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a/SynthData0523/main/m4/tabbyflow/tabbyflow-m4-20260505_002825/_tabbyflow_gen.py b/SynthData0523/main/m4/tabbyflow/tabbyflow-m4-20260505_002825/_tabbyflow_gen.py new file mode 100644 index 0000000000000000000000000000000000000000..ba4fa3f8f32d4373e69ee42b105477ed78a7b2d4 --- /dev/null +++ b/SynthData0523/main/m4/tabbyflow/tabbyflow-m4-20260505_002825/_tabbyflow_gen.py @@ -0,0 +1,34 @@ + +import os, shutil, subprocess, sys +root = r"/workspace/ef-vfm" +name = r"pipeline_m4" +src = r"/work/output-Benchmark-trainonly-v1/m4/tabbyflow/tabbyflow-m4-20260505_002825/tabular_bundle/pipeline_m4" +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", "") +os.environ.setdefault("EFVFM_SAMPLE_BATCH_SIZE", "128") +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_m4/adapter_efvfm/model_100.pt", + "--num_samples_to_generate", str(int(2217)), +]) +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/m4/tabbyflow/tabbyflow-m4-20260505_002825/tabbyflow-m4-2217-20260505_003023.csv") diff --git a/SynthData0523/main/m4/tabbyflow/tabbyflow-m4-20260505_002825/_tabbyflow_train.py b/SynthData0523/main/m4/tabbyflow/tabbyflow-m4-20260505_002825/_tabbyflow_train.py new file mode 100644 index 0000000000000000000000000000000000000000..8c74a2194b73d6dfb20f494bb7f7de1d2e616ff6 --- /dev/null +++ b/SynthData0523/main/m4/tabbyflow/tabbyflow-m4-20260505_002825/_tabbyflow_train.py @@ -0,0 +1,24 @@ + +import os, shutil, subprocess, sys +root = r"/workspace/ef-vfm" +name = r"pipeline_m4" +src = r"/work/output-Benchmark-trainonly-v1/m4/tabbyflow/tabbyflow-m4-20260505_002825/tabular_bundle/pipeline_m4" +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"] = "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, "main.py", + "--dataname", name, "--mode", "train", "--gpu", "0", + "--no_wandb", "--exp_name", r"adapter_efvfm", +]) diff --git a/SynthData0523/main/m4/tabbyflow/tabbyflow-m4-20260505_002825/gen_20260505_003023.log b/SynthData0523/main/m4/tabbyflow/tabbyflow-m4-20260505_002825/gen_20260505_003023.log new file mode 100644 index 0000000000000000000000000000000000000000..3aa4f47fb0215250d022dbcc157a4c1eeec39dcc --- /dev/null +++ b/SynthData0523/main/m4/tabbyflow/tabbyflow-m4-20260505_002825/gen_20260505_003023.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:3ff00cc474b015a53ab657d830e3523511cf89aa66dc64522d232be3ad411379 +size 3570 diff --git a/SynthData0523/main/m4/tabbyflow/tabbyflow-m4-20260505_002825/input_snapshot.json 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mode 100644 index 0000000000000000000000000000000000000000..0a5ed2fcf2a49dd28edbd6b08de6fba2b1f3300c --- /dev/null +++ b/SynthData0523/main/m4/tabbyflow/tabbyflow-m4-20260505_003100/_tabbyflow_gen.py @@ -0,0 +1,34 @@ + +import os, shutil, subprocess, sys +root = r"/workspace/ef-vfm" +name = r"pipeline_m4" +src = r"/work/output-Benchmark-trainonly-v1/m4/tabbyflow/tabbyflow-m4-20260505_003100/tabular_bundle/pipeline_m4" +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", "") +os.environ.setdefault("EFVFM_SAMPLE_BATCH_SIZE", "128") +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_m4/adapter_efvfm/model_200.pt", + "--num_samples_to_generate", str(int(2217)), +]) +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/m4/tabbyflow/tabbyflow-m4-20260505_003100/tabbyflow-m4-2217-20260505_003434.csv") diff --git a/SynthData0523/main/m4/tabbyflow/tabbyflow-m4-20260505_003100/_tabbyflow_train.py b/SynthData0523/main/m4/tabbyflow/tabbyflow-m4-20260505_003100/_tabbyflow_train.py new file mode 100644 index 0000000000000000000000000000000000000000..7369e4e838bbdaaac506bdcaba17882ed8cee658 --- /dev/null +++ b/SynthData0523/main/m4/tabbyflow/tabbyflow-m4-20260505_003100/_tabbyflow_train.py @@ -0,0 +1,24 @@ + +import os, shutil, subprocess, sys +root = r"/workspace/ef-vfm" +name = r"pipeline_m4" +src = r"/work/output-Benchmark-trainonly-v1/m4/tabbyflow/tabbyflow-m4-20260505_003100/tabular_bundle/pipeline_m4" +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"] = "200" +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, "main.py", + "--dataname", name, "--mode", "train", "--gpu", "0", + "--no_wandb", "--exp_name", r"adapter_efvfm", +]) diff --git a/SynthData0523/main/m4/tabbyflow/tabbyflow-m4-20260505_003100/gen_20260505_003434.log b/SynthData0523/main/m4/tabbyflow/tabbyflow-m4-20260505_003100/gen_20260505_003434.log new file mode 100644 index 0000000000000000000000000000000000000000..0681dbc10d0151a138c1c0f9a0630a5cc1a5c7e5 --- /dev/null +++ b/SynthData0523/main/m4/tabbyflow/tabbyflow-m4-20260505_003100/gen_20260505_003434.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:0ad83c4056da0e2c40f7683dc166cc05de3c3da16a2baa62e8fa5a5b4cecb9e1 +size 3571 diff --git a/SynthData0523/main/m4/tabbyflow/tabbyflow-m4-20260505_003100/input_snapshot.json b/SynthData0523/main/m4/tabbyflow/tabbyflow-m4-20260505_003100/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..3b67aeb611d1a2ccb83cbfb9523d159921e32463 --- 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b/SynthData0523/main/m4/tabbyflow/tabbyflow-m4-20260505_003507/_tabbyflow_gen.py @@ -0,0 +1,34 @@ + +import os, shutil, subprocess, sys +root = r"/workspace/ef-vfm" +name = r"pipeline_m4" +src = r"/work/output-Benchmark-trainonly-v1/m4/tabbyflow/tabbyflow-m4-20260505_003507/tabular_bundle/pipeline_m4" +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", "") +os.environ.setdefault("EFVFM_SAMPLE_BATCH_SIZE", "128") +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_m4/adapter_efvfm/model_300.pt", + "--num_samples_to_generate", str(int(2217)), +]) +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/m4/tabbyflow/tabbyflow-m4-20260505_003507/tabbyflow-m4-2217-20260505_004055.csv") diff --git a/SynthData0523/main/m4/tabbyflow/tabbyflow-m4-20260505_003507/_tabbyflow_train.py b/SynthData0523/main/m4/tabbyflow/tabbyflow-m4-20260505_003507/_tabbyflow_train.py new file mode 100644 index 0000000000000000000000000000000000000000..6218a0f02f6a9586b6b53dd56df0b58092541475 --- /dev/null +++ b/SynthData0523/main/m4/tabbyflow/tabbyflow-m4-20260505_003507/_tabbyflow_train.py @@ -0,0 +1,24 @@ + +import os, shutil, subprocess, sys +root = r"/workspace/ef-vfm" +name = r"pipeline_m4" +src = r"/work/output-Benchmark-trainonly-v1/m4/tabbyflow/tabbyflow-m4-20260505_003507/tabular_bundle/pipeline_m4" +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"] = "300" +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, "main.py", + "--dataname", name, "--mode", "train", "--gpu", "0", + 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b/SynthData0523/main/m4/tabbyflow/tabbyflow-m4-20260505_004140/_tabbyflow_gen.py @@ -0,0 +1,34 @@ + +import os, shutil, subprocess, sys +root = r"/workspace/ef-vfm" +name = r"pipeline_m4" +src = r"/work/output-Benchmark-trainonly-v1/m4/tabbyflow/tabbyflow-m4-20260505_004140/tabular_bundle/pipeline_m4" +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", "") +os.environ.setdefault("EFVFM_SAMPLE_BATCH_SIZE", "128") +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_m4/adapter_efvfm/model_500.pt", + "--num_samples_to_generate", str(int(2217)), +]) +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/m4/tabbyflow/tabbyflow-m4-20260505_004140/tabbyflow-m4-2217-20260505_005022.csv") diff --git a/SynthData0523/main/m4/tabbyflow/tabbyflow-m4-20260505_004140/_tabbyflow_train.py b/SynthData0523/main/m4/tabbyflow/tabbyflow-m4-20260505_004140/_tabbyflow_train.py new file mode 100644 index 0000000000000000000000000000000000000000..fd64179df4109c651c643ca1e269b10fb088abad --- /dev/null +++ b/SynthData0523/main/m4/tabbyflow/tabbyflow-m4-20260505_004140/_tabbyflow_train.py @@ -0,0 +1,24 @@ + +import os, shutil, subprocess, sys +root = r"/workspace/ef-vfm" +name = r"pipeline_m4" +src = r"/work/output-Benchmark-trainonly-v1/m4/tabbyflow/tabbyflow-m4-20260505_004140/tabular_bundle/pipeline_m4" +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" +os.environ.setdefault("EFVFM_SAMPLE_BATCH_SIZE", "128") +os.environ.setdefault("EFVFM_EVAL_NUM_SAMPLES", "512") +subprocess.check_call([ + sys.executable, "main.py", + "--dataname", name, "--mode", "train", "--gpu", "0", + 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b/SynthData0523/main/m4/tabbyflow/tabbyflow-m4-20260505_005055/_tabbyflow_gen.py @@ -0,0 +1,34 @@ + +import os, shutil, subprocess, sys +root = r"/workspace/ef-vfm" +name = r"pipeline_m4" +src = r"/work/output-Benchmark-trainonly-v1/m4/tabbyflow/tabbyflow-m4-20260505_005055/tabular_bundle/pipeline_m4" +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", "") +os.environ.setdefault("EFVFM_SAMPLE_BATCH_SIZE", "128") +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_m4/adapter_efvfm/model_700.pt", + "--num_samples_to_generate", str(int(2217)), +]) +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/m4/tabbyflow/tabbyflow-m4-20260505_005055/tabbyflow-m4-2217-20260505_010225.csv") diff --git a/SynthData0523/main/m4/tabbyflow/tabbyflow-m4-20260505_005055/_tabbyflow_train.py b/SynthData0523/main/m4/tabbyflow/tabbyflow-m4-20260505_005055/_tabbyflow_train.py new file mode 100644 index 0000000000000000000000000000000000000000..0ccdd159ae1979542da4684eb4dd6c46d57bcbe9 --- /dev/null +++ b/SynthData0523/main/m4/tabbyflow/tabbyflow-m4-20260505_005055/_tabbyflow_train.py @@ -0,0 +1,24 @@ + +import os, shutil, subprocess, sys +root = r"/workspace/ef-vfm" +name = r"pipeline_m4" +src = r"/work/output-Benchmark-trainonly-v1/m4/tabbyflow/tabbyflow-m4-20260505_005055/tabular_bundle/pipeline_m4" +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"] = "700" +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, "main.py", + "--dataname", name, "--mode", "train", "--gpu", "0", + "--no_wandb", "--exp_name", r"adapter_efvfm", +]) diff --git a/SynthData0523/main/m4/tabbyflow/tabbyflow-m4-20260505_005055/gen_20260505_010225.log b/SynthData0523/main/m4/tabbyflow/tabbyflow-m4-20260505_005055/gen_20260505_010225.log new file mode 100644 index 0000000000000000000000000000000000000000..902609bac9a81482caad0884657c812ddda54f4a --- /dev/null +++ b/SynthData0523/main/m4/tabbyflow/tabbyflow-m4-20260505_005055/gen_20260505_010225.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:56844fc5cc51695c0d9b3fb2374ed0c7477d0ff694ab291546050190ed5e305c +size 3571 diff --git a/SynthData0523/main/m4/tabbyflow/tabbyflow-m4-20260505_005055/input_snapshot.json b/SynthData0523/main/m4/tabbyflow/tabbyflow-m4-20260505_005055/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..3b67aeb611d1a2ccb83cbfb9523d159921e32463 --- /dev/null +++ b/SynthData0523/main/m4/tabbyflow/tabbyflow-m4-20260505_005055/input_snapshot.json @@ -0,0 +1,3 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b/SynthData0523/main/m4/tabddpm/tabddpm-m4-20260501_002012/_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 2217 rows") +ret = subprocess.run( + [sys.executable, wrapper, "scripts/pipeline.py", + "--config", "/work/output-Benchmark-trainonly-v1/m4/tabddpm/tabddpm-m4-20260501_002012/config_sample_20260501_002103_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/m4/tabddpm/tabddpm-m4-20260501_002012/data/info.json" +with