brain_hemorrhage_classification.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "50b39148",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "from os.path import exists\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "from PIL import Image, ImageDraw, ImageFilter\n",
    "from skimage.transform import rescale, resize, downscale_local_mean\n",
    "from skimage import color\n",
    "import json\n",
    "import csv"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "793d4842",
   "metadata": {},
   "source": [
    "Test Downsampling An Image"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0648f2ca",
   "metadata": {},
   "source": [
    "Initial Image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c5dc7353",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7fc8862c3760>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sample_image_path = 'epidural/max_contrast_window/ID_0a3f30540.jpg'\n",
    "sample_image = plt.imread(sample_image_path)\n",
    "plt.imshow(sample_image)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "25459e67",
   "metadata": {},
   "source": [
    "Grayscale Image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "29ef8cd6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7fc88b3cff70>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sample_image_gray = color.rgb2gray(sample_image)\n",
    "plt.imshow(sample_image_gray, cmap=plt.cm.gray)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7f03fbcd",
   "metadata": {},
   "source": [
    "Rescaled Image No Aliasing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "32066824",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7fc88c111b20>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sample_image_rescaled = rescale(sample_image_gray, 0.25, anti_aliasing=True)\n",
    "plt.imshow(sample_image_rescaled, cmap=plt.cm.gray)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f7931b52",
   "metadata": {},
   "source": [
    "Resized Image Aliasing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "01d424b2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7fc88b2d5160>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sample_image_resized = resize(sample_image_gray, (sample_image_gray.shape[0] / 4, sample_image_gray.shape[1] / 4), anti_aliasing=False)\n",
    "plt.imshow(sample_image_resized, cmap=plt.cm.gray)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "faee10b1",
   "metadata": {},
   "source": [
    "Downscaled Image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c789d6c7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7fc88b13a1c0>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sample_image_downscaled = downscale_local_mean(sample_image_gray, (4, 4))\n",
    "plt.imshow(sample_image_downscaled, cmap=plt.cm.gray)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2dababfc",
   "metadata": {},
   "source": [
    "Building Dataset of Image + Classifications"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "27b36631",
   "metadata": {},
   "outputs": [],
   "source": [
    "positive_examples_per_type = 3145\n",
    "test_set_size_per_model = int(positive_examples_per_type * 2 * 0.2)\n",
    "max_training_set_size_per_model = int(positive_examples_per_type * 2 * 0.8)\n",
    "standard_height, standard_width = 512, 512\n",
    "view = 'max_contrast_window'\n",
    "hemorrhage_types = ['epidural', 'intraparenchymal', 'intraventricular', 'subarachnoid', 'subdural']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b7b1b7e7",
   "metadata": {},
   "source": [
    "All of the labels are in this csv file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "27fcc5a8",
   "metadata": {
    "scrolled": true
   },
   "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>Image</th>\n",
       "      <th>any</th>\n",
       "      <th>epidural</th>\n",
       "      <th>intraparenchymal</th>\n",
       "      <th>intraventricular</th>\n",
       "      <th>subarachnoid</th>\n",
       "      <th>subdural</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ID_000012eaf</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ID_000039fa0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ID_00005679d</td>\n",
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       "      <th>752798</th>\n",
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       "      <th>752800</th>\n",
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       "<p>752803 rows × 7 columns</p>\n",
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      ],
      "text/plain": [
       "               Image  any  epidural  intraparenchymal  intraventricular  \\\n",
       "0       ID_000012eaf    0         0                 0                 0   \n",
       "1       ID_000039fa0    0         0                 0                 0   \n",
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       "...              ...  ...       ...               ...               ...   \n",
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       "752799  ID_ffff922b9    1         0                 0                 1   \n",
       "752800  ID_ffffb670a    1         0                 0                 0   \n",
       "752801  ID_ffffcbff8    0         0                 0                 0   \n",
       "752802  ID_fffff9393    0         0                 0                 0   \n",
       "\n",
       "        subarachnoid  subdural  \n",
       "0                  0         0  \n",
       "1                  0         0  \n",
       "2                  0         0  \n",
       "3                  0         0  \n",
       "4                  0         0  \n",
       "...              ...       ...  \n",
       "752798             0         0  \n",
       "752799             0         0  \n",
       "752800             1         0  \n",
       "752801             0         0  \n",
       "752802             0         0  \n",
       "\n",
       "[752803 rows x 7 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_labels = pd.read_csv('segmentations/hemorrhage-labels.csv')\n",
    "all_labels"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "31deae72",
   "metadata": {},
   "source": [
    "get_image_path determines what directory the image will reside in and return the corresponding path: e.g. images with no hemorrhage are in the normal directory, images with a hemorrhage of multiple different types are in the multi directory, the images of only a single hemorrhage type are in that corresponding hemorrhage directory  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "64ddfec1",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_image_path(row, view):\n",
    "    if row[1] == 0:\n",
    "        return f'normal/{view}/{row[0]}.jpg'\n",
    "    elif row[2:].sum() > 1:\n",
    "        return f'multi/{view}/{row[0]}.jpg'\n",
    "    else:\n",
    "        for i in range(len(hemorrhage_types)):\n",
    "            if row[2 + i] == 1:\n",
    "                return f'{hemorrhage_types[i]}/{view}/{row[0]}.jpg'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7cd5bc9f",
   "metadata": {},
   "source": [
    "using get_image_path, we input a column in each row describing the image path for that image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "15a10267",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>752799</th>\n",
       "      <td>ID_ffff922b9</td>\n",
       "      <td>intraventricular/max_contrast_window/ID_ffff92...</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
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       "      <td>ID_fffff9393</td>\n",
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       "<p>752803 rows × 8 columns</p>\n",
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      ],
      "text/plain": [
       "               Image                                          ImagePath  any  \\\n",
       "0       ID_000012eaf        normal/max_contrast_window/ID_000012eaf.jpg    0   \n",
       "1       ID_000039fa0        normal/max_contrast_window/ID_000039fa0.jpg    0   \n",
       "2       ID_00005679d        normal/max_contrast_window/ID_00005679d.jpg    0   \n",
       "3       ID_00008ce3c        normal/max_contrast_window/ID_00008ce3c.jpg    0   \n",
       "4       ID_0000950d7        normal/max_contrast_window/ID_0000950d7.jpg    0   \n",
       "...              ...                                                ...  ...   \n",
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       "752802  ID_fffff9393        normal/max_contrast_window/ID_fffff9393.jpg    0   \n",
       "\n",
       "        epidural  intraparenchymal  intraventricular  subarachnoid  subdural  \n",
       "0              0                 0                 0             0         0  \n",
       "1              0                 0                 0             0         0  \n",
       "2              0                 0                 0             0         0  \n",
       "3              0                 0                 0             0         0  \n",
       "4              0                 0                 0             0         0  \n",
       "...          ...               ...               ...           ...       ...  \n",
       "752798         0                 0                 0             0         0  \n",
       "752799         0                 0                 1             0         0  \n",
       "752800         0                 0                 0             1         0  \n",
       "752801         0                 0                 0             0         0  \n",
       "752802         0                 0                 0             0         0  \n",
       "\n",
       "[752803 rows x 8 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_labels.insert(1, 'ImagePath', all_labels.apply(lambda row: get_image_path(row, view), axis=1))\n",
    "all_labels"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5cac8948",
   "metadata": {},
   "source": [
    "Remove the rows whose images are not available. We determine which images are not available by checking if they are in the file directories using the exists function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "625695e2",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
       "      <th>752769</th>\n",
       "      <td>ID_fffd00949</td>\n",
       "      <td>subarachnoid/max_contrast_window/ID_fffd00949.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
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      ],
      "text/plain": [
       "               Image                                          ImagePath  any  \\\n",
       "0       ID_000012eaf        normal/max_contrast_window/ID_000012eaf.jpg    0   \n",
       "1       ID_000039fa0        normal/max_contrast_window/ID_000039fa0.jpg    0   \n",
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       "4       ID_0000950d7        normal/max_contrast_window/ID_0000950d7.jpg    0   \n",
       "...              ...                                                ...  ...   \n",
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       "752769  ID_fffd00949  subarachnoid/max_contrast_window/ID_fffd00949.jpg    1   \n",
       "752783  ID_fffe2edb8         multi/max_contrast_window/ID_fffe2edb8.jpg    1   \n",
       "752799  ID_ffff922b9  intraventricular/max_contrast_window/ID_ffff92...    1   \n",
       "752800  ID_ffffb670a  subarachnoid/max_contrast_window/ID_ffffb670a.jpg    1   \n",
       "\n",
       "        epidural  intraparenchymal  intraventricular  subarachnoid  subdural  \n",
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       "...          ...               ...               ...           ...       ...  \n",
       "752755         0                 1                 1             0         0  \n",
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       "\n",
       "[116532 rows x 8 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "available_labels = all_labels[list(map(exists, all_labels['ImagePath']))]\n",
    "available_labels"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "69f08d9e",
   "metadata": {},
   "source": [
    "If the image corresponding to an image path does not have the standard size we're expecting (512 x 512), remove that row"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b9af14b7",
   "metadata": {},
   "outputs": [],
   "source": [
    "def is_correct_size(image_path):\n",
    "    width, height = Image.open(image_path).size\n",
    "    return width == standard_width and height == standard_height"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "8f26c8c3",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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      ],
      "text/plain": [
       "               Image                                          ImagePath  any  \\\n",
       "0       ID_000012eaf        normal/max_contrast_window/ID_000012eaf.jpg    0   \n",
       "1       ID_000039fa0        normal/max_contrast_window/ID_000039fa0.jpg    0   \n",
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       "\n",
       "        epidural  intraparenchymal  intraventricular  subarachnoid  subdural  \n",
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       "752800         0                 0                 0             1         0  \n",
       "\n",
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     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "usable_labels = available_labels[list(map(is_correct_size, available_labels['ImagePath']))]\n",
    "usable_labels"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "443860a0",
   "metadata": {},
   "source": [
    "We will only be getting a certain # of training examples for each type of classification. First, get a set amount of images that are classfied as no hemorrhage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a75fc754",
   "metadata": {},
   "outputs": [
    {
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       "             Image                                    ImagePath  any  \\\n",
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      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "normal_labels = usable_labels[usable_labels['any'] == 0].sample(positive_examples_per_type, random_state=0)\n",
    "normal_labels"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "525dd855",
   "metadata": {},
   "source": [
    "Then, get a set amount of training examples for each distinct type of hemorrhage. The epidural DataFrame is printed as an example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "572cae6f",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "      <td>epidural/max_contrast_window/ID_385bfea1f.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75143</th>\n",
       "      <td>ID_197585b09</td>\n",
       "      <td>multi/max_contrast_window/ID_197585b09.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186120</th>\n",
       "      <td>ID_3f36d865e</td>\n",
       "      <td>epidural/max_contrast_window/ID_3f36d865e.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201737</th>\n",
       "      <td>ID_4482a0ddf</td>\n",
       "      <td>epidural/max_contrast_window/ID_4482a0ddf.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>393391</th>\n",
       "      <td>ID_8586e65da</td>\n",
       "      <td>epidural/max_contrast_window/ID_8586e65da.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>624833</th>\n",
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       "      <td>1</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>3145 rows × 8 columns</p>\n",
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      ],
      "text/plain": [
       "               Image                                      ImagePath  any  \\\n",
       "100708  ID_2238a16d2     multi/max_contrast_window/ID_2238a16d2.jpg    1   \n",
       "733641  ID_f9758e16f     multi/max_contrast_window/ID_f9758e16f.jpg    1   \n",
       "278751  ID_5e830ddd1     multi/max_contrast_window/ID_5e830ddd1.jpg    1   \n",
       "165982  ID_385bfea1f  epidural/max_contrast_window/ID_385bfea1f.jpg    1   \n",
       "75143   ID_197585b09     multi/max_contrast_window/ID_197585b09.jpg    1   \n",
       "...              ...                                            ...  ...   \n",
       "186120  ID_3f36d865e  epidural/max_contrast_window/ID_3f36d865e.jpg    1   \n",
       "201737  ID_4482a0ddf  epidural/max_contrast_window/ID_4482a0ddf.jpg    1   \n",
       "393391  ID_8586e65da  epidural/max_contrast_window/ID_8586e65da.jpg    1   \n",
       "624833  ID_d458e0d0b  epidural/max_contrast_window/ID_d458e0d0b.jpg    1   \n",
       "652076  ID_dd9c305f7     multi/max_contrast_window/ID_dd9c305f7.jpg    1   \n",
       "\n",
       "        epidural  intraparenchymal  intraventricular  subarachnoid  subdural  \n",
       "100708         1                 0                 0             1         0  \n",
       "733641         1                 0                 0             0         1  \n",
       "278751         1                 0                 1             0         1  \n",
       "165982         1                 0                 0             0         0  \n",
       "75143          1                 0                 0             0         1  \n",
       "...          ...               ...               ...           ...       ...  \n",
       "186120         1                 0                 0             0         0  \n",
       "201737         1                 0                 0             0         0  \n",
       "393391         1                 0                 0             0         0  \n",
       "624833         1                 0                 0             0         0  \n",
       "652076         1                 0                 0             0         1  \n",
       "\n",
       "[3145 rows x 8 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hemorrhage_labels = dict()\n",
    "for hemorrhage in hemorrhage_types:\n",
    "    hemorrhage_labels[hemorrhage] = usable_labels[usable_labels[hemorrhage] == 1].sample(positive_examples_per_type, random_state=0)\n",
    "hemorrhage_labels['epidural']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "790b3167",
   "metadata": {},
   "source": [
    "Combine all the labels that were selected into one DataFrame to make it easier to read the image data all at once"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "31562436",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>ID_0057098ae</td>\n",
       "      <td>subdural/max_contrast_window/ID_0057098ae.jpg</td>\n",
       "      <td>1</td>\n",
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       "      <td>multi/max_contrast_window/ID_aa412d966.jpg</td>\n",
       "      <td>1</td>\n",
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      "text/plain": [
       "               Image                                      ImagePath  any  \\\n",
       "5667    ID_01f4d6cfa    normal/max_contrast_window/ID_01f4d6cfa.jpg    0   \n",
       "5914    ID_020b5fdc2    normal/max_contrast_window/ID_020b5fdc2.jpg    0   \n",
       "158     ID_000e1b81f    normal/max_contrast_window/ID_000e1b81f.jpg    0   \n",
       "3710    ID_01472d4a6    normal/max_contrast_window/ID_01472d4a6.jpg    0   \n",
       "8345    ID_02e0469a3    normal/max_contrast_window/ID_02e0469a3.jpg    0   \n",
       "...              ...                                            ...  ...   \n",
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       "425933  ID_9088273b7  subdural/max_contrast_window/ID_9088273b7.jpg    1   \n",
       "\n",
       "        epidural  intraparenchymal  intraventricular  subarachnoid  subdural  \n",
       "5667           0                 0                 0             0         0  \n",
       "5914           0                 0                 0             0         0  \n",
       "158            0                 0                 0             0         0  \n",
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       "\n",
       "[18870 rows x 8 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels = normal_labels\n",
    "for hemorrhage in hemorrhage_types:\n",
    "    labels = labels.append(hemorrhage_labels[hemorrhage])\n",
    "labels"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "645db6f3",
   "metadata": {},
   "source": [
    "Some examples may have been added multiple times because they are classified as multiple types of hemorrhages, so remove the duplicate rows of data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "c0eeca1f",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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      ],
      "text/plain": [
       "              Image                                      ImagePath  any  \\\n",
       "0      ID_01f4d6cfa    normal/max_contrast_window/ID_01f4d6cfa.jpg    0   \n",
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       "...             ...                                            ...  ...   \n",
       "18296  ID_d1ef577cf  subdural/max_contrast_window/ID_d1ef577cf.jpg    1   \n",
       "18297  ID_0057098ae  subdural/max_contrast_window/ID_0057098ae.jpg    1   \n",
       "18298  ID_aa412d966     multi/max_contrast_window/ID_aa412d966.jpg    1   \n",
       "18299  ID_4065a3887  subdural/max_contrast_window/ID_4065a3887.jpg    1   \n",
       "18300  ID_9088273b7  subdural/max_contrast_window/ID_9088273b7.jpg    1   \n",
       "\n",
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       "\n",
       "[18301 rows x 8 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels = labels.drop_duplicates().reset_index(drop=True)\n",
    "labels"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f265c749",
   "metadata": {},
   "source": [
    "Now that we have a master list of images + labels to use, let's actually fill each row of the DataFrame with the corresponding image data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "23611e26",
   "metadata": {},
   "source": [
    "get_image_data applies a vector operation to each row in the labels DataFrame that retrieves the image data based on the image path. the function returns a new DataFrame with each row representing one image. the index of each row in the new image data DataFrame corresponds to the index of each row in the old labels DataFrame, making it seamless to combine the two"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c172bb69",
   "metadata": {},
   "source": [
    "get_data allows for customization of what type of image to read (grayscale vs rgb, max_contrast_window vs subdural vs different view, how much to downsample each image, etc.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "f4eb1281",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_image_data(image_labels, grayscale=True, downsample_factor=None):\n",
    "    return image_labels.apply(lambda row: get_data(row, grayscale, downsample_factor), axis=1, result_type='expand')\n",
    "def get_data(row, grayscale, downsample_factor):\n",
    "    if int(row.name) % 100 == 0:\n",
    "        print(f'{row.name} Images Read')\n",
    "        \n",
    "    image = plt.imread(row[1])\n",
    "    if image.shape != (standard_height, standard_width, 3):\n",
    "        image = np.zeros((standard_height, standard_width, 3))\n",
    "    \n",
    "    if grayscale:\n",
    "        image = color.rgb2gray(image)\n",
    "    \n",
    "    if downsample_factor is not None:\n",
    "        image = rescale(image, downsample_factor, anti_aliasing=True)\n",
    "        \n",
    "    return image.flatten()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3c382771",
   "metadata": {},
   "source": [
    "retrieve pixel data for each image in labels file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "68554013",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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       "      <td>0.209305</td>\n",
       "      <td>0.208801</td>\n",
       "      <td>0.209126</td>\n",
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       "  </tbody>\n",
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       "<p>18301 rows × 16384 columns</p>\n",
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       "          0         1         2         3         4         5         6      \\\n",
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       "0      0.177472  0.174163  1.665602e-01  ...  0.097850  0.108344  0.125145   \n",
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       "...         ...       ...           ...  ...       ...       ...       ...   \n",
       "18296  0.000000  0.000000  0.000000e+00  ...  0.000000  0.000000  0.000000   \n",
       "18297  0.000000  0.000000  0.000000e+00  ...  0.000000  0.000000  0.000000   \n",
       "18298  0.094848  0.093858  1.031995e-01  ...  0.150922  0.169018  0.167451   \n",
       "18299  0.102748  0.088685  9.467082e-02  ...  0.218619  0.210665  0.231792   \n",
       "18300  0.102309  0.099694  1.045909e-01  ...  0.149299  0.152500  0.157374   \n",
       "\n",
       "          16377     16378     16379     16380     16381     16382     16383  \n",
       "0      0.105169  0.121053  0.123569  0.126484  0.130485  0.120768  0.118604  \n",
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       "3      0.108986  0.104655  0.093633  0.094241  0.120462  0.107320  0.102138  \n",
       "4      0.225333  0.214360  0.233186  0.216167  0.223220  0.217947  0.218500  \n",
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       "18299  0.255857  0.259348  0.260824  0.260744  0.276087  0.291454  0.293790  \n",
       "18300  0.172733  0.188803  0.200190  0.208734  0.209305  0.208801  0.209126  \n",
       "\n",
       "[18301 rows x 16384 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "image_data = get_image_data(labels, downsample_factor=0.25)\n",
    "image_data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a481274",
   "metadata": {},
   "source": [
    "combine the labels DataFrame and the image data DataFrame to form one unified DataFrame with all the data. remove the image data that unable to be parsed (all pixels sum up to 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "36050cec",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Image</th>\n",
       "      <th>ImagePath</th>\n",
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       "      <th>3</th>\n",
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       "      <td>normal/max_contrast_window/ID_01472d4a6.jpg</td>\n",
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       "      <td>normal/max_contrast_window/ID_02e0469a3.jpg</td>\n",
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       "      <td>0.162278</td>\n",
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       "    <tr>\n",
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       "      <td>ID_4065a3887</td>\n",
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       "    <tr>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>18301 rows × 16392 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              Image                                      ImagePath  any  \\\n",
       "0      ID_01f4d6cfa    normal/max_contrast_window/ID_01f4d6cfa.jpg    0   \n",
       "1      ID_020b5fdc2    normal/max_contrast_window/ID_020b5fdc2.jpg    0   \n",
       "2      ID_000e1b81f    normal/max_contrast_window/ID_000e1b81f.jpg    0   \n",
       "3      ID_01472d4a6    normal/max_contrast_window/ID_01472d4a6.jpg    0   \n",
       "4      ID_02e0469a3    normal/max_contrast_window/ID_02e0469a3.jpg    0   \n",
       "...             ...                                            ...  ...   \n",
       "18296  ID_d1ef577cf  subdural/max_contrast_window/ID_d1ef577cf.jpg    1   \n",
       "18297  ID_0057098ae  subdural/max_contrast_window/ID_0057098ae.jpg    1   \n",
       "18298  ID_aa412d966     multi/max_contrast_window/ID_aa412d966.jpg    1   \n",
       "18299  ID_4065a3887  subdural/max_contrast_window/ID_4065a3887.jpg    1   \n",
       "18300  ID_9088273b7  subdural/max_contrast_window/ID_9088273b7.jpg    1   \n",
       "\n",
       "       epidural  intraparenchymal  intraventricular  subarachnoid  subdural  \\\n",
       "0             0                 0                 0             0         0   \n",
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       "...         ...               ...               ...           ...       ...   \n",
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       "\n",
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       "\n",
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       "1      0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  \n",
       "2      0.367718  0.371026  0.400871  0.436808  0.442879  0.448418  \n",
       "3      0.104655  0.093633  0.094241  0.120462  0.107320  0.102138  \n",
       "4      0.214360  0.233186  0.216167  0.223220  0.217947  0.218500  \n",
       "...         ...       ...       ...       ...       ...       ...  \n",
       "18296  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  \n",
       "18297  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  \n",
       "18298  0.153922  0.166517  0.172181  0.167094  0.158782  0.162278  \n",
       "18299  0.259348  0.260824  0.260744  0.276087  0.291454  0.293790  \n",
       "18300  0.188803  0.200190  0.208734  0.209305  0.208801  0.209126  \n",
       "\n",
       "[18301 rows x 16392 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_data = pd.concat([labels, image_data], axis=1)\n",
    "image_data_column_list = [i for i in range(image_data.shape[1])]\n",
    "total_data = total_data[(total_data[image_data_column_list].sum(axis=1) != 0)]\n",
    "total_data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "34ac415b",
   "metadata": {},
   "source": [
    "we now have our dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a4954ae",
   "metadata": {},
   "source": [
    "Building Logistic Regression Models to Classify Types of Hemorrhages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "607feb4b",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import confusion_matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f6524204",
   "metadata": {},
   "source": [
    "we will be building 6 different logistic regression classifiers. each will determine whether or not the image belongs to a given class"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc693026",
   "metadata": {},
   "source": [
    "on average for each classification in the dataset, there are about 17% of training examples that belong to that classification and 83% that don't. it is inefficient to input a lot of training examples that belong to the negative class into a classifier, so for each classifier we build a custom dataset where 50% of the training examples belong to the positive class and 50% to the negative."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "ba671640",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_balanced_dataset(data, column, num_positive_examples=positive_examples_per_type):\n",
    "    positive_data = data[data[column] == 1].sample(num_positive_examples, random_state=0)\n",
