Recommendations_with_IBM.ipynb

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    "# Recommendations with IBM\n",
    "\n",
    "In this notebook, you will be putting your recommendation skills to use on real data from the IBM Watson Studio platform. \n",
    "\n",
    "\n",
    "You may either submit your notebook through the workspace here, or you may work from your local machine and submit through the next page.  Either way assure that your code passes the project [RUBRIC](https://review.udacity.com/#!/rubrics/2322/view).  **Please save regularly.**\n",
    "\n",
    "By following the table of contents, you will build out a number of different methods for making recommendations that can be used for different situations. \n",
    "\n",
    "\n",
    "## Table of Contents\n",
    "\n",
    "I. [Exploratory Data Analysis](#Exploratory-Data-Analysis)<br>\n",
    "II. [Rank Based Recommendations](#Rank)<br>\n",
    "III. [User-User Based Collaborative Filtering](#User-User)<br>\n",
    "IV. [Content Based Recommendations (EXTRA - NOT REQUIRED)](#Content-Recs)<br>\n",
    "V. [Matrix Factorization](#Matrix-Fact)<br>\n",
    "VI. [Extras & Concluding](#conclusions)\n",
    "\n",
    "At the end of the notebook, you will find directions for how to submit your work.  Let's get started by importing the necessary libraries and reading in the data."
   ]
  },
  {
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   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import project_tests as t\n",
    "import pickle\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "df = pd.read_csv('data/user-item-interactions.csv')\n",
    "df_content = pd.read_csv('data/articles_community.csv')\n",
    "del df['Unnamed: 0']\n",
    "del df_content['Unnamed: 0']\n",
    "\n",
    "# Show df to get an idea of the data\n",
    "df.head()"
   ]
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   "cell_type": "code",
   "execution_count": 42,
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    "# Show df_content to get an idea of the data\n",
    "df_content.head()"
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   "source": [
    "### <a class=\"anchor\" id=\"Exploratory-Data-Analysis\">Part I : Exploratory Data Analysis</a>\n",
    "\n",
    "Use the dictionary and cells below to provide some insight into the descriptive statistics of the data.\n",
    "\n",
    "`1.` What is the distribution of how many articles a user interacts with in the dataset?  Provide a visual and descriptive statistics to assist with giving a look at the number of times each user interacts with an article.  "
   ]
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   "cell_type": "code",
   "execution_count": 43,
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    "df.groupby('email')['article_id'].count()"
   ]
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>0108ce3220657a9a89a85bdec959b0f2976dd51c</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>011455e91a24c1fb815a4deac6b6eaf5ad16819e</th>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01198c58d684d79c9026abe355cfb532cb524dc5</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>011ae4de07ffb332b0f51c155a35c23c80294962</th>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>011fcfb582be9534e9a275336f7e7c3717100381</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0129dfcdb701b6e1d309934be6393004c6683a2d</th>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01327bbc4fd7bfe8ad62e599453d2876b928e725</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01455f0ab0a5a22a93d94ad35f6e78431aa90625</th>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>014dedab269f1453c647598c92a3fa37b39eed97</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>014e4fe6e6c5eb3fe5ca0b16c16fb4599df6375c</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01560f88312a91894d254e6406c25df19f0ad5e8</th>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fe5396e3762c36767c9c915f7ed1731691d7e4b4</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fe5480ff15f0ac51eeb2314a192351f168d7aad7</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fe56a49b62752708ed2f6e30677c57881f7b78d1</th>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fe5885b80e91be887510a0b6dd04e011178d6364</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fe5f9d7528518e00b0a73c7a3994afc335496961</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fe66aa534c7824eca663b84b99a437a98a9b026e</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fe69c72c964a8346dbc7763309c4e07d818d360f</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fe88d1f683f308b32fb3d7554f007cc55cc48df5</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fe8c1cb974e39d8ea8c005044e927b3f0de8acd0</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fe90d98b0287090fe8e653bafba6ed3eff19331e</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fe9327be39fd457df70e83d3fc8cba9b8b3f95b1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>feaea388105a4ccc48795b191bbf0c26a23b1356</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fef0c6be3a2ed226e1fb8a811b0ee68a389f6f3c</th>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fef28e45f7217026b2684d1783a2e18b061bdffb</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fef3bc88def1aa787c99957ded7d5b2c0edc040e</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ff27ffd93e21154b8a9cf2722f2cc0f75dc39eff</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ff288722b76eba5209cdbf9158c6dfbf229b9129</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ff452614b91f4c9bd965150b1a82e7bf18f59334</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ff4d3e1c359cfbb73bcae07fa1eb62c45da2b161</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ff55d0c0b2a4f56aae87c2a21afb7070ab34383d</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ff6e82c763fe2443643e48a03e239eb635f406dc</th>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ff7a0f59ba022102ad22981141a7182c4d8273c3</th>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ff833869969184d86f870f98405e7988eccc2309</th>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ff979e07f9d906a32ba35a9b75fd9585f6306dbc</th>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ffaefa3a1bc2d074d9a14c9924d4e67a46c35410</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ffc6cfa435937ca0df967b44e9178439d04e3537</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ffc96f8fbb35aac4cb0029332b0fc78e7766bb5d</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ffe3d0543c9046d35c2ee3724ea9d774dff98a32</th>\n",
       "      <td>27</td>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fff9fc3ec67bd18ed57a34ed1e67410942c4cd81</th>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fffb93a166547448a0ff0232558118d59395fecd</th>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5148 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                          article_id  title  email\n",
       "email                                                             \n",
       "0000b6387a0366322d7fbfc6434af145adf7fed1          12     12      1\n",
       "001055fc0bb67f71e8fa17002342b256a30254cd           4      4      1\n",
       "00148e4911c7e04eeff8def7bbbdaf1c59c2c621           3      3      1\n",
       "001a852ecbd6cc12ab77a785efa137b2646505fe           5      5      1\n",
       "001fc95b90da5c3cb12c501d201a915e4f093290           2      2      1\n",
       "0042719415c4fca7d30bd2d4e9d17c5fc570de13           2      2      1\n",
       "00772abe2d0b269b2336fc27f0f4d7cb1d2b65d7           2      2      1\n",
       "008ba1d5b4ebf54babf516a2d5aa43e184865da5          10     10      1\n",
       "008ca24b82c41d513b3799d09ae276d37f92ce72           1      1      1\n",
       "008dfc7a327b5186244caec48e0ab61610a0c660          10     10      1\n",
       "009af4e0537378bf8e8caf0ad0e2994f954d822e           1      1      1\n",
       "00bda305223d05f6df5d77de41abd2a0c7d895fe           4      4      1\n",
       "00c2d5190e8c6b821b0e3848bf56f6e47e428994           3      3      1\n",
       "00ced21f957bbcee5edf7b107b2bd05628b04774           4      4      1\n",
       "00d9337ecd5f70fba1c4c7a78e21b3532e0112c4           1      1      1\n",
