diff --git a/tutorials/tutorial-medical-classification.ipynb b/tutorials/tutorial-medical-classification.ipynb index cdfe4d8..621c8c6 100644 --- a/tutorials/tutorial-medical-classification.ipynb +++ b/tutorials/tutorial-medical-classification.ipynb @@ -36,79 +36,41 @@ "source": [ "from tira.third_party_integrations import ensure_pyterrier_is_loaded\n", "from tira.rest_api_client import Client\n", + "import pandas as pd\n", "\n", "import pyterrier as pt\n", + "import matplotlib.pyplot as plt\n", "\n", "ensure_pyterrier_is_loaded()\n", "tira = Client()" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# ClueWeb09 Web Track Results\n", + "\n", + "Over all web tracks, 16 queries are health related and 184 are not health-related.\n", + "\n", + "Purely lexical models (Dirichlet and BM25) models seem to perform better than semantic neural models on health related topics compared to not health-related topics." + ] + }, { "cell_type": "code", "execution_count": 2, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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qidquery
01obama family tree
12french lick resort and casino
23getting organized
\n", - "
" - ], - "text/plain": [ - " qid query\n", - "0 1 obama family tree\n", - "1 2 french lick resort and casino\n", - "2 3 getting organized" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "dataset = pt.get_dataset(\"irds:clueweb09/en/trec-web-2009\")\n", - "topics = dataset.get_topics(variant='query')\n", - "topics.head(3)" + "clueweb_dataset_ids = [\n", + " \"irds:clueweb09/en/trec-web-2009\",\n", + " \"irds:clueweb09/en/trec-web-2010\",\n", + " \"irds:clueweb09/en/trec-web-2011\",\n", + " \"irds:clueweb09/en/trec-web-2012\",\n", + "]\n", + "all_datasets = [pt.get_dataset(dataset) for dataset in clueweb_dataset_ids]\n", + "all_topics = [dataset.get_topics(variant='query') for dataset in all_datasets]\n", + "all_query_health_classifications = [tira.pt.transform_queries('ir-benchmarks/fschlatt/query-health-classification', dataset) for dataset in all_datasets]" ] }, { @@ -147,563 +109,563 @@ " \n", " \n", " \n", - " 3\n", - " 4\n", - " toilet\n", - " 226.5945\n", - " 226.5945\n", - " 24.5363\n", - " 24.5363\n", + " 45\n", + " 196\n", + " sore throat\n", + " 411.7695\n", + " 411.7695\n", + " 99.3674\n", + " 99.3674\n", " \n", " \n", - " 29\n", - " 30\n", - " diabetes education\n", - " 203.9913\n", - " 203.9913\n", - " 243.6227\n", - " 243.6227\n", + " 37\n", + " 88\n", + " forearm pain\n", + " 354.0813\n", + " 354.0813\n", + " 310.8832\n", + " 310.8832\n", " \n", " \n", - " 25\n", - " 26\n", - " lower heart rate\n", - " 168.4160\n", - " 130.3137\n", - " 198.0859\n", - " 184.8707\n", + " 31\n", + " 82\n", + " joints\n", + " 331.5549\n", + " 331.5549\n", + " 240.9360\n", + " 240.9360\n", " \n", " \n", - " 33\n", - " 34\n", - " cell phones\n", - " 114.1950\n", - " 114.1950\n", - " 194.8462\n", - " 194.8462\n", + " 27\n", + " 128\n", + " atypical squamous cells\n", + " 272.0022\n", + " 277.4749\n", + " 452.8779\n", + " 399.4028\n", " \n", " \n", - " 2\n", - " 3\n", - " getting organized\n", - " 74.7692\n", - 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" 37\n", - " 38\n", - " dogs for adoption\n", - " 27.9989\n", - " 38.0222\n", - " 119.2782\n", - " 93.8006\n", + " 29\n", + " 30\n", + " diabetes education\n", + " 203.9913\n", + " 203.9913\n", + " 243.6227\n", + " 243.6227\n", " \n", " \n", - " 18\n", - " 19\n", - " the current\n", - " 23.1269\n", - " 23.1269\n", - " 83.3028\n", - " 83.3028\n", + " 12\n", + " 63\n", + " flushing\n", + " 201.4836\n", + " 201.4836\n", + " 94.9256\n", + " 94.9256\n", " \n", " \n", - " 10\n", - " 11\n", - " gmat prep classes\n", - " 22.5833\n", - " 18.3837\n", - " 57.1493\n", - " 75.9148\n", + " 22\n", + " 123\n", + " von willebrand disease\n", + " 200.9725\n", + " 226.8965\n", + " 279.1960\n", + " 335.1087\n", " \n", " \n", - " 15\n", - " 16\n", - " arizona game and fish\n", - " 20.1810\n", - " 14.9945\n", - " 39.9599\n", - " 29.2368\n", + " 17\n", + " 168\n", + " lipoma\n", + " 181.9301\n", + " 181.9301\n", + " 496.2743\n", + " 496.2743\n", " \n", " \n", - " 0\n", - " 1\n", - " obama family tree\n", - " 17.3931\n", - " 10.2143\n", - " 28.3937\n", - " 41.8619\n", + " 31\n", + " 182\n", + " quit smoking\n", + " 177.5335\n", + " 177.5335\n", + " 190.5365\n", + " 190.5365\n", " \n", " \n", - " 43\n", - " 44\n", - " map of the united states\n", - " 17.2940\n", - " 16.4337\n", - " 36.5626\n", - " 32.4568\n", + " 25\n", + " 26\n", + " lower heart rate\n", + " 168.4160\n", + " 130.3137\n", + " 198.0859\n", + " 184.8707\n", " \n", " \n", - " 32\n", - " 33\n", - " elliptical trainer\n", - " 14.7771\n", - " 14.7771\n", - " 32.5055\n", - " 32.5055\n", + " 6\n", + " 57\n", + " ct jobs\n", + " 167.2068\n", + " 167.2068\n", + " 179.6955\n", + " 179.6955\n", " \n", " \n", - " 40\n", - " 41\n", - " orange county convention center\n", - " 13.9195\n", - " 12.3219\n", - " 13.8007\n", - " 10.9814\n", + " 24\n", + " 125\n", + " butter and margarine\n", + " 136.5154\n", + " 125.0510\n", + " 52.6185\n", + " 54.7226\n", " \n", " \n", - " 14\n", - " 15\n", - " espn sports\n", - " 11.6742\n", - " 11.6742\n", - " 5.5440\n", - " 5.5440\n", + " 44\n", + " 195\n", + " pressure washers\n", + " 129.7544\n", + " 129.7544\n", + " 126.1504\n", + " 126.1504\n", " \n", " \n", - " 1\n", - " 2\n", - " french lick resort and casino\n", - " 10.4784\n", - " 7.7701\n", - " 16.4415\n", - " 5.8377\n", + " 10\n", + " 61\n", + " worm\n", + " 129.3127\n", + " 129.3127\n", + " 99.3838\n", + " 99.3838\n", " \n", " \n", - " 41\n", - " 42\n", - " the music man\n", - " 10.2074\n", - " 6.5388\n", - " 18.7398\n", - " 22.1102\n", + " 10\n", + " 111\n", + " lymphoma in dogs\n", + " 123.1295\n", + " 39.4860\n", + " 228.4353\n", + " 198.1618\n", " \n", " \n", - " 42\n", - " 43\n", - " the secret garden\n", - " 8.8980\n", - " 2.9900\n", - " 12.3074\n", - " 2.8687\n", + " 20\n", + " 121\n", + " sit and reach test\n", + " 121.9814\n", + " 78.1871\n", + " 80.8871\n", + " 54.8896\n", " \n", " \n", - " 23\n", - " 24\n", - " diversity\n", - " 7.4336\n", - " 7.4336\n", - " 204.5207\n", - " 204.5207\n", + " 18\n", + " 69\n", + " sewing instructions\n", + " 114.4124\n", + " 114.4124\n", + " 38.1157\n", + " 38.1157\n", " \n", " \n", - " 12\n", - " 13\n", - " map\n", - " 7.1708\n", - " 7.1708\n", - " 66.2089\n", - " 66.2089\n", + " 33\n", + " 34\n", + " cell phones\n", + " 114.1950\n", + " 114.1950\n", + " 194.8462\n", + " 194.8462\n", " \n", " \n", " 11\n", - " 12\n", - " djs\n", - " 4.5480\n", - " 4.5480\n", - " 4.3716\n", - " 4.3716\n", + " 162\n", + " dnr\n", + " 112.1133\n", + " 112.1133\n", + " 193.3705\n", + " 193.3705\n", " \n", " \n", - " 47\n", - " 48\n", - " wilson antenna\n", - " 4.4472\n", - " 4.4472\n", - " 24.4462\n", - " 24.4462\n", + " 17\n", + " 118\n", + " poem in your pocket day\n", + " 107.4421\n", + " 34.8868\n", + " 40.2603\n", + " 48.4656\n", " \n", " \n", - " 48\n", - " 49\n", - " flame designs\n", - " 3.9968\n", - " 