part2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import warnings\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import train_test_split, cross_val_score, GridSearchCV\n",
    "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, roc_auc_score, roc_curve\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.metrics import ConfusionMatrixDisplay\n",
    "pd.set_option('display.max_columns', None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Turn off while experimenting\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Ensure reproducibility\n",
    "REP = 1\n",
    "np.random.seed(REP)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Group 7\n",
    "### 012205927 - Ana Terović\n",
    "### 012206591 - Fani Sentinella-Jerbić\n",
    "### 012228451 - Jasper De Landsheere"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def evaluate(clfs, X, y, plot=True):\n",
    "    results = []\n",
    "    \n",
    "    for clf in clfs:\n",
    "        # Split the data into training and testing sets\n",
    "        X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)\n",
    "    \n",
    "        # Fit the model\n",
    "        clf.fit(X_train, y_train)\n",
    "    \n",
    "        # Make predictions\n",
    "        y_pred = clf.predict(X_test)\n",
    "    \n",
    "        # Get name of the classifier\n",
    "        name = type(clf).__name__\n",
    "        \n",
    "        # Calculate different scores\n",
    "        res = {\n",
    "            'Algorithm': name,\n",
    "            'Accuracy': accuracy_score(y_test, y_pred),\n",
    "            'F1 macro': f1_score(y_test, y_pred, average='macro'),\n",
    "            'F1 micro': f1_score(y_test, y_pred, average='micro')\n",
    "        }\n",
    "        \n",
    "        if plot:\n",
    "            ConfusionMatrixDisplay.from_estimator(clf, X_test, y_test,  cmap='rocket_r')\n",
    "        \n",
    "        results.append(res)\n",
    "        \n",
    "    return pd.DataFrame(results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>a0</th>\n",
       "      <th>a1</th>\n",
       "      <th>a2</th>\n",
       "      <th>a3</th>\n",
       "      <th>a45c</th>\n",
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       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>52c</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>54c</td>\n",
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       "      <th>2</th>\n",
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       "      <td>8</td>\n",
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       "      <th>3</th>\n",
       "      <td>3</td>\n",
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       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "      <td>3c</td>\n",
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       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
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      "text/plain": [
       "   a0  a1  a2  a3 a45c\n",
       "0   2   4   8   6  52c\n",
       "1   2   4   8   6  54c\n",
       "2   2   4   8   6  54c\n",
       "3   3   4   9   4   3c\n",
       "4   3   4   9   4   3c"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"diterpene_shuf.csv\")\n",
    "\n",
    "# Consider only multiplicity counts\n",
    "df_simple = df[['a0', 'a1', 'a2', 'a3', 'a45c']]\n",
    "df_simple.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Rename columns for better readability\n",
    "df_simple = df_simple.rename(columns={'a0':'s_cnt', 'a1':'d_cnt', 'a2':'t_cnt', 'a3':'q_cnt', 'a45c':'Class'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df_simple[['s_cnt', 'd_cnt', 't_cnt', 'q_cnt']]\n",
    "y = df_simple['Class']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>a5</th>\n",
       "      <th>a6</th>\n",
       "      <th>a7</th>\n",
       "      <th>a8</th>\n",
       "      <th>a9</th>\n",
       "      <th>a10</th>\n",
       "      <th>a11</th>\n",
       "      <th>a12</th>\n",
       "      <th>a13</th>\n",
       "      <th>a14</th>\n",
       "      <th>a15</th>\n",
       "      <th>a16</th>\n",
       "      <th>a17</th>\n",
