mirror of
https://github.com/velocitatem/PHANTOM.git
synced 2026-05-31 08:33:36 +00:00
37b2099ee0
* 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
1173 lines
46 KiB
Plaintext
1173 lines
46 KiB
Plaintext
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"metadata": {},
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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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"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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"Data columns (total 19 columns):\n",
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" # Column Non-Null Count Dtype \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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|
|
" <tr>\n",
|
|
" <th>432</th>\n",
|
|
" <td>214d9fad-9b00-40c3-bd0e-7739b6acd654</td>\n",
|
|
" <td>click</td>\n",
|
|
" <td>1762448192425</td>\n",
|
|
" <td>DIV</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>/</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>1623.0</td>\n",
|
|
" <td>493.0</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>433</th>\n",
|
|
" <td>214d9fad-9b00-40c3-bd0e-7739b6acd654</td>\n",
|
|
" <td>click</td>\n",
|
|
" <td>1762448192645</td>\n",
|
|
" <td>DIV</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>/</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>1623.0</td>\n",
|
|
" <td>493.0</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>434</th>\n",
|
|
" <td>214d9fad-9b00-40c3-bd0e-7739b6acd654</td>\n",
|
|
" <td>pageview</td>\n",
|
|
" <td>1762448205850</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>/</td>\n",
|
|
" <td></td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>435</th>\n",
|
|
" <td>214d9fad-9b00-40c3-bd0e-7739b6acd654</td>\n",
|
|
" <td>click</td>\n",
|
|
" <td>1762448207922</td>\n",
|
|
" <td>DIV</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>/</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>421.0</td>\n",
|
|
" <td>216.0</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>438</th>\n",
|
|
" <td>f0d40ca6-c1d3-4ecd-beb3-796adc74349d</td>\n",
|
|
" <td>pageview</td>\n",
|
|
" <td>1762448283244</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>/</td>\n",
|
|
" <td></td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>439</th>\n",
|
|
" <td>f0d40ca6-c1d3-4ecd-beb3-796adc74349d</td>\n",
|
|
" <td>click</td>\n",
|
|
" <td>1762448295524</td>\n",
|
|
" <td>HTML</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>/</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>614.0</td>\n",
|
|
" <td>720.0</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>440</th>\n",
|
|
" <td>f0d40ca6-c1d3-4ecd-beb3-796adc74349d</td>\n",
|
|
" <td>click</td>\n",
|
|
" <td>1762448342763</td>\n",
|
|
" <td>DIV</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>/</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>416.0</td>\n",
|
|
" <td>397.0</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>441</th>\n",
|
|
" <td>f0d40ca6-c1d3-4ecd-beb3-796adc74349d</td>\n",
|
|
" <td>pageview</td>\n",
|
|
" <td>1762448343396</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>/</td>\n",
|
|
" <td></td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>442</th>\n",
|
|
" <td>f0d40ca6-c1d3-4ecd-beb3-796adc74349d</td>\n",
|
|
" <td>click</td>\n",
|
|
" <td>1762448829631</td>\n",
|
|
" <td>DIV</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>/</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>45.0</td>\n",
|
|
" <td>44.0</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
|
"</table>\n",
|
|
"</div>"
|
|
],
|
|
"text/plain": [
