{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# LAB 06.01 - Clustering companies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "!wget --no-cache -O init.py -q https://raw.githubusercontent.com/rramosp/ai4eng.v1/main/content/init.py\n",
    "import init; init.init(force_download=False); init.get_weblink()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from local.lib.rlxmoocapi import submit, session\n",
    "session.LoginSequence(endpoint=init.endpoint, course_id=init.course_id, lab_id=\"L06.01\", varname=\"student\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dataset\n",
    "\n",
    "observe the following dataset with daily stock data from different companies"
   ]
  },
  {
   "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",
    "from IPython.display import Image\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60, 963)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "d = pd.read_csv(\"local/data/company-stock-movements-2010-2015-incl.csv.gz\", index_col=0)\n",
    "d.shape\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "d.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Apple', 'AIG', 'Amazon', 'American express', 'Boeing',\n",
       "       'Bank of America', 'British American Tobacco', 'Canon', 'Caterpillar',\n",
       "       'Colgate-Palmolive', 'ConocoPhillips', 'Cisco', 'Chevron',\n",
       "       'DuPont de Nemours', 'Dell', 'Ford', 'General Electrics',\n",
       "       'Google/Alphabet', 'Goldman Sachs', 'GlaxoSmithKline', 'Home Depot',\n",
       "       'Honda', 'HP', 'IBM', 'Intel', 'Johnson & Johnson', 'JPMorgan Chase',\n",
       "       'Kimberly-Clark', 'Coca Cola', 'Lookheed Martin', 'MasterCard',\n",
       "       'McDonalds', '3M', 'Microsoft', 'Mitsubishi', 'Navistar',\n",
       "       'Northrop Grumman', 'Novartis', 'Pepsi', 'Pfizer', 'Procter Gamble',\n",
       "       'Philip Morris', 'Royal Dutch Shell', 'SAP', 'Schlumberger', 'Sony',\n",
       "       'Sanofi-Aventis', 'Symantec', 'Toyota', 'Total',\n",
       "       'Taiwan Semiconductor Manufacturing', 'Texas instruments', 'Unilever',\n",
       "       'Valero Energy', 'Walgreen', 'Wells Fargo', 'Wal-Mart', 'Exxon',\n",
       "       'Xerox', 'Yahoo'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "d.index\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## TASK 1: convert `d` into a signed dataframe\n",
    "\n",
    "turn all values to 1 if >0 and -1 otherwise\n",
    "\n",
    "the resulting dataset must look like the following image\n",
    "\n",
    "**HINT**: use [`numpy.sign`](https://numpy.org/doc/stable/reference/generated/numpy.sign.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "Image(\"local/imgs/stock_signed.png\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def signed(d):\n",
    "    result = ....\n",
    "    return result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "manually check your code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "signed(d)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**submit your code**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "student.submit_task(globals(), task_id=\"task_01\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercise 2: implement custom metric\n",
    "\n",
    "the intuition is that we want our metric to reward sets of symbols moving in sync.\n",
    "\n",
    "So, given a matrix:\n",
    "    \n",
    "1. for each column compute the max number of positions with equal value.\n",
    "1.  divide by the number of elements in the column\n",
    "1. average the value for all columns\n",
    "\n",
    "for instance, for the following matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "m  = np.array([[-1,  1,  1, -1],\n",
    "               [-1, -1,  1, -1],\n",
    "               [-1, -1,  1,  1],\n",
    "               [-1, -1, -1,  1],\n",
    "               [-1,  1,  1, -1],\n",
    "               [-1, -1,  1, -1]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. the number of positions of equal value for each column is `6,4,5,4`\n",
    "2. normalizing by the number of elements per column (6) is `1,0.6667,.8333,.6667`\n",
    "3. the mean of the previous result is about `0.792`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**complete the following function** to compute this metric, assuming the input matrix `m` is a numpy array containing only values of -1 and 1\n",
    "\n",
    "**suggested strategy**: loop over the columns, compute the number of 1's and -1's and keep whichever is grater in each column, divide by the length of m (you will have then one number per column), and take the average.\n",
    "\n",
    "**challenge 1**: solve it with one line\n",
