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mirea-projects/Third term/Artificial intelligence systems and big data/5.ipynb
NKTKLN 82611254b0 many
2024-09-27 08:31:03 +03:00

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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Рабочая тетрадь No 5"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"from sklearn import tree, metrics\n",
"from sklearn.tree import DecisionTreeRegressor\n",
"from sklearn.tree import DecisionTreeClassifier\n",
"from sklearn.model_selection import train_test_split\n",
"from sklearn.metrics import classification_report, confusion_matrix"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 1.2.3 Задание\n",
"\n",
"Создайте класс по работе с тригонометрическими функциями. В классе \n",
"должны быть реализованы функции вычисления: \n",
"- косинуса; \n",
"- синуса; \n",
"- тангенса; \n",
"- арксинуса; \n",
"- арккосинуса; \n",
"- арктангенса; \n",
"- перевода из градусов в радианы."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"class TrigFunctions:\n",
" def __init__(self, precision=10):\n",
" # Константа для pi\n",
" self.pi = 3.141592653589793\n",
" # Переменная точности для вычислений\n",
" self.precision = precision\n",
"\n",
" # Факториал для ряда Тейлора\n",
" def factorial(self, n):\n",
" result = 1\n",
" for i in range(2, n + 1):\n",
" result *= i\n",
" return result\n",
"\n",
" # Приближенное вычисление синуса с помощью ряда Тейлора\n",
" def sin(self, angle_radians):\n",
" sin_approx = 0\n",
" \n",
" for n in range(self.precision):\n",
" sign = (-1) ** n\n",
" term = (angle_radians ** (2 * n + 1)) / self.factorial(2 * n + 1)\n",
" sin_approx += sign * term\n",
" \n",
" return sin_approx\n",
"\n",
" # Приближенное вычисление косинуса с помощью ряда Тейлора\n",
" def cos(self, angle_radians):\n",
" cos_approx = 0\n",
" \n",
" for n in range(self.precision):\n",
" sign = (-1) ** n\n",
" term = (angle_radians ** (2 * n)) / self.factorial(2 * n)\n",
" cos_approx += sign * term\n",
" \n",
" return cos_approx\n",
"\n",
" # Приближенное вычисление тангенса как sin/cos\n",
" def tan(self, angle_radians):\n",
" return self.sin(angle_radians) / self.cos(angle_radians)\n",
"\n",
" # Приближенное вычисление арксинуса с использованием метода Ньютона\n",
" def arcsin(self, value):\n",
" if value < -1 or value > 1:\n",
" return None # Арксинус определен только на отрезке [-1, 1]\n",
" \n",
" x = value\n",
" \n",
" for _ in range(self.precision):\n",
" x -= (self.sin(x) - value) / self.cos(x)\n",
" \n",
" return x\n",
"\n",
" # Арккосинус как pi/2 - арксинус\n",
" def arccos(self, value):\n",
" return self.pi / 2 - self.arcsin(value)\n",
"\n",
" # Приближенное вычисление арктангенса с использованием метода Ньютона\n",
" def arctan(self, value):\n",
" x = value\n",
" \n",
" for _ in range(self.precision):\n",
" x -= (self.tan(x) - value) / (1 + value ** 2)\n",
" \n",
" return x"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Угол (рад): 0.7853981633974483\n",
"sin: 0.7071067811865475\n",
"cos: 0.7071067811865475\n",
"tan: 1.0\n",
"arcsin (рад): 0.7853981633974483\n",
"arccos (рад): 0.7853981633974483\n",
"arctan (рад): 0.7853981633974483\n"
]
}
],
"source": [
"trig_functions = TrigFunctions(precision=10)\n",
"\n",
"angle = 3.141592653589793 / 4 # pi/4 радиан (45 градусов)\n",
"\n",
"sin_value = trig_functions.sin(angle)\n",
"cos_value = trig_functions.cos(angle)\n",
"tan_value = trig_functions.tan(angle)\n",
"arcsin_value = trig_functions.arcsin(sin_value)\n",
"arccos_value = trig_functions.arccos(cos_value)\n",
"arctan_value = trig_functions.arctan(tan_value)\n",
"\n",
"print(\"Угол (рад):\", angle)\n",
"print(\"sin:\", sin_value)\n",
