Recommendations_with_IBM.ipynb

{
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   "source": [
    "# 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",
    "## 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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "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",
    "from scipy.stats.stats import pearsonr\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "df = pd.read_csv('data/user-item-interactions.csv', dtype=str)\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.article_id = df.article_id.apply(lambda x: str(int(float(x))))\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>article_id</th>\n",
       "      <th>title</th>\n",
       "      <th>email</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>ef5f11f77ba020cd36e1105a00ab868bbdbf7fe7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1314.0</td>\n",
       "      <td>healthcare python streaming application demo</td>\n",
       "      <td>083cbdfa93c8444beaa4c5f5e0f5f9198e4f9e0b</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>b96a4f2e92d8572034b1e9b28f9ac673765cd074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1338.0</td>\n",
       "      <td>ml optimization using cognitive assistant</td>\n",
       "      <td>06485706b34a5c9bf2a0ecdac41daf7e7654ceb7</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>f01220c46fc92c6e6b161b1849de11faacd7ccb2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "  article_id                                              title  \\\n",
       "0     1430.0  using pixiedust for fast, flexible, and easier...   \n",
       "1     1314.0       healthcare python streaming application demo   \n",
       "2     1429.0         use deep learning for image classification   \n",
       "3     1338.0          ml optimization using cognitive assistant   \n",
       "4     1276.0          deploy your python model as a restful api   \n",
       "\n",
       "                                      email  \n",
       "0  ef5f11f77ba020cd36e1105a00ab868bbdbf7fe7  \n",
       "1  083cbdfa93c8444beaa4c5f5e0f5f9198e4f9e0b  \n",
       "2  b96a4f2e92d8572034b1e9b28f9ac673765cd074  \n",
       "3  06485706b34a5c9bf2a0ecdac41daf7e7654ceb7  \n",
       "4  f01220c46fc92c6e6b161b1849de11faacd7ccb2  "
      ]
     },
     "execution_count": 1,
     "metadata": {},
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    }
   ],
   "source": [
    "!tar -zcvf project"
   ]
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       "      <td>Detect Malfunctioning IoT Sensors with Streami...</td>\n",
       "      <td>Live</td>\n",
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       "      <td>No Free Hunch Navigation * kaggle.com\\r\\n\\r\\n ...</td>\n",
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       "      <td>Live</td>\n",
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       "      <td>Here’s this week’s news in Data Science and Bi...</td>\n",
       "      <td>This Week in Data Science (April 18, 2017)</td>\n",
       "      <td>Live</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>DATALAYER: HIGH THROUGHPUT, LOW LATENCY AT SCA...</td>\n",
       "      <td>Learn how distributed DBs solve the problem of...</td>\n",
       "      <td>DataLayer Conference: Boost the performance of...</td>\n",
       "      <td>Live</td>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Skip navigation Sign in SearchLoading...\\r\\n\\r...</td>\n",
       "      <td>This video demonstrates the power of IBM DataS...</td>\n",
       "      <td>Analyze NY Restaurant data using Spark in DSX</td>\n",
       "      <td>Live</td>\n",
       "      <td>4</td>\n",
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       "1  No Free Hunch Navigation * kaggle.com\\r\\n\\r\\n ...   \n",
       "2  ☰ * Login\\r\\n * Sign Up\\r\\n\\r\\n * Learning Pat...   \n",
       "3  DATALAYER: HIGH THROUGHPUT, LOW LATENCY AT SCA...   \n",
       "4  Skip navigation Sign in SearchLoading...\\r\\n\\r...   \n",
       "\n",
       "                                     doc_description  \\\n",
       "0  Detect bad readings in real time using Python ...   \n",
       "1  See the forest, see the trees. Here lies the c...   \n",
       "2  Here’s this week’s news in Data Science and Bi...   \n",
       "3  Learn how distributed DBs solve the problem of...   \n",
       "4  This video demonstrates the power of IBM DataS...   \n",
       "\n",
       "                                       doc_full_name doc_status  article_id  \n",
       "0  Detect Malfunctioning IoT Sensors with Streami...       Live           0  \n",
       "1  Communicating data science: A guide to present...       Live           1  \n",
       "2         This Week in Data Science (April 18, 2017)       Live           2  \n",
       "3  DataLayer Conference: Boost the performance of...       Live           3  \n",
       "4      Analyze NY Restaurant data using Spark in DSX       Live           4  "
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     "execution_count": 2,
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    }
   ],
   "source": [
    "# Show df_content to get an idea of the data\n",
    "df_content.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>article_id</th>\n",
       "      <th>title</th>\n",
       "      <th>email</th>\n",
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       "      <th>count</th>\n",
       "      <td>45993</td>\n",
       "      <td>45993</td>\n",
       "      <td>45976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>714</td>\n",
       "      <td>714</td>\n",
       "      <td>5148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>1429.0</td>\n",
