no2klbd.ipynb

{
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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyOiLfndhMP5W+oh7QYIW2Pv",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/gist/syambahardi/6da381ecc071a92ceba41336db7baa17/no2klbd.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CHlG1ZFQ9gGT",
        "outputId": "d62f4f10-bd5b-4b63-a4be-2be19026815d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2023-01-02 14:50:20--  https://github.com/nzhinusoftcm/review-on-collaborative-filtering/raw/master/recsys.zip\n",
            "Resolving github.com (github.com)... 140.82.112.3\n",
            "Connecting to github.com (github.com)|140.82.112.3|:443... connected.\n",
            "HTTP request sent, awaiting response... 302 Found\n",
            "Location: https://raw.githubusercontent.com/nzhinusoftcm/review-on-collaborative-filtering/master/recsys.zip [following]\n",
            "--2023-01-02 14:50:20--  https://raw.githubusercontent.com/nzhinusoftcm/review-on-collaborative-filtering/master/recsys.zip\n",
            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n",
            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 15312323 (15M) [application/zip]\n",
            "Saving to: ‘recsys.zip’\n",
            "\n",
            "recsys.zip          100%[===================>]  14.60M  --.-KB/s    in 0.1s    \n",
            "\n",
            "2023-01-02 14:50:20 (130 MB/s) - ‘recsys.zip’ saved [15312323/15312323]\n",
            "\n",
            "Archive:  recsys.zip\n",
            "   creating: recsys/\n",
            "  inflating: recsys/datasets.py      \n",
            "  inflating: recsys/preprocessing.py  \n",
            "  inflating: recsys/utils.py         \n",
            "  inflating: recsys/requirements.txt  \n",
            "   creating: recsys/.vscode/\n",
            "  inflating: recsys/.vscode/settings.json  \n",
            "   creating: recsys/__pycache__/\n",
            "  inflating: recsys/__pycache__/datasets.cpython-36.pyc  \n",
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            "  inflating: recsys/__pycache__/preprocessing.cpython-37.pyc  \n",
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            "  inflating: recsys/__pycache__/preprocessing.cpython-38.pyc  \n",
            "   creating: recsys/memories/\n",
            "  inflating: recsys/memories/ItemToItem.py  \n",
            "  inflating: recsys/memories/UserToUser.py  \n",
            "   creating: recsys/memories/__pycache__/\n",
            "  inflating: recsys/memories/__pycache__/UserToUser.cpython-36.pyc  \n",
            "  inflating: recsys/memories/__pycache__/UserToUser.cpython-37.pyc  \n",
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            "  inflating: recsys/memories/__pycache__/user2user.cpython-36.pyc  \n",
            "  inflating: recsys/memories/__pycache__/ItemToItem.cpython-36.pyc  \n",
            "   creating: recsys/models/\n",
            "  inflating: recsys/models/SVD.py    \n",
            "  inflating: recsys/models/MatrixFactorization.py  \n",
            "  inflating: recsys/models/ExplainableMF.py  \n",
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            "  inflating: recsys/metrics/EvaluationMetrics.py  \n",
            "   creating: recsys/img/\n",
            "  inflating: recsys/img/MF-and-NNMF.png  \n",
            "  inflating: recsys/img/svd.png      \n",
            "  inflating: recsys/img/MF.png       \n",
            "   creating: recsys/predictions/\n",
            "   creating: recsys/predictions/item2item/\n",
            "   creating: recsys/weights/\n",
            "   creating: recsys/weights/item2item/\n",
            "   creating: recsys/weights/item2item/ml1m/\n",
            "  inflating: recsys/weights/item2item/ml1m/similarities.npy  \n",
            "  inflating: recsys/weights/item2item/ml1m/neighbors.npy  \n",
            "   creating: recsys/weights/item2item/ml100k/\n",
            "  inflating: recsys/weights/item2item/ml100k/similarities.npy  \n",
            "  inflating: recsys/weights/item2item/ml100k/neighbors.npy  \n"
          ]
        }
      ],
      "source": [
        "import os\n",
        "\n",
        "if not (os.path.exists(\"recsys.zip\") or os.path.exists(\"recsys\")):\n",
        "    !wget https://github.com/nzhinusoftcm/review-on-collaborative-filtering/raw/master/recsys.zip    \n",
        "    !unzip recsys.zip"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.neighbors import NearestNeighbors\n",
        "from scipy.sparse import csr_matrix\n",
        "\n",
        "from recsys.datasets import ml100k\n",
        "from recsys.preprocessing import ids_encoder\n",
        "\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import zipfile"
      ],
      "metadata": {
        "id": "_Bj52qUd9rCp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ratings, movies = ml100k.load()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TiCKuPc19s6_",
        "outputId": "816d7605-b1bd-4a59-ca9a-b6561e2c87a4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Download data 100.2%\n",
            "Successfully downloaded ml-100k.zip 4924029 bytes.\n",
