{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.pyplot import savefig\n",
    "\n",
    "sns.set(style=\"white\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Useful function\n",
    "\n",
    "def get_highest_values(arr, n):\n",
    "    return np.array(arr).argsort()[-n:][::-1]\n",
    "\n",
    "def get_lowest_values(arr, n):\n",
    "    return np.array(arr).argsort()[::-1][-n:][::-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_file = \"data/temp.train\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Correlation analysis between SVD features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>...</th>\n",
       "      <th>191</th>\n",
       "      <th>192</th>\n",
       "      <th>193</th>\n",
       "      <th>194</th>\n",
       "      <th>195</th>\n",
       "      <th>196</th>\n",
       "      <th>197</th>\n",
       "      <th>198</th>\n",
       "      <th>199</th>\n",
       "      <th>200</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.747867</td>\n",
       "      <td>0.015906</td>\n",
       "      <td>0.013435</td>\n",
       "      <td>0.010133</td>\n",
       "      <td>0.009132</td>\n",
       "      <td>0.006958</td>\n",
       "      <td>0.006652</td>\n",
       "      <td>0.006193</td>\n",
       "      <td>0.005855</td>\n",
       "      <td>0.005532</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000031</td>\n",
       "      <td>0.000028</td>\n",
       "      <td>0.000023</td>\n",
       "      <td>0.000020</td>\n",
       "      <td>0.000014</td>\n",
       "      <td>0.000014</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>0.000007</td>\n",
       "      <td>0.000003</td>\n",
       "      <td>2.854226e-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.742556</td>\n",
       "      <td>0.016040</td>\n",
       "      <td>0.013593</td>\n",
       "      <td>0.010234</td>\n",
       "      <td>0.009399</td>\n",
       "      <td>0.007116</td>\n",
       "      <td>0.006650</td>\n",
       "      <td>0.006278</td>\n",
       "      <td>0.006005</td>\n",
       "      <td>0.005751</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000057</td>\n",
       "      <td>0.000053</td>\n",
       "      <td>0.000043</td>\n",
       "      <td>0.000033</td>\n",
       "      <td>0.000029</td>\n",
       "      <td>0.000025</td>\n",
       "      <td>0.000022</td>\n",
       "      <td>0.000012</td>\n",
       "      <td>0.000006</td>\n",
       "      <td>3.012061e-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.741817</td>\n",
       "      <td>0.016078</td>\n",
       "      <td>0.013635</td>\n",
       "      <td>0.010261</td>\n",
       "      <td>0.009387</td>\n",
       "      <td>0.007099</td>\n",
       "      <td>0.006711</td>\n",
       "      <td>0.006291</td>\n",
       "      <td>0.006031</td>\n",
       "      <td>0.005788</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000067</td>\n",
       "      <td>0.000060</td>\n",
       "      <td>0.000060</td>\n",
       "      <td>0.000048</td>\n",
       "      <td>0.000036</td>\n",
       "      <td>0.000033</td>\n",
       "      <td>0.000017</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>0.000006</td>\n",
       "      <td>1.667818e-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.728526</td>\n",
       "      <td>0.016202</td>\n",
       "      <td>0.014147</td>\n",
       "      <td>0.010591</td>\n",
       "      <td>0.009438</td>\n",
       "      <td>0.007605</td>\n",
       "      <td>0.006869</td>\n",
       "      <td>0.006649</td>\n",
       "      <td>0.006454</td>\n",
       "      <td>0.006113</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000118</td>\n",
       "      <td>0.000107</td>\n",
       "      <td>0.000101</td>\n",
       "      <td>0.000078</td>\n",
       "      <td>0.000072</td>\n",
       "      <td>0.000056</td>\n",
       "      <td>0.000033</td>\n",
       "      <td>0.000020</td>\n",
       "      <td>0.000006</td>\n",
       "      <td>3.226420e-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.747843</td>\n",
       "      <td>0.015910</td>\n",
       "      <td>0.013440</td>\n",
       "      <td>0.010138</td>\n",
       "      <td>0.009140</td>\n",
       "      <td>0.006962</td>\n",
       "      <td>0.006653</td>\n",
       "      <td>0.006193</td>\n",
       "      <td>0.005857</td>\n",
       "      <td>0.005531</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000032</td>\n",
       "      <td>0.000029</td>\n",
       "      <td>0.000023</td>\n",
       "      <td>0.000019</td>\n",
       "      <td>0.000015</td>\n",
       "      <td>0.000012</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>3.281675e-16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 200 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        1         2         3         4         5         6         7    \\\n",
       "0  0.747867  0.015906  0.013435  0.010133  0.009132  0.006958  0.006652   \n",
       "1  0.742556  0.016040  0.013593  0.010234  0.009399  0.007116  0.006650   \n",
       "2  0.741817  0.016078  0.013635  0.010261  0.009387  0.007099  0.006711   \n",
       "3  0.728526  0.016202  0.014147  0.010591  0.009438  0.007605  0.006869   \n",
       "4  0.747843  0.015910  0.013440  0.010138  0.009140  0.006962  0.006653   \n",
