CTG_RP_Train_Model_Shuffled.ipynb

{
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  "metadata": {
    "colab": {
      "name": "CTG_RP Train Model Shuffled.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.6.8"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/williamsdoug/CTG_RP/blob/master/CTG_RP_Train_Model_Shuffled.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rdV-UfCWAbe1",
        "colab_type": "text"
      },
      "source": [
        "# Generate Datasets and Train Model\n",
        "\n",
        "\n",
        "Random selection for Train and Valid without regard for source file"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7ytg9ggCMvGs",
        "colab_type": "code",
        "outputId": "3cdab4a8-6797-4fa7-d640-426c0329f005",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "! ls"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "basic_denoise.py     config_local.py  ctu-uhb-ctgdb  libRP.py\t  sample_data\n",
            "compute_metadata.py  ctg_utils.py     images\t     __pycache__  test.py\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Waljt_cvAbe2",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "%reload_ext autoreload\n",
        "%autoreload 2\n",
        "%matplotlib inline"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r3uofxVyAbe6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import config_local\n",
        "from config_local import *"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c75SbCPBAbe8",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import gc\n",
        "\n",
        "from fastai.vision import *\n",
        "from fastai.metrics import error_rate\n",
        "\n",
        "import torch\n",
        "from torch import nn\n",
        "\n",
        "\n",
        "import collections\n",
        "import pprint"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BWT1_z0IAbe9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from compute_metadata import get_splits, generate_label_file, generate_lists"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-Ai9ZTafGiL2",
        "colab_type": "text"
      },
      "source": [
        "## Config"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V7IcSKzIq98B",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "np.random.seed(1234)\n",
        "random.seed(1234)\n",
        "\n",
        "binary_image_stats = ([0.5]*3, [0.5]*3)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XO7NNv_NAbfH",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "tfms=[]\n",
        "size=64\n",
        "bs=64\n",
        "workers=4\n",
        "\n",
        "path = Path() / 'images'"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IXcz4vuVAbfA",
        "colab_type": "text"
      },
      "source": [
        "## Generate Train and Valid Label Files"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L8Kt2t7-hltb",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def generate_label_file(group, image_dir='images', \n",
        "                        csv_file='labels.csv', header='fname, label'):\n",
        "    if csv_file is None:\n",
        "        results = []\n",
        "        for v in group.values():\n",
        "            for label, all_files in v.items():\n",
        "                for fname in all_files:\n",
        "                    results.append((os.path.join(image_dir, fname), 1 if label else 0))\n",
        "        return results\n",
        "    else:\n",
        "        with open(os.path.join(image_dir, csv_file), 'wt') as outfile:\n",
        "            if header:\n",
        "                print(header, file=outfile)\n",
        "            for v in group.values():\n",
        "                for label, all_files in v.items():\n",
        "                    for fname in all_files:\n",
        "                        line = '{}, {}'.format(fname, 1 if label else 0)\n",
        "                        print(line, file=outfile)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9lEivw5PAbfB",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_valid_groups_full = get_splits(image_dir='images', image_file='rp_images_index.json', \n",
        "                                     exclude=['_clipped'],\n",
        "                                     thresh = 7.15)\n",
        "              \n",
        "generate_label_file(train_valid_groups_full[0], csv_file='labels.csv')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7pRyYW8LAbfD",
        "colab_type": "code",
        "outputId": "315fb65b-044e-4b79-f033-c26fa319dda2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        }
      },
      "source": [
        "!ls images/*.csv\n",
        "#!cat images/labels.csv"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "images/labels.csv   images/train_2.csv\timages/valid_0.csv  images/valid_3.csv\n",
            "images/train_0.csv  images/train_3.csv\timages/valid_1.csv  images/valid_4.csv\n",
            "images/train_1.csv  images/train_4.csv\timages/valid_2.csv\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zTto_DwSil7w",
        "colab_type": "code",
        "outputId": "e4c83500-0030-431b-d2ed-49cf236518ae",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 306
        }
      },
      "source": [
        "lls = ImageList.from_csv(path, 'labels.csv').split_by_rand_pct(0.2).label_from_df(cols=1).transform(tfms, size=size)\n",
        "db = lls.databunch(bs=bs, num_workers=workers)\n",
