GraphRec_POC.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oiyRqSoIuoF8"
      },
      "source": [
        "# Przygotowanie środowiska Collab"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_jrVzsdPEaqq"
      },
      "outputs": [],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "q0IYnhf0ukEn",
        "outputId": "be83ba30-aac7-467d-ce46-4d2780e02593"
      },
      "outputs": [],
      "source": [
        "!apt-get install lzop --quiet"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LgMlxagVCeRQ"
      },
      "source": [
        "# GraphRec demo"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a5mY9T_oEQn8"
      },
      "source": [
        "## Data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Xq6WMX88EPzR"
      },
      "outputs": [],
      "source": [
        "import pickle\n",
        "from functools import reduce\n",
        "\n",
        "history_u_lists, \\\n",
        "    history_ur_lists, \\\n",
        "    history_v_lists, \\\n",
        "    history_vr_lists, \\\n",
        "    train_u, train_v, \\\n",
        "    train_r, \\\n",
        "    test_u, \\\n",
        "    test_v, \\\n",
        "    test_r, \\\n",
        "    social_adj_lists, \\\n",
        "    ratings_list = pickle.load(open('/content/drive/Shareddrives/RecSys21/GraphRec-WWW19/data/toy_dataset.pickle', 'rb'))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZDuHHpmLHDyO",
        "outputId": "759c9ea9-108a-48e5-cb84-ff6e48c116e2"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Total amount of purchased items in the dataset: 14091\n"
          ]
        }
      ],
      "source": [
        "c = 0\n",
        "for i in range(len(history_u_lists)):\n",
        "    c += len(history_u_lists[i])\n",
        "\n",
        "print(\"Total amount of purchased items in the dataset: %d\" % c)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qrmMUgQqJnAM",
        "outputId": "48c1ceb5-90d7-4f03-cf78-12d5afa69fc7"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "{0.5: 7, 1.0: 1, 1.5: 6, 2.0: 0, 2.5: 4, 3.0: 2, 3.5: 5, 4.0: 3}"
            ]
          },
          "execution_count": 57,
          "metadata": {
            "tags": []
          },
          "output_type": "execute_result"
        }
      ],
      "source": [
        "ratings_list"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QmMhPxwNER03"
      },
      "source": [
        "## Training"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 732
        },
        "id": "rqU1pd4ECg6_",
        "outputId": "0080fb24-90c3-4aab-e809-dbd5f37ad38f"
      },
      "outputs": [],
      "source": [
        "%%time\n",
        "import os\n",
        "os.chdir('/content/drive/Shareddrives/RecSys21/GraphRec-WWW19/')\n",
        "\n",
        "%run run_GraphRec_example.py"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cOKdN4urq-5v"
      },
      "source": [
        "# LZOP\n",
        "\n",
        "Parsowanie danych do postaci .pickle dla potrzeb GraphRec korzystająć z paczek lzop\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3_lPxkCZ0wPf"
      },
      "outputs": [],
      "source": [
        "import subprocess\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import itertools\n",
        "import seaborn as sns\n",
        "import pickle\n",
        "import random\n",
        "import os\n",
        "from tqdm.notebook import tqdm\n",
        "from sklearn.model_selection import train_test_split"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "kxg7GXjCWmUx"
      },
      "outputs": [],
      "source": [
        "all_features = [\"text_tokens\", \"hashtags\", \"tweet_id\", \"present_media\", \"present_links\", \"present_domains\",\\\n",
        "                \"tweet_type\",\"language\", \"tweet_timestamp\", \"engaged_with_user_id\", \"engaged_with_user_follower_count\",\\\n",
        "               \"engaged_with_user_following_count\", \"engaged_with_user_is_verified\", \"engaged_with_user_account_creation\",\\\n",
        "               \"enaging_user_id\", \"enaging_user_follower_count\", \"enaging_user_following_count\", \"enaging_user_is_verified\",\\\n",
        "               \"enaging_user_account_creation\", \"engagee_follows_engager\"]\n",
        "labels = [\"reply_timestamp\", \"retweet_timestamp\", \"retweet_with_comment_timestamp\", \"like_timestamp\"]\n",
        "\n",
        "all_features_to_idx = dict(zip(all_features, range(len(all_features))))\n",
        "labels_to_idx = dict(zip(labels, range(len(all_features), len(all_features)+len(labels))))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "pK-llj5qjQWc"
      },
      "outputs": [],
      "source": [
        "def unpack_to_df(package_name, sslice=None):\n",
        "    out = subprocess.Popen(['lzop', '-dc', package_name],\n",
        "            stdout=subprocess.PIPE,\n",
        "            # stderr=subprocess.DEVNULL,\n",
        "            )\n",
        "    lines, stderr = out.communicate()\n",
        "    lines = lines.decode('utf-8').split('\\n')\n",
        "\n",