       "      <td>use deep learning for image classification</td>\n",
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       "      <th>freq</th>\n",
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       "      <td>364</td>\n",
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      "text/plain": [
       "       article_id                                       title  \\\n",
       "count       45993                                       45993   \n",
       "unique        714                                         714   \n",
       "top        1429.0  use deep learning for image classification   \n",
       "freq          937                                         937   \n",
       "\n",
       "                                           email  \n",
       "count                                      45976  \n",
       "unique                                      5148  \n",
       "top     2b6c0f514c2f2b04ad3c4583407dccd0810469ee  \n",
       "freq                                         364  "
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     "execution_count": 3,
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   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>email</th>\n",
       "      <th>article_id</th>\n",
       "      <th>title</th>\n",
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       "      <td>00148e4911c7e04eeff8def7bbbdaf1c59c2c621</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
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       "      <th>3</th>\n",
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       "      <td>6</td>\n",
       "      <td>6</td>\n",
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       "      <th>4</th>\n",
       "      <td>001fc95b90da5c3cb12c501d201a915e4f093290</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
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      "text/plain": [
       "                                      email  article_id  title\n",
       "0  0000b6387a0366322d7fbfc6434af145adf7fed1          13     13\n",
       "1  001055fc0bb67f71e8fa17002342b256a30254cd           4      4\n",
       "2  00148e4911c7e04eeff8def7bbbdaf1c59c2c621           3      3\n",
       "3  001a852ecbd6cc12ab77a785efa137b2646505fe           6      6\n",
       "4  001fc95b90da5c3cb12c501d201a915e4f093290           2      2"
      ]
     },
     "execution_count": 4,
     "metadata": {},
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    }
   ],
   "source": [
    "grouped = df.groupby('email').count().reset_index()\n",
    "grouped.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f20ae69d320>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(grouped.article_id, bins=30)\n",
    "plt.xlabel(\"Number of Intractions\")\n",
    "plt.ylabel(\"Number of Users\")\n",
    "plt.title(\"User vs Number Interactions \");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f20ae6c94a8>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "grouped_by_arts = df.groupby('article_id')['email'].count().reset_index()\n",
    "plt.hist(grouped_by_arts.email, bins=30)\n",
    "plt.xlabel(\"Number of Intractions\")\n",
    "plt.ylabel(\"Number of articles\")\n",
    "plt.title(\"Articles vs Number Interactions \");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fill in the median and maximum number of user_article interactios below\n",
    "median_val = 3 # 50% of individuals interact with ____ number of articles or fewer.\n",
    "max_views_by_user = 364.0 # The maximum number of user-article interactions by any 1 user is ______."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`2.` Explore and remove duplicate articles from the **df_content** dataframe.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# duplicate docs\n",
    "sum(df_content.article_id.value_counts() >=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Remove any rows that have the same article_id - only keep the first\n",
    "df_content.drop_duplicates(subset='doc_full_name',inplace=True)\n",
    "sum(df_content.article_id.value_counts() >=2)"
   ]
  },
  {
   "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": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "714\n",
      "1051\n",
      "5149\n",
      "45993\n",
      "article_id     0\n",
      "title          0\n",
      "email         17\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(len(set(np.array(df.article_id))))\n",
    "print(len(set(np.array(df_content.article_id))))\n",
    "print(len(set(np.array(df.email))))\n",
    "print(df.shape[0])\n",
    "print(df.isnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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 (17 of those users email is unknown)"
   ]
  },
  {
   "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": 12,
   "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": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.article_id.value_counts()[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": 14,
     "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": 15,
   "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": 16,
   "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().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",
    "    # Your code here\n",
    "    top_articles = list(df.article_id.value_counts().index)[:n]\n",
    "    return top_articles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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": 18,
   "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": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create the user-article matrix with 1's and 0's\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",
    "    user_item = df.groupby(['user_id', 'article_id'])['title'].count().unstack()\n",