"print(\"cos:\", cos_value)\n",
"print(\"tan:\", tan_value)\n",
"print(\"arcsin (рад):\", arcsin_value)\n",
"print(\"arccos (рад):\", arccos_value)\n",
"print(\"arctan (рад):\", arctan_value)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 1.2.2 Задание 1\n",
"\n",
"Представьте дерево показанное на рисунке с использованием списка из \n",
"списков. Выведите на печать корень дерева, а также его левое и правое \n",
"поддеревья."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Корень: a\n",
"Левое поддерево: ['b', ['d', []], ['e', []]]\n",
"Правое поддерево: ['c', ['f', []]]\n"
]
}
],
"source": [
"tr = ['a', ['b', ['d', []], ['e', []]], ['c', ['f', []]]]\n",
"\n",
"print(f\"Корень: {tr[0]}\")\n",
"print(f\"Левое поддерево: {tr[1]}\")\n",
"print(f\"Правое поддерево: {tr[2]}\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 1.2.2 Задание 2\n",
"\n",
"Дан класс, описывающий бинарное дерево. \n",
"\n",
"\n",
"```python\n",
"class Tree: \n",
" def __init__(self, data): \n",
" self.left = None \n",
" self.right = None \n",
" self.data = data \n",
" def PrintTree(self): \n",
" print(self.data) \n",
"```\n",
"Реализуйте в классе функцию для вставки нового элемента в дерево по \n",
"следующим правилам: \n",
" \n",
"- Левое поддерево узла содержит только узлы со значениями меньше, \n",
"чем значение в узле. \n",
"- Правое поддерево узла содержит только узлы со значениями меньше, \n",
"чем значение в узле. \n",
"- Каждое из левого и правого поддеревьев также должно быть \n",
"бинарным деревом поиска. \n",
"- Не должно быть повторяющихся узлов. \n",
"\n",
"Метод вставки сравнивает значение узла с родительским узлом и решает \n",
"куда доваить элемент (в левое или правое поддерево). Перепишите, метод \n",
"PrintTree для печати полной версии дерева."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"class BinaryTree: \n",
" def __init__(self, data): \n",
" self.left = None \n",
" self.right = None \n",
" self.data = data\n",
"\n",
" def insert(self, data):\n",
" if data < self.data:\n",
" if self.left is None:\n",
" self.left = BinaryTree(data)\n",
" else:\n",
" self.left.insert(data)\n",
" elif data > self.data:\n",
" if self.right is None:\n",
" self.right = BinaryTree(data)\n",
" else:\n",
" self.right.insert(data)\n",
"\n",
" def Print(self, level=0):\n",
" if self.right:\n",
" self.right.Print(level + 1)\n",
" \n",
" print(' ' * 4 * level + '->', self.data)\n",
" if self.left:\n",
" self.left.Print(level + 1) "
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" -> 17\n",
" -> 15\n",
" -> 12\n",
"-> 10\n",
" -> 7\n",
" -> 5\n",
" -> 3\n",
" -> 1\n"
]
}
],
"source": [
"root = BinaryTree(10)\n",
"root.insert(5)\n",
"root.insert(1)\n",
"root.insert(15)\n",
"root.insert(7)\n",
"root.insert(3)\n",
"root.insert(7)\n",
"root.insert(12)\n",
"root.insert(17)\n",
"\n",
"root.Print()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 1.3.1 Задание\n",
"\n",
"Постройте классификатор на основе дерева принятия решений следующего датасета:\n",
"\n",
"```python\n",
"X = np.array([[-1, -1], [-2, -1], [-3, -2], [1, 1], [2, 1], [3, 2]])\n",
"target = [0, 0, 0, 1, 1, 1]\n",
"```"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"ds = pd.DataFrame(np.array([[-1, -1], [-2, -1], [-3, -2], [1, 1], [2, 1], [3, 2]]))\n",
"target = [0, 0, 0, 1, 1, 1]"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"x_train, x_test, y_train, y_test = train_test_split(ds, target, test_size=0.2)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[Text(0.5, 0.75, 'x[1] <= 0.0\\ngini = 0.375\\nsamples = 4\\nvalue = [3, 1]'),\n",
" Text(0.25, 0.25, 'gini = 0.0\\nsamples = 3\\nvalue = [3, 0]'),\n",
" Text(0.375, 0.5, 'True '),\n",
