From c943260db9562d89698090e7eed899464f4f4b8c Mon Sep 17 00:00:00 2001
From: DmitriyAntonov <dmitriy.antonov@simbirsoft.com>
Date: Sat, 30 Sep 2023 20:26:46 +0400
Subject: [PATCH 1/5] =?UTF-8?q?=D0=BB=D0=B0=D0=B1=D0=B0=202=20done?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 antonov_dmitry_lab_2/README.md  | 104 ++++++++++++++++++++++++++++++++
 antonov_dmitry_lab_2/example.py |  50 +++++++++++++++
 antonov_dmitry_lab_2/lab2.py    |  71 ++++++++++++++++++++++
 3 files changed, 225 insertions(+)
 create mode 100644 antonov_dmitry_lab_2/README.md
 create mode 100644 antonov_dmitry_lab_2/example.py
 create mode 100644 antonov_dmitry_lab_2/lab2.py

diff --git a/antonov_dmitry_lab_2/README.md b/antonov_dmitry_lab_2/README.md
new file mode 100644
index 0000000..4dc1ae4
--- /dev/null
+++ b/antonov_dmitry_lab_2/README.md
@@ -0,0 +1,104 @@
+# Лаб 2
+
+Ранжирование признаков
+
+Используя код из (пункт «Решение задачи ранжирования признаков», стр. 205), 
+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 3
+
+Данные: make_classification (n_samples=500, n_features=2,
+n_redundant=0, n_informative=2, random_state=rs, n_clusters_per_class=1)
+
+# Запуск
+
+Выполнением скрипта файла (вывод в консоль + рисует графики).
+
+# Модели:
+
+1. Линейная регрессия
+1. Полиномиальная регрессия (со степенью 3)
+1. Гребневая полиномиальная регрессия (со степенью 3, alpha = 1.0)
+
+# Графики
+
+<div>
+Качество каждой модели может быть оценено на основе среднеквадратичной ошибки (MSE). 
+Более низкая MSE указывает на лучшее соответствие данным. 
+Однако выбор модели зависит от набора данных и лежащей в основе взаимосвязи между объектами и целевой переменной.
+
+Линейная регрессия: Линейная регрессия предполагает линейную зависимость между признаками и целевой переменной.
+Это хорошо работает, когда взаимосвязь линейна, а шум в наборе данных минимален.
+Лучше всего сработала на наборе лун. Хуже всего на кругах.
+На линейном наборе показала себя на равне с остальными.
+
+Полиномиальная и гребневая показали примерно одинаково на всех наборах.
+
+Полиномиальная регрессия (степень=3):
+Полиномиальная регрессия обеспечивает более гибкую подгонку за счет полинома более высокого порядка(кубическая кривая).
+Она может выявить более сложные взаимосвязи между объектами и целевой переменной.
+Она может сработать лучше, чем линейная регрессия, если истинная взаимосвязь нелинейна.
+
+Гребневая регрессия (степень= 3, альфа=1,0):
+В случае полиномиальной регрессии с регуляризацией (альфа=1,0) модель добавляет коэффициент регуляризации
+для управления сложностью обучения. Регуляризация помогает предотвратить переобучение, когда набор
+данных содержит шум или когда он ограничен.
