deepwalk_recommender.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# DEEPWALK RECOMMENDER"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Hoćemo da primenimo deepwalk algoritam na sistem za preporučivanje. Konkretno, koristićemo skup movie-lense 100k.\n",
    "- DeepWalk je pristup za učenje reprezentacija čvorova u mrežama. Naučene reprezentacije čvorova su vektori u neprekidnom vektorskom prostoru.\n",
    "- DeepWalk koristi kratke slučajne šetnje kroz graf, za svaki čvor, tretirajući šetnje kao ulazne rečenice za word2vec, kako bi dobio latentnu reprezentaciju čvorova.\n",
    "\n",
    "<img src='assets/embedding.jpg'>\n",
    "\n",
    "- Koraci:\n",
    "   - Kreiranje grafa na osnovu movie lense skupa podataka\n",
    "   - Formiranje korpusa - skupa slučajnih šetnji kroz graf, za svaki čvor (moguće je krenuti više puta iz istog čvora)\n",
    "   - Primena word2vec na korpus u cilju dobijanja vektorskih reprezentacija svakog čvora\n",
    "   - Testiranje dobijenog modela na test skupu, korišćenjem k najblizih suseda u dobijenoj reprezentaciji čvorova"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import random\n",
    "from tqdm.notebook import tqdm\n",
    "from gensim.models import Word2Vec\n",
    "from sklearn import metrics\n",
    "import numpy as np\n",
    "import networkx as nx\n",
    "from matplotlib import pyplot as plt\n",
    "import pandas as pd\n",
    "from surprise import SVD\n",
    "from surprise import Dataset\n",
    "from surprise import model_selection\n",
    "from surprise import accuracy\n",
    "from surprise import Reader\n",
    "from surprise.prediction_algorithms.predictions import Prediction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Definišemo dve pomoćne funkcije koje pretvaraju n-torku u string i obrnuto, zbog upotrebe word2vec koji prihvata listu rečenica."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def stringToTuple(s):\n",
    "    strings=s.split(\"_\")\n",
    "    if len(strings)==2:\n",
    "        return (strings[0],int(strings[1]))\n",
    "    else:\n",
    "        return (strings[0],int(strings[1]),float(strings[2]))\n",
    "\n",
    "def tupleToString(t):\n",
    "    if len(t)==2:\n",
    "        return str(t[0])+\"_\"+str(t[1])\n",
    "    else:\n",
    "        return str(t[0])+\"_\"+str(t[1])+\"_\"+str(t[2])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Klasa kojom je predstavljen sistem za preporučivanje\n",
    "\n",
    "- Klasa sadrži potrebne metode za kreiranje i upotrebu sistema za preporučivanje korišćenjem DeepWalk algoritma. \n",
    "- Srž je funkcija **fit**, koja kreira Word2Vec model.\n",
    "- Ideja da reprezentacije učimo na osnovu verovatnoća istovremenih pojavljivanja čvorova. Za čvor $v_i$ procenjujemo verovatnoće pojavljivanja ostalih čvorova grafa unutra njegovog konteksta $\\{v_{i-w}, \\dots, v_{i-1}, v_{i+1}, \\dots v_{i+w}\\}$ prilikom slučajne šetnje.\n",
    "- S obzirom da radimo sa grafom koji ima skup čvorova $V$, hoćemo da nađemo funkciju \n",
    "$\\Phi: V \\mapsto R^{|V|\\times d}$, koja svakom čvoru dodelju reprezentaciju u prostoru odgovarajuće dimenzije ($d$ je metaparametar). Drugim rečima, dobijamo matricu dimenzije \n",
    "$R^{|V|\\times d}$ u kojoj redovi predstavljaju reprezentacije odgovarajućih čvorova.\n",
    "- Ono što Word2Vec radi je upravo to, za svaku slučajnu šetnju preračunava matricu $\\Phi$ koristeći neuronsku mrežu sa jednim skrivenim slojem veličine $d$. \n",
    "<img src='assets/word2vec-net.png'>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "class DeepWalkRecommender():\n",
    "    \"\"\"\n",
    "    Parametri:\n",
    "    w: dužina prozora\n",
    "    epochs: broj epoha\n",
    "    t: dužina slučajnih šetnji\n",
    "    latent_dim: dimenzija izlaznog prostora atributa\n",
    "    k: broj najbližih suseda u izlaznom prostoru atributa\n",
    "    \"\"\"\n",
    "    def __init__(self, w=8, epochs = 10, t = 30,latent_dim=32,k=50):        \n",
    "        self.w = w\n",
    "        self.epochs = epochs\n",
    "        self.t = t\n",
    "        self.latent_dim = latent_dim\n",
    "        self.k=k\n",
    "        \n",
    "    def __build_graph__(self, data):\n",
    "        \"\"\"\n",
    "        Funkcija koja kreira graf iz skupa podataka. \n",
    "        Čvorovi grafa su oblika (oznaka,redniBroj[, ocena]),\n",
    "        pri čemu je oznaka 'u' za korisnika, 'm' za film,\n",
    "        redniBroj je identifikator filma/korisnika,\n",
    "        a ocena predstavlja ocenu kojom je ocenjen film\n",
    "        Ukoliko je korisnik 'u' ocenio film 'm', onda dodajemo granu izmedju 'u' i 'm'\n",
    "        \"\"\"\n",
    "        G = nx.Graph()\n",
    "        \n",
    "        for user, movie, rating in data.all_ratings():\n",
    "            user_node = ('u', int(data.to_raw_uid(user)))\n",
    "            movie_node = ('m', int(data.to_raw_iid(movie)), rating)\n",
    "            G.add_edge(user_node, movie_node)\n",
    "        \n",
    "        return G\n",
    "    \n",
    "    def __build_val_map__(self, data):\n",
    "        return {(int(user), int(movie)) : float(rating) for (user, movie, rating) in data}\n",
    "    \n",
    "    def make_corpus(self,G):\n",
    "        \"\"\"\n",
    "        Funkcija koja kreira korpus koristeći graf G. \n",
    "        Korpus je lista slučajnih šetnji sa početkom u čvorovima grafa, moguće više puta,\n",
