From c184c960e8ce6d0dbacebdb72dec65c1f413ee41 Mon Sep 17 00:00:00 2001 From: Sheeba Samuel Date: Fri, 27 Sep 2024 01:56:50 +0200 Subject: [PATCH] Update the notebooks for SPARQL queries and evaluation with benchmark and performance metrics. --- notebooks/FAIRJupyter_SPARQL.ipynb | 754 +++++++++++++++++++++----- notebooks/FAIRJupyter_benchmark.ipynb | 654 ++++++++++++++++++++++ 2 files changed, 1267 insertions(+), 141 deletions(-) create mode 100644 notebooks/FAIRJupyter_benchmark.ipynb diff --git a/notebooks/FAIRJupyter_SPARQL.ipynb b/notebooks/FAIRJupyter_SPARQL.ipynb index 98c5fa5..6ec22ae 100644 --- a/notebooks/FAIRJupyter_SPARQL.ipynb +++ b/notebooks/FAIRJupyter_SPARQL.ipynb @@ -18,7 +18,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 1, "id": "93739f34-2a55-4882-baa8-1085998fadf6", "metadata": {}, "outputs": [ @@ -40,7 +40,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 2, "id": "13e5348b-58ee-4c63-98f6-c16a79fd2c34", "metadata": {}, "outputs": [ @@ -63,7 +63,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 3, "id": "bb9f2020-8098-4da9-804b-62b0c9ef9120", "metadata": {}, "outputs": [], @@ -86,7 +86,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 4, "id": "10ba0428-5784-47c6-8a78-5dfcaad886cc", "metadata": {}, "outputs": [], @@ -102,7 +102,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 5, "id": "70c896be-d1b3-4bdc-9276-00afb61585ba", "metadata": {}, "outputs": [], @@ -163,7 +163,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 6, "id": "256ab636-f768-4448-a2a1-4fcd6010f548", "metadata": {}, "outputs": [ @@ -268,7 +268,7 @@ "9 Health Care Quality, Access, and Evaluation 361" ] }, - "execution_count": 21, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -303,7 +303,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 7, "id": "1875525d-ea87-40d1-b540-69a8ea1c58af", "metadata": {}, "outputs": [ @@ -431,7 +431,7 @@ "9 343 " ] }, - "execution_count": 22, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -476,7 +476,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 8, "id": "533365a8-9baf-4e03-a5c4-ff8fc1a186e7", "metadata": {}, "outputs": [ @@ -581,7 +581,7 @@ "9 Sensors (Basel, Switzerland) 51" ] }, - "execution_count": 23, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -612,7 +612,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 9, "id": "14559a81-bf60-4884-b28f-5cfcfe619456", "metadata": {}, "outputs": [ @@ -740,7 +740,7 @@ "9 42 " ] }, - "execution_count": 24, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -781,7 +781,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 10, "id": "a39a85ea-e908-484d-b0a2-2c09bc4fe35a", "metadata": {}, "outputs": [ @@ -909,7 +909,7 @@ "9 9 " ] }, - "execution_count": 25, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -956,7 +956,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 11, "id": "fb266223-0e09-414a-ada5-fdff0a9bfea1", "metadata": {}, "outputs": [ @@ -1061,7 +1061,7 @@ "9 Java 8" ] }, - "execution_count": 26, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -1092,26 +1092,152 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "id": "7b9e3806-f28b-473d-8e77-ca8bb029311b", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Results written to results/fig_10_top_programming_languages_per_year.csv\n" + ] + }, + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " created_year minor_version count_minor_version\n", + "0 2010 3.4 27\n", + "1 2010 3.6 27\n", + "2 2010 3.7 1\n", + "3 2010 3.8 2\n", + "4 2010 3.9 1\n", + ".. ... ... ...