gitea_admin/sientia-dataops-laborious_temporal
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity
Files
23e41ac097521e6cb88fa3603af1cfaf4f488d09
sientia-dataops-laborious_t…/tests.ipynb
vitor-aignosi 33ee1e02cf SIENTIAPDE-1478 
Update sonar-project.properties to exclude all worker files from coverage and modify execution counts and timestamps in tests.ipynb. Add a new test for empty DataFrame handling in test_model_repository.py.
2026-01-19 09:55:46 -03:00

1921 lines
246 KiB
Plaintext
Raw Blame History

This file contains ambiguous Unicode characters
This file contains Unicode characters that might be confused with other characters. If you think that this is intentional, you can safely ignore this warning. Use the Escape button to reveal them.
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "b10e5c25",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[ 0.5479121 -0.12224312 0.71719584 0.39473606 -0.8116453 0.9512447\n",
" 0.5222794 0.57212861 -0.74377273 -0.09922812 -0.25840395 0.85352998\n",
" 0.28773024 0.64552323 -0.1131716 -0.54552256 0.10916957 -0.87236549\n",
" 0.65526234 0.2633288 0.51617548 -0.29094806 0.94139605 0.78624224\n",
" 0.55676699 -0.61072258 -0.06655799 -0.91239247 -0.69142102 0.36609791\n",
" 0.48952431 0.93501946 -0.34834928 -0.25908059 -0.06088838 -0.62105728\n",
" -0.74015699 -0.04859015 -0.5461813 0.33962799 -0.12569616 0.66535639\n",
" 0.4005302 -0.37526672 0.6645196 0.60952871 -0.22504324 -0.42334379\n",
" 0.36499101 -0.72049503 -0.6001836 -0.98527546 0.57384876 0.32970171\n",
" 0.41033076 0.56145806 -0.08216845 0.13748239 -0.720406 -0.77093985\n",
" 0.33680592 -0.05780759 0.13047221 0.52999771 0.26943664 0.1071588\n",
" 0.11841432 -0.3920998 -0.93836433 -0.12656522 -0.57083065 -0.18294271\n",
" 0.70680615 -0.53212103 -0.88339452 -0.43723222 -0.41281248 0.32383303\n",
" 0.1140643 0.56779642 0.32862708 -0.18722628 0.62804077 -0.66605416\n",
" -0.95457585 -0.81990428 0.4447187 -0.07624554 -0.67745644 0.00208955\n",
" -0.69537579 0.39264075 -0.10768745 -0.23795755 -0.39697582 0.26056519\n",
" -0.27637478 -0.82470016 -0.7639882 0.92379533 0.81716138 0.39941427\n",
" -0.46826008 0.93835275 0.55750181 0.43378038 -0.101277 -0.45551688\n",
" -0.80721808 0.80520479 -0.08844742 -0.59527327 -0.38808675 0.15843914\n",
" -0.64645443 0.71322857 0.51703906 0.43892591 -0.13581392 0.25461768\n",
" 0.16819594 0.2996932 -0.83111136 -0.1683852 -0.91677165 -0.01201836\n",
" -0.34027758 -0.71095162 -0.79319406 0.17528914 -0.65881406 0.85024024\n",
" 0.16212228 -0.30626039 0.18183098 -0.95439226 0.91711843 -0.03539313\n",
" 0.56547045 -0.83454 -0.02668334 -0.01858601 0.87565291 0.1434561\n",
" -0.0530212 -0.46604867 -0.33686201 0.0413448 -0.12217708 -0.95677584\n",
" 0.65258385 0.79232154 -0.71950182 0.10807229 -0.78284852 0.34448019\n",
" -0.43753243 0.31884527 0.45398923 0.53729498 -0.78451811 0.83202369\n",
" -0.53957202 -0.92517489 0.10970494 -0.25815543 0.65957949 0.61650294\n",
" -0.36572221 0.90579879 -0.41816432 0.03011426 -0.48806982 0.87208714\n",
" -0.67078436 -0.91017876 -0.12980588 0.98475113 0.78335453 0.49721604\n",
" 0.78158498 0.78689328 0.03771672 -0.3681419 0.54402486 0.32332253\n",
" -0.25268454 -0.81106666 0.49357922 -0.47507897 0.8736263 -0.51805885\n",
" -0.75448414 0.66222534 -0.69343137 -0.64146338 0.19876558 0.74912408\n",
" -0.60713067 -0.37935265 0.55480968 0.94365285 0.00148237 -0.71220499\n",
" -0.97212742 -0.54068794 -0.73635556 0.35531735 -0.75633499 0.01265986\n",
" 0.38852487 0.16223322 -0.6004487 0.60824905 0.43081426 0.47796801\n",
" -0.7378845 -0.75249239 0.8551251 -0.20484361 -0.39810262 -0.02283191\n",
" 0.32572843 0.91124651 -0.42710755 0.84961686 -0.95028102 0.11039608\n",
" 0.26795022 -0.78820519 -0.71932081 -0.16177136 0.93246382 0.19208511\n",
" 0.86604644 0.60872183 -0.0652368 0.5695269 -0.96432643 -0.78171201\n",
" 0.65885723 0.59363418 -0.53471852 0.06153918 0.21203164 0.73547791\n",
" 0.20621431 -0.17485686 -0.25163191 -0.14823583 0.30386205 0.73498126\n",
" -0.09220624 -0.50432087 -0.52667527 0.49202856 0.63313753 -0.78944384\n",
" -0.86688229 0.18886733 -0.70765351 0.64932838 -0.37933065 -0.71225613\n",
" 0.84194094 -0.66893655 -0.43055984 -0.69277321 -0.76901987 -0.95770397\n",
" -0.88920918 -0.65071706 -0.89323613 0.18228763 0.36142905 -0.21273909\n",
" -0.36401781 0.00905247 0.75000988 0.70226325 -0.91304988 -0.63700318\n",
" -0.52651026 -0.50122485 0.1424653 -0.16747515 -0.90149176 -0.25277172\n",
" 0.0475059 -0.79665619 0.66691711 -0.89607627 0.84968374 -0.80177372\n",
" 0.6871499 0.80530629 0.95914136 0.60405176 0.55895508]\n",
"[ 0.28496655 0.55799271 -0.73089558 0.07213607 0.02844574 0.71514429\n",
" -0.07440127 -0.22982101 0.27912654 -0.46707336 -0.72046318 -0.04424545\n",
" -0.16622126 -0.53486012 -0.26497638 -0.2672151 -0.34500887 -0.24107184\n",
" 0.37148669 -0.40624705 0.89771585 0.83269604 -0.03817914 -0.34327759\n",
" 0.07086958 0.69712098 0.30517468 0.60878366 0.06544455 0.26583526\n",
" -0.42368877 0.46978632 -0.59519081 0.38959626 0.72143814 -0.73579433\n",
" 0.22875948 -0.8098085 0.45143126 -0.83101356 0.87187965 -0.72518414\n",
" 0.91776049 0.60176835 0.18736401 0.56524821 0.59022968 0.89205413\n",
" -0.49323329 0.18015179 -0.8099016 0.2323314 -0.65741739 0.12990122\n",
" 0.14486103 -0.06802969 0.04526355 0.52784678 0.59848943 -0.01569357\n",
" 0.19918688 0.86247247 -0.76053282 -0.76579287 -0.82458198 0.31572657\n",
" -0.1627834 0.54864283 0.34246283 -0.33272448 0.79673309 0.52506429\n",
" -0.45893012 -0.27161596 -0.37112004 -0.6847767 -0.70443325 0.87225493\n",
" -0.12419193 -0.23336035 0.45937142 0.10598613 0.87227997 0.56060299\n",
" -0.04126087 -0.24728105 0.97326309 0.43552047 0.90238932 -0.76304285\n",
" 0.70106736 0.27414777 -0.75615664 0.176516 0.37219273 -0.97539463\n",
