Unsupervised.Learning.in.Python/Visualization-with-hierarchical-clustering-and-t-SNE.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualization with hierarchical clustering and t-SNE\n",
    "> A Summary of lecture \"Unsupervised Learning with scikit-learn\", via datacamp\n",
    "\n",
    "- toc: true \n",
    "- badges: true\n",
    "- comments: true\n",
    "- author: Chanseok Kang\n",
    "- categories: [Python, Datacamp, Machine Learning, Visualization]\n",
    "- image: "
   ]
  },
  {
   "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"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualizing hierarchies\n",
    "- Visualizations communicate insight\n",
    "    - 't-SNE': Creates a 2D map of a dataset\n",
    "    - 'Hierarchical clustering'\n",
    "- A hierarchy of groups\n",
    "    - Groups of living things can form a hierarchy\n",
    "    - Cluster are contained in one another\n",
    "- Hierarchical clustering\n",
    "    - Every element begins in a separate cluster\n",
    "    - At each step, the two closest clusters are merged\n",
    "    - Continue until all elements in a single cluster\n",
    "    - This is **\"agglomerative\"(or divisive)** hierarchical clustering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Hierarchical clustering of the grain data\n",
    "In the video, you learned that the SciPy ```linkage()``` function performs hierarchical clustering on an array of samples. Use the ```linkage()``` function to obtain a hierarchical clustering of the grain samples, and use ```dendrogram()``` to visualize the result. A sample of the grain measurements is provided in the array ```samples```, while the variety of each grain sample is given by the list ```varieties```."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Preprocess"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <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",
       "    </tr>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>15.26</td>\n",
       "      <td>14.84</td>\n",
       "      <td>0.8710</td>\n",
       "      <td>5.763</td>\n",
       "      <td>3.312</td>\n",
       "      <td>2.221</td>\n",
       "      <td>5.220</td>\n",
       "      <td>Kama wheat</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>14.88</td>\n",
       "      <td>14.57</td>\n",
       "      <td>0.8811</td>\n",
       "      <td>5.554</td>\n",
       "      <td>3.333</td>\n",
       "      <td>1.018</td>\n",
       "      <td>4.956</td>\n",
       "      <td>Kama wheat</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>14.29</td>\n",
       "      <td>14.09</td>\n",
       "      <td>0.9050</td>\n",
       "      <td>5.291</td>\n",
       "      <td>3.337</td>\n",
       "      <td>2.699</td>\n",
       "      <td>4.825</td>\n",
       "      <td>Kama wheat</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>13.84</td>\n",
       "      <td>13.94</td>\n",
       "      <td>0.8955</td>\n",
       "      <td>5.324</td>\n",
       "      <td>3.379</td>\n",
       "      <td>2.259</td>\n",
       "      <td>4.805</td>\n",
       "      <td>Kama wheat</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>16.14</td>\n",
       "      <td>14.99</td>\n",
       "      <td>0.9034</td>\n",
       "      <td>5.658</td>\n",
       "      <td>3.562</td>\n",
       "      <td>1.355</td>\n",
       "      <td>5.175</td>\n",
       "      <td>Kama wheat</td>\n",
       "    </tr>\n",
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      "text/plain": [
       "       0      1       2      3      4      5      6           7\n",
       "0  15.26  14.84  0.8710  5.763  3.312  2.221  5.220  Kama wheat\n",
       "1  14.88  14.57  0.8811  5.554  3.333  1.018  4.956  Kama wheat\n",
       "2  14.29  14.09  0.9050  5.291  3.337  2.699  4.825  Kama wheat\n",
       "3  13.84  13.94  0.8955  5.324  3.379  2.259  4.805  Kama wheat\n",
       "4  16.14  14.99  0.9034  5.658  3.562  1.355  5.175  Kama wheat"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('./dataset/seeds.csv', header=None)\n",
    "df[7] = df[7].map({1:'Kama wheat', 2:'Rosa wheat', 3:'Canadian wheat'})\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "samples = df.iloc[:, :-1].values\n",
    "varieties = df.iloc[:, -1].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from scipy.cluster.hierarchy import linkage, dendrogram\n",
    "\n",
    "# Calculate the linkage: mergings\n",
    "mergings = linkage(samples, method='complete')\n",
    "\n",
    "# Plot the dendrogram, using varieties as labels\n",
    "plt.figure(figsize=(15, 5))\n",
    "dendrogram(mergings,\n",
