CBSD Attempt MLP weights.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "2b751281",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-12-05T15:02:50.172032Z",
     "start_time": "2022-12-05T15:02:46.767662Z"
    },
    "id": "2b751281"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from matplotlib import pyplot\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn.svm import LinearSVC\n",
    "from scipy.stats import mannwhitneyu\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.linear_model import LassoCV\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "UXZLJew-XX9-",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "UXZLJew-XX9-",
    "outputId": "d7a3a6b4-a40d-48dc-bdd3-9e65e0b6708f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mounted at /content/drive\n"
     ]
    }
   ],
   "source": [
    "from google.colab import drive\n",
    "drive.mount('/content/drive')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "8cd313cf",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-12-05T15:03:32.778976Z",
     "start_time": "2022-12-05T15:03:32.630871Z"
    },
    "id": "8cd313cf"
   },
   "outputs": [],
   "source": [
    "path = \".\\\\data\\\\\"\n",
    "BF_df_CTU = pd.read_parquet(path +\"BF_df_CTU.parquet\")\n",
    "#BF_df_OU = pd.read_feather(\"BF_df_OU.feather\")\n",
    "BF_df_V = pd.read_parquet(path +\"BF_df_V.parquet\")\n",
    "#DT_df_CC = pd.read_feather(\"DT_df_CC.feather\")\n",
    "#DT_df_JI = pd.read_feather(\"DT_df_JI.feather\")\n",
    "#IADQ_df = pd.read_feather(\"IADQ_df.feather\")\n",
    "IESR_df = pd.read_parquet(path +\"IESR_df.parquet\")\n",
    "#NAQ_R_df = pd.read_feather(\"NAQ_R_df.feather\")\n",
    "PCL5_df = pd.read_parquet(path +\"PCL5_df.parquet\")\n",
    "PHQ9_GAD7_df = pd.read_parquet(path +\"PHQ9_GAD7_df.parquet\")\n",
    "#PID5_df = pd.read_feather(\"PID5_df.feather\")\n",
    "#PRFQ_df = pd.read_feather(\"PRFQ_df.feather\")\n",
    "PRMQ_df = pd.read_parquet(path +\"PRMQ_df.parquet\")\n",
    "#R_NEO_PI = pd.read_feather(\"R_NEO_PI.feather\")\n",
    "#RAW_DDDT = pd.read_feather(\"RAW_DDDT.feather\")\n",
    "#sPID_5_df = pd.read_feather(\"sPID-5_df.feather\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "dc305fb1",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-12-05T15:03:37.253096Z",
     "start_time": "2022-12-05T15:03:37.248110Z"
    },
    "id": "dc305fb1"
   },
   "outputs": [],
   "source": [
    "dfs = [BF_df_CTU, BF_df_V, IESR_df, PCL5_df, PHQ9_GAD7_df, PRMQ_df]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1b428729",
   "metadata": {
    "id": "1b428729"
   },
   "outputs": [],
   "source": [
    "# dfs_2 = [BF_df_CTU, BF_df_OU, BF_df_V, DT_df_CC, DT_df_JI, IADQ_df, IESR_df, NAQ_R_df, PCL5_df, PHQ9_GAD7_df, PID5_df, PRFQ_df, PRMQ_df, R_NEO_PI, RAW_DDDT, sPID_5_df]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "eq5rQgPx0N9h",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-12-05T15:03:39.296996Z",
     "start_time": "2022-12-05T15:03:39.277038Z"
    },
    "id": "eq5rQgPx0N9h"
   },
   "outputs": [],
   "source": [
    "dfs_names = ['BF_df_CTU', 'BF_df_V', 'IESR_df', 'PCL5_df', 'PHQ9_GAD7_df','PRMQ_df']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "95f1cbe8",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-12-05T15:03:41.010135Z",
     "start_time": "2022-12-05T15:03:40.991124Z"
    },
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "95f1cbe8",
    "outputId": "1222f92c-1ee4-4112-a196-79ef78ee3dc9"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "442 11\n",
      "486 11\n",
      "358 23\n",
      "402 21\n",
      "1118 17\n",
      "1404 17\n"
     ]
    }
   ],
   "source": [
    "for i in dfs:\n",
    "    n, p = i.shape\n",
    "    print(n, p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "87f9ccdd",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-12-05T15:03:43.384192Z",
     "start_time": "2022-12-05T15:03:43.331008Z"
    },
    "id": "87f9ccdd"
   },
   "outputs": [],
   "source": [
    "dataframe_list = []\n",
    "for i in dfs:\n",
    "    i['CONDITION'] = i['CONDITION'].replace(['H', 'D', 'HONEST', 'DISHONEST', 'FAKE'], ['1', '0', '1', '0', '0'])\n",
    "    temp = (i[i['CONDITION'] == \"1\"].iloc[:, :-1] - i[i['CONDITION'] == \"0\"].iloc[:, :-1].reset_index()).iloc[:, :-1]\n",
    "    dataframe_list.append(temp)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "8af59048",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-12-05T15:03:45.638033Z",
     "start_time": "2022-12-05T15:03:45.628059Z"
    },
    "id": "8af59048"
   },
   "outputs": [],
   "source": [
    "def custom_loss(y_true, y_pred):\n",
    "    loss = tf.norm(y_true-y_pred, ord=1)\n",
    "    return loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a3bb0aa5",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-12-05T15:04:47.140545Z",
     "start_time": "2022-12-05T15:03:51.648254Z"
    },
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "a3bb0aa5",
    "outputId": "9084086d-d19a-4a2b-efd6-842259bdebf0"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/500\n",
      "7/7 [==============================] - 0s 1ms/step - loss: 389.4434\n",
      "Epoch 2/500\n",
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      "Epoch 3/500\n",
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      "Epoch 4/500\n",
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      "Epoch 5/500\n",
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      "Epoch 6/500\n",
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      "Epoch 7/500\n",
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      "Epoch 8/500\n",
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      "Epoch 9/500\n",
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      "Epoch 10/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 375.8939\n",
      "Epoch 11/500\n",
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      "Epoch 13/500\n",
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      "Epoch 14/500\n",
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      "Epoch 15/500\n",
      "7/7 [==============================] - 0s 1ms/step - loss: 368.6001\n",
      "Epoch 16/500\n",
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      "Epoch 17/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 365.8759\n",
      "Epoch 18/500\n",
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      "Epoch 19/500\n",
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      "Epoch 20/500\n",
      "7/7 [==============================] - 0s 997us/step - loss: 361.4666\n",
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      "Epoch 50/500\n",
      "7/7 [==============================] - 0s 1ms/step - loss: 318.9745\n",
      "Epoch 51/500\n",
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      "Epoch 55/500\n",
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      "Epoch 56/500\n",
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      "Epoch 57/500\n",
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      "Epoch 58/500\n",
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      "Epoch 59/500\n",
      "7/7 [==============================] - 0s 3ms/step - loss: 306.2692\n",
      "Epoch 60/500\n",
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      "Epoch 61/500\n",
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      "Epoch 63/500\n",
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      "Epoch 64/500\n",
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      "Epoch 65/500\n",
      "7/7 [==============================] - 0s 997us/step - loss: 297.7196\n",
      "Epoch 66/500\n",
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      "Epoch 67/500\n",
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      "Epoch 68/500\n",
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      "Epoch 69/500\n",
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      "Epoch 70/500\n",
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      "Epoch 71/500\n",
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      "Epoch 72/500\n",
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      "Epoch 73/500\n",
      "7/7 [==============================] - 0s 831us/step - loss: 286.1655\n",
      "Epoch 74/500\n",
      "7/7 [==============================] - 0s 997us/step - loss: 284.8050\n",
      "Epoch 75/500\n",
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      "Epoch 76/500\n",
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      "Epoch 77/500\n",
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      "Epoch 78/500\n",
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      "Epoch 79/500\n",
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      "Epoch 80/500\n",
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      "Epoch 81/500\n",
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      "Epoch 83/500\n",
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      "Epoch 84/500\n",
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      "Epoch 85/500\n",
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      "Epoch 86/500\n",
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      "Epoch 87/500\n",
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      "Epoch 88/500\n",
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      "Epoch 89/500\n",
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      "Epoch 90/500\n",
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      "Epoch 91/500\n",
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      "Epoch 92/500\n",
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      "Epoch 93/500\n",
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      "Epoch 94/500\n",
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      "Epoch 95/500\n",
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      "Epoch 96/500\n",
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      "Epoch 97/500\n",
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      "Epoch 98/500\n",
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      "Epoch 99/500\n",
      "7/7 [==============================] - 0s 1ms/step - loss: 249.5517\n",
      "Epoch 100/500\n",
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      "Epoch 101/500\n",
      "7/7 [==============================] - 0s 1ms/step - loss: 246.3633\n",
      "Epoch 102/500\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7/7 [==============================] - 0s 1ms/step - loss: 245.0085\n",
      "Epoch 103/500\n",
      "7/7 [==============================] - 0s 1ms/step - loss: 243.3045\n",
      "Epoch 104/500\n",
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      "Epoch 105/500\n",
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      "Epoch 106/500\n",
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      "Epoch 107/500\n",
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      "Epoch 108/500\n",
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      "Epoch 109/500\n",
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      "Epoch 110/500\n",
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      "Epoch 111/500\n",
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      "Epoch 112/500\n",
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      "Epoch 113/500\n",
      "7/7 [==============================] - 0s 831us/step - loss: 229.5779\n",
      "Epoch 114/500\n",
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      "Epoch 115/500\n",
      "7/7 [==============================] - 0s 1ms/step - loss: 226.4380\n",
      "Epoch 116/500\n",
      "7/7 [==============================] - 0s 1ms/step - loss: 225.2664\n",
      "Epoch 117/500\n",
      "7/7 [==============================] - 0s 1ms/step - loss: 223.9019\n",
      "Epoch 118/500\n",
      "7/7 [==============================] - 0s 999us/step - loss: 222.5224\n",
      "Epoch 119/500\n",
      "7/7 [==============================] - 0s 1ms/step - loss: 221.3797\n",
      "Epoch 120/500\n",
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      "Epoch 121/500\n",
      "7/7 [==============================] - 0s 831us/step - loss: 218.7670\n",
      "Epoch 122/500\n",
      "7/7 [==============================] - 0s 997us/step - loss: 217.4350\n",
      "Epoch 123/500\n",
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      "Epoch 124/500\n",
      "7/7 [==============================] - 0s 1ms/step - loss: 214.7727\n",
      "Epoch 125/500\n",
      "7/7 [==============================] - 0s 1ms/step - loss: 213.7548\n",
      "Epoch 126/500\n",
      "7/7 [==============================] - 0s 1ms/step - loss: 212.4130\n",
      "Epoch 127/500\n",
      "7/7 [==============================] - 0s 997us/step - loss: 210.6940\n",
      "Epoch 128/500\n",
      "7/7 [==============================] - 0s 1ms/step - loss: 209.7913\n",
      "Epoch 129/500\n",
      "7/7 [==============================] - 0s 1ms/step - loss: 208.3710\n",
      "Epoch 130/500\n",
      "7/7 [==============================] - 0s 1ms/step - loss: 207.4894\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
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    {
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     "output_type": "stream",
     "text": [
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
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    {
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     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 301/500\n",
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     ]
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    {
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     "text": [
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
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    {
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     "text": [
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      "Epoch 467/500\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "22/22 [==============================] - 0s 807us/step - loss: 106.3326\n",
      "Epoch 468/500\n",
      "22/22 [==============================] - 0s 836us/step - loss: 106.4540\n",
      "Epoch 469/500\n",
      "22/22 [==============================] - 0s 855us/step - loss: 106.4275\n",