       "00e524e4f13137a6fac54f9c71d7769c6507ecde           8      8      1\n",
       "00f8341cbecd6af00ba8c78b3bb6ec49adf83248           2      2      1\n",
       "00f946b14100f0605fa25089437ee9486378872c           1      1      1\n",
       "01041260c97ab9221d923b0a2c525437f148d589           2      2      1\n",
       "0108ce3220657a9a89a85bdec959b0f2976dd51c           3      3      1\n",
       "011455e91a24c1fb815a4deac6b6eaf5ad16819e           9      9      1\n",
       "01198c58d684d79c9026abe355cfb532cb524dc5           1      1      1\n",
       "011ae4de07ffb332b0f51c155a35c23c80294962          29     29      1\n",
       "011fcfb582be9534e9a275336f7e7c3717100381           4      4      1\n",
       "0129dfcdb701b6e1d309934be6393004c6683a2d          12     12      1\n",
       "01327bbc4fd7bfe8ad62e599453d2876b928e725           3      3      1\n",
       "01455f0ab0a5a22a93d94ad35f6e78431aa90625           6      6      1\n",
       "014dedab269f1453c647598c92a3fa37b39eed97           2      2      1\n",
       "014e4fe6e6c5eb3fe5ca0b16c16fb4599df6375c           1      1      1\n",
       "01560f88312a91894d254e6406c25df19f0ad5e8           9      9      1\n",
       "...                                              ...    ...    ...\n",
       "fe5396e3762c36767c9c915f7ed1731691d7e4b4           1      1      1\n",
       "fe5480ff15f0ac51eeb2314a192351f168d7aad7           1      1      1\n",
       "fe56a49b62752708ed2f6e30677c57881f7b78d1          10     10      1\n",
       "fe5885b80e91be887510a0b6dd04e011178d6364           3      3      1\n",
       "fe5f9d7528518e00b0a73c7a3994afc335496961           3      3      1\n",
       "fe66aa534c7824eca663b84b99a437a98a9b026e           2      2      1\n",
       "fe69c72c964a8346dbc7763309c4e07d818d360f           2      2      1\n",
       "fe88d1f683f308b32fb3d7554f007cc55cc48df5           1      1      1\n",
       "fe8c1cb974e39d8ea8c005044e927b3f0de8acd0           2      2      1\n",
       "fe90d98b0287090fe8e653bafba6ed3eff19331e           1      1      1\n",
       "fe9327be39fd457df70e83d3fc8cba9b8b3f95b1           1      1      1\n",
       "feaea388105a4ccc48795b191bbf0c26a23b1356           4      4      1\n",
       "fef0c6be3a2ed226e1fb8a811b0ee68a389f6f3c          11     11      1\n",
       "fef28e45f7217026b2684d1783a2e18b061bdffb           3      3      1\n",
       "fef3bc88def1aa787c99957ded7d5b2c0edc040e           3      3      1\n",
       "ff27ffd93e21154b8a9cf2722f2cc0f75dc39eff           1      1      1\n",
       "ff288722b76eba5209cdbf9158c6dfbf229b9129           1      1      1\n",
       "ff452614b91f4c9bd965150b1a82e7bf18f59334           2      2      1\n",
       "ff4d3e1c359cfbb73bcae07fa1eb62c45da2b161           3      3      1\n",
       "ff55d0c0b2a4f56aae87c2a21afb7070ab34383d           1      1      1\n",
       "ff6e82c763fe2443643e48a03e239eb635f406dc          13     13      1\n",
       "ff7a0f59ba022102ad22981141a7182c4d8273c3           5      5      1\n",
       "ff833869969184d86f870f98405e7988eccc2309           9      9      1\n",
       "ff979e07f9d906a32ba35a9b75fd9585f6306dbc          15     15      1\n",
       "ffaefa3a1bc2d074d9a14c9924d4e67a46c35410           1      1      1\n",
       "ffc6cfa435937ca0df967b44e9178439d04e3537           1      1      1\n",
       "ffc96f8fbb35aac4cb0029332b0fc78e7766bb5d           2      2      1\n",
       "ffe3d0543c9046d35c2ee3724ea9d774dff98a32          27     27      1\n",
       "fff9fc3ec67bd18ed57a34ed1e67410942c4cd81           7      7      1\n",
       "fffb93a166547448a0ff0232558118d59395fecd           6      6      1\n",
       "\n",
       "[5148 rows x 3 columns]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('email').nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1429.0    937\n",
       "1330.0    927\n",
       "1431.0    671\n",
       "1427.0    643\n",
       "1364.0    627\n",
       "Name: article_id, dtype: int64"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#most read articles\n",
    "df['article_id'].value_counts().nlargest(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "364"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The maximum number of user-article interactions by any 1 user is 364\n",
    "df.groupby('email')['article_id'].count().max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2b6c0f514c2f2b04ad3c4583407dccd0810469ee    364\n",
       "77959baaa9895a7e2bdc9297f8b27c1b6f2cb52a    363\n",
       "2f5c7feae533ce046f2cb16fb3a29fe00528ed66    170\n",
       "a37adec71b667b297ed2440a9ff7dad427c7ac85    169\n",
       "8510a5010a5d4c89f5b07baac6de80cd12cfaf93    160\n",
       "Name: email, dtype: int64"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Users most active\n",
    "df['email'].value_counts().nlargest(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.0"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# median value for articles read by a user\n",
    "df['email'].value_counts().median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fill in the median and maximum number of user_article interactions below\n",
    "median_val = 3 #50% of individuals interact with _3_ number of articles or fewer.\n",
    "max_views_by_user = 364 # The maximum number of user-article interactions by any 1 user is _364."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/matplotlib/cbook/deprecation.py:106: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "  warnings.warn(message, mplDeprecation, stacklevel=1)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9157c7f630>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualization to show the number of times each user interacts with an article\n",
    "\n",
    "plt_1 = df['email'].value_counts()\n",
    "plt_1.plot(kind='bar',figsize=(15,8),color='green')\n",
    "ax1 = plt.axes()\n",
    "x_axis = ax1.axes.get_xaxis()\n",
    "x_axis.set_visible(False)\n",
    "plt.title('The distribution of number of articles read by each user')\n",
    "plt.ylabel('Number of articles read')\n",
    "plt.ylim(top=170)\n",
    "plt.show()\n",
    "plt.close();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`2.` Explore and remove duplicate articles from the **df_content** dataframe.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "doc_body duplicates: 19\n",
      "doc_description duplicates: 33\n",
      "doc_full_name duplicates: 5\n",
      "doc_status duplicates: 1055\n",
      "article_id duplicates: 5\n"
     ]
    }
   ],
   "source": [
    "# Find and explore duplicate articles\n",
    "for col in df_content.columns:\n",
    "    print('{} duplicates: {}'.format(col, df_content.duplicated(col).sum()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Remove any rows that have the same article_id - only keep the first\n",
    "df_content.drop_duplicates(subset='article_id', keep='first', inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`3.` Use the cells below to find:\n",
    "\n",
    "**a.** The number of unique articles that have an interaction with a user.  \n",
    "**b.** The number of unique articles in the dataset (whether they have any interactions or not).<br>\n",
    "**c.** The number of unique users in the dataset. (excluding null values) <br>\n",
    "**d.** The number of user-article interactions in the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "714"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Number of unique articles\n",
    "df['article_id'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1051"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The number of unique articles on the IBM platform\n",
    "df_content['article_id'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5148"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Number of unique users\n",
    "df['email'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "45993"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The number of user-article interactions\n",
    "df.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "unique_articles = 714# The number of unique articles that have at least one interaction\n",
    "total_articles = 1051# The number of unique articles on the IBM platform\n",
