From 27d25c8f147eac2a6cbbe9efef9018530335c687 Mon Sep 17 00:00:00 2001 From: rita Date: Wed, 22 Nov 2023 00:31:35 +0400 Subject: [PATCH 1/3] mashkova_margarita_lab_2 ready --- mashkova_margarita_lab_2/README.md | 61 +++++++++++++ mashkova_margarita_lab_2/main.py | 100 ++++++++++++++++++++++ mashkova_margarita_lab_2/means.png | Bin 0 -> 9991 bytes mashkova_margarita_lab_2/ranks.png | Bin 0 -> 26209 bytes mashkova_margarita_lab_2/ranks_sorted.png | Bin 0 -> 33308 bytes 5 files changed, 161 insertions(+) create mode 100644 mashkova_margarita_lab_2/README.md create mode 100644 mashkova_margarita_lab_2/main.py create mode 100644 mashkova_margarita_lab_2/means.png create mode 100644 mashkova_margarita_lab_2/ranks.png create mode 100644 mashkova_margarita_lab_2/ranks_sorted.png diff --git a/mashkova_margarita_lab_2/README.md b/mashkova_margarita_lab_2/README.md new file mode 100644 index 0000000..bb424af --- /dev/null +++ b/mashkova_margarita_lab_2/README.md @@ -0,0 +1,61 @@ +# Лабораторная работа №2 +## ПИбд-42 Машкова Маргарита (Вариант 19) +## Задание +Выполнить ранжирование признаков с помощью указанных по варианту моделей. +Отобразить получившиеся значения\оценки каждого признака каждым методом\моделью и среднюю оценку. +Провести анализ получившихся результатов. Какие четыре признака оказались самыми важными по среднему значению? + +### Модели: + +> - Линейная регрессия (LinearRegression) +> - Гребневая регрессия (Ridge) +> - Лассо (Lasso) +> - Случайное Лассо (RandomizedLasso) + +> **Important** +> +> Модель `RandomizedLasso` была признана устаревшей в scikit-learn 0.19 и удалена в 0.21. +Вместо нее будет использоваться регрессор случайного леса `RandomForestRegressor`. + +## Запуск программы +Для запуска программы необходимо запустить файл main.py + +## Используемые технологии +> **Язык программирования:** python +> +> **Библиотеки:** +> - `numpy` - используется для работы с массивами. +> - `sklearn` - предоставляет широкий спектр инструментов для машинного обучения, статистики и анализа данных. +## Описание работы программы + +Для начала необходимо сгенерировать исходные данные (Х) - 750 строк-наблюдений и 14 столбцов-признаков. +Затем задать функцию-выход (Y): регрессионную проблему Фридмана, когда на вход моделей подается 14 факторов, +выход рассчитывается по формуле, использующей только пять факторов, но факторы 11-14 зависят от факторов 1-4. +Соотвественно, далее добавляется зависимость для признаков (факторов) х11, х12, х13, х14 от х1, х2, х3, х4. + +Далее создаются модели, указанные в варианте задания, и выполняется их обучение. + +После чего в единый массив размера 4×14 (количество_моделей и количество_признаков) выгружаются все оценки +моделей по признакам. Находятся средние оценки и выводится результат в формате списка пар `{номер_признака – средняя_оценка}`, +отсортированном по убыванию. Оценки признаков получаются через поле `coef_` у моделей LinearRegression, Ridge и Lasso. +У модели RandomForestRegressor - через поле `feature_importances_`. +Для удобства отображения данных оценки помещаются в конструкцию вида: +`[имя_модели : [{имя_признака : оценка},{имя_признака : оценка}...]]`. +Таким образом, получаем словарь, в котором располагаются 4 записи из четырнадцати пар каждая. +Ключом является имя модели. + +## Тесты + +### Оценки важности признаков моделями +![Оценки важности признаков моделями](ranks.png) +### Оценки важности признаков моделями, отсортированные по убыванию +![Оценки важности признаков моделями, отсортированные по убыванию](ranks_sorted.png) +### Средние оценки важности признаков +![Средние оценки важности признаков](means.png) + +**Вывод:** основываясь на средних оценках, четырьмя наиболее важными празнаками оказались: +`x4 (0.86), x1 (0.8), x2 (0.73), x14 (0.51)`. +Все модели оценили как наиболее важные признаки x1, x2, x4, и четвертым важным признаком выбрали зависимые признаки: +LinearRegression - х11, Ridge - х14, RandomForestRegressor - х14. Модель Lasso включила также независимый признак - х5. + + diff --git a/mashkova_margarita_lab_2/main.py b/mashkova_margarita_lab_2/main.py new file mode 100644 index 0000000..8a03237 --- /dev/null +++ b/mashkova_margarita_lab_2/main.py @@ -0,0 +1,100 @@ +from sklearn.linear_model