       "      <th>a18</th>\n",
       "      <th>a19</th>\n",
       "      <th>a20</th>\n",
       "      <th>a21</th>\n",
       "      <th>a22</th>\n",
       "      <th>a23</th>\n",
       "      <th>a24</th>\n",
       "      <th>a25</th>\n",
       "      <th>a26</th>\n",
       "      <th>a27</th>\n",
       "      <th>a28</th>\n",
       "      <th>a29</th>\n",
       "      <th>a30</th>\n",
       "      <th>a31</th>\n",
       "      <th>a32</th>\n",
       "      <th>a33</th>\n",
       "      <th>a34</th>\n",
       "      <th>a35</th>\n",
       "      <th>a36</th>\n",
       "      <th>a37</th>\n",
       "      <th>a38</th>\n",
       "      <th>a39</th>\n",
       "      <th>a40</th>\n",
       "      <th>a41</th>\n",
       "      <th>a42</th>\n",
       "      <th>a43</th>\n",
       "      <th>a44</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>t</td>\n",
       "      <td>139.2</td>\n",
       "      <td>q</td>\n",
       "      <td>33.2</td>\n",
       "      <td>d</td>\n",
       "      <td>30.7</td>\n",
       "      <td>d</td>\n",
       "      <td>49.6</td>\n",
       "      <td>t</td>\n",
       "      <td>38.6</td>\n",
       "      <td>t</td>\n",
       "      <td>39.6</td>\n",
       "      <td>q</td>\n",
       "      <td>19.9</td>\n",
       "      <td>t</td>\n",
       "      <td>26.0</td>\n",
       "      <td>q</td>\n",
       "      <td>21.3</td>\n",
       "      <td>q</td>\n",
       "      <td>61.0</td>\n",
       "      <td>d</td>\n",
       "      <td>51.3</td>\n",
       "      <td>s</td>\n",
       "      <td>32.8</td>\n",
       "      <td>t</td>\n",
       "      <td>39.6</td>\n",
       "      <td>s</td>\n",
       "      <td>37.0</td>\n",
       "      <td>q</td>\n",
       "      <td>173.5</td>\n",
       "      <td>d</td>\n",
       "      <td>134.5</td>\n",
       "      <td>t</td>\n",
       "      <td>24.2</td>\n",
       "      <td>q</td>\n",
       "      <td>14.4</td>\n",
       "      <td>t</td>\n",
       "      <td>42.2</td>\n",
       "      <td>t</td>\n",
       "      <td>18.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>d</td>\n",
       "      <td>144.0</td>\n",
       "      <td>q</td>\n",
       "      <td>66.7</td>\n",
       "      <td>d</td>\n",
       "      <td>46.2</td>\n",
       "      <td>t</td>\n",
       "      <td>32.7</td>\n",
       "      <td>q</td>\n",
       "      <td>19.1</td>\n",
       "      <td>t</td>\n",
       "      <td>17.7</td>\n",
       "      <td>d</td>\n",
       "      <td>40.7</td>\n",
       "      <td>t</td>\n",
       "      <td>120.5</td>\n",
       "      <td>q</td>\n",
       "      <td>59.2</td>\n",
       "      <td>t</td>\n",
       "      <td>36.6</td>\n",
       "      <td>t</td>\n",
       "      <td>36.0</td>\n",
       "      <td>q</td>\n",
       "      <td>17.9</td>\n",
       "      <td>d</td>\n",
       "      <td>140.2</td>\n",
       "      <td>t</td>\n",
       "      <td>122.7</td>\n",
       "      <td>s</td>\n",
       "      <td>38.0</td>\n",
       "      <td>t</td>\n",
       "      <td>22.4</td>\n",
       "      <td>s</td>\n",
       "      <td>37.7</td>\n",
       "      <td>q</td>\n",
       "      <td>19.7</td>\n",
       "      <td>q</td>\n",
       "      <td>16.5</td>\n",
       "      <td>t</td>\n",
       "      <td>26.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>d</td>\n",
       "      <td>30.7</td>\n",
       "      <td>t</td>\n",
       "      <td>26.8</td>\n",
       "      <td>t</td>\n",
       "      <td>41.2</td>\n",
       "      <td>t</td>\n",
       "      <td>139.3</td>\n",
       "      <td>q</td>\n",
       "      <td>19.8</td>\n",
       "      <td>q</td>\n",
       "      <td>170.1</td>\n",
       "      <td>q</td>\n",
       "      <td>173.3</td>\n",
       "      <td>d</td>\n",
       "      <td>36.0</td>\n",
       "      <td>t</td>\n",
       "      <td>31.6</td>\n",
       "      <td>s</td>\n",
       "      <td>38.4</td>\n",
       "      <td>d</td>\n",
       "      <td>46.2</td>\n",
       "      <td>q</td>\n",
       "      <td>15.6</td>\n",
       "      <td>t</td>\n",
       "      <td>29.2</td>\n",
       "      <td>t</td>\n",
