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moinalasys
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| { | |
| "cells": [ | |
| { | |
| "cell_type": "markdown", | |
| "id": "f1cb0878-39d0-43c3-8dbd-c2a770fd12b8", | |
| "metadata": {}, | |
| "source": [ | |
| "# moinalasys" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 1, | |
| "id": "45d8d273-1843-4681-80ad-ec449a8f647b", | |
| "metadata": { | |
| "tags": [] | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "import os\n", | |
| "\n", | |
| "from IPython.display import display, Markdown\n", | |
| "from matplotlib.ticker import MaxNLocator\n", | |
| "from natsort import natsorted, ns" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 2, | |
| "id": "62173e94-754f-45b6-991b-27e9a957ed03", | |
| "metadata": { | |
| "tags": [] | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "natsort = lambda l: natsorted(l, alg=ns.IGNORECASE)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 3, | |
| "id": "3fb482a7-f03b-491a-9089-9ec8caf97a94", | |
| "metadata": { | |
| "tags": [] | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "moin_user = os.environ['MOIN_USER']\n", | |
| "moin_pass = os.environ['MOIN_PASS']" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 4, | |
| "id": "a0182ca7-2fd4-4e54-86a8-8abe659a0f48", | |
| "metadata": { | |
| "tags": [] | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "# Create a backup if there's already downloaded data\n", | |
| "!mv moin-full.json{,.bak} 2>/dev/null\n", | |
| "# Download all the moins, or use backup if it fails\n", | |
| "# Lol why don't shorthands work here?\n", | |
| "!curl -fsSL \"https://$moin_user:$moin_pass@moin.hsmr.cc/api/full.json\" -o moin-full.json || mv moin-full.json.bak moin-full.json" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 5, | |
| "id": "0acfc196-93dd-4b45-8c58-7b20d81b46b1", | |
| "metadata": { | |
| "tags": [] | |
| }, | |
| "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>user</th>\n", | |
| " <th>variation</th>\n", | |
| " <th>channel</th>\n", | |
| " <th>moins</th>\n", | |
| " <th>nick</th>\n", | |
| " <th>day</th>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>timestamp</th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>2023-02-13 15:20:58.594449997</th>\n", | |
| " <td><xkey!~xkey@krebs/xkey></td>\n", | |
| " <td>moin</td>\n", | |
| " <td>#hsmr-moin</td>\n", | |
| " <td>1</td>\n", | |
| " <td>xkey</td>\n", | |
| " <td>2023-02-13</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>2023-02-13 16:35:47.441560030</th>\n", | |
| " <td><xkey!~xkey@krebs/xkey></td>\n", | |
| " <td>MOIN</td>\n", | |
| " <td>#hsmr-moin</td>\n", | |
| " <td>1</td>\n", | |
| " <td>xkey</td>\n", | |
| " <td>2023-02-13</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>2023-02-13 16:45:55.468576908</th>\n", | |
| " <td><xkey!~xkey@krebs/xkey></td>\n", | |
| " <td>moin</td>\n", | |
| " <td>#hsmr-moin</td>\n", | |
| " <td>1</td>\n", | |
| " <td>xkey</td>\n", | |
| " <td>2023-02-13</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>2023-02-13 16:46:39.605882645</th>\n", | |
| " <td><xkey!~xkey@krebs/xkey></td>\n", | |
| " <td>moin</td>\n", | |
| " <td>#hsmr-moin</td>\n", | |
| " <td>1</td>\n", | |
| " <td>xkey</td>\n", | |
| " <td>2023-02-13</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>2023-02-13 16:46:41.758325338</th>\n", | |
| " <td><xkey!~xkey@krebs/xkey></td>\n", | |
| " <td>moin</td>\n", | |
| " <td>#hsmr-moin</td>\n", | |
| " <td>1</td>\n", | |
| " <td>xkey</td>\n", | |
| " <td>2023-02-13</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "</div>" | |
| ], | |
| "text/plain": [ | |
| " user variation channel \\\n", | |
| "timestamp \n", | |
| "2023-02-13 15:20:58.594449997 <xkey!~xkey@krebs/xkey> moin #hsmr-moin \n", | |
| "2023-02-13 16:35:47.441560030 <xkey!~xkey@krebs/xkey> MOIN #hsmr-moin \n", | |
| "2023-02-13 16:45:55.468576908 <xkey!~xkey@krebs/xkey> moin #hsmr-moin \n", | |
| "2023-02-13 16:46:39.605882645 <xkey!~xkey@krebs/xkey> moin #hsmr-moin \n", | |
| "2023-02-13 16:46:41.758325338 <xkey!~xkey@krebs/xkey> moin #hsmr-moin \n", | |
| "\n", | |
| " moins nick day \n", | |
| "timestamp \n", | |
| "2023-02-13 15:20:58.594449997 1 xkey 2023-02-13 \n", | |
| "2023-02-13 16:35:47.441560030 1 xkey 2023-02-13 \n", | |
| "2023-02-13 16:45:55.468576908 1 xkey 2023-02-13 \n", | |
| "2023-02-13 16:46:39.605882645 1 xkey 2023-02-13 \n", | |
| "2023-02-13 16:46:41.758325338 1 xkey 2023-02-13 " | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| }, | |
| { | |
| "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>user</th>\n", | |
| " <th>variation</th>\n", | |
| " <th>channel</th>\n", | |
| " <th>moins</th>\n", | |
| " <th>nick</th>\n", | |
| " <th>day</th>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>timestamp</th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>2023-04-02 06:56:10.402448654</th>\n", | |
| " <td>moinbot</td>\n", | |
| " <td>moin</td>\n", | |
| " <td>#hsmr-moin</td>\n", | |
| " <td>1</td>\n", | |
| " <td>moinbot</td>\n", | |
| " <td>2023-04-02</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>2023-04-03 10:31:17.375585318</th>\n", | |
| " <td>moinbot</td>\n", | |
| " <td>moin</td>\n", | |
| " <td>#hsmr-moin</td>\n", | |
| " <td>1</td>\n", | |
| " <td>moinbot</td>\n", | |
| " <td>2023-04-03</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>2023-04-04 09:42:17.669587851</th>\n", | |
| " <td>oibot</td>\n", | |
| " <td>oi</td>\n", | |
| " <td>#hsmr-moin</td>\n", | |
| " <td>1</td>\n", | |
| " <td>oibot</td>\n", | |
| " <td>2023-04-04</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>2023-04-07 07:40:00.369472742</th>\n", | |
| " <td>moinbot</td>\n", | |
| " <td>moin</td>\n", | |
| " <td>#hsmr-moin</td>\n", | |
| " <td>1</td>\n", | |
| " <td>moinbot</td>\n", | |
| " <td>2023-04-07</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>2023-04-07 14:08:31.263088225</th>\n", | |
| " <td>xkey</td>\n", | |
| " <td>moin</td>\n", | |
| " <td>#hsmr-moin</td>\n", | |
| " <td>1</td>\n", | |
| " <td>xkey</td>\n", | |
| " <td>2023-04-07</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "</div>" | |
| ], | |
| "text/plain": [ | |
| " user variation channel moins nick \\\n", | |
| "timestamp \n", | |
| "2023-04-02 06:56:10.402448654 moinbot moin #hsmr-moin 1 moinbot \n", | |
| "2023-04-03 10:31:17.375585318 moinbot moin #hsmr-moin 1 moinbot \n", | |
| "2023-04-04 09:42:17.669587851 oibot oi #hsmr-moin 1 oibot \n", | |
| "2023-04-07 07:40:00.369472742 moinbot moin #hsmr-moin 1 moinbot \n", | |
| "2023-04-07 14:08:31.263088225 xkey moin #hsmr-moin 1 xkey \n", | |
| "\n", | |
| " day \n", | |
| "timestamp \n", | |
| "2023-04-02 06:56:10.402448654 2023-04-02 \n", | |
| "2023-04-03 10:31:17.375585318 2023-04-03 \n", | |
| "2023-04-04 09:42:17.669587851 2023-04-04 \n", | |
| "2023-04-07 07:40:00.369472742 2023-04-07 \n", | |
| "2023-04-07 14:08:31.263088225 2023-04-07 " | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "df = pd.read_json('moin-full.json', convert_dates='timestamp').set_index('timestamp')\n", | |
| "df['moins'] = 1\n", | |
| "df['nick'] = df.user.str.replace(r'<?([^!]+)!.+', r'\\1', regex=True)\n", | |
| "df['day'] = df.index.date\n", | |
| "display(df.head())\n", | |
| "display(df.tail())" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 6, | |
| "id": "49d73d78-1662-46f2-93de-4cc6694052d2", | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "# Kick out all the bots\n", | |
| "df = df[~df['nick'].str.contains('bot')]" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 7, | |
| "id": "12cd8f29-ae2e-44a7-8b33-ff15364bbe70", | |
| "metadata": { | |
| "tags": [] | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/markdown": [ | |
