KleimanPhD_Chp3.ipynb

{
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  "metadata": {
    "colab": {
      "name": "KleimanPhD_Chp3",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "TPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/mjkleiman/streamlineAD/blob/master/KleimanPhD_Chp3.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aMSvyhqBweVD",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "\n",
        "from google.colab import drive\n",
        "drive.mount('/content/gdrive')\n",
        "dir='/content/gdrive/My Drive/Dissertation/'\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PSgL08Q3Hwsc",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import dill"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "B0fF3sjv3SnK",
        "colab_type": "text"
      },
      "source": [
        "# Cost-Sum Feature Selection"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DMz8jWHe3T2L",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "adni_pair_X = pd.read_csv(f'{dir}/Data/adni_pair_X.csv')\n",
        "adni_pair_y2 = pd.read_csv(f'{dir}/Data/adni_pair_y2.csv')\n",
        "adni_pair_y3 = pd.read_csv(f'{dir}/Data/adni_pair_y3.csv')\n",
        "\n",
        "features = pd.read_csv(f'{dir}/features_paired.csv')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Hd0D1NLg4Bxj",
        "colab_type": "text"
      },
      "source": [
        "## Efficiency Costs"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dOl02ZZ48i8L",
        "colab_type": "text"
      },
      "source": [
        "### Time\n",
        "\n",
        "Must be considered on its own. Any required delay is counted\n",
        "\n",
        "- 1 = Less than a minute\n",
        "- 2 = 1 min to 5 minutes\n",
        "- 3 = 5 minutes to 20 minutes\n",
        "- 4 = 20 minutes to 45 mins\n",
        "- 5 = over 45 mins"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d2fRrm6V3_2i",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df = pd.DataFrame(columns=adni_pair_X.columns.values)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "PD6RdYXk8nII",
        "colab": {}
      },
      "source": [
        "df.loc['time',['moca_visuo_exec', 'moca_clock']]=2\n",
        "df.loc['time',['moca_naming']]=1\n",
        "df.loc['time',['moca_digits']]=2\n",
        "df.loc['time',['moca_letters']]=2\n",
        "df.loc['time',['moca_serial7']]=2\n",
        "df.loc['time',['moca_repeat']]=1\n",
        "df.loc['time',['moca_fluency']]=2\n",
        "df.loc['time',['moca_recall']]=1\n",
        "df.loc['time',['moca_similarities', 'moca_orient']]=1\n",
        "df.loc['time',['faq1', 'faq2', 'faq3', 'faq4',\n",
        "       'faq5', 'faq6', 'faq7', 'faq8', 'faq9', 'faq10']]=1\n",
        "df.loc['time',['MMNAME']]=1\n",
        "df.loc['time',['MMREPEAT']]=1\n",
        "df.loc['time',['MMREAD']]=2\n",
        "df.loc['time',['hach1', 'hach2', 'hach3', 'hach4', 'hach5', 'hach6', 'hach7',\n",
        "       'hach8']]=1\n",
        "df.loc['time',['PXHEADEY', 'PXNECK', 'PXHEART']]=2\n",
        "df.loc['time',['vlt_recogn']]=2\n",
        "df.loc['time',['vlt_delay']]=4\n",
        "df.loc['time',['nbspan_forward', 'nbspan_backward']]=3\n",
        "df.loc['time',['verbal_fluency']]=2\n",
        "df.loc['time',['tma_time', 'tmb_time','tmb_mistakes']]=2\n",
        "df.loc['time',['vlt_recall']]=2\n",
        "df.loc['time',['npi1_sev', 'npi2_sev', 'npi3_sev', 'npi4_sev',\n",
        "       'npi5_sev', 'npi6_sev', 'npi7_sev', 'npi8_sev', 'npi9_sev',\n",
        "       'npi10_sev', 'npi11_sev', 'npi12_sev']]=1\n",
        "df.loc['time',['PTGENDER', 'PTHAND',\n",
        "       'PTEDUCAT', 'PTRETIRED', 'PTHOME', 'PTRACE']]=1\n",
        "df.loc['time',['FAMHISTAD', 'FAMHISTDEM']]=1\n",
        "df.loc['time',['q_memory_pt', 'q_orient_pt',\n",
        "       'q_judgmt_pt', 'q_outsideact_pt', 'q_homeact_pt', 'q_language_pt',\n",
        "       'q_attention_pt', 'q_memory_cg', 'q_orient_cg',\n",
        "       'q_judgmt_cg', 'q_outsideact_cg', 'q_homeact_cg', 'q_language_cg',\n",
        "       'q_attention_cg']]=1"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "TkGl87yY8nIJ"
      },
      "source": [
        "### Difficulty\n",
        "\n",
        "- 1 = Minimal/neglible effort/doctor not required\n",
        "- 2 = Some effort required to analyze/administer (30 seconds or less to analyze), minimal focus\n",
        "- 3 = Up to a few minutes required to analyze / requires focus or effort (cannot analyze with patient present) (minimum for needing specialist)\n",
        "- 4 = Up to 5 minutes to analyze per question / requires significant focus or effort\n",
        "- 5 = More than 5 minutes to analyze, extreme focus/effort required"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gSrpfou3-FKS",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df.loc['diff',['moca_visuo_exec', 'moca_clock']]=2\n",
        "df.loc['diff',['moca_naming']]=2\n",
        "df.loc['diff',['moca_digits']]=3\n",
        "df.loc['diff',['moca_letters']]=3\n",
        "df.loc['diff',['moca_serial7']]=3\n",
        "df.loc['diff',['moca_repeat']]=2\n",
        "df.loc['diff',['moca_fluency']]=3\n",
        "df.loc['diff',['moca_recall']]=2\n",
        "df.loc['diff',['moca_similarities', 'moca_orient']]=1\n",
        "df.loc['diff',['faq1', 'faq2', 'faq3', 'faq4',\n",
        "       'faq5', 'faq6', 'faq7', 'faq8', 'faq9', 'faq10']]=2\n",
        "df.loc['diff',['MMNAME']]=1\n",
        "df.loc['diff',['MMREPEAT']]=1\n",
        "df.loc['diff',['MMREAD']]=2\n",
        "df.loc['diff',['hach1', 'hach2', 'hach3', 'hach4', 'hach5', 'hach6', 'hach7',\n",
        "       'hach8']]=2\n",
        "df.loc['diff',['PXHEADEY', 'PXNECK', 'PXHEART']]=3\n",
        "df.loc['diff',['vlt_recogn']]=2\n",
        "df.loc['diff',['vlt_delay']]=2\n",
        "df.loc['diff',['nbspan_forward', 'nbspan_backward']]=2\n",
        "df.loc['diff',['verbal_fluency']]=1\n",
        "df.loc['diff',['tma_time', 'tmb_time','tmb_mistakes']]=3\n",
        "df.loc['diff',['vlt_recall']]=2\n",
        "df.loc['diff',['npi1_sev', 'npi2_sev', 'npi3_sev', 'npi4_sev',\n",
        "       'npi5_sev', 'npi6_sev', 'npi7_sev', 'npi8_sev', 'npi9_sev',\n",
        "       'npi10_sev', 'npi11_sev', 'npi12_sev']]=3\n",
        "df.loc['diff',['PTGENDER', 'PTHAND',\n",
        "       'PTEDUCAT', 'PTRETIRED', 'PTHOME', 'PTRACE']]=1\n",
        "df.loc['diff',['FAMHISTAD', 'FAMHISTDEM']]=1\n",
        "df.loc['diff',['q_memory_pt', 'q_orient_pt',\n",
        "       'q_judgmt_pt', 'q_outsideact_pt', 'q_homeact_pt', 'q_language_pt',\n",
        "       'q_attention_pt', 'q_memory_cg', 'q_orient_cg',\n",
        "       'q_judgmt_cg', 'q_outsideact_cg', 'q_homeact_cg', 'q_language_cg',\n",
        "       'q_attention_cg']]=1"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "xrVZC8Nl8dDr"
      },
      "source": [
        "### Parsing"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "BbYZQfih8dDr",
        "colab": {}
      },
      "source": [
        "df = df.transpose()\n",
        "df.dropna(inplace=True)\n",
        "\n",
        "df['cost'] = df['time']+df['diff']\n",
        "df['cost_reverse'] = df['cost']/10\n",
        "\n",
        "'''Need to reverse sort cost, so that we can maximize both avg_score and\n",
        "efficiency, instead of needing to maximize one and minimize the other'''\n",
        "features['cost_reverse'] = 1 - features['cost_reverse']\n",
        "\n",
        "df.sort_values('cost_reverse', ascending=False, inplace=True)\n",
        "df = df.reset_index().rename({'index':'feature'}, axis=1)\n",
        "\n",
        "df.to_csv(f'{dir}/features_costs_pair.csv', index=False)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eFNrLhEPBY9a",
        "colab_type": "text"
      },
      "source": [
        ""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "lqQY7EEj3iUK"
      },
      "source": [
        "## Feature importances"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eso40DlF57XY",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# features = pd.read_csv(f'{dir}/features_costs_pair.csv')\n",
        "features = df\n",
        "features.set_index('feature', inplace=True)\n",
        "features = features.loc[adni_pair_X.columns.values]\n",
        "features.reset_index(inplace=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "outputId": "9d1c7f02-5ea5-4a81-ae05-945e0aae04ec",
        "id": "9n50UcHW3iUM",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.model_selection import cross_val_score, StratifiedKFold\n",
        "\n",
        "cv = StratifiedKFold(n_splits=5)\n",
        "\n",
        "clf = RandomForestClassifier(n_estimators=100, max_features=.5)\n",
        "\n",
        "from mlxtend.feature_selection import ExhaustiveFeatureSelector\n",
        "\n",
        "efs = ExhaustiveFeatureSelector(clf, \n",
        "                                min_features=1, \n",
        "                                max_features=1, \n",
        "                                print_progress=True,\n",
        "                                scoring='accuracy',\n",
        "                                cv=5,\n",
        "                                n_jobs=-1)\n",
        "efs = efs.fit(adni_pair_X, adni_pair_y2.values.ravel())"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Features: 83/83"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "pE0nytYB3iUU",
        "colab": {}
      },
      "source": [
        "f_imp = pd.DataFrame.from_dict(efs.subsets_)\n",
        "f_imp = f_imp.transpose()\n",
        "f_imp['feature']=adni_pair_X.columns.values\n",
        "f_imp = f_imp.sort_values('avg_score', ascending=False)\n",
        "f_imp.drop(labels=['cv_scores','feature_idx','feature_names'], axis=1, inplace=True)\n",
        "\n",
        "features = f_imp.merge(features, on='feature', how='left')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "ppcEDJvP3iUe",
        "colab": {}
      },
      "source": [
        "\n",
        "\n",
        "features.to_csv(f'{dir}/features_paired.csv', index=False)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "MX034en93iUc"
      },
      "source": [
        "## Select features"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yt4ztwn8O2S6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def cost_sum(feature_row, weight, start):\n",
        "  feature_row_index = feature_row - 1\n",
        "  print(feature_row, ' features.')\n",
        "  print('Accuracy: ', f_costs.iloc[feature_row_index,0])\n",
        "  print('Features: ', f_costs.iloc[feature_row_index,3])\n",
        "  ilocs = list([int(s) for s in f_costs.iloc[feature_row_index,2].split(',')])\n",
        "  print('Cost Sum: ', features.iloc[ilocs,4].sum(), ' Avg: ', features.iloc[ilocs,4].sum() / feature_row)\n",
        "  print('Cost Time: ', features.iloc[ilocs,2].sum(), ' Avg: ', features.iloc[ilocs,2].sum() / feature_row)\n",
        "  print('Cost Diff: ', features.iloc[ilocs,3].sum(), ' Avg: ', features.iloc[ilocs,3].sum() / feature_row)\n",
        "  df = pd.DataFrame({'weighted':weight,'start_#':start,'f_#':feature_row,\n",
        "                     'features':str(f_costs.iloc[feature_row_index,3]),\n",
        "                     'accuracy':f_costs.iloc[feature_row_index,0],\n",
        "                     'cost_sum':[features.iloc[ilocs,4].sum()]})\n",
        "  \n",
        "  return df"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2cWe3jx53T34",
        "colab_type": "text"
      },
      "source": [
        "### Even weights"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "62a59w6NOnXW"
      },
      "source": [
        "#### 50 starting features"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "zdOq-Np9OnXS",
        "colab": {}
      },
      "source": [
        "# features = pd.read_csv(f'{dir}/features_paired.csv')\n",
        "features['weight'] = (features['avg_score'] + (features['cost_reverse']))/2\n",
        "features.sort_values('weight', ascending=False, inplace=True)\n",
        "features = features.head(50)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "jDWWyxfROnXX",
        "colab": {}
      },
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.model_selection import cross_val_score, StratifiedKFold\n",
        "\n",
        "cv = StratifiedKFold(n_splits=5)\n",
        "\n",
        "clf = RandomForestClassifier(n_estimators=100, max_features=.5)\n",
        "\n",
        "adni_pair_i_X = adni_pair_X.loc[:,features['feature']]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "vHQzSKxCOnXZ",
        "colab": {}
      },
      "source": [
        "from mlxtend.feature_selection import SequentialFeatureSelector as SFS\n",
        "\n",
        "sfs1 = SFS(clf, \n",
        "           k_features=(30), \n",
        "           forward=True, \n",
        "           floating=False, \n",
        "           verbose=2,\n",
        "           scoring='accuracy',\n",
        "           cv=cv,\n",
        "           n_jobs=-1)\n",
        "\n",
        "sfs1 = sfs1.fit(adni_pair_i_X, adni_pair_y2.values.ravel())"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1wsS7pz_PoXh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "f_costs = pd.DataFrame.from_dict(sfs1.subsets_)\n",
        "f_costs = f_costs.transpose()\n",
        "f_costs['feature_idx'] = f_costs['feature_idx'].apply(str).str.replace('(','').str.replace(')','')\n",
        "f_costs.to_csv(f'{dir}/Output/eff_features_paired_even50.csv', index=False)\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "acFpzZKrHz8D",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "dill.dump_session(f'{dir}/Sessions/costs_even50.db')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CGo7ZXM0n1hS",
        "colab_type": "code",
        "outputId": "d669edf2-cd3b-4222-ed23-7ca17018d6be",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "f_costs"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<div>\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>avg_score</th>\n",
              "      <th>cv_scores</th>\n",
              "      <th>feature_idx</th>\n",
              "      <th>feature_names</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.803048</td>\n",
              "      <td>[0.8721461187214612, 0.730593607305936, 0.8401...</td>\n",
              "      <td>0,</td>\n",
              "      <td>(q_memory_cg,)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.814049</td>\n",
              "      <td>[0.8812785388127854, 0.7442922374429224, 0.844...</td>\n",
              "      <td>0, 10</td>\n",
              "      <td>(q_memory_cg, moca_orient)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.819549</td>\n",
              "      <td>[0.8812785388127854, 0.7488584474885844, 0.849...</td>\n",
              "      <td>0, 10, 12</td>\n",
              "      <td>(q_memory_cg, moca_orient, q_outsideact_pt)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.824132</td>\n",
              "      <td>[0.8767123287671232, 0.7579908675799086, 0.858...</td>\n",
              "      <td>0, 3, 10, 12</td>\n",
              "      <td>(q_memory_cg, q_language_cg, moca_orient, q_ou...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0.829624</td>\n",
              "      <td>[0.8858447488584474, 0.7579908675799086, 0.867...</td>\n",
              "      <td>0, 3, 10, 12, 29</td>\n",
              "      <td>(q_memory_cg, q_language_cg, moca_orient, q_ou...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>0.831455</td>\n",
              "      <td>[0.8858447488584474, 0.7625570776255708, 0.867...</td>\n",
              "      <td>0, 3, 10, 12, 29, 42</td>\n",
              "      <td>(q_memory_cg, q_language_cg, moca_orient, q_ou...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>0.831455</td>\n",
              "      <td>[0.8858447488584474, 0.7625570776255708, 0.867...</td>\n",
              "      <td>0, 3, 10, 12, 14, 29, 42</td>\n",
              "      <td>(q_memory_cg, q_language_cg, moca_orient, q_ou...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>0.832381</td>\n",
              "      <td>[0.8812785388127854, 0.7625570776255708, 0.867...</td>\n",
              "      <td>0, 3, 10, 12, 14, 29, 30, 42</td>\n",
              "      <td>(q_memory_cg, q_language_cg, moca_orient, q_ou...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>0.832381</td>\n",
              "      <td>[0.8812785388127854, 0.7625570776255708, 0.867...</td>\n",
              "      <td>0, 3, 10, 12, 14, 29, 30, 42, 43</td>\n",
              "      <td>(q_memory_cg, q_language_cg, moca_orient, q_ou...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>0.830554</td>\n",
              "      <td>[0.8721461187214612, 0.7625570776255708, 0.867...</td>\n",
              "      <td>0, 3, 10, 12, 14, 29, 30, 38, 42, 43</td>\n",
              "      <td>(q_memory_cg, q_language_cg, moca_orient, q_ou...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>0.831468</td>\n",
              "      <td>[0.8767123287671232, 0.7625570776255708, 0.867...</td>\n",
              "      <td>0, 3, 10, 12, 14, 29, 30, 38, 41, 42, 43</td>\n",
              "      <td>(q_memory_cg, q_language_cg, moca_orient, q_ou...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>0.827815</td>\n",
              "      <td>[0.867579908675799, 0.7534246575342466, 0.8675...</td>\n",
              "      <td>0, 3, 10, 12, 14, 29, 30, 31, 38, 41, 42, 43</td>\n",
              "      <td>(q_memory_cg, q_language_cg, moca_orient, q_ou...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>0.825984</td>\n",
              "      <td>[0.8721461187214612, 0.7488584474885844, 0.867...</td>\n",
              "      <td>0, 3, 10, 12, 14, 29, 30, 31, 38, 39, 41, 42, 43</td>\n",
              "      <td>(q_memory_cg, q_language_cg, moca_orient, q_ou...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>0.826901</td>\n",
              "      <td>[0.867579908675799, 0.7488584474885844, 0.8675...</td>\n",
              "      <td>0, 3, 10, 12, 14, 29, 30, 31, 38, 39, 41, 42, ...</td>\n",
              "      <td>(q_memory_cg, q_language_cg, moca_orient, q_ou...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>0.824157</td>\n",
              "      <td>[0.867579908675799, 0.7442922374429224, 0.8630...</td>\n",
              "      <td>0, 3, 10, 12, 14, 29, 30, 31, 32, 38, 39, 41, ...</td>\n",
              "      <td>(q_memory_cg, q_language_cg, moca_orient, q_ou...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>0.820509</td>\n",
              "      <td>[0.8584474885844748, 0.7442922374429224, 0.853...</td>\n",
              "      <td>0, 3, 10, 12, 14, 16, 29, 30, 31, 32, 38, 39, ...</td>\n",
              "      <td>(q_memory_cg, q_language_cg, moca_orient, q_ou...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>0.817773</td>\n",
              "      <td>[0.8584474885844748, 0.7397260273972602, 0.840...</td>\n",
              "      <td>0, 3, 10, 12, 14, 16, 29, 30, 31, 32, 37, 38, ...</td>\n",
              "      <td>(q_memory_cg, q_language_cg, moca_orient, q_ou...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>0.823274</td>\n",
              "      <td>[0.8721461187214612, 0.730593607305936, 0.8447...</td>\n",
              "      <td>0, 3, 10, 12, 14, 16, 29, 30, 31, 32, 36, 37, ...</td>\n",
              "      <td>(q_memory_cg, q_language_cg, moca_orient, q_ou...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>0.81686</td>\n",
              "      <td>[0.863013698630137, 0.730593607305936, 0.84018...</td>\n",
              "      <td>0, 3, 6, 10, 12, 14, 16, 29, 30, 31, 32, 36, 3...</td>\n",
              "      <td>(q_memory_cg, q_language_cg, PTHAND, moca_orie...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>0.81412</td>\n",
              "      <td>[0.863013698630137, 0.7214611872146118, 0.8310...</td>\n",
              "      <td>0, 3, 6, 10, 12, 14, 16, 29, 30, 31, 32, 35, 3...</td>\n",
              "      <td>(q_memory_cg, q_language_cg, PTHAND, moca_orie...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>0.812268</td>\n",
              "      <td>[0.867579908675799, 0.7442922374429224, 0.8219...</td>\n",
              "      <td>0, 3, 6, 10, 11, 12, 14, 16, 29, 30, 31, 32, 3...</td>\n",
              "      <td>(q_memory_cg, q_language_cg, PTHAND, moca_orie...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>0.809554</td>\n",
              "      <td>[0.8447488584474886, 0.7351598173515982, 0.817...</td>\n",
              "      <td>0, 3, 6, 10, 11, 12, 14, 16, 29, 30, 31, 32, 3...</td>\n",
              "      <td>(q_memory_cg, q_language_cg, PTHAND, moca_orie...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>0.817786</td>\n",
              "      <td>[0.8493150684931506, 0.7397260273972602, 0.840...</td>\n",
              "      <td>0, 3, 6, 10, 11, 12, 13, 14, 16, 29, 30, 31, 3...</td>\n",
              "      <td>(q_memory_cg, q_language_cg, PTHAND, moca_orie...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>0.821468</td>\n",
              "      <td>[0.8356164383561644, 0.7534246575342466, 0.840...</td>\n",
              "      <td>0, 3, 6, 10, 11, 12, 13, 14, 16, 29, 30, 31, 3...</td>\n",
              "      <td>(q_memory_cg, q_language_cg, PTHAND, moca_orie...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>0.817803</td>\n",
              "      <td>[0.8264840182648402, 0.7579908675799086, 0.840...</td>\n",
              "      <td>0, 3, 6, 10, 11, 12, 13, 14, 16, 29, 30, 31, 3...</td>\n",
              "      <td>(q_memory_cg, q_language_cg, PTHAND, moca_orie...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>0.82603</td>\n",
              "      <td>[0.8538812785388128, 0.771689497716895, 0.8356...</td>\n",
              "      <td>0, 2, 3, 6, 10, 11, 12, 13, 14, 16, 29, 30, 31...</td>\n",
              "      <td>(q_memory_cg, q_memory_pt, q_language_cg, PTHA...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>0.832431</td>\n",
              "      <td>[0.867579908675799, 0.771689497716895, 0.84931...</td>\n",
              "      <td>0, 1, 2, 3, 6, 10, 11, 12, 13, 14, 16, 29, 30,...</td>\n",
              "      <td>(q_memory_cg, q_attention_cg, q_memory_pt, q_l...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>0.837923</td>\n",
              "      <td>[0.867579908675799, 0.776255707762557, 0.85844...</td>\n",
              "      <td>0, 1, 2, 3, 6, 10, 11, 12, 13, 14, 16, 29, 30,...</td>\n",
              "      <td>(q_memory_cg, q_attention_cg, q_memory_pt, q_l...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>0.837911</td>\n",
              "      <td>[0.863013698630137, 0.771689497716895, 0.87214...</td>\n",
              "      <td>0, 1, 2, 3, 6, 10, 11, 12, 13, 14, 16, 19, 29,...</td>\n",
              "      <td>(q_memory_cg, q_attention_cg, q_memory_pt, q_l...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>0.838807</td>\n",
              "      <td>[0.8721461187214612, 0.771689497716895, 0.8767...</td>\n",
              "      <td>0, 1, 2, 3, 6, 10, 11, 12, 13, 14, 16, 19, 21,...</td>\n",
              "      <td>(q_memory_cg, q_attention_cg, q_memory_pt, q_l...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   avg_score  ...                                      feature_names\n",
              "1   0.803048  ...                                     (q_memory_cg,)\n",
              "2   0.814049  ...                         (q_memory_cg, moca_orient)\n",
              "3   0.819549  ...        (q_memory_cg, moca_orient, q_outsideact_pt)\n",
              "4   0.824132  ...  (q_memory_cg, q_language_cg, moca_orient, q_ou...\n",
              "5   0.829624  ...  (q_memory_cg, q_language_cg, moca_orient, q_ou...\n",
              "6   0.831455  ...  (q_memory_cg, q_language_cg, moca_orient, q_ou...\n",
              "7   0.831455  ...  (q_memory_cg, q_language_cg, moca_orient, q_ou...\n",
              "8   0.832381  ...  (q_memory_cg, q_language_cg, moca_orient, q_ou...\n",
              "9   0.832381  ...  (q_memory_cg, q_language_cg, moca_orient, q_ou...\n",
              "10  0.830554  ...  (q_memory_cg, q_language_cg, moca_orient, q_ou...\n",
              "11  0.831468  ...  (q_memory_cg, q_language_cg, moca_orient, q_ou...\n",
              "12  0.827815  ...  (q_memory_cg, q_language_cg, moca_orient, q_ou...\n",
              "13  0.825984  ...  (q_memory_cg, q_language_cg, moca_orient, q_ou...\n",
              "14  0.826901  ...  (q_memory_cg, q_language_cg, moca_orient, q_ou...\n",
              "15  0.824157  ...  (q_memory_cg, q_language_cg, moca_orient, q_ou...\n",
              "16  0.820509  ...  (q_memory_cg, q_language_cg, moca_orient, q_ou...\n",
              "17  0.817773  ...  (q_memory_cg, q_language_cg, moca_orient, q_ou...\n",
              "18  0.823274  ...  (q_memory_cg, q_language_cg, moca_orient, q_ou...\n",
              "19   0.81686  ...  (q_memory_cg, q_language_cg, PTHAND, moca_orie...\n",
              "20   0.81412  ...  (q_memory_cg, q_language_cg, PTHAND, moca_orie...\n",
              "21  0.812268  ...  (q_memory_cg, q_language_cg, PTHAND, moca_orie...\n",
              "22  0.809554  ...  (q_memory_cg, q_language_cg, PTHAND, moca_orie...\n",
              "23  0.817786  ...  (q_memory_cg, q_language_cg, PTHAND, moca_orie...\n",
              "24  0.821468  ...  (q_memory_cg, q_language_cg, PTHAND, moca_orie...\n",
              "25  0.817803  ...  (q_memory_cg, q_language_cg, PTHAND, moca_orie...\n",
              "26   0.82603  ...  (q_memory_cg, q_memory_pt, q_language_cg, PTHA...\n",
              "27  0.832431  ...  (q_memory_cg, q_attention_cg, q_memory_pt, q_l...\n",
              "28  0.837923  ...  (q_memory_cg, q_attention_cg, q_memory_pt, q_l...\n",
              "29  0.837911  ...  (q_memory_cg, q_attention_cg, q_memory_pt, q_l...\n",
              "30  0.838807  ...  (q_memory_cg, q_attention_cg, q_memory_pt, q_l...\n",
              "\n",
              "[30 rows x 4 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GdupzkOsnuU1",
        "colab_type": "text"
      },
      "source": [
        "#### Cost Sums"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jBtx8lTHIvyR",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "dill.load_session(f'{dir}/Sessions/costs_even50.db')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BrLnIBs8mmrN",
        "colab_type": "code",
        "outputId": "63e1059c-2892-4694-9af6-ac9146c7e987",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        }
      },
      "source": [
        "df = cost_sum(4, 'even', 50)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "4  features.\n",
            "Accuracy:  0.8241321874661558\n",
            "Features:  ('q_memory_cg', 'q_language_cg', 'moca_orient', 'q_outsideact_pt')\n",
            "Cost Sum:  4.0  Avg:  1.0\n",
            "Cost Time:  4  Avg:  1.0\n",
            "Cost Diff:  4  Avg:  1.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nAGdJHGKiLpU",
        "colab_type": "code",
        "outputId": "d16496d3-ead9-40a7-c93f-93356bf1fcdf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        }
      },
      "source": [
        "df = df.append(cost_sum(5,'even', 50))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "5  features.\n",
            "Accuracy:  0.8296242456815213\n",
            "Features:  ('q_memory_cg', 'q_language_cg', 'moca_orient', 'q_outsideact_pt', 'faq3')\n",
            "Cost Sum:  5.5  Avg:  1.1\n",
            "Cost Time:  5  Avg:  1.0\n",
            "Cost Diff:  6  Avg:  1.2\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rQEmK5KHsHju",
        "colab_type": "code",
        "outputId": "01992c87-ef40-4bc9-a517-59f81a3fbbdc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        }
      },
      "source": [
        "df = df.append(cost_sum(12,'even', 50))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "12  features.\n",
            "Accuracy:  0.8278145570073555\n",
            "Features:  ('q_memory_cg', 'q_language_cg', 'moca_orient', 'q_outsideact_pt', 'MMNAME', 'faq3', 'faq4', 'hach7', 'hach3', 'hach1', 'hach2', 'hach4')\n",
            "Cost Sum:  15.5  Avg:  1.2916666666666667\n",
            "Cost Time:  12  Avg:  1.0\n",
            "Cost Diff:  19  Avg:  1.5833333333333333\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rfwx7YWbNTBk",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df.to_csv(f'{dir}/Output/cost_sum.csv', index=False)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KhZQ5tgSSVwd",
        "colab_type": "text"
      },
      "source": [
        "#### 30 starting features"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "oh2Sv4ORQiQR",
        "colab": {}
      },
      "source": [
        "features = pd.read_csv(f'{dir}/features_paired.csv')\n",
        "features['weight'] = (features['avg_score'] + (features['cost_reverse']))/2\n",
        "features.sort_values('weight', ascending=False, inplace=True)\n",
        "features = features.head(30)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "MAONrWgHQiQT",
        "colab": {}
      },
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.model_selection import cross_val_score, StratifiedKFold\n",
        "\n",
        "cv = StratifiedKFold(n_splits=5)\n",
        "\n",
        "clf = RandomForestClassifier(n_estimators=100, max_features=.5)\n",
        "\n",
        "adni_pair_i_X = adni_pair_X.loc[:,features['feature']]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "yrtJpw9jQiQX",
        "colab": {}
      },
      "source": [
        "from mlxtend.feature_selection import SequentialFeatureSelector as SFS\n",
        "\n",
        "sfs1 = SFS(clf, \n",
        "           k_features=(30), \n",
        "           forward=True, \n",
        "           floating=False, \n",
        "           verbose=2,\n",
        "           scoring='accuracy',\n",
        "           cv=cv,\n",
        "           n_jobs=-1)\n",
        "\n",
        "sfs1 = sfs1.fit(adni_pair_i_X, adni_pair_y2.values.ravel())"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "20Tw1DQjQiQc",
        "colab": {}
      },
      "source": [
        "f_costs = pd.DataFrame.from_dict(sfs1.subsets_)\n",
        "f_costs = f_costs.transpose()\n",
        "f_costs['feature_idx'] = f_costs['feature_idx'].apply(str).str.replace('(','').str.replace(')','')\n",
        "f_costs.to_csv(f'{dir}/Output/eff_features_paired_even30.csv', index=False)\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fFAhBfYLIA0h",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "dill.dump_session(f'{dir}/Sessions/costs_even30.db')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "outputId": "2702d38a-b011-4aa2-f8d7-6134111f6341",
        "id": "vCNvF9mhQiQh",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "f_costs"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "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",
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              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>avg_score</th>\n",
              "      <th>cv_scores</th>\n",
              "      <th>feature_idx</th>\n",
              "      <th>feature_names</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.803048</td>\n",
              "      <td>[0.8721461187214612, 0.730593607305936, 0.8401...</td>\n",
              "      <td>0,</td>\n",
              "      <td>(q_memory_cg,)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.814966</td>\n",
