{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Predicting Schizophrenia Diagnosis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebooks contains an analysis of the COBRE dataset available on Nilearn. The dataset contains resting state fMRI data from 146 participants. Approximately half of the subjects are patients diagnosed with schizophrenia and the remainder are healthy controls. The anlaysis in this notebook attempt to predict schizophrenia diagnosis using resting state fMRI data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#import data\n",
    "from nilearn import datasets\n",
    "data = datasets.fetch_cobre(n_subjects=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Phenotypic info for the subjects is included with the data ut requires some cleaning first."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#import phenotypic data\n",
    "import pandas\n",
    "pheno = pandas.DataFrame(data.phenotypic)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll extract subject ID from the niifti file names using index slicing and then merge the fMRI file paths to the phenotypic data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#extract participant id from file paths\n",
    "file_names = []\n",
    "for path in data.func:\n",
    "    \n",
    "    file_names.append(path[40:45])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#create dataframe of file paths and ids\n",
    "files = pandas.DataFrame(data.func, columns = ['path'])\n",
    "files['id'] = file_names\n",
    "files['id'] = files.id.astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#merge phenotypic data with file paths\n",
    "import pandas\n",
    "pheno = pandas.merge(pheno, files, on = 'id')\n",
    "\n",
    "#fix string decoding\n",
    "pheno['gender'] = pheno['gender'].map(lambda x: x.decode('utf-8'))\n",
    "pheno['handedness'] = pheno['handedness'].map(lambda x: x.decode('utf-8'))\n",
    "pheno['subject_type'] = pheno['subject_type'].map(lambda x: x.decode('utf-8'))\n",
    "pheno['diagnosis'] = pheno['diagnosis'].map(lambda x: x.decode('utf-8'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's take a look at what we have now. And also sve the cleaned phenotypic data to a csv."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>id</th>\n",
       "      <th>current_age</th>\n",
       "      <th>gender</th>\n",
       "      <th>handedness</th>\n",
       "      <th>subject_type</th>\n",
       "      <th>diagnosis</th>\n",
       "      <th>frames_ok</th>\n",
       "      <th>fd</th>\n",
       "      <th>fd_scrubbed</th>\n",
       "      <th>path</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>40061</td>\n",
       "      <td>18</td>\n",
       "      <td>Male</td>\n",
       "      <td>Right</td>\n",
       "      <td>Control</td>\n",
       "      <td>None</td>\n",
       "      <td>133</td>\n",
       "      <td>0.25512</td>\n",
       "      <td>0.22657</td>\n",
       "      <td>/home/aalbury/nilearn_data/cobre/fmri_0040061....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>40090</td>\n",
       "      <td>18</td>\n",
       "      <td>Female</td>\n",
       "      <td>Right</td>\n",
       "      <td>Control</td>\n",
       "      <td>None</td>\n",
       "      <td>150</td>\n",
       "      <td>0.16963</td>\n",
       "      <td>0.16963</td>\n",
       "      <td>/home/aalbury/nilearn_data/cobre/fmri_0040090....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>40046</td>\n",
       "      <td>18</td>\n",
       "      <td>Male</td>\n",
       "      <td>Left</td>\n",
       "      <td>Patient</td>\n",
       "      <td>295.70 depressed type</td>\n",
       "      <td>76</td>\n",
       "      <td>0.37504</td>\n",
       "      <td>0.30042</td>\n",
       "      <td>/home/aalbury/nilearn_data/cobre/fmri_0040046....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>40002</td>\n",
       "      <td>19</td>\n",
       "      <td>Male</td>\n",
       "      <td>Right</td>\n",
       "      <td>Patient</td>\n",
       "      <td>295.3</td>\n",
       "      <td>67</td>\n",
       "      <td>0.40006</td>\n",
       "      <td>0.21575</td>\n",
       "      <td>/home/aalbury/nilearn_data/cobre/fmri_0040002....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>40117</td>\n",
       "      <td>19</td>\n",
       "      <td>Male</td>\n",
       "      <td>Right</td>\n",
       "      <td>Patient</td>\n",
       "      <td>295.3</td>\n",
       "      <td>133</td>\n",
       "      <td>0.20975</td>\n",
       "      <td>0.18410</td>\n",
       "      <td>/home/aalbury/nilearn_data/cobre/fmri_0040117....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>40089</td>\n",
       "      <td>62</td>\n",
       "      <td>Male</td>\n",
       "      <td>Right</td>\n",
       "      <td>Patient</td>\n",
       "      <td>295.3</td>\n",
       "      <td>40</td>\n",
       "      <td>0.70368</td>\n",
       "      <td>0.72439</td>\n",
       "      <td>/home/aalbury/nilearn_data/cobre/fmri_0040089....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>40040</td>\n",
       "      <td>63</td>\n",
       "      <td>Male</td>\n",
       "      <td>Right</td>\n",
       "      <td>Patient</td>\n",
       "      <td>295.3</td>\n",
       "      <td>42</td>\n",
       "      <td>0.58301</td>\n",
       "      <td>0.40646</td>\n",
       "      <td>/home/aalbury/nilearn_data/cobre/fmri_0040040....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>40028</td>\n",
       "      <td>64</td>\n",
       "      <td>Male</td>\n",
       "      <td>Right</td>\n",
       "      <td>Patient</td>\n",
       "      <td>295.3</td>\n",
       "      <td>55</td>\n",
       "      <td>0.42364</td>\n",
       "      <td>0.26393</td>\n",
       "      <td>/home/aalbury/nilearn_data/cobre/fmri_0040028....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>40086</td>\n",
       "      <td>65</td>\n",
       "      <td>Male</td>\n",
       "      <td>Right</td>\n",
       "      <td>Control</td>\n",
       "      <td>None</td>\n",
       "      <td>48</td>\n",
       "      <td>0.39595</td>\n",
       "      <td>0.32296</td>\n",
       "      <td>/home/aalbury/nilearn_data/cobre/fmri_0040086....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>40007</td>\n",
       "      <td>65</td>\n",
       "      <td>Female</td>\n",
       "      <td>Right</td>\n",
       "      <td>Patient</td>\n",
       "      <td>295.3</td>\n",
       "      <td>40</td>\n",
       "      <td>0.70044</td>\n",
       "      <td>0.72077</td>\n",
       "      <td>/home/aalbury/nilearn_data/cobre/fmri_0040007....</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>146 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        id  current_age  gender handedness subject_type  \\\n",
       "0    40061           18    Male      Right      Control   \n",
       "1    40090           18  Female      Right      Control   \n",
       "2    40046           18    Male       Left      Patient   \n",
       "3    40002           19    Male      Right      Patient   \n",
       "4    40117           19    Male      Right      Patient   \n",
       "..     ...          ...     ...        ...          ...   \n",
       "141  40089           62    Male      Right      Patient   \n",
       "142  40040           63    Male      Right      Patient   \n",
       "143  40028           64    Male      Right      Patient   \n",
       "144  40086           65    Male      Right      Control   \n",
       "145  40007           65  Female      Right      Patient   \n",
       "\n",
       "                 diagnosis  frames_ok       fd  fd_scrubbed  \\\n",
       "0                     None        133  0.25512      0.22657   \n",
       "1                     None        150  0.16963      0.16963   \n",
       "2    295.70 depressed type         76  0.37504      0.30042   \n",
       "3                    295.3         67  0.40006      0.21575   \n",
