Patrik Kenfack

!pip install fairtfm

Collecting fairtfm

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Installing collected packages: mpmath, tqdm, sympy, setuptools, networkx, narwhals, hf-xet, fsspec, filelock, click, torch, fairlearn, huggingface_hub, fairtfm

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Successfully installed click-8.5.0 fairlearn-0.14.0 fairtfm-1.0.0 filelock-3.32.6 fsspec-2026.7.0 hf-xet-1.6.0 huggingface_hub-1.30.0 mpmath-1.3.0 narwhals-2.26.0 networkx-3.4.2 setuptools-84.0.0 sympy-1.14.0 torch-2.14.0 tqdm-4.70.0



[notice] A new release of pip is available: 25.1.1 -> 26.2.1

[notice] To update, run: pip install --upgrade pip

from fairtfm import FairTFMClassifier

from fairlearn.datasets import fetch_adult
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler, LabelEncoder

from sklearn.linear_model import LogisticRegression
import pandas as pd

X_raw, y = fetch_adult(as_frame=True, return_X_y=True)

X_raw.head(5)

	age	workclass	fnlwgt	education	education-num	marital-status	occupation	relationship	race	sex	capital-gain	hours-per-week	native-country
0	25	Private	226802	11th	7	Never-married	Machine-op-inspct	Own-child	Black	Male	0	40	United-States
1	38	Private	89814	HS-grad	9	Married-civ-spouse	Farming-fishing	Husband	White	Male	0	50	United-States
2	28	Local-gov	336951	Assoc-acdm	12	Married-civ-spouse	Protective-serv	Husband	White	Male	0	40	United-States
3	44	Private	160323	Some-college	10	Married-civ-spouse	Machine-op-inspct	Husband	Black	Male	7688	40	United-States
4	18	NaN	103497	Some-college	10	Never-married	NaN	Own-child	White	Female	0	30	United-States

s = X_raw['sex']
X = X_raw.drop(columns=['sex'])
X = pd.get_dummies(X, drop_first=True)
 
y = LabelEncoder().fit_transform(y)
s = LabelEncoder().fit_transform(s)





X_train, X_test, y_train, y_test, s_train, s_test = train_test_split(X, y, s, test_size=0.3, random_state=42)

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

# subsample for faster inference
train_sample_size, test_sample_size = 1000, 500 
y_train = y_train[:train_sample_size] 
s_train = s_train[:train_sample_size]
X_train = X_train[:train_sample_size]
X_test = X_test[:test_sample_size]
y_test = y_test[:test_sample_size]
s_test = s_test[:test_sample_size]

lr = LogisticRegression()

lr.fit(X_train, y_train)
y_prob = lr.predict_proba(X_test)[:, 1]
y_pred = (y_prob >= 0.5).astype(int)

from fairlearn.metrics import demographic_parity_difference 
from sklearn.metrics import accuracy_score, roc_auc_score

acc= accuracy_score(y_test, y_pred)
dp_diff = demographic_parity_difference(y_test, y_pred, sensitive_features=s_test)
auc = roc_auc_score(y_test, y_prob)

print(f"Accuracy: {acc:.4f}")
print(f"Demographic Parity Difference: {dp_diff:.4f}")
print(f"AUC: {auc:.4f}")

Accuracy: 0.8240
Demographic Parity Difference: 0.0823
AUC: 0.8661