    "           labels=varieties,\n",
    "           leaf_rotation=90,\n",
    "           leaf_font_size=6,\n",
    "          );"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Hierarchies of stocks\n",
    "In chapter 1, you used k-means clustering to cluster companies according to their stock price movements. Now, you'll perform hierarchical clustering of the companies. You are given a NumPy array of price movements ```movements```, where the rows correspond to companies, and a list of the company names companies. SciPy hierarchical clustering doesn't fit into a sklearn pipeline, so you'll need to use the ```normalize()``` function from ```sklearn.preprocessing``` instead of ```Normalizer```."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Preprocess"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>2010-01-04</th>\n",
       "      <th>2010-01-05</th>\n",
       "      <th>2010-01-06</th>\n",
       "      <th>2010-01-07</th>\n",
       "      <th>2010-01-08</th>\n",
       "      <th>2010-01-11</th>\n",
       "      <th>2010-01-12</th>\n",
       "      <th>2010-01-13</th>\n",
       "      <th>2010-01-14</th>\n",
       "      <th>2010-01-15</th>\n",
       "      <th>...</th>\n",
       "      <th>2013-10-16</th>\n",
       "      <th>2013-10-17</th>\n",
       "      <th>2013-10-18</th>\n",
       "      <th>2013-10-21</th>\n",
       "      <th>2013-10-22</th>\n",
       "      <th>2013-10-23</th>\n",
       "      <th>2013-10-24</th>\n",
       "      <th>2013-10-25</th>\n",
       "      <th>2013-10-28</th>\n",
       "      <th>2013-10-29</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Apple</th>\n",
       "      <td>0.580000</td>\n",
       "      <td>-0.220005</td>\n",
       "      <td>-3.409998</td>\n",
       "      <td>-1.170000</td>\n",
       "      <td>1.680011</td>\n",
       "      <td>-2.689994</td>\n",
       "      <td>-1.469994</td>\n",
       "      <td>2.779997</td>\n",
       "      <td>-0.680003</td>\n",
       "      <td>-4.999995</td>\n",
       "      <td>...</td>\n",
       "      <td>0.320008</td>\n",
       "      <td>4.519997</td>\n",
       "      <td>2.899987</td>\n",
       "      <td>9.590019</td>\n",
       "      <td>-6.540016</td>\n",
       "      <td>5.959976</td>\n",
       "      <td>6.910011</td>\n",
       "      <td>-5.359962</td>\n",
       "      <td>0.840019</td>\n",
       "      <td>-19.589981</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AIG</th>\n",
       "      <td>-0.640002</td>\n",
       "      <td>-0.650000</td>\n",
       "      <td>-0.210001</td>\n",
       "      <td>-0.420000</td>\n",
       "      <td>0.710001</td>\n",
       "      <td>-0.200001</td>\n",
       "      <td>-1.130001</td>\n",
       "      <td>0.069999</td>\n",
       "      <td>-0.119999</td>\n",
       "      <td>-0.500000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.919998</td>\n",
       "      <td>0.709999</td>\n",
       "      <td>0.119999</td>\n",
       "      <td>-0.480000</td>\n",
       "      <td>0.010002</td>\n",
       "      <td>-0.279998</td>\n",
       "      <td>-0.190003</td>\n",
       "      <td>-0.040001</td>\n",
       "      <td>-0.400002</td>\n",
       "      <td>0.660000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Amazon</th>\n",
       "      <td>-2.350006</td>\n",
       "      <td>1.260009</td>\n",
       "      <td>-2.350006</td>\n",
       "      <td>-2.009995</td>\n",
       "      <td>2.960006</td>\n",
       "      <td>-2.309997</td>\n",
       "      <td>-1.640007</td>\n",
       "      <td>1.209999</td>\n",
       "      <td>-1.790001</td>\n",
       "      <td>-2.039994</td>\n",
       "      <td>...</td>\n",
       "      <td>2.109985</td>\n",
       "      <td>3.699982</td>\n",
       "      <td>9.570008</td>\n",
       "      <td>-3.450013</td>\n",
       "      <td>4.820008</td>\n",
       "      <td>-4.079986</td>\n",
       "      <td>2.579986</td>\n",
       "      <td>4.790009</td>\n",
       "      <td>-1.760009</td>\n",
       "      <td>3.740021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>American express</th>\n",
       "      <td>0.109997</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.260002</td>\n",
       "      <td>0.720002</td>\n",
       "      <td>0.190003</td>\n",
       "      <td>-0.270001</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.300004</td>\n",
       "      <td>0.639999</td>\n",
       "      <td>-0.130001</td>\n",
       "      <td>...</td>\n",
       "      <td>0.680001</td>\n",
       "      <td>2.290001</td>\n",
       "      <td>0.409996</td>\n",