    "unique_users = 5148 # The number of unique users\n",
    "user_article_interactions = 45993 # The number of user-article interactions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`4.` Use the cells below to find the most viewed **article_id**, as well as how often it was viewed.  After talking to the company leaders, the `email_mapper` function was deemed a reasonable way to map users to ids.  There were a small number of null values, and it was found that all of these null values likely belonged to a single user (which is how they are stored using the function below)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "# The most viewed article\n",
    "articles_viewed = df['article_id'].value_counts().nlargest(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'1429.0'"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "articles_viewed.index[0].astype(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "937"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Number of times most viewed article was viewed\n",
    "articles_viewed.iloc[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "most_viewed_article_id = '1429.0' # The most viewed article in the dataset as a string with one value following the decimal \n",
    "max_views = 937 # The most viewed article in the dataset was viewed how many times?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "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>article_id</th>\n",
       "      <th>title</th>\n",
       "      <th>user_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1430.0</td>\n",
       "      <td>using pixiedust for fast, flexible, and easier...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1314.0</td>\n",
       "      <td>healthcare python streaming application demo</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1429.0</td>\n",
       "      <td>use deep learning for image classification</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1338.0</td>\n",
       "      <td>ml optimization using cognitive assistant</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1276.0</td>\n",
       "      <td>deploy your python model as a restful api</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   article_id                                              title  user_id\n",
       "0      1430.0  using pixiedust for fast, flexible, and easier...        1\n",
       "1      1314.0       healthcare python streaming application demo        2\n",
       "2      1429.0         use deep learning for image classification        3\n",
       "3      1338.0          ml optimization using cognitive assistant        4\n",
       "4      1276.0          deploy your python model as a restful api        5"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## No need to change the code here - this will be helpful for later parts of the notebook\n",
    "# Run this cell to map the user email to a user_id column and remove the email column\n",
    "\n",
    "def email_mapper():\n",
    "    coded_dict = dict()\n",
    "    cter = 1\n",
    "    email_encoded = []\n",
    "    \n",
    "    for val in df['email']:\n",
    "        if val not in coded_dict:\n",
    "            coded_dict[val] = cter\n",
    "            cter+=1\n",
    "        \n",
    "        email_encoded.append(coded_dict[val])\n",
    "    return email_encoded\n",
    "\n",
    "email_encoded = email_mapper()\n",
    "del df['email']\n",
    "df['user_id'] = email_encoded\n",
    "\n",
    "# show header\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "It looks like you have everything right here! Nice job!\n"
     ]
    }
   ],
   "source": [
    "## If you stored all your results in the variable names above, \n",
    "## you shouldn't need to change anything in this cell\n",
    "\n",
    "sol_1_dict = {\n",
    "    '`50% of individuals have _____ or fewer interactions.`': median_val,\n",
    "    '`The total number of user-article interactions in the dataset is ______.`': user_article_interactions,\n",
    "    '`The maximum number of user-article interactions by any 1 user is ______.`': max_views_by_user,\n",
    "    '`The most viewed article in the dataset was viewed _____ times.`': max_views,\n",
    "    '`The article_id of the most viewed article is ______.`': most_viewed_article_id,\n",
    "    '`The number of unique articles that have at least 1 rating ______.`': unique_articles,\n",
    "    '`The number of unique users in the dataset is ______`': unique_users,\n",
    "    '`The number of unique articles on the IBM platform`': total_articles\n",
    "}\n",
    "\n",
    "# Test your dictionary against the solution\n",
    "t.sol_1_test(sol_1_dict)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <a class=\"anchor\" id=\"Rank\">Part II: Rank-Based Recommendations</a>\n",
    "\n",
    "Unlike in the earlier lessons, we don't actually have ratings for whether a user liked an article or not.  We only know that a user has interacted with an article.  In these cases, the popularity of an article can really only be based on how often an article was interacted with.\n",
    "\n",
    "`1.` Fill in the function below to return the **n** top articles ordered with most interactions as the top. Test your function using the tests below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_top_articles(n, df=df):\n",
    "    '''\n",
    "    INPUT:\n",
    "    n - (int) the number of top articles to return\n",
    "    df - (pandas dataframe) df as defined at the top of the notebook \n",
    "    \n",
    "    OUTPUT:\n",
    "    top_articles - (list) A list of the top 'n' article titles \n",
    "    \n",
    "    '''\n",
    "    top_articles = list(df['title'].value_counts().nlargest(n).index[:n])\n",
    "    \n",
    "    return top_articles # Return the top article titles from df (not df_content)\n",
    "\n",
    "def get_top_article_ids(n, df=df):\n",
    "    '''\n",
    "    INPUT:\n",
    "    n - (int) the number of top articles to return\n",
    "    df - (pandas dataframe) df as defined at the top of the notebook \n",
    "    \n",
    "    OUTPUT:\n",
    "    top_articles - (list) A list of the top 'n' article titles \n",
    "    \n",
    "    '''\n",
    "    top_articles = list(df['article_id'].value_counts().nlargest(n).index[:n].astype(str))\n",
    " \n",
    "    return top_articles # Return the top article ids"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['use deep learning for image classification', 'insights from new york car accident reports', 'visualize car data with brunel', 'use xgboost, scikit-learn & ibm watson machine learning apis', 'predicting churn with the spss random tree algorithm', 'healthcare python streaming application demo', 'finding optimal locations of new store using decision optimization', 'apache spark lab, part 1: basic concepts', 'analyze energy consumption in buildings', 'gosales transactions for logistic regression model']\n",
      "['1429.0', '1330.0', '1431.0', '1427.0', '1364.0', '1314.0', '1293.0', '1170.0', '1162.0', '1304.0']\n"
     ]
    }
   ],
   "source": [
    "print(get_top_articles(10))\n",
    "print(get_top_article_ids(10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Your top_5 looks like the solution list! Nice job.\n",
      "Your top_10 looks like the solution list! Nice job.\n",
      "Your top_20 looks like the solution list! Nice job.\n"
     ]
    }
   ],
   "source": [
    "# Test your function by returning the top 5, 10, and 20 articles\n",
    "top_5 = get_top_articles(5)\n",
    "top_10 = get_top_articles(10)\n",
    "top_20 = get_top_articles(20)\n",
    "\n",
    "# Test each of your three lists from above\n",
    "t.sol_2_test(get_top_articles)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <a class=\"anchor\" id=\"User-User\">Part III: User-User Based Collaborative Filtering</a>\n",
    "\n",
    "\n",
    "`1.` Use the function below to reformat the **df** dataframe to be shaped with users as the rows and articles as the columns.  \n",
    "\n",
    "* Each **user** should only appear in each **row** once.\n",
    "\n",
    "\n",