+</div>
+
+<p>
+    <div>Набор лун (moon_dataset)</div>
+    <img src="screens/myplot1.png" width="650" title="датасет 1">
+</p>
+<p>
+    <div>Графики регрессии</div>
+    <img src="screens/myplot2.png" width="450" title="линейная модель">
+    <img src="screens/myplot3.png" width="450" title="полиномиальная модель">
+    <img src="screens/myplot4.png" width="450" title="гребневая модель">
+    <div>
+        Линейная MSE: 0.0936
+        Полиномиальная (degree=3) MSE: 0.0674
+        Гребневая (degree=3, alpha=1.0) MSE: 0.0682
+    </div>
+</p>
+
+<p>
+    <div>Набор кругов (circles_dataset)</div>
+    <img src="screens/myplot5.png" width="650" title="датасет 2">
+</p>
+<p>
+  <div>Графики регрессии</div>
+    <img src="screens/myplot6.png" width="450" title="линейная модель">
+    <img src="screens/myplot7.png" width="450" title="полиномиальная модель">
+    <img src="screens/myplot8.png" width="450" title="гребневая модель">
+    <div>
+        Линейная MSE: 0.2684
+        Полиномиальная (degree=3) MSE: 0.1341
+        Гребневая (degree=3, alpha=1.0) MSE: 0.1312
+    </div>
+</p>
+
+<p>
+    <div>Набор линейный (linearly_dataset)</div>
+    <img src="screens/myplot9.png" width="650" title="датасет 3">
+</p>
+<p>
+    <div>Графики регрессии</div>
+    <img src="screens/myplot10.png" width="450" title="линейная модель">
+    <img src="screens/myplot11.png" width="450" title="полиномиальная модель">
+    <img src="screens/myplot12.png" width="450" title="гребневая модель">
+    <div>
+        Линейная MSE: 0.1101
+        Полиномиальная (degree=3) MSE: 0.1045
+        Гребневая (degree=3, alpha=1.0) MSE: 0.1078
+    </div>
+</p>
+
+<div>
+Итоговая модель подбирается учитывая зависимость в данных,
+как правило полиномиальная регрессия справляется лучше, а коэф регуляризации в гребневой регрессии помогает избежать
+переобучения.
+</div>
\ No newline at end of file
diff --git a/antonov_dmitry_lab_2/example.py b/antonov_dmitry_lab_2/example.py
new file mode 100644
index 0000000..0d6b861
--- /dev/null
+++ b/antonov_dmitry_lab_2/example.py
@@ -0,0 +1,50 @@
+import numpy as np
+from sklearn.datasets import make_regression
+from sklearn.feature_selection import RFECV, f_regression
+from sklearn.linear_model import LinearRegression
+from sklearn.ensemble import RandomForestRegressor
+
+# генерируем исходные данные: 100 строк-наблюдений и 10 столбцов-признаков
+X, y = make_regression(n_samples=100, n_features=10, random_state=42)
+
+# линейная модель
+linear_reg = LinearRegression()
+linear_reg.fit(X, y)
+linear_ranking_lr = np.abs(linear_reg.coef_)
+
+# cокращение признаков cлучайными деревьями (Random Forest Regressor)
+rf_reg = RandomForestRegressor()
+rfecv = RFECV(estimator=rf_reg)
+rfecv.fit(X, y)
+rfecv_ranking = rfecv.ranking_
+
+# линейная корреляция (f_regression)
+f_reg, _ = f_regression(X, y)
+linear_corr_ranking = f_reg
+
+# ранжирование признаков и вычисление средней оценки
+all_rankings = np.vstack((linear_ranking_lr, rfecv_ranking, linear_corr_ranking))
+average_ranking = np.mean(all_rankings, axis=0)
+
+# средние показатели четырех наиболее важных характеристик
+most_important_indices = np.argsort(average_ranking)[-4:]
+
+# результаты
+print("ранги линейной модели:")
+print(linear_ranking_lr)
+print("")
+
+print("ранги после сокращения признаков Random Forest:")
+print(rfecv_ranking)
+print("")
+
+print("ранги линейнейной корреляции (f_regression):")
+print(linear_corr_ranking)
+print("")
+
+print("ранги по средней оценке:")
