03B-spotlight.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Spotlight\n",
    "\n",
    "The Quickstart Example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from spotlight.cross_validation import random_train_test_split\n",
    "from spotlight.datasets.movielens import get_movielens_dataset\n",
    "from spotlight.evaluation import mrr_score\n",
    "from spotlight.factorization.implicit import ImplicitFactorizationModel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = get_movielens_dataset(variant='100K')\n",
    "\n",
    "train, test = random_train_test_split(dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Interactions dataset (944 users x 1683 items x 80000 interactions)>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Not helpful... need to go deeper:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([416, 493, 654, ..., 293, 207, 415], dtype=int32)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.user_ids"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([313, 881, 250, ..., 210, 997, 323], dtype=int32)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.item_ids"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([5., 1., 1., ..., 3., 1., 2.], dtype=float32)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.ratings"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And peek inside:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "matrix([[0., 0., 0., ..., 0., 0., 0.],\n",
       "        [0., 5., 0., ..., 0., 0., 0.],\n",
       "        [0., 4., 0., ..., 0., 0., 0.],\n",
       "        ...,\n",
       "        [0., 5., 0., ..., 0., 0., 0.],\n",
       "        [0., 0., 0., ..., 0., 0., 0.],\n",
       "        [0., 0., 0., ..., 0., 0., 0.]], dtype=float32)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.tocsr().todense()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = ImplicitFactorizationModel(n_iter=3,\n",
    "                                   loss='bpr')\n",
    "model.fit(train)\n",
    "\n",
    "mrr = mrr_score(model, test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.01792003, 0.07894809, 0.05453556, 0.00520833, 0.00964695,\n",
       "       0.03831481, 0.01286256, 0.04568794, 0.12909755, 0.0457502 ])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mrr[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### GitHub Stars"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data retrived from scraping GitHub:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "\n",
    "df = pd.read_csv('data/stars.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user</th>\n",
       "      <th>repo</th>\n",
       "      <th>description</th>\n",
       "      <th>language</th>\n",
       "      <th>stargazers</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>45306</th>\n",
       "      <td>Kiphen</td>\n",
       "      <td>brigadecore/brigade</td>\n",
       "      <td>Event-based Scripting for Kubernetes.</td>\n",
       "      <td>Go</td>\n",
       "      <td>1901</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5232</th>\n",
       "      <td>andreztz</td>\n",
       "      <td>brettvanderwerff/Flaskerizer</td>\n",
       "      <td>Automatically create Flask apps from Bootstrap...</td>\n",
       "      <td>JavaScript</td>\n",
       "      <td>116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41667</th>\n",
       "      <td>GarrettMooney</td>\n",
       "      <td>IRkernel/IRkernel</td>\n",
       "      <td>R kernel for Jupyter</td>\n",
       "      <td>Jupyter Notebook</td>\n",
       "      <td>1252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47792</th>\n",
       "      <td>viseshrp</td>\n",
       "      <td>agiliq/building-api-django</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Python</td>\n",
       "      <td>189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55794</th>\n",
       "      <td>StevenLOL</td>\n",
       "      <td>Tencent/PocketFlow</td>\n",
       "      <td>An Automatic Model Compression (AutoMC) framew...</td>\n",
       "      <td>Python</td>\n",
       "      <td>2416</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                user                          repo  \\\n",
       "45306         Kiphen           brigadecore/brigade   \n",
       "5232        andreztz  brettvanderwerff/Flaskerizer   \n",
       "41667  GarrettMooney             IRkernel/IRkernel   \n",
       "47792       viseshrp    agiliq/building-api-django   \n",
       "55794      StevenLOL            Tencent/PocketFlow   \n",
       "\n",
       "                                             description          language  \\\n",
       "45306              Event-based Scripting for Kubernetes.                Go   \n",
       "5232   Automatically create Flask apps from Bootstrap...        JavaScript   \n",
       "41667                               R kernel for Jupyter  Jupyter Notebook   \n",
       "47792                                                NaN            Python   \n",
       "55794  An Automatic Model Compression (AutoMC) framew...            Python   \n",
       "\n",
       "       stargazers  \n",
       "45306        1901  \n",
       "5232          116  \n",
       "41667        1252  \n",
       "47792         189  \n",
       "55794        2416  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Python              25702\n",
       "JavaScript           6459\n",
       "Jupyter Notebook     3777\n",
       "Go                   2571\n",
       "C++                  2544\n",
       "                    ...  \n",
       "Limbo                   1\n",
       "Vim Snippet             1\n",
       "Oz                      1\n",
       "Mercury                 1\n",
       "Ada                     1\n",
       "Name: language, Length: 178, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['language'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df[df.language == 'Python']\n",
