{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "# Recommendation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "This notebook shows how to apply [scikit-network](https://scikit-network.readthedocs.io/) for content recommendation.\n",
    "\n",
    "We use consider the [Movielens](https://netset.telecom-paris.fr/pages/movielens.html) dataset of the [netset](https://netset.telecom-paris.fr) collection, corresponding to ratings of 9066 movies by 671 users."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "from IPython.display import SVG"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from scipy.cluster.hierarchy import linkage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "from sknetwork.data import load_netset\n",
    "from sknetwork.ranking import PageRank, top_k\n",
    "from sknetwork.embedding import Spectral\n",
    "from sknetwork.utils import get_neighbors\n",
    "from sknetwork.visualization import visualize_dendrogram"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parsing files...\n",
      "Done.\n"
     ]
    }
   ],
   "source": [
    "dataset = load_netset('movielens')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "biadjacency = dataset.biadjacency\n",
    "names = dataset.names\n",
    "labels = dataset.labels\n",
    "names_labels = dataset.names_labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<9066x671 sparse matrix of type '<class 'numpy.float64'>'\n",
       "\twith 100004 stored elements in Compressed Sparse Row format>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "biadjacency"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "n_movies, n_users = biadjacency.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0.5, 1. , 1.5, 2. , 2.5, 3. , 3.5, 4. , 4.5, 5. ]),\n",
       " array([ 1101,  3326,  1687,  7271,  4449, 20064, 10538, 28750,  7723,\n",
       "        15095]))"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# ratings\n",
    "np.unique(biadjacency.data, return_counts=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# positive ratings\n",
    "positive = biadjacency >= 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<9066x671 sparse matrix of type '<class 'numpy.bool_'>'\n",
       "\twith 82170 stored elements in Compressed Sparse Row format>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "positive"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Action', 'Adventure', 'Animation', 'Children', 'Comedy', 'Crime',\n",
       "       'Documentary', 'Drama', 'Fantasy', 'Film-Noir', 'Horror', 'IMAX',\n",
       "       'Musical', 'Mystery', 'Romance', 'Sci-Fi', 'Thriller', 'War',\n",
       "       'Western'], dtype='<U11')"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "names_labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9066, 19)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## PageRank\n",
    "\n",
    "We first use (personalized) PageRank to get the most popular movies of each category."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "pagerank = PageRank()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Forrest Gump (1994)', 'Pulp Fiction (1994)',\n",
       "       'Shawshank Redemption, The (1994)',\n",
       "       'Silence of the Lambs, The (1991)',\n",
       "       'Star Wars: Episode IV - A New Hope (1977)', 'Matrix, The (1999)',\n",
       "       'Jurassic Park (1993)', \"Schindler's List (1993)\",\n",
       "       'Back to the Future (1985)',\n",
       "       'Star Wars: Episode V - The Empire Strikes Back (1980)'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# top-10 movies\n",
    "scores = pagerank.fit_predict(positive)\n",
    "names[top_k(scores, 10)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# number of movies per genre\n",
    "n_selection = 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# selection\n",
    "selection = []\n",
    "for label in np.arange(len(names_labels)):\n",
    "    ppr = pagerank.fit_predict(positive, weights=labels[:, label])\n",
    "    scores = ppr * labels[:, label]\n",
    "    selection.append(top_k(scores, n_selection))\n",
    "selection = np.array(selection)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---\n",