|
|
" sessionId eventType ts targetEl \\\n",
|
|
"0 1762434923440-66hdhq8qicd pageview 1762434924107 NaN \n",
|
|
"1 1762434923440-66hdhq8qicd click 1762434925198 DIV \n",
|
|
"2 1762434923440-66hdhq8qicd click 1762434925371 MAIN \n",
|
|
"3 1762434923440-66hdhq8qicd pageview 1762437192910 NaN \n",
|
|
"4 1762434923440-66hdhq8qicd pageview 1762437198539 NaN \n",
|
|
"390 d423ce8a-77aa-4c9a-94d4-d1adddcc3472 click 1762443115648 DIV \n",
|
|
"391 d423ce8a-77aa-4c9a-94d4-d1adddcc3472 click 1762443174606 DIV \n",
|
|
"392 d423ce8a-77aa-4c9a-94d4-d1adddcc3472 click 1762443183406 INPUT \n",
|
|
"393 d423ce8a-77aa-4c9a-94d4-d1adddcc3472 click 1762443208588 DIV \n",
|
|
"394 d423ce8a-77aa-4c9a-94d4-d1adddcc3472 click 1762443225474 DIV \n",
|
|
"407 1762444018243-0120z6z5u42f pageview 1762444018256 NaN \n",
|
|
"408 1762444018243-0120z6z5u42f click 1762445774344 DIV \n",
|
|
"431 214d9fad-9b00-40c3-bd0e-7739b6acd654 pageview 1762448190973 NaN \n",
|
|
"432 214d9fad-9b00-40c3-bd0e-7739b6acd654 click 1762448192425 DIV \n",
|
|
"433 214d9fad-9b00-40c3-bd0e-7739b6acd654 click 1762448192645 DIV \n",
|
|
"434 214d9fad-9b00-40c3-bd0e-7739b6acd654 pageview 1762448205850 NaN \n",
|
|
"435 214d9fad-9b00-40c3-bd0e-7739b6acd654 click 1762448207922 DIV \n",
|
|
"438 f0d40ca6-c1d3-4ecd-beb3-796adc74349d pageview 1762448283244 NaN \n",
|
|
"439 f0d40ca6-c1d3-4ecd-beb3-796adc74349d click 1762448295524 HTML \n",
|
|
"440 f0d40ca6-c1d3-4ecd-beb3-796adc74349d click 1762448342763 DIV \n",
|
|
"441 f0d40ca6-c1d3-4ecd-beb3-796adc74349d pageview 1762448343396 NaN \n",
|
|
"442 f0d40ca6-c1d3-4ecd-beb3-796adc74349d click 1762448829631 DIV \n",
|
|
"\n",
|
|
" eventName page storeMode userAgent productId metadata_path \\\n",
|
|
"0 NaN NaN NaN NaN NaN / \n",
|
|
"1 NaN NaN NaN NaN NaN / \n",
|
|
"2 NaN NaN NaN NaN NaN / \n",
|
|
"3 NaN NaN NaN NaN NaN / \n",
|
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"4 NaN NaN NaN NaN NaN / \n",
|
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"390 NaN NaN NaN NaN NaN / \n",
|
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"391 NaN NaN NaN NaN NaN / \n",
|
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"392 NaN NaN NaN NaN NaN / \n",
|
|
"393 NaN NaN NaN NaN NaN / \n",
|
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"394 NaN NaN NaN NaN NaN / \n",
|
|
"407 NaN NaN NaN NaN NaN / \n",
|
|
"408 NaN NaN NaN NaN NaN / \n",
|
|
"431 NaN NaN NaN NaN NaN / \n",
|
|
"432 NaN NaN NaN NaN NaN / \n",
|
|
"433 NaN NaN NaN NaN NaN / \n",
|
|
"434 NaN NaN NaN NaN NaN / \n",
|
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"435 NaN NaN NaN NaN NaN / \n",
|
|
"438 NaN NaN NaN NaN NaN / \n",
|
|
"439 NaN NaN NaN NaN NaN / \n",
|
|
"440 NaN NaN NaN NaN NaN / \n",
|
|
"441 NaN NaN NaN NaN NaN / \n",
|
|
"442 NaN NaN NaN NaN NaN / \n",
|
|
"\n",
|
|
" metadata_referrer metadata_x metadata_y metadata_event \\\n",
|
|
"0 NaN NaN NaN \n",
|
|
"1 NaN 1098.0 663.0 NaN \n",
|
|
"2 NaN 1098.0 663.0 NaN \n",
|
|
"3 NaN NaN NaN \n",
|
|
"4 NaN NaN NaN \n",
|
|
"390 NaN 245.0 595.0 NaN \n",
|
|
"391 NaN 475.0 428.0 NaN \n",
|
|
"392 NaN 832.0 219.0 NaN \n",
|
|
"393 NaN 485.0 155.0 NaN \n",
|
|
"394 NaN 281.0 281.0 NaN \n",
|
|
"407 NaN NaN NaN \n",
|
|
"408 NaN 299.0 214.0 NaN \n",
|
|
"431 NaN NaN NaN \n",
|
|
"432 NaN 1623.0 493.0 NaN \n",
|
|
"433 NaN 1623.0 493.0 NaN \n",
|
|
"434 NaN NaN NaN \n",
|
|
"435 NaN 421.0 216.0 NaN \n",
|
|
"438 NaN NaN NaN \n",
|
|
"439 NaN 614.0 720.0 NaN \n",
|
|
"440 NaN 416.0 397.0 NaN \n",
|
|
"441 NaN NaN NaN \n",
|
|
"442 NaN 45.0 44.0 NaN \n",
|
|
"\n",
|
|
" metadata_targetEl metadata_roomType metadata_price metadata_nights \\\n",
|
|
