From 55b79c339e5948251111585c67b1c138d41cba0c Mon Sep 17 00:00:00 2001
From: acidmikk <sv.acid6@gmail.com>
Date: Sun, 29 Oct 2023 14:03:33 +0400
Subject: [PATCH 1/4] start work

---
 .gitignore                       |  2 ++
 basharin_sevastyan_lab_2/main.py | 60 ++++++++++++++++++++++++++++++++
 2 files changed, 62 insertions(+)
 create mode 100644 .gitignore
 create mode 100644 basharin_sevastyan_lab_2/main.py

diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000..dd5f60f
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,2 @@
+# Specify filepatterns you want git to ignore.
+.idea/
diff --git a/basharin_sevastyan_lab_2/main.py b/basharin_sevastyan_lab_2/main.py
new file mode 100644
index 0000000..f767893
--- /dev/null
+++ b/basharin_sevastyan_lab_2/main.py
@@ -0,0 +1,60 @@
+from random import randrange
+import numpy as np
+from matplotlib import pyplot as plt
+from matplotlib.colors import ListedColormap
+from sklearn import metrics
+from sklearn.model_selection import train_test_split
+from sklearn.linear_model import LinearRegression, LogisticRegression
+from sklearn.pipeline import make_pipeline
+from sklearn.preprocessing import StandardScaler, PolynomialFeatures
+from sklearn.datasets import make_circles
+
+rs = randrange(50)
+X, y = make_circles(noise=0.2, factor=0.5, random_state=rs)  # Сгенерируем данные
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2,
+                                                    random_state=rs)  # Разделим данные на обучающий и тестовый наборы
+
+# Линейная модель
+linear_reg = LinearRegression()
+# Полиномиальная регрессия (со степенью 4)
+poly_reg = make_pipeline(PolynomialFeatures(degree=4), StandardScaler(), LogisticRegression(random_state=rs))
+# Гребневая полиномиальная регрессия (со степенью 4 и alpha=1.0)
+ridge_poly_reg = make_pipeline(PolynomialFeatures(degree=4), StandardScaler(), LogisticRegression(penalty='l2', C=1.0,
+                                                                                                  random_state=rs))
+
+
+# Обучение моделей
+def mid_sq_n_det(name, model):
+    model.fit(X_train, y_train)
+    y_predict = model.predict(X_test)
+    print(f'Рассчёт среднеквадратичной ошибки для {name}: '
+          f'{np.round(np.sqrt(metrics.mean_squared_error(y_test, y_predict)),3)}')  # Рассчёт среднеквадратичной ошибки модели
+    print(f'Рассчёт коэфициента детерминации для {name}: {np.round(metrics.r2_score(y_test, y_predict), 2)}')  # Рассчёт коэфициента детерминации модели
+    return name, model
+
+
+# Графики
+models = [mid_sq_n_det("Линейная регрессия", linear_reg),
+          mid_sq_n_det("Полиномиальная регрессия (со степенью 4)", poly_reg),
+          mid_sq_n_det("Гребневая полиномиальная регрессия (со степенью 4, alpha = 1.0)", ridge_poly_reg)]
+
+cmap_background = ListedColormap(['#FFAAAA', '#AAAAFF'])
+cmap_points = ListedColormap(['#FF0000', '#0000FF'])
+
+plt.figure(figsize=(15, 4))
+for i, (name, model) in enumerate(models):
+    plt.subplot(1, 3, i + 1)
+    xx, yy = np.meshgrid(np.linspace(X[:, 0].min() - 1, X[:, 0].max() + 1, 100),
+                         np.linspace(X[:, 1].min() - 1, X[:, 1].max() + 1, 100))
+    Z = model.predict(np.c_[xx.ravel(), yy.ravel()])
+    Z = Z.reshape(xx.shape)
+    plt.contourf(xx, yy, Z, cmap=cmap_background, alpha=0.5)
+    plt.scatter(X_test[:, 0], X_test[:, 1], c=y_test, cmap=cmap_points, marker='o', label='Тестовые точки')
+    plt.scatter(X_train[:, 0], X_train[:, 1], c=y_train, cmap=cmap_points, marker='x', label='Обучающие точки')
+    plt.legend()
+    plt.title(name)
+    plt.text(0.5, -1.2, 'Красный класс', color='r', fontsize=12)
+    plt.text(0.5, -1.7, 'Синий класс', color='b', fontsize=12)
+
+plt.tight_layout()
+plt.show()

