minor design changes

Reviewed-on: #9

lab_10/lab10.ipynb

@@ -0,0 +1,937 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "ae6b4270",
+   "metadata": {},
+   "source": [
+    "# Лабораторная работа 10"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b4b9ee35",
+   "metadata": {},
+   "source": [
+    "В качестве задачи оптимизации была выбрана классическая вариация задачи о рюкзаке: дан набор предметов, каждый с определенным весом и ценностью. Требуется определить, какие предметы взять с собой в рюкзак, чтобы их суммарная ценность была максимальной, а суммарный вес не превышал заданную грузоподъемность рюкзака. При этом каждый предмет можно взять только один раз или не брать вовсе (0/1).\n",
+    "\n",
+    "Используем соответствующий датасет, в котором имеется большое число вариантов задачи с различными параметрами: https://www.kaggle.com/datasets/warcoder/knapsack-problem?select=knapsack_5_items.csv"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "id": "80d638c3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.microsoft.datawrangler.viewer.v0+json": {
+       "columns": [
+        {
+         "name": "index",
+         "rawType": "int64",
+         "type": "integer"
+        },
+        {
+         "name": "Weights",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "Prices",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "Capacity",
+         "rawType": "int64",
+         "type": "integer"
+        },
+        {
+         "name": "Best picks",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "Best price",
+         "rawType": "float64",
+         "type": "float"
+        }
+       ],
+       "conversionMethod": "pd.DataFrame",
+       "ref": "f950228b-7bd0-4f67-b42e-48be43e881b9",
+       "rows": [
+        [
+         "0",
+         "[46 40 42 38 10]",
+         "[12 19 19 15  8]",
+         "40",
+         "[0. 1. 0. 0. 0.]",
+         "19.0"
+        ],
+        [
+         "1",
+         "[11 31  4  6  7]",
+         "[ 2  8 18 16  3]",
+         "64",
+         "[1. 1. 1. 1. 1.]",
+         "47.0"
+        ],
+        [
+         "2",
+         "[32 49 27 37 24]",
+         "[19 16 16  4  1]",
+         "87",
+         "[1. 0. 1. 0. 1.]",
+         "36.0"
+        ],
+        [
+         "3",
+         "[20 35 22 23 16]",
+         "[19 17 19  9  1]",
+         "21",
+         "[1. 0. 0. 0. 0.]",
+         "19.0"
+        ],
+        [
+         "4",
+         "[ 7 12 19 13 20]",
+         "[10 11 18 15  5]",
+         "50",
+         "[0. 1. 1. 1. 0.]",
+         "44.0"
+        ],
+        [
+         "9995",
+         "[18 12 11 49 32]",
+         "[12  3 17 19  7]",
+         "41",
+         "[1. 1. 1. 0. 0.]",
+         "32.0"
+        ],
+        [
+         "9996",
+         "[20  2 24  7  7]",
+         "[17 12  4  3  8]",
+         "17",
+         "[0. 1. 0. 1. 1.]",
+         "23.0"
+        ],
+        [
+         "9997",
+         "[43 43  5 15 23]",
+         "[15  5  7  2  7]",
+         "62",
+         "[1. 0. 1. 0. 0.]",
+         "22.0"
+        ],
+        [
+         "9998",
+         "[49  9 15 21 39]",
+         "[11 15  3 12 19]",
+         "65",
+         "[0. 1. 1. 0. 1.]",
+         "37.0"
+        ],
+        [
+         "9999",
+         "[25 36 42 19 39]",
+         "[15 12  7 18 12]",
+         "79",
+         "[1. 0. 0. 1. 0.]",
+         "33.0"
+        ]
+       ],
+       "shape": {
+        "columns": 5,
+        "rows": 10
+       }
+      },
+      "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>Weights</th>\n",
+       "      <th>Prices</th>\n",
+       "      <th>Capacity</th>\n",
+       "      <th>Best picks</th>\n",
+       "      <th>Best price</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>[46 40 42 38 10]</td>\n",
+       "      <td>[12 19 19 15  8]</td>\n",
+       "      <td>40</td>\n",
+       "      <td>[0. 1. 0. 0. 0.]</td>\n",
+       "      <td>19.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>[11 31  4  6  7]</td>\n",
+       "      <td>[ 2  8 18 16  3]</td>\n",
+       "      <td>64</td>\n",
+       "      <td>[1. 1. 1. 1. 1.]</td>\n",
+       "      <td>47.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>[32 49 27 37 24]</td>\n",