" -0.09136408 0.65079902 -0.40928195 -0.08290384 -0.11537175 -0.39614522\n",
" 0.83688379 0.56258807 -0.77882318 0.99406932 0.75840005 -0.43218312\n",
" 0.67379316 -0.78716094 0.99820946 0.33136947 0.30025003 -0.81911855\n",
" 0.7940668 -0.94200099 -0.51834388 -0.71395625 0.55353588 -0.60359155\n",
" 0.82127645 0.31253808 -0.92767458 -0.98914033 -0.89668417 0.21185036\n",
" 0.60296362 -0.52289436 0.69881769 -0.88553612 0.60192771 0.85559086\n",
" 0.5442168 0.39624157 0.67596044 -0.9196974 -0.59643578 -0.75015264\n",
" 0.00906198 0.49037626 0.26002369 0.7022622 -0.68957402 0.46924218\n",
" -0.61391702 -0.4584825 0.41980939 0.96040957 0.22308721 -0.89099937\n",
" 0.23261794 -0.9152989 0.76829142 0.41915657 -0.65374431 -0.81655799\n",
" -0.63293354 0.96005436 -0.08287872 0.5681619 0.27281668 0.1448263\n",
" -0.70973949 0.89204891 -0.39731473 0.15603443 0.39955189 0.29846631\n",
" 0.88118882 -0.70312202 0.01670548 -0.19193122 -0.05166254 -0.76156495\n",
" -0.73181078 -0.44384891 -0.39059079 -0.14419357 0.22197509 0.26925823\n",
" -0.17637821 -0.18243378 -0.56474295 0.1766125 -0.36591818 -0.92788033\n",
" -0.16319991 -0.05173465 -0.54881426 0.14491587 0.1315438 0.40400436\n",
" 0.29589696 0.30486611 -0.3675717 0.57486444 0.09828877 -0.13716361\n",
" 0.25202496 -0.27868533 0.02547849 0.47341138 0.77280577 0.84211439\n",
" 0.00726585 0.04055023 0.59974082 -0.37109862 0.67476472 -0.01171671\n",
" -0.76828655 -0.85588171 0.68398642 -0.88886417 -0.43877713 -0.33173992\n",
" -0.65401111 -0.37221326 0.48538513 -0.97063431 0.65434685 0.71309605\n",
" -0.25547685 -0.6927742 0.20168082 -0.76065489 -0.27016128 0.91685836\n",
" 0.99092895 0.54420978 -0.37807698 0.3753301 0.41081273 -0.22431661\n",
" 0.28177727 -0.97854471 -0.58188468 0.05017661 -0.67249739 -0.66818626\n",
" 0.67260858 0.97826601 0.11193886 0.67813946 0.98064333 -0.71680822\n",
" -0.10350877 -0.21485457 -0.83990143 0.51066035 -0.13244195 -0.06134613\n",
" -0.69865405 -0.6381467 0.81420724 -0.91070182 -0.53429543 -0.41588134\n",
" -0.01960492 0.17289035 -0.01342005 -0.83176933 -0.51266509 0.68717677\n",
" 0.2751774 0.2982981 0.34040651 0.52580604 -0.88378304 -0.26678323\n",
" 0.07905487 -0.32308703 0.68895775 -0.03485498 0.53725518 0.70403103\n",
" 0.00958297 0.81910449 0.17424788 0.7005486 -0.31881841 -0.00236608\n",
" 0.06282208 -0.79004057 -0.20289499 0.83467535 0.26166448 -0.64498684\n",
" -0.32228873 -0.61679398 -0.95035374 0.85492092 -0.10358534 -0.38492986\n",
" 0.19695438 -0.98537109 -0.44395579 0.40606693 0.26753955]\n",
" Counter Rollout CounterPlusRollout Timestamp\n",
"0 37.270658 -59.474345 -22.203688 2025-01-01 00:00:00\n",
"1 43.561734 -54.184658 -10.622924 2025-01-01 00:00:01\n",
"2 42.158150 -43.826926 -1.668776 2025-01-01 00:00:02\n",
"3 50.392926 -57.394165 -7.001239 2025-01-01 00:00:03\n",
"4 54.925249 -56.055140 -1.129892 2025-01-01 00:00:04\n"
]
}
],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"\n",
"# Set random seed for reproducibility\n",
"rng = np.random.default_rng(42)\n",
"\n",
"# Generate random walks starting at 0\n",
"counter = np.zeros(300)\n",
"rollout = np.zeros(300)\n",
"\n",
"# Generate random steps between -1 and 1\n",
"counter_steps = rng.uniform(-1, 1, 299)\n",
"rollout_steps = rng.uniform(-1, 1, 299)\n",
"\n",
"print(counter_steps)\n",
"print(rollout_steps)\n",
"\n",
"# Calculate cumulative sum and scale to -100 to 100 range\n",
"for i in range(1, 300):\n",
" counter[i] = counter[i-1] + counter_steps[i-1]\n",
" rollout[i] = rollout[i-1] + rollout_steps[i-1]\n",
"\n",
"# normalize values between -100 and 100, lowest value is -100, highest value is 100\n",
"counter = (counter - min(counter)) / (max(counter) - min(counter)) * 200 - 100\n",
"rollout = (rollout - min(rollout)) / (max(rollout) - min(rollout)) * 200 - 100\n",
"\n",
"# Create DataFrame\n",
"df = pd.DataFrame({\n",
" 'Counter': counter,\n",
" 'Rollout': rollout,\n",
" 'CounterPlusRollout': counter + rollout\n",
"})\n",
"\n",
"# add a timestamp column\n",
"df['Timestamp'] = pd.date_range(start='2025-01-01', periods=300, freq='1s')\n",
"\n",
"# Save to CSV\n",
"df.to_csv('random_walks.csv', index=False)\n",
"\n",
"# Display first few rows\n",
"print(df.head())\n"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "c61be7ab",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[ 0.5479121 -0.12224312 0.71719584 0.39473606 -0.8116453 0.9512447\n",
" 0.5222794 0.57212861 -0.74377273 -0.09922812 -0.25840395 0.85352998\n",
" 0.28773024 0.64552323 -0.1131716 -0.54552256 0.10916957 -0.87236549\n",
" 0.65526234 0.2633288 0.51617548 -0.29094806 0.94139605 0.78624224\n",
" 0.55676699 -0.61072258 -0.06655799 -0.91239247 -0.69142102 0.36609791\n",
" 0.48952431 0.93501946 -0.34834928 -0.25908059 -0.06088838 -0.62105728\n",
" -0.74015699 -0.04859015 -0.5461813 0.33962799 -0.12569616 0.66535639\n",
" 0.4005302 -0.37526672 0.6645196 0.60952871 -0.22504324 -0.42334379\n",
" 0.36499101]\n",
"[-0.72049503 -0.6001836 -0.98527546 0.57384876 0.32970171 0.41033076\n",
" 0.56145806 -0.08216845 0.13748239 -0.720406 -0.77093985 0.33680592\n",
" -0.05780759 0.13047221 0.52999771 0.26943664 0.1071588 0.11841432\n",
" -0.3920998 -0.93836433 -0.12656522 -0.57083065 -0.18294271 0.70680615\n",
" -0.53212103 -0.88339452 -0.43723222 -0.41281248 0.32383303 0.1140643\n",
" 0.56779642 0.32862708 -0.18722628 0.62804077 -0.66605416 -0.95457585\n",
" -0.81990428 0.4447187 -0.07624554 -0.67745644 0.00208955 -0.69537579\n",
" 0.39264075 -0.10768745 -0.23795755 -0.39697582 0.26056519 -0.27637478\n",
" -0.82470016]\n",
" Counter Rollout Square Timestamp\n",
"0 -100.000000 100.000000 -37.646304 2025-01-01 00:00:00\n",
"1 -79.942159 79.589040 -72.994086 2025-01-01 00:00:05\n",
