task-5.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "# Задача 5 (бонусная): экспериментальное исследование алгоритмов для регулярных запросов #"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "7yk54_Ej32Bt",
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## Подготовка окружения ##\n",
    "\n",
    "Note: Linux only\n",
    "\n",
    "Установка необходимых зависимостей:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "YK4kVHI-13X8",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "!pip install -r requirements.txt "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "!pip install pycubool matplotlib"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "HngcVASm4LCH",
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## Введение \n",
    "В эксперименте рассматривается задача достижимости в графе с регулярными ограничениями. Используем библиотеки для работы с разреженными матрицами [scipy.sparse](https://github.com/scipy/scipy) и [pycubool](https://github.com/JetBrains-Research/cuBool) для исследования производительности решений.\n",
    "\n",
    "### О задаче достижимости \n",
    "Пусть дан конечный ориентированный помеченный граф $G = (V, E, L)$. Каждому пути $\\pi \\in E^*$, состоящему из рёбер с метками $l_1, ..., l_n \\in L$, сопоставляется слово по правилу $\\omega(\\pi) = l_1 + ... + l_n$, где $+$ --- конкатенация.\n",
    "\n",
    "Возьмем $R$ --- регулярное выражение.\n",
    "\n",
    "Рассмотрим следующие варианты:\n",
    "\n",
    "#### 1. Достижимость между всеми парами вершин\n",
    "Необходимо найти пары вершин $G$, такие, что между ними существует путь, которому сопоставляется слово из языка, задаваемого $R$. \n",
    "Далее обозначим $V_S$ множество стартовых вершин, $V_F$ множество финальных вершин. \n",
    "\n",
    "#### 2. Достижимость для всего множества заданных вершин\n",
    "\n",
    "Необходимо найти все финальные вершины, такие, что до них существуют пути из стартовых, которым сопоставляются слова из языка, задаваемого $R$.\n",
    "\n",
    "#### 3. Достижимость для каждой вершины из заданного множества стартовых вершин\n",
    "\n",
    "Необходимо найти множество пар вершин-начал и вершин-концов путей, которым сопоставляются слова из языка, задаваемого $R$ \n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "6EsyfZQF9oS2",
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## Исследуемые решения ##\n",
    "\n",
    "Рассмотрим краткие идеи решения каждой задачи:\n",
    "\n",
    "#### 1. Достижимость между всеми парами вершин\n",
    "По графу и регулярному выражению строятся конечные автоматы, представленные в виде разреженных булевых матриц, а затем ищется их пересечение с помощью тензорного произведения. Финальном шагом является подсчет транзитивного замыкания пересечения автоматов.\n",
    "\n",
    "#### 2. Достижимость для всего множества заданных вершин ####\n",
    "Аналогично по графу и регулярному выражению строятся конечные автоматы, а затем производится синхронный поиск в ширину с помощью матричного произведения.\n",
    "\n",
    "#### 3. Достижимость для каждой вершины из заданного множества стартовых вершин ####\n",
    "Аналогично задаче 2, однако матрица, отражающая в каких состояниях мы находимся на текущей итерации поиска в ширину имеет расширенный вид, так как необходимо поддерживать информацию для каждой стартовой вершины графа отдельно.\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "FO4YQ-Yz_Kg8",
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## Реализация\n",
    "\n",
    "Представленные реализации алгоритмов с помощью библиотек **scipy.sparse** и **pycubool** идентичны. \n",
    "\n",
    "На основе **scipy.sparse** представлена в модуле `project.rpq`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "YTautIrD-Hfd",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "from project.rpq import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "PkTJAG_wCtWL",
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "На основе **pycubool**:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "vUpz_x14CwVd",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "from typing import Dict, Set, Any, List\n",
    "\n",
    "from pyformlang.finite_automaton import State, EpsilonNFA\n",
    "\n",
    "from pycubool import Matrix\n",
    "\n",
    "\n",
    "class BoolMatrixAutomatonPyCuBool:\n",
    "    def __init__(\n",
    "        self,\n",
    "        state_to_idx: Dict[State, int],\n",
    "        start_states: Set[State],\n",
    "        final_states: Set[State],\n",
    "        b_mtx: Dict[Any, Matrix],\n",
    "    ):\n",
    "        self.state_to_idx = state_to_idx\n",
    "        self.start_states = start_states\n",
    "        self.final_states = final_states\n",
    "        self.b_mtx = b_mtx\n",
    "\n",
    "    def __and__(\n",
    "        self, other: \"BoolMatrixAutomatonPyCuBool\"\n",
    "    ) -> \"BoolMatrixAutomatonPyCuBool\":\n",
    "        \n",
    "        inter_labels = self.b_mtx.keys() & other.b_mtx.keys()\n",
    "        inter_b_mtx = {\n",
    "            label: self.b_mtx[label].kronecker(other.b_mtx[label])\n",
    "            for label in inter_labels\n",
    "        }\n",
    "        inter_state_to_idx = dict()\n",
    "        inter_start_states = set()\n",
    "        inter_final_states = set()\n",
    "        for self_state, self_idx in self.state_to_idx.items():\n",
    "            for other_state, other_idx in other.state_to_idx.items():\n",
    "                state = State((self_state.value, other_state.value))\n",
    "                idx = self_idx * len(other.state_to_idx) + other_idx\n",
    "                inter_state_to_idx[state] = idx\n",
    "                if (\n",
    "                    self_state in self.start_states\n",
    "                    and other_state in other.start_states\n",
    "                ):\n",
    "                    inter_start_states.add(state)\n",
    "                if (\n",
    "                    self_state in self.final_states\n",
    "                    and other_state in other.final_states\n",
    "                ):\n",
    "                    inter_final_states.add(state)\n",
    "        return BoolMatrixAutomatonPyCuBool(\n",
    "            state_to_idx=inter_state_to_idx,\n",
    "            start_states=inter_start_states,\n",
    "            final_states=inter_final_states,\n",
    "            b_mtx=inter_b_mtx,\n",
    "        )\n",
    "\n",
    "    def transitive_closure(self) -> Matrix:\n",
    "\n",