import LinearRegression, Ridge, Lasso +from sklearn.ensemble import RandomForestRegressor +from sklearn.preprocessing import MinMaxScaler +import numpy as np + +# Генерация исходных данных: 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)) + +# Добавление зависимости признаков +X[:, 10:] = X[:, :4] + np.random.normal(0, .025, (size, 4)) + +# Создание моделей и их обучение +# Линейная модель +lr = LinearRegression() +lr.fit(X, Y) +# Гребневая модель +ridge = Ridge(alpha=7) +ridge.fit(X, Y) +# Лассо +lasso = Lasso(alpha=.05) +lasso.fit(X, Y) +# Регрессор случайного леса +rfr = RandomForestRegressor() +rfr.fit(X, Y) + +# Список, содержащий имена признаков +names = ["x%s" % i for i in range(1, 15)] + + +# Функция создания записи в словаре оценок важности признаков +def rank_to_dict(ranks): + 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)) + + +# Словарь, содержащий оценки важности признаков +ranks_dict = dict() +# Добавление записей в словарь +ranks_dict["Linear regression"] = rank_to_dict(lr.coef_) +ranks_dict["Ridge"] = rank_to_dict(ridge.coef_) +ranks_dict["Lasso"] = rank_to_dict(lasso.coef_) +ranks_dict["Random Forest Regressor"] = rank_to_dict(rfr.feature_importances_) + + +def print_ranks(): + for key, value in ranks_dict.items(): + print(key) + print(value) + + +def print_ranks_sorted(): + for key, value in ranks_dict.items(): + print(key) + value_sorted = sorted(value.items(), key=lambda x: x[1], reverse=True) + print(value_sorted) + + +def get_means(): + # Создаем пустой список для средних оценок + mean = {} + for key, value in ranks_dict.items(): + # Пробегаемся по словарю значений ranks, которые являются парой имя:оценка + for item in value.items(): + # Имя будет ключом для нашего mean + # Если элемента с текущим ключом в mean нет - добавляем + if item[0] not in mean: + mean[item[0]] = 0 + # Суммируем значения по каждому ключу-имени признака + mean[item[0]] += item[1] + + # Находим среднее по каждому признаку + for key, value in mean.items(): + res = value / len(ranks_dict) + mean[key] = round(res, 2) + # сортируем список + mean_sorted = sorted(mean.items(), key=lambda x: x[1], reverse=True) + return mean_sorted + + +def print_means(): + for item in get_means(): + print(item) + + +print("Оценки каждого признака каждой моделью:") +print_ranks() +print("\nОценки 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z34j|IexByv8I1O398jq~oXf?Eo#+szr=ng+G!Z}wnW726_Ft+U6x?w{EtNH zBw&d^fRhwDjX0G~Qgwi}!Zy~^@piuH2-^)VOXlcfGyL_Y-u|0qY*yPlMMiE0#F{@h zWuKZtRWb6R@gPk4f)-rwz+uMb);?G@yU=W(FAG7E3O9z?`eXzyiH(`r3 v{~*30W@-A?zax?U2mhbnr}_USnKMDn0}U+P$YeSf@b8S-Mboz?SMUFS!xG3$ literal 0 HcmV?d00001 -- 2.25.1 From 481a18c68df1f10d879008c382a5e0c987f22d77 Mon Sep 17 00:00:00 2001 From: rita Date: Wed, 22 Nov 2023 00:35:41 +0400 Subject: [PATCH 2/3] mashkova_margarita_lab_2 change md --- mashkova_margarita_lab_2/README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/mashkova_margarita_lab_2/README.md b/mashkova_margarita_lab_2/README.md index bb424af..efd4daf 100644 --- a/mashkova_margarita_lab_2/README.md +++ b/mashkova_margarita_lab_2/README.md @@ -12,8 +12,8 @@ > - Лассо (Lasso) > - Случайное Лассо (RandomizedLasso) -> **Important** -> +> [!IMPORTANT] +> > Модель `RandomizedLasso` была признана устаревшей в scikit-learn 0.19 и удалена в 0.21. Вместо нее будет использоваться регрессор случайного леса `RandomForestRegressor`. -- 2.25.1 From 3a316d94a1cfff62ad70c997c2231935002f2640 Mon Sep 17 00:00:00 2001 From: rita Date: Wed, 22 Nov 2023 00:40:05 +0400 Subject: [PATCH 3/3] mashkova_margarita_lab_2 change md --- mashkova_margarita_lab_2/README.md | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/mashkova_margarita_lab_2/README.md b/mashkova_margarita_lab_2/README.md index efd4daf..a3ec462 100644 --- a/mashkova_margarita_lab_2/README.md +++ b/mashkova_margarita_lab_2/README.md @@ -12,11 +12,13 @@ > - Лассо (Lasso) > - Случайное Лассо (RandomizedLasso) -> [!IMPORTANT] + +> **Note** > > Модель `RandomizedLasso` была признана устаревшей в scikit-learn 0.19 и удалена в 0.21. Вместо нее будет использоваться регрессор случайного леса `RandomForestRegressor`. + ## Запуск программы Для запуска программы необходимо запустить файл main.py -- 2.25.1