woe-credit-scoring 3.0.1

A toolkit for Credit Scoring using Weight of Evidence (WoE) and Logistic Regression

WOE Credit Scoring Toolkit

A production-ready Python library for building Weight of Evidence (WoE) based credit scorecards. Provides a complete pipeline from data profiling and IV analysis to scorecard generation, model serialization, explainability, validation reports, HTML export, and an MCP server for LLM integration.

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Features

• End-to-end pipeline — AutoCreditScoring handles partition, outlier treatment, normalization, feature selection, WoE transformation, logistic regression training, PDO scaling, and reporting in a single call.
• sklearn-compatible — All classes implement get_params() / set_params() and __repr__() for seamless integration with scikit-learn pipelines and grid search.
• Serialization — Save and load fitted models with to_pickle() / from_pickle().
• Isotonic calibration — Calibrate probabilities via predict_proba() with optional isotonic regression.
• Per-observation explainability — explain() returns a feature-level score breakdown (WoE, beta, points) for any single client.
• Feature analysis — feature_analysis() provides IV, number of bins, monotonicity flag, type, and score contribution percentage per feature.
• Built-in plots — plot_roc(), plot_ks(), plot_score_distribution(), plot_iv(), plot_discretized_features(), plot_woe_bins() directly on the fitted model.
• Validation reports — validation_report() returns KS, Gini, AUC train/valid, overfitting gap, confusion matrix, optimal threshold, and cumulative gains chart.
• MCP server — 5 LLM-callable tools: analyze_dataset, calculate_iv, build_scorecard, score_clients, explain_decision.
• HTML export — export_scorecard_html() generates a self-contained report with metrics, scorecard table, and embedded plots.
• EDA module — dataset_profile(), psi(), event_rate_by_feature(), woe_profile(), vif().
• Pydantic v2 models — PipelineConfig, FeatureInfo, ScorecardResult, DatasetProfile for validated configuration and structured results.

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Installation

pip install woe-credit-scoring

To enable the MCP server (LLM integration):

pip install woe-credit-scoring[mcp]

The MCP server is also available as a console script:

woe-mcp

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Quick Start

import pandas as pd
from woe_credit_scoring import (
    AutoCreditScoring, PipelineConfig, dataset_profile,
)

# Load data
train = pd.read_csv("example_data/train.csv")

# Profile the dataset
profile = dataset_profile(train, target="TARGET")
print(f"Rows: {profile['basic_info']['rows']}, Target rate: {profile['target_distribution']['proportion'].values[1]:.2%}")

# Build and fit the model
acs = AutoCreditScoring(
    data=train,
    target="TARGET",
    continuous_features=[c for c in train.columns if c.startswith("C_")],
    discrete_features=[c for c in train.columns if c.startswith("D_")],
)

acs.fit(
    iv_feature_threshold=0.05,
    max_discretization_bins=6,
    strictly_monotonic=True,
    discretization_method="quantile",
    calibrate=True,
    verbose=True,
)

# Score new clients
valid = pd.read_csv("example_data/valid.csv")
scores = acs.predict(valid)
print(scores[["score"]].head())

# Calibrated probabilities
probas = acs.predict_proba(valid)

# Explain a single decision
explanation = acs.explain({
    "C_INCOME": 45000,
    "C_AGE": 34,
    "D_JOB": "Self",
})
print(explanation)

# Plot
acs.plot_roc()
acs.plot_ks()
acs.plot_score_distribution()
acs.plot_iv()

# Validation report
report = acs.validation_report()
print(report.summarize())

# Feature analysis
acs.feature_analysis()

# Serialize
acs.to_pickle("scorecard.pkl")

# Load later
from woe_credit_scoring import AutoCreditScoring
loaded = AutoCreditScoring.from_pickle("scorecard.pkl")

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Pipeline Classes

DiscreteNormalizer

Normalizes discrete features by grouping infrequent categories below a relative frequency threshold into a default bucket ("OTHER" by default). Missing values are assigned to a "MISSING" category. If the default group still falls below the threshold, it is mapped to the most frequent category.

from woe_credit_scoring import DiscreteNormalizer

dn = DiscreteNormalizer(
    normalization_threshold=0.05,
    default_category="OTHER",
)
dn.fit(train[discrete_features])
normalized = dn.transform(valid[discrete_features])

Discretizer

Bins continuous features using uniform, quantile, kmeans, or gaussian strategies. Supports parallel execution via n_threads. The gaussian strategy uses GaussianMixture from scikit-learn. Missing values are handled and binned as "MISSING".

from woe_credit_scoring import Discretizer

disc = Discretizer(
    min_segments=2,
    max_segments=6,
    strategy="quantile",
)
disc.fit(train[continuous_features], n_threads=4)
discretized = disc.transform(valid[continuous_features])

WoeEncoder

Transforms discrete/binned features into Weight of Evidence values. Supports fit(), transform(), and inverse_transform() (back to original categories).

from woe_credit_scoring import WoeEncoder

encoder = WoeEncoder()
encoder.fit(X_discrete, y_target)
X_woe = encoder.transform(X_discrete)
X_back = encoder.inverse_transform(X_woe)

WoeBaseFeatureSelector

Base class providing _information_value() and _check_monotonic() static methods used by both continuous and discrete feature selectors.

