auto-sync 2026-07-02T20:07:13Z workspace (part 8)
Browse files- workspace/results/h16_transport_field_reground_fieldonly_k6clean_dropnoopwg_b12_v1_besttransport_margin0p00_k6_srcscore_task_pick001_stack005_policyanchor_advw4p0_summary.json +487 -0
- workspace/results/h16_transport_field_reground_fieldonly_k6clean_dropnoopwg_b12_v1_besttransport_margin0p00_k6_srcscore_task_pick001_stack005_policyanchor_advw4p0_summary.md +19 -0
- workspace/results/paper_analysis.json +1789 -1
- workspace/results/paper_analysis.md +35 -1
- workspace/results/v1_generator_next_submitted.json +36 -0
- workspace/scripts/build_paper_analysis.py +342 -0
- workspace/scripts/eval_positive_tangent_memory.py +81 -0
- workspace/scripts/slurm/eval_positive_tangent_memory.sbatch +39 -0
- workspace/scripts/slurm/train_positive_tangent_cvae.sbatch +70 -0
- workspace/scripts/slurm/train_positive_tangent_cvae_cpu.sbatch +68 -0
- workspace/scripts/summarize_positive_tangent_cvae_sweep.py +125 -0
- workspace/scripts/train_positive_tangent_cvae.py +119 -0
workspace/results/h16_transport_field_reground_fieldonly_k6clean_dropnoopwg_b12_v1_besttransport_margin0p00_k6_srcscore_task_pick001_stack005_policyanchor_advw4p0_summary.json
ADDED
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| 1 |
+
{
|
| 2 |
+
"run_root": "/scratch/knguy52/dovla/experiments/dovla_h16_policy_ckpt_runs",
|
| 3 |
+
"objective": "transport_field_reground_fieldonly_k6clean_dropnoopwg_b12_v1",
|
| 4 |
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"out_name": "policy_rollout_fieldonly_k6clean_dropnoopwg_b12_v1_besttransport_margin0p00_k6_srcscore_task_pick001_stack005_policyanchor_advw4p0.json",
|
| 5 |
+
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| 6 |
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| 7 |
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| 8 |
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| 9 |
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| 10 |
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| 11 |
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| 12 |
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| 13 |
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| 14 |
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"rows": [
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| 15 |
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{
|
| 16 |
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"seed": 0,
|
| 17 |
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"path": "/scratch/knguy52/dovla/experiments/dovla_h16_policy_ckpt_runs/transport_field_reground_fieldonly_k6clean_dropnoopwg_b12_v1/seed_0/policy_rollout_fieldonly_k6clean_dropnoopwg_b12_v1_besttransport_margin0p00_k6_srcscore_task_pick001_stack005_policyanchor_advw4p0.json",
|
| 18 |
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|
| 19 |
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|
| 20 |
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| 21 |
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| 22 |
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| 23 |
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| 24 |
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| 25 |
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| 26 |
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| 27 |
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| 28 |
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| 29 |
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| 30 |
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| 31 |
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| 32 |
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| 33 |
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| 34 |
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| 35 |
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| 36 |
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| 38 |
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| 39 |
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| 40 |
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| 41 |
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|
| 42 |
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| 43 |
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|
| 44 |
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|
| 45 |
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|
| 46 |
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|
| 47 |
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],
|
| 48 |
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"retrieval_residual_anchor": "policy",
|
| 49 |
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"retrieval_residual_direction": "candidate_minus_anchor",
|
| 50 |
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"retrieval_residual_reduce": "compose_mean_by_type",
|
| 51 |
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| 52 |
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| 53 |
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| 54 |
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| 55 |
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| 56 |
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| 57 |
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|
| 58 |
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|
| 59 |
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|
| 60 |
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|
| 61 |
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|
| 62 |
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| 63 |
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| 64 |
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| 87 |
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| 88 |
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| 89 |
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| 90 |
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| 92 |
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| 95 |
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| 99 |
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| 100 |
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| 101 |
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| 102 |
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| 103 |
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| 113 |
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},
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| 114 |
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|
| 422 |
+
"transport_field_edges": 0.0,
|
| 423 |
+
"transport_field_loss": 0.0,
|
| 424 |
+
"transport_field_potential_loss": 0.0,
|
| 425 |
+
"transport_field_preference_loss": 0.0,
|
| 426 |
+
"transport_field_rank_acc": 0.0
|
| 427 |
+
},
|
| 428 |
+
"per_task": {
|
| 429 |
+
"LiftPegUpright-v1": {
|
| 430 |
+
"action_mse_to_best": 0.44579967874839593,
|
| 431 |
+
"expert_success_rate": 0.8229166666666666,
|
| 432 |
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"num_groups": 96,
|
| 433 |
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"oracle_success_rate": 0.9270833333333334,
|
| 434 |
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"policy_expert_regret": 0.8196508556914827,
|
| 435 |
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"policy_oracle_regret": 0.9392079638006786,
|
| 436 |
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"policy_rollout_progress": 0.6506505594588816,
|
| 437 |
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"policy_rollout_success_rate": 0.34375,
|
| 438 |
+
"restore_max_error": 1.955777406692505e-07
|
| 439 |
+
},
|
| 440 |
+
"PickCube-v1": {
|
| 441 |
+
"action_mse_to_best": 0.4667625424894269,
|
| 442 |
+
"expert_success_rate": 0.9444444444444444,
|
| 443 |
+
"num_groups": 198,
|
| 444 |
+
"oracle_success_rate": 0.9595959595959596,
|
| 445 |
+
"policy_expert_regret": 1.0138591957852394,
|
| 446 |
+
"policy_oracle_regret": 1.020942646313948,
|
| 447 |
+
"policy_rollout_progress": 0.6228315381502564,
|
| 448 |
+
"policy_rollout_success_rate": 0.3181818181818182,
|
| 449 |
+
"restore_max_error": 2.384185791015625e-07
|
| 450 |
+
},
|
| 451 |
+
"PullCube-v1": {
|
| 452 |
+
"action_mse_to_best": 0.6834456741809845,
|
| 453 |
+
"expert_success_rate": 0.24444444444444444,
|
| 454 |
+
"num_groups": 90,
|
| 455 |
+
"oracle_success_rate": 0.4666666666666667,
|
| 456 |
+
"policy_expert_regret": 0.3312447832793825,
|
| 457 |
+
"policy_oracle_regret": 0.4773602064595454,
|
| 458 |
+
"policy_rollout_progress": 0.35248699988135035,
|
| 459 |
+
"policy_rollout_success_rate": 0.24444444444444444,
|
| 460 |
+
"restore_max_error": 2.384185791015625e-07
|
| 461 |
+
},
|
| 462 |
+
"PushCube-v1": {
|
| 463 |
+
"action_mse_to_best": 0.512414920669381,
|
| 464 |
+
"expert_success_rate": 0.8514851485148515,
|
| 465 |
+
"num_groups": 101,
|
| 466 |
+
"oracle_success_rate": 1.0,
|
| 467 |
+
"policy_expert_regret": 0.26510606558606176,
|
| 468 |
+
"policy_oracle_regret": 0.2836854187863888,
|
| 469 |
+
"policy_rollout_progress": 0.8648294326987597,
|
| 470 |
+
"policy_rollout_success_rate": 0.8514851485148515,
|
| 471 |
+
"restore_max_error": 2.384185791015625e-07
|
| 472 |
+
},
|
| 473 |
+
"StackCube-v1": {
|
| 474 |
+
"action_mse_to_best": 0.6296677116718558,
|
| 475 |
+
"expert_success_rate": 0.7666666666666667,
|
| 476 |
+
"num_groups": 90,
|
| 477 |
+
"oracle_success_rate": 0.9111111111111111,
|
| 478 |
+
"policy_expert_regret": 1.117056666314602,
|
| 479 |
+
"policy_oracle_regret": 1.2662078713377316,
|
| 480 |
+
"policy_rollout_progress": 0.4551861916979154,
|
| 481 |
+
"policy_rollout_success_rate": 0.17777777777777778,
|
| 482 |
+
"restore_max_error": 2.2351741790771484e-07
|
| 483 |
+
}
|
| 484 |
+
}
|
| 485 |
+
}
|
| 486 |
+
]
|
| 487 |
+
}
|
workspace/results/h16_transport_field_reground_fieldonly_k6clean_dropnoopwg_b12_v1_besttransport_margin0p00_k6_srcscore_task_pick001_stack005_policyanchor_advw4p0_summary.md
ADDED
|
@@ -0,0 +1,19 @@
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|
| 1 |
+
# h=16 Best-Policy Checkpoint Rollout
|
| 2 |
+
|
| 3 |
+
Run root: `/scratch/knguy52/dovla/experiments/dovla_h16_policy_ckpt_runs`
|
| 4 |
+
Objective: `transport_field_reground_fieldonly_k6clean_dropnoopwg_b12_v1`
|
| 5 |
+
Result file: `policy_rollout_fieldonly_k6clean_dropnoopwg_b12_v1_besttransport_margin0p00_k6_srcscore_task_pick001_stack005_policyanchor_advw4p0.json`
|
| 6 |
+
Completed seeds: 3
|
| 7 |
+
Baseline h=4 policy success: 29.67%
|
| 8 |
+
Baseline h=16 rank-checkpoint success: 29.74%
|
| 9 |
+
|
| 10 |
+
Mean success: 37.16% +/- 0.96%
|
| 11 |
+
Gain vs h=16 rank checkpoint: +7.42%
|
| 12 |
+
Mean progress: 58.72%
|
| 13 |
+
Mean action MSE to best: 0.514
|
| 14 |
+
|
| 15 |
+
| seed | mode | k | policy cand | retrieval K | retrieval metric | residual anchor | residual direction | residual reduce | min type success | type success bonus | consensus penalty | min source progress | source progress bonus | source score bonus | source adv bonus | source adv weight | residual scale | residual scales | margin | sigma | opt steps | trust | success | progress | oracle | candidate oracle | oracle gain | action MSE |
|
| 16 |
+
|---:|---|---:|---|---:|---|---|---|---|---:|---:|---:|---:|---:|---:|---:|---:|---:|---|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|
|
| 17 |
+