+print(average_ranking)
+print("")
+
+print("4 выделенных главных признака:")
+print(most_important_indices)
\ No newline at end of file
diff --git a/antonov_dmitry_lab_2/lab2.py b/antonov_dmitry_lab_2/lab2.py
new file mode 100644
index 0000000..fe975dc
--- /dev/null
+++ b/antonov_dmitry_lab_2/lab2.py
@@ -0,0 +1,71 @@
+from operator import itemgetter
+
+from sklearn.feature_selection import f_regression
+from sklearn.linear_model import LinearRegression, Ridge, Lasso
+from sklearn.preprocessing import MinMaxScaler
+import numpy as np
+
+# 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))
+
+# 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)
+
+names = ["x%s" % i for i in range(1, 15)]
+
+
+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))
+
+
+ranks = {}
+ranks["Linear reg"] = rank_to_dict(lr.coef_, names)
+ranks["Ridge"] = rank_to_dict(ridge.coef_, names)
+ranks["Lasso"] = rank_to_dict(lasso.coef_, names)
+
+# Создаем пустой список для данных
+mean = {}
+# «Бежим» по списку ranks
+for key, value in ranks.items():
+    # «Пробегаемся» по списку значений ranks, которые являются парой имя:оценка
+    for item in value.items():
+        # имя будет ключом для нашего mean
+        # если элемента с текущим ключем 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)
+    mean[key] = round(res, 2)
+# сортируем и распечатываем список
+mean = sorted(mean.items(), key=itemgetter(1), reverse=True)
+print("MEAN")
+print(mean)
+
+for key, value in ranks.items():
+    ranks[key] = sorted(value.items(), key=itemgetter(1), reverse=True)
+for key, value in ranks.items():
+    print(key)
+    print(value)
+
+f, pval = f_regression(X, Y, center=True)

From 059d5b0b126201dac096ec8a65cfdfd084a6005b Mon Sep 17 00:00:00 2001
From: DmitriyAntonov <dmitriy.antonov@simbirsoft.com>
Date: Sat, 30 Sep 2023 20:35:16 +0400
Subject: [PATCH 2/5] =?UTF-8?q?=D0=BB=D0=B0=D0=B1=D0=B0=203=20done?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 antonov_dmitry_lab_3/example.py       |   40 +
 antonov_dmitry_lab_3/titanic_data.csv | 1310 +++++++++++++++++++++++++
 2 files changed, 1350 insertions(+)
 create mode 100644 antonov_dmitry_lab_3/example.py
 create mode 100644 antonov_dmitry_lab_3/titanic_data.csv

diff --git a/antonov_dmitry_lab_3/example.py b/antonov_dmitry_lab_3/example.py
new file mode 100644
index 0000000..25d3c69
--- /dev/null
+++ b/antonov_dmitry_lab_3/example.py
@@ -0,0 +1,40 @@
+import pandas as pd
+from sklearn.model_selection import train_test_split
+from sklearn.tree import DecisionTreeClassifier
+from sklearn.metrics import accuracy_score
+
+# прочитали датасет
+data = pd.read_csv("titanic_data.csv")
+
+# определение признаков
+features = ['Sex', 'Age', 'sibsp']
+
+# целевая переменная - выжившие
+target = 'Survived'
+
+# разделили данные на тренировочную и тестовую выборки
+train_data, test_data, train_labels, test_labels = train_test_split(
+    data[features],
+    data[target],
+    test_size=0.2,
+    random_state=42
+)
+
+# создали модель decision tree classifier
+model = DecisionTreeClassifier()
+
+# натренировали модель
+model.fit(train_data, train_labels)
+
+# получили значения модели для проверки точности
+predictions = model.predict(test_data)
+
+# вычислили точность модели
+accuracy = accuracy_score(test_labels, predictions)
+print("точность модели:", accuracy)
+
+# нашли два самых важных признака
+importances = model.feature_importances_
+indices = (-importances).argsort()[:2]