    "* Each **article** should only show up in one **column**.  \n",
    "\n",
    "\n",
    "* **If a user has interacted with an article, then place a 1 where the user-row meets for that article-column**.  It does not matter how many times a user has interacted with the article, all entries where a user has interacted with an article should be a 1.  \n",
    "\n",
    "\n",
    "* **If a user has not interacted with an item, then place a zero where the user-row meets for that article-column**. \n",
    "\n",
    "Use the tests to make sure the basic structure of your matrix matches what is expected by the solution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create the user-article matrix with 1's and 0's\n",
    "\n",
    "def create_user_item_matrix(df):\n",
    "    '''\n",
    "    INPUT:\n",
    "    df - pandas dataframe with article_id, title, user_id columns\n",
    "    \n",
    "    OUTPUT:\n",
    "    user_item - user item matrix \n",
    "    \n",
    "    Description:\n",
    "    Return a matrix with user ids as rows and article ids on the columns with 1 values where a user interacted with \n",
    "    an article and a 0 otherwise\n",
    "    '''\n",
    "    # Fill in the function here\n",
    "    \n",
    "    new_df = df.drop('title',axis=1)\n",
    "    new_df = new_df.groupby(['user_id','article_id'])['article_id'].max().unstack()\n",
    "    user_item = new_df.notnull().astype('int')\n",
    "    \n",
    "    return user_item # return the user_item matrix \n",
    "\n",
    "user_item = create_user_item_matrix(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "You have passed our quick tests!  Please proceed!\n"
     ]
    }
   ],
   "source": [
    "## Tests: You should just need to run this cell.  Don't change the code.\n",
    "assert user_item.shape[0] == 5149, \"Oops!  The number of users in the user-article matrix doesn't look right.\"\n",
    "assert user_item.shape[1] == 714, \"Oops!  The number of articles in the user-article matrix doesn't look right.\"\n",
    "assert user_item.sum(axis=1)[1] == 36, \"Oops!  The number of articles seen by user 1 doesn't look right.\"\n",
    "print(\"You have passed our quick tests!  Please proceed!\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`2.` Complete the function below which should take a user_id and provide an ordered list of the most similar users to that user (from most similar to least similar).  The returned result should not contain the provided user_id, as we know that each user is similar to him/herself. Because the results for each user here are binary, it (perhaps) makes sense to compute similarity as the dot product of two users. \n",
    "\n",
    "Use the tests to test your function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "def find_similar_users(user_id, user_item=user_item):\n",
    "    '''\n",
    "    INPUT:\n",
    "    user_id - (int) a user_id\n",
    "    user_item - (pandas dataframe) matrix of users by articles: \n",
    "                1's when a user has interacted with an article, 0 otherwise\n",
    "    \n",
    "    OUTPUT:\n",
    "    similar_users - (list) an ordered list where the closest users (largest dot product users)\n",
    "                    are listed first\n",
    "    \n",
    "    Description:\n",
    "    Computes the similarity of every pair of users based on the dot product\n",
    "    Returns an ordered\n",
    "    \n",
    "    '''\n",
    "    # compute similarity of each user to the provided user\n",
    "    matrix = user_item.dot(np.transpose(user_item))\n",
    "    \n",
    "    # sort by similarity and create list of just the ids\n",
    "    mat_lst_user_id = list(matrix.loc[(user_id),:].sort_values(ascending=False).keys())\n",
    "     \n",
    "    # remove the own user's id\n",
    "    mat_lst_user_id.remove(user_id)\n",
    "    most_similar_users = mat_lst_user_id\n",
    "       \n",
    "    return most_similar_users # return a list of the users in order from most to least similar\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The 10 most similar users to user 1 are: [3933, 23, 3782, 203, 4459, 131, 3870, 46, 4201, 5041]\n",
      "The 5 most similar users to user 3933 are: [1, 23, 3782, 4459, 203]\n",
      "The 3 most similar users to user 46 are: [4201, 23, 3782]\n"
     ]
    }
   ],
   "source": [
    "# Do a spot check of your function\n",
    "print(\"The 10 most similar users to user 1 are: {}\".format(find_similar_users(1)[:10]))\n",
    "print(\"The 5 most similar users to user 3933 are: {}\".format(find_similar_users(3933)[:5]))\n",
    "print(\"The 3 most similar users to user 46 are: {}\".format(find_similar_users(46)[:3]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`3.` Now that you have a function that provides the most similar users to each user, you will want to use these users to find articles you can recommend.  Complete the functions below to return the articles you would recommend to each user. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_article_names(article_ids, df=df):\n",
    "    '''\n",
    "    INPUT:\n",
    "    article_ids - (list) a list of article ids\n",
    "    df - (pandas dataframe) df as defined at the top of the notebook\n",
    "    \n",
    "    OUTPUT:\n",
    "    article_names - (list) a list of article names associated with the list of article ids \n",
    "                    (this is identified by the title column)\n",
    "    '''\n",
    "    article_names = []\n",
    "    for article in article_ids:\n",
    "        name = df[df.article_id == float(article)].iloc[0,1]\n",
    "        article_names.append(name)\n",
    "           \n",
    "    return article_names # Return the article names associated with list of article ids\n",
    "\n",
    "\n",
    "def get_user_articles(user_id, user_item=user_item):\n",
    "    '''\n",
    "    INPUT:\n",
    "    user_id - (int) a user id\n",
    "    user_item - (pandas dataframe) matrix of users by articles: \n",
    "                1's when a user has interacted with an article, 0 otherwise\n",
    "    \n",
    "    OUTPUT:\n",
    "    article_ids - (list) a list of the article ids seen by the user\n",
    "    article_names - (list) a list of article names associated with the list of article ids \n",
    "                    (this is identified by the doc_full_name column in df_content)\n",
    "    \n",
    "    Description:\n",
    "    Provides a list of the article_ids and article titles that have been seen by a user\n",
    "    '''\n",
    "    article_ids = (user_item.iloc[user_id-1][user_item.iloc[user_id-1]==1].index).astype(str)\n",
    "    article_ids = list(article_ids)\n",
    "    article_names = get_article_names(article_ids)\n",
    "    \n",
    "    return article_ids, article_names # return the ids and names\n",
    "\n",
    "\n",
    "def user_user_recs(user_id, m=10):\n",
    "    '''\n",
    "    INPUT:\n",
    "    user_id - (int) a user id\n",
    "    m - (int) the number of recommendations you want for the user\n",
    "    \n",
    "    OUTPUT:\n",
    "    recs - (list) a list of recommendations for the user\n",
    "    \n",
    "    Description:\n",
    "    Loops through the users based on closeness to the input user_id\n",
    "    For each user - finds articles the user hasn't seen before and provides them as recs\n",
    "    Does this until m recommendations are found\n",
    "    \n",
    "    Notes:\n",
    "    Users who are the same closeness are chosen arbitrarily as the 'next' user\n",
    "    \n",
    "    For the user where the number of recommended articles starts below m \n",
    "    and ends exceeding m, the last items are chosen arbitrarily\n",
    "    \n",
    "    '''\n",
    "    most_similar_users = find_similar_users(user_id)\n",
    "    \n",
    "    recs = []\n",
    "    count = 0\n",
    "    \n",
    "    for user in range(len(most_similar_users)):\n",
    "        sim_id, sim_name = get_user_articles(user)\n",
    "        us_id, us_name = get_user_articles(user_id)\n",
    "        diff = np.setdiff1d(sim_id,us_id)\n",
    "        diff = diff.astype(str)\n",
    "    \n",
    "        for rec in diff:\n",