    "    user_item.fillna(0, inplace=True)\n",
    "    user_item[user_item > 0] = 1                     \n",
    "    return user_item # return the user_item matrix \n",
    "\n",
    "user_item = create_user_item_matrix(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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": 21,
   "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",
    "    \n",
    "    similarity = {}\n",
    "    for user in range(1,user_item.shape[0]+1):\n",
    "        similarity[user] = np.dot(user_item.loc[user_id], user_item.loc[user]) \n",
    "    similarity = sorted(similarity.items(), key = lambda kv: kv[1], reverse=True)\n",
    "    # remove the own user's id\n",
    "    most_similar_users = [k[0] for k in similarity]\n",
    "    most_similar_users.remove(user_id)\n",
    "    return most_similar_users # return a list of the users in order from most to least similar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 23, 3782, 203, 4459]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "find_similar_users(3933)[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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, 49]\n",
      "The 5 most similar users to user 3933 are: [1, 23, 3782, 203, 4459]\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": 24,
   "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 id in article_ids:\n",
    "        article_names.extend(list(np.unique(df[df.article_id == id]['title'])))\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",
    "    # Your code here\n",
    "    article_ids = []\n",
    "    for col in user_item.columns:\n",
    "        if user_item.loc[user_id,col]:\n",
    "            article_ids.append(col)\n",
    "    article_names = get_article_names(article_ids, df)\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",
    "    # Your code here\n",
    "    # get article Ids that user has seen\n",
    "    this_article_ids, this_article_names = get_user_articles(user_id)\n",
    "    sim_users = find_similar_users(user_id, user_item)\n",
    "    recs = []\n",
    "    for u in sim_users:\n",
    "        if len(recs) == m:\n",
    "                break\n",
    "        article_ids, _ = get_user_articles(u, user_item)\n",
    "        for id in this_article_ids:\n",
    "            if id not in article_ids:\n",
    "                recs.append(id)\n",
    "            if len(recs) == m:\n",
    "                break\n",
    "    return recs # return your recommendations for this user_id    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['jupyter notebook tutorial',\n",
       " 'categorize urban density',\n",
       " 'country statistics: life expectancy at birth',\n",
       " 'predict loan applicant behavior with tensorflow neural networking',\n",
       " 'uci ml repository: chronic kidney disease data set',\n",
       " 'uci: iris',\n",
       " 'jupyter notebook tutorial',\n",
       " 'sector correlations shiny app',\n",
       " 'time series prediction using recurrent neural networks (lstms)',\n",
       " 'introduction to market basket analysis in\\xa0python']"
      ]
     },
     "execution_count": 25,
     "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": 26,
   "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": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "def find_user_sim_df(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",
    "    user-similarity df - data frame correlation of the dataframe\n",
    "    \n",
    "    Description:\n",
    "    Computes the similarity of every pair of users based on the dot product\n",
    "    Returns an ordered\n",
    "    '''\n",
    "    similarity = {}\n",
    "    for user in range(1,user_item.shape[0]+1):\n",
    "        similarity[user] = np.dot(user_item.loc[user_id], user_item.loc[user]) \n",
    "    similarity.pop(user_id)\n",
    "    similarity_list = sorted(similarity.items(), key = lambda kv: kv[1], reverse=True)\n",
    "    # remove the own user's id\n",
    "    sim_df = pd.DataFrame( similarity_list, columns=['user_id', 'similarity'])\n",
    "    return sim_df # return a list of the users in order from most to least similar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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",
    "    # Your code here\n",
    "    # find similar users\n",
    "    sim_df  = find_user_sim_df(1)\n",
    "    df_int_count = df.groupby('user_id')['article_id'].count().reset_index()\n",
    "    df_merged = sim_df.merge(right=df_int_count, how='inner', on='user_id')\n",
    "    neighbors_df = df_merged[['user_id','similarity', 'article_id' ]].sort_values(by=['similarity', 'article_id'], ascending=False)\n",
    "    neighbors_df.columns = ['user_id','similarity', 'num_interactions' ]\n",
    "    return neighbors_df # Return the dataframe specified in the doc_string"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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>user_id</th>\n",
       "      <th>similarity</th>\n",
       "      <th>num_interactions</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3933</td>\n",
       "      <td>35.0</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>23</td>\n",
       "      <td>17.0</td>\n",
       "      <td>364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3782</td>\n",
       "      <td>17.0</td>\n",
       "      <td>363</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>203</td>\n",
       "      <td>15.0</td>\n",
       "      <td>160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4459</td>\n",
       "      <td>15.0</td>\n",
       "      <td>158</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id  similarity  num_interactions\n",