"2 -84.417207 62.586392 -30.252436 2025-01-01 00:00:10\n",
"3 -58.162265 34.674447 7.555532 2025-01-01 00:00:15\n",
"4 -43.711857 50.931053 26.035410 2025-01-01 00:00:20\n"
]
}
],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"\n",
"# Set random seed for reproducibility\n",
"rng = np.random.default_rng(42)\n",
"\n",
"size = 50\n",
"\n",
"# Generate random walks starting at 0\n",
"counter = np.zeros(size)\n",
"rollout = np.zeros(size)\n",
"square = np.zeros(size)\n",
"\n",
"# Generate random steps between -1 and 1\n",
"counter_steps = rng.uniform(-1, 1, size-1)\n",
"rollout_steps = rng.uniform(-1, 1, size-1)\n",
"square_steps = rng.uniform(-1, 1, size-1)\n",
"\n",
"print(counter_steps)\n",
"print(rollout_steps)\n",
"\n",
"# Calculate cumulative sum and scale to -100 to 100 range\n",
"for i in range(1, size):\n",
" counter[i] = counter[i-1] + counter_steps[i-1]\n",
" rollout[i] = rollout[i-1] + rollout_steps[i-1]\n",
" square[i] = square[i-1] + square_steps[i-1]\n",
"\n",
"# normalize values between -100 and 100, lowest value is -100, highest value is 100\n",
"counter = (counter - min(counter)) / (max(counter) - min(counter)) * 200 - 100\n",
"rollout = (rollout - min(rollout)) / (max(rollout) - min(rollout)) * 200 - 100\n",
"square = (square - min(square)) / (max(square) - min(square)) * 200 - 100\n",
"\n",
"# Create DataFrame\n",
"df = pd.DataFrame({\n",
" 'Counter': counter,\n",
" 'Rollout': rollout,\n",
" 'Square': square\n",
"})\n",
"\n",
"# add a timestamp column\n",
"df['Timestamp'] = pd.date_range(start='2025-01-01', periods=size, freq='5s')\n",
"\n",
"# Save to CSV\n",
"df.to_csv('random_walks_demo.csv', index=False)\n",
"\n",
"# Display first few rows\n",
"print(df.head())\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "e7c8eeb1",
"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>a</th>\n",
" <th>b</th>\n",
" </tr>\n",
" <tr>\n",
" <th>timestamp</th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2025-01-01</th>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2025-01-02</th>\n",
" <td>2</td>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2025-01-03</th>\n",
" <td>3</td>\n",
" <td>6</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" a b\n",
"timestamp \n",
"2025-01-01 1 4\n",
"2025-01-02 2 5\n",
"2025-01-03 3 6"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"True\n"
]
},
{
"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>a</th>\n",
" <th>b</th>\n",
" </tr>\n",
" <tr>\n",
" <th>timestamp</th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2025-01-03</th>\n",
" <td>3</td>\n",
" <td>6</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" a b\n",
"timestamp \n",
"2025-01-03 3 6"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"3\n",
"1\n"
]
}
],
"source": [
"from pandas import DataFrame\n",
"\n",
"a = DataFrame({\n",
" \"a\": {\"2025-01-01\": 1, \"2025-01-02\": 2, \"2025-01-03\": 3},\n",
" \"b\": {\"2025-01-01\": 4, \"2025-01-02\": 5, \"2025-01-03\": 6},\n",
"})\n",
"\n",
"a.index.name = \"timestamp\"\n",
"\n",
"display(a)\n",
"\n",
"print(\"a\" in a.columns)\n",
"\n",
"b = a.tail(1)\n",
"\n",
"display(b)\n",
"\n",
"print(len(a))\n",
"print(len(b))\n",
"print(b.size)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f3374174",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'str'>\n",
"All elements in index are of the same type\n"
]
}
],
"source": [
"from pandas import DataFrame\n",
"\n",
"data = DataFrame({\n",
" \"a\": {\"2025-01-01\": 1, \"2025-01-02\": 2, \"2025-01-03\": 3},\n",
" \"b\": {\"2025-01-01\": 4, \"2025-01-02\": 5, \"2025-01-03\": 6},\n",
"})\n",
"\n",
"index = data.index\n",
"\n",
"# Get type of first element of index\n",
"index_type = type(index[0])\n",
"\n",
"print(index_type)\n",
"\n",
"# Check if all in index are of the same type\n",
"if all(isinstance(i, index_type) for i in index):\n",
" print(\"All elements in index are of the same type\")\n",
"else:\n",
" print(\"Elements in index are of different types\")\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "40e72c60",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 6,
"id": "1fbb3788",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"test\n"
]
}
],
"source": [
"from unittest.mock import MagicMock\n",
"from asyncua.ua.uaerrors import BadAlreadyExists\n",
"\n",
"mock1 = MagicMock(\n",
" side_effect = Exception(\"test\")\n",
")\n",
"\n",
"mock2 = MagicMock(\n",
" side_effect = BadAlreadyExists(\"test\")\n",
")\n",
"\n",
"try:\n",
" mock1()\n",
"except ValueError as e:\n",
" try:\n",
" mock2()\n",
" except BadAlreadyExists as e:\n",
" print(e)\n",
"except Exception as e:\n",
" print(e)\n"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "771ab4ee",
"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>a</th>\n",
" <th>b</th>\n",
" <th>target</th>\n",
" <th>prediction</th>\n",
" <th>timestamp</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>7</td>\n",
" <td>1</td>\n",
" <td>2025-01-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2</td>\n",
" <td>5</td>\n",
" <td>8</td>\n",
" <td>2</td>\n",
" <td>2025-01-02</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3</td>\n",
" <td>6</td>\n",
" <td>9</td>\n",
" <td>3</td>\n",
" <td>2025-01-03</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" a b target prediction timestamp\n",
"0 1 4 7 1 2025-01-01\n",
"1 2 5 8 2 2025-01-02\n",
"2 3 6 9 3 2025-01-03"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from pandas import DataFrame, merge\n",
"\n",
"retrain_dataset = DataFrame({\n",
" 'a': {'2025-01-01': 1, '2025-01-02': 2, '2025-01-03': 3},\n",
" 'b': {'2025-01-01': 4, '2025-01-02': 5, '2025-01-03': 6},\n",
" 'c': {'2025-01-01': 7, '2025-01-02': 8, '2025-01-03': 9},\n",
"})\n",
"\n",
"prediction_data = DataFrame({\n",
" 'prediction': {'1': 1, '2': 2, '3': 3},\n",