+       "      <td>[19 16 16  4  1]</td>\n",
+       "      <td>87</td>\n",
+       "      <td>[1. 0. 1. 0. 1.]</td>\n",
+       "      <td>36.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>[20 35 22 23 16]</td>\n",
+       "      <td>[19 17 19  9  1]</td>\n",
+       "      <td>21</td>\n",
+       "      <td>[1. 0. 0. 0. 0.]</td>\n",
+       "      <td>19.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>[ 7 12 19 13 20]</td>\n",
+       "      <td>[10 11 18 15  5]</td>\n",
+       "      <td>50</td>\n",
+       "      <td>[0. 1. 1. 1. 0.]</td>\n",
+       "      <td>44.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9995</th>\n",
+       "      <td>[18 12 11 49 32]</td>\n",
+       "      <td>[12  3 17 19  7]</td>\n",
+       "      <td>41</td>\n",
+       "      <td>[1. 1. 1. 0. 0.]</td>\n",
+       "      <td>32.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9996</th>\n",
+       "      <td>[20  2 24  7  7]</td>\n",
+       "      <td>[17 12  4  3  8]</td>\n",
+       "      <td>17</td>\n",
+       "      <td>[0. 1. 0. 1. 1.]</td>\n",
+       "      <td>23.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9997</th>\n",
+       "      <td>[43 43  5 15 23]</td>\n",
+       "      <td>[15  5  7  2  7]</td>\n",
+       "      <td>62</td>\n",
+       "      <td>[1. 0. 1. 0. 0.]</td>\n",
+       "      <td>22.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9998</th>\n",
+       "      <td>[49  9 15 21 39]</td>\n",
+       "      <td>[11 15  3 12 19]</td>\n",
+       "      <td>65</td>\n",
+       "      <td>[0. 1. 1. 0. 1.]</td>\n",
+       "      <td>37.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9999</th>\n",
+       "      <td>[25 36 42 19 39]</td>\n",
+       "      <td>[15 12  7 18 12]</td>\n",
+       "      <td>79</td>\n",
+       "      <td>[1. 0. 0. 1. 0.]</td>\n",
+       "      <td>33.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               Weights            Prices  Capacity        Best picks  \\\n",
+       "0     [46 40 42 38 10]  [12 19 19 15  8]        40  [0. 1. 0. 0. 0.]   \n",
+       "1     [11 31  4  6  7]  [ 2  8 18 16  3]        64  [1. 1. 1. 1. 1.]   \n",
+       "2     [32 49 27 37 24]  [19 16 16  4  1]        87  [1. 0. 1. 0. 1.]   \n",
+       "3     [20 35 22 23 16]  [19 17 19  9  1]        21  [1. 0. 0. 0. 0.]   \n",
+       "4     [ 7 12 19 13 20]  [10 11 18 15  5]        50  [0. 1. 1. 1. 0.]   \n",
+       "9995  [18 12 11 49 32]  [12  3 17 19  7]        41  [1. 1. 1. 0. 0.]   \n",
+       "9996  [20  2 24  7  7]  [17 12  4  3  8]        17  [0. 1. 0. 1. 1.]   \n",
+       "9997  [43 43  5 15 23]  [15  5  7  2  7]        62  [1. 0. 1. 0. 0.]   \n",
+       "9998  [49  9 15 21 39]  [11 15  3 12 19]        65  [0. 1. 1. 0. 1.]   \n",
+       "9999  [25 36 42 19 39]  [15 12  7 18 12]        79  [1. 0. 0. 1. 0.]   \n",
+       "\n",
+       "      Best price  \n",
+       "0           19.0  \n",
+       "1           47.0  \n",
+       "2           36.0  \n",
+       "3           19.0  \n",
+       "4           44.0  \n",
+       "9995        32.0  \n",
+       "9996        23.0  \n",
+       "9997        22.0  \n",
+       "9998        37.0  \n",
+       "9999        33.0  "
+      ]
+     },
+     "execution_count": 70,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "df = pd.read_csv(\"..//..//static//csv//knapsack_5_items.csv\")\n",
+    "\n",
+    "pd.concat([df.head(5), df.tail(5)])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "884f0cd1",
+   "metadata": {},
+   "source": [
+    "### Структура хромосомы и тип данных гена\n",
+    "\n",
+    "В данном случае хромосома будет представлять из себя список длины n (количество предметов в конкректной задаче), который представляет собой решение задачи рюкзака — то есть указывает, какие предметы включить в рюкзак.\n",
+    "\n",
+    "Пример: [1, 0, 1, 0, 0]. В примере выбраны первый и третий предметы.\n",
+    "\n",
+    "Ген же — это одно значение в хромосоме. \n",
+    "\n",
+    "Тип данных: int.\n",
+    " \n",
+    "Возможные значения:\n",
+    "* 1 — предмет в рюкзаке;\n",
+    "* 0 — предмет не в рюкзаке."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "92661ad8",
+   "metadata": {},