    "    negative_data = data[data[column] == 0].sample(num_positive_examples, random_state=0)\n",
    "    return positive_data.append(negative_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "00cc3b52",
   "metadata": {},
   "source": [
    "epidural custom dataset for example: 50% of images are of class epidural, 50% aren't"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "c38222ee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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>Image</th>\n",
       "      <th>ImagePath</th>\n",
       "      <th>any</th>\n",
       "      <th>epidural</th>\n",
       "      <th>intraparenchymal</th>\n",
       "      <th>intraventricular</th>\n",
       "      <th>subarachnoid</th>\n",
       "      <th>subdural</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>...</th>\n",
       "      <th>16374</th>\n",
       "      <th>16375</th>\n",
       "      <th>16376</th>\n",
       "      <th>16377</th>\n",
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       "      <th>16380</th>\n",
       "      <th>16381</th>\n",
       "      <th>16382</th>\n",
       "      <th>16383</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3567</th>\n",
       "      <td>ID_3548b6e47</td>\n",
       "      <td>epidural/max_contrast_window/ID_3548b6e47.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>0.110203</td>\n",
       "      <td>...</td>\n",
       "      <td>0.231457</td>\n",
       "      <td>0.181929</td>\n",
       "      <td>0.210159</td>\n",
       "      <td>0.235625</td>\n",
       "      <td>0.243665</td>\n",
       "      <td>0.226798</td>\n",
       "      <td>0.217793</td>\n",
       "      <td>0.200388</td>\n",
       "      <td>0.210863</td>\n",
       "      <td>0.240653</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6204</th>\n",
       "      <td>ID_4e8abd707</td>\n",
       "      <td>epidural/max_contrast_window/ID_4e8abd707.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>0.000000</td>\n",
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       "      <td>0.000000</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4327</th>\n",
       "      <td>ID_6da0fbd46</td>\n",
       "      <td>epidural/max_contrast_window/ID_6da0fbd46.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3828</th>\n",
       "      <td>ID_404a9e458</td>\n",
       "      <td>epidural/max_contrast_window/ID_404a9e458.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.091563</td>\n",
       "      <td>0.091536</td>\n",
       "      <td>...</td>\n",
       "      <td>0.337795</td>\n",
       "      <td>0.336784</td>\n",
       "      <td>0.327188</td>\n",
       "      <td>0.329424</td>\n",
       "      <td>0.334011</td>\n",
       "      <td>0.329989</td>\n",
       "      <td>0.329940</td>\n",
       "      <td>0.325051</td>\n",
       "      <td>0.322101</td>\n",
       "      <td>0.325606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3450</th>\n",
       "      <td>ID_34d6c8f77</td>\n",
       "      <td>multi/max_contrast_window/ID_34d6c8f77.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0.000000</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
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       "    <tr>\n",
       "      <th>16683</th>\n",
       "      <td>ID_fd587ed00</td>\n",
       "      <td>multi/max_contrast_window/ID_fd587ed00.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>0.059985</td>\n",
       "      <td>0.051891</td>\n",
       "      <td>...</td>\n",
       "      <td>0.253925</td>\n",
       "      <td>0.257570</td>\n",
       "      <td>0.251759</td>\n",
       "      <td>0.252825</td>\n",
       "      <td>0.254025</td>\n",
       "      <td>0.249340</td>\n",
       "      <td>0.254413</td>\n",
       "      <td>0.260373</td>\n",
       "      <td>0.257957</td>\n",
       "      <td>0.257166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10425</th>\n",
       "      <td>ID_64a3ac30b</td>\n",
       "      <td>intraventricular/max_contrast_window/ID_64a3ac...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>15305</th>\n",
       "      <td>ID_1b8b7c9c1</td>\n",
       "      <td>multi/max_contrast_window/ID_1b8b7c9c1.jpg</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.370127</td>\n",
       "      <td>0.375757</td>\n",
       "      <td>...</td>\n",
       "      <td>0.463157</td>\n",
       "      <td>0.462986</td>\n",
       "      <td>0.460520</td>\n",
       "      <td>0.454764</td>\n",
       "      <td>0.453640</td>\n",
       "      <td>0.453459</td>\n",
       "      <td>0.454346</td>\n",
       "      <td>0.452885</td>\n",
       "      <td>0.448481</td>\n",
       "      <td>0.448336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15487</th>\n",
       "      <td>ID_c74607597</td>\n",
       "      <td>subdural/max_contrast_window/ID_c74607597.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>17809</th>\n",
       "      <td>ID_d4cb5f2ba</td>\n",
       "      <td>multi/max_contrast_window/ID_d4cb5f2ba.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.070540</td>\n",
       "      <td>0.098648</td>\n",
       "      <td>...</td>\n",
       "      <td>0.362523</td>\n",
       "      <td>0.361389</td>\n",
       "      <td>0.343615</td>\n",
       "      <td>0.341581</td>\n",
       "      <td>0.383424</td>\n",
       "      <td>0.333179</td>\n",
       "      <td>0.262174</td>\n",
       "      <td>0.262607</td>\n",
       "      <td>0.250234</td>\n",
       "      <td>0.214728</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6290 rows × 16392 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              Image                                          ImagePath  any  \\\n",
       "3567   ID_3548b6e47      epidural/max_contrast_window/ID_3548b6e47.jpg    1   \n",
       "6204   ID_4e8abd707      epidural/max_contrast_window/ID_4e8abd707.jpg    1   \n",
       "4327   ID_6da0fbd46      epidural/max_contrast_window/ID_6da0fbd46.jpg    1   \n",
       "3828   ID_404a9e458      epidural/max_contrast_window/ID_404a9e458.jpg    1   \n",
       "3450   ID_34d6c8f77         multi/max_contrast_window/ID_34d6c8f77.jpg    1   \n",
       "...             ...                                                ...  ...   \n",
       "16683  ID_fd587ed00         multi/max_contrast_window/ID_fd587ed00.jpg    1   \n",
       "10425  ID_64a3ac30b  intraventricular/max_contrast_window/ID_64a3ac...    1   \n",
       "15305  ID_1b8b7c9c1         multi/max_contrast_window/ID_1b8b7c9c1.jpg    1   \n",
       "15487  ID_c74607597      subdural/max_contrast_window/ID_c74607597.jpg    1   \n",
       "17809  ID_d4cb5f2ba         multi/max_contrast_window/ID_d4cb5f2ba.jpg    1   \n",
       "\n",
       "       epidural  intraparenchymal  intraventricular  subarachnoid  subdural  \\\n",
       "3567          1                 0                 0             0         0   \n",
       "6204          1                 0                 0             0         0   \n",
       "4327          1                 0                 0             0         0   \n",
       "3828          1                 0                 0             0         0   \n",
       "3450          1                 0                 0             1         0   \n",
       "...         ...               ...               ...           ...       ...   \n",
       "16683         0                 1                 0             1         1   \n",
       "10425         0                 0                 1             0         0   \n",
       "15305         0                 1                 0             1         0   \n",
       "15487         0                 0                 0             0         1   \n",
       "17809         0                 1                 0             0         1   \n",
       "\n",
       "              0         1  ...     16374     16375     16376     16377  \\\n",
       "3567   0.097827  0.110203  ...  0.231457  0.181929  0.210159  0.235625   \n",
       "6204   0.000000  0.000000  ...  0.000000  0.000000  0.000000  0.000000   \n",
       "4327   0.000000  0.000000  ...  0.000000  0.000000  0.000000  0.000000   \n",
       "3828   0.091563  0.091536  ...  0.337795  0.336784  0.327188  0.329424   \n",
       "3450   0.000000  0.000000  ...  0.000000  0.000000  0.000000  0.000000   \n",
       "...         ...       ...  ...       ...       ...       ...       ...   \n",
       "16683  0.059985  0.051891  ...  0.253925  0.257570  0.251759  0.252825   \n",
       "10425  0.000000  0.000000  ...  0.000000  0.000000  0.000000  0.000000   \n",
       "15305  0.370127  0.375757  ...  0.463157  0.462986  0.460520  0.454764   \n",
       "15487  0.000000  0.000000  ...  0.000000  0.000000  0.000000  0.000000   \n",
       "17809  0.070540  0.098648  ...  0.362523  0.361389  0.343615  0.341581   \n",
       "\n",
       "          16378     16379     16380     16381     16382     16383  \n",
       "3567   0.243665  0.226798  0.217793  0.200388  0.210863  0.240653  \n",
       "6204   0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  \n",
       "4327   0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  \n",
       "3828   0.334011  0.329989  0.329940  0.325051  0.322101  0.325606  \n",
       "3450   0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  \n",
       "...         ...       ...       ...       ...       ...       ...  \n",
       "16683  0.254025  0.249340  0.254413  0.260373  0.257957  0.257166  \n",
       "10425  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  \n",
       "15305  0.453640  0.453459  0.454346  0.452885  0.448481  0.448336  \n",
       "15487  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  \n",
       "17809  0.383424  0.333179  0.262174  0.262607  0.250234  0.214728  \n",
       "\n",
       "[6290 rows x 16392 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_balanced_dataset(total_data, 'epidural')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2a6d9369",
   "metadata": {},
   "source": [
    "The following function builds a logistic regression model for a given positive class (type of hemorrhage: epidural, subdural, etc.) and prints relevant statistics about the accuracy of predicting that type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "4e084e54",
   "metadata": {},
   "outputs": [],
   "source": [
    "def build_logistic_model(data, column, iterations=100, solve_with='newton-cg'):\n",
    "    balanced_data = get_balanced_dataset(data, column)\n",
    "    X_train, X_test, y_train, y_test = train_test_split(balanced_data[image_data_column_list], balanced_data[column], test_size=0.2, random_state=0)\n",
    "    print(f'Training Set Size: {X_train.shape[0]}')\n",
    "    print(f'Test Set Size: {X_test.shape[0]}')\n",
    "    model = LogisticRegression(random_state=0, max_iter=iterations, solver=solve_with,verbose=1).fit(X_train, y_train)\n",
    "    print(f'Score: {model.score(X_test, y_test)}')\n",
    "    y_predict = model.predict(X_test)\n",
    "    results = confusion_matrix(y_test, y_predict)\n",
    "    print(f'Precision: {results[1][1]/(results[1][1] + results[0][1])}')\n",
    "    print(f'Recall: {results[1][1]/(results[1][1] + results[1][0])}')\n",
    "    print(results)\n",
    "    return model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6eadd836",
   "metadata": {},
   "source": [
    "Detecting if Image Has Any Hemorrhage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "5ebda9e3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Set Size: 5032\n",
      "Test Set Size: 1258\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:  1.3min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Score: 0.6550079491255962\n",
      "Precision: 0.6466165413533834\n",
      "Recall: 0.6836248012718601\n",
      "[[394 235]\n",
      " [199 430]]\n"
     ]
    }
   ],
   "source": [
    "any_hemorrhage_logistic_model = build_logistic_model(total_data, 'any', iterations=50)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff38bca1",
   "metadata": {},
   "source": [
    "Determining if Image Specifically Has Hemorrhage of Type Epidural"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "8a7e8b7d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Set Size: 5032\n",
      "Test Set Size: 1258\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:  1.7min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Score: 0.7289348171701113\n",
      "Precision: 0.7051282051282052\n",
      "Recall: 0.7869634340222575\n",
      "[[422 207]\n",
      " [134 495]]\n"
     ]
    }
   ],
   "source": [
    "epidural_logistic_model = build_logistic_model(total_data, 'epidural', iterations=50)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3289ef8e",
   "metadata": {},
   "source": [
    "Detecting Other Types of Hemorrhages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "6d947b02",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Set Size: 5032\n",
      "Test Set Size: 1258\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:  1.5min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Score: 0.5691573926868044\n",
      "Precision: 0.5682888540031397\n",
      "Recall: 0.575516693163752\n",
      "[[354 275]\n",
      " [267 362]]\n"
     ]
    }
   ],
   "source": [
    "intraparenchymal_logistic_model = build_logistic_model(total_data, 'intraparenchymal', iterations=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "f689b1a7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Set Size: 5032\n",
      "Test Set Size: 1258\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:  1.8min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Score: 0.6335453100158982\n",
      "Precision: 0.6300309597523219\n",
      "Recall: 0.6470588235294118\n",
      "[[390 239]\n",
      " [222 407]]\n"
     ]
    }
   ],
   "source": [
    "intraventricular_logistic_model = build_logistic_model(total_data, 'intraventricular', iterations=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "28526607",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Set Size: 5032\n",
      "Test Set Size: 1258\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:  6.3min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Score: 0.5572337042925278\n",
      "Precision: 0.5545454545454546\n",
      "Recall: 0.5818759936406995\n",
      "[[335 294]\n",
      " [263 366]]\n"
     ]
    }
   ],
   "source": [
    "subarachnoid_logistic_model = build_logistic_model(total_data, 'subarachnoid', iterations=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "d6053614",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Set Size: 5032\n",
      "Test Set Size: 1258\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:  1.1min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Score: 0.5993640699523053\n",
      "Precision: 0.5963020030816641\n",
      "Recall: 0.615262321144674\n",
      "[[367 262]\n",
      " [242 387]]\n"
     ]
    }
   ],
   "source": [
    "subdural_logistic_model = build_logistic_model(total_data, 'subdural', iterations=50)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "688d31cf",
   "metadata": {},
   "source": [
    "Things to Try: different number of examples, different picture types (max_contrast vs other types), RGB vs grayscale, all pixels vs downsampling, different solvers for logistic regression, different number of iterations"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c46d3cf",
   "metadata": {},
   "source": [
    "Building Support Vector Machines to Classify Hemorrhages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "8d07e92d",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import LinearSVC\n",
    "from sklearn.linear_model import SGDClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "ff26f04c",
   "metadata": {},
   "outputs": [],
   "source": [
    "def build_svm_model(data, column, iterations=1000, training_set_size = max_training_set_size_per_model):\n",
    "    positive_examples_to_use = int((training_set_size + test_set_size_per_model)/2)\n",
    "    balanced_data = get_balanced_dataset(data, column, num_positive_examples=positive_examples_to_use)\n",
    "    test_size_percentage = test_set_size_per_model/(positive_examples_to_use * 2)\n",
    "    X_train, X_test, y_train, y_test = train_test_split(balanced_data[image_data_column_list], balanced_data[column], test_size=test_size_percentage, random_state=0)\n",
    "    print(f'Training Set Size: {X_train.shape[0]}')\n",
    "    print(f'Test Set Size: {X_test.shape[0]}')    \n",
    "    model = LinearSVC(random_state=0, verbose=1, max_iter=iterations).fit(X_train, y_train)\n",
    "    print(f'Score: {model.score(X_test, y_test)}')\n",
    "    y_predict = model.predict(X_test)\n",
    "    results = confusion_matrix(y_test, y_predict)\n",
    "    print(f'Precision: {results[1][1]/(results[1][1] + results[0][1])}')\n",
    "    print(f'Recall: {results[1][1]/(results[1][1] + results[1][0])}')\n",
    "    print(results)\n",
    "    return model\n",
    "def build_stochastic_svm_model(data, column):\n",
    "    balanced_data = get_balanced_dataset(data, column)\n",
    "    X_train, X_test, y_train, y_test = train_test_split(balanced_data[image_data_column_list], balanced_data[column], test_size=0.2, random_state=0)\n",
    "    print(f'Training Set Size: {X_train.shape[0]}')\n",
    "    print(f'Test Set Size: {X_test.shape[0]}')\n",
    "    model = SGDClassifier(random_state=0, verbose=1).fit(X_train, y_train)\n",
    "    print(f'Score: {model.score(X_test, y_test)}')\n",
    "    y_predict = model.predict(X_test)\n",
    "    results = confusion_matrix(y_test, y_predict)\n",
    "    print(f'Precision: {results[1][1]/(results[1][1] + results[0][1])}')\n",
    "    print(f'Recall: {results[1][1]/(results[1][1] + results[1][0])}')\n",
    "    print(results)\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "29b4e07d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Set Size: 750\n",
      "Test Set Size: 1258\n",
      "[LibLinear]Score: 0.6406995230524642\n",
      "Precision: 0.6506410256410257\n",
      "Recall: 0.634375\n",
      "[[400 218]\n",
      " [234 406]]\n"
     ]
    }
   ],
   "source": [
    "any_hemorrhage_svm_model = build_svm_model(total_data, 'any', iterations = 5000, training_set_size = 750)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "8bf3c8b7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Set Size: 5032\n",
      "Test Set Size: 1258\n",
      "-- Epoch 1\n",
      "Norm: 1373.82, NNZs: 16384, Bias: -75.148910, T: 5032, Avg. loss: 2709.655146\n",
      "Total training time: 0.15 seconds.\n",
      "-- Epoch 2\n",
      "Norm: 973.48, NNZs: 16384, Bias: -89.581754, T: 10064, Avg. loss: 844.223655\n",
      "Total training time: 0.30 seconds.\n",
      "-- Epoch 3\n",
      "Norm: 816.47, NNZs: 16384, Bias: -97.717904, T: 15096, Avg. loss: 481.124287\n",
      "Total training time: 0.45 seconds.\n",
      "-- Epoch 4\n",
      "Norm: 725.99, NNZs: 16384, Bias: -100.895847, T: 20128, Avg. loss: 346.377637\n",
      "Total training time: 0.59 seconds.\n",
      "-- Epoch 5\n",
      "Norm: 666.44, NNZs: 16384, Bias: -104.803993, T: 25160, Avg. loss: 271.099554\n",
      "Total training time: 0.74 seconds.\n",
      "-- Epoch 6\n",
      "Norm: 627.17, NNZs: 16384, Bias: -109.255605, T: 30192, Avg. loss: 216.960587\n",
      "Total training time: 0.88 seconds.\n",
      "-- Epoch 7\n",
      "Norm: 589.45, NNZs: 16384, Bias: -111.049224, T: 35224, Avg. loss: 182.349625\n",
      "Total training time: 1.02 seconds.\n",
      "-- Epoch 8\n",
      "Norm: 558.19, NNZs: 16384, Bias: -111.794162, T: 40256, Avg. loss: 151.851583\n",
      "Total training time: 1.17 seconds.\n",
      "-- Epoch 9\n",
      "Norm: 535.66, NNZs: 16384, Bias: -112.722819, T: 45288, Avg. loss: 134.645036\n",
      "Total training time: 1.31 seconds.\n",
      "-- Epoch 10\n",
      "Norm: 517.52, NNZs: 16384, Bias: -114.770191, T: 50320, Avg. loss: 120.233339\n",
      "Total training time: 1.45 seconds.\n",
      "-- Epoch 11\n",
      "Norm: 495.33, NNZs: 16384, Bias: -116.075078, T: 55352, Avg. loss: 106.751649\n",
      "Total training time: 1.59 seconds.\n",
      "-- Epoch 12\n",
      "Norm: 479.77, NNZs: 16384, Bias: -117.246158, T: 60384, Avg. loss: 92.274352\n",
      "Total training time: 1.73 seconds.\n",
      "-- Epoch 13\n",
      "Norm: 463.27, NNZs: 16384, Bias: -118.165938, T: 65416, Avg. loss: 88.280365\n",
      "Total training time: 1.87 seconds.\n",
      "-- Epoch 14\n",
      "Norm: 451.03, NNZs: 16384, Bias: -118.593879, T: 70448, Avg. loss: 79.654755\n",
      "Total training time: 2.01 seconds.\n",
      "-- Epoch 15\n",
      "Norm: 439.87, NNZs: 16384, Bias: -118.864181, T: 75480, Avg. loss: 74.546084\n",
      "Total training time: 2.15 seconds.\n",
      "-- Epoch 16\n",
      "Norm: 431.25, NNZs: 16384, Bias: -119.378086, T: 80512, Avg. loss: 67.136348\n",
      "Total training time: 2.30 seconds.\n",
      "-- Epoch 17\n",
      "Norm: 422.11, NNZs: 16384, Bias: -120.342561, T: 85544, Avg. loss: 64.053888\n",
      "Total training time: 2.44 seconds.\n",
      "-- Epoch 18\n",
      "Norm: 413.70, NNZs: 16384, Bias: -120.902201, T: 90576, Avg. loss: 58.912072\n",
      "Total training time: 2.58 seconds.\n",
      "-- Epoch 19\n",
      "Norm: 405.18, NNZs: 16384, Bias: -120.905534, T: 95608, Avg. loss: 55.335262\n",
      "Total training time: 2.72 seconds.\n",
      "-- Epoch 20\n",
      "Norm: 396.79, NNZs: 16384, Bias: -120.908487, T: 100640, Avg. loss: 49.216544\n",
      "Total training time: 2.86 seconds.\n",
      "-- Epoch 21\n",
      "Norm: 389.31, NNZs: 16384, Bias: -121.191964, T: 105672, Avg. loss: 47.792157\n",
      "Total training time: 2.99 seconds.\n",
      "-- Epoch 22\n",
      "Norm: 382.65, NNZs: 16384, Bias: -121.383446, T: 110704, Avg. loss: 45.171742\n",
      "Total training time: 3.13 seconds.\n",
      "-- Epoch 23\n",
      "Norm: 376.79, NNZs: 16384, Bias: -121.821468, T: 115736, Avg. loss: 42.417873\n",
      "Total training time: 3.27 seconds.\n",
      "-- Epoch 24\n",
      "Norm: 370.94, NNZs: 16384, Bias: -122.159070, T: 120768, Avg. loss: 40.591984\n",
      "Total training time: 3.41 seconds.\n",
      "-- Epoch 25\n",
      "Norm: 365.91, NNZs: 16384, Bias: -122.479175, T: 125800, Avg. loss: 38.233088\n",
      "Total training time: 3.55 seconds.\n",
      "-- Epoch 26\n",
      "Norm: 359.93, NNZs: 16384, Bias: -122.319837, T: 130832, Avg. loss: 36.734332\n",
      "Total training time: 3.68 seconds.\n",
      "-- Epoch 27\n",
      "Norm: 355.48, NNZs: 16384, Bias: -122.396741, T: 135864, Avg. loss: 34.693137\n",
      "Total training time: 3.82 seconds.\n",
      "-- Epoch 28\n",
      "Norm: 350.14, NNZs: 16384, Bias: -122.612869, T: 140896, Avg. loss: 32.565962\n",
      "Total training time: 3.96 seconds.\n",
      "-- Epoch 29\n",
      "Norm: 345.43, NNZs: 16384, Bias: -122.818635, T: 145928, Avg. loss: 32.935120\n",
      "Total training time: 4.09 seconds.\n",
      "-- Epoch 30\n",
      "Norm: 341.30, NNZs: 16384, Bias: -122.886734, T: 150960, Avg. loss: 30.474614\n",
      "Total training time: 4.23 seconds.\n",
      "-- Epoch 31\n",
      "Norm: 337.78, NNZs: 16384, Bias: -123.078657, T: 155992, Avg. loss: 30.988009\n",
      "Total training time: 4.37 seconds.\n",
      "-- Epoch 32\n",
      "Norm: 333.88, NNZs: 16384, Bias: -123.393054, T: 161024, Avg. loss: 27.721344\n",
      "Total training time: 4.51 seconds.\n",
      "-- Epoch 33\n",
      "Norm: 329.92, NNZs: 16384, Bias: -123.574073, T: 166056, Avg. loss: 27.021482\n",
      "Total training time: 4.64 seconds.\n",
      "-- Epoch 34\n",
      "Norm: 326.12, NNZs: 16384, Bias: -123.811922, T: 171088, Avg. loss: 26.858718\n",
      "Total training time: 4.78 seconds.\n",
      "-- Epoch 35\n",
      "Norm: 323.10, NNZs: 16384, Bias: -123.985059, T: 176120, Avg. loss: 24.728669\n",
      "Total training time: 4.92 seconds.\n",
      "-- Epoch 36\n",
      "Norm: 320.10, NNZs: 16384, Bias: -124.042883, T: 181152, Avg. loss: 23.716738\n",
      "Total training time: 5.06 seconds.\n",
      "-- Epoch 37\n",
      "Norm: 316.94, NNZs: 16384, Bias: -124.205570, T: 186184, Avg. loss: 22.881284\n",
      "Total training time: 5.20 seconds.\n",
      "-- Epoch 38\n",
      "Norm: 314.12, NNZs: 16384, Bias: -124.257033, T: 191216, Avg. loss: 22.871170\n",
      "Total training time: 5.33 seconds.\n",
      "-- Epoch 39\n",
      "Norm: 310.84, NNZs: 16384, Bias: -124.257658, T: 196248, Avg. loss: 22.203072\n",
      "Total training time: 5.47 seconds.\n",
      "-- Epoch 40\n",
      "Norm: 307.50, NNZs: 16384, Bias: -124.406925, T: 201280, Avg. loss: 21.475065\n",
      "Total training time: 5.61 seconds.\n",
      "-- Epoch 41\n",
      "Norm: 304.72, NNZs: 16384, Bias: -124.408139, T: 206312, Avg. loss: 20.284641\n",
      "Total training time: 5.74 seconds.\n",
      "-- Epoch 42\n",
      "Norm: 302.29, NNZs: 16384, Bias: -124.454956, T: 211344, Avg. loss: 19.952839\n",
      "Total training time: 5.88 seconds.\n",
      "-- Epoch 43\n",
      "Norm: 299.56, NNZs: 16384, Bias: -124.454154, T: 216376, Avg. loss: 19.268526\n",
      "Total training time: 6.01 seconds.\n",
      "-- Epoch 44\n",
      "Norm: 296.80, NNZs: 16384, Bias: -124.318569, T: 221408, Avg. loss: 18.427986\n",
      "Total training time: 6.17 seconds.\n",
      "-- Epoch 45\n",
      "Norm: 294.09, NNZs: 16384, Bias: -124.362820, T: 226440, Avg. loss: 18.381517\n",
      "Total training time: 6.30 seconds.\n",
      "-- Epoch 46\n",
      "Norm: 291.92, NNZs: 16384, Bias: -124.276127, T: 231472, Avg. loss: 17.663610\n",
      "Total training time: 6.43 seconds.\n",
      "-- Epoch 47\n",
      "Norm: 289.90, NNZs: 16384, Bias: -124.276995, T: 236504, Avg. loss: 17.546091\n",
      "Total training time: 6.57 seconds.\n",
      "-- Epoch 48\n",
      "Norm: 287.33, NNZs: 16384, Bias: -124.359914, T: 241536, Avg. loss: 16.053392\n",
      "Total training time: 6.70 seconds.\n",
      "-- Epoch 49\n",
      "Norm: 285.50, NNZs: 16384, Bias: -124.481930, T: 246568, Avg. loss: 16.159614\n",
      "Total training time: 6.84 seconds.\n",
      "-- Epoch 50\n",
      "Norm: 283.34, NNZs: 16384, Bias: -124.362427, T: 251600, Avg. loss: 15.923786\n",
      "Total training time: 6.98 seconds.\n",
      "-- Epoch 51\n",
      "Norm: 281.41, NNZs: 16384, Bias: -124.283841, T: 256632, Avg. loss: 15.206629\n",
      "Total training time: 7.11 seconds.\n",
      "-- Epoch 52\n",
      "Norm: 279.24, NNZs: 16384, Bias: -124.208469, T: 261664, Avg. loss: 14.704405\n",
      "Total training time: 7.24 seconds.\n",
      "-- Epoch 53\n",
      "Norm: 277.65, NNZs: 16384, Bias: -124.208169, T: 266696, Avg. loss: 14.880865\n",
      "Total training time: 7.38 seconds.\n",
      "-- Epoch 54\n",
      "Norm: 275.86, NNZs: 16384, Bias: -124.245429, T: 271728, Avg. loss: 14.629422\n",
      "Total training time: 7.51 seconds.\n",
      "-- Epoch 55\n",
      "Norm: 273.90, NNZs: 16384, Bias: -124.136337, T: 276760, Avg. loss: 13.759377\n",
      "Total training time: 7.65 seconds.\n",
      "-- Epoch 56\n",
      "Norm: 272.44, NNZs: 16384, Bias: -124.136522, T: 281792, Avg. loss: 13.533482\n",
      "Total training time: 7.78 seconds.\n",
      "-- Epoch 57\n",
      "Norm: 270.80, NNZs: 16384, Bias: -124.101504, T: 286824, Avg. loss: 13.277839\n",
      "Total training time: 7.91 seconds.\n",
      "-- Epoch 58\n",
      "Norm: 268.75, NNZs: 16384, Bias: -124.066889, T: 291856, Avg. loss: 12.719558\n",
      "Total training time: 8.05 seconds.\n",
      "-- Epoch 59\n",
      "Norm: 266.97, NNZs: 16384, Bias: -124.033473, T: 296888, Avg. loss: 13.087843\n",
      "Total training time: 8.18 seconds.\n",
      "-- Epoch 60\n",
      "Norm: 265.39, NNZs: 16384, Bias: -123.900686, T: 301920, Avg. loss: 12.072086\n",
      "Total training time: 8.31 seconds.\n",
      "-- Epoch 61\n",
      "Norm: 264.13, NNZs: 16384, Bias: -123.900853, T: 306952, Avg. loss: 12.089654\n",
      "Total training time: 8.44 seconds.\n",
      "-- Epoch 62\n",
      "Norm: 262.46, NNZs: 16384, Bias: -123.868373, T: 311984, Avg. loss: 11.356826\n",
      "Total training time: 8.58 seconds.\n",
      "-- Epoch 63\n",
      "Norm: 261.01, NNZs: 16384, Bias: -123.741244, T: 317016, Avg. loss: 12.159280\n",
      "Total training time: 8.71 seconds.\n",
      "-- Epoch 64\n",
      "Norm: 259.41, NNZs: 16384, Bias: -123.741110, T: 322048, Avg. loss: 11.321377\n",
      "Total training time: 8.84 seconds.\n",
      "-- Epoch 65\n",
      "Norm: 258.05, NNZs: 16384, Bias: -123.679284, T: 327080, Avg. loss: 11.591154\n",
      "Total training time: 8.98 seconds.\n",
      "-- Epoch 66\n",
      "Norm: 256.70, NNZs: 16384, Bias: -123.619140, T: 332112, Avg. loss: 11.124257\n",
      "Total training time: 9.11 seconds.\n",
      "-- Epoch 67\n",
      "Norm: 255.28, NNZs: 16384, Bias: -123.530114, T: 337144, Avg. loss: 10.431429\n",
      "Total training time: 9.24 seconds.\n",
      "-- Epoch 68\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Norm: 253.90, NNZs: 16384, Bias: -123.559071, T: 342176, Avg. loss: 10.218916\n",
      "Total training time: 9.37 seconds.\n",
      "-- Epoch 69\n",
      "Norm: 252.56, NNZs: 16384, Bias: -123.414282, T: 347208, Avg. loss: 10.153664\n",
      "Total training time: 9.50 seconds.\n",
      "-- Epoch 70\n",
      "Norm: 251.22, NNZs: 16384, Bias: -123.357208, T: 352240, Avg. loss: 9.626727\n",
      "Total training time: 9.64 seconds.\n",
      "-- Epoch 71\n",
      "Norm: 249.98, NNZs: 16384, Bias: -123.272734, T: 357272, Avg. loss: 9.394479\n",
      "Total training time: 9.77 seconds.\n",
      "-- Epoch 72\n",
      "Norm: 248.78, NNZs: 16384, Bias: -123.189874, T: 362304, Avg. loss: 9.527937\n",
      "Total training time: 9.90 seconds.\n",
      "-- Epoch 73\n",
      "Norm: 247.51, NNZs: 16384, Bias: -123.135704, T: 367336, Avg. loss: 9.443876\n",
      "Total training time: 10.03 seconds.\n",
      "-- Epoch 74\n",
      "Norm: 246.43, NNZs: 16384, Bias: -123.054333, T: 372368, Avg. loss: 9.053276\n",
      "Total training time: 10.16 seconds.\n",
      "-- Epoch 75\n",
      "Norm: 245.08, NNZs: 16384, Bias: -123.001341, T: 377400, Avg. loss: 8.821585\n",
      "Total training time: 10.29 seconds.\n",
      "-- Epoch 76\n",
      "Norm: 243.77, NNZs: 16384, Bias: -122.896267, T: 382432, Avg. loss: 8.659317\n",
      "Total training time: 10.42 seconds.\n",
      "-- Epoch 77\n",
      "Norm: 242.34, NNZs: 16384, Bias: -122.844868, T: 387464, Avg. loss: 8.577235\n",
      "Total training time: 10.55 seconds.\n",
      "-- Epoch 78\n",
      "Norm: 241.29, NNZs: 16384, Bias: -122.768436, T: 392496, Avg. loss: 8.513026\n",
      "Total training time: 10.69 seconds.\n",
      "-- Epoch 79\n",
      "Norm: 240.16, NNZs: 16384, Bias: -122.642458, T: 397528, Avg. loss: 8.714101\n",
      "Total training time: 10.82 seconds.\n",
      "-- Epoch 80\n",
      "Norm: 239.02, NNZs: 16384, Bias: -122.617605, T: 402560, Avg. loss: 7.979477\n",
      "Total training time: 10.96 seconds.\n",
      "-- Epoch 81\n",
      "Norm: 237.99, NNZs: 16384, Bias: -122.494599, T: 407592, Avg. loss: 8.335624\n",
      "Total training time: 11.09 seconds.\n",
      "-- Epoch 82\n",
      "Norm: 236.92, NNZs: 16384, Bias: -122.373304, T: 412624, Avg. loss: 7.718603\n",
