Metadata-Version: 2.4
Name: bitbullet
Version: 0.5.0
Summary: Composable data-science building blocks for transformation, training, forecasting, clustering, evaluation, and model metadata.
Author-email: BitBullet <contact@bitbullet.ai>
License-Expression: MIT
Project-URL: Homepage, https://developer.bitbullet.co.uk
Project-URL: Documentation, https://developer.bitbullet.co.uk
Keywords: machine-learning,forecasting,time-series,clustering,data-science,feature-engineering,model-evaluation
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
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Classifier: Programming Language :: Python :: 3.11
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Dynamic: license-file

# BitBullet

BitBullet is a Python data-science SDK for tabular machine learning and
forecasting workflows. It provides composable building blocks for transformation
pipelines, model training, forecasting, clustering, evaluation, and reproducible
artifact metadata.

Use individual modules step by step or compose them into notebooks, batch jobs,
and Python services.

Full documentation and practical tutorials are available at
[developer.bitbullet.co.uk](https://developer.bitbullet.co.uk).

## Modules

| Module | Purpose |
| --- | --- |
| `bitbullet.transform` | Fitted transformation pipelines for numerical, categorical, and datetime features. |
| `bitbullet.train` | Supervised classification/regression training utilities, Optuna-backed search, feature selection, sample weights, threshold optimization, and training reports. |
| `bitbullet.forecast` | Ordered and panel forecasting schemas, feature framing, reduction strategies, rolling-origin backtesting, intervals, and reconciliation. |
| `bitbullet.model_selection` | Ordered holdouts and expanding or rolling temporal windows with explicit gaps and audit metadata. |
| `bitbullet.cluster` | K-Means, K-Modes, K-Prototypes, DBSCAN, GMM, gamma estimation, categorical weighting, K selection, profiling, and clustering metrics. |
| `bitbullet.evaluate` | Structured classification and regression evaluation metrics for reports and metadata. |
| `bitbullet.model` | Model wrappers, model metadata, dataset metadata, and serialization helpers. |

## Installation

```bash
pip install bitbullet
```

Optional extras keep installations lean:

```bash
pip install "bitbullet[inference-models]"   # LightGBM and XGBoost wrappers
pip install "bitbullet[inference-cluster]"  # clustering extras such as kmodes
pip install "bitbullet[forecast-statistical]"  # optional StatsForecast adapter
pip install "bitbullet[train,viz]"          # training, SHAP, and plotting tools
pip install "bitbullet[all]"                # complete SDK
```

## Transform Data

```python
from bitbullet.transform import TransformPipeline

pipeline = TransformPipeline(name="credit_features")
pipeline.add("numerical", "standard_scale", columns=["income", "balance"])
pipeline.add("categorical", "onehot_encode", columns=["region"])

X_transformed = pipeline.fit_transform(X_train)
X_new = pipeline.transform(X_new_raw)
pipeline.save("artifacts/transform_pipeline.joblib")
```

Target-aware encoders receive `y` directly. `target_encode` is leakage-aware:
`fit_transform(..., y=...)` returns out-of-fold training encodings, while later
`transform(...)` calls use the stored full-training smoothed mapping.

```python
pipeline = TransformPipeline()
pipeline.add(
    "categorical",
    "target_encode",
    columns=["merchant_category"],
    params={"target_type": "classification", "cv_folds": 5, "cv_strategy": "stratified"},
)
X_encoded = pipeline.fit_transform(X_train, y=y_train)
```

## Train A Classifier

```python
from bitbullet.train import TrainConfig, OptunaTrainer

config = TrainConfig(
    name="default_risk_lgbm",
    model_type="lgbm",
    task="binary_classification",
    n_trials=30,
    optimization_metric="roc_auc",
    optuna_sampler="tpe",  # tpe, random, grid, cmaes
)

trainer = OptunaTrainer(config)
model = trainer.fit(X_train, y_train, X_val=X_val, y_val=y_val)

print(trainer.best_params)
print(trainer.state.optimal_threshold)
```

