbyflow-m8-20260501_034536/train_20260501_034537.log b/SynthData0523/main/m8/tabbyflow/tabbyflow-m8-20260501_034536/train_20260501_034537.log new file mode 100644 index 0000000000000000000000000000000000000000..1a54e09fb8b828056967a234a466af6801368206 --- /dev/null +++ b/SynthData0523/main/m8/tabbyflow/tabbyflow-m8-20260501_034536/train_20260501_034537.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:91d5fc185b9bcd99cf2c5a0cd9dd917fb0374178061eed8797dcb047400ec818 +size 1005474 diff --git a/SynthData0523/main/m8/tabddpm/tabddpm-m8-20260424_033725/_tabddpm_sample.py b/SynthData0523/main/m8/tabddpm/tabddpm-m8-20260424_033725/_tabddpm_sample.py new file mode 100644 index 0000000000000000000000000000000000000000..144a7a5945e75718addb67c0700c2302b48a97af --- /dev/null +++ b/SynthData0523/main/m8/tabddpm/tabddpm-m8-20260424_033725/_tabddpm_sample.py @@ -0,0 +1,67 @@ +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 36168 rows") +ret = subprocess.run( + [sys.executable, wrapper, "scripts/pipeline.py", + "--config", "/work/output-SpecializedModels/m8/tabddpm/tabddpm-m8-20260424_033725/config_sample_20260424_034117.toml", + "--sample"], + cwd=tabddpm_root, + env=env +) +if ret.returncode != 0: + sys.exit(ret.returncode) + +# 将 .npy 输出转为 CSV +work_dir = "/work/output-SpecializedModels/m8/tabddpm/tabddpm-m8-20260424_033725" +info_path = os.path.join(work_dir, "data", "info.json") +with open(info_path) as f: + info = json.load(f) + +output_dir = os.path.join(work_dir, "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/m8/tabddpm/tabddpm-m8-20260424_033725/tabddpm-m8-36168-20260424_034117.csv", index=False) + print(f"[TabDDPM] Saved {len(df)} rows -> /work/output-SpecializedModels/m8/tabddpm/tabddpm-m8-20260424_033725/tabddpm-m8-36168-20260424_034117.csv") +else: + print("[TabDDPM] WARNING: No output .npy files found") + sys.exit(1) diff --git a/SynthData0523/main/m8/tabddpm/tabddpm-m8-20260424_033725/_tabddpm_train.py b/SynthData0523/main/m8/tabddpm/tabddpm-m8-20260424_033725/_tabddpm_train.py new file mode 100644 index 0000000000000000000000000000000000000000..9131c7848f60dc652dca294a62ea7bad304b57cc --- /dev/null +++ b/SynthData0523/main/m8/tabddpm/tabddpm-m8-20260424_033725/_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/m8/tabddpm/tabddpm-m8-20260424_033725/config.toml") +ret = subprocess.run( + [sys.executable, wrapper, "scripts/pipeline.py", + "--config", "/work/output-SpecializedModels/m8/tabddpm/tabddpm-m8-20260424_033725/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/m8/tabddpm/tabddpm-m8-20260424_033725/config.toml b/SynthData0523/main/m8/tabddpm/tabddpm-m8-20260424_033725/config.toml new file mode 100644 index 0000000000000000000000000000000000000000..6b9575c823850c58813ecf191ea9fee0225065de --- /dev/null +++ b/SynthData0523/main/m8/tabddpm/tabddpm-m8-20260424_033725/config.toml @@ -0,0 +1,39 @@ +seed = 0 +parent_dir = "/work/output-SpecializedModels/m8/tabddpm/tabddpm-m8-20260424_033725/output" +real_data_path = "/work/output-SpecializedModels/m8/tabddpm/tabddpm-m8-20260424_033725/data" +model_type = "mlp" +num_numerical_features = 7 +device = "cuda:0" + +[model_params] +d_in = 16 +num_classes = 2 +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/m8/tabddpm/tabddpm-m8-20260424_033725/config_sample_20260424_034117.toml b/SynthData0523/main/m8/tabddpm/tabddpm-m8-20260424_033725/config_sample_20260424_034117.toml new file mode 100644 index 0000000000000000000000000000000000000000..81fd820c811d6999d0e2afd852fe6cb3208970b9 --- /dev/null +++ b/SynthData0523/main/m8/tabddpm/tabddpm-m8-20260424_033725/config_sample_20260424_034117.toml @@ -0,0 +1,39 @@ +seed = 0 +parent_dir = "/work/output-SpecializedModels/m8/tabddpm/tabddpm-m8-20260424_033725/output" +real_data_path = "/work/output-SpecializedModels/m8/tabddpm/tabddpm-m8-20260424_033725/data" +model_type = "mlp" +num_numerical_features = 7 +device = "cuda:0" + +[model_params] +d_in = 16 +num_classes = 2 +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 = 36168 +batch_size = 1000 +seed = 0 diff --git a/SynthData0523/main/m8/tabddpm/tabddpm-m8-20260424_033725/data/X_cat_train.npy b/SynthData0523/main/m8/tabddpm/tabddpm-m8-20260424_033725/data/X_cat_train.npy new file mode 100644 index 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0000000000000000000000000000000000000000..1bd717269bf6985a22ab81027f21e9239655f40a --- /dev/null +++ b/SynthData0523/main/m8/tabddpm/tabddpm-m8-20260501_000003/_tabddpm_sample_r0.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 36168 rows") +ret = subprocess.run( + [sys.executable, wrapper, "scripts/pipeline.py", + "--config", "/work/output-Benchmark-trainonly-v1/m8/tabddpm/tabddpm-m8-20260501_000003/config_sample_20260501_000111_r0.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-Benchmark-trainonly-v1/m8/tabddpm/tabddpm-m8-20260501_000003/data/info.json" +with open(info_path) as f: + info = json.load(f) + +output_dir = "/work/output-Benchmark-trainonly-v1/m8/tabddpm/tabddpm-m8-20260501_000003/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-Benchmark-trainonly-v1/m8/tabddpm/tabddpm-m8-20260501_000003/tabddpm-m8-36168-20260501_000111.csv", index=False) + print(f"[TabDDPM] Saved {len(df)} rows -> /work/output-Benchmark-trainonly-v1/m8/tabddpm/tabddpm-m8-20260501_000003/tabddpm-m8-36168-20260501_000111.csv") +else: + print("[TabDDPM] WARNING: No output .npy files found") + sys.exit(1) diff --git a/SynthData0523/main/m8/tabddpm/tabddpm-m8-20260501_000003/_tabddpm_train.py b/SynthData0523/main/m8/tabddpm/tabddpm-m8-20260501_000003/_tabddpm_train.py new file mode 100644 index 0000000000000000000000000000000000000000..5b98669dc3848f2c9d39f3862ac6d02a94a19806 --- /dev/null +++ b/SynthData0523/main/m8/tabddpm/tabddpm-m8-20260501_000003/_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-Benchmark-trainonly-v1/m8/tabddpm/tabddpm-m8-20260501_000003/config.toml") +ret = subprocess.run( + [sys.executable, wrapper, "scripts/pipeline.py", + "--config", "/work/output-Benchmark-trainonly-v1/m8/tabddpm/tabddpm-m8-20260501_000003/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/m8/tabddpm/tabddpm-m8-20260501_000003/config.toml b/SynthData0523/main/m8/tabddpm/tabddpm-m8-20260501_000003/config.toml new file mode 100644 index 0000000000000000000000000000000000000000..bc9603ea00334ef5c1ab75f954f5a517a8b2ffd3 --- /dev/null +++ b/SynthData0523/main/m8/tabddpm/tabddpm-m8-20260501_000003/config.toml @@ 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b/SynthData0523/main/m8/tabddpm/tabddpm-m8-20260501_000003/train_20260501_000003.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:080263f158d37c7293f67d5dd43b99e65af6da72a35fb06f5ea1c65d20554b2d +size 1157 diff --git a/SynthData0523/main/m8/tabddpm/tabddpm-m8-20260501_035531/_tabddpm_sample_r0.py b/SynthData0523/main/m8/tabddpm/tabddpm-m8-20260501_035531/_tabddpm_sample_r0.py new file mode 100644 index 0000000000000000000000000000000000000000..4c0174996b0c9b9cca57dc568b6502824dd0d4bb --- /dev/null +++ b/SynthData0523/main/m8/tabddpm/tabddpm-m8-20260501_035531/_tabddpm_sample_r0.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 36168 rows") +ret = subprocess.run( + [sys.executable, wrapper, "scripts/pipeline.py", + "--config", "/work/output-Benchmark-trainonly-v1/m8/tabddpm/tabddpm-m8-20260501_035531/config_sample_20260501_035632_r0.