pbiecek / challenges-xai-aging-aaai23

Supplementary materials to "Challenges facing the explainability of age prediction models: case study for two modalities"

Challenges facing the explainability of age prediction models: case study for two modalities

by Mikolaj Spytek, Weronika Hryniewska, Jarosław Żygierewicz, Jacek Rogala, Przemyslaw Biecek

Supplementary materials

EEG

Models

• Linear Regression Model
• Multi Layer Perceptron

Sample data for inference

• 10 sample EEG Recordings

Code example

import pickle
import pandas as pd

from sklearn.linear_model import LinearRegression
from sklearn.neural_network import MLPRegressor


data = pd.read_csv("sample_data.csv", index_col=0)

X = data.iloc[:, 1:].values
y = data.iloc[:, 0].values

with open("LinearRegression.obj", "rb") as f:
    linreg = pickle.load(f)

with open("MultiLayerPerceptron.obj", "rb") as f:
    mlp = pickle.load(f)


prediction_linreg = linreg.predict(X)
prediction_mlp = mlp.predict(X)

X-ray

Model

• XGBoost Regression Model
• Catboost Regression Model

Sample data for inference

• sample X-ray embeddings

Code example

import joblib
import pandas as pd

import xgboost as xgb
from catboost import CatBoostRegressor

data = pd.read_csv("sample_data_xray.csv")

X = data.iloc[:, 3:].values
y = data.iloc[:, 2].values

xgb = xgb.XGBRegressor()
xgb.load_model("model_xgb_regressor.json")

with open("model_catboost_regressor.json", "rb") as f:
    cat = joblib.load(f)

prediction_xgb = xgb.predict(X)
prediction_cat = cat.predict(X)