      "Total training time: 11.23 seconds.\n",
      "-- Epoch 83\n",
      "Norm: 235.60, NNZs: 16384, Bias: -122.277739, T: 417656, Avg. loss: 7.734756\n",
      "Total training time: 11.35 seconds.\n",
      "-- Epoch 84\n",
      "Norm: 234.51, NNZs: 16384, Bias: -122.230177, T: 422688, Avg. loss: 7.558516\n",
      "Total training time: 11.48 seconds.\n",
      "-- Epoch 85\n",
      "Norm: 233.48, NNZs: 16384, Bias: -122.136503, T: 427720, Avg. loss: 7.580072\n",
      "Total training time: 11.61 seconds.\n",
      "-- Epoch 86\n",
      "Norm: 232.56, NNZs: 16384, Bias: -122.020651, T: 432752, Avg. loss: 7.182815\n",
      "Total training time: 11.75 seconds.\n",
      "-- Epoch 87\n",
      "Norm: 231.64, NNZs: 16384, Bias: -121.975015, T: 437784, Avg. loss: 7.159793\n",
      "Total training time: 11.87 seconds.\n",
      "-- Epoch 88\n",
      "Norm: 230.60, NNZs: 16384, Bias: -121.929653, T: 442816, Avg. loss: 7.391653\n",
      "Total training time: 12.01 seconds.\n",
      "-- Epoch 89\n",
      "Norm: 229.51, NNZs: 16384, Bias: -121.862268, T: 447848, Avg. loss: 7.168520\n",
      "Total training time: 12.14 seconds.\n",
      "-- Epoch 90\n",
      "Norm: 228.60, NNZs: 16384, Bias: -121.751305, T: 452880, Avg. loss: 7.064503\n",
      "Total training time: 12.27 seconds.\n",
      "-- Epoch 91\n",
      "Norm: 227.54, NNZs: 16384, Bias: -121.620114, T: 457912, Avg. loss: 6.989880\n",
      "Total training time: 12.40 seconds.\n",
      "-- Epoch 92\n",
      "Norm: 226.64, NNZs: 16384, Bias: -121.490171, T: 462944, Avg. loss: 6.626219\n",
      "Total training time: 12.53 seconds.\n",
      "-- Epoch 93\n",
      "Norm: 225.60, NNZs: 16384, Bias: -121.404431, T: 467976, Avg. loss: 6.449304\n",
      "Total training time: 12.65 seconds.\n",
      "-- Epoch 94\n",
      "Norm: 224.69, NNZs: 16384, Bias: -121.319780, T: 473008, Avg. loss: 6.504186\n",
      "Total training time: 12.78 seconds.\n",
      "-- Epoch 95\n",
      "Norm: 223.70, NNZs: 16384, Bias: -121.256792, T: 478040, Avg. loss: 6.487192\n",
      "Total training time: 12.91 seconds.\n",
      "-- Epoch 96\n",
      "Norm: 222.77, NNZs: 16384, Bias: -121.215038, T: 483072, Avg. loss: 6.475702\n",
      "Total training time: 13.04 seconds.\n",
      "-- Epoch 97\n",
      "Norm: 221.99, NNZs: 16384, Bias: -121.070982, T: 488104, Avg. loss: 6.408238\n",
      "Total training time: 13.17 seconds.\n",
      "-- Epoch 98\n",
      "Norm: 221.06, NNZs: 16384, Bias: -121.030499, T: 493136, Avg. loss: 6.277336\n",
      "Total training time: 13.30 seconds.\n",
      "-- Epoch 99\n",
      "Norm: 220.11, NNZs: 16384, Bias: -120.990113, T: 498168, Avg. loss: 6.032213\n",
      "Total training time: 13.43 seconds.\n",
      "-- Epoch 100\n",
      "Norm: 219.19, NNZs: 16384, Bias: -120.890349, T: 503200, Avg. loss: 5.680788\n",
      "Total training time: 13.56 seconds.\n",
      "-- Epoch 101\n",
      "Norm: 218.26, NNZs: 16384, Bias: -120.752246, T: 508232, Avg. loss: 5.827060\n",
      "Total training time: 13.69 seconds.\n",
      "-- Epoch 102\n",
      "Norm: 217.52, NNZs: 16384, Bias: -120.752421, T: 513264, Avg. loss: 5.926157\n",
      "Total training time: 13.82 seconds.\n",
      "-- Epoch 103\n",
      "Norm: 216.76, NNZs: 16384, Bias: -120.636427, T: 518296, Avg. loss: 5.732437\n",
      "Total training time: 13.95 seconds.\n",
      "-- Epoch 104\n",
      "Norm: 215.92, NNZs: 16384, Bias: -120.540611, T: 523328, Avg. loss: 5.538163\n",
      "Total training time: 14.08 seconds.\n",
      "-- Epoch 105\n",
      "Norm: 215.00, NNZs: 16384, Bias: -120.464566, T: 528360, Avg. loss: 5.618520\n",
      "Total training time: 14.21 seconds.\n",
      "-- Epoch 106\n",
      "Norm: 214.22, NNZs: 16384, Bias: -120.351792, T: 533392, Avg. loss: 5.491290\n",
      "Total training time: 14.34 seconds.\n",
      "-- Epoch 107\n",
      "Norm: 213.44, NNZs: 16384, Bias: -120.221350, T: 538424, Avg. loss: 5.552781\n",
      "Total training time: 14.46 seconds.\n",
      "-- Epoch 108\n",
      "Norm: 212.71, NNZs: 16384, Bias: -120.129083, T: 543456, Avg. loss: 5.408176\n",
      "Total training time: 14.59 seconds.\n",
      "-- Epoch 109\n",
      "Norm: 211.89, NNZs: 16384, Bias: -120.037872, T: 548488, Avg. loss: 5.341146\n",
      "Total training time: 14.72 seconds.\n",
      "-- Epoch 110\n",
      "Norm: 211.17, NNZs: 16384, Bias: -119.929322, T: 553520, Avg. loss: 5.278246\n",
      "Total training time: 14.85 seconds.\n",
      "-- Epoch 111\n",
      "Norm: 210.52, NNZs: 16384, Bias: -119.839495, T: 558552, Avg. loss: 5.143789\n",
      "Total training time: 14.98 seconds.\n",
      "-- Epoch 112\n",
      "Norm: 209.80, NNZs: 16384, Bias: -119.732722, T: 563584, Avg. loss: 4.972549\n",
      "Total training time: 15.11 seconds.\n",
      "-- Epoch 113\n",
      "Norm: 209.05, NNZs: 16384, Bias: -119.644555, T: 568616, Avg. loss: 4.716600\n",
      "Total training time: 15.24 seconds.\n",
      "-- Epoch 114\n",
      "Norm: 208.33, NNZs: 16384, Bias: -119.574777, T: 573648, Avg. loss: 4.860639\n",
      "Total training time: 15.36 seconds.\n",
      "-- Epoch 115\n",
      "Norm: 207.63, NNZs: 16384, Bias: -119.488114, T: 578680, Avg. loss: 4.754716\n",
      "Total training time: 15.49 seconds.\n",
      "-- Epoch 116\n",
      "Norm: 206.88, NNZs: 16384, Bias: -119.402207, T: 583712, Avg. loss: 4.647410\n",
      "Total training time: 15.62 seconds.\n",
      "-- Epoch 117\n",
      "Norm: 206.19, NNZs: 16384, Bias: -119.300071, T: 588744, Avg. loss: 4.708450\n",
      "Total training time: 15.75 seconds.\n",
      "-- Epoch 118\n",
      "Norm: 205.49, NNZs: 16384, Bias: -119.181901, T: 593776, Avg. loss: 4.521224\n",
      "Total training time: 15.87 seconds.\n",
      "-- Epoch 119\n",
      "Norm: 204.80, NNZs: 16384, Bias: -119.081476, T: 598808, Avg. loss: 4.648614\n",
      "Total training time: 16.00 seconds.\n",
      "-- Epoch 120\n",
      "Norm: 204.11, NNZs: 16384, Bias: -118.981962, T: 603840, Avg. loss: 4.463370\n",
      "Total training time: 16.13 seconds.\n",
      "-- Epoch 121\n",
      "Norm: 203.40, NNZs: 16384, Bias: -118.932735, T: 608872, Avg. loss: 4.564072\n",
      "Total training time: 16.26 seconds.\n",
      "-- Epoch 122\n",
      "Norm: 202.74, NNZs: 16384, Bias: -118.851074, T: 613904, Avg. loss: 4.468227\n",
      "Total training time: 16.39 seconds.\n",
      "-- Epoch 123\n",
      "Norm: 202.03, NNZs: 16384, Bias: -118.737731, T: 618936, Avg. loss: 4.313331\n",
      "Total training time: 16.51 seconds.\n",
      "-- Epoch 124\n",
      "Norm: 201.33, NNZs: 16384, Bias: -118.641430, T: 623968, Avg. loss: 4.553461\n",
      "Total training time: 16.64 seconds.\n",
      "-- Epoch 125\n",
      "Norm: 200.72, NNZs: 16384, Bias: -118.545743, T: 629000, Avg. loss: 4.096233\n",
      "Total training time: 16.77 seconds.\n",
      "-- Epoch 126\n",
      "Norm: 200.03, NNZs: 16384, Bias: -118.466700, T: 634032, Avg. loss: 4.093903\n",
      "Total training time: 16.90 seconds.\n",
      "-- Epoch 127\n",
      "Norm: 199.40, NNZs: 16384, Bias: -118.404002, T: 639064, Avg. loss: 3.992182\n",
      "Total training time: 17.02 seconds.\n",
      "-- Epoch 128\n",
      "Norm: 198.72, NNZs: 16384, Bias: -118.310614, T: 644096, Avg. loss: 4.143759\n",
      "Total training time: 17.15 seconds.\n",
      "-- Epoch 129\n",
      "Norm: 198.15, NNZs: 16384, Bias: -118.202639, T: 649128, Avg. loss: 4.094202\n",
      "Total training time: 17.28 seconds.\n",
      "-- Epoch 130\n",
      "Norm: 197.56, NNZs: 16384, Bias: -118.110852, T: 654160, Avg. loss: 4.136488\n",
      "Total training time: 17.41 seconds.\n",
      "-- Epoch 131\n",
      "Norm: 196.92, NNZs: 16384, Bias: -118.065311, T: 659192, Avg. loss: 4.041889\n",
      "Total training time: 17.53 seconds.\n",
      "-- Epoch 132\n",
      "Norm: 196.31, NNZs: 16384, Bias: -117.944724, T: 664224, Avg. loss: 3.783026\n",
      "Total training time: 17.66 seconds.\n",
      "-- Epoch 133\n",
      "Norm: 195.74, NNZs: 16384, Bias: -117.884944, T: 669256, Avg. loss: 3.971461\n",
      "Total training time: 17.79 seconds.\n",
      "-- Epoch 134\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Norm: 195.18, NNZs: 16384, Bias: -117.825590, T: 674288, Avg. loss: 3.737891\n",
      "Total training time: 17.92 seconds.\n",
      "-- Epoch 135\n",
      "Norm: 194.61, NNZs: 16384, Bias: -117.737085, T: 679320, Avg. loss: 3.962012\n",
      "Total training time: 18.04 seconds.\n",
      "-- Epoch 136\n",
      "Norm: 194.01, NNZs: 16384, Bias: -117.663800, T: 684352, Avg. loss: 3.806116\n",
      "Total training time: 18.17 seconds.\n",
      "-- Epoch 137\n",
      "Norm: 193.47, NNZs: 16384, Bias: -117.605594, T: 689384, Avg. loss: 3.960065\n",
      "Total training time: 18.30 seconds.\n",
      "-- Epoch 138\n",
      "Norm: 192.84, NNZs: 16384, Bias: -117.519002, T: 694416, Avg. loss: 3.603606\n",
      "Total training time: 18.42 seconds.\n",
      "-- Epoch 139\n",
      "Norm: 192.30, NNZs: 16384, Bias: -117.418710, T: 699448, Avg. loss: 3.896110\n",
      "Total training time: 18.55 seconds.\n",
      "-- Epoch 140\n",
      "Norm: 191.77, NNZs: 16384, Bias: -117.347548, T: 704480, Avg. loss: 3.404442\n",
      "Total training time: 18.68 seconds.\n",
      "-- Epoch 141\n",
      "Norm: 191.22, NNZs: 16384, Bias: -117.234502, T: 709512, Avg. loss: 3.606736\n",
      "Total training time: 18.80 seconds.\n",
      "-- Epoch 142\n",
      "Norm: 190.65, NNZs: 16384, Bias: -117.164460, T: 714544, Avg. loss: 3.597788\n",
      "Total training time: 18.93 seconds.\n",
      "-- Epoch 143\n",
      "Norm: 190.17, NNZs: 16384, Bias: -117.066978, T: 719576, Avg. loss: 3.495585\n",
      "Total training time: 19.06 seconds.\n",
      "-- Epoch 144\n",
      "Norm: 189.72, NNZs: 16384, Bias: -116.956332, T: 724608, Avg. loss: 3.669645\n",
      "Total training time: 19.19 seconds.\n",
      "-- Epoch 145\n",
      "Norm: 189.24, NNZs: 16384, Bias: -116.860139, T: 729640, Avg. loss: 3.402657\n",
      "Total training time: 19.31 seconds.\n",
      "-- Epoch 146\n",
      "Norm: 188.80, NNZs: 16384, Bias: -116.778363, T: 734672, Avg. loss: 3.470141\n",
      "Total training time: 19.44 seconds.\n",
      "-- Epoch 147\n",
      "Norm: 188.29, NNZs: 16384, Bias: -116.697098, T: 739704, Avg. loss: 3.498388\n",
      "Total training time: 19.57 seconds.\n",
      "-- Epoch 148\n",
      "Norm: 187.80, NNZs: 16384, Bias: -116.575988, T: 744736, Avg. loss: 3.359878\n",
      "Total training time: 19.70 seconds.\n",
      "-- Epoch 149\n",
      "Norm: 187.31, NNZs: 16384, Bias: -116.522597, T: 749768, Avg. loss: 3.393357\n",
      "Total training time: 19.82 seconds.\n",
      "-- Epoch 150\n",
      "Norm: 186.76, NNZs: 16384, Bias: -116.442973, T: 754800, Avg. loss: 3.338840\n",
      "Total training time: 19.95 seconds.\n",
      "-- Epoch 151\n",
      "Norm: 186.30, NNZs: 16384, Bias: -116.337521, T: 759832, Avg. loss: 3.275658\n",
      "Total training time: 20.07 seconds.\n",
      "-- Epoch 152\n",
      "Norm: 185.81, NNZs: 16384, Bias: -116.272046, T: 764864, Avg. loss: 3.195873\n",
      "Total training time: 20.20 seconds.\n",
      "-- Epoch 153\n",
      "Norm: 185.36, NNZs: 16384, Bias: -116.206947, T: 769896, Avg. loss: 3.087099\n",
      "Total training time: 20.33 seconds.\n",
      "-- Epoch 154\n",
      "Norm: 184.89, NNZs: 16384, Bias: -116.103487, T: 774928, Avg. loss: 3.154426\n",
      "Total training time: 20.46 seconds.\n",
      "-- Epoch 155\n",
      "Norm: 184.39, NNZs: 16384, Bias: -116.013588, T: 779960, Avg. loss: 3.055040\n",
      "Total training time: 20.58 seconds.\n",
      "-- Epoch 156\n",
      "Norm: 183.88, NNZs: 16384, Bias: -115.962539, T: 784992, Avg. loss: 3.147563\n",
      "Total training time: 20.71 seconds.\n",
      "-- Epoch 157\n",
      "Norm: 183.41, NNZs: 16384, Bias: -115.873747, T: 790024, Avg. loss: 3.128375\n",
      "Total training time: 20.84 seconds.\n",
      "-- Epoch 158\n",
      "Norm: 182.92, NNZs: 16384, Bias: -115.798044, T: 795056, Avg. loss: 2.912327\n",
      "Total training time: 20.97 seconds.\n",
      "-- Epoch 159\n",
      "Norm: 182.49, NNZs: 16384, Bias: -115.710362, T: 800088, Avg. loss: 3.071949\n",
      "Total training time: 21.10 seconds.\n",
      "-- Epoch 160\n",
      "Norm: 182.07, NNZs: 16384, Bias: -115.610864, T: 805120, Avg. loss: 3.077427\n",
      "Total training time: 21.23 seconds.\n",
      "-- Epoch 161\n",
      "Norm: 181.64, NNZs: 16384, Bias: -115.524310, T: 810152, Avg. loss: 2.858325\n",
      "Total training time: 21.35 seconds.\n",
      "-- Epoch 162\n",
      "Norm: 181.21, NNZs: 16384, Bias: -115.450623, T: 815184, Avg. loss: 3.043424\n",
      "Total training time: 21.48 seconds.\n",
      "-- Epoch 163\n",
      "Norm: 180.77, NNZs: 16384, Bias: -115.365141, T: 820216, Avg. loss: 2.875135\n",
      "Total training time: 21.61 seconds.\n",
      "-- Epoch 164\n",
      "Norm: 180.34, NNZs: 16384, Bias: -115.292370, T: 825248, Avg. loss: 2.860929\n",
      "Total training time: 21.74 seconds.\n",
      "-- Epoch 165\n",
      "Norm: 179.87, NNZs: 16384, Bias: -115.195851, T: 830280, Avg. loss: 2.850353\n",
      "Total training time: 21.86 seconds.\n",
      "-- Epoch 166\n",
      "Norm: 179.46, NNZs: 16384, Bias: -115.123900, T: 835312, Avg. loss: 2.791253\n",
      "Total training time: 21.99 seconds.\n",
      "-- Epoch 167\n",
      "Norm: 179.03, NNZs: 16384, Bias: -115.040457, T: 840344, Avg. loss: 2.721841\n",
      "Total training time: 22.11 seconds.\n",
      "-- Epoch 168\n",
      "Norm: 178.62, NNZs: 16384, Bias: -114.969346, T: 845376, Avg. loss: 2.634395\n",
      "Total training time: 22.24 seconds.\n",
      "-- Epoch 169\n",
      "Norm: 178.21, NNZs: 16384, Bias: -114.886839, T: 850408, Avg. loss: 2.705754\n",
      "Total training time: 22.37 seconds.\n",
      "-- Epoch 170\n",
      "Norm: 177.78, NNZs: 16384, Bias: -114.804863, T: 855440, Avg. loss: 2.687027\n",
      "Total training time: 22.49 seconds.\n",
      "-- Epoch 171\n",
      "Norm: 177.38, NNZs: 16384, Bias: -114.735001, T: 860472, Avg. loss: 2.622138\n",
      "Total training time: 22.62 seconds.\n",
      "-- Epoch 172\n",
      "Norm: 177.00, NNZs: 16384, Bias: -114.654023, T: 865504, Avg. loss: 2.632276\n",
      "Total training time: 22.74 seconds.\n",
      "-- Epoch 173\n",
      "Norm: 176.56, NNZs: 16384, Bias: -114.596422, T: 870536, Avg. loss: 2.562936\n",
      "Total training time: 22.87 seconds.\n",
      "-- Epoch 174\n",
      "Norm: 176.16, NNZs: 16384, Bias: -114.527743, T: 875568, Avg. loss: 2.572172\n",
      "Total training time: 23.00 seconds.\n",
      "-- Epoch 175\n",
      "Norm: 175.73, NNZs: 16384, Bias: -114.459449, T: 880600, Avg. loss: 2.509517\n",
      "Total training time: 23.12 seconds.\n",
      "-- Epoch 176\n",
      "Norm: 175.31, NNZs: 16384, Bias: -114.369004, T: 885632, Avg. loss: 2.518720\n",
      "Total training time: 23.25 seconds.\n",
      "-- Epoch 177\n",
      "Norm: 174.89, NNZs: 16384, Bias: -114.312776, T: 890664, Avg. loss: 2.478191\n",
      "Total training time: 23.37 seconds.\n",
      "-- Epoch 178\n",
      "Norm: 174.54, NNZs: 16384, Bias: -114.200897, T: 895696, Avg. loss: 2.480855\n",
      "Total training time: 23.50 seconds.\n",
      "-- Epoch 179\n",
      "Norm: 174.12, NNZs: 16384, Bias: -114.123053, T: 900728, Avg. loss: 2.484257\n",
      "Total training time: 23.62 seconds.\n",
      "-- Epoch 180\n",
      "Norm: 173.70, NNZs: 16384, Bias: -114.034547, T: 905760, Avg. loss: 2.558003\n",
      "Total training time: 23.75 seconds.\n",
      "-- Epoch 181\n",
      "Norm: 173.32, NNZs: 16384, Bias: -113.957592, T: 910792, Avg. loss: 2.422710\n",
      "Total training time: 23.87 seconds.\n",
      "-- Epoch 182\n",
      "Norm: 172.94, NNZs: 16384, Bias: -113.913877, T: 915824, Avg. loss: 2.422688\n",
      "Total training time: 24.00 seconds.\n",
      "-- Epoch 183\n",
      "Norm: 172.56, NNZs: 16384, Bias: -113.848679, T: 920856, Avg. loss: 2.440358\n",
      "Total training time: 24.12 seconds.\n",
      "-- Epoch 184\n",
      "Norm: 172.18, NNZs: 16384, Bias: -113.762138, T: 925888, Avg. loss: 2.316979\n",
      "Total training time: 24.25 seconds.\n",
      "-- Epoch 185\n",
      "Norm: 171.79, NNZs: 16384, Bias: -113.697549, T: 930920, Avg. loss: 2.444271\n",
      "Total training time: 24.37 seconds.\n",
      "-- Epoch 186\n",
      "Norm: 171.44, NNZs: 16384, Bias: -113.622568, T: 935952, Avg. loss: 2.360556\n",
      "Total training time: 24.50 seconds.\n",
      "-- Epoch 187\n",
      "Norm: 171.06, NNZs: 16384, Bias: -113.558718, T: 940984, Avg. loss: 2.300422\n",
      "Total training time: 24.62 seconds.\n",
      "-- Epoch 188\n",
      "Norm: 170.66, NNZs: 16384, Bias: -113.484601, T: 946016, Avg. loss: 2.304665\n",
      "Total training time: 24.75 seconds.\n",
      "-- Epoch 189\n",
      "Norm: 170.30, NNZs: 16384, Bias: -113.410883, T: 951048, Avg. loss: 2.275075\n",
      "Total training time: 24.87 seconds.\n",
      "-- Epoch 190\n",
      "Norm: 169.94, NNZs: 16384, Bias: -113.358477, T: 956080, Avg. loss: 2.280868\n",
      "Total training time: 25.00 seconds.\n",
      "-- Epoch 191\n",
      "Norm: 169.62, NNZs: 16384, Bias: -113.275114, T: 961112, Avg. loss: 2.243536\n",
      "Total training time: 25.13 seconds.\n",
      "-- Epoch 192\n",
      "Norm: 169.17, NNZs: 16384, Bias: -113.202533, T: 966144, Avg. loss: 2.161154\n",
      "Total training time: 25.25 seconds.\n",
      "-- Epoch 193\n",
      "Norm: 168.86, NNZs: 16384, Bias: -113.120007, T: 971176, Avg. loss: 2.099775\n",
      "Total training time: 25.37 seconds.\n",
      "-- Epoch 194\n",
      "Norm: 168.51, NNZs: 16384, Bias: -113.048181, T: 976208, Avg. loss: 2.158346\n",
      "Total training time: 25.50 seconds.\n",
      "-- Epoch 195\n",
      "Norm: 168.15, NNZs: 16384, Bias: -112.997092, T: 981240, Avg. loss: 2.154989\n",
      "Total training time: 25.62 seconds.\n",
      "-- Epoch 196\n",
      "Norm: 167.82, NNZs: 16384, Bias: -112.915853, T: 986272, Avg. loss: 2.228165\n",
      "Total training time: 25.75 seconds.\n",
      "-- Epoch 197\n",
      "Norm: 167.46, NNZs: 16384, Bias: -112.855282, T: 991304, Avg. loss: 2.107822\n",
      "Total training time: 25.88 seconds.\n",
      "-- Epoch 198\n",
      "Norm: 167.12, NNZs: 16384, Bias: -112.815120, T: 996336, Avg. loss: 2.129097\n",
      "Total training time: 26.00 seconds.\n",
      "Convergence after 198 epochs took 26.00 seconds\n",
      "Score: 0.6422893481717011\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Precision: 0.6698292220113852\n",
      "Recall: 0.56120826709062\n",
      "[[455 174]\n",
      " [276 353]]\n"
     ]
    }
   ],
   "source": [
    "any_hemorrhage_svm_model = build_stochastic_svm_model(total_data, 'any')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "996210e0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Set Size: 750\n",
      "Test Set Size: 1258\n",
      "[LibLinear]Score: 0.6136724960254372\n",
      "Precision: 0.6075418994413407\n",
      "Recall: 0.6796875\n",
      "[[337 281]\n",
      " [205 435]]\n"
     ]
    }
   ],
   "source": [
    "epidural_svm_model = build_svm_model(total_data, 'epidural', iterations = 5000, training_set_size =750)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "abbcb05e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Set Size: 5032\n",
      "Test Set Size: 1258\n",
      "-- Epoch 1\n",
      "Norm: 1339.68, NNZs: 16384, Bias: -18.917567, T: 5032, Avg. loss: 3166.031913\n",
      "Total training time: 0.16 seconds.\n",
      "-- Epoch 2\n",
      "Norm: 1070.34, NNZs: 16384, Bias: -23.974018, T: 10064, Avg. loss: 938.979546\n",
      "Total training time: 0.32 seconds.\n",
      "-- Epoch 3\n",
      "Norm: 928.21, NNZs: 16384, Bias: -27.309210, T: 15096, Avg. loss: 558.619092\n",
      "Total training time: 0.47 seconds.\n",
      "-- Epoch 4\n",
      "Norm: 847.76, NNZs: 16384, Bias: -30.574236, T: 20128, Avg. loss: 393.836097\n",
      "Total training time: 0.62 seconds.\n",
      "-- Epoch 5\n",
      "Norm: 786.81, NNZs: 16384, Bias: -31.470388, T: 25160, Avg. loss: 291.985422\n",
      "Total training time: 0.77 seconds.\n",
      "-- Epoch 6\n",
      "Norm: 740.88, NNZs: 16384, Bias: -33.544844, T: 30192, Avg. loss: 238.772495\n",
      "Total training time: 0.92 seconds.\n",
      "-- Epoch 7\n",
      "Norm: 699.44, NNZs: 16384, Bias: -34.709091, T: 35224, Avg. loss: 191.226454\n",
      "Total training time: 1.06 seconds.\n",
      "-- Epoch 8\n",
      "Norm: 670.60, NNZs: 16384, Bias: -34.992131, T: 40256, Avg. loss: 167.157503\n",
      "Total training time: 1.21 seconds.\n",
      "-- Epoch 9\n",
      "Norm: 639.92, NNZs: 16384, Bias: -36.602000, T: 45288, Avg. loss: 144.063579\n",
      "Total training time: 1.36 seconds.\n",
      "-- Epoch 10\n",
      "Norm: 612.57, NNZs: 16384, Bias: -36.403235, T: 50320, Avg. loss: 122.146071\n",
      "Total training time: 1.50 seconds.\n",
      "-- Epoch 11\n",
      "Norm: 593.06, NNZs: 16384, Bias: -37.324533, T: 55352, Avg. loss: 110.139183\n",
      "Total training time: 1.64 seconds.\n",
      "-- Epoch 12\n",
      "Norm: 572.07, NNZs: 16384, Bias: -38.154883, T: 60384, Avg. loss: 97.948706\n",
      "Total training time: 1.79 seconds.\n",
      "-- Epoch 13\n",
      "Norm: 552.39, NNZs: 16384, Bias: -38.459121, T: 65416, Avg. loss: 91.745638\n",
      "Total training time: 1.93 seconds.\n",
      "-- Epoch 14\n",
      "Norm: 537.35, NNZs: 16384, Bias: -39.043932, T: 70448, Avg. loss: 82.208850\n",
      "Total training time: 2.08 seconds.\n",
      "-- Epoch 15\n",
      "Norm: 522.92, NNZs: 16384, Bias: -39.325724, T: 75480, Avg. loss: 75.426775\n",
      "Total training time: 2.22 seconds.\n",
      "-- Epoch 16\n",
      "Norm: 511.15, NNZs: 16384, Bias: -39.711993, T: 80512, Avg. loss: 69.279518\n",
      "Total training time: 2.36 seconds.\n",
      "-- Epoch 17\n",
      "Norm: 499.25, NNZs: 16384, Bias: -40.067068, T: 85544, Avg. loss: 64.460523\n",
      "Total training time: 2.50 seconds.\n",
      "-- Epoch 18\n",
      "Norm: 488.06, NNZs: 16384, Bias: -40.175301, T: 90576, Avg. loss: 59.527507\n",
      "Total training time: 2.64 seconds.\n",
      "-- Epoch 19\n",
      "Norm: 478.81, NNZs: 16384, Bias: -40.385327, T: 95608, Avg. loss: 57.158724\n",
      "Total training time: 2.78 seconds.\n",
      "-- Epoch 20\n",
      "Norm: 469.21, NNZs: 16384, Bias: -40.792686, T: 100640, Avg. loss: 52.469661\n",
      "Total training time: 2.93 seconds.\n",
      "-- Epoch 21\n",
      "Norm: 459.34, NNZs: 16384, Bias: -40.890579, T: 105672, Avg. loss: 48.016484\n",
      "Total training time: 3.06 seconds.\n",
      "-- Epoch 22\n",
      "Norm: 450.99, NNZs: 16384, Bias: -41.077688, T: 110704, Avg. loss: 46.603255\n",
      "Total training time: 3.20 seconds.\n",
      "-- Epoch 23\n",
      "Norm: 442.32, NNZs: 16384, Bias: -41.689178, T: 115736, Avg. loss: 43.995211\n",
      "Total training time: 3.34 seconds.\n",
      "-- Epoch 24\n",
      "Norm: 435.32, NNZs: 16384, Bias: -42.020314, T: 120768, Avg. loss: 41.360515\n",
      "Total training time: 3.48 seconds.\n",
      "-- Epoch 25\n",
      "Norm: 428.08, NNZs: 16384, Bias: -42.418101, T: 125800, Avg. loss: 37.066193\n",
      "Total training time: 3.62 seconds.\n",
      "-- Epoch 26\n",
      "Norm: 421.96, NNZs: 16384, Bias: -42.648717, T: 130832, Avg. loss: 36.776463\n",
      "Total training time: 3.76 seconds.\n",
      "-- Epoch 27\n",
      "Norm: 415.47, NNZs: 16384, Bias: -42.871630, T: 135864, Avg. loss: 35.705848\n",
      "Total training time: 3.90 seconds.\n",
      "-- Epoch 28\n",
      "Norm: 409.72, NNZs: 16384, Bias: -42.940767, T: 140896, Avg. loss: 32.930879\n",
      "Total training time: 4.04 seconds.\n",
      "-- Epoch 29\n",
      "Norm: 404.59, NNZs: 16384, Bias: -43.285080, T: 145928, Avg. loss: 32.212105\n",
      "Total training time: 4.18 seconds.\n",
      "-- Epoch 30\n",
      "Norm: 399.36, NNZs: 16384, Bias: -43.550827, T: 150960, Avg. loss: 31.062680\n",
      "Total training time: 4.32 seconds.\n",
      "-- Epoch 31\n",
      "Norm: 394.28, NNZs: 16384, Bias: -43.679757, T: 155992, Avg. loss: 28.488254\n",
      "Total training time: 4.45 seconds.\n",
      "-- Epoch 32\n",
      "Norm: 388.85, NNZs: 16384, Bias: -44.118373, T: 161024, Avg. loss: 27.260786\n",
      "Total training time: 4.59 seconds.\n",
      "-- Epoch 33\n",
      "Norm: 383.92, NNZs: 16384, Bias: -44.118807, T: 166056, Avg. loss: 26.554724\n",
      "Total training time: 4.72 seconds.\n",
      "-- Epoch 34\n",
      "Norm: 379.48, NNZs: 16384, Bias: -44.179249, T: 171088, Avg. loss: 25.805412\n",
      "Total training time: 4.86 seconds.\n",
      "-- Epoch 35\n",
      "Norm: 374.66, NNZs: 16384, Bias: -44.524915, T: 176120, Avg. loss: 24.770652\n",
      "Total training time: 5.00 seconds.\n",
      "-- Epoch 36\n",
      "Norm: 370.63, NNZs: 16384, Bias: -44.749429, T: 181152, Avg. loss: 23.399756\n",
      "Total training time: 5.14 seconds.\n",
      "-- Epoch 37\n",
      "Norm: 366.56, NNZs: 16384, Bias: -45.019631, T: 186184, Avg. loss: 22.533839\n",
      "Total training time: 5.28 seconds.\n",
      "-- Epoch 38\n",
      "Norm: 362.81, NNZs: 16384, Bias: -45.126497, T: 191216, Avg. loss: 21.595852\n",
      "Total training time: 5.41 seconds.\n",
      "-- Epoch 39\n",
      "Norm: 359.25, NNZs: 16384, Bias: -45.280102, T: 196248, Avg. loss: 20.757379\n",
      "Total training time: 5.55 seconds.\n",
      "-- Epoch 40\n",
      "Norm: 356.00, NNZs: 16384, Bias: -45.278106, T: 201280, Avg. loss: 20.729027\n",
      "Total training time: 5.68 seconds.\n",
      "-- Epoch 41\n",
      "Norm: 352.25, NNZs: 16384, Bias: -45.522612, T: 206312, Avg. loss: 19.586660\n",
      "Total training time: 5.82 seconds.\n",
      "-- Epoch 42\n",
      "Norm: 349.15, NNZs: 16384, Bias: -45.666121, T: 211344, Avg. loss: 19.880088\n",
      "Total training time: 5.95 seconds.\n",
      "-- Epoch 43\n",
      "Norm: 345.81, NNZs: 16384, Bias: -45.758532, T: 216376, Avg. loss: 18.050330\n",
      "Total training time: 6.09 seconds.\n",
      "-- Epoch 44\n",
      "Norm: 342.41, NNZs: 16384, Bias: -45.894837, T: 221408, Avg. loss: 18.476285\n",
      "Total training time: 6.22 seconds.\n",
      "-- Epoch 45\n",
      "Norm: 339.58, NNZs: 16384, Bias: -45.939765, T: 226440, Avg. loss: 17.471612\n",
      "Total training time: 6.35 seconds.\n",
      "-- Epoch 46\n",
      "Norm: 336.19, NNZs: 16384, Bias: -46.200974, T: 231472, Avg. loss: 16.503112\n",
      "Total training time: 6.49 seconds.\n",
      "-- Epoch 47\n",
      "Norm: 333.64, NNZs: 16384, Bias: -46.244141, T: 236504, Avg. loss: 16.366728\n",
      "Total training time: 6.62 seconds.\n",
      "-- Epoch 48\n",
      "Norm: 330.60, NNZs: 16384, Bias: -46.327392, T: 241536, Avg. loss: 15.763475\n",
      "Total training time: 6.77 seconds.\n",
      "-- Epoch 49\n",
      "Norm: 327.77, NNZs: 16384, Bias: -46.450939, T: 246568, Avg. loss: 15.930995\n",
      "Total training time: 6.90 seconds.\n",
      "-- Epoch 50\n",
      "Norm: 325.35, NNZs: 16384, Bias: -46.651471, T: 251600, Avg. loss: 15.302337\n",
      "Total training time: 7.04 seconds.\n",
      "-- Epoch 51\n",
      "Norm: 323.01, NNZs: 16384, Bias: -46.730621, T: 256632, Avg. loss: 14.679884\n",
      "Total training time: 7.17 seconds.\n",
      "-- Epoch 52\n",
      "Norm: 320.21, NNZs: 16384, Bias: -46.808437, T: 261664, Avg. loss: 13.421447\n",
      "Total training time: 7.30 seconds.\n",
      "-- Epoch 53\n",
      "Norm: 317.73, NNZs: 16384, Bias: -46.997169, T: 266696, Avg. loss: 13.573445\n",
      "Total training time: 7.44 seconds.\n",
      "-- Epoch 54\n",
      "Norm: 315.28, NNZs: 16384, Bias: -47.107717, T: 271728, Avg. loss: 14.306673\n",
      "Total training time: 7.57 seconds.\n",
      "-- Epoch 55\n",
      "Norm: 312.92, NNZs: 16384, Bias: -47.216409, T: 276760, Avg. loss: 13.182876\n",
      "Total training time: 7.70 seconds.\n",
      "-- Epoch 56\n",
      "Norm: 310.75, NNZs: 16384, Bias: -47.323243, T: 281792, Avg. loss: 13.069944\n",
      "Total training time: 7.84 seconds.\n",
      "-- Epoch 57\n",
      "Norm: 308.44, NNZs: 16384, Bias: -47.428767, T: 286824, Avg. loss: 12.039040\n",
      "Total training time: 7.97 seconds.\n",
      "-- Epoch 58\n",
      "Norm: 306.06, NNZs: 16384, Bias: -47.429489, T: 291856, Avg. loss: 12.495371\n",
      "Total training time: 8.10 seconds.\n",
      "-- Epoch 59\n",
      "Norm: 303.54, NNZs: 16384, Bias: -47.632741, T: 296888, Avg. loss: 11.647090\n",
      "Total training time: 8.23 seconds.\n",
      "-- Epoch 60\n",
      "Norm: 301.43, NNZs: 16384, Bias: -47.699567, T: 301920, Avg. loss: 11.910708\n",
      "Total training time: 8.37 seconds.\n",
      "-- Epoch 61\n",
      "Norm: 299.44, NNZs: 16384, Bias: -47.732138, T: 306952, Avg. loss: 11.255381\n",
      "Total training time: 8.50 seconds.\n",
      "-- Epoch 62\n",
      "Norm: 297.57, NNZs: 16384, Bias: -47.828611, T: 311984, Avg. loss: 10.687272\n",
      "Total training time: 8.63 seconds.\n",
      "-- Epoch 63\n",
      "Norm: 295.46, NNZs: 16384, Bias: -47.860405, T: 317016, Avg. loss: 10.352101\n",
      "Total training time: 8.76 seconds.\n",
      "-- Epoch 64\n",
      "Norm: 293.52, NNZs: 16384, Bias: -47.922556, T: 322048, Avg. loss: 10.522459\n",
      "Total training time: 8.89 seconds.\n",
      "-- Epoch 65\n",
      "Norm: 291.43, NNZs: 16384, Bias: -48.107197, T: 327080, Avg. loss: 9.526077\n",
      "Total training time: 9.02 seconds.\n",
      "-- Epoch 66\n",
      "Norm: 289.38, NNZs: 16384, Bias: -48.198242, T: 332112, Avg. loss: 9.991590\n",
      "Total training time: 9.15 seconds.\n",
      "-- Epoch 67\n",
      "Norm: 287.78, NNZs: 16384, Bias: -48.347329, T: 337144, Avg. loss: 9.695587\n",
      "Total training time: 9.29 seconds.\n",
      "-- Epoch 68\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Norm: 285.88, NNZs: 16384, Bias: -48.376745, T: 342176, Avg. loss: 9.654509\n",
      "Total training time: 9.42 seconds.\n",
      "-- Epoch 69\n",
      "Norm: 284.02, NNZs: 16384, Bias: -48.492244, T: 347208, Avg. loss: 9.346248\n",
      "Total training time: 9.55 seconds.\n",
      "-- Epoch 70\n",
      "Norm: 282.30, NNZs: 16384, Bias: -48.549288, T: 352240, Avg. loss: 9.054414\n",
      "Total training time: 9.68 seconds.\n",
      "-- Epoch 71\n",
      "Norm: 280.57, NNZs: 16384, Bias: -48.605573, T: 357272, Avg. loss: 9.205021\n",
      "Total training time: 9.81 seconds.\n",
      "-- Epoch 72\n",
      "Norm: 278.79, NNZs: 16384, Bias: -48.633177, T: 362304, Avg. loss: 8.917682\n",
      "Total training time: 9.94 seconds.\n",
      "-- Epoch 73\n",
      "Norm: 277.44, NNZs: 16384, Bias: -48.660268, T: 367336, Avg. loss: 8.639894\n",
      "Total training time: 10.07 seconds.\n",
      "-- Epoch 74\n",
      "Norm: 275.82, NNZs: 16384, Bias: -48.686903, T: 372368, Avg. loss: 8.342579\n",
      "Total training time: 10.20 seconds.\n",
      "-- Epoch 75\n",
      "Norm: 274.38, NNZs: 16384, Bias: -48.687083, T: 377400, Avg. loss: 8.068344\n",
      "Total training time: 10.33 seconds.\n",
      "-- Epoch 76\n",
      "Norm: 272.59, NNZs: 16384, Bias: -48.791873, T: 382432, Avg. loss: 7.853618\n",