    "        state_count = max(len(self.state_to_idx), 1)\n",
    "        transitive_closure = Matrix.empty((state_count, state_count))\n",
    "        for b_mtx in self.b_mtx.values():\n",
    "            transitive_closure = transitive_closure.ewiseadd(b_mtx)\n",
    "\n",
    "        prev_nnz, cur_nnz = None, transitive_closure.nvals\n",
    "        if not cur_nnz:\n",
    "            return transitive_closure\n",
    "\n",
    "        while prev_nnz != cur_nnz:\n",
    "            transitive_closure.mxm(\n",
    "                transitive_closure, out=transitive_closure, accumulate=True\n",
    "            )\n",
    "            prev_nnz, cur_nnz = cur_nnz, transitive_closure.nvals\n",
    "        return transitive_closure\n",
    "\n",
    "    @classmethod\n",
    "    def from_nfa(cls, nfa: EpsilonNFA) -> \"BoolMatrixAutomatonPyCuBool\":\n",
    "\n",
    "        state_to_idx = {state: idx for idx, state in enumerate(nfa.states)}\n",
    "        return cls(\n",
    "            state_to_idx=state_to_idx,\n",
    "            start_states=nfa.start_states.copy(),\n",
    "            final_states=nfa.final_states.copy(),\n",
    "            b_mtx=cls._b_mtx_from_nfa(\n",
    "                nfa=nfa,\n",
    "                state_to_idx=state_to_idx,\n",
    "            ),\n",
    "        )\n",
    "\n",
    "    @staticmethod\n",
    "    def _b_mtx_from_nfa(\n",
    "        nfa: EpsilonNFA, state_to_idx: Dict[State, int]\n",
    "    ) -> Dict[Any, Matrix]:\n",
    "\n",
    "        b_mtx = dict()\n",
    "        state_from_to_transitions = nfa.to_dict()\n",
    "        for label in nfa.symbols:\n",
    "            mtx = Matrix.empty((len(nfa.states), len(nfa.states)))\n",
    "            for state_from, transitions in state_from_to_transitions.items():\n",
    "                states_to = transitions.get(label, set())\n",
    "                if not isinstance(states_to, set):\n",
    "                    states_to = {states_to}\n",
    "                for state_to in states_to:\n",
    "                    mtx[state_to_idx[state_from], state_to_idx[state_to]] = True\n",
    "            b_mtx[label] = mtx\n",
    "        return b_mtx\n",
    "\n",
    "    def _direct_sum(\n",
    "        self, other: \"BoolMatrixAutomatonPyCuBool\"\n",
    "    ) -> \"BoolMatrixAutomatonPyCuBool\":\n",
    "\n",
    "        shifted_state_to_idx = {\n",
    "            state: len(self.state_to_idx) + idx\n",
    "            for state, idx in other.state_to_idx.items()\n",
    "        }\n",
    "        state_to_idx = {**self.state_to_idx, **shifted_state_to_idx}\n",
    "        start_states = self.start_states | other.start_states\n",
    "        final_states = self.final_states | other.final_states\n",
    "        b_mtx = dict()\n",
    "        for label in self.b_mtx.keys() & other.b_mtx.keys():\n",
    "            mtx = Matrix.empty((len(state_to_idx), len(state_to_idx)))\n",
    "            for i, j in self.b_mtx[label]:\n",
    "                mtx[i, j] = True\n",
    "            for i, j in other.b_mtx[label]:\n",
    "                mtx[len(self.state_to_idx) + i, len(self.state_to_idx) + j] = True\n",
    "            b_mtx[label] = mtx\n",
    "        return BoolMatrixAutomatonPyCuBool(\n",
    "            state_to_idx=state_to_idx,\n",
    "            start_states=start_states,\n",
    "            final_states=final_states,\n",
    "            b_mtx=b_mtx,\n",
    "        )\n",
    "\n",
    "    def sync_bfs(\n",
    "        self,\n",
    "        other: \"BoolMatrixAutomatonPyCuBool\",\n",
    "        reachable_per_node: bool,\n",
    "    ) -> Set[Any]:\n",
    "\n",
    "        if not self.state_to_idx or not other.state_to_idx:\n",
    "            return set()\n",
    "\n",
    "        ordered_start_states = list(self.start_states)\n",
    "\n",
    "        direct_sum = other._direct_sum(self)\n",
    "        initial_front = self._init_sync_bfs_front(\n",
    "            other=other,\n",
    "            reachable_per_node=reachable_per_node,\n",
    "            ordered_start_states=ordered_start_states,\n",
    "        )\n",
    "\n",
    "        front = self._copy_mtx(initial_front)\n",
    "        visited = self._copy_mtx(front)\n",
    "\n",
    "        other_states_num = len(other.state_to_idx)\n",
    "\n",
    "        while True:\n",
    "            visited_nnz = visited.nvals\n",
    "            new_front = self._copy_mtx(front)\n",
    "\n",
    "            for _, mtx in direct_sum.b_mtx.items():\n",
    "                product: Matrix = front.mxm(mtx)\n",
    "                new_front_step = Matrix.empty(product.shape)\n",
    "                for i, j in product:\n",
    "                    if j >= other_states_num:\n",
    "                        continue\n",
    "                    row = product[i : i + 1, other_states_num:]\n",
    "                    if not row.nvals:\n",
    "                        continue\n",
    "                    row_shift = i // other_states_num * other_states_num\n",
    "                    new_front_step[row_shift + j, j] = True\n",
    "                    for _, rj in row:\n",
    "                        new_front_step[row_shift + j, other_states_num + rj] = True\n",
    "                new_front = new_front.ewiseadd(new_front_step)\n",
    "\n",
    "            new_front_without_visited = Matrix.empty(new_front.shape)\n",
    "            for i, j in set(new_front.to_list()).difference(set(visited.to_list())):\n",
    "              new_front_without_visited[i, j] = True\n",
    "\n",
    "            visited = visited.ewiseadd(new_front_without_visited)\n",
    "            front = new_front_without_visited\n",
    "\n",
    "            if visited_nnz == visited.nvals:\n",
    "                break\n",
    "\n",
    "        self_idx_to_state = {idx: state for state, idx in self.state_to_idx.items()}\n",
    "        other_idx_to_state = {idx: state for state, idx in other.state_to_idx.items()}\n",
    "\n",
    "        result = set()\n",
    "        nonzero = set(visited.to_list()).difference(set(initial_front.to_list()))\n",
    "        for i, j in nonzero:\n",
    "            if (\n",
    "                other_idx_to_state[i % other_states_num] not in other.final_states\n",
    "                or j < other_states_num\n",
    "            ):\n",
    "                continue\n",
    "            self_state = self_idx_to_state[j - other_states_num]\n",
    "            if self_state not in self.final_states:\n",