       "      <td>26.8</td>\n",
       "      <td>q</td>\n",
       "      <td>18.6</td>\n",
       "      <td>t</td>\n",
       "      <td>16.7</td>\n",
       "      <td>t</td>\n",
       "      <td>35.4</td>\n",
       "      <td>q</td>\n",
       "      <td>65.8</td>\n",
       "      <td>s</td>\n",
       "      <td>42.4</td>\n",
       "      <td>d</td>\n",
       "      <td>139.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>t</td>\n",
       "      <td>37.2</td>\n",
       "      <td>t</td>\n",
       "      <td>17.4</td>\n",
       "      <td>q</td>\n",
       "      <td>16.7</td>\n",
       "      <td>s</td>\n",
       "      <td>58.1</td>\n",
       "      <td>t</td>\n",
       "      <td>41.6</td>\n",
       "      <td>d</td>\n",
       "      <td>78.8</td>\n",
       "      <td>t</td>\n",
       "      <td>25.5</td>\n",
       "      <td>t</td>\n",
       "      <td>212.4</td>\n",
       "      <td>q</td>\n",
       "      <td>68.9</td>\n",
       "      <td>t</td>\n",
       "      <td>40.1</td>\n",
       "      <td>t</td>\n",
       "      <td>18.1</td>\n",
       "      <td>t</td>\n",
       "      <td>45.1</td>\n",
       "      <td>q</td>\n",
       "      <td>32.7</td>\n",
       "      <td>d</td>\n",
       "      <td>53.7</td>\n",
       "      <td>s</td>\n",
       "      <td>40.1</td>\n",
       "      <td>s</td>\n",
       "      <td>33.5</td>\n",
       "      <td>d</td>\n",
       "      <td>44.9</td>\n",
       "      <td>d</td>\n",
       "      <td>56.6</td>\n",
       "      <td>q</td>\n",
       "      <td>20.8</td>\n",
       "      <td>t</td>\n",
       "      <td>36.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>q</td>\n",
       "      <td>12.2</td>\n",
       "      <td>d</td>\n",
       "      <td>154.6</td>\n",
       "      <td>t</td>\n",
       "      <td>18.7</td>\n",
       "      <td>t</td>\n",
       "      <td>37.1</td>\n",
       "      <td>t</td>\n",
       "      <td>79.1</td>\n",
       "      <td>t</td>\n",
       "      <td>43.3</td>\n",
       "      <td>t</td>\n",
       "      <td>74.7</td>\n",
       "      <td>d</td>\n",
       "      <td>39.8</td>\n",
       "      <td>t</td>\n",
       "      <td>22.8</td>\n",
       "      <td>q</td>\n",
       "      <td>109.7</td>\n",
       "      <td>t</td>\n",
       "      <td>24.0</td>\n",
       "      <td>s</td>\n",
       "      <td>40.0</td>\n",
       "      <td>d</td>\n",
       "      <td>79.2</td>\n",
       "      <td>q</td>\n",
       "      <td>17.0</td>\n",
       "      <td>q</td>\n",
       "      <td>64.5</td>\n",
       "      <td>d</td>\n",
       "      <td>49.5</td>\n",
       "      <td>t</td>\n",
       "      <td>38.8</td>\n",
       "      <td>t</td>\n",
       "      <td>71.4</td>\n",
       "      <td>s</td>\n",
       "      <td>36.8</td>\n",
       "      <td>s</td>\n",
       "      <td>46.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  a5     a6 a7     a8 a9   a10 a11    a12 a13   a14 a15    a16 a17    a18 a19  \\\n",
       "0  t  139.2  q   33.2  d  30.7   d   49.6   t  38.6   t   39.6   q   19.9   t   \n",
       "1  d  144.0  q   66.7  d  46.2   t   32.7   q  19.1   t   17.7   d   40.7   t   \n",
       "2  d   30.7  t   26.8  t  41.2   t  139.3   q  19.8   q  170.1   q  173.3   d   \n",
       "3  t   37.2  t   17.4  q  16.7   s   58.1   t  41.6   d   78.8   t   25.5   t   \n",
       "4  q   12.2  d  154.6  t  18.7   t   37.1   t  79.1   t   43.3   t   74.7   d   \n",
       "\n",
       "     a20 a21   a22 a23    a24 a25   a26 a27   a28 a29    a30 a31    a32 a33  \\\n",
       "0   26.0   q  21.3   q   61.0   d  51.3   s  32.8   t   39.6   s   37.0   q   \n",
       "1  120.5   q  59.2   t   36.6   t  36.0   q  17.9   d  140.2   t  122.7   s   \n",
       "2   36.0   t  31.6   s   38.4   d  46.2   q  15.6   t   29.2   t   26.8   q   \n",
       "3  212.4   q  68.9   t   40.1   t  18.1   t  45.1   q   32.7   d   53.7   s   \n",
       "4   39.8   t  22.8   q  109.7   t  24.0   s  40.0   d   79.2   q   17.0   q   \n",
       "\n",
       "     a34 a35    a36 a37   a38 a39   a40 a41   a42 a43    a44  \n",
       "0  173.5   d  134.5   t  24.2   q  14.4   t  42.2   t   18.6  \n",