\n", + "108 2022 unk 42\n", + "109 2023 3.6 1\n", + "110 2023 3.8 5\n", + "111 2023 3.9 14\n", + "112 2023 unk 1\n", + "\n", + "[113 rows x 3 columns]" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "query_string = \"\"\"\n", "SELECT ?created_year ?minor_version (COUNT(?notebook) as ?count_minor_version)\n", @@ -1140,7 +1393,7 @@ " \"python\" ;\n", " ?version .\n", " ?repository ?created_at .\n", - " BIND(REPLACE(str(?created_at), \"(\\\\d*)-.*\", \"$1\") AS ?created_year) \n", + " BIND(REPLACE(str(?created_at), \"^([0-9]{4})-.*\", \"$1\") AS ?created_year) \n", " BIND(SUBSTR(?version, 1, 3) AS ?minor_version)\n", " FILTER(?version != \"3\" && ?version != \"1\" && ?version != \"ES2015\")\n", "}\n", @@ -1164,10 +1417,316 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "id": "68c9782b-f2f8-44a8-82c4-7447edb564f5", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Results written to results/fig_12_python_notebooks_by_major_version_first_commit.csv\n" + ] + }, + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " created_year major_version count_major_version\n", + "0 2010 3 58\n", + "1 2011 2 2\n", + "2 2011 3 38\n", + "3 2012 2 4\n", + "4 2012 3 178\n", + "5 2013 2 41\n", + "6 2013 3 282\n", + "7 2013 u 40\n", + "8 2014 2 436\n", + "9 2014 3 195\n", + "10 2014 u 294\n", + "11 2015 2 303\n", + "12 2015 3 705\n", + "13 2015 u 28\n", + "14 2016 2 374\n", + "15 2016 3 920\n", + "16 2016 u 53\n", + "17 2017 2 323\n", + "18 2017 3 1368\n", + "19 2017 u 9\n", + "20 2018 2 256\n", + "21 2018 3 2371\n", + "22 2018 u 295\n", + "23 2019 2 164\n", + "24 2019 3 3915\n", + "25 2019 u 45\n", + "26 2020 2 92\n", + "27 2020 3 5707\n", + "28 2020 u 29\n", + "29 2021 2 80\n", + "30 2021 3 2604\n", + "31 2021 u 118\n", + "32 2022 2 2\n", + "33 2022 3 1146\n", + "34 2022 u 42\n", + "35 2023 3 20\n", + "36 2023 u 1" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "query_string = \"\"\"\n", "SELECT ?created_year ?major_version (COUNT(?notebook) as ?count_major_version)\n", @@ -1177,7 +1736,7 @@ " \"python\" ;\n", " ?version .\n", " ?repository ?created_at .\n", - " BIND(REPLACE(str(?created_at), \"(\\\\d*)-.*\", \"$1\") AS ?created_year) \n", + " BIND(REPLACE(str(?created_at), \"^([0-9]{4})-.*\", \"$1\") AS ?created_year) \n", " BIND(SUBSTR(?version, 1, 1) AS ?major_version)\n", " FILTER(?version != \"3\" && ?version != \"1\" && ?version != \"ES2015\")\n", "}\n", @@ -1201,7 +1760,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 15, "id": "1d5e330c-890a-46aa-898a-50613100ffd3", "metadata": {}, "outputs": [ @@ -1306,7 +1865,7 @@ "9 CalledProcessError 68" ] }, - "execution_count": 31, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } @@ -1338,7 +1897,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 16, "id": "0a397aca-e6f5-4f6d-b8c5-6cff90719e5b", "metadata": {}, "outputs": [ @@ -1443,7 +2002,7 @@ "9 Natural Science Disciplines 3663" ] }, - "execution_count": 32, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } @@ -1485,7 +2044,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 17, "id": "0b92d735-d8b2-4ebe-81be-0cd3d974cb36", "metadata": {}, "outputs": [ @@ -1538,7 +2097,7 @@ "0 324 879 1203" ] }, - "execution_count": 33, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } @@ -1577,7 +2136,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 18, "id": "32a2f2fb-d951-4b4f-9c30-e01484f485f7", "metadata": {}, "outputs": [ @@ -1717,7 +2276,7 @@ "9 comparison to None should be 'if cond is None:' " ] }, - "execution_count": 34, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" } @@ -1745,93 +2304,6 @@ "# Other queries over the FAIR Jupyter graph" ] }, - { - "cell_type": "markdown", - "id": "027b66fc-8279-4d14-840b-f1b284048bfd", - "metadata": {}, - "source": [ - "## Notebooks by search term: 'immun' AND ('stem' OR 'differentiation')" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "id": "8cddcac4-1898-470d-b12f-a960727b6943", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Results written to results/notebooks:by_search_term.csv\n" - ] - }, - { - "data": { - "text/html": [ - "