| "Participating moiners: **Fail-X**, **feliks**, **feliks-**, **kmein**, **moooooin**, **oxzi**, **xkey**" | |
| ], | |
| "text/plain": [ | |
| "<IPython.core.display.Markdown object>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "all_nicks = pd.Series(natsort(list(df['nick'].unique())))\n", | |
| "display(Markdown(f\"Participating moiners: **{'**, **'.join(all_nicks)}**\"))" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "id": "bf30cded-02bd-4834-a8f2-6841152a4c10", | |
| "metadata": {}, | |
| "source": [ | |
| "## Moins by time of day" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "id": "cfff32c3-ffbf-4341-8f93-a3609d6ffdc6", | |
| "metadata": {}, | |
| "source": [ | |
| "Moins' metadata allow to analyze a user's daily activity. Below it is shown in a KDE plot and a logarithmic histogram." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 8, | |
| "id": "cd611668-a815-418e-bd4c-394ff38b994b", | |
| "metadata": { | |
| "tags": [] | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "image/png": 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| |
| "text/plain": [ | |
| "<Figure size 2500x800 with 1 Axes>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| }, | |
| { | |
| "data": { | |
| "text/markdown": [ | |
| "> π Skipped the following users because of a lack of moins: **feliks-**, **kmein**, **moooooin**" | |
| ], | |
| "text/plain": [ | |
| "<IPython.core.display.Markdown object>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "dfs = pd.DataFrame(data=zip(df.index.hour, df['nick']), columns=['hour', 'nick'])\n", | |
| "\n", | |
| "# Keep on moinin' π€\n", | |
| "bad_nicks_s = dfs.groupby('nick').std(numeric_only=True).isna()\n", | |
| "bad_nicks = natsort(bad_nicks_s.loc[bad_nicks_s['hour'] == True].index.values)\n", | |
| "\n", | |
| "# Select only users with valid std\n", | |
| "dfv = df[~df['nick'].isin(bad_nicks)]\n", | |
| "# FIXME Wrap around daily 24 hours\n", | |
| "# KDE plot over time of day disabling warnings for zero variance\n", | |
| "sns.kdeplot(data=dfv, x=dfv.index.hour, hue='nick', warn_singular=False, common_norm=False)\n", | |
| "plt.title(\"Frequency of moins over time of day\")\n", | |
| "plt.xlabel(\"Time of day [h]\")\n", | |
| "plt.ylabel(\"Probability of moin [%]\")\n", | |
| "plt.xlim(-1, 24)\n", | |
| "plt.ylim(-1e-10, None)\n", | |
| "plt.gca().set_xticks(range(24))\n", | |
| "plt.yticks(plt.gca().get_yticks(), [int(tick) for tick in plt.gca().get_yticks() * 100])\n", | |
| "plt.show()\n", | |
| "display(Markdown(f\"> π Skipped the following users because of a lack of moins: **{'**, **'.join(bad_nicks)}**\"))" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 9, | |
| "id": "ab324128-762c-4404-a582-13fb01c3cd87", | |
| "metadata": { | |
| "tags": [] | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "image/png": 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| |
| "text/plain": [ | |
| "<Figure size 2500x800 with 1 Axes>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| }, | |
| { | |
| "data": { | |
| "text/markdown": [ | |
| "> π Skipped the following users because of a lack of moins: **feliks-**, **kmein**, **moooooin**" | |
| ], | |
| "text/plain": [ | |
| "<IPython.core.display.Markdown object>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "sns.histplot(data=dfv, x=dfv.index.hour, hue='nick', multiple='dodge', edgecolor=None, bins=[float(i) for i in range(24)], shrink=.8, log_scale=(False, True))\n", | |
| "plt.title(\"Total moins over time of day\")\n", | |
| "plt.xlabel(\"Time of day [h]\")\n", | |
| "plt.ylabel(\"Number of moins\")\n", | |
| "plt.gca().set_xticks(range(24))\n", | |
| "plt.show()\n", | |
| "display(Markdown(f\"> π Skipped the following users because of a lack of moins: **{'**, **'.join(bad_nicks)}**\"))" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "id": "c7a6b621-aef7-47db-aaf6-9917150cae57", | |
| "metadata": {}, | |
| "source": [ | |
| "## Total moins" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "id": "752a345b-c881-4570-b7e8-057aeea10623", | |
| "metadata": {}, | |
| "source": [ | |
| "Total moins are a simple aggregate of a user's moins over the whole dataset." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 10, | |
| "id": "10b78509-ad7e-4e89-9515-43bca838f6aa", | |
| "metadata": { | |
| "tags": [] | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/markdown": [ | |
| "#### π Highest total moins" | |
| ], | |
| "text/plain": [ | |
| "<IPython.core.display.Markdown object>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| }, | |
| { | |
| "data": { | |
| "text/markdown": [ | |
| "> π Skipped the following users because of a lack of moins: **feliks-**, **kmein**, **moooooin**" | |
| ], | |
| "text/plain": [ | |
| "<IPython.core.display.Markdown object>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| }, | |
| { | |
| "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>moins</th>\n", | |
| " <th>medal</th>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>nick</th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>feliks</th>\n", | |
| " <td>166</td>\n", | |
| " <td>π₯</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>xkey</th>\n", | |
| " <td>135</td>\n", | |
| " <td>π₯</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>oxzi</th>\n", | |
| " <td>38</td>\n", | |
| " <td>π₯</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>Fail-X</th>\n", | |
| " <td>2</td>\n", | |
| " <td></td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "</div>" | |
| ], | |
| "text/plain": [ | |
| " moins medal\n", | |
| "nick \n", | |
| "feliks 166 π₯\n", | |
| "xkey 135 π₯\n", | |
| "oxzi 38 π₯\n", | |
| "Fail-X 2 " | |
| ] | |
| }, | |
| "execution_count": 10, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "# Dataframe holding total moins\n", | |
| "dftm = dfv[['nick', 'moins']].groupby('nick').sum('moins').sort_values('moins', ascending=False)\n", | |
| "dftm['medal'] = ''\n", | |
| "display(Markdown(\"#### π Highest total moins\"))\n", | |
| "if len(dftm) >= 3:\n", | |
| " dftm.iloc[:3, -1] = ['π₯', 'π₯', 'π₯']\n", | |
| "else:\n", | |
| " display(Markdown(f\"> π Not enough contestants for medal\"))\n", | |
| "display(Markdown(f\"> π Skipped the following users because of a lack of moins: **{'**, **'.join(bad_nicks)}**\"))\n", | |
| "dftm" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "id": "ce691b04-43d8-4c22-8db9-f220e001f81d", | |
| "metadata": {}, | |
| "source": [ | |
| "## User dissimilarity" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "id": "5a2a683a-836a-48a7-b5bb-c5bcb5f90c05", | |
| "metadata": {}, | |
| "source": [ | |
| "User dissimilarity compares the individual users' frequency of moins over the time of day. The more strongly a user diverges from everyone else, the more special they are and the better is their score. Dissimilarity is measured using standardized DTW." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 11, | |
| "id": "82ac85a8-bfd8-46dd-bbe7-b6160a5f26f2", | |
| "metadata": { | |
| "tags": [] | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "from dtaidistance import dtw" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 12, | |
| "id": "d1c83d29-0b3f-457b-b15d-2961b3b2327c", | |
| "metadata": { | |
| "tags": [] | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "hstud = {}\n", | |
| "for user in natsort(dfv['nick'].unique()):\n", | |
| " dfu = dfv[dfv['nick'] == user]\n", | |
| " hst, _ = np.histogram(dfu.index.hour, bins=range(24), density=True)\n", | |
| " hstn = (hst - hst.mean()) / hst.std()\n", | |
| " hstud[user] = hstn" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 13, | |