       "4                    295.3        133  0.20975      0.18410   \n",
       "..                     ...        ...      ...          ...   \n",
       "141                  295.3         40  0.70368      0.72439   \n",
       "142                  295.3         42  0.58301      0.40646   \n",
       "143                  295.3         55  0.42364      0.26393   \n",
       "144                   None         48  0.39595      0.32296   \n",
       "145                  295.3         40  0.70044      0.72077   \n",
       "\n",
       "                                                  path  \n",
       "0    /home/aalbury/nilearn_data/cobre/fmri_0040061....  \n",
       "1    /home/aalbury/nilearn_data/cobre/fmri_0040090....  \n",
       "2    /home/aalbury/nilearn_data/cobre/fmri_0040046....  \n",
       "3    /home/aalbury/nilearn_data/cobre/fmri_0040002....  \n",
       "4    /home/aalbury/nilearn_data/cobre/fmri_0040117....  \n",
       "..                                                 ...  \n",
       "141  /home/aalbury/nilearn_data/cobre/fmri_0040089....  \n",
       "142  /home/aalbury/nilearn_data/cobre/fmri_0040040....  \n",
       "143  /home/aalbury/nilearn_data/cobre/fmri_0040028....  \n",
       "144  /home/aalbury/nilearn_data/cobre/fmri_0040086....  \n",
       "145  /home/aalbury/nilearn_data/cobre/fmri_0040007....  \n",
       "\n",
       "[146 rows x 10 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#pheno.to_csv('pheno.csv', index=False)\n",
    "pheno"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have the file paths matched with the phenotypic data, we can easily make subsets for patients and controls."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#create lists of filepaths for patients and controls\n",
    "patients = []\n",
    "controls = []\n",
    "\n",
    "for i in pheno.index:\n",
    "    if pheno.loc[i, 'subject_type']=='Patient':\n",
    "    \n",
    "        patients.append(pheno.loc[i, 'path'])\n",
    "    else:\n",
    "        controls.append(pheno.loc[i, 'path'])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The code below generates an interactive app using plotly express that will plot a histogram of subject age."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"800\"\n",
       "            height=\"650\"\n",
       "            src=\"http://127.0.0.1:8050/\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.IFrame at 0x7f02388c9b50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import plotly.express as px\n",
    "from jupyter_dash import JupyterDash\n",
    "import dash_core_components as dcc\n",
    "import dash_html_components as html\n",
    "from dash.dependencies import Input, Output\n",
    "# Load Data\n",
    "df = pheno\n",
    "# Build App\n",
    "app = JupyterDash(__name__)\n",
    "app.layout = html.Div([\n",
    "    html.H1(\"Age\"),\n",
    "    dcc.Graph(id='graph'),\n",
    "    html.Label([\n",
    "        \"Participant type\",\n",
    "        dcc.Dropdown(\n",
    "            id='subject_type', clearable=False,\n",
    "            value='Patient', options=[\n",
    "                {'label': c, 'value': c}\n",
    "                for c in df.subject_type.unique() #get all unique values from column\n",
    "            ])\n",
    "    ]),\n",
    "])\n",
    "# Define callback to update graph\n",
    "@app.callback(\n",
    "    Output('graph', 'figure'),\n",
    "    [Input(\"subject_type\", \"value\")]\n",
    ")\n",
    "def update_figure(subject_type):\n",
    "    return px.histogram(\n",
    "        df[df[\"subject_type\"]==subject_type], x=\"current_age\", color=\"gender\"\n",
    "        \n",
    "    )\n",
    "# Run app and display result inline in the notebook\n",
    "app.run_server(mode='inline')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Connectivity"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This anlaysis uses the BASC atlas to defin ROIs. We'll focus on 64 ROIs for this analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#import atlas\n",
    "parcellations = datasets.fetch_atlas_basc_multiscale_2015(version='sym')\n",
    "atlas_filename = parcellations.scale064"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<nilearn.plotting.displays.OrthoSlicer at 0x7f0230866f10>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 475.2x187.2 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualize atlas\n",
    "from nilearn import plotting\n",
    "plotting.plot_roi(atlas_filename, draw_cross = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's generate correlation matrices for each subject and then merge them to the phenotypic data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "from nilearn.input_data import NiftiLabelsMasker\n",
    "from nilearn.connectome import ConnectivityMeasure\n",
    "\n",
    "# create mask\n",
    "mask = NiftiLabelsMasker(labels_img=atlas_filename, \n",
    "                           standardize=True, \n",
    "                           memory='nilearn_cache', \n",
    "                           verbose=1)\n",
    "\n",
    "# initialize correlation measure\n",
    "correlation_measure = ConnectivityMeasure(kind='correlation', vectorize=True,\n",
    "                                         discard_diagonal=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NiftiLabelsMasker.fit_transform] loading data from /home/aalbury/nilearn_data/basc_multiscale_2015/template_cambridge_basc_multiscale_nii_sym/template_cambridge_basc_multiscale_sym_scale064.nii.gz\n",
      "Resampling labels\n",
      "[NiftiLabelsMasker.fit_transform] loading data from /home/aalbury/nilearn_data/basc_multiscale_2015/template_cambridge_basc_multiscale_nii_sym/template_cambridge_basc_multiscale_sym_scale064.nii.gz\n",
      "[NiftiLabelsMasker.fit_transform] loading data from /home/aalbury/nilearn_data/basc_multiscale_2015/template_cambridge_basc_multiscale_nii_sym/template_cambridge_basc_multiscale_sym_scale064.nii.gz\n",
      "[NiftiLabelsMasker.fit_transform] loading data from /home/aalbury/nilearn_data/basc_multiscale_2015/template_cambridge_basc_multiscale_nii_sym/template_cambridge_basc_multiscale_sym_scale064.nii.gz\n",
      "[NiftiLabelsMasker.fit_transform] loading data from /home/aalbury/nilearn_data/basc_multiscale_2015/template_cambridge_basc_multiscale_nii_sym/template_cambridge_basc_multiscale_sym_scale064.nii.gz\n",
      "[NiftiLabelsMasker.fit_transform] loading data from /home/aalbury/nilearn_data/basc_multiscale_2015/template_cambridge_basc_multiscale_nii_sym/template_cambridge_basc_multiscale_sym_scale064.nii.gz\n",
      "[NiftiLabelsMasker.fit_transform] loading data from /home/aalbury/nilearn_data/basc_multiscale_2015/template_cambridge_basc_multiscale_nii_sym/template_cambridge_basc_multiscale_sym_scale064.nii.gz\n",
      "[NiftiLabelsMasker.fit_transform] loading data from /home/aalbury/nilearn_data/basc_multiscale_2015/template_cambridge_basc_multiscale_nii_sym/template_cambridge_basc_multiscale_sym_scale064.nii.gz\n",
      "[NiftiLabelsMasker.fit_transform] loading data from /home/aalbury/nilearn_data/basc_multiscale_2015/template_cambridge_basc_multiscale_nii_sym/template_cambridge_basc_multiscale_sym_scale064.nii.gz\n",
      "[NiftiLabelsMasker.fit_transform] loading data from /home/aalbury/nilearn_data/basc_multiscale_2015/template_cambridge_basc_multiscale_nii_sym/template_cambridge_basc_multiscale_sym_scale064.nii.gz\n",
      "[NiftiLabelsMasker.fit_transform] loading data from /home/aalbury/nilearn_data/basc_multiscale_2015/template_cambridge_basc_multiscale_nii_sym/template_cambridge_basc_multiscale_sym_scale064.nii.gz\n",
      "[NiftiLabelsMasker.fit_transform] loading data from /home/aalbury/nilearn_data/basc_multiscale_2015/template_cambridge_basc_multiscale_nii_sym/template_cambridge_basc_multiscale_sym_scale064.nii.gz\n",
      "[NiftiLabelsMasker.fit_transform] loading data from /home/aalbury/nilearn_data/basc_multiscale_2015/template_cambridge_basc_multiscale_nii_sym/template_cambridge_basc_multiscale_sym_scale064.nii.gz\n",
