{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "1c23a3e3-332b-4712-a2b8-1788cd14087e",
"metadata": {},
"outputs": [],
"source": [
"%reset -f"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "207f38ab-618d-42fa-accb-f400dfc8be34",
"metadata": {},
"outputs": [],
"source": [
"user = \"HHegde\"\n",
"db = f\"/Users/{user}/.data/oaklib/phenio.db\""
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "2e2ce839-b8cc-4f1a-a931-67b85ba0df4d",
"metadata": {},
"outputs": [],
"source": [
"%reload_ext sql\n",
"%sql sqlite:///{db}"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "87025967-554f-4c9d-9967-97ce1e40acf7",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" * sqlite:////Users/HHegde/.data/oaklib/phenio.db\n",
"Done.\n"
]
},
{
"data": {
"text/html": [
"
\n",
" \n",
" \n",
" id | \n",
" subject | \n",
" predicate | \n",
" object | \n",
" evidence_type | \n",
" publication | \n",
" source | \n",
"
\n",
" \n",
" \n",
" \n",
" uuid:70269c5a-42a9-11ee-be37-31ef105c25ea | \n",
" MONDO:0023659 | \n",
" biolink:has_phenotype | \n",
" HP:0011097 | \n",
" ECO:0000269 | \n",
" PMID:31675180 | \n",
" infores:hpo-annotations | \n",
"
\n",
" \n",
" uuid:70269c5b-42a9-11ee-be37-31ef105c25ea | \n",
" MONDO:0023659 | \n",
" biolink:has_phenotype | \n",
" HP:0002187 | \n",
" ECO:0000269 | \n",
" PMID:31675180 | \n",
" infores:hpo-annotations | \n",
"
\n",
" \n",
"
"
],
"text/plain": [
"[('uuid:70269c5a-42a9-11ee-be37-31ef105c25ea', 'MONDO:0023659', 'biolink:has_phenotype', 'HP:0011097', 'ECO:0000269', 'PMID:31675180', 'infores:hpo-annotations'),\n",
" ('uuid:70269c5b-42a9-11ee-be37-31ef105c25ea', 'MONDO:0023659', 'biolink:has_phenotype', 'HP:0002187', 'ECO:0000269', 'PMID:31675180', 'infores:hpo-annotations')]"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%sql SELECT * FROM term_association LIMIT 2;"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "e639def1-00e1-4113-9da6-fb2f32701952",
"metadata": {},
"outputs": [],
"source": [
"import sqlite3\n",
"import pandas as pd\n",
"from semsimian import Semsimian\n",
"from collections import Counter"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "bb1e79e6-2b18-466f-95fb-d219f8c64431",
"metadata": {},
"outputs": [],
"source": [
"conn = sqlite3.connect(db)\n",
"res = conn.execute(\"SELECT name FROM sqlite_master WHERE type='table';\")\n",
"# tables = res.fetchall()\n",
"\n",
"# tables"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "e406c2fa-43e1-4f57-b8be-52334edfdbd6",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" id | \n",
" subject | \n",
" predicate | \n",
" object | \n",
" evidence_type | \n",
" publication | \n",
" source | \n",
"
\n",
" \n",
" \n",
" \n",
" 0 | \n",
" uuid:70269c5a-42a9-11ee-be37-31ef105c25ea | \n",
" MONDO:0023659 | \n",
" biolink:has_phenotype | \n",
" HP:0011097 | \n",
" ECO:0000269 | \n",
" PMID:31675180 | \n",
" infores:hpo-annotations | \n",
"
\n",
" \n",
" 1 | \n",
" uuid:70269c5b-42a9-11ee-be37-31ef105c25ea | \n",
" MONDO:0023659 | \n",
" biolink:has_phenotype | \n",
" HP:0002187 | \n",
" ECO:0000269 | \n",
" PMID:31675180 | \n",
" infores:hpo-annotations | \n",
"
\n",
" \n",
" 2 | \n",
" uuid:70269c5c-42a9-11ee-be37-31ef105c25ea | \n",