       "1   38.0   t   22.4   s  37.7   q  19.7   q  16.5   t   26.6  \n",
       "2   18.6   t   16.7   t  35.4   q  65.8   s  42.4   d  139.8  \n",
       "3   40.1   s   33.5   d  44.9   d  56.6   q  20.8   t   36.5  \n",
       "4   64.5   d   49.5   t  38.8   t  71.4   s  36.8   s   46.2  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the rest of features\n",
    "df_rest = df.drop(columns=['a0', 'a1', 'a2', 'a3','a4', 'a45c'])\n",
    "df_rest.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "t_sum = []\n",
    "q_sum = []\n",
    "s_sum = []\n",
    "d_sum = []\n",
    "t_mean = []\n",
    "q_mean = []\n",
    "s_mean = []\n",
    "d_mean = []\n",
    "t_std = []\n",
    "q_std = []\n",
    "s_std = []\n",
    "d_std = []\n",
    "t_min = []\n",
    "q_min = []\n",
    "s_min = []\n",
    "d_min = []\n",
    "t_max = []\n",
    "q_max = []\n",
    "s_max = []\n",
    "d_max = []\n",
    "cols = df_rest.columns.values\n",
    "\n",
    "# Consider each structure individually\n",
    "for index, row in df_rest.iterrows():\n",
    "    tvals = []\n",
    "    qvals = []\n",
    "    svals = []\n",
    "    dvals = []\n",
    "    # Gather up frequencies for each multiplicity\n",
    "    for i in range(1, len(cols), 2):\n",
    "        mul = row[cols[i-1]]\n",
    "        freq = row[cols[i]]\n",
    "        if mul == 't':\n",
    "            tvals.append(freq)\n",
    "        elif mul == 'q':\n",
    "            qvals.append(freq)\n",
    "        elif mul == 's':\n",
    "            svals.append(freq)\n",
    "        elif mul == 'd':\n",
    "            dvals.append(freq)\n",
    "            \n",
    "    t_sum.append(sum(tvals))\n",
    "    q_sum.append(sum(qvals))\n",
    "    s_sum.append(sum(svals))\n",
    "    d_sum.append(sum(dvals))\n",
    "    \n",
    "    t_mean.append(np.mean(tvals))\n",
    "    q_mean.append(np.mean(qvals))\n",
    "    s_mean.append(np.mean(svals))\n",
    "    d_mean.append(np.mean(dvals))\n",
    "    \n",
    "    t_std.append(np.std(tvals))\n",
    "    q_std.append(np.std(qvals))\n",
    "    s_std.append(np.std(svals))\n",
    "    d_std.append(np.std(dvals))\n",
    "    \n",
    "    t_min.append(np.min(tvals))\n",
    "    q_min.append(np.min(qvals))\n",
    "    s_min.append(np.min(svals))\n",
    "    d_min.append(np.min(dvals))\n",
    "    \n",
    "    t_max.append(np.max(tvals))\n",
    "    q_max.append(np.max(qvals))\n",
    "    s_max.append(np.max(svals))\n",
    "    d_max.append(np.max(dvals))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "X['t_freq_sum'] = t_sum\n",
    "X['q_freq_sum'] = q_sum\n",
    "X['s_freq_sum'] = s_sum\n",
    "X['d_freq_sum'] = d_sum\n",
    "X['t_freq_mean'] = t_mean\n",
    "X['q_freq_mean'] = q_mean\n",
    "X['s_freq_mean'] = s_mean\n",
    "X['d_freq_mean'] = d_mean\n",
    "X['t_freq_std'] = t_std\n",
    "X['q_freq_std'] = q_std\n",
    "X['s_freq_std'] = s_std\n",
    "X['d_freq_std'] = d_std\n",
    "X['t_freq_min'] = t_min\n",
    "X['q_freq_min'] = q_min\n",
    "X['s_freq_min'] = s_min\n",
    "X['d_freq_min'] = d_min\n",
    "X['t_freq_max'] = t_max\n",
    "X['q_freq_max'] = q_max\n",
    "X['s_freq_max'] = s_max\n",
    "X['d_freq_max'] = d_max"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>s_cnt</th>\n",
       "      <th>d_cnt</th>\n",
       "      <th>t_cnt</th>\n",
       "      <th>q_cnt</th>\n",
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       "      <th>d_freq_mean</th>\n",
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       "      <th>q_freq_std</th>\n",
       "      <th>s_freq_std</th>\n",
       "      <th>d_freq_std</th>\n",
       "      <th>t_freq_min</th>\n",
       "      <th>q_freq_min</th>\n",
       "      <th>s_freq_min</th>\n",
       "      <th>d_freq_min</th>\n",
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       "      <th>s_freq_max</th>\n",
       "      <th>d_freq_max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>368.0</td>\n",
       "      <td>323.3</td>\n",
       "      <td>69.8</td>\n",
       "      <td>266.1</td>\n",
       "      <td>46.000000</td>\n",