       "      <td>-0.069999</td>\n",
       "      <td>0.100006</td>\n",
       "      <td>0.069999</td>\n",
       "      <td>0.130005</td>\n",
       "      <td>1.849999</td>\n",
       "      <td>0.040001</td>\n",
       "      <td>0.540001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Boeing</th>\n",
       "      <td>0.459999</td>\n",
       "      <td>1.770000</td>\n",
       "      <td>1.549999</td>\n",
       "      <td>2.690003</td>\n",
       "      <td>0.059997</td>\n",
       "      <td>-1.080002</td>\n",
       "      <td>0.360000</td>\n",
       "      <td>0.549999</td>\n",
       "      <td>0.530002</td>\n",
       "      <td>-0.709999</td>\n",
       "      <td>...</td>\n",
       "      <td>1.559997</td>\n",
       "      <td>2.480003</td>\n",
       "      <td>0.019997</td>\n",
       "      <td>-1.220001</td>\n",
       "      <td>0.480003</td>\n",
       "      <td>3.020004</td>\n",
       "      <td>-0.029999</td>\n",
       "      <td>1.940002</td>\n",
       "      <td>1.130005</td>\n",
       "      <td>0.309998</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 963 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  2010-01-04  2010-01-05  2010-01-06  2010-01-07  2010-01-08  \\\n",
       "Apple               0.580000   -0.220005   -3.409998   -1.170000    1.680011   \n",
       "AIG                -0.640002   -0.650000   -0.210001   -0.420000    0.710001   \n",
       "Amazon             -2.350006    1.260009   -2.350006   -2.009995    2.960006   \n",
       "American express    0.109997    0.000000    0.260002    0.720002    0.190003   \n",
       "Boeing              0.459999    1.770000    1.549999    2.690003    0.059997   \n",
       "\n",
       "                  2010-01-11  2010-01-12  2010-01-13  2010-01-14  2010-01-15  \\\n",
       "Apple              -2.689994   -1.469994    2.779997   -0.680003   -4.999995   \n",
       "AIG                -0.200001   -1.130001    0.069999   -0.119999   -0.500000   \n",
       "Amazon             -2.309997   -1.640007    1.209999   -1.790001   -2.039994   \n",
       "American express   -0.270001    0.750000    0.300004    0.639999   -0.130001   \n",
       "Boeing             -1.080002    0.360000    0.549999    0.530002   -0.709999   \n",
       "\n",
       "                  ...  2013-10-16  2013-10-17  2013-10-18  2013-10-21  \\\n",
       "Apple             ...    0.320008    4.519997    2.899987    9.590019   \n",
       "AIG               ...    0.919998    0.709999    0.119999   -0.480000   \n",
       "Amazon            ...    2.109985    3.699982    9.570008   -3.450013   \n",
       "American express  ...    0.680001    2.290001    0.409996   -0.069999   \n",
       "Boeing            ...    1.559997    2.480003    0.019997   -1.220001   \n",
       "\n",
       "                  2013-10-22  2013-10-23  2013-10-24  2013-10-25  2013-10-28  \\\n",
       "Apple              -6.540016    5.959976    6.910011   -5.359962    0.840019   \n",
       "AIG                 0.010002   -0.279998   -0.190003   -0.040001   -0.400002   \n",
       "Amazon              4.820008   -4.079986    2.579986    4.790009   -1.760009   \n",
       "American express    0.100006    0.069999    0.130005    1.849999    0.040001   \n",
       "Boeing              0.480003    3.020004   -0.029999    1.940002    1.130005   \n",
       "\n",
       "                  2013-10-29  \n",
       "Apple             -19.589981  \n",
       "AIG                 0.660000  \n",
       "Amazon              3.740021  \n",
       "American express    0.540001  \n",
       "Boeing              0.309998  \n",
       "\n",
       "[5 rows x 963 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('./dataset/company-stock-movements-2010-2015-incl.csv', index_col=0)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "movements = df.values\n",
    "companies = df.index.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.preprocessing import normalize\n",
    "\n",
    "# Normalize the movements: normalize_movements\n",
    "normalized_movements = normalize(movements)\n",
    "\n",
    "# Calculate the linkage: mergings\n",
    "mergings = linkage(normalized_movements, method='complete')\n",
    "\n",
    "# Plot the dendrogram\n",
    "plt.figure(figsize=(15, 5))\n",
    "dendrogram(mergings, \n",
    "           labels=companies,\n",
    "           leaf_rotation=90,\n",
    "          leaf_font_size=6);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cluster labels in hierarchical clustering\n",
    "- Intermediate clusterings & height on dendrogram\n",
    "    - Height on dendrogram specifies max. distance between merging clusters\n",