" MONDO:0023659 | \n",
" biolink:has_phenotype | \n",
" HP:0001518 | \n",
" ECO:0000269 | \n",
" PMID:31675180 | \n",
" infores:hpo-annotations | \n",
"
\n",
" \n",
" 3 | \n",
" uuid:70269c5d-42a9-11ee-be37-31ef105c25ea | \n",
" MONDO:0023659 | \n",
" biolink:has_phenotype | \n",
" HP:0032792 | \n",
" ECO:0000269 | \n",
" PMID:31675180 | \n",
" infores:hpo-annotations | \n",
"
\n",
" \n",
" 4 | \n",
" uuid:70269c5e-42a9-11ee-be37-31ef105c25ea | \n",
" MONDO:0023659 | \n",
" biolink:has_phenotype | \n",
" HP:0011451 | \n",
" ECO:0000269 | \n",
" PMID:31675180 | \n",
" infores:hpo-annotations | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" id subject \\\n",
"0 uuid:70269c5a-42a9-11ee-be37-31ef105c25ea MONDO:0023659 \n",
"1 uuid:70269c5b-42a9-11ee-be37-31ef105c25ea MONDO:0023659 \n",
"2 uuid:70269c5c-42a9-11ee-be37-31ef105c25ea MONDO:0023659 \n",
"3 uuid:70269c5d-42a9-11ee-be37-31ef105c25ea MONDO:0023659 \n",
"4 uuid:70269c5e-42a9-11ee-be37-31ef105c25ea MONDO:0023659 \n",
"\n",
" predicate object evidence_type publication \\\n",
"0 biolink:has_phenotype HP:0011097 ECO:0000269 PMID:31675180 \n",
"1 biolink:has_phenotype HP:0002187 ECO:0000269 PMID:31675180 \n",
"2 biolink:has_phenotype HP:0001518 ECO:0000269 PMID:31675180 \n",
"3 biolink:has_phenotype HP:0032792 ECO:0000269 PMID:31675180 \n",
"4 biolink:has_phenotype HP:0011451 ECO:0000269 PMID:31675180 \n",
"\n",
" source \n",
"0 infores:hpo-annotations \n",
"1 infores:hpo-annotations \n",
"2 infores:hpo-annotations \n",
"3 infores:hpo-annotations \n",
"4 infores:hpo-annotations "
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_term_association = pd.read_sql_query(\"SELECT * FROM term_association\", conn)\n",
"df_term_association.head()"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "76a44b5a-2532-44a4-8226-e74322e289c9",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array(['MONDO', 'HGNC', 'WB', 'MGI', 'RGD', 'Xenbase', 'ZFIN'],\n",
" dtype=object)"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_term_association['subject'].str.split(\":\").str[0].unique()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "23cd279e-77fd-472c-bd13-d21753d3cf9f",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array(['HP', 'WBPhenotype', 'MP', 'XPO', 'ZP'], dtype=object)"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_term_association['object'].str.split(\":\").str[0].unique()"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "f8af957d-612c-4922-998e-760553f171ad",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" subject | \n",
" predicate | \n",
" object | \n",
"
\n",
" \n",
" \n",
" \n",
" 396014 | \n",
" MGI:1261425 | \n",
" biolink:has_phenotype | \n",
" MP:0004974 | \n",
"
\n",
" \n",
" 434353 | \n",
" MGI:1858212 | \n",
" biolink:has_phenotype | \n",
" MP:0005331 | \n",
"
\n",
" \n",
" 374300 | \n",
" MGI:109207 | \n",
" biolink:has_phenotype | \n",
" MP:0005560 | \n",
"
\n",
" \n",
" 387558 | \n",
" MGI:98475 | \n",
" biolink:has_phenotype | \n",
" MP:0008872 | \n",
"
\n",
" \n",
" 315680 | \n",
" MGI:97512 | \n",
" biolink:has_phenotype | \n",
" MP:0005293 | \n",
"
\n",
" \n",
" ... | \n",
" ... | \n",
" ... | \n",
" ... | \n",
"