       "\n",
       "        8         9         10       ...            191       192       193  \\\n",
       "0  0.006193  0.005855  0.005532      ...       0.000031  0.000028  0.000023   \n",
       "1  0.006278  0.006005  0.005751      ...       0.000057  0.000053  0.000043   \n",
       "2  0.006291  0.006031  0.005788      ...       0.000067  0.000060  0.000060   \n",
       "3  0.006649  0.006454  0.006113      ...       0.000118  0.000107  0.000101   \n",
       "4  0.006193  0.005857  0.005531      ...       0.000032  0.000029  0.000023   \n",
       "\n",
       "        194       195       196       197       198       199           200  \n",
       "0  0.000020  0.000014  0.000014  0.000010  0.000007  0.000003  2.854226e-16  \n",
       "1  0.000033  0.000029  0.000025  0.000022  0.000012  0.000006  3.012061e-16  \n",
       "2  0.000048  0.000036  0.000033  0.000017  0.000013  0.000006  1.667818e-16  \n",
       "3  0.000078  0.000072  0.000056  0.000033  0.000020  0.000006  3.226420e-16  \n",
       "4  0.000019  0.000015  0.000012  0.000010  0.000004  0.000002  3.281675e-16  \n",
       "\n",
       "[5 rows x 200 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(data_file, sep=';', header=None)\n",
    "df = df.drop(df.columns[[0]], axis=1)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute the correlation matrix\n",
    "corr = df[1:200].corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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\n",
      "text/plain": [
       "<Figure size 2160x1440 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Generate a mask for the upper triangle\n",
    "mask = np.zeros_like(corr, dtype=np.bool)\n",
    "mask[np.triu_indices_from(mask)] = True\n",
    "\n",
    "# Set up the matplotlib figure\n",
    "f, ax = plt.subplots(figsize=(30, 20))\n",
    "\n",
    "# Generate a custom diverging colormap\n",
    "cmap = sns.diverging_palette(220, 10, as_cmap=True)\n",
    "\n",
    "# Draw the heatmap with the mask and correct aspect ratio\n",
    "sns.heatmap(corr, mask=mask, cmap=cmap,\n",
    "            square=True, linewidths=.5, cbar_kws={\"shrink\": .5})\n",
    "savefig('corr_no_label.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "features_corr = []\n",
    "\n",
    "for id_row, row in enumerate(corr):\n",
    "    correlation_score = 0\n",
    "    for id_col, val in enumerate(corr[row]):\n",
    "        if id_col != id_row:\n",
    "            correlation_score += abs(val)\n",
    "\n",
    "    features_corr.append(correlation_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([63, 64, 62, 65, 66, 61, 67, 68, 69, 60, 70, 59, 71, 58, 72, 57, 73,\n",
       "       74, 75, 76])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_highest_values(features_corr, 20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  0,   7,   3,   2,   1,   6,   4,   8,  10,   5,  11,   9,  12,\n",
       "       199,  18,  14,  17,  13,  19,  16])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_lowest_values(features_corr, 20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Correlation analysis between SVD features and labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>191</th>\n",
       "      <th>192</th>\n",
       "      <th>193</th>\n",
       "      <th>194</th>\n",
       "      <th>195</th>\n",
       "      <th>196</th>\n",
       "      <th>197</th>\n",
       "      <th>198</th>\n",
       "      <th>199</th>\n",
       "      <th>200</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.747867</td>\n",
       "      <td>0.015906</td>\n",
       "      <td>0.013435</td>\n",
       "      <td>0.010133</td>\n",
       "      <td>0.009132</td>\n",
       "      <td>0.006958</td>\n",
       "      <td>0.006652</td>\n",
       "      <td>0.006193</td>\n",
       "      <td>0.005855</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000031</td>\n",
       "      <td>0.000028</td>\n",
       "      <td>0.000023</td>\n",
       "      <td>0.000020</td>\n",
       "      <td>0.000014</td>\n",
       "      <td>0.000014</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>0.000007</td>\n",
       "      <td>0.000003</td>\n",
       "      <td>2.854226e-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.742556</td>\n",
       "      <td>0.016040</td>\n",
       "      <td>0.013593</td>\n",
       "      <td>0.010234</td>\n",
       "      <td>0.009399</td>\n",
       "      <td>0.007116</td>\n",
       "      <td>0.006650</td>\n",
       "      <td>0.006278</td>\n",
       "      <td>0.006005</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000057</td>\n",
       "      <td>0.000053</td>\n",
       "      <td>0.000043</td>\n",
       "      <td>0.000033</td>\n",
       "      <td>0.000029</td>\n",
       "      <td>0.000025</td>\n",
       "      <td>0.000022</td>\n",
       "      <td>0.000012</td>\n",
       "      <td>0.000006</td>\n",
       "      <td>3.012061e-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0.741817</td>\n",
       "      <td>0.016078</td>\n",
       "      <td>0.013635</td>\n",
       "      <td>0.010261</td>\n",
       "      <td>0.009387</td>\n",
       "      <td>0.007099</td>\n",
       "      <td>0.006711</td>\n",
       "      <td>0.006291</td>\n",
       "      <td>0.006031</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000067</td>\n",
