cccntu/mc4-timestamp-analysis.ipynb

## mc4-timestamp-analysis.ipynb
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            "_view_module": "@jupyter-widgets/base",
            "overflow": null,
            "_model_module_version": "1.2.0",
            "_view_count": null,
            "flex_flow": null,
            "width": null,
            "min_width": null,
            "border": null,
            "align_items": null,
            "bottom": null,
            "_model_module": "@jupyter-widgets/base",
            "top": null,
            "grid_column": null,
            "overflow_y": null,
            "overflow_x": null,
            "grid_auto_flow": null,
            "grid_area": null,
            "grid_template_columns": null,
            "flex": null,
            "_model_name": "LayoutModel",
            "justify_items": null,
            "grid_row": null,
            "max_height": null,
            "align_content": null,
            "visibility": null,
            "align_self": null,
            "height": null,
            "min_height": null,
            "padding": null,
            "grid_auto_rows": null,
            "grid_gap": null,
            "max_width": null,
            "order": null,
            "_view_module_version": "1.2.0",
            "grid_template_areas": null,
            "object_position": null,
            "object_fit": null,
            "grid_auto_columns": null,
            "margin": null,
            "display": null,
            "left": null
          }
        }
      }
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/gist/cccntu/39705400f50d834f7a904f5417ad3329/mc4-timestamp-analysis.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DYPXhTLWQmxD"
      },
      "source": [
        "!pip install -q datasets[streaming]"
      ],
      "id": "DYPXhTLWQmxD",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jobi6-WJRprV",
        "outputId": "7efed37a-18e9-46b8-fe47-589d1aac6e2f"
      },
      "source": [
        "\n",
        "!git clone https://github.com/cccntu/dateutil"
      ],
      "id": "jobi6-WJRprV",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cloning into 'dateutil'...\n",
            "remote: Enumerating objects: 6540, done.\u001b[K\n",
            "remote: Counting objects: 100% (239/239), done.\u001b[K\n",
            "remote: Compressing objects: 100% (157/157), done.\u001b[K\n",
            "remote: Total 6540 (delta 110), reused 167 (delta 74), pack-reused 6301\u001b[K\n",
            "Receiving objects: 100% (6540/6540), 5.85 MiB | 11.54 MiB/s, done.\n",
            "Resolving deltas: 100% (4208/4208), done.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nPL4coMgR1Dq",
        "outputId": "a3b20f32-700f-4f60-abf8-077af8d24fcb"
      },
      "source": [
        "!cd dateutil && git checkout date-only && cd .."
      ],
      "id": "nPL4coMgR1Dq",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Branch 'date-only' set up to track remote branch 'date-only' from 'origin'.\n",
            "Switched to a new branch 'date-only'\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sMFxpNxfQ-6q",
        "outputId": "3672a4af-fd50-45bf-f463-1e3c78fa171a"
      },
      "source": [
        "!pip install -e dateutil"
      ],
      "id": "sMFxpNxfQ-6q",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Obtaining file:///content/dateutil\n",
            "  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
            "    Preparing wheel metadata ... \u001b[?25l\u001b[?25hdone\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil==2.8.3.dev11+g9d40189) (1.15.0)\n",
            "Installing collected packages: python-dateutil\n",
            "  Attempting uninstall: python-dateutil\n",
            "    Found existing installation: python-dateutil 2.8.2\n",
            "    Uninstalling python-dateutil-2.8.2:\n",
            "      Successfully uninstalled python-dateutil-2.8.2\n",