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-Benchmark-trainonly-v1/m8/tabddpm/tabddpm-m8-20260501_035531/data/info.json" +with open(info_path) as f: + info = json.load(f) + +output_dir = "/work/output-Benchmark-trainonly-v1/m8/tabddpm/tabddpm-m8-20260501_035531/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-Benchmark-trainonly-v1/m8/tabddpm/tabddpm-m8-20260501_035531/tabddpm-m8-36168-20260501_035632.csv", index=False) + print(f"[TabDDPM] Saved {len(df)} rows -> /work/output-Benchmark-trainonly-v1/m8/tabddpm/tabddpm-m8-20260501_035531/tabddpm-m8-36168-20260501_035632.csv") +else: + print("[TabDDPM] WARNING: No output .npy files found") + sys.exit(1) diff --git a/SynthData0523/main/m8/tabddpm/tabddpm-m8-20260501_035531/_tabddpm_train.py b/SynthData0523/main/m8/tabddpm/tabddpm-m8-20260501_035531/_tabddpm_train.py new file mode 100644 index 0000000000000000000000000000000000000000..c2a062abf0c95d17cc50cffc19e3299faaa292db --- /dev/null +++ b/SynthData0523/main/m8/tabddpm/tabddpm-m8-20260501_035531/_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-Benchmark-trainonly-v1/m8/tabddpm/tabddpm-m8-20260501_035531/config.toml") +ret = subprocess.run( + [sys.executable, wrapper, "scripts/pipeline.py", + "--config", "/work/output-Benchmark-trainonly-v1/m8/tabddpm/tabddpm-m8-20260501_035531/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/m8/tabddpm/tabddpm-m8-20260501_035531/config.toml b/SynthData0523/main/m8/tabddpm/tabddpm-m8-20260501_035531/config.toml new file mode 100644 index 0000000000000000000000000000000000000000..1853ac2eba7f19facfc03f54ba9c07429912953d --- /dev/null +++ b/SynthData0523/main/m8/tabddpm/tabddpm-m8-20260501_035531/config.toml @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:8981731079f47e2fc6db6b05dffaf0c2318cfe44e06f60940ad49c1ab9affb4e +size 769 diff --git a/SynthData0523/main/m8/tabddpm/tabddpm-m8-20260501_035531/config_sample_20260501_035632_r0.toml b/SynthData0523/main/m8/tabddpm/tabddpm-m8-20260501_035531/config_sample_20260501_035632_r0.toml new file mode 100644 index 0000000000000000000000000000000000000000..17b9c5d78fb9614aae4d7216f12cef34a663ab33 --- /dev/null +++ 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+1,36 @@ + +import os, shutil, subprocess, sys +td = r"/workspace/TabDiff" +name = r"pipeline_m8" +src = r"/work/output-Benchmark-trainonly-v1/m8/tabdiff/tabdiff-m8-20260501_010036/tabular_bundle/pipeline_m8" +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_m8/adapter_learnable/model_500.pt", + "--num_samples_to_generate", str(int(36168)), +]) +# 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/m8/tabdiff/tabdiff-m8-20260501_010036/tabdiff-m8-36168-20260501_011423.csv") diff --git a/SynthData0523/main/m8/tabdiff/tabdiff-m8-20260501_010036/_tabdiff_train.py b/SynthData0523/main/m8/tabdiff/tabdiff-m8-20260501_010036/_tabdiff_train.py new file mode 100644 index 0000000000000000000000000000000000000000..508e8e7e8b3bd48863c0b417c32ffe35a3748e93 --- /dev/null +++ b/SynthData0523/main/m8/tabdiff/tabdiff-m8-20260501_010036/_tabdiff_train.py @@ -0,0 +1,21 @@ + +import os, shutil, subprocess, sys +td = r"/workspace/TabDiff" +name = r"pipeline_m8" +src = r"/work/output-Benchmark-trainonly-v1/m8/tabdiff/tabdiff-m8-20260501_010036/tabular_bundle/pipeline_m8" +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 a/SynthData0523/main/m8/tabdiff/tabdiff-m8-20260501_010036/gen_20260501_011423.log b/SynthData0523/main/m8/tabdiff/tabdiff-m8-20260501_010036/gen_20260501_011423.log new file mode 100644 index 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b/SynthData0523/main/m8/tabdiff/tabdiff-m8-20260501_010036/train_20260501_010037.log new file mode 100644 index 0000000000000000000000000000000000000000..03480d906e8cfe17e1bf5af5e8641eaa208eca4e --- /dev/null +++ b/SynthData0523/main/m8/tabdiff/tabdiff-m8-20260501_010036/train_20260501_010037.