# Complex demo: Load Iris, split, classify, evaluate with accuracy/confusion matrix/precision/recall, visualize heatmap
import pandas as pd
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score, confusion_matrix, precision_score, recall_score
import matplotlib.pyplot as plt
import seaborn as sns

def advanced_classification_eval():
    # Load and prep
    iris = load_iris()
    df = pd.DataFrame(iris.data, columns=iris.feature_names)
    df['Target'] = iris.target
    X = df.drop('Target', axis=1)
    y = df['Target']
    # Split
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42, stratify=y)
    # Train and predict
    model = RandomForestClassifier(n_estimators=100, random_state=42)
    model.fit(X_train, y_train)
    y_pred = model.predict(X_test)
    # Evaluate with multiple metrics
    acc = accuracy_score(y_test, y_pred)
    cm = confusion_matrix(y_test, y_pred)
    precision = precision_score(y_test, y_pred, average='weighted')
    recall = recall_score(y_test, y_pred, average='weighted')
    print(f"Accuracy: {acc:.2f}")
    print(f"Precision: {precision:.2f}")
    print(f"Recall: {recall:.2f}")
    print("Confusion Matrix:\n", cm)
    # Visualize confusion matrix with heatmap
    sns.heatmap(cm, annot=True, cmap='Blues', fmt='d')
    plt.title('Confusion Matrix for Iris Classification')
    plt.xlabel('Predicted')
    plt.ylabel('True')
    plt.annotate('High Accuracy', xy=(1.5, 0.5), xytext=(2.5, 1.5), arrowprops=dict(facecolor='black', shrink=0.05))
    plt.show()

advanced_classification_eval()