open(info_path) as f: + info = json.load(f) + +output_dir = "/work/output-Benchmark-trainonly-v1/m4/tabddpm/tabddpm-m4-20260501_002012/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/m4/tabddpm/tabddpm-m4-20260501_002012/tabddpm-m4-2217-20260501_002103.csv", index=False) + print(f"[TabDDPM] Saved {len(df)} rows -> /work/output-Benchmark-trainonly-v1/m4/tabddpm/tabddpm-m4-20260501_002012/tabddpm-m4-2217-20260501_002103.csv") +else: + print("[TabDDPM] WARNING: No output .npy files found") + sys.exit(1) diff --git a/SynthData0523/main/m4/tabddpm/tabddpm-m4-20260501_002012/_tabddpm_train.py b/SynthData0523/main/m4/tabddpm/tabddpm-m4-20260501_002012/_tabddpm_train.py new file mode 100644 index 0000000000000000000000000000000000000000..417fdb96cffb192101af75c03ef22fb0fe6f9c37 --- /dev/null +++ b/SynthData0523/main/m4/tabddpm/tabddpm-m4-20260501_002012/_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/m4/tabddpm/tabddpm-m4-20260501_002012/config.toml") +ret = subprocess.run( + [sys.executable, wrapper, "scripts/pipeline.py", + "--config", "/work/output-Benchmark-trainonly-v1/m4/tabddpm/tabddpm-m4-20260501_002012/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/m4/tabddpm/tabddpm-m4-20260501_002012/config.toml b/SynthData0523/main/m4/tabddpm/tabddpm-m4-20260501_002012/config.toml new file mode 100644 index 0000000000000000000000000000000000000000..046cbf1cac02f7cef9d5c1193db9e417c3237576 --- /dev/null +++ b/SynthData0523/main/m4/tabddpm/tabddpm-m4-20260501_002012/config.toml @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid 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+size 9176 diff --git a/SynthData0523/main/m4/tabdiff/tabdiff-m4-20260501_004659/_tabdiff_gen.py b/SynthData0523/main/m4/tabdiff/tabdiff-m4-20260501_004659/_tabdiff_gen.py new file mode 100644 index 0000000000000000000000000000000000000000..8558e989d232d1b665a25c80607d47a0ca9a9191 --- /dev/null +++ b/SynthData0523/main/m4/tabdiff/tabdiff-m4-20260501_004659/_tabdiff_gen.py @@ -0,0 +1,36 @@ + +import os, shutil, subprocess, sys +td = r"/workspace/TabDiff" +name = r"pipeline_m4" +src = r"/work/output-Benchmark-trainonly-v1/m4/tabdiff/tabdiff-m4-20260501_004659/tabular_bundle/pipeline_m4" +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_m4/adapter_learnable/model_500.pt", + "--num_samples_to_generate", str(int(2217)), +]) +# 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/m4/tabdiff/tabdiff-m4-20260501_004659/tabdiff-m4-2217-20260501_005309.csv") diff --git a/SynthData0523/main/m4/tabdiff/tabdiff-m4-20260501_004659/_tabdiff_train.py b/SynthData0523/main/m4/tabdiff/tabdiff-m4-20260501_004659/_tabdiff_train.py new file mode 100644 index 0000000000000000000000000000000000000000..8a3b26184cee7eed4375238f2069bf1ee6309387 --- /dev/null +++ b/SynthData0523/main/m4/tabdiff/tabdiff-m4-20260501_004659/_tabdiff_train.py @@ -0,0 +1,21 @@ + +import os, shutil, subprocess, sys +td = r"/workspace/TabDiff" +name = r"pipeline_m4" +src = r"/work/output-Benchmark-trainonly-v1/m4/tabdiff/tabdiff-m4-20260501_004659/tabular_bundle/pipeline_m4" +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/m4/tabdiff/tabdiff-m4-20260501_004659/gen_20260501_005309.log b/SynthData0523/main/m4/tabdiff/tabdiff-m4-20260501_004659/gen_20260501_005309.log new file mode 100644 index 0000000000000000000000000000000000000000..5a690f4fd1c621b3e91540189a5d2933fd62ad29 --- /dev/null +++ b/SynthData0523/main/m4/tabdiff/tabdiff-m4-20260501_004659/gen_20260501_005309.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:d00869193a3d6da4619a20b3b2d02f2071ba38b7cf188eaebfe2e30e8e6f546d +size 4557 diff --git a/SynthData0523/main/m4/tabdiff/tabdiff-m4-20260501_004659/input_snapshot.json b/SynthData0523/main/m4/tabdiff/tabdiff-m4-20260501_004659/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..28cd3a4d75d18efdf20f8d2e0b931a2ecdef48e9 --- /dev/null +++ b/SynthData0523/main/m4/tabdiff/tabdiff-m4-20260501_004659/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "m4", + "model": "tabdiff", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-train.csv", + "exists": true, + "size": 92191, + "sha256": "396b9d409ca21bf4a4cd329bdf5b7796aa0ae6356fa8d89b8eb669b5880b81f1" + }, + "val_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-val.csv", + "exists": true, + "size": 11482, + "sha256": "ee3c247d02f56e1687d03c381e13125d6a3a2a411ac7f202ba8520a4be9f1784" + }, + "test_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-test.csv", + "exists": true, + "size": 11559, + "sha256": "cadb9941124001b8fa7cb1ebae43b70a9ca56294f4df4d3c2f22c164c41757d4" + }, + "profile_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/m4/m4-dataset_profile.json", + "exists": true, + "size": 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newline at end of file diff --git a/SynthData0523/main/m4/tabdiff/tabdiff-m4-20260501_004659/public_gate/public_gate_report.json b/SynthData0523/main/m4/tabdiff/tabdiff-m4-20260501_004659/public_gate/public_gate_report.json new file mode 100644 index 0000000000000000000000000000000000000000..e7a25408e2574124629ea9437356551e4d6f4790 --- /dev/null +++ b/SynthData0523/main/m4/tabdiff/tabdiff-m4-20260501_004659/public_gate/public_gate_report.json @@ -0,0 +1,37 @@ +{ + "dataset_id": "m4", + "status": "pass", + "checks": [ + { + "check_id": "PG001_csv_parse_ok", + "status": "pass" + }, + { + "check_id": "PG002_split_header_consistent", + "status": "pass" + }, + { + "check_id": "PG003_profile_header_match", + "status": "pass" + }, + { + "check_id": "PG004_missing_token_normalized", + "status": "pass" + }, + { + "check_id": "PG005_semantic_type_validated", + "status": "pass" + }, + { + "check_id": "PG006_target_defined_and_valid", + "status": "pass" + } + ], + "target_column": "charges", + "task_type": "regression", + "input_splits": { + "train": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-train.csv", + "val": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-val.csv", + "test": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-test.csv" + } +} \ No newline at end of file diff --git a/SynthData0523/main/m4/tabdiff/tabdiff-m4-20260501_004659/public_gate/staged_input_manifest.json b/SynthData0523/main/m4/tabdiff/tabdiff-m4-20260501_004659/public_gate/staged_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..1590344d2101ed87d7b9f7b7941a5c9dacad00a9 --- /dev/null +++ b/SynthData0523/main/m4/tabdiff/tabdiff-m4-20260501_004659/public_gate/staged_input_manifest.json @@ -0,0 +1,152 @@ +{ + "dataset_id": "m4", + "target_column": "charges", + "task_type": "regression", + "train_csv": 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b/SynthData0523/main/m4/tabdiff/tabdiff-m4-20260501_004659/tabular_bundle/pipeline_m4/y_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..717f965e62bd5595d6df01d1ad7851fc5768dc29 --- /dev/null +++ b/SynthData0523/main/m4/tabdiff/tabdiff-m4-20260501_004659/tabular_bundle/pipeline_m4/y_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:0c64fea6ad5ed2f8441ef5212c726195dbf53e1c81298427bddd18eb20e35cf2 +size 8996 diff --git a/SynthData0523/main/m4/tabdiff/tabdiff-m4-20260501_004659/tabular_bundle/pipeline_m4/y_val.npy b/SynthData0523/main/m4/tabdiff/tabdiff-m4-20260501_004659/tabular_bundle/pipeline_m4/y_val.npy new file mode 100644 index 0000000000000000000000000000000000000000..717f965e62bd5595d6df01d1ad7851fc5768dc29 --- /dev/null +++ b/SynthData0523/main/m4/tabdiff/tabdiff-m4-20260501_004659/tabular_bundle/pipeline_m4/y_val.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:0c64fea6ad5ed2f8441ef5212c726195dbf53e1c81298427bddd18eb20e35cf2 +size 8996 diff --git a/SynthData0523/main/m4/tabdiff/tabdiff-m4-20260501_004659/train_20260501_004659.log b/SynthData0523/main/m4/tabdiff/tabdiff-m4-20260501_004659/train_20260501_004659.log new file mode 100644 index 0000000000000000000000000000000000000000..ccd8d5f878ad3a19cbb3567447853c0bcf31d2c8 --- /dev/null +++ b/SynthData0523/main/m4/tabdiff/tabdiff-m4-20260501_004659/train_20260501_004659.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:a2a12f73486cd9d00b992f6e238a862d617a8acbb201d9e42306dc2cc94aa62c +size 292966 diff --git a/SynthData0523/main/m4/tabpfgen/tabpfgen-m4-20260501_005403/_tabpfgen_generate.py b/SynthData0523/main/m4/tabpfgen/tabpfgen-m4-20260501_005403/_tabpfgen_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..b849aba2d7577b09e1ec71efab398c568f41bbb7 --- /dev/null +++ b/SynthData0523/main/m4/tabpfgen/tabpfgen-m4-20260501_005403/_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/m4/tabpfgen/tabpfgen-m4-20260501_005403/staged/public/train.csv") +target_col = "charges" + +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(2217) + +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_regression") +X_syn, y_syn = gen.generate_regression(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/m4/tabpfgen/tabpfgen-m4-20260501_005403/tabpfgen-m4-2217-20260501_005403.csv", index=False) +print(f"[TabPFGen] Saved {len(syn_df)} rows -> /work/output-Benchmark-trainonly-v1/m4/tabpfgen/tabpfgen-m4-20260501_005403/tabpfgen-m4-2217-20260501_005403.csv") diff --git a/SynthData0523/main/m4/tabpfgen/tabpfgen-m4-20260501_005403/gen_20260501_005403.log b/SynthData0523/main/m4/tabpfgen/tabpfgen-m4-20260501_005403/gen_20260501_005403.log new file mode 100644 index 0000000000000000000000000000000000000000..8dabb9bfeb17f6585cbad340648bc085e92a2a3e --- /dev/null +++ b/SynthData0523/main/m4/tabpfgen/tabpfgen-m4-20260501_005403/gen_20260501_005403.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:96a46fe5ac21305487900ec49903c345aa1a96959e97976873b1b757cf174d2c +size 951 diff --git a/SynthData0523/main/m4/tabpfgen/tabpfgen-m4-20260501_005403/input_snapshot.json b/SynthData0523/main/m4/tabpfgen/tabpfgen-m4-20260501_005403/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..0e4ec9d0bc32394ab638c94bcf784b81478f2f39 --- /dev/null +++ b/SynthData0523/main/m4/tabpfgen/tabpfgen-m4-20260501_005403/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "m4", + "model": "tabpfgen", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-train.csv", + "exists": true, + "size": 92191, + "sha256": "396b9d409ca21bf4a4cd329bdf5b7796aa0ae6356fa8d89b8eb669b5880b81f1" + }, + "val_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-val.csv", + "exists": true, + "size": 11482, + "sha256": "ee3c247d02f56e1687d03c381e13125d6a3a2a411ac7f202ba8520a4be9f1784" + }, + "test_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-test.csv", + "exists": true, + "size": 11559, + "sha256": "cadb9941124001b8fa7cb1ebae43b70a9ca56294f4df4d3c2f22c164c41757d4" + }, + "profile_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/m4/m4-dataset_profile.json", + "exists": true, + "size": 3336, + "sha256": "83e2764810e4a0e8cdece3a28dbd9134b7c9df6f2e56953e46d024ad2c4e035f" + }, + "contract_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/m4/m4-dataset_contract_v1.json", + "exists": true, + "size": 3810, + "sha256": "c23641b258629a845b164099bd0132886f8f6d0100e990494e0f92540f8987d9" + } + } +} \ No newline at end of file diff --git a/SynthData0523/main/m4/tabpfgen/tabpfgen-m4-20260501_005403/public_gate/normalized_schema_snapshot.json b/SynthData0523/main/m4/tabpfgen/tabpfgen-m4-20260501_005403/public_gate/normalized_schema_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..31ed9818686b11907df75e872e4ffbe010330880 --- /dev/null +++ b/SynthData0523/main/m4/tabpfgen/tabpfgen-m4-20260501_005403/public_gate/normalized_schema_snapshot.json @@ -0,0 +1,147 @@ +{ + "dataset_id": "m4", + "target_column": "charges", + "task_type": "regression", + "columns": [ + { + "name": "age", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 47, + "unique_ratio": 0.0212, + "example_values": [ + "46", + "38", + "19", + "27", + "26" + ] + } + }, + { + "name": "sex", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "female", + "male" + ] + } + }, + { + "name": "bmi", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 538, + "unique_ratio": 0.24267, + "example_values": [ + "23.655", + "19.3", + "30.59", + "32.67", + "29.45" + ] + } + }, + { + "name": "children", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 6, + "unique_ratio": 0.002706, + "example_values": [ + "1", + "0", + "3", + "2", + "5" + ] + } + }, + { + "name": "smoker", + "role": "feature", + "semantic_type": "boolean", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "yes", + "no" + ] + } + }, + { + "name": "region", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.001804, + "example_values": [ + "northwest", + "southwest", + "southeast", + "northeast" + ] + } + }, + { + "name": "charges", + "role": "target", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 1281, + "unique_ratio": 0.577808, + "example_values": [ + "21677.28345", + "15820.699", + "1639.5631", + "2497.0383", + "2897.3235" + ] + } + } + ] +} \ No newline at end of file diff --git a/SynthData0523/main/m4/tabpfgen/tabpfgen-m4-20260501_005403/public_gate/public_gate_report.json b/SynthData0523/main/m4/tabpfgen/tabpfgen-m4-20260501_005403/public_gate/public_gate_report.json new file mode 100644 index 0000000000000000000000000000000000000000..e7a25408e2574124629ea9437356551e4d6f4790 --- /dev/null +++ b/SynthData0523/main/m4/tabpfgen/tabpfgen-m4-20260501_005403/public_gate/public_gate_report.json @@ -0,0 +1,37 @@ +{ + "dataset_id": "m4", + "status": "pass", + "checks": [ + { + "check_id": "PG001_csv_parse_ok", + "status": "pass" + }, + { + "check_id": "PG002_split_header_consistent", + "status": "pass" + }, + { + "check_id": "PG003_profile_header_match", + "status": "pass" + }, + { + "check_id": "PG004_missing_token_normalized", + "status": "pass" + }, + { + "check_id": "PG005_semantic_type_validated", + "status": "pass" + }, + { + "check_id": "PG006_target_defined_and_valid", + "status": "pass" + } + ], + "target_column": "charges", + "task_type": "regression", + "input_splits": { + "train": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-train.csv", + "val": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-val.csv", + "test": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-test.csv" + } +} \ No newline at end of file diff --git a/SynthData0523/main/m4/tabpfgen/tabpfgen-m4-20260501_005403/public_gate/staged_input_manifest.json b/SynthData0523/main/m4/tabpfgen/tabpfgen-m4-20260501_005403/public_gate/staged_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..2f421eb9ce8e3dc6ab98883a4ccc7a24d321d222 --- /dev/null +++ b/SynthData0523/main/m4/tabpfgen/tabpfgen-m4-20260501_005403/public_gate/staged_input_manifest.json @@ -0,0 +1,152 @@ +{ + "dataset_id": "m4", + "target_column": "charges", + "task_type": "regression", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabpfgen/tabpfgen-m4-20260501_005403/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabpfgen/tabpfgen-m4-20260501_005403/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabpfgen/tabpfgen-m4-20260501_005403/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabpfgen/tabpfgen-m4-20260501_005403/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabpfgen/tabpfgen-m4-20260501_005403/public_gate/public_gate_report.json", + "column_schema": [ + { + "name": "age", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 47, + "unique_ratio": 0.0212, + "example_values": [ + "46", + "38", + "19", + "27", + "26" + ] + } + }, + { + "name": "sex", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "female", + "male" + ] + } + }, + { + "name": "bmi", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 538, + "unique_ratio": 0.24267, + "example_values": [ + "23.655", + "19.3", + "30.59", + "32.67", + "29.45" + ] + } + }, + { + "name": "children", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 6, + "unique_ratio": 0.002706, + "example_values": [ + "1", + "0", + "3", + "2", + "5" + ] + } + }, + { + "name": "smoker", + "role": "feature", + "semantic_type": "boolean", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "yes", + "no" + ] + } + }, + { + "name": "region", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.001804, + "example_values": [ + "northwest", + "southwest", + "southeast", + "northeast" + ] + } + }, + { + "name": "charges", + "role": "target", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 1281, + "unique_ratio": 0.577808, + "example_values": [ + "21677.28345", + "15820.699", + "1639.5631", + "2497.0383", + "2897.3235" + ] + } + } + ] +} \ No newline at end of file diff --git a/SynthData0523/main/m4/tabpfgen/tabpfgen-m4-20260501_005403/runtime_result.json