{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from random import sample\n",
    "\n",
    "from itertools import chain\n",
    "from random import sample \n",
    "import scipy\n",
    "\n",
    "import sklearn.model_selection as skl"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## First read in the dataframe. You'll notice it's similar to the dataframe that you ended the final solution with in Lesson 2, Exercise 4, only with more data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "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>Unnamed: 0</th>\n",
       "      <th>Finding Labels</th>\n",
       "      <th>Patient ID</th>\n",
       "      <th>Patient Age</th>\n",
       "      <th>Patient Gender</th>\n",
       "      <th>Atelectasis</th>\n",
       "      <th>Cardiomegaly</th>\n",
       "      <th>Consolidation</th>\n",
       "      <th>Edema</th>\n",
       "      <th>Effusion</th>\n",
       "      <th>Emphysema</th>\n",
       "      <th>Fibrosis</th>\n",
       "      <th>Hernia</th>\n",
       "      <th>Infiltration</th>\n",
       "      <th>Mass</th>\n",
       "      <th>No Finding</th>\n",
       "      <th>Nodule</th>\n",
       "      <th>Pleural_Thickening</th>\n",
       "      <th>Pneumonia</th>\n",
       "      <th>Pneumothorax</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Cardiomegaly|Emphysema</td>\n",
       "      <td>1</td>\n",
       "      <td>57</td>\n",
       "      <td>M</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>No Finding</td>\n",
       "      <td>2</td>\n",
       "      <td>77</td>\n",
       "      <td>M</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Atelectasis</td>\n",
       "      <td>3</td>\n",
       "      <td>79</td>\n",
       "      <td>M</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Cardiomegaly|Edema|Effusion</td>\n",
       "      <td>4</td>\n",
       "      <td>55</td>\n",
       "      <td>F</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Consolidation|Mass</td>\n",
       "      <td>5</td>\n",
       "      <td>68</td>\n",
       "      <td>M</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0               Finding Labels  Patient ID  Patient Age  \\\n",
       "0           1       Cardiomegaly|Emphysema           1           57   \n",
       "1           2                   No Finding           2           77   \n",
       "2           3                  Atelectasis           3           79   \n",
       "3           4  Cardiomegaly|Edema|Effusion           4           55   \n",
       "4           5           Consolidation|Mass           5           68   \n",
       "\n",
       "  Patient Gender  Atelectasis  Cardiomegaly  Consolidation  Edema  Effusion  \\\n",
       "0              M          0.0           1.0            0.0    0.0       0.0   \n",
       "1              M          0.0           0.0            0.0    0.0       0.0   \n",
       "2              M          1.0           0.0            0.0    0.0       0.0   \n",
       "3              F          0.0           1.0            0.0    1.0       1.0   \n",
       "4              M          0.0           0.0            1.0    0.0       0.0   \n",
       "\n",
       "   Emphysema  Fibrosis  Hernia  Infiltration  Mass  No Finding  Nodule  \\\n",
       "0        1.0       0.0     0.0           0.0   0.0         0.0     0.0   \n",
       "1        0.0       0.0     0.0           0.0   0.0         1.0     0.0   \n",
       "2        0.0       0.0     0.0           0.0   0.0         0.0     0.0   \n",
       "3        0.0       0.0     0.0           0.0   0.0         0.0     0.0   \n",
       "4        0.0       0.0     0.0           0.0   1.0         0.0     0.0   \n",
       "\n",
       "   Pleural_Thickening  Pneumonia  Pneumothorax  \n",
       "0                 0.0        0.0           0.0  \n",
       "1                 0.0        0.0           0.0  \n",
       "2                 0.0        0.0           0.0  \n",
       "3                 0.0        0.0           0.0  \n",
       "4                 0.0        0.0           0.0  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d = pd.read_csv('findings_data_5000.csv')\n",
    "d.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Just like in Lesson 2, Exercise 4, we want to see how different diseases are distributed with our disease of interest, as well as how age and gender are distributed:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_labels = np.unique(list(chain(*d['Finding Labels'].map(lambda x: x.split('|')).tolist())))\n",
    "all_labels = [x for x in all_labels if len(x)>0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0, 0.5, 'Number of Images with Label')]"
      ]
     },
     "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": [
    "ax = d[all_labels].sum().plot(kind='bar')\n",
    "ax.set(ylabel = 'Number of Images with Label')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f0ed604d450>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "##Since there are many combinations of potential findings, I'm going to look at the 30 most common co-occurrences:\n",
    "plt.figure(figsize=(16,6))\n",
    "d[d.Pneumothorax==1]['Finding Labels'].value_counts()[0:30].plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f0ed60def10>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "##Since there are many combinations of potential findings, I'm going to look at the 30 most common co-occurrences:\n",
    "plt.figure(figsize=(6,6))\n",
    "d[d.Pneumothorax ==1]['Patient Gender'].value_counts().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([12., 18., 13., 21., 20., 47., 50., 26.,  9.,  4.]),\n",
       " array([ 7. , 14.6, 22.2, 29.8, 37.4, 45. , 52.6, 60.2, 67.8, 75.4, 83. ]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "plt.hist(d[d.Pneumothorax==1]['Patient Age'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Now, knowing what we know from above, let's create the appropriate training and validation sets for a model that we want to train to classify the presence of a Pneumothorax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df, valid_df = skl.train_test_split(d, \n",
    "                                   test_size = 0.2, \n",
    "                                   stratify = d['Pneumothorax'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.04401100275068767"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df['Pneumothorax'].sum()/len(train_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.044"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "valid_df['Pneumothorax'].sum()/len(valid_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Great, our train_test_split made sure that we had the same proportions of Pneumothorax in both sets!\n",
    "\n",
    "But.... we know that we want our model to be trained on a set that has _equal_ proportions of pneumothorax and no pneumothorax, so we're going to have to throw away some data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "p_inds = train_df[train_df.Pneumothorax==1].index.tolist()\n",
    "np_inds = train_df[train_df.Pneumothorax==0].index.tolist()\n",
    "\n",
    "np_sample = sample(np_inds,len(p_inds))\n",
    "train_df = train_df.loc[p_inds + np_sample]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df['Pneumothorax'].sum()/len(train_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ta-da! We randomly chose a set of non-Pneumothorax images using the sample() function that was the same length as the number of true Pneumothorax cases we had, and then we threw out the rest of the non-Pneumothorax cases. Now our training dataset is balanced 50-50.\n",
    "\n",
    "Finally, we want to make the balance in our validation set more like 20-80 since our exercise told us that the prevalence of Pneumothorax in this clinical situation is about 20%:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "p_inds = valid_df[valid_df.Pneumothorax==1].index.tolist()\n",
    "np_inds = valid_df[valid_df.Pneumothorax==0].index.tolist()\n",
    "\n",
    "np_sample = sample(np_inds,4*len(p_inds))\n",
    "valid_df = valid_df.loc[p_inds + np_sample]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.2"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "valid_df['Pneumothorax'].sum()/len(valid_df)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
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