Quickstart

This tutorial trains a scikit-learn classifier, fits FPDE prototypes from the training data, and explains one test sample. The example uses the breast cancer dataset bundled with scikit-learn.

Prerequisites

Python 3.12 or newer
fpde
A classifier that exposes predict_proba and classes_

Install FPDE

Install the stable package from PyPI.

python -m pip install fpde

Train a classifier

Train the model on the same feature space you will explain.

from sklearn.datasets import load_breast_cancer
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler

data = load_breast_cancer()
X_train, X_test, y_train, _ = train_test_split(
    data.data,
    data.target,
    test_size=0.25,
    random_state=7,
    stratify=data.target,
)

scaler = StandardScaler()
X_train = scaler.fit_transform(X_train)
X_test = scaler.transform(X_test)

model = LogisticRegression(max_iter=2000, random_state=7)
model.fit(X_train, y_train)

Fit FPDE

Fit reusable class-mean prototypes from the training data.

from fpde import FPDEEngine

engine = FPDEEngine.fit(X_train, y_train, model=model)

Explain one sample

Compute a Hyb-FPDE explanation with a fixed lambda_hyb.

import numpy as np

attributions, details = engine.explain_one(X_test[0], lambda_hyb=0.5)

print(np.asarray(attributions))
print(details["target_label"], details["rival_label"], details["evidence"])

Complete example

import numpy as np
from sklearn.datasets import load_breast_cancer
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler

from fpde import FPDEEngine

data = load_breast_cancer()
X_train, X_test, y_train, _ = train_test_split(
    data.data,
    data.target,
    test_size=0.25,
    random_state=7,
    stratify=data.target,
)

scaler = StandardScaler()
X_train = scaler.fit_transform(X_train)
X_test = scaler.transform(X_test)

model = LogisticRegression(max_iter=2000, random_state=7)
model.fit(X_train, y_train)

engine = FPDEEngine.fit(X_train, y_train, model=model)
attributions, details = engine.explain_one(X_test[0], lambda_hyb=0.5)

print(np.asarray(attributions))
print(details["target_label"], details["rival_label"], details["evidence"])

Interpret the output

details["target_label"] is the model’s highest-probability class. details["rival_label"] is the second-highest-probability class. details["evidence"] is the target-versus-rival contrast decomposed by FPDE.

Positive attribution values support the target class. Negative attribution values support the rival class.

Next steps

Use Explain one sample for a slower walkthrough of the result fields.
Use Explain batches when you need an attribution matrix.
Use Select lambda_hyb to choose a fixed Hyb-FPDE mixture with validation data.

Getting started

Guides

Concepts

Reference

Changelog

Prerequisites

Complete example

Interpret the output

Next steps

​Prerequisites

​Complete example

​Interpret the output

​Next steps

Prerequisites

Complete example

Interpret the output

Next steps