    "df = df[~df['repo'].isin(['maxhumber/gif', 'maxhumber/gazpacho'])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(25380, 5)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12222"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df['repo'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "326"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df['user'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user</th>\n",
       "      <th>repo</th>\n",
       "      <th>description</th>\n",
       "      <th>language</th>\n",
       "      <th>stargazers</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>sbarman-mi9</td>\n",
       "      <td>as-ideas/ForwardTacotron</td>\n",
       "      <td>⏩ Generating speech in a single forward pass w...</td>\n",
       "      <td>Python</td>\n",
       "      <td>97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>sbarman-mi9</td>\n",
       "      <td>abhishekkrthakur/bert-sentiment</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Python</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>sbarman-mi9</td>\n",
       "      <td>EmilyAlsentzer/clinicalBERT</td>\n",
       "      <td>repository for Publicly Available Clinical BER...</td>\n",
       "      <td>Python</td>\n",
       "      <td>160</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          user                             repo  \\\n",
       "0  sbarman-mi9         as-ideas/ForwardTacotron   \n",
       "1  sbarman-mi9  abhishekkrthakur/bert-sentiment   \n",
       "4  sbarman-mi9      EmilyAlsentzer/clinicalBERT   \n",
       "\n",
       "                                         description language  stargazers  \n",
       "0  ⏩ Generating speech in a single forward pass w...   Python          97  \n",
       "1                                                NaN   Python          21  \n",
       "4  repository for Publicly Available Clinical BER...   Python         160  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "from spotlight.interactions import Interactions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "must be str, not int",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-19-b910af9b4cf8>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# won't work\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0minteractions\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mInteractions\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'user'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'repo'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/anaconda3/envs/spot/lib/python3.6/site-packages/spotlight/interactions.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, user_ids, item_ids, ratings, timestamps, weights, num_users, num_items)\u001b[0m\n\u001b[1;32m    100\u001b[0m                  num_items=None):\n\u001b[1;32m    101\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 102\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnum_users\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnum_users\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0muser_ids\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    103\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnum_items\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnum_items\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem_ids\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    104\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: must be str, not int"
     ]
    }
   ],
   "source": [
    "# won't work\n",
    "interactions = Interactions(df['user'], df['repo'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\"Everything must be a number\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "user_encoder = LabelEncoder()\n",
    "item_encoder = LabelEncoder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "users = user_encoder.fit_transform(df['user'])\n",
    "items = item_encoder.fit_transform(df['repo'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "interactions = Interactions(users, items)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Interactions dataset (326 users x 12222 items x 25380 interactions)>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "interactions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Be a good Data Scientist:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from spotlight.cross_validation import random_train_test_split\n",
    "\n",
    "train, test = random_train_test_split(interactions, test_percentage=0.2, random_state=np.random.RandomState(42))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = ImplicitFactorizationModel(loss='pointwise', n_iter=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.fit(train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user</th>\n",
       "      <th>repo</th>\n",
       "      <th>description</th>\n",
       "      <th>language</th>\n",
       "      <th>stargazers</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>sbarman-mi9</td>\n",
       "      <td>as-ideas/ForwardTacotron</td>\n",
       "      <td>⏩ Generating speech in a single forward pass w...</td>\n",
       "      <td>Python</td>\n",
       "      <td>97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>sbarman-mi9</td>\n",
       "      <td>abhishekkrthakur/bert-sentiment</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Python</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>sbarman-mi9</td>\n",
       "      <td>EmilyAlsentzer/clinicalBERT</td>\n",
       "      <td>repository for Publicly Available Clinical BER...</td>\n",
       "      <td>Python</td>\n",
       "      <td>160</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          user                             repo  \\\n",
       "0  sbarman-mi9         as-ideas/ForwardTacotron   \n",
       "1  sbarman-mi9  abhishekkrthakur/bert-sentiment   \n",
       "4  sbarman-mi9      EmilyAlsentzer/clinicalBERT   \n",
       "\n",
       "                                         description language  stargazers  \n",
       "0  ⏩ Generating speech in a single forward pass w...   Python          97  \n",