+important_features = [features[i] for i in indices]
+print("два самых важных признака:", important_features)
\ No newline at end of file
diff --git a/antonov_dmitry_lab_3/titanic_data.csv b/antonov_dmitry_lab_3/titanic_data.csv
new file mode 100644
index 0000000..d6127fe
--- /dev/null
+++ b/antonov_dmitry_lab_3/titanic_data.csv
@@ -0,0 +1,1310 @@
+Passengerid,Age,Fare,Sex,sibsp,zero,zero,zero,zero,zero,zero,zero,Parch,zero,zero,zero,zero,zero,zero,zero,zero,Pclass,zero,zero,Embarked,zero,zero,Survived
+1,22,7.25,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,2,0,0,0
+2,38,71.2833,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1
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From a847058d449c56281cf5fd0ed07819d7d4ee0d64 Mon Sep 17 00:00:00 2001
From: DmitriyAntonov <dmitriy.antonov@simbirsoft.com>
Date: Sat, 7 Oct 2023 22:30:34 +0400
Subject: [PATCH 3/5] =?UTF-8?q?=D0=BB=D0=B0=D0=B1=D0=B0=203=20=D0=BF=D0=BE?=
 =?UTF-8?q?=D1=87=D1=82=D0=B8?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 antonov_dmitry_lab_3/dataset.csv              | 4425 +++++++++++++++++
 antonov_dmitry_lab_3/lab3.py                  |   35 +
 .../{example.py => titanic.py}                |    0
 3 files changed, 4460 insertions(+)
 create mode 100644 antonov_dmitry_lab_3/dataset.csv
 create mode 100644 antonov_dmitry_lab_3/lab3.py
 rename antonov_dmitry_lab_3/{example.py => titanic.py} (100%)

diff --git a/antonov_dmitry_lab_3/dataset.csv b/antonov_dmitry_lab_3/dataset.csv
new file mode 100644
index 0000000..02c2f85
--- /dev/null
+++ b/antonov_dmitry_lab_3/dataset.csv
@@ -0,0 +1,4425 @@
+Marital status,Application mode,Application order,Course,Daytime/evening attendance,Previous qualification,Nacionality,Mother's qualification,Father's qualification,Mother's occupation,Father's occupation,Displaced,Educational special needs,Debtor,Tuition fees up to date,Gender,Scholarship holder,Age at enrollment,International,Curricular units 1st sem (credited),Curricular units 1st sem (enrolled),Curricular units 1st sem (evaluations),Curricular units 1st sem (approved),Curricular units 1st sem (grade),Curricular units 1st sem (without evaluations),Curricular units 2nd sem (credited),Curricular units 2nd sem (enrolled),Curricular units 2nd sem (evaluations),Curricular units 2nd sem (approved),Curricular units 2nd sem (grade),Curricular units 2nd sem (without evaluations),Unemployment rate,Inflation rate,GDP,Target
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diff --git a/antonov_dmitry_lab_3/lab3.py b/antonov_dmitry_lab_3/lab3.py
new file mode 100644
index 0000000..27223cd
--- /dev/null
+++ b/antonov_dmitry_lab_3/lab3.py
@@ -0,0 +1,35 @@
+import pandas as pd
+from sklearn.metrics import accuracy_score
+from sklearn.model_selection import train_test_split
+from sklearn.tree import DecisionTreeClassifier
+
+# прочитали датасет
+data = pd.read_csv('dataset.csv')
+
+# определение признаков
+# целевая переменная - Target
+X = data[['Gender', 'Debtor', 'International']]
+y = data['Target']  # Assuming 'Dropout' is the target variable
+
+# разделили данные на тренировочную и тестовую выборки
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+# создали модель decision tree classifier
+dt_classifier = DecisionTreeClassifier(random_state=42)
+dt_classifier.fit(X_train, y_train)
+
+# получили значения модели для 2ух самых важных признаков
+feature_importances = dt_classifier.feature_importances_
+
+top_features_indices = feature_importances.argsort()[-2:][::-1]