    "        u zavisnosti od parametra epochs\n",
    "        \"\"\"\n",
    "        corpus=[]\n",
    "        nodes=list(G.nodes)\n",
    "        for _ in tqdm(range(self.epochs),total=self.epochs):\n",
    "            np.random.shuffle(nodes)\n",
    "            for node in nodes:\n",
    "                walk = [node]\n",
    "                for i in range(1,self.t):\n",
    "                    if len(list(G.neighbors(walk[-1]))) == 0:\n",
    "                        break\n",
    "                    walk.append(random.choice(list(G.neighbors(walk[-1]))))\n",
    "                corpus.append([tupleToString(word) for word in walk])    \n",
    "        return corpus\n",
    "    \n",
    "    def fit(self, data):\n",
    "        \"\"\"\n",
    "        Funkcija koja predstavlja sam DeepWalk algoritam. Funkcija kreira word2vec model,\n",
    "        koristeći prethodne funkcije i čuva ga u \n",
    "        self.word2vec za kasniju upotrebu.\n",
    "        \"\"\"\n",
    "        self.possible_ratings = sorted(set([rating for (_, _, rating) in data.all_ratings()]))\n",
    "        self.mean_rating = data.global_mean\n",
    "        \n",
    "        G = self.__build_graph__(data)\n",
    "        self.G_train=G\n",
    "        \n",
    "        corpus=self.make_corpus(G)\n",
    "        \n",
    "        self.word2vec=Word2Vec(corpus, size = self.latent_dim, window = self.w, min_count = 0, sg = 1, hs = 1, workers = 4)\n",
    "        return self.word2vec\n",
    "        \n",
    "    def test(self,test_data):\n",
    "        \"\"\"\n",
    "        Funkcija koja predviđa ocene za svaki par korisnik - film u test skupu.\n",
    "        Dakle, na osnovu modela dobijenog na skupu za treniranje,\n",
    "        nalazi k najbližih suseda, računa njihovu prosečnu ocenu za odgovarajući film\n",
    "        i nju prijavljuje kao predviđanje. \n",
    "        U slučaju da od k najbližih suseda niko nije ocenio odgovarajući film,\n",
    "        prijavljuje se ukupna prosečna ocena\n",
    "        \"\"\"\n",
    "        predictions=[]\n",
    "        for user,movie,rating in test_data:\n",
    "            user=int(user)\n",
    "            movie=int(movie)\n",
    "            \n",
    "            similar_users=self.word2vec.wv.most_similar(positive = [tupleToString(('u',user))], topn = self.k)\n",
    "            \n",
    "            rating_sum=0\n",
    "            rating_cnt=0\n",
    "            for similar_user in similar_users:\n",
    "                for possible_rating in self.possible_ratings:\n",
    "                    if ('m',movie,possible_rating) in self.G_train.neighbors(stringToTuple(similar_user[0])):\n",
    "                        rating_sum+=possible_rating\n",
    "                        rating_cnt+=1\n",
    "            \n",
    "            if rating_cnt>0:\n",
    "                predicted_rating=rating_sum/rating_cnt\n",
    "            else:\n",
    "                predicted_rating = self.mean_rating\n",
    "            details={'s':True}\n",
    "            predictions.append(Prediction(uid = user,\n",
    "                                          iid = movie,\n",
    "                                          r_ui = rating,\n",
    "                                          est = float(predicted_rating),\n",
    "                                          details = details))\n",
    "        return predictions\n",
    "\n",
    "    def gridSearch(self,train_data,val_data):\n",
    "        \"\"\"\n",
    "        Pomoćna funkcija koja pronalazi optimalne vrednosti parametara,\n",
    "        pomoću skupa za validaciju\n",
    "        \"\"\"\n",
    "        ws=[8,6,4,2]\n",
    "        ts=[30,20,10]\n",
    "        ks=[1600,800,100,50]\n",
    "        epochs=[30,10]\n",
    "        dimensions=[16,8,4,2]\n",
    "        best_params={}\n",
    "        best_rmse=1000\n",
    "        out=open('gridSearch.txt','a')\n",
    "        i=1\n",
    "        iterations=len(ws)*len(ts)*len(ks)*len(epochs)*len(dimensions)\n",
    "        \n",
    "        for w in ws:\n",
    "            for t in ts:\n",
    "                for epoch in epochs:\n",
    "                    for dim in dimensions:\n",
    "                        for k in ks:\n",
    "                            self.w=w\n",
    "                            self.k=k\n",
    "                            self.latent_dim=dim\n",
    "                            self.epochs=epoch\n",
    "                            self.t=t\n",
    "                            \n",
    "                            s='Korak: '+str(i)+'/'+str(iterations)+'\\n'+'w='+str(w)+'\\n'+'t='+str(t)+'\\n'+'epochs='+str(epoch)+'\\n'+'dim='+str(dim)+'\\n'+'k='+str(k)+'\\n'\n",
    "                            out.write(s)\n",
    "                            out.flush()\n",
    "                            \n",
    "                            self.fit(train_data)\n",
    "                            predictions=self.test(val_data)\n",
    "                            rmse=accuracy.rmse(predictions)\n",
    "                            \n",
    "                            out.write('RMSE='+str(rmse)+'\\n')\n",
    "                            out.write('------------------------\\n')\n",
    "                            out.flush()\n",
    "                            i+=1\n",
    "                            \n",