    "            if count < m:\n",
    "                recs.append(rec)\n",
    "                count += 1\n",
    "        \n",
    "    return recs # return your recommendations for this user_id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['higher-order logistic regression for large datasets',\n",
       " 'using deep learning to reconstruct high-resolution audio',\n",
       " 'build a python app on the streaming analytics service',\n",
       " 'healthcare python streaming application demo',\n",
       " 'use r dataframes & ibm watson natural language understanding',\n",
       " 'machine learning for the enterprise.',\n",
       " 'adolescent fertility rate (births per 1,000 women ages 15-19), worldwide',\n",
       " 'airbnb data for analytics: amsterdam calendar',\n",
       " 'practical tutorial on random forest and parameter tuning in r',\n",
       " 'analyze precipitation data']"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check Results\n",
    "get_article_names(user_user_recs(1, 10)) # Return 10 recommendations for user 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "If this is all you see, you passed all of our tests!  Nice job!\n"
     ]
    }
   ],
   "source": [
    "# Test your functions here - No need to change this code - just run this cell\n",
    "assert set(get_article_names(['1024.0', '1176.0', '1305.0', '1314.0', '1422.0', '1427.0'])) == set(['using deep learning to reconstruct high-resolution audio', 'build a python app on the streaming analytics service', 'gosales transactions for naive bayes model', 'healthcare python streaming application demo', 'use r dataframes & ibm watson natural language understanding', 'use xgboost, scikit-learn & ibm watson machine learning apis']), \"Oops! Your the get_article_names function doesn't work quite how we expect.\"\n",
    "assert set(get_article_names(['1320.0', '232.0', '844.0'])) == set(['housing (2015): united states demographic measures','self-service data preparation with ibm data refinery','use the cloudant-spark connector in python notebook']), \"Oops! Your the get_article_names function doesn't work quite how we expect.\"\n",
    "assert set(get_user_articles(20)[0]) == set(['1320.0', '232.0', '844.0'])\n",
    "assert set(get_user_articles(20)[1]) == set(['housing (2015): united states demographic measures', 'self-service data preparation with ibm data refinery','use the cloudant-spark connector in python notebook'])\n",
    "assert set(get_user_articles(2)[0]) == set(['1024.0', '1176.0', '1305.0', '1314.0', '1422.0', '1427.0'])\n",
    "assert set(get_user_articles(2)[1]) == set(['using deep learning to reconstruct high-resolution audio', 'build a python app on the streaming analytics service', 'gosales transactions for naive bayes model', 'healthcare python streaming application demo', 'use r dataframes & ibm watson natural language understanding', 'use xgboost, scikit-learn & ibm watson machine learning apis'])\n",
    "print(\"If this is all you see, you passed all of our tests!  Nice job!\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`4.` Now we are going to improve the consistency of the **user_user_recs** function from above.  \n",
    "\n",
    "* Instead of arbitrarily choosing when we obtain users who are all the same closeness to a given user - choose the users that have the most total article interactions before choosing those with fewer article interactions.\n",
    "\n",
    "\n",
    "* Instead of arbitrarily choosing articles from the user where the number of recommended articles starts below m and ends exceeding m, choose articles with the articles with the most total interactions before choosing those with fewer total interactions. This ranking should be  what would be obtained from the **top_articles** function you wrote earlier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_top_sorted_users(user_id, df=df, user_item=user_item):\n",
    "    '''\n",
    "    INPUT:\n",
    "    user_id - (int)\n",
    "    df - (pandas dataframe) df as defined at the top of the notebook \n",
    "    user_item - (pandas dataframe) matrix of users by articles: \n",
    "            1's when a user has interacted with an article, 0 otherwise\n",
    "    \n",
    "            \n",
    "    OUTPUT:\n",
    "    neighbors_df - (pandas dataframe) a dataframe with:\n",
    "                    neighbor_id - is a neighbor user_id\n",
    "                    similarity - measure of the similarity of each user to the provided user_id\n",
    "                    num_interactions - the number of articles viewed by the user - if a u\n",
    "                    \n",
    "    Other Details - sort the neighbors_df by the similarity and then by number of interactions where \n",
    "                    highest of each is higher in the dataframe\n",
    "     \n",
    "    '''\n",
    "    user_interactions = df.groupby(('user_id')).count()['article_id']\n",
    "    \n",
    "    num_users = user_item.shape[0]\n",
    "    \n",
    "    neighbor_id = [usr_id for usr_id in range(1,num_users) if usr_id != user_id] \n",
    "    # find_similar_users(user_id)\n",
    "    similarity = []\n",
    "    num_interactions = []\n",
    "    \n",
    "    for id in neighbor_id:\n",
    "        mat = np.dot(user_item.loc[user_id], user_item.loc[id])\n",
    "        similarity.append(mat)\n",
    "        \n",
    "        num_interactions.append(user_interactions.loc[id])\n",
    "        \n",
    "    #dataframe\n",
    "    neighbors_df = pd.DataFrame({\n",
    "        'neighbor_id': neighbor_id,\n",
    "        'similarity': similarity,\n",
    "        'num_interactions': num_interactions\n",
    "    })\n",
    "    \n",
    "    # sort dataframe by similarity\n",
    "    neighbors_df.sort_values('similarity', ascending=False, inplace=True)\n",
    "    \n",
    "    return neighbors_df # Return the dataframe specified in the doc_string\n",
    "\n",
    "\n",
    "def user_user_recs_part2(user_id, m=10):\n",
    "    '''\n",
    "    INPUT:\n",
    "    user_id - (int) a user id\n",
    "    m - (int) the number of recommendations you want for the user\n",
    "    \n",
    "    OUTPUT:\n",
    "    recs - (list) a list of recommendations for the user by article id\n",
    "    rec_names - (list) a list of recommendations for the user by article title\n",
    "    \n",
    "    Description:\n",
    "    Loops through the users based on closeness to the input user_id\n",
    "    For each user - finds articles the user hasn't seen before and provides them as recs\n",
    "    Does this until m recommendations are found\n",
    "    \n",
    "    Notes:\n",
    "    * Choose the users that have the most total article interactions \n",
    "    before choosing those with fewer article interactions.\n",
    "\n",
    "    * Choose articles with the articles with the most total interactions \n",
    "    before choosing those with fewer total interactions. \n",
    "   \n",
    "    '''\n",
    "    # Your code here\n",
    "    neighbors_df = get_top_sorted_users(user_id)\n",
    "    \n",
    "    top_user_list = list(neighbors_df['neighbor_id'][:m])  \n",
    "    \n",
    "    recs = []\n",
    "   \n",
    "    count = 0\n",
    "    \n",
    "    for user in top_user_list:\n",
    "        sim_id, sim_name = get_user_articles(user)\n",
    "        us_id, us_name = get_user_articles(user_id)\n",
    "        diff = np.setdiff1d(sim_id,us_id)\n",
    "        diff = diff.astype(str)       \n",
    "    \n",
    "        for rec in diff:\n",
    "            if count < m:\n",
    "                recs.append(rec)\n",
    "                count += 1\n",
    "    rec_names = []\n",
    "    for name in recs:\n",
    "        rec_name = df[df.article_id == float(name)].iloc[0,1]\n",
    "        rec_names.append(rec_name)\n",
    "    \n",
    "    return recs, rec_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The top 10 recommendations for user 20 are the following article ids:\n",
      "['1053.0', '1186.0', '1271.0', '1296.0', '495.0', '53.0', '727.0', '793.0', '89.0', '981.0']\n",
      "\n",
      "The top 10 recommendations for user 20 are the following article names:\n",