            "  Running setup.py develop for python-dateutil\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "albumentations 0.1.12 requires imgaug<0.2.7,>=0.2.5, but you have imgaug 0.2.9 which is incompatible.\u001b[0m\n",
            "Successfully installed python-dateutil-2.8.3.dev11+g9d40189\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DHsXeGUqTLmV"
      },
      "source": [
        "# restart the runtime to use the new dateutil version "
      ],
      "id": "DHsXeGUqTLmV",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5d8vUrryS8l-",
        "outputId": "d2d82b25-cf0c-409f-8654-07f297d4c9f4"
      },
      "source": [
        "# make sure this cell runs without error\n",
        "from dateutil.parser import parser, parse, ParserError\n",
        "parse('/2021/1/1/some text', fuzzy=True, date_only=True)"
      ],
      "id": "5d8vUrryS8l-",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "datetime.datetime(2021, 1, 1, 0, 0)"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c41bab1c-daf1-4183-8e02-545e0975b578"
      },
      "source": [
        "# many part of this notebook is borrowed from Mike's notebook\n",
        "# https://github.com/bigscience-workshop/metadata/blob/e72b7245fadd588b94a8811265f45c7d60084471/playground.ipynb"
      ],
      "id": "c41bab1c-daf1-4183-8e02-545e0975b578",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c3c232c6-9327-4251-82bd-4ddaf340a0d3"
      },
      "source": [
        "import requests\n",
        "import time\n",
        "from tqdm.auto import tqdm\n",
        "import pandas as pd\n",
        "import json\n",
        "import dateutil\n",
        "from json import JSONDecodeError\n",
        "from urllib.parse import urlsplit\n",
        "from urllib.parse import unquote_plus\n",
        "import datetime\n",
        "import seaborn as sns\n",
        "from matplotlib import pyplot as plt\n",
        "from datasets import load_dataset"
      ],
      "id": "c3c232c6-9327-4251-82bd-4ddaf340a0d3",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "57e9d195-e4df-43c9-851f-6e2d2eb2aed8"
      },
      "source": [
        "mc4_en_trn_ds = load_dataset(\n",
        "    \"mc4\",\n",
        "    \"en\",\n",
        "    split=\"train\",\n",
        "    streaming=True,\n",
        ")"
      ],
      "id": "57e9d195-e4df-43c9-851f-6e2d2eb2aed8",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7f48177c-0392-425c-8cf9-8b5ab2dbfbdd"
      },
      "source": [
        "ds = mc4_en_trn_ds.take(1000)\n",
        "data = list(ds)\n",
        "df = pd.DataFrame(data)"
      ],
      "id": "7f48177c-0392-425c-8cf9-8b5ab2dbfbdd",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8f3c4846-1fd5-461a-95e4-7c1058a2e394"
      },
      "source": [
        "def parse_url(url):\n",
        "    parts = urlsplit(url)\n",
        "    return {\n",
        "        \"url\": url,\n",
        "        \"netloc\": parts.netloc,\n",
        "        \"hostname\": parts.hostname if parts.hostname else \"\",\n",
        "        \"port\": parts.port if parts.port else \"\",\n",
        "        \"path\": unquote_plus(parts.path),\n",
        "        \"query\": unquote_plus(parts.query),\n",
        "        \"fragment\": unquote_plus(parts.fragment),\n",
        "    }"
      ],
      "id": "8f3c4846-1fd5-461a-95e4-7c1058a2e394",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a9a9e3cf-32c1-444c-801b-d2830eb5d7ab"
      },
      "source": [
        "urldata = df[\"url\"].apply(parse_url).tolist()\n",
        "url_df = pd.DataFrame(urldata)\n",
        "df = df.merge(url_df)"
      ],
      "id": "a9a9e3cf-32c1-444c-801b-d2830eb5d7ab",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ddbe68a1-a862-494c-8e52-23622a94bb08",
        "outputId": "85743d55-664c-401b-c7b9-f694c5cad355"
      },
      "source": [
        "# I modified the source and installed locally, such that it only parse date\n",
        "# otherwise it can fail if it fails to parse time\n",
        "from dateutil.parser import parser, parse, ParserError\n",
        "\n",