       "1                                                NaN   Python          21  \n",
       "4  repository for Publicly Available Clinical BER...   Python         160  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Examining one user:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "garrry = df[df['user'] == 'garrrychan']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['mnielsen/neural-networks-and-deep-learning',\n",
       "       'brendan-rius/jupyter-c-kernel', 'google-research/uda',\n",
       "       'hhatto/autopep8', 'MaxHalford/prince',\n",
       "       'scikit-learn-contrib/sklearn-pandas', 'modin-project/modin',\n",
       "       'VikParuchuri/apartment-finder', 'uber-research/parallax',\n",
       "       'prabhupant/python-ds', 'tensorflow/nmt',\n",
       "       'huggingface/transformers', 'h5py/h5py', 'google-research/bert',\n",
       "       'sloria/TextBlob', 'openai/gpt-2', 'matsui528/rii',\n",
       "       'ResidentMario/missingno', 'lmcinnes/enstop',\n",
       "       'phatpiglet/autocorrect', 'barrust/pyspellchecker',\n",
       "       'seatgeek/fuzzywuzzy', 'maxhumber/chart', 'apache/airflow',\n",
       "       'graphql-python/graphene', 'maxhumber/marc', 'spotify/luigi',\n",
       "       'garrrychan/recipe_recommender_system',\n",
       "       'CamDavidsonPilon/lifetimes', 'maciejkula/spotlight',\n",
       "       'lyst/lightfm', 'practical-recommender-systems/moviegeek',\n",
       "       'uwescience/TrafficCruising-DSSG2017'], dtype=object)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "garrry['repo'].values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looking at the user_id:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "u = user_encoder.transform(['garrrychan'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And all of the items:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['00111000/Imports-in-Python', '05bit/peewee-async',\n",
       "       '0Kee-Team/WatchAD', ..., 'zzw922cn/Automatic_Speech_Recognition',\n",
       "       'zzzDavid/ICDAR-2019-SROIE', 'zzzeek/sqlalchemy'], dtype=object)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "item_encoder.classes_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "preds = model.predict(u, np.arange(len(item_encoder.classes_)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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>repo</th>\n",
       "      <th>pred</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9120</th>\n",
       "      <td>plotly/dash</td>\n",
       "      <td>14.197499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11599</th>\n",
       "      <td>vinta/awesome-python</td>\n",
       "      <td>11.208200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9238</th>\n",
       "      <td>psf/black</td>\n",
       "      <td>8.538667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11292</th>\n",
       "      <td>tqdm/tqdm</td>\n",
       "      <td>8.492222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5597</th>\n",
       "      <td>google/trax</td>\n",
       "      <td>8.418111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0voice/interview_internal_reference</td>\n",
       "      <td>-14.476551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11438</th>\n",
       "      <td>uiri/toml</td>\n",
       "      <td>-14.862925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2409</th>\n",
       "      <td>aidlearning/AidLearning-FrameWork</td>\n",
       "      <td>-14.865212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4236</th>\n",
       "      <td>davidteather/TikTok-Api</td>\n",
       "      <td>-15.087893</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>951</th>\n",
       "      <td>JelteF/PyLaTeX</td>\n",
       "      <td>-17.409521</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>12222 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                      repo       pred\n",
       "9120                           plotly/dash  14.197499\n",
       "11599                 vinta/awesome-python  11.208200\n",
       "9238                             psf/black   8.538667\n",
       "11292                            tqdm/tqdm   8.492222\n",
       "5597                           google/trax   8.418111\n",
       "...                                    ...        ...\n",
       "8      0voice/interview_internal_reference -14.476551\n",
       "11438                            uiri/toml -14.862925\n",
       "2409     aidlearning/AidLearning-FrameWork -14.865212\n",
       "4236               davidteather/TikTok-Api -15.087893\n",
       "951                         JelteF/PyLaTeX -17.409521\n",
       "\n",
       "[12222 rows x 2 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame({\n",
    "    'repo': item_encoder.classes_,\n",
    "    'pred': preds\n",
    "}).sort_values('pred', ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Evaulating the model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "from spotlight.evaluation import precision_recall_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "precision, recall = precision_recall_score(model, test, train, k=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.03431372549019608"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "precision.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.03234916539212286"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recall.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Serialize:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch \n",
    "\n",
    "torch.save(model, 'model.spot')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "del model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = torch.load('model.spot')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Predict on another random user:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "u = user_encoder.transform(['RandomOS'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-8.888058 , -3.227444 , -7.1882577, ..., -3.6611528, -7.4558845,\n",