      "Total training time: 10.46 seconds.\n",
      "-- Epoch 77\n",
      "Norm: 271.03, NNZs: 16384, Bias: -48.791592, T: 387464, Avg. loss: 7.879617\n",
      "Total training time: 10.59 seconds.\n",
      "-- Epoch 78\n",
      "Norm: 269.57, NNZs: 16384, Bias: -48.842942, T: 392496, Avg. loss: 7.756160\n",
      "Total training time: 10.72 seconds.\n",
      "-- Epoch 79\n",
      "Norm: 268.18, NNZs: 16384, Bias: -48.944150, T: 397528, Avg. loss: 7.810383\n",
      "Total training time: 10.85 seconds.\n",
      "-- Epoch 80\n",
      "Norm: 266.79, NNZs: 16384, Bias: -48.968835, T: 402560, Avg. loss: 7.295712\n",
      "Total training time: 10.98 seconds.\n",
      "-- Epoch 81\n",
      "Norm: 265.07, NNZs: 16384, Bias: -49.091876, T: 407592, Avg. loss: 7.467619\n",
      "Total training time: 11.11 seconds.\n",
      "-- Epoch 82\n",
      "Norm: 263.66, NNZs: 16384, Bias: -49.189249, T: 412624, Avg. loss: 7.398782\n",
      "Total training time: 11.24 seconds.\n",
      "-- Epoch 83\n",
      "Norm: 262.49, NNZs: 16384, Bias: -49.261311, T: 417656, Avg. loss: 7.311566\n",
      "Total training time: 11.37 seconds.\n",
      "-- Epoch 84\n",
      "Norm: 261.12, NNZs: 16384, Bias: -49.332539, T: 422688, Avg. loss: 6.989054\n",
      "Total training time: 11.50 seconds.\n",
      "-- Epoch 85\n",
      "Norm: 259.87, NNZs: 16384, Bias: -49.355919, T: 427720, Avg. loss: 6.636704\n",
      "Total training time: 11.64 seconds.\n",
      "-- Epoch 86\n",
      "Norm: 258.60, NNZs: 16384, Bias: -49.448674, T: 432752, Avg. loss: 6.355777\n",
      "Total training time: 11.77 seconds.\n",
      "-- Epoch 87\n",
      "Norm: 257.45, NNZs: 16384, Bias: -49.448759, T: 437784, Avg. loss: 6.735049\n",
      "Total training time: 11.90 seconds.\n",
      "-- Epoch 88\n",
      "Norm: 256.22, NNZs: 16384, Bias: -49.539454, T: 442816, Avg. loss: 6.597093\n",
      "Total training time: 12.03 seconds.\n",
      "-- Epoch 89\n",
      "Norm: 254.90, NNZs: 16384, Bias: -49.561559, T: 447848, Avg. loss: 6.507867\n",
      "Total training time: 12.16 seconds.\n",
      "-- Epoch 90\n",
      "Norm: 253.66, NNZs: 16384, Bias: -49.561393, T: 452880, Avg. loss: 6.251805\n",
      "Total training time: 12.29 seconds.\n",
      "-- Epoch 91\n",
      "Norm: 252.35, NNZs: 16384, Bias: -49.583178, T: 457912, Avg. loss: 6.068419\n",
      "Total training time: 12.42 seconds.\n",
      "-- Epoch 92\n",
      "Norm: 251.15, NNZs: 16384, Bias: -49.605099, T: 462944, Avg. loss: 5.963894\n",
      "Total training time: 12.55 seconds.\n",
      "-- Epoch 93\n",
      "Norm: 249.95, NNZs: 16384, Bias: -49.669437, T: 467976, Avg. loss: 5.888043\n",
      "Total training time: 12.68 seconds.\n",
      "-- Epoch 94\n",
      "Norm: 248.71, NNZs: 16384, Bias: -49.712038, T: 473008, Avg. loss: 5.819736\n",
      "Total training time: 12.81 seconds.\n",
      "-- Epoch 95\n",
      "Norm: 247.48, NNZs: 16384, Bias: -49.775080, T: 478040, Avg. loss: 5.683489\n",
      "Total training time: 12.93 seconds.\n",
      "-- Epoch 96\n",
      "Norm: 246.41, NNZs: 16384, Bias: -49.775094, T: 483072, Avg. loss: 5.499719\n",
      "Total training time: 13.07 seconds.\n",
      "-- Epoch 97\n",
      "Norm: 245.26, NNZs: 16384, Bias: -49.816159, T: 488104, Avg. loss: 5.436265\n",
      "Total training time: 13.19 seconds.\n",
      "-- Epoch 98\n",
      "Norm: 244.21, NNZs: 16384, Bias: -49.856742, T: 493136, Avg. loss: 5.549824\n",
      "Total training time: 13.32 seconds.\n",
      "-- Epoch 99\n",
      "Norm: 243.11, NNZs: 16384, Bias: -49.916990, T: 498168, Avg. loss: 5.280910\n",
      "Total training time: 13.45 seconds.\n",
      "-- Epoch 100\n",
      "Norm: 241.93, NNZs: 16384, Bias: -49.976807, T: 503200, Avg. loss: 5.268906\n",
      "Total training time: 13.58 seconds.\n",
      "-- Epoch 101\n",
      "Norm: 240.90, NNZs: 16384, Bias: -49.976966, T: 508232, Avg. loss: 5.310263\n",
      "Total training time: 13.71 seconds.\n",
      "-- Epoch 102\n",
      "Norm: 239.72, NNZs: 16384, Bias: -49.996755, T: 513264, Avg. loss: 5.280335\n",
      "Total training time: 13.84 seconds.\n",
      "-- Epoch 103\n",
      "Norm: 238.58, NNZs: 16384, Bias: -50.035467, T: 518296, Avg. loss: 4.837659\n",
      "Total training time: 13.97 seconds.\n",
      "-- Epoch 104\n",
      "Norm: 237.54, NNZs: 16384, Bias: -50.035486, T: 523328, Avg. loss: 4.837209\n",
      "Total training time: 14.10 seconds.\n",
      "-- Epoch 105\n",
      "Norm: 236.41, NNZs: 16384, Bias: -50.073241, T: 528360, Avg. loss: 4.578208\n",
      "Total training time: 14.22 seconds.\n",
      "-- Epoch 106\n",
      "Norm: 235.53, NNZs: 16384, Bias: -50.054437, T: 533392, Avg. loss: 4.853984\n",
      "Total training time: 14.35 seconds.\n",
      "-- Epoch 107\n",
      "Norm: 234.51, NNZs: 16384, Bias: -50.073236, T: 538424, Avg. loss: 4.453393\n",
      "Total training time: 14.48 seconds.\n",
      "-- Epoch 108\n",
      "Norm: 233.48, NNZs: 16384, Bias: -50.128726, T: 543456, Avg. loss: 4.770940\n",
      "Total training time: 14.61 seconds.\n",
      "-- Epoch 109\n",
      "Norm: 232.60, NNZs: 16384, Bias: -50.128790, T: 548488, Avg. loss: 4.710983\n",
      "Total training time: 14.74 seconds.\n",
      "-- Epoch 110\n",
      "Norm: 231.69, NNZs: 16384, Bias: -50.201270, T: 553520, Avg. loss: 4.556401\n",
      "Total training time: 14.87 seconds.\n",
      "-- Epoch 111\n",
      "Norm: 230.83, NNZs: 16384, Bias: -50.201268, T: 558552, Avg. loss: 4.366312\n",
      "Total training time: 14.99 seconds.\n",
      "-- Epoch 112\n",
      "Norm: 229.95, NNZs: 16384, Bias: -50.183598, T: 563584, Avg. loss: 4.251479\n",
      "Total training time: 15.12 seconds.\n",
      "-- Epoch 113\n",
      "Norm: 228.97, NNZs: 16384, Bias: -50.201428, T: 568616, Avg. loss: 4.305600\n",
      "Total training time: 15.25 seconds.\n",
      "-- Epoch 114\n",
      "Norm: 228.05, NNZs: 16384, Bias: -50.218842, T: 573648, Avg. loss: 4.355345\n",
      "Total training time: 15.38 seconds.\n",
      "-- Epoch 115\n",
      "Norm: 227.23, NNZs: 16384, Bias: -50.201538, T: 578680, Avg. loss: 4.251444\n",
      "Total training time: 15.50 seconds.\n",
      "-- Epoch 116\n",
      "Norm: 226.40, NNZs: 16384, Bias: -50.235925, T: 583712, Avg. loss: 4.052756\n",
      "Total training time: 15.63 seconds.\n",
      "-- Epoch 117\n",
      "Norm: 225.66, NNZs: 16384, Bias: -50.236110, T: 588744, Avg. loss: 4.332463\n",
      "Total training time: 15.76 seconds.\n",
      "-- Epoch 118\n",
      "Norm: 224.84, NNZs: 16384, Bias: -50.253171, T: 593776, Avg. loss: 4.052674\n",
      "Total training time: 15.89 seconds.\n",
      "-- Epoch 119\n",
      "Norm: 224.00, NNZs: 16384, Bias: -50.253185, T: 598808, Avg. loss: 4.042157\n",
      "Total training time: 16.02 seconds.\n",
      "-- Epoch 120\n",
      "Norm: 223.12, NNZs: 16384, Bias: -50.269909, T: 603840, Avg. loss: 3.842225\n",
      "Total training time: 16.15 seconds.\n",
      "-- Epoch 121\n",
      "Norm: 222.14, NNZs: 16384, Bias: -50.319362, T: 608872, Avg. loss: 3.591044\n",
      "Total training time: 16.27 seconds.\n",
      "-- Epoch 122\n",
      "Norm: 221.20, NNZs: 16384, Bias: -50.335740, T: 613904, Avg. loss: 3.569850\n",
      "Total training time: 16.40 seconds.\n",
      "-- Epoch 123\n",
      "Norm: 220.41, NNZs: 16384, Bias: -50.352027, T: 618936, Avg. loss: 3.678390\n",
      "Total training time: 16.52 seconds.\n",
      "-- Epoch 124\n",
      "Norm: 219.61, NNZs: 16384, Bias: -50.336035, T: 623968, Avg. loss: 3.833546\n",
      "Total training time: 16.66 seconds.\n",
      "-- Epoch 125\n",
      "Norm: 218.87, NNZs: 16384, Bias: -50.367912, T: 629000, Avg. loss: 3.726839\n",
      "Total training time: 16.78 seconds.\n",
      "-- Epoch 126\n",
      "Norm: 218.02, NNZs: 16384, Bias: -50.352117, T: 634032, Avg. loss: 3.539859\n",
      "Total training time: 16.91 seconds.\n",
      "-- Epoch 127\n",
      "Norm: 217.20, NNZs: 16384, Bias: -50.367826, T: 639064, Avg. loss: 3.581011\n",
      "Total training time: 17.03 seconds.\n",
      "-- Epoch 128\n",
      "Norm: 216.40, NNZs: 16384, Bias: -50.430074, T: 644096, Avg. loss: 3.522616\n",
      "Total training time: 17.16 seconds.\n",
      "-- Epoch 129\n",
      "Norm: 215.61, NNZs: 16384, Bias: -50.399265, T: 649128, Avg. loss: 3.396825\n",
      "Total training time: 17.29 seconds.\n",
      "-- Epoch 130\n",
      "Norm: 214.95, NNZs: 16384, Bias: -50.414657, T: 654160, Avg. loss: 3.428638\n",
      "Total training time: 17.42 seconds.\n",
      "-- Epoch 131\n",
      "Norm: 214.18, NNZs: 16384, Bias: -50.460314, T: 659192, Avg. loss: 3.268933\n",
      "Total training time: 17.54 seconds.\n",
      "-- Epoch 132\n",
      "Norm: 213.46, NNZs: 16384, Bias: -50.490510, T: 664224, Avg. loss: 3.246398\n",
      "Total training time: 17.67 seconds.\n",
      "-- Epoch 133\n",
      "Norm: 212.76, NNZs: 16384, Bias: -50.520484, T: 669256, Avg. loss: 3.411173\n",
      "Total training time: 17.79 seconds.\n",
      "-- Epoch 134\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Norm: 212.00, NNZs: 16384, Bias: -50.490738, T: 674288, Avg. loss: 3.285606\n",
      "Total training time: 17.92 seconds.\n",
      "-- Epoch 135\n",
      "Norm: 211.32, NNZs: 16384, Bias: -50.505486, T: 679320, Avg. loss: 3.286649\n",
      "Total training time: 18.05 seconds.\n",
      "-- Epoch 136\n",
      "Norm: 210.60, NNZs: 16384, Bias: -50.490855, T: 684352, Avg. loss: 3.130788\n",
      "Total training time: 18.17 seconds.\n",
      "-- Epoch 137\n",
      "Norm: 209.94, NNZs: 16384, Bias: -50.476283, T: 689384, Avg. loss: 3.279410\n",
      "Total training time: 18.30 seconds.\n",
      "-- Epoch 138\n",
      "Norm: 209.23, NNZs: 16384, Bias: -50.476159, T: 694416, Avg. loss: 3.078744\n",
      "Total training time: 18.42 seconds.\n",
      "-- Epoch 139\n",
      "Norm: 208.54, NNZs: 16384, Bias: -50.476127, T: 699448, Avg. loss: 3.019805\n",
      "Total training time: 18.55 seconds.\n",
      "-- Epoch 140\n",
      "Norm: 207.83, NNZs: 16384, Bias: -50.490351, T: 704480, Avg. loss: 2.883117\n",
      "Total training time: 18.67 seconds.\n",
      "-- Epoch 141\n",
      "Norm: 207.10, NNZs: 16384, Bias: -50.490284, T: 709512, Avg. loss: 2.950468\n",
      "Total training time: 18.80 seconds.\n",
      "-- Epoch 142\n",
      "Norm: 206.43, NNZs: 16384, Bias: -50.532345, T: 714544, Avg. loss: 3.075221\n",
      "Total training time: 18.93 seconds.\n",
      "-- Epoch 143\n",
      "Norm: 205.75, NNZs: 16384, Bias: -50.518435, T: 719576, Avg. loss: 2.821156\n",
      "Total training time: 19.05 seconds.\n",
      "-- Epoch 144\n",
      "Norm: 205.12, NNZs: 16384, Bias: -50.559944, T: 724608, Avg. loss: 3.071850\n",
      "Total training time: 19.18 seconds.\n",
      "-- Epoch 145\n",
      "Norm: 204.43, NNZs: 16384, Bias: -50.532442, T: 729640, Avg. loss: 2.561897\n",
      "Total training time: 19.30 seconds.\n",
      "-- Epoch 146\n",
      "Norm: 203.82, NNZs: 16384, Bias: -50.518877, T: 734672, Avg. loss: 2.864101\n",
      "Total training time: 19.43 seconds.\n",
      "-- Epoch 147\n",
      "Norm: 203.20, NNZs: 16384, Bias: -50.545915, T: 739704, Avg. loss: 2.742001\n",
      "Total training time: 19.56 seconds.\n",
      "-- Epoch 148\n",
      "Norm: 202.54, NNZs: 16384, Bias: -50.545989, T: 744736, Avg. loss: 2.616779\n",
      "Total training time: 19.68 seconds.\n",
      "-- Epoch 149\n",
      "Norm: 201.96, NNZs: 16384, Bias: -50.559395, T: 749768, Avg. loss: 2.730552\n",
      "Total training time: 19.81 seconds.\n",
      "-- Epoch 150\n",
      "Norm: 201.29, NNZs: 16384, Bias: -50.572714, T: 754800, Avg. loss: 2.605786\n",
      "Total training time: 19.93 seconds.\n",
      "Convergence after 150 epochs took 19.93 seconds\n",
      "Score: 0.665341812400636\n",
      "Precision: 0.6048387096774194\n",
      "Recall: 0.9538950715421304\n",
      "[[237 392]\n",
      " [ 29 600]]\n"
     ]
    }
   ],
   "source": [
    "epidural_svm_model = build_stochastic_svm_model(total_data, 'epidural')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "6da8bf95",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Set Size: 750\n",
      "Test Set Size: 1258\n",
      "[LibLinear]Score: 0.5731319554848967\n",
      "Precision: 0.5877342419080068\n",
      "Recall: 0.5390625\n",
      "[[376 242]\n",
      " [295 345]]\n"
     ]
    }
   ],
   "source": [
    "intraparenchymal_svm_model = build_svm_model(total_data, 'intraparenchymal',iterations = 5000, training_set_size = 750)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "ce7288c7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Set Size: 5032\n",
      "Test Set Size: 1258\n",
      "-- Epoch 1\n",
      "Norm: 1105.29, NNZs: 16384, Bias: -23.756328, T: 5032, Avg. loss: 3261.562109\n",
      "Total training time: 0.15 seconds.\n",
      "-- Epoch 2\n",
      "Norm: 848.33, NNZs: 16384, Bias: -24.208231, T: 10064, Avg. loss: 1044.602086\n",
      "Total training time: 0.31 seconds.\n",
      "-- Epoch 3\n",
      "Norm: 749.17, NNZs: 16384, Bias: -22.080800, T: 15096, Avg. loss: 630.984385\n",
      "Total training time: 0.46 seconds.\n",
      "-- Epoch 4\n",
      "Norm: 691.99, NNZs: 16384, Bias: -22.299134, T: 20128, Avg. loss: 448.242011\n",
      "Total training time: 0.62 seconds.\n",
      "-- Epoch 5\n",
      "Norm: 637.08, NNZs: 16384, Bias: -22.701409, T: 25160, Avg. loss: 347.138020\n",
      "Total training time: 0.77 seconds.\n",
      "-- Epoch 6\n",
      "Norm: 603.18, NNZs: 16384, Bias: -22.606728, T: 30192, Avg. loss: 278.878843\n",
      "Total training time: 0.92 seconds.\n",
      "-- Epoch 7\n",
      "Norm: 580.99, NNZs: 16384, Bias: -23.718841, T: 35224, Avg. loss: 237.386471\n",
      "Total training time: 1.07 seconds.\n",
      "-- Epoch 8\n",
      "Norm: 550.26, NNZs: 16384, Bias: -23.170791, T: 40256, Avg. loss: 200.116272\n",
      "Total training time: 1.22 seconds.\n",
      "-- Epoch 9\n",
      "Norm: 529.50, NNZs: 16384, Bias: -23.397593, T: 45288, Avg. loss: 173.289734\n",
      "Total training time: 1.37 seconds.\n",
      "-- Epoch 10\n",
      "Norm: 509.10, NNZs: 16384, Bias: -22.991905, T: 50320, Avg. loss: 151.146797\n",
      "Total training time: 1.53 seconds.\n",
      "-- Epoch 11\n",
      "Norm: 490.59, NNZs: 16384, Bias: -22.635883, T: 55352, Avg. loss: 138.882958\n",
      "Total training time: 1.67 seconds.\n",
      "-- Epoch 12\n",
      "Norm: 475.66, NNZs: 16384, Bias: -22.620660, T: 60384, Avg. loss: 119.944936\n",
      "Total training time: 1.83 seconds.\n",
      "-- Epoch 13\n",
      "Norm: 462.80, NNZs: 16384, Bias: -22.631032, T: 65416, Avg. loss: 113.626727\n",
      "Total training time: 1.98 seconds.\n",
      "-- Epoch 14\n",
      "Norm: 451.51, NNZs: 16384, Bias: -22.501792, T: 70448, Avg. loss: 100.744852\n",
      "Total training time: 2.13 seconds.\n",
      "-- Epoch 15\n",
      "Norm: 440.54, NNZs: 16384, Bias: -22.102056, T: 75480, Avg. loss: 97.544693\n",
      "Total training time: 2.28 seconds.\n",
      "-- Epoch 16\n",
      "Norm: 432.04, NNZs: 16384, Bias: -22.480704, T: 80512, Avg. loss: 87.334432\n",
      "Total training time: 2.43 seconds.\n",
      "-- Epoch 17\n",
      "Norm: 422.04, NNZs: 16384, Bias: -22.480584, T: 85544, Avg. loss: 84.336962\n",
      "Total training time: 2.58 seconds.\n",
      "-- Epoch 18\n",
      "Norm: 415.91, NNZs: 16384, Bias: -22.257230, T: 90576, Avg. loss: 77.929562\n",
      "Total training time: 2.72 seconds.\n",
      "-- Epoch 19\n",
      "Norm: 407.44, NNZs: 16384, Bias: -22.567445, T: 95608, Avg. loss: 72.163342\n",
      "Total training time: 2.87 seconds.\n",
      "-- Epoch 20\n",
      "Norm: 401.57, NNZs: 16384, Bias: -22.363276, T: 100640, Avg. loss: 66.439692\n",
      "Total training time: 3.02 seconds.\n",
      "-- Epoch 21\n",
      "Norm: 395.43, NNZs: 16384, Bias: -22.172506, T: 105672, Avg. loss: 63.152841\n",
      "Total training time: 3.16 seconds.\n",
      "-- Epoch 22\n",
      "Norm: 389.17, NNZs: 16384, Bias: -22.084661, T: 110704, Avg. loss: 61.672737\n",
      "Total training time: 3.31 seconds.\n",
      "-- Epoch 23\n",
      "Norm: 383.59, NNZs: 16384, Bias: -22.168483, T: 115736, Avg. loss: 58.550036\n",
      "Total training time: 3.45 seconds.\n",
      "-- Epoch 24\n",
      "Norm: 379.61, NNZs: 16384, Bias: -21.750796, T: 120768, Avg. loss: 54.504695\n",
      "Total training time: 3.60 seconds.\n",
      "-- Epoch 25\n",
      "Norm: 375.10, NNZs: 16384, Bias: -21.507979, T: 125800, Avg. loss: 52.955031\n",
      "Total training time: 3.74 seconds.\n",
      "-- Epoch 26\n",
      "Norm: 370.96, NNZs: 16384, Bias: -21.435764, T: 130832, Avg. loss: 48.913524\n",
      "Total training time: 3.89 seconds.\n",
      "-- Epoch 27\n",
      "Norm: 367.39, NNZs: 16384, Bias: -20.984244, T: 135864, Avg. loss: 48.133699\n",
      "Total training time: 4.03 seconds.\n",
      "-- Epoch 28\n",
      "Norm: 362.95, NNZs: 16384, Bias: -20.699608, T: 140896, Avg. loss: 44.643725\n",
      "Total training time: 4.18 seconds.\n",
      "-- Epoch 29\n",
      "Norm: 358.69, NNZs: 16384, Bias: -20.699374, T: 145928, Avg. loss: 43.276442\n",
      "Total training time: 4.32 seconds.\n",
      "-- Epoch 30\n",
      "Norm: 354.41, NNZs: 16384, Bias: -20.566398, T: 150960, Avg. loss: 41.665143\n",
      "Total training time: 4.47 seconds.\n",
      "-- Epoch 31\n",
      "Norm: 350.54, NNZs: 16384, Bias: -20.694976, T: 155992, Avg. loss: 39.015089\n",
      "Total training time: 4.62 seconds.\n",
      "-- Epoch 32\n",
      "Norm: 346.48, NNZs: 16384, Bias: -20.567149, T: 161024, Avg. loss: 38.682079\n",
      "Total training time: 4.76 seconds.\n",
      "-- Epoch 33\n",
      "Norm: 342.63, NNZs: 16384, Bias: -20.383623, T: 166056, Avg. loss: 38.558601\n",
      "Total training time: 4.90 seconds.\n",
      "-- Epoch 34\n",
      "Norm: 339.60, NNZs: 16384, Bias: -20.207056, T: 171088, Avg. loss: 35.766813\n",
      "Total training time: 5.05 seconds.\n",
      "-- Epoch 35\n",
      "Norm: 336.47, NNZs: 16384, Bias: -20.150164, T: 176120, Avg. loss: 33.507957\n",
      "Total training time: 5.19 seconds.\n",
      "-- Epoch 36\n",
      "Norm: 332.90, NNZs: 16384, Bias: -20.041542, T: 181152, Avg. loss: 33.660975\n",
      "Total training time: 5.34 seconds.\n",
      "-- Epoch 37\n",
      "Norm: 329.65, NNZs: 16384, Bias: -20.097530, T: 186184, Avg. loss: 32.201988\n",
      "Total training time: 5.52 seconds.\n",
      "-- Epoch 38\n",
      "Norm: 326.68, NNZs: 16384, Bias: -19.991683, T: 191216, Avg. loss: 31.130339\n",
      "Total training time: 5.67 seconds.\n",
      "-- Epoch 39\n",
      "Norm: 323.93, NNZs: 16384, Bias: -19.888549, T: 196248, Avg. loss: 29.785732\n",
      "Total training time: 5.81 seconds.\n",
      "-- Epoch 40\n",
      "Norm: 321.27, NNZs: 16384, Bias: -19.889293, T: 201280, Avg. loss: 28.777572\n",
      "Total training time: 5.95 seconds.\n",
      "-- Epoch 41\n",
      "Norm: 318.53, NNZs: 16384, Bias: -19.694338, T: 206312, Avg. loss: 27.566579\n",
      "Total training time: 6.10 seconds.\n",
      "-- Epoch 42\n",
      "Norm: 316.07, NNZs: 16384, Bias: -19.837589, T: 211344, Avg. loss: 27.142913\n",
      "Total training time: 6.25 seconds.\n",
      "-- Epoch 43\n",
      "Norm: 313.45, NNZs: 16384, Bias: -19.650466, T: 216376, Avg. loss: 25.530088\n",
      "Total training time: 6.39 seconds.\n",
      "-- Epoch 44\n",
      "Norm: 310.83, NNZs: 16384, Bias: -19.695476, T: 221408, Avg. loss: 25.735574\n",
      "Total training time: 6.53 seconds.\n",
      "-- Epoch 45\n",
      "Norm: 308.43, NNZs: 16384, Bias: -19.383849, T: 226440, Avg. loss: 24.139067\n",
      "Total training time: 6.67 seconds.\n",
      "-- Epoch 46\n",
      "Norm: 306.29, NNZs: 16384, Bias: -19.426327, T: 231472, Avg. loss: 24.407764\n",
      "Total training time: 6.82 seconds.\n",
      "-- Epoch 47\n",
      "Norm: 304.23, NNZs: 16384, Bias: -19.298426, T: 236504, Avg. loss: 23.408610\n",
      "Total training time: 6.96 seconds.\n",
      "-- Epoch 48\n",
      "Norm: 302.12, NNZs: 16384, Bias: -19.298338, T: 241536, Avg. loss: 23.523659\n",
      "Total training time: 7.10 seconds.\n",
      "-- Epoch 49\n",
      "Norm: 300.13, NNZs: 16384, Bias: -19.257641, T: 246568, Avg. loss: 22.826181\n",
      "Total training time: 7.24 seconds.\n",
      "-- Epoch 50\n",
      "Norm: 298.24, NNZs: 16384, Bias: -19.097804, T: 251600, Avg. loss: 21.432009\n",
      "Total training time: 7.38 seconds.\n",
      "-- Epoch 51\n",
      "Norm: 296.09, NNZs: 16384, Bias: -18.980642, T: 256632, Avg. loss: 21.510113\n",
      "Total training time: 7.52 seconds.\n",
      "-- Epoch 52\n",
      "Norm: 294.44, NNZs: 16384, Bias: -18.943451, T: 261664, Avg. loss: 20.412100\n",
      "Total training time: 7.66 seconds.\n",
      "-- Epoch 53\n",
      "Norm: 292.66, NNZs: 16384, Bias: -18.830836, T: 266696, Avg. loss: 20.220591\n",
      "Total training time: 7.80 seconds.\n",
      "-- Epoch 54\n",
      "Norm: 290.82, NNZs: 16384, Bias: -18.793459, T: 271728, Avg. loss: 20.375671\n",
      "Total training time: 7.94 seconds.\n",
      "-- Epoch 55\n",
      "Norm: 289.14, NNZs: 16384, Bias: -18.684157, T: 276760, Avg. loss: 19.213889\n",
      "Total training time: 8.08 seconds.\n",
      "-- Epoch 56\n",
      "Norm: 287.26, NNZs: 16384, Bias: -18.684371, T: 281792, Avg. loss: 19.109316\n",
      "Total training time: 8.23 seconds.\n",
      "-- Epoch 57\n",
      "Norm: 285.54, NNZs: 16384, Bias: -18.648740, T: 286824, Avg. loss: 18.096124\n",
      "Total training time: 8.38 seconds.\n",
      "-- Epoch 58\n",
      "Norm: 283.77, NNZs: 16384, Bias: -18.511056, T: 291856, Avg. loss: 17.762255\n",
      "Total training time: 8.52 seconds.\n",
      "-- Epoch 59\n",
      "Norm: 282.32, NNZs: 16384, Bias: -18.545120, T: 296888, Avg. loss: 17.383219\n",
      "Total training time: 8.66 seconds.\n",
      "-- Epoch 60\n",
      "Norm: 280.78, NNZs: 16384, Bias: -18.411763, T: 301920, Avg. loss: 17.265383\n",
      "Total training time: 8.80 seconds.\n",
      "-- Epoch 61\n",
      "Norm: 279.04, NNZs: 16384, Bias: -18.445123, T: 306952, Avg. loss: 16.896395\n",
      "Total training time: 8.94 seconds.\n",
      "-- Epoch 62\n",
      "Norm: 277.51, NNZs: 16384, Bias: -18.477225, T: 311984, Avg. loss: 16.510706\n",
      "Total training time: 9.08 seconds.\n",
      "-- Epoch 63\n",
      "Norm: 276.14, NNZs: 16384, Bias: -18.318844, T: 317016, Avg. loss: 16.193287\n",
      "Total training time: 9.22 seconds.\n",
      "-- Epoch 64\n",
      "Norm: 274.66, NNZs: 16384, Bias: -18.288397, T: 322048, Avg. loss: 15.881621\n",
      "Total training time: 9.36 seconds.\n",
      "-- Epoch 65\n",
      "Norm: 273.09, NNZs: 16384, Bias: -18.288500, T: 327080, Avg. loss: 15.006681\n",
      "Total training time: 9.50 seconds.\n",
      "-- Epoch 66\n",
      "Norm: 271.64, NNZs: 16384, Bias: -18.136659, T: 332112, Avg. loss: 15.427818\n",
      "Total training time: 9.64 seconds.\n",
      "-- Epoch 67\n",
      "Norm: 270.11, NNZs: 16384, Bias: -18.136825, T: 337144, Avg. loss: 14.607913\n",
      "Total training time: 9.78 seconds.\n",
      "-- Epoch 68\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Norm: 268.64, NNZs: 16384, Bias: -18.078511, T: 342176, Avg. loss: 14.814745\n",
      "Total training time: 9.91 seconds.\n",
      "-- Epoch 69\n",
      "Norm: 267.27, NNZs: 16384, Bias: -18.078179, T: 347208, Avg. loss: 14.084069\n",
      "Total training time: 10.05 seconds.\n",
      "-- Epoch 70\n",
      "Norm: 266.04, NNZs: 16384, Bias: -18.020842, T: 352240, Avg. loss: 14.450584\n",
      "Total training time: 10.19 seconds.\n",
      "-- Epoch 71\n",
      "Norm: 264.60, NNZs: 16384, Bias: -17.936495, T: 357272, Avg. loss: 13.724559\n",
      "Total training time: 10.33 seconds.\n",
      "-- Epoch 72\n",
      "Norm: 263.37, NNZs: 16384, Bias: -17.936557, T: 362304, Avg. loss: 13.631523\n",
      "Total training time: 10.48 seconds.\n",
      "-- Epoch 73\n",
      "Norm: 262.08, NNZs: 16384, Bias: -17.881849, T: 367336, Avg. loss: 12.976347\n",
      "Total training time: 10.62 seconds.\n",
      "-- Epoch 74\n",
      "Norm: 260.89, NNZs: 16384, Bias: -17.720001, T: 372368, Avg. loss: 13.320379\n",
      "Total training time: 10.76 seconds.\n",
      "-- Epoch 75\n",
      "Norm: 259.55, NNZs: 16384, Bias: -17.773371, T: 377400, Avg. loss: 12.332697\n",
      "Total training time: 10.89 seconds.\n",
      "-- Epoch 76\n",
      "Norm: 258.17, NNZs: 16384, Bias: -17.694318, T: 382432, Avg. loss: 12.450175\n",
      "Total training time: 11.03 seconds.\n",
      "-- Epoch 77\n",
      "Norm: 257.06, NNZs: 16384, Bias: -17.642680, T: 387464, Avg. loss: 12.533787\n",
      "Total training time: 11.17 seconds.\n",
      "-- Epoch 78\n",
      "Norm: 255.79, NNZs: 16384, Bias: -17.565715, T: 392496, Avg. loss: 11.829036\n",
      "Total training time: 11.31 seconds.\n",
      "-- Epoch 79\n",
      "Norm: 254.83, NNZs: 16384, Bias: -17.540332, T: 397528, Avg. loss: 11.981814\n",
      "Total training time: 11.45 seconds.\n",
      "-- Epoch 80\n",
      "Norm: 253.66, NNZs: 16384, Bias: -17.465488, T: 402560, Avg. loss: 11.662764\n",
      "Total training time: 11.59 seconds.\n",
      "-- Epoch 81\n",
      "Norm: 252.59, NNZs: 16384, Bias: -17.416116, T: 407592, Avg. loss: 11.397179\n",
      "Total training time: 11.72 seconds.\n",
      "-- Epoch 82\n",
      "Norm: 251.39, NNZs: 16384, Bias: -17.367350, T: 412624, Avg. loss: 11.375346\n",
      "Total training time: 11.86 seconds.\n",
      "-- Epoch 83\n",
      "Norm: 250.32, NNZs: 16384, Bias: -17.318932, T: 417656, Avg. loss: 11.048387\n",
      "Total training time: 12.00 seconds.\n",
      "-- Epoch 84\n",
      "Norm: 249.44, NNZs: 16384, Bias: -17.247769, T: 422688, Avg. loss: 10.923215\n",
      "Total training time: 12.14 seconds.\n",
      "-- Epoch 85\n",
      "Norm: 248.49, NNZs: 16384, Bias: -17.177337, T: 427720, Avg. loss: 10.835722\n",
      "Total training time: 12.28 seconds.\n",
      "-- Epoch 86\n",
      "Norm: 247.46, NNZs: 16384, Bias: -17.084348, T: 432752, Avg. loss: 10.692717\n",
      "Total training time: 12.42 seconds.\n",
      "-- Epoch 87\n",
      "Norm: 246.42, NNZs: 16384, Bias: -17.061487, T: 437784, Avg. loss: 10.256235\n",
      "Total training time: 12.56 seconds.\n",
      "-- Epoch 88\n",
      "Norm: 245.40, NNZs: 16384, Bias: -17.106875, T: 442816, Avg. loss: 10.341138\n",
      "Total training time: 12.69 seconds.\n",
      "-- Epoch 89\n",
      "Norm: 244.55, NNZs: 16384, Bias: -17.061937, T: 447848, Avg. loss: 9.968863\n",
      "Total training time: 12.83 seconds.\n",
      "-- Epoch 90\n",
      "Norm: 243.62, NNZs: 16384, Bias: -17.061838, T: 452880, Avg. loss: 10.058515\n",
      "Total training time: 12.97 seconds.\n",
      "-- Epoch 91\n",
      "Norm: 242.66, NNZs: 16384, Bias: -16.996170, T: 457912, Avg. loss: 9.734073\n",
      "Total training time: 13.10 seconds.\n",
      "-- Epoch 92\n",
      "Norm: 241.66, NNZs: 16384, Bias: -16.974506, T: 462944, Avg. loss: 9.173676\n",
      "Total training time: 13.24 seconds.\n",
      "-- Epoch 93\n",
      "Norm: 240.67, NNZs: 16384, Bias: -16.867485, T: 467976, Avg. loss: 9.367282\n",
      "Total training time: 13.37 seconds.\n",
      "-- Epoch 94\n",
      "Norm: 239.69, NNZs: 16384, Bias: -16.825249, T: 473008, Avg. loss: 8.832401\n",
      "Total training time: 13.51 seconds.\n",
      "-- Epoch 95\n",
      "Norm: 238.85, NNZs: 16384, Bias: -16.762409, T: 478040, Avg. loss: 9.135653\n",
      "Total training time: 13.64 seconds.\n",
      "-- Epoch 96\n",
      "Norm: 237.85, NNZs: 16384, Bias: -16.699986, T: 483072, Avg. loss: 8.737073\n",
      "Total training time: 13.78 seconds.\n",
      "-- Epoch 97\n",
      "Norm: 237.12, NNZs: 16384, Bias: -16.638117, T: 488104, Avg. loss: 8.703520\n",
      "Total training time: 13.92 seconds.\n",
      "-- Epoch 98\n",
      "Norm: 236.25, NNZs: 16384, Bias: -16.638173, T: 493136, Avg. loss: 8.831401\n",
      "Total training time: 14.05 seconds.\n",
      "-- Epoch 99\n",
      "Norm: 235.32, NNZs: 16384, Bias: -16.617778, T: 498168, Avg. loss: 8.454863\n",
      "Total training time: 14.19 seconds.\n",
      "-- Epoch 100\n",
      "Norm: 234.49, NNZs: 16384, Bias: -16.517969, T: 503200, Avg. loss: 8.098376\n",
      "Total training time: 14.32 seconds.\n",
      "-- Epoch 101\n",
      "Norm: 233.65, NNZs: 16384, Bias: -16.478385, T: 508232, Avg. loss: 8.409821\n",
      "Total training time: 14.46 seconds.\n",
      "-- Epoch 102\n",
      "Norm: 232.81, NNZs: 16384, Bias: -16.439402, T: 513264, Avg. loss: 8.184286\n",
      "Total training time: 14.59 seconds.\n",
      "-- Epoch 103\n",
      "Norm: 232.00, NNZs: 16384, Bias: -16.361984, T: 518296, Avg. loss: 8.125217\n",
      "Total training time: 14.73 seconds.\n",
      "-- Epoch 104\n",
      "Norm: 231.07, NNZs: 16384, Bias: -16.304406, T: 523328, Avg. loss: 7.899984\n",
      "Total training time: 14.86 seconds.\n",
      "-- Epoch 105\n",
      "Norm: 230.34, NNZs: 16384, Bias: -16.228558, T: 528360, Avg. loss: 8.250726\n",
      "Total training time: 15.00 seconds.\n",
      "-- Epoch 106\n",
      "Norm: 229.49, NNZs: 16384, Bias: -16.209631, T: 533392, Avg. loss: 7.504086\n",
      "Total training time: 15.13 seconds.\n",
      "-- Epoch 107\n",
      "Norm: 228.72, NNZs: 16384, Bias: -16.191017, T: 538424, Avg. loss: 7.436909\n",
      "Total training time: 15.27 seconds.\n",
      "-- Epoch 108\n",
      "Norm: 227.96, NNZs: 16384, Bias: -16.153960, T: 543456, Avg. loss: 7.752962\n",
      "Total training time: 15.40 seconds.\n",
      "-- Epoch 109\n",
      "Norm: 227.20, NNZs: 16384, Bias: -16.080860, T: 548488, Avg. loss: 7.758167\n",
      "Total training time: 15.54 seconds.\n",
      "-- Epoch 110\n",
      "Norm: 226.38, NNZs: 16384, Bias: -16.099058, T: 553520, Avg. loss: 7.440432\n",
      "Total training time: 15.67 seconds.\n",
      "-- Epoch 111\n",
      "Norm: 225.58, NNZs: 16384, Bias: -16.009412, T: 558552, Avg. loss: 7.170870\n",
      "Total training time: 15.81 seconds.\n",
      "-- Epoch 112\n",
      "Norm: 224.86, NNZs: 16384, Bias: -16.045078, T: 563584, Avg. loss: 7.040585\n",
      "Total training time: 15.94 seconds.\n",
      "-- Epoch 113\n",
      "Norm: 224.07, NNZs: 16384, Bias: -15.992312, T: 568616, Avg. loss: 7.121813\n",
      "Total training time: 16.08 seconds.\n",
      "-- Epoch 114\n",
      "Norm: 223.32, NNZs: 16384, Bias: -15.992471, T: 573648, Avg. loss: 7.152967\n",
      "Total training time: 16.21 seconds.\n",
      "-- Epoch 115\n",
      "Norm: 222.63, NNZs: 16384, Bias: -15.940486, T: 578680, Avg. loss: 7.067814\n",
      "Total training time: 16.35 seconds.\n",
      "-- Epoch 116\n",
      "Norm: 221.88, NNZs: 16384, Bias: -15.871727, T: 583712, Avg. loss: 6.659173\n",
      "Total training time: 16.48 seconds.\n",
      "-- Epoch 117\n",
      "Norm: 221.20, NNZs: 16384, Bias: -15.837610, T: 588744, Avg. loss: 6.595485\n",
      "Total training time: 16.62 seconds.\n",
      "-- Epoch 118\n",
      "Norm: 220.47, NNZs: 16384, Bias: -15.770156, T: 593776, Avg. loss: 6.628355\n",
      "Total training time: 16.75 seconds.\n",
      "-- Epoch 119\n",
      "Norm: 219.75, NNZs: 16384, Bias: -15.770241, T: 598808, Avg. loss: 6.288263\n",