    "                continue\n",
    "            result.add(\n",
    "                self_state.value\n",
    "                if not reachable_per_node\n",
    "                else (\n",
    "                    ordered_start_states[i // other_states_num].value,\n",
    "                    self_state.value,\n",
    "                )\n",
    "            )\n",
    "        return result\n",
    "\n",
    "    @staticmethod\n",
    "    def _copy_mtx(matrix: Matrix) -> Matrix:\n",
    "        \n",
    "        copy = Matrix.empty(matrix.shape)\n",
    "        for i, j in matrix:\n",
    "            copy[i, j] = True\n",
    "        return copy\n",
    "\n",
    "    def _init_sync_bfs_front(\n",
    "        self,\n",
    "        other: \"BoolMatrixAutomatonPyCuBool\",\n",
    "        reachable_per_node: bool,\n",
    "        ordered_start_states: List[State],\n",
    "    ) -> Matrix:\n",
    "\n",
    "        def front_with_self_start_row(self_start_row: List):\n",
    "            front = Matrix.empty(\n",
    "                (\n",
    "                    len(other.state_to_idx),\n",
    "                    len(self.state_to_idx) + len(other.state_to_idx),\n",
    "                )\n",
    "            )\n",
    "            for state in other.start_states:\n",
    "                idx = other.state_to_idx[state]\n",
    "                front[idx, idx] = True\n",
    "                for i, item in enumerate(self_start_row):\n",
    "                    if item:\n",
    "                        front[idx, len(other.state_to_idx) + i] = item\n",
    "            return front\n",
    "\n",
    "        if not reachable_per_node:\n",
    "            start_indices = set(\n",
    "                self.state_to_idx[state] for state in ordered_start_states\n",
    "            )\n",
    "            return front_with_self_start_row(\n",
    "                [idx in start_indices for idx in range(len(self.state_to_idx))]\n",
    "            )\n",
    "\n",
    "        fronts = [\n",
    "            front_with_self_start_row(\n",
    "                [\n",
    "                    idx == self.state_to_idx[start]\n",
    "                    for idx in range(len(self.state_to_idx))\n",
    "                ]\n",
    "            )\n",
    "            for start in ordered_start_states\n",
    "        ]\n",
    "\n",
    "        mtx = Matrix.empty(\n",
    "            (\n",
    "                len(fronts) * len(other.state_to_idx),\n",
    "                len(other.state_to_idx) + len(self.state_to_idx),\n",
    "            )\n",
    "        )\n",
    "        for front_number, front in enumerate(fronts):\n",
    "            for i, j in front:\n",
    "                mtx[len(other.state_to_idx) * front_number + i, j] = True\n",
    "        return mtx\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "id": "DjRZElIGC5aO",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "from typing import Set, Optional, Tuple, Any, Callable\n",
    "\n",
    "from networkx import MultiDiGraph\n",
    "from pyformlang.regular_expression import Regex\n",
    "\n",
    "from project import (\n",
    "    graph_to_epsilon_nfa,\n",
    "    generate_min_dfa_by_regex,\n",
    ")\n",
    "\n",
    "def rpq_tensor_pycubool(\n",
    "    graph: MultiDiGraph,\n",
    "    query: Regex,\n",
    "    start_states: Optional[Set],\n",
    "    final_states: Optional[Set],\n",
    ") -> Set[Tuple[Any, Any]]:\n",
    "    \n",
    "    nfa_bool_mtx = BoolMatrixAutomatonPyCuBool.from_nfa(\n",
    "        graph_to_epsilon_nfa(\n",
    "            graph=graph,\n",
    "            start_states=start_states,\n",
    "            final_states=final_states,\n",
    "        )\n",
    "    )\n",
    "    query_bool_mtx = BoolMatrixAutomatonPyCuBool.from_nfa(\n",
    "        generate_min_dfa_by_regex(regex=query),\n",
    "    )\n",
    "    intersection_bool_mtx = nfa_bool_mtx & query_bool_mtx\n",
    "    idx_to_state = {\n",
    "        idx: state for state, idx in intersection_bool_mtx.state_to_idx.items()\n",
    "    }\n",
    "    transitive_closure = intersection_bool_mtx.transitive_closure()\n",
    "    result = set()\n",
    "    for state_from_idx, state_to_idx in transitive_closure:\n",
    "        state_from, state_to = idx_to_state[state_from_idx], idx_to_state[state_to_idx]\n",
    "        if (\n",
    "            state_from in intersection_bool_mtx.start_states\n",
    "            and state_to in intersection_bool_mtx.final_states\n",
    "        ):\n",
    "            state_from_graph_value, _ = state_from.value\n",
    "            state_to_graph_value, _ = state_to.value\n",
    "            result.add(\n",
    "                (state_from_graph_value, state_to_graph_value),\n",
    "            )\n",
    "    return result\n",
    "\n",
    "\n",
    "def rpq_bfs_pycubool(\n",
    "    graph: MultiDiGraph,\n",
    "    query: Regex,\n",
    "    start_states: Optional[Set],\n",
    "    final_states: Optional[Set],\n",
    "    mode: RpqMode,\n",
    ") -> Set[Any]:\n",
    "\n",
    "    nfa_bool_mtx = BoolMatrixAutomatonPyCuBool.from_nfa(\n",
    "        graph_to_epsilon_nfa(\n",
    "            graph=graph,\n",
    "            start_states=start_states,\n",
    "            final_states=final_states,\n",
    "        )\n",
    "    )\n",
    "    query_bool_mtx = BoolMatrixAutomatonPyCuBool.from_nfa(\n",
    "        generate_min_dfa_by_regex(regex=query),\n",
    "    )\n",
    "    return nfa_bool_mtx.sync_bfs(\n",
    "        other=query_bool_mtx,\n",
    "        reachable_per_node=mode\n",
    "        == RpqMode.FIND_REACHABLE_FOR_EACH_START_NODE,\n",
    "    )\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "X5D9uVr1HCHf",
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## Цель работы ##\n",
    "\n",
    "В качестве цели работы необходимо найти ответы на следующие вопросы:\n",
    "\n",
    "* Когда использование специализированных библиотек разреженной линейной алгебры даёт выигрыш в производительности? На примере каждой из трех задач.\n",
    "* Если использовать специализированные библиотеки, то начиная с какого размера стартового множества выгоднее решать задачу для всех пар и выбирать нужные?\n",
    "* Если использовать специализированные библиотеки, то на сколько решение второй задачи медленнее решения третьей при одинаковых начальных условиях? "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "UN60nNpvN4rL",
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## Условия эксперимента ##\n",