+top_features = X.columns[top_features_indices]
+
+# вывод результата
+print("2 самых важных признака:", top_features)
+
+# получили значения модели для проверки точности
+predictions = dt_classifier.predict(X_test)
+
+# вычислили точность модели
+accuracy = accuracy_score(y_test, predictions)
+print("точность модели:", accuracy)
diff --git a/antonov_dmitry_lab_3/example.py b/antonov_dmitry_lab_3/titanic.py
similarity index 100%
rename from antonov_dmitry_lab_3/example.py
rename to antonov_dmitry_lab_3/titanic.py

From 401a5454eeb0eceeee538c196a7000993536402e Mon Sep 17 00:00:00 2001
From: DmitriyAntonov <dmitriy.antonov@simbirsoft.com>
Date: Sun, 8 Oct 2023 10:49:00 +0400
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---
 antonov_dmitry_lab_3/README.md              |  85 ++++++++++++++++++++
 antonov_dmitry_lab_3/lab3.py                |   2 +-
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 antonov_dmitry_lab_3/screens/mydataset2.png | Bin 0 -> 16053 bytes
 antonov_dmitry_lab_3/screens/titanic.png    | Bin 0 -> 10846 bytes
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diff --git a/antonov_dmitry_lab_3/README.md b/antonov_dmitry_lab_3/README.md
new file mode 100644
index 0000000..1f8db32
--- /dev/null
+++ b/antonov_dmitry_lab_3/README.md
@@ -0,0 +1,85 @@
+# Лаб 3
+
+Деревья решений
+
+Часть 1. По данным о пассажирах Титаника решите задачу классификации
+(с помощью дерева решений), в которой по различным характеристикам
+пассажиров требуется найти у выживших пассажиров два наиболее важных
+признака из трех рассматриваемых (по варианту). Пример решения задачи
+можно посмотреть здесь: [1] (стр.188). Скачать данные можно по ссылке:
+https://www.kaggle.com/datasets/heptapod/titanic
+
+Часть 2. Решите с помощью библиотечной реализации дерева решений
+задачу из лабораторной работы «Веб-сервис «Дерево решений» по предмету
+«Методы искусственного интеллекта» на 99% ваших данных. Проверьте
+работу модели на оставшемся проценте, сделайте вывод.
+
+# Вариант 3
+
+Признаки Sex,Age,SibSp
+
+# Запуск
+
+Выполнением скрипта файла (вывод в консоль).
+
+# Описание модели:
+
+DecisionTreeClassifier - это алгоритм машинного обучения, используемый для задач классификации и регрессии.
+Он представляет собой дерево решений, где на каждом узле дерева решается, какой вопрос задать дальше 
+(признак для дальнейшего разбиения данных), а в листьях находятся окончательные ответы.
+
+# Результаты
+
+На данных для Титаника модель определяет важность признаков с точность 75% (исключает 'sibsp').
+Эти два признака обладают статистической важностью.
+<p>
+    <div>Титаник</div>
+    <img src="screens/titanic.png" width="650" title="Титаник 1">
+</p>
+
+На данных моего датасета модель справляется на 52.768%, если в качестве предлагаемых параметров
+на вход идут ['Gender', 'Debtor', 'International'] (исключает 'International').
+
+<p>
+    <div>Мой датасет 1</div>
+    <img src="screens/mydataset1.png" width="650" title="Мой датасет 1">
+</p>
+
+И на 70.961, если на вход идут ['Gender', 'Debtor', 'Curricular units 2nd sem (approved)']
+(исключает 'Gender'). 
+
+<p>
+    <div>Мой датасет 2</div>
+    <img src="screens/mydataset2.png" width="650" title="Мой датасет 2">
+</p>
+
+Такой результат можно объяснить большей значимостью признака 'Curricular units 2nd sem (approved)' 
+вместо 'International' (было показано в предыдущей лабораторной).
+
+Из-за того, что мы взяли статистически более значимый признак, модель выдает нам большую точность.