        "my_stats = db.batch_stats()\n",
        "db = lls.databunch(bs=bs, num_workers=workers).normalize(my_stats)\n",
        "db"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ImageDataBunch;\n",
              "\n",
              "Train: LabelList (490 items)\n",
              "x: ImageList\n",
              "Image (3, 64, 64),Image (3, 64, 64),Image (3, 64, 64),Image (3, 64, 64),Image (3, 64, 64)\n",
              "y: CategoryList\n",
              "0,0,0,0,0\n",
              "Path: images;\n",
              "\n",
              "Valid: LabelList (122 items)\n",
              "x: ImageList\n",
              "Image (3, 64, 64),Image (3, 64, 64),Image (3, 64, 64),Image (3, 64, 64),Image (3, 64, 64)\n",
              "y: CategoryList\n",
              "0,1,1,1,0\n",
              "Path: images;\n",
              "\n",
              "Test: None"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z97vKXrkxn5q",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "3bdedb9c-eac3-4ba4-f08f-4ac4bdf16a63"
      },
      "source": [
        "db.batch_stats()"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[tensor([-0.0890, -0.0890, -0.0890]), tensor([0.8887, 0.8887, 0.8887])]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h_teKLeJAbfJ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# train = ImageList.from_csv(path, 'train_0.csv')\n",
        "# valid = ImageList.from_csv(path, 'valid_0.csv')\n",
        "\n",
        "# lls = ItemLists(path, train, valid).label_from_df(cols=1).transform(tfms, size=size) \n",
        "# db = lls.databunch(bs=bs, num_workers=workers)#.normalize(imagenet_stats)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xSCAnpBdAbff",
        "colab_type": "text"
      },
      "source": [
        "### Examine Results"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Bk14YOW7Abfg",
        "colab_type": "code",
        "outputId": "618f433f-e801-4186-f470-da8a92bcbb3a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 323
        }
      },
      "source": [
        "print('nClass: {}    classes: {}'.format(db.c, db.classes))\n",
        "db"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "nClass: 2    classes: [0, 1]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ImageDataBunch;\n",
              "\n",
              "Train: LabelList (490 items)\n",
              "x: ImageList\n",
              "Image (3, 64, 64),Image (3, 64, 64),Image (3, 64, 64),Image (3, 64, 64),Image (3, 64, 64)\n",
              "y: CategoryList\n",
              "0,0,0,0,0\n",
              "Path: images;\n",
              "\n",
              "Valid: LabelList (122 items)\n",
              "x: ImageList\n",
              "Image (3, 64, 64),Image (3, 64, 64),Image (3, 64, 64),Image (3, 64, 64),Image (3, 64, 64)\n",
              "y: CategoryList\n",
              "0,1,1,1,0\n",
              "Path: images;\n",
              "\n",
              "Test: None"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0AgeX_E6AbfP",
        "colab_type": "code",
        "outputId": "ff8cce31-e42a-4cbd-d12d-1c3b982aa5fa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 232
        }
      },
      "source": [
        "im = lls.train.get(-1)\n",
        "print(len(lls.train), im.size)\n",
        "im.show()"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "490 torch.Size([599, 599])\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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d7cJyt4jI9ZPwbDxjOp26hbdFEZCZFUk6GYh2lbwBgnA+9rowkOd6pBzaQ9q9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\nynoVbxBfEAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 216x216 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Vg5CLziZHC1J",
        "colab_type": "text"
      },
      "source": [
        "## Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KdG9Pe1wIXLw",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "trial_model = nn.Sequential(\n",
        "    nn.Sequential(\n",
        "          nn.Conv2d(3,8,5),     # 60 × 60 × 8\n",
        "          nn.ReLU(),\n",
        "          nn.AvgPool2d(3, stride=2),    # 29 × 29 × 8\n",
        "        \n",
        "          #nn.Dropout(p=0.25),\n",
        "          nn.Conv2d(8,8,5),         # 25 × 25 × 8\n",
        "          nn.ReLU(),\n",
        "          nn.AvgPool2d(3, stride=2),    # 12 × 12 × 8\n",
        "        \n",
        "          Flatten()         # 1152\n",
        "    ),\n",
        "    # removed model head to compute flatten size\n",
        ")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hKLvyhRrIXQ8",
        "colab_type": "code",
        "outputId": "4c6e3911-3a62-4747-bce7-87ca0a92fd65",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 374
        }
      },
      "source": [
        "trial_learn = Learner(db, trial_model, loss_func = nn.CrossEntropyLoss(), metrics=accuracy)\n",
        "trial_learn.summary()"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "======================================================================\n",
              "Layer (type)         Output Shape         Param #    Trainable \n",
              "======================================================================\n",
              "Conv2d               [8, 60, 60]          608        True      \n",
              "______________________________________________________________________\n",
              "ReLU                 [8, 60, 60]          0          False     \n",
