mirror of
https://github.com/velocitatem/PHANTOM.git
synced 2026-05-31 08:33:36 +00:00
ab8b8787a8
* baseline setup of agent abstract * feat: new implementation of simple AI agent that can follow a goal and return * refactored import structure and created full tests * pytest setup a github workflow to run tests + more ignores * singularity for pushing * fixing builds of PDFs * inital structure of docs * init styles and docs * basic style implementation * 13 create outline for research paper draft (#18) * updated outline for paper from issue * extra paper sections and some formalization of series data * algorithms and acknowledgements * updated outline for paper from issue * Refactor docker-compose services to use individual Dockerfiles (#20) * Initial plan * Refactor services into individual Dockerfiles Co-authored-by: velocitatem <60182044+velocitatem@users.noreply.github.com> * Add EXPOSE directives to all Dockerfiles with port documentation Co-authored-by: velocitatem <60182044+velocitatem@users.noreply.github.com> --------- Co-authored-by: copilot-swe-agent[bot] <198982749+Copilot@users.noreply.github.com> Co-authored-by: velocitatem <60182044+velocitatem@users.noreply.github.com> * 2 nextjs scaffold with store mode shop and admin session experiment wiring event emission v1 (#17) * chore: cleaning gitignore * formating and env documentation * feat: context switching of hotel/airline depndent on env var via middleware * fixed alignment and building * wrong file * prods * fixed applying style * better session cookie management * tentative session storage with maybe using airtable * migrated api of ingestion * events and products apge * fixing build * 13 create outline for research paper draft (#18) * updated outline for paper from issue * extra paper sections and some formalization of series data * algorithms and acknowledgements * updated outline for paper from issue * upadted text formating * event unification * refactor tracking to ues callbacks instead of refs * implement a pricing display api with session passing * moved middleware to proxy according to new changes in Nextjs * refactoed kafka ingestion to go via backend not web-db * Refactor docker-compose services to use individual Dockerfiles (#20) * Initial plan * Refactor services into individual Dockerfiles Co-authored-by: velocitatem <60182044+velocitatem@users.noreply.github.com> * Add EXPOSE directives to all Dockerfiles with port documentation Co-authored-by: velocitatem <60182044+velocitatem@users.noreply.github.com> --------- Co-authored-by: copilot-swe-agent[bot] <198982749+Copilot@users.noreply.github.com> Co-authored-by: velocitatem <60182044+velocitatem@users.noreply.github.com> * fixing small bugs and adding exepriments to tracking * added some doc * fixing prod * prod kafka server logging * topic auto create * pytest setup a github workflow to run tests + more ignores * getting data from agents properly * proper pipeline to handle data and build matrices * fixing backend dumping * fixing agents and ignore * fixing import for tests --------- Co-authored-by: Copilot <198982749+Copilot@users.noreply.github.com>
958 lines
42 KiB
Plaintext
958 lines
42 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 10,
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"id": "62eafcd9-5462-4063-8873-0e7fb9add907",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"True"
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]
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},
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"execution_count": 10,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"from kafka import KafkaConsumer\n",
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"import pandas as pd\n",
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"import json\n",
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"import numpy as np\n",
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"import os\n",
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"from dotenv import load_dotenv\n",
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"import matplotlib.pyplot as plt\n",
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"from IPython.display import display, SVG, Image\n",
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"load_dotenv()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"id": "4af65cb4-e8cf-4877-b2db-13ac19f3838f",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"<class 'pandas.core.frame.DataFrame'>\n",
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"RangeIndex: 73 entries, 0 to 72\n",
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"Data columns (total 13 columns):\n",
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" # Column Non-Null Count Dtype \n",
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"--- ------ -------------- ----- \n",
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" 0 sessionId 73 non-null object \n",
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" 1 eventName 73 non-null object \n",
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" 2 page 73 non-null object \n",
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" 3 productId 67 non-null object \n",
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" 4 storeMode 73 non-null object \n",
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" 5 userAgent 73 non-null object \n",
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" 6 ts 73 non-null object \n",
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" 7 metadata_referrer 6 non-null object \n",
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" 8 metadata_roomType 45 non-null object \n",
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" 9 metadata_price 45 non-null float64\n",