+
+Точность 52.768% указывает на то, что модель работает на уровне случайности, что означает, что она 
+работает не лучше, чем случайное угадывание. Для этого может быть несколько причин:
+
+1. Признаки все имеет малое значение: то есть для сравнения подаются признаки статистически малозначимые. 
+
+2. Недостаточно данных: Набор данных может содержать недостаточно информации или примеров для 
+изучения моделью. Если набор данных невелик или нерепрезентативен, модель, возможно, не сможет 
+хорошо обобщить новые данные.
+
+3. Несбалансированные классы: Если классы в вашей целевой переменной несбалансированы 
+(например, случаев, не связанных с отсевом, гораздо больше, чем случаев отсева), модель может 
+быть смещена в сторону прогнозирования класса большинства.
+
+4. Переобучение: Модель может быть переобучена обучающими данным, что означает, что она изучает шум 
+в данных, а не лежащие в их основе закономерности. Это может произойти, если модель слишком сложна по 
+сравнению с объемом доступных данных.
+
+5. Недостаточное соответствие: С другой стороны, модель может быть слишком простой, чтобы отразить 
+взаимосвязи в данных. Важно выбрать соответствующий уровень сложности модели.
+
+<div>
+При отборе признаков должна учитываться их статистическая значимость, вычисленная различными способами
+(например с помощью лин регрессии, Random Forest Regressor, линейной корреляции f_regression или других).
+Так же должно быть достаточно данных, в модели должно быть сведено к минимуму переобучение.
+</div>
\ No newline at end of file
diff --git a/antonov_dmitry_lab_3/lab3.py b/antonov_dmitry_lab_3/lab3.py
index 27223cd..e2c9d22 100644
--- a/antonov_dmitry_lab_3/lab3.py
+++ b/antonov_dmitry_lab_3/lab3.py
@@ -8,7 +8,7 @@ data = pd.read_csv('dataset.csv')
 
 # определение признаков
 # целевая переменная - Target
-X = data[['Gender', 'Debtor', 'International']]
+X = data[['Gender', 'Debtor', 'Curricular units 2nd sem (approved)']]
 y = data['Target']  # Assuming 'Dropout' is the target variable
 
 # разделили данные на тренировочную и тестовую выборки
diff --git a/antonov_dmitry_lab_3/screens/mydataset1.png b/antonov_dmitry_lab_3/screens/mydataset1.png
new file mode 100644
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From 8c47411bf1b5219ae548d797a023de85cd643b08 Mon Sep 17 00:00:00 2001
From: DmitriyAntonov <dmitriy.antonov@simbirsoft.com>
Date: Sun, 8 Oct 2023 10:52:30 +0400
Subject: [PATCH 5/5] =?UTF-8?q?=D0=BB=D0=B0=D0=B1=D0=B0=203=20=D1=80=D0=B5?=
 =?UTF-8?q?=D0=B0=D0=B4=D0=B81?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 antonov_dmitry_lab_2/README.md  | 104 --------------------------------
 antonov_dmitry_lab_2/example.py |  50 ---------------
 antonov_dmitry_lab_2/lab2.py    |  71 ----------------------
 3 files changed, 225 deletions(-)
 delete mode 100644 antonov_dmitry_lab_2/README.md
 delete mode 100644 antonov_dmitry_lab_2/example.py
 delete mode 100644 antonov_dmitry_lab_2/lab2.py

diff --git a/antonov_dmitry_lab_2/README.md b/antonov_dmitry_lab_2/README.md
deleted file mode 100644
index 4dc1ae4..0000000
--- a/antonov_dmitry_lab_2/README.md
+++ /dev/null
@@ -1,104 +0,0 @@
-# Лаб 2
-
-Ранжирование признаков
-
-Используя код из (пункт «Решение задачи ранжирования признаков», стр. 205), 
-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 3
-
-Данные: make_classification (n_samples=500, n_features=2,
-n_redundant=0, n_informative=2, random_state=rs, n_clusters_per_class=1)
-
-# Запуск
-
-Выполнением скрипта файла (вывод в консоль + рисует графики).