              "______________________________________________________________________\n",
              "AvgPool2d            [8, 29, 29]          0          False     \n",
              "______________________________________________________________________\n",
              "Conv2d               [8, 25, 25]          1,608      True      \n",
              "______________________________________________________________________\n",
              "ReLU                 [8, 25, 25]          0          False     \n",
              "______________________________________________________________________\n",
              "AvgPool2d            [8, 12, 12]          0          False     \n",
              "______________________________________________________________________\n",
              "Flatten              [1152]               0          False     \n",
              "______________________________________________________________________\n",
              "\n",
              "Total params: 2,216\n",
              "Total trainable params: 2,216\n",
              "Total non-trainable params: 0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yqADfj8XrSly",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "3c082fa1-0ec5-41da-cccc-e0c08fdadc8c"
      },
      "source": [
        "del trial_model\n",
        "trial_learn.destroy()\n",
        "gc.collect()"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "this Learner object self-destroyed - it still exists, but no longer usable\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U36mS4qBIXZ_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "mymodel = nn.Sequential(\n",
        "    nn.Sequential(\n",
        "          nn.Conv2d(3,8,5),     # 60 × 60 × 8\n",
        "          nn.ReLU(),\n",
        "          nn.AvgPool2d(3, stride=2),    # 29 × 29 × 8\n",
        "        \n",
        "          #nn.Dropout(p=0.25),\n",
        "          nn.Conv2d(8,8,5),         # 25 × 25 × 8\n",
        "          nn.ReLU(),\n",
        "          nn.AvgPool2d(3, stride=2),    # 12 × 12 × 8\n",
        "        \n",
        "          Flatten()         # 1152\n",
        "    ),\n",
        "    nn.Sequential(\n",
        "#           nn.Dropout(p=0.25),\n",
        "          nn.Linear(1152, 144),\n",
        "          nn.ReLU(),\n",
        "          nn.Dropout(p=0.8),\n",
        "          nn.Linear(144, db.c) \n",
        "    )\n",
        ")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7Z5EyEC2JDkv",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "learn = Learner(db, mymodel, loss_func = nn.CrossEntropyLoss(), metrics=accuracy)\n",
        "learn.summary()\n",
        "learn.save('initial')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0t4sSMWIKgk2",
        "colab_type": "text"
      },
      "source": [
        "# Train Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FteSA2jlJDxH",
        "colab_type": "code",
        "outputId": "77c869da-f516-4645-83ea-dee39e1f77a3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 80
        }
      },
      "source": [
        "learn.fit_one_cycle(1, 1e-6)  # learn.fit_one_cycle(1, 0.01)\n",
        "# learn.save('save-1')"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: left;\">\n",
              "      <th>epoch</th>\n",
              "      <th>train_loss</th>\n",
              "      <th>valid_loss</th>\n",
              "      <th>accuracy</th>\n",
              "      <th>time</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>0</td>\n",
              "      <td>0.694697</td>\n",
              "      <td>0.691843</td>\n",
              "      <td>0.557377</td>\n",
              "      <td>00:04</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UNY7L1jFK3jS",
        "colab_type": "code",
        "outputId": "835c24ee-9e1b-449a-9bd2-7615e965be2a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        }
      },
      "source": [
        "learn.lr_find(start_lr=1e-8, end_lr=1)\n",
        "learn.recorder.plot()"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              ""
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "LR Finder is complete, type {learner_name}.recorder.plot() to see the graph.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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5to6ZmSUCjwFTgAJgvpnNcPeVYfvkAvcCZ7h7sZn1CcovASYBE4FU4B0zey1YNx4zywNi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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7Pkb1Vo3NYz9",
        "colab_type": "code",
        "outputId": "3fa2d40e-96e1-4c92-f7a7-80830a40e231",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 3149
        }
      },
      "source": [
        "learn.load('initial')\n",
        "learn.fit_one_cycle(100, 3e-3)  # learn.fit_one_cycle(1, 0.01)"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: left;\">\n",
              "      <th>epoch</th>\n",
              "      <th>train_loss</th>\n",
              "      <th>valid_loss</th>\n",
              "      <th>accuracy</th>\n",
              "      <th>time</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>0</td>\n",
              "      <td>0.693327</td>\n",
              "      <td>0.692420</td>\n",
              "      <td>0.508197</td>\n",
              "      <td>00:03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>0.693667</td>\n",
              "      <td>0.692282</td>\n",
              "      <td>0.467213</td>\n",
              "      <td>00:03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>0.693996</td>\n",