| "id": "3a32448e-403f-4db3-9887-876ce8f0d052", | |
| "metadata": { | |
| "tags": [] | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "hstu = np.array(list(hstud.values()))" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 14, | |
| "id": "a2687170-4113-43c8-81ab-fbfc0cdc3edf", | |
| "metadata": { | |
| "tags": [] | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "['Fail-X', 'feliks', 'oxzi', 'xkey']" | |
| ] | |
| }, | |
| "execution_count": 14, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "users = list(hstud.keys())\n", | |
| "users" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 15, | |
| "id": "759351d8-d562-43b4-833e-14b0b4fec2c4", | |
| "metadata": { | |
| "tags": [] | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "ds = dtw.distance_matrix_fast(hstu)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 16, | |
| "id": "f51b4d3d-915d-4082-80f1-eaad03a99114", | |
| "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>Fail-X</th>\n", | |
| " <th>feliks</th>\n", | |
| " <th>oxzi</th>\n", | |
| " <th>xkey</th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>Fail-X</th>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.819453</td>\n", | |
| " <td>1.304066</td>\n", | |
| " <td>3.226283</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>feliks</th>\n", | |
| " <td>0.819453</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>1.312479</td>\n", | |
| " <td>2.392033</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>oxzi</th>\n", | |
| " <td>1.304066</td>\n", | |
| " <td>1.312479</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>2.597538</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>xkey</th>\n", | |
| " <td>3.226283</td>\n", | |
| " <td>2.392033</td>\n", | |
| " <td>2.597538</td>\n", | |
| " <td>0.000000</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "</div>" | |
| ], | |
| "text/plain": [ | |
| " Fail-X feliks oxzi xkey\n", | |
| "Fail-X 0.000000 0.819453 1.304066 3.226283\n", | |
| "feliks 0.819453 0.000000 1.312479 2.392033\n", | |
| "oxzi 1.304066 1.312479 0.000000 2.597538\n", | |
| "xkey 3.226283 2.392033 2.597538 0.000000" | |
| ] | |
| }, | |
| "execution_count": 16, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "dfds = pd.DataFrame(data=ds, columns=users, index=users)\n", | |
| "dfds" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 17, | |
| "id": "68ac6996-3d93-48cd-95d8-e75a534facaf", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
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\n", | |
| "text/plain": [ | |
| "<Figure size 600x500 with 2 Axes>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "plt.figure(figsize=(6, 5))\n", | |
| "sns.heatmap(dfds, cmap='turbo', annot=True, cbar_kws={'label': 'DTW distance [a.u.]'})\n", | |
| "plt.title(\"Dissimilarity of users' post time\")\n", | |
| "ax = plt.gca()\n", | |
| "plt.grid(which='minor', ls='-', lw=1)\n", | |
| "plt.show()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 18, | |
| "id": "ba035df0-342a-49bf-b07d-2393aad751f4", | |
| "metadata": { | |
| "tags": [] | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/markdown": [ | |
| "#### π Highest degree of dissimilarity" | |
| ], | |
| "text/plain": [ | |
| "<IPython.core.display.Markdown object>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| }, | |
| { | |
| "data": { | |
| "text/markdown": [ | |
| "> π Skipped the following users because of a lack of moins: **feliks-**, **kmein**, **moooooin**" | |
| ], | |
| "text/plain": [ | |
| "<IPython.core.display.Markdown object>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| }, | |
| { | |
| "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>dtwsum</th>\n", | |
| " <th>medal</th>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>nick</th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>xkey</th>\n", | |
| " <td>2.053964</td>\n", | |
| " <td>π₯</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>Fail-X</th>\n", | |
| " <td>1.337451</td>\n", | |
| " <td>π₯</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>oxzi</th>\n", | |
| " <td>1.303521</td>\n", | |
| " <td>π₯</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>feliks</th>\n", | |
| " <td>1.130991</td>\n", | |
| " <td></td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "</div>" | |
| ], | |
| "text/plain": [ | |
| " dtwsum medal\n", | |
| "nick \n", | |
| "xkey 2.053964 π₯\n", | |
| "Fail-X 1.337451 π₯\n", | |
| "oxzi 1.303521 π₯\n", | |
| "feliks 1.130991 " | |
| ] | |
| }, | |
| "execution_count": 18, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "# Dataframe holding DTW distances\n", | |
| "dfdd = pd.DataFrame(data=zip(users, ds.mean(axis=1)), columns=['nick', 'dtwsum']).set_index('nick').sort_values('dtwsum', ascending=False)\n", | |
| "dfdd['medal'] = ''\n", | |
| "display(Markdown(\"#### π Highest degree of dissimilarity\"))\n", | |
| "if len(dfdd) >= 3:\n", | |
| " dfdd.iloc[:3, -1] = ['π₯', 'π₯', 'π₯']\n", | |
| "else:\n", | |
| " display(Markdown(f\"> π Not enough contestants for medal\"))\n", | |
| "display(Markdown(f\"> π Skipped the following users because of a lack of moins: **{'**, **'.join(bad_nicks)}**\"))\n", | |
| "dfdd" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "id": "2c57cb71-9daa-47fa-898f-7bfa08358305", | |
| "metadata": {}, | |
| "source": [ | |
| "## Users' daily inconsistency" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "id": "608ed256-db21-44d8-bb19-c5a6ece11f81", | |
| "metadata": {}, | |
| "source": [ | |
| "Daily moin inconsistency scores how well of a daily rhythm the moins of a user keep. The more regular their pattern is in relation to a 24 hour shift, operationalized by the autocorrelation of their moin timestamps, aggregated by hour, the lower the user scores. Hail Eris!" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 19, | |
| "id": "db448101-9f13-41a4-911a-bc2c9e74e0ca", | |
| "metadata": { | |
| "tags": [] | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "# https://stackoverflow.com/a/7981132\n", | |
| "def acf(x, length):\n", | |
| " return np.array([1] + [\n", | |
| " np.corrcoef(x[:-i], x[i:])[0,1]\n", | |
| " for i in range(1, length)\n", | |
| " ])" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 20, | |
| "id": "0a050311-e153-4c29-8e96-b9132071e72c", | |
| "metadata": { | |
| "tags": [] | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "def encode_ts(s):\n", | |
| " # Convert from format:\n", | |
| " # [1970-01-01T00:00:00, 1970-01-01T00:30:00, 1970-01-01-T01:00:00, 1970-01-01T05:00:00, ...]\n", | |
| " # To format (binned and summed by hour):\n", | |
| " # [2, 1, 0, 0, 0, 1, ...]\n", | |
| " d = s.diff().astype('timedelta64[h]')\n", | |
| " agg = 0\n", | |
| " ret = []\n", | |
| " for v in d[1:]:\n", | |
| " if v == 0:\n", | |
| " agg += 1\n", | |
| " else:\n", | |
| " ret += [agg]\n", | |
| " agg = 0\n", | |
| " return np.array(ret)\n", | |
| "\n", | |
| "def get_acd24(df, plot=False, print_se=False):\n", | |
| " # Gets the autocorrelation dissimilarity for the 24 hour shift\n", | |
| " min_observations = 25\n", | |
| " acds = {}\n", | |
| " acd24s = {}\n", | |
| " users_skip = []\n", | |
| " for user in df['nick'].unique():\n", | |
| " dfu = df[df['nick'] == user]\n", | |
| " s = pd.Series(dfu.index.round('h'))\n", | |
| " # 'One-hot' encode series\n", | |
| " se = encode_ts(s)\n", | |
| " if print_se:\n", | |
| " print(f'{user}: {se}')\n", | |
| " # Autocorrelation as dissimilarity\n", | |
| " acd = 1 - acf(se, length=len(se) - 1)\n", | |
| " acd24 = f'{acd[24]:.4f}' if len(acd) >= min_observations else 'n/a'\n", | |
| " if plot and len(acd) > 1:\n", | |