3.9968\n", - " 52.7193\n", - " 52.7193\n", + " 18\n", + " 119\n", + " interview thank you\n", + " 91.8932\n", + " 1.8060\n", + " 19.2758\n", + " 6.6938\n", " \n", " \n", - " 28\n", - " 29\n", - " ps 2 games\n", - " 2.8709\n", - " 1.0395\n", - " 57.1064\n", - " 1.5486\n", + " 4\n", + " 55\n", + " iron\n", + " 88.1704\n", + " 88.1704\n", + " 106.5617\n", + " 106.5617\n", " \n", " \n", - " 17\n", - " 18\n", - " wedding budget calculator\n", - " 2.8128\n", - " 0.3303\n", - " 26.0799\n", - " 14.1282\n", + " 7\n", + " 158\n", + " septic system design\n", + " 87.3629\n", + " 74.0658\n", + " 199.0964\n", + " 117.9455\n", " \n", " \n", - " 44\n", - " 45\n", - " solar panels\n", - " 2.4109\n", - " 2.4109\n", - " 43.2100\n", - " 43.2100\n", + " 26\n", + " 177\n", + " best long term care insurance\n", + " 86.1771\n", + " 47.4360\n", + " 83.9122\n", + " 44.2507\n", " \n", " \n", - " 9\n", - " 10\n", - " cheap internet\n", - " 2.2672\n", - " 2.2672\n", - " 37.0891\n", - " 37.0891\n", + " 3\n", + " 104\n", + " indiana child support\n", + " 84.3150\n", + " 57.3328\n", + " 56.8610\n", + " 75.9244\n", " \n", " \n", - " 31\n", - " 32\n", - " website design hosting\n", - " 2.0071\n", - " 1.3135\n", - " 29.1524\n", - " 4.9398\n", + " 42\n", + " 193\n", + " dog clean up bags\n", + " 83.9026\n", + " 81.7193\n", + " 43.2959\n", + " 40.5533\n", " \n", " \n", - " 13\n", - " 14\n", - " dinosaurs\n", - " 1.6772\n", - " 1.6772\n", - " 9.7717\n", - " 9.7717\n", + " 24\n", + " 175\n", + " signs of a heartattack\n", + " 78.6511\n", + " 33.5019\n", + " 66.7424\n", + " 49.2636\n", " \n", " \n", - " 35\n", - " 36\n", - " gps\n", - " 1.5202\n", - " 1.5202\n", - " 162.8659\n", - " 162.8659\n", + " 1\n", + " 152\n", + " angular cheilitis\n", + " 76.9094\n", + " 76.9094\n", + " 201.6837\n", + " 201.6837\n", " \n", " \n", - " 4\n", - " 5\n", - " mitchell college\n", - " 1.1339\n", - " 1.1339\n", - " 4.0976\n", - " 4.0976\n", + " 2\n", + " 3\n", + " getting organized\n", + " 74.7692\n", + " 74.7692\n", + " 22.8042\n", + " 22.8042\n", " \n", " \n", - " 24\n", - " 25\n", - " euclid\n", - " 0.9195\n", - " 0.9195\n", - " 2.3739\n", - " 2.3739\n", + " 19\n", + " 70\n", + " to be or not to be that is the question\n", + " 68.8508\n", + " 59.1396\n", + " 70.9016\n", + " 80.7897\n", " \n", " \n", - " 46\n", - " 47\n", - " indexed annuity\n", - " 0.6762\n", - " 0.6762\n", - " 56.6894\n", - " 56.6894\n", + " 27\n", + " 178\n", + " pork tenderloin\n", + " 66.2645\n", + " 66.2645\n", + " 41.0316\n", + " 41.0316\n", " \n", " \n", - " 21\n", - " 22\n", - " rick warren\n", - " 0.3939\n", - " 0.3939\n", - " 0.5514\n", - " 0.5514\n", + " 32\n", + " 83\n", + " memory\n", + " 64.2118\n", + " 64.2118\n", + " 145.4128\n", + " 145.4128\n", " \n", " \n", - " 38\n", - " 39\n", - " disneyland hotel\n", - " 0.2791\n", - " 0.2791\n", - " 0.3598\n", - " 0.3598\n", + " 16\n", + " 117\n", + " dangers of asbestos\n", + " 63.6375\n", + " 48.6819\n", + " 89.2323\n", + " 53.1681\n", " \n", " \n", - " 39\n", - " 40\n", - " michworks\n", - " 0.0000\n", - " 0.0000\n", - " 0.0000\n", - " 0.0000\n", + " 31\n", + " 132\n", + " mothers day songs\n", + " 60.7894\n", + " 57.7526\n", + " 98.9898\n", + " 52.5622\n", " \n", " \n", - " 7\n", - " 8\n", - " appraisals\n", - " 0.0000\n", - " 0.0000\n", - " 231.0034\n", - " 231.0034\n", + " 42\n", + " 143\n", + " arkadelphia health club\n", + " 60.6634\n", + " 0.4916\n", + " 62.4479\n", + " 0.5497\n", " \n", " \n", - " 36\n", - " 37\n", - " pampered chef\n", - " 0.0000\n", - " 0.0000\n", - " 0.8015\n", - " 0.8015\n", + " 8\n", + " 9\n", + " used car parts\n", + " 57.5809\n", + " 75.1587\n", + " 49.4786\n", + " 32.9048\n", " \n", " \n", - " 34\n", - " 35\n", - " hoboken\n", - " 0.0000\n", - " 0.0000\n", - " 0.4462\n", - " 0.4462\n", + " 6\n", + " 7\n", + " air travel information\n", + " 56.5040\n", + " 44.4720\n", + " 55.5602\n", + " 23.8385\n", " \n", " \n", - " 30\n", - " 31\n", - " atari\n", - " 0.0000\n", - " 0.0000\n", - " 0.0893\n", - " 0.0893\n", + " 10\n", + " 161\n", + " furniture for small spaces\n", + " 55.1753\n", + " 39.9681\n", + " 52.5583\n", + " 59.6951\n", " \n", " \n", - " 27\n", - " 28\n", - " inuyasha\n", - " 0.0000\n", - " 0.0000\n", - " 0.0000\n", - " 0.0000\n", + " 38\n", + " 189\n", + " gs pay rate\n", + " 54.8410\n", + " 23.4032\n", + " 123.0190\n", + " 101.9324\n", " \n", " \n", - " 26\n", - " 27\n", - " starbucks\n", - " 0.0000\n", - " 0.0000\n", - " 0.3505\n", - " 0.3505\n", + " 14\n", + " 115\n", + " pacific northwest laboratory\n", + " 54.6976\n", + " 2.7565\n", + " 70.4378\n", + " 11.0099\n", " \n", " \n", - 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" qid query mean_health_score \\\n", - "3 4 toilet 226.5945 \n", - "29 30 diabetes education 203.9913 \n", - "25 26 lower heart rate 168.4160 \n", - "33 34 cell phones 114.1950 \n", - "2 3 getting organized 74.7692 \n", - "8 9 used car parts 57.5809 \n", - "6 7 air travel information 56.5040 \n", - "49 50 dog heat 44.3673 \n", - "45 46 alexian brothers hospital 35.2337 \n", - "37 38 dogs for adoption 27.9989 \n", - "18 19 the current 23.1269 \n", - "10 11 gmat prep classes 22.5833 \n", - "15 16 arizona game and fish 20.1810 \n", - "0 1 obama family tree 17.3931 \n", - "43 44 map of the united states 17.2940 \n", - "32 33 elliptical trainer 14.7771 \n", - "40 41 orange county convention center 13.9195 \n", - "14 15 espn sports 11.6742 \n", - "1 2 french lick resort and casino 10.4784 \n", - "41 42 the music man 10.2074 \n", - "42 43 the secret garden 8.8980 \n", - "23 24 diversity 7.4336 \n", - "12 13 map 7.1708 \n", - "11 12 djs 4.5480 \n", - "47 48 wilson antenna 4.4472 \n", - "48 49 flame designs 3.9968 \n", - "28 29 ps 2 games 2.8709 \n", - "17 18 wedding budget calculator 2.8128 \n", - "44 45 solar panels 2.4109 \n", - "9 10 cheap internet 2.2672 \n", - "31 32 website design hosting 2.0071 \n", - "13 14 dinosaurs 1.6772 \n", - "35 36 gps 1.5202 \n", - "4 5 mitchell college 1.1339 \n", - "24 25 euclid 0.9195 \n", - "46 47 indexed annuity 0.6762 \n", - "21 22 rick warren 0.3939 \n", - "38 39 disneyland hotel 0.2791 \n", - "39 40 michworks 0.0000 \n", - "7 8 appraisals 0.0000 \n", - "36 37 pampered chef 0.0000 \n", - "34 35 hoboken 0.0000 \n", - "30 31 atari 0.0000 \n", - "27 28 inuyasha 0.0000 \n", - "26 27 starbucks 0.0000 \n", - "22 23 yahoo 0.0000 \n", - "20 21 volvo 0.0000 \n", - "19 20 defender 0.0000 \n", - "16 17 poker tournaments 0.0000 \n", - "5 6 kcs 0.0000 \n", + " qid query mean_health_score \\\n", + "45 196 sore throat 411.7695 \n", + "37 88 forearm pain 354.0813 \n", + "31 82 joints 331.5549 \n", + "27 128 atypical squamous cells 272.0022 \n", + "16 67 vldl levels 267.6323 \n", + "22 173 hip fractures 233.0622 \n", + "3 4 toilet 226.5945 \n", + "27 78 dieting 220.2939 \n", + "38 89 ocd 218.6667 \n", + "29 30 diabetes education 203.9913 \n", + "12 63 flushing 201.4836 \n", + "22 123 von willebrand disease 200.9725 \n", + "17 168 lipoma 181.9301 \n", + "31 182 quit smoking 177.5335 \n", + "25 26 lower