WoeContinuousFeatureSelector

Selects continuous features by discretizing them with one or more strategies and ranking by IV. Supports two combination methods:

Method	Description
dcc	Combination of discretizations (selects the best binning per feature across all strategies)
dec	Ensemble of discretizations (selects the best strategy per feature, preserving the method name)

Single-strategy methods: quantile, uniform, kmeans, gaussian.

Can enforce strictly monotonic WoE behavior.

from woe_credit_scoring import WoeContinuousFeatureSelector

selector = WoeContinuousFeatureSelector()
selector.fit(
    X=train[continuous_features],
    y=train["TARGET"],
    method="dcc",
    iv_threshold=0.05,
    max_bins=6,
    strictly_monotonic=True,
    n_threads=4,
)
candidates = selector.transform(valid[continuous_features])

# Inspect IV report
print(selector.iv_report)

WoeDiscreteFeatureSelector

Selects discrete features based on their IV. Evaluates each normalized discrete feature and keeps those exceeding the IV threshold.

from woe_credit_scoring import WoeDiscreteFeatureSelector

selector = WoeDiscreteFeatureSelector()
selector.fit(
    X=normalized_discrete,
    y=train["TARGET"],
    iv_threshold=0.05,
)
candidates = selector.transform(normalized_discrete)
print(selector.selected_features)

CreditScoring

Implements the PDO (Points to Double the Odds) scoring methodology from Siddiqi (2012). Takes a fitted WoeEncoder and LogisticRegression model, and produces a scorecard mapping each feature attribute to points. Scores can be linearly scaled to a custom range.

from woe_credit_scoring import CreditScoring

cs = CreditScoring(
    pdo=20,
    base_score=400,
    base_odds=1,
)
cs.fit(X_woe, woe_encoder, logistic_model)
scored = cs.transform(X_discrete)
print(cs.scorecard)

AutoCreditScoring

Fully automated pipeline class. Handles the entire workflow:

1. Partition — train/validation split with target proportion tolerance
2. Outlier treatment — winsorization via scipy.stats.mstats.winsorize
3. Discrete normalization — infrequent category grouping
4. Feature selection — IV-based filtering for continuous and discrete features
5. WoE transformation — encoding into log-odds space
6. Logistic regression — model training with AUC tracking and overfitting detection
7. PDO scoring — scorecard generation with linear scaling to a target range
8. Optional calibration — isotonic regression for calibrated probabilities

Key methods:

Method	Description
fit(**kwargs)	Train the full pipeline
fit_predict(**kwargs)	Fit and return scores for all data
predict(X)	Score new raw data (returns points per feature + total)
predict_proba(X)	Return calibrated (or raw) probabilities
explain(observation)	Per-feature score breakdown for one client
feature_analysis()	DataFrame with IV, bins, monotonicity, contribution %
validation_report()	ValidationReport object (KS, Gini, AUC, confusion matrix)
plot_roc()	ROC curve for train and validation
plot_ks()	KS chart on validation data
plot_score_distribution()	Score histogram (train + validation)
plot_iv()	IV barplot for selected features
plot_discretized_features()	Event rate bars with WoE annotations per binned feature
plot_woe_bins(feature)	Dual-axis plot (WoE bars + event rate line) for a single feature
to_pickle(path) / from_pickle(path)	Serialization
save_reports(folder)	Save PNG reports (requires create_reporting=True)

IVCalculator

Quick IV calculation without the full pipeline. Handles discretization and normalization internally.

from woe_credit_scoring import IVCalculator

ivc = IVCalculator(
    data=train,
    target="TARGET",
    continuous_features=varc,
    discrete_features=vard,
)
report = ivc.calculate_iv(
    max_discretization_bins=5,
    discretization_method="quantile",
    discrete_normalization_threshold=0.05,
)
print(report)  # columns: feature, iv, feature_type

frequency_table

Prints absolute frequency, relative frequency, and cumulative statistics for one or more columns.

from woe_credit_scoring import frequency_table

frequency_table(train, ["D_JOB", "D_REASON"])

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Modules

eda.py — Exploratory Data Analysis

from woe_credit_scoring import (
    dataset_profile, psi, event_rate_by_feature, woe_profile, vif,
)