      "['access mysql with python', 'connect to db2 warehouse on cloud and db2 using scala', 'customer demographics and sales', 'fortune 100 companies', 'top 10 machine learning algorithms for beginners', 'introducing streams designer', 'from python nested lists to multidimensional numpy arrays', '10 powerful features on watson data platform, no coding necessary', 'top 20 r machine learning and data science packages', 'super fast string matching in python']\n"
     ]
    }
   ],
   "source": [
    "# Quick spot check - don't change this code - just use it to test your functions\n",
    "rec_ids, rec_names = user_user_recs_part2(20, 10)\n",
    "print(\"The top 10 recommendations for user 20 are the following article ids:\")\n",
    "print(rec_ids)\n",
    "print()\n",
    "print(\"The top 10 recommendations for user 20 are the following article names:\")\n",
    "print(rec_names)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`5.` Use your functions from above to correctly fill in the solutions to the dictionary below.  Then test your dictionary against the solution.  Provide the code you need to answer each following the comments below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "neighbor_id         3933\n",
       "similarity            35\n",
       "num_interactions      45\n",
       "Name: 3931, dtype: int64"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find the user that is most similar to user 1\n",
    "get_top_sorted_users(1).iloc[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "neighbor_id         242\n",
       "similarity           25\n",
       "num_interactions    148\n",
       "Name: 240, dtype: int64"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find the 10th most similar user to user 131\n",
    "get_top_sorted_users(131).iloc[9]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Tests with a dictionary of results\n",
    "\n",
    "user1_most_sim = 3933# Find the user that is most similar to user 1 \n",
    "user131_10th_sim = 242# Find the 10th most similar user to user 131"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This all looks good!  Nice job!\n"
     ]
    }
   ],
   "source": [
    "## Dictionary Test Here\n",
    "sol_5_dict = {\n",
    "    'The user that is most similar to user 1.': user1_most_sim, \n",
    "    'The user that is the 10th most similar to user 131': user131_10th_sim,\n",
    "}\n",
    "\n",
    "t.sol_5_test(sol_5_dict)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`6.` If we were given a new user, which of the above functions would you be able to use to make recommendations?  Explain.  Can you think of a better way we might make recommendations?  Use the cell below to explain a better method for new users."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Provide your response here.**\n",
    "\n",
    "At this point, we would not be able to make any recommendation for new users that relies only on user similarity. Since a new user has no prior browsing history of the articles, so we can't look for similarities with other users based on browsing history. The best way could be to use \"get_top_article\" which is a rank-based recommendation of the most popular articles for all the user interactions.\n",
    "\n",
    "\n",
    "This makes sense for new users since they would want to explore the platform, and would prefer starting with the more popular articles. The tradeoff of this is that it may skew our recommendation algorithm later on as it would see users who have interacted with all the same articles and think they are similar. When in reality, they might not be similar. A better way may also be based on a matrix based on metadata tags of the similarities between the registered users. We can compare the information from a new user with information about our current users and find users with a similar profile, thereby increasing the chances of making better recommendations."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`7.` Using your existing functions, provide the top 10 recommended articles you would provide for the a new user below.  You can test your function against our thoughts to make sure we are all on the same page with how we might make a recommendation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_user = '0.0'\n",
    "\n",
    "# What would your recommendations be for this new user '0.0'?  As a new user, they have no observed articles.\n",
    "# Provide a list of the top 10 article ids you would give to \n",
    "new_user_recs = get_top_article_ids(10) # Your recommendations here"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "That's right!  Nice job!\n"
     ]
    }
   ],
   "source": [
    "assert set(new_user_recs) == set(['1314.0','1429.0','1293.0','1427.0','1162.0','1364.0','1304.0','1170.0','1431.0','1330.0']), \"Oops!  It makes sense that in this case we would want to recommend the most popular articles, because we don't know anything about these users.\"\n",
    "\n",
    "print(\"That's right!  Nice job!\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <a class=\"anchor\" id=\"Content-Recs\">Part IV: Content Based Recommendations (EXTRA - NOT REQUIRED)</a>\n",
    "\n",
    "Another method we might use to make recommendations is to perform a ranking of the highest ranked articles associated with some term.  You might consider content to be the **doc_body**, **doc_description**, or **doc_full_name**.  There isn't one way to create a content based recommendation, especially considering that each of these columns hold content related information.  \n",
    "\n",
    "`1.` Use the function body below to create a content based recommender.  Since there isn't one right answer for this recommendation tactic, no test functions are provided.  Feel free to change the function inputs if you decide you want to try a method that requires more input values.  The input values are currently set with one idea in mind that you may use to make content based recommendations.  One additional idea is that you might want to choose the most popular recommendations that meet your 'content criteria', but again, there is a lot of flexibility in how you might make these recommendations.\n",
    "\n",
    "### This part is NOT REQUIRED to pass this project.  However, you may choose to take this on as an extra way to show off your skills."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def make_content_recs():\n",
    "    '''\n",
    "    INPUT:\n",
    "    \n",
    "    OUTPUT:\n",
    "    \n",
    "    '''"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`2.` Now that you have put together your content-based recommendation system, use the cell below to write a summary explaining how your content based recommender works.  Do you see any possible improvements that could be made to your function?  Is there anything novel about your content based recommender?\n",
    "\n",
    "### This part is NOT REQUIRED to pass this project.  However, you may choose to take this on as an extra way to show off your skills."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Write an explanation of your content based recommendation system here.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`3.` Use your content-recommendation system to make recommendations for the below scenarios based on the comments.  Again no tests are provided here, because there isn't one right answer that could be used to find these content based recommendations.\n",
    "\n",
    "### This part is NOT REQUIRED to pass this project.  However, you may choose to take this on as an extra way to show off your skills."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# make recommendations for a brand new user\n",
    "\n",
    "\n",
    "# make a recommendations for a user who only has interacted with article id '1427.0'\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <a class=\"anchor\" id=\"Matrix-Fact\">Part V: Matrix Factorization</a>\n",
    "\n",
    "In this part of the notebook, you will build use matrix factorization to make article recommendations to the users on the IBM Watson Studio platform.\n",
    "\n",