        "    out.terminate() \n",
        "    del out, stderr\n",
        "\n",
        "    if sslice is not None:\n",
        "        lines = lines[sslice]\n",
        "    for i in range(len(lines)-1):\n",
        "        line = lines[i].split(\"\\x01\")\n",
        "        line = [\n",
        "            line[all_features_to_idx['tweet_id']],\n",
        "            line[all_features_to_idx['engaged_with_user_id']],\n",
        "            line[all_features_to_idx['enaging_user_id']],\n",
        "            line[all_features_to_idx['engagee_follows_engager']],\n",
        "            [\n",
        "             bool(line[labels_to_idx['reply_timestamp']]),\n",
        "             bool(line[labels_to_idx['retweet_timestamp']]),\n",
        "             bool(line[labels_to_idx['retweet_with_comment_timestamp']]),\n",
        "             bool(line[labels_to_idx['like_timestamp']]),\n",
        "            ]]\n",
        "        lines[i] = line\n",
        "        \n",
        "    lines = pd.DataFrame(lines, columns=[\n",
        "        'tweet_id',\n",
        "        'engaged_with_user_id',\n",
        "        'enaging_user_id',\n",
        "        'engagee_follows_engager',\n",
        "        'reaction'\n",
        "    ])\n",
        "    # lines['reaction'] = lines[\"reaction\"].apply(lambda r: reduce(lambda out, bit: (out << 1) | bit, r)) \n",
        "    return lines"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ozhEC01fVGoG"
      },
      "outputs": [],
      "source": [
        "df = unpack_to_df(\"/content/drive/Shareddrives/RecSys21/training/part-00000.lzo\")\n",
        "df.memory_usage(deep=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "L5Hd3O3PCp5-"
      },
      "source": [
        "## Write pickle"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 83,
          "referenced_widgets": [
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      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "CPU times: user 4 µs, sys: 0 ns, total: 4 µs\n",
            "Wall time: 9.54 µs\n"
          ]
        },
        {
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            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=3011125.0), HTML(value='')))"
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          },
          "metadata": {
            "tags": []
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "%time\n",
        "\n",
        "random.seed(4)\n",
        "\n",
        "p = 0.8\n",
        "parts = [\"part-00000.lzo\", \"part-00001.lzo\"]\n",
        "\n",
        "history_u_lists = {}\n",
        "history_ur_lists = {}\n",
        "history_v_lists = {}\n",
        "history_vr_lists = {}\n",
        "train_u  = []\n",
        "test_u  = []\n",
        "train_v  = []\n",
        "test_v  = []\n",
        "train_r = [] \n",
        "test_r = []\n",
        "social_adj_lists = {}\n",
        "ratings = list(itertools.product([False, True], repeat=4))\n",
        "ratings_list = dict(zip(ratings, np.arange(16)))\n",
        "\n",
        "for part in parts:\n",
        "    df = unpack_to_df(\"/content/drive/Shareddrives/RecSys21/training/\" + part)\n",
        "    n = len(df)\n",
        "    train_samples = np.ones(n, dtype=bool)\n",
        "    train_samples[int(n*p):] = False\n",
        "    random.shuffle(train_samples)\n",
        "    pbar = tqdm(total=n)\n",
        "\n",
        "    for i, row in df.loc[:n-1, :].iterrows():\n",
        "        v_id = row[\"tweet_id\"]\n",
        "        u_id = row[\"enaging_user_id\"]\n",
        "        n_id = row[\"engaged_with_user_id\"]\n",
        "        undirected = row[\"engagee_follows_engager\"]\n",
        "        r_value = row[\"reaction\"]\n",
        "        is_train_sample = train_samples[i]\n",
        "        \n",
        "        if is_train_sample:\n",
        "            # build user-item graph\n",
        "            if u_id not in history_u_lists:\n",
        "                history_u_lists[u_id] = []\n",
        "                history_ur_lists[u_id] = []\n",
        "            history_u_lists[u_id].append(v_id)\n",
        "            history_ur_lists[u_id].append(r_value)\n",
        "\n",
        "            # build item-user graph\n",
        "            if v_id not in history_v_lists:\n",
        "                history_v_lists[v_id] = []\n",
        "                history_vr_lists[v_id] = []\n",
        "            history_v_lists[v_id].append(u_id)\n",
        "            history_vr_lists[v_id].append(r_value)\n",
        "\n",
        "            # build train set\n",
        "            train_u.append(u_id)\n",
        "            train_v.append(v_id)\n",
        "            train_r.append(r_value)\n",
        "        else:\n",
        "            # build test set\n",
        "            test_u.append(u_id)\n",
        "            test_v.append(v_id)\n",
        "            test_r.append(r_value)\n",
        "        \n",
        "        # build social graph\n",