| " plt.plot(range(1, len(acd[1:]) + 1), acd[1:], '+:', markersize=10, label=f'{user} ({acd24})')\n", | |
| " acd_nn = acd[~np.isnan(acd)]\n", | |
| " acds[user] = acd_nn\n", | |
| " acd24s[user] = acd24\n", | |
| " # Keep on moinin' π€\n", | |
| " if len(acd) < min_observations:\n", | |
| " users_skip.append(user)\n", | |
| " continue\n", | |
| " if plot:\n", | |
| " # FIXME Here is the problem of division by zero\n", | |
| " acd_min_agg = [v[v > 0].min() for v in acds.values() if len(v[v > 0]) > 0]\n", | |
| " acd_max_agg = [v.max() for v in acds.values() if len(v) > 0]\n", | |
| " acd_min = min(acd_min_agg) if len(acd_min_agg) > 0 else 0\n", | |
| " acd_max = max(acd_max_agg) if len(acd_max_agg) > 0 else 1\n", | |
| " plt.legend()\n", | |
| " plt.vlines(24, acd_min, acd_max, colors='w', linestyles=':', label='24 hour mark', alpha=.5)\n", | |
| " plt.title(\"Autocorrelation of posts' times binned hourly\")\n", | |
| " plt.xlabel(\"Timeseries shift [hours]\")\n", | |
| " plt.ylabel(\"Autocorrelation dissimilarity\")\n", | |
| " plt.xlim(0, 48)\n", | |
| " plt.show()\n", | |
| " df_acd24s = pd.DataFrame(data=acd24s.items(), columns=['nick', 'acd24']).set_index('nick')\n", | |
| " return df_acd24s, natsort(users_skip)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 21, | |
| "id": "00cee3e8-b16a-4661-ab94-a3e4f72e700c", | |
| "metadata": { | |
| "tags": [] | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "# Required to temporarily suppress warnings\n", | |
| "import warnings" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 22, | |
| "id": "5267e578-1724-46bd-81de-386974a7f6da", | |
| "metadata": { | |
| "tags": [] | |
| }, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "xkey: [ 0 8 4 0 0 2 0 4 0 1 0 3 0 0 0 0 2 0 0 0 0 1 0 0\n", | |
| " 1 2 0 0 3 0 0 1 0 1 0 0 0 0 0 1 0 0 0 0 0 1 0 0\n", | |
| " 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 24 0 0\n", | |
| " 0]\n", | |
| "oxzi: [ 0 0 0 0 0 17 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n", | |
| "feliks: [ 4 0 0 0 3 0 0 1 0 0 1 0 1 5 0 0 0 0 1 1 1 0 0 0\n", | |
| " 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 9 90]\n", | |
| "kmein: []\n", | |
| "moooooin: []\n", | |
| "Fail-X: [0]\n", | |
| "feliks-: []\n" | |
| ] | |
| }, | |
| { | |
| "data": { | |
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| |
| "text/plain": [ | |
| "<Figure size 2500x800 with 1 Axes>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| }, | |
| { | |
| "data": { | |
| "text/markdown": [ | |
| "#### π Highest degree of daily inconsistency" | |
| ], | |
| "text/plain": [ | |
| "<IPython.core.display.Markdown object>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| }, | |
| { | |
| "data": { | |
| "text/markdown": [ | |
| "> π Not enough contestants for medal" | |
| ], | |
| "text/plain": [ | |
| "<IPython.core.display.Markdown object>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| }, | |
| { | |
| "data": { | |
| "text/markdown": [ | |
| "> π Skipped the following users because of a lack of moins: **Fail-X**, **feliks-**, **kmein**, **moooooin**, **oxzi**" | |
| ], | |
| "text/plain": [ | |
| "<IPython.core.display.Markdown object>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| }, | |
| { | |
| "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>acd24</th>\n", | |
| " <th>medal</th>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>nick</th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>feliks</th>\n", | |
| " <td>1.1304</td>\n", | |
| " <td></td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>xkey</th>\n", | |
| " <td>0.9182</td>\n", | |
| " <td></td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "</div>" | |
| ], | |
| "text/plain": [ | |
| " acd24 medal\n", | |
| "nick \n", | |
| "feliks 1.1304 \n", | |
| "xkey 0.9182 " | |
| ] | |
| }, | |
| "execution_count": 22, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "# Dataframe holding autocorrelations\n", | |
| "with warnings.catch_warnings():\n", | |
| " warnings.simplefilter(\"ignore\")\n", | |
| " dfac, users_skip = get_acd24(df, plot=True, print_se=True)\n", | |
| "# Filter n/a\n", | |
| "dfac = dfac[dfac != 'n/a'].dropna().astype(float)\n", | |
| "dfac = dfac.sort_values('acd24', ascending=False)\n", | |
| "dfac['medal'] = ''\n", | |
| "display(Markdown(\"#### π Highest degree of daily inconsistency\"))\n", | |
| "if len(dfac) >= 3:\n", | |
| " dfac.iloc[:3, -1] = ['π₯', 'π₯', 'π₯']\n", | |
| "else:\n", | |
| " display(Markdown(f\"> π Not enough contestants for medal\"))\n", | |
| "display(Markdown(f\"> π Skipped the following users because of a lack of moins: **{'**, **'.join(users_skip)}**\"))\n", | |
| "dfac" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "id": "2be9a7e0-575d-4873-9f79-03c3abd86d82", | |
| "metadata": {}, | |
| "source": [ | |
| "## Steady diversity of moins" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "id": "e2b48caf-6e3a-4eb8-a8fe-00d0e5ba8e0e", | |
| "metadata": {}, | |
| "source": [ | |
| "All moins are beautiful. Moin diversity is defined for each user by the standard deviation of the moin variants used. It should be low implying diverse use of moins. This score is then divided by the users total number of moin variants used." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 23, | |
| "id": "f4f6b828-5901-41b5-9029-0717ef3e59dd", | |
| "metadata": { | |
| "tags": [] | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "variation_count = df[['nick', 'variation']].groupby(['nick', 'variation']).size().to_frame(name='count') #.reset_index()\n", | |
| "# Number of different variations, like moin, mOin, MOIN, oi, etc\n", | |
| "variation_num = variation_count.reset_index().drop(['count'], axis=1).groupby('nick').size()\n", | |
| "# Standard deviation between these different variations' counts\n", | |
| "variation_std = variation_count.groupby(['nick']).std()\n", | |
| "# Score diversity of moins: std / num\n", | |
| "variation_score = (variation_std['count'] / variation_num).dropna()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 24, | |
| "id": "7315dbf4-28cc-4e02-82ec-0a1137482108", | |
| "metadata": { | |
| "tags": [] | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/markdown": [ | |
| "#### π Highest degree of steady diversity" | |
| ], | |
| "text/plain": [ | |
| "<IPython.core.display.Markdown object>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| }, | |
| { | |
| "data": { | |
| "text/markdown": [ | |
| "> π Skipped the following users because of a lack of moins: **Fail-X**, **feliks-**, **kmein**, **moooooin**, **oxzi**" | |
| ], | |
| "text/plain": [ | |
| "<IPython.core.display.Markdown object>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| }, | |
| { | |
| "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>diversity</th>\n", | |
| " <th>medal</th>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>nick</th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>Fail-X</th>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>π₯</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>feliks</th>\n", | |
| " <td>2.222749</td>\n", | |
| " <td>π₯</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>xkey</th>\n", | |
| " <td>3.890476</td>\n", | |
| " <td>π₯</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>oxzi</th>\n", | |
| " <td>12.727922</td>\n", | |
| " <td></td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "</div>" | |
| ], | |
| "text/plain": [ | |
| " diversity medal\n", | |
| "nick \n", | |
| "Fail-X 0.000000 π₯\n", | |
| "feliks 2.222749 π₯\n", | |
| "xkey 3.890476 π₯\n", | |
| "oxzi 12.727922 " | |
| ] | |
| }, | |
| "execution_count": 24, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "# Create according dataframe & medals\n", | |