              "      <td>0.691893</td>\n",
              "      <td>0.459016</td>\n",
              "      <td>00:03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>0.693211</td>\n",
              "      <td>0.691155</td>\n",
              "      <td>0.475410</td>\n",
              "      <td>00:03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>0.692800</td>\n",
              "      <td>0.690164</td>\n",
              "      <td>0.516393</td>\n",
              "      <td>00:03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5</td>\n",
              "      <td>0.691713</td>\n",
              "      <td>0.688191</td>\n",
              "      <td>0.573770</td>\n",
              "      <td>00:03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6</td>\n",
              "      <td>0.690287</td>\n",
              "      <td>0.685161</td>\n",
              "      <td>0.549180</td>\n",
              "      <td>00:04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7</td>\n",
              "      <td>0.688371</td>\n",
              "      <td>0.678960</td>\n",
              "      <td>0.557377</td>\n",
              "      <td>00:04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>8</td>\n",
              "      <td>0.687793</td>\n",
              "      <td>0.669602</td>\n",
              "      <td>0.573770</td>\n",
              "      <td>00:04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>9</td>\n",
              "      <td>0.686679</td>\n",
              "      <td>0.660444</td>\n",
              "      <td>0.565574</td>\n",
              "      <td>00:03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>10</td>\n",
              "      <td>0.685147</td>\n",
              "      <td>0.667196</td>\n",
              "      <td>0.532787</td>\n",
              "      <td>00:03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>11</td>\n",
              "      <td>0.684087</td>\n",
              "      <td>0.669038</td>\n",
              "      <td>0.573770</td>\n",
              "      <td>00:03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>12</td>\n",
              "      <td>0.679933</td>\n",
              "      <td>0.653756</td>\n",
              "      <td>0.614754</td>\n",
              "      <td>00:03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>13</td>\n",
              "      <td>0.672798</td>\n",
              "      <td>0.626938</td>\n",
              "      <td>0.688525</td>\n",
              "      <td>00:03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>14</td>\n",
              "      <td>0.669417</td>\n",
              "      <td>0.669755</td>\n",
              "      <td>0.540984</td>\n",
              "      <td>00:04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>15</td>\n",
              "      <td>0.665489</td>\n",
              "      <td>0.632473</td>\n",
              "      <td>0.622951</td>\n",
              "      <td>00:03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>16</td>\n",
              "      <td>0.658222</td>\n",
              "      <td>0.593025</td>\n",
              "      <td>0.647541</td>\n",
              "      <td>00:03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>17</td>\n",
              "      <td>0.646409</td>\n",
              "      <td>0.587669</td>\n",
              "      <td>0.704918</td>\n",
              "      <td>00:03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>18</td>\n",
              "      <td>0.635161</td>\n",
              "      <td>0.581825</td>\n",
              "      <td>0.713115</td>\n",
              "      <td>00:03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>19</td>\n",
              "      <td>0.626777</td>\n",
              "      <td>0.582403</td>\n",
              "      <td>0.688525</td>\n",
              "      <td>00:03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>20</td>\n",
              "      <td>0.615703</td>\n",
              "      <td>0.596147</td>\n",
              "      <td>0.688525</td>\n",
              "      <td>00:03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>21</td>\n",
              "      <td>0.602857</td>\n",
              "      <td>0.586757</td>\n",
              "      <td>0.688525</td>\n",
              "      <td>00:03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>22</td>\n",
              "      <td>0.587635</td>\n",
              "      <td>0.572640</td>\n",
              "      <td>0.729508</td>\n",
              "      <td>00:03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>23</td>\n",
              "      <td>0.569287</td>\n",
              "      <td>0.569918</td>\n",
              "      <td>0.737705</td>\n",
              "      <td>00:03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>24</td>\n",
              "      <td>0.555949</td>\n",
              "      <td>0.515780</td>\n",
              "      <td>0.754098</td>\n",
              "      <td>00:03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>25</td>\n",
              "      <td>0.540863</td>\n",
              "      <td>0.613805</td>\n",
              "      <td>0.721311</td>\n",
              "      <td>00:04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>26</td>\n",
              "      <td>0.523340</td>\n",
              "      <td>0.571728</td>\n",
              "      <td>0.762295</td>\n",