    "                            if rmse<best_rmse:\n",
    "                                best_rmse=rmse\n",
    "                                best_params['w']=w\n",
    "                                best_params['t']=t\n",
    "                                best_params['epochs']=epoch\n",
    "                                best_params['k']=k\n",
    "                                best_params['dim']=dim\n",
    "        out.close()                        \n",
    "        return best_params,best_rmse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'w': 6, 't': 10, 'epochs': 30, 'k': 1600, 'dim': 16}"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d=DeepWalkRecommender()\n",
    "\n",
    "data = Dataset.load_builtin('ml-100k')\n",
    "train_data, test_data = model_selection.train_test_split(data, test_size = 0.3,random_state=7)\n",
    "val_data=test_data[:15000]\n",
    "\n",
    "# best_params,best_rmse=d.gridSearch(train_data,val_data)\n",
    "# Da se ne bi izvrsavao ceo dan svaki put kad napravimo izmenu\n",
    "best_params = {'w': 6, 't': 10, 'epochs': 30, 'k': 1600, 'dim': 16}\n",
    "best_params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "98cbd8f743464cc3bef73ecc18191310",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=30.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "FINAL:\n",
      "RMSE: 0.9764\n"
     ]
    }
   ],
   "source": [
    "train_data1, test_data1 = model_selection.train_test_split(data, test_size = 0.15,random_state=7)\n",
    "\n",
    "d_final=DeepWalkRecommender(w=best_params['w'],\n",
    "                            epochs=best_params['epochs'],\n",
    "                            t=best_params['t'],\n",
    "                            latent_dim=best_params['dim'],\n",
    "                            k=best_params['k'])\n",
    "\n",
    "#test_data1 je isto kao test_data[15000:], provereno                            \n",
    "model=d_final.fit(train_data1)\n",
    "predictions=d_final.test(test_data1)\n",
    "print(\"FINAL:\")\n",
    "final_rmse=accuracy.rmse(predictions)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Možemo uporediti naš sistem sa bibliotečkim SVD:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE: 0.9352\n",
      "MAE:  0.7364\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.7364496371841713"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svd = SVD()\n",
    "svd.fit(train_data1)\n",
    "predictionsSVD = svd.test(test_data1)\n",
    "accuracy.rmse(predictionsSVD)\n",
    "accuracy.mae(predictionsSVD)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.save('deepwalk.model')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Učitavamo dodatne podatke i biramo jednog korisnika i ispisujemo sve njegove ocene."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>Title</th>\n",
       "      <th>Rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>640</th>\n",
       "      <td>641</td>\n",
       "      <td>Paths of Glory (1957)</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>184</th>\n",
       "      <td>185</td>\n",
       "      <td>Psycho (1960)</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>237</th>\n",
       "      <td>238</td>\n",
       "      <td>Raising Arizona (1987)</td>\n",
       "      <td>5.0</td>\n",
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       "    <tr>\n",
       "      <th>639</th>\n",
       "      <td>640</td>\n",
       "      <td>Cook the Thief His Wife &amp; Her Lover, The (1989)</td>\n",
       "      <td>5.0</td>\n",
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       "      <th>473</th>\n",
       "      <td>474</td>\n",
       "      <td>Dr. Strangelove or: How I Learned to Stop Worr...</td>\n",
       "      <td>5.0</td>\n",
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       "      <th>199</th>\n",
       "      <td>200</td>\n",
       "      <td>Shining, The (1980)</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>197</td>\n",
       "      <td>Graduate, The (1967)</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186</th>\n",
       "      <td>187</td>\n",
       "      <td>Godfather: Part II, The (1974)</td>\n",
       "      <td>5.0</td>\n",
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       "    <tr>\n",
       "      <th>181</th>\n",
       "      <td>182</td>\n",
       "      <td>GoodFellas (1990)</td>\n",
       "      <td>5.0</td>\n",
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       "    <tr>\n",
       "      <th>174</th>\n",
       "      <td>175</td>\n",
       "      <td>Brazil (1985)</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>173</th>\n",
       "      <td>174</td>\n",
       "      <td>Raiders of the Lost Ark (1981)</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>479</th>\n",
       "      <td>480</td>\n",
       "      <td>North by Northwest (1959)</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>89</td>\n",
       "      <td>Blade Runner (1982)</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>522</th>\n",
       "      <td>523</td>\n",
       "      <td>Cool Hand Luke (1967)</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>487</th>\n",
       "      <td>488</td>\n",
       "      <td>Sunset Blvd. (1950)</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>481</th>\n",
       "      <td>482</td>\n",
       "      <td>Some Like It Hot (1959)</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>478</th>\n",