            "Unzipping the ml-100k.zip zip file ...\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ratings, uencoder, iencoder = ids_encoder(ratings)"
      ],
      "metadata": {
        "id": "vE2jmus-9z8P"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def ratings_matrix(ratings):    \n",
        "    return csr_matrix(pd.crosstab(ratings.userid, ratings.itemid, ratings.rating, aggfunc=sum).fillna(0).values)    \n",
        "\n",
        "R = ratings_matrix(ratings)"
      ],
      "metadata": {
        "id": "sjjr1j-e93zx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def create_model(rating_matrix, metric):\n",
        "    \"\"\"\n",
        "    - create the nearest neighbors model with the corresponding similarity metric\n",
        "    - fit the model\n",
        "    \"\"\"\n",
        "    model = NearestNeighbors(metric=metric, n_neighbors=21, algorithm='brute')\n",
        "    model.fit(rating_matrix)    \n",
        "    return model"
      ],
      "metadata": {
        "id": "R2_wbNq4-DpY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def nearest_neighbors(rating_matrix, model):\n",
        "    \"\"\"    \n",
        "    :param rating_matrix : rating matrix of shape (nb_users, nb_items)\n",
        "    :param model : nearest neighbors model    \n",
        "    :return\n",
        "        - similarities : distances of the neighbors from the referenced user\n",
        "        - neighbors : neighbors of the referenced user in decreasing order of similarities\n",
        "    \"\"\"    \n",
        "    similarities, neighbors = model.kneighbors(rating_matrix)        \n",
        "    return similarities[:, 1:], neighbors[:, 1:]"
      ],
      "metadata": {
        "id": "t32f8DS--FB5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = create_model(rating_matrix=R, metric='cosine') # we can also use the 'euclidian' distance\n",
        "similarities, neighbors = nearest_neighbors(R, model)"
      ],
      "metadata": {
        "id": "3lqYBQgj-InC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def find_candidate_items(userid):\n",
        "    \"\"\"\n",
        "    Find candidate items for an active user\n",
        "    \n",
        "    :param userid : active user\n",
        "    :param neighbors : users similar to the active user        \n",
        "    :return candidates : top 30 of candidate items\n",
        "    \"\"\"\n",
        "    user_neighbors = neighbors[userid]\n",
        "    activities = ratings.loc[ratings.userid.isin(user_neighbors)]\n",
        "    \n",
        "    # sort items in decreasing order of frequency\n",
        "    frequency = activities.groupby('itemid')['rating'].count().reset_index(name='count').sort_values(['count'],ascending=False)\n",
        "    Gu_items = frequency.itemid\n",
        "    active_items = ratings.loc[ratings.userid == userid].itemid.to_list()\n",
        "    candidates = np.setdiff1d(Gu_items, active_items, assume_unique=True)[:30]\n",
        "        \n",
        "    return candidates"
      ],
      "metadata": {
        "id": "iHoCvzZH-LfP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# mean ratings for each user\n",
        "mean = ratings.groupby(by='userid', as_index=False)['rating'].mean()\n",
        "mean_ratings = pd.merge(ratings, mean, suffixes=('','_mean'), on='userid')\n",
        "\n",
        "# normalized ratings for each items\n",
        "mean_ratings['norm_rating'] = mean_ratings['rating'] - mean_ratings['rating_mean']\n",
        "\n",
        "mean = mean.to_numpy()[:, 1]"
      ],
      "metadata": {
        "id": "ANTccGXv-OlL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "np_ratings = mean_ratings.to_numpy()"
      ],
      "metadata": {
        "id": "wML6r-u8-R83"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def predict(userid, itemid):\n",
        "    \"\"\"\n",
        "    predict what score userid would have given to itemid.\n",
        "    \n",
        "    :param\n",
        "        - userid : user id for which we want to make prediction\n",
        "        - itemid : item id on which we want to make prediction\n",
        "        \n",
        "    :return\n",
        "        - r_hat : predicted rating of user userid on item itemid\n",
        "    \"\"\"\n",
        "    user_similarities = similarities[userid]\n",
        "    user_neighbors = neighbors[userid]\n",
        "    # get mean rating of user userid\n",
        "    user_mean = mean[userid]\n",
        "    \n",
        "    # find users who rated item 'itemid'\n",
        "    iratings = np_ratings[np_ratings[:, 1].astype('int') == itemid]\n",
        "    \n",
        "    # find similar users to 'userid' who rated item 'itemid'\n",
        "    suri = iratings[np.isin(iratings[:, 0], user_neighbors)]\n",
        "    \n",
        "    # similar users who rated current item (surci)\n",
        "    normalized_ratings = suri[:,4]\n",
        "    indexes = [np.where(user_neighbors == uid)[0][0] for uid in suri[:, 0].astype('int')]\n",
        "    sims = user_similarities[indexes]\n",
        "    \n",
        "    num = np.dot(normalized_ratings, sims)\n",
        "    den = np.sum(np.abs(sims))\n",
        "    \n",
        "    if num == 0 or den == 0:\n",
        "        return user_mean\n",
        "    \n",
