bike-sharing-tabular-models

Bike Sharing - Tabular Models (8 architectures + interpretability, Poisson)

Pre-trained models for the t22000t/bike-sharing-tabular dataset, covering all eight architectures from the tabular-data-modelling-pipeline with a Poisson loss matching the count target.

v3 release - adds the full interpretability stack: SHAP TreeExplainer for the GBMs, Captum Integrated Gradients for the DL models, per-layer attention for FT-Transformer, residual analysis for CANN / CANN-GBM, per-row coefficients for LocalGLMnet, and full distributional outputs (mean, variance, quantiles, VaR) for DRN. See INTERPRETABILITY.md and dashboard_dl_interpretability.html.

Results

All 8 architectures trained with default hyperparameters (no Optuna tuning), 3-seed ensembles for the DL models, Poisson family + log link.

† DRN has a calibration issue on this dataset: rank-order discrimination (Gini=0.29) is reasonable but predicted magnitudes are ~20× the actuals (MAE 3819 vs target mean ~190). The model's distributional output scaling is off. Treat DRN's predictions as rank-only; do not interpret them on the count scale.

• Test set: 3,470 hourly rows (20% of 17,379)
• Target: cnt (hourly bike rental count, 1-977)
• Loss: Poisson NLL (count:poisson for XGBoost, Poisson for CatBoost)
• Link: log
• Random seed: 42

MAE units are bikes. A test MAE of ~23 on a target with median 142 is genuinely useful.

Interpretability (new in v3)

Multiple methods applied side by side. With 17,379 hourly rows the data is rich enough that all methods agree closely on the dominant drivers:

Cross-method consensus (the high-confidence finding)

Both CatBoost and XGBoost ranked these five features in their top-5:

Note that hr dominates so heavily (importance ~5x the next feature) that any model failing to use it heavily is essentially blind. This explains why TabM and FT-Transformer underperform on this dataset despite 17k rows - their attention/aggregation mechanisms appear to dilute the strong univariate signal in hr.

Full breakdown - per-method top-10 tables, LocalGLMnet coefficient distributions, sign-stability analysis - is in INTERPRETABILITY.md. For interactive plots see dashboard_dl_interpretability.html.

Files

Loading and inference

CatBoost

from huggingface_hub import hf_hub_download
from catboost import CatBoostRegressor
import pandas as pd

path = hf_hub_download("t22000t/bike-sharing-tabular-models", "catboost.cbm")
model = CatBoostRegressor()
model.load_model(path)

df = pd.read_csv("hf://datasets/t22000t/bike-sharing-tabular/hour.csv")
features = [
    "temp", "atemp", "hum", "windspeed", "hr", "yr", "mnth",
    "season", "holiday", "weekday", "workingday", "weathersit",
]
preds = model.predict(df[features])  # predicted hourly rental count

XGBoost (best overall)

from huggingface_hub import hf_hub_download
import xgboost as xgb

path = hf_hub_download("t22000t/bike-sharing-tabular-models", "xgboost.json")
booster = xgb.Booster()
booster.load_model(path)

Deep-learning architectures

Each ships as a 3-seed ensemble of PyTorch state-dicts. To reconstruct, install the pipeline package and load via the matching architecture class:

git clone https://github.com/timothy22000/tabular_data_modelling_pipeline
cd tabular_data_modelling_pipeline
pip install -e ".[all]"

Training configuration

To reproduce:

git clone https://github.com/timothy22000/tabular_data_modelling_pipeline
cd tabular_data_modelling_pipeline
pip install -e ".[all]"
python scripts/download_data.py --dataset bike_sharing

OMP_NUM_THREADS=1 python train.py \
    --config configs/example_bike_sharing.py \
    --input data/bike_sharing.csv \
    --skip-tuning --skip-interpretability \
    --architectures catboost xgboost cann cann_gbm ft_transformer tabm localglmnet drn

(OMP_NUM_THREADS=1 is only needed on macOS arm64.)

Limitations

• No Optuna tuning. Defaults only.
• DRN calibration off. Rank-only on this dataset (see results note).
• FT-Transformer & TabM underperform the GBMs even with 17k rows. These architectures usually need 50k+ rows + tuning to be competitive on tabular tasks. Reported here for completeness, not as a recommendation.
• No interpretability artefacts. --skip-interpretability was set for wall-clock; re-run without it for Captum attributions and partial-dependence plots.
• Random split, not chronological. Bike rental data has obvious seasonality; a date-based split (train on 2011, test on 2012) would be more realistic.
• casual and registered excluded as features (they sum to cnt, i.e. label leakage).

Intended use

• Demonstrating the pipeline on count/Poisson data alongside the gamma-family House Prices model collection.
• Baseline for tabular DL research on count regression.
• Teaching Poisson regression with a realistic mid-sized dataset.

Citation

@article{fanaee2014event,
  title   = {Event labeling combining ensemble detectors and background knowledge},
  author  = {Fanaee-T, Hadi and Gama, Jo{\~a}o},
  journal = {Progress in Artificial Intelligence},
  year    = {2014}
}

@software{tabular_data_modelling_pipeline,
  author = {Mun, Timothy},
  title  = {tabular-data-modelling-pipeline},
  url    = {https://github.com/timothy22000/tabular_data_modelling_pipeline},
  year   = {2026}
}

Please also cite the individual architecture papers - see the main repo README.

License

MIT for the model code and pipeline. Underlying dataset under CC BY 4.0.

Related

• 📂 Dataset: t22000t/bike-sharing-tabular
• 🤖 Companion: t22000t/house-prices-tabular-models - gamma family, 1.5k rows
• 📦 Pipeline: tabular-data-modelling-pipeline