       "      <td>53.883333</td>\n",
       "      <td>34.900</td>\n",
       "      <td>66.525000</td>\n",
       "      <td>36.165177</td>\n",
       "      <td>55.620842</td>\n",
       "      <td>2.100000</td>\n",
       "      <td>40.069588</td>\n",
       "      <td>18.6</td>\n",
       "      <td>14.4</td>\n",
       "      <td>32.8</td>\n",
       "      <td>30.7</td>\n",
       "      <td>139.2</td>\n",
       "      <td>173.5</td>\n",
       "      <td>37.0</td>\n",
       "      <td>134.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>415.2</td>\n",
       "      <td>199.1</td>\n",
       "      <td>75.7</td>\n",
       "      <td>371.1</td>\n",
       "      <td>51.900000</td>\n",
       "      <td>33.183333</td>\n",
       "      <td>37.850</td>\n",
       "      <td>92.775000</td>\n",
       "      <td>40.703378</td>\n",
       "      <td>21.182887</td>\n",
       "      <td>0.150000</td>\n",
       "      <td>49.381594</td>\n",
       "      <td>17.7</td>\n",
       "      <td>16.5</td>\n",
       "      <td>37.7</td>\n",
       "      <td>40.7</td>\n",
       "      <td>122.7</td>\n",
       "      <td>66.7</td>\n",
       "      <td>38.0</td>\n",
       "      <td>144.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>347.0</td>\n",
       "      <td>463.2</td>\n",
       "      <td>80.8</td>\n",
       "      <td>252.7</td>\n",
       "      <td>43.375000</td>\n",
       "      <td>77.200000</td>\n",
       "      <td>40.400</td>\n",
       "      <td>63.175000</td>\n",
       "      <td>36.862133</td>\n",
       "      <td>68.943044</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>44.588809</td>\n",
       "      <td>16.7</td>\n",
       "      <td>15.6</td>\n",
       "      <td>38.4</td>\n",
       "      <td>30.7</td>\n",
       "      <td>139.3</td>\n",
       "      <td>173.3</td>\n",
       "      <td>42.4</td>\n",
       "      <td>139.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "      <td>473.9</td>\n",
       "      <td>139.1</td>\n",
       "      <td>131.7</td>\n",
       "      <td>234.0</td>\n",
       "      <td>52.655556</td>\n",
       "      <td>34.775000</td>\n",
       "      <td>43.900</td>\n",
       "      <td>58.500000</td>\n",
       "      <td>57.280579</td>\n",
       "      <td>20.559836</td>\n",
       "      <td>10.396153</td>\n",
       "      <td>12.486993</td>\n",
       "      <td>17.4</td>\n",
       "      <td>16.7</td>\n",
       "      <td>33.5</td>\n",
       "      <td>44.9</td>\n",
       "      <td>212.4</td>\n",
       "      <td>68.9</td>\n",
       "      <td>58.1</td>\n",
       "      <td>78.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "      <td>409.9</td>\n",
       "      <td>203.4</td>\n",
       "      <td>123.0</td>\n",
       "      <td>323.1</td>\n",
       "      <td>45.544444</td>\n",
       "      <td>50.850000</td>\n",
       "      <td>41.000</td>\n",
       "      <td>80.775000</td>\n",
       "      <td>22.282186</td>\n",
       "      <td>39.652522</td>\n",
       "      <td>3.902136</td>\n",
       "      <td>45.026902</td>\n",
       "      <td>18.7</td>\n",
       "      <td>12.2</td>\n",
       "      <td>36.8</td>\n",
       "      <td>39.8</td>\n",
       "      <td>79.1</td>\n",
       "      <td>109.7</td>\n",
       "      <td>46.2</td>\n",
       "      <td>154.6</td>\n",
       "    </tr>\n",
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       "      <th>...</th>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>1197</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>609.8</td>\n",
       "      <td>321.9</td>\n",
       "      <td>78.8</td>\n",
       "      <td>225.1</td>\n",
       "      <td>76.225000</td>\n",
       "      <td>45.985714</td>\n",
       "      <td>39.400</td>\n",
       "      <td>75.033333</td>\n",
       "      <td>60.015076</td>\n",
       "      <td>53.764657</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>48.296400</td>\n",
       "      <td>27.0</td>\n",
       "      <td>15.7</td>\n",
       "      <td>38.8</td>\n",
       "      <td>36.1</td>\n",
       "      <td>200.1</td>\n",
       "      <td>172.1</td>\n",
       "      <td>40.0</td>\n",