| 0 | retrieval_residual | 48 | no | 6 | raw | policy | candidate_minus_anchor | compose_mean_by_type | 0.00 | 0.000 | 0.000 | 0.00 | 0.000 | 0.000 | 0.000 | 4.000 | 1.00 | 0.35,0.40,0.45 | 0.000 | 0.00 | 0 | 0.00 | 36.52% | 57.67% | 85.74% | n/a | n/a | 0.497 |
|
| 18 |
+
| 1 | retrieval_residual | 48 | no | 6 | raw | policy | candidate_minus_anchor | compose_mean_by_type | 0.00 | 0.000 | 0.000 | 0.00 | 0.000 | 0.000 | 0.000 | 4.000 | 1.00 | 0.35,0.40,0.45 | 0.000 | 0.00 | 0 | 0.00 | 36.70% | 58.35% | 86.96% | n/a | n/a | 0.515 |
|
| 19 |
+
| 2 | retrieval_residual | 48 | no | 6 | raw | policy | candidate_minus_anchor | compose_mean_by_type | 0.00 | 0.000 | 0.000 | 0.00 | 0.000 | 0.000 | 0.000 | 4.000 | 1.00 | 0.35,0.40,0.45 | 0.000 | 0.00 | 0 | 0.00 | 38.26% | 60.14% | 87.65% | n/a | n/a | 0.531 |
|
workspace/results/paper_analysis.json
CHANGED
|
@@ -23,7 +23,1795 @@
|
|
| 23 |
"selected_success_for_65pct_gap_closure": 0.47449275362318843,
|
| 24 |
"selected_success_for_75pct_gap_closure": 0.5017391304347827
|
| 25 |
},
|
| 26 |
-
"generated_utc": "2026-07-
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|
| 27 |
"mechanism_gap": {
|
| 28 |
"best_clean_vs_direct_same_ckpt": 0.1060869565217391,
|
| 29 |
"best_clean_vs_h16": 0.09159420289855075,
|
|
|
|
| 23 |
"selected_success_for_65pct_gap_closure": 0.47449275362318843,
|
| 24 |
"selected_success_for_75pct_gap_closure": 0.5017391304347827
|
| 25 |
},
|
| 26 |
+
"generated_utc": "2026-07-02T20:35:07+00:00",
|
| 27 |
+
"generator_v2_cvae_support_proxy": {
|
| 28 |
+
"config": {
|
| 29 |
+
"batch_size": 128,
|
| 30 |
+
"beta": 0.02,
|
| 31 |
+
"diversity_temperature": 0.5,
|
| 32 |
+
"epochs": 300,
|
| 33 |
+
"hidden_dim": 256,
|
| 34 |
+
"latent_dim": 24,
|
| 35 |
+
"learning_rate": 0.001,
|
| 36 |
+
"obs_dim": 96,
|
| 37 |
+
"seed": 0,
|
| 38 |
+
"text_dim": 64,
|
| 39 |
+
"val_fraction": 0.2
|
| 40 |
+
},
|
| 41 |
+
"label_counts": {
|
| 42 |
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"negative": 35225,
|
| 43 |
+
"neutral": 6310,
|
| 44 |
+
"positive": 1560
|
| 45 |
+
},
|
| 46 |
+
"metric_scope": "offline_support_proxy",
|
| 47 |
+
"missing": false,
|
| 48 |
+
"note": "CVAE proposals are sampled from a train-only positive tangent model and evaluated against hidden heldout same-state positives.",
|
| 49 |
+
"num_examples": 43095,
|
| 50 |
+
"num_groups": 2873,
|
| 51 |
+
"num_train_examples": 34275,
|
| 52 |
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"num_train_positive": 1198,
|
| 53 |
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"num_val_examples": 8820,
|
| 54 |
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"num_val_groups_with_positive": 93,
|
| 55 |
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"overall": {
|
| 56 |
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"mean_positive_min_rms_l2_at_1": 0.7824125163314778,
|
| 57 |
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"mean_positive_min_rms_l2_at_16": 0.641030304113575,
|
| 58 |
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"mean_positive_min_rms_l2_at_2": 0.7445953670123056,
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| 59 |
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|
| 60 |
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|
| 61 |
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"median_positive_min_rms_l2_at_1": 0.7363222502525835,
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| 62 |
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| 63 |
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| 64 |
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|
| 65 |
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|
| 66 |
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"negative_near_at_16_thr_0p05": 0.0,
|
| 67 |
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|
| 68 |
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|
| 69 |
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"negative_near_at_16_thr_0p4": 0.24,
|
| 70 |
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|
| 71 |
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"negative_near_at_1_thr_0p1": 0.0,
|
| 72 |
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| 73 |
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"negative_near_at_1_thr_0p4": 0.14666666666666667,
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| 74 |
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"negative_near_at_2_thr_0p05": 0.0,
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| 75 |
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| 76 |
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| 77 |
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"negative_near_at_2_thr_0p4": 0.18666666666666668,
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| 78 |
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| 79 |
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"negative_near_at_4_thr_0p1": 0.0,
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| 80 |
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"negative_near_at_4_thr_0p2": 0.0,
|
| 81 |
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"negative_near_at_4_thr_0p4": 0.2,
|
| 82 |
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|
| 83 |
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"negative_near_at_8_thr_0p1": 0.0,
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| 84 |
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| 85 |
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"negative_near_at_8_thr_0p4": 0.21333333333333335,
|
| 86 |
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"num_groups": 93,
|
| 87 |
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"positive_closer_than_negative_rate_at_1": 0.6266666666666667,
|
| 88 |
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"positive_closer_than_negative_rate_at_16": 0.6533333333333333,
|
| 89 |
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"positive_closer_than_negative_rate_at_2": 0.6266666666666667,
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| 90 |
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"positive_closer_than_negative_rate_at_4": 0.6666666666666666,
|
| 91 |
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"positive_closer_than_negative_rate_at_8": 0.64,
|
| 92 |
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"ptr_proxy_at_16_thr_0p05": 0.0,
|
| 93 |
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| 94 |
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"ptr_proxy_at_16_thr_0p2": 0.07526881720430108,
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| 95 |
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"ptr_proxy_at_16_thr_0p4": 0.34408602150537637,
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| 96 |
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|
| 97 |
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| 98 |
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"ptr_proxy_at_1_thr_0p2": 0.021505376344086023,
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| 99 |
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"ptr_proxy_at_1_thr_0p4": 0.1935483870967742,
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| 100 |
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| 101 |
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| 102 |
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"ptr_proxy_at_2_thr_0p2": 0.043010752688172046,
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| 103 |
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"ptr_proxy_at_2_thr_0p4": 0.21505376344086022,
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| 104 |
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"ptr_proxy_at_4_thr_0p05": 0.0,
|
| 105 |
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"ptr_proxy_at_4_thr_0p1": 0.0,
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| 106 |
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"ptr_proxy_at_4_thr_0p2": 0.07526881720430108,
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| 107 |
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"ptr_proxy_at_4_thr_0p4": 0.24731182795698925,
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| 108 |
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| 109 |
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| 110 |
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|
| 111 |
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"ptr_proxy_at_8_thr_0p4": 0.26881720430107525
|
| 112 |
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},
|
| 113 |
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"per_task": {
|
| 114 |
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"LiftPegUpright-v1": {
|
| 115 |
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"mean_positive_min_rms_l2_at_1": 0.39111942974351616,
|
| 116 |
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"mean_positive_min_rms_l2_at_16": 0.3512190545690644,
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| 117 |
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| 118 |
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| 119 |
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"mean_positive_min_rms_l2_at_8": 0.3572936568478917,
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| 120 |
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"median_positive_min_rms_l2_at_1": 0.36694583942625314,
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| 121 |
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| 122 |
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"median_positive_min_rms_l2_at_2": 0.36583500576650796,
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| 123 |
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"median_positive_min_rms_l2_at_4": 0.329721585304501,
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| 124 |
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| 125 |
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|
| 126 |
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|
| 127 |
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|
| 128 |
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| 129 |
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| 130 |
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| 131 |
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|
| 132 |
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"negative_near_at_1_thr_0p4": 0.25,
|
| 133 |
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|
| 134 |
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|
| 135 |
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|
| 136 |
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"negative_near_at_2_thr_0p4": 0.5,
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| 137 |
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| 138 |
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|
| 139 |
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|
| 140 |
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| 141 |
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|
| 142 |
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| 143 |
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|
| 144 |
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|
| 145 |
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"num_groups": 8,