    "`1.` You should have already created a **user_item** matrix above in **question 1** of **Part III** above.  This first question here will just require that you run the cells to get things set up for the rest of **Part V** of the notebook. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load the matrix here\n",
    "user_item_matrix = pd.read_pickle('user_item_matrix.p')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
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       "article_id  0.0  100.0  1000.0  1004.0  1006.0  1008.0  101.0  1014.0  1015.0  \\\n",
       "user_id                                                                         \n",
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       "\n",
       "article_id  1016.0  ...    977.0  98.0  981.0  984.0  985.0  986.0  990.0  \\\n",
       "user_id             ...                                                     \n",
       "1              0.0  ...      0.0   0.0    1.0    0.0    0.0    0.0    0.0   \n",
       "2              0.0  ...      0.0   0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "3              0.0  ...      1.0   0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "4              0.0  ...      0.0   0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "5              0.0  ...      0.0   0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "\n",
       "article_id  993.0  996.0  997.0  \n",
       "user_id                          \n",
       "1             0.0    0.0    0.0  \n",
       "2             0.0    0.0    0.0  \n",
       "3             0.0    0.0    0.0  \n",
       "4             0.0    0.0    0.0  \n",
       "5             0.0    0.0    0.0  \n",
       "\n",
       "[5 rows x 714 columns]"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# quick look at the matrix\n",
    "user_item_matrix.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`2.` In this situation, you can use Singular Value Decomposition from [numpy](https://docs.scipy.org/doc/numpy-1.14.0/reference/generated/numpy.linalg.svd.html) on the user-item matrix.  Use the cell to perform SVD, and explain why this is different than in the lesson."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Perform SVD on the User-Item Matrix Here\n",
    "u, s, vt = np.linalg.svd(user_item_matrix) # use the built in to get the three matrices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(5149, 5149) (714,) (714, 714)\n"
     ]
    }
   ],
   "source": [
    "print(u.shape, s.shape, vt.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Provide your response here.**\n",
    "\n",
    "\n",
    "FunkSVD is not required because there are no null values in user_item_matrix. In the lectures, user_item_matrix is made using user ratings. Due to this, we have a sparse matrix and hence a different approach is necessary. Our model has user interactions which are either 0 or 1 and hence don't include null values which FunkSVD can resolve."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`3.` Now for the tricky part, how do we choose the number of latent features to use?  Running the below cell, you can see that as the number of latent features increases, we obtain a lower error rate on making predictions for the 1 and 0 values in the user-item matrix.  Run the cell below to get an idea of how the accuracy improves as we increase the number of latent features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9152a29198>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "num_latent_feats = np.arange(10,700+10,20)\n",
    "sum_errs = []\n",
    "\n",
    "for k in num_latent_feats:\n",
    "    # restructure with k latent features\n",
    "    s_new, u_new, vt_new = np.diag(s[:k]), u[:, :k], vt[:k, :]\n",
    "    \n",
    "    # take dot product\n",
    "    user_item_est = np.around(np.dot(np.dot(u_new, s_new), vt_new))\n",
    "    \n",
    "    # compute error for each prediction to actual value\n",
    "    diffs = np.subtract(user_item_matrix, user_item_est)\n",
    "    \n",
    "    # total errors and keep track of them\n",
    "    err = np.sum(np.sum(np.abs(diffs)))\n",
    "    sum_errs.append(err)\n",
    "    \n",
    "    \n",
    "plt.plot(num_latent_feats, 1 - np.array(sum_errs)/df.shape[0]);\n",
    "plt.xlabel('Number of Latent Features');\n",
    "plt.ylabel('Accuracy');\n",
    "plt.title('Accuracy vs. Number of Latent Features');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`4.` From the above, we can't really be sure how many features to use, because simply having a better way to predict the 1's and 0's of the matrix doesn't exactly give us an indication of if we are able to make good recommendations.  Instead, we might split our dataset into a training and test set of data, as shown in the cell below.  \n",
    "\n",
    "Use the code from question 3 to understand the impact on accuracy of the training and test sets of data with different numbers of latent features. Using the split below: \n",
    "\n",
    "* How many users can we make predictions for in the test set?  \n",
    "* How many users are we not able to make predictions for because of the cold start problem?\n",
    "* How many articles can we make predictions for in the test set?  \n",
    "* How many articles are we not able to make predictions for because of the cold start problem?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train = df.head(40000)\n",
    "df_test = df.tail(5993)\n",
    "\n",
    "def create_test_and_train_user_item(df_train, df_test):\n",
    "    '''\n",
    "    INPUT:\n",
    "    df_train - training dataframe\n",
    "    df_test - test dataframe\n",
    "    \n",
    "    OUTPUT:\n",
    "    user_item_train - a user-item matrix of the training dataframe \n",
    "                      (unique users for each row and unique articles for each column)\n",
    "    user_item_test - a user-item matrix of the testing dataframe \n",
    "                    (unique users for each row and unique articles for each column)\n",
    "    test_idx - all of the test user ids\n",
    "    test_arts - all of the test article ids\n",
    "    \n",
    "    '''\n",
    "    # Your code here\n",
    "    user_item_train = create_user_item_matrix(df_train)\n",
    "    user_item_test = create_user_item_matrix(df_test)\n",
    "    \n",
    "    test_idx = list(user_item_test.index)\n",
    "    test_arts = list(user_item_test.columns)\n",
    "    \n",
    "    return user_item_train, user_item_test, test_idx, test_arts\n",
    "\n",
    "user_item_train, user_item_test, test_idx, test_arts = create_test_and_train_user_item(df_train, df_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(4487, 714) (682, 574)\n"
     ]
    }
   ],
   "source": [
    "print(user_item_train.shape,\n",
    "      user_item_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "# The train articles and user ids\n",
    "train_idx = user_item_train.index\n",
    "train_arts = user_item_train.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "574"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# How many movies can we make predictions for in the test set?'- basically the articles of the test set contained also in the training set\n",
    "test_articles_subset = np.intersect1d(test_arts, train_arts)\n",
    "len(np.intersect1d(test_arts, train_arts))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#How many users can we make predictions for in the test set?\n",
    "test_user_subset = np.intersect1d(test_idx, train_idx)\n",
    "len(np.intersect1d(test_idx, train_idx))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "662"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# How many users in the test set are we not able to make predictions for because of the cold start problem?\n",
    "# The number of users in the test set that we can't make predictions for\n",
    "user_item_test.shape[0] - len(test_user_subset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# How many movies in the test set are we not able to make predictions for because of the cold start problem?\n",