       "      <td>479</td>\n",
       "      <td>Vertigo (1958)</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>483</th>\n",
       "      <td>484</td>\n",
       "      <td>Maltese Falcon, The (1941)</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>61</td>\n",
       "      <td>Three Colors: White (1994)</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>514</th>\n",
       "      <td>515</td>\n",
       "      <td>Boot, Das (1981)</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>526</th>\n",
       "      <td>527</td>\n",
       "      <td>Gandhi (1982)</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>650</th>\n",
       "      <td>651</td>\n",
       "      <td>Glory (1989)</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>707</th>\n",
       "      <td>708</td>\n",
       "      <td>Sex, Lies, and Videotape (1989)</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>805</th>\n",
       "      <td>806</td>\n",
       "      <td>Menace II Society (1993)</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>918</th>\n",
       "      <td>919</td>\n",
       "      <td>City of Lost Children, The (1995)</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>474</th>\n",
       "      <td>475</td>\n",
       "      <td>Trainspotting (1996)</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>356</th>\n",
       "      <td>357</td>\n",
       "      <td>One Flew Over the Cuckoo's Nest (1975)</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>210</th>\n",
       "      <td>211</td>\n",
       "      <td>M*A*S*H (1970)</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>301</th>\n",
       "      <td>302</td>\n",
       "      <td>L.A. Confidential (1997)</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151</th>\n",
       "      <td>152</td>\n",
       "      <td>Sleeper (1973)</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>171</th>\n",
       "      <td>172</td>\n",
       "      <td>Empire Strikes Back, The (1980)</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>285</th>\n",
       "      <td>286</td>\n",
       "      <td>English Patient, The (1996)</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>413</th>\n",
       "      <td>414</td>\n",
       "      <td>My Favorite Year (1982)</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>461</th>\n",
       "      <td>462</td>\n",
       "      <td>Like Water For Chocolate (Como agua para choco...</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>143</td>\n",
       "      <td>Sound of Music, The (1965)</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>873</th>\n",
       "      <td>874</td>\n",
       "      <td>Career Girls (1997)</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201</th>\n",
       "      <td>202</td>\n",
       "      <td>Groundhog Day (1993)</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>641</th>\n",
       "      <td>642</td>\n",
       "      <td>Grifters, The (1990)</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>241</th>\n",
       "      <td>242</td>\n",
       "      <td>Kolya (1996)</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>257</th>\n",
       "      <td>258</td>\n",
       "      <td>Contact (1997)</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>495</th>\n",
       "      <td>496</td>\n",
       "      <td>It's a Wonderful Life (1946)</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>332</th>\n",
       "      <td>333</td>\n",
       "      <td>Game, The (1997)</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>944</th>\n",
       "      <td>945</td>\n",
       "      <td>Charade (1963)</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>221</th>\n",
       "      <td>222</td>\n",
       "      <td>Star Trek: First Contact (1996)</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>288</th>\n",
       "      <td>289</td>\n",
       "      <td>Evita (1996)</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>323</th>\n",
       "      <td>324</td>\n",
       "      <td>Lost Highway (1997)</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Id                                              Title  Rating\n",
       "640  641                              Paths of Glory (1957)     5.0\n",
       "184  185                                      Psycho (1960)     5.0\n",
       "237  238                             Raising Arizona (1987)     5.0\n",
       "639  640    Cook the Thief His Wife & Her Lover, The (1989)     5.0\n",
       "473  474  Dr. Strangelove or: How I Learned to Stop Worr...     5.0\n",
       "199  200                                Shining, The (1980)     5.0\n",
       "196  197                               Graduate, The (1967)     5.0\n",
       "186  187                     Godfather: Part II, The (1974)     5.0\n",
       "181  182                                  GoodFellas (1990)     5.0\n",
       "174  175                                      Brazil (1985)     5.0\n",
       "173  174                     Raiders of the Lost Ark (1981)     5.0\n",
       "479  480                          North by Northwest (1959)     5.0\n",
       "88    89                                Blade Runner (1982)     5.0\n",
       "522  523                              Cool Hand Luke (1967)     5.0\n",
       "487  488                                Sunset Blvd. (1950)     4.0\n",
