featurely 0.1.1

Reusable feature engineering utilities

featurely

Reusable feature engineering utilities for tabular machine learning with pandas and scikit-learn.

featurely provides function-based helpers for the screen-then-commit feature engineering loop: build candidate features, test whether they explain variance your current model misses, and keep only the winners.

• Pipeline evaluation: cross-validated stage-over-stage comparison with persisted, rerun-safe results and progressive box plots.
• Candidate screening: residual correlation scans with Benjamini-Hochberg false discovery rate correction, for individual features and grouped feature sets.
• Feature builders: outlier handling, monotonic transforms, geographic encodings (haversine distances, geohash cells, rotated coordinates), quantile bin aggregates, k-means cluster memberships, Gaussian kernel spatial smoothing, and polynomial expansion with PCA component selection.
• Diagnostics and EDA: distribution plots, pairwise correlation analysis, and variance inflation factors.

All helpers accept a pandas DataFrame, take explicit column names, and return transformed copies without mutating input.

Install

pip install featurely

Requires Python 3.10 or newer.

Quick start

import pandas as pd
import featurely as fl

df = pd.read_csv("my_data.csv")
target = "price"
features = [c for c in df.columns if c != target]

# Establish a baseline
results = fl.add_pipeline_step(None, "raw", df[features], df[target])

# Clean outliers and measure the effect
df_clean = fl.clip_outliers(df, features, threshold=2.25)

results = fl.add_pipeline_step(
    results, "+ cleaned", df_clean[features], df_clean[target]
)

fl.plot_pipeline_steps(results, title="Effect of outlier clipping")

# Build candidates and screen them against baseline residuals
candidates = fl.compute_bin_aggregates(df_clean, "latitude", ["income"], n_bins=10)
scan = fl.run_candidate_scan(df_clean, candidates, target=target)
significant = fl.plot_candidate_scan(scan, title="Candidate scan")

keep = [name for name, is_sig in significant.items() if is_sig]
df_clean = pd.concat([df_clean, candidates[keep]], axis=1)

Documentation

Full API reference, getting-started guide, and a complete worked example on the California housing dataset:

• Documentation: gperdrizet.github.io/featurely
• Source and example notebooks: github.com/gperdrizet/featurely

License

MIT