"0 NaN NaN NaN NaN \n",
|
|
"1 NaN NaN NaN NaN \n",
|
|
"2 NaN NaN NaN NaN \n",
|
|
"3 NaN NaN NaN NaN \n",
|
|
"4 NaN NaN NaN NaN \n",
|
|
"390 NaN NaN NaN NaN \n",
|
|
"391 NaN NaN NaN NaN \n",
|
|
"392 NaN NaN NaN NaN \n",
|
|
"393 NaN NaN NaN NaN \n",
|
|
"394 NaN NaN NaN NaN \n",
|
|
"407 NaN NaN NaN NaN \n",
|
|
"408 NaN NaN NaN NaN \n",
|
|
"431 NaN NaN NaN NaN \n",
|
|
"432 NaN NaN NaN NaN \n",
|
|
"433 NaN NaN NaN NaN \n",
|
|
"434 NaN NaN NaN NaN \n",
|
|
"435 NaN NaN NaN NaN \n",
|
|
"438 NaN NaN NaN NaN \n",
|
|
"439 NaN NaN NaN NaN \n",
|
|
"440 NaN NaN NaN NaN \n",
|
|
"441 NaN NaN NaN NaN \n",
|
|
"442 NaN NaN NaN NaN \n",
|
|
"\n",
|
|
" metadata_targetUrl \n",
|
|
"0 NaN \n",
|
|
"1 NaN \n",
|
|
"2 NaN \n",
|
|
"3 NaN \n",
|
|
"4 NaN \n",
|
|
"390 NaN \n",
|
|
"391 NaN \n",
|
|
"392 NaN \n",
|
|
"393 NaN \n",
|
|
"394 NaN \n",
|
|
"407 NaN \n",
|
|
"408 NaN \n",
|
|
"431 NaN \n",
|
|
"432 NaN \n",
|
|
"433 NaN \n",
|
|
"434 NaN \n",
|
|
"435 NaN \n",
|
|
"438 NaN \n",
|
|
"439 NaN \n",
|
|
"440 NaN \n",
|
|
"441 NaN \n",
|
|
"442 NaN "
|
|
]
|
|
},
|
|
"execution_count": 11,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"df.groupby('sessionId').head()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 12,
|
|
"id": "380eca5f-8304-4fb2-be32-e8bcfd312085",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"['214d9fad-9b00-40c3-bd0e-7739b6acd654',\n",
|
|
" '1762444018243-0120z6z5u42f',\n",
|
|
" 'f0d40ca6-c1d3-4ecd-beb3-796adc74349d',\n",
|
|
" 'd423ce8a-77aa-4c9a-94d4-d1adddcc3472',\n",
|
|
" '1762434923440-66hdhq8qicd']"
|
|
]
|
|
},
|
|
"execution_count": 12,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"sessions = list(set(df['sessionId'])); sessions"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 13,
|
|
"id": "f4ae6f81-dcb8-44be-aee7-30dbc3a6bae1",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"# map sessions to experiments"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 17,
|
|
"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": 18,
|
|
"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",
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" g.attr(rankdir=\"LR\", size=\"30\")\n",
|
|
" g.attr(\"node\", shape=\"circle\")\n",
|
|
"\n",
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" # 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",
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|
"\n",
|
|
" g.render(fname, view=view, cleanup=True)\n",
|
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" return g\n"
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]
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},
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|
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"def explore_session(session_id: str):\n",
|
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" subset = df[df['sessionId'] == session_id]\n",
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|
" P, labels = build_transition_prob_matrix(subset)\n",
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" g = render_graph(f\"session_{session_id}\", P, ls_index=labels, threshold=0.01, fmt=\"svg\", view=False)\n",
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|
" display(g)\n",
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|
" return P\n",
|
|
"for session in sessions:\n",
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|
" print(explore_session(session))"
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]
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},
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{
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