" Text(0.75, 0.25, 'gini = 0.0\\nsamples = 1\\nvalue = [0, 1]'),\n",
" Text(0.625, 0.5, ' False')]"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"classifier = DecisionTreeClassifier()\n",
"classifier.fit(x_train, y_train)\n",
"\n",
"tree.plot_tree(classifier)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[1 1]\n",
"[[2]]\n",
" precision recall f1-score support\n",
"\n",
" 1 1.00 1.00 1.00 2\n",
"\n",
" accuracy 1.00 2\n",
" macro avg 1.00 1.00 1.00 2\n",
"weighted avg 1.00 1.00 1.00 2\n",
"\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/nktkln/.local/lib/python3.12/site-packages/sklearn/metrics/_classification.py:409: UserWarning: A single label was found in 'y_true' and 'y_pred'. For the confusion matrix to have the correct shape, use the 'labels' parameter to pass all known labels.\n",
" warnings.warn(\n"
]
}
],
"source": [
"y_pred = classifier.predict(x_test)\n",
"\n",
"print(y_pred)\n",
"print(confusion_matrix(y_test, y_pred))\n",
"print(classification_report(y_test, y_pred))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 1.4.1 Задание\n",
"\n",
"Задание. Постройте модель регрессии для данных из предыдущей рабочей \n",
"тетради.Для примера можно взять потребления газа (в миллионах \n",
"галлонов) в 48 штатах США или набор данных о качестве красного вина: \n",
"https://raw.githubusercontent.com/likarajo/petrol_consumption/master/data/petrol_consumption.csv \n",
"https://raw.githubusercontent.com/aniruddhachoudhury/Red-Wine-Quality/master/winequality-red.csv \n",
"\n",
"Постройте прогноз. Оцените точность модели."
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>fixed acidity</th>\n",
" <th>volatile acidity</th>\n",
" <th>citric acid</th>\n",
" <th>residual sugar</th>\n",
" <th>chlorides</th>\n",
" <th>free sulfur dioxide</th>\n",
" <th>total sulfur dioxide</th>\n",
" <th>density</th>\n",
" <th>pH</th>\n",
" <th>sulphates</th>\n",
" <th>alcohol</th>\n",
" <th>quality</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>1599.000000</td>\n",
" <td>1599.000000</td>\n",
" <td>1599.000000</td>\n",
" <td>1599.000000</td>\n",
" <td>1599.000000</td>\n",
" <td>1599.000000</td>\n",
" <td>1599.000000</td>\n",
" <td>1599.000000</td>\n",
" <td>1599.000000</td>\n",
" <td>1599.000000</td>\n",
" <td>1599.000000</td>\n",
" <td>1599.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>8.319637</td>\n",
" <td>0.527821</td>\n",
" <td>0.270976</td>\n",
" <td>2.538806</td>\n",
" <td>0.087467</td>\n",
" <td>15.874922</td>\n",
" <td>46.467792</td>\n",
" <td>0.996747</td>\n",
" <td>3.311113</td>\n",
" <td>0.658149</td>\n",
" <td>10.422983</td>\n",
" <td>5.636023</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>1.741096</td>\n",
" <td>0.179060</td>\n",
" <td>0.194801</td>\n",
" <td>1.409928</td>\n",
" <td>0.047065</td>\n",
" <td>10.460157</td>\n",
" <td>32.895324</td>\n",
" <td>0.001887</td>\n",
" <td>0.154386</td>\n",
" <td>0.169507</td>\n",
" <td>1.065668</td>\n",
" <td>0.807569</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>4.600000</td>\n",
" <td>0.120000</td>\n",
" <td>0.000000</td>\n",
" <td>0.900000</td>\n",
" <td>0.012000</td>\n",
" <td>1.000000</td>\n",
" <td>6.000000</td>\n",
" <td>0.990070</td>\n",
" <td>2.740000</td>\n",
" <td>0.330000</td>\n",
" <td>8.400000</td>\n",
" <td>3.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>7.100000</td>\n",
" <td>0.390000</td>\n",
" <td>0.090000</td>\n",
" <td>1.900000</td>\n",
" <td>0.070000</td>\n",
" <td>7.000000</td>\n",
" <td>22.000000</td>\n",
" <td>0.995600</td>\n",
" <td>3.210000</td>\n",
" <td>0.550000</td>\n",
" <td>9.500000</td>\n",
" <td>5.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>7.900000</td>\n",
" <td>0.520000</td>\n",
" <td>0.260000</td>\n",
" <td>2.200000</td>\n",