              "      <td>[0.8812785388127854, 0.7442922374429224, 0.844...</td>\n",
              "      <td>0, 13</td>\n",
              "      <td>(q_memory_cg, moca_orient)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.819549</td>\n",
              "      <td>[0.8812785388127854, 0.7488584474885844, 0.849...</td>\n",
              "      <td>0, 11, 13</td>\n",
              "      <td>(q_memory_cg, q_outsideact_pt, moca_orient)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.824132</td>\n",
              "      <td>[0.8767123287671232, 0.7579908675799086, 0.858...</td>\n",
              "      <td>0, 3, 11, 13</td>\n",
              "      <td>(q_memory_cg, q_language_cg, q_outsideact_pt, ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0.825963</td>\n",
              "      <td>[0.8767123287671232, 0.7625570776255708, 0.858...</td>\n",
              "      <td>0, 3, 11, 13, 28</td>\n",
              "      <td>(q_memory_cg, q_language_cg, q_outsideact_pt, ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>0.825963</td>\n",
              "      <td>[0.8767123287671232, 0.7625570776255708, 0.858...</td>\n",
              "      <td>0, 3, 9, 11, 13, 28</td>\n",
              "      <td>(q_memory_cg, q_language_cg, MMNAME, q_outside...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>0.825963</td>\n",
              "      <td>[0.8767123287671232, 0.7625570776255708, 0.858...</td>\n",
              "      <td>0, 3, 9, 11, 13, 17, 28</td>\n",
              "      <td>(q_memory_cg, q_language_cg, MMNAME, q_outside...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>0.82231</td>\n",
              "      <td>[0.863013698630137, 0.7625570776255708, 0.8538...</td>\n",
              "      <td>0, 3, 7, 9, 11, 13, 17, 28</td>\n",
              "      <td>(q_memory_cg, q_language_cg, q_orient_cg, MMNA...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>0.821409</td>\n",
              "      <td>[0.8493150684931506, 0.7442922374429224, 0.863...</td>\n",
              "      <td>0, 2, 3, 7, 9, 11, 13, 17, 28</td>\n",
              "      <td>(q_memory_cg, q_memory_pt, q_language_cg, q_or...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>0.816826</td>\n",
              "      <td>[0.8447488584474886, 0.7488584474885844, 0.858...</td>\n",
              "      <td>0, 2, 3, 7, 9, 11, 13, 17, 24, 28</td>\n",
              "      <td>(q_memory_cg, q_memory_pt, q_language_cg, q_or...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>0.815905</td>\n",
              "      <td>[0.8447488584474886, 0.7351598173515982, 0.867...</td>\n",
              "      <td>0, 2, 3, 7, 9, 10, 11, 13, 17, 24, 28</td>\n",
              "      <td>(q_memory_cg, q_memory_pt, q_language_cg, q_or...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>0.820517</td>\n",
              "      <td>[0.8401826484018264, 0.7579908675799086, 0.844...</td>\n",
              "      <td>0, 2, 3, 7, 9, 10, 11, 13, 17, 24, 27, 28</td>\n",
              "      <td>(q_memory_cg, q_memory_pt, q_language_cg, q_or...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>0.825071</td>\n",
              "      <td>[0.8356164383561644, 0.7853881278538812, 0.863...</td>\n",
              "      <td>0, 2, 3, 7, 9, 10, 11, 13, 17, 23, 24, 27, 28</td>\n",
              "      <td>(q_memory_cg, q_memory_pt, q_language_cg, q_or...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>0.821422</td>\n",
              "      <td>[0.821917808219178, 0.7899543378995434, 0.8493...</td>\n",
              "      <td>0, 2, 3, 7, 8, 9, 10, 11, 13, 17, 23, 24, 27, 28</td>\n",
              "      <td>(q_memory_cg, q_memory_pt, q_language_cg, q_or...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>0.823269</td>\n",
              "      <td>[0.8584474885844748, 0.7534246575342466, 0.844...</td>\n",
              "      <td>0, 2, 3, 7, 8, 9, 10, 11, 13, 17, 23, 24, 26, ...</td>\n",
              "      <td>(q_memory_cg, q_memory_pt, q_language_cg, q_or...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>0.82691</td>\n",
              "      <td>[0.8538812785388128, 0.7579908675799086, 0.872...</td>\n",
              "      <td>0, 2, 3, 7, 8, 9, 10, 11, 12, 13, 17, 23, 24, ...</td>\n",
              "      <td>(q_memory_cg, q_memory_pt, q_language_cg, q_or...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>0.833298</td>\n",
              "      <td>[0.863013698630137, 0.771689497716895, 0.87214...</td>\n",
              "      <td>0, 2, 3, 7, 8, 9, 10, 11, 12, 13, 17, 23, 24, ...</td>\n",
              "      <td>(q_memory_cg, q_memory_pt, q_language_cg, q_or...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>0.839746</td>\n",
              "      <td>[0.8721461187214612, 0.7625570776255708, 0.872...</td>\n",
              "      <td>0, 1, 2, 3, 7, 8, 9, 10, 11, 12, 13, 17, 23, 2...</td>\n",
              "      <td>(q_memory_cg, q_attention_cg, q_memory_pt, q_l...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>0.842485</td>\n",
              "      <td>[0.8721461187214612, 0.7899543378995434, 0.863...</td>\n",
              "      <td>0, 1, 2, 3, 4, 7, 8, 9, 10, 11, 12, 13, 17, 23...</td>\n",
              "      <td>(q_memory_cg, q_attention_cg, q_memory_pt, q_l...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>0.844312</td>\n",
              "      <td>[0.8812785388127854, 0.7899543378995434, 0.853...</td>\n",
              "      <td>0, 1, 2, 3, 4, 6, 7, 8, 9, 10, 11, 12, 13, 17,...</td>\n",
              "      <td>(q_memory_cg, q_attention_cg, q_memory_pt, q_l...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>0.843411</td>\n",
              "      <td>[0.8812785388127854, 0.771689497716895, 0.8675...</td>\n",
              "      <td>0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, ...</td>\n",
              "      <td>(q_memory_cg, q_attention_cg, q_memory_pt, q_l...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>0.842498</td>\n",
              "      <td>[0.8721461187214612, 0.776255707762557, 0.8630...</td>\n",
              "      <td>0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, ...</td>\n",
              "      <td>(q_memory_cg, q_attention_cg, q_memory_pt, q_l...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>0.843373</td>\n",
              "      <td>[0.8904109589041096, 0.776255707762557, 0.8767...</td>\n",
              "      <td>0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, ...</td>\n",
              "      <td>(q_memory_cg, q_attention_cg, q_memory_pt, q_l...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>0.839725</td>\n",
              "      <td>[0.8721461187214612, 0.771689497716895, 0.8767...</td>\n",
              "      <td>0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, ...</td>\n",
              "      <td>(q_memory_cg, q_attention_cg, q_memory_pt, q_l...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>0.838803</td>\n",
              "      <td>[0.8858447488584474, 0.7579908675799086, 0.881...</td>\n",
              "      <td>0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, ...</td>\n",
              "      <td>(q_memory_cg, q_attention_cg, q_memory_pt, q_l...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>0.844299</td>\n",
              "      <td>[0.8904109589041096, 0.7671232876712328, 0.876...</td>\n",
              "      <td>0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, ...</td>\n",
              "      <td>(q_memory_cg, q_attention_cg, q_memory_pt, q_l...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>0.840634</td>\n",
              "      <td>[0.8858447488584474, 0.7671232876712328, 0.872...</td>\n",
              "      <td>0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, ...</td>\n",
              "      <td>(q_memory_cg, q_attention_cg, q_memory_pt, q_l...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>0.836088</td>\n",
              "      <td>[0.8904109589041096, 0.7579908675799086, 0.853...</td>\n",
              "      <td>0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, ...</td>\n",
              "      <td>(q_memory_cg, q_attention_cg, q_memory_pt, q_l...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>0.833298</td>\n",
              "      <td>[0.8858447488584474, 0.7625570776255708, 0.858...</td>\n",
              "      <td>0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, ...</td>\n",
              "      <td>(q_memory_cg, q_attention_cg, q_memory_pt, q_l...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>0.829629</td>\n",
              "      <td>[0.8767123287671232, 0.771689497716895, 0.8675...</td>\n",
              "      <td>0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, ...</td>\n",
              "      <td>(q_memory_cg, q_attention_cg, q_memory_pt, q_l...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   avg_score  ...                                      feature_names\n",
              "1   0.803048  ...                                     (q_memory_cg,)\n",
              "2   0.814966  ...                         (q_memory_cg, moca_orient)\n",
              "3   0.819549  ...        (q_memory_cg, q_outsideact_pt, moca_orient)\n",
              "4   0.824132  ...  (q_memory_cg, q_language_cg, q_outsideact_pt, ...\n",
              "5   0.825963  ...  (q_memory_cg, q_language_cg, q_outsideact_pt, ...\n",
              "6   0.825963  ...  (q_memory_cg, q_language_cg, MMNAME, q_outside...\n",
              "7   0.825963  ...  (q_memory_cg, q_language_cg, MMNAME, q_outside...\n",
              "8    0.82231  ...  (q_memory_cg, q_language_cg, q_orient_cg, MMNA...\n",
              "9   0.821409  ...  (q_memory_cg, q_memory_pt, q_language_cg, q_or...\n",
              "10  0.816826  ...  (q_memory_cg, q_memory_pt, q_language_cg, q_or...\n",
              "11  0.815905  ...  (q_memory_cg, q_memory_pt, q_language_cg, q_or...\n",
              "12  0.820517  ...  (q_memory_cg, q_memory_pt, q_language_cg, q_or...\n",
              "13  0.825071  ...  (q_memory_cg, q_memory_pt, q_language_cg, q_or...\n",
              "14  0.821422  ...  (q_memory_cg, q_memory_pt, q_language_cg, q_or...\n",
              "15  0.823269  ...  (q_memory_cg, q_memory_pt, q_language_cg, q_or...\n",
              "16   0.82691  ...  (q_memory_cg, q_memory_pt, q_language_cg, q_or...\n",
              "17  0.833298  ...  (q_memory_cg, q_memory_pt, q_language_cg, q_or...\n",
              "18  0.839746  ...  (q_memory_cg, q_attention_cg, q_memory_pt, q_l...\n",
              "19  0.842485  ...  (q_memory_cg, q_attention_cg, q_memory_pt, q_l...\n",
              "20  0.844312  ...  (q_memory_cg, q_attention_cg, q_memory_pt, q_l...\n",
              "21  0.843411  ...  (q_memory_cg, q_attention_cg, q_memory_pt, q_l...\n",
              "22  0.842498  ...  (q_memory_cg, q_attention_cg, q_memory_pt, q_l...\n",
              "23  0.843373  ...  (q_memory_cg, q_attention_cg, q_memory_pt, q_l...\n",
              "24  0.839725  ...  (q_memory_cg, q_attention_cg, q_memory_pt, q_l...\n",
              "25  0.838803  ...  (q_memory_cg, q_attention_cg, q_memory_pt, q_l...\n",
              "26  0.844299  ...  (q_memory_cg, q_attention_cg, q_memory_pt, q_l...\n",
              "27  0.840634  ...  (q_memory_cg, q_attention_cg, q_memory_pt, q_l...\n",
              "28  0.836088  ...  (q_memory_cg, q_attention_cg, q_memory_pt, q_l...\n",
              "29  0.833298  ...  (q_memory_cg, q_attention_cg, q_memory_pt, q_l...\n",
              "30  0.829629  ...  (q_memory_cg, q_attention_cg, q_memory_pt, q_l...\n",
              "\n",
              "[30 rows x 4 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "LUlGeREvQiQl"
      },
      "source": [
        "#### Cost Sums"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mPhZ200AIyE2",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "dill.load_session(f'{dir}/Sessions/costs_even30.db')\n",
        "df = pd.read_csv(f'{dir}/Output/cost_sum.csv')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "outputId": "10a72372-ac2a-43d2-e92e-369f77fcc415",
        "id": "SVBxKebEQiQl",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        }
      },
      "source": [
        "df = df.append(cost_sum(5,'even', 30))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "5  features.\n",
            "Accuracy:  0.8259628606679488\n",
            "Features:  ('q_memory_cg', 'q_language_cg', 'q_outsideact_pt', 'moca_orient', 'faq8')\n",
            "Cost Sum:  5.5  Avg:  1.1\n",
            "Cost Time:  5  Avg:  1.0\n",
            "Cost Diff:  6  Avg:  1.2\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vt1yhu2nttL_",
        "colab_type": "code",
        "outputId": "34cd65a7-68c1-42f5-eb02-c9f3837be0da",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        }
      },
      "source": [
        "df = df.append(cost_sum(6,'even', 30))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "6  features.\n",
            "Accuracy:  0.8259628606679488\n",
            "Features:  ('q_memory_cg', 'q_language_cg', 'MMNAME', 'q_outsideact_pt', 'moca_orient', 'faq8')\n",
            "Cost Sum:  6.5  Avg:  1.0833333333333333\n",
            "Cost Time:  6  Avg:  1.0\n",
            "Cost Diff:  7  Avg:  1.1666666666666667\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iaRaJzRBPnCo",
        "colab_type": "code",
        "outputId": "455388a7-2491-4f00-e7dc-abe4cb909d06",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        }
      },
      "source": [
        "df = df.append(cost_sum(7,'even', 30))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "7  features.\n",
            "Accuracy:  0.8259628606679488\n",
            "Features:  ('q_memory_cg', 'q_language_cg', 'MMNAME', 'q_outsideact_pt', 'moca_orient', 'PTHAND', 'faq8')\n",
            "Cost Sum:  7.5  Avg:  1.0714285714285714\n",
            "Cost Time:  7  Avg:  1.0\n",
            "Cost Diff:  8  Avg:  1.1428571428571428\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ObVVeD1wWrV9",
        "colab_type": "code",
        "outputId": "3c654856-1e5e-4094-fee0-3c62e71cfdcc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        }
      },
      "source": [
        "df = df.append(cost_sum(8,'even', 30))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "8  features.\n",
            "Accuracy:  0.8223098926314194\n",
            "Features:  ('q_memory_cg', 'q_language_cg', 'q_orient_cg', 'MMNAME', 'q_outsideact_pt', 'moca_orient', 'PTHAND', 'faq8')\n",
            "Cost Sum:  8.5  Avg:  1.0625\n",
            "Cost Time:  8  Avg:  1.0\n",
            "Cost Diff:  9  Avg:  1.125\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "63Ne4OScNaBI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df.to_csv(f'{dir}/Output/cost_sum.csv', index=False)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ufEdBdaLPnZk",
        "colab_type": "text"
      },
      "source": [
        "#### 15 starting features"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "iZsoJlKLQj4Y",
        "colab": {}
      },
      "source": [
        "features = pd.read_csv(f'{dir}/features_paired.csv')\n",
        "features['weight'] = (features['avg_score'] + (features['cost_reverse']))/2\n",
        "features.sort_values('weight', ascending=False, inplace=True)\n",
        "features = features.head(15)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "cvD9UnkDQj4a",
        "colab": {}
      },
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.model_selection import cross_val_score, StratifiedKFold\n",
        "\n",
        "cv = StratifiedKFold(n_splits=5)\n",
        "\n",
        "clf = RandomForestClassifier(n_estimators=100, max_features=.5)\n",
        "\n",
        "adni_pair_i_X = adni_pair_X.loc[:,features['feature']]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "ETBquV0CQj4b",
        "colab": {}
      },
      "source": [
        "from mlxtend.feature_selection import SequentialFeatureSelector as SFS\n",
        "\n",
        "sfs1 = SFS(clf, \n",
        "           k_features=(15), \n",
        "           forward=True, \n",
        "           floating=False, \n",
        "           verbose=2,\n",
        "           scoring='accuracy',\n",
        "           cv=cv,\n",
        "           n_jobs=-1)\n",
        "\n",
        "sfs1 = sfs1.fit(adni_pair_i_X, adni_pair_y2.values.ravel())"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "mx-YGg8pQj4d",
        "colab": {}
      },
      "source": [
        "f_costs = pd.DataFrame.from_dict(sfs1.subsets_)\n",
        "f_costs = f_costs.transpose()\n",
        "f_costs['feature_idx'] = f_costs['feature_idx'].apply(str).str.replace('(','').str.replace(')','')\n",
        "f_costs.to_csv(f'{dir}/Output/eff_features_paired_even15.csv', index=False)\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZUbYquRgIDEj",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "dill.dump_session(f'{dir}/Sessions/costs_even15.db')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "outputId": "99c542e2-caa0-41d8-fc82-8724a9399789",
        "id": "ZGZnF6mRQj4e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 757
        }
      },
      "source": [
        "f_costs"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>avg_score</th>\n",
              "      <th>cv_scores</th>\n",
              "      <th>feature_idx</th>\n",
              "      <th>feature_names</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.803048</td>\n",
              "      <td>[0.8721461187214612, 0.730593607305936, 0.8401...</td>\n",
              "      <td>0,</td>\n",
              "      <td>(q_memory_cg,)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.814966</td>\n",
              "      <td>[0.8812785388127854, 0.7442922374429224, 0.844...</td>\n",
              "      <td>0, 10</td>\n",
              "      <td>(q_memory_cg, moca_orient)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.819549</td>\n",
              "      <td>[0.8767123287671232, 0.7534246575342466, 0.853...</td>\n",
              "      <td>0, 3, 10</td>\n",
              "      <td>(q_memory_cg, q_language_cg, moca_orient)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.823215</td>\n",
              "      <td>[0.8767123287671232, 0.7579908675799086, 0.858...</td>\n",
              "      <td>0, 3, 10, 12</td>\n",
              "      <td>(q_memory_cg, q_language_cg, moca_orient, q_ou...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0.824132</td>\n",
              "      <td>[0.8767123287671232, 0.7579908675799086, 0.858...</td>\n",
              "      <td>0, 3, 7, 10, 12</td>\n",
              "      <td>(q_memory_cg, q_language_cg, PTHAND, moca_orie...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>0.821388</td>\n",
              "      <td>[0.867579908675799, 0.7579908675799086, 0.8584...</td>\n",
              "      <td>0, 3, 7, 10, 12, 14</td>\n",
              "      <td>(q_memory_cg, q_language_cg, PTHAND, moca_orie...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>0.819562</td>\n",
              "      <td>[0.867579908675799, 0.7534246575342466, 0.8493...</td>\n",
              "      <td>0, 2, 3, 7, 10, 12, 14</td>\n",
              "      <td>(q_memory_cg, q_memory_pt, q_language_cg, PTHA...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>0.825066</td>\n",
              "      <td>[0.863013698630137, 0.7397260273972602, 0.8675...</td>\n",
              "      <td>0, 2, 3, 7, 10, 12, 13, 14</td>\n",
              "      <td>(q_memory_cg, q_memory_pt, q_language_cg, PTHA...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>0.81867</td>\n",
              "      <td>[0.8447488584474886, 0.7442922374429224, 0.863...</td>\n",
              "      <td>0, 2, 3, 7, 10, 11, 12, 13, 14</td>\n",
              "      <td>(q_memory_cg, q_memory_pt, q_language_cg, PTHA...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>0.819591</td>\n",
              "      <td>[0.8584474885844748, 0.7442922374429224, 0.849...</td>\n",
              "      <td>0, 2, 3, 5, 7, 10, 11, 12, 13, 14</td>\n",
              "      <td>(q_memory_cg, q_memory_pt, q_language_cg, moca...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>0.815</td>\n",
              "      <td>[0.863013698630137, 0.7579908675799086, 0.8219...</td>\n",
              "      <td>0, 2, 3, 4, 5, 7, 10, 11, 12, 13, 14</td>\n",
              "      <td>(q_memory_cg, q_memory_pt, q_language_cg, FAMH...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>0.823274</td>\n",
              "      <td>[0.8493150684931506, 0.776255707762557, 0.8356...</td>\n",
              "      <td>0, 2, 3, 4, 5, 7, 8, 10, 11, 12, 13, 14</td>\n",
              "      <td>(q_memory_cg, q_memory_pt, q_language_cg, FAMH...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>0.811368</td>\n",
              "      <td>[0.8447488584474886, 0.7488584474885844, 0.826...</td>\n",
              "      <td>0, 2, 3, 4, 5, 7, 8, 9, 10, 11, 12, 13, 14</td>\n",
              "      <td>(q_memory_cg, q_memory_pt, q_language_cg, FAMH...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>0.815038</td>\n",
              "      <td>[0.8447488584474886, 0.7351598173515982, 0.844...</td>\n",
              "      <td>0, 1, 2, 3, 4, 5, 7, 8, 9, 10, 11, 12, 13, 14</td>\n",
              "      <td>(q_memory_cg, q_attention_cg, q_memory_pt, q_l...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>0.804916</td>\n",
              "      <td>[0.8447488584474886, 0.7488584474885844, 0.826...</td>\n",
              "      <td>0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14</td>\n",
              "      <td>(q_memory_cg, q_attention_cg, q_memory_pt, q_l...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   avg_score  ...                                      feature_names\n",
              "1   0.803048  ...                                     (q_memory_cg,)\n",
              "2   0.814966  ...                         (q_memory_cg, moca_orient)\n",
              "3   0.819549  ...          (q_memory_cg, q_language_cg, moca_orient)\n",
              "4   0.823215  ...  (q_memory_cg, q_language_cg, moca_orient, q_ou...\n",
              "5   0.824132  ...  (q_memory_cg, q_language_cg, PTHAND, moca_orie...\n",
              "6   0.821388  ...  (q_memory_cg, q_language_cg, PTHAND, moca_orie...\n",
              "7   0.819562  ...  (q_memory_cg, q_memory_pt, q_language_cg, PTHA...\n",
              "8   0.825066  ...  (q_memory_cg, q_memory_pt, q_language_cg, PTHA...\n",
              "9    0.81867  ...  (q_memory_cg, q_memory_pt, q_language_cg, PTHA...\n",
              "10  0.819591  ...  (q_memory_cg, q_memory_pt, q_language_cg, moca...\n",
              "11     0.815  ...  (q_memory_cg, q_memory_pt, q_language_cg, FAMH...\n",
              "12  0.823274  ...  (q_memory_cg, q_memory_pt, q_language_cg, FAMH...\n",
              "13  0.811368  ...  (q_memory_cg, q_memory_pt, q_language_cg, FAMH...\n",
              "14  0.815038  ...  (q_memory_cg, q_attention_cg, q_memory_pt, q_l...\n",
              "15  0.804916  ...  (q_memory_cg, q_attention_cg, q_memory_pt, q_l...\n",
              "\n",
              "[15 rows x 4 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "loiuGPcdQj4h"
      },
      "source": [
        "#### Cost Sums"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LlPznHsYIzfy",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "dill.load_session(f'{dir}/Sessions/costs_even15.db')\n",
        "df = pd.read_csv(f'{dir}/Output/cost_sum.csv')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "outputId": "e6ee0453-b22e-4a8f-9b19-0317977d172c",
        "id": "P1l9lSNnQj4i",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        }
      },
      "source": [
        "df = df.append(cost_sum(5,'even', 15))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "5  features.\n",
            "Accuracy:  0.8241321874661558\n",
            "Features:  ('q_memory_cg', 'q_language_cg', 'PTHAND', 'moca_orient', 'q_outsideact_pt')\n",
            "Cost Sum:  5.0  Avg:  1.0\n",
            "Cost Time:  5  Avg:  1.0\n",
            "Cost Diff:  5  Avg:  1.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pnHS519Ku_xf",
        "colab_type": "code",
        "outputId": "39fb76e0-b7c0-4fa2-a3e0-253fc5f3ad7d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        }
      },
      "source": [
        "df = df.append(cost_sum(8,'even', 15))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "8  features.\n",
            "Accuracy:  0.8250663367829425\n",
            "Features:  ('q_memory_cg', 'q_memory_pt', 'q_language_cg', 'PTHAND', 'moca_orient', 'q_outsideact_pt', 'q_judgmt_pt', 'MMNAME')\n",
            "Cost Sum:  8.0  Avg:  1.0\n",
            "Cost Time:  8  Avg:  1.0\n",
            "Cost Diff:  8  Avg:  1.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Uzu2IqNcPqK5",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df.to_csv(f'{dir}/Output/cost_sum.csv', index=False)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "5IYkhwv1Sxxy"
      },
      "source": [
        "### Importance over cost"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "Xh6bILIMS112"
      },
      "source": [
        "#### 50 starting features"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "hLSN-TpPSxxz",
        "colab": {}
      },
      "source": [
        "features = pd.read_csv(f'{dir}/features_paired.csv')\n",
        "features['weight_x.5'] = (features['avg_score'] + (features['cost_reverse']*.5))/2\n",
        "features.sort_values('weight_x.5', ascending=False)\n",
        "features = features.head(50)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "oI1VHBkyS112",
        "colab": {}
      },
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.model_selection import cross_val_score, StratifiedKFold\n",
        "\n",
        "cv = StratifiedKFold(n_splits=5)\n",
        "\n",
        "clf = RandomForestClassifier(n_estimators=100, max_features=.5)\n",
        "\n",
        "adni_pair_i_X = adni_pair_X.loc[:,features['feature']]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "jBX0tuoXS114",
        "colab": {}
      },
      "source": [
        "from mlxtend.feature_selection import SequentialFeatureSelector as SFS\n",
        "\n",
        "sfs1 = SFS(clf, \n",
        "           k_features=(30), \n",
        "           forward=True, \n",
        "           floating=False, \n",
        "           verbose=2,\n",
        "           scoring='accuracy',\n",
        "           cv=cv,\n",
        "           n_jobs=-1)\n",
        "\n",
        "sfs1 = sfs1.fit(adni_pair_i_X, adni_pair_y2.values.ravel())"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "Tx6WmqiQoX6T",
        "colab": {}
      },
      "source": [
        "f_costs = pd.DataFrame.from_dict(sfs1.subsets_)\n",
        "f_costs = f_costs.transpose()\n",
        "f_costs['feature_idx'] = f_costs['feature_idx'].apply(str).str.replace('(','').str.replace(')','')\n",
        "f_costs.to_csv(f'{dir}/Output/eff_features_paired_importweight_50.csv', index=False)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YogjLbnrIEst",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "dill.dump_session(f'{dir}/Sessions/costs_importweight50.db')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "outputId": "09fb6943-1322-461d-ab6d-869258a6de5f",
        "id": "a05ByYsgoX6V",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "f_costs"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "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",
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              "    }\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>avg_score</th>\n",
              "      <th>cv_scores</th>\n",
              "      <th>feature_idx</th>\n",
              "      <th>feature_names</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.803048</td>\n",
              "      <td>[0.8721461187214612, 0.730593607305936, 0.8401...</td>\n",
              "      <td>0,</td>\n",
              "      <td>(q_memory_cg,)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.809432</td>\n",
              "      <td>[0.8767123287671232, 0.7534246575342466, 0.840...</td>\n",
              "      <td>0, 3</td>\n",
              "      <td>(q_memory_cg, q_memory_pt)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.809432</td>\n",
              "      <td>[0.8767123287671232, 0.7534246575342466, 0.840...</td>\n",
              "      <td>0, 3, 15</td>\n",
              "      <td>(q_memory_cg, q_memory_pt, npi1_sev)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.809432</td>\n",
              "      <td>[0.8767123287671232, 0.7534246575342466, 0.840...</td>\n",
              "      <td>0, 3, 15, 17</td>\n",
              "      <td>(q_memory_cg, q_memory_pt, npi1_sev, npi2_sev)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0.807605</td>\n",
              "      <td>[0.867579908675799, 0.7534246575342466, 0.8401...</td>\n",
              "      <td>0, 3, 15, 17, 21</td>\n",
              "      <td>(q_memory_cg, q_memory_pt, npi1_sev, npi2_sev,...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>0.807605</td>\n",
              "      <td>[0.867579908675799, 0.7534246575342466, 0.8401...</td>\n",
              "      <td>0, 3, 15, 17, 21, 36</td>\n",
              "      <td>(q_memory_cg, q_memory_pt, npi1_sev, npi2_sev,...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>0.807605</td>\n",
              "      <td>[0.867579908675799, 0.7534246575342466, 0.8401...</td>\n",
              "      <td>0, 3, 15, 17, 21, 36, 45</td>\n",
              "      <td>(q_memory_cg, q_memory_pt, npi1_sev, npi2_sev,...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>0.810379</td>\n",
              "      <td>[0.863013698630137, 0.7442922374429224, 0.8538...</td>\n",
              "      <td>0, 1, 3, 15, 17, 21, 36, 45</td>\n",
              "      <td>(q_memory_cg, q_attention_cg, q_memory_pt, npi...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>0.814937</td>\n",
              "      <td>[0.8721461187214612, 0.7671232876712328, 0.844...</td>\n",
              "      <td>0, 1, 3, 15, 17, 21, 28, 36, 45</td>\n",
              "      <td>(q_memory_cg, q_attention_cg, q_memory_pt, npi...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>0.818594</td>\n",
              "      <td>[0.8721461187214612, 0.7808219178082192, 0.849...</td>\n",
              "      <td>0, 1, 3, 15, 17, 21, 28, 36, 44, 45</td>\n",
              "      <td>(q_memory_cg, q_attention_cg, q_memory_pt, npi...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>0.81859</td>\n",
              "      <td>[0.867579908675799, 0.7853881278538812, 0.8493...</td>\n",
              "      <td>0, 1, 3, 15, 17, 21, 27, 28, 36, 44, 45</td>\n",
              "      <td>(q_memory_cg, q_attention_cg, q_memory_pt, npi...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>0.815841</td>\n",
              "      <td>[0.8721461187214612, 0.771689497716895, 0.8493...</td>\n",
              "      <td>0, 1, 3, 15, 17, 21, 27, 28, 34, 36, 44, 45</td>\n",
              "      <td>(q_memory_cg, q_attention_cg, q_memory_pt, npi...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>0.815863</td>\n",
              "      <td>[0.8767123287671232, 0.7671232876712328, 0.835...</td>\n",
              "      <td>0, 1, 3, 15, 17, 21, 24, 27, 28, 34, 36, 44, 45</td>\n",
              "      <td>(q_memory_cg, q_attention_cg, q_memory_pt, npi...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>0.814015</td>\n",
              "      <td>[0.8721461187214612, 0.7671232876712328, 0.840...</td>\n",
