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Updated visualize decision tree functions
Update 2_5_0_Machine_Learning.ipynb
2026-03-02 09:48:17 +00:00 · 2026-02-25 17:19:08 +01:00 · 2026-02-25 16:32:10 +01:00 · 2026-02-25 16:27:04 +01:00 · 2026-02-25 16:21:44 +01:00 · 2026-02-19 12:34:40 +01:00
12 changed files with 274 additions and 98 deletions
--- a/lod/01_1_SPARQL_Server.ipynb
+++ b/lod/01_1_SPARQL_Server.ipynb
@@ -0,0 +1,157 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<header style=\"width:100%;position:relative\">\n",
+    "  <div style=\"width:80%;float:right;\">\n",
+    "    <h1>Course Notes for Learning Intelligent Systems</h1>\n",
+    "    <h2>Department of Telematic Engineering Systems</h2>\n",
+    "    <h3>Universidad Politécnica de Madrid. © Carlos A. Iglesias </h3>\n",
+    "  </div>\n",
+    "        <img style=\"width:15%;\" src=\"https://github.com/gsi-upm/sitc/blob/9844820e6653b0e169113a06538f8e54554c4fbc/images/EscUpmPolit_p.gif?raw=true\" alt=\"UPM\" />\n",
+    "</header>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Introduction\n",
+    "\n",
+    "This lecture explains how to run the SPARQL Server Fuseki using Docker."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Installing Fuseki with Docker\n",
+    "This section is taken from [[1](#1), [2](#2)].\n",
+    "\n",
+    "## Install Docker if not installed\n",
+    "You should have installed **Docker**. If not, refer to the [docker install guide](https://docs.docker.com/engine/install/).\n",
+    "\n",
+    "## Install fuseki image\n",
+    "In a terminal, run\n",
+    "```\n",
+    "docker run -p 3030:3030 --name fuseki -e ADMIN_PASSWORD=fuseki -it stain/jena-fuseki\n",
+    "```\n",
+    "You can change the admin password to anyone that you want, or the ports.\n",
+    "\n",
+    "You should see the logs in the terminal.\n",
+    "\n",
+    "![alt text](docker-run-jena-fuseki.jpg \"Fuseki running in Docker\")\n",
+    "\n",
+    "## Access admin UI\n",
+    "Now open a browser to [http://localhost:3030](http://localhost:3030) and log in as user *admin* with password *fuseki* (or the password you set earlier).\n",
+    "\n",
+    "![alt text](fuseki-running.jpg \"UI Admin Fuseki\")\n",
+    "\n",
+    "## Load data\n",
+    "Download this dataset to your computer.\n",
+    "\n",
+    "[Beatles dataset](https://github.com/gsi-upm/sitc/blob/master/lod/BeatlesMusicians.ttl).\n",
+    "\n",
+    "At the bottom of the UI, you can see 'No datasets created - add one'. Click on *add one*. Set *beatles* as the dataset name and in-memory as the dataset type.\n",
+    "\n",
+    "![alt text](new-dataset.jpg \"create dataset\")\n",
+    "\n",
+    "\n",
+    "Click the *create dataset* button.\n",
+    "\n",
+    "![alt text](created-dataset.jpg \"created dataset\")\n",
+    "\n",
+    "Click the *add data* button. In the new screen, select the button *select files* and click on the file you have previously downloaded, and click on the *upload now* button.\n",
+    "\n",
+    "![alt text](upload-data.jpg \"upload data\")\n",
+    "\n",
+    "Now the *query* tab.\n",
+    "\n",
+    "![alt text](query-ui.jpg \"query ui\")\n",
+    "\n",
+    "You see a generic query to list triples, click on the *Play* button (a triangle on the SPARQL query's right).\n",
+    "\n",
+    "Scroll down to see the query results.\n",
+    "\n",
+    "![alt text](query-results.jpg \"query results\")\n",
+    "\n",
+    "\n",
+    "Congratulations! You have a SPARQL server running, serving a dataset!!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## References"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* <a id=\"1\">[1]</a> [SPARQL by Example. A Tutorial. Lee Feigenbaum. W3C, 2009](https://hub.docker.com/r/stain/jena-fuseki)\n",
+    "* <a id=\"2\">[2]</a> [SPARQL queries of Beatles recording sessions](http://www.snee.com/bobdc.blog/2017/11/sparql-queries-of-beatles-reco.html)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Licence\n",
+    "The notebook is freely licensed under the [Creative Commons Attribution Share-Alike license](https://creativecommons.org/licenses/by/2.0/).  \n",
+    "\n",
+    "©  Carlos A. Iglesias, Universidad Politécnica de Madrid."