        "        if u_id not in social_adj_lists:\n",
        "            social_adj_lists[u_id] = set()\n",
        "        social_adj_lists[u_id].add(n_id)\n",
        "\n",
        "        if undirected:\n",
        "            if n_id not in social_adj_lists:\n",
        "                social_adj_lists[n_id] = set()\n",
        "\n",
        "            social_adj_lists[n_id].add(u_id)\n",
        "        \n",
        "        pbar.update(1)\n",
        "    \n",
        "    pbar.close()\n",
        "    del df\n",
        "\n",
        "    with open('dataset.pickle', 'wb') as handle:\n",
        "        pickle.dump(\n",
        "            [history_u_lists, history_ur_lists, history_v_lists, history_vr_lists, train_u, train_v, train_r, test_u, test_v, test_r, social_adj_lists, ratings_list]\n",
        "        , handle, protocol=pickle.HIGHEST_PROTOCOL)\n",
        "    del handle\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "QqSV6APKFuCt"
      },
      "outputs": [],
      "source": [
        "# !cp dataset.pickle \"/content/drive/Shareddrives/RecSys21/pickle/part-0000[0_1].pickle\""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iugm2LsQCnNl"
      },
      "source": [
        "## Read pickle"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "_bjEE1PnnDeQ"
      },
      "outputs": [],
      "source": [
        "# with open('/content/drive/Shareddrives/RecSys21/pickle/part-00000.pickle', 'rb') as handle:\n",
        "with open('dataset.pickle', 'rb') as handle:\n",
        "    history_u_lists, history_ur_lists, history_v_lists, history_vr_lists, \\\n",
        "     train_u, train_v, train_r, test_u, test_v, test_r, social_adj_lists, ratings_list = pickle.load(handle)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SAr32AaeWtjC"
      },
      "source": [
        "## Memory test"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "izckRGuBPq5d",
        "outputId": "230f5923-5a27-497b-e64a-47f3608c7ded"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(40112, 80096, 80072, 80072)"
            ]
          },
          "execution_count": 44,
          "metadata": {
            "tags": []
          },
          "output_type": "execute_result"
        }
      ],
      "source": [
        "size = 10000\n",
        "a = np.random.choice(a=[False, True], size=(size,4))\n",
        "b = np.random.randint(0,15,size)\n",
        "sys.getsizeof(a), sys.getsizeof(b), sys.getsizeof(a.tolist()), sys.getsizeof(b.tolist())"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2XM17LNopYKS"
      },
      "source": [
        "# NEO4J API\n",
        "Wyciągnięcie danych dla potrzeb GraphRec korzystająć z paczek neo4j API"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 107,
      "metadata": {
        "id": "hhHqDCcdpzrD"
      },
      "outputs": [],
      "source": [
        "!pip install neo4j --quiet"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 108,
      "metadata": {
        "id": "MB9qPksCpyTc"
      },
      "outputs": [],
      "source": [
        "from neo4j import GraphDatabase"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 111,
      "metadata": {
        "id": "ALtn_eaUpfaG"
      },
      "outputs": [],
      "source": [
        "host = '46.101.246.118'\n",
        "user = 'neo4j'\n",
        "password = 'kochamPWR411'\n",
        "uri = f'bolt://{host}:7687'\n",
        "driver = GraphDatabase.driver(uri, auth=(user, password))\n",
        "db = driver.session()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 112,
      "metadata": {
        "id": "qpwcUh94NFBO"
      },
      "outputs": [],
      "source": [
        "DATASET_SIZE = 10000"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 114,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 66,
          "referenced_widgets": [
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        },
        "id": "Gbk6tb18pvIM",
        "outputId": "9e26e7dc-994f-4b84-b144-645a422c8d85"
      },
      "outputs": [
        {
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              "model_id": "74d411b048454a69b83118dbfecc563e",
              "version_major": 2,
              "version_minor": 0
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            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=10000.0), HTML(value='')))"
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          "metadata": {
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        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        }
      ],
      "source": [
        "# ==== u, v, r ====\n",
        "query = 'MATCH (u:User)-[r:Reply|Retweet|RetweetComment|Like]->(t:Tweet)\\\n",
        "         RETURN u.id as user, t.id as tweet, [type(r)=\"Reply\", type(r)=\"Retweet\", type(r)=\"RetweetComment\",type(r)=\"Like\"] as engagement \\\n",
        "         LIMIT %d' % (DATASET_SIZE)\n",
        "results = db.run(query)\n",
        "\n",
        "u, v, r = np.array([]), np.array([]), np.empty((0, 4), bool)\n",
        "\n",
        "pbar = tqdm(total=DATASET_SIZE)\n",