| "dfdm = variation_score.to_frame(name='diversity').sort_values('diversity', ascending=True)\n", | |
| "dfdm['medal'] = ''\n", | |
| "display(Markdown(\"#### π Highest degree of steady diversity\"))\n", | |
| "if len(dfdm) >= 3:\n", | |
| " dfdm.iloc[:3, -1] = ['π₯', 'π₯', 'π₯']\n", | |
| "else:\n", | |
| " display(Markdown(f\"> π Not enough contestants for medal\"))\n", | |
| "display(Markdown(f\"> π Skipped the following users because of a lack of moins: **{'**, **'.join(users_skip)}**\"))\n", | |
| "dfdm" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "id": "7949c26d-1cae-48ca-a5f6-630f5a4c3565", | |
| "metadata": {}, | |
| "source": [ | |
| "## Moin chain gang" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "id": "01ecd00e-0aca-43d1-8343-f8fc0937ebc3", | |
| "metadata": {}, | |
| "source": [ | |
| "A moin chain is a relatively quick succession of at least 3 moins. It is broken when nobody replies within 3 hours. Gang members that moin twice without another member's moin in between are kicked out and all the points are lost. Other members still in the gang share their points within the chain. At the end, all chains are counted and user scores are compared." | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "id": "d7bc072a-e2e1-4dd8-a00f-03cc3dead1b3", | |
| "metadata": {}, | |
| "source": [ | |
| "> π Moin chains don't get collected correctly" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 25, | |
| "id": "04b52ece-89a6-4a2f-a44a-9b0b0b9ea5e1", | |
| "metadata": { | |
| "tags": [] | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "mcg_max_hours = 3\n", | |
| "mcg_min_len = 3" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 26, | |
| "id": "b4356d20-7954-41a8-839b-d7a28fb92d15", | |
| "metadata": { | |
| "tags": [] | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "def get_nick_switch(s_nick):\n", | |
| " return s_nick.ne(s_nick.shift().ffill()).astype(int)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 27, | |
| "id": "82f16959-a01e-4f0b-8fa8-ee573352deeb", | |
| "metadata": { | |
| "tags": [] | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "Int64Index([159, 165, 166, 171, 172, 173, 174, 175, 178, 182,\n", | |
| " ...\n", | |
| " 424, 425, 426, 427, 428, 429, 430, 498, 499, 500],\n", | |
| " dtype='int64', length=157)" | |
| ] | |
| }, | |
| "execution_count": 27, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "# Prepare dataframe with new column indicating a user switch between moins\n", | |
| "dfcg = df['nick'].copy().to_frame().reset_index()\n", | |
| "dfcg['switch'] = get_nick_switch(dfcg['nick'])\n", | |
| "dfcg = dfcg.sort_values('timestamp')\n", | |
| "# Select non-repetetive users' moins\n", | |
| "dfcgn = dfcg[dfcg['switch'] != 0].drop('switch', axis=1)\n", | |
| "# Find indices for successive moin chain gang\n", | |
| "dts = pd.Series(dfcgn['timestamp'].round('h')).diff()\n", | |
| "# Split indices\n", | |
| "# FIXME This line might not correctly compare by minimum time\n", | |
| "si = [0] + (dts > f'{mcg_max_hours}h').index + [len(dts) + 2]\n", | |
| "# Aggregate dataframes with proposed splits\n", | |
| "dfcgns = [\n", | |
| " dfcg.loc[si[n]:si[n + 1]]\n", | |
| " for n in range(len(si) - 1)\n", | |
| " if si[n + 1] - si[n] >= mcg_min_len\n", | |
| "]\n", | |
| "si" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 28, | |
| "id": "d15bb57d-9c91-417d-8684-1a7d4b2958c3", | |
| "metadata": { | |
| "tags": [] | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "def select_dfs_min_length(dfs):\n", | |
| " df_count = len(dfcgns)\n", | |
| " dfs = [\n", | |
| " _df\n", | |
| " for _df in dfs\n", | |
| " # Select only ones that have minimum length\n", | |
| " if len(_df) >= mcg_min_len\n", | |
| " ]\n", | |
| " shrink_pct = 1 - (len(dfs) / df_count)\n", | |
| " return dfs, shrink_pct" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 29, | |
| "id": "5a89f14d-ba72-40d8-9c8a-63cfa5f185ce", | |
| "metadata": { | |
| "tags": [] | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/markdown": [ | |
| "> π Removed about 35% of total chains because they were too short. A total of 13 chains is left," | |
| ], | |
| "text/plain": [ | |
| "<IPython.core.display.Markdown object>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "dfcgns, shrink_pct = select_dfs_min_length(dfcgns)\n", | |
| "display(Markdown(f\"> π Removed about {shrink_pct * 100:.0f}% of total chains because they were too short. A total of {len(dfcgns)} chains is left,\"))" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 30, | |
| "id": "1622b4d4-7736-4969-82c0-7eb65bfcb694", | |
| "metadata": { | |
| "tags": [] | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/markdown": [ | |
| "> π Removed about 23% of total chains because they were too short. A total of 10 chains is left," | |
| ], | |
| "text/plain": [ | |
| "<IPython.core.display.Markdown object>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "# Filter and check selected dataframes for updated nick switches\n", | |
| "# (If a person posted twice or more in a chain)\n", | |
| "dfcgnsf = []\n", | |
| "for _df in dfcgns:\n", | |
| " # Only valid nick switches allowed\n", | |
| " _df = _df[_df['switch'] > 0]\n", | |
| " # When now minimum length not met anymore kick it\n", | |
| " if len(_df) < mcg_min_len:\n", | |
| " continue\n", | |
| " # TODO Use this to let the assertion kick in\n", | |
| " # _df = _df.append(_df.iloc[len(_df) - 1])\n", | |
| " _ns = get_nick_switch(_df['nick'])\n", | |
| " # TODO If this assertion kicks in, dataframes have to be refiltered\n", | |
| " assert (_ns == _df['switch']).all(), f\"Mismatch at indices: {', '.join([str(idx) for idx in (_df.index[_ns != _df['switch']])])}\"\n", | |
| " # _df['switch'] = _ns\n", | |
| " dfcgnsf.append(_df)\n", | |
| "shrink_pct = 1 - (len(dfcgnsf) / len(dfcgns))\n", | |
| "display(Markdown(f\"> π Removed about {shrink_pct * 100:.0f}% of total chains because they were too short. A total of {len(dfcgnsf)} chains is left,\"))" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "id": "35ff3fa6-c157-4117-a180-2b3f8b28f366", | |
| "metadata": {}, | |
| "source": [ | |
| "### Plot moin chains" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 31, | |
| "id": "30ede203-820f-4bc6-aaf0-29dbaa88db80", | |
| "metadata": { | |
| "tags": [] | |
| }, | |
| "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>timestamp</th>\n", | |
| " <th>nick</th>\n", | |
| " <th>relatime</th>\n", | |
| " <th>chain_idx</th>\n", | |
| " <th>strttime</th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>260</th>\n", | |
| " <td>2023-03-24 07:53:15.236446619</td>\n", | |
| " <td>xkey</td>\n", | |
| " <td>0 days 00:00:01</td>\n", | |
| " <td>1</td>\n", | |
| " <td>2023-03-24 07:53</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>261</th>\n", | |
| " <td>2023-03-24 07:53:15.497602701</td>\n", | |
| " <td>feliks</td>\n", | |
| " <td>0 days 00:00:01.261156082</td>\n", | |
| " <td>1</td>\n", | |
| " <td>2023-03-24 07:53</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>262</th>\n", | |
| " <td>2023-03-24 07:53:17.236378908</td>\n", | |
| " <td>xkey</td>\n", | |
| " <td>0 days 00:00:02.999932289</td>\n", | |
| " <td>1</td>\n", | |
| " <td>2023-03-24 07:53</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>263</th>\n", | |
| " <td>2023-03-24 07:53:17.497668505</td>\n", | |
| " <td>feliks</td>\n", | |
| " <td>0 days 00:00:03.261221886</td>\n", | |
| " <td>1</td>\n", | |
| " <td>2023-03-24 07:53</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>264</th>\n", | |
| " <td>2023-03-24 07:53:19.242156029</td>\n", | |
| " <td>xkey</td>\n", | |