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notebook_urlarticle_labelkeywords
\n", - "
" - ], - "text/plain": [ - "Empty DataFrame\n", - "Columns: [notebook_url, article_label, keywords]\n", - "Index: []" - ] - }, - "execution_count": 35, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "query_string = \"\"\"\n", - "SELECT DISTINCT ?notebook_url ?article_label ?keywords WHERE { \n", - " ?article ?keywords .\n", - " ?article ?article_label . \n", - " ?article ?journal .\n", - " ?journal ?journal_label . \n", - " FILTER (REGEX(LCASE(CONCAT(?keywords, \" \", ?article_label, \" \", ?journal_label)), \"immun\"))\n", - " FILTER (REGEX(LCASE(CONCAT(?keywords, \" \", ?article_label, \" \", ?journal_label)), \"\\\\b(stem|differentiation)\"))\n", - " ?article ^ ?repository .\n", - " ?notebook ?repository .\n", - " ?notebook .\n", - " ?notebook ?notebook_label . # filename\n", - " ?repository ?repo_url_base . # find repo on GitHub\n", - " BIND(URI(CONCAT( ?repo_url_base, \"/blob/master/\", ?notebook_label)) AS ?notebook_url) # create clickable link to notebook on GitHub\n", - " FILTER (?notebook_url != \"\")\n", - "}\n", - "\n", - "\"\"\"\n", - "csv_filename = 'notebooks:by_search_term'\n", - "results = query_and_display_results(query_string, csv_filename)\n", - "results" - ] - }, { "cell_type": "markdown", "id": "35153af4-ae10-4515-a642-1ac9645902ff", @@ -1842,7 +2314,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 19, "id": "bc348244-0855-4627-b397-7a209b7439f3", "metadata": {}, "outputs": [ @@ -2267,7 +2739,7 @@ "53 African cities;Cote d’Ivoire;diarrhoea;landsca... " ] }, - "execution_count": 36, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" } @@ -2295,7 +2767,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 20, "id": "7ecf1873-71ed-4d02-b775-c199b40ecd56", "metadata": {}, "outputs": [ @@ -2382,7 +2854,7 @@ "6 RuntimeError 1" ] }, - "execution_count": 37, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" } @@ -2417,7 +2889,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 21, "id": "46e9655b-96f7-4c5b-9bd5-e40a1a6c968a", "metadata": {}, "outputs": [ @@ -2534,7 +3006,7 @@ "11 TypeError 1" ] }, - "execution_count": 38, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" } @@ -2571,7 +3043,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 22, "id": "f0071524-0106-4fd5-b0c7-9214a604befa", "metadata": {}, "outputs": [ @@ -2685,7 +3157,7 @@ "[106 rows x 2 columns]" ] }, - "execution_count": 39, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" } @@ -2725,7 +3197,7 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 23, "id": "161d8410-b2c9-497b-8d61-3b2ac5b3e9fe", "metadata": {}, "outputs": [ @@ -2839,7 +3311,7 @@ "[4162 rows x 2 columns]" ] }, - "execution_count": 40, + "execution_count": 23, "metadata": {}, "output_type": "execute_result" } @@ -2877,7 +3349,7 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 24, "id": "82e41f9c-e992-4c4c-bc11-00a9044d97d5", "metadata": {}, "outputs": [ @@ -3054,7 +3526,7 @@ "[100 rows x 4 columns]" ] }, - "execution_count": 41, + "execution_count": 24, "metadata": {}, "output_type": "execute_result" } @@ -3099,7 +3571,7 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 