              "      <td>0, 1, 3, 15, 17, 21, 24, 27, 28, 34, 35, 36, 4...</td>\n",
              "      <td>(q_memory_cg, q_attention_cg, q_memory_pt, npi...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>0.812184</td>\n",
              "      <td>[0.8721461187214612, 0.7579908675799086, 0.840...</td>\n",
              "      <td>0, 1, 3, 15, 17, 20, 21, 24, 27, 28, 34, 35, 3...</td>\n",
              "      <td>(q_memory_cg, q_attention_cg, q_memory_pt, npi...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>0.811296</td>\n",
              "      <td>[0.867579908675799, 0.7534246575342466, 0.8356...</td>\n",
              "      <td>0, 1, 3, 13, 15, 17, 20, 21, 24, 27, 28, 34, 3...</td>\n",
              "      <td>(q_memory_cg, q_attention_cg, q_memory_pt, npi...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>0.810421</td>\n",
              "      <td>[0.8721461187214612, 0.726027397260274, 0.8264...</td>\n",
              "      <td>0, 1, 3, 13, 15, 17, 18, 20, 21, 24, 27, 28, 3...</td>\n",
              "      <td>(q_memory_cg, q_attention_cg, q_memory_pt, npi...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>0.810421</td>\n",
              "      <td>[0.8721461187214612, 0.7351598173515982, 0.817...</td>\n",
              "      <td>0, 1, 3, 13, 15, 17, 18, 20, 21, 24, 25, 27, 2...</td>\n",
              "      <td>(q_memory_cg, q_attention_cg, q_memory_pt, npi...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>0.813169</td>\n",
              "      <td>[0.863013698630137, 0.7488584474885844, 0.8219...</td>\n",
              "      <td>0, 1, 3, 13, 15, 17, 18, 19, 20, 21, 24, 25, 2...</td>\n",
              "      <td>(q_memory_cg, q_attention_cg, q_memory_pt, npi...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>0.809503</td>\n",
              "      <td>[0.8767123287671232, 0.726027397260274, 0.8173...</td>\n",
              "      <td>0, 1, 3, 11, 13, 15, 17, 18, 19, 20, 21, 24, 2...</td>\n",
              "      <td>(q_memory_cg, q_attention_cg, q_memory_pt, npi...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>0.814996</td>\n",
              "      <td>[0.867579908675799, 0.7488584474885844, 0.8264...</td>\n",
              "      <td>0, 1, 3, 11, 12, 13, 15, 17, 18, 19, 20, 21, 2...</td>\n",
              "      <td>(q_memory_cg, q_attention_cg, q_memory_pt, npi...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>0.804929</td>\n",
              "      <td>[0.8584474885844748, 0.7671232876712328, 0.789...</td>\n",
              "      <td>0, 1, 3, 11, 12, 13, 15, 17, 18, 19, 20, 21, 2...</td>\n",
              "      <td>(q_memory_cg, q_attention_cg, q_memory_pt, npi...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>0.807694</td>\n",
              "      <td>[0.863013698630137, 0.7534246575342466, 0.7990...</td>\n",
              "      <td>0, 1, 3, 10, 11, 12, 13, 15, 17, 18, 19, 20, 2...</td>\n",
              "      <td>(q_memory_cg, q_attention_cg, q_memory_pt, npi...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>0.808561</td>\n",
              "      <td>[0.8949771689497716, 0.7625570776255708, 0.794...</td>\n",
              "      <td>0, 1, 3, 10, 11, 12, 13, 15, 17, 18, 19, 20, 2...</td>\n",
              "      <td>(q_memory_cg, q_attention_cg, q_memory_pt, npi...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>0.81221</td>\n",
              "      <td>[0.8721461187214612, 0.7625570776255708, 0.835...</td>\n",
              "      <td>0, 1, 3, 6, 10, 11, 12, 13, 15, 17, 18, 19, 20...</td>\n",
              "      <td>(q_memory_cg, q_attention_cg, q_memory_pt, q_l...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>0.809461</td>\n",
              "      <td>[0.867579908675799, 0.7534246575342466, 0.8356...</td>\n",
              "      <td>0, 1, 3, 6, 10, 11, 12, 13, 14, 15, 17, 18, 19...</td>\n",
              "      <td>(q_memory_cg, q_attention_cg, q_memory_pt, q_l...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>0.812226</td>\n",
              "      <td>[0.863013698630137, 0.7534246575342466, 0.8356...</td>\n",
              "      <td>0, 1, 3, 6, 10, 11, 12, 13, 14, 15, 17, 18, 19...</td>\n",
              "      <td>(q_memory_cg, q_attention_cg, q_memory_pt, q_l...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>0.813123</td>\n",
              "      <td>[0.8812785388127854, 0.7488584474885844, 0.840...</td>\n",
              "      <td>0, 1, 3, 6, 10, 11, 12, 13, 14, 15, 17, 18, 19...</td>\n",
              "      <td>(q_memory_cg, q_attention_cg, q_memory_pt, q_l...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>0.812197</td>\n",
              "      <td>[0.8721461187214612, 0.7534246575342466, 0.849...</td>\n",
              "      <td>0, 1, 3, 6, 7, 10, 11, 12, 13, 14, 15, 17, 18,...</td>\n",
              "      <td>(q_memory_cg, q_attention_cg, q_memory_pt, q_l...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>0.820467</td>\n",
              "      <td>[0.8767123287671232, 0.7671232876712328, 0.831...</td>\n",
              "      <td>0, 1, 3, 5, 6, 7, 10, 11, 12, 13, 14, 15, 17, ...</td>\n",
              "      <td>(q_memory_cg, q_attention_cg, q_memory_pt, moc...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   avg_score  ...                                      feature_names\n",
              "1   0.803048  ...                                     (q_memory_cg,)\n",
              "2   0.809432  ...                         (q_memory_cg, q_memory_pt)\n",
              "3   0.809432  ...               (q_memory_cg, q_memory_pt, npi1_sev)\n",
              "4   0.809432  ...     (q_memory_cg, q_memory_pt, npi1_sev, npi2_sev)\n",
              "5   0.807605  ...  (q_memory_cg, q_memory_pt, npi1_sev, npi2_sev,...\n",
              "6   0.807605  ...  (q_memory_cg, q_memory_pt, npi1_sev, npi2_sev,...\n",
              "7   0.807605  ...  (q_memory_cg, q_memory_pt, npi1_sev, npi2_sev,...\n",
              "8   0.810379  ...  (q_memory_cg, q_attention_cg, q_memory_pt, npi...\n",
              "9   0.814937  ...  (q_memory_cg, q_attention_cg, q_memory_pt, npi...\n",
              "10  0.818594  ...  (q_memory_cg, q_attention_cg, q_memory_pt, npi...\n",
              "11   0.81859  ...  (q_memory_cg, q_attention_cg, q_memory_pt, npi...\n",
              "12  0.815841  ...  (q_memory_cg, q_attention_cg, q_memory_pt, npi...\n",
              "13  0.815863  ...  (q_memory_cg, q_attention_cg, q_memory_pt, npi...\n",
              "14  0.814015  ...  (q_memory_cg, q_attention_cg, q_memory_pt, npi...\n",
              "15  0.812184  ...  (q_memory_cg, q_attention_cg, q_memory_pt, npi...\n",
              "16  0.811296  ...  (q_memory_cg, q_attention_cg, q_memory_pt, npi...\n",
              "17  0.810421  ...  (q_memory_cg, q_attention_cg, q_memory_pt, npi...\n",
              "18  0.810421  ...  (q_memory_cg, q_attention_cg, q_memory_pt, npi...\n",
              "19  0.813169  ...  (q_memory_cg, q_attention_cg, q_memory_pt, npi...\n",
              "20  0.809503  ...  (q_memory_cg, q_attention_cg, q_memory_pt, npi...\n",
              "21  0.814996  ...  (q_memory_cg, q_attention_cg, q_memory_pt, npi...\n",
              "22  0.804929  ...  (q_memory_cg, q_attention_cg, q_memory_pt, npi...\n",
              "23  0.807694  ...  (q_memory_cg, q_attention_cg, q_memory_pt, npi...\n",
              "24  0.808561  ...  (q_memory_cg, q_attention_cg, q_memory_pt, npi...\n",
              "25   0.81221  ...  (q_memory_cg, q_attention_cg, q_memory_pt, q_l...\n",
              "26  0.809461  ...  (q_memory_cg, q_attention_cg, q_memory_pt, q_l...\n",
              "27  0.812226  ...  (q_memory_cg, q_attention_cg, q_memory_pt, q_l...\n",
              "28  0.813123  ...  (q_memory_cg, q_attention_cg, q_memory_pt, q_l...\n",
              "29  0.812197  ...  (q_memory_cg, q_attention_cg, q_memory_pt, q_l...\n",
              "30  0.820467  ...  (q_memory_cg, q_attention_cg, q_memory_pt, moc...\n",
              "\n",
              "[30 rows x 4 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "42RSS3VaoX6Y"
      },
      "source": [
        "#### Cost Sums"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r_KNf_wOI1Eg",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "dill.load_session(f'{dir}/Sessions/costs_importweight50.db')\n",
        "df = pd.read_csv(f'{dir}/Output/cost_sum.csv')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "outputId": "2a343e10-532d-4310-9391-bc0fcbe7a863",
        "id": "toc1dM4RoX6Y",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        }
      },
      "source": [
        "df = df.append(cost_sum(7,'import', 50))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "7  features.\n",
            "Accuracy:  0.8076054723478172\n",
            "Features:  ('q_memory_cg', 'q_memory_pt', 'npi1_sev', 'npi2_sev', 'hach8', 'hach1', 'MMREAD')\n",
            "Cost Sum:  11.0  Avg:  1.5714285714285714\n",
            "Cost Time:  8  Avg:  1.1428571428571428\n",
            "Cost Diff:  14  Avg:  2.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "asRWdJ39SzGv",
        "colab_type": "code",
        "outputId": "40ec0aff-baaf-40df-860f-860d478b2cb9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        }
      },
      "source": [
        "df = df.append(cost_sum(10,'import', 50))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "10  features.\n",
            "Accuracy:  0.818593855182049\n",
            "Features:  ('q_memory_cg', 'q_attention_cg', 'q_memory_pt', 'npi1_sev', 'npi2_sev', 'hach8', 'FAMHISTAD', 'hach1', 'moca_letters', 'MMREAD')\n",
            "Cost Sum:  15.5  Avg:  1.55\n",
            "Cost Time:  12  Avg:  1.2\n",
            "Cost Diff:  19  Avg:  1.9\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_gbbxLEyNsWW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df.to_csv(f'{dir}/Output/cost_sum.csv', index=False)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kgqiWJjQPxem",
        "colab_type": "text"
      },
      "source": [
        "#### 30 starting features"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "hGpqORMwP77v",
        "colab": {}
      },
      "source": [
        "features = pd.read_csv(f'{dir}/features_paired.csv')\n",
        "features['weight_x.5'] = (features['avg_score'] + (features['cost_reverse']*.5))/2\n",
        "features.sort_values('weight_x.5', ascending=False)\n",
        "features = features.head(30)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "CUcqNZU8P77y",
        "colab": {}
      },
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.model_selection import cross_val_score, StratifiedKFold\n",
        "\n",
        "cv = StratifiedKFold(n_splits=5)\n",
        "\n",
        "clf = RandomForestClassifier(n_estimators=100, max_features=.5)\n",
        "\n",
        "adni_pair_i_X = adni_pair_X.loc[:,features['feature']]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "PdGYs0IRP77z",
        "colab": {}
      },
      "source": [
        "from mlxtend.feature_selection import SequentialFeatureSelector as SFS\n",
        "\n",
        "sfs1 = SFS(clf, \n",
        "           k_features=(30), \n",
        "           forward=True, \n",
        "           floating=False, \n",
        "           verbose=2,\n",
        "           scoring='accuracy',\n",
        "           cv=cv,\n",
        "           n_jobs=-1)\n",
        "\n",
        "sfs1 = sfs1.fit(adni_pair_i_X, adni_pair_y2.values.ravel())"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "G4dFSEvcP772",
        "colab": {}
      },
      "source": [
        "f_costs = pd.DataFrame.from_dict(sfs1.subsets_)\n",
        "f_costs = f_costs.transpose()\n",
        "f_costs['feature_idx'] = f_costs['feature_idx'].apply(str).str.replace('(','').str.replace(')','')\n",
        "f_costs.to_csv(f'{dir}/Output/eff_features_paired_importweight_30.csv', index=False)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g44-vTEqIHtt",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "dill.dump_session(f'{dir}/Sessions/costs_importweight30.db')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "outputId": "eb590ad8-a4ef-4fcc-83c6-d2ea435a1aee",
        "id": "WqxZDmldP773",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "f_costs"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>avg_score</th>\n",
              "      <th>cv_scores</th>\n",
              "      <th>feature_idx</th>\n",
              "      <th>feature_names</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.803048</td>\n",
              "      <td>[0.8721461187214612, 0.730593607305936, 0.8401...</td>\n",
              "      <td>0,</td>\n",
              "      <td>(q_memory_cg,)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.80673</td>\n",
              "      <td>[0.867579908675799, 0.7351598173515982, 0.8401...</td>\n",
              "      <td>0, 22</td>\n",
              "      <td>(q_memory_cg, npi3_sev)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.808561</td>\n",
              "      <td>[0.867579908675799, 0.7397260273972602, 0.8401...</td>\n",
              "      <td>0, 22, 27</td>\n",
              "      <td>(q_memory_cg, npi3_sev, hach2)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.810387</td>\n",
              "      <td>[0.8584474885844748, 0.7488584474885844, 0.849...</td>\n",
              "      <td>0, 6, 22, 27</td>\n",
              "      <td>(q_memory_cg, q_language_cg, npi3_sev, hach2)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0.809461</td>\n",
              "      <td>[0.8584474885844748, 0.7488584474885844, 0.849...</td>\n",
              "      <td>0, 6, 14, 22, 27</td>\n",
              "      <td>(q_memory_cg, q_language_cg, q_orient_pt, npi3...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>0.814949</td>\n",
              "      <td>[0.8584474885844748, 0.7579908675799086, 0.858...</td>\n",
              "      <td>0, 6, 14, 15, 22, 27</td>\n",
              "      <td>(q_memory_cg, q_language_cg, q_orient_pt, npi1...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>0.81404</td>\n",
              "      <td>[0.8538812785388128, 0.7534246575342466, 0.858...</td>\n",
              "      <td>0, 6, 14, 15, 19, 22, 27</td>\n",
              "      <td>(q_memory_cg, q_language_cg, q_orient_pt, npi1...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>0.813123</td>\n",
              "      <td>[0.8538812785388128, 0.7534246575342466, 0.858...</td>\n",
              "      <td>0, 6, 14, 15, 17, 19, 22, 27</td>\n",
              "      <td>(q_memory_cg, q_language_cg, q_orient_pt, npi1...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>0.808557</td>\n",
              "      <td>[0.8493150684931506, 0.7534246575342466, 0.840...</td>\n",
              "      <td>0, 6, 14, 15, 17, 19, 21, 22, 27</td>\n",
              "      <td>(q_memory_cg, q_language_cg, q_orient_pt, npi1...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>0.807635</td>\n",
              "      <td>[0.8493150684931506, 0.7534246575342466, 0.840...</td>\n",
              "      <td>0, 6, 13, 14, 15, 17, 19, 21, 22, 27</td>\n",
              "      <td>(q_memory_cg, q_language_cg, npi6_sev, q_orien...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>0.805813</td>\n",
              "      <td>[0.8493150684931506, 0.7488584474885844, 0.835...</td>\n",
              "      <td>0, 6, 13, 14, 15, 17, 19, 20, 21, 22, 27</td>\n",
              "      <td>(q_memory_cg, q_language_cg, npi6_sev, q_orien...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>0.803081</td>\n",
              "      <td>[0.8447488584474886, 0.7351598173515982, 0.831...</td>\n",
              "      <td>0, 6, 11, 13, 14, 15, 17, 19, 20, 21, 22, 27</td>\n",
              "      <td>(q_memory_cg, q_language_cg, npi8_sev, npi6_se...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>0.804883</td>\n",
              "      <td>[0.8447488584474886, 0.7671232876712328, 0.835...</td>\n",
              "      <td>0, 6, 11, 12, 13, 14, 15, 17, 19, 20, 21, 22, 27</td>\n",
              "      <td>(q_memory_cg, q_language_cg, npi8_sev, npi7_se...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>0.800316</td>\n",
              "      <td>[0.8447488584474886, 0.7534246575342466, 0.826...</td>\n",
              "      <td>0, 6, 11, 12, 13, 14, 15, 17, 19, 20, 21, 22, ...</td>\n",
              "      <td>(q_memory_cg, q_language_cg, npi8_sev, npi7_se...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>0.799386</td>\n",
              "      <td>[0.8538812785388128, 0.7397260273972602, 0.835...</td>\n",
              "      <td>0, 6, 11, 12, 13, 14, 15, 17, 19, 20, 21, 22, ...</td>\n",
              "      <td>(q_memory_cg, q_language_cg, npi8_sev, npi7_se...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>0.800342</td>\n",
              "      <td>[0.821917808219178, 0.7442922374429224, 0.8356...</td>\n",
              "      <td>0, 3, 6, 11, 12, 13, 14, 15, 17, 19, 20, 21, 2...</td>\n",
              "      <td>(q_memory_cg, q_memory_pt, q_language_cg, npi8...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>0.812285</td>\n",
              "      <td>[0.8082191780821918, 0.7534246575342466, 0.853...</td>\n",
              "      <td>0, 3, 6, 11, 12, 13, 14, 15, 16, 17, 19, 20, 2...</td>\n",
              "      <td>(q_memory_cg, q_memory_pt, q_language_cg, npi8...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>0.81226</td>\n",
              "      <td>[0.8401826484018264, 0.7488584474885844, 0.840...</td>\n",
              "      <td>0, 3, 6, 10, 11, 12, 13, 14, 15, 16, 17, 19, 2...</td>\n",
              "      <td>(q_memory_cg, q_memory_pt, q_language_cg, npi9...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>0.812256</td>\n",
              "      <td>[0.867579908675799, 0.7442922374429224, 0.8356...</td>\n",
              "      <td>0, 3, 5, 6, 10, 11, 12, 13, 14, 15, 16, 17, 19...</td>\n",
              "      <td>(q_memory_cg, q_memory_pt, moca_recall, q_lang...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>0.814983</td>\n",
              "      <td>[0.8721461187214612, 0.7579908675799086, 0.835...</td>\n",
              "      <td>0, 3, 5, 6, 10, 11, 12, 13, 14, 15, 16, 17, 19...</td>\n",
              "      <td>(q_memory_cg, q_memory_pt, moca_recall, q_lang...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>0.816801</td>\n",
              "      <td>[0.867579908675799, 0.7488584474885844, 0.8675...</td>\n",
              "      <td>0, 3, 5, 6, 10, 11, 12, 13, 14, 15, 16, 17, 19...</td>\n",
              "      <td>(q_memory_cg, q_memory_pt, moca_recall, q_lang...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>0.813165</td>\n",
              "      <td>[0.8356164383561644, 0.7579908675799086, 0.858...</td>\n",
              "      <td>0, 3, 5, 6, 9, 10, 11, 12, 13, 14, 15, 16, 17,...</td>\n",
              "      <td>(q_memory_cg, q_memory_pt, moca_recall, q_lang...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>0.811317</td>\n",
              "      <td>[0.8538812785388128, 0.7625570776255708, 0.831...</td>\n",
              "      <td>0, 3, 4, 5, 6, 9, 10, 11, 12, 13, 14, 15, 16, ...</td>\n",
              "      <td>(q_memory_cg, q_memory_pt, vlt_recall, moca_re...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>0.823219</td>\n",
              "      <td>[0.863013698630137, 0.776255707762557, 0.84474...</td>\n",
              "      <td>0, 3, 4, 5, 6, 7, 9, 10, 11, 12, 13, 14, 15, 1...</td>\n",
              "      <td>(q_memory_cg, q_memory_pt, vlt_recall, moca_re...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>0.820475</td>\n",
              "      <td>[0.867579908675799, 0.771689497716895, 0.83561...</td>\n",
              "      <td>0, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15...</td>\n",
              "      <td>(q_memory_cg, q_memory_pt, vlt_recall, moca_re...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>0.826859</td>\n",
              "      <td>[0.8858447488584474, 0.776255707762557, 0.8447...</td>\n",
              "      <td>0, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15...</td>\n",
              "      <td>(q_memory_cg, q_memory_pt, vlt_recall, moca_re...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>0.825967</td>\n",
              "      <td>[0.8721461187214612, 0.776255707762557, 0.8493...</td>\n",
              "      <td>0, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15...</td>\n",
              "      <td>(q_memory_cg, q_memory_pt, vlt_recall, moca_re...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>0.822318</td>\n",
              "      <td>[0.8767123287671232, 0.7397260273972602, 0.858...</td>\n",
              "      <td>0, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14,...</td>\n",
              "      <td>(q_memory_cg, vlt_delay, q_memory_pt, vlt_reca...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>0.820488</td>\n",
              "      <td>[0.863013698630137, 0.7488584474885844, 0.8493...</td>\n",
              "      <td>0, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14,...</td>\n",
              "      <td>(q_memory_cg, vlt_delay, q_memory_pt, vlt_reca...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>0.814966</td>\n",
              "      <td>[0.8721461187214612, 0.7671232876712328, 0.835...</td>\n",
              "      <td>0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, ...</td>\n",
              "      <td>(q_memory_cg, q_attention_cg, vlt_delay, q_mem...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   avg_score  ...                                      feature_names\n",
              "1   0.803048  ...                                     (q_memory_cg,)\n",
              "2    0.80673  ...                            (q_memory_cg, npi3_sev)\n",
              "3   0.808561  ...                     (q_memory_cg, npi3_sev, hach2)\n",
              "4   0.810387  ...      (q_memory_cg, q_language_cg, npi3_sev, hach2)\n",
              "5   0.809461  ...  (q_memory_cg, q_language_cg, q_orient_pt, npi3...\n",
              "6   0.814949  ...  (q_memory_cg, q_language_cg, q_orient_pt, npi1...\n",
              "7    0.81404  ...  (q_memory_cg, q_language_cg, q_orient_pt, npi1...\n",
              "8   0.813123  ...  (q_memory_cg, q_language_cg, q_orient_pt, npi1...\n",
              "9   0.808557  ...  (q_memory_cg, q_language_cg, q_orient_pt, npi1...\n",
              "10  0.807635  ...  (q_memory_cg, q_language_cg, npi6_sev, q_orien...\n",
              "11  0.805813  ...  (q_memory_cg, q_language_cg, npi6_sev, q_orien...\n",
              "12  0.803081  ...  (q_memory_cg, q_language_cg, npi8_sev, npi6_se...\n",
              "13  0.804883  ...  (q_memory_cg, q_language_cg, npi8_sev, npi7_se...\n",
              "14  0.800316  ...  (q_memory_cg, q_language_cg, npi8_sev, npi7_se...\n",
              "15  0.799386  ...  (q_memory_cg, q_language_cg, npi8_sev, npi7_se...\n",
              "16  0.800342  ...  (q_memory_cg, q_memory_pt, q_language_cg, npi8...\n",
              "17  0.812285  ...  (q_memory_cg, q_memory_pt, q_language_cg, npi8...\n",
              "18   0.81226  ...  (q_memory_cg, q_memory_pt, q_language_cg, npi9...\n",
              "19  0.812256  ...  (q_memory_cg, q_memory_pt, moca_recall, q_lang...\n",
              "20  0.814983  ...  (q_memory_cg, q_memory_pt, moca_recall, q_lang...\n",
              "21  0.816801  ...  (q_memory_cg, q_memory_pt, moca_recall, q_lang...\n",
              "22  0.813165  ...  (q_memory_cg, q_memory_pt, moca_recall, q_lang...\n",
              "23  0.811317  ...  (q_memory_cg, q_memory_pt, vlt_recall, moca_re...\n",
              "24  0.823219  ...  (q_memory_cg, q_memory_pt, vlt_recall, moca_re...\n",
              "25  0.820475  ...  (q_memory_cg, q_memory_pt, vlt_recall, moca_re...\n",
              "26  0.826859  ...  (q_memory_cg, q_memory_pt, vlt_recall, moca_re...\n",
              "27  0.825967  ...  (q_memory_cg, q_memory_pt, vlt_recall, moca_re...\n",
              "28  0.822318  ...  (q_memory_cg, vlt_delay, q_memory_pt, vlt_reca...\n",
              "29  0.820488  ...  (q_memory_cg, vlt_delay, q_memory_pt, vlt_reca...\n",
              "30  0.814966  ...  (q_memory_cg, q_attention_cg, vlt_delay, q_mem...\n",
              "\n",
              "[30 rows x 4 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "zkXPH2i9P777"
      },
      "source": [
        "#### Cost Sums"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4vPgJ1NYI4Ji",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "dill.load_session(f'{dir}/Sessions/costs_importweight30.db')\n",
        "df = pd.read_csv(f'{dir}/Output/cost_sum.csv')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "outputId": "46bc8bd8-adaa-49b8-8f67-8efd99154eac",
        "id": "-VhIIX3uP777",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        }
      },
      "source": [
        "df = df.append(cost_sum(4,'import', 30))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "4  features.\n",
            "Accuracy:  0.8103873218704145\n",
            "Features:  ('q_memory_cg', 'q_language_cg', 'npi3_sev', 'hach2')\n",
            "Cost Sum:  5.5  Avg:  1.375\n",
            "Cost Time:  4  Avg:  1.0\n",
            "Cost Diff:  7  Avg:  1.75\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "piv9U20NPxmJ",
        "colab_type": "code",
        "outputId": "819d7a10-8147-4748-f656-8019d5957902",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        }
      },
      "source": [
        "df = df.append(cost_sum(6,'import', 30))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "6  features.\n",
            "Accuracy:  0.8149492655125755\n",
            "Features:  ('q_memory_cg', 'q_language_cg', 'q_orient_pt', 'npi1_sev', 'npi3_sev', 'hach2')\n",
            "Cost Sum:  8.5  Avg:  1.4166666666666667\n",
            "Cost Time:  6  Avg:  1.0\n",
            "Cost Diff:  11  Avg:  1.8333333333333333\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ywKCmm_Qv44j",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XiqBm_52Nti7",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df.to_csv(f'{dir}/Output/cost_sum.csv', index=False)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "06raLgk5Pxo6",
        "colab_type": "text"
      },
      "source": [
        "#### 15 starting features"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "nTBTH9IPP_Fx",
        "colab": {}
      },
      "source": [
        "features = pd.read_csv(f'{dir}/features_paired.csv')\n",
        "features['weight_x.5'] = (features['avg_score'] + (features['cost_reverse']*.5))/2\n",
        "features.sort_values('weight_x.5', ascending=False)\n",
        "features = features.head(15)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "Q-nBD4EmP_F3",
        "colab": {}
      },
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.model_selection import cross_val_score, StratifiedKFold\n",
        "\n",
        "cv = StratifiedKFold(n_splits=5)\n",
        "\n",
        "clf = RandomForestClassifier(n_estimators=100, max_features=.5)\n",
        "\n",
        "adni_pair_i_X = adni_pair_X.loc[:,features['feature']]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "37vFcMHPP_F8",
        "colab": {}
      },
      "source": [
        "from mlxtend.feature_selection import SequentialFeatureSelector as SFS\n",
        "\n",
        "sfs1 = SFS(clf, \n",
        "           k_features=(15), \n",
        "           forward=True, \n",
        "           floating=False, \n",
        "           verbose=2,\n",
        "           scoring='accuracy',\n",
        "           cv=cv,\n",
        "           n_jobs=-1)\n",
        "\n",
        "sfs1 = sfs1.fit(adni_pair_i_X, adni_pair_y2.values.ravel())"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "tOL3TJnkP_GB",
        "colab": {}
      },
      "source": [
        "f_costs = pd.DataFrame.from_dict(sfs1.subsets_)\n",
        "f_costs = f_costs.transpose()\n",
        "f_costs['feature_idx'] = f_costs['feature_idx'].apply(str).str.replace('(','').str.replace(')','')\n",
        "f_costs.to_csv(f'{dir}/Output/eff_features_paired_importweight_15.csv', index=False)\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QXuqX6JjIJ-X",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "dill.dump_session(f'{dir}/Sessions/costs_importweight15.db')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "outputId": "a2c4d5eb-fa77-4d7e-9e45-f3a7bcc2e000",
        "id": "-YuL7vdNP_GE",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 757
        }
      },
      "source": [
        "f_costs"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>avg_score</th>\n",
              "      <th>cv_scores</th>\n",
              "      <th>feature_idx</th>\n",
              "      <th>feature_names</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.803048</td>\n",
              "      <td>[0.8721461187214612, 0.730593607305936, 0.8401...</td>\n",
              "      <td>0,</td>\n",
              "      <td>(q_memory_cg,)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.810366</td>\n",
              "      <td>[0.867579908675799, 0.7488584474885844, 0.8447...</td>\n",
              "      <td>0, 14</td>\n",
              "      <td>(q_memory_cg, q_orient_pt)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.814949</td>\n",
              "      <td>[0.8584474885844748, 0.7579908675799086, 0.858...</td>\n",
              "      <td>0, 6, 14</td>\n",
              "      <td>(q_memory_cg, q_language_cg, q_orient_pt)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.814941</td>\n",
              "      <td>[0.8584474885844748, 0.7625570776255708, 0.858...</td>\n",
              "      <td>0, 6, 13, 14</td>\n",
              "      <td>(q_memory_cg, q_language_cg, npi6_sev, q_orien...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0.811284</td>\n",
              "      <td>[0.8538812785388128, 0.7534246575342466, 0.858...</td>\n",
              "      <td>0, 6, 11, 13, 14</td>\n",
              "      <td>(q_memory_cg, q_language_cg, npi8_sev, npi6_se...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>0.811275</td>\n",
              "      <td>[0.8538812785388128, 0.7671232876712328, 0.853...</td>\n",
              "      <td>0, 6, 11, 12, 13, 14</td>\n",
              "      <td>(q_memory_cg, q_language_cg, npi8_sev, npi7_se...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>0.810354</td>\n",
              "      <td>[0.8447488584474886, 0.7625570776255708, 0.863...</td>\n",
              "      <td>0, 6, 10, 11, 12, 13, 14</td>\n",
              "      <td>(q_memory_cg, q_language_cg, npi9_sev, npi8_se...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>0.806688</td>\n",
              "      <td>[0.8584474885844748, 0.7442922374429224, 0.867...</td>\n",
              "      <td>0, 5, 6, 10, 11, 12, 13, 14</td>\n",
              "      <td>(q_memory_cg, moca_recall, q_language_cg, npi9...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>0.809449</td>\n",
              "      <td>[0.8493150684931506, 0.7625570776255708, 0.853...</td>\n",
              "      <td>0, 5, 6, 9, 10, 11, 12, 13, 14</td>\n",
              "      <td>(q_memory_cg, moca_recall, q_language_cg, npi5...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>0.802147</td>\n",
              "      <td>[0.8401826484018264, 0.7351598173515982, 0.858...</td>\n",
              "      <td>0, 3, 5, 6, 9, 10, 11, 12, 13, 14</td>\n",
              "      <td>(q_memory_cg, q_memory_pt, moca_recall, q_lang...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>0.803969</td>\n",