        "    r_hat = user_mean + np.dot(normalized_ratings, sims) / np.sum(np.abs(sims))\n",
        "    \n",
        "    return r_hat"
      ],
      "metadata": {
        "id": "t1miUGXQ-UJZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def user2userPredictions(userid, pred_path):\n",
        "    \"\"\"\n",
        "    Make rating prediction for the active user on each candidate item and save in file prediction.csv\n",
        "    \n",
        "    :param\n",
        "        - userid : id of the active user\n",
        "        - pred_path : where to save predictions\n",
        "    \"\"\"    \n",
        "    # find candidate items for the active user\n",
        "    candidates = find_candidate_items(userid)\n",
        "    \n",
        "    # loop over candidates items to make predictions\n",
        "    for itemid in candidates:\n",
        "        \n",
        "        # prediction for userid on itemid\n",
        "        r_hat = predict(userid, itemid)\n",
        "        \n",
        "        # save predictions\n",
        "        with open(pred_path, 'a+') as file:\n",
        "            line = '{},{},{}\\n'.format(userid, itemid, r_hat)\n",
        "            file.write(line)"
      ],
      "metadata": {
        "id": "l13VBtAO-Xzo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import sys\n",
        "\n",
        "def user2userCF():\n",
        "    \"\"\"\n",
        "    Make predictions for each user in the database.    \n",
        "    \"\"\"\n",
        "    # get list of users in the database\n",
        "    users = ratings.userid.unique()\n",
        "    \n",
        "    def _progress(count):\n",
        "        sys.stdout.write('\\rRating predictions. Progress status : %.1f%%' % (float(count/len(users))*100.0))\n",
        "        sys.stdout.flush()\n",
        "    \n",
        "    saved_predictions = 'predictions.csv'    \n",
        "    if os.path.exists(saved_predictions):\n",
        "        os.remove(saved_predictions)\n",
        "    \n",
        "    for count, userid in enumerate(users):        \n",
        "        # make rating predictions for the current user\n",
        "        user2userPredictions(userid, saved_predictions)\n",
        "        _progress(count)"
      ],
      "metadata": {
        "id": "YUTMX6UD-a0a"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "user2userCF()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6_zQoWsW-dhG",
        "outputId": "c6ab8cfa-5351-4440-bc23-da449c1ba4df"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Rating predictions. Progress status : 99.9%"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def user2userRecommendation(userid):\n",
        "    \"\"\"\n",
        "    \"\"\"\n",
        "    # encode the userid\n",
        "    uid = uencoder.transform([userid])[0]\n",
        "    saved_predictions = 'predictions.csv'\n",
        "    \n",
        "    predictions = pd.read_csv(saved_predictions, sep=',', names=['userid', 'itemid', 'predicted_rating'])\n",
        "    predictions = predictions[predictions.userid==uid]\n",
        "    List = predictions.sort_values(by=['predicted_rating'], ascending=False)\n",
        "    \n",
        "    List.userid = uencoder.inverse_transform(List.userid.tolist())\n",
        "    List.itemid = iencoder.inverse_transform(List.itemid.tolist())\n",
        "    \n",
        "    List = pd.merge(List, movies, on='itemid', how='inner')\n",
        "    \n",
        "    return List"
      ],
      "metadata": {
        "id": "TeVQ9XRj-oNI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "user2userRecommendation(212)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 990
        },
        "id": "3qxCY-jr-rl-",
        "outputId": "cefe0b77-bd60-4ec5-c10c-3d6952782c04"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    userid  itemid  predicted_rating  \\\n",
              "0      212     483          4.871495   \n",
              "1      212     357          4.764547   \n",
              "2      212      50          4.660002   \n",
              "3      212      98          4.613636   \n",
              "4      212      64          4.550733   \n",
              "5      212     194          4.522336   \n",
              "6      212     174          4.521300   \n",
              "7      212     134          4.414819   \n",
              "8      212     187          4.344531   \n",
              "9      212     196          4.303696   \n",
              "10     212     523          4.281802   \n",
              "11     212     216          4.278246   \n",
              "12     212     100          4.260087   \n",
              "13     212     168          4.206139   \n",
              "14     212     435          4.122984   \n",
              "15     212     135          4.115228   \n",
              "16     212      83          4.106995   \n",
              "17     212      69          4.086366   \n",
              "18     212      70          4.086328   \n",
              "19     212     275          3.985037   \n",
              "20     212     153          3.981619   \n",
              "21     212     514          3.956640   \n",
              "22     212     521          3.937792   \n",