heart rate 168.4160 \n", + "6 57 ct jobs 167.2068 \n", + "24 125 butter and margarine 136.5154 \n", + "44 195 pressure washers 129.7544 \n", + "10 61 worm 129.3127 \n", + "10 111 lymphoma in dogs 123.1295 \n", + "20 121 sit and reach test 121.9814 \n", + "18 69 sewing instructions 114.4124 \n", + "33 34 cell phones 114.1950 \n", + "11 162 dnr 112.1133 \n", + "17 118 poem in your pocket day 107.4421 \n", + "18 119 interview thank you 91.8932 \n", + "4 55 iron 88.1704 \n", + "7 158 septic system design 87.3629 \n", + "26 177 best long term care insurance 86.1771 \n", + "3 104 indiana child support 84.3150 \n", + "42 193 dog clean up bags 83.9026 \n", + "24 175 signs of a heartattack 78.6511 \n", + "1 152 angular cheilitis 76.9094 \n", + "2 3 getting organized 74.7692 \n", + "19 70 to be or not to be that is the question 68.8508 \n", + "27 178 pork tenderloin 66.2645 \n", + "32 83 memory 64.2118 \n", + "16 117 dangers of asbestos 63.6375 \n", + "31 132 mothers day songs 60.7894 \n", + "42 143 arkadelphia health club 60.6634 \n", + "8 9 used car parts 57.5809 \n", + "6 7 air travel information 56.5040 \n", + "10 161 furniture for small spaces 55.1753 \n", + "38 189 gs pay rate 54.8410 \n", + "14 115 pacific northwest laboratory 54.6976 \n", + "8 59 how to build a fence 54.1619 \n", + "41 92 the wall 51.8888 \n", + "33 184 civil right movement 50.0213 \n", + "25 176 weather strip 48.1532 \n", + "11 112 kenmore gas water heater 46.8892 \n", "\n", " median_health_score mean_medical_score median_medical_score \n", + "45 411.7695 99.3674 99.3674 \n", + "37 354.0813 310.8832 310.8832 \n", + "31 331.5549 240.9360 240.9360 \n", + "27 277.4749 452.8779 399.4028 \n", + "16 267.6323 434.0579 434.0579 \n", + "22 233.0622 308.1734 308.1734 \n", "3 226.5945 24.5363 24.5363 \n", + "27 220.2939 245.9618 245.9618 \n", + "38 218.6667 328.5885 328.5885 \n", "29 203.9913 243.6227 243.6227 \n", + "12 201.4836 94.9256 94.9256 \n", + "22 226.8965 279.1960 335.1087 \n", + "17 181.9301 496.2743 496.2743 \n", + "31 177.5335 190.5365 190.5365 \n", "25 130.3137 198.0859 184.8707 \n", + "6 167.2068 179.6955 179.6955 \n", + "24 125.0510 52.6185 54.7226 \n", + "44 129.7544 126.1504 126.1504 \n", + "10 129.3127 99.3838 99.3838 \n", + "10 39.4860 228.4353 198.1618 \n", + "20 78.1871 80.8871 54.8896 \n", + "18 114.4124 38.1157 38.1157 \n", "33 114.1950 194.8462 194.8462 \n", + "11 112.1133 193.3705 193.3705 \n", + "17 34.8868 40.2603 48.4656 \n", + "18 1.8060 19.2758 6.6938 \n", + "4 88.1704 106.5617 106.5617 \n", + "7 74.0658 199.0964 117.9455 \n", + "26 47.4360 83.9122 44.2507 \n", + "3 57.3328 56.8610 75.9244 \n", + "42 81.7193 43.2959 40.5533 \n", + "24 33.5019 66.7424 49.2636 \n", + "1 76.9094 201.6837 201.6837 \n", "2 74.7692 22.8042 22.8042 \n", + "19 59.1396 70.9016 80.7897 \n", + "27 66.2645 41.0316 41.0316 \n", + "32 64.2118 145.4128 145.4128 \n", + "16 48.6819 89.2323 53.1681 \n", + "31 57.7526 98.9898 52.5622 \n", + "42 0.4916 62.4479 0.5497 \n", "8 75.1587 49.4786 32.9048 \n", "6 44.4720 55.5602 23.8385 \n", - "49 44.3673 78.6086 78.6086 \n", - "45 1.1535 46.1671 8.7453 \n", - "37 38.0222 119.2782 93.8006 \n", - "18 23.1269 83.3028 83.3028 \n", - "10 18.3837 57.1493 75.9148 \n", - "15 14.9945 39.9599 29.2368 \n", - "0 10.2143 28.3937 41.8619 \n", - "43 16.4337 36.5626 32.4568 \n", - "32 14.7771 32.5055 32.5055 \n", - "40 12.3219 13.8007 10.9814 \n", - "14 11.6742 5.5440 5.5440 \n", - "1 7.7701 16.4415 5.8377 \n", - "41 6.5388 18.7398 22.1102 \n", - "42 2.9900 12.3074 2.8687 \n", - "23 7.4336 204.5207 204.5207 \n", - "12 7.1708 66.2089 66.2089 \n", - "11 4.5480 4.3716 4.3716 \n", - "47 4.4472 24.4462 24.4462 \n", - "48 3.9968 52.7193 52.7193 \n", - "28 1.0395 57.1064 1.5486 \n", - "17 0.3303 26.0799 14.1282 \n", - "44 2.4109 43.2100 43.2100 \n", - "9 2.2672 37.0891 37.0891 \n", - "31 1.3135 29.1524 4.9398 \n", - "13 1.6772 9.7717 9.7717 \n", - "35 1.5202 162.8659 162.8659 \n", - "4 1.1339 4.0976 4.0976 \n", - "24 0.9195 2.3739 2.3739 \n", - "46 0.6762 56.6894 56.6894 \n", - "21 0.3939 0.5514 0.5514 \n", - "38 0.2791 0.3598 0.3598 \n", - "39 0.0000 0.0000 0.0000 \n", - "7 0.0000 231.0034 231.0034 \n", - "36 0.0000 0.8015 0.8015 \n", - "34 0.0000 0.4462 0.4462 \n", - "30 0.0000 0.0893 0.0893 \n", - "27 0.0000 0.0000 0.0000 \n", - "26 0.0000 0.3505 0.3505 \n", - "22 0.0000 2.0731 2.0731 \n", - "20 0.0000 0.4028 0.4028 \n", - "19 0.0000 0.6087 0.6087 \n", - "16 0.0000 0.9231 0.9231 \n", - "5 0.0000 192.6268 192.6268 " + "10 39.9681 52.5583 59.6951 \n", + "38 23.4032 123.0190 101.9324 \n", + "14 2.7565 70.4378 11.0099 \n", + "8 38.2836 45.7611 45.3592 \n", + "41 51.8888 64.8467 64.8467 \n", + "33 73.1061 40.7264 53.3459 \n", + "25 48.1532 18.7130 18.7130 \n", + "11 54.1085 54.4762 61.0496 " ] }, "execution_count": 3, @@ -712,49060 +674,191 @@ } ], "source": [ - "query_health_classification = tira.pt.transform_queries('ir-benchmarks/fschlatt/query-health-classification', dataset)\n", - "query_health_classification(topics).sort_values('mean_health_score', ascending=False)" + "# all topics with mean health score > 150 are health-related\n", + "tira.pt.transform_queries('ir-benchmarks/fschlatt/query-health-classification', all_datasets[0])\n", + "pd.concat(\n", + " [query_health_classification(topics) for query_health_classification, topics in zip(all_query_health_classifications, all_topics)]\n", + ").sort_values(\"mean_health_score\", ascending=False).head(50)" ] }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ - "bm25 = tira.pt.from_submission('ir-benchmarks/tira-ir-starter/BM25 Re-Rank (tira-ir-starter-pyterrier)', dataset)\n", - "\n", - "dirichlet = tira.pt.from_submission('ir-benchmarks/tira-ir-starter/DirichletLM Re-Rank (tira-ir-starter-pyterrier)', dataset)\n", - "\n", - "mont53b = tira.pt.from_submission('ir-benchmarks/tira-ir-starter/MonoT5 3b (tira-ir-starter-gygaggle)', dataset)\n", - "\n", - "splade = tira.pt.from_submission('workshop-on-open-web-search/naverlabseurope/Splade (re-ranker)', dataset)\n", - "\n", - "#lit5 = tira.pt.from_submission('ir-benchmarks/fschlatt/castorini-list-in-t5-150', dataset)\n", - "\n", - "\t" + "approaches = {\n", + " \"BM25\": \"ir-benchmarks/tira-ir-starter/BM25 Re-Rank (tira-ir-starter-pyterrier)\",\n", + " \"Dirichlet\": \"ir-benchmarks/tira-ir-starter/DirichletLM Re-Rank (tira-ir-starter-pyterrier)\",\n", + " \"MonoT5 3b\": \"ir-benchmarks/tira-ir-starter/MonoT5 3b (tira-ir-starter-gygaggle)\",\n", + " \"Splade\": \"workshop-on-open-web-search/naverlabseurope/Splade (re-ranker)\",\n", + "}" ] }, { "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'/root/.tira/extracted_runs/ir-benchmarks/clueweb09-en-trec-web-2009-20230107-training/fschlatt/2024-02-14-13-05-04/output'" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "tira.get_run_output('ir-benchmarks/fschlatt/document-health-classification', \"clueweb09-en-trec-web-2009-20230107-training\")" - ] - }, - { - "cell_type": "code", - "execution_count": 