From 1f35af8f8fa64bb53c40dce267960eed045aeaec Mon Sep 17 00:00:00 2001 From: admal Date: Wed, 1 Nov 2023 23:53:45 +0400 Subject: [PATCH] lab1 ready --- malkova_anastasia_lab_1/README.md | 44 ++++++++++++++++++ malkova_anastasia_lab_1/dataset.py | 16 +++++++ malkova_anastasia_lab_1/main.py | 19 ++++++++ malkova_anastasia_lab_1/models.py | 37 +++++++++++++++ malkova_anastasia_lab_1/plots.jpg | Bin 0 -> 198557 bytes malkova_anastasia_lab_1/plots.py | 71 +++++++++++++++++++++++++++++ 6 files changed, 187 insertions(+) create mode 100644 malkova_anastasia_lab_1/README.md create mode 100644 malkova_anastasia_lab_1/dataset.py create mode 100644 malkova_anastasia_lab_1/main.py create mode 100644 malkova_anastasia_lab_1/models.py create mode 100644 malkova_anastasia_lab_1/plots.jpg create mode 100644 malkova_anastasia_lab_1/plots.py diff --git a/malkova_anastasia_lab_1/README.md b/malkova_anastasia_lab_1/README.md new file mode 100644 index 0000000..a0f26a5 --- /dev/null +++ b/malkova_anastasia_lab_1/README.md @@ -0,0 +1,44 @@ +# Лабораторная работа №1 + +> Работа с типовыми наборами данных и различными моделями + +# Задание + +Сгенерировать определённый тип данных, сравнить на нём разные модели и отобразить качество на графиках. + +Данные: make_classification (n_samples=500, n_features=2, n_redundant=0, n_informative=2, random_state=rs, n_clusters_per_class=1) +Модели: +* Линейную регрессию +* Персептрон +* Гребневую полиномиальную регрессию (со степенью 3, alpha= 1.0) + +### Как запустить лабораторную работу + +1. Установить python, numpy, sklearn, matplotlib +2. Запустить команду `python main.py` в корне проекта + +### Использованные технологии + +* Язык программирования `python` +* Библиотеки `numpy, sklearn, matplotlib` +* Среда разработки `PyCharm` + +### Что делает программа? + +Генерирует набор данных для классификации с помощью make_classification. +Обучает на них 3 модели: + +- Линейную регрессию +- Персептрон +- Гребневую полиномиальную регрессию (со степенью 3, alpha = 1.0) + +Собирает итоговые оценки моделей: + +- Линейная регрессия - коэффициент детерминации R2 +- Персептрон - средняя точность по заданным тестовым данным +- Гребневая полиномиальная регрессия - Перекрёстная проверка + +![plots screen](plots.jpg) + +Лучший результат показала модель персептрона + diff --git a/malkova_anastasia_lab_1/dataset.py b/malkova_anastasia_lab_1/dataset.py new file mode 100644 index 0000000..547d726 --- /dev/null +++ b/malkova_anastasia_lab_1/dataset.py @@ -0,0 +1,16 @@ +import numpy as np +from sklearn.datasets import make_classification +from sklearn.model_selection import train_test_split + + +def generate_dataset(): + x, y = make_classification(n_samples=500, n_features=2, n_redundant=0, + n_informative=2, random_state=0, n_clusters_per_class=1) + random = np.random.RandomState(2) + x += 2.5 * random.uniform(size=x.shape) + return x, y + + +def split_dataset(x, y): + return train_test_split( + x, y, test_size=.05, random_state=42) diff --git a/malkova_anastasia_lab_1/main.py b/malkova_anastasia_lab_1/main.py new file mode 100644 index 0000000..0e12982 --- /dev/null +++ b/malkova_anastasia_lab_1/main.py @@ -0,0 +1,19 @@ +from dataset import generate_dataset, split_dataset +from models import launch_linear_regression, launch_perceptron, launch_ridge_poly_regression +from plots import show_plot + +x, y = generate_dataset() + +x_train, x_test, y_train, y_test = split_dataset(x, y) + +my_linear_model, linear_model_score = launch_linear_regression( + x_train, x_test, y_train, y_test) +my_perceptron_model, perceptron_model_score = launch_perceptron( + x_train, x_test, y_train, y_test) +my_polynomial_model, polynomial_model_score = launch_ridge_poly_regression( + x_train, x_test, y_train, y_test) + +show_plot(x, x_train, x_test, y_train, y_test, + my_linear_model, linear_model_score, + my_perceptron_model, perceptron_model_score, + my_polynomial_model, polynomial_model_score) diff --git a/malkova_anastasia_lab_1/models.py b/malkova_anastasia_lab_1/models.py new file mode 100644 index 0000000..97fa9ec --- /dev/null +++ b/malkova_anastasia_lab_1/models.py @@ -0,0 +1,37 @@ +from sklearn.linear_model import LinearRegression, Perceptron, Ridge +from sklearn.preprocessing import PolynomialFeatures +from sklearn.model_selection import cross_val_score +from sklearn.pipeline import Pipeline + + +def launch_linear_regression(x_train, x_test, y_train, y_test): + my_linear_model = LinearRegression() + my_linear_model.fit(x_train, y_train) + linear_model_score = my_linear_model.score( + x_test, y_test) + print('linear_model_score: ', linear_model_score) + return my_linear_model, linear_model_score + + +# Perceptron +def launch_perceptron(x_train, x_test, y_train, y_test): + my_perceptron_model = Perceptron() + my_perceptron_model.fit(x_train, y_train) + perceptron_model_score = my_perceptron_model.score( + x_test, y_test) + print('perceptron_model_score: ', perceptron_model_score) + return my_perceptron_model, perceptron_model_score + + +# RidgePolyRegression +def launch_ridge_poly_regression(x_train, x_test, y_train, y_test): + my_polynomial_model = PolynomialFeatures(degree=3, include_bias=False) + ridge = Ridge(alpha=1) + pipeline = Pipeline( + [("polynomial_features", my_polynomial_model), ("ridge_regression", ridge)]) + pipeline.fit(x_train, y_train) + scores = cross_val_score(pipeline, x_test, y_test, + scoring="neg_mean_squared_error", cv=5) + polynomial_model_score = -scores.mean() + print('mean polynomial_model_score: ', polynomial_model_score) + return my_polynomial_model, 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