      "[NiftiLabelsMasker.fit_transform] loading data from /home/aalbury/nilearn_data/basc_multiscale_2015/template_cambridge_basc_multiscale_nii_sym/template_cambridge_basc_multiscale_sym_scale064.nii.gz\n",
      "[NiftiLabelsMasker.fit_transform] loading data from /home/aalbury/nilearn_data/basc_multiscale_2015/template_cambridge_basc_multiscale_nii_sym/template_cambridge_basc_multiscale_sym_scale064.nii.gz\n",
      "[NiftiLabelsMasker.fit_transform] loading data from /home/aalbury/nilearn_data/basc_multiscale_2015/template_cambridge_basc_multiscale_nii_sym/template_cambridge_basc_multiscale_sym_scale064.nii.gz\n",
      "[NiftiLabelsMasker.fit_transform] loading data from /home/aalbury/nilearn_data/basc_multiscale_2015/template_cambridge_basc_multiscale_nii_sym/template_cambridge_basc_multiscale_sym_scale064.nii.gz\n",
      "[NiftiLabelsMasker.fit_transform] loading data from /home/aalbury/nilearn_data/basc_multiscale_2015/template_cambridge_basc_multiscale_nii_sym/template_cambridge_basc_multiscale_sym_scale064.nii.gz\n",
      "________________________________________________________________________________\n",
      "[Memory] Calling nilearn.input_data.base_masker.filter_and_extract...\n",
      "filter_and_extract('/home/aalbury/nilearn_data/cobre/fmri_0040072.nii.gz', <nilearn.input_data.nifti_labels_masker._ExtractionFunctor object at 0x7f02300f3dd0>, \n",
      "{ 'background_label': 0,\n",
      "  'detrend': False,\n",
      "  'dtype': None,\n",
      "  'high_pass': None,\n",
      "  'labels_img': '/home/aalbury/nilearn_data/basc_multiscale_2015/template_cambridge_basc_multiscale_nii_sym/template_cambridge_basc_multiscale_sym_scale064.nii.gz',\n",
      "  'low_pass': None,\n",
      "  'mask_img': None,\n",
      "  'smoothing_fwhm': None,\n",
      "  'standardize': True,\n",
      "  'strategy': 'mean',\n",
      "  't_r': None,\n",
      "  'target_affine': None,\n",
      "  'target_shape': None}, confounds='/home/aalbury/nilearn_data/cobre/fmri_0040072.tsv', dtype=None, memory=Memory(location=nilearn_cache/joblib), memory_level=1, verbose=1)\n",
      "[NiftiLabelsMasker.transform_single_imgs] Loading data from /home/aalbury/nilearn_data/cobre/fmri_0040072.nii.gz\n",
      "[NiftiLabelsMasker.transform_single_imgs] Extracting region signals\n",
      "[NiftiLabelsMasker.transform_single_imgs] Cleaning extracted signals\n",
      "_______________________________________________filter_and_extract - 0.9s, 0.0min\n",
      "[NiftiLabelsMasker.fit_transform] loading data from /home/aalbury/nilearn_data/basc_multiscale_2015/template_cambridge_basc_multiscale_nii_sym/template_cambridge_basc_multiscale_sym_scale064.nii.gz\n",
      "[NiftiLabelsMasker.fit_transform] loading data from /home/aalbury/nilearn_data/basc_multiscale_2015/template_cambridge_basc_multiscale_nii_sym/template_cambridge_basc_multiscale_sym_scale064.nii.gz\n",
      "[NiftiLabelsMasker.fit_transform] loading data from /home/aalbury/nilearn_data/basc_multiscale_2015/template_cambridge_basc_multiscale_nii_sym/template_cambridge_basc_multiscale_sym_scale064.nii.gz\n",
      "[NiftiLabelsMasker.fit_transform] loading data from /home/aalbury/nilearn_data/basc_multiscale_2015/template_cambridge_basc_multiscale_nii_sym/template_cambridge_basc_multiscale_sym_scale064.nii.gz\n",
      "[NiftiLabelsMasker.fit_transform] loading data from /home/aalbury/nilearn_data/basc_multiscale_2015/template_cambridge_basc_multiscale_nii_sym/template_cambridge_basc_multiscale_sym_scale064.nii.gz\n",
      "[NiftiLabelsMasker.fit_transform] loading data from /home/aalbury/nilearn_data/basc_multiscale_2015/template_cambridge_basc_multiscale_nii_sym/template_cambridge_basc_multiscale_sym_scale064.nii.gz\n",
      "[NiftiLabelsMasker.fit_transform] loading data from /home/aalbury/nilearn_data/basc_multiscale_2015/template_cambridge_basc_multiscale_nii_sym/template_cambridge_basc_multiscale_sym_scale064.nii.gz\n",
      "[NiftiLabelsMasker.fit_transform] loading data from /home/aalbury/nilearn_data/basc_multiscale_2015/template_cambridge_basc_multiscale_nii_sym/template_cambridge_basc_multiscale_sym_scale064.nii.gz\n",
      "[NiftiLabelsMasker.fit_transform] loading data from /home/aalbury/nilearn_data/basc_multiscale_2015/template_cambridge_basc_multiscale_nii_sym/template_cambridge_basc_multiscale_sym_scale064.nii.gz\n",
      "[NiftiLabelsMasker.fit_transform] loading data from /home/aalbury/nilearn_data/basc_multiscale_2015/template_cambridge_basc_multiscale_nii_sym/template_cambridge_basc_multiscale_sym_scale064.nii.gz\n",
      "[NiftiLabelsMasker.fit_transform] loading data from /home/aalbury/nilearn_data/basc_multiscale_2015/template_cambridge_basc_multiscale_nii_sym/template_cambridge_basc_multiscale_sym_scale064.nii.gz\n",
      "[NiftiLabelsMasker.fit_transform] loading data from /home/aalbury/nilearn_data/basc_multiscale_2015/template_cambridge_basc_multiscale_nii_sym/template_cambridge_basc_multiscale_sym_scale064.nii.gz\n",
      "[NiftiLabelsMasker.fit_transform] loading data from /home/aalbury/nilearn_data/basc_multiscale_2015/template_cambridge_basc_multiscale_nii_sym/template_cambridge_basc_multiscale_sym_scale064.nii.gz\n",
      "[NiftiLabelsMasker.fit_transform] loading data from /home/aalbury/nilearn_data/basc_multiscale_2015/template_cambridge_basc_multiscale_nii_sym/template_cambridge_basc_multiscale_sym_scale064.nii.gz\n",
      "[NiftiLabelsMasker.fit_transform] loading data from /home/aalbury/nilearn_data/basc_multiscale_2015/template_cambridge_basc_multiscale_nii_sym/template_cambridge_basc_multiscale_sym_scale064.nii.gz\n",
      "[NiftiLabelsMasker.fit_transform] loading data from /home/aalbury/nilearn_data/basc_multiscale_2015/template_cambridge_basc_multiscale_nii_sym/template_cambridge_basc_multiscale_sym_scale064.nii.gz\n",
      "[NiftiLabelsMasker.fit_transform] loading data from /home/aalbury/nilearn_data/basc_multiscale_2015/template_cambridge_basc_multiscale_nii_sym/template_cambridge_basc_multiscale_sym_scale064.nii.gz\n",
      "[NiftiLabelsMasker.fit_transform] loading data from /home/aalbury/nilearn_data/basc_multiscale_2015/template_cambridge_basc_multiscale_nii_sym/template_cambridge_basc_multiscale_sym_scale064.nii.gz\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NiftiLabelsMasker.fit_transform] loading data from /home/aalbury/nilearn_data/basc_multiscale_2015/template_cambridge_basc_multiscale_nii_sym/template_cambridge_basc_multiscale_sym_scale064.nii.gz\n",
      "[NiftiLabelsMasker.fit_transform] loading data from /home/aalbury/nilearn_data/basc_multiscale_2015/template_cambridge_basc_multiscale_nii_sym/template_cambridge_basc_multiscale_sym_scale064.nii.gz\n",
      "[NiftiLabelsMasker.fit_transform] loading data from /home/aalbury/nilearn_data/basc_multiscale_2015/template_cambridge_basc_multiscale_nii_sym/template_cambridge_basc_multiscale_sym_scale064.nii.gz\n",
      "[NiftiLabelsMasker.fit_transform] loading data from /home/aalbury/nilearn_data/basc_multiscale_2015/template_cambridge_basc_multiscale_nii_sym/template_cambridge_basc_multiscale_sym_scale064.nii.gz\n",
      "[NiftiLabelsMasker.fit_transform] loading data from /home/aalbury/nilearn_data/basc_multiscale_2015/template_cambridge_basc_multiscale_nii_sym/template_cambridge_basc_multiscale_sym_scale064.nii.gz\n",
      "[NiftiLabelsMasker.fit_transform] loading data from /home/aalbury/nilearn_data/basc_multiscale_2015/template_cambridge_basc_multiscale_nii_sym/template_cambridge_basc_multiscale_sym_scale064.nii.gz\n",
      "[NiftiLabelsMasker.fit_transform] loading data from /home/aalbury/nilearn_data/basc_multiscale_2015/template_cambridge_basc_multiscale_nii_sym/template_cambridge_basc_multiscale_sym_scale064.nii.gz\n",
      "[NiftiLabelsMasker.fit_transform] loading data from /home/aalbury/nilearn_data/basc_multiscale_2015/template_cambridge_basc_multiscale_nii_sym/template_cambridge_basc_multiscale_sym_scale064.nii.gz\n",