       "0     3933        35.0                45\n",
       "1       23        17.0               364\n",
       "2     3782        17.0               363\n",
       "3      203        15.0               160\n",
       "4     4459        15.0               158"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "neighbors_df = get_top_sorted_users(1)\n",
    "neighbors_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_ordered_article_id_interactions(article_ids):\n",
    "    \"\"\"\n",
    "    \"\"\"\n",
    "    top_df = pd.DataFrame(df.article_id.value_counts().reset_index())\n",
    "    top_df.columns = ['article_id', 'num_interactions']\n",
    "    article_id_list = list(top_df.loc[top_df['article_id'].isin(article_ids)].article_id.values)\n",
    "    return article_id_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['1330.0', '1364.0', '1314.0', '1162.0', '1304.0', '1351.0', '1393.0', '1160.0', '1354.0', '1338.0']\n"
     ]
    }
   ],
   "source": [
    "user_id = 1\n",
    "m = 10\n",
    "rec = []\n",
    "top_sorted_users = list(get_top_sorted_users(user_id)['user_id'].values)\n",
    "this_article_ids, this_article_names = get_user_articles(user_id)\n",
    "for u  in top_sorted_users[1:3]:\n",
    "    if len(rec) >= m:\n",
    "        break\n",
    "    art_ids, art_names = get_user_articles(u)\n",
    "    sorted_ids = get_ordered_article_id_interactions(list(set(art_ids) - set(this_article_ids)))\n",
    "    rec.extend(sorted_ids)\n",
    "print(rec[:10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "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",
    "    top_sorted_users = list(get_top_sorted_users(user_id)['user_id'].values)\n",
    "    this_article_ids, this_article_names = get_user_articles(user_id)\n",
    "    for u  in top_sorted_users:\n",
    "        if len(rec) >= m:\n",
    "            break\n",
    "        art_ids, art_names = get_user_articles(u)\n",
    "        sorted_ids = get_ordered_article_id_interactions(list(set(art_ids) - set(this_article_ids)))\n",
    "        rec.extend(sorted_ids)\n",
    "    \n",
    "    recs = rec[:10]\n",
    "    rec_names = get_article_names(recs, df=df)\n",
    "    return recs, rec_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The top 10 recommendations for user 20 are the following article ids:\n",
      "['1330.0', '1364.0', '1314.0', '1162.0', '1304.0', '1351.0', '1393.0', '1160.0', '1354.0', '1338.0']\n",
      "\n",
      "The top 10 recommendations for user 20 are the following article names:\n",
      "['insights from new york car accident reports', 'predicting churn with the spss random tree algorithm', 'healthcare python streaming application demo', 'analyze energy consumption in buildings', 'gosales transactions for logistic regression model', 'model bike sharing data with spss', 'the nurse assignment problem', 'analyze accident reports on amazon emr spark', 'movie recommender system with spark machine learning', 'ml optimization using cognitive assistant']\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": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This all looks good!  Nice job!\n"
     ]
    }
   ],
   "source": [
    "### Tests with a dictionary of results\n",
    "sol_5_dict= {\n",
    "    'The user that is most similar to user 1.': find_similar_users(1)[:1], \n",
    "    'The user that is the 10th most similar to user 131': find_similar_users(131)[9:10]\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": [
    "Recommending the most popular articles is one method of providing recommendations to a new user. Knowlegde based or content based recommendations can also be used to provide initial recommendations for a new user."
   ]
  },
  {
   "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": 35,
   "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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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=\"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": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load the matrix here\n",
    "user_item_matrix = pd.read_pickle('user_item_matrix.p')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>article_id</th>\n",
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       "      <th>997.0</th>\n",
       "    </tr>\n",
       "    <tr>\n",
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       "      <th></th>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "    <tr>\n",
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       "<p>5 rows × 714 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "article_id  0.0  100.0  1000.0  1004.0  1006.0  1008.0  101.0  1014.0  1015.0  \\\n",
       "user_id                                                                         \n",
       "1           0.0    0.0     0.0     0.0     0.0     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     0.0   \n",
       "3           0.0    0.0     0.0     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     0.0   \n",
       "5           0.0    0.0     0.0     0.0     0.0     0.0    0.0     0.0     0.0   \n",
       "\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": 40,
     "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": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((714,), (5149, 5149), (714, 714))"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Perform SVD on the User-Item Matrix Here\n",
    "u, s, vt = np.linalg.svd(user_item_matrix)\n",
    "s.shape, u.shape, vt.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "SVD doesn't work with missing values. The user-movie matrix in the lesson has missing values but this data doesn't.If there were missing vlaues we can use FunkSVD."