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:6725e0d8a189e38fcae9a0944c90d93381668aac8a507352115ce526fff6a314 +size 1094773 diff --git a/SynthData0523/main/m8/tabdiff/tabdiff-m8-20260501_035904/_tabdiff_gen.py b/SynthData0523/main/m8/tabdiff/tabdiff-m8-20260501_035904/_tabdiff_gen.py new file mode 100644 index 0000000000000000000000000000000000000000..03ae207e8fe16b4dea0c42509f67b0e1f9d1c89d --- /dev/null +++ b/SynthData0523/main/m8/tabdiff/tabdiff-m8-20260501_035904/_tabdiff_gen.py @@ -0,0 +1,36 @@ + +import os, shutil, subprocess, sys +td = r"/workspace/TabDiff" +name = r"pipeline_m8" +src = r"/work/output-Benchmark-trainonly-v1/m8/tabdiff/tabdiff-m8-20260501_035904/tabular_bundle/pipeline_m8" +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_m8/adapter_learnable/model_500.pt", + "--num_samples_to_generate", str(int(36168)), +]) +# 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/m8/tabdiff/tabdiff-m8-20260501_035904/tabdiff-m8-36168-20260501_040953.csv") diff --git a/SynthData0523/main/m8/tabdiff/tabdiff-m8-20260501_035904/_tabdiff_train.py b/SynthData0523/main/m8/tabdiff/tabdiff-m8-20260501_035904/_tabdiff_train.py new file mode 100644 index 0000000000000000000000000000000000000000..ed0bf514e6bb98c8a5c9b95c11bba4e13150eb22 --- /dev/null +++ b/SynthData0523/main/m8/tabdiff/tabdiff-m8-20260501_035904/_tabdiff_train.py @@ -0,0 +1,21 @@ + +import os, shutil, subprocess, sys +td = r"/workspace/TabDiff" +name = r"pipeline_m8" +src = r"/work/output-Benchmark-trainonly-v1/m8/tabdiff/tabdiff-m8-20260501_035904/tabular_bundle/pipeline_m8" +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 a/SynthData0523/main/m8/tabdiff/tabdiff-m8-20260501_035904/gen_20260501_040953.log b/SynthData0523/main/m8/tabdiff/tabdiff-m8-20260501_035904/gen_20260501_040953.log new file mode 100644 index 0000000000000000000000000000000000000000..d11024e9b42a2de1e6318684aca20f5c09e02700 --- /dev/null +++ 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sha256:3fe448bbe15f46b1714e7d0ebca94f3df952a9ef5364159dda69ccefcdce02c6 +size 289472 diff --git a/SynthData0523/main/m8/tabdiff/tabdiff-m8-20260501_035904/tabular_bundle/pipeline_m8/y_val.npy b/SynthData0523/main/m8/tabdiff/tabdiff-m8-20260501_035904/tabular_bundle/pipeline_m8/y_val.npy new file mode 100644 index 0000000000000000000000000000000000000000..6de42d9b9d2e98ac13fbeb62391fe185ffdd6210 --- /dev/null +++ b/SynthData0523/main/m8/tabdiff/tabdiff-m8-20260501_035904/tabular_bundle/pipeline_m8/y_val.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:3fe448bbe15f46b1714e7d0ebca94f3df952a9ef5364159dda69ccefcdce02c6 +size 289472 diff --git a/SynthData0523/main/m8/tabdiff/tabdiff-m8-20260501_035904/train_20260501_035905.log b/SynthData0523/main/m8/tabdiff/tabdiff-m8-20260501_035904/train_20260501_035905.log new file mode 100644 index 0000000000000000000000000000000000000000..0cb7247bfb789e3deef6aab7198b1770d6b42cfc --- /dev/null +++ b/SynthData0523/main/m8/tabdiff/tabdiff-m8-20260501_035904/train_20260501_035905.