Automated feature engineering in one function, 100% Polars-native
Project description
Installation
pip install polarsmith
# atau dengan uv:
uv add polarsmith
Quick Start
import polars as pl
from polarsmith import forge
df = pl.DataFrame({
"age": [25, 30, 35, 40],
"income": [50000, 60000, 70000, 80000],
"churn": [0, 1, 0, 1]
})
df_out = forge(
df,
target="churn",
strategy="smart", # auto-detect fitur yang relevan
config={
"binning": {"max_bins": 10},
"interactions": ["age*income"],
"encoding": {"method": "james_stein"},
}
)
Feature Matrix
| Fitur | Status |
|---|---|
| Auto Binning | ✅ v0.1 |
| Cyclical Features | ✅ v0.1 |
| Interaction Terms | ✅ v0.1 |
| James-Stein Enc. | ✅ v0.1 |
| WoE Encoding | ✅ v0.1 |
| Smart Detection | ✅ v0.1 |
| Pandas Compat | ✅ v0.1 |
Performance
Benchmark pada MacBook Air M-series, Python 3.12:
| Dataset Size | Polarsmith | Pandas equivalent | Speedup |
|---|---|---|---|
| 10,000 rows | 0.0xs | 0.0xs | Xx |
| 100,000 rows | 0.0xs | 0.0xs | Xx |
| 1,000,000 rows | 0.0xs | 0.0xs | Xx |
Polarsmith menggunakan Polars 1.x dengan lazy evaluation dan SIMD.
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