              "      <td>[0.817351598173516, 0.776255707762557, 0.84931...</td>\n",
              "      <td>0, 3, 5, 6, 7, 9, 10, 11, 12, 13, 14</td>\n",
              "      <td>(q_memory_cg, q_memory_pt, moca_recall, q_lang...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>0.814065</td>\n",
              "      <td>[0.8401826484018264, 0.7442922374429224, 0.863...</td>\n",
              "      <td>0, 3, 4, 5, 6, 7, 9, 10, 11, 12, 13, 14</td>\n",
              "      <td>(q_memory_cg, q_memory_pt, vlt_recall, moca_re...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>0.823194</td>\n",
              "      <td>[0.8538812785388128, 0.7853881278538812, 0.867...</td>\n",
              "      <td>0, 1, 3, 4, 5, 6, 7, 9, 10, 11, 12, 13, 14</td>\n",
              "      <td>(q_memory_cg, q_attention_cg, q_memory_pt, vlt...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>0.819553</td>\n",
              "      <td>[0.8447488584474886, 0.7625570776255708, 0.863...</td>\n",
              "      <td>0, 1, 2, 3, 4, 5, 6, 7, 9, 10, 11, 12, 13, 14</td>\n",
              "      <td>(q_memory_cg, q_attention_cg, vlt_delay, q_mem...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>0.811317</td>\n",
              "      <td>[0.8447488584474886, 0.7625570776255708, 0.844...</td>\n",
              "      <td>0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14</td>\n",
              "      <td>(q_memory_cg, q_attention_cg, vlt_delay, q_mem...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   avg_score  ...                                      feature_names\n",
              "1   0.803048  ...                                     (q_memory_cg,)\n",
              "2   0.810366  ...                         (q_memory_cg, q_orient_pt)\n",
              "3   0.814949  ...          (q_memory_cg, q_language_cg, q_orient_pt)\n",
              "4   0.814941  ...  (q_memory_cg, q_language_cg, npi6_sev, q_orien...\n",
              "5   0.811284  ...  (q_memory_cg, q_language_cg, npi8_sev, npi6_se...\n",
              "6   0.811275  ...  (q_memory_cg, q_language_cg, npi8_sev, npi7_se...\n",
              "7   0.810354  ...  (q_memory_cg, q_language_cg, npi9_sev, npi8_se...\n",
              "8   0.806688  ...  (q_memory_cg, moca_recall, q_language_cg, npi9...\n",
              "9   0.809449  ...  (q_memory_cg, moca_recall, q_language_cg, npi5...\n",
              "10  0.802147  ...  (q_memory_cg, q_memory_pt, moca_recall, q_lang...\n",
              "11  0.803969  ...  (q_memory_cg, q_memory_pt, moca_recall, q_lang...\n",
              "12  0.814065  ...  (q_memory_cg, q_memory_pt, vlt_recall, moca_re...\n",
              "13  0.823194  ...  (q_memory_cg, q_attention_cg, q_memory_pt, vlt...\n",
              "14  0.819553  ...  (q_memory_cg, q_attention_cg, vlt_delay, q_mem...\n",
              "15  0.811317  ...  (q_memory_cg, q_attention_cg, vlt_delay, q_mem...\n",
              "\n",
              "[15 rows x 4 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "Imm5NK8lP_GM"
      },
      "source": [
        "#### Cost Sums"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UZ-lhefkI5oD",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "dill.load_session(f'{dir}/Sessions/costs_importweight15.db')\n",
        "df = pd.read_csv(f'{dir}/Output/cost_sum.csv')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "outputId": "5dceea4f-1f15-4b62-d6e2-8fd3c9be16c2",
        "id": "E02vxfGzP_GN",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        }
      },
      "source": [
        "df = df.append(cost_sum(3,'import', 15))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "3  features.\n",
            "Accuracy:  0.8149492655125755\n",
            "Features:  ('q_memory_cg', 'q_language_cg', 'q_orient_pt')\n",
            "Cost Sum:  3.0  Avg:  1.0\n",
            "Cost Time:  3  Avg:  1.0\n",
            "Cost Diff:  3  Avg:  1.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5hGJWQeEPxrD",
        "colab_type": "code",
        "outputId": "6560efc6-de89-4548-cb3e-2df441896574",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        }
      },
      "source": [
        "df = df.append(cost_sum(4,'import', 15))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "4  features.\n",
            "Accuracy:  0.8149408485355327\n",
            "Features:  ('q_memory_cg', 'q_language_cg', 'npi6_sev', 'q_orient_pt')\n",
            "Cost Sum:  5.0  Avg:  1.25\n",
            "Cost Time:  4  Avg:  1.0\n",
            "Cost Diff:  6  Avg:  1.5\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "glim3X1VwRLL",
        "colab_type": "code",
        "outputId": "af675aab-bc1d-4954-9774-5d4153277c8c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        }
      },
      "source": [
        "df = df.append(cost_sum(6,'import', 15))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "6  features.\n",
            "Accuracy:  0.8112753129484187\n",
            "Features:  ('q_memory_cg', 'q_language_cg', 'npi8_sev', 'npi7_sev', 'npi6_sev', 'q_orient_pt')\n",
            "Cost Sum:  9.0  Avg:  1.5\n",
            "Cost Time:  6  Avg:  1.0\n",
            "Cost Diff:  12  Avg:  2.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dN5DimcFNunV",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df.to_csv(f'{dir}/Output/cost_sum.csv', index=False)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "j5ti0oksSxx1"
      },
      "source": [
        "### Cost over importance"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "jiDY-YnJTBdt"
      },
      "source": [
        "#### 50 starting features"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "u_j1jrjnSxx3",
        "colab": {}
      },
      "source": [
        "features = pd.read_csv(f'{dir}/features_paired.csv')\n",
        "features['weight_x2'] = (features['avg_score'] + (features['cost_reverse']*2))/2\n",
        "features.sort_values('weight_x2', ascending=False, inplace=True)\n",
        "features = features.head(50)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "ZJ5HS11KTBdt",
        "colab": {}
      },
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.model_selection import cross_val_score, StratifiedKFold\n",
        "\n",
        "cv = StratifiedKFold(n_splits=5)\n",
        "\n",
        "clf = RandomForestClassifier(n_estimators=100, max_features=.5)\n",
        "\n",
        "adni_pair_i_X = adni_pair_X.loc[:,features['feature']]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "-cA1WNfPTBdu",
        "colab": {}
      },
      "source": [
        "from mlxtend.feature_selection import SequentialFeatureSelector as SFS\n",
        "\n",
        "sfs1 = SFS(clf, \n",
        "           k_features=(30), \n",
        "           forward=True, \n",
        "           floating=False, \n",
        "           verbose=2,\n",
        "           scoring='accuracy',\n",
        "           cv=cv,\n",
        "           n_jobs=-1)\n",
        "\n",
        "sfs1 = sfs1.fit(adni_pair_i_X, adni_pair_y2.values.ravel())"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "7olHhN3YofEg",
        "colab": {}
      },
      "source": [
        "f_costs = pd.DataFrame.from_dict(sfs1.subsets_)\n",
        "f_costs = f_costs.transpose()\n",
        "f_costs['feature_idx'] = f_costs['feature_idx'].apply(str).str.replace('(','').str.replace(')','')\n",
        "f_costs.to_csv(f'{dir}/Output/eff_features_paired_costweight_50.csv', index=False)\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OYROghdyIL_l",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "dill.dump_session(f'{dir}/Sessions/costs_costweight50.db')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "outputId": "817f6672-b09f-4a83-ff46-b6b9e99c59e6",
        "id": "U2ireGJRofEi",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "f_costs"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>avg_score</th>\n",
              "      <th>cv_scores</th>\n",
              "      <th>feature_idx</th>\n",
              "      <th>feature_names</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.803048</td>\n",
              "      <td>[0.8721461187214612, 0.730593607305936, 0.8401...</td>\n",
              "      <td>0,</td>\n",
              "      <td>(q_memory_cg,)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.814966</td>\n",
              "      <td>[0.8812785388127854, 0.7442922374429224, 0.844...</td>\n",
              "      <td>0, 10</td>\n",
              "      <td>(q_memory_cg, moca_orient)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.820467</td>\n",
              "      <td>[0.8767123287671232, 0.7534246575342466, 0.853...</td>\n",
              "      <td>0, 10, 47</td>\n",
              "      <td>(q_memory_cg, moca_orient, faq2)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.823215</td>\n",
              "      <td>[0.8767123287671232, 0.7534246575342466, 0.863...</td>\n",
              "      <td>0, 3, 10, 47</td>\n",
              "      <td>(q_memory_cg, q_language_cg, moca_orient, faq2)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0.825954</td>\n",
              "      <td>[0.8767123287671232, 0.7671232876712328, 0.863...</td>\n",
              "      <td>0, 3, 10, 42, 47</td>\n",
              "      <td>(q_memory_cg, q_language_cg, moca_orient, hach...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>0.826872</td>\n",
              "      <td>[0.8767123287671232, 0.7671232876712328, 0.863...</td>\n",
              "      <td>0, 3, 10, 41, 42, 47</td>\n",
              "      <td>(q_memory_cg, q_language_cg, moca_orient, hach...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>0.826872</td>\n",
              "      <td>[0.8767123287671232, 0.7671232876712328, 0.863...</td>\n",
              "      <td>0, 3, 10, 14, 41, 42, 47</td>\n",
              "      <td>(q_memory_cg, q_language_cg, moca_orient, MMNA...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>0.825967</td>\n",
              "      <td>[0.8767123287671232, 0.7579908675799086, 0.863...</td>\n",
              "      <td>0, 3, 6, 10, 14, 41, 42, 47</td>\n",
              "      <td>(q_memory_cg, q_language_cg, PTHAND, moca_orie...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>0.825054</td>\n",
              "      <td>[0.8721461187214612, 0.7579908675799086, 0.863...</td>\n",
              "      <td>0, 3, 6, 10, 14, 31, 41, 42, 47</td>\n",
              "      <td>(q_memory_cg, q_language_cg, PTHAND, moca_orie...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>0.824132</td>\n",
              "      <td>[0.8721461187214612, 0.7579908675799086, 0.863...</td>\n",
              "      <td>0, 3, 6, 10, 14, 31, 41, 42, 43, 47</td>\n",
              "      <td>(q_memory_cg, q_language_cg, PTHAND, moca_orie...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>0.823215</td>\n",
              "      <td>[0.8721461187214612, 0.7579908675799086, 0.863...</td>\n",
              "      <td>0, 3, 6, 10, 14, 31, 41, 42, 43, 45, 47</td>\n",
              "      <td>(q_memory_cg, q_language_cg, PTHAND, moca_orie...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>0.822318</td>\n",
              "      <td>[0.867579908675799, 0.7488584474885844, 0.8584...</td>\n",
              "      <td>0, 3, 6, 10, 14, 21, 31, 41, 42, 43, 45, 47</td>\n",
              "      <td>(q_memory_cg, q_language_cg, PTHAND, moca_orie...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>0.81957</td>\n",
              "      <td>[0.867579908675799, 0.7442922374429224, 0.8584...</td>\n",
              "      <td>0, 3, 6, 10, 14, 21, 31, 38, 41, 42, 43, 45, 47</td>\n",
              "      <td>(q_memory_cg, q_language_cg, PTHAND, moca_orie...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>0.81774</td>\n",
              "      <td>[0.8493150684931506, 0.7488584474885844, 0.867...</td>\n",
              "      <td>0, 3, 6, 10, 14, 21, 31, 34, 38, 41, 42, 43, 4...</td>\n",
              "      <td>(q_memory_cg, q_language_cg, PTHAND, moca_orie...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>0.819595</td>\n",
              "      <td>[0.863013698630137, 0.730593607305936, 0.86301...</td>\n",
              "      <td>0, 3, 6, 10, 14, 21, 24, 31, 34, 38, 41, 42, 4...</td>\n",
              "      <td>(q_memory_cg, q_language_cg, PTHAND, moca_orie...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>0.823253</td>\n",
              "      <td>[0.863013698630137, 0.7442922374429224, 0.8630...</td>\n",
              "      <td>0, 3, 6, 10, 14, 21, 24, 31, 34, 36, 38, 41, 4...</td>\n",
              "      <td>(q_memory_cg, q_language_cg, PTHAND, moca_orie...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>0.821426</td>\n",
              "      <td>[0.8538812785388128, 0.7534246575342466, 0.858...</td>\n",
              "      <td>0, 3, 6, 10, 14, 21, 24, 31, 32, 34, 36, 38, 4...</td>\n",
              "      <td>(q_memory_cg, q_language_cg, PTHAND, moca_orie...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>0.820505</td>\n",
              "      <td>[0.8584474885844748, 0.7442922374429224, 0.863...</td>\n",
              "      <td>0, 3, 6, 10, 14, 21, 24, 31, 32, 34, 36, 38, 3...</td>\n",
              "      <td>(q_memory_cg, q_language_cg, PTHAND, moca_orie...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>0.821451</td>\n",
              "      <td>[0.8493150684931506, 0.7397260273972602, 0.858...</td>\n",
              "      <td>0, 3, 6, 10, 14, 21, 24, 25, 31, 32, 34, 36, 3...</td>\n",
              "      <td>(q_memory_cg, q_language_cg, PTHAND, moca_orie...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>0.818695</td>\n",
              "      <td>[0.863013698630137, 0.7351598173515982, 0.8447...</td>\n",
              "      <td>0, 3, 6, 10, 12, 14, 21, 24, 25, 31, 32, 34, 3...</td>\n",
              "      <td>(q_memory_cg, q_language_cg, PTHAND, moca_orie...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>0.825997</td>\n",
              "      <td>[0.8767123287671232, 0.7397260273972602, 0.863...</td>\n",
              "      <td>0, 3, 6, 10, 12, 14, 21, 24, 25, 29, 31, 32, 3...</td>\n",
              "      <td>(q_memory_cg, q_language_cg, PTHAND, moca_orie...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>0.819616</td>\n",
              "      <td>[0.8538812785388128, 0.730593607305936, 0.8584...</td>\n",
              "      <td>0, 3, 6, 10, 12, 14, 15, 21, 24, 25, 29, 31, 3...</td>\n",
              "      <td>(q_memory_cg, q_language_cg, PTHAND, moca_orie...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>0.814112</td>\n",
              "      <td>[0.8584474885844748, 0.7397260273972602, 0.840...</td>\n",
              "      <td>0, 3, 6, 10, 12, 14, 15, 21, 24, 25, 29, 31, 3...</td>\n",
              "      <td>(q_memory_cg, q_language_cg, PTHAND, moca_orie...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>0.818665</td>\n",
              "      <td>[0.8721461187214612, 0.7351598173515982, 0.849...</td>\n",
              "      <td>0, 3, 6, 10, 12, 14, 15, 21, 24, 25, 29, 31, 3...</td>\n",
              "      <td>(q_memory_cg, q_language_cg, PTHAND, moca_orie...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>0.829671</td>\n",
              "      <td>[0.8858447488584474, 0.7488584474885844, 0.849...</td>\n",
              "      <td>0, 1, 3, 6, 10, 12, 14, 15, 21, 24, 25, 29, 31...</td>\n",
              "      <td>(q_memory_cg, q_attention_cg, q_language_cg, P...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>0.830567</td>\n",
              "      <td>[0.867579908675799, 0.771689497716895, 0.85844...</td>\n",
              "      <td>0, 1, 3, 6, 10, 12, 14, 15, 21, 23, 24, 25, 29...</td>\n",
              "      <td>(q_memory_cg, q_attention_cg, q_language_cg, P...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>0.830533</td>\n",
              "      <td>[0.8812785388127854, 0.7625570776255708, 0.872...</td>\n",
              "      <td>0, 1, 3, 6, 10, 12, 14, 15, 19, 21, 23, 24, 25...</td>\n",
              "      <td>(q_memory_cg, q_attention_cg, q_language_cg, P...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>0.834199</td>\n",
              "      <td>[0.8812785388127854, 0.7579908675799086, 0.881...</td>\n",
              "      <td>0, 1, 3, 6, 10, 12, 14, 15, 19, 21, 23, 24, 25...</td>\n",
              "      <td>(q_memory_cg, q_attention_cg, q_language_cg, P...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>0.841534</td>\n",
              "      <td>[0.8949771689497716, 0.7671232876712328, 0.881...</td>\n",
              "      <td>0, 1, 3, 6, 10, 12, 14, 15, 18, 19, 21, 23, 24...</td>\n",
              "      <td>(q_memory_cg, q_attention_cg, q_language_cg, P...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>0.835125</td>\n",
              "      <td>[0.8904109589041096, 0.771689497716895, 0.8630...</td>\n",
              "      <td>0, 1, 3, 6, 10, 12, 14, 15, 18, 19, 21, 23, 24...</td>\n",
              "      <td>(q_memory_cg, q_attention_cg, q_language_cg, P...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   avg_score  ...                                      feature_names\n",
              "1   0.803048  ...                                     (q_memory_cg,)\n",
              "2   0.814966  ...                         (q_memory_cg, moca_orient)\n",
              "3   0.820467  ...                   (q_memory_cg, moca_orient, faq2)\n",
              "4   0.823215  ...    (q_memory_cg, q_language_cg, moca_orient, faq2)\n",
              "5   0.825954  ...  (q_memory_cg, q_language_cg, moca_orient, hach...\n",
              "6   0.826872  ...  (q_memory_cg, q_language_cg, moca_orient, hach...\n",
              "7   0.826872  ...  (q_memory_cg, q_language_cg, moca_orient, MMNA...\n",
              "8   0.825967  ...  (q_memory_cg, q_language_cg, PTHAND, moca_orie...\n",
              "9   0.825054  ...  (q_memory_cg, q_language_cg, PTHAND, moca_orie...\n",
              "10  0.824132  ...  (q_memory_cg, q_language_cg, PTHAND, moca_orie...\n",
              "11  0.823215  ...  (q_memory_cg, q_language_cg, PTHAND, moca_orie...\n",
              "12  0.822318  ...  (q_memory_cg, q_language_cg, PTHAND, moca_orie...\n",
              "13   0.81957  ...  (q_memory_cg, q_language_cg, PTHAND, moca_orie...\n",
              "14   0.81774  ...  (q_memory_cg, q_language_cg, PTHAND, moca_orie...\n",
              "15  0.819595  ...  (q_memory_cg, q_language_cg, PTHAND, moca_orie...\n",
              "16  0.823253  ...  (q_memory_cg, q_language_cg, PTHAND, moca_orie...\n",
              "17  0.821426  ...  (q_memory_cg, q_language_cg, PTHAND, moca_orie...\n",
              "18  0.820505  ...  (q_memory_cg, q_language_cg, PTHAND, moca_orie...\n",
              "19  0.821451  ...  (q_memory_cg, q_language_cg, PTHAND, moca_orie...\n",
              "20  0.818695  ...  (q_memory_cg, q_language_cg, PTHAND, moca_orie...\n",
              "21  0.825997  ...  (q_memory_cg, q_language_cg, PTHAND, moca_orie...\n",
              "22  0.819616  ...  (q_memory_cg, q_language_cg, PTHAND, moca_orie...\n",
              "23  0.814112  ...  (q_memory_cg, q_language_cg, PTHAND, moca_orie...\n",
              "24  0.818665  ...  (q_memory_cg, q_language_cg, PTHAND, moca_orie...\n",
              "25  0.829671  ...  (q_memory_cg, q_attention_cg, q_language_cg, P...\n",
              "26  0.830567  ...  (q_memory_cg, q_attention_cg, q_language_cg, P...\n",
              "27  0.830533  ...  (q_memory_cg, q_attention_cg, q_language_cg, P...\n",
              "28  0.834199  ...  (q_memory_cg, q_attention_cg, q_language_cg, P...\n",
              "29  0.841534  ...  (q_memory_cg, q_attention_cg, q_language_cg, P...\n",
              "30  0.835125  ...  (q_memory_cg, q_attention_cg, q_language_cg, P...\n",
              "\n",
              "[30 rows x 4 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "ir4BWpVcofEl"
      },
      "source": [
        "#### Cost Sums"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IjEis3DaI7Td",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "dill.load_session(f'{dir}/Sessions/costs_costweight50.db')\n",
        "df = pd.read_csv(f'{dir}/Output/cost_sum.csv')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "outputId": "c46c5038-8ae0-4a26-fef8-2a58bc78937b",
        "id": "qpjABhz8ofEm",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        }
      },
      "source": [
        "df = df.append(cost_sum(3,'cost', 50))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "3  features.\n",
            "Accuracy:  0.8204666132690553\n",
            "Features:  ('q_memory_cg', 'moca_orient', 'faq2')\n",
            "Cost Sum:  3.5  Avg:  1.1666666666666667\n",
            "Cost Time:  3  Avg:  1.0\n",
            "Cost Diff:  4  Avg:  1.3333333333333333\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JH_GmLVUSzpI",
        "colab_type": "code",
        "outputId": "3693c11a-41ad-4613-ed3d-69148bf98dcb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        }
      },
      "source": [
        "df = df.append(cost_sum(4,'cost', 50))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "4  features.\n",
            "Accuracy:  0.8232147562734953\n",
            "Features:  ('q_memory_cg', 'q_language_cg', 'moca_orient', 'faq2')\n",
            "Cost Sum:  4.5  Avg:  1.125\n",
            "Cost Time:  4  Avg:  1.0\n",
            "Cost Diff:  5  Avg:  1.25\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sU_RAGyTfdWw",
        "colab_type": "code",
        "outputId": "26dae102-2538-4a71-dfd5-8c88797542c6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        }
      },
      "source": [
        "df = df.append(cost_sum(5,'cost', 50))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "5  features.\n",
            "Accuracy:  0.8259544823008926\n",
            "Features:  ('q_memory_cg', 'q_language_cg', 'moca_orient', 'hach2', 'faq2')\n",
            "Cost Sum:  6.0  Avg:  1.2\n",
            "Cost Time:  5  Avg:  1.0\n",
            "Cost Diff:  7  Avg:  1.4\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ldZSBOx2wkW0",
        "colab_type": "code",
        "outputId": "6124e65c-215b-48cd-9452-0548b9fab743",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        }
      },
      "source": [
        "df = df.append(cost_sum(6,'cost', 50))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "6  features.\n",
            "Accuracy:  0.826871913493553\n",
            "Features:  ('q_memory_cg', 'q_language_cg', 'moca_orient', 'hach1', 'hach2', 'faq2')\n",
            "Cost Sum:  7.5  Avg:  1.25\n",
            "Cost Time:  6  Avg:  1.0\n",
            "Cost Diff:  9  Avg:  1.5\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yXGO-Uyafe7A",
        "colab_type": "code",
        "outputId": "042158ce-d3e2-4a41-f2d1-b8d873a684f4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        }
      },
      "source": [
        "df = df.append(cost_sum(7,'cost', 50))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "7  features.\n",
            "Accuracy:  0.826871913493553\n",
            "Features:  ('q_memory_cg', 'q_language_cg', 'moca_orient', 'MMNAME', 'hach1', 'hach2', 'faq2')\n",
            "Cost Sum:  8.5  Avg:  1.2142857142857142\n",
            "Cost Time:  7  Avg:  1.0\n",
            "Cost Diff:  10  Avg:  1.4285714285714286\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TNdn-cA3wzTL",
        "colab_type": "code",
        "outputId": "9948f560-847b-479d-8f3c-2b1bc8833912",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        }
      },
      "source": [
        "df = df.append(cost_sum(8,'cost', 50))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "8  features.\n",
            "Accuracy:  0.8259670884614636\n",
            "Features:  ('q_memory_cg', 'q_language_cg', 'PTHAND', 'moca_orient', 'MMNAME', 'hach1', 'hach2', 'faq2')\n",
            "Cost Sum:  9.5  Avg:  1.1875\n",
            "Cost Time:  8  Avg:  1.0\n",
            "Cost Diff:  11  Avg:  1.375\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AVdV9i0LNvyl",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df.to_csv(f'{dir}/Output/cost_sum.csv', index=False)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "RiIRqV95QMr5"
      },
      "source": [
        "#### 30 starting features"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "T4jHB-1cQMr8",
        "colab": {}
      },
      "source": [
        "features = pd.read_csv(f'{dir}/features_paired.csv')\n",
        "features['weight_x2'] = (features['avg_score'] + (features['cost_reverse']*2))/2\n",
        "features.sort_values('weight_x2', ascending=False, inplace=True)\n",
        "features = features.head(30)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "cvS-arElQMr-",
        "colab": {}
      },
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.model_selection import cross_val_score, StratifiedKFold\n",
        "\n",
        "cv = StratifiedKFold(n_splits=5)\n",
        "\n",
        "clf = RandomForestClassifier(n_estimators=100, max_features=.5)\n",
        "\n",
        "adni_pair_i_X = adni_pair_X.loc[:,features['feature']]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "RcFkAfGkQMsC",
        "colab": {}
      },
      "source": [
        "from mlxtend.feature_selection import SequentialFeatureSelector as SFS\n",
        "\n",
        "sfs1 = SFS(clf, \n",
        "           k_features=(30), \n",
        "           forward=True, \n",
        "           floating=False, \n",
        "           verbose=2,\n",
        "           scoring='accuracy',\n",
        "           cv=cv,\n",
        "           n_jobs=-1)\n",
        "\n",
        "sfs1 = sfs1.fit(adni_pair_i_X, adni_pair_y2.values.ravel())"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "hBQGO9NnQMsE",
        "colab": {}
      },
      "source": [
        "f_costs = pd.DataFrame.from_dict(sfs1.subsets_)\n",
        "f_costs = f_costs.transpose()\n",
        "f_costs['feature_idx'] = f_costs['feature_idx'].apply(str).str.replace('(','').str.replace(')','')\n",
        "f_costs.to_csv(f'{dir}/Output/eff_features_paired_costweight_30.csv', index=False)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CMKgwizGIOkV",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "dill.dump_session(f'{dir}/Sessions/costs_costweight30.db')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "outputId": "03cbed98-26cd-4ed3-a9a5-34b7afc5b977",
        "id": "KTsaZ4xTQMsG",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "f_costs"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>avg_score</th>\n",
              "      <th>cv_scores</th>\n",
              "      <th>feature_idx</th>\n",
              "      <th>feature_names</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.803048</td>\n",
              "      <td>[0.8721461187214612, 0.730593607305936, 0.8401...</td>\n",
              "      <td>0,</td>\n",
              "      <td>(q_memory_cg,)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.814966</td>\n",
              "      <td>[0.8812785388127854, 0.7442922374429224, 0.844...</td>\n",
              "      <td>0, 10</td>\n",
              "      <td>(q_memory_cg, moca_orient)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.819549</td>\n",
              "      <td>[0.8767123287671232, 0.7534246575342466, 0.853...</td>\n",
              "      <td>0, 3, 10</td>\n",
              "      <td>(q_memory_cg, q_language_cg, moca_orient)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.824132</td>\n",
              "      <td>[0.8767123287671232, 0.7579908675799086, 0.858...</td>\n",
              "      <td>0, 3, 10, 12</td>\n",
              "      <td>(q_memory_cg, q_language_cg, moca_orient, q_ou...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0.829624</td>\n",
              "      <td>[0.8858447488584474, 0.7579908675799086, 0.867...</td>\n",
              "      <td>0, 3, 10, 12, 29</td>\n",
              "      <td>(q_memory_cg, q_language_cg, moca_orient, q_ou...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>0.829624</td>\n",
              "      <td>[0.8858447488584474, 0.7579908675799086, 0.867...</td>\n",
              "      <td>0, 3, 10, 12, 14, 29</td>\n",
              "      <td>(q_memory_cg, q_language_cg, moca_orient, q_ou...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>0.828711</td>\n",
              "      <td>[0.8812785388127854, 0.7579908675799086, 0.867...</td>\n",
              "      <td>0, 3, 10, 12, 13, 14, 29</td>\n",
              "      <td>(q_memory_cg, q_language_cg, moca_orient, q_ou...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>0.829658</td>\n",
              "      <td>[0.863013698630137, 0.7351598173515982, 0.8858...</td>\n",
              "      <td>0, 2, 3, 10, 12, 13, 14, 29</td>\n",
              "      <td>(q_memory_cg, q_memory_pt, q_language_cg, moca...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>0.829645</td>\n",
              "      <td>[0.863013698630137, 0.7579908675799086, 0.8767...</td>\n",
              "      <td>0, 2, 3, 4, 10, 12, 13, 14, 29</td>\n",
              "      <td>(q_memory_cg, q_memory_pt, q_language_cg, moca...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>0.828715</td>\n",
              "      <td>[0.867579908675799, 0.7534246575342466, 0.8721...</td>\n",
              "      <td>0, 2, 3, 4, 9, 10, 12, 13, 14, 29</td>\n",
              "      <td>(q_memory_cg, q_memory_pt, q_language_cg, moca...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>0.826889</td>\n",
              "      <td>[0.8767123287671232, 0.7671232876712328, 0.858...</td>\n",
              "      <td>0, 2, 3, 4, 9, 10, 12, 13, 14, 27, 29</td>\n",
              "      <td>(q_memory_cg, q_memory_pt, q_language_cg, moca...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>0.833281</td>\n",
              "      <td>[0.8812785388127854, 0.776255707762557, 0.8767...</td>\n",
              "      <td>0, 2, 3, 4, 9, 10, 12, 13, 14, 21, 27, 29</td>\n",
              "      <td>(q_memory_cg, q_memory_pt, q_language_cg, moca...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>0.83236</td>\n",
              "      <td>[0.867579908675799, 0.7808219178082192, 0.8858...</td>\n",
              "      <td>0, 1, 2, 3, 4, 9, 10, 12, 13, 14, 21, 27, 29</td>\n",
              "      <td>(q_memory_cg, q_attention_cg, q_memory_pt, q_l...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>0.836034</td>\n",
              "      <td>[0.8812785388127854, 0.776255707762557, 0.8721...</td>\n",
              "      <td>0, 1, 2, 3, 4, 9, 10, 11, 12, 13, 14, 21, 27, 29</td>\n",
              "      <td>(q_memory_cg, q_attention_cg, q_memory_pt, q_l...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>0.845183</td>\n",
              "      <td>[0.8858447488584474, 0.7853881278538812, 0.885...</td>\n",
              "      <td>0, 1, 2, 3, 4, 9, 10, 11, 12, 13, 14, 19, 21, ...</td>\n",
              "      <td>(q_memory_cg, q_attention_cg, q_memory_pt, q_l...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>0.846096</td>\n",
              "      <td>[0.8767123287671232, 0.7990867579908676, 0.885...</td>\n",
              "      <td>0, 1, 2, 3, 4, 9, 10, 11, 12, 13, 14, 19, 21, ...</td>\n",
              "      <td>(q_memory_cg, q_attention_cg, q_memory_pt, q_l...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>0.843352</td>\n",
              "      <td>[0.8812785388127854, 0.7990867579908676, 0.872...</td>\n",
