From 23bc64c816796f3ee3fba58a3b71ef23203ab80d Mon Sep 17 00:00:00 2001 From: yulia Date: Fri, 27 Oct 2023 05:35:21 +0400 Subject: [PATCH] podkorytova_yulia_lab2 --- podkorytova_yulia_lab_2/README.md | 42 +++++++++++ podkorytova_yulia_lab_2/f_regression.JPG | Bin 0 -> 20129 bytes podkorytova_yulia_lab_2/lasso.JPG | Bin 0 -> 18488 bytes podkorytova_yulia_lab_2/linear_regression.JPG | Bin 0 -> 21914 bytes podkorytova_yulia_lab_2/lr2.py | 67 ++++++++++++++++++ podkorytova_yulia_lab_2/mean.JPG | Bin 0 -> 21071 bytes podkorytova_yulia_lab_2/ridge.JPG | Bin 0 -> 20440 bytes 7 files changed, 109 insertions(+) create mode 100644 podkorytova_yulia_lab_2/README.md create mode 100644 podkorytova_yulia_lab_2/f_regression.JPG create mode 100644 podkorytova_yulia_lab_2/lasso.JPG create mode 100644 podkorytova_yulia_lab_2/linear_regression.JPG create mode 100644 podkorytova_yulia_lab_2/lr2.py create mode 100644 podkorytova_yulia_lab_2/mean.JPG create mode 100644 podkorytova_yulia_lab_2/ridge.JPG diff --git a/podkorytova_yulia_lab_2/README.md b/podkorytova_yulia_lab_2/README.md new file mode 100644 index 0000000..a53d173 --- /dev/null +++ b/podkorytova_yulia_lab_2/README.md @@ -0,0 +1,42 @@ +# Лабораторная работа 2. Ранжирование признаков +### Задание на лабораторную: +Используя код из пункта «Решение задачи ранжирования признаков», выполните ранжирование признаков с помощью указанных по варианту моделей. Отобразите получившиеся значения\оценки каждого признака каждым методом\моделью и среднюю оценку. Проведите анализ получившихся результатов. Какие четыре признака оказались самыми важными по среднему значению? (Названия\индексы признаков и будут ответом на задание). + +**Вариант 20.** Линейная регрессия (LinearRegression), Гребневая регрессия +(Ridge), Лассо (Lasso), Линейная корреляция (f_regression). +*** +### Как запустить лабораторную работу: +Для запуска лабораторной работы необходимо открыть файл `lr2.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. + +**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 генерируются исходные данные (750 строк-наблюдений и 14 столбцов-признаков), задается зависимость для функции-выхода Y с использованием формулы регрессионной проблемы Фридмана, а также добавляется зависимость между признаками. +Далее создаются 4 модели (линейная регрессия, гребневая регрессия, лассо и линейная корреляция). +Для каждой модели происходит тренировка и оценка рангов признаков с помощью функции *rank_to_dict*. +Средние оценки признаков вычисляются при помощи функции *mean_ranks*. + +Результатом работы являются оценки признаков для каждой из 4 моделей и средние оценки признаков, выведенные в консоль. +*** +### Пример выходных данных: +**ОЦЕНКИ ПРИЗНАКОВ** +![](linear_regression.jpg) +![](ridge.jpg) +![](lasso.jpg) +![](f_regression.jpg) + +**СРЕДНИЕ ОЦЕНКИ ПРИЗНАКОВ** +![](mean.jpg) +*** +**Вывод**: результаты показали, что признаки "х4", "х1", "х2" и "х14" - это четыре самых важных признака по среднему значению, при этом признак "х14" был признан ошибочно значимым. + +Модель *Лассо* выявила все значимые признаки (х1-х5), не взяв никакого лишнего. + +Модель *Линейная регрессия* верно выявила значимость признаков *x1, x2, x3, х4*, но потеряла значимый признак *x5* и ошибочно включила признак *x11* в значимые. + +Модель *Гребневая регрессия* верно выявила значимость признаков *x1, x2, x4, х5*, но потеряла значимый признак *x3* и ошибочно включила признак *x14* в значимые. + +Модель *Линейная корреляция* верно выявила значимость признаков *x1, x2, x4*, но потеряла значимые признак *x3, х5* и ошибочно включила признаки *x12, х14* в значимые. \ No newline at end of file diff --git a/podkorytova_yulia_lab_2/f_regression.JPG b/podkorytova_yulia_lab_2/f_regression.JPG new file mode 100644 index 0000000000000000000000000000000000000000..d3438c75854634109012f0e6e3134dd54cf050a1 GIT binary 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рангов признаков + 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) + + sorted_mean = sorted(mean.items(), key=lambda x: x[1], reverse=True) + return sorted_mean + + +# 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)) + +# создаем модели +linear_regression = LinearRegression() +ridge = Ridge() +lasso = Lasso(alpha=.05) +f, pval = f_regression(X, Y, center=True) +models = [("Линейная регрессия", linear_regression), + ("Гребневая регрессия", ridge), + ("Лассо", lasso), + ("Линейная корреляция", f)] + +names = ["x%s" % i for i in range(1,15)] # список содержащий, имена признаков +ranks = {} # словарь для хранения рангов признаков + +for (name, model) in models: + if name != "Линейная корреляция": + model.fit(X, Y) # тренируем модель + ranks[name] = rank_to_dict(model.coef_, names) # преобразуем признаки в словарь + else: + ranks[name] = rank_to_dict(model, names) + +print("-----ОЦЕНКИ ПРИЗНАКОВ-----") +for key, value in ranks.items(): + print(key, ":") + for item in value.items(): + print(item[0], "-", item[1]) + print() + +print("---СРЕДНИЕ ОЦЕНКИ ПРИЗНАКОВ---") +for r in mean_ranks(ranks): + print(r[0], "-", r[1]) \ No newline at end of file diff --git a/podkorytova_yulia_lab_2/mean.JPG b/podkorytova_yulia_lab_2/mean.JPG new file mode 100644 index 0000000000000000000000000000000000000000..d13b9d195691e3509c9a57c345beb332b1cad773 GIT binary patch literal 21071 zcmeHt1z6PE+V7uE1EgC*I)?6$O^b9$DV@UrLnDGHs0f3A4AR{U-60|+DLLQ_DV@>+ 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