25, "id": "ed701131-6ff3-45d7-ad1e-fa7635e8ebab", "metadata": {}, "outputs": [ @@ -3263,7 +3735,7 @@ "[100 rows x 4 columns]" ] }, - "execution_count": 42, + "execution_count": 25, "metadata": {}, "output_type": "execute_result" } @@ -3307,7 +3779,7 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 26, "id": "fe8e4dbc-006a-4721-8dae-132bc6cc89e6", "metadata": {}, "outputs": [ @@ -3458,7 +3930,7 @@ "[100 rows x 4 columns]" ] }, - "execution_count": 43, + "execution_count": 26, "metadata": {}, "output_type": "execute_result" } diff --git a/notebooks/FAIRJupyter_benchmark.ipynb b/notebooks/FAIRJupyter_benchmark.ipynb new file mode 100644 index 0000000..e03f346 --- /dev/null +++ b/notebooks/FAIRJupyter_benchmark.ipynb @@ -0,0 +1,654 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "809c0a99-64fd-4f7f-87ca-3398dc6b1646", + "metadata": {}, + "source": [ + "# FAIR Jupyter Evaluation: Benchmark and Performance Metrics\n", + "FAIR Jupyter is a knowledge graph for semantic sharing and granular exploration of a computational notebook reproducibility dataset. This notebook provides some SPARQL queries to query the FAIR Jupyter SPARQL Endpoint.\n", + "More Information on FAIR Jupyter Ontology and Knowledge Graph: https://w3id.org/fairjupyter\n", + "\n", + "This notebook runs ten SPARQL queries on FAIR Jupyter and federated queries over Wikidata. It runs the notebook two times with different LIMITs. It logs the performance metrics when the queries are run with limit (e.g, 10, 100) and also logs the memory usage for each query execution.\n", + "\n", + "## Prerequisites\n", + "The notebook is written in Python and besides Jupyter and Wikidata, it has the following direct dependencies:\n", + "* sparqlwrapper to run SPARQL queries on Wikidata's SPARQL endpoint\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "fe8b7723-9822-4ed6-aa0e-3323c07855ca", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Requirement already satisfied: sparqlwrapper in /home/shsam/miniconda3/lib/python3.12/site-packages (2.0.0)\n", + "Requirement already satisfied: rdflib>=6.1.1 in /home/shsam/miniconda3/lib/python3.12/site-packages (from sparqlwrapper) (7.0.0)\n", + "Requirement already satisfied: isodate<0.7.0,>=0.6.0 in /home/shsam/miniconda3/lib/python3.12/site-packages (from rdflib>=6.1.1->sparqlwrapper) (0.6.1)\n", + "Requirement already satisfied: pyparsing<4,>=2.1.0 in /home/shsam/miniconda3/lib/python3.12/site-packages (from rdflib>=6.1.1->sparqlwrapper) (3.1.2)\n", + "Requirement already satisfied: six in /home/shsam/miniconda3/lib/python3.12/site-packages (from isodate<0.7.0,>=0.6.0->rdflib>=6.1.1->sparqlwrapper) (1.16.0)\n" + ] + } + ], + "source": [ + "!pip install sparqlwrapper" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "bae6f238-2313-48cd-ae82-55c2ff01b63e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Requirement already satisfied: memory-profiler in /home/shsam/miniconda3/lib/python3.12/site-packages (0.61.0)\n", + "Requirement already satisfied: psutil in /home/shsam/miniconda3/lib/python3.12/site-packages (from memory-profiler) (5.9.8)\n" + ] + } + ], + "source": [ + "!pip install memory-profiler" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "196d57d4-d0e1-4b3d-963a-1cb55bcc0ab2", + "metadata": {}, + "outputs": [], + "source": [ + "import time\n", + "import pandas as pd\n", + "from SPARQLWrapper import SPARQLWrapper, JSON\n", + "import psutil\n" + ] + }, + { + "cell_type": "markdown", + "id": "27d8c428-12af-4990-839b-6b8b1492292b", + "metadata": {}, + "source": [ + "# SPARQL