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      "[NiftiLabelsMasker.fit_transform] loading data from /home/aalbury/nilearn_data/basc_multiscale_2015/template_cambridge_basc_multiscale_nii_sym/template_cambridge_basc_multiscale_sym_scale064.nii.gz\n",
      "[NiftiLabelsMasker.fit_transform] loading data from /home/aalbury/nilearn_data/basc_multiscale_2015/template_cambridge_basc_multiscale_nii_sym/template_cambridge_basc_multiscale_sym_scale064.nii.gz\n",
      "[NiftiLabelsMasker.fit_transform] loading data from /home/aalbury/nilearn_data/basc_multiscale_2015/template_cambridge_basc_multiscale_nii_sym/template_cambridge_basc_multiscale_sym_scale064.nii.gz\n",
      "[NiftiLabelsMasker.fit_transform] loading data from /home/aalbury/nilearn_data/basc_multiscale_2015/template_cambridge_basc_multiscale_nii_sym/template_cambridge_basc_multiscale_sym_scale064.nii.gz\n",
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      "[NiftiLabelsMasker.fit_transform] loading data from /home/aalbury/nilearn_data/basc_multiscale_2015/template_cambridge_basc_multiscale_nii_sym/template_cambridge_basc_multiscale_sym_scale064.nii.gz\n",
      "[NiftiLabelsMasker.fit_transform] loading data from /home/aalbury/nilearn_data/basc_multiscale_2015/template_cambridge_basc_multiscale_nii_sym/template_cambridge_basc_multiscale_sym_scale064.nii.gz\n",
      "[NiftiLabelsMasker.fit_transform] loading data from /home/aalbury/nilearn_data/basc_multiscale_2015/template_cambridge_basc_multiscale_nii_sym/template_cambridge_basc_multiscale_sym_scale064.nii.gz\n",
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     ]
    },
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     "output_type": "stream",
     "text": [
      "[NiftiLabelsMasker.fit_transform] loading data from /home/aalbury/nilearn_data/basc_multiscale_2015/template_cambridge_basc_multiscale_nii_sym/template_cambridge_basc_multiscale_sym_scale064.nii.gz\n",
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      "[NiftiLabelsMasker.fit_transform] loading data from /home/aalbury/nilearn_data/basc_multiscale_2015/template_cambridge_basc_multiscale_nii_sym/template_cambridge_basc_multiscale_sym_scale064.nii.gz\n",
      "[NiftiLabelsMasker.fit_transform] loading data from /home/aalbury/nilearn_data/basc_multiscale_2015/template_cambridge_basc_multiscale_nii_sym/template_cambridge_basc_multiscale_sym_scale064.nii.gz\n",
      "[NiftiLabelsMasker.fit_transform] loading data from /home/aalbury/nilearn_data/basc_multiscale_2015/template_cambridge_basc_multiscale_nii_sym/template_cambridge_basc_multiscale_sym_scale064.nii.gz\n",
      "[NiftiLabelsMasker.fit_transform] loading data from /home/aalbury/nilearn_data/basc_multiscale_2015/template_cambridge_basc_multiscale_nii_sym/template_cambridge_basc_multiscale_sym_scale064.nii.gz\n",
      "[NiftiLabelsMasker.fit_transform] loading data from /home/aalbury/nilearn_data/basc_multiscale_2015/template_cambridge_basc_multiscale_nii_sym/template_cambridge_basc_multiscale_sym_scale064.nii.gz\n",
      "[NiftiLabelsMasker.fit_transform] loading data from /home/aalbury/nilearn_data/basc_multiscale_2015/template_cambridge_basc_multiscale_nii_sym/template_cambridge_basc_multiscale_sym_scale064.nii.gz\n",
      "[NiftiLabelsMasker.fit_transform] loading data from /home/aalbury/nilearn_data/basc_multiscale_2015/template_cambridge_basc_multiscale_nii_sym/template_cambridge_basc_multiscale_sym_scale064.nii.gz\n",
      "[NiftiLabelsMasker.fit_transform] loading data from /home/aalbury/nilearn_data/basc_multiscale_2015/template_cambridge_basc_multiscale_nii_sym/template_cambridge_basc_multiscale_sym_scale064.nii.gz\n",
      "[NiftiLabelsMasker.fit_transform] loading data from /home/aalbury/nilearn_data/basc_multiscale_2015/template_cambridge_basc_multiscale_nii_sym/template_cambridge_basc_multiscale_sym_scale064.nii.gz\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NiftiLabelsMasker.fit_transform] loading data from /home/aalbury/nilearn_data/basc_multiscale_2015/template_cambridge_basc_multiscale_nii_sym/template_cambridge_basc_multiscale_sym_scale064.nii.gz\n",
      "[NiftiLabelsMasker.fit_transform] loading data from /home/aalbury/nilearn_data/basc_multiscale_2015/template_cambridge_basc_multiscale_nii_sym/template_cambridge_basc_multiscale_sym_scale064.nii.gz\n",
      "[NiftiLabelsMasker.fit_transform] loading data from /home/aalbury/nilearn_data/basc_multiscale_2015/template_cambridge_basc_multiscale_nii_sym/template_cambridge_basc_multiscale_sym_scale064.nii.gz\n",
      "[NiftiLabelsMasker.fit_transform] loading data from /home/aalbury/nilearn_data/basc_multiscale_2015/template_cambridge_basc_multiscale_nii_sym/template_cambridge_basc_multiscale_sym_scale064.nii.gz\n",
      "[NiftiLabelsMasker.fit_transform] loading data from /home/aalbury/nilearn_data/basc_multiscale_2015/template_cambridge_basc_multiscale_nii_sym/template_cambridge_basc_multiscale_sym_scale064.nii.gz\n",
      "[NiftiLabelsMasker.fit_transform] loading data from /home/aalbury/nilearn_data/basc_multiscale_2015/template_cambridge_basc_multiscale_nii_sym/template_cambridge_basc_multiscale_sym_scale064.nii.gz\n",
      "[NiftiLabelsMasker.fit_transform] loading data from /home/aalbury/nilearn_data/basc_multiscale_2015/template_cambridge_basc_multiscale_nii_sym/template_cambridge_basc_multiscale_sym_scale064.nii.gz\n",
      "[NiftiLabelsMasker.fit_transform] loading data from /home/aalbury/nilearn_data/basc_multiscale_2015/template_cambridge_basc_multiscale_nii_sym/template_cambridge_basc_multiscale_sym_scale064.nii.gz\n",
      "[NiftiLabelsMasker.fit_transform] loading data from /home/aalbury/nilearn_data/basc_multiscale_2015/template_cambridge_basc_multiscale_nii_sym/template_cambridge_basc_multiscale_sym_scale064.nii.gz\n",
      "[NiftiLabelsMasker.fit_transform] loading data from /home/aalbury/nilearn_data/basc_multiscale_2015/template_cambridge_basc_multiscale_nii_sym/template_cambridge_basc_multiscale_sym_scale064.nii.gz\n",
      "[NiftiLabelsMasker.fit_transform] loading data from /home/aalbury/nilearn_data/basc_multiscale_2015/template_cambridge_basc_multiscale_nii_sym/template_cambridge_basc_multiscale_sym_scale064.nii.gz\n",
      "[NiftiLabelsMasker.fit_transform] loading data from /home/aalbury/nilearn_data/basc_multiscale_2015/template_cambridge_basc_multiscale_nii_sym/template_cambridge_basc_multiscale_sym_scale064.nii.gz\n",
      "[NiftiLabelsMasker.fit_transform] loading data from /home/aalbury/nilearn_data/basc_multiscale_2015/template_cambridge_basc_multiscale_nii_sym/template_cambridge_basc_multiscale_sym_scale064.nii.gz\n",
      "[NiftiLabelsMasker.fit_transform] loading data from /home/aalbury/nilearn_data/basc_multiscale_2015/template_cambridge_basc_multiscale_nii_sym/template_cambridge_basc_multiscale_sym_scale064.nii.gz\n",
      "[NiftiLabelsMasker.fit_transform] loading data from /home/aalbury/nilearn_data/basc_multiscale_2015/template_cambridge_basc_multiscale_nii_sym/template_cambridge_basc_multiscale_sym_scale064.nii.gz\n",
      "[NiftiLabelsMasker.fit_transform] loading data from /home/aalbury/nilearn_data/basc_multiscale_2015/template_cambridge_basc_multiscale_nii_sym/template_cambridge_basc_multiscale_sym_scale064.nii.gz\n",
      "[NiftiLabelsMasker.fit_transform] loading data from /home/aalbury/nilearn_data/basc_multiscale_2015/template_cambridge_basc_multiscale_nii_sym/template_cambridge_basc_multiscale_sym_scale064.nii.gz\n",
      "[NiftiLabelsMasker.fit_transform] loading data from /home/aalbury/nilearn_data/basc_multiscale_2015/template_cambridge_basc_multiscale_nii_sym/template_cambridge_basc_multiscale_sym_scale064.nii.gz\n",
      "[NiftiLabelsMasker.fit_transform] loading data from /home/aalbury/nilearn_data/basc_multiscale_2015/template_cambridge_basc_multiscale_nii_sym/template_cambridge_basc_multiscale_sym_scale064.nii.gz\n",
