ce-library 0.1.1

CEL: Counterfactual Explanations Library

CEL: Counterfactual Explanations Library

A comprehensive Python framework for generating and evaluating counterfactual explanations in machine learning models. CEL (Counterfactual Explanations Library) provides a unified interface for multiple state-of-the-art counterfactual methods, including local (instance-level), global (model-level), and group (cohort-level) approaches.

Overview

Counterfactual explanations offer a way to understand machine learning model decisions by explaining what minimal changes would alter a prediction. This library provides a unified framework for generating, evaluating, and comparing different counterfactual explanation methods across various datasets and model types.

The library includes multiple counterfactual methods, from gradient-based approaches like Wachter to advanced methods using normalizing flows for density estimation. It emphasizes plausibility, ensuring that generated explanations are coherent and realistic within the context of the original data.

Table of Contents

• Key Features
• Installation
• Quick Start
• Library Structure
• Counterfactual Methods
• Datasets
• Models
• Metrics
• Running Experiments
• Documentation
• Contributing
• Citation
• Contact

Key Features

• Multiple CF Method Families: Local, global, and group counterfactual methods
• Normalizing Flow Integration: State-of-the-art density estimation for plausibility
• Comprehensive Metrics: 17+ evaluation metrics for counterfactual quality
• Hydra Configuration: Flexible experiment management with YAML configs
• 18 Built-in Datasets: Classification and regression tasks
• Extensible Architecture: Easy to add new methods, models, and metrics
• PyTorch-based: Modern deep learning framework
• Cross-validation Support: Robust evaluation with k-fold CV
• Preprocessing Pipeline: Composable feature transformations

Installation

Clone the repository and set up the environment:

git clone [e-mail]:ofurman/cel.git
cd cel
./setup_env.sh

Or install dependencies manually with uv:

uv sync

Requirements: Python >= 3.10

Quick Start

import torch
from cel.datasets import FileDataset, MethodDataset
from cel.cf_methods import PPCEF
from cel.models import MaskedAutoregressiveFlow, MLPClassifier
from cel.losses import BinaryDiscLoss
from cel.metrics.orchestrator import MetricsOrchestrator
import numpy as np

# Note: You may need to convert data to float32 for PyTorch compatibility
# X_train = dataset.X_train.astype(np.float32)
# y_train = dataset.y_train.astype(np.float32)

# Load dataset with preprocessing
file_dataset = FileDataset(config_path="config/datasets/moons.yaml")
dataset = MethodDataset(file_dataset=file_dataset)
train_loader = dataset.train_dataloader(batch_size=128, shuffle=True)
test_loader = dataset.test_dataloader(batch_size=128, shuffle=False)

# Train discriminative model (classifier)
disc_model = MLPClassifier(
    num_inputs=dataset.X_train.shape[1],
    num_targets=1,
    hidden_layer_sizes=[256, 256],
    dropout=0.2,
)
disc_model.fit(train_loader, test_loader, epochs=5000, patience=300, lr=1e-3)

# Train generative model (normalizing flow)
gen_model = MaskedAutoregressiveFlow(
    features=dataset.X_train.shape[1],
    hidden_features=8,
    context_features=1,
)
gen_model.fit(train_loader, test_loader, epochs=1000)

# Generate counterfactuals
cf_method = PPCEF(
    gen_model=gen_model,
    disc_model=disc_model,
    disc_model_criterion=BinaryDiscLoss(),
)
log_prob_threshold = torch.quantile(gen_model.predict_log_prob(test_loader), 0.25)
result = cf_method.explain_dataloader(
    test_loader,
    alpha=100,
    log_prob_threshold=log_prob_threshold,
    epochs=4000,
)

# Evaluate results using MetricsOrchestrator
orchestrator = MetricsOrchestrator(
    X_cf=result.x_cfs,
    y_target=result.y_cf_targets,
    X_train=dataset.X_train,
    y_train=dataset.y_train,
    X_test=result.x_origs,
    y_test=result.y_origs,
    gen_model=gen_model,
    disc_model=disc_model,
    continuous_features=dataset.numerical_features_indices,
    categorical_features=dataset.categorical_features_indices,
    prob_plausibility_threshold=log_prob_threshold,
    metrics_conf_path="cel/pipelines/conf/metrics/default.yaml",
)
metrics = orchestrator.calculate_all_metrics()

Library Structure

counterfactuals/
├── cf_methods/           # Counterfactual explanation methods
│   ├── local/            # Instance-level methods (PPCEF, DiCE, WACH, etc.)
│   ├── global_/          # Model-level methods (GLOBE-CE, AReS)
│   └── group/            # Cohort-level methods (GLANCE, T-CREx)
├── models/               # ML models
│   ├── discriminative/   # Classifiers (MLP, LogisticRegression, NODE)
│   ├── generative/       # Density estimators (MAF, RealNVP, NICE, KDE)
│   └── regression/       # Regressors (MLP, LinearRegression)
├── datasets/             # Dataset loading and configuration
├── preprocessing/        # Feature transformation pipeline
├── dequantization/       # Categorical feature handling for flows
├── losses/               # Loss functions for CF optimization
├── metrics/              # Evaluation metrics
├── pipelines/            # Experiment orchestration
│   ├── nodes/            # Pipeline components
│   └── conf/             # Hydra configuration files
├── plotting/             # Visualization utilities
└── utils.py              # Helper functions

config/
└── datasets/             # Dataset YAML configurations (18 datasets)

docs/
├── library_overview.md   # Comprehensive package documentation
└── ppcef_pipeline.md     # Pipeline guide