9, + "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "{\"doc_id\":\"clueweb09-en0044-22-32198\",\"mean_health_score\":12.3156,\"median_health_score\":0.0,\"mean_medical_score\":17.6455,\"median_medical_score\":0.6109}\n", - "{\"doc_id\":\"clueweb09-en0059-35-02945\",\"mean_health_score\":13.0598,\"median_health_score\":0.2051,\"mean_medical_score\":18.6768,\"median_medical_score\":0.8151}\n", - "{\"doc_id\":\"clueweb09-en0054-92-07350\",\"mean_health_score\":19.792,\"median_health_score\":1.0088,\"mean_medical_score\":24.5251,\"median_medical_score\":4.3065}\n", - "{\"doc_id\":\"clueweb09-en0022-20-06969\",\"mean_health_score\":5.502,\"median_health_score\":0.0,\"mean_medical_score\":16.0128,\"median_medical_score\":0.6093}\n", - "{\"doc_id\":\"clueweb09-en0001-02-21241\",\"mean_health_score\":19.6664,\"median_health_score\":0.8715,\"mean_medical_score\":27.5347,\"median_medical_score\":4.4065}\n", - "{\"doc_id\":\"clueweb09-en0040-54-11247\",\"mean_health_score\":21.8902,\"median_health_score\":1.7685,\"mean_medical_score\":26.9394,\"median_medical_score\":6.4771}\n", - "{\"doc_id\":\"clueweb09-en0115-47-13635\",\"mean_health_score\":26.7494,\"median_health_score\":6.9237,\"mean_medical_score\":42.8403,\"median_medical_score\":22.7407}\n", - "{\"doc_id\":\"clueweb09-en0015-42-15430\",\"mean_health_score\":28.6099,\"median_health_score\":10.1074,\"mean_medical_score\":34.4586,\"median_medical_score\":15.379}\n", - "{\"doc_id\":\"clueweb09-en0043-86-05608\",\"mean_health_score\":24.6904,\"median_health_score\":3.1752,\"mean_medical_score\":43.3637,\"median_medical_score\":20.2396}\n", - "{\"doc_id\":\"clueweb09-en0122-65-39729\",\"mean_health_score\":27.694,\"median_health_score\":6.9202,\"mean_medical_score\":34.9916,\"median_medical_score\":12.6025}\n", - "{\"doc_id\":\"clueweb09-en0012-99-21831\",\"mean_health_score\":32.0267,\"median_health_score\":9.0734,\"mean_medical_score\":47.9388,\"median_medical_score\":21.3434}\n", - "{\"doc_id\":\"clueweb09-ja0003-15-08172\",\"mean_health_score\":14.6613,\"median_health_score\":0.0,\"mean_medical_score\":24.0083,\"median_medical_score\":1.5409}\n", - "{\"doc_id\":\"clueweb09-en0035-62-02938\",\"mean_health_score\":36.2538,\"median_health_score\":15.9602,\"mean_medical_score\":73.0141,\"median_medical_score\":45.4343}\n", - "{\"doc_id\":\"clueweb09-en0010-57-32932\",\"mean_health_score\":20.2457,\"median_health_score\":0.9727,\"mean_medical_score\":25.7647,\"median_medical_score\":4.8663}\n", - "{\"doc_id\":\"clueweb09-en0130-58-46659\",\"mean_health_score\":24.7653,\"median_health_score\":3.8056,\"mean_medical_score\":40.8693,\"median_medical_score\":19.0596}\n", - "{\"doc_id\":\"clueweb09-en0044-44-24156\",\"mean_health_score\":36.9209,\"median_health_score\":12.7641,\"mean_medical_score\":44.577,\"median_medical_score\":17.9878}\n", - "{\"doc_id\":\"clueweb09-en0123-51-37433\",\"mean_health_score\":35.8018,\"median_health_score\":16.3692,\"mean_medical_score\":56.0158,\"median_medical_score\":40.0652}\n", - "{\"doc_id\":\"clueweb09-en0058-75-26623\",\"mean_health_score\":32.8025,\"median_health_score\":9.5401,\"mean_medical_score\":45.0378,\"median_medical_score\":18.5875}\n", - "{\"doc_id\":\"clueweb09-en0010-72-26577\",\"mean_health_score\":46.1659,\"median_health_score\":16.5048,\"mean_medical_score\":84.6176,\"median_medical_score\":48.4107}\n", - "{\"doc_id\":\"clueweb09-en0123-51-37906\",\"mean_health_score\":37.1963,\"median_health_score\":16.8193,\"mean_medical_score\":50.9477,\"median_medical_score\":37.1016}\n", - "{\"doc_id\":\"clueweb09-en0003-80-11840\",\"mean_health_score\":30.5264,\"median_health_score\":5.8493,\"mean_medical_score\":46.5188,\"median_medical_score\":13.3813}\n", - "{\"doc_id\":\"clueweb09-en0066-83-09895\",\"mean_health_score\":29.3676,\"median_health_score\":7.2068,\"mean_medical_score\":44.2939,\"median_medical_score\":23.7099}\n", - "{\"doc_id\":\"clueweb09-en0012-72-25185\",\"mean_health_score\":35.008,\"median_health_score\":12.1425,\"mean_medical_score\":46.886,\"median_medical_score\":24.6199}\n", - "{\"doc_id\":\"clueweb09-en0044-22-32024\",\"mean_health_score\":23.2165,\"median_health_score\":1.7073,\"mean_medical_score\":35.4721,\"median_medical_score\":10.1076}\n", - "{\"doc_id\":\"clueweb09-en0024-00-19391\",\"mean_health_score\":29.6855,\"median_health_score\":7.4648,\"mean_medical_score\":53.6601,\"median_medical_score\":26.575}\n", - "{\"doc_id\":\"clueweb09-en0064-22-06169\",\"mean_health_score\":27.1356,\"median_health_score\":8.5066,\"mean_medical_score\":44.6217,\"median_medical_score\":20.8591}\n", - "{\"doc_id\":\"clueweb09-en0114-65-00612\",\"mean_health_score\":27.1356,\"median_health_score\":8.5066,\"mean_medical_score\":44.6217,\"median_medical_score\":20.8591}\n", - 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"{\"doc_id\":\"clueweb09-en0083-37-28781\",\"mean_health_score\":60.6293,\"median_health_score\":36.371,\"mean_medical_score\":58.2997,\"median_medical_score\":42.7559}\n", - "{\"doc_id\":\"clueweb09-en0123-03-38288\",\"mean_health_score\":49.4992,\"median_health_score\":23.2229,\"mean_medical_score\":66.4787,\"median_medical_score\":43.2958}\n", - "{\"doc_id\":\"clueweb09-en0026-86-07763\",\"mean_health_score\":49.4992,\"median_health_score\":23.2229,\"mean_medical_score\":66.4787,\"median_medical_score\":43.2958}\n", - "{\"doc_id\":\"clueweb09-en0013-21-27288\",\"mean_health_score\":64.3185,\"median_health_score\":40.1172,\"mean_medical_score\":79.921,\"median_medical_score\":55.1097}\n", - "{\"doc_id\":\"clueweb09-en0026-41-07795\",\"mean_health_score\":37.1625,\"median_health_score\":18.9171,\"mean_medical_score\":50.2549,\"median_medical_score\":28.0204}\n", - "{\"doc_id\":\"clueweb09-en0025-05-07243\",\"mean_health_score\":73.7552,\"median_health_score\":43.5619,\"mean_medical_score\":70.1329,\"median_medical_score\":49.8763}\n", - "{\"doc_id\":\"clueweb09-en0096-32-17703\",\"mean_health_score\":60.6293,\"median_health_score\":36.371,\"mean_medical_score\":58.2997,\"median_medical_score\":42.7559}\n", - "{\"doc_id\":\"clueweb09-en0063-99-31606\",\"mean_health_score\":45.2429,\"median_health_score\":25.8195,\"mean_medical_score\":64.1122,\"median_medical_score\":46.9601}\n", - "{\"doc_id\":\"clueweb09-en0066-40-13573\",\"mean_health_score\":40.7707,\"median_health_score\":13.8786,\"mean_medical_score\":45.9688,\"median_medical_score\":17.7591}\n", - "{\"doc_id\":\"clueweb09-en0036-08-08562\",\"mean_health_score\":81.9842,\"median_health_score\":43.6129,\"mean_medical_score\":99.5095,\"median_medical_score\":65.6418}\n", - "{\"doc_id\":\"clueweb09-en0006-60-32198\",\"mean_health_score\":43.4839,\"median_health_score\":18.0318,\"mean_medical_score\":64.6578,\"median_medical_score\":46.6899}\n", - "{\"doc_id\":\"clueweb09-en0016-99-07937\",\"mean_health_score\":32.9992,\"median_health_score\":5.1805,\"mean_medical_score\":37.0822,\"median_medical_score\":10.2125}\n", - "{\"doc_id\":\"clueweb09-en0120-11-26264\",\"mean_health_score\":99.0815,\"median_health_score\":