|
| 146 |
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|
| 147 |
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|
| 148 |
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|
| 149 |
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|
| 150 |
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|
| 151 |
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|
| 152 |
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|
| 153 |
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| 154 |
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|
| 155 |
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|
| 156 |
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|
| 157 |
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|
| 158 |
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|
| 159 |
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|
| 160 |
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|
| 161 |
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|
| 162 |
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|
| 163 |
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|
| 164 |
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|
| 165 |
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|
| 166 |
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|
| 167 |
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|
| 168 |
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|
| 169 |
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|
| 170 |
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"ptr_proxy_at_8_thr_0p4": 0.75
|
| 171 |
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},
|
| 172 |
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"PickCube-v1": {
|
| 173 |
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|
| 174 |
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"mean_positive_min_rms_l2_at_16": 0.40976408842272166,
|
| 175 |
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"mean_positive_min_rms_l2_at_2": 0.5831845872132485,
|
| 176 |
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"mean_positive_min_rms_l2_at_4": 0.4808842074614948,
|
| 177 |
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|
| 178 |
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|
| 179 |
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"median_positive_min_rms_l2_at_16": 0.4070159486917856,
|
| 180 |
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"median_positive_min_rms_l2_at_2": 0.5704718658537254,
|
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"median_positive_min_rms_l2_at_4": 0.4804083273804754,
|
| 182 |
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"median_positive_min_rms_l2_at_8": 0.42884823945946465,
|
| 183 |
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| 184 |
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|
| 185 |
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+
"positive_closer_than_negative_rate_at_4": 0.375,
|
| 1714 |
+
"positive_closer_than_negative_rate_at_8": 0.875,
|
| 1715 |
+
"ptr_proxy_at_16_thr_0p05": 0.0,
|
| 1716 |
+
"ptr_proxy_at_16_thr_0p1": 0.0,
|
| 1717 |
+
"ptr_proxy_at_16_thr_0p2": 0.06666666666666667,
|
| 1718 |
+
"ptr_proxy_at_16_thr_0p4": 0.6,
|
| 1719 |
+
"ptr_proxy_at_1_thr_0p05": 0.0,
|
| 1720 |
+
"ptr_proxy_at_1_thr_0p1": 0.0,
|
| 1721 |
+
"ptr_proxy_at_1_thr_0p2": 0.0,
|
| 1722 |
+
"ptr_proxy_at_1_thr_0p4": 0.0,
|
| 1723 |
+
"ptr_proxy_at_2_thr_0p05": 0.0,
|
| 1724 |
+
"ptr_proxy_at_2_thr_0p1": 0.0,
|
| 1725 |
+
"ptr_proxy_at_2_thr_0p2": 0.0,
|
| 1726 |
+
"ptr_proxy_at_2_thr_0p4": 0.0,
|
| 1727 |
+
"ptr_proxy_at_4_thr_0p05": 0.0,
|
| 1728 |
+
"ptr_proxy_at_4_thr_0p1": 0.0,
|
| 1729 |
+
"ptr_proxy_at_4_thr_0p2": 0.06666666666666667,
|
| 1730 |
+
"ptr_proxy_at_4_thr_0p4": 0.2,
|
| 1731 |
+
"ptr_proxy_at_8_thr_0p05": 0.0,
|
| 1732 |
+
"ptr_proxy_at_8_thr_0p1": 0.0,
|
| 1733 |
+
"ptr_proxy_at_8_thr_0p2": 0.06666666666666667,
|
| 1734 |
+
"ptr_proxy_at_8_thr_0p4": 0.5333333333333333
|
| 1735 |
+
},
|
| 1736 |
+
"StackCube-v1": {
|
| 1737 |
+
"mean_positive_min_rms_l2_at_1": 0.3757673745128833,
|
| 1738 |
+
"mean_positive_min_rms_l2_at_16": 0.30107246631048973,
|
| 1739 |
+
"mean_positive_min_rms_l2_at_2": 0.37266978493957653,
|
| 1740 |
+
"mean_positive_min_rms_l2_at_4": 0.36921076916278317,
|
| 1741 |
+
"mean_positive_min_rms_l2_at_8": 0.36737043893860366,
|
| 1742 |
+
"median_positive_min_rms_l2_at_1": 0.3155970042305564,
|
| 1743 |
+
"median_positive_min_rms_l2_at_16": 0.27981834400490646,
|
| 1744 |
+
"median_positive_min_rms_l2_at_2": 0.3155970042305564,
|
| 1745 |
+
"median_positive_min_rms_l2_at_4": 0.3155970042305564,
|
| 1746 |
+
"median_positive_min_rms_l2_at_8": 0.3155970042305564,
|
| 1747 |
+
"negative_near_at_16_thr_0p05": 0.0,
|
| 1748 |
+
"negative_near_at_16_thr_0p1": 0.0,
|
| 1749 |
+
"negative_near_at_16_thr_0p2": 0.0,
|
| 1750 |
+
"negative_near_at_16_thr_0p4": 0.75,
|
| 1751 |
+
"negative_near_at_1_thr_0p05": 0.0,
|
| 1752 |
+
"negative_near_at_1_thr_0p1": 0.0,
|
| 1753 |
+
"negative_near_at_1_thr_0p2": 0.0,
|
| 1754 |
+
"negative_near_at_1_thr_0p4": 0.4375,
|
| 1755 |
+
"negative_near_at_2_thr_0p05": 0.0,
|
| 1756 |
+
"negative_near_at_2_thr_0p1": 0.0,
|
| 1757 |
+
"negative_near_at_2_thr_0p2": 0.0,
|
| 1758 |
+
"negative_near_at_2_thr_0p4": 0.4375,
|
| 1759 |
+
"negative_near_at_4_thr_0p05": 0.0,
|
| 1760 |
+
"negative_near_at_4_thr_0p1": 0.0,
|
| 1761 |
+
"negative_near_at_4_thr_0p2": 0.0,
|
| 1762 |
+
"negative_near_at_4_thr_0p4": 0.4375,
|
| 1763 |
+
"negative_near_at_8_thr_0p05": 0.0,
|
| 1764 |
+
"negative_near_at_8_thr_0p1": 0.0,
|
| 1765 |
+
"negative_near_at_8_thr_0p2": 0.0,
|
| 1766 |
+
"negative_near_at_8_thr_0p4": 0.4375,
|
| 1767 |
+
"num_groups": 16,
|
| 1768 |
+
"positive_closer_than_negative_rate_at_1": 0.875,
|
| 1769 |
+
"positive_closer_than_negative_rate_at_16": 0.875,
|
| 1770 |
+
"positive_closer_than_negative_rate_at_2": 0.875,
|
| 1771 |
+
"positive_closer_than_negative_rate_at_4": 0.875,
|
| 1772 |
+
"positive_closer_than_negative_rate_at_8": 0.875,
|
| 1773 |
+
"ptr_proxy_at_16_thr_0p05": 0.0,
|
| 1774 |
+
"ptr_proxy_at_16_thr_0p1": 0.0,
|
| 1775 |
+
"ptr_proxy_at_16_thr_0p2": 0.1875,
|
| 1776 |
+
"ptr_proxy_at_16_thr_0p4": 0.875,
|
| 1777 |
+
"ptr_proxy_at_1_thr_0p05": 0.0,
|
| 1778 |
+
"ptr_proxy_at_1_thr_0p1": 0.0,
|
| 1779 |
+
"ptr_proxy_at_1_thr_0p2": 0.1875,
|
| 1780 |
+
"ptr_proxy_at_1_thr_0p4": 0.625,
|
| 1781 |
+
"ptr_proxy_at_2_thr_0p05": 0.0,
|
| 1782 |
+
"ptr_proxy_at_2_thr_0p1": 0.0,
|
| 1783 |
+
"ptr_proxy_at_2_thr_0p2": 0.1875,
|
| 1784 |
+
"ptr_proxy_at_2_thr_0p4": 0.625,
|
| 1785 |
+
"ptr_proxy_at_4_thr_0p05": 0.0,
|
| 1786 |
+
"ptr_proxy_at_4_thr_0p1": 0.0,
|
| 1787 |
+
"ptr_proxy_at_4_thr_0p2": 0.1875,
|
| 1788 |
+
"ptr_proxy_at_4_thr_0p4": 0.625,
|
| 1789 |
+
"ptr_proxy_at_8_thr_0p05": 0.0,
|
| 1790 |
+
"ptr_proxy_at_8_thr_0p1": 0.0,
|
| 1791 |
+
"ptr_proxy_at_8_thr_0p2": 0.1875,
|
| 1792 |
+
"ptr_proxy_at_8_thr_0p4": 0.625
|
| 1793 |
+
}
|
| 1794 |
+
},
|
| 1795 |
+
"proposal_count_by_task": {
|
| 1796 |
+
"LiftPegUpright-v1": 16,
|
| 1797 |
+
"PickCube-v1": 15,
|
| 1798 |
+
"PullCube-v1": 16,
|
| 1799 |
+
"PushCube-v1": 16,
|
| 1800 |
+
"StackCube-v1": 16
|
| 1801 |
+
},
|
| 1802 |
+
"report_type": "positive_tangent_memory_generator_eval",
|
| 1803 |
+
"seed": 0,
|
| 1804 |
+
"source": "results/generator_v2_positive_tangent_memory_eval.json",
|
| 1805 |
+
"targets": "/lustre09/project/6037638/knguy52/vla/results/generator_v2_positive_tangent_targets.json",
|
| 1806 |
+
"train_positive_by_task": {
|
| 1807 |
+
"LiftPegUpright-v1": 50,
|
| 1808 |
+
"PickCube-v1": 15,
|
| 1809 |
+
"PullCube-v1": 723,
|
| 1810 |
+
"PushCube-v1": 308,
|
| 1811 |
+
"StackCube-v1": 102
|
| 1812 |
+
},
|
| 1813 |
+
"val_fraction": 0.2
|
| 1814 |
+
},
|
| 1815 |
"mechanism_gap": {
|
| 1816 |
"best_clean_vs_direct_same_ckpt": 0.1060869565217391,
|
| 1817 |
"best_clean_vs_h16": 0.09159420289855075,
|
workspace/results/paper_analysis.md
CHANGED
|
@@ -1,6 +1,6 @@
|
|
| 1 |
# Paper Analysis
|
| 2 |
|
| 3 |
-
Generated: `2026-07-
|
| 4 |
|
| 5 |
## Main Seed Statistics
|
| 6 |
|
|
@@ -233,6 +233,40 @@ Generated: `2026-07-02T19:30:26+00:00`
|
|
| 233 |
- Branch success by prefix rank: 38.43%, 37.39%, 36.06%, 33.80%, 27.36%, 26.38%, 25.39%, 23.22%.
|
| 234 |
- Branch score gain by prefix rank: +0.000, -0.030, -0.054, -0.095, -0.225, -0.247, -0.267, -0.301.
|
| 235 |
|
|
|
|
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|
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|
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|
|
| 236 |
## Selection Histograms
|
| 237 |
|
| 238 |
- `same_state_near_miss`: lattice_near_miss=1725 (100.0%)
|
|
|
|
| 1 |
# Paper Analysis
|
| 2 |
|
| 3 |
+
Generated: `2026-07-02T20:35:07+00:00`
|
| 4 |
|
| 5 |
## Main Seed Statistics
|
| 6 |
|
|
|
|
| 233 |
- Branch success by prefix rank: 38.43%, 37.39%, 36.06%, 33.80%, 27.36%, 26.38%, 25.39%, 23.22%.
|
| 234 |
- Branch score gain by prefix rank: +0.000, -0.030, -0.054, -0.095, -0.225, -0.247, -0.267, -0.301.
|
| 235 |
|
| 236 |
+
## Generator V2 Support Proxy
|
| 237 |
+
|
| 238 |
+
- Artifact `results/generator_v2_positive_tangent_memory_eval.json` evaluates train-only positive tangent memory proposals on 93 heldout groups with positive support.
|
| 239 |
+
- Train positives by task: {'LiftPegUpright-v1': 50, 'PickCube-v1': 15, 'PullCube-v1': 723, 'PushCube-v1': 308, 'StackCube-v1': 102}; prototype count by task: {'LiftPegUpright-v1': 16, 'PickCube-v1': 15, 'PullCube-v1': 16, 'PushCube-v1': 16, 'StackCube-v1': 16}.
|
| 240 |
+
|
| 241 |
+
| metric | K1 | K2 | K4 | K8 | K16 |
|
| 242 |
+
|---|---:|---:|---:|---:|---:|
|
| 243 |
+
| PTR proxy @ RMS<=0.10 | 0.00% | 0.00% | 0.00% | 1.08% | 3.23% |
|
| 244 |
+
| PTR proxy @ RMS<=0.20 | 6.45% | 7.53% | 8.60% | 8.60% | 11.83% |
|
| 245 |
+
| Negative-near @ RMS<=0.20 | 0.00% | 5.33% | 6.67% | 6.67% | 8.00% |
|
| 246 |
+
| Positive closer than negative | 62.67% | 57.33% | 48.00% | 57.33% | 61.33% |
|
| 247 |
+
|
| 248 |
+
| task | eval groups | K8 PTR@0.20 | K16 PTR@0.20 | K16 pos<neg |
|
| 249 |
+
|---|---:|---:|---:|---:|
|
| 250 |
+
| LiftPegUpright-v1 | 8 | 12.50% | 50.00% | 37.50% |
|
| 251 |
+
| PickCube-v1 | 3 | 100.00% | 100.00% | 100.00% |
|
| 252 |
+
| PullCube-v1 | 51 | 0.00% | 0.00% | 47.50% |
|
| 253 |
+
| PushCube-v1 | 15 | 6.67% | 6.67% | 87.50% |
|
| 254 |
+
| StackCube-v1 | 16 | 18.75% | 18.75% | 87.50% |
|
| 255 |
+
|
| 256 |
+
### Trainable CVAE Diagnostic
|
| 257 |
+
|
| 258 |
+
- Artifact `results/generator_v2_positive_tangent_cvae_temp0p5_eval.json` samples from a train-only positive-tangent CVAE trained on 1198 positive targets.
|
| 259 |
+
- Final training snapshot: epoch 300, loss 0.0627, reconstruction MSE 0.0332, KL 1.4739.
|
| 260 |
+
|
| 261 |
+
| generator | heldout groups | K16 PTR@0.20 | K16 PTR@0.40 | K16 neg@0.20 | K16 pos<neg |
|
| 262 |
+
|---|---:|---:|---:|---:|---:|
|
| 263 |
+
| memory | 93 | 11.83% | 41.94% | 8.00% | 61.33% |
|
| 264 |
+
| raw-cvae | 93 | 7.53% | 34.41% | 0.00% | 65.33% |
|
| 265 |
+
| spline-cvae | 93 | 9.68% | 34.41% | 1.33% | 66.67% |
|
| 266 |
+
| spline-flow | 93 | 1.08% | 29.03% | 0.00% | 66.67% |
|
| 267 |
+
| guided-spline-flow | missing | missing | missing | missing | missing |
|
| 268 |
+
- Spline-CVAE source `results/generator_v2_positive_tangent_spline_cvae_eval.json` uses 21D keyframe codes decoded to 16x7 chunks.