| " <td>0 days 00:00:05.005709410</td>\n", | |
| " <td>1</td>\n", | |
| " <td>2023-03-24 07:53</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "</div>" | |
| ], | |
| "text/plain": [ | |
| " timestamp nick relatime \\\n", | |
| "260 2023-03-24 07:53:15.236446619 xkey 0 days 00:00:01 \n", | |
| "261 2023-03-24 07:53:15.497602701 feliks 0 days 00:00:01.261156082 \n", | |
| "262 2023-03-24 07:53:17.236378908 xkey 0 days 00:00:02.999932289 \n", | |
| "263 2023-03-24 07:53:17.497668505 feliks 0 days 00:00:03.261221886 \n", | |
| "264 2023-03-24 07:53:19.242156029 xkey 0 days 00:00:05.005709410 \n", | |
| "\n", | |
| " chain_idx strttime \n", | |
| "260 1 2023-03-24 07:53 \n", | |
| "261 1 2023-03-24 07:53 \n", | |
| "262 1 2023-03-24 07:53 \n", | |
| "263 1 2023-03-24 07:53 \n", | |
| "264 1 2023-03-24 07:53 " | |
| ] | |
| }, | |
| "execution_count": 31, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "dfcgnsl = [\n", | |
| " len(_df)\n", | |
| " for _df in dfcgnsf\n", | |
| "]\n", | |
| "# Iterate over moin chains from longest to shortest\n", | |
| "_dfpcg = []\n", | |
| "for idxn, (idx, chain_len) in enumerate(sorted(list(enumerate(dfcgnsl)), key=lambda tup: tup[1], reverse=True)):\n", | |
| " # Get chain by index\n", | |
| " _df = dfcgnsf[idx].drop('switch', axis=1)\n", | |
| " _df['relatime'] = _df['timestamp'] - _df['timestamp'].min() + pd.Timedelta('1s')\n", | |
| " _df['chain_idx'] = idxn + 1\n", | |
| " _df['strttime'] = _df['timestamp'].dt.strftime('%Y-%m-%d %H:%M')\n", | |
| " _dfpcg.append(_df)\n", | |
| "dfpcg = pd.concat(_dfpcg)\n", | |
| "dfpcg.head()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 32, | |
| "id": "e04c6c08-3efb-4c66-b4db-2102bb5e9ec1", | |
| "metadata": { | |
| "tags": [] | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "image/png": 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\n", | |
| "text/plain": [ | |
| "<Figure size 2500x800 with 1 Axes>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "vlines = {k: v * 1e9 for k, v in {\n", | |
| " '1 m': 60, # 1 minute\n", | |
| " '1 h': 60 * 60, # 1 hour\n", | |
| " '1 d': 60 * 60 * 24, # 1 day\n", | |
| " '1 w': 60 * 60 * 24 * 7, # 1 week\n", | |
| "}.items()}\n", | |
| "max_relatime = dfpcg['relatime'].max() / np.timedelta64(1, 'ns')\n", | |
| "\n", | |
| "fig = plt.figure()\n", | |
| "ax = plt.gca()\n", | |
| "sns.lineplot(data=dfpcg, x='relatime', y='chain_idx', hue='chain_idx', dashes=(1, 2), legend=False, ax=ax, palette=['w'] * (max(dfpcg['chain_idx'])))\n", | |
| "sp = sns.scatterplot(data=dfpcg, x='relatime', y='chain_idx', hue='nick', s=170, zorder=2, marker='p', linewidth=.8)\n", | |
| "sns.move_legend(sp, \"upper left\", bbox_to_anchor=(1, .7), title='Nickname')\n", | |
| "ax.invert_yaxis()\n", | |
| "yticks_idx = [int(tick) for tick in plt.gca().get_yticks() if tick == int(tick)]\n", | |
| "yticks_date = [dfpcg['strttime'][dfpcg['chain_idx'] == int(tick)].min() for tick in plt.gca().get_yticks() if tick == int(tick)]\n", | |
| "plt.yticks(yticks_idx, yticks_date)\n", | |
| "plt.title(\"Moin chains in relative time\")\n", | |
| "plt.xlabel(\"Relative time [nanoseconds]\")\n", | |
| "plt.ylabel(\"Chain index [id]\")\n", | |
| "# plt.xscale('log')\n", | |
| "\n", | |
| "# Plot vertical lines and annotations\n", | |
| "for label, pos in vlines.items():\n", | |
| " if pos > max_relatime:\n", | |
| " continue\n", | |
| " plt.vlines(pos, dfpcg['chain_idx'].min(), dfpcg['chain_idx'].max(), colors='w', linestyles=':', label='24 hour mark', alpha=.3)\n", | |
| " ax.text(pos * 1.1, 1.5, label)\n", | |
| "\n", | |
| "plt.show()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "id": "85081441-bab3-41f1-bd3a-06ed22fd8227", | |
| "metadata": {}, | |
| "source": [ | |
| "### Gang score" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "id": "39580795-5b71-4229-b973-c4e344cdb2d6", | |
| "metadata": {}, | |
| "source": [ | |
| "Every users' gang score is computed by counting all the moin chains that they were involved in (and not kicked out of) and multiplying it by the according chain's length." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 33, | |
| "id": "3dd284f0-566f-4509-8000-3b52baa305f1", | |
| "metadata": { | |
| "tags": [] | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "# Compute scores for chain gangs' members\n", | |
| "member_scores = {}\n", | |
| "for _df in dfcgnsf:\n", | |
| " score = _df['switch'].sum()\n", | |
| " members = _df['nick'].unique()\n", | |
| " for member in members:\n", | |
| " if member not in member_scores:\n", | |
| " member_scores[member] = score\n", | |
| " else:\n", | |
| " member_scores[member] += score" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 34, | |
| "id": "3938999e-65e4-47a1-a4e6-728917240ac2", | |
| "metadata": { | |
| "tags": [] | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/markdown": [ | |
| "#### π Highest gang score from moin chains" | |
| ], | |
| "text/plain": [ | |
| "<IPython.core.display.Markdown object>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| }, | |
| { | |
| "data": { | |
| "text/markdown": [ | |
| "> π Skipped the following users because of a lack of moins: **Fail-X**, **kmein**, **moooooin**" | |
| ], | |
| "text/plain": [ | |
| "<IPython.core.display.Markdown object>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| }, | |
| { | |
| "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>gang_score</th>\n", | |
| " <th>medal</th>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>nick</th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>xkey</th>\n", | |
| " <td>47</td>\n", | |
| " <td>π₯</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>feliks</th>\n", | |
| " <td>47</td>\n", | |
| " <td>π₯</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>oxzi</th>\n", | |
| " <td>18</td>\n", | |
| " <td>π₯</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>feliks-</th>\n", | |
| " <td>3</td>\n", | |
| " <td></td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "</div>" | |
| ], | |
| "text/plain": [ | |
| " gang_score medal\n", | |
| "nick \n", | |
| "xkey 47 π₯\n", | |
| "feliks 47 π₯\n", | |
| "oxzi 18 π₯\n", | |
| "feliks- 3 " | |
| ] | |
| }, | |
| "execution_count": 34, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "dfmc = pd.DataFrame(data=zip(member_scores.keys(), member_scores.values()), columns=['nick', 'gang_score']).set_index('nick').sort_values('gang_score', ascending=False)\n", | |
| "nochain_nicks = natsort(all_nicks[~all_nicks.isin(dfmc.index)])\n", | |
| "\n", | |
| "dfmc['medal'] = ''\n", | |
| "display(Markdown(\"#### π Highest gang score from moin chains\"))\n", | |
| "if len(dfmc) >= 3:\n", | |
| " dfmc.iloc[:3, -1] = ['π₯', 'π₯', 'π₯']\n", | |
| "else:\n", | |
| " display(Markdown(f\"> π Not enough contestants for medal\"))\n", | |
| "display(Markdown(f\"> π Skipped the following users because of a lack of moins: **{'**, **'.join(nochain_nicks)}**\"))\n", | |
| "dfmc" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "id": "e7ec480b-1149-4762-932d-9e843d8dc3f0", | |
| "metadata": {}, | |
| "source": [ | |
| "### Chain starters" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "id": "6917329e-8f64-4b17-8b9c-d4e460b0b198", | |
| "metadata": {}, | |
| "source": [ | |
| "Each chain needs a person to start it. These people get credit for starting the longest chains. It's as simple as that." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 35, | |
| "id": "49aa2774-9115-4c68-93b6-297154a3414a", | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "chain_starters = dfpcg[dfpcg['chain_idx'].diff().fillna(1) == 1][['nick', 'chain_idx']]\n", | |