      "[NiftiLabelsMasker.fit_transform] loading data from /home/aalbury/nilearn_data/basc_multiscale_2015/template_cambridge_basc_multiscale_nii_sym/template_cambridge_basc_multiscale_sym_scale064.nii.gz\n",
      "[NiftiLabelsMasker.fit_transform] loading data from /home/aalbury/nilearn_data/basc_multiscale_2015/template_cambridge_basc_multiscale_nii_sym/template_cambridge_basc_multiscale_sym_scale064.nii.gz\n",
      "[NiftiLabelsMasker.fit_transform] loading data from /home/aalbury/nilearn_data/basc_multiscale_2015/template_cambridge_basc_multiscale_nii_sym/template_cambridge_basc_multiscale_sym_scale064.nii.gz\n",
      "[NiftiLabelsMasker.fit_transform] loading data from /home/aalbury/nilearn_data/basc_multiscale_2015/template_cambridge_basc_multiscale_nii_sym/template_cambridge_basc_multiscale_sym_scale064.nii.gz\n",
      "[NiftiLabelsMasker.fit_transform] loading data from /home/aalbury/nilearn_data/basc_multiscale_2015/template_cambridge_basc_multiscale_nii_sym/template_cambridge_basc_multiscale_sym_scale064.nii.gz\n",
      "[NiftiLabelsMasker.fit_transform] loading data from /home/aalbury/nilearn_data/basc_multiscale_2015/template_cambridge_basc_multiscale_nii_sym/template_cambridge_basc_multiscale_sym_scale064.nii.gz\n",
      "[NiftiLabelsMasker.fit_transform] loading data from /home/aalbury/nilearn_data/basc_multiscale_2015/template_cambridge_basc_multiscale_nii_sym/template_cambridge_basc_multiscale_sym_scale064.nii.gz\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "#initialize empty dataframe\n",
    "all_features = pd.DataFrame(columns=['features', 'file'])\n",
    "\n",
    "for i,sub in enumerate(data.func):\n",
    "    # extract the timeseries from the ROIs in the atlas\n",
    "    time_series = mask.fit_transform(sub, confounds=data.confounds[i])\n",
    "    # create a region x region correlation matrix\n",
    "    correlation_matrix = correlation_measure.fit_transform([time_series])[0]\n",
    "    # add features and file name to dataframe\n",
    "    all_features = all_features.append({'features': correlation_matrix, 'file': data.func[i]}, ignore_index=True)\n",
    "    # uncomment below to keep track of status\n",
    "    #print('finished %s of %s'%(i+1,len(data.func)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create pandas dataframe of features and phenotypic data\n",
    "full = pandas.merge(pheno, all_features, left_on = 'path', right_on = 'file')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we have a Pandas dataframe with all of our demographic data and a column that contains the correlation matrix for each subject as an array."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>id</th>\n",
       "      <th>current_age</th>\n",
       "      <th>gender</th>\n",
       "      <th>handedness</th>\n",
       "      <th>subject_type</th>\n",
       "      <th>diagnosis</th>\n",
       "      <th>frames_ok</th>\n",
       "      <th>fd</th>\n",
       "      <th>fd_scrubbed</th>\n",
       "      <th>path</th>\n",
       "      <th>features</th>\n",
       "      <th>file</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>40061</td>\n",
       "      <td>18</td>\n",
       "      <td>Male</td>\n",
       "      <td>Right</td>\n",
       "      <td>Control</td>\n",
       "      <td>None</td>\n",
       "      <td>133</td>\n",
       "      <td>0.25512</td>\n",
       "      <td>0.22657</td>\n",
       "      <td>/home/aalbury/nilearn_data/cobre/fmri_0040061....</td>\n",
       "      <td>[0.12785325357282862, 0.24422311479417322, 0.0...</td>\n",
       "      <td>/home/aalbury/nilearn_data/cobre/fmri_0040061....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>40090</td>\n",
       "      <td>18</td>\n",
       "      <td>Female</td>\n",
       "      <td>Right</td>\n",
       "      <td>Control</td>\n",
       "      <td>None</td>\n",
       "      <td>150</td>\n",
       "      <td>0.16963</td>\n",
       "      <td>0.16963</td>\n",
       "      <td>/home/aalbury/nilearn_data/cobre/fmri_0040090....</td>\n",
       "      <td>[0.05584897620355883, 0.11246991477285287, 0.0...</td>\n",
       "      <td>/home/aalbury/nilearn_data/cobre/fmri_0040090....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>40046</td>\n",
       "      <td>18</td>\n",
       "      <td>Male</td>\n",
       "      <td>Left</td>\n",
       "      <td>Patient</td>\n",
       "      <td>295.70 depressed type</td>\n",
       "      <td>76</td>\n",
       "      <td>0.37504</td>\n",
       "      <td>0.30042</td>\n",
       "      <td>/home/aalbury/nilearn_data/cobre/fmri_0040046....</td>\n",
       "      <td>[0.08678037911430761, 0.06380639929297223, 0.2...</td>\n",
       "      <td>/home/aalbury/nilearn_data/cobre/fmri_0040046....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>40002</td>\n",
       "      <td>19</td>\n",
       "      <td>Male</td>\n",
       "      <td>Right</td>\n",
       "      <td>Patient</td>\n",
       "      <td>295.3</td>\n",
       "      <td>67</td>\n",
       "      <td>0.40006</td>\n",
       "      <td>0.21575</td>\n",
       "      <td>/home/aalbury/nilearn_data/cobre/fmri_0040002....</td>\n",
       "      <td>[0.1456258349041035, -0.06313977045762048, 0.0...</td>\n",
       "      <td>/home/aalbury/nilearn_data/cobre/fmri_0040002....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>40117</td>\n",
       "      <td>19</td>\n",
       "      <td>Male</td>\n",
       "      <td>Right</td>\n",
       "      <td>Patient</td>\n",
       "      <td>295.3</td>\n",
       "      <td>133</td>\n",
       "      <td>0.20975</td>\n",
       "      <td>0.18410</td>\n",
       "      <td>/home/aalbury/nilearn_data/cobre/fmri_0040117....</td>\n",
       "      <td>[0.17462308889197792, -0.11825290188441862, -0...</td>\n",
       "      <td>/home/aalbury/nilearn_data/cobre/fmri_0040117....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>40089</td>\n",
       "      <td>62</td>\n",
       "      <td>Male</td>\n",
       "      <td>Right</td>\n",
       "      <td>Patient</td>\n",
       "      <td>295.3</td>\n",
       "      <td>40</td>\n",
       "      <td>0.70368</td>\n",
       "      <td>0.72439</td>\n",
       "      <td>/home/aalbury/nilearn_data/cobre/fmri_0040089....</td>\n",
       "      <td>[0.010289989396440208, 0.05385253837186407, 0....</td>\n",
       "      <td>/home/aalbury/nilearn_data/cobre/fmri_0040089....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>40040</td>\n",
       "      <td>63</td>\n",
       "      <td>Male</td>\n",
       "      <td>Right</td>\n",
       "      <td>Patient</td>\n",
       "      <td>295.3</td>\n",
       "      <td>42</td>\n",
       "      <td>0.58301</td>\n",
       "      <td>0.40646</td>\n",
       "      <td>/home/aalbury/nilearn_data/cobre/fmri_0040040....</td>\n",
       "      <td>[-0.05942811380550243, 0.005379451667081145, 0...</td>\n",
       "      <td>/home/aalbury/nilearn_data/cobre/fmri_0040040....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>40028</td>\n",
       "      <td>64</td>\n",
       "      <td>Male</td>\n",
       "      <td>Right</td>\n",
       "      <td>Patient</td>\n",
       "      <td>295.3</td>\n",
       "      <td>55</td>\n",
       "      <td>0.42364</td>\n",
       "      <td>0.26393</td>\n",
       "      <td>/home/aalbury/nilearn_data/cobre/fmri_0040028....</td>\n",
       "      <td>[0.1526906170886252, 0.2197449376315366, 0.301...</td>\n",
       "      <td>/home/aalbury/nilearn_data/cobre/fmri_0040028....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>40086</td>\n",
       "      <td>65</td>\n",
       "      <td>Male</td>\n",
       "      <td>Right</td>\n",
       "      <td>Control</td>\n",
       "      <td>None</td>\n",
       "      <td>48</td>\n",
       "      <td>0.39595</td>\n",
       "      <td>0.32296</td>\n",
       "      <td>/home/aalbury/nilearn_data/cobre/fmri_0040086....</td>\n",
       "      <td>[0.44868677474053265, -0.1957847795785304, -0....</td>\n",