      "Total training time: 16.88 seconds.\n",
      "-- Epoch 120\n",
      "Norm: 219.16, NNZs: 16384, Bias: -15.753737, T: 603840, Avg. loss: 6.743767\n",
      "Total training time: 17.02 seconds.\n",
      "-- Epoch 121\n",
      "Norm: 218.49, NNZs: 16384, Bias: -15.688006, T: 608872, Avg. loss: 6.516333\n",
      "Total training time: 17.15 seconds.\n",
      "-- Epoch 122\n",
      "Norm: 217.92, NNZs: 16384, Bias: -15.671753, T: 613904, Avg. loss: 6.497648\n",
      "Total training time: 17.29 seconds.\n",
      "-- Epoch 123\n",
      "Norm: 217.22, NNZs: 16384, Bias: -15.655693, T: 618936, Avg. loss: 6.230383\n",
      "Total training time: 17.42 seconds.\n",
      "-- Epoch 124\n",
      "Norm: 216.60, NNZs: 16384, Bias: -15.639600, T: 623968, Avg. loss: 6.162589\n",
      "Total training time: 17.56 seconds.\n",
      "-- Epoch 125\n",
      "Norm: 216.01, NNZs: 16384, Bias: -15.576011, T: 629000, Avg. loss: 6.155813\n",
      "Total training time: 17.70 seconds.\n",
      "-- Epoch 126\n",
      "Norm: 215.33, NNZs: 16384, Bias: -15.655068, T: 634032, Avg. loss: 5.984117\n",
      "Total training time: 17.84 seconds.\n",
      "-- Epoch 127\n",
      "Norm: 214.66, NNZs: 16384, Bias: -15.576618, T: 639064, Avg. loss: 5.767753\n",
      "Total training time: 17.97 seconds.\n",
      "-- Epoch 128\n",
      "Norm: 214.03, NNZs: 16384, Bias: -15.545522, T: 644096, Avg. loss: 5.962659\n",
      "Total training time: 18.11 seconds.\n",
      "-- Epoch 129\n",
      "Norm: 213.35, NNZs: 16384, Bias: -15.483779, T: 649128, Avg. loss: 5.872675\n",
      "Total training time: 18.24 seconds.\n",
      "-- Epoch 130\n",
      "Norm: 212.76, NNZs: 16384, Bias: -15.437836, T: 654160, Avg. loss: 5.879013\n",
      "Total training time: 18.38 seconds.\n",
      "-- Epoch 131\n",
      "Norm: 212.14, NNZs: 16384, Bias: -15.453007, T: 659192, Avg. loss: 5.368651\n",
      "Total training time: 18.51 seconds.\n",
      "-- Epoch 132\n",
      "Norm: 211.53, NNZs: 16384, Bias: -15.407668, T: 664224, Avg. loss: 5.619948\n",
      "Total training time: 18.64 seconds.\n",
      "-- Epoch 133\n",
      "Norm: 210.97, NNZs: 16384, Bias: -15.362685, T: 669256, Avg. loss: 5.768315\n",
      "Total training time: 18.78 seconds.\n",
      "-- Epoch 134\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Norm: 210.30, NNZs: 16384, Bias: -15.303347, T: 674288, Avg. loss: 5.387892\n",
      "Total training time: 18.91 seconds.\n",
      "-- Epoch 135\n",
      "Norm: 209.73, NNZs: 16384, Bias: -15.288535, T: 679320, Avg. loss: 5.404614\n",
      "Total training time: 19.04 seconds.\n",
      "-- Epoch 136\n",
      "Norm: 209.11, NNZs: 16384, Bias: -15.229951, T: 684352, Avg. loss: 5.287292\n",
      "Total training time: 19.18 seconds.\n",
      "-- Epoch 137\n",
      "Norm: 208.56, NNZs: 16384, Bias: -15.215344, T: 689384, Avg. loss: 5.372923\n",
      "Total training time: 19.31 seconds.\n",
      "-- Epoch 138\n",
      "Norm: 207.98, NNZs: 16384, Bias: -15.186334, T: 694416, Avg. loss: 5.354438\n",
      "Total training time: 19.45 seconds.\n",
      "-- Epoch 139\n",
      "Norm: 207.41, NNZs: 16384, Bias: -15.157536, T: 699448, Avg. loss: 5.275025\n",
      "Total training time: 19.58 seconds.\n",
      "-- Epoch 140\n",
      "Norm: 206.83, NNZs: 16384, Bias: -15.114774, T: 704480, Avg. loss: 5.232973\n",
      "Total training time: 19.71 seconds.\n",
      "-- Epoch 141\n",
      "Norm: 206.28, NNZs: 16384, Bias: -15.086475, T: 709512, Avg. loss: 5.027145\n",
      "Total training time: 19.85 seconds.\n",
      "-- Epoch 142\n",
      "Norm: 205.73, NNZs: 16384, Bias: -15.058362, T: 714544, Avg. loss: 5.233170\n",
      "Total training time: 19.98 seconds.\n",
      "-- Epoch 143\n",
      "Norm: 205.24, NNZs: 16384, Bias: -15.030377, T: 719576, Avg. loss: 4.911610\n",
      "Total training time: 20.11 seconds.\n",
      "-- Epoch 144\n",
      "Norm: 204.73, NNZs: 16384, Bias: -15.002763, T: 724608, Avg. loss: 4.952424\n",
      "Total training time: 20.25 seconds.\n",
      "-- Epoch 145\n",
      "Norm: 204.19, NNZs: 16384, Bias: -15.002850, T: 729640, Avg. loss: 5.018778\n",
      "Total training time: 20.38 seconds.\n",
      "-- Epoch 146\n",
      "Norm: 203.75, NNZs: 16384, Bias: -14.989249, T: 734672, Avg. loss: 5.083073\n",
      "Total training time: 20.51 seconds.\n",
      "-- Epoch 147\n",
      "Norm: 203.21, NNZs: 16384, Bias: -14.962125, T: 739704, Avg. loss: 4.783463\n",
      "Total training time: 20.65 seconds.\n",
      "-- Epoch 148\n",
      "Norm: 202.69, NNZs: 16384, Bias: -14.908407, T: 744736, Avg. loss: 4.652980\n",
      "Total training time: 20.78 seconds.\n",
      "-- Epoch 149\n",
      "Norm: 202.18, NNZs: 16384, Bias: -14.935213, T: 749768, Avg. loss: 4.668863\n",
      "Total training time: 20.91 seconds.\n",
      "-- Epoch 150\n",
      "Norm: 201.64, NNZs: 16384, Bias: -14.895380, T: 754800, Avg. loss: 4.816478\n",
      "Total training time: 21.05 seconds.\n",
      "-- Epoch 151\n",
      "Norm: 201.13, NNZs: 16384, Bias: -14.842602, T: 759832, Avg. loss: 4.677314\n",
      "Total training time: 21.18 seconds.\n",
      "-- Epoch 152\n",
      "Norm: 200.61, NNZs: 16384, Bias: -14.816406, T: 764864, Avg. loss: 4.477288\n",
      "Total training time: 21.31 seconds.\n",
      "-- Epoch 153\n",
      "Norm: 200.18, NNZs: 16384, Bias: -14.751252, T: 769896, Avg. loss: 4.761627\n",
      "Total training time: 21.44 seconds.\n",
      "-- Epoch 154\n",
      "Norm: 199.64, NNZs: 16384, Bias: -14.712460, T: 774928, Avg. loss: 4.559134\n",
      "Total training time: 21.58 seconds.\n",
      "-- Epoch 155\n",
      "Norm: 199.20, NNZs: 16384, Bias: -14.686735, T: 779960, Avg. loss: 4.538332\n",
      "Total training time: 21.71 seconds.\n",
      "-- Epoch 156\n",
      "Norm: 198.66, NNZs: 16384, Bias: -14.635637, T: 784992, Avg. loss: 4.110022\n",
      "Total training time: 21.84 seconds.\n",
      "-- Epoch 157\n",
      "Norm: 198.18, NNZs: 16384, Bias: -14.635627, T: 790024, Avg. loss: 4.495585\n",
      "Total training time: 21.98 seconds.\n",
      "-- Epoch 158\n",
      "Norm: 197.68, NNZs: 16384, Bias: -14.597757, T: 795056, Avg. loss: 4.250266\n",
      "Total training time: 22.12 seconds.\n",
      "-- Epoch 159\n",
      "Norm: 197.15, NNZs: 16384, Bias: -14.572654, T: 800088, Avg. loss: 4.062080\n",
      "Total training time: 22.25 seconds.\n",
      "-- Epoch 160\n",
      "Norm: 196.64, NNZs: 16384, Bias: -14.597510, T: 805120, Avg. loss: 4.330599\n",
      "Total training time: 22.38 seconds.\n",
      "-- Epoch 161\n",
      "Norm: 196.14, NNZs: 16384, Bias: -14.572745, T: 810152, Avg. loss: 4.110203\n",
      "Total training time: 22.52 seconds.\n",
      "-- Epoch 162\n",
      "Norm: 195.64, NNZs: 16384, Bias: -14.523590, T: 815184, Avg. loss: 4.030775\n",
      "Total training time: 22.65 seconds.\n",
      "-- Epoch 163\n",
      "Norm: 195.19, NNZs: 16384, Bias: -14.474845, T: 820216, Avg. loss: 4.146344\n",
      "Total training time: 22.78 seconds.\n",
      "-- Epoch 164\n",
      "Norm: 194.69, NNZs: 16384, Bias: -14.426349, T: 825248, Avg. loss: 4.110680\n",
      "Total training time: 22.91 seconds.\n",
      "-- Epoch 165\n",
      "Norm: 194.20, NNZs: 16384, Bias: -14.414327, T: 830280, Avg. loss: 4.061092\n",
      "Total training time: 23.05 seconds.\n",
      "-- Epoch 166\n",
      "Norm: 193.78, NNZs: 16384, Bias: -14.378380, T: 835312, Avg. loss: 4.071508\n",
      "Total training time: 23.18 seconds.\n",
      "-- Epoch 167\n",
      "Norm: 193.35, NNZs: 16384, Bias: -14.390251, T: 840344, Avg. loss: 3.903874\n",
      "Total training time: 23.31 seconds.\n",
      "-- Epoch 168\n",
      "Norm: 192.95, NNZs: 16384, Bias: -14.366483, T: 845376, Avg. loss: 3.877984\n",
      "Total training time: 23.45 seconds.\n",
      "-- Epoch 169\n",
      "Norm: 192.54, NNZs: 16384, Bias: -14.331148, T: 850408, Avg. loss: 4.057230\n",
      "Total training time: 23.58 seconds.\n",
      "-- Epoch 170\n",
      "Norm: 192.09, NNZs: 16384, Bias: -14.284328, T: 855440, Avg. loss: 3.912511\n",
      "Total training time: 23.71 seconds.\n",
      "-- Epoch 171\n",
      "Norm: 191.54, NNZs: 16384, Bias: -14.249423, T: 860472, Avg. loss: 3.722233\n",
      "Total training time: 23.84 seconds.\n",
      "-- Epoch 172\n",
      "Norm: 191.13, NNZs: 16384, Bias: -14.237868, T: 865504, Avg. loss: 3.706640\n",
      "Total training time: 23.98 seconds.\n",
      "-- Epoch 173\n",
      "Norm: 190.74, NNZs: 16384, Bias: -14.203386, T: 870536, Avg. loss: 3.865859\n",
      "Total training time: 24.11 seconds.\n",
      "-- Epoch 174\n",
      "Norm: 190.28, NNZs: 16384, Bias: -14.191864, T: 875568, Avg. loss: 3.608596\n",
      "Total training time: 24.25 seconds.\n",
      "-- Epoch 175\n",
      "Norm: 189.85, NNZs: 16384, Bias: -14.146313, T: 880600, Avg. loss: 3.722746\n",
      "Total training time: 24.38 seconds.\n",
      "-- Epoch 176\n",
      "Norm: 189.45, NNZs: 16384, Bias: -14.101074, T: 885632, Avg. loss: 3.576715\n",
      "Total training time: 24.51 seconds.\n",
      "-- Epoch 177\n",
      "Norm: 189.00, NNZs: 16384, Bias: -14.078620, T: 890664, Avg. loss: 3.520026\n",
      "Total training time: 24.64 seconds.\n",
      "-- Epoch 178\n",
      "Norm: 188.61, NNZs: 16384, Bias: -14.056295, T: 895696, Avg. loss: 3.586545\n",
      "Total training time: 24.77 seconds.\n",
      "-- Epoch 179\n",
      "Norm: 188.22, NNZs: 16384, Bias: -14.034063, T: 900728, Avg. loss: 3.728779\n",
      "Total training time: 24.91 seconds.\n",
      "-- Epoch 180\n",
      "Norm: 187.78, NNZs: 16384, Bias: -14.023049, T: 905760, Avg. loss: 3.528049\n",
      "Total training time: 25.04 seconds.\n",
      "-- Epoch 181\n",
      "Norm: 187.38, NNZs: 16384, Bias: -14.001052, T: 910792, Avg. loss: 3.476382\n",
      "Total training time: 25.17 seconds.\n",
      "-- Epoch 182\n",
      "Norm: 186.95, NNZs: 16384, Bias: -13.990126, T: 915824, Avg. loss: 3.481785\n",
      "Total training time: 25.30 seconds.\n",
      "-- Epoch 183\n",
      "Norm: 186.57, NNZs: 16384, Bias: -13.946597, T: 920856, Avg. loss: 3.292216\n",
      "Total training time: 25.43 seconds.\n",
      "-- Epoch 184\n",
      "Norm: 186.18, NNZs: 16384, Bias: -13.946603, T: 925888, Avg. loss: 3.284056\n",
      "Total training time: 25.56 seconds.\n",
      "-- Epoch 185\n",
      "Norm: 185.76, NNZs: 16384, Bias: -13.914268, T: 930920, Avg. loss: 3.356443\n",
      "Total training time: 25.69 seconds.\n",
      "-- Epoch 186\n",
      "Norm: 185.39, NNZs: 16384, Bias: -13.892813, T: 935952, Avg. loss: 3.330145\n",
      "Total training time: 25.83 seconds.\n",
      "-- Epoch 187\n",
      "Norm: 185.02, NNZs: 16384, Bias: -13.839594, T: 940984, Avg. loss: 3.166285\n",
      "Total training time: 25.96 seconds.\n",
      "-- Epoch 188\n",
      "Norm: 184.62, NNZs: 16384, Bias: -13.839535, T: 946016, Avg. loss: 3.161699\n",
      "Total training time: 26.09 seconds.\n",
      "-- Epoch 189\n",
      "Norm: 184.22, NNZs: 16384, Bias: -13.839503, T: 951048, Avg. loss: 3.248759\n",
      "Total training time: 26.22 seconds.\n",
      "-- Epoch 190\n",
      "Norm: 183.86, NNZs: 16384, Bias: -13.818499, T: 956080, Avg. loss: 3.259129\n",
      "Total training time: 26.35 seconds.\n",
      "-- Epoch 191\n",
      "Norm: 183.50, NNZs: 16384, Bias: -13.787230, T: 961112, Avg. loss: 3.219167\n",
      "Total training time: 26.49 seconds.\n",
      "-- Epoch 192\n",
      "Norm: 183.06, NNZs: 16384, Bias: -13.745794, T: 966144, Avg. loss: 3.144368\n",
      "Total training time: 26.62 seconds.\n",
      "-- Epoch 193\n",
      "Norm: 182.69, NNZs: 16384, Bias: -13.704589, T: 971176, Avg. loss: 3.088526\n",
      "Total training time: 26.75 seconds.\n",
      "-- Epoch 194\n",
      "Norm: 182.30, NNZs: 16384, Bias: -13.673818, T: 976208, Avg. loss: 3.053967\n",
      "Total training time: 26.88 seconds.\n",
      "-- Epoch 195\n",
      "Norm: 181.95, NNZs: 16384, Bias: -13.653430, T: 981240, Avg. loss: 3.189934\n",
      "Total training time: 27.01 seconds.\n",
      "-- Epoch 196\n",
      "Norm: 181.59, NNZs: 16384, Bias: -13.633141, T: 986272, Avg. loss: 3.118161\n",
      "Total training time: 27.14 seconds.\n",
      "-- Epoch 197\n",
      "Norm: 181.20, NNZs: 16384, Bias: -13.612919, T: 991304, Avg. loss: 2.941713\n",
      "Total training time: 27.27 seconds.\n",
      "-- Epoch 198\n",
      "Norm: 180.85, NNZs: 16384, Bias: -13.602844, T: 996336, Avg. loss: 3.069401\n",
      "Total training time: 27.40 seconds.\n",
      "-- Epoch 199\n",
      "Norm: 180.50, NNZs: 16384, Bias: -13.552796, T: 1001368, Avg. loss: 2.965506\n",
      "Total training time: 27.53 seconds.\n",
      "-- Epoch 200\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Norm: 180.16, NNZs: 16384, Bias: -13.522991, T: 1006400, Avg. loss: 2.945397\n",
      "Total training time: 27.66 seconds.\n",
      "-- Epoch 201\n",
      "Norm: 179.80, NNZs: 16384, Bias: -13.503168, T: 1011432, Avg. loss: 3.113882\n",
      "Total training time: 27.79 seconds.\n",
      "-- Epoch 202\n",
      "Norm: 179.44, NNZs: 16384, Bias: -13.483425, T: 1016464, Avg. loss: 2.828225\n",
      "Total training time: 27.93 seconds.\n",
      "-- Epoch 203\n",
      "Norm: 179.09, NNZs: 16384, Bias: -13.444165, T: 1021496, Avg. loss: 2.867368\n",
      "Total training time: 28.06 seconds.\n",
      "-- Epoch 204\n",
      "Norm: 178.71, NNZs: 16384, Bias: -13.405095, T: 1026528, Avg. loss: 2.755769\n",
      "Total training time: 28.19 seconds.\n",
      "-- Epoch 205\n",
      "Norm: 178.31, NNZs: 16384, Bias: -13.375954, T: 1031560, Avg. loss: 2.784976\n",
      "Total training time: 28.32 seconds.\n",
      "-- Epoch 206\n",
      "Norm: 177.96, NNZs: 16384, Bias: -13.366353, T: 1036592, Avg. loss: 2.959802\n",
      "Total training time: 28.45 seconds.\n",
      "-- Epoch 207\n",
      "Norm: 177.60, NNZs: 16384, Bias: -13.327914, T: 1041624, Avg. loss: 2.795848\n",
      "Total training time: 28.58 seconds.\n",
      "-- Epoch 208\n",
      "Norm: 177.26, NNZs: 16384, Bias: -13.308818, T: 1046656, Avg. loss: 2.936620\n",
      "Total training time: 28.71 seconds.\n",
      "-- Epoch 209\n",
      "Norm: 176.87, NNZs: 16384, Bias: -13.299337, T: 1051688, Avg. loss: 2.798213\n",
      "Total training time: 28.84 seconds.\n",
      "Convergence after 209 epochs took 28.84 seconds\n",
      "Score: 0.5914149443561209\n",
      "Precision: 0.5953565505804311\n",
      "Recall: 0.5707472178060413\n",
      "[[385 244]\n",
      " [270 359]]\n"
     ]
    }
   ],
   "source": [
    "intraparenchymal_svm_model = build_stochastic_svm_model(total_data, 'intraparenchymal')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "55234b6e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Set Size: 750\n",
      "Test Set Size: 1258\n",
      "[LibLinear]Score: 0.5739268680445151\n",
      "Precision: 0.5878378378378378\n",
      "Recall: 0.54375\n",
      "[[374 244]\n",
      " [292 348]]\n"
     ]
    }
   ],
   "source": [
    "intraventricular_svm_model = build_svm_model(total_data, 'intraventricular', iterations = 5000, training_set_size =750)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "c76c2d9b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Set Size: 5032\n",
      "Test Set Size: 1258\n",
      "-- Epoch 1\n",
      "Norm: 1201.54, NNZs: 16384, Bias: -40.545135, T: 5032, Avg. loss: 2916.709446\n",
      "Total training time: 0.15 seconds.\n",
      "-- Epoch 2\n",
      "Norm: 954.58, NNZs: 16384, Bias: -53.248797, T: 10064, Avg. loss: 942.895292\n",
      "Total training time: 0.31 seconds.\n",
      "-- Epoch 3\n",
      "Norm: 839.21, NNZs: 16384, Bias: -56.726405, T: 15096, Avg. loss: 563.843111\n",
      "Total training time: 0.47 seconds.\n",
      "-- Epoch 4\n",
      "Norm: 757.02, NNZs: 16384, Bias: -60.732148, T: 20128, Avg. loss: 393.852838\n",
      "Total training time: 0.62 seconds.\n",
      "-- Epoch 5\n",
      "Norm: 693.81, NNZs: 16384, Bias: -63.728231, T: 25160, Avg. loss: 301.977360\n",
      "Total training time: 0.76 seconds.\n",
      "-- Epoch 6\n",
      "Norm: 649.01, NNZs: 16384, Bias: -67.198144, T: 30192, Avg. loss: 240.208787\n",
      "Total training time: 0.91 seconds.\n",
      "-- Epoch 7\n",
      "Norm: 615.88, NNZs: 16384, Bias: -68.661411, T: 35224, Avg. loss: 198.308130\n",
      "Total training time: 1.05 seconds.\n",
      "-- Epoch 8\n",
      "Norm: 587.88, NNZs: 16384, Bias: -69.445227, T: 40256, Avg. loss: 166.523356\n",
      "Total training time: 1.19 seconds.\n",
      "-- Epoch 9\n",
      "Norm: 563.60, NNZs: 16384, Bias: -70.138490, T: 45288, Avg. loss: 153.175986\n",
      "Total training time: 1.34 seconds.\n",
      "-- Epoch 10\n",
      "Norm: 543.20, NNZs: 16384, Bias: -71.373536, T: 50320, Avg. loss: 132.088081\n",
      "Total training time: 1.48 seconds.\n",
      "-- Epoch 11\n",
      "Norm: 523.73, NNZs: 16384, Bias: -72.511481, T: 55352, Avg. loss: 120.700960\n",
      "Total training time: 1.63 seconds.\n",
      "-- Epoch 12\n",
      "Norm: 507.05, NNZs: 16384, Bias: -73.163720, T: 60384, Avg. loss: 105.603626\n",
      "Total training time: 1.77 seconds.\n",
      "-- Epoch 13\n",
      "Norm: 494.82, NNZs: 16384, Bias: -73.325852, T: 65416, Avg. loss: 98.947912\n",
      "Total training time: 1.91 seconds.\n",
      "-- Epoch 14\n",
      "Norm: 481.38, NNZs: 16384, Bias: -74.050307, T: 70448, Avg. loss: 90.383201\n",
      "Total training time: 2.06 seconds.\n",
      "-- Epoch 15\n",
      "Norm: 468.84, NNZs: 16384, Bias: -74.593231, T: 75480, Avg. loss: 81.906432\n",
      "Total training time: 2.20 seconds.\n",
      "-- Epoch 16\n",
      "Norm: 459.60, NNZs: 16384, Bias: -74.978388, T: 80512, Avg. loss: 73.386792\n",
      "Total training time: 2.34 seconds.\n",
      "-- Epoch 17\n",
      "Norm: 449.75, NNZs: 16384, Bias: -75.332379, T: 85544, Avg. loss: 70.892904\n",
      "Total training time: 2.48 seconds.\n",
      "-- Epoch 18\n",
      "Norm: 440.87, NNZs: 16384, Bias: -76.004023, T: 90576, Avg. loss: 66.225371\n",
      "Total training time: 2.63 seconds.\n",
      "-- Epoch 19\n",
      "Norm: 432.77, NNZs: 16384, Bias: -76.208376, T: 95608, Avg. loss: 61.690363\n",
      "Total training time: 2.77 seconds.\n",
      "-- Epoch 20\n",
      "Norm: 424.43, NNZs: 16384, Bias: -76.505838, T: 100640, Avg. loss: 55.895884\n",
      "Total training time: 2.91 seconds.\n",
      "-- Epoch 21\n",
      "Norm: 415.85, NNZs: 16384, Bias: -76.793525, T: 105672, Avg. loss: 51.834141\n",
      "Total training time: 3.05 seconds.\n",
      "-- Epoch 22\n",
      "Norm: 408.75, NNZs: 16384, Bias: -76.977689, T: 110704, Avg. loss: 54.181544\n",
      "Total training time: 3.19 seconds.\n",
      "-- Epoch 23\n",
      "Norm: 403.26, NNZs: 16384, Bias: -77.238165, T: 115736, Avg. loss: 48.202032\n",
      "Total training time: 3.33 seconds.\n",
      "-- Epoch 24\n",
      "Norm: 396.14, NNZs: 16384, Bias: -77.488906, T: 120768, Avg. loss: 46.194910\n",
      "Total training time: 3.47 seconds.\n",
      "-- Epoch 25\n",
      "Norm: 389.85, NNZs: 16384, Bias: -77.893946, T: 125800, Avg. loss: 42.860414\n",
      "Total training time: 3.61 seconds.\n",
      "-- Epoch 26\n",
      "Norm: 384.78, NNZs: 16384, Bias: -77.816558, T: 130832, Avg. loss: 40.954882\n",
      "Total training time: 3.75 seconds.\n",
      "-- Epoch 27\n",
      "Norm: 379.29, NNZs: 16384, Bias: -78.039451, T: 135864, Avg. loss: 39.520270\n",
      "Total training time: 3.89 seconds.\n",
      "-- Epoch 28\n",
      "Norm: 374.12, NNZs: 16384, Bias: -78.326917, T: 140896, Avg. loss: 38.381621\n",
      "Total training time: 4.03 seconds.\n",
      "-- Epoch 29\n",
      "Norm: 370.03, NNZs: 16384, Bias: -78.395152, T: 145928, Avg. loss: 35.133431\n",
      "Total training time: 4.17 seconds.\n",
      "-- Epoch 30\n",
      "Norm: 364.79, NNZs: 16384, Bias: -78.528059, T: 150960, Avg. loss: 33.536132\n",
      "Total training time: 4.31 seconds.\n",
      "-- Epoch 31\n",
      "Norm: 360.35, NNZs: 16384, Bias: -78.592271, T: 155992, Avg. loss: 33.473508\n",
      "Total training time: 4.45 seconds.\n",
      "-- Epoch 32\n",
      "Norm: 355.78, NNZs: 16384, Bias: -78.715534, T: 161024, Avg. loss: 31.406499\n",
      "Total training time: 4.59 seconds.\n",
      "-- Epoch 33\n",
      "Norm: 352.11, NNZs: 16384, Bias: -78.838202, T: 166056, Avg. loss: 31.492307\n",
      "Total training time: 4.73 seconds.\n",
      "-- Epoch 34\n",
      "Norm: 348.70, NNZs: 16384, Bias: -78.896963, T: 171088, Avg. loss: 30.026628\n",
      "Total training time: 4.87 seconds.\n",
      "-- Epoch 35\n",
      "Norm: 345.21, NNZs: 16384, Bias: -79.012293, T: 176120, Avg. loss: 28.403593\n",
      "Total training time: 5.00 seconds.\n",
      "-- Epoch 36\n",
      "Norm: 341.62, NNZs: 16384, Bias: -79.289336, T: 181152, Avg. loss: 27.216532\n",
      "Total training time: 5.15 seconds.\n",
      "-- Epoch 37\n",
      "Norm: 338.24, NNZs: 16384, Bias: -79.126518, T: 186184, Avg. loss: 25.682013\n",
      "Total training time: 5.28 seconds.\n",
      "-- Epoch 38\n",
      "Norm: 334.94, NNZs: 16384, Bias: -79.179695, T: 191216, Avg. loss: 26.061628\n",
      "Total training time: 5.42 seconds.\n",
      "-- Epoch 39\n",
      "Norm: 332.14, NNZs: 16384, Bias: -79.231603, T: 196248, Avg. loss: 24.482751\n",
      "Total training time: 5.56 seconds.\n",
      "-- Epoch 40\n",
      "Norm: 329.21, NNZs: 16384, Bias: -79.532546, T: 201280, Avg. loss: 24.009352\n",
      "Total training time: 5.70 seconds.\n",
      "-- Epoch 41\n",
      "Norm: 326.37, NNZs: 16384, Bias: -79.580189, T: 206312, Avg. loss: 22.681671\n",
      "Total training time: 5.83 seconds.\n",
      "-- Epoch 42\n",
      "Norm: 323.65, NNZs: 16384, Bias: -79.770020, T: 211344, Avg. loss: 22.466223\n",
      "Total training time: 5.97 seconds.\n",
      "-- Epoch 43\n",
      "Norm: 320.80, NNZs: 16384, Bias: -79.862182, T: 216376, Avg. loss: 21.423021\n",
      "Total training time: 6.11 seconds.\n",
      "-- Epoch 44\n",
      "Norm: 318.27, NNZs: 16384, Bias: -79.862847, T: 221408, Avg. loss: 21.003634\n",
      "Total training time: 6.25 seconds.\n",
      "-- Epoch 45\n",
      "Norm: 315.47, NNZs: 16384, Bias: -79.817785, T: 226440, Avg. loss: 20.360091\n",
      "Total training time: 6.39 seconds.\n",
      "-- Epoch 46\n",
      "Norm: 313.11, NNZs: 16384, Bias: -79.904816, T: 231472, Avg. loss: 19.956305\n",
      "Total training time: 6.53 seconds.\n",
      "-- Epoch 47\n",
      "Norm: 310.25, NNZs: 16384, Bias: -79.946986, T: 236504, Avg. loss: 19.067144\n",
      "Total training time: 6.66 seconds.\n",
      "-- Epoch 48\n",
      "Norm: 307.76, NNZs: 16384, Bias: -79.987955, T: 241536, Avg. loss: 18.391373\n",
      "Total training time: 6.80 seconds.\n",
      "-- Epoch 49\n",
      "Norm: 305.78, NNZs: 16384, Bias: -80.191257, T: 246568, Avg. loss: 18.128018\n",
      "Total training time: 6.93 seconds.\n",
      "-- Epoch 50\n",
      "Norm: 303.51, NNZs: 16384, Bias: -80.111014, T: 251600, Avg. loss: 17.935557\n",
      "Total training time: 7.07 seconds.\n",
      "-- Epoch 51\n",
      "Norm: 301.41, NNZs: 16384, Bias: -80.070890, T: 256632, Avg. loss: 17.364730\n",
      "Total training time: 7.20 seconds.\n",
      "-- Epoch 52\n",
      "Norm: 299.13, NNZs: 16384, Bias: -80.147731, T: 261664, Avg. loss: 16.525648\n",
      "Total training time: 7.34 seconds.\n",
      "-- Epoch 53\n",
      "Norm: 297.23, NNZs: 16384, Bias: -80.410426, T: 266696, Avg. loss: 15.765061\n",
      "Total training time: 7.47 seconds.\n",
      "-- Epoch 54\n",
      "Norm: 295.13, NNZs: 16384, Bias: -80.483497, T: 271728, Avg. loss: 16.031968\n",
      "Total training time: 7.61 seconds.\n",
      "-- Epoch 55\n",
      "Norm: 293.22, NNZs: 16384, Bias: -80.628192, T: 276760, Avg. loss: 15.283587\n",
      "Total training time: 7.74 seconds.\n",
      "-- Epoch 56\n",
      "Norm: 291.15, NNZs: 16384, Bias: -80.627874, T: 281792, Avg. loss: 15.513494\n",
      "Total training time: 7.88 seconds.\n",
      "-- Epoch 57\n",
      "Norm: 289.41, NNZs: 16384, Bias: -80.593041, T: 286824, Avg. loss: 14.473459\n",
      "Total training time: 8.01 seconds.\n",
      "-- Epoch 58\n",
      "Norm: 287.37, NNZs: 16384, Bias: -80.628583, T: 291856, Avg. loss: 14.203375\n",
      "Total training time: 8.15 seconds.\n",
      "-- Epoch 59\n",
      "Norm: 285.36, NNZs: 16384, Bias: -80.729866, T: 296888, Avg. loss: 14.081805\n",
      "Total training time: 8.28 seconds.\n",
      "-- Epoch 60\n",
      "Norm: 283.52, NNZs: 16384, Bias: -80.729694, T: 301920, Avg. loss: 13.866129\n",
      "Total training time: 8.45 seconds.\n",
      "-- Epoch 61\n",
      "Norm: 281.63, NNZs: 16384, Bias: -80.762215, T: 306952, Avg. loss: 13.758669\n",
      "Total training time: 8.58 seconds.\n",
      "-- Epoch 62\n",
      "Norm: 280.10, NNZs: 16384, Bias: -80.858771, T: 311984, Avg. loss: 13.202276\n",
      "Total training time: 8.72 seconds.\n",
      "-- Epoch 63\n",
      "Norm: 278.45, NNZs: 16384, Bias: -80.890185, T: 317016, Avg. loss: 12.912617\n",
      "Total training time: 8.85 seconds.\n",
      "-- Epoch 64\n",
      "Norm: 276.88, NNZs: 16384, Bias: -80.858580, T: 322048, Avg. loss: 11.998341\n",
      "Total training time: 8.99 seconds.\n",
      "-- Epoch 65\n",
      "Norm: 275.18, NNZs: 16384, Bias: -80.827662, T: 327080, Avg. loss: 12.343091\n",
      "Total training time: 9.12 seconds.\n",
      "-- Epoch 66\n",
      "Norm: 273.78, NNZs: 16384, Bias: -80.827643, T: 332112, Avg. loss: 11.686053\n",
      "Total training time: 9.25 seconds.\n",
      "-- Epoch 67\n",
      "Norm: 272.30, NNZs: 16384, Bias: -80.917449, T: 337144, Avg. loss: 11.647527\n",
      "Total training time: 9.39 seconds.\n",
      "-- Epoch 68\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Norm: 270.76, NNZs: 16384, Bias: -80.975502, T: 342176, Avg. loss: 11.503156\n",
      "Total training time: 9.52 seconds.\n",
      "-- Epoch 69\n",
      "Norm: 269.31, NNZs: 16384, Bias: -80.888632, T: 347208, Avg. loss: 11.030455\n",
      "Total training time: 9.65 seconds.\n",
      "-- Epoch 70\n",
      "Norm: 267.71, NNZs: 16384, Bias: -80.917062, T: 352240, Avg. loss: 10.981377\n",
      "Total training time: 9.79 seconds.\n",
      "-- Epoch 71\n",
      "Norm: 265.99, NNZs: 16384, Bias: -80.973367, T: 357272, Avg. loss: 10.927925\n",
      "Total training time: 9.92 seconds.\n",
      "-- Epoch 72\n",
      "Norm: 264.55, NNZs: 16384, Bias: -80.973169, T: 362304, Avg. loss: 10.751986\n",
      "Total training time: 10.05 seconds.\n",
      "-- Epoch 73\n",
      "Norm: 263.17, NNZs: 16384, Bias: -80.945975, T: 367336, Avg. loss: 10.335449\n",
      "Total training time: 10.18 seconds.\n",
      "-- Epoch 74\n",
      "Norm: 262.00, NNZs: 16384, Bias: -80.973305, T: 372368, Avg. loss: 10.426074\n",
      "Total training time: 10.32 seconds.\n",
      "-- Epoch 75\n",
      "Norm: 260.57, NNZs: 16384, Bias: -80.946846, T: 377400, Avg. loss: 10.123573\n",
      "Total training time: 10.45 seconds.\n",
      "-- Epoch 76\n",
      "Norm: 259.39, NNZs: 16384, Bias: -80.947049, T: 382432, Avg. loss: 9.927345\n",
      "Total training time: 10.58 seconds.\n",
      "-- Epoch 77\n",
      "Norm: 258.07, NNZs: 16384, Bias: -80.868802, T: 387464, Avg. loss: 9.488790\n",
      "Total training time: 10.72 seconds.\n",
      "-- Epoch 78\n",
      "Norm: 256.76, NNZs: 16384, Bias: -80.843225, T: 392496, Avg. loss: 9.593406\n",
      "Total training time: 10.85 seconds.\n",
      "-- Epoch 79\n",
      "Norm: 255.49, NNZs: 16384, Bias: -80.843130, T: 397528, Avg. loss: 9.440449\n",
      "Total training time: 10.98 seconds.\n",
      "-- Epoch 80\n",
      "Norm: 254.12, NNZs: 16384, Bias: -80.768419, T: 402560, Avg. loss: 9.124757\n",
      "Total training time: 11.11 seconds.\n",
      "-- Epoch 81\n",
      "Norm: 252.92, NNZs: 16384, Bias: -80.793427, T: 407592, Avg. loss: 8.607811\n",
      "Total training time: 11.25 seconds.\n",
      "-- Epoch 82\n",
      "Norm: 251.64, NNZs: 16384, Bias: -80.793530, T: 412624, Avg. loss: 8.085436\n",
      "Total training time: 11.38 seconds.\n",
      "-- Epoch 83\n",
      "Norm: 250.31, NNZs: 16384, Bias: -80.793433, T: 417656, Avg. loss: 9.018595\n",
      "Total training time: 11.51 seconds.\n",
      "-- Epoch 84\n",
      "Norm: 249.27, NNZs: 16384, Bias: -80.769877, T: 422688, Avg. loss: 8.607741\n",
      "Total training time: 11.64 seconds.\n",
      "-- Epoch 85\n",
      "Norm: 248.06, NNZs: 16384, Bias: -80.793496, T: 427720, Avg. loss: 8.363000\n",
      "Total training time: 11.78 seconds.\n",
      "-- Epoch 86\n",
      "Norm: 246.86, NNZs: 16384, Bias: -80.816628, T: 432752, Avg. loss: 8.211548\n",
      "Total training time: 11.91 seconds.\n",
      "-- Epoch 87\n",
      "Norm: 245.65, NNZs: 16384, Bias: -80.839405, T: 437784, Avg. loss: 8.130658\n",
      "Total training time: 12.04 seconds.\n",
      "Convergence after 87 epochs took 12.04 seconds\n",
      "Score: 0.609697933227345\n",
      "Precision: 0.6864864864864865\n",
      "Recall: 0.40381558028616854\n",
      "[[513 116]\n",
      " [375 254]]\n"
     ]
    }
   ],
   "source": [
    "intraventricular_svm_model = build_stochastic_svm_model(total_data, 'intraventricular')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "9519584e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Set Size: 750\n",
      "Test Set Size: 1258\n",
      "[LibLinear]Score: 0.5453100158982512\n",
      "Precision: 0.554140127388535\n",
      "Recall: 0.54375\n",
      "[[338 280]\n",
      " [292 348]]\n"
     ]
    }
   ],
   "source": [
    "subarachnoid_svm_model = build_svm_model(total_data, 'subarachnoid', iterations = 12500, training_set_size =750)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "7f19f349",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Set Size: 5032\n",
      "Test Set Size: 1258\n",
      "-- Epoch 1\n",
      "Norm: 1025.65, NNZs: 16384, Bias: -26.814266, T: 5032, Avg. loss: 3341.761014\n",
      "Total training time: 0.15 seconds.\n",
      "-- Epoch 2\n",
      "Norm: 849.90, NNZs: 16384, Bias: -31.958587, T: 10064, Avg. loss: 1076.942960\n",
      "Total training time: 0.31 seconds.\n",
      "-- Epoch 3\n",
      "Norm: 748.57, NNZs: 16384, Bias: -35.512452, T: 15096, Avg. loss: 642.699789\n",
      "Total training time: 0.46 seconds.\n",
      "-- Epoch 4\n",
      "Norm: 695.42, NNZs: 16384, Bias: -38.815462, T: 20128, Avg. loss: 452.863616\n",
      "Total training time: 0.61 seconds.\n",
      "-- Epoch 5\n",
      "Norm: 654.95, NNZs: 16384, Bias: -40.059421, T: 25160, Avg. loss: 354.716520\n",
      "Total training time: 0.76 seconds.\n",
      "-- Epoch 6\n",
      "Norm: 601.53, NNZs: 16384, Bias: -41.407335, T: 30192, Avg. loss: 290.128657\n",
      "Total training time: 0.91 seconds.\n",
      "-- Epoch 7\n",
      "Norm: 571.23, NNZs: 16384, Bias: -42.276881, T: 35224, Avg. loss: 237.693704\n",
      "Total training time: 1.06 seconds.\n",
      "-- Epoch 8\n",
      "Norm: 552.83, NNZs: 16384, Bias: -43.286626, T: 40256, Avg. loss: 201.815830\n",
      "Total training time: 1.21 seconds.\n",
      "-- Epoch 9\n",
      "Norm: 531.26, NNZs: 16384, Bias: -44.201009, T: 45288, Avg. loss: 181.193823\n",
      "Total training time: 1.36 seconds.\n",
      "-- Epoch 10\n",