    "\n",
    "Данный раздел посвящен описанию характеристик оборудования, на котором производителись замеры\n",
    "\n",
    "### Характеристики оборудования ###\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "EqiDK5FiOGt7",
    "outputId": "e61980ff-2c7e-4efa-9ff0-8615e1c65625",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "========== OS  ==========\n",
      "No LSB modules are available.\n",
      "Distributor ID:\tUbuntu\n",
      "Description:\tUbuntu 20.04 LTS\n",
      "Release:\t20.04\n",
      "Codename:\tfocal\n",
      "\n",
      "========== CPU ==========\n",
      "Architecture:                    x86_64\n",
      "CPU op-mode(s):                  32-bit, 64-bit\n",
      "Byte Order:                      Little Endian\n",
      "Address sizes:                   39 bits physical, 48 bits virtual\n",
      "CPU(s):                          12\n",
      "On-line CPU(s) list:             0-11\n",
      "Thread(s) per core:              2\n",
      "Core(s) per socket:              6\n",
      "Socket(s):                       1\n",
      "Vendor ID:                       GenuineIntel\n",
      "CPU family:                      6\n",
      "Model:                           165\n",
      "Model name:                      Intel(R) Core(TM) i5-10400F CPU @ 2.90GHz\n",
      "Stepping:                        3\n",
      "CPU MHz:                         2903.998\n",
      "BogoMIPS:                        5807.99\n",
      "Hypervisor vendor:               Microsoft\n",
      "Virtualization type:             full\n",
      "L1d cache:                       192 KiB\n",
      "L1i cache:                       192 KiB\n",
      "L2 cache:                        1.5 MiB\n",
      "L3 cache:                        12 MiB\n",
      "Vulnerability Itlb multihit:     KVM: Mitigation: VMX unsupported\n",
      "Vulnerability L1tf:              Not affected\n",
      "Vulnerability Mds:               Not affected\n",
      "Vulnerability Meltdown:          Not affected\n",
      "Vulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled v\n",
      "                                 ia prctl and seccomp\n",
      "Vulnerability Spectre v1:        Mitigation; usercopy/swapgs barriers and __user\n",
      "                                  pointer sanitization\n",
      "Vulnerability Spectre v2:        Mitigation; Enhanced IBRS, IBPB conditional, RS\n",
      "                                 B filling\n",
      "Vulnerability Srbds:             Not affected\n",
      "Vulnerability Tsx async abort:   Not affected\n",
      "Flags:                           fpu vme de pse tsc msr pae mce cx8 apic sep mtr\n",
      "                                 r pge mca cmov pat pse36 clflush mmx fxsr sse s\n",
      "                                 se2 ss ht syscall nx pdpe1gb rdtscp lm constant\n",
      "                                 _tsc rep_good nopl xtopology cpuid pni pclmulqd\n",
      "                                 q ssse3 fma cx16 pcid sse4_1 sse4_2 movbe popcn\n",
      "                                 t aes xsave avx f16c rdrand hypervisor lahf_lm \n",
      "                                 abm 3dnowprefetch invpcid_single ssbd ibrs ibpb\n",
      "                                  stibp ibrs_enhanced fsgsbase bmi1 avx2 smep bm\n",
      "                                 i2 erms invpcid rdseed adx smap clflushopt xsav\n",
      "                                 eopt xsavec xgetbv1 xsaves flush_l1d arch_capab\n",
      "                                 ilities\n",
      "\n",
      "========== GPU ==========\n",
      "GPU 0: NVIDIA GeForce RTX 3060 Ti (UUID: GPU-467269b8-367e-ff86-eaa8-3eb21925f1d6)\n",
      "Sat Oct 29 22:17:18 2022       \n",
      "+-----------------------------------------------------------------------------+\n",
      "| NVIDIA-SMI 520.56.05    Driver Version: 522.25       CUDA Version: 11.8     |\n",
      "|-------------------------------+----------------------+----------------------+\n",
      "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
      "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
      "|                               |                      |               MIG M. |\n",
      "|===============================+======================+======================|\n",
      "|   0  NVIDIA GeForce ...  On   | 00000000:01:00.0  On |                  N/A |\n",
      "| 30%   54C    P0    54W / 200W |    601MiB /  8192MiB |      3%      Default |\n",
      "|                               |                      |                  N/A |\n",
      "+-------------------------------+----------------------+----------------------+\n",
      "                                                                               \n",
      "+-----------------------------------------------------------------------------+\n",
      "| Processes:                                                                  |\n",
      "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n",
      "|        ID   ID                                                   Usage      |\n",
      "|=============================================================================|\n",
      "|  No running processes found                                                 |\n",
      "+-----------------------------------------------------------------------------+\n",
      "\n",
      "========== RAM ==========\n",
      "              total        used        free      shared  buff/cache   available\n",
      "Mem:          12707         305       12173           0         229       12156\n",
      "Swap:          4096           0        4096\n"
     ]
    }
   ],
   "source": [
    "!printf '========== OS  ==========\\n'\n",
    "!lsb_release -a\n",
    "\n",
    "!printf '\\n========== CPU ==========\\n'\n",
    "!lscpu\n",
    "\n",
    "!printf '\\n========== GPU ==========\\n'\n",
    "!nvidia-smi -L\n",
    "!nvidia-smi\n",
    "\n",
    "!printf '\\n========== RAM ==========\\n'\n",
    "!free -m"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "fKZBt-UQP2Ap",
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "### Описание используемых данных ###\n",
    "\n",
    "Здесь приведено описание используемых графов и запросов к ним\n",
    "\n",
    "#### Используемые графы ####\n",
    "Эксперимент проводился на девяти графах из датасета [CPFQ-data](https://jetbrains-research.github.io/CFPQ_Data/dataset/index.html): `pizza`, `skos`, `travel`, `atom`, `bzip`, `pr`, `ls`, `gzip`, `funding`. Графы с большим числом вершин не были взяты из-за медленных замеров на личном оборудовании.\n",