              "23     212      97          3.906106   \n",
              "24     212     173          3.879325   \n",
              "25     212     660          3.847897   \n",
              "26     212     215          3.709920   \n",
              "27     212     258          3.583718   \n",
              "28     212     202          3.508617   \n",
              "29     212     237          3.039041   \n",
              "\n",
              "                                        title  \n",
              "0                           Casablanca (1942)  \n",
              "1      One Flew Over the Cuckoo's Nest (1975)  \n",
              "2                            Star Wars (1977)  \n",
              "3            Silence of the Lambs, The (1991)  \n",
              "4            Shawshank Redemption, The (1994)  \n",
              "5                           Sting, The (1973)  \n",
              "6              Raiders of the Lost Ark (1981)  \n",
              "7                         Citizen Kane (1941)  \n",
              "8              Godfather: Part II, The (1974)  \n",
              "9                   Dead Poets Society (1989)  \n",
              "10                      Cool Hand Luke (1967)  \n",
              "11             When Harry Met Sally... (1989)  \n",
              "12                               Fargo (1996)  \n",
              "13     Monty Python and the Holy Grail (1974)  \n",
              "14  Butch Cassidy and the Sundance Kid (1969)  \n",
              "15               2001: A Space Odyssey (1968)  \n",
              "16              Much Ado About Nothing (1993)  \n",
              "17                        Forrest Gump (1994)  \n",
              "18         Four Weddings and a Funeral (1994)  \n",
              "19               Sense and Sensibility (1995)  \n",
              "20                Fish Called Wanda, A (1988)  \n",
              "21                          Annie Hall (1977)  \n",
              "22                    Deer Hunter, The (1978)  \n",
              "23                  Dances with Wolves (1990)  \n",
              "24                 Princess Bride, The (1987)  \n",
              "25                Fried Green Tomatoes (1991)  \n",
              "26                     Field of Dreams (1989)  \n",
              "27                             Contact (1997)  \n",
              "28                       Groundhog Day (1993)  \n",
              "29                       Jerry Maguire (1996)  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-483d410b-6fea-40be-89b3-9835045089f2\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>userid</th>\n",
              "      <th>itemid</th>\n",
              "      <th>predicted_rating</th>\n",
              "      <th>title</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>212</td>\n",
              "      <td>483</td>\n",
              "      <td>4.871495</td>\n",
              "      <td>Casablanca (1942)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>212</td>\n",
              "      <td>357</td>\n",
              "      <td>4.764547</td>\n",
              "      <td>One Flew Over the Cuckoo's Nest (1975)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>212</td>\n",
              "      <td>50</td>\n",
              "      <td>4.660002</td>\n",
              "      <td>Star Wars (1977)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>212</td>\n",
              "      <td>98</td>\n",
              "      <td>4.613636</td>\n",
              "      <td>Silence of the Lambs, The (1991)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>212</td>\n",
              "      <td>64</td>\n",
              "      <td>4.550733</td>\n",
              "      <td>Shawshank Redemption, The (1994)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>212</td>\n",
              "      <td>194</td>\n",
              "      <td>4.522336</td>\n",
              "      <td>Sting, The (1973)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>212</td>\n",
              "      <td>174</td>\n",
              "      <td>4.521300</td>\n",
              "      <td>Raiders of the Lost Ark (1981)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>212</td>\n",
              "      <td>134</td>\n",
              "      <td>4.414819</td>\n",
              "      <td>Citizen Kane (1941)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>212</td>\n",
              "      <td>187</td>\n",
              "      <td>4.344531</td>\n",
              "      <td>Godfather: Part II, The (1974)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>212</td>\n",
              "      <td>196</td>\n",
              "      <td>4.303696</td>\n",
              "      <td>Dead Poets Society (1989)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>212</td>\n",
              "      <td>523</td>\n",
              "      <td>4.281802</td>\n",
              "      <td>Cool Hand Luke (1967)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>212</td>\n",
              "      <td>216</td>\n",
              "      <td>4.278246</td>\n",
              "      <td>When Harry Met Sally... (1989)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>212</td>\n",
              "      <td>100</td>\n",
              "      <td>4.260087</td>\n",