b/SynthData0523/main/m4/tabpfgen/tabpfgen-m4-20260501_005403/runtime_result.json new file mode 100644 index 0000000000000000000000000000000000000000..7fae3acd256c90f33b3674e66b3dc94ce875e757 --- /dev/null +++ b/SynthData0523/main/m4/tabpfgen/tabpfgen-m4-20260501_005403/runtime_result.json @@ -0,0 +1,27 @@ +{ + "dataset_id": "m4", + "model": "tabpfgen", + "run_id": "tabpfgen-m4-20260501_005403", + "public_gate_status": "pass", + "adapter_ready_status": "pass", + "train_status": "success", + "generate_status": "success", + "reason_code": null, + "reason_detail": null, + "artifacts": { + "synthetic_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabpfgen/tabpfgen-m4-20260501_005403/tabpfgen-m4-2217-20260501_005403.csv", + "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabpfgen/tabpfgen-m4-20260501_005403" + }, + "timings": { + "train": { + "started_at": "2026-05-01T00:54:03", + "ended_at": "2026-05-01T00:54:03", + "duration_sec": 0.006 + }, + "generate": { + "started_at": "2026-05-01T00:54:03", + "ended_at": "2026-05-01T00:54:23", + "duration_sec": 19.969 + } + } +} \ No newline at end of file diff --git a/SynthData0523/main/m4/tabpfgen/tabpfgen-m4-20260501_005403/staged/public/staged_features.json b/SynthData0523/main/m4/tabpfgen/tabpfgen-m4-20260501_005403/staged/public/staged_features.json new file mode 100644 index 0000000000000000000000000000000000000000..d6acd098c35111f423f3ffff2cfd1754bc5dfebc --- /dev/null +++ b/SynthData0523/main/m4/tabpfgen/tabpfgen-m4-20260501_005403/staged/public/staged_features.json @@ -0,0 +1,37 @@ +[ + { + "feature_name": "age", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "sex", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "bmi", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "children", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "smoker", + "data_type": "binary", + "is_target": false + }, + { + "feature_name": "region", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "charges", + "data_type": "continuous", + "is_target": true + } +] \ No newline at end of file diff --git a/SynthData0523/main/m4/tabpfgen/tabpfgen-m4-20260501_005403/staged/public/test.csv b/SynthData0523/main/m4/tabpfgen/tabpfgen-m4-20260501_005403/staged/public/test.csv new file mode 100644 index 0000000000000000000000000000000000000000..c88a2e7cfcd715bb421cb56eb078033da45c4dc8 --- /dev/null +++ b/SynthData0523/main/m4/tabpfgen/tabpfgen-m4-20260501_005403/staged/public/test.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4c541b677fb2d45c5bc79338eeee9fd8c91484b195a0c2c546c2fbabf113b7ea +size 11298 diff --git a/SynthData0523/main/m4/tabpfgen/tabpfgen-m4-20260501_005403/staged/public/train.csv b/SynthData0523/main/m4/tabpfgen/tabpfgen-m4-20260501_005403/staged/public/train.csv new file mode 100644 index 0000000000000000000000000000000000000000..5a05019d786529e0617291766c4fbe016ca01bf5 --- /dev/null +++ b/SynthData0523/main/m4/tabpfgen/tabpfgen-m4-20260501_005403/staged/public/train.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:614ebfac5f41d6f661aff675fa18c0f933a8d9bcc77ea71b7b2360c2f0155837 +size 90069 diff --git a/SynthData0523/main/m4/tabpfgen/tabpfgen-m4-20260501_005403/staged/public/val.csv b/SynthData0523/main/m4/tabpfgen/tabpfgen-m4-20260501_005403/staged/public/val.csv new file mode 100644 index 0000000000000000000000000000000000000000..d78fe5ad1537c437ba9ac2629f2aa60a6c17e708 --- /dev/null +++ b/SynthData0523/main/m4/tabpfgen/tabpfgen-m4-20260501_005403/staged/public/val.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e55f5edbe7942ff01630de91a9c70dfbc6445eba76c7b970350364546b67c2d7 +size 11218 diff --git a/SynthData0523/main/m4/tabpfgen/tabpfgen-m4-20260501_005403/staged/tabpfgen/adapter_report.json b/SynthData0523/main/m4/tabpfgen/tabpfgen-m4-20260501_005403/staged/tabpfgen/adapter_report.json new file mode 100644 index 0000000000000000000000000000000000000000..5bab4b213666513b1d6984199c924b867ba6da62 --- /dev/null +++ b/SynthData0523/main/m4/tabpfgen/tabpfgen-m4-20260501_005403/staged/tabpfgen/adapter_report.json @@ -0,0 +1,7 @@ +{ + "adapter_ready_status": "pass", + "adapter_fail_reason_code": null, + "adapter_fail_detail": null, + "adapter_transforms_applied": [], + "model_input_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabpfgen/tabpfgen-m4-20260501_005403/staged/tabpfgen/model_input_manifest.json" +} \ No newline at end of file diff --git a/SynthData0523/main/m4/tabpfgen/tabpfgen-m4-20260501_005403/staged/tabpfgen/adapter_transforms_applied.json b/SynthData0523/main/m4/tabpfgen/tabpfgen-m4-20260501_005403/staged/tabpfgen/adapter_transforms_applied.json new file mode 100644 index 0000000000000000000000000000000000000000..0637a088a01e8ddab3bf3fa98dbe804cbde1a0dc --- /dev/null +++ b/SynthData0523/main/m4/tabpfgen/tabpfgen-m4-20260501_005403/staged/tabpfgen/adapter_transforms_applied.json @@ -0,0 +1 @@ +[] \ No newline at end of file diff --git a/SynthData0523/main/m4/tabpfgen/tabpfgen-m4-20260501_005403/staged/tabpfgen/model_input_manifest.json b/SynthData0523/main/m4/tabpfgen/tabpfgen-m4-20260501_005403/staged/tabpfgen/model_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..911959d0545938ed704c81ed6c65d04c5457cc3d --- /dev/null +++ b/SynthData0523/main/m4/tabpfgen/tabpfgen-m4-20260501_005403/staged/tabpfgen/model_input_manifest.json @@ -0,0 +1,154 @@ +{ + "dataset_id": "m4", + "model": "tabpfgen", + "target_column": "charges", + "task_type": "regression", + "column_schema": [ + { + "name": "age", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 47, + "unique_ratio": 