From dab82f11eef438479d11f3805de37953e1c89b59 Mon Sep 17 00:00:00 2001
From: acidmikk <sv.acid6@gmail.com>
Date: Sun, 29 Oct 2023 15:23:51 +0400
Subject: [PATCH 2/4] fix ingore

---
 .gitignore                       |  2 +-
 basharin_sevastyan_lab_2/main.py | 81 ++++++++++++--------------------
 2 files changed, 31 insertions(+), 52 deletions(-)

diff --git a/.gitignore b/.gitignore
index dd5f60f..40ada36 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,2 +1,2 @@
 # Specify filepatterns you want git to ignore.
-.idea/
+.idea
diff --git a/basharin_sevastyan_lab_2/main.py b/basharin_sevastyan_lab_2/main.py
index f767893..e0f801e 100644
--- a/basharin_sevastyan_lab_2/main.py
+++ b/basharin_sevastyan_lab_2/main.py
@@ -1,60 +1,39 @@
-from random import randrange
 import numpy as np
-from matplotlib import pyplot as plt
-from matplotlib.colors import ListedColormap
-from sklearn import metrics
-from sklearn.model_selection import train_test_split
-from sklearn.linear_model import LinearRegression, LogisticRegression
-from sklearn.pipeline import make_pipeline
-from sklearn.preprocessing import StandardScaler, PolynomialFeatures
-from sklearn.datasets import make_circles
+from sklearn.linear_model import Ridge, LinearRegression
+from sklearn.ensemble import RandomForestRegressor
+from sklearn.feature_selection import RFE
 
-rs = randrange(50)
-X, y = make_circles(noise=0.2, factor=0.5, random_state=rs)  # Сгенерируем данные
-X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2,
-                                                    random_state=rs)  # Разделим данные на обучающий и тестовый наборы
+''' Задание
+Используя код из [1](пункт «Решение задачи ранжирования признаков», стр. 205), выполните ранжирование признаков с 
+помощью указанных по вариантумоделей. Отобразите получившиеся значения\оценки каждого признака каждым методом\моделью и
+среднюю  оценку. Проведите анализ получившихся результатов. Какие четырепризнака оказались самыми важными по среднему 
+значению? (Названия\индексы  признаков и будут ответом на задание).
 
-# Линейная модель
-linear_reg = LinearRegression()
-# Полиномиальная регрессия (со степенью 4)
-poly_reg = make_pipeline(PolynomialFeatures(degree=4), StandardScaler(), LogisticRegression(random_state=rs))
-# Гребневая полиномиальная регрессия (со степенью 4 и alpha=1.0)
-ridge_poly_reg = make_pipeline(PolynomialFeatures(degree=4), StandardScaler(), LogisticRegression(penalty='l2', C=1.0,
-                                                                                                  random_state=rs))
+Вариант 5.
+Гребневая регрессия (Ridge), Рекурсивное сокращение признаков (Recursive Feature Elimination – RFE),
+Сокращение признаков Случайными деревьями (Random Forest Regressor).
+'''
 
+# создание данных
+rs = np.random.RandomState(2)
+X, y = make_regression(n_samples=750, n_features=15, noise=0.1, random_state=random_state)
+data = pd.DataFrame(X, columns=[f'Признак {i}' for i in range(X.shape[1])])
+data['Целевая переменная'] = y
+X = data.drop('Целевая переменная', axis=1)
+y = data['Целевая переменная']
 