       "      <td>143.1</td>\n",
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       "    <tr>\n",
       "      <th>1198</th>\n",
       "      <td>2</td>\n",
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       "      <td>6</td>\n",
       "      <td>485.3</td>\n",
       "      <td>254.8</td>\n",
       "      <td>88.0</td>\n",
       "      <td>370.3</td>\n",
       "      <td>60.662500</td>\n",
       "      <td>42.466667</td>\n",
       "      <td>44.000</td>\n",
       "      <td>92.575000</td>\n",
       "      <td>41.474358</td>\n",
       "      <td>22.851671</td>\n",
       "      <td>4.300000</td>\n",
       "      <td>50.837111</td>\n",
       "      <td>19.1</td>\n",
       "      <td>16.4</td>\n",
       "      <td>39.7</td>\n",
       "      <td>36.2</td>\n",
       "      <td>127.8</td>\n",
       "      <td>74.2</td>\n",
       "      <td>48.3</td>\n",
       "      <td>143.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1199</th>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>401.5</td>\n",
       "      <td>407.4</td>\n",
       "      <td>75.3</td>\n",
       "      <td>416.5</td>\n",
       "      <td>50.187500</td>\n",
       "      <td>67.900000</td>\n",
       "      <td>37.650</td>\n",
       "      <td>104.125000</td>\n",
       "      <td>29.571415</td>\n",
       "      <td>53.503987</td>\n",
       "      <td>3.050000</td>\n",
       "      <td>48.252947</td>\n",
       "      <td>22.6</td>\n",
       "      <td>15.3</td>\n",
       "      <td>34.6</td>\n",
       "      <td>55.6</td>\n",
       "      <td>117.3</td>\n",
       "      <td>166.1</td>\n",
       "      <td>40.7</td>\n",
       "      <td>157.2</td>\n",
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       "    <tr>\n",
       "      <th>1200</th>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>335.3</td>\n",
       "      <td>153.5</td>\n",
       "      <td>76.4</td>\n",
       "      <td>257.5</td>\n",
       "      <td>41.912500</td>\n",
       "      <td>25.583333</td>\n",
       "      <td>38.200</td>\n",
       "      <td>64.375000</td>\n",
       "      <td>30.313258</td>\n",
       "      <td>16.691656</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>46.549241</td>\n",
       "      <td>17.3</td>\n",
       "      <td>15.7</td>\n",
       "      <td>38.1</td>\n",
       "      <td>30.6</td>\n",
       "      <td>120.4</td>\n",
       "      <td>62.8</td>\n",
       "      <td>38.3</td>\n",
       "      <td>144.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1201</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>500.3</td>\n",
       "      <td>188.1</td>\n",
       "      <td>231.3</td>\n",
       "      <td>273.2</td>\n",
       "      <td>71.471429</td>\n",
       "      <td>47.025000</td>\n",
       "      <td>57.825</td>\n",
       "      <td>54.640000</td>\n",
       "      <td>65.550193</td>\n",
       "      <td>20.188286</td>\n",
       "      <td>24.011078</td>\n",
       "      <td>12.455778</td>\n",
       "      <td>24.4</td>\n",
       "      <td>21.9</td>\n",
       "      <td>34.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>224.7</td>\n",
       "      <td>68.9</td>\n",
       "      <td>95.6</td>\n",
       "      <td>62.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1202 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      s_cnt  d_cnt  t_cnt  q_cnt  t_freq_sum  q_freq_sum  s_freq_sum  \\\n",
       "0         2      4      8      6       368.0       323.3        69.8   \n",
       "1         2      4      8      6       415.2       199.1        75.7   \n",
       "2         2      4      8      6       347.0       463.2        80.8   \n",
       "3         3      4      9      4       473.9       139.1       131.7   \n",
       "4         3      4      9      4       409.9       203.4       123.0   \n",
       "...     ...    ...    ...    ...         ...         ...         ...   \n",
       "1197      2      3      8      7       609.8       321.9        78.8   \n",
       "1198      2      4      8      6       485.3       254.8        88.0   \n",
       "1199      2      4      8      6       401.5       407.4        75.3   \n",