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" 10 metadata_nights 45 non-null float64\n",
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" 11 metadata_elementText 22 non-null object \n",
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" 12 metadata_dwellTime 22 non-null float64\n",
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"dtypes: float64(3), object(10)\n",
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"memory usage: 7.5+ KB\n"
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]
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}
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],
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"source": [
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"KAFKA_PORT=os.getenv(\"KAFKA_PORT\", 9092)\n",
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"topic = \"user-interactions\"\n",
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"consumer = KafkaConsumer(\n",
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" topic, \n",
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" enable_auto_commit=True,\n",
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" value_deserializer=lambda x: json.loads(x.decode('utf-8')),\n",
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" auto_offset_reset='earliest', \n",
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" bootstrap_servers=['localhost:9092'])\n",
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"messages=consumer.poll(timeout_ms=1000,max_records=10000)\n",
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"df = []\n",
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"for m in messages.values():\n",
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" for i in m:\n",
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" df.append(i.value)\n",
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"df = pd.DataFrame(df)\n",
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"# explode metadata col json\n",
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"df = df.join(pd.json_normalize(df.pop(\"metadata\"), sep=\".\").add_prefix(\"metadata_\"))\n",
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"df.info()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 12,
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"id": "f6819a1c-32ab-49c7-845b-5df7bf60f561",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>sessionId</th>\n",
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" <th>eventName</th>\n",
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" <th>storeMode</th>\n",
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" <th>metadata_roomType</th>\n",
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" <td>/products</td>\n",
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" <td>Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/53...</td>\n",
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" <td>2025-11-14T13:23:46.270Z</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>1</th>\n",
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" <td>f0317a5d-e424-44e9-b784-c8f7291ffe31</td>\n",
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" <td>page_view</td>\n",
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" <td>None</td>\n",
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" <td>Mozilla/5.0 (X11; Linux x86_64; rv:143.0) Geck...</td>\n",
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" <td>2025-11-14T13:26:00.291Z</td>\n",
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" <td></td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2</th>\n",
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" <td>f0317a5d-e424-44e9-b784-c8f7291ffe31</td>\n",
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" <td>page_view</td>\n",
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" <td>/products</td>\n",
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" <td>None</td>\n",
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" <td>hotel</td>\n",
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" <td>Mozilla/5.0 (X11; Linux x86_64; rv:143.0) Geck...</td>\n",
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" <td>2025-11-14T13:26:07.769Z</td>\n",
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" <td></td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>3</th>\n",
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" <td>f0317a5d-e424-44e9-b784-c8f7291ffe31</td>\n",
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" <td>view_item_page</td>\n",
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" <td>/products</td>\n",
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" <td>htl-0</td>\n",
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" <td>hotel</td>\n",
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" <td>Mozilla/5.0 (X11; Linux x86_64; rv:143.0) Geck...</td>\n",
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" <td>2025-11-14T13:26:15.010Z</td>\n",
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" <td>NaN</td>\n",
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" <td>Premium Room</td>\n",
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" <td>269.0</td>\n",
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" <td>1.0</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>4</th>\n",
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" <td>238dc588-a7ab-4c0e-bccd-6abca5076c66</td>\n",
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" <td>page_view</td>\n",
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" <td>/products</td>\n",
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" <td>None</td>\n",
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" <td>hotel</td>\n",
