autoseqmodels 0.1.0

Automated weekly sequence-model workflow (LSTM / Transformer) for customer transaction prediction.

autoseqmodels

Automated weekly sequence-model workflow for customer transaction prediction. Provides an end-to-end pipeline from raw transaction tables to trained LSTM / Transformer models, with column-type inference, encoding strategy proposal, per-customer sequence construction, training, tuning (Optuna), and holdout evaluation.

Installation

pip install autoseqmodels

Or from a local clone:

pip install -e .

Workflow

from autoseqmodels import (
    loader, inspection, encoders, sequence_builder,
    training, sequence_lstm, sequence_transformer,
)

# 1. Load data (CSV / Excel / RData)
df = loader.load_table("transactions.csv")

# 2. Aggregate raw transactions to a (customer, week) panel
panel = loader.build_transaction_panel(df, ...)

# 3. Detect column types (user-editable)
detected = inspection.infer_column_types(panel)
panel = inspection.cast_columns_by_detected_type(panel, detected)

# 4. Resolve entity / date / target + covariate plan
structure, plan = inspection.analyze_structure(panel, detected)

# 5. Propose encoding strategy (user-editable)
strategy = encoders.propose_encodings(panel, detected, plan)

# 6. Fit encoders on training rows only
enc_df, spec = encoders.apply_encodings(panel, strategy, ...)
plan = encoders.expand_plan(plan, spec)

# 7. Build per-customer sequences
seqs = sequence_builder.build_transaction_sequences(enc_df, ...)

# 8. Train / tune / evaluate
model = sequence_lstm.train_tuned_lstm(seqs, ...)
preds = sequence_lstm.predict_holdout(model, seqs)

Modules

• loader — read CSV / Excel / RData and build the (customer, week) panel
• inspection — column-type inference, structural analysis, casting
• encoders — encoding strategy proposal and fitting (scale / one-hot / embed)
• sequence_builder — per-customer sequence construction with calibration / holdout split
• training — generic dataset, train/val split, training loop, evaluation, plotting
• sequence_lstm — SequenceLSTM, predict_holdout, tune_lstm, train_tuned_lstm
• sequence_transformer — SequenceTransformer and matching tune / train / predict helpers

Requirements

Python ≥ 3.10. See pyproject.toml for the full dependency list.

License

MIT — see LICENSE.