From be1b6a74ae569a3f90fd002be77bc99573ec3a2d Mon Sep 17 00:00:00 2001 From: yulia Date: Fri, 27 Oct 2023 01:23:28 +0400 Subject: [PATCH] podkorytova_yulia_lab1 --- podkorytova_yulia_lab_1/README.md | 37 ++++++++++++++++++++++ podkorytova_yulia_lab_1/lr1.py | 45 +++++++++++++++++++++++++++ podkorytova_yulia_lab_1/plotRes.JPG | Bin 0 -> 123712 bytes podkorytova_yulia_lab_1/scoreRes.JPG | Bin 0 -> 22706 bytes 4 files changed, 82 insertions(+) create mode 100644 podkorytova_yulia_lab_1/README.md create mode 100644 podkorytova_yulia_lab_1/lr1.py create mode 100644 podkorytova_yulia_lab_1/plotRes.JPG create mode 100644 podkorytova_yulia_lab_1/scoreRes.JPG diff --git a/podkorytova_yulia_lab_1/README.md b/podkorytova_yulia_lab_1/README.md new file mode 100644 index 0000000..70b0929 --- /dev/null +++ b/podkorytova_yulia_lab_1/README.md @@ -0,0 +1,37 @@ +# Лабораторная работа 1. Работа с типовыми наборами данных и различными моделями +### Задание на лабораторную: +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. + +**Вариант 20.** +Данные: make_circles (noise=0.2, factor=0.5, random_state=rs) + +Модели: +* Линейную регрессию +* Полиномиальную регрессию (со степенью 5) +* Гребневую полиномиальную регрессию (со степенью 5, alpha= 1.0) +*** +### Как запустить лабораторную работу: +Для запуска лабораторной работы необходимо открыть файл `lr1.py`, нажать на ПКМ и в выпадающем списке выбрать опцию "Run". +*** +### Технологии: +**NumPy (Numerical Python)** - это библиотека для научных вычислений в Python, которая обеспечивает эффективные вычисления и манипуляции с данными. + +**Matplotlib** - это библиотека визуализации данных для языка программирования Python, которая предоставляет широкий спектр инструментов для создания различных типов графиков, диаграмм и визуализаций данных. + +**Scikit-learn (Sklearn)** - это библиотека для языка программирования Python, которая предоставляет инструменты для разработки и применения различных алгоритмов машинного обучения, включая классификацию, регрессию, кластеризацию, снижение размерности и многое другое. Scikit-learn также предлагает функции для предобработки данных, оценки моделей и выбора наилучших параметров. +*** +### Что делает лабораторная работа: +В лабораторной работе генерируется набор данных при помощи функции *make_circles* с параметрами из задания. +Далее происходит разделение данных на обучащий и тестовый наборы при помощи функции *train_test_split*. +После создаются 3 модели (линейная, полиномиальная и гребневая полиномиальная), а дальше происходит обучение моделей, предсказание и оценка качества. + +Результатом работы являются показатели качества моделей, выведенные в консоль, и 3 графика, отображающие данные, их классификацию и границы решения моделей. +*** +### Пример выходных данных: +***Качество моделей:*** +![](scoreRes.jpg) + +***Графики:*** +![](plotRes.jpg) +*** +**Вывод**: результаты показали, что для сгенерированного набора данных из 3 моделей наиболее точной оказалась полиномиальная регрессия (со степенью 5), гребневая полиномиальная регрессия (со степенью 5, alpha= 1.0), которая идентична полиномиальной регрессии, стала второй по точности, а линейная регрессия показала самую низкую точность. \ No newline at end of file diff --git a/podkorytova_yulia_lab_1/lr1.py b/podkorytova_yulia_lab_1/lr1.py new file mode 100644 index 0000000..ea5473c --- /dev/null +++ b/podkorytova_yulia_lab_1/lr1.py @@ -0,0 +1,45 @@ +import numpy as np +from matplotlib import pyplot as plt +from matplotlib.colors import ListedColormap +from sklearn.model_selection import train_test_split +from sklearn.pipeline import make_pipeline +from sklearn.preprocessing import PolynomialFeatures +from sklearn.datasets import make_circles +from sklearn.linear_model import LinearRegression, Ridge + +rs = np.random.RandomState(50) +X, y = make_circles(noise=0.2, factor=0.5, random_state=rs) # генерация данных +X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.4, random_state=42) + +# создание моделей +linear_regression = LinearRegression() +polynomial_regression = make_pipeline(PolynomialFeatures(degree=5), LinearRegression()) +ridge_polynomial_regression = make_pipeline(PolynomialFeatures(degree=5), Ridge(alpha=1.0)) +models = [("Линейная регрессия", linear_regression), + ("Полиномиальная регрессия", polynomial_regression), + ("Гребневая полиномиальная регрессия", ridge_polynomial_regression)] + +# тренируем модель +for name, model in models: + model.fit(X_train, y_train) # обучение модели + y_predict = model.predict(X_test) # предсказание + score = model.score(X_train, y_train) # оценка качества + print(name + ': качество модели = ' + str(score)) + +# построение графиков +cm = plt.cm.RdBu +cm_bright = ListedColormap(['#FF0000', '#0000FF']) +for i, (name, model) in enumerate(models): + current_subplot = plt.subplot(1, 3, i + 1) + h = .02 # шаг регулярной сетки + x0_min, x0_max = X[:, 0].min() - .5, X[:, 0].max() + .5 + x1_min, x1_max = X[:, 1].min() - .5, X[:, 1].max() + .5 + xx0, xx1 = np.meshgrid(np.arange(x0_min, x0_max, h), np.arange(x1_min, x1_max, h)) + Z = model.predict(np.c_[xx0.ravel(), xx1.ravel()]) + Z = Z.reshape(xx0.shape) + current_subplot.contourf(xx0, xx1, Z, cmap=cm, alpha=.8) + current_subplot.scatter(X_train[:, 0], X_train[:, 1], c=y_train, cmap=cm_bright) + current_subplot.scatter(X_test[:, 0], X_test[:, 1], c=y_test, cmap=cm_bright, alpha=0.4) + plt.title(name) + +plt.show() \ No newline at end of file diff --git a/podkorytova_yulia_lab_1/plotRes.JPG b/podkorytova_yulia_lab_1/plotRes.JPG new file mode 100644 index 0000000000000000000000000000000000000000..5bfea2ab34d6fedb5976c25e620d51116f8ee2a0 GIT binary patch literal 123712 zcmeFa2Ut{1wl=yEv;mQvvjjn+faK67vg9aHCC4Vy1ZhCY0s;bx3P=#il5r=iJ}+*|6EWx~kS&YrVB%y{6yd<0{=H6F!*e+bnx9^11S61C%Av{ zB`86kA>#?&ulJSlaeukT6%_%1`1s%MfAcHwlfGm3)35lyQGe9gCjvha z_zeN!E5agDLRX}O#aXXhkrEY?0-c8+Jp%w*JYl>mc(P#R;uLwF!PdjWMM_Z6*@D>q(x!DK`NlHozUJ(`)7Ul<2@Vomsd)$NbJG-<0 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