-# Обучение моделей
-def mid_sq_n_det(name, model):
-    model.fit(X_train, y_train)
-    y_predict = model.predict(X_test)
-    print(f'Рассчёт среднеквадратичной ошибки для {name}: '
-          f'{np.round(np.sqrt(metrics.mean_squared_error(y_test, y_predict)),3)}')  # Рассчёт среднеквадратичной ошибки модели
-    print(f'Рассчёт коэфициента детерминации для {name}: {np.round(metrics.r2_score(y_test, y_predict), 2)}')  # Рассчёт коэфициента детерминации модели
-    return name, model
+ridge = Ridge(alpha=1)  # Создаём модель гребневой регрессии и обучаем её
+ridge.fit(X, Y)
 
+recFE = RFE(LinearRegression(), n_features_to_select=1)  #
+recFE.fit(X, Y)
 
-# Графики
-models = [mid_sq_n_det("Линейная регрессия", linear_reg),
-          mid_sq_n_det("Полиномиальная регрессия (со степенью 4)", poly_reg),
-          mid_sq_n_det("Гребневая полиномиальная регрессия (со степенью 4, alpha = 1.0)", ridge_poly_reg)]
+rfr = RandomForestRegressor()  # Создаём и обучаем регрессор случайного леса
+rfr.fit(X, Y)
 
-cmap_background = ListedColormap(['#FFAAAA', '#AAAAFF'])
-cmap_points = ListedColormap(['#FF0000', '#0000FF'])
+models = [('Гребневая регрессия', ridge),
+          ('RFE', recFE),
+          ('RFR', rfr)]
 
-plt.figure(figsize=(15, 4))
-for i, (name, model) in enumerate(models):
-    plt.subplot(1, 3, i + 1)
-    xx, yy = np.meshgrid(np.linspace(X[:, 0].min() - 1, X[:, 0].max() + 1, 100),
-                         np.linspace(X[:, 1].min() - 1, X[:, 1].max() + 1, 100))
-    Z = model.predict(np.c_[xx.ravel(), yy.ravel()])
-    Z = Z.reshape(xx.shape)
-    plt.contourf(xx, yy, Z, cmap=cmap_background, alpha=0.5)
-    plt.scatter(X_test[:, 0], X_test[:, 1], c=y_test, cmap=cmap_points, marker='o', label='Тестовые точки')
-    plt.scatter(X_train[:, 0], X_train[:, 1], c=y_train, cmap=cmap_points, marker='x', label='Обучающие точки')
-    plt.legend()
-    plt.title(name)
-    plt.text(0.5, -1.2, 'Красный класс', color='r', fontsize=12)
-    plt.text(0.5, -1.7, 'Синий класс', color='b', fontsize=12)
-
-plt.tight_layout()
-plt.show()
+for name, model in models:
+    pass

From faeeecf1ef8e91a81bdea4177b49fce61cfb8332 Mon Sep 17 00:00:00 2001
From: acidmikk <sv.acid6@gmail.com>
Date: Sun, 29 Oct 2023 15:26:49 +0400
Subject: [PATCH 3/4] fix ignore

---
 .gitignore | 142 ++++++++++++++++++++++++++++++++++++++++++++++++++++-
 1 file changed, 140 insertions(+), 2 deletions(-)

diff --git a/.gitignore b/.gitignore
index 40ada36..7e86cf0 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,2 +1,140 @@
-# Specify filepatterns you want git to ignore.
-.idea
+### Python template
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+cover/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+.pybuilder/
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+#   For a library or package, you might want to ignore these files since the code is
+#   intended to run in multiple environments; otherwise, check them in:
+# .python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# pytype static type analyzer
+.pytype/
+
+# Cython debug symbols
+cython_debug/
+