"})\n",
"\n",
"prediction_data.index = retrain_dataset.index\n",
"\n",
"prediction_data = merge(\n",
" retrain_dataset, prediction_data, left_index=True, right_index=True, how='left')\n",
"\n",
"prediction_data.rename(columns={'c': 'target'}, inplace=True)\n",
"\n",
"prediction_data['timestamp'] = prediction_data.index\n",
"\n",
"prediction_data.reset_index(drop=True, inplace=True)\n",
"\n",
"display(prediction_data)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "486b95b3",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[]"
]
},
"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>a</th>\n",
" <th>b</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2025-01-01</th>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2025-01-02</th>\n",
" <td>2</td>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2025-01-03</th>\n",
" <td>3</td>\n",
" <td>6</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" a b\n",
"2025-01-01 1 4\n",
"2025-01-02 2 5\n",
"2025-01-03 3 6"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"{'index': ['2025-01-01', '2025-01-02', '2025-01-03'],\n",
" 'columns': ['a', 'b'],\n",
" 'data': [[1, 4], [2, 5], [3, 6]]}"
]
},
"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>0</th>\n",
" <th>1</th>\n",
" <th>2</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>index</th>\n",
" <td>2025-01-01</td>\n",
" <td>2025-01-02</td>\n",
" <td>2025-01-03</td>\n",
" </tr>\n",
" <tr>\n",
" <th>columns</th>\n",
" <td>a</td>\n",
" <td>b</td>\n",
" <td>None</td>\n",
" </tr>\n",
" <tr>\n",
" <th>data</th>\n",
" <td>[1, 4]</td>\n",
" <td>[2, 5]</td>\n",
" <td>[3, 6]</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" 0 1 2\n",
"index 2025-01-01 2025-01-02 2025-01-03\n",
"columns a b None\n",
"data [1, 4] [2, 5] [3, 6]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"3"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from pandas import DataFrame\n",
"\n",
"data = DataFrame()\n",
"\n",
"display(data.to_dict(orient='records'))\n",
"\n",
"data = DataFrame({\n",
" \"a\": {\"2025-01-01\": 1, \"2025-01-02\": 2, \"2025-01-03\": 3},\n",
" \"b\": {\"2025-01-01\": 4, \"2025-01-02\": 5, \"2025-01-03\": 6},\n",
"})\n",
"\n",
"display(data)\n",
"\n",
"data_list = data.to_dict('split')\n",
"\n",
"display(data_list)\n",
"\n",
"data_rec = DataFrame.from_dict(data_list, orient='index')\n",
"\n",
"display(data_rec)\n",
"\n",
"data.shape[0]"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "d67d551f",
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"import shutil\n",
"import mlflow\n",
"from mlflow.tracking import MlflowClient\n",
"from rich.console import Console\n",
"import sys\n",
"\n",
"if \"src\" not in sys.path:\n",
" sys.path.insert(0, \"src\")\n",
"\n",
"console = Console()\n",
"\n",
"os.environ[\"MLFLOW_TRACKING_URI\"] = \"http://localhost:35785/\"\n",
"os.environ[\"MLFLOW_TRACKING_USERNAME\"] = \"aignosi\"\n",
"os.environ[\"MLFLOW_TRACKING_PASSWORD\"] = \"1L0FP50j3ncp123\"\n",
"\n",
"client = MlflowClient()"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "98bdd6af",
"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>CI-J3J01S1</th>\n",
" <th>CI-J3P01T1A</th>\n",
" <th>CI-J3P03S1</th>\n",
" <th>CI-W3A05F1</th>\n",
" <th>CI-W3A50A1</th>\n",
" <th>CI-W3A50A2</th>\n",
" <th>CI-W3A50A3</th>\n",
" <th>CI-W3A50P1</th>\n",
" <th>CI-W3A50T1</th>\n",
" <th>CI-W3A55P1</th>\n",
" <th>...</th>\n",
" <th>CI-W3W01G1</th>\n",
" <th>CI-W3W01P1</th>\n",
" <th>CI-W3W01P2</th>\n",
" <th>CI-W3W03I1</th>\n",
" <th>CI-W3W03S1</th>\n",
" <th>CI-W3_C3S</th>\n",
" <th>CI-W3_CAO</th>\n",
" <th>CI-W3_MA</th>\n",
" <th>CI-W3_MS</th>\n",
" <th>CI-W3_PL</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>91.99121</td>\n",
" <td>139.699982</td>\n",
" <td>100.442688</td>\n",
" <td>281.293549</td>\n",
" <td>0.135894</td>\n",
" <td>2.580241</td>\n",
" <td>366.93277</td>\n",
" <td>-0.200209</td>\n",
" <td>405.9825</td>\n",
" <td>-18.968868</td>\n",
" <td>...</td>\n",
" <td>818.09340</td>\n",
" <td>-4.743839</td>\n",
" <td>0.521077</td>\n",
" <td>63.926727</td>\n",
" <td>1796.58582</td>\n",
" <td>62.16332</td>\n",
" <td>63.70447</td>\n",
" <td>1.12104</td>\n",
" <td>2.50506</td>\n",
" <td>1020</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>91.99121</td>\n",
" <td>139.699982</td>\n",
" <td>100.442688</td>\n",
" <td>281.293549</td>\n",
" <td>0.135894</td>\n",
" <td>2.580241</td>\n",
" <td>366.93277</td>\n",
" <td>-0.200209</td>\n",
" <td>405.9825</td>\n",
" <td>-18.968868</td>\n",
" <td>...</td>\n",
" <td>818.09340</td>\n",
" <td>-4.935149</td>\n",
" <td>0.521077</td>\n",
" <td>63.926727</td>\n",
" <td>1796.58582</td>\n",
" <td>62.16332</td>\n",
" <td>63.70447</td>\n",
" <td>1.12104</td>\n",
" <td>2.50506</td>\n",
" <td>1020</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>91.99121</td>\n",
" <td>139.699982</td>\n",
" <td>100.442688</td>\n",
" <td>281.819700</td>\n",
" <td>0.135894</td>\n",
" <td>2.580241</td>\n",
" <td>366.93277</td>\n",
" <td>-0.200209</td>\n",
" <td>405.9825</td>\n",
" <td>-18.968868</td>\n",
" <td>...</td>\n",
" <td>770.03894</td>\n",
" <td>-4.935149</td>\n",
" <td>0.580711</td>\n",
" <td>63.926727</td>\n",
" <td>1796.58582</td>\n",
" <td>62.16332</td>\n",
" <td>63.70447</td>\n",
" <td>1.12104</td>\n",
" <td>2.50506</td>\n",
" <td>1020</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>3 rows × 39 columns</p>\n",
"</div>"
],
"text/plain": [
" CI-J3J01S1 CI-J3P01T1A CI-J3P03S1 CI-W3A05F1 CI-W3A50A1 CI-W3A50A2 \\\n",
"0 91.99121 139.699982 100.442688 281.293549 0.135894 2.580241 \n",
"1 91.99121 139.699982 100.442688 281.293549 0.135894 2.580241 \n",
"2 91.99121 139.699982 100.442688 281.819700 0.135894 2.580241 \n",
"\n",
" CI-W3A50A3 CI-W3A50P1 CI-W3A50T1 CI-W3A55P1 ... CI-W3W01G1 \\\n",