" <td>0.079000</td>\n",
" <td>14.000000</td>\n",
" <td>38.000000</td>\n",
" <td>0.996750</td>\n",
" <td>3.310000</td>\n",
" <td>0.620000</td>\n",
" <td>10.200000</td>\n",
" <td>6.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>9.200000</td>\n",
" <td>0.640000</td>\n",
" <td>0.420000</td>\n",
" <td>2.600000</td>\n",
" <td>0.090000</td>\n",
" <td>21.000000</td>\n",
" <td>62.000000</td>\n",
" <td>0.997835</td>\n",
" <td>3.400000</td>\n",
" <td>0.730000</td>\n",
" <td>11.100000</td>\n",
" <td>6.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>15.900000</td>\n",
" <td>1.580000</td>\n",
" <td>1.000000</td>\n",
" <td>15.500000</td>\n",
" <td>0.611000</td>\n",
" <td>72.000000</td>\n",
" <td>289.000000</td>\n",
" <td>1.003690</td>\n",
" <td>4.010000</td>\n",
" <td>2.000000</td>\n",
" <td>14.900000</td>\n",
" <td>8.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" fixed acidity volatile acidity citric acid residual sugar \\\n",
"count 1599.000000 1599.000000 1599.000000 1599.000000 \n",
"mean 8.319637 0.527821 0.270976 2.538806 \n",
"std 1.741096 0.179060 0.194801 1.409928 \n",
"min 4.600000 0.120000 0.000000 0.900000 \n",
"25% 7.100000 0.390000 0.090000 1.900000 \n",
"50% 7.900000 0.520000 0.260000 2.200000 \n",
"75% 9.200000 0.640000 0.420000 2.600000 \n",
"max 15.900000 1.580000 1.000000 15.500000 \n",
"\n",
" chlorides free sulfur dioxide total sulfur dioxide density \\\n",
"count 1599.000000 1599.000000 1599.000000 1599.000000 \n",
"mean 0.087467 15.874922 46.467792 0.996747 \n",
"std 0.047065 10.460157 32.895324 0.001887 \n",
"min 0.012000 1.000000 6.000000 0.990070 \n",
"25% 0.070000 7.000000 22.000000 0.995600 \n",
"50% 0.079000 14.000000 38.000000 0.996750 \n",
"75% 0.090000 21.000000 62.000000 0.997835 \n",
"max 0.611000 72.000000 289.000000 1.003690 \n",
"\n",
" pH sulphates alcohol quality \n",
"count 1599.000000 1599.000000 1599.000000 1599.000000 \n",
"mean 3.311113 0.658149 10.422983 5.636023 \n",
"std 0.154386 0.169507 1.065668 0.807569 \n",
"min 2.740000 0.330000 8.400000 3.000000 \n",
"25% 3.210000 0.550000 9.500000 5.000000 \n",
"50% 3.310000 0.620000 10.200000 6.000000 \n",
"75% 3.400000 0.730000 11.100000 6.000000 \n",
"max 4.010000 2.000000 14.900000 8.000000 "
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"url = 'https://raw.githubusercontent.com/aniruddhachoudhury/Red-Wine-Quality/master/winequality-red.csv'\n",
"\n",
"ds = pd.read_csv(url)\n",
"\n",
"ds.describe()"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"X = ds.iloc[:, :-1].values\n",
"y = ds.iloc[:, -1].values\n",
"\n",
"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"regressor = DecisionTreeRegressor()\n",
"regressor.fit(X_train, y_train)\n",
"tree.plot_tree(regressor)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" Actual Predicted\n",
"0 6 5.0\n",
"1 5 6.0\n",
"2 7 7.0\n",
"3 6 5.0\n",
"4 5 5.0\n",
".. ... ...\n",
"315 6 6.0\n",
"316 4 6.0\n",
"317 5 5.0\n",
"318 4 5.0\n",
"319 6 7.0\n",
"\n",
"[320 rows x 2 columns]\n"
]
}
],
"source": [
"y_pred = regressor.predict(X_test)\n",
"df = pd.DataFrame({'Actual':y_test, 'Predicted':y_pred})\n",
"print(df)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"MSE: 0.675\n",
"MAE: 0.475\n"
]
},
{
"data": {
"text/plain": [
"np.float64(8.427929427430094)"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"print('MSE:', metrics.mean_squared_error(y_test, y_pred))\n",
"print('MAE:', metrics.mean_absolute_error(y_test, y_pred))\n",
"\n",
"metrics.mean_absolute_error(y_test, y_pred) / np.average(y) * 100"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.12.5"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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