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:bc2d3ec932d73ac9b7bf6f6c42dddda146dc6291ab972c1ef9c1d9904b3c0d25 +size 1075205 diff --git a/SynthData0523/main/m8/tabpfgen/tabpfgen-m8-20260501_012611/_tabpfgen_generate.py b/SynthData0523/main/m8/tabpfgen/tabpfgen-m8-20260501_012611/_tabpfgen_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..dff1a68b0f83f3aba8492758878992e74e83ef9d --- /dev/null +++ b/SynthData0523/main/m8/tabpfgen/tabpfgen-m8-20260501_012611/_tabpfgen_generate.py @@ -0,0 +1,100 @@ +import os +import numpy as np +import pandas as pd +import json +from tabpfgen import TabPFGen + +df = pd.read_csv("/work/output-Benchmark-trainonly-v1/m8/tabpfgen/tabpfgen-m8-20260501_012611/staged/public/train.csv") +target_col = "y" + +target_missing = df[target_col].isna() +if target_missing.any(): + dropped = int(target_missing.sum()) + df = df.loc[~target_missing].copy() + print( + f"[TabPFGen] Dropped {dropped} rows with missing target '{target_col}'" + ) +if df.empty: + raise ValueError( + f"[TabPFGen] No rows remain after dropping missing target '{target_col}'" + ) + +feature_cols = [c for c in df.columns if c != target_col] + +cat_encodings = {} +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)") + +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 +target_n = int(36168) + +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 + +# TabPFGen v0.1.x API：仅支持 n_sgld_steps / sgld_* / device。 +# （旧版脚本中的 energy_*_chunk 与上游 TabPFGen 不一致，会导致 TypeError。） +gen = TabPFGen( + n_sgld_steps=1000, + sgld_step_size=0.01, + sgld_noise_scale=0.01, + device="auto", +) + +print(f"[TabPFGen] Generating {target_n} rows via generate_classification") +X_syn, y_syn = gen.generate_classification(X, y, n_samples=target_n) + +syn_df = pd.DataFrame(X_syn, columns=feature_cols) +syn_df[target_col] = y_syn + +for col, cats in cat_encodings.items(): + 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] + +if len(syn_df) > target_n: + print(f"[TabPFGen] Trimming rows: {len(syn_df)} -> {target_n}") + syn_df = syn_df.iloc[:target_n].copy() +elif len(syn_df) < target_n: + deficit = target_n - len(syn_df) + print(f"[TabPFGen] Padding rows: {len(syn_df)} -> {target_n} (deficit={deficit})") + if len(syn_df) > 0: + extra = syn_df.sample(n=deficit, replace=True, random_state=42) + syn_df = pd.concat( + [syn_df.reset_index(drop=True), extra.reset_index(drop=True)], + ignore_index=True, + ) + else: + syn_df = df[feature_cols + [target_col]].sample( + n=target_n, replace=True, random_state=42 + ).reset_index(drop=True) + +syn_df = syn_df[list(df.columns)] +if len(syn_df) != target_n: + raise RuntimeError( + f"[TabPFGen] Row alignment failed: got {len(syn_df)}, expected {target_n}" + ) +syn_df.to_csv("/work/output-Benchmark-trainonly-v1/m8/tabpfgen/tabpfgen-m8-20260501_012611/tabpfgen-m8-36168-20260501_012611.csv", index=False) +print(f"[TabPFGen] Saved {len(syn_df)} rows -> /work/output-Benchmark-trainonly-v1/m8/tabpfgen/tabpfgen-m8-20260501_012611/tabpfgen-m8-36168-20260501_012611.csv") diff --git a/SynthData0523/main/m8/tabpfgen/tabpfgen-m8-20260501_012611/gen_20260501_012611.log b/SynthData0523/main/m8/tabpfgen/tabpfgen-m8-20260501_012611/gen_20260501_012611.log new file mode 100644 index 0000000000000000000000000000000000000000..d8ca6ccd4e81aec53ac27272777e22fe6465dd6a --- /dev/null +++ b/SynthData0523/main/m8/tabpfgen/tabpfgen-m8-20260501_012611/gen_20260501_012611.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:42f3e87ada5a412f0cf341c625689d022a95d40db325a9ce4d047ad17a770897 +size 1400 diff --git a/SynthData0523/main/m8/tabpfgen/tabpfgen-m8-20260501_012611/input_snapshot.json 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442 diff --git a/SynthData0523/main/m8/tabpfgen/tabpfgen-m8-20260501_012611/train_20260501_012611.log b/SynthData0523/main/m8/tabpfgen/tabpfgen-m8-20260501_012611/train_20260501_012611.log new file mode 100644 index 0000000000000000000000000000000000000000..88537154202a98ca178f9870e6e4d428164dc669 --- /dev/null +++ b/SynthData0523/main/m8/tabpfgen/tabpfgen-m8-20260501_012611/train_20260501_012611.