+   ]
+  }
+ ],
+ "metadata": {
+  "datacleaner": {
+   "position": {
+    "top": "50px"
+   },
+   "python": {
+    "varRefreshCmd": "try:\n    print(_datacleaner.dataframe_metadata())\nexcept:\n    print([])"
+   },
+   "window_display": false
+  },
+  "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": "3.12.2"
+  },
+  "latex_envs": {
+   "LaTeX_envs_menu_present": true,
+   "autocomplete": true,
+   "bibliofile": "biblio.bib",
+   "cite_by": "apalike",
+   "current_citInitial": 1,
+   "eqLabelWithNumbers": true,
+   "eqNumInitial": 1,
+   "hotkeys": {
+    "equation": "Ctrl-E",
+    "itemize": "Ctrl-I"
+   },
+   "labels_anchors": false,
+   "latex_user_defs": false,
+   "report_style_numbering": false,
+   "user_envs_cfg": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
--- a/lod/01_SPARQL_Introduction.ipynb
+++ b/lod/01_SPARQL_Introduction.ipynb
@@ -7,7 +7,7 @@
    "editable": false,
    "nbgrader": {
     "cell_type": "markdown",
-     "checksum": "7276f055a8c504d3c80098c62ed41a4f",
+     "checksum": "91ddca8e4ff354f86a8bdbfee755a886",
     "grade": false,
     "grade_id": "cell-0bfe38f97f6ab2d2",
     "locked": true,
@@ -19,10 +19,10 @@
    "<header style=\"width:100%;position:relative\">\n",
    "  <div style=\"width:80%;float:right;\">\n",
    "    <h1>Course Notes for Learning Intelligent Systems</h1>\n",
-    "    <h3>Department of Telematic Engineering Systems</h3>\n",
-    "    <h5>Universidad Politécnica de Madrid</h5>\n",
+    "    <h2>Department of Telematic Engineering Systems</h2>\n",
+    "    <h3>Universidad Politécnica de Madrid</h3>\n",
    "  </div>\n",
-    "        <img style=\"width:15%;\" src=\"../logo.jpg\" alt=\"UPM\" />\n",
+    "        <img style=\"width:15%;\" src=\"https://github.com/gsi-upm/sitc/blob/9844820e6653b0e169113a06538f8e54554c4fbc/images/EscUpmPolit_p.gif?raw=true\" alt=\"UPM\" />\n",
    "</header>"
   ]
  },
@@ -58,7 +58,7 @@
    "editable": false,
    "nbgrader": {
     "cell_type": "markdown",
-     "checksum": "40ccd05ad0704781327031a84dfb9939",
+     "checksum": "5760eec341771cfd3ebd91627da0a481",
     "grade": false,
     "grade_id": "cell-4f8492996e74bf20",
     "locked": true,
@@ -71,10 +71,10 @@
    "\n",
    "* This notebook\n",
    "* External SPARQL editors (optional)\n",
-    "    * YASGUI-GSI http://yasgui.gsi.upm.es\n",
-    "    * DBpedia virtuoso http://dbpedia.org/sparql\n",
+    "    * *YASGUI* https://yasgui.org/\n",
+    "    * *DBpedia Virtuoso* http://dbpedia.org/sparql\n",
    "\n",
-    "Using the YASGUI-GSI editor has several advantages over other options.\n",
+    "Using the YASGUI editor has several advantages over other options.\n",
    "It features:\n",
    "\n",
    "* Selection of data source, either by specifying the URL or by selecting from a dropdown menu\n",
@@ -96,7 +96,7 @@
    "editable": false,
    "nbgrader": {
     "cell_type": "markdown",
-     "checksum": "81894e9d65e5dd9f3b6e1c5f66804bf6",
+     "checksum": "b1285fb82e4438a22e05ff134d1e080d",
     "grade": false,
     "grade_id": "cell-70ac24910356c3cf",
     "locked": true,
@@ -107,13 +107,15 @@
   "source": [
    "## Instructions\n",
    "\n",
-    "We will be using a semantic server, available at: http://fuseki.gsi.upm.es/sitc.\n",
+    "We will use an available semantic server. There are two possible settings:\n",
+    "* A local server available at [http://fuseki.gsi.upm.es/sitc]( http://fuseki.gsi.upm.es/sitc/)\n",
+    "* Install the SPARQL server yourself and run it locally. To do this, follow the instructions in the notebook [how to install a SPARQL Server](01_1_SPARQL_Server.ipynb). In this case, your server is available at  [http://localhost:3030](http://localhost:3030).\n",
    "\n",
    "This server contains a dataset about [Beatles songs](http://www.snee.com/bobdc.blog/2017/11/sparql-queries-of-beatles-reco.html), which we will query with SPARQL.\n",
    "\n",
-    "We will provide you some example code to get you started, the *question* you will have to answer using SPARQL, a template for the answer.\n",
+    "We will provide you with some example code to get you started, along with the *question* you will have to answer using SPARQL and a template for the answer.\n",
    "\n",
-    "After every query, you will find some python code to test the results of the query.\n",
+    "After every query, you will find some Python code to test the results of the query.\n",
    "**Make sure you've run the tests before moving to the next exercise**.\n",
    "If the test gives you an error, you've probably done something wrong.\n",
    "You do not need to understand or modify the test code."