Function	Description
dataset_profile(df, target)	Returns rows, columns, memory, missing %, target distribution, feature type counts
psi(expected, actual, feature)	Population Stability Index between two distributions
event_rate_by_feature(df, target, feature)	Event rate per category, sorted descending
woe_profile(df, target, feature)	WoE and IV contribution per category
vif(X)	Variance Inflation Factor for each numeric feature

models.py — Pydantic v2 Models

from woe_credit_scoring import PipelineConfig, FeatureInfo, ScorecardResult, DatasetProfile

Model	Fields
PipelineConfig	iv_threshold, pdo, base_score, base_odds, min_score, max_score, discretization_method, max_discretization_bins, strictly_monotonic, n_threads, treat_outliers, outlier_threshold, target_proportion_tolerance, train_proportion — with full validation
FeatureInfo	feature, iv, feature_type, status
ScorecardResult	features, auc_train, auc_valid, n_features_total, n_features_selected, overfitting_warning, score_range, created_at
DatasetProfile	n_rows, n_columns, n_continuous, n_discrete, target_rate, missing_pct, timestamp

plots.py — Visualization

from woe_credit_scoring import (
    roc_curve_plot, roc_comparison_plot, ks_plot, iv_barplot,
    event_rate_plot, score_distribution_plot, event_rate_by_score_plot,
)

All functions return matplotlib.figure.Figure and accept an optional ax parameter for subplot composition. Uses a violet + blue color palette.

Function	Description
roc_curve_plot(y_true, y_score)	Single ROC curve with AUC
roc_comparison_plot(y_true_train, y_score_train, y_true_valid, y_score_valid)	Train vs. validation ROC overlay
ks_plot(y_true, y_score)	Kolmogorov-Smirnov chart with decile annotation
iv_barplot(iv_report, top_n=15)	Horizontal bar chart of top N features by IV
event_rate_plot(df, target, feature)	Event rate per category with sample size
score_distribution_plot(scores_train, scores_valid)	Score histogram (train + validation)
event_rate_by_score_plot(df, target)	Stacked bar of good/bad proportions by score range

validation.py — Model Validation

from woe_credit_scoring import ValidationReport

Lazily-computed properties: ks, gini, auc_train, auc_valid, overfitting_gap, confusion_matrix, psi_report.

Methods: summarize(), to_dict(), plot_cumulative_gains().

export_html.py — Stakeholder Reporting

from woe_credit_scoring.export_html import export_scorecard_html

acs.fit(...)
export_scorecard_html(acs, "scorecard_report.html")

Generates a self-contained HTML file with:

• Summary metric cards (AUC train, AUC valid, KS)
• Full scorecard table
• Embedded base64 plots (ROC, KS, score distribution, IV, discretized features)

mcp_server.py — LLM Integration

pip install woe-credit-scoring[mcp]
woe-mcp

Exposes 5 tools callable from any MCP-compatible client (Claude Desktop, Continue, etc.):

Tool	Description
analyze_dataset(data_path, target)	Profile a CSV: rows, columns, target distribution, missing values, feature types
calculate_iv(data_path, target, max_bins, method)	IV calculation for all features
build_scorecard(data_path, target, model_path, iv_threshold, max_bins, method, calibrate)	Train and optionally save a scorecard
score_clients(model_path, data_path)	Score new clients using a saved model
explain_decision(model_path, client_data)	Per-feature score breakdown for one client

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Dependencies

• Python >= 3.10
• numpy >= 1.21.0
• pandas >= 1.3.0
• scikit-learn >= 1.0.0
• scipy >= 1.7.0
• matplotlib >= 3.4.0
• seaborn >= 0.11.0
• pydantic >= 2.0 (models)
• fastmcp >= 2.0 (optional, for MCP server)

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Contributing

If you have a suggestion that would make this better, please fork the repo and create a pull request. You can also open an issue with the tag "enhancement".

Don't forget to give the project a star!

1. Fork the Project
2. Create your Feature Branch (git checkout -b feature/AmazingFeature)
3. Commit your Changes (git commit -m 'Add some AmazingFeature')
4. Push to the Branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

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License

Distributed under the GNU General Public License v3.0 License. See LICENSE for more information.

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Contact

Jose G Fuentes — @jgusteacher

Project Link: https://github.com/JGFuentesC/woe_credit_scoring

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Citing

If you use this software in scientific publications, we would appreciate citations to the following paper:

Combination of Unsupervised Discretization Methods for Credit Risk Jose G. Fuentes Cabrera, Hugo A. Perez Vicente, Sebastian Maldonado, Jonas Velasco

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Acknowledgments

• Siddiqi, N. (2012). Credit risk scorecards: developing and implementing intelligent credit scoring (Vol. 3). John Wiley & Sons.. For his amazing textbook.
• @othneildrew. For his amazing README template.
• Demo data. For providing example data.

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