        "# _parser = parser()\n",
        "# _parser.parse(datelike_paths[i], fuzzy=True)\n",
        "def parse_date(path):\n",
        "    try:\n",
        "        return parse(path, fuzzy=True, date_only=True)\n",
        "    except ParserError:\n",
        "        return None\n",
        "    except OverflowError:\n",
        "        # this happens, I don't know why, just ignore it\n",
        "        return None\n",
        "\n",
        "\n",
        "def remove_improbable_date(x):\n",
        "    if x is not None and (x.year < 1983 or x.year > 2021):\n",
        "        return None\n",
        "    return x\n",
        "\n",
        "\n",
        "parse_date(\"/2021/2/1/some name some number 123, 12\")"
      ],
      "id": "ddbe68a1-a862-494c-8e52-23622a94bb08",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "datetime.datetime(2021, 2, 1, 0, 0)"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4649c7b7-5721-4bca-8f3a-537229a5a0ad"
      },
      "source": [
        "df[\"parsed_date\"] = df[\"path\"].apply(parse_date).apply(remove_improbable_date)"
      ],
      "id": "4649c7b7-5721-4bca-8f3a-537229a5a0ad",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2d342003-3df9-499c-957a-8388ef242d71",
        "outputId": "93b70612-0caf-4988-f71f-06acd76e2ea0"
      },
      "source": [
        "df[\"parsed_date\"].notna().mean()"
      ],
      "id": "2d342003-3df9-499c-957a-8388ef242d71",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.136"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c793ea4e-aaa0-46df-ad32-7fbbd287b136",
        "outputId": "af769616-636b-4d71-8ced-88c5d5450ab7"
      },
      "source": [
        "df[\"parsed_date\"].dropna().head()"
      ],
      "id": "c793ea4e-aaa0-46df-ad32-7fbbd287b136",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "10   2005-03-22\n",
              "15   2013-01-19\n",
              "18   2014-08-18\n",
              "20   2011-11-24\n",
              "24   2019-09-17\n",
              "Name: parsed_date, dtype: datetime64[ns]"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "55355619-14cb-44c7-9799-37893afaf801"
      },
      "source": [
        "# I don't really know if retry is necessary\n",
        "# copied from:\n",
        "# https://findwork.dev/blog/advanced-usage-python-requests-timeouts-retries-hooks/\n",
        "from requests.adapters import HTTPAdapter\n",
        "from requests.packages.urllib3.util.retry import Retry\n",
        "\n",
        "\n",
        "class WayBachMachine:\n",
        "    def __init__(self):\n",
        "        retry_strategy = Retry(total=3)\n",
        "        adapter = HTTPAdapter(max_retries=retry_strategy)\n",
        "        sess = requests.Session()\n",
        "        sess.mount(\"https://\", adapter)\n",
        "        sess.mount(\"http://\", adapter)\n",
        "        self.sess = sess\n",
        "\n",
        "    @staticmethod\n",
        "    def get_api_url(url):\n",
        "        return (\n",
        "            \"http://archive.org/wayback/available?url=\"\n",
        "            + url\n",
        "            + \"&timestamp=19000101000000\"\n",
        "        )\n",
        "\n",
        "    def __call__(self, url):\n",
        "        response = self.sess.get(self.get_api_url(url))\n",
        "        if response.status_code == 200:\n",
        "            try:\n",
        "                return json.loads(response.text)\n",
        "            except JSONDecodeError:\n",
        "                return None\n",
        "        return None\n",
        "\n",
        "    def get_timestamp(self, url):\n",
        "        result = self.__call__(url)\n",
        "        try:\n",
        "            return result[\"archived_snapshots\"][\"closest\"][\"timestamp\"]\n",
        "        except KeyError:\n",
        "            return None\n",
        "\n",
        "\n",
        "wbm = WayBachMachine()"
      ],
      "id": "55355619-14cb-44c7-9799-37893afaf801",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81,
