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philipphager/ips.ipynb

Created October 9, 2024 13:53
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CMIP IPS Example
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ips.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "58c27abe-1bb2-4bd0-aead-e21401a66096",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"zsh:1: no matches found: vegafusion[embed]\n"
]
}
],
"source": [
"!pip install --quiet altair cmip-metric numpy pandas \"vegafusion[embed]\""
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "4e9bd232-3c58-40fd-99c2-0ba699611bc2",
"metadata": {},
"outputs": [],
"source": [
"import altair as alt\n",
"import numpy as np\n",
"import pandas as pd\n",
"\n",
"from theme import theme\n",
"\n",
"from cmip_metric import CMIP"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "6d275d6b-9a76-4457-b434-bf40da0dad3e",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"DataTransformerRegistry.enable('vegafusion')"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"alt.themes.register(\"latex\", theme)\n",
"alt.themes.enable(\"latex\")\n",
"alt.data_transformers.enable(\"vegafusion\")"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "0c33c210-fa10-4ebe-bb6b-b0e62796b0f4",
"metadata": {},
"outputs": [],
"source": [
"np.random.seed(2024)\n",
"\n",
"n_queries = 500\n",
"n_items = 20\n",
"n_sessions = 250\n",
"\n",
"labels = np.random.randint(1, 5, size=(n_queries, n_items))\n",
"logging_policy = labels + 0.5 * np.random.randn(n_queries, n_items)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "f8d2b858-3d85-426d-b13b-ebf4790d5793",
"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>query</th>\n",
" <th>session</th>\n",
" <th>item</th>\n",
" <th>bias</th>\n",
" <th>misspecified_bias</th>\n",
" <th>y</th>\n",
" <th>y_logging_policy</th>\n",
" <th>click</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>9</td>\n",
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" <td>1.000000</td>\n",
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" <td>4.589312</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>4</td>\n",
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" <td>0.577350</td>\n",
" <td>4</td>\n",
" <td>4.240276</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0.250000</td>\n",
" <td>0.500000</td>\n",
" <td>3</td>\n",
" <td>3.704135</td>\n",
" <td>0</td>\n",
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" <tr>\n",
" <th>4</th>\n",
" <td>0</td>\n",
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" <td>12</td>\n",
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"</table>\n",
"</div>"
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"text/plain": [
" query session item bias misspecified_bias y y_logging_policy \\\n",
"0 0 0 9 1.000000 1.000000 4 4.589312 \n",
"1 0 0 7 0.500000 0.707107 4 4.462886 \n",
"2 0 0 4 0.333333 0.577350 4 4.240276 \n",
"3 0 0 1 0.250000 0.500000 3 3.704135 \n",
"4 0 0 12 0.200000 0.447214 4 3.551111 \n",
"\n",
" click \n",
"0 1 \n",
"1 1 \n",
"2 0 \n",
"3 0 \n",
"4 0 "
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sessions = []\n",
"\n",
"for i in range(n_queries * n_sessions):\n",
" query = i % n_queries\n",