+   "source": [
+    "### Реализация функции генерации начальной популяции и ее тест:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "id": "9a7b0970",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[[0, 1, 0, 0, 0],\n",
+       " [1, 0, 0, 0, 1],\n",
+       " [1, 0, 0, 0, 0],\n",
+       " [1, 0, 1, 0, 1],\n",
+       " [1, 1, 1, 1, 0],\n",
+       " [0, 1, 0, 0, 1],\n",
+       " [1, 0, 1, 1, 1],\n",
+       " [1, 0, 0, 0, 1],\n",
+       " [1, 0, 1, 1, 0],\n",
+       " [1, 1, 0, 1, 1]]"
+      ]
+     },
+     "execution_count": 71,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import random\n",
+    "\n",
+    "def create_individual(elements_num): \n",
+    "    # Генерирует случайную двоичную строку той же длины, что и список элементов\n",
+    "    return [random.randint(0, 1) for _ in range(elements_num)]\n",
+    "\n",
+    "def create_population(elements_num, population_size):  \n",
+    "    return [create_individual(elements_num) for _ in range(population_size)]\n",
+    "\n",
+    "create_population(5, 10)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bc114201",
+   "metadata": {},
+   "source": [
+    "### Реализация фитнес-функции и ее тест:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "id": "0809a0b1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "44"
+      ]
+     },
+     "execution_count": 72,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "def evaluate_fitness(individual, weights, prices, capacity):\n",
+    "    total_value = total_weight = 0\n",
+    "    for i in range(len(individual)):\n",
+    "        if individual[i] == 1:\n",
+    "            total_value += prices[i]\n",
+    "            total_weight += weights[i]\n",
+    "    # Если общий вес превышает вместимость ранца, устанавливается значение 0 (неверное решение)\n",
+    "    return total_value if total_weight <= capacity else 0\n",
+    "\n",
+    "evaluate_fitness([0, 1, 1, 1, 0], [7, 12, 19, 13, 20], [10, 11, 18, 15, 5], 50)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cccc6557",
+   "metadata": {},
+   "source": [
+    "### Реализация оператора кроссинговера и его тест:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "id": "4d5a13d7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "([0, 1, 1, 0, 0], [1, 0, 1, 1, 0])"
+      ]
+     },
+     "execution_count": 73,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# одноточечный кроссинговер\n",
+    "def crossover(parent1, parent2):\n",
+    "    point = random.randint(1, len(parent1) - 1)\n",
+    "    return (parent1[:point] + parent2[point:], parent2[:point] + parent1[point:])\n",
+    "\n",
+    "crossover([0, 1, 1, 1, 0], [1, 0, 1, 0, 0])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "08c626b5",
+   "metadata": {},
+   "source": [
+    "### Реализация двух операторов мутации и их тест:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "id": "66021b53",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0, 1, 1, 1, 0]\n",
+      "================\n",
+      "[0, 1, 0, 1, 0]\n",
+      "================\n",
+      "[0, 0, 0, 1, 1]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Мутация 1: побитовая замена\n",
+    "def mutate_flip_bits(individual, mutation_rate):\n",
+    "    for i in range(len(individual)):\n",
+    "        # Сработает с некоторой вероятностью\n",
+    "        if random.random() < mutation_rate:\n",
+    "            individual[i] = 1 - individual[i]\n",
+    "\n",
+    "# Мутация 2: случайный свап двух генов\n",
+    "def mutate_swap_genes(individual, mutation_rate):\n",
+    "    if random.random() < mutation_rate:\n",
+    "        i, j = random.sample(range(len(individual)), 2)\n",
+    "        individual[i], individual[j] = individual[j], individual[i]\n",
+    "\n",
+    "individual = [0, 1, 1, 1, 0]\n",
+    "print(individual)\n",
+    "mutate_flip_bits(individual, 0.5)\n",
+    "print(\"================\")\n",
+    "print(individual)\n",
+    "print(\"================\")\n",
+    "mutate_swap_genes(individual, 1)\n",
+    "print(individual)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d199e789",
+   "metadata": {},
+   "source": [