Counterfactual Methods

Local Methods (Instance-level)

Method	Class	Description
WACH	WACH	Wachter-style gradient-based CF
Artelt	Artelt	Heuristic-based CF method
DiCE	DICE	Diverse Counterfactual Explanations
CCHVAE	CCHVAE	Conditional Heterogeneous VAE
PPCEF	PPCEF	Probabilistically Plausible CF with normalizing flows
CEM	CEM_CF	Contrastive Explanation Method
CEGP	CEGP	Counterfactual with Gaussian Processes
CADEX	CADEX	Counterfactual explanations via optimization
SACE	SACE	Several SACE variants
CEARM	CEARM	Counterfactual explanation through association rule mining

Global Methods (Model-level)

Method	Class	Description
GLOBE-CE	GLOBE_CE	Global Counterfactual Explanations
AReS	AReS	Actionable Recourse Summaries

Group Methods (Cohort-level)

Method	Class	Description
GLANCE	GLANCE	Group-level CF method
T-CREx	TCREx	Temporal Counterfactual Rule Extraction

Datasets

The library includes 18 pre-configured datasets:

Classification (13): adult_census, audit, bank_marketing, blobs, credit_default, digits, german_credit, give_me_some_credit (GMC), heloc, law, lending_club, moons, wine

Regression (5): concrete, diabetes, yacht, synthetic, scm20d

Dataset configurations are in config/datasets/*.yaml and support:

• Automatic feature type detection (continuous/categorical)
• Actionability flags for features
• Cross-validation splits
• Train/test split configuration

Models

Discriminative Models

Model	Class	Use Case
MLP Classifier	MLPClassifier	General classification
Logistic Regression	LogisticRegression	Binary classification
Multinomial LR	MultinomialLogisticRegression	Multiclass
NODE	NODE	Neural Oblivious Decision Ensembles

Generative Models

Model	Class	Description
MAF	MaskedAutoregressiveFlow	Primary normalizing flow
RealNVP	RealNVP	Real-valued Non-Volume Preserving
NICE	NICE	Non-linear Independent Components
KDE	KDE	Kernel Density Estimation baseline

Regression Models

Model	Class
MLP Regressor	MLPRegressor
Linear Regression	LinearRegression

Metrics

The library provides comprehensive evaluation metrics:

Category	Metrics
Validity	coverage, validity, actionability
Sparsity	sparsity
Distance	proximity_continuous_euclidean, proximity_continuous_manhattan, proximity_continuous_mad, proximity_categorical_hamming, proximity_categorical_jaccard, proximity_l2_jaccard, proximity_mad_hamming
Plausibility	prob_plausibility, log_density_cf, log_density_test
Outlier Detection	lof_scores_cf, lof_scores_test, isolation_forest_scores_cf, isolation_forest_scores_test

Running Experiments

Using Hydra Pipelines

# Run PPCEF pipeline
uv run python cel/pipelines/run_ppcef_pipeline.py

# With custom configuration
uv run python cel/pipelines/run_ppcef_pipeline.py \
  dataset.config_path=config/datasets/heloc.yaml \
  disc_model.model=disc_model/mlp_large \
  counterfactuals_params.target_class=1

Available Pipelines

Pipeline	Method
run_ppcef_pipeline.py	PPCEF
run_dice_pipeline.py	DiCE
run_cem_pipeline.py	CEM
run_cchvae_pipeline.py	C-CHVAE
run_wach_pipeline.py	WACH
run_artelt_pipeline.py	Artelt
run_cegp_pipeline.py	CEGP
run_cadex_pipeline.py	CADEX
run_sace_pipeline.py	SACE
run_cearm_pipeline.py	CEARM
run_globe_ce_pipeline.py	GLOBE-CE
run_ares_pipeline.py	AReS
run_glance_pipeline.py	GLANCE
run_tcrex_pipeline.py	T-CREx

Documentation

Live Docs: https://ofurman.github.io/counterfactuals/

Contributing

Contributions are welcome! Before opening a PR:

1. Read AGENTS.md and docs/ppcef_pipeline.md to understand the workflow
2. Use uv for all operations:

   uv sync                     # Install dependencies
   uv run ruff check --fix     # Lint and fix
   uv run pytest               # Run tests
   

3. Follow the coding standards:
   • Python 3.10+, PEP 8 compliant
   • Full type hints everywhere
   • Google-style docstrings
   • Line length: 100 characters
4. Keep patches small and well-documented
5. Update or add tests when behavior changes

To add new dependencies:

uv add <package>

Contact

For questions or comments, please contact via LinkedIn: TBA