   ]
  },
  {
   "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": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f20ae8c5e48>"
      ]
     },
     "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": 43,
   "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",
    "    user_item_train = create_user_item_matrix(df_train)\n",
    "    user_item_test = create_user_item_matrix(df_test)\n",
    "    test_idx = user_item_test.index.values\n",
    "    test_arts = user_item_test.columns.values\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": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "662\n",
      "682\n",
      "0\n",
      "574\n"
     ]
    }
   ],
   "source": [
    "print(len(set(test_idx) - set(user_item_train.index.values)))\n",
    "print(len(test_idx))\n",
    "print(len(set(test_arts) - set(user_item_train.columns.values)))\n",
    "print(len(test_arts))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "common_idxs  = set.intersection(set(test_idx), set(user_item_train.index.values))\n",
    "len(common_idxs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "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",
    "# the test script says movies , it should have been articles.\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, \n",
    "    'How many users in the test set are we not able to make predictions for because of the cold start problem?':a, \n",
    "    'How many articles can we make predictions for in the test set?': b,\n",
    "    'How many articles in the test set are we not able to make predictions for because of the cold start problem?': d\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": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((4487, 4487), (714,), (714, 714))"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# fit SVD on the user_item_train matrix\n",
    "u_train, s_train, vt_train = np.linalg.svd(user_item_train)\n",
    "u_train.shape, s_train.shape, vt_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "714"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(user_item_train.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(20, 4487)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "row_idxs = user_item_train.index.isin(test_idx)\n",
    "col_idxs = user_item_train.columns.isin(test_arts)\n",
    "u_test = u_train[row_idxs, :]\n",
    "u_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(714, 574)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vt_test = vt_train[:, col_idxs]\n",
    "vt_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(20, 574)"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "common = set(test_idx).intersection(set(user_item_train.index))\n",
    "# filter out common user_item_test to both trianing and test data\n",
    "user_item_test_selected = user_item_test[user_item_test.index.isin(common)]\n",
    "user_item_test_selected.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_latent_feats = np.arange(10,710, 20)\n",
    "all_errs, train_errs, test_errs  = [],[],[]\n",
    "for k in num_latent_feats:\n",
    "    s_train_lat, u_train_lat, vt_train_lat = np.diag(s_train[:k]), u_train[:, :k], vt_train[:k, :]\n",
    "    s_test_lat, u_test_lat, vt_test_lat = np.diag(s_train[:k]), u_test[:, :k], vt_test[:k, :]\n",
    "    user_item_train_preds = np.around(np.dot(np.dot(u_train_lat, s_train_lat), vt_train_lat))\n",
    "    user_item_test_preds = np.around(np.dot(np.dot(u_test_lat, s_test_lat), vt_test_lat))\n",
    "    all_errs.append(1 - ((np.sum(user_item_test_preds)+np.sum(np.sum(user_item_test_selected)))/(user_item_test_selected.shape[0]*user_item_test_selected.shape[1])))\n",
    "    train_diffs = np.subtract(user_item_train, user_item_train_preds)\n",
    "    test_diffs= np.subtract(user_item_test_selected, user_item_test_preds)\n",
    "    train_errs.append(np.sum(np.sum(np.abs(train_diffs))))\n",
    "    test_errs.append(np.sum(np.sum(np.abs(test_diffs))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f20ae5b3860>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(num_latent_feats, 1 - np.array(train_errs)/(user_item_train.shape[0]*user_item_train.shape[1]));\n",
    "plt.plot(num_latent_feats, 1 - np.array(test_errs)/(user_item_test_selected.shape[0]*user_item_test_selected.shape[1]))\n",
    "plt.plot(num_latent_feats,all_errs)\n",
    "plt.legend(['Train','Test' , 'All Errors'])\n",
    "plt.xlabel('Number of Latent Features');\n",
    "plt.ylabel('Accuracy');\n",
    "plt.title('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": [
    "The dashboard shown at the project details section shows that currents users are provided with the newest articles everytime they visit the page. This approach seems generic and doesn't put into consideration the taste specific to each user. To compare the current approach with one of the above recommendation systems we can setup A/B testing.In setting up A/B testing, let's say we have a rating setup ( from 1 to 10) to determine how satified users are for a given recommended articles. We can setup this both for the control ( the current system) and experiment (the new recommendation system). We can direct 50% of traffice to current system and 50% to the new system. We can then compare average rating over a course of time (may be a month) to verfiy which one works better.\n",
    "\n",
    "In the last recommender system, we can directly measure ( or have some estimate) of how accurate our recommender system is by splitting the data into training and test data as shown above and measure the peformance on the test data. We can tune on the number of latent factors to use based on the model accuracy on both training and test data. So it looks like the SVD approach is better than the other methods we looked at so far in telling us how accurate is  our recommender."
   ]
  }
 ],
 "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": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}