|
| 269 |
+
|
| 270 |
## Selection Histograms
|
| 271 |
|
| 272 |
- `same_state_near_miss`: lattice_near_miss=1725 (100.0%)
|
workspace/results/v1_generator_next_submitted.json
ADDED
|
@@ -0,0 +1,36 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
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|
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|
|
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|
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|
|
|
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|
|
|
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|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"generated_utc": "2026-07-02T19:13:05.819439+00:00",
|
| 3 |
+
"recommendation": "submit_wider_advantage_weight_support_sweep",
|
| 4 |
+
"submitted": [
|
| 5 |
+
{
|
| 6 |
+
"key": "advw0p5",
|
| 7 |
+
"eval_job": "15069074",
|
| 8 |
+
"summary_job": "15069075"
|
| 9 |
+
},
|
| 10 |
+
{
|
| 11 |
+
"key": "advw4p0",
|
| 12 |
+
"eval_job": "15069076",
|
| 13 |
+
"summary_job": "15069077"
|
| 14 |
+
},
|
| 15 |
+
{
|
| 16 |
+
"key": "policyanchor_advw1p0",
|
| 17 |
+
"eval_job": "15069078",
|
| 18 |
+
"summary_job": "15069079"
|
| 19 |
+
},
|
| 20 |
+
{
|
| 21 |
+
"key": "policyanchor_advw4p0",
|
| 22 |
+
"eval_job": "15069080",
|
| 23 |
+
"summary_job": "15069081"
|
| 24 |
+
},
|
| 25 |
+
{
|
| 26 |
+
"key": "advw2p0_gate0",
|
| 27 |
+
"eval_job": "15069082",
|
| 28 |
+
"summary_job": "15069083"
|
| 29 |
+
},
|
| 30 |
+
{
|
| 31 |
+
"key": "policyanchor_advw2p0_gate0",
|
| 32 |
+
"eval_job": "15069084",
|
| 33 |
+
"summary_job": "15069085"
|
| 34 |
+
}
|
| 35 |
+
]
|
| 36 |
+
}
|
workspace/scripts/build_paper_analysis.py
CHANGED
|
@@ -26,6 +26,15 @@ OUT_JSON = RESULTS_DIR / "paper_analysis.json"
|
|
| 26 |
OUT_MD = RESULTS_DIR / "paper_analysis.md"
|
| 27 |
LATEX_TABLES_DIR = Path("latex") / "tables"
|
| 28 |
OUT_CAR_TABLE = LATEX_TABLES_DIR / "car_decomposition.tex"
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 29 |
CANONICAL_H16_ROLLOUT = Path("/scratch/knguy52/dovla/experiments/dovla_h16_rollout_runs")
|
| 30 |
FALLBACK_BEST_CLEAN_KEY = "residual_k4_consensus_grid035040045_noopbonus003"
|
| 31 |
NON_DEPLOYMENT_KEYS = {
|
|
@@ -1808,6 +1817,201 @@ def _load_methods() -> dict[str, dict[str, Any]]:
|
|
| 1808 |
return methods
|
| 1809 |
|
| 1810 |
|
|
|
|
|
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|
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| 1811 |
def _headline_metric_label(metric: str) -> str:
|
| 1812 |
if metric == "mean_candidate_oracle_success_rate":
|
| 1813 |
return "candidate-oracle"
|
|
@@ -2104,6 +2308,104 @@ def _render_markdown(report: dict[str, Any]) -> str:
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| 2104 |
),
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| 2105 |
]
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| 2106 |
)
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| 2107 |
lines.extend(
|
| 2108 |
[
|
| 2109 |
"",
|
|
@@ -2227,9 +2529,49 @@ def build_report() -> dict[str, Any]:
|
|
| 2227 |
},
|
| 2228 |
"best_candidate_oracle_key": oracle_key,
|
| 2229 |
"best_clean_key": best_clean_key,
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|
| 2230 |
}
|
| 2231 |
|
| 2232 |
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|
| 2233 |
def _latex_pct(value: float | None) -> str:
|
| 2234 |
if value is None:
|
| 2235 |
return "--"
|
|
|
|
| 26 |
OUT_MD = RESULTS_DIR / "paper_analysis.md"
|
| 27 |
LATEX_TABLES_DIR = Path("latex") / "tables"
|
| 28 |
OUT_CAR_TABLE = LATEX_TABLES_DIR / "car_decomposition.tex"
|
| 29 |
+
GENERATOR_V2_MEMORY_EVAL = RESULTS_DIR / "generator_v2_positive_tangent_memory_eval.json"
|
| 30 |
+
GENERATOR_V2_CVAE_EVAL = RESULTS_DIR / "generator_v2_positive_tangent_cvae_eval.json"
|
| 31 |
+
GENERATOR_V2_CVAE_SWEEP = RESULTS_DIR / "generator_v2_positive_tangent_cvae_sweep_summary.json"
|
| 32 |
+
GENERATOR_V2_SPLINE_CVAE_EVAL = RESULTS_DIR / "generator_v2_positive_tangent_spline_cvae_eval.json"
|
| 33 |
+
GENERATOR_V2_SPLINE_CVAE_SWEEP = RESULTS_DIR / "generator_v2_positive_tangent_spline_cvae_sweep_summary.json"
|
| 34 |
+
GENERATOR_V2_SPLINE_FLOW_EVAL = RESULTS_DIR / "generator_v2_positive_tangent_spline_flow_eval.json"
|
| 35 |
+
GENERATOR_V2_SPLINE_FLOW_SWEEP = RESULTS_DIR / "generator_v2_positive_tangent_spline_flow_sweep_summary.json"
|
| 36 |
+
GENERATOR_V2_GUIDED_SPLINE_FLOW_EVAL = RESULTS_DIR / "generator_v2_positive_tangent_guided_spline_flow_eval.json"
|
| 37 |
+
GENERATOR_V2_GUIDED_SPLINE_FLOW_SWEEP = RESULTS_DIR / "generator_v2_positive_tangent_guided_spline_flow_sweep_summary.json"
|
| 38 |
CANONICAL_H16_ROLLOUT = Path("/scratch/knguy52/dovla/experiments/dovla_h16_rollout_runs")
|
| 39 |
FALLBACK_BEST_CLEAN_KEY = "residual_k4_consensus_grid035040045_noopbonus003"
|
| 40 |
NON_DEPLOYMENT_KEYS = {
|
|
|
|
| 1817 |
return methods
|
| 1818 |
|
| 1819 |
|
| 1820 |
+
def _load_generator_v2_support_proxy() -> dict[str, Any]:
|
| 1821 |
+
if not GENERATOR_V2_MEMORY_EVAL.exists():
|
| 1822 |
+
return {"missing": True, "source": str(GENERATOR_V2_MEMORY_EVAL)}
|
| 1823 |
+
data = _load_json(GENERATOR_V2_MEMORY_EVAL)
|
| 1824 |
+
return {
|
| 1825 |
+
"missing": False,
|
| 1826 |
+
"source": str(GENERATOR_V2_MEMORY_EVAL),
|
| 1827 |
+
"report_type": data.get("report_type"),
|
| 1828 |
+
"metric_scope": data.get("metric_scope"),
|
| 1829 |
+
"note": data.get("note"),
|
| 1830 |
+
"targets": data.get("targets"),
|
| 1831 |
+
"seed": data.get("seed"),
|
| 1832 |
+
"val_fraction": data.get("val_fraction"),
|
| 1833 |
+
"diversity_weight": data.get("diversity_weight"),
|
| 1834 |
+
"num_examples": data.get("num_examples"),
|
| 1835 |
+
"num_train_examples": data.get("num_train_examples"),
|
| 1836 |
+
"num_val_examples": data.get("num_val_examples"),
|
| 1837 |
+
"num_groups": data.get("num_groups"),
|
| 1838 |
+
"num_val_groups": data.get("num_val_groups"),
|
| 1839 |
+
"num_eval_groups": data.get("num_eval_groups"),
|
| 1840 |
+
"num_eval_groups_with_positive": data.get("num_eval_groups_with_positive"),
|
| 1841 |
+
"label_counts": data.get("label_counts", {}),
|
| 1842 |
+
"train_positive_by_task": data.get("train_positive_by_task", {}),
|
| 1843 |
+
"proposal_count_by_task": data.get("proposal_count_by_task", {}),
|
| 1844 |
+
"overall": data.get("overall", {}),
|
| 1845 |
+
"per_task": data.get("per_task", {}),
|
| 1846 |
+
}
|
| 1847 |
+
|
| 1848 |
+
|
| 1849 |
+
def _load_generator_v2_cvae_support_proxy() -> dict[str, Any]:
|
| 1850 |
+
source = GENERATOR_V2_CVAE_EVAL
|
| 1851 |
+
sweep_best: dict[str, Any] | None = None
|
| 1852 |
+
if GENERATOR_V2_CVAE_SWEEP.exists():
|
| 1853 |
+
sweep = _load_json(GENERATOR_V2_CVAE_SWEEP)
|
| 1854 |
+
if sweep.get("best", {}).get("path"):
|
| 1855 |
+
candidate = Path(str(sweep["best"]["path"]))
|
| 1856 |
+
if candidate.exists():
|
| 1857 |
+
source = candidate
|
| 1858 |
+
sweep_best = sweep["best"]
|
| 1859 |
+
if not source.exists():
|
| 1860 |
+
return {"missing": True, "source": str(source)}
|
| 1861 |
+
data = _load_json(source)
|
| 1862 |
+
return {
|
| 1863 |
+
"missing": False,
|
| 1864 |
+
"source": str(source),
|
| 1865 |
+
"sweep_summary": str(GENERATOR_V2_CVAE_SWEEP)
|
| 1866 |
+
if GENERATOR_V2_CVAE_SWEEP.exists()
|
| 1867 |
+
else None,
|
| 1868 |
+
"sweep_best": sweep_best,
|
| 1869 |
+
"report_type": data.get("report_type"),
|
| 1870 |
+
"metric_scope": data.get("metric_scope"),
|
| 1871 |
+
"note": data.get("note"),
|
| 1872 |
+
"targets": data.get("targets"),
|
| 1873 |
+
"config": data.get("config", {}),
|
| 1874 |
+
"num_examples": data.get("num_examples"),
|
| 1875 |
+
"num_groups": data.get("num_groups"),
|
| 1876 |
+
"num_train_examples": data.get("num_train_examples"),
|
| 1877 |
+
"num_val_examples": data.get("num_val_examples"),
|
| 1878 |
+
"num_train_positive": data.get("num_train_positive"),
|
| 1879 |
+
"num_val_groups_with_positive": data.get("num_val_groups_with_positive"),
|
| 1880 |
+
"label_counts": data.get("label_counts", {}),
|
| 1881 |
+
"train_positive_by_task": data.get("train_positive_by_task", {}),
|
| 1882 |
+
"overall": data.get("overall", {}),
|
| 1883 |
+
"per_task": data.get("per_task", {}),
|
| 1884 |
+
"train_history": data.get("train_history", []),
|
| 1885 |
+
}
|
| 1886 |
+
|
| 1887 |
+
|
| 1888 |
+
def _load_generator_v2_spline_cvae_support_proxy() -> dict[str, Any]:
|
| 1889 |
+
source = GENERATOR_V2_SPLINE_CVAE_EVAL
|
| 1890 |
+
sweep_best: dict[str, Any] | None = None
|
| 1891 |
+
if GENERATOR_V2_SPLINE_CVAE_SWEEP.exists():
|
| 1892 |
+
sweep = _load_json(GENERATOR_V2_SPLINE_CVAE_SWEEP)
|
| 1893 |
+
if sweep.get("best", {}).get("path"):
|
| 1894 |
+
candidate = Path(str(sweep["best"]["path"]))
|
| 1895 |
+
if candidate.exists():
|
| 1896 |
+
source = candidate
|
| 1897 |
+
sweep_best = sweep["best"]
|
| 1898 |
+
if not source.exists():
|
| 1899 |
+
return {"missing": True, "source": str(source)}
|
| 1900 |
+
data = _load_json(source)
|
| 1901 |
+
return {
|
| 1902 |
+
"missing": False,
|
| 1903 |
+
"source": str(source),
|
| 1904 |
+
"sweep_summary": str(GENERATOR_V2_SPLINE_CVAE_SWEEP)
|
| 1905 |
+
if GENERATOR_V2_SPLINE_CVAE_SWEEP.exists()
|
| 1906 |
+
else None,
|
| 1907 |
+
"sweep_best": sweep_best,
|
| 1908 |
+
"report_type": data.get("report_type"),
|
| 1909 |
+
"metric_scope": data.get("metric_scope"),
|
| 1910 |
+
"note": data.get("note"),
|
| 1911 |
+
"targets": data.get("targets"),
|
| 1912 |
+
"config": data.get("config", {}),
|
| 1913 |
+
"code_dim": data.get("code_dim"),
|
| 1914 |
+
"horizon": data.get("horizon"),
|
| 1915 |
+
"action_dim": data.get("action_dim"),
|
| 1916 |
+
"num_examples": data.get("num_examples"),
|
| 1917 |
+
"num_groups": data.get("num_groups"),