Endpoint" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "b078a8b6-e755-4d15-8f5c-4bdfcb0af9ba", + "metadata": {}, + "outputs": [], + "source": [ + "# Define the SPARQL endpoint\n", + "sparql = SPARQLWrapper(\"https://reproduceme.uni-jena.de/fairjupyter/sparql\")" + ] + }, + { + "cell_type": "markdown", + "id": "e99839aa-3f6b-434e-a35b-0ef1e1404eda", + "metadata": {}, + "source": [ + "# Selected SPARQL queries and federated queries over Wikidata" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "0324f264-da4b-43a4-afdf-f245464bfab2", + "metadata": {}, + "outputs": [], + "source": [ + "# List of queries with placeholders for LIMIT\n", + "queries = [\n", + " \"\"\"\n", + " SELECT ?research_field (COUNT(DISTINCT ?article) AS ?number_of_articles)\n", + " WHERE {{ \n", + " ?repository ?article .\n", + " ?article ?mesh .\n", + " ?mesh ?top_mesh .\n", + " ?top_mesh ?research_field\n", + " \n", + " }}\n", + " GROUP BY ?research_field\n", + " ORDER BY DESC(?number_of_articles)\n", + " LIMIT {}\n", + " \"\"\",\n", + " \"\"\"\n", + " SELECT ?journal_name (COUNT(?article) as ?article_count)\n", + " WHERE {{\n", + " ?article ?journal .\n", + " ?journal ?journal_name .\n", + " }}\n", + " GROUP BY ?journal_name\n", + " ORDER BY DESC(?article_count)\n", + " LIMIT {}\n", + " \"\"\",\n", + " \"\"\"\n", + " SELECT ?language (COUNT(?notebook) as ?notebook_count)\n", + " WHERE {{\n", + " ?notebook a ;\n", + " ?language .\n", + " }}\n", + " GROUP BY ?language\n", + " ORDER BY DESC(?notebook_count)\n", + " LIMIT {}\n", + " \"\"\",\n", + " \"\"\"\n", + " SELECT ?exception (COUNT(?exception) AS ?count)\n", + " WHERE {{\n", + " ?execution a ;\n", + " ?exception .\n", + " }}\n", + " GROUP BY ?exception\n", + " ORDER BY DESC(?count)\n", + " LIMIT {}\n", + " \"\"\",\n", + " \"\"\"\n", + " PREFIX xsd: \n", + " SELECT DISTINCT ?research_field (COUNT(?exception) AS ?exception_count)\n", + " WHERE {{ \n", + " ?execution a ;\n", + " ?exception ;\n", + " ?repository .\n", + " ?repository a ;\n", + " \t\t\t ?article ;\n", + " \t\t\t ?notebooks_count .\n", + " ?article a ; \n", + " \t\t ?mesh .\n", + " ?mesh ?top_mesh .\n", + " ?top_mesh ?research_field . \n", + " FILTER (xsd:integer(?notebooks_count)>0)\n", + " }}\n", + " GROUP BY ?research_field\n", + " ORDER BY DESC(?exception_count)\n", + " LIMIT {}\n", + " \"\"\",\n", + " \"\"\"\n", + " SELECT ?notebook ?error ?description\n", + " WHERE {{\n", + " ?error a ;\n", + " ?description ;\n", + " ?notebook .\n", + " }}\n", + " LIMIT {}\n", + " \"\"\",\n", + " \"\"\"\n", + " SELECT DISTINCT ?article ?keywords WHERE {{ \n", + " ?article ?keywords .\n", + " FILTER (REGEX(LCASE(?keywords), \"open(.)source\"))\n", + " }}\n", + " LIMIT {}\n", + " \"\"\",\n", + " \"\"\"\n", + " PREFIX xsd: \n", + " SELECT DISTINCT ?repo ?stargazers_count WHERE {{\n", + " ?repo ?count. \n", + " BIND(xsd:float(?count) AS ?stargazers_count)\n", + " FILTER ((?stargazers_count) > 0)\n", + " }}\n", + " ORDER BY DESC(?stargazers_count)\n", + " LIMIT {}\n", + " \"\"\",\n", + " \"\"\"\n", + " PREFIX rdfs: \n", + "\n", + " PREFIX wikidata_wd: \n", + " PREFIX wikidata_wdt: \n", + " \n", + " SELECT DISTINCT\n", + " \n", + " ?fj_article\n", + " ?wikidata\n", + " ?wikidata_label\n", + " ?DOI\n", + " \n", + " WHERE {{\n", + " ?fj_article ?doi .\n", + " BIND(UCASE(?doi) AS ?DOI)\n", + " service {{\n", + " ?wikidata wikidata_wdt:P356 ?DOI .\n", + " ?wikidata rdfs:label ?wikidata_label .\n", + " FILTER (LANG(?wikidata_label) = \"en\")\n", + " }}\n", + " }}\n", + " LIMIT {}\n", + " \"\"\",\n", + " \"\"\"\n", + " PREFIX rdfs: \n", + "\n", + " PREFIX wikidata_wd: \n", + " PREFIX wikidata_wdt: \n", + " \n", + " SELECT DISTINCT\n", + " \n", + " ?fj_article\n", + " ?wikidata\n", + " ?wikidata_label\n", + " ?pmc\n", + " \n", + " WHERE {{\n", + " ?fj_article ?pmc .