62.4942,\"mean_medical_score\":111.1026,\"median_medical_score\":61.8343}\n", - "{\"doc_id\":\"clueweb09-en0026-86-16441\",\"mean_health_score\":60.2622,\"median_health_score\":34.8354,\"mean_medical_score\":58.7139,\"median_medical_score\":33.4317}\n", - "{\"doc_id\":\"clueweb09-en0105-47-43464\",\"mean_health_score\":43.2281,\"median_health_score\":14.6852,\"mean_medical_score\":61.1554,\"median_medical_score\":29.6107}\n", - "{\"doc_id\":\"clueweb09-en0008-42-31162\",\"mean_health_score\":48.8638,\"median_health_score\":25.0677,\"mean_medical_score\":63.6413,\"median_medical_score\":41.8619}\n", - "{\"doc_id\":\"clueweb09-en0021-55-30116\",\"mean_health_score\":58.605,\"median_health_score\":33.4018,\"mean_medical_score\":71.1082,\"median_medical_score\":50.2561}\n", - "{\"doc_id\":\"clueweb09-en0019-73-19267\",\"mean_health_score\":40.8102,\"median_health_score\":22.527,\"mean_medical_score\":64.0136,\"median_medical_score\":45.3559}\n", - "{\"doc_id\":\"clueweb09-en0091-21-31606\",\"mean_health_score\":62.4228,\"median_health_score\":42.424,\"mean_medical_score\":72.8954,\"median_medical_score\":49.2232}\n", - "{\"doc_id\":\"clueweb09-en0102-11-01521\",\"mean_health_score\":51.3283,\"median_health_score\":28.8415,\"mean_medical_score\":53.763,\"median_medical_score\":35.5739}\n", - "gzip: head.gz: No such file or directory\n" + "There are multiple qrel fields available: ['relevance', 'method', 'iprob']. Defaulting to \"relevance\", but to use a different one, supply variant\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.10/dist-packages/pyterrier/pipelines.py:129: UserWarning: 2 topic(s) not found in qrels. Scores for these topics are given as NaN and should not contribute to averages.\n", + " warn(f'{backfill_count} topic(s) not found in qrels. Scores for these topics are given as NaN and should not contribute to averages.')\n", + "/usr/local/lib/python3.10/dist-packages/pyterrier/pipelines.py:129: UserWarning: 2 topic(s) not found in qrels. Scores for these topics are given as NaN and should not contribute to averages.\n", + " warn(f'{backfill_count} topic(s) not found in qrels. Scores for these topics are given as NaN and should not contribute to averages.')\n", + "/usr/local/lib/python3.10/dist-packages/pyterrier/pipelines.py:129: UserWarning: 2 topic(s) not found in qrels. Scores for these topics are given as NaN and should not contribute to averages.\n", + " warn(f'{backfill_count} topic(s) not found in qrels. Scores for these topics are given as NaN and should not contribute to averages.')\n", + "/usr/local/lib/python3.10/dist-packages/pyterrier/pipelines.py:129: UserWarning: 2 topic(s) not found in qrels. Scores for these topics are given as NaN and should not contribute to averages.\n", + " warn(f'{backfill_count} topic(s) not found in qrels. Scores for these topics are given as NaN and should not contribute to averages.')\n" ] } ], "source": [ - "!zcat /root/.tira/extracted_runs/ir-benchmarks/clueweb09-en-trec-web-2009-20230107-training/fschlatt/2024-02-14-13-05-04/output/documents.jsonl.gz head -2" + "non_health_results = []\n", + "health_results = []\n", + "iterator = zip(all_topics, all_datasets, all_query_health_classifications)\n", + "for topics, dataset, query_health_classification in iterator:\n", + " topics = query_health_classification(topics)\n", + " qrels = dataset.get_qrels(variant=\"relevance\")\n", + " non_health_topics = topics.loc[topics[\"mean_health_score\"] <= 150]\n", + " health_topics = topics.loc[topics[\"mean_health_score\"] >= 150]\n", + " rankings = [\n", + " tira.pt.from_submission(approach, dataset) for approach in approaches.values()\n", + " ]\n", + " non_health_results.append(\n", + " pt.Experiment(\n", + " rankings,\n", + " non_health_topics,\n", + " qrels,\n", + " ['ndcg_cut.10', 'recip_rank'],\n", + " names=list(approaches.keys()),\n", + " perquery=True\n", + " )\n", + " )\n", + " health_results.append(\n", + " pt.Experiment(\n", + " rankings,\n", + " health_topics,\n", + " qrels,\n", + " ['ndcg_cut.10', 'recip_rank'],\n", + " names=list(approaches.keys()),\n", + " perquery=True\n", + " )\n", + " )" ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 6, "metadata": {}, "outputs": [ { - "ename": "KeyError", - "evalue": "'docno'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", - "File \u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/indexes/base.py:3790\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 3789\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 3790\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_engine\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_loc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcasted_key\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 3791\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n", - "File \u001b[0;32mindex.pyx:152\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n", - "File \u001b[0;32mindex.pyx:181\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n", - "File \u001b[0;32mpandas/_libs/hashtable_class_helper.pxi:7080\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n", - "File \u001b[0;32mpandas/_libs/hashtable_class_helper.pxi:7088\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n", - "\u001b[0;31mKeyError\u001b[0m: 'docno'", - "\nThe above exception was the direct cause of the following exception:\n", - "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[10], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m document_health_classification \u001b[38;5;241m=\u001b[39m \u001b[43mtira\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpt\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtransform_documents\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mir-benchmarks/fschlatt/document-health-classification\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdataset\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2\u001b[0m bm25_health \u001b[38;5;241m=\u001b[39m bm25 \u001b[38;5;241m%\u001b[39m \u001b[38;5;241m10\u001b[39m \u001b[38;5;241m>>\u001b[39m document_health_classification\n\u001b[1;32m 3\u001b[0m \u001b[38;5;66;03m# bm25_health(topics).sort_values('mean_health_score', ascending=False)\u001b[39;00m\n", - "File \u001b[0;32m/usr/local/lib/python3.10/dist-packages/tira/pyterrier_integration.py:126\u001b[0m, in \u001b[0;36mPyTerrierIntegration.transform_documents\u001b[0;34m(self, approach, dataset, file_selection)\u001b[0m\n\u001b[1;32m 124\u001b[0m ret \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpd\u001b[38;5;241m.\u001b[39mtransform_documents(approach, dataset, file_selection)\n\u001b[1;32m 125\u001b[0m cols \u001b[38;5;241m=\u001b[39m [i \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m ret\u001b[38;5;241m.