       "      <td>/home/aalbury/nilearn_data/cobre/fmri_0040086....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>40007</td>\n",
       "      <td>65</td>\n",
       "      <td>Female</td>\n",
       "      <td>Right</td>\n",
       "      <td>Patient</td>\n",
       "      <td>295.3</td>\n",
       "      <td>40</td>\n",
       "      <td>0.70044</td>\n",
       "      <td>0.72077</td>\n",
       "      <td>/home/aalbury/nilearn_data/cobre/fmri_0040007....</td>\n",
       "      <td>[0.08900283860731034, 0.11650348658751322, 0.3...</td>\n",
       "      <td>/home/aalbury/nilearn_data/cobre/fmri_0040007....</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>146 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        id  current_age  gender handedness subject_type  \\\n",
       "0    40061           18    Male      Right      Control   \n",
       "1    40090           18  Female      Right      Control   \n",
       "2    40046           18    Male       Left      Patient   \n",
       "3    40002           19    Male      Right      Patient   \n",
       "4    40117           19    Male      Right      Patient   \n",
       "..     ...          ...     ...        ...          ...   \n",
       "141  40089           62    Male      Right      Patient   \n",
       "142  40040           63    Male      Right      Patient   \n",
       "143  40028           64    Male      Right      Patient   \n",
       "144  40086           65    Male      Right      Control   \n",
       "145  40007           65  Female      Right      Patient   \n",
       "\n",
       "                 diagnosis  frames_ok       fd  fd_scrubbed  \\\n",
       "0                     None        133  0.25512      0.22657   \n",
       "1                     None        150  0.16963      0.16963   \n",
       "2    295.70 depressed type         76  0.37504      0.30042   \n",
       "3                    295.3         67  0.40006      0.21575   \n",
       "4                    295.3        133  0.20975      0.18410   \n",
       "..                     ...        ...      ...          ...   \n",
       "141                  295.3         40  0.70368      0.72439   \n",
       "142                  295.3         42  0.58301      0.40646   \n",
       "143                  295.3         55  0.42364      0.26393   \n",
       "144                   None         48  0.39595      0.32296   \n",
       "145                  295.3         40  0.70044      0.72077   \n",
       "\n",
       "                                                  path  \\\n",
       "0    /home/aalbury/nilearn_data/cobre/fmri_0040061....   \n",
       "1    /home/aalbury/nilearn_data/cobre/fmri_0040090....   \n",
       "2    /home/aalbury/nilearn_data/cobre/fmri_0040046....   \n",
       "3    /home/aalbury/nilearn_data/cobre/fmri_0040002....   \n",
       "4    /home/aalbury/nilearn_data/cobre/fmri_0040117....   \n",
       "..                                                 ...   \n",
       "141  /home/aalbury/nilearn_data/cobre/fmri_0040089....   \n",
       "142  /home/aalbury/nilearn_data/cobre/fmri_0040040....   \n",
       "143  /home/aalbury/nilearn_data/cobre/fmri_0040028....   \n",
       "144  /home/aalbury/nilearn_data/cobre/fmri_0040086....   \n",
       "145  /home/aalbury/nilearn_data/cobre/fmri_0040007....   \n",
       "\n",
       "                                              features  \\\n",
       "0    [0.12785325357282862, 0.24422311479417322, 0.0...   \n",
       "1    [0.05584897620355883, 0.11246991477285287, 0.0...   \n",
       "2    [0.08678037911430761, 0.06380639929297223, 0.2...   \n",
       "3    [0.1456258349041035, -0.06313977045762048, 0.0...   \n",
       "4    [0.17462308889197792, -0.11825290188441862, -0...   \n",
       "..                                                 ...   \n",
       "141  [0.010289989396440208, 0.05385253837186407, 0....   \n",
       "142  [-0.05942811380550243, 0.005379451667081145, 0...   \n",
       "143  [0.1526906170886252, 0.2197449376315366, 0.301...   \n",
       "144  [0.44868677474053265, -0.1957847795785304, -0....   \n",
       "145  [0.08900283860731034, 0.11650348658751322, 0.3...   \n",
       "\n",
       "                                                  file  \n",
       "0    /home/aalbury/nilearn_data/cobre/fmri_0040061....  \n",
       "1    /home/aalbury/nilearn_data/cobre/fmri_0040090....  \n",
       "2    /home/aalbury/nilearn_data/cobre/fmri_0040046....  \n",
       "3    /home/aalbury/nilearn_data/cobre/fmri_0040002....  \n",
       "4    /home/aalbury/nilearn_data/cobre/fmri_0040117....  \n",
       "..                                                 ...  \n",
       "141  /home/aalbury/nilearn_data/cobre/fmri_0040089....  \n",
       "142  /home/aalbury/nilearn_data/cobre/fmri_0040040....  \n",
       "143  /home/aalbury/nilearn_data/cobre/fmri_0040028....  \n",
       "144  /home/aalbury/nilearn_data/cobre/fmri_0040086....  \n",
       "145  /home/aalbury/nilearn_data/cobre/fmri_0040007....  \n",
       "\n",
       "[146 rows x 12 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "full"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualizing Connectivity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x432 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.pyplot import figure, savefig\n",
    "\n",
    "patient_features = list(full.loc[full['subject_type']=='Patient']['features'])\n",
    "control_features = list(full.loc[full['subject_type']=='Control']['features'])\n",
    "\n",
    "figure(figsize=(16,6))\n",
    "\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.imshow(patient_features, aspect='auto')\n",
    "plt.colorbar()\n",
    "plt.title('Patients')\n",
    "plt.xlabel('features')\n",
    "plt.ylabel('subjects')\n",
    "\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.imshow(control_features, aspect='auto')\n",
    "plt.colorbar()\n",
    "plt.title('Controls')\n",
    "plt.xlabel('features')\n",
    "plt.ylabel('subjects')\n",
    "\n",
    "savefig('features.png', transparent=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Classification"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This section contains the main analysis of this notebook. Namely, predicting schizophrenia diagnosis. The features used are the correlation matrices generated previously, and diagnosis labels are contained in the `subject_type` column from our phenotypic data.\n",
    "\n",
    "We first split the data into training and validation sets, with a ratio of 80/20."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# Split the sample to training/validation with a 80/20 ratio\n",
    "\n",
    "x_train, x_val, y_train, y_val = train_test_split(\n",
    "                                                    list(full['features']), # x\n",
    "                                                    full['subject_type'], # y\n",
    "                                                    test_size = 0.2, # 80%/20% split  \n",
    "                                                    shuffle = True, # shuffle dataset\n",
    "                                                    stratify = full['subject_type'],\n",
    "                                                    random_state = 242 \n",
    "                                                                       )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our starting classifier with be a linear support vector machine, specified as `SVC()` in Nilearn. This is often the [first recommendation](https://scikit-learn.org/stable/tutorial/machine_learning_map/index.html) for clssification problems with small sample sizes. \n",
    "\n",
    "We'll be using 10-fold corss validation to get a rough benchmark of performance for each classifier. We'll use F1 as our performance metric. After each run we'll look at the preformance of the classifier across the folds as well as the average performance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# build SVC classifier\n",
