dantabnn 0.3.1

Dual-Attention Neural Networks for tabular data classification and regression

DanTabNN — Dual-Attention Neural Networks for Tabular Data

PyTorch pipeline for tabular classification and regression with Dual-Attention Networks, automated preprocessing, memory-efficient streaming, and Optuna hyperparameter tuning.

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Installation

git clone https://github.com/alex-rybin-ml/DanTabNN.git && cd DanTabNN
uv sync

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Quick Start

from dantabnn.regression import RegressionPipeline

pipe = RegressionPipeline(
    numeric_features=["age", "income", "rooms"],
    categorical_features=["city"],
    target_column="price",
)
pipe.fit(df_train)                    # auto-runs: impute → clip outliers → x²+log1p → scale → encode
preds = pipe.predict(df_test)

# Hyperparameter tuning with CV
best = pipe.hyperparameters_tuning(df_train,
    n_iter=500, n_jobs=14, direction="maximize")
print(best.hyperparameters)

Binary with imbalanced classes:

from dantabnn.binary import BinaryClassificationPipeline

pipe = BinaryClassificationPipeline(
    numeric_features=[...], categorical_features=[...],
    target_column="churn",
    pos_weight=9.0,              # for 90/10 imbalance
    threshold_tuning=True,        # auto-find optimal F2 threshold
)
pipe.fit(df_train, df_val=df_val)
print(f"Optimal threshold: {pipe.optimal_threshold:.3f}")
classes = pipe.predict_classes(df_test)  # uses optimal threshold

Large datasets (10M+ rows) — chunked from Parquet:

pipe = RegressionPipeline(...)
pipe.fit_from_parquet(
    "/data/30gb_dataset.parquet",   # local or S3 path
    df_val=df_val,                  # optional validation data
    chunk_size=100_000,             # rows per chunk (peak memory ≈ 15 GB for 30 GB dataset)
    sample_size=100_000,            # rows for fitting preprocessor statistics
)
preds = pipe.predict(df_test)

Other tasks: BinaryClassificationPipeline, MulticlassClassificationPipeline — same API.

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Architecture

DataFrame → [NaNImputer → OutlierClipper → AutoFeatureEngineer → StandardScaler → OneHotEncoder]
         → Feature Gating → Feature Attention → Cross Network → Feed-Forward → Output

Component	Purpose
Preprocessing chain	IQR outlier clipping → median imputation → x²+log1p engineering → scale → encode
preprocessing_mode="auto"	Auto-skips IQR + feature eng on small/clean datasets (n<1000, d<20)
Feature Gating	Gumbel-Softmax differentiable feature selection
Feature Attention	4-head self-attention across feature dimensions
Cross Network	Explicit DCN-style pairwise feature crosses

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Benchmark Results (500-trial Optuna tuning, EarlyStoppingCallback + MedianPruner)

Dataset	Task	Baseline (v2)	v7 (20tr)	v10 (500tr)
Boston Housing	R²	0.628	0.816	0.855
Diabetes Progression	R²	0.362	0.509	0.548
Energy Efficiency	R²	0.845	0.989	0.992
Wine Quality	R²	-0.023	0.479	0.512
Breast Cancer	AUC	0.994	0.996	0.997
German Credit	AUC	0.703	0.801	0.815
Pima Diabetes	AUC	0.823	0.822	0.828
Spambase	AUC	0.972	0.986	0.989
Iris	F1	0.898	0.967	1.000
Digits	F1	0.964	0.980	0.983
Vehicle	F1	0.940	0.985	0.992
Segment	F1	0.978	1.000	1.000
Wine	F1	1.000	1.000	1.000

500 trials with EarlyStoppingCallback (100-round patience) and MedianPruner consistently outperforms 20 trials (+0.01-0.04).

---

Configuration

Parameter	Default	Description
hidden_dims	[128,64,32]	3-layer MLP (adaptive per dataset)
dropout	0.2	Dropout rate
gating_type	"soft"	Feature gating: "soft" or "none"
clip_outliers	True	IQR winsorization [Q1-1.5×IQR, Q3+1.5×IQR] (in-place for memory)
impute_missing	True	Median imputation (handles pd.NA + np.nan)
engineer_features	True	x² for all features + log1p for
engineer_max_features	100	Max generated features (capped for memory safety)
preprocessing_mode	"auto"	"auto" skips IQR+engineering on small/clean data
pos_weight	None	BCEWithLogitsLoss class weight (use 9.0 for 90/10 imbalance)
threshold_tuning	True	Auto-optimize F2 decision threshold on validation data
use_batch_norm	False	Harmful for regression
epochs	100	With early stopping (patience=10)
learning_rate	1e-3	Adam optimizer (log-tuned)
weight_decay	1e-5	L2 regularization (log-tuned)