       "481  482                            Some Like It Hot (1959)     4.0\n",
       "478  479                                     Vertigo (1958)     4.0\n",
       "483  484                         Maltese Falcon, The (1941)     4.0\n",
       "60    61                         Three Colors: White (1994)     4.0\n",
       "514  515                                   Boot, Das (1981)     4.0\n",
       "526  527                                      Gandhi (1982)     4.0\n",
       "650  651                                       Glory (1989)     4.0\n",
       "707  708                    Sex, Lies, and Videotape (1989)     4.0\n",
       "805  806                           Menace II Society (1993)     4.0\n",
       "918  919                  City of Lost Children, The (1995)     4.0\n",
       "474  475                               Trainspotting (1996)     4.0\n",
       "356  357             One Flew Over the Cuckoo's Nest (1975)     4.0\n",
       "210  211                                     M*A*S*H (1970)     4.0\n",
       "301  302                           L.A. Confidential (1997)     4.0\n",
       "151  152                                     Sleeper (1973)     4.0\n",
       "171  172                    Empire Strikes Back, The (1980)     4.0\n",
       "285  286                        English Patient, The (1996)     4.0\n",
       "413  414                            My Favorite Year (1982)     4.0\n",
       "461  462  Like Water For Chocolate (Como agua para choco...     3.0\n",
       "142  143                         Sound of Music, The (1965)     3.0\n",
       "873  874                                Career Girls (1997)     3.0\n",
       "201  202                               Groundhog Day (1993)     3.0\n",
       "641  642                               Grifters, The (1990)     3.0\n",
       "241  242                                       Kolya (1996)     3.0\n",
       "257  258                                     Contact (1997)     3.0\n",
       "495  496                       It's a Wonderful Life (1946)     3.0\n",
       "332  333                                   Game, The (1997)     3.0\n",
       "944  945                                     Charade (1963)     3.0\n",
       "221  222                    Star Trek: First Contact (1996)     2.0\n",
       "288  289                                       Evita (1996)     2.0\n",
       "323  324                                Lost Highway (1997)     2.0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_data = pd.read_csv('ml-100k/u.user', sep = '|', encoding = 'latin-1', header = None, names = ['Id', 'Age', 'Gender', 'Occupation', 'Zip'] )\n",
    "item_data = pd.read_csv('ml-100k/u.item', sep = '|', encoding = 'latin-1', header = None, names = ['Id', 'Title', 'Date', 'Video date', 'Url', 'unknown', 'Action', 'Adventure', 'Animation', 'Children', 'Comedy', 'Crime', 'Documentary', 'Drama', 'Fantasy', 'Film-Noir', 'Horror', 'Musical', 'Mystery', 'Romance', 'Sci-Fi', 'Thriller', 'War', 'Western'])\n",
    "\n",
    "\n",
    "user = 154\n",
    "\n",
    "pd.set_option('display.max_rows', 50)\n",
    "\n",
    "user_data[user_data['Id'] == int(user)]\n",
    "rated_movie_ids = {int(train_data1.to_raw_iid(m)) : r for (u, m, r) in train_data1.all_ratings() if int(train_data1.to_raw_uid(u)) == user}\n",
    "user_movies = item_data[item_data['Id'].isin(rated_movie_ids)][['Id', 'Title']]\n",
    "user_movies['Rating'] = user_movies.apply(lambda row : rated_movie_ids[row['Id']], axis = 1)\n",
    "user_movies.sort_values('Rating', ascending = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ispisujemo predviđanja modela za sve te filmove. U oba slučaja su filmovi uređeni po oceni."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>Title</th>\n",
       "      <th>Rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>473</th>\n",
       "      <td>474</td>\n",
       "      <td>Dr. Strangelove or: How I Learned to Stop Worr...</td>\n",
       "      <td>4.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>241</th>\n",
       "      <td>242</td>\n",
       "      <td>Kolya (1996)</td>\n",
       "      <td>4.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>479</th>\n",
       "      <td>480</td>\n",
       "      <td>North by Northwest (1959)</td>\n",
       "      <td>4.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>356</th>\n",
       "      <td>357</td>\n",
       "      <td>One Flew Over the Cuckoo's Nest (1975)</td>\n",
       "      <td>4.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>640</th>\n",
       "      <td>641</td>\n",
       "      <td>Paths of Glory (1957)</td>\n",
       "      <td>4.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>478</th>\n",
       "      <td>479</td>\n",
       "      <td>Vertigo (1958)</td>\n",
       "      <td>4.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>487</th>\n",
       "      <td>488</td>\n",
       "      <td>Sunset Blvd. (1950)</td>\n",
       "      <td>4.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>197</td>\n",
       "      <td>Graduate, The (1967)</td>\n",
       "      <td>4.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186</th>\n",
       "      <td>187</td>\n",
       "      <td>Godfather: Part II, The (1974)</td>\n",
       "      <td>4.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>89</td>\n",
       "      <td>Blade Runner (1982)</td>\n",
