featranker 0.1.1

Rank feature importance across multiple ML models.

FeatRanker

Rank feature importance across multiple ML models using permutation importance.

FeatRanker trains a configurable set of scikit-learn, XGBoost, LightGBM, and CatBoost models on your data, computes permutation importance for every trained model, and returns per-model rankings plus an aggregated average ranking.

Item	Value
Package name	featranker
Import module	featranker
CLI command	featranker
Model config	featranker/importance_config.yaml
Default prep file	featureCalc.py (project root)

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Table of Contents

• Installation
• How It Works
• Data Preparation
• Quick Start
• CLI Reference
• Python API
• Model Configuration
• Available Models
• Output Format
• Troubleshooting

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Installation

Install dependencies:

pip install -r requirements.txt

Install the package in editable (development) mode:

pip install -e .

Or install from PyPI:

pip install featranker

Requirements

• Python ≥ 3.10
• numpy, scikit-learn, pyyaml, tqdm, xgboost, lightgbm, catboost

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How It Works

1. Load data — A user-defined prep class returns a feature dict with a "label" key.
2. Initialize models — Model definitions are read from importance_config.yaml and instantiated for the requested task and group.
3. Train models — Every initialized model is fitted on the feature matrix.
4. Rank features — Permutation importance is computed per model, and an overall average ranking is produced.

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Data Preparation

Before running FeatRanker you need a prep class — a Python class with a _calc_features() method that returns your data as a dict.

Expected return format

{
    "feature_1": [v1, v2, v3, ...],
    "feature_2": [v1, v2, v3, ...],
    ...
    "label":     [y1, y2, y3, ...],
}

• Every feature key maps to a list of numeric values.
• All lists (including "label") must have the same length.
• The "label" key is required.

Where to put it

Option A — Edit the default file (simplest)

Define your class in featureCalc.py at the project root. The default class name is prepFeature, but you can name it anything and select it with --prep-class.

Option B — Use a separate file (no reinstall needed)

Keep your prep logic in any Python file and point to it at runtime:

featranker --prep-file ./my_features.py --prep-class MyPrepClass --task clf

Example prep class

from sklearn.datasets import load_iris

class IrisPrep:
    def _calc_features(self):
        data = load_iris()
        features = {
            data.feature_names[i]: data.data[:, i].tolist()
            for i in range(data.data.shape[1])
        }
        features["label"] = data.target.tolist()
        return features

---

Quick Start

1. Implement _calc_features() in featureCalc.py (or your own file).
2. Run the CLI:

# Classification with all model families, using the default prepFeature class
featranker --task clf --group all

# Regression with tree models only, custom prep file and class
featranker --task reg --group tree \
    --prep-file ./my_features.py --prep-class DiabetesPrep

# Save results to a JSON file
featranker --task clf --group linear --output results

---

CLI Reference

featranker --task {clf,reg} [--group {linear,tree,all}]
           [--prep-file PATH] [--prep-class NAME]
           [--output PATH]

Flag	Description	Default
--task	clf (classification) or reg (regression)	required
--group	linear, tree, or all (both)	all
--prep-class	Name of the prep class to instantiate	prepFeature
--prep-file	Path to the Python file containing the prep class	featureCalc.py in the current working directory
--output	File path for JSON output (.json appended if missing)	print to stdout

---

Python API

Using FeatureRanker directly (default prep file)

When your default prepFeature class lives in featureCalc.py at the project root:

from featranker import FeatureRanker

ranker = FeatureRanker(task="clf", group="all")
results = ranker.rankFeatures()

Using build_ranker with a custom prep file

build_ranker is a convenience factory that returns a fully initialized FeatureRanker instance (features loaded, models trained, ready to rank):

from featranker import build_ranker

ranker = build_ranker(
    task="reg",
    group="tree",
    prep_file="./my_features.py",
    prep_class="DiabetesPrep",
)
results = ranker.rankFeatures()

Constructor parameters

Parameter	Type	Description
task	"clf" | "reg"	Classification or regression
group	"linear" | "tree" | "all"	Which model family to use
prep_file	str or None	Path to prep file (defaults to featureCalc.py)
prep_class	str	Name of the prep class (defaults to "prepFeature")

---

Model Configuration

Models are defined in featranker/importance_config.yaml, organized by task and group:

classification:
  linear:
    - name: logistic_regression
      import: sklearn.linear_model
      class: LogisticRegression
      params:
        max_iter: 2000
  tree:
    - name: random_forest
      import: sklearn.ensemble
      class: RandomForestClassifier
      params:
        random_state: 42

regression:
  linear:
    - ...
  tree:
    - ...