From 5e0058b82ebdc55a4178904a0697821322200612 Mon Sep 17 00:00:00 2001
From: acidmikk <sv.acid6@gmail.com>
Date: Sun, 29 Oct 2023 17:07:56 +0400
Subject: [PATCH 4/4] basharin_sevastyan_lab_2 is ready

---
 .gitignore                         |   1 +
 basharin_sevastyan_lab_2/README.md |  42 ++++++++++++++++++++++++++
 basharin_sevastyan_lab_2/main.py   |  46 +++++++++++++++++++++++------
 basharin_sevastyan_lab_2/res.png   | Bin 0 -> 6134 bytes
 basharin_sevastyan_lab_2/rfe.png   | Bin 0 -> 11475 bytes
 basharin_sevastyan_lab_2/rfr.png   | Bin 0 -> 11407 bytes
 basharin_sevastyan_lab_2/ridge.png | Bin 0 -> 13923 bytes
 7 files changed, 80 insertions(+), 9 deletions(-)
 create mode 100644 basharin_sevastyan_lab_2/README.md
 create mode 100644 basharin_sevastyan_lab_2/res.png
 create mode 100644 basharin_sevastyan_lab_2/rfe.png
 create mode 100644 basharin_sevastyan_lab_2/rfr.png
 create mode 100644 basharin_sevastyan_lab_2/ridge.png

diff --git a/.gitignore b/.gitignore
index 7e86cf0..d13d270 100644
--- a/.gitignore
+++ b/.gitignore
@@ -138,3 +138,4 @@ dmypy.json
 # Cython debug symbols
 cython_debug/
 
+.idea
\ No newline at end of file
diff --git a/basharin_sevastyan_lab_2/README.md b/basharin_sevastyan_lab_2/README.md
new file mode 100644
index 0000000..9aa5309
--- /dev/null
+++ b/basharin_sevastyan_lab_2/README.md
@@ -0,0 +1,42 @@
+## Лабораторная работа 2. Вариант 5.
+### Задание 
+Выполнить ранжирование признаков. 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:
+
+- Гребневая регрессия `Ridge`, 
+- Рекурсивное сокращение признаков `Recursive Feature Elimination – RFE`, 
+- Сокращение признаков Случайными деревьями `Random Forest Regressor`
+
+### Как запустить
+Для запуска программы необходимо с помощью командной строки в корневой директории файлов прокета прописать:
+```
+python main.py
+```
+
+### Используемые технологии
+- `numpy` (псевдоним `np`): NumPy - это библиотека для научных вычислений в Python.
+- `sklearn` (scikit-learn): Scikit-learn - это библиотека для машинного обучения и анализа данных в Python. Из данной библиотеки были использованы следующие модули:
+  - `LinearRegression` - линейная регрессия - это алгоритм машинного обучения, используемый для задач бинарной классификации.
+  - `Ridge` - инструмент работы с моделью "Гребневая регрессия"
+  - `RFE` - инструмент оценки важности признаков "Рекурсивное сокращение признаков"
+  - `RandomForestRegressor` - инструмент работы с моделью "Регрессор случайного леса"
+
+### Описание работы
+1. Программа генерирует данные для обучения моделей, содержащие матрицу признаков X и вектор целевой переменной y.
+1. Создает DataFrame data, в котором столбцы представляют признаки, а последний столбец - целевую переменную.
+1. Разделяет данные на матрицу признаков X и вектор целевой переменной y
+1. Создает список обученных моделей для ранжирования признаков: гребневой регрессии, рекурсивного сокращения признаков и сокращения признаков случайными деревьями.
+1. Создает словарь model_scores для хранения оценок каждой модели.
+1. Выводит оценки признаков каждой модели и их средние оценки.
+1. Находит четыре наиболее важных признака по средней оценке и выводит их индексы и значения.
+
+### Результат работы
+![](ridge.png "Гребневая регрессия")
+![](rfe.png "Рекурсивное сокращение признаков")
+![](rfr.png "Сокращение признаков Случайными деревьями")
+![](res.png "Четыре самых важных")
+
+### Вывод
+Четыре наиболее важных признака, определенных на основе средних оценок, включают 
+Признак 1, Признак 3, Признак 12 и Признак 6.
\ No newline at end of file
diff --git a/basharin_sevastyan_lab_2/main.py b/basharin_sevastyan_lab_2/main.py
index e0f801e..8603b2a 100644
--- a/basharin_sevastyan_lab_2/main.py
+++ b/basharin_sevastyan_lab_2/main.py
@@ -1,7 +1,10 @@
 import numpy as np
+import pandas as pd
+from sklearn.datasets import make_regression
 from sklearn.linear_model import Ridge, LinearRegression
 from sklearn.ensemble import RandomForestRegressor
 from sklearn.feature_selection import RFE
+from sklearn.preprocessing import MinMaxScaler
 