      "Epoch 470/500\n",
      "22/22 [==============================] - 0s 855us/step - loss: 106.4054\n",
      "Epoch 471/500\n",
      "22/22 [==============================] - 0s 807us/step - loss: 106.3316\n",
      "Epoch 472/500\n",
      "22/22 [==============================] - 0s 807us/step - loss: 106.3979\n",
      "Epoch 473/500\n",
      "22/22 [==============================] - 0s 855us/step - loss: 106.3643\n",
      "Epoch 474/500\n",
      "22/22 [==============================] - 0s 808us/step - loss: 106.3574\n",
      "Epoch 475/500\n",
      "22/22 [==============================] - 0s 807us/step - loss: 106.5059\n",
      "Epoch 476/500\n",
      "22/22 [==============================] - 0s 756us/step - loss: 106.3466\n",
      "Epoch 477/500\n",
      "22/22 [==============================] - 0s 807us/step - loss: 106.3799\n",
      "Epoch 478/500\n",
      "22/22 [==============================] - 0s 855us/step - loss: 106.4416\n",
      "Epoch 479/500\n",
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      "Epoch 480/500\n",
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      "Epoch 481/500\n",
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      "Epoch 484/500\n",
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      "Epoch 485/500\n",
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      "Epoch 486/500\n",
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      "Epoch 487/500\n",
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      "Epoch 488/500\n",
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      "Epoch 489/500\n",
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      "Epoch 490/500\n",
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      "Epoch 491/500\n",
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      "Epoch 492/500\n",
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      "Epoch 494/500\n",
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      "Epoch 495/500\n",
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      "Epoch 496/500\n",
      "22/22 [==============================] - 0s 855us/step - loss: 106.3712\n",
      "Epoch 497/500\n",
      "22/22 [==============================] - 0s 902us/step - loss: 106.3908\n",
      "Epoch 498/500\n",
      "22/22 [==============================] - 0s 856us/step - loss: 106.3775\n",
      "Epoch 499/500\n",
      "22/22 [==============================] - 0s 855us/step - loss: 106.4290\n",
      "Epoch 500/500\n",
      "22/22 [==============================] - 0s 809us/step - loss: 106.3774\n"
     ]
    }
   ],
   "source": [
    "# TensorFlow and tf.keras\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "\n",
    "columns = {}\n",
    "for df, name in zip(dataframe_list, dfs_names):\n",
    "    model = keras.Sequential([\n",
    "        keras.Input(shape=(len(df.columns),)),\n",
    "        keras.layers.Dense(1, use_bias=False)\n",
    "    ])\n",
    "\n",
    "    model.compile(optimizer='adam',\n",
    "                  loss=custom_loss)\n",
    "    y = []\n",
    "    for _ in range(len(df)):\n",
    "    y.append(10.)\n",
    "    \n",
    "    model.fit(np.asarray(df).astype(np.float64), np.asarray(y).astype(np.float64) , epochs = 500)\n",
    "    w_index_value = {}\n",
    "    for i, x in enumerate(model.get_weights()[0]):\n",
    "        w_index_value[i] = x[0]\n",
    "\n",
    "    w_index_value = {k: v for k, v in sorted(w_index_value.items(), key=lambda item: abs(item[1]), reverse=True)}\n",
    "    columns[name] = w_index_value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "pq9C0_psEzgA",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-12-05T15:06:25.253233Z",
     "start_time": "2022-12-05T15:06:25.244254Z"
    },
    "id": "pq9C0_psEzgA"
   },
   "outputs": [],
   "source": [
    "columns_reduced = {}\n",
    "for name, index_list in columns.items():\n",
    "    index_red = []\n",
    "    for ix, weight in index_list.items(): \n",
    "        index_red.append(ix)\n",
    "    #if abs(weight) >= 0.40:\n",
    "    #    index_red.append(ix\n",
    "        if len(index_red) >= len(index_list)/3:\n",
    "            break\n",
    "        columns_reduced[name] = index_red"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "UOL2gt8-HfXW",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-12-05T15:06:26.777677Z",
     "start_time": "2022-12-05T15:06:26.761901Z"
    },
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "UOL2gt8-HfXW",
    "outputId": "3ee85d9a-2d71-4fbc-8cd8-730d54fd13cd"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'BF_df_CTU': [4, 6, 0, 2],\n",
       " 'BF_df_V': [5, 6, 2, 4],\n",
       " 'IESR_df': [10, 19, 3, 4, 8, 5, 11, 18],\n",
       " 'PCL5_df': [15, 5, 3, 17, 6, 11, 19],\n",
       " 'PHQ9_GAD7_df': [14, 13, 8, 3, 7, 12],\n",
       " 'PRMQ_df': [10, 12, 2, 3, 9, 6]}"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "columns_reduced"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4d4f1c2d",
   "metadata": {
    "id": "4d4f1c2d"
   },
   "source": [
    "### Accuracy of complete datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "02b2102e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-12-05T15:06:37.118583Z",
     "start_time": "2022-12-05T15:06:37.104873Z"
    },
    "id": "02b2102e"
   },
   "outputs": [],
   "source": [
    "def complete_ds_acc(dfs, dfs_names, data):\n",
    "    for i in range(len(dfs)):\n",
    "        \n",
    "        X = dfs[i].iloc[:,:-1]\n",
    "        y = dfs[i].iloc[:,-1]\n",
    "        center_function = lambda x: x - x.mean()\n",
    "        X = center_function(X)\n",
    "        X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)\n",
    "\n",
    "        # LOGISTIC REGRESSION\n",
    "        lr = LogisticRegression(C=0.01, random_state=42)\n",
    "        lr.fit(X_train, y_train)\n",
    "        y_lr_pred = lr.predict(X_test)\n",
    "        acc_lr = accuracy_score(y_test, y_lr_pred)\n",
    "\n",
    "        # RANDOM FOREST\n",
    "        rf = RandomForestClassifier(random_state=42)\n",
    "        rf.fit(X_train, y_train)\n",
    "        y_rf_pred = rf.predict(X_test)\n",
    "        acc_rf = accuracy_score(y_test, y_rf_pred)\n",
    "\n",
    "        #SVM\n",
    "        svm = LinearSVC(loss='hinge',random_state=42, max_iter = 100000)\n",
    "        svm.fit(X_train, y_train)\n",
    "        y_pred_svm = svm.predict(X_test)\n",
    "        acc_svm = accuracy_score(y_pred_svm,y_test)\n",
    "\n",
    "        data[dfs_names[i]] = [acc_lr,acc_rf, acc_svm]\n",
    "\n",
    "        \n",
    "    return data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "3f46ed43",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-12-05T15:06:40.497937Z",
     "start_time": "2022-12-05T15:06:39.385658Z"
    },
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 144
    },
    "id": "3f46ed43",
    "outputId": "16c98b47-d07f-43fa-c14e-abe9db85c835"
   },
   "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>BF_df_CTU</th>\n",
       "      <th>BF_df_V</th>\n",
       "      <th>IESR_df</th>\n",
       "      <th>PCL5_df</th>\n",
       "      <th>PHQ9_GAD7_df</th>\n",
       "      <th>PRMQ_df</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.797</td>\n",
       "      <td>0.740</td>\n",
       "      <td>0.963</td>\n",
       "      <td>0.835</td>\n",
       "      <td>0.988</td>\n",
       "      <td>0.912</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.827</td>\n",
       "      <td>0.747</td>\n",
       "      <td>0.907</td>\n",
       "      <td>0.843</td>\n",
       "      <td>0.982</td>\n",
       "      <td>0.931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.805</td>\n",
       "      <td>0.733</td>\n",
       "      <td>0.898</td>\n",
       "      <td>0.851</td>\n",
       "      <td>0.985</td>\n",
       "      <td>0.903</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   BF_df_CTU  BF_df_V  IESR_df  PCL5_df  PHQ9_GAD7_df  PRMQ_df\n",
       "0      0.797    0.740    0.963    0.835         0.988    0.912\n",
       "1      0.827    0.747    0.907    0.843         0.982    0.931\n",
       "2      0.805    0.733    0.898    0.851         0.985    0.903"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dic_acc = {}\n",
    "results_acc = complete_ds_acc(dfs, dfs_names, dic_acc)\n",
    "acc_df = pd.DataFrame(results_acc)\n",
    "acc_df.round(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "V_EmG0nLFNBB",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-12-05T15:06:42.711237Z",
     "start_time": "2022-12-05T15:06:42.695282Z"
    },
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "V_EmG0nLFNBB",
    "outputId": "f069b9db-73b4-474c-e961-91cbaf6d44b9"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BF_df_CTU       0.809524\n",
       "BF_df_V         0.739726\n",
       "IESR_df         0.922840\n",
       "PCL5_df         0.842975\n",
       "PHQ9_GAD7_df    0.985119\n",
       "PRMQ_df         0.915482\n",
       "dtype: float64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_acc_df = acc_df.mean()\n",
    "mean_acc_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "-EWFutVc-84x",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-12-05T15:06:49.069957Z",
     "start_time": "2022-12-05T15:06:49.052917Z"
    },
    "id": "-EWFutVc-84x"
   },
   "outputs": [],
   "source": [
    "data_reduce = []\n",
    "for df, name in zip(dfs, dfs_names):\n",
    "    columns_reduced[name].append(-1)\n",
    "    data_reduce.append(df.iloc[:, columns_reduced[name]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "ET22QBmw-64y",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-12-05T15:06:53.429915Z",
     "start_time": "2022-12-05T15:06:52.443532Z"
    },
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 144
    },
    "id": "ET22QBmw-64y",
    "outputId": "eed9568d-91e1-441d-a175-bfcfc2b0904d"
   },
   "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>BF_df_CTU</th>\n",
       "      <th>BF_df_V</th>\n",
       "      <th>IESR_df</th>\n",
       "      <th>PCL5_df</th>\n",
       "      <th>PHQ9_GAD7_df</th>\n",
       "      <th>PRMQ_df</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.789</td>\n",
       "      <td>0.753</td>\n",
       "      <td>0.926</td>\n",
       "      <td>0.818</td>\n",
       "      <td>0.958</td>\n",
       "      <td>0.834</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.835</td>\n",
       "      <td>0.747</td>\n",
       "      <td>0.898</td>\n",
       "      <td>0.752</td>\n",
       "      <td>0.961</td>\n",
       "      <td>0.872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.797</td>\n",
       "      <td>0.753</td>\n",
       "      <td>0.907</td>\n",
       "      <td>0.860</td>\n",
       "      <td>0.955</td>\n",
       "      <td>0.829</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   BF_df_CTU  BF_df_V  IESR_df  PCL5_df  PHQ9_GAD7_df  PRMQ_df\n",
       "0      0.789    0.753    0.926    0.818         0.958    0.834\n",
       "1      0.835    0.747    0.898    0.752         0.961    0.872\n",
       "2      0.797    0.753    0.907    0.860         0.955    0.829"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dic_acc = {}\n",
    "results_acc = complete_ds_acc(data_reduce, dfs_names, dic_acc)\n",
    "acc_df = pd.DataFrame(results_acc)\n",
    "acc_df.round(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "4dE4rCnJFVuL",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-12-05T15:06:55.278572Z",
     "start_time": "2022-12-05T15:06:55.261269Z"
    },
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "4dE4rCnJFVuL",
    "outputId": "f3b8dfb9-7057-49f3-ebc1-89942102d6b7"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BF_df_CTU       0.807018\n",
       "BF_df_V         0.751142\n",
       "IESR_df         0.910494\n",
       "PCL5_df         0.809917\n",
       "PHQ9_GAD7_df    0.958333\n",
       "PRMQ_df         0.845182\n",
       "dtype: float64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_acc_df = acc_df.mean()\n",
    "mean_acc_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "UTV-0AtPGx9-",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-12-05T15:06:57.179415Z",
     "start_time": "2022-12-05T15:06:57.163346Z"
    },
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "UTV-0AtPGx9-",
    "outputId": "27ca4e90-93d0-4a09-8ba1-77ed6fab76fc"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'BF_df_CTU': [4, 6, 0, 2, -1],\n",
       " 'BF_df_V': [5, 6, 2, 4, -1],\n",
       " 'IESR_df': [10, 19, 3, 4, 8, 5, 11, 18, -1],\n",
       " 'PCL5_df': [15, 5, 3, 17, 6, 11, 19, -1],\n",
       " 'PHQ9_GAD7_df': [14, 13, 8, 3, 7, 12, -1],\n",
       " 'PRMQ_df': [10, 12, 2, 3, 9, 6, -1]}"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "columns_reduced"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2a64f36d",
   "metadata": {
    "id": "2a64f36d"
   },
   "source": [
    "### Mann-Whitney U test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "9eda071c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-12-05T15:07:02.852653Z",
     "start_time": "2022-12-05T15:07:02.729952Z"
    },
    "id": "9eda071c",
    "outputId": "eaf59bdb-18d2-42fb-80a2-79ba9a11c667"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10\n",
      "8\n",
      " \n",
      "10\n",
      "9\n",
      " \n",
      "22\n",
      "21\n",
      " \n",
      "20\n",
      "20\n",
      " \n",
      "16\n",
      "16\n",
      " \n",
      "16\n",
      "16\n",
      " \n"
     ]
    }
   ],
   "source": [
    "### soglia 0.01 o 0.05 ? \n",
    "\n",
    "\n",
    "for i in range(len(dfs)):\n",
    "    n, p = dfs[i].shape\n",
    "    print(p-1)\n",
    "    X_honest = dfs[i][dfs[i]['CONDITION'] == '1'].iloc[:, :-1]\n",
    "    X_dishonest = dfs[i][dfs[i]['CONDITION'] == '0'].iloc[:, :-1]\n",
    "    indices = []\n",
    "    for k in range(0, (p-1)):\n",
    "        U1, s = mannwhitneyu(X_honest.iloc[:, k], X_dishonest.iloc[:, k])\n",
    "        if s < 0.01:\n",
    "            indices.append(k)\n",
    "    print(len(indices))\n",
    "    print(' ')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "9c0307f5",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-12-05T15:07:04.547762Z",
     "start_time": "2022-12-05T15:07:04.527816Z"
    },
    "id": "9c0307f5"