"0 366.93277 -0.200209 405.9825 -18.968868 ... 818.09340 \n",
"1 366.93277 -0.200209 405.9825 -18.968868 ... 818.09340 \n",
"2 366.93277 -0.200209 405.9825 -18.968868 ... 770.03894 \n",
"\n",
" CI-W3W01P1 CI-W3W01P2 CI-W3W03I1 CI-W3W03S1 CI-W3_C3S CI-W3_CAO \\\n",
"0 -4.743839 0.521077 63.926727 1796.58582 62.16332 63.70447 \n",
"1 -4.935149 0.521077 63.926727 1796.58582 62.16332 63.70447 \n",
"2 -4.935149 0.580711 63.926727 1796.58582 62.16332 63.70447 \n",
"\n",
" CI-W3_MA CI-W3_MS CI-W3_PL \n",
"0 1.12104 2.50506 1020 \n",
"1 1.12104 2.50506 1020 \n",
"2 1.12104 2.50506 1020 \n",
"\n",
"[3 rows x 39 columns]"
]
},
"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>CI-J3J01S1</th>\n",
" <th>CI-J3P01T1A</th>\n",
" <th>CI-J3P03S1</th>\n",
" <th>CI-W3A05F1</th>\n",
" <th>CI-W3A50A1</th>\n",
" <th>CI-W3A50A2</th>\n",
" <th>CI-W3A50A3</th>\n",
" <th>CI-W3A50P1</th>\n",
" <th>CI-W3A50T1</th>\n",
" <th>CI-W3A55P1</th>\n",
" <th>...</th>\n",
" <th>CI-W3W01P1</th>\n",
" <th>CI-W3W01P2</th>\n",
" <th>CI-W3W03I1</th>\n",
" <th>CI-W3W03S1</th>\n",
" <th>CI-W3_C3S</th>\n",
" <th>CI-W3_CAO</th>\n",
" <th>CI-W3_MA</th>\n",
" <th>CI-W3_MS</th>\n",
" <th>CI-W3_PL</th>\n",
" <th>timestamp</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>91.99121</td>\n",
" <td>139.699982</td>\n",
" <td>100.442688</td>\n",
" <td>281.293549</td>\n",
" <td>0.135894</td>\n",
" <td>2.580241</td>\n",
" <td>366.93277</td>\n",
" <td>-0.200209</td>\n",
" <td>405.9825</td>\n",
" <td>-18.968868</td>\n",
" <td>...</td>\n",
" <td>-4.743839</td>\n",
" <td>0.521077</td>\n",
" <td>63.926727</td>\n",
" <td>1796.58582</td>\n",
" <td>62.16332</td>\n",
" <td>63.70447</td>\n",
" <td>1.12104</td>\n",
" <td>2.50506</td>\n",
" <td>1020</td>\n",
" <td>2026-01-14 19:52:14</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>91.99121</td>\n",
" <td>139.699982</td>\n",
" <td>100.442688</td>\n",
" <td>281.293549</td>\n",
" <td>0.135894</td>\n",
" <td>2.580241</td>\n",
" <td>366.93277</td>\n",
" <td>-0.200209</td>\n",
" <td>405.9825</td>\n",
" <td>-18.968868</td>\n",
" <td>...</td>\n",
" <td>-4.935149</td>\n",
" <td>0.521077</td>\n",
" <td>63.926727</td>\n",
" <td>1796.58582</td>\n",
" <td>62.16332</td>\n",
" <td>63.70447</td>\n",
" <td>1.12104</td>\n",
" <td>2.50506</td>\n",
" <td>1020</td>\n",
" <td>2026-01-14 19:52:17</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>91.99121</td>\n",
" <td>139.699982</td>\n",
" <td>100.442688</td>\n",
" <td>281.819700</td>\n",
" <td>0.135894</td>\n",
" <td>2.580241</td>\n",
" <td>366.93277</td>\n",
" <td>-0.200209</td>\n",
" <td>405.9825</td>\n",
" <td>-18.968868</td>\n",
" <td>...</td>\n",
" <td>-4.935149</td>\n",
" <td>0.580711</td>\n",
" <td>63.926727</td>\n",
" <td>1796.58582</td>\n",
" <td>62.16332</td>\n",
" <td>63.70447</td>\n",
" <td>1.12104</td>\n",
" <td>2.50506</td>\n",
" <td>1020</td>\n",
" <td>2026-01-14 19:52:20</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>3 rows × 40 columns</p>\n",
"</div>"
],
"text/plain": [
" CI-J3J01S1 CI-J3P01T1A CI-J3P03S1 CI-W3A05F1 CI-W3A50A1 CI-W3A50A2 \\\n",
"0 91.99121 139.699982 100.442688 281.293549 0.135894 2.580241 \n",
"1 91.99121 139.699982 100.442688 281.293549 0.135894 2.580241 \n",
"2 91.99121 139.699982 100.442688 281.819700 0.135894 2.580241 \n",
"\n",
" CI-W3A50A3 CI-W3A50P1 CI-W3A50T1 CI-W3A55P1 ... CI-W3W01P1 \\\n",
"0 366.93277 -0.200209 405.9825 -18.968868 ... -4.743839 \n",
"1 366.93277 -0.200209 405.9825 -18.968868 ... -4.935149 \n",
"2 366.93277 -0.200209 405.9825 -18.968868 ... -4.935149 \n",
"\n",
" CI-W3W01P2 CI-W3W03I1 CI-W3W03S1 CI-W3_C3S CI-W3_CAO CI-W3_MA \\\n",
"0 0.521077 63.926727 1796.58582 62.16332 63.70447 1.12104 \n",
"1 0.521077 63.926727 1796.58582 62.16332 63.70447 1.12104 \n",
"2 0.580711 63.926727 1796.58582 62.16332 63.70447 1.12104 \n",
"\n",
" CI-W3_MS CI-W3_PL timestamp \n",
"0 2.50506 1020 2026-01-14 19:52:14 \n",
"1 2.50506 1020 2026-01-14 19:52:17 \n",
"2 2.50506 1020 2026-01-14 19:52:20 \n",
"\n",
"[3 rows x 40 columns]"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from pandas import DataFrame, read_json\n",
"\n",
"# Load from data file (json)\n",
"data = read_json('data.json')\n",
"\n",
"column_order = [\n",
" 'CI-J3J01S1', 'CI-J3P01T1A', 'CI-J3P03S1', 'CI-W3A05F1',\n",
" 'CI-W3A50A1', 'CI-W3A50A2', 'CI-W3A50A3', 'CI-W3A50P1', 'CI-W3A50T1',\n",
" 'CI-W3A55P1', 'CI-W3A55T1', 'CI-W3A65_Cl', 'CI-W3A65_SO3', 'CI-W3A71P1',\n",
" 'CI-W3A71P2', 'CI-W3A71P3', 'CI-W3E01F1', 'CI-W3K01S1', 'CI-W3K01T1',\n",
" 'CI-W3K01T2', 'CI-W3K01T4', 'CI-W3K14P1', 'CI-W3P17S1', 'CI-W3V04P1',\n",
" 'CI-W3V04P3', 'CI-W3V21F1', 'CI-W3V21P1', 'CI-W3V30F1', 'CI-W3V33P1',\n",
" 'CI-W3W01G1', 'CI-W3W01P1', 'CI-W3W01P2', 'CI-W3W03I1', 'CI-W3W03S1',\n",
" 'CI-W3_C3S', 'CI-W3_CAO', 'CI-W3_MA', 'CI-W3_MS', 'CI-W3_PL']\n",
"\n",
"ordered_data = data[column_order]\n",
"\n",
"display(ordered_data.head(3))\n",
"display(data.head(3))\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "d7e73c30",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/grezewave/Documents/projects/sientia/sientia-dataops-laborious_temporal/venv/lib/python3.11/site-packages/mlflow/store/artifact/utils/models.py:32: FutureWarning: ``mlflow.tracking.client.MlflowClient.get_latest_versions`` is deprecated since 2.9.0. Model registry stages will be removed in a future major release. To learn more about the deprecation of model registry stages, see our migration guide here: https://mlflow.org/docs/2.10.1/model-registry.html#migrating-from-stages\n",
" latest = client.get_latest_versions(name, None if stage is None else [stage])\n",
"/home/grezewave/Documents/projects/sientia/sientia-dataops-laborious_temporal/venv/lib/python3.11/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
