Otimizacoes.ipynb

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      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
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      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/jurandi82/Python/blob/main/Otimizacoes.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nTGWgXlLs3pF"
      },
      "source": [
        " <font size=\"6\">Testes de otimização</font>\n",
        "\n",
        "Usando Numba.Jit e Cython **Magic**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6c_03CAQ7DQf"
      },
      "source": [
        "#Preparando o ambiente"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kaWqH0SRs-Ow"
      },
      "source": [
        "#para poder utilizar o jit\n",
        "from numba import jit\n",
        "\n",
        "#para medir o tempo de execução\n",
        "from time import time\n",
        "\n",
        "#para produção dos valores de exemplo\n",
        "import numpy as np\n",
        "\n",
        "#extras para visualização\n",
        "from tqdm.notebook import trange"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mQtekWxh1jgE"
      },
      "source": [
        "%load_ext Cython\n",
        "#Cython é uma extenção, precisa ser carregada dessa forma."
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MpS0MiYW7h8D"
      },
      "source": [
        "# Valores para teste"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yeRCeSLMaOE6"
      },
      "source": [
        "valores = np.random.rand(1_000)\n",
        "loops = 1_000"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aRvg0aMG65S2"
      },
      "source": [
        "#Criando funções"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RssBM8_rbGtc"
      },
      "source": [
        "#Python Puro\n",
        "def media_mv(x, w=3):\n",
        "  r =[]\n",
        "  for i in range(len(x)):\n",
        "    if i+1 < w:\n",
        "      r.append(0)\n",
        "    else:\n",
        "      r.append((x[i-w+1:i+1]).sum()/w)\n",
        "  return r"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LkP7rJi71aS_"
      },
      "source": [
        "%%cython\n",
        "#Cython cell\n",
        "def media_mv2(x, w=3):\n",
        "  r =[]\n",
        "  for i in range(len(x)):\n",
        "    if i+1 < w:\n",
        "      r.append(0)\n",
        "    else:\n",
        "      r.append((x[i-w+1:i+1]).sum()/w)\n",
        "  return r\n"
      ],
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      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fTMbB6Uy3k9F"
      },
      "source": [
        "#Usando Numba.jit\n",
        "\n",
        "@jit\n",
        "def media_mv3(x, w=3):\n",
        "  r =[]\n",
        "  for i in range(len(x)):\n",
        "    if i+1 < w:\n",
        "      r.append(0)\n",
        "    else:\n",
        "      r.append((x[i-w+1:i+1]).sum()/w)\n",
        "  return r"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "V6dg22jY5Xzl"
      },
      "source": [
        "#Testanto numero de interações"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "k7Ko4FcN6DQ-",
        "outputId": "57a74f48-793a-4a62-9439-4906ffff4570"
      },
      "source": [
        "print('Teste 1 - Python Puro')\n",
        "i=1\n",
        "inicio = time()\n",
        "while(time()-inicio<2):\n",
        "  _ = media_mv(valores)\n",
        "  i+=1\n",
        "print(f'Interações = {i}')"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Teste 1 - Python Puro\n",
            "Interações = 545\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "g7ehXpA96DC-",
        "outputId": "e7659fe4-f7ff-491a-d409-171f4f5808fb"
      },
      "source": [
        "print('Teste 2 - com Cython')\n",
        "i=1\n",
        "inicio = time()\n",
        "while(time()-inicio<2):\n",
        "  _ = media_mv2(valores)\n",
        "  i+=1\n",
        "print(f'Interações = {i}')"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Teste 2 - com Cython\n",
            "Interações = 596\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TKPVasG7zcSj",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "298d7a45-6ee0-4043-c429-8e5ffca29bc6"
      },
      "source": [
        "print('Teste 3 - com JIT')\n",
        "i=1\n",
        "inicio = time()\n",
        "while(time()-inicio<2):\n",
        "  _ = media_mv3(valores)\n",
        "  i+=1\n",
        "print(f'Interações = {i}')"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Teste 3 - com JIT\n",
            "Interações = 25614\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "V3Pkbksa6zV2"
      },
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      ]
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        "print('Teste 1 - Python Puro')\n",
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        "for i in trange(loops):\n",
        "  _ = media_mv(valores)\n",
        "tempo1=time()-inicio"
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            "Teste 1 - Python Puro\n"
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            "11cca45b956843cb9bd8b018a7f38333",
            "9c4a9e40811e46a7abfc3d2b9ebd2462"
          ]
        },
        "outputId": "7f51f759-294e-4bc9-ab26-bff430ac2033"
      },
      "source": [
        "%%time\n",
        "print('Teste 2 - com Cython')\n",
        "inicio = time()\n",
        "for i in trange(loops):\n",
        "  _ = media_mv2(valores)\n",
        "tempo2=time()-inicio"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Teste 2 - com Cython\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "4842dd8de8014a91a4150070e211bc05",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0/1000 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "CPU times: user 3.81 s, sys: 35 ms, total: 3.85 s\n",
            "Wall time: 3.9 s\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wwu2jXtTbYFu",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 103,
          "referenced_widgets": [
            "61bd803aa05c498a966d305a975f1f8e",
            "d2a5c7ec3a1c45d0858bcfd5cae0377b",
            "7efbbce015914133974d5eed9ffd4704",
            "ba79141fdb5c478096ffb0ed32a6c701",
            "dd30754e86f74a87904233cf12b233b8",
            "6765df9c0dd44883931084093205d8a8",
            "77c5c14ed86b49079857f53a19afcfd8",
            "5c644ccb2cb5447ca0188960c8425f1f",
            "37b26133035c4ceca4e7287fd4f5c810",
            "9af1d8123e4049c08244082a38e390d6",
            "947ab3648d964fb4b516bce57d0afbcf"
          ]
        },
        "outputId": "2c78dce1-9527-4171-a662-787e2c9d2d60"
      },
      "source": [
        "%%time\n",
        "print('Teste 3 - com JIT')\n",
        "inicio = time()\n",
        "for i in trange(loops):\n",
        "  _ = media_mv3(valores)\n",
        "tempo3=time()-inicio"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Teste 3 - com JIT\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "61bd803aa05c498a966d305a975f1f8e",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0/1000 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "CPU times: user 136 ms, sys: 12.5 ms, total: 148 ms\n",
            "Wall time: 161 ms\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KoGNNEfPycuS",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "84a9eca8-7af6-4b50-d163-847ad54719d2"
      },
      "source": [
        "print(f'Economia de tempo  Cython contra Python Puro {round((1-tempo2/tempo1)*100,2)}%')\n",
        "print(f'Economia de tempo  JIT    contra Cython      {round((1-tempo3/tempo2)*100,2)}%')\n",
        "print(f'Economia de tempo  JIT    contra Python Puro {round((1-tempo3/tempo1)*100,2)}%')"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Economia de tempo  Cython contra Python Puro 5.86%\n ",
            "Economia de tempo  JIT    contra Cython      95.88%\n ",
            "Economia de tempo  JIT    contra Python Puro 96.12%\n "
          ]
        }
      ]
    }
  ]
}