    "    - Don't merge clusters further apart than this.\n",
    "- Distance between clusters\n",
    "    - Defined by \"linkage method\"\n",
    "    - In \"complete\" linkage: distance between clusters is max. distance between their samples\n",
    "    - Different linkage method, different hierarchical clustering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Different linkage, different hierarchical clustering!\n",
    "In the video, you saw a hierarchical clustering of the voting countries at the Eurovision song contest using ```'complete'``` linkage. Now, perform a hierarchical clustering of the voting countries with ```'single'``` linkage, and compare the resulting dendrogram with the one in the video. Different linkage, different hierarchical clustering!\n",
    "\n",
    "You are given an array ```samples```. Each row corresponds to a voting country, and each column corresponds to a performance that was voted for. The list ```country_names``` gives the name of each voting country. This dataset was obtained from [Eurovision](http://www.eurovision.tv/page/results)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Preprocess\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>From country</th>\n",
       "      <th>To country</th>\n",
       "      <th>Jury A</th>\n",
       "      <th>Jury B</th>\n",
       "      <th>Jury C</th>\n",
       "      <th>Jury D</th>\n",
       "      <th>Jury E</th>\n",
       "      <th>Jury Rank</th>\n",
       "      <th>Televote Rank</th>\n",
       "      <th>Jury Points</th>\n",
       "      <th>Televote Points</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Albania</td>\n",
       "      <td>Belgium</td>\n",
       "      <td>20</td>\n",
       "      <td>16</td>\n",
       "      <td>24</td>\n",
       "      <td>22</td>\n",
       "      <td>24</td>\n",
       "      <td>25</td>\n",
       "      <td>14</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>Czech Republic</td>\n",
       "      <td>21</td>\n",
       "      <td>15</td>\n",
       "      <td>25</td>\n",
       "      <td>23</td>\n",
       "      <td>16</td>\n",
       "      <td>22</td>\n",
       "      <td>22</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Albania</td>\n",
       "      <td>The Netherlands</td>\n",
       "      <td>22</td>\n",
       "      <td>14</td>\n",
       "      <td>23</td>\n",
       "      <td>24</td>\n",
       "      <td>21</td>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Albania</td>\n",
       "      <td>Azerbaijan</td>\n",
       "      <td>19</td>\n",
       "      <td>12</td>\n",
       "      <td>11</td>\n",
       "      <td>21</td>\n",
       "      <td>11</td>\n",
       "      <td>13</td>\n",
       "      <td>19</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Albania</td>\n",
       "      <td>Hungary</td>\n",
       "      <td>8</td>\n",
       "      <td>13</td>\n",
       "      <td>9</td>\n",
       "      <td>14</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1061</th>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>Ukraine</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>10.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1062</th>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>Malta</td>\n",
       "      <td>18</td>\n",
       "      <td>13</td>\n",
       "      <td>8</td>\n",
       "      <td>17</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>15</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1063</th>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>Georgia</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>19</td>\n",
       "      <td>12.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1064</th>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>Austria</td>\n",
       "      <td>23</td>\n",
       "      <td>15</td>\n",
       "      <td>12</td>\n",
       "      <td>18</td>\n",
       "      <td>13</td>\n",
       "      <td>17</td>\n",
       "      <td>13</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1065</th>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>Armenia</td>\n",
       "      <td>15</td>\n",
       "      <td>22</td>\n",
       "      <td>7</td>\n",
       "      <td>24</td>\n",
       "      <td>18</td>\n",
       "      <td>20</td>\n",
       "      <td>18</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1066 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        From country       To country  Jury A  Jury B  Jury C  Jury D  Jury E  \\\n",
       "0            Albania          Belgium      20      16      24      22      24   \n",