       "      <td>4.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>514</th>\n",
       "      <td>515</td>\n",
       "      <td>Boot, Das (1981)</td>\n",
       "      <td>4.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>184</th>\n",
       "      <td>185</td>\n",
       "      <td>Psycho (1960)</td>\n",
       "      <td>4.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>301</th>\n",
       "      <td>302</td>\n",
       "      <td>L.A. Confidential (1997)</td>\n",
       "      <td>4.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>483</th>\n",
       "      <td>484</td>\n",
       "      <td>Maltese Falcon, The (1941)</td>\n",
       "      <td>4.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>173</th>\n",
       "      <td>174</td>\n",
       "      <td>Raiders of the Lost Ark (1981)</td>\n",
       "      <td>4.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>481</th>\n",
       "      <td>482</td>\n",
       "      <td>Some Like It Hot (1959)</td>\n",
       "      <td>4.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>171</th>\n",
       "      <td>172</td>\n",
       "      <td>Empire Strikes Back, The (1980)</td>\n",
       "      <td>4.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181</th>\n",
       "      <td>182</td>\n",
       "      <td>GoodFellas (1990)</td>\n",
       "      <td>4.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>474</th>\n",
       "      <td>475</td>\n",
       "      <td>Trainspotting (1996)</td>\n",
       "      <td>4.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>650</th>\n",
       "      <td>651</td>\n",
       "      <td>Glory (1989)</td>\n",
       "      <td>4.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>495</th>\n",
       "      <td>496</td>\n",
       "      <td>It's a Wonderful Life (1946)</td>\n",
       "      <td>4.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174</th>\n",
       "      <td>175</td>\n",
       "      <td>Brazil (1985)</td>\n",
       "      <td>4.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>237</th>\n",
       "      <td>238</td>\n",
       "      <td>Raising Arizona (1987)</td>\n",
       "      <td>4.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>522</th>\n",
       "      <td>523</td>\n",
       "      <td>Cool Hand Luke (1967)</td>\n",
       "      <td>4.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>526</th>\n",
       "      <td>527</td>\n",
       "      <td>Gandhi (1982)</td>\n",
       "      <td>4.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>210</th>\n",
       "      <td>211</td>\n",
       "      <td>M*A*S*H (1970)</td>\n",
       "      <td>4.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>461</th>\n",
       "      <td>462</td>\n",
       "      <td>Like Water For Chocolate (Como agua para choco...</td>\n",
       "      <td>4.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>944</th>\n",
       "      <td>945</td>\n",
       "      <td>Charade (1963)</td>\n",
       "      <td>3.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>918</th>\n",
       "      <td>919</td>\n",
       "      <td>City of Lost Children, The (1995)</td>\n",
       "      <td>3.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>200</td>\n",
       "      <td>Shining, The (1980)</td>\n",
       "      <td>3.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>61</td>\n",
       "      <td>Three Colors: White (1994)</td>\n",
       "      <td>3.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>641</th>\n",
       "      <td>642</td>\n",
       "      <td>Grifters, The (1990)</td>\n",
       "      <td>3.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>707</th>\n",
       "      <td>708</td>\n",
       "      <td>Sex, Lies, and Videotape (1989)</td>\n",
       "      <td>3.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>285</th>\n",
       "      <td>286</td>\n",
       "      <td>English Patient, The (1996)</td>\n",
       "      <td>3.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201</th>\n",
       "      <td>202</td>\n",
       "      <td>Groundhog Day (1993)</td>\n",
       "      <td>3.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>413</th>\n",
       "      <td>414</td>\n",
       "      <td>My Favorite Year (1982)</td>\n",
       "      <td>3.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>257</th>\n",
       "      <td>258</td>\n",
       "      <td>Contact (1997)</td>\n",
       "      <td>3.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151</th>\n",
       "      <td>152</td>\n",
       "      <td>Sleeper (1973)</td>\n",
       "      <td>3.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>221</th>\n",
       "      <td>222</td>\n",
       "      <td>Star Trek: First Contact (1996)</td>\n",
       "      <td>3.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>143</td>\n",
       "      <td>Sound of Music, The (1965)</td>\n",
       "      <td>3.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>805</th>\n",
       "      <td>806</td>\n",
       "      <td>Menace II Society (1993)</td>\n",
       "      <td>3.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>323</th>\n",
       "      <td>324</td>\n",
       "      <td>Lost Highway (1997)</td>\n",
       "      <td>3.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>873</th>\n",
       "      <td>874</td>\n",
       "      <td>Career Girls (1997)</td>\n",