       "       -4.7885914], dtype=float32)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict(u, np.arange(len(item_encoder.classes_)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "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>repo</th>\n",
       "      <th>pred</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9426</th>\n",
       "      <td>python-poetry/poetry</td>\n",
       "      <td>15.067130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11142</th>\n",
       "      <td>tiangolo/fastapi</td>\n",
       "      <td>14.133257</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3927</th>\n",
       "      <td>cookiecutter/cookiecutter</td>\n",
       "      <td>13.093539</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9348</th>\n",
       "      <td>pypa/pipenv</td>\n",
       "      <td>12.918298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9446</th>\n",
       "      <td>python/cpython</td>\n",
       "      <td>12.113222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982</th>\n",
       "      <td>TheAlgorithms/Python</td>\n",
       "      <td>11.538029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10121</th>\n",
       "      <td>satwikkansal/wtfpython</td>\n",
       "      <td>11.168749</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>30-seconds/30-seconds-of-python</td>\n",
       "      <td>11.077389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3929</th>\n",
       "      <td>cool-RR/PySnooper</td>\n",
       "      <td>10.525791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9090</th>\n",
       "      <td>pipxproject/pipx</td>\n",
       "      <td>9.955636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5580</th>\n",
       "      <td>google/python-fire</td>\n",
       "      <td>9.726627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5897</th>\n",
       "      <td>home-assistant/core</td>\n",
       "      <td>9.668429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4333</th>\n",
       "      <td>deezer/spleeter</td>\n",
       "      <td>9.543584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9240</th>\n",
       "      <td>psf/requests-html</td>\n",
       "      <td>9.480227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6161</th>\n",
       "      <td>iodide-project/pyodide</td>\n",
       "      <td>9.416597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7889</th>\n",
       "      <td>mherrmann/helium</td>\n",
       "      <td>9.218766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9448</th>\n",
       "      <td>python/mypy</td>\n",
       "      <td>9.112749</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5107</th>\n",
       "      <td>falconry/falcon</td>\n",
       "      <td>8.911641</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>843</th>\n",
       "      <td>HypothesisWorks/hypothesis</td>\n",
       "      <td>8.722430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4833</th>\n",
       "      <td>encode/httpx</td>\n",
       "      <td>8.173861</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  repo       pred\n",
       "9426              python-poetry/poetry  15.067130\n",
       "11142                 tiangolo/fastapi  14.133257\n",
       "3927         cookiecutter/cookiecutter  13.093539\n",
       "9348                       pypa/pipenv  12.918298\n",
       "9446                    python/cpython  12.113222\n",
       "1982              TheAlgorithms/Python  11.538029\n",
       "10121           satwikkansal/wtfpython  11.168749\n",
       "33     30-seconds/30-seconds-of-python  11.077389\n",
       "3929                 cool-RR/PySnooper  10.525791\n",
       "9090                  pipxproject/pipx   9.955636\n",
       "5580                google/python-fire   9.726627\n",
       "5897               home-assistant/core   9.668429\n",
       "4333                   deezer/spleeter   9.543584\n",
       "9240                 psf/requests-html   9.480227\n",
       "6161            iodide-project/pyodide   9.416597\n",
       "7889                  mherrmann/helium   9.218766\n",
       "9448                       python/mypy   9.112749\n",
       "5107                   falconry/falcon   8.911641\n",
       "843         HypothesisWorks/hypothesis   8.722430\n",
       "4833                      encode/httpx   8.173861"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame({\n",
    "    'repo': item_encoder.classes_,\n",
    "    'pred': model.predict(u, np.arange(len(item_encoder.classes_)))\n",
    "}).sort_values('pred', ascending=False).head(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Actual likes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "58885               tortoise/tortoise-orm\n",
       "58887                 mingrammer/diagrams\n",
       "58905                       spulec/uncurl\n",
       "58913         mozilla-iot/webthing-python\n",
       "58922                       spotify/luigi\n",
       "58924                    python-trio/trio\n",
       "58925                       ranger/ranger\n",
       "58928              googlefonts/noto-emoji\n",
       "58937                Synss/python-mbedtls\n",
       "58940                    pipxproject/pipx\n",
       "58941                       linkedin/shiv\n",
       "58942                      pantsbuild/pex\n",
       "58946        PythonCharmers/python-future\n",
       "58947     alan-turing-institute/CleverCSV\n",
       "58948                 emeryberger/scalene\n",
       "58949                 aouinizied/nfstream\n",
       "58950               ionelmc/python-hunter\n",
       "58951                      wolever/pip2pi\n",
       "58956          fossasia/open-event-server\n",
       "58994                python-poetry/poetry\n",
       "58997                     thumbor/thumbor\n",
       "59004                       holoviz/panel\n",
       "59019             0xInfection/Awesome-WAF\n",