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b5927a1d9e97d405c0bf308d01bca24b77253fa8c84043d31cd0ca2d7c624af5 +size 597 diff --git a/SynthData0523/main/m8/tabpfgen/tabpfgen-m8-20260501_041014/_tabpfgen_generate.py b/SynthData0523/main/m8/tabpfgen/tabpfgen-m8-20260501_041014/_tabpfgen_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..65c854c132d9f6d26fbe156c838c0083a2072849 --- /dev/null +++ b/SynthData0523/main/m8/tabpfgen/tabpfgen-m8-20260501_041014/_tabpfgen_generate.py @@ -0,0 +1,100 @@ +import os +import numpy as np +import pandas as pd +import json +from tabpfgen import TabPFGen + +df = pd.read_csv("/work/output-Benchmark-trainonly-v1/m8/tabpfgen/tabpfgen-m8-20260501_041014/staged/public/train.csv") +target_col = "y" + +target_missing = df[target_col].isna() +if target_missing.any(): + dropped = int(target_missing.sum()) + df = df.loc[~target_missing].copy() + print( + f"[TabPFGen] Dropped {dropped} rows with missing target '{target_col}'" + ) +if df.empty: + raise ValueError( + f"[TabPFGen] No rows remain after dropping missing target '{target_col}'" + ) + +feature_cols = [c for c in df.columns if c != target_col] + +cat_encodings = {} +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)") + +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 +target_n = int(36168) + +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 + +# TabPFGen v0.1.x API：仅支持 n_sgld_steps / sgld_* / device。 +# （旧版脚本中的 energy_*_chunk 与上游 TabPFGen 不一致，会导致 TypeError。） +gen = TabPFGen( + n_sgld_steps=1000, + sgld_step_size=0.01, + sgld_noise_scale=0.01, + device="auto", +) + +print(f"[TabPFGen] Generating {target_n} rows via generate_classification") +X_syn, y_syn = gen.generate_classification(X, y, n_samples=target_n) + +syn_df = pd.DataFrame(X_syn, columns=feature_cols) +syn_df[target_col] = y_syn + +for col, cats in cat_encodings.items(): + 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] + +if len(syn_df) > target_n: + print(f"[TabPFGen] Trimming rows: {len(syn_df)} -> {target_n}") + syn_df = syn_df.iloc[:target_n].copy() +elif len(syn_df) < target_n: + deficit = target_n - len(syn_df) + print(f"[TabPFGen] Padding rows: {len(syn_df)} -> {target_n} (deficit={deficit})") + if len(syn_df) > 0: + extra = syn_df.sample(n=deficit, replace=True, random_state=42) + syn_df = pd.concat( + [syn_df.reset_index(drop=True), extra.reset_index(drop=True)], + ignore_index=True, + ) + else: + syn_df = df[feature_cols + [target_col]].sample( + n=target_n, replace=True, random_state=42 + ).reset_index(drop=True) + +syn_df = syn_df[list(df.columns)] +if len(syn_df) != target_n: + raise RuntimeError( + f"[TabPFGen] Row alignment failed: got {len(syn_df)}, expected {target_n}" + ) +syn_df.to_csv("/work/output-Benchmark-trainonly-v1/m8/tabpfgen/tabpfgen-m8-20260501_041014/tabpfgen-m8-36168-20260501_041015.csv", index=False) +print(f"[TabPFGen] Saved {len(syn_df)} rows -> /work/output-Benchmark-trainonly-v1/m8/tabpfgen/tabpfgen-m8-20260501_041014/tabpfgen-m8-36168-20260501_041015.csv") diff --git a/SynthData0523/main/m8/tabpfgen/tabpfgen-m8-20260501_041014/gen_20260501_041015.log b/SynthData0523/main/m8/tabpfgen/tabpfgen-m8-20260501_041014/gen_20260501_041015.log new file mode 100644 index 0000000000000000000000000000000000000000..e0ff3cc8bc275f735f328411a6a5d9aaa188a00c --- /dev/null +++ b/SynthData0523/main/m8/tabpfgen/tabpfgen-m8-20260501_041014/gen_20260501_041015.log @@ -0,0 +1,3 @@ +version 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+++ b/SynthData0523/main/m8/tabpfgen/tabpfgen-m8-20260501_041014/public_gate/normalized_schema_snapshot.json @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:d733310afeedb79582ccecc72b050f6a9a712817177584d3824924c50e502e38 +size 7627