      "0 Action\n",
      "['Star Wars: Episode IV - A New Hope (1977)' 'Matrix, The (1999)'\n",
      " 'Jurassic Park (1993)'\n",
      " 'Star Wars: Episode V - The Empire Strikes Back (1980)'\n",
      " 'Terminator 2: Judgment Day (1991)']\n",
      "---\n",
      "1 Adventure\n",
      "['Star Wars: Episode IV - A New Hope (1977)' 'Jurassic Park (1993)'\n",
      " 'Star Wars: Episode V - The Empire Strikes Back (1980)'\n",
      " 'Back to the Future (1985)' 'Toy Story (1995)']\n",
      "---\n",
      "2 Animation\n",
      "['Gnomeo & Juliet (2011)' 'Hop (2011)'\n",
      " \"Lion King II: Simba's Pride, The (1998)\" 'Mars Needs Moms (2011)'\n",
      " 'Once Upon a Forest (1993)']\n",
      "---\n",
      "3 Children\n",
      "['Spy Kids 3-D: Game Over (2003)' 'Race to Witch Mountain (2009)'\n",
      " 'G-Force (2009)' 'Prancer (1989)' 'Diary of a Wimpy Kid (2010)']\n",
      "---\n",
      "4 Comedy\n",
      "['Forrest Gump (1994)' 'Pulp Fiction (1994)' 'Back to the Future (1985)'\n",
      " 'Toy Story (1995)' 'Fargo (1996)']\n",
      "---\n",
      "5 Crime\n",
      "['Pulp Fiction (1994)' 'Shawshank Redemption, The (1994)'\n",
      " 'Silence of the Lambs, The (1991)' 'Fargo (1996)' 'Godfather, The (1972)']\n",
      "---\n",
      "6 Documentary\n",
      "['Queen of Versailles, The (2012)' 'Powaqqatsi (1988)'\n",
      " 'Fragile Trust: Plagiarism, Power, and Jayson Blair at the New York Times, A (2013)'\n",
      " 'African Cats (2011)' 'Eddie Murphy Delirious (1983)']\n",
      "---\n",
      "7 Drama\n",
      "['Pulp Fiction (1994)' 'Forrest Gump (1994)'\n",
      " 'Shawshank Redemption, The (1994)' \"Schindler's List (1993)\"\n",
      " 'American Beauty (1999)']\n",
      "---\n",
      "8 Fantasy\n",
      "['The Pumaman (1980)'\n",
      " 'Golem, The (Golem, wie er in die Welt kam, Der) (1920)'\n",
      " 'Twilight Saga: New Moon, The (2009)'\n",
      " 'Highlander: Endgame (Highlander IV) (2000)'\n",
      " 'Ghost Rider: Spirit of Vengeance (2012)']\n",
      "---\n",
      "9 Film-Noir\n",
      "['No Way Out (1950)' 'Johnny Eager (1942)'\n",
      " 'Lady from Shanghai, The (1947)' 'This World, Then the Fireworks (1997)'\n",
      " 'T-Men (1947)']\n",
      "---\n",
      "10 Horror\n",
      "['Silence of the Lambs, The (1991)' 'Carnosaur 3: Primal Species (1996)'\n",
      " \"Devil's Chair, The (2006)\" 'AVPR: Aliens vs. Predator - Requiem (2007)'\n",
      " 'Jason Goes to Hell: The Final Friday (1993)']\n",
      "---\n",
      "11 IMAX\n",
      "[\"Madagascar 3: Europe's Most Wanted (2012)\" 'Final Destination 5 (2011)'\n",
      " 'Jack the Giant Slayer (2013)' \"Dr. Seuss' The Lorax (2012)\"\n",
      " 'After Earth (2013)']\n",
      "---\n",
      "12 Musical\n",
      "['Gypsy (1993)' \"Breakin' (1984)\" \"Breakin' 2: Electric Boogaloo (1984)\"\n",
      " 'True Stories (1986)' 'Camp Rock (2008)']\n",
      "---\n",
      "13 Mystery\n",
      "['Double, The (2011)' 'In the Electric Mist (2009)'\n",
      " 'Spirits of the Dead (1968)' 'Nomads (1986)'\n",
      " 'Fast and the Furious, The (1955)']\n",
      "---\n",
      "14 Romance\n",
      "['Forrest Gump (1994)' 'American Beauty (1999)'\n",
      " 'Princess Bride, The (1987)' 'Beauty and the Beast (1991)'\n",
      " 'Good Will Hunting (1997)']\n",
      "---\n",
      "15 Sci-Fi\n",
      "['Star Wars: Episode IV - A New Hope (1977)' 'Matrix, The (1999)'\n",
      " 'Star Wars: Episode V - The Empire Strikes Back (1980)'\n",
      " 'Jurassic Park (1993)' 'Back to the Future (1985)']\n",
      "---\n",
      "16 Thriller\n",
      "['Pulp Fiction (1994)' 'Silence of the Lambs, The (1991)'\n",
      " 'Matrix, The (1999)' 'Jurassic Park (1993)' 'Fargo (1996)']\n",
      "---\n",
      "17 War\n",
      "['Pathfinder (2007)' 'Green Berets, The (1968)'\n",
      " 'They Were Expendable (1945)' 'Legionnaire (1998)' 'Iron Eagle II (1988)']\n",
      "---\n",
      "18 Western\n",
      "['Bandidas (2006)' \"'Neath the Arizona Skies (1934)\"\n",
      " 'American Outlaws (2001)' 'The Missouri Breaks (1976)'\n",
      " 'Stagecoach (1966)']\n"
     ]
    }
   ],
   "source": [