\n", + " service {{\n", + " ?wikidata wikidata_wdt:P932 ?pmc .\n", + " ?wikidata rdfs:label ?wikidata_label .\n", + " FILTER (LANG(?wikidata_label) = \"en\")\n", + " }}\n", + " }}\n", + " LIMIT {}\n", + " \"\"\",\n", + "]\n" + ] + }, + { + "cell_type": "markdown", + "id": "cb7135c1-ca3f-42e2-834c-530edb9b8dfb", + "metadata": {}, + "source": [ + "# Functions" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "3f78a669-65f6-4ba1-953e-4470b39ff200", + "metadata": {}, + "outputs": [], + "source": [ + "# Function to execute the SPARQL query and manually measure memory\n", + "def execute_query(query, limit):\n", + " query_with_limit = query.format(limit)\n", + " \n", + " # Measure memory and time\n", + " start_time = time.time()\n", + " process = psutil.Process() # Get current process\n", + " mem_before = process.memory_info().rss / (1024 * 1024) # Memory in MB\n", + "\n", + " sparql.setQuery(query_with_limit)\n", + " sparql.setReturnFormat(JSON)\n", + " sparql.query().convert()\n", + " \n", + " mem_after = process.memory_info().rss / (1024 * 1024) # Memory in MB\n", + " end_time = time.time()\n", + " \n", + " execution_time = end_time - start_time\n", + " memory_used = mem_after - mem_before\n", + " \n", + " return execution_time, memory_used" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "821cb71d-8e36-49ee-b669-2dfe838e318e", + "metadata": {}, + "outputs": [], + "source": [ + "# Function to run all queries for a specific LIMIT and log time/memory\n", + "def run_queries_with_limit(limit):\n", + " results = []\n", + " total_start_time = time.time()\n", + " total_mem_usage = []\n", + "\n", + " for i, query in enumerate(queries):\n", + " print(f\"Running Query {i+1} with LIMIT {limit}...\")\n", + " \n", + " # Run the query and log time/memory\n", + " execution_time, mem_usage = execute_query(query, limit) \n", + " print(f\"execution_time: {execution_time}\")\n", + " print(f\"mem_usage: {mem_usage}\")\n", + " time.sleep(2) # Delay for 2 seconds between queries\n", + " \n", + " if execution_time is not None and mem_usage is not None:\n", + " total_mem_usage.append(mem_usage)\n", + " results.append({\n", + " 'Query': i + 1,\n", + " 'LIMIT': limit,\n", + " 'Execution Time (s)': execution_time,\n", + " 'Memory Usage (MB)': mem_usage\n", + " })\n", + " else:\n", + " print(f\"Skipping Query {i+1} due to an error.\")\n", + " \n", + " total_end_time = time.time()\n", + " total_execution_time = total_end_time - total_start_time\n", + " total_memory_usage = max(total_mem_usage) if total_mem_usage else None # Overall peak memory usage\n", + " \n", + " # Log the total memory/time for the whole notebook\n", + " results.append({\n", + " 'Query': 'Total',\n", + " 'LIMIT': limit,\n", + " 'Execution Time (s)': total_execution_time,\n", + " 'Memory Usage (MB)': total_memory_usage\n", + " })\n", + " \n", + " return results" + ] + }, + { + "cell_type": "markdown", + "id": "4c035a27-1ccc-47b8-8b6c-903360435835", + "metadata": {}, + "source": [ + "# Results" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "2cb8f825-3974-41b5-b10d-ab2ba7ac8a91", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Running Query 1 with LIMIT 10...\n", + "execution_time: 4.4106605052948\n", + "mem_usage: 1.5\n", + "Running Query 2 with LIMIT 10...\n", + "execution_time: 0.16930508613586426\n", + "mem_usage: 0.0\n", + "Running Query 3 with LIMIT 10...\n", + "execution_time: 0.3337666988372803\n", + "mem_usage: 0.0\n", + "Running Query 4 with LIMIT 10...