              "      <td>00:04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>27</td>\n",
              "      <td>0.512441</td>\n",
              "      <td>0.582286</td>\n",
              "      <td>0.721311</td>\n",
              "      <td>00:04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>28</td>\n",
              "      <td>0.499139</td>\n",
              "      <td>0.517941</td>\n",
              "      <td>0.737705</td>\n",
              "      <td>00:04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>29</td>\n",
              "      <td>0.485297</td>\n",
              "      <td>0.467071</td>\n",
              "      <td>0.795082</td>\n",
              "      <td>00:03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>30</td>\n",
              "      <td>0.465541</td>\n",
              "      <td>0.526552</td>\n",
              "      <td>0.737705</td>\n",
              "      <td>00:03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>31</td>\n",
              "      <td>0.449582</td>\n",
              "      <td>0.597458</td>\n",
              "      <td>0.745902</td>\n",
              "      <td>00:03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>32</td>\n",
              "      <td>0.433068</td>\n",
              "      <td>0.537581</td>\n",
              "      <td>0.786885</td>\n",
              "      <td>00:03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>33</td>\n",
              "      <td>0.412181</td>\n",
              "      <td>0.572914</td>\n",
              "      <td>0.754098</td>\n",
              "      <td>00:03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>34</td>\n",
              "      <td>0.397310</td>\n",
              "      <td>0.551374</td>\n",
              "      <td>0.819672</td>\n",
              "      <td>00:03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>35</td>\n",
              "      <td>0.380799</td>\n",
              "      <td>0.518006</td>\n",
              "      <td>0.827869</td>\n",
              "      <td>00:03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>36</td>\n",
              "      <td>0.365889</td>\n",
              "      <td>0.484920</td>\n",
              "      <td>0.836066</td>\n",
              "      <td>00:03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>37</td>\n",
              "      <td>0.346956</td>\n",
              "      <td>0.507253</td>\n",
              "      <td>0.844262</td>\n",
              "      <td>00:03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>38</td>\n",
              "      <td>0.331075</td>\n",
              "      <td>0.537868</td>\n",
              "      <td>0.827869</td>\n",
              "      <td>00:03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>39</td>\n",
              "      <td>0.326048</td>\n",
              "      <td>0.481716</td>\n",
              "      <td>0.844262</td>\n",
              "      <td>00:03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>40</td>\n",
              "      <td>0.314643</td>\n",
              "      <td>0.438615</td>\n",
              "      <td>0.860656</td>\n",
              "      <td>00:03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>41</td>\n",
              "      <td>0.301784</td>\n",
              "      <td>0.462500</td>\n",
              "      <td>0.827869</td>\n",
              "      <td>00:03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>42</td>\n",
              "      <td>0.292196</td>\n",
              "      <td>0.473262</td>\n",
              "      <td>0.852459</td>\n",
              "      <td>00:03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>43</td>\n",
              "      <td>0.275773</td>\n",
              "      <td>0.381732</td>\n",
              "      <td>0.860656</td>\n",
              "      <td>00:03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>44</td>\n",
              "      <td>0.261762</td>\n",
              "      <td>0.372367</td>\n",
              "      <td>0.852459</td>\n",
              "      <td>00:03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>45</td>\n",
              "      <td>0.246564</td>\n",
              "      <td>0.358652</td>\n",
              "      <td>0.860656</td>\n",
              "      <td>00:03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>46</td>\n",
              "      <td>0.239783</td>\n",
              "      <td>0.445270</td>\n",
              "      <td>0.852459</td>\n",
              "      <td>00:03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>47</td>\n",
              "      <td>0.232306</td>\n",
              "      <td>0.541846</td>\n",
              "      <td>0.786885</td>\n",
              "      <td>00:04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>48</td>\n",
              "      <td>0.220670</td>\n",
              "      <td>0.327060</td>\n",
              "      <td>0.877049</td>\n",
              "      <td>00:04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>49</td>\n",
              "      <td>0.209627</td>\n",
              "      <td>0.349661</td>\n",
              "      <td>0.885246</td>\n",
              "      <td>00:04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>50</td>\n",
              "      <td>0.196855</td>\n",
              "      <td>0.341417</td>\n",
              "      <td>0.852459</td>\n",
              "      <td>00:03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>51</td>\n",
              "      <td>0.189521</td>\n",