    "from sklearn.svm import SVC\n",
    "svc = SVC(kernel='linear')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7981132756132755\n",
      "[0.82857143 0.74825175 0.74825175 0.74825175 1.         0.74825175\n",
      " 0.90598291 0.81666667 0.80357143 0.63333333]\n"
     ]
    }
   ],
   "source": [
    "# F1 score by averaging each fold\n",
    "from sklearn.model_selection import cross_val_score\n",
    "import numpy as np\n",
    "svc_score = cross_val_score(svc, x_train, y_train, cv=10, scoring = 'f1_macro')\n",
    "print(np.mean(svc_score))\n",
    "print(svc_score)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Linear SCV seems to perform very strongly, with an average F1 score of ~0.80\n",
    "\n",
    "We'll try gradient boosting next. The gradient boost model will use a greater number of estimators and a larger max depth than the defaults in order to try and improve performance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# build gradient boost classifier\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "boost = GradientBoostingClassifier(n_estimators=500,\n",
    "                                   max_depth=4, \n",
    "                                   random_state=242\n",
    "                                  )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5752838827838829\n",
      "[0.48571429 0.5        0.74825175 0.625      0.24475524 0.82857143\n",
      " 0.60714286 0.71794872 0.54545455 0.45      ]\n"
     ]
    }
   ],
   "source": [
    "#train model\n",
    "boost.fit(x_train, y_train)\n",
    "\n",
    "# F1 score by averaging each fold\n",
    "from sklearn.model_selection import cross_val_score\n",
    "import numpy as np\n",
    "boost_score = cross_val_score(boost, x_train, y_train, cv=10, scoring = 'f1_macro')\n",
    "print(np.mean(boost_score))\n",
    "print(boost_score)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The gradient boost model seems to be highly variable and doesn't come close to matching the performance of the SVC. \n",
    "We'll try K Nearest Neighbors next."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6219476356976357\n",
      "[0.73333333 0.625      0.58041958 0.48571429 0.48571429 0.4375\n",
      " 0.71794872 0.71794872 0.71794872 0.71794872]\n"
     ]
    }
   ],
   "source": [
    "# K Nearest Neighbours\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "knn = KNeighborsClassifier()\n",
    "\n",
    "knn_score = cross_val_score(knn, x_train, y_train, cv=10, scoring = 'f1_macro')\n",
    "print(np.mean(knn_score))\n",
    "print(knn_score)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "K Nearest Neighbors performs poorly with default paramaters. Given the large difference between KNN and the other classifiers I won't try to tweak this alogrithm.\n",
    "\n",
    "Lastly we'll try a Random Forest classifier. We'll increase the numebr of estimators like we did with the gradient boost model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7322227772227772\n",
      "[0.83333333 0.73333333 0.65714286 0.58041958 0.74825175 0.91608392\n",
      " 0.68571429 0.81666667 0.63333333 0.71794872]\n"
     ]
    }
   ],
   "source": [
    "# Random Forest\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "rfc = RandomForestClassifier(n_estimators = 500, random_state = 242)\n",
    "\n",
    "rfc_score = cross_val_score(rfc, x_train, y_train, cv=10, scoring = 'f1_macro')\n",
    "print(np.mean(rfc_score))\n",
    "print(rfc_score)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Random Forest model seems to perform well but not as well as the linear SVC. With some hyperparameter tweaking it might be possible to achieve the same performance but considering the random forest classifier is more complex, and takes longer to train, we'll use SVC as the final model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Hyperparameter Tuning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we've committed to a model, let's see if we can get a little more out of it by tweaking the hyperparameters. Unfortunately, the only option for a linear SVC is the `C` parameter.\n",
    "\n",
    "We can create a range of values for `C` and then compare each using cross validation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import validation_curve\n",
    "\n",
    "C_range = 10. ** np.arange(-3, 8) # A range of different values for C\n",
    "\n",
    "train_scores, valid_scores = validation_curve(svc, x_train, y_train, \n",
    "                                              param_name= \"C\",\n",
    "                                              param_range = C_range,\n",
    "                                              cv=10,\n",
    "                                             scoring='f1_macro')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating a Pandas dataframe of the results\n",
    "tScores = pandas.DataFrame(train_scores).stack().reset_index()\n",
    "tScores.columns = ['C','Fold','Score']\n",
    "tScores.loc[:,'Type'] = ['Train' for x in range(len(tScores))]\n",
    "\n",
    "vScores = pandas.DataFrame(valid_scores).stack().reset_index()\n",
    "vScores.columns = ['C','Fold','Score']\n",
    "vScores.loc[:,'Type'] = ['Validate' for x in range(len(vScores))]\n",
    "\n",
    "ValCurves = pandas.concat([tScores,vScores]).reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f0229bccc50>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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\n",
      "text/plain": [
       "<Figure size 437.25x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting the performance of different values of C\n",
    "import seaborn as sns\n",
    "g = sns.catplot(x='C', y='Score', hue='Type', data=ValCurves, kind='point')\n",
    "\n",
    "g.set_xticklabels(C_range, rotation=90)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The best performance seems to be at a C value of 0.1 but it's a negligible difference. But there's one more thing to try.\n",
    "\n",
    "What if we changed the SVC kernel to the default 'rbf' which would let us adjust C and gamma? Let's use a grid search to see if optimizing an rbf kernel would perform better than a linear kernel."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=10, error_score=nan,\n",
       "             estimator=SVC(C=1.0, break_ties=False, cache_size=200,\n",
       "                           class_weight=None, coef0=0.0,\n",
       "                           decision_function_shape='ovr', degree=3,\n",
       "                           gamma='scale', kernel='rbf', max_iter=-1,\n",
       "                           probability=False, random_state=None, shrinking=True,\n",
       "                           tol=0.001, verbose=False),\n",
       "             iid='deprecated', n_jobs=None,\n",
       "             param_grid={'C': array([1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02, 1.e+03, 1.e+04,\n",
       "       1.e+05, 1.e+06, 1.e+07]),\n",
       "                         'gamma': array([1.e-08, 1.e-07, 1.e-06, 1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01,\n",
       "       1.e+00, 1.e+01, 1.e+02])},\n",
       "             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n",
       "             scoring=None, verbose=0)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# RBF SVC model\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "svc_rbf = SVC(kernel='rbf')\n",
    "\n",
    "C_range = 10. ** np.arange(-3, 8)\n",
    "gamma_range = 10. ** np.arange(-8, 3)\n",
    "\n",
    "param_grid = dict(gamma=gamma_range, C=C_range)\n",
    "\n",
    "grid = GridSearchCV(svc_rbf, param_grid=param_grid, cv=10)\n",
    "\n",
    "grid.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'C': 100.0, 'gamma': 0.001}\n"
     ]
    }
   ],
   "source": [
    "print(grid.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8061452436452436\n",
      "[0.82857143 0.82857143 0.74825175 0.74825175 1.         0.74825175\n",