 ''' Задание
 Используя код из [1](пункт «Решение задачи ранжирования признаков», стр. 205), выполните ранжирование признаков с 
@@ -15,25 +18,50 @@ from sklearn.feature_selection import RFE
 '''
 
 # создание данных
-rs = np.random.RandomState(2)
+random_state = np.random.RandomState(2)
 X, y = make_regression(n_samples=750, n_features=15, noise=0.1, random_state=random_state)
 data = pd.DataFrame(X, columns=[f'Признак {i}' for i in range(X.shape[1])])
 data['Целевая переменная'] = y
 X = data.drop('Целевая переменная', axis=1)
 y = data['Целевая переменная']
 
-ridge = Ridge(alpha=1)  # Создаём модель гребневой регрессии и обучаем её
-ridge.fit(X, Y)
+ridge = Ridge(alpha=1)  # Гребневая регрессия
+ridge.fit(X, y)
 
-recFE = RFE(LinearRegression(), n_features_to_select=1)  #
-recFE.fit(X, Y)
+recFE = RFE(LinearRegression(), n_features_to_select=1)  # Рекурсивное сокращение признаков
+recFE.fit(X, y)
 
-rfr = RandomForestRegressor()  # Создаём и обучаем регрессор случайного леса
-rfr.fit(X, Y)
+rfr = RandomForestRegressor()  # Сокращение признаков Случайными деревьями
+rfr.fit(X, y)
 
-models = [('Гребневая регрессия', ridge),
+models = [('Ridge', ridge),
           ('RFE', recFE),
           ('RFR', rfr)]
+model_scores = []
 
 for name, model in models:
-    pass
+    if name == 'Ridge':
+        coef = model.coef_
+        normalized_coef = MinMaxScaler().fit_transform(coef.reshape(-1, 1))
+        model_scores.append((name, normalized_coef.flatten()))
+    elif name == 'RFE':
+        rankings = model.ranking_
+        normalized_rankings = 1 - (rankings - 1) / (np.max(rankings) - 1)
+        model_scores.append((name, normalized_rankings))
+    elif name == 'RFR':
+        feature_importances = model.feature_importances_
+        normalized_importances = MinMaxScaler().fit_transform(feature_importances.reshape(-1, 1))
+        model_scores.append((name, normalized_importances.flatten()))
+
+for name, scores in model_scores:
+    print(f"{name} оценки признаков:")
+    for feature, score in enumerate(scores, start=1):
+        print(f"Признак {feature}: {score:.2f}")
+    print(f"Средняя оценка: {np.mean(scores):.2f}")
+
+all_feature_scores = np.mean(list(map(lambda x: x[1], model_scores)), axis=0)
+sorted_features = sorted(enumerate(all_feature_scores, start=1), key=lambda x: x[1], reverse=True)
+top_features = sorted_features[:4]
+print("Четыре наиболее важных признака:")
+for feature, score in top_features:
+    print(f"Признак {feature}: {score:.2f}")
diff --git a/basharin_sevastyan_lab_2/res.png b/basharin_sevastyan_lab_2/res.png
new file mode 100644
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