From c24c21caf3d7220d8149448fd855e2c0c0722d94 Mon Sep 17 00:00:00 2001 From: KirillTepechin Date: Wed, 29 Nov 2023 17:43:31 +0400 Subject: [PATCH] tepechin_kirill_lab_2 --- tepechin_kirill_lab_2/README.md | 27 +++++++++++ tepechin_kirill_lab_2/lab2.py | 75 ++++++++++++++++++++++++++++++ tepechin_kirill_lab_2/results.png | Bin 0 -> 72506 bytes 3 files changed, 102 insertions(+) create mode 100644 tepechin_kirill_lab_2/README.md create mode 100644 tepechin_kirill_lab_2/lab2.py create mode 100644 tepechin_kirill_lab_2/results.png diff --git a/tepechin_kirill_lab_2/README.md b/tepechin_kirill_lab_2/README.md new file mode 100644 index 0000000..3245653 --- /dev/null +++ b/tepechin_kirill_lab_2/README.md @@ -0,0 +1,27 @@ +## Лабораторная работа №2, ПИбд-42 Тепечин Кирилл, Вариант 26(6) + +### Задание + +#### Модели: +* Гребневая регрессия (Ridge) +* Сокращение признаков случайными деревьями (Random Forest Regressor) +* Линейная корреляция (f_regression) + +### Как запустить лабораторную работу: + +Для запуска лабораторной работы необходимо запустить файл lab2.py + +### Используемые технологии: + +* Python 3.12 +* numpy +* scikit-learn + +### Что делает лабораторная работа: + +Эта лабораторная работа генерирует данные, создает и обучает три модели по варианту, далее выполняется ранжирование признаков и отображение получившиеся оценки каждой +признака каждой моделью и среднюю оценку. Также отображаются 4 самых важных признака. + +### Результат: +![Результат](results.png) + diff --git a/tepechin_kirill_lab_2/lab2.py b/tepechin_kirill_lab_2/lab2.py new file mode 100644 index 0000000..82b2d72 --- /dev/null +++ b/tepechin_kirill_lab_2/lab2.py @@ -0,0 +1,75 @@ +import numpy as np +from sklearn.ensemble import RandomForestRegressor +from sklearn.feature_selection import f_regression +from sklearn.linear_model import Ridge +from sklearn.preprocessing import MinMaxScaler + + +def rank_to_dict(ranks, names): + ranks = np.abs(ranks) + minmax = MinMaxScaler() + ranks = minmax.fit_transform(np.array(ranks).reshape(14, 1)).ravel() + ranks = map(lambda x: round(x, 2), ranks) + return dict(zip(names, ranks)) + + +# генерируем исходные данные: 750 строк-наблюдений и 14 столбцов-признаков +np.random.seed(0) +size = 750 +X = np.random.uniform(0, 1, (size, 14)) +# Задаем функцию-выход: регрессионную проблему Фридмана +Y = (10 * np.sin(np.pi * X[:, 0] * X[:, 1]) + 20 * (X[:, 2] - .5) ** 2 + + 10 * X[:, 3] + 5 * X[:, 4] ** 5 + np.random.normal(0, 1)) +# Добавляем зависимость признаков +X[:, 10:] = X[:, :4] + np.random.normal(0, .025, (size, 4)) + + +# Гребневая регрессия +ridge = Ridge(alpha=7) +ridge.fit(X, Y) +# Сокращение признаков случайными деревьями +rf = RandomForestRegressor() +rf.fit(X, Y) +# Линейная корреляция +f_values, _ = f_regression(X, Y) + +names = ["x%s" % i for i in range(1, 15)] + +ranks = dict() +ranks["Гребневая регрессия"] = rank_to_dict(ridge.coef_, names) +ranks["Сокращение признаков случайными деревьями"] = rank_to_dict(rf.feature_importances_, names) +ranks["Линейная корреляция"] = rank_to_dict(f_values, names) + +# Создаем пустой список для данных +mean = {} +# «Бежим» по списку ranks +for key, value in ranks.items(): + for item in value.items(): + if item[0] not in mean: + mean[item[0]] = 0 + mean[item[0]] += item[1] +for key, value in mean.items(): + res = value / len(ranks) + mean[key] = round(res, 2) + +mean = sorted(mean.items(), key=lambda x: x[1], reverse=True) +print("Средние оценки:") +print(mean) +print() + +for key, value in ranks.items(): + ranks[key] = sorted(value.items(), key=lambda x: x[1], reverse=True) +for key, value in ranks.items(): + print(key) + print(value) + +print() + +print("Средние оценки:") +print(mean[:4]) + +for key, value in ranks.items(): + print(f"Главные признаки для модели '{key}':") + print(value[:4]) + + diff --git a/tepechin_kirill_lab_2/results.png b/tepechin_kirill_lab_2/results.png new file mode 100644 index 0000000000000000000000000000000000000000..8c6376fbfca0970a4f36294876fcc2e781951f32 GIT binary patch literal 72506 zcmeFZXFyYFxA=?W2s0u&BPt34Gme0Q5b4rYL{zHu5)!0$qy(f}aRd<&F|<%bIt1w? zKw_gyB8CuJhzcQ)P=pX5fsnhSuR8BJ=RNm+xc~d<<`a9fv-h*tdY)&s-^x8pb7PU6 z;yVQd1Vk=gIAy20@npDo;zc64Khb&TU&eccuR{0k52Bq z)%!Ow#FtSv82EGZS%2HlQq4|^Lr-DQipG}re(glNBxl898`Le~djWq2u{WVL!FzXk zXkA2#nm>`nOG~NtqFRvYYf*@Fs@WqR4VkF~JnrkKz-ANsM*7>4fB?@+?Zx*aODmu2 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