IIS_2023_1/savenkov_alexander_lab_1/app.py

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2023-10-12 15:17:22 +04:00
import numpy as np
from flask import Flask, request, render_template
from sklearn.datasets import make_moons
from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import PolynomialFeatures
from sklearn.linear_model import Ridge
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
app = Flask(__name__)
@app.route('/')
def home():
return render_template('index.html')
@app.route('/compare_models', methods=['POST'])
def compare_models():
# Генерация данных
rs = 0
X, y = make_moons(noise=0.3, random_state=rs)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.4, random_state=rs)
# Линейная регрессия
lr = LinearRegression()
lr.fit(X_train, y_train)
lr_score = lr.score(X_test, y_test)
# Полиномиальная регрессия (степень 3)
poly = PolynomialFeatures(degree=3)
X_poly = poly.fit_transform(X_train)
poly_reg = LinearRegression()
poly_reg.fit(X_poly, y_train)
poly_score = poly_reg.score(poly.transform(X_test), y_test)
# Гребневая полиномиальная регрессия (степень 3, alpha=1.0)
ridge = Ridge(alpha=1.0)
ridge.fit(X_poly, y_train)
ridge_score = ridge.score(poly.transform(X_test), y_test)
# Создание графиков
plt.figure(figsize=(12, 4))
plt.subplot(131)
plt.scatter(X_test[:, 0], X_test[:, 1], c=y_test, cmap=plt.cm.RdBu)
plt.title('Линейная регрессия\n(Score: {:.2f})'.format(lr_score))
plt.subplot(132)
plt.scatter(X_test[:, 0], X_test[:, 1], c=y_test, cmap=plt.cm.RdBu)
plt.title('Полиномиальная регрессия\n(Score: {:.2f})'.format(poly_score))
plt.subplot(133)
plt.scatter(X_test[:, 0], X_test[:, 1], c=y_test, cmap=plt.cm.RdBu)
plt.title('Гребневая полиномиальная регрессия\n(Score: {:.2f})'.format(ridge_score))
plt.tight_layout()
plt.savefig('static/models_comparison.png')
return render_template('index.html', result=True)
if __name__ == '__main__':
app.run(debug=True)