+    "### И наконец реализуем сам генетический алгоритм:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "id": "17093d62",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Параметры алгоритма\n",
+    "population_size = 100\n",
+    "num_generations = 10\n",
+    "mutation_rate = 0.1\n",
+    "mutation_strategy = 'flip'\n",
+    "\n",
+    "# Выбор участников кроссинговера с помощью селекции на основе рулетки\n",
+    "def select_parents(population, weights, prices, capacity):\n",
+    "    fitness_values = [evaluate_fitness(ind, weights, prices, capacity) for ind in population]\n",
+    "    total_fitness = sum(fitness_values)\n",
+    "    if total_fitness == 0:\n",
+    "        return random.choice(population), random.choice(population)\n",
+    "    # чем выше значение фитнес-функции, тем больше шанс на выбор\n",
+    "    probabilities = [f / total_fitness for f in fitness_values]\n",
+    "    return random.choices(population, weights=probabilities, k=2)\n",
+    "\n",
+    "def genetic_algorithm(weights, prices, capacity, population_size = 100, num_generations = 10, mutation_rate = 0.1, mutation_strategy='flip'):\n",
+    "    elements_num = len(weights)\n",
+    "    population = create_population(elements_num, population_size)\n",
+    "\n",
+    "    for _ in range(num_generations):\n",
+    "        new_population = []\n",
+    "        for _ in range(population_size // 2):\n",
+    "            p1, p2 = select_parents(population, weights, prices, capacity)\n",
+    "            c1, c2 = crossover(p1, p2)\n",
+    "            if mutation_strategy == 'flip':\n",
+    "                mutate_flip_bits(c1, mutation_rate)\n",
+    "                mutate_flip_bits(c2, mutation_rate)\n",
+    "            elif mutation_strategy == 'swap':\n",
+    "                mutate_swap_genes(c1, mutation_rate)\n",
+    "                mutate_swap_genes(c2, mutation_rate)\n",
+    "            new_population.extend([c1, c2])\n",
+    "        population = new_population\n",
+    "\n",
+    "    best = max(population, key=lambda ind: evaluate_fitness(ind, weights, prices, capacity))\n",
+    "    best_value = evaluate_fitness(best, weights, prices, capacity)\n",
+    "    return best, best_value"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f241602a",
+   "metadata": {},
+   "source": [
+    "Применим его для всех случаев из датасета:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "id": "9ef718ac",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.microsoft.datawrangler.viewer.v0+json": {
+       "columns": [
+        {
+         "name": "index",
+         "rawType": "int64",
+         "type": "integer"
+        },
+        {
+         "name": "Weights",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "Prices",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "Capacity",
+         "rawType": "int64",
+         "type": "integer"
+        },
+        {
+         "name": "Best picks",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "Best price",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "algorithmPicks",
+         "rawType": "object",
+         "type": "unknown"
+        },
+        {
+         "name": "algorithmPrice",
+         "rawType": "int64",
+         "type": "integer"
+        }
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+       "<div>\n",
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+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Weights</th>\n",
+       "      <th>Prices</th>\n",
+       "      <th>Capacity</th>\n",
+       "      <th>Best picks</th>\n",
+       "      <th>Best price</th>\n",
+       "      <th>algorithmPicks</th>\n",
+       "      <th>algorithmPrice</th>\n",
+       "    </tr>\n",
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+       "      <td>19</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>[11 31  4  6  7]</td>\n",
+       "      <td>[ 2  8 18 16  3]</td>\n",
+       "      <td>64</td>\n",
+       "      <td>[1. 1. 1. 1. 1.]</td>\n",
+       "      <td>47.0</td>\n",
+       "      <td>[1, 1, 1, 1, 1]</td>\n",
+       "      <td>47</td>\n",
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+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>[32 49 27 37 24]</td>\n",
