plot_results.ipynb

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     "data": {
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       "        <script type=\"text/javascript\">\n",
       "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
       "        if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
       "        if (typeof require !== 'undefined') {\n",
       "        require.undef(\"plotly\");\n",
       "        requirejs.config({\n",
       "            paths: {\n",
       "                'plotly': ['https://cdn.plot.ly/plotly-latest.min']\n",
       "            }\n",
       "        });\n",
       "        require(['plotly'], function(Plotly) {\n",
       "            window._Plotly = Plotly;\n",
       "        });\n",
       "        }\n",
       "        </script>\n",
       "        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from scipy import stats\n",
    "from glob import glob\n",
    "import os\n",
    "import pickle\n",
    "import sys\n",
    "from collections import defaultdict\n",
    "\n",
    "# visualizations with plotly\n",
    "from _plotly_future_ import v4_subplots\n",
    "import plotly.offline as py\n",
    "py.init_notebook_mode(connected=True)\n",
    "import plotly.graph_objs as go\n",
    "import plotly.subplots as tls\n",
    "import plotly.figure_factory as ff\n",
    "import plotly.io as pio\n",
    "pio.templates.default = 'plotly_white'\n",
    "\n",
    "# new default\n",
    "colors = [\"#636efa\", \"#EF553B\", \"#00cc96\", \"#ab63fa\", \"#FFA15A\", \n",
    "          \"#19d3f3\", \"#FF6692\", \"#B6E880\", \"#FF97FF\", \"#FECB52\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "HOME_DIR = '/mnt/c/umd/demos/FC_geodesic'\n",
    "        \n",
    "conditions = ['condition1']\n",
    "N = 20\n",
    "kROI = 300\n",
    "methods = ['demo']\n",
    "distance = ['pearson', 'geodesic']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def _load_accuracy(files):\n",
    "    accuracy = []\n",
    "    sem = []\n",
    "    for file in files:\n",
    "        with open(file, 'rb') as f:\n",
    "            a = pickle.load(f)\n",
    "        accuracy.append(a)\n",
    "        \n",
    "    results = {}\n",
    "    ind = np.argmax(accuracy)    \n",
    "    results['accuracy'] = accuracy[ind]\n",
    "    \n",
    "    return results\n",
    "            \n",
    "def _get_accuracy(cond, dist, trim_method, N, kROI):\n",
    "    '''\n",
    "    inputs -\n",
    "    cond(ition): RS WM ...\n",
    "    dist(ance): pearson or geodesic\n",
    "    trim_method: trim or truncated or full blocks\n",
    "    N: number of subjects\n",
    "    kROI: number of ROI\n",
    "    \n",
    "    output -\n",
    "    accuracy['LR'] when train data has phase LR\n",
    "    accuracy['RL'] when train data has phase RL\n",
    "    test data always has opposite phase\n",
    "    '''\n",
    "    LOAD_DIR = (HOME_DIR + \n",
    "                '/results/%s/N_%d_kROI_%d/whole_brain/accuracy' %(\n",
    "               trim_method, N, kROI))\n",
    "    \n",
    "    regex = '*%s*.pkl' %(dist)\n",
    "    files = [y for x in os.walk(LOAD_DIR) \n",
    "             for y in glob(os.path.join(x[0], regex))]\n",
    "    # filter by bootstrap and by condition\n",
    "    LR_files = []\n",
    "    RL_files = []\n",
    "    for file in files:\n",
    "        if 'B_' not in file:\n",
    "            LR = 'train_%s_LR' %(cond)\n",
    "            RL = 'train_%s_RL' %(cond)\n",
    "            if LR in file:\n",
    "                LR_files.append(file)\n",
    "            elif RL in file:\n",
    "                RL_files.append(file)\n",
    "                           \n",
    "    accuracy = {}\n",
    "    accuracy['LR'] = _load_accuracy(LR_files)\n",
    "    accuracy['RL'] = _load_accuracy(RL_files)\n",
    "    \n",
    "    return accuracy\n",
    "\n",
    "def _plot_accuracy(accuracy, trim_method):\n",
    "    \n",
    "    fig = go.Figure()\n",
    "    for dist in accuracy:\n",
    "        acc = accuracy[dist]\n",
    "        x = [ii for ii in acc]\n",
    "        y = []\n",
    "        error = []\n",
    "        for cond in acc:\n",
    "            #\n",
    "            a = (acc[cond]['LR']['accuracy'] +\n",
    "                 acc[cond]['RL']['accuracy'])/2               \n",
    "            y.append(a)\n",
    "                \n",
    "        fig.add_trace(go.Bar(x=x, \n",
    "                             y=y,\n",
    "                             name=dist.capitalize()))\n",
    "    \n",
    "    title = trim_method\n",
    "    fig.layout.update(title=title,\n",
    "                      font=dict(family='helvetica'),\n",
    "                      legend=go.layout.Legend(\n",
    "                              x=0, y=-0.08,\n",
    "                              font=dict(size=16,\n",
    "                                        color=\"black\")\n",
    "                             ),\n",
    "                      legend_orientation='h',\n",
    "                      height=500,\n",
    "                      width=400\n",
    "                     )\n",
    "    fig.update_xaxes(tickfont=dict(size=16))\n",
    "    fig.update_yaxes(title=dict(text='Accuracy', font=dict(size=20)),\n",
    "                     tickfont=dict(size=16),\n",
    "                     range= [0.2, 1])\n",
    "\n",
    "    py.iplot(fig)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## no bootstrap analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
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   ],
   "source": [
    "for trim_method in methods:    \n",
    "    accuracy = defaultdict(dict)\n",
    "    for cond in conditions:\n",
    "        for dist in distance:\n",
    "            accuracy[dist][cond] = _get_accuracy(cond, dist, trim_method,\n",
    "                                                 N, kROI)\n",
    "    _plot_accuracy(accuracy, trim_method)"
   ]
  },
  {
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   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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