              "      <td>Fargo (1996)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>212</td>\n",
              "      <td>168</td>\n",
              "      <td>4.206139</td>\n",
              "      <td>Monty Python and the Holy Grail (1974)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>212</td>\n",
              "      <td>435</td>\n",
              "      <td>4.122984</td>\n",
              "      <td>Butch Cassidy and the Sundance Kid (1969)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>212</td>\n",
              "      <td>135</td>\n",
              "      <td>4.115228</td>\n",
              "      <td>2001: A Space Odyssey (1968)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>212</td>\n",
              "      <td>83</td>\n",
              "      <td>4.106995</td>\n",
              "      <td>Much Ado About Nothing (1993)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>212</td>\n",
              "      <td>69</td>\n",
              "      <td>4.086366</td>\n",
              "      <td>Forrest Gump (1994)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>212</td>\n",
              "      <td>70</td>\n",
              "      <td>4.086328</td>\n",
              "      <td>Four Weddings and a Funeral (1994)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>212</td>\n",
              "      <td>275</td>\n",
              "      <td>3.985037</td>\n",
              "      <td>Sense and Sensibility (1995)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>212</td>\n",
              "      <td>153</td>\n",
              "      <td>3.981619</td>\n",
              "      <td>Fish Called Wanda, A (1988)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>212</td>\n",
              "      <td>514</td>\n",
              "      <td>3.956640</td>\n",
              "      <td>Annie Hall (1977)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>212</td>\n",
              "      <td>521</td>\n",
              "      <td>3.937792</td>\n",
              "      <td>Deer Hunter, The (1978)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>212</td>\n",
              "      <td>97</td>\n",
              "      <td>3.906106</td>\n",
              "      <td>Dances with Wolves (1990)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>212</td>\n",
              "      <td>173</td>\n",
              "      <td>3.879325</td>\n",
              "      <td>Princess Bride, The (1987)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>212</td>\n",
              "      <td>660</td>\n",
              "      <td>3.847897</td>\n",
              "      <td>Fried Green Tomatoes (1991)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>212</td>\n",
              "      <td>215</td>\n",
              "      <td>3.709920</td>\n",
              "      <td>Field of Dreams (1989)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>212</td>\n",
              "      <td>258</td>\n",
              "      <td>3.583718</td>\n",
              "      <td>Contact (1997)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>212</td>\n",
              "      <td>202</td>\n",
              "      <td>3.508617</td>\n",
              "      <td>Groundhog Day (1993)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>212</td>\n",
              "      <td>237</td>\n",
              "      <td>3.039041</td>\n",
              "      <td>Jerry Maguire (1996)</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-483d410b-6fea-40be-89b3-9835045089f2')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
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              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "    [theme=dark] .colab-df-convert:hover {\n",
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              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-483d410b-6fea-40be-89b3-9835045089f2');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from recsys.preprocessing import train_test_split, get_examples\n",
        "\n",
        "# get examples as tuples of userids and itemids and labels from normalize ratings\n",
        "raw_examples, raw_labels = get_examples(ratings, labels_column='rating')\n",
        "\n",
        "# train test split\n",
        "(x_train, x_test), (y_train, y_test) = train_test_split(examples=raw_examples, labels=raw_labels)\n",
        "\n",
        "def evaluate(x_test, y_test):\n",
        "    print('Evaluate the model on {} test data ...'.format(x_test.shape[0]))\n",
        "    preds = list(predict(u,i) for (u,i) in x_test)\n",
        "    mae = np.sum(np.absolute(y_test - np.array(preds))) / x_test.shape[0]\n",
        "    print('\\nMAE :', mae)\n",
        "    return mae"
      ],
      "metadata": {
        "id": "9iojo5Mk-u7v"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "evaluate(x_test, y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qA85xcj1-ycZ",
        "outputId": "b66b5146-8717-46fa-8263-3327d354d2de"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Evaluate the model on 10000 test data ...\n",
            "\n",
            "MAE : 0.7505910931068639\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7505910931068639"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    }
  ]
}