       "1            Albania   Czech Republic      21      15      25      23      16   \n",
       "2            Albania  The Netherlands      22      14      23      24      21   \n",
       "3            Albania       Azerbaijan      19      12      11      21      11   \n",
       "4            Albania          Hungary       8      13       9      14       9   \n",
       "...              ...              ...     ...     ...     ...     ...     ...   \n",
       "1061  United Kingdom          Ukraine      11       1       1       1       5   \n",
       "1062  United Kingdom            Malta      18      13       8      17       4   \n",
       "1063  United Kingdom          Georgia       2       2       3       2       1   \n",
       "1064  United Kingdom          Austria      23      15      12      18      13   \n",
       "1065  United Kingdom          Armenia      15      22       7      24      18   \n",
       "\n",
       "      Jury Rank  Televote Rank  Jury Points  Televote Points  \n",
       "0            25             14          NaN              NaN  \n",
       "1            22             22          NaN              NaN  \n",
       "2            24             24          NaN              NaN  \n",
       "3            13             19          NaN              NaN  \n",
       "4            10             10          1.0              1.0  \n",
       "...         ...            ...          ...              ...  \n",
       "1061          2              6         10.0              5.0  \n",
       "1062         12             15          NaN              NaN  \n",
       "1063          1             19         12.0              NaN  \n",
       "1064         17             13          NaN              NaN  \n",
       "1065         20             18          NaN              NaN  \n",
       "\n",
       "[1066 rows x 11 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('./dataset/eurovision-2016.csv')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "samples = df.iloc[:, 2:7].values[:42]\n",
    "country_names = df.iloc[:, 1].values[:42]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculate the linkage: mergings\n",
    "mergings = linkage(samples, method='single')\n",
    "\n",
    "# Plot the dendrogram\n",
    "plt.figure(figsize=(15, 5))\n",
    "dendrogram(mergings,\n",
    "           labels=country_names,\n",
    "           leaf_rotation=90, \n",
    "           leaf_font_size=6);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Extracting the cluster labels\n",
    "In the previous exercise, you saw that the intermediate clustering of the grain samples at height 6 has 3 clusters. Now, use the ```fcluster()``` function to extract the cluster labels for this intermediate clustering, and compare the labels with the grain varieties using a cross-tabulation."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Preprocess"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>15.26</td>\n",
       "      <td>14.84</td>\n",
       "      <td>0.8710</td>\n",
       "      <td>5.763</td>\n",
       "      <td>3.312</td>\n",
       "      <td>2.221</td>\n",
       "      <td>5.220</td>\n",
       "      <td>Kama wheat</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>14.88</td>\n",
       "      <td>14.57</td>\n",
       "      <td>0.8811</td>\n",
       "      <td>5.554</td>\n",
       "      <td>3.333</td>\n",
       "      <td>1.018</td>\n",
       "      <td>4.956</td>\n",
       "      <td>Kama wheat</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>14.29</td>\n",
       "      <td>14.09</td>\n",
       "      <td>0.9050</td>\n",
       "      <td>5.291</td>\n",
       "      <td>3.337</td>\n",
       "      <td>2.699</td>\n",
       "      <td>4.825</td>\n",
       "      <td>Kama wheat</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>13.84</td>\n",
       "      <td>13.94</td>\n",
       "      <td>0.8955</td>\n",
       "      <td>5.324</td>\n",
       "      <td>3.379</td>\n",
       "      <td>2.259</td>\n",
       "      <td>4.805</td>\n",
       "      <td>Kama wheat</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>16.14</td>\n",
       "      <td>14.99</td>\n",
       "      <td>0.9034</td>\n",
       "      <td>5.658</td>\n",
       "      <td>3.562</td>\n",
       "      <td>1.355</td>\n",
       "      <td>5.175</td>\n",
       "      <td>Kama wheat</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       0      1       2      3      4      5      6           7\n",
       "0  15.26  14.84  0.8710  5.763  3.312  2.221  5.220  Kama wheat\n",