| "chain_lengths = dfpcg.groupby('chain_idx')['nick'].count().to_frame(name='chain_len')\n", | |
| "dfcs = chain_starters.join(chain_lengths, on='chain_idx')[['nick', 'chain_len']].groupby('nick').max() \\\n", | |
| " .rename({'chain_len': 'max_chain_len'}).sort_values('chain_len', ascending=False)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 36, | |
| "id": "68f83a42-6a91-4846-bf26-a9027fe3bbe3", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/markdown": [ | |
| "#### π Starter of longest moin chains" | |
| ], | |
| "text/plain": [ | |
| "<IPython.core.display.Markdown object>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| }, | |
| { | |
| "data": { | |
| "text/markdown": [ | |
| "> π Not enough contestants for medal" | |
| ], | |
| "text/plain": [ | |
| "<IPython.core.display.Markdown object>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| }, | |
| { | |
| "data": { | |
| "text/markdown": [ | |
| "> π Skipped the following users because of a lack of moins: **Fail-X**, **feliks-**, **kmein**, **moooooin**, **oxzi**" | |
| ], | |
| "text/plain": [ | |
| "<IPython.core.display.Markdown object>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| }, | |
| { | |
| "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>chain_len</th>\n", | |
| " <th>medal</th>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>nick</th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>xkey</th>\n", | |
| " <td>8</td>\n", | |
| " <td></td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>feliks</th>\n", | |
| " <td>5</td>\n", | |
| " <td></td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "</div>" | |
| ], | |
| "text/plain": [ | |
| " chain_len medal\n", | |
| "nick \n", | |
| "xkey 8 \n", | |
| "feliks 5 " | |
| ] | |
| }, | |
| "execution_count": 36, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "nostart_nicks = natsort(all_nicks[~all_nicks.isin(dfcs.index)])\n", | |
| "\n", | |
| "dfcs['medal'] = ''\n", | |
| "display(Markdown(\"#### π Starter of longest moin chains\"))\n", | |
| "if len(dfcs) >= 3:\n", | |
| " dfcs.iloc[:3, -1] = ['π₯', 'π₯', 'π₯']\n", | |
| "else:\n", | |
| " display(Markdown(f\"> π Not enough contestants for medal\"))\n", | |
| "display(Markdown(f\"> π Skipped the following users because of a lack of moins: **{'**, **'.join(nostart_nicks)}**\"))\n", | |
| "dfcs" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "id": "c241cf01-7eae-4bb0-bb78-bef8bbc7afe4", | |
| "metadata": {}, | |
| "source": [ | |
| "## Rising star" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "id": "daadddbf-e3cb-4575-b767-6f179ac98297", | |
| "metadata": {}, | |
| "source": [ | |
| "A person that moins is considered a rising star when they have gained traction with their relative share of moining recently." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 37, | |
| "id": "f7915d7b-8022-49e3-8c28-39bed318b336", | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "from matplotlib import dates" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 38, | |
| "id": "26e639da-e489-481f-a85a-78bddd3c83cb", | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "@plt.FuncFormatter\n", | |
| "def fake_dates(x, pos):\n", | |
| " \"\"\" Custom formater to turn floats into e.g., 2016-05-08\"\"\"\n", | |
| " return dates.num2date(x).strftime('%Y-%m-%d')" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 39, | |
| "id": "49fb06f6-b92d-433b-9526-53df99263188", | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "dfud = df.reset_index()[['day', 'nick', 'moins']].groupby(['nick', 'day']).sum().reset_index()\n", | |
| "dfud['date'] = dfud['day'].apply(lambda x: pd.to_datetime(x))\n", | |
| "dfud['datenum'] = dfud['date'].astype(str).apply(lambda x: dates.datestr2num(x))\n", | |
| "dfud = dfud[dfud['moins'] > 1]" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 40, | |
| "id": "da09a009-2a9d-4c00-811a-5f2ea03cc585", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
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| |
| "text/plain": [ | |
| "<Figure size 1500x500 with 3 Axes>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "fg = sns.lmplot(data=dfud, x='datenum', y='moins', hue='nick', col='nick')\n", | |
| "with warnings.catch_warnings():\n", | |
| " warnings.simplefilter(\"ignore\")\n", | |
| " for ax in fg.axes.flat:\n", | |
| " ax.set_xticklabels(ax.get_xticks(), rotation=90)\n", | |
| " ax.xaxis.set_major_formatter(fake_dates)\n", | |
| " ax.set_ylim(-5, 20)\n", | |
| "plt.show()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 41, | |
| "id": "e8c7f1c3-733c-4bf4-98f7-d19a1c3418c6", | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "# dfudd = dfud.groupby('nick').diff().join(dfud['nick']).dropna()[['nick', 'moins']].join(dfud[['day', 'datenum']])" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 42, | |
| "id": "35ab3b12-6920-455a-8e35-7d88d5d5fce3", | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "# fg = sns.lmplot(data=dfudd, x='datenum', y='moins', hue='nick', col='nick')#, logistic=(False, False))#, ax=ax)\n", | |
| "# for ax in fg.axes.flat:\n", | |
| "# ax.set_xticklabels(ax.get_xticks(), rotation=90)\n", | |
| "# ax.xaxis.set_major_formatter(fake_dates)\n", | |
| "# ax.set_ylim(-5, 20)\n", | |
| "# plt.show()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 43, | |
| "id": "8321ccae-f968-4913-b03c-a7bf9798e5e4", | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "from scipy import stats" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 44, | |
| "id": "47e3c219-c5aa-4172-ad05-7df9c925bcdb", | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "nick_slopes = []\n", | |
| "for nick in all_nicks:\n", | |
| " _df = dfud[dfud['nick'] == nick]\n", | |
| " if len(_df) < 1:\n", | |
| " continue\n", | |
| " slope, intercept, r_value, p_value, std_err = stats.linregress(_df['datenum'], _df['moins'])\n", | |
| " nick_slopes.append([nick, slope])\n", | |
| "dfns = pd.DataFrame(data=nick_slopes, columns=['nick', 'slope']).sort_values('slope', ascending=False).set_index('nick')" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 45, | |
| "id": "1171bc66-b321-4476-8686-9b14496e311b", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/markdown": [ | |
| "#### π Highest underdog score from trend slope" | |
| ], | |
| "text/plain": [ | |
| "<IPython.core.display.Markdown object>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| }, | |
| { | |
| "data": { | |
| "text/markdown": [ | |
| "> π Skipped the following users because of a lack of moins: **Fail-X**, **feliks-**, **kmein**, **moooooin**" | |
| ], | |
| "text/plain": [ | |
| "<IPython.core.display.Markdown object>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| }, | |
| { | |
| "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>slope</th>\n", | |
| " <th>medal</th>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>nick</th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>feliks</th>\n", | |
| " <td>1.911452</td>\n", | |
| " <td>π₯</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>xkey</th>\n", | |
| " <td>0.051433</td>\n", | |
| " <td>π₯</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>oxzi</th>\n", | |
| " <td>-0.098649</td>\n", | |
| " <td>π₯</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "</div>" | |
| ], | |
| "text/plain": [ | |
| " slope medal\n", | |
| "nick \n", | |
| "feliks 1.911452 π₯\n", | |
| "xkey 0.051433 π₯\n", | |
| "oxzi -0.098649 π₯" | |
| ] | |
| }, | |
| "execution_count": 45, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "noslope_nicks = natsort(all_nicks[~all_nicks.isin(dfns.index)])\n", | |
| "\n", | |
| "dfns['medal'] = ''\n", | |
| "display(Markdown(\"#### π Highest underdog score from trend slope\"))\n", | |
