softauto 0.3.3

One-call EDA + preprocessing + feature selection + plotting + advisor + tuning (robust CV).

softauto 0.3.1 – tiny-data safe SMOTE, advisor, plots

softauto 0.3.2 (Pro+Fix)

EDA → Clean → Select → Train → Tune → Plot → Advise
A lightweight AutoML helper that makes it easy to run an end-to-end experiment with just one call.

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✨ Features

• Model Zoo (classification & regression):
  logreg, linear, ridge, lasso, random_forest, gb, svm_rbf, knn, mlp
  (XGBoost / LightGBM available if installed).

• Preprocessing

  • Missing-value imputation (median / most_frequent).
  • Scaling: standard, minmax, robust, or None.
  • Rare-category binning (cat_min_freq=...).
  • Outlier clipping (num_clip_quantiles=(low, high)).

• Feature Selection

  • mutual_info → keep top-k informative features.
  • rfecv → recursive elimination with CV.

• Imbalance Handling

  • imbalance="auto" → tries SMOTE (safe on small folds) else falls back to class weights.
  • imbalance="class_weight" or "smote" explicit.
  • New: smote_k_neighbors param (default 5, auto-reduced on small datasets).

• Advisor

  • If user-chosen model underperforms (e.g., accuracy < 0.75), evaluates a shortlist and:
    • Returns best alternatives.
    • Suggests practical tuning/feature/imbalance tips.
    • Auto-fixes to best alternative if advisor_auto_fix=True.

• Plots & Report

  • Target distribution, missingness, correlation, feature importance.
  • Auto-generated report.html with embedded charts.

• Tuning

  • Lightweight RandomizedSearchCV with sensible param grids.
  • Safe defaults (error_score=0.0 → failed folds don’t crash).

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🔧 Install

pip install -e .
# optional extras:
pip install -e .[boosters]   # xgboost, lightgbm
pip install -e .[imbalance]  # imbalanced-learn for SMOTE