+       "      <td>[19 16 16  4  1]</td>\n",
+       "      <td>87</td>\n",
+       "      <td>[1. 0. 1. 0. 1.]</td>\n",
+       "      <td>36.0</td>\n",
+       "      <td>[1, 0, 1, 0, 1]</td>\n",
+       "      <td>36</td>\n",
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+       "      <th>3</th>\n",
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+       "      <td>[ 7 12 19 13 20]</td>\n",
+       "      <td>[10 11 18 15  5]</td>\n",
+       "      <td>50</td>\n",
+       "      <td>[0. 1. 1. 1. 0.]</td>\n",
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+       "      <td>[0, 1, 1, 1, 0]</td>\n",
+       "      <td>44</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9995</th>\n",
+       "      <td>[18 12 11 49 32]</td>\n",
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+       "  </tbody>\n",
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+       "</div>"
+      ],
+      "text/plain": [
+       "               Weights            Prices  Capacity        Best picks  \\\n",
+       "0     [46 40 42 38 10]  [12 19 19 15  8]        40  [0. 1. 0. 0. 0.]   \n",
+       "1     [11 31  4  6  7]  [ 2  8 18 16  3]        64  [1. 1. 1. 1. 1.]   \n",
+       "2     [32 49 27 37 24]  [19 16 16  4  1]        87  [1. 0. 1. 0. 1.]   \n",
+       "3     [20 35 22 23 16]  [19 17 19  9  1]        21  [1. 0. 0. 0. 0.]   \n",
+       "4     [ 7 12 19 13 20]  [10 11 18 15  5]        50  [0. 1. 1. 1. 0.]   \n",
+       "9995  [18 12 11 49 32]  [12  3 17 19  7]        41  [1. 1. 1. 0. 0.]   \n",
+       "9996  [20  2 24  7  7]  [17 12  4  3  8]        17  [0. 1. 0. 1. 1.]   \n",
+       "9997  [43 43  5 15 23]  [15  5  7  2  7]        62  [1. 0. 1. 0. 0.]   \n",
+       "9998  [49  9 15 21 39]  [11 15  3 12 19]        65  [0. 1. 1. 0. 1.]   \n",
+       "9999  [25 36 42 19 39]  [15 12  7 18 12]        79  [1. 0. 0. 1. 0.]   \n",
+       "\n",
+       "      Best price   algorithmPicks  algorithmPrice  \n",
+       "0           19.0  [0, 1, 0, 0, 0]              19  \n",
+       "1           47.0  [1, 1, 1, 1, 1]              47  \n",
+       "2           36.0  [1, 0, 1, 0, 1]              36  \n",
+       "3           19.0  [1, 0, 0, 0, 0]              19  \n",
+       "4           44.0  [0, 1, 1, 1, 0]              44  \n",
+       "9995        32.0  [1, 1, 1, 0, 0]              32  \n",
+       "9996        23.0  [0, 1, 0, 1, 1]              23  \n",
+       "9997        22.0  [1, 0, 1, 0, 0]              22  \n",
+       "9998        37.0  [0, 1, 1, 0, 1]              37  \n",
+       "9999        33.0  [1, 0, 0, 1, 0]              33  "
+      ]
+     },
+     "execution_count": 80,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import ast\n",
+    "import re\n",
+    "\n",
+    "picks = []\n",
+    "best_prices = []\n",
+    "\n",
+    "def fix_list_string(s):\n",
+    "    # Удалить пробел сразу после [ и сразу перед ]\n",
+    "    s = re.sub(r'\\[\\s*', '[', s)\n",
+    "    s = re.sub(r'\\s*\\]', ']', s)\n",
+    "    # Заменить все группы пробелов на запятую\n",
+    "    s = re.sub(r'\\s+', ',', s)\n",
+    "    return s\n",
+    "\n",
+    "for _, row in df.iterrows():\n",
+    "    weights = ast.literal_eval(fix_list_string(row['Weights']))\n",
+    "    prices = ast.literal_eval(fix_list_string(row['Prices']))\n",
+    "    capacity = row['Capacity']\n",
+    "\n",
+    "    best_individual, best_value = genetic_algorithm(weights, prices, capacity, population_size, num_generations, mutation_rate, mutation_strategy)\n",
+    "    \n",
+    "    picks.append(best_individual)\n",
+    "    best_prices.append(best_value)\n",
+    "\n",
+    "df['algorithmPicks'] = picks\n",
+    "df['algorithmPrice'] = best_prices\n",
+    "\n",
+    "pd.concat([df.head(5), df.tail(5)])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "72958862",
+   "metadata": {},
+   "source": [
+    "По полученным результатам видно, что ответы алгоритма совпадают с теми ответами, которые уже имелись в наборе данных. Поэтому, можно сказать, что для таких условий задачи алгоритм работает успешно даже с 10 поколениями"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "aimenv",
+   "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": 5
+}