      "Norm: 511.30, NNZs: 16384, Bias: -45.018303, T: 50320, Avg. loss: 157.693903\n",
      "Total training time: 1.50 seconds.\n",
      "-- Epoch 11\n",
      "Norm: 494.97, NNZs: 16384, Bias: -46.686993, T: 55352, Avg. loss: 138.551296\n",
      "Total training time: 1.65 seconds.\n",
      "-- Epoch 12\n",
      "Norm: 483.46, NNZs: 16384, Bias: -47.181527, T: 60384, Avg. loss: 123.380311\n",
      "Total training time: 1.80 seconds.\n",
      "-- Epoch 13\n",
      "Norm: 472.26, NNZs: 16384, Bias: -47.658530, T: 65416, Avg. loss: 119.047574\n",
      "Total training time: 1.95 seconds.\n",
      "-- Epoch 14\n",
      "Norm: 460.98, NNZs: 16384, Bias: -48.401703, T: 70448, Avg. loss: 104.868604\n",
      "Total training time: 2.09 seconds.\n",
      "-- Epoch 15\n",
      "Norm: 453.55, NNZs: 16384, Bias: -48.543063, T: 75480, Avg. loss: 97.599213\n",
      "Total training time: 2.24 seconds.\n",
      "-- Epoch 16\n",
      "Norm: 444.47, NNZs: 16384, Bias: -49.056539, T: 80512, Avg. loss: 91.428753\n",
      "Total training time: 2.39 seconds.\n",
      "-- Epoch 17\n",
      "Norm: 436.96, NNZs: 16384, Bias: -49.539450, T: 85544, Avg. loss: 82.779876\n",
      "Total training time: 2.53 seconds.\n",
      "-- Epoch 18\n",
      "Norm: 429.92, NNZs: 16384, Bias: -50.101244, T: 90576, Avg. loss: 80.572472\n",
      "Total training time: 2.68 seconds.\n",
      "-- Epoch 19\n",
      "Norm: 422.85, NNZs: 16384, Bias: -50.637701, T: 95608, Avg. loss: 73.951657\n",
      "Total training time: 2.84 seconds.\n",
      "-- Epoch 20\n",
      "Norm: 414.52, NNZs: 16384, Bias: -51.343842, T: 100640, Avg. loss: 67.151591\n",
      "Total training time: 3.01 seconds.\n",
      "-- Epoch 21\n",
      "Norm: 408.03, NNZs: 16384, Bias: -51.821752, T: 105672, Avg. loss: 63.852020\n",
      "Total training time: 3.15 seconds.\n",
      "-- Epoch 22\n",
      "Norm: 402.41, NNZs: 16384, Bias: -51.915181, T: 110704, Avg. loss: 63.134526\n",
      "Total training time: 3.30 seconds.\n",
      "-- Epoch 23\n",
      "Norm: 398.07, NNZs: 16384, Bias: -52.533329, T: 115736, Avg. loss: 57.446801\n",
      "Total training time: 3.44 seconds.\n",
      "-- Epoch 24\n",
      "Norm: 392.86, NNZs: 16384, Bias: -52.782520, T: 120768, Avg. loss: 57.256392\n",
      "Total training time: 3.59 seconds.\n",
      "-- Epoch 25\n",
      "Norm: 387.84, NNZs: 16384, Bias: -53.260241, T: 125800, Avg. loss: 52.534095\n",
      "Total training time: 3.74 seconds.\n",
      "-- Epoch 26\n",
      "Norm: 382.90, NNZs: 16384, Bias: -53.569650, T: 130832, Avg. loss: 49.558805\n",
      "Total training time: 3.88 seconds.\n",
      "-- Epoch 27\n",
      "Norm: 377.98, NNZs: 16384, Bias: -54.087338, T: 135864, Avg. loss: 48.347882\n",
      "Total training time: 4.02 seconds.\n",
      "-- Epoch 28\n",
      "Norm: 373.19, NNZs: 16384, Bias: -54.157097, T: 140896, Avg. loss: 45.932580\n",
      "Total training time: 87.09 seconds.\n",
      "-- Epoch 29\n",
      "Norm: 369.54, NNZs: 16384, Bias: -54.364064, T: 145928, Avg. loss: 43.664079\n",
      "Total training time: 87.28 seconds.\n",
      "-- Epoch 30\n",
      "Norm: 365.87, NNZs: 16384, Bias: -54.431102, T: 150960, Avg. loss: 41.600459\n",
      "Total training time: 87.43 seconds.\n",
      "-- Epoch 31\n",
      "Norm: 361.87, NNZs: 16384, Bias: -54.688311, T: 155992, Avg. loss: 39.908297\n",
      "Total training time: 87.58 seconds.\n",
      "-- Epoch 32\n",
      "Norm: 358.26, NNZs: 16384, Bias: -54.750335, T: 161024, Avg. loss: 37.819527\n",
      "Total training time: 87.73 seconds.\n",
      "-- Epoch 33\n",
      "Norm: 354.51, NNZs: 16384, Bias: -54.994103, T: 166056, Avg. loss: 37.806639\n",
      "Total training time: 87.89 seconds.\n",
      "-- Epoch 34\n",
      "Norm: 351.61, NNZs: 16384, Bias: -55.170305, T: 171088, Avg. loss: 36.756538\n",
      "Total training time: 88.13 seconds.\n",
      "-- Epoch 35\n",
      "Norm: 347.95, NNZs: 16384, Bias: -55.286242, T: 176120, Avg. loss: 33.909765\n",
      "Total training time: 88.90 seconds.\n",
      "-- Epoch 36\n",
      "Norm: 344.63, NNZs: 16384, Bias: -55.619861, T: 181152, Avg. loss: 33.476017\n",
      "Total training time: 89.09 seconds.\n",
      "-- Epoch 37\n",
      "Norm: 341.31, NNZs: 16384, Bias: -55.728167, T: 186184, Avg. loss: 32.612077\n",
      "Total training time: 89.68 seconds.\n",
      "-- Epoch 38\n",
      "Norm: 338.87, NNZs: 16384, Bias: -55.886640, T: 191216, Avg. loss: 31.150525\n",
      "Total training time: 90.10 seconds.\n",
      "-- Epoch 39\n",
      "Norm: 335.98, NNZs: 16384, Bias: -55.938761, T: 196248, Avg. loss: 29.041860\n",
      "Total training time: 90.36 seconds.\n",
      "-- Epoch 40\n",
      "Norm: 333.50, NNZs: 16384, Bias: -56.137919, T: 201280, Avg. loss: 30.677822\n",
      "Total training time: 90.59 seconds.\n",
      "-- Epoch 41\n",
      "Norm: 330.50, NNZs: 16384, Bias: -56.138178, T: 206312, Avg. loss: 27.168386\n",
      "Total training time: 91.17 seconds.\n",
      "-- Epoch 42\n",
      "Norm: 327.82, NNZs: 16384, Bias: -56.330463, T: 211344, Avg. loss: 27.808056\n",
      "Total training time: 91.65 seconds.\n",
      "-- Epoch 43\n",
      "Norm: 325.50, NNZs: 16384, Bias: -56.282782, T: 216376, Avg. loss: 26.533741\n",
      "Total training time: 91.86 seconds.\n",
      "-- Epoch 44\n",
      "Norm: 322.80, NNZs: 16384, Bias: -56.374606, T: 221408, Avg. loss: 25.123093\n",
      "Total training time: 92.03 seconds.\n",
      "-- Epoch 45\n",
      "Norm: 320.23, NNZs: 16384, Bias: -56.551634, T: 226440, Avg. loss: 24.857391\n",
      "Total training time: 92.44 seconds.\n",
      "-- Epoch 46\n",
      "Norm: 317.86, NNZs: 16384, Bias: -56.639042, T: 231472, Avg. loss: 24.282119\n",
      "Total training time: 92.66 seconds.\n",
      "-- Epoch 47\n",
      "Norm: 315.54, NNZs: 16384, Bias: -56.724225, T: 236504, Avg. loss: 23.949817\n",
      "Total training time: 92.83 seconds.\n",
      "-- Epoch 48\n",
      "Norm: 312.95, NNZs: 16384, Bias: -56.848702, T: 241536, Avg. loss: 22.580177\n",
      "Total training time: 93.00 seconds.\n",
      "-- Epoch 49\n",
      "Norm: 310.90, NNZs: 16384, Bias: -56.971077, T: 246568, Avg. loss: 22.734580\n",
      "Total training time: 93.16 seconds.\n",
      "-- Epoch 50\n",
      "Norm: 309.09, NNZs: 16384, Bias: -57.091980, T: 251600, Avg. loss: 22.310189\n",
      "Total training time: 93.33 seconds.\n",
      "-- Epoch 51\n",
      "Norm: 306.99, NNZs: 16384, Bias: -57.092523, T: 256632, Avg. loss: 21.041654\n",
      "Total training time: 93.52 seconds.\n",
      "-- Epoch 52\n",
      "Norm: 305.16, NNZs: 16384, Bias: -57.246647, T: 261664, Avg. loss: 20.518051\n",
      "Total training time: 93.69 seconds.\n",
      "-- Epoch 53\n",
      "Norm: 303.36, NNZs: 16384, Bias: -57.359700, T: 266696, Avg. loss: 20.622396\n",
      "Total training time: 93.85 seconds.\n",
      "-- Epoch 54\n",
      "Norm: 301.06, NNZs: 16384, Bias: -57.321704, T: 271728, Avg. loss: 19.331039\n",
      "Total training time: 94.02 seconds.\n",
      "-- Epoch 55\n",
      "Norm: 299.14, NNZs: 16384, Bias: -57.430239, T: 276760, Avg. loss: 19.687000\n",
      "Total training time: 94.17 seconds.\n",
      "-- Epoch 56\n",
      "Norm: 297.50, NNZs: 16384, Bias: -57.501022, T: 281792, Avg. loss: 18.413598\n",
      "Total training time: 94.32 seconds.\n",
      "-- Epoch 57\n",
      "Norm: 295.47, NNZs: 16384, Bias: -57.571070, T: 286824, Avg. loss: 17.233425\n",
      "Total training time: 94.46 seconds.\n",
      "-- Epoch 58\n",
      "Norm: 293.83, NNZs: 16384, Bias: -57.708843, T: 291856, Avg. loss: 17.738994\n",
      "Total training time: 94.61 seconds.\n",
      "-- Epoch 59\n",
      "Norm: 292.28, NNZs: 16384, Bias: -57.709354, T: 296888, Avg. loss: 17.291930\n",
      "Total training time: 94.76 seconds.\n",
      "-- Epoch 60\n",
      "Norm: 290.71, NNZs: 16384, Bias: -57.808626, T: 301920, Avg. loss: 17.480808\n",
      "Total training time: 94.92 seconds.\n",
      "-- Epoch 61\n",
      "Norm: 289.11, NNZs: 16384, Bias: -57.808922, T: 306952, Avg. loss: 16.943926\n",
      "Total training time: 95.06 seconds.\n",
      "-- Epoch 62\n",
      "Norm: 287.39, NNZs: 16384, Bias: -57.873048, T: 311984, Avg. loss: 16.066000\n",
      "Total training time: 95.22 seconds.\n",
      "-- Epoch 63\n",
      "Norm: 285.85, NNZs: 16384, Bias: -57.809620, T: 317016, Avg. loss: 16.245112\n",
      "Total training time: 95.36 seconds.\n",
      "-- Epoch 64\n",
      "Norm: 284.08, NNZs: 16384, Bias: -57.871943, T: 322048, Avg. loss: 15.369973\n",
      "Total training time: 95.50 seconds.\n",
      "-- Epoch 65\n",
      "Norm: 282.32, NNZs: 16384, Bias: -57.964081, T: 327080, Avg. loss: 15.261361\n",
      "Total training time: 95.64 seconds.\n",
      "-- Epoch 66\n",
      "Norm: 280.99, NNZs: 16384, Bias: -58.024877, T: 332112, Avg. loss: 15.180941\n",
      "Total training time: 95.78 seconds.\n",
      "-- Epoch 67\n",
      "Norm: 279.46, NNZs: 16384, Bias: -58.084407, T: 337144, Avg. loss: 14.577192\n",
      "Total training time: 95.93 seconds.\n",
      "-- Epoch 68\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Norm: 278.13, NNZs: 16384, Bias: -58.172536, T: 342176, Avg. loss: 14.411509\n",
      "Total training time: 96.08 seconds.\n",
      "-- Epoch 69\n",
      "Norm: 276.56, NNZs: 16384, Bias: -58.201206, T: 347208, Avg. loss: 14.310075\n",
      "Total training time: 96.23 seconds.\n",
      "-- Epoch 70\n",
      "Norm: 275.12, NNZs: 16384, Bias: -58.314656, T: 352240, Avg. loss: 13.815110\n",
      "Total training time: 96.37 seconds.\n",
      "-- Epoch 71\n",
      "Norm: 273.71, NNZs: 16384, Bias: -58.257780, T: 357272, Avg. loss: 13.348682\n",
      "Total training time: 96.52 seconds.\n",
      "-- Epoch 72\n",
      "Norm: 272.41, NNZs: 16384, Bias: -58.313519, T: 362304, Avg. loss: 13.886235\n",
      "Total training time: 96.68 seconds.\n",
      "-- Epoch 73\n",
      "Norm: 271.04, NNZs: 16384, Bias: -58.368216, T: 367336, Avg. loss: 13.045253\n",
      "Total training time: 96.82 seconds.\n",
      "-- Epoch 74\n",
      "Norm: 269.71, NNZs: 16384, Bias: -58.341280, T: 372368, Avg. loss: 12.837244\n",
      "Total training time: 96.96 seconds.\n",
      "-- Epoch 75\n",
      "Norm: 268.22, NNZs: 16384, Bias: -58.341100, T: 377400, Avg. loss: 12.271854\n",
      "Total training time: 97.11 seconds.\n",
      "-- Epoch 76\n",
      "Norm: 266.69, NNZs: 16384, Bias: -58.315236, T: 382432, Avg. loss: 12.376525\n",
      "Total training time: 97.26 seconds.\n",
      "-- Epoch 77\n",
      "Norm: 265.57, NNZs: 16384, Bias: -58.393255, T: 387464, Avg. loss: 12.237200\n",
      "Total training time: 97.40 seconds.\n",
      "-- Epoch 78\n",
      "Norm: 264.41, NNZs: 16384, Bias: -58.419271, T: 392496, Avg. loss: 11.866355\n",
      "Total training time: 97.55 seconds.\n",
      "-- Epoch 79\n",
      "Norm: 263.32, NNZs: 16384, Bias: -58.469785, T: 397528, Avg. loss: 11.826068\n",
      "Total training time: 97.71 seconds.\n",
      "-- Epoch 80\n",
      "Norm: 262.02, NNZs: 16384, Bias: -58.444674, T: 402560, Avg. loss: 11.556232\n",
      "Total training time: 97.85 seconds.\n",
      "-- Epoch 81\n",
      "Norm: 260.81, NNZs: 16384, Bias: -58.469223, T: 407592, Avg. loss: 10.663808\n",
      "Total training time: 97.99 seconds.\n",
      "-- Epoch 82\n",
      "Norm: 259.41, NNZs: 16384, Bias: -58.420441, T: 412624, Avg. loss: 11.295387\n",
      "Total training time: 98.14 seconds.\n",
      "-- Epoch 83\n",
      "Norm: 258.24, NNZs: 16384, Bias: -58.372468, T: 417656, Avg. loss: 10.687140\n",
      "Total training time: 98.28 seconds.\n",
      "-- Epoch 84\n",
      "Norm: 257.12, NNZs: 16384, Bias: -58.467335, T: 422688, Avg. loss: 11.048496\n",
      "Total training time: 98.43 seconds.\n",
      "-- Epoch 85\n",
      "Norm: 256.01, NNZs: 16384, Bias: -58.467328, T: 427720, Avg. loss: 10.328737\n",
      "Total training time: 98.57 seconds.\n",
      "-- Epoch 86\n",
      "Norm: 254.89, NNZs: 16384, Bias: -58.490391, T: 432752, Avg. loss: 10.114936\n",
      "Total training time: 98.72 seconds.\n",
      "-- Epoch 87\n",
      "Norm: 253.90, NNZs: 16384, Bias: -58.490301, T: 437784, Avg. loss: 10.148973\n",
      "Total training time: 98.87 seconds.\n",
      "-- Epoch 88\n",
      "Norm: 252.75, NNZs: 16384, Bias: -58.535375, T: 442816, Avg. loss: 9.872432\n",
      "Total training time: 99.02 seconds.\n",
      "-- Epoch 89\n",
      "Norm: 251.67, NNZs: 16384, Bias: -58.580105, T: 447848, Avg. loss: 9.729743\n",
      "Total training time: 99.17 seconds.\n",
      "-- Epoch 90\n",
      "Norm: 250.58, NNZs: 16384, Bias: -58.624562, T: 452880, Avg. loss: 9.664208\n",
      "Total training time: 99.33 seconds.\n",
      "-- Epoch 91\n",
      "Norm: 249.40, NNZs: 16384, Bias: -58.689989, T: 457912, Avg. loss: 9.445184\n",
      "Total training time: 99.47 seconds.\n",
      "-- Epoch 92\n",
      "Norm: 248.38, NNZs: 16384, Bias: -58.668510, T: 462944, Avg. loss: 9.444869\n",
      "Total training time: 99.62 seconds.\n",
      "-- Epoch 93\n",
      "Norm: 247.49, NNZs: 16384, Bias: -58.689993, T: 467976, Avg. loss: 9.078347\n",
      "Total training time: 99.76 seconds.\n",
      "-- Epoch 94\n",
      "Norm: 246.48, NNZs: 16384, Bias: -58.732160, T: 473008, Avg. loss: 8.698481\n",
      "Total training time: 99.91 seconds.\n",
      "-- Epoch 95\n",
      "Norm: 245.57, NNZs: 16384, Bias: -58.711140, T: 478040, Avg. loss: 9.136421\n",
      "Total training time: 100.06 seconds.\n",
      "-- Epoch 96\n",
      "Norm: 244.64, NNZs: 16384, Bias: -58.731786, T: 483072, Avg. loss: 8.829419\n",
      "Total training time: 100.20 seconds.\n",
      "-- Epoch 97\n",
      "Norm: 243.61, NNZs: 16384, Bias: -58.711250, T: 488104, Avg. loss: 9.041622\n",
      "Total training time: 100.35 seconds.\n",
      "-- Epoch 98\n",
      "Norm: 242.67, NNZs: 16384, Bias: -58.731496, T: 493136, Avg. loss: 8.295005\n",
      "Total training time: 100.49 seconds.\n",
      "-- Epoch 99\n",
      "Norm: 241.79, NNZs: 16384, Bias: -58.670928, T: 498168, Avg. loss: 8.436787\n",
      "Total training time: 100.63 seconds.\n",
      "-- Epoch 100\n",
      "Norm: 240.78, NNZs: 16384, Bias: -58.710587, T: 503200, Avg. loss: 8.296688\n",
      "Total training time: 100.77 seconds.\n",
      "-- Epoch 101\n",
      "Norm: 239.95, NNZs: 16384, Bias: -58.671015, T: 508232, Avg. loss: 8.397176\n",
      "Total training time: 100.91 seconds.\n",
      "-- Epoch 102\n",
      "Norm: 239.00, NNZs: 16384, Bias: -58.710040, T: 513264, Avg. loss: 7.775467\n",
      "Total training time: 101.05 seconds.\n",
      "-- Epoch 103\n",
      "Norm: 238.00, NNZs: 16384, Bias: -58.729309, T: 518296, Avg. loss: 8.185986\n",
      "Total training time: 101.18 seconds.\n",
      "-- Epoch 104\n",
      "Norm: 237.13, NNZs: 16384, Bias: -58.690925, T: 523328, Avg. loss: 8.010676\n",
      "Total training time: 101.34 seconds.\n",
      "-- Epoch 105\n",
      "Norm: 236.20, NNZs: 16384, Bias: -58.671861, T: 528360, Avg. loss: 7.583922\n",
      "Total training time: 101.49 seconds.\n",
      "-- Epoch 106\n",
      "Norm: 235.41, NNZs: 16384, Bias: -58.634256, T: 533392, Avg. loss: 7.601115\n",
      "Total training time: 101.64 seconds.\n",
      "-- Epoch 107\n",
      "Norm: 234.59, NNZs: 16384, Bias: -58.652969, T: 538424, Avg. loss: 7.555184\n",
      "Total training time: 101.78 seconds.\n",
      "-- Epoch 108\n",
      "Norm: 233.77, NNZs: 16384, Bias: -58.634432, T: 543456, Avg. loss: 7.601007\n",
      "Total training time: 101.93 seconds.\n",
      "-- Epoch 109\n",
      "Norm: 232.97, NNZs: 16384, Bias: -58.634421, T: 548488, Avg. loss: 7.406477\n",
      "Total training time: 102.08 seconds.\n",
      "-- Epoch 110\n",
      "Norm: 232.25, NNZs: 16384, Bias: -58.634361, T: 553520, Avg. loss: 7.272028\n",
      "Total training time: 102.23 seconds.\n",
      "-- Epoch 111\n",
      "Norm: 231.49, NNZs: 16384, Bias: -58.616367, T: 558552, Avg. loss: 7.265505\n",
      "Total training time: 102.37 seconds.\n",
      "-- Epoch 112\n",
      "Norm: 230.64, NNZs: 16384, Bias: -58.616292, T: 563584, Avg. loss: 7.066395\n",
      "Total training time: 102.52 seconds.\n",
      "-- Epoch 113\n",
      "Norm: 229.87, NNZs: 16384, Bias: -58.563555, T: 568616, Avg. loss: 6.971231\n",
      "Total training time: 102.65 seconds.\n",
      "-- Epoch 114\n",
      "Norm: 229.14, NNZs: 16384, Bias: -58.616044, T: 573648, Avg. loss: 7.041884\n",
      "Total training time: 102.80 seconds.\n",
      "-- Epoch 115\n",
      "Norm: 228.38, NNZs: 16384, Bias: -58.616162, T: 578680, Avg. loss: 6.828895\n",
      "Total training time: 102.93 seconds.\n",
      "-- Epoch 116\n",
      "Norm: 227.63, NNZs: 16384, Bias: -58.633398, T: 583712, Avg. loss: 6.908803\n",
      "Total training time: 103.08 seconds.\n",
      "-- Epoch 117\n",
      "Norm: 226.91, NNZs: 16384, Bias: -58.582539, T: 588744, Avg. loss: 6.625150\n",
      "Total training time: 103.23 seconds.\n",
      "-- Epoch 118\n",
      "Norm: 226.13, NNZs: 16384, Bias: -58.599397, T: 593776, Avg. loss: 6.407722\n",
      "Total training time: 103.36 seconds.\n",
      "-- Epoch 119\n",
      "Norm: 225.38, NNZs: 16384, Bias: -58.599426, T: 598808, Avg. loss: 6.505444\n",
      "Total training time: 103.51 seconds.\n",
      "-- Epoch 120\n",
      "Norm: 224.71, NNZs: 16384, Bias: -58.549575, T: 603840, Avg. loss: 6.602463\n",
      "Total training time: 103.65 seconds.\n",
      "-- Epoch 121\n",
      "Norm: 223.98, NNZs: 16384, Bias: -58.599064, T: 608872, Avg. loss: 6.330398\n",
      "Total training time: 103.79 seconds.\n",
      "-- Epoch 122\n",
      "Norm: 223.24, NNZs: 16384, Bias: -58.582664, T: 613904, Avg. loss: 6.296738\n",
      "Total training time: 103.93 seconds.\n",
      "-- Epoch 123\n",
      "Norm: 222.47, NNZs: 16384, Bias: -58.566439, T: 618936, Avg. loss: 5.834574\n",
      "Total training time: 104.07 seconds.\n",
      "-- Epoch 124\n",
      "Norm: 221.79, NNZs: 16384, Bias: -58.550571, T: 623968, Avg. loss: 6.339927\n",
      "Total training time: 104.21 seconds.\n",
      "-- Epoch 125\n",
      "Norm: 221.04, NNZs: 16384, Bias: -58.550669, T: 629000, Avg. loss: 6.100694\n",
      "Total training time: 104.35 seconds.\n",
      "-- Epoch 126\n",
      "Norm: 220.40, NNZs: 16384, Bias: -58.566448, T: 634032, Avg. loss: 6.136716\n",
      "Total training time: 104.49 seconds.\n",
      "-- Epoch 127\n",
      "Norm: 219.75, NNZs: 16384, Bias: -58.550611, T: 639064, Avg. loss: 5.890986\n",
      "Total training time: 104.63 seconds.\n",
      "-- Epoch 128\n",
      "Norm: 219.11, NNZs: 16384, Bias: -58.503899, T: 644096, Avg. loss: 5.708062\n",
      "Total training time: 104.78 seconds.\n",
      "-- Epoch 129\n",
      "Norm: 218.40, NNZs: 16384, Bias: -58.457644, T: 649128, Avg. loss: 5.771630\n",
      "Total training time: 104.91 seconds.\n",
      "-- Epoch 130\n",
      "Norm: 217.68, NNZs: 16384, Bias: -58.457783, T: 654160, Avg. loss: 5.890947\n",
      "Total training time: 105.05 seconds.\n",
      "-- Epoch 131\n",
      "Norm: 217.02, NNZs: 16384, Bias: -58.488177, T: 659192, Avg. loss: 5.756709\n",
      "Total training time: 105.21 seconds.\n",
      "-- Epoch 132\n",
      "Norm: 216.52, NNZs: 16384, Bias: -58.473175, T: 664224, Avg. loss: 5.733699\n",
      "Total training time: 105.37 seconds.\n",
      "-- Epoch 133\n",
      "Norm: 215.88, NNZs: 16384, Bias: -58.458184, T: 669256, Avg. loss: 5.605364\n",
      "Total training time: 105.52 seconds.\n",
      "-- Epoch 134\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Norm: 215.32, NNZs: 16384, Bias: -58.443361, T: 674288, Avg. loss: 5.545473\n",
      "Total training time: 105.68 seconds.\n",
      "-- Epoch 135\n",
      "Norm: 214.73, NNZs: 16384, Bias: -58.428616, T: 679320, Avg. loss: 5.475592\n",
      "Total training time: 105.83 seconds.\n",
      "-- Epoch 136\n",
      "Norm: 214.13, NNZs: 16384, Bias: -58.384708, T: 684352, Avg. loss: 5.417801\n",
      "Total training time: 105.97 seconds.\n",
      "-- Epoch 137\n",
      "Norm: 213.49, NNZs: 16384, Bias: -58.384705, T: 689384, Avg. loss: 5.420900\n",
      "Total training time: 106.11 seconds.\n",
      "-- Epoch 138\n",
      "Norm: 212.83, NNZs: 16384, Bias: -58.355806, T: 694416, Avg. loss: 5.127114\n",
      "Total training time: 106.24 seconds.\n",
      "-- Epoch 139\n",
      "Norm: 212.20, NNZs: 16384, Bias: -58.341447, T: 699448, Avg. loss: 5.202128\n",
      "Total training time: 106.38 seconds.\n",
      "-- Epoch 140\n",
      "Norm: 211.53, NNZs: 16384, Bias: -58.327179, T: 704480, Avg. loss: 4.994701\n",
      "Total training time: 106.52 seconds.\n",
      "-- Epoch 141\n",
      "Norm: 210.92, NNZs: 16384, Bias: -58.313040, T: 709512, Avg. loss: 4.939430\n",
      "Total training time: 106.65 seconds.\n",
      "-- Epoch 142\n",
      "Norm: 210.34, NNZs: 16384, Bias: -58.298972, T: 714544, Avg. loss: 5.299302\n",
      "Total training time: 106.79 seconds.\n",
      "-- Epoch 143\n",
      "Norm: 209.81, NNZs: 16384, Bias: -58.271066, T: 719576, Avg. loss: 4.819886\n",
      "Total training time: 106.93 seconds.\n",
      "-- Epoch 144\n",
      "Norm: 209.19, NNZs: 16384, Bias: -58.257327, T: 724608, Avg. loss: 4.859943\n",
      "Total training time: 107.06 seconds.\n",
      "-- Epoch 145\n",
      "Norm: 208.64, NNZs: 16384, Bias: -58.216122, T: 729640, Avg. loss: 4.881342\n",
      "Total training time: 107.19 seconds.\n",
      "-- Epoch 146\n",
      "Norm: 208.11, NNZs: 16384, Bias: -58.175303, T: 734672, Avg. loss: 4.791142\n",
      "Total training time: 107.33 seconds.\n",
      "-- Epoch 147\n",
      "Norm: 207.55, NNZs: 16384, Bias: -58.175416, T: 739704, Avg. loss: 4.815823\n",
      "Total training time: 107.46 seconds.\n",
      "-- Epoch 148\n",
      "Norm: 207.02, NNZs: 16384, Bias: -58.162077, T: 744736, Avg. loss: 4.649925\n",
      "Total training time: 107.59 seconds.\n",
      "-- Epoch 149\n",
      "Norm: 206.45, NNZs: 16384, Bias: -58.162080, T: 749768, Avg. loss: 4.567552\n",
      "Total training time: 107.73 seconds.\n",
      "-- Epoch 150\n",
      "Norm: 205.86, NNZs: 16384, Bias: -58.108997, T: 754800, Avg. loss: 4.529306\n",
      "Total training time: 107.86 seconds.\n",
      "-- Epoch 151\n",
      "Norm: 205.33, NNZs: 16384, Bias: -58.095769, T: 759832, Avg. loss: 4.692028\n",
      "Total training time: 107.99 seconds.\n",
      "-- Epoch 152\n",
      "Norm: 204.78, NNZs: 16384, Bias: -58.095753, T: 764864, Avg. loss: 4.536169\n",
      "Total training time: 108.13 seconds.\n",
      "-- Epoch 153\n",
      "Norm: 204.21, NNZs: 16384, Bias: -58.056761, T: 769896, Avg. loss: 4.328112\n",
      "Total training time: 108.26 seconds.\n",
      "-- Epoch 154\n",
      "Norm: 203.66, NNZs: 16384, Bias: -58.043821, T: 774928, Avg. loss: 4.232120\n",
      "Total training time: 108.39 seconds.\n",
      "-- Epoch 155\n",
      "Norm: 203.13, NNZs: 16384, Bias: -58.018026, T: 779960, Avg. loss: 4.405802\n",
      "Total training time: 108.53 seconds.\n",
      "-- Epoch 156\n",
      "Norm: 202.61, NNZs: 16384, Bias: -57.979606, T: 784992, Avg. loss: 4.402904\n",
      "Total training time: 108.67 seconds.\n",
      "-- Epoch 157\n",
      "Norm: 202.15, NNZs: 16384, Bias: -57.941512, T: 790024, Avg. loss: 4.281758\n",
      "Total training time: 108.80 seconds.\n",
      "-- Epoch 158\n",
      "Norm: 201.66, NNZs: 16384, Bias: -57.916224, T: 795056, Avg. loss: 4.215894\n",
      "Total training time: 108.94 seconds.\n",
      "-- Epoch 159\n",
      "Norm: 201.17, NNZs: 16384, Bias: -57.903655, T: 800088, Avg. loss: 4.223537\n",
      "Total training time: 109.07 seconds.\n",
      "-- Epoch 160\n",
      "Norm: 200.68, NNZs: 16384, Bias: -57.878815, T: 805120, Avg. loss: 4.238347\n",
      "Total training time: 109.21 seconds.\n",
      "-- Epoch 161\n",
      "Norm: 200.13, NNZs: 16384, Bias: -57.866400, T: 810152, Avg. loss: 4.218887\n",
      "Total training time: 109.35 seconds.\n",
      "-- Epoch 162\n",
      "Norm: 199.65, NNZs: 16384, Bias: -57.829544, T: 815184, Avg. loss: 4.071143\n",
      "Total training time: 109.48 seconds.\n",
      "-- Epoch 163\n",
      "Norm: 199.17, NNZs: 16384, Bias: -57.805110, T: 820216, Avg. loss: 4.192281\n",
      "Total training time: 109.62 seconds.\n",
      "-- Epoch 164\n",
      "Norm: 198.64, NNZs: 16384, Bias: -57.793016, T: 825248, Avg. loss: 3.889588\n",
      "Total training time: 109.75 seconds.\n",
      "-- Epoch 165\n",
      "Norm: 198.16, NNZs: 16384, Bias: -57.793069, T: 830280, Avg. loss: 4.005301\n",
      "Total training time: 109.88 seconds.\n",
      "-- Epoch 166\n",
      "Norm: 197.62, NNZs: 16384, Bias: -57.781087, T: 835312, Avg. loss: 3.886569\n",
      "Total training time: 110.01 seconds.\n",
      "-- Epoch 167\n",
      "Norm: 197.17, NNZs: 16384, Bias: -57.757247, T: 840344, Avg. loss: 3.988883\n",
      "Total training time: 110.15 seconds.\n",
      "-- Epoch 168\n",
      "Norm: 196.70, NNZs: 16384, Bias: -57.745349, T: 845376, Avg. loss: 3.967251\n",
      "Total training time: 110.30 seconds.\n",
      "-- Epoch 169\n",
      "Norm: 196.26, NNZs: 16384, Bias: -57.710026, T: 850408, Avg. loss: 3.834025\n",
      "Total training time: 110.43 seconds.\n",
      "-- Epoch 170\n",
      "Norm: 195.79, NNZs: 16384, Bias: -57.686606, T: 855440, Avg. loss: 3.696586\n",
      "Total training time: 110.56 seconds.\n",
      "-- Epoch 171\n",
      "Norm: 195.39, NNZs: 16384, Bias: -57.675036, T: 860472, Avg. loss: 3.948351\n",
      "Total training time: 110.70 seconds.\n",
      "-- Epoch 172\n",
      "Norm: 194.97, NNZs: 16384, Bias: -57.651907, T: 865504, Avg. loss: 3.866392\n",
      "Total training time: 110.83 seconds.\n",
      "-- Epoch 173\n",
      "Norm: 194.53, NNZs: 16384, Bias: -57.617316, T: 870536, Avg. loss: 3.912340\n",
      "Total training time: 110.96 seconds.\n",
      "-- Epoch 174\n",
      "Norm: 194.10, NNZs: 16384, Bias: -57.605874, T: 875568, Avg. loss: 3.826975\n",
      "Total training time: 111.11 seconds.\n",
      "-- Epoch 175\n",
      "Norm: 193.65, NNZs: 16384, Bias: -57.549012, T: 880600, Avg. loss: 3.723549\n",
      "Total training time: 111.24 seconds.\n",
      "Convergence after 175 epochs took 111.24 seconds\n",
      "Score: 0.534181240063593\n",
      "Precision: 0.5186147186147186\n",
      "Recall: 0.9523052464228935\n",
      "[[ 73 556]\n",
      " [ 30 599]]\n"
     ]
    }
   ],
   "source": [
    "subarachnoid_svm_model = build_stochastic_svm_model(total_data, 'subarachnoid')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "7388df60",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Set Size: 750\n",
      "Test Set Size: 1258\n",
      "[LibLinear]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/dev/opt/anaconda3/lib/python3.8/site-packages/sklearn/svm/_base.py:985: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\"Liblinear failed to converge, increase \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Score: 0.575516693163752\n",
      "Precision: 0.5791044776119403\n",
      "Recall: 0.60625\n",
      "[[336 282]\n",
      " [252 388]]\n"
     ]
    }
   ],
   "source": [
    "subdural_svm_model = build_svm_model(total_data, 'subdural',iterations = 5000, training_set_size =750)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "2873e664",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Set Size: 5032\n",
      "Test Set Size: 1258\n",
      "-- Epoch 1\n",
      "Norm: 1278.61, NNZs: 16384, Bias: -2.717241, T: 5032, Avg. loss: 3157.314676\n",
      "Total training time: 0.15 seconds.\n",
      "-- Epoch 2\n",
      "Norm: 969.96, NNZs: 16384, Bias: 1.191717, T: 10064, Avg. loss: 1027.422492\n",
      "Total training time: 0.32 seconds.\n",
      "-- Epoch 3\n",
      "Norm: 822.00, NNZs: 16384, Bias: -0.805889, T: 15096, Avg. loss: 604.889790\n",
      "Total training time: 0.47 seconds.\n",
      "-- Epoch 4\n",
      "Norm: 750.62, NNZs: 16384, Bias: 0.197096, T: 20128, Avg. loss: 429.391427\n",
      "Total training time: 0.62 seconds.\n",
      "-- Epoch 5\n",
      "Norm: 674.12, NNZs: 16384, Bias: 1.137339, T: 25160, Avg. loss: 332.120456\n",
      "Total training time: 0.77 seconds.\n",
      "-- Epoch 6\n",
      "Norm: 637.86, NNZs: 16384, Bias: 0.409774, T: 30192, Avg. loss: 274.105336\n",
      "Total training time: 0.93 seconds.\n",
      "-- Epoch 7\n",
      "Norm: 601.67, NNZs: 16384, Bias: 0.763420, T: 35224, Avg. loss: 218.371076\n",
      "Total training time: 1.07 seconds.\n",
      "-- Epoch 8\n",
      "Norm: 572.17, NNZs: 16384, Bias: 0.757275, T: 40256, Avg. loss: 189.442874\n",
      "Total training time: 1.22 seconds.\n",
      "-- Epoch 9\n",
      "Norm: 548.82, NNZs: 16384, Bias: 0.991898, T: 45288, Avg. loss: 166.276692\n",
      "Total training time: 1.37 seconds.\n",
      "-- Epoch 10\n",
      "Norm: 526.56, NNZs: 16384, Bias: 0.984138, T: 50320, Avg. loss: 145.102127\n",
      "Total training time: 1.52 seconds.\n",
      "-- Epoch 11\n",
      "Norm: 505.11, NNZs: 16384, Bias: 1.138077, T: 55352, Avg. loss: 134.455962\n",
      "Total training time: 1.67 seconds.\n",
      "-- Epoch 12\n",
      "Norm: 490.44, NNZs: 16384, Bias: 0.120123, T: 60384, Avg. loss: 117.833563\n",
      "Total training time: 1.82 seconds.\n",
      "-- Epoch 13\n",
      "Norm: 479.54, NNZs: 16384, Bias: 0.745862, T: 65416, Avg. loss: 107.641599\n",
      "Total training time: 1.97 seconds.\n",
      "-- Epoch 14\n",
      "Norm: 465.79, NNZs: 16384, Bias: 0.603702, T: 70448, Avg. loss: 101.242468\n",
      "Total training time: 2.12 seconds.\n",
      "-- Epoch 15\n",
      "Norm: 457.44, NNZs: 16384, Bias: 1.003917, T: 75480, Avg. loss: 92.014984\n",
      "Total training time: 2.27 seconds.\n",
      "-- Epoch 16\n",
      "Norm: 446.36, NNZs: 16384, Bias: 0.874978, T: 80512, Avg. loss: 82.132819\n",
      "Total training time: 2.42 seconds.\n",
      "-- Epoch 17\n",
      "Norm: 436.93, NNZs: 16384, Bias: 0.752646, T: 85544, Avg. loss: 81.419763\n",
      "Total training time: 2.57 seconds.\n",
      "-- Epoch 18\n",
      "Norm: 428.89, NNZs: 16384, Bias: 0.423014, T: 90576, Avg. loss: 73.768558\n",
      "Total training time: 2.72 seconds.\n",
      "-- Epoch 19\n",
      "Norm: 422.32, NNZs: 16384, Bias: 0.425237, T: 95608, Avg. loss: 67.949409\n",
      "Total training time: 2.86 seconds.\n",
      "-- Epoch 20\n",
      "Norm: 415.59, NNZs: 16384, Bias: 0.614888, T: 100640, Avg. loss: 64.617481\n",
      "Total training time: 3.02 seconds.\n",
      "-- Epoch 21\n",
      "Norm: 408.85, NNZs: 16384, Bias: 0.137369, T: 105672, Avg. loss: 60.070390\n",
      "Total training time: 3.16 seconds.\n",
      "-- Epoch 22\n",
      "Norm: 402.21, NNZs: 16384, Bias: 0.316627, T: 110704, Avg. loss: 57.942412\n",
      "Total training time: 3.31 seconds.\n",
      "-- Epoch 23\n",
      "Norm: 395.95, NNZs: 16384, Bias: -0.032526, T: 115736, Avg. loss: 55.493580\n",
      "Total training time: 3.46 seconds.\n",
      "-- Epoch 24\n",
      "Norm: 390.98, NNZs: 16384, Bias: -0.110711, T: 120768, Avg. loss: 52.637618\n",
      "Total training time: 3.61 seconds.\n",
      "-- Epoch 25\n",
      "Norm: 384.35, NNZs: 16384, Bias: -0.106411, T: 125800, Avg. loss: 49.264961\n",
      "Total training time: 3.75 seconds.\n",
