Boost.py

import numpy as np
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.ensemble import AdaBoostClassifier,GradientBoostingClassifier
from xgboost import XGBClassifier
from sklearn.metrics import accuracy_score, classification_report
from sklearn.datasets import make_classification

# Generate synthetic dataset
X, y = make_classification(n_samples=1000, n_features=20,n_informative=15,n_redundant=5, random_state=42)

# Split dataset into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2,
random_state=42)

# 1. Implementing AdaBoost Classifier
ada_boost = AdaBoostClassifier(n_estimators=50, learning_rate=1.0,random_state=42)
ada_boost.fit(X_train, y_train)
y_pred_ada = ada_boost.predict(X_test)

print("AdaBoost Classifier Accuracy:", accuracy_score(y_test,y_pred_ada))
print("AdaBoost Classification Report:\n",classification_report(y_test, y_pred_ada))

# 2. Implementing Gradient Boosting Classifier
grad_boost = GradientBoostingClassifier(n_estimators=100,learning_rate=0.1,max_depth=3, random_state=42)
grad_boost.fit(X_train, y_train)
y_pred_grad = grad_boost.predict(X_test)

print("Gradient Boosting Classifier Accuracy:",accuracy_score(y_test, y_pred_grad))
print("Gradient Boosting Classification Report:\n",classification_report(y_test, y_pred_grad))

# 3. Implementing XGBoost Classifier
xgb_model = XGBClassifier(n_estimators=100, learning_rate=0.1,max_depth=3, random_state=42, eval_metric='logloss')
xgb_model.fit(X_train, y_train)
y_pred_xgb = xgb_model.predict(X_test)

print("XGBoost Classifier Accuracy:", accuracy_score(y_test,y_pred_xgb))

print("XGBoost Classification Report:\n",classification_report(y_test, y_pred_xgb))

# Plot feature importance for Gradient Boosting and XGBoost
plt.figure(figsize=(12, 6))

plt.subplot(1, 2, 1)
plt.barh(range(len(grad_boost.feature_importances_)),grad_boost.feature_importances_)
plt.title("Gradient Boosting Feature Importance")

plt.subplot(1, 2, 2)
plt.barh(range(len(xgb_model.feature_importances_)),
xgb_model.feature_importances_)
plt.title("XGBoost Feature Importance")

plt.show()