       "1200      2      4      8      6       335.3       153.5        76.4   \n",
       "1201      4      5      7      4       500.3       188.1       231.3   \n",
       "\n",
       "      d_freq_sum  t_freq_mean  q_freq_mean  s_freq_mean  d_freq_mean  \\\n",
       "0          266.1    46.000000    53.883333       34.900    66.525000   \n",
       "1          371.1    51.900000    33.183333       37.850    92.775000   \n",
       "2          252.7    43.375000    77.200000       40.400    63.175000   \n",
       "3          234.0    52.655556    34.775000       43.900    58.500000   \n",
       "4          323.1    45.544444    50.850000       41.000    80.775000   \n",
       "...          ...          ...          ...          ...          ...   \n",
       "1197       225.1    76.225000    45.985714       39.400    75.033333   \n",
       "1198       370.3    60.662500    42.466667       44.000    92.575000   \n",
       "1199       416.5    50.187500    67.900000       37.650   104.125000   \n",
       "1200       257.5    41.912500    25.583333       38.200    64.375000   \n",
       "1201       273.2    71.471429    47.025000       57.825    54.640000   \n",
       "\n",
       "      t_freq_std  q_freq_std  s_freq_std  d_freq_std  t_freq_min  q_freq_min  \\\n",
       "0      36.165177   55.620842    2.100000   40.069588        18.6        14.4   \n",
       "1      40.703378   21.182887    0.150000   49.381594        17.7        16.5   \n",
       "2      36.862133   68.943044    2.000000   44.588809        16.7        15.6   \n",
       "3      57.280579   20.559836   10.396153   12.486993        17.4        16.7   \n",
       "4      22.282186   39.652522    3.902136   45.026902        18.7        12.2   \n",
       "...          ...         ...         ...         ...         ...         ...   \n",
       "1197   60.015076   53.764657    0.600000   48.296400        27.0        15.7   \n",
       "1198   41.474358   22.851671    4.300000   50.837111        19.1        16.4   \n",
       "1199   29.571415   53.503987    3.050000   48.252947        22.6        15.3   \n",
       "1200   30.313258   16.691656    0.100000   46.549241        17.3        15.7   \n",
       "1201   65.550193   20.188286   24.011078   12.455778        24.4        21.9   \n",
       "\n",
       "      s_freq_min  d_freq_min  t_freq_max  q_freq_max  s_freq_max  d_freq_max  \n",
       "0           32.8        30.7       139.2       173.5        37.0       134.5  \n",
       "1           37.7        40.7       122.7        66.7        38.0       144.0  \n",
       "2           38.4        30.7       139.3       173.3        42.4       139.8  \n",
       "3           33.5        44.9       212.4        68.9        58.1        78.8  \n",
       "4           36.8        39.8        79.1       109.7        46.2       154.6  \n",
       "...          ...         ...         ...         ...         ...         ...  \n",
       "1197        38.8        36.1       200.1       172.1        40.0       143.1  \n",
       "1198        39.7        36.2       127.8        74.2        48.3       143.9  \n",
       "1199        34.6        55.6       117.3       166.1        40.7       157.2  \n",
       "1200        38.1        30.6       120.4        62.8        38.3       144.4  \n",
       "1201        34.0        30.0       224.7        68.9        95.6        62.9  \n",
       "\n",
       "[1202 rows x 24 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Algorithm</th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>F1 macro</th>\n",
       "      <th>F1 micro</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>0.834025</td>\n",
       "      <td>0.465376</td>\n",
       "      <td>0.834025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>KNeighborsClassifier</td>\n",
       "      <td>0.788382</td>\n",
       "      <td>0.585168</td>\n",
       "      <td>0.788382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>DecisionTreeClassifier</td>\n",
       "      <td>0.887967</td>\n",
       "      <td>0.519531</td>\n",
       "      <td>0.887967</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Algorithm  Accuracy  F1 macro  F1 micro\n",