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" <td>Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7...</td>\n",
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" <td>2025-11-14T13:27:15.457Z</td>\n",
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" <td></td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>5</th>\n",
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" <td>238dc588-a7ab-4c0e-bccd-6abca5076c66</td>\n",
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" <td>view_item_page</td>\n",
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" <td>/products</td>\n",
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" <td>htl-0</td>\n",
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" <td>hotel</td>\n",
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" <td>Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7...</td>\n",
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" <td>2025-11-14T13:27:15.591Z</td>\n",
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" <td>NaN</td>\n",
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" <td>Premium Room</td>\n",
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" <td>264.0</td>\n",
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" <td>2.0</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>432</th>\n",
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" <td>214d9fad-9b00-40c3-bd0e-7739b6acd654</td>\n",
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" <td>click</td>\n",
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" <td>1762448192425</td>\n",
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" <td>DIV</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>/</td>\n",
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" <td>NaN</td>\n",
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" <td>1623.0</td>\n",
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" <td>493.0</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>6</th>\n",
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" <td>238dc588-a7ab-4c0e-bccd-6abca5076c66</td>\n",
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" <td>view_item_page</td>\n",
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" <td>/products</td>\n",
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" <td>htl-0</td>\n",
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" <td>hotel</td>\n",
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" <td>Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7...</td>\n",
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" <td>2025-11-14T13:27:21.483Z</td>\n",
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" <td>NaN</td>\n",
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" <td>Premium Room</td>\n",
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" <td>264.0</td>\n",
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" <td>2.0</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>7</th>\n",
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" <td>238dc588-a7ab-4c0e-bccd-6abca5076c66</td>\n",
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" <td>hover_over_title</td>\n",
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" <td>/products</td>\n",
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" <td>htl-0</td>\n",
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" <td>hotel</td>\n",
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" <td>Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7...</td>\n",
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" <td>2025-11-14T13:27:22.646Z</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>Grand Plaza Hotel</td>\n",
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" <td>1200.0</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>8</th>\n",
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" <td>238dc588-a7ab-4c0e-bccd-6abca5076c66</td>\n",
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" <td>view_item_page</td>\n",
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" <td>/products</td>\n",
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" <td>htl-0</td>\n",
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" <td>hotel</td>\n",
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" <td>Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7...</td>\n",
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" <td>2025-11-14T13:27:25.889Z</td>\n",
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" <td>NaN</td>\n",
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" <td>Premium Room</td>\n",
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" <td>264.0</td>\n",
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" <td>2.0</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>35</th>\n",
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" <td>013fc334-4045-4d5a-8739-dd0a8766a63b</td>\n",
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" <td>page_view</td>\n",
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" <td>/products</td>\n",
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" <td>None</td>\n",
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" <td>hotel</td>\n",
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" <td>Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/53...</td>\n",
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" <td>2025-11-14T13:53:59.993Z</td>\n",
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" <td></td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>36</th>\n",
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" <td>013fc334-4045-4d5a-8739-dd0a8766a63b</td>\n",
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" <td>view_item_page</td>\n",