      " 0.90598291 0.81666667 0.80357143 0.63333333]\n"
     ]
    }
   ],
   "source": [
    "svc_rbf = SVC(kernel='rbf', C=100.0, gamma=0.001)\n",
    "\n",
    "svc_rbf_score = cross_val_score(svc_rbf, x_train, y_train, cv=10, scoring = 'f1_macro')\n",
    "print(np.mean(svc_rbf_score))\n",
    "print(svc_rbf_score)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It seems like SVC with an RBF kernel and tuned hyperparameters performs slightly better than linear SVC, so we'll use this as the final model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Testing The Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now run the model on the left out data and see how it performs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1: 0.6875\n",
      "Accuracy: 0.6666666666666666\n"
     ]
    }
   ],
   "source": [
    "# Validation\n",
    "from sklearn.metrics import f1_score, accuracy_score\n",
    "svc_rbf.fit(x_train, y_train)\n",
    "final_pred = svc_rbf.predict(x_val)\n",
    "print('F1:', f1_score(y_val, final_pred, pos_label='Patient'))\n",
    "print('Accuracy:', accuracy_score(y_val, final_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "An F1 score of .69 isn't too bad for a binary classification problem. Let's see how the model is handling the labels by taking a look at the confusion matrix."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 9  6]\n",
      " [ 4 11]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import plot_confusion_matrix\n",
    "\n",
    "disp = plot_confusion_matrix(svc_rbf, x_val, y_val,\n",
    "                                 cmap=plt.cm.Blues,\n",
    "                                 normalize=None)\n",
    "disp.ax_.set_title('SVC Schizophrenia Labels')\n",
    "\n",
    "print(disp.confusion_matrix)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The model seems to handle each class equally well."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Predicting Schizophrenia Subtype"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The phenotypic data also includes the schizophrenia subtype that each patient was diagnosed with. Maybe we can predict subtype as well. Let's take a look at how they are distributed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "None                     72\n",
       "295.3                    41\n",
       "295.6                    12\n",
       "295.7                     5\n",
       "295.9                     5\n",
       "295.1                     3\n",
       "296.26                    1\n",
       "296.4                     1\n",
       "290.3                     1\n",
       "295.92                    1\n",
       "311                       1\n",
       "295.70 bipolar type       1\n",
       "295.70 depressed type     1\n",
       "295.2                     1\n",
       "Name: diagnosis, dtype: int64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "full.diagnosis.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The distribution of schizohprenia subtypes seems highly unbalanced. Most of the patients were diagnosed with the label \"295.3\" which refers to paranoid schizophrenia. There are very few observations for the other subtypes and so it's unlikely that any model could predict these with so little data. Maybe we can predict paranoid schizophrenia from the other subtypes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# creating a new variable for subtype\n",
    "diagnosis=[]\n",
    "\n",
    "for i in full.index:\n",
    "    if full.loc[i, 'diagnosis']=='295.3':\n",
    "        diagnosis.append('Paranoid')\n",
    "    elif full.loc[i, 'diagnosis']=='None':\n",
    "        diagnosis.append('None')\n",
    "    else:\n",
    "        diagnosis.append('Other')\n",
    "        \n",
    "full['type'] = diagnosis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll split the data again. Stratified by our new subtype variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# Split the sample to training/validation with a 80/20 ratio\n",
    "\n",
    "x_train2, x_val2, y_train2, y_val2 = train_test_split(\n",
    "                                                    list(full['features']), # x\n",
    "                                                    full['type'], # y\n",
    "                                                    test_size = 0.2, # 80%/20% split  \n",
    "                                                    shuffle = True, # shuffle dataset\n",
    "                                                    stratify = full['type'],\n",
    "                                                    random_state = 242 \n",
    "                                                                       )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's avoid running all of the models separately again. It would be much easier to compare a lot of models at once. The cell below defines several models and then loops over them to generate cross validated performance metrics. A more detailed example of this can be found [here]()."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Nearest Neighbors 0.39345543345543355\n",
      "Linear SVM 0.4076200651200651\n",
      "RBF SVM 0.21960784313725487\n",
      "Gaussian Process 0.1431135531135531\n",
      "Decision Tree 0.34980260480260483\n",
      "Random Forest 0.36240516564045977\n",
      "Neural Net 0.3840762723115664\n",
      "AdaBoost 0.3908056540409482\n",
      "Naive Bayes 0.41458892958892957\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.gaussian_process import GaussianProcessClassifier\n",
    "from sklearn.gaussian_process.kernels import RBF\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "\n",
    "np.random.seed(242)\n",
    "\n",
    "names = [\"Nearest Neighbors\", \"Linear SVM\", \"RBF SVM\", \"Gaussian Process\",\n",
    "         \"Decision Tree\", \"Random Forest\", \"Neural Net\", \"AdaBoost\",\n",
    "         \"Naive Bayes\"]\n",
    "\n",
    "classifiers = [\n",
    "    KNeighborsClassifier(3),\n",
    "    SVC(kernel=\"linear\"),\n",
    "    SVC(gamma=2, C=1),\n",
    "    GaussianProcessClassifier(1.0 * RBF(1.0)),\n",
    "    DecisionTreeClassifier(max_depth=5),\n",
    "    RandomForestClassifier(max_depth=5, n_estimators=10, max_features=1),\n",
    "    MLPClassifier(alpha=1, max_iter=1000),\n",
    "    AdaBoostClassifier(),\n",
    "    GaussianNB()]\n",
    "\n",
    "for name, clf in zip(names, classifiers):\n",
    "        \n",
    "        score = cross_val_score(clf, x_train2, y_train2, cv=10, scoring='f1_macro')\n",
    "        \n",
    "        print(name, np.mean(score))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A Gaussian Naive Bayes model performs slightly better than linear SVC, so we'll use it in this case. But I think this is another example of how powerful SVM is as an approach."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.4464646464646464"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Validation\n",
    "NB = GaussianNB()\n",
    "\n",
    "NB.fit(x_train2, y_train2)\n",
    "type_pred = NB.predict(x_val2)\n",
    "f1_score(y_val2, type_pred, average='macro')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[10  3  2]\n",
      " [ 4  3  0]\n",
      " [ 4  2  2]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import plot_confusion_matrix\n",
    "\n",
    "\n",
    "disp = plot_confusion_matrix(NB, x_val2, y_val2,\n",
    "                                 #display_labels=class_names,\n",
    "                                 cmap=plt.cm.Blues,\n",
    "                                 normalize=None)\n",
    "disp.ax_.set_title('Naive Bayes: Schizophrenia Type')\n",
    "\n",
    "\n",
    "print(disp.confusion_matrix)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It seems like the model is performing very well predicting controls, but poorly differentiating schizophrenia patients."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}