From d62368540ec3bcfcd9140ba452a46e3bde249dfd Mon Sep 17 00:00:00 2001 From: KondrashinMikhail Date: Sat, 25 Nov 2023 16:34:00 +0400 Subject: [PATCH] kondrashin_mikhail_lab_1_ready --- kondrashin_mikhail_lab_1/README.md | 44 ++++++++++++++++ kondrashin_mikhail_lab_1/funcs.py | 47 ++++++++++++++++++ .../images/greb_polynom.png | Bin 0 -> 53492 bytes kondrashin_mikhail_lab_1/images/linear.png | Bin 0 -> 19316 bytes .../images/polynomial.png | Bin 0 -> 65932 bytes kondrashin_mikhail_lab_1/images/result.png | Bin 0 -> 55256 bytes kondrashin_mikhail_lab_1/main.py | 16 ++++++ 7 files changed, 107 insertions(+) create mode 100644 kondrashin_mikhail_lab_1/README.md create mode 100644 kondrashin_mikhail_lab_1/funcs.py create mode 100644 kondrashin_mikhail_lab_1/images/greb_polynom.png create mode 100644 kondrashin_mikhail_lab_1/images/linear.png create mode 100644 kondrashin_mikhail_lab_1/images/polynomial.png create mode 100644 kondrashin_mikhail_lab_1/images/result.png create mode 100644 kondrashin_mikhail_lab_1/main.py diff --git a/kondrashin_mikhail_lab_1/README.md b/kondrashin_mikhail_lab_1/README.md new file mode 100644 index 0000000..e59ac29 --- /dev/null +++ b/kondrashin_mikhail_lab_1/README.md @@ -0,0 +1,44 @@ +#### Кондрашин Михаил ПИбд-41 + +## Лабораторная работа 1. Работа с типовыми наборами данных и различными моделями + +### Задание: + +**Данные:** make_classification (n_samples=500, n_features=2, n_redundant=0, n_informative=2, random_state=rs, +n_clusters_per_class=1) + +**Модели:** + +* Линейная регрессия +* Полиномиальная регрессия (со степенью 3) +* Гребневая полиномиальная регрессия (со степенью 4, alpha = 1.0) + +### Запуск лабораторной работы: + +* установить `python`, `numpy`, `matplotlib`, `sklearn` +* запустить проект (стартовая точка класс `main.py`) + +### Используемые технологии: + +* Язык программирования `Python`, +* Библиотеки `numpy`, `matplotlib`, `sklearn` +* Среда разработки `IntelliJ IDEA` (В версии "Ultimate edition" можно писать на python) + +### Описание решения: + +* Программа генерирует данные с make_classification (n_samples=500, n_features=2, n_redundant=0, n_informative=2, + random_state=rs, n_clusters_per_class=1) +* Сравнивает три типа моделей: линейная, полиномиальная, гребневая полиномиальная регрессии +* Выдает графики и оценки качества по коэффициенту детерминации для каждой модели + +### Результат: + +![Linear](images/linear.png) +![Polynomial](images/polynomial.png) +![Greb](images/greb_polynom.png) + +* Результат расчета оценки качества: + ![Result](images/result.png) + +По результатам оценки качества можно сказать, что **полиномиальная регрессия** показала наибольшую оценку + diff --git a/kondrashin_mikhail_lab_1/funcs.py b/kondrashin_mikhail_lab_1/funcs.py new file mode 100644 index 0000000..30a91ef --- /dev/null +++ b/kondrashin_mikhail_lab_1/funcs.py @@ -0,0 +1,47 @@ +from matplotlib import pyplot as plt +from sklearn.linear_model import LinearRegression +from sklearn.linear_model import Ridge +from sklearn.pipeline import Pipeline +from sklearn.preprocessing import PolynomialFeatures + + +def lin(x_train, x_test, y_train, y_test): + plt.scatter(x_test, y_test) + model = LinearRegression().fit(x_train, y_train) + y_predict = model.intercept_ + model.coef_ * x_test + plt.plot(x_test, y_predict, color='red') + plt.title('Линейная регрессия') + plt.savefig('images/linear.png') + plt.show() + + print('Линейная регрессия') + print('Оценка качества:', model.score(x_train, y_train)) + + +def polynom(x_train, y_train): + plt.scatter(x_train, y_train) + x_poly = PolynomialFeatures(degree=4).fit_transform(x_train) + pol_reg = LinearRegression() + model = pol_reg.fit(x_poly, y_train) + y_predict = pol_reg.predict(x_poly) + plt.plot(x_train, y_predict, color='green') + plt.title('Полиномиальная регрессия') + plt.savefig('images/polynomial.png') + plt.show() + + print('Полиномиальная регрессия') + print('Оценка качества:', model.score(x_poly, y_train)) + + +def greb_polynom(x_train, x_test, y_train, y_test): + plt.scatter(x_test, y_test) + pipeline = Pipeline([("polynomial_features", PolynomialFeatures(degree=4)), ("ridge", Ridge(alpha=1.0))]) + model = pipeline.fit(x_train, y_train) + y_predict = pipeline.predict(x_test) + plt.plot(x_test, y_predict, 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