              "      <td>0, 1, 2, 3, 4, 9, 10, 11, 12, 13, 14, 15, 19, ...</td>\n",
              "      <td>(q_memory_cg, q_attention_cg, q_memory_pt, q_l...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>0.8406</td>\n",
              "      <td>[0.8858447488584474, 0.7808219178082192, 0.876...</td>\n",
              "      <td>0, 1, 2, 3, 4, 9, 10, 11, 12, 13, 14, 15, 17, ...</td>\n",
              "      <td>(q_memory_cg, q_attention_cg, q_memory_pt, q_l...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>0.840604</td>\n",
              "      <td>[0.8812785388127854, 0.7990867579908676, 0.867...</td>\n",
              "      <td>0, 1, 2, 3, 4, 5, 9, 10, 11, 12, 13, 14, 15, 1...</td>\n",
              "      <td>(q_memory_cg, q_attention_cg, q_memory_pt, q_l...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>0.837869</td>\n",
              "      <td>[0.8949771689497716, 0.7534246575342466, 0.876...</td>\n",
              "      <td>0, 1, 2, 3, 4, 5, 9, 10, 11, 12, 13, 14, 15, 1...</td>\n",
              "      <td>(q_memory_cg, q_attention_cg, q_memory_pt, q_l...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>0.844291</td>\n",
              "      <td>[0.8995433789954338, 0.7671232876712328, 0.876...</td>\n",
              "      <td>0, 1, 2, 3, 4, 5, 6, 9, 10, 11, 12, 13, 14, 15...</td>\n",
              "      <td>(q_memory_cg, q_attention_cg, q_memory_pt, q_l...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>0.837864</td>\n",
              "      <td>[0.9041095890410958, 0.7625570776255708, 0.867...</td>\n",
              "      <td>0, 1, 2, 3, 4, 5, 6, 9, 10, 11, 12, 13, 14, 15...</td>\n",
              "      <td>(q_memory_cg, q_attention_cg, q_memory_pt, q_l...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>0.832393</td>\n",
              "      <td>[0.8721461187214612, 0.771689497716895, 0.8630...</td>\n",
              "      <td>0, 1, 2, 3, 4, 5, 6, 9, 10, 11, 12, 13, 14, 15...</td>\n",
              "      <td>(q_memory_cg, q_attention_cg, q_memory_pt, q_l...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>0.840608</td>\n",
              "      <td>[0.8767123287671232, 0.7853881278538812, 0.885...</td>\n",
              "      <td>0, 1, 2, 3, 4, 5, 6, 9, 10, 11, 12, 13, 14, 15...</td>\n",
              "      <td>(q_memory_cg, q_attention_cg, q_memory_pt, q_l...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>0.838778</td>\n",
              "      <td>[0.8858447488584474, 0.771689497716895, 0.8812...</td>\n",
              "      <td>0, 1, 2, 3, 4, 5, 6, 8, 9, 10, 11, 12, 13, 14,...</td>\n",
              "      <td>(q_memory_cg, q_attention_cg, q_memory_pt, q_l...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>0.833294</td>\n",
              "      <td>[0.8812785388127854, 0.7625570776255708, 0.872...</td>\n",
              "      <td>0, 1, 2, 3, 4, 5, 6, 8, 9, 10, 11, 12, 13, 14,...</td>\n",
              "      <td>(q_memory_cg, q_attention_cg, q_memory_pt, q_l...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>0.836934</td>\n",
              "      <td>[0.8995433789954338, 0.771689497716895, 0.8812...</td>\n",
              "      <td>0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, ...</td>\n",
              "      <td>(q_memory_cg, q_attention_cg, q_memory_pt, q_l...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>0.830537</td>\n",
              "      <td>[0.8721461187214612, 0.7671232876712328, 0.876...</td>\n",
              "      <td>0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, ...</td>\n",
              "      <td>(q_memory_cg, q_attention_cg, q_memory_pt, q_l...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>0.822306</td>\n",
              "      <td>[0.863013698630137, 0.7625570776255708, 0.8584...</td>\n",
              "      <td>0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, ...</td>\n",
              "      <td>(q_memory_cg, q_attention_cg, q_memory_pt, q_l...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>0.814966</td>\n",
              "      <td>[0.8538812785388128, 0.7579908675799086, 0.863...</td>\n",
              "      <td>0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, ...</td>\n",
              "      <td>(q_memory_cg, q_attention_cg, q_memory_pt, q_l...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   avg_score  ...                                      feature_names\n",
              "1   0.803048  ...                                     (q_memory_cg,)\n",
              "2   0.814966  ...                         (q_memory_cg, moca_orient)\n",
              "3   0.819549  ...          (q_memory_cg, q_language_cg, moca_orient)\n",
              "4   0.824132  ...  (q_memory_cg, q_language_cg, moca_orient, q_ou...\n",
              "5   0.829624  ...  (q_memory_cg, q_language_cg, moca_orient, q_ou...\n",
              "6   0.829624  ...  (q_memory_cg, q_language_cg, moca_orient, q_ou...\n",
              "7   0.828711  ...  (q_memory_cg, q_language_cg, moca_orient, q_ou...\n",
              "8   0.829658  ...  (q_memory_cg, q_memory_pt, q_language_cg, moca...\n",
              "9   0.829645  ...  (q_memory_cg, q_memory_pt, q_language_cg, moca...\n",
              "10  0.828715  ...  (q_memory_cg, q_memory_pt, q_language_cg, moca...\n",
              "11  0.826889  ...  (q_memory_cg, q_memory_pt, q_language_cg, moca...\n",
              "12  0.833281  ...  (q_memory_cg, q_memory_pt, q_language_cg, moca...\n",
              "13   0.83236  ...  (q_memory_cg, q_attention_cg, q_memory_pt, q_l...\n",
              "14  0.836034  ...  (q_memory_cg, q_attention_cg, q_memory_pt, q_l...\n",
              "15  0.845183  ...  (q_memory_cg, q_attention_cg, q_memory_pt, q_l...\n",
              "16  0.846096  ...  (q_memory_cg, q_attention_cg, q_memory_pt, q_l...\n",
              "17  0.843352  ...  (q_memory_cg, q_attention_cg, q_memory_pt, q_l...\n",
              "18    0.8406  ...  (q_memory_cg, q_attention_cg, q_memory_pt, q_l...\n",
              "19  0.840604  ...  (q_memory_cg, q_attention_cg, q_memory_pt, q_l...\n",
              "20  0.837869  ...  (q_memory_cg, q_attention_cg, q_memory_pt, q_l...\n",
              "21  0.844291  ...  (q_memory_cg, q_attention_cg, q_memory_pt, q_l...\n",
              "22  0.837864  ...  (q_memory_cg, q_attention_cg, q_memory_pt, q_l...\n",
              "23  0.832393  ...  (q_memory_cg, q_attention_cg, q_memory_pt, q_l...\n",
              "24  0.840608  ...  (q_memory_cg, q_attention_cg, q_memory_pt, q_l...\n",
              "25  0.838778  ...  (q_memory_cg, q_attention_cg, q_memory_pt, q_l...\n",
              "26  0.833294  ...  (q_memory_cg, q_attention_cg, q_memory_pt, q_l...\n",
              "27  0.836934  ...  (q_memory_cg, q_attention_cg, q_memory_pt, q_l...\n",
              "28  0.830537  ...  (q_memory_cg, q_attention_cg, q_memory_pt, q_l...\n",
              "29  0.822306  ...  (q_memory_cg, q_attention_cg, q_memory_pt, q_l...\n",
              "30  0.814966  ...  (q_memory_cg, q_attention_cg, q_memory_pt, q_l...\n",
              "\n",
              "[30 rows x 4 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "1VGO5Q5yQMsJ"
      },
      "source": [
        "#### Cost Sums"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gnN1hAorI-Wg",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "dill.load_session(f'{dir}/Sessions/costs_costweight30.db')\n",
        "df = pd.read_csv(f'{dir}/Output/cost_sum.csv')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "outputId": "6b33789d-14d0-46ed-e90d-7a7c8cfff4ab",
        "id": "0_lZUQXEQMsJ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        }
      },
      "source": [
        "df = df.append(cost_sum(4,'cost', 30))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "4  features.\n",
            "Accuracy:  0.8241321874661558\n",
            "Features:  ('q_memory_cg', 'q_language_cg', 'moca_orient', 'q_outsideact_pt')\n",
            "Cost Sum:  4.0  Avg:  1.0\n",
            "Cost Time:  4  Avg:  1.0\n",
            "Cost Diff:  4  Avg:  1.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b-QGuiivQQic",
        "colab_type": "code",
        "outputId": "dbbef010-cfe5-4ac9-da08-7d8488dfedd3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        }
      },
      "source": [
        "df = df.append(cost_sum(5,'cost', 30))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "5  features.\n",
            "Accuracy:  0.8296242456815213\n",
            "Features:  ('q_memory_cg', 'q_language_cg', 'moca_orient', 'q_outsideact_pt', 'faq3')\n",
            "Cost Sum:  5.5  Avg:  1.1\n",
            "Cost Time:  5  Avg:  1.0\n",
            "Cost Diff:  6  Avg:  1.2\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DF7h5WkxjeYP",
        "colab_type": "code",
        "outputId": "2f20bbe8-41e6-46ab-b3ba-f012bf25a0b6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        }
      },
      "source": [
        "df = df.append(cost_sum(6,'cost', 30))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "6  features.\n",
            "Accuracy:  0.8296242456815213\n",
            "Features:  ('q_memory_cg', 'q_language_cg', 'moca_orient', 'q_outsideact_pt', 'MMNAME', 'faq3')\n",
            "Cost Sum:  6.5  Avg:  1.0833333333333333\n",
            "Cost Time:  6  Avg:  1.0\n",
            "Cost Diff:  7  Avg:  1.1666666666666667\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QPJ6Z9ncxI0l",
        "colab_type": "code",
        "outputId": "71dc0c28-29fa-420e-cb94-bc173b9363ac",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        }
      },
      "source": [
        "df = df.append(cost_sum(7,'cost', 30))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "7  features.\n",
            "Accuracy:  0.8287110036723888\n",
            "Features:  ('q_memory_cg', 'q_language_cg', 'moca_orient', 'q_outsideact_pt', 'q_judgmt_pt', 'MMNAME', 'faq3')\n",
            "Cost Sum:  7.5  Avg:  1.0714285714285714\n",
            "Cost Time:  7  Avg:  1.0\n",
            "Cost Diff:  8  Avg:  1.1428571428571428\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d1jHCp-FxNXM",
        "colab_type": "code",
        "outputId": "46c5183c-d6a8-4b4e-9d03-c122c3ee9137",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        }
      },
      "source": [
        "df = df.append(cost_sum(12,'cost', 30))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "12  features.\n",
            "Accuracy:  0.833281480121552\n",
            "Features:  ('q_memory_cg', 'q_memory_pt', 'q_language_cg', 'moca_similarities', 'q_attention_pt', 'moca_orient', 'q_outsideact_pt', 'q_judgmt_pt', 'MMNAME', 'PTRACE', 'verbal_fluency', 'faq3')\n",
            "Cost Sum:  13.0  Avg:  1.0833333333333333\n",
            "Cost Time:  13  Avg:  1.0833333333333333\n",
            "Cost Diff:  13  Avg:  1.0833333333333333\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WaI3QOh6xU3r",
        "colab_type": "code",
        "outputId": "c1cfda27-207d-4b2f-c707-0d05bb32bfcf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 139
        }
      },
      "source": [
        "df = df.append(cost_sum(15,'cost', 30))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "15  features.\n",
            "Accuracy:  0.8451829119149838\n",
            "Features:  ('q_memory_cg', 'q_attention_cg', 'q_memory_pt', 'q_language_cg', 'moca_similarities', 'q_attention_pt', 'moca_orient', 'FAMHISTAD', 'q_outsideact_pt', 'q_judgmt_pt', 'MMNAME', 'q_orient_pt', 'PTRACE', 'verbal_fluency', 'faq3')\n",
            "Cost Sum:  16.0  Avg:  1.0666666666666667\n",
            "Cost Time:  16  Avg:  1.0666666666666667\n",
            "Cost Diff:  16  Avg:  1.0666666666666667\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TdYHIxffxYtL",
        "colab_type": "code",
        "outputId": "7ab373d1-fbd2-454b-a59c-0289f7e3bc3e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 139
        }
      },
      "source": [
        "df = df.append(cost_sum(16,'cost', 30))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "16  features.\n",
            "Accuracy:  0.8460961539241163\n",
            "Features:  ('q_memory_cg', 'q_attention_cg', 'q_memory_pt', 'q_language_cg', 'moca_similarities', 'q_attention_pt', 'moca_orient', 'FAMHISTAD', 'q_outsideact_pt', 'q_judgmt_pt', 'MMNAME', 'q_orient_pt', 'PTRACE', 'q_judgmt_cg', 'verbal_fluency', 'faq3')\n",
            "Cost Sum:  17.0  Avg:  1.0625\n",
            "Cost Time:  17  Avg:  1.0625\n",
            "Cost Diff:  17  Avg:  1.0625\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2DTxpmOKNxDd",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df.to_csv(f'{dir}/Output/cost_sum.csv', index=False)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "BLuoBTv7QQs4"
      },
      "source": [
        "#### 15 starting features"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "8B1mus1bQQs4",
        "colab": {}
      },
      "source": [
        "features = pd.read_csv(f'{dir}/features_paired.csv')\n",
        "features['weight_x2'] = (features['avg_score'] + (features['cost_reverse']*2))/2\n",
        "features.sort_values('weight_x2', ascending=False, inplace=True)\n",
        "features = features.head(15)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "qGeEgKjRQQs5",
        "colab": {}
      },
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.model_selection import cross_val_score, StratifiedKFold\n",
        "\n",
        "cv = StratifiedKFold(n_splits=5)\n",
        "\n",
        "clf = RandomForestClassifier(n_estimators=100, max_features=.5)\n",
        "\n",
        "adni_pair_i_X = adni_pair_X.loc[:,features['feature']]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "exeG31g1QQs6",
        "colab": {}
      },
      "source": [
        "from mlxtend.feature_selection import SequentialFeatureSelector as SFS\n",
        "\n",
        "sfs1 = SFS(clf, \n",
        "           k_features=(15), \n",
        "           forward=True, \n",
        "           floating=False, \n",
        "           verbose=2,\n",
        "           scoring='accuracy',\n",
        "           cv=cv,\n",
        "           n_jobs=-1)\n",
        "\n",
        "sfs1 = sfs1.fit(adni_pair_i_X, adni_pair_y2.values.ravel())"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "V-kCKPZOQQs7",
        "colab": {}
      },
      "source": [
        "f_costs = pd.DataFrame.from_dict(sfs1.subsets_)\n",
        "f_costs = f_costs.transpose()\n",
        "f_costs['feature_idx'] = f_costs['feature_idx'].apply(str).str.replace('(','').str.replace(')','')\n",
        "f_costs.to_csv(f'{dir}/Output/eff_features_paired_costweight_15.csv', index=False)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VvYbb-pbIQK8",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "dill.dump_session(f'{dir}/Sessions/costs_costweight15.db')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "outputId": "93c4d426-6adf-40e0-f4d2-0f042a5c30d8",
        "id": "iLEbgR1PQQs9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 757
        }
      },
      "source": [
        "f_costs"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>avg_score</th>\n",
              "      <th>cv_scores</th>\n",
              "      <th>feature_idx</th>\n",
              "      <th>feature_names</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.803048</td>\n",
              "      <td>[0.8721461187214612, 0.730593607305936, 0.8401...</td>\n",
              "      <td>0,</td>\n",
              "      <td>(q_memory_cg,)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.814966</td>\n",
              "      <td>[0.8812785388127854, 0.7442922374429224, 0.844...</td>\n",
              "      <td>0, 10</td>\n",
              "      <td>(q_memory_cg, moca_orient)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.820462</td>\n",
              "      <td>[0.8812785388127854, 0.7534246575342466, 0.853...</td>\n",
              "      <td>0, 3, 10</td>\n",
              "      <td>(q_memory_cg, q_language_cg, moca_orient)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.823219</td>\n",
              "      <td>[0.8767123287671232, 0.7579908675799086, 0.853...</td>\n",
              "      <td>0, 3, 10, 12</td>\n",
              "      <td>(q_memory_cg, q_language_cg, moca_orient, q_ou...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0.824132</td>\n",
              "      <td>[0.8767123287671232, 0.7579908675799086, 0.858...</td>\n",
              "      <td>0, 3, 6, 10, 12</td>\n",
              "      <td>(q_memory_cg, q_language_cg, PTHAND, moca_orie...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>0.822306</td>\n",
              "      <td>[0.867579908675799, 0.7579908675799086, 0.8584...</td>\n",
              "      <td>0, 3, 6, 10, 12, 14</td>\n",
              "      <td>(q_memory_cg, q_language_cg, PTHAND, moca_orie...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>0.817723</td>\n",
              "      <td>[0.8538812785388128, 0.7579908675799086, 0.858...</td>\n",
              "      <td>0, 2, 3, 6, 10, 12, 14</td>\n",
              "      <td>(q_memory_cg, q_memory_pt, q_language_cg, PTHA...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>0.82231</td>\n",
              "      <td>[0.863013698630137, 0.7442922374429224, 0.8675...</td>\n",
              "      <td>0, 2, 3, 6, 10, 12, 13, 14</td>\n",
              "      <td>(q_memory_cg, q_memory_pt, q_language_cg, PTHA...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>0.822314</td>\n",
              "      <td>[0.8538812785388128, 0.7671232876712328, 0.853...</td>\n",
              "      <td>0, 2, 3, 4, 6, 10, 12, 13, 14</td>\n",
              "      <td>(q_memory_cg, q_memory_pt, q_language_cg, moca...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>0.819574</td>\n",
              "      <td>[0.863013698630137, 0.776255707762557, 0.82648...</td>\n",
              "      <td>0, 2, 3, 4, 6, 10, 11, 12, 13, 14</td>\n",
              "      <td>(q_memory_cg, q_memory_pt, q_language_cg, moca...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>0.824158</td>\n",
              "      <td>[0.8721461187214612, 0.7808219178082192, 0.835...</td>\n",
              "      <td>0, 2, 3, 4, 6, 7, 10, 11, 12, 13, 14</td>\n",
              "      <td>(q_memory_cg, q_memory_pt, q_language_cg, moca...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>0.821426</td>\n",
              "      <td>[0.8584474885844748, 0.7808219178082192, 0.826...</td>\n",
              "      <td>0, 2, 3, 4, 6, 7, 8, 10, 11, 12, 13, 14</td>\n",
              "      <td>(q_memory_cg, q_memory_pt, q_language_cg, moca...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>0.812268</td>\n",
              "      <td>[0.8447488584474886, 0.7625570776255708, 0.821...</td>\n",
              "      <td>0, 2, 3, 4, 6, 7, 8, 9, 10, 11, 12, 13, 14</td>\n",
              "      <td>(q_memory_cg, q_memory_pt, q_language_cg, moca...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>0.807677</td>\n",
              "      <td>[0.8447488584474886, 0.7488584474885844, 0.831...</td>\n",
              "      <td>0, 1, 2, 3, 4, 6, 7, 8, 9, 10, 11, 12, 13, 14</td>\n",
              "      <td>(q_memory_cg, q_attention_cg, q_memory_pt, q_l...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>0.801259</td>\n",
              "      <td>[0.8401826484018264, 0.7397260273972602, 0.821...</td>\n",
              "      <td>0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14</td>\n",
              "      <td>(q_memory_cg, q_attention_cg, q_memory_pt, q_l...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   avg_score  ...                                      feature_names\n",
              "1   0.803048  ...                                     (q_memory_cg,)\n",
              "2   0.814966  ...                         (q_memory_cg, moca_orient)\n",
              "3   0.820462  ...          (q_memory_cg, q_language_cg, moca_orient)\n",
              "4   0.823219  ...  (q_memory_cg, q_language_cg, moca_orient, q_ou...\n",
              "5   0.824132  ...  (q_memory_cg, q_language_cg, PTHAND, moca_orie...\n",
              "6   0.822306  ...  (q_memory_cg, q_language_cg, PTHAND, moca_orie...\n",
              "7   0.817723  ...  (q_memory_cg, q_memory_pt, q_language_cg, PTHA...\n",
              "8    0.82231  ...  (q_memory_cg, q_memory_pt, q_language_cg, PTHA...\n",
              "9   0.822314  ...  (q_memory_cg, q_memory_pt, q_language_cg, moca...\n",
              "10  0.819574  ...  (q_memory_cg, q_memory_pt, q_language_cg, moca...\n",
              "11  0.824158  ...  (q_memory_cg, q_memory_pt, q_language_cg, moca...\n",
              "12  0.821426  ...  (q_memory_cg, q_memory_pt, q_language_cg, moca...\n",
              "13  0.812268  ...  (q_memory_cg, q_memory_pt, q_language_cg, moca...\n",
              "14  0.807677  ...  (q_memory_cg, q_attention_cg, q_memory_pt, q_l...\n",
              "15  0.801259  ...  (q_memory_cg, q_attention_cg, q_memory_pt, q_l...\n",
              "\n",
              "[15 rows x 4 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "-hLIaAadQQs_"
      },
      "source": [
        "#### Cost Sums"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UEGujbgeI_3-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "dill.load_session(f'{dir}/Sessions/costs_costweight15.db')\n",
        "df = pd.read_csv(f'{dir}/Output/cost_sum.csv')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "outputId": "e8084049-eef7-459e-d4a8-7daf51fb89c4",
        "id": "FCkGcsODQQtA",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        }
      },
      "source": [
        "df = df.append(cost_sum(3,'cost', 15))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "3  features.\n",
            "Accuracy:  0.8204624240855273\n",
            "Features:  ('q_memory_cg', 'q_language_cg', 'moca_orient')\n",
            "Cost Sum:  3.0  Avg:  1.0\n",
            "Cost Time:  3  Avg:  1.0\n",
            "Cost Diff:  3  Avg:  1.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sBVeRxVSj_Qk",
        "colab_type": "code",
        "outputId": "d88f69af-010f-4f06-b96f-c65706aed940",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        }
      },
      "source": [
        "df = df.append(cost_sum(4,'cost', 15))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "4  features.\n",
            "Accuracy:  0.8232189454570236\n",
            "Features:  ('q_memory_cg', 'q_language_cg', 'moca_orient', 'q_outsideact_pt')\n",
            "Cost Sum:  4.0  Avg:  1.0\n",
            "Cost Time:  4  Avg:  1.0\n",
            "Cost Diff:  4  Avg:  1.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iEcKhtiPkDou",
        "colab_type": "code",
        "outputId": "13e576a4-e24c-4f92-cae4-e047e72fc0db",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        }
      },
      "source": [
        "df = df.append(cost_sum(5,'cost', 15))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "5  features.\n",
            "Accuracy:  0.8241321874661558\n",
            "Features:  ('q_memory_cg', 'q_language_cg', 'PTHAND', 'moca_orient', 'q_outsideact_pt')\n",
            "Cost Sum:  5.0  Avg:  1.0\n",
            "Cost Time:  5  Avg:  1.0\n",
            "Cost Diff:  5  Avg:  1.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Jb88dlSExrTD",
        "colab_type": "code",
        "outputId": "eb31f8ea-3818-4d3c-e5f5-09ca205b9bca",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        }
      },
      "source": [
        "df = df.append(cost_sum(6,'cost', 15))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "6  features.\n",
            "Accuracy:  0.8223057034478909\n",
            "Features:  ('q_memory_cg', 'q_language_cg', 'PTHAND', 'moca_orient', 'q_outsideact_pt', 'MMNAME')\n",
            "Cost Sum:  6.0  Avg:  1.0\n",
            "Cost Time:  6  Avg:  1.0\n",
            "Cost Diff:  6  Avg:  1.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FxqWIrfMxtbL",
        "colab_type": "code",
        "outputId": "8b12d6d3-1dac-444a-ff72-cbb130ef9c0d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        }
      },
      "source": [
        "df = df.append(cost_sum(11,'cost', 15))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "11  features.\n",
            "Accuracy:  0.8241575156172569\n",
            "Features:  ('q_memory_cg', 'q_memory_pt', 'q_language_cg', 'moca_similarities', 'PTHAND', 'PTRETIRED', 'moca_orient', 'FAMHISTAD', 'q_outsideact_pt', 'q_judgmt_pt', 'MMNAME')\n",
            "Cost Sum:  11.0  Avg:  1.0\n",
            "Cost Time:  11  Avg:  1.0\n",
            "Cost Diff:  11  Avg:  1.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9se8vRr5NyCc",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df.to_csv(f'{dir}/Output/cost_sum.csv', index=False)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zUjzvy_30opV",
        "colab_type": "text"
      },
      "source": [
        "# Feature Testing (ADNI/Clinic)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8313VazYTRcf",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "adni_pair_X = pd.read_csv(f'{dir}/Data/adni_pair_X.csv')\n",
        "clinic_pair_X = pd.read_csv(f'{dir}/Data/sm_clinic_pair_X.csv')\n",
        "\n",
        "adni_pair_y2 = pd.read_csv(f'{dir}/Data/adni_pair_y2.csv')\n",
        "adni_pair_y3 = pd.read_csv(f'{dir}/Data/adni_pair_y3.csv')\n",
        "\n",
        "clinic_pair_y2 = pd.read_csv(f'{dir}/Data/sm_clinic_pair_y2.csv', header=None)\n",
        "clinic_pair_y3 = pd.read_csv(f'{dir}/Data/sm_clinic_pair_y3.csv', header=None)\n",
        "\n",
        "\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "import xgboost\n",
        "from sklearn.metrics import roc_auc_score, accuracy_score\n",
        "from sklearn.neural_network import MLPClassifier\n",
        "\n",
        "def costsum_traintest(feature_row_index, X_train, y_train, X_test, y_test, \n",
        "                      random_state=0):\n",
        "  features = df_costsum.iloc[feature_row_index,3]\n",
        "  X_train = X_train.loc[:,features]\n",
        "  X_test = X_test.loc[:,features]\n",
        "  \n",
        "  rf = RandomForestClassifier(n_estimators=100, max_features=.5, \n",
        "                              random_state=random_state)\n",
        "  rf.fit(X_train, y_train.values.ravel())\n",
        "  y_pred = rf.predict(X_test)\n",
        "  rf_testacc = accuracy_score(y_test, y_pred)\n",
        "  rf_auc = roc_auc_score(y_test, y_pred)\n",
        "  \n",
        "  mlp = MLPClassifier(hidden_layer_sizes=(105,60,15), activation='relu', \n",
        "                      solver='sgd', alpha=.0001, learning_rate_init=.0005, \n",
        "                      max_iter=5000, random_state=random_state)\n",
        "  mlp.fit(X_train, y_train.values.ravel())\n",
        "  y_pred = mlp.predict(X_test)\n",
        "  mlp_testacc = accuracy_score(y_test, y_pred)\n",
        "  mlp_auc = roc_auc_score(y_test, y_pred)\n",
        "  return rf_testacc, rf_auc, mlp_testacc, mlp_auc\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rTxNCTfZ4Z-k",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "for j in [0,10,20,30,40]:\n",
        "  \n",
        "  df_costsum = pd.read_csv(f'{dir}/Output/cost_sum.csv')\n",
        "  df_costsum.sort_values(['cost_sum','accuracy'], ascending=[True, False], \n",
        "                         inplace=True)\n",
        "  df_costsum.drop_duplicates(subset=['features'], inplace=True) # Remove duplicate\n",
        "  df_costsum.reset_index(drop=True, inplace=True) # Fix index order\n",
        "  df_costsum.features = df_costsum.features.str.replace(' ', '')\\\n",
        "                      .str.replace('\\'','').str.replace('\\(','')\\\n",
        "                      .str.replace('\\)','').str.split(',')\n",
        "  for i in df_costsum.index:\n",
        "    df_costsum.loc[i,'rf_score'], df_costsum.loc[i,'rf_auc'], \\\n",
        "    df_costsum.loc[i,'mlp_score'], df_costsum.loc[i,'mlp_auc'] = \\\n",
        "    costsum_traintest(i, adni_pair_X, adni_pair_y2, clinic_pair_X, clinic_pair_y2,\n",
        "                     random_state=j)\n",
        "    \n",
        "    df_costsum.to_csv(f'{dir}/Output/cost_sum_output_s_'+str(j)+'.csv', index=False)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qCzRKCIPeWUe",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# df_costsum.to_csv(f'{dir}/Output/cost_sum_output2_0.csv', index=False)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HRLTNy3Uy-Ky",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df_s_0 = pd.read_csv(f'{dir}/Output/cost_sum_output_s_0.csv')\n",
        "df_s_10 = pd.read_csv(f'{dir}/Output/cost_sum_output_s_10.csv')\n",
        "df_s_20 = pd.read_csv(f'{dir}/Output/cost_sum_output_s_20.csv')\n",
        "df_s_30 = pd.read_csv(f'{dir}/Output/cost_sum_output_s_30.csv')\n",
        "df_s_40 = pd.read_csv(f'{dir}/Output/cost_sum_output_s_40.csv')\n",
        "df_us_0 = pd.read_csv(f'{dir}/Output/cost_sum_output_0.csv')\n",
        "df_us_10 = pd.read_csv(f'{dir}/Output/cost_sum_output_10.csv')\n",
        "df_us_20 = pd.read_csv(f'{dir}/Output/cost_sum_output_20.csv')\n",
        "df_us_30 = pd.read_csv(f'{dir}/Output/cost_sum_output_30.csv')\n",
        "df_us_40 = pd.read_csv(f'{dir}/Output/cost_sum_output_40.csv')\n",
        "\n",
        "df = pd.concat([df_s_0, df_s_10, df_s_20, df_s_30, df_s_40, df_us_0, df_us_10,\n",
        "               df_us_20, df_us_30, df_us_40], axis=0)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rIJvBa773M8B",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df_costsum['rf_score'] = df['rf_score'].groupby(df.index).mean()\n",
        "df_costsum['rf_auc'] = df['rf_auc'].groupby(df.index).mean()\n",
        "df_costsum['mlp_score'] = df['mlp_score'].groupby(df.index).mean()\n",
        "df_costsum['mlp_auc'] = df['mlp_auc'].groupby(df.index).mean()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RmG0D7SU1n0O",
        "colab_type": "code",
        "outputId": "15616816-5aca-4d3b-b344-591fe383f5d7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "df_costsum = df_costsum[df_costsum['rf_score']>0.65]\n",
        "df_costsum"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>weighted</th>\n",
              "      <th>start_#</th>\n",
              "      <th>f_#</th>\n",
              "      <th>features</th>\n",
              "      <th>accuracy</th>\n",
              "      <th>cost_sum</th>\n",
              "      <th>rf_score</th>\n",
              "      <th>rf_auc</th>\n",
              "      <th>mlp_score</th>\n",
              "      <th>mlp_auc</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>cost</td>\n",
              "      <td>15</td>\n",
              "      <td>3</td>\n",
              "      <td>[q_memory_cg, q_language_cg, moca_orient]</td>\n",
              "      <td>0.820462</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.839934</td>\n",
              "      <td>0.844708</td>\n",
              "      <td>0.745545</td>\n",
              "      <td>0.755557</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>import</td>\n",
              "      <td>15</td>\n",
              "      <td>3</td>\n",
              "      <td>[q_memory_cg, q_language_cg, q_orient_pt]</td>\n",
              "      <td>0.814949</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.752475</td>\n",
              "      <td>0.764910</td>\n",
              "      <td>0.762706</td>\n",
              "      <td>0.763971</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>cost</td>\n",
