From 1324019882a293080246ba74cab5befa6dc795f9 Mon Sep 17 00:00:00 2001 From: Nelikey Date: Fri, 12 Jan 2024 11:28:32 +0400 Subject: [PATCH] lab1 --- kozlov_alexey_lab_1/README.md | 62 ++++++++++++++++++++++++++++++++ kozlov_alexey_lab_1/lab1.py | 64 ++++++++++++++++++++++++++++++++++ kozlov_alexey_lab_1/lr.png | Bin 0 -> 34056 bytes kozlov_alexey_lab_1/pers.png | Bin 0 -> 51290 bytes kozlov_alexey_lab_1/pr.png | Bin 0 -> 51498 bytes 5 files changed, 126 insertions(+) create mode 100644 kozlov_alexey_lab_1/README.md create mode 100644 kozlov_alexey_lab_1/lab1.py create mode 100644 kozlov_alexey_lab_1/lr.png create mode 100644 kozlov_alexey_lab_1/pers.png create mode 100644 kozlov_alexey_lab_1/pr.png diff --git a/kozlov_alexey_lab_1/README.md b/kozlov_alexey_lab_1/README.md new file mode 100644 index 0000000..ea3c96b --- /dev/null +++ b/kozlov_alexey_lab_1/README.md @@ -0,0 +1,62 @@ +# Лабораторная работа №1. Работа с типовыми наборами данных и различными моделями +## 14 вариант +___ + +### Задание: +Используя код из пункта «Регуляризация и сеть прямого распространения», сгенерируйте определенный тип данных и сравните на нем 3 модели (по варианту). Постройте графики, отобразите качество моделей, объясните полученные результаты. + +### Данные по варианту: +- make_circles (noise=0.2, factor=0.5, random_state=rs) + +### Модели по варианту: +- Линейная регрессия +- Персептрон +- Гребневая полиномиальная регрессия (со степенью 4, alpha = 1.0) + +___ + +### Запуск +- Запустить файл lab1.py + +### Используемые технологии +- Язык программирования **Python** +- Среда разработки **PyCharm** +- Библиотеки: + * sklearn + * matplotlib + +### Описание программы +Программа генерирует набор данных с помощью функции make_circles() +с заданными по варианту параметрами. После этого происходит создание и обучение моделй, вывод в консоль +качества данных моделей по варианту и построение графикиков для этих моделей. + +Оценка точности происходит при помощи MAE (средняя абсолютная ошибка, измеряет среднюю абсолютную разницу между прогнозируемыми значениями модели и фактическими значениями целевой переменной) и MSE (средняя квадратическая ошибка, измеряет среднюю квадратичную разницу между прогнозируемыми значениями модели и фактическими значениями целевой переменной). Модель с наименьшими показателями MSE и MAE считается наиболее приспособленной к задаче предсказания. + +___ +### Пример работы + +![Graphics](lr.png) +```text +===> Линейная регрессия <=== +MAE 0.5039063025033765 +MSE 0.254199973993164 +``` +___ + +![Graphics](pers.png) +```text +===> Персептрон <=== +MAE 0.5 +MSE 0.5 +``` +___ + +![Graphics](pr.png) +```text +===> Гребневая полиномиальная регрессия <=== +MAE 0.24796914724994906 +MSE 0.07704666136671298 +``` + +### Вывод +Моделью с наименьшими значениями MAE и MSE оказалась модель гребневой полиномиальной регресссии, следоватьельно ее можно назвать наиболее подходящей для задачи регрессии при данной конфигурации исходных данных. \ No newline at end of file diff --git a/kozlov_alexey_lab_1/lab1.py b/kozlov_alexey_lab_1/lab1.py new file mode 100644 index 0000000..776927f --- /dev/null +++ b/kozlov_alexey_lab_1/lab1.py @@ -0,0 +1,64 @@ +from matplotlib import pyplot as plt +from matplotlib.colors import ListedColormap +from sklearn.linear_model import LinearRegression, Ridge, Perceptron +from sklearn.model_selection import train_test_split +from sklearn.pipeline import Pipeline +from sklearn.preprocessing import PolynomialFeatures +from sklearn.datasets import make_circles +from sklearn import metrics + +cm_bright = ListedColormap(['#8B0000', '#FF0000']) +cm_bright1 = ListedColormap(['#FF4500', '#FFA500']) + + +def create_circles(): + x, y = make_circles(noise=0.2, factor=0.5, random_state=0) + X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=42) + + linear_regression(X_train, X_test, y_train, y_test) + perceptron(X_train, X_test, y_train, y_test) + ridge_regression(X_train, X_test, y_train, y_test) + + +def linear_regression(x_train, x_test, y_train, y_test): + model = LinearRegression().fit(x_train, y_train) + y_predict = model.intercept_ + model.coef_ * x_test + plt.title('Линейная регрессия') + print('Линейная регрессия') + plt.scatter(x_train[:, 0], x_train[:, 1], c=y_train, cmap=cm_bright) + plt.scatter(x_test[:, 0], x_test[:, 1], c=y_test, cmap=cm_bright1, alpha=0.7) + plt.plot(x_test, y_predict, color='red') + print('MAE', metrics.mean_absolute_error(y_test, y_predict[:, 1])) + print('MSE', metrics.mean_squared_error(y_test, y_predict[:, 1])) + plt.show() + + +def perceptron(x_train, x_test, y_train, y_test): + model = Perceptron() + model.fit(x_train, y_train) + y_predict = model.predict(x_test) + plt.title('Персептрон') + plt.scatter(x_train[:, 0], x_train[:, 1], c=y_train, cmap=cm_bright) + plt.scatter(x_test[:, 0], x_test[:, 1], c=y_test, cmap=cm_bright1, alpha=0.8) + plt.plot(x_test, y_predict, color='red', linewidth=1) + plt.show() + print('Персептрон') + print('MAE', metrics.mean_absolute_error(y_test, y_predict)) + print('MSE', metrics.mean_squared_error(y_test, y_predict)) + + +def ridge_regression(X_train, X_test, y_train, y_test): + model = Pipeline([('poly', PolynomialFeatures(degree=3)), ('ridge', Ridge(alpha=1.0))]) + model.fit(X_train, y_train) + y_predict = model.predict(X_test) + plt.title('Гребневая полиномиальная регрессия') + plt.scatter(X_train[:, 0], X_train[:, 1], c=y_train, cmap=cm_bright) + plt.scatter(X_test[:, 0], X_test[:, 1], c=y_test, cmap=cm_bright1, alpha=0.7) + plt.plot(X_test, y_predict, color='blue') + plt.show() + print('Гребневая полиномиальная регрессия') + print('MAE', metrics.mean_absolute_error(y_test, y_predict)) + print('MSE', metrics.mean_squared_error(y_test, y_predict)) + + +create_circles() \ No newline at end of file diff --git a/kozlov_alexey_lab_1/lr.png b/kozlov_alexey_lab_1/lr.png new file mode 100644 index 0000000000000000000000000000000000000000..99a7ce58cc65ea41a15c06e90b39d3703583b99d GIT binary patch literal 34056 zcmd?R^;?x)6fH_AA|)W9bPFh*(%m7_9ZE=Sx=UJ7x*KU}iA~ogL{gDWv*{2v-FX+^ zIrrT2!#RJ!<#~7ncD?UfbIm#C7-RZLLros{@$<(hC@8p!3No4~C}=?_D5#5%9)iE{ z{hnI|{|LFs>bbpzSh;zcySztHHFtBehq&3>TF`jBcX73aIP$OyvU9(rv2k;Aa(&Ig 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