" positions = np.argsort(-logging_policy[query])\n",
" \n",
" relevance = labels[query][positions] / 4\n",
" bias = 1 / np.arange(1, n_items + 1)\n",
" misspecified_bias = (1 / np.arange(1, n_items + 1)) ** 0.5\n",
" clicks = np.random.binomial(n=1, p=(bias * relevance))\n",
"\n",
" sessions.append(pd.DataFrame({\n",
" \"query\": query,\n",
" \"session\": i,\n",
" \"item\": positions.ravel(),\n",
" \"bias\": bias.ravel(),\n",
" \"misspecified_bias\": misspecified_bias.ravel(),\n",
" \"y\": labels[query][positions].ravel(),\n",
" \"y_logging_policy\": logging_policy[query][positions].ravel(),\n",
" \"click\": clicks.ravel(),\n",
" }))\n",
"\n",
"df = pd.concat(sessions)\n",
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "8066cc4a-94d7-4b9c-8290-4db8b10bf3f9",
"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>query</th>\n",
" <th>item</th>\n",
" <th>y</th>\n",
" <th>y_logging_policy</th>\n",
" <th>y_ips</th>\n",
" <th>y_ips_misspecified</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
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" <td>1.429634</td>\n",
" <td>0.392</td>\n",
" <td>0.104766</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>3.704135</td>\n",
" <td>0.624</td>\n",
" <td>0.312000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>1.279415</td>\n",
" <td>0.256</td>\n",
" <td>0.064000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>0.266676</td>\n",
" <td>0.240</td>\n",
" <td>0.053666</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0</td>\n",
" <td>4</td>\n",
" <td>4</td>\n",
" <td>4.240276</td>\n",
" <td>0.924</td>\n",
" <td>0.533472</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" query item y y_logging_policy y_ips y_ips_misspecified\n",
"0 0 0 1 1.429634 0.392 0.104766\n",
"1 0 1 3 3.704135 0.624 0.312000\n",
"2 0 2 1 1.279415 0.256 0.064000\n",
"3 0 3 1 0.266676 0.240 0.053666\n",
"4 0 4 4 4.240276 0.924 0.533472"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df[\"y_ips\"] = df[\"click\"] / df[\"bias\"]\n",
"df[\"y_ips_misspecified\"] = df[\"click\"] / df[\"misspecified_bias\"]\n",
"\n",
"agg_df = df.groupby([\"query\", \"item\", \"y\"])[[\"y_logging_policy\", \"y_ips\", \"y_ips_misspecified\"]].mean().reset_index()\n",
"agg_df.head()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "e93fd621-31f9-4ff6-b649-94c9f2c361b5",
"metadata": {},
"outputs": [],
"source": [
"def compute_cmip(df, y_predict, groupby):\n",
" session_df = df.groupby(groupby).agg(\n",
" y=(\"y\", list),\n",
" y_logging_policy=(\"y_logging_policy\", list),\n",
" y_predict=(y_predict, list),\n",
" ).reset_index()\n",
"\n",
" session_df[\"n\"] = session_df[\"y\"].map(len)\n",
" \n",
" metric = CMIP(n_bootstrap=5)\n",
" value = metric(\n",
" y_predict=np.array(session_df[\"y_predict\"].tolist()),\n",
" y_logging_policy=np.array(session_df[\"y_logging_policy\"].tolist()),\n",
" y_true=np.array(session_df[\"y\"].tolist()),\n",
" n=np.array(session_df[\"n\"].tolist())\n",
" )\n",
"\n",
" return pd.DataFrame({\n",
" \"metric\": [\"CMIP\"],\n",
" \"value\": [value],\n",
" })"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "a6e993c7-3113-4ae4-8d4d-0e2d1266b3be",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"<style>\n",
