import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score, classification_report, confusion_matrix
import matplotlib.pyplot as plt
from sklearn.datasets import load_iris

data = load_iris()
X = data.data
y = data.target

binary_indices = np.where(y != 2)
X = X[binary_indices]
y = y[binary_indices]

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)

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

y_pred = model.predict(X_test)

accuracy = accuracy_score(y_test, y_pred)
print(f'Accuracy: {accuracy:.2f}')

print('Classification Report:')
print(classification_report(y_test, y_pred))

print('Confusion Matrix:')
print(confusion_matrix(y_test, y_pred))
X_train_2D = X_train[:, :2]
X_test_2D = X_test[:, :2]

model_2D = LogisticRegression()
model_2D.fit(X_train_2D, y_train)

h = .02
x_min, x_max = X_train_2D[:, 0].min() - 1, X_train_2D[:, 0].max() + 1
y_min, y_max = X_train_2D[:, 1].min() - 1, X_train_2D[:, 1].max() + 1
xx, yy = np.meshgrid(np.arange(x_min, x_max, h),
                     np.arange(y_min, y_max, h))
Z = model_2D.predict(np.c_[xx.ravel(), yy.ravel()])
Z = Z.reshape(xx.shape)

plt.contourf(xx, yy, Z, alpha=0.8)
plt.scatter(X_train_2D[:, 0], X_train_2D[:, 1], c=y_train, edgecolor='k', marker='o')
plt.xlabel('Feature 1')
plt.ylabel('Feature 2')
plt.title('Decision Boundary')
plt.show()