        "for res in results:\n",
        "    values = res.values()\n",
        "    u = np.append(u, values[0])\n",
        "    v = np.append(v, values[1])\n",
        "    r = np.append(r, [values[2]], axis=0)\n",
        "    pbar.update(1)\n",
        "    \n",
        "pbar.close()\n",
        "\n",
        "# ==== SPLIT TO TRAIN AND TEST ====\n",
        "train_u, test_u, train_v, test_v, train_r, test_r = train_test_split(u, v, r, test_size=0.33, random_state=42)\n",
        "del u, v, r"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 115,
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        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        }
      ],
      "source": [
        "# ==== history_u_lists, history_ur_lists ====\n",
        "\n",
        "# UWAGA: Wektory one-hot. Trzeba dać OR dla wektorów rankingu z tym samym id\n",
        "query_template = 'MATCH (u:User {id: \"%s\"})-[r:Like|Reply|Retweet|RetweetComment]->(t:Tweet)\\\n",
        "         RETURN collect(t.id), collect([type(r)=\"Reply\", type(r)=\"Retweet\", type(r)=\"RetweetComment\",type(r)=\"Like\"])'\n",
        "\n",
        "history_u_lists = {}\n",
        "history_ur_lists = {}\n",
        "\n",
        "ids = set(train_u)\n",
        "pbar = tqdm(total=len(ids))\n",
        "\n",
        "for u_id in ids:\n",
        "    results = db.run(query_template % (u_id))\n",
        "\n",
        "    for res in results:\n",
        "        values = res.values()\n",
        "        v_list = values[0]\n",
        "        r_list = values[1]\n",
        "\n",
        "        history_u_lists[u_id] = v_list\n",
        "        history_ur_lists[u_id] = r_list \n",
        "\n",
        "    pbar.update(1)\n",
        "pbar.close()"
      ]
    },
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        "id": "EhE862M3EvEK",
        "outputId": "b19537a5-9cde-49c8-ab80-9ebbf499f320"
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              "HBox(children=(FloatProgress(value=0.0, max=6407.0), HTML(value='')))"
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      ],
      "source": [
        "# ==== history_v_lists, history_vr_lists ====\n",
        "\n",
        "query_template = 'MATCH (u:User)-[r:Like|Reply|Retweet|RetweetComment]->(t:Tweet {id: \"%s\"}) \\\n",
        "                  RETURN collect(u.id), collect([type(r)=\"Reply\", type(r)=\"Retweet\", type(r)=\"RetweetComment\", type(r)=\"Like\"])'\n",
        "\n",
        "history_v_lists = {}\n",
        "history_vr_lists = {}\n",
        "\n",
        "ids = set(train_v)\n",
        "pbar = tqdm(total=len(ids))\n",
        "\n",
        "for v_id in ids:\n",
        "    results = db.run(query_template % (v_id))\n",
        "\n",
        "    for res in results:\n",
        "        values = res.values()\n",
        "        u_list = values[0]\n",
        "        r_list = values[1]\n",
        "\n",
        "        history_v_lists[v_id] = v_list\n",
        "        history_ur_lists[u_id] = r_list\n",
        "\n",
        "    pbar.update(1)\n",
        "pbar.close() "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 77,
      "metadata": {
        "id": "QodQempvFmzR"
      },
      "outputs": [],
      "source": [
        "# ==== social_adj_lists ====\n",
        "\n",
        "query_template = 'MATCH (u:User {id: \"%s\"})-[:Follow]->(n: User) RETURN collect(n.id)'\n",
        "\n",
        "social_adj_lists = {}\n",
        "\n",
        "ids = set(train_u)\n",
        "pbar = tqdm(total=len(ids))\n",
        "\n",
        "for u_id in ids:\n",
        "    results = db.run(query_template % (u_id))\n",
        "\n",
        "    for res in results:\n",
        "        values = res.values()\n",
        "        n_list = values[0]\n",
        "        social_adj_lists[u_id] = n_list\n",
        "    pbar.update(1)\n",
        "pbar.close() "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "pFDSzRljNprO"
      },
      "outputs": [],
      "source": [
        "# ==== ratings_list ====\n",
        "\n",
        "ratings = list(itertools.product([False, True], repeat=4))\n",
        "ratings_list = dict(zip(ratings, np.arange(16)))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kQh4fThbQmbq"
      },
      "source": [
        "## Write pickle"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jHotPlFZQltt"
      },
      "outputs": [],
      "source": [
        "with open('/content/drive/Shareddrives/RecSys21/pickle/neo4j_%d.pickle' % (DATASET_SIZE), 'wb') as handle:\n",
        "    pickle.dump(\n",
        "        [history_u_lists, history_ur_lists, history_v_lists, history_vr_lists, train_u, train_v, train_r, test_u, test_v, test_r, social_adj_lists, ratings_list]\n",
        "    , handle, protocol=pickle.HIGHEST_PROTOCOL)\n",
        "del handle"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "eW6un04wprMd"
      },
      "outputs": [],
      "source": [
        "driver.close()"
      ]
    }
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