Each entry has four fields:

Field	Description
name	Display name used in output
import	Python module to import (e.g., sklearn.ensemble)
class	Class name to instantiate from that module
params	Dict of keyword arguments passed to the constructor (optional)

Edit this file to add, remove, or tune models. Changes take effect on the next run — no reinstall required.

---

Available Models

Classification — Linear

Name	Class
logistic_regression	LogisticRegression
logistic_regression_l1	LogisticRegression (L1)
logistic_regression_l2	LogisticRegression (L2)
logistic_regression_elasticnet	LogisticRegression (ElasticNet)
linear_svm	LinearSVC
sgd_classifier	SGDClassifier
ridge_classifier	RidgeClassifier
perceptron	Perceptron
passive_aggressive	PassiveAggressiveClassifier
lda	LinearDiscriminantAnalysis
qda	QuadraticDiscriminantAnalysis
naive_bayes_gaussian	GaussianNB
naive_bayes_bernoulli	BernoulliNB
naive_bayes_multinomial	MultinomialNB
pls_da	PLSRegression

Classification — Tree

Name	Class
decision_tree	DecisionTreeClassifier
random_forest	RandomForestClassifier
extra_trees	ExtraTreesClassifier
bagging_tree	BaggingClassifier
adaboost	AdaBoostClassifier
gradient_boosting	GradientBoostingClassifier
hist_gradient_boosting	HistGradientBoostingClassifier
xgboost	XGBClassifier
catboost	CatBoostClassifier

Regression — Linear

Name	Class
linear_regression	LinearRegression
ridge_regression	Ridge
lasso_regression	Lasso
elasticnet_regression	ElasticNet
elasticnet_cv_regression	ElasticNetCV
pls_regression	PLSRegression
huber_regression	HuberRegressor
ransac_regression	RANSACRegressor
kernel_ridge_regression	KernelRidge
svr_regression	SVR

Regression — Tree

Name	Class
decision_tree_regressor	DecisionTreeRegressor
random_forest_regressor	RandomForestRegressor
extra_trees_regressor	ExtraTreesRegressor
adaboost_regressor	AdaBoostRegressor
gradient_boosting_regressor	GradientBoostingRegressor
hist_gradient_boosting_regressor	HistGradientBoostingRegressor
xgboost_regressor	XGBRegressor
catboost_regressor	CatBoostRegressor

---

Output Format

The result is a dict (or JSON object) keyed by model name, with an additional "average" entry that aggregates across all models. Each value is a list of single-entry dicts sorted by score in descending order. Scores are rounded to four decimal places.

{
  "logistic_regression": [
    {"feature_a": 0.1234},
    {"feature_b": 0.0567},
    {"feature_c": 0.0012}
  ],
  "random_forest": [
    {"feature_b": 0.0890},
    {"feature_a": 0.0745},
    {"feature_c": 0.0023}
  ],
  "average": [
    {"feature_a": 0.0990},
    {"feature_b": 0.0729},
    {"feature_c": 0.0018}
  ]
}

---

Troubleshooting

Symptom	Cause	Fix
Prep file not found	FeatRanker can't locate featureCalc.py	Run the command from the directory that contains featureCalc.py, or pass an explicit path with --prep-file
AttributeError: … has no attribute 'X'	The prep class name doesn't match what's in the file	Check spelling of --prep-class against the class defined in your prep file
'label' key missing	_calc_features() didn't include a "label" entry	Add features["label"] = ... to your return dict
Feature length mismatch	Feature lists have different lengths	Ensure every feature list and "label" have the same number of elements
Model training errors (printed, not fatal)	A model failed to converge or doesn't support the data	Check the printed warning; consider removing or tuning that model in importance_config.yaml