" from .autonotebook import tqdm as notebook_tqdm\n",
"Downloading artifacts: 0%| | 0/20 [00:00<?, ?it/s]2026/01/14 17:31:16 INFO mlflow.store.artifact.artifact_repo: The progress bar can be disabled by setting the environment variable MLFLOW_ENABLE_ARTIFACTS_PROGRESS_BAR to false\n",
"Downloading artifacts: 100%|██████████| 20/20 [00:05<00:00, 3.49it/s] \n",
"2026/01/14 17:31:23 WARNING mlflow.utils.requirements_utils: Detected one or more mismatches between the model's dependencies and the current Python environment:\n",
" - pandas (current: 2.2.2, required: pandas==2.3.3)\n",
" - scikit-learn (current: 1.4.2, required: scikit-learn==1.8.0)\n",
" - scipy (current: 1.13.0, required: scipy==1.16.3)\n",
"To fix the mismatches, call `mlflow.pyfunc.get_model_dependencies(model_uri)` to fetch the model's environment and install dependencies using the resulting environment file.\n",
"/tmp/tmp7i9wovag/code/utils/models/xgboost_model.py:235: UserWarning: [17:31:29] WARNING: /workspace/src/gbm/gbtree.cc:377: Changing updater from `grow_gpu_hist` to `grow_quantile_histmaker`.\n",
" return pickle.load(f)\n",
"/tmp/tmp7i9wovag/code/utils/models/xgboost_model.py:235: UserWarning: [17:31:29] WARNING: /workspace/src/context.cc:53: No visible GPU is found, setting device to CPU.\n",
" return pickle.load(f)\n",
"/tmp/tmp7i9wovag/code/utils/models/xgboost_model.py:235: UserWarning: [17:31:29] WARNING: /workspace/src/context.cc:207: Device is changed from GPU to CPU as we couldn't find any available GPU on the system.\n",
" return pickle.load(f)\n"
]
},
{
"data": {
"text/html": [
"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">[17:31:29] </span><span style=\"color: #008000; text-decoration-color: #008000\">Model loaded from context</span> <a href=\"file:///tmp/tmp7i9wovag/code/utils/mlflow/pyfunc_wrappers.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">pyfunc_wrappers.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///tmp/tmp7i9wovag/code/utils/mlflow/pyfunc_wrappers.py#36\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">36</span></a>\n",
"</pre>\n"
],
"text/plain": [
"\u001b[2;36m[17:31:29]\u001b[0m\u001b[2;36m \u001b[0m\u001b[32mModel loaded from context\u001b[0m \u001b]8;id=70872;file:///tmp/tmp7i9wovag/code/utils/mlflow/pyfunc_wrappers.py\u001b\\\u001b[2mpyfunc_wrappers.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=188021;file:///tmp/tmp7i9wovag/code/utils/mlflow/pyfunc_wrappers.py#36\u001b\\\u001b[2m36\u001b[0m\u001b]8;;\u001b\\\n"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import mlflow\n",
"\n",
"model_name = \"vcm-o2-vanilla-ice\"\n",
"\n",
"model = mlflow.pyfunc.load_model(f'models:/{model_name}/production')"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "4e5bae06",
"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>CI-W3W01A2</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>6.094787</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>5.987316</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>5.787229</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>5.780445</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>6.448903</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>5.631461</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>5.726437</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>5.669214</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>5.909435</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>5.845652</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>5.875139</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>6.048102</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>5.918375</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>5.859076</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>5.841918</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>6.265776</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>6.311982</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>6.792706</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>6.873636</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" CI-W3W01A2\n",
"0 6.094787\n",
"1 5.987316\n",
"2 5.787229\n",
"3 5.780445\n",
"4 6.448903\n",
"5 5.631461\n",
"6 5.726437\n",
"7 5.669214\n",
"8 5.909435\n",
"9 5.845652\n",
"10 5.875139\n",
"11 6.048102\n",
"12 5.918375\n",
"13 5.859076\n",
"14 5.841918\n",
"15 6.265776\n",
"16 6.311982\n",
"17 6.792706\n",
"18 6.873636"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model.predict(ordered_data)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "cd1bfaa0",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/grezewave/Documents/projects/sientia/sientia-dataops-laborious_temporal/venv/lib/python3.11/site-packages/mlflow/store/artifact/utils/models.py:32: FutureWarning: ``mlflow.tracking.client.MlflowClient.get_latest_versions`` is deprecated since 2.9.0. Model registry stages will be removed in a future major release. To learn more about the deprecation of model registry stages, see our migration guide here: https://mlflow.org/docs/2.10.1/model-registry.html#migrating-from-stages\n",
" latest = client.get_latest_versions(name, None if stage is None else [stage])\n",
"Downloading artifacts: 0%| | 0/20 [00:00<?, ?it/s]2026/01/14 17:17:04 INFO mlflow.store.artifact.artifact_repo: The progress bar can be disabled by setting the environment variable MLFLOW_ENABLE_ARTIFACTS_PROGRESS_BAR to false\n",
"Downloading artifacts: 100%|██████████| 20/20 [00:00<00:00, 28.20it/s] "
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"/home/grezewave/Documents/projects/sientia/sientia-dataops-laborious_temporal/backup-model/\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"\n"
]
}
],
"source": [
"# Download pkl file from mlflow\n",