          "referenced_widgets": [
            "3de5e9efb9b64deb8c8cc714aa6973fd",
            "25062f1dc92c493c8932e3261b11f14c",
            "8f46c49a404d425991f0340bf24cb7d7",
            "494c044badaa4da6b8917239ff938ef9",
            "f01cf99d310442fda2a51352949448aa",
            "40edb60a9082432aae3f74b5492cee09",
            "2ff3f198b4b0432b8c932afff8f8eab0",
            "cba02c08c9cc4010b2ad44aa3a7736ea",
            "f3ae88cded0c46af97bb0d308c47b341",
            "93ba755b9c8c42f7ae926f076533fec3",
            "2a5f0b24f84c49ffb3dfd83aff220471",
            "3f29f5352798423b8edf262be2a4c3b8",
            "b7d667ee641646bcb0c12f81c71da495",
            "7d6b6b4a749849a1ba2f9336dff56ff3",
            "7bf8b4fcb8d248bebb2ffc07dd919fcf",
            "c61215a8519c470db62682c13b02d857",
            "6f672b6a5e564fa09f7cfbc2f0f14470",
            "aa07965806754bfeaf7137cd3f141c64",
            "3c7eb04d62964c4f8adf70d4be5a954c",
            "92ba5bc4e5ac4e9fb4ba57e469c76b92",
            "52fb9015cb31461390123467f26c6781",
            "ea810b2b2bf44647acc070cb659b7b72"
          ]
        },
        "id": "029eeb60-7485-4cc3-a193-6d7eb35d1758",
        "outputId": "a895b674-8c70-4e02-cbbd-8f85deb84457"
      },
      "source": [
        "timestamps = []\n",
        "success_count = tqdm(desc=\"success count\")\n",
        "for url in tqdm(df[\"url\"]):\n",
        "    timestamp = wbm.get_timestamp(url)\n",
        "    timestamps.append(timestamp)\n",
        "    if timestamp is not None:\n",
        "        success_count.update(1)"
      ],
      "id": "029eeb60-7485-4cc3-a193-6d7eb35d1758",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "3de5e9efb9b64deb8c8cc714aa6973fd",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "success count: 0it [00:00, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "3f29f5352798423b8edf262be2a4c3b8",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0/1000 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c6dda847-7a21-4d5c-8f0e-7dcb61028e08"
      },
      "source": [
        "df[\"wbm_timestamps\"] = timestamps"
      ],
      "id": "c6dda847-7a21-4d5c-8f0e-7dcb61028e08",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d472120d-8fc6-42db-8ddf-ca7f99f4f778"
      },
      "source": [
        "def parse_datetime(s):\n",
        "    try:\n",
        "        return dateutil.parser.parse(s)\n",
        "    except TypeError:\n",
        "        pass"
      ],
      "id": "d472120d-8fc6-42db-8ddf-ca7f99f4f778",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5d1efdbb-9566-4ac7-bee0-8a39ded79091",
        "outputId": "da1dff65-eba8-40cf-8b09-dddff50491c2"
      },
      "source": [
        "df[\"parsed_date\"].notna().mean()"
      ],
      "id": "5d1efdbb-9566-4ac7-bee0-8a39ded79091",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.136"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "19211581-cd98-4748-830f-aadaac4d1283"
      },
      "source": [
        "parsed_datetime = df[\"parsed_date\"]\n",
        "\n",
        "wbm_datetime = df[\"wbm_timestamps\"].apply(parse_datetime)\n",
        "\n",
        "crawl_datetime = df[\"timestamp\"].apply(parse_datetime)"
      ],
      "id": "19211581-cd98-4748-830f-aadaac4d1283",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "222a6781-48eb-4b93-b929-95f3f0f936ec"
      },
      "source": [
        "start = datetime.datetime(2001, 1, 1).toordinal()\n",
        "end = datetime.datetime(2021, 9, 1).toordinal()\n",
        "sample_dates = [datetime.datetime.fromordinal(x) for x in range(start, end, 7)]"
      ],
      "id": "222a6781-48eb-4b93-b929-95f3f0f936ec",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "dabefe0f-1ba5-4d3b-b9b2-7ae2f4000f9a",
        "outputId": "0fb1c50c-29f6-4fbd-f2ac-f03d6c68b5a9"
      },
      "source": [
        "sns.relplot(crawl_datetime, wbm_datetime)\n",
        "plt.plot(sample_dates, sample_dates)\n",
        "plt.show()\n",