    "\n",
    "Загрузка графов:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 332
    },
    "id": "yDOwmxBJRX32",
    "outputId": "fc442c67-7129-4109-bd05-4f2485984cb7",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "import cfpq_data\n",
    "import pandas as pd\n",
    "from collections import Counter\n",
    "from project.graph_utils import get_graph_info, load_graph\n",
    "\n",
    "def get_most_common(l, n):\n",
    "    return sorted(Counter(l).most_common(n), key=lambda x: x[1], reverse=True)\n",
    "\n",
    "graph_names = ['pizza', 'skos', 'travel', 'atom', 'bzip', 'pr', 'ls', 'gzip', 'funding']\n",
    "GRAPHS = []\n",
    "stats = []\n",
    "for name in graph_names:\n",
    "  graph = load_graph(name)\n",
    "  graph.name = name\n",
    "  GRAPHS.append(graph)\n",
    "  \n",
    "  info = get_graph_info(graph)\n",
    "  \n",
    "  stats.append([\n",
    "      graph.name,\n",
    "      info.nodes,\n",
    "      info.edges,\n",
    "      get_most_common(map(lambda edge: edge[2], graph.edges(data='label')), 4),\n",
    "  ])\n",
    "    \n",
    "pd.DataFrame(\n",
    "  sorted(stats, key=lambda st: st[2]),\n",
    "  columns=[\"Name\", \"Nodes\", \"Edges\", \"Most common labels\"],\n",
    ")\n",
    "    \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "G25luXMffPVM",
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "#### Используемые регулярные выражения ###\n",
    "\n",
    "Для запросов были выбраны регулярные выражения вида\n",
    "\n",
    "* $(l1|l2)* l3$\n",
    "* $(l1|l2)+ l3*$\n",
    "* $l1 l2 l3 (l4|l1)$\n",
    "* $(l1|l2|l3)*$\n",
    "\n",
    "Если в графе недостаточно уникальных меток, то будем использовать функцию `next_label_if_not_enough` для их предоставления по очереди.\n",
    "\n",
    "Автоматы, представляющие эти выражения:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 175
    },
    "id": "Hg96YxrThGoD",
    "outputId": "3b1cad92-aa40-47f0-964b-3def5e9b26f2",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "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>Regex</th>\n",
       "      <th>Symbols</th>\n",
       "      <th>States</th>\n",
       "      <th>Transitions</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>(((a|b))*.c)</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>(((a|b).((a|b))*).(c)*)</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>(((a.b).c).((d|a))*)</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>(((a|b)|c))*</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Regex  Symbols  States  Transitions\n",
       "0             (((a|b))*.c)        3       2            3\n",
       "1  (((a|b).((a|b))*).(c)*)        3       3            6\n",
       "2     (((a.b).c).((d|a))*)        4       4            5\n",
       "3             (((a|b)|c))*        3       1            3"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pyformlang.regular_expression import Regex\n",
    "from pyformlang.regular_expression.regex_objects import Symbol\n",
    "\n",
    "MAX_LABELS_IN_REGEX = 4\n",
    "REGEXES_STR = [\n",
    "    \"(l1|l2)* l3\",\n",
    "    \"(l1|l2)+ l3*\",\n",
    "    \"l1 l2 l3 (l4|l1)*\",\n",
    "    \"(l1|l2|l3)*\",\n",
    "]\n",
    "\n",
    "def next_label_if_not_enough(labels):\n",
    "    ls = []\n",
    "    for i in range(MAX_LABELS_IN_REGEX):\n",
    "        ls.append(labels[i % len(labels)])\n",
    "    return ls\n",
    "        \n",
    "\n",
    "def label_to_regex(label):\n",
    "  regex = Regex(\"\")\n",
    "  regex.head = Symbol(str(label))\n",
    "  return regex\n",
    "\n",
    "def create_first_regex(labels):\n",
    "  labels = next_label_if_not_enough(labels)\n",
    "  \"\"\"(l1|l2)* l3\"\"\"\n",
    "  r1 = label_to_regex(labels[0])\n",
    "  r2 = label_to_regex(labels[1])\n",
    "  r3 = label_to_regex(labels[2])\n",
    "  return r1.union(r2).kleene_star().concatenate(r3)\n",
    "\n",
    "def create_second_regex(labels):\n",
    "  labels = next_label_if_not_enough(labels)\n",
    "  \"\"\"(l1|l2)+ l3*\"\"\"\n",
    "  r1 = label_to_regex(labels[0]) \n",
    "  r2 = label_to_regex(labels[1])\n",
    "  r3 = label_to_regex(labels[2])\n",
    "  return r1.union(r2).concatenate(r1.union(r2).kleene_star()).concatenate(r3.kleene_star())\n",
    "\n",
    "def create_third_regex(labels):\n",
    "  labels = next_label_if_not_enough(labels)\n",
    "  \"\"\"l1 l2 l3 (l4|l1)*\"\"\"\n",
    "  r1 = label_to_regex(labels[0])\n",
    "  r2 = label_to_regex(labels[1])\n",
    "  r3 = label_to_regex(labels[2])\n",
    "  r4 = label_to_regex(labels[3])\n",
    "  return r1.concatenate(r2).concatenate(r3).concatenate(r4.union(r1).kleene_star())\n",
    "\n",
    "def create_fourth_regex(labels):\n",
    "  labels = next_label_if_not_enough(labels)\n",
    "  \"\"\"(l1|l2|l3)*\"\"\"\n",
    "  r1 = label_to_regex(labels[0])\n",
    "  r2 = label_to_regex(labels[1])\n",
    "  r3 = label_to_regex(labels[2])\n",
    "  return r1.union(r2).union(r3).kleene_star()\n",
    "\n",
    "def create_queries(labels):\n",
    "  return [\n",
    "      create_first_regex(labels),\n",
    "      create_second_regex(labels),\n",
    "      create_third_regex(labels),\n",
    "      create_fourth_regex(labels)\n",
    "  ]\n",
    "\n",
    "def create_queries_by_graph(graph):\n",
    "    most_common_labels = get_most_common(\n",
    "        map(lambda edge: edge[2], graph.edges(data='label')),\n",
    "        MAX_LABELS_IN_REGEX\n",
    "    )\n",
    "    return create_queries(most_common_labels)\n",
    "\n",
    "stats = []\n",
    "for query in create_queries([\"a\", \"b\", \"c\", \"d\"]):\n",
    "    dfa = generate_min_dfa_by_regex(query)\n",
    "    stats.append([\n",
    "        str(query),\n",
    "        len(dfa.symbols),\n",
    "        len(dfa.states),\n",
    "        dfa.get_number_transitions(),\n",
    "    ])\n",
    "\n",
    "pd.DataFrame(stats, columns=[\"Regex\", \"Symbols\", \"States\", \"Transitions\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "3vheYLJhhHq_",
    "outputId": "531e8845-d7d8-402e-d91e-7afa9f504cab",
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "#### Выбор стартовых вершин ###\n",
    "\n",
    "Множества стартовых вершин генерировались при помощи функции generate_multiple_source из библиотеки CFPQ_Data с `seed = 10`\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "id": "06VBxe3WveMV",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "SEED = 10"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "2_NccMmEXkUG",