              "      <td>50</td>\n",
              "      <td>3</td>\n",
              "      <td>[q_memory_cg, moca_orient, faq2]</td>\n",
              "      <td>0.820467</td>\n",
              "      <td>3.5</td>\n",
              "      <td>0.765677</td>\n",
              "      <td>0.753517</td>\n",
              "      <td>0.701980</td>\n",
              "      <td>0.694539</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>even</td>\n",
              "      <td>50</td>\n",
              "      <td>4</td>\n",
              "      <td>[q_memory_cg, q_language_cg, moca_orient, q_ou...</td>\n",
              "      <td>0.824132</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.845875</td>\n",
              "      <td>0.849764</td>\n",
              "      <td>0.769307</td>\n",
              "      <td>0.781142</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>cost</td>\n",
              "      <td>50</td>\n",
              "      <td>4</td>\n",
              "      <td>[q_memory_cg, q_language_cg, moca_orient, faq2]</td>\n",
              "      <td>0.823215</td>\n",
              "      <td>4.5</td>\n",
              "      <td>0.781188</td>\n",
              "      <td>0.766719</td>\n",
              "      <td>0.722112</td>\n",
              "      <td>0.721083</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>even</td>\n",
              "      <td>15</td>\n",
              "      <td>5</td>\n",
              "      <td>[q_memory_cg, q_language_cg, PTHAND, moca_orie...</td>\n",
              "      <td>0.824132</td>\n",
              "      <td>5.0</td>\n",
              "      <td>0.846205</td>\n",
              "      <td>0.850045</td>\n",
              "      <td>0.774587</td>\n",
              "      <td>0.782301</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>import</td>\n",
              "      <td>15</td>\n",
              "      <td>4</td>\n",
              "      <td>[q_memory_cg, q_language_cg, npi6_sev, q_orien...</td>\n",
              "      <td>0.814941</td>\n",
              "      <td>5.0</td>\n",
              "      <td>0.752475</td>\n",
              "      <td>0.764910</td>\n",
              "      <td>0.759736</td>\n",
              "      <td>0.761443</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>even</td>\n",
              "      <td>50</td>\n",
              "      <td>5</td>\n",
              "      <td>[q_memory_cg, q_language_cg, moca_orient, q_ou...</td>\n",
              "      <td>0.829624</td>\n",
              "      <td>5.5</td>\n",
              "      <td>0.797030</td>\n",
              "      <td>0.795447</td>\n",
              "      <td>0.753795</td>\n",
              "      <td>0.745191</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>even</td>\n",
              "      <td>30</td>\n",
              "      <td>5</td>\n",
              "      <td>[q_memory_cg, q_language_cg, q_outsideact_pt, ...</td>\n",
              "      <td>0.825963</td>\n",
              "      <td>5.5</td>\n",
              "      <td>0.821452</td>\n",
              "      <td>0.820045</td>\n",
              "      <td>0.753135</td>\n",
              "      <td>0.754038</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>import</td>\n",
              "      <td>30</td>\n",
              "      <td>4</td>\n",
              "      <td>[q_memory_cg, q_language_cg, npi3_sev, hach2]</td>\n",
              "      <td>0.810387</td>\n",
              "      <td>5.5</td>\n",
              "      <td>0.729373</td>\n",
              "      <td>0.721427</td>\n",
              "      <td>0.787789</td>\n",
              "      <td>0.789488</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>cost</td>\n",
              "      <td>50</td>\n",
              "      <td>5</td>\n",
              "      <td>[q_memory_cg, q_language_cg, moca_orient, hach...</td>\n",
              "      <td>0.825954</td>\n",
              "      <td>6.0</td>\n",
              "      <td>0.785479</td>\n",
              "      <td>0.770371</td>\n",
              "      <td>0.722442</td>\n",
              "      <td>0.705166</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>cost</td>\n",
              "      <td>15</td>\n",
              "      <td>6</td>\n",
              "      <td>[q_memory_cg, q_language_cg, PTHAND, moca_orie...</td>\n",
              "      <td>0.822306</td>\n",
              "      <td>6.0</td>\n",
              "      <td>0.836634</td>\n",
              "      <td>0.841899</td>\n",
              "      <td>0.768647</td>\n",
              "      <td>0.771647</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>cost</td>\n",
              "      <td>30</td>\n",
              "      <td>6</td>\n",
              "      <td>[q_memory_cg, q_language_cg, moca_orient, q_ou...</td>\n",
              "      <td>0.829624</td>\n",
              "      <td>6.5</td>\n",
              "      <td>0.784818</td>\n",
              "      <td>0.784220</td>\n",
              "      <td>0.742574</td>\n",
              "      <td>0.735283</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>even</td>\n",
              "      <td>30</td>\n",
              "      <td>6</td>\n",
              "      <td>[q_memory_cg, q_language_cg, MMNAME, q_outside...</td>\n",
              "      <td>0.825963</td>\n",
              "      <td>6.5</td>\n",
              "      <td>0.811551</td>\n",
              "      <td>0.811618</td>\n",
              "      <td>0.747855</td>\n",
              "      <td>0.746566</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>cost</td>\n",
              "      <td>30</td>\n",
              "      <td>7</td>\n",
              "      <td>[q_memory_cg, q_language_cg, moca_orient, q_ou...</td>\n",
              "      <td>0.828711</td>\n",
              "      <td>7.5</td>\n",
              "      <td>0.797690</td>\n",
              "      <td>0.797796</td>\n",
              "      <td>0.780198</td>\n",
              "      <td>0.782670</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>cost</td>\n",
              "      <td>50</td>\n",
              "      <td>6</td>\n",
              "      <td>[q_memory_cg, q_language_cg, moca_orient, hach...</td>\n",
              "      <td>0.826872</td>\n",
              "      <td>7.5</td>\n",
              "      <td>0.784488</td>\n",
              "      <td>0.769528</td>\n",
              "      <td>0.710891</td>\n",
              "      <td>0.694858</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>even</td>\n",
              "      <td>30</td>\n",
              "      <td>7</td>\n",
              "      <td>[q_memory_cg, q_language_cg, MMNAME, q_outside...</td>\n",
              "      <td>0.825963</td>\n",
              "      <td>7.5</td>\n",
              "      <td>0.812871</td>\n",
              "      <td>0.812742</td>\n",
              "      <td>0.753795</td>\n",
              "      <td>0.762461</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>even</td>\n",
              "      <td>15</td>\n",
              "      <td>8</td>\n",
              "      <td>[q_memory_cg, q_memory_pt, q_language_cg, PTHA...</td>\n",
              "      <td>0.825066</td>\n",
              "      <td>8.0</td>\n",
              "      <td>0.810891</td>\n",
              "      <td>0.823562</td>\n",
              "      <td>0.787789</td>\n",
              "      <td>0.813070</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>cost</td>\n",
              "      <td>50</td>\n",
              "      <td>7</td>\n",
              "      <td>[q_memory_cg, q_language_cg, moca_orient, MMNA...</td>\n",
              "      <td>0.826872</td>\n",
              "      <td>8.5</td>\n",
              "      <td>0.782178</td>\n",
              "      <td>0.767562</td>\n",
              "      <td>0.714851</td>\n",
              "      <td>0.703827</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>even</td>\n",
              "      <td>30</td>\n",
              "      <td>8</td>\n",
              "      <td>[q_memory_cg, q_language_cg, q_orient_cg, MMNA...</td>\n",
              "      <td>0.822310</td>\n",
              "      <td>8.5</td>\n",
              "      <td>0.812541</td>\n",
              "      <td>0.812461</td>\n",
              "      <td>0.746535</td>\n",
              "      <td>0.741393</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>import</td>\n",
              "      <td>30</td>\n",
              "      <td>6</td>\n",
              "      <td>[q_memory_cg, q_language_cg, q_orient_pt, npi1...</td>\n",
              "      <td>0.814949</td>\n",
              "      <td>8.5</td>\n",
              "      <td>0.759076</td>\n",
              "      <td>0.770528</td>\n",
              "      <td>0.775578</td>\n",
              "      <td>0.765993</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>import</td>\n",
              "      <td>15</td>\n",
              "      <td>6</td>\n",
              "      <td>[q_memory_cg, q_language_cg, npi8_sev, npi7_se...</td>\n",
              "      <td>0.811275</td>\n",
              "      <td>9.0</td>\n",
              "      <td>0.819472</td>\n",
              "      <td>0.831699</td>\n",
              "      <td>0.773597</td>\n",
              "      <td>0.760258</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>cost</td>\n",
              "      <td>50</td>\n",
              "      <td>8</td>\n",
              "      <td>[q_memory_cg, q_language_cg, PTHAND, moca_orie...</td>\n",
              "      <td>0.825967</td>\n",
              "      <td>9.5</td>\n",
              "      <td>0.782178</td>\n",
              "      <td>0.767562</td>\n",
              "      <td>0.714851</td>\n",
              "      <td>0.706685</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>cost</td>\n",
              "      <td>15</td>\n",
              "      <td>11</td>\n",
              "      <td>[q_memory_cg, q_memory_pt, q_language_cg, moca...</td>\n",
              "      <td>0.824158</td>\n",
              "      <td>11.0</td>\n",
              "      <td>0.815842</td>\n",
              "      <td>0.832063</td>\n",
              "      <td>0.774917</td>\n",
              "      <td>0.805330</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>import</td>\n",
              "      <td>50</td>\n",
              "      <td>7</td>\n",
              "      <td>[q_memory_cg, q_memory_pt, npi1_sev, npi2_sev,...</td>\n",
              "      <td>0.807605</td>\n",
              "      <td>11.0</td>\n",
              "      <td>0.747525</td>\n",
              "      <td>0.773202</td>\n",
              "      <td>0.720462</td>\n",
              "      <td>0.762079</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>cost</td>\n",
              "      <td>30</td>\n",
              "      <td>12</td>\n",
              "      <td>[q_memory_cg, q_memory_pt, q_language_cg, moca...</td>\n",
              "      <td>0.833281</td>\n",
              "      <td>13.0</td>\n",
              "      <td>0.824092</td>\n",
              "      <td>0.835155</td>\n",
              "      <td>0.782838</td>\n",
              "      <td>0.802901</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>cost</td>\n",
              "      <td>15</td>\n",
              "      <td>13</td>\n",
              "      <td>[q_memory_cg, q_memory_pt, q_language_cg, moca...</td>\n",
              "      <td>0.812268</td>\n",
              "      <td>13.0</td>\n",
              "      <td>0.828383</td>\n",
              "      <td>0.844285</td>\n",
              "      <td>0.766337</td>\n",
              "      <td>0.796717</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>even</td>\n",
              "      <td>50</td>\n",
              "      <td>12</td>\n",
              "      <td>[q_memory_cg, q_language_cg, moca_orient, q_ou...</td>\n",
              "      <td>0.827815</td>\n",
              "      <td>15.5</td>\n",
              "      <td>0.817162</td>\n",
              "      <td>0.820085</td>\n",
              "      <td>0.754455</td>\n",
              "      <td>0.748135</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>import</td>\n",
              "      <td>50</td>\n",
              "      <td>10</td>\n",
              "      <td>[q_memory_cg, q_attention_cg, q_memory_pt, npi...</td>\n",
              "      <td>0.818594</td>\n",
              "      <td>15.5</td>\n",
              "      <td>0.752805</td>\n",
              "      <td>0.782461</td>\n",
              "      <td>0.769967</td>\n",
              "      <td>0.804094</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>cost</td>\n",
              "      <td>30</td>\n",
              "      <td>15</td>\n",
              "      <td>[q_memory_cg, q_attention_cg, q_memory_pt, q_l...</td>\n",
              "      <td>0.845183</td>\n",
              "      <td>16.0</td>\n",
              "      <td>0.820792</td>\n",
              "      <td>0.829845</td>\n",
              "      <td>0.800990</td>\n",
              "      <td>0.816564</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>cost</td>\n",
              "      <td>30</td>\n",
              "      <td>16</td>\n",
              "      <td>[q_memory_cg, q_attention_cg, q_memory_pt, q_l...</td>\n",
              "      <td>0.846096</td>\n",
              "      <td>17.0</td>\n",
              "      <td>0.816172</td>\n",
              "      <td>0.825555</td>\n",
              "      <td>0.811551</td>\n",
              "      <td>0.828173</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   weighted  start_#  f_#  ...    rf_auc  mlp_score   mlp_auc\n",
              "0      cost       15    3  ...  0.844708   0.745545  0.755557\n",
              "1    import       15    3  ...  0.764910   0.762706  0.763971\n",
              "2      cost       50    3  ...  0.753517   0.701980  0.694539\n",
              "3      even       50    4  ...  0.849764   0.769307  0.781142\n",
              "4      cost       50    4  ...  0.766719   0.722112  0.721083\n",
              "5      even       15    5  ...  0.850045   0.774587  0.782301\n",
              "6    import       15    4  ...  0.764910   0.759736  0.761443\n",
              "7      even       50    5  ...  0.795447   0.753795  0.745191\n",
              "8      even       30    5  ...  0.820045   0.753135  0.754038\n",
              "9    import       30    4  ...  0.721427   0.787789  0.789488\n",
              "10     cost       50    5  ...  0.770371   0.722442  0.705166\n",
              "11     cost       15    6  ...  0.841899   0.768647  0.771647\n",
              "12     cost       30    6  ...  0.784220   0.742574  0.735283\n",
              "13     even       30    6  ...  0.811618   0.747855  0.746566\n",
              "14     cost       30    7  ...  0.797796   0.780198  0.782670\n",
              "15     cost       50    6  ...  0.769528   0.710891  0.694858\n",
              "16     even       30    7  ...  0.812742   0.753795  0.762461\n",
              "17     even       15    8  ...  0.823562   0.787789  0.813070\n",
              "18     cost       50    7  ...  0.767562   0.714851  0.703827\n",
              "19     even       30    8  ...  0.812461   0.746535  0.741393\n",
              "20   import       30    6  ...  0.770528   0.775578  0.765993\n",
              "21   import       15    6  ...  0.831699   0.773597  0.760258\n",
              "22     cost       50    8  ...  0.767562   0.714851  0.706685\n",
              "23     cost       15   11  ...  0.832063   0.774917  0.805330\n",
              "24   import       50    7  ...  0.773202   0.720462  0.762079\n",
              "25     cost       30   12  ...  0.835155   0.782838  0.802901\n",
              "26     cost       15   13  ...  0.844285   0.766337  0.796717\n",
              "27     even       50   12  ...  0.820085   0.754455  0.748135\n",
              "28   import       50   10  ...  0.782461   0.769967  0.804094\n",
              "29     cost       30   15  ...  0.829845   0.800990  0.816564\n",
              "30     cost       30   16  ...  0.825555   0.811551  0.828173\n",
              "\n",
              "[31 rows x 10 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 207
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x0pchKKL36Qc",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df_costsum.to_csv(f'{dir}/Output/cost_sum_output_avg.csv', index=False)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "H363IaRR1_1j",
        "colab_type": "text"
      },
      "source": [
        "# In-depth analysis"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EEgSptO78QTu",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "import xgboost\n",
        "from sklearn.metrics import roc_auc_score, accuracy_score, confusion_matrix, classification_report\n",
        "from sklearn.neural_network import MLPClassifier\n",
        "\n",
        "df_costsum = pd.read_csv(f'{dir}/Output/cost_sum_output_avg.csv')\n",
        "df_costsum.features = df_costsum.features.str.replace(' ', '')\\\n",
        "                      .str.replace('\\'','').str.replace('\\[','')\\\n",
        "                      .str.replace('\\]','').str.split(',')\n",
        "\n",
        "adni_X = pd.read_csv(f'{dir}/Data/adni_pair_unscaled_X.csv')\n",
        "adni_y = pd.read_csv(f'{dir}/Data/adni_pair_us_y2.csv')\n",
        "clinic_X = pd.read_csv(f'{dir}/Data/sm_clinic_pair_us_X.csv')\n",
        "clinic_y = pd.read_csv(f'{dir}/Data/sm_clinic_pair_y2.csv', header=None)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HHrf5tve_r23",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!pip install scikit-plot\n",
        "import scikitplot as skplt"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uDrhPM6o6akm",
        "colab_type": "text"
      },
      "source": [
        "## Row 0: q_memory_cg, q_language_cg, moca_orient"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r1fMymzN2Bes",
        "colab_type": "code",
        "outputId": "ca897bfd-1ddb-4730-d724-389ea79c3a37",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 289
        }
      },
      "source": [
        "features = df_costsum.iloc[0,3]\n",
        "\n",
        "X_train = adni_X.loc[:,features]\n",
        "X_test = clinic_X.loc[:,features]\n",
        "\n",
        "clf = RandomForestClassifier(n_estimators=300, max_features=.5, \n",
        "                            min_samples_leaf=2, random_state=0)\n",
        "clf.fit(X_train, adni_y.values.ravel())\n",
        "y_pred = clf.predict(X_test)\n",
        "print('AUC: ', roc_auc_score(clinic_y, y_pred))\n",
        "print('\\n')\n",
        "print(classification_report(clinic_y, y_pred, digits=3))\n",
        "print('\\n')\n",
        "print(confusion_matrix(clinic_y, y_pred))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "AUC:  0.8368314606741574\n",
            "\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "         0.0      0.734     0.904     0.810       125\n",
            "         1.0      0.919     0.770     0.838       178\n",
            "\n",
            "    accuracy                          0.825       303\n",
            "   macro avg      0.827     0.837     0.824       303\n",
            "weighted avg      0.843     0.825     0.826       303\n",
            "\n",
            "\n",
            "\n",
            "[[113  12]\n",
            " [ 41 137]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Fr3Q89wT9acH",
        "colab_type": "code",
        "outputId": "70c4086b-04fb-4fc9-b4c7-480b4600e39f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        }
      },
      "source": [
        "\n",
        "adni_proba = clf.predict_proba(X_test)\n",
        "roc = skplt.metrics.plot_roc(clinic_y, adni_proba)\n",
        "# fig = roc.get_figure()\n",
        "# fig.savefig(f'{dir}/Figures/roc_eff_row0_3', dpi=300)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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XJvz69a/kxB3DmPtg+K1bIDfOu5WQyxowsOTTQGTNhKxpmsFut7NgwQImT55M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\n1354bEn02T8RHR0NqVSKZcuWobCwEJMmTUJUVBSGDx9eK5ck5VQPixY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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PhRpNKCL_Efa",
        "colab_type": "code",
        "outputId": "61906185-b9c5-4bf6-9965-502dff69faff",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 289
        }
      },
      "source": [
        "clf = MLPClassifier(hidden_layer_sizes=(105,60,15), activation='relu', \n",
        "                      solver='sgd', alpha=.0001, learning_rate_init=.0005, \n",
        "                      max_iter=5000, random_state=0)\n",
        "clf.fit(X_train, adni_y.values.ravel())\n",
        "y_pred = clf.predict(X_test)\n",
        "print('AUC: ', roc_auc_score(clinic_y, y_pred))\n",
        "print('\\n')\n",
        "print(classification_report(clinic_y, y_pred, digits=3))\n",
        "print('\\n')\n",
        "print(confusion_matrix(clinic_y, y_pred))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "AUC:  0.8044044943820223\n",
            "\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "         0.0      0.709     0.856     0.775       125\n",
            "         1.0      0.882     0.753     0.812       178\n",
            "\n",
            "    accuracy                          0.795       303\n",
            "   macro avg      0.795     0.804     0.794       303\n",
            "weighted avg      0.810     0.795     0.797       303\n",
            "\n",
            "\n",
            "\n",
            "[[107  18]\n",
            " [ 44 134]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nL7un35K2Bhh",
        "colab_type": "code",
        "outputId": "ac5ff33d-c774-4817-c431-78a7e7cc4ada",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "from sklearn.tree import DecisionTreeClassifier\n",
        "\n",
        "clf = DecisionTreeClassifier(random_state=0)\n",
        "clf.fit(X_train, adni_y)\n",
        "\n",
        "import graphviz\n",
        "from sklearn.tree import export_graphviz\n",
        "\n",
        "dot = export_graphviz(clf, out_file=None,\n",
        "                     feature_names=features,\n",
        "                     class_names=['Normal','Impaired'],\n",
        "                     filled=True, rounded=True,\n",
        "                     rotate=True, max_depth=3)\n",
        "\n",
        "graph = graphviz.Source(dot)\n",
        "graph.render(filename='eff_tree_row0_3.gv', directory=f'{dir}/Output/', format='pdf')\n",
        "graph"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<graphviz.files.Source at 0x7f9777b81ef0>"
            ],
            "image/svg+xml": "<?xml version=\"1.0\" encoding=\"UTF-8\" standalone=\"no\"?>\n<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n<!-- Generated by graphviz version 2.40.1 (20161225.0304)\n -->\n<!-- Title: Tree Pages: 1 -->\n<svg width=\"838pt\" height=\"815pt\"\n viewBox=\"0.00 0.00 838.00 815.00\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n<g id=\"graph0\" class=\"graph\" transform=\"scale(1 1) rotate(0) translate(4 811)\">\n<title>Tree</title>\n<polygon fill=\"#ffffff\" stroke=\"transparent\" points=\"-4,4 -4,-811 834,-811 834,4 -4,4\"/>\n<!-- 0 -->\n<g id=\"node1\" class=\"node\">\n<title>0</title>\n<path fill=\"#a8d4f4\" stroke=\"#000000\" d=\"M142,-456.5C142,-456.5 12,-456.5 12,-456.5 6,-456.5 0,-450.5 0,-444.5 0,-444.5 0,-385.5 0,-385.5 0,-379.5 6,-373.5 12,-373.5 12,-373.5 142,-373.5 142,-373.5 148,-373.5 154,-379.5 154,-385.5 154,-385.5 154,-444.5 154,-444.5 154,-450.5 148,-456.5 142,-456.5\"/>\n<text text-anchor=\"middle\" x=\"77\" y=\"-441.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">q_memory_cg &lt;= 0.5</text>\n<text text-anchor=\"middle\" x=\"77\" y=\"-426.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">gini = 0.461</text>\n<text text-anchor=\"middle\" x=\"77\" y=\"-411.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 1167</text>\n<text text-anchor=\"middle\" x=\"77\" y=\"-396.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [420, 747]</text>\n<text text-anchor=\"middle\" x=\"77\" y=\"-381.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">class = Impaired</text>\n</g>\n<!-- 1 -->\n<g id=\"node2\" class=\"node\">\n<title>1</title>\n<path fill=\"#efb489\" stroke=\"#000000\" d=\"M325.5,-546.5C325.5,-546.5 208.5,-546.5 208.5,-546.5 202.5,-546.5 196.5,-540.5 196.5,-534.5 196.5,-534.5 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          },
          "metadata": {
            "tags": []
          },
          "execution_count": 312
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2WpQvQS36grF",
        "colab_type": "text"
      },
      "source": [
        "## Row 3: ['q_memory_cg', 'q_language_cg', 'moca_orient', 'q_outsideact_pt']"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LqzafR376gwL",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "features = df_costsum.iloc[3,3]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "outputId": "642c3a92-0c65-40c1-a952-d52c3a8e76fd",
        "id": "tWGdP-ApBPks",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 289
        }
      },
      "source": [
        "X_train = adni_X.loc[:,features]\n",
        "X_test = clinic_X.loc[:,features]\n",
        "\n",
        "clf = RandomForestClassifier(n_estimators=300, max_features=.5, \n",
        "                            min_samples_leaf=5, bootstrap=True, random_state=0)\n",
        "clf.fit(X_train, adni_y.values.ravel())\n",
        "y_pred = clf.predict(X_test)\n",
        "print('AUC: ', roc_auc_score(clinic_y, y_pred))\n",
        "print('\\n')\n",
        "print(classification_report(clinic_y, y_pred, digits=3))\n",
        "print('\\n')\n",
        "print(confusion_matrix(clinic_y, y_pred))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "AUC:  0.8424494382022473\n",
            "\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "         0.0      0.743     0.904     0.816       125\n",
            "         1.0      0.921     0.781     0.845       178\n",
            "\n",
            "    accuracy                          0.832       303\n",
            "   macro avg      0.832     0.842     0.830       303\n",
            "weighted avg      0.847     0.832     0.833       303\n",
            "\n",
            "\n",
            "\n",
            "[[113  12]\n",
            " [ 39 139]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "outputId": "2e639201-6e48-4f63-e0e4-08b2bc5e73ee",
        "id": "zhPziX36BPky",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        }
      },
      "source": [
        "adni_proba = clf.predict_proba(X_test)\n",
        "roc = skplt.metrics.plot_roc(clinic_y, adni_proba)\n",
        "# fig = roc.get_figure()\n",
        "# fig.savefig(f'{dir}/Figures/roc_eff_row0_3', dpi=300)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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nzp2MGjWKgwe1eJ9HH32U2bNnU69eBctI4ASUUFRCgr8QQB92/qVj9eqjZZ6a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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "outputId": "ea81ecf1-a8d1-45d3-ad29-62adf204844e",
        "id": "K4kvF-OsBPk2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 289
        }
      },
      "source": [
        "clf = MLPClassifier(hidden_layer_sizes=(105,60,15), activation='relu', \n",
        "                      solver='adam', alpha=.0001, learning_rate_init=.005, \n",
        "                      max_iter=5000, random_state=0)\n",
        "clf.fit(X_train, adni_y.values.ravel())\n",
        "y_pred = clf.predict(X_test)\n",
        "print('AUC: ', roc_auc_score(clinic_y, y_pred))\n",
        "print('\\n')\n",
        "print(classification_report(clinic_y, y_pred, digits=3))\n",
        "print('\\n')\n",
        "print(confusion_matrix(clinic_y, y_pred))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "AUC:  0.8532584269662922\n",
            "\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "         0.0      0.752     0.920     0.827       125\n",
            "         1.0      0.933     0.787     0.854       178\n",
            "\n",
            "    accuracy                          0.842       303\n",
            "   macro avg      0.842     0.853     0.840       303\n",
            "weighted avg      0.858     0.842     0.843       303\n",
            "\n",
            "\n",
            "\n",
            "[[115  10]\n",
            " [ 38 140]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4AuMHeNtIE73",
        "colab_type": "code",
        "outputId": "36115f85-b451-4376-da9e-68b7a960f195",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        }
      },
      "source": [
        "adni_proba = clf.predict_proba(X_test)\n",
        "roc = skplt.metrics.plot_roc(clinic_y, adni_proba)\n",
        "fig = roc.get_figure()\n",
        "fig.savefig(f'{dir}/Figures/roc_eff_row3_4_mlp', dpi=300)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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y/ZZr2kejBhVWKrkjR44watQohBBs2LABIQTh4eHMmDHD1aGVSypROFmmOZMr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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "outputId": "cd451f2c-5cb9-4e1d-a43d-1c136ad0e222",
        "id": "1sG6C5clBPk5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "from sklearn.tree import DecisionTreeClassifier\n",
        "\n",
        "clf = DecisionTreeClassifier(random_state=0)\n",
        "clf.fit(X_train, adni_y)\n",
        "\n",
        "import graphviz\n",
        "from sklearn.tree import export_graphviz\n",
        "\n",
        "dot = export_graphviz(clf, out_file=None,\n",
        "                     feature_names=features,\n",
        "                     class_names=['Normal','Impaired'],\n",
        "                     filled=True, rounded=True,\n",
        "                     rotate=True, max_depth=3)\n",
        "\n",
        "graph = graphviz.Source(dot)\n",
        "graph.render(filename='eff_tree_row3_4.gv', directory=f'{dir}/Output/', format='pdf')\n",
        "graph"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<graphviz.files.Source at 0x7f9777b7d198>"
            ],
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          },
          "metadata": {
            "tags": []
          },
          "execution_count": 339
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "6jId-s_FsyS4"
      },
      "source": [
        "## Row X: ['q_memory_cg', 'q_language_cg',  'moca_orient', 'q_memory_pt, q_judgement_pt', 'q_outsideact_pt']"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-TrmPxD4tE2s",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "features = ['q_memory_cg', 'q_language_cg', 'q_memory_pt', 'moca_orient', 'q_judgmt_pt','q_outsideact_pt']"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "outputId": "de8f86d4-f7db-49ff-ad60-a3911bea58bc",
        "id": "jW3bWQYWsyS6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 308
        }
      },
      "source": [
        "X_train = adni_X.loc[:,features]\n",
        "X_test = clinic_X.loc[:,features]\n",
        "\n",
        "clf = RandomForestClassifier(n_estimators=500, max_features=.5, \n",
        "                            min_samples_leaf=1, bootstrap=True, random_state=0)\n",
        "clf.fit(X_train, adni_y.values.ravel())\n",
        "y_pred = clf.predict(X_test)\n",
        "print('AUC: ', roc_auc_score(clinic_y, y_pred))\n",