\n",
" \n",
" 402503 | \n",
" MGI:88417 | \n",
" biolink:has_phenotype | \n",
" MP:0001923 | \n",
"
\n",
" \n",
" 350599 | \n",
" MGI:104510 | \n",
" biolink:has_phenotype | \n",
" MP:0006358 | \n",
"
\n",
" \n",
" 349262 | \n",
" MGI:103289 | \n",
" biolink:has_phenotype | \n",
" MP:0008476 | \n",
"
\n",
" \n",
" 331444 | \n",
" MGI:98834 | \n",
" biolink:has_phenotype | \n",
" MP:0003789 | \n",
"
\n",
" \n",
" 472334 | \n",
" MGI:1920719 | \n",
" biolink:has_phenotype | \n",
" MP:0001491 | \n",
"
\n",
" \n",
"
\n",
"
100 rows × 3 columns
\n",
"
"
],
"text/plain": [
" subject predicate object\n",
"396014 MGI:1261425 biolink:has_phenotype MP:0004974\n",
"434353 MGI:1858212 biolink:has_phenotype MP:0005331\n",
"374300 MGI:109207 biolink:has_phenotype MP:0005560\n",
"387558 MGI:98475 biolink:has_phenotype MP:0008872\n",
"315680 MGI:97512 biolink:has_phenotype MP:0005293\n",
"... ... ... ...\n",
"402503 MGI:88417 biolink:has_phenotype MP:0001923\n",
"350599 MGI:104510 biolink:has_phenotype MP:0006358\n",
"349262 MGI:103289 biolink:has_phenotype MP:0008476\n",
"331444 MGI:98834 biolink:has_phenotype MP:0003789\n",
"472334 MGI:1920719 biolink:has_phenotype MP:0001491\n",
"\n",
"[100 rows x 3 columns]"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# MGI:1261425 => drosha, ribonuclease type III\n",
"df_100 = df_term_association[df_term_association['subject'].str.startswith(\"MGI:\")].sample(n=100, random_state=1)\n",
"df_100 = df_100[['subject', 'predicate', 'object']]\n",
"df_100"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "68078a59-7ac9-419a-af3b-9b4e089c3e4f",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"396014 MGI:1261425\n",
"434353 MGI:1858212\n",
"374300 MGI:109207\n",
"387558 MGI:98475\n",
"315680 MGI:97512\n",
" ... \n",
"402503 MGI:88417\n",
"350599 MGI:104510\n",
"349262 MGI:103289\n",
"331444 MGI:98834\n",
"472334 MGI:1920719\n",
"Name: subject, Length: 100, dtype: object"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_100['subject'].drop_duplicates()"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "b0440238-5be2-4d0d-b017-ce6e0e82deaa",
"metadata": {},
"outputs": [],
"source": [
"subject_object_dict = {}\n",
"for subject in df_100['subject'].drop_duplicates():\n",
" objects = set(df_100[df_100['subject']==subject]['object'])\n",
" subject_object_dict[subject] = objects\n"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "33d93d2c-a3cd-4a97-8c5c-51f8a874c24b",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 13.5 s, sys: 1.03 s, total: 14.5 s\n",
"Wall time: 15.1 s\n"
]
}
],
"source": [
"%%time\n",
"predicates = [\"rdfs:subClassOf\", \"BFO:0000050\"]\n",
"semsimian = Semsimian(\n",
" spo=None,\n",
" predicates=predicates,\n",
" pairwise_similarity_attributes=None,\n",
" resource_path=db,\n",
" )\n"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "f5ffa45a-0223-426b-966b-3ddcebd5b180",
"metadata": {},
"outputs": [],
"source": [
"\n",
"subject_prefixes = [\"MGI:\"]\n",
"# object_terms = set(df_100['object'].drop_duplicates())\n",
"assoc_predicate = {\"biolink:has_phenotype\"}\n",
"include_similarity_object = True\n",
"limit = 50\n"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "e17e45a8-a80b-451c-a8a8-064bcfdb979e",