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" <td>/products</td>\n",
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" <td>htl-0</td>\n",
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" <td>hotel</td>\n",
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" <td>Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/53...</td>\n",
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" <td>2025-11-14T13:54:10.705Z</td>\n",
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" <td>NaN</td>\n",
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" <td>Premium Room</td>\n",
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" <td>223.0</td>\n",
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" <td>3.0</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>37</th>\n",
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" <td>013fc334-4045-4d5a-8739-dd0a8766a63b</td>\n",
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" <td>hover_over_title</td>\n",
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" <td>/products</td>\n",
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" <td>htl-0</td>\n",
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" <td>hotel</td>\n",
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" <td>Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/53...</td>\n",
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" <td>2025-11-14T13:54:11.771Z</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>416.0</td>\n",
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" <td>397.0</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>Grand Plaza Hotel</td>\n",
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" <td>1200.0</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>38</th>\n",
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" <td>013fc334-4045-4d5a-8739-dd0a8766a63b</td>\n",
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" <td>view_item_page</td>\n",
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" <td>/products</td>\n",
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" <td>htl-1</td>\n",
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" <td>hotel</td>\n",
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" <td>Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/53...</td>\n",
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" <td>2025-11-14T13:54:29.772Z</td>\n",
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" <td>NaN</td>\n",
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" <td>Standard Room</td>\n",
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" <td>267.0</td>\n",
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" <td>5.0</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>39</th>\n",
|
|
" <td>013fc334-4045-4d5a-8739-dd0a8766a63b</td>\n",
|
|
" <td>hover_over_title</td>\n",
|
|
" <td>/products</td>\n",
|
|
" <td>htl-1</td>\n",
|
|
" <td>hotel</td>\n",
|
|
" <td>Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/53...</td>\n",
|
|
" <td>2025-11-14T13:54:30.833Z</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>Seaside Resort</td>\n",
|
|
" <td>1200.0</td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
|
"</table>\n",
|
|
"</div>"
|
|
],
|
|
"text/plain": [
|
|
" sessionId eventName page \\\n",
|
|
"0 d176d7c9-4027-4702-9e31-2a71395cdda0 page_view /products \n",
|
|
"1 f0317a5d-e424-44e9-b784-c8f7291ffe31 page_view / \n",
|
|
"2 f0317a5d-e424-44e9-b784-c8f7291ffe31 page_view /products \n",
|
|
"3 f0317a5d-e424-44e9-b784-c8f7291ffe31 view_item_page /products \n",
|
|
"4 238dc588-a7ab-4c0e-bccd-6abca5076c66 page_view /products \n",
|
|
"5 238dc588-a7ab-4c0e-bccd-6abca5076c66 view_item_page /products \n",
|
|
"6 238dc588-a7ab-4c0e-bccd-6abca5076c66 view_item_page /products \n",
|
|
"7 238dc588-a7ab-4c0e-bccd-6abca5076c66 hover_over_title /products \n",
|
|
"8 238dc588-a7ab-4c0e-bccd-6abca5076c66 view_item_page /products \n",
|
|
"35 013fc334-4045-4d5a-8739-dd0a8766a63b page_view /products \n",
|
|
"36 013fc334-4045-4d5a-8739-dd0a8766a63b view_item_page /products \n",
|
|
"37 013fc334-4045-4d5a-8739-dd0a8766a63b hover_over_title /products \n",
|
|
"38 013fc334-4045-4d5a-8739-dd0a8766a63b view_item_page /products \n",
|
|
"39 013fc334-4045-4d5a-8739-dd0a8766a63b hover_over_title /products \n",
|
|
"\n",
|
|
" productId storeMode userAgent \\\n",
|
|
"0 None hotel Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/53... \n",
|
|
"1 None hotel Mozilla/5.0 (X11; Linux x86_64; rv:143.0) Geck... \n",
|
|
"2 None hotel Mozilla/5.0 (X11; Linux x86_64; rv:143.0) Geck... \n",
|
|
"3 htl-0 hotel Mozilla/5.0 (X11; Linux x86_64; rv:143.0) Geck... \n",
|
|
"4 None hotel Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7... \n",
|
|
"5 htl-0 hotel Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7... \n",
|
|
"6 htl-0 hotel Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7... \n",
|
|
"7 htl-0 hotel Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7... \n",
|
|
"8 htl-0 hotel Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7... \n",
|
|
"35 None hotel Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/53... \n",
|
|
"36 htl-0 hotel Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/53... \n",
|
|
"37 htl-0 hotel Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/53... \n",
|
|
"38 htl-1 hotel Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/53... \n",
|
|
"39 htl-1 hotel Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/53... \n",
|
|
"\n",
|
|
" ts metadata_referrer metadata_roomType \\\n",
|
|
"0 2025-11-14T13:23:46.270Z NaN \n",
|
|
"1 2025-11-14T13:26:00.291Z NaN \n",
|
|
"2 2025-11-14T13:26:07.769Z NaN \n",
|
|
"3 2025-11-14T13:26:15.010Z NaN Premium Room \n",
|
|
"4 2025-11-14T13:27:15.457Z NaN \n",
|
|
"5 2025-11-14T13:27:15.591Z NaN Premium Room \n",