   },
   "outputs": [],
   "source": [
    "def new_mwu_data(dfs):\n",
    "    for i in range(len(dfs)):\n",
    "        n, p = dfs[i].shape\n",
    "        X_honest = dfs[i][dfs[i]['CONDITION'] == '1'].iloc[:, :-1]\n",
    "        X_dishonest = dfs[i][dfs[i]['CONDITION'] == '0'].iloc[:, :-1]\n",
    "        indices = []\n",
    "        for k in range(0, (p-1)):\n",
    "            U1, s = mannwhitneyu(X_honest.iloc[:, k], X_dishonest.iloc[:, k])\n",
    "            if s < 0.01:\n",
    "                indices.append(k)\n",
    "        indices.append(-1)\n",
    "        dfs[i] = dfs[i].iloc[:, indices]\n",
    "        print(dfs[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "5f2420f1",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-12-05T15:07:06.444308Z",
     "start_time": "2022-12-05T15:07:06.319372Z"
    },
    "id": "5f2420f1",
    "outputId": "1c41f2ba-7b14-47ab-b540-0c07d38b833a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     EX1G  A1G  A2G  C1G  C2G  ES1G  ES2G  O2G CONDITION\n",
      "0       4    4    4    4    5     4     4    4         1\n",
      "1       4    2    4    4    5     3     4    4         1\n",
      "2       4    2    4    4    4     3     4    4         1\n",
      "3       4    2    4    2    4     5     5    2         1\n",
      "4       3    3    4    5    5     4     4    5         1\n",
      "..    ...  ...  ...  ...  ...   ...   ...  ...       ...\n",
      "437     4    3    3    5    5     3     4    4         0\n",
      "438     4    4    4    5    4     5     4    4         0\n",
      "439     4    4    4    5    5     4     4    5         0\n",
      "440     4    3    4    5    5     5     4    4         0\n",
      "441     5    4    3    5    5     5     5    4         0\n",
      "\n",
      "[442 rows x 9 columns]\n",
      "     EX1G  EX2G  A1G  C1G  C2G  ES1G  ES2G  O1G  O2G CONDITION\n",
      "0       4     4    4    5    5     4     4    4    5         1\n",
      "1       4     3    3    4    4     3     3    3    5         1\n",
      "2       4     4    2    2    5     4     3    2    1         1\n",
      "3       4     4    2    2    5     4     3    2    1         1\n",
      "4       4     2    2    1    5     2     3    5    5         1\n",
      "..    ...   ...  ...  ...  ...   ...   ...  ...  ...       ...\n",
      "481     5     2    5    5    5     5     5    4    5         0\n",
      "482     3     2    2    4    4     4     4    4    4         0\n",
      "483     5     2    4    5    5     4     4    4    5         0\n",
      "484     2     4    4    4    2     4     2    5    2         0\n",
      "485     5     1    1    5    5     5     5    5    5         0\n",
      "\n",
      "[486 rows x 10 columns]\n",
      "     IESR1  IESR2  IESR3  IESR4  IESR6  IESR7  IESR8  IESR9  IESR10  IESR11  \\\n",
      "0        1      1      1      1      1      1      1      1       1       1   \n",
      "1        1      1      1      1      1      1      1      1       1       1   \n",
      "2        1      1      1      1      1      1      1      1       1       1   \n",
      "3        1      1      1      1      1      1      1      1       1       1   \n",
      "4        1      1      1      1      1      1      1      1       1       1   \n",
      "..     ...    ...    ...    ...    ...    ...    ...    ...     ...     ...   \n",
      "353      5      5      4      5      5      5      5      4       5       5   \n",
      "354      4      3      2      5      3      5      5      5       4       5   \n",
      "355      5      5      4      4      4      4      2      5       4       4   \n",
      "356      5      5      5      5      5      1      5      5       5       5   \n",
      "357      5      5      5      5      5      1      1      5       5       1   \n",
      "\n",
      "     ...  IESR14  IESR15  IESR16  IESR17  IESR18  IESR19  IESR20  IESR21  \\\n",
      "0    ...       1       1       1       1       1       1       1       1   \n",
      "1    ...       1       1       1       1       1       1       1       1   \n",
      "2    ...       1       1       1       1       1       1       1       1   \n",
      "3    ...       1       1       1       1       1       1       1       1   \n",
      "4    ...       1       1       1       1       1       1       1       1   \n",
      "..   ...     ...     ...     ...     ...     ...     ...     ...     ...   \n",
      "353  ...       4       5       4       5       5       4       4       5   \n",
      "354  ...       5       4       4       5       4       3       5       5   \n",
      "355  ...       4       4       5       4       4       5       4       4   \n",
      "356  ...       5       5       5       5       5       5       5       5   \n",
      "357  ...       5       5       5       1       5       5       5       5   \n",
      "\n",
      "     IESR22  CONDITION  \n",
      "0         1          1  \n",
      "1         1          1  \n",
      "2         1          1  \n",
      "3         1          1  \n",
      "4         1          1  \n",
      "..      ...        ...  \n",
      "353       5          0  \n",
      "354       3          0  \n",
      "355       4          0  \n",
      "356       5          0  \n",
      "357       1          0  \n",
      "\n",
      "[358 rows x 22 columns]\n",
      "     PCL1  PCL2  PCL3  PCL4  PCL5  PCL6  PCL7  PCL8  PCL9  PCL10  ...  PCL12  \\\n",
      "0       1     1     3     2     2     0     1     0     1      2  ...      3   \n",
      "1       0     0     0     0     0     0     0     0     0      0  ...      0   \n",
      "2       0     0     0     0     0     0     2     0     3      0  ...      3   \n",
      "3       0     1     0     1     0     0     1     2     1      1  ...      2   \n",
      "4       2     1     1     1     0     3     2     0     2      1  ...      1   \n",
      "..    ...   ...   ...   ...   ...   ...   ...   ...   ...    ...  ...    ...   \n",
      "397     0     0     0     0     1     1     1     1     1      1  ...      3   \n",
      "398     4     4     3     4     4     1     1     1     3      4  ...      3   \n",
      "399     4     4     4     4     4     4     4     3     3      4  ...      4   \n",
      "400     3     3     4     4     4     3     4     4     3      4  ...      3   \n",
      "401     3     3     4     3     3     4     4     4     4      4  ...      4   \n",
      "\n",
      "     PCL13  PCL14  PCL15  PCL16  PCL17  PCL18  PCL19  PCL20  CONDITION  \n",
      "0        2      0      0      1      2      1      0      1          1  \n",
      "1        0      0      1      0      0      0      0      0          1  \n",
      "2        3      4      0      0      1      0      2      2          1  \n",
      "3        1      0      2      1      0      0      3      1          1  \n",
      "4        3      0      1      0      1      1      4      1          1  \n",
      "..     ...    ...    ...    ...    ...    ...    ...    ...        ...  \n",
      "397      3      1      2      0      2      2      2      2          0  \n",
      "398      4      4      4      0      3      4      3      4          0  \n",
      "399      3      3      4      2      4      3      3      4          0  \n",
      "400      4      3      4      4      4      4      3      4          0  \n",
      "401      4      4      3      4      4      3      4      4          0  \n",
      "\n",
      "[402 rows x 21 columns]\n",
      "      PHQ1  PHQ2  PHQ3  PHQ4  PHQ5  PHQ6  PHQ7  PHQ8  PHQ9  GAD1  GAD2  GAD3  \\\n",
      "0        3     1     1     3     1     1     2     1     2     2     3     2   \n",
      "1        2     1     3     3     1     1     3     1     5     3     3     3   \n",
      "2        3     2     2     3     4     3     2     2     2     3     2     3   \n",
      "3        2     2     4     3     2     4     1     4     4     2     4     4   \n",
      "4        2     2     5     2     4     3     4     2     4     3     2     5   \n",
      "...    ...   ...   ...   ...   ...   ...   ...   ...   ...   ...   ...   ...   \n",
      "1113     5     5     5     5     4     5     5     5     5     5     5     5   \n",
      "1114     4     4     5     4     4     4     2     4     5     5     5     5   \n",
      "1115     5     5     4     5     5     4     4     4     4     5     5     5   \n",
      "1116     4     4     5     5     4     4     3     4     2     5     4     4   \n",
      "1117     5     5     5     5     4     5     4     4     5     5     3     5   \n",
      "\n",
      "      GAD4  GAD5  GAD6  GAD7 CONDITION  \n",
      "0        2     2     3     2         1  \n",
      "1        2     1     3     5         1  \n",
      "2        3     3     3     3         1  \n",
      "3        1     1     1     3         1  \n",
      "4        3     1     5     2         1  \n",
      "...    ...   ...   ...   ...       ...  \n",
      "1113     5     4     5     5         0  \n",
      "1114     5     4     3     5         0  \n",
      "1115     5     4     4     5         0  \n",
      "1116     5     4     4     5         0  \n",
      "1117     5     4     4     5         0  \n",
      "\n",
      "[1118 rows x 17 columns]\n",
      "      PRMQ_PR_SH_CU1  PRMQ_RE _LO_ENV2  PRMQ_PR_SH_ENV3  PRMQ_RE_SH_CU4  \\\n",
      "0                4.0               3.0              2.0             4.0   \n",
      "1                3.0               1.0              1.0             3.0   \n",
      "2                3.0               2.0              2.0             4.0   \n",
      "3                4.0               1.0              2.0             4.0   \n",
      "4                4.0               3.0              3.0             4.0   \n",
      "...              ...               ...              ...             ...   \n",
      "1399             2.0               4.0              4.0             2.0   \n",
      "1400             5.0               5.0              4.0             5.0   \n",
      "1401             4.0               4.0              4.0             5.0   \n",
      "1402             4.0               4.0              4.0             5.0   \n",
      "1403             4.0               5.0              4.0             4.0   \n",
      "\n",
      "      PRMQ_PR_LO_CU5  PRMQ_RE_SH_ENV6  PRMQ_PR_LO_ENV7  PRMQ_RE_LO_CU8  \\\n",
      "0                4.0              2.0              3.0             2.0   \n",
      "1                1.0              4.0              4.0             2.0   \n",
      "2                5.0              1.0              1.0             4.0   \n",
      "3                3.0              1.0              1.0             3.0   \n",
      "4                3.0              2.0              5.0             3.0   \n",
      "...              ...              ...              ...             ...   \n",
      "1399             4.0              5.0              5.0             5.0   \n",
      "1400             5.0              4.0              5.0             5.0   \n",
      "1401             5.0              5.0              5.0             5.0   \n",
      "1402             2.0              4.0              4.0             3.0   \n",
      "1403             4.0              5.0              5.0             4.0   \n",
      "\n",
      "      PRMQ_RE_LO_ENV9  PRMQ_PR_SH_ENV10  PRMQ _RE_SH_CU11  PRMQ_PR_LO_ENV12  \\\n",
      "0                 3.0               3.0               4.0               4.0   \n",
      "1                 5.0               3.0               4.0               1.0   \n",
      "2                 2.0               4.0               5.0               2.0   \n",
      "3                 4.0               3.0               4.0               3.0   \n",
      "4                 4.0               5.0               4.0               3.0   \n",
      "...               ...               ...               ...               ...   \n",
      "1399              1.0               5.0               1.0               5.0   \n",
      "1400              5.0               5.0               5.0               5.0   \n",
      "1401              1.0               5.0               5.0               5.0   \n",
      "1402              2.0               5.0               3.0               2.0   \n",
      "1403              5.0               5.0               4.0               4.0   \n",
      "\n",
      "      PRMQ _RE_SH_ENV13  PRMQ _PR_LO_CU14  PRMQ_RE_LO_CU15  PRMQ_PR_SH_CU16  \\\n",
      "0                   2.0               4.0              4.0              4.0   \n",
      "1                   2.0               4.0              3.0              4.0   \n",
      "2                   1.0               5.0              2.0              4.0   \n",
      "3                   2.0               5.0              4.0              4.0   \n",
      "4                   3.0               3.0              2.0              5.0   \n",
      "...                 ...               ...              ...              ...   \n",
      "1399                4.0               4.0              5.0              4.0   \n",
      "1400                5.0               5.0              5.0              5.0   \n",
      "1401                4.0               4.0              4.0              5.0   \n",
      "1402                5.0               5.0              4.0              4.0   \n",
      "1403                4.0               4.0              4.0              5.0   \n",
      "\n",
      "     CONDITION  \n",
      "0            1  \n",
      "1            1  \n",
      "2            1  \n",
      "3            1  \n",
      "4            1  \n",
      "...        ...  \n",
      "1399         0  \n",
      "1400         0  \n",
      "1401         0  \n",
      "1402         0  \n",
      "1403         0  \n",
      "\n",
      "[1404 rows x 17 columns]\n"
     ]
    }
   ],
   "source": [
    "new_mwu_data(dfs)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bf2453c2",
   "metadata": {
    "id": "bf2453c2"
   },
   "source": [
    "### PCA plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "03e08fe1",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "03e08fe1",
    "outputId": "c80f3301-c03a-45bf-dac0-70076260d23d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.35474923 0.13560575 0.10520792 0.08881985 0.07868395]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.36039243 0.12621939 0.11882242 0.08839938 0.07792358]\n"