\n", + "execution_time: 0.19837260246276855\n", + "mem_usage: 0.0\n", + "Running Query 5 with LIMIT 10...\n", + "execution_time: 1.2284893989562988\n", + "mem_usage: 0.0\n", + "Running Query 6 with LIMIT 10...\n", + "execution_time: 0.12428760528564453\n", + "mem_usage: 0.0\n", + "Running Query 7 with LIMIT 10...\n", + "execution_time: 0.12464475631713867\n", + "mem_usage: 0.0\n", + "Running Query 8 with LIMIT 10...\n", + "execution_time: 0.1638171672821045\n", + "mem_usage: 0.0\n", + "Running Query 9 with LIMIT 10...\n", + "execution_time: 2.3390748500823975\n", + "mem_usage: 0.0\n", + "Running Query 10 with LIMIT 10...\n", + "execution_time: 2.6051535606384277\n", + "mem_usage: 0.0\n", + "Running Query 1 with LIMIT 100...\n", + "execution_time: 4.086849927902222\n", + "mem_usage: 0.125\n", + "Running Query 2 with LIMIT 100...\n", + "execution_time: 0.22158551216125488\n", + "mem_usage: 0.0\n", + "Running Query 3 with LIMIT 100...\n", + "execution_time: 0.3401978015899658\n", + "mem_usage: 0.0\n", + "Running Query 4 with LIMIT 100...\n", + "execution_time: 0.23847579956054688\n", + "mem_usage: 0.0\n", + "Running Query 5 with LIMIT 100...\n", + "execution_time: 1.41654634475708\n", + "mem_usage: 0.0\n", + "Running Query 6 with LIMIT 100...\n", + "execution_time: 0.18763065338134766\n", + "mem_usage: 0.0\n", + "Running Query 7 with LIMIT 100...\n", + "execution_time: 0.16681241989135742\n", + "mem_usage: 0.0\n", + "Running Query 8 with LIMIT 100...\n", + "execution_time: 0.4047880172729492\n", + "mem_usage: 0.0\n", + "Running Query 9 with LIMIT 100...\n", + "execution_time: 15.250993251800537\n", + "mem_usage: 0.25\n", + "Running Query 10 with LIMIT 100...\n", + "execution_time: 23.499578714370728\n", + "mem_usage: 0.0\n", + "Running Query 1 with LIMIT 1000...\n", + "execution_time: 4.16658878326416\n", + "mem_usage: 0.0\n", + "Running Query 2 with LIMIT 1000...\n", + "execution_time: 0.4732682704925537\n", + "mem_usage: 0.875\n", + "Running Query 3 with LIMIT 1000...\n", + "execution_time: 0.3411281108856201\n", + "mem_usage: 0.0\n", + "Running Query 4 with LIMIT 1000...\n", + "execution_time: 0.27576780319213867\n", + "mem_usage: 0.0\n", + "Running Query 5 with LIMIT 1000...\n", + "execution_time: 1.2777421474456787\n", + "mem_usage: 0.0\n", + "Running Query 6 with LIMIT 1000...\n", + "execution_time: 1.1799492835998535\n", + "mem_usage: 0.625\n", + "Running Query 7 with LIMIT 1000...\n", + "execution_time: 0.16412019729614258\n", + "mem_usage: 0.0\n", + "Running Query 8 with LIMIT 1000...\n", + "execution_time: 0.6738555431365967\n", + "mem_usage: 0.0\n", + "Running Query 9 with LIMIT 1000...\n", + "execution_time: 127.23007845878601\n", + "mem_usage: -0.05078125\n", + "Running Query 10 with LIMIT 1000...\n", + "execution_time: 223.4653778076172\n", + "mem_usage: 0.0859375\n", + "Benchmarking complete. Results saved to 'sparql_benchmark_results.csv'.\n" + ] + } + ], + "source": [ + "\n", + "# Run the notebook two times with different LIMITs\n", + "all_results = []\n", + "for limit in [10, 100, 1000]:\n", + " run_results = run_queries_with_limit(limit)\n", + " all_results.extend(run_results)\n", + "\n", + "# Save results to a DataFrame and export to CSV\n", + "df_results = pd.DataFrame(all_results)\n", + "df_results.to_csv('sparql_benchmark_results.csv', index=False)\n", + "\n", + "print(\"Benchmarking complete. Results saved to 'sparql_benchmark_results.csv'.\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "83eb843a-4ab3-422a-9f45-579843968581", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Collecting seaborn\n", + " Downloading seaborn-0.13.2-py3-none-any.whl.metadata (5.4 kB)\n", + "Requirement already satisfied: numpy!