Manual fixed-parameter training is available when you do not want a search:

```python
config = TrainConfig(
    name="fixed_rf",
    model_type="random_forest",
    optimizer="manual",
    model_params={"n_estimators": 300, "max_depth": 20},
)
```

Optuna-backed grid and random search are explicit sampler choices:

```python
config = TrainConfig(
    name="small_grid",
    model_type="lgbm",
    optuna_sampler="grid",
    search_space={
        "num_leaves": [31, 63],
        "learning_rate": [0.05, 0.1],
    },
)
```

## Evaluate Classification

```python
from bitbullet.evaluate import evaluate_classification

report = evaluate_classification(
    y_true=y_test,
    y_pred_proba=model.predict_proba(X_test),
    threshold=trainer.state.optimal_threshold or 0.5,
)

metadata_ready = report.to_dict()
```

## Train And Evaluate A Regressor

```python
from bitbullet.evaluate import evaluate_regression
from bitbullet.train import TrainConfig, OptunaTrainer

config = TrainConfig(
    name="house_price_lgbm",
    model_type="lgbm",
    task="regression",
    n_trials=30,
    optimization_metric="rmse",  # minimize by default for regression
    optuna_sampler="tpe",
)

trainer = OptunaTrainer(config)
model = trainer.fit(X_train, y_train)

y_pred = model.predict(X_test)
report = evaluate_regression(
    y_true=y_test,
    y_pred=y_pred,
    n_features=X_train.shape[1],
)

metadata_ready = report.to_dict()
```

## Forecast Ordered And Panel Data

```python
from sklearn.linear_model import Ridge

from bitbullet.forecast import (
    ForecastConfig,
    ForecastFeatureBuilder,
    ForecastFrame,
    ForecastSchema,
    RollingFeature,
    TabularForecaster,
)

schema = ForecastSchema(
    time="date",
    targets="sales",
    entities="store",
    future_covariates=("promotion", "temperature"),
    cadence="D",
)
history = ForecastFrame(history_df, schema)
features = ForecastFeatureBuilder(
    target_lags=(1, 7),
    rolling_features=(RollingFeature("sales", window=7, lag=1),),
    calendar_features=("day_of_week_sin", "day_of_week_cos"),
)

forecaster = TabularForecaster(
    Ridge(alpha=1.0),
    config=ForecastConfig(horizons=(1, 2, 3), strategy="direct"),
    feature_builder=features,
).fit(history)

predictions = forecaster.forecast(future_df)
```

`ForecastBacktester` adds synchronized rolling-origin evaluation, while
`ConformalIntervalCalibrator` and `HierarchicalReconciler` provide optional
interval and aggregation layers. See the
[forecasting API guide](https://developer.bitbullet.co.uk/api/forecast/) and
advanced forecasting tutorial for the complete workflow.

## Cluster Data

```python
from bitbullet.cluster.core import ClusterConfig
from bitbullet.cluster.algorithms.partitional import KPrototypesClusterer

config = ClusterConfig(
    name="customer_segments",
    algorithm_type="partitional",
    method="kprototypes",
    n_clusters=5,
    numerical_columns=["income", "spend"],
    categorical_columns=["region", "channel"],
    params={
        "gamma": "huang",
        "categorical_weights": "relevance",
        "init": "Cao",
        "n_init": 10,
    },
)

clusterer = KPrototypesClusterer(config)
labels = clusterer.fit_predict(df)

print(clusterer.state.fitted_params["gamma_by_column"])
print(clusterer.state.fitted_params["categorical_weights_by_column"])
```

## Save Models With Metadata

```python
from bitbullet.model import ModelMetadata, ModelSerializer

metadata = ModelMetadata(
    name="default_risk_lgbm",
    model_type=model.model_type,
    framework=model.framework,
    task="binary_classification",
    metrics=report.metrics,
)
metadata.add_feature_schema(X_train)

ModelSerializer.save(
    model=model,
    path="artifacts/default_risk_lgbm.pkl",
    metadata=metadata,
    train_data=(X_train, y_train),
    test_data=(X_test, y_test),
    include_datasets=False,
)
```

## License

MIT

## Support

Questions and problem reports can be sent to contact@bitbullet.ai.