     ]
    },
    {
     "data": {
      "image/png": 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2pXbt2rRt2zbfL6y8XHjhhcydO5fWrVvj7tSuXZuXXnqJ008/nbfffpumTZvSoEGDPL9wtjrhhBM499xzueCCC7L/Irn99tvp0aMHNWvWpGPHjtnJuSDXX389ffr0YeDAgXTt2nW729euXZuhQ4fSvXt3tmzZwl577cUbb7zBLbfcQv/+/WnevDlbtmyhUaNG29wXkRh+/RUmTAg1+1degTVroGZNOOOM0LLv1AlylCTTTZmYczcjI8Nz913/+uuvVfcs5bZOoFMS3T/Tjf77z8O6dTB+fCjjjBsHa9dCrVpw+ukh2XfsCGk2sJ2Zferu2/Q5VotfRMquNWvg1VdDsh8/PiT/2rXhnHNCsj/uuLRL9nEo8Utitva+ESlWq1eHFv3o0fDaa6GGv/fe0KcP9OgBxxwDlZTaClKmfzvurgGrJO2UhfJssVu5MtTqR4+G118PvXPq1IELLwwt+6OPhujek2xfmU38VatW5ZdfftHQzJJWto7HX7Vq1VSHkrzly0NvnMxMmDgx9LvfZ5/wRO2ZZ8JRR0EFdUwsjDKb+OvVq8e8efPQtIySbrbOwFUuLVsWulpmZsKbb4YnauvXh8svD2Wctm2V7IvBdhO/mVUAWgB1gV+BL9290DNnFZfKlStrBiKR8mDp0pDsR4+Gt98OY+U0bAhXXRWS/eGHg/6qL1b5Jn4zawzcQJgwZTawBKgKHGhm64DHgBHuviW/Y4iI5Gnx4jAmTmZmGCNn82bYbz+49tpQxjnsMCX7BBXU4h8IPAJc4rnuJpnZXsDZQG8g3qONIpLeFi4MQxtnZsK774bJTA44AG64IST7li2V7EtIvom/gBm0iEo9JT9AjoiULfPnw5gxIdm/9x64Q5MmcNNNoYxz6KFK9ikQp8bfA5jg7qvN7BagFTDQ3aclHp2IlD0//RRa9qNHwwcfhGTfrBncemtI9k2bKtmnWJxePbe4+2gzO5owX+59hBJQ20QjE5Gy44cfQst+9Gj46KOw7tBD4Y47QhlHw0uUKnES/+boZ1dgqLu/amYDE4xJRMqCuXNDos/MhClTwrqWLeGuu8JgaBpautSKk/h/NrPHgBOAe8ysChrOWSQ9rV4dkv3w4aFmD6EHzj/+EVr2+++f0vAknjiJ/49AF+B+d19hZnWAvyQbloiUGlu2hC6Xw4eH2v26dXDggaFlf9ZZoRumlCnbTfzuvs7MFgNHE/rzZ0U/RaQ8++47GDECnn4afvwRatQIk5n07RueoNUN2jIrTq+e24AMoAnwFFAZeAZoV9B+IlIGrVz5Wynn/ffD8AgnnAD33JNWM1SVd3FKPacTunBOA3D3+WZWLdGoRKTkbN4chkoYPjw8Tfvrr3DQQXD33WFc+2hKSSk/4iT+je7uZuYAZpb3bNMiUrbMmvVbKWfePNh991DG6dMH2rRRKacci5P4R0W9enY3s4uA84HHkw1LRBKxYgWMGhVa9x9+GEo5nTvDoEFh8vF0GO5ZYt3cvd/MTgBWEer8t7r7G4lHJiLFY/PmMMTxiBGhlLN+fXh69t574U9/grp1Ux2hlLBY4/FHiV7JXqQs+eabkOz//W/4+WeoWRPOPz+UczIyVMpJY3F69awGto7OuROhV89ad6+eZGAiUgjLl8Pzz4eE/9FHYTrCLl1gyBA49VSoUiXVEUopEKfUk92Dx8Ich6cBRyQZlIjsgM2b4Y03Qt3+pZfCfLTNmsH994dSzv/9X6ojlFJmh6ZejMblfynq2z8gmZBEJJaZM38r5SxYAHvsARddFEo5rVurlCP5ilPq6Z5jsQLhYa71iUUkIvlbtgxGjgwJf8qUUMo5+eSQ7Lt2VSlHYonT4j81x/ssYC6h3FNoZnY1cCHh3sEM4Dx315eJSF6ysmDixFDKefll2LgxDHk8eDCcfTbsvXeqI5QyJk6N/7ziPKGZ7QNcCTR191/NbBRwFjC8OM8jUuZ99VVI9s88E6YtrFULLr00tO41TaEUQUGTrT/Mb715tuHuVxbxvDub2SZgF2B+EY4lUn788gs891wo5UydCpUqhRJO376hpLPTTqmOUMqBglr8U5M4obv/bGb3Az8CvwIT3X1i7u3M7GLgYoAGDRokEYpI6bBpE7z+emjdjx0bllu2hAceCKWcvfZKdYRSzljoqFOCJzSrCYwBegIrgNFAprs/k98+GRkZPnVqIt9DIqkzY0ZI9s8+C4sWQe3aoftlnz4h8YsUkZl96u4ZudfH6dVTG7gBaApkD+Th7h0LGUsnYI67L4mO/wJwFGGoZ5HybenSUMoZPhymTQulnFNPDaWck06CypVTHaGkgTi9ep4FnifMuXsp0AdYUoRz/ggcYWa7EEo9x5NQWUmkVNi0CV57LST7cePCcuvW8OCD0KtXaOmLlKA4ib+Wuw8zs6vc/V3gXTP7pLAndPePzSyTML5/FvAZMLSwxxMptb74ItykfeYZWLIk1Or79QulnObNUx2dpLE4iX9T9HOBmXUl9MDZoygndffbgNuKcgyRUmnJEvjPf0Lr/vPPQ+mmW7dQyuncWaUcKRXiJP6BZlYDuBZ4GKgOXJ1oVCJlycaNMH58SPavvhoeuDrsMHj44VDKqVUr1RGK/E6cxP+xu68EVgIdEo5HpOyYNQv+3/8LvXKWLg1P0PbvH0o5hxyS6uhE8hUn8b9vZnMJN3hfcPflyYYkUsrNmgV33hlKOpUqhUnI+/aFE08MyyKlXIXtbeDuBwI3A82AT81snJmdk3hkIqXN7NmhNX/wwTBmDFx9Nfz4Y5jK8OSTlfSlzNhu4gdw9ynufg3QBlgGjEg0KpHS5H//Cy36gw8OSb5/f5gzJ4x3rwHSpAyK8wBXdeB0wkBqjYEXCV8AIuXb99/DwIHw9NOhN06/fnDDDZrYRMq8OH+bfgG8BPzN3T9MOB6R1JszJyT8ESNC+eaKK0LCr1Mn1ZGJFIs4iX8/L+kBfURSYe5cuOuu0C2zYkW47DIYMADq1k11ZCLFKk7iP8DMrgMa5ty+CGP1iJQuP/wQEv5TT0GFCmHM+wEDYJ99Uh2ZSCLiJP7RwKPAE8DmZMMRKUE//gh//zs8+WSY1OTii+HGG6FevVRHJpKoOIk/y90fSTwSkZLy008h4Q8bFpYvvDAk/Pr1UxuXSAmJk/hfMbPLCL15Nmxd6e7LEotKJAnz5sE//gFPPAHucMEFIeFroh9JM3ESf5/o519yrHNgv+IPRyQBP/8cEv7jj8OWLXD++XDTTbDvvqmOTCQl4ky23qgkAhEpdvPnw913w9ChsHkznHdeSPgNG6Y6MpGUivMA1y7ANUADd7/YzA4Amrj7uMSjEymMBQtCwn/ssTBSZt++8Ne/QiO1YUQg3pANTwEbCdMjAvwMDEwsIpHCWrgwjJ+z337wr3+F+WtnzQo1fSV9kWxxavyN3b2nmfUCcPd1ZmYJxyUS36JFcM898MgjYVrD3r3h5puhceNURyZSKsVJ/BvNbGfCDV3MrDE5eveIpMzixXDvvWFM/A0bfkv4+++f6shESrU4if82YAJQ38yeBdoBfZMMSqRAixfDffeFhL9+fSjp3HILHHBAqiMTKRPi9Op5w8ymAUcABlzl7ksTj0wktyVLwlDI//xnSPhnnx0S/oEHpjoykTIl7swRVYHl0fZNzQx3n5xcWCI5LF36W8Jft+63hN+kSaojEymT4nTnvAfoCXwFbIlWO6DEL8n65ZeQ8B9+OCT8s86CW2+Fgw5KdWQiZVqcFv8fCP32dUNXSsayZTBoEDz0EKxdCz17hhZ+06apjkykXIiT+L8HKqOePJK0Zctg8OCQ8NesgR49Qgu/WbNURyZSrsRJ/OuAz83sLX4/SNuViUUl6WX5cnjgAXjwQVi16reEf8ghqY5MpFyKk/jHRi+R4rViRUj4Q4aEhH/GGXDbbXDooamOTKRci9Odc0RJBCJpZOXKkOwfeCC87949tPBbtEh1ZCJpIU6vnhlET+3msBKYCgx091+SCEzKoZUrQznngQdCa/8Pfwgt/JYtUx2ZSFqJU+p5jTDl4n+i5bOAXYCFwHDg1EQik/Jj1apww3bw4FDPP+20kPBbtUp1ZCJpKU7i7+TurXMszzCzae7e2szOSSowKQdWrw4Jf9CgkPC7dQsJv3Xr7e8rIomJMyxzRTNrs3XBzA4HKkaLWYlEJWXb6tVhxquGDcOgae3awdSp8PLLSvoipUCcFv+FwJNmththrJ5VwIVmtivwjySDkzJmzZowrML994enbrt2DS38ww9PdWQikkOcXj2fAIeaWY1oeWWOj0clFZiUIWvXholP7rsvjKtz0klw++3Qps12dxWRkpdv4jezc9z9GTO7Jtd6ANx9cMKxSWn3669haOR77gkjZ3bpElr4RxyR6shEpAAF1fh3jX5Wy+dVaGa2u5llmtk3Zva1mR1ZlONJCcvKgmHDwnDI110Xeud88AG89pqSvkgZkG+L390fi37ekcB5HwQmuPuZZrYToXuolHbu8OKLYeLyb76Btm3hmWfguONSHZmI7IDt9uoxswPN7C0z+zJabm5mNxf2hNG9gmOBYQDuvtHdVxT2eFJCJk0KrfkzzgCz8AXw4YdK+iJlUJzunI8DNwKbANx9OuEhrsJqBCwBnjKzz8zsiaiH0O+Y2cVmNtXMpi5ZsqQIp5Mi+eyzULvv2BHmzw8lnunTw1O30f0eESlb4iT+Xdx9Sq51Rem/XwloDTzi7q2AtcCA3Bu5+1B3z3D3jNq1axfhdFIo//tfmOmqdWv45JPQRXPWLDj/fKgUd+I2ESmN4iT+pWbWmGi8HjM7E1hQhHPOA+a5+8fRcibhi0BKg4UL4fLLwyxXL78MN90UvgSuvRZ23jnV0YlIMYjTdLscGAocZGY/A3OAPxX2hO6+0Mx+MrMm7v4tcDwws7DHk2KycmVo1Q8eDBs3wkUXhVmv6tRJdWQiUsziPMD1PdApqsNXcPfVxXDefsCzUY+e74HziuGYUhjr14e++H//e3jatmdPGDgQ9t8/1ZGJSEIKfIAL+I+7bwFw97W5Pm8M1HH3/+7oSd39cyBjR/eTYrR5Mzz9dHjg6qef4MQTw/g6GktHpNwrqMVfC/jMzD4FPiX0xKkK7A8cBywlj5uyUsq5w9ixoXY/c2YYR2f48NBrR0TSQr43d939QcJN1+eA2oRafGvgZ6C3u5/h7rNLJEopHpMnh5Ey//CH8PRtZiZ8/LGSvkiaKbDG7+6bgTeil5RV06fDjTfC+PFQty4MHQrnnadumSJpKk53Timr5syB3r3D1IYffBAGU/vuu9BjR0lfJG3p//7yaPHi0DPn0UehYkW4/nq44QaoWTPVkYlIKaDEX56sWhX64Q8aFIZMvuACuPVW2GefVEcmIqVInEHa9jazYWb2WrTc1MwuSD40iW3DBnjwQWjcGO64I0yE8tVX8NhjSvoiso04Nf7hwOtA3Wh5FtA/qYBkB2zeDP/+dxheoX9/aNECpkyBUaOgSZNURycipVScxL+nu48Ctj7IlQVsTjQqKZg7jBsXJkA591zYYw+YOBHefFPz24rIdsVJ/GvNrBa/DdJ2BLCy4F0kMe+/D8ceC6eeGur4zz8fRs884YRURyYiZUScm7vXAGOBxmb2PuFhrjMTjUq29eWX4WnbV16B//s/eOSRcPO2cuVURyYiZUycQdqmmdlxQBPAgG/dfVPikUnwww9hPJ2nn4bq1cNgaldeCbtuM3eNiEgscXr1XA7s5u5fufuXwG5mdlnyoaW5pUvh6qvDhOYjR4ZJzb//PjyBq6QvIkUQp8Z/Uc45cd19OXBRciGluTVr4M47Yb/94KGHwpO3330H994bbuKKiBRRnBp/RTMzd996c7cisFOyYaWhjRvDGDp33hmevO3ePTx9e/DBqY5MRMqZOIl/AvC8mT0WLV8SrZPisGULPPdcmO1qzhxo3z4Mm9y2baojE5FyKk7iv4GQ7P8cLb8BPJFYROnCHSZMCDX7L74IA6m99hp07gxmqY5ORMqxOL16tgCPRC8pDh99BAMGwLvvhlr+f/4TpjysoMFSRSR5cXr1tDOzN8xslpl9b2ZzzOz7kgiu3Pn6azj9dDjySPjmG/jXv8K6Xr2U9EWkxMQp9QwDrkb0NbQAAA+RSURBVCZMv6ihGgrjp59CX/wRI0JXzDvvDGPr7LZbqiMTkTQUJ/GvdPfXEo+kPPrllzCB+T//GWr6V10Vnr7dc89URyYiaSxO4p9kZvcBLwAbtq5092mJRVXWrV0bhkm+557QL//cc8NwyQ0apDoyEZFYiX9rv8KMHOsc0AzduW3aBE88AX/7GyxcCKedBnfdBc2apToyEZFscXr1dCiJQMq0LVtg9Gi4+ebwlO0xx8CYMXDUUamOTERkG7GmXjSzrkAzoOrWde7+t6SCKlM++gguvxymTYPmzeHVV8MMWOqLLyKl1HYTv5k9CuwCdCA8uHUmMCXhuIpNwwGvJnr8jHlf8cD/fmbQKdfyctPj8MkOk8cncq65d3dN5Lgikl7itPiPcvfmZjbd3e8ws0GAevlEptZrRvuLh7K5QsVUhyIiEkucp4Z+jX6uM7O6wCagTnIhlT1K+iJSlsRp8Y8zs92B+4BphB49GqtHRKSMitOr587o7RgzGwdUdXfNuSsiUkblm/jNrKO7v21m3fP4DHd/IdnQREQkCQW1+I8D3gZOzeMzJzzJKyIiZUy+id/dbzOzCsBr7j6qBGMSEZEEFdirJxqL//okTmxmFc3ss+i+gYiIlJA43TnfNLPrzKy+me2x9VUM574K+LoYjiMiIjsgTnfOntHPy3Osc2C/wp7UzOoBXYG7gGsKexwREdlxcbpzNkrgvEMIJaRq+W1gZhcDFwM00HDGIiLFJu4gbYcATfn9IG1PF+aEZnYKsNjdPzWz9vlt5+5DgaEAGRkZXphziYjItuIM0nYb0J6Q+McDJwH/BQqV+IF2QDczO5nwRVLdzJ5x93MKeTwREdkBcW7ungkcDyx09/OAFkCNwp7Q3W9093ru3hA4C3hbSV9EpOTEGqQt6taZZWbVgcVA/WTDEhGRpMSp8U+NBml7HPgUWAN8WBwnd/d3gHeK41giIhJPnF49l0VvHzWzCUB1d5+ebFgiIpKU7ZZ6zGysmZ1tZru6+1wlfRGRsi1OjX8QcDQw08wyzexMM6u6vZ1ERKR0ilPqeRd418wqAh2Bi4AngeoJxyYiIgmI+wDXzoThmXsCrYERSQYlIiLJifMA1yigDTAB+CfwbtS9U0REyqA4Lf5hQC9335x0MCIikrw4Nf7XSyIQEREpGXF69YiISDmixC8ikmbyLfWYWeuCdnT3acUfjoiIJK2gGv+g6GdVIAP4AjCgOTAVODLZ0EREJAn5lnrcvYO7dwAWAK3dPcPdDwNaAT+XVIAiIlK84tT4m7j7jK0L7v4lcHByIYmISJLi9OOfbmZPAM9Ey38CNFCbiEgZFSfxnwf8GbgqWp4MPJJYRCIikqg4D3CtN7NHgfHu/m0JxCQiIgmKMx5/N+Bzwlg9mFlLMxubdGAiIpKMODd3byMM0rYCwN0/BxolGZSIiCQnTuLf5O4rc63zJIIREZHkxbm5+5WZnQ1UNLMDgCuBD5INS0REkhKnxd8PaAZsAJ4DVgH9kwxKRESSE6dXzzrgr9FLRETKuDgzcB0IXAc0zLm9u3dMLiwREUlKnBr/aOBR4AlAs3CJiJRxcRJ/lrvrSV0RkXIizs3dV8zsMjOrY2Z7bH0lHpmIiCQiTou/T/TzLznWObBf8YcjIiJJi9OrR0/pioiUIwVNvdjR3d82s+55fe7uLyQXloiIJKWgFv9xwNvAqXl85oASv4hIGZRv4nf326Kf55VcOCIikrQ4N3cxs66EYRuqbl3n7n9LKigREUlOnPH4HwV6EsbsMaAHsG/CcYmISELi9OM/yt3PBZa7+x3AkcCBhT2hmdU3s0lmNtPMvjKzq7a/l4iIFJc4pZ5fo5/rzKwu8AtQpwjnzAKudfdpZlYN+NTM3nD3mUU4poiIxBQn8Y8zs92B+4BphB49TxT2hO6+AFgQvV9tZl8D+wBK/CIiJSDOA1x3Rm/HmNk4oGoeM3IVipk1BFoBH+fx2cXAxQANGjQojtOJiAgFP8CV54Nb0WdFfoDLzHYDxgD93X1V7s/dfSgwFCAjI0NTPYqIFJOCWvx5Pbi1VZEe4DKzyoSk/6yeABYRKVkFPcCVyINbZmbAMOBrdx+cxDlERCR/cfrx1zKzh8xsmpl9amYPmlmtIpyzHdAb6Ghmn0evk4twPBER2QFxevWMBCYDZ0TLfwKeBzoV5oTu/l/Cg2AiIpICcRJ/nRw9ewAGmlnPpAISEZFkxXlyd6KZnWVmFaLXH4HXkw5MRESSESfxXwT8B9gQvUYCl5jZajPbphumiIiUbnEe4KpWEoFI8Ws44NVUh1Bs5t7dNdUhiJQbcXr1XJBruaKZ3ZZcSCIikqQ4N3ePN7MzgAuAWsBTwLuJRiVSDPQXj0je4pR6zo568cwA1gJnu/v7iUcmIiKJiFPqOQC4ijDEwg9AbzPbJenAREQkGXF69bwC3OLulxAmYJ8NfJJoVCIikpg4Nf42W0fPdHcHBpnZK8mGJSIiScm3xW9m1wO4+yoz65Hr475JBiUiIskpqNRzVo73N+b6rEsCsYiISAkoKPFbPu/zWhYRkTKioBq/5/M+r2URKUX0DIMUpKDE3yIai8eAnXOMy2NA1cQjExGRRBQ0A1fFkgxERERKRpzunCIiZUp5KXUlVeaK8wCXiIiUI0r8IiJpRolfRCTNKPGLiKQZJX4RkTSjxC8ikmaU+EVE0owSv4hImlHiFxFJM0r8IiJpRolfRCTNKPGLiKQZJX4RkTSjxC8ikmaU+EVE0owSv4hImlHiFxFJMylJ/GbWxcy+NbPvzGxAKmIQEUlXJZ74zawi8C/gJKAp0MvMmpZ0HCIi6SoVLf42wHfu/r27bwRGAqelIA4RkbRk7l6yJzQ7E+ji7hdGy72Btu5+Ra7tLgYujhabAN+WaKA7Zk9gaaqDSKF0vv50vnZI7+svC9e+r7vXzr2yUioiicPdhwJDUx1HHGY21d0zUh1HqqTz9afztUN6X39ZvvZUlHp+BurnWK4XrRMRkRKQisT/CXCAmTUys52As4CxKYhDRCQtlXipx92zzOwK4HWgIvCku39V0nEUszJRkkpQOl9/Ol87pPf1l9lrL/GbuyIiklp6cldEJM0o8YuIpBkl/iIwsyfNbLGZfZnqWEqamdU3s0lmNtPMvjKzq1IdU0kys6pmNsXMvoiu/45Ux1TSzKyimX1mZuNSHUtJM7O5ZjbDzD43s6mpjmdHqcZfBGZ2LLAGeNrdD0l1PCXJzOoAddx9mplVAz4F/uDuM1McWokwMwN2dfc1ZlYZ+C9wlbt/lOLQSoyZXQNkANXd/ZRUx1OSzGwukOHupf0BrjypxV8E7j4ZWJbqOFLB3Re4+7To/Wrga2Cf1EZVcjxYEy1Wjl5p04oys3pAV+CJVMciO06JX4rMzBoCrYCPUxtJyYpKHZ8Di4E33D2drn8IcD2wJdWBpIgDE83s02h4mTJFiV+KxMx2A8YA/d19VarjKUnuvtndWxKePm9jZmlR7jOzU4DF7v5pqmNJoaPdvTVhlOHLo7JvmaHEL4UW1bbHAM+6+wupjidV3H0FMAnokupYSkg7oFtU5x4JdDSzZ1IbUsly95+jn4uBFwmjDpcZSvxSKNHNzWHA1+4+ONXxlDQzq21mu0fvdwZOAL5JbVQlw91vdPd67t6QMOTK2+5+TorDKjFmtmvUoQEz2xU4EShTPfuU+IvAzJ4DPgSamNk8M7sg1TGVoHZAb0Jr7/PodXKqgypBdYBJZjadMP7UG+6edt0a09TewH/N7AtgCvCqu09IcUw7RN05RUTSjFr8IiJpRolfRCTNKPGLiKQZJX4RkTSjxC8ikmaU+CUxZuZmNijH8nVmdnsxHXu4mZ1ZHMfaznl6mNnXZjYp6XOlmpndlOoYpGQo8UuSNgDdzWzPVAeSk5ntyJSjFwAXuXuHpOIpRZT404QSvyQpizAv6dW5P8jdYjezNdHP9mb2rpm9bGbfm9ndZvanaOz7GWbWOMdhOpnZVDObFY0fs3XgtPvM7BMzm25ml+Q47ntmNhbYZuhoM+sVHf9LM7snWncrcDQwzMzuy2OfG6J9vjCzu6N1Lc3so+jcL5pZzWj9O2b2QBTv12Z2uJm9YGazzWxgtE1DM/vGzJ6Ntsk0s12iz46Pxr6fEc0DUSVaP9fM7jCzadFnB0Xrd422mxLtd1q0vm903gnRue+N1t8N7Bw9iPdstP+r0bV9aWY9d+DfXUo7d9dLr0RehLkKqgNzgRrAdcDt0WfDgTNzbhv9bA+sIDwZWwX4Gbgj+uwqYEiO/ScQGi8HAPOAqsDFwM3RNlWAqUCj6LhrgUZ5xFkX+BGoDVQC3ibMLQDwDmHc9dz7nAR8AOwSLe8R/ZwOHBe9/1uOeN8B7slxHfNzXOM8oBbQkDDqY7touyej31lV4CfgwGj904RB8Yh+t/2i95cBT0Tv/w6cE73fHZgF7Ar0Bb6P/j2qAj8A9XP+G0TvzwAez7FcI9X/PelVfC+1+CVRHkbsfBq4cgd2+8TDeP8bgP8BE6P1MwjJcatR7r7F3WcTktlBhHFTzo2GS/6YkFAPiLaf4u5z8jjf4cA77r7E3bOAZ4HtjbbYCXjK3ddF17nMzGoAu7v7u9E2I3IdZ2yO6/gqxzV+D9SPPvvJ3d+P3j9D+IujCTDH3Wflc9ytA+R9ym+/nxOBAdHv4R1Ckm8QffaWu6909/WEv372zeP6ZgAnmNk9ZnaMu6/czu9DypAdqXWKFNYQYBrwVI51WUSlRjOrAOyU47MNOd5vybG8hd//N5t7vBEHjNACfj3nB2bWntDiT6Wc15H7GrdeV17XFPe4m3Mcx4Az3P3bnBuaWdtc5865z28ndZ9lZq2Bk4GBZvaWu/8tRixSBqjFL4lz92XAKMKN0q3mAodF77sRZrDaUT3MrEJU998P+BZ4HfhzNGQ0ZnZgNIJiQaYAx5nZnmZWEegFvLudfd4AzstRg98jahUvN7Njom16xzhObg3M7Mjo/dmEKR2/BRqa2f47cNzXgX5mZlF8rWKce1OO31tdYJ27PwPcB7TescuQ0kwtfikpg4Arciw/DrwcjXA4gcK1xn8kJO3qwKXuvt7MniCUO6ZFSW8J8IeCDuLuC8xsAGFMfSOMtvjydvaZYGYtgalmthEYT+gV0wd4NPpC+B44bwev6VvCxB5PEsowj0TXdR4wOuqR9Anw6HaOcyfhL63p0V9Uc4DtzYs7NNp+GqE8d5+ZbQE2AX/eweuQUkyjc4qUEhamsBzn7mkxk5ekjko9IiJpRi1+EZE0oxa/iEiaUeIXEUkzSvwiImlGiV9EJM0o8YuIpJn/DwEjh7Nbv6iHAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.68261088 0.04066235]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.65526556 0.05371404]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.65351624 0.05593097]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.61566447 0.05633069 0.03502169]\n"
     ]
    },
    {
     "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": [
    "for i in dfs:\n",
    "    X = i.iloc[:,:-1]\n",
    "    y = i.iloc[:,-1]\n",
    "    center_function = lambda x: x - x.mean()\n",
    "    X = center_function(X)\n",
    "    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)\n",
    "    pca = PCA(0.70)\n",
    "    X_train_pca = pca.fit_transform(X_train)\n",
    "    X_test_pca = pca.transform(X_test)\n",
    "    \n",
    "    print(pca.explained_variance_ratio_)\n",
    "    plt.bar(\n",
    "    range(1,len(pca.explained_variance_)+1),\n",
    "    pca.explained_variance_\n",
    "    )\n",
    " \n",
    "    plt.plot(\n",
    "    range(1,len(pca.explained_variance_ )+1),\n",
    "    np.cumsum(pca.explained_variance_),\n",
    "    c='red',\n",
    "    label='Cumulative Explained Variance')\n",
    " \n",
    "    plt.legend(loc='upper left')\n",
    "    plt.xlabel('Number of components')\n",
    "    plt.ylabel('Explained variance (eignenvalues)')\n",
    "    plt.title('Scree plot')\n",
    " \n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "43df2a59",
   "metadata": {
    "id": "43df2a59"
   },
   "source": [
    "### Accuracy of datasets after PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3fdd764f",
   "metadata": {
    "id": "3fdd764f"
   },
   "outputs": [],
   "source": [
    "def pca_ds_acc(dfs,dfs_names,data):\n",
    "    for i in range(len(dfs)):\n",
    "        \n",
    "        X = dfs[i].iloc[:,:-1]\n",
    "        y = dfs[i].iloc[:,-1]\n",
    "        center_function = lambda x: x - x.mean()\n",
    "        X = center_function(X)\n",
    "        X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)\n",
    "        pca = PCA(0.70)\n",
    "        X_train_pca = pca.fit_transform(X_train)\n",
    "        X_test_pca = pca.transform(X_test)\n",
    "\n",
    "        n_pcs= pca.components_.shape[0]\n",
    "        pca_comp = [abs(pca.components_[x]) for x in range(n_pcs)]\n",
    "        pca_comp = [list(pca_comp[l]) for l in range(n_pcs)]\n",
    "        pca_comp_sort = [sorted(pca_comp[p])[-3:] for p in range(n_pcs)]\n",
    "\n",
    "        most_important_index = []\n",
    "        for k in range(len(pca_comp_sort)):\n",
    "          for j in range(len(pca_comp_sort[k])):\n",
    "            most_important_index.append(pca_comp[k].index(pca_comp_sort[k][j]))\n",
    "        \n",