| "if len(dfns) >= 3:\n", | |
| " dfns.iloc[:3, -1] = ['π₯', 'π₯', 'π₯']\n", | |
| "else:\n", | |
| " display(Markdown(f\"> π Not enough contestants for medal\"))\n", | |
| "display(Markdown(f\"> π Skipped the following users because of a lack of moins: **{'**, **'.join(noslope_nicks)}**\"))\n", | |
| "dfns" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "id": "1f5adbd6-3af5-45ce-9bbe-1cdae9fd62dc", | |
| "metadata": {}, | |
| "source": [ | |
| "# Winners' rostrum" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 46, | |
| "id": "74593fd0-4b9b-49d0-94b1-3e59d9ae8c31", | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "def get_rank(s_score):\n", | |
| " base_rank = ((s_score.max() - s_score).astype('category').cat.codes + 1).diff()\n", | |
| " agg, rank = 0, 1\n", | |
| " ranks = []\n", | |
| " for idx, br in base_rank.items():\n", | |
| " if br == 1:\n", | |
| " rank += agg\n", | |
| " agg = 0\n", | |
| " ranks.append(rank)\n", | |
| " agg += 1\n", | |
| " return pd.Series(ranks, index=s_score.index)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 47, | |
| "id": "284ab85c-5c55-41fd-8818-a21b27215cca", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/markdown": [ | |
| "#### π Winners' rostrum" | |
| ], | |
| "text/plain": [ | |
| "<IPython.core.display.Markdown object>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| }, | |
| { | |
| "data": { | |
| "text/markdown": [ | |
| "> π₯³ moin **xkey**" | |
| ], | |
| "text/plain": [ | |
| "<IPython.core.display.Markdown object>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| }, | |
| { | |
| "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>win</th>\n", | |
| " <th>rank</th>\n", | |
| " <th>score</th>\n", | |
| " <th>medals</th>\n", | |
| " <th>medals_gold</th>\n", | |
| " <th>medals_silver</th>\n", | |
| " <th>medals_bronze</th>\n", | |
| " <th>tm</th>\n", | |
| " <th>dd</th>\n", | |
| " <th>ac</th>\n", | |
| " <th>dm</th>\n", | |
| " <th>mc</th>\n", | |
| " <th>cs</th>\n", | |
| " <th>ns</th>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>nick</th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>xkey</th>\n", | |
| " <td>πͺ</td>\n", | |
| " <td>1</td>\n", | |
| " <td>11</td>\n", | |
| " <td>π₯π₯π₯π₯π₯</td>\n", | |
| " <td>2</td>\n", | |
| " <td>2</td>\n", | |
| " <td>1</td>\n", | |
| " <td>π₯</td>\n", | |
| " <td>π₯</td>\n", | |
| " <td>π</td>\n", | |
| " <td>π₯</td>\n", | |
| " <td>π₯</td>\n", | |
| " <td>π</td>\n", | |
| " <td>π₯</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>feliks</th>\n", | |
| " <td></td>\n", | |
| " <td>2</td>\n", | |
| " <td>10</td>\n", | |
| " <td>π₯π₯π₯π₯</td>\n", | |
| " <td>2</td>\n", | |
| " <td>2</td>\n", | |
| " <td>0</td>\n", | |
| " <td>π₯</td>\n", | |
| " <td></td>\n", | |
| " <td>π</td>\n", | |
| " <td>π₯</td>\n", | |
| " <td>π₯</td>\n", | |
| " <td>π</td>\n", | |
| " <td>π₯</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>Fail-X</th>\n", | |
| " <td></td>\n", | |
| " <td>3</td>\n", | |
| " <td>5</td>\n", | |
| " <td>π₯π₯</td>\n", | |
| " <td>1</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td></td>\n", | |
| " <td>π₯</td>\n", | |
| " <td>π</td>\n", | |
| " <td>π₯</td>\n", | |
| " <td></td>\n", | |
| " <td>π</td>\n", | |
| " <td></td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>oxzi</th>\n", | |
| " <td></td>\n", | |
| " <td>4</td>\n", | |
| " <td>4</td>\n", | |
| " <td>π₯π₯π₯π₯</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>4</td>\n", | |
| " <td>π₯</td>\n", | |
| " <td>π₯</td>\n", | |
| " <td>π</td>\n", | |
| " <td></td>\n", | |
| " <td>π₯</td>\n", | |
| " <td>π</td>\n", | |
| " <td>π₯</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>feliks-</th>\n", | |
| " <td></td>\n", | |
| " <td>5</td>\n", | |
| " <td>0</td>\n", | |
| " <td></td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td></td>\n", | |
| " <td></td>\n", | |
| " <td>π</td>\n", | |
| " <td></td>\n", | |
| " <td></td>\n", | |
| " <td>π</td>\n", | |
| " <td></td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "</div>" | |
| ], | |
| "text/plain": [ | |
| " win rank score medals medals_gold medals_silver medals_bronze tm \\\n", | |
| "nick \n", | |
| "xkey πͺ 1 11 π₯π₯π₯π₯π₯ 2 2 1 π₯ \n", | |
| "feliks 2 10 π₯π₯π₯π₯ 2 2 0 π₯ \n", | |
| "Fail-X 3 5 π₯π₯ 1 1 0 \n", | |
| "oxzi 4 4 π₯π₯π₯π₯ 0 0 4 π₯ \n", | |
| "feliks- 5 0 0 0 0 \n", | |
| "\n", | |
| " dd ac dm mc cs ns \n", | |
| "nick \n", | |
| "xkey π₯ π π₯ π₯ π π₯ \n", | |
| "feliks π π₯ π₯ π π₯ \n", | |
| "Fail-X π₯ π π₯ π \n", | |
| "oxzi π₯ π π₯ π π₯ \n", | |
| "feliks- π π " | |
| ] | |
| }, | |
| "execution_count": 47, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "# Dataframe holding winners' medals\n", | |
| "mcols = ['tm', 'dd', 'ac', 'dm', 'mc', 'cs', 'ns']\n", | |
| "dfwm = pd.DataFrame([\n", | |
| " df['medal']\n", | |
| " for df in [dftm, dfdd, dfac, dfdm, dfmc, dfcs, dfns]\n", | |
| "]).T.fillna('').set_axis(mcols, axis=1)\n", | |
| "\n", | |
| "# Series holding score\n", | |
| "ss = dfwm.sum(axis=1)\n", | |
| "\n", | |
| "# Series holding medals\n", | |
| "sm = dfwm.replace({'π₯': 3, 'π₯': 2, 'π₯': 1, '': 0}).astype(int).sum(axis=1)\n", | |
| "sm1 = dfwm.replace({'π₯': 1, 'π₯': 0, 'π₯': 0, '': 0}).astype(int).sum(axis=1)\n", | |
| "sm2 = dfwm.replace({'π₯': 0, 'π₯': 1, 'π₯': 0, '': 0}).astype(int).sum(axis=1)\n", | |
| "sm3 = dfwm.replace({'π₯': 0, 'π₯': 0, 'π₯': 1, '': 0}).astype(int).sum(axis=1)\n", | |
| "\n", | |
| "# Dataframe holding winners' rostrum\n", | |
| "cols = ['score', 'medals', 'medals_gold', 'medals_silver', 'medals_bronze']\n", | |
| "dfwr = pd.DataFrame(\n", | |
| " data=zip(sm, ss, sm1, sm2, sm3),\n", | |
| " columns=cols,\n", | |
| " index=ss.index).sort_values('score', ascending=False)\n", | |
| "dfwr['rank'] = get_rank(dfwr['score'])\n", | |
| "\n", | |
| "# Determine winner(s)\n", | |
| "winners = dfwr['rank'] == 1\n", | |
| "winners_names = dfwr.index[winners]\n", | |
| "winners_symbol = 'πͺ' if len(winners_names) <= 1 else 'β'\n", | |
| "dfwr['win'] = ''\n", | |
| "dfwr.loc[winners, 'win'] = [winners_symbol] * len(dfwr.loc[winners])\n", | |
| "dfwr = dfwr[['win', 'rank'] + cols]\n", | |
| "\n", | |
| "# Annotate yet unavailable medals\n", | |
| "for mcol in mcols:\n", | |
| " if len(dfwm[mcol].unique()) == 1:\n", | |
| " dfwm[mcol] = 'π'\n", | |
| "\n", | |
| "# Display winners' stats\n", | |
| "display(Markdown(\"#### π Winners' rostrum\"))\n", | |
| "display(Markdown(f\"> π₯³ moin **{', '.join(sorted(winners_names))}**\"))\n", | |
| "dfwr.join(dfwm)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "id": "a2dfb536-4261-47cb-8b3e-76f37c73d88f", | |
| "metadata": {}, | |
| "source": [ | |
| "m | desc\n", | |
| "-|-\n", | |
| "tm | Total moins\n", | |
| "dd | User dissimilarity\n", | |
| "ac | Daily inconsistency\n", | |
| "dm | Diversity of moins\n", | |
| "mc | Chain gang\n", | |
| "cs | Chain starter\n", | |
| "ns | Underdog\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "id": "6414473d-0d7d-4a96-8657-e772af2b37b2", | |
| "metadata": {}, | |
| "source": [ | |
| " " | |
| ] | |
| } | |
| ], | |
| "metadata": { | |
| "kernelspec": { | |
| "display_name": "Python 3 (ipykernel)", | |
| "language": "python", | |
| "name": "python3" | |
| }, | |
| "language_info": { | |
| "codemirror_mode": { | |
| "name": "ipython", | |
| "version": 3 | |
| }, | |
| "file_extension": ".py", | |
| "mimetype": "text/x-python", | |
| "name": "python", | |
| "nbconvert_exporter": "python", | |
| "pygments_lexer": "ipython3", | |
| "version": "3.10.8" | |
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| "nbformat_minor": 5 | |
| } |
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