\u001b[39mcolumns \u001b[38;5;28;01mif\u001b[39;00m i \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m [\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdocno\u001b[39m\u001b[38;5;124m'\u001b[39m]]\n\u001b[0;32m--> 126\u001b[0m ret \u001b[38;5;241m=\u001b[39m {\u001b[38;5;28mstr\u001b[39m(i[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdocno\u001b[39m\u001b[38;5;124m'\u001b[39m]): i \u001b[38;5;28;01mfor\u001b[39;00m _, i \u001b[38;5;129;01min\u001b[39;00m ret\u001b[38;5;241m.\u001b[39miterrows()}\n\u001b[1;32m 128\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__transform_df\u001b[39m(df):\n\u001b[1;32m 129\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m col \u001b[38;5;129;01min\u001b[39;00m cols:\n", - "File \u001b[0;32m/usr/local/lib/python3.10/dist-packages/tira/pyterrier_integration.py:126\u001b[0m, in \u001b[0;36m\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m 124\u001b[0m ret \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpd\u001b[38;5;241m.\u001b[39mtransform_documents(approach, dataset, file_selection)\n\u001b[1;32m 125\u001b[0m cols \u001b[38;5;241m=\u001b[39m [i \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m ret\u001b[38;5;241m.\u001b[39mcolumns \u001b[38;5;28;01mif\u001b[39;00m i \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m [\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdocno\u001b[39m\u001b[38;5;124m'\u001b[39m]]\n\u001b[0;32m--> 126\u001b[0m ret \u001b[38;5;241m=\u001b[39m {\u001b[38;5;28mstr\u001b[39m(\u001b[43mi\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mdocno\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m): i \u001b[38;5;28;01mfor\u001b[39;00m _, i \u001b[38;5;129;01min\u001b[39;00m ret\u001b[38;5;241m.\u001b[39miterrows()}\n\u001b[1;32m 128\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__transform_df\u001b[39m(df):\n\u001b[1;32m 129\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m col \u001b[38;5;129;01min\u001b[39;00m cols:\n", - "File \u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/series.py:1040\u001b[0m, in \u001b[0;36mSeries.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 1037\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_values[key]\n\u001b[1;32m 1039\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m key_is_scalar:\n\u001b[0;32m-> 1040\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_get_value\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1042\u001b[0m \u001b[38;5;66;03m# Convert generator to list before going through hashable part\u001b[39;00m\n\u001b[1;32m 1043\u001b[0m \u001b[38;5;66;03m# (We will iterate through the generator there to check for slices)\u001b[39;00m\n\u001b[1;32m 1044\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_iterator(key):\n", - "File \u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/series.py:1156\u001b[0m, in \u001b[0;36mSeries._get_value\u001b[0;34m(self, label, takeable)\u001b[0m\n\u001b[1;32m 1153\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_values[label]\n\u001b[1;32m 1155\u001b[0m \u001b[38;5;66;03m# Similar to Index.get_value, but we do not fall back to positional\u001b[39;00m\n\u001b[0;32m-> 1156\u001b[0m loc \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mindex\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_loc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mlabel\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1158\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_integer(loc):\n\u001b[1;32m 1159\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_values[loc]\n", - "File \u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/indexes/base.py:3797\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 3792\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(casted_key, \u001b[38;5;28mslice\u001b[39m) \u001b[38;5;129;01mor\u001b[39;00m (\n\u001b[1;32m 3793\u001b[0m \u001b[38;5;28misinstance\u001b[39m(casted_key, abc\u001b[38;5;241m.\u001b[39mIterable)\n\u001b[1;32m 3794\u001b[0m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28many\u001b[39m(\u001b[38;5;28misinstance\u001b[39m(x, \u001b[38;5;28mslice\u001b[39m) \u001b[38;5;28;01mfor\u001b[39;00m x \u001b[38;5;129;01min\u001b[39;00m casted_key)\n\u001b[1;32m 3795\u001b[0m ):\n\u001b[1;32m 3796\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m InvalidIndexError(key)\n\u001b[0;32m-> 3797\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(key) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01merr\u001b[39;00m\n\u001b[1;32m 3798\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m:\n\u001b[1;32m 3799\u001b[0m \u001b[38;5;66;03m# If we have a listlike key, _check_indexing_error will raise\u001b[39;00m\n\u001b[1;32m 3800\u001b[0m \u001b[38;5;66;03m# InvalidIndexError. Otherwise we fall through and re-raise\u001b[39;00m\n\u001b[1;32m 3801\u001b[0m \u001b[38;5;66;03m# the TypeError.\u001b[39;00m\n\u001b[1;32m 3802\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_indexing_error(key)\n", - "\u001b[0;31mKeyError\u001b[0m: 'docno'" - ] + "data": { + "text/plain": [ + "(184, 16)" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ - "document_health_classification = tira.pt.transform_documents('ir-benchmarks/fschlatt/document-health-classification', dataset)\n", - "bm25_health = bm25 % 10 >> document_health_classification\n", - "# bm25_health(topics).sort_values('mean_health_score', ascending=False)" + "pd.concat(non_health_results)[\"qid\"].unique().shape[0], pd.concat(health_results)[\"qid\"].unique().shape[0]" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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measurendcg_cut.10recip_rank
name
Dirichlet0.2498430.485740
MonoT5 3b0.2008920.406660
Splade0.1924590.418589
BM250.1916530.438125
\n", + "
" + ], + "text/plain": [ + "measure ndcg_cut.10 recip_rank\n", + "name \n", + "Dirichlet 0.249843 0.485740\n", + "MonoT5 3b 0.200892 0.406660\n", + "Splade 0.192459 0.418589\n", + "BM25 0.191653 0.438125" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "document_health_classification = tira.pt.transform_documents('ir-benchmarks/fschlatt/document-health-classification', dataset)\n", - "bm25_health = bm25 >> document_health_classification\n", - "bm25_health(topics).sort_values('mean_health_score', ascending=False)" + "pd.concat(non_health_results).groupby([\"name\", \"measure\"])[\"value\"].mean().unstack(\"measure\").sort_values(\"ndcg_cut.10\", ascending=False)" ] }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 8, "metadata": {}, "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "There are multiple qrel fields available: ['relevance', 'method', 'iprob']. Defaulting to \"relevance\", but to use a different one, supply variant\n" - ] - }, { "data": { "text/html": [ @@ -49786,67 +879,93 @@ "\n", " \n", " \n", - " \n", - " \n", + " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", "
namemeasurendcg_cut.10recip_rank
name
0BM250.1783020.371874Dirichlet0.2755070.542262
2Splade0.1999860.417381BM250.2401640.523327
3MonoT50.2065870.407017MonoT5 3b0.1837880.391410
1Dirichlet0.2748750.516681Splade0.1669800.457813