Disable preprocessing: clip_outliers=False, impute_missing=False, engineer_features=False

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Experiment Tracking (MLflow)

# Run experiments
uv run python experiments/run_experiments.py --version v1 --epochs 100

# Optuna tuning (13 datasets, 14 parallel trials, CV mode)
uv run python experiments/tune_pipeline.py --all --n-trials 500

# Compare versions in browser
uv run python experiments/compare_runs.py --exp dantabnn --ver1 v2-baseline --ver2 v7

# MLflow UI
mlflow ui --backend-store-uri sqlite:///mlruns/mlflow.db

# Terminal dump
uv run python experiments/analyze_db.py

---

Hyperparameter Tuning (built-in API)

pipe = BinaryClassificationPipeline(...)
best = pipe.hyperparameters_tuning(
    df_train,
    n_iter=500,          # trials (pruner stops bad ones early)
    n_jobs=14,           # parallel workers
    direction="maximize" # maximize AUC/R²/F1
)

Tunable: dropout, learning_rate, weight_decay, hidden_dims_choice (adaptive/narrow/wide), gating_type (soft/none).

Uses TPE sampler + MedianPruner (10 startup trials). CV mode auto-activates when df_val=None.

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Advanced: Feature Generation

from dantabnn.feature_generation import DomainRatioGenerator

gen = DomainRatioGenerator(max_features=20)
gen.fit(X_train, y_train)           # auto-discovers skewed, cyclic, ratio features
new_features = gen.transform(X_val) # produces log1p, sin/cos, ratio columns

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Save / Load

pipe.save("models/my_pipeline")
pipe2 = BinaryClassificationPipeline(...).load("models/my_pipeline")
preds = pipe2.predict(df)  # ready immediately

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Project Structure

src/dantabnn/
├── base.py, binary.py, regression.py, multiclass.py   # Pipelines
├── models/danet.py, gating.py, cross.py               # Neural modules
├── preprocessing/encoder.py, scaler.py, imputer.py,   # Preprocessing
│   outlier.py, feature_engineer.py
├── feature_generation/                                 # Advanced feature eng
├── tuning/hyperparam.py, tune_utils.py                # Optuna tuner + param grids
└── utils/metrics.py, logger.py, hardware.py
experiments/run_experiments.py, tune_pipeline.py        # Runners
tests/ (230 tests, 83% coverage)

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Key Findings

1. 500-trial tuning outperforms 20-trial: +0.01-0.04 across all datasets (avg|Δ|=0.014)
2. fit_from_parquet() enables 30 GB datasets in 32 GB Docker containers — 15 GB peak vs 140 GB with fit()
3. Preprocessing helps without tuning: wine_quality -0.02→0.34, iris F1=0.90→0.97
4. EarlyStoppingCallback saves time: studies stop at 120-380 trials (never run full 500)
5. Threshold tuning matters for imbalanced data: F2-optimal threshold ≠ 0.5 for skewed classes
6. pos_weight in BCEWithLogitsLoss addresses class imbalance without oversampling
7. Sparse OneHotEncoder saves 80-95% RAM — production-ready for large feature sets
8. No hard dimension limits — escalating warnings only (>256/512/1024) for production safety
9. In-place outlier clipping + intermediate gc — 25-32 GB RAM saved on large datasets
10. Better than LightAutoML: DanTabNN wins 8-1-4 across 13 benchmark datasets

Memory Optimizations

Optimization	Impact	Location
fit_from_parquet() chunked streaming	30 GB → 15 GB peak RAM	base.py
Sparse OneHotEncoder	80-95% RAM savings for categorical	encoder.py
In-place outlier clipping	25 GB saved on large arrays	outlier.py
del df_train after numpy extraction	60-90 GB pandas overhead freed	base.py:fit()
gc.collect() between preprocessing steps	25-32 GB freed between stages	base.py:_prepare_features()
float64→float32 conversion	50% GPU transfer reduction	base.py:_prepare_features()
pd.isna() + to_numpy(na_value=nan)	Handles pandas NAType (nullable Int columns)	base.py

License

MIT — see LICENSE.