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"CPU times: user 1min 53s, sys: 2.26 s, total: 1min 56s\n",
"Wall time: 1min 34s\n"
]
}
],
"source": [
"%%time\n",
"\n",
"search_type = \"flat\"\n",
"flat_result = {}\n",
"for subj, obj in subject_object_dict.items():\n",
" flat_result[subj] = semsimian.associations_search(\n",
" assoc_predicate,\n",
" set(obj),\n",
" include_similarity_object,\n",
" search_type,\n",
" None,\n",
" subject_prefixes,\n",
" limit,\n",
" )"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "28cc65ae-5384-4930-abd5-c8ca6a2ac425",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"100"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(flat_result)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "64c75fc3-e80c-4e2e-8f6a-dfc73067cd86",
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"CPU times: user 2h 11min 22s, sys: 3min 48s, total: 2h 15min 10s\n",
"Wall time: 2h 14min 31s\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n"
]
}
],
"source": [
"%%time\n",
"\n",
"search_type = \"full\"\n",
"full_result = {}\n",
"for subj, obj in subject_object_dict.items():\n",
" full_result[subj] = semsimian.associations_search(\n",
" assoc_predicate,\n",
" set(obj),\n",
" include_similarity_object,\n",
" search_type,\n",
" None,\n",
" subject_prefixes,\n",
" limit,\n",
" )"
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "d7893cfb",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"100"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(full_result)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"id": "2fa0aa3c-1c15-42b0-b183-d0696bb3539c",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"CPU times: user 21min 27s, sys: 16.5 s, total: 21min 43s\n",
"Wall time: 21min 16s\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n",
"Using cache! \"MGI:biolink:has_phenotypeflat\"\n",
"Using cache! \"MGI:biolink:has_phenotypefull\"\n"
]
}
],
"source": [
"%%time\n",
"\n",
"search_type = \"hybrid\"\n",
"hybrid_result = {}\n",
"for subj, obj in subject_object_dict.items():\n",
" hybrid_result[subj] = semsimian.associations_search(\n",
" assoc_predicate,\n",
" set(obj),\n",
" include_similarity_object,\n",
" search_type,\n",
" None,\n",
" subject_prefixes,\n",
" limit,\n",
" )"
]
},
{
"cell_type": "code",
"execution_count": 20,
"id": "af58c33b",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"100"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(hybrid_result)"
]
},
{
"cell_type": "code",
"execution_count": 21,
"id": "ce99423f-6e90-440d-8d7f-eb4689d6f9ed",
"metadata": {},
"outputs": [],
"source": [
"def get_search_terms_for_input_term(dictionary:dict):\n",
" result_dict = {}\n",
" for k, v in dictionary.items():\n",
" result_dict[k] = [curie for _,_,curie in v]\n",
" return result_dict\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 22,
"id": "154aedab-3fc5-4f15-aade-74efbbd3c826",
"metadata": {},
"outputs": [],
"source": [
"full_result_terms = get_search_terms_for_input_term(full_result)\n",
"flat_result_terms = get_search_terms_for_input_term(flat_result)\n",
"hybrid_result_terms = get_search_terms_for_input_term(hybrid_result)"