\n", "" ], "text/plain": [ - " name ndcg_cut.10 recip_rank\n", - "0 BM25 0.178302 0.371874\n", - "2 Splade 0.199986 0.417381\n", - "3 MonoT5 0.206587 0.407017\n", - "1 Dirichlet 0.274875 0.516681" + "measure ndcg_cut.10 recip_rank\n", + "name \n", + "Dirichlet 0.275507 0.542262\n", + "BM25 0.240164 0.523327\n", + "MonoT5 3b 0.183788 0.391410\n", + "Splade 0.166980 0.457813" ] }, - "execution_count": 30, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "pt.Experiment(\n", - " [bm25, dirichlet, splade, mont53b],\n", - " topics,\n", - " dataset.get_qrels('relevance'),\n", - " ['ndcg_cut.10', 'recip_rank'],\n", - " names=['BM25', \"Dirichlet\", \"Splade\", \"MonoT5\"]\n", - ").sort_values('ndcg_cut.10')" + "pd.concat(health_results).groupby([\"name\", \"measure\"])[\"value\"].mean().unstack(\"measure\").sort_values(\"ndcg_cut.10\", ascending=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# TREC Health Misinformation Results\n", + "\n", + "Filtering documents by median health relatedness score can improve the effectiveness of BM25 on the TREC Health Misinformation task." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "There are multiple qrel fields available: ['relevance', 'effectiveness', 'redibility']. Defaulting to \"relevance\", but to use a different one, supply variant\n" + ] + } + ], + "source": [ + "dataset = \"clueweb12-trec-misinfo-2019-20240214-training\"\n", + "topics = pt.get_dataset(\"irds:clueweb12/b13/trec-misinfo-2019\").get_topics(variant=\"title\")\n", + "qrels = pt.get_dataset(\"irds:clueweb12/b13/trec-misinfo-2019\").get_qrels(variant=\"relevance\")\n", + "document_health_classification = tira.pt.transform_documents(\"ir-benchmarks/fschlatt/document-health-classification\", dataset)\n", + "bm25 = tira.pt.from_submission(\"ir-benchmarks/tira-ir-starter/BM25 Re-Rank (tira-ir-starter-pyterrier)\", dataset)\n", + "bm25_health = bm25 % 1000 >> document_health_classification" ] }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -49872,6 +991,14 @@ " \n", " qid\n", " query\n", + " q0\n", + " rank\n", + " score\n", + " system\n", + " docno\n", + " tira_task\n", + " tira_dataset\n", + " tira_first_stage_run_id\n", " mean_health_score\n", " median_health_score\n", " mean_medical_score\n", @@ -49880,72 +1007,306 @@ " \n", " \n", " \n", - " 3\n", - " 4\n", - " toilet\n", - " 226.5945\n", - " 226.5945\n", - " 24.5363\n", - " 24.5363\n", + " 0\n", + " 1\n", + " cranberries urinary tract infections\n", + " Q0\n", + " 1\n", + " 23.569565\n", + " pyterrier.default_pipelines.wmodel_text_scorer\n", + " clueweb12-1712wb-84-02961\n", + " ir-benchmarks\n", + " clueweb12-trec-misinfo-2019-20240214-training\n", + " 2024-03-18-10-10-30\n", + " 108.1876\n", + " 67.2081\n", + " 114.9722\n", + " 74.7917\n", " \n", " \n", - " 29\n", - " 30\n", - " diabetes education\n", - " 203.9913\n", - " 203.9913\n", - " 243.6227\n", - " 243.6227\n", + " 1\n", + " 1\n", + " cranberries urinary tract infections\n", + " Q0\n", + " 2\n", + " 23.476046\n", + " pyterrier.default_pipelines.wmodel_text_scorer\n", + " clueweb12-1304wb-51-24177\n", + " ir-benchmarks\n", + " clueweb12-trec-misinfo-2019-20240214-training\n", + " 2024-03-18-10-10-30\n", + " 100.8895\n", + " 57.2659\n", + " 111.6515\n", + " 76.4557\n", " \n", " \n", - " 25\n", - " 26\n", - " lower heart rate\n", - " 168.4160\n", - " 130.3137\n", - " 198.0859\n", - " 184.8707\n", + " 2\n", + " 1\n", + " cranberries urinary tract infections\n", + " Q0\n", + " 3\n", + " 23.404021\n", + " pyterrier.default_pipelines.wmodel_text_scorer\n", + " clueweb12-1304wb-88-12518\n", + " ir-benchmarks\n", + " clueweb12-trec-misinfo-2019-20240214-training\n", + " 2024-03-18-10-10-30\n", + " 108.0479\n", + " 66.5542\n", + " 113.0515\n", + " 74.5736\n", + " \n", + " \n", + " 3\n", + " 1\n", + " cranberries urinary tract infections\n", + " Q0\n", + " 4\n", + " 23.380401\n", + " pyterrier.default_pipelines.wmodel_text_scorer\n", + " clueweb12-0109wb-83-09551\n", + " ir-benchmarks\n", + " clueweb12-trec-misinfo-2019-20240214-training\n", + " 2024-03-18-10-10-30\n", + " 107.0973\n", + " 55.9035\n", + " 124.5779\n", + " 84.6786\n", + " \n", + " \n", + " 4\n", + " 1\n", + " cranberries urinary tract infections\n", + " Q0\n", + " 5\n", + " 23.370749\n", + " pyterrier.default_pipelines.wmodel_text_scorer\n", + " clueweb12-1912wb-62-07565\n", + " ir-benchmarks\n", + " clueweb12-trec-misinfo-2019-20240214-training\n", + " 2024-03-18-10-10-30\n", + " 92.6918\n", + " 46.1817\n", + " 100.2450\n", + " 60.0157\n", + " \n", + " \n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " \n", + " \n", + " 50994\n", + " 51\n", + " dehumidifiers asthma\n", + " Q0\n", + " 995\n", + " 10.490712\n", + " pyterrier.default_pipelines.wmodel_text_scorer\n", + " clueweb12-0500wb-06-07863\n", + " ir-benchmarks\n", + " clueweb12-trec-misinfo-2019-20240214-training\n", + " 2024-03-18-10-10-30\n", + " 68.3214\n", + " 35.4775\n", + " 93.5654\n", + " 59.5807\n", + " \n", + " \n", + " 50995\n", + " 51\n", + " dehumidifiers asthma\n", + " Q0\n", + " 996\n", + " 10.487029\n", + " pyterrier.default_pipelines.wmodel_text_scorer\n", + " clueweb12-1307wb-71-08120\n", + " ir-benchmarks\n", + " clueweb12-trec-misinfo-2019-20240214-training\n", + " 2024-03-18-10-10-30\n", + " 79.5897\n", + " 42.9657\n", + " 102.9494\n", + " 61.0742\n", + " \n", + " \n", + " 50996\n", + " 51\n", + " dehumidifiers asthma\n", + " Q0\n", + " 997\n", + " 10.462628\n", + " pyterrier.default_pipelines.wmodel_text_scorer\n", + " clueweb12-0100wb-15-07532\n", + " ir-benchmarks\n", + " clueweb12-trec-misinfo-2019-20240214-training\n", + " 2024-03-18-10-10-30\n", + " 101.0621\n", + " 69.3955\n", + " 127.3782\n", + " 91.2131\n", + " \n", + " \n", + " 50997\n", + " 51\n", + " dehumidifiers asthma\n", + " Q0\n", + " 998\n", + " 10.440274\n", + " pyterrier.default_pipelines.wmodel_text_scorer\n", + " clueweb12-0600tw-25-01456\n", + " ir-benchmarks\n", + " clueweb12-trec-misinfo-2019-20240214-training\n", + " 2024-03-18-10-10-30\n", + " 99.0062\n", + " 72.4115\n", + " 93.6922\n", + " 60.6180\n", + " \n", + " \n", + " 50998\n", + " 51\n", + " dehumidifiers asthma\n", + " Q0\n", + " 999\n", + " 9.650459\n", + " pyterrier.default_pipelines.wmodel_text_scorer\n", + " clueweb12-1611wb-80-00880\n", + " ir-benchmarks\n", + " clueweb12-trec-misinfo-2019-20240214-training\n", + " 2024-03-18-10-10-30\n", + " 61.0268\n", + " 38.0222\n", + " 81.9152\n", + " 64.4986\n", " \n", " \n", "\n", + "

50949 rows × 14 columns