"model_uri = f'models:/{model_name}/production'\n",
"model_path = mlflow.artifacts.download_artifacts(model_uri, dst_path='./backup-model')\n",
"\n",
"print(model_path)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "0ef9c913",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"b'\\xfd7zXZ\\x00\\x00\\x04\\xe6\\xd6'\n"
]
}
],
"source": [
"file_path = f'{model_path}/artifacts/xgboost_model.pkl'\n",
"\n",
"# Verificar os primeiros bytes do arquivo\n",
"with open(file_path, 'rb') as f:\n",
" first_bytes = f.read(10)\n",
" print(first_bytes)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "8ae302f3",
"metadata": {},
"outputs": [],
"source": [
"from pandas import DataFrame, to_datetime\n",
"from sientia_do.temporal.activities.postgres import Postgres\n",
"from unittest.mock import MagicMock, AsyncMock\n",
"\n",
"postgres = Postgres(\n",
" host=\"localhost\",\n",
" port=5432,\n",
" dbname=\"sientia\",\n",
" user=\"sientia\",\n",
" password=\"sientia\",\n",
" min_connections=1,\n",
" max_connections=10,\n",
" logger=MagicMock(),\n",
" notification_handler=MagicMock(),\n",
" metrics_controller=AsyncMock()\n",
")\n"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "51dd2dbf",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/grezewave/Documents/projects/sientia/sientia-dataops-laborious_temporal/venv/lib/python3.11/site-packages/sientia_do/observability/sientia_monitoring.py:80: RuntimeWarning: coroutine 'AsyncMockMixin._execute_mock_call' was never awaited\n",
" self.metrics_controller.start()\n",
"RuntimeWarning: Enable tracemalloc to get the object allocation traceback\n"
]
},
{
"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",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2026-01-19 12:38:40+00:00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2026-01-18 06:00:47+00:00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2026-01-18 05:32:44+00:00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2026-01-18 04:59:56+00:00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2026-01-18 04:29:47+00:00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>2026-01-18 04:00:47+00:00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>2026-01-18 03:29:56+00:00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>2026-01-18 03:03:08+00:00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>2026-01-18 02:29:55+00:00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>2026-01-18 02:02:56+00:00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>2026-01-18 01:29:56+00:00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>2026-01-18 00:59:56+00:00</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" timestamp\n",
"0 2026-01-19 12:38:40+00:00\n",
"1 2026-01-18 06:00:47+00:00\n",
"2 2026-01-18 05:32:44+00:00\n",
"3 2026-01-18 04:59:56+00:00\n",
"4 2026-01-18 04:29:47+00:00\n",
"5 2026-01-18 04:00:47+00:00\n",
"6 2026-01-18 03:29:56+00:00\n",
"7 2026-01-18 03:03:08+00:00\n",
"8 2026-01-18 02:29:55+00:00\n",
"9 2026-01-18 02:02:56+00:00\n",
"10 2026-01-18 01:29:56+00:00\n",
"11 2026-01-18 00:59:56+00:00"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/grezewave/Documents/projects/sientia/sientia-dataops-laborious_temporal/venv/lib/python3.11/site-packages/sientia_do/observability/sientia_monitoring.py:80: RuntimeWarning: coroutine 'AsyncMockMixin._execute_mock_call' was never awaited\n",
" self.metrics_controller.start()\n",
"RuntimeWarning: Enable tracemalloc to get the object allocation traceback\n"
]
},
{
"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>prediction</th>\n",
" <th>value</th>\n",
" <th>timestamp</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>7199</th>\n",
" <td>344.890411</td>\n",
" <td>399.286682</td>\n",
" <td>2026-01-18 00:56:32+00:00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7197</th>\n",
" <td>345.693787</td>\n",
" <td>399.286682</td>\n",
" <td>2026-01-18 00:56:35+00:00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7195</th>\n",
" <td>382.127563</td>\n",
" <td>399.286682</td>\n",
" <td>2026-01-18 00:56:38+00:00</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" prediction value timestamp\n",
"7199 344.890411 399.286682 2026-01-18 00:56:32+00:00\n",
"7197 345.693787 399.286682 2026-01-18 00:56:35+00:00\n",
"7195 382.127563 399.286682 2026-01-18 00:56:38+00:00"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/grezewave/Documents/projects/sientia/sientia-dataops-laborious_temporal/venv/lib/python3.11/site-packages/sientia_do/observability/sientia_monitoring.py:80: RuntimeWarning: coroutine 'AsyncMockMixin._execute_mock_call' was never awaited\n",
" self.metrics_controller.start()\n",
"RuntimeWarning: Enable tracemalloc to get the object allocation traceback\n"
]
},
{
"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",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2026-01-19 12:40:28+00:00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2026-01-18 05:59:55+00:00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2026-01-18 05:29:57+00:00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2026-01-18 05:01:02+00:00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2026-01-18 04:30:59+00:00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>2026-01-18 03:59:56+00:00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>2026-01-18 03:33:17+00:00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>2026-01-18 02:59:57+00:00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>2026-01-18 02:33:19+00:00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>2026-01-18 01:59:56+00:00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>2026-01-18 01:31:03+00:00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>2026-01-18 01:03:14+00:00</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" timestamp\n",