0.0212, + "example_values": [ + "46", + "38", + "19", + "27", + "26" + ] + } + }, + { + "name": "sex", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "female", + "male" + ] + } + }, + { + "name": "bmi", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 538, + "unique_ratio": 0.24267, + "example_values": [ + "23.655", + "19.3", + "30.59", + "32.67", + "29.45" + ] + } + }, + { + "name": "children", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 6, + "unique_ratio": 0.002706, + "example_values": [ + "1", + "0", + "3", + "2", + "5" + ] + } + }, + { + "name": "smoker", + "role": "feature", + "semantic_type": "boolean", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "yes", + "no" + ] + } + }, + { + "name": "region", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.001804, + "example_values": [ + "northwest", + "southwest", + "southeast", + "northeast" + ] + } + }, + { + "name": "charges", + "role": "target", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 1281, + "unique_ratio": 0.577808, + "example_values": [ + "21677.28345", + "15820.699", + "1639.5631", + "2497.0383", + "2897.3235" + ] + } + } + ], + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabpfgen/tabpfgen-m4-20260501_005403/public_gate/staged_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabpfgen/tabpfgen-m4-20260501_005403/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabpfgen/tabpfgen-m4-20260501_005403/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabpfgen/tabpfgen-m4-20260501_005403/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabpfgen/tabpfgen-m4-20260501_005403/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabpfgen/tabpfgen-m4-20260501_005403/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/SynthData0523/main/m4/tabpfgen/tabpfgen-m4-20260501_005403/tabpfgen-m4-2217-20260501_005403.csv b/SynthData0523/main/m4/tabpfgen/tabpfgen-m4-20260501_005403/tabpfgen-m4-2217-20260501_005403.csv new file mode 100644 index 0000000000000000000000000000000000000000..dc40bd37f64a7b5021507ad9eafdaec0feb99a33 --- /dev/null +++ b/SynthData0523/main/m4/tabpfgen/tabpfgen-m4-20260501_005403/tabpfgen-m4-2217-20260501_005403.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e988aee8dd9ccc9813359e624a373d154cc48bdeffe5f5d0a5817b130fbd4aad +size 130729 diff --git a/SynthData0523/main/m4/tabpfgen/tabpfgen-m4-20260501_005403/tabpfgen_meta.json b/SynthData0523/main/m4/tabpfgen/tabpfgen-m4-20260501_005403/tabpfgen_meta.json new file mode 100644 index 0000000000000000000000000000000000000000..b7d04611615bf3b7afd5f5e71ce493a235046145 --- /dev/null +++ b/SynthData0523/main/m4/tabpfgen/tabpfgen-m4-20260501_005403/tabpfgen_meta.json @@ -0,0 +1,9 @@ +{ + "csv_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabpfgen/tabpfgen-m4-20260501_005403/staged/public/train.csv", + "json_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabpfgen/tabpfgen-m4-20260501_005403/staged/public/staged_features.json", + "target_col": "charges", + "is_classification": false, + "task_type": "regression", + "n_rows": 2217, + "n_cols": 7 +} \ No newline at end of file diff --git a/SynthData0523/main/m4/tabpfgen/tabpfgen-m4-20260501_005403/train_20260501_005403.log b/SynthData0523/main/m4/tabpfgen/tabpfgen-m4-20260501_005403/train_20260501_005403.log new file mode 100644 index 0000000000000000000000000000000000000000..2bc33582165aff2ed37bc5f569b0741f3ac7be47 --- /dev/null +++ b/SynthData0523/main/m4/tabpfgen/tabpfgen-m4-20260501_005403/train_20260501_005403.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:8cafc6bd1a580da66b0197a0595821f6de2ce962295c27af83354e897305bbcc +size 595 diff --git a/SynthData0523/main/m4/tabpfgen/tabpfgen-m4-20260505_030045/_tabpfgen_generate.py b/SynthData0523/main/m4/tabpfgen/tabpfgen-m4-20260505_030045/_tabpfgen_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..d977c8cd6fa00b8209a876eef79c658b9c2d0a05 --- /dev/null +++ b/SynthData0523/main/m4/tabpfgen/tabpfgen-m4-20260505_030045/_tabpfgen_generate.py @@ -0,0 +1,122 @@ +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/m4/tabpfgen/tabpfgen-m4-20260505_030045/staged/public/train.csv") +target_col = "charges" + +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 +fit_rows_cap = max(1, int(os.environ.get("TABPFGEN_FIT_MAX_ROWS", "50000"))) +if len(X) > fit_rows_cap: + rng = np.random.default_rng(42) + idx = np.sort(rng.choice(len(X), size=fit_rows_cap, replace=False)) + X = X[idx] + y = y[idx] + print(f"[TabPFGen] Downsampled fit rows -> {len(X)} (cap={fit_rows_cap})") +target_n = int(2217) + +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 + +chunk_rows = max(1, int(os.environ.get("TABPFGEN_GEN_CHUNK_ROWS", "256"))) +device = (os.environ.get("TABPFGEN_DEVICE") or "auto").strip() or "auto" + +# 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=device, +) + +print(f"[TabPFGen] Generating {target_n} rows via generate_regression (chunk_rows={chunk_rows}, device={device})") +x_parts = [] +y_parts = [] +remaining = target_n +while remaining > 0: + take = min(chunk_rows, remaining) + X_part, y_part = gen.generate_regression(X, y, n_samples=take) + x_parts.append(np.asarray(X_part)) + y_parts.append(np.asarray(y_part)) + remaining -= take + print(f"[TabPFGen] chunk done: take={take}, remaining={remaining}") + +X_syn = np.concatenate(x_parts, axis=0) +y_syn = np.concatenate(y_parts, axis=0) + +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/m4/tabpfgen/tabpfgen-m4-20260505_030045/tabpfgen-m4-2217-20260505_030045.csv", index=False) +print(f"[TabPFGen] Saved {len(syn_df)} rows -> /work/output-Benchmark-trainonly-v1/m4/tabpfgen/tabpfgen-m4-20260505_030045/tabpfgen-m4-2217-20260505_030045.csv") diff --git a/SynthData0523/main/m4/tabpfgen/tabpfgen-m4-20260505_030045/gen_20260505_030045.log 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b/SynthData0523/main/m4/tabpfgen/tabpfgen-m4-20260505_030225/_tabpfgen_generate.py @@ -0,0 +1,122 @@ +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/m4/tabpfgen/tabpfgen-m4-20260505_030225/staged/public/train.csv") +target_col = "charges" + +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 +fit_rows_cap = max(1, int(os.environ.get("TABPFGEN_FIT_MAX_ROWS", "50000"))) +if len(X) > fit_rows_cap: + rng = np.random.default_rng(42) + idx = np.sort(rng.choice(len(X), size=fit_rows_cap, replace=False)) + X = X[idx] + y = y[idx] + print(f"[TabPFGen] Downsampled fit rows -> {len(X)} (cap={fit_rows_cap})") +target_n = int(2217) + +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 + +chunk_rows = max(1, int(os.environ.get("TABPFGEN_GEN_CHUNK_ROWS", "256"))) +device = (os.environ.get("TABPFGEN_DEVICE") or "auto").strip() or "auto" + +# 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=device, +) + +print(f"[TabPFGen] Generating {target_n} rows via generate_regression (chunk_rows={chunk_rows}, device={device})") +x_parts = [] +y_parts = [] +remaining = target_n +while remaining > 0: + take = min(chunk_rows, remaining) + X_part, y_part = gen.generate_regression(X, y, n_samples=take) + x_parts.append(np.asarray(X_part)) + y_parts.append(np.asarray(y_part)) + remaining -= take + print(f"[TabPFGen] chunk done: take={take}, remaining={remaining}") + +X_syn = np.concatenate(x_parts, axis=0) +y_syn = np.concatenate(y_parts, axis=0) + +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/m4/tabpfgen/tabpfgen-m4-20260505_030225/tabpfgen-m4-2217-20260505_030225.csv", index=False) +print(f"[TabPFGen] Saved {len(syn_df)} rows -> /work/output-Benchmark-trainonly-v1/m4/tabpfgen/tabpfgen-m4-20260505_030225/tabpfgen-m4-2217-20260505_030225.csv") diff --git a/SynthData0523/main/m4/tabpfgen/tabpfgen-m4-20260505_030225/gen_20260505_030225.log b/SynthData0523/main/m4/tabpfgen/tabpfgen-m4-20260505_030225/gen_20260505_030225.log new file mode 100644 index 0000000000000000000000000000000000000000..0e5c7ca54079b38f3c50b055f614a8950b22c598 --- /dev/null +++ b/SynthData0523/main/m4/tabpfgen/tabpfgen-m4-20260505_030225/gen_20260505_030225.log @@ -0,0 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0000000000000000000000000000000000000000..c7e45383d6da258cf12355c175b327638c1418eb --- /dev/null +++ b/SynthData0523/main/m4/tabpfgen/tabpfgen-m4-20260505_030225/train_20260505_030225.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e0e080b1b30975dc81ec4667ba1a26d54aa1e5166e8667083ad70b642286aad4 +size 595 diff --git a/SynthData0523/main/m4/tabpfgen/tabpfgen-m4-20260505_030403/_tabpfgen_generate.py b/SynthData0523/main/m4/tabpfgen/tabpfgen-m4-20260505_030403/_tabpfgen_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..ce27a9003d44421bc4724f5c380b2fe3fe5fa51f --- /dev/null +++ b/SynthData0523/main/m4/tabpfgen/tabpfgen-m4-20260505_030403/_tabpfgen_generate.py @@ -0,0 +1,122 @@ +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/m4/tabpfgen/tabpfgen-m4-20260505_030403/staged/public/train.csv") +target_col = "charges" + +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 +fit_rows_cap = max(1, int(os.environ.get("TABPFGEN_FIT_MAX_ROWS", "50000"))) +if len(X) > fit_rows_cap: + rng = np.random.default_rng(42) + idx = np.sort(rng.choice(len(X), size=fit_rows_cap, replace=False)) + X = X[idx] + y = y[idx] + print(f"[TabPFGen] Downsampled fit rows -> {len(X)} (cap={fit_rows_cap})") +target_n = int(2217) + +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 + +chunk_rows = max(1, int(os.environ.get("TABPFGEN_GEN_CHUNK_ROWS", "256"))) +device = (os.environ.get("TABPFGEN_DEVICE") or "auto").strip() or "auto" + +# 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=device, +) + +print(f"[TabPFGen] Generating {target_n} rows via generate_regression (chunk_rows={chunk_rows}, device={device})") +x_parts = [] +y_parts = [] +remaining = target_n +while remaining > 0: + take = min(chunk_rows, remaining) + X_part, y_part = gen.generate_regression(X, y, n_samples=take) + x_parts.append(np.asarray(X_part)) + y_parts.append(np.asarray(y_part)) + remaining -= take + print(f"[TabPFGen] chunk done: take={take}, remaining={remaining}") + +X_syn = np.concatenate(x_parts, axis=0) +y_syn = np.concatenate(y_parts, axis=0) + +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/m4/tabpfgen/tabpfgen-m4-20260505_030403/tabpfgen-m4-2217-20260505_030403.csv", index=False) +print(f"[TabPFGen] Saved {len(syn_df)} rows -> /work/output-Benchmark-trainonly-v1/m4/tabpfgen/tabpfgen-m4-20260505_030403/tabpfgen-m4-2217-20260505_030403.csv") diff --git a/SynthData0523/main/m4/tabpfgen/tabpfgen-m4-20260505_030403/gen_20260505_030403.log b/SynthData0523/main/m4/tabpfgen/tabpfgen-m4-20260505_030403/gen_20260505_030403.log new file mode 100644 index 0000000000000000000000000000000000000000..8a99c95e77b5ad36a2a8505e9052a76a74cad371 --- /dev/null +++ b/SynthData0523/main/m4/tabpfgen/tabpfgen-m4-20260505_030403/gen_20260505_030403.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:14041ed1dbffad8712f876083db1ad8631d5ab7fdc27a4074407bbc2c6508992 +size 4318 diff --git a/SynthData0523/main/m4/tabpfgen/tabpfgen-m4-20260505_030403/input_snapshot.json 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