    "user_item_test.shape[1] - len(test_articles_subset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Awesome job!  That's right!  All of the test movies are in the training data, but there are only 20 test users that were also in the training set.  All of the other users that are in the test set we have no data on.  Therefore, we cannot make predictions for these users using SVD.\n"
     ]
    }
   ],
   "source": [
    "# Replace the values in the dictionary below\n",
    "a = 662 \n",
    "b = 574 \n",
    "c = 20 \n",
    "d = 0 \n",
    "\n",
    "\n",
    "sol_4_dict = {\n",
    "    'How many users can we make predictions for in the test set?': c ,# letter here, \n",
    "    'How many users in the test set are we not able to make predictions for because of the cold start problem?':a, # letter here, \n",
    "    'How many movies can we make predictions for in the test set?': b ,# letter here,\n",
    "    'How many movies in the test set are we not able to make predictions for because of the cold start problem?': d # letter here\n",
    "}\n",
    "\n",
    "t.sol_4_test(sol_4_dict)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`5.` Now use the **user_item_train** dataset from above to find U, S, and V transpose using SVD. Then find the subset of rows in the **user_item_test** dataset that you can predict using this matrix decomposition with different numbers of latent features to see how many features makes sense to keep based on the accuracy on the test data. This will require combining what was done in questions `2` - `4`.\n",
    "\n",
    "Use the cells below to explore how well SVD works towards making predictions for recommendations on the test data.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "# fit SVD on the user_item_train matrix\n",
    "u_train, s_train, vt_train = np.linalg.svd(user_item_matrix) # fit svd similar to above then use the cells below"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Use these cells to see how well you can use the training \n",
    "# decomposition to predict on test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(5149, 5149)\n",
      "(714,)\n",
      "(714, 714)\n"
     ]
    }
   ],
   "source": [
    "print(u_train.shape)\n",
    "print(s_train.shape)\n",
    "print(vt_train.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(20, 5149)\n",
      "(714, 574)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/ipykernel_launcher.py:1: VisibleDeprecationWarning: boolean index did not match indexed array along dimension 0; dimension is 5149 but corresponding boolean dimension is 4487\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "#Subset for making predictions\n",
    "u_test = u_train[train_idx.isin(test_idx), :]\n",
    "vt_test = vt_train[:, train_arts.isin(test_arts)]\n",
    "print(u_test.shape)\n",
    "print(vt_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Test matrix for making predictions\n",
    "user_item_test_mat = user_item_test.loc[test_user_subset,test_articles_subset]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f915854ffd0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "num_latent_feats = np.arange(10,570+10,20)\n",
    "sum_errs = []\n",
    "\n",
    "for k in num_latent_feats:\n",
    "    # restructure with k latent features\n",
    "    s_train_new, u_train_new, vt_train_new = np.diag(s_train[:k]), u_train[:, :k], vt_train[:k, :]\n",
    "    s_test_new, u_test_new, vt_test_new = s_train_new, u_test[:, :k], vt_test[:k, :]\n",
    "    \n",
    "    # take dot product\n",
    "    user_item_test_est = np.around(np.dot(np.dot(u_test_new, s_test_new), vt_test_new))\n",
    "    \n",
    "    # compute error for each prediction to actual value\n",
    "    diffs = np.subtract(user_item_test_mat, user_item_test_est)\n",
    "    \n",
    "    # total errors and keep track of them\n",
    "    err = np.sum(np.sum(np.abs(diffs)))\n",
    "    sum_errs.append(err)\n",
    "    \n",
    "    \n",
    "plt.plot(num_latent_feats, 1 - np.array(sum_errs)/df.shape[0]);\n",
    "plt.grid();\n",
    "plt.xlabel('Number of Latent Features');\n",
    "plt.ylabel('Test Set Accuracy');\n",
    "plt.title('Test Set Accuracy vs. Number of Latent Features');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "`6.` Use the cell below to comment on the results you found in the previous question. Given the circumstances of your results, discuss what you might do to determine if the recommendations you make with any of the above recommendation systems are an improvement to how users currently find articles? "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Your response here.**\n",
    "\n",
    "\n",
    "\n",
    "The critical factor here is that we only have a just sample of 20 users to make predictions on and generalize the model. This is a very small sample to base confident conclusions. The model accuracy is high because this is mostly a sparse matrix, therefore we might not need to use very many latent features to correctly reproduce the original matrix. \n",
    "I would not yet implement a recommendation system based solely using SVD as the training and testing sample is very small. However, the number of latent features of around 150 ( between 100 and 200) could be a good value to use for SVD in our recommendation model.\n",
    "\n",
    "Nevertheless, considering the small sample, a better approach might be to do an online test. We could use a type of A/B testing where we split the users into 2 groups- \n",
    "\n",
    "- A- first with those using old recommenadation system\n",
    "- B-second group using the new recommendation functions\n",
    "\n",
    "We can measure the mean interactions between the two groups and use hypothesis testing to see if the second group has an increased interaction with the articles for a given confidence level. Similar training and testing methodologies can be applied across the other recommendation methods such as collaborative filtering and content based recommendations."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "<a id='conclusions'></a>\n",
    "### Extras\n",
    "Using your workbook, you could now save your recommendations for each user, develop a class to make new predictions and update your results, and make a flask app to deploy your results.  These tasks are beyond what is required for this project.  However, from what you learned in the lessons, you certainly capable of taking these tasks on to improve upon your work here!\n",
    "\n",
    "\n",
    "## Conclusion\n",
    "\n",
    "> Congratulations!  You have reached the end of the Recommendations with IBM project! \n",
    "\n",
    "> **Tip**: Once you are satisfied with your work here, check over your report to make sure that it is satisfies all the areas of the [rubric](https://review.udacity.com/#!/rubrics/2322/view). You should also probably remove all of the \"Tips\" like this one so that the presentation is as polished as possible.\n",
    "\n",
    "\n",
    "## Directions to Submit\n",
    "\n",
    "> Before you submit your project, you need to create a .html or .pdf version of this notebook in the workspace here. To do that, run the code cell below. If it worked correctly, you should get a return code of 0, and you should see the generated .html file in the workspace directory (click on the orange Jupyter icon in the upper left).\n",
    "\n",
    "> Alternatively, you can download this report as .html via the **File** > **Download as** submenu, and then manually upload it into the workspace directory by clicking on the orange Jupyter icon in the upper left, then using the Upload button.\n",
    "\n",
    "> Once you've done this, you can submit your project by clicking on the \"Submit Project\" button in the lower right here. This will create and submit a zip file with this .ipynb doc and the .html or .pdf version you created. Congratulations! "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
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       "0"
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     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "from subprocess import call\n",
    "call(['python', '-m', 'nbconvert', 'Recommendations_with_IBM.ipynb'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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