Facing issue while using Tpot #1378

!pip install tpot[sklearnex]


import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.metrics import roc_auc_score

import tpot
from tpot import TPOTClassifier

df = df_copy

# 1. Preprocess anxiety column
df['anxiety_binary'] = df['anxiety'].apply(lambda x: 0 if x == 0 else 1)

# 2. Define features and target
X = df.drop(columns=['anxiety', 'anxiety_binary'])  # Drop original and binary if you want
y = df['anxiety_binary']

# 3. Split into train and test sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42, stratify=y)

# 4. Train model

# Option 1: TPOT
tpot = TPOTClassifier(generations=5, population_size=20, verbosity=2, random_state=42, scoring='roc_auc')
tpot.fit(X_train, y_train)
y_pred_proba = tpot.predict_proba(X_test)[:, 1]  # probability of class 1

roc_score = roc_auc_score(y_test, y_pred_proba)
print(f"ROC-AUC Score: {roc_score:.4f}")