" #altair-viz-a9bb81dd76ca4d9ea53f82c2104a3502.vega-embed {\n",
" width: 100%;\n",
" display: flex;\n",
" }\n",
"\n",
" #altair-viz-a9bb81dd76ca4d9ea53f82c2104a3502.vega-embed details,\n",
" #altair-viz-a9bb81dd76ca4d9ea53f82c2104a3502.vega-embed details summary {\n",
" position: relative;\n",
" }\n",
"</style>\n",
"<div id=\"altair-viz-a9bb81dd76ca4d9ea53f82c2104a3502\"></div>\n",
"<script type=\"text/javascript\">\n",
" var VEGA_DEBUG = (typeof VEGA_DEBUG == \"undefined\") ? {} : VEGA_DEBUG;\n",
" (function(spec, embedOpt){\n",
" let outputDiv = document.currentScript.previousElementSibling;\n",
" if (outputDiv.id !== \"altair-viz-a9bb81dd76ca4d9ea53f82c2104a3502\") {\n",
" outputDiv = document.getElementById(\"altair-viz-a9bb81dd76ca4d9ea53f82c2104a3502\");\n",
" }\n",
" const paths = {\n",
" \"vega\": \"https://cdn.jsdelivr.net/npm/vega@5?noext\",\n",
" \"vega-lib\": \"https://cdn.jsdelivr.net/npm/vega-lib?noext\",\n",
" \"vega-lite\": \"https://cdn.jsdelivr.net/npm/[email protected]?noext\",\n",
" \"vega-embed\": \"https://cdn.jsdelivr.net/npm/vega-embed@6?noext\",\n",
" };\n",
"\n",
" function maybeLoadScript(lib, version) {\n",
" var key = `${lib.replace(\"-\", \"\")}_version`;\n",
" return (VEGA_DEBUG[key] == version) ?\n",
" Promise.resolve(paths[lib]) :\n",
" new Promise(function(resolve, reject) {\n",
" var s = document.createElement('script');\n",
" document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
" s.async = true;\n",
" s.onload = () => {\n",
" VEGA_DEBUG[key] = version;\n",
" return resolve(paths[lib]);\n",
" };\n",
" s.onerror = () => reject(`Error loading script: ${paths[lib]}`);\n",
" s.src = paths[lib];\n",
" });\n",
" }\n",
"\n",
" function showError(err) {\n",
" outputDiv.innerHTML = `<div class=\"error\" style=\"color:red;\">${err}</div>`;\n",
" throw err;\n",
" }\n",
"\n",
" function displayChart(vegaEmbed) {\n",
" vegaEmbed(outputDiv, spec, embedOpt)\n",
" .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
" }\n",
"\n",
" if(typeof define === \"function\" && define.amd) {\n",
" requirejs.config({paths});\n",
" require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n",
" } else {\n",
" maybeLoadScript(\"vega\", \"5\")\n",
" .then(() => maybeLoadScript(\"vega-lite\", \"5.20.1\"))\n",
" .then(() => maybeLoadScript(\"vega-embed\", \"6\"))\n",
" .catch(showError)\n",
" .then(() => displayChart(vegaEmbed));\n",
" }\n",
" })({\"$schema\": \"https://vega.github.io/schema/vega/v5.json\", \"data\": [{\"name\": \"source_0\", \"values\": [{\"query\": 375, \"session\": 36875, \"item\": 19, \"bias\": 0.25, \"misspecified_bias\": 0.5, \"y\": 4, \"y_logging_policy\": 3.44750364000054, \"click\": 1, \"y_ips\": 4.0, \"y_ips_misspecified\": 2.0}, {\"query\": 345, \"session\": 69345, \"item\": 3, \"bias\": 0.125, \"misspecified_bias\": 0.3535533905932738, \"y\": 3, \"y_logging_policy\": 2.8408739417479207, \"click\": 1, \"y_ips\": 8.0, \"y_ips_misspecified\": 2.82842712474619}, {\"query\": 146, \"session\": 44646, \"item\": 0, \"bias\": 0.16666666666666666, \"misspecified_bias\": 0.408248290463863, \"y\": 4, \"y_logging_policy\": 3.347983851571966, \"click\": 0, \"y_ips\": 0.0, \"y_ips_misspecified\": 0.0}, {\"query\": 424, \"session\": 