-
-# Модели:
-
-1. Линейная регрессия
-1. Полиномиальная регрессия (со степенью 3)
-1. Гребневая полиномиальная регрессия (со степенью 3, alpha = 1.0)
-
-# Графики
-
-<div>
-Качество каждой модели может быть оценено на основе среднеквадратичной ошибки (MSE). 
-Более низкая MSE указывает на лучшее соответствие данным. 
-Однако выбор модели зависит от набора данных и лежащей в основе взаимосвязи между объектами и целевой переменной.
-
-Линейная регрессия: Линейная регрессия предполагает линейную зависимость между признаками и целевой переменной.
-Это хорошо работает, когда взаимосвязь линейна, а шум в наборе данных минимален.
-Лучше всего сработала на наборе лун. Хуже всего на кругах.
-На линейном наборе показала себя на равне с остальными.
-
-Полиномиальная и гребневая показали примерно одинаково на всех наборах.
-
-Полиномиальная регрессия (степень=3):
-Полиномиальная регрессия обеспечивает более гибкую подгонку за счет полинома более высокого порядка(кубическая кривая).
-Она может выявить более сложные взаимосвязи между объектами и целевой переменной.
-Она может сработать лучше, чем линейная регрессия, если истинная взаимосвязь нелинейна.
-
-Гребневая регрессия (степень= 3, альфа=1,0):
-В случае полиномиальной регрессии с регуляризацией (альфа=1,0) модель добавляет коэффициент регуляризации
-для управления сложностью обучения. Регуляризация помогает предотвратить переобучение, когда набор
-данных содержит шум или когда он ограничен.
-</div>
-
-<p>
-    <div>Набор лун (moon_dataset)</div>
-    <img src="screens/myplot1.png" width="650" title="датасет 1">
-</p>
-<p>
-    <div>Графики регрессии</div>
-    <img src="screens/myplot2.png" width="450" title="линейная модель">
-    <img src="screens/myplot3.png" width="450" title="полиномиальная модель">
-    <img src="screens/myplot4.png" width="450" title="гребневая модель">
-    <div>
-        Линейная MSE: 0.0936
-        Полиномиальная (degree=3) MSE: 0.0674
-        Гребневая (degree=3, alpha=1.0) MSE: 0.0682
-    </div>
-</p>
-
-<p>
-    <div>Набор кругов (circles_dataset)</div>
-    <img src="screens/myplot5.png" width="650" title="датасет 2">
-</p>
-<p>
-  <div>Графики регрессии</div>
-    <img src="screens/myplot6.png" width="450" title="линейная модель">
-    <img src="screens/myplot7.png" width="450" title="полиномиальная модель">
-    <img src="screens/myplot8.png" width="450" title="гребневая модель">
-    <div>
-        Линейная MSE: 0.2684
-        Полиномиальная (degree=3) MSE: 0.1341
-        Гребневая (degree=3, alpha=1.0) MSE: 0.1312
-    </div>
-</p>
-
-<p>
-    <div>Набор линейный (linearly_dataset)</div>
-    <img src="screens/myplot9.png" width="650" title="датасет 3">
-</p>
-<p>
-    <div>Графики регрессии</div>
-    <img src="screens/myplot10.png" width="450" title="линейная модель">
-    <img src="screens/myplot11.png" width="450" title="полиномиальная модель">
-    <img src="screens/myplot12.png" width="450" title="гребневая модель">
-    <div>
-        Линейная MSE: 0.1101
-        Полиномиальная (degree=3) MSE: 0.1045
-        Гребневая (degree=3, alpha=1.0) MSE: 0.1078
-    </div>
-</p>
-
-<div>
-Итоговая модель подбирается учитывая зависимость в данных,
-как правило полиномиальная регрессия справляется лучше, а коэф регуляризации в гребневой регрессии помогает избежать
-переобучения.