|
| 1918 |
+
"num_train_examples": data.get("num_train_examples"),
|
| 1919 |
+
"num_val_examples": data.get("num_val_examples"),
|
| 1920 |
+
"num_train_positive": data.get("num_train_positive"),
|
| 1921 |
+
"num_val_groups_with_positive": data.get("num_val_groups_with_positive"),
|
| 1922 |
+
"label_counts": data.get("label_counts", {}),
|
| 1923 |
+
"train_positive_by_task": data.get("train_positive_by_task", {}),
|
| 1924 |
+
"overall": data.get("overall", {}),
|
| 1925 |
+
"per_task": data.get("per_task", {}),
|
| 1926 |
+
"train_history": data.get("train_history", []),
|
| 1927 |
+
}
|
| 1928 |
+
|
| 1929 |
+
|
| 1930 |
+
def _load_generator_v2_spline_flow_support_proxy() -> dict[str, Any]:
|
| 1931 |
+
source = GENERATOR_V2_SPLINE_FLOW_EVAL
|
| 1932 |
+
sweep_best: dict[str, Any] | None = None
|
| 1933 |
+
if GENERATOR_V2_SPLINE_FLOW_SWEEP.exists():
|
| 1934 |
+
sweep = _load_json(GENERATOR_V2_SPLINE_FLOW_SWEEP)
|
| 1935 |
+
if sweep.get("best", {}).get("path"):
|
| 1936 |
+
candidate = Path(str(sweep["best"]["path"]))
|
| 1937 |
+
if candidate.exists():
|
| 1938 |
+
source = candidate
|
| 1939 |
+
sweep_best = sweep["best"]
|
| 1940 |
+
if not source.exists():
|
| 1941 |
+
return {"missing": True, "source": str(source)}
|
| 1942 |
+
data = _load_json(source)
|
| 1943 |
+
return {
|
| 1944 |
+
"missing": False,
|
| 1945 |
+
"source": str(source),
|
| 1946 |
+
"sweep_summary": str(GENERATOR_V2_SPLINE_FLOW_SWEEP)
|
| 1947 |
+
if GENERATOR_V2_SPLINE_FLOW_SWEEP.exists()
|
| 1948 |
+
else None,
|
| 1949 |
+
"sweep_best": sweep_best,
|
| 1950 |
+
"report_type": data.get("report_type"),
|
| 1951 |
+
"metric_scope": data.get("metric_scope"),
|
| 1952 |
+
"note": data.get("note"),
|
| 1953 |
+
"targets": data.get("targets"),
|
| 1954 |
+
"config": data.get("config", {}),
|
| 1955 |
+
"code_dim": data.get("code_dim"),
|
| 1956 |
+
"horizon": data.get("horizon"),
|
| 1957 |
+
"action_dim": data.get("action_dim"),
|
| 1958 |
+
"num_examples": data.get("num_examples"),
|
| 1959 |
+
"num_groups": data.get("num_groups"),
|
| 1960 |
+
"num_train_examples": data.get("num_train_examples"),
|
| 1961 |
+
"num_val_examples": data.get("num_val_examples"),
|
| 1962 |
+
"num_train_positive": data.get("num_train_positive"),
|
| 1963 |
+
"num_val_groups_with_positive": data.get("num_val_groups_with_positive"),
|
| 1964 |
+
"overall": data.get("overall", {}),
|
| 1965 |
+
"per_task": data.get("per_task", {}),
|
| 1966 |
+
"train_history": data.get("train_history", []),
|
| 1967 |
+
}
|
| 1968 |
+
|
| 1969 |
+
|
| 1970 |
+
def _load_generator_v2_guided_spline_flow_support_proxy() -> dict[str, Any]:
|
| 1971 |
+
source = GENERATOR_V2_GUIDED_SPLINE_FLOW_EVAL
|
| 1972 |
+
sweep_best: dict[str, Any] | None = None
|
| 1973 |
+
if GENERATOR_V2_GUIDED_SPLINE_FLOW_SWEEP.exists():
|
| 1974 |
+
sweep = _load_json(GENERATOR_V2_GUIDED_SPLINE_FLOW_SWEEP)
|
| 1975 |
+
if sweep.get("best", {}).get("path"):
|
| 1976 |
+
candidate = Path(str(sweep["best"]["path"]))
|
| 1977 |
+
if candidate.exists():
|
| 1978 |
+
source = candidate
|
| 1979 |
+
sweep_best = sweep["best"]
|
| 1980 |
+
if not source.exists():
|
| 1981 |
+
return {"missing": True, "source": str(source)}
|
| 1982 |
+
data = _load_json(source)
|
| 1983 |
+
return {
|
| 1984 |
+
"missing": False,
|
| 1985 |
+
"source": str(source),
|
| 1986 |
+
"sweep_summary": str(GENERATOR_V2_GUIDED_SPLINE_FLOW_SWEEP)
|
| 1987 |
+
if GENERATOR_V2_GUIDED_SPLINE_FLOW_SWEEP.exists()
|
| 1988 |
+
else None,
|
| 1989 |
+
"sweep_best": sweep_best,
|
| 1990 |
+
"report_type": data.get("report_type"),
|
| 1991 |
+
"metric_scope": data.get("metric_scope"),
|
| 1992 |
+
"note": data.get("note"),
|
| 1993 |
+
"targets": data.get("targets"),
|
| 1994 |
+
"config": data.get("config", {}),
|
| 1995 |
+
"code_dim": data.get("code_dim"),
|
| 1996 |
+
"horizon": data.get("horizon"),
|
| 1997 |
+
"action_dim": data.get("action_dim"),
|
| 1998 |
+
"num_examples": data.get("num_examples"),
|
| 1999 |
+
"num_groups": data.get("num_groups"),
|
| 2000 |
+
"num_train_examples": data.get("num_train_examples"),
|
| 2001 |
+
"num_val_examples": data.get("num_val_examples"),
|
| 2002 |
+
"num_train_positive": data.get("num_train_positive"),
|
| 2003 |
+
"num_train_negative": data.get("num_train_negative"),
|
| 2004 |
+
"num_val_groups_with_positive": data.get("num_val_groups_with_positive"),
|
| 2005 |
+
"label_counts": data.get("label_counts", {}),
|
| 2006 |
+
"train_positive_by_task": data.get("train_positive_by_task", {}),
|
| 2007 |
+
"train_negative_by_task": data.get("train_negative_by_task", {}),
|
| 2008 |
+
"overall": data.get("overall", {}),
|
| 2009 |
+
"per_task": data.get("per_task", {}),
|
| 2010 |
+
"train_history": data.get("train_history", []),
|
| 2011 |
+
"utility_train_history": data.get("utility_train_history", []),
|
| 2012 |
+
}
|
| 2013 |
+
|
| 2014 |
+
|
| 2015 |
def _headline_metric_label(metric: str) -> str:
|
| 2016 |
if metric == "mean_candidate_oracle_success_rate":
|
| 2017 |
return "candidate-oracle"
|
|
|
|
| 2308 |
),
|
| 2309 |
]
|
| 2310 |
)
|
| 2311 |
+
support_proxy = report.get("generator_v2_support_proxy", {})
|
| 2312 |
+
cvae_proxy = report.get("generator_v2_cvae_support_proxy", {})
|
| 2313 |
+
spline_proxy = report.get("generator_v2_spline_cvae_support_proxy", {})
|
| 2314 |
+
flow_proxy = report.get("generator_v2_spline_flow_support_proxy", {})
|
| 2315 |
+
guided_flow_proxy = report.get("generator_v2_guided_spline_flow_support_proxy", {})
|
| 2316 |
+
lines.extend(
|
| 2317 |
+
[
|
| 2318 |
+
"",
|
| 2319 |
+
"## Generator V2 Support Proxy",
|
| 2320 |
+
"",
|
| 2321 |
+
]
|
| 2322 |
+
)
|
| 2323 |
+
if support_proxy.get("missing", True):
|
| 2324 |
+
lines.append(
|
| 2325 |
+
f"- Pending: `{support_proxy.get('source', GENERATOR_V2_MEMORY_EVAL)}` has not been generated yet."
|
| 2326 |
+
)
|
| 2327 |
+
else:
|
| 2328 |
+
overall = support_proxy.get("overall", {})
|
| 2329 |
+
lines.extend(
|
| 2330 |
+
[
|
| 2331 |
+
(
|
| 2332 |
+
f"- Artifact `{support_proxy['source']}` evaluates train-only positive "
|
| 2333 |
+
f"tangent memory proposals on {support_proxy.get('num_eval_groups_with_positive', 0)} "
|
| 2334 |
+
"heldout groups with positive support."
|
| 2335 |
+
),
|
| 2336 |
+
(
|
| 2337 |
+
f"- Train positives by task: {support_proxy.get('train_positive_by_task', {})}; "
|
| 2338 |
+
f"prototype count by task: {support_proxy.get('proposal_count_by_task', {})}."
|
| 2339 |
+
),
|
| 2340 |
+
"",
|
| 2341 |
+
"| metric | K1 | K2 | K4 | K8 | K16 |",
|
| 2342 |
+
"|---|---:|---:|---:|---:|---:|",
|
| 2343 |
+
_support_proxy_row(overall, "PTR proxy @ RMS<=0.10", "ptr_proxy", "0p1"),
|
| 2344 |
+
_support_proxy_row(overall, "PTR proxy @ RMS<=0.20", "ptr_proxy", "0p2"),
|
| 2345 |
+
_support_proxy_row(
|
| 2346 |
+
overall,
|
| 2347 |
+
"Negative-near @ RMS<=0.20",
|
| 2348 |
+
"negative_near",
|
| 2349 |
+
"0p2",
|
| 2350 |
+
),
|
| 2351 |
+
_support_proxy_row(
|
| 2352 |
+
overall,
|
| 2353 |
+
"Positive closer than negative",
|
| 2354 |
+
"positive_closer_than_negative_rate",
|
| 2355 |
+
None,
|
| 2356 |
+
),
|
| 2357 |
+
"",
|
| 2358 |
+
"| task | eval groups | K8 PTR@0.20 | K16 PTR@0.20 | K16 pos<neg |",
|
| 2359 |
+
"|---|---:|---:|---:|---:|",
|
| 2360 |
+
]
|
| 2361 |
+
)
|
| 2362 |
+
for task_id, values in sorted(support_proxy.get("per_task", {}).items()):
|
| 2363 |
+
lines.append(
|
| 2364 |
+
"| {task} | {groups} | {k8} | {k16} | {closer} |".format(
|
| 2365 |
+
task=task_id,
|
| 2366 |
+
groups=int(values.get("num_groups", 0)),
|
| 2367 |
+
k8=_pct(values.get("ptr_proxy_at_8_thr_0p2")),
|
| 2368 |
+
k16=_pct(values.get("ptr_proxy_at_16_thr_0p2")),
|
| 2369 |
+
closer=_pct(values.get("positive_closer_than_negative_rate_at_16")),
|
| 2370 |
+
)
|
| 2371 |
+
)
|
| 2372 |
+
lines.extend(["", "### Trainable CVAE Diagnostic", ""])
|
| 2373 |
+
if cvae_proxy.get("missing", True):
|
| 2374 |
+
lines.append(
|
| 2375 |
+
f"- Pending: `{cvae_proxy.get('source', GENERATOR_V2_CVAE_EVAL)}` has not been generated yet."
|
| 2376 |
+
)
|
| 2377 |
+
else:
|
| 2378 |
+
cvae_history = cvae_proxy.get("train_history", [])
|
| 2379 |
+
last_epoch = cvae_history[-1] if cvae_history else {}
|
| 2380 |
+
lines.extend(
|
| 2381 |
+
[
|
| 2382 |
+
(
|
| 2383 |
+
f"- Artifact `{cvae_proxy['source']}` samples from a train-only "
|
| 2384 |
+
f"positive-tangent CVAE trained on {cvae_proxy.get('num_train_positive', 0)} "
|
| 2385 |
+
"positive targets."
|
| 2386 |
+
),
|
| 2387 |
+
(
|
| 2388 |
+
f"- Final training snapshot: epoch {int(last_epoch.get('epoch', 0))}, "
|
| 2389 |
+
f"loss {float(last_epoch.get('loss', float('nan'))):.4f}, "
|
| 2390 |
+
f"reconstruction MSE {float(last_epoch.get('reconstruction_mse', float('nan'))):.4f}, "
|
| 2391 |
+
f"KL {float(last_epoch.get('kl', float('nan'))):.4f}."
|
| 2392 |
+
),
|
| 2393 |
+
"",
|
| 2394 |
+
"| generator | heldout groups | K16 PTR@0.20 | K16 PTR@0.40 | K16 neg@0.20 | K16 pos<neg |",
|
| 2395 |
+
"|---|---:|---:|---:|---:|---:|",
|
| 2396 |
+
_generator_support_compare_row("memory", support_proxy),
|
| 2397 |
+
_generator_support_compare_row("raw-cvae", cvae_proxy),
|
| 2398 |
+
_generator_support_compare_row("spline-cvae", spline_proxy),
|
| 2399 |
+
_generator_support_compare_row("spline-flow", flow_proxy),
|
| 2400 |
+
_generator_support_compare_row("guided-spline-flow", guided_flow_proxy),
|
| 2401 |
+
]
|
| 2402 |
+
)
|
| 2403 |
+
if not spline_proxy.get("missing", True):
|
| 2404 |
+
lines.append(
|
| 2405 |
+
f"- Spline-CVAE source `{spline_proxy['source']}` uses "
|
| 2406 |
+
f"{spline_proxy.get('code_dim')}D keyframe codes decoded to "
|
| 2407 |
+
f"{spline_proxy.get('horizon')}x{spline_proxy.get('action_dim')} chunks."
|
| 2408 |
+
)
|
| 2409 |
lines.extend(
|
| 2410 |
[
|
| 2411 |
"",
|
|
|
|
| 2529 |
},
|
| 2530 |
"best_candidate_oracle_key": oracle_key,
|
| 2531 |
"best_clean_key": best_clean_key,
|
| 2532 |
+
"generator_v2_support_proxy": _load_generator_v2_support_proxy(),
|
| 2533 |
+
"generator_v2_cvae_support_proxy": _load_generator_v2_cvae_support_proxy(),
|
| 2534 |
+
"generator_v2_spline_cvae_support_proxy": (
|
| 2535 |
+
_load_generator_v2_spline_cvae_support_proxy()
|
| 2536 |
+
),
|
| 2537 |
+
"generator_v2_spline_flow_support_proxy": (
|
| 2538 |
+
_load_generator_v2_spline_flow_support_proxy()
|
| 2539 |
+
),
|
| 2540 |
+
"generator_v2_guided_spline_flow_support_proxy": (
|
| 2541 |
+
_load_generator_v2_guided_spline_flow_support_proxy()
|
| 2542 |
+
),
|
| 2543 |
}
|
| 2544 |
|
| 2545 |
|
| 2546 |
+
def _generator_support_compare_row(name: str, proxy: dict[str, Any]) -> str:
|
| 2547 |
+
if proxy.get("missing", True):
|
| 2548 |
+
return f"| {name} | missing | missing | missing | missing | missing |"
|
| 2549 |
+
overall = proxy.get("overall", {})
|
| 2550 |
+
return (
|
| 2551 |
+
f"| {name} | {int(proxy.get('num_val_groups_with_positive') or proxy.get('num_eval_groups_with_positive') or 0)} | "
|
| 2552 |
+
f"{_pct(overall.get('ptr_proxy_at_16_thr_0p2'))} | "
|
| 2553 |
+
f"{_pct(overall.get('ptr_proxy_at_16_thr_0p4'))} | "
|
| 2554 |
+
f"{_pct(overall.get('negative_near_at_16_thr_0p2'))} | "
|
| 2555 |
+
f"{_pct(overall.get('positive_closer_than_negative_rate_at_16'))} |"
|
| 2556 |
+
)
|
| 2557 |
+
|
| 2558 |
+
|
| 2559 |
+
def _support_proxy_row(
|
| 2560 |
+
values: dict[str, Any],
|
| 2561 |
+
label: str,
|
| 2562 |
+
metric_prefix: str,
|
| 2563 |
+
threshold_key: str | None,
|
| 2564 |
+
) -> str:
|
| 2565 |
+
cells = []
|
| 2566 |
+
for k in (1, 2, 4, 8, 16):
|
| 2567 |
+
if threshold_key is None:
|
| 2568 |
+
key = f"{metric_prefix}_at_{k}"
|
| 2569 |
+
else:
|
| 2570 |
+
key = f"{metric_prefix}_at_{k}_thr_{threshold_key}"
|
| 2571 |
+
cells.append(_pct(values.get(key)))
|
| 2572 |
+
return f"| {label} | " + " | ".join(cells) + " |"
|
| 2573 |
+
|
| 2574 |
+
|
| 2575 |
def _latex_pct(value: float | None) -> str:
|
| 2576 |
if value is None:
|
| 2577 |
return "--"
|
workspace/scripts/eval_positive_tangent_memory.py
ADDED
|
@@ -0,0 +1,81 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
#!/usr/bin/env python
|
| 2 |
+
from __future__ import annotations
|
| 3 |
+
|
| 4 |
+
import argparse
|
| 5 |
+
import json
|
| 6 |
+
import sys
|
| 7 |
+
from pathlib import Path
|
| 8 |
+
|
| 9 |
+
PROJECT_ROOT = Path(__file__).resolve().parents[1]
|
| 10 |
+
if str(PROJECT_ROOT) not in sys.path:
|
| 11 |
+
sys.path.insert(0, str(PROJECT_ROOT))
|
| 12 |
+
|
| 13 |
+
from dovla_cil.generation.tangent_memory import ( # noqa: E402
|
| 14 |
+
evaluate_tangent_memory_generator,
|
| 15 |
+
load_tangent_targets,
|
| 16 |
+
)
|
| 17 |
+
|
| 18 |
+
|
| 19 |
+
def main(argv: list[str] | None = None) -> int:
|
| 20 |
+
parser = argparse.ArgumentParser(
|
| 21 |
+
description=(
|
| 22 |
+
"Evaluate a train-only positive-tangent memory generator against heldout "
|
| 23 |
+
"same-state positive tangent support."
|
| 24 |
+
)
|
| 25 |
+
)
|
| 26 |
+
parser.add_argument("--targets", type=Path, required=True)
|
| 27 |
+
parser.add_argument("--out", type=Path, required=True)
|
| 28 |
+
parser.add_argument("--k-values", default="1,2,4,8,16")
|
| 29 |
+
parser.add_argument("--thresholds", default="0.05,0.1,0.2,0.4")
|
| 30 |
+
parser.add_argument("--val-fraction", type=float, default=0.2)
|
| 31 |
+
parser.add_argument("--seed", type=int, default=0)
|
| 32 |
+
parser.add_argument("--diversity-weight", type=float, default=0.25)
|
| 33 |
+
parser.add_argument("--no-groups", action="store_true", help="Drop per-group rows from output.")
|
| 34 |
+
args = parser.parse_args(argv)
|
| 35 |
+
|
| 36 |
+
try:
|
| 37 |
+
k_values = _parse_ints(args.k_values)
|
| 38 |
+
thresholds = _parse_floats(args.thresholds)
|
| 39 |
+
except ValueError as exc:
|
| 40 |
+
parser.error(str(exc))
|
| 41 |
+
|
| 42 |
+
examples = load_tangent_targets(args.targets)
|
| 43 |
+
report = evaluate_tangent_memory_generator(
|
| 44 |
+
examples,
|
| 45 |
+
k_values=k_values,
|
| 46 |
+
thresholds=thresholds,
|
| 47 |
+
val_fraction=args.val_fraction,
|
| 48 |
+
seed=args.seed,
|
| 49 |
+
diversity_weight=args.diversity_weight,
|
| 50 |
+
)
|
| 51 |
+
report["targets"] = str(args.targets)
|
| 52 |
+
if args.no_groups:
|
| 53 |
+
report.pop("groups", None)
|
| 54 |
+
args.out.parent.mkdir(parents=True, exist_ok=True)
|
| 55 |
+
args.out.write_text(json.dumps(report, indent=2) + "\n")
|
| 56 |
+
summary = {
|
| 57 |
+
key: value
|
| 58 |
+
for key, value in report.items()
|
| 59 |
+
if key not in {"groups"}
|
| 60 |
+
}
|
| 61 |
+
print(json.dumps(summary, indent=2))
|
| 62 |
+
print(f"Wrote {args.out}")
|
| 63 |
+
return 0
|
| 64 |
+
|
| 65 |
+
|
| 66 |
+
def _parse_ints(value: str) -> tuple[int, ...]:
|
| 67 |
+
items = tuple(int(item.strip()) for item in value.split(",") if item.strip())
|
| 68 |
+
if not items or any(item <= 0 for item in items):
|
| 69 |
+
raise ValueError("integer list must contain positive values")
|
| 70 |
+
return items
|
| 71 |
+
|
| 72 |
+
|
| 73 |
+
def _parse_floats(value: str) -> tuple[float, ...]:
|
| 74 |
+
items = tuple(float(item.strip()) for item in value.split(",") if item.strip())
|
| 75 |
+
if not items or any(item < 0 for item in items):
|
| 76 |
+
raise ValueError("float list must contain non-negative values")
|
| 77 |
+
return items
|
| 78 |
+
|
| 79 |
+
|
| 80 |
+
if __name__ == "__main__":
|
| 81 |
+
raise SystemExit(main())
|
workspace/scripts/slurm/eval_positive_tangent_memory.sbatch
ADDED
|
@@ -0,0 +1,39 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
#!/bin/bash
|
| 2 |
+
#SBATCH --job-name=eval_pos_tangent_mem
|
| 3 |
+
#SBATCH --account=def-yalda
|
| 4 |
+
#SBATCH --time=00:20:00
|
| 5 |
+
#SBATCH --cpus-per-task=1
|
| 6 |
+
#SBATCH --mem=4G
|
| 7 |
+
#SBATCH --output=outputs/hpc/logs/%x_%j.out
|
| 8 |
+
#SBATCH --error=outputs/hpc/logs/%x_%j.err
|
| 9 |
+
|
| 10 |
+
set -euo pipefail
|
| 11 |
+
|
| 12 |
+
PROJECT_DIR="${PROJECT_DIR:-$SLURM_SUBMIT_DIR}"
|
| 13 |
+
PYTHON="${PYTHON:-python3}"
|
| 14 |
+
TARGETS="${TARGETS:-$PROJECT_DIR/results/generator_v2_positive_tangent_targets.json}"
|
| 15 |
+
OUT="${OUT:-$PROJECT_DIR/results/generator_v2_positive_tangent_memory_eval.json}"
|
| 16 |
+
K_VALUES="${K_VALUES:-1,2,4,8,16}"
|
| 17 |
+
THRESHOLDS="${THRESHOLDS:-0.05,0.1,0.2,0.4}"
|
| 18 |
+
VAL_FRACTION="${VAL_FRACTION:-0.2}"
|
| 19 |
+
SEED="${SEED:-0}"
|
| 20 |
+
DIVERSITY_WEIGHT="${DIVERSITY_WEIGHT:-0.25}"
|
| 21 |
+
NO_GROUPS="${NO_GROUPS:-0}"
|
| 22 |
+
|
| 23 |
+
cd "$PROJECT_DIR"
|
| 24 |
+
mkdir -p outputs/hpc/logs "$(dirname "$OUT")"
|
| 25 |
+
|
| 26 |
+
ARGS=(
|
| 27 |
+
--targets "$TARGETS"
|
| 28 |
+
--out "$OUT"
|
| 29 |
+
--k-values "$K_VALUES"
|
| 30 |
+
--thresholds "$THRESHOLDS"
|
| 31 |
+
--val-fraction "$VAL_FRACTION"
|
| 32 |
+
--seed "$SEED"
|
| 33 |
+
--diversity-weight "$DIVERSITY_WEIGHT"
|
| 34 |
+
)
|
| 35 |
+
if [[ "$NO_GROUPS" == "1" ]]; then
|
| 36 |
+
ARGS+=(--no-groups)
|
| 37 |
+
fi
|
| 38 |
+
|
| 39 |
+
"$PYTHON" scripts/eval_positive_tangent_memory.py "${ARGS[@]}"
|
workspace/scripts/slurm/train_positive_tangent_cvae.sbatch
ADDED
|
@@ -0,0 +1,70 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
#!/bin/bash
|
| 2 |
+
#SBATCH --job-name=train_pos_tangent_cvae
|
| 3 |
+
#SBATCH --account=def-yalda_gpu
|
| 4 |
+
#SBATCH --nodes=1
|
| 5 |
+
#SBATCH --ntasks=1
|
| 6 |
+
#SBATCH --cpus-per-task=2
|
| 7 |
+
#SBATCH --gres=gpu:nvidia_h100_80gb_hbm3_1g.10gb:1
|
| 8 |
+
#SBATCH --mem=16G
|
| 9 |
+
#SBATCH --time=00:30:00
|
| 10 |
+
#SBATCH --output=outputs/hpc/logs/%x_%j.out
|
| 11 |
+
#SBATCH --error=outputs/hpc/logs/%x_%j.err
|
| 12 |
+
|
| 13 |
+
set -euo pipefail
|
| 14 |
+
|
| 15 |
+
PROJECT_DIR="${PROJECT_DIR:-$SLURM_SUBMIT_DIR}"
|
| 16 |
+
SCRATCH_ROOT="/scratch/$USER/dovla"
|
| 17 |
+
SIF="${SIF:-$SCRATCH_ROOT/containers/pytorch_2.7.1_cuda12.8.sif}"
|
| 18 |
+
PYTHON="${PYTHON:-$SCRATCH_ROOT/envs/maniskill/bin/python}"
|
| 19 |
+
TARGETS="${TARGETS:-$PROJECT_DIR/results/generator_v2_positive_tangent_targets.json}"
|
| 20 |
+
OUT="${OUT:-$PROJECT_DIR/results/generator_v2_positive_tangent_cvae_eval.json}"
|
| 21 |
+
CHECKPOINT="${CHECKPOINT:-$PROJECT_DIR/results/generator_v2_positive_tangent_cvae.pt}"
|
| 22 |
+
K_VALUES="${K_VALUES:-1,2,4,8,16}"
|
| 23 |
+
THRESHOLDS="${THRESHOLDS:-0.05,0.1,0.2,0.4}"
|
| 24 |
+
EPOCHS="${EPOCHS:-300}"
|
| 25 |
+
LATENT_DIM="${LATENT_DIM:-24}"
|
| 26 |
+
HIDDEN_DIM="${HIDDEN_DIM:-256}"
|
| 27 |
+
BATCH_SIZE="${BATCH_SIZE:-128}"
|
| 28 |
+
LR="${LR:-0.001}"
|
| 29 |
+
BETA="${BETA:-0.02}"
|
| 30 |
+
VAL_FRACTION="${VAL_FRACTION:-0.2}"
|
| 31 |
+
SEED="${SEED:-0}"
|
| 32 |
+
TEMPERATURE="${TEMPERATURE:-1.0}"
|
| 33 |
+
DEVICE="${DEVICE:-cuda}"
|
| 34 |
+
NO_GROUPS="${NO_GROUPS:-0}"
|
| 35 |
+
|
| 36 |
+
module load StdEnv/2023 apptainer/1.4.5
|
| 37 |
+
cd "$PROJECT_DIR"
|
| 38 |
+
mkdir -p outputs/hpc/logs "$(dirname "$OUT")" "$(dirname "$CHECKPOINT")"
|
| 39 |
+
|
| 40 |
+
export OMP_NUM_THREADS=1
|
| 41 |
+
export OPENBLAS_NUM_THREADS=1
|
| 42 |
+
export MKL_NUM_THREADS=1
|
| 43 |
+
export DOVLA_TORCH_THREADS=1
|
| 44 |
+
|
| 45 |
+
ARGS=(
|
| 46 |
+
--targets "$TARGETS"
|
| 47 |
+
--out "$OUT"
|
| 48 |
+
--checkpoint "$CHECKPOINT"
|
| 49 |
+
--k-values "$K_VALUES"
|
| 50 |
+
--thresholds "$THRESHOLDS"
|
| 51 |
+
--epochs "$EPOCHS"
|
| 52 |
+
--latent-dim "$LATENT_DIM"
|
| 53 |
+
--hidden-dim "$HIDDEN_DIM"
|
| 54 |
+
--batch-size "$BATCH_SIZE"
|
| 55 |
+
--lr "$LR"
|
| 56 |
+
--beta "$BETA"
|
| 57 |
+
--val-fraction "$VAL_FRACTION"
|
| 58 |
+
--seed "$SEED"
|
| 59 |
+
--temperature "$TEMPERATURE"
|
| 60 |
+
--device "$DEVICE"
|
| 61 |
+
)
|
| 62 |
+
if [[ "$NO_GROUPS" == "1" ]]; then
|
| 63 |
+
ARGS+=(--no-groups)
|
| 64 |
+
fi
|
| 65 |
+
|
| 66 |
+
apptainer exec --nv \
|
| 67 |
+
--env "OMP_NUM_THREADS=1,OPENBLAS_NUM_THREADS=1,MKL_NUM_THREADS=1,DOVLA_TORCH_THREADS=1,PYTHONDONTWRITEBYTECODE=1" \
|
| 68 |
+
-B "$PROJECT_DIR:$PROJECT_DIR" \
|
| 69 |
+
-B "/scratch/$USER:/scratch/$USER" \
|
| 70 |
+
"$SIF" "$PYTHON" scripts/train_positive_tangent_cvae.py "${ARGS[@]}"
|
workspace/scripts/slurm/train_positive_tangent_cvae_cpu.sbatch
ADDED
|
@@ -0,0 +1,68 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
#!/bin/bash
|
| 2 |
+
#SBATCH --job-name=train_pos_tangent_cvae_cpu
|
| 3 |
+
#SBATCH --account=def-yalda
|
| 4 |
+
#SBATCH --nodes=1
|
| 5 |
+
#SBATCH --ntasks=1
|
| 6 |
+
#SBATCH --cpus-per-task=4
|
| 7 |
+
#SBATCH --mem=16G
|
| 8 |
+
#SBATCH --time=00:30:00
|
| 9 |
+
#SBATCH --output=outputs/hpc/logs/%x_%j.out
|
| 10 |
+
#SBATCH --error=outputs/hpc/logs/%x_%j.err
|
| 11 |
+
|
| 12 |
+
set -euo pipefail
|
| 13 |
+
|
| 14 |
+
PROJECT_DIR="${PROJECT_DIR:-$SLURM_SUBMIT_DIR}"
|
| 15 |
+
SCRATCH_ROOT="/scratch/$USER/dovla"
|
| 16 |
+
SIF="${SIF:-$SCRATCH_ROOT/containers/pytorch_2.7.1_cuda12.8.sif}"
|
| 17 |
+
PYTHON="${PYTHON:-$SCRATCH_ROOT/envs/maniskill/bin/python}"
|
| 18 |
+
TARGETS="${TARGETS:-$PROJECT_DIR/results/generator_v2_positive_tangent_targets.json}"
|
| 19 |
+
OUT="${OUT:-$PROJECT_DIR/results/generator_v2_positive_tangent_cvae_eval.json}"
|
| 20 |
+
CHECKPOINT="${CHECKPOINT:-$PROJECT_DIR/results/generator_v2_positive_tangent_cvae.pt}"
|
| 21 |
+
K_VALUES="${K_VALUES:-1,2,4,8,16}"
|
| 22 |
+
THRESHOLDS="${THRESHOLDS:-0.05,0.1,0.2,0.4}"
|
| 23 |
+
EPOCHS="${EPOCHS:-300}"
|
| 24 |
+
LATENT_DIM="${LATENT_DIM:-24}"
|
| 25 |
+
HIDDEN_DIM="${HIDDEN_DIM:-256}"
|
| 26 |
+
BATCH_SIZE="${BATCH_SIZE:-128}"
|
| 27 |
+
LR="${LR:-0.001}"
|
| 28 |
+
BETA="${BETA:-0.02}"
|
| 29 |
+
VAL_FRACTION="${VAL_FRACTION:-0.2}"
|
| 30 |
+
SEED="${SEED:-0}"
|
| 31 |
+
TEMPERATURE="${TEMPERATURE:-1.0}"
|
| 32 |
+
NO_GROUPS="${NO_GROUPS:-0}"
|
| 33 |
+
|
| 34 |
+
module load StdEnv/2023 apptainer/1.4.5
|
| 35 |
+
cd "$PROJECT_DIR"
|
| 36 |
+
mkdir -p outputs/hpc/logs "$(dirname "$OUT")" "$(dirname "$CHECKPOINT")"
|
| 37 |
+
|
| 38 |
+
export OMP_NUM_THREADS=1
|
| 39 |
+
export OPENBLAS_NUM_THREADS=1
|
| 40 |
+
export MKL_NUM_THREADS=1
|
| 41 |
+
export DOVLA_TORCH_THREADS=1
|
| 42 |
+
|
| 43 |
+
ARGS=(
|
| 44 |
+
--targets "$TARGETS"
|
| 45 |
+
--out "$OUT"
|
| 46 |
+
--checkpoint "$CHECKPOINT"
|
| 47 |
+
--k-values "$K_VALUES"
|
| 48 |
+
--thresholds "$THRESHOLDS"
|
| 49 |
+
--epochs "$EPOCHS"
|
| 50 |
+
--latent-dim "$LATENT_DIM"
|
| 51 |
+
--hidden-dim "$HIDDEN_DIM"
|
| 52 |
+
--batch-size "$BATCH_SIZE"
|
| 53 |
+
--lr "$LR"
|
| 54 |
+
--beta "$BETA"
|
| 55 |
+
--val-fraction "$VAL_FRACTION"
|
| 56 |
+
--seed "$SEED"
|
| 57 |
+
--temperature "$TEMPERATURE"
|
| 58 |
+
--device cpu
|
| 59 |
+
)
|
| 60 |
+
if [[ "$NO_GROUPS" == "1" ]]; then
|
| 61 |
+
ARGS+=(--no-groups)
|
| 62 |
+
fi
|
| 63 |
+
|
| 64 |
+
apptainer exec \
|
| 65 |
+
--env "OMP_NUM_THREADS=1,OPENBLAS_NUM_THREADS=1,MKL_NUM_THREADS=1,DOVLA_TORCH_THREADS=1,PYTHONDONTWRITEBYTECODE=1" \
|
| 66 |
+
-B "$PROJECT_DIR:$PROJECT_DIR" \
|
| 67 |
+
-B "/scratch/$USER:/scratch/$USER" \
|
| 68 |
+
"$SIF" "$PYTHON" scripts/train_positive_tangent_cvae.py "${ARGS[@]}"
|
workspace/scripts/summarize_positive_tangent_cvae_sweep.py
ADDED
|
@@ -0,0 +1,125 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
#!/usr/bin/env python
|
| 2 |
+
from __future__ import annotations
|
| 3 |
+
|
| 4 |
+
import argparse
|
| 5 |
+
import json
|
| 6 |
+
from pathlib import Path
|
| 7 |
+
from typing import Any
|
| 8 |
+
|
| 9 |
+
|
| 10 |
+
def main() -> int:
|
| 11 |
+
parser = argparse.ArgumentParser(
|
| 12 |
+
description="Summarize positive-tangent CVAE support-proxy sweep results."
|
| 13 |
+
)
|
| 14 |
+
parser.add_argument(
|
| 15 |
+
"--glob",
|
| 16 |
+
default="results/generator_v2_positive_tangent_cvae*_eval.json",
|
| 17 |
+
)
|
| 18 |
+
parser.add_argument(
|
| 19 |
+
"--out-json",
|
| 20 |
+
type=Path,
|
| 21 |
+
default=Path("results/generator_v2_positive_tangent_cvae_sweep_summary.json"),
|
| 22 |
+
)
|
| 23 |
+
parser.add_argument(
|
| 24 |
+
"--out-md",
|
| 25 |
+
type=Path,
|
| 26 |
+
default=Path("results/generator_v2_positive_tangent_cvae_sweep_summary.md"),
|
| 27 |
+
)
|
| 28 |
+
args = parser.parse_args()
|
| 29 |
+
|
| 30 |
+
rows = []
|
| 31 |
+
for path in sorted(Path().glob(args.glob)):
|
| 32 |
+
data = json.loads(path.read_text())
|
| 33 |
+
if data.get("report_type") != "positive_tangent_cvae_generator_eval":
|
| 34 |
+
continue
|
| 35 |
+
rows.append(_summarize(path, data))
|
| 36 |
+
rows = sorted(rows, key=_rank_key)
|
| 37 |
+
summary = {
|
| 38 |
+
"num_runs": len(rows),
|
| 39 |
+
"best": rows[0] if rows else None,
|
| 40 |
+
"rows": rows,
|
| 41 |
+
}
|
| 42 |
+
args.out_json.parent.mkdir(parents=True, exist_ok=True)
|
| 43 |
+
args.out_json.write_text(json.dumps(summary, indent=2) + "\n")
|
| 44 |
+
args.out_md.write_text(_render_markdown(summary), encoding="utf-8")
|
| 45 |
+
print(json.dumps(summary, indent=2))
|
| 46 |
+
print(f"Wrote {args.out_json}")
|
| 47 |
+
print(f"Wrote {args.out_md}")
|
| 48 |
+
return 0
|
| 49 |
+
|
| 50 |
+
|
| 51 |
+
def _summarize(path: Path, data: dict[str, Any]) -> dict[str, Any]:
|
| 52 |
+
overall = data.get("overall", {})
|
| 53 |
+
config = data.get("config", {})
|
| 54 |
+
history = data.get("train_history", [])
|
| 55 |
+
final = history[-1] if history else {}
|
| 56 |
+
return {
|
| 57 |
+
"path": str(path),
|
| 58 |
+
"checkpoint": str(path).replace("_eval.json", ".pt"),
|
| 59 |
+
"temperature": config.get("diversity_temperature"),
|
| 60 |
+
"beta": config.get("beta"),
|
| 61 |
+
"latent_dim": config.get("latent_dim"),
|
| 62 |
+
"epochs": config.get("epochs"),
|
| 63 |
+
"num_val_groups_with_positive": data.get("num_val_groups_with_positive"),
|
| 64 |
+
"ptr_proxy_at_16_thr_0p2": overall.get("ptr_proxy_at_16_thr_0p2"),
|
| 65 |
+
"ptr_proxy_at_16_thr_0p4": overall.get("ptr_proxy_at_16_thr_0p4"),
|
| 66 |
+
"negative_near_at_16_thr_0p2": overall.get("negative_near_at_16_thr_0p2"),
|
| 67 |
+
"positive_closer_than_negative_rate_at_16": overall.get(
|
| 68 |
+
"positive_closer_than_negative_rate_at_16"
|
| 69 |
+
),
|
| 70 |
+
"mean_positive_min_rms_l2_at_16": overall.get("mean_positive_min_rms_l2_at_16"),
|
| 71 |
+
"final_loss": final.get("loss"),
|
| 72 |
+
"final_reconstruction_mse": final.get("reconstruction_mse"),
|
| 73 |
+
"final_kl": final.get("kl"),
|
| 74 |
+
}
|
| 75 |
+
|
| 76 |
+
|
| 77 |
+
def _rank_key(row: dict[str, Any]) -> tuple[float, float, float, float, float]:
|
| 78 |
+
return (
|
| 79 |
+
-float(row.get("ptr_proxy_at_16_thr_0p2") or 0.0),
|
| 80 |
+
-float(row.get("ptr_proxy_at_16_thr_0p4") or 0.0),
|
| 81 |
+
float(row.get("negative_near_at_16_thr_0p2") or 0.0),
|
| 82 |
+
-float(row.get("positive_closer_than_negative_rate_at_16") or 0.0),
|
| 83 |
+
float(row.get("mean_positive_min_rms_l2_at_16") or 1.0e9),
|
| 84 |
+
)
|
| 85 |
+
|
| 86 |
+
|
| 87 |
+
def _render_markdown(summary: dict[str, Any]) -> str:
|
| 88 |
+
lines = [
|
| 89 |
+
"# Positive Tangent CVAE Sweep",
|
| 90 |
+
"",
|
| 91 |
+
"| rank | file | temp | beta | K16 PTR@0.20 | K16 PTR@0.40 | K16 neg@0.20 | K16 pos<neg | mean pos dist | final recon |",
|
| 92 |
+
"|---:|---|---:|---:|---:|---:|---:|---:|---:|---:|",
|
| 93 |
+
]
|
| 94 |
+
for index, row in enumerate(summary.get("rows", []), start=1):
|
| 95 |
+
lines.append(
|
| 96 |
+
"| {rank} | {file} | {temp} | {beta} | {ptr02} | {ptr04} | {neg02} | {closer} | {dist} | {recon} |".format(
|
| 97 |
+
rank=index,
|
| 98 |
+
file=row["path"],
|
| 99 |
+
temp=_fmt(row.get("temperature")),
|
| 100 |
+
beta=_fmt(row.get("beta")),
|
| 101 |
+
ptr02=_pct(row.get("ptr_proxy_at_16_thr_0p2")),
|
| 102 |
+
ptr04=_pct(row.get("ptr_proxy_at_16_thr_0p4")),
|
| 103 |
+
neg02=_pct(row.get("negative_near_at_16_thr_0p2")),
|
| 104 |
+
closer=_pct(row.get("positive_closer_than_negative_rate_at_16")),
|
| 105 |
+
dist=_fmt(row.get("mean_positive_min_rms_l2_at_16")),
|
| 106 |
+
recon=_fmt(row.get("final_reconstruction_mse")),
|
| 107 |
+
)
|
| 108 |
+
)
|
| 109 |
+
return "\n".join(lines) + "\n"
|
| 110 |
+
|
| 111 |
+
|
| 112 |
+
def _pct(value: Any) -> str:
|
| 113 |
+
if value is None:
|
| 114 |
+
return "n/a"
|
| 115 |
+
return f"{float(value) * 100:.2f}%"
|
| 116 |
+
|
| 117 |
+
|
| 118 |
+
def _fmt(value: Any) -> str:
|
| 119 |
+
if value is None:
|
| 120 |
+
return "n/a"
|
| 121 |
+
return f"{float(value):.4f}"
|
| 122 |
+
|
| 123 |
+
|
| 124 |
+
if __name__ == "__main__":
|
| 125 |
+
raise SystemExit(main())
|
workspace/scripts/train_positive_tangent_cvae.py
ADDED
|
@@ -0,0 +1,119 @@
|
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|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
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|
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|
|
|
|
|
|
|
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|
|
|
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|
|
|
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|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
#!/usr/bin/env python
|
| 2 |
+
from __future__ import annotations
|
| 3 |
+
|
| 4 |
+
import argparse
|
| 5 |
+
import json
|
| 6 |
+
import sys
|
| 7 |
+
from pathlib import Path
|
| 8 |
+
|
| 9 |
+
PROJECT_ROOT = Path(__file__).resolve().parents[1]
|
| 10 |
+
if str(PROJECT_ROOT) not in sys.path:
|
| 11 |
+
sys.path.insert(0, str(PROJECT_ROOT))
|
| 12 |
+
|
| 13 |
+
from dovla_cil.generation.tangent_cvae import ( # noqa: E402
|
| 14 |
+
TangentCVAEConfig,
|
| 15 |
+
build_tangent_cvae_rows,
|
| 16 |
+
hash_bow,
|
| 17 |
+
train_and_evaluate_tangent_cvae,
|
| 18 |
+
)
|
| 19 |
+
|
| 20 |
+
|
| 21 |
+
def main(argv: list[str] | None = None) -> int:
|
| 22 |
+
parser = argparse.ArgumentParser(
|
| 23 |
+
description=(
|
| 24 |
+
"Train a conditional VAE over measured positive action tangents and "
|
| 25 |
+
"evaluate heldout positive support recall."
|
| 26 |
+
)
|
| 27 |
+
)
|
| 28 |
+
parser.add_argument("--targets", type=Path, required=True)
|
| 29 |
+
parser.add_argument("--out", type=Path, required=True)
|
| 30 |
+
parser.add_argument("--checkpoint", type=Path, required=True)
|
| 31 |
+
parser.add_argument("--k-values", default="1,2,4,8,16")
|
| 32 |
+
parser.add_argument("--thresholds", default="0.05,0.1,0.2,0.4")
|
| 33 |
+
parser.add_argument("--obs-dim", type=int, default=96)
|
| 34 |
+
parser.add_argument("--text-dim", type=int, default=64)
|
| 35 |
+
parser.add_argument("--hidden-dim", type=int, default=256)
|
| 36 |
+
parser.add_argument("--latent-dim", type=int, default=24)
|
| 37 |
+
parser.add_argument("--batch-size", type=int, default=128)
|
| 38 |
+
parser.add_argument("--epochs", type=int, default=300)
|
| 39 |
+
parser.add_argument("--lr", type=float, default=1.0e-3)
|
| 40 |
+
parser.add_argument("--beta", type=float, default=0.02)
|
| 41 |
+
parser.add_argument("--val-fraction", type=float, default=0.2)
|
| 42 |
+
parser.add_argument("--seed", type=int, default=0)
|
| 43 |
+
parser.add_argument("--temperature", type=float, default=1.0)
|
| 44 |
+
parser.add_argument("--device", default="auto")
|
| 45 |
+
parser.add_argument("--no-groups", action="store_true", help="Drop per-group rows from output.")
|
| 46 |
+
args = parser.parse_args(argv)
|
| 47 |
+
|
| 48 |
+
k_values = _parse_ints(args.k_values)
|
| 49 |
+
thresholds = _parse_floats(args.thresholds)
|
| 50 |
+
payload = json.loads(args.targets.read_text())
|
| 51 |
+
targets = list(payload.get("targets", []))
|
| 52 |
+
config = TangentCVAEConfig(
|
| 53 |
+
obs_dim=args.obs_dim,
|
| 54 |
+
text_dim=args.text_dim,
|
| 55 |
+
hidden_dim=args.hidden_dim,
|
| 56 |
+
latent_dim=args.latent_dim,
|
| 57 |
+
batch_size=args.batch_size,
|
| 58 |
+
epochs=args.epochs,
|
| 59 |
+
learning_rate=args.lr,
|
| 60 |
+
beta=args.beta,
|
| 61 |
+
val_fraction=args.val_fraction,
|
| 62 |
+
seed=args.seed,
|
| 63 |
+
diversity_temperature=args.temperature,
|
| 64 |
+
)
|
| 65 |
+
report, artifact = train_and_evaluate_tangent_cvae(
|
| 66 |
+
targets,
|
| 67 |
+
config=config,
|
| 68 |
+
k_values=k_values,
|
| 69 |
+
thresholds=thresholds,
|
| 70 |
+
device=args.device,
|
| 71 |
+
)
|
| 72 |
+
report["targets"] = str(args.targets)
|
| 73 |
+
if args.no_groups:
|
| 74 |
+
report.pop("groups", None)
|
| 75 |
+
args.out.parent.mkdir(parents=True, exist_ok=True)
|
| 76 |
+
args.checkpoint.parent.mkdir(parents=True, exist_ok=True)
|
| 77 |
+
args.out.write_text(json.dumps(report, indent=2) + "\n")
|
| 78 |
+
|
| 79 |
+
try:
|
| 80 |
+
import torch
|
| 81 |
+
except ImportError as exc: # pragma: no cover - handled in train function first
|
| 82 |
+
raise RuntimeError("PyTorch disappeared after training") from exc
|
| 83 |
+
torch.save(artifact, args.checkpoint)
|
| 84 |
+
|
| 85 |
+
summary = {
|
| 86 |
+
key: value
|
| 87 |
+
for key, value in report.items()
|
| 88 |
+
if key not in {"groups"}
|
| 89 |
+
}
|
| 90 |
+
print(json.dumps(summary, indent=2))
|
| 91 |
+
print(f"Wrote {args.out}")
|
| 92 |
+
print(f"Wrote {args.checkpoint}")
|
| 93 |
+
return 0
|
| 94 |
+
|
| 95 |
+
|
| 96 |
+
def _parse_ints(value: str) -> tuple[int, ...]:
|
| 97 |
+
items = tuple(int(item.strip()) for item in value.split(",") if item.strip())
|
| 98 |
+
if not items or any(item <= 0 for item in items):
|
| 99 |
+
raise ValueError("integer list must contain positive values")
|
| 100 |
+
return items
|
| 101 |
+
|
| 102 |
+
|
| 103 |
+
def _parse_floats(value: str) -> tuple[float, ...]:
|
| 104 |
+
items = tuple(float(item.strip()) for item in value.split(",") if item.strip())
|
| 105 |
+
if not items or any(item < 0 for item in items):
|
| 106 |
+
raise ValueError("float list must contain non-negative values")
|
| 107 |
+
return items
|
| 108 |
+
|
| 109 |
+
|
| 110 |
+
__all__ = [
|
| 111 |
+
"TangentCVAEConfig",
|
| 112 |
+
"build_tangent_cvae_rows",
|
| 113 |
+
"hash_bow",
|
| 114 |
+
"main",
|
| 115 |
+
]
|
| 116 |
+
|
| 117 |
+
|
| 118 |
+
if __name__ == "__main__":
|
| 119 |
+
raise SystemExit(main())
|