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "Определим две величины: \n",
    "* Размер стартового множества, если замеры производятся с фиксированным числом стартовых вершин\n",
    "* Размеры стартовых множеств, если если замеры производятся с различным числом стартовых вершин"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "id": "UB-ZwynKXkUH",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "START_SIZE = 25\n",
    "START_SIZES = [25, 50, 100, 200, 400, 800, 1500, 2000, 2500]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "8LrR0eP0XkUH",
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## Проводимые замеры ##\n",
    "Рассмотрим, какие замеры необходимо провести, чтобы ответить на поставленные вопросы.\n",
    "\n",
    "* **Вопрос 1**:\n",
    "При каких условиях использование pycubool даёт выигрыш в производительности против использования scipy.sparse? Необходимо определить при каком размере матрицы и степени ее разреженности получается выигрыш. Поэтому следует замерить скорость работы алгоритмов на всевозможных комбинациях выбранных графов и регулярных выражений с фиксированным размером стартового множества.\n",
    "\n",
    "* **Вопрос 2**:\n",
    "При каких размерах множества стартовых вершин, используя pycubool, эффективнее решать задачу в её первой постановке, а при каких в третьей? Поэтому требуется замерить скорость работы данных решений на pycubool на всевозможных комбинациях выбранных графов и регулярных выражений при разных размерах стартового множества вершин. \n",
    "\n",
    "* **Вопрос 3**:\n",
    "Насколько решение задачи на pycubool в её второй постановке быстрее решения в третьей при равных начальных условиях нужно замерить время их работы на одинаковых графах и регулярных запросах с равными стартовыми множествами."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "hRum3PtqXkUH",
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## Замеры ##\n",
    "Время работы каждого алгоритма на каждый входных данных замерялось пять раз для получения среднего и среднего отклонения."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "id": "39qtyYIQXkUH",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "import timeit\n",
    "from statistics import fmean\n",
    "from statistics import stdev\n",
    "import signal\n",
    "from typing import Callable\n",
    "\n",
    "RUN_TIMES = 5\n",
    "\n",
    "\n",
    "class RunTimeoutError(Exception):\n",
    "    pass\n",
    "\n",
    "\n",
    "def timeout_handler(_signum, _frame):\n",
    "    raise RunTimeoutError\n",
    "\n",
    "\n",
    "def run_timed(f: Callable, run_times: int, timeout_each_s: int = 600):\n",
    "    timeout_total_s = timeout_each_s * run_times\n",
    "\n",
    "    signal.signal(signal.SIGALRM, timeout_handler)\n",
    "    try:\n",
    "        signal.alarm(timeout_total_s)\n",
    "        times = timeit.repeat(f, repeat=run_times, number=1)\n",
    "    except RunTimeoutError:\n",
    "        times = [float(\"inf\")]\n",
    "    finally:\n",
    "        signal.alarm(0)\n",
    "\n",
    "    mean = fmean(times)\n",
    "    std = stdev(times) if len(times) > 1 else 0\n",
    "    return mean, std"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "nRpIyjY1XkUI",
    "pycharm": {
     "name": "#%%\n"
    },
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "from cfpq_data import *\n",
    "import scipy.sparse\n",
    "\n",
    "def run_fixed_starts():\n",
    "    results = []\n",
    "\n",
    "    for graph in GRAPHS:\n",
    "        regexes = create_queries_by_graph(graph)\n",
    "        starts = cfpq_data.generate_multiple_source(graph, START_SIZE, seed=SEED)\n",
    "\n",
    "        graph_results = []\n",
    "        for regex in regexes:\n",
    "            by_tensor = run_timed(\n",
    "                lambda: rpq_tensor(\n",
    "                    graph,\n",
    "                    regex,\n",
    "                    starts,\n",
    "                    None,\n",
    "                ),\n",
    "                run_times=RUN_TIMES,\n",
    "            )\n",
    "            by_bfs_all = run_timed(\n",
    "                lambda: rpq_bfs(\n",
    "                    graph,\n",
    "                    regex,\n",
    "                    start_states=starts,\n",
    "                    final_states=None,\n",
    "                    mode=RpqMode.FIND_ALL_REACHABLE\n",
    "                ),\n",
    "                run_times=RUN_TIMES,\n",
    "            )\n",
    "            by_bfs_each = run_timed(\n",
    "                lambda: rpq_bfs(\n",
    "                    graph,\n",
    "                    regex,\n",
    "                    start_states=starts,\n",
    "                    final_states=None,\n",
    "                    mode=RpqMode.FIND_REACHABLE_FOR_EACH_START_NODE\n",
    "                ),\n",
    "                run_times=RUN_TIMES,\n",
    "            )\n",
    "            by_tensor_cuda = run_timed(\n",
    "                lambda: rpq_tensor_pycubool(\n",
    "                    graph,\n",
    "                    regex,\n",
    "                    start_states=starts,\n",
    "                    final_states=None,\n",
    "                ),\n",
    "                run_times=RUN_TIMES,\n",
    "            )\n",
    "            by_bfs_all_cuda = run_timed(\n",
    "                lambda: rpq_bfs_pycubool(\n",
    "                    graph,\n",
    "                    regex,\n",
    "                    start_states=starts,\n",
    "                    final_states=None,\n",
    "                    mode=RpqMode.FIND_ALL_REACHABLE\n",
    "                ),\n",
    "                run_times=RUN_TIMES,\n",
    "            )\n",
    "            by_bfs_each_cuda = run_timed(\n",
    "                lambda: rpq_bfs_pycubool(\n",
    "                    graph,\n",
    "                    regex,\n",
    "                    start_states=starts,\n",
    "                    final_states=None,\n",
    "                    mode=RpqMode.FIND_REACHABLE_FOR_EACH_START_NODE\n",
    "                ),\n",
    "                run_times=RUN_TIMES,\n",
    "            )\n",
    "            graph_results.append(\n",
    "                ((by_tensor, by_bfs_all, by_bfs_each), (by_tensor_cuda, by_bfs_all_cuda, by_bfs_each_cuda))\n",
    "            )\n",
    "\n",
    "        results.append(graph_results)\n",
    "\n",
    "    return results\n",
    "\n",
    "\n",
    "def run_varied_starts():\n",