=1.24.0,>=1.20 in /home/shsam/miniconda3/lib/python3.12/site-packages (from seaborn) (1.26.4)\n", + "Requirement already satisfied: pandas>=1.2 in /home/shsam/miniconda3/lib/python3.12/site-packages (from seaborn) (2.2.2)\n", + "Requirement already satisfied: matplotlib!=3.6.1,>=3.4 in /home/shsam/miniconda3/lib/python3.12/site-packages (from seaborn) (3.9.2)\n", + "Requirement already satisfied: contourpy>=1.0.1 in /home/shsam/miniconda3/lib/python3.12/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (1.3.0)\n", + "Requirement already satisfied: cycler>=0.10 in 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"code", + "execution_count": 11, + "id": "81175f37-5a6f-427c-b3ec-ab9d1b195d06", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "\n", + "# Load the benchmark results from the CSV file\n", + "df_results = pd.read_csv('sparql_benchmark_results.csv')\n", + "\n", + "# Set seaborn style for better aesthetics\n", + "sns.set(style=\"whitegrid\")\n", + "\n", + "# Create a figure with two subplots: one for execution time, one for memory usage\n", + "fig, axs = plt.subplots(2, 1, figsize=(10, 8))\n", + "\n", + "# Plot Execution Time\n", + "sns.lineplot(\n", + " data=df_results, \n", + " x='LIMIT', \n", + " y='Execution Time (s)', \n", + " hue='Query', \n", + " marker=\"o\", \n", + " ax=axs[0]\n", + ")\n", + "axs[0].set_title('SPARQL Query Execution Time by LIMIT')\n", + "axs[0].set_ylabel('Execution Time (s)')\n", + "axs[0].set_xlabel('LIMIT')\n", + "axs[0].legend(title='Query', bbox_to_anchor=(1.05, 1), loc='upper left')\n", + "\n", + "# Plot Memory Usage\n", + "sns.lineplot(\n", + " data=df_results, \n", + " x='LIMIT', \n", + " y='Memory Usage (MB)', \n", + " hue='Query', \n", + " marker=\"o\", \n", + " ax=axs[1]\n", + ")\n", + "axs[1].set_title('SPARQL Query Memory Usage by LIMIT')\n", + "axs[1].set_ylabel('Memory Usage (MB)')\n", + "axs[1].set_xlabel('LIMIT')\n", + "axs[1].legend(title='Query', bbox_to_anchor=(1.05, 1), loc='upper left')\n", + "\n", + "# Adjust layout\n", + "plt.tight_layout()\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "74382bce-26a7-482c-b040-6ef768de32c8", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Plot has been saved as 'sparql_benchmark_results.pdf'\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "\n", + "# Load the benchmark results from the CSV file (replace with your actual file path)\n", + "df_results = pd.read_csv('sparql_benchmark_results.csv')\n", + "\n", + "# Set seaborn style for better aesthetics\n", + "sns.set(style=\"whitegrid\")\n", + "\n", + "# Create a figure with two subplots: one for execution time, one for memory usage\n", + "fig, axs = plt.subplots(1, 1, figsize=(8, 4))\n", + "\n", + "# Plot Execution Time\n", + "sns.lineplot(\n", + " data=df_results, \n", + " x='LIMIT', \n", + " y='Execution Time (s)', \n", + " hue='Query', \n", + " marker=\"o\", \n", + " ax=axs\n", + ")\n", + "axs.set_title('SPARQL Query Execution Time by LIMIT')\n", + "axs.set_ylabel('Execution Time (s)')\n", + "axs.set_xlabel('LIMIT')\n", + "axs.legend(title='Query', bbox_to_anchor=(1.05, 1), loc='upper left')\n", + "\n", + "\n", + "\n", + "# Adjust layout\n", + "plt.tight_layout()\n", + "\n", + "# Save the figure as a PDF\n", + "fig.savefig('sparql_benchmark_results.pdf')\n", + "\n", + "print(\"Plot has been saved as 'sparql_benchmark_results.pdf'\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "45ac8db6-2b8d-421d-8152-42d179259feb", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +}