diff --git a/.gitattributes b/.gitattributes index 30be4bb4bda6579ba37c664598cfe03277be6efc..437e750eeb07498b2216454607e1890f9cd12fe2 100644 --- a/.gitattributes +++ b/.gitattributes @@ -35,3 +35,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text *tfevents* filter=lfs diff=lfs merge=lfs -text models/zero_shot_lgbm.txt filter=lfs diff=lfs merge=lfs -text results/f1_vs_n_full_city.png filter=lfs diff=lfs merge=lfs -text +reports/nnlab/f1_vs_n_overnight.png filter=lfs diff=lfs merge=lfs -text diff --git a/README.md b/README.md index df5401cef658bbd80d41cf043f20eb5246e2585f..57137f510e58ec32cb865fdfb925d16e1e1bf14b 100644 --- a/README.md +++ b/README.md @@ -11,9 +11,11 @@ library_name: lightgbm # Building age from Landsat: Madrid → Amsterdam (Hack4Dev 2026) -This repo holds the two winning models from our Iraq Hackathon 2026 / Hack4Dev -entry "Building Age Classification from Satellite Imagery", together with -the code, data and notebooks needed to reproduce them or compare against them. +This repo holds the models from our Iraq Hackathon 2026 / Hack4Dev entry +"Building Age Classification from Satellite Imagery", together with the code, +data and notebooks needed to reproduce them or compare against them. It has +our best pipeline (FINAL-2, added on 11 November 2026) and the two LightGBM +models of the first release. **The task.** Predict a building-age class (1–4) for each 30 m Landsat pixel. Train on Madrid and transfer to Amsterdam. Scoring is macro F1 on Amsterdam @@ -27,7 +29,151 @@ class** (the few-shot protocol). | 3 | 1984–2004 | 1984–2004 | | 4 | 2004–2024 | 2004–2024 | -## The two winners +## Update, 11 November 2026: the best pipeline (FINAL-2) + +After the first release we scored about 130 more model variants in one night +on a laptop CPU: neural networks with a frozen backbone, metric learning, +SVMs and other classical models, and ideas from the domain-adaptation +literature. The best result combines two models with a fixed weighting and a +fixed decision rule: + +1. **Zero-shot anchor.** A teammate's XGBoost + DANN design on the organisers' + 60 features, re-implemented by us on CPU. It is trained on Madrid labels + and unlabelled Amsterdam features, and never sees an Amsterdam label. +2. **LightGBM with domain copies.** The retrained LightGBM from the first + release with one change, Daumé's "frustratingly easy" domain adaptation. + The 120 most important features each get two extra copies: one filled only + on Madrid rows and one filled only on Amsterdam rows. +3. **Combine.** Average the two models' log-probabilities. The zero-shot model + gets weight 10/(n+10) and the LightGBM gets the rest. No weight is fitted. +4. **Decide.** Smooth each pixel's probabilities with its neighbours (radius 6 + pixels, the teammate's setting, which they report choosing on Madrid). + Then apply our decision rule: tau = 0.5 logit adjustment with the Madrid + class prior, then the macro-F1 plug-in. The rule was chosen on Madrid. + +Macro F1 on all of Amsterdam, on draws 5–11 (seven per n). These draws were +never used to choose anything. + +| pipeline | n=5 | n=25 | n=50 | n=100 | n=200 | average | +|---|---|---|---|---|---|---| +| **FINAL-2, with neighbour smoothing** | 0.672 | 0.705 | 0.716 | 0.732 | 0.744 | **0.714** | +| FINAL-2 without coordinates (no smoothing) | 0.652 | 0.694 | 0.702 | 0.720 | 0.734 | 0.700 | +| LightGBM with domain copies alone, with smoothing | 0.623 | 0.699 | 0.712 | 0.730 | 0.744 | 0.702 | +| LightGBM with domain copies alone, no smoothing | 0.607 | 0.687 | 0.698 | 0.718 | 0.734 | 0.689 | +| Round-1 five-model ensemble + label propagation, with smoothing | 0.677 | 0.705 | 0.714 | 0.725 | 0.728 | 0.710 | +| Retrained LightGBM (first release), with smoothing | 0.587 | 0.652 | 0.680 | 0.712 | 0.732 | 0.673 | +| Retrained LightGBM (first release), no smoothing | 0.559 | 0.629 | 0.660 | 0.697 | 0.721 | 0.653 | +| Teammate's zero-shot XGBoost + DANN (our re-implementation), with smoothing and our decision rule | 0.654 | 0.653 | 0.653 | 0.652 | 0.651 | 0.653 | + +- **Compare like with like.** With smoothing on both sides FINAL-2 averages + 0.714 against 0.673 for the retrained LightGBM. Without coordinates on both + sides it averages 0.700 against 0.653. +- **No pixel coordinates?** Quote the second row, 0.700. +- **What the anchor adds.** Against the LightGBM with domain copies alone, + both smoothed, FINAL-2 gains about 0.05 at n=5 and nothing at n=200 (0.744 + for both). +- **The margin over the round-1 ensemble is small.** FINAL-2 is 0.004 ahead + on average. At n=5 the ensemble is 0.004 ahead, which is inside the draw + noise. We prefer FINAL-2 because it is simpler: two models and no network + fitted per draw. +- **The draws differ from the first-release table.** The retrained LightGBM + averages 0.653 here and 0.656 in the table further down, which uses draws + 0–4. +- **Standard deviation over the seven draws** for FINAL-2: 0.007 / 0.012 / + 0.015 / 0.004 / 0.003 at n = 5 / 25 / 50 / 100 / 200. + +![macro F1 against labelling budget, overnight search](reports/nnlab/f1_vs_n_overnight.png) + +Run it for one draw. It takes one to two minutes. Set `NNLAB_THREADS` to your +core count, because the default is 3. + +```bash +python tools/nnlab/model_create2.py --n 25 --rep 6 +python tools/nnlab/model_create2.py --n 200 --rep 3 --no-spatial +``` + +Expected output, from a run inside a clean copy of this repo: + +``` +FINAL-2 n=25 rep=6 spatial=True: macro F1 0.696921 +FINAL-2 n=200 rep=3 spatial=False: macro F1 0.735816 +``` + +From Python: `model_create(shot_idx, shot_labels).predict()` in +`tools/nnlab/model_create2.py`. The full write-up is +`reports/nnlab/OVERNIGHT_RESULTS.md`. + +### Caveats for FINAL-2 + +- **The choice of models was made on Amsterdam.** About 130 variants were + scored on all of Amsterdam on draws 0–4. The LightGBM with domain copies + was picked from those full-city scores, and the two-model combination was + picked on half of the city. FINAL-2 is the second of two rounds of picks. A + round-1 rule (switch pipelines at n=100) failed its confirmation and was + dropped. Only draws 5–11 were never used for choosing. FINAL-2 reads 0.005 + lower on them than on draws 0–4, and the review estimated 0.005 to 0.01 of + optimism for the top picks. +- **It uses more of the unlabelled target city.** The DANN inside the anchor + is trained against unlabelled Amsterdam features, each city is scaled with + its own statistics, and the smoothing needs pixel coordinates. +- **Batch-level.** The decision rule and the smoothing label the whole city at + once. +- **The anchor weight 10/(n+10) was fixed in advance** and was not validated + on Madrid. +- **The anchor's settings are the teammate's.** Their XGBoost and DANN + hyper-parameters and the smoothing radius are copied, not re-tuned here, and + this repo cannot show how they were chosen. +- **The anchor ships as cached Amsterdam probabilities** + (`data/processed/zeroshot_xgb_dann_ams.npz`). The code that builds it + follows the teammate's code and is not in this repo. +- **Fresh draws are not a fresh city.** They are new labelled pixels in the + same Amsterdam. +- **The neural networks use one seed.** +- **The checks were automated.** `verification*.json` and + `script_reviews.json` come from separate AI review agents, not from human or + external review. + +### What else was tried overnight + +Selected variants of each kind. Draws 0–4, all of Amsterdam, our decision +rule, no smoothing. + +| model | script | n=5 | n=25 | n=50 | n=100 | n=200 | average | +|---|---|---|---|---|---|---|---| +| **LightGBM with domain copies** | `lgbm_var.py` | 0.634 | 0.678 | 0.712 | 0.719 | 0.736 | **0.696** | +| DANN network, last layer fine-tuned, then the whole network | `lpft.py` | 0.601 | 0.642 | 0.674 | 0.690 | 0.703 | 0.662 | +| Retrained LightGBM (first release) | `baselines.py` | 0.566 | 0.628 | 0.670 | 0.697 | 0.720 | 0.656 | +| Metric learning: cosine encoder, Mahalanobis head | `metric.py` | 0.583 | 0.638 | 0.678 | 0.688 | 0.691 | 0.656 | +| Metric learning: prototypical net + CORAL, Mahalanobis head | `metric.py` | 0.605 | 0.640 | 0.671 | 0.679 | 0.682 | 0.655 | +| Shrinkage LDA on 64 PCA components | `classical.py` | 0.600 | 0.625 | 0.648 | 0.673 | 0.688 | 0.647 | +| Metric learning: prototypical net + CORAL, projection layer fine-tuned on the shots | `metric.py` | 0.597 | 0.627 | 0.656 | 0.673 | 0.677 | 0.646 | +| Logistic regression | `classical.py` | 0.564 | 0.608 | 0.658 | 0.679 | 0.694 | 0.640 | +| DANN network, backbone frozen, last layer fine-tuned | `tab_nn.py` | 0.605 | 0.628 | 0.643 | 0.650 | 0.660 | 0.637 | +| RBF SVM, domain-augmented features | `classical.py` | 0.556 | 0.611 | 0.651 | 0.661 | 0.674 | 0.631 | +| ResNet, backbone frozen, last layer fine-tuned | `tab_nn.py` | 0.552 | 0.603 | 0.642 | 0.662 | 0.673 | 0.626 | +| 1D CNN + DANN on the raw yearly series, last layer fine-tuned | `ts_nn.py` | 0.516 | 0.601 | 0.620 | 0.649 | 0.658 | 0.609 | +| DANN network, same backbone, last layer refit on Madrid + shots | `tab_nn.py` | 0.567 | 0.572 | 0.578 | 0.587 | 0.603 | 0.581 | + +- **Frozen backbone, last layer fine-tuned.** Starting the last layer from its + Madrid weights and fine-tuning it on the shots beats refitting it on Madrid + plus shots: 0.637 against 0.581 for the same DANN backbone. +- **Unfreezing afterwards helps.** Fine-tuning the whole network for 60 small + steps after the last layer lifts the DANN to 0.662. That is level with the + retrained LightGBM. A variant with an entropy term scores 0.663. +- **Metric learning matches the retrained LightGBM on average.** It is ahead + at n=5 and n=25 and about 0.03 behind at n=200. The Mahalanobis head does + better than fine-tuning the projection layer on three of the four encoders. +- **None of them beats the LightGBM with domain copies**, and adding them to + the combination lowered it. +- **Did not help:** prior re-estimation by EM, neural embeddings as LightGBM + inputs, networks on the raw time series. +- **Not recommended:** self-training (+0.006 on one ensemble, but below its + teacher on its own) and label propagation (+0.006 on Amsterdam, but worse on + a Madrid stand-in). + +All 134 scored methods are in `reports/nnlab/leaderboard.csv`. + +## The two winners of the first release | | **Zero-shot LightGBM** | **Retrained LightGBM** | |---|---|---| @@ -60,7 +206,12 @@ notebook. Their draws and evaluation set are not ours, so compare them loosely. Their prototype scores higher on their 60 features than on our 393. Both of our winners still beat it at every n. -**Best by prize category:** +**Best by prize category, within the first release.** FINAL-2 above is higher +than each of these: 0.714 average, 0.744 peak, 0.705 at n=25, 0.672 at n=5. +Those figures use smoothing, which needs pixel coordinates, and draws 5–11. +Without coordinates they are 0.700 / 0.734 / 0.694 / 0.652. The draws and the +amount of unlabelled Amsterdam information differ from the rows below, so +this is not a like-for-like ranking. - **Average:** retrained LightGBM, 0.6563. - **Peak:** retrained LightGBM at n=200, 0.7204. - **n=25:** the prototype, 0.6361. It is implemented in `src/gbdt.py` @@ -86,16 +237,20 @@ winners. Details are in `results/llm_protocol_results_colab.md`. ## Quick start ```bash -pip install numpy pandas pyarrow scikit-learn lightgbm matplotlib +pip install numpy pandas pyarrow scikit-learn scipy lightgbm matplotlib python winners.py zero-shot # ~15-40 s python winners.py retrained --n 25 --rep 0 # ~2-4 min +python tools/nnlab/model_create2.py --n 25 --rep 6 # FINAL-2, 1-2 min ``` +The neural and metric-learning scripts in `tools/nnlab/` also need `torch`. + Expected output, from the verification run on our machine: ``` zero-shot, all 25,992 Amsterdam pixels: macro F1 0.5928 retrained, n=25 rep=0: macro F1 0.6303 +FINAL-2 n=25 rep=6 spatial=True: macro F1 0.696921 ``` To re-run the whole protocol: `python winners.py protocol --model retrained @@ -278,6 +433,8 @@ models/ retrained_example_shot_indices.json which Amsterdam rows they were trained on meta.json feature order, Madrid prior, verification results data/processed/ madrid_features.parquet, amsterdam_features.parquet + *_official60_first.parquet the organisers' 60 features on the same pixels + zeroshot_xgb_dann_ams.npz cached Amsterdam probabilities of the zero-shot anchor notebooks/ winners_demo.ipynb executed walk-through of both winners (start here) 03_gbdt_models.ipynb full model study: CV, normalisation, decision rule, all 14 variants @@ -285,9 +442,36 @@ notebooks/ src/ the same notebooks as plain .py src/ project code the notebooks import (gbdt.py, evaluation.py, ...) tools/full_score.py full-city re-scoring of the two winners -results/ per-draw JSON, logs of every run, the LLM results +tools/nnlab/ the overnight search (11 November) + model_create2.py FINAL-2 from a set of shots (start here) + lab.py shared harness: draws, prediction store, scoring + lgbm_var.py LightGBM variants, including the domain copies + tab_nn.py, lpft.py tabular networks: frozen backbone, last-layer and whole-network fine-tuning + metric.py metric-learning encoders and heads + classical.py SVM, logistic regression, kNN, LDA + ts_nn.py networks on the raw yearly series (needs the hackathon raw data) + transductive.py, ensemble.py, baselines.py, confirm.py, plot_confirm.py, model_create.py, lpft_steps.py +tools/build_official60_first.py builds the 60-feature tables (needs the hackathon raw data) +results/ per-draw JSON, logs of every run, the LLM results (first release) +reports/nnlab/ results of the overnight search + OVERNIGHT_RESULTS.md the full write-up + confirmation.csv the fresh-draw tables + leaderboard.csv every scored method (leaderboard_A.csv: half A of the city only) + f1_vs_n_overnight.png the chart above + research_shortlist.json the literature sweep behind the ideas tried + scores/ per-draw scores of every method + PREREGISTRATION.md the picks, written before their confirmation + verification*.json, script_reviews.json, verify/ checks by separate AI review agents + cache/ the knobs chosen on Madrid, with their evidence + logs/ logs of every run ``` +The per-draw prediction files and the trained network weights of the overnight +search are not in this repo (about 3 GB). The scripts regenerate them into +`reports/nnlab/preds/` and `reports/nnlab/cache/`. The scripts in +`reports/nnlab/verify/` are the record of the checks as they were run; they +read those prediction files. + `models/meta.json` → `verification` records the checks run when these files were exported: - the ported code in `winners.py` gives the same probabilities as the project diff --git a/data/processed/amsterdam_official60_first.parquet b/data/processed/amsterdam_official60_first.parquet new file mode 100644 index 0000000000000000000000000000000000000000..af29402808065966ac9bf4ebea1ad125bc5cda2c --- /dev/null +++ b/data/processed/amsterdam_official60_first.parquet @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4b89183fe348067ad37fdc85c2194049695c0829c02d1cb9fae0eb4131c89ef4 +size 8858304 diff --git a/data/processed/madrid_official60_first.parquet b/data/processed/madrid_official60_first.parquet new file mode 100644 index 0000000000000000000000000000000000000000..62991809e245a205278307b00c39cefe114777dd --- /dev/null +++ b/data/processed/madrid_official60_first.parquet @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4dc24ffd0dfcb05590919900a7ab1951b9aa46954f844fcc1ee179be455e5bd7 +size 26294722 diff --git a/data/processed/zeroshot_xgb_dann_ams.npz b/data/processed/zeroshot_xgb_dann_ams.npz new file mode 100644 index 0000000000000000000000000000000000000000..0258657369dc16bf7d04ba9d7642ec6962fb49d1 --- /dev/null +++ b/data/processed/zeroshot_xgb_dann_ams.npz @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:847cd046aceedc89352ad7eb4394031b3ef854dd7d5c799e1f7ca80ef158ef06 +size 2080116 diff --git a/reports/nnlab/OVERNIGHT_RESULTS.md b/reports/nnlab/OVERNIGHT_RESULTS.md new file mode 100644 index 0000000000000000000000000000000000000000..93ecf9380194e7ff38e8a9112dffb801dabc0cc5 --- /dev/null +++ b/reports/nnlab/OVERNIGHT_RESULTS.md @@ -0,0 +1,223 @@ +# Overnight model search, 2026-11-11 + +About 130 model variants were scored on the Amsterdam protocol in one night, on +the laptop CPU. The best result is a two-model pipeline: the teammate's +zero-shot XGBoost+DANN combined with a LightGBM that is retrained on Madrid plus +the shots **with domain-specific feature copies**. On draws never used for +choosing it averages **0.714** macro F1 with neighbour smoothing (0.700 without +coordinates), against 0.673 (0.653) for the retrained LightGBM that was the +previous best. + +![Macro F1 against labelling budget](f1_vs_n_overnight.png) + +## The result + +Macro F1 on all of Amsterdam except each draw's labelled pixels. These are the +"fresh" draws 5-11, seven per n, which were never used to choose anything. + +| pipeline | n=5 | n=25 | n=50 | n=100 | n=200 | average | +|---|---|---|---|---|---|---| +| **FINAL-2: zero-shot + LightGBM with domain copies, smoothing** | **0.672** | **0.705** | **0.716** | **0.732** | **0.744** | **0.714** | +| FINAL-2 without coordinates (no smoothing) | 0.652 | 0.694 | 0.702 | 0.720 | 0.734 | 0.700 | +| LightGBM with domain copies alone, smoothing | 0.623 | 0.699 | 0.712 | 0.730 | 0.744 | 0.702 | +| LightGBM with domain copies alone, no smoothing | 0.607 | 0.687 | 0.698 | 0.718 | 0.734 | 0.689 | +| Five-model ensemble (round 1), smoothing | 0.669 | 0.700 | 0.706 | 0.721 | 0.725 | 0.704 | +| Five-model ensemble + label propagation, smoothing | 0.677 | 0.705 | 0.714 | 0.725 | 0.728 | 0.710 | +| Five-model ensemble, no coordinates | 0.647 | 0.683 | 0.690 | 0.706 | 0.713 | 0.688 | +| Retrained LightGBM, smoothing | 0.587 | 0.652 | 0.680 | 0.712 | 0.732 | 0.673 | +| Retrained LightGBM, no smoothing (previous best, as published) | 0.559 | 0.629 | 0.660 | 0.697 | 0.721 | 0.653 | +| Teammate's zero-shot XGBoost+DANN, smoothing, our rule | 0.654 | 0.653 | 0.653 | 0.652 | 0.651 | 0.653 | + +Standard deviation over the seven draws for FINAL-2: 0.007 / 0.012 / 0.015 / +0.004 / 0.003. All 12 draws and the half-B view are in `confirmation.csv`. + +What the comparison says: + +- **FINAL-2 has the best average on the fresh draws (0.714).** Its peak, 0.744 + at n=200, is the same as the LightGBM with domain copies alone. At n=25 it + scores 0.705, level with the five-model ensemble plus label propagation. At + n=5 that ensemble is 0.004 ahead, which is inside the draw noise. +- **Like for like against the previous best.** With smoothing on both, FINAL-2 + gains +0.085 / +0.053 / +0.035 / +0.020 / +0.012 at n = 5 / 25 / 50 / 100 / + 200. Without coordinates on both, +0.047 on the average (0.700 against 0.653). +- **Where the gain comes from.** At n=5 most of the level is the zero-shot model + (0.654 with no labels at all); from n=25 up it is the LightGBM with domain + copies, which alone beats the previous LightGBM by +0.036 on average. +- **Against the round-1 five-model ensemble** the margin is small on average + (+0.010; +0.004 if label propagation is added to the ensemble) and widens + with n. FINAL-2 needs two models and no neural network fitted per draw. +- **Selection optimism is visible and small**: the LightGBM with domain copies + scored 0.696 on the first five draws, where it was picked, and 0.689 on the + fresh ones. The retrained LightGBM, which was never selected, also reads + 0.003 lower on the fresh draws, so the net optimism is about 0.004. +- **The gain does not depend on the decision rule.** The domain copies beat the + plain retrained LightGBM on 35 of 35 fresh draws under plain argmax (+0.039), + our rule (+0.036) and rule plus smoothing (+0.029). + +### FINAL-2, step by step + +1. **Zero-shot anchor**: the teammate's XGBoost + DANN on the official 60 + features. Madrid labels, unlabelled Amsterdam features, no Amsterdam labels. +2. **LightGBM with domain copies** (`lgbm_feda`): our retrained LightGBM + (Madrid + shots weighted 30x, classes balanced, each city robust-z scaled + with its own median and IQR) with one change, Daume's "frustratingly easy" + domain adaptation. The 120 most important features each get two extra + copies, one filled only for Madrid rows and one filled only for Amsterdam + rows. Trees can then learn Madrid-specific splits that Amsterdam rows + bypass through the missing-value branch. +3. **Combine**: average of log-probabilities, the zero-shot model weighted + 10/(n+10) and the LightGBM the rest. No weight is fitted. +4. **Decide**: neighbour smoothing (radius 6 pixels, the teammate's setting, + which they report choosing on Madrid), then our rule (tau = 0.5 logit adjustment and the + macro-F1 plug-in), both chosen on Madrid. + +`tools/nnlab/model_create2.py` runs it from a set of shots: +`model_create(shot_idx, shot_labels).predict()`. It reproduces the stored +scores to six decimals on the two draws tested. One call is one LightGBM fit, +about one to two minutes. + +### Evidence for the domain copies beyond the Amsterdam score + +- **Madrid only.** With a spatial quarter of Madrid standing in as the target + city, the domain copies beat the plain retrained LightGBM in 8 of 9 draws: + +0.005 at n=5, +0.018 at n=50, +0.020 at n=200 + (`verify/feda_madrid_check.json`). Caveat: the list of 120 features came from + a model trained on all of Madrid, the stand-in part included. +- **Shuffled labels.** Trained with the shot labels shuffled, the model + collapses to 0.32 at n=5 and 0.23 at n=200. The gain comes from the shot + labels, not from a leak. +- **Fixed knobs.** 120 features and shot weight 30 were set before any score; + shot weight 100 on top of the copies changed nothing (0.694 against 0.696). +- **What does the work** (one draw, n=50, argmax, by an independent verifier): + plain retrained LightGBM 0.641; one Madrid/Amsterdam indicator column 0.647; + Madrid-only copies 0.662; Amsterdam-only copies 0.659; both 0.664. So most of + the gain comes from letting the trees tell the cities apart, and about 29% of + the split gain sits on the Madrid-only copies against 5% on the + Amsterdam-only ones. + +## Every family, single models + +Draws 0-4, all pixels, our decision rule, no smoothing. + +| family | best variant | n=5 | n=25 | n=50 | n=100 | n=200 | average | +|---|---|---|---|---|---|---|---| +| **LightGBM variants** | **domain copies (Daume)** | 0.634 | 0.678 | 0.712 | 0.719 | 0.736 | **0.696** | +| LightGBM variants | shot weight 100 instead of 30 | 0.571 | 0.636 | 0.680 | 0.706 | 0.731 | 0.665 | +| Tabular network | DANN, last layer then whole network fine-tuned | 0.601 | 0.642 | 0.674 | 0.690 | 0.703 | 0.662 | +| Retrained LightGBM | Madrid + shots x30 (previous best) | 0.566 | 0.628 | 0.670 | 0.697 | 0.720 | 0.656 | +| Metric learning | cosine encoder, Mahalanobis head | 0.583 | 0.638 | 0.678 | 0.688 | 0.691 | 0.656 | +| Metric learning | prototypical net + CORAL, Mahalanobis head | 0.605 | 0.640 | 0.671 | 0.679 | 0.682 | 0.655 | +| Classical | shrinkage LDA on 64 PCA components | 0.600 | 0.625 | 0.648 | 0.673 | 0.688 | 0.647 | +| Classical | logistic regression | 0.564 | 0.608 | 0.658 | 0.679 | 0.695 | 0.640 | +| Tabular network | DANN, frozen, last layer fine-tuned | 0.605 | 0.628 | 0.643 | 0.650 | 0.660 | 0.637 | +| Classical | RBF SVM, domain-augmented features | 0.556 | 0.611 | 0.651 | 0.661 | 0.674 | 0.631 | +| LightGBM variants | neural embeddings as extra columns | 0.568 | 0.598 | 0.626 | 0.657 | 0.689 | 0.628 | +| Tabular network | ResNet, frozen, last layer fine-tuned | 0.552 | 0.603 | 0.642 | 0.662 | 0.673 | 0.626 | +| Sequence network | 1D CNN + DANN on raw yearly series, last layer fine-tuned | 0.516 | 0.601 | 0.620 | 0.649 | 0.658 | 0.609 | +| Tabular network | CORAL, frozen, last layer fine-tuned | 0.544 | 0.562 | 0.576 | 0.595 | 0.614 | 0.578 | + +Findings: + +- **Frozen backbone, fine-tune the last layer.** On every backbone, starting the + last layer from the Madrid weights and fine-tuning it on the shots (with a + pull back toward the Madrid weights) beats re-fitting the last layer on + Madrid + shots. For the DANN: 0.637 against 0.582. +- **Then unfreeze, gently.** Fine-tuning the last layer first and then the + whole network for 60 small steps (Madrid examples mixed in, a penalty for + drifting from the start) lifts the DANN from 0.637 to 0.662, more at large n. + On the fresh draws it scores 0.658, the best neural model of the night and + level with the old LightGBM. The 60 steps were fixed in advance. A Madrid + stand-in preferred 300 steps, which was worse on Amsterdam (0.644 on draws + 0-4, 0.640 fresh), so 60 was kept. +- **Domain-adversarial training is the best backbone.** DANN 0.637, ResNet + 0.626, plain MLP 0.603, CORAL 0.578. +- **Metric learning: the head matters more than the encoder.** A within-class + Mahalanobis head is best on three of the four encoders (on the supervised + contrastive one, fine-tuning the projection wins, 0.641 against 0.630). Plain prototypes reach 0.56 to + 0.60, soft k-means refinement with unlabelled pixels 0.54 to 0.60, and + fine-tuning the projection layer on the shots about 0.64. +- **Raw time series transfer worst.** The sequence networks see the 42-year + series directly and are the weakest family. Their zero-shot scores (0.32 to + 0.53) show how city-specific the raw signal is. +- **Feeding neural embeddings to LightGBM hurt** (0.628 against 0.656). +- **Prior re-estimation by EM hurt on every base on Amsterdam** (for example + 0.656 to 0.646 on the retrained LightGBM), although it helped slightly on + the Madrid stand-in run tonight (+0.010). +- **Self-training** (retrain LightGBM on the ensemble's confident Amsterdam + predictions) added +0.006 to the five-model ensemble without label + propagation and +0.001 with it; on its own it scored below its teacher. +- **Label propagation** added about +0.006 on top of the five-model ensemble on + Amsterdam but lowered the score on a Madrid stand-in (0.475 against 0.498), + so it is reported and not recommended. + +The full list is in `leaderboard.csv`. + +## How far to trust it + +Round 1 was checked by three separate AI review agents (`verification.json`): a +leakage audit (none found; Amsterdam labels are read only for the shots of the +draw being built and by the scorer), an independent recomputation (exact to +float32 precision; the harness reproduces the published retrained-LightGBM +numbers 0.5658 / 0.6284 / 0.6697 / 0.6973 / 0.7204 with zero difference), and a +methodology critique. Round 2 was checked by two more +(`verification_round2.json`): an independent refit of the LightGBM with domain +copies (identical to 3e-8, no leakage found) and a cell-by-cell fact check of +this report, whose corrections are applied. Every model script was also +reviewed by a second agent before its full run (`script_reviews.json`). + +Caveats that belong next to any headline number: + +1. **Chosen on Amsterdam, not Madrid:** which models go into the combination + (FINAL-2 was picked on half A of the first five draws, where adding the + metric-learning and neural models back lowered it). Fixed in advance but + not validated on Madrid: the anchor weight 10/(n+10), a heuristic from the + literature shortlist, and the label-propagation settings. Fixed on Madrid: + the smoothing radius, the decision rule, the teammate's XGBoost and DANN + settings (their own Madrid tuning), and the training lengths and + fine-tuning strength of the networks. +2. **Selection optimism.** About 130 variants were scored. The reviewers + estimated 0.005 to 0.01 of optimism for the top picks; the picks read 0.004 + to 0.007 lower on the fresh draws, of which 0.003 is shared by the + unselected reference. The fresh-draw table is the one to quote. +3. **Fresh draws are not a fresh city.** They use new labelled pixels but the + same Amsterdam. Half A / half B (69 interleaved blocks) is only a sanity + split; on half B FINAL-2 averages 0.700 against 0.696 for the five-model + ensemble (0.699 with label propagation) and 0.656 for the retrained + LightGBM, all with smoothing. +4. **Madrid is a weak guide for adaptation strength.** Three times a Madrid + stand-in and Amsterdam disagreed (label propagation, fine-tuning length, EM + prior correction). They agree on the domain copies and on unfreezing the + network after the last-layer fine-tune. +5. **What the organisers' harness must provide.** All unlabelled Amsterdam + features when the model is built (per-city scaling, the DANN in the anchor), + pixel coordinates for smoothing, and batch prediction of the whole city. + Without coordinates use the "no coordinates" row: 0.700. +6. **A rule that failed.** "Switch pipelines from n=100", chosen on half A in + round 1, did not hold on half B. It is dropped. +7. **The neural networks use one seed** (three were estimated at four hours). + +## Reproducing + +``` +python -I tools/full_score_combo.py zeroshot # teammate's zero-shot cache (once; this script is not in the published repo, the cache is) +python tools/nnlab/baselines.py all # references + retrained LightGBM per draw +python tools/nnlab/lgbm_var.py # LightGBM variants, incl. lgbm_feda +python tools/nnlab/ensemble.py e12 --zs zs_xgb_dann --shot lgbm_feda # FINAL-2 +python tools/nnlab/metric.py # metric-learning encoders and heads +python tools/nnlab/tab_nn.py --seeds 1 # tabular networks, frozen backbone +python tools/nnlab/lpft.py # whole-network fine-tuning +python tools/nnlab/ts_nn.py # sequence networks +python tools/nnlab/classical.py # SVM, logistic regression, kNN, LDA +python tools/nnlab/lab.py score # score everything in the store +python tools/nnlab/confirm.py # the tables above +python tools/nnlab/model_create2.py --n 25 --rep 0 # FINAL-2 for one draw, from the shots +``` + +Set `NNLAB_REPS=12` for all twelve draws. `model_create.py` is the same kind of +entry point for the round-1 five-model ensemble. + +Files: `PREREGISTRATION.md` (both picks, written before their confirmation), +`research_shortlist.json` (the literature sweep; the domain copies are its item +10), `verification.json`, `verification_round2.json`, `script_reviews.json`, +`verify/` (shuffle test, Madrid checks), `logs/`, +`cache/*/selection*.json` (every knob chosen on Madrid, with its evidence). diff --git a/reports/nnlab/PREREGISTRATION.md b/reports/nnlab/PREREGISTRATION.md new file mode 100644 index 0000000000000000000000000000000000000000..058f5e145a3b8d1c5a397dc6365adb9537e7294c --- /dev/null +++ b/reports/nnlab/PREREGISTRATION.md @@ -0,0 +1,54 @@ +# Final picks, fixed before confirmation (2026-11-11 01:36) + +Written before any score on half B alone was tabulated and before draws 5-11 +existed for the ensemble members. + +## What was used to choose +- Half A of Amsterdam (checkerboard of 2 km blocks, `lab.half_A`), draws 0-4, + `python tools/nnlab/lab.py board _A`. +- Honest caveat: full-city scores (A + B together) of the single models and of + ensembles e1-e3 were printed earlier in the night, so B was not perfectly + blind. B on its own was never looked at. + +## The picks +| name | pipeline | decision | +|---|---|---| +| FINAL-AVG | `ens_e4__lp`: fixed-rule ensemble (zero-shot XGBoost+DANN with weight 10/(n+10); the rest shared equally by retrained LightGBM, met_protoda_maha, met_cos_maha, tab_dann_rz_ft), then label propagation with the shots clamped | rule + neighbour smoothing | +| FINAL-PEAK | `ens_e2__lapshot`: zero-shot XGBoost+DANN (weight 10/(n+10)) + retrained LightGBM, then LaplacianShot-style graph refinement | rule + neighbour smoothing | +| FINAL-SWITCH | n <= 50: FINAL-AVG; n >= 100: FINAL-PEAK (n is an input of modelCreate, so this is one method) | as above | + +Half A, draws 0-4 (the evidence): ens_e4__lp 0.7033 / 0.7109 / 0.7290 / 0.7355 / 0.7392; +ens_e2__lapshot 0.6856 / 0.6952 / 0.7172 / 0.7376 / 0.7524. + +Why e4 and not e6 / e7 (which add a self-trained LightGBM or LDA): they differ +from e4 by under 0.001 on half A, which is noise, and e4 is one stage simpler. + +## Confirmation to be reported, whatever it shows +1. Half B, draws 0-11. +2. All pixels, draws 5-11 only (draws never used for choosing). +3. The same for the reference: retrained LightGBM with the rule (0.6563 average). + +## Round 2 pick, fixed before its confirmation (2026-11-11 03:26) + +A LightGBM variant from round 2, `lgbm_feda` (Daume feature augmentation: shared, +Madrid-only and Amsterdam-only copies of the top 120 features; everything else as +the retrained LightGBM), scored well above the others on draws 0-4. + +| name | pipeline | decision | +|---|---|---| +| FINAL-2 | `ens_e12`: zero-shot XGBoost+DANN with weight 10/(n+10) + `lgbm_feda` with the rest | rule + neighbour smoothing | +| FINAL-2 without coordinates | the same | rule only | +| single model | `lgbm_feda` | rule + smoothing, and rule only | + +Evidence used: half A, draws 0-4. ens_e12 rule_smooth 0.7052 / 0.7161 / 0.7410 / +0.7481 / 0.7591 (avg 0.734) against ens_e4 0.7139 avg; lgbm_feda alone 0.7236 avg. +Ensembles that keep the metric-learning and neural members next to lgbm_feda +(e10, e11) were lower on half A (0.721, 0.724) and are not picked. + +Sanity check done before confirmation: training lgbm_feda with the shot labels +shuffled collapses macro F1 to 0.32 (n=5) and 0.23 (n=200), so the gain comes +from the shot labels. + +Confirmation to be reported whatever it shows: all pixels on draws 5-11, on which +lgbm_feda has never been run, and half B on draws 0-11. +`lgbm_fedaw100` (feda + shot weight 100) is exploratory only, draws 0-4. diff --git a/reports/nnlab/cache/lpft/base_check.json b/reports/nnlab/cache/lpft/base_check.json new file mode 100644 index 0000000000000000000000000000000000000000..9c1c94b00d6387f90312a82e1af432fee89ed482 --- /dev/null +++ b/reports/nnlab/cache/lpft/base_check.json @@ -0,0 +1,7 @@ +{ + "compared_with": "reports\\nnlab\\cache\\tab_nn\\emb_dann_rz_s0.npz", + "base_threads": 3, + "Za_maxabs": 1.430511474609375e-06, + "W0_maxabs": 0.0, + "match_1e-3": true +} \ No newline at end of file diff --git a/reports/nnlab/cache/lpft/madrid_check.json b/reports/nnlab/cache/lpft/madrid_check.json new file mode 100644 index 0000000000000000000000000000000000000000..cf18a3748e82e515302c5e15df43aecef7166dcb --- /dev/null +++ b/reports/nnlab/cache/lpft/madrid_check.json @@ -0,0 +1,94 @@ +{ + "ns": [ + 5, + 50, + 200 + ], + "reps": 3, + "shot_rng": "default_rng(9000 + 100 n + rep)", + "epochs": 15, + "lam": 10.0, + "draws": [ + { + "n": 5, + "rep": 0, + "lp": 0.4570928762811606, + "lpft": 0.4752690893967487, + "lpft_ent": 0.48058559187233046 + }, + { + "n": 5, + "rep": 1, + "lp": 0.4962300677451825, + "lpft": 0.47896254829584733, + "lpft_ent": 0.48623655425208534 + }, + { + "n": 5, + "rep": 2, + "lp": 0.49912649201630965, + "lpft": 0.5194485521018687, + "lpft_ent": 0.5218634141305216 + }, + { + "n": 50, + "rep": 0, + "lp": 0.5469353536909174, + "lpft": 0.5490712016882752, + "lpft_ent": 0.5508626235873457 + }, + { + "n": 50, + "rep": 1, + "lp": 0.5190261997622546, + "lpft": 0.5527083307285695, + "lpft_ent": 0.5516332670404107 + }, + { + "n": 50, + "rep": 2, + "lp": 0.5177433426465476, + 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0000000000000000000000000000000000000000..2e1ab8088e34992cdfde3e14240a0a6d82576357 --- /dev/null +++ b/reports/nnlab/cache/metric/protoda_select.json @@ -0,0 +1,16 @@ +{ + "candidates": [ + 400, + 800, + 1600 + ], + "pseudo_target_f1": { + "400": 0.5346068260062208, + "800": 0.5117476445589391, + "1600": 0.5269532337806007 + }, + "chosen": 400, + "seed": 100, + "split": "lab.madrid_split(pm) default", + "da_target_in_selection": "pseudo-target (unlabelled)" +} \ No newline at end of file diff --git a/reports/nnlab/cache/metric/supcon_select.json b/reports/nnlab/cache/metric/supcon_select.json new file mode 100644 index 0000000000000000000000000000000000000000..d0502de989ea6f29899a02160c71126300042eab --- /dev/null +++ b/reports/nnlab/cache/metric/supcon_select.json @@ -0,0 +1,16 @@ +{ + "candidates": [ + 300, + 700, + 1500 + ], + "pseudo_target_f1": { + "300": 0.5113092490498672, + "700": 0.5227456085770579, + "1500": 0.5148359504081166 + }, + "chosen": 700, + "seed": 100, + "split": "lab.madrid_split(pm) default", + "da_target_in_selection": null +} \ No newline at end of file diff --git a/reports/nnlab/cache/tab_nn/selection.json b/reports/nnlab/cache/tab_nn/selection.json new file mode 100644 index 0000000000000000000000000000000000000000..852080b0499030ec8eba1866315df1dca0ae03f9 --- /dev/null +++ b/reports/nnlab/cache/tab_nn/selection.json @@ -0,0 +1,71 @@ +{ + "epochs": { + "mlp": 30, + "res": 15, + "dann": 15, + "coral": 45 + }, + "epoch_scores": { + "mlp": { + "15": 0.4999554636261916, + "30": 0.5049793102098873, + "45": 0.5036779222509986 + }, + "res": { + "15": 0.5075904273140687, + "30": 0.49876532366138193, + "45": 0.48974432628349995 + }, + "dann": { + "15": 0.47348192372436493, + "30": 0.4588599874942516, + "45": 0.4520852473361714 + }, + "coral": { + "15": 0.5034106048326475, + "30": 0.49598761101812194, + "45": 0.5052691962026917 + } + }, + "lam": 10.0, + "lam_scores": { + "0.1": 0.47686438636808737, + "1.0": 0.5023598505755028, + "10.0": 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0.5093685357526514, + "15": 0.514016324587161 + }, + "epochs": 15, + "lam_grid": [ + 0.1, + 1.0, + 10.0, + 100.0 + ], + "pseudo_target_ft_f1_by_lam": { + "0.1": 0.4312605201838526, + "1.0": 0.4417334872748878, + "10.0": 0.4598041504441417, + "100.0": 0.48013005722863794 + }, + "lam": 100.0, + "lam_sim": "n per class in (5, 50) x 5 reps on the pseudo-target, argmax macro F1 on the rest", + "sec_per_full_epoch": 67.5705314318339, + "n_src": 61932, + "n_pseudo_target": 14331 +} \ No newline at end of file diff --git a/reports/nnlab/cache/ts_nn/select_tcnn_ry.json b/reports/nnlab/cache/ts_nn/select_tcnn_ry.json new file mode 100644 index 0000000000000000000000000000000000000000..438859b8fe9a09fe478ce59905fd6b221b3d6806 --- /dev/null +++ b/reports/nnlab/cache/ts_nn/select_tcnn_ry.json @@ -0,0 +1,34 @@ +{ + "backbone": "tcnn", + "variant": "ry", + "seed_used": 0, + "split": "lab.madrid_split(pm) defaults (frac .25, axis 0, high)", + "epoch_grid": [ + 5, + 10, + 15 + ], + "pseudo_target_f1_by_epoch": { + "5": 0.5340027718176347, + "10": 0.5362399360770098, + "15": 0.5545017383586993 + }, + "epochs": 15, + "lam_grid": [ + 0.1, + 1.0, + 10.0, + 100.0 + ], + "pseudo_target_ft_f1_by_lam": { + "0.1": 0.48627533687387514, + "1.0": 0.5195767800933085, + "10.0": 0.5493702887227292, + "100.0": 0.5540445428257714 + }, + "lam": 100.0, + "lam_sim": "n per class in (5, 50) x 5 reps on the pseudo-target, argmax macro F1 on the rest", + "sec_per_full_epoch": 33.42512311935425, + "n_src": 61932, + "n_pseudo_target": 14331 +} \ No newline at end of file diff --git a/reports/nnlab/cache/ts_nn/select_tcnn_st.json b/reports/nnlab/cache/ts_nn/select_tcnn_st.json new file mode 100644 index 0000000000000000000000000000000000000000..82e5c22011e6d410ed72476908ca69b1f2d3956c --- /dev/null +++ b/reports/nnlab/cache/ts_nn/select_tcnn_st.json @@ -0,0 +1,34 @@ +{ + "backbone": "tcnn", + "variant": "st", + "seed_used": 0, + "split": "lab.madrid_split(pm) defaults (frac .25, axis 0, high)", + "epoch_grid": [ + 5, + 10, + 15 + ], + "pseudo_target_f1_by_epoch": { + "5": 0.526023912531838, + "10": 0.5385065163765071, + "15": 0.5474806666222004 + }, + "epochs": 15, + "lam_grid": [ + 0.1, + 1.0, + 10.0, + 100.0 + ], + "pseudo_target_ft_f1_by_lam": { + "0.1": 0.47930721991287895, + "1.0": 0.5090665027050114, + "10.0": 0.5454646105613945, + "100.0": 0.554988512762798 + }, + "lam": 100.0, + "lam_sim": "n per class in (5, 50) x 5 reps on the pseudo-target, argmax macro F1 on the rest", + "sec_per_full_epoch": 35.32085949579875, + "n_src": 61932, + "n_pseudo_target": 14331 +} \ No newline at end of file diff --git a/reports/nnlab/confirmation.csv b/reports/nnlab/confirmation.csv new file mode 100644 index 0000000000000000000000000000000000000000..e150113e3037e73503b30b8d42694a09b1ec1d87 --- /dev/null +++ b/reports/nnlab/confirmation.csv @@ -0,0 +1,65 @@ +method,decision,view,desc,draws_per_n,n5,n25,n50,n100,n200,sd5,sd25,sd50,sd100,sd200,avg,peak 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+ens_e12,rule_smooth,first5,"FINAL-2: zero-shot anchor + LightGBM with domain copies, smoothing",5,0.6878594142590512,0.700558705065584,0.7282409898426059,0.7330910288424988,0.7445541144477235,0.009369197099419522,0.008414653752560104,0.0089721983797269,0.0029137566853388915,0.006137249373050134,0.7188608504914926,0.7445541144477235 +ens_e12,rule,fresh,FINAL-2 without coordinates,7,0.6516885123651744,0.693797220653644,0.7021475444659664,0.7195121117797006,0.7343699773574539,0.012212803354992453,0.013891042304498263,0.01581522936874291,0.004241910523961266,0.002770748028318377,0.7003030733243879,0.7343699773574539 +ens_e12,rule,B,FINAL-2 without coordinates,12,0.6392308173266427,0.6751414668474016,0.6926853048553282,0.7032561079631837,0.7208019501214507,0.010789283170353445,0.011787671993686173,0.014337748317707606,0.004760529058473119,0.006033071617100818,0.6862231294228014,0.7208019501214507 +ens_e12,rule,all,FINAL-2 without coordinates,12,0.6581184151032142,0.6916508913430035,0.7076119674665423,0.7195498421304539,0.735108629399027,0.013005091638930509,0.012009882553406733,0.014470594175165122,0.0044122055691860375,0.004074468051105237,0.7024079490884482,0.735108629399027 +ens_e12,rule,first5,FINAL-2 without coordinates,5,0.6671202789364699,0.6886460303081068,0.7152621596673485,0.7196026646215085,0.7361427422572293,0.00823054968203162,0.009373508878982978,0.008674726783001862,0.0051516112125037285,0.00564326426345011,0.7053547751581327,0.7361427422572293 +lgbm_feda,rule_smooth,fresh,single model: LightGBM with domain copies + smoothing,7,0.6225215988073963,0.6987180472657218,0.7117810874254776,0.7302946783914661,0.7442753480620923,0.0286171373738811,0.014437439688972821,0.015818057636161422,0.003977961611314101,0.0034275753829592676,0.7015181519904308,0.7442753480620923 +lgbm_feda,rule_smooth,B,single model: LightGBM with domain copies + smoothing,12,0.6181771043949493,0.6787718645922062,0.7030722589903492,0.7153855785703288,0.7306202195898068,0.029386425041193903,0.012936623659237619,0.015967345918769565,0.004846691346137096,0.00731989164743793,0.689205405227528,0.7306202195898068 +lgbm_feda,rule_smooth,all,single model: LightGBM with domain copies + smoothing,12,0.6344174583550974,0.6956395257372289,0.7176313726745821,0.7309265706578091,0.7441859203220972,0.030987330440029034,0.013163777578211404,0.014991614797866972,0.003542693927368331,0.004359711473597244,0.704560169549363,0.7441859203220972 +lgbm_feda,rule_smooth,first5,single model: LightGBM with domain copies + smoothing,5,0.6510716617218788,0.6913295955973388,0.7258217720233281,0.7318112198306893,0.7440607214861039,0.028597749244392248,0.011138659031892529,0.009949546503577466,0.003016801939031133,0.005883322455053052,0.7088189941318678,0.7440607214861039 +lgbm_feda,rule,fresh,single model: LightGBM with domain copies,7,0.6068064907339805,0.6865433879814257,0.6978570759264401,0.7182891453372162,0.7340105004959818,0.030806604830152108,0.0172002023875822,0.01813868075010362,0.004443684453089558,0.0027368647529825694,0.688701320095009,0.7340105004959818 +lgbm_feda,rule,B,single model: LightGBM with domain copies,12,0.602663850027922,0.6666432437898443,0.6890515635985178,0.7022168476683185,0.7205734858550638,0.029499212847983382,0.01440777885566807,0.015885497702506506,0.004896987522407284,0.005983854154559139,0.6762297981879333,0.7205734858550638 +lgbm_feda,rule,all,single model: LightGBM with domain copies,12,0.6179690339594646,0.6830595979741189,0.7038838195807409,0.7184562345920446,0.7347887799261619,0.0307051778830208,0.015388864703454018,0.016459283438953774,0.00458140603355678,0.00419473500196923,0.6916314932065062,0.7347887799261619 +lgbm_feda,rule,first5,single model: LightGBM with domain copies,5,0.6335965944751423,0.6781822919638897,0.7123212606967619,0.7186901595488039,0.735878371128414,0.02541301926650535,0.012510084263689629,0.009946282621948807,0.005289968535235071,0.0058829238055425935,0.6957337355626023,0.735878371128414 +tab_dann_rz_lpft,rule,fresh,"single model: DANN, last layer then whole network fine-tuned",7,0.5932221557189944,0.6503028904656575,0.6584299522248689,0.6864550465487932,0.6993375177971751,0.011100498482265965,0.018116311824433993,0.016537925888728613,0.009169442937689311,0.001820490918382736,0.6575495125510978,0.6993375177971751 +tab_dann_rz_lpft,rule,B,"single model: DANN, last layer then whole network fine-tuned",12,0.5898668678096975,0.6397950263758413,0.6583727546169323,0.6825387949941787,0.6953526153663443,0.01491814667234172,0.016688917197257812,0.014978510681864291,0.008237294415316855,0.00366544871443404,0.6531852118325988,0.6953526153663443 +tab_dann_rz_lpft,rule,all,"single model: DANN, last layer then whole network fine-tuned",12,0.5963733968985947,0.6468589190108454,0.6647318290794766,0.6878619475633201,0.7007834713174289,0.01570019936095448,0.01690866624695203,0.01511421575059053,0.007991903031818825,0.003003142089115108,0.6593219127739331,0.7007834713174289 +tab_dann_rz_lpft,rule,first5,"single model: DANN, last layer then whole network fine-tuned",5,0.600785134550035,0.6420373589741085,0.6735544566759273,0.6898316089836578,0.7028078062457841,0.021244429081834586,0.01562421702380619,0.007152401473284613,0.00641969875146283,0.0033240499690942106,0.6618032730859026,0.7028078062457841 +ens_e4__lp,rule_smooth,fresh,FINAL-AVG as pre-registered: ensemble + label propagation + smoothing,7,0.6768457570277929,0.7046501196233966,0.7137423584365991,0.7246133955872265,0.7284902150148829,0.008721904733148569,0.008428447379451663,0.009542978640932986,0.004621388020941063,0.00445863903919215,0.7096683691379797,0.7284902150148829 +ens_e4__lp,rule_smooth,B,FINAL-AVG as pre-registered: ensemble + label propagation + smoothing,12,0.6719915853181985,0.6924151637836582,0.7051837526854823,0.7123632875785991,0.7146859985388013,0.013048519814050715,0.008997019302678605,0.009674094468344845,0.005915144940895477,0.0072342302081197865,0.6993279575809479,0.7146859985388013 +ens_e4__lp,rule_smooth,all,FINAL-AVG as pre-registered: ensemble + label propagation + smoothing,12,0.6840473093405883,0.7038279491367313,0.71639443300221,0.7247136175582963,0.7278244561748326,0.01185205913059215,0.0075418024463774,0.008068664511018252,0.0041484573243592985,0.005434747568186179,0.7113615530425317,0.7278244561748326 +ens_e4__lp,rule_smooth,first5,FINAL-AVG as pre-registered: ensemble + label propagation + smoothing,5,0.6941294825785019,0.7026769104553997,0.7201073373940654,0.724853928317794,0.7268923937987621,0.0073733820758668436,0.006857077981533023,0.0035903821952188494,0.0039049510176568858,0.007038788509785335,0.7137320105089048,0.7268923937987621 +ens_e4,rule_smooth,fresh,ensemble + smoothing (no label propagation),7,0.6687499694029553,0.6995427983568853,0.7059857993622197,0.7205085631245615,0.725464165138276,0.009325201751519418,0.012436499355543026,0.006058458327959933,0.005256988142917495,0.0033962739202174717,0.7040502590769795,0.725464165138276 +ens_e4,rule_smooth,B,ensemble + smoothing (no label propagation),12,0.6634531641304235,0.6874755161468822,0.702065889113855,0.712409439874463,0.7169275491105283,0.011182794394758727,0.012027901088742686,0.007002809370420917,0.004994466241767745,0.004416079699906983,0.6964663116752304,0.7169275491105283 +ens_e4,rule_smooth,all,ensemble + smoothing (no label propagation),12,0.674401995605212,0.6961771577557442,0.7099124427641058,0.7206348121086751,0.7254093646317085,0.010994352861178856,0.011324747409215897,0.0067869540519492145,0.00431061282428305,0.0041041019538490394,0.7053071545730891,0.7254093646317085 +ens_e4,rule_smooth,first5,ensemble + smoothing (no label propagation),5,0.6823148322883712,0.6914652609141468,0.7154097435267461,0.7208115606864343,0.7253326439225136,0.008233994986476242,0.008550544274544908,0.0026186550580667525,0.003094830131862326,0.005385667787559618,0.7070668082676423,0.7253326439225136 +ens_e4,rule,fresh,"ensemble, no coordinates used",7,0.6465751493319589,0.6833292312537262,0.6898034210079995,0.706245986902281,0.7131636536261885,0.009345035915703424,0.014295504265282696,0.006119337575231637,0.005408978326102121,0.002232989025031671,0.6878234884244309,0.7131636536261885 +ens_e4,rule,B,"ensemble, no coordinates used",12,0.6394250094414752,0.668878059365579,0.6835747677001226,0.69645382913262,0.7026027487751983,0.01098563328102197,0.012422269042199053,0.005982028867070602,0.004059093182501103,0.003268755122546141,0.6781868828829991,0.7026027487751983 +ens_e4,rule,all,"ensemble, no coordinates used",12,0.6519397192963646,0.6792924772161718,0.6935719636267647,0.7064385647158286,0.7133183128535028,0.010776702476115356,0.013051802719372597,0.006660278590003988,0.004725770104129584,0.0033307703589248254,0.6889122075417264,0.7133183128535028 +ens_e4,rule,first5,"ensemble, no coordinates used",5,0.6594501172465327,0.6736410215635955,0.6988479232930359,0.7067081736547951,0.7135348357717427,0.008216623973394226,0.009668428080667852,0.0024840752513013765,0.004168231227233318,0.004788398894739578,0.6904364143059404,0.7135348357717427 +ens_e2__lapshot,rule_smooth,fresh,"FINAL-PEAK as pre-registered: zero-shot + LightGBM, graph refinement + smoothing",7,0.6686536646547763,0.6816532046885747,0.6951857214146546,0.7206055971997382,0.7374120599540205,0.002463763631428892,0.0083120821554866,0.005765376516195115,0.006160247970887419,0.004463529749615921,0.7007020495823528,0.7374120599540205 +ens_e2__lapshot,rule_smooth,B,"FINAL-PEAK as pre-registered: zero-shot + LightGBM, graph refinement + smoothing",12,0.6561908371726143,0.6663640486027115,0.6819525278258015,0.7009470046597029,0.7209545044945992,0.005166844883804179,0.0074962076602055035,0.008248233884150661,0.006141985125033626,0.006633220933080744,0.6852817845510859,0.7209545044945992 +ens_e2__lapshot,rule_smooth,all,"FINAL-PEAK as pre-registered: zero-shot + LightGBM, graph refinement + smoothing",12,0.670967575803796,0.6821005482838619,0.6985253375166253,0.7200842603212064,0.7370038220653504,0.004130158027865544,0.0072038990328673735,0.006912698465907201,0.0047486961785936236,0.0045939852843930795,0.7017363087981681,0.7370038220653504 +ens_e2__lapshot,rule_smooth,first5,"FINAL-PEAK as pre-registered: zero-shot + LightGBM, graph refinement + smoothing",5,0.6742070514124234,0.6827268293172637,0.7032008000593839,0.7193543886912621,0.7364322890212123,0.00391373116616002,0.006183679361152368,0.005891328431297651,0.00198703491527846,0.00523959121621748,0.7031842717003091,0.7364322890212123 +lgbm_upw__lp,rule_smooth,fresh,retrained LightGBM + label propagation + smoothing,7,0.5332826718559118,0.6860908842993512,0.7020830674695988,0.7241251629789701,0.7366129454097383,0.046613172483015004,0.00846274247260879,0.005667813623528082,0.005745510725552485,0.004148989844138986,0.676438946402714,0.7366129454097383 +lgbm_upw__lp,rule_smooth,B,retrained LightGBM + label propagation + smoothing,12,0.5223299976669146,0.6695038836392128,0.6882573601504917,0.705891179278817,0.7195916410498672,0.030230393775252423,0.008852061742802543,0.009081452146300116,0.0064992529978443025,0.006123455532624046,0.6611148123570606,0.7195916410498672 +lgbm_upw__lp,rule_smooth,all,retrained LightGBM + label propagation + smoothing,12,0.5346177958775603,0.6857636253509423,0.7046945969273692,0.723997052596177,0.7351489112880073,0.036544447895441264,0.00845979808095178,0.0065195661426676,0.004441789348001175,0.00483738104022122,0.6768443964080112,0.7351489112880073 +lgbm_upw__lp,rule_smooth,first5,retrained 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+tab_mlp_o60_joint,argmax,0.473057823304248,0.49344967967922104,0.520676611016921,0.5521754387621189,0.5762052427624631,0.5231129591049944,0.5762052427624631,25 +cls_lr_zs,rule,0.5235372265050621,0.5234453131254976,0.5228386859966806,0.5222686541501756,0.5210245623723537,0.522622888429954,0.5235372265050621,25 +zs_lgbm__em,argmax,0.520495613115607,0.5203915360432558,0.5202813863250955,0.5200655748178984,0.5190796573150406,0.5200627535233795,0.520495613115607,25 +tab_mlp_rz_joint,argmax,0.479708186091169,0.4951142582252224,0.513723423741525,0.535862379370695,0.5671177975413197,0.5183052089939862,0.5671177975413197,25 +tab_mlp_rz_zs,rule,0.5184626741266707,0.5182577146669474,0.5179587130009444,0.5169627275162081,0.5162473622888384,0.5175778383199219,0.5184626741266707,25 +tab_mlp_o60_zs,rule,0.5178427405139988,0.5174745008650438,0.5170566038310154,0.5158964015920963,0.5152315080600651,0.5167003509724439,0.5178427405139988,25 +tab_mlp_rg_zs,argmax,0.5143432686501652,0.5142128595614801,0.5139514875691689,0.5133820043792188,0.5119351171466573,0.5135649474613381,0.5143432686501652,25 +cls_svm_zs,rule_smooth,0.5144808447650704,0.514122870693057,0.51393658444761,0.5129051832255159,0.5114944345341883,0.5133879835330883,0.5144808447650704,25 +zs_dann__em,argmax,0.5095529920630784,0.5093244145203577,0.5088643968362854,0.5081930227220106,0.5070934064788868,0.5086056465241238,0.5095529920630784,25 +cls_lr_zs,argmax,0.5071928969398255,0.5070409909633208,0.5066202022279211,0.5061268181128608,0.5050185347984114,0.506399888608468,0.5071928969398255,25 +cls_svm_zs,rule,0.5068830067503021,0.5065160506953371,0.5060681319151491,0.5049895375181583,0.5036542103446898,0.5056221874447272,0.5068830067503021,25 +ts_tcnn_dann_st_zs,argmax,0.5042768321985243,0.5042599168936553,0.5041291165292223,0.5034090469564967,0.503141651817059,0.5038433128789915,0.5042768321985243,25 +cls_svm_zs,argmax,0.5043305697290678,0.5041058954506041,0.5036848442670435,0.502722384970548,0.501559188905013,0.5032805766644552,0.5043305697290678,25 +ts_tcnn_ry_ft,rule,0.4442134015613249,0.4800798853987934,0.49500239200494967,0.5344938549411132,0.5602795788101694,0.5028138225432702,0.5602795788101694,25 +ts_tcnn_st_ft,rule_smooth,0.3849735700301594,0.45049642325350464,0.48692744625958523,0.5666975946346359,0.6213227617869286,0.5020835591929627,0.6213227617869286,25 +tab_res_rz_zs,rule_smooth,0.500277997552148,0.5001815443406576,0.49994486229488455,0.4985094400822395,0.4974395052654684,0.4992706699070796,0.500277997552148,25 +ts_tcnn_ry_joint,rule_smooth,0.4476530218723226,0.4720736271159609,0.48734719427106077,0.5237621413183058,0.5577322858163569,0.4977136540788013,0.5577322858163569,25 +tab_mlp_rz_zs,argmax,0.49298959051400687,0.4929866441070624,0.49258034217169194,0.4917165077208045,0.49109837174166404,0.49227429125104594,0.49298959051400687,25 +ts_tcnn_ry_ft,argmax,0.4227104818353789,0.4693237048197593,0.4848932490907078,0.5284136789160943,0.553434860758324,0.49175519508405285,0.553434860758324,25 +tab_mlp_o60_zs,argmax,0.4901116330789149,0.48993380075539594,0.489558429695607,0.4889279217518842,0.48813356713023703,0.4893330704824078,0.4901116330789149,25 +zs_xgb_dann__em,argmax,0.485539836947286,0.4852956300582393,0.48517782249803154,0.4845205789836843,0.48400772521411256,0.4849083187402707,0.485539836947286,25 +tab_res_rz_zs,rule,0.47676984023270635,0.4767097638644103,0.47648146518072343,0.4755389174319225,0.4746046212232817,0.4760209215866088,0.47676984023270635,25 +ts_tcnn_st_ft,rule,0.37606410961365716,0.42676587186436626,0.4568425529006495,0.5224998927122958,0.5742682028115271,0.4712881259804992,0.5742682028115271,25 +ts_tcnn_dann_st_zs,rule_smooth,0.4702146305262637,0.4701171751582107,0.4699968147002703,0.46907004775835015,0.46710686306975147,0.46930110624256927,0.4702146305262637,25 +tab_mlp_o60_zs,rule_smooth,0.4684821166599134,0.46841079247601974,0.4673568221763336,0.4668206109902687,0.4658934822718649,0.46739276491488013,0.4684821166599134,25 +ts_tcnn_ry_joint,rule,0.43018034541593486,0.44748111667618795,0.459926385356016,0.4855397018715715,0.5127931803730279,0.4671841459385476,0.5127931803730279,25 +ts_tcnn_st_joint,rule_smooth,0.3472525368684599,0.41316957690357176,0.46025492978814403,0.525519255365836,0.5750076138472174,0.46424078255464585,0.5750076138472174,25 +tab_res_rz_zs,argmax,0.46022420540715075,0.46015806157587474,0.45990035456699274,0.4591981500781886,0.45837213662257714,0.45957058165015685,0.46022420540715075,25 +ts_tcnn_st_ft,argmax,0.3254537664591867,0.41036924151193893,0.4420264127542253,0.5177915233693039,0.5695884649299956,0.45304588180493005,0.5695884649299956,25 +ts_tcnn_ry_joint,argmax,0.40346407545916063,0.4218738372905843,0.440118264083979,0.4682224495869568,0.4963012566563168,0.4459959766153996,0.4963012566563168,25 +ts_tcnn_st_joint,rule,0.3395608421763471,0.3972383580294001,0.4351096221085064,0.48856009315452276,0.5314350957109835,0.4383808022359519,0.5314350957109835,25 +ts_tcnn_ry_zs,rule_smooth,0.4290246965719352,0.42865235687002057,0.4282164795580082,0.42809009383090213,0.42639720732426356,0.42807616683102595,0.4290246965719352,25 +ts_tcnn_ry_zs,rule,0.41615335184216623,0.41586389810393587,0.41549566760663065,0.4148243157987472,0.41242544362442457,0.4149525353951809,0.41615335184216623,25 +ts_tcnn_ry_zs,argmax,0.38629812800964947,0.3860265203322294,0.38566732080214844,0.3854704044510031,0.38360976863291946,0.38541442844558993,0.38629812800964947,25 +ts_tcnn_st_joint,argmax,0.27087947317958694,0.33929121857491473,0.3779464505506491,0.43857362861245874,0.4801821347871006,0.381374581140942,0.4801821347871006,25 +ts_tcnn_st_zs,rule_smooth,0.30250545928879924,0.3023094829382745,0.3019170541441609,0.3015930478588448,0.30036258800756854,0.3017375264475296,0.30250545928879924,25 +ts_tcnn_st_zs,rule,0.2983428984282512,0.2981478861054498,0.2977132054260889,0.29717192363657996,0.29587490547380496,0.297450163814035,0.2983428984282512,25 +ts_tcnn_st_zs,argmax,0.22717336812791186,0.22700384115189814,0.22670153269422197,0.22617943600989948,0.22511777559997367,0.22643519071678103,0.22717336812791186,25 diff --git a/reports/nnlab/logs/classical.log b/reports/nnlab/logs/classical.log new file mode 100644 index 0000000000000000000000000000000000000000..ccaccec8f935efabc6da9c68b4adb40148297c88 --- /dev/null +++ b/reports/nnlab/logs/classical.log @@ -0,0 +1,179 @@ +[ 6.7s] loaded Madrid (76263, 393) Amsterdam (25992, 393) +[ 17.4s] cls_lr_zs n=0 r=0 done in 10.7s +[ 38.8s] cls_svm_zs n=0 r=0 done in 21.4s +[ 46.8s] cls_lr n=5 r=0 done in 8.0s +[ 54.6s] cls_lr n=5 r=1 done in 7.8s +[ 62.8s] cls_lr n=5 r=2 done in 8.2s +[ 72.1s] cls_lr n=5 r=3 done in 9.3s +[ 81.0s] cls_lr n=5 r=4 done in 8.9s +[ 89.4s] cls_lr n=25 r=0 done in 8.4s +[ 98.2s] cls_lr n=25 r=1 done in 8.7s +[ 106.3s] cls_lr n=25 r=2 done in 8.1s +[ 114.9s] cls_lr n=25 r=3 done in 8.6s +[ 123.7s] cls_lr n=25 r=4 done in 8.8s +[ 132.6s] cls_lr n=50 r=0 done in 8.9s +[ 141.6s] cls_lr n=50 r=1 done in 9.1s +[ 150.0s] cls_lr n=50 r=2 done in 8.4s +[ 159.0s] cls_lr n=50 r=3 done in 8.9s +[ 168.3s] cls_lr n=50 r=4 done in 9.3s +[ 177.8s] cls_lr n=100 r=0 done in 9.5s +[ 187.2s] cls_lr n=100 r=1 done in 9.4s +[ 196.2s] cls_lr n=100 r=2 done in 8.9s +[ 204.5s] cls_lr n=100 r=3 done in 8.4s +[ 212.6s] cls_lr n=100 r=4 done in 8.1s +[ 220.9s] cls_lr n=200 r=0 done in 8.3s +[ 229.5s] cls_lr n=200 r=1 done in 8.6s +[ 237.2s] cls_lr n=200 r=2 done in 7.6s +[ 244.8s] cls_lr n=200 r=3 done in 7.6s +[ 252.4s] cls_lr n=200 r=4 done in 7.6s +[ 273.7s] cls_lr_feda n=5 r=0 done in 21.3s +[ 294.6s] cls_lr_feda n=5 r=1 done in 20.9s +[ 317.3s] cls_lr_feda n=5 r=2 done in 22.7s +[ 338.6s] cls_lr_feda n=5 r=3 done in 21.3s +[ 358.2s] cls_lr_feda n=5 r=4 done in 19.6s +[ 378.7s] cls_lr_feda n=25 r=0 done in 20.5s +[ 399.2s] cls_lr_feda n=25 r=1 done in 20.5s +[ 419.1s] cls_lr_feda n=25 r=2 done in 19.9s +[ 437.9s] cls_lr_feda n=25 r=3 done in 18.9s +[ 457.3s] cls_lr_feda n=25 r=4 done in 19.4s +[ 475.9s] cls_lr_feda n=50 r=0 done in 18.6s +[ 494.7s] cls_lr_feda n=50 r=1 done in 18.9s +[ 513.4s] cls_lr_feda n=50 r=2 done in 18.7s +[ 531.6s] cls_lr_feda n=50 r=3 done in 18.2s +[ 549.8s] cls_lr_feda n=50 r=4 done in 18.3s +[ 568.9s] cls_lr_feda n=100 r=0 done in 19.1s +[ 589.1s] cls_lr_feda n=100 r=1 done in 20.2s +[ 608.8s] cls_lr_feda n=100 r=2 done in 19.8s +[ 628.3s] cls_lr_feda n=100 r=3 done in 19.4s +[ 647.7s] cls_lr_feda n=100 r=4 done in 19.4s +[ 666.4s] cls_lr_feda n=200 r=0 done in 18.7s +[ 683.8s] cls_lr_feda n=200 r=1 done in 17.4s +[ 704.1s] cls_lr_feda n=200 r=2 done in 20.3s +[ 726.2s] cls_lr_feda n=200 r=3 done in 22.1s +[ 748.6s] cls_lr_feda n=200 r=4 done in 22.3s +[ 773.2s] cls_svm n=5 r=0 done in 24.7s +[ 796.0s] cls_svm n=5 r=1 done in 22.8s +[ 820.8s] cls_svm n=5 r=2 done in 24.7s +[ 845.7s] cls_svm n=5 r=3 done in 24.9s +[ 870.1s] cls_svm n=5 r=4 done in 24.4s +[ 895.0s] cls_svm n=25 r=0 done in 24.9s +[ 918.4s] cls_svm n=25 r=1 done in 23.4s +[ 943.2s] cls_svm n=25 r=2 done in 24.9s +[ 968.3s] cls_svm n=25 r=3 done in 25.1s +[ 992.7s] cls_svm n=25 r=4 done in 24.4s +[ 1018.6s] cls_svm n=50 r=0 done in 25.9s +[ 1045.4s] cls_svm n=50 r=1 done in 26.8s +[ 1070.9s] cls_svm n=50 r=2 done in 25.5s +[ 1097.6s] cls_svm n=50 r=3 done in 26.8s +[ 1123.8s] cls_svm n=50 r=4 done in 26.2s +[ 1149.6s] cls_svm n=100 r=0 done in 25.7s +[ 1176.5s] cls_svm n=100 r=1 done in 27.0s +[ 1203.2s] cls_svm n=100 r=2 done in 26.7s +[ 1229.2s] cls_svm n=100 r=3 done in 26.0s +[ 1255.8s] cls_svm n=100 r=4 done in 26.5s +[ 1281.1s] cls_svm n=200 r=0 done in 25.3s +[ 1309.2s] cls_svm n=200 r=1 done in 28.1s +[ 1339.1s] cls_svm n=200 r=2 done in 29.9s +[ 1366.6s] cls_svm n=200 r=3 done in 27.5s +[ 1394.7s] cls_svm n=200 r=4 done in 28.1s +[ 1447.7s] cls_svm_feda n=5 r=0 done in 53.1s +[ 1501.8s] cls_svm_feda n=5 r=1 done in 54.1s +[ 1551.7s] cls_svm_feda n=5 r=2 done in 49.9s +[ 1600.2s] cls_svm_feda n=5 r=3 done in 48.5s +[ 1652.6s] cls_svm_feda n=5 r=4 done in 52.4s +[ 1710.2s] cls_svm_feda n=25 r=0 done in 57.6s +[ 1762.1s] cls_svm_feda n=25 r=1 done in 51.9s +[ 1810.4s] cls_svm_feda n=25 r=2 done in 48.3s +[ 1864.6s] cls_svm_feda n=25 r=3 done in 54.2s +[ 1920.8s] cls_svm_feda n=25 r=4 done in 56.3s +[ 1970.7s] cls_svm_feda n=50 r=0 done in 49.8s +[ 2021.9s] cls_svm_feda n=50 r=1 done in 51.3s +[ 2079.2s] cls_svm_feda n=50 r=2 done in 57.3s +[ 2136.3s] cls_svm_feda n=50 r=3 done in 57.1s +[ 2186.3s] cls_svm_feda n=50 r=4 done in 49.9s +[ 2240.0s] cls_svm_feda n=100 r=0 done in 53.7s +[ 2298.0s] cls_svm_feda n=100 r=1 done in 58.1s +[ 2354.1s] cls_svm_feda n=100 r=2 done in 56.0s +[ 2411.0s] cls_svm_feda n=100 r=3 done in 56.9s +[ 2467.6s] cls_svm_feda n=100 r=4 done in 56.6s +[ 2512.3s] cls_svm_feda n=200 r=0 done in 44.7s +[ 2542.7s] cls_svm_feda n=200 r=1 done in 30.5s +[ 2577.2s] cls_svm_feda n=200 r=2 done in 34.4s +[ 2608.2s] cls_svm_feda n=200 r=3 done in 31.0s +[ 2639.8s] cls_svm_feda n=200 r=4 done in 31.6s +[ 2644.7s] cls_nys_lr n=5 r=0 done in 4.9s +[ 2648.8s] cls_nys_lr n=5 r=1 done in 4.1s +[ 2652.6s] cls_nys_lr n=5 r=2 done in 3.8s +[ 2657.5s] cls_nys_lr n=5 r=3 done in 4.9s +[ 2664.1s] cls_nys_lr n=5 r=4 done in 6.5s +[ 2668.8s] cls_nys_lr n=25 r=0 done in 4.8s +[ 2674.7s] cls_nys_lr n=25 r=1 done in 5.9s +[ 2680.5s] cls_nys_lr n=25 r=2 done in 5.8s +[ 2684.6s] cls_nys_lr n=25 r=3 done in 4.1s +[ 2687.8s] cls_nys_lr n=25 r=4 done in 3.2s +[ 2692.5s] cls_nys_lr n=50 r=0 done in 4.7s +[ 2697.4s] cls_nys_lr n=50 r=1 done in 4.9s +[ 2701.6s] cls_nys_lr n=50 r=2 done in 4.2s +[ 2705.0s] cls_nys_lr n=50 r=3 done in 3.4s +[ 2709.4s] cls_nys_lr n=50 r=4 done in 4.4s +[ 2713.2s] cls_nys_lr n=100 r=0 done in 3.8s +[ 2717.9s] cls_nys_lr n=100 r=1 done in 4.7s +[ 2720.8s] cls_nys_lr n=100 r=2 done in 2.9s +[ 2723.3s] cls_nys_lr n=100 r=3 done in 2.5s +[ 2727.8s] cls_nys_lr n=100 r=4 done in 4.5s +[ 2733.9s] cls_nys_lr n=200 r=0 done in 6.0s +[ 2738.1s] cls_nys_lr n=200 r=1 done in 4.2s +[ 2742.2s] cls_nys_lr n=200 r=2 done in 4.1s +[ 2744.6s] cls_nys_lr n=200 r=3 done in 2.4s +[ 2750.3s] cls_nys_lr n=200 r=4 done in 5.7s +[ 2750.4s] cls_knn n=5 r=0 done in 0.1s +[ 2750.5s] cls_knn n=5 r=1 done in 0.1s +[ 2750.6s] cls_knn n=5 r=2 done in 0.1s +[ 2750.7s] cls_knn n=5 r=3 done in 0.1s +[ 2750.8s] cls_knn n=5 r=4 done in 0.1s +[ 2751.0s] cls_knn n=25 r=0 done in 0.2s +[ 2751.2s] cls_knn n=25 r=1 done in 0.2s +[ 2751.4s] cls_knn n=25 r=2 done in 0.2s +[ 2751.5s] cls_knn n=25 r=3 done in 0.2s +[ 2751.7s] cls_knn n=25 r=4 done in 0.2s +[ 2752.0s] cls_knn n=50 r=0 done in 0.3s +[ 2752.3s] cls_knn n=50 r=1 done in 0.3s +[ 2752.6s] cls_knn n=50 r=2 done in 0.3s +[ 2752.9s] cls_knn n=50 r=3 done in 0.3s +[ 2753.2s] cls_knn n=50 r=4 done in 0.3s +[ 2753.5s] cls_knn n=100 r=0 done in 0.4s +[ 2753.9s] cls_knn n=100 r=1 done in 0.4s +[ 2754.4s] cls_knn n=100 r=2 done in 0.4s +[ 2754.8s] cls_knn n=100 r=3 done in 0.4s +[ 2755.1s] cls_knn n=100 r=4 done in 0.3s +[ 2755.7s] cls_knn n=200 r=0 done in 0.6s +[ 2756.3s] cls_knn n=200 r=1 done in 0.6s +[ 2756.9s] cls_knn n=200 r=2 done in 0.6s +[ 2757.6s] cls_knn n=200 r=3 done in 0.7s +[ 2758.2s] cls_knn n=200 r=4 done in 0.6s +[ 2758.5s] cls_lda n=5 r=0 done in 0.3s +[ 2758.7s] cls_lda n=5 r=1 done in 0.3s +[ 2759.0s] cls_lda n=5 r=2 done in 0.2s +[ 2759.2s] cls_lda n=5 r=3 done in 0.2s +[ 2759.4s] cls_lda n=5 r=4 done in 0.2s +[ 2759.6s] cls_lda n=25 r=0 done in 0.2s +[ 2759.9s] cls_lda n=25 r=1 done in 0.2s +[ 2760.1s] cls_lda n=25 r=2 done in 0.3s +[ 2760.4s] cls_lda n=25 r=3 done in 0.2s +[ 2760.6s] cls_lda n=25 r=4 done in 0.2s +[ 2760.9s] cls_lda n=50 r=0 done in 0.2s +[ 2761.1s] cls_lda n=50 r=1 done in 0.2s +[ 2761.3s] cls_lda n=50 r=2 done in 0.3s +[ 2761.6s] cls_lda n=50 r=3 done in 0.2s +[ 2761.8s] cls_lda n=50 r=4 done in 0.2s +[ 2762.0s] cls_lda n=100 r=0 done in 0.2s +[ 2762.3s] cls_lda n=100 r=1 done in 0.3s +[ 2762.5s] cls_lda n=100 r=2 done in 0.2s +[ 2762.7s] cls_lda n=100 r=3 done in 0.2s +[ 2763.0s] cls_lda n=100 r=4 done in 0.2s +[ 2763.2s] cls_lda n=200 r=0 done in 0.3s +[ 2763.5s] cls_lda n=200 r=1 done in 0.2s +[ 2763.7s] cls_lda n=200 r=2 done in 0.3s +[ 2764.0s] cls_lda n=200 r=3 done in 0.3s +[ 2764.3s] cls_lda n=200 r=4 done in 0.3s +[ 2764.3s] all done diff --git a/reports/nnlab/logs/feda_madrid_check.log b/reports/nnlab/logs/feda_madrid_check.log new file mode 100644 index 0000000000000000000000000000000000000000..53cc90528a1d2a9a84fd8fbf9b6587ef09c10854 --- /dev/null +++ b/reports/nnlab/logs/feda_madrid_check.log @@ -0,0 +1,14 @@ +[ 244s] n=5 rep=0 base=0.5113 feda=0.5166 (argmax 0.5212 / 0.5165) +[ 502s] n=5 rep=1 base=0.5072 feda=0.5274 (argmax 0.5159 / 0.5312) +[ 748s] n=5 rep=2 base=0.5110 feda=0.5003 (argmax 0.5224 / 0.5039) +[ 998s] n=50 rep=0 base=0.5426 feda=0.5629 (argmax 0.5585 / 0.5663) +[ 1250s] n=50 rep=1 base=0.5449 feda=0.5559 (argmax 0.5528 / 0.5581) +[ 1502s] n=50 rep=2 base=0.5445 feda=0.5674 (argmax 0.5509 / 0.5685) +[ 1754s] n=200 rep=0 base=0.5867 feda=0.5985 (argmax 0.6000 / 0.6082) +[ 2009s] n=200 rep=1 base=0.5915 feda=0.6173 (argmax 0.6082 / 0.6243) +[ 2263s] n=200 rep=2 base=0.5894 feda=0.6131 (argmax 0.6071 / 0.6258) + base lgbm_feda base_argmax lgbm_feda_argmax +n +5 0.5098 0.5147 0.5199 0.5172 +50 0.5440 0.5621 0.5541 0.5643 +200 0.5892 0.6096 0.6051 0.6195 diff --git a/reports/nnlab/logs/lgbm_emb.log b/reports/nnlab/logs/lgbm_emb.log new file mode 100644 index 0000000000000000000000000000000000000000..f609f828e4b07d0e9d597962c441fa2861cc0763 --- /dev/null +++ b/reports/nnlab/logs/lgbm_emb.log @@ -0,0 +1,26 @@ +lgbm_emb n=5 rep=0 115s (total 115s) +lgbm_emb n=5 rep=1 131s (total 246s) +lgbm_emb n=5 rep=2 128s (total 374s) +lgbm_emb n=5 rep=3 129s (total 503s) +lgbm_emb n=5 rep=4 129s (total 632s) +lgbm_emb n=25 rep=0 124s (total 756s) +lgbm_emb n=25 rep=1 129s (total 885s) +lgbm_emb n=25 rep=2 129s (total 1014s) +lgbm_emb n=25 rep=3 129s (total 1143s) +lgbm_emb n=25 rep=4 136s (total 1278s) +lgbm_emb n=50 rep=0 139s (total 1417s) +lgbm_emb n=50 rep=1 130s (total 1547s) +lgbm_emb n=50 rep=2 131s (total 1678s) +lgbm_emb n=50 rep=3 126s (total 1804s) +lgbm_emb n=50 rep=4 114s (total 1918s) +lgbm_emb n=100 rep=0 113s (total 2031s) +lgbm_emb n=100 rep=1 113s (total 2144s) +lgbm_emb n=100 rep=2 113s (total 2257s) +lgbm_emb n=100 rep=3 113s (total 2371s) +lgbm_emb n=100 rep=4 113s (total 2484s) +lgbm_emb n=200 rep=0 97s (total 2581s) +lgbm_emb n=200 rep=1 89s (total 2670s) +lgbm_emb n=200 rep=2 88s (total 2758s) +lgbm_emb n=200 rep=3 91s (total 2849s) +lgbm_emb n=200 rep=4 90s (total 2939s) +done diff --git a/reports/nnlab/logs/lgbm_feda.log b/reports/nnlab/logs/lgbm_feda.log new file mode 100644 index 0000000000000000000000000000000000000000..03c771a15aef90a32de15d84a597c8186d749128 --- /dev/null +++ b/reports/nnlab/logs/lgbm_feda.log @@ -0,0 +1,26 @@ +lgbm_feda n=5 rep=0 125s (total 125s) +lgbm_feda n=5 rep=1 141s (total 267s) +lgbm_feda n=5 rep=2 139s (total 405s) +lgbm_feda n=5 rep=3 140s (total 545s) +lgbm_feda n=5 rep=4 136s (total 681s) +lgbm_feda n=25 rep=0 143s (total 824s) +lgbm_feda n=25 rep=1 146s (total 970s) +lgbm_feda n=25 rep=2 146s (total 1116s) +lgbm_feda n=25 rep=3 150s (total 1266s) +lgbm_feda n=25 rep=4 154s (total 1420s) +lgbm_feda n=50 rep=0 150s (total 1571s) +lgbm_feda n=50 rep=1 150s (total 1720s) +lgbm_feda n=50 rep=2 140s (total 1860s) +lgbm_feda n=50 rep=3 131s (total 1991s) +lgbm_feda n=50 rep=4 131s (total 2121s) +lgbm_feda n=100 rep=0 133s (total 2255s) +lgbm_feda n=100 rep=1 132s (total 2387s) +lgbm_feda n=100 rep=2 131s (total 2518s) +lgbm_feda n=100 rep=3 104s (total 2622s) +lgbm_feda n=100 rep=4 103s (total 2725s) +lgbm_feda n=200 rep=0 105s (total 2830s) +lgbm_feda n=200 rep=1 105s (total 2935s) +lgbm_feda n=200 rep=2 65s (total 2999s) +lgbm_feda n=200 rep=3 63s (total 3063s) +lgbm_feda n=200 rep=4 64s (total 3127s) +done diff --git a/reports/nnlab/logs/lgbm_feda_r12.log b/reports/nnlab/logs/lgbm_feda_r12.log new file mode 100644 index 0000000000000000000000000000000000000000..1db634e618284dcceec8c6d650f6b55b0c888dbe --- /dev/null +++ b/reports/nnlab/logs/lgbm_feda_r12.log @@ -0,0 +1,36 @@ +lgbm_feda n=5 rep=5 60s (total 60s) +lgbm_feda n=5 rep=6 66s (total 127s) +lgbm_feda n=5 rep=7 71s (total 198s) +lgbm_feda n=5 rep=8 71s (total 269s) +lgbm_feda n=5 rep=9 70s (total 339s) +lgbm_feda n=5 rep=10 67s (total 406s) +lgbm_feda n=5 rep=11 58s (total 464s) +lgbm_feda n=25 rep=5 92s (total 557s) +lgbm_feda n=25 rep=6 102s (total 659s) +lgbm_feda n=25 rep=7 107s (total 766s) +lgbm_feda n=25 rep=8 116s (total 882s) +lgbm_feda n=25 rep=9 111s (total 993s) +lgbm_feda n=25 rep=10 99s (total 1092s) +lgbm_feda n=25 rep=11 104s (total 1196s) +lgbm_feda n=50 rep=5 105s (total 1301s) +lgbm_feda n=50 rep=6 103s (total 1403s) +lgbm_feda n=50 rep=7 101s (total 1504s) +lgbm_feda n=50 rep=8 99s (total 1603s) +lgbm_feda n=50 rep=9 102s (total 1705s) +lgbm_feda n=50 rep=10 105s (total 1811s) +lgbm_feda n=50 rep=11 102s (total 1913s) +lgbm_feda n=100 rep=5 109s (total 2021s) +lgbm_feda n=100 rep=6 103s (total 2125s) +lgbm_feda n=100 rep=7 104s (total 2229s) +lgbm_feda n=100 rep=8 102s (total 2331s) +lgbm_feda n=100 rep=9 107s (total 2439s) +lgbm_feda n=100 rep=10 104s (total 2543s) +lgbm_feda n=100 rep=11 105s (total 2648s) +lgbm_feda n=200 rep=5 105s (total 2753s) +lgbm_feda n=200 rep=6 83s (total 2836s) +lgbm_feda n=200 rep=7 77s (total 2914s) +lgbm_feda n=200 rep=8 78s (total 2991s) +lgbm_feda n=200 rep=9 76s (total 3068s) +lgbm_feda n=200 rep=10 80s (total 3147s) +lgbm_feda n=200 rep=11 75s (total 3222s) +done diff --git a/reports/nnlab/logs/lgbm_fedaw100.log b/reports/nnlab/logs/lgbm_fedaw100.log new file mode 100644 index 0000000000000000000000000000000000000000..21ba8cfd69d883ce8ede10999428cd09831ac972 --- /dev/null +++ b/reports/nnlab/logs/lgbm_fedaw100.log @@ -0,0 +1,26 @@ +lgbm_fedaw100 n=5 rep=0 108s (total 108s) +lgbm_fedaw100 n=5 rep=1 111s (total 219s) +lgbm_fedaw100 n=5 rep=2 118s (total 337s) +lgbm_fedaw100 n=5 rep=3 123s (total 460s) +lgbm_fedaw100 n=5 rep=4 112s (total 571s) +lgbm_fedaw100 n=25 rep=0 112s (total 683s) +lgbm_fedaw100 n=25 rep=1 117s (total 801s) +lgbm_fedaw100 n=25 rep=2 115s (total 916s) +lgbm_fedaw100 n=25 rep=3 112s (total 1028s) +lgbm_fedaw100 n=25 rep=4 111s (total 1139s) +lgbm_fedaw100 n=50 rep=0 114s (total 1253s) +lgbm_fedaw100 n=50 rep=1 117s (total 1370s) +lgbm_fedaw100 n=50 rep=2 118s (total 1488s) +lgbm_fedaw100 n=50 rep=3 115s (total 1603s) +lgbm_fedaw100 n=50 rep=4 115s (total 1718s) +lgbm_fedaw100 n=100 rep=0 115s (total 1833s) +lgbm_fedaw100 n=100 rep=1 117s (total 1950s) +lgbm_fedaw100 n=100 rep=2 114s (total 2063s) +lgbm_fedaw100 n=100 rep=3 114s (total 2177s) +lgbm_fedaw100 n=100 rep=4 114s (total 2291s) +lgbm_fedaw100 n=200 rep=0 91s (total 2382s) +lgbm_fedaw100 n=200 rep=1 88s (total 2470s) +lgbm_fedaw100 n=200 rep=2 88s (total 2559s) +lgbm_fedaw100 n=200 rep=3 88s (total 2647s) +lgbm_fedaw100 n=200 rep=4 90s (total 2737s) +done diff --git a/reports/nnlab/logs/lgbm_upw.log b/reports/nnlab/logs/lgbm_upw.log new file mode 100644 index 0000000000000000000000000000000000000000..6619cb669aef1590b1fdb844980d0f1247b6b014 --- /dev/null +++ b/reports/nnlab/logs/lgbm_upw.log @@ -0,0 +1,26 @@ +lgbm_upw n=5 rep=0 122s +lgbm_upw n=5 rep=1 120s +lgbm_upw n=5 rep=2 408s +lgbm_upw n=5 rep=3 340s +lgbm_upw n=5 rep=4 161s +lgbm_upw n=25 rep=0 174s +lgbm_upw n=25 rep=1 145s +lgbm_upw n=25 rep=2 73s +lgbm_upw n=25 rep=3 67s +lgbm_upw n=25 rep=4 100s +lgbm_upw n=50 rep=0 62s +lgbm_upw n=50 rep=1 66s +lgbm_upw n=50 rep=2 82s +lgbm_upw n=50 rep=3 63s +lgbm_upw n=50 rep=4 58s +lgbm_upw n=100 rep=0 53s +lgbm_upw n=100 rep=1 55s +lgbm_upw n=100 rep=2 61s +lgbm_upw n=100 rep=3 61s +lgbm_upw n=100 rep=4 59s +lgbm_upw n=200 rep=0 52s +lgbm_upw n=200 rep=1 96s +lgbm_upw n=200 rep=2 100s +lgbm_upw n=200 rep=3 87s +lgbm_upw n=200 rep=4 82s +done diff --git a/reports/nnlab/logs/lgbm_upw_r12.log b/reports/nnlab/logs/lgbm_upw_r12.log new file mode 100644 index 0000000000000000000000000000000000000000..42ee34f6fe3338aa0efaae3ba89e5d168d5487f7 --- /dev/null +++ b/reports/nnlab/logs/lgbm_upw_r12.log @@ -0,0 +1,36 @@ +lgbm_upw n=5 rep=5 64s +lgbm_upw n=5 rep=6 61s +lgbm_upw n=5 rep=7 66s +lgbm_upw n=5 rep=8 73s +lgbm_upw n=5 rep=9 98s +lgbm_upw n=5 rep=10 68s +lgbm_upw n=5 rep=11 63s +lgbm_upw n=25 rep=5 61s +lgbm_upw n=25 rep=6 82s +lgbm_upw n=25 rep=7 91s +lgbm_upw n=25 rep=8 73s +lgbm_upw n=25 rep=9 58s +lgbm_upw n=25 rep=10 49s +lgbm_upw n=25 rep=11 47s +lgbm_upw n=50 rep=5 49s +lgbm_upw n=50 rep=6 49s +lgbm_upw n=50 rep=7 50s +lgbm_upw n=50 rep=8 49s +lgbm_upw n=50 rep=9 49s +lgbm_upw n=50 rep=10 49s +lgbm_upw n=50 rep=11 43s +lgbm_upw n=100 rep=5 37s +lgbm_upw n=100 rep=6 49s +lgbm_upw n=100 rep=7 49s +lgbm_upw n=100 rep=8 48s +lgbm_upw n=100 rep=9 47s +lgbm_upw n=100 rep=10 48s +lgbm_upw n=100 rep=11 48s +lgbm_upw n=200 rep=5 49s +lgbm_upw n=200 rep=6 47s +lgbm_upw n=200 rep=7 51s +lgbm_upw n=200 rep=8 49s +lgbm_upw n=200 rep=9 48s +lgbm_upw n=200 rep=10 39s +lgbm_upw n=200 rep=11 48s +done diff --git a/reports/nnlab/logs/lgbm_w100.log b/reports/nnlab/logs/lgbm_w100.log new file mode 100644 index 0000000000000000000000000000000000000000..82c05809be5dd37551d4a12239ae5079a9e807f6 --- /dev/null +++ b/reports/nnlab/logs/lgbm_w100.log @@ -0,0 +1,26 @@ +lgbm_w100 n=5 rep=0 92s (total 92s) +lgbm_w100 n=5 rep=1 105s (total 197s) +lgbm_w100 n=5 rep=2 106s (total 303s) +lgbm_w100 n=5 rep=3 104s (total 406s) +lgbm_w100 n=5 rep=4 105s (total 512s) +lgbm_w100 n=25 rep=0 104s (total 616s) +lgbm_w100 n=25 rep=1 100s (total 716s) +lgbm_w100 n=25 rep=2 105s (total 821s) +lgbm_w100 n=25 rep=3 103s (total 924s) +lgbm_w100 n=25 rep=4 105s (total 1029s) +lgbm_w100 n=50 rep=0 105s (total 1133s) +lgbm_w100 n=50 rep=1 110s (total 1244s) +lgbm_w100 n=50 rep=2 114s (total 1358s) +lgbm_w100 n=50 rep=3 108s (total 1466s) +lgbm_w100 n=50 rep=4 105s (total 1571s) +lgbm_w100 n=100 rep=0 106s (total 1677s) +lgbm_w100 n=100 rep=1 103s (total 1780s) +lgbm_w100 n=100 rep=2 94s (total 1874s) +lgbm_w100 n=100 rep=3 92s (total 1966s) +lgbm_w100 n=100 rep=4 91s (total 2057s) +lgbm_w100 n=200 rep=0 92s (total 2148s) +lgbm_w100 n=200 rep=1 94s (total 2242s) +lgbm_w100 n=200 rep=2 92s (total 2334s) +lgbm_w100 n=200 rep=3 92s (total 2426s) +lgbm_w100 n=200 rep=4 92s (total 2518s) +done diff --git a/reports/nnlab/logs/lp_madrid_check.log b/reports/nnlab/logs/lp_madrid_check.log new file mode 100644 index 0000000000000000000000000000000000000000..1480e08bed076f3919b5041aa4ab7e39dbf34c0a --- /dev/null +++ b/reports/nnlab/logs/lp_madrid_check.log @@ -0,0 +1,10 @@ +[ 1.0s] loaded: Madrid (76263, 393), Amsterdam (25992, 393) +[ 169.2s] madrid-check: base model fitted on 61932 src rows +[ 169.3s] madrid-check: pseudo-target 14331 rows, mean degree 23.4 +[ 177.6s] madrid-check: rule macro F1 on pseudo-target (non-shot rows), mean over reps + base n5=0.5009 n25=0.4996 n50=0.4991 n100=0.4974 n200=0.4936 avg=0.4981 + clamp n5=0.5033 n25=0.5041 n50=0.5046 n100=0.5072 n200=0.5094 avg=0.5057 + lp n5=0.4621 n25=0.4660 n50=0.4739 n100=0.4803 n200=0.4910 avg=0.4747 + lapshot_row n5=0.5044 n25=0.5044 n50=0.5051 n100=0.5060 n200=0.5055 avg=0.5051 + lapshot_raw n5=0.4308 n25=0.4316 n50=0.4335 n100=0.4369 n200=0.4394 avg=0.4344 + em n5=0.5101 n25=0.5091 n50=0.5088 n100=0.5078 n200=0.5042 avg=0.5080 diff --git a/reports/nnlab/logs/lpft.log b/reports/nnlab/logs/lpft.log new file mode 100644 index 0000000000000000000000000000000000000000..fdb783a1daae95fce544ffc25ff11878c4647388 --- /dev/null +++ b/reports/nnlab/logs/lpft.log @@ -0,0 +1,39 @@ +[ 4.5s] data loaded +[ 100.2s] trained base dann_rz_src_s0.pt (3 threads): 15 epochs on 61932 rows 88s +[ 100.3s] madrid-check base vs tab_nn sel_dann.npz Zt: max abs diff 0.00e+00 +[ 109.1s] madrid-check n=5 rep=0: lp=0.4571 lpft=0.4753 lpft_ent=0.4806 8.7s +[ 113.6s] madrid-check n=5 rep=1: lp=0.4962 lpft=0.4790 lpft_ent=0.4862 4.5s +[ 117.8s] madrid-check n=5 rep=2: lp=0.4991 lpft=0.5194 lpft_ent=0.5219 4.1s +[ 123.1s] madrid-check n=50 rep=0: lp=0.5469 lpft=0.5491 lpft_ent=0.5509 5.3s +[ 127.7s] madrid-check n=50 rep=1: lp=0.5190 lpft=0.5527 lpft_ent=0.5516 4.6s +[ 133.5s] madrid-check n=50 rep=2: lp=0.5177 lpft=0.5471 lpft_ent=0.5486 5.8s +[ 139.6s] madrid-check n=200 rep=0: lp=0.5293 lpft=0.5695 lpft_ent=0.5711 6.2s +[ 146.0s] madrid-check n=200 rep=1: lp=0.5289 lpft=0.5797 lpft_ent=0.5784 6.3s +[ 152.5s] madrid-check n=200 rep=2: lp=0.5229 lpft=0.5713 lpft_ent=0.5704 6.6s +[ 152.6s] madrid-check means: {'lp': {5: 0.4841498120142176, 50: 0.5279016320332399, 200: 0.5270376259955917}, 'lpft': {5: 0.49122672993148825, 50: 0.5496386272948383, 200: 0.5735218920183637}, 'lpft_ent': {5: 0.49622852008497914, 50: 0.5503723327589127, 200: 0.5732739094886868}} +[ 157.9s] tab_dann_rz_lpft+tab_dann_rz_lpft_ent n=5 rep=0 5.3s +[ 163.8s] tab_dann_rz_lpft+tab_dann_rz_lpft_ent n=5 rep=1 5.9s +[ 168.9s] tab_dann_rz_lpft+tab_dann_rz_lpft_ent n=5 rep=2 5.1s +[ 174.1s] tab_dann_rz_lpft+tab_dann_rz_lpft_ent n=5 rep=3 5.2s +[ 179.7s] tab_dann_rz_lpft+tab_dann_rz_lpft_ent n=5 rep=4 5.6s +[ 185.6s] tab_dann_rz_lpft+tab_dann_rz_lpft_ent n=25 rep=0 5.9s +[ 191.9s] tab_dann_rz_lpft+tab_dann_rz_lpft_ent n=25 rep=1 6.3s +[ 197.2s] tab_dann_rz_lpft+tab_dann_rz_lpft_ent n=25 rep=2 5.3s +[ 202.8s] tab_dann_rz_lpft+tab_dann_rz_lpft_ent n=25 rep=3 5.6s +[ 207.9s] tab_dann_rz_lpft+tab_dann_rz_lpft_ent n=25 rep=4 5.1s +[ 213.7s] tab_dann_rz_lpft+tab_dann_rz_lpft_ent n=50 rep=0 5.8s +[ 220.3s] tab_dann_rz_lpft+tab_dann_rz_lpft_ent n=50 rep=1 6.6s +[ 226.1s] tab_dann_rz_lpft+tab_dann_rz_lpft_ent n=50 rep=2 5.8s +[ 231.9s] tab_dann_rz_lpft+tab_dann_rz_lpft_ent n=50 rep=3 5.8s +[ 237.6s] tab_dann_rz_lpft+tab_dann_rz_lpft_ent n=50 rep=4 5.8s +[ 243.8s] tab_dann_rz_lpft+tab_dann_rz_lpft_ent n=100 rep=0 6.2s +[ 251.1s] tab_dann_rz_lpft+tab_dann_rz_lpft_ent n=100 rep=1 7.3s +[ 256.9s] tab_dann_rz_lpft+tab_dann_rz_lpft_ent n=100 rep=2 5.8s +[ 262.8s] tab_dann_rz_lpft+tab_dann_rz_lpft_ent n=100 rep=3 5.9s +[ 269.0s] tab_dann_rz_lpft+tab_dann_rz_lpft_ent n=100 rep=4 6.2s +[ 275.8s] tab_dann_rz_lpft+tab_dann_rz_lpft_ent n=200 rep=0 6.8s +[ 283.5s] tab_dann_rz_lpft+tab_dann_rz_lpft_ent n=200 rep=1 7.7s +[ 290.7s] tab_dann_rz_lpft+tab_dann_rz_lpft_ent n=200 rep=2 7.2s +[ 297.9s] tab_dann_rz_lpft+tab_dann_rz_lpft_ent n=200 rep=3 7.2s +[ 305.4s] tab_dann_rz_lpft+tab_dann_rz_lpft_ent n=200 rep=4 7.4s +[ 305.4s] done diff --git a/reports/nnlab/logs/lpft_r12.log b/reports/nnlab/logs/lpft_r12.log new file mode 100644 index 0000000000000000000000000000000000000000..1995025947c2e9b79a192804a75fa879fda359ff --- /dev/null +++ b/reports/nnlab/logs/lpft_r12.log @@ -0,0 +1,37 @@ +[ 5.1s] data loaded +[ 24.3s] tab_dann_rz_lpft+tab_dann_rz_lpft_ent n=5 rep=5 12.0s +[ 29.8s] tab_dann_rz_lpft+tab_dann_rz_lpft_ent n=5 rep=6 5.5s +[ 35.1s] tab_dann_rz_lpft+tab_dann_rz_lpft_ent n=5 rep=7 5.3s +[ 39.5s] tab_dann_rz_lpft+tab_dann_rz_lpft_ent n=5 rep=8 4.4s +[ 45.0s] tab_dann_rz_lpft+tab_dann_rz_lpft_ent n=5 rep=9 5.5s +[ 50.4s] tab_dann_rz_lpft+tab_dann_rz_lpft_ent n=5 rep=10 5.4s +[ 56.7s] tab_dann_rz_lpft+tab_dann_rz_lpft_ent n=5 rep=11 6.2s +[ 62.4s] tab_dann_rz_lpft+tab_dann_rz_lpft_ent n=25 rep=5 5.7s +[ 68.9s] tab_dann_rz_lpft+tab_dann_rz_lpft_ent n=25 rep=6 6.5s +[ 74.7s] tab_dann_rz_lpft+tab_dann_rz_lpft_ent n=25 rep=7 5.8s +[ 81.0s] tab_dann_rz_lpft+tab_dann_rz_lpft_ent n=25 rep=8 6.3s +[ 87.1s] tab_dann_rz_lpft+tab_dann_rz_lpft_ent n=25 rep=9 6.1s +[ 92.5s] tab_dann_rz_lpft+tab_dann_rz_lpft_ent n=25 rep=10 5.5s +[ 98.3s] tab_dann_rz_lpft+tab_dann_rz_lpft_ent n=25 rep=11 5.7s +[ 104.4s] tab_dann_rz_lpft+tab_dann_rz_lpft_ent n=50 rep=5 6.1s +[ 110.2s] tab_dann_rz_lpft+tab_dann_rz_lpft_ent n=50 rep=6 5.8s +[ 116.5s] tab_dann_rz_lpft+tab_dann_rz_lpft_ent n=50 rep=7 6.3s +[ 122.9s] tab_dann_rz_lpft+tab_dann_rz_lpft_ent n=50 rep=8 6.3s +[ 128.9s] tab_dann_rz_lpft+tab_dann_rz_lpft_ent n=50 rep=9 6.1s +[ 134.7s] tab_dann_rz_lpft+tab_dann_rz_lpft_ent n=50 rep=10 5.8s +[ 140.6s] tab_dann_rz_lpft+tab_dann_rz_lpft_ent n=50 rep=11 5.9s +[ 147.2s] tab_dann_rz_lpft+tab_dann_rz_lpft_ent n=100 rep=5 6.6s +[ 154.2s] tab_dann_rz_lpft+tab_dann_rz_lpft_ent n=100 rep=6 7.0s +[ 160.6s] tab_dann_rz_lpft+tab_dann_rz_lpft_ent n=100 rep=7 6.4s +[ 167.1s] tab_dann_rz_lpft+tab_dann_rz_lpft_ent n=100 rep=8 6.5s +[ 174.0s] tab_dann_rz_lpft+tab_dann_rz_lpft_ent n=100 rep=9 6.9s +[ 179.6s] tab_dann_rz_lpft+tab_dann_rz_lpft_ent n=100 rep=10 5.6s +[ 186.0s] tab_dann_rz_lpft+tab_dann_rz_lpft_ent n=100 rep=11 6.4s +[ 193.7s] tab_dann_rz_lpft+tab_dann_rz_lpft_ent n=200 rep=5 7.7s +[ 201.6s] tab_dann_rz_lpft+tab_dann_rz_lpft_ent n=200 rep=6 7.8s +[ 209.9s] tab_dann_rz_lpft+tab_dann_rz_lpft_ent n=200 rep=7 8.3s +[ 217.7s] tab_dann_rz_lpft+tab_dann_rz_lpft_ent n=200 rep=8 7.9s +[ 225.3s] tab_dann_rz_lpft+tab_dann_rz_lpft_ent n=200 rep=9 7.6s +[ 233.6s] tab_dann_rz_lpft+tab_dann_rz_lpft_ent n=200 rep=10 8.2s +[ 241.3s] tab_dann_rz_lpft+tab_dann_rz_lpft_ent n=200 rep=11 7.7s +[ 241.3s] done diff --git a/reports/nnlab/logs/lpft_steps.log b/reports/nnlab/logs/lpft_steps.log new file mode 100644 index 0000000000000000000000000000000000000000..087d52283a7516989e44a13c8068fdfaebe7f463 --- /dev/null +++ b/reports/nnlab/logs/lpft_steps.log @@ -0,0 +1,98 @@ +[ 23s] madrid steps=60 n=5 rep=0 f1=0.4753 +[ 26s] madrid steps=60 n=5 rep=1 f1=0.4790 +[ 28s] madrid steps=60 n=5 rep=2 f1=0.5194 +[ 31s] madrid steps=60 n=50 rep=0 f1=0.5491 +[ 33s] madrid steps=60 n=50 rep=1 f1=0.5527 +[ 35s] madrid steps=60 n=50 rep=2 f1=0.5471 +[ 38s] madrid steps=60 n=200 rep=0 f1=0.5695 +[ 41s] madrid steps=60 n=200 rep=1 f1=0.5797 +[ 44s] madrid steps=60 n=200 rep=2 f1=0.5713 +[ 48s] madrid steps=150 n=5 rep=0 f1=0.4853 +[ 53s] madrid steps=150 n=5 rep=1 f1=0.4868 +[ 56s] madrid steps=150 n=5 rep=2 f1=0.5172 +[ 61s] madrid steps=150 n=50 rep=0 f1=0.5500 +[ 65s] madrid steps=150 n=50 rep=1 f1=0.5535 +[ 70s] madrid steps=150 n=50 rep=2 f1=0.5493 +[ 76s] madrid steps=150 n=200 rep=0 f1=0.5818 +[ 84s] madrid steps=150 n=200 rep=1 f1=0.5931 +[ 90s] madrid steps=150 n=200 rep=2 f1=0.5912 +[ 98s] madrid steps=300 n=5 rep=0 f1=0.4898 +[ 105s] madrid steps=300 n=5 rep=1 f1=0.4852 +[ 114s] madrid steps=300 n=5 rep=2 f1=0.5120 +[ 122s] madrid steps=300 n=50 rep=0 f1=0.5512 +[ 130s] madrid steps=300 n=50 rep=1 f1=0.5555 +[ 139s] madrid steps=300 n=50 rep=2 f1=0.5496 +[ 152s] madrid steps=300 n=200 rep=0 f1=0.5823 +[ 165s] madrid steps=300 n=200 rep=1 f1=0.5953 +[ 177s] madrid steps=300 n=200 rep=2 f1=0.5921 +[ 191s] madrid steps=600 n=5 rep=0 f1=0.4934 +[ 207s] madrid steps=600 n=5 rep=1 f1=0.4865 +[ 221s] madrid steps=600 n=5 rep=2 f1=0.5096 +[ 239s] madrid steps=600 n=50 rep=0 f1=0.5495 +[ 256s] madrid steps=600 n=50 rep=1 f1=0.5549 +[ 278s] madrid steps=600 n=50 rep=2 f1=0.5508 +[ 303s] madrid steps=600 n=200 rep=0 f1=0.5805 +[ 327s] madrid steps=600 n=200 rep=1 f1=0.5912 +[ 352s] madrid steps=600 n=200 rep=2 f1=0.5928 +madrid selection: {60: 0.5381290830815634, 150: 0.5453455986355832, 300: 0.545893932403143, 600: 0.5454710417072588} by n: {60: {5: 0.49122672993148825, 50: 0.5496386272948383, 200: 0.5735218920183637}, 150: {5: 0.49642611088751853, 50: 0.5509334248570606, 200: 0.5886772601621703}, 300: {5: 0.4956672067590944, 50: 0.5521068084327269, 200: 0.5899077820176076}, 600: {5: 0.4965136951898914, 50: 0.5517205898817409, 200: 0.5881788400501441}} -> chosen 300 +[ 365s] tab_dann_rz_lpft_s300 n=5 rep=0 8.4s +[ 374s] tab_dann_rz_lpft_s300 n=5 rep=1 8.9s +[ 384s] tab_dann_rz_lpft_s300 n=5 rep=2 10.1s +[ 393s] tab_dann_rz_lpft_s300 n=5 rep=3 8.4s +[ 401s] tab_dann_rz_lpft_s300 n=5 rep=4 8.3s +[ 410s] tab_dann_rz_lpft_s300 n=5 rep=5 9.4s +[ 418s] tab_dann_rz_lpft_s300 n=5 rep=6 7.4s +[ 426s] tab_dann_rz_lpft_s300 n=5 rep=7 8.4s +[ 433s] tab_dann_rz_lpft_s300 n=5 rep=8 7.2s +[ 442s] tab_dann_rz_lpft_s300 n=5 rep=9 8.6s +[ 449s] tab_dann_rz_lpft_s300 n=5 rep=10 7.4s +[ 457s] tab_dann_rz_lpft_s300 n=5 rep=11 8.0s +[ 466s] tab_dann_rz_lpft_s300 n=25 rep=0 8.6s +[ 479s] tab_dann_rz_lpft_s300 n=25 rep=1 13.3s +[ 490s] tab_dann_rz_lpft_s300 n=25 rep=2 11.3s +[ 501s] tab_dann_rz_lpft_s300 n=25 rep=3 10.4s +[ 513s] tab_dann_rz_lpft_s300 n=25 rep=4 12.3s +[ 526s] tab_dann_rz_lpft_s300 n=25 rep=5 12.6s +[ 539s] tab_dann_rz_lpft_s300 n=25 rep=6 13.6s +[ 551s] tab_dann_rz_lpft_s300 n=25 rep=7 12.0s +[ 562s] tab_dann_rz_lpft_s300 n=25 rep=8 11.0s +[ 573s] tab_dann_rz_lpft_s300 n=25 rep=9 10.8s +[ 584s] tab_dann_rz_lpft_s300 n=25 rep=10 10.9s +[ 596s] tab_dann_rz_lpft_s300 n=25 rep=11 11.4s +[ 609s] tab_dann_rz_lpft_s300 n=50 rep=0 13.4s +[ 622s] tab_dann_rz_lpft_s300 n=50 rep=1 13.1s +[ 636s] tab_dann_rz_lpft_s300 n=50 rep=2 13.4s +[ 651s] tab_dann_rz_lpft_s300 n=50 rep=3 15.3s +[ 664s] tab_dann_rz_lpft_s300 n=50 rep=4 12.8s +[ 678s] tab_dann_rz_lpft_s300 n=50 rep=5 14.0s +[ 690s] tab_dann_rz_lpft_s300 n=50 rep=6 12.2s +[ 702s] tab_dann_rz_lpft_s300 n=50 rep=7 12.6s +[ 712s] tab_dann_rz_lpft_s300 n=50 rep=8 9.9s +[ 720s] tab_dann_rz_lpft_s300 n=50 rep=9 7.6s +[ 729s] tab_dann_rz_lpft_s300 n=50 rep=10 9.1s +[ 740s] tab_dann_rz_lpft_s300 n=50 rep=11 10.5s +[ 753s] tab_dann_rz_lpft_s300 n=100 rep=0 13.4s +[ 767s] tab_dann_rz_lpft_s300 n=100 rep=1 13.7s +[ 777s] tab_dann_rz_lpft_s300 n=100 rep=2 10.6s +[ 788s] tab_dann_rz_lpft_s300 n=100 rep=3 10.8s +[ 799s] tab_dann_rz_lpft_s300 n=100 rep=4 11.3s +[ 809s] tab_dann_rz_lpft_s300 n=100 rep=5 10.0s +[ 820s] tab_dann_rz_lpft_s300 n=100 rep=6 10.4s +[ 832s] tab_dann_rz_lpft_s300 n=100 rep=7 11.8s +[ 842s] tab_dann_rz_lpft_s300 n=100 rep=8 10.5s +[ 852s] tab_dann_rz_lpft_s300 n=100 rep=9 9.8s +[ 868s] tab_dann_rz_lpft_s300 n=100 rep=10 16.0s +[ 879s] tab_dann_rz_lpft_s300 n=100 rep=11 10.9s +[ 892s] tab_dann_rz_lpft_s300 n=200 rep=0 13.3s +[ 905s] tab_dann_rz_lpft_s300 n=200 rep=1 13.2s +[ 919s] tab_dann_rz_lpft_s300 n=200 rep=2 13.4s +[ 934s] tab_dann_rz_lpft_s300 n=200 rep=3 15.5s +[ 948s] tab_dann_rz_lpft_s300 n=200 rep=4 13.6s +[ 961s] tab_dann_rz_lpft_s300 n=200 rep=5 12.8s +[ 973s] tab_dann_rz_lpft_s300 n=200 rep=6 12.9s +[ 985s] tab_dann_rz_lpft_s300 n=200 rep=7 12.0s +[ 999s] tab_dann_rz_lpft_s300 n=200 rep=8 13.0s +[ 1012s] tab_dann_rz_lpft_s300 n=200 rep=9 13.4s +[ 1024s] tab_dann_rz_lpft_s300 n=200 rep=10 11.9s +[ 1037s] tab_dann_rz_lpft_s300 n=200 rep=11 13.5s +done diff --git a/reports/nnlab/logs/metric.log b/reports/nnlab/logs/metric.log new file mode 100644 index 0000000000000000000000000000000000000000..a31b22eda7e199da4f63b37792a63ebffc5d09eb --- /dev/null +++ b/reports/nnlab/logs/metric.log @@ -0,0 +1,138 @@ +[ 5.2s] loaded Madrid (76263, 393) Amsterdam (25992, 393); encoders ['supcon', 'proto', 'protoda', 'cos'] +[ 18.0s] supcon: selecting length on Madrid split (src 61932, pseudo-target 14331) from [300, 700, 1500] +[ 45.0s] supcon seed100 step 300: loss 5.9978 pseudo-target F1 0.5113 (20s) +[ 60.5s] supcon seed100 step 700: loss 5.9759 pseudo-target F1 0.5227 (36s) +[ 99.1s] supcon seed100 step 1500: loss 5.9317 pseudo-target F1 0.5148 (75s) +[ 99.1s] supcon: chose 700 steps +[ 99.1s] supcon: training seed 0 on all Madrid for 700 steps +[ 131.0s] supcon seed0 step 700: loss 5.9621 (32s) +[ 131.8s] supcon: training seed 1 on all Madrid for 700 steps +[ 180.3s] supcon seed1 step 700: loss 6.0183 (48s) +[ 186.0s] supcon n=5 rep=0: saved 5 heads (3.5s) +[ 189.5s] supcon n=5 rep=1: saved 5 heads (3.5s) +[ 192.9s] supcon n=5 rep=2: saved 5 heads (3.4s) +[ 196.3s] supcon n=5 rep=3: saved 5 heads (3.4s) +[ 199.6s] supcon n=5 rep=4: saved 5 heads (3.2s) +[ 203.3s] supcon n=25 rep=0: saved 5 heads (3.8s) +[ 207.1s] supcon n=25 rep=1: saved 5 heads (3.7s) +[ 210.8s] supcon n=25 rep=2: saved 5 heads (3.7s) +[ 214.1s] supcon n=25 rep=3: saved 5 heads (3.3s) +[ 216.2s] supcon n=25 rep=4: saved 5 heads (2.1s) +[ 220.1s] supcon n=50 rep=0: saved 5 heads (3.9s) +[ 224.4s] supcon n=50 rep=1: saved 5 heads (4.3s) +[ 228.8s] supcon n=50 rep=2: saved 5 heads (4.4s) +[ 232.9s] supcon n=50 rep=3: saved 5 heads (4.0s) +[ 237.1s] supcon n=50 rep=4: saved 5 heads (4.2s) +[ 242.2s] supcon n=100 rep=0: saved 5 heads (5.0s) +[ 246.7s] supcon n=100 rep=1: saved 5 heads (4.5s) +[ 251.6s] supcon n=100 rep=2: saved 5 heads (4.9s) +[ 256.3s] supcon n=100 rep=3: saved 5 heads (4.7s) +[ 260.7s] supcon n=100 rep=4: saved 5 heads (4.4s) +[ 265.4s] supcon n=200 rep=0: saved 5 heads (4.8s) +[ 271.0s] supcon n=200 rep=1: saved 5 heads (5.6s) +[ 276.5s] supcon n=200 rep=2: saved 5 heads (5.5s) +[ 279.4s] supcon n=200 rep=3: saved 5 heads (2.8s) +[ 284.6s] supcon n=200 rep=4: saved 5 heads (5.2s) +[ 288.8s] proto: selecting length on Madrid split (src 61932, pseudo-target 14331) from [400, 800, 1600] +[ 301.0s] proto seed100 step 400: loss 0.8636 pseudo-target F1 0.4932 (12s) +[ 311.7s] proto seed100 step 800: loss 0.9729 pseudo-target F1 0.5287 (23s) +[ 335.6s] proto seed100 step 1600: loss 0.8058 pseudo-target F1 0.5097 (47s) +[ 335.6s] proto: chose 800 steps +[ 335.6s] proto: training seed 0 on all Madrid for 800 steps +[ 356.8s] proto seed0 step 800: loss 0.9539 (21s) +[ 358.0s] proto: training seed 1 on all Madrid for 800 steps +[ 386.6s] proto seed1 step 800: loss 1.0048 (29s) +[ 393.0s] proto n=5 rep=0: saved 5 heads (4.1s) +[ 396.3s] proto n=5 rep=1: saved 5 heads (3.3s) +[ 399.5s] proto n=5 rep=2: saved 5 heads (3.2s) +[ 402.8s] proto n=5 rep=3: saved 5 heads (3.3s) +[ 406.4s] proto n=5 rep=4: saved 5 heads (3.6s) +[ 410.5s] proto n=25 rep=0: saved 5 heads (4.1s) +[ 414.3s] proto n=25 rep=1: saved 5 heads (3.8s) +[ 418.1s] proto n=25 rep=2: saved 5 heads (3.8s) +[ 421.8s] proto n=25 rep=3: saved 5 heads (3.7s) +[ 424.7s] proto n=25 rep=4: saved 5 heads (2.9s) +[ 426.9s] proto n=50 rep=0: saved 5 heads (2.2s) +[ 430.1s] proto n=50 rep=1: saved 5 heads (3.2s) +[ 434.6s] proto n=50 rep=2: saved 5 heads (4.4s) +[ 439.2s] proto n=50 rep=3: saved 5 heads (4.7s) +[ 443.5s] proto n=50 rep=4: saved 5 heads (4.2s) +[ 448.3s] proto n=100 rep=0: saved 5 heads (4.8s) +[ 453.2s] proto n=100 rep=1: saved 5 heads (4.9s) +[ 458.0s] proto n=100 rep=2: saved 5 heads (4.8s) +[ 462.8s] proto n=100 rep=3: saved 5 heads (4.8s) +[ 467.5s] proto n=100 rep=4: saved 5 heads (4.6s) +[ 472.8s] proto n=200 rep=0: saved 5 heads (5.3s) +[ 478.2s] proto n=200 rep=1: saved 5 heads (5.4s) +[ 483.6s] proto n=200 rep=2: saved 5 heads (5.4s) +[ 487.9s] proto n=200 rep=3: saved 5 heads (4.3s) +[ 490.6s] proto n=200 rep=4: saved 5 heads (2.7s) +[ 495.0s] protoda: selecting length on Madrid split (src 61932, pseudo-target 14331) from [400, 800, 1600] +[ 513.7s] protoda seed100 step 400: loss 0.9048 pseudo-target F1 0.5346 (19s) +[ 533.2s] protoda seed100 step 800: loss 0.9346 pseudo-target F1 0.5117 (38s) +[ 567.8s] protoda seed100 step 1600: loss 0.8521 pseudo-target F1 0.5270 (73s) +[ 567.8s] protoda: chose 400 steps +[ 567.8s] protoda: training seed 0 on all Madrid for 400 steps +[ 587.5s] protoda seed0 step 400: loss 1.0030 (20s) +[ 588.5s] protoda: training seed 1 on all Madrid for 400 steps +[ 603.6s] protoda seed1 step 400: loss 1.0488 (15s) +[ 609.0s] protoda n=5 rep=0: saved 5 heads (3.4s) +[ 612.2s] protoda n=5 rep=1: saved 5 heads (3.1s) +[ 615.3s] protoda n=5 rep=2: saved 5 heads (3.1s) +[ 618.7s] protoda n=5 rep=3: saved 5 heads (3.4s) +[ 621.8s] protoda n=5 rep=4: saved 5 heads (3.2s) +[ 625.6s] protoda n=25 rep=0: saved 5 heads (3.8s) +[ 629.3s] protoda n=25 rep=1: saved 5 heads (3.7s) +[ 633.1s] protoda n=25 rep=2: saved 5 heads (3.8s) +[ 636.5s] protoda n=25 rep=3: saved 5 heads (3.4s) +[ 639.9s] protoda n=25 rep=4: saved 5 heads (3.4s) +[ 644.4s] protoda n=50 rep=0: saved 5 heads (4.5s) +[ 648.7s] protoda n=50 rep=1: saved 5 heads (4.3s) +[ 652.6s] protoda n=50 rep=2: saved 5 heads (3.9s) +[ 654.9s] protoda n=50 rep=3: saved 5 heads (2.4s) +[ 657.6s] protoda n=50 rep=4: saved 5 heads (2.6s) +[ 661.9s] protoda n=100 rep=0: saved 5 heads (4.3s) +[ 666.7s] protoda n=100 rep=1: saved 5 heads (4.8s) +[ 671.6s] protoda n=100 rep=2: saved 5 heads (4.9s) +[ 676.2s] protoda n=100 rep=3: saved 5 heads (4.6s) +[ 680.8s] protoda n=100 rep=4: saved 5 heads (4.7s) +[ 685.9s] protoda n=200 rep=0: saved 5 heads (5.0s) +[ 691.2s] protoda n=200 rep=1: saved 5 heads (5.3s) +[ 696.8s] protoda n=200 rep=2: saved 5 heads (5.5s) +[ 701.6s] protoda n=200 rep=3: saved 5 heads (4.8s) +[ 706.9s] protoda n=200 rep=4: saved 5 heads (5.3s) +[ 711.3s] cos: selecting length on Madrid split (src 61932, pseudo-target 14331) from [300, 700, 1500] +[ 717.6s] cos seed100 step 300: loss 0.8239 pseudo-target F1 0.5167 (6s) +[ 731.6s] cos seed100 step 700: loss 0.8426 pseudo-target F1 0.5191 (20s) +[ 760.8s] cos seed100 step 1500: loss 0.7345 pseudo-target F1 0.5268 (50s) +[ 760.8s] cos: chose 1500 steps +[ 760.8s] cos: training seed 0 on all Madrid for 1500 steps +[ 814.0s] cos seed0 step 1500: loss 0.7407 (53s) +[ 815.2s] cos: training seed 1 on all Madrid for 1500 steps +[ 865.5s] cos seed1 step 1500: loss 0.7244 (50s) +[ 870.7s] cos n=5 rep=0: saved 5 heads (3.0s) +[ 873.8s] cos n=5 rep=1: saved 5 heads (3.1s) +[ 876.9s] cos n=5 rep=2: saved 5 heads (3.0s) +[ 879.9s] cos n=5 rep=3: saved 5 heads (3.0s) +[ 882.9s] cos n=5 rep=4: saved 5 heads (3.0s) +[ 886.5s] cos n=25 rep=0: saved 5 heads (3.6s) +[ 888.2s] cos n=25 rep=1: saved 5 heads (1.7s) +[ 890.3s] cos n=25 rep=2: saved 5 heads (2.1s) +[ 893.6s] cos n=25 rep=3: saved 5 heads (3.3s) +[ 897.0s] cos n=25 rep=4: saved 5 heads (3.5s) +[ 901.1s] cos n=50 rep=0: saved 5 heads (4.1s) +[ 905.3s] cos n=50 rep=1: saved 5 heads (4.1s) +[ 909.0s] cos n=50 rep=2: saved 5 heads (3.8s) +[ 913.3s] cos n=50 rep=3: saved 5 heads (4.2s) +[ 918.7s] cos n=50 rep=4: saved 5 heads (5.4s) +[ 925.9s] cos n=100 rep=0: saved 5 heads (7.1s) +[ 933.4s] cos n=100 rep=1: saved 5 heads (7.6s) +[ 938.9s] cos n=100 rep=2: saved 5 heads (5.4s) +[ 944.6s] cos n=100 rep=3: saved 5 heads (5.8s) +[ 950.2s] cos n=100 rep=4: saved 5 heads (5.5s) +[ 956.7s] cos n=200 rep=0: saved 5 heads (6.5s) +[ 964.2s] cos n=200 rep=1: saved 5 heads (7.5s) +[ 971.7s] cos n=200 rep=2: saved 5 heads (7.5s) +[ 978.4s] cos n=200 rep=3: saved 5 heads (6.7s) +[ 984.5s] cos n=200 rep=4: saved 5 heads (6.1s) +[ 984.5s] done diff --git a/reports/nnlab/logs/metric_r12.log b/reports/nnlab/logs/metric_r12.log new file mode 100644 index 0000000000000000000000000000000000000000..f8daf9c2fe09e11882756dce2caf13144fc13aa1 --- /dev/null +++ b/reports/nnlab/logs/metric_r12.log @@ -0,0 +1,74 @@ +[ 5.5s] loaded Madrid (76263, 393) Amsterdam (25992, 393); encoders ['protoda'] +[ 27.2s] protoda n=5 rep=5: saved 5 heads (11.8s) +[ 30.7s] protoda n=5 rep=6: saved 5 heads (3.5s) +[ 34.1s] protoda n=5 rep=7: saved 5 heads (3.4s) +[ 37.4s] protoda n=5 rep=8: saved 5 heads (3.4s) +[ 40.9s] protoda n=5 rep=9: saved 5 heads (3.4s) +[ 44.2s] protoda n=5 rep=10: saved 5 heads (3.3s) +[ 47.5s] protoda n=5 rep=11: saved 5 heads (3.3s) +[ 51.5s] protoda n=25 rep=5: saved 5 heads (3.9s) +[ 55.5s] protoda n=25 rep=6: saved 5 heads (4.0s) +[ 59.6s] protoda n=25 rep=7: saved 5 heads (4.1s) +[ 63.3s] protoda n=25 rep=8: saved 5 heads (3.7s) +[ 67.5s] protoda n=25 rep=9: saved 5 heads (4.2s) +[ 71.3s] protoda n=25 rep=10: saved 5 heads (3.9s) +[ 75.3s] protoda n=25 rep=11: saved 5 heads (4.0s) +[ 79.7s] protoda n=50 rep=5: saved 5 heads (4.4s) +[ 84.8s] protoda n=50 rep=6: saved 5 heads (5.1s) +[ 87.4s] protoda n=50 rep=7: saved 5 heads (2.6s) +[ 90.9s] protoda n=50 rep=8: saved 5 heads (3.5s) +[ 95.2s] protoda n=50 rep=9: saved 5 heads (4.3s) +[ 99.6s] protoda n=50 rep=10: saved 5 heads (4.4s) +[ 105.8s] protoda n=50 rep=11: saved 5 heads (6.1s) +[ 111.8s] protoda n=100 rep=5: saved 5 heads (6.0s) +[ 118.1s] protoda n=100 rep=6: saved 5 heads (6.3s) +[ 123.2s] protoda n=100 rep=7: saved 5 heads (5.2s) +[ 129.6s] protoda n=100 rep=8: saved 5 heads (6.4s) +[ 136.5s] protoda n=100 rep=9: saved 5 heads (6.9s) +[ 143.3s] protoda n=100 rep=10: saved 5 heads (6.9s) +[ 148.7s] protoda n=100 rep=11: saved 5 heads (5.4s) +[ 154.8s] protoda n=200 rep=5: saved 5 heads (6.1s) +[ 161.0s] protoda n=200 rep=6: saved 5 heads (6.2s) +[ 166.6s] protoda n=200 rep=7: saved 5 heads (5.6s) +[ 172.4s] protoda n=200 rep=8: saved 5 heads (5.8s) +[ 178.0s] protoda n=200 rep=9: saved 5 heads (5.6s) +[ 183.7s] protoda n=200 rep=10: saved 5 heads (5.6s) +[ 186.3s] protoda n=200 rep=11: saved 5 heads (2.6s) +[ 186.3s] done +[ 4.6s] loaded Madrid (76263, 393) Amsterdam (25992, 393); encoders ['cos'] +[ 25.9s] cos n=5 rep=5: saved 5 heads (10.9s) +[ 29.4s] cos n=5 rep=6: saved 5 heads (3.6s) +[ 32.9s] cos n=5 rep=7: saved 5 heads (3.5s) +[ 35.9s] cos n=5 rep=8: saved 5 heads (3.0s) +[ 39.1s] cos n=5 rep=9: saved 5 heads (3.2s) +[ 42.2s] cos n=5 rep=10: saved 5 heads (3.0s) +[ 45.1s] cos n=5 rep=11: saved 5 heads (3.0s) +[ 49.1s] cos n=25 rep=5: saved 5 heads (3.9s) +[ 52.9s] cos n=25 rep=6: saved 5 heads (3.8s) +[ 56.6s] cos n=25 rep=7: saved 5 heads (3.7s) +[ 60.2s] cos n=25 rep=8: saved 5 heads (3.6s) +[ 62.8s] cos n=25 rep=9: saved 5 heads (2.6s) +[ 64.7s] cos n=25 rep=10: saved 5 heads (1.9s) +[ 67.7s] cos n=25 rep=11: saved 5 heads (3.0s) +[ 72.0s] cos n=50 rep=5: saved 5 heads (4.3s) +[ 76.4s] cos n=50 rep=6: saved 5 heads (4.4s) +[ 80.7s] cos n=50 rep=7: saved 5 heads (4.3s) +[ 85.1s] cos n=50 rep=8: saved 5 heads (4.4s) +[ 89.7s] cos n=50 rep=9: saved 5 heads (4.6s) +[ 94.2s] cos n=50 rep=10: saved 5 heads (4.5s) +[ 98.4s] cos n=50 rep=11: saved 5 heads (4.2s) +[ 104.9s] cos n=100 rep=5: saved 5 heads (6.5s) +[ 109.6s] cos n=100 rep=6: saved 5 heads (4.7s) +[ 114.1s] cos n=100 rep=7: saved 5 heads (4.5s) +[ 118.7s] cos n=100 rep=8: saved 5 heads (4.6s) +[ 123.4s] cos n=100 rep=9: saved 5 heads (4.7s) +[ 126.3s] cos n=100 rep=10: saved 5 heads (2.9s) +[ 128.7s] cos n=100 rep=11: saved 5 heads (2.3s) +[ 134.0s] cos n=200 rep=5: saved 5 heads (5.3s) +[ 139.3s] cos n=200 rep=6: saved 5 heads (5.3s) +[ 144.9s] cos n=200 rep=7: saved 5 heads (5.6s) +[ 150.3s] cos n=200 rep=8: saved 5 heads (5.4s) +[ 155.6s] cos n=200 rep=9: saved 5 heads (5.3s) +[ 160.7s] cos n=200 rep=10: saved 5 heads (5.1s) +[ 165.9s] cos n=200 rep=11: saved 5 heads (5.2s) +[ 165.9s] done diff --git a/reports/nnlab/logs/st_e1lp.log b/reports/nnlab/logs/st_e1lp.log new file mode 100644 index 0000000000000000000000000000000000000000..f7852776adba8523afc57002c887d03a6c78efee --- /dev/null +++ b/reports/nnlab/logs/st_e1lp.log @@ -0,0 +1,28 @@ +[ 0.7s] loaded: Madrid (76263, 393), Amsterdam (25992, 393) +[ 0.8s] graph: 583310 edges, mean degree 22.4, min 10 +[ 82.5s] ens_e1__lp__st n=5 rep=0 base=ens_e1__lp pseudo=[1199, 1230, 724, 742] done in 81.5s +[ 152.1s] ens_e1__lp__st n=5 rep=1 base=ens_e1__lp pseudo=[1182, 1356, 664, 694] done in 69.7s +[ 222.9s] ens_e1__lp__st n=5 rep=2 base=ens_e1__lp pseudo=[1206, 1288, 835, 566] done in 70.8s +[ 295.3s] ens_e1__lp__st n=5 rep=3 base=ens_e1__lp pseudo=[1059, 1467, 662, 708] done in 72.4s +[ 371.9s] ens_e1__lp__st n=5 rep=4 base=ens_e1__lp pseudo=[1274, 1299, 676, 648] done in 76.6s +[ 447.5s] ens_e1__lp__st n=25 rep=0 base=ens_e1__lp pseudo=[1297, 1168, 832, 587] done in 75.6s +[ 517.2s] ens_e1__lp__st n=25 rep=1 base=ens_e1__lp pseudo=[1338, 997, 858, 691] done in 69.7s +[ 585.8s] ens_e1__lp__st n=25 rep=2 base=ens_e1__lp pseudo=[1526, 875, 756, 727] done in 68.6s +[ 655.0s] ens_e1__lp__st n=25 rep=3 base=ens_e1__lp pseudo=[1301, 1060, 807, 715] done in 69.2s +[ 724.6s] ens_e1__lp__st n=25 rep=4 base=ens_e1__lp pseudo=[1261, 1272, 755, 596] done in 69.6s +[ 799.3s] ens_e1__lp__st n=50 rep=0 base=ens_e1__lp pseudo=[1164, 1236, 906, 562] done in 74.7s +[ 871.5s] ens_e1__lp__st n=50 rep=1 base=ens_e1__lp pseudo=[1232, 1152, 908, 578] done in 72.2s +[ 942.0s] ens_e1__lp__st n=50 rep=2 base=ens_e1__lp pseudo=[1292, 1166, 749, 662] done in 70.5s +[ 1016.6s] ens_e1__lp__st n=50 rep=3 base=ens_e1__lp pseudo=[1311, 1163, 810, 584] done in 74.6s +[ 1087.1s] ens_e1__lp__st n=50 rep=4 base=ens_e1__lp pseudo=[1188, 1253, 795, 633] done in 70.5s +[ 1157.9s] ens_e1__lp__st n=100 rep=0 base=ens_e1__lp pseudo=[1331, 1121, 788, 598] done in 70.8s +[ 1230.3s] ens_e1__lp__st n=100 rep=1 base=ens_e1__lp pseudo=[1326, 1093, 809, 611] done in 72.4s +[ 1300.3s] ens_e1__lp__st n=100 rep=2 base=ens_e1__lp pseudo=[1285, 1093, 895, 565] done in 70.0s +[ 1374.4s] ens_e1__lp__st n=100 rep=3 base=ens_e1__lp pseudo=[1372, 1030, 830, 606] done in 74.2s +[ 1448.0s] ens_e1__lp__st n=100 rep=4 base=ens_e1__lp pseudo=[1124, 1267, 907, 541] done in 73.5s +[ 1518.4s] ens_e1__lp__st n=200 rep=0 base=ens_e1__lp pseudo=[1210, 1174, 873, 522] done in 70.4s +[ 1590.9s] ens_e1__lp__st n=200 rep=1 base=ens_e1__lp pseudo=[1300, 1031, 876, 572] done in 72.6s +[ 1659.8s] ens_e1__lp__st n=200 rep=2 base=ens_e1__lp pseudo=[1143, 1210, 863, 563] done in 68.8s +[ 1728.9s] ens_e1__lp__st n=200 rep=3 base=ens_e1__lp pseudo=[1247, 1148, 811, 573] done in 69.1s +[ 1800.7s] ens_e1__lp__st n=200 rep=4 base=ens_e1__lp pseudo=[1240, 1154, 868, 517] done in 71.9s +[ 1800.7s] done diff --git a/reports/nnlab/logs/tab_nn.log b/reports/nnlab/logs/tab_nn.log new file mode 100644 index 0000000000000000000000000000000000000000..71c8abf59a35aa1bb373b8c9b49e0633837da5e1 --- /dev/null +++ b/reports/nnlab/logs/tab_nn.log @@ -0,0 +1,777 @@ +[ 1.2s] data loaded +[ 257.4s] select epochs mlp: pseudo-target rule F1 {15: 0.4999554636261916, 30: 0.5049793102098873, 45: 0.5036779222509986} -> 30 (251s) +[ 522.5s] select epochs res: pseudo-target rule F1 {15: 0.5075904273140687, 30: 0.49876532366138193, 45: 0.48974432628349995} -> 15 (265s) +[ 995.5s] select epochs dann: pseudo-target rule F1 {15: 0.47348192372436493, 30: 0.4588599874942516, 45: 0.4520852473361714} -> 15 (473s) +[ 1254.3s] select epochs coral: pseudo-target rule F1 {15: 0.5034106048326475, 30: 0.49598761101812194, 45: 0.5052691962026917} -> 45 (259s) +[ 1288.5s] select lam: {'0.1': 0.47686438636808737, '1.0': 0.5023598505755028, '10.0': 0.5164665414687039, '100.0': 0.5110354354278966} -> 10.0 (34s) +[ 1398.7s] train mlp/rz seed 0: 30 epochs 106s +[ 1398.8s] tab_mlp_rz_zs n=0 saved +[ 1398.9s] tab_mlp_rz_ft n=5 rep=0 0.1s +[ 1407.0s] tab_mlp_rz_joint n=5 rep=0 8.2s +[ 1407.2s] tab_mlp_rz_proto n=5 rep=0 0.2s +[ 1407.3s] tab_mlp_rz_ft n=5 rep=1 0.1s +[ 1410.0s] tab_mlp_rz_joint n=5 rep=1 2.7s +[ 1410.1s] tab_mlp_rz_proto n=5 rep=1 0.1s +[ 1410.2s] tab_mlp_rz_ft n=5 rep=2 0.1s +[ 1411.6s] tab_mlp_rz_joint n=5 rep=2 1.4s +[ 1411.7s] tab_mlp_rz_proto n=5 rep=2 0.1s +[ 1411.8s] tab_mlp_rz_ft n=5 rep=3 0.1s +[ 1413.8s] tab_mlp_rz_joint n=5 rep=3 2.1s +[ 1413.9s] tab_mlp_rz_proto n=5 rep=3 0.1s +[ 1414.0s] tab_mlp_rz_ft n=5 rep=4 0.1s +[ 1416.6s] tab_mlp_rz_joint n=5 rep=4 2.6s +[ 1416.7s] tab_mlp_rz_proto n=5 rep=4 0.1s +[ 1416.7s] tab_mlp_rz_ft n=25 rep=0 0.1s +[ 1418.9s] tab_mlp_rz_joint n=25 rep=0 2.2s +[ 1419.0s] tab_mlp_rz_proto n=25 rep=0 0.1s +[ 1419.1s] tab_mlp_rz_ft n=25 rep=1 0.1s +[ 1421.4s] tab_mlp_rz_joint n=25 rep=1 2.4s +[ 1421.5s] tab_mlp_rz_proto n=25 rep=1 0.1s +[ 1421.6s] tab_mlp_rz_ft n=25 rep=2 0.1s +[ 1424.4s] tab_mlp_rz_joint n=25 rep=2 2.7s +[ 1424.4s] tab_mlp_rz_proto n=25 rep=2 0.1s +[ 1424.5s] tab_mlp_rz_ft n=25 rep=3 0.1s +[ 1428.1s] tab_mlp_rz_joint n=25 rep=3 3.6s +[ 1428.2s] tab_mlp_rz_proto n=25 rep=3 0.1s +[ 1428.3s] tab_mlp_rz_ft n=25 rep=4 0.1s +[ 1431.1s] tab_mlp_rz_joint n=25 rep=4 2.8s +[ 1431.2s] tab_mlp_rz_proto n=25 rep=4 0.1s +[ 1431.3s] tab_mlp_rz_ft n=50 rep=0 0.1s +[ 1434.9s] tab_mlp_rz_joint n=50 rep=0 3.5s +[ 1435.0s] tab_mlp_rz_proto n=50 rep=0 0.1s +[ 1435.1s] tab_mlp_rz_ft n=50 rep=1 0.1s +[ 1437.2s] tab_mlp_rz_joint n=50 rep=1 2.1s +[ 1437.3s] tab_mlp_rz_proto n=50 rep=1 0.1s +[ 1437.4s] tab_mlp_rz_ft n=50 rep=2 0.1s +[ 1440.9s] tab_mlp_rz_joint n=50 rep=2 3.5s +[ 1441.0s] tab_mlp_rz_proto n=50 rep=2 0.1s +[ 1441.1s] tab_mlp_rz_ft n=50 rep=3 0.2s +[ 1444.3s] tab_mlp_rz_joint n=50 rep=3 3.2s +[ 1444.5s] tab_mlp_rz_proto n=50 rep=3 0.2s +[ 1446.0s] tab_mlp_rz_ft n=50 rep=4 1.5s +[ 1451.6s] tab_mlp_rz_joint n=50 rep=4 5.6s +[ 1451.7s] tab_mlp_rz_proto n=50 rep=4 0.1s +[ 1451.9s] tab_mlp_rz_ft n=100 rep=0 0.3s +[ 1456.8s] tab_mlp_rz_joint n=100 rep=0 4.8s +[ 1456.9s] tab_mlp_rz_proto n=100 rep=0 0.1s +[ 1457.1s] tab_mlp_rz_ft n=100 rep=1 0.2s +[ 1461.2s] tab_mlp_rz_joint n=100 rep=1 4.1s +[ 1461.3s] tab_mlp_rz_proto n=100 rep=1 0.1s +[ 1461.5s] tab_mlp_rz_ft n=100 rep=2 0.2s +[ 1467.0s] tab_mlp_rz_joint n=100 rep=2 5.5s +[ 1467.1s] tab_mlp_rz_proto n=100 rep=2 0.1s +[ 1467.3s] tab_mlp_rz_ft n=100 rep=3 0.3s +[ 1471.1s] tab_mlp_rz_joint n=100 rep=3 3.8s +[ 1471.2s] tab_mlp_rz_proto n=100 rep=3 0.1s +[ 1471.4s] tab_mlp_rz_ft n=100 rep=4 0.2s +[ 1475.7s] tab_mlp_rz_joint n=100 rep=4 4.3s +[ 1475.8s] tab_mlp_rz_proto n=100 rep=4 0.1s +[ 1475.9s] tab_mlp_rz_ft n=200 rep=0 0.2s +[ 1479.9s] tab_mlp_rz_joint n=200 rep=0 3.9s +[ 1479.9s] tab_mlp_rz_proto n=200 rep=0 0.1s +[ 1480.2s] tab_mlp_rz_ft n=200 rep=1 0.2s +[ 1484.5s] tab_mlp_rz_joint n=200 rep=1 4.3s +[ 1484.6s] tab_mlp_rz_proto n=200 rep=1 0.1s +[ 1484.8s] tab_mlp_rz_ft n=200 rep=2 0.2s +[ 1488.5s] tab_mlp_rz_joint n=200 rep=2 3.7s +[ 1488.6s] tab_mlp_rz_proto n=200 rep=2 0.1s +[ 1488.8s] tab_mlp_rz_ft n=200 rep=3 0.2s +[ 1492.7s] tab_mlp_rz_joint n=200 rep=3 4.0s +[ 1492.8s] tab_mlp_rz_proto n=200 rep=3 0.1s +[ 1492.9s] tab_mlp_rz_ft n=200 rep=4 0.1s +[ 1496.5s] tab_mlp_rz_joint n=200 rep=4 3.5s +[ 1496.5s] tab_mlp_rz_proto n=200 rep=4 0.1s +[ 1619.4s] train mlp/rg seed 0: 30 epochs 112s +[ 1619.5s] tab_mlp_rg_zs n=0 saved +[ 1619.6s] tab_mlp_rg_ft n=5 rep=0 0.1s +[ 1626.5s] tab_mlp_rg_joint n=5 rep=0 6.9s +[ 1626.7s] tab_mlp_rg_proto n=5 rep=0 0.2s +[ 1626.8s] tab_mlp_rg_ft n=5 rep=1 0.1s +[ 1629.8s] tab_mlp_rg_joint n=5 rep=1 3.0s +[ 1629.9s] tab_mlp_rg_proto n=5 rep=1 0.1s +[ 1630.0s] tab_mlp_rg_ft n=5 rep=2 0.1s +[ 1631.6s] tab_mlp_rg_joint n=5 rep=2 1.6s +[ 1631.7s] tab_mlp_rg_proto n=5 rep=2 0.1s +[ 1631.8s] tab_mlp_rg_ft n=5 rep=3 0.1s +[ 1633.2s] tab_mlp_rg_joint n=5 rep=3 1.4s +[ 1633.2s] tab_mlp_rg_proto n=5 rep=3 0.1s +[ 1633.3s] tab_mlp_rg_ft n=5 rep=4 0.1s +[ 1634.6s] tab_mlp_rg_joint n=5 rep=4 1.2s +[ 1634.6s] tab_mlp_rg_proto n=5 rep=4 0.1s +[ 1634.7s] tab_mlp_rg_ft n=25 rep=0 0.1s +[ 1636.5s] tab_mlp_rg_joint n=25 rep=0 1.7s +[ 1636.5s] tab_mlp_rg_proto n=25 rep=0 0.1s +[ 1636.6s] tab_mlp_rg_ft n=25 rep=1 0.1s +[ 1639.9s] tab_mlp_rg_joint n=25 rep=1 3.2s +[ 1639.9s] tab_mlp_rg_proto n=25 rep=1 0.1s +[ 1640.1s] tab_mlp_rg_ft n=25 rep=2 0.1s +[ 1642.3s] tab_mlp_rg_joint n=25 rep=2 2.2s +[ 1642.4s] tab_mlp_rg_proto n=25 rep=2 0.1s +[ 1642.6s] tab_mlp_rg_ft n=25 rep=3 0.2s +[ 1650.3s] tab_mlp_rg_joint n=25 rep=3 7.8s +[ 1650.4s] tab_mlp_rg_proto n=25 rep=3 0.1s +[ 1650.5s] tab_mlp_rg_ft n=25 rep=4 0.1s +[ 1652.7s] tab_mlp_rg_joint n=25 rep=4 2.2s +[ 1652.8s] tab_mlp_rg_proto n=25 rep=4 0.1s +[ 1652.9s] tab_mlp_rg_ft n=50 rep=0 0.1s +[ 1655.7s] tab_mlp_rg_joint n=50 rep=0 2.8s +[ 1655.8s] tab_mlp_rg_proto n=50 rep=0 0.1s +[ 1655.9s] tab_mlp_rg_ft n=50 rep=1 0.1s +[ 1662.7s] tab_mlp_rg_joint n=50 rep=1 6.8s +[ 1662.8s] tab_mlp_rg_proto n=50 rep=1 0.1s +[ 1662.9s] tab_mlp_rg_ft n=50 rep=2 0.1s +[ 1667.1s] tab_mlp_rg_joint n=50 rep=2 4.1s +[ 1667.2s] tab_mlp_rg_proto n=50 rep=2 0.1s +[ 1667.3s] tab_mlp_rg_ft n=50 rep=3 0.1s +[ 1670.9s] tab_mlp_rg_joint n=50 rep=3 3.6s +[ 1671.0s] tab_mlp_rg_proto n=50 rep=3 0.1s +[ 1671.1s] tab_mlp_rg_ft n=50 rep=4 0.1s +[ 1674.0s] tab_mlp_rg_joint n=50 rep=4 2.9s +[ 1674.1s] tab_mlp_rg_proto n=50 rep=4 0.1s +[ 1674.2s] tab_mlp_rg_ft n=100 rep=0 0.1s +[ 1678.6s] tab_mlp_rg_joint n=100 rep=0 4.4s +[ 1678.7s] tab_mlp_rg_proto n=100 rep=0 0.1s +[ 1678.9s] tab_mlp_rg_ft n=100 rep=1 0.1s +[ 1682.4s] tab_mlp_rg_joint n=100 rep=1 3.5s +[ 1682.5s] tab_mlp_rg_proto n=100 rep=1 0.1s +[ 1682.6s] tab_mlp_rg_ft n=100 rep=2 0.1s +[ 1686.1s] tab_mlp_rg_joint n=100 rep=2 3.5s +[ 1686.2s] tab_mlp_rg_proto n=100 rep=2 0.1s +[ 1686.4s] tab_mlp_rg_ft n=100 rep=3 0.2s +[ 1690.6s] tab_mlp_rg_joint n=100 rep=3 4.2s +[ 1690.7s] tab_mlp_rg_proto n=100 rep=3 0.1s +[ 1690.8s] tab_mlp_rg_ft n=100 rep=4 0.1s +[ 1694.3s] tab_mlp_rg_joint n=100 rep=4 3.5s +[ 1694.4s] tab_mlp_rg_proto n=100 rep=4 0.1s +[ 1694.6s] tab_mlp_rg_ft n=200 rep=0 0.2s +[ 1698.9s] tab_mlp_rg_joint n=200 rep=0 4.3s +[ 1699.0s] tab_mlp_rg_proto n=200 rep=0 0.1s +[ 1699.2s] tab_mlp_rg_ft n=200 rep=1 0.2s +[ 1703.6s] tab_mlp_rg_joint n=200 rep=1 4.3s +[ 1703.7s] tab_mlp_rg_proto n=200 rep=1 0.1s +[ 1703.9s] tab_mlp_rg_ft n=200 rep=2 0.2s +[ 1707.4s] tab_mlp_rg_joint n=200 rep=2 3.5s +[ 1707.4s] tab_mlp_rg_proto n=200 rep=2 0.1s +[ 1707.6s] tab_mlp_rg_ft n=200 rep=3 0.2s +[ 1720.3s] tab_mlp_rg_joint n=200 rep=3 12.7s +[ 1720.4s] tab_mlp_rg_proto n=200 rep=3 0.1s +[ 1720.6s] tab_mlp_rg_ft n=200 rep=4 0.2s +[ 1734.0s] tab_mlp_rg_joint n=200 rep=4 13.4s +[ 1734.1s] tab_mlp_rg_proto n=200 rep=4 0.1s +[ 1824.2s] train mlp/o60 seed 0: 30 epochs 90s +[ 1824.3s] tab_mlp_o60_zs n=0 saved +[ 1824.4s] tab_mlp_o60_ft n=5 rep=0 0.1s +[ 1830.9s] tab_mlp_o60_joint n=5 rep=0 6.6s +[ 1831.2s] tab_mlp_o60_proto n=5 rep=0 0.3s +[ 1831.3s] tab_mlp_o60_ft n=5 rep=1 0.1s +[ 1832.9s] tab_mlp_o60_joint n=5 rep=1 1.6s +[ 1833.0s] tab_mlp_o60_proto n=5 rep=1 0.1s +[ 1833.1s] tab_mlp_o60_ft n=5 rep=2 0.1s +[ 1834.6s] tab_mlp_o60_joint n=5 rep=2 1.5s +[ 1834.7s] tab_mlp_o60_proto n=5 rep=2 0.1s +[ 1834.8s] tab_mlp_o60_ft n=5 rep=3 0.1s +[ 1836.3s] tab_mlp_o60_joint n=5 rep=3 1.6s +[ 1836.4s] tab_mlp_o60_proto n=5 rep=3 0.1s +[ 1836.5s] tab_mlp_o60_ft n=5 rep=4 0.1s +[ 1838.1s] tab_mlp_o60_joint n=5 rep=4 1.6s +[ 1838.2s] tab_mlp_o60_proto n=5 rep=4 0.1s +[ 1838.3s] tab_mlp_o60_ft n=25 rep=0 0.1s +[ 1841.4s] tab_mlp_o60_joint n=25 rep=0 3.1s +[ 1841.4s] tab_mlp_o60_proto n=25 rep=0 0.1s +[ 1841.5s] tab_mlp_o60_ft n=25 rep=1 0.1s +[ 1844.4s] tab_mlp_o60_joint n=25 rep=1 2.9s +[ 1844.5s] tab_mlp_o60_proto n=25 rep=1 0.1s +[ 1844.6s] tab_mlp_o60_ft n=25 rep=2 0.1s +[ 1848.1s] tab_mlp_o60_joint n=25 rep=2 3.5s +[ 1848.2s] tab_mlp_o60_proto n=25 rep=2 0.1s +[ 1848.3s] tab_mlp_o60_ft n=25 rep=3 0.1s +[ 1850.9s] tab_mlp_o60_joint n=25 rep=3 2.6s +[ 1850.9s] tab_mlp_o60_proto n=25 rep=3 0.1s +[ 1851.0s] tab_mlp_o60_ft n=25 rep=4 0.1s +[ 1852.8s] tab_mlp_o60_joint n=25 rep=4 1.7s +[ 1852.8s] tab_mlp_o60_proto n=25 rep=4 0.1s +[ 1852.9s] tab_mlp_o60_ft n=50 rep=0 0.1s +[ 1856.1s] tab_mlp_o60_joint n=50 rep=0 3.1s +[ 1856.2s] tab_mlp_o60_proto n=50 rep=0 0.1s +[ 1856.3s] tab_mlp_o60_ft n=50 rep=1 0.1s +[ 1858.4s] tab_mlp_o60_joint n=50 rep=1 2.1s +[ 1858.5s] tab_mlp_o60_proto n=50 rep=1 0.1s +[ 1858.6s] tab_mlp_o60_ft n=50 rep=2 0.1s +[ 1862.0s] tab_mlp_o60_joint n=50 rep=2 3.4s +[ 1862.1s] tab_mlp_o60_proto n=50 rep=2 0.1s +[ 1862.2s] tab_mlp_o60_ft n=50 rep=3 0.1s +[ 1864.3s] tab_mlp_o60_joint n=50 rep=3 2.1s +[ 1864.4s] tab_mlp_o60_proto n=50 rep=3 0.1s +[ 1864.5s] tab_mlp_o60_ft n=50 rep=4 0.1s +[ 1868.0s] tab_mlp_o60_joint n=50 rep=4 3.5s +[ 1868.0s] tab_mlp_o60_proto n=50 rep=4 0.1s +[ 1868.2s] tab_mlp_o60_ft n=100 rep=0 0.1s +[ 1873.8s] tab_mlp_o60_joint n=100 rep=0 5.7s +[ 1873.9s] tab_mlp_o60_proto n=100 rep=0 0.1s +[ 1874.1s] tab_mlp_o60_ft n=100 rep=1 0.1s +[ 1877.6s] tab_mlp_o60_joint n=100 rep=1 3.6s +[ 1877.7s] tab_mlp_o60_proto n=100 rep=1 0.1s +[ 1877.9s] tab_mlp_o60_ft n=100 rep=2 0.2s +[ 1881.5s] tab_mlp_o60_joint n=100 rep=2 3.5s +[ 1881.6s] tab_mlp_o60_proto n=100 rep=2 0.1s +[ 1881.7s] tab_mlp_o60_ft n=100 rep=3 0.2s +[ 1884.6s] tab_mlp_o60_joint n=100 rep=3 2.9s +[ 1884.7s] tab_mlp_o60_proto n=100 rep=3 0.1s +[ 1884.8s] tab_mlp_o60_ft n=100 rep=4 0.1s +[ 1888.2s] tab_mlp_o60_joint n=100 rep=4 3.4s +[ 1888.3s] tab_mlp_o60_proto n=100 rep=4 0.1s +[ 1888.5s] tab_mlp_o60_ft n=200 rep=0 0.2s +[ 1891.3s] tab_mlp_o60_joint n=200 rep=0 2.9s +[ 1891.4s] tab_mlp_o60_proto n=200 rep=0 0.1s +[ 1891.5s] tab_mlp_o60_ft n=200 rep=1 0.1s +[ 1894.4s] tab_mlp_o60_joint n=200 rep=1 2.9s +[ 1894.5s] tab_mlp_o60_proto n=200 rep=1 0.1s +[ 1894.7s] tab_mlp_o60_ft n=200 rep=2 0.2s +[ 1898.2s] tab_mlp_o60_joint n=200 rep=2 3.5s +[ 1898.3s] tab_mlp_o60_proto n=200 rep=2 0.1s +[ 1898.5s] tab_mlp_o60_ft n=200 rep=3 0.2s +[ 1904.0s] tab_mlp_o60_joint n=200 rep=3 5.6s +[ 1904.1s] tab_mlp_o60_proto n=200 rep=3 0.1s +[ 1904.4s] tab_mlp_o60_ft n=200 rep=4 0.2s +[ 1907.2s] tab_mlp_o60_joint n=200 rep=4 2.9s +[ 1907.3s] tab_mlp_o60_proto n=200 rep=4 0.1s +[ 2021.0s] train res/rz seed 0: 15 epochs 114s +[ 2021.1s] tab_res_rz_zs n=0 saved +[ 2021.2s] tab_res_rz_ft n=5 rep=0 0.1s +[ 2027.5s] tab_res_rz_joint n=5 rep=0 6.3s +[ 2027.7s] tab_res_rz_proto n=5 rep=0 0.2s +[ 2027.8s] tab_res_rz_ft n=5 rep=1 0.1s +[ 2030.5s] tab_res_rz_joint n=5 rep=1 2.7s +[ 2030.6s] tab_res_rz_proto n=5 rep=1 0.1s +[ 2030.7s] tab_res_rz_ft n=5 rep=2 0.1s +[ 2032.1s] tab_res_rz_joint n=5 rep=2 1.4s +[ 2032.2s] tab_res_rz_proto n=5 rep=2 0.1s +[ 2032.3s] tab_res_rz_ft n=5 rep=3 0.1s +[ 2033.7s] tab_res_rz_joint n=5 rep=3 1.4s +[ 2033.8s] tab_res_rz_proto n=5 rep=3 0.1s +[ 2033.9s] tab_res_rz_ft n=5 rep=4 0.1s +[ 2036.1s] tab_res_rz_joint n=5 rep=4 2.2s +[ 2036.2s] tab_res_rz_proto n=5 rep=4 0.1s +[ 2036.3s] tab_res_rz_ft n=25 rep=0 0.1s +[ 2039.2s] tab_res_rz_joint n=25 rep=0 2.9s +[ 2039.3s] tab_res_rz_proto n=25 rep=0 0.1s +[ 2039.4s] tab_res_rz_ft n=25 rep=1 0.1s +[ 2041.6s] tab_res_rz_joint n=25 rep=1 2.2s +[ 2041.7s] tab_res_rz_proto n=25 rep=1 0.1s +[ 2041.8s] tab_res_rz_ft n=25 rep=2 0.1s +[ 2045.9s] tab_res_rz_joint n=25 rep=2 4.2s +[ 2046.1s] tab_res_rz_proto n=25 rep=2 0.1s +[ 2046.5s] tab_res_rz_ft n=25 rep=3 0.4s +[ 2051.0s] tab_res_rz_joint n=25 rep=3 4.5s +[ 2051.1s] tab_res_rz_proto n=25 rep=3 0.1s +[ 2051.2s] tab_res_rz_ft n=25 rep=4 0.1s +[ 2054.1s] tab_res_rz_joint n=25 rep=4 2.9s +[ 2054.2s] tab_res_rz_proto n=25 rep=4 0.1s +[ 2054.3s] tab_res_rz_ft n=50 rep=0 0.2s +[ 2057.4s] tab_res_rz_joint n=50 rep=0 3.1s +[ 2057.5s] tab_res_rz_proto n=50 rep=0 0.1s +[ 2057.6s] tab_res_rz_ft n=50 rep=1 0.1s +[ 2061.5s] tab_res_rz_joint n=50 rep=1 3.9s +[ 2061.6s] tab_res_rz_proto n=50 rep=1 0.1s +[ 2061.7s] tab_res_rz_ft n=50 rep=2 0.2s +[ 2064.2s] tab_res_rz_joint n=50 rep=2 2.4s +[ 2064.2s] tab_res_rz_proto n=50 rep=2 0.1s +[ 2064.4s] tab_res_rz_ft n=50 rep=3 0.2s +[ 2066.5s] tab_res_rz_joint n=50 rep=3 2.1s +[ 2066.5s] tab_res_rz_proto n=50 rep=3 0.1s +[ 2066.6s] tab_res_rz_ft n=50 rep=4 0.1s +[ 2068.5s] tab_res_rz_joint n=50 rep=4 1.8s +[ 2068.5s] tab_res_rz_proto n=50 rep=4 0.1s +[ 2068.6s] tab_res_rz_ft n=100 rep=0 0.1s +[ 2073.1s] tab_res_rz_joint n=100 rep=0 4.5s +[ 2073.2s] tab_res_rz_proto n=100 rep=0 0.1s +[ 2073.4s] tab_res_rz_ft n=100 rep=1 0.2s +[ 2079.6s] tab_res_rz_joint n=100 rep=1 6.2s +[ 2079.7s] tab_res_rz_proto n=100 rep=1 0.1s +[ 2079.9s] tab_res_rz_ft n=100 rep=2 0.2s +[ 2084.2s] tab_res_rz_joint n=100 rep=2 4.3s +[ 2084.2s] tab_res_rz_proto n=100 rep=2 0.1s +[ 2084.4s] tab_res_rz_ft n=100 rep=3 0.2s +[ 2088.0s] tab_res_rz_joint n=100 rep=3 3.5s +[ 2088.0s] tab_res_rz_proto n=100 rep=3 0.1s +[ 2088.2s] tab_res_rz_ft n=100 rep=4 0.2s +[ 2092.4s] tab_res_rz_joint n=100 rep=4 4.2s +[ 2092.5s] tab_res_rz_proto n=100 rep=4 0.1s +[ 2092.8s] tab_res_rz_ft n=200 rep=0 0.3s +[ 2096.3s] tab_res_rz_joint n=200 rep=0 3.6s +[ 2096.4s] tab_res_rz_proto n=200 rep=0 0.1s +[ 2096.7s] tab_res_rz_ft n=200 rep=1 0.3s +[ 2100.3s] tab_res_rz_joint n=200 rep=1 3.6s +[ 2100.4s] tab_res_rz_proto n=200 rep=1 0.1s +[ 2100.6s] tab_res_rz_ft n=200 rep=2 0.2s +[ 2104.1s] tab_res_rz_joint n=200 rep=2 3.5s +[ 2104.2s] tab_res_rz_proto n=200 rep=2 0.1s +[ 2104.3s] tab_res_rz_ft n=200 rep=3 0.2s +[ 2108.0s] tab_res_rz_joint n=200 rep=3 3.7s +[ 2108.1s] tab_res_rz_proto n=200 rep=3 0.1s +[ 2108.3s] tab_res_rz_ft n=200 rep=4 0.2s +[ 2111.2s] tab_res_rz_joint n=200 rep=4 2.9s +[ 2111.3s] tab_res_rz_proto n=200 rep=4 0.1s +[ 2216.9s] train res/o60 seed 0: 15 epochs 106s +[ 2217.0s] tab_res_o60_zs n=0 saved +[ 2217.1s] tab_res_o60_ft n=5 rep=0 0.1s +[ 2223.3s] tab_res_o60_joint n=5 rep=0 6.2s +[ 2223.4s] tab_res_o60_proto n=5 rep=0 0.2s +[ 2223.5s] tab_res_o60_ft n=5 rep=1 0.1s +[ 2226.2s] tab_res_o60_joint n=5 rep=1 2.7s +[ 2226.3s] tab_res_o60_proto n=5 rep=1 0.1s +[ 2226.4s] tab_res_o60_ft n=5 rep=2 0.1s +[ 2227.8s] tab_res_o60_joint n=5 rep=2 1.4s +[ 2227.8s] tab_res_o60_proto n=5 rep=2 0.1s +[ 2227.9s] tab_res_o60_ft n=5 rep=3 0.1s +[ 2229.2s] tab_res_o60_joint n=5 rep=3 1.3s +[ 2229.3s] tab_res_o60_proto n=5 rep=3 0.1s +[ 2229.4s] tab_res_o60_ft n=5 rep=4 0.1s +[ 2230.7s] tab_res_o60_joint n=5 rep=4 1.3s +[ 2230.7s] tab_res_o60_proto n=5 rep=4 0.1s +[ 2230.9s] tab_res_o60_ft n=25 rep=0 0.1s +[ 2232.9s] tab_res_o60_joint n=25 rep=0 2.0s +[ 2232.9s] tab_res_o60_proto n=25 rep=0 0.1s +[ 2233.1s] tab_res_o60_ft n=25 rep=1 0.1s +[ 2235.0s] tab_res_o60_joint n=25 rep=1 2.0s +[ 2235.1s] tab_res_o60_proto n=25 rep=1 0.1s +[ 2235.2s] tab_res_o60_ft n=25 rep=2 0.1s +[ 2237.2s] tab_res_o60_joint n=25 rep=2 2.0s +[ 2237.3s] tab_res_o60_proto n=25 rep=2 0.1s +[ 2237.4s] tab_res_o60_ft n=25 rep=3 0.1s +[ 2240.1s] tab_res_o60_joint n=25 rep=3 2.7s +[ 2240.1s] tab_res_o60_proto n=25 rep=3 0.1s +[ 2240.2s] tab_res_o60_ft n=25 rep=4 0.1s +[ 2243.5s] tab_res_o60_joint n=25 rep=4 3.2s +[ 2243.5s] tab_res_o60_proto n=25 rep=4 0.1s +[ 2243.7s] tab_res_o60_ft n=50 rep=0 0.1s +[ 2247.7s] tab_res_o60_joint n=50 rep=0 4.0s +[ 2247.8s] tab_res_o60_proto n=50 rep=0 0.1s +[ 2247.9s] tab_res_o60_ft n=50 rep=1 0.1s +[ 2250.0s] tab_res_o60_joint n=50 rep=1 2.1s +[ 2250.1s] tab_res_o60_proto n=50 rep=1 0.1s +[ 2250.2s] tab_res_o60_ft n=50 rep=2 0.1s +[ 2253.3s] tab_res_o60_joint n=50 rep=2 3.1s +[ 2253.4s] tab_res_o60_proto n=50 rep=2 0.1s +[ 2253.5s] tab_res_o60_ft n=50 rep=3 0.1s +[ 2256.1s] tab_res_o60_joint n=50 rep=3 2.5s +[ 2256.2s] tab_res_o60_proto n=50 rep=3 0.1s +[ 2256.3s] tab_res_o60_ft n=50 rep=4 0.2s +[ 2259.7s] tab_res_o60_joint n=50 rep=4 3.4s +[ 2259.8s] tab_res_o60_proto n=50 rep=4 0.1s +[ 2259.9s] tab_res_o60_ft n=100 rep=0 0.2s +[ 2266.2s] tab_res_o60_joint n=100 rep=0 6.2s +[ 2266.2s] tab_res_o60_proto n=100 rep=0 0.1s +[ 2266.4s] tab_res_o60_ft n=100 rep=1 0.1s +[ 2270.5s] tab_res_o60_joint n=100 rep=1 4.1s +[ 2270.6s] tab_res_o60_proto n=100 rep=1 0.1s +[ 2270.8s] tab_res_o60_ft n=100 rep=2 0.2s +[ 2274.9s] tab_res_o60_joint n=100 rep=2 4.1s +[ 2274.9s] tab_res_o60_proto n=100 rep=2 0.1s +[ 2275.1s] tab_res_o60_ft n=100 rep=3 0.1s +[ 2277.2s] tab_res_o60_joint n=100 rep=3 2.1s +[ 2277.2s] tab_res_o60_proto n=100 rep=3 0.1s +[ 2277.4s] tab_res_o60_ft n=100 rep=4 0.1s +[ 2280.1s] tab_res_o60_joint n=100 rep=4 2.8s +[ 2280.2s] tab_res_o60_proto n=100 rep=4 0.1s +[ 2280.4s] tab_res_o60_ft n=200 rep=0 0.2s +[ 2284.2s] tab_res_o60_joint n=200 rep=0 3.8s +[ 2284.3s] tab_res_o60_proto n=200 rep=0 0.1s +[ 2284.4s] tab_res_o60_ft n=200 rep=1 0.1s +[ 2287.3s] tab_res_o60_joint n=200 rep=1 2.9s +[ 2287.4s] tab_res_o60_proto n=200 rep=1 0.1s +[ 2287.5s] tab_res_o60_ft n=200 rep=2 0.1s +[ 2294.2s] tab_res_o60_joint n=200 rep=2 6.7s +[ 2294.3s] tab_res_o60_proto n=200 rep=2 0.1s +[ 2294.4s] tab_res_o60_ft n=200 rep=3 0.2s +[ 2298.5s] tab_res_o60_joint n=200 rep=3 4.1s +[ 2298.6s] tab_res_o60_proto n=200 rep=3 0.1s +[ 2298.8s] tab_res_o60_ft n=200 rep=4 0.2s +[ 2302.9s] tab_res_o60_joint n=200 rep=4 4.1s +[ 2303.0s] tab_res_o60_proto n=200 rep=4 0.1s +[ 2396.6s] train dann/rz seed 0: 15 epochs 94s +[ 2396.7s] tab_dann_rz_zs n=0 saved +[ 2396.8s] tab_dann_rz_ft n=5 rep=0 0.1s +[ 2403.0s] tab_dann_rz_joint n=5 rep=0 6.2s +[ 2403.2s] tab_dann_rz_proto n=5 rep=0 0.2s +[ 2403.3s] tab_dann_rz_ft n=5 rep=1 0.1s +[ 2405.3s] tab_dann_rz_joint n=5 rep=1 2.1s +[ 2405.4s] tab_dann_rz_proto n=5 rep=1 0.1s +[ 2405.5s] tab_dann_rz_ft n=5 rep=2 0.1s +[ 2407.6s] tab_dann_rz_joint n=5 rep=2 2.1s +[ 2407.7s] tab_dann_rz_proto n=5 rep=2 0.1s +[ 2407.7s] tab_dann_rz_ft n=5 rep=3 0.1s +[ 2409.1s] tab_dann_rz_joint n=5 rep=3 1.4s +[ 2409.2s] tab_dann_rz_proto n=5 rep=3 0.1s +[ 2409.3s] tab_dann_rz_ft n=5 rep=4 0.1s +[ 2411.3s] tab_dann_rz_joint n=5 rep=4 2.0s +[ 2411.4s] tab_dann_rz_proto n=5 rep=4 0.1s +[ 2411.6s] tab_dann_rz_ft n=25 rep=0 0.2s +[ 2415.1s] tab_dann_rz_joint n=25 rep=0 3.5s +[ 2415.2s] tab_dann_rz_proto n=25 rep=0 0.1s +[ 2415.3s] tab_dann_rz_ft n=25 rep=1 0.1s +[ 2417.4s] tab_dann_rz_joint n=25 rep=1 2.1s +[ 2417.4s] tab_dann_rz_proto n=25 rep=1 0.1s +[ 2417.5s] tab_dann_rz_ft n=25 rep=2 0.1s +[ 2420.8s] tab_dann_rz_joint n=25 rep=2 3.2s +[ 2420.9s] tab_dann_rz_proto n=25 rep=2 0.1s +[ 2420.9s] tab_dann_rz_ft n=25 rep=3 0.1s +[ 2422.6s] tab_dann_rz_joint n=25 rep=3 1.7s +[ 2422.7s] tab_dann_rz_proto n=25 rep=3 0.1s +[ 2422.8s] tab_dann_rz_ft n=25 rep=4 0.1s +[ 2425.1s] tab_dann_rz_joint n=25 rep=4 2.3s +[ 2425.1s] tab_dann_rz_proto n=25 rep=4 0.1s +[ 2425.3s] tab_dann_rz_ft n=50 rep=0 0.1s +[ 2427.9s] tab_dann_rz_joint n=50 rep=0 2.6s +[ 2428.0s] tab_dann_rz_proto n=50 rep=0 0.1s +[ 2428.1s] tab_dann_rz_ft n=50 rep=1 0.1s +[ 2430.8s] tab_dann_rz_joint n=50 rep=1 2.7s +[ 2430.9s] tab_dann_rz_proto n=50 rep=1 0.1s +[ 2431.1s] tab_dann_rz_ft n=50 rep=2 0.2s +[ 2433.8s] tab_dann_rz_joint n=50 rep=2 2.8s +[ 2433.9s] tab_dann_rz_proto n=50 rep=2 0.1s +[ 2434.0s] tab_dann_rz_ft n=50 rep=3 0.1s +[ 2436.8s] tab_dann_rz_joint n=50 rep=3 2.8s +[ 2436.9s] tab_dann_rz_proto n=50 rep=3 0.1s +[ 2437.0s] tab_dann_rz_ft n=50 rep=4 0.2s +[ 2440.4s] tab_dann_rz_joint n=50 rep=4 3.4s +[ 2440.5s] tab_dann_rz_proto n=50 rep=4 0.1s +[ 2440.7s] tab_dann_rz_ft n=100 rep=0 0.2s +[ 2444.1s] tab_dann_rz_joint n=100 rep=0 3.5s +[ 2444.2s] tab_dann_rz_proto n=100 rep=0 0.1s +[ 2444.4s] tab_dann_rz_ft n=100 rep=1 0.2s +[ 2447.2s] tab_dann_rz_joint n=100 rep=1 2.9s +[ 2447.3s] tab_dann_rz_proto n=100 rep=1 0.1s +[ 2447.5s] tab_dann_rz_ft n=100 rep=2 0.2s +[ 2451.1s] tab_dann_rz_joint n=100 rep=2 3.6s +[ 2451.2s] tab_dann_rz_proto n=100 rep=2 0.1s +[ 2451.4s] tab_dann_rz_ft n=100 rep=3 0.2s +[ 2455.6s] tab_dann_rz_joint n=100 rep=3 4.2s +[ 2455.6s] tab_dann_rz_proto n=100 rep=3 0.1s +[ 2455.8s] tab_dann_rz_ft n=100 rep=4 0.2s +[ 2459.3s] tab_dann_rz_joint n=100 rep=4 3.5s +[ 2459.4s] tab_dann_rz_proto n=100 rep=4 0.1s +[ 2459.6s] tab_dann_rz_ft n=200 rep=0 0.2s +[ 2463.9s] tab_dann_rz_joint n=200 rep=0 4.4s +[ 2464.0s] tab_dann_rz_proto n=200 rep=0 0.1s +[ 2464.3s] tab_dann_rz_ft n=200 rep=1 0.2s +[ 2467.8s] tab_dann_rz_joint n=200 rep=1 3.5s +[ 2467.9s] tab_dann_rz_proto n=200 rep=1 0.1s +[ 2468.0s] tab_dann_rz_ft n=200 rep=2 0.2s +[ 2471.3s] tab_dann_rz_joint n=200 rep=2 3.3s +[ 2471.4s] tab_dann_rz_proto n=200 rep=2 0.1s +[ 2471.5s] tab_dann_rz_ft n=200 rep=3 0.2s +[ 2474.9s] tab_dann_rz_joint n=200 rep=3 3.4s +[ 2475.0s] tab_dann_rz_proto n=200 rep=3 0.1s +[ 2475.2s] tab_dann_rz_ft n=200 rep=4 0.2s +[ 2485.7s] tab_dann_rz_joint n=200 rep=4 10.5s +[ 2485.8s] tab_dann_rz_proto n=200 rep=4 0.1s +[ 2541.7s] train dann/rg seed 0: 15 epochs 56s +[ 2541.7s] tab_dann_rg_zs n=0 saved +[ 2541.8s] tab_dann_rg_ft n=5 rep=0 0.1s +[ 2546.2s] tab_dann_rg_joint n=5 rep=0 4.4s +[ 2546.5s] tab_dann_rg_proto n=5 rep=0 0.2s +[ 2546.6s] tab_dann_rg_ft n=5 rep=1 0.1s +[ 2548.1s] tab_dann_rg_joint n=5 rep=1 1.6s +[ 2548.2s] tab_dann_rg_proto n=5 rep=1 0.1s +[ 2548.3s] tab_dann_rg_ft n=5 rep=2 0.1s +[ 2549.4s] tab_dann_rg_joint n=5 rep=2 1.1s +[ 2549.4s] tab_dann_rg_proto n=5 rep=2 0.1s +[ 2549.5s] tab_dann_rg_ft n=5 rep=3 0.1s +[ 2550.4s] tab_dann_rg_joint n=5 rep=3 0.9s +[ 2550.4s] tab_dann_rg_proto n=5 rep=3 0.1s +[ 2550.5s] tab_dann_rg_ft n=5 rep=4 0.1s +[ 2551.4s] tab_dann_rg_joint n=5 rep=4 0.8s +[ 2551.4s] tab_dann_rg_proto n=5 rep=4 0.1s +[ 2551.5s] tab_dann_rg_ft n=25 rep=0 0.1s +[ 2552.8s] tab_dann_rg_joint n=25 rep=0 1.3s +[ 2552.8s] tab_dann_rg_proto n=25 rep=0 0.0s +[ 2552.9s] tab_dann_rg_ft n=25 rep=1 0.1s +[ 2554.1s] tab_dann_rg_joint n=25 rep=1 1.2s +[ 2554.1s] tab_dann_rg_proto n=25 rep=1 0.0s +[ 2554.2s] tab_dann_rg_ft n=25 rep=2 0.1s +[ 2555.4s] tab_dann_rg_joint n=25 rep=2 1.2s +[ 2555.4s] tab_dann_rg_proto n=25 rep=2 0.0s +[ 2555.5s] tab_dann_rg_ft n=25 rep=3 0.1s +[ 2556.7s] tab_dann_rg_joint n=25 rep=3 1.2s +[ 2556.7s] tab_dann_rg_proto n=25 rep=3 0.0s +[ 2556.8s] tab_dann_rg_ft n=25 rep=4 0.1s +[ 2558.0s] tab_dann_rg_joint n=25 rep=4 1.2s +[ 2558.1s] tab_dann_rg_proto n=25 rep=4 0.0s +[ 2558.1s] tab_dann_rg_ft n=50 rep=0 0.1s +[ 2559.3s] tab_dann_rg_joint n=50 rep=0 1.2s +[ 2559.4s] tab_dann_rg_proto n=50 rep=0 0.0s +[ 2559.5s] tab_dann_rg_ft n=50 rep=1 0.1s +[ 2560.7s] tab_dann_rg_joint n=50 rep=1 1.2s +[ 2560.7s] tab_dann_rg_proto n=50 rep=1 0.1s +[ 2560.8s] tab_dann_rg_ft n=50 rep=2 0.1s +[ 2562.4s] tab_dann_rg_joint n=50 rep=2 1.6s +[ 2562.5s] tab_dann_rg_proto n=50 rep=2 0.0s +[ 2562.6s] tab_dann_rg_ft n=50 rep=3 0.1s +[ 2563.8s] tab_dann_rg_joint n=50 rep=3 1.2s +[ 2563.8s] tab_dann_rg_proto n=50 rep=3 0.0s +[ 2563.9s] tab_dann_rg_ft n=50 rep=4 0.1s +[ 2566.0s] tab_dann_rg_joint n=50 rep=4 2.0s +[ 2566.0s] tab_dann_rg_proto n=50 rep=4 0.1s +[ 2566.1s] tab_dann_rg_ft n=100 rep=0 0.1s +[ 2568.2s] tab_dann_rg_joint n=100 rep=0 2.1s +[ 2568.2s] tab_dann_rg_proto n=100 rep=0 0.0s +[ 2568.3s] tab_dann_rg_ft n=100 rep=1 0.1s +[ 2570.0s] tab_dann_rg_joint n=100 rep=1 1.7s +[ 2570.1s] tab_dann_rg_proto n=100 rep=1 0.1s +[ 2570.2s] tab_dann_rg_ft n=100 rep=2 0.1s +[ 2571.8s] tab_dann_rg_joint n=100 rep=2 1.6s +[ 2571.8s] tab_dann_rg_proto n=100 rep=2 0.1s +[ 2571.9s] tab_dann_rg_ft n=100 rep=3 0.1s +[ 2573.2s] tab_dann_rg_joint n=100 rep=3 1.3s +[ 2573.3s] tab_dann_rg_proto n=100 rep=3 0.0s +[ 2573.4s] tab_dann_rg_ft n=100 rep=4 0.1s +[ 2574.7s] tab_dann_rg_joint n=100 rep=4 1.3s +[ 2574.7s] tab_dann_rg_proto n=100 rep=4 0.1s +[ 2574.9s] tab_dann_rg_ft n=200 rep=0 0.1s +[ 2576.6s] tab_dann_rg_joint n=200 rep=0 1.7s +[ 2576.6s] tab_dann_rg_proto n=200 rep=0 0.1s +[ 2576.7s] tab_dann_rg_ft n=200 rep=1 0.1s +[ 2578.5s] tab_dann_rg_joint n=200 rep=1 1.8s +[ 2578.6s] tab_dann_rg_proto n=200 rep=1 0.1s +[ 2578.7s] tab_dann_rg_ft n=200 rep=2 0.2s +[ 2580.9s] tab_dann_rg_joint n=200 rep=2 2.2s +[ 2581.0s] tab_dann_rg_proto n=200 rep=2 0.1s +[ 2581.1s] tab_dann_rg_ft n=200 rep=3 0.1s +[ 2582.9s] tab_dann_rg_joint n=200 rep=3 1.7s +[ 2582.9s] tab_dann_rg_proto n=200 rep=3 0.1s +[ 2583.0s] tab_dann_rg_ft n=200 rep=4 0.1s +[ 2584.7s] tab_dann_rg_joint n=200 rep=4 1.7s +[ 2584.8s] tab_dann_rg_proto n=200 rep=4 0.0s +[ 2628.3s] train dann/o60 seed 0: 15 epochs 44s +[ 2628.4s] tab_dann_o60_zs n=0 saved +[ 2628.4s] tab_dann_o60_ft n=5 rep=0 0.0s +[ 2631.8s] tab_dann_o60_joint n=5 rep=0 3.4s +[ 2632.0s] tab_dann_o60_proto n=5 rep=0 0.1s +[ 2632.0s] tab_dann_o60_ft n=5 rep=1 0.0s +[ 2632.9s] tab_dann_o60_joint n=5 rep=1 0.9s +[ 2633.0s] tab_dann_o60_proto n=5 rep=1 0.1s +[ 2633.0s] tab_dann_o60_ft n=5 rep=2 0.1s +[ 2633.9s] tab_dann_o60_joint n=5 rep=2 0.8s +[ 2633.9s] tab_dann_o60_proto n=5 rep=2 0.1s +[ 2634.0s] tab_dann_o60_ft n=5 rep=3 0.0s +[ 2634.9s] tab_dann_o60_joint n=5 rep=3 0.9s +[ 2634.9s] tab_dann_o60_proto n=5 rep=3 0.0s +[ 2635.0s] tab_dann_o60_ft n=5 rep=4 0.1s +[ 2635.8s] tab_dann_o60_joint n=5 rep=4 0.9s +[ 2635.9s] tab_dann_o60_proto n=5 rep=4 0.1s +[ 2636.0s] tab_dann_o60_ft n=25 rep=0 0.1s +[ 2637.6s] tab_dann_o60_joint n=25 rep=0 1.7s +[ 2637.7s] tab_dann_o60_proto n=25 rep=0 0.1s +[ 2637.7s] tab_dann_o60_ft n=25 rep=1 0.1s +[ 2639.0s] tab_dann_o60_joint n=25 rep=1 1.3s +[ 2639.1s] tab_dann_o60_proto n=25 rep=1 0.1s +[ 2639.2s] tab_dann_o60_ft n=25 rep=2 0.1s +[ 2640.5s] tab_dann_o60_joint n=25 rep=2 1.3s +[ 2640.5s] tab_dann_o60_proto n=25 rep=2 0.1s +[ 2640.6s] tab_dann_o60_ft n=25 rep=3 0.1s +[ 2642.4s] tab_dann_o60_joint n=25 rep=3 1.8s +[ 2642.5s] tab_dann_o60_proto n=25 rep=3 0.1s +[ 2642.6s] tab_dann_o60_ft n=25 rep=4 0.1s +[ 2645.0s] tab_dann_o60_joint n=25 rep=4 2.4s +[ 2645.1s] tab_dann_o60_proto n=25 rep=4 0.1s +[ 2645.3s] tab_dann_o60_ft n=50 rep=0 0.1s +[ 2647.1s] tab_dann_o60_joint n=50 rep=0 1.9s +[ 2647.2s] tab_dann_o60_proto n=50 rep=0 0.1s +[ 2647.3s] tab_dann_o60_ft n=50 rep=1 0.1s +[ 2651.8s] tab_dann_o60_joint n=50 rep=1 4.5s +[ 2651.9s] tab_dann_o60_proto n=50 rep=1 0.1s +[ 2652.0s] tab_dann_o60_ft n=50 rep=2 0.1s +[ 2653.1s] tab_dann_o60_joint n=50 rep=2 1.1s +[ 2653.1s] tab_dann_o60_proto n=50 rep=2 0.1s +[ 2653.2s] tab_dann_o60_ft n=50 rep=3 0.1s +[ 2654.5s] tab_dann_o60_joint n=50 rep=3 1.3s +[ 2654.6s] tab_dann_o60_proto n=50 rep=3 0.1s +[ 2654.8s] tab_dann_o60_ft n=50 rep=4 0.1s +[ 2657.1s] tab_dann_o60_joint n=50 rep=4 2.3s +[ 2657.1s] tab_dann_o60_proto n=50 rep=4 0.1s +[ 2657.3s] tab_dann_o60_ft n=100 rep=0 0.1s +[ 2659.9s] tab_dann_o60_joint n=100 rep=0 2.7s +[ 2660.0s] tab_dann_o60_proto n=100 rep=0 0.1s +[ 2660.1s] tab_dann_o60_ft n=100 rep=1 0.1s +[ 2662.5s] tab_dann_o60_joint n=100 rep=1 2.4s +[ 2662.6s] tab_dann_o60_proto n=100 rep=1 0.1s +[ 2662.7s] tab_dann_o60_ft n=100 rep=2 0.1s +[ 2665.6s] tab_dann_o60_joint n=100 rep=2 3.0s +[ 2665.7s] tab_dann_o60_proto n=100 rep=2 0.1s +[ 2665.8s] tab_dann_o60_ft n=100 rep=3 0.1s +[ 2668.4s] tab_dann_o60_joint n=100 rep=3 2.6s +[ 2668.5s] tab_dann_o60_proto n=100 rep=3 0.1s +[ 2668.6s] tab_dann_o60_ft n=100 rep=4 0.2s +[ 2670.9s] tab_dann_o60_joint n=100 rep=4 2.3s +[ 2671.0s] tab_dann_o60_proto n=100 rep=4 0.1s +[ 2671.2s] tab_dann_o60_ft n=200 rep=0 0.2s +[ 2674.2s] tab_dann_o60_joint n=200 rep=0 3.0s +[ 2674.3s] tab_dann_o60_proto n=200 rep=0 0.1s +[ 2674.4s] tab_dann_o60_ft n=200 rep=1 0.1s +[ 2676.9s] tab_dann_o60_joint n=200 rep=1 2.4s +[ 2676.9s] tab_dann_o60_proto n=200 rep=1 0.1s +[ 2677.1s] tab_dann_o60_ft n=200 rep=2 0.1s +[ 2680.9s] tab_dann_o60_joint n=200 rep=2 3.8s +[ 2681.0s] tab_dann_o60_proto n=200 rep=2 0.1s +[ 2681.2s] tab_dann_o60_ft n=200 rep=3 0.1s +[ 2684.4s] tab_dann_o60_joint n=200 rep=3 3.2s +[ 2684.5s] tab_dann_o60_proto n=200 rep=3 0.1s +[ 2684.6s] tab_dann_o60_ft n=200 rep=4 0.2s +[ 2692.3s] tab_dann_o60_joint n=200 rep=4 7.7s +[ 2692.5s] tab_dann_o60_proto n=200 rep=4 0.1s +[ 2966.5s] train coral/rz seed 0: 45 epochs 274s +[ 2966.6s] tab_coral_rz_zs n=0 saved +[ 2966.6s] tab_coral_rz_ft n=5 rep=0 0.0s +[ 2973.9s] tab_coral_rz_joint n=5 rep=0 7.3s +[ 2974.1s] tab_coral_rz_proto n=5 rep=0 0.2s +[ 2974.2s] tab_coral_rz_ft n=5 rep=1 0.1s +[ 2977.1s] tab_coral_rz_joint n=5 rep=1 2.9s +[ 2977.2s] tab_coral_rz_proto n=5 rep=1 0.1s +[ 2977.3s] tab_coral_rz_ft n=5 rep=2 0.1s +[ 2978.8s] tab_coral_rz_joint n=5 rep=2 1.5s +[ 2978.9s] tab_coral_rz_proto n=5 rep=2 0.1s +[ 2979.0s] tab_coral_rz_ft n=5 rep=3 0.1s +[ 2982.1s] tab_coral_rz_joint n=5 rep=3 3.1s +[ 2982.1s] tab_coral_rz_proto n=5 rep=3 0.1s +[ 2982.3s] tab_coral_rz_ft n=5 rep=4 0.1s +[ 2985.4s] tab_coral_rz_joint n=5 rep=4 3.1s +[ 2985.5s] tab_coral_rz_proto n=5 rep=4 0.1s +[ 2985.6s] tab_coral_rz_ft n=25 rep=0 0.1s +[ 2989.4s] tab_coral_rz_joint n=25 rep=0 3.8s +[ 2989.5s] tab_coral_rz_proto n=25 rep=0 0.1s +[ 2989.6s] tab_coral_rz_ft n=25 rep=1 0.1s +[ 2991.9s] tab_coral_rz_joint n=25 rep=1 2.3s +[ 2992.0s] tab_coral_rz_proto n=25 rep=1 0.1s +[ 2992.1s] tab_coral_rz_ft n=25 rep=2 0.1s +[ 2995.8s] tab_coral_rz_joint n=25 rep=2 3.7s +[ 2995.9s] tab_coral_rz_proto n=25 rep=2 0.1s +[ 2996.0s] tab_coral_rz_ft n=25 rep=3 0.1s +[ 2999.0s] tab_coral_rz_joint n=25 rep=3 3.1s +[ 2999.1s] tab_coral_rz_proto n=25 rep=3 0.1s +[ 2999.3s] tab_coral_rz_ft n=25 rep=4 0.1s +[ 3003.1s] tab_coral_rz_joint n=25 rep=4 3.9s +[ 3003.2s] tab_coral_rz_proto n=25 rep=4 0.1s +[ 3003.4s] tab_coral_rz_ft n=50 rep=0 0.1s +[ 3020.1s] tab_coral_rz_joint n=50 rep=0 16.7s +[ 3020.2s] tab_coral_rz_proto n=50 rep=0 0.1s +[ 3020.4s] tab_coral_rz_ft n=50 rep=1 0.2s +[ 3024.0s] tab_coral_rz_joint n=50 rep=1 3.6s +[ 3024.1s] tab_coral_rz_proto n=50 rep=1 0.1s +[ 3024.2s] tab_coral_rz_ft n=50 rep=2 0.2s +[ 3028.0s] tab_coral_rz_joint n=50 rep=2 3.7s +[ 3028.1s] tab_coral_rz_proto n=50 rep=2 0.1s +[ 3028.3s] tab_coral_rz_ft n=50 rep=3 0.2s +[ 3032.1s] tab_coral_rz_joint n=50 rep=3 3.9s +[ 3032.2s] tab_coral_rz_proto n=50 rep=3 0.1s +[ 3032.4s] tab_coral_rz_ft n=50 rep=4 0.2s +[ 3036.2s] tab_coral_rz_joint n=50 rep=4 3.8s +[ 3036.3s] tab_coral_rz_proto n=50 rep=4 0.1s +[ 3036.5s] tab_coral_rz_ft n=100 rep=0 0.3s +[ 3040.1s] tab_coral_rz_joint n=100 rep=0 3.6s +[ 3040.2s] tab_coral_rz_proto n=100 rep=0 0.1s +[ 3040.3s] tab_coral_rz_ft n=100 rep=1 0.1s +[ 3045.1s] tab_coral_rz_joint n=100 rep=1 4.8s +[ 3045.2s] tab_coral_rz_proto n=100 rep=1 0.1s +[ 3045.4s] tab_coral_rz_ft n=100 rep=2 0.2s +[ 3050.9s] tab_coral_rz_joint n=100 rep=2 5.5s +[ 3051.0s] tab_coral_rz_proto n=100 rep=2 0.1s +[ 3051.2s] tab_coral_rz_ft n=100 rep=3 0.2s +[ 3056.4s] tab_coral_rz_joint n=100 rep=3 5.2s +[ 3056.5s] tab_coral_rz_proto n=100 rep=3 0.1s +[ 3056.9s] tab_coral_rz_ft n=100 rep=4 0.4s +[ 3060.9s] tab_coral_rz_joint n=100 rep=4 4.0s +[ 3061.0s] tab_coral_rz_proto n=100 rep=4 0.1s +[ 3061.3s] tab_coral_rz_ft n=200 rep=0 0.3s +[ 3065.3s] tab_coral_rz_joint n=200 rep=0 4.0s +[ 3065.4s] tab_coral_rz_proto n=200 rep=0 0.1s +[ 3066.1s] tab_coral_rz_ft n=200 rep=1 0.6s +[ 3073.0s] tab_coral_rz_joint n=200 rep=1 7.0s +[ 3073.2s] tab_coral_rz_proto n=200 rep=1 0.1s +[ 3073.5s] tab_coral_rz_ft n=200 rep=2 0.4s +[ 3077.5s] tab_coral_rz_joint n=200 rep=2 4.0s +[ 3077.6s] tab_coral_rz_proto n=200 rep=2 0.1s +[ 3078.0s] tab_coral_rz_ft n=200 rep=3 0.4s +[ 3082.6s] tab_coral_rz_joint n=200 rep=3 4.6s +[ 3082.7s] tab_coral_rz_proto n=200 rep=3 0.1s +[ 3083.3s] tab_coral_rz_ft n=200 rep=4 0.7s +[ 3104.3s] tab_coral_rz_joint n=200 rep=4 21.0s +[ 3104.4s] tab_coral_rz_proto n=200 rep=4 0.1s +[ 3468.9s] train coral/o60 seed 0: 45 epochs 364s +[ 3469.0s] tab_coral_o60_zs n=0 saved +[ 3469.1s] tab_coral_o60_ft n=5 rep=0 0.1s +[ 3476.6s] tab_coral_o60_joint n=5 rep=0 7.5s +[ 3476.9s] tab_coral_o60_proto n=5 rep=0 0.3s +[ 3477.0s] tab_coral_o60_ft n=5 rep=1 0.1s +[ 3480.3s] tab_coral_o60_joint n=5 rep=1 3.4s +[ 3480.4s] tab_coral_o60_proto n=5 rep=1 0.1s +[ 3480.5s] tab_coral_o60_ft n=5 rep=2 0.1s +[ 3482.2s] tab_coral_o60_joint n=5 rep=2 1.7s +[ 3482.3s] tab_coral_o60_proto n=5 rep=2 0.1s +[ 3482.4s] tab_coral_o60_ft n=5 rep=3 0.1s +[ 3484.1s] tab_coral_o60_joint n=5 rep=3 1.7s +[ 3484.2s] tab_coral_o60_proto n=5 rep=3 0.1s +[ 3484.2s] tab_coral_o60_ft n=5 rep=4 0.1s +[ 3486.0s] tab_coral_o60_joint n=5 rep=4 1.8s +[ 3486.1s] tab_coral_o60_proto n=5 rep=4 0.1s +[ 3486.3s] tab_coral_o60_ft n=25 rep=0 0.1s +[ 3490.3s] tab_coral_o60_joint n=25 rep=0 4.0s +[ 3490.4s] tab_coral_o60_proto n=25 rep=0 0.1s +[ 3490.5s] tab_coral_o60_ft n=25 rep=1 0.1s +[ 3495.3s] tab_coral_o60_joint n=25 rep=1 4.8s +[ 3495.4s] tab_coral_o60_proto n=25 rep=1 0.1s +[ 3495.5s] tab_coral_o60_ft n=25 rep=2 0.1s +[ 3498.1s] tab_coral_o60_joint n=25 rep=2 2.6s +[ 3498.2s] tab_coral_o60_proto n=25 rep=2 0.1s +[ 3498.3s] tab_coral_o60_ft n=25 rep=3 0.1s +[ 3503.3s] tab_coral_o60_joint n=25 rep=3 5.0s +[ 3503.4s] tab_coral_o60_proto n=25 rep=3 0.1s +[ 3503.5s] tab_coral_o60_ft n=25 rep=4 0.1s +[ 3505.3s] tab_coral_o60_joint n=25 rep=4 1.8s +[ 3505.4s] tab_coral_o60_proto n=25 rep=4 0.1s +[ 3505.5s] tab_coral_o60_ft n=50 rep=0 0.1s +[ 3509.1s] tab_coral_o60_joint n=50 rep=0 3.6s +[ 3509.2s] tab_coral_o60_proto n=50 rep=0 0.1s +[ 3509.4s] tab_coral_o60_ft n=50 rep=1 0.2s +[ 3512.5s] tab_coral_o60_joint n=50 rep=1 3.1s +[ 3512.6s] tab_coral_o60_proto n=50 rep=1 0.1s +[ 3512.7s] tab_coral_o60_ft n=50 rep=2 0.1s +[ 3518.4s] tab_coral_o60_joint n=50 rep=2 5.7s +[ 3518.5s] tab_coral_o60_proto n=50 rep=2 0.1s +[ 3518.7s] tab_coral_o60_ft n=50 rep=3 0.1s +[ 3521.1s] tab_coral_o60_joint n=50 rep=3 2.4s +[ 3521.2s] tab_coral_o60_proto n=50 rep=3 0.1s +[ 3521.3s] tab_coral_o60_ft n=50 rep=4 0.1s +[ 3523.8s] tab_coral_o60_joint n=50 rep=4 2.5s +[ 3523.9s] tab_coral_o60_proto n=50 rep=4 0.1s +[ 3524.1s] tab_coral_o60_ft n=100 rep=0 0.2s +[ 3529.0s] tab_coral_o60_joint n=100 rep=0 4.9s +[ 3529.1s] tab_coral_o60_proto n=100 rep=0 0.1s +[ 3529.3s] tab_coral_o60_ft n=100 rep=1 0.2s +[ 3536.3s] tab_coral_o60_joint n=100 rep=1 7.0s +[ 3536.4s] tab_coral_o60_proto n=100 rep=1 0.1s +[ 3536.5s] tab_coral_o60_ft n=100 rep=2 0.2s +[ 3540.5s] tab_coral_o60_joint n=100 rep=2 4.0s +[ 3540.6s] tab_coral_o60_proto n=100 rep=2 0.1s +[ 3540.8s] tab_coral_o60_ft n=100 rep=3 0.2s +[ 3543.2s] tab_coral_o60_joint n=100 rep=3 2.4s +[ 3543.3s] tab_coral_o60_proto n=100 rep=3 0.1s +[ 3543.5s] tab_coral_o60_ft n=100 rep=4 0.2s +[ 3547.4s] tab_coral_o60_joint n=100 rep=4 3.9s +[ 3547.5s] tab_coral_o60_proto n=100 rep=4 0.1s +[ 3547.7s] tab_coral_o60_ft n=200 rep=0 0.2s +[ 3555.5s] tab_coral_o60_joint n=200 rep=0 7.8s +[ 3555.6s] tab_coral_o60_proto n=200 rep=0 0.1s +[ 3555.8s] tab_coral_o60_ft n=200 rep=1 0.2s +[ 3560.5s] tab_coral_o60_joint n=200 rep=1 4.6s +[ 3560.6s] tab_coral_o60_proto n=200 rep=1 0.1s +[ 3560.8s] tab_coral_o60_ft n=200 rep=2 0.2s +[ 3569.0s] tab_coral_o60_joint n=200 rep=2 8.2s +[ 3569.1s] tab_coral_o60_proto n=200 rep=2 0.1s +[ 3569.3s] tab_coral_o60_ft n=200 rep=3 0.2s +[ 3575.0s] tab_coral_o60_joint n=200 rep=3 5.7s +[ 3575.1s] tab_coral_o60_proto n=200 rep=3 0.1s +[ 3575.2s] tab_coral_o60_ft n=200 rep=4 0.1s +[ 3577.3s] tab_coral_o60_joint n=200 rep=4 2.1s +[ 3577.4s] tab_coral_o60_proto n=200 rep=4 0.1s +[ 3577.4s] done diff --git a/reports/nnlab/logs/tab_nn_r12.log b/reports/nnlab/logs/tab_nn_r12.log new file mode 100644 index 0000000000000000000000000000000000000000..e858c0e38af2bd01a626ba03e01e560ec8227196 --- /dev/null +++ b/reports/nnlab/logs/tab_nn_r12.log @@ -0,0 +1,107 @@ +[ 1.1s] data loaded +[ 17.5s] tab_dann_rz_ft n=5 rep=5 12.2s +[ 28.7s] tab_dann_rz_joint n=5 rep=5 11.3s +[ 29.0s] tab_dann_rz_proto n=5 rep=5 0.3s +[ 29.1s] tab_dann_rz_ft n=5 rep=6 0.1s +[ 30.8s] tab_dann_rz_joint n=5 rep=6 1.7s +[ 30.8s] tab_dann_rz_proto n=5 rep=6 0.1s +[ 30.9s] tab_dann_rz_ft n=5 rep=7 0.1s +[ 32.6s] tab_dann_rz_joint n=5 rep=7 1.7s +[ 32.7s] tab_dann_rz_proto n=5 rep=7 0.1s +[ 32.8s] tab_dann_rz_ft n=5 rep=8 0.1s +[ 34.5s] tab_dann_rz_joint n=5 rep=8 1.7s +[ 34.6s] tab_dann_rz_proto n=5 rep=8 0.1s +[ 34.7s] tab_dann_rz_ft n=5 rep=9 0.1s +[ 36.3s] tab_dann_rz_joint n=5 rep=9 1.7s +[ 36.4s] tab_dann_rz_proto n=5 rep=9 0.1s +[ 36.5s] tab_dann_rz_ft n=5 rep=10 0.1s +[ 38.2s] tab_dann_rz_joint n=5 rep=10 1.7s +[ 38.3s] tab_dann_rz_proto n=5 rep=10 0.1s +[ 38.4s] tab_dann_rz_ft n=5 rep=11 0.1s +[ 40.0s] tab_dann_rz_joint n=5 rep=11 1.6s +[ 40.1s] tab_dann_rz_proto n=5 rep=11 0.1s +[ 40.3s] tab_dann_rz_ft n=25 rep=5 0.2s +[ 43.4s] tab_dann_rz_joint n=25 rep=5 3.2s +[ 43.5s] tab_dann_rz_proto n=25 rep=5 0.1s +[ 43.7s] tab_dann_rz_ft n=25 rep=6 0.1s +[ 46.2s] tab_dann_rz_joint n=25 rep=6 2.5s +[ 46.2s] tab_dann_rz_proto n=25 rep=6 0.1s +[ 46.4s] tab_dann_rz_ft n=25 rep=7 0.1s +[ 50.5s] tab_dann_rz_joint n=25 rep=7 4.2s +[ 50.6s] tab_dann_rz_proto n=25 rep=7 0.1s +[ 50.8s] tab_dann_rz_ft n=25 rep=8 0.2s +[ 54.3s] tab_dann_rz_joint n=25 rep=8 3.5s +[ 54.4s] tab_dann_rz_proto n=25 rep=8 0.1s +[ 54.6s] tab_dann_rz_ft n=25 rep=9 0.1s +[ 57.7s] tab_dann_rz_joint n=25 rep=9 3.1s +[ 57.8s] tab_dann_rz_proto n=25 rep=9 0.1s +[ 57.9s] tab_dann_rz_ft n=25 rep=10 0.2s +[ 63.4s] tab_dann_rz_joint n=25 rep=10 5.4s +[ 63.4s] tab_dann_rz_proto n=25 rep=10 0.1s +[ 63.7s] tab_dann_rz_ft n=25 rep=11 0.3s +[ 67.0s] tab_dann_rz_joint n=25 rep=11 3.3s +[ 67.0s] tab_dann_rz_proto n=25 rep=11 0.1s +[ 67.3s] tab_dann_rz_ft n=50 rep=5 0.2s +[ 71.4s] tab_dann_rz_joint n=50 rep=5 4.1s +[ 71.5s] tab_dann_rz_proto n=50 rep=5 0.1s +[ 71.7s] tab_dann_rz_ft n=50 rep=6 0.2s +[ 76.9s] tab_dann_rz_joint n=50 rep=6 5.2s +[ 77.0s] tab_dann_rz_proto n=50 rep=6 0.1s +[ 77.2s] tab_dann_rz_ft n=50 rep=7 0.2s +[ 81.8s] tab_dann_rz_joint n=50 rep=7 4.6s +[ 81.9s] tab_dann_rz_proto n=50 rep=7 0.1s +[ 82.1s] tab_dann_rz_ft n=50 rep=8 0.2s +[ 85.0s] tab_dann_rz_joint n=50 rep=8 2.8s +[ 85.0s] tab_dann_rz_proto n=50 rep=8 0.1s +[ 85.2s] tab_dann_rz_ft n=50 rep=9 0.2s +[ 88.1s] tab_dann_rz_joint n=50 rep=9 2.9s +[ 88.2s] tab_dann_rz_proto n=50 rep=9 0.1s +[ 88.4s] tab_dann_rz_ft n=50 rep=10 0.2s +[ 93.1s] tab_dann_rz_joint n=50 rep=10 4.6s +[ 93.1s] tab_dann_rz_proto n=50 rep=10 0.1s +[ 93.3s] tab_dann_rz_ft n=50 rep=11 0.2s +[ 98.5s] tab_dann_rz_joint n=50 rep=11 5.2s +[ 98.6s] tab_dann_rz_proto n=50 rep=11 0.1s +[ 98.7s] tab_dann_rz_ft n=100 rep=5 0.2s +[ 109.8s] tab_dann_rz_joint n=100 rep=5 11.1s +[ 109.9s] tab_dann_rz_proto n=100 rep=5 0.1s +[ 110.2s] tab_dann_rz_ft n=100 rep=6 0.3s +[ 114.2s] tab_dann_rz_joint n=100 rep=6 4.0s +[ 114.3s] tab_dann_rz_proto n=100 rep=6 0.1s +[ 114.6s] tab_dann_rz_ft n=100 rep=7 0.3s +[ 119.5s] tab_dann_rz_joint n=100 rep=7 4.9s +[ 119.6s] tab_dann_rz_proto n=100 rep=7 0.1s +[ 119.9s] tab_dann_rz_ft n=100 rep=8 0.3s +[ 125.4s] tab_dann_rz_joint n=100 rep=8 5.6s +[ 125.5s] tab_dann_rz_proto n=100 rep=8 0.1s +[ 125.8s] tab_dann_rz_ft n=100 rep=9 0.3s +[ 131.1s] tab_dann_rz_joint n=100 rep=9 5.3s +[ 131.2s] tab_dann_rz_proto n=100 rep=9 0.1s +[ 131.4s] tab_dann_rz_ft n=100 rep=10 0.2s +[ 135.8s] tab_dann_rz_joint n=100 rep=10 4.4s +[ 135.9s] tab_dann_rz_proto n=100 rep=10 0.1s +[ 136.2s] tab_dann_rz_ft n=100 rep=11 0.3s +[ 140.1s] tab_dann_rz_joint n=100 rep=11 3.9s +[ 140.2s] tab_dann_rz_proto n=100 rep=11 0.1s +[ 140.5s] tab_dann_rz_ft n=200 rep=5 0.4s +[ 145.1s] tab_dann_rz_joint n=200 rep=5 4.6s +[ 145.2s] tab_dann_rz_proto n=200 rep=5 0.1s +[ 145.5s] tab_dann_rz_ft n=200 rep=6 0.3s +[ 149.1s] tab_dann_rz_joint n=200 rep=6 3.5s +[ 149.2s] tab_dann_rz_proto n=200 rep=6 0.1s +[ 149.5s] tab_dann_rz_ft n=200 rep=7 0.4s +[ 153.8s] tab_dann_rz_joint n=200 rep=7 4.3s +[ 153.9s] tab_dann_rz_proto n=200 rep=7 0.1s +[ 154.2s] tab_dann_rz_ft n=200 rep=8 0.3s +[ 159.0s] tab_dann_rz_joint n=200 rep=8 4.8s +[ 159.1s] tab_dann_rz_proto n=200 rep=8 0.1s +[ 159.5s] tab_dann_rz_ft n=200 rep=9 0.4s +[ 163.8s] tab_dann_rz_joint n=200 rep=9 4.3s +[ 163.9s] tab_dann_rz_proto n=200 rep=9 0.1s +[ 164.3s] tab_dann_rz_ft n=200 rep=10 0.4s +[ 169.7s] tab_dann_rz_joint n=200 rep=10 5.4s +[ 169.8s] tab_dann_rz_proto n=200 rep=10 0.1s +[ 170.2s] tab_dann_rz_ft n=200 rep=11 0.4s +[ 175.7s] tab_dann_rz_joint n=200 rep=11 5.5s +[ 175.8s] tab_dann_rz_proto n=200 rep=11 0.1s +[ 175.8s] done diff --git a/reports/nnlab/logs/transductive.log b/reports/nnlab/logs/transductive.log new file mode 100644 index 0000000000000000000000000000000000000000..c9c39e7ef100a2deefc2bbce583cef6b400c21f9 --- /dev/null +++ b/reports/nnlab/logs/transductive.log @@ -0,0 +1,758 @@ +[ 0.8s] loaded: Madrid (76263, 393), Amsterdam (25992, 393) +[ 0.9s] graph: 583310 edges, mean degree 22.4, min 10 +[ 1.4s] lgbm_upw__clamp n=5 rep=0 base=lgbm_upw done in 0.5s +[ 1.5s] lgbm_upw__clamp n=5 rep=1 base=lgbm_upw done in 0.0s +[ 1.5s] lgbm_upw__clamp n=5 rep=2 base=lgbm_upw done in 0.1s +[ 1.6s] lgbm_upw__clamp n=5 rep=3 base=lgbm_upw done in 0.0s +[ 1.6s] lgbm_upw__clamp n=5 rep=4 base=lgbm_upw done in 0.0s +[ 1.6s] lgbm_upw__clamp n=25 rep=0 base=lgbm_upw done in 0.0s +[ 1.7s] lgbm_upw__clamp n=25 rep=1 base=lgbm_upw done in 0.0s +[ 1.7s] lgbm_upw__clamp n=25 rep=2 base=lgbm_upw done in 0.0s +[ 1.8s] lgbm_upw__clamp n=25 rep=3 base=lgbm_upw done in 0.0s +[ 1.8s] lgbm_upw__clamp n=25 rep=4 base=lgbm_upw done in 0.0s +[ 1.9s] lgbm_upw__clamp n=50 rep=0 base=lgbm_upw done in 0.0s +[ 1.9s] lgbm_upw__clamp n=50 rep=1 base=lgbm_upw done in 0.1s +[ 2.0s] lgbm_upw__clamp n=50 rep=2 base=lgbm_upw done in 0.0s +[ 2.0s] lgbm_upw__clamp n=50 rep=3 base=lgbm_upw done in 0.0s +[ 2.0s] lgbm_upw__clamp n=50 rep=4 base=lgbm_upw done in 0.0s +[ 2.1s] lgbm_upw__clamp n=100 rep=0 base=lgbm_upw done in 0.1s +[ 2.1s] lgbm_upw__clamp n=100 rep=1 base=lgbm_upw done in 0.0s +[ 2.2s] lgbm_upw__clamp n=100 rep=2 base=lgbm_upw done in 0.0s +[ 2.2s] lgbm_upw__clamp n=100 rep=3 base=lgbm_upw done in 0.0s +[ 2.2s] lgbm_upw__clamp n=100 rep=4 base=lgbm_upw done in 0.0s +[ 2.3s] lgbm_upw__clamp n=200 rep=0 base=lgbm_upw done in 0.0s +[ 2.3s] lgbm_upw__clamp n=200 rep=1 base=lgbm_upw done in 0.0s +[ 2.3s] lgbm_upw__clamp n=200 rep=2 base=lgbm_upw done in 0.0s +[ 2.4s] lgbm_upw__clamp n=200 rep=3 base=lgbm_upw done in 0.0s +[ 2.4s] lgbm_upw__clamp n=200 rep=4 base=lgbm_upw done in 0.0s +[ 2.5s] zs_xgb_dann__clamp n=5 rep=0 base=zs_xgb_dann done in 0.1s +[ 2.5s] zs_xgb_dann__clamp n=5 rep=1 base=zs_xgb_dann done in 0.0s +[ 2.6s] zs_xgb_dann__clamp n=5 rep=2 base=zs_xgb_dann done in 0.0s +[ 2.6s] zs_xgb_dann__clamp n=5 rep=3 base=zs_xgb_dann done in 0.1s +[ 2.7s] zs_xgb_dann__clamp n=5 rep=4 base=zs_xgb_dann done in 0.0s +[ 2.7s] zs_xgb_dann__clamp n=25 rep=0 base=zs_xgb_dann done in 0.0s +[ 2.8s] zs_xgb_dann__clamp n=25 rep=1 base=zs_xgb_dann done in 0.1s +[ 2.8s] zs_xgb_dann__clamp n=25 rep=2 base=zs_xgb_dann done in 0.1s +[ 2.9s] zs_xgb_dann__clamp n=25 rep=3 base=zs_xgb_dann done in 0.1s +[ 2.9s] zs_xgb_dann__clamp n=25 rep=4 base=zs_xgb_dann done in 0.1s +[ 3.0s] zs_xgb_dann__clamp n=50 rep=0 base=zs_xgb_dann done in 0.1s +[ 3.1s] zs_xgb_dann__clamp n=50 rep=1 base=zs_xgb_dann done in 0.1s +[ 3.1s] zs_xgb_dann__clamp n=50 rep=2 base=zs_xgb_dann done in 0.1s +[ 3.2s] zs_xgb_dann__clamp n=50 rep=3 base=zs_xgb_dann done in 0.1s +[ 3.2s] zs_xgb_dann__clamp n=50 rep=4 base=zs_xgb_dann done in 0.0s +[ 3.3s] zs_xgb_dann__clamp n=100 rep=0 base=zs_xgb_dann done in 0.1s +[ 3.3s] zs_xgb_dann__clamp n=100 rep=1 base=zs_xgb_dann done in 0.0s +[ 3.4s] zs_xgb_dann__clamp n=100 rep=2 base=zs_xgb_dann done in 0.1s +[ 3.4s] zs_xgb_dann__clamp n=100 rep=3 base=zs_xgb_dann done in 0.0s +[ 3.5s] zs_xgb_dann__clamp n=100 rep=4 base=zs_xgb_dann done in 0.0s +[ 3.5s] zs_xgb_dann__clamp n=200 rep=0 base=zs_xgb_dann done in 0.0s +[ 3.6s] zs_xgb_dann__clamp n=200 rep=1 base=zs_xgb_dann done in 0.1s +[ 3.6s] zs_xgb_dann__clamp n=200 rep=2 base=zs_xgb_dann done in 0.1s +[ 3.7s] zs_xgb_dann__clamp n=200 rep=3 base=zs_xgb_dann done in 0.1s +[ 3.8s] zs_xgb_dann__clamp n=200 rep=4 base=zs_xgb_dann done in 0.0s +[ 3.8s] zs_lgbm__clamp n=5 rep=0 base=zs_lgbm done in 0.1s +[ 3.9s] zs_lgbm__clamp n=5 rep=1 base=zs_lgbm done in 0.1s +[ 3.9s] zs_lgbm__clamp n=5 rep=2 base=zs_lgbm done in 0.0s +[ 4.0s] zs_lgbm__clamp n=5 rep=3 base=zs_lgbm done in 0.1s +[ 4.0s] zs_lgbm__clamp n=5 rep=4 base=zs_lgbm done in 0.1s +[ 4.1s] zs_lgbm__clamp n=25 rep=0 base=zs_lgbm done in 0.1s +[ 4.2s] zs_lgbm__clamp n=25 rep=1 base=zs_lgbm done in 0.1s +[ 4.2s] zs_lgbm__clamp n=25 rep=2 base=zs_lgbm done in 0.1s +[ 4.3s] zs_lgbm__clamp n=25 rep=3 base=zs_lgbm done in 0.1s +[ 4.3s] zs_lgbm__clamp n=25 rep=4 base=zs_lgbm done in 0.0s +[ 4.4s] zs_lgbm__clamp n=50 rep=0 base=zs_lgbm done in 0.1s +[ 4.4s] zs_lgbm__clamp n=50 rep=1 base=zs_lgbm done in 0.1s +[ 4.5s] zs_lgbm__clamp n=50 rep=2 base=zs_lgbm done in 0.0s +[ 4.5s] zs_lgbm__clamp n=50 rep=3 base=zs_lgbm done in 0.0s +[ 4.6s] zs_lgbm__clamp n=50 rep=4 base=zs_lgbm done in 0.1s +[ 4.6s] zs_lgbm__clamp n=100 rep=0 base=zs_lgbm done in 0.1s +[ 4.6s] zs_lgbm__clamp n=100 rep=1 base=zs_lgbm done in 0.0s +[ 4.7s] zs_lgbm__clamp n=100 rep=2 base=zs_lgbm done in 0.0s +[ 4.7s] zs_lgbm__clamp n=100 rep=3 base=zs_lgbm done in 0.0s +[ 4.8s] zs_lgbm__clamp n=100 rep=4 base=zs_lgbm done in 0.0s +[ 4.8s] zs_lgbm__clamp n=200 rep=0 base=zs_lgbm done in 0.0s +[ 4.9s] zs_lgbm__clamp n=200 rep=1 base=zs_lgbm done in 0.0s +[ 4.9s] zs_lgbm__clamp n=200 rep=2 base=zs_lgbm done in 0.1s +[ 5.0s] zs_lgbm__clamp n=200 rep=3 base=zs_lgbm done in 0.0s +[ 5.0s] zs_lgbm__clamp n=200 rep=4 base=zs_lgbm done in 0.0s +[ 5.0s] zs_dann__clamp n=5 rep=0 base=zs_dann done in 0.0s +[ 5.1s] zs_dann__clamp n=5 rep=1 base=zs_dann done in 0.0s +[ 5.1s] zs_dann__clamp n=5 rep=2 base=zs_dann done in 0.0s +[ 5.2s] zs_dann__clamp n=5 rep=3 base=zs_dann done in 0.0s +[ 5.2s] zs_dann__clamp n=5 rep=4 base=zs_dann done in 0.0s +[ 5.2s] zs_dann__clamp n=25 rep=0 base=zs_dann done in 0.0s +[ 5.3s] zs_dann__clamp n=25 rep=1 base=zs_dann done in 0.0s +[ 5.3s] zs_dann__clamp n=25 rep=2 base=zs_dann done in 0.0s +[ 5.3s] zs_dann__clamp n=25 rep=3 base=zs_dann done in 0.0s +[ 5.4s] zs_dann__clamp n=25 rep=4 base=zs_dann done in 0.0s +[ 5.4s] zs_dann__clamp n=50 rep=0 base=zs_dann done in 0.0s +[ 5.4s] zs_dann__clamp n=50 rep=1 base=zs_dann done in 0.0s +[ 5.4s] zs_dann__clamp n=50 rep=2 base=zs_dann done in 0.0s +[ 5.5s] zs_dann__clamp n=50 rep=3 base=zs_dann done in 0.0s +[ 5.5s] zs_dann__clamp n=50 rep=4 base=zs_dann done in 0.0s +[ 5.6s] zs_dann__clamp n=100 rep=0 base=zs_dann done in 0.0s +[ 5.6s] zs_dann__clamp n=100 rep=1 base=zs_dann done in 0.0s +[ 5.6s] zs_dann__clamp n=100 rep=2 base=zs_dann done in 0.0s +[ 5.6s] zs_dann__clamp n=100 rep=3 base=zs_dann done in 0.0s +[ 5.7s] zs_dann__clamp n=100 rep=4 base=zs_dann done in 0.0s +[ 5.7s] zs_dann__clamp n=200 rep=0 base=zs_dann done in 0.0s +[ 5.7s] zs_dann__clamp n=200 rep=1 base=zs_dann done in 0.0s +[ 5.8s] zs_dann__clamp n=200 rep=2 base=zs_dann done in 0.0s +[ 5.8s] zs_dann__clamp n=200 rep=3 base=zs_dann done in 0.0s +[ 5.8s] zs_dann__clamp n=200 rep=4 base=zs_dann done in 0.0s +[ 5.8s] done +[ 0.8s] loaded: Madrid (76263, 393), Amsterdam (25992, 393) +[ 0.9s] graph: 583310 edges, mean degree 22.4, min 10 +[ 1.0s] lgbm_upw__lp n=5 rep=0 base=lgbm_upw done in 0.1s +[ 1.2s] lgbm_upw__lp n=5 rep=1 base=lgbm_upw done in 0.1s +[ 1.3s] lgbm_upw__lp n=5 rep=2 base=lgbm_upw done in 0.1s +[ 1.5s] lgbm_upw__lp n=5 rep=3 base=lgbm_upw done in 0.2s +[ 1.6s] lgbm_upw__lp n=5 rep=4 base=lgbm_upw done in 0.1s +[ 1.8s] lgbm_upw__lp n=25 rep=0 base=lgbm_upw done in 0.2s +[ 1.9s] lgbm_upw__lp n=25 rep=1 base=lgbm_upw done in 0.2s +[ 2.1s] lgbm_upw__lp n=25 rep=2 base=lgbm_upw done in 0.2s +[ 2.2s] lgbm_upw__lp n=25 rep=3 base=lgbm_upw done in 0.1s +[ 2.4s] lgbm_upw__lp n=25 rep=4 base=lgbm_upw done in 0.2s +[ 2.5s] lgbm_upw__lp n=50 rep=0 base=lgbm_upw done in 0.2s +[ 2.7s] lgbm_upw__lp n=50 rep=1 base=lgbm_upw done in 0.2s +[ 2.8s] lgbm_upw__lp n=50 rep=2 base=lgbm_upw done in 0.2s +[ 3.0s] lgbm_upw__lp n=50 rep=3 base=lgbm_upw done in 0.1s +[ 3.1s] lgbm_upw__lp n=50 rep=4 base=lgbm_upw done in 0.2s +[ 3.3s] lgbm_upw__lp n=100 rep=0 base=lgbm_upw done in 0.1s +[ 3.4s] lgbm_upw__lp n=100 rep=1 base=lgbm_upw done in 0.1s +[ 3.6s] lgbm_upw__lp n=100 rep=2 base=lgbm_upw done in 0.1s +[ 3.7s] lgbm_upw__lp n=100 rep=3 base=lgbm_upw done in 0.1s +[ 3.8s] lgbm_upw__lp n=100 rep=4 base=lgbm_upw done in 0.1s +[ 4.0s] lgbm_upw__lp n=200 rep=0 base=lgbm_upw done in 0.1s +[ 4.1s] lgbm_upw__lp n=200 rep=1 base=lgbm_upw done in 0.1s +[ 4.2s] lgbm_upw__lp n=200 rep=2 base=lgbm_upw done in 0.1s +[ 4.4s] lgbm_upw__lp n=200 rep=3 base=lgbm_upw done in 0.1s +[ 4.5s] lgbm_upw__lp n=200 rep=4 base=lgbm_upw done in 0.1s +[ 4.7s] zs_xgb_dann__lp n=5 rep=0 base=zs_xgb_dann done in 0.1s +[ 4.8s] zs_xgb_dann__lp n=5 rep=1 base=zs_xgb_dann done in 0.1s +[ 5.0s] zs_xgb_dann__lp n=5 rep=2 base=zs_xgb_dann done in 0.1s +[ 5.1s] zs_xgb_dann__lp n=5 rep=3 base=zs_xgb_dann done in 0.2s +[ 5.3s] zs_xgb_dann__lp n=5 rep=4 base=zs_xgb_dann done in 0.2s +[ 5.4s] zs_xgb_dann__lp n=25 rep=0 base=zs_xgb_dann done in 0.1s +[ 5.6s] zs_xgb_dann__lp n=25 rep=1 base=zs_xgb_dann done in 0.2s +[ 5.7s] zs_xgb_dann__lp n=25 rep=2 base=zs_xgb_dann done in 0.1s +[ 5.9s] zs_xgb_dann__lp n=25 rep=3 base=zs_xgb_dann done in 0.2s +[ 6.0s] zs_xgb_dann__lp n=25 rep=4 base=zs_xgb_dann done in 0.1s +[ 6.2s] zs_xgb_dann__lp n=50 rep=0 base=zs_xgb_dann done in 0.1s +[ 6.3s] zs_xgb_dann__lp n=50 rep=1 base=zs_xgb_dann done in 0.2s +[ 6.5s] zs_xgb_dann__lp n=50 rep=2 base=zs_xgb_dann done in 0.2s +[ 6.6s] zs_xgb_dann__lp n=50 rep=3 base=zs_xgb_dann done in 0.1s +[ 6.7s] zs_xgb_dann__lp n=50 rep=4 base=zs_xgb_dann done in 0.1s +[ 6.9s] zs_xgb_dann__lp n=100 rep=0 base=zs_xgb_dann done in 0.1s +[ 7.0s] zs_xgb_dann__lp n=100 rep=1 base=zs_xgb_dann done in 0.1s +[ 7.2s] zs_xgb_dann__lp n=100 rep=2 base=zs_xgb_dann done in 0.1s +[ 7.3s] zs_xgb_dann__lp n=100 rep=3 base=zs_xgb_dann done in 0.1s +[ 7.4s] zs_xgb_dann__lp n=100 rep=4 base=zs_xgb_dann done in 0.2s +[ 7.6s] zs_xgb_dann__lp n=200 rep=0 base=zs_xgb_dann done in 0.1s +[ 7.7s] zs_xgb_dann__lp n=200 rep=1 base=zs_xgb_dann done in 0.1s +[ 7.9s] zs_xgb_dann__lp n=200 rep=2 base=zs_xgb_dann done in 0.1s +[ 8.0s] zs_xgb_dann__lp n=200 rep=3 base=zs_xgb_dann done in 0.1s +[ 8.1s] zs_xgb_dann__lp n=200 rep=4 base=zs_xgb_dann done in 0.1s +[ 8.3s] zs_lgbm__lp n=5 rep=0 base=zs_lgbm done in 0.1s +[ 8.4s] zs_lgbm__lp n=5 rep=1 base=zs_lgbm done in 0.1s +[ 8.5s] zs_lgbm__lp n=5 rep=2 base=zs_lgbm done in 0.1s +[ 8.7s] zs_lgbm__lp n=5 rep=3 base=zs_lgbm done in 0.1s +[ 8.8s] zs_lgbm__lp n=5 rep=4 base=zs_lgbm done in 0.1s +[ 9.0s] zs_lgbm__lp n=25 rep=0 base=zs_lgbm done in 0.1s +[ 9.1s] zs_lgbm__lp n=25 rep=1 base=zs_lgbm done in 0.2s +[ 9.3s] zs_lgbm__lp n=25 rep=2 base=zs_lgbm done in 0.1s +[ 9.4s] zs_lgbm__lp n=25 rep=3 base=zs_lgbm done in 0.2s +[ 9.6s] zs_lgbm__lp n=25 rep=4 base=zs_lgbm done in 0.2s +[ 9.7s] zs_lgbm__lp n=50 rep=0 base=zs_lgbm done in 0.1s +[ 9.9s] zs_lgbm__lp n=50 rep=1 base=zs_lgbm done in 0.2s +[ 10.1s] zs_lgbm__lp n=50 rep=2 base=zs_lgbm done in 0.2s +[ 10.2s] zs_lgbm__lp n=50 rep=3 base=zs_lgbm done in 0.1s +[ 10.3s] zs_lgbm__lp n=50 rep=4 base=zs_lgbm done in 0.1s +[ 10.5s] zs_lgbm__lp n=100 rep=0 base=zs_lgbm done in 0.1s +[ 10.6s] zs_lgbm__lp n=100 rep=1 base=zs_lgbm done in 0.1s +[ 10.7s] zs_lgbm__lp n=100 rep=2 base=zs_lgbm done in 0.1s +[ 10.9s] zs_lgbm__lp n=100 rep=3 base=zs_lgbm done in 0.1s +[ 11.0s] zs_lgbm__lp n=100 rep=4 base=zs_lgbm done in 0.1s +[ 11.1s] zs_lgbm__lp n=200 rep=0 base=zs_lgbm done in 0.1s +[ 11.2s] zs_lgbm__lp n=200 rep=1 base=zs_lgbm done in 0.1s +[ 11.4s] zs_lgbm__lp n=200 rep=2 base=zs_lgbm done in 0.1s +[ 11.5s] zs_lgbm__lp n=200 rep=3 base=zs_lgbm done in 0.1s +[ 11.7s] zs_lgbm__lp n=200 rep=4 base=zs_lgbm done in 0.1s +[ 11.8s] zs_dann__lp n=5 rep=0 base=zs_dann done in 0.1s +[ 11.9s] zs_dann__lp n=5 rep=1 base=zs_dann done in 0.1s +[ 12.1s] zs_dann__lp n=5 rep=2 base=zs_dann done in 0.1s +[ 12.2s] zs_dann__lp n=5 rep=3 base=zs_dann done in 0.1s +[ 12.4s] zs_dann__lp n=5 rep=4 base=zs_dann done in 0.2s +[ 12.6s] zs_dann__lp n=25 rep=0 base=zs_dann done in 0.2s +[ 12.7s] zs_dann__lp n=25 rep=1 base=zs_dann done in 0.1s +[ 12.8s] zs_dann__lp n=25 rep=2 base=zs_dann done in 0.2s +[ 13.0s] zs_dann__lp n=25 rep=3 base=zs_dann done in 0.2s +[ 13.2s] zs_dann__lp n=25 rep=4 base=zs_dann done in 0.2s +[ 13.3s] zs_dann__lp n=50 rep=0 base=zs_dann done in 0.2s +[ 13.4s] zs_dann__lp n=50 rep=1 base=zs_dann done in 0.1s +[ 13.6s] zs_dann__lp n=50 rep=2 base=zs_dann done in 0.1s +[ 13.7s] zs_dann__lp n=50 rep=3 base=zs_dann done in 0.1s +[ 13.9s] zs_dann__lp n=50 rep=4 base=zs_dann done in 0.1s +[ 14.0s] zs_dann__lp n=100 rep=0 base=zs_dann done in 0.1s +[ 14.1s] zs_dann__lp n=100 rep=1 base=zs_dann done in 0.1s +[ 14.3s] zs_dann__lp n=100 rep=2 base=zs_dann done in 0.1s +[ 14.4s] zs_dann__lp n=100 rep=3 base=zs_dann done in 0.1s +[ 14.5s] zs_dann__lp n=100 rep=4 base=zs_dann done in 0.1s +[ 14.6s] zs_dann__lp n=200 rep=0 base=zs_dann done in 0.1s +[ 14.8s] zs_dann__lp n=200 rep=1 base=zs_dann done in 0.1s +[ 14.9s] zs_dann__lp n=200 rep=2 base=zs_dann done in 0.1s +[ 15.0s] zs_dann__lp n=200 rep=3 base=zs_dann done in 0.1s +[ 15.2s] zs_dann__lp n=200 rep=4 base=zs_dann done in 0.1s +[ 15.2s] done +[ 0.9s] loaded: Madrid (76263, 393), Amsterdam (25992, 393) +[ 0.9s] graph: 583310 edges, mean degree 22.4, min 10 +[ 1.0s] lgbm_upw__lapshot n=5 rep=0 base=lgbm_upw done in 0.1s +[ 1.2s] lgbm_upw__lapshot n=5 rep=1 base=lgbm_upw done in 0.1s +[ 1.3s] lgbm_upw__lapshot n=5 rep=2 base=lgbm_upw done in 0.1s +[ 1.5s] lgbm_upw__lapshot n=5 rep=3 base=lgbm_upw done in 0.2s +[ 1.6s] lgbm_upw__lapshot n=5 rep=4 base=lgbm_upw done in 0.1s +[ 1.8s] lgbm_upw__lapshot n=25 rep=0 base=lgbm_upw done in 0.2s +[ 1.9s] lgbm_upw__lapshot n=25 rep=1 base=lgbm_upw done in 0.1s +[ 2.1s] lgbm_upw__lapshot n=25 rep=2 base=lgbm_upw done in 0.2s +[ 2.2s] lgbm_upw__lapshot n=25 rep=3 base=lgbm_upw done in 0.1s +[ 2.4s] lgbm_upw__lapshot n=25 rep=4 base=lgbm_upw done in 0.1s +[ 2.5s] lgbm_upw__lapshot n=50 rep=0 base=lgbm_upw done in 0.1s +[ 2.6s] lgbm_upw__lapshot n=50 rep=1 base=lgbm_upw done in 0.1s +[ 2.8s] lgbm_upw__lapshot n=50 rep=2 base=lgbm_upw done in 0.2s +[ 2.9s] lgbm_upw__lapshot n=50 rep=3 base=lgbm_upw done in 0.1s +[ 3.1s] lgbm_upw__lapshot n=50 rep=4 base=lgbm_upw done in 0.2s +[ 3.2s] lgbm_upw__lapshot n=100 rep=0 base=lgbm_upw done in 0.1s +[ 3.4s] lgbm_upw__lapshot n=100 rep=1 base=lgbm_upw done in 0.1s +[ 3.5s] lgbm_upw__lapshot n=100 rep=2 base=lgbm_upw done in 0.2s +[ 3.6s] lgbm_upw__lapshot n=100 rep=3 base=lgbm_upw done in 0.1s +[ 3.8s] lgbm_upw__lapshot n=100 rep=4 base=lgbm_upw done in 0.1s +[ 3.9s] lgbm_upw__lapshot n=200 rep=0 base=lgbm_upw done in 0.1s +[ 4.0s] lgbm_upw__lapshot n=200 rep=1 base=lgbm_upw done in 0.1s +[ 4.2s] lgbm_upw__lapshot n=200 rep=2 base=lgbm_upw done in 0.1s +[ 4.3s] lgbm_upw__lapshot n=200 rep=3 base=lgbm_upw done in 0.1s +[ 4.5s] lgbm_upw__lapshot n=200 rep=4 base=lgbm_upw done in 0.1s +[ 4.6s] zs_xgb_dann__lapshot n=5 rep=0 base=zs_xgb_dann done in 0.2s +[ 4.8s] zs_xgb_dann__lapshot n=5 rep=1 base=zs_xgb_dann done in 0.1s +[ 4.9s] zs_xgb_dann__lapshot n=5 rep=2 base=zs_xgb_dann done in 0.1s +[ 5.0s] zs_xgb_dann__lapshot n=5 rep=3 base=zs_xgb_dann done in 0.1s +[ 5.2s] zs_xgb_dann__lapshot n=5 rep=4 base=zs_xgb_dann done in 0.1s +[ 5.3s] zs_xgb_dann__lapshot n=25 rep=0 base=zs_xgb_dann done in 0.1s +[ 5.4s] zs_xgb_dann__lapshot n=25 rep=1 base=zs_xgb_dann done in 0.1s +[ 5.6s] zs_xgb_dann__lapshot n=25 rep=2 base=zs_xgb_dann done in 0.2s +[ 5.8s] zs_xgb_dann__lapshot n=25 rep=3 base=zs_xgb_dann done in 0.2s +[ 5.9s] zs_xgb_dann__lapshot n=25 rep=4 base=zs_xgb_dann done in 0.2s +[ 6.0s] zs_xgb_dann__lapshot n=50 rep=0 base=zs_xgb_dann done in 0.1s +[ 6.2s] zs_xgb_dann__lapshot n=50 rep=1 base=zs_xgb_dann done in 0.1s +[ 6.3s] zs_xgb_dann__lapshot n=50 rep=2 base=zs_xgb_dann done in 0.1s +[ 6.5s] zs_xgb_dann__lapshot n=50 rep=3 base=zs_xgb_dann done in 0.2s +[ 6.6s] zs_xgb_dann__lapshot n=50 rep=4 base=zs_xgb_dann done in 0.1s +[ 6.8s] zs_xgb_dann__lapshot n=100 rep=0 base=zs_xgb_dann done in 0.2s +[ 6.9s] zs_xgb_dann__lapshot n=100 rep=1 base=zs_xgb_dann done in 0.1s +[ 7.1s] zs_xgb_dann__lapshot n=100 rep=2 base=zs_xgb_dann done in 0.1s +[ 7.2s] zs_xgb_dann__lapshot n=100 rep=3 base=zs_xgb_dann done in 0.1s +[ 7.4s] zs_xgb_dann__lapshot n=100 rep=4 base=zs_xgb_dann done in 0.1s +[ 7.5s] zs_xgb_dann__lapshot n=200 rep=0 base=zs_xgb_dann done in 0.1s +[ 7.6s] zs_xgb_dann__lapshot n=200 rep=1 base=zs_xgb_dann done in 0.1s +[ 7.8s] zs_xgb_dann__lapshot n=200 rep=2 base=zs_xgb_dann done in 0.2s +[ 7.9s] zs_xgb_dann__lapshot n=200 rep=3 base=zs_xgb_dann done in 0.1s +[ 8.0s] zs_xgb_dann__lapshot n=200 rep=4 base=zs_xgb_dann done in 0.1s +[ 8.2s] zs_lgbm__lapshot n=5 rep=0 base=zs_lgbm done in 0.1s +[ 8.3s] zs_lgbm__lapshot n=5 rep=1 base=zs_lgbm done in 0.1s +[ 8.5s] zs_lgbm__lapshot n=5 rep=2 base=zs_lgbm done in 0.2s +[ 8.6s] zs_lgbm__lapshot n=5 rep=3 base=zs_lgbm done in 0.1s +[ 8.7s] zs_lgbm__lapshot n=5 rep=4 base=zs_lgbm done in 0.1s +[ 8.9s] zs_lgbm__lapshot n=25 rep=0 base=zs_lgbm done in 0.2s +[ 9.0s] zs_lgbm__lapshot n=25 rep=1 base=zs_lgbm done in 0.2s +[ 9.2s] zs_lgbm__lapshot n=25 rep=2 base=zs_lgbm done in 0.2s +[ 9.4s] zs_lgbm__lapshot n=25 rep=3 base=zs_lgbm done in 0.2s +[ 9.5s] zs_lgbm__lapshot n=25 rep=4 base=zs_lgbm done in 0.2s +[ 9.7s] zs_lgbm__lapshot n=50 rep=0 base=zs_lgbm done in 0.2s +[ 9.8s] zs_lgbm__lapshot n=50 rep=1 base=zs_lgbm done in 0.1s +[ 10.0s] zs_lgbm__lapshot n=50 rep=2 base=zs_lgbm done in 0.2s +[ 10.2s] zs_lgbm__lapshot n=50 rep=3 base=zs_lgbm done in 0.2s +[ 10.3s] zs_lgbm__lapshot n=50 rep=4 base=zs_lgbm done in 0.2s +[ 10.5s] zs_lgbm__lapshot n=100 rep=0 base=zs_lgbm done in 0.2s +[ 10.6s] zs_lgbm__lapshot n=100 rep=1 base=zs_lgbm done in 0.1s +[ 10.8s] zs_lgbm__lapshot n=100 rep=2 base=zs_lgbm done in 0.1s +[ 10.9s] zs_lgbm__lapshot n=100 rep=3 base=zs_lgbm done in 0.1s +[ 11.0s] zs_lgbm__lapshot n=100 rep=4 base=zs_lgbm done in 0.1s +[ 11.2s] zs_lgbm__lapshot n=200 rep=0 base=zs_lgbm done in 0.1s +[ 11.3s] zs_lgbm__lapshot n=200 rep=1 base=zs_lgbm done in 0.1s +[ 11.4s] zs_lgbm__lapshot n=200 rep=2 base=zs_lgbm done in 0.1s +[ 11.6s] zs_lgbm__lapshot n=200 rep=3 base=zs_lgbm done in 0.1s +[ 11.7s] zs_lgbm__lapshot n=200 rep=4 base=zs_lgbm done in 0.1s +[ 11.9s] zs_dann__lapshot n=5 rep=0 base=zs_dann done in 0.1s +[ 12.0s] zs_dann__lapshot n=5 rep=1 base=zs_dann done in 0.1s +[ 12.1s] zs_dann__lapshot n=5 rep=2 base=zs_dann done in 0.1s +[ 12.3s] zs_dann__lapshot n=5 rep=3 base=zs_dann done in 0.1s +[ 12.4s] zs_dann__lapshot n=5 rep=4 base=zs_dann done in 0.1s +[ 12.6s] zs_dann__lapshot n=25 rep=0 base=zs_dann done in 0.2s +[ 12.7s] zs_dann__lapshot n=25 rep=1 base=zs_dann done in 0.1s +[ 12.8s] zs_dann__lapshot n=25 rep=2 base=zs_dann done in 0.1s +[ 13.0s] zs_dann__lapshot n=25 rep=3 base=zs_dann done in 0.1s +[ 13.1s] zs_dann__lapshot n=25 rep=4 base=zs_dann done in 0.1s +[ 13.2s] zs_dann__lapshot n=50 rep=0 base=zs_dann done in 0.1s +[ 13.4s] zs_dann__lapshot n=50 rep=1 base=zs_dann done in 0.1s +[ 13.5s] zs_dann__lapshot n=50 rep=2 base=zs_dann done in 0.1s +[ 13.6s] zs_dann__lapshot n=50 rep=3 base=zs_dann done in 0.1s +[ 13.7s] zs_dann__lapshot n=50 rep=4 base=zs_dann done in 0.1s +[ 13.9s] zs_dann__lapshot n=100 rep=0 base=zs_dann done in 0.1s +[ 14.0s] zs_dann__lapshot n=100 rep=1 base=zs_dann done in 0.1s +[ 14.1s] zs_dann__lapshot n=100 rep=2 base=zs_dann done in 0.1s +[ 14.2s] zs_dann__lapshot n=100 rep=3 base=zs_dann done in 0.1s +[ 14.4s] zs_dann__lapshot n=100 rep=4 base=zs_dann done in 0.1s +[ 14.5s] zs_dann__lapshot n=200 rep=0 base=zs_dann done in 0.1s +[ 14.6s] zs_dann__lapshot n=200 rep=1 base=zs_dann done in 0.1s +[ 14.7s] zs_dann__lapshot n=200 rep=2 base=zs_dann done in 0.1s +[ 14.9s] zs_dann__lapshot n=200 rep=3 base=zs_dann done in 0.1s +[ 15.0s] zs_dann__lapshot n=200 rep=4 base=zs_dann done in 0.1s +[ 15.0s] done +[ 0.9s] loaded: Madrid (76263, 393), Amsterdam (25992, 393) +[ 0.9s] graph: 583310 edges, mean degree 22.4, min 10 +[ 1.1s] lgbm_upw__em n=5 rep=0 base=lgbm_upw done in 0.2s +[ 1.3s] lgbm_upw__em n=5 rep=1 base=lgbm_upw done in 0.2s +[ 1.4s] lgbm_upw__em n=5 rep=2 base=lgbm_upw done in 0.2s +[ 1.6s] lgbm_upw__em n=5 rep=3 base=lgbm_upw done in 0.2s +[ 1.8s] lgbm_upw__em n=5 rep=4 base=lgbm_upw done in 0.1s +[ 1.9s] lgbm_upw__em n=25 rep=0 base=lgbm_upw done in 0.2s +[ 2.1s] lgbm_upw__em n=25 rep=1 base=lgbm_upw done in 0.2s +[ 2.3s] lgbm_upw__em n=25 rep=2 base=lgbm_upw done in 0.2s +[ 2.5s] lgbm_upw__em n=25 rep=3 base=lgbm_upw done in 0.2s +[ 2.6s] lgbm_upw__em n=25 rep=4 base=lgbm_upw done in 0.1s +[ 2.8s] lgbm_upw__em n=50 rep=0 base=lgbm_upw done in 0.2s +[ 3.0s] lgbm_upw__em n=50 rep=1 base=lgbm_upw done in 0.2s +[ 3.1s] lgbm_upw__em n=50 rep=2 base=lgbm_upw done in 0.2s +[ 3.3s] lgbm_upw__em n=50 rep=3 base=lgbm_upw done in 0.2s +[ 3.4s] lgbm_upw__em n=50 rep=4 base=lgbm_upw done in 0.1s +[ 3.6s] lgbm_upw__em n=100 rep=0 base=lgbm_upw done in 0.2s +[ 3.7s] lgbm_upw__em n=100 rep=1 base=lgbm_upw done in 0.1s +[ 3.9s] lgbm_upw__em n=100 rep=2 base=lgbm_upw done in 0.1s +[ 4.0s] lgbm_upw__em n=100 rep=3 base=lgbm_upw done in 0.1s +[ 4.2s] lgbm_upw__em n=100 rep=4 base=lgbm_upw done in 0.2s +[ 4.4s] lgbm_upw__em n=200 rep=0 base=lgbm_upw done in 0.2s +[ 4.5s] lgbm_upw__em n=200 rep=1 base=lgbm_upw done in 0.1s +[ 4.7s] lgbm_upw__em n=200 rep=2 base=lgbm_upw done in 0.2s +[ 4.9s] lgbm_upw__em n=200 rep=3 base=lgbm_upw done in 0.1s +[ 5.0s] lgbm_upw__em n=200 rep=4 base=lgbm_upw done in 0.2s +[ 5.2s] zs_xgb_dann__em n=0 rep=0 base=zs_xgb_dann done in 0.1s +[ 5.4s] zs_lgbm__em n=0 rep=0 base=zs_lgbm done in 0.2s +[ 5.5s] zs_dann__em n=0 rep=0 base=zs_dann done in 0.2s +[ 5.5s] done +[ 0.9s] loaded: Madrid (76263, 393), Amsterdam (25992, 393) +[ 1.0s] graph: 583310 edges, mean degree 22.4, min 10 +[ 1.6s] ens_e1__clamp n=5 rep=0 base=ens_e1 done in 0.6s +[ 1.6s] ens_e1__clamp n=5 rep=1 base=ens_e1 done in 0.0s +[ 1.7s] ens_e1__clamp n=5 rep=2 base=ens_e1 done in 0.0s +[ 1.7s] ens_e1__clamp n=5 rep=3 base=ens_e1 done in 0.0s +[ 1.7s] ens_e1__clamp n=5 rep=4 base=ens_e1 done in 0.0s +[ 1.8s] ens_e1__clamp n=25 rep=0 base=ens_e1 done in 0.0s +[ 1.8s] ens_e1__clamp n=25 rep=1 base=ens_e1 done in 0.0s +[ 1.9s] ens_e1__clamp n=25 rep=2 base=ens_e1 done in 0.0s +[ 1.9s] ens_e1__clamp n=25 rep=3 base=ens_e1 done in 0.0s +[ 1.9s] ens_e1__clamp n=25 rep=4 base=ens_e1 done in 0.0s +[ 2.0s] ens_e1__clamp n=50 rep=0 base=ens_e1 done in 0.0s +[ 2.0s] ens_e1__clamp n=50 rep=1 base=ens_e1 done in 0.0s +[ 2.0s] ens_e1__clamp n=50 rep=2 base=ens_e1 done in 0.0s +[ 2.1s] ens_e1__clamp n=50 rep=3 base=ens_e1 done in 0.0s +[ 2.1s] ens_e1__clamp n=50 rep=4 base=ens_e1 done in 0.0s +[ 2.2s] ens_e1__clamp n=100 rep=0 base=ens_e1 done in 0.1s +[ 2.2s] ens_e1__clamp n=100 rep=1 base=ens_e1 done in 0.0s +[ 2.2s] ens_e1__clamp n=100 rep=2 base=ens_e1 done in 0.0s +[ 2.3s] ens_e1__clamp n=100 rep=3 base=ens_e1 done in 0.0s +[ 2.3s] ens_e1__clamp n=100 rep=4 base=ens_e1 done in 0.0s +[ 2.4s] ens_e1__clamp n=200 rep=0 base=ens_e1 done in 0.0s +[ 2.4s] ens_e1__clamp n=200 rep=1 base=ens_e1 done in 0.0s +[ 2.4s] ens_e1__clamp n=200 rep=2 base=ens_e1 done in 0.0s +[ 2.4s] ens_e1__clamp n=200 rep=3 base=ens_e1 done in 0.0s +[ 2.5s] ens_e1__clamp n=200 rep=4 base=ens_e1 done in 0.0s +[ 2.5s] ens_e2__clamp n=5 rep=0 base=ens_e2 done in 0.0s +[ 2.6s] ens_e2__clamp n=5 rep=1 base=ens_e2 done in 0.0s +[ 2.6s] ens_e2__clamp n=5 rep=2 base=ens_e2 done in 0.0s +[ 2.6s] ens_e2__clamp n=5 rep=3 base=ens_e2 done in 0.0s +[ 2.7s] ens_e2__clamp n=5 rep=4 base=ens_e2 done in 0.0s +[ 2.7s] ens_e2__clamp n=25 rep=0 base=ens_e2 done in 0.0s +[ 2.8s] ens_e2__clamp n=25 rep=1 base=ens_e2 done in 0.0s +[ 2.8s] ens_e2__clamp n=25 rep=2 base=ens_e2 done in 0.0s +[ 2.8s] ens_e2__clamp n=25 rep=3 base=ens_e2 done in 0.0s +[ 2.9s] ens_e2__clamp n=25 rep=4 base=ens_e2 done in 0.0s +[ 2.9s] ens_e2__clamp n=50 rep=0 base=ens_e2 done in 0.0s +[ 3.0s] ens_e2__clamp n=50 rep=1 base=ens_e2 done in 0.0s +[ 3.0s] ens_e2__clamp n=50 rep=2 base=ens_e2 done in 0.1s +[ 3.1s] ens_e2__clamp n=50 rep=3 base=ens_e2 done in 0.1s +[ 3.1s] ens_e2__clamp n=50 rep=4 base=ens_e2 done in 0.0s +[ 3.2s] ens_e2__clamp n=100 rep=0 base=ens_e2 done in 0.0s +[ 3.2s] ens_e2__clamp n=100 rep=1 base=ens_e2 done in 0.0s +[ 3.2s] ens_e2__clamp n=100 rep=2 base=ens_e2 done in 0.0s +[ 3.3s] ens_e2__clamp n=100 rep=3 base=ens_e2 done in 0.0s +[ 3.3s] ens_e2__clamp n=100 rep=4 base=ens_e2 done in 0.0s +[ 3.3s] ens_e2__clamp n=200 rep=0 base=ens_e2 done in 0.0s +[ 3.3s] ens_e2__clamp n=200 rep=1 base=ens_e2 done in 0.0s +[ 3.4s] ens_e2__clamp n=200 rep=2 base=ens_e2 done in 0.0s +[ 3.4s] ens_e2__clamp n=200 rep=3 base=ens_e2 done in 0.0s +[ 3.4s] ens_e2__clamp n=200 rep=4 base=ens_e2 done in 0.0s +[ 3.4s] ens_e3__clamp n=5 rep=0 base=ens_e3 done in 0.0s +[ 3.5s] ens_e3__clamp n=5 rep=1 base=ens_e3 done in 0.0s +[ 3.5s] ens_e3__clamp n=5 rep=2 base=ens_e3 done in 0.0s +[ 3.5s] ens_e3__clamp n=5 rep=3 base=ens_e3 done in 0.0s +[ 3.5s] ens_e3__clamp n=5 rep=4 base=ens_e3 done in 0.0s +[ 3.5s] ens_e3__clamp n=25 rep=0 base=ens_e3 done in 0.0s +[ 3.6s] ens_e3__clamp n=25 rep=1 base=ens_e3 done in 0.0s +[ 3.6s] ens_e3__clamp n=25 rep=2 base=ens_e3 done in 0.0s +[ 3.6s] ens_e3__clamp n=25 rep=3 base=ens_e3 done in 0.0s +[ 3.7s] ens_e3__clamp n=25 rep=4 base=ens_e3 done in 0.0s +[ 3.7s] ens_e3__clamp n=50 rep=0 base=ens_e3 done in 0.0s +[ 3.7s] ens_e3__clamp n=50 rep=1 base=ens_e3 done in 0.0s +[ 3.7s] ens_e3__clamp n=50 rep=2 base=ens_e3 done in 0.0s +[ 3.7s] ens_e3__clamp n=50 rep=3 base=ens_e3 done in 0.0s +[ 3.8s] ens_e3__clamp n=50 rep=4 base=ens_e3 done in 0.0s +[ 3.8s] ens_e3__clamp n=100 rep=0 base=ens_e3 done in 0.0s +[ 3.8s] ens_e3__clamp n=100 rep=1 base=ens_e3 done in 0.0s +[ 3.8s] ens_e3__clamp n=100 rep=2 base=ens_e3 done in 0.0s +[ 3.8s] ens_e3__clamp n=100 rep=3 base=ens_e3 done in 0.0s +[ 3.9s] ens_e3__clamp n=100 rep=4 base=ens_e3 done in 0.0s +[ 3.9s] ens_e3__clamp n=200 rep=0 base=ens_e3 done in 0.0s +[ 3.9s] ens_e3__clamp n=200 rep=1 base=ens_e3 done in 0.0s +[ 3.9s] ens_e3__clamp n=200 rep=2 base=ens_e3 done in 0.0s +[ 4.0s] ens_e3__clamp n=200 rep=3 base=ens_e3 done in 0.0s +[ 4.0s] ens_e3__clamp n=200 rep=4 base=ens_e3 done in 0.0s +[ 4.0s] done +[ 0.9s] loaded: Madrid (76263, 393), Amsterdam (25992, 393) +[ 0.9s] graph: 583310 edges, mean degree 22.4, min 10 +[ 1.1s] ens_e1__lp n=5 rep=0 base=ens_e1 done in 0.1s +[ 1.2s] ens_e1__lp n=5 rep=1 base=ens_e1 done in 0.1s +[ 1.3s] ens_e1__lp n=5 rep=2 base=ens_e1 done in 0.1s +[ 1.4s] ens_e1__lp n=5 rep=3 base=ens_e1 done in 0.1s +[ 1.6s] ens_e1__lp n=5 rep=4 base=ens_e1 done in 0.1s +[ 1.7s] ens_e1__lp n=25 rep=0 base=ens_e1 done in 0.1s +[ 1.8s] ens_e1__lp n=25 rep=1 base=ens_e1 done in 0.1s +[ 2.0s] ens_e1__lp n=25 rep=2 base=ens_e1 done in 0.1s +[ 2.1s] ens_e1__lp n=25 rep=3 base=ens_e1 done in 0.1s +[ 2.3s] ens_e1__lp n=25 rep=4 base=ens_e1 done in 0.1s +[ 2.4s] ens_e1__lp n=50 rep=0 base=ens_e1 done in 0.1s +[ 2.5s] ens_e1__lp n=50 rep=1 base=ens_e1 done in 0.1s +[ 2.7s] ens_e1__lp n=50 rep=2 base=ens_e1 done in 0.1s +[ 2.8s] ens_e1__lp n=50 rep=3 base=ens_e1 done in 0.1s +[ 2.9s] ens_e1__lp n=50 rep=4 base=ens_e1 done in 0.2s +[ 3.1s] ens_e1__lp n=100 rep=0 base=ens_e1 done in 0.1s +[ 3.2s] ens_e1__lp n=100 rep=1 base=ens_e1 done in 0.2s +[ 3.3s] ens_e1__lp n=100 rep=2 base=ens_e1 done in 0.1s +[ 3.5s] ens_e1__lp n=100 rep=3 base=ens_e1 done in 0.1s +[ 3.6s] ens_e1__lp n=100 rep=4 base=ens_e1 done in 0.1s +[ 3.7s] ens_e1__lp n=200 rep=0 base=ens_e1 done in 0.1s +[ 3.9s] ens_e1__lp n=200 rep=1 base=ens_e1 done in 0.1s +[ 4.0s] ens_e1__lp n=200 rep=2 base=ens_e1 done in 0.1s +[ 4.1s] ens_e1__lp n=200 rep=3 base=ens_e1 done in 0.1s +[ 4.2s] ens_e1__lp n=200 rep=4 base=ens_e1 done in 0.1s +[ 4.3s] ens_e2__lp n=5 rep=0 base=ens_e2 done in 0.1s +[ 4.5s] ens_e2__lp n=5 rep=1 base=ens_e2 done in 0.1s +[ 4.6s] ens_e2__lp n=5 rep=2 base=ens_e2 done in 0.1s +[ 4.7s] ens_e2__lp n=5 rep=3 base=ens_e2 done in 0.1s +[ 4.9s] ens_e2__lp n=5 rep=4 base=ens_e2 done in 0.1s +[ 5.0s] ens_e2__lp n=25 rep=0 base=ens_e2 done in 0.1s +[ 5.1s] ens_e2__lp n=25 rep=1 base=ens_e2 done in 0.1s +[ 5.3s] ens_e2__lp n=25 rep=2 base=ens_e2 done in 0.1s +[ 5.4s] ens_e2__lp n=25 rep=3 base=ens_e2 done in 0.1s +[ 5.5s] ens_e2__lp n=25 rep=4 base=ens_e2 done in 0.1s +[ 5.6s] ens_e2__lp n=50 rep=0 base=ens_e2 done in 0.1s +[ 5.8s] ens_e2__lp n=50 rep=1 base=ens_e2 done in 0.1s +[ 5.9s] ens_e2__lp n=50 rep=2 base=ens_e2 done in 0.1s +[ 6.1s] ens_e2__lp n=50 rep=3 base=ens_e2 done in 0.1s +[ 6.2s] ens_e2__lp n=50 rep=4 base=ens_e2 done in 0.2s +[ 6.4s] ens_e2__lp n=100 rep=0 base=ens_e2 done in 0.2s +[ 6.5s] ens_e2__lp n=100 rep=1 base=ens_e2 done in 0.1s +[ 6.7s] ens_e2__lp n=100 rep=2 base=ens_e2 done in 0.1s +[ 6.8s] ens_e2__lp n=100 rep=3 base=ens_e2 done in 0.2s +[ 7.0s] ens_e2__lp n=100 rep=4 base=ens_e2 done in 0.2s +[ 7.1s] ens_e2__lp n=200 rep=0 base=ens_e2 done in 0.2s +[ 7.3s] ens_e2__lp n=200 rep=1 base=ens_e2 done in 0.2s +[ 7.4s] ens_e2__lp n=200 rep=2 base=ens_e2 done in 0.2s +[ 7.6s] ens_e2__lp n=200 rep=3 base=ens_e2 done in 0.1s +[ 7.7s] ens_e2__lp n=200 rep=4 base=ens_e2 done in 0.1s +[ 7.9s] ens_e3__lp n=5 rep=0 base=ens_e3 done in 0.1s +[ 8.0s] ens_e3__lp n=5 rep=1 base=ens_e3 done in 0.2s +[ 8.2s] ens_e3__lp n=5 rep=2 base=ens_e3 done in 0.2s +[ 8.3s] ens_e3__lp n=5 rep=3 base=ens_e3 done in 0.1s +[ 8.4s] ens_e3__lp n=5 rep=4 base=ens_e3 done in 0.1s +[ 8.6s] ens_e3__lp n=25 rep=0 base=ens_e3 done in 0.2s +[ 8.7s] ens_e3__lp n=25 rep=1 base=ens_e3 done in 0.2s +[ 8.9s] ens_e3__lp n=25 rep=2 base=ens_e3 done in 0.2s +[ 9.1s] ens_e3__lp n=25 rep=3 base=ens_e3 done in 0.2s +[ 9.2s] ens_e3__lp n=25 rep=4 base=ens_e3 done in 0.2s +[ 9.4s] ens_e3__lp n=50 rep=0 base=ens_e3 done in 0.2s +[ 9.6s] ens_e3__lp n=50 rep=1 base=ens_e3 done in 0.2s +[ 9.7s] ens_e3__lp n=50 rep=2 base=ens_e3 done in 0.2s +[ 9.8s] ens_e3__lp n=50 rep=3 base=ens_e3 done in 0.1s +[ 10.0s] ens_e3__lp n=50 rep=4 base=ens_e3 done in 0.1s +[ 10.1s] ens_e3__lp n=100 rep=0 base=ens_e3 done in 0.1s +[ 10.3s] ens_e3__lp n=100 rep=1 base=ens_e3 done in 0.2s +[ 10.5s] ens_e3__lp n=100 rep=2 base=ens_e3 done in 0.1s +[ 10.6s] ens_e3__lp n=100 rep=3 base=ens_e3 done in 0.2s +[ 10.8s] ens_e3__lp n=100 rep=4 base=ens_e3 done in 0.1s +[ 10.9s] ens_e3__lp n=200 rep=0 base=ens_e3 done in 0.1s +[ 11.0s] ens_e3__lp n=200 rep=1 base=ens_e3 done in 0.1s +[ 11.2s] ens_e3__lp n=200 rep=2 base=ens_e3 done in 0.1s +[ 11.3s] ens_e3__lp n=200 rep=3 base=ens_e3 done in 0.1s +[ 11.4s] ens_e3__lp n=200 rep=4 base=ens_e3 done in 0.1s +[ 11.4s] done +[ 0.9s] loaded: Madrid (76263, 393), Amsterdam (25992, 393) +[ 1.0s] graph: 583310 edges, mean degree 22.4, min 10 +[ 1.1s] ens_e1__lapshot n=5 rep=0 base=ens_e1 done in 0.1s +[ 1.2s] ens_e1__lapshot n=5 rep=1 base=ens_e1 done in 0.1s +[ 1.3s] ens_e1__lapshot n=5 rep=2 base=ens_e1 done in 0.1s +[ 1.5s] ens_e1__lapshot n=5 rep=3 base=ens_e1 done in 0.1s +[ 1.6s] ens_e1__lapshot n=5 rep=4 base=ens_e1 done in 0.1s +[ 1.7s] ens_e1__lapshot n=25 rep=0 base=ens_e1 done in 0.2s +[ 1.9s] ens_e1__lapshot n=25 rep=1 base=ens_e1 done in 0.1s +[ 2.0s] ens_e1__lapshot n=25 rep=2 base=ens_e1 done in 0.1s +[ 2.1s] ens_e1__lapshot n=25 rep=3 base=ens_e1 done in 0.1s +[ 2.3s] ens_e1__lapshot n=25 rep=4 base=ens_e1 done in 0.1s +[ 2.4s] ens_e1__lapshot n=50 rep=0 base=ens_e1 done in 0.1s +[ 2.5s] ens_e1__lapshot n=50 rep=1 base=ens_e1 done in 0.1s +[ 2.7s] ens_e1__lapshot n=50 rep=2 base=ens_e1 done in 0.1s +[ 2.8s] ens_e1__lapshot n=50 rep=3 base=ens_e1 done in 0.1s +[ 2.9s] ens_e1__lapshot n=50 rep=4 base=ens_e1 done in 0.1s +[ 3.0s] ens_e1__lapshot n=100 rep=0 base=ens_e1 done in 0.1s +[ 3.2s] ens_e1__lapshot n=100 rep=1 base=ens_e1 done in 0.1s +[ 3.3s] ens_e1__lapshot n=100 rep=2 base=ens_e1 done in 0.1s +[ 3.4s] ens_e1__lapshot n=100 rep=3 base=ens_e1 done in 0.1s +[ 3.5s] ens_e1__lapshot n=100 rep=4 base=ens_e1 done in 0.1s +[ 3.7s] ens_e1__lapshot n=200 rep=0 base=ens_e1 done in 0.1s +[ 3.8s] ens_e1__lapshot n=200 rep=1 base=ens_e1 done in 0.1s +[ 3.9s] ens_e1__lapshot n=200 rep=2 base=ens_e1 done in 0.1s +[ 4.1s] ens_e1__lapshot n=200 rep=3 base=ens_e1 done in 0.1s +[ 4.2s] ens_e1__lapshot n=200 rep=4 base=ens_e1 done in 0.1s +[ 4.3s] ens_e2__lapshot n=5 rep=0 base=ens_e2 done in 0.1s +[ 4.5s] ens_e2__lapshot n=5 rep=1 base=ens_e2 done in 0.1s +[ 4.6s] ens_e2__lapshot n=5 rep=2 base=ens_e2 done in 0.1s +[ 4.7s] ens_e2__lapshot n=5 rep=3 base=ens_e2 done in 0.1s +[ 4.9s] ens_e2__lapshot n=5 rep=4 base=ens_e2 done in 0.1s +[ 5.0s] ens_e2__lapshot n=25 rep=0 base=ens_e2 done in 0.1s +[ 5.1s] ens_e2__lapshot n=25 rep=1 base=ens_e2 done in 0.1s +[ 5.2s] ens_e2__lapshot n=25 rep=2 base=ens_e2 done in 0.1s +[ 5.3s] ens_e2__lapshot n=25 rep=3 base=ens_e2 done in 0.1s +[ 5.5s] ens_e2__lapshot n=25 rep=4 base=ens_e2 done in 0.1s +[ 5.6s] ens_e2__lapshot n=50 rep=0 base=ens_e2 done in 0.1s +[ 5.7s] ens_e2__lapshot n=50 rep=1 base=ens_e2 done in 0.1s +[ 5.9s] ens_e2__lapshot n=50 rep=2 base=ens_e2 done in 0.1s +[ 6.0s] ens_e2__lapshot n=50 rep=3 base=ens_e2 done in 0.1s +[ 6.1s] ens_e2__lapshot n=50 rep=4 base=ens_e2 done in 0.1s +[ 6.3s] ens_e2__lapshot n=100 rep=0 base=ens_e2 done in 0.1s +[ 6.4s] ens_e2__lapshot n=100 rep=1 base=ens_e2 done in 0.1s +[ 6.5s] ens_e2__lapshot n=100 rep=2 base=ens_e2 done in 0.1s +[ 6.6s] ens_e2__lapshot n=100 rep=3 base=ens_e2 done in 0.1s +[ 6.8s] ens_e2__lapshot n=100 rep=4 base=ens_e2 done in 0.1s +[ 6.9s] ens_e2__lapshot n=200 rep=0 base=ens_e2 done in 0.1s +[ 7.0s] ens_e2__lapshot n=200 rep=1 base=ens_e2 done in 0.1s +[ 7.2s] ens_e2__lapshot n=200 rep=2 base=ens_e2 done in 0.2s +[ 7.3s] ens_e2__lapshot n=200 rep=3 base=ens_e2 done in 0.1s +[ 7.4s] ens_e2__lapshot n=200 rep=4 base=ens_e2 done in 0.1s +[ 7.6s] ens_e3__lapshot n=5 rep=0 base=ens_e3 done in 0.1s +[ 7.7s] ens_e3__lapshot n=5 rep=1 base=ens_e3 done in 0.1s +[ 7.8s] ens_e3__lapshot n=5 rep=2 base=ens_e3 done in 0.1s +[ 8.0s] ens_e3__lapshot n=5 rep=3 base=ens_e3 done in 0.1s +[ 8.1s] ens_e3__lapshot n=5 rep=4 base=ens_e3 done in 0.1s +[ 8.2s] ens_e3__lapshot n=25 rep=0 base=ens_e3 done in 0.1s +[ 8.4s] ens_e3__lapshot n=25 rep=1 base=ens_e3 done in 0.1s +[ 8.5s] ens_e3__lapshot n=25 rep=2 base=ens_e3 done in 0.1s +[ 8.6s] ens_e3__lapshot n=25 rep=3 base=ens_e3 done in 0.1s +[ 8.8s] ens_e3__lapshot n=25 rep=4 base=ens_e3 done in 0.1s +[ 8.9s] ens_e3__lapshot n=50 rep=0 base=ens_e3 done in 0.1s +[ 9.0s] ens_e3__lapshot n=50 rep=1 base=ens_e3 done in 0.1s +[ 9.1s] ens_e3__lapshot n=50 rep=2 base=ens_e3 done in 0.1s +[ 9.2s] ens_e3__lapshot n=50 rep=3 base=ens_e3 done in 0.1s +[ 9.3s] ens_e3__lapshot n=50 rep=4 base=ens_e3 done in 0.1s +[ 9.4s] ens_e3__lapshot n=100 rep=0 base=ens_e3 done in 0.1s +[ 9.5s] ens_e3__lapshot n=100 rep=1 base=ens_e3 done in 0.1s +[ 9.7s] ens_e3__lapshot n=100 rep=2 base=ens_e3 done in 0.1s +[ 9.8s] ens_e3__lapshot n=100 rep=3 base=ens_e3 done in 0.2s +[ 10.0s] ens_e3__lapshot n=100 rep=4 base=ens_e3 done in 0.2s +[ 10.1s] ens_e3__lapshot n=200 rep=0 base=ens_e3 done in 0.1s +[ 10.3s] ens_e3__lapshot n=200 rep=1 base=ens_e3 done in 0.1s +[ 10.4s] ens_e3__lapshot n=200 rep=2 base=ens_e3 done in 0.2s +[ 10.6s] ens_e3__lapshot n=200 rep=3 base=ens_e3 done in 0.1s +[ 10.7s] ens_e3__lapshot n=200 rep=4 base=ens_e3 done in 0.1s +[ 10.7s] done +[ 0.7s] loaded: Madrid (76263, 393), Amsterdam (25992, 393) +[ 0.7s] graph: 583310 edges, mean degree 22.4, min 10 +[ 0.9s] ens_e4__lp n=5 rep=0 base=ens_e4 done in 0.1s +[ 1.0s] ens_e4__lp n=5 rep=1 base=ens_e4 done in 0.1s +[ 1.1s] ens_e4__lp n=5 rep=2 base=ens_e4 done in 0.1s +[ 1.2s] ens_e4__lp n=5 rep=3 base=ens_e4 done in 0.1s +[ 1.4s] ens_e4__lp n=5 rep=4 base=ens_e4 done in 0.1s +[ 1.5s] ens_e4__lp n=25 rep=0 base=ens_e4 done in 0.1s +[ 1.6s] ens_e4__lp n=25 rep=1 base=ens_e4 done in 0.2s +[ 1.8s] ens_e4__lp n=25 rep=2 base=ens_e4 done in 0.2s +[ 1.9s] ens_e4__lp n=25 rep=3 base=ens_e4 done in 0.1s +[ 2.1s] ens_e4__lp n=25 rep=4 base=ens_e4 done in 0.1s +[ 2.2s] ens_e4__lp n=50 rep=0 base=ens_e4 done in 0.1s +[ 2.3s] ens_e4__lp n=50 rep=1 base=ens_e4 done in 0.1s +[ 2.5s] ens_e4__lp n=50 rep=2 base=ens_e4 done in 0.1s +[ 2.6s] ens_e4__lp n=50 rep=3 base=ens_e4 done in 0.1s +[ 2.7s] ens_e4__lp n=50 rep=4 base=ens_e4 done in 0.1s +[ 2.9s] ens_e4__lp n=100 rep=0 base=ens_e4 done in 0.1s +[ 2.9s] ens_e4__lp n=100 rep=1 base=ens_e4 done in 0.1s +[ 3.1s] ens_e4__lp n=100 rep=2 base=ens_e4 done in 0.1s +[ 3.2s] ens_e4__lp n=100 rep=3 base=ens_e4 done in 0.1s +[ 3.4s] ens_e4__lp n=100 rep=4 base=ens_e4 done in 0.2s +[ 3.6s] ens_e4__lp n=200 rep=0 base=ens_e4 done in 0.2s +[ 3.7s] ens_e4__lp n=200 rep=1 base=ens_e4 done in 0.2s +[ 3.9s] ens_e4__lp n=200 rep=2 base=ens_e4 done in 0.1s +[ 4.0s] ens_e4__lp n=200 rep=3 base=ens_e4 done in 0.1s +[ 4.2s] ens_e4__lp n=200 rep=4 base=ens_e4 done in 0.2s +[ 4.3s] ens_e5__lp n=5 rep=0 base=ens_e5 done in 0.1s +[ 4.4s] ens_e5__lp n=5 rep=1 base=ens_e5 done in 0.1s +[ 4.6s] ens_e5__lp n=5 rep=2 base=ens_e5 done in 0.2s +[ 4.7s] ens_e5__lp n=5 rep=3 base=ens_e5 done in 0.1s +[ 4.9s] ens_e5__lp n=5 rep=4 base=ens_e5 done in 0.1s +[ 5.0s] ens_e5__lp n=25 rep=0 base=ens_e5 done in 0.2s +[ 5.2s] ens_e5__lp n=25 rep=1 base=ens_e5 done in 0.1s +[ 5.3s] ens_e5__lp n=25 rep=2 base=ens_e5 done in 0.1s +[ 5.4s] ens_e5__lp n=25 rep=3 base=ens_e5 done in 0.1s +[ 5.5s] ens_e5__lp n=25 rep=4 base=ens_e5 done in 0.1s +[ 5.6s] ens_e5__lp n=50 rep=0 base=ens_e5 done in 0.1s +[ 5.7s] ens_e5__lp n=50 rep=1 base=ens_e5 done in 0.1s +[ 5.8s] ens_e5__lp n=50 rep=2 base=ens_e5 done in 0.1s +[ 5.9s] ens_e5__lp n=50 rep=3 base=ens_e5 done in 0.1s +[ 6.0s] ens_e5__lp n=50 rep=4 base=ens_e5 done in 0.1s +[ 6.1s] ens_e5__lp n=100 rep=0 base=ens_e5 done in 0.1s +[ 6.3s] ens_e5__lp n=100 rep=1 base=ens_e5 done in 0.1s +[ 6.4s] ens_e5__lp n=100 rep=2 base=ens_e5 done in 0.1s +[ 6.4s] ens_e5__lp n=100 rep=3 base=ens_e5 done in 0.1s +[ 6.6s] ens_e5__lp n=100 rep=4 base=ens_e5 done in 0.1s +[ 6.7s] ens_e5__lp n=200 rep=0 base=ens_e5 done in 0.1s +[ 6.8s] ens_e5__lp n=200 rep=1 base=ens_e5 done in 0.1s +[ 6.9s] ens_e5__lp n=200 rep=2 base=ens_e5 done in 0.1s +[ 7.0s] ens_e5__lp n=200 rep=3 base=ens_e5 done in 0.1s +[ 7.1s] ens_e5__lp n=200 rep=4 base=ens_e5 done in 0.1s +[ 7.2s] ens_e6__lp n=5 rep=0 base=ens_e6 done in 0.1s +[ 7.3s] ens_e6__lp n=5 rep=1 base=ens_e6 done in 0.1s +[ 7.4s] ens_e6__lp n=5 rep=2 base=ens_e6 done in 0.1s +[ 7.5s] ens_e6__lp n=5 rep=3 base=ens_e6 done in 0.1s +[ 7.6s] ens_e6__lp n=5 rep=4 base=ens_e6 done in 0.1s +[ 7.7s] ens_e6__lp n=25 rep=0 base=ens_e6 done in 0.1s +[ 7.8s] ens_e6__lp n=25 rep=1 base=ens_e6 done in 0.1s +[ 7.9s] ens_e6__lp n=25 rep=2 base=ens_e6 done in 0.1s +[ 8.0s] ens_e6__lp n=25 rep=3 base=ens_e6 done in 0.1s +[ 8.1s] ens_e6__lp n=25 rep=4 base=ens_e6 done in 0.1s +[ 8.2s] ens_e6__lp n=50 rep=0 base=ens_e6 done in 0.1s +[ 8.3s] ens_e6__lp n=50 rep=1 base=ens_e6 done in 0.1s +[ 8.4s] ens_e6__lp n=50 rep=2 base=ens_e6 done in 0.1s +[ 8.5s] ens_e6__lp n=50 rep=3 base=ens_e6 done in 0.1s +[ 8.7s] ens_e6__lp n=50 rep=4 base=ens_e6 done in 0.1s +[ 8.8s] ens_e6__lp n=100 rep=0 base=ens_e6 done in 0.1s +[ 8.9s] ens_e6__lp n=100 rep=1 base=ens_e6 done in 0.1s +[ 9.0s] ens_e6__lp n=100 rep=2 base=ens_e6 done in 0.1s +[ 9.1s] ens_e6__lp n=100 rep=3 base=ens_e6 done in 0.1s +[ 9.2s] ens_e6__lp n=100 rep=4 base=ens_e6 done in 0.1s +[ 9.3s] ens_e6__lp n=200 rep=0 base=ens_e6 done in 0.1s +[ 9.4s] ens_e6__lp n=200 rep=1 base=ens_e6 done in 0.1s +[ 9.5s] ens_e6__lp n=200 rep=2 base=ens_e6 done in 0.1s +[ 9.6s] ens_e6__lp n=200 rep=3 base=ens_e6 done in 0.1s +[ 9.6s] ens_e6__lp n=200 rep=4 base=ens_e6 done in 0.1s +[ 9.6s] done +[ 0.8s] loaded: Madrid (76263, 393), Amsterdam (25992, 393) +[ 0.8s] graph: 583310 edges, mean degree 22.4, min 10 +[ 0.9s] ens_e4__lapshot n=5 rep=0 base=ens_e4 done in 0.1s +[ 1.0s] ens_e4__lapshot n=5 rep=1 base=ens_e4 done in 0.1s +[ 1.1s] ens_e4__lapshot n=5 rep=2 base=ens_e4 done in 0.1s +[ 1.2s] ens_e4__lapshot n=5 rep=3 base=ens_e4 done in 0.1s +[ 1.3s] ens_e4__lapshot n=5 rep=4 base=ens_e4 done in 0.1s +[ 1.4s] ens_e4__lapshot n=25 rep=0 base=ens_e4 done in 0.1s +[ 1.5s] ens_e4__lapshot n=25 rep=1 base=ens_e4 done in 0.1s +[ 1.6s] ens_e4__lapshot n=25 rep=2 base=ens_e4 done in 0.1s +[ 1.7s] ens_e4__lapshot n=25 rep=3 base=ens_e4 done in 0.1s +[ 1.8s] ens_e4__lapshot n=25 rep=4 base=ens_e4 done in 0.1s +[ 1.9s] ens_e4__lapshot n=50 rep=0 base=ens_e4 done in 0.1s +[ 2.0s] ens_e4__lapshot n=50 rep=1 base=ens_e4 done in 0.1s +[ 2.1s] ens_e4__lapshot n=50 rep=2 base=ens_e4 done in 0.1s +[ 2.2s] ens_e4__lapshot n=50 rep=3 base=ens_e4 done in 0.1s +[ 2.3s] ens_e4__lapshot n=50 rep=4 base=ens_e4 done in 0.1s +[ 2.4s] ens_e4__lapshot n=100 rep=0 base=ens_e4 done in 0.1s +[ 2.5s] ens_e4__lapshot n=100 rep=1 base=ens_e4 done in 0.1s +[ 2.6s] ens_e4__lapshot n=100 rep=2 base=ens_e4 done in 0.1s +[ 2.7s] ens_e4__lapshot n=100 rep=3 base=ens_e4 done in 0.1s +[ 2.8s] ens_e4__lapshot n=100 rep=4 base=ens_e4 done in 0.1s +[ 2.8s] ens_e4__lapshot n=200 rep=0 base=ens_e4 done in 0.1s +[ 3.0s] ens_e4__lapshot n=200 rep=1 base=ens_e4 done in 0.1s +[ 3.1s] ens_e4__lapshot n=200 rep=2 base=ens_e4 done in 0.1s +[ 3.1s] ens_e4__lapshot n=200 rep=3 base=ens_e4 done in 0.1s +[ 3.2s] ens_e4__lapshot n=200 rep=4 base=ens_e4 done in 0.1s +[ 3.3s] ens_e5__lapshot n=5 rep=0 base=ens_e5 done in 0.1s +[ 3.4s] ens_e5__lapshot n=5 rep=1 base=ens_e5 done in 0.1s +[ 3.5s] ens_e5__lapshot n=5 rep=2 base=ens_e5 done in 0.1s +[ 3.6s] ens_e5__lapshot n=5 rep=3 base=ens_e5 done in 0.1s +[ 3.7s] ens_e5__lapshot n=5 rep=4 base=ens_e5 done in 0.1s +[ 3.8s] ens_e5__lapshot n=25 rep=0 base=ens_e5 done in 0.1s +[ 3.9s] ens_e5__lapshot n=25 rep=1 base=ens_e5 done in 0.1s +[ 4.0s] ens_e5__lapshot n=25 rep=2 base=ens_e5 done in 0.1s +[ 4.1s] ens_e5__lapshot n=25 rep=3 base=ens_e5 done in 0.1s +[ 4.2s] ens_e5__lapshot n=25 rep=4 base=ens_e5 done in 0.1s +[ 4.2s] ens_e5__lapshot n=50 rep=0 base=ens_e5 done in 0.1s +[ 4.3s] ens_e5__lapshot n=50 rep=1 base=ens_e5 done in 0.1s +[ 4.4s] ens_e5__lapshot n=50 rep=2 base=ens_e5 done in 0.1s +[ 4.5s] ens_e5__lapshot n=50 rep=3 base=ens_e5 done in 0.1s +[ 4.6s] ens_e5__lapshot n=50 rep=4 base=ens_e5 done in 0.1s +[ 4.7s] ens_e5__lapshot n=100 rep=0 base=ens_e5 done in 0.1s +[ 4.8s] ens_e5__lapshot n=100 rep=1 base=ens_e5 done in 0.1s +[ 4.9s] ens_e5__lapshot n=100 rep=2 base=ens_e5 done in 0.1s +[ 4.9s] ens_e5__lapshot n=100 rep=3 base=ens_e5 done in 0.1s +[ 5.0s] ens_e5__lapshot n=100 rep=4 base=ens_e5 done in 0.1s +[ 5.1s] ens_e5__lapshot n=200 rep=0 base=ens_e5 done in 0.1s +[ 5.2s] ens_e5__lapshot n=200 rep=1 base=ens_e5 done in 0.1s +[ 5.3s] ens_e5__lapshot n=200 rep=2 base=ens_e5 done in 0.1s +[ 5.4s] ens_e5__lapshot n=200 rep=3 base=ens_e5 done in 0.1s +[ 5.5s] ens_e5__lapshot n=200 rep=4 base=ens_e5 done in 0.1s +[ 5.6s] ens_e6__lapshot n=5 rep=0 base=ens_e6 done in 0.1s +[ 5.7s] ens_e6__lapshot n=5 rep=1 base=ens_e6 done in 0.1s +[ 5.8s] ens_e6__lapshot n=5 rep=2 base=ens_e6 done in 0.1s +[ 5.9s] ens_e6__lapshot n=5 rep=3 base=ens_e6 done in 0.1s +[ 5.9s] ens_e6__lapshot n=5 rep=4 base=ens_e6 done in 0.1s +[ 6.0s] ens_e6__lapshot n=25 rep=0 base=ens_e6 done in 0.1s +[ 6.1s] ens_e6__lapshot n=25 rep=1 base=ens_e6 done in 0.1s +[ 6.2s] ens_e6__lapshot n=25 rep=2 base=ens_e6 done in 0.1s +[ 6.3s] ens_e6__lapshot n=25 rep=3 base=ens_e6 done in 0.1s +[ 6.4s] ens_e6__lapshot n=25 rep=4 base=ens_e6 done in 0.1s +[ 6.5s] ens_e6__lapshot n=50 rep=0 base=ens_e6 done in 0.1s +[ 6.6s] ens_e6__lapshot n=50 rep=1 base=ens_e6 done in 0.1s +[ 6.7s] ens_e6__lapshot n=50 rep=2 base=ens_e6 done in 0.1s +[ 6.8s] ens_e6__lapshot n=50 rep=3 base=ens_e6 done in 0.1s +[ 6.9s] ens_e6__lapshot n=50 rep=4 base=ens_e6 done in 0.1s +[ 7.0s] ens_e6__lapshot n=100 rep=0 base=ens_e6 done in 0.1s +[ 7.1s] ens_e6__lapshot n=100 rep=1 base=ens_e6 done in 0.1s +[ 7.2s] ens_e6__lapshot n=100 rep=2 base=ens_e6 done in 0.1s +[ 7.3s] ens_e6__lapshot n=100 rep=3 base=ens_e6 done in 0.1s +[ 7.4s] ens_e6__lapshot n=100 rep=4 base=ens_e6 done in 0.1s +[ 7.5s] ens_e6__lapshot n=200 rep=0 base=ens_e6 done in 0.1s +[ 7.7s] ens_e6__lapshot n=200 rep=1 base=ens_e6 done in 0.1s +[ 7.8s] ens_e6__lapshot n=200 rep=2 base=ens_e6 done in 0.1s +[ 7.9s] ens_e6__lapshot n=200 rep=3 base=ens_e6 done in 0.1s +[ 8.0s] ens_e6__lapshot n=200 rep=4 base=ens_e6 done in 0.1s +[ 8.0s] done +[ 1.0s] loaded: Madrid (76263, 393), Amsterdam (25992, 393) +[ 1.0s] graph: 583310 edges, mean degree 22.4, min 10 +[ 1.2s] ens_e7__lp n=5 rep=0 base=ens_e7 done in 0.2s +[ 1.4s] ens_e7__lp n=5 rep=1 base=ens_e7 done in 0.2s +[ 1.6s] ens_e7__lp n=5 rep=2 base=ens_e7 done in 0.1s +[ 1.7s] ens_e7__lp n=5 rep=3 base=ens_e7 done in 0.1s +[ 1.9s] ens_e7__lp n=5 rep=4 base=ens_e7 done in 0.2s +[ 2.1s] ens_e7__lp n=25 rep=0 base=ens_e7 done in 0.2s +[ 2.2s] ens_e7__lp n=25 rep=1 base=ens_e7 done in 0.1s +[ 2.4s] ens_e7__lp n=25 rep=2 base=ens_e7 done in 0.2s +[ 2.5s] ens_e7__lp n=25 rep=3 base=ens_e7 done in 0.2s +[ 2.7s] ens_e7__lp n=25 rep=4 base=ens_e7 done in 0.2s +[ 2.8s] ens_e7__lp n=50 rep=0 base=ens_e7 done in 0.2s +[ 3.0s] ens_e7__lp n=50 rep=1 base=ens_e7 done in 0.1s +[ 3.1s] ens_e7__lp n=50 rep=2 base=ens_e7 done in 0.2s +[ 3.3s] ens_e7__lp n=50 rep=3 base=ens_e7 done in 0.2s +[ 3.4s] ens_e7__lp n=50 rep=4 base=ens_e7 done in 0.2s +[ 3.6s] ens_e7__lp n=100 rep=0 base=ens_e7 done in 0.2s +[ 3.8s] ens_e7__lp n=100 rep=1 base=ens_e7 done in 0.2s +[ 3.9s] ens_e7__lp n=100 rep=2 base=ens_e7 done in 0.2s +[ 4.1s] ens_e7__lp n=100 rep=3 base=ens_e7 done in 0.1s +[ 4.2s] ens_e7__lp n=100 rep=4 base=ens_e7 done in 0.1s +[ 4.4s] ens_e7__lp n=200 rep=0 base=ens_e7 done in 0.2s +[ 4.5s] ens_e7__lp n=200 rep=1 base=ens_e7 done in 0.1s +[ 4.7s] ens_e7__lp n=200 rep=2 base=ens_e7 done in 0.1s +[ 4.8s] ens_e7__lp n=200 rep=3 base=ens_e7 done in 0.2s +[ 5.0s] ens_e7__lp n=200 rep=4 base=ens_e7 done in 0.1s +[ 5.0s] done diff --git a/reports/nnlab/logs/transductive_r12.log b/reports/nnlab/logs/transductive_r12.log new file mode 100644 index 0000000000000000000000000000000000000000..dd2f5979dc362f74f0e7b8e330255c3b888186d8 --- /dev/null +++ b/reports/nnlab/logs/transductive_r12.log @@ -0,0 +1,111 @@ +[ 0.5s] loaded: Madrid (76263, 393), Amsterdam (25992, 393) +[ 0.5s] graph: 583310 edges, mean degree 22.4, min 10 +[ 0.6s] ens_e4__lp n=5 rep=5 base=ens_e4 done in 0.1s +[ 0.7s] ens_e4__lp n=5 rep=6 base=ens_e4 done in 0.1s +[ 0.7s] ens_e4__lp n=5 rep=7 base=ens_e4 done in 0.1s +[ 0.8s] ens_e4__lp n=5 rep=8 base=ens_e4 done in 0.1s +[ 0.9s] ens_e4__lp n=5 rep=9 base=ens_e4 done in 0.1s +[ 1.0s] ens_e4__lp n=5 rep=10 base=ens_e4 done in 0.1s +[ 1.1s] ens_e4__lp n=5 rep=11 base=ens_e4 done in 0.1s +[ 1.1s] ens_e4__lp n=25 rep=5 base=ens_e4 done in 0.1s +[ 1.2s] ens_e4__lp n=25 rep=6 base=ens_e4 done in 0.1s +[ 1.3s] ens_e4__lp n=25 rep=7 base=ens_e4 done in 0.1s +[ 1.4s] ens_e4__lp n=25 rep=8 base=ens_e4 done in 0.1s +[ 1.4s] ens_e4__lp n=25 rep=9 base=ens_e4 done in 0.1s +[ 1.5s] ens_e4__lp n=25 rep=10 base=ens_e4 done in 0.1s +[ 1.6s] ens_e4__lp n=25 rep=11 base=ens_e4 done in 0.1s +[ 1.7s] ens_e4__lp n=50 rep=5 base=ens_e4 done in 0.1s +[ 1.7s] ens_e4__lp n=50 rep=6 base=ens_e4 done in 0.1s +[ 1.8s] ens_e4__lp n=50 rep=7 base=ens_e4 done in 0.1s +[ 1.9s] ens_e4__lp n=50 rep=8 base=ens_e4 done in 0.1s +[ 2.0s] ens_e4__lp n=50 rep=9 base=ens_e4 done in 0.1s +[ 2.1s] ens_e4__lp n=50 rep=10 base=ens_e4 done in 0.1s +[ 2.2s] ens_e4__lp n=50 rep=11 base=ens_e4 done in 0.1s +[ 2.2s] ens_e4__lp n=100 rep=5 base=ens_e4 done in 0.1s +[ 2.3s] ens_e4__lp n=100 rep=6 base=ens_e4 done in 0.1s +[ 2.4s] ens_e4__lp n=100 rep=7 base=ens_e4 done in 0.1s +[ 2.5s] ens_e4__lp n=100 rep=8 base=ens_e4 done in 0.1s +[ 2.5s] ens_e4__lp n=100 rep=9 base=ens_e4 done in 0.1s +[ 2.6s] ens_e4__lp n=100 rep=10 base=ens_e4 done in 0.1s +[ 2.7s] ens_e4__lp n=100 rep=11 base=ens_e4 done in 0.1s +[ 2.8s] ens_e4__lp n=200 rep=5 base=ens_e4 done in 0.1s +[ 2.9s] ens_e4__lp n=200 rep=6 base=ens_e4 done in 0.1s +[ 3.0s] ens_e4__lp n=200 rep=7 base=ens_e4 done in 0.1s +[ 3.0s] ens_e4__lp n=200 rep=8 base=ens_e4 done in 0.1s +[ 3.1s] ens_e4__lp n=200 rep=9 base=ens_e4 done in 0.1s +[ 3.2s] ens_e4__lp n=200 rep=10 base=ens_e4 done in 0.1s +[ 3.3s] ens_e4__lp n=200 rep=11 base=ens_e4 done in 0.1s +[ 3.4s] lgbm_upw__lp n=5 rep=5 base=lgbm_upw done in 0.1s +[ 3.5s] lgbm_upw__lp n=5 rep=6 base=lgbm_upw done in 0.1s +[ 3.5s] lgbm_upw__lp n=5 rep=7 base=lgbm_upw done in 0.1s +[ 3.6s] lgbm_upw__lp n=5 rep=8 base=lgbm_upw done in 0.1s +[ 3.7s] lgbm_upw__lp n=5 rep=9 base=lgbm_upw done in 0.1s +[ 3.8s] lgbm_upw__lp n=5 rep=10 base=lgbm_upw done in 0.1s +[ 3.9s] lgbm_upw__lp n=5 rep=11 base=lgbm_upw done in 0.1s +[ 3.9s] lgbm_upw__lp n=25 rep=5 base=lgbm_upw done in 0.1s +[ 4.0s] lgbm_upw__lp n=25 rep=6 base=lgbm_upw done in 0.1s +[ 4.1s] lgbm_upw__lp n=25 rep=7 base=lgbm_upw done in 0.1s +[ 4.2s] lgbm_upw__lp n=25 rep=8 base=lgbm_upw done in 0.1s +[ 4.2s] lgbm_upw__lp n=25 rep=9 base=lgbm_upw done in 0.1s +[ 4.3s] lgbm_upw__lp n=25 rep=10 base=lgbm_upw done in 0.1s +[ 4.4s] lgbm_upw__lp n=25 rep=11 base=lgbm_upw done in 0.1s +[ 4.5s] lgbm_upw__lp n=50 rep=5 base=lgbm_upw done in 0.1s +[ 4.5s] lgbm_upw__lp n=50 rep=6 base=lgbm_upw done in 0.1s +[ 4.6s] lgbm_upw__lp n=50 rep=7 base=lgbm_upw done in 0.1s +[ 4.7s] lgbm_upw__lp n=50 rep=8 base=lgbm_upw done in 0.1s +[ 4.8s] lgbm_upw__lp n=50 rep=9 base=lgbm_upw done in 0.1s +[ 4.9s] lgbm_upw__lp n=50 rep=10 base=lgbm_upw done in 0.1s +[ 4.9s] lgbm_upw__lp n=50 rep=11 base=lgbm_upw done in 0.1s +[ 5.0s] lgbm_upw__lp n=100 rep=5 base=lgbm_upw done in 0.1s +[ 5.1s] lgbm_upw__lp n=100 rep=6 base=lgbm_upw done in 0.1s +[ 5.2s] lgbm_upw__lp n=100 rep=7 base=lgbm_upw done in 0.1s +[ 5.2s] lgbm_upw__lp n=100 rep=8 base=lgbm_upw done in 0.1s +[ 5.3s] lgbm_upw__lp n=100 rep=9 base=lgbm_upw done in 0.1s +[ 5.4s] lgbm_upw__lp n=100 rep=10 base=lgbm_upw done in 0.1s +[ 5.5s] lgbm_upw__lp n=100 rep=11 base=lgbm_upw done in 0.1s +[ 5.5s] lgbm_upw__lp n=200 rep=5 base=lgbm_upw done in 0.1s +[ 5.6s] lgbm_upw__lp n=200 rep=6 base=lgbm_upw done in 0.1s +[ 5.7s] lgbm_upw__lp n=200 rep=7 base=lgbm_upw done in 0.1s +[ 5.8s] lgbm_upw__lp n=200 rep=8 base=lgbm_upw done in 0.1s +[ 5.8s] lgbm_upw__lp n=200 rep=9 base=lgbm_upw done in 0.1s +[ 5.9s] lgbm_upw__lp n=200 rep=10 base=lgbm_upw done in 0.1s +[ 6.0s] lgbm_upw__lp n=200 rep=11 base=lgbm_upw done in 0.1s +[ 6.0s] done +[ 0.5s] loaded: Madrid (76263, 393), Amsterdam (25992, 393) +[ 0.5s] graph: 583310 edges, mean degree 22.4, min 10 +[ 0.6s] ens_e2__lapshot n=5 rep=5 base=ens_e2 done in 0.1s +[ 0.7s] ens_e2__lapshot n=5 rep=6 base=ens_e2 done in 0.1s +[ 0.8s] ens_e2__lapshot n=5 rep=7 base=ens_e2 done in 0.1s +[ 0.8s] ens_e2__lapshot n=5 rep=8 base=ens_e2 done in 0.1s +[ 0.9s] ens_e2__lapshot n=5 rep=9 base=ens_e2 done in 0.1s +[ 1.0s] ens_e2__lapshot n=5 rep=10 base=ens_e2 done in 0.1s +[ 1.1s] ens_e2__lapshot n=5 rep=11 base=ens_e2 done in 0.1s +[ 1.1s] ens_e2__lapshot n=25 rep=5 base=ens_e2 done in 0.1s +[ 1.2s] ens_e2__lapshot n=25 rep=6 base=ens_e2 done in 0.1s +[ 1.3s] ens_e2__lapshot n=25 rep=7 base=ens_e2 done in 0.1s +[ 1.4s] ens_e2__lapshot n=25 rep=8 base=ens_e2 done in 0.1s +[ 1.5s] ens_e2__lapshot n=25 rep=9 base=ens_e2 done in 0.1s +[ 1.5s] ens_e2__lapshot n=25 rep=10 base=ens_e2 done in 0.1s +[ 1.6s] ens_e2__lapshot n=25 rep=11 base=ens_e2 done in 0.1s +[ 1.7s] ens_e2__lapshot n=50 rep=5 base=ens_e2 done in 0.1s +[ 1.8s] ens_e2__lapshot n=50 rep=6 base=ens_e2 done in 0.1s +[ 1.8s] ens_e2__lapshot n=50 rep=7 base=ens_e2 done in 0.1s +[ 1.9s] ens_e2__lapshot n=50 rep=8 base=ens_e2 done in 0.1s +[ 2.0s] ens_e2__lapshot n=50 rep=9 base=ens_e2 done in 0.1s +[ 2.1s] ens_e2__lapshot n=50 rep=10 base=ens_e2 done in 0.1s +[ 2.2s] ens_e2__lapshot n=50 rep=11 base=ens_e2 done in 0.1s +[ 2.2s] ens_e2__lapshot n=100 rep=5 base=ens_e2 done in 0.1s +[ 2.3s] ens_e2__lapshot n=100 rep=6 base=ens_e2 done in 0.1s +[ 2.4s] ens_e2__lapshot n=100 rep=7 base=ens_e2 done in 0.1s +[ 2.5s] ens_e2__lapshot n=100 rep=8 base=ens_e2 done in 0.1s +[ 2.5s] ens_e2__lapshot n=100 rep=9 base=ens_e2 done in 0.1s +[ 2.6s] ens_e2__lapshot n=100 rep=10 base=ens_e2 done in 0.1s +[ 2.7s] ens_e2__lapshot n=100 rep=11 base=ens_e2 done in 0.1s +[ 2.7s] ens_e2__lapshot n=200 rep=5 base=ens_e2 done in 0.1s +[ 2.8s] ens_e2__lapshot n=200 rep=6 base=ens_e2 done in 0.1s +[ 2.9s] ens_e2__lapshot n=200 rep=7 base=ens_e2 done in 0.1s +[ 3.0s] ens_e2__lapshot n=200 rep=8 base=ens_e2 done in 0.1s +[ 3.0s] ens_e2__lapshot n=200 rep=9 base=ens_e2 done in 0.1s +[ 3.1s] ens_e2__lapshot n=200 rep=10 base=ens_e2 done in 0.1s +[ 3.2s] ens_e2__lapshot n=200 rep=11 base=ens_e2 done in 0.1s +[ 3.2s] done diff --git a/reports/nnlab/logs/ts_nn.log b/reports/nnlab/logs/ts_nn.log new file mode 100644 index 0000000000000000000000000000000000000000..3573e87e6903c2328d9d53a8ff565d9d337c023f --- /dev/null +++ b/reports/nnlab/logs/ts_nn.log @@ -0,0 +1,98 @@ +[ 0.0s] methods: ['ts_tcnn_st_zs', 'ts_tcnn_st_ft', 'ts_tcnn_st_joint', 'ts_tcnn_ry_zs', 'ts_tcnn_ry_ft', 'ts_tcnn_ry_joint', 'ts_tcnn_dann_st_zs', 'ts_tcnn_dann_st_ft', 'ts_tcnn_dann_st_joint'] rss=0.09G peak=0.09G +[ 9.0s] inputs st: Madrid (76263, 10, 42) Amsterdam (25992, 10, 42) rss=0.83G peak=1.15G +[ 234.5s] tcnn seed0 epoch 5/15 40.7s/full-epoch eval=0.5260 rss=1.33G peak=1.33G +[ 416.0s] tcnn seed0 epoch 10/15 29.2s/full-epoch eval=0.5385 rss=1.33G peak=1.38G +[ 561.8s] tcnn seed0 epoch 15/15 28.7s/full-epoch eval=0.5475 rss=1.34G peak=1.38G +[ 570.7s] select tcnn_st: epochs=15 {'5': 0.526023912531838, '10': 0.5385065163765071, '15': 0.5474806666222004} lam=100.0 {0.1: 0.4793, 1.0: 0.5091, 10.0: 0.5455, 100.0: 0.555} rss=1.17G peak=1.38G +[ 578.5s] backbone tcnn_st seed0: 35.3s/full-epoch, 569s rss=1.22G peak=1.38G +[ 578.5s] ts_tcnn_st_zs saved rss=1.04G peak=1.38G +[ 580.9s] tcnn_st n=5 rep=0 heads=['ft', 'joint'] 2.3s rss=1.08G peak=1.38G +[ 582.7s] tcnn_st n=5 rep=1 heads=['ft', 'joint'] 1.8s rss=1.08G peak=1.38G +[ 584.6s] tcnn_st n=5 rep=2 heads=['ft', 'joint'] 1.9s rss=1.07G peak=1.38G +[ 586.4s] tcnn_st n=5 rep=3 heads=['ft', 'joint'] 1.8s rss=1.07G peak=1.38G +[ 588.0s] tcnn_st n=5 rep=4 heads=['ft', 'joint'] 1.6s rss=1.07G peak=1.38G +[ 589.5s] tcnn_st n=25 rep=0 heads=['ft', 'joint'] 1.5s rss=1.07G peak=1.38G +[ 591.1s] tcnn_st n=25 rep=1 heads=['ft', 'joint'] 1.5s rss=1.07G peak=1.38G +[ 592.9s] tcnn_st n=25 rep=2 heads=['ft', 'joint'] 1.8s rss=1.07G peak=1.38G +[ 594.4s] tcnn_st n=25 rep=3 heads=['ft', 'joint'] 1.6s rss=1.07G peak=1.38G +[ 596.2s] tcnn_st n=25 rep=4 heads=['ft', 'joint'] 1.8s rss=1.07G peak=1.38G +[ 597.8s] tcnn_st n=50 rep=0 heads=['ft', 'joint'] 1.5s rss=1.07G peak=1.38G +[ 599.4s] tcnn_st n=50 rep=1 heads=['ft', 'joint'] 1.6s rss=1.07G peak=1.38G +[ 601.1s] tcnn_st n=50 rep=2 heads=['ft', 'joint'] 1.7s rss=1.07G peak=1.38G +[ 602.7s] tcnn_st n=50 rep=3 heads=['ft', 'joint'] 1.5s rss=1.07G peak=1.38G +[ 603.9s] tcnn_st n=50 rep=4 heads=['ft', 'joint'] 1.2s rss=1.07G peak=1.38G +[ 605.5s] tcnn_st n=100 rep=0 heads=['ft', 'joint'] 1.6s rss=1.07G peak=1.38G +[ 607.0s] tcnn_st n=100 rep=1 heads=['ft', 'joint'] 1.5s rss=1.07G peak=1.38G +[ 608.3s] tcnn_st n=100 rep=2 heads=['ft', 'joint'] 1.3s rss=1.07G peak=1.38G +[ 609.9s] tcnn_st n=100 rep=3 heads=['ft', 'joint'] 1.6s rss=1.07G peak=1.38G +[ 611.5s] tcnn_st n=100 rep=4 heads=['ft', 'joint'] 1.5s rss=1.07G peak=1.38G +[ 612.9s] tcnn_st n=200 rep=0 heads=['ft', 'joint'] 1.4s rss=1.08G peak=1.38G +[ 614.4s] tcnn_st n=200 rep=1 heads=['ft', 'joint'] 1.5s rss=1.08G peak=1.38G +[ 615.9s] tcnn_st n=200 rep=2 heads=['ft', 'joint'] 1.5s rss=1.08G peak=1.38G +[ 617.5s] tcnn_st n=200 rep=3 heads=['ft', 'joint'] 1.6s rss=1.08G peak=1.38G +[ 619.0s] tcnn_st n=200 rep=4 heads=['ft', 'joint'] 1.5s rss=1.08G peak=1.38G +[ 626.0s] inputs ry: Madrid (76263, 10, 42) Amsterdam (25992, 10, 42) rss=1.24G peak=1.55G +[ 784.7s] tcnn seed0 epoch 5/15 32.9s/full-epoch eval=0.5340 rss=1.42G peak=1.55G +[ 953.3s] tcnn seed0 epoch 10/15 33.9s/full-epoch eval=0.5362 rss=1.42G peak=1.55G +[ 1131.4s] tcnn seed0 epoch 15/15 36.1s/full-epoch eval=0.5545 rss=1.22G peak=1.55G +[ 1140.4s] select tcnn_ry: epochs=15 {'5': 0.5340027718176347, '10': 0.5362399360770098, '15': 0.5545017383586993} lam=100.0 {0.1: 0.4863, 1.0: 0.5196, 10.0: 0.5494, 100.0: 0.554} rss=1.03G peak=1.55G +[ 1148.4s] backbone tcnn_ry seed0: 33.4s/full-epoch, 522s rss=1.06G peak=1.55G +[ 1148.5s] ts_tcnn_ry_zs saved rss=0.89G peak=1.55G +[ 1150.7s] tcnn_ry n=5 rep=0 heads=['ft', 'joint'] 2.1s rss=0.87G peak=1.55G +[ 1152.5s] tcnn_ry n=5 rep=1 heads=['ft', 'joint'] 1.8s rss=0.87G peak=1.55G +[ 1154.6s] tcnn_ry n=5 rep=2 heads=['ft', 'joint'] 2.1s rss=0.87G peak=1.55G +[ 1156.6s] tcnn_ry n=5 rep=3 heads=['ft', 'joint'] 2.0s rss=0.87G peak=1.55G +[ 1158.4s] tcnn_ry n=5 rep=4 heads=['ft', 'joint'] 1.8s rss=0.87G peak=1.55G +[ 1160.2s] tcnn_ry n=25 rep=0 heads=['ft', 'joint'] 1.8s rss=0.86G peak=1.55G +[ 1162.1s] tcnn_ry n=25 rep=1 heads=['ft', 'joint'] 1.9s rss=0.86G peak=1.55G +[ 1164.5s] tcnn_ry n=25 rep=2 heads=['ft', 'joint'] 2.4s rss=0.86G peak=1.55G +[ 1166.5s] tcnn_ry n=25 rep=3 heads=['ft', 'joint'] 2.0s rss=0.86G peak=1.55G +[ 1168.2s] tcnn_ry n=25 rep=4 heads=['ft', 'joint'] 1.7s rss=0.86G peak=1.55G +[ 1169.8s] tcnn_ry n=50 rep=0 heads=['ft', 'joint'] 1.7s rss=0.86G peak=1.55G +[ 1171.9s] tcnn_ry n=50 rep=1 heads=['ft', 'joint'] 2.0s rss=0.86G peak=1.55G +[ 1174.0s] tcnn_ry n=50 rep=2 heads=['ft', 'joint'] 2.2s rss=0.86G peak=1.55G +[ 1175.8s] tcnn_ry n=50 rep=3 heads=['ft', 'joint'] 1.7s rss=0.86G peak=1.55G +[ 1177.4s] tcnn_ry n=50 rep=4 heads=['ft', 'joint'] 1.7s rss=0.86G peak=1.55G +[ 1179.1s] tcnn_ry n=100 rep=0 heads=['ft', 'joint'] 1.7s rss=0.86G peak=1.55G +[ 1180.9s] tcnn_ry n=100 rep=1 heads=['ft', 'joint'] 1.8s rss=0.86G peak=1.55G +[ 1182.6s] tcnn_ry n=100 rep=2 heads=['ft', 'joint'] 1.6s rss=0.86G peak=1.55G +[ 1184.3s] tcnn_ry n=100 rep=3 heads=['ft', 'joint'] 1.7s rss=0.86G peak=1.55G +[ 1186.0s] tcnn_ry n=100 rep=4 heads=['ft', 'joint'] 1.7s rss=0.86G peak=1.55G +[ 1187.7s] tcnn_ry n=200 rep=0 heads=['ft', 'joint'] 1.7s rss=0.86G peak=1.55G +[ 1189.3s] tcnn_ry n=200 rep=1 heads=['ft', 'joint'] 1.6s rss=0.86G peak=1.55G +[ 1191.2s] tcnn_ry n=200 rep=2 heads=['ft', 'joint'] 1.9s rss=0.86G peak=1.55G +[ 1192.7s] tcnn_ry n=200 rep=3 heads=['ft', 'joint'] 1.6s rss=0.86G peak=1.55G +[ 1194.2s] tcnn_ry n=200 rep=4 heads=['ft', 'joint'] 1.5s rss=0.86G peak=1.55G +[ 1199.5s] inputs st: Madrid (76263, 10, 42) Amsterdam (25992, 10, 42) rss=1.02G peak=1.55G +[ 1530.9s] tcnn_dann seed0 epoch 5/15 66.4s/full-epoch eval=0.5044 rss=1.20G peak=1.55G +[ 1875.5s] tcnn_dann seed0 epoch 10/15 68.2s/full-epoch eval=0.5094 rss=1.20G peak=1.55G +[ 2217.2s] tcnn_dann seed0 epoch 15/15 68.0s/full-epoch eval=0.5140 rss=1.22G peak=1.55G +[ 2251.4s] select tcnn_dann_st: epochs=15 {'5': 0.5044430346339247, '10': 0.5093685357526514, '15': 0.514016324587161} lam=100.0 {0.1: 0.4313, 1.0: 0.4417, 10.0: 0.4598, 100.0: 0.4801} rss=1.02G peak=1.55G +[ 2260.2s] backbone tcnn_dann_st seed0: 67.6s/full-epoch, 1061s rss=1.06G peak=1.55G +[ 2260.2s] ts_tcnn_dann_st_zs saved rss=0.89G peak=1.55G +[ 2267.7s] tcnn_dann_st n=5 rep=0 heads=['ft', 'joint'] 7.4s rss=0.85G peak=1.55G +[ 2275.3s] tcnn_dann_st n=5 rep=1 heads=['ft', 'joint'] 7.6s rss=0.85G peak=1.55G +[ 2282.6s] tcnn_dann_st n=5 rep=2 heads=['ft', 'joint'] 7.3s rss=0.85G peak=1.55G +[ 2288.9s] tcnn_dann_st n=5 rep=3 heads=['ft', 'joint'] 6.4s rss=0.85G peak=1.55G +[ 2296.1s] tcnn_dann_st n=5 rep=4 heads=['ft', 'joint'] 7.2s rss=0.85G peak=1.55G +[ 2303.5s] tcnn_dann_st n=25 rep=0 heads=['ft', 'joint'] 7.4s rss=0.85G peak=1.55G +[ 2311.6s] tcnn_dann_st n=25 rep=1 heads=['ft', 'joint'] 8.1s rss=0.85G peak=1.55G +[ 2319.1s] tcnn_dann_st n=25 rep=2 heads=['ft', 'joint'] 7.5s rss=0.85G peak=1.55G +[ 2326.5s] tcnn_dann_st n=25 rep=3 heads=['ft', 'joint'] 7.4s rss=0.85G peak=1.55G +[ 2334.5s] tcnn_dann_st n=25 rep=4 heads=['ft', 'joint'] 8.0s rss=0.85G peak=1.55G +[ 2342.9s] tcnn_dann_st n=50 rep=0 heads=['ft', 'joint'] 8.3s rss=0.85G peak=1.55G +[ 2351.2s] tcnn_dann_st n=50 rep=1 heads=['ft', 'joint'] 8.4s rss=0.85G peak=1.55G +[ 2357.9s] tcnn_dann_st n=50 rep=2 heads=['ft', 'joint'] 6.7s rss=0.85G peak=1.55G +[ 2366.2s] tcnn_dann_st n=50 rep=3 heads=['ft', 'joint'] 8.3s rss=0.85G peak=1.55G +[ 2374.6s] tcnn_dann_st n=50 rep=4 heads=['ft', 'joint'] 8.4s rss=0.85G peak=1.55G +[ 2383.5s] tcnn_dann_st n=100 rep=0 heads=['ft', 'joint'] 8.9s rss=0.85G peak=1.55G +[ 2392.6s] tcnn_dann_st n=100 rep=1 heads=['ft', 'joint'] 9.0s rss=0.85G peak=1.55G +[ 2401.1s] tcnn_dann_st n=100 rep=2 heads=['ft', 'joint'] 8.5s rss=0.85G peak=1.55G +[ 2409.9s] tcnn_dann_st n=100 rep=3 heads=['ft', 'joint'] 8.8s rss=0.85G peak=1.55G +[ 2418.1s] tcnn_dann_st n=100 rep=4 heads=['ft', 'joint'] 8.1s rss=0.85G peak=1.55G +[ 2426.3s] tcnn_dann_st n=200 rep=0 heads=['ft', 'joint'] 8.3s rss=0.85G peak=1.55G +[ 2436.8s] tcnn_dann_st n=200 rep=1 heads=['ft', 'joint'] 10.5s rss=0.85G peak=1.55G +[ 2446.7s] tcnn_dann_st n=200 rep=2 heads=['ft', 'joint'] 9.9s rss=0.85G peak=1.55G +[ 2455.6s] tcnn_dann_st n=200 rep=3 heads=['ft', 'joint'] 8.9s rss=0.85G peak=1.55G +[ 2464.3s] tcnn_dann_st n=200 rep=4 heads=['ft', 'joint'] 8.6s rss=0.85G peak=1.55G +[ 2464.3s] done rss=0.85G peak=1.55G diff --git a/reports/nnlab/logs/ts_nn_smoke.log b/reports/nnlab/logs/ts_nn_smoke.log new file mode 100644 index 0000000000000000000000000000000000000000..6092fc0592507cb04259aadbb06a78dc1c4b3cd6 --- /dev/null +++ b/reports/nnlab/logs/ts_nn_smoke.log @@ -0,0 +1,28 @@ +[ 0.0s] methods: ['ts_tcnn_st_zs_SMOKE', 'ts_tcnn_st_ft_SMOKE', 'ts_tcnn_st_joint_SMOKE', 'ts_tcnn_ry_zs_SMOKE', 'ts_tcnn_ry_ft_SMOKE', 'ts_tcnn_ry_joint_SMOKE', 'ts_tcnn_dann_st_zs_SMOKE', 'ts_tcnn_dann_st_ft_SMOKE', 'ts_tcnn_dann_st_joint_SMOKE'] rss=0.09G peak=0.09G +[ 17.9s] gru seed0 epoch 1/1 3.4s/full-epoch rss=0.95G peak=1.02G +[ 18.2s] gru check ok: ~204s per Madrid-source epoch (extrapolated) rss=0.97G peak=1.02G +[ 25.1s] inputs st: Madrid (76263, 10, 42) Amsterdam (25992, 10, 42) rss=1.12G peak=1.44G +[ 34.9s] tcnn seed0 epoch 1/2 47.8s/full-epoch eval=0.4092 rss=1.33G peak=1.44G +[ 39.9s] tcnn seed0 epoch 2/2 48.0s/full-epoch eval=0.4755 rss=1.34G peak=1.44G +[ 45.0s] select tcnn_st_SMOKE: epochs=2 {'1': 0.40919231367742104, '2': 0.4754845381892462} lam=1.0 {0.1: 0.5026, 1.0: 0.5153, 10.0: 0.509, 100.0: 0.4977} rss=1.17G peak=1.44G +[ 59.0s] backbone tcnn_st_SMOKE seed0: 47.9s/full-epoch, 34s rss=1.20G peak=1.44G +[ 59.1s] ts_tcnn_st_zs_SMOKE saved rss=1.04G peak=1.44G +[ 67.0s] tcnn_st n=5 rep=0 heads=['ft', 'joint'] 7.9s rss=1.08G peak=1.44G +[ 76.8s] tcnn_st n=200 rep=0 heads=['ft', 'joint'] 9.8s rss=1.07G peak=1.44G +[ 85.5s] inputs ry: Madrid (76263, 10, 42) Amsterdam (25992, 10, 42) rss=1.23G peak=1.55G +[ 90.8s] tcnn seed0 epoch 1/2 51.9s/full-epoch eval=0.3879 rss=1.41G peak=1.55G +[ 96.2s] tcnn seed0 epoch 2/2 46.3s/full-epoch eval=0.4781 rss=1.41G peak=1.55G +[ 102.1s] select tcnn_ry_SMOKE: epochs=2 {'1': 0.38785458289169794, '2': 0.4780757221716626} lam=1.0 {0.1: 0.4986, 1.0: 0.518, 10.0: 0.5106, 100.0: 0.5018} rss=1.24G peak=1.55G +[ 116.2s] backbone tcnn_ry_SMOKE seed0: 49.1s/full-epoch, 31s rss=1.28G peak=1.55G +[ 116.2s] ts_tcnn_ry_zs_SMOKE saved rss=1.10G peak=1.55G +[ 123.3s] tcnn_ry n=5 rep=0 heads=['ft', 'joint'] 7.0s rss=1.08G peak=1.55G +[ 131.2s] tcnn_ry n=200 rep=0 heads=['ft', 'joint'] 7.9s rss=1.07G peak=1.55G +[ 138.4s] inputs st: Madrid (76263, 10, 42) Amsterdam (25992, 10, 42) rss=1.23G peak=1.55G +[ 147.2s] tcnn_dann seed0 epoch 1/2 105.1s/full-epoch eval=0.4122 rss=1.41G peak=1.55G +[ 156.8s] tcnn_dann seed0 epoch 2/2 108.4s/full-epoch eval=0.4255 rss=1.17G peak=1.55G +[ 166.1s] select tcnn_dann_st_SMOKE: epochs=2 {'1': 0.41219350565994745, '2': 0.42554601012067195} lam=100.0 {0.1: 0.4255, 1.0: 0.4625, 10.0: 0.4735, 100.0: 0.4767} rss=1.06G peak=1.55G +[ 179.7s] backbone tcnn_dann_st_SMOKE seed0: 106.7s/full-epoch, 41s rss=1.12G peak=1.55G +[ 180.1s] ts_tcnn_dann_st_zs_SMOKE saved rss=0.94G peak=1.55G +[ 195.0s] tcnn_dann_st n=5 rep=0 heads=['ft', 'joint'] 14.9s rss=0.91G peak=1.55G +[ 208.8s] tcnn_dann_st n=200 rep=0 heads=['ft', 'joint'] 13.8s rss=0.91G peak=1.55G +[ 208.8s] done rss=0.91G peak=1.55G diff --git a/reports/nnlab/logs/ts_nn_smoke_review.log b/reports/nnlab/logs/ts_nn_smoke_review.log new file mode 100644 index 0000000000000000000000000000000000000000..4d18dfa86bb6eac2c638b5d814038ead15f3a011 --- /dev/null +++ b/reports/nnlab/logs/ts_nn_smoke_review.log @@ -0,0 +1,28 @@ +[ 0.0s] methods: ['ts_tcnn_st_zs_SMOKE', 'ts_tcnn_st_ft_SMOKE', 'ts_tcnn_st_joint_SMOKE', 'ts_tcnn_ry_zs_SMOKE', 'ts_tcnn_ry_ft_SMOKE', 'ts_tcnn_ry_joint_SMOKE', 'ts_tcnn_dann_st_zs_SMOKE', 'ts_tcnn_dann_st_ft_SMOKE', 'ts_tcnn_dann_st_joint_SMOKE'] rss=0.09G peak=0.09G +[ 18.9s] gru seed0 epoch 1/1 3.6s/full-epoch rss=0.96G peak=1.02G +[ 19.2s] gru check ok: ~216s per Madrid-source epoch (extrapolated) rss=0.97G peak=1.02G +[ 25.7s] inputs st: Madrid (76263, 10, 42) Amsterdam (25992, 10, 42) rss=1.12G peak=1.44G +[ 35.2s] tcnn seed0 epoch 1/2 50.3s/full-epoch eval=0.4092 rss=1.33G peak=1.44G +[ 40.5s] tcnn seed0 epoch 2/2 50.8s/full-epoch eval=0.4755 rss=1.34G peak=1.44G +[ 44.7s] select tcnn_st_SMOKE: epochs=2 {'1': 0.40919231367742104, '2': 0.4754845381892462} lam=1.0 {0.1: 0.5026, 1.0: 0.5153, 10.0: 0.509, 100.0: 0.4977} rss=1.17G peak=1.44G +[ 57.0s] backbone tcnn_st_SMOKE seed0: 50.5s/full-epoch, 31s rss=1.20G peak=1.44G +[ 57.1s] ts_tcnn_st_zs_SMOKE saved rss=1.04G peak=1.44G +[ 65.1s] tcnn_st n=5 rep=0 heads=['ft', 'joint'] 8.0s rss=1.08G peak=1.44G +[ 75.3s] tcnn_st n=200 rep=0 heads=['ft', 'joint'] 10.2s rss=1.07G peak=1.44G +[ 83.3s] inputs ry: Madrid (76263, 10, 42) Amsterdam (25992, 10, 42) rss=1.23G peak=1.55G +[ 88.1s] tcnn seed0 epoch 1/2 47.3s/full-epoch eval=0.3879 rss=1.41G peak=1.55G +[ 92.9s] tcnn seed0 epoch 2/2 44.7s/full-epoch eval=0.4781 rss=1.42G peak=1.55G +[ 97.6s] select tcnn_ry_SMOKE: epochs=2 {'1': 0.38785458289169794, '2': 0.4780757221716626} lam=1.0 {0.1: 0.4986, 1.0: 0.518, 10.0: 0.5106, 100.0: 0.5018} rss=1.24G peak=1.55G +[ 106.5s] backbone tcnn_ry_SMOKE seed0: 46.0s/full-epoch, 23s rss=1.29G peak=1.55G +[ 106.6s] ts_tcnn_ry_zs_SMOKE saved rss=1.10G peak=1.55G +[ 113.6s] tcnn_ry n=5 rep=0 heads=['ft', 'joint'] 7.0s rss=1.08G peak=1.55G +[ 121.3s] tcnn_ry n=200 rep=0 heads=['ft', 'joint'] 7.7s rss=1.07G peak=1.55G +[ 126.9s] inputs st: Madrid (76263, 10, 42) Amsterdam (25992, 10, 42) rss=1.23G peak=1.55G +[ 134.7s] tcnn_dann seed0 epoch 1/2 89.6s/full-epoch eval=0.4122 rss=1.41G peak=1.55G +[ 142.9s] tcnn_dann seed0 epoch 2/2 96.6s/full-epoch eval=0.4255 rss=1.41G peak=1.55G +[ 150.7s] select tcnn_dann_st_SMOKE: epochs=2 {'1': 0.41219350565994745, '2': 0.42554601012067195} lam=100.0 {0.1: 0.4255, 1.0: 0.4625, 10.0: 0.4735, 100.0: 0.4767} rss=1.24G peak=1.55G +[ 161.9s] backbone tcnn_dann_st_SMOKE seed0: 93.1s/full-epoch, 35s rss=1.29G peak=1.55G +[ 161.9s] ts_tcnn_dann_st_zs_SMOKE saved rss=1.11G peak=1.55G +[ 174.2s] tcnn_dann_st n=5 rep=0 heads=['ft', 'joint'] 12.2s rss=1.07G peak=1.55G +[ 187.6s] tcnn_dann_st n=200 rep=0 heads=['ft', 'joint'] 13.4s rss=1.08G peak=1.55G +[ 187.6s] done rss=1.08G peak=1.55G diff --git a/reports/nnlab/research_shortlist.json b/reports/nnlab/research_shortlist.json new file mode 100644 index 0000000000000000000000000000000000000000..be9169d88157af04485d25ef912cf73b8f569c1d --- /dev/null +++ b/reports/nnlab/research_shortlist.json @@ -0,0 +1,227 @@ +{ + "shortlist": [ + { + "rank": 1, + "name": "Per-city input normalisation (robust z-score or quantile transform fitted separately per city, with an optional prior-reweighted Madrid pass)", + "family": "input-transform", + "source": "Merged: per-city standardisation (idea 'Per-city input normalisation', TimeMatch Nyborg et al. 2022 arXiv 2111.02682 for the motivation); surgical fine-tuning init (Lee et al. ICLR 2023, arXiv 2210.11466); AdaBN / CL2N re-centring (SimpleShot, arXiv 1911.04623)", + "why": "It costs seconds, every other item here builds on it, and it goes straight at the input-level shift (the city detector reaches AUC 1.0). With 20 labels at n=5 the shots cannot absorb a city-wide offset themselves. Expected +0.01 to +0.04, mostly at n<=25.", + "recipe": "Default A: for each of the 393 features, x' = (x - median_city) / IQR_city, computed on ALL pixels of that city (76,263 Madrid, 25,992 Amsterdam unlabelled). Clip to [-5,5]. Variant B: sklearn QuantileTransformer(output_distribution='normal', n_quantiles=200), fitted separately per city. Label-shift fix (one pass, fixed in advance): after you have an EM prior estimate pi_A (item 2), recompute the Madrid median/IQR with each Madrid row weighted by pi_A[y]/pi_M[y] (weighted quantiles), so both cities' statistics refer to the same class mix, then refit downstream models once. Keep a raw-feature copy, because trees may still want the raw scale. Choosing A vs B vs raw: run the Madrid spatial-block pseudo-target harness (see notes) on the 3 options and keep the winner for every n.", + "rule_compliance": "Uses no target labels. It uses unlabelled Amsterdam features only, which is allowed. The choice between variants is made on the Madrid harness, not on Amsterdam labels.", + "est_cpu_minutes": 2, + "uses_shots": "No", + "uses_unlabelled_target": "Yes (per-city statistics)" + }, + { + "name": "EM (MLLS) prior re-estimation on unlabelled Amsterdam, with the correct source prior and a partial-exponent logit adjustment", + "rank": 2, + "family": "head", + "source": "Merged from 4 near-duplicates: Saerens et al. 2002; Lipton et al. ICML 2018 (arXiv 1802.03916); Garg et al. NeurIPS 2020 (arXiv 2003.07554: MLLS beats BBSE); Sipka et al. WACV 2022 (MAP/Dirichlet variant)", + "why": "The label shift is stated outright and macro F1 is sensitive to per-class thresholds. This is essentially free and applies to every model and every n. The balanced shots carry no prior information, so the prior has to come from the unlabelled pool. Expected +0.01 to +0.03, with the most room at n=5/25.", + "recipe": "1) Get the probabilities P (25,992 x 4) from any model. 2) Set pi_S to the model's EFFECTIVE training prior: for Madrid + weighted shots, pi_S[c] = (Madrid count_c*w_M + n*w_shot) / total weight; for a head fitted on balanced shots, pi_S = uniform; for a class-balanced loss, pi_S = uniform. 3) Calibrate with one scalar temperature T fitted on held-out Madrid folds (not on the shots at n=5). 4) EM: start pi = pi_S; repeat 200 times or until change < 1e-6: q \u221d P*pi/pi_S; pi = (sum_i q_i + alpha_dir) / (N + 4*alpha_dir), with Dirichlet floor alpha_dir = 0.01*N; clip pi/pi_S to [0.25, 4]. 5) Final logits = log P + gamma*log(pi/pi_S). Fix gamma ahead of time from {0, 0.5, 1} on the Madrid harness with the held-out block resampled to the EM-estimated Amsterdam prior. If you skip the harness, default gamma = 0.5 (the macro-F1 compromise). 6) Apply BEFORE graph smoothing (item 4). Sanity check: the predicted class histogram should move toward pi. If EM puts <5% on any class, fall back to gamma=0.", + "rule_compliance": "Allowed. The prior comes from unlabelled pixels; gamma is chosen on Madrid. Do NOT use idea 3's suggestion to tune gamma on 'Amsterdam shots simulated via n=200 splits' scored on other Amsterdam pixels (that uses Amsterdam labels beyond n). Also do not estimate the prior from the shots.", + "est_cpu_minutes": 1, + "uses_shots": "Only to define pi_S when shots are in the training set", + "uses_unlabelled_target": "Yes (EM prior)" + }, + { + "name": "n-dependent anchoring to the zero-shot model: shrink the shot-fitted head toward the Madrid/DANN head (tau = c/n) and schedule the LightGBM shot weight with n", + "rank": 3, + "family": "head", + "source": "Source-regularised head (L2-SP applied to the head: Li et al. ICML 2018, arXiv 1802.01483; DFR, Kirichenko et al. ICLR 2023, arXiv 2204.02937); prototype mixing alpha = n/(n+k) from SimpleShot-style ideas", + "why": "The current n=5 result (0.566) is BELOW zero-shot LightGBM (0.593) and far below zero-shot XGB+DANN+smoothing (0.639). Weighting 20 shots 30x overrides the source model, so a rule where shot influence grows with n should recover at least the zero-shot level at n=5 and keep the gains at large n. This is probably the biggest single gain at n=5 for the least work.", + "recipe": "(a) LightGBM/XGB: shot weight w(n) = r * N_madrid / (4n) * n/(n+k0), so the shots' total mass is a fraction r*n/(n+k0) of Madrid's mass. Defaults r=1, k0=25 (n=5 gives about 17% of r, n=200 gives about 89%). Warm-start from the zero-shot DANN+XGB model where possible (init_model / base_margin = zero-shot logits). (b) Convex head on frozen embeddings (item 5): minimise sum_shots CE + (tau/2)(||W-W_0||^2+||b-b_0||^2), L-BFGS, tau = 50/n. W_0 is the Madrid head, or (better under shift) the Madrid class prototypes after per-city centring. (c) Choose r, k0 and c on the Madrid harness over 20 simulated shot draws per n, from the grids r in {0.3,1,3}, k0 in {10,25,50}, c in {5,50,500}, then freeze them.", + "rule_compliance": "Allowed if c/r/k0 come from the Madrid harness. Idea 6's suggestion to tune tau 'using simulated n-shot draws from the labelled Amsterdam pool' BREAKS the rule and must not be used.", + "est_cpu_minutes": 20, + "uses_shots": "Yes", + "uses_unlabelled_target": "Indirectly (through the DANN warm start)" + }, + { + "name": "Graph label spreading on a mixed spatial-kNN + feature-kNN Amsterdam graph, with shots clamped as seeds (replaces plain spatial smoothing)", + "rank": 4, + "family": "transductive-refinement", + "source": "Zhou et al. NIPS 2003 (Learning with Local and Global Consistency); BD-CSPN/Prototype Rectification (Liu et al., arXiv 1911.10713); merged with the label-propagation parts of the self-training ideas", + "why": "Smoothing already gives +0.02. Adding the shots as seeds and a feature graph lets labels spread along both space and spectrum, which helps most at n=5-50. It runs in seconds with scipy.sparse, and its hyperparameters can be tuned on Madrid, which also has coordinates.", + "recipe": "Graph features: 30-d PCA of the per-city-standardised features (item 1), or the 128-d frozen embedding (item 5). W_feat: kNN with k=15, Gaussian weights with sigma = mean kNN distance. W_spat: kNN on coordinates, k=8, weight 1. W = a*W_feat + (1-a)*W_spat; symmetrise; S = D^-1/2 W D^-1/2. Y0 = P_model after item 2's prior correction, with each shot row replaced by its one-hot label times 5. Iterate F <- alpha*S F + (1-alpha)*Y0 30 times. Divide each column of F by the class mass sum_i F_ic, multiply by pi_EM, then argmax. Defaults a=0.3, alpha=0.9. Pick (a, alpha) from {0.0,0.3,0.6} x {0.8,0.9,0.99} on the Madrid harness, with the graph built on the held-out Madrid block only.", + "rule_compliance": "Allowed. Shot pixels are excluded from scoring anyway; the hyperparameters come from Madrid. Do not use leave-shots-out on Amsterdam to tune a at n=5; it is legal but too noisy.", + "est_cpu_minutes": 3, + "uses_shots": "Yes (clamped seeds)", + "uses_unlabelled_target": "Yes (graph over all 25,992 pixels)" + }, + { + "name": "Frozen DANN-MLP backbone (importance-weighted DANN, per-city inputs, AdaBN) + CL2N embeddings + shrunk prototype / logistic head", + "rank": 5, + "family": "backbone", + "source": "Merged from 9 ideas: SimpleShot CL2N (arXiv 1911.04623); DFR last-layer retraining (arXiv 2204.02937); IWDAN (Tachet des Combes et al. NeurIPS 2020, arXiv 2003.04475); AdaBN/SHOT-lite (arXiv 2002.08546); MME cosine-head init (arXiv 1904.06487); TabM/RealMLP training tricks; frozen-head plan (a)", + "why": "This is plan (a), built so it can work. The backbone is trained with domain-invariance pressure, because Madrid-only CE embeddings may throw away cues Amsterdam needs. The head fits few parameters and is shrunk toward Madrid as n gets small. It gives a strong n=5/25 member that differs from the tree models. Expected +0.02 to +0.05 at n=5/25 over the current results, about parity at n>=100.", + "recipe": "Input: the 60 DANN features plus the top 100 LightGBM-importance features, per-city standardised (item 1). Network: 160 -> 256 -> 128 (BatchNorm, SiLU, dropout 0.2) -> 4. Loss: class-balanced CE on Madrid (label smoothing 0.1) + DANN with a GRL on the 128-d layer, lambda ramp 2/(1+exp(-10p))-1 scaled by 0.3, discriminator 128-64-1. Discriminator loss on Madrid rows weighted by w_y = pi_EM/pi_M (IWDAN; pi_EM from item 2 run on the zero-shot model). AdamW lr 1e-3, wd 1e-4, batch 512 per domain, a FIXED 30 epochs, 5 seeds. AdaBN: recompute BN running statistics on all Amsterdam pixels (train mode, no gradients). Embed both cities; CL2N: z = (e - mu_city)/||e - mu_city||, where mu_M is the prior-reweighted Madrid mean. Head (default, closed form): cosine prototypes p_c = alpha*mean(shots_c) + (1-alpha)*mean(Madrid_c), alpha = n/(n+k), k=10, logits = 15*cos(z, p_c). Alternative head: item 3(b) shrunk logistic regression. Report the average over the 5 seeds. Pipeline: item 2 prior correction, then item 4 graph spreading.", + "rule_compliance": "Allowed. k, temperature and the epoch count are fixed or chosen on the Madrid harness. Do NOT run idea 'Warning' as stated: its 20-seed diagnostic scores methods on the remaining Amsterdam pixels, which is selection on test labels. Run that diagnostic on the Madrid harness instead.", + "est_cpu_minutes": 20, + "uses_shots": "Yes (head only)", + "uses_unlabelled_target": "Yes (DANN, AdaBN, centring)" + }, + { + "name": "Probability-level ensemble: zero-shot XGB+DANN, LightGBM with the item-3 schedule, frozen-MLP head, Mahalanobis prototype; uniform log-prob average, then EM, then graph spreading", + "rank": 6, + "family": "ensemble", + "source": "Merged ensemble ideas (cheap ensembling/calibration layer; stacking idea without TrAdaBoost); TableShift (arXiv 2312.07577: no single method dominates under shift)", + "why": "Trees, MLPs and prototypes make errors in different places, and averaging is the most reliable +0.01 to +0.03 at every n. The base models already exist, so it costs nothing more.", + "recipe": "Members: (1) zero-shot XGB+DANN, (2) LightGBM with the item-3 schedule, (3) item-5 frozen MLP head (5-seed mean), (4) Mahalanobis prototype with shrinkage to the Madrid class means (alpha = n/(n+10)), (5) item 7 if it is ready. Calibrate each member with one temperature fitted on held-out Madrid. Combine with an unweighted mean of log-probabilities. Fixed n-dependent weight rule: member 1 gets weight 10/(n+10), and the rest share the remainder equally. Then run item 2 EM on the ensemble output with pi_S = mean of the members' effective priors (or uniform after each member's own correction; simpler: prior-correct each member to uniform first, then use pi_S = uniform), then item 4 graph spreading. No learned stacking weights at n<=25.", + "rule_compliance": "Allowed. The weights are a fixed rule; optional stacking by CV log-loss on the shots is legal only for n>=50 and is not recommended.", + "est_cpu_minutes": 3, + "uses_shots": "Through the members", + "uses_unlabelled_target": "Through EM and the graph" + }, + { + "name": "Cross-fitted self-training of LightGBM on class-quota pseudo-labels from the graph-refined ensemble", + "rank": 7, + "family": "transductive-refinement", + "source": "Merged from 3 ideas: self-training with class-balanced pseudo-labels (FixMatch/CDAC-inspired, arXiv 2104.09415); LightGBM pseudo-label retraining; TimeMatch's semi-supervised stage (arXiv 2111.02682)", + "why": "It turns the 26k unlabelled pixels into Amsterdam-distributed training data, so the trees learn Amsterdam-specific splits that the graph cannot. Expected +0.02 to +0.05 when the starting point is the ensemble; riskiest at n=5.", + "recipe": "Start: Q = item 6 output (prior-corrected, graph-spread). Round r = 1, 2 (FIXED at 2 rounds): for each class c, take the top fraction f_r of pixels by Q_c among pixels whose argmax is c, with f_1=0.2 and f_2=0.35. Cap the per-class counts proportional to pi_EM. Train LightGBM on Madrid (weight 0.3) + shots (item-3 schedule weight) + pseudo-labels (weight 1, hard labels), num_leaves 15, lr 0.05, 300 rounds, feature_fraction 0.5, min_data_in_leaf 50. Cross-fit: split Amsterdam into 5 SPATIAL blocks; predict block b with a model that excludes block b's pseudo-labels. Then repeat the EM -> graph step for the next round. Final output = mean of the round-2 cross-fitted model and Q (log-prob average).", + "rule_compliance": "Allowed (pseudo-labels are not ground-truth labels). The number of rounds and the fractions are fixed in advance, not chosen on Amsterdam; if you validate, do it on the Madrid harness.", + "est_cpu_minutes": 40, + "uses_shots": "Yes (heavily weighted rows)", + "uses_unlabelled_target": "Yes (pseudo-labels)" + }, + { + "name": "Transductive prototype rectification with marginal constraints (soft k-means / Sinkhorn toward EM priors, TIM-lite) on the frozen embeddings", + "rank": 8, + "family": "head", + "source": "Merged: TIM (Boudiaf et al. NeurIPS 2020, arXiv 2008.11297); LaplacianShot (arXiv 2006.15486); BD-CSPN (arXiv 1911.10713); PT-MAP (arXiv 2006.03806); T3A (NeurIPS 2021); the entropy part of MME", + "why": "Prototypes from 5 shots are noisy, and 26k unlabelled embeddings can rectify them. Sinkhorn with the EM prior as the column marginal avoids TIM's uniform-marginal mistake under label shift. Closed form, seconds, few hyperparameters. Expected +0.01 to +0.04 at n=5/25.", + "recipe": "On the item-5 CL2N embeddings: initialise p_c from item 5's shrunk prototypes. Iterate 10 times: logits = 15*cos(z, p_c); P = Sinkhorn(softmax(logits)) with 5 iterations, row sums 1, column sums N*pi_EM; mask pixels with max P < 0.6; p_c <- 0.5*p_c_init + 0.5*normalise(sum_i P_ic z_i). Shots stay hard-assigned in every iteration. Output P, then item 4. A TIM variant is optional: CE(shots) + 0.3*mean H(p_i) + 1.0*KL(mean p || pi_EM), 100 Adam steps on W only. Use one variant, not both. Default to Sinkhorn.", + "rule_compliance": "Allowed. Constants are fixed in advance or set on the Madrid harness; nothing is tuned on the shots.", + "est_cpu_minutes": 3, + "uses_shots": "Yes", + "uses_unlabelled_target": "Yes" + }, + { + "name": "Ordinal head (CORN / cumulative logits) on the frozen embedding", + "rank": 9, + "family": "head", + "source": "Ordinal / change-point idea (BATSCCD, IJDE 2024; LandTrendr building-age, IEEE JSTARS 2024); CORN/CORAL ordinal regression (general)", + "why": "Age classes are ordered. A shared direction plus 3 thresholds has far fewer free parameters than 4 class vectors, which matters at n=5. It also reduces errors between non-adjacent classes, and it is a cheap extra head on the same embedding.", + "recipe": "On the item-5 embedding (128-d CL2N): fit 3 binary logistic regressions for y>1, y>2, y>3, sharing one weight vector w (cumulative link, mord.LogisticAT or a small torch model) on shots + Madrid rows at weight 1/w(n) from the item-3 rule. L2 C=0.1. p(y=k) = P(>k-1) - P(>k), clipped at 1e-4 and renormalised. Then EM (item 2) with pi_S matching the training mix, then use it as an extra member in item 6.", + "rule_compliance": "Allowed. C is fixed in advance or chosen on the Madrid harness.", + "est_cpu_minutes": 3, + "uses_shots": "Yes", + "uses_unlabelled_target": "Via the embedding and EM" + }, + { + "name": "Daume feature augmentation (shared / Madrid-only / Amsterdam-only copies) for LightGBM", + "rank": 10, + "family": "classical", + "source": "Daume III, Frustratingly Easy Domain Adaptation, ACL 2007 (arXiv 0907.1815)", + "why": "Nearly free: the trees can split on city-specific copies instead of having 30x weights fight 76k Madrid rows. Likely +0.01 to +0.02 at n=25-100; small at n=5. It adds a diverse tree member to the ensemble.", + "recipe": "Use the 60 DANN features + top 60 by importance = 120 features. Madrid rows -> [X, X, NaN]; Amsterdam shots and pseudo-labels -> [X, NaN, X]; predict Amsterdam with [X, NaN, X]. LightGBM num_leaves 15, lr 0.05, 400 rounds, feature_fraction 0.5, min_child_samples 5, shot weight from the item-3 rule. Then item 2, and use it as a member in item 6.", + "rule_compliance": "Allowed. Shot weight comes from the Madrid-tuned rule, not from CV on Amsterdam.", + "est_cpu_minutes": 10, + "uses_shots": "Yes", + "uses_unlabelled_target": "No (optionally pseudo-labels from item 7)" + }, + { + "name": "Second backbone: small TempCNN on the 41-year x 6-band series (per-city normalised, AdaBN), used frozen with the item-5 head", + "rank": 11, + "family": "backbone", + "source": "Pelletier et al. 2019 TempCNN (arXiv 1811.10166); Russwurm & Korner 2020 (arXiv 1910.10536); 'Frozen sequence backbone + target-BN' idea (Modular Adaptation, arXiv 2104.00619)", + "why": "It sees the raw temporal trajectory rather than hand-built summaries, so it adds ensemble diversity beyond the MLP. Only worth doing after items 1-8 work.", + "recipe": "Input: 41 x (6 bands + NDVI, NDBI, MNDWI + missing mask), yearly mean of up to 3 observations, per-city per-band robust z-score. Three Conv1d layers (64 filters, k=5, BN, ReLU, dropout 0.3, no pooling) -> flatten -> 128-d -> 4. Training: class-balanced CE + label smoothing 0.1, CORAL loss (weight 0.5) between Madrid and Amsterdam 128-d batch covariances, augmentations: random year masking 15% and band gain U(0.9,1.1). AdamW 1e-3, 20 epochs fixed, 3 seeds. AdaBN on Amsterdam. Then the same CL2N + shrunk prototype head as item 5, and add it as a member in item 6.", + "rule_compliance": "Allowed. The epoch count and CORAL weight are fixed in advance.", + "est_cpu_minutes": 35, + "uses_shots": "Head only", + "uses_unlabelled_target": "Yes (CORAL, AdaBN, centring)" + }, + { + "name": "LP-FT with L2-SP for n>=50 only (unfreeze the item-5 MLP gently from the probed head)", + "rank": 12, + "family": "backbone", + "source": "Kumar et al. ICLR 2022 (arXiv 2202.10054); L2-SP Li et al. ICML 2018 (arXiv 1802.01483)", + "why": "Optional, for the n=100/200 regime where frozen features saturate: a controlled fine-tune can add +0.01 to +0.02. Skip it at n<=25.", + "recipe": "Start from item 5 with the head fitted (LP). Unfreeze everything; AdamW lr 1e-4 for the backbone and 1e-3 for the head, 60 full-batch steps FIXED. Loss = class-balanced CE on the shots + 0.1*CE on a 5k Madrid replay subsample (resampled each step) + 0.01*sum||theta - theta_0||^2. No early stopping. Re-run AdaBN afterwards. Apply only for n in {50,100,200}, and add it as an ensemble member, not a replacement.", + "rule_compliance": "Allowed. Step count and lambda are fixed in advance or set on the Madrid harness; there is no shot-based early stopping at small n.", + "est_cpu_minutes": 10, + "uses_shots": "Yes", + "uses_unlabelled_target": "Via AdaBN" + } + ], + "dropped": [ + { + "name": "Duplicate EM/BBSE prior-correction entries (4 copies across scouts)", + "reason": "Merged into shortlist item 2." + }, + { + "name": "Duplicate TIM / LaplacianShot entries (2 copies)", + "reason": "Merged into item 8 (Sinkhorn-constrained rectification, TIM-lite variant) and item 4 (the Laplacian/graph part)." + }, + { + "name": "Duplicate self-training entries (3 copies, incl. Tent-style entropy head tuning)", + "reason": "Merged into item 7. The Tent entropy-only update was dropped as unstable at about 55% initial accuracy." + }, + { + "name": "Duplicate frozen-MLP last-layer entries (SimpleShot/DFR, MME frozen variant, LP head of LP-FT, TabM frozen backbone, sequence BN-head)", + "reason": "Merged into item 5 (MLP) and item 11 (TempCNN)." + }, + { + "name": "BBSE confusion-matrix inversion", + "reason": "A 4x4 confusion matrix from 20-100 shots is too noisy, and Garg et al. show MLLS/EM is better; EM is kept in item 2." + }, + { + "name": "Warning / negative-result protocol", + "reason": "Not a method; kept as the Madrid harness in notes. As written, its diagnostic scores methods on the remaining Amsterdam pixels, which is selection on test labels and breaks the rule." + }, + { + "name": "Surgical first-layer FiLM tuning", + "reason": "Its init (per-city standardisation) is item 1. Training 786 affine parameters on 20 shots adds overfitting risk for a small, unverified extra gain." + }, + { + "name": "MME minimax entropy (full adversarial version)", + "reason": "Known to be unstable, and it collapses to the majority class under label shift; its useful part (prototype-init cosine head + entropy) lives in items 5 and 8." + }, + { + "name": "Full SHOT target adaptation of the feature extractor", + "reason": "Entropy-driven backbone updates are a gamble at about 60% pseudo-label accuracy; the cheap AdaBN part is kept in item 5." + }, + { + "name": "Class-conditional alignment with d-SNE / FADA contrastive training", + "reason": "Metric learning overfits with 5 positives per class. The closed-form shrinkage (prototype mixing alpha = n/(n+k)) is already in item 5." + }, + { + "name": "Separate IW-DANN / MCD backbone idea", + "reason": "IW-DANN is folded into item 5's backbone. MCD adds another unstable adversarial method with no unsupervised model-selection criterion." + }, + { + "name": "Episodic meta-training on Madrid spatial blocks (ProtoNet / ANIL)", + "reason": "Costs 20-40 min. Madrid blocks are a weak proxy for the Madrid-to-Amsterdam shift, and supervised pretraining + probe is usually as good." + }, + { + "name": "Multi-detector change-point features (LandTrendr/CCDC/BIC breaks)", + "reason": "Moderate engineering cost, and you already have a step detector; the gain (+0.01 to +0.03) is uncertain for a single night. The cheap ordinal-head part is kept as item 9." + }, + { + "name": "TimeMatch-style year shift of the break-year feature chosen by max confidence", + "reason": "Break year IS the age signal, so shifting it moves the target directly, and confidence-maximisation can pick a wrong shift. Risky for small expected gain." + }, + { + "name": "Classifier-based density-ratio / KMM source reweighting", + "reason": "With domain AUC 1.0 the weights are degenerate; expected gain about 0 to +0.015." + }, + { + "name": "TrAdaBoost", + "reason": "Old and unstable (source weights collapse), the multiclass extension is unverified, and the shot error signal is too thin at n=5." + }, + { + "name": "TabPFN v2 in-context on the shots", + "reason": "Needs a weight download (possibly with a terms prompt). CPU inference over 26k queries x 5 n takes about an hour, a large share of the 5-hour budget, and it is weak at n=5/25, which are the priority settings." + }, + { + "name": "Separate 'architecture choice: GRU/transformer' idea", + "reason": "The GRU is about 3x slower and the transformer is riskier with no evidence for 41 yearly points. Only the TempCNN is kept (item 11)." + }, + { + "name": "Separate 'cheap ensembling/calibration' and 'stacking' ideas", + "reason": "Merged into item 6. Learned stacking weights at n<=25 would overfit and are replaced by a fixed rule." + } + ], + "notes": "BIGGEST RED FLAG IN THE CURRENT RESULTS: at n=5, LightGBM+30x shots (0.566) is worse than zero-shot LightGBM (0.593) and far worse than zero-shot XGB+DANN+smoothing (0.639). Twenty shots weighted 30x override the source model (and also push its effective prior toward uniform). So at n=5 every pipeline should START from the 0.639 zero-shot model and let shots in gently (item 3). That alone should lift n=5 by about +0.07, and the other items then build on that level, not on 0.566.\n\nSUGGESTED ORDER TONIGHT (about 5 h): items 1+2+3+4 in the first hour on the existing XGB-DANN/LightGBM. Then item 5 (backbone, about 20 min of CPU) and items 6 and 8. Then item 7 (self-training, the largest CPU block). Items 9-12 only if time remains.\n\nCOMPOSITION: per-city inputs (1) -> backbones (5, 11, trees with 3/10) -> heads (3, 8, 9) -> ensemble (6) -> EM prior (2) -> graph spreading (4) -> optional self-training loop (7) -> EM + graph again. Always smooth AFTER prior correction, and compare everything with smoothing on.\n\nHYPERPARAMETER RULE: tune ONLY on a Madrid pseudo-target harness. Build it like this:\n1. Cluster the Madrid coordinates into 5 spatial blocks with KMeans.\n2. Hold out one block and re-standardise it with its own statistics, mimicking the per-city transform.\n3. Resample its labels to the EM-estimated Amsterdam prior to imitate label shift.\n4. Draw n shots per class from it over 20 seeds, train on the other 4 blocks (plus unlabelled = the held-out block), and score macro F1 on the rest of the block.\n\nThis underestimates the real city shift, so prefer conservative defaults when the harness is flat. After that, freeze one pipeline per n and evaluate on Amsterdam once.\n\nRULE FLAGS IN THE SCOUTS' RECIPES:\n(a) Idea 3 (label-shift head) says to tune gamma 'on Amsterdam shots simulated via your n=200 splits'. This uses Amsterdam labels beyond n for n<200, so it breaks the rule.\n(b) Idea 6 (source-regularised head) says to tune tau 'using simulated n-shot draws from the labelled Amsterdam pool'. This breaks the rule.\n(c) The Warning idea's 20-seed diagnostic scores on the remaining Amsterdam pixels, which is test-set model selection.\n(d) Choosing among existing pipelines by their reported Amsterdam test F1 is already mild test leakage; pre-register choices with the harness.\n\nLeave-shots-out CV on the n shots is legal but too noisy at n=5 (std about 0.03-0.05), so it is used nowhere for tuning here. EM priors, AdaBN, centring, graphs and pseudo-labels use only unlabelled Amsterdam features and coordinates, which is allowed.\n\nLABEL-SHIFT TRAPS:\n- Centring or standardising on the unlabelled Amsterdam mean bakes in the prior difference. Item 1 handles it with a prior-reweighted Madrid pass.\n- When EM is used, pi_S must be the model's effective training prior (Madrid + weighted shots, or uniform for balanced heads).\n\nGains quoted are guesses from the literature, not measured; expect them to overlap rather than add, especially items 4/7/8, which all exploit the same unlabelled structure." +} \ No newline at end of file diff --git a/reports/nnlab/scores/cls_knn.json b/reports/nnlab/scores/cls_knn.json new file mode 100644 index 0000000000000000000000000000000000000000..f417a18556d03489a428c8d4fcfcbeec84f7b78d --- /dev/null +++ b/reports/nnlab/scores/cls_knn.json @@ -0,0 +1,461 @@ +{ + "method": "cls_knn", + "draws": [ + { + "n": 5, + "rep": 0, + "argmax": 0.5221903557289336, + "argmax_A": 0.5302732879082349, + "argmax_B": 0.5137141488882171, + "rule": 0.5454851293814139, + "rule_A": 0.5515532967625634, + "rule_B": 0.5389193256438298, + "rule_smooth": 0.4988736472287242, + "rule_smooth_A": 0.5009881027559154, + 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--git a/reports/nnlab/script_reviews.json b/reports/nnlab/script_reviews.json new file mode 100644 index 0000000000000000000000000000000000000000..505ac7ea3561864241b03afe62ea72bd3ae180bf --- /dev/null +++ b/reports/nnlab/script_reviews.json @@ -0,0 +1,193 @@ +[ + { + "verdict": "clean", + "leakage_findings": [ + "None found. A grep of classical.py for _ya, age_class, weighted_mean_year, usable_label and held_out finds nothing. Amsterdam labels are read only in the main loop, through lab.shot_labels(d, lab.shots_for(d, n, rep)), and are used only to fit that draw's model.", + "Labels from one draw never reach another draw. Every cache (cls_pca64, cls_nys500, cls_knn_madrid) holds only label-free Madrid and Amsterdam features or Madrid neighbour indices, so no shot labels are stored in them.", + "No hyper-parameter was chosen from an Amsterdam score. The two departures from the spec (SVC subsample 6000, LR max_iter=100 with tol=1e-3) are compute-budget cuts based on timing only. Both are recorded in the meta CFG." + ], + "correctness_findings": [ + "robustz is applied to Madrid and to Amsterdam separately (Zm and Za). PCA(64) and Nystroem are fitted label-free on vstack(Zm, Za), as the spec asks.", + "Class indexing is right. Labels 1..4 index balanced_class_weights with y-1. LR, LDA and SVC column order comes from the sorted classes_ [1,2,3,4]. kNN builds column c from votes where lbl == c+1.", + "Every unit of work predicts all 25,992 Amsterdam rows in table order. The kNN shot-label matrix broadcast_to(ys) lines up column by column with S = Pa[idx]. Merging the cached Madrid top-25 with the distances to the shots and then taking argpartition gives exactly the joint top-25 search.", + "Shot rows predict themselves through a self-distance of 0 (kNN) or by being in the training set. This does no harm because the scorer drops shot rows.", + "Resume works per (method, n, rep) through lab.have_pred. Smoke outputs carry the _SMOKE suffix, so they cannot mix with full outputs. Zero-shot methods are saved once, at n=0, rep=0.", + "The caches are keyed well enough for this script. The PCA and kNN settings are the same in smoke and full runs, and the Nystroem cache name includes the component count (smoke 60 vs full 500). One edge case: the kNN cache depends on the PCA cache, so if cls_pca64.npz is deleted, cls_knn_madrid.npz must be deleted with it. Today PCA is deterministic (random_state=0), so this is harmless.", + "Minor, in lab.py and not a bug: save_pred is not atomic. A run killed in the middle of np.save could leave a truncated .npy that have_pred then treats as done.", + "There is no backbone or seed averaging to check in this family. Seeds are fixed (PCA, Nystroem and the SVC subsample all use seed 0), so runs are deterministic." + ], + "fixes_applied": [], + "full_run_command": "cd && NNLAB_THREADS=3 python tools/nnlab/classical.py", + "est_full_minutes": 45, + "smoke_rerun_passed": true, + "notes": "I changed nothing, but I re-ran the smoke test to check RAM and the code paths. It passed with NNLAB_THREADS=2: all 9 methods ran without error in 61 s. Peak working set was 1.68 GB, measured with psutil peak_wset; the largest step is the 1179-dimensional FEDA LR. Memory for that step does not depend on the iteration count, so the full run should also stay under 2.5 GB.\n\nI scored cls_lda_SMOKE and cls_svm_SMOKE (rule: lda 0.597 / 0.686, svm 0.524 / 0.689 at n=5 / n=200). These are smoke configs, not results. Afterwards I deleted every _SMOKE prediction folder, the _SMOKE score files and the smoke-only cache cls_nys60.npz. The valid full-run caches are kept under reports/nnlab/cache: cls_pca64.npz, cls_nys500.npz and cls_knn_madrid.npz.\n\nMethods the full run produces: cls_lr_zs, cls_svm_zs, cls_lr, cls_lr_feda, cls_svm, cls_svm_feda, cls_nys_lr, cls_knn, cls_lda.\n\nRun time: the author estimates about 42 min on 3 threads, from per-draw timings at n=200 (lr 16 s, lr_feda 25 s, nys 12 s, svm about 20-24 s, svm_feda about 40-47 s). That estimate is plausible, but it sits close to the 45-minute budget, and the shared laptop could push it to about 50 min. I report 45 as a realistic central figure. I did not re-time the full SVC at 6000 myself.\n\nDisclosed deviations from the spec, all acceptable:\n- SVC subsample is 6000, not 12000 or the fallback of 8000.\n- LR runs max_iter=100 with tol=1e-3, so it is not fully converged.\n- kNN has no class balancing.\n- LDA uses empirical priors.\n- SVC probabilities are a plain softmax over the ovr decision function.\n\nI found no bug in lab.py." + }, + { + "verdict": "fixed", + "leakage_findings": [ + "None found. grep for _ya, age_class, weighted_mean_year, usable_label and held_out in transductive.py matches only a docstring line that explains why W.load_data is avoided.", + "The script reads Amsterdam labels only as lab.shot_labels(d, lab.shots_for(d, n, rep)), and only inside that same draw's refinement or st refit. No labels carry over from one draw to another.", + "Every knob is a module constant that matches the spec. The lapshot row-normalisation is a structural deviation. The author justifies it a priori with the graph degree and checks it on the Madrid pseudo-target only (base 0.485, lapshot_row 0.493, lapshot_raw 0.464, from a 40-tree smoke run). It is recorded in the meta as graph_norm=row. No Amsterdam score feeds into any choice.", + "--madrid-check draws its pseudo-shots from Madrid labels on the madrid_split pseudo-target, with a different rng seed (7+1000n+rep). It robust-z scales source and pseudo-target separately." + ], + "correctness_findings": [ + "The per-city transforms are right. The graph uses PCA-32 of lab.robustz(Xa) on Amsterdam alone. st scales Madrid with its own stats and Amsterdam with stats from the whole unlabelled Amsterdam matrix, which matches winners.train_retrained exactly (same LGBM_PARAMS+RETRAIN_OVERRIDES, shots x30, then balanced_class_weights over the union).", + "Class indexing is right. onehot uses y-1, pseudo-label classes are k+1, the weight lookup is [y-1], and _fit_lgbm subtracts 1 internally.", + "Every save is (25992,4) in table order. save_pred asserts the shape. Clamp, lp and lapshot work on all rows. EM and st predict all rows.", + "LP iterates F=aSF+(1-a)Y0 from Y0 for 20 steps with S=D^-1/2 A D^-1/2, which is correct. A has its self-loops removed after the union with lab.adjacency, which includes self.", + "EM (Saerens) is right. It reweights by pi/pi_s and renormalises, sets pi to the mean posterior, shrinks 50% to the Madrid prior and reweights by pi_hat/pi_s.", + "Resume is safe. Smoke outputs carry the _SMOKE suffix, so smoke and full runs never mix. The zero-shot EM is saved once at n=0. Per-draw bases are checked with have_pred before load_pred (lab.load_pred silently falls back to n0_r0). In the full run, missing lgbm_upw draws are skipped, not substituted.", + "Caches are keyed correctly. The graph cache name carries r, k and pca and does not depend on the draw. The Madrid base cache is keyed SMOKE/full.", + "Minor issue, fixed: the docstring said the union graph had about 35-45 neighbours. The measured mean degree is 22.4 (min 10). Code was correct.", + "Nit, not fixed: knn_graph assumes the first neighbour returned is the point itself. With exact duplicate PCA vectors one row loses one neighbour. The effect is negligible, and any self-loop is removed later anyway.", + "Race to watch, not a code bug: lgbm_upw is still being written by baselines.py (only n5_r0..r2 existed at review time). save_pred is not atomic, so start the full run only after baselines.py finishes." + ], + "fixes_applied": [ + "tools/nnlab/transductive.py docstring (DEVIATIONS, lapshot): changed the neighbour count from '~35-45, logit up to ~40' to the measured '~22 on average (min 10), logit up to ~22'. Documentation only; no code changed." + ], + "full_run_command": "cd && NNLAB_THREADS=3 python tools/nnlab/transductive.py --base lgbm_upw,zs_xgb_dann,zs_lgbm,zs_dann (start only after baselines.py has written all 25 lgbm_upw draws)", + "est_full_minutes": 100, + "smoke_rerun_passed": true, + "notes": "Reviewed all of lab.py, transductive.py and the winners.train_retrained / robustz / LGBM params code it reuses. The change was documentation only. I re-ran a light smoke on the fast path: NNLAB_THREADS=2, --base zs_xgb_dann --smoke --only lapshot. It used the cached graph (583,310 edges, mean degree 22.4) and took 0.1-0.2 s per draw. lab.py score ran on it (rule avg 0.637 for 2 draws; smoke number only, not used for anything). I then deleted its _SMOKE preds and score JSON. I did not re-run the st smoke because 5 other python jobs were already running on the shared machine, and a 40-tree LightGBM fit would have been a second heavy process; the st code did not change. Budget: the fast methods for all 4 bases take about 1-2 min. st costs one 400-tree LightGBM fit per draw on about 76k Madrid rows, plus the shots, plus about 15% of Amsterdam (about 3.9k pseudo-labelled rows), x393 features. Observed lgbm_upw fits under contention took 2-7 min per draw (file times 00:08, 00:10, 00:17), so 25 draws come to about 75-125 min. The 100 min estimate is plausible but could reach about 150 min if the machine stays contended. Peak RAM estimate is about 1.1 GB (Madrid and Amsterdam float64 plus their robust-z copies plus the vstack), under 2.5 GB. Methods produced: lgbm_upw__{clamp,lp,lapshot,em,st}, and for each of zs_xgb_dann, zs_lgbm and zs_dann the suffixes __clamp, __lp, __lapshot and __em (the zs __em ones are saved once at n=0). For __clamp read the rule column. lab.py bug-risk (not modified): load_pred silently falls back to n0_r0; the script guards against it with have_pred. st reads features as float32 values cast to float64, so they can differ from lgbm_upw's float64 W.load_data features at rounding level (documented deviation)." + }, + { + "verdict": "fixed", + "leakage_findings": [ + "No leakage found. ts_nn.py never mentions _ya, age_class, weighted_mean_year, usable_label or held_out (grep is empty). Amsterdam labels are read only at main() lines ~480-481, via lab.shots_for(d, n, rep) followed by lab.shot_labels(d, shot). They are used only to fit that draw's ft and joint heads, and are not shared between draws.", + "The raw-parquet reader asks only for px_key, py_key, year, the 18 band columns and qa_valid_1..3, so it never reads a label column. Madrid labels come from lab.load()['ym'].", + "Epoch count and lam are both chosen on Madrid only. The backbone trains on the madrid_split source part. Epochs are scored by argmax macro F1 on the pseudo-target. lam is scored with simulated n=5 and n=50 shots drawn from the pseudo-target, which the backbone never saw. No Amsterdam score is computed anywhere in the script. The choices are written to select_*.json and copied into meta['selection']." + ], + "correctness_findings": [ + "Row alignment and observation selection are verified by experiment. For both cities, interp_time(T)*SR_SCALE+SR_OFFSET averaged over years matches the feature table's sr_{band}_mean columns to within 3e-7 absolute (correlation 1.0) for all 6 bands. So the tensor rows follow the feature-table order, and the selection matches make_flat_view(agg='median') plus to_year_matrices: per-slot qa_valid, nanmedian, dropping years with any band NaN or Blue > BLUE_MAX, linear interpolation inside the series and nearest values at the edges.", + "make_inputs is called separately for each city, so the mean/std standardisation and the ry per-year median use that city's own label-free statistics. The observed-mask channel is left out of the ry subtraction.", + "Class indices are correct. Training uses ys-1, the class weights come from bincount[1:], and finetune_last_layer and joint_head take labels 1..4. Columns 0..3 map to classes 1..4: the softmax head is ordered that way, and joint_head's predict_proba is too, because all 4 classes appear in Madrid.", + "P has shape (25992, 4) and covers every Amsterdam row in table order (proba(Za...) / predict_proba(Sa)). Probabilities are averaged over seeds with np.mean over embs. The backbone stays frozen: ft starts from the backbone's (W0, b0) and only touches the last layer on the cached raw embeddings, and joint is an LR on the frozen embeddings, standardised with the Madrid embedding mean and std (label-free).", + "Smoke and full outputs cannot mix: method names carry the _SMOKE suffix and cache files carry the _SMOKE tag. The tensor cache is shared, but it is deterministic and its shape is asserted. Resume works per (method, n, rep).", + "BUG (fixed). When select_*.json exists but the embedding npz does not (a crash between the two writes, or any seed beyond the first), backbone() retrains for sel['epochs'] epochs. For tcnn_dann the GRL lambda schedule was then stretched over sel['epochs'] instead of max(epoch_grid), so the rebuilt backbone was not the model that was selected. I added train(..., sched_epochs=...), and the retrain branch now passes max(sel['epoch_grid']). Verified: after deleting the DANN npz, the retrain branch reproduces the zs predictions with a max abs diff of 0.0.", + "Minor, not changed. The cache files are keyed by backbone, variant, seed and smoke tag, not by hyper-parameters such as EPOCH_GRID or LR. If those constants are edited later, the reports/nnlab/cache/ts_nn/*.npz and select_*.json files must be deleted by hand.", + "Documented deviations that I accept as honest: 42 years (1984-2025, as the feature pipeline uses), epoch grid {5,10,15}, 1 seed, the selected checkpoint used directly with no retrain on all Madrid, and gru and tcnn_dann_ry only with --extended. All of these are recorded in meta. Re-running the smoke test gave the same selection scores as the author's run, so it is deterministic." + ], + "fixes_applied": [ + "tools/nnlab/ts_nn.py: train() now takes sched_epochs (the length of the DANN lambda schedule; the default is unchanged). The retrain-from-selection branch in backbone() passes sched_epochs=max(sel['epoch_grid']), so it reproduces the selected checkpoint exactly. The normal path is unchanged." + ], + "full_run_command": "cd && NNLAB_THREADS=3 python tools/nnlab/ts_nn.py > reports/nnlab/logs/ts_nn.log 2>&1", + "est_full_minutes": 66, + "smoke_rerun_passed": true, + "notes": "Methods the full run produces: ts_tcnn_st_{zs,ft,joint}, ts_tcnn_ry_{zs,ft,joint}, ts_tcnn_dann_st_{zs,ft,joint}. --extended adds tcnn_dann_ry, gru_st and gru_ry, which takes about 2.5 h more and is beyond budget.\n\nSmoke re-run with NNLAB_THREADS=2: 188 s, peak RSS 1.55 GB. The --only tcnn_dann retrain-branch check took 75 s. Scoring ts_tcnn_ry_ft_SMOKE and ts_tcnn_dann_st_joint_SMOKE worked and gave the same numbers as the author's report. Afterwards I deleted every ts_*_SMOKE prediction folder, every score json and cache/ts_nn/*SMOKE*. The tab_mlp_*_SMOKE folders belong to another job and were left alone. The smoke log is at reports/nnlab/logs/ts_nn_smoke_review.log.\n\nBudget, measured at 2 threads under contention: tcnn about 47-50 s per full source epoch, so 15 epochs plus evals, embedding and lam selection come to about 13 min per variant. tcnn_dann is about 90-107 s per epoch, about 25-28 min. Heads are about 7-10 s per draw for tcnn and 12-17 s for dann, about 12 min over 75 draw-combos. With loading, the total is about 60-70 min on 3 threads, so it should fit 70 min but with little margin if the machine stays contended. Peak RAM is about 1.6 GB, under 2.5 GB.\n\nNo bug found in lab.py." + }, + { + "verdict": "fixed", + "leakage_findings": [ + "None found. The script never mentions _ya, age_class, weighted_mean_year, usable_label, held_out or read_parquet. It reads Amsterdam labels only through lab.shot_labels(d, lab.shots_for(d, n, rep)), and only to fit that one draw's ft/joint/proto head; nothing carries over between draws.", + "Epochs and ft lam are chosen only on lab.madrid_split. Training uses the source part, scoring uses macro F1 of W.decide on the Madrid pseudo-target, the prior comes from the source part, and the shots are left out of the pseudo-target score. Unlabelled Amsterdam rows are used only as the DANN/CORAL adversary target in the final backbones, which the rules allow.", + "Fixed-in-advance knobs (shot_weight 30, C=1, proto k=10 and T=20, CORAL weight 1, the optimiser settings) and the selection scores all go into every meta.json." + ], + "correctness_findings": [ + "RAM (real problem): with the first smoke run (your version), peak RSS of the process was 2710 MB, over the 2.5 GB cap. Cause: lab.rankgauss on the full 76k x 393 Madrid matrix creates about 1.5 GB of float64/rank temporaries (lab.robustz adds about 0.5 GB), on top of about 1 GB already resident. Fixed by running the transforms in column blocks.", + "Cache key gap (minor, fixed): emb_{bb}_{inp}_s{seed}.npz is keyed without the epoch count. If selection.json were regenerated with a different choice, a stale net would be reused silently while meta reported the new count. The cached 'epochs' field is now checked against selection.json and the net is retrained if they differ.", + "Efficiency (minor, fixed): head_proto used dict.setdefault(key, _unit(...)), which runs the full Madrid and Amsterdam normalisation on every call.", + "Verified correct: each city is transformed separately (main run Madrid and Amsterdam; selection source part and pseudo-target). Row order is kept, because embed works in order and Xm60/Xa60 are aligned by lab's merge. Labels 1..4 map to columns 0..3 consistently (Y = ys - 1, predict_proba classes 1..4, head columns go to W.decide). Every save is (25992, 4) for all rows. The 3 seeds' probabilities are averaged before saving. The backbone stays frozen: the heads only use the cached embeddings and W0/b0. zs is saved once at n=0, rep=0. Smoke and full runs use separate _SMOKE names and a separate cache dir, so they cannot mix. Resume skip works (a re-run of the smoke skipped everything in 4 s). The DANN loop matches tools/compare_teammate.py train_dann. The '45-epoch run evaluated at 15/30/45' shortcut really equals separate runs (constant lr, same generator, eval uses no RNG). sklearn 1.9 LogisticRegression is multinomial, and newton-cholesky warm start from coef_ works. Runs are deterministic: smoke scores after my fixes are identical to the author's.", + "Documented deviation, kept as is: the joint head does not call lab.joint_head. It standardises the embeddings with Madrid mean/sd and uses newton-cholesky with a warm start. Because the C=1 L2 penalty then acts on standardised weights, the objective is not literally the same as lab.joint_head on raw embeddings, despite the docstring's claim. It is still a legitimate, fixed-in-advance, label-free choice, and the author's convergence argument for it holds.", + "lab.py: no bug found." + ], + "fixes_applied": [ + "tools/nnlab/tab_nn.py: new _by_columns() wrapper; TRANSFORM rz and rg now run lab.robustz / lab.rankgauss in 48-column blocks. Output is bit-identical (np.array_equal checked on Xa for both), and smoke peak RSS fell from 2710 MB to 1546 MB. The selection inputs use the same wrapper.", + "tools/nnlab/tab_nn.py main(): a cached embedding file whose stored 'epochs' differs from selection.json is deleted and retrained, not reused.", + "tools/nnlab/tab_nn.py head_proto: the unit-normalised embeddings are computed once per seed, not on every call (setdefault ran its argument eagerly)." + ], + "full_run_command": "cd && NNLAB_THREADS=3 python tools/nnlab/tab_nn.py", + "est_full_minutes": 240, + "smoke_rerun_passed": true, + "notes": "The smoke rerun after the fixes (NNLAB_THREADS=2, from scratch) took 182 s with peak RSS 1546 MB. A re-run skipped every saved unit in 4 s. lab.py score gave the same numbers as the author's smoke: tab_mlp_rz_ft_SMOKE avg 0.635 rule / 0.662 smooth, tab_dann_rz_joint_SMOKE 0.607 / 0.611, tab_coral_o60_proto_SMOKE 0.524 / 0.542. I then deleted all _SMOKE preds, scores and cache/tab_nn_SMOKE. The full run was not started.\n\nBudget: the full run will not fit the brief's 60-minute target. With rg already dropped for res and coral it is 40 methods (the author's list is correct). The author's estimate of about 240 min (range 150-330) is consistent with the smoke timings: selection is about 40 min, final training about 90/180/275 min if the chosen epoch count is 15/30/45, and heads about 40-50 min (joint is about 2.5-6 s per seed per draw). The fallback `--seeds 1` gives about 100-120 min but changes the spec, since method names stay the same and the seed count is recorded only in meta. Resident RAM in the full run should stay around 1.1-1.6 GB, under the cap.\n\nShared-machine note: three other Python processes (about 0.9-1.3 GB each) were running during my test, so wall times will vary.\n\nMethod list: tab_{mlp,dann}_{rz,rg,o60}_{zs,ft,joint,proto} and tab_{res,coral}_{rz,o60}_{zs,ft,joint,proto}." + }, + { + "verdict": "clean", + "leakage_findings": [ + "No leakage found. metric.py never mentions _ya, age_class, weighted_mean_year or held_out. Amsterdam labels are read only at line 461, as lab.shot_labels(d, lab.shots_for(d, n, r)).", + "tpn (head_tpn) uses only the shot labels plus soft assignments of the non-shot Amsterdam rows. No hidden labels go in, and the cap w = min(1, 5*n_c/mass) is implemented as specified.", + "ftproj (head_ftproj) trains only on the shots' cached activations plus Madrid episodes, which use Madrid labels. Nothing carries from one draw to the next: Wt/bt start from torch.tensor(W0), a copy of the cached source weights that is never mutated. The shared Madrid sampler gets a fresh rng each draw (seed+10*rep+1000*n), so results do not depend on resume order.", + "The only data-driven knob is training length. It is picked from the proto F1 on the Madrid pseudo-target (madrid_split, rng 7, Madrid labels) and saved to {enc}_select.json and into meta. protoda's CORAL target in the selection run is the unlabelled pseudo-target, not Amsterdam. Every head knob is a fixed constant. Nothing is chosen from an Amsterdam score." + ], + "correctness_findings": [ + "robustz is applied separately per city: Xm and Xa in the main run, and src and pseudo-target separately in selection. CL2N centres each city on its own mean: cl2n is called separately on Em and Ea, and on the pseudo-target in selection.", + "Class indexing is consistent. Labels 1..4 map to 0..3 via y-1, ysh-1 and the lab.CLASSES order in the Sampler, episodes, means and kNN. Every head returns (25992, 4) in table order, with rows summing to 1 (checked).", + "The encoder stays frozen where it should. embed_H calls model.eval(), so BN uses running stats and dropout is off. ftproj trains only the 256x64 W and b on those cached eval-mode activations.", + "Cross-block episodes are disjoint. One random block half is drawn per episode and shared by all classes; support comes from one half, queries from the other. The fallback to any pixel (which breaks disjointness) never triggers on Madrid: I measured 0 of 8000 class-episodes, since each class covers 90-122 blocks.", + "Seeds are averaged with np.mean(Ps, 0) for pn, maha, tpn and knn. ftproj uses seed 0 only, and meta.seeds says so.", + "Resume keeps smoke and full runs apart: pred names, select json and encoder npz all carry the _SMOKE suffix. The encoder cache is keyed by (encoder, seed, chosen length, smoke).", + "Minor (not fixed): select json and npz are not keyed by CAND or other encoder config, so they go stale if the script is edited. Clear reports/nnlab/cache/metric after any config change.", + "Minor (by design, not a bug): in protoda the separate train-mode forward of the Amsterdam batch also updates the BN running stats, so the eval-time BN stats are a Madrid/Amsterdam blend. This uses only unlabelled data, so it is allowed.", + "No bug found in lab.py." + ], + "fixes_applied": [], + "full_run_command": "cd && NNLAB_THREADS=3 python tools/nnlab/metric.py 2>&1 | tee reports/nnlab/logs/metric.log ; python tools/nnlab/lab.py score met_supcon_pn met_supcon_maha met_supcon_tpn met_supcon_knn met_supcon_ftproj met_proto_pn met_proto_maha met_proto_tpn met_proto_knn met_proto_ftproj met_protoda_pn met_protoda_maha met_protoda_tpn met_protoda_knn met_protoda_ftproj met_cos_pn met_cos_maha met_cos_tpn met_cos_knn met_cos_ftproj", + "est_full_minutes": 58, + "notes": "I changed nothing. I re-ran the smoke test anyway (NNLAB_THREADS=2, 131 s, on a CPU shared with 4 other python jobs) and it passed on every code path. Scoring met_cos_ftproj_SMOKE and met_protoda_tpn_SMOKE with lab.py score also worked. I then deleted all met_*_SMOKE preds and scores and the smoke caches in reports/nnlab/cache/metric, which is now empty.\n\nFine-tuning the metric is covered, as the user asked: the ftproj head fine-tunes the encoder's projection layer on the shots, with a Madrid episode and an L2-SP penalty.\n\nHead cost per seed, measured: pn 0.03 s, maha 0.2-1.3 s, tpn 0.4 s, knn up to 0.9 s, ftproj about 2.7 s once warm (9.7 s on the very first call). That is about 5-6 s per draw per encoder, so about 9-10 min for heads over 100 draw-encoder units.\n\nEncoder training measured 0.10-0.13 s/step for proto, protoda and cos, and 0.13-0.25 s/step for supcon. If every encoder picks its longest candidate, the selection run plus 2 retrains cost about 4.5-4.8k steps each, roughly 35-45 min. My estimate for the full run is 45-65 min at 3 threads, central about 58, so it may go slightly over 60 when the machine is loaded.\n\nRAM: peak about 1 GB in my estimate. Raw plus robust-z Madrid is about 240 MB; the robustz float64 temporaries are short-lived; 2 seeds of cached H are about 210 MB; the kNN similarity matrix is up to 166 MB. That is well under 2.5 GB.\n\nReminder: --only 'proto' also matches protoda." + }, + { + "verdict": "fixed", + "leakage_findings": [ + "None found. Amsterdam labels are read only in main(), as lab.shot_labels(d, lab.shots_for(d, n, rep)) for the current draw. d['_ya'] is never touched. The Madrid check uses only Madrid labels (d['ym']).", + "No state carries over between draws. lpft() rebuilds the network each draw from the cached state_dict with make_net(state), deep-copies it per variant, and builds new optimisers and generators. The base state dict is never modified in place.", + "No knob is chosen from Amsterdam scores. STEPS, the learning rates, replay, L2-SP and entropy weights are fixed by the brief. epochs=15 and lam=10 come from tab_nn's Madrid-only selection.json. The Madrid check is logged and stored but not used to pick anything." + ], + "correctness_findings": [ + "Variant confound, now fixed: both variants drew from one torch.Generator. In the _ent variant the extra Amsterdam-batch draws shifted the replay batch indices on every later step, so lpft and lpft_ent differed by more than the entropy term. The entropy batch now has its own generator (seed + 7777777), so both variants see the same replay batches.", + "Smoke-mode threads, now fixed: in --smoke the base nets were trained with NNLAB_BASE_THREADS=3, which breaks the NNLAB_THREADS=2 smoke rule. The thread override only exists to reproduce tab_nn's cache, and the smoke base is never compared with it. In smoke mode BASE_THREADS now equals lab.THREADS. The full run is unchanged.", + "I checked the cached base myself. Loading reports/nnlab/cache/lpft/dann_rz_s0.pt and embedding gives Za max abs diff 1.43e-06 and Zm 3.3e-06 against emb_dann_rz_s0.npz, and b0 matches exactly, so it is the right net and the right epoch count. Every module is in eval mode.", + "BatchNorm: net.eval() is set on the copy before fine-tuning and net.train() is never called, so running stats stay frozen and dropout stays off. logits_of uses no_grad on the eval-mode net. BN affine parameters are trained and included in L2-SP, which matches the spec.", + "Labels and columns line up: Ys = ys - 1, the replay targets are arange(4) for CLASSES 1..4, and finetune_last_layer gets labels 1..4. X_pred = Xa in full, so P is (25992, 4) in feature-table order. Scaling is per city via tab_nn.city_inputs (robustz on Madrid and Amsterdam separately). The Madrid check transforms the source and pseudo-target parts separately, as tab_nn's selection does.", + "The L2-SP anchor is theta_LP: it is cloned after the LP head is copied in. The domain head is frozen and receives zero gradient (GradReverse with lam=0). The entropy term is the information-maximisation loss as specified: mean entropy minus entropy of the mean prediction.", + "Resume works per variant via lab.have_pred. Each variant has its own deterministic generators, so running the variants separately with --only gives the same draws.", + "Minor, left as is: the shot CE is a weighted mean, not a sum. With n shots per class every class weight is equal, so this is plain mean CE. The spec does not say sum." + ], + "fixes_applied": [ + "tools/nnlab/lpft.py lpft(): added g_ent = torch.Generator().manual_seed(seed + 7_777_777) and switched the entropy-batch randint to it, so the replay batches are identical across variants.", + "tools/nnlab/lpft.py main(): under --smoke, set the global BASE_THREADS = lab.THREADS." + ], + "full_run_command": "cd && NNLAB_THREADS=3 python tools/nnlab/lpft.py", + "est_full_minutes": 3, + "notes": "I re-ran the smoke test after the fixes with NNLAB_THREADS=2: 24 s wall time in total. Bases were trained at 2 threads, and each draw took 2.0 s at n=5 and 2.9 s at n=200 for both variants together. lab.py score worked for tab_dann_rz_lpft_SMOKE and tab_dann_rz_lpft_ent_SMOKE. I then deleted their preds, their scores and cache/lpft_SMOKE. The pre-existing lgbm_*_SMOKE dirs are not mine and were left alone.\n\nMethods are tab_dann_rz_lpft and tab_dann_rz_lpft_ent. The Amsterdam base (reports/nnlab/cache/lpft/dann_rz_s0.pt) is already cached and verified. The full run trains only the Madrid-check source base, about 50 s at 3 threads, then runs 9 check draws and 25 protocol draws at about 2.5-3 s each, so about 3 min in total at 3 threads. There are no predictions under reports/nnlab/preds yet, so the full run starts clean.\n\nScript: tools/nnlab/lpft.py" + }, + { + "verdict": "fixed", + "leakage_findings": [ + "No Amsterdam score is used to pick any setting. Shot labels are read only through lab.shot_labels(d, lab.shots_for(d, n, rep)) for the current draw. A fresh LGBMClassifier is built for every draw (random_state fixed), so no state carries over between draws. TOPK=120 and shot weight 100 are fixed by the spec. The protoda length L400 comes from protoda_select.json, which was chosen on the Madrid pseudo-target.", + "Minor rule-1 issue (fixed): the script loaded data with W.load_data(), which reads Amsterdam's age_class column into wd['y_ams']. That column was never used, but the spec says never to read Amsterdam age_class. The script now reads the same parquet files with columns= set to the features (plus px/py keys for Amsterdam, plus age_class for Madrid only). Values, dtype and order are unchanged: the base check still gives a 2.98e-08 max diff.", + "Process issue in the author's cleanup (not in the script): they deleted with the broad glob reports/nnlab/preds/*_SMOKE, which could have removed another agent's smoke outputs. I deleted only lgbm_{feda,emb,w100}_SMOKE preds and scores." + ], + "correctness_findings": [ + "Baseline match: the robust-z per city, Amsterdam stats from all 25,992 rows, weights of 1 for Madrid and 30 for shots times balanced_class_weights(y)[y-1], params {**LGBM_PARAMS, **RETRAIN_OVERRIDES} with n_jobs=lab.THREADS, and W.predict on the scaled matrix all match W.train_retrained and baselines.py. lgbm_base_check at 400 rounds on draw (5,0) with 2 threads: max abs diff 2.98e-08, argmax agreement 100%. PASS.", + "lgbm_w100 changes only the shot weight. lgbm_emb appends unscaled [DANN Zm|Za (64), l2n(H @ W.T + b) of protoda_s0_L400 (64)] for 521 columns. The formula matches metric.py's Enc.forward (proj(body)) with l2n. The cache arrays are (76263,64)/(25992,64) and (.,256) in feature-table order, and the fit uses Ea[shot] for the shot rows.", + "lgbm_feda: the column order [X(393), Madrid copy(120), Amsterdam copy(120)] and the NaN placement match at fit time (Madrid rows [x,x,NaN], shots [x,NaN,x]) and at predict time (all Amsterdam rows [x,NaN,x]). The top-120 come from zero_shot_lgbm.txt gain importance (393 features asserted) and are sorted by index.", + "Labels and columns: y-1 goes into LightGBM and predict_proba columns 0..3 map to classes 1..4. Predictions cover all 25,992 rows in table order. I added an assert that the Amsterdam px/py keys equal lab's d['pa']. Resume uses lab.have_pred per draw.", + "BUDGET (fixed): the smoke run's peak RAM measured 2.68 GB, above the 2.5 GB limit. I computed the per-city robust-z once in main instead of on every draw; it is label-free and doesn't depend on the draw, so results are bit-identical. I also built the feda matrices in one preallocated array instead of nested hstack/vstack. Peak is now 2.28 GB. The author's '~100 s smoke' and '1.5-2 GB' claims were unmeasured or wrong: their smoke took 167 s and peaked at 2.68 GB." + ], + "fixes_applied": [ + "tools/nnlab/lgbm_var.py: replaced W.load_data() with pd.read_parquet(W.DATA/..., columns=feats(+PID for Amsterdam, +age_class for Madrid)), so Amsterdam labels are never loaded. Added an assert that the Amsterdam row order equals lab's pa.", + "tools/nnlab/lgbm_var.py: the per-city robust-z (W.robustz_fit/W.robustz) is now done once in main. fit_predict takes the scaled matrices. Peak RAM went from 2.68 GB to 2.28 GB, about 3 s are saved per fit, and the output is identical (base check still 2.98e-08).", + "tools/nnlab/lgbm_var.py: the feda train and predict matrices are built in one preallocated NaN array. The layout is the same and there are fewer temporaries.", + "Re-ran the smoke test with NNLAB_THREADS=2: 150 s wall, base check PASS. Scored all three _SMOKE methods with lab.py score (rule n5/n200: feda 0.640/0.722, emb 0.571/0.666, w100 0.593/0.724 at 40 trees). Then deleted only these three methods' _SMOKE preds dirs and score files." + ], + "full_run_command": "cd && NNLAB_THREADS=2 python tools/nnlab/lgbm_var.py (methods: lgbm_feda, lgbm_emb, lgbm_w100; 25 draws each; resumable; optional --only lgbm_feda|lgbm_emb|lgbm_w100 to split across sessions)", + "est_full_minutes": 100, + "notes": "I recommend NNLAB_THREADS=2, because that is the setting I measured. Full 400-round fit plus predict at 2 threads: lgbm_feda 96 s, lgbm_emb 81 s, lgbm_w100 about 64 s (the base-check fit). Over 25 draws that is about 6,000 s, or roughly 100 min for all 75 fits. I did not measure 3 threads; I would guess about 70-80 min. Peak RAM at 2 threads is 2.28 GB, measured with psutil peak_wset over the whole smoke run. The full run was not started. My timing script is scratchpad/time_full.py; it saves nothing. The only file modified is tools/nnlab/lgbm_var.py." + }, + { + "verdict": "reproduces-after-fix", + "evidence": "Both untested draws reproduce exactly, and so do all the members. Shot order is the one real problem: it changes the LightGBM member by much more than floating-point noise.\n(2) Draws the author did not test, with NNLAB_THREADS=2, about 174 s each:\n- n=50, rep=3, spatial: macro F1 rule_smooth = 0.725269. The stored ens_e4__lp.json entry is 0.7252687, a difference of +0.00e+00. max |proba - stored ens_e4__lp| = 2.98e-08, the float32 rounding of the stored file. Every member (zs_xgb_dann, lgbm_upw, met_protoda_maha, met_cos_maha, tab_dann_rz_ft) and the pre-LP ensemble differ by 0.0.\n- n=200, rep=4, --no-spatial: macro F1 rule = 0.719744. The stored ens_e4.json entry is 0.7197439, a difference of +0.00e+00. max |proba - stored ens_e4| = 0.0, and all members differ by 0.0.\n(3) Shuffled shot order (n=25, rep=2, permutation from rng 7, spatial): macro F1 = 0.705238 against 0.705882 stored, a drop of 0.0006. max |proba - stored ens_e4__lp| = 0.0209. Per member: lgbm_upw 0.331, met_protoda_maha 1.3e-06, met_cos_maha 2.1e-06, tab_dann_rz_ft 0.0, zs 0.0. So the metric heads, tab head and LP only show rounding noise, but LightGBM depends on order. The cause is LGBM_PARAMS subsample=0.8 with subsample_freq=1, which bags rows by position, so the same shots in a different order give a different fit. lab.shots_for does not return sorted indices (draw_shots joins rng.choice output class by class), so no canonical order can match the stored files.\nAfter the fix: two different permutations of the n=5, rep=0 shots with sort_shots=True and spatial=False give max |dproba| = 0.0 and identical F1 (0.650188 for both).", + "leakage_findings": [ + "No leakage found. The only Amsterdam labels model_create() sees are the shot_labels passed in. lab.load does hold d['_ya'] in memory, but the only code that reads it is the CLI, through lab.shots_for / lab.shot_labels, and score_only(). The float64 LightGBM features are read from parquet as feature columns only, without age_class.", + "Label-free transductive inputs, as allowed by the protocol: the Amsterdam robust-z statistics inside W.train_retrained, the CL2N centring of the metric embeddings, the cached union graph (spatial r=3 plus feature kNN; union_graph builds it from features and coordinates only), and lab.smooth over the non-shot rows.", + "metric.py is imported with sys.argv swapped out. Its import-time work (lab.load, robustz) uses no Amsterdam labels. select_length and encoder_cache only read caches here, because model_create asserts that the json and npz files exist first. SFX is '' because no --smoke is passed, so the paths checked are the right ones.", + "reports/nnlab/preds is read only inside main()'s comparison block, through lab.pred_path. reports/nnlab/scores is only read to print the stored F1.", + "keep comes from the shot indices alone (setdiff1d). predict() smooths and decides on every non-shot row at once, so the only thing that depends on which rows are scored is the batch-level W.decide plug-in, which is allowed." + ], + "fixes_applied": [ + "tools/nnlab/model_create.py: model_create() takes a new argument sort_shots=False. With True it sorts (shot, label) pairs by row index (stable argsort) before fitting, so the method no longer depends on input order. The default False keeps the caller's order, so the stored per-draw files are still reproduced bit for bit. The docstring now describes the order dependence with the measured numbers.", + "The same function now asserts that there are no duplicate shot indices; a duplicate would silently double-weight that shot in LightGBM and in the heads.", + "Nothing else was changed. No other tools/nnlab file was touched, and nothing was written to reports/nnlab/preds or reports/nnlab/scores. The test scripts are in the scratchpad: scratchpad/shuf.py and scratchpad/sort.py." + ], + "notes": "Recommendation for the organiser-facing modelCreate wrapper: call model_create(..., sort_shots=True) so the output does not depend on the order the samples arrive in. The cost is that lgbm_upw no longer matches the stored files bit for bit, and the expected F1 change is of LightGBM-seed size (about 1e-3). Keep the default when reproducing stored numbers. The stored-file reproduction checks above all used the default path; with sort_shots=True I only checked permutation invariance, on n=5, rep=0. Not checked: whether sort_shots=True changes the averaged F1 over all draws. The whole order dependence comes from winners.train_retrained bagging rows by position. The stored lab results have the same property, because they were made in lab.shots_for order. The author's other claims hold: about 175 s per call with 2 threads, LightGBM with n_jobs=2 gives exactly the stored lgbm_upw, and no encoder or backbone was retrained." + } +] \ No newline at end of file diff --git a/reports/nnlab/verification.json b/reports/nnlab/verification.json new file mode 100644 index 0000000000000000000000000000000000000000..d7608ad8be687eb4aaa8e4ba9444fd3b9cd29574 --- /dev/null +++ b/reports/nnlab/verification.json @@ -0,0 +1,164 @@ +[ + { + "verdict": "holds-with-caveats", + "summary": "I traced every route by which an Amsterdam label could reach a scored prediction and found no leak. Amsterdam labels are read in exactly two places: lab.shot_labels(shots_for(n, rep)) for the same draw that is being written, and the scorer. The transductive clamps (__lp, __lapshot, __clamp) seed only the current draw's shots, and the scorer drops those rows with setdiff. No shot-dependent method has an n0_r0 file it could fall back to. The graph and the zero-shot XGBoost+DANN cache use no labels. My own macro F1, using my own shot draws, matches the stored argmax score to 5 decimals. The headline numbers reproduce exactly. The +0.12 jump at n=5 has two legitimate sources: (a) the zero-shot anchor zs_xgb_dann already scores rule_smooth 0.654 at every n, and (b) the headline compares the new method's rule_smooth column against the old method's rule column. Read on the same column, the gain is +0.099 at n=5 and +0.038 on average, not +0.12 / +0.055. The caveats are about how the comparison is reported and one hyper-parameter origin I could not check; neither is a leak.", + "findings": [ + { + "severity": "note", + "claim": "Amsterdam labels enter only through lab.shot_labels(lab.shots_for(d,n,rep)) for the draw being written. The scorer is the only other reader.", + "evidence": "grep across tools/nnlab: _ya appears only at lab.py:88 (load), :105 (shots_for), :110 (shot_labels), :288 (score_method), plus an equality assert at baselines.py:17. Every family calls shots_for and shot_labels with the loop's own (n, rep) right before save_pred(name, n, rep): baselines.py:67-69, metric.py:460-475, tab_nn.py:466-479, transductive.py:339-350 and :375-381, classical.py:245-246. ensemble.py reads no labels at all (lines 27-34)." + }, + { + "severity": "note", + "claim": "__lp, __lapshot and __clamp clamp only the current draw's shot rows, and the scorer excludes exactly those rows.", + "evidence": "transductive.py:339-348: shot = shots_for(d, n, rep), then r_lp, r_lapshot or r_clamp is applied to the base file of the same draw (base_pred(B, n, rep), :308-315), then saved as (n, rep) at :350. r_lp :147-148 and r_lapshot :160/:163 write one-hot values only at index `shot`. Scorer lab.py:295-297: keep = setdiff1d(arange, shots_for(d, n, rep)). In the scorer's smoothing (lab.py:274-275) shots are also not neighbours: A[keep][:, keep]." + }, + { + "severity": "note", + "claim": "No draw can silently read another draw's or another method's file. The load_pred fallback has no guard, but it never fires for any shot-dependent method in the store.", + "evidence": "load_pred (lab.py:242-246) falls back only to the SAME method's n0_r0.npy. I listed all preds/* dirs: n0_r0.npy exists only in the shot-free *_zs, zs_* and zs_*__em dirs. Every shot-dependent dir (ens_*, lgbm_upw, met_*, tab_*_ft/joint/proto, ts_*_ft/joint, cls_*) has only per-draw files. ensemble.py:30 requires a per-draw file for every --shot member. An md5 check over the 25 draws gives 25 distinct files for each of ens_e4__lp, ens_e2__lapshot, lgbm_upw, met_protoda_maha, met_cos_maha and tab_dann_rz_ft. Latent hazard only: if a shot-dependent method were ever given an n0_r0 file, or passed via --zs, the fallback would hide a missing draw. A guard in load_pred would close this." + }, + { + "severity": "note", + "claim": "The transductive graph uses no labels.", + "evidence": "transductive.py:108-123: PCA-32 of lab.robustz(Xa), a cosine kNN graph (k=10) union lab.adjacency(pa, r=3); only features and coordinates go in. Graph() at :126-134 is also label-free. The em variant estimates its prior on the non-shot rows from predictions only (:176-181)." + }, + { + "severity": "note", + "claim": "The zero-shot cache data/processed/zeroshot_xgb_dann_ams.npz was trained without Amsterdam labels.", + "evidence": "tools/full_score_combo.py:55-70 build_cache: XGBoost is fit on (Xm60, ym) with balanced weights and predicts Xa60; DANN is CT.train_dann(region_norm(Xm60), ym, region_norm(Xa60)), and compare_teammate.py:109-137 uses Xt only as unlabelled domain-classifier input (bce against ones). d['ya'] is loaded by CT.load (compare_teammate.py:196) but build_cache never uses it. In CT.load's 'first' path, age_class_o is used only in an alignment assert (:187). The 393 + 60 feature names contain no label-like column (my name scan flagged only 'coverage')." + }, + { + "severity": "minor", + "claim": "I could not verify where the teammate's zero-shot hyper-parameters came from. If they were tuned on Amsterdam, that would be an indirect (selection) leak into the strongest ensemble member.", + "evidence": "compare_teammate.py:57-60: XGB_PARAMS are commented \"their best_config.json\", and DANN_EPOCHS=40 is fixed. No best_config.json exists in the repo outside held_out (I did not open those). This is a provenance question, not a leak I found.", + "suggested_fix": "Ask the teammate whether best_config.json was tuned on Madrid CV or on Amsterdam. If on Amsterdam, re-tune on lab.madrid_split or say so in PREREGISTRATION.md." + }, + { + "severity": "minor", + "claim": "The headline gain mixes decision columns: the new method's rule_smooth against the old method's rule. Read on the same column, the gain is smaller.", + "evidence": "scores/*.json, n=5/25/50/100/200, then the average. lgbm_upw rule = 0.566/0.628/0.670/0.697/0.720 (0.656), but lgbm_upw rule_smooth = 0.595/0.654/0.689/0.712/0.731 (0.676). ens_e1__lp rule_smooth = 0.689/0.699/0.720/0.723/0.726 (0.711) and ens_e4__lp rule_smooth = 0.694/0.703/0.720/0.725/0.727 (0.714). On the same column the gain at n=5 is 0.689-0.595 = +0.094 (e1) / +0.099 (e4), not +0.12. On average it is +0.035 / +0.038, not +0.055. At n=200 it is about -0.005 (0.726/0.727 vs 0.731).", + "suggested_fix": "Report both methods on the same column (rule_smooth vs rule_smooth)." + }, + { + "severity": "note", + "claim": "The jump at n=5 comes mostly from the zero-shot anchor, which has weight 10/15 = 0.67 at n=5. That is legitimate, not leakage.", + "evidence": "zs_xgb_dann rule_smooth = 0.654/0.653/0.653/0.652/0.651 (it uses no shots; the values differ slightly only because the keep set changes). Steps at n=5, rule_smooth: ens_e2 = 0.669 (zs + lgbm_upw), ens_e4 = 0.682 (adds the metric and tab members), ens_e4__lp = 0.694 (LP adds +0.012)." + }, + { + "severity": "note", + "claim": "Empirical test: shot rows are fit perfectly as expected, and my independent macro F1 on non-shot rows equals the scorer's argmax value exactly, so the keep set excludes the shots.", + "evidence": "Script reports/nnlab/verify/leak_check.py re-implements the shot draw (rng 42+1000n+rep) and reads labels from amsterdam_features.parquet. ens_e4__lp n5 r0: acc_shot=1.000 (20 rows), acc_nonshot=0.6706, F1 mine=0.66695, json argmax=0.66695, F1 with shots included=0.66723. n5 r1: 1.000 / 0.6976 / 0.68851 = json 0.68851. n200 r0: 1.000 / 0.7373 / 0.73287 = json 0.73287 (with shots included it would be 0.74254, so excluding them matters at n=200). ens_e2__lapshot n5 r0: 1.000 / 0.6479 / 0.64479 = json. ens_e4, before LP: acc_shot 0.95 / 0.90 / 0.892, F1 matches json in every case." + }, + { + "severity": "note", + "claim": "half_A and the scorer's decision rule use no labels.", + "evidence": "lab.half_A (lab.py:280-284) is a coordinate checkerboard. W.decide (winners.py:121-126) applies tau-logit adjustment with the Madrid prior, then the plug-in rule (:95-118), which uses only the predicted class shares of the batch it is given, here the keep rows. Labels are used only in W.macro_f1 for scoring. Metric encoder length selection (metric.py:274-291) and tab_nn selection (tab_nn.py:317+) use lab.madrid_split on Madrid only." + } + ], + "numbers": "My own numpy/sklearn scorer (reports/nnlab/verify/leak_check.py, 2 threads, about 1 min) re-draws the shots with rng(42+1000n+rep) and reads labels from amsterdam_features.parquet. On non-shot rows its argmax macro F1 equals scores/.json \"argmax\" to 5 decimals for ens_e4__lp, ens_e2__lapshot, ens_e1__lp, ens_e4 and lgbm_upw at draws (5,0), (5,1) and (200,0); for example ens_e4__lp n5 r0 gives 0.66695 in both. Shot-row accuracy is 1.000 for clamped methods and lgbm_upw, and 0.95 / 0.90 / 0.892 for ens_e4 before LP. All 25 draw files are distinct by md5 for the six methods checked. Leaderboard means from scores/*.json (n=5/25/50/100/200, then the average): ens_e1__lp rule_smooth 0.689/0.699/0.720/0.723/0.726 (0.711) and lgbm_upw rule 0.566/0.628/0.670/0.697/0.720 (0.656), both reproducing the headline. Also: ens_e4__lp rule_smooth 0.694/0.703/0.720/0.725/0.727 (0.714); lgbm_upw rule_smooth 0.595/0.654/0.689/0.712/0.731 (0.676); zs_xgb_dann rule_smooth 0.654 flat; ens_e2 rule_smooth n5 0.669; ens_e4 rule_smooth n5 0.682." + }, + { + "verdict": "holds-with-caveats", + "summary": "I rebuilt everything independently and it matches. The ensemble, the label propagation, the graph, the shot draws and the scorer all reproduce the stored files to float32 precision, and the macro F1 values match to 4 decimals. The reference numbers also reproduce: lgbm_upw per-draw scores differ from the originals by exactly 0, and zs_lgbm is 0.5928 at n=5. The headline numbers are correct: ens_e1__lp averages 0.7113 (0.6888/0.6986/0.7200/0.7232/0.7256) and ens_e4__lp averages 0.7137. I found no leakage or computation bug. The only caveat is how the +0.128 at n=5 is framed. About +0.088 of it does not come from tonight's work. It comes from the teammate's zero-shot XGB+DANN model (+0.053, Madrid labels plus unlabelled Amsterdam only) and from neighbour smoothing (+0.035). The headline also compares a smoothed score with an unsmoothed one. Compared fairly against the best pipeline that existed before tonight (zs_xgb_dann, rule_smooth 0.6536), the net gain at n=5 is +0.040. That gain is consistent across all 5 draws and also shows up on half B, which was not used for choosing.", + "findings": [ + { + "severity": "note", + "claim": "(1) My own re-implementation of the ens_e4 rule matches the stored ens_e4 file for all three draws. The rule is w0=10/(n+10), mean of clipped log-probs of zero-shot members vs shot-using members, then softmax.", + "evidence": "tools/nnlab/ensemble.py:26-33 rule. max|own - stored| = 2.98e-08 for n5_r0, n25_r1 and n200_r2 (float32 storage). Script: reports/nnlab/verify/recompute.py" + }, + { + "severity": "note", + "claim": "(2) I rebuilt the union graph from scratch and wrote my own label propagation. Both match the stored ens_e4__lp file. The graph is robust-z with clip 10, PCA-32 (random_state=0), cosine 10-NN made symmetric, union with the r=3 spatial graph, no self-loops, 0/1 weights. The propagation is S=D^-1/2 A D^-1/2, Y0 = probabilities with shot rows set to 10 x one-hot, 20 iterations of F = 0.8 S F + 0.2 Y0, then row-normalised.", + "evidence": "Own graph: nnz=583310, identical to cache/transd_graph_ams_r3_k10_pca32.npz (0 differing entries), mean degree 22.44. LP max|own - stored| = 3.45e-08 (n5_r0), 3.38e-08 (n25_r1), 3.32e-08 (n200_r2). argmax agreement 1.000000. Code: transductive.py:96-123, 146-152" + }, + { + "severity": "note", + "claim": "(3) My own macro F1 matches the stored score file on every draw and every column. Shots are re-derived with default_rng(42+1000n+rep) and removed before scoring. They equal lab.shots_for (20/100/800 unique rows).", + "evidence": "ens_e4__lp. n5_r0: argmax 0.6669, rule 0.6815, rule_smooth 0.6919. n25_r1: 0.6814 / 0.6906 / 0.6961. n200_r2: 0.7208 / 0.7156 / 0.7163. ens_e4. n5_r0: 0.6432 / 0.6504 / 0.6748. n25_r1: 0.6576 / 0.6629 / 0.6794. n200_r2: 0.7120 / 0.7083 / 0.7171. Own and stored are equal to 4 decimals in every cell." + }, + { + "severity": "note", + "claim": "(4) The reference numbers reproduce.", + "evidence": "lgbm_upw stored 'rule' per draw vs reports/full_score_upweighted.json 'full': max abs diff 0.0 over all 25 draws. Means 0.5658 / 0.6284 / 0.6697 / 0.6973 / 0.7204, avg 0.6563. zs_lgbm rule at n=5 with my scorer: 0.5928 on every rep 0-4." + }, + { + "severity": "minor", + "claim": "(5) The '+0.12 at n=5' is real, but most of it comes from work done before tonight. The headline also compares rule_smooth with an unsmoothed 'rule'. Against lgbm_upw with the same smoothing (rule_smooth 0.5950) the gain is +0.099. Against the teammate's zero-shot model with smoothing (0.6536), which already beat lgbm_upw at n=5 (reports/teammate_comparison.md lines 84-88), the gain is +0.040.", + "evidence": "Means over n=5 draws 0-4, with per-rep values. Each step is plausible. (a) lgbm_upw rule 0.5658 to zs_xgb_dann rule 0.6188 (+0.053): a zero-shot model trained on Madrid labels plus unlabelled Amsterdam (tools/full_score_combo.py:55-69), constant across reps (0.6186-0.6189). (b) Smoothing to 0.6536 (+0.035): consistent with the earlier +0.02 to +0.05 reported for smoothing. (c) Ensemble ens_e4 rule 0.6595 (+0.041 over the zero-shot rule). It is consistent across reps (0.6504 / 0.6687 / 0.6520 / 0.6598 / 0.6664) even though every shot-using member is weaker alone (protoda_maha 0.605, cos_maha 0.583, tab_dann_rz_ft 0.605, lgbm_upw 0.566) and the zero-shot weight is 0.667. This is ordinary ensemble diversity and I found no leakage path (by contrast, ens_e2 alone is 0.6397). (d) ens_e4 rule_smooth 0.6823 (+0.023). (e) ens_e4__lp rule 0.6863 and rule_smooth 0.6941: LP plus smoothing adds +0.012 over (d)." + }, + { + "severity": "minor", + "claim": "Variants were chosen on half A and the full-city headline includes half A, so it is mildly optimistic. The ranking still holds on half B.", + "evidence": "PREREGISTRATION.md:5-12 (e1-e3 full-city scores were seen earlier). Half-B rule_smooth at n=5: ens_e4__lp 0.6825, zs_xgb_dann 0.6354, lgbm_upw 0.5767 (lgbm_upw rule_B 0.5512). Half-B average over n: ens_e4__lp 0.7020 vs lgbm_upw rule 0.6390." + }, + { + "severity": "note", + "claim": "Small mismatch: the teammate table gives the zero-shot FINAL as rule 0.6142 / rule+smooth 0.6506. nnlab gives 0.6188 / 0.6536. The likely causes are the 3-seed DANN averaging (full_score_combo.py:20) and nnlab's smoothing excluding shots. This does not affect the conclusions.", + "evidence": "reports/teammate_comparison.md:63 vs reports/nnlab/scores/zs_xgb_dann.json mean at n=5" + } + ], + "numbers": "Recomputed in reports/nnlab/verify/recompute.py (my own ensemble, my own graph, my own LP, my own F1; lab.W.decide and lab.smooth used for the rule columns) and reports/nnlab/verify/decomp.py (n=5 decomposition from the score files, plus my own scoring of zs_lgbm, lgbm_upw and zs_xgb_dann at n=5). Rows below are n = 5/25/50/100/200, means over draws 0-4. ens_e4__lp rule_smooth: 0.6941/0.7027/0.7201/0.7249/0.7269, avg 0.7137. ens_e1__lp rule_smooth: 0.6888/0.6986/0.7200/0.7232/0.7256, avg 0.7113 (matches the claim). lgbm_upw rule: 0.5658/0.6284/0.6697/0.6973/0.7204, avg 0.6563. lgbm_upw rule_smooth: 0.5950/0.6540/0.6887/0.7116/0.7310, avg 0.6761. zs_xgb_dann rule_smooth: 0.6536 at n=5. Only the n=5 decomposition steps are recomputed from the score files: +0.0530 (zero-shot model), +0.0348 (smoothing), +0.0059 (ens_e4 rule_smooth vs zs rule_smooth: +0.0059 = 0.6823 - 0.6536 - ... namely ens_e4 rule 0.6595, rule_smooth 0.6823), +0.0118 (LP plus smoothing over ens_e4 rule_smooth). Total from lgbm_upw rule to ens_e4__lp rule_smooth: +0.1283." + }, + { + "verdict": "holds-with-caveats", + "summary": "The stored scores reproduce the headline numbers. The 5 numbers are per-n means (n = 5, 25, 50, 100, 200) over reps 0-4, not per-draw values. The large gain at n=5 is real: it shows up on half B (+0.131) at least as strongly as on half A (+0.122), and it adds up from parts that make sense. Going from lgbm_upw \"rule\" at 0.566 to ens_e4__lp \"rule_smooth\" at 0.694: the zero-shot XGB+DANN anchor alone adds +0.053 (0.619 rule), mixing it with the shot-using members adds +0.041 (ens_e4 rule 0.660), neighbour smoothing adds +0.023 (0.682) and label propagation adds +0.012 (0.694). So the \"+0.12\" is mostly an apples-to-oranges comparison, not leakage. The reference ignores both the zero-shot model, which already beat it at n<=25, and the smoothing step that the pick gets. Against lgbm_upw read with the same rule_smooth column, the gain is +0.099 / +0.049 / +0.031 / +0.013 / -0.004. At n=200 the pick is slightly WORSE than lgbm_upw with smoothing, and worse than lgbm_upw__lp. Selection optimism is small next to the gain: the top picks on half A lose about 0.005-0.008 more from A to B than the typical variant does. The checkerboard guard is weak, though: only 69 blocks; A and B share the same shot draws; part of B was effectively seen. One preregistered choice does not hold up on B: the n>=100 switch to FINAL-PEAK. Several pipeline components were chosen on Amsterdam: the member list, the zero-shot anchor, the post-processor, the decision column and the switch point. The draws 5-11 that the preregistration needs for confirmation do not exist yet: every score file has 25 draws. The whole gain on top of non-spatial methods also depends on having coordinates for all test pixels and predicting them all at once.", + "findings": [ + { + "severity": "major", + "claim": "The +0.12-0.13 jump at n=5 is real (it holds on half B), but it comes from comparing different column choices and a reference that leaves out the zero-shot model. It is not evidence that the few-shot method is better. The fair 'previous best' at n=5 was the teammate's zero-shot FINAL (0.639 argmax+smooth as built; 0.654 rule_smooth). Against that, ens_e4__lp gains about +0.04-0.055 at n=5.", + "evidence": "From scores/*.json, n=5 full city: lgbm_upw rule 0.5658; zs_xgb_dann rule 0.6188; ens_e4 rule 0.6595; ens_e4 rule_smooth 0.6823; ens_e4__lp rule_smooth 0.6941. Paired difference ens_e4__lp rule_smooth minus lgbm_upw rule at n=5: all +0.1284+-0.0037, A +0.1224, B +0.1313. Minus lgbm_upw rule_smooth: +0.0991 / +0.0487 / +0.0314 / +0.0132 / -0.0041 for n = 5..200. Minus zs_xgb_dann rule_smooth at n=5: +0.0406. reports/teammate_comparison.md lists their FINAL zero-shot at 0.639 for every n.", + "suggested_fix": "Report the gain against the strongest earlier pipeline at each n, read with the same decision column (lgbm_upw rule_smooth, and zs_xgb_dann rule_smooth / teammate FINAL 0.639). Do not report it against lgbm_upw 'rule'." + }, + { + "severity": "major", + "claim": "The FINAL-SWITCH threshold (move to FINAL-PEAK at n>=100) was chosen on half A and does not hold on half B at n=100. On B it would cost about 0.013 at n=100. Only n=200 holds up.", + "evidence": "ens_e2__lapshot minus ens_e4__lp (both rule_smooth), per n = 5/25/50/100/200. Half A: -0.0177 -0.0157 -0.0118 +0.0020 +0.0132. Half B: -0.0224 -0.0241 -0.0218 -0.0128 +0.0063. Full: ... -0.0055 +0.0095. At n=100 the top 3 on A (lgbm_upw__lp, ens_e2__lp, ens_e2__lapshot) rank 23, 22 and 33 on B.", + "suggested_fix": "Use FINAL-AVG for every n, or switch only at n=200. Say that the switch point was picked on Amsterdam. Draws 5-11 can then confirm it without having been used to choose it." + }, + { + "severity": "major", + "claim": "The preregistered confirmation has not been run yet. Every score file contains only draws 0-4 (25 draws). The protection given by half B is also weaker than it looks: full-city scores (which include B) were printed during selection (as PREREGISTRATION.md admits). A and B share the same 25 shot draws, so a lucky draw helps both halves. This verification has now also tabulated B for draws 0-4.", + "evidence": "All 114 scores/*.json have n_draws = 25. PREREGISTRATION.md, 'Honest caveat' paragraph. lab.py score_method computes rule_A / rule_B from the same P_all per (n, rep).", + "suggested_fix": "Run NNLAB_REPS=12 and report all pixels on draws 5-11 for ens_e4__lp, ens_e2__lapshot and lgbm_upw (both rule and rule_smooth), as the preregistration lists. Treat these as the only clean numbers." + }, + { + "severity": "minor", + "claim": "Selection bias from scoring about 110 methods (221 method x decision variants) on Amsterdam is measurable but small, about 0.005-0.01 on the average score. That is far below the average gain of about +0.057 over lgbm_upw rule, or +0.038 over lgbm_upw rule_smooth. Choosing between e4, e6 and e7 is pure noise: they differ by under 0.001 on A, they reorder on B, and the paired standard errors are about 0.002-0.004.", + "evidence": "Across 221 variants: Spearman(A avg, B avg) = 0.983; the A-B gap has median 0.0179 and SD 0.0142. Excess of the mean gap over the median gap for the top k on A: top1 +0.0076, top3 +0.0056, top10 +0.0050, top20 +0.0051. ens_e4__lp: A 0.7236, B 0.7020, B rank 2 of 221. ens_e6__lp, ranked 1st on A, is 8th on B. The best on B is ens_e4__lapshot (A rank 13). Per-draw std of ens_e4__lp rule_smooth by n: .0074 .0069 .0036 .0039 .0070, so the SE of a 5-rep mean is .0015-.0033. The gain over the reference on B is larger than on A at every n (e.g. vs lgbm_upw rule: A avg +0.0505, B +0.0630), so the gain is not an artefact of selecting on A.", + "suggested_fix": "Quote the result as about 0.71 +-0.01 and not to three decimals. Name e4 as a representative of a group of equivalent ensembles, not as the winner." + }, + { + "severity": "minor", + "claim": "The checkerboard is a guard against pixel-level label noise. It is not an independent test. There are only 69 blocks of 2 km (about 35 per half), and the halves interleave the same districts. The spatial operators cross the A/B border, but that leaks no labels, because they carry predictions and allowed shots, not B labels. Half B is also systematically harder for every method, so absolute levels on another subset of pixels can easily move by about 0.02.", + "evidence": "Computed from amsterdam_features.parquet coordinates only (no labels): 69 blocks; A share 0.481. r=3: 11.3% of pixels have a neighbour in the other half; on average 3.1% of a pixel's neighbours are in the other half. r=6: 26.2% and 6.8%. The kNN feature graph spans the whole city anyway. The A-B gap is positive for nearly all variants (median +0.018; lgbm_upw rule +0.034).", + "suggested_fix": "Describe A/B as a sanity split. Claim robustness from draws 5-11, and if possible from a blocked hold-out (whole contiguous regions), not from B." + }, + { + "severity": "minor", + "claim": "Knob provenance. Fixed in advance from Madrid or the literature: K=10 in w0=K/(n+K) (research_shortlist.json item 6, a heuristic rather than a published value); smoothing r=6, alpha=0.5 (teammate, Madrid; lab.py:61); the tau-0.5 rule (Madrid); the within-family knobs (metric.py:16-18 training steps, tab_nn.py:34-45 epochs and lam, both on lab.madrid_split). Fixed ad hoc and not Madrid-validated, but not searched on Amsterdam either: the LP knobs alpha 0.8, seed weight x10, k=10 cosine 0/1, spatial r=3, 20 iterations (transductive.py:67-69). They depart from shortlist item 4 (alpha 0.9, seed x5, k=15 Gaussian, spatial kNN 8, choose on the Madrid harness). The Madrid graph cache exists, but no madrid-check output is in the logs. Selected on Amsterdam: the anchor zs_xgb_dann (best zero-shot in the teammate_comparison Amsterdam table); the shot members met_protoda_maha, met_cos_maha, tab_dann_rz_ft (out of about 100 family variants); the e1-e7 compositions; the post-processor per base (lp / lapshot / clamp / em / st); reading rule_smooth rather than rule for __lp; and the n>=100 switch.", + "evidence": "ensemble.py docstring; transductive.py:67-69 and its DEVIATIONS block; research_shortlist.json items 4 and 6; preds/ens_e*/meta.json member lists; lab.py:61 'teammate's choice, made on Madrid'; no 'madrid-check' string anywhere in reports/nnlab/logs/.", + "suggested_fix": "List the knobs selected on Amsterdam explicitly in the write-up. Run transductive.py --madrid-check (LP knobs) and log the output, so the LP setting gets a Madrid justification after the fact." + }, + { + "severity": "major", + "claim": "Protocol fairness: the spatial and transductive parts need things a standard harness may not provide. They need coordinates of the test pixels (smoothing, the LP spatial graph). They need ALL unlabelled test features at fit time (the DANN inside zs_xgb_dann, tab_dann, protoda CORAL, the PCA-32 kNN graph over 25,992 rows, per-city normalisation). And they need batch prediction (smoothing and LP couple the test pixels). Scored one pixel at a time, smoothing reduces to the identity and LP to the base, so the result falls to about ens_e4 'rule'. If held-out pixels are not among the 25,992 rows, the graph and the DANN would have to be refit with them included. Spatial distance from the shots matters little at small n and somewhat more at n=200.", + "evidence": "Without spatial steps, ens_e4 rule = 0.6595 / 0.6736 / 0.6988 / 0.7067 / 0.7135 (avg 0.6904), still +0.034 avg over lgbm_upw rule (0.6563). Smoothing is worth +0.017 avg for ens_e4 (0.7071 vs 0.6904), and LP a further +0.007 (0.7137). Median distance to the nearest shot, using random-placement proxies: 20 shots 1342 m, 200 shots 356 m, 800 shots 155 m. At n=5 the LP spatial channel (r=3, 90 m) hardly reaches any test pixel. LP gain at n=200: e4 0.7253 to e4__lp 0.7269.", + "suggested_fix": "Confirm the organisers' interface: does modelCreate receive the unlabelled pool plus coordinates, and does prediction run as one batch? Prepare a fallback (ens_e4 + rule, no spatial steps) and report its numbers next to the main ones." + }, + { + "severity": "note", + "claim": "Reading rule_smooth on a __lp output does not reuse any labels. The scorer's smooth() drops shot rows as neighbours (lab.py:268-277, keep=). It does apply spatial structure twice (LP r=3 graph, then r=6 averaging), which amounts to a wider kernel, and the column was chosen on A. The effect is small and holds on B, so it does not invalidate the claim. It should be described as one combined post-processing step and not as 'label propagation' alone.", + "evidence": "ens_e4__lp rule to rule_smooth: full 0.7084 to 0.7137 (+0.0053); A +0.0050; B 0.6966 to 0.7020 (+0.0054). The transductive.py docstring forbids double smoothing only for __clamp.", + "suggested_fix": "Describe the method as 'ensemble, then LP (r=3 + feature kNN), then r=6 smoothing, then rule', and say the final smoothing was kept after looking at A." + }, + { + "severity": "note", + "claim": "The headline quotes ens_e1__lp, which is not the preregistered pick. The FINAL-AVG pick ens_e4__lp is slightly higher. Its numbers should be the ones quoted.", + "evidence": "ens_e1__lp rule_smooth: 0.6888 0.6986 0.7200 0.7232 0.7256 (avg 0.7113), matching the headline. ens_e4__lp rule_smooth: 0.6941 0.7027 0.7201 0.7249 0.7269 (avg 0.7137). lgbm_upw rule: 0.5658 0.6284 0.6697 0.6973 0.7204 (avg 0.6563), matching.", + "suggested_fix": "Quote ens_e4__lp." + } + ], + "numbers": "Scripts: reports/nnlab/verify/meth_skeptic.py, meth_skeptic2.py and meth_skeptic3.py. They read only scores/*.json, plus coordinate columns of amsterdam_features.parquet for the border and shot-distance statistics; no labels were read.\n\n(a) Reproduction: ens_e1__lp, ens_e4__lp and lgbm_upw match the headline exactly (see the last finding).\n\n(b) n=5 decomposition on all pixels: 0.5658 (lgbm_upw rule), then 0.6188 (+0.053, zs_xgb_dann rule), then 0.6595 (+0.041, ens_e4 rule), then 0.6823 (+0.023, smoothing), then 0.6941 (+0.012, LP). Total +0.128.\n\n(c) Paired difference per draw, mean +- SE over 5 reps, ens_e4__lp rule_smooth minus lgbm_upw rule_smooth:\n- all pixels: +.0991+-.0027, +.0487+-.0053, +.0314+-.0032, +.0132+-.0015, -.0041+-.0021\n- half B: +.1058, +.0572, +.0379, +.0218, -.0010\n\n(d) A vs B across 221 variants: Spearman 0.983. Median A-B gap 0.0179 (SD 0.0142). The top 1/3/10/20 on A have a gap larger than the median by 0.0076 / 0.0056 / 0.0050 / 0.0051, which estimates the winner's-curse optimism. ens_e4__lp: A 0.7236, B 0.7020 (B rank 2). ens_e2__lapshot: A 0.7176, B 0.6870 (B rank 26 on the average; rank 3 at n=200).\n\n(e) Switch check, ens_e2__lapshot minus ens_e4__lp for n = 5/25/50/100/200:\n- half A: -.0177, -.0157, -.0118, +.0020, +.0132\n- half B: -.0224, -.0241, -.0218, -.0128, +.0063\n\n(f) Fallbacks without spatial steps: ens_e4 rule averages 0.6904; ens_e2 rule averages 0.6780.\n\n(g) Geometry: 69 blocks of 2 km. Pixels with a neighbour in the other half: 11.3% at r=3, 26.2% at r=6. Median distance to the nearest of k random pixels: 1342 m for k=20, 526 m for k=100, 356 m for k=200, 155 m for k=800.\n\n(h) Draws: n_draws = 25 in all 114 score files, so draws 5-11 have not been scored.\n\nWording I would accept: \"On Amsterdam (all pixels except the shots; 5 shot draws per n, not yet confirmed on the preregistered draws 5-11), a fixed-rule ensemble anchored on a zero-shot domain-adapted model, followed by graph label propagation and neighbour smoothing, reaches macro F1 0.694 / 0.703 / 0.720 / 0.725 / 0.727 for n = 5 / 25 / 50 / 100 / 200 (average 0.714). The strongest earlier pipelines, read with the same smoothing and decision rule, reach 0.654 (zero-shot) at n=5 and 0.654 / 0.676 / 0.689 / 0.712 / 0.731 (retrained LightGBM). The gain is about +0.04 to +0.10 for n <= 50, about +0.01 at n=100 and none at n=200. Members, post-processing and the decision column were selected on half of Amsterdam; we estimate the resulting optimism at about 0.005-0.01. The method is transductive: it needs the coordinates and features of all test pixels at once. Without the spatial steps it scores 0.660-0.714 (average 0.690).\"" + } +] \ No newline at end of file diff --git a/reports/nnlab/verification_round2.json b/reports/nnlab/verification_round2.json new file mode 100644 index 0000000000000000000000000000000000000000..4929229d31d7a74ac9b2a08abce4de5cef6701dd --- /dev/null +++ b/reports/nnlab/verification_round2.json @@ -0,0 +1,156 @@ +[ + { + "verdict": "holds-with-caveats", + "summary": "I recomputed every cell of both tables from reports/nnlab/scores/*.json; my script is reports/nnlab/verify/factcheck_tables.py. The fresh view uses draws 5-11, the single-models table uses draws 0-4 with the \"rule\" column, and half B uses draws 0-11. No cell is off by more than 0.0006. Three cells are rounded up when they should round down: two in the results table and one in the single-models table. The half-B figure for the five-model ensemble in caveat 3 is also rounded wrong. These checks all match the sources: the gains in the bullets, the standard deviations, the count of about 130 variants (134 score files), the half-A selection numbers, the Madrid domain-copies check (8 of 9 draws; +0.005, +0.018 and +0.020), the shuffle test, and the reviewers' statements. I re-ran model_create2.py --n 5 --rep 0 and it reproduced the stored ens_e12 result: F1 0.688960 against 0.688960 stored, max probability difference 3e-8. Every command in the Reproducing section exists with the arguments shown. The substantive problems are wording that goes beyond the evidence. \"Best in peak\" is a tie with lgbm_feda alone. The supercon encoder breaks the Mahalanobis-head claim. EM helped on this night's Madrid stand-in, which contradicts two sentences. Caveat 1 puts the anchor weight among the things chosen on Amsterdam, but the scripts say it was fixed in advance. The line-by-line review claim has no record. The self-training and \"one step where both agree\" statements are too strong.", + "findings": [ + { + "severity": "minor", + "claim": "Results table, five-model ensemble with smoothing, n=200: the report says 0.726. The source value is 0.7255, which rounds to 0.725.", + "evidence": "confirmation.csv ens_e4 rule_smooth fresh n200 = 0.725464; the per-draw mean from scores/ens_e4.json for reps 5-11 is 0.7255. The difference is 0.0005, inside the 0.0006 tolerance, but the rounding is wrong.", + "suggested_fix": "0.725" + }, + { + "severity": "minor", + "claim": "Results table, five-model ensemble plus label propagation, n=200: the report says 0.729. The source value is 0.7285, which rounds to 0.728.", + "evidence": "ens_e4__lp rule_smooth fresh n200 = 0.728490. The difference is 0.0005; the rounding is wrong.", + "suggested_fix": "0.728" + }, + { + "severity": "minor", + "claim": "Single-models table, metric learning with the cosine encoder and Mahalanobis head, n=100: the report says 0.689. The source value is 0.68846, which rounds to 0.688.", + "evidence": "scores/met_cos_maha.json, rule, reps 0-4, n=100 gives 0.68846. Every other cell in the 14 single-model rows matches to within 0.0005, and all averages are correct.", + "suggested_fix": "0.688" + }, + { + "severity": "minor", + "claim": "Caveat 3 says the five-model ensemble averages 0.697 on half B. No row gives 0.697: ens_e4 with smoothing is 0.696, and ens_e4 plus label propagation is 0.699.", + "evidence": "confirmation.csv view B: ens_e4 rule_smooth avg 0.696466 and ens_e4__lp rule_smooth avg 0.699328. FINAL-2 at 0.7005 (0.700) and lgbm_upw at 0.6560 (0.656) are correct.", + "suggested_fix": "'0.696 for the five-model ensemble (0.699 with label propagation)'" + }, + { + "severity": "minor", + "claim": "'FINAL-2 is best on the fresh draws in ... the peak (0.744 at n=200)' is effectively a tie: lgbm_feda alone with smoothing reaches the same peak.", + "evidence": "Fresh view, n=200: ens_e12 rule_smooth 0.744281 against lgbm_feda rule_smooth 0.744275, a difference of 0.000006. FINAL-2's peak gain comes entirely from the LightGBM with domain copies. The average claim (0.714 against 0.710 for the next best) is correct, as are the n=25 tie (0.70499 against 0.70465) and the n=5 deficit of 0.004 (paired difference -0.0043, SE 0.0039, ens_e4__lp ahead in 5 of 7 draws, so inside the noise).", + "suggested_fix": "'... and ties the LightGBM with domain copies alone at the peak (0.744 at n=200).'" + }, + { + "severity": "minor", + "claim": "'Against the round-1 five-model ensemble the margin ... comes from n >= 100' holds against the version with label propagation. Against the plain ensemble FINAL-2 is ahead at every n, and n=50 contributes too.", + "evidence": "Fresh, smoothing, FINAL-2 minus ens_e4: +0.004 / +0.006 / +0.010 / +0.011 / +0.019. FINAL-2 minus ens_e4__lp: -0.004 / +0.000 / +0.002 / +0.007 / +0.016. The averages +0.010 and +0.004 are correct.", + "suggested_fix": "Say the n>=100 statement refers to the ensemble with label propagation, or say the gap widens with n." + }, + { + "severity": "minor", + "claim": "Finding 'a within-class Mahalanobis head is best on all four encoders' is false for the supcon encoder.", + "evidence": "Draws 0-4, rule: met_supcon_maha 0.6304 < met_supcon_ftproj 0.6407. On cos, proto and protoda, Mahalanobis is best (0.656, 0.648, 0.655). The ranges quoted for prototypes (0.56-0.60), soft k-means (0.54-0.60) and the projection fine-tune (about 0.64) are correct.", + "suggested_fix": "'best on three of four encoders (supcon: projection fine-tune 0.641 against Mahalanobis 0.630)'." + }, + { + "severity": "minor", + "claim": "'Prior re-estimation by EM hurt on every base, as it did on the Madrid shift harness earlier' contradicts this night's Madrid stand-in. There EM improved the score.", + "evidence": "logs/lp_madrid_check.log: base avg 0.4981 against em 0.5080 (+0.010). On Amsterdam, EM did hurt on all four bases: lgbm_upw 0.656 to 0.646, zs_dann 0.576 to 0.571, zs_lgbm 0.592 to 0.583, zs_xgb_dann 0.618 to 0.600. Caveat 4 ('Twice a Madrid stand-in and Amsterdam disagreed') therefore leaves out a third disagreement, EM. Clamp was also positive on Madrid (0.5057).", + "suggested_fix": "Drop 'as it did on the Madrid shift harness', or cite which harness, and add EM to the disagreements in caveat 4." + }, + { + "severity": "minor", + "claim": "Caveat 4 says 'the domain copies are the one adaptation step where both agree'. That is not the only one: LP-FT against LP also agreed on Madrid and Amsterdam.", + "evidence": "cache/lpft/madrid_check.json: lp 0.484/0.528/0.527 against lpft 0.491/0.550/0.574 at n=5/50/200. On Amsterdam, draws 0-4: tab_dann_rz_ft 0.637 against tab_dann_rz_lpft 0.662. The fine-tune strength from tab_nn selection.json (lam=10) is also chosen on Madrid.", + "suggested_fix": "'one of the adaptation steps where both agree'." + }, + { + "severity": "minor", + "claim": "Caveat 1 lists the anchor weight 10/(n+10) under 'Chosen on Amsterdam, not Madrid'. The scripts and the reviewer say it was fixed in advance from the literature shortlist, not chosen on Amsterdam. Separately, the claim that 'every knob inside the models' was fixed on Madrid or in advance overstates it.", + "evidence": "ensemble.py docstring: 'Rule, fixed before any ensemble was scored (from the literature shortlist, item 6)'. The verification.json methodology agent lists K=10 as 'Fixed in advance ... a heuristic rather than a published value'. On the knobs: the leakage agent could not establish where the teammate's XGBoost and DANN hyper-parameters came from ('best_config.json' is not in the repo). The transductive LP knobs were ad hoc and never checked on Madrid. TOPK=120 and shot weight 30 were fixed in advance (lgbm_var.py docstring) but not tuned on Madrid. They also depart from shortlist item 10, which specifies 60 DANN + top-60 features. Separately, the Amsterdam-chosen list correctly includes the member list, i.e. picking lgbm_feda over w100/emb/fedaw100 and e12 over e10/e11 on half A; that part is consistent with PREREGISTRATION.md.", + "suggested_fix": "Move the anchor weight to 'fixed in advance (heuristic, not Madrid-validated)'. Add the teammate's zero-shot hyper-parameters as 'provenance unknown'." + }, + { + "severity": "minor", + "claim": "'Self-training ... added nothing' is only true once label propagation follows. Without it, adding the self-trained LightGBM raised the ensemble, and on its own the self-trained model scored below its pseudo-label source.", + "evidence": "Half A, draws 0-4, rule_smooth: ens_e6 (e4 + ens_e1__lp__st) 0.7199 against ens_e4 0.7139 (+0.006). Draws 0-4 all pixels, rule: 0.6971 against 0.6904. With LP: ens_e6__lp 0.7243 against ens_e4__lp 0.7236 (+0.0007), which matches the pre-registration's 'under 0.001'. ens_e1__lp__st alone scores 0.690 against its base ens_e1__lp at 0.706.", + "suggested_fix": "'added nothing once label propagation is applied (+0.001); +0.006 without it'." + }, + { + "severity": "minor", + "claim": "In the finding on LP-FT, '300 steps ... was worse on Amsterdam (0.640)' quotes the fresh-draw value inside a paragraph whose other numbers are draws 0-4.", + "evidence": "tab_dann_rz_lpft_s300 rule: draws 0-4 0.644, fresh 0.640. The comparison values 0.637 and 0.662 are draws 0-4, and 0.658 is fresh (0.6575). Madrid preferred 300 (steps_selection.json mean 0.5459 against 0.5381 for 60), but its margin over 150 and 600 is under 0.001. STEPS=60 is the a-priori value from shortlist item 12, so the reported 60-step model was not chosen on Madrid.", + "suggested_fix": "'(0.644 on draws 0-4, 0.640 fresh)'. Also note that 60 steps was the a-priori value and was kept against the Madrid choice." + }, + { + "severity": "minor", + "claim": "'The round-2 scripts each had an independent line-by-line review before running' has no supporting record in the repository.", + "evidence": "verification.json holds only the three round-1 agents (leakage, recomputation, methodology). The only review log is logs/ts_nn_smoke_review.log (00:36, a round-1 sequence-network smoke test). Nothing records reviews of lgbm_var.py, lpft.py or the e10-e12 ensembles.", + "suggested_fix": "Remove it, or point to where the reviews are recorded." + }, + { + "severity": "minor", + "claim": "Selection optimism (bullet and caveat 2): 0.696 to 0.689 for lgbm_feda and '0.004 to 0.007' are correct. But reference models that were never selected also drop by about 0.003 from draws 0-4 to fresh, so not all of the gap is selection.", + "evidence": "First 5 minus fresh: lgbm_feda rule 0.6957-0.6887 = 0.007; FINAL-2 smooth 0.7189-0.7138 = 0.005; ens_e4__lp 0.7137-0.7097 = 0.004. The never-selected lgbm_upw rule 0.6563-0.6532 = 0.003 and rule_smooth 0.6761-0.6727 = 0.003. The reviewers' 0.005-0.01 estimate comes from verification.json (top-k excess A-B gap +0.005 to +0.008).", + "suggested_fix": "Mention that the unselected reference drops by 0.003, so net selection optimism is about 0.001-0.004." + }, + { + "severity": "note", + "claim": "'Shuffled labels ... The gain comes from the shot labels, not from a leak' slightly overstates what the test shows. The numbers are correct.", + "evidence": "Shuffled 0.3152 (n=5) and 0.2285 (n=200) against true 0.6154 and 0.7380 (scores/lgbm_feda.json rep 0 rule: 0.6154, 0.7380). The test shows the model depends on the shot labels. Ruling out leaks rests on the code audit (round 1, verification.json), and that audit predates lgbm_feda.", + "suggested_fix": "'... so the gain depends on the shot labels' and cite the leakage audit separately." + }, + { + "severity": "note", + "claim": "Several context claims check out: the Madrid domain-copies check, its caveat, the step-by-step description, the half-A selection numbers and the verification quotes.", + "evidence": "feda_madrid_check.json: feda wins 8/9 (loses only n=5 rep 2: 0.5003 against 0.5110), means +0.0049 / +0.0181 / +0.0204. The top-120 list comes from zero_shot_lgbm.txt trained on all Madrid (lgbm_var.top_gain_idx), as the caveat says. Half A in PREREGISTRATION.md: ens_e12 0.7052/0.7161/0.7410/0.7481/0.7591 (avg 0.734), e10 0.721, e11 0.724, ens_e4 0.7139, lgbm_feda 0.7236, all reproduced. Feature layout [x, x_top, NaN] / [x, NaN, x_top], weight 30, balanced classes and per-city robust-z all appear in fit_predict. SMOOTH_R=6, alpha 0.5, 'teammate's choice, made on Madrid' is at lab.py:61. There are 69 2-km blocks (computed from px_key/py_key). lgbm_upw draws 0-4 = 0.5658/0.6284/0.6697/0.6973/0.7204 and reproduces with zero difference according to verification.json. Variant count: 134 score files. Shot weight 100 on top of the copies: 0.6943 against 0.6957. The tabular-network row lists lpft (0.662) as the best variant, but lpft_ent is marginally higher (0.663)." + }, + { + "severity": "note", + "claim": "Most of the Reproducing section is correct, but it leaves out the steps that build several compared rows. The 'cache/*/selection*.json' glob also misses most of the Madrid selection files.", + "evidence": "All named scripts exist and their arguments parse: full_score_combo.py zeroshot (inserts its own sys.path, so it works under -I), baselines.py all, ensemble.py NAME --zs --shot [--k], tab_nn.py --seeds, lab.py score, model_create2.py --n --rep [--no-spatial]. model_create.py exists with --n/--rep. Missing are tools/nnlab/transductive.py (it builds ens_e4__lp, lgbm_upw__lp and __st) and the ensemble.py calls for e4 (round 1). The Madrid choices are in lpft/steps_selection.json, metric/*_select.json and ts_nn/select_*.json, which 'selection*' does not match; only tab_nn/selection.json does.", + "suggested_fix": "Add 'python tools/nnlab/transductive.py' and the e4 ensemble command, and change the glob to cache/*/*select*.json." + }, + { + "severity": "note", + "claim": "The headline numbers and the remaining derived figures are correct.", + "evidence": "Fresh: FINAL-2 0.7138 (0.7003), lgbm_upw 0.6727 (0.6532). FINAL-2 SDs 0.0074/0.0116/0.0146/0.0037/0.0033 match '0.007 / 0.012 / 0.015 / 0.004 / 0.003'. Like-for-like gains +0.085/+0.053/+0.035/+0.020/+0.012, no-coordinates +0.047, LightGBM with copies against the old LightGBM +0.036 (rule; +0.029 with smoothing). LP +0.006 on fresh smoothed draws, though +0.018 in the draws 0-4 rule view. LP on Madrid 0.475 against 0.498. Finding numbers 0.637/0.582, 0.662, 0.658, DANN/ResNet/MLP/CORAL 0.637/0.626/0.603/0.578, sequence-network zero-shot 0.32-0.53, embeddings 0.628 against 0.656. The fine-tune-beats-refit pattern holds on every backbone, including the sequence networks." + } + ] + }, + { + "verdict": "holds-with-caveats", + "summary": "I found no leakage in lgbm_feda or ens_e12 (FINAL-2), and an independent refit reproduces the stored numbers. The only Amsterdam labels that reach the fit are the current draw's shots. The top-120 feature list comes from a Madrid-only booster. The train and predict matrices have the same layout, and shot and non-shot rows are built the same way at predict time. I refit the draw n=50, rep=7 with my own matrix construction, using 3 threads instead of the original 6. It matches the stored lgbm_feda to within 3e-8, with 100% argmax agreement. Rebuilding ens_e12 from the stored members also matches to within 3e-8. My own scorer, with the shots re-derived, gives argmax macro F1 0.671204 and rule_smooth 0.699202, the same as scores/ens_e12.json to every printed digit. The gain is not an artefact of the scorer: on all 35 fresh draws (reps 5-11) lgbm_feda beats lgbm_upw under all three decision rules and on half B. The gain is largest under plain argmax (+0.039, against +0.036 rule and +0.029 rule_smooth), so the decision rule shrinks it rather than creating it. The caveat is the mechanism: \"Amsterdam pixels get branches of their own, fitted on the shots\" does not describe what happens. Most of the split gain sits on the Madrid-only copies, and an ablation shows that almost any way of letting the trees tell the two cities apart recovers most of the gain.", + "findings": [ + { + "severity": "note", + "claim": "No Amsterdam label other than the current draw's shots reaches the lgbm_feda fit.", + "evidence": "In tools/nnlab/lgbm_var.py main(), Amsterdam is read with columns=feats+PID only, without age_class. The labels come only from lab.shot_labels(d, shot) for lab.shots_for(d, n, rep). Amsterdam robust-z uses W.robustz_fit over all Amsterdam rows' features, which needs no labels, does not depend on the draw, and is transductive use the protocol allows. model_create2.py calls the same fit_predict and reads non-shot labels only in its CLI scoring line. In my refit I re-derived the shots with default_rng(42+1000n+rep) and per-class rng.choice; they are identical to W.shots_for." + }, + { + "severity": "note", + "claim": "The top-120 feature list uses no Amsterdam labels: it comes from a Madrid-only zero-shot booster.", + "evidence": "tools/export_winners.py calls W.train_zero_shot(d['X_mad'], d['y_mad']), which trains on Madrid only with class-balanced weights and raw features, and saves models/zero_shot_lgbm.txt. In the saved booster, tree 0 has a root internal_count of 60929, about 0.8 (bagging) times the 76263 Madrid rows; a Madrid+Amsterdam fit would show about 82k. The feature list drops columns whose std is 0 in either city, which also needs no labels. top_gain_idx() equals the feda_idx stored in preds/lgbm_feda/meta.json (checked: True)." + }, + { + "severity": "note", + "claim": "Train and predict matrices have the same column layout, and shot rows are not treated differently from other Amsterdam rows at predict time.", + "evidence": "Training rows are Madrid [x, x_top, NaN] and shots [x, NaN, x_top]. Prediction rows are all 25,992 Amsterdam rows as [x, NaN, x_top], so the shots get exactly the same row format as every other Amsterdam row. Shots are removed by keep before scoring and before smoothing (lab.smooth with keep slices the adjacency matrix, so shots are never neighbours). The refit model classifies the shots 100% correctly against 66% on non-shot rows, but the shots are never scored, so this does not inflate the result." + }, + { + "severity": "note", + "claim": "An independent refit of lgbm_feda for n=50, rep=7 reproduces the stored predictions, and the ens_e12 rebuild reproduces the stored file and the score file.", + "evidence": "Refit with my own matrix code, {**LGBM_PARAMS, **RETRAIN_OVERRIDES, n_jobs=3} (the stored run used n_jobs=6), took 66 s. Against the stored file: max abs diff 2.98e-08, mean 4.6e-09, argmax agreement 1.0. The zs_xgb_dann store equals (P_xgb + mean DANN seeds)/2 from the npz exactly. My ens_e12 rebuild with w0=10/60 differs from the stored file by at most 2.98e-08. My macro F1 on non-shot rows is 0.671204 (argmax) and 0.699202 (rule_smooth), equal to scores/ens_e12.json (argmax 0.6712044, rule_smooth 0.6992015). Outputs are in reports/nnlab/verify/leak_recompute_e12_n50_r7.json." + }, + { + "severity": "minor", + "claim": "The report's explanation of the mechanism is not supported: most of the gain comes from the Madrid-only copies, and simply letting the trees separate the two domains reproduces most of it.", + "evidence": "Split-gain shares in the refit model (n=50, rep=7): shared block 0.670, Madrid-only copies 0.285, Amsterdam-only copies 0.045. Split-count shares: Madrid copies 0.297, Amsterdam copies 0.019. Ablation on the same draw (argmax macro F1, non-shot rows): lgbm_upw 0.6410; a single 0/1 domain-indicator column 0.6471; Madrid copies only 0.6616; Amsterdam copies only 0.6590; full feda 0.6637. Outputs are in reports/nnlab/verify/leak_feda_ablation_n50_r7.json. Either copy on its own gives the trees missing-value routing that separates the cities: in a Madrid-copy split, Madrid rows split on the value and all Amsterdam rows go to the missing-value branch. This also fits the structure of the hyper-parameters. min_child_samples=80 counts rows, so an Amsterdam-only copy can hardly be split with 20 shots (n=5) or 100 shots (n=25), yet the largest gains are at n=5 (+0.055) and n=25 (+0.062). That contradicts 'branches of their own, fitted on the shots'. The gain is real and label-driven (the shuffled-label check collapses it), so this is a wording problem, not a leak.", + "suggested_fix": "Change step 2 of OVERNIGHT_RESULTS.md to something like: 'the copies let trees learn Madrid-specific splits that Amsterdam rows bypass through the missing-value branch; one domain indicator column alone gives about a quarter of the gain, and Madrid-only or Amsterdam-only copies alone give most of it (one draw, n=50 rep 7)'. Mark this as a single-draw observation." + }, + { + "severity": "note", + "claim": "The gain is not an artefact of the decision rule or of the class-balanced shots: it holds under plain argmax, on every fresh draw, and on half B.", + "evidence": "lgbm_feda minus lgbm_upw, mean over draws 5-11 (35 draws), from the scores/*.json files: argmax +0.0388, rule +0.0355, rule_smooth +0.0288. Half B: +0.0402 / +0.0372 / +0.0315. By n (argmax): n5 +0.055, n25 +0.062, n50 +0.041, n100 +0.022, n200 +0.014. lgbm_feda is better on 35 of 35 draws under every rule and on both halves. The rule and smoothing shrink the gain, so they are not producing it. Draws 0-4 show the same pattern (argmax +0.078 at n=5, down to +0.016 at n=200)." + }, + { + "severity": "note", + "claim": "A minor point about data provenance: lgbm_var and model_create2 read features from release/hack4dev-building-age-winners/data/processed (W.DATA), while lab.load reads data/processed (lab.PROC).", + "evidence": "lgbm_var.main asserts that the row order of the two tables is the same (px/py keys and Madrid labels). My refit used W.DATA, and its labels reproduce the scores exactly, so the two copies agree as far as this check reaches. This is not a correctness problem." + } + ] + } +] \ No newline at end of file diff --git a/reports/nnlab/verify/decomp.py b/reports/nnlab/verify/decomp.py new file mode 100644 index 0000000000000000000000000000000000000000..1bda6f7f704b66ee12db6cac4245f1f69d933632 --- /dev/null +++ b/reports/nnlab/verify/decomp.py @@ -0,0 +1,29 @@ +import os,sys,json +os.environ["NNLAB_THREADS"]="2"; os.environ["OMP_NUM_THREADS"]="2" +import numpy as np, pandas as pd +sys.path.insert(0,"./tools/nnlab") +import lab +S="./reports/nnlab/scores/" +steps=[("lgbm_upw","rule"),("zs_xgb_dann","rule"),("zs_xgb_dann","rule_smooth"),("ens_e2","rule"),("ens_e4","rule"),("ens_e4","rule_smooth"),("ens_e4__lp","rule"),("ens_e4__lp","rule_smooth")] +tab={} +for m,c in steps: + s=json.load(open(S+m+".json")) + tab[f"{m}:{c}"]=[r[c] for r in sorted(s["draws"],key=lambda r:(r["n"],r["rep"])) if r["n"]==5] +df=pd.DataFrame(tab).T; df.columns=[f"r{i}" for i in range(5)]; df["mean"]=df.mean(1); df["d_prev"]=df["mean"].diff() +pd.set_option("display.width",200); print(df.round(4)) +# own anchor recomputation at n=5 +ams=pd.read_parquet(lab.PROC/"amsterdam_features.parquet",columns=["age_class"]); ya=ams.age_class.to_numpy().astype(int) +ym=pd.read_parquet(lab.PROC/"madrid_features.parquet",columns=["age_class"]).age_class.to_numpy().astype(int) +prior=np.array([(ym==c).mean() for c in (1,2,3,4)]) +def shots(n,rep): + rng=np.random.default_rng(42+1000*n+rep) + return np.concatenate([rng.choice(np.flatnonzero(ya==c),n,replace=False) for c in (1,2,3,4)]) +from sklearn.metrics import f1_score +for m in ("zs_lgbm","lgbm_upw","zs_xgb_dann"): + v=[] + for r in range(5): + P=lab.load_pred(m,5,r); s=shots(5,r); keep=np.setdiff1d(np.arange(len(ya)),s) + v.append(f1_score(ya[keep],lab.W.decide(P[keep],prior),labels=[1,2,3,4],average="macro")) + print(m,"own rule n5:",np.round(v,4),"mean",round(float(np.mean(v)),4)) +# member agreement / zero-shot weight at n=5 +w0=10/15; print("w0 at n=5 =",round(w0,3),"; each shot member weight =",round((1-w0)/4,3)) diff --git a/reports/nnlab/verify/factcheck_tables.py b/reports/nnlab/verify/factcheck_tables.py new file mode 100644 index 0000000000000000000000000000000000000000..a22333ccd4a8b9724095928a2d6d97529cc23561 --- /dev/null +++ b/reports/nnlab/verify/factcheck_tables.py @@ -0,0 +1,20 @@ +import json, glob, os, numpy as np +N=[5,25,50,100,200] +def view(m,col,reps): + d=json.load(open(f'reports/nnlab/scores/{m}.json'))['draws'] + out=[] + for n in N: + v=[r[col] for r in d if r['n']==n and r['rep'] in reps and col in r] + out.append((np.mean(v) if v else np.nan, len(v), np.std(v,ddof=1) if len(v)>1 else np.nan)) + return out +for f in sorted(glob.glob('reports/nnlab/scores/*.json')): + m=os.path.basename(f)[:-5] + r=view(m,'rule',range(5)) + means=[x[0] for x in r] + print(f"{m:28s} first5 rule "+" ".join(f"{x:.4f}" for x in means)+f" avg {np.mean(means):.4f} cnt {[x[1] for x in r]}") +print() +for m,c in [('ens_e12','rule_smooth'),('ens_e12','rule'),('lgbm_feda','rule_smooth'),('lgbm_feda','rule'),('ens_e4','rule_smooth'),('ens_e4__lp','rule_smooth'),('ens_e4','rule'),('lgbm_upw','rule_smooth'),('lgbm_upw','rule'),('zs_xgb_dann','rule_smooth'),('tab_dann_rz_lpft','rule'),('tab_dann_rz_lpft_s300','rule')]: + r=view(m,c,range(5,12)); means=[x[0] for x in r] + print(f"fresh {m:22s}{c:12s} "+" ".join(f"{x:.4f}" for x in means)+f" avg {np.mean(means):.4f} sd "+" ".join(f"{x[2]:.4f}" for x in r)+f" cnt {[x[1] for x in r]}") + r=view(m,c+'_B',range(12)); means=[x[0] for x in r] + print(f" B "+" ".join(f"{x:.4f}" for x in means)+f" avg {np.mean(means):.4f}") diff --git a/reports/nnlab/verify/feda_madrid_check.json b/reports/nnlab/verify/feda_madrid_check.json new file mode 100644 index 0000000000000000000000000000000000000000..a83314e1ee8af04a557e7880ee05370a679596f1 --- /dev/null +++ b/reports/nnlab/verify/feda_madrid_check.json @@ -0,0 +1,98 @@ +{ + "rows": [ + { + "n": 5, + "rep": 0, + "base": 0.5112909757419573, + "base_argmax": 0.5212239015332509, + "lgbm_feda": 0.5165852812927493, + "lgbm_feda_argmax": 0.5165388003706562 + }, + { + "n": 5, + "rep": 1, + "base": 0.5071879809252857, + "base_argmax": 0.5159222739580802, + "lgbm_feda": 0.5273800955124958, + "lgbm_feda_argmax": 0.5312224386386708 + }, + { + "n": 5, + "rep": 2, + "base": 0.5110492517459505, + "base_argmax": 0.5224134291918237, + "lgbm_feda": 0.5002520159037596, + "lgbm_feda_argmax": 0.5038965008668661 + }, + { + "n": 50, + "rep": 0, + "base": 0.5425683537376056, + "base_argmax": 0.5585137296714877, + "lgbm_feda": 0.5629221419826955, + "lgbm_feda_argmax": 0.566261784267049 + }, + { + "n": 50, + "rep": 1, + "base": 0.5449103532928878, + "base_argmax": 0.5528065504153683, + "lgbm_feda": 0.5559370130220953, + "lgbm_feda_argmax": 0.5580697864507776 + }, + { + "n": 50, + "rep": 2, + "base": 0.5445297255328264, + "base_argmax": 0.5508593904182901, + "lgbm_feda": 0.5674457280388097, + "lgbm_feda_argmax": 0.5684731730878443 + }, + { + "n": 200, + "rep": 0, + "base": 0.5866932582537123, + "base_argmax": 0.5999832226697881, + "lgbm_feda": 0.59851203258147, + "lgbm_feda_argmax": 0.6082464674161475 + }, + { + "n": 200, + "rep": 1, + "base": 0.59152516194576, + "base_argmax": 0.6081797662738705, + "lgbm_feda": 0.6172543351714332, + "lgbm_feda_argmax": 0.6242938802111315 + }, + { + "n": 200, + "rep": 2, + "base": 0.589399251356872, + "base_argmax": 0.6071173681438173, + "lgbm_feda": 0.6131371569406152, + "lgbm_feda_argmax": 0.6258432237880246 + } + ], + "mean": { + "base": { + "5": 0.5098427361377311, + "50": 0.5440028108544399, + "200": 0.5892058905187815 + }, + "lgbm_feda": { + "5": 0.5147391309030015, + "50": 0.5621016276812002, + "200": 0.6096345082311728 + }, + "base_argmax": { + "5": 0.5198532015610516, + "50": 0.5540598901683821, + "200": 0.605093452362492 + }, + "lgbm_feda_argmax": { + "5": 0.5172192466253978, + "50": 0.5642682479352237, + "200": 0.6194611904717678 + } + } +} \ No newline at end of file diff --git a/reports/nnlab/verify/feda_madrid_check.py b/reports/nnlab/verify/feda_madrid_check.py new file mode 100644 index 0000000000000000000000000000000000000000..d8c9279a8b758abdfd572d728ffe38e7129494fe --- /dev/null +++ b/reports/nnlab/verify/feda_madrid_check.py @@ -0,0 +1,36 @@ +"""Madrid-only evidence for lgbm_feda: a spatial part of Madrid plays the target city +(lab.madrid_split). Baseline (retrained LightGBM, shots x30) against the feda variant, +n in (5, 50, 200), 3 draws each. No Amsterdam data is used at all. +Caveat: the top-120 feature list comes from the zero-shot model trained on ALL Madrid.""" +import json, os, sys, time +os.environ.setdefault("NNLAB_THREADS", "3") +sys.path.insert(0, "tools/nnlab") +import numpy as np, pandas as pd +import lab, lgbm_var as V +W = lab.W +d = lab.load(official60=False) +feats = W.feature_names() +X = pd.read_parquet(W.DATA / "madrid_features.parquet", columns=feats)[feats].to_numpy(float) +src, tgt = lab.madrid_split(d["pm"]) +Zs, Zt = W.robustz(X[src], W.robustz_fit(X[src])), W.robustz(X[tgt], W.robustz_fit(X[tgt])) +ys, yt = d["ym"][src], d["ym"][tgt] +prior = np.array([(ys == c).mean() for c in lab.CLASSES]) +ctx = {"top": V.top_gain_idx()} +params = {**W.LGBM_PARAMS, **W.RETRAIN_OVERRIDES, "n_jobs": lab.THREADS} +rows, t0 = [], time.time() +for n in (5, 50, 200): + for rep in range(3): + rng = np.random.default_rng(9000 + 100 * n + rep) + shot = np.concatenate([rng.choice(np.flatnonzero(yt == c), n, replace=False) for c in lab.CLASSES]) + keep = np.setdiff1d(np.arange(len(yt)), shot) + r = dict(n=n, rep=rep) + for v in ("base", "lgbm_feda"): + P = V.fit_predict(v, Zs, ys, Zt, shot, yt[shot], ctx, params) + r[v] = W.macro_f1(yt[keep], W.decide(P[keep], prior)) + r[v + "_argmax"] = W.macro_f1(yt[keep], lab.CLASSES[P[keep].argmax(1)]) + rows.append(r) + print(f"[{time.time()-t0:5.0f}s] n={n} rep={rep} base={r['base']:.4f} feda={r['lgbm_feda']:.4f} (argmax {r['base_argmax']:.4f} / {r['lgbm_feda_argmax']:.4f})", flush=True) +df = pd.DataFrame(rows) +m = df.groupby("n")[["base", "lgbm_feda", "base_argmax", "lgbm_feda_argmax"]].mean() +print(m.round(4).to_string()) +json.dump(dict(rows=rows, mean=m.to_dict()), open("reports/nnlab/verify/feda_madrid_check.json", "w"), indent=1) diff --git a/reports/nnlab/verify/feda_shuffle.py b/reports/nnlab/verify/feda_shuffle.py new file mode 100644 index 0000000000000000000000000000000000000000..644b65e3d5e69592a326e9e85714d0d3e2089b3e --- /dev/null +++ b/reports/nnlab/verify/feda_shuffle.py @@ -0,0 +1,29 @@ +"""Sanity test for lgbm_feda: train with the SHOT LABELS SHUFFLED. If the score stays +high, the gain does not come from the shots' labels and something is wrong.""" +import os, sys +os.environ.setdefault("NNLAB_THREADS", "4") +sys.path.insert(0, "tools/nnlab") +import numpy as np, pandas as pd +import lab, lgbm_var as V +W = lab.W +d = lab.load(official60=False) +feats = W.feature_names() +mad = pd.read_parquet(W.DATA / "madrid_features.parquet", columns=feats) +ams = pd.read_parquet(W.DATA / "amsterdam_features.parquet", columns=feats) +Xm, Xa = mad[feats].to_numpy(float), ams[feats].to_numpy(float) +Xm, Xa = W.robustz(Xm, W.robustz_fit(Xm)), W.robustz(Xa, W.robustz_fit(Xa)) +ctx = {"top": V.top_gain_idx()} +params = {**W.LGBM_PARAMS, **W.RETRAIN_OVERRIDES, "n_jobs": lab.THREADS} +ya = d["_ya"] # scoring only +for n in (5, 200): + shot = lab.shots_for(d, n, 0) + ys = lab.shot_labels(d, shot) + keep = np.setdiff1d(np.arange(len(ya)), shot) + ref = np.load(lab.pred_path("lgbm_feda", n, 0)).astype(float) + f_ref = W.macro_f1(ya[keep], W.decide(ref[keep], d["prior"])) + rng = np.random.default_rng(123 + n) + P = V.fit_predict("lgbm_feda", Xm, d["ym"], Xa, shot, rng.permutation(ys), ctx, params) + f_shuf = W.macro_f1(ya[keep], W.decide(P[keep], d["prior"])) + Pt = V.fit_predict("lgbm_feda", Xm, d["ym"], Xa, shot, ys, ctx, params) + print(f"n={n}: stored true-label F1 {f_ref:.4f} | refit true-label F1 {W.macro_f1(ya[keep], W.decide(Pt[keep], d['prior'])):.4f} " + f"(max abs diff vs stored {np.abs(Pt-ref).max():.2e}) | SHUFFLED-label F1 {f_shuf:.4f}", flush=True) diff --git a/reports/nnlab/verify/leak_check.py b/reports/nnlab/verify/leak_check.py new file mode 100644 index 0000000000000000000000000000000000000000..4e11fa9953ce453f189e5d6653e917faa853f3fc --- /dev/null +++ b/reports/nnlab/verify/leak_check.py @@ -0,0 +1,40 @@ +import os, sys, json +os.environ["NNLAB_THREADS"]="2"; os.environ["OMP_NUM_THREADS"]="2" +import numpy as np, pandas as pd +from sklearn.metrics import f1_score +R="." +sys.path.insert(0,R+"/release/hack4dev-building-age-winners"); sys.path.insert(0,R+"/src") +import winners as W, config as C +feats=W.feature_names(); f60=list(C.FEATURE_COLS_60) +bad=[f for f in feats+f60 if any(s in f.lower() for s in ("age","year_built","weighted_mean","class","label","city"))] +print("n feats",len(feats),len(f60),"suspicious names:",bad) +ams=pd.read_parquet(R+"/data/processed/amsterdam_features.parquet",columns=["age_class","px_key","py_key"]) +y=ams["age_class"].to_numpy().astype(int) +# own shot draw, independent re-implementation +def draw(n,rep): + rng=np.random.default_rng(42+1000*n+rep); out=[] + for c in (1,2,3,4): + out.append(rng.choice(np.flatnonzero(y==c),n,replace=False)) + return np.concatenate(out) +CL=np.array([1,2,3,4]) +for meth in ("ens_e4__lp","ens_e2__lapshot","ens_e1__lp","ens_e4","lgbm_upw"): + sc=json.load(open(f"{R}/reports/nnlab/scores/{meth}.json")) + rows={(r["n"],r["rep"]):r for r in sc["draws"]} + for n,rep in ((5,0),(5,1),(200,0)): + P=np.load(f"{R}/reports/nnlab/preds/{meth}/n{n}_r{rep}.npy") + s=draw(n,rep); mask=np.ones(len(y),bool); mask[s]=False + am=CL[P.argmax(1)] + acc_s=(am[s]==y[s]).mean(); acc_k=(am[mask]==y[mask]).mean() + f_keep=f1_score(y[mask],am[mask],labels=CL,average="macro") + f_all=f1_score(y,am,labels=CL,average="macro") + print(f"{meth:18s} n={n:3d} r={rep} shots={len(s)} acc_shot={acc_s:.3f} acc_nonshot={acc_k:.4f} " + f"F1keep(mine)={f_keep:.5f} json_argmax={rows[(n,rep)]['argmax']:.5f} F1incl_shots={f_all:.5f} " + f"json_rule_smooth={rows[(n,rep)]['rule_smooth']:.4f}") +# cross-draw identity check: are any two draws' files identical? +import hashlib +for meth in ("ens_e4__lp","ens_e2__lapshot","lgbm_upw","met_protoda_maha","met_cos_maha","tab_dann_rz_ft"): + hs={} + for n in (5,25,50,100,200): + for r in range(5): + hs[(n,r)]=hashlib.md5(np.load(f"{R}/reports/nnlab/preds/{meth}/n{n}_r{r}.npy").tobytes()).hexdigest() + print(meth,"distinct files among 25:",len(set(hs.values()))) diff --git a/reports/nnlab/verify/leak_feda_ablation.py b/reports/nnlab/verify/leak_feda_ablation.py new file mode 100644 index 0000000000000000000000000000000000000000..4031f2752b7868e45b23794cb634124ff4c570c1 --- /dev/null +++ b/reports/nnlab/verify/leak_feda_ablation.py @@ -0,0 +1,38 @@ +"""Mechanism ablation for one draw: Madrid-copy only; Amsterdam-copy only; 1 domain-indicator column.""" +import sys, json, time +from pathlib import Path +ROOT = Path(r".") +sys.path.insert(0, str(ROOT / "tools" / "nnlab")) +import lab +import numpy as np, pandas as pd +from sklearn.metrics import f1_score +W = lab.W +N, REP = int(sys.argv[1]), int(sys.argv[2]) +feats = W.feature_names() +mad = pd.read_parquet(W.DATA / "madrid_features.parquet", columns=feats + ["age_class"]) +ams = pd.read_parquet(W.DATA / "amsterdam_features.parquet", columns=feats + ["age_class"]) +Xm = mad[feats].to_numpy(float); ym = mad["age_class"].to_numpy().astype(int) +Xa = ams[feats].to_numpy(float); ya = ams["age_class"].to_numpy().astype(int) +shot = W.shots_for(ya, N, REP); ys = ya[shot] +keep = np.setdiff1d(np.arange(len(ya)), shot) +import lightgbm as lgb +g = lgb.Booster(model_file=str(W.MODELS / "zero_shot_lgbm.txt")).feature_importance("gain") +top = np.sort(np.argsort(-g, kind="stable")[:120]) +Zm = W.robustz(Xm, W.robustz_fit(Xm)); Za = W.robustz(Xa, W.robustz_fit(Xa)); Zs = Za[shot] +K = 120 +y = np.r_[ym, ys] +w = np.r_[np.ones(len(ym)), np.full(len(ys), 30.0)]; w = w * W.balanced_class_weights(y)[y - 1] +params = {**W.LGBM_PARAMS, **W.RETRAIN_OVERRIDES, "n_jobs": 3} +mf = lambda p: float(f1_score(ya[keep], p[keep].argmax(1) + 1, labels=[1, 2, 3, 4], average="macro", zero_division=0)) +res = {} +def run(name, Xtr, Xpr): + t0 = time.time(); m = W._fit_lgbm(Xtr, y, params, sample_weight=w); P = m.predict_proba(Xpr) + res[name] = mf(P); print(name, res[name], f"{time.time()-t0:.0f}s", flush=True) +nm, ns, na = np.full((len(Zm), K), np.nan), np.full((len(Zs), K), np.nan), np.full((len(Za), K), np.nan) +run("madrid_copy_only", np.vstack([np.hstack([Zm, Zm[:, top]]), np.hstack([Zs, ns])]), np.hstack([Za, na])) +run("ams_copy_only", np.vstack([np.hstack([Zm, nm]), np.hstack([Zs, Zs[:, top]])]), np.hstack([Za, Za[:, top]])) +run("domain_indicator", np.vstack([np.c_[Zm, np.zeros(len(Zm))], np.c_[Zs, np.ones(len(Zs))]]), np.c_[Za, np.ones(len(Za))]) +res["stored_feda"] = mf(np.load(lab.pred_path("lgbm_feda", N, REP))) +res["stored_upw"] = mf(np.load(lab.pred_path("lgbm_upw", N, REP))) +print(json.dumps(res, indent=1)) +(ROOT / "reports/nnlab/verify" / f"leak_feda_ablation_n{N}_r{REP}.json").write_text(json.dumps(res, indent=1)) diff --git a/reports/nnlab/verify/leak_feda_ablation_n50_r7.json b/reports/nnlab/verify/leak_feda_ablation_n50_r7.json new file mode 100644 index 0000000000000000000000000000000000000000..012ea6ccb79b72d4a72a6e7b3d05a53d748c6549 --- /dev/null +++ b/reports/nnlab/verify/leak_feda_ablation_n50_r7.json @@ -0,0 +1,7 @@ +{ + "madrid_copy_only": 0.6616285322961823, + "ams_copy_only": 0.6590384203553862, + "domain_indicator": 0.6471181864888529, + "stored_feda": 0.6637443083538547, + "stored_upw": 0.6409536273441575 +} \ No newline at end of file diff --git a/reports/nnlab/verify/leak_recompute_e12.py b/reports/nnlab/verify/leak_recompute_e12.py new file mode 100644 index 0000000000000000000000000000000000000000..ae5afe66e698098a9463fbd4604908990fafe3e8 --- /dev/null +++ b/reports/nnlab/verify/leak_recompute_e12.py @@ -0,0 +1,94 @@ +"""Independent refit of lgbm_feda for one draw + rebuild of ens_e12 (verification scratch).""" +import sys, json, time, os +from pathlib import Path +ROOT = Path(r".") +sys.path.insert(0, str(ROOT / "tools" / "nnlab")) +import lab +import numpy as np, pandas as pd +from sklearn.metrics import f1_score +W = lab.W +N, REP = int(sys.argv[1]), int(sys.argv[2]) +out = {} +feats = W.feature_names() +mad = pd.read_parquet(W.DATA / "madrid_features.parquet", columns=feats + ["age_class"]) +ams = pd.read_parquet(W.DATA / "amsterdam_features.parquet", columns=feats + ["px_key", "py_key", "age_class"]) +print("W.DATA", W.DATA, "lab.PROC", lab.PROC) +Xm = mad[feats].to_numpy(float); ym = mad["age_class"].to_numpy().astype(int) +Xa = ams[feats].to_numpy(float); ya = ams["age_class"].to_numpy().astype(int) # ya: shots + scoring only +out["n_madrid"] = len(Xm); out["root_internal_count_expect_0.8"] = 0.8 * len(Xm) +# shots re-derived by my own code +rng = np.random.default_rng(42 + 1000 * N + REP) +shot = np.concatenate([rng.choice(np.flatnonzero(ya == c), size=N, replace=False) for c in (1, 2, 3, 4)]) +assert np.array_equal(shot, W.shots_for(ya, N, REP)) +ys = ya[shot] +# top-120 from zero-shot booster (Madrid-only model) +import lightgbm as lgb +b0 = lgb.Booster(model_file=str(W.MODELS / "zero_shot_lgbm.txt")) +g = b0.feature_importance("gain") +top = np.sort(np.argsort(-g, kind="stable")[:120]) +meta = json.loads((lab.PRED / "lgbm_feda" / "meta.json").read_text()) +out["top_matches_stored_meta"] = bool(np.array_equal(top, np.array(meta["feda_idx"]))) +d0 = b0.dump_model() +out["zs_root_count_tree0"] = d0["tree_info"][0]["tree_structure"].get("internal_count") +# per-city robust-z +Zm = W.robustz(Xm, W.robustz_fit(Xm)); Za = W.robustz(Xa, W.robustz_fit(Xa)) +F = Zm.shape[1]; K = len(top) +nan_m = np.full((len(Zm), K), np.nan); nan_s = np.full((len(shot), K), np.nan) +Xtr = np.vstack([np.hstack([Zm, Zm[:, top], nan_m]), + np.hstack([Za[shot], nan_s, Za[shot][:, top]])]) +Xpr = np.hstack([Za, np.full((len(Za), K), np.nan), Za[:, top]]) +assert Xtr.shape[1] == Xpr.shape[1] == F + 2 * K +y = np.r_[ym, ys] +w = np.r_[np.ones(len(ym)), np.full(len(ys), 30.0)] +w = w * W.balanced_class_weights(y)[y - 1] +params = {**W.LGBM_PARAMS, **W.RETRAIN_OVERRIDES, "n_jobs": 3} +t0 = time.time() +m = W._fit_lgbm(Xtr, y, params, sample_weight=w) +out["fit_s"] = time.time() - t0 +P = m.predict_proba(Xpr) +ref = np.load(lab.pred_path("lgbm_feda", N, REP)).astype(np.float64) +out["feda_max_abs_diff"] = float(np.abs(P - ref).max()) +out["feda_mean_abs_diff"] = float(np.abs(P - ref).mean()) +out["feda_argmax_agree"] = float((P.argmax(1) == ref.argmax(1)).mean()) +# shot rows vs: does prediction on shots look memorised? +keep = np.setdiff1d(np.arange(len(ya)), shot) +out["shot_train_acc"] = float((P[shot].argmax(1) + 1 == ys).mean()) +out["nonshot_acc"] = float((P[keep].argmax(1) + 1 == ya[keep]).mean()) +# gain split by block +gi = m.booster_.feature_importance("gain") +tot = gi.sum() +out["gain_share_shared"] = float(gi[:F].sum() / tot) +out["gain_share_shared_top120"] = float(gi[top].sum() / tot) +out["gain_share_madrid_copy"] = float(gi[F:F + K].sum() / tot) +out["gain_share_ams_copy"] = float(gi[F + K:].sum() / tot) +sp = m.booster_.feature_importance("split") +out["split_share_madrid_copy"] = float(sp[F:F + K].sum() / sp.sum()) +out["split_share_ams_copy"] = float(sp[F + K:].sum() / sp.sum()) +# how the NaN default direction matters: Madrid-copy splits route Amsterdam rows by missing direction +# ens_e12 rebuild from stored members +z = np.load(lab.PROC / "zeroshot_xgb_dann_ams.npz") +zs_path = lab.pred_path("zs_xgb_dann", N, REP) +zs_store = lab.load_pred("zs_xgb_dann", N, REP) +Pzs_npz = (z["P_xgb"] + z["P_dann_seeds"].mean(0)) / 2 +out["zs_store_vs_npz_maxdiff"] = float(np.abs(zs_store - Pzs_npz.astype(np.float32).astype(np.float64)).max()) +w0 = 10 / (N + 10) +def ens(Pz, Pf): + L = w0 * np.log(np.clip(Pz, 1e-6, 1)) + (1 - w0) * np.log(np.clip(Pf, 1e-6, 1)) + L -= L.max(1, keepdims=True); E = np.exp(L); return E / E.sum(1, keepdims=True) +E_mine = ens(zs_store, ref) +E_ref = np.load(lab.pred_path("ens_e12", N, REP)).astype(np.float64) +out["ens_rebuild_max_abs_diff"] = float(np.abs(E_mine - E_ref).max()) +E_refit = ens(zs_store, P) +mf = lambda yy, pp: float(f1_score(yy, pp, labels=[1, 2, 3, 4], average="macro", zero_division=0)) +out["my_f1_argmax_ens_stored"] = mf(ya[keep], E_ref[keep].argmax(1) + 1) +out["my_f1_argmax_ens_refit"] = mf(ya[keep], E_refit[keep].argmax(1) + 1) +out["my_f1_argmax_feda_refit"] = mf(ya[keep], P[keep].argmax(1) + 1) +out["my_f1_argmax_feda_stored"] = mf(ya[keep], ref[keep].argmax(1) + 1) +prior = np.array([(ym == c).mean() for c in (1, 2, 3, 4)]) +pa = ams[["px_key", "py_key"]].to_numpy().astype(np.int64) +out["f1_rule_smooth_ens_refit"] = mf(ya[keep], W.decide(lab.smooth(E_refit[keep], pa, keep=keep), prior)) +sc = json.loads((lab.SCORES / "ens_e12.json").read_text()) +row = [r for r in sc["draws"] if r["n"] == N and r["rep"] == REP][0] +out["scorefile_ens_e12"] = {k: row[k] for k in ("argmax", "rule", "rule_smooth")} +print(json.dumps(out, indent=1)) +(ROOT / "reports/nnlab/verify" / f"leak_recompute_e12_n{N}_r{REP}.json").write_text(json.dumps(out, indent=1)) diff --git a/reports/nnlab/verify/leak_recompute_e12_n50_r7.json b/reports/nnlab/verify/leak_recompute_e12_n50_r7.json new file mode 100644 index 0000000000000000000000000000000000000000..10aaa6c3316031335d36da0d73bddfdd940a90a6 --- /dev/null +++ b/reports/nnlab/verify/leak_recompute_e12_n50_r7.json @@ -0,0 +1,30 @@ +{ + "n_madrid": 76263, + "root_internal_count_expect_0.8": 61010.4, + "top_matches_stored_meta": true, + "zs_root_count_tree0": 60929, + "fit_s": 65.83379554748535, + "feda_max_abs_diff": 2.9799862355517348e-08, + "feda_mean_abs_diff": 4.64469949421147e-09, + "feda_argmax_agree": 1.0, + "shot_train_acc": 1.0, + "nonshot_acc": 0.6638880272952854, + "gain_share_shared": 0.6699033034205327, + "gain_share_shared_top120": 0.48701220863732003, + "gain_share_madrid_copy": 0.2854266510716563, + "gain_share_ams_copy": 0.04467004550781095, + "split_share_madrid_copy": 0.2966875, + "split_share_ams_copy": 0.0189375, + "zs_store_vs_npz_maxdiff": 0.0, + "ens_rebuild_max_abs_diff": 2.9800773959642868e-08, + "my_f1_argmax_ens_stored": 0.6712043772201366, + "my_f1_argmax_ens_refit": 0.6712043772201366, + "my_f1_argmax_feda_refit": 0.6637443083538547, + "my_f1_argmax_feda_stored": 0.6637443083538547, + "f1_rule_smooth_ens_refit": 0.6992015371128085, + "scorefile_ens_e12": { + "argmax": 0.6712043772201366, + "rule": 0.6771065640024653, + "rule_smooth": 0.6992015371128085 + } +} \ No newline at end of file diff --git a/reports/nnlab/verify/meth_skeptic.py b/reports/nnlab/verify/meth_skeptic.py new file mode 100644 index 0000000000000000000000000000000000000000..71b8182aae1bf1c1dd7625c8e7e54c4ff0cec7a2 --- /dev/null +++ b/reports/nnlab/verify/meth_skeptic.py @@ -0,0 +1,11 @@ +import json,glob,os,numpy as np +S={os.path.basename(f)[:-5]:json.load(open(f)) for f in glob.glob('reports/nnlab/scores/*.json')} +print(len(S),'score files; draws:',sorted(set(s['n_draws'] for s in S.values()))) +N=['5','25','50','100','200'] +def row(m,c): + s=S[m]; return [s['mean'][c].get(n,np.nan) for n in N] +for m,c in [('lgbm_upw','rule'),('lgbm_upw','rule_smooth'),('zs_xgb_dann','rule'),('zs_xgb_dann','rule_smooth'),('lgbm_upw__lp','rule_smooth'),('ens_e1','rule_smooth'),('ens_e4','rule_smooth'),('ens_e1__lp','rule_smooth'),('ens_e4__lp','rule'),('ens_e4__lp','rule_smooth'),('ens_e2__lapshot','rule_smooth'),('ens_e2__lapshot','rule'),('zs_xgb_dann__lp','rule_smooth')]: + if m not in S: print('missing',m); continue + for suf in ['','_A','_B']: + v=row(m,c+suf); print(f"{m:18s}{c+suf:15s}"," ".join(f"{x:.4f}" for x in v),f"avg={np.nanmean(v):.4f}") + sd=[S[m]['std'][c].get(n,np.nan) for n in N]; print(' '*33,'std',' '.join(f"{x:.4f}" for x in sd), 'n_draws',S[m]['n_draws']) diff --git a/reports/nnlab/verify/meth_skeptic2.py b/reports/nnlab/verify/meth_skeptic2.py new file mode 100644 index 0000000000000000000000000000000000000000..e49cb21794c53839fd2425c57dadf9c073f2f77a --- /dev/null +++ b/reports/nnlab/verify/meth_skeptic2.py @@ -0,0 +1,20 @@ +import json,glob,os,numpy as np +S={os.path.basename(f)[:-5]:json.load(open(f)) for f in glob.glob('reports/nnlab/scores/*.json')} +N=['5','25','50','100','200'] +rows=[] +for m,s in S.items(): + for c in ['rule','rule_smooth']: + if m.endswith('__clamp') and c=='rule_smooth': continue + a=np.mean([s['mean'][c+'_A'][n] for n in N]); b=np.mean([s['mean'][c+'_B'][n] for n in N]); f=np.mean([s['mean'][c][n] for n in N]) + rows.append((m,c,a,b,f)) +rows.sort(key=lambda r:-r[2]) +A=np.array([r[2] for r in rows]);B=np.array([r[3] for r in rows]);F=np.array([r[4] for r in rows]) +from scipy.stats import spearmanr +print('variants',len(rows),'spearman A vs B',spearmanr(A,B)[0]) +g=A-B; print('A-B gap: median %.4f sd %.4f'%(np.median(g),g.std())) +for k in (1,3,5,10,20): print(f'top{k} on A: mean gap {g[:k].mean():.4f} (excess over median {g[:k].mean()-np.median(g):+.4f})') +rB=np.argsort(np.argsort(-B)) +print('top 15 by A: method, decision, A, B, full, rank_on_B') +for i in range(15): print(f'{rows[i][0]:22s}{rows[i][1]:12s}{A[i]:.4f} {B[i]:.4f} {F[i]:.4f} {rB[i]+1}') +iB=np.argsort(-B)[:8]; print('top 8 by B:'); [print(f' {rows[i][0]:22s}{rows[i][1]:12s}B={B[i]:.4f} A-rank={i+1}') for i in iB] +# family grouping of top diff --git a/reports/nnlab/verify/meth_skeptic3.py b/reports/nnlab/verify/meth_skeptic3.py new file mode 100644 index 0000000000000000000000000000000000000000..87cb75333d1dbef6d1eede2c20cc4d18c27677e9 --- /dev/null +++ b/reports/nnlab/verify/meth_skeptic3.py @@ -0,0 +1,26 @@ +import json,numpy as np +L=lambda m:json.load(open(f'reports/nnlab/scores/{m}.json')) +def per(m,c): + d={} + for r in L(m)['draws']: d[(r['n'],r['rep'])]=r[c] + return d +for ref,rc in [('lgbm_upw','rule'),('lgbm_upw','rule_smooth'),('lgbm_upw__lp','rule_smooth'),('zs_xgb_dann','rule_smooth')]: + for pick in ['ens_e4__lp','ens_e2__lapshot']: + print(f'{pick} rule_smooth minus {ref} {rc}') + for suf in ['','_A','_B']: + a=per(pick,'rule_smooth'+suf); b=per(ref,rc+suf) + out=[] + for n in (5,25,50,100,200): + dd=np.array([a[(n,r)]-b[(n,r)] for r in range(5)]); out.append(f'n{n}:{dd.mean():+.4f}±{dd.std(ddof=1)/np.sqrt(5):.4f}') + print(' ',suf or 'all',' '.join(out)) +# switch: at n>=100, e2__lapshot vs e4__lp on A and B +for suf in ['_A','_B','']: + a=per('ens_e2__lapshot','rule_smooth'+suf); b=per('ens_e4__lp','rule_smooth'+suf) + print('peak-avg',suf or 'all',[f'{np.mean([a[(n,r)]-b[(n,r)] for r in range(5)]):+.4f}' for n in (5,25,50,100,200)]) +# best at each n on A among all, and its B +import glob,os +S={os.path.basename(f)[:-5]:json.load(open(f)) for f in glob.glob('reports/nnlab/scores/*.json')} +for n in ['5','100','200']: + lst=sorted([(s['mean'][c+'_A'][n],s['mean'][c+'_B'][n],m,c) for m,s in S.items() for c in ('rule','rule_smooth') if not(m.endswith('__clamp') and c=='rule_smooth')],reverse=True) + Bs=sorted([x[1] for x in lst],reverse=True) + print('n',n,'top3 on A:',[(m,c,round(a,4),round(b,4),Bs.index(b)+1) for a,b,m,c in lst[:3]]) diff --git a/reports/nnlab/verify/recompute.py b/reports/nnlab/verify/recompute.py new file mode 100644 index 0000000000000000000000000000000000000000..0fc5f86f8b889ef52b6d6085c6b2a89831531a99 --- /dev/null +++ b/reports/nnlab/verify/recompute.py @@ -0,0 +1,83 @@ +import os, sys, json, time +os.environ["NNLAB_THREADS"]="2"; os.environ["OMP_NUM_THREADS"]="2"; os.environ["MKL_NUM_THREADS"]="2"; os.environ["OPENBLAS_NUM_THREADS"]="2" +import numpy as np, pandas as pd +from pathlib import Path +from scipy import sparse +from scipy.spatial import cKDTree +ROOT=Path("."); PRED=ROOT/"reports/nnlab/preds"; SC=ROOT/"reports/nnlab/scores" +t0=time.time() +ams=pd.read_parquet(ROOT/"data/processed/amsterdam_features.parquet") +mad=pd.read_parquet(ROOT/"data/processed/madrid_features.parquet", columns=["age_class"]) +ya=ams["age_class"].to_numpy().astype(int); N=len(ya) +pa=ams[["px_key","py_key"]].to_numpy().astype(np.int64) +ym=mad["age_class"].to_numpy().astype(int) +prior=np.array([(ym==c).mean() for c in (1,2,3,4)]) +print("N",N,"prior",prior.round(4)) +def shots(n,rep): + rng=np.random.default_rng(42+1000*n+rep) + return np.concatenate([rng.choice(np.flatnonzero(ya==c), size=min(n,(ya==c).sum()), replace=False) for c in (1,2,3,4)]) +def ld(m,n,r): + p=PRED/m/f"n{n}_r{r}.npy" + return np.load(p).astype(np.float64) +def mysoftmax(L): + L=L-L.max(1,keepdims=True); E=np.exp(L); return E/E.sum(1,keepdims=True) +def myf1(y,yh): + f=[] + for c in (1,2,3,4): + tp=((y==c)&(yh==c)).sum(); fp=((y!=c)&(yh==c)).sum(); fn=((y==c)&(yh!=c)).sum() + f.append(0 if tp==0 else 2*tp/(2*tp+fp+fn)) + return float(np.mean(f)) +SH=["lgbm_upw","met_protoda_maha","met_cos_maha","tab_dann_rz_ft"] +DR=[(5,0),(25,1),(200,2)] +# (2) own graph +from sklearn.decomposition import PCA +sys.path.insert(0,str(ROOT/"release/hack4dev-building-age-winners")) +import winners as W +feats=W.feature_names() +X=ams[feats].to_numpy(np.float32).astype(np.float64) +med=np.median(X,0); q75,q25=np.percentile(X,[75,25],0); iqr=np.where((q75-q25)>1e-12,q75-q25,1.0) +Z=np.clip((X-med)/iqr,-10,10).astype(np.float32) +Zp=PCA(32,random_state=0).fit_transform(Z) +U=Zp/np.clip(np.linalg.norm(Zp,axis=1,keepdims=True),1e-12,None) +_,nb=cKDTree(U).query(U,k=11) +rows=np.repeat(np.arange(N),10); cols=nb[:,1:].ravel() +K=sparse.coo_matrix((np.ones(len(rows)),(rows,cols)),shape=(N,N)).tocsr(); K=K.maximum(K.T) +pr=cKDTree(pa.astype(float)).query_pairs(3,output_type="ndarray") +Sp=sparse.coo_matrix((np.ones(len(pr)),(pr[:,0],pr[:,1])),shape=(N,N)).tocsr(); Sp=Sp+Sp.T +A=Sp.maximum(K).tocsr(); A.setdiag(0); A.eliminate_zeros(); A.data[:]=1.0 +Ac=sparse.load_npz(ROOT/"reports/nnlab/cache/transd_graph_ams_r3_k10_pca32.npz").tocsr() +dA=(A!=Ac).nnz +print(f"own graph nnz={A.nnz} cached nnz={Ac.nnz} differing entries={dA} meandeg={A.sum()/N:.2f} [{time.time()-t0:.0f}s]") +deg=np.asarray(A.sum(1)).ravel(); dinv=1/np.sqrt(deg); S=sparse.diags(dinv)@A@sparse.diags(dinv) +sys.path.insert(0,str(ROOT/"tools/nnlab")) +import lab +res={} +sc=json.load(open(SC/"ens_e4__lp.json")); scd={(r["n"],r["rep"]):r for r in sc["draws"]} +sc4=json.load(open(SC/"ens_e4.json")); scd4={(r["n"],r["rep"]):r for r in sc4["draws"]} +for n,rep in DR: + zs=np.log(np.clip(ld("zs_xgb_dann",0,0),1e-6,1)) + sh=np.mean([np.log(np.clip(ld(m,n,rep),1e-6,1)) for m in SH],0) + w0=10/(n+10) + E=mysoftmax(w0*zs+(1-w0)*sh) + Es=ld("ens_e4",n,rep) + s=shots(n,rep); s_lab=lab.W.shots_for(ya,n,rep) + ys=ya[s] + Y0=E.copy(); Y0[s]=0; Y0[s,ys-1]=10.0 + F=Y0.copy() + for _ in range(20): F=0.8*(S@F)+0.2*Y0 + F=np.clip(F,1e-12,None); F/=F.sum(1,keepdims=True) + Fs=ld("ens_e4__lp",n,rep) + keep=np.setdiff1d(np.arange(N),s) + out={} + for nm,P in (("ens_e4",E),("ens_e4__lp",F)): + Pk=P[keep]; y=ya[keep] + out[nm]=dict(argmax=myf1(y,Pk.argmax(1)+1), rule=myf1(y,lab.W.decide(Pk,prior)), + rule_smooth=myf1(y,lab.W.decide(lab.smooth(Pk,pa,keep=keep),prior))) + # also F1 on stored files + print(f"n={n} rep={rep}: shots equal lab={np.array_equal(np.sort(s),np.sort(s_lab))} |shots|={len(s)} overlap-check uniq={len(np.unique(s))}") + print(f" ens_e4 maxabs(own-stored)={np.abs(E-Es).max():.2e} ens_e4__lp maxabs(own-stored)={np.abs(F-Fs).max():.2e} argmax agree={(F.argmax(1)==Fs.argmax(1)).mean():.6f}") + for nm,d_ in (("ens_e4",scd4),("ens_e4__lp",scd)): + st=d_[(n,rep)] + print(" "+nm.ljust(11)+" ".join(f"{k}: own={out[nm][k]:.4f} stored={st[k]:.4f}" for k in ("argmax","rule","rule_smooth"))) + # leakage probe: shot-row mass of F away from shot label vs non-shots +print(f"done [{time.time()-t0:.0f}s]") diff --git a/tools/build_official60_first.py b/tools/build_official60_first.py new file mode 100644 index 0000000000000000000000000000000000000000..734393b22ad8d564eadf76fa9b0d11746f386d0c --- /dev/null +++ b/tools/build_official60_first.py @@ -0,0 +1,51 @@ +""" +Build the 60 official features exactly as the organisers do: the FIRST QA-valid +observation per pixel-year (features.make_flat_view(agg="first")), rather than +the per-year median our feature table uses. + +Needed to run a teammate's pipeline, which was built on the organisers' +preprocessed_data.pkl, on the same inputs it was built for. The organisers' +arrays are additionally z-scored with Madrid's scaler; that is one affine map +per feature shared by both cities, which tree models and per-region +standardisation are invariant to, so it is not reproduced here. + + python -I tools/build_official60_first.py +writes data/processed/{madrid,amsterdam}_official60_first.parquet (PID + 60 + age_class) +""" +from __future__ import annotations + +import os +import sys +import time +from pathlib import Path + +ROOT = Path(__file__).resolve().parent.parent +sys.path.insert(0, str(ROOT / "src")) +os.chdir(ROOT) + +import pandas as pd + +import config as C +import features as F + +for city, path in (("madrid", ROOT / "data/raw/madrid_train.parquet"), + ("amsterdam", ROOT / "data/raw/amsterdam_data.parquet")): + t0 = time.time() + df = pd.read_parquet(path) + df = F.assign_age_class(df) + flat = F.make_flat_view(df, agg="first") + del df + indicators = F.period_coverage(flat) + labels = (flat.sort_values(C.PID + ["year"]) + .groupby(C.PID, as_index=False)["age_class"].first()) + pix, years, mats, observed, sat = F.to_year_matrices(flat) + del flat + off = F.build_official_60(pix, years, mats, indicators) + out = off.merge(labels, on=C.PID, how="left") + out = out[out["age_class"].notna()].reset_index(drop=True) + out["age_class"] = out["age_class"].astype(int) + assert all(c in out.columns for c in C.FEATURE_COLS_60) + dst = C.PROC_DIR / f"{city}_official60_first.parquet" + out[C.PID + list(C.FEATURE_COLS_60) + ["age_class"]].to_parquet(dst, index=False) + print(f"{city}: {len(out):,} labelled pixels -> {dst} ({time.time() - t0:.0f}s)", + flush=True) diff --git a/tools/nnlab/baselines.py b/tools/nnlab/baselines.py new file mode 100644 index 0000000000000000000000000000000000000000..dbad4c44dc585cb13d827d778480171c44137d38 --- /dev/null +++ b/tools/nnlab/baselines.py @@ -0,0 +1,35 @@ +"""Reference methods written into the nnlab prediction store, so every new model +can be compared and ensembled with them draw by draw. + zs_lgbm zero-shot LightGBM (saved model, raw 393 features) + zs_xgb_dann teammate's FINAL before smoothing: (XGBoost + mean of 3 DANN seeds) / 2 + zs_dann teammate's DANN alone (mean of 3 seeds) + lgbm_upw our retrained LightGBM (Madrid + shots x30), one fit per draw +""" +import sys, time +from pathlib import Path +sys.path.insert(0, str(Path(__file__).resolve().parent)) +import lab +import numpy as np +W = lab.W + +d = W.load_data() # float64, exactly as the reported numbers +ld = lab.load(official60=False) +assert (d["y_ams"] == ld["_ya"]).all() and np.allclose(d["coords_ams"], ld["pa"]) +if not lab.have_pred("zs_lgbm", 0, 0): + lab.save_pred("zs_lgbm", 0, 0, W.predict(W.load_zero_shot(), d["X_ams"])) +z = np.load(lab.PROC / "zeroshot_xgb_dann_ams.npz") +assert (z["pa"] == ld["pa"]).all() +Pd = z["P_dann_seeds"].mean(0) +lab.save_pred("zs_dann", 0, 0, Pd) +lab.save_pred("zs_xgb_dann", 0, 0, (z["P_xgb"] + Pd) / 2) +which = sys.argv[1] if len(sys.argv) > 1 else "all" +if which == "all": + for n, rep in lab.draws(): + if lab.have_pred("lgbm_upw", n, rep): + continue + t0 = time.time() + shot = lab.shots_for(ld, n, rep) + m, st = W.train_retrained(d["X_mad"], d["y_mad"], d["X_ams"], d["X_ams"][shot], lab.shot_labels(ld, shot)) + lab.save_pred("lgbm_upw", n, rep, W.predict_retrained(m, st, d["X_ams"])) + print(f"lgbm_upw n={n} rep={rep} {time.time()-t0:.0f}s", flush=True) +print("done") diff --git a/tools/nnlab/classical.py b/tools/nnlab/classical.py new file mode 100644 index 0000000000000000000000000000000000000000..c46c241980f06fc989e022c85731432c6acb6f1b --- /dev/null +++ b/tools/nnlab/classical.py @@ -0,0 +1,250 @@ +""" +classical.py - non-neural, non-boosting models that use the shots. + + python tools/nnlab/classical.py [--smoke] [--only ] + +Methods: cls_lr cls_lr_feda cls_svm cls_svm_feda cls_nys_lr cls_knn cls_lda + (+ zero-shot cls_lr_zs cls_svm_zs saved once at n=0, rep=0) + +All hyper-parameters are fixed in advance (nothing is chosen on Amsterdam, nothing +was tuned at all). Inputs: lab.robustz per city; PCA(64) fitted on Madrid+Amsterdam +features together (label-free); Nystroem fitted on Madrid+Amsterdam (label-free). +Amsterdam labels are read only via lab.shot_labels(lab.shots_for(...)). +""" +from __future__ import annotations + +import argparse +import sys +import time +import warnings +from pathlib import Path + +sys.path.insert(0, str(Path(__file__).resolve().parent)) +import lab # noqa: E402 +import numpy as np # noqa: E402 + +warnings.filterwarnings("ignore") +T0 = time.time() +SHOT_W = 30.0 +KNN_K = 25 +ALL = ["cls_lr_zs", "cls_svm_zs", "cls_lr", "cls_lr_feda", "cls_svm", "cls_svm_feda", + "cls_nys_lr", "cls_knn", "cls_lda"] + + +def log(msg): + print(f"[{time.time() - T0:7.1f}s] {msg}", flush=True) + + +ap = argparse.ArgumentParser() +ap.add_argument("--smoke", action="store_true") +ap.add_argument("--only", default=None) +args = ap.parse_args() +SMOKE = args.smoke +SFX = "_SMOKE" if SMOKE else "" + +# configuration (smoke = tiny) ------------------------------------------------ # +LR_C = 1.0 +LR_ITER = 15 if SMOKE else 100 +SVM_SUB = 1500 if SMOKE else 6000 # class-balanced Madrid subsample for SVC +SVM_C = 3.0 +NYS_COMP = 60 if SMOKE else 500 +PCA_K = 64 +SEED = 0 + +CFG = dict(shot_weight=SHOT_W, robustz_clip=10.0, pca=PCA_K, lr_C=LR_C, lr_max_iter=LR_ITER, + svm_sub=SVM_SUB, svm_C=SVM_C, svm_gamma="scale", nystroem=NYS_COMP, + nys_gamma="1/(393*var(Zm))", knn_k=KNN_K, lda="lsqr auto shrinkage, shots duplicated 30x", + madrid_choices="none - everything fixed in advance", smoke=SMOKE) + +# data ------------------------------------------------------------------------ # +d = lab.load(official60=False) +ym, pm = d["ym"], d["pm"] +Zm = lab.robustz(d["Xm"]) +Za = lab.robustz(d["Xa"]) +nM, nA = len(Zm), len(Za) +log(f"loaded Madrid {Zm.shape} Amsterdam {Za.shape}") + + +def cached(name, fn): + p = lab.CACHE / f"cls_{name}.npz" + if p.exists(): + z = np.load(p) + return tuple(z[k] for k in z.files) + out = fn() + np.savez(p, *out) + return out + + +def _pca(): + from sklearn.decomposition import PCA + pca = PCA(PCA_K, svd_solver="randomized", random_state=SEED).fit(np.vstack([Zm, Za])) + return pca.transform(Zm).astype(np.float32), pca.transform(Za).astype(np.float32) + + +_pcs = {} + + +def pca64(): + if not _pcs: + _pcs["v"] = cached("pca64", _pca) + return _pcs["v"] + + +def nys(): + def f(): + from sklearn.kernel_approximation import Nystroem + gamma = 1.0 / (Zm.shape[1] * float(Zm.var())) + ny = Nystroem(gamma=gamma, n_components=NYS_COMP, random_state=SEED).fit(np.vstack([Zm, Za])) + return ny.transform(Zm).astype(np.float32), ny.transform(Za).astype(np.float32) + return cached(f"nys{NYS_COMP}", f) + + +def feda(X, is_madrid): + z = np.zeros_like(X) + return np.hstack([X, X, z]) if is_madrid else np.hstack([X, z, X]) + + +def weights(y_all, n_src): + w = np.r_[np.ones(n_src), np.full(len(y_all) - n_src, SHOT_W)] + return w * lab.balanced_class_weights(y_all)[y_all - 1] + + +def lr_fit(X, y, w): + from sklearn.linear_model import LogisticRegression + return LogisticRegression(C=LR_C, max_iter=LR_ITER, tol=1e-3).fit(X, y, sample_weight=w) + + +# SVC Madrid subsample (fixed seed, class balanced) --------------------------- # +def svm_subsample(): + rng = np.random.default_rng(SEED) + q = SVM_SUB // 4 + idx = [] + for c in lab.CLASSES: + ii = np.flatnonzero(ym == c) + idx.append(rng.choice(ii, min(q, len(ii)), replace=False)) + return np.sort(np.concatenate(idx)) + + +def svm_fit_predict(Xs, ys, Xt, shots_X=None, ys_shots=None): + from sklearn.svm import SVC + if shots_X is not None: + X = np.vstack([Xs, shots_X]) + y = np.r_[ys, ys_shots] + w = weights(y, len(ys)) + else: + X, y = Xs, ys + w = lab.balanced_class_weights(y)[y - 1] + m = SVC(C=SVM_C, kernel="rbf", gamma="scale", decision_function_shape="ovr", cache_size=1000) + m.fit(X, y, sample_weight=w) + return lab.softmax(m.decision_function(Xt)) + + +# unit-of-work functions ------------------------------------------------------ # +# each: f(idx, ys) -> P (nA, 4) ; idx/ys None for zero-shot +def f_lr(idx, ys): + if idx is None: + m = lr_fit(Zm, ym, lab.balanced_class_weights(ym)[ym - 1]) + else: + y = np.r_[ym, ys] + m = lr_fit(np.vstack([Zm, Za[idx]]), y, weights(y, nM)) + return m.predict_proba(Za) + + +def f_lr_feda(idx, ys): + y = np.r_[ym, ys] + X = np.vstack([feda(Zm, True), feda(Za[idx], False)]) + m = lr_fit(X, y, weights(y, nM)) + return m.predict_proba(feda(Za, False)) + + +def f_svm(idx, ys): + Pm, Pa = pca64() + s = svm_subsample() + if idx is None: + return svm_fit_predict(Pm[s], ym[s], Pa) + return svm_fit_predict(Pm[s], ym[s], Pa, Pa[idx], ys) + + +def f_svm_feda(idx, ys): + Pm, Pa = pca64() + s = svm_subsample() + return svm_fit_predict(feda(Pm[s], True), ym[s], feda(Pa, False), + feda(Pa[idx], False), ys) + + +def f_nys_lr(idx, ys): + Nm, Na = nys() + y = np.r_[ym, ys] + m = lr_fit(np.vstack([Nm, Na[idx]]), y, weights(y, nM)) + return m.predict_proba(Na) + + +def _knn_madrid(): + def f(): + from sklearn.neighbors import NearestNeighbors + Pm, Pa = pca64() + nn = NearestNeighbors(n_neighbors=KNN_K, algorithm="brute", n_jobs=lab.THREADS).fit(Pm) + dist, ind = nn.kneighbors(Pa) + return dist.astype(np.float32), ind.astype(np.int32) + return cached("knn_madrid", f) + + +def f_knn(idx, ys): + # Madrid neighbours are shot-independent (cached); merge with distances to the shots, + # keep the 25 closest of the union, vote with weight (1 or 30) / distance. + Pm, Pa = pca64() + dm, im = _knn_madrid() + S = Pa[idx] + ds = np.sqrt(np.maximum(((Pa ** 2).sum(1)[:, None] + (S ** 2).sum(1)[None, :] + - 2.0 * Pa @ S.T), 0.0)).astype(np.float32) + D = np.hstack([dm, ds]) + lbl = np.hstack([ym[im], np.broadcast_to(ys, (nA, len(ys)))]) + wt = np.hstack([np.ones_like(dm), np.full_like(ds, SHOT_W)]) + o = np.argpartition(D, KNN_K - 1, axis=1)[:, :KNN_K] + r = np.arange(nA)[:, None] + D, lbl, wt = D[r, o], lbl[r, o], wt[r, o] + v = wt / np.maximum(D, 1e-6) + P = np.zeros((nA, 4)) + for c in range(4): + P[:, c] = (v * (lbl == c + 1)).sum(1) + P += 1e-3 * P.sum(1, keepdims=True) / 4 + return P / P.sum(1, keepdims=True) + + +def f_lda(idx, ys): + from sklearn.discriminant_analysis import LinearDiscriminantAnalysis + Pm, Pa = pca64() + X = np.vstack([Pm] + [Pa[idx]] * int(SHOT_W)) + y = np.r_[ym, np.tile(ys, int(SHOT_W))] + m = LinearDiscriminantAnalysis(solver="lsqr", shrinkage="auto").fit(X, y) + return m.predict_proba(Pa) + + +METHODS = { + "cls_lr_zs": (f_lr, True), "cls_svm_zs": (f_svm, True), + "cls_lr": (f_lr, False), "cls_lr_feda": (f_lr_feda, False), + "cls_svm": (f_svm, False), "cls_svm_feda": (f_svm_feda, False), + "cls_nys_lr": (f_nys_lr, False), "cls_knn": (f_knn, False), "cls_lda": (f_lda, False), +} + +# main loop ------------------------------------------------------------------- # +todo = [m for m in ALL if args.only is None or args.only in m] +draw_list = [(5, 0), (200, 0)] if SMOKE else list(lab.draws()) +for name in todo: + fn, zs = METHODS[name] + out_name = name + SFX + units = [(0, 0)] if zs else draw_list + for n, rep in units: + if lab.have_pred(out_name, n, rep): + log(f"skip {out_name} n={n} r={rep}") + continue + t = time.time() + if zs: + P = fn(None, None) + else: + idx = lab.shots_for(d, n, rep) + ys = lab.shot_labels(d, idx) + P = fn(idx, ys) + lab.save_pred(out_name, n, rep, P, meta=dict(CFG, method=out_name)) + log(f"{out_name} n={n} r={rep} done in {time.time() - t:.1f}s") +log("all done") diff --git a/tools/nnlab/confirm.py b/tools/nnlab/confirm.py new file mode 100644 index 0000000000000000000000000000000000000000..2bf4da7468976237d9eac6cda0e27f92d4820810 --- /dev/null +++ b/tools/nnlab/confirm.py @@ -0,0 +1,48 @@ +"""Confirmation tables from the per-draw score files (reports/nnlab/scores/*.json). + fresh = all pixels, draws 5-11 (never used for choosing) + B = half B, draws 0-11 + all = all pixels, draws 0-11 +""" +import json, sys +from pathlib import Path +import numpy as np, pandas as pd +S = Path(__file__).resolve().parent.parent.parent / "reports" / "nnlab" / "scores" +ROWS = [("ens_e12", "rule_smooth", "FINAL-2: zero-shot anchor + LightGBM with domain copies, smoothing"), + ("ens_e12", "rule", "FINAL-2 without coordinates"), + ("lgbm_feda", "rule_smooth", "single model: LightGBM with domain copies + smoothing"), + ("lgbm_feda", "rule", "single model: LightGBM with domain copies"), + ("tab_dann_rz_lpft", "rule", "single model: DANN, last layer then whole network fine-tuned"), + ("ens_e4__lp", "rule_smooth", "FINAL-AVG as pre-registered: ensemble + label propagation + smoothing"), + ("ens_e4", "rule_smooth", "ensemble + smoothing (no label propagation)"), + ("ens_e4", "rule", "ensemble, no coordinates used"), + ("ens_e2__lapshot", "rule_smooth", "FINAL-PEAK as pre-registered: zero-shot + LightGBM, graph refinement + smoothing"), + ("lgbm_upw__lp", "rule_smooth", "retrained LightGBM + label propagation + smoothing"), + ("lgbm_upw", "rule_smooth", "reference: retrained LightGBM + smoothing"), + ("lgbm_upw", "rule", "reference: retrained LightGBM (published 0.656)"), + ("zs_xgb_dann", "rule_smooth", "reference: teammate zero-shot + smoothing + our rule"), + ("met_protoda_maha", "rule", "member: metric learning (protoda, Mahalanobis)"), + ("met_cos_maha", "rule", "member: metric learning (cosine, Mahalanobis)"), + ("tab_dann_rz_ft", "rule", "member: DANN MLP, last layer fine-tuned")] +N = [5, 25, 50, 100, 200] +out = [] +for m, k, desc in ROWS: + f = S / f"{m}.json" + if not f.exists(): + continue + df = pd.DataFrame(json.loads(f.read_text())["draws"]) + for view, sub, col in (("fresh", df[df.rep >= 5], k), ("B", df, k + "_B"), ("all", df, k), ("first5", df[df.rep < 5], k)): + g = sub.groupby("n")[col] + mean, sd, cnt = g.mean(), g.std(), g.count() + r = {"method": m, "decision": k, "view": view, "desc": desc, "draws_per_n": int(cnt.min())} + r.update({f"n{n}": mean.get(n, np.nan) for n in N}) + r.update({f"sd{n}": sd.get(n, np.nan) for n in N}) + r["avg"] = float(np.mean([r[f"n{n}"] for n in N])); r["peak"] = float(np.max([r[f"n{n}"] for n in N])) + out.append(r) +df = pd.DataFrame(out) +df.to_csv(S.parent / "confirmation.csv", index=False) +pd.set_option("display.width", 250) +for view in ("fresh", "B", "all", "first5"): + print(f"\n== {view} ==") + print(df[df["view"] == view][["method", "decision", "draws_per_n"] + [f"n{n}" for n in N] + ["avg"]].round(4).to_string(index=False)) +print("\n== sd over draws, fresh ==") +print(df[df["view"] == "fresh"][["method", "decision"] + [f"sd{n}" for n in N]].round(4).to_string(index=False)) diff --git a/tools/nnlab/ensemble.py b/tools/nnlab/ensemble.py new file mode 100644 index 0000000000000000000000000000000000000000..114ad5280926471f82562742af47cb70a54870a0 --- /dev/null +++ b/tools/nnlab/ensemble.py @@ -0,0 +1,36 @@ +"""Fixed-rule ensembles of methods already in the prediction store. + +Rule, fixed before any ensemble was scored (from the literature shortlist, item 6): + log p = w0 * mean(log p of zero-shot members) + (1 - w0) * mean(log p of shot-using members) + w0 = K / (n + K), K = 10 (the zero-shot anchor fades as the labelled sample grows) +No weights are fitted, so no Amsterdam label is used beyond what the members used. + + python tools/nnlab/ensemble.py NAME --zs zs_xgb_dann --shot lgbm_upw,tab_dann_rz_joint [--k 10] +writes method ens_NAME. +""" +import argparse, sys +from pathlib import Path +sys.path.insert(0, str(Path(__file__).resolve().parent)) +import lab +import numpy as np + +ap = argparse.ArgumentParser() +ap.add_argument("name") +ap.add_argument("--zs", default="") +ap.add_argument("--shot", default="") +ap.add_argument("--k", type=float, default=10.0) +a = ap.parse_args() +zs = [m for m in a.zs.split(",") if m] +sh = [m for m in a.shot.split(",") if m] +assert zs or sh +name = "ens_" + a.name +logm = lambda ms, n, r: np.mean([np.log(np.clip(lab.load_pred(m, n, r), 1e-6, 1)) for m in ms], 0) +done = 0 +for n, rep in lab.draws(): + if any(not lab.have_pred(m, n, rep) for m in sh) or any(not (lab.have_pred(m, n, rep) or lab.have_pred(m, 0, 0)) for m in zs): + continue + w0 = (a.k / (n + a.k) if sh else 1.0) if zs else 0.0 + L = (w0 * logm(zs, n, rep) if zs else 0) + ((1 - w0) * logm(sh, n, rep) if sh else 0) + lab.save_pred(name, n, rep, lab.softmax(L), meta=dict(zs=zs, shot=sh, k=a.k, rule="w0=k/(n+k), mean of log-probs")) + done += 1 +print(f"{name}: {done} draws written") diff --git a/tools/nnlab/lab.py b/tools/nnlab/lab.py new file mode 100644 index 0000000000000000000000000000000000000000..295e04bd7c60f50c85b5bbb3f82a90f84be553e6 --- /dev/null +++ b/tools/nnlab/lab.py @@ -0,0 +1,357 @@ +""" +nnlab - shared harness for the overnight model search (2026-11-11). + +Every model family writes, for each protocol draw (n, rep), ONE array of class +probabilities over ALL 25,992 Amsterdam pixels (row order = the feature table): + + lab.save_pred("my_method", n, rep, P) # P: (25992, 4), columns = class 1..4 + +and nothing else. Scoring is done here, identically for everybody: + + python tools/nnlab/lab.py score my_method [other_method ...] + python tools/nnlab/lab.py board + +RULES every module must keep (reviewers check these): + * Amsterdam LABELS may be read only through lab.shot_labels(d, idx) for the + indices returned by lab.shots_for(d, n, rep). Never touch d["ya"] directly. + * Unlabelled Amsterdam features / coordinates may be used freely (transductive). + * Hyper-parameters are fixed in advance or chosen on Madrid (lab.madrid_split + gives a spatial pseudo-target). Never chosen by looking at an Amsterdam score. + * The held-out organisers' data is never opened. + +The draws are the protocol's: seed 42, rng(42 + 1000 n + rep), n in +(5, 25, 50, 100, 200). Shots are removed from the scored set. +""" +from __future__ import annotations + +import json +import os +import sys +import time +from pathlib import Path + +HERE = Path(__file__).resolve().parent +ROOT = HERE.parent.parent +# ROOT itself holds winners.py when these scripts run from the published bundle +for _p in (ROOT, HERE, ROOT / "src", ROOT / "tools"): + if str(_p) not in sys.path: + sys.path.insert(0, str(_p)) +THREADS = int(os.environ.get("NNLAB_THREADS", "3")) +os.environ.setdefault("OMP_NUM_THREADS", str(THREADS)) +os.environ.setdefault("MKL_NUM_THREADS", str(THREADS)) + +import numpy as np # noqa: E402 +import pandas as pd # noqa: E402 + +import winners as W # noqa: E402 (verified port of the protocol + rule) + +PROC = ROOT / "data" / "processed" +OUT = ROOT / "reports" / "nnlab" +PRED = OUT / "preds" +SCORES = OUT / "scores" +CACHE = OUT / "cache" # embeddings, time-series tensors, ... +for _d in (PRED, SCORES, CACHE): + _d.mkdir(parents=True, exist_ok=True) + +SEED = 42 +N_LIST = (5, 25, 50, 100, 200) +REPS = int(os.environ.get("NNLAB_REPS", "5")) # reps 0..4 by default; NNLAB_REPS=12 adds the confirmation draws +CLASSES = np.array([1, 2, 3, 4]) +PID = ["px_key", "py_key"] +BLOCK = 67 # 67 px = 2 km +SMOOTH_R, SMOOTH_ALPHA = 6, 0.5 # teammate's choice, made on Madrid + + +def torch_setup(seed: int = 0): + import torch + torch.set_num_threads(THREADS) + torch.manual_seed(seed) + np.random.seed(seed) + return torch + + +# --------------------------------------------------------------------------- # +# Data +# --------------------------------------------------------------------------- # +def load(official60: bool = True) -> dict: + """Madrid + Amsterdam tables. Xm/Xa: our 393 features (float32, raw scale); + Xm60/Xa60: the organisers' official 60 (first valid observation per year); + pm/pa: integer pixel coordinates (30 m units); ym: Madrid labels; + prior: Madrid class shares. Amsterdam labels are kept under a private key.""" + feats = W.feature_names() + mad = pd.read_parquet(PROC / "madrid_features.parquet") + ams = pd.read_parquet(PROC / "amsterdam_features.parquet") + d = dict( + feats=feats, + Xm=mad[feats].to_numpy(np.float32), Xa=ams[feats].to_numpy(np.float32), + ym=mad["age_class"].to_numpy().astype(int), + pm=mad[PID].to_numpy().astype(np.int64), pa=ams[PID].to_numpy().astype(np.int64), + _ya=ams["age_class"].to_numpy().astype(int), + ) + d["prior"] = np.array([(d["ym"] == c).mean() for c in CLASSES]) + if official60: + import config as C + f60 = list(C.FEATURE_COLS_60) + for city, ref, key in (("madrid", mad, "Xm60"), ("amsterdam", ams, "Xa60")): + o = pd.read_parquet(PROC / f"{city}_official60_first.parquet") + o = ref[PID].merge(o, on=PID, how="left") + assert len(o) == len(ref) and o[f60].notna().all().all() + d[key] = o[f60].to_numpy(np.float32) + d["feats60"] = f60 + return d + + +def shots_for(d: dict, n: int, rep: int) -> np.ndarray: + """Row indices of the n-per-class labelled Amsterdam pixels of draw (n, rep).""" + return W.shots_for(d["_ya"], n, rep) + + +def shot_labels(d: dict, idx: np.ndarray) -> np.ndarray: + """Labels (1..4) of shot rows. The ONLY sanctioned way to read Amsterdam labels.""" + return d["_ya"][np.asarray(idx)] + + +def draws(reps: int = REPS, n_list=N_LIST): + for n in n_list: + for r in range(reps): + yield n, r + + +def balanced_class_weights(y) -> np.ndarray: + return W.balanced_class_weights(y) + + +# per-city input transforms (label-free) ------------------------------------- # +def robustz(X: np.ndarray, clip: float | None = 10.0) -> np.ndarray: + """Median/IQR scaling with this matrix's own statistics (call once per city).""" + X = np.asarray(X, np.float64) + med = np.median(X, 0) + q75, q25 = np.percentile(X, [75, 25], 0) + iqr = np.where((q75 - q25) > 1e-12, q75 - q25, 1.0) + Z = (X - med) / iqr + if clip: + Z = np.clip(Z, -clip, clip) + return Z.astype(np.float32) + + +def standardise(X: np.ndarray) -> np.ndarray: + X = np.asarray(X, np.float64) + sd = X.std(0) + sd[sd == 0] = 1 + return ((X - X.mean(0)) / sd).astype(np.float32) + + +def rankgauss(X: np.ndarray) -> np.ndarray: + """Per-column rank -> standard normal quantile, within this matrix (one city).""" + from scipy.special import ndtri + from scipy.stats import rankdata + X = np.asarray(X, np.float64) + R = rankdata(X, axis=0, method="average") + return ndtri((R - 0.5) / len(X)).astype(np.float32) + + +# Madrid-only model selection ------------------------------------------------- # +def block_ids(p: np.ndarray, block: int = BLOCK) -> np.ndarray: + b = p // block + return (b[:, 0] * 100_003 + b[:, 1]).astype(np.int64) + + +def madrid_split(pm: np.ndarray, frac: float = 0.25, axis: int = 0, high: bool = True): + """A spatial pseudo-target inside Madrid: the `frac` of 2 km blocks furthest + along one axis. Returns (source_idx, pseudo_target_idx). Use it to choose + epochs / regularisation without touching Amsterdam labels.""" + b = pm[:, axis] // BLOCK + cut = np.quantile(b, 1 - frac) if high else np.quantile(b, frac) + tgt = b > cut if high else b < cut + return np.flatnonzero(~tgt), np.flatnonzero(tgt) + + +# --------------------------------------------------------------------------- # +# Heads on a frozen backbone +# --------------------------------------------------------------------------- # +def softmax(L: np.ndarray) -> np.ndarray: + L = L - L.max(1, keepdims=True) + E = np.exp(L) + return E / E.sum(1, keepdims=True) + + +def finetune_last_layer(W0, b0, Zs, ys, lam: float = 1.0, class_balanced: bool = True, + max_iter: int = 200): + """True last-layer fine-tuning: start from the Madrid-trained last layer + (W0: (4, k), b0: (4,)) and minimise, on the labelled shots only, + sum_i w_i CE(softmax(W z_i + b), y_i) + lam * (||W - W0||^2 + ||b - b0||^2). + The CE is a SUM, so the pull toward the source weights fades as n grows. + Returns (W, b). Probabilities: softmax(Z @ W.T + b).""" + torch = torch_setup(0) + Z = torch.tensor(np.asarray(Zs), dtype=torch.float64) + y = torch.tensor(np.asarray(ys) - 1, dtype=torch.long) + w = torch.ones(len(y), dtype=torch.float64) + if class_balanced: + w = torch.tensor(balanced_class_weights(np.asarray(ys))[np.asarray(ys) - 1], dtype=torch.float64) + W0t = torch.tensor(np.asarray(W0), dtype=torch.float64) + b0t = torch.tensor(np.asarray(b0), dtype=torch.float64) + Wt, bt = W0t.clone().requires_grad_(True), b0t.clone().requires_grad_(True) + opt = torch.optim.LBFGS([Wt, bt], max_iter=max_iter, line_search_fn="strong_wolfe") + + def closure(): + opt.zero_grad() + ce = torch.nn.functional.cross_entropy(Z @ Wt.T + bt, y, reduction="none") + loss = (w * ce).sum() + lam * (((Wt - W0t) ** 2).sum() + ((bt - b0t) ** 2).sum()) + loss.backward() + return loss + + opt.step(closure) + return Wt.detach().numpy(), bt.detach().numpy() + + +def joint_head(Zm, ym, Zs, ys, shot_weight: float = 30.0, C: float = 1.0, max_iter: int = 400): + """Last layer re-fitted on frozen embeddings of Madrid + shots (shots + weighted `shot_weight`x, classes balanced) - the frozen-backbone analogue + of the retrained LightGBM. Returns a fitted sklearn LogisticRegression + (predict_proba columns = classes 1..4).""" + from sklearn.linear_model import LogisticRegression + X = np.vstack([Zm, Zs]) + y = np.r_[np.asarray(ym), np.asarray(ys)] + w = np.r_[np.ones(len(ym)), np.full(len(ys), shot_weight)] + w = w * balanced_class_weights(y)[y - 1] + return LogisticRegression(C=C, max_iter=max_iter).fit(X, y, sample_weight=w) + + +# --------------------------------------------------------------------------- # +# Prediction store +# --------------------------------------------------------------------------- # +def pred_path(method: str, n: int, rep: int) -> Path: + return PRED / method / f"n{n}_r{rep}.npy" + + +def have_pred(method: str, n: int, rep: int) -> bool: + return pred_path(method, n, rep).exists() + + +def save_pred(method: str, n: int, rep: int, P: np.ndarray, meta: dict | None = None): + """P: (25992, 4) probabilities for ALL Amsterdam pixels, feature-table row + order. A method that ignores the shots saves once with n=0, rep=0.""" + P = np.asarray(P, np.float32) + assert P.shape == (25992, 4) and np.isfinite(P).all(), P.shape + p = pred_path(method, n, rep) + p.parent.mkdir(parents=True, exist_ok=True) + np.save(p, P) + if meta is not None: + (p.parent / "meta.json").write_text(json.dumps(meta, indent=1, default=str)) + + +def load_pred(method: str, n: int, rep: int) -> np.ndarray: + p = pred_path(method, n, rep) + if not p.exists(): + p = pred_path(method, 0, 0) # shot-independent method + return np.load(p).astype(np.float64) + + +# --------------------------------------------------------------------------- # +# Scoring (the only place Amsterdam labels are compared with predictions) +# --------------------------------------------------------------------------- # +_ADJ = {} + + +def adjacency(coords: np.ndarray, r: float = SMOOTH_R): + """Sparse 0/1 matrix: pixels within r (Euclidean, pixel units), self included.""" + from scipy.sparse import coo_matrix, identity + from scipy.spatial import cKDTree + key = (coords.shape[0], float(r), int(coords[:, 0].sum()), int(coords[:, 1].sum())) + if key not in _ADJ: + pr = cKDTree(coords.astype(float)).query_pairs(r, output_type="ndarray") + n = len(coords) + A = coo_matrix((np.ones(len(pr)), (pr[:, 0], pr[:, 1])), shape=(n, n)).tocsr() + _ADJ[key] = (A + A.T + identity(n, format="csr")).tocsr() + return _ADJ[key] + + +def smooth(P: np.ndarray, coords: np.ndarray, r: float = SMOOTH_R, alpha: float = SMOOTH_ALPHA, + keep: np.ndarray | None = None) -> np.ndarray: + """Teammate's neighbour smoothing: alpha * own + (1 - alpha) * mean over + pixels within r (self included). With `keep`, only those rows take part + (P must then already be P_all[keep]) - shots are never neighbours.""" + A = adjacency(coords, r) + if keep is not None: + A = A[keep][:, keep] + deg = np.asarray(A.sum(1)).ravel() + return alpha * P + (1 - alpha) * (A @ P) / deg[:, None] + + +def half_A(pa: np.ndarray) -> np.ndarray: + """Checkerboard of 2 km blocks. Half A is for comparing variants; half B is + looked at only to confirm the final picks.""" + b = pa // BLOCK + return ((b[:, 0] + b[:, 1]) % 2) == 0 + + +def score_method(d: dict, method: str, reps: int = REPS, n_list=N_LIST, verbose: bool = True) -> dict: + ya, pa, prior = d["_ya"], d["pa"], d["prior"] + inA = half_A(pa) + rows = [] + for n, rep in draws(reps, n_list): + if not (have_pred(method, n, rep) or have_pred(method, 0, 0)): + continue + P_all = load_pred(method, n, rep) + shot = shots_for(d, n, rep) + keep = np.setdiff1d(np.arange(len(ya)), shot) + P, y = P_all[keep], ya[keep] + lab = { + "argmax": CLASSES[P.argmax(1)], + "rule": W.decide(P, prior), + "rule_smooth": W.decide(smooth(P, pa, keep=keep), prior), + } + row = {"n": n, "rep": rep} + for k, l in lab.items(): + row[k] = W.macro_f1(y, l) + row[k + "_A"] = W.macro_f1(y[inA[keep]], l[inA[keep]]) + row[k + "_B"] = W.macro_f1(y[~inA[keep]], l[~inA[keep]]) + rows.append(row) + if not rows: + return {} + df = pd.DataFrame(rows) + summ = df.groupby("n").mean(numeric_only=True).drop(columns="rep") + std = df.groupby("n").std(numeric_only=True).drop(columns="rep") + out = {"method": method, "draws": rows, "n_draws": int(len(rows)), + "mean": {c: {int(n): float(v) for n, v in summ[c].items()} for c in summ.columns}, + "std": {c: {int(n): float(v) for n, v in std[c].items()} for c in std.columns}} + (SCORES / f"{method}.json").write_text(json.dumps(out, indent=1)) + if verbose: + for k in ("argmax", "rule", "rule_smooth"): + m = summ[k] + print(f"{method:<34s} {k:<12s} " + " ".join(f"n{n}={m[n]:.4f}" for n in m.index) + + f" avg={m.mean():.4f} peak={m.max():.4f} ({len(rows)} draws)", flush=True) + return out + + +def board(metric_suffix: str = "") -> pd.DataFrame: + """All scored methods, one row per (method, decision). suffix '' / '_A' / '_B'.""" + rows = [] + for f in sorted(SCORES.glob("*.json")): + s = json.loads(f.read_text()) + for k in ("argmax", "rule", "rule_smooth"): + m = s["mean"].get(k + metric_suffix) + if not m: + continue + r = {"method": s["method"], "decision": k, **{f"n{n}": m.get(str(n), np.nan) for n in N_LIST}} + v = [r[f"n{n}"] for n in N_LIST] + r["avg"], r["peak"], r["draws"] = float(np.nanmean(v)), float(np.nanmax(v)), s["n_draws"] + rows.append(r) + df = pd.DataFrame(rows).sort_values("avg", ascending=False) + df.to_csv(OUT / f"leaderboard{metric_suffix}.csv", index=False) + return df + + +if __name__ == "__main__": + cmd = sys.argv[1] if len(sys.argv) > 1 else "board" + if cmd == "score": + d = load(official60=False) + names = sys.argv[2:] or sorted(p.name for p in PRED.iterdir() if p.is_dir()) + t0 = time.time() + for m in names: + score_method(d, m) + print(f"scored {len(names)} methods in {time.time() - t0:.0f}s") + else: + suffix = sys.argv[2] if len(sys.argv) > 2 else "" + pd.set_option("display.width", 250) + print(board(suffix).round(4).to_string(index=False)) diff --git a/tools/nnlab/lgbm_var.py b/tools/nnlab/lgbm_var.py new file mode 100644 index 0000000000000000000000000000000000000000..026ac762ae0c47137564002b49646cd1d7e4ff8b --- /dev/null +++ b/tools/nnlab/lgbm_var.py @@ -0,0 +1,135 @@ +"""Retrained-LightGBM variants (each = lgbm_upw baseline with ONE change). + lgbm_feda Daume feature augmentation: [X, X_madrid_only, X_amsterdam_only] for the 120 top-gain features + lgbm_emb + 64-d DANN embedding + 64-d protoda(seed0) projected L2-normalised embedding (not robust-z scaled) + lgbm_w100 shot weight 100 instead of 30 +Usage: python tools/nnlab/lgbm_var.py [--smoke] [--only ] +Hyper-parameters are fixed in advance (baseline params; TOPK=120; protoda length = Madrid-chosen in protoda_select.json). +""" +import argparse, json, sys, time +from pathlib import Path +sys.path.insert(0, str(Path(__file__).resolve().parent)) +import lab +import numpy as np + +W = lab.W +TOPK = 120 +SMOKE_TREES = 40 +VARIANTS = ["lgbm_feda", "lgbm_emb", "lgbm_w100", "lgbm_fedaw100"] # fedaw100 (added 03:40, exploratory): both changes together + + +def l2n(Z): + return Z / np.maximum(np.linalg.norm(Z, axis=1, keepdims=True), 1e-12) + + +def load_embeddings(): + z = np.load(lab.CACHE / "tab_nn" / "emb_dann_rz_s0.npz") + dann_m, dann_a = z["Zm"].astype(np.float32), z["Za"].astype(np.float32) + sel = json.loads((lab.CACHE / "metric" / "protoda_select.json").read_text()) + L = int(sel["chosen"]) + e = np.load(lab.CACHE / "metric" / f"protoda_s0_L{L}.npz") + Wp, bp = e["W"].astype(np.float64), e["b"].astype(np.float64) + pm_ = l2n(e["Hm"].astype(np.float64) @ Wp.T + bp).astype(np.float32) # metric.py: proj(body), L2-normalised + pa_ = l2n(e["Ha"].astype(np.float64) @ Wp.T + bp).astype(np.float32) + return np.hstack([dann_m, pm_]), np.hstack([dann_a, pa_]), L + + +def top_gain_idx(): + import lightgbm as lgb + b = lgb.Booster(model_file=str(W.MODELS / "zero_shot_lgbm.txt")) + g = b.feature_importance("gain") + assert len(g) == 393 + return np.sort(np.argsort(-g, kind="stable")[:TOPK]) + + +def fit_predict(variant, Zm, ym, Za, shot, ys, ctx, params): + """Zm, Za: float64, each city robust-z scaled with its own median/IQR (label-free, + draw-independent, computed once in main). Returns (25992,4) probabilities.""" + shot_w = 100.0 if variant.endswith("w100") else W.SHOT_WEIGHT + Zs = Za[shot] + if variant in ("lgbm_feda", "lgbm_fedaw100"): + k = ctx["top"] + F, nm = Zm.shape[1], len(Zm) + Xtr = np.full((nm + len(Zs), F + 2 * TOPK), np.nan) # [X, X_madrid_only, X_ams_only] + Xtr[:nm, :F] = Zm; Xtr[nm:, :F] = Zs + Xtr[:nm, F:F + TOPK] = Zm[:, k] # Madrid rows: [x, x, NaN] + Xtr[nm:, F + TOPK:] = Zs[:, k] # Amsterdam rows: [x, NaN, x] + Xpr = np.full((len(Za), F + 2 * TOPK), np.nan) + Xpr[:, :F] = Za; Xpr[:, F + TOPK:] = Za[:, k] + elif variant == "lgbm_emb": + Em, Ea = ctx["emb"] + Xtr = np.hstack([np.vstack([Zm, Zs]), np.vstack([Em, Ea[shot]])]) + Xpr = np.hstack([Za, Ea]) + else: + Xtr, Xpr = np.vstack([Zm, Zs]), Za + y = np.r_[ym, ys] + w = np.r_[np.ones(len(ym)), np.full(len(ys), shot_w)] + w = w * W.balanced_class_weights(y)[y - 1] + m = W._fit_lgbm(Xtr, y, params, sample_weight=w) + return W.predict(m, Xpr) + + +def main(): + ap = argparse.ArgumentParser() + ap.add_argument("--smoke", action="store_true") + ap.add_argument("--only", default=None) + a = ap.parse_args() + sfx = "_SMOKE" if a.smoke else "" + base = {**W.LGBM_PARAMS, **W.RETRAIN_OVERRIDES, "n_jobs": lab.THREADS} + params = dict(base, n_estimators=SMOKE_TREES) if a.smoke else base + d = lab.load(official60=False) + ym, ya_fn = d["ym"], lab.shot_labels + # exact float64 matrices as used for the stored baseline + # (same files/order/dtype as W.load_data, but Amsterdam read WITHOUT its label column) + import pandas as pd + feats = W.feature_names() + mad = pd.read_parquet(W.DATA / "madrid_features.parquet", columns=feats + ["age_class"]) + ams = pd.read_parquet(W.DATA / "amsterdam_features.parquet", columns=feats + lab.PID) + Xm, Xa = mad[feats].to_numpy(float), ams[feats].to_numpy(float) + assert (mad["age_class"].to_numpy() == ym).all() + assert (ams[lab.PID].to_numpy().astype(np.int64) == d["pa"]).all() # row order = lab table + del mad, ams + del d["Xm"], d["Xa"] + # per-city robust-z exactly as W.train_retrained (Amsterdam stats from ALL its rows); draw-independent + Xm = W.robustz(Xm, W.robustz_fit(Xm)) + Xa = W.robustz(Xa, W.robustz_fit(Xa)) + ctx = {"top": top_gain_idx()} + names = [v for v in VARIANTS if not a.only or a.only in v] + if a.smoke: + names = names + (["lgbm_base_check"] if (not a.only or a.only in "lgbm_base_check") else []) + if "lgbm_emb" in names: + Em, Ea, L = load_embeddings() + ctx["emb"] = (Em, Ea) + else: + L = None + draws = [(5, 0), (200, 0)] if a.smoke else list(lab.draws()) + meta_common = dict(base_params=base, n_estimators_used=params["n_estimators"], topk_feda=TOPK, + feda_idx=ctx["top"].tolist(), protoda_len=L, smoke=a.smoke, + baseline="winners.train_retrained: per-city robust-z, shots x30, balanced weights", + hyperparameters="fixed in advance; none tuned on Amsterdam") + t00 = time.time() + for v in names: + if v == "lgbm_base_check": + t0 = time.time() + shot = lab.shots_for(d, 5, 0) + P = fit_predict("base", Xm, ym, Xa, shot, lab.shot_labels(d, shot), ctx, base) + ref = np.load(lab.PRED / "lgbm_upw" / "n5_r0.npy").astype(np.float64) + diff = np.abs(P - ref) + agree = float((P.argmax(1) == ref.argmax(1)).mean()) + print(f"base_check max_abs_diff={diff.max():.3e} mean_abs_diff={diff.mean():.3e} " + f"argmax_agree={agree:.5f} {'PASS(<1e-3)' if diff.max() < 1e-3 else 'DIFFERS'} " + f"[{time.time()-t0:.0f}s]", flush=True) + continue + method = v + sfx + for n, rep in draws: + if lab.have_pred(method, n, rep): + continue + t0 = time.time() + shot = lab.shots_for(d, n, rep) + P = fit_predict(v, Xm, ym, Xa, shot, lab.shot_labels(d, shot), ctx, params) + lab.save_pred(method, n, rep, P, dict(meta_common, method=method)) + print(f"{method} n={n} rep={rep} {time.time()-t0:.0f}s (total {time.time()-t00:.0f}s)", flush=True) + print("done", flush=True) + + +if __name__ == "__main__": + main() diff --git a/tools/nnlab/lpft.py b/tools/nnlab/lpft.py new file mode 100644 index 0000000000000000000000000000000000000000..e79bac4414b823bbd92a27372a2dc28fb0d30964 --- /dev/null +++ b/tools/nnlab/lpft.py @@ -0,0 +1,296 @@ +""" +lpft - whole-network adaptation of the DANN MLP with the shots: LP-FT (Kumar et al. +2022) with an L2-SP anchor (Li et al. 2018), optionally plus SHOT-style information +maximisation on unlabelled Amsterdam rows (Liang et al. 2020). + + python tools/nnlab/lpft.py [--smoke] [--only ] [--base-only] + +Base network: tab_nn.py's "dann" backbone on input "rz" (lab.robustz per city), seed 0, +trained exactly as tab_nn.train does (epochs from CACHE/tab_nn/selection.json), once, +with NNLAB_BASE_THREADS (default 3) torch threads - the count tab_nn's cache was made with; +CPU results depend on it (2 threads gave Za max abs diff 14) - then the run's NNLAB_THREADS; +state_dict cached at CACHE/lpft/dann_rz_s0.pt. Its Amsterdam embedding is compared with +CACHE/tab_nn/emb_dann_rz_s0.npz Za (max abs diff logged and stored in meta). + +Per protocol draw (fresh copy of the base each time): + LP last Linear <- lab.finetune_last_layer(W0, b0, Z_shot, y_shot, lam), lam from + selection.json (= tab_dann_rz_ft for seed 0 only; not saved). + FT everything unfrozen (domain head unused); BatchNorm in eval mode (running stats + frozen), dropout off (net.eval() throughout); AdamW lr 1e-4 backbone, 1e-3 last + layer, weight decay 0; STEPS=60 full-batch steps; loss = + class-balanced CE(shots) + + 0.1 * CE(fresh class-balanced Madrid replay batch, 128 per class = 512) + + 0.01 * sum_params ||theta - theta_LP||^2 + [+ 0.1 * (mean entropy - entropy of mean prediction) on 512 random Amsterdam + rows, variant "_ent"]. No early stopping. +Methods: tab_dann_rz_lpft, tab_dann_rz_lpft_ent (suffix _SMOKE under --smoke). + +Madrid check (evidence only, run once, CACHE/lpft/madrid_check.json, copied into meta): +lab.madrid_split; base trained on the source part with the unlabelled pseudo-target +as adversary (transforms per part, as tab_nn's selection); for n in (5, 50, 200) and +3 draws (rng(9000 + 100 n + rep)) from the pseudo-target: LP vs LP-FT vs LP-FT-ent, +macro F1 of W.decide (source-part prior) on the remaining pseudo-target rows. +Replay batches come from the source part; the entropy batch from the pseudo-target. +""" +from __future__ import annotations + +import argparse +import copy +import json +import os +import sys +import time +from pathlib import Path + +sys.path.insert(0, str(Path(__file__).resolve().parent)) +import lab # noqa: E402 (sets thread env before torch) +import numpy as np # noqa: E402 +import tab_nn # noqa: E402 + +W = lab.W +T0 = time.time() + +KIND, INP, SEED = "dann", "rz", 0 +STEPS = 60 +LR_BACKBONE, LR_HEAD = 1e-4, 1e-3 +REPLAY_W, REPLAY_PER_CLASS = 0.1, 128 +L2SP_W = 0.01 +ENT_W, ENT_BATCH = 0.1, 512 +CHECK_NS, CHECK_REPS = (5, 50, 200), 3 +BASE_THREADS = int(os.environ.get("NNLAB_BASE_THREADS", "3")) # thread count tab_nn's cache was made with +METHODS = {"lpft": False, "lpft_ent": True} # suffix -> use entropy term + + +def log(msg): + print(f"[{time.time() - T0:7.1f}s] {msg}", flush=True) + + +# --------------------------------------------------------------------------- # +# Base network +# --------------------------------------------------------------------------- # +def make_net(state, d_in): + net = tab_nn.build_net(KIND, d_in) + net.load_state_dict(state) + net.eval() + return net + + +def logits_of(net, X, bs=8192): + import torch + out = [] + with torch.no_grad(): + for k in range(0, len(X), bs): + out.append(net(torch.from_numpy(np.ascontiguousarray(X[k:k + bs], np.float32)))[0].double().numpy()) + return np.vstack(out) + + +def get_base(path, Xs, ys, Xt, epochs): + import torch + if path.exists(): + return torch.load(path), None + t0 = time.time() + # tab_nn trains on lab.torch_setup's thread count; its cache was made with 3 threads and + # CPU torch results depend on the thread count (2 threads: Za max abs diff 14, argmax + # agreement 0.87), so the base is trained with BASE_THREADS to reproduce it exactly. + run_threads, lab.THREADS = lab.THREADS, BASE_THREADS + try: + net, _ = tab_nn.train(KIND, Xs, ys, Xt, epochs, SEED) + finally: + lab.THREADS = run_threads + torch.set_num_threads(run_threads) + state = {k: v.detach().clone() for k, v in net.state_dict().items()} + torch.save(state, path) + log(f"trained base {path.name} ({BASE_THREADS} threads): {epochs} epochs on {min(len(Xs), tab_nn.SMOKE_ROWS or len(Xs))} rows {time.time() - t0:.0f}s") + return state, net + + +# --------------------------------------------------------------------------- # +# LP-FT for one draw +# --------------------------------------------------------------------------- # +def lpft(state, X_shot, ys, X_pred, X_rep, y_rep, X_unl, lam, seed, variants): + """Returns {"lp": P, suffix: P ...} probabilities on X_pred (columns classes 1..4).""" + import torch + import torch.nn.functional as F + lab.torch_setup(seed) + base = make_net(state, X_shot.shape[1]) + Z_shot = tab_nn.embed(base, X_shot) + W0, b0 = tab_nn.last_layer(base) + Wf, bf = lab.finetune_last_layer(W0, b0, Z_shot, ys, lam) + with torch.no_grad(): + base.head.weight.copy_(torch.tensor(Wf, dtype=torch.float32)) + base.head.bias.copy_(torch.tensor(bf, dtype=torch.float32)) + out = {"lp": lab.softmax(logits_of(base, X_pred))} + + Xs = torch.from_numpy(np.ascontiguousarray(X_shot, np.float32)) + Ys = torch.from_numpy(np.asarray(ys) - 1).long() + cw = torch.tensor(lab.balanced_class_weights(np.asarray(ys)), dtype=torch.float32) + Xr = torch.from_numpy(np.ascontiguousarray(X_rep, np.float32)) + by_cls = [torch.from_numpy(np.flatnonzero(np.asarray(y_rep) == c)) for c in lab.CLASSES] + Xu = torch.from_numpy(np.ascontiguousarray(X_unl, np.float32)) + + for sfx, use_ent in variants.items(): + net = copy.deepcopy(base) + net.eval() # BN running stats frozen, dropout off + if net.dom is not None: + for p in net.dom.parameters(): + p.requires_grad_(False) # domain head is not in the classifier path + named = [(k, p) for k, p in net.named_parameters() if p.requires_grad] + anchor = {k: p.detach().clone() for k, p in named} + head_p = [p for k, p in named if k.startswith("head.")] + body_p = [p for k, p in named if not k.startswith("head.")] + opt = torch.optim.AdamW([{"params": body_p, "lr": LR_BACKBONE}, + {"params": head_p, "lr": LR_HEAD}], weight_decay=0.0) + g = torch.Generator().manual_seed(seed) + # reviewer: separate stream for the entropy batch so both variants get the SAME + # replay batches (the only difference between them is the entropy term) + g_ent = torch.Generator().manual_seed(seed + 7_777_777) + for _ in range(STEPS): + loss = F.cross_entropy(net(Xs)[0], Ys, weight=cw) + ri = torch.cat([ix[torch.randint(0, len(ix), (REPLAY_PER_CLASS,), generator=g)] for ix in by_cls]) + yr = torch.repeat_interleave(torch.arange(4), REPLAY_PER_CLASS) + loss = loss + REPLAY_W * F.cross_entropy(net(Xr[ri])[0], yr) + loss = loss + L2SP_W * sum(((p - anchor[k]) ** 2).sum() for k, p in named) + if use_ent: + j = torch.randint(0, len(Xu), (ENT_BATCH,), generator=g_ent) + pu = F.softmax(net(Xu[j])[0], 1) + ment = -(pu * torch.log(pu + 1e-8)).sum(1).mean() + pm = pu.mean(0) + hm = -(pm * torch.log(pm + 1e-8)).sum() + loss = loss + ENT_W * (ment - hm) + opt.zero_grad() + loss.backward() + opt.step() + net.eval() + out[sfx] = lab.softmax(logits_of(net, X_pred)) + return out + + +# --------------------------------------------------------------------------- # +# Madrid check +# --------------------------------------------------------------------------- # +def madrid_check(d, cache, epochs, lam, smoke): + path = cache / "madrid_check.json" + if path.exists(): + return json.loads(path.read_text()) + src, tgt = lab.madrid_split(d["pm"]) + Xs, Xt = tab_nn.TRANSFORM[INP](d["Xm"][src]), tab_nn.TRANSFORM[INP](d["Xm"][tgt]) + ys, yt = d["ym"][src], d["ym"][tgt] + prior = np.array([(ys == c).mean() for c in lab.CLASSES]) + state, net = get_base(cache / f"{KIND}_{INP}_src_s{SEED}.pt", Xs, ys, Xt, epochs) + res = {"ns": list(CHECK_NS), "reps": CHECK_REPS, "shot_rng": "default_rng(9000 + 100 n + rep)", + "epochs": epochs, "lam": lam, "draws": []} + sel_f = lab.CACHE / "tab_nn" / f"sel_{KIND}.npz" + if net is not None and sel_f.exists() and not smoke: + with np.load(sel_f) as z: + diff = float(np.abs(tab_nn.embed(net, Xt) - z["Zt"]).max()) + res["src_base_vs_tab_nn_sel_Zt_maxabs"] = diff + log(f"madrid-check base vs tab_nn sel_{KIND}.npz Zt: max abs diff {diff:.2e}") + ns = (5, 200) if smoke else CHECK_NS + reps = 1 if smoke else CHECK_REPS + for n in ns: + for rep in range(reps): + t0 = time.time() + rng = np.random.default_rng(9000 + 100 * n + rep) + shot = np.concatenate([rng.choice(np.flatnonzero(yt == c), n, replace=False) for c in lab.CLASSES]) + keep = np.setdiff1d(np.arange(len(yt)), shot) + P = lpft(state, Xt[shot], yt[shot], Xt[keep], Xs, ys, Xt, lam, 10_000 + 1000 * n + rep, METHODS) + row = {"n": n, "rep": rep} + for k, Pk in P.items(): + row[k] = W.macro_f1(yt[keep], W.decide(Pk, prior)) + res["draws"].append(row) + log(f"madrid-check n={n} rep={rep}: " + " ".join(f"{k}={v:.4f}" for k, v in row.items() + if k not in ("n", "rep")) + f" {time.time() - t0:.1f}s") + import pandas as pd + df = pd.DataFrame(res["draws"]) + res["mean"] = {k: {int(n): float(v) for n, v in df.groupby("n")[k].mean().items()} + for k in ["lp", *METHODS]} + path.write_text(json.dumps(res, indent=1)) + log(f"madrid-check means: {res['mean']}") + return res + + +# --------------------------------------------------------------------------- # +# Main +# --------------------------------------------------------------------------- # +def main(): + ap = argparse.ArgumentParser() + ap.add_argument("--smoke", action="store_true") + ap.add_argument("--only", default="") + ap.add_argument("--base-only", action="store_true", + help="train/cache the Amsterdam base net, compare with tab_nn's embedding, stop") + a = ap.parse_args() + smoke = a.smoke + sfx = "_SMOKE" if smoke else "" + if smoke: + tab_nn.SMOKE_ROWS = 3000 + global BASE_THREADS # reviewer: smoke base is never compared, keep NNLAB_THREADS + BASE_THREADS = lab.THREADS + cache = lab.CACHE / ("lpft" + sfx) + cache.mkdir(parents=True, exist_ok=True) + sel = json.loads((lab.CACHE / "tab_nn" / "selection.json").read_text()) + epochs = 2 if smoke else int(sel["epochs"][KIND]) + lam = float(sel["lam"]) + draws = [(5, 0), (200, 0)] if smoke else list(lab.draws()) + names = {s: f"tab_{KIND}_{INP}_{s}{sfx}" for s in METHODS} + variants = {s: e for s, e in METHODS.items() if a.only in names[s]} + + d = lab.load(official60=False) + Xm, Xa = tab_nn.city_inputs(d, INP) + log("data loaded") + + # base on all Madrid, adversary = unlabelled Amsterdam + base_path = cache / f"{KIND}_{INP}_s{SEED}.pt" + state, net = get_base(base_path, Xm, d["ym"], Xa, epochs) + chk_path = cache / "base_check.json" + if net is not None or not chk_path.exists(): + if net is None: + net = make_net(state, Xm.shape[1]) + emb_f = lab.CACHE / "tab_nn" / f"emb_{KIND}_{INP}_s{SEED}.npz" + chk = {"compared_with": str(emb_f.relative_to(lab.ROOT)), "base_threads": BASE_THREADS} + if smoke: + chk["note"] = "smoke base (3000 rows, 2 epochs): no comparison" + else: + with np.load(emb_f) as z: + Za = tab_nn.embed(net, Xa) + chk["Za_maxabs"] = float(np.abs(Za - z["Za"]).max()) + chk["W0_maxabs"] = float(np.abs(tab_nn.last_layer(net)[0] - z["W0"]).max()) + chk["match_1e-3"] = bool(chk["Za_maxabs"] <= 1e-3) + assert int(z["epochs"]) == epochs + msg = "MATCH" if chk["match_1e-3"] else "MISMATCH (continuing; threading?)" + log(f"base vs tab_nn {emb_f.name}: Za max abs diff {chk['Za_maxabs']:.2e}, " + f"W0 {chk['W0_maxabs']:.2e} -> {msg}") + chk_path.write_text(json.dumps(chk, indent=1)) + base_chk = json.loads(chk_path.read_text()) + del net + if a.base_only: + log("base only: done") + return + if not variants: + log("nothing selected by --only") + return + + check = madrid_check(d, cache, epochs, lam, smoke) + + meta = dict(script="tools/nnlab/lpft.py", backbone=KIND, input=INP, seed=SEED, epochs=epochs, + lp_lam=lam, steps=STEPS, lr_backbone=LR_BACKBONE, lr_head=LR_HEAD, weight_decay=0.0, + replay_weight=REPLAY_W, replay_batch=4 * REPLAY_PER_CLASS, l2sp_weight=L2SP_W, + ent_weight=ENT_W, ent_batch=ENT_BATCH, bn="eval (running stats frozen)", dropout="off", + draw_seed="torch seed / generator 1000 n + rep", + base_threads=BASE_THREADS, base_check=base_chk, madrid_check=check, smoke=smoke, + hyperparameters="fixed in advance by the brief; epochs and lam from tab_nn Madrid selection") + for n, rep in draws: + todo = {s: e for s, e in variants.items() if not lab.have_pred(names[s], n, rep)} + if not todo: + continue + t0 = time.time() + shot = lab.shots_for(d, n, rep) + ys = lab.shot_labels(d, shot) + P = lpft(state, Xa[shot], ys, Xa, Xm, d["ym"], Xa, lam, 1000 * n + rep, todo) + for s in todo: + lab.save_pred(names[s], n, rep, P[s], {**meta, "method": names[s], "entropy_term": todo[s]}) + log(f"{'+'.join(names[s] for s in todo)} n={n} rep={rep} {time.time() - t0:.1f}s") + log("done") + + +if __name__ == "__main__": + main() diff --git a/tools/nnlab/lpft_steps.py b/tools/nnlab/lpft_steps.py new file mode 100644 index 0000000000000000000000000000000000000000..f0fee00d75d563627d324354981866d05b80b8c2 --- /dev/null +++ b/tools/nnlab/lpft_steps.py @@ -0,0 +1,74 @@ +"""LP-FT with the number of fine-tuning steps chosen on Madrid. + +tools/nnlab/lpft.py fixed STEPS = 60 in advance. Its Madrid check showed the gain of +whole-network fine-tuning growing with n, so this driver asks, on the Madrid +pseudo-target only (lab.madrid_split, the same cached source-part base network), +which of STEPS in {60, 150, 300, 600} is best, and then runs that one setting on +the Amsterdam protocol as tab_dann_rz_lpft_s{STEPS}. +Everything else (learning rates, replay, L2-SP, lam, epochs) is unchanged. +Amsterdam labels: only lab.shot_labels(lab.shots_for(...)) of the current draw. +""" +import json, sys, time +from pathlib import Path +sys.path.insert(0, str(Path(__file__).resolve().parent)) +import lab +import numpy as np +import lpft as L +import tab_nn +W = lab.W +GRID = (60, 150, 300, 600) +cache = lab.CACHE / "lpft" +sel = json.loads((lab.CACHE / "tab_nn" / "selection.json").read_text()) +epochs, lam = int(sel["epochs"][L.KIND]), float(sel["lam"]) +d = lab.load(official60=False) +t00 = time.time() +plain = {"lpft": L.METHODS["lpft"]} +sel_path = cache / "steps_selection.json" +if sel_path.exists(): + S = json.loads(sel_path.read_text()) +else: + src, tgt = lab.madrid_split(d["pm"]) + Xs, Xt = tab_nn.TRANSFORM[L.INP](d["Xm"][src]), tab_nn.TRANSFORM[L.INP](d["Xm"][tgt]) + ys, yt = d["ym"][src], d["ym"][tgt] + prior = np.array([(ys == c).mean() for c in lab.CLASSES]) + state, _ = L.get_base(cache / f"{L.KIND}_{L.INP}_src_s{L.SEED}.pt", Xs, ys, Xt, epochs) + rows = [] + for steps in GRID: + L.STEPS = steps + for n in L.CHECK_NS: + for rep in range(L.CHECK_REPS): + rng = np.random.default_rng(9000 + 100 * n + rep) # same draws as lpft.madrid_check + shot = np.concatenate([rng.choice(np.flatnonzero(yt == c), n, replace=False) for c in lab.CLASSES]) + keep = np.setdiff1d(np.arange(len(yt)), shot) + P = L.lpft(state, Xt[shot], yt[shot], Xt[keep], Xs, ys, Xt, lam, 10_000 + 1000 * n + rep, plain) + f = W.macro_f1(yt[keep], W.decide(P["lpft"], prior)) + rows.append(dict(steps=steps, n=n, rep=rep, f1=f)) + print(f"[{time.time()-t00:6.0f}s] madrid steps={steps} n={n} rep={rep} f1={f:.4f}", flush=True) + import pandas as pd + df = pd.DataFrame(rows) + by = df.groupby(["steps", "n"]).f1.mean().unstack() + mean = df.groupby("steps").f1.mean() + best = int(mean.idxmax()) + S = dict(grid=list(GRID), rows=rows, mean_by_steps={int(k): float(v) for k, v in mean.items()}, + by_n={int(s): {int(n): float(v) for n, v in by.loc[s].items()} for s in by.index}, chosen=best, + rule="highest mean macro F1 over n in (5, 50, 200) x 3 draws on the Madrid pseudo-target") + sel_path.write_text(json.dumps(S, indent=1)) +print("madrid selection:", S["mean_by_steps"], "by n:", S["by_n"], "-> chosen", S["chosen"], flush=True) +steps = int(S["chosen"]) +if steps == 60: + print("60 steps stays best on Madrid: nothing new to run") + sys.exit(0) +L.STEPS = steps +name = f"tab_{L.KIND}_{L.INP}_lpft_s{steps}" +Xm, Xa = tab_nn.city_inputs(d, L.INP) +state, _ = L.get_base(cache / f"{L.KIND}_{L.INP}_s{L.SEED}.pt", Xm, d["ym"], Xa, epochs) +for n, rep in lab.draws(): + if lab.have_pred(name, n, rep): + continue + t0 = time.time() + shot = lab.shots_for(d, n, rep) + P = L.lpft(state, Xa[shot], lab.shot_labels(d, shot), Xa, Xm, d["ym"], Xa, lam, 1000 * n + rep, plain) + lab.save_pred(name, n, rep, P["lpft"], dict(script="tools/nnlab/lpft_steps.py", steps=steps, steps_selection=S, + lam=lam, epochs=epochs, base="lpft.py base, seed 0")) + print(f"[{time.time()-t00:6.0f}s] {name} n={n} rep={rep} {time.time()-t0:.1f}s", flush=True) +print("done") diff --git a/tools/nnlab/metric.py b/tools/nnlab/metric.py new file mode 100644 index 0000000000000000000000000000000000000000..c3638bcd52f9bce23e7af7cd3db954e9af5a3b04 --- /dev/null +++ b/tools/nnlab/metric.py @@ -0,0 +1,488 @@ +""" +metric.py - METRIC LEARNING family: an embedding trained on Madrid so that distance +means "same age class", then adapted per protocol draw with the n Amsterdam shots. + + python tools/nnlab/metric.py [--smoke] [--only ] + +Encoders (MLP 393-256-256-64, BN/ReLU/Dropout 0.2, AdamW lr 1e-3 wd 1e-4, 2 seeds): + supcon supervised contrastive (T = 0.1), class-balanced batches of 512 + proto prototypical episodes (4-way, k in {5, 25}, 50 queries/class, scale 10), + cross-block: support from one random half of the 2 km blocks, queries from the other + protoda proto + deep-CORAL (weight 1) to an unlabelled Amsterdam batch every step + cos Baseline++ cosine classifier (scale 16), class-balanced batches +Heads (encoder frozen, CL2N per city, seeds' probabilities averaged): + pn, maha, tpn, knn, ftproj -> methods met_{encoder}_{head} + +Training length per encoder is chosen ONCE on Madrid (lab.madrid_split pseudo-target, +25-shot prototype macro F1, candidates evaluated as checkpoints of one constant-LR run), +then the encoder is retrained on all Madrid. Amsterdam labels: only lab.shot_labels. +""" +from __future__ import annotations + +import argparse +import json +import sys +import time +from pathlib import Path + +sys.path.insert(0, str(Path(__file__).resolve().parent)) +import lab # noqa: E402 +import numpy as np # noqa: E402 + +T0 = time.time() + + +def log(msg): + print(f"[{time.time() - T0:7.1f}s] {msg}", flush=True) + + +ap = argparse.ArgumentParser() +ap.add_argument("--smoke", action="store_true") +ap.add_argument("--only", default=None) +args = ap.parse_args() +SMOKE = args.smoke +SFX = "_SMOKE" if SMOKE else "" + +ENCODERS = ["supcon", "proto", "protoda", "cos"] +HEADS = ["pn", "maha", "tpn", "knn", "ftproj"] + +# configuration ---------------------------------------------------------------- # +# candidate training lengths (optimizer steps; one step = one batch / one episode) +# (sized to the measured ~0.09-0.2 s/step on the shared CPU so the full run fits ~60 min; +# 1500 steps x 512 = ~10 Madrid epochs) +CAND = {"supcon": [300, 700, 1500], "cos": [300, 700, 1500], + "proto": [400, 800, 1600], "protoda": [400, 800, 1600]} +if SMOKE: + CAND = {k: [15, 30] for k in CAND} +SEEDS = [0] if SMOKE else [0, 1] +FT_SEEDS = 1 # ftproj uses only the first seed's encoder (time budget) +SEL_SEED = 100 # seed of the Madrid-only selection run +SEL_DRAWS = 2 if SMOKE else 5 # pseudo-target 25-shot draws per checkpoint +DRAWS = [(5, 0), (200, 0)] if SMOKE else list(lab.draws()) +BATCH = 512 +TEMP = 0.1 +PROTO_SCALE = 10.0 +PROTO_K = (5, 25) +PROTO_Q = 50 +CORAL_W = 1.0 +DA_BATCH = 300 # Amsterdam batch for CORAL ~ size of a Madrid episode +COS_SCALE = 16.0 +LR, WD = 1e-3, 1e-4 +# heads +K_MIX, S_PN = 5.0, 20.0 +SHRINK, RIDGE = 50.0, 1e-3 +TPN_IT, TPN_CAP = 10, 5.0 +KNN_K = 15 +FT_STEPS, FT_LR, FT_L2SP = 100, 1e-3, 1e-2 + +MC = lab.CACHE / "metric" +MC.mkdir(parents=True, exist_ok=True) + + +def mname(enc, head): + return f"met_{enc}_{head}{SFX}" + + +WANT = {e: [h for h in HEADS if args.only is None or args.only in f"met_{e}_{h}"] for e in ENCODERS} +WANT = {e: hs for e, hs in WANT.items() if hs} + +# data ------------------------------------------------------------------------- # +d = lab.load(official60=False) +ym, pm = d["ym"], d["pm"] +Xm = lab.robustz(d["Xm"]) +Xa = lab.robustz(d["Xa"]) +del d["Xa"] # raw Madrid kept for the selection split +nM, nA = len(Xm), len(Xa) +log(f"loaded Madrid {Xm.shape} Amsterdam {Xa.shape}; encoders {list(WANT)}") + +torch = lab.torch_setup(0) +nn = torch.nn +F = torch.nn.functional + + +# --------------------------------------------------------------------------- # +# Encoder + losses +# --------------------------------------------------------------------------- # +class Enc(nn.Module): + def __init__(self, din=393): + super().__init__() + self.body = nn.Sequential( + nn.Linear(din, 256), nn.BatchNorm1d(256), nn.ReLU(), nn.Dropout(0.2), + nn.Linear(256, 256), nn.BatchNorm1d(256), nn.ReLU(), nn.Dropout(0.2)) + self.proj = nn.Linear(256, 64) + + def forward(self, x): + return self.proj(self.body(x)) + + +def supcon_loss(z, y, T=TEMP): + z = F.normalize(z, dim=1) + sim = z @ z.T / T + n = len(y) + eye = torch.eye(n, dtype=torch.bool) + sim = sim.masked_fill(eye, -1e9) + logp = sim - torch.logsumexp(sim, 1, keepdim=True) + pos = (y[:, None] == y[None, :]) & ~eye + return -((logp * pos).sum(1) / pos.sum(1).clamp(min=1)).mean() + + +def proto_loss(zs, ys, zq, yq, scale=PROTO_SCALE, ncls=4): + zs, zq = F.normalize(zs, dim=1), F.normalize(zq, dim=1) + P = torch.stack([zs[ys == c].mean(0) for c in range(ncls)]) + d2 = ((zq[:, None, :] - P[None]) ** 2).sum(-1) + return F.cross_entropy(-scale * d2, yq) + + +def coral(a, b): + def cov(x): + x = x - x.mean(0, keepdim=True) + return x.T @ x / (len(x) - 1) + dd = a.shape[1] + return ((cov(a) - cov(b)) ** 2).sum() / (4 * dd * dd) + + +class Sampler: + """Class-balanced batches and cross-block episodes over one labelled pool.""" + + def __init__(self, y, p, rng): + self.y, self.rng = y, rng + self.cls = [np.flatnonzero(y == c) for c in lab.CLASSES] + b = lab.block_ids(p) + _, self.code = np.unique(b, return_inverse=True) + self.nb = self.code.max() + 1 + self.ccode = [self.code[ix] for ix in self.cls] + + def batch(self, size=BATCH): + m = size // 4 + return np.concatenate([ix[self.rng.integers(0, len(ix), m)] for ix in self.cls]) + + def episode(self, k=None): + """Cross-block episode. Sampling is with replacement inside pools of thousands of + pixels (duplicates are rare; it avoids a full permutation per class per step).""" + k = k or int(self.rng.choice(PROTO_K)) + half = self.rng.random(self.nb) < 0.5 + S, Q = [], [] + for ix, cc in zip(self.cls, self.ccode): + m = half[cc] + a, b = ix[m], ix[~m] + if len(a) < k or len(b) < PROTO_Q: # degenerate split: fall back to any pixel + a = b = ix + S.append(a[self.rng.integers(0, len(a), k)]) + Q.append(b[self.rng.integers(0, len(b), PROTO_Q)]) + ys = np.repeat(np.arange(4), k) + yq = np.repeat(np.arange(4), PROTO_Q) + return np.concatenate(S), ys, np.concatenate(Q), yq + + +@torch.no_grad() +def embed_H(model, X, bs=8192): + model.eval() + out = [model.body(torch.from_numpy(X[i:i + bs])).numpy() for i in range(0, len(X), bs)] + return np.vstack(out).astype(np.float32) + + +def train_encoder(kind, X, y, p, Xt, lengths, seed, eval_fn=None): + """Constant-LR training; returns (model, {length: eval score}) with checkpoints at + `lengths` (the model returned is the one at max(lengths)).""" + torch.manual_seed(seed) + rng = np.random.default_rng(seed) + model = Enc(X.shape[1]) + params = list(model.parameters()) + Wc = None + if kind == "cos": + Wc = nn.Parameter(torch.randn(4, 64) * 0.1) + params.append(Wc) + opt = torch.optim.AdamW(params, lr=LR, weight_decay=WD) + smp = Sampler(y, p, rng) + Xt_t = torch.from_numpy(X) + yt = torch.from_numpy(y - 1) + scores = {} + t0 = time.time() + for step in range(1, max(lengths) + 1): + model.train() + if kind in ("supcon", "cos"): + ix = smp.batch() + z = model(Xt_t[ix]) + if kind == "supcon": + loss = supcon_loss(z, yt[ix]) + else: + logits = COS_SCALE * F.normalize(z, dim=1) @ F.normalize(Wc, dim=1).T + loss = F.cross_entropy(logits, yt[ix]) + else: + S, ys, Q, yq = smp.episode() + z = model(Xt_t[np.concatenate([S, Q])]) + zs, zq = z[:len(S)], z[len(S):] + loss = proto_loss(zs, torch.from_numpy(ys), zq, torch.from_numpy(yq)) + if kind == "protoda": + it = rng.integers(0, len(Xt), DA_BATCH) + zt = model(torch.from_numpy(Xt[it])) + loss = loss + CORAL_W * coral(z, zt) + opt.zero_grad() + loss.backward() + opt.step() + if step in lengths: + if eval_fn is not None: + scores[step] = eval_fn(model) + log(f" {kind} seed{seed} step {step}: loss {loss.item():.4f} " + f"pseudo-target F1 {scores[step]:.4f} ({time.time() - t0:.0f}s)") + else: + log(f" {kind} seed{seed} step {step}: loss {loss.item():.4f} ({time.time() - t0:.0f}s)") + return model, scores + + +# --------------------------------------------------------------------------- # +# Embedding utilities +# --------------------------------------------------------------------------- # +def l2n(Z): + return Z / np.maximum(np.linalg.norm(Z, axis=1, keepdims=True), 1e-12) + + +def cl2n(Z): + """SimpleShot CL2N with this city's own mean (label-free).""" + E = l2n(np.asarray(Z, np.float64)) + return l2n(E - E.mean(0)) + + +def macro_f1(y, yhat): + from sklearn.metrics import f1_score + return float(f1_score(y, yhat, average="macro")) + + +def proto_f1_eval(Xpt, ypt, ndraw=SEL_DRAWS, shots=25): + """Pseudo-target score: 25-shot pure prototype classifier on CL2N embeddings.""" + rs = np.random.default_rng(7) + draws_ = [] + for _ in range(ndraw): + sh = np.concatenate([rs.choice(np.flatnonzero(ypt == c), shots, replace=False) for c in lab.CLASSES]) + draws_.append(sh) + + def fn(model): + H = embed_H(model, Xpt) + with torch.no_grad(): + Z = model.proj(torch.from_numpy(H)).numpy() + E = cl2n(Z) + out = [] + for sh in draws_: + P = np.stack([E[sh][ypt[sh] == c].mean(0) for c in lab.CLASSES]) + keep = np.setdiff1d(np.arange(len(E)), sh) + pred = lab.CLASSES[(-((E[keep, None, :] - P[None]) ** 2).sum(-1)).argmax(1)] + out.append(macro_f1(ypt[keep], pred)) + return float(np.mean(out)) + return fn + + +def select_length(kind): + p = MC / f"{kind}_select{SFX}.json" + if p.exists(): + return json.loads(p.read_text()) + src, pt = lab.madrid_split(pm) + Xs, Xpt = lab.robustz(d["Xm"][src]), lab.robustz(d["Xm"][pt]) + log(f"{kind}: selecting length on Madrid split (src {len(src)}, pseudo-target {len(pt)}) " + f"from {CAND[kind]}") + # protoda: the unlabelled 'target' of the selection run is the pseudo-target itself + _, sc = train_encoder(kind, Xs, ym[src], pm[src], Xpt, CAND[kind], SEL_SEED, + eval_fn=proto_f1_eval(Xpt, ym[pt])) + best = max(sc, key=lambda s: (sc[s], -s)) + res = {"candidates": CAND[kind], "pseudo_target_f1": {str(k): v for k, v in sc.items()}, + "chosen": int(best), "seed": SEL_SEED, "split": "lab.madrid_split(pm) default", + "da_target_in_selection": "pseudo-target (unlabelled)" if kind == "protoda" else None} + p.write_text(json.dumps(res, indent=1)) + log(f"{kind}: chose {best} steps") + return res + + +def encoder_cache(kind, seed, length): + p = MC / f"{kind}_s{seed}_L{length}{SFX}.npz" + if p.exists(): + z = np.load(p) + return {k: z[k] for k in z.files} + log(f"{kind}: training seed {seed} on all Madrid for {length} steps") + model, _ = train_encoder(kind, Xm, ym, pm, Xa, [length], seed) + out = dict(Hm=embed_H(model, Xm), Ha=embed_H(model, Xa), + W=model.proj.weight.detach().numpy().copy(), b=model.proj.bias.detach().numpy().copy()) + np.savez(p, **out) + return out + + +# --------------------------------------------------------------------------- # +# Heads +# --------------------------------------------------------------------------- # +def class_means(E, y): + return np.stack([E[y == c].mean(0) for c in lab.CLASSES]) + + +def within_cov(E, y): + R = np.vstack([E[y == c] - E[y == c].mean(0) for c in lab.CLASSES]) + return R.T @ R / max(len(R) - 4, 1) + + +def pn_protos(Ea, sh, ysh, mu_m): + P = [] + for j, c in enumerate(lab.CLASSES): + S = Ea[sh[ysh == c]] + P.append((len(S) * S.mean(0) + K_MIX * mu_m[j]) / (len(S) + K_MIX)) + return np.stack(P) + + +def sqd(E, P): + return (E ** 2).sum(1)[:, None] - 2 * E @ P.T + (P ** 2).sum(1)[None] + + +def head_pn(Ea, sh, ysh, mu_m): + return lab.softmax(-S_PN * sqd(Ea, pn_protos(Ea, sh, ysh, mu_m))) + + +def head_maha(Ea, sh, ysh, mu_m, S_m): + from scipy.linalg import cho_factor, cho_solve + P = pn_protos(Ea, sh, ysh, mu_m) + nt = len(sh) + S = (nt * within_cov(Ea[sh], ysh) + SHRINK * S_m) / (nt + SHRINK) + RIDGE * np.eye(Ea.shape[1]) + cf = cho_factor(S) + D = np.empty((len(Ea), 4)) + for j in range(4): + R = Ea - P[j] + D[:, j] = (R * cho_solve(cf, R.T).T).sum(1) + return lab.softmax(-0.5 * D) + + +def head_tpn(Ea, sh, ysh, mu_m): + P = pn_protos(Ea, sh, ysh, mu_m) + unl = np.setdiff1d(np.arange(len(Ea)), sh) + Eu = Ea[unl] + sums = np.stack([Ea[sh[ysh == c]].sum(0) for c in lab.CLASSES]) + nc = np.array([(ysh == c).sum() for c in lab.CLASSES], float) + for _ in range(TPN_IT): + R = lab.softmax(-S_PN * sqd(Eu, P)) # soft assignments of the unlabelled pool + mass = R.sum(0) + w = np.minimum(1.0, TPN_CAP * nc / np.maximum(mass, 1e-12)) + P = (sums + w[:, None] * (R.T @ Eu)) / (nc + w * mass)[:, None] + return lab.softmax(-S_PN * sqd(Ea, P)) + + +def head_knn(Ea, sh, ysh, mu_m, n): + if n < 25: + return head_pn(Ea, sh, ysh, mu_m) + Es = Ea[sh] + C = Ea @ Es.T # cosine (unit vectors) + k = min(KNN_K, len(sh)) + nb = np.argpartition(-C, k - 1, axis=1)[:, :k] + cs = np.take_along_axis(C, nb, 1) + w = 1.0 / (1.0 - cs + 1e-3) # inverse cosine distance + lab_nb = ysh[nb] - 1 + P = np.zeros((len(Ea), 4)) + for j in range(4): + P[:, j] = (w * (lab_nb == j)).sum(1) + P += 1e-3 + return P / P.sum(1, keepdims=True) + + +def head_ftproj(enc, Hm, Ha, W0, b0, sh, ysh, n, seed): + """Fine-tune ONLY the projection Linear(256,64) on cached frozen activations.""" + torch.manual_seed(seed + 1000 * n) + rng = np.random.default_rng(seed + 1000 * n) + Wt = torch.tensor(W0, requires_grad=True) + bt = torch.tensor(b0, requires_grad=True) + W0t, b0t = torch.tensor(W0), torch.tensor(b0) + opt = torch.optim.Adam([Wt, bt], lr=FT_LR) + Hs = torch.from_numpy(Ha[sh]) + Hm_t = torch.from_numpy(Hm) + smp = MAD_SMP + smp.rng = rng + cls_ix = [np.flatnonzero(ysh == c) for c in lab.CLASSES] + for _ in range(FT_STEPS): + S, Q = [], [] + for ix in cls_ix: + pr = rng.permutation(ix) + ns = 3 if len(ix) <= 5 else len(ix) // 2 + S.append(pr[:ns]); Q.append(pr[ns:]) + ys = torch.from_numpy(np.concatenate([np.full(len(s), j) for j, s in enumerate(S)])) + yq = torch.from_numpy(np.concatenate([np.full(len(q), j) for j, q in enumerate(Q)])) + S, Q = np.concatenate(S), np.concatenate(Q) + loss = proto_loss(Hs[S] @ Wt.T + bt, ys, Hs[Q] @ Wt.T + bt, yq) + mS, mys, mQ, myq = smp.episode() + loss = loss + proto_loss(Hm_t[mS] @ Wt.T + bt, torch.from_numpy(mys), + Hm_t[mQ] @ Wt.T + bt, torch.from_numpy(myq)) + loss = loss + FT_L2SP * (((Wt - W0t) ** 2).sum() + ((bt - b0t) ** 2).sum()) + opt.zero_grad() + loss.backward() + opt.step() + W, b = Wt.detach().numpy(), bt.detach().numpy() + Em, Ea = cl2n(Hm @ W.T + b), cl2n(Ha @ W.T + b) + return head_pn(Ea, sh, ysh, class_means(Em, ym)) + + +# --------------------------------------------------------------------------- # +# Main loop +# --------------------------------------------------------------------------- # +def meta_for(enc, head, sel): + return dict(family="metric", encoder=enc, head=head, smoke=SMOKE, + seeds=SEEDS[:FT_SEEDS] if head == "ftproj" else SEEDS, + input="lab.robustz per city (clip 10)", + encoder_arch="393-256-BN-ReLU-Do0.2-256-BN-ReLU-Do0.2-64, L2-normalised", + optim=f"AdamW lr {LR} wd {WD}, constant LR", + encoder_cfg=dict(batch=BATCH, supcon_T=TEMP, proto_scale=PROTO_SCALE, proto_k=PROTO_K, + proto_q=PROTO_Q, cross_block="2 km blocks random halves per episode", + coral_w=CORAL_W, da_batch=DA_BATCH, cos_scale=COS_SCALE, + cos_balance="class-balanced batches (128 per class), plain CE"), + length_selection=sel, + madrid_choices=f"training steps chosen on lab.madrid_split pseudo-target: {sel['chosen']}", + head_cfg=dict(CL2N="per-city mean, re-normalised", k_mix=K_MIX, s=S_PN, shrink=SHRINK, + ridge=RIDGE, tpn_iters=TPN_IT, tpn_cap=TPN_CAP, knn_k=KNN_K, + knn_weight="1/(1-cos+1e-3)", knn_n5="pn fallback", + ft_steps=FT_STEPS, ft_lr=FT_LR, ft_l2sp=FT_L2SP, + ft_episode="shots half/half (n=5: 3/2) + one Madrid cross-block episode"), + hyperparams_from_amsterdam="none") + + +MAD_SMP = None + + +def main(): + global MAD_SMP + MAD_SMP = Sampler(ym, pm, np.random.default_rng(0)) + for enc, heads in WANT.items(): + todo = [(n, r) for n, r in DRAWS if not all(lab.have_pred(mname(enc, h), n, r) for h in heads)] + if not todo: + log(f"{enc}: all predictions exist, skipping") + continue + sel = select_length(enc) + L = sel["chosen"] + caches = [encoder_cache(enc, s, L) for s in SEEDS] + prep = [] + for c in caches: + Zm, Za = c["Hm"] @ c["W"].T + c["b"], c["Ha"] @ c["W"].T + c["b"] + Em, Ea = cl2n(Zm), cl2n(Za) + prep.append(dict(Ea=Ea, mu=class_means(Em, ym), Sm=within_cov(Em, ym))) + del Em + metas = {h: meta_for(enc, h, sel) for h in heads} + for n, r in todo: + t0 = time.time() + sh = lab.shots_for(d, n, r) + ysh = lab.shot_labels(d, sh) + for h in heads: + if lab.have_pred(mname(enc, h), n, r): + continue + Ps = [] + for si, (c, pp) in enumerate(zip(caches, prep)): + if h == "ftproj" and si >= FT_SEEDS: + break + Ea, mu = pp["Ea"], pp["mu"] + if h == "pn": + P = head_pn(Ea, sh, ysh, mu) + elif h == "maha": + P = head_maha(Ea, sh, ysh, mu, pp["Sm"]) + elif h == "tpn": + P = head_tpn(Ea, sh, ysh, mu) + elif h == "knn": + P = head_knn(Ea, sh, ysh, mu, n) + else: + P = head_ftproj(enc, c["Hm"], c["Ha"], c["W"], c["b"], sh, ysh, n, SEEDS[si] + 10 * r) + Ps.append(P) + lab.save_pred(mname(enc, h), n, r, np.mean(Ps, 0), metas[h]) + log(f"{enc} n={n} rep={r}: saved {len(heads)} heads ({time.time() - t0:.1f}s)") + del caches, prep + log("done") + + +if __name__ == "__main__": + main() diff --git a/tools/nnlab/model_create.py b/tools/nnlab/model_create.py new file mode 100644 index 0000000000000000000000000000000000000000..8af9d81ef7af658c08314a283c44081fd9d73798 --- /dev/null +++ b/tools/nnlab/model_create.py @@ -0,0 +1,348 @@ +""" +model_create - ONE entry point for FINAL-AVG ("ens_e4__lp", read with rule_smooth): +a METHOD that turns n labelled Amsterdam pixels per class into a classifier for +every other Amsterdam pixel. + + from model_create import model_create + model = model_create(shot_idx, shot_labels, n=None, spatial=True) + labels = model.predict() # classes 1..4 for model.keep (all NON-shot rows, in order) + model.proba # (25992, 4) probabilities before smoothing / the rule + model.keep # the non-shot row indices predict() labels + + python tools/nnlab/model_create.py --n 25 --rep 0 [--no-spatial] + +WHAT THE METHOD IS + Five members, each giving p(class | pixel) for all 25,992 Amsterdam rows: + zs_xgb_dann zero-shot: (XGBoost + mean of 3 DANN seeds) / 2, Madrid-trained, + no shots (cached in data/processed/zeroshot_xgb_dann_ams.npz) + lgbm_upw LightGBM retrained per call on Madrid + the shots (weight 30), + each city robust-z scaled by its own median/IQR + (winners.train_retrained / predict_retrained) + met_protoda_maha metric-learning encoder (prototypical + deep-CORAL, 400 steps, + seeds 0 and 1) + within-class Mahalanobis head on the shots + (metric.head_maha, prototypes shrunk to the Madrid class means) + met_cos_maha same head on the cosine-classifier encoder (1500 steps, seeds 0, 1) + tab_dann_rz_ft DANN MLP backbone (15 epochs, seed 0), last layer fine-tuned on + the shots with an L2 pull to the Madrid weights, lam = 10 + (lab.finetune_last_layer via tab_nn.head_ft) + Ensemble (ensemble.py rule): log p = w0 log p_zs + (1 - w0) mean(log p of the four + shot-using members), w0 = 10 / (n + 10); softmax. + spatial=True (FINAL-AVG): label propagation (transductive.r_lp: Zhou et al., a = 0.8, + 20 iterations, shots clamped as 10 x one-hot) on the union of a spatial + r = 3 px graph and a cosine 10-NN graph on PCA-32 of robust-z features, + then the lab's neighbour smoothing (r = 6, alpha = 0.5) over the non-shot + rows only, then the decision rule (winners.decide: tau = 0.5 logit + adjustment toward the Madrid prior + macro-F1 plug-in). + spatial=False (fallback when pixel coordinates are not available, "ens_e4" + rule): + ensemble -> decision rule. No graph, no smoothing. + The decision rule is BATCH-LEVEL (the plug-in looks at the predicted class shares of + the set it labels), so predict() labels all non-shot rows at once. + +WHAT IT NEEDS AT FIT TIME + * the n-per-class shot rows (indices into the Amsterdam feature table) and labels; + * ALL unlabelled Amsterdam features (393 columns of amsterdam_features.parquet): + the per-city robust-z statistics, the CL2N centring of the metric embeddings and the + kNN graph are computed from them (transductive, label-free); + * spatial=True only: the pixel coordinates (px_key, py_key) for the r=3 graph and the + r=6 smoothing; + * the Madrid table (features + labels) - LightGBM is refitted on it each call, and + the Madrid class means / covariances / prior come from it; + * cached Madrid-trained encoders (NOT retrained here; a missing cache is an error): + reports/nnlab/cache/metric/{protoda_s{0,1}_L400, cos_s{0,1}_L1500}.npz, + reports/nnlab/cache/tab_nn/emb_dann_rz_s0.npz + selection.json (lam), + data/processed/zeroshot_xgb_dann_ams.npz; the union graph is read from + reports/nnlab/cache/transd_graph_ams_r3_k10_pca32.npz (rebuilt label-free if absent). + Everything shot-independent is loaded lazily, once per process (STATE below). + No file under reports/nnlab/preds is read, except by the CLI's comparison step. + +REPRODUCING THE STORED NUMBERS + The lab stored every member as float32 and the ensemble read them back; the ensemble + was stored as float32 before label propagation. model_create reproduces those float32 + round trips (ROUND32 = True) so model.proba matches the stored files; they change the + probabilities by < 1e-6 and do not matter for the method itself. + LightGBM runs with n_jobs = NNLAB_THREADS (default 2 here) instead of winners' -1: + that is the only intended difference from the stored lgbm_upw. + ensemble.py and baselines.py are argparse scripts with side effects at import, so + their few lines (the log-mean rule; the zero-shot average) are restated here; + everything else is imported from lab / winners / metric / tab_nn / transductive. + +Amsterdam labels: model_create never reads them. The CLI draws shots with +lab.shots_for and reads their labels with lab.shot_labels; score_only() (clearly +separate, used only by the CLI) reads non-shot labels to compute macro F1, exactly +like lab.score_method. +""" +from __future__ import annotations + +import os +import sys +import time +from pathlib import Path + +os.environ.setdefault("NNLAB_THREADS", "2") +sys.path.insert(0, str(Path(__file__).resolve().parent)) +import lab # noqa: E402 +import numpy as np # noqa: E402 + +W = lab.W +K_ENS = 10.0 # ensemble.py --k default, as used for ens_e4 +METRIC_MEMBERS = ("protoda", "cos") # met_protoda_maha, met_cos_maha +TAB_BACKBONE, TAB_INPUT, TAB_SEEDS = "dann", "rz", (0,) +GRAPH_TAG = "ams_r3_k10_pca32" +ROUND32 = True # mimic the lab's float32 prediction store + + +def _r32(P): + P = np.asarray(P, np.float64) + return P.astype(np.float32).astype(np.float64) if ROUND32 else P + + +# --------------------------------------------------------------------------- # +# Shot-independent state, loaded once per process +# --------------------------------------------------------------------------- # +class _State: + def __init__(self): + self._c = {} + + def get(self, key, fn): + if key not in self._c: + t = time.time() + self._c[key] = fn() + if os.environ.get("MC_VERBOSE"): + print(f" [model_create] loaded {key} in {time.time() - t:.1f}s", flush=True) + return self._c[key] + + # labels-free tables ------------------------------------------------------ # + def data(self): + """lab.load (float32 features, coords, Madrid labels/prior).""" + return self.get("data", lambda: lab.load(official60=False)) + + def X64(self): + """Madrid / Amsterdam features as float64, exactly as winners.load_data builds them + (parquet -> to_numpy(float)) but without reading Amsterdam age_class.""" + def f(): + import pandas as pd + feats = W.feature_names() + Xm = pd.read_parquet(W.DATA / "madrid_features.parquet", columns=feats)[feats].to_numpy(float) + Xa = pd.read_parquet(W.DATA / "amsterdam_features.parquet", columns=feats)[feats].to_numpy(float) + return Xm, Xa + return self.get("X64", f) + + def zero_shot(self): + """zs_xgb_dann exactly as baselines.py builds it.""" + def f(): + z = np.load(lab.PROC / "zeroshot_xgb_dann_ams.npz") + assert (z["pa"] == self.data()["pa"]).all() + return _r32((z["P_xgb"] + z["P_dann_seeds"].mean(0)) / 2) + return self.get("zs", f) + + # metric encoders ---------------------------------------------------------- # + def metric(self): + """Import tools/nnlab/metric.py (it parses argv and loads data at import).""" + def f(): + argv = sys.argv + sys.argv = [str(lab.HERE / "metric.py")] + try: + import metric as M + finally: + sys.argv = argv + return M + return self.get("metric_module", f) + + def metric_prep(self, enc): + """Per seed: CL2N Amsterdam embeddings, Madrid class means and within-class + covariance - exactly metric.main()'s `prep`, from the cached encoders.""" + def f(): + M = self.metric() + sel_p = M.MC / f"{enc}_select.json" + assert sel_p.exists(), f"missing {sel_p} (would re-run the length selection)" + L = M.select_length(enc)["chosen"] + prep = [] + for s in M.SEEDS: + p = M.MC / f"{enc}_s{s}_L{L}.npz" + assert p.exists(), f"missing encoder cache {p} (model_create never retrains)" + c = M.encoder_cache(enc, s, L) + Zm, Za = c["Hm"] @ c["W"].T + c["b"], c["Ha"] @ c["W"].T + c["b"] + Em, Ea = M.cl2n(Zm), M.cl2n(Za) + prep.append(dict(Ea=Ea, mu=M.class_means(Em, M.ym), Sm=M.within_cov(Em, M.ym))) + return prep + return self.get(f"metric_{enc}", f) + + # tab_nn backbone ------------------------------------------------------------ # + def tab(self): + def f(): + import json + import tab_nn as T + cache = lab.CACHE / "tab_nn" + sel = json.loads((cache / "selection.json").read_text()) + embs = [] + for s in TAB_SEEDS: + p = cache / f"emb_{TAB_BACKBONE}_{TAB_INPUT}_s{s}.npz" + assert p.exists(), f"missing backbone cache {p} (model_create never retrains)" + z = np.load(p) + assert int(z["epochs"]) == int(sel["epochs"][TAB_BACKBONE]), "stale backbone cache" + embs.append(dict(Zm=z["Zm"], Za=z["Za"], W0=z["W0"], b0=z["b0"], ym=self.data()["ym"])) + return T, embs, float(sel["lam"]) + return self.get("tab", f) + + # graph ---------------------------------------------------------------------- # + def graph(self): + def f(): + import transductive as TR + d = self.data() + return TR, TR.Graph(TR.union_graph(d["Xa"], d["pa"], tag=GRAPH_TAG), d["pa"]) + return self.get("graph", f) + + +STATE = _State() + + +# --------------------------------------------------------------------------- # +# Members (each: shots -> (25992, 4) probabilities) +# --------------------------------------------------------------------------- # +def member_lgbm_upw(shot, ys): + Xm, Xa = STATE.X64() + ym = STATE.data()["ym"] + saved = dict(W.RETRAIN_OVERRIDES) + W.RETRAIN_OVERRIDES["n_jobs"] = lab.THREADS # shared laptop; winners uses -1 + try: + m, st = W.train_retrained(Xm, ym, Xa, Xa[shot], ys) + finally: + W.RETRAIN_OVERRIDES.clear() + W.RETRAIN_OVERRIDES.update(saved) + return W.predict_retrained(m, st, Xa) + + +def member_metric_maha(enc, shot, ys): + M = STATE.metric() + return np.mean([M.head_maha(pp["Ea"], shot, ys, pp["mu"], pp["Sm"]) + for pp in STATE.metric_prep(enc)], 0) + + +def member_tab_ft(shot, ys): + T, embs, lam = STATE.tab() + return np.mean([T.head_ft(e, e["Za"][shot], ys, lam) for e in embs], 0) + + +def ensemble(P_zs, P_shot, n): + """ensemble.py's rule (log-prob mean, w0 = K/(n+K)).""" + logm = lambda Ps: np.mean([np.log(np.clip(P, 1e-6, 1)) for P in Ps], 0) + w0 = K_ENS / (n + K_ENS) + return lab.softmax(w0 * logm([P_zs]) + (1 - w0) * logm(P_shot)) + + +# --------------------------------------------------------------------------- # +class FinalAvgModel: + def __init__(self, proba, keep, spatial, members, proba_ensemble): + self.proba = proba # (25992, 4), before smoothing / the rule + self.keep = keep # non-shot rows, in order + self.spatial = spatial + self.members = members # name -> (25992, 4) + self.proba_ensemble = proba_ensemble # before label propagation + + def predict(self) -> np.ndarray: + """Class labels 1..4 for every row in self.keep (batch-level decision).""" + d = STATE.data() + P = self.proba[self.keep] + if self.spatial: + P = lab.smooth(P, d["pa"], keep=self.keep) + return W.decide(P, d["prior"]) + + +def model_create(shot_idx, shot_labels, n=None, spatial=True, verbose=False, + sort_shots=False) -> FinalAvgModel: + """Build FINAL-AVG (spatial=True) or its coordinate-free fallback (spatial=False) + from the labelled shots. shot_idx: row indices into the Amsterdam feature table; + shot_labels: their classes 1..4; n: shots per class (default len(shot_idx) / 4), + used only for the zero-shot weight w0 = 10 / (n + 10). + + SHOT ORDER: lgbm_upw bags rows by position (subsample=0.8, subsample_freq=1), so + the same shots in a different order give a different LightGBM fit (seen: max + |dp| 0.33 on lgbm_upw, ~0.02 after the ensemble + LP, macro F1 -0.0006 on + n=25 rep=2) - noise of the size of a LightGBM seed change, not float rounding. + sort_shots=True sorts (shot, label) by row index first, which makes the method + invariant to input order; the default False keeps the caller's order so the + stored per-draw files (drawn in lab.shots_for order) are reproduced exactly.""" + shot = np.asarray(shot_idx, dtype=np.int64) + ys = np.asarray(shot_labels).astype(int) + assert len(shot) == len(ys) and set(np.unique(ys)) <= {1, 2, 3, 4} + assert len(np.unique(shot)) == len(shot), "duplicate shot indices" + if sort_shots: + o = np.argsort(shot, kind="stable") + shot, ys = shot[o], ys[o] + if n is None: + n = len(shot) / 4.0 + t0 = time.time() + + def tick(name): + if verbose: + print(f" [model_create] {name:<18s} {time.time() - t0:6.1f}s", flush=True) + + members = {"zs_xgb_dann": STATE.zero_shot()} + tick("zero-shot") + members["lgbm_upw"] = _r32(member_lgbm_upw(shot, ys)); tick("lgbm_upw") + for enc in METRIC_MEMBERS: + members[f"met_{enc}_maha"] = _r32(member_metric_maha(enc, shot, ys)); tick(f"met_{enc}_maha") + members["tab_dann_rz_ft"] = _r32(member_tab_ft(shot, ys)); tick("tab_dann_rz_ft") + shot_members = [members[k] for k in ("lgbm_upw", "met_protoda_maha", "met_cos_maha", "tab_dann_rz_ft")] + P_ens = _r32(ensemble(members["zs_xgb_dann"], shot_members, n)) + proba = P_ens + if spatial: + TR, g = STATE.graph() + proba = TR.r_lp(P_ens, shot, ys, g); tick("label propagation") + keep = np.setdiff1d(np.arange(len(proba)), shot) + return FinalAvgModel(proba, keep, spatial, members, P_ens) + + +# --------------------------------------------------------------------------- # +# Score-only helper (the only code that reads non-shot Amsterdam labels) +# --------------------------------------------------------------------------- # +def score_only(d, model) -> float: + """Macro F1 of model.predict() on its non-shot rows, as lab.score_method computes + rule (spatial=False) / rule_smooth (spatial=True).""" + return W.macro_f1(d["_ya"][model.keep], model.predict()) + + +def main(): + import argparse + import json + ap = argparse.ArgumentParser() + ap.add_argument("--n", type=int, required=True) + ap.add_argument("--rep", type=int, required=True) + ap.add_argument("--no-spatial", action="store_true") + a = ap.parse_args() + spatial = not a.no_spatial + d = STATE.data() + shot = lab.shots_for(d, a.n, a.rep) + ys = lab.shot_labels(d, shot) + t = time.time() + model = model_create(shot, ys, n=a.n, spatial=spatial, verbose=True) + labels = model.predict() + dt = time.time() - t + method, col = ("ens_e4__lp", "rule_smooth") if spatial else ("ens_e4", "rule") + f1 = score_only(d, model) + print(f"n={a.n} rep={a.rep} spatial={spatial}: {len(labels)} rows labelled in {dt:.1f}s " + f"(incl. one-off loading)") + print(f"macro F1 ({col}) = {f1:.6f}") + sp = lab.SCORES / f"{method}.json" + if sp.exists(): + rows = [r for r in json.loads(sp.read_text())["draws"] if r["n"] == a.n and r["rep"] == a.rep] + if rows: + print(f"stored {method} {col} = {rows[0][col]:.6f} diff {f1 - rows[0][col]:+.2e}") + pp = lab.pred_path(method, a.n, a.rep) # comparison only + if pp.exists(): + S = np.load(pp).astype(np.float64) + print(f"max |proba - stored {method}| = {np.abs(model.proba - S).max():.3e}") + for k in model.members: + mp = lab.pred_path(k, a.n, a.rep) if k != "zs_xgb_dann" else lab.pred_path(k, 0, 0) + if mp.exists(): + print(f" member {k:<18s} max |diff| = {np.abs(model.members[k] - np.load(mp)).max():.3e}") + if spatial: + ep = lab.pred_path("ens_e4", a.n, a.rep) + if ep.exists(): + print(f" ensemble (pre-LP) max |diff| = {np.abs(model.proba_ensemble - np.load(ep)).max():.3e}") + else: + print(f"no stored {pp} to compare with") + + +if __name__ == "__main__": + main() diff --git a/tools/nnlab/model_create2.py b/tools/nnlab/model_create2.py new file mode 100644 index 0000000000000000000000000000000000000000..18be921db0fa57d815d609f95dab865e813ef3a1 --- /dev/null +++ b/tools/nnlab/model_create2.py @@ -0,0 +1,94 @@ +""" +modelCreate for the round-2 pick (FINAL-2): two models and one fixed rule. + + 1. zero-shot anchor teammate's XGBoost + DANN, trained on Madrid labels and + UNLABELLED Amsterdam features (cached probabilities, + data/processed/zeroshot_xgb_dann_ams.npz) + 2. lgbm_feda LightGBM on Madrid + the labelled shots (weight 30, classes + balanced, each city robust-z scaled with its own median/IQR) + with Daume's feature augmentation: the top 120 features get + a Madrid-only copy and an Amsterdam-only copy + 3. ensemble log p = w0 * log p_zero_shot + (1 - w0) * log p_feda, w0 = 10 / (n + 10) + 4. decision neighbour smoothing (r = 6 px, alpha 0.5, shots excluded), then + tau-0.5 logit adjustment + macro-F1 plug-in rule + (spatial=False skips the smoothing: no coordinates needed) + + from model_create2 import model_create + m = model_create(shot_idx, shot_labels) # rows of the Amsterdam feature table, labels 1..4 + labels = m.predict() # labels 1..4 for m.keep (every non-shot row) + +Needs at fit time: the Madrid table, ALL unlabelled Amsterdam features (per-city +scaling, the DANN inside the anchor), pixel coordinates for the smoothing. The +decision is batch-level: it labels the whole city at once. + +CLI (scores one protocol draw; the only place non-shot labels are read): + python tools/nnlab/model_create2.py --n 25 --rep 0 [--no-spatial] +""" +from __future__ import annotations +import argparse, sys +from pathlib import Path +sys.path.insert(0, str(Path(__file__).resolve().parent)) +import lab +import numpy as np +import pandas as pd +W = lab.W +K = 10.0 +_S = {} + + +def _state(): + if not _S: + import lgbm_var as V + d = lab.load(official60=False) + feats = W.feature_names() + Xm = pd.read_parquet(W.DATA / "madrid_features.parquet", columns=feats)[feats].to_numpy(float) + Xa = pd.read_parquet(W.DATA / "amsterdam_features.parquet", columns=feats)[feats].to_numpy(float) + z = np.load(lab.PROC / "zeroshot_xgb_dann_ams.npz") + assert (z["pa"] == d["pa"]).all() + _S.update(V=V, d=d, Zm=W.robustz(Xm, W.robustz_fit(Xm)), Za=W.robustz(Xa, W.robustz_fit(Xa)), + ctx={"top": V.top_gain_idx()}, + P_zs=((z["P_xgb"] + z["P_dann_seeds"].mean(0)) / 2).astype(np.float32).astype(np.float64), + params={**W.LGBM_PARAMS, **W.RETRAIN_OVERRIDES, "n_jobs": lab.THREADS}) + return _S + + +class Model: + def __init__(self, proba, keep, spatial, members): + self.proba, self.keep, self.spatial, self.members = proba, keep, spatial, members + + def predict(self): + S = _state() + P = self.proba[self.keep] + if self.spatial: + P = lab.smooth(P, S["d"]["pa"], keep=self.keep) + return W.decide(P, S["d"]["prior"]) + + +def model_create(shot_idx, shot_labels, n=None, spatial=True): + S = _state() + shot = np.asarray(shot_idx) + ys = np.asarray(shot_labels) + n = len(shot) / 4 if n is None else n + P_feda = S["V"].fit_predict("lgbm_feda", S["Zm"], S["d"]["ym"], S["Za"], shot, ys, S["ctx"], S["params"]) + P_feda = P_feda.astype(np.float32).astype(np.float64) # as stored by the lab + w0 = K / (n + K) + L = w0 * np.log(np.clip(S["P_zs"], 1e-6, 1)) + (1 - w0) * np.log(np.clip(P_feda, 1e-6, 1)) + keep = np.setdiff1d(np.arange(len(S["Za"])), shot) + return Model(lab.softmax(L), keep, spatial, {"zs_xgb_dann": S["P_zs"], "lgbm_feda": P_feda}) + + +if __name__ == "__main__": + ap = argparse.ArgumentParser() + ap.add_argument("--n", type=int, default=25) + ap.add_argument("--rep", type=int, default=0) + ap.add_argument("--no-spatial", action="store_true") + a = ap.parse_args() + S = _state() + d = S["d"] + shot = lab.shots_for(d, a.n, a.rep) + m = model_create(shot, lab.shot_labels(d, shot), n=a.n, spatial=not a.no_spatial) + f1 = W.macro_f1(d["_ya"][m.keep], m.predict()) # scoring only + print(f"FINAL-2 n={a.n} rep={a.rep} spatial={m.spatial}: macro F1 {f1:.6f}") + ref = lab.pred_path("ens_e12", a.n, a.rep) + if ref.exists(): + print(f"max |proba - stored ens_e12| = {np.abs(m.proba - np.load(ref)).max():.2e}") diff --git a/tools/nnlab/plot_confirm.py b/tools/nnlab/plot_confirm.py new file mode 100644 index 0000000000000000000000000000000000000000..b3ab410bab0ebf4cca0418f2997b3aa2c30c7ea0 --- /dev/null +++ b/tools/nnlab/plot_confirm.py @@ -0,0 +1,46 @@ +"""F1 against labelling budget for the overnight picks (reports/nnlab/confirmation.csv, view = all 12 draws).""" +from pathlib import Path +import numpy as np, pandas as pd +import matplotlib +matplotlib.use("Agg") +import matplotlib.pyplot as plt + +R = Path(__file__).resolve().parent.parent.parent / "reports" / "nnlab" +df = pd.read_csv(R / "confirmation.csv") +N = [5, 25, 50, 100, 200] +INK, INK2, MUTED, GRID, SURF = "#0b0b0b", "#52514e", "#898781", "#e1e0d9", "#fcfcfb" +def row(m, k, view="all"): + r = df[(df.method == m) & (df.decision == k) & (df["view"] == view)].iloc[0] + return np.array([r[f"n{n}"] for n in N]), np.array([r[f"sd{n}"] for n in N]) +series = [ # fixed categorical order: blue, orange, aqua + ("ens_e12", "rule_smooth", "Zero-shot + LightGBM with domain copies", "#2a78d6", 6), + ("ens_e4", "rule_smooth", "Five-model ensemble", "#eb6834", -8), + ("lgbm_upw", "rule_smooth", "Retrained LightGBM", "#1baf7a", -3), +] +fig, ax = plt.subplots(figsize=(9, 5.2), facecolor=SURF) +ax.set_facecolor(SURF) +x = np.log2(N) +for m, k, name, col, dy in series: + mean, sd = row(m, k) + ax.fill_between(x, mean - sd, mean + sd, color=col, alpha=0.12, lw=0) + ax.plot(x, mean, color=col, lw=2, marker="o", ms=8, mec=SURF, mew=2, label=name) + ax.annotate(name, (x[-1], mean[-1]), xytext=(10, dy), textcoords="offset points", va="center", color=INK2, fontsize=9) +for m, k, name, ls, dy in [("lgbm_upw", "rule", "Retrained LightGBM, no smoothing (previous best)", "--", -14), + ("zs_xgb_dann", "rule_smooth", "Teammate zero-shot (no labels)", ":", 0)]: + mean, _ = row(m, k) + ax.plot(x, mean, color=MUTED, lw=1.5, ls=ls, label=name) + ax.annotate(name, (x[-1], mean[-1]), xytext=(10, dy), textcoords="offset points", va="center", color=MUTED, fontsize=9) +ax.set_xticks(x, [str(n) for n in N]) +ax.set_xlabel("Labelled Amsterdam pixels per class (log scale)", color=INK2) +ax.set_ylabel("Macro F1, all of Amsterdam (12 draws per n)", color=INK2) +ax.text(0, 1.02, "All solid lines and the dotted line use neighbour smoothing; bands are one standard deviation over draws", transform=ax.transAxes, color=MUTED, fontsize=8) +ax.set_title("Macro F1 against labelling budget", loc="left", color=INK, fontsize=12, pad=18) +ax.grid(axis="y", color=GRID, lw=0.8); ax.set_axisbelow(True) +for sp in ("top", "right"): ax.spines[sp].set_visible(False) +for sp in ("left", "bottom"): ax.spines[sp].set_color(GRID) +ax.tick_params(colors=MUTED) +ax.set_xlim(x[0] - 0.3, x[-1] + 3.9) +ax.legend(loc="lower right", frameon=False, fontsize=8, labelcolor=INK2) +plt.tight_layout() +fig.savefig(R / "f1_vs_n_overnight.png", dpi=160, facecolor=SURF) +print("saved") diff --git a/tools/nnlab/tab_nn.py b/tools/nnlab/tab_nn.py new file mode 100644 index 0000000000000000000000000000000000000000..af84aa4edf164fd5360005a04431ab39bb4816b1 --- /dev/null +++ b/tools/nnlab/tab_nn.py @@ -0,0 +1,485 @@ +""" +tab_nn - tabular neural networks with a FROZEN backbone and last-layer adaptation. + + python tools/nnlab/tab_nn.py [--smoke] [--only ] + +Backbones (trained ONCE on all Madrid labels, class-balanced CE, AdamW lr 1e-3, +wd 1e-4, batch 512, seeds 0,1,2): + mlp Linear-BN-ReLU-Drop(0.2) x2 (256) -> Linear(64)-ReLU -> Linear(64,64)-ReLU -> Linear(64,4) + res ResNet-MLP (Gorishniy et al. 2021): Linear(256), 3 x [x + Lin(Drop(ReLU(Lin(BN(x)))))], + BN-ReLU -> Linear(64)-ReLU -> Linear(64,4) + dann mlp + domain head (Lin(64,64)-ReLU-Lin(64,1)) on the first 64-d features through + gradient reversal, lambda = 2/(1+exp(-10p))-1, adversary vs UNLABELLED Amsterdam rows + (as in the teammate's DANN; Madrid and Amsterdam batches go through + the net separately) + coral mlp + deep-CORAL loss (weight 1.0) between the penultimate features of a Madrid + batch and an unlabelled Amsterdam batch +Inputs: rz = 393 feats lab.robustz per city; rg = 393 feats lab.rankgauss per city; + o60 = official 60 feats lab.standardise per city. +The EMBEDDING is the input of the final Linear(.,4); (W0, b0) are that layer's weights. + +Heads per protocol draw (probabilities averaged over the 3 seeds before saving): + zs source head unchanged (saved once, n=0 rep=0) + ft lab.finetune_last_layer(W0, b0, Z_shot, y_shot, lam) lam chosen on Madrid + joint the objective of lab.joint_head(Z_madrid, y_madrid, Z_shot, y_shot, shot_weight=30) + (same weights, C=1) on embeddings standardised with the Madrid embedding mean/sd + (same affine map for the shots), solved with newton-cholesky warm-started from + the Madrid-only fit (see head_joint: lab.joint_head's lbfgs does not converge + in 400 iterations and would cost ~2 h over the run) + proto prototypes (n_c * shot mean + k * Madrid class mean)/(n_c + k), k=10, embeddings + centred by their own city's mean and L2-normalised; P = softmax(-20 * sq. distance) +Method names: tab_{backbone}_{input}_{head}. "rg" is run for mlp and dann only (time budget), +so 10 backbone x input combos x 4 heads = 40 methods. + +Madrid-only choices (lab.madrid_split, pseudo-target = the 25 % of 2 km blocks furthest +along axis 0; transforms computed separately on the source part and on the pseudo-target, +exactly as they are per city; adversary / CORAL target = the unlabelled pseudo-target): + * epochs per backbone type from {15, 30, 45}: input rz, seed 0, trained on the source + part, scored by macro F1 of the decision rule (W.decide, source-part prior) on the + pseudo-target. Non-adversarial nets: one 45-epoch run evaluated after 15/30/45 epochs + (identical to separate runs: constant lr, same seed). dann: separate runs, since its + lambda schedule depends on the total epoch count. + * lam for ft, ONE value for all backbones and n, from {0.1, 1, 10, 100}: on each + backbone's selected source-part net (rz, seed 0), draw n in {5, 50} per class from the + pseudo-target (5 draws, rng(7000 + 100 n + rep)), fine-tune, score the remaining + pseudo-target with the rule; pick the lam with the best mean over backbones x n x draws. + Both are cached in CACHE/tab_nn/selection.json and copied into every meta.json. + +CACHE LAYOUT (lab.CACHE / "tab_nn", or "tab_nn_SMOKE" for --smoke): + selection.json epochs per backbone, lam, all pseudo-target scores + sel_{backbone}.npz source-part (madrid_split) net at the chosen epochs: + Zs, Zt (source / pseudo-target embeddings), W0, b0 + emb_{backbone}_{input}_s{seed}.npz one file per (backbone, input, seed); inputs rz,o60 + for all backbones, rg for mlp and dann; seeds 0,1,2: + Zm (76263, 64) float32 Madrid embeddings (feature-table row order, eval mode) + Za (25992, 64) float32 Amsterdam embeddings (feature-table row order) + W0 (4, 64) float32 final layer weight; b0 (4,) final layer bias + epochs () int epochs it was trained for + Logits = Z @ W0.T + b0, columns = classes 1..4. +""" +from __future__ import annotations + +import argparse +import json +import sys +import time +from pathlib import Path + +sys.path.insert(0, str(Path(__file__).resolve().parent)) +import lab # noqa: E402 +import numpy as np # noqa: E402 + +W = lab.W +T0 = time.time() + +BACKBONES = ("mlp", "res", "dann", "coral") +INPUTS = ("rz", "rg", "o60") +# The full run on 3 shared CPU threads is far above 60 min, so per the brief the +# "rg" input is dropped for "res" and "coral" (10 backbone x input combos, 40 methods). +INPUTS_FOR = {"mlp": INPUTS, "dann": INPUTS, "res": ("rz", "o60"), "coral": ("rz", "o60")} +HEADS = ("zs", "ft", "joint", "proto") +SEEDS = (0, 1, 2) +EPOCH_GRID = (15, 30, 45) +LAM_GRID = (0.1, 1.0, 10.0, 100.0) +LAM_NS = (5, 50) +LAM_REPS = 5 +SHOT_WEIGHT = 30.0 +PROTO_K, PROTO_T = 10.0, 20.0 +CORAL_W = 1.0 +BATCH, LR, WD = 512, 1e-3, 1e-4 + + +def log(msg): + print(f"[{time.time() - T0:7.1f}s] {msg}", flush=True) + + +# --------------------------------------------------------------------------- # +# Networks +# --------------------------------------------------------------------------- # +def build_net(kind: str, d_in: int): + import torch.nn as nn + from torch.autograd import Function + + class GradReverse(Function): + @staticmethod + def forward(ctx, x, lam): + ctx.lam = lam + return x.view_as(x) + + @staticmethod + def backward(ctx, g): + return -ctx.lam * g, None + + class ResBlock(nn.Module): + def __init__(self, h, p=0.2): + super().__init__() + self.f = nn.Sequential(nn.BatchNorm1d(h), nn.Linear(h, h), nn.ReLU(), nn.Dropout(p), nn.Linear(h, h)) + + def forward(self, x): + return x + self.f(x) + + class Net(nn.Module): + def __init__(self): + super().__init__() + if kind == "res": + self.f1 = nn.Sequential(nn.Linear(d_in, 256), ResBlock(256), ResBlock(256), ResBlock(256), + nn.BatchNorm1d(256), nn.ReLU(), nn.Linear(256, 64), nn.ReLU()) + self.f2 = nn.Identity() + else: + self.f1 = nn.Sequential(nn.Linear(d_in, 256), nn.BatchNorm1d(256), nn.ReLU(), nn.Dropout(0.2), + nn.Linear(256, 256), nn.BatchNorm1d(256), nn.ReLU(), nn.Dropout(0.2), + nn.Linear(256, 64), nn.ReLU()) + self.f2 = nn.Sequential(nn.Linear(64, 64), nn.ReLU()) + self.head = nn.Linear(64, 4) + self.dom = nn.Sequential(nn.Linear(64, 64), nn.ReLU(), nn.Linear(64, 1)) if kind == "dann" else None + + def forward(self, x, lam=0.0): + f = self.f1(x) + z = self.f2(f) + dl = self.dom(GradReverse.apply(f, lam)) if self.dom is not None else None + return self.head(z), z, dl + + return Net() + + +def coral_loss(a, b): + import torch + d = a.shape[1] + + def cov(x): + x = x - x.mean(0, keepdim=True) + return x.T @ x / max(len(x) - 1, 1) + return ((cov(a) - cov(b)) ** 2).sum() / (4 * d * d) + + +SMOKE_ROWS = None # --smoke: train on a random subset of this many rows + + +def train(kind, Xs, ys, Xt, epochs, seed, eval_at=(), eval_fn=None): + """Train a backbone. Xt = unlabelled target rows (dann / coral). If eval_at is + given, eval_fn(net) is called after those epochs; returns (net, {ep: result}).""" + torch = lab.torch_setup(seed) + if SMOKE_ROWS and len(Xs) > SMOKE_ROWS: + sub = np.random.default_rng(seed).choice(len(Xs), SMOKE_ROWS, replace=False) + Xs, ys = Xs[sub], np.asarray(ys)[sub] + import torch.nn as nn + net = build_net(kind, Xs.shape[1]) + opt = torch.optim.AdamW(net.parameters(), lr=LR, weight_decay=WD) + cw = torch.tensor(lab.balanced_class_weights(ys), dtype=torch.float32) + ce, bce = nn.CrossEntropyLoss(weight=cw), nn.BCEWithLogitsLoss() + S, Y = torch.from_numpy(np.ascontiguousarray(Xs, np.float32)), torch.from_numpy(np.asarray(ys) - 1).long() + T = torch.from_numpy(np.ascontiguousarray(Xt, np.float32)) + g = torch.Generator().manual_seed(1000 + seed) + steps = int(np.ceil(len(S) / BATCH)) + res = {} + for ep in range(epochs): + net.train() + perm = torch.randperm(len(S), generator=g) + for k in range(steps): + i = perm[k * BATCH:(k + 1) * BATCH] + if len(i) < 2: + continue + if kind == "dann": + p = (ep * steps + k) / (epochs * steps) + lam = 2 / (1 + np.exp(-10 * p)) - 1 + j = torch.randint(0, len(T), (len(i),), generator=g) + ls, _, ds = net(S[i], lam) + _, _, dt = net(T[j], lam) + loss = ce(ls, Y[i]) + bce(ds.squeeze(1), torch.zeros(len(i))) + bce(dt.squeeze(1), torch.ones(len(j))) + elif kind == "coral": + j = torch.randint(0, len(T), (len(i),), generator=g) + ls, zs, _ = net(S[i]) + _, zt, _ = net(T[j]) + loss = ce(ls, Y[i]) + CORAL_W * coral_loss(zs, zt) + else: + ls, _, _ = net(S[i]) + loss = ce(ls, Y[i]) + opt.zero_grad() + loss.backward() + opt.step() + if ep + 1 in eval_at: + res[ep + 1] = eval_fn(net) + net.eval() + return net, res + + +def embed(net, X): + import torch + net.eval() + out = [] + with torch.no_grad(): + for k in range(0, len(X), 8192): + out.append(net(torch.from_numpy(np.ascontiguousarray(X[k:k + 8192], np.float32)))[1].numpy()) + return np.vstack(out).astype(np.float32) + + +def last_layer(net): + return (net.head.weight.detach().numpy().astype(np.float32).copy(), + net.head.bias.detach().numpy().astype(np.float32).copy()) + + +# --------------------------------------------------------------------------- # +# Inputs +# --------------------------------------------------------------------------- # +def _by_columns(f, chunk=48): + """Apply a per-column lab transform in column blocks: identical output (every lab + transform is column-wise), but bounds the float64 / rank temporaries - lab.rankgauss on + the full 76k x 393 Madrid matrix alone peaks at ~1.5 GB extra (reviewer fix, RAM cap).""" + def g(X): + return np.hstack([f(X[:, k:k + chunk]) for k in range(0, X.shape[1], chunk)]) + return g + + +TRANSFORM = {"rz": _by_columns(lab.robustz), "rg": _by_columns(lab.rankgauss), "o60": lab.standardise} + + +def raw_inputs(d, inp): + return (d["Xm60"], d["Xa60"]) if inp == "o60" else (d["Xm"], d["Xa"]) + + +_INPUT_CACHE = {} + + +def city_inputs(d, inp): + """Per-city transformed inputs, kept in memory for the run (~150 MB per 393-feat input).""" + if inp not in _INPUT_CACHE: + Xm, Xa = raw_inputs(d, inp) + f = TRANSFORM[inp] + _INPUT_CACHE[inp] = (f(Xm), f(Xa)) + return _INPUT_CACHE[inp] + + +# --------------------------------------------------------------------------- # +# Heads +# --------------------------------------------------------------------------- # +def head_ft(e, Zs, ys, lam): + Wf, bf = lab.finetune_last_layer(e["W0"], e["b0"], Zs, ys, lam) + return lab.softmax(e["Za"].astype(np.float64) @ Wf.T + bf) + + +def joint_init(e): + """Standardise embeddings with the Madrid mean/sd and fit the Madrid-only + weighted LR once per seed (warm start for every draw's joint head).""" + from sklearn.linear_model import LogisticRegression + if "_Zm_std" not in e: + mu, sd = e["Zm"].mean(0), e["Zm"].std(0) + sd = np.where(sd > 1e-8, sd, 1.0) + e["_Zm_std"] = ((e["Zm"] - mu) / sd).astype(np.float64) + e["_Za_std"] = ((e["Za"] - mu) / sd).astype(np.float64) + w = lab.balanced_class_weights(e["ym"])[e["ym"] - 1] + e["_m0"] = LogisticRegression(C=1.0, solver="newton-cholesky", max_iter=100).fit( + e["_Zm_std"], e["ym"], sample_weight=w) + return e + + +def head_joint(e, shot, ys): + """Same objective as lab.joint_head(Zm, ym, Zs, ys, shot_weight=30, C=1) + (identical sample weights), solved with sklearn's newton-cholesky solver + warm-started from the Madrid-only fit: lab.joint_head's lbfgs stops at + max_iter=400 unconverged on these embeddings and takes ~9 s per fit; this + converges in 2-4 Newton steps (~2 s) and lands closer to the true optimum.""" + from sklearn.linear_model import LogisticRegression + e = joint_init(e) + Zm, Za = e["_Zm_std"], e["_Za_std"] + X = np.vstack([Zm, Za[shot]]) + y = np.r_[np.asarray(e["ym"]), np.asarray(ys)] + w = np.r_[np.ones(len(e["ym"])), np.full(len(ys), SHOT_WEIGHT)] + w = w * lab.balanced_class_weights(y)[y - 1] + m = LogisticRegression(C=1.0, solver="newton-cholesky", max_iter=100, warm_start=True) + m.coef_, m.intercept_ = e["_m0"].coef_.copy(), e["_m0"].intercept_.copy() + m.fit(X, y, sample_weight=w) + return m.predict_proba(Za) + + +def _unit(Z): + Z = Z - Z.mean(0) + return Z / np.clip(np.linalg.norm(Z, axis=1, keepdims=True), 1e-12, None) + + +def head_proto(e, shot, ys): + if "_Um" not in e: # computed once per seed (setdefault would recompute every call) + e["_Um"], e["_Ua"] = _unit(e["Zm"].astype(np.float64)), _unit(e["Za"].astype(np.float64)) + Um, Ua = e["_Um"], e["_Ua"] + protos = [] + for c in lab.CLASSES: + mm = Um[e["ym"] == c].mean(0) + sc = Ua[shot[ys == c]] + nc = len(sc) + protos.append((nc * sc.mean(0) + PROTO_K * mm) / (nc + PROTO_K) if nc else mm) + Pr = np.stack(protos) + d2 = (Ua ** 2).sum(1)[:, None] - 2 * Ua @ Pr.T + (Pr ** 2).sum(1)[None] + return lab.softmax(-PROTO_T * d2) + + +# --------------------------------------------------------------------------- # +# Madrid-only selection +# --------------------------------------------------------------------------- # +def selection(d, cache, smoke): + path = cache / "selection.json" + sel = json.loads(path.read_text()) if path.exists() else {"epochs": {}, "epoch_scores": {}} + grid = (1, 2) if smoke else EPOCH_GRID + src, tgt = lab.madrid_split(d["pm"]) + Xs, Xt = TRANSFORM["rz"](d["Xm"][src]), TRANSFORM["rz"](d["Xm"][tgt]) + ys, yt = d["ym"][src], d["ym"][tgt] + prior = np.array([(ys == c).mean() for c in lab.CLASSES]) + + def f1(net): + P = lab.softmax(embed(net, Xt).astype(np.float64) @ last_layer(net)[0].T.astype(np.float64) + + last_layer(net)[1]) + return W.macro_f1(yt, W.decide(P, prior)) + + emb = {} + for kind in BACKBONES: + ep_path = cache / f"sel_{kind}.npz" + if kind in sel["epochs"] and ep_path.exists(): + z = np.load(ep_path) + emb[kind] = dict(Zs=z["Zs"], Zt=z["Zt"], W0=z["W0"], b0=z["b0"]) + continue + t0 = time.time() + nets = {} + if kind == "dann": + scores = {} + for E in grid: + net, _ = train(kind, Xs, ys, Xt, E, 0) + scores[E], nets[E] = f1(net), (embed(net, Xs), embed(net, Xt), *last_layer(net)) + else: + snap = {} + + def ev(net): + r = f1(net) + snap[len(snap)] = (embed(net, Xs), embed(net, Xt), *last_layer(net)) + net.train() + return r + _, scores = train(kind, Xs, ys, Xt, max(grid), 0, eval_at=grid, eval_fn=ev) + for k, E in enumerate(grid): + nets[E] = snap[k] + best = max(grid, key=lambda E: (scores[E], -E)) + sel["epochs"][kind] = best + sel["epoch_scores"][kind] = {str(k): v for k, v in scores.items()} + Zs_, Zt_, W0, b0 = nets[best] + np.savez(ep_path, Zs=Zs_, Zt=Zt_, W0=W0, b0=b0) + emb[kind] = dict(Zs=Zs_, Zt=Zt_, W0=W0, b0=b0) + path.write_text(json.dumps(sel, indent=1)) + log(f"select epochs {kind}: pseudo-target rule F1 {scores} -> {best} ({time.time() - t0:.0f}s)") + + if "lam" not in sel: + t0 = time.time() + reps = 1 if smoke else LAM_REPS + tab = {str(l): [] for l in LAM_GRID} + per = {} + for kind in BACKBONES: + e = emb[kind] + for n in LAM_NS: + for rep in range(reps): + rng = np.random.default_rng(7000 + 100 * n + rep) + shot = np.concatenate([rng.choice(np.flatnonzero(yt == c), n, replace=False) for c in lab.CLASSES]) + keep = np.setdiff1d(np.arange(len(yt)), shot) + for l in LAM_GRID: + Wf, bf = lab.finetune_last_layer(e["W0"], e["b0"], e["Zt"][shot], yt[shot], l) + P = lab.softmax(e["Zt"][keep].astype(np.float64) @ Wf.T + bf) + s = W.macro_f1(yt[keep], W.decide(P, prior)) + tab[str(l)].append(s) + per.setdefault(f"{kind}_n{n}_lam{l}", []).append(s) + means = {l: float(np.mean(v)) for l, v in tab.items()} + sel["lam"] = float(max(LAM_GRID, key=lambda l: (means[str(l)], l))) + sel["lam_scores"] = means + sel["lam_scores_detail"] = {k: float(np.mean(v)) for k, v in per.items()} + path.write_text(json.dumps(sel, indent=1)) + log(f"select lam: {means} -> {sel['lam']} ({time.time() - t0:.0f}s)") + return sel + + +# --------------------------------------------------------------------------- # +# Main +# --------------------------------------------------------------------------- # +def main(): + ap = argparse.ArgumentParser() + ap.add_argument("--smoke", action="store_true") + ap.add_argument("--only", default="") + ap.add_argument("--seeds", type=int, default=len(SEEDS), + help="use only the first k of seeds 0,1,2 (default 3; method names unchanged, " + "recorded in meta) - a time-saving fallback, not the planned run") + a = ap.parse_args() + smoke = a.smoke + global SMOKE_ROWS + SMOKE_ROWS = 3000 if smoke else None + sfx = "_SMOKE" if smoke else "" + cache = lab.CACHE / ("tab_nn" + sfx) + cache.mkdir(parents=True, exist_ok=True) + seeds = (0,) if smoke else SEEDS[:a.seeds] + draws = [(5, 0), (200, 0)] if smoke else list(lab.draws()) + + d = lab.load(official60=True) + log("data loaded") + + def mname(bb, inp, h): + return f"tab_{bb}_{inp}_{h}{sfx}" + + combos = [(bb, inp) for bb in BACKBONES for inp in INPUTS_FOR[bb] + if any(a.only in mname(bb, inp, h) for h in HEADS)] + sel = selection(d, cache, smoke) + lam = sel["lam"] + + for bb, inp in combos: + heads = [h for h in HEADS if a.only in mname(bb, inp, h)] + todo_draws = [(n, r) for n, r in draws + if any(not lab.have_pred(mname(bb, inp, h), n, r) for h in heads if h != "zs")] + need_zs = "zs" in heads and not lab.have_pred(mname(bb, inp, "zs"), 0, 0) + if not todo_draws and not need_zs: + log(f"{bb}/{inp}: all saved, skip") + continue + epochs = int(sel["epochs"][bb]) + embs, Xin = [], None + for s in seeds: + f = cache / f"emb_{bb}_{inp}_s{s}.npz" + if f.exists(): # reviewer: never reuse a net trained for another epoch count + with np.load(f) as z0: + stale = int(z0["epochs"]) != epochs + if stale: + log(f"cached {f.name} has a different epoch count than selection.json: retraining") + f.unlink() + if not f.exists(): + if Xin is None: + Xin = city_inputs(d, inp) + t0 = time.time() + net, _ = train(bb, Xin[0], d["ym"], Xin[1], epochs, s) + W0, b0 = last_layer(net) + np.savez(f, Zm=embed(net, Xin[0]), Za=embed(net, Xin[1]), W0=W0, b0=b0, epochs=epochs) + log(f"train {bb}/{inp} seed {s}: {epochs} epochs {time.time() - t0:.0f}s") + z = np.load(f) + embs.append(dict(Zm=z["Zm"], Za=z["Za"], W0=z["W0"], b0=z["b0"], ym=d["ym"])) + del Xin + meta = dict(script="tools/nnlab/tab_nn.py", backbone=bb, input=inp, seeds=list(seeds), + epochs=epochs, epoch_choice=sel["epoch_scores"].get(bb), ft_lam=lam, + lam_scores=sel.get("lam_scores"), joint_shot_weight=SHOT_WEIGHT, + proto_k=PROTO_K, proto_temp=PROTO_T, coral_weight=CORAL_W if bb == "coral" else None, + optim=dict(opt="AdamW", lr=LR, wd=WD, batch=BATCH), + joint_solver="newton-cholesky, warm start from Madrid-only fit, C=1", + selection="lab.madrid_split pseudo-target, rule macro F1, input rz seed 0", + smoke=smoke) + if need_zs: + P = np.mean([lab.softmax(e["Za"].astype(np.float64) @ e["W0"].T + e["b0"]) for e in embs], 0) + lab.save_pred(mname(bb, inp, "zs"), 0, 0, P, {**meta, "head": "zs"}) + log(f"{mname(bb, inp, 'zs')} n=0 saved") + for n, rep in todo_draws: + t0 = time.time() + shot = lab.shots_for(d, n, rep) + ys = lab.shot_labels(d, shot) + for h in heads: + if h == "zs" or lab.have_pred(mname(bb, inp, h), n, rep): + continue + th = time.time() + if h == "ft": + P = np.mean([head_ft(e, e["Za"][shot], ys, lam) for e in embs], 0) + elif h == "joint": + P = np.mean([head_joint(e, shot, ys) for e in embs], 0) + else: + P = np.mean([head_proto(e, shot, ys) for e in embs], 0) + lab.save_pred(mname(bb, inp, h), n, rep, P, {**meta, "head": h}) + log(f"{mname(bb, inp, h)} n={n} rep={rep} {time.time() - th:.1f}s") + del embs + log("done") + + +if __name__ == "__main__": + main() diff --git a/tools/nnlab/transductive.py b/tools/nnlab/transductive.py new file mode 100644 index 0000000000000000000000000000000000000000..5cbd3184dbd4d6464c324942b3aa534875f807ef --- /dev/null +++ b/tools/nnlab/transductive.py @@ -0,0 +1,388 @@ +""" +nnlab / transductive - refine probabilities ALREADY in the prediction store with +the unlabelled Amsterdam pool, the pixel coordinates and the n labelled shots. + + python tools/nnlab/transductive.py --base lgbm_upw [--base zs_xgb_dann,zs_lgbm] + [--smoke] [--only lp] [--madrid-check] + +For every base B and protocol draw (n, rep) it writes: + + B__clamp shot rows -> one-hot of their known label, then the lab's neighbour + smoothing (r=6, alpha=0.5) over ALL pixels, shots included as + neighbours. The smoothing is already inside the prediction, so READ + THE "rule" COLUMN of `lab.py score` for these methods: its + "rule_smooth" column would smooth a second time. + B__lp label propagation (Zhou et al. 2004): F <- a S F + (1-a) Y0, 20 power + iterations from F = Y0 (-> (1-a)(I-aS)^-1 Y0), S = D^-1/2 A D^-1/2, + A = spatial graph (lab.adjacency(pa, r=3)) UNION feature kNN graph + (k=10, cosine, PCA-32 of lab.robustz(Xa)), self-loops removed, + Y0 = base probabilities with shot rows = 10 x one-hot, a = 0.8. + F row-normalised. + B__lapshot LaplacianShot-style (Ziko et al. 2020): y <- softmax(log p + lam * G y), + 10 iterations from y = p, lam = 1.0, shots clamped to one-hot every + iteration. G = the same union graph, ROW-NORMALISED (D^-1 A, i.e. the + mean over neighbours) - see DEVIATIONS. + B__em label-shift EM (Saerens et al. 2002), training prior = Madrid prior, + 50 iterations on the non-shot rows, estimated target prior shrunk 50% + toward the Madrid prior, posteriors re-weighted with that prior. + For a shot-independent base it is label-free and saved once (n=0). + B__st (only B == lgbm_upw) one round of self-training: per class c, among + non-shot pixels whose base argmax is c, the top 15% by base p(c) + become pseudo-labels (weight 3.0); Madrid weight 1, shots weight 30, + then class-balanced over the union; refit with winners.train_retrained's + robust-z scaling and LightGBM parameters; predict all Amsterdam rows. + ~1-2 min per draw, run LAST. + +The "rule" column is the one to read for __clamp; for the others both columns are +legitimate (rule_smooth is a further, separate smoothing). + +DEVIATIONS (all fixed in advance, none chosen on Amsterdam): + * lapshot uses the row-normalised graph. With the raw 0/1 union graph a pixel has + ~22 neighbours on average (min 10; measured), so lam=1 puts a pairwise logit of up to ~22 against a unary of + a few nats: the unary term is drowned and the result is a hard spatial majority + vote. `--madrid-check` compares both on the Madrid pseudo-target. + * Self-loops are dropped from the union graph (Zhou et al. set W_ii = 0). + * LightGBM in __st uses n_jobs = NNLAB_THREADS instead of -1 (shared laptop). + * __st reads the features from lab.load() (float32 parquet columns -> float64), + not winners.load_data (which also loads Amsterdam age_class). + +Hard rules kept: Amsterdam labels only via lab.shot_labels(lab.shots_for(...)). +""" +from __future__ import annotations + +import argparse +import sys +import time +from pathlib import Path + +sys.path.insert(0, str(Path(__file__).resolve().parent)) +import lab # noqa: E402 +import numpy as np # noqa: E402 +from scipy import sparse # noqa: E402 + +W = lab.W +T0 = time.time() + +# fixed knobs (spec) ---------------------------------------------------------- # +CLAMP_R, CLAMP_ALPHA = lab.SMOOTH_R, lab.SMOOTH_ALPHA # 6, 0.5 +GRAPH_R, KNN_K, PCA_K = 3, 10, 32 +LP_ALPHA, LP_ITERS, LP_SEED_W = 0.8, 20, 10.0 +LS_LAM, LS_ITERS = 1.0, 10 +LS_NORM = "row" # see DEVIATIONS +EM_ITERS, EM_SHRINK = 50, 0.5 +ST_SHARE, ST_W, ST_SHOT_W = 0.15, 3.0, 30.0 +FAST = ("clamp", "lp", "lapshot", "em") +SHOT_FREE_BASES = {"zs_lgbm", "zs_xgb_dann", "zs_dann"} + + +def log(msg): + print(f"[{time.time() - T0:7.1f}s] {msg}", flush=True) + + +def onehot(y): + Y = np.zeros((len(y), 4)) + Y[np.arange(len(y)), np.asarray(y) - 1] = 1.0 + return Y + + +def rownorm(P): + P = np.clip(P, 1e-12, None) + return P / P.sum(1, keepdims=True) + + +# --------------------------------------------------------------------------- # +# Graphs (draw independent) +# --------------------------------------------------------------------------- # +def knn_graph(Z, k=KNN_K): + """Symmetric 0/1 kNN graph, cosine similarity (= euclidean on unit vectors).""" + from scipy.spatial import cKDTree + U = Z / np.clip(np.linalg.norm(Z, axis=1, keepdims=True), 1e-12, None) + _, nb = cKDTree(U).query(U, k=k + 1) + n = len(U) + rows = np.repeat(np.arange(n), k) + cols = nb[:, 1:].ravel() + A = sparse.coo_matrix((np.ones(len(rows)), (rows, cols)), shape=(n, n)).tocsr() + return A.maximum(A.T).tocsr() + + +def union_graph(X, coords, tag=None): + """Spatial r=3 graph UNION feature kNN graph on PCA-32 of robustz(X); no self-loops. + X must be ONE city's (or one pseudo-city's) raw features.""" + path = lab.CACHE / f"transd_graph_{tag}.npz" if tag else None + if path is not None and path.exists(): + return sparse.load_npz(path).tocsr() + from sklearn.decomposition import PCA + Z = PCA(PCA_K, random_state=0).fit_transform(lab.robustz(X)) + A_sp = lab.adjacency(coords, GRAPH_R) + A = A_sp.maximum(knn_graph(Z)).tocsr() + A.setdiag(0) + A.eliminate_zeros() + A.data[:] = 1.0 + if path is not None: + sparse.save_npz(path, A) + return A + + +class Graph: + def __init__(self, A, coords): + self.A = A + deg = np.asarray(A.sum(1)).ravel() + self.deg = deg + dinv = 1.0 / np.sqrt(np.clip(deg, 1e-12, None)) + self.S = sparse.diags(dinv) @ A @ sparse.diags(dinv) # Zhou + self.R = sparse.diags(1.0 / np.clip(deg, 1e-12, None)) @ A # mean over nbrs + self.coords = coords + + +# --------------------------------------------------------------------------- # +# Refinements (all take base P (N,4), shot idx, shot labels) +# --------------------------------------------------------------------------- # +def r_clamp(P, shot, ys, g): + Q = P.copy() + Q[shot] = onehot(ys) + return lab.smooth(Q, g.coords, r=CLAMP_R, alpha=CLAMP_ALPHA) + + +def r_lp(P, shot, ys, g, a=LP_ALPHA, iters=LP_ITERS): + Y0 = P.copy() + Y0[shot] = LP_SEED_W * onehot(ys) + F = Y0.copy() + for _ in range(iters): + F = a * (g.S @ F) + (1 - a) * Y0 + return rownorm(F) + + +def r_lapshot(P, shot, ys, g, lam=LS_LAM, iters=LS_ITERS, norm=LS_NORM): + G = g.R if norm == "row" else g.A + U = np.log(np.clip(P, 1e-6, None)) + Ys = onehot(ys) + Y = P.copy() + Y[shot] = Ys + for _ in range(iters): + Y = lab.softmax(U + lam * (G @ Y)) + Y[shot] = Ys + return Y + + +def em_prior(P, prior_s, iters=EM_ITERS): + pi_s = np.asarray(prior_s, float) / np.sum(prior_s) + pi = pi_s.copy() + for _ in range(iters): + Q = rownorm(P * (pi / pi_s)) + pi = Q.mean(0) + return pi + + +def r_em(P, shot, prior_s): + pi_s = np.asarray(prior_s, float) / np.sum(prior_s) + keep = np.setdiff1d(np.arange(len(P)), shot) if shot is not None else slice(None) + pi_t = em_prior(P[keep], pi_s) + pi_hat = (1 - EM_SHRINK) * pi_t + EM_SHRINK * pi_s + return rownorm(P * (pi_hat / pi_s)), pi_t, pi_hat + + +def pseudo_labels(P, shot, share=ST_SHARE): + """Per class c: among non-shot rows with argmax c, the top `share` by p(c).""" + free = np.ones(len(P), bool) + free[shot] = False + am = P.argmax(1) + idx, lab_ = [], [] + for k in range(4): + cand = np.flatnonzero(free & (am == k)) + m = int(round(share * len(cand))) + if m == 0: + continue + top = cand[np.argsort(-P[cand, k], kind="stable")[:m]] + idx.append(top) + lab_.append(np.full(m, k + 1)) + return np.concatenate(idx), np.concatenate(lab_) + + +def r_st(P, shot, ys, Xm, ym, Xa, params): + pidx, plab = pseudo_labels(P, shot) + ms, ams = W.robustz_fit(Xm), W.robustz_fit(Xa) + Za = W.robustz(Xa, ams) + X = np.vstack([W.robustz(Xm, ms), Za[shot], Za[pidx]]) + y = np.r_[ym, ys, plab] + w = np.r_[np.ones(len(ym)), np.full(len(ys), ST_SHOT_W), np.full(len(plab), ST_W)] + w = w * W.balanced_class_weights(y)[y - 1] + m = W._fit_lgbm(X, y, params, sample_weight=w) + del X + return W.predict(m, Za), np.bincount(plab, minlength=5)[1:] + + +# --------------------------------------------------------------------------- # +# Madrid validation (pseudo-target, Madrid labels only) +# --------------------------------------------------------------------------- # +def madrid_check(d, smoke): + """Train a retrained-style LightGBM on the Madrid source split, predict the + spatial pseudo-target, draw pseudo-shots there, apply every refinement and + print the rule macro F1 on the pseudo-target's non-shot rows.""" + src, tgt = lab.madrid_split(d["pm"]) + Xm = d["Xm"].astype(np.float64) + ym = d["ym"] + tag = "SMOKE" if smoke else "full" + cp = lab.CACHE / f"transd_madrid_base_{tag}.npy" + params = {**W.LGBM_PARAMS, **W.RETRAIN_OVERRIDES, "n_jobs": lab.THREADS} + if smoke: + params.update(n_estimators=40) + if cp.exists(): + Pt = np.load(cp) + else: + ss, ts = W.robustz_fit(Xm[src]), W.robustz_fit(Xm[tgt]) + cw = W.balanced_class_weights(ym[src]) + m = W._fit_lgbm(W.robustz(Xm[src], ss), ym[src], params, sample_weight=cw[ym[src] - 1]) + Pt = W.predict(m, W.robustz(Xm[tgt], ts)) + np.save(cp, Pt) + log(f"madrid-check: base model fitted on {len(src)} src rows") + yt, pt = ym[tgt], d["pm"][tgt] + prior = np.array([(ym[src] == c).mean() for c in lab.CLASSES]) + g = Graph(union_graph(Xm[tgt], pt, tag=f"madrid_tgt_{len(tgt)}"), pt) + log(f"madrid-check: pseudo-target {len(tgt)} rows, mean degree {g.deg.mean():.1f}") + variants = { + "base": lambda P, s, y: P, + "clamp": lambda P, s, y: r_clamp(P, s, y, g), + "lp": lambda P, s, y: r_lp(P, s, y, g), + "lapshot_row": lambda P, s, y: r_lapshot(P, s, y, g, norm="row"), + "lapshot_raw": lambda P, s, y: r_lapshot(P, s, y, g, norm="raw"), + "em": lambda P, s, y: r_em(P, s, prior)[0], + } + res = {k: {} for k in variants} + for n in ((5, 200) if smoke else lab.N_LIST): + for rep in range(1 if smoke else 3): + rng = np.random.default_rng(7 + 1000 * n + rep) + shot = np.concatenate([rng.choice(np.flatnonzero(yt == c), n, replace=False) + for c in lab.CLASSES]) + keep = np.setdiff1d(np.arange(len(yt)), shot) + for k, f in variants.items(): + Q = f(Pt, shot, yt[shot]) + res[k].setdefault(n, []).append(W.macro_f1(yt[keep], W.decide(Q[keep], prior))) + log("madrid-check: rule macro F1 on pseudo-target (non-shot rows), mean over reps") + out = {} + for k, r in res.items(): + m = {n: float(np.mean(v)) for n, v in r.items()} + out[k] = m + print(f" {k:<12s} " + " ".join(f"n{n}={v:.4f}" for n, v in m.items()) + + f" avg={np.mean(list(m.values())):.4f}", flush=True) + return out + + +# --------------------------------------------------------------------------- # +def main(): + ap = argparse.ArgumentParser() + ap.add_argument("--base", action="append", default=None, + help="base method(s); repeatable or comma-separated") + ap.add_argument("--smoke", action="store_true") + ap.add_argument("--only", default=None, help="substring filter on method names") + ap.add_argument("--madrid-check", action="store_true", + help="validate the refinements on the Madrid pseudo-target and exit") + a = ap.parse_args() + bases = [b for s in (a.base or ["lgbm_upw"]) for b in s.split(",") if b] + sfx = "_SMOKE" if a.smoke else "" + draws = [(5, 0), (200, 0)] if a.smoke else list(lab.draws()) + + d = lab.load(official60=False) + log(f"loaded: Madrid {d['Xm'].shape}, Amsterdam {d['Xa'].shape}") + if a.madrid_check: + madrid_check(d, a.smoke) + return + + g = Graph(union_graph(d["Xa"], d["pa"], tag=f"ams_r{GRAPH_R}_k{KNN_K}_pca{PCA_K}"), d["pa"]) + log(f"graph: {g.A.nnz} edges, mean degree {g.deg.mean():.1f}, min {g.deg.min():.0f}") + + knobs = dict(clamp=dict(r=CLAMP_R, alpha=CLAMP_ALPHA), + graph=dict(spatial_r=GRAPH_R, knn_k=KNN_K, knn_metric="cosine", + pca=PCA_K, on="lab.robustz(Xa)", self_loops=False, weights="0/1 union"), + lp=dict(a=LP_ALPHA, iters=LP_ITERS, seed_weight=LP_SEED_W, S="D^-1/2 A D^-1/2"), + lapshot=dict(lam=LS_LAM, iters=LS_ITERS, graph_norm=LS_NORM, clamp=True), + em=dict(iters=EM_ITERS, shrink_to_madrid_prior=EM_SHRINK, train_prior="Madrid"), + st=dict(share=ST_SHARE, pseudo_w=ST_W, shot_w=ST_SHOT_W, madrid_w=1.0, + class_balanced=True, params="winners LGBM_PARAMS+RETRAIN_OVERRIDES")) + choice_note = ("all knobs fixed in advance by the task spec; lapshot row-normalisation " + "fixed a priori (raw 0/1 graph with lam=1 drowns the unary term); " + "checked on Madrid pseudo-target via --madrid-check, never on Amsterdam") + + def want(name): + return a.only is None or a.only in name + + def base_pred(B, n, rep): + """Base probabilities for a draw. In smoke mode a missing per-draw base + falls back to zs_xgb_dann (lgbm_upw is still being written).""" + if lab.have_pred(B, n, rep) or lab.have_pred(B, 0, 0): + return lab.load_pred(B, n, rep), B + if a.smoke: + return lab.load_pred("zs_xgb_dann", 0, 0), "zs_xgb_dann" + return None, None + + # ---- fast methods ---------------------------------------------------- # + for B in bases: + for kind in FAST: + name = f"{B}__{kind}{sfx}" + if not want(name): + continue + zero = kind == "em" and B in SHOT_FREE_BASES + for n, rep in ([(0, 0)] if zero else draws): + if lab.have_pred(name, n, rep): + continue + t = time.time() + P, used = base_pred(B, max(n, 0), rep) if not zero else (lab.load_pred(B, 0, 0), B) + if P is None: + log(f"{name} n={n} rep={rep}: base {B} missing, skipped") + continue + meta = dict(base=B, base_used=used, kind=kind, knobs=knobs[kind] if kind in knobs else None, + graph=knobs["graph"] if kind in ("lp", "lapshot") else None, + choices=choice_note, read_column="rule" if kind == "clamp" else "rule or rule_smooth") + if zero: + Q, pi_t, pi_hat = r_em(P, None, d["prior"]) + meta.update(pi_t=pi_t.tolist(), pi_hat=pi_hat.tolist()) + else: + shot = lab.shots_for(d, n, rep) + ys = lab.shot_labels(d, shot) + if kind == "clamp": + Q = r_clamp(P, shot, ys, g) + elif kind == "lp": + Q = r_lp(P, shot, ys, g) + elif kind == "lapshot": + Q = r_lapshot(P, shot, ys, g) + else: + Q, pi_t, pi_hat = r_em(P, shot, d["prior"]) + meta.update(pi_t=pi_t.tolist(), pi_hat=pi_hat.tolist()) + lab.save_pred(name, n, rep, Q, meta) + log(f"{name} n={n} rep={rep} base={used} done in {time.time() - t:.1f}s") + + # ---- self-training (last, slow) -------------------------------------- # + st_bases = [B for B in bases if B == "lgbm_upw" or B in __import__("os").environ.get("NNLAB_ST_BASES", "").split(",")] # pseudo-labels may come from any stored method + if not st_bases and a.smoke: + st_bases = [bases[0]] # exercise the path in smoke mode + params = {**W.LGBM_PARAMS, **W.RETRAIN_OVERRIDES, "n_jobs": lab.THREADS} + if a.smoke: + params.update(n_estimators=40) + Xm = Xa = None + for B in st_bases: + name = f"{B}__st{sfx}" + if not want(name): + continue + for n, rep in draws: + if lab.have_pred(name, n, rep): + continue + P, used = base_pred(B, n, rep) + if P is None: + log(f"{name} n={n} rep={rep}: base {B} missing, skipped") + continue + if Xm is None: + Xm, Xa = d["Xm"].astype(np.float64), d["Xa"].astype(np.float64) + t = time.time() + shot = lab.shots_for(d, n, rep) + ys = lab.shot_labels(d, shot) + Q, counts = r_st(P, shot, ys, Xm, d["ym"], Xa, params) + meta = dict(base=B, base_used=used, kind="st", knobs=knobs["st"], + n_estimators=params["n_estimators"], n_jobs=params["n_jobs"], + choices=choice_note, pseudo_counts_last=counts.tolist()) + lab.save_pred(name, n, rep, Q, meta) + log(f"{name} n={n} rep={rep} base={used} pseudo={counts.tolist()} " + f"done in {time.time() - t:.1f}s") + log("done") + + +if __name__ == "__main__": + main() diff --git a/tools/nnlab/ts_nn.py b/tools/nnlab/ts_nn.py new file mode 100644 index 0000000000000000000000000000000000000000..0e904a0d3eee71f8755b9b2364fe3b222717b75d --- /dev/null +++ b/tools/nnlab/ts_nn.py @@ -0,0 +1,501 @@ +"""Sequence networks on the RAW yearly Landsat time series, frozen backbone + +last-layer adaptation. + + python tools/nnlab/ts_nn.py [--smoke] [--only ] + +Step 1 tensor cache lab.CACHE/ts/{city}_T.npy [N, 42, 6] float32 DN, median of + QA-valid observations per year (src/features.py make_flat_view(agg="median"): + a year is kept only if every band has a valid median and Blue <= BLUE_MAX), + NaN where no kept observation; rows aligned with the processed feature table. + NOTE: the raw data covers 1984-2025 (42 years), as the feature pipeline uses. +Step 2 inputs 6 SR bands + NDVI, NDBI, MNDWI + observed mask = 10 ch x 42 steps, + linear interpolation in time (edges nearest, as to_year_matrices), indices from + the interpolated SR bands; per-city standardisation per channel. Variant "ry" + subtracts the city's per-year median over pixels (9 continuous channels) first. +Step 3 backbones tcnn, gru, tcnn_dann; class-balanced CE, AdamW(1e-3, wd 1e-4), + batch 256, constant LR, brightness/offset augmentation on the 6 band channels. + CPU budget: per (backbone, variant) ONE run on the lab.madrid_split source part, + checkpoints at EPOCH_GRID = (5, 10, 15), the checkpoint with the best + pseudo-target macro F1 IS the backbone (no retrain on all Madrid); lam for "ft" + chosen on the same model. 1 seed. Default combos: tcnn_st, tcnn_ry, + tcnn_dann_st; --extended adds tcnn_dann_ry, gru_st, gru_ry (~2.5 h more). +Step 4 heads zs (n=0), ft (lab.finetune_last_layer), joint (lab.joint_head); + probabilities averaged over seeds. +Methods: ts_{backbone}_{variant}_{head}. +""" +from __future__ import annotations + +import argparse +import copy +import json +import sys +import time +import warnings +from pathlib import Path + +sys.path.insert(0, str(Path(__file__).resolve().parent)) +import lab # noqa: E402 +import numpy as np # noqa: E402 +import config as C # noqa: E402 (src/config.py, put on sys.path by lab) + +T0 = time.time() +YEARS = np.arange(1984, 2026) # 42 years present in both raw files +NY = len(YEARS) +BANDS = list(C.BASE_BANDS) +TS_DIR = lab.CACHE / "ts" +NN_DIR = lab.CACHE / "ts_nn" +for _d in (TS_DIR, NN_DIR): + _d.mkdir(parents=True, exist_ok=True) + +HEADS = ("zs", "ft", "joint") +EPOCH_GRID = (5, 10, 15) # spec: (10, 20, 30) - halved to fit the CPU budget +LAM_GRID = (0.1, 1.0, 10.0, 100.0) +SEEDS = (0,) # spec: 2 seeds - cut to 1 to fit the CPU budget +DEFAULT_COMBOS = (("tcnn", "st"), ("tcnn", "ry"), ("tcnn_dann", "st")) +EXTENDED_COMBOS = (("tcnn_dann", "ry"), ("gru", "st"), ("gru", "ry")) +BATCH = 256 +LR, WD = 1e-3, 1e-4 +SHOT_WEIGHT = 30.0 + + +def _rss(): + try: + import psutil + mi = psutil.Process().memory_info() + return f" rss={mi.rss / 2**30:.2f}G peak={getattr(mi, 'peak_wset', mi.rss) / 2**30:.2f}G" + except Exception: + return "" + + +def log(msg): + print(f"[{time.time() - T0:7.1f}s] {msg}{_rss()}", flush=True) + + +# --------------------------------------------------------------------------- # +# Step 1: tensor cache +# --------------------------------------------------------------------------- # +RAW = {"madrid": lab.ROOT / "data" / "raw" / "madrid_train.parquet", + "amsterdam": lab.ROOT / "data" / "raw" / "amsterdam_data.parquet"} + + +def build_tensor(city: str, P: np.ndarray) -> np.ndarray: + """P: (N, 2) px_key, py_key of the feature table (row order).""" + import pyarrow.parquet as pq + path = TS_DIR / f"{city}_T.npy" + if path.exists(): + T = np.load(path) + assert T.shape == (len(P), NY, 6) + return T + t0 = time.time() + cols = ["px_key", "py_key", "year"] + [f"{b}_{k}" for k in (1, 2, 3) for b in BANDS] \ + + [f"qa_valid_{k}" for k in (1, 2, 3)] # no label columns are read + key_f = P[:, 0].astype(np.int64) * 10_000_000 + P[:, 1].astype(np.int64) + assert P[:, 1].min() >= 0 and P[:, 1].max() < 10_000_000 + order = np.argsort(key_f) + ks = key_f[order] + assert (np.diff(ks) > 0).all(), "duplicate pixel keys in feature table" + T = np.full((len(P), NY, 6), np.nan, np.float32) + seen = np.zeros((len(P), NY), bool) + f = pq.ParquetFile(RAW[city]) + n_rows = n_kept = 0 + for batch in f.iter_batches(batch_size=200_000, columns=cols): + tb = {c: batch.column(c) for c in cols} + n = batch.num_rows + n_rows += n + oks = [np.asarray(tb[f"qa_valid_{k}"].fill_null(False).to_numpy(zero_copy_only=False), bool) + for k in (1, 2, 3)] + med = np.empty((n, 6), np.float32) + with warnings.catch_warnings(): + warnings.simplefilter("ignore", RuntimeWarning) + for j, b in enumerate(BANDS): + st = [] + for k in (1, 2, 3): + v = np.asarray(tb[f"{b}_{k}"].to_numpy(zero_copy_only=False), np.float32).copy() + v[~oks[k - 1]] = np.nan + st.append(v) + med[:, j] = np.nanmedian(np.stack(st), axis=0) + good = ~np.isnan(med).any(1) + good &= ~(med[:, 0] > C.BLUE_MAX) # make_flat_view: keep Blue <= BLUE_MAX + px = np.asarray(tb["px_key"].to_numpy(zero_copy_only=False), np.int64) + py = np.asarray(tb["py_key"].to_numpy(zero_copy_only=False), np.int64) + yr = np.asarray(tb["year"].to_numpy(zero_copy_only=False), np.int64) + key = px * 10_000_000 + py + pos = np.searchsorted(ks, key).clip(0, len(ks) - 1) + hit = (ks[pos] == key) & good + assert ((yr[hit] >= YEARS[0]) & (yr[hit] <= YEARS[-1])).all() + r, c = order[pos[hit]], yr[hit] - YEARS[0] + assert not seen[r, c].any(), "duplicate pixel-year" + seen[r, c] = True + T[r, c] = med[hit] + n_kept += int(hit.sum()) + del batch, tb, med, st + found = seen.any(1) + assert found.all(), f"{(~found).sum()} feature-table pixels have no observation" + np.save(path, T) + log(f"tensor {city}: {n_rows:,} raw rows, {n_kept:,} pixel-years kept, " + f"shape {T.shape}, observed {seen.mean():.3f}, {time.time() - t0:.0f}s") + return T + + +# --------------------------------------------------------------------------- # +# Step 2: inputs +# --------------------------------------------------------------------------- # +def interp_time(T: np.ndarray) -> np.ndarray: + """Linear interpolation over the year axis, nearest at the edges (per pixel, band).""" + N, Y, B = T.shape + out = T.copy() + x = np.arange(Y, dtype=np.float32) + for b in range(B): + M = out[:, :, b] + obs = ~np.isnan(M) + # index of previous / next observed year for every cell + idx = np.where(obs, np.arange(Y)[None, :], -1) + prev = np.maximum.accumulate(idx, axis=1) + idx2 = np.where(obs, np.arange(Y)[None, :], Y) + nxt = np.minimum.accumulate(idx2[:, ::-1], axis=1)[:, ::-1] + rows = np.arange(N)[:, None] + pv = np.where(prev >= 0, M[rows, prev.clip(0)], np.nan) + nv = np.where(nxt < Y, M[rows, nxt.clip(None, Y - 1)], np.nan) + w = np.where((prev >= 0) & (nxt < Y) & (nxt > prev), + (x[None, :] - prev) / np.maximum(nxt - prev, 1), 0.0).astype(np.float32) + lin = pv + w * (nv - pv) + fill = np.where(np.isnan(pv), nv, np.where(np.isnan(nv), pv, lin)) + out[:, :, b] = np.where(obs, M, fill) + assert not np.isnan(out).any() + return out + + +def make_inputs(T: np.ndarray, variant: str) -> np.ndarray: + """[N, Y, 6] DN -> [N, 10, Y] float32, standardised with this city's statistics.""" + obs = (~np.isnan(T).any(2)).astype(np.float32) # year observed + S = interp_time(T) * np.float32(C.SR_SCALE) + np.float32(C.SR_OFFSET) + B, G, R, N_, S1 = S[..., 0], S[..., 1], S[..., 2], S[..., 3], S[..., 4] + eps = np.float32(1e-6) + ndvi = (N_ - R) / (N_ + R + eps) + ndbi = (S1 - N_) / (S1 + N_ + eps) + mndwi = (G - S1) / (G + S1 + eps) + X = np.concatenate([S, ndvi[..., None], ndbi[..., None], mndwi[..., None], obs[..., None]], 2) + del S + X = np.clip(X, -5, 5) # index denominators near 0 on bad pixels + if variant == "ry": + X[..., :9] -= np.median(X[..., :9], axis=0, keepdims=True) + mu = X.reshape(-1, 10).mean(0, dtype=np.float64) + sd = X.reshape(-1, 10).std(0, dtype=np.float64) + sd[sd < 1e-8] = 1 + X = ((X - mu.astype(np.float32)) / sd.astype(np.float32)).astype(np.float32) + return np.ascontiguousarray(X.transpose(0, 2, 1)) # [N, C, Y] + + +# --------------------------------------------------------------------------- # +# Step 3: backbones +# --------------------------------------------------------------------------- # +def build_net(kind: str): + import torch + nn = torch.nn + + class GradReverse(torch.autograd.Function): + @staticmethod + def forward(ctx, x, lam): + ctx.lam = lam + return x.view_as(x) + + @staticmethod + def backward(ctx, g): + return -ctx.lam * g, None + + class TempCNN(nn.Module): + def __init__(self, c_in=10, T=NY, dann=False): + super().__init__() + layers, c = [], c_in + for _ in range(3): + layers += [nn.Conv1d(c, 64, 5, padding=2), nn.BatchNorm1d(64), nn.ReLU(), nn.Dropout(0.2)] + c = 64 + self.conv = nn.Sequential(*layers) + self.emb = nn.Sequential(nn.Flatten(), nn.Linear(64 * T, 128), nn.ReLU(), + nn.Linear(128, 64), nn.ReLU()) + self.head = nn.Linear(64, 4) + self.dom = nn.Sequential(nn.Linear(64, 64), nn.ReLU(), nn.Linear(64, 1)) if dann else None + + def embed(self, x): + return self.emb(self.conv(x)) + + def forward(self, x, lam=0.0): + z = self.embed(x) + dom = self.dom(GradReverse.apply(z, lam)) if self.dom is not None else None + return self.head(z), dom + + class GRUNet(nn.Module): + def __init__(self, c_in=10): + super().__init__() + self.gru = nn.GRU(c_in, 64, num_layers=2, bidirectional=True, batch_first=True) + self.emb = nn.Sequential(nn.Linear(128, 64), nn.ReLU()) + self.head = nn.Linear(64, 4) + self.dom = None + + def embed(self, x): + _, h = self.gru(x.transpose(1, 2)) # h: [4, B, 64] + return self.emb(torch.cat([h[-2], h[-1]], 1)) + + def forward(self, x, lam=0.0): + return self.head(self.embed(x)), None + + if kind == "tcnn": + return TempCNN() + if kind == "tcnn_dann": + return TempCNN(dann=True) + if kind == "gru": + return GRUNet() + raise ValueError(kind) + + +def augment(torch, xb, gen): + """Per-sample brightness scale U(0.9,1.1) and offset N(0,0.1) on the 6 band channels.""" + B = xb.shape[0] + s = torch.empty(B, 1, 1).uniform_(0.9, 1.1, generator=gen) + o = torch.randn(B, 1, 1, generator=gen) * 0.1 + xb = xb.clone() + xb[:, :6] = xb[:, :6] * s + o + return xb + + +def train(kind, Xs, ys, Xt, epochs, seed, eval_fn=None, eval_at=(), max_rows=None, sched_epochs=None): + """Returns (net, {epoch: eval result}, {epoch: state_dict copy}, sec/epoch). + sched_epochs: length of the DANN lambda schedule (default `epochs`); a retrain to a + selected checkpoint passes the selection run's length so it reproduces that model.""" + sched = sched_epochs or epochs + torch = lab.torch_setup(seed) + gen = torch.Generator().manual_seed(seed) + net = build_net(kind) + opt = torch.optim.AdamW(net.parameters(), lr=LR, weight_decay=WD) + counts = np.bincount(ys, minlength=5)[1:] + w = torch.tensor(len(ys) / (4 * np.maximum(counts, 1)), dtype=torch.float32) + ce, bce = torch.nn.CrossEntropyLoss(weight=w), torch.nn.BCEWithLogitsLoss() + S, Y = torch.from_numpy(Xs), torch.from_numpy(ys - 1).long() + Tt = torch.from_numpy(Xt) if Xt is not None else None + dann = kind.endswith("_dann") + n = len(S) if max_rows is None else min(len(S), max_rows) + steps = int(np.ceil(n / BATCH)) + evals, states, t_ep = {}, {}, [] + for ep in range(1, epochs + 1): + te = time.time() + net.train() + perm = torch.randperm(len(S), generator=gen)[:n] + for k in range(steps): + i = perm[k * BATCH:(k + 1) * BATCH] + if len(i) < 2: + continue + xb = augment(torch, S[i], gen) + if dann: + p = ((ep - 1) * steps + k) / (sched * steps) + lam = float(2 / (1 + np.exp(-10 * p)) - 1) + logit, dom_s = net(xb, lam) + j = torch.randint(0, len(Tt), (len(i),), generator=gen) + _, dom_t = net(augment(torch, Tt[j], gen), lam) + loss = ce(logit, Y[i]) + bce(dom_s.squeeze(1), torch.zeros(len(i))) + bce(dom_t.squeeze(1), torch.ones(len(i))) + else: + logit, _ = net(xb, 0.0) + loss = ce(logit, Y[i]) + opt.zero_grad() + loss.backward() + opt.step() + t_ep.append((time.time() - te) * len(S) / n) # extrapolated to a full epoch + if ep in eval_at: + states[ep] = copy.deepcopy(net.state_dict()) + if eval_fn is not None: + evals[ep] = eval_fn(net) + log(f" {kind} seed{seed} epoch {ep}/{epochs} {t_ep[-1]:.1f}s/full-epoch" + + (f" eval={evals[ep]:.4f}" if ep in evals else "")) + net.eval() + return net, evals, states, float(np.mean(t_ep)) + + +def embed(net, X, bs=2048): + import torch + net.eval() + out = [] + with torch.no_grad(): + for k in range(0, len(X), bs): + out.append(net.embed(torch.from_numpy(X[k:k + bs])).numpy()) + return np.concatenate(out).astype(np.float32) + + +def last_layer(net): + return (net.head.weight.detach().numpy().astype(np.float64).copy(), + net.head.bias.detach().numpy().astype(np.float64).copy()) + + +def proba(Z, Wt, b): + return lab.softmax(Z.astype(np.float64) @ Wt.T + b) + + +def mf1(y, P): + return lab.W.macro_f1(y, lab.CLASSES[P.argmax(1)]) + +# --------------------------------------------------------------------------- # +# Backbone per (backbone, variant): one run on the Madrid spatial source split, +# checkpoints at the epoch grid, pick the epoch on the pseudo-target, keep that +# checkpoint (budget: no retrain on all Madrid), then choose lam on the same model. +# --------------------------------------------------------------------------- # +def simulate_lam(Zt, yt, W0, b0, reps): + lam_score = {} + for lam in LAM_GRID: + sc = [] + for n in (5, 50): + for rep in range(reps): + rng = np.random.default_rng(7_000 + 1000 * n + rep) + sh = np.concatenate([rng.choice(np.flatnonzero(yt == c), min(n, int((yt == c).sum())), + replace=False) for c in lab.CLASSES]) + rest = np.setdiff1d(np.arange(len(yt)), sh) + Wf, bf = lab.finetune_last_layer(W0, b0, Zt[sh], yt[sh], lam) + sc.append(mf1(yt[rest], proba(Zt[rest], Wf, bf))) + lam_score[lam] = float(np.mean(sc)) + return lam_score + + +def backbone(kind, var, d, Xm, Xa, cfg): + """Returns (selection dict, [(Zm, Za, W0, b0) per seed]).""" + tag = f"{kind}_{var}{cfg['tag']}" + sel_path = NN_DIR / f"select_{tag}.json" + src, tgt = lab.madrid_split(d["pm"]) + ym = d["ym"] + grid = cfg["epoch_grid"] + embs = [] + sel = json.loads(sel_path.read_text()) if sel_path.exists() else None + for seed in cfg["seeds"]: + path = NN_DIR / f"{tag}_s{seed}.npz" + if path.exists() and sel is not None: + z = np.load(path) + embs.append((z["Zm"], z["Za"], z["W0"], z["b0"])) + continue + t0 = time.time() + if sel is None: + def ev(net): + Wt, b = last_layer(net) + return mf1(ym[tgt], proba(embed(net, Xm[tgt]), Wt, b)) + net, evals, states, sec = train(kind, Xm[src], ym[src], Xa, max(grid), seed, eval_fn=ev, + eval_at=grid, max_rows=cfg["max_rows"]) + E = max(grid, key=lambda e: (evals[e], -e)) # ties -> fewer epochs + net.load_state_dict(states[E]) + net.eval() + W0, b0 = last_layer(net) + lam_score = simulate_lam(embed(net, Xm[tgt]), ym[tgt], W0, b0, cfg["lam_reps"]) + lam = max(LAM_GRID, key=lambda l: (lam_score[l], l)) # ties -> stronger pull + sel = {"backbone": kind, "variant": var, "seed_used": int(seed), + "split": "lab.madrid_split(pm) defaults (frac .25, axis 0, high)", + "epoch_grid": list(grid), + "pseudo_target_f1_by_epoch": {str(k): float(v) for k, v in evals.items()}, + "epochs": int(E), "lam_grid": list(LAM_GRID), + "pseudo_target_ft_f1_by_lam": {str(k): v for k, v in lam_score.items()}, + "lam": float(lam), + "lam_sim": f"n per class in (5, 50) x {cfg['lam_reps']} reps on the pseudo-target, " + "argmax macro F1 on the rest", + "sec_per_full_epoch": sec, "n_src": int(len(src)), "n_pseudo_target": int(len(tgt))} + sel_path.write_text(json.dumps(sel, indent=1)) + scores = {k: round(v, 4) for k, v in lam_score.items()} + log(f"select {tag}: epochs={E} {sel['pseudo_target_f1_by_epoch']} lam={lam} {scores}") + else: + net, _, _, sec = train(kind, Xm[src], ym[src], Xa, sel["epochs"], seed, + eval_at=(sel["epochs"],), max_rows=cfg["max_rows"], + sched_epochs=max(sel["epoch_grid"])) + Zm, Za = embed(net, Xm), embed(net, Xa) + W0, b0 = last_layer(net) + np.savez(path, Zm=Zm, Za=Za, W0=W0, b0=b0) + embs.append((Zm, Za, W0, b0)) + log(f"backbone {tag} seed{seed}: {sec:.1f}s/full-epoch, {time.time() - t0:.0f}s") + return sel, embs + + +def gru_check(): + """Smoke-only: the GRU network trains and embeds (the rest of its path is shared).""" + rng = np.random.default_rng(0) + X = rng.standard_normal((1024, 10, NY)).astype(np.float32) + y = rng.integers(1, 5, 1024) + net, _, _, sec = train("gru", X, y, None, 1, 0, eval_at=(1,)) + Z = embed(net, X[:300]) + assert Z.shape == (300, 64) and np.isfinite(Z).all() + log(f"gru check ok: ~{sec * 61932 / 1024:.0f}s per Madrid-source epoch (extrapolated)") + + +# --------------------------------------------------------------------------- # +# main +# --------------------------------------------------------------------------- # +def main(): + ap = argparse.ArgumentParser() + ap.add_argument("--smoke", action="store_true") + ap.add_argument("--only", default="") + ap.add_argument("--extended", action="store_true", + help="also tcnn_dann_ry, gru_st, gru_ry (beyond the 70 min budget)") + a = ap.parse_args() + combos = list(DEFAULT_COMBOS) + (list(EXTENDED_COMBOS) if a.extended else []) + if a.smoke: + cfg = dict(tag="_SMOKE", suffix="_SMOKE", epoch_grid=(1, 2), max_rows=4096, lam_reps=1, + seeds=(0,), draws=[(5, 0), (200, 0)]) + else: + cfg = dict(tag="", suffix="", epoch_grid=EPOCH_GRID, max_rows=None, lam_reps=5, + seeds=SEEDS, draws=list(lab.draws())) + + def mname(bb, var, head): + return f"ts_{bb}_{var}_{head}{cfg['suffix']}" + + todo = {(bb, var): [h for h in HEADS if a.only in mname(bb, var, h)] for bb, var in combos} + todo = {k: v for k, v in todo.items() if v} + log(f"methods: {[mname(bb, var, h) for (bb, var), hs in todo.items() for h in hs]}") + + d = lab.load(official60=False) + Tm = build_tensor("madrid", d["pm"]) + Ta = build_tensor("amsterdam", d["pa"]) + if a.smoke: + gru_check() + + for (bb, var), heads in todo.items(): + need = [h for h in heads for (n, rep) in ([(0, 0)] if h == "zs" else cfg["draws"]) + if not lab.have_pred(mname(bb, var, h), n, rep)] + if not need: + log(f"{bb}_{var}: all predictions present, skip") + continue + Xm, Xa = make_inputs(Tm, var), make_inputs(Ta, var) + log(f"inputs {var}: Madrid {Xm.shape} Amsterdam {Xa.shape}") + sel, embs = backbone(bb, var, d, Xm, Xa, cfg) + del Xm, Xa + E, lam = sel["epochs"], sel["lam"] + meta = {"family": "ts_nn", "backbone": bb, "variant": var, "seeds": list(cfg["seeds"]), + "epochs": E, "lam": lam, "shot_weight": SHOT_WEIGHT, "selection": sel, + "backbone_training_rows": "Madrid spatial source split only (lab.madrid_split)", + "joint_input": "embeddings standardised with Madrid embedding mean/std", + "input": f"10 ch x {NY} years (1984-2025), median of QA-valid obs, Blue<=BLUE_MAX", + "augment": "band scale U(0.9,1.1), offset N(0,0.1)", + "train": f"AdamW lr={LR} wd={WD} batch={BATCH} class-balanced CE, constant LR", + "smoke": bool(a.smoke)} + if "zs" in heads and not lab.have_pred(mname(bb, var, "zs"), 0, 0): + P = np.mean([proba(Za, W0, b0) for _, Za, W0, b0 in embs], 0) + lab.save_pred(mname(bb, var, "zs"), 0, 0, P, {**meta, "head": "zs"}) + log(f"{mname(bb, var, 'zs')} saved") + scaled = [] + for Zm, Za, _, _ in embs: + mu, sd = Zm.mean(0), Zm.std(0) + 1e-6 + scaled.append(((Zm - mu) / sd, (Za - mu) / sd)) + for n, rep in cfg["draws"]: + hs = [h for h in heads if h != "zs" and not lab.have_pred(mname(bb, var, h), n, rep)] + if not hs: + continue + t0 = time.time() + shot = lab.shots_for(d, n, rep) + ys = lab.shot_labels(d, shot) + for h in hs: + Ps = [] + for (Zm, Za, W0, b0), (Sm, Sa) in zip(embs, scaled): + if h == "ft": + Wf, bf = lab.finetune_last_layer(W0, b0, Za[shot], ys, lam) + Ps.append(proba(Za, Wf, bf)) + else: + with warnings.catch_warnings(): + warnings.simplefilter("ignore") + m = lab.joint_head(Sm, d["ym"], Sa[shot], ys, shot_weight=SHOT_WEIGHT) + Ps.append(m.predict_proba(Sa)) + lab.save_pred(mname(bb, var, h), n, rep, np.mean(Ps, 0), {**meta, "head": h}) + log(f"{bb}_{var} n={n} rep={rep} heads={hs} {time.time() - t0:.1f}s") + log("done") + + +if __name__ == "__main__": + main()