    "\n",
    "**challenge 2**: solve it without a loop, but using the `axis` argument  when using `np.sum` and `np.max`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "def mean_sync_move(m):\n",
    "    result = ...\n",
    "    return result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "manually check your code "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "mean_sync_move(m)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "the following must return `1`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "mean_sync_move(np.ones((10,2)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "obtain the `mean_sync_move` of the full dataset **AFTER** converting it into signed. It should be around `0.72`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "mean_sync_move(signed(d).values)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**submit your code**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "student.submit_task(globals(), task_id=\"task_02\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Task 3: Cluster the full dataset \n",
    "\n",
    "complete the following function such that upon receiving a dataset:\n",
    "\n",
    "- obtains the signed dataset\n",
    "- uses `sklearn` `KMeans` to cluster the dataset with the given number of clusters (`n_clusters`)\n",
    "- predicts the cluster number of each company\n",
    "- for each cluster number:\n",
    "    - filters the signed dataset so that it keeps the companies belonging to that cluster.\n",
    "    - computes the `mean_sync_move` of the resulting filtered signed dataset.\n",
    "\n",
    "your function must return a dataframe with one row per cluster containing \n",
    "\n",
    "   - the cluster number\n",
    "   - the number of companies in the cluster\n",
    "   - the `mean_sync_move` of each cluster\n",
    "   \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "for instance, for 4 clusters your resulting dataframe should look like the following one. The cluster numbers might be in a different order, but the index and the columns **MUST** be as illustrated.\n",
    "\n",
    "**important**: use `random_state=2` when creating the KMeans instance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "Image(filename='local/imgs/labclusters.png')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "\n",
    "def cluster_dataset(d, n_clusters):\n",
    "    \n",
    "    def signed(d):\n",
    "        return ... # your code here\n",
    "    \n",
    "    def mean_sync_move(m):\n",
    "        return ... # your code here\n",
    "    \n",
    "    d = signed(d)\n",
    "    \n",
    "    c = KMeans(....)\n",
    "    y = c.fit_predict(...)\n",
    "    r = pd.DataFrame(...., columns=[\"cluster\", \"nb_companies\", \"mean_sync_move\"])    \n",
    "   \n",
    "    r.index = r.cluster.values.astype(int)\n",
    "    r.index.name = \"cluster\"\n",
    "    \n",
    "    del(r[\"cluster\"])\n",
    "    return r"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "cluster_dataset(d, n_clusters=4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**submit your code**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "student.submit_task(globals(), task_id=\"task_03\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Compare with PCA\n",
    "\n",
    "we apply PCA with 2 components to +1/-1 dataset and visualize clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.decomposition import PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_clusters = 7\n",
    "\n",
    "X = PCA(n_components=2).fit_transform(signed(d).values)\n",
    "y = KMeans(n_clusters=10).fit_predict(signed(d).values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "cmap = plt.cm.hot\n",
    "plt.figure(figsize=(20,10))\n",
    "plt.scatter(X[:,0], X[:,1], color=cmap((y*255./(n_clusters-1)).astype(int)), s=100, edgecolor=\"black\", lw=2)\n",
    "for i in range(len(d)):\n",
    "    name = d.index[i]\n",
    "    plt.text(X[i,0]+.1, X[i,1]+.1,d.index[i], fontsize=14)\n",
    "plt.axis(\"off\");\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "---\n",
    "\n",
    "## Other suggested exercises\n",
    "\n",
    "Try the following exercises from O'Reilly's [Python Data Science Handbook](https://jakevdp.github.io/PythonDataScienceHandbook/), and its associated [Notebook Repository](https://github.com/jakevdp/PythonDataScienceHandbook) at [Chapter about Clustering](https://github.com/jakevdp/PythonDataScienceHandbook/blob/main/notebooks/05.11-K-Means.ipynb) :\n",
    "\n",
    "- Clustering, **Exercise 1: k-Means on Digits**\n",
    "- Clustering, **Exercise 2: k-Means for Color Compression**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}