        "print('\\n')\n",
        "print(classification_report(clinic_y, y_pred, digits=3))\n",
        "print('\\n')\n",
        "print(confusion_matrix(clinic_y, y_pred))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "AUC:  0.7975056179775282\n",
            "\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "         0.0      0.673     0.904     0.771       125\n",
            "         1.0      0.911     0.691     0.786       178\n",
            "\n",
            "    accuracy                          0.779       303\n",
            "   macro avg      0.792     0.798     0.779       303\n",
            "weighted avg      0.813     0.779     0.780       303\n",
            "\n",
            "\n",
            "\n",
            "[[113  12]\n",
            " [ 55 123]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "outputId": "49d19622-bc02-4312-dec8-8602826b6c38",
        "id": "jH2YhAPxsyS9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        }
      },
      "source": [
        "adni_proba = clf.predict_proba(X_test)\n",
        "roc = skplt.metrics.plot_roc(clinic_y, adni_proba)\n",
        "# fig = roc.get_figure()\n",
        "# fig.savefig(f'{dir}/Figures/roc_eff_row0_3', dpi=300)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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280/i3LkkNm16Al0hXUNPnIhn+fJd9u3D+y/BqiOw5yLM6OhQNijI7JAooi1W2hqAdScd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\nl4t9ECmULqdUil2LIrJiLLf6OfLX6YKX70hKSmLBggVERUUB5mic77/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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "outputId": "bbbae035-8c94-41dc-f118-98ed2172e44c",
        "id": "AXjq2m8_syS_",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 308
        }
      },
      "source": [
        "clf = MLPClassifier(hidden_layer_sizes=(105,60,15), activation='relu', \n",
        "                      solver='adam', alpha=.0001, learning_rate_init=.0005, \n",
        "                      max_iter=5000, random_state=0)\n",
        "clf.fit(X_train, adni_y.values.ravel())\n",
        "y_pred = clf.predict(X_test)\n",
        "print('AUC: ', roc_auc_score(clinic_y, y_pred))\n",
        "print('\\n')\n",
        "print(classification_report(clinic_y, y_pred, digits=3))\n",
        "print('\\n')\n",
        "print(confusion_matrix(clinic_y, y_pred))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "AUC:  0.8464494382022472\n",
            "\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "         0.0      0.745     0.912     0.820       125\n",
            "         1.0      0.927     0.781     0.848       178\n",
            "\n",
            "    accuracy                          0.835       303\n",
            "   macro avg      0.836     0.846     0.834       303\n",
            "weighted avg      0.852     0.835     0.836       303\n",
            "\n",
            "\n",
            "\n",
            "[[114  11]\n",
            " [ 39 139]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "outputId": "5bd4f245-9074-4fa8-d3ba-01ecc4f40a49",
        "id": "PL3nC6H_syTC",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "from sklearn.tree import DecisionTreeClassifier\n",
        "\n",
        "clf = DecisionTreeClassifier(random_state=0)\n",
        "clf.fit(X_train, adni_y)\n",
        "\n",
        "import graphviz\n",
        "from sklearn.tree import export_graphviz\n",
        "\n",
        "dot = export_graphviz(clf, out_file=None,\n",
        "                     feature_names=features,\n",
        "                     class_names=['Normal','Impaired'],\n",
        "                     filled=True, rounded=True,\n",
        "                     rotate=True, max_depth=3)\n",
        "\n",
        "graph = graphviz.Source(dot)\n",
        "graph.render(filename='eff_tree_row5_5.gv', directory=f'{dir}/Output/', format='pdf')\n",
        "graph"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<graphviz.files.Source at 0x7f976c3f8dd8>"
            ],
            "image/svg+xml": "<?xml version=\"1.0\" encoding=\"UTF-8\" standalone=\"no\"?>\n<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n<!-- Generated by graphviz version 2.40.1 (20161225.0304)\n -->\n<!-- Title: Tree Pages: 1 -->\n<svg width=\"842pt\" height=\"817pt\"\n viewBox=\"0.00 0.00 842.00 817.00\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n<g id=\"graph0\" class=\"graph\" transform=\"scale(1 1) rotate(0) translate(4 813)\">\n<title>Tree</title>\n<polygon fill=\"#ffffff\" stroke=\"transparent\" points=\"-4,4 -4,-813 838,-813 838,4 -4,4\"/>\n<!-- 0 -->\n<g id=\"node1\" class=\"node\">\n<title>0</title>\n<path fill=\"#a8d4f4\" stroke=\"#000000\" d=\"M142,-465.5C142,-465.5 12,-465.5 12,-465.5 6,-465.5 0,-459.5 0,-453.5 0,-453.5 0,-394.5 0,-394.5 0,-388.5 6,-382.5 12,-382.5 12,-382.5 142,-382.5 142,-382.5 148,-382.5 154,-388.5 154,-394.5 154,-394.5 154,-453.5 154,-453.5 154,-459.5 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fill=\"#000000\">q_memory_cg &lt;= 1.5</text>\n<text text-anchor=\"middle\" x=\"267\" y=\"-334.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">gini = 0.266</text>\n<text text-anchor=\"middle\" x=\"267\" y=\"-319.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 742</text>\n<text text-anchor=\"middle\" x=\"267\" y=\"-304.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [117, 625]</text>\n<text text-anchor=\"middle\" x=\"267\" y=\"-289.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">class = Impaired</text>\n</g>\n<!-- 0&#45;&gt;28 -->\n<g id=\"edge14\" class=\"edge\">\n<title>0&#45;&gt;28</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M154.0064,-383.065C162.8786,-378.3488 171.9681,-373.5169 180.95,-368.7424\"/>\n<polygon fill=\"#000000\" stroke=\"#000000\" points=\"182.6969,-371.7776 189.884,-363.9933 179.4112,-365.5966 182.6969,-371.7776\"/>\n<text 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30</text>\n<text text-anchor=\"middle\" x=\"662\" y=\"-745.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [10, 20]</text>\n<text text-anchor=\"middle\" x=\"662\" y=\"-730.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">class = Impaired</text>\n</g>\n<!-- 2&#45;&gt;3 -->\n<g id=\"edge3\" class=\"edge\">\n<title>2&#45;&gt;3</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M544.2135,-708.6403C553.2636,-712.8939 562.5146,-717.2418 571.6569,-721.5387\"/>\n<polygon fill=\"#000000\" stroke=\"#000000\" points=\"570.2124,-724.7271 580.7515,-725.8132 573.19,-718.3919 570.2124,-724.7271\"/>\n</g>\n<!-- 14 -->\n<g id=\"node7\" class=\"node\">\n<title>14</title>\n<path fill=\"#399de5\" stroke=\"#000000\" d=\"M711,-704C711,-704 613,-704 613,-704 607,-704 601,-698 601,-692 601,-692 601,-648 601,-648 601,-642 607,-636 613,-636 613,-636 711,-636 711,-636 717,-636 723,-642 723,-648 723,-648 723,-692 723,-692 723,-698 717,-704 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          },
          "metadata": {
            "tags": []
          },
          "execution_count": 354
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WMkzA6FD6zNf",
        "colab_type": "text"
      },
      "source": [
        "## Row 5: ['q_memory_cg', 'q_language_cg', 'PTHAND', 'moca_orient', 'q_outsideact_pt']"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mAtdJBKK619-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "features = df_costsum.iloc[5,3]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "outputId": "b55e6765-d45e-407b-fc12-382e6510e112",
        "id": "3kuGBbkCEz_6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 289
        }
      },
      "source": [
        "X_train = adni_X.loc[:,features]\n",
        "X_test = clinic_X.loc[:,features]\n",
        "\n",
        "clf = RandomForestClassifier(n_estimators=300, max_features=.5, \n",
        "                            min_samples_leaf=7, bootstrap=True, random_state=0)\n",
        "clf.fit(X_train, adni_y.values.ravel())\n",
        "y_pred = clf.predict(X_test)\n",
        "print('AUC: ', roc_auc_score(clinic_y, y_pred))\n",
        "print('\\n')\n",
        "print(classification_report(clinic_y, y_pred, digits=3))\n",
        "print('\\n')\n",
        "print(confusion_matrix(clinic_y, y_pred))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "AUC:  0.8424494382022473\n",
            "\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "         0.0      0.743     0.904     0.816       125\n",
            "         1.0      0.921     0.781     0.845       178\n",
            "\n",
            "    accuracy                          0.832       303\n",
            "   macro avg      0.832     0.842     0.830       303\n",
            "weighted avg      0.847     0.832     0.833       303\n",
            "\n",
            "\n",
            "\n",
            "[[113  12]\n",
            " [ 39 139]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "outputId": "49d19622-bc02-4312-dec8-8602826b6c38",
        "id": "r7oBRnB1Ez_9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        }
      },
      "source": [
        "adni_proba = clf.predict_proba(X_test)\n",
        "roc = skplt.metrics.plot_roc(clinic_y, adni_proba)\n",
        "# fig = roc.get_figure()\n",
        "# fig.savefig(f'{dir}/Figures/roc_eff_row0_3', dpi=300)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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280/i3LkkNm16Al0hXUNPnIhn+fJd9u3D+y/BqiOw5yLM6OhQNijI7JAooi1W2hqAdScd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\nl4t9ECmULqdUil2LIrJiLLf6OfLX6YKX70hKSmLBggVERUUB5mic77/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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "outputId": "33b451eb-793e-4bda-a273-6280b643335c",
        "id": "zEScE6vYEz__",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 289
        }
      },
      "source": [
        "clf = MLPClassifier(hidden_layer_sizes=(105,60,15), activation='relu', \n",
        "                      solver='adam', alpha=.0001, learning_rate_init=.0005, \n",
        "                      max_iter=5000, random_state=0)\n",
        "clf.fit(X_train, adni_y.values.ravel())\n",
        "y_pred = clf.predict(X_test)\n",
        "print('AUC: ', roc_auc_score(clinic_y, y_pred))\n",
        "print('\\n')\n",
        "print(classification_report(clinic_y, y_pred, digits=3))\n",
        "print('\\n')\n",
        "print(confusion_matrix(clinic_y, y_pred))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "AUC:  0.8021123595505618\n",
            "\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "         0.0      0.754     0.784     0.769       125\n",
            "         1.0      0.844     0.820     0.832       178\n",
            "\n",
            "    accuracy                          0.805       303\n",
            "   macro avg      0.799     0.802     0.800       303\n",
            "weighted avg      0.807     0.805     0.806       303\n",
            "\n",
            "\n",
            "\n",
            "[[ 98  27]\n",
            " [ 32 146]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "outputId": "5bd4f245-9074-4fa8-d3ba-01ecc4f40a49",
        "id": "Uq1TZyyFE0AC",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "from sklearn.tree import DecisionTreeClassifier\n",
        "\n",
        "clf = DecisionTreeClassifier(random_state=0)\n",
        "clf.fit(X_train, adni_y)\n",
        "\n",
        "import graphviz\n",
        "from sklearn.tree import export_graphviz\n",
        "\n",
        "dot = export_graphviz(clf, out_file=None,\n",
        "                     feature_names=features,\n",
        "                     class_names=['Normal','Impaired'],\n",
        "                     filled=True, rounded=True,\n",
        "                     rotate=True, max_depth=3)\n",
        "\n",
        "graph = graphviz.Source(dot)\n",
        "graph.render(filename='eff_tree_row5_5.gv', directory=f'{dir}/Output/', format='pdf')\n",
        "graph"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<graphviz.files.Source at 0x7f976c3f8dd8>"
            ],
            "image/svg+xml": "<?xml version=\"1.0\" encoding=\"UTF-8\" standalone=\"no\"?>\n<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n<!-- Generated by graphviz version 2.40.1 (20161225.0304)\n -->\n<!-- Title: Tree Pages: 1 -->\n<svg width=\"842pt\" height=\"817pt\"\n viewBox=\"0.00 0.00 842.00 817.00\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n<g id=\"graph0\" class=\"graph\" transform=\"scale(1 1) rotate(0) translate(4 813)\">\n<title>Tree</title>\n<polygon fill=\"#ffffff\" stroke=\"transparent\" points=\"-4,4 -4,-813 838,-813 838,4 -4,4\"/>\n<!-- 0 -->\n<g id=\"node1\" class=\"node\">\n<title>0</title>\n<path fill=\"#a8d4f4\" stroke=\"#000000\" d=\"M142,-465.5C142,-465.5 12,-465.5 12,-465.5 6,-465.5 0,-459.5 0,-453.5 0,-453.5 0,-394.5 0,-394.5 0,-388.5 6,-382.5 12,-382.5 12,-382.5 142,-382.5 142,-382.5 148,-382.5 154,-388.5 154,-394.5 154,-394.5 154,-453.5 154,-453.5 154,-459.5 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fill=\"#000000\">q_memory_cg &lt;= 1.5</text>\n<text text-anchor=\"middle\" x=\"267\" y=\"-334.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">gini = 0.266</text>\n<text text-anchor=\"middle\" x=\"267\" y=\"-319.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 742</text>\n<text text-anchor=\"middle\" x=\"267\" y=\"-304.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [117, 625]</text>\n<text text-anchor=\"middle\" x=\"267\" y=\"-289.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">class = Impaired</text>\n</g>\n<!-- 0&#45;&gt;28 -->\n<g id=\"edge14\" class=\"edge\">\n<title>0&#45;&gt;28</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M154.0064,-383.065C162.8786,-378.3488 171.9681,-373.5169 180.95,-368.7424\"/>\n<polygon fill=\"#000000\" stroke=\"#000000\" points=\"182.6969,-371.7776 189.884,-363.9933 179.4112,-365.5966 182.6969,-371.7776\"/>\n<text 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30</text>\n<text text-anchor=\"middle\" x=\"662\" y=\"-745.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [10, 20]</text>\n<text text-anchor=\"middle\" x=\"662\" y=\"-730.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">class = Impaired</text>\n</g>\n<!-- 2&#45;&gt;3 -->\n<g id=\"edge3\" class=\"edge\">\n<title>2&#45;&gt;3</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M544.2135,-708.6403C553.2636,-712.8939 562.5146,-717.2418 571.6569,-721.5387\"/>\n<polygon fill=\"#000000\" stroke=\"#000000\" points=\"570.2124,-724.7271 580.7515,-725.8132 573.19,-718.3919 570.2124,-724.7271\"/>\n</g>\n<!-- 14 -->\n<g id=\"node7\" class=\"node\">\n<title>14</title>\n<path fill=\"#399de5\" stroke=\"#000000\" d=\"M711,-704C711,-704 613,-704 613,-704 607,-704 601,-698 601,-692 601,-692 601,-648 601,-648 601,-642 607,-636 613,-636 613,-636 711,-636 711,-636 717,-636 723,-642 723,-648 723,-648 723,-692 723,-692 723,-698 717,-704 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          },
          "metadata": {
            "tags": []
          },
          "execution_count": 354
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yiXv7dpd62F_",
        "colab_type": "text"
      },
      "source": [
        "## Row 11: ['q_memory_cg', 'q_language_cg', 'PTHAND', 'moca_orient', 'q_outsideact_pt', 'MMNAME']"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rqsFQ0uw62lN",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "features = df_costsum.iloc[11,3]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "outputId": "e4c42ca9-4f5c-4089-e095-143236ae183a",
        "id": "Xdf0NHNVFTsq",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 289
        }
      },
      "source": [
        "X_train = adni_X.loc[:,features]\n",
        "X_test = clinic_X.loc[:,features]\n",
        "\n",
        "clf = RandomForestClassifier(n_estimators=300, max_features=.5, \n",
        "                            min_samples_leaf=3, bootstrap=True, random_state=0)\n",
        "clf.fit(X_train, adni_y.values.ravel())\n",
        "y_pred = clf.predict(X_test)\n",
        "print('AUC: ', roc_auc_score(clinic_y, y_pred))\n",
        "print('\\n')\n",
        "print(classification_report(clinic_y, y_pred, digits=3))\n",
        "print('\\n')\n",
        "print(confusion_matrix(clinic_y, y_pred))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "AUC:  0.8424494382022473\n",
            "\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "         0.0      0.743     0.904     0.816       125\n",
            "         1.0      0.921     0.781     0.845       178\n",
            "\n",
            "    accuracy                          0.832       303\n",
            "   macro avg      0.832     0.842     0.830       303\n",
            "weighted avg      0.847     0.832     0.833       303\n",
            "\n",
            "\n",
            "\n",
            "[[113  12]\n",
            " [ 39 139]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "outputId": "4d92b8b5-f740-4a77-8960-1c4cfa8c4a4c",
        "id": "5tMG7sxOFTss",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        }
      },
      "source": [
        "adni_proba = clf.predict_proba(X_test)\n",
        "roc = skplt.metrics.plot_roc(clinic_y, adni_proba)\n",
        "# fig = roc.get_figure()\n",
        "# fig.savefig(f'{dir}/Figures/roc_eff_row0_3', dpi=300)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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0hkK6+GqaM2zatIlBgwaxc6fR1te3b19mz55N9eq63UsnirIs+rSRJAC7XTFk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\n4jalSDl79izvvfdesZxbiYXxvSUZp+p15JTN7dmJT8Qv5li9hDxDIBY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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "outputId": "9bc9362e-2d87-4c5e-f92e-1103d44c1c3f",
        "id": "0ky40AmyFTsw",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 289
        }
      },
      "source": [
        "clf = MLPClassifier(hidden_layer_sizes=(105,60,15), activation='relu', \n",
        "                      solver='sgd', alpha=.0001, learning_rate_init=.0005, \n",
        "                      max_iter=5000, random_state=0)\n",
        "clf.fit(X_train, adni_y.values.ravel())\n",
        "y_pred = clf.predict(X_test)\n",
        "print('AUC: ', roc_auc_score(clinic_y, y_pred))\n",
        "print('\\n')\n",
        "print(classification_report(clinic_y, y_pred, digits=3))\n",
        "print('\\n')\n",
        "print(confusion_matrix(clinic_y, y_pred))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "AUC:  0.8175955056179776\n",
            "\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "         0.0      0.712     0.888     0.790       125\n",
            "         1.0      0.905     0.747     0.818       178\n",
            "\n",
            "    accuracy                          0.805       303\n",
            "   macro avg      0.808     0.818     0.804       303\n",
            "weighted avg      0.825     0.805     0.807       303\n",
            "\n",
            "\n",
            "\n",
            "[[111  14]\n",
            " [ 45 133]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "outputId": "b0bb30a3-04fa-4f92-eeda-cd8c7cc3fb51",
        "id": "jcrkbVEkFTsy",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "from sklearn.tree import DecisionTreeClassifier\n",
        "\n",
        "clf = DecisionTreeClassifier(random_state=0)\n",
        "clf.fit(X_train, adni_y)\n",
        "\n",
        "import graphviz\n",
        "from sklearn.tree import export_graphviz\n",
        "\n",
        "dot = export_graphviz(clf, out_file=None,\n",
        "                     feature_names=features,\n",
        "                     class_names=['Normal','Impaired'],\n",
        "                     filled=True, rounded=True,\n",
        "                     rotate=True, max_depth=3)\n",
        "\n",
        "graph = graphviz.Source(dot)\n",
        "graph.render(filename='eff_tree_row11_6.gv', directory=f'{dir}/Output/', format='pdf')\n",
        "graph"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<graphviz.files.Source at 0x7f976a1aa048>"
            ],
            "image/svg+xml": "<?xml version=\"1.0\" encoding=\"UTF-8\" standalone=\"no\"?>\n<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n<!-- Generated by graphviz version 2.40.1 (20161225.0304)\n -->\n<!-- Title: Tree Pages: 1 -->\n<svg width=\"842pt\" height=\"773pt\"\n viewBox=\"0.00 0.00 842.00 773.00\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n<g id=\"graph0\" class=\"graph\" transform=\"scale(1 1) rotate(0) translate(4 769)\">\n<title>Tree</title>\n<polygon fill=\"#ffffff\" stroke=\"transparent\" points=\"-4,4 -4,-769 838,-769 838,4 -4,4\"/>\n<!-- 0 -->\n<g id=\"node1\" class=\"node\">\n<title>0</title>\n<path fill=\"#a8d4f4\" stroke=\"#000000\" d=\"M142,-425.5C142,-425.5 12,-425.5 12,-425.5 6,-425.5 0,-419.5 0,-413.5 0,-413.5 0,-354.5 0,-354.5 0,-348.5 6,-342.5 12,-342.5 12,-342.5 142,-342.5 142,-342.5 148,-342.5 154,-348.5 154,-354.5 154,-354.5 154,-413.5 154,-413.5 154,-419.5 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fill=\"#000000\">value = [1, 0]</text>\n<text text-anchor=\"middle\" x=\"662\" y=\"-704.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">class = Normal</text>\n</g>\n<!-- 2&#45;&gt;3 -->\n<g id=\"edge3\" class=\"edge\">\n<title>2&#45;&gt;3</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M525.5561,-666.8713C548.0747,-677.4551 573.4498,-689.3814 596.1549,-700.0528\"/>\n<polygon fill=\"#000000\" stroke=\"#000000\" points=\"594.8342,-703.2993 605.3732,-704.3854 597.8118,-696.9642 594.8342,-703.2993\"/>\n</g>\n<!-- 4 -->\n<g id=\"node5\" class=\"node\">\n<title>4</title>\n<path fill=\"#86c3ef\" stroke=\"#000000\" d=\"M732,-678.5C732,-678.5 592,-678.5 592,-678.5 586,-678.5 580,-672.5 580,-666.5 580,-666.5 580,-607.5 580,-607.5 580,-601.5 586,-595.5 592,-595.5 592,-595.5 732,-595.5 732,-595.5 738,-595.5 744,-601.5 744,-607.5 744,-607.5 744,-666.5 744,-666.5 744,-672.5 738,-678.5 732,-678.5\"/>\n<text text-anchor=\"middle\" x=\"662\" y=\"-663.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">q_outsideact_pt &lt;= 1.5</text>\n<text text-anchor=\"middle\" x=\"662\" y=\"-648.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">gini = 0.404</text>\n<text text-anchor=\"middle\" x=\"662\" y=\"-633.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 32</text>\n<text text-anchor=\"middle\" x=\"662\" y=\"-618.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [9, 23]</text>\n<text text-anchor=\"middle\" x=\"662\" y=\"-603.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">class = Impaired</text>\n</g>\n<!-- 2&#45;&gt;4 -->\n<g id=\"edge4\" class=\"edge\">\n<title>2&#45;&gt;4</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M525.5561,-637C539.5891,-637 554.7314,-637 569.6456,-637\"/>\n<polygon fill=\"#000000\" stroke=\"#000000\" points=\"569.918,-640.5001 579.918,-637 569.9179,-633.5001 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          },
          "metadata": {
            "tags": []
          },
          "execution_count": 372
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "azasZHNShPmW"
      },
      "source": [
        "## Row 13: ['q_memory_cg', 'q_language_cg', 'MMNAME', 'q_outsideact_pt', 'moca_orient', 'faq8']"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "KmL1FcArhPmY",
        "colab": {}
      },
      "source": [
        "features = df_costsum.iloc[13,3]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "outputId": "0e07fde2-c975-4483-c3fc-e00c9b0456de",
        "id": "qGkbp6PshPmc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 308
        }
      },
      "source": [
        "X_train = adni_X.loc[:,features]\n",
        "X_test = clinic_X.loc[:,features]\n",
        "\n",
        "clf = RandomForestClassifier(n_estimators=300, max_features=None, \n",
        "                            min_samples_leaf=5, bootstrap=True, random_state=0)\n",
        "clf.fit(X_train, adni_y.values.ravel())\n",
        "y_pred = clf.predict(X_test)\n",
        "print('AUC: ', roc_auc_score(clinic_y, y_pred))\n",
        "print('\\n')\n",
        "print(classification_report(clinic_y, y_pred, digits=3))\n",
        "print('\\n')\n",
        "print(confusion_matrix(clinic_y, y_pred))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "AUC:  0.8424494382022473\n",
            "\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "         0.0      0.743     0.904     0.816       125\n",
            "         1.0      0.921     0.781     0.845       178\n",
            "\n",
            "    accuracy                          0.832       303\n",
            "   macro avg      0.832     0.842     0.830       303\n",
            "weighted avg      0.847     0.832     0.833       303\n",
            "\n",
            "\n",
            "\n",
            "[[113  12]\n",
            " [ 39 139]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "outputId": "4d92b8b5-f740-4a77-8960-1c4cfa8c4a4c",
        "id": "rg5gmfxThPmh",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        }
      },
      "source": [
        "adni_proba = clf.predict_proba(X_test)\n",
        "roc = skplt.metrics.plot_roc(clinic_y, adni_proba)\n",
        "# fig = roc.get_figure()\n",
        "# fig.savefig(f'{dir}/Figures/roc_eff_row0_3', dpi=300)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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0hkK6+GqaM2zatIlBgwaxc6fR1te3b19mz55N9eq63UsnirIs+rSRJAC7XTFk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\n4jalSDl79izvvfdesZxbiYXxvSUZp+p15JTN7dmJT8Qv5li9hDxDIBY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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "outputId": "ec3c87fd-1f85-4acf-b934-e51e81ddf9d7",
        "id": "wKFdPyk4hPmn",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 308
        }
      },
      "source": [
        "clf = MLPClassifier(hidden_layer_sizes=(90,50,15), activation='relu', \n",
        "                      solver='adam', alpha=.0001, learning_rate_init=.0005, \n",
        "                      max_iter=10000, random_state=0)\n",
        "clf.fit(X_train, adni_y.values.ravel())\n",
        "y_pred = clf.predict(X_test)\n",
        "print('AUC: ', roc_auc_score(clinic_y, y_pred))\n",
        "print('\\n')\n",
        "print(classification_report(clinic_y, y_pred, digits=3))\n",
        "print('\\n')\n",
        "print(confusion_matrix(clinic_y, y_pred))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "AUC:  0.7667865168539326\n",
            "\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "         0.0      0.683     0.792     0.733       125\n",
            "         1.0      0.835     0.742     0.786       178\n",
            "\n",
            "    accuracy                          0.762       303\n",
            "   macro avg      0.759     0.767     0.760       303\n",
            "weighted avg      0.772     0.762     0.764       303\n",
            "\n",
            "\n",
            "\n",
            "[[ 99  26]\n",
            " [ 46 132]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "outputId": "74c034f2-d906-4a30-a999-606b4e0bca13",
        "id": "JwHJfHRBhPmu",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "from sklearn.tree import DecisionTreeClassifier\n",
        "\n",
        "clf = DecisionTreeClassifier(random_state=0)\n",
        "clf.fit(X_train, adni_y)\n",
        "\n",
        "import graphviz\n",
        "from sklearn.tree import export_graphviz\n",
        "\n",
        "dot = export_graphviz(clf, out_file=None,\n",
        "                     feature_names=features,\n",
        "                     class_names=['Normal','Impaired'],\n",
        "                     filled=True, rounded=True,\n",
        "                     rotate=True, max_depth=3)\n",
        "\n",
        "graph = graphviz.Source(dot)\n",
        "graph.render(filename='eff_tree_row13_6.gv', directory=f'{dir}/Output/', format='pdf')\n",
        "graph"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<graphviz.files.Source at 0x7f2979821080>"
            ],
            "image/svg+xml": "<?xml version=\"1.0\" encoding=\"UTF-8\" standalone=\"no\"?>\n<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n<!-- Generated by graphviz version 2.40.1 (20161225.0304)\n -->\n<!-- Title: Tree Pages: 1 -->\n<svg width=\"842pt\" height=\"759pt\"\n viewBox=\"0.00 0.00 842.00 759.00\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n<g id=\"graph0\" class=\"graph\" transform=\"scale(1 1) rotate(0) translate(4 755)\">\n<title>Tree</title>\n<polygon fill=\"#ffffff\" stroke=\"transparent\" points=\"-4,4 -4,-755 838,-755 838,4 -4,4\"/>\n<!-- 0 -->\n<g id=\"node1\" class=\"node\">\n<title>0</title>\n<path fill=\"#a8d4f4\" stroke=\"#000000\" d=\"M142,-418.5C142,-418.5 12,-418.5 12,-418.5 6,-418.5 0,-412.5 0,-406.5 0,-406.5 0,-347.5 0,-347.5 0,-341.5 6,-335.5 12,-335.5 12,-335.5 142,-335.5 142,-335.5 148,-335.5 154,-341.5 154,-347.5 154,-347.5 154,-406.5 154,-406.5 154,-412.5 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font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [1, 0]</text>\n<text text-anchor=\"middle\" x=\"662\" y=\"-690.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">class = Normal</text>\n</g>\n<!-- 2&#45;&gt;3 -->\n<g id=\"edge3\" class=\"edge\">\n<title>2&#45;&gt;3</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M525.5561,-652.8713C548.0747,-663.4551 573.4498,-675.3814 596.1549,-686.0528\"/>\n<polygon fill=\"#000000\" stroke=\"#000000\" points=\"594.8342,-689.2993 605.3732,-690.3854 597.8118,-682.9642 594.8342,-689.2993\"/>\n</g>\n<!-- 4 -->\n<g id=\"node5\" class=\"node\">\n<title>4</title>\n<path fill=\"#86c3ef\" stroke=\"#000000\" d=\"M732,-664.5C732,-664.5 592,-664.5 592,-664.5 586,-664.5 580,-658.5 580,-652.5 580,-652.5 580,-593.5 580,-593.5 580,-587.5 586,-581.5 592,-581.5 592,-581.5 732,-581.5 732,-581.5 738,-581.5 744,-587.5 744,-593.5 744,-593.5 744,-652.5 744,-652.5 744,-658.5 738,-664.5 732,-664.5\"/>\n<text text-anchor=\"middle\" x=\"662\" y=\"-649.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">q_outsideact_pt &lt;= 1.5</text>\n<text text-anchor=\"middle\" x=\"662\" y=\"-634.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">gini = 0.404</text>\n<text text-anchor=\"middle\" x=\"662\" y=\"-619.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 32</text>\n<text text-anchor=\"middle\" x=\"662\" y=\"-604.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [9, 23]</text>\n<text text-anchor=\"middle\" x=\"662\" y=\"-589.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">class = Impaired</text>\n</g>\n<!-- 2&#45;&gt;4 -->\n<g id=\"edge4\" class=\"edge\">\n<title>2&#45;&gt;4</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M525.5561,-623C539.5891,-623 554.7314,-623 569.6456,-623\"/>\n<polygon fill=\"#000000\" stroke=\"#000000\" 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fill=\"#c0c0c0\" stroke=\"#000000\" d=\"M822,-629C822,-629 792,-629 792,-629 786,-629 780,-623 780,-617 780,-617 780,-605 780,-605 780,-599 786,-593 792,-593 792,-593 822,-593 822,-593 828,-593 834,-599 834,-605 834,-605 834,-617 834,-617 834,-623 828,-629 822,-629\"/>\n<text text-anchor=\"middle\" x=\"807\" y=\"-607.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">(...)</text>\n</g>\n<!-- 4&#45;&gt;14 -->\n<g id=\"edge6\" class=\"edge\">\n<title>4&#45;&gt;14</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M744.2442,-616.1936C753.133,-615.458 761.8228,-614.7388 769.766,-614.0814\"/>\n<polygon fill=\"#000000\" stroke=\"#000000\" points=\"770.0547,-617.5696 779.7319,-613.2567 769.4773,-610.5935 770.0547,-617.5696\"/>\n</g>\n<!-- 16 -->\n<g id=\"node9\" class=\"node\">\n<title>16</title>\n<path fill=\"#eda978\" stroke=\"#000000\" d=\"M732,-563.5C732,-563.5 592,-563.5 592,-563.5 586,-563.5 580,-557.5 580,-551.5 580,-551.5 580,-492.5 580,-492.5 580,-486.5 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class=\"edge\">\n<title>16&#45;&gt;28</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M744.2442,-498.1775C753.2329,-495.5739 762.0181,-493.0292 770.0335,-490.7076\"/>\n<polygon fill=\"#000000\" stroke=\"#000000\" points=\"771.1005,-494.0424 779.7319,-487.8983 769.153,-487.3188 771.1005,-494.0424\"/>\n</g>\n<!-- 31 -->\n<g id=\"node14\" class=\"node\">\n<title>31</title>\n<path fill=\"#83c2ef\" stroke=\"#000000\" d=\"M532,-324.5C532,-324.5 392,-324.5 392,-324.5 386,-324.5 380,-318.5 380,-312.5 380,-312.5 380,-253.5 380,-253.5 380,-247.5 386,-241.5 392,-241.5 392,-241.5 532,-241.5 532,-241.5 538,-241.5 544,-247.5 544,-253.5 544,-253.5 544,-312.5 544,-312.5 544,-318.5 538,-324.5 532,-324.5\"/>\n<text text-anchor=\"middle\" x=\"462\" y=\"-309.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">q_outsideact_pt &lt;= 0.5</text>\n<text text-anchor=\"middle\" x=\"462\" y=\"-294.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">gini = 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font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">gini = 0.442</text>\n<text text-anchor=\"middle\" x=\"662\" y=\"-330.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 285</text>\n<text text-anchor=\"middle\" x=\"662\" y=\"-315.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [94, 191]</text>\n<text text-anchor=\"middle\" x=\"662\" y=\"-300.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">class = Impaired</text>\n</g>\n<!-- 31&#45;&gt;32 -->\n<g id=\"edge14\" class=\"edge\">\n<title>31&#45;&gt;32</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M544.2135,-303.9644C552.9808,-306.2001 561.9366,-308.4838 570.7995,-310.7439\"/>\n<polygon fill=\"#000000\" stroke=\"#000000\" points=\"570.1967,-314.2021 580.7515,-313.2816 571.9264,-307.4192 570.1967,-314.2021\"/>\n</g>\n<!-- 59 -->\n<g id=\"node18\" class=\"node\">\n<title>59</title>\n<path fill=\"#4ba6e7\" 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font-size=\"14.00\" fill=\"#000000\">class = Impaired</text>\n</g>\n<!-- 31&#45;&gt;59 -->\n<g id=\"edge17\" class=\"edge\">\n<title>31&#45;&gt;59</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M544.2135,-262.4466C552.6542,-260.3365 561.2696,-258.1826 569.8087,-256.0478\"/>\n<polygon fill=\"#000000\" stroke=\"#000000\" points=\"570.9301,-259.3753 579.7826,-253.5544 569.2323,-252.5843 570.9301,-259.3753\"/>\n</g>\n<!-- 33 -->\n<g id=\"node16\" class=\"node\">\n<title>33</title>\n<path fill=\"#c0c0c0\" stroke=\"#000000\" d=\"M822,-383C822,-383 792,-383 792,-383 786,-383 780,-377 780,-371 780,-371 780,-359 780,-359 780,-353 786,-347 792,-347 792,-347 822,-347 822,-347 828,-347 834,-353 834,-359 834,-359 834,-371 834,-371 834,-377 828,-383 822,-383\"/>\n<text text-anchor=\"middle\" x=\"807\" y=\"-361.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">(...)</text>\n</g>\n<!-- 32&#45;&gt;33 -->\n<g id=\"edge15\" 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          },