78924, \"item\": 6, \"bias\": 0.07692307692307693, \"misspecified_bias\": 0.2773500981126146, \"y\": 2, \"y_logging_policy\": 1.5243365310227963, \"click\": 0, \"y_ips\": 0.0, \"y_ips_misspecified\": 0.0}, {\"query\": 496, \"session\": 40996, \"item\": 11, \"bias\": 0.05263157894736842, \"misspecified_bias\": 0.22941573387056177, \"y\": 1, \"y_logging_policy\": 1.0053734560019298, \"click\": 0, \"y_ips\": 0.0, \"y_ips_misspecified\": 0.0}, {\"query\": 227, \"session\": 123227, \"item\": 15, \"bias\": 0.5, \"misspecified_bias\": 0.7071067811865476, \"y\": 4, \"y_logging_policy\": 4.022501538656022, \"click\": 1, \"y_ips\": 2.0, \"y_ips_misspecified\": 1.414213562373095}, {\"query\": 426, \"session\": 99426, \"item\": 8, \"bias\": 0.25, \"misspecified_bias\": 0.5, \"y\": 4, \"y_logging_policy\": 3.9661133493024745, \"click\": 0, \"y_ips\": 0.0, \"y_ips_misspecified\": 0.0}, {\"query\": 406, \"session\": 78906, \"item\": 1, \"bias\": 0.3333333333333333, \"misspecified_bias\": 0.5773502691896257, \"y\": 4, \"y_logging_policy\": 4.1305538875739085, \"click\": 0, \"y_ips\": 0.0, \"y_ips_misspecified\": 0.0}, {\"query\": 209, \"session\": 123709, \"item\": 17, \"bias\": 0.3333333333333333, \"misspecified_bias\": 0.5773502691896257, \"y\": 4, \"y_logging_policy\": 3.9717489445954803, \"click\": 0, \"y_ips\": 0.0, \"y_ips_misspecified\": 0.0}, {\"query\": 345, \"session\": 83845, \"item\": 8, \"bias\": 1.0, \"misspecified_bias\": 1.0, \"y\": 4, \"y_logging_policy\": 4.6130825036420555, \"click\": 1, \"y_ips\": 1.0, \"y_ips_misspecified\": 1.0}, {\"query\": 244, \"session\": 56744, \"item\": 1, \"bias\": 0.14285714285714285, \"misspecified_bias\": 0.3779644730092272, \"y\": 4, \"y_logging_policy\": 3.6342571226748004, \"click\": 1, \"y_ips\": 7.0, \"y_ips_misspecified\": 2.6457513110645907}, {\"query\": 226, \"session\": 40726, \"item\": 18, \"bias\": 0.05, \"misspecified_bias\": 0.22360679774997896, \"y\": 2, \"y_logging_policy\": 1.246617171171907, \"click\": 0, \"y_ips\": 0.0, \"y_ips_misspecified\": 0.0}, {\"query\": 296, \"session\": 8296, \"item\": 2, \"bias\": 0.06666666666666667, \"misspecified_bias\": 0.2581988897471611, \"y\": 1, \"y_logging_policy\": 1.361877500361588, \"click\": 0, \"y_ips\": 0.0, \"y_ips_misspecified\": 0.0}, {\"query\": 275, \"session\": 101275, \"item\": 13, \"bias\": 0.5, \"misspecified_bias\": 0.7071067811865476, \"y\": 4, \"y_logging_policy\": 4.176507575391922, \"click\": 0, \"y_ips\": 0.0, \"y_ips_misspecified\": 0.0}, {\"query\": 346, \"session\": 46346, \"item\": 14, \"bias\": 0.3333333333333333, \"misspecified_bias\": 0.5773502691896257, \"y\": 4, \"y_logging_policy\": 4.477376897114054, \"click\": 0, \"y_ips\": 0.0, \"y_ips_misspecified\": 0.0}, {\"query\": 316, \"session\": 57816, \"item\": 19, \"bias\": 0.09090909090909091, \"misspecified_bias\": 0.30151134457776363, \"y\": 1, \"y_logging_policy\": 1.7060393148646016, \"click\": 0, \"y_ips\": 0.0, \"y_ips_misspecified\": 0.0}, {\"query\": 306, \"session\": 25306, \"item\": 2, \"bias\": 0.16666666666666666, \"misspecified_bias\": 0.408248290463863, \"y\": 4, \"y_logging_policy\": 3.763012980902503, \"click\": 0, \"y_ips\": 0.0, 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