       "      <td>0.000060</td>\n",
       "      <td>0.000060</td>\n",
       "      <td>0.000048</td>\n",
       "      <td>0.000036</td>\n",
       "      <td>0.000033</td>\n",
       "      <td>0.000017</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>0.000006</td>\n",
       "      <td>1.667818e-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0.728526</td>\n",
       "      <td>0.016202</td>\n",
       "      <td>0.014147</td>\n",
       "      <td>0.010591</td>\n",
       "      <td>0.009438</td>\n",
       "      <td>0.007605</td>\n",
       "      <td>0.006869</td>\n",
       "      <td>0.006649</td>\n",
       "      <td>0.006454</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000118</td>\n",
       "      <td>0.000107</td>\n",
       "      <td>0.000101</td>\n",
       "      <td>0.000078</td>\n",
       "      <td>0.000072</td>\n",
       "      <td>0.000056</td>\n",
       "      <td>0.000033</td>\n",
       "      <td>0.000020</td>\n",
       "      <td>0.000006</td>\n",
       "      <td>3.226420e-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0.747843</td>\n",
       "      <td>0.015910</td>\n",
       "      <td>0.013440</td>\n",
       "      <td>0.010138</td>\n",
       "      <td>0.009140</td>\n",
       "      <td>0.006962</td>\n",
       "      <td>0.006653</td>\n",
       "      <td>0.006193</td>\n",
       "      <td>0.005857</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000032</td>\n",
       "      <td>0.000029</td>\n",
       "      <td>0.000023</td>\n",
       "      <td>0.000019</td>\n",
       "      <td>0.000015</td>\n",
       "      <td>0.000012</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>3.281675e-16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 201 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   0         1         2         3         4         5         6         7    \\\n",
       "0    0  0.747867  0.015906  0.013435  0.010133  0.009132  0.006958  0.006652   \n",
       "1    1  0.742556  0.016040  0.013593  0.010234  0.009399  0.007116  0.006650   \n",
       "2    1  0.741817  0.016078  0.013635  0.010261  0.009387  0.007099  0.006711   \n",
       "3    1  0.728526  0.016202  0.014147  0.010591  0.009438  0.007605  0.006869   \n",
       "4    0  0.747843  0.015910  0.013440  0.010138  0.009140  0.006962  0.006653   \n",
       "\n",
       "        8         9        ...            191       192       193       194  \\\n",
       "0  0.006193  0.005855      ...       0.000031  0.000028  0.000023  0.000020   \n",
       "1  0.006278  0.006005      ...       0.000057  0.000053  0.000043  0.000033   \n",
       "2  0.006291  0.006031      ...       0.000067  0.000060  0.000060  0.000048   \n",
       "3  0.006649  0.006454      ...       0.000118  0.000107  0.000101  0.000078   \n",
       "4  0.006193  0.005857      ...       0.000032  0.000029  0.000023  0.000019   \n",
       "\n",
       "        195       196       197       198       199           200  \n",
       "0  0.000014  0.000014  0.000010  0.000007  0.000003  2.854226e-16  \n",
       "1  0.000029  0.000025  0.000022  0.000012  0.000006  3.012061e-16  \n",
       "2  0.000036  0.000033  0.000017  0.000013  0.000006  1.667818e-16  \n",
       "3  0.000072  0.000056  0.000033  0.000020  0.000006  3.226420e-16  \n",
       "4  0.000015  0.000012  0.000010  0.000004  0.000002  3.281675e-16  \n",
       "\n",
       "[5 rows x 201 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(data_file, sep=';', header=None)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute the correlation matrix\n",
    "corr = df.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 2160x1440 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Generate a mask for the upper triangle\n",
    "mask = np.zeros_like(corr, dtype=np.bool)\n",
    "mask[np.triu_indices_from(mask)] = True\n",
    "\n",
    "# Set up the matplotlib figure\n",
    "f, ax = plt.subplots(figsize=(30, 20))\n",
    "\n",
    "# Generate a custom diverging colormap\n",
    "cmap = sns.diverging_palette(220, 10, as_cmap=True)\n",
    "\n",
    "# Draw the heatmap with the mask and correct aspect ratio\n",
    "sns.heatmap(corr, mask=mask, cmap=cmap,\n",
    "            square=True, linewidths=.5, cbar_kws={\"shrink\": .5})\n",
    "savefig('corr_with_label.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "features_corr = []\n",
    "\n",
    "for id_row, row in enumerate(corr):\n",
    "    for id_col, val in enumerate(corr[row]):\n",
    "        if id_col == 0 and id_row != 0:\n",
    "            features_corr.append(abs(val))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0, 31, 33, 36, 40, 41, 37, 43, 39, 32, 42, 35, 45, 34, 44, 30, 38,\n",
       "       50, 48, 49])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_highest_values(features_corr, 20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([123, 124, 125, 126, 122, 127, 128, 121, 129, 130, 120, 131, 119,\n",
       "       118, 132, 133, 117, 134, 116, 135])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_lowest_values(features_corr, 20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "thesis-venv",
   "language": "python",
   "name": "thesis-venv"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}