    "# show selection (some movies may have several genres)\n",
    "for label, name_label in enumerate(names_labels):\n",
    "    print('---')\n",
    "    print(label, name_label)\n",
    "    print(names[selection[label, :5]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "We now apply PageRank to get the most relevant movies associated with a given movie."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "target = {i: name for i, name in enumerate(names) if 'Cherbourg' in name}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{175: 'Umbrellas of Cherbourg, The (Parapluies de Cherbourg, Les) (1964)'}"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "scores_ppr = pagerank.fit_predict(positive, weights={175:1})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Umbrellas of Cherbourg, The (Parapluies de Cherbourg, Les) (1964)',\n",
       "       'Three Colors: White (Trzy kolory: Bialy) (1994)',\n",
       "       'Wings of Desire (Himmel über Berlin, Der) (1987)',\n",
       "       'Three Colors: Red (Trois couleurs: Rouge) (1994)',\n",
       "       'Like Water for Chocolate (Como agua para chocolate) (1992)',\n",
       "       'Piano, The (1993)', 'L.A. Confidential (1997)', 'Fargo (1996)',\n",
       "       'Beauty of the Day (Belle de jour) (1967)',\n",
       "       'Boot, Das (Boat, The) (1981)'], dtype=object)"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "names[top_k(scores_ppr - scores, 10)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "We can also apply PageRank to make recommend movies to a user."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    },
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "user = 1\n",
    "targets = get_neighbors(positive, user, transpose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['GoldenEye (1995)', 'Sense and Sensibility (1995)',\n",
       "       'Clueless (1995)', 'Seven (a.k.a. Se7en) (1995)',\n",
       "       'Usual Suspects, The (1995)', 'Mighty Aphrodite (1995)',\n",
       "       \"Mr. Holland's Opus (1995)\", 'Braveheart (1995)',\n",
       "       'Brothers McMullen, The (1995)', 'Apollo 13 (1995)'], dtype=object)"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# seen movies (sample)\n",
    "names[targets][:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "mask = np.zeros(len(names), dtype=bool)\n",
    "mask[targets] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "scores_ppr = pagerank.fit_predict(positive, weights=mask)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Shawshank Redemption, The (1994)', 'True Lies (1994)',\n",
       "       'Star Wars: Episode IV - A New Hope (1977)',\n",
       "       'Beauty and the Beast (1991)', 'Toy Story (1995)',\n",
       "       'Twelve Monkeys (a.k.a. 12 Monkeys) (1995)', 'Fargo (1996)',\n",
       "       'Independence Day (a.k.a. ID4) (1996)', 'Matrix, The (1999)',\n",
       "       'Star Wars: Episode V - The Empire Strikes Back (1980)'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# top-10 recommendation\n",
    "names[top_k((scores_ppr - scores) * (1 - mask), 10)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## Embedding"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "We now represent each movie by a vector in low dimension, and use hierarchical clustering to visualize the structure of this embedding for top-100 movies."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# embedding\n",
    "spectral = Spectral(10)\n",
    "embedding = spectral.fit_transform(positive)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# top-100 movies\n",
    "scores = pagerank.fit_predict(positive)\n",
    "index = top_k(scores, 100)\n",
    "dendrogram = linkage(embedding[index], method='ward')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
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      ],
      "text/plain": [
       "<IPython.core.display.SVG object>"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# visualization\n",
    "image = visualize_dendrogram(dendrogram, names=names[index], rotate=True, width=200, height=1000, n_clusters=6)\n",
    "SVG(image)\n"
   ]
  }
 ],
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