       "0      LogisticRegression  0.834025  0.465376  0.834025\n",
       "1    KNeighborsClassifier  0.788382  0.585168  0.788382\n",
       "2  DecisionTreeClassifier  0.887967  0.519531  0.887967"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clfs_to_try = [LogisticRegression(), \n",
    "               KNeighborsClassifier(), \n",
    "               DecisionTreeClassifier()]\n",
    "evaluate(clfs_to_try, X, y, plot=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Algorithm</th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>F1 macro</th>\n",
       "      <th>F1 micro</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>DecisionTreeClassifier</td>\n",
       "      <td>0.883817</td>\n",
       "      <td>0.559123</td>\n",
       "      <td>0.883817</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Algorithm  Accuracy  F1 macro  F1 micro\n",
       "0  DecisionTreeClassifier  0.883817  0.559123  0.883817"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Do a grid search\n",
    "clf = DecisionTreeClassifier(random_state=REP)\n",
    "grid_values = {'ccp_alpha': [0.1, 0.01, 0.001, 0.0001, 0],\n",
    "               'criterion': ['gini', 'entropy'],\n",
    "               'max_depth': [5, 6, 7, 8, 9, 10, None],\n",
    "               'max_features': ['auto', 'sqrt', 'log2']\n",
    "              }\n",
    "grid_clf_acc = GridSearchCV(clf, param_grid = grid_values,scoring = 'accuracy', n_jobs=-1)\n",
    "grid_clf_acc.fit(X_train, y_train)\n",
    "evaluate([DecisionTreeClassifier(**grid_clf_acc.best_params_)], X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Algorithm</th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>F1 macro</th>\n",
       "      <th>F1 micro</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>KNeighborsClassifier</td>\n",
       "      <td>0.896266</td>\n",
       "      <td>0.603864</td>\n",
       "      <td>0.896266</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Algorithm  Accuracy  F1 macro  F1 micro\n",
       "0  KNeighborsClassifier  0.896266  0.603864  0.896266"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Do a grid search\n",
    "clf = KNeighborsClassifier()\n",
    "grid_values = {'n_neighbors':[6,8,10,12,14,16,18,20],\n",
    "               'leaf_size':list(range(1,50,5)),\n",
    "               \"metric\":[\"euclidean\", \"manhattan\", \"chebyshev\"],\n",
    "               'weights':['uniform', 'distance'],\n",
    "               'algorithm':['auto', 'ball_tree', 'kd_tree', 'brute']\n",
    "              }\n",
    "grid_clf_acc = GridSearchCV(clf, param_grid = grid_values,scoring = 'accuracy', n_jobs=-1)\n",
    "grid_clf_acc.fit(X_train, y_train)\n",
    "evaluate([KNeighborsClassifier(**grid_clf_acc.best_params_)], X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Algorithm</th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>F1 macro</th>\n",
       "      <th>F1 micro</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>0.929461</td>\n",
       "      <td>0.698864</td>\n",
       "      <td>0.929461</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Algorithm  Accuracy  F1 macro  F1 micro\n",
       "0  LogisticRegression  0.929461  0.698864  0.929461"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Do a grid search\n",
    "clf = LogisticRegression(random_state=REP)\n",
    "grid_values = {'penalty': ['l2'], \n",
    "               'C':[0.001,.009,0.01,.09,1,5,10,25], \n",
    "               'solver':['lbfgs'], \n",
    "               'max_iter':[100,1000,2500]\n",
    "              }\n",
    "grid_clf_acc = GridSearchCV(clf, param_grid = grid_values,scoring = 'accuracy', n_jobs=-1)\n",
    "grid_clf_acc.fit(X_train, y_train)\n",
    "evaluate([LogisticRegression(**grid_clf_acc.best_params_)], X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.906 +/- 0.01089\n"
     ]
    }
   ],
   "source": [
    "score = cross_val_score(LogisticRegression(**grid_clf_acc.best_params_), X=X, y=y)\n",
    "print(\"{:.4} +/- {:.4}\".format(np.mean(score), np.std(score)))"
   ]
  }
 ],
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