       "      <td>3.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>332</th>\n",
       "      <td>333</td>\n",
       "      <td>Game, The (1997)</td>\n",
       "      <td>3.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>639</th>\n",
       "      <td>640</td>\n",
       "      <td>Cook the Thief His Wife &amp; Her Lover, The (1989)</td>\n",
       "      <td>3.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>288</th>\n",
       "      <td>289</td>\n",
       "      <td>Evita (1996)</td>\n",
       "      <td>2.83</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Id                                              Title  Rating\n",
       "473  474  Dr. Strangelove or: How I Learned to Stop Worr...    4.47\n",
       "241  242                                       Kolya (1996)    4.45\n",
       "479  480                          North by Northwest (1959)    4.45\n",
       "356  357             One Flew Over the Cuckoo's Nest (1975)    4.41\n",
       "640  641                              Paths of Glory (1957)    4.37\n",
       "478  479                                     Vertigo (1958)    4.35\n",
       "487  488                                Sunset Blvd. (1950)    4.33\n",
       "196  197                               Graduate, The (1967)    4.30\n",
       "186  187                     Godfather: Part II, The (1974)    4.29\n",
       "88    89                                Blade Runner (1982)    4.28\n",
       "514  515                                   Boot, Das (1981)    4.28\n",
       "184  185                                      Psycho (1960)    4.27\n",
       "301  302                           L.A. Confidential (1997)    4.26\n",
       "483  484                         Maltese Falcon, The (1941)    4.26\n",
       "173  174                     Raiders of the Lost Ark (1981)    4.21\n",
       "481  482                            Some Like It Hot (1959)    4.19\n",
       "171  172                    Empire Strikes Back, The (1980)    4.18\n",
       "181  182                                  GoodFellas (1990)    4.11\n",
       "474  475                               Trainspotting (1996)    4.07\n",
       "650  651                                       Glory (1989)    4.06\n",
       "495  496                       It's a Wonderful Life (1946)    4.05\n",
       "174  175                                      Brazil (1985)    4.05\n",
       "237  238                             Raising Arizona (1987)    4.04\n",
       "522  523                              Cool Hand Luke (1967)    4.04\n",
       "526  527                                      Gandhi (1982)    4.02\n",
       "210  211                                     M*A*S*H (1970)    4.02\n",
       "461  462  Like Water For Chocolate (Como agua para choco...    4.00\n",
       "944  945                                     Charade (1963)    3.95\n",
       "918  919                  City of Lost Children, The (1995)    3.93\n",
       "199  200                                Shining, The (1980)    3.88\n",
       "60    61                         Three Colors: White (1994)    3.86\n",
       "641  642                               Grifters, The (1990)    3.83\n",
       "707  708                    Sex, Lies, and Videotape (1989)    3.82\n",
       "285  286                        English Patient, The (1996)    3.81\n",
       "201  202                               Groundhog Day (1993)    3.74\n",
       "413  414                            My Favorite Year (1982)    3.72\n",
       "257  258                                     Contact (1997)    3.69\n",
       "151  152                                     Sleeper (1973)    3.66\n",
       "221  222                    Star Trek: First Contact (1996)    3.55\n",
       "142  143                         Sound of Music, The (1965)    3.47\n",
       "805  806                           Menace II Society (1993)    3.41\n",
       "323  324                                Lost Highway (1997)    3.31\n",
       "873  874                                Career Girls (1997)    3.29\n",
       "332  333                                   Game, The (1997)    3.20\n",
       "639  640    Cook the Thief His Wife & Her Lover, The (1989)    3.19\n",
       "288  289                                       Evita (1996)    2.83"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies = user_movies['Id']\n",
    "test_movies = [(str(user), str(movie), 0) for movie in movies]\n",
    "suggested_ratings = {int(prediction.iid) : prediction.est for prediction in d_final.test(test_movies)}\n",
    "predictions = item_data[item_data['Id'].isin(suggested_ratings)][['Id', 'Title']]\n",
    "predictions['Rating'] = predictions.apply(lambda row : round(suggested_ratings[row['Id']], 2), axis = 1)\n",
    "predictions.sort_values('Rating', ascending = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Konačno ispisujemo i predviđanja za 20 nasumično odabranih filmova radi provere koliko se predviđanja uklapaju u ono što znamo o korisniku."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>Id</th>\n",
       "      <th>Title</th>\n",
       "      <th>Rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>178</th>\n",
       "      <td>179</td>\n",
       "      <td>Clockwork Orange, A (1971)</td>\n",
       "      <td>4.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>208</th>\n",
       "      <td>209</td>\n",