       "59020    swisskyrepo/PayloadsAllTheThings\n",
       "59029                    ekzhu/datasketch\n",
       "59035               rytilahti/python-miio\n",
       "59041                    trailofbits/algo\n",
       "59047                TheAlgorithms/Python\n",
       "59053                     dephell/dephell\n",
       "59057            LyleMi/Learn-Web-Hacking\n",
       "59068                joerick/cibuildwheel\n",
       "59074                       intoli/exodus\n",
       "59075                   piwheels/piwheels\n",
       "59111                 bomquote/transistor\n",
       "59120                     coleifer/ucache\n",
       "59122                  wooparadog/pystack\n",
       "59147                       django/daphne\n",
       "59148                  MasterOdin/crayons\n",
       "59153                     vstinner/pyperf\n",
       "59178                    tiangolo/fastapi\n",
       "59179                 taoufik07/responder\n",
       "Name: repo, dtype: object"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['user'] == 'RandomOS']['repo']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### But what if the user is brand new?~"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "y contains previously unseen labels: ['maxhumber']",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-48-0dd7a477fb62>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0muser_encoder\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtransform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'maxhumber'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/anaconda3/envs/spot/lib/python3.6/site-packages/sklearn/preprocessing/_label.py\u001b[0m in \u001b[0;36mtransform\u001b[0;34m(self, y)\u001b[0m\n\u001b[1;32m    271\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    272\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 273\u001b[0;31m         \u001b[0m_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_encode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0muniques\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclasses_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mencode\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    274\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    275\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/spot/lib/python3.6/site-packages/sklearn/preprocessing/_label.py\u001b[0m in \u001b[0;36m_encode\u001b[0;34m(values, uniques, encode, check_unknown)\u001b[0m\n\u001b[1;32m    116\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    117\u001b[0m         return _encode_numpy(values, uniques, encode,\n\u001b[0;32m--> 118\u001b[0;31m                              check_unknown=check_unknown)\n\u001b[0m\u001b[1;32m    119\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    120\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/spot/lib/python3.6/site-packages/sklearn/preprocessing/_label.py\u001b[0m in \u001b[0;36m_encode_numpy\u001b[0;34m(values, uniques, encode, check_unknown)\u001b[0m\n\u001b[1;32m     48\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mdiff\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     49\u001b[0m                 raise ValueError(\"y contains previously unseen labels: %s\"\n\u001b[0;32m---> 50\u001b[0;31m                                  % str(diff))\n\u001b[0m\u001b[1;32m     51\u001b[0m         \u001b[0mencoded\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msearchsorted\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0muniques\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     52\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0muniques\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mencoded\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: y contains previously unseen labels: ['maxhumber']"
     ]
    }
   ],
   "source": [
    "user_encoder.transform(['maxhumber'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Maximum user id greater than number of users in model.",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-49-fb1ffee07023>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1993\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem_encoder\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclasses_\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/anaconda3/envs/spot/lib/python3.6/site-packages/spotlight/factorization/implicit.py\u001b[0m in \u001b[0;36mpredict\u001b[0;34m(self, user_ids, item_ids)\u001b[0m\n\u001b[1;32m    300\u001b[0m         \"\"\"\n\u001b[1;32m    301\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 302\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_check_input\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0muser_ids\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mitem_ids\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mallow_items_none\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    303\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_net\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    304\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/spot/lib/python3.6/site-packages/spotlight/factorization/implicit.py\u001b[0m in \u001b[0;36m_check_input\u001b[0;34m(self, user_ids, item_ids, allow_items_none)\u001b[0m\n\u001b[1;32m    167\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    168\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0muser_id_max\u001b[0m \u001b[0;34m>=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_num_users\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 169\u001b[0;31m             raise ValueError('Maximum user id greater '\n\u001b[0m\u001b[1;32m    170\u001b[0m                              'than number of users in model.')\n\u001b[1;32m    171\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: Maximum user id greater than number of users in model."
     ]
    }
   ],
   "source": [
    "model.predict(1993, np.arange(len(item_encoder.classes_)))"
   ]
  }
 ],
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