       "1  14.88  14.57  0.8811  5.554  3.333  1.018  4.956  Kama wheat\n",
       "2  14.29  14.09  0.9050  5.291  3.337  2.699  4.825  Kama wheat\n",
       "3  13.84  13.94  0.8955  5.324  3.379  2.259  4.805  Kama wheat\n",
       "4  16.14  14.99  0.9034  5.658  3.562  1.355  5.175  Kama wheat"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('./dataset/seeds.csv', header=None)\n",
    "df[7] = df[7].map({1:'Kama wheat', 2:'Rosa wheat', 3:'Canadian wheat'})\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "samples = df.iloc[:, :-1].values\n",
    "varieties = df.iloc[:, -1].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "varieties  Canadian wheat  Kama wheat  Rosa wheat\n",
      "labels                                           \n",
      "1                       0           0          47\n",
      "2                       0          52          23\n",
      "3                      13           1           0\n",
      "4                      57          17           0\n"
     ]
    }
   ],
   "source": [
    "from scipy.cluster.hierarchy import fcluster\n",
    "\n",
    "mergings = linkage(samples, method='complete')\n",
    "\n",
    "# Use fcluster to extract labels: labels\n",
    "labels = fcluster(mergings, 6, criterion='distance')\n",
    "\n",
    "# Create a DataFrame with labels and varieties as columns: df\n",
    "df = pd.DataFrame({'labels': labels, 'varieties': varieties})\n",
    "\n",
    "# Create crosstab\n",
    "ct = pd.crosstab(df['labels'], df['varieties'])\n",
    "\n",
    "# Display ct\n",
    "print(ct)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## t-SNE for 2-dimensional maps\n",
    "- t-SNE for 2-dimensional maps\n",
    "    - t-SNE = \"t-distributed stochastic neighbor embedding\"\n",
    "    - Maps samples to 2D space (or 3D)\n",
    "    - Map approximately preserves nearness of samples\n",
    "    - Great for inspecting dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### t-SNE visualization of grain dataset\n",
    "In the video, you saw t-SNE applied to the iris dataset. In this exercise, you'll apply t-SNE to the grain samples data and inspect the resulting t-SNE features using a scatter plot."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Preprocess"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('./dataset/seeds.csv', header=None)\n",
    "\n",
    "samples = df.iloc[:, :-1].values\n",
    "variety_numbers = df.iloc[:, -1].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.manifold import TSNE\n",
    "\n",
    "# Create a TSNE instance: model\n",
    "model = TSNE(learning_rate=200)\n",
    "\n",
    "# Apply fit_transform to samples: tsne_features\n",
    "tsne_features = model.fit_transform(samples)\n",
    "\n",
    "# Select the 0th feature: xs\n",
    "xs = tsne_features[:, 0]\n",
    "\n",
    "# Select the 1st feature: ys\n",
    "ys = tsne_features[:, 1]\n",
    "\n",
    "# Scatter plot, coloring by variety_numbers\n",
    "plt.scatter(xs, ys, c=variety_numbers);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### A t-SNE map of the stock market\n",
    "t-SNE provides great visualizations when the individual samples can be labeled. In this exercise, you'll apply t-SNE to the company stock price data. A scatter plot of the resulting t-SNE features, labeled by the company names, gives you a map of the stock market! The stock price movements for each company are available as the array ```normalized_movements``` (these have already been normalized for you). The list ```companies``` gives the name of each company."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Preprocess"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('./dataset/company-stock-movements-2010-2015-incl.csv', index_col=0)\n",
    "movements = df.values\n",
    "companies = df.index.values\n",
    "normalized_movements = normalize(movements)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a TSNE instance: model\n",
    "model = TSNE(learning_rate=50)\n",
    "\n",
    "# Apply fit_transform to normalized_movements: tsne_features\n",
    "tsne_features = model.fit_transform(normalized_movements)\n",
    "\n",
    "# Select the 0th feature: xs\n",
    "xs = tsne_features[:, 0]\n",
    "\n",
    "# Select the 1st feature: ys\n",
    "ys = tsne_features[:, 1]\n",
    "\n",
    "# Scatter plot\n",
    "plt.figure(figsize=(10, 10))\n",
    "plt.scatter(xs, ys, alpha=0.5)\n",
    "\n",
    "# Annotate the points\n",
    "for x, y, company in zip(xs, ys, companies):\n",
    "    plt.annotate(company, (x, y), fontsize=8, alpha=0.75)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}