    "        initial_feature_names = dfs[i].columns\n",
    "        most_important_index = list(set(most_important_index))\n",
    "        most_important_names = [initial_feature_names[most_important_index[q]] for q in range(len(most_important_index))]\n",
    "        \n",
    "        sub_col = list(most_important_names)\n",
    "        sub_col.append(\"CONDITION\")\n",
    "        df_5 = dfs[i][sub_col]    \n",
    "        print(dfs_names[i])\n",
    "        print([dfs[i].columns.get_loc(sub_col[j]) for j in range(len(sub_col))])\n",
    "\n",
    "        X_sub = df_5.iloc[:,:-1]\n",
    "        y_sub = df_5.iloc[:,-1]\n",
    "        X_sub_train, X_sub_test, y_train, y_test = train_test_split(X_sub, y_sub, test_size=0.3, random_state=42)\n",
    "\n",
    "        # LOGISTIC REGRESSION\n",
    "        lr = LogisticRegression(C=0.01, random_state=42)\n",
    "        lr.fit(X_sub_train, y_train)\n",
    "        y_lr_pred = lr.predict(X_sub_test)\n",
    "        acc_lr = accuracy_score(y_test, y_lr_pred)\n",
    "        #feture importance LR \n",
    "        importances = pd.DataFrame(data={\n",
    "            'Attribute': X_sub_train.columns,\n",
    "            'Importance': lr.coef_[0]\n",
    "        })\n",
    "        importances = importances.sort_values(by='Importance', ascending=False)\n",
    "        plt.bar(x=importances['Attribute'], height=importances['Importance'], color='#087E8B')\n",
    "        plt.title('Feature importances obtained from coefficients- LOGISTIC REGRESSION', size=20)\n",
    "        plt.xticks(rotation='vertical')\n",
    "        plt.show()\n",
    "\n",
    "        # RANDOM FOREST\n",
    "        rf = RandomForestClassifier(random_state=42)\n",
    "        rf.fit(X_sub_train, y_train)\n",
    "        y_rf_pred = rf.predict(X_sub_test)\n",
    "        acc_rf = accuracy_score(y_test, y_rf_pred)\n",
    "        #feture importance RF\n",
    "        importance = rf.feature_importances_\n",
    "\n",
    "        print(pd.DataFrame(np.c_[X_sub_train.columns, rf.feature_importances_],\n",
    "                          columns = [\"Feature\", \"Importance\"]).sort_values(\"Importance\", ascending = False))\n",
    "        plt.bar(X_sub_train.columns, importance)\n",
    "        plt.xticks(rotation='vertical')\n",
    "        plt.show()\n",
    "\n",
    "        #SVM\n",
    "        svm = LinearSVC(loss='hinge',random_state=42)\n",
    "        svm.fit(X_sub_train, y_train)\n",
    "        y_pred_svm = svm.predict(X_sub_test)\n",
    "        acc_svm = accuracy_score(y_pred_svm,y_test)\n",
    "        importances = pd.DataFrame(data={\n",
    "            'Attribute': X_sub_train.columns,\n",
    "            'Importance': svm.coef_[0]\n",
    "        })\n",
    "        #feture importance SVM\n",
    "        importances = importances.sort_values(by='Importance', ascending=False)\n",
    "        plt.bar(x=importances['Attribute'], height=importances['Importance'], color='#087E8B')\n",
    "        plt.title('Feature importances obtained from coefficients- SVM', size=20)\n",
    "        plt.xticks(rotation='vertical')\n",
    "        plt.show()\n",
    "\n",
    "        data[dfs_names[i]] = [acc_lr,acc_rf, acc_svm]\n",
    "\n",
    "        \n",
    "    return data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6387e190",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "6387e190",
    "outputId": "c2cd9afa-1a1a-436f-9d3f-a842e8ef2693"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "BF_df_CTU\n",
      "[0, 1, 2, 3, 4, 6, 7, 8, 9, 10]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Feature Importance\n",
      "5    ES1G    0.22008\n",
      "2     A1G   0.161914\n",
      "4     C1G   0.146109\n",
      "6    ES2G   0.146043\n",
      "0    EX1G   0.087475\n",
      "8     O2G    0.07593\n",
      "7     O1G   0.058984\n",
      "3     A2G   0.052429\n",
      "1    EX2G   0.051036\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.8/dist-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "BF_df_V\n",
      "[1, 2, 3, 4, 6, 7, 8, 9, 10]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Feature Importance\n",
      "5    ES2G   0.291373\n",
      "4    ES1G    0.19254\n",
      "3     C1G   0.152187\n",
      "6     O1G   0.082885\n",
      "1     A1G   0.079785\n",
      "0    EX2G   0.071792\n",
      "7     O2G   0.068555\n",
      "2     A2G   0.060881\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.8/dist-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "IESR_df\n",
      "[8, 10, 13, 16, 18, 21, 22]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Feature Importance\n",
      "0   IESR9   0.285382\n",
      "2  IESR14   0.255034\n",
      "4  IESR19   0.253441\n",
      "3  IESR17   0.112531\n",
      "5  IESR22   0.054866\n",
      "1  IESR11   0.038746\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.8/dist-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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GrbszMWzdnm67zbXefm2UM1vjbvPTaa/ZNE7dhunV4VGZ+f0ZxjNtmXkacFo96D+O0jn8/wBfioidM/PSLI/eOHoW55nA1yLiSEqfvKeyYkdcKHcTHUpJKt5DSUjuB3xoyNnpwXncFREfBo6gnL2/mtHO2q1kNh8z33sa325jfOchlA6KgwemNYAnDvnOvQzPTnunnR4wOCIiHsIYvyKrn1A2iMfXu3Dmk5UO7BHxIMqyWZrLH3TTexjO7g3lF7C8PZt6pQ/TO2AOa6fptPu4zqXcWbDXiGU3qpcyxpKZyzLzO5l5FOUWOBjtYD7Vcn8IZcO+Mld+INECyt1Dg3rT6X+40Sjt8JvBhKSajcRwmPMp2/E4+4qLKPurpvVj91mIaVTTbbe51tv/PnOEsuNu89Npr9nU2w/vFBFbTXMa0zk+Tcc9jHD2JDN/l5nfqEnemymXiEdpu5m4rb6u1A8hy11HF1KW8WOZ+tkkw5xASea3oSQzTWd0V2nWkpIstwJ9C9g3Il7aVCYiHhkR9+8btBTYvn9Fq6eajqbcVdHkJhqSjuonlNPP+/TPJyLWo3SgHEtm3k3JGrcE3l2ns4KI2DIihsU6SS+vt2gDfzzgv53S5h/uK/d5Su//59fT+f3+lXK9++s53oPferembTtk/FLGb/dxvae+/ldErPQLa2DYO+vr8U07vYi4b/+yiYi/bFoXKGenYLSnUX6ovh4ZEb3+VkTEmpRHcq9B6SzW5C31DE3vOxtTznrAim17M7XD4pDpLKWcfdipf2BE/APl+vOcqH01Pg4siojX1TqvICIeHBEP7BvUq9ebImLdvnL9dV8dZtJuc+mLlPZ8dkQ8f3BkRPT3PRtrm59me82aenB7P+Ws3gf61/0677X722KID1MSm9fXg+4KImKNmJ3/lXMTpf9Z07HiSUP6V4yz3xgqIvaKiH2bfkBHxPosv1357CGT6F1ueQelj9L3M/O8Uedf+0ztRblFe+zjbc+sPqeEcgvSN4APRsTLKLca3ULJnHai3Hq1K+UZEFAOBh8ALo6Iz1I6Gz6BcmD6IuV5CYPOpHSm+SIlk78LODszz66nkN5FuV324oj4XK3jnpQOZdPpJPVGSu/yfwSeFRHfoFzbvD+lr8kTKH0LBjtHTdq3gUtqh6pbKQeZR1Gy4bf1CmXm7TWJ/AxwVu2k9EvKJYmnU66tHzrmvC+jLKP9I+IuyjMQEvhoZv6C6bX7WDLzaxHxH5QD1o8jovecks0pv7bPpdy1QWaeGRFHUG5fvTwivky5A2F9Sv+bJ1M6WPbOurwSeGpEfKuWux14BOWXzs2M8OsiM78TEW+r0/phlA6zv6vT+PM6v7c3fPU6Sj+PH0bEqZTrt/tREuf3Z+Yfdzi1bc+jPJvm48BPKb/kTq2nsI+lrBfnRMSnKevJorp8TqnTnSuHUbafNwAvjohzKNekt6J0NtyF8hyPK2v5T1A6zz271v0LfXW/AHjwHMb6RzNot7mO684onbS/RrmEeChlHV+Xsjz3oO7vp7nNj9tes+0YyuWOZwE/jYgvUX75P6DG/O9M0eciM2+KiP0od9qdGxFnUh68mHUau1L6nQx29B7XmZRlcXqUDsPLgO9l5hcpB+qtI+LblATyTsoyfyplH/nJGc774ZR9681133Q5pe/LNsBfU7ovnEd5xk+TkykJSe9s0th9QjLza+N+p2kio9xmlAy5fauh7P2A11AOfrdT7k65kvLQmEOotwz1lT+Q5U8IvJGy0jySIU/loyQDJ1M2iHsYuPWUcmrqCMr13jspG9vbKA8RWsoUT6Ocok5B6cx4JuUXxp2Ug+45ta4PGHHZLB5cjkxx+yyrfs7ESrdBsuLTVv+N5U90vYZyEBr2RNdd6rK/oW+5/TcNt+D1zWPhFHXdpS6vWymnfgdv0Ru33Yfe8jlVPJSHd51e2633RNfP0XwL5hMpD+i6luVPmb0E+H/03adP2Ql+mJKI3lrrcBllp7PdKOtC37T2r+vRbbWdfkRJctdtKLuU5U90fV9t02WUpyev8ETXvu88hJLo3dTXDgf2jd+bcvC6jfID4muUO3EOHCzbH8M429CwtmP5E2W/U5fjsrrenUn5VbdJQ/mjKLfRLquxvIkxnug61bY4yro2k3abRhxD1+sp4tqWclbhyroO30Q5EL2moezI2/w022u69R62viyo8z+fcmz5HeXAexzwkL5yR9OwD6njFlIOyr0n5/6Wso/8KPXpuqMsf4bstykdPP+b0qfibvr235SnAH+izvv2Ou8f1nV4s6ZlMc4f5WnXL63zuJTyA+mu2r7fpDygbe1VTKN3y/vvaXiSdcMyXul26SHtNvItwVG/pI6I8h9hD6A81GzpZKORJGl0s9nRVZIkadpMSiRJUiuYlEiSpFawT4kkSWqF2b4lWHNo0003zYULF046DEmaVy688MIbM3NVzzJRC5iUzCMLFy5kyZKm/w0lSRomIn4x6Rg0GvuUSJKkVjApkSRJrWBSIkmSWsGkRJIktYJJiSRJagWTEkmS1AomJZIkqRVMSiRJUiv48LQ/Eeu9/h2TDmEkdxxz+KRDkCRNiGdKJElSK5iUSJKkVjApkSRJrWBSMkciYq+IuCwiroiIIxrGPykiLoqIuyNiv0nEKElSm5iUzIGIWBN4H/BMYEfg+RGx40CxXwIHAiev3ugkSWon776ZG48FrsjMnwNExCeBfYBLewUyc2kdd+8kApQkqW08UzI3tgau6vt8dR0mSZKGMClpuYg4JCKWRMSSG264YdLhSJI0Z0xK5sY1wAP6Pm9Th40tM4/LzEWZuWizzTableAkSWojk5K5cQGwfUQ8MCLWBvYHTp1wTJIktZpJyRzIzLuBw4CvAj8GPp2ZP4qIN0TEswEiYpeIuBp4HvA/EfGjyUUsSdLkeffNHMnMLwNfHhh2VN/7CyiXdSRJEp4pkSRJLWFSIkmSWsGkRJIktYJJiSRJagWTEkmS1AomJZIkqRVMSiRJUiuYlEiSpFYwKZEkSa1gUiJJklrBpESSJLWCSYkkSWoFkxJJktQKJiWSJKkVTEokSVIrmJRIkqRWMCmRJEmtYFIiSZJawaREkiS1gkmJJElqBZMSSZLUCiYlkiSpFUxKJElSK5iUSJKkVjApkSRJrWBSIkmSWsGkRJIktYJJiSRJagWTEkmS1AomJZIkqRVMSiRJUiuYlEiSpFYwKZEkSa1gUjJHImKviLgsIq6IiCMaxq8TEZ+q48+LiIWrP0pJktrDpGQORMSawPuAZwI7As+PiB0Hiv0DcHNmPgR4J/CfqzdKSZLaxaRkbjwWuCIzf56ZdwKfBPYZKLMPcFJ9fwqwR0TEaoxRkqRWWTDpADpqa+Cqvs9XA48bViYz746IW4FNgBv7C0XEIcAhANtuu+20A7rjmMOn/d22Wu/175h0CCMZddl3rT7QvTp1rT7QzTpp/vJMSctl5nGZuSgzF2222WaTDkeSpDljUjI3rgEe0Pd5mzqssUxELAA2AG5aLdFJktRCJiVz4wJg+4h4YESsDewPnDpQ5lTggPp+P+AbmZmrMUZJklrFPiVzoPYROQz4KrAm8KHM/FFEvAFYkpmnAh8EPhoRVwC/oSQukiT9yTIpmSOZ+WXgywPDjup7/wfgeas7LkmS2srLN5IkqRVMSiRJUiuYlEiSpFYwKZEkSa1gUiJJklrBpESSJLWCSYkkSWoFkxJJktQKJiWSJKkVTEokSVIrmJRIkqRW8H/faN6645jDJx2CJGkWeaZEkiS1gkmJJElqBZMSSZLUCiYlkiSpFUxKJElSK5iUSJKkVjApkSRJrWBSIkmSWsGkRJIktYJJiSRJagWTEkmS1AomJZIkqRVMSiRJUiuYlEiSpFYwKZEkSa1gUiJJklrBpESSJLWCSYkkSWoFkxJJktQKJiWSJKkVTEpmWURsHBFnRMTl9XWjIeVOj4hbIuJLqztGSZLayKRk9h0BnJmZ2wNn1s9N3g68eLVFJUlSy5mUzL59gJPq+5OA5zQVyswzgdtWV1CSJLWdScns2zwzr6vvfwVsPslgJEmaLxZMOoD5KCK+DmzRMOq1/R8yMyMiZzivQ4BDALbddtuZTEqSpFYzKZmGzHzasHER8euI2DIzr4uILYHrZziv44DjABYtWjSjBEeSpDbz8s3sOxU4oL4/APjCBGORJGneMCmZfW8F9oyIy4Gn1c9ExKKIOKFXKCK+BXwG2CMiro6IZ0wkWkmSWsLLN7MsM28C9mgYvgQ4qO/zbqszLkmS2s4zJZIkqRVMSiRJUiuYlEiSpFYwKZEkSa1gUiJJklrBpESSJLWCSYkkSWoFkxJJktQKJiWSJKkVTEokSVIrmJRIkqRWMCmRJEmtYFIiSZJawaREkiS1gkmJJElqBZMSSZLUCiYlkiSpFUxKJElSK5iUSJKkVjApkSRJrWBSIkmSWsGkRJIktYJJiSRJagWTEkmS1AomJZIkqRVMSiRJUiuYlEiSpFYwKZEkSa1gUiJJklphwaQDkFTccczhkw5BkibKMyWSJKkVTEokSVIrmJRIkqRWMCmZZRGxcUScERGX19eNGso8OiK+GxE/iojvR8TfTSJWSZLaxKRk9h0BnJmZ2wNn1s+Dfg/8fWY+AtgLODYiNlyNMUqS1DomJbNvH+Ck+v4k4DmDBTLzp5l5eX1/LXA9sNlqi1CSpBYyKZl9m2fmdfX9r4DNpyocEY8F1gZ+NmT8IRGxJCKW3HDDDbMbqSRJLeJzSqYhIr4ObNEw6rX9HzIzIyKnmM6WwEeBAzLz3qYymXkccBzAokWLhk5LkqT5zqRkGjLzacPGRcSvI2LLzLyuJh3XDyn3Z8BpwGsz89w5ClWSpHnDyzez71TggPr+AOALgwUiYm3gc8BHMvOU1RibJEmtZVIy+94K7BkRlwNPq5+JiEURcUIt87fAk4ADI+KS+vfoyYQrSVI7ePlmlmXmTcAeDcOXAAfV9x8DPraaQ5MkqdU8UyJJklrBpESSJLWCl28kzZk7jjl80iFImkc8UyJJklrBpESSJLWCSYkkSWoFkxJJktQKJiWSJKkVTEokSVIrmJRIkqRWMCmRJEmtYFIiSZJawaREkiS1gkmJJElqBZMSSZLUCiYlkiSpFUxKJElSK5iUSJKkVjApkSRJrbBg0gFI0nxxxzGHTzoEqdM8UyJJklrBpESSJLWCSYkkSWoFkxJJktQKJiWSJKkVTEokSVIrmJRIkqRWMCmRJEmtYFIiSZJawaREkiS1gkmJJElqBZMSSZLUCiYlsywiNo6IMyLi8vq6UUOZ7SLiooi4JCJ+FBH/OIlYJUlqE5OS2XcEcGZmbg+cWT8Pug7YNTMfDTwOOCIitlqNMUqS1DomJbNvH+Ck+v4k4DmDBTLzzsxcVj+ug+0gSZIHwzmweWZeV9//Cti8qVBEPCAivg9cBfxnZl47pNwhEbEkIpbccMMNcxOxJEktsGDSAcxHEfF1YIuGUa/t/5CZGRHZNI3MvArYqV62+XxEnJKZv24odxxwHMCiRYsapyVJUheYlExDZj5t2LiI+HVEbJmZ10XElsD1q5jWtRHxQ2A34JRZDlWSpHnDyzez71TggPr+AOALgwUiYpuIWK++3wh4InDZaotQkqQWMimZfW8F9oyIy4Gn1c9ExKKIOKGW2QE4LyK+B5wFvCMzfzCRaCVJagkv38yyzLwJ2KNh+BLgoPr+DGCn1RyaJEmt5pkSSZLUCiYlkiSpFUxKJElSK5iUSJKkVjApkSRJrWBSIkmSWsGkRJIktYJJiSRJagWTEkmS1AomJZIkqRVMSiRJUiuYlEiSpFYwKZEkSa1gUiJJklrBpESSJLWCSYkkSWoFkxJJktQKJiWSJKkVTEokSVIrLJh0AJKkybnjmMMnHYL0R54pkSRJrWBSIkmSWsGkRJIktYJJiSRJagWTEkmS1AomJZIkqRVMSiRJUiuYlEiSpFYwKZEkSa0QmTnpGDSiiLgB+MWk4+izKXDjpIOYZV2rU9fqA92rU9fqA+2r03aZudmkg9CqmZRo2iJiSWYumnQcs6lrdepafaB7depafaCbddLq4eUbSZLUCiYlkiSpFUxKNBPHTTqAOdC1OnWtPkSm13MAAAcKSURBVNC9OnWtPtDNOmk1sE+JJElqBc+USJKkVjApkSRJrWBSIkmSWsGkRJIktYJJiWYsItafdAyCiNhw0jHMtohYIyJeGhGnRcT3IuKiiPhkROw+6dhmIiI2i4idI2KnLm4/EfHsSceg+WnBpANQJ1wKbDvpIMYVEY8Ejge2Br4CvCozb67jzs/Mx04yvmm4MSIWA58APpuZt0w4ntnwQcq/VngLsB/wW+BbwJER8cjMfM8kgxtXROwIvBtYSNlmLgbuHxFnAS/PzFsnGN60RMS+g4OA90XEAoDM/N/VH5XmK28J1kgi4hXDRgGvzcyNV2c8syEizgH+AzgXOAh4CfDszPxZRFycmTtPNMAxRcQPgFcDzwf2As6hJChfyMw7JhnbdEXE9zNzp77P52bm4yNiHeCSzNxhguGNLSLOBQ7IzMsi4rHAP2fmARFxMPCMzNxvwiGOLSLuAr4KXE/ZH0BJIE8BMjNfOqnYNP94+UajejOwEXC/gb/1mb/r0f0y8/TMvCUz3wEcBpweEY8H5mO2fldmfikzXwhsA3wc+Fvg6og4ebKhTdtdEfFggIh4DHAnQGYuY3620XqZeRlAZp4PPLK+Px54xCQDm4G/BNYDLsjMl2TmS4Ab63sTEo3Fyzca1UXA5zPzwsEREXHQBOKZFRGxQe+UeWZ+MyKeC3wWmHdnflj+K5V6ZuTTwKcjYgPgOROLamb+HfhmRCyj7K/2h9InA/jSJAObpp9FxOuAbwD7ApcARMRazNPkPjMviIg9gX+JiG8Cr2J+JoxqAS/faCQR8TDgpsxc6d+RR8TmmfnrCYQ1IxHxAuDnmXnuwPBtgddl5sGTiWx6IuLwesanUyIigE2a1r35pnZGfg2wI/A94K2ZeVtNHHcYXBfnm4jYCjgWWJSZD5p0PJp/TEokzTv1l/krM3PPScciafbMy9OFapeImJf/fCsi1oyIQyPijRHxhIFxR04qrrkwj9voqRHx04i4PSI+FhGPjIglwFuB/550fLNpvrbRVLpYJ80tz5RoJBExrI9FAN/LzG1WZzyzISJOAO4DnA+8GDgrM19Rx12UmY+ZZHzj6mgbXQz8X+C7wDOBjwFHZOZ7JxrYNHW0jTpXJ02OSYlGEhH3UJ4XEX2Ds37eOjPXnkhgM9B/u2l9psL7gU0pt9SeOw9vCe5iG62QHEbEZZn5sEnGNBMdbaPO1UmT4903GtXPgT0y85eDIyLiqgnEMxv+uLPMzLuBQyLiKMqdEfPxKZtdbKMNBx7OtaD/8zx8MFcX26iLddKEmJRoVMdSnlOy0o4HeNtqjmW2LImIvTLz9N6AzHxDRFzL/Oyv0MU2Ogt4Vt/ns/s+JzDfkpIutlEX66QJ8fKNJElqBe++0YxExJ4Rccak45hNXavTfK5PRDwrIrbr+3xU/cd8p0bEAycZ22yaz200TBfrpLlnUqKRdPHWzK7VqWv1qd4E3AAQEXsDLwJeCpwKfGCCcU1LF9uoi3XS5JiUaFT/BRwCbEL5R1vfBU7MzL+Yh50Ne7pWp67VB8o/dPt9fb8v8MHMvDAzTwA2m2Bc09XFNupinTQh9inRSLp2ayZ0r05dqw+U27Yp//Dt98CVwHMzc0kdd2lm7jjJ+MbV0TbqXJ00Od59o1F17dZM6F6dulYfKHd2XAL8FvhxX0KyM3DdJAObpi62URfrpAnxTIlGEhEfnmJ0zsd/Ud61OnWtPj0RsTVwf8rTQe+tw7YE1mp6NkabdbGNulgnTY5JiaTWiogXZebH6vsnZOa3+8YdNl8fNy+pmR1dNZIu3prZtTp1rT7VK/rev2dg3Lz7Bd7FNupinTQ5JiUaVaduzay6Vqeu1QdW/H8qMcW4+aKLbdTFOmlCTEo0qq7dmgndq1PX6gPlUfJN75s+zwedbKMO1kkTYlKiUUVErB8RawB7AGf2jVt3QjHNVNfq1LX6ADw8Ir4fET/oe9/7PB9vO+1iG3WxTpoQbwnWqLp2ayZ0r05dqw/ADpMOYJZ1sY26WCdNiHffaGRdujWzp2t16lp9uqiLbdTFOmkyvHyjkdRbM6/JzIuBXXvDM/M64NmTi2z6ulanrtUHICJui4jfNvzdFhG/nXR84+poG3WuTpocz5RoJP2Pkm54rPQKn+eLrtWpa/Xpoi62URfrpMnxTIlG1bVbM6F7depafbqoi23UxTppQkxKNKqu3ZoJ3atT1+rTRV1soy7WSRPi5RuNJCJ+D1xB+eXz4Pqe+vlBmXnfScU2XV2rU9fq00VdbKMu1kmT4y3BGlXXbs2E7tWpa/Xpoi62URfrpAnxTIkkSWoFz5RoJBFxG83Xh4PymOk/W80hzVjX6tS1+nRRF9uoi3XS5HimRJIktYJ330iSpFYwKZEkSa1gUiJJklrBpESSJLXC/wdWjWxFMa5tJAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PCL5_df\n",
      "[1, 3, 4, 10, 12, 20]\n"
     ]
    },
    {
     "data": {