    "    results = []\n",
    "\n",
    "    for graph in GRAPHS:\n",
    "        regexes = create_queries_by_graph(graph)\n",
    "        graph_results = []\n",
    "        for regex in regexes:\n",
    "            regex_results = {}\n",
    "            for start_size in START_SIZES:\n",
    "                if start_size > graph.number_of_nodes():\n",
    "                    continue\n",
    "                starts = generate_multiple_source(graph, start_size, seed=SEED)\n",
    "                by_tensor_cuda = run_timed(\n",
    "                    lambda: rpq_tensor_pycubool(\n",
    "                        graph,\n",
    "                        regex,\n",
    "                        start_states=starts,\n",
    "                        final_states=None\n",
    "                    ),\n",
    "                    run_times=RUN_TIMES,\n",
    "                )\n",
    "                by_bfs_each_cuda = run_timed(\n",
    "                    lambda: rpq_bfs_pycubool(\n",
    "                        graph,\n",
    "                        regex,\n",
    "                        start_states=starts,\n",
    "                        final_states=None,\n",
    "                        mode=RpqMode.FIND_REACHABLE_FOR_EACH_START_NODE\n",
    "                    ),\n",
    "                    run_times=RUN_TIMES,\n",
    "                )\n",
    "                regex_results[start_size] = (by_tensor_cuda, by_bfs_each_cuda)\n",
    "            graph_results.append(regex_results)\n",
    "        results.append(graph_results)\n",
    "\n",
    "    return results\n",
    "\n",
    "\n",
    "fixed_starts_res = run_fixed_starts()\n",
    "varied_starts_res = run_varied_starts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "NGiTA9C2XkUI",
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## Результаты ##\n",
    "\n",
    "В данном разделе приведены полученные графики, а также приведены ответы на поставленные вопросы."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "goBi-peHkGrm",
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## Ответы на вопросы ##\n",
    "\n",
    "**При каких условиях использование специализированных библиотек разреженной линейной алгебры даёт выигрыш в производительности? Для каждой из трёх задач.**\n",
    "\n",
    "Из графиков 1.1.1, 1.1.2, 1.1.3 следует, что для постановок 1 и 2 всех графов и для всех запросов, основанное на `pycubool` решение показывает результаты лучше, чем решение, основанное на `scipy.sparse`.\n",
    "Однако на графе `gzip`, при регулярном запросе, который задается первым регулярным выражением, решение на `scipy` показало себя лучше. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "id": "AWXoLrI8XkUI",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "ALGORITHMS = [\"Tensor\", \"BFS\", \"Separated BFS\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "XUex6P8qXkUJ",
    "outputId": "a932753f-79be-4b14-cf5f-0ac45ced4ef6",
    "pycharm": {
     "name": "#%%\n"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1000x900 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1000x900 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x900 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def batch_plots_1(res):\n",
    "    fig_size = (2.5 * len(REGEXES_STR), len(GRAPHS))\n",
    "    label_locations = np.arange(len(REGEXES_STR))\n",
    "    bar_width = 0.25\n",
    "    cap_size = 4\n",
    "\n",
    "    for algo_i, algo_name in enumerate(ALGORITHMS):\n",
    "        fig: plt.Figure\n",
    "        fig, axs = plt.subplots(len(GRAPHS) // 2, 2, figsize=fig_size)\n",
    "\n",
    "        fig.suptitle(\n",
    "            f\"Plot 1.1.{algo_i + 1}. Absolute difference scipy.sparse vs pycubool performance for {algo_name}\")\n",
    "        fig.tight_layout()\n",
    "\n",
    "        for g_i, ax in enumerate(axs.flat):\n",
    "            graph_name = GRAPHS[g_i].name\n",
    "            graph_results = res[g_i]\n",
    "\n",
    "            ax.set_xlabel(graph_name, weight=\"bold\")\n",
    "            ax.xaxis.set_label_position(\"top\")\n",
    "\n",
    "            ax.set_xticks(label_locations, REGEXES_STR)\n",
    "            ax.tick_params(axis='x', which='both', bottom=False)\n",
    "            if not g_i % len(axs[0]):\n",
    "                ax.set_ylabel(\"Time, s\")\n",
    "\n",
    "            scipy_means = [rs[0][algo_i][0] for rs in graph_results]\n",
    "            scipy_stdevs = [rs[0][algo_i][1] for rs in graph_results]\n",
    "            ax.bar(\n",
    "                label_locations - bar_width / 2,\n",
    "                scipy_means,\n",
    "                bar_width,\n",
    "                yerr=scipy_stdevs,\n",
    "                color=\"tab:green\",\n",
    "                label=\"scipy.sparse\",\n",
    "                capsize=cap_size,\n",
    "            )\n",
    "\n",
    "            pycubool_means = [rs[1][algo_i][0] for rs in graph_results]\n",
    "            pycubool_stdevs = [rs[1][algo_i][1] for rs in graph_results]\n",
    "            ax.bar(\n",
    "                label_locations + bar_width / 2,\n",
    "                pycubool_means,\n",
    "                bar_width,\n",
    "                yerr=pycubool_stdevs,\n",
    "                color=\"tab:blue\",\n",
    "                label=\"pycubool\",\n",
    "                capsize=cap_size,\n",
    "            )\n",
    "\n",
    "        handles, labels = axs[-1][-1].get_legend_handles_labels()\n",
    "        fig.legend(handles, labels, bbox_to_anchor=(1, 1), loc=\"upper left\")\n",
    "\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "batch_plots_1(fixed_starts_res)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Начиная с какого размера стартового множества выгоднее решать задачу для всех пар и выбирать нужные? (При использовании специализированных библиотек)**\n",
    "\n",
    "Для ответа на этот вопрос рассмотрим график 2. Как можно заметить, для всех выбранных графов эффективнее решать задачу в первой постановке, используя тензорное произведение, и выбирать из всех найденных пар нужные. Причем, при увеличении числа стартовых вершин разрыв во времени увеличивается."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "2XiNX92TXkUJ",
    "outputId": "724b32b5-7c5a-4fb5-c7a4-e1622c505da9",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x2700 with 36 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def batch_plots_2(res):\n",