              "      <td>0.288280</td>\n",
              "      <td>0.893443</td>\n",
              "      <td>00:04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>52</td>\n",
              "      <td>0.177972</td>\n",
              "      <td>0.271521</td>\n",
              "      <td>0.893443</td>\n",
              "      <td>00:04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>53</td>\n",
              "      <td>0.168781</td>\n",
              "      <td>0.308220</td>\n",
              "      <td>0.885246</td>\n",
              "      <td>00:04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>54</td>\n",
              "      <td>0.161524</td>\n",
              "      <td>0.290750</td>\n",
              "      <td>0.893443</td>\n",
              "      <td>00:03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>55</td>\n",
              "      <td>0.156216</td>\n",
              "      <td>0.292264</td>\n",
              "      <td>0.893443</td>\n",
              "      <td>00:03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>56</td>\n",
              "      <td>0.148924</td>\n",
              "      <td>0.283055</td>\n",
              "      <td>0.893443</td>\n",
              "      <td>00:03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>57</td>\n",
              "      <td>0.138190</td>\n",
              "      <td>0.262015</td>\n",
              "      <td>0.885246</td>\n",
              "      <td>00:03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>58</td>\n",
              "      <td>0.130408</td>\n",
              "      <td>0.288977</td>\n",
              "      <td>0.885246</td>\n",
              "      <td>00:03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>59</td>\n",
              "      <td>0.121740</td>\n",
              "      <td>0.261681</td>\n",
              "      <td>0.918033</td>\n",
              "      <td>00:03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>60</td>\n",
              "      <td>0.112958</td>\n",
              "      <td>0.227427</td>\n",
              "      <td>0.942623</td>\n",
              "      <td>00:03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>61</td>\n",
              "      <td>0.107858</td>\n",
              "      <td>0.239338</td>\n",
              "      <td>0.918033</td>\n",
              "      <td>00:03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>62</td>\n",
              "      <td>0.105899</td>\n",
              "      <td>0.379067</td>\n",
              "      <td>0.901639</td>\n",
              "      <td>00:03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>63</td>\n",
              "      <td>0.102010</td>\n",
              "      <td>0.357062</td>\n",
              "      <td>0.901639</td>\n",
              "      <td>00:03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>64</td>\n",
              "      <td>0.098958</td>\n",
              "      <td>0.324806</td>\n",
              "      <td>0.909836</td>\n",
              "      <td>00:03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>65</td>\n",
              "      <td>0.095695</td>\n",
              "      <td>0.275259</td>\n",
              "      <td>0.918033</td>\n",
              "      <td>00:03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>66</td>\n",
              "      <td>0.090429</td>\n",
              "      <td>0.274218</td>\n",
              "      <td>0.901639</td>\n",
              "      <td>00:04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>67</td>\n",
              "      <td>0.088064</td>\n",
              "      <td>0.275650</td>\n",
              "      <td>0.918033</td>\n",
              "      <td>00:04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>68</td>\n",
              "      <td>0.081980</td>\n",
              "      <td>0.226376</td>\n",
              "      <td>0.918033</td>\n",
              "      <td>00:04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>69</td>\n",
              "      <td>0.078974</td>\n",
              "      <td>0.186952</td>\n",
              "      <td>0.909836</td>\n",
              "      <td>00:03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>70</td>\n",
              "      <td>0.074536</td>\n",
              "      <td>0.182593</td>\n",
              "      <td>0.934426</td>\n",
              "      <td>00:03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>71</td>\n",
              "      <td>0.072571</td>\n",
              "      <td>0.185881</td>\n",
              "      <td>0.926230</td>\n",
              "      <td>00:03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>72</td>\n",
              "      <td>0.068878</td>\n",
              "      <td>0.201311</td>\n",
              "      <td>0.934426</td>\n",
              "      <td>00:03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>73</td>\n",
              "      <td>0.070722</td>\n",
              "      <td>0.218396</td>\n",
              "      <td>0.926230</td>\n",
              "      <td>00:03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>74</td>\n",
              "      <td>0.065602</td>\n",
              "      <td>0.235972</td>\n",
              "      <td>0.934426</td>\n",
              "      <td>00:03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>75</td>\n",
              "      <td>0.062092</td>\n",
              "      <td>0.265400</td>\n",
              "      <td>0.909836</td>\n",