      "-- Epoch 26\n",
      "Norm: 379.89, NNZs: 16384, Bias: -0.026336, T: 130832, Avg. loss: 47.685816\n",
      "Total training time: 3.90 seconds.\n",
      "-- Epoch 27\n",
      "Norm: 374.32, NNZs: 16384, Bias: 0.043888, T: 135864, Avg. loss: 45.601071\n",
      "Total training time: 4.04 seconds.\n",
      "-- Epoch 28\n",
      "Norm: 369.50, NNZs: 16384, Bias: -0.031153, T: 140896, Avg. loss: 42.354839\n",
      "Total training time: 4.19 seconds.\n",
      "-- Epoch 29\n",
      "Norm: 364.66, NNZs: 16384, Bias: -0.171646, T: 145928, Avg. loss: 41.934767\n",
      "Total training time: 4.33 seconds.\n",
      "-- Epoch 30\n",
      "Norm: 360.16, NNZs: 16384, Bias: -0.441502, T: 150960, Avg. loss: 38.445950\n",
      "Total training time: 4.48 seconds.\n",
      "-- Epoch 31\n",
      "Norm: 356.51, NNZs: 16384, Bias: -0.636197, T: 155992, Avg. loss: 37.063132\n",
      "Total training time: 4.63 seconds.\n",
      "-- Epoch 32\n",
      "Norm: 352.74, NNZs: 16384, Bias: -0.386620, T: 161024, Avg. loss: 37.041865\n",
      "Total training time: 4.78 seconds.\n",
      "-- Epoch 33\n",
      "Norm: 349.10, NNZs: 16384, Bias: -0.444179, T: 166056, Avg. loss: 34.665396\n",
      "Total training time: 4.93 seconds.\n",
      "-- Epoch 34\n",
      "Norm: 345.57, NNZs: 16384, Bias: -0.618639, T: 171088, Avg. loss: 33.002413\n",
      "Total training time: 5.07 seconds.\n",
      "-- Epoch 35\n",
      "Norm: 342.34, NNZs: 16384, Bias: -0.620119, T: 176120, Avg. loss: 33.132011\n",
      "Total training time: 5.22 seconds.\n",
      "-- Epoch 36\n",
      "Norm: 339.24, NNZs: 16384, Bias: -0.788931, T: 181152, Avg. loss: 31.471184\n",
      "Total training time: 5.37 seconds.\n",
      "-- Epoch 37\n",
      "Norm: 336.11, NNZs: 16384, Bias: -0.679538, T: 186184, Avg. loss: 29.337205\n",
      "Total training time: 5.51 seconds.\n",
      "-- Epoch 38\n",
      "Norm: 332.71, NNZs: 16384, Bias: -0.839657, T: 191216, Avg. loss: 29.268403\n",
      "Total training time: 5.65 seconds.\n",
      "-- Epoch 39\n",
      "Norm: 329.58, NNZs: 16384, Bias: -0.736236, T: 196248, Avg. loss: 27.803705\n",
      "Total training time: 5.80 seconds.\n",
      "-- Epoch 40\n",
      "Norm: 326.72, NNZs: 16384, Bias: -0.886324, T: 201280, Avg. loss: 28.462874\n",
      "Total training time: 5.94 seconds.\n",
      "-- Epoch 41\n",
      "Norm: 323.68, NNZs: 16384, Bias: -0.836949, T: 206312, Avg. loss: 26.258680\n",
      "Total training time: 6.08 seconds.\n",
      "-- Epoch 42\n",
      "Norm: 320.31, NNZs: 16384, Bias: -0.789465, T: 211344, Avg. loss: 25.647185\n",
      "Total training time: 6.22 seconds.\n",
      "-- Epoch 43\n",
      "Norm: 318.02, NNZs: 16384, Bias: -1.020484, T: 216376, Avg. loss: 24.795322\n",
      "Total training time: 6.36 seconds.\n",
      "-- Epoch 44\n",
      "Norm: 315.81, NNZs: 16384, Bias: -0.928767, T: 221408, Avg. loss: 23.523915\n",
      "Total training time: 6.51 seconds.\n",
      "-- Epoch 45\n",
      "Norm: 313.07, NNZs: 16384, Bias: -0.839516, T: 226440, Avg. loss: 24.395282\n",
      "Total training time: 6.65 seconds.\n",
      "-- Epoch 46\n",
      "Norm: 310.45, NNZs: 16384, Bias: -0.796199, T: 231472, Avg. loss: 22.780249\n",
      "Total training time: 6.79 seconds.\n",
      "-- Epoch 47\n",
      "Norm: 308.35, NNZs: 16384, Bias: -0.796926, T: 236504, Avg. loss: 21.813383\n",
      "Total training time: 6.93 seconds.\n",
      "-- Epoch 48\n",
      "Norm: 306.19, NNZs: 16384, Bias: -0.879569, T: 241536, Avg. loss: 21.115824\n",
      "Total training time: 7.07 seconds.\n",
      "-- Epoch 49\n",
      "Norm: 304.28, NNZs: 16384, Bias: -0.920089, T: 246568, Avg. loss: 20.766700\n",
      "Total training time: 7.21 seconds.\n",
      "-- Epoch 50\n",
      "Norm: 302.49, NNZs: 16384, Bias: -0.840166, T: 251600, Avg. loss: 19.925941\n",
      "Total training time: 7.35 seconds.\n",
      "-- Epoch 51\n",
      "Norm: 300.33, NNZs: 16384, Bias: -0.879781, T: 256632, Avg. loss: 19.901945\n",
      "Total training time: 7.49 seconds.\n",
      "-- Epoch 52\n",
      "Norm: 298.25, NNZs: 16384, Bias: -0.918349, T: 261664, Avg. loss: 19.487881\n",
      "Total training time: 7.63 seconds.\n",
      "-- Epoch 53\n",
      "Norm: 296.37, NNZs: 16384, Bias: -1.068984, T: 266696, Avg. loss: 18.871293\n",
      "Total training time: 7.77 seconds.\n",
      "-- Epoch 54\n",
      "Norm: 294.59, NNZs: 16384, Bias: -0.994561, T: 271728, Avg. loss: 18.486688\n",
      "Total training time: 7.91 seconds.\n",
      "-- Epoch 55\n",
      "Norm: 292.37, NNZs: 16384, Bias: -0.993728, T: 276760, Avg. loss: 18.499805\n",
      "Total training time: 8.05 seconds.\n",
      "-- Epoch 56\n",
      "Norm: 290.63, NNZs: 16384, Bias: -1.028587, T: 281792, Avg. loss: 17.277311\n",
      "Total training time: 8.19 seconds.\n",
      "-- Epoch 57\n",
      "Norm: 288.61, NNZs: 16384, Bias: -0.992501, T: 286824, Avg. loss: 17.480199\n",
      "Total training time: 8.33 seconds.\n",
      "-- Epoch 58\n",
      "Norm: 286.80, NNZs: 16384, Bias: -1.061615, T: 291856, Avg. loss: 16.554965\n",
      "Total training time: 8.47 seconds.\n",
      "-- Epoch 59\n",
      "Norm: 285.15, NNZs: 16384, Bias: -1.060977, T: 296888, Avg. loss: 16.584634\n",
      "Total training time: 8.61 seconds.\n",
      "-- Epoch 60\n",
      "Norm: 283.55, NNZs: 16384, Bias: -1.060858, T: 301920, Avg. loss: 16.047161\n",
      "Total training time: 8.75 seconds.\n",
      "-- Epoch 61\n",
      "Norm: 281.97, NNZs: 16384, Bias: -1.093447, T: 306952, Avg. loss: 16.232462\n",
      "Total training time: 8.89 seconds.\n",
      "-- Epoch 62\n",
      "Norm: 280.17, NNZs: 16384, Bias: -1.028822, T: 311984, Avg. loss: 15.164260\n",
      "Total training time: 9.03 seconds.\n",
      "-- Epoch 63\n",
      "Norm: 278.65, NNZs: 16384, Bias: -1.028256, T: 317016, Avg. loss: 14.693477\n",
      "Total training time: 9.17 seconds.\n",
      "-- Epoch 64\n",
      "Norm: 277.27, NNZs: 16384, Bias: -1.028615, T: 322048, Avg. loss: 14.676176\n",
      "Total training time: 9.31 seconds.\n",
      "-- Epoch 65\n",
      "Norm: 275.71, NNZs: 16384, Bias: -1.120297, T: 327080, Avg. loss: 14.731063\n",
      "Total training time: 9.45 seconds.\n",
      "-- Epoch 66\n",
      "Norm: 273.98, NNZs: 16384, Bias: -1.180580, T: 332112, Avg. loss: 13.994358\n",
      "Total training time: 9.59 seconds.\n",
      "-- Epoch 67\n",
      "Norm: 272.50, NNZs: 16384, Bias: -1.121007, T: 337144, Avg. loss: 14.175797\n",
      "Total training time: 9.73 seconds.\n",
      "-- Epoch 68\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Norm: 270.95, NNZs: 16384, Bias: -1.120948, T: 342176, Avg. loss: 13.498774\n",
      "Total training time: 9.87 seconds.\n",
      "-- Epoch 69\n",
      "Norm: 269.55, NNZs: 16384, Bias: -1.063097, T: 347208, Avg. loss: 13.509278\n",
      "Total training time: 10.01 seconds.\n",
      "-- Epoch 70\n",
      "Norm: 268.21, NNZs: 16384, Bias: -1.091739, T: 352240, Avg. loss: 12.814313\n",
      "Total training time: 10.15 seconds.\n",
      "-- Epoch 71\n",
      "Norm: 266.80, NNZs: 16384, Bias: -1.091456, T: 357272, Avg. loss: 12.485006\n",
      "Total training time: 10.29 seconds.\n",
      "-- Epoch 72\n",
      "Norm: 265.72, NNZs: 16384, Bias: -1.008436, T: 362304, Avg. loss: 12.262329\n",
      "Total training time: 10.43 seconds.\n",
      "-- Epoch 73\n",
      "Norm: 264.15, NNZs: 16384, Bias: -1.062935, T: 367336, Avg. loss: 12.232814\n",
      "Total training time: 10.56 seconds.\n",
      "-- Epoch 74\n",
      "Norm: 263.06, NNZs: 16384, Bias: -0.982275, T: 372368, Avg. loss: 11.830999\n",
      "Total training time: 10.70 seconds.\n",
      "-- Epoch 75\n",
      "Norm: 261.75, NNZs: 16384, Bias: -0.955823, T: 377400, Avg. loss: 11.639939\n",
      "Total training time: 10.84 seconds.\n",
      "-- Epoch 76\n",
      "Norm: 260.47, NNZs: 16384, Bias: -1.008333, T: 382432, Avg. loss: 11.653679\n",
      "Total training time: 10.98 seconds.\n",
      "-- Epoch 77\n",
      "Norm: 259.36, NNZs: 16384, Bias: -0.982354, T: 387464, Avg. loss: 11.541028\n",
      "Total training time: 11.11 seconds.\n",
      "-- Epoch 78\n",
      "Norm: 258.21, NNZs: 16384, Bias: -0.930990, T: 392496, Avg. loss: 11.118862\n",
      "Total training time: 11.25 seconds.\n",
      "-- Epoch 79\n",
      "Norm: 257.01, NNZs: 16384, Bias: -0.956487, T: 397528, Avg. loss: 10.935831\n",
      "Total training time: 11.39 seconds.\n",
      "-- Epoch 80\n",
      "Norm: 255.80, NNZs: 16384, Bias: -0.931654, T: 402560, Avg. loss: 10.982976\n",
      "Total training time: 11.53 seconds.\n",
      "-- Epoch 81\n",
      "Norm: 254.75, NNZs: 16384, Bias: -0.956209, T: 407592, Avg. loss: 10.873237\n",
      "Total training time: 11.67 seconds.\n",
      "-- Epoch 82\n",
      "Norm: 253.63, NNZs: 16384, Bias: -0.956102, T: 412624, Avg. loss: 10.556848\n",
      "Total training time: 11.80 seconds.\n",
      "-- Epoch 83\n",
      "Norm: 252.58, NNZs: 16384, Bias: -1.004117, T: 417656, Avg. loss: 10.399836\n",
      "Total training time: 11.94 seconds.\n",
      "-- Epoch 84\n",
      "Norm: 251.48, NNZs: 16384, Bias: -0.956701, T: 422688, Avg. loss: 10.227243\n",
      "Total training time: 12.08 seconds.\n",
      "-- Epoch 85\n",
      "Norm: 250.27, NNZs: 16384, Bias: -0.956895, T: 427720, Avg. loss: 10.079267\n",
      "Total training time: 12.21 seconds.\n",
      "-- Epoch 86\n",
      "Norm: 249.23, NNZs: 16384, Bias: -1.003446, T: 432752, Avg. loss: 9.541502\n",
      "Total training time: 12.35 seconds.\n",
      "-- Epoch 87\n",
      "Norm: 248.00, NNZs: 16384, Bias: -1.049016, T: 437784, Avg. loss: 9.617343\n",
      "Total training time: 12.49 seconds.\n",
      "-- Epoch 88\n",
      "Norm: 247.03, NNZs: 16384, Bias: -0.980642, T: 442816, Avg. loss: 9.131691\n",
      "Total training time: 12.63 seconds.\n",
      "-- Epoch 89\n",
      "Norm: 245.94, NNZs: 16384, Bias: -0.980683, T: 447848, Avg. loss: 9.291891\n",
      "Total training time: 12.77 seconds.\n",
      "-- Epoch 90\n",
      "Norm: 244.98, NNZs: 16384, Bias: -1.002636, T: 452880, Avg. loss: 8.957727\n",
      "Total training time: 12.92 seconds.\n",
      "-- Epoch 91\n",
      "Norm: 243.88, NNZs: 16384, Bias: -1.024277, T: 457912, Avg. loss: 8.961055\n",
      "Total training time: 13.05 seconds.\n",
      "-- Epoch 92\n",
      "Norm: 242.81, NNZs: 16384, Bias: -0.980949, T: 462944, Avg. loss: 8.612045\n",
      "Total training time: 13.19 seconds.\n",
      "-- Epoch 93\n",
      "Norm: 241.81, NNZs: 16384, Bias: -0.980921, T: 467976, Avg. loss: 9.197038\n",
      "Total training time: 13.33 seconds.\n",
      "-- Epoch 94\n",
      "Norm: 240.93, NNZs: 16384, Bias: -0.938312, T: 473008, Avg. loss: 8.451206\n",
      "Total training time: 13.47 seconds.\n",
      "-- Epoch 95\n",
      "Norm: 239.98, NNZs: 16384, Bias: -0.938186, T: 478040, Avg. loss: 8.716251\n",
      "Total training time: 13.60 seconds.\n",
      "-- Epoch 96\n",
      "Norm: 238.98, NNZs: 16384, Bias: -0.855070, T: 483072, Avg. loss: 8.100628\n",
      "Total training time: 13.74 seconds.\n",
      "-- Epoch 97\n",
      "Norm: 238.03, NNZs: 16384, Bias: -0.854991, T: 488104, Avg. loss: 8.246143\n",
      "Total training time: 13.88 seconds.\n",
      "-- Epoch 98\n",
      "Norm: 237.15, NNZs: 16384, Bias: -0.834358, T: 493136, Avg. loss: 8.177655\n",
      "Total training time: 14.01 seconds.\n",
      "-- Epoch 99\n",
      "Norm: 236.16, NNZs: 16384, Bias: -0.814312, T: 498168, Avg. loss: 7.915170\n",
      "Total training time: 14.15 seconds.\n",
      "-- Epoch 100\n",
      "Norm: 235.22, NNZs: 16384, Bias: -0.834404, T: 503200, Avg. loss: 7.771713\n",
      "Total training time: 14.29 seconds.\n",
      "-- Epoch 101\n",
      "Norm: 234.34, NNZs: 16384, Bias: -0.834407, T: 508232, Avg. loss: 7.560592\n",
      "Total training time: 14.42 seconds.\n",
      "-- Epoch 102\n",
      "Norm: 233.50, NNZs: 16384, Bias: -0.873482, T: 513264, Avg. loss: 7.698356\n",
      "Total training time: 14.56 seconds.\n",
      "-- Epoch 103\n",
      "Norm: 232.63, NNZs: 16384, Bias: -0.854180, T: 518296, Avg. loss: 7.532862\n",
      "Total training time: 14.69 seconds.\n",
      "-- Epoch 104\n",
      "Norm: 231.68, NNZs: 16384, Bias: -0.777730, T: 523328, Avg. loss: 7.024478\n",
      "Total training time: 14.83 seconds.\n",
      "-- Epoch 105\n",
      "Norm: 230.83, NNZs: 16384, Bias: -0.796586, T: 528360, Avg. loss: 7.323224\n",
      "Total training time: 14.96 seconds.\n",
      "-- Epoch 106\n",
      "Norm: 229.93, NNZs: 16384, Bias: -0.815316, T: 533392, Avg. loss: 7.304295\n",
      "Total training time: 15.10 seconds.\n",
      "-- Epoch 107\n",
      "Norm: 229.16, NNZs: 16384, Bias: -0.852588, T: 538424, Avg. loss: 6.926239\n",
      "Total training time: 15.23 seconds.\n",
      "-- Epoch 108\n",
      "Norm: 228.35, NNZs: 16384, Bias: -0.834185, T: 543456, Avg. loss: 6.990405\n",
      "Total training time: 15.37 seconds.\n",
      "-- Epoch 109\n",
      "Norm: 227.73, NNZs: 16384, Bias: -0.815714, T: 548488, Avg. loss: 7.249432\n",
      "Total training time: 15.51 seconds.\n",
      "-- Epoch 110\n",
      "Norm: 226.94, NNZs: 16384, Bias: -0.815630, T: 553520, Avg. loss: 6.811640\n",
      "Total training time: 15.64 seconds.\n",
      "-- Epoch 111\n",
      "Norm: 226.07, NNZs: 16384, Bias: -0.797639, T: 558552, Avg. loss: 6.599448\n",
      "Total training time: 15.78 seconds.\n",
      "-- Epoch 112\n",
      "Norm: 225.37, NNZs: 16384, Bias: -0.779911, T: 563584, Avg. loss: 6.880769\n",
      "Total training time: 15.91 seconds.\n",
      "-- Epoch 113\n",
      "Norm: 224.60, NNZs: 16384, Bias: -0.762194, T: 568616, Avg. loss: 6.588810\n",
      "Total training time: 16.04 seconds.\n",
      "-- Epoch 114\n",
      "Norm: 223.85, NNZs: 16384, Bias: -0.779665, T: 573648, Avg. loss: 6.597573\n",
      "Total training time: 16.18 seconds.\n",
      "-- Epoch 115\n",
      "Norm: 223.06, NNZs: 16384, Bias: -0.814242, T: 578680, Avg. loss: 6.220290\n",
      "Total training time: 16.32 seconds.\n",
      "-- Epoch 116\n",
      "Norm: 222.33, NNZs: 16384, Bias: -0.779823, T: 583712, Avg. loss: 6.120570\n",
      "Total training time: 16.45 seconds.\n",
      "-- Epoch 117\n",
      "Norm: 221.65, NNZs: 16384, Bias: -0.779929, T: 588744, Avg. loss: 6.290917\n",
      "Total training time: 16.58 seconds.\n",
      "-- Epoch 118\n",
      "Norm: 220.85, NNZs: 16384, Bias: -0.763004, T: 593776, Avg. loss: 5.994283\n",
      "Total training time: 16.72 seconds.\n",
      "-- Epoch 119\n",
      "Norm: 220.02, NNZs: 16384, Bias: -0.763177, T: 598808, Avg. loss: 5.887720\n",
      "Total training time: 16.85 seconds.\n",
      "-- Epoch 120\n",
      "Norm: 219.47, NNZs: 16384, Bias: -0.763205, T: 603840, Avg. loss: 6.142421\n",
      "Total training time: 16.99 seconds.\n",
      "-- Epoch 121\n",
      "Norm: 218.68, NNZs: 16384, Bias: -0.763219, T: 608872, Avg. loss: 5.685388\n",
      "Total training time: 17.12 seconds.\n",
      "-- Epoch 122\n",
      "Norm: 217.99, NNZs: 16384, Bias: -0.714235, T: 613904, Avg. loss: 5.766613\n",
      "Total training time: 17.25 seconds.\n",
      "-- Epoch 123\n",
      "Norm: 217.26, NNZs: 16384, Bias: -0.730468, T: 618936, Avg. loss: 5.883499\n",
      "Total training time: 17.39 seconds.\n",
      "-- Epoch 124\n",
      "Norm: 216.63, NNZs: 16384, Bias: -0.762767, T: 623968, Avg. loss: 5.854852\n",
      "Total training time: 17.52 seconds.\n",
      "-- Epoch 125\n",
      "Norm: 216.01, NNZs: 16384, Bias: -0.730885, T: 629000, Avg. loss: 5.544155\n",
      "Total training time: 17.66 seconds.\n",
      "-- Epoch 126\n",
      "Norm: 215.29, NNZs: 16384, Bias: -0.730908, T: 634032, Avg. loss: 5.880398\n",
      "Total training time: 17.79 seconds.\n",
      "-- Epoch 127\n",
      "Norm: 214.64, NNZs: 16384, Bias: -0.652605, T: 639064, Avg. loss: 5.585634\n",
      "Total training time: 17.93 seconds.\n",
      "-- Epoch 128\n",
      "Norm: 213.98, NNZs: 16384, Bias: -0.652763, T: 644096, Avg. loss: 5.429565\n",
      "Total training time: 18.06 seconds.\n",
      "-- Epoch 129\n",
      "Norm: 213.38, NNZs: 16384, Bias: -0.668240, T: 649128, Avg. loss: 5.300502\n",
      "Total training time: 18.20 seconds.\n",
      "-- Epoch 130\n",
      "Norm: 212.72, NNZs: 16384, Bias: -0.668330, T: 654160, Avg. loss: 5.404634\n",
      "Total training time: 18.33 seconds.\n",
      "-- Epoch 131\n",
      "Norm: 212.09, NNZs: 16384, Bias: -0.729177, T: 659192, Avg. loss: 5.241678\n",
      "Total training time: 18.47 seconds.\n",
      "-- Epoch 132\n",
      "Norm: 211.51, NNZs: 16384, Bias: -0.714068, T: 664224, Avg. loss: 5.110550\n",
      "Total training time: 18.60 seconds.\n",
      "-- Epoch 133\n",
      "Norm: 210.85, NNZs: 16384, Bias: -0.669231, T: 669256, Avg. loss: 5.315044\n",
      "Total training time: 18.73 seconds.\n",
      "-- Epoch 134\n",
      "Norm: 210.21, NNZs: 16384, Bias: -0.654506, T: 674288, Avg. loss: 5.210525\n",
      "Total training time: 18.87 seconds.\n",
      "-- Epoch 135\n",
      "Norm: 209.61, NNZs: 16384, Bias: -0.669359, T: 679320, Avg. loss: 5.031195\n",
      "Total training time: 19.00 seconds.\n",
      "-- Epoch 136\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Norm: 208.98, NNZs: 16384, Bias: -0.625344, T: 684352, Avg. loss: 5.071103\n",
      "Total training time: 19.13 seconds.\n",
      "-- Epoch 137\n",
      "Norm: 208.39, NNZs: 16384, Bias: -0.639957, T: 689384, Avg. loss: 5.192552\n",
      "Total training time: 19.27 seconds.\n",
      "-- Epoch 138\n",
      "Norm: 207.78, NNZs: 16384, Bias: -0.697648, T: 694416, Avg. loss: 5.053525\n",
      "Total training time: 19.40 seconds.\n",
      "-- Epoch 139\n",
      "Norm: 207.20, NNZs: 16384, Bias: -0.668911, T: 699448, Avg. loss: 4.794111\n",
      "Total training time: 19.53 seconds.\n",
      "-- Epoch 140\n",
      "Norm: 206.59, NNZs: 16384, Bias: -0.683145, T: 704480, Avg. loss: 4.787694\n",
      "Total training time: 19.67 seconds.\n",
      "-- Epoch 141\n",
      "Norm: 206.03, NNZs: 16384, Bias: -0.739613, T: 709512, Avg. loss: 4.704107\n",
      "Total training time: 19.80 seconds.\n",
      "-- Epoch 142\n",
      "Norm: 205.45, NNZs: 16384, Bias: -0.711540, T: 714544, Avg. loss: 4.665250\n",
      "Total training time: 19.93 seconds.\n",
      "-- Epoch 143\n",
      "Norm: 204.89, NNZs: 16384, Bias: -0.725457, T: 719576, Avg. loss: 4.566626\n",
      "Total training time: 20.06 seconds.\n",
      "-- Epoch 144\n",
      "Norm: 204.31, NNZs: 16384, Bias: -0.725467, T: 724608, Avg. loss: 4.560635\n",
      "Total training time: 20.20 seconds.\n",
      "-- Epoch 145\n",
      "Norm: 203.67, NNZs: 16384, Bias: -0.739277, T: 729640, Avg. loss: 4.536744\n",
      "Total training time: 20.33 seconds.\n",
      "-- Epoch 146\n",
      "Norm: 203.10, NNZs: 16384, Bias: -0.712022, T: 734672, Avg. loss: 4.529360\n",
      "Total training time: 20.47 seconds.\n",
      "-- Epoch 147\n",
      "Norm: 202.53, NNZs: 16384, Bias: -0.712040, T: 739704, Avg. loss: 4.391642\n",
      "Total training time: 20.60 seconds.\n",
      "-- Epoch 148\n",
      "Norm: 201.94, NNZs: 16384, Bias: -0.685179, T: 744736, Avg. loss: 4.279628\n",
      "Total training time: 20.73 seconds.\n",
      "-- Epoch 149\n",
      "Norm: 201.41, NNZs: 16384, Bias: -0.671884, T: 749768, Avg. loss: 4.359832\n",
      "Total training time: 20.86 seconds.\n",
      "-- Epoch 150\n",
      "Norm: 200.91, NNZs: 16384, Bias: -0.698388, T: 754800, Avg. loss: 4.315541\n",
      "Total training time: 20.99 seconds.\n",
      "-- Epoch 151\n",
      "Norm: 200.30, NNZs: 16384, Bias: -0.698421, T: 759832, Avg. loss: 4.266862\n",
      "Total training time: 21.13 seconds.\n",
      "-- Epoch 152\n",
      "Norm: 199.72, NNZs: 16384, Bias: -0.737806, T: 764864, Avg. loss: 4.166006\n",
      "Total training time: 21.26 seconds.\n",
      "-- Epoch 153\n",
      "Norm: 199.20, NNZs: 16384, Bias: -0.698780, T: 769896, Avg. loss: 4.106349\n",
      "Total training time: 21.39 seconds.\n",
      "-- Epoch 154\n",
      "Norm: 198.72, NNZs: 16384, Bias: -0.685816, T: 774928, Avg. loss: 4.109649\n",
      "Total training time: 21.52 seconds.\n",
      "-- Epoch 155\n",
      "Norm: 198.22, NNZs: 16384, Bias: -0.685884, T: 779960, Avg. loss: 4.111537\n",
      "Total training time: 21.65 seconds.\n",
      "-- Epoch 156\n",
      "Norm: 197.83, NNZs: 16384, Bias: -0.698764, T: 784992, Avg. loss: 4.248341\n",
      "Total training time: 21.79 seconds.\n",
      "-- Epoch 157\n",
      "Norm: 197.30, NNZs: 16384, Bias: -0.698749, T: 790024, Avg. loss: 4.101533\n",
      "Total training time: 21.92 seconds.\n",
      "-- Epoch 158\n",
      "Norm: 196.80, NNZs: 16384, Bias: -0.698714, T: 795056, Avg. loss: 3.986156\n",
      "Total training time: 22.05 seconds.\n",
      "-- Epoch 159\n",
      "Norm: 196.34, NNZs: 16384, Bias: -0.673625, T: 800088, Avg. loss: 3.974119\n",
      "Total training time: 22.18 seconds.\n",
      "-- Epoch 160\n",
      "Norm: 195.82, NNZs: 16384, Bias: -0.636310, T: 805120, Avg. loss: 3.824002\n",
      "Total training time: 22.32 seconds.\n",
      "-- Epoch 161\n",
      "Norm: 195.38, NNZs: 16384, Bias: -0.636294, T: 810152, Avg. loss: 3.873143\n",
      "Total training time: 22.45 seconds.\n",
      "-- Epoch 162\n",
      "Norm: 194.86, NNZs: 16384, Bias: -0.623972, T: 815184, Avg. loss: 3.737906\n",
      "Total training time: 22.58 seconds.\n",
      "-- Epoch 163\n",
      "Norm: 194.34, NNZs: 16384, Bias: -0.599495, T: 820216, Avg. loss: 3.828930\n",
      "Total training time: 22.71 seconds.\n",
      "-- Epoch 164\n",
      "Norm: 193.92, NNZs: 16384, Bias: -0.575276, T: 825248, Avg. loss: 3.678542\n",
      "Total training time: 22.84 seconds.\n",
      "-- Epoch 165\n",
      "Norm: 193.39, NNZs: 16384, Bias: -0.587356, T: 830280, Avg. loss: 3.661643\n",
      "Total training time: 22.97 seconds.\n",
      "-- Epoch 166\n",
      "Norm: 192.90, NNZs: 16384, Bias: -0.587441, T: 835312, Avg. loss: 3.796606\n",
      "Total training time: 23.11 seconds.\n",
      "-- Epoch 167\n",
      "Norm: 192.43, NNZs: 16384, Bias: -0.587413, T: 840344, Avg. loss: 3.703890\n",
      "Total training time: 23.24 seconds.\n",
      "-- Epoch 168\n",
      "Norm: 191.99, NNZs: 16384, Bias: -0.587421, T: 845376, Avg. loss: 3.573962\n",
      "Total training time: 23.37 seconds.\n",
      "-- Epoch 169\n",
      "Norm: 191.57, NNZs: 16384, Bias: -0.575704, T: 850408, Avg. loss: 3.623760\n",
      "Total training time: 23.50 seconds.\n",
      "-- Epoch 170\n",
      "Norm: 191.14, NNZs: 16384, Bias: -0.610902, T: 855440, Avg. loss: 3.466188\n",
      "Total training time: 23.63 seconds.\n",
      "-- Epoch 171\n",
      "Norm: 190.69, NNZs: 16384, Bias: -0.622505, T: 860472, Avg. loss: 3.559551\n",
      "Total training time: 23.76 seconds.\n",
      "-- Epoch 172\n",
      "Norm: 190.24, NNZs: 16384, Bias: -0.599329, T: 865504, Avg. loss: 3.669222\n",
      "Total training time: 23.90 seconds.\n",
      "-- Epoch 173\n",
      "Norm: 189.82, NNZs: 16384, Bias: -0.610836, T: 870536, Avg. loss: 3.554604\n",
      "Total training time: 24.03 seconds.\n",
      "-- Epoch 174\n",
      "Norm: 189.42, NNZs: 16384, Bias: -0.622272, T: 875568, Avg. loss: 3.467315\n",
      "Total training time: 24.16 seconds.\n",
      "-- Epoch 175\n",
      "Norm: 188.98, NNZs: 16384, Bias: -0.610856, T: 880600, Avg. loss: 3.359134\n",
      "Total training time: 24.29 seconds.\n",
      "-- Epoch 176\n",
      "Norm: 188.56, NNZs: 16384, Bias: -0.599474, T: 885632, Avg. loss: 3.556654\n",
      "Total training time: 24.43 seconds.\n",
      "-- Epoch 177\n",
      "Norm: 188.11, NNZs: 16384, Bias: -0.576994, T: 890664, Avg. loss: 3.317355\n",
      "Total training time: 24.56 seconds.\n",
      "-- Epoch 178\n",
      "Norm: 187.66, NNZs: 16384, Bias: -0.588119, T: 895696, Avg. loss: 3.220232\n",
      "Total training time: 24.69 seconds.\n",
      "-- Epoch 179\n",
      "Norm: 187.22, NNZs: 16384, Bias: -0.576995, T: 900728, Avg. loss: 3.329918\n",
      "Total training time: 24.82 seconds.\n",
      "-- Epoch 180\n",
      "Norm: 186.81, NNZs: 16384, Bias: -0.610166, T: 905760, Avg. loss: 3.202189\n",
      "Total training time: 24.95 seconds.\n",
      "-- Epoch 181\n",
      "Norm: 186.41, NNZs: 16384, Bias: -0.610174, T: 910792, Avg. loss: 3.183257\n",
      "Total training time: 25.08 seconds.\n",
      "-- Epoch 182\n",
      "Norm: 185.99, NNZs: 16384, Bias: -0.610170, T: 915824, Avg. loss: 3.195155\n",
      "Total training time: 25.21 seconds.\n",
      "-- Epoch 183\n",
      "Norm: 185.57, NNZs: 16384, Bias: -0.599338, T: 920856, Avg. loss: 3.253312\n",
      "Total training time: 25.35 seconds.\n",
      "-- Epoch 184\n",
      "Norm: 185.14, NNZs: 16384, Bias: -0.599405, T: 925888, Avg. loss: 3.098283\n",
      "Total training time: 25.48 seconds.\n",
      "-- Epoch 185\n",
      "Norm: 184.76, NNZs: 16384, Bias: -0.577877, T: 930920, Avg. loss: 3.067825\n",
      "Total training time: 25.61 seconds.\n",
      "-- Epoch 186\n",
      "Norm: 184.33, NNZs: 16384, Bias: -0.599286, T: 935952, Avg. loss: 2.949219\n",
      "Total training time: 25.74 seconds.\n",
      "-- Epoch 187\n",
      "Norm: 183.95, NNZs: 16384, Bias: -0.588648, T: 940984, Avg. loss: 3.221279\n",
      "Total training time: 25.87 seconds.\n",
      "-- Epoch 188\n",
      "Norm: 183.57, NNZs: 16384, Bias: -0.609763, T: 946016, Avg. loss: 3.015627\n",
      "Total training time: 26.00 seconds.\n",
      "-- Epoch 189\n",
      "Norm: 183.15, NNZs: 16384, Bias: -0.599245, T: 951048, Avg. loss: 2.867340\n",
      "Total training time: 26.13 seconds.\n",
      "-- Epoch 190\n",
      "Norm: 182.75, NNZs: 16384, Bias: -0.630629, T: 956080, Avg. loss: 2.828282\n",
      "Total training time: 26.26 seconds.\n",
      "-- Epoch 191\n",
      "Norm: 182.39, NNZs: 16384, Bias: -0.620187, T: 961112, Avg. loss: 3.097958\n",
      "Total training time: 26.40 seconds.\n",
      "-- Epoch 192\n",
      "Norm: 182.01, NNZs: 16384, Bias: -0.589093, T: 966144, Avg. loss: 2.917849\n",
      "Total training time: 26.53 seconds.\n",
      "-- Epoch 193\n",
      "Norm: 181.62, NNZs: 16384, Bias: -0.609737, T: 971176, Avg. loss: 2.977429\n",
      "Total training time: 26.67 seconds.\n",
      "-- Epoch 194\n",
      "Norm: 181.19, NNZs: 16384, Bias: -0.599513, T: 976208, Avg. loss: 2.678153\n",
      "Total training time: 26.79 seconds.\n",
      "-- Epoch 195\n",
      "Norm: 180.82, NNZs: 16384, Bias: -0.599537, T: 981240, Avg. loss: 2.913973\n",
      "Total training time: 26.92 seconds.\n",
      "-- Epoch 196\n",
      "Norm: 180.45, NNZs: 16384, Bias: -0.609661, T: 986272, Avg. loss: 2.844418\n",
      "Total training time: 27.05 seconds.\n",
      "-- Epoch 197\n",
      "Norm: 180.08, NNZs: 16384, Bias: -0.599549, T: 991304, Avg. loss: 2.806214\n",
      "Total training time: 27.18 seconds.\n",
      "-- Epoch 198\n",
      "Norm: 179.70, NNZs: 16384, Bias: -0.599558, T: 996336, Avg. loss: 2.643192\n",
      "Total training time: 27.31 seconds.\n",
      "-- Epoch 199\n",
      "Norm: 179.36, NNZs: 16384, Bias: -0.599561, T: 1001368, Avg. loss: 2.791873\n",
      "Total training time: 27.44 seconds.\n",
      "-- Epoch 200\n",
      "Norm: 178.94, NNZs: 16384, Bias: -0.609512, T: 1006400, Avg. loss: 2.652424\n",
      "Total training time: 27.57 seconds.\n",
      "-- Epoch 201\n",
      "Norm: 178.56, NNZs: 16384, Bias: -0.619432, T: 1011432, Avg. loss: 2.680640\n",
      "Total training time: 27.70 seconds.\n",
      "-- Epoch 202\n",
      "Norm: 178.21, NNZs: 16384, Bias: -0.609575, T: 1016464, Avg. loss: 2.736765\n",
      "Total training time: 27.83 seconds.\n",
      "-- Epoch 203\n",
      "Norm: 177.79, NNZs: 16384, Bias: -0.609581, T: 1021496, Avg. loss: 2.684732\n",
      "Total training time: 27.96 seconds.\n",
      "Convergence after 203 epochs took 27.96 seconds\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Score: 0.6025437201907791\n",
      "Precision: 0.6205607476635514\n",
      "Recall: 0.5278219395866455\n",
      "[[426 203]\n",
      " [297 332]]\n"
     ]
    }
   ],
   "source": [
    "subdural_svm_model = build_stochastic_svm_model(total_data, 'subdural')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b4296b75",
   "metadata": {},
   "source": [
    "Using Neural Networks for Classifying Hemorrhages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "17886cb9",
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras import regularizers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "bd544177",
   "metadata": {},
   "outputs": [],
   "source": [
    "def build_neural_network(data, column, training_set_size = max_training_set_size_per_model):\n",
    "    positive_examples_to_use = int((training_set_size + test_set_size_per_model)/2)\n",
    "    balanced_data = get_balanced_dataset(data, column, num_positive_examples=positive_examples_to_use)\n",
    "    test_size_percentage = test_set_size_per_model/(positive_examples_to_use * 2)\n",
    "    X_train, X_test, y_train, y_test = train_test_split(balanced_data[image_data_column_list], balanced_data[column], test_size=test_size_percentage, random_state=0)\n",
    "    X_train, X_valid, y_train, y_valid = train_test_split(X_train, y_train, test_size=0.1, random_state=0)\n",
    "    print(f'Training Set Size: {X_train.shape[0]}')\n",
    "    print(f'Validation Set Size: {X_valid.shape[0]}')\n",
    "    print(f'Test Set Size: {X_test.shape[0]}')\n",
    "    model = keras.models.Sequential([keras.layers.Dense(4096, activation=\"relu\"), keras.layers.Dense(1024, activation=\"relu\"), keras.layers.Dense(256, activation=\"relu\"),keras.layers.Dense(64, activation=\"relu\"),keras.layers.Dense(16, activation=\"relu\"),keras.layers.Dense(4, activation=\"relu\"), keras.layers.Dense(1, activation=\"sigmoid\")]) \n",
    "    model.compile(loss=\"binary_crossentropy\", optimizer=\"sgd\", metrics=[\"accuracy\"])\n",
    "    history = model.fit(X_train, y_train, epochs=50, verbose=1, validation_data=(X_valid, y_valid), callbacks=[keras.callbacks.EarlyStopping(monitor='val_loss',patience=3)])\n",
    "    model.summary()\n",
    "    test_loss, test_accuracy = model.evaluate(X_test, y_test, verbose=1)\n",
    "    print(f'Test accuracy: {test_accuracy}')\n",
    "    y_predict = model.predict(X_test)\n",
    "    y_predict = list(map(lambda y : int(y >= 0.5), y_predict))\n",
    "    results = confusion_matrix(y_test, y_predict)\n",
    "    print(f'Precision: {results[1][1]/(results[1][1] + results[0][1])}')\n",
    "    print(f'Recall: {results[1][1]/(results[1][1] + results[1][0])}')\n",
    "    print(results)\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "f30c11f4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Set Size: 4528\n",
      "Validation Set Size: 504\n",
      "Test Set Size: 1258\n",
      "Epoch 1/50\n",
      "142/142 [==============================] - 30s 198ms/step - loss: 0.6907 - accuracy: 0.5234 - val_loss: 0.6869 - val_accuracy: 0.6091\n",
      "Epoch 2/50\n",
      "142/142 [==============================] - 29s 202ms/step - loss: 0.6805 - accuracy: 0.6064 - val_loss: 0.6716 - val_accuracy: 0.6250\n",
      "Epoch 3/50\n",
      "142/142 [==============================] - 28s 196ms/step - loss: 0.6640 - accuracy: 0.6440 - val_loss: 0.6543 - val_accuracy: 0.6726\n",