      "image/png": 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hz1VqWVsi6cMu73XpETWVaYQWlQxr9AKwOCKeaBq3LL+v1nVQ9vPmARFxk+1bJU23PSnSHey75dGXlKR/3PalSjusF2n1eihbZm0ZYb0viXzpR5Nb8/tkpRYsSXqp0kZQdvlKsz2VgpzX2y67K3+ipE1tbxIRK5V2Ru+T9H3b5yhdBnJZRPy5jd9qaLXcb7B9m6RtbW8cEfcXRj+utNE3G8zvq3XV1Eru8eADSgd605T+bIu27GB2ZetwsW4adlcKqKq6o1wvvxe3q92UdsC/LEk/2EEe27U0Iu4qGT7SeruvYv24XekA5Tcl45YpBcOba2h7r/LS/P6jYdJJnW/zI6mv0bSD0tmYK2LkPdM0/p9eWFGG5+b3HZWuTS1qd70ezplKl7hcYftspYOryyLitmKi3EXajKZpl0bEGR38VjedUDLsDA3T40ik6OU/c28TByit07sptXYeLunttv81nnrpai+YXTLs9Ih4xzDTbRsRS9fwt2do9fqYr9V7g5HGOJ8jqd+IuFfS65y6zjxA6b643ZUuCd1F0r/aPjAirlL93q/0X/x5De1/2zaa13xvkt/3z68qf+8z0vZrlE4rPKrUsvVnpdbNJ5VWmlconVKswx0lwxpl2l7lO5SG0n4wO3R/ybDHq8blP0lp6I+vWVXXO3cotZpvrLQz3DgPr7qeqjF8UsW8OrYG9V61824sp2L/q5Mk3RsRj7SRpU2UtoVWdSzl0/URcaXtvZVukDtU+Zp/29dLOjEivt3Gb7az3LdWKkex/u8uORCThupi45JxpWy/VCkAm6B0U/QCpVOCT2qo265Otr/V6qewnhbrprFd7Z5fVYrb1caS7omIsmsPR7QeDqNqniOtt7LtW8rrblOg/pRxqt7Gixrb53BButT5Nj+S+hpNnZStSqMMR7RMVV6Gsv1O2T6npYj4Xr7W/v1K15O+S5Js/0bShyLiopx0hlbfF/1cT73PaU001u1ntZG2keYxDV2z25h+i7IJio10tn+pdDNj23Kj0Nn5JdtPl3Ss0r00J9teEBFtdS3XgeVKZ4a3VO6LfhTtExGD+f6xHZXOsrzd9k0R8fE1nPc9Sg1H6ynVx03FkRExV6k1W7ZnKv3fdiOfxTx1XL85uP+f/JJTf+VfU7o/6RSl/6tWRrLOtzzQj4hf58a3Q22/Mdq8N6dhNJ9w2fjzeF9EuMXr7YVpPqa0UQ9ExCER8f6IOD6vMNePIA9P5veqg4qyALIhSoY1ynTeMGXadgR5HWtTK4Zvnt/vb3rfvCStlFpDi+mKypZZO0a73svcJ2lyvrloOPcrBeqt6tgRcUtjgoj4dUQcpNTy9bJcpqlKN5/NbPM3pc6X+zNd/pCH5nptx4clbah0+cyrIuKoQj1c0cF8OtXI40nDLO99mqaZkv8YmlUtwzVRtW6vyfYylhoBYjtnKjotw0jqazR1UrYqjTK8cJgyzG85lzUUERdGxL5K+439lAKcnSX9wPZOOc3cknzNGMVsXKV0WdUWtoc7W9HYl11eOOi/LL/PsD3mT8mOiIci4iPK97qow2C+TY0zamP2HIOI+FtEXK0UMN6i1Od2R2cqS+b5uIb21aOS97HI5zC/13H95rNFhynFES+0PWWYSRr1O6Pi/7Oosc5f1jJV8iGlg59P5ZvC2zaaG07j6WR7dzDNdkrX1fyxODBv0HtVTPOkqlsb7s3vqx3d2N5OHbQKZn9S2vG/tOJPf232iuYBtp+ttGyWxtBDExod2s8oST9BQ/VZ1gtAlcZOuqqeRlLvnbpc6RqzA9tMOzlfgtGRiFgVEb+KiOOVuvuTUovxcFot9+2ULie6OVZ/uMUEpd5amjXmU3x4Qjv1cE9EDJaMW239GUVXKm3Hnewrfqu0vypbP2aMQp7aNdJ6G2uN/e+r2kjb6TY/kvoaTY398C75QR0jMZL/p5F4Qm20hueA45KIOFrpOtiJaq/u1lg+G3hW/vrhqnS54eLo/HVeYdSgUk8xz1K69r0uf83vLZ+6OUKN8h3ppicmNrO9RmfjI+JhpRub11G68XBNNXrveL/tUXuI3xjkczid1u8qpeC7nWl+rrTObqF01qlUjgFeo3Rmq52efG5UaoHfVqnb5baNWvAdEYuUuhd8re3Swtl+ge3NCoOWStq+uEPNXYrNVerFosxKVZ86+JPSafNZxd/JO5Evt1eSIfmo8mSllqAvl7Wi2p7WaLFYy7zPqetHSX8PbD+nVOenF9J9X+nU1ZvyZQhFRymtVBdHRCfX3TdOT25dMX6pOq/3Tp2c379ge7UWs6ZhJ+X3U8r+3G0/vbhsbP9jRYt6Y6fdztP5Ghv2h2037odQPir/vFI9nVYx7aeKfwD5qL/xJ1qs23uVb5yrmM9SpdbkXYoDbb9T6fq6MRHpWuozJQ3Y/khZS4Tt59gunlFqlOsTLjzat6nsdViTehtLFyjV56ttv6l5pJ/6WOmOtvkR1teoyS2uX1M6S/PfzcGP7YnFuqhwulIAf4LtPZpH2l7HFY+j7tBKpftDyv4rXu7yx053st8YLR9WujTxzbY/1pwv25OVLg3cTumelr9fSpfr491KAcrJtt9e1gKeG6zaDgZtf6CqAcT2Xkq9Uj2u1OPRqIqIy5QuX9hEqVvk7UvysE7etr45Cj/5HaV+42eOwnr3LaXLBp8n6QKXPEI+a3Xmv8qo5bPT+s3/ux9pcTB0lNJlYn+IdC9WpbzOvkepEeFL+dLX5jzsqHTp5XqSPlY80z2MjyrtW45TB5fejWo/30rdgV0i6TTb/650OuQ+pdagXZS6dNpTqQ9lKQU9/y3pd7bPVWq+f5lSAHaB0mmPZgslHWb7AqWWmb9JujQiLo2Iv9n+klK3Mr+zfV4u4/5K1++M5Gadjyndzf9uSQfbvkTp2sPNlK4Ff5nSQm++SafbLpO02OnGnvuVgqkXKt3Y9fej2Ih4MB8sfVfSz21/V+kmqxcrddd3h/K1iR24XmkZHWb7b0qnrkKpa6pbNLJ670hE/NT2x5X+ZP7o1HfnrUp/dHsptYTNyWkX2j5WqVu8JbZ/qHQH9jOUro9/hdJpq0Yr+n9I2tf2L3K6B5VOHb9KKeAtthJV5e9XTjee/Ieka/O1Yw/leTw//97nSiZdrnRq7lrbjR3FoUoHiF+LiEsLv/Gg7SuU+nY/U+khDE9IWpBPK35Rab34pe3vKK0nA3n5nKPOHqHcqfcqbT8flfRWp2tD71RqmdhR6driNyktXyn9+b9RqS/4a22fXyj7VUqPsx5za1BvY52vx5xuFv6p0qVP71JaxzdQWp77Ke/vR7jNd1pfo+1EpRuzDpZ0g+0fKLWUPSvn+QNqcU10RKx0erT0eZIut71Q6QFdkeexp1Lg1XzDcacWKi2LHzvduLpK0u8j4gKlBqAtbV+mdKD0mNIy31dpH/m/a/jbRZ+3Xfb8B0k6PiL+4tS//AKlfeS/2P6x0kFZ4/Hyk5UC79dHxJPFGeR9ZuNR3V+XdLztnyv9x26gtF7MVFqmV6uNx7sr9cLzWdt/Ulp3lys9J2RnpWVkSe+Pkd90O5x/U9o/vlvpP2NQqVeNVUqXPO2rFMucs6Y/FBFh+3il9fGTKj+beZTtquU22DhjGRFP2H6tUteosyTdlOviWqUDuk2VluE/Kq1zbV9SOJr5VOf1u57S/uYE21cqdVd9r1InGC9Ter7DQ0r11U5ZLrL9FqX19Xt5no3nhuys9F+4ntL++2OVM1p9vvc4Pfuhs7MD0V6XOaGKbqFK0v6DpP9UCvIeVOoN5GalhwscqaYnMCkFQI0npt2tVMkvUMVTypSC3rOUdvxP5DRzC+OtdPH+n/NC/UteKE/TME/na1EmK91Ut1Bp5/SYUnD5y1zW0se5lsxnsHk5qkW3fBq+n+ay7prOyMOfrXRjT+MJl8uUgq2qJ1zunpf9isJy+y+VdO1V+I3pLcq6e15e92uom6AZhfGd1vtqZW0nP0oPeWn8qTSecHmeyrt220vpSP92DT11c7Gk/6d0fXoj3SuVWtL+kMv3kNIBx5fV9FSsNtaJw/J69NdcT9cpHcyVdRu1VENPuPxqrtNVSk+TfcoTLgvTbKd0QLOyUA9zCuMPUtoR/lXpD/KnSj2fzGlOW8xDJ9tQVd1p6Ambv8rLcVVe7xYqtWpsUpL+eKWbilblvHxCHTzhstW22M66tib1NoJ8VK7XLfK1tVIr8c0auknuCjX1Pd7pNj/C+hppuavWlwn5969U+m95SKknqnmStiukm6uSfUgeN13pAU2NJ4k+oLSP/KZW74KtcvmrYr+tFEz8l9KT8B5XYf+t1MXft/NvP5h/+9q8Dm9atiw6fam97t92bcrv0Xldbvy33aH0QKM3qGSf0vR7myhd93ppXo/+prRN/FEpMD9Iqz+avrHsBpuGv0jpQOCSvP4+oqGnvZ6pQpduFXmZoxb/l4X6j6r1Mqd5idLZqxtyPRX/N97QXJ4W8xmsWg8LaRblNAd3WIdzK+a3n1IQfqPS9tF4iuslSrFK2XMdaslnp/WrdBbxQKWufa9Q+l9urF+NxqOybbNleZQOoD6n1KLf2H8vzevrHi2WQajQ1WDTuPU11EVrqI2uBp0nxDhh+wylLoNGo5siAAAAjKIxv1MZAAAAQELwjZ5j+0Db19u+MV+r3Tx+fdtn5/FXOHXQ3xj3oTz8ettjdlMhAABAGYJv9JTc08JXlW5w20mpx4bmHlLeqdRv93ZKN3d+Jk+7k9K1ujsrXUv2tbKeGwAAAMYKwfc4ExFzIj2UYWm38zJG9pB0Y0TcFBGPKfUQ0Nyv9iylmyekdGf6fradh/9vpL65b1a6KWW1bscAAADGymh3NQiMtS2V7jxvuE3p7vTSNJEeb36/0l35W2roYRuNacv6AD9SqWcePf3pT3/xDjvsMOLM/vb20X4KcnfstkXL506UGi9llzovfz+XXRo/5e/nsksjK3/Db37zm7sjYrj+14G+RPANNImIecp9dQ8MDMSiRYtGPK8NT/j8aGWrqxadeEzH04yXskudl7+fyy6Nn/L3c9mlkZW/wXa7DykB+g6XnaDXLNNTn3C6VR5WmiY/uW1jpf6O25kWAABgzBB8o9dcpfRo+m1tT1S6gXJBU5oFSn2dS+kJiJdE6tB+gdJTN9fPj8LeXumBHQAAALXgshP0lHwN93sl/UTSupK+HhHX2f6opEURsUDp6WTftH2j0lPbDsvTXpcfo/4HpafP/VtEPNGVggAAgL5E8I2eExE/lPTDpmHHFz4/Kun1FdN+QulxzgAAALXjshMAAACgJgTfAAAAQE0IvgEAAICaEHwDAAAANSH4BgAAAGpC8A0AAADUhOAbAAAAqAnBNwAAAFATgm8AAACgJgTfAAAAQE0IvgEAAICaEHwDAAAANSH4BgAAAGpC8A0AAADUhOAbAAAAqAnBNwAAAFATgm8AAACgJgTfAAAAQE0IvgEAAICaEHwDAAAANSH4BgAAAGpC8A0AAADUhOAbAAAAqAnBNwAAAFATgm/0DNtTbF9ke0l+n1yRbnZOs8T27MLwQdvX216cX5vVl3sAAACCb/SWYyUtjIjtJS3M35/C9hRJJ0h6iaQ9JJ3QFKS/JSJ2za+76sg0AABAA8E3esksSfPz5/mSDilJc4CkiyLinoi4V9JFkg6sKX8AAAAtEXyjl0yNiOX58x2Sppak2VLSrYXvt+VhDafnS04+YttlP2L7SNuLbC9asWLFqGQcAABAkiZ0OwNAke2LJW1eMuq44peICNvR4ezfEhHLbP+DpHMlvVXSN5oTRcQ8SfMkaWBgoNPfAAAAqETwjbVKRMysGmf7TtvTImK57WmSyq7ZXiZpRuH7VpIG87yX5fe/2j5L6Zrw1YJvAACAscJlJ+glCyQ1ei+ZLen8kjQ/kfRK25PzjZavlPQT2xNsP1OSbK8n6SBJ19aQZwAAgL8j+EYv+bSk/W0vkTQzf5ftAdunSlJE3CPpY5Kuyq+P5mHrKwXhV0tarNRCfkr9RQAAAP2My07QMyJipaT9SoYvknR44fvXJX29Kc1Dkl481nkEAABohZZvAAAAoCYE3wAAAEBNCL4BAACAmhB8AwAAADUh+AYAAABqQvANAAAA1ITgG0qF92wAABDwSURBVAAAAKgJwTcAAABQE4JvAAAAoCYE3wAAAEBNCL4BAACAmhB8AwAAADUh+AYAAABqQvANAAAA1ITgGwAAAKgJwTcAAABQE4JvAAAAoCYE3wAAAEBNCL4BAACAmhB8AwAAADUh+AYAAABqQvANAAAA1ITgGwAAAKgJwTd6hu0pti+yvSS/T65I92Pb99n+QdPwbW1fYftG22fbnlhPzgEAABKCb/SSYyUtjIjtJS3M38t8TtJbS4Z/RtJJEbGdpHslvXNMcgkAAFCB4Bu9ZJak+fnzfEmHlCWKiIWS/locZtuS9pV0znDTAwAAjBWCb/SSqRGxPH++Q9LUDqbdRNJ9EfF4/n6bpC3LEto+0vYi24tWrFgx8twCAAA0mdDtDABFti+WtHnJqOOKXyIibMdY5CEi5kmaJ0kDAwNj8hsAAKA/EXxjrRIRM6vG2b7T9rSIWG57mqS7Opj1SkmTbE/Ird9bSVq2htkFAADoCJedoJcskDQ7f54t6fx2J4yIkPQzSYeOZHoAAIDRQPCNXvJpSfvbXiJpZv4u2wO2T20ksv0LSd+VtJ/t22wfkEd9UNLRtm9Uugb8tFpzDwAA+h6XnaBnRMRKSfuVDF8k6fDC970rpr9J0h5jlkEAAIBh0PINAAAA1ITgGwAAAKgJwTcAAABQE675BsbQIyce0+0sAACAtQgt3wAAAEBNCL4BAACAmhB8AwAAADUh+AYAAABqQvANAAAA1ITgGwAAAKgJwTcAAABQE4JvAAAAoCYE3wAAAEBNCL4BAACAmhB8AwAAADUh+AYAAABqQvANAAAA1ITgGwAAAKgJwTcAAABQE4JvAAAAoCYE3wAAAEBNCL4BAACAmhB8AwAAADUh+AYAAABqQvCNnmF7iu2LbC/J75Mr0v3Y9n22f9A0/AzbN9tenF+71pNzAACAhOAbveRYSQsjYntJC/P3Mp+T9NaKcR+IiF3za/FYZBIAAKAKwTd6ySxJ8/Pn+ZIOKUsUEQsl/bWuTAEAALSL4Bu9ZGpELM+f75A0dQTz+ITtq22fZHv9sgS2j7S9yPaiFStWjDizAAAAzQi+sVaxfbHta0tes4rpIiIkRYez/5CkHSTtLmmKpA+WJYqIeRExEBEDm2666UiKAQAAUGpCtzMAFEXEzKpxtu+0PS0iltueJumuDufdaDVfZft0ScesQVYBAAA6Rss3eskCSbPz59mSzu9k4hywy7aVrhe/dlRzBwAAMAyCb/SST0va3/YSSTPzd9kesH1qI5HtX0j6rqT9bN9m+4A86kzb10i6RtIzJX281twDAIC+x2Un6BkRsVLSfiXDF0k6vPB974rp9x273AEAAAyPlm8AAACgJgTfAAAAQE247ATAmHjkRDqTAQCgGS3fAAAAQE0IvgEAAICaEHwDAAAANSH4BgAAAGrCDZcAMMq42RQAUIWWbwAAAKAmBN8AAABATQi+AQAAgJoQfAMAAAA1IfgGAAAAakLwDQAAANSE4BsAAACoCcE3AAAAUBOCbwAAAKAmBN8AAABATQi+AQAAgJoQfAMAAAA1IfgGAAAAakLwDQAAANSE4BsAAACoCcE3eobtKbYvsr0kv08uSbOr7V/bvs721bbfWBi3re0rbN9o+2zbE+stAQAA6HcE3+glx0paGBHbS1qYvzd7WNLbImJnSQdK+qLtSXncZySdFBHbSbpX0jtryDMAAMDfEXyjl8ySND9/ni/pkOYEEXFDRCzJn2+XdJekTW1b0r6Szmk1PQAAwFgi+EYvmRoRy/PnOyRNbZXY9h6SJkr6s6RNJN0XEY/n0bdJ2rJiuiNtL7K9aMWKFaOTcwAAAEkTup0BoMj2xZI2Lxl1XPFLRITtaDGfaZK+KWl2RDyZGr7bExHzJM2TpIGBgcrfAAAA6BTBN9YqETGzapztO21Pi4jlObi+qyLdRpIulHRcRFyeB6+UNMn2hNz6vZWkZaOcfQAAgJa47AS9ZIGk2fnzbEnnNyfIPZicJ+kbEdG4vlsREZJ+JunQVtMDAACMJYJv9JJPS9rf9hJJM/N32R6wfWpO8wZJL5c0x/bi/No1j/ugpKNt36h0Dfhp9WYfAAD0Oy47Qc+IiJWS9isZvkjS4fnztyR9q2L6myTtMZZ5BAAAaIWWbwAAAKAmBN8AAABATQi+AQAAgJoQfAMAAAA1IfgGAAAAakLwDQAAANSE4BsAAACoCcE3AAAAUBOCbwAAAKAmBN8AAABATQi+AQAAgJoQfAMAAAA1IfgGAAAAakLwDQAAANSE4BsAAACoCcE3AAAAUBOCbwAAAKAmBN8AAABATQi+AQAAgJoQfAMAAAA1IfgGAAAAakLwDQAAANSE4BsAAACoCcE3AAAAUBOCb/QM21NsX2R7SX6fXJJmV9u/tn2d7attv7Ew7gzbN9tenF+71lsCAADQ7wi+0UuOlbQwIraXtDB/b/awpLdFxM6SDpT0RduTCuM/EBG75tfisc8yAADAEIJv9JJZkubnz/MlHdKcICJuiIgl+fPtku6StGltOQQAAGiB4Bu9ZGpELM+f75A0tVVi23tImijpz4XBn8iXo5xke/2K6Y60vcj2ohUrVoxKxgEAACSCb6xlbF9s+9qS16xiuogISdFiPtMkfVPS2yPiyTz4Q5J2kLS7pCmSPlg2bUTMi4iBiBjYdFMazQEAwOiZ0O0MAEURMbNqnO07bU+LiOU5uL6rIt1Gki6UdFxEXF6Yd6PVfJXt0yUdM4pZBwAAGBYt3+glCyTNzp9nSzq/OYHtiZLOk/SNiDinady0/G6l68WvHdPcAgAANCH4Ri/5tKT9bS+RNDN/l+0B26fmNG+Q9HJJc0q6FDzT9jWSrpH0TEkfrzf7AACg33HZCXpGRKyUtF/J8EWSDs+fvyXpWxXT7zumGQQAABgGLd8AAABATQi+AQAAgJoQfAMAAAA1IfgGAAAAakLwDQAAANSE4BsAAACoCcE3AAAAUBOCbwAAAKAmPGQHADCqHjnxmG5nAQDWWrR8AwAAADUh+AYAAABqQvANAAAA1ITgGwAAAKgJwTcAAABQE4JvAAAAoCYE3wAAAEBNCL4BAACAmhB8AwAAADUh+AYAAABqQvANAAAA1ITgGwAAAKgJwTcAAABQE4JvAAAAoCYE3wAAAEBNCL7RM2xPsX2R7SX5fXJJmm1s/9b2YtvX2X53YdyLbV9j+0bbX7bteksAAAD6HcE3esmxkhZGxPaSFubvzZZL2jMidpX0EknH2t4ij/svSUdI2j6/Dhz7LAMAAAwh+EYvmSVpfv48X9IhzQki4rGIWJW/rq+8jtueJmmjiLg8IkLSN8qmBwAAGEsE3+glUyNief58h6SpZYlsP8v21ZJulfSZiLhd0paSbiskuy0PK5v+SNuLbC9asWLF6OUeAAD0vQndzgBQZPtiSZuXjDqu+CUiwnaUzSMibpW0S77c5Pu2z+kkDxExT9I8SRoYGCj9DQAAgJEg+MZaJSJmVo2zfaftaRGxPF9Gctcw87rd9rWS9pZ0maStCqO3krRsNPIMAADQLi47QS9ZIGl2/jxb0vnNCWxvZXvD/HmypL0kXZ8vV3nA9ktzLydvK5seAABgLBF8o5d8WtL+tpdImpm/y/aA7VNzmh0lXWH795J+LunzEXFNHvceSadKulHSnyX9qM7MAwAAcNkJekZErJS0X8nwRZIOz58vkrRLxfSLJD1/LPMIAADQCi3fAAAAQE0IvgEAAICaEHwDAAAANSH4BgAAAGpC8A0AAADUhN5OAAAYJY+ceEy3swBgLUfLNwAAAFATgm8AAACgJgTfAAAAQE0IvgEAAICaEHwDAAAANSH4BgAAAGpC8A0AAADUhOAbAAAAqAnBNwAAAFATgm8AAACgJo6IbucBWGvZXiHplm7nYxjPlHR3tzPRJf1cdqm/y9/PZZfW/vJvExGbdjsTwNqI4BvocbYXRcRAt/PRDf1cdqm/y9/PZZcoP9DLuOwEAAAAqAnBNwAAAFATgm+g983rdga6qJ/LLvV3+fu57BLlB3oW13wDAAAANaHlGwAAAKgJwTcAAABQE4JvAAAAoCYE3wB6lu3Nup0HAAA6QfAN9Bjb69heJ3+eaHs321O6na+xZntK02sTSVfantwP5W/F9g7dzsNYsr2R7U/Z/qbtNzeN+1q38lUX2wcWPm9s+zTbV9s+y/bUbuYNQOcIvoEeYvsQScslLbM9S9IvJH1O0tW2D+5q5sbe3ZJ+U3gtkrSlpN/mz/3sp93OwBg7XZIlnSvpMNvn2l4/j3tp97JVm08WPn9BaR9wsKSrJP1PV3IEYMQmdDsDADpygqQXStpQ0u8l7R4R19veRikwuaCbmRtjH5C0v6QPRMQ1kmT75ojYtrvZqoftL1eNkjSpzrx0wXMi4nX58/dtHyfpEtuv7mamumQgInbNn0+yPburuQHQMYJvoMdExB2SZPsvEXF9HnZL41KU8SoivmD7bKWA41alA5F+elDB2yW9X9KqknFvqjkvdVvf9joR8aQkRcQnbC+TdKmkZ3Q3a7XYzPbRSgdaG9l2DD2kY1xv98B4RPAN9JhCEPKOwrB1JU3sXq7qERG3SXp9bvG8SNLTupylOl0l6dqI+FXzCNtz689OrS6QtK+kixsDIuIM23dIOrlruarPKZL+IX+eL+mZklbY3lzS4q7lCsCI8IRLoIfY3l3SNRHxaNPw6ZL2iohvdSNf3WB7Q6XLEa61/bqIOLfbeRpL+abSRyPi4W7nZW3SD3XfSr+XH+hFBN8Ael6+BGfrbucD9ev3uu/38gO9iGvFgHHC9o+6nYcucrcz0E3UfV/r9/IDPYdrvoEeYnu3qlGSdq0Y1w/G/Sk86r7SuK/7YfR7+YGeQ/AN9JarJP1c5a1d47q7OdvXqDzQsKR+eNAIdV8ySn1Q9/1efmC8IfgGessfJb0rIpY0j8jd741nB3U7A11G3fevfi8/MK4QfAO9Za6q79X4PzXmoxvWkzQ1Ii4rDrT9Mkl3dCdLtZor6r5f677fyw+MK9xwCfSQiDin8WCdEuvWmpn6fVHSAyXDH8jjxjXqvn/rXpQfGFcIvoHx46RuZ2CMTW08Vr4oD5tef3bWKtT9+Nbv5QfGFYJvYPwY712OtbqpcMPacrF2ou7Ht34vPzCuEHwD48d473Jske0jmgfaPlzSb7qQn7UJdT++9Xv5gXGFJ1wCPWSYLseeGxHr15yl2tieKuk8SY9pKOAYkDRR0msiYlzfeEbd93Xd93X5gfGG4BvoIba3aTU+Im6pKy/dYnsfSc/PX6+LiEu6mZ+6UPf9W/cN/V5+YLygq0Ggt/Rtl2O2N5D0bknbSbpG0mkR8Xh3c1Ur6r5P677fyw+MN1zzDfSWfu5ybL7SqfZrJL1K0ue7m53aUff9W/f9Xn5gXKHlG+gtlV2O2Z5ef3ZqtVNEvECSbJ8m6cou56du1L36tu77vfzAuELLN9Bb+rnLsb81PvTpKXfqXn1b9/1efmBcoeUb6C2LbB8REacUB/ZJl2MvtN247MKSNszfLSkiYqPuZa0W1H3Sj3Xf7+UHxhV6OwF6CF2O9S/qHgDGB4JvoAfR5Vj/ou4BoLdx2QnQQ+hyrH9R9wAwPtDyDfQQ22cr3Xz1C6Uux5ZGxFHdzRXqQN0DwPhA8A30ENvXFLocmyDpyojYrcvZQg2oewAYH+hqEOgtdDnWv6h7ABgHaPkGeojtJyQ91Piq1L/zw6LLsXGPugeA8YHgGwAAAKgJl50AAAAANSH4BgAAAGpC8A0AAADUhOAbAAAAqMn/B09pc6UcBUe3AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Feature Importance\n",
      "0    PCL2   0.291109\n",
      "2    PCL5    0.24327\n",
      "3   PCL11    0.18705\n",
      "1    PCL4   0.152922\n",
      "4   PCL13    0.12565\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.8/dist-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PHQ9_GAD7_df\n",
      "[1, 2, 6, 7, 8, 13, 16]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Feature Importance\n",
      "3    PHQ8   0.371373\n",
      "0    PHQ2   0.302289\n",
      "2    PHQ7   0.195588\n",
      "5    GAD5   0.065109\n",
      "1    PHQ3   0.053018\n",
      "4    PHQ9   0.012623\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.8/dist-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PRMQ_df\n",
      "[1, 2, 5, 6, 7, 10, 12, 14, 16]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             Feature Importance\n",
      "0   PRMQ_RE _LO_ENV2   0.218299\n",
      "4     PRMQ_RE_LO_CU8   0.189241\n",
      "7    PRMQ_RE_LO_CU15   0.128127\n",
      "1    PRMQ_PR_SH_ENV3   0.119704\n",
      "2    PRMQ_RE_SH_ENV6   0.118786\n",
      "6  PRMQ _RE_SH_ENV13   0.098118\n",
      "3    PRMQ_PR_LO_ENV7   0.075065\n",
      "5   PRMQ _RE_SH_CU11    0.05266\n"
     ]
    },
    {
     "data": {