]
},
{
"cell_type": "code",
"execution_count": 23,
"id": "bcbb7a29-a37c-4f29-b93b-620a84fa6f2c",
"metadata": {},
"outputs": [],
"source": [
"def get_overlap(dict1, dict2):\n",
" input_term_overlap = {}\n",
" for term, result in dict1.items():\n",
" common = set(result).intersection(set(dict2[term]))\n",
" total = len(set(dict2[term]) | set(result))\n",
" overlap = len(common) / total * 100\n",
" input_term_overlap[term] = overlap\n",
" return input_term_overlap\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 24,
"id": "d1a6a3cf-935c-48d8-b585-d7e14845c821",
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"{'MGI:1261425': 38.88888888888889,\n",
" 'MGI:1858212': 26.582278481012654,\n",
" 'MGI:109207': 29.87012987012987,\n",
" 'MGI:98475': 11.11111111111111,\n",
" 'MGI:97512': 33.33333333333333,\n",
" 'MGI:88291': 42.857142857142854,\n",
" 'MGI:1914362': 35.13513513513514,\n",
" 'MGI:1924285': 20.481927710843372,\n",
" 'MGI:2385958': 11.11111111111111,\n",
" 'MGI:1313275': 17.647058823529413,\n",
" 'MGI:2177178': 19.047619047619047,\n",
" 'MGI:98973': 13.636363636363635,\n",
" 'MGI:1341163': 8.695652173913043,\n",
" 'MGI:97311': 35.13513513513514,\n",
" 'MGI:88258': 25.0,\n",
" 'MGI:1915325': 40.845070422535215,\n",
" 'MGI:96083': 23.456790123456788,\n",
" 'MGI:95808': 33.33333333333333,\n",
" 'MGI:104779': 9.89010989010989,\n",
" 'MGI:104673': 42.857142857142854,\n",
" 'MGI:1321392': 53.84615384615385,\n",
" 'MGI:97348': 42.857142857142854,\n",
" 'MGI:1339999': 35.13513513513514,\n",
" 'MGI:88145': 53.84615384615385,\n",
" 'MGI:1098280': 40.845070422535215,\n",
" 'MGI:1891740': 2.0408163265306123,\n",
" 'MGI:1346872': 25.0,\n",
" 'MGI:105098': 25.0,\n",
" 'MGI:97503': 31.57894736842105,\n",
" 'MGI:2673128': 25.0,\n",
" 'MGI:1196326': 29.87012987012987,\n",
" 'MGI:102780': 2.0408163265306123,\n",
" 'MGI:95664': 38.88888888888889,\n",
" 'MGI:2385599': 61.29032258064516,\n",
" 'MGI:894293': 20.481927710843372,\n",
" 'MGI:1918686': 31.57894736842105,\n",
" 'MGI:97740': 23.456790123456788,\n",
" 'MGI:1859183': 6.382978723404255,\n",
" 'MGI:2145823': 38.88888888888889,\n",
" 'MGI:1920999': 14.942528735632186,\n",
" 'MGI:107543': 26.582278481012654,\n",
" 'MGI:1347470': 13.636363636363635,\n",
" 'MGI:1261768': 29.87012987012987,\n",
" 'MGI:1914523': 38.88888888888889,\n",
" 'MGI:1335098': 66.66666666666666,\n",
" 'MGI:892003': 29.87012987012987,\n",
" 'MGI:88342': 29.87012987012987,\n",
" 'MGI:108417': 47.05882352941176,\n",
" 'MGI:1195272': 25.0,\n",
" 'MGI:2137356': 1.0101010101010102,\n",
" 'MGI:107364': 11.11111111111111,\n",
" 'MGI:2135607': 17.647058823529413,\n",
" 'MGI:1346013': 25.0,\n",
" 'MGI:96958': 13.636363636363635,\n",
" 'MGI:96606': 13.636363636363635,\n",
" 'MGI:96721': 7.526881720430108,\n",
" 'MGI:1194993': 88.67924528301887,\n",
" 'MGI:2684139': 31.57894736842105,\n",
" 'MGI:1339753': 47.05882352941176,\n",
" 'MGI:1916296': 42.857142857142854,\n",
" 'MGI:97525': 26.582278481012654,\n",
" 'MGI:96551': 13.636363636363635,\n",
" 'MGI:1098590': 44.927536231884055,\n",
" 'MGI:1891124': 26.582278481012654,\n",
" 'MGI:1096335': 16.27906976744186,\n",
" 'MGI:87986': 25.0,\n",
" 'MGI:1276116': 29.87012987012987,\n",