      "Epoch 4/50\n",
      "142/142 [==============================] - 32s 226ms/step - loss: 0.6483 - accuracy: 0.6645 - val_loss: 0.6404 - val_accuracy: 0.6687\n",
      "Epoch 5/50\n",
      "142/142 [==============================] - 32s 227ms/step - loss: 0.6365 - accuracy: 0.6665 - val_loss: 0.6395 - val_accuracy: 0.6766\n",
      "Epoch 6/50\n",
      "142/142 [==============================] - 33s 231ms/step - loss: 0.6256 - accuracy: 0.6784 - val_loss: 0.6184 - val_accuracy: 0.6806\n",
      "Epoch 7/50\n",
      "142/142 [==============================] - 33s 234ms/step - loss: 0.6158 - accuracy: 0.6915 - val_loss: 0.6160 - val_accuracy: 0.6944\n",
      "Epoch 8/50\n",
      "142/142 [==============================] - 30s 212ms/step - loss: 0.6107 - accuracy: 0.6873 - val_loss: 0.6356 - val_accuracy: 0.6825\n",
      "Epoch 9/50\n",
      "142/142 [==============================] - 30s 210ms/step - loss: 0.6063 - accuracy: 0.6959 - val_loss: 0.6207 - val_accuracy: 0.6766\n",
      "Epoch 10/50\n",
      "142/142 [==============================] - 28s 196ms/step - loss: 0.5973 - accuracy: 0.6943 - val_loss: 0.5999 - val_accuracy: 0.6885\n",
      "Epoch 11/50\n",
      "142/142 [==============================] - 28s 196ms/step - loss: 0.5914 - accuracy: 0.7038 - val_loss: 0.5975 - val_accuracy: 0.6885\n",
      "Epoch 12/50\n",
      "142/142 [==============================] - 28s 196ms/step - loss: 0.5793 - accuracy: 0.7160 - val_loss: 0.6406 - val_accuracy: 0.6766\n",
      "Epoch 13/50\n",
      "142/142 [==============================] - 28s 197ms/step - loss: 0.5765 - accuracy: 0.7147 - val_loss: 0.5832 - val_accuracy: 0.7063\n",
      "Epoch 14/50\n",
      "142/142 [==============================] - 28s 196ms/step - loss: 0.5744 - accuracy: 0.7102 - val_loss: 0.5816 - val_accuracy: 0.7083\n",
      "Epoch 15/50\n",
      "142/142 [==============================] - 28s 197ms/step - loss: 0.5680 - accuracy: 0.7222 - val_loss: 0.6301 - val_accuracy: 0.6825\n",
      "Epoch 16/50\n",
      "142/142 [==============================] - 29s 201ms/step - loss: 0.5563 - accuracy: 0.7392 - val_loss: 0.5740 - val_accuracy: 0.7063\n",
      "Epoch 17/50\n",
      "142/142 [==============================] - 28s 194ms/step - loss: 0.5498 - accuracy: 0.7354 - val_loss: 0.6264 - val_accuracy: 0.6944\n",
      "Epoch 18/50\n",
      "142/142 [==============================] - 26s 185ms/step - loss: 0.5493 - accuracy: 0.7350 - val_loss: 0.5915 - val_accuracy: 0.6726\n",
      "Epoch 19/50\n",
      "142/142 [==============================] - 26s 185ms/step - loss: 0.5399 - accuracy: 0.7414 - val_loss: 0.5990 - val_accuracy: 0.6905\n",
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " dense_7 (Dense)             (None, 4096)              67112960  \n",
      "                                                                 \n",
      " dense_8 (Dense)             (None, 1024)              4195328   \n",
      "                                                                 \n",
      " dense_9 (Dense)             (None, 256)               262400    \n",
      "                                                                 \n",
      " dense_10 (Dense)            (None, 64)                16448     \n",
      "                                                                 \n",
      " dense_11 (Dense)            (None, 16)                1040      \n",
      "                                                                 \n",
      " dense_12 (Dense)            (None, 4)                 68        \n",
      "                                                                 \n",
      " dense_13 (Dense)            (None, 1)                 5         \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 71,588,249\n",
      "Trainable params: 71,588,249\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "40/40 [==============================] - 2s 46ms/step - loss: 0.6181 - accuracy: 0.6645\n",
      "Test accuracy: 0.6645469069480896\n",
      "Precision: 0.6042296072507553\n",
      "Recall: 0.9538950715421304\n",
      "[[236 393]\n",
      " [ 29 600]]\n"
     ]
    }
   ],
   "source": [
    "any_hemorrhage_neural_network = build_neural_network(total_data, 'any')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "2322ad51",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Set Size: 4528\n",
      "Validation Set Size: 504\n",
      "Test Set Size: 1258\n",
      "Epoch 1/50\n",
      "142/142 [==============================] - 34s 233ms/step - loss: 0.6919 - accuracy: 0.5278 - val_loss: 0.6845 - val_accuracy: 0.5575\n",
      "Epoch 2/50\n",
      "142/142 [==============================] - 40s 281ms/step - loss: 0.6792 - accuracy: 0.5786 - val_loss: 0.6722 - val_accuracy: 0.6151\n",
      "Epoch 3/50\n",
      "142/142 [==============================] - 36s 251ms/step - loss: 0.6674 - accuracy: 0.5917 - val_loss: 0.6749 - val_accuracy: 0.5476\n",
      "Epoch 4/50\n",
      "142/142 [==============================] - 38s 268ms/step - loss: 0.6591 - accuracy: 0.6095 - val_loss: 0.6477 - val_accuracy: 0.5794\n",
      "Epoch 5/50\n",
      "142/142 [==============================] - 34s 242ms/step - loss: 0.6532 - accuracy: 0.6133 - val_loss: 0.6409 - val_accuracy: 0.6190\n",
      "Epoch 6/50\n",
      "142/142 [==============================] - 38s 271ms/step - loss: 0.6467 - accuracy: 0.6325 - val_loss: 0.6772 - val_accuracy: 0.5238\n",
      "Epoch 7/50\n",
      "142/142 [==============================] - 35s 244ms/step - loss: 0.6401 - accuracy: 0.6279 - val_loss: 0.6452 - val_accuracy: 0.6448\n",
      "Epoch 8/50\n",
      "142/142 [==============================] - 33s 233ms/step - loss: 0.6314 - accuracy: 0.6418 - val_loss: 0.6148 - val_accuracy: 0.6448\n",
      "Epoch 9/50\n",
      "142/142 [==============================] - 33s 229ms/step - loss: 0.6257 - accuracy: 0.6424 - val_loss: 0.6045 - val_accuracy: 0.6607\n",
      "Epoch 10/50\n",
      "142/142 [==============================] - 32s 226ms/step - loss: 0.6213 - accuracy: 0.6484 - val_loss: 0.6788 - val_accuracy: 0.5833\n",
      "Epoch 11/50\n",
      "142/142 [==============================] - 35s 250ms/step - loss: 0.6112 - accuracy: 0.6608 - val_loss: 0.7871 - val_accuracy: 0.5516\n",
      "Epoch 12/50\n",
      "142/142 [==============================] - 36s 255ms/step - loss: 0.6044 - accuracy: 0.6681 - val_loss: 0.6079 - val_accuracy: 0.6667\n",
      "Model: \"sequential_5\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " dense_35 (Dense)            (None, 4096)              67112960  \n",
      "                                                                 \n",
      " dense_36 (Dense)            (None, 1024)              4195328   \n",
      "                                                                 \n",
      " dense_37 (Dense)            (None, 256)               262400    \n",
      "                                                                 \n",
      " dense_38 (Dense)            (None, 64)                16448     \n",
      "                                                                 \n",
      " dense_39 (Dense)            (None, 16)                1040      \n",
      "                                                                 \n",
      " dense_40 (Dense)            (None, 4)                 68        \n",
      "                                                                 \n",
      " dense_41 (Dense)            (None, 1)                 5         \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 71,588,249\n",
      "Trainable params: 71,588,249\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "40/40 [==============================] - 3s 68ms/step - loss: 0.6541 - accuracy: 0.6161\n",
      "Test accuracy: 0.6160572171211243\n",
      "Precision: 0.5717092337917485\n",
      "Recall: 0.9252782193958664\n",
      "[[193 436]\n",
      " [ 47 582]]\n"
     ]
    }
   ],
   "source": [
    "epidural_neural_network = build_neural_network(total_data, 'epidural')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "9c497821",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Set Size: 4528\n",
      "Validation Set Size: 504\n",
      "Test Set Size: 1258\n",
      "Epoch 1/50\n",
      "142/142 [==============================] - 36s 231ms/step - loss: 0.6851 - accuracy: 0.5382 - val_loss: 0.7093 - val_accuracy: 0.4921\n",
      "Epoch 2/50\n",
      "142/142 [==============================] - 40s 271ms/step - loss: 0.6765 - accuracy: 0.5656 - val_loss: 0.6815 - val_accuracy: 0.5476\n",
      "Epoch 3/50\n",
      "142/142 [==============================] - 35s 243ms/step - loss: 0.6717 - accuracy: 0.5685 - val_loss: 0.6810 - val_accuracy: 0.5496\n",
      "Epoch 4/50\n",
      "142/142 [==============================] - 31s 218ms/step - loss: 0.6655 - accuracy: 0.5797 - val_loss: 0.7096 - val_accuracy: 0.5139\n",
      "Epoch 5/50\n",
      "142/142 [==============================] - 30s 212ms/step - loss: 0.6685 - accuracy: 0.5808 - val_loss: 0.6906 - val_accuracy: 0.5238\n",
      "Epoch 6/50\n",
      "142/142 [==============================] - 37s 258ms/step - loss: 0.6589 - accuracy: 0.5919 - val_loss: 0.6748 - val_accuracy: 0.5615\n",
      "Epoch 7/50\n",
      "142/142 [==============================] - 33s 233ms/step - loss: 0.6566 - accuracy: 0.5972 - val_loss: 0.6712 - val_accuracy: 0.5655\n",
      "Epoch 8/50\n",
      "142/142 [==============================] - 31s 216ms/step - loss: 0.6522 - accuracy: 0.6106 - val_loss: 0.7573 - val_accuracy: 0.5119\n",
      "Epoch 9/50\n",
      "142/142 [==============================] - 30s 208ms/step - loss: 0.6508 - accuracy: 0.6053 - val_loss: 0.6730 - val_accuracy: 0.5635\n",
      "Epoch 10/50\n",
      "142/142 [==============================] - 31s 216ms/step - loss: 0.6464 - accuracy: 0.6148 - val_loss: 0.6716 - val_accuracy: 0.5516\n",
      "Model: \"sequential_6\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " dense_42 (Dense)            (None, 4096)              67112960  \n",
      "                                                                 \n",
      " dense_43 (Dense)            (None, 1024)              4195328   \n",
      "                                                                 \n",
      " dense_44 (Dense)            (None, 256)               262400    \n",
      "                                                                 \n",
      " dense_45 (Dense)            (None, 64)                16448     \n",
      "                                                                 \n",
      " dense_46 (Dense)            (None, 16)                1040      \n",
      "                                                                 \n",
      " dense_47 (Dense)            (None, 4)                 68        \n",
      "                                                                 \n",
      " dense_48 (Dense)            (None, 1)                 5         \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 71,588,249\n",
      "Trainable params: 71,588,249\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "40/40 [==============================] - 2s 62ms/step - loss: 0.6638 - accuracy: 0.5962\n",
      "Test accuracy: 0.5961844325065613\n",
      "Precision: 0.5799207397622193\n",
      "Recall: 0.6979332273449921\n",
      "[[311 318]\n",
      " [190 439]]\n"
     ]
    }
   ],
   "source": [
    "intraparenchymal_neural_network = build_neural_network(total_data, 'intraparenchymal')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "c888c5d8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Set Size: 4528\n",
      "Validation Set Size: 504\n",
      "Test Set Size: 1258\n",
      "Epoch 1/50\n",
      "142/142 [==============================] - 34s 234ms/step - loss: 0.6760 - accuracy: 0.5674 - val_loss: 0.6544 - val_accuracy: 0.5952\n",
      "Epoch 2/50\n",
      "142/142 [==============================] - 32s 225ms/step - loss: 0.6394 - accuracy: 0.6135 - val_loss: 0.6308 - val_accuracy: 0.6369\n",
      "Epoch 3/50\n",
      "142/142 [==============================] - 31s 215ms/step - loss: 0.6231 - accuracy: 0.6290 - val_loss: 0.6528 - val_accuracy: 0.5972\n",
      "Epoch 4/50\n",
      "142/142 [==============================] - 30s 209ms/step - loss: 0.6159 - accuracy: 0.6378 - val_loss: 0.6154 - val_accuracy: 0.6448\n",
      "Epoch 5/50\n",
      "142/142 [==============================] - 30s 215ms/step - loss: 0.6062 - accuracy: 0.6413 - val_loss: 0.6077 - val_accuracy: 0.6548\n",
      "Epoch 6/50\n",
      "142/142 [==============================] - 32s 227ms/step - loss: 0.5958 - accuracy: 0.6581 - val_loss: 0.6026 - val_accuracy: 0.6627\n",
      "Epoch 7/50\n",
      "142/142 [==============================] - 38s 269ms/step - loss: 0.5899 - accuracy: 0.6639 - val_loss: 0.6214 - val_accuracy: 0.6448\n",
      "Epoch 8/50\n",
      "142/142 [==============================] - 37s 261ms/step - loss: 0.5877 - accuracy: 0.6643 - val_loss: 0.6703 - val_accuracy: 0.6091\n",
      "Epoch 9/50\n",
      "142/142 [==============================] - 42s 296ms/step - loss: 0.5810 - accuracy: 0.6745 - val_loss: 0.5986 - val_accuracy: 0.6647\n",
      "Epoch 10/50\n",
      "142/142 [==============================] - 43s 301ms/step - loss: 0.5728 - accuracy: 0.6811 - val_loss: 0.5969 - val_accuracy: 0.6845\n",
      "Epoch 11/50\n",
      "142/142 [==============================] - 42s 292ms/step - loss: 0.5680 - accuracy: 0.6890 - val_loss: 0.7115 - val_accuracy: 0.5833\n",
      "Epoch 12/50\n",
      "142/142 [==============================] - 40s 282ms/step - loss: 0.5686 - accuracy: 0.6917 - val_loss: 0.6086 - val_accuracy: 0.6746\n",
      "Epoch 13/50\n",
      "142/142 [==============================] - 54s 382ms/step - loss: 0.5623 - accuracy: 0.6882 - val_loss: 0.6095 - val_accuracy: 0.6647\n",
      "Model: \"sequential_7\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " dense_49 (Dense)            (None, 4096)              67112960  \n",
      "                                                                 \n",
      " dense_50 (Dense)            (None, 1024)              4195328   \n",
      "                                                                 \n",
      " dense_51 (Dense)            (None, 256)               262400    \n",
      "                                                                 \n",
      " dense_52 (Dense)            (None, 64)                16448     \n",
      "                                                                 \n",
      " dense_53 (Dense)            (None, 16)                1040      \n",
      "                                                                 \n",
      " dense_54 (Dense)            (None, 4)                 68        \n",
      "                                                                 \n",
      " dense_55 (Dense)            (None, 1)                 5         \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 71,588,249\n",
      "Trainable params: 71,588,249\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "40/40 [==============================] - 6s 159ms/step - loss: 0.6155 - accuracy: 0.6471\n",
      "Test accuracy: 0.6470588445663452\n",
      "Precision: 0.6134969325153374\n",
      "Recall: 0.794912559618442\n",
      "[[314 315]\n",
      " [129 500]]\n"
     ]
    }
   ],
   "source": [
    "intraventricular_neural_network = build_neural_network(total_data, 'intraventricular')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "6b72913b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Set Size: 1\n",
      "Validation Set Size: 1\n",
      "Test Set Size: 1258\n",
      "Epoch 1/50\n",
      "1/1 [==============================] - 1s 1s/step - loss: 0.6912 - accuracy: 1.0000 - val_loss: 0.7591 - val_accuracy: 0.0000e+00\n",
      "Epoch 2/50\n",
      "1/1 [==============================] - 0s 334ms/step - loss: 0.6046 - accuracy: 1.0000 - val_loss: 0.8177 - val_accuracy: 0.0000e+00\n",
      "Epoch 3/50\n",
      "1/1 [==============================] - 0s 281ms/step - loss: 0.5507 - accuracy: 1.0000 - val_loss: 0.8728 - val_accuracy: 0.0000e+00\n",
      "Epoch 4/50\n",
      "1/1 [==============================] - 0s 317ms/step - loss: 0.4672 - accuracy: 1.0000 - val_loss: 0.9942 - val_accuracy: 0.0000e+00\n",
      "Model: \"sequential_2\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " dense_2 (Dense)             (None, 4096)              67112960  \n",
      "                                                                 \n",
      " dense_3 (Dense)             (None, 1024)              4195328   \n",
      "                                                                 \n",
      " dense_4 (Dense)             (None, 256)               262400    \n",
      "                                                                 \n",
      " dense_5 (Dense)             (None, 64)                16448     \n",
      "                                                                 \n",
      " dense_6 (Dense)             (None, 16)                1040      \n",
      "                                                                 \n",
      " dense_7 (Dense)             (None, 4)                 68        \n",
      "                                                                 \n",
      " dense_8 (Dense)             (None, 1)                 5         \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 71,588,249\n",
      "Trainable params: 71,588,249\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "40/40 [==============================] - 3s 66ms/step - loss: 0.7278 - accuracy: 0.5000\n",
      "Test accuracy: 0.5\n",
      "Precision: 0.5\n",
      "Recall: 1.0\n",
      "[[  0 629]\n",
      " [  0 629]]\n"
     ]
    }
   ],
   "source": [
    "subarachnoid_neural_network = build_neural_network(total_data, 'subarachnoid')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "a45ec77c",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Set Size: 1\n",
      "Validation Set Size: 1\n",
      "Test Set Size: 1258\n",
      "Epoch 1/50\n",
      "1/1 [==============================] - 1s 1s/step - loss: 0.7802 - accuracy: 0.0000e+00 - val_loss: 0.6959 - val_accuracy: 0.0000e+00\n",
      "Epoch 2/50\n",
      "1/1 [==============================] - 0s 294ms/step - loss: 0.6960 - accuracy: 0.0000e+00 - val_loss: 0.7013 - val_accuracy: 0.0000e+00\n",
      "Epoch 3/50\n",
      "1/1 [==============================] - 0s 275ms/step - loss: 0.6857 - accuracy: 1.0000 - val_loss: 0.7043 - val_accuracy: 0.0000e+00\n",
      "Epoch 4/50\n",
      "1/1 [==============================] - 0s 296ms/step - loss: 0.6825 - accuracy: 1.0000 - val_loss: 0.7074 - val_accuracy: 0.0000e+00\n",
      "Model: \"sequential_3\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " dense_9 (Dense)             (None, 4096)              67112960  \n",
      "                                                                 \n",
      " dense_10 (Dense)            (None, 1024)              4195328   \n",
      "                                                                 \n",
      " dense_11 (Dense)            (None, 256)               262400    \n",
      "                                                                 \n",
      " dense_12 (Dense)            (None, 64)                16448     \n",
      "                                                                 \n",
      " dense_13 (Dense)            (None, 16)                1040      \n",
      "                                                                 \n",
      " dense_14 (Dense)            (None, 4)                 68        \n",
      "                                                                 \n",
      " dense_15 (Dense)            (None, 1)                 5         \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 71,588,249\n",
      "Trainable params: 71,588,249\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "40/40 [==============================] - 2s 60ms/step - loss: 0.6932 - accuracy: 0.5000\n",
      "Test accuracy: 0.5\n",
      "Precision: 0.5\n",
      "Recall: 1.0\n",
      "[[  0 629]\n",
      " [  0 629]]\n"
     ]
    }
   ],
   "source": [
    "subdural_neural_network = build_neural_network(total_data, 'subdural')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b3284dbc",
   "metadata": {},
   "source": [
    "Using Convolutional Neural Networks for Classifying Hemorrhages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "ced57e18",
   "metadata": {},
   "outputs": [],
   "source": [
    "def build_cnn(data, column, training_set_size = max_training_set_size_per_model, conv_patience=3):\n",
    "    positive_examples_to_use = int((training_set_size + test_set_size_per_model)/2)\n",
    "    balanced_data = get_balanced_dataset(data, column, num_positive_examples=positive_examples_to_use)\n",
    "    test_size_percentage = test_set_size_per_model/(positive_examples_to_use * 2)\n",
    "    X_train, X_test, y_train, y_test = train_test_split(balanced_data[image_data_column_list], balanced_data[column], test_size=test_size_percentage, random_state=0)\n",
    "    X_train, X_valid, y_train, y_valid = train_test_split(X_train, y_train, test_size=0.1, random_state=0)\n",
    "    X_train = X_train.to_numpy().reshape(-1, 128, 128, 1)\n",
    "    X_valid = X_valid.to_numpy().reshape(-1, 128, 128, 1)\n",
    "    X_test = X_test.to_numpy().reshape(-1, 128, 128, 1)\n",
    "    print(f'Training Set Shape: {X_train.shape}')\n",
    "    print(f'Validation Set Shape: {X_valid.shape}')\n",
    "    print(f'Test Set Shape: {X_test.shape}')\n",
    "    print(f'Training Set Size: {X_train.shape[0]}')\n",
    "    print(f'Validation Set Size: {X_valid.shape[0]}')\n",
    "    print(f'Test Set Size: {X_test.shape[0]}')\n",
    "    model = keras.models.Sequential([keras.layers.Conv2D(32, (3,3), activation='relu',input_shape=(128, 128, 1)), keras.layers.MaxPooling2D((2, 2)), keras.layers.Conv2D(64, (3,3), activation='relu'),keras.layers.MaxPooling2D((2, 2)), keras.layers.Conv2D(128, (3,3), activation='relu'), keras.layers.Flatten(), keras.layers.Dense(100, activation='relu'), keras.layers.Dense(1, activation='sigmoid')])\n",
    "    model.compile(loss=\"binary_crossentropy\", optimizer=\"sgd\", metrics=[\"accuracy\"])\n",
    "    history = model.fit(X_train, y_train, epochs=50, verbose=1, validation_data=(X_valid, y_valid), callbacks=[keras.callbacks.EarlyStopping(monitor='val_loss',patience=conv_patience)])\n",
    "    model.summary()\n",
    "    test_loss, test_accuracy = model.evaluate(X_test, y_test, verbose=1)\n",
    "    print(f'Test accuracy: {test_accuracy}')\n",
    "    y_predict = model.predict(X_test)\n",
    "    y_predict = list(map(lambda y : int(y >= 0.5), y_predict))\n",
    "    results = confusion_matrix(y_test, y_predict)\n",
    "    print(f'Precision: {results[1][1]/(results[1][1] + results[0][1])}')\n",
    "    print(f'Recall: {results[1][1]/(results[1][1] + results[1][0])}')\n",
    "    print(results)\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "6ec0ef75",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Set Shape: (4528, 128, 128, 1)\n",
      "Validation Set Shape: (504, 128, 128, 1)\n",
      "Test Set Shape: (1258, 128, 128, 1)\n",
      "Training Set Size: 4528\n",
      "Validation Set Size: 504\n",
      "Test Set Size: 1258\n",
      "Epoch 1/50\n",
      "142/142 [==============================] - 55s 383ms/step - loss: 0.6704 - accuracy: 0.5890 - val_loss: 0.6377 - val_accuracy: 0.6587\n",
      "Epoch 2/50\n",
      "142/142 [==============================] - 56s 395ms/step - loss: 0.6314 - accuracy: 0.6413 - val_loss: 0.6536 - val_accuracy: 0.5595\n",
      "Epoch 3/50\n",
      "142/142 [==============================] - 55s 389ms/step - loss: 0.6132 - accuracy: 0.6608 - val_loss: 0.5964 - val_accuracy: 0.6766\n",
      "Epoch 4/50\n",
      "142/142 [==============================] - 54s 383ms/step - loss: 0.5964 - accuracy: 0.6782 - val_loss: 0.6900 - val_accuracy: 0.5476\n",
      "Epoch 5/50\n",
      "142/142 [==============================] - 57s 403ms/step - loss: 0.5878 - accuracy: 0.6851 - val_loss: 0.6629 - val_accuracy: 0.5694\n",
      "Epoch 6/50\n",
      "142/142 [==============================] - 54s 383ms/step - loss: 0.5765 - accuracy: 0.6886 - val_loss: 0.6030 - val_accuracy: 0.6905\n",
      "Model: \"sequential_3\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " conv2d_3 (Conv2D)           (None, 126, 126, 32)      320       \n",
      "                                                                 \n",
      " max_pooling2d_2 (MaxPooling  (None, 63, 63, 32)       0         \n",
      " 2D)                                                             \n",
      "                                                                 \n",
      " conv2d_4 (Conv2D)           (None, 61, 61, 64)        18496     \n",
      "                                                                 \n",
      " max_pooling2d_3 (MaxPooling  (None, 30, 30, 64)       0         \n",
      " 2D)                                                             \n",
      "                                                                 \n",
      " conv2d_5 (Conv2D)           (None, 28, 28, 128)       73856     \n",
      "                                                                 \n",
      " flatten_1 (Flatten)         (None, 100352)            0         \n",
      "                                                                 \n",
      " dense_16 (Dense)            (None, 100)               10035300  \n",
      "                                                                 \n",
      " dense_17 (Dense)            (None, 1)                 101       \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 10,128,073\n",
      "Trainable params: 10,128,073\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "40/40 [==============================] - 4s 104ms/step - loss: 0.6040 - accuracy: 0.6876\n",
      "Test accuracy: 0.6875993609428406\n",
      "Precision: 0.6239495798319328\n",
      "Recall: 0.9443561208267091\n",
      "[[271 358]\n",
      " [ 35 594]]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.engine.sequential.Sequential at 0x7fa7ae023af0>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "build_cnn(total_data, 'any')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "21603922",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Set Shape: (4528, 128, 128, 1)\n",
      "Validation Set Shape: (504, 128, 128, 1)\n",
      "Test Set Shape: (1258, 128, 128, 1)\n",
      "Training Set Size: 4528\n",
      "Validation Set Size: 504\n",
      "Test Set Size: 1258\n",
      "Epoch 1/50\n",
      "142/142 [==============================] - 57s 397ms/step - loss: 0.6910 - accuracy: 0.5342 - val_loss: 0.6978 - val_accuracy: 0.4921\n",
      "Epoch 2/50\n",
      "142/142 [==============================] - 55s 386ms/step - loss: 0.6860 - accuracy: 0.5537 - val_loss: 0.6936 - val_accuracy: 0.5139\n",
      "Epoch 3/50\n",
      "142/142 [==============================] - 51s 358ms/step - loss: 0.6821 - accuracy: 0.5585 - val_loss: 0.6848 - val_accuracy: 0.5397\n",
      "Epoch 4/50\n",
      "142/142 [==============================] - 52s 363ms/step - loss: 0.6759 - accuracy: 0.5760 - val_loss: 0.6783 - val_accuracy: 0.5516\n",
      "Epoch 5/50\n",
      "142/142 [==============================] - 58s 409ms/step - loss: 0.6719 - accuracy: 0.5799 - val_loss: 0.6861 - val_accuracy: 0.5397\n",
      "Epoch 6/50\n",
      "142/142 [==============================] - 53s 370ms/step - loss: 0.6690 - accuracy: 0.5870 - val_loss: 0.6889 - val_accuracy: 0.6091\n",
      "Epoch 7/50\n",
      "142/142 [==============================] - 55s 386ms/step - loss: 0.6652 - accuracy: 0.5941 - val_loss: 0.6716 - val_accuracy: 0.5734\n",
      "Epoch 8/50\n",
      "142/142 [==============================] - 73s 518ms/step - loss: 0.6636 - accuracy: 0.5912 - val_loss: 0.6698 - val_accuracy: 0.5655\n",
      "Epoch 9/50\n",
      "142/142 [==============================] - 75s 532ms/step - loss: 0.6598 - accuracy: 0.6036 - val_loss: 0.6694 - val_accuracy: 0.5893\n",
      "Epoch 10/50\n",
      "116/142 [=======================>......] - ETA: 11s - loss: 0.6540 - accuracy: 0.6086"
     ]
    }
   ],
   "source": [
    "build_cnn(total_data, 'epidural')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9e0651c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "build_cnn(total_data, 'intraparenchymal')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "25259a10",
   "metadata": {},
   "outputs": [],
   "source": [
    "build_cnn(total_data, 'intraventricular')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5fdb02a0",
   "metadata": {},
   "outputs": [],
   "source": [
    "build_cnn(total_data, 'subarachnoid')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "04d44b1a",
   "metadata": {},
   "outputs": [],
   "source": [
    "build_cnn(total_data, 'subdural')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "519de0a3",
   "metadata": {},
   "source": [
    "Annotating a Sample Image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8933fcce",
   "metadata": {},
   "outputs": [],
   "source": [
    "epidural_csv_file = 'segmentations/Results_Epidural Hemorrhage Detection_2020-11-16_21.31.26.148.csv'\n",
    "epidural_image_repo = 'epidural/max_contrast_window'\n",
    "epidural_image_file = 'ID_004c4b319.jpg'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "125306b4",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_coords(image, image_csv_file, image_file_name):\n",
    "    labels = get_labels_csv(image_csv_file, image_file_name)\n",
    "    coord_list = []\n",
    "    for label in labels:\n",
    "        coord = (label['x'] * image.size[0], label['y'] * image.size[1])\n",
    "        coord_list.append(coord)\n",
    "    return coord_list\n",
    "def get_labels_csv(image_csv_file, image_file_name):\n",
    "    with open(image_csv_file) as csv_file:\n",
    "        data_reader = csv.reader(csv_file, dialect='excel')\n",
    "        for row in data_reader:\n",
    "            if row[1] == image_file_name:\n",
    "                return json.loads(row[7])\n",
    "    return None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4015b6be",
   "metadata": {},
   "outputs": [],
   "source": [
    "im = Image.open(f'{epidural_image_repo}/{epidural_image_file}')\n",
    "features = []\n",
    "for x in range(im.size[0]):\n",
    "    for y in range(20):\n",
    "        features.append(im.getpixel((x, y))[0])\n",
    "        features.append(im.getpixel((x, y))[1])\n",
    "        features.append(im.getpixel((x, y))[2])\n",
    "draw = ImageDraw.Draw(im)\n",
    "draw.line(get_coords(im, epidural_csv_file, epidural_image_file), fill='red')\n",
    "im.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "97da8374",
   "metadata": {},
   "outputs": [],
   "source": [
    "# the line of code below loads the 'all labels' field in the csv file in json\n",
    "# coords_lists = json.loads(row[4].replace(\"'[\", \"[\").replace(\"]'\", \"],\").replace(',]', ']'))"
   ]
  }
 ],
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