-</div>
\ No newline at end of file
diff --git a/antonov_dmitry_lab_2/example.py b/antonov_dmitry_lab_2/example.py
deleted file mode 100644
index 0d6b861..0000000
--- a/antonov_dmitry_lab_2/example.py
+++ /dev/null
@@ -1,50 +0,0 @@
-import numpy as np
-from sklearn.datasets import make_regression
-from sklearn.feature_selection import RFECV, f_regression
-from sklearn.linear_model import LinearRegression
-from sklearn.ensemble import RandomForestRegressor
-
-# генерируем исходные данные: 100 строк-наблюдений и 10 столбцов-признаков
-X, y = make_regression(n_samples=100, n_features=10, random_state=42)
-
-# линейная модель
-linear_reg = LinearRegression()
-linear_reg.fit(X, y)
-linear_ranking_lr = np.abs(linear_reg.coef_)
-
-# cокращение признаков cлучайными деревьями (Random Forest Regressor)
-rf_reg = RandomForestRegressor()
-rfecv = RFECV(estimator=rf_reg)
-rfecv.fit(X, y)
-rfecv_ranking = rfecv.ranking_
-
-# линейная корреляция (f_regression)
-f_reg, _ = f_regression(X, y)
-linear_corr_ranking = f_reg
-
-# ранжирование признаков и вычисление средней оценки
-all_rankings = np.vstack((linear_ranking_lr, rfecv_ranking, linear_corr_ranking))
-average_ranking = np.mean(all_rankings, axis=0)
-
-# средние показатели четырех наиболее важных характеристик
-most_important_indices = np.argsort(average_ranking)[-4:]
-
-# результаты
-print("ранги линейной модели:")
-print(linear_ranking_lr)
-print("")
-
-print("ранги после сокращения признаков Random Forest:")
-print(rfecv_ranking)
-print("")
-
-print("ранги линейнейной корреляции (f_regression):")
-print(linear_corr_ranking)
-print("")
-
-print("ранги по средней оценке:")
-print(average_ranking)
-print("")
-
-print("4 выделенных главных признака:")
-print(most_important_indices)
\ No newline at end of file
diff --git a/antonov_dmitry_lab_2/lab2.py b/antonov_dmitry_lab_2/lab2.py
deleted file mode 100644
index fe975dc..0000000
--- a/antonov_dmitry_lab_2/lab2.py
+++ /dev/null
@@ -1,71 +0,0 @@
-from operator import itemgetter
-
-from sklearn.feature_selection import f_regression
-from sklearn.linear_model import LinearRegression, Ridge, Lasso
-from sklearn.preprocessing import MinMaxScaler
-import numpy as np
-
-# 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))
-
-# 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)
-
-names = ["x%s" % i for i in range(1, 15)]
-
-
-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))
-
-
-ranks = {}
-ranks["Linear reg"] = rank_to_dict(lr.coef_, names)
-ranks["Ridge"] = rank_to_dict(ridge.coef_, names)
-ranks["Lasso"] = rank_to_dict(lasso.coef_, names)
-
-# Создаем пустой список для данных
-mean = {}
-# «Бежим» по списку ranks
-for key, value in ranks.items():
-    # «Пробегаемся» по списку значений ranks, которые являются парой имя:оценка
-    for item in value.items():
-        # имя будет ключом для нашего mean
-        # если элемента с текущим ключем 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)
-    mean[key] = round(res, 2)
-# сортируем и распечатываем список
-mean = sorted(mean.items(), key=itemgetter(1), reverse=True)
-print("MEAN")
-print(mean)
-
-for key, value in ranks.items():
-    ranks[key] = sorted(value.items(), key=itemgetter(1), reverse=True)
-for key, value in ranks.items():
-    print(key)
-    print(value)
-
-f, pval = f_regression(X, Y, center=True)