              "      <td>00:03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>76</td>\n",
              "      <td>0.060193</td>\n",
              "      <td>0.253992</td>\n",
              "      <td>0.934426</td>\n",
              "      <td>00:03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>77</td>\n",
              "      <td>0.059364</td>\n",
              "      <td>0.220470</td>\n",
              "      <td>0.934426</td>\n",
              "      <td>00:03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>78</td>\n",
              "      <td>0.059196</td>\n",
              "      <td>0.174635</td>\n",
              "      <td>0.918033</td>\n",
              "      <td>00:03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>79</td>\n",
              "      <td>0.056240</td>\n",
              "      <td>0.156860</td>\n",
              "      <td>0.926230</td>\n",
              "      <td>00:03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>80</td>\n",
              "      <td>0.053705</td>\n",
              "      <td>0.154915</td>\n",
              "      <td>0.918033</td>\n",
              "      <td>00:03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>81</td>\n",
              "      <td>0.051555</td>\n",
              "      <td>0.155969</td>\n",
              "      <td>0.934426</td>\n",
              "      <td>00:03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>82</td>\n",
              "      <td>0.048242</td>\n",
              "      <td>0.162600</td>\n",
              "      <td>0.918033</td>\n",
              "      <td>00:03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>83</td>\n",
              "      <td>0.045945</td>\n",
              "      <td>0.165775</td>\n",
              "      <td>0.926230</td>\n",
              "      <td>00:03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>84</td>\n",
              "      <td>0.042672</td>\n",
              "      <td>0.170409</td>\n",
              "      <td>0.926230</td>\n",
              "      <td>00:03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>85</td>\n",
              "      <td>0.039991</td>\n",
              "      <td>0.174946</td>\n",
              "      <td>0.918033</td>\n",
              "      <td>00:03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>86</td>\n",
              "      <td>0.037802</td>\n",
              "      <td>0.176688</td>\n",
              "      <td>0.918033</td>\n",
              "      <td>00:04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>87</td>\n",
              "      <td>0.035632</td>\n",
              "      <td>0.180678</td>\n",
              "      <td>0.918033</td>\n",
              "      <td>00:04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>88</td>\n",
              "      <td>0.035644</td>\n",
              "      <td>0.183300</td>\n",
              "      <td>0.918033</td>\n",
              "      <td>00:04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>89</td>\n",
              "      <td>0.033723</td>\n",
              "      <td>0.182546</td>\n",
              "      <td>0.918033</td>\n",
              "      <td>00:03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>90</td>\n",
              "      <td>0.035698</td>\n",
              "      <td>0.183879</td>\n",
              "      <td>0.918033</td>\n",
              "      <td>00:03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>91</td>\n",
              "      <td>0.034619</td>\n",
              "      <td>0.183288</td>\n",
              "      <td>0.918033</td>\n",
              "      <td>00:03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>92</td>\n",
              "      <td>0.035067</td>\n",
              "      <td>0.182259</td>\n",
              "      <td>0.918033</td>\n",
              "      <td>00:03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>93</td>\n",
              "      <td>0.033919</td>\n",
              "      <td>0.180371</td>\n",
              "      <td>0.918033</td>\n",
              "      <td>00:03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>94</td>\n",
              "      <td>0.033159</td>\n",
              "      <td>0.179331</td>\n",
              "      <td>0.918033</td>\n",
              "      <td>00:03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>95</td>\n",
              "      <td>0.032420</td>\n",
              "      <td>0.178945</td>\n",
              "      <td>0.926230</td>\n",
              "      <td>00:03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>96</td>\n",
              "      <td>0.031248</td>\n",
              "      <td>0.179156</td>\n",
              "      <td>0.926230</td>\n",
              "      <td>00:03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>97</td>\n",
              "      <td>0.031407</td>\n",
              "      <td>0.179180</td>\n",
              "      <td>0.926230</td>\n",
              "      <td>00:03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>98</td>\n",
              "      <td>0.029980</td>\n",
              "      <td>0.179211</td>\n",
              "      <td>0.926230</td>\n",
              "      <td>00:03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>99</td>\n",
              "      <td>0.029561</td>\n",
              "      <td>0.179225</td>\n",
              "      <td>0.926230</td>\n",
              "      <td>00:03</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}