"0 2026-01-19 12:40:28+00:00\n",
"1 2026-01-18 05:59:55+00:00\n",
"2 2026-01-18 05:29:57+00:00\n",
"3 2026-01-18 05:01:02+00:00\n",
"4 2026-01-18 04:30:59+00:00\n",
"5 2026-01-18 03:59:56+00:00\n",
"6 2026-01-18 03:33:17+00:00\n",
"7 2026-01-18 02:59:57+00:00\n",
"8 2026-01-18 02:33:19+00:00\n",
"9 2026-01-18 01:59:56+00:00\n",
"10 2026-01-18 01:31:03+00:00\n",
"11 2026-01-18 01:03:14+00:00"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/grezewave/Documents/projects/sientia/sientia-dataops-laborious_temporal/venv/lib/python3.11/site-packages/sientia_do/observability/sientia_monitoring.py:80: RuntimeWarning: coroutine 'AsyncMockMixin._execute_mock_call' was never awaited\n",
" self.metrics_controller.start()\n",
"RuntimeWarning: Enable tracemalloc to get the object allocation traceback\n"
]
},
{
"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>prediction</th>\n",
" <th>value</th>\n",
" <th>timestamp</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>7199</th>\n",
" <td>6.191058</td>\n",
" <td>2.347790</td>\n",
" <td>2026-01-18 00:56:32+00:00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7197</th>\n",
" <td>6.168668</td>\n",
" <td>2.379994</td>\n",
" <td>2026-01-18 00:56:35+00:00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7195</th>\n",
" <td>6.180912</td>\n",
" <td>2.379994</td>\n",
" <td>2026-01-18 00:56:38+00:00</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" prediction value timestamp\n",
"7199 6.191058 2.347790 2026-01-18 00:56:32+00:00\n",
"7197 6.168668 2.379994 2026-01-18 00:56:35+00:00\n",
"7195 6.180912 2.379994 2026-01-18 00:56:38+00:00"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"\n",
"period_hours = 6\n",
"\n",
"samples = 20*60*period_hours\n",
"retrain_samples = 2*period_hours\n",
"\n",
"query_nox = f\"\"\"\n",
"select p.prediction, ld.value, p.\"timestamp\" from sientia_data.predictions p\n",
"join sientia_data.laborious_data ld on p.\"timestamp\" = ld.\"timestamp\"\n",
"where p.model_id = '4' and variable='CI-W3W01A3' order by p.created_at desc limit {samples};\n",
"\"\"\"\n",
"\n",
"retrain_query = f\"\"\"\n",
"select \"timestamp\" from sientia_data.log_retrain lr where model_id = '4' order by lr.\"timestamp\" desc limit {retrain_samples};\n",
"\"\"\"\n",
"\n",
"retrain_data_nox = DataFrame(await postgres.load_custom_query(\n",
" {\n",
" \"query\": retrain_query,\n",
" \"metadata\": {},\n",
" }\n",
"))\n",
"\n",
"retrain_data_nox['timestamp'] = to_datetime(retrain_data_nox['timestamp'])\n",
"\n",
"display(retrain_data_nox)\n",
"\n",
"data_nox = DataFrame(await postgres.load_custom_query(\n",
" {\n",
" \"query\": query_nox,\n",
" \"metadata\": {},\n",
" \"datetime_columns\": [\"timestamp\"],\n",
" }\n",
"))\n",
"\n",
"data_nox.drop_duplicates(subset=['timestamp'], inplace=True, keep='first')\n",
"data_nox.sort_values(by='timestamp', inplace=True)\n",
"data_nox['timestamp'] = to_datetime(data_nox['timestamp'])\n",
"\n",
"display(data_nox.head(3))\n",
"\n",
"query_o2 = f\"\"\"\n",
"select p.prediction, ld.value, p.\"timestamp\" from sientia_data.predictions p\n",
"join sientia_data.laborious_data ld on p.\"timestamp\" = ld.\"timestamp\"\n",
"where p.model_id = '5' and variable='CI-W3W01A2' order by p.created_at desc limit {samples};\n",
"\"\"\"\n",
"\n",
"retrain_query_o2 = f\"\"\"\n",
"select \"timestamp\" from sientia_data.log_retrain lr where model_id = '5' order by lr.\"timestamp\" desc limit {retrain_samples};\n",
"\"\"\"\n",
"\n",
"retrain_data_o2 = DataFrame(await postgres.load_custom_query(\n",
" {\n",
" \"query\": retrain_query_o2,\n",
" \"metadata\": {},\n",
" }\n",
"))\n",
"\n",
"retrain_data_o2['timestamp'] = to_datetime(retrain_data_o2['timestamp'])\n",
"\n",
"display(retrain_data_o2)\n",
"\n",
"data_o2 = DataFrame(await postgres.load_custom_query(\n",
" {\n",
" \"query\": query_o2,\n",
" \"metadata\": {},\n",
" \"datetime_columns\": [\"timestamp\"],\n",
" }\n",
"))\n",
"\n",
"data_o2.drop_duplicates(subset=['timestamp'], inplace=True, keep='first')\n",
"data_o2.sort_values(by='timestamp', inplace=True)\n",
"data_o2['timestamp'] = to_datetime(data_o2['timestamp'])\n",
"\n",
"display(data_o2.head(3))\n"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "7b82e5a7",
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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",
"text/plain": [
"<Figure size 1000x1000 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"\n",
"import matplotlib.pyplot as plt\n",
"\n",
"plt.figure(figsize=(10, 10))\n",
"plt.subplot(2, 1, 1)\n",
"plt.plot(data_nox['timestamp'], data_nox['value'])\n",
"plt.plot(data_nox['timestamp'], data_nox['prediction'])\n",
"plt.vlines(\n",
" x=retrain_data_nox['timestamp'],\n",
" ymin=plt.ylim()[0],\n",
" ymax=plt.ylim()[1],\n",
" colors='k',\n",
" linestyles='--'\n",
")\n",
"\n",
"\n",
"plt.legend(['real', 'prediction', 'retrain'])\n",
"plt.title('NOx')\n",
"plt.xlim(\n",
" data_nox['timestamp'].min(),\n",
" data_nox['timestamp'].max()\n",
")\n",
"\n",
"plt.subplot(2, 1, 2)\n",
"plt.plot(data_o2['timestamp'], data_o2['value'])\n",
"plt.plot(data_o2['timestamp'], data_o2['prediction'])\n",
"plt.vlines(\n",
" x=retrain_data_o2['timestamp'],\n",
" ymin=plt.ylim()[0],\n",
" ymax=plt.ylim()[1],\n",
" colors='k',\n",
" linestyles='--'\n",
")\n",
"\n",
"plt.xlim(\n",
" data_o2['timestamp'].min(),\n",
" data_o2['timestamp'].max()\n",
")\n",
"\n",
"plt.legend(['real', 'prediction', 'retrain'])\n",
"plt.title('O2')\n",
"plt.show()\n",
"\n",
"\n"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "venv",
"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": "3.11.14"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
Reference in New Issue View Git Blame Copy Permalink