        "\n",
        "sns.relplot(crawl_datetime, parsed_datetime)\n",
        "plt.plot(sample_dates, sample_dates)\n",
        "plt.show()\n",
        "\n",
        "sns.relplot(parsed_datetime, crawl_datetime)\n",
        "plt.plot(sample_dates, sample_dates)\n",
        "plt.show()\n",
        "\n",
        "\n",
        "sns.relplot(parsed_datetime, wbm_datetime)\n",
        "plt.plot(sample_dates, sample_dates)\n",
        "plt.show()"
      ],
      "id": "dabefe0f-1ba5-4d3b-b9b2-7ae2f4000f9a",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "85f5b7ca-1405-4dd8-a91c-86cbaf94a660"
      },
      "source": [
        "* parsed date is usually ealier than other dates, and matches very closely sometimes (on the diagnol) suggesting that it's more accurate\n",
        "* but only ~9% of urls have dates extracted\n",
        "\n",
        "* compared to the crawl dates,  the dates from wayback machine (wbm_date) is overall closer to the dates on url\n",
        "\n",
        "* 40% of url are on wbm"
      ],
      "id": "85f5b7ca-1405-4dd8-a91c-86cbaf94a660"
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1106e438-97f9-4557-a584-d68ebc66a785"
      },
      "source": [
        "def filter_newer_dates(x):\n",
        "    if isinstance(x, datetime.datetime) and x > datetime.datetime(2020, 1, 1):\n",
        "        return None\n",
        "    return x"
      ],
      "id": "1106e438-97f9-4557-a584-d68ebc66a785",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "79526171-fc83-462b-84f5-446bcdf7bb48",
        "outputId": "c9a00b25-333b-4326-abf2-bde50a8f2af6"
      },
      "source": [
        "wbm_datetime.apply(filter_newer_dates).notna().mean()"
      ],
      "id": "79526171-fc83-462b-84f5-446bcdf7bb48",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.287"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 438
        },
        "id": "748fc946-8d15-44ec-94bb-6ef648a836a7",
        "outputId": "7bc9df48-9ab5-4081-8fea-bd572f41415d"
      },
      "source": [
        "sns.relplot(parsed_datetime, list(map(filter_newer_dates, wbm_datetime)))\n",
        "plt.plot(sample_dates, sample_dates)\n",
        "plt.show()"
      ],
      "id": "748fc946-8d15-44ec-94bb-6ef648a836a7",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "e6538ec1-715b-4bee-a0bb-80c00126bb76"
      },
      "source": [
        "* if we filter out wbm_date newer than 2020, the dates are probably more accurate, and we still have ~28% of data with date.\n",
        "* the only problem is the ~2 example/second rate limit (single process)"
      ],
      "id": "e6538ec1-715b-4bee-a0bb-80c00126bb76"
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9fa1c1c6-9bc9-4764-90f8-47f86c86dc19"
      },
      "source": [
        "df.to_parquet(\"mc4_en_dates.parquet\")"
      ],
      "id": "9fa1c1c6-9bc9-4764-90f8-47f86c86dc19",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6a19027c-5f8b-4667-84c5-a4c3bce6fc44"
      },
      "source": [
        "## load it later"
      ],
      "id": "6a19027c-5f8b-4667-84c5-a4c3bce6fc44"
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7B_veh_hVEif"
      },
      "source": [
        "df = pd.read_parquet(\"mc4_en_dates.parquet\")\n",
        "# in case this fail, run the next cell"
      ],
      "id": "7B_veh_hVEif",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "08605593-5520-46b5-ae9e-01d8a4a86f77"
      },
      "source": [
        "# the loading failed somehow, so I found this workaround to load only string colunns\n",
        "# (the parsed date need to be parsed again)\n",
        "from pyarrow.parquet import ParquetFile\n",
        "metadata = ParquetFile(\"mc4_en_dates.parquet\").metadata\n",
        "cols = [col.name for col in metadata.schema if str(col.logical_type) == \"String\"]\n",
        "\n",
        "df = pd.read_parquet(\"mc4_en_dates.parquet\", columns=cols)"
      ],
      "id": "08605593-5520-46b5-ae9e-01d8a4a86f77",
      "execution_count": null,
      "outputs": []
    }
  ]
}