<s>[INST] import os
import time
import requests
import pandas as pd
import math
import numpy as np

# First, I extract data from a .csv file which is a survey where responders are asked about their:
# gambling frequency, response to stress, types of activities they enjoy, and other related attributes.
data = pd.read_csv('gambling_data.csv')

# After manipulating and cleaning the data I come up with 7 relevant features that I believe
# effect the dependent variable (frequency of gambling)
# features = ['age', 'stress_level', 'education_level', 'income_level', 'favorite_activity',
# 'average_spending', 'problematic_behavior']

# I split my data in training and testing sets in a ratio of 80:20 using the following code:
random_seed = 123
X_train, X_test, y_train, y_test = train_test_split(data.drop('frequency_of_gambling', axis=1),
                                                    data['frequency_of_gambling'], test_size=0.2, random_state=random_seed)

# I use these features for a Logistic Regression model to predict the frequency of gambling:
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression

model = LogisticRegression()
model.fit(X_train, y_train)

# Then I make predictions using the model:
predictions = model.predict(X_test)

# Evaluation
from sklearn.metrics import accuracy_score
from sklearn.metrics import classification_report
from sklearn.metrics import confusion_matrix
from sklearn.metrics import roc_auc_score

# Define a function to get the evaluation metrics
def model_evaluation(true_values, predicted_values):
    accuracy = accuracy_score(true_values, predicted_values)
    report = classification_report(true_values, predicted_values)
    cm = confusion_matrix(true_values, predicted_values)
    auc_score = roc_auc_score(true_values, predicted_values)
    return accuracy, report, cm, auc_score

# Use the function to evaluate the model
accuracy, report, cm, auc_score = model_evaluation(y_test, predictions)

# Output the evaluation metrics
print("Accuracy:", accuracy)
print("Classification Report:\n", report)
print("Confusion Matrix:\n", cm)
print("AUC Score:", auc_score)

# And here is where I get stuck.

# I'm trying to perform a feature selection based on a mutual information score. 
# I'm trying to use mutual information to quantify the dependency between my features and 
# my target variable and keep only the features with the highest dependencies.

from sklearn.feature_selection import mutual_info_classif

# Calculate the mutual information between the features and the target variable
mutual_info = mutual_info_classif(X_train, y_train)

# I'm not sure how to use the output of mutual_info_classif
# to select the top 'k' features (let's say k=5).

# I'm also not sure how to use the selected features to retrain the 
# model and evaluate its performance.

# How do I continue from here?[/INST]