" 'MGI:1098767': 35.13513513513514,\n",
" 'MGI:1321395': 16.27906976744186,\n",
" 'MGI:1261423': 38.88888888888889,\n",
" 'MGI:1917579': 33.33333333333333,\n",
" 'MGI:1929601': 20.481927710843372,\n",
" 'MGI:1196389': 92.3076923076923,\n",
" 'MGI:101884': 21.951219512195124,\n",
" 'MGI:98923': 25.0,\n",
" 'MGI:2444248': 38.88888888888889,\n",
" 'MGI:95514': 25.0,\n",
" 'MGI:1346060': 23.456790123456788,\n",
" 'MGI:107537': 14.942528735632186,\n",
" 'MGI:1914701': 13.636363636363635,\n",
" 'MGI:108520': 58.730158730158735,\n",
" 'MGI:1924238': 40.845070422535215,\n",
" 'MGI:1914155': 44.927536231884055,\n",
" 'MGI:88084': 16.27906976744186,\n",
" 'MGI:2684944': 21.951219512195124,\n",
" 'MGI:2141861': 35.13513513513514,\n",
" 'MGI:1919202': 12.359550561797752,\n",
" 'MGI:1353592': 2.0408163265306123,\n",
" 'MGI:1206581': 33.33333333333333,\n",
" 'MGI:1277238': 42.857142857142854,\n",
" 'MGI:3045253': 17.647058823529413,\n",
" 'MGI:103560': 36.986301369863014,\n",
" 'MGI:2387643': 21.951219512195124,\n",
" 'MGI:95481': 61.29032258064516,\n",
" 'MGI:104644': 13.636363636363635,\n",
" 'MGI:88417': 42.857142857142854,\n",
" 'MGI:104510': 47.05882352941176,\n",
" 'MGI:103289': 23.456790123456788,\n",
" 'MGI:98834': 16.27906976744186,\n",
" 'MGI:1920719': 33.33333333333333}"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# input_term_full_flat_overlap = get_overlap(full_result_terms, flat_result_terms)\n",
"input_term_full_hybrid_overlap = get_overlap(full_result_terms, hybrid_result_terms)\n",
"input_term_full_flat_overlap = get_overlap(full_result_terms, flat_result_terms)\n",
"input_term_flat_hybrid_overlap = get_overlap(flat_result_terms, hybrid_result_terms)\n",
"\n",
"input_term_flat_hybrid_overlap\n",
"# print(len(set(full_result_terms['MGI:1261425']).intersection(set(full_result_terms['MGI:1261425']))))"
]
},
{
"cell_type": "markdown",
"id": "c5c76fc6-4ed9-4c30-8085-9da558f50031",
"metadata": {},
"source": [
"### Plot params"
]
},
{
"cell_type": "code",
"execution_count": 25,
"id": "ebc0af67-8b45-45bf-9196-9a878c10766d",
"metadata": {},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
"\n",
"\n",
"\n",
"def plot_graph(data, title):\n",
" # Create lists for the plot\n",
" keys = list(data.keys())\n",
" values = list(data.values())\n",
" \n",
" # Create a bar chart\n",
" plt.figure(figsize=(20,10)) # Increase the size as needed\n",
" bars = plt.bar(range(len(data)), values, tick_label=keys)\n",
" \n",
" # Rotate the x-axis labels so they don't overlap\n",
" plt.xticks(rotation=90)\n",
" \n",
" # Add title and labels\n",
" plt.title(title)\n",
" plt.xlabel('Keys')\n",
" plt.ylabel('Values')\n",
"\n",
" # Loop over the bars and add the value on top\n",
" for bar in bars:\n",
" yval = bar.get_height()\n",
" plt.text(bar.get_x() + bar.get_width()/2.0, yval, round(yval, 2), va='bottom', rotation=45) # va: vertical alignment\n",
" \n",
" # Show the plot\n",
" plt.show()\n"
]
},
{
"cell_type": "code",
"execution_count": 26,
"id": "8ea8aa39-02c1-48e8-a319-cee7723733d7",
"metadata": {},
"outputs": [
{
"data": {
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",
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