      "image/png": 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skLSd7V/YfuFgpqT7Av9VsK6B7s5mq3aUXmz/RNKdhWq6i1k+dbMbTSj8cMSyA21/qUBZ3RpeTPN1/hdD8+8HHGX7pWUqA0n7AmcCl7ezDgS+QLOD/GfbHyxY28jTbpJ2AJ5m+4wRvzYRknYHXk0TDn9P863jSOB64JjB0X6MR5JcOMAk3QH8kubbzzbAHra/3w4Xs6L0qaWBWQ7682i+0n9sOExrIOmhti8uXcdc2gfIPB64P82R/I3ABaXPiUs6xvabStYQkyHpYNufKV3HKG1TwANtf7l0LTDbQX8ozVf6g1h78e689uEqxUm6lOZC7NnAWbavLlzSGu0F2LNs31i6lmGSng18wvavStcyTNI7WPervmlOfV1Y+hvkXCRdabuGU5l3Iem7theXrmMazGzQD0i6O2vP4/4R8CnggzUcKUj6PZq6ngv8lmZndLbtVYXregvNKKWr2po+bPvHJWsakPRvNKeSLqCp7YJaBtKTdMSI2TsDzwE+ZPutEy4JAEnPmGsRcIrtRZOsZ50CKu4IWkhNO8mZD/qu9tzzGcC+tqt6tq2kh9CE/nOAH9g+sHA9Ah7Z1vQ0mvP1ZwEftV30Ipmk7WlaPg8D9qO5X+KsURcba9BeNP7PUi29kn4LfIB1v20MPMv2PSZc0hqSbmXujqAP2b735KvqFFHxTrJrlrtuAJB0b5rwPAy4D00XxAtK1jSsbb27F82NI78L/KhsRU17G/B54PNqhrF+HE2//7uAuxeu7ec0O+wzJN2T5tvH2yXtbHv3+X978trW1JIlXAG8qe2iWoekxxWop6vmjiBo7m2Zayd5twnXMqeZDXpJL6Lpevg94CPAK2z/Z9mq1iXpETQ1Pg24kuZ8/V/b/lnRwjokPZi1p5d+Qtke+nVI2gl4Bk1tO9PcFFcVNU9kez7NxexSXgr8fI5lT59kIcNsz3m3te1HTrKWOdS8k1xjZk/dSDqN5lTDZ21X0+86IOkGmra7s4FzbBc/ih+QtDdNuB8G3EFT49m2rytaGKBm/JOn0+wg96d5ytnZwEUVtOL9F3c98rud5pvRS21/b/JV1U3SyTTXzGq9WP0I4Hrb3x2xbKntFQXKuotZDvp5r9aP+g83SZL2sH19yRrmIunbrL0wfJcjmZIk/QT4d5pwv8D2bwuXVL15uoE+Z/uLZapqSDqadU+rnmX70pI1TaNZDvoraTbo7slRA4uAe5W+GCvpfYw+7wfNKfIjJ1nPtJC0bQVDHYwk6Qk0Q1ucOzT/WcDPSnV61doN1NUOTTL4FrktzYHGWba/WbiuaneSXTMb9MPagc1eSXNR8e2231G4nmeOmL078Nc0Y83sNuGS1pD0HdbduNV5bdv3nXxVbSHS55h/B1lyMLgv0dyd++Oh+bvQ9P7/UZnKRivdDTQXSfvTDFVSvDtuGnaSkKAfnG/+W+DhwJuBM2r7ui9pL+BvaNoZ3wK8t+SNXW0nS9cWNBv2McDXbY/aSU2EpANGzP5D4FjgR7YfOuGS1pC0wvbSOZZdUcvt8l1zDSlRoI6tgCcydJOj7Y+XrGsute0kZ7nr5vdpAv5BwBuBI2u5sWZAzZjWr6a5qHgS8BLbq8tWBbZvhjVtn88HXgFcRjPsbtE7eG1fMpiW9Cjg72ja3F5i+1PFCmtsL2mr4f+Gkn6H5nRENSrpBho8+ORw4E+Ar9Fce1lm+5cl61pIBS2z65jZI/p2MKIbaB5mcJeAt/1XEy+qQ9KHgQNovmWcw1CNtm8pUResCaYX0pxG+iJwgu2VpeoZ1p4LfzXwa+AfbX+ucEkAqBnG+d40g9L9sp23HfA24Ce2X1mormq7gSRdSHM+/lyvHRq7ap2d5DNsP6V0PTDbQf8C5j6XS8lRDgEkraJz3nswe/Da9l4TL2pQhHQjsJpm3Pe7dCfZ/ujEi2pJupjmgvpJwF0GlLL99YkX1WoD4PXAX9C0zgIspnmK09/VdsowFlbzTrJrZoN+PqO+Xsdakk5n/gueL5xj2WYn6SLmr+2xEyxnpPb87f3alyuHu4QmPSpjrd1AbQ3dIF1zoENz2nlr2zN7+nl9zGzQS/qi7f/RTp9p+/mdZV93oce6dWp4nu33t9PrjI8v6Sjb7yxXXWxOk97+pqkbqD3V9ZfAi4F/s/3ywvVUu5PsKv00oJK6o949aGhZDVdRXtaZHm71LHbEDCDprZ3po4eWnT7xgtb9/GM7088eWlb8oe9jmvT2t81wyEPzlCTW/TspRtKOkv6eZsiBewAPLR3yrdfQnKYZdhFw/GRLmdssB/18X2Vq+JqjOaZHvZ607hgjw33EpVsED+tMD4+7c8gkC9kIk97+tm+vH6yjhm4gSbtIegPNs4hXA/vbfvWg86sC1e8kYYbbK4EdJT2dZme3Y2e4UQE7lCtrjeG77eZaVsJ8O6HSat5B1uqjwHvaU4LD3UDFLqy3rgd+DLwPuA04stu2aHv4ecqTNhUts7Mc9J8HntqZ7rZBfWHy5dzFAyRdQRNO922naV8X67hpbdGODLlFZ3rw11d6HP+ad5DjWjXhz3s1TTfQ9ZLu0g004VqGncTa/27FxsWfR807yTVm9mLsuCQdUaLVsh3bY04lBzxrWz/vZPQRcunWz+7DmrelOQqkfX03279TqjYASfeiuZg4uC50FfAvHvGQ+kmrrRtoGkxLy2yCfgE1dODMR9KXa+qK6JL0INtXla5jFEk7TfoGHEkHAh8ETgcGd/AeQHOd489qHYp3oMTfgqRzbD+nnT6xe1OZpE/bfvwk65lL7TvJWT51M67az+tW8xSbEc4Eat1JfpbJ1/ZmmjbG7jC7y9U85/bdNOMt1azE38LenemDaQYeHKjiMX3QDHlA83CguZwIFAv6We66GVftX3lqrq/mnWSJ2rYfNZa67cuo8/zzsBLbWu3dceMq+reQI/qF1RxWtav5D7FEbRp1ykjSzuSgay53b4cl3gLYtp0Wa6/BTIuifwsJ+oVVfd6U7IimyVuAT0s6hqYvHJpz9CfSjBtUu1UFPvP7wKCF8ged6cHrGMNMB307VPGxwD7trKuAN9setDJi+6gSta2H5y/8lmKKjZk/honvIG2fKul7wOtoum4MXA283vYnJl3PQOcekpEGg9TZnvd9m4Ptx4zzvtIXO8ewquSHz2zXjaRDgTcBbwAGD/BdSnM35TGlH2gwx6h40ASUbW8/4ZLWFiDNexGz5AiRA5IeQ6eFcXioYkk7lxzqeZikL9k+sNBnv6/z8ilAd6dTdJC6cZXsjqu5ZXZgloP+cuBQ26uG5i8BPm77IQXKGqmWp/wMtI/rGziAZkfZHUK52AiRknaluVHlV6zbwrgt8HTbN5WqbT6SbrC9ewV1VLWtjatU3dPSMjvLp262Gg55ANur2tuXa1LV3rj7dbr9Ays+9G/HO4F32T69O1PS/wT+BTi0RFFjqOW/cS11rK9SdU9Fy+wsB/1qSYttr/PgjPaO1IxFP77agmEf208fnmn7XyX9bYmCBuY5Fz5tHSSx1pwts5KqaZmd5aB/LfAf7dC1g69cS4Hj2p+ihkJhx+GQKPkUp8qNbFNU83zb0uPwzPdYuU9OrIohkj7B2h32XpKWd5fbfupdf6s6qwp97lS0zM7sOXoASQ8BXs66F1HebPvyclU1hi6QDSv9FKd3sDYYDqN5YPMaLvi8XUlvAbajeYzbYJCp36VpbfxVydrGNenxldQ8RH1OtkeNtz4R43YElSJpGfAiYFTL7Ptsn1Kqtq6ZDvq5SPqu7cWl66iVpOEx6NdRYhC4gfb6yhuAF7DuIFNnAK+qZZCp+ZQeX6ldh78P3GT7R6XqaGupviNI0pNp2rS7LbMnlWyZHZagH6GGDghJTwGuGIxSKek1wDNpwuto298pWd+wdqjin7qSDWpokKlv275N0ptsH1OyrnFMuoNE0inAO2xfJWkHmoeq3wHsTNNqfNakapnPtHUElWyZHVbNOaTK1BBW/0jzwIXBEcPzaB4huBwo+nVQ0mskPaCd3kbShcC3gR9KelzJ2gZs3277yvZnMFTxc4oWNb5Jb3+P6Iwy+ufAN20/mOYUxLFz/9rE1fB3uT6qOSswsxdjJb1srkU053hLcyegngG81/YlwCWS/lfBugCeS3N3JzT9wqIZSfD+NKdI/qNQXQuZluEiJl1n9w7mg4EPA9j+QfdpTrHeqtkxzWzQM/9ogW+bWBVzU/ukmtuAg2h6wAdKD038m84pmicAZ9u+A7hm1LNHJ6ntdhi5iOkJ+knfZPPT9lvjTcCBwJGw5qEapZ8ZW3VH0LS0zM5s0Nv+h3HeJ+lVtt+wuesZ4a3AZcDPgWtsr2jr2Z9moKeSft2OE/RD4DE0HQcDdy9T0hqX0ATDqFAvOvaOpLfafmk7fbTtt3WWnW77BVBkfKUXA28H/htNt9JgsLCDgPMmXMuwN3Wm31ysirlV2TI7LBdjF1B4DI1dgXsBl9u+s513H+B3Bjd6lXiKk6SH05yiWQS81fbr2vl/Ajzf9uGTrGdDFFpva7al4e2qdKfNOAoe9HRrqKYjaH1MumV2WC7GLqzY133bN9m+dBDy7bzvD93Ne2aBur5q+wG27zkI+Xb++d2QX6gNs7CJrzfW3Zam5TRS17Mn/YGSTpH0oHZ6B+By4F+BSyVVf0DRcXTJD0/QL6z2rzw1B0bRjXsBJdbbFpJ2knTPzvTO7XWF0nftjqPEOpuWjqCF5AlTlas5SKHuHVHN667EetuB5hrCYL0UH855PZVYZ33pCMoTpir34dIFTLGad0ITZ3tJ6Ro2UolkrbYjaD0V3Svl1M0CbP9T6RoWkKc4bZhq1puk+0t6T+k6xlDioOfFwFHA+6ivI2h9FB2XPkE/ZYZDwfYflqxnAVU8dAHqWG+S9pX0aUnfkPR6SfeR9BHgQprxUYqQdE5n+sShZZ8eTJc46LH9TduH2N6v+4wB2xfYfvngtaRXTbq29nPf2pk+emjZ6YPpAi2z60jQV6rWUGhrq3bjrnm9Ae+heRrRM2mGt7iMZuiI+9l+S8G69u5MHzy0bNEkC9kIE+8Iaj2yMz3cZbbvJAuZz8wG/bhhVVCtoQB1b9w1r7dtbJ9u+9r2Zqlf2j7W9q8K1zXftZRpuc5S6jThVLTMzvLF2OGw6g57UDqsoA2Fdvra9k7KWtrJat64a15vd2vvbB6ss193X7vcQ9Xv3taxBbBtp6aqbuNfQKkd0hbtyK1bdKYH/32raZmd5aCvOayg3lCAujfumtfb94F/7rz+Qee1gVLP3u3W1a1p8HoalPobnoqW2ZkdAkHS5cCjacLqwnZ68B/rc7YfUqayhqSLmPsoxS74QG5Jq4A7meOPy/aeEy2oo+b1Ni5JB9v+TOk6pomkv5mCDrliZjnoV1FpWMVsq2XcG0kHA8faHr5AO8kazrH9nHb6RNuv7Cz7tO3Hl6ptPpLuD7zC9otK1wIzfDHW9hLbe9nec9RP6fok7S3pY233yFntAGdVq6EffBrX2wgTPQ0h6bGSvinpF5LeL+nBklYAJwDvmmQtI1TdEVR5l9caMxv0c6khrFqn0dwQ8kya837vKFvOWpVv3NWut/Uw6a/ZbwaWAfcEzqV5lODptg9w4YdvU39HUM1dXmvM7MVYSfvSjHX934GPAScD7wQeTh3jXt/D9mCHc5Kkmi7yvIfmSO/LwCE0G/cZwJ9V0CpY83qrlW1f1E5/TNJNtt9ZsqCO2juCau7yWmNmg566wwru2j3S3chLd4/UvHHXvN7GtWrCn7ej1n1S0lbd14WP6mvvCKq5y2uNWb4Ye5nt/Tqvr7O9V8mauiR9bp7Fpbtu/h9wOGs37g8Af0oFG3fN6w3WPDjmL4F92lkrgHfbvrlgTe+bZ7Ftv3BixUyZ2re3gVkO+mrDan2UaMWblo17PoXW26OA99NcR7iknX0A8LT253jbz59kTetDhZ+S1FVDR9D6KN0yO8tBP/VhBfW04o1SeuOeT4n1JulrwIttXzo0fz/gC8C/2a72qVyF1tljgVNYey3tRJqRLAX8YwUXi8dS+u90Zs/R237MOO+rOaxaNd7VO3AiUOu6K7HethsOeQDbl0n6Ic0TlGpWYp0NOoK+DDyx/d/jKrpYPK6MR1+5E6eNWBkAAAYoSURBVBd+S1E1fyWreSdUYr2pHS5ieObOwOrus4ErVWKd2fZFtn9t+2M0DwWftpCHPGGqejWHVe1q3gmV8Bbg05KOYe2YKAfQHExU03M9jxJ/CzV3BE2NBP3Cag+rVaULmFKrJv2Btk+V9D3gdcCD2tlXAa+3/YlJ17MBSjxI5vPAUzqvv9B5bWBagn5VyQ+f2Yux4yp5EaXGVrz1Iemjtp+x8Ds3+edO9XqL9VeyI2gatrcE/QIKhlXVrXi1btw1rzdJb59vue2/mlQtfVPqgKzm7a1rpoO+1rCCulvxat64K19vvwG+AZwDfI+hc9619KhPI0mX2t6/wOdWu72tU8+sBn3NYQUg6Wrb+8yx7FvA75Xq0qh54658vd2T5tmmzwVWAx8CzrX90xL1dNV80DOOgkf01W5vXbPcXnkS8FTbr7W9vP15Lc1jBS+nGau+pJpb8ebsBwdK94NXu95s32z7lPYejj8HdgSullT0q3170PM14A7g9PZnG+BCSXtKOrNcdWMr9szYWre3rlkO+prDCta24j1K0j3an0cDn6J8K17NG3fN6w0ASX8AHA08j6auS+b/jc2u9oOecZToCIIp2N4AsD2TP8A1wE4j5u8MXFO6vraWJ9OcCrm5/fkC8JQK6loGXAw8CrhH+/No4KvAsgrqq3W9HU8T6u9va9yqdE1tXVfPs+xbwBala6z5p9btrfszy+folwEvAkbdvPJe26eWqm0aSHoycCzr9oOf5OnoBy9C0p3Ad4Db2lmDPz7R3AG6b6G6rgH+2PatQ/N3Br5k+4El6opNZ2aDHuoOq7TibZia15ukPeZbbvv6SdXSlYOeDVfz9tY100Ffs5pb8WreuGteb3ORtAVwuO0PFKyh5oOeajuCpmV7m9mgrzmsoPpWvGo37srX2/Y0gbUrsJxmZM+jgJcDl9s+tGB5VZqCNuhqt7euWQ76asNqmKTdgMOAlwGvtF203W1qNu761tvHgVtphto9CLgXzXZ3tJtur1J1VXvQU/M9G8Nq2966ZjnopyWs/oDmSVgH0xzRvNn21WWrWqvWjbvG9SbpStsPbqe3pHke6mIXfkZxzQc903JDUo3bW9fMjl7Znt87BTilE1ZXS6oirCQdDzyJpg30bOBVtleXrWpdQxt3Df3gta+33w4mbN8h6cbSId+6D/Ue9EjSTnN0BJW+Z6P27W2NmT2iH6h1T1xrKx6M3Lj/vZaNu/L1dgfwS9YeMW9LU+egtu1L1TZQ2ze02juCat7eumY26GsOK6i3FQ/q3rhrXm+1q/igp+aOoKnY3mY56KsNq/lU0oo3FRt3VyXr7W7AS4D7AVcAp9VwcFH7Qc80qmF765rloK86rKaxFa+Gjbvm9SbpQzTn6f8vzYOur7d9dKl6Bmo+6Km5Iwjq3t66Zjbo51JDWLV1VNmK19ZW7cZd+Xrrdt1sBXzNhZ5e1lXzQU/NHUFQ9/bWNbNBX3NYQb2teG091W7cla+3dcZMH35dmxoOempvg655e+ua2fZK4EzWhtVfAH9DE1ZPKx1WrVpb8QD26mzc/4e6Nu6a19tDJP28nRawbfu6aNfNQgc9QLGgr70Nmrq3tzVm+Yi+6j1xza14NR+Z1rzealXzN7SBijuCpmJ7m+WgrzasajctG3eMp+aDnnQEbRqzHPRVh1WtrXi1y3pbfzUf9NTcEQTTs73NbNDXrtZWPKh74655vdWq5oOemjuCYHq2t5kN+prDCuptxYO6N+6a11tsOjV0BLV1TMX2NstdN2ewNqz+hOb26irCqtW9mr9aKvWQ+5H26Wzc7wW+VrierprXW5VqPuipuSOoNRXb2ywf0Ve9J+58nYa2FY96vk7XfE632vVWq8q/oVXdETQt29ssH9FXvSe2vWXpGuZRZT84VL/ealXzN7Sa79mYmu1tloO+2rCq3bRs3DG2mg96puKGpNrN7KmbiGjUfPqh5o6gaZKgj4jouVk+dRMRlau5I2ia5Ig+IqpVc0fQNEnQR0S1am+DnhZblC4gImIe63QElSxkmuWIPiKqVXNH0DRJ0EdE9FxO3URE9FyCPiKi5xL0ERE9l6CPiOi5BH1ERM8l6CMieu7/AyEPwsApJP6/AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.8/dist-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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HeEYsIs4BzsmV/htJjcP/H/BrSetHxE2Rbr1x4CjOM4DzJX2B1CZvc+ZsiAupN9FepKTiKFJC8jLgJx3OTrfP41lJxwP7k87e30V3Z+0GGc3bzLfuxrfJMD6zGqmBYnvFNA54c4fPPE/n7LR12mml9hGSVmMYR5HZX0kbxJtyL5x+Mqhil7QqqWxmxsCNblo3w9msZvrxDCzPulbpnbQqzE7LaSTLfbj+SOpZsHWX0y6VL2UMS0Q8HRF/iIgvkbrAQXeV+VDlvhppw749Bt+QaDyp91C71vdUb27UzXL4V3tCko1GYtjJVaTteDj7imtI+6u69WOzUYipWyNdbmOttf/dpotph7vNj2R5jabWfngdSa8c4XeMpH4aiefo4uxJRDweERflJO8bpEvE3Sy7efGf/H9QO4RIvY6uJpXxGxj63iSd/JiUzK9ISmbqzujO1aglJZG6Av0e2EHSR+qmkfQ6SS+vDJoJrF5d0fKppgNJvSrqPERN0pH9lXT6eUp1PpIWJTWgHJaImE3KGpcHvpu/Zw6SlpfUKdYm7ZO7aAMvVPiHkpb58ZXpziS1/t8ln86v+iTpevfvYng3fmt1TZvYYfxMhr/ch+uo/P9wSYOOsNqGHZH/H1u305P00mrZSPqvunWBdHYKursb5U/y/y9IarW3QtICpFtyjyM1FqvzzXyGpvWZCaSzHjDnsn2Y3GCxw/fMJJ19WKc6UNJHSdefx0Ruq/FzYLKkL+bfPAdJr5K0SmVQ63d9XdIilemqv70X5mW5jaWzScvzXZJ2aR8pqdr2bFjb/AiX16jJldsPSGf1jq6u+3neC1WXRQfHkxKbL+dKdw6Sxml0npXzEKn9WV1d8ZYO7SuGs9/oSNLWknaoO4CWtBgD3ZUv7fAVrcsth5HaKN0QEVd2O//cZmprUhftYde3LaN6nxJSF6SLgOMkfYLU1egRUua0Dqnr1Uake0BAqgyOBq6V9EtSY8ONSRXT2aT7JbS7kNSY5mxSJv8scGlEXJpPIX2H1F32Wkln5N+4FalB2UgaSX2V1Lr8Y8B2ki4iXdt8OamtycaktgXtjaOadjlwXW5Q9SipklmXlA1/qzVRRDyWk8hfAJfkRkr/JF2SeBvp2vpew5z3LaQy2lnSs6R7IARwYkTcwciW+7BExPmSvkaqsG6W1LpPyXKko+0/knptEBEXStqf1H31VknnknogLEZqf7MpqYFl66zLfsDmkn6fp3sMWJt0pPMwXRxdRMQfJH0rf9dflBrMPp6/47V5fofWfPReUjuPv0iaRrp+uyMpcf5BRLyww8nL9krSvWl+DvyNdCQ3LZ/CPpK0Xlwm6TTSejI5l8/p+XvHyt6k7ecrwIckXUa6Jv1KUmPDDUn38bg9T38yqfHcu/JvP6vy2/8EvGoMY33BPCy3sY7rGaVG2ueTLiHuRVrHFyGV5xbk/f0It/nhLq/RdhDpcsd2wN8k/Zp05L9SjvkzDNHmIiIekrQjqafdHyVdSLrxYuTv2IjU7qS9ofdwXUgqi98qNRh+Grg+Is4mVdQrSLqclEA+QyrzzUn7yFPmcd5rkvatD+d9062kti8rAu8kNV+4knSPnzonkRKS1tmkYbcJiYjzh/uZui/ppptR0KH7Vs20LwM+T6r8HiP1TrmddNOYPcldhirT78bAHQIfJK00r6PDXflIycBJpA3iOdq6npJOTe1Put77DGlj+xbpJkIzGeJulEP8JpEaM15IOsJ4hlTpXpZ/60pdls309nJkiO6zzP0+E4O6QTLn3VY/zcAdXe8mVUKd7ui6YS77WZVy+yE1XfAq85g0xG/dMJfXo6RTv+1d9Ia73Dt2+RwqHtLNu36bl1vrjq5nUN8F882kG3Tdw8BdZq8Dvk2lnz5pJ3g8KRF9NP+GW0g7nZW7WRcq37VzXo/+k5fTjaQkd5GaaWcycEfX7+dl+jTp7slz3NG18pnVSIneQ5XlsFtl/Lakyus/pAOI80k9cXZrn7Yaw3C2oU7LjoE7yv4hl+PTeb27kHRUt3TN9F8idaN9OsfydYZxR9ehtsVu1rV5WW4jiKPjej1EXBNJZxVuz+vwQ6SK6PM103a9zY9weY30d3daX8bn+V9FqlseJ1W8xwCrVaY7kJp9SB43iVQpt+6c+2/SPvJE8t11uyl/Ouy3SQ08f0hqUzGbyv6bdBfgk/O8H8vz/kteh5etK4vh/JHudv2RPI+bSAdIz+blezHpBm0LzeU7Wl3en6DmTtY1ZTyou3SH5dZ1l2DlD9l8QumJsLuSbmo2s9lozMzMujeaDV3NzMzMRsxJiZmZmRXBSYmZmZkVwW1KzMzMrAg+U2JmZmZFGO37lNgYWmaZZWLSpElNh2Fm1leuvvrqByNibjdYswI4KekjkyZNYsaMugdWmplZJ5LuaDoG644v35iZmVkRnJSYmZlZEZyUmJmZWRGclJiZmVkRnJSYmZlZEZyUmJmZWRGclJiZmVkRnJSYmZlZEXzztBeJRb98WKPzf/KgfRudv5mZlc9nSszMzKwITkrMzMysCE5KzMzMrAhOSszMzKwITkrMzMysCE5KzMzMrAhOSszMzKwITkrMzMysCE5KxoikrSXdIuk2SfvXjP+UpJsk3SDpQkkrNxGnmZlZKZyUjAFJCwDfB7YB1gJ2kbRW22TXApMjYh3gdOBbvY3SzMysLE5KxsYbgNsi4h8R8QxwCjClOkFEXBwRT+S3fwRW7HGMZmZmRXFSMjZWAO6svL8rD+vko8BvxjQiMzOzwvmBfA2T9EFgMrBph/F7AnsCTJw4sYeRmZmZ9ZbPlIyNu4GVKu9XzMPmIGlL4ADgXRHxdN0XRcQxETE5IiYvu+yyYxKsmZlZCZyUjI0/AatLWkXSQsDOwLTqBJLWB35ESkgeaCBGMzOzojgpGQMRMRvYGzgPuBk4LSJulPQVSe/Kkx0KLAb8QtJ1kqZ1+DozM7MXBbcpGSMRcS5wbtuwL1Veb9nzoMzMzArmMyVmZmZWBCclZmZmVgQnJWZmZlYEJyVmZmZWBCclZmZmVgQnJWZmZlYEJyVmZmZWBCclZmZmVgQnJWZmZlYEJyVmZmZWBCclZmZmVgQnJWZmZlYEJyVmZmZWBCclZmZmVgQnJWZmZlYEJyVmZmZWBCclZmZmVgQnJWZmZlYEJyVmZmZWBCclY0TS1pJukXSbpP1rxr9F0jWSZkvasYkYzczMSuKkZAxIWgD4PrANsBawi6S12ib7J7AbcFJvozMzMyvT+KYDmE+9AbgtIv4BIOkUYApwU2uCiJiZxz3fRIBmZmal8ZmSsbECcGfl/V15mJmZmXXgpKRwkvaUNEPSjFmzZjUdjpmZ2ZhxUjI27gZWqrxfMQ8btog4JiImR8TkZZdddlSCMzMzK5GTkrHxJ2B1SatIWgjYGZjWcExmZmZFc1IyBiJiNrA3cB5wM3BaRNwo6SuS3gUgaUNJdwE7AT+SdGNzEZuZmTXPvW/GSEScC5zbNuxLldd/Il3WMTMzM3ymxMzMzArhpMTMzMyK4KTEzMzMiuCkxMzMzIrgpMTMzMyK4KTEzMzMiuCkxMzMzIrgpMTMzMyK4KTEzMzMiuCkxMzMzIrgpMTMzMyK4KTEzMzMiuCkxMzMzIrgpMTMzMyK4KTEzMzMiuCkxMzMzIrgpMTMzMyK4KTEzMzMiuCkxMzMzIrgpGSMSNpa0i2SbpO0f834hSWdmsdfKWlS76M0MzMrh5OSMSBpAeD7wDbAWsAuktZqm+yjwMMRsRpwBHBIb6M0MzMri5OSsfEG4LaI+EdEPAOcAkxpm2YKcEJ+fTqwhST1MEYzM7OiOCkZGysAd1be35WH1U4TEbOBR4GlexKdmZlZgcY3HYANTdKewJ4AEydOHPH3PHnQvqMV0qhb9MuHNTr/ocqm5Nig2fgc28g4tpEpeR9mo8dnSsbG3cBKlfcr5mG100gaDywBPNT+RRFxTERMjojJyy677BiFa2Zm1jwnJWPjT8DqklaRtBCwMzCtbZppwK759Y7ARRERPYzRzMysKL58MwYiYrakvYHzgAWAn0TEjZK+AsyIiGnAccCJkm4D/kVKXMzMzF60nJSMkYg4Fzi3bdiXKq+fAnbqdVxmZmal8uUbMzMzK4KTEjMzMyuCkxIzMzMrgpMSMzMzK4KTEjMzMyuCkxIzMzMrgpMSMzMzK4KTEjMzMyuCkxIzMzMrgpMSMzMzK4KTEjMzMyuCkxIzMzMrgpMSMzMzK4KTEjMzMyuCkxIzMzMrgpMSMzMzK4KTEjMzMyuCkxIzMzMrgpMSMzMzK8L4pgOY30iaAJwKTAJmAu+NiIdrpvst8CbgsojYtpcxlubJg/ZtOgQzMyuAz5SMvv2BCyNideDC/L7OocCHehaVmZlZ4ZyUjL4pwAn59QnA9nUTRcSFwH96FZSZmVnpnJSMvuUi4t78+j5guSaDMTMz6xduUzICkn4HvKJm1AHVNxERkmIe57UnsCfAxIkT5+WrzMzMiuakZAQiYstO4yTdL2n5iLhX0vLAA/M4r2OAYwAmT548TwmODZ8b4ZqZ9Y4v34y+acCu+fWuwFkNxmJmZtY3nJSMvoOBrSTdCmyZ3yNpsqQftyaS9HvgF8AWku6S9PZGojUzMyuEL9+Msoh4CNiiZvgMYPfK+016GZeZmVnpfKbEzMzMiuCkxMzMzIrgpMTMzMyK4KTEzMzMiuCkxMzMzIrgpMTMzMyK4KTEzMzMiuCkxMzMzIrgpMTMzMyK4Du6mvUxPzDQzOYnPlNiZmZmRXBSYmZmZkVwUmJmZmZFcFJiZmZmRXBSYmZmZkVwUmJmZmZFcFJiZmZmRXBSYmZmZkVwUmJmZmZFcFJiZmZmRXBSMsokTZB0gaRb8/+laqZZT9IVkm6UdIOk9zURq5mZWUmclIy+/YELI2J14ML8vt0TwH9HxNrA1sCRkpbsYYxmZmbFcVIy+qYAJ+TXJwDbt08QEX+LiFvz63uAB4BlexahmZlZgZyUjL7lIuLe/Po+YLmhJpb0BmAh4O8dxu8paYakGbNmzRrdSM3MzAoyvukA+pGk3wGvqBl1QPVNRISkGOJ7lgdOBHaNiOfrpomIY4BjACZPntzxu8xK8+RB+zYdgpn1GSclIxARW3YaJ+l+SctHxL056Xigw3SLA+cAB0TEH8coVDMzs77hyzejbxqwa369K3BW+wSSFgLOAH4aEaf3MDYzM7NiOSkZfQcDW0m6Fdgyv0fSZEk/ztO8F3gLsJuk6/Lfes2Ea2ZmVgZfvhllEfEQsEXN8BnA7vn1z4Cf9Tg0MzOzovlMiZmZmRXBSYmZmZkVwUmJmZmZFcFJiZmZmRXBSYmZmZkVwUmJmZmZFcFJiZmZmRXBSYmZmZkVwUmJmZmZFcFJiZmZmRXBSYmZmZkVwUmJmZmZFcEP5DOzF50nD9q36RDMrIbPlJiZmVkRnJSYmZlZEZyUmJmZWRHcpsTMrCBu72IvZj5TYmZmZkVwUmJmZmZFcFIyyiRNkHSBpFvz/6VqpllZ0jWSrpN0o6SPNRGrmZlZSZyUjL79gQsjYnXgwvy+3b3ARhGxHvBGYH9Jr+xhjGZmZsVxUjL6pgAn5NcnANu3TxARz0TE0/ntwng5mJmZuTIcA8tFxL359X3AcnUTSVpJ0g3AncAhEXFPh+n2lDRD0oxZs2aNTcRmZmYFcJfgEZD0O+AVNaMOqL6JiJAUdd8REXcC6+TLNmdKOj0i7q+Z7hjgGIDJkyfXfpeZmdn8wEnJCETElp3GSbpf0vIRca+k5YEH5vJd90j6C7AJcPooh2pmZtY3fPlm9E0Dds2vdwXOap9A0oqSFs2vlwLeDNzSswjNzMwK5KRk9B0MbCXpVmDL/B5JkyX9OE/zGuBKSdcDlwCHRcSfG4nWzMysEL58M8oi4iFgi5rhM4Dd8+sLgHV6HJqZmVnRfKbEzMzMiuCkxMzMzIrgpMTMzMyK4KTEzMzMiuCkxMzMzIrgpMTMzMyK4KTEzMzMiuCkxMzMzIrgpMTMzMyK4KTEzMzMiuCkxMzMzIrgpMTMzMyK4KTEzMzMiuCkxMzMzIrgpMTMzMyK4KTEzMzMiuCkxMzMzIrgpMTMzMyK4KTEzMzMiuCkZJRJmiDpAq5M2oIAAB02SURBVEm35v9LDTHt4pLukvS9XsZoZmZWIiclo29/4MKIWB24ML/v5KvApT2JyszMrHBOSkbfFOCE/PoEYPu6iSRtACwHnN+juMzMzIrmpGT0LRcR9+bX95ESjzlIGgccDuw7ty+TtKekGZJmzJo1a3QjNTMzK8j4pgPoR5J+B7yiZtQB1TcREZKiZrqPA+dGxF2ShpxXRBwDHAMwefLkuu8yMzObLzgpGYGI2LLTOEn3S1o+Iu6VtDzwQM1kGwGbSPo4sBiwkKTHImKo9idmZmbzNSclo28asCtwcP5/VvsEEfGB1mtJuwGTnZCYmdmLnduUjL6Dga0k3Qpsmd8jabKkHzcamZmZWcF8pmSURcRDwBY1w2cAu9cMnwpMHfPAzMzMCuczJWZmZlYEJyVmZmZWBCclZmZmVgQnJWZmZlYEJyVmZmZWBCclZmZmVgQnJWZmZlYEJyVmZmZWBCclZmZmVgQnJWZmZlYE32bezMy68uRB+zYdgs3nfKbEzMzMiuCkxMzMzIrgpMTMzMyK4KTEzMzMiuCkxMzMzIrgpMTMzMyK4KTEzMzMiuCkxMzMzIrgpMTMzMyKoIhoOgbrkqRZwB0NzX4Z4MGG5j03jm1kHNvIOLaRaTK2lSNi2YbmbcPgpMS6ImlGRExuOo46jm1kHNvIOLaRKTk2K4cv35iZmVkRnJSYmZlZEZyUWLeOaTqAITi2kXFsI+PYRqbk2KwQblNiZmZmRfCZEjMzMyuCkxIzMzMrgpMSMzMzK4KTEhsWSVs1HYO9OEhapukYSibpJZL2k/QZSYtI2k3SNEnfkrRYAfGtKWmL9lgkbd1UTFY+JyU2XMc1HYCkJZuOoRNJC0lS5f1bJX1a0jZNxlVH0iqSdpC0ZgGxbCPpdkmXSVpf0o3AlZLukrRFw7GtKek3ks6R9CpJUyU9IukqSa9pMLSpwHLAKsA5wGTgUEDAD5sLCyR9AjgL+F/gL5KmVEZ/o5morB+4940NImlap1HA5hHx0l7GMygIaTYwHTgZ+GVEPNJkPFWSrgc2i4iHJX0GeDdwLrApMCMiPtdgbGdGxPb59RTgSFI5/hfwzYiY2mBs1wG7AEsCvwbeGRF/zJX+zyPi9Q3Gdimpsl8MOBj4LHAqsC3wyYhoJGmSdF1ErJeT4HuB5SMi8vvrI2KdJuLKsf0Z2CgiHpM0CTgdODEiviPp2ohYv6nYrGzjmw7AirQJ8EHgsbbhAt7Q+3AGuZlUoe4CfEvSZaQE5ayIeLLRyGCBiHg4v34fsElEPCnpYOAaoLGkBFi58vqzpATz9nyZ5ELSkXdTno+ImwEkPRERfwSIiJslNX1G92URcTaApK9GxCl5+NmSDmowLgByInJu5CPM/L7po81xEfFYjmempM2A0yWtTNqPmNVyUmJ1/gg8ERGXtI+QdEsD8bR7NiJ+Dfxa0qLAdsDOwPclnRcR728wtn9Lem1E/IX08LFFgCdJ21rTlWu1ohofEbcDRMSDkp5vKKaWRyTtBSwOPCzp/4DTgC0ZnBz32gKV199uG7dQLwNpM0PSYhHxWER8pDVQ0quA/zQYF8D9ktaLiOsA8hmTbYGfAK9rNjQrmS/fWN/pdPpX0hLA9hFxQgNhtWJYBzgRuD4P2hi4lLQj/nZEnNRgbM8Bj5OOVBcmPTn1XkkLkS4tNXm6fyXgC6TE6UDSWbCPkp6KvW/rLEpDse1FuoT0WNvw1YC9I+KTzUTWmSRFgzt3SSuSDh7urxm3