\n", "" ], "text/plain": [ - " qid query mean_health_score median_health_score \\\n", - "3 4 toilet 226.5945 226.5945 \n", - "29 30 diabetes education 203.9913 203.9913 \n", - "25 26 lower heart rate 168.4160 130.3137 \n", + " qid query q0 rank score \\\n", + "0 1 cranberries urinary tract infections Q0 1 23.569565 \n", + "1 1 cranberries urinary tract infections Q0 2 23.476046 \n", + "2 1 cranberries urinary tract infections Q0 3 23.404021 \n", + "3 1 cranberries urinary tract infections Q0 4 23.380401 \n", + "4 1 cranberries urinary tract infections Q0 5 23.370749 \n", + "... .. ... .. ... ... \n", + "50994 51 dehumidifiers asthma Q0 995 10.490712 \n", + "50995 51 dehumidifiers asthma Q0 996 10.487029 \n", + "50996 51 dehumidifiers asthma Q0 997 10.462628 \n", + "50997 51 dehumidifiers asthma Q0 998 10.440274 \n", + "50998 51 dehumidifiers asthma Q0 999 9.650459 \n", + "\n", + " system \\\n", + "0 pyterrier.default_pipelines.wmodel_text_scorer \n", + "1 pyterrier.default_pipelines.wmodel_text_scorer \n", + "2 pyterrier.default_pipelines.wmodel_text_scorer \n", + "3 pyterrier.default_pipelines.wmodel_text_scorer \n", + "4 pyterrier.default_pipelines.wmodel_text_scorer \n", + "... ... \n", + "50994 pyterrier.default_pipelines.wmodel_text_scorer \n", + "50995 pyterrier.default_pipelines.wmodel_text_scorer \n", + "50996 pyterrier.default_pipelines.wmodel_text_scorer \n", + "50997 pyterrier.default_pipelines.wmodel_text_scorer \n", + "50998 pyterrier.default_pipelines.wmodel_text_scorer \n", + "\n", + " docno tira_task \\\n", + "0 clueweb12-1712wb-84-02961 ir-benchmarks \n", + "1 clueweb12-1304wb-51-24177 ir-benchmarks \n", + "2 clueweb12-1304wb-88-12518 ir-benchmarks \n", + "3 clueweb12-0109wb-83-09551 ir-benchmarks \n", + "4 clueweb12-1912wb-62-07565 ir-benchmarks \n", + "... ... ... \n", + "50994 clueweb12-0500wb-06-07863 ir-benchmarks \n", + "50995 clueweb12-1307wb-71-08120 ir-benchmarks \n", + "50996 clueweb12-0100wb-15-07532 ir-benchmarks \n", + "50997 clueweb12-0600tw-25-01456 ir-benchmarks \n", + "50998 clueweb12-1611wb-80-00880 ir-benchmarks \n", + "\n", + " tira_dataset tira_first_stage_run_id \\\n", + "0 clueweb12-trec-misinfo-2019-20240214-training 2024-03-18-10-10-30 \n", + "1 clueweb12-trec-misinfo-2019-20240214-training 2024-03-18-10-10-30 \n", + "2 clueweb12-trec-misinfo-2019-20240214-training 2024-03-18-10-10-30 \n", + "3 clueweb12-trec-misinfo-2019-20240214-training 2024-03-18-10-10-30 \n", + "4 clueweb12-trec-misinfo-2019-20240214-training 2024-03-18-10-10-30 \n", + "... ... ... \n", + "50994 clueweb12-trec-misinfo-2019-20240214-training 2024-03-18-10-10-30 \n", + "50995 clueweb12-trec-misinfo-2019-20240214-training 2024-03-18-10-10-30 \n", + "50996 clueweb12-trec-misinfo-2019-20240214-training 2024-03-18-10-10-30 \n", + "50997 clueweb12-trec-misinfo-2019-20240214-training 2024-03-18-10-10-30 \n", + "50998 clueweb12-trec-misinfo-2019-20240214-training 2024-03-18-10-10-30 \n", + "\n", + " mean_health_score median_health_score mean_medical_score \\\n", + "0 108.1876 67.2081 114.9722 \n", + "1 100.8895 57.2659 111.6515 \n", + "2 108.0479 66.5542 113.0515 \n", + "3 107.0973 55.9035 124.5779 \n", + "4 92.6918 46.1817 100.2450 \n", + "... ... ... ... \n", + "50994 68.3214 35.4775 93.5654 \n", + "50995 79.5897 42.9657 102.9494 \n", + "50996 101.0621 69.3955 127.3782 \n", + "50997 99.0062 72.4115 93.6922 \n", + "50998 61.0268 38.0222 81.9152 \n", "\n", - " mean_medical_score median_medical_score \n", - "3 24.5363 24.5363 \n", - "29 243.6227 243.6227 \n", - "25 198.0859 184.8707 " + " median_medical_score \n", + "0 74.7917 \n", + "1 76.4557 \n", + "2 74.5736 \n", + "3 84.6786 \n", + "4 60.0157 \n", + "... ... \n", + "50994 59.5807 \n", + "50995 61.0742 \n", + "50996 91.2131 \n", + "50997 60.6180 \n", + "50998 64.4986 \n", + "\n", + "[50949 rows x 14 columns]" ] }, - "execution_count": 14, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "health_topics = query_health_classification(topics).sort_values('mean_health_score', ascending=False)\n", - "health_topics = health_topics.head(3)\n", - "\n", - "health_topics" + "bm25_ranking = bm25_health(topics)\n", + "bm25_ranking" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "rankings = []\n", + "thresholds = list(range(0, 60, 5))\n", + "for threshold in thresholds:\n", + " rankings.append(bm25_ranking.loc[bm25_ranking[\"median_health_score\"] >= threshold])" ] }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 12, "metadata": {}, "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "There are multiple qrel fields available: ['relevance', 'method', 'iprob']. Defaulting to \"relevance\", but to use a different one, supply variant\n" - ] - }, { "data": { "text/html": [ @@ -49967,61 +1328,140 @@ " \n", " \n", " \n", - " name\n", - " ndcg_cut.10\n", - " recip_rank\n", + " nDCG@10\n", + " MRR\n", + " \n", + " \n", + " Health Score Threshold\n", + " \n", + " \n", " \n", " \n", " \n", " \n", " 0\n", - " BM25\n", - " 0.057330\n", - " 0.203704\n", + " 0.467323\n", + " 0.720945\n", " \n", " \n", - " 3\n", - " MonoT5\n", - " 0.089858\n", - " 0.127778\n", + " 5\n", + " 0.467232\n", + " 0.723088\n", " \n", " \n", - " 2\n", - " Splade\n", - " 0.115139\n", - " 0.208333\n", + " 10\n", + " 0.473522\n", + " 0.728755\n", " \n", " \n", - " 1\n", - " Dirichlet\n", - " 0.249066\n", - " 0.666667\n", + " 15\n", + " 0.475634\n", + " 0.738773\n", + " \n", + " \n", + " 20\n", + " 0.485593\n", + " 0.743662\n", + " \n", + " \n", + " 25\n", + " 0.496041\n", + " 0.757014\n", + " \n", + " \n", + " 30\n", + " 0.490500\n", + " 0.770645\n", + " \n", + " \n", + " 35\n", + " 0.479722\n", + " 0.767978\n", + " \n", + " \n", + " 40\n", + " 0.479449\n", + " 0.765714\n", + " \n", + " \n", + " 45\n", + " 0.482691\n", + " 0.816157\n", + " \n", + " \n", + " 50\n", + " 0.455525\n", + " 0.783242\n", + " \n", + " \n", + " 55\n", + " 0.407818\n", + " 0.716444\n", " \n", " \n", "\n", "" ], "text/plain": [ - " name ndcg_cut.10 recip_rank\n", - "0 BM25 0.057330 0.203704\n", - "3 MonoT5 0.089858 0.127778\n", - "2 Splade 0.115139 0.208333\n", - "1 Dirichlet 0.249066 0.666667" + " nDCG@10 MRR\n", + "Health Score Threshold \n", + "0 0.467323 0.720945\n", + "5 0.467232 0.723088\n", + "10 0.473522 0.728755\n", + "15 0.475634 0.738773\n", + "20 0.485593 0.743662\n", + "25 0.496041 0.757014\n", + "30 0.490500 0.770645\n", + "35 0.479722 0.767978\n", + "40 0.479449 0.765714\n", + "45 0.482691 0.816157\n", + "50 0.455525 0.783242\n", + "55 0.407818 0.716444" ] }, - "execution_count": 31, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "pt.Experiment(\n", - " [bm25, dirichlet, splade, mont53b],\n", - " health_topics,\n", - " dataset.get_qrels('relevance'),\n", + "results = pt.Experiment(\n", + " rankings,\n", + " topics,\n", + " qrels.rename({\"label\": \"relevance\"}, axis=1),\n", " ['ndcg_cut.10', 'recip_rank'],\n", - " names=['BM25', \"Dirichlet\", \"Splade\", \"MonoT5\"]\n", - ").sort_values('ndcg_cut.10')" + " names=thresholds,\n", + ").rename({\"name\": \"Health Score Threshold\", \"ndcg_cut.10\": \"nDCG@10\", \"recip_rank\": \"MRR\"}, axis=1)\n", + "results = results.set_index(\"Health Score Threshold\")\n", + "results" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "scale = 0.7\n", + "fig, axes = plt.subplots(1, 2, figsize=(8 * scale, 4 * scale))\n", + "for idx, metric in enumerate((\"nDCG@10\", \"MRR\")):\n", + " ax = axes[idx]\n", + " ax.plot(results.index, results.loc[:, metric], marker=\"o\")\n", + " ax.set_ylabel(metric)\n", + " ax.set_xlabel(\"Health Score Threshold\")\n", + "fig.suptitle(\"TREC Health Misinformation\")\n", + "fig.tight_layout()" ] } ],