|
|
"6 2025-11-14T13:27:21.483Z NaN Premium Room \n",
|
|
"7 2025-11-14T13:27:22.646Z NaN NaN \n",
|
|
"8 2025-11-14T13:27:25.889Z NaN Premium Room \n",
|
|
"35 2025-11-14T13:53:59.993Z NaN \n",
|
|
"36 2025-11-14T13:54:10.705Z NaN Premium Room \n",
|
|
"37 2025-11-14T13:54:11.771Z NaN NaN \n",
|
|
"38 2025-11-14T13:54:29.772Z NaN Standard Room \n",
|
|
"39 2025-11-14T13:54:30.833Z NaN NaN \n",
|
|
"\n",
|
|
" metadata_price metadata_nights metadata_elementText metadata_dwellTime \n",
|
|
"0 NaN NaN NaN NaN \n",
|
|
"1 NaN NaN NaN NaN \n",
|
|
"2 NaN NaN NaN NaN \n",
|
|
"3 269.0 1.0 NaN NaN \n",
|
|
"4 NaN NaN NaN NaN \n",
|
|
"5 264.0 2.0 NaN NaN \n",
|
|
"6 264.0 2.0 NaN NaN \n",
|
|
"7 NaN NaN Grand Plaza Hotel 1200.0 \n",
|
|
"8 264.0 2.0 NaN NaN \n",
|
|
"35 NaN NaN NaN NaN \n",
|
|
"36 223.0 3.0 NaN NaN \n",
|
|
"37 NaN NaN Grand Plaza Hotel 1200.0 \n",
|
|
"38 267.0 5.0 NaN NaN \n",
|
|
"39 NaN NaN Seaside Resort 1200.0 "
|
|
]
|
|
},
|
|
"execution_count": 12,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"df.groupby('sessionId').head()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 13,
|
|
"id": "380eca5f-8304-4fb2-be32-e8bcfd312085",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"['013fc334-4045-4d5a-8739-dd0a8766a63b',\n",
|
|
" '238dc588-a7ab-4c0e-bccd-6abca5076c66',\n",
|
|
" 'd176d7c9-4027-4702-9e31-2a71395cdda0',\n",
|
|
" 'f0317a5d-e424-44e9-b784-c8f7291ffe31']"
|
|
]
|
|
},
|
|
"execution_count": 13,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"sessions = list(set(df['sessionId'])); sessions # 238dc588-a7ab-4c0e-bccd-6abca5076c66"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 14,
|
|
"id": "f4ae6f81-dcb8-44be-aee7-30dbc3a6bae1",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"# map sessions to experiments"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 15,
|
|
"id": "050d90a4-20a9-47f5-b998-c31178a54cb3",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"def build_transition_prob_matrix(df: pd.DataFrame):\n",
|
|
" df = df.dropna(subset=['eventName'])\n",
|
|
" events = df['eventName'].tolist()\n",
|
|
" labels = pd.Index(events).unique().tolist()\n",
|
|
" idx = {e:i for i,e in enumerate(labels)}\n",
|
|
" M = np.zeros((len(labels), len(labels)), dtype=float)\n",
|
|
" for a, b in zip(events, events[1:]):\n",
|
|
" M[idx[a], idx[b]] += 1\n",
|
|
" row_sums = M.sum(axis=1, keepdims=True)\n",
|
|
" with np.errstate(divide='ignore', invalid='ignore'):\n",
|
|
" P = np.divide(M, row_sums, where=row_sums>0) # row-normalized\n",
|
|
" return P, labels"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 16,
|
|
"id": "e68f9004-82f5-4826-aece-e3dc6e15a18f",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"# https://medium.com/data-science/time-series-data-markov-transition-matrices-7060771e362b\n",
|
|
"from graphviz import Digraph\n",
|
|
"import numpy as np\n",
|
|
"import pandas as pd\n",
|
|
"\n",
|
|
"def _as_prob_df(matrix, labels=None):\n",
|
|
" \"\"\"Return a square DataFrame with index=columns=labels.\"\"\"\n",
|
|
" if isinstance(matrix, pd.DataFrame):\n",
|
|
" # Ensure square and aligned\n",
|
|
" assert (matrix.index == matrix.columns).all(), \"Index/columns must match.\"\n",
|
|
" return matrix\n",
|
|
" matrix = np.asarray(matrix, dtype=float)\n",
|
|
" assert matrix.shape[0] == matrix.shape[1], \"Matrix must be square.\"\n",
|
|
" if labels is None:\n",
|
|
" raise ValueError(\"labels are required when matrix is not a DataFrame\")\n",
|
|
" assert len(labels) == matrix.shape[0], \"labels length must match matrix size.\"\n",
|
|
" return pd.DataFrame(matrix, index=list(labels), columns=list(labels))\n",
|
|
"\n",
|
|
"def _df_to_edgelist(P: pd.DataFrame, threshold=0.0, round_digits=2):\n",
|
|
" \"\"\"Build weighted edges > threshold.\"\"\"\n",
|
|
" edges = []\n",
|
|
" for src in P.index:\n",
|
|
" for dst in P.columns:\n",
|
|
" w = float(P.loc[src, dst])\n",
|
|
" if w > threshold:\n",
|
|
" edges.append((str(src), str(dst), f\"{w:.{round_digits}f}\"))\n",
|
|
" return edges\n",
|
|
"\n",
|
|
"def render_graph(fname, matrix, ls_index=None, threshold=0.0, fmt=\"svg\", view=False):\n",
|
|
" \"\"\"\n",
|
|
" fname: output file stem (no extension)\n",
|
|
" matrix: NumPy array or pandas DataFrame of transition PROBABILITIES\n",
|
|
" ls_index: ordered labels (required if matrix is not a DataFrame)\n",
|
|
" threshold: hide edges with weight <= threshold\n",
|
|
" fmt: 'svg'|'png'|'pdf' etc.\n",
|
|
" view: open after rendering\n",
|
|
" \"\"\"\n",
|
|
" P = _as_prob_df(matrix, labels=ls_index)\n",
|
|
" edges = _df_to_edgelist(P, threshold=threshold)\n",
|
|
"\n",
|
|
" g = Digraph(format=fmt)\n",
|
|
" g.attr(rankdir=\"LR\", size=\"30\")\n",
|
|
" g.attr(\"node\", shape=\"circle\")\n",
|
|
"\n",
|
|
" # ensure isolated nodes appear\n",
|
|
" for node in P.index:\n",
|
|
" g.node(str(node), width=\"1\", height=\"1\")\n",
|
|
"\n",
|
|
" for src, dst, label in edges:\n",
|
|
" g.edge(src, dst, label=label)\n",
|
|
"\n",
|
|
" g.render(fname, view=view, cleanup=True)\n",
|
|
" return g\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 17,
|
|
"id": "e255a2c1-6454-4e5e-89f6-ef8ac51ab6cc",
|
|
"metadata": {},
|
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"outputs": [
|
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{
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|
"name": "stdout",
|
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"output_type": "stream",
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"text": [
|
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"013fc334-4045-4d5a-8739-dd0a8766a63b\n"
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"<!-- view_item_page->hover_over_paragraph -->\n",
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