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Welcome to NoBias’s documentation!¶

NoBias is a Python library for responsible AI

Note

This project is under active development.

Installation¶

To install nobias, run this command in your terminal:

$ pip install nobias

Usage: Equal Treatment Inspector¶

Synthetic example¶

Importing libraries

from sklearn.model_selection import train_test_split
from sklearn.datasets import make_blobs
from nobias import ExplanationAudit
from xgboost import XGBClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import roc_auc_score
import pandas as pd
import numpy as np
import random
import matplotlib.pyplot as plt
random.seed(0)

Let’s generate a synthetic dataset with a protected attribute and a target variable.

X, y = make_blobs(n_samples=2000, centers=2, n_features=5, random_state=0)
X = pd.DataFrame(X, columns=["a", "b", "c", "d", "e"])
# Protected att
X["a"] = np.where(X["a"] > X["a"].mean(), 1, 0)

# Train Val Holdout Split
X_tr, X_te, y_tr, y_te = train_test_split(X, y, test_size=0.5, random_state=0)
X_hold, X_te, y_hold, y_te = train_test_split(X_te, y_te, test_size=0.5, random_state=0)

z_tr = X_tr["a"]
z_te = X_te["a"]
z_hold = X_hold["a"]
X_tr = X_tr.drop("a", axis=1)
X_te = X_te.drop("a", axis=1)
X_hold = X_hold.drop("a", axis=1)
# Random
z_tr_ = np.random.randint(0, 2, size=X_tr.shape[0])
z_te_ = np.random.randint(0, 2, size=X_te.shape[0])
z_hold_ = np.random.randint(0, 2, size=X_hold.shape[0])

Now there is two training options that are equivalent, either passing a trained model and just training the Inspector

Fit ET Inspector where the classifier is a Gradient Boosting Decision Tree and the Detector a logistic regression. Any other classifier or detector can be used.

# Option 1: fit the auditor when there is a trained model
model = XGBClassifier().fit(X_tr, y_tr)

auditor = ExplanationAudit(model=model, gmodel=LogisticRegression())

auditor.fit_inspector(X_hold, z_hold)
print(roc_auc_score(z_te, auditor.predict_proba(X_te)[:, 1]))
# 0.84

Or fit the whole pipeline without previous retraining. If the AUC is above 0.5 then we can expect and change on the model predictions.

# Option 2: fit the whole pipeline of model and auditor at once
auditor.fit_pipeline(X=X_tr, y=y_tr, z=z_tr)
print(roc_auc_score(z_te, auditor.predict_proba(X_te)[:, 1]))
# 0.83

# If we fit to random protected att, there is no performance
# We fit in the previous generated random data
auditor.fit_pipeline(X=X_tr, y=y_tr, z=z_tr_)
print(roc_auc_score(z_te_, auditor.predict_proba(X_te)[:, 1]))
# 0.5

Features¶

Here’s a list of features that sktools currently offers:

nobias.audits.ExplanationAudit performs equal treatment audits.
nobias.distributionshift.ExplanationShiftDetector Detector for explanation shift.

Welcome to NoBias’s documentation!¶

Installation¶

Usage: Equal Treatment Inspector¶

Synthetic example¶

Features¶

Tutorial¶

Documentation¶

Indices and tables¶