semvec/pypi-packages huggingface dataset:
hippo 0.1.0b2
Hippo is a lightweight machine-learning framework that unifies declarative preprocessing pipelines, automatic hyperparameter optimisation, and local experiment tracking behind a single, friendly CLI.
License: MIT
Requires Python: >=3.10

# hippo

*Lightweight ML workflow engine: declarative preprocessing ▸ hyper-parameter optimisation ▸ experiment tracking*

<img src="https://i.ibb.co/69jrJSv/CA30-BEF0-6-D8-B-4-FD4-A7-E8-D18427-D94-F87.png" width="220" alt="hippo logo">

`hippo` brings together three everyday ML chores under one friendly roof:

| What you need                                      | How hippo helps |
|----------------------------------------------------|-----------------|
| **Clean, consistent data**                         | Build declarative preprocessing pipelines with Sklearn primitives |
| **Good hyper-parameters without the grind**        | Run Optuna-powered Bayesian optimisation (TPE, CMA-ES, …) |
| **Remember what actually worked**                  | Log parameters & metrics to a local SQLite DB with zero setup |

**Why hippo?**

* ⚡ **Efficient** – sensible defaults, parallel Optuna trials  
* 🛡️ **Reliable** – minimal dependencies, 100 % typed codebase  
* 🔌 **Extensible** – register your own models or transformations in a single line  
* 🐚 **Simple CLI** – `hippo train config.yml` is all you need

## Installation

```bash
pip install hippo
```

## Quick Start

```python
from sklearn.datasets import load_breast_cancer
from hippo.data.preprocessing import build_pipeline
from hippo.models import get_model
from hippo.tuning import optimise
from optuna import Trial

# 1  Load toy data
data = load_breast_cancer(as_frame=True)
X, y = data.data, data.target

# 2  Preprocessing pipeline
pipe = build_pipeline(num_feats=X.columns.tolist(), cat_feats=[])

# 3  Objective builder for Optuna
def make_objective(trial: Trial):
    n_estimators = trial.suggest_int("n_estimators", 50, 400)
    max_depth = trial.suggest_int("max_depth", 3, 10)

    model = get_model("rf", n_estimators=n_estimators, max_depth=max_depth, random_state=0)

    def objective(trial_: Trial):
        from sklearn.model_selection import cross_val_score
        score = cross_val_score(model, pipe.fit_transform(X), y, cv=3).mean()
        return score
    return objective

study = optimise(make_objective, n_trials=50, direction="maximize")
print("Best accuracy:", study.best_value)
```

Or the same via CLI:

```bash
python -m hippo.cli train config.yml
```

`config.yml` example:

```yaml
run_name: demo_rf
data:
  path: cancer.joblib         # joblib-dumped DataFrame
  target: target
  numerical: [mean radius, mean area, ...]
  categorical: []
model:
  name: rf
  params:
    n_estimators: 200
```