       "      <td>This Is Spinal Tap (1984)</td>\n",
       "      <td>4.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1237</th>\n",
       "      <td>1238</td>\n",
       "      <td>Full Speed (1996)</td>\n",
       "      <td>4.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>274</th>\n",
       "      <td>275</td>\n",
       "      <td>Sense and Sensibility (1995)</td>\n",
       "      <td>4.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1546</th>\n",
       "      <td>1547</td>\n",
       "      <td>Show, The (1995)</td>\n",
       "      <td>4.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1534</th>\n",
       "      <td>1535</td>\n",
       "      <td>Enfer, L' (1994)</td>\n",
       "      <td>4.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1368</th>\n",
       "      <td>1369</td>\n",
       "      <td>Forbidden Christ, The (Cristo proibito, Il) (1...</td>\n",
       "      <td>4.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1122</th>\n",
       "      <td>1123</td>\n",
       "      <td>Last Time I Saw Paris, The (1954)</td>\n",
       "      <td>4.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1186</th>\n",
       "      <td>1187</td>\n",
       "      <td>Switchblade Sisters (1975)</td>\n",
       "      <td>3.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1177</th>\n",
       "      <td>1178</td>\n",
       "      <td>Major Payne (1994)</td>\n",
       "      <td>3.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1638</th>\n",
       "      <td>1639</td>\n",
       "      <td>Bitter Sugar (Azucar Amargo) (1996)</td>\n",
       "      <td>3.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1348</th>\n",
       "      <td>1349</td>\n",
       "      <td>Mille bolle blu (1993)</td>\n",
       "      <td>3.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1660</th>\n",
       "      <td>1661</td>\n",
       "      <td>New Age, The (1994)</td>\n",
       "      <td>3.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>68</td>\n",
       "      <td>Crow, The (1994)</td>\n",
       "      <td>3.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>147</td>\n",
       "      <td>Long Kiss Goodnight, The (1996)</td>\n",
       "      <td>3.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1454</th>\n",
       "      <td>1455</td>\n",
       "      <td>Outlaw, The (1943)</td>\n",
       "      <td>3.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>599</th>\n",
       "      <td>600</td>\n",
       "      <td>Daniel Defoe's Robinson Crusoe (1996)</td>\n",
       "      <td>3.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>688</th>\n",
       "      <td>689</td>\n",
       "      <td>Jackal, The (1997)</td>\n",
       "      <td>2.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1111</th>\n",
       "      <td>1112</td>\n",
       "      <td>Cobb (1994)</td>\n",
       "      <td>2.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>363</th>\n",
       "      <td>364</td>\n",
       "      <td>Ace Ventura: When Nature Calls (1995)</td>\n",
       "      <td>2.67</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Id                                              Title  Rating\n",
       "178    179                         Clockwork Orange, A (1971)    4.17\n",
       "208    209                          This Is Spinal Tap (1984)    4.01\n",
       "1237  1238                                  Full Speed (1996)    4.00\n",
       "274    275                       Sense and Sensibility (1995)    4.00\n",
       "1546  1547                                   Show, The (1995)    4.00\n",
       "1534  1535                                   Enfer, L' (1994)    4.00\n",
       "1368  1369  Forbidden Christ, The (Cristo proibito, Il) (1...    4.00\n",
       "1122  1123                  Last Time I Saw Paris, The (1954)    4.00\n",
       "1186  1187                         Switchblade Sisters (1975)    3.80\n",
       "1177  1178                                 Major Payne (1994)    3.53\n",
       "1638  1639                Bitter Sugar (Azucar Amargo) (1996)    3.53\n",
       "1348  1349                             Mille bolle blu (1993)    3.53\n",
       "1660  1661                                New Age, The (1994)    3.53\n",
       "67      68                                   Crow, The (1994)    3.29\n",
       "146    147                    Long Kiss Goodnight, The (1996)    3.15\n",
       "1454  1455                                 Outlaw, The (1943)    3.00\n",
       "599    600              Daniel Defoe's Robinson Crusoe (1996)    3.00\n",
       "688    689                                 Jackal, The (1997)    2.82\n",
       "1111  1112                                        Cobb (1994)    2.75\n",
       "363    364              Ace Ventura: When Nature Calls (1995)    2.67"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies = item_data.sample(20)['Id']\n",
    "test_movies = [(str(user), str(movie), 0) for movie in movies]\n",
    "suggested_ratings = {int(prediction.iid) : prediction.est for prediction in d_final.test(test_movies)}\n",
    "predictions = item_data[item_data['Id'].isin(suggested_ratings)][['Id', 'Title']]\n",
    "predictions['Rating'] = predictions.apply(lambda row : round(suggested_ratings[row['Id']], 2), axis = 1)\n",
    "predictions.sort_values('Rating', ascending = False)"
   ]
  }
 ],
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