cURc3kBY1geclFjfkbRvRBzWdBydSFoAeBuwBukMyV3AeSW1fanKDYdfExFXNB1LiSRtGBF/ajqO4ZC0VURc0OD8zyFdUj2zPZkzG4qTEhsWSX+OiEZPv0raCTg7Ip5qMo46uXHryRFxV9Ox9BNJRzHn5aUgXf66qOmjaknXkhq5nkJatjc1GU83JP0zIiY2OP8ppEuqWzDQKP2ciHimqZisPzgpsUEk7dBpFHB0RCzby3gGBSGdQbosch5pZ3deRDzXZEwtko4AdgRmkmL7RUTMajSoLjSdbEratWbwBOC9wKkRcWSPQ5qDpFeTKtn3Ac+Slu0pETGzwZiK7iUH6V4qDLT52gj4DXBSk2dxrGxOSmwQSc8CP2fOI9eWHSPiZT0OaRBJi5O62+4MrEe6J8LJdY1zey13yXwLKbbtSe1LTgZ+FRGNNUAsPdmskxsy/6GkLqSS1iUt2/cC90XExg3F8TCde8mdGhHL9T6qznJ7qxOAdSJigblNby9O7n1jdW4ADss9SOYgacsG4hkkIv5N2sGdIGlp0tmJ70qaEBErNRxbAJcAl0jam9SD5GDSDa1e0mBop9I52Vykx7F0JXenbjqMF+TuyS8n3bTspcADDYZTei85JC1HSt52BpYn9ajarcmYrGxOSqzOJ4F/dxj37l4GMjeSlgJ2IJ1Wn0C6SVMRJL2OgVP+D9LsPUqgD5LNKknjgQ+RGgo3HcsmpB5B2wN/JrUv+b+IeLSpmCKi412CI+ItvYylnaQ9SOX1auCXwGci4g9NxmT9wZdvrO/kZ2m8m7TTWx+YRqokpjfZDTLHtjopEdkZeC7HdUpE/KPJuOCFivWOiPhnzbjJETGjgbBa8/8Pg8/gPEk64/TJiLin91Elku4kdU0+BTgtIpo8O/ICSd8ntc8ornutpJ+QLlleGBFN3wPH+oiTEhtkiJ4QF0fEZc1ENUDSg8BvSZXEeRHxbMMhvUDS3xloBDnojIT1H0krR8QdTcfRTtI+zHlZ5OSIuLbZqBJJQ/b8qUuMzcBJidXog54QixZwO/m+U3KyKentpNu5n942fEfg0YbvuXE89e1wIDUh+mgv42mXb93eOju3KCkpPjki/tZgTH8mlVm1QVAAywIvd0NX68RJiXWtlJ4Qki5m6EqisafKSrqdOWNT5X1ExKt6H1UOpOBkU9LlpLvxzmobvgzpnjQbNRMZSHpPzeCVgP8jPetoxR6H1JGk9Um3ci+qh0t+KN9nSY2+vxsRRzUakBXLSYkNSwlP+JS0Qc3gNwH7AQ9ExIY9DukFuSdQ1ThSpb8vcE1E1FVwjSoh2ZQ0IyImdxh3Q5O3wK+StCrweVKX7yOA45q+IVhuELwNbTcri4izmowLXmhjdQDwRuBw4ISSLrdaedz7xrpSUk+IiLi69VrSpsAXSV1aPxYRv2ksMCAiHspxjSOV12eA64B3lnon0EK63S4uaXxEzK4OlLQg6ZJEoyStSXo2z/rAoaR1bfbQnxrzmLYiNfZ+B3AVqY3VnhHxeJNxAUh6LSkZWRv4FvDRUm5waGXzmRIbpOSeEC25DcIXgKeBr0fExQ2HBLxQiX6EdGr/MuDgiLit2ag6qySbO0TEdg3GcTDp3h97tyrV3MvqO8CDEfHZBmP7BbAB6Uj/NFKvqhdExL8aiusiUvuR0yPi4SZi6CQ//PFO4BzaygsgIj7R86CsLzgpsb4j6U+kBnOHAoMeIhcR1/Q8qEzSXcBs4EhgUA+DiPhVz4PKSk42c3L0NWB3UvdbgInAccAXmzzlL2kmlXZBrcGt9xGxas+DKpyk3ejc7oto8EneVjYnJTZIyT0hchzTGbqh6+Y9DGcOkqYydGwf6WE4fSe3b1ktv72tvZdV00+/LUlbkvlCkkS6LL9QRBR5eb7uMp1Zi5MSG6TknhA2cqUnm92QdE1EvL7H8/xgRPwsv964erMySXtHxPd6GU8n+XLX/wB7AWdExKcbjOWyiHhzfn1iRHyoMq7ny9D6x7imA7AiLdyekABExIOk5300StJ+ldc7tY37Ru8jmmP+R1Ze79M2bmrPA5rTl0iXatpNB77S21BGrIkWuZ+qvG7vytr4mS9JS0o6kPQYgZcBGzaZkGTV/cTabeMab1Vt5XJSYnUWz9f451BKTwhS18eW9ufJbN3LQGpUnznSfl+Qpru1Fp1sdqmJU7vq8Lrufc9IWkbSN4FrSO2Y1o+IL7R6gDVsqOXk0/PWUZHXHK1xvwKOzaem23tCNNZQs6LISqJm/k3H0q7obrcFa78LbqdxvXYHMAs4HngC+Gi1a3dEfLuhuACWlPRu0oHvkpJ2yMMFLNFcWFY6JyVW5wuknhB3SBrUE6KxqAaUWkkAjMtPLh5Xed2qKZq+w2bpyWY3ZjYwzzUl3UBajq/Kr8nvm+x5cygD6/vLGoyjziXAuyqvq93NL+19ONYv3NDVOiq1J0S+B8LjpEphUdJRIvn9IhGxYK9jqsQ2E3ie+rMkjXYfLbnbLYCkl5MaarbaINwI/CAi7m8uqheeLdNRiQ/r6xeSdnX3YKtyUmIjVnoreklLlXZTqRZJa0fEjQ3Nu7hkU9LGwEnAVKB1x94NSO1yPlDt8VIqSVf0smeapNMi4r359SHVG8xJOj8i3tarWEaq9H2I9Z4v39i8KK3NRLsLgVJ3eCfSUGw5CfnzEJMcAvT6DNjhpG7o11aGTZN0BvAj0rNTSrdIj+e3euX1VqQH3rUs2+NYRqr0fYj1mHvf2Lwo/TRbyTs8xzanxdsSEgAi4jrKay/RSa+3h/mhh0u/xGk94jMlNj8reYfn2Oakusttkibgg6dOXiJpfVL5LJpfi4G2Vv2g5OTcGuCkxObFzKYDsPnGEcD5kvYl3XcDUpuSQ0jPEeoHva5g7wVa3X7vq7xuve8HxbcVst5yUmKDVO4pUKv1ULmIGHK6ApR8FPZM0wEMYWavZxgRx0i6B/gqqfdNADcBX4uIs3sdzwh9aO6TjJ6IeGs30zXYS+61wH7AWnnQjcDhEdHqUk1E7N3ruKxs7n1jg0g6vvJ2O6BaKRTzUDlJb6XSfTQiLm4bP6HXj5WXNGTj1SafYAzldrsdiqTLI2LjBudf93RlSElvRMTiPQ5pWBp6XtAU4DDgm8CMPHgy6Q7M+0bEWb2Mx/qHkxIbkqRrI2L9puOokrQC6WZfTzFn99FFgXdHxN0NxlZNjDYg7ZCrj7lv8gnGfdntVtKdEbFS03FAmdvD3DQRs6TrgSkRMbNt+CTgrIhYt5fxWP/w5RubmxKz1u8BP4yIqdWBkv4b+AEwpYmgYM5T6rkyaCwJqdGv3W5LWgdLiqVbTcQ8vj0hAYiImfmxBma1nJRYP1orIt7dPjAifirpgCYC6qC0Cqxjt1tJjXa7HaIdUz/1JLEBsyVNjIh/Vgfmu+PO7vAZMyclNpiksxmoUFeVNK06PiLeNfhTPVXbRVTSOJp/vkzJSu52u90Q437dsyhqtCVMS7YnUK2G3wWb2cA8vwz8TtI3GLhUOBnYP/+Z1XKbEhtE0qZDjY+IS3oVSx1JRwCLAZ+sPFjupaRupU9FxCcajO0oBhK6nYFTquMbjm1PYA+grtvt8RFxdFOxdauJZ6W0Nfxu11jD7257yTVF0rrAp5mzUfXhEXF9c1FZ6ZyU2Fzla8CvBe6OiAcKieebwG7M+WC5E4DPNflgOUm7DjW+6YePSdqW1E2z2u320H7pdutnpQzol15y7ST9MyImNh2HlclJiQ0i6WjgqIi4UdISwBXAc8AEUne+kxsNMGt7sNzfI+IJSYdFxL5NxtVO0lLAI1HwxtZ0t9tuNdSTZDvghtbTgCV9CXgPKSHeJyJu72U8dfqpV1BJvamsPE1fR7YybVJ5gu2Hgb9FxOtIp/r3ay6sOUXEkxHx5/z3RB783iZjkvQlSWvm1wtLugj4O3C/pC2bjG0u+uXItYnE7uvALHjhTNMHgY8A04BSLnkVm/DW6KdYrcfc0NXqVO82uhXwC4CIuE8q+SapQPN3cX0f6a6kkO7/IdITW9cgXV76XUNxzU2/VBRNLN+oJL07AMdFxNXA1ZI+3kA8xZP0qU6jSO3BzGo5KbE6j+QjwruBjYGPAkgaTwHdM3NvkdpRNJ+UPFO5TPN24JSIeA64OZdfY+aTbrdN3OBNkhYDngC2IN0Lp2WRBuIBiu8lN1QX8+/0LArrO05KrM5ewHeBV5B6uLQe7rUFcE5jUQ24mrQzrktAmn6mzNP5mR/3A28l9XRpeUkzIb2g5G63R0bEJ/PrfSLiO5VxUyNiN2jsWSlHAtcB/wZujogZOa71SQ/Fa8phldeHNxZFjYg4qJvpJH0uIr451vFY/3BDVxux0ncoktautI3p1TzfSLpMsyxwZER8NQ9/B/ChiNill/GMREPdbl/oVdPew6aEHjf50QYvB66PiOfzsOWBBVs3CGtifWuLsahect0oYdlaWdzQ1ebFTk0HMBcn9nqGEXFlRKwZEUu3EpI8/NxqQjK3rsMN26eBearD6yJExN0RcW0rIcnD7m27Y2lP1zdJR0taO79eArge+ClwraTik9+suGVtzXJSYvOi9B1KyfE1UfF3q4lyGydpKUlLV15PyO2H+uUuvb0ut77oJTcXPlVvc3CbEpsXpe9QSo6v5ISpiXJbgtRWqFUu1wwxbal6XW793EuupW8Ctd5wUmLzwjuUkXPCVBERk3o9z/lA0b3kuvSLpgOwsvjyjc2L0ncoTffEGUrJCV0T3W5rSVpD0rFNx9GlXq9vewF7A8dTZi+5uYqIbzQdg5XFSYkNIum0yutD2sad33pd2g6lvQKLiDc1Gc9c9Lzil3Rk5fU+beOmtl430e1W0jqSzpf0F0lfk7S8pF8CF5Gez1Ocpte3iPhbRGwdEetFxNTK8PMi4tOVOD/Xy7jM5oWTEquzeuX1Vm3jlu1lIHVKrsBKrviBt1Ret/f+WaeXgdQ4FjiJ9EyZWaT7gvwdWC0ijmgysJLXty6V3kvO7AVOSqzOUO0dSmgLUWwFRtkVf8ndbheOiKkRcUu+cdrjEbFfRDzVdGCUvb51o+fLutvk3KydG7panZfku1WOAxbNr1u3cC+hAd3CldPVt+Q7gJbSBbLkin9cfmLxuMrrVoxNd7tdpLKeQboz7gvvI6LJ3jglr2/daOJAoj05r95avunk3ArmpMTq3At8O7++r/K69b5pJVdgJVf8JXe7ra5zMOd6F8DmPY9oQMnrWzeaSI5LTs6tYL7NvPUdSdPpfPQXEdFYBSZpJvA8HXbEEbFKTwOaz0jaKiIu6PE8p1Po+tYNSZ/vdaN0SdcDm5GS84vy69Y2cXFErNvLeKx/OCmxrknaCtgvItobv1ofk7QG8JmI2KPpWObGz0oZIOm0iHhvfn1IRHy2Mu78iHhbg7HNxMm5jYAbutogkjaX9DdJj0n6maTXSZoBHAz8sID4Vpd0Zu4NcXJ+WFrRSrjfxnzQiwSaabRZ6vpWbC+5iJgUEatGxCp1f03GZmVzUmJ1Dgf2BJYGTgeuAKZGxAYR8atGI0t+Qro51HtI7SKOajacAYVX/P3eiwSaabRZ6vpWei+5QUpIzq1sbuhqdSIipufXZ0q6OyK+12RAbV4WEa0d26GSSmpoeCzpbNIVwNakiv8E4AMFdG/t914kTSl1fSu2l5ykdYDDgFcCZwLfB74HvJF00GNWy0mJ1VlS0g6V9+Or7ws4W9LeG6K6Q266N0TJFX+/9yIBmNnAPEtd30ruJVdycm4Fc0NXG0TS8UOMjoj4SM+CqSHp4iFGN9375q/ALgxUYD8H3k/zFVjR5QYgaXngf4C18qAZwI8i4qHmoiq/3Eok6bqIWK/y/h8RsWqTMVl/cFJiIyZp14g4oek4Ommo+2jfV2ANldumwM9I7TeuzoM3ALbPf1+JiA/1MqbhaqLcOsVBw73kSk7OrWxOSmzESu+eWXJ8pVRgdZooN0lXAXtFxLVtw9cDLgXOiIj22/YXpdflJmlz4GgG2m0cQnpisICvN3mZdX5Izq0ZblNi86L0OzWWHN8hQJFJCc2U22LtCQlARFwn6X7gww3ENFy9LrdWL7krgG3y//1LaJQeEW/tZrqSk3NrhrsE27wo/TRbyfGVnDA1UW7Kt+RvHzgBmB0RzzcQ03D1utwiIqZHxNMRcSZQWi+5bhzSdABWFp8psXlRcsVaupITpiYcAZwvaV8GnsmzAanS6pd7qPRa6b3kuuF9iM3BSYnNi8ubDmAuZjYdQJ+a2esZRsQxku4BvgqsnQffCHwtIs7udTwjNLPH87sE2K7y/tLK+wD6ISlxcm5zcENX60uldh/tlqRfRcQOc59y1Ofb1+XWlH4ut5J7yZXcGN2a4aTE+k7p3UdLrcBKLjdJ3x1qfER8olextCu53LpRcsXfVHJu5XJSYn2n5O6jJVdghZfbM8BfgNOAe2hra9DkkX7J5dYNSddGxPoNzLfI5NzK5qTEapW8Q5F0U0Ss1WHcrcCrm+qtUXIFVni5LQ3sBLwPmA2cCpweEY80EU9VyeXWjYbuO1Nscm5lc5dgGyTvUK4CngOm5r+FgYskrSLpxOaiA8ruPtrxfhtA0/fbKLbcIuKhiDg639/iw8CSwE2SSqi4ii23LjXRw+VQ4F0R8eWImJb/vgzsClwPlF5m1hAnJVan9B1Kq/voppJelv82A35D891HS67ASi43ACS9HtgH+CAprquH/kRPFF9uc9FEL7mSk3MrmC/f2CD9cLpa0rbAfszZffTQpruPStoT2AOou9/GcRFxTFOxQdHl9hXgncDNwCnAbyNidpMxVZVabqWSdDPwXxHxcNvwCcDlEfGaZiKz0jkpsUG8Q5k3rsCGT9LzwO3AE3lQa8ck0p1L12kkMBuR0pNzK5eTEhuk9B1Kyd1HS1ZyuUlaeajxEXFHr2JpV3K5lczJuY2EkxKrVfIOpfDuo8VWYCWXWyeSxgG7RMTPG4yh2HIruZec2Ug4KbG+U3j30ZIrsJLLbXFS5boCMI30BOW9gU8D10fElAZjK7LcSu52W3JybmVzUmKD9NMORdKKwM7Ap4DPRkSj3ZVLrcDaFVhuZwEPA1cAWwAvJyV0++QeG0UoqdwKvydOscm5lc1JiQ3SLzuU3H10F2Ar0pHi4RFxU7NRDSipAqsqsdwk/TkiXpdfLwDcC0yMiKeajKuqtHIruZdcvyTnVh4/JdjqLE/BO5Sa7qOfK6n7KAyqwIq430bh5fZs60VEPCfprlISkoLLTZKW6tBLrvGb4QFHA0dXkvObJBWTnFuZfKbEhlTi0X7J3UdLvt9G4eX2HPA4A2flFiXF2Ypt8QZjK7LcSu8lB+WdXbLyOSmxjkrdoRTefbTICgzKLreSlVxupfaSKzk5t7I5KbFB+nWHUkj30WIrsE4KKbdFgI8BqwE3AD8pfZ0rodxKVXJybmVzUmKDlL5DKbn7aCclVGAll5ukU0ntSn4PbAPcERH7NBVPVanlVnIvuX5Mzq0MTkpskNJ3KCV3Hy21AsuxlVxu1d4344GrIuL1TcbUUmq59UsvuaoSknMrm5MS61opO5SSu4+WWoHl2Eout2uqSUj7+yaVWm4ld7stOTm3srlLsA0ytx0K0PRRTrHdR4FVKxXYjymkAstKLrd1Jf07vxawaH7feO8bCi23wrvdnshAcr478HnSsty+6eTcyuYzJTZIyUf7UHz30ZKP+Istt5KVXm4l9pIr9eySlc9JiQ3iHcrIlV6B2fyj5F5yJSfnVjYnJTZI6TuUfuw+WgKX28iUWm4l95Jzcm4j5aTEBil9h1J499EiKzAou9xKVmq5ld5LzmwknJRY3ym8+2iRFRiUXW4l67dyK6GXXMnJuZXNvW9skD7YoVR7Q8yWNNS0vbZWpQI7Driq4XiqSi63khVZboX3kjuBgeT8HaTb4BeRnFvZfKbEBin5aB/muLwEufsohVxeKrk9TsnlVrJSy63kXnL9dnbJyuEzJVan5KN9ImKBpmMYQrH32yi83IpVcLn1yz1xijm7ZOVzUmJ1vEMZoYIrMJv/FHlTt6zY5NzK5ss3Nkipp6vNbEDpveTMRsJJiZmZmRXBl2/MzPpQH/SSMxs2nykxM+tDpfeSMxsJJyVmZn3I3W5tfjSu6QDMzGxE5ugl12QgZqPFZ0rMzPqQe8nZ/MhJiZmZmRXBl2/MzMysCE5KzMzMrAhOSszMzKwITkrMzMysCE5KzMzMrAhOSszMzKwI/x84MmKO0OkfGAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'BF_df_CTU': [0.8045112781954887, 0.8195488721804511, 0.7819548872180451], 'BF_df_V': [0.7534246575342466, 0.7465753424657534, 0.7123287671232876], 'IESR_df': [0.9074074074074074, 0.8981481481481481, 0.9259259259259259], 'PCL5_df': [0.7933884297520661, 0.7768595041322314, 0.8347107438016529], 'PHQ9_GAD7_df': [0.9761904761904762, 0.9702380952380952, 0.9761904761904762], 'PRMQ_df': [0.9004739336492891, 0.919431279620853, 0.9028436018957346]}\n"
     ]
    }
   ],
   "source": [
    "data = {}\n",
    "results_acc = pca_ds_acc(dfs,dfs_names,data)\n",
    "print(results_acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "KrJLcB7m2hUX",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 144
    },
    "id": "KrJLcB7m2hUX",
    "outputId": "cbb70383-74ef-488b-9ea2-bdf197f52992"
   },
   "outputs": [
    {
     "data": {
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>BF_df_CTU</th>\n",
       "      <th>BF_df_V</th>\n",
       "      <th>IESR_df</th>\n",
       "      <th>PCL5_df</th>\n",
       "      <th>PHQ9_GAD7_df</th>\n",
       "      <th>PRMQ_df</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.804511</td>\n",
       "      <td>0.753425</td>\n",
       "      <td>0.907407</td>\n",
       "      <td>0.793388</td>\n",
       "      <td>0.976190</td>\n",
       "      <td>0.900474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.819549</td>\n",
       "      <td>0.746575</td>\n",
       "      <td>0.898148</td>\n",
       "      <td>0.776860</td>\n",
       "      <td>0.970238</td>\n",
       "      <td>0.919431</td>\n",
       "    </tr>\n",
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       "      <th>2</th>\n",
       "      <td>0.781955</td>\n",
       "      <td>0.712329</td>\n",
       "      <td>0.925926</td>\n",
       "      <td>0.834711</td>\n",
       "      <td>0.976190</td>\n",
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       "\n",
       "    [theme=dark] .colab-df-convert {\n",
       "      background-color: #3B4455;\n",
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       "\n",
       "    [theme=dark] .colab-df-convert:hover {\n",
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       "\n",
       "      <script>\n",
       "        const buttonEl =\n",
       "          document.querySelector('#df-2119ed30-1ea5-4f10-a1e6-279e737a64e6 button.colab-df-convert');\n",
       "        buttonEl.style.display =\n",
       "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "        async function convertToInteractive(key) {\n",
       "          const element = document.querySelector('#df-2119ed30-1ea5-4f10-a1e6-279e737a64e6');\n",
       "          const dataTable =\n",
       "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                     [key], {});\n",
       "          if (!dataTable) return;\n",
       "\n",
       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "            + ' to learn more about interactive tables.';\n",
       "          element.innerHTML = '';\n",
       "          dataTable['output_type'] = 'display_data';\n",
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       "        }\n",
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       "    </div>\n",
       "  </div>\n",
       "  "
      ],
      "text/plain": [
       "   BF_df_CTU   BF_df_V   IESR_df   PCL5_df  PHQ9_GAD7_df   PRMQ_df\n",
       "0   0.804511  0.753425  0.907407  0.793388      0.976190  0.900474\n",
       "1   0.819549  0.746575  0.898148  0.776860      0.970238  0.919431\n",
       "2   0.781955  0.712329  0.925926  0.834711      0.976190  0.902844"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "acc_df = pd.DataFrame(results_acc)\n",
    "acc_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "x_9lP0fw23lc",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "x_9lP0fw23lc",
    "outputId": "945a7144-96d0-44cf-b91d-543d1f10cd2d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BF_df_CTU       0.802005\n",
       "BF_df_V         0.737443\n",
       "IESR_df         0.910494\n",
       "PCL5_df         0.801653\n",
       "PHQ9_GAD7_df    0.974206\n",
       "PRMQ_df         0.907583\n",
       "dtype: float64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_acc_df = acc_df.mean()\n",
    "mean_acc_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "rZZrWfbP3L14",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "rZZrWfbP3L14",
    "outputId": "55b74ee5-42b1-4967-efb3-4147b4c653a2"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_axis = np.arange(len(dfs_names))\n",
    "X_axis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1yjXidrr3PpW",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "1yjXidrr3PpW",
    "outputId": "4973787b-7176-4b29-8a81-84393e9134f0"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BF_df_CTU       0.024170\n",
       "BF_df_OU        0.025102\n",
       "BF_df_V         0.011863\n",
       "DT_df_CC        0.027872\n",
       "DF_df_JI        0.011750\n",
       "IADQ_df         0.004277\n",
       "IESR_df         0.016038\n",
       "NAQ_R_df        0.000000\n",
       "PCL5_df         0.029798\n",
       "PHQ9_GAD7_df    0.003437\n",
       "PID5_df         0.016788\n",
       "PRFQ_df         0.010204\n",
       "PRMQ_df         0.010329\n",
       "R_NEO_PI        0.020013\n",
       "RAQ_DDDT        0.008502\n",
       "sPID-5_df       0.001850\n",
       "dtype: float64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "std_acc = acc_df.std()\n",
    "std_acc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "zexckWGP3X58",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 734
    },
    "id": "zexckWGP3X58",
    "outputId": "ca8a8b76-fc49-441d-bd9e-8d85deb7d03f"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1728x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f = plt.figure()\n",
    "f.set_figwidth(24)\n",
    "f.set_figheight(10)\n",
    "plt.rc('xtick', labelsize=20) \n",
    "plt.rc('ytick', labelsize=20)\n",
    "plt.bar(X_axis, mean_acc_df,0.4, yerr = std_acc)\n",
    "plt.xticks(X_axis, dfs_names, rotation=90)\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}