    "    fig, axs = plt.subplots(len(GRAPHS), len(REGEXES_STR), figsize=(5 * len(REGEXES_STR), 3 * len(GRAPHS)))\n",
    "\n",
    "    fig.suptitle(\"Plot 2. Start set size influence on algorithm 1 vs algorithm 3\")\n",
    "    fig.tight_layout()\n",
    "    fig.subplots_adjust(top=0.95)\n",
    "\n",
    "    bar_width = 0.2\n",
    "    cap_size = 4\n",
    "\n",
    "    for g_i, axs_row in enumerate(axs):\n",
    "        graph_name = GRAPHS[g_i].name\n",
    "        graph_results = res[g_i]\n",
    "\n",
    "        for r_i, ax in enumerate(axs_row):\n",
    "            regex_name = list(REGEXES_STR)[r_i]\n",
    "            regex_results = graph_results[r_i]\n",
    "\n",
    "            if not g_i:\n",
    "                ax.set_xlabel(regex_name, weight=\"bold\")\n",
    "                ax.xaxis.set_label_position(\"top\")\n",
    "\n",
    "            if not r_i:\n",
    "                ax.set_ylabel(\"Time, s\")\n",
    "\n",
    "            if r_i == len(REGEXES_STR) - 1:\n",
    "                ax.set_ylabel(graph_name, weight=\"bold\")\n",
    "                ax.yaxis.set_label_position(\"right\")\n",
    "\n",
    "            label_locations = np.arange(len(regex_results))\n",
    "            ax.set_xticks(label_locations, regex_results.keys())\n",
    "\n",
    "            algo1_results = [rs[0] for rs in regex_results.values()]\n",
    "            algo1_means, algo1_stdevs = zip(*algo1_results)\n",
    "            ax.bar(\n",
    "                label_locations - bar_width / 2,\n",
    "                algo1_means,\n",
    "                bar_width,\n",
    "                yerr=algo1_stdevs,\n",
    "                color=\"tab:red\",\n",
    "                label=ALGORITHMS[0],\n",
    "                capsize=cap_size,\n",
    "            )\n",
    "\n",
    "            algo3_results = [rs[1] for rs in regex_results.values()]\n",
    "            algo3_means, algo3_stdevs = zip(*algo3_results)\n",
    "            ax.bar(\n",
    "                label_locations + bar_width / 2,\n",
    "                algo3_means,\n",
    "                bar_width,\n",
    "                yerr=algo3_stdevs,\n",
    "                color=\"tab:purple\",\n",
    "                label=ALGORITHMS[2],\n",
    "                capsize=cap_size,\n",
    "            )\n",
    "\n",
    "            ax.tick_params(axis='x', which='both', bottom=False)\n",
    "\n",
    "    handles, labels = axs[-1][-1].get_legend_handles_labels()\n",
    "    fig.legend(handles, labels, bbox_to_anchor=(1, 1), loc=\"upper left\")\n",
    "\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "batch_plots_2(varied_starts_res)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**На сколько решение второй задачи медленнее решения третьей при одинаковых начальных условиях? (При использовании специализированных библиотек)**\n",
    "\n",
    "Из графика 3 следует, что для всех графов и для всех регулярных запросов, время выполнения задачи 2 быстрее, чем время выполнения задачи 3. В среднем разница в 2 раза."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 915
    },
    "id": "DBGWhS-wXkUJ",
    "outputId": "0baafac9-4aa3-447c-a33a-4f2d3a6c2988",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1000x900 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def batch_plots_3(res):\n",
    "    fig_size = (2.5 * len(REGEXES_STR), len(GRAPHS))\n",
    "    label_locations = np.arange(len(REGEXES_STR))\n",
    "    bar_width = 0.2\n",
    "    cap_size = 4\n",
    "\n",
    "    fig, axs = plt.subplots(len(GRAPHS) // 2, 2, figsize=fig_size)\n",
    "\n",
    "    fig.suptitle(\"Plot 3. BFS-based algorithms comparison\")\n",
    "    fig.tight_layout()\n",
    "\n",
    "    for g_i, ax in enumerate(axs.flat):\n",
    "        graph_name = GRAPHS[g_i].name\n",
    "        graph_results = res[g_i]\n",
    "\n",
    "        ax.set_xlabel(graph_name, weight=\"bold\")\n",
    "        ax.xaxis.set_label_position(\"top\")\n",
    "\n",
    "        ax.set_xticks(label_locations, REGEXES_STR)\n",
    "        ax.tick_params(axis='x', which='both', bottom=False)\n",
    "        if not g_i % len(axs[0]):\n",
    "            ax.set_ylabel(\"Time, s\")\n",
    "\n",
    "        pycubool_algo2_means = [rs[1][1][0] for rs in graph_results]\n",
    "        pycubool_algo2_stdevs = [rs[1][1][1] for rs in graph_results]\n",
    "        ax.bar(\n",
    "            label_locations - bar_width / 2,\n",
    "            pycubool_algo2_means,\n",
    "            bar_width,\n",
    "            yerr=pycubool_algo2_stdevs,\n",
    "            color=\"tab:blue\",\n",
    "            label=ALGORITHMS[1],\n",
    "            capsize=cap_size,\n",
    "        )\n",
    "\n",
    "        pycubool_algo3_means = [rs[1][2][0] for rs in graph_results]\n",
    "        pycubool_algo3_stdevs = [rs[1][2][1] for rs in graph_results]\n",
    "        ax.bar(\n",
    "            label_locations + bar_width / 2,\n",
    "            pycubool_algo3_means,\n",
    "            bar_width,\n",
    "            yerr=pycubool_algo3_stdevs,\n",
    "            color=\"tab:green\",\n",
    "            label=ALGORITHMS[2],\n",
    "            capsize=cap_size,\n",
    "        )\n",
    "\n",
    "    handles, labels = axs[-1][-1].get_legend_handles_labels()\n",
    "    fig.legend(handles, labels, bbox_to_anchor=(1, 1), loc=\"upper left\")\n",
    "\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "batch_plots_3(fixed_starts_res)"
   ]
  },
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   "source": [
    "## Заключение ##\n",
    "\n",
    "Исходя из полученных результатов можно сформировать следующие ответы на поставленные вопросы относительно задачи достижимости в графе с регулярными ограничениями и её решений, основанных на линейной алгебре:\n",
    "\n",
    "* Для решений задач достижимости между всеми парами вершин, а также для всего множества стартовых вершин, реализации, использующие ядря CUDA показывают себя лучше реализаций на `scipy.sparse`. Однако, с увеличением размеров графа, выигрыш от применения CUDA снижается.\n",
    "* При использовании `pycubool`, для всех размеров множества стартовых вершин выгоднее решать задачу достижимости между всеми парами вершин с помощью тензорного произведения и выбирать нужные, нежели искать пути лишь из стартовых вершин через раздельный BFS. Помимо этого, нахождение достижимых вершин для всего стартового множества сразу быстрее нахождения достижимых вершин для каждой стартовой вершины отдельно."
   ]
  }
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