          "metadata": {
            "tags": []
          },
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tOcMFRdC62n8",
        "colab_type": "text"
      },
      "source": [
        "## Row 25: ['q_memory_cg', 'q_memory_pt', 'q_language_cg', 'moca_similarities', 'q_attention_pt', 'moca_orient', 'q_outsideact_pt', 'q_judgmt_pt', 'MMNAME', 'verbal_fluency', 'faq3']"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Uje194KM75J-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "features = df_costsum.iloc[25,3]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L4CdYhqAitL1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Remove Race\n",
        "features = ['q_memory_cg',\n",
        " 'q_memory_pt',\n",
        " 'q_language_cg',\n",
        " 'moca_similarities',\n",
        " 'q_attention_pt',\n",
        " 'moca_orient',\n",
        " 'q_outsideact_pt',\n",
        " 'q_judgmt_pt',\n",
        " 'MMNAME',\n",
        " 'verbal_fluency',\n",
        " 'faq3']"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "outputId": "2f3b26eb-287a-4292-9d70-a05854c40027",
        "id": "EYJT2vSZFXvF",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 308
        }
      },
      "source": [
        "X_train = adni_X.loc[:,features]\n",
        "X_test = clinic_X.loc[:,features]\n",
        "\n",
        "clf = RandomForestClassifier(n_estimators=300, max_features=None, \n",
        "                            min_samples_leaf=18, bootstrap=False, random_state=0)\n",
        "clf.fit(X_train, adni_y.values.ravel())\n",
        "y_pred = clf.predict(X_test)\n",
        "print('AUC: ', roc_auc_score(clinic_y, y_pred))\n",
        "print('\\n')\n",
        "print(classification_report(clinic_y, y_pred, digits=3))\n",
        "print('\\n')\n",
        "print(confusion_matrix(clinic_y, y_pred))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "AUC:  0.8199775280898876\n",
            "\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "         0.0      0.706     0.904     0.793       125\n",
            "         1.0      0.916     0.736     0.816       178\n",
            "\n",
            "    accuracy                          0.805       303\n",
            "   macro avg      0.811     0.820     0.805       303\n",
            "weighted avg      0.830     0.805     0.807       303\n",
            "\n",
            "\n",
            "\n",
            "[[113  12]\n",
            " [ 47 131]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "soiKoD9lFXvI",
        "colab": {}
      },
      "source": [
        "adni_proba = clf.predict_proba(X_test)\n",
        "roc = skplt.metrics.plot_roc(clinic_y, adni_proba)\n",
        "# fig = roc.get_figure()\n",
        "# fig.savefig(f'{dir}/Figures/roc_eff_row0_3', dpi=300)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "outputId": "29dea990-470a-467a-ed23-2011a018f651",
        "id": "XYqTzCqtFXvL",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 308
        }
      },
      "source": [
        "clf = MLPClassifier(hidden_layer_sizes=(105,80,20), activation='relu', \n",
        "                      solver='sgd', alpha=.0001, learning_rate_init=.001, \n",
        "                      max_iter=5000, random_state=0)\n",
        "clf.fit(X_train, adni_y.values.ravel())\n",
        "y_pred = clf.predict(X_test)\n",
        "print('AUC: ', roc_auc_score(clinic_y, y_pred))\n",
        "print('\\n')\n",
        "print(classification_report(clinic_y, y_pred, digits=3))\n",
        "print('\\n')\n",
        "print(confusion_matrix(clinic_y, y_pred))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "AUC:  0.8272134831460674\n",
            "\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "         0.0      0.723     0.896     0.800       125\n",
            "         1.0      0.912     0.758     0.828       178\n",
            "\n",
            "    accuracy                          0.815       303\n",
            "   macro avg      0.817     0.827     0.814       303\n",
            "weighted avg      0.834     0.815     0.817       303\n",
            "\n",
            "\n",
            "\n",
            "[[112  13]\n",
            " [ 43 135]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "outputId": "31790a60-57d8-4ff7-aaca-9bac53217af3",
        "id": "sUm1-BcjFXvN",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "from sklearn.tree import DecisionTreeClassifier\n",
        "\n",
        "clf = DecisionTreeClassifier(random_state=0)\n",
        "clf.fit(X_train, adni_y)\n",
        "\n",
        "import graphviz\n",
        "from sklearn.tree import export_graphviz\n",
        "\n",
        "dot = export_graphviz(clf, out_file=None,\n",
        "                     feature_names=features,\n",
        "                     class_names=['Normal','Impaired'],\n",
        "                     filled=True, rounded=True,\n",
        "                     rotate=True, max_depth=4)\n",
        "\n",
        "graph = graphviz.Source(dot)\n",
        "graph.render(filename='eff_tree_row25_11_4.gv', directory=f'{dir}/Output/', format='pdf')\n",
        "graph"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<graphviz.files.Source at 0x7f2977fc6e80>"
            ],
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x=\"866\" y=\"-1093.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">moca_similarities &lt;= 0.5</text>\n<text text-anchor=\"middle\" x=\"866\" y=\"-1078.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">gini = 0.5</text>\n<text text-anchor=\"middle\" x=\"866\" y=\"-1063.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 4</text>\n<text text-anchor=\"middle\" x=\"866\" y=\"-1048.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [2, 2]</text>\n<text text-anchor=\"middle\" x=\"866\" y=\"-1033.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">class = Normal</text>\n</g>\n<!-- 6&#45;&gt;12 -->\n<g id=\"edge10\" class=\"edge\">\n<title>6&#45;&gt;12</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M743.2124,-1095.2893C751.9311,-1093.2806 760.8615,-1091.2231 769.7339,-1089.179\"/>\n<polygon fill=\"#000000\" stroke=\"#000000\" 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d=\"M1030,-1129C1030,-1129 1000,-1129 1000,-1129 994,-1129 988,-1123 988,-1117 988,-1117 988,-1105 988,-1105 988,-1099 994,-1093 1000,-1093 1000,-1093 1030,-1093 1030,-1093 1036,-1093 1042,-1099 1042,-1105 1042,-1105 1042,-1117 1042,-1117 1042,-1123 1036,-1129 1030,-1129\"/>\n<text text-anchor=\"middle\" x=\"1015\" y=\"-1107.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">(...)</text>\n</g>\n<!-- 12&#45;&gt;13 -->\n<g id=\"edge11\" class=\"edge\">\n<title>12&#45;&gt;13</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M952.2424,-1092.4676C961.2926,-1095.1401 970.1056,-1097.7426 978.129,-1100.1119\"/>\n<polygon fill=\"#000000\" stroke=\"#000000\" points=\"977.2463,-1103.5006 987.8281,-1102.9761 979.2288,-1096.7872 977.2463,-1103.5006\"/>\n</g>\n<!-- 14 -->\n<g id=\"node13\" class=\"node\">\n<title>14</title>\n<path fill=\"#c0c0c0\" stroke=\"#000000\" d=\"M1030,-1075C1030,-1075 1000,-1075 1000,-1075 994,-1075 988,-1069 988,-1063 988,-1063 988,-1051 988,-1051 988,-1045 994,-1039 1000,-1039 1000,-1039 1030,-1039 1030,-1039 1036,-1039 1042,-1045 1042,-1051 1042,-1051 1042,-1063 1042,-1063 1042,-1069 1036,-1075 1030,-1075\"/>\n<text text-anchor=\"middle\" x=\"1015\" y=\"-1053.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">(...)</text>\n</g>\n<!-- 12&#45;&gt;14 -->\n<g id=\"edge12\" class=\"edge\">\n<title>12&#45;&gt;14</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M952.2424,-1061.2119C961.0915,-1060.618 969.7138,-1060.0393 977.593,-1059.5105\"/>\n<polygon fill=\"#000000\" stroke=\"#000000\" points=\"978.085,-1062.9855 987.8281,-1058.8236 977.6162,-1056.0012 978.085,-1062.9855\"/>\n</g>\n<!-- 18 -->\n<g id=\"node15\" class=\"node\">\n<title>18</title>\n<path fill=\"#eca26e\" stroke=\"#000000\" d=\"M725,-906.5C725,-906.5 599,-906.5 599,-906.5 593,-906.5 587,-900.5 587,-894.5 587,-894.5 587,-835.5 587,-835.5 587,-829.5 593,-823.5 599,-823.5 599,-823.5 725,-823.5 725,-823.5 731,-823.5 737,-829.5 737,-835.5 737,-835.5 737,-894.5 737,-894.5 737,-900.5 731,-906.5 725,-906.5\"/>\n<text text-anchor=\"middle\" x=\"662\" y=\"-891.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">q_memory_pt &lt;= 0.5</text>\n<text text-anchor=\"middle\" x=\"662\" y=\"-876.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">gini = 0.332</text>\n<text text-anchor=\"middle\" x=\"662\" y=\"-861.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 343</text>\n<text text-anchor=\"middle\" x=\"662\" y=\"-846.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [271, 72]</text>\n<text text-anchor=\"middle\" x=\"662\" y=\"-831.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">class = Normal</text>\n</g>\n<!-- 17&#45;&gt;18 -->\n<g id=\"edge14\" class=\"edge\">\n<title>17&#45;&gt;18</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M537.0416,-833.1356C550.0257,-836.4465 563.6094,-839.9104 576.8388,-843.2839\"/>\n<polygon fill=\"#000000\" stroke=\"#000000\" points=\"576.3157,-846.7624 586.8705,-845.842 578.0454,-839.9795 576.3157,-846.7624\"/>\n</g>\n<!-- 209 -->\n<g id=\"node22\" class=\"node\">\n<title>209</title>\n<path fill=\"#daedfa\" stroke=\"#000000\" d=\"M732,-805.5C732,-805.5 592,-805.5 592,-805.5 586,-805.5 580,-799.5 580,-793.5 580,-793.5 580,-734.5 580,-734.5 580,-728.5 586,-722.5 592,-722.5 592,-722.5 732,-722.5 732,-722.5 738,-722.5 744,-728.5 744,-734.5 744,-734.5 744,-793.5 744,-793.5 744,-799.5 738,-805.5 732,-805.5\"/>\n<text text-anchor=\"middle\" x=\"662\" y=\"-790.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">verbal_fluency &lt;= 23.5</text>\n<text text-anchor=\"middle\" x=\"662\" y=\"-775.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">gini = 0.495</text>\n<text text-anchor=\"middle\" x=\"662\" y=\"-760.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 49</text>\n<text text-anchor=\"middle\" x=\"662\" y=\"-745.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [22, 27]</text>\n<text text-anchor=\"middle\" x=\"662\" y=\"-730.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">class = Impaired</text>\n</g>\n<!-- 17&#45;&gt;209 -->\n<g id=\"edge21\" class=\"edge\">\n<title>17&#45;&gt;209</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M537.0416,-795.2396C547.7344,-792.5664 558.8338,-789.7915 569.8071,-787.0482\"/>\n<polygon fill=\"#000000\" stroke=\"#000000\" points=\"570.9814,-790.3624 579.8339,-784.5415 569.2836,-783.5715 570.9814,-790.3624\"/>\n</g>\n<!-- 19 -->\n<g id=\"node16\" class=\"node\">\n<title>19</title>\n<path fill=\"#e99456\" stroke=\"#000000\" d=\"M936,-1007.5C936,-1007.5 796,-1007.5 796,-1007.5 790,-1007.5 784,-1001.5 784,-995.5 784,-995.5 784,-936.5 784,-936.5 784,-930.5 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class=\"edge\">\n<title>82&#45;&gt;208</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M947.0404,-847.0515C957.8674,-844.6535 968.5089,-842.2967 978.0404,-840.1857\"/>\n<polygon fill=\"#000000\" stroke=\"#000000\" points=\"978.8973,-843.5808 987.9038,-838.0012 977.3836,-836.7464 978.8973,-843.5808\"/>\n</g>\n<!-- 210 -->\n<g id=\"node23\" class=\"node\">\n<title>210</title>\n<path fill=\"#b6dbf5\" stroke=\"#000000\" d=\"M924.5,-805.5C924.5,-805.5 807.5,-805.5 807.5,-805.5 801.5,-805.5 795.5,-799.5 795.5,-793.5 795.5,-793.5 795.5,-734.5 795.5,-734.5 795.5,-728.5 801.5,-722.5 807.5,-722.5 807.5,-722.5 924.5,-722.5 924.5,-722.5 930.5,-722.5 936.5,-728.5 936.5,-734.5 936.5,-734.5 936.5,-793.5 936.5,-793.5 936.5,-799.5 930.5,-805.5 924.5,-805.5\"/>\n<text text-anchor=\"middle\" x=\"866\" y=\"-790.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">q_judgmt_pt &lt;= 0.5</text>\n<text text-anchor=\"middle\" x=\"866\" y=\"-775.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">gini = 0.474</text>\n<text text-anchor=\"middle\" x=\"866\" y=\"-760.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 44</text>\n<text text-anchor=\"middle\" x=\"866\" y=\"-745.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [17, 27]</text>\n<text text-anchor=\"middle\" x=\"866\" y=\"-730.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">class = Impaired</text>\n</g>\n<!-- 209&#45;&gt;210 -->\n<g id=\"edge22\" class=\"edge\">\n<title>209&#45;&gt;210</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M744.0928,-764C757.5914,-764 771.574,-764 785.0446,-764\"/>\n<polygon fill=\"#000000\" stroke=\"#000000\" points=\"785.2294,-767.5001 795.2294,-764 785.2293,-760.5001 785.2294,-767.5001\"/>\n</g>\n<!-- 255 -->\n<g id=\"node26\" class=\"node\">\n<title>255</title>\n<path fill=\"#e58139\" stroke=\"#000000\" 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font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">MMNAME &lt;= 1.5</text>\n<text text-anchor=\"middle\" x=\"462\" y=\"-284.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">gini = 0.082</text>\n<text text-anchor=\"middle\" x=\"462\" y=\"-269.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 372</text>\n<text text-anchor=\"middle\" x=\"462\" y=\"-254.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [16, 356]</text>\n<text text-anchor=\"middle\" x=\"462\" y=\"-239.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">class = Impaired</text>\n</g>\n<!-- 256&#45;&gt;500 -->\n<g id=\"edge42\" class=\"edge\">\n<title>256&#45;&gt;500</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M321.8373,-379.3799C345.8236,-361.1749 374.1009,-339.7132 398.9516,-320.8521\"/>\n<polygon fill=\"#000000\" stroke=\"#000000\" points=\"401.3872,-323.3975 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text-anchor=\"middle\" x=\"866\" y=\"-557.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [21, 90]</text>\n<text text-anchor=\"middle\" x=\"866\" y=\"-542.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">class = Impaired</text>\n</g>\n<!-- 258&#45;&gt;259 -->\n<g id=\"edge29\" class=\"edge\">\n<title>258&#45;&gt;259</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M744.0928,-515.644C753.5737,-520.3379 763.2933,-525.1501 772.9065,-529.9096\"/>\n<polygon fill=\"#000000\" stroke=\"#000000\" points=\"771.5648,-533.1508 782.0795,-534.4511 774.6707,-526.8775 771.5648,-533.1508\"/>\n</g>\n<!-- 338 -->\n<g id=\"node33\" class=\"node\">\n<title>338</title>\n<path fill=\"#c8e4f8\" stroke=\"#000000\" d=\"M929,-516.5C929,-516.5 803,-516.5 803,-516.5 797,-516.5 791,-510.5 791,-504.5 791,-504.5 791,-445.5 791,-445.5 791,-439.5 797,-433.5 803,-433.5 803,-433.5 929,-433.5 929,-433.5 935,-433.5 941,-439.5 941,-445.5 941,-445.5 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d=\"M1030,-539C1030,-539 1000,-539 1000,-539 994,-539 988,-533 988,-527 988,-527 988,-515 988,-515 988,-509 994,-503 1000,-503 1000,-503 1030,-503 1030,-503 1036,-503 1042,-509 1042,-515 1042,-515 1042,-527 1042,-527 1042,-533 1036,-539 1030,-539\"/>\n<text text-anchor=\"middle\" x=\"1015\" y=\"-517.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">(...)</text>\n</g>\n<!-- 338&#45;&gt;339 -->\n<g id=\"edge33\" class=\"edge\">\n<title>338&#45;&gt;339</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M941.373,-498.2695C954.0837,-502.1936 966.7604,-506.1072 977.9231,-509.5534\"/>\n<polygon fill=\"#000000\" stroke=\"#000000\" points=\"977.1188,-512.9681 987.7063,-512.5738 979.1838,-506.2796 977.1188,-512.9681\"/>\n</g>\n<!-- 366 -->\n<g id=\"node35\" class=\"node\">\n<title>366</title>\n<path fill=\"#c0c0c0\" stroke=\"#000000\" d=\"M1030,-485C1030,-485 1000,-485 1000,-485 994,-485 988,-479 988,-473 988,-473 988,-461 988,-461 988,-455 994,-449 1000,-449 1000,-449 1030,-449 1030,-449 1036,-449 1042,-455 1042,-461 1042,-461 1042,-473 1042,-473 1042,-479 1036,-485 1030,-485\"/>\n<text text-anchor=\"middle\" x=\"1015\" y=\"-463.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">(...)</text>\n</g>\n<!-- 338&#45;&gt;366 -->\n<g id=\"edge34\" class=\"edge\">\n<title>338&#45;&gt;366</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M941.373,-470.9531C953.954,-470.2776 966.5017,-469.6039 977.5809,-469.0091\"/>\n<polygon fill=\"#000000\" stroke=\"#000000\" points=\"977.9084,-472.4966 987.7063,-468.4654 977.533,-465.5067 977.9084,-472.4966\"/>\n</g>\n<!-- 470 -->\n<g id=\"node37\" class=\"node\">\n<title>470</title>\n<path fill=\"#46a4e7\" stroke=\"#000000\" d=\"M936,-415.5C936,-415.5 796,-415.5 796,-415.5 790,-415.5 784,-409.5 784,-403.5 784,-403.5 784,-344.5 784,-344.5 784,-338.5 790,-332.5 796,-332.5 796,-332.5 936,-332.5 936,-332.5 942,-332.5 948,-338.5 948,-344.5 948,-344.5 948,-403.5 948,-403.5 948,-409.5 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text-anchor=\"middle\" x=\"866\" y=\"-254.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [2, 4]</text>\n<text text-anchor=\"middle\" x=\"866\" y=\"-239.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">class = Impaired</text>\n</g>\n<!-- 469&#45;&gt;497 -->\n<g id=\"edge39\" class=\"edge\">\n<title>469&#45;&gt;497</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M723.2067,-343.6967C739.3972,-335.6808 757.2295,-326.8521 774.6433,-318.2305\"/>\n<polygon fill=\"#000000\" stroke=\"#000000\" points=\"776.2918,-321.3199 783.7006,-313.7462 773.1859,-315.0466 776.2918,-321.3199\"/>\n</g>\n<!-- 471 -->\n<g id=\"node38\" class=\"node\">\n<title>471</title>\n<path fill=\"#c0c0c0\" stroke=\"#000000\" d=\"M1030,-430C1030,-430 1000,-430 1000,-430 994,-430 988,-424 988,-418 988,-418 988,-406 988,-406 988,-400 994,-394 1000,-394 1000,-394 1030,-394 1030,-394 1036,-394 1042,-400 1042,-406 1042,-406 1042,-418 1042,-418 1042,-424 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          },
          "metadata": {
            "tags": []
          },
          "execution_count": 98
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6IiVDuVQ75NJ",
        "colab_type": "text"
      },
      "source": [
        "## Row 26: ['q_memory_cg', 'q_memory_pt', 'q_language_cg', 'moca_similarities', 'PTHAND', 'PTRETIRED', 'PTHOME', 'q_attention_pt', 'moca_orient', 'FAMHISTAD', 'q_outsideact_pt', 'q_judgmt_pt', 'MMNAME']"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zWA-luJ98J7W",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "features = df_costsum.iloc[26,3]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "outputId": "1de78897-ceb3-4352-86f9-838a7fec5eda",
        "id": "qwHYK1ztFbP7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 289
        }
      },
      "source": [
        "X_train = adni_X.loc[:,features]\n",
        "X_test = clinic_X.loc[:,features]\n",
        "\n",
        "clf = RandomForestClassifier(n_estimators=300, max_features=None, \n",
        "                            min_samples_leaf=30, bootstrap=False, random_state=0)\n",
        "clf.fit(X_train, adni_y.values.ravel())\n",
        "y_pred = clf.predict(X_test)\n",
        "print('AUC: ', roc_auc_score(clinic_y, y_pred))\n",
        "print('\\n')\n",
        "print(classification_report(clinic_y, y_pred, digits=3))\n",
        "print('\\n')\n",
        "print(confusion_matrix(clinic_y, y_pred))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "AUC:  0.8143595505617979\n",
            "\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "         0.0      0.698     0.904     0.787       125\n",
            "         1.0      0.915     0.725     0.809       178\n",
            "\n",
            "    accuracy                          0.799       303\n",
            "   macro avg      0.806     0.814     0.798       303\n",
            "weighted avg      0.825     0.799     0.800       303\n",
            "\n",
            "\n",
            "\n",
            "[[113  12]\n",
            " [ 49 129]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "outputId": "3134eaad-0694-4951-b92a-87b9f43461e5",
        "id": "Q08k8PWqFbP-",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        }
      },
      "source": [
        "adni_proba = clf.predict_proba(X_test)\n",
        "roc = skplt.metrics.plot_roc(clinic_y, adni_proba)\n",
        "# fig = roc.get_figure()\n",
        "# fig.savefig(f'{dir}/Figures/roc_eff_row0_3', dpi=300)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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wsAP/8A4nwvEUOJuhXdDHdrJ9zANgNMPPxab9qKiNoGmpmkJFZQum0mjmo9Uw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\nIyQkhHLlymE0Gov8/KGhoXh4eDBnzhzS0tIYNWoUISEhPPfccw9kSFK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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "outputId": "706ef71b-bd90-41da-c1dc-a36070f84939",
        "id": "tynesDz_FbQA",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 308
        }
      },
      "source": [
        "clf = MLPClassifier(hidden_layer_sizes=(105,75,60,15), activation='relu', \n",
        "                      solver='sgd', alpha=.0001, learning_rate_init=.001, \n",
        "                      max_iter=10000, random_state=0)\n",
        "clf.fit(X_train, adni_y.values.ravel())\n",
        "y_pred = clf.predict(X_test)\n",
        "print('AUC: ', roc_auc_score(clinic_y, y_pred))\n",
        "print('\\n')\n",
        "print(classification_report(clinic_y, y_pred, digits=3))\n",
        "print('\\n')\n",
        "print(confusion_matrix(clinic_y, y_pred))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "AUC:  0.7956404494382022\n",
            "\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "         0.0      0.718     0.816     0.764       125\n",
            "         1.0      0.857     0.775     0.814       178\n",
            "\n",
            "    accuracy                          0.792       303\n",
            "   macro avg      0.788     0.796     0.789       303\n",
            "weighted avg      0.800     0.792     0.793       303\n",
            "\n",
            "\n",
            "\n",
            "[[102  23]\n",
            " [ 40 138]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "outputId": "d307dc2a-ca8f-4e42-cf61-f1ebcb18038c",
        "id": "dW1Jl5naFbQE",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "from sklearn.tree import DecisionTreeClassifier\n",
        "\n",
        "clf = DecisionTreeClassifier(random_state=0)\n",
        "clf.fit(X_train, adni_y)\n",
        "\n",
        "import graphviz\n",
        "from sklearn.tree import export_graphviz\n",
        "\n",
        "dot = export_graphviz(clf, out_file=None,\n",
        "                     feature_names=features,\n",
        "                     class_names=['Normal','Impaired'],\n",
        "                     filled=True, rounded=True,\n",
        "                     rotate=True, max_depth=3)\n",
        "\n",
        "graph = graphviz.Source(dot)\n",
        "graph.render(filename='eff_tree_row26_13.gv', directory=f'{dir}/Output/', format='pdf')\n",
        "graph"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<graphviz.files.Source at 0x7f976eded470>"
            ],
            "image/svg+xml": "<?xml version=\"1.0\" encoding=\"UTF-8\" standalone=\"no\"?>\n<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n<!-- Generated by graphviz version 2.40.1 (20161225.0304)\n -->\n<!-- Title: Tree Pages: 1 -->\n<svg width=\"850pt\" height=\"817pt\"\n viewBox=\"0.00 0.00 850.00 817.00\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n<g id=\"graph0\" class=\"graph\" transform=\"scale(1 1) rotate(0) translate(4 813)\">\n<title>Tree</title>\n<polygon fill=\"#ffffff\" stroke=\"transparent\" points=\"-4,4 -4,-813 846,-813 846,4 -4,4\"/>\n<!-- 0 -->\n<g id=\"node1\" class=\"node\">\n<title>0</title>\n<path fill=\"#a8d4f4\" stroke=\"#000000\" d=\"M142,-425.5C142,-425.5 12,-425.5 12,-425.5 6,-425.5 0,-419.5 0,-413.5 0,-413.5 0,-354.5 0,-354.5 0,-348.5 6,-342.5 12,-342.5 12,-342.5 142,-342.5 142,-342.5 148,-342.5 154,-348.5 154,-354.5 154,-354.5 154,-413.5 154,-413.5 154,-419.5 148,-425.5 142,-425.5\"/>\n<text text-anchor=\"middle\" x=\"77\" y=\"-410.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">q_memory_cg &lt;= 0.5</text>\n<text text-anchor=\"middle\" x=\"77\" y=\"-395.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">gini = 0.461</text>\n<text text-anchor=\"middle\" x=\"77\" y=\"-380.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 1167</text>\n<text text-anchor=\"middle\" x=\"77\" y=\"-365.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [420, 747]</text>\n<text text-anchor=\"middle\" x=\"77\" y=\"-350.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">class = Impaired</text>\n</g>\n<!-- 1 -->\n<g id=\"node2\" class=\"node\">\n<title>1</title>\n<path fill=\"#efb489\" stroke=\"#000000\" d=\"M325.5,-526.5C325.5,-526.5 208.5,-526.5 208.5,-526.5 202.5,-526.5 196.5,-520.5 196.5,-514.5 196.5,-514.5 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font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [101, 269]</text>\n<text text-anchor=\"middle\" x=\"462\" y=\"-253.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">class = Impaired</text>\n</g>\n<!-- 262&#45;&gt;263 -->\n<g id=\"edge17\" class=\"edge\">\n<title>262&#45;&gt;263</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M344.0692,-287C352.4859,-287 361.1153,-287 369.691,-287\"/>\n<polygon fill=\"#000000\" stroke=\"#000000\" points=\"369.7176,-290.5001 379.7175,-287 369.7175,-283.5001 369.7176,-290.5001\"/>\n</g>\n<!-- 482 -->\n<g id=\"node25\" class=\"node\">\n<title>482</title>\n<path fill=\"#42a1e6\" stroke=\"#000000\" d=\"M513.5,-180.5C513.5,-180.5 410.5,-180.5 410.5,-180.5 404.5,-180.5 398.5,-174.5 398.5,-168.5 398.5,-168.5 398.5,-109.5 398.5,-109.5 398.5,-103.5 404.5,-97.5 410.5,-97.5 410.5,-97.5 513.5,-97.5 513.5,-97.5 519.5,-97.5 525.5,-103.5 525.5,-109.5 525.5,-109.5 525.5,-168.5 525.5,-168.5 525.5,-174.5 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fill=\"#000000\">samples = 285</text>\n<text text-anchor=\"middle\" x=\"666\" y=\"-315.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [94, 191]</text>\n<text text-anchor=\"middle\" x=\"666\" y=\"-300.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">class = Impaired</text>\n</g>\n<!-- 263&#45;&gt;264 -->\n<g id=\"edge18\" class=\"edge\">\n<title>263&#45;&gt;264</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M544.0928,-305.9135C552.5279,-307.8569 561.152,-309.8438 569.7221,-311.8183\"/>\n<polygon fill=\"#000000\" stroke=\"#000000\" points=\"569.2127,-315.2926 579.7432,-314.1271 570.7843,-308.4713 569.2127,-315.2926\"/>\n</g>\n<!-- 445 -->\n<g id=\"node22\" class=\"node\">\n<title>445</title>\n<path fill=\"#4ba6e7\" stroke=\"#000000\" d=\"M715,-274.5C715,-274.5 617,-274.5 617,-274.5 611,-274.5 605,-268.5 605,-262.5 605,-262.5 605,-203.5 605,-203.5 605,-197.5 611,-191.5 617,-191.5 617,-191.5 715,-191.5 715,-191.5 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id=\"edge27\" class=\"edge\">\n<title>484&#45;&gt;485</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M736.5729,-57.7884C750.7939,-60.3653 765.2063,-62.977 777.7296,-65.2463\"/>\n<polygon fill=\"#000000\" stroke=\"#000000\" points=\"777.2953,-68.7245 787.7591,-67.0637 778.5435,-61.8367 777.2953,-68.7245\"/>\n</g>\n<!-- 498 -->\n<g id=\"node29\" class=\"node\">\n<title>498</title>\n<path fill=\"#c0c0c0\" stroke=\"#000000\" d=\"M830,-36C830,-36 800,-36 800,-36 794,-36 788,-30 788,-24 788,-24 788,-12 788,-12 788,-6 794,0 800,0 800,0 830,0 830,0 836,0 842,-6 842,-12 842,-12 842,-24 842,-24 842,-30 836,-36 830,-36\"/>\n<text text-anchor=\"middle\" x=\"815\" y=\"-14.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">(...)</text>\n</g>\n<!-- 484&#45;&gt;498 -->\n<g id=\"edge28\" class=\"edge\">\n<title>484&#45;&gt;498</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M736.5729,-32.2116C750.7939,-29.6347 765.2063,-27.023 777.7296,-24.7537\"/>\n<polygon 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          },
          "metadata": {
            "tags": []
          },
          "execution_count": 420
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "daXImRewkt4j",
        "colab_type": "text"
      },
      "source": [
        "# Conclusions\n",
        "\n",
        "The most accurate feature set using the constrained cost values happened to also be the most efficient feature set, at four features and 84.2% accuracy (AUC = 0.853)"
      ]
    }
  ]
}