@@ -126,7 +128,7 @@
    "editable": false,
    "nbgrader": {
     "cell_type": "markdown",
-     "checksum": "1d332d3d11fd6b57f0ec0ac3c358c6cb",
+     "checksum": "a5918241a0b402cb091a85d245aaa3fd",
     "grade": false,
     "grade_id": "cell-eb13908482825e42",
     "locked": true,
@@ -138,7 +140,7 @@
    "For convenience, the examples in the notebook are executable (using the `%%sparql` magic command), and they are accompanied by some code to test the results.\n",
    "If the tests pass, you probably got the answer right.\n",
    "\n",
-    "**Run this line to enable the `%%sparql` magic command.**"
+    "### Run this line to enable the `%%sparql` magic command.**"
   ]
  },
  {
@@ -200,7 +202,7 @@
    "editable": false,
    "nbgrader": {
     "cell_type": "markdown",
-     "checksum": "34710d3bb8e2cf826833a43adb7fb448",
+     "checksum": "77cd823ca5a1556311f3dcf7e1533bce",
     "grade": false,
     "grade_id": "cell-2a44c0da2c206d01",
     "locked": true,
@@ -216,10 +218,23 @@
    "In addition to running queries from your browser, they provide useful features such as syntax highlighting and autocompletion.\n",
    "Some examples are:\n",
    "\n",
-    "* DBpedia's virtuoso query editor https://dbpedia.org/sparql\n",
-    "* A javascript based client hosted at GSI: http://yasgui.gsi.upm.es/\n",
+    "* DBpedia's Virtuoso query editor https://dbpedia.org/sparql\n",
+    "* A JavaScript-based client: https://yasgui.org/\n",
    "\n",
-    "[^1]: http://www.snee.com/bobdc.blog/2017/11/sparql-queries-of-beatles-reco.html"
+    "[^1]: http://www.snee.com/bobdc.blog/2017/11/sparql-queries-of-beatles-reco.html\n",
+    "\n",
+    "### Set your SPARQL server"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "## Set your Fuseki server URL\n",
+    "\n",
+    "fuseki = 'http://localhost:3030/beatles/sparql'"
   ]
  },
  {
@@ -250,7 +265,7 @@
    "editable": false,
    "nbgrader": {
     "cell_type": "markdown",
-     "checksum": "f7428fe79cd33383dfd3b09a0d951b6e",
+     "checksum": "1d6bd8b03621fb0b2d74c80b91e9b91a",
     "grade": false,
     "grade_id": "cell-8391a5322a9ad4a7",
     "locked": true,
@@ -259,7 +274,7 @@
    }
   },
   "source": [
-    "#### First select - Exploring the dataset\n",
+    "### First select - Exploring the dataset\n",
    "\n"
   ]
  },
@@ -324,7 +339,7 @@
    "editable": false,
    "nbgrader": {
     "cell_type": "code",
-     "checksum": "3bc71f851a33fa401d18ea3ab02cf61f",
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     "grade_id": "cell-8ce8c954513f17e7",
     "locked": true,
@@ -334,7 +349,7 @@
   },
   "outputs": [],
   "source": [
-    "%%sparql http://fuseki.gsi.upm.es/sitc/\n",
+    "%%sparql $fuseki\n",
    "\n",
    "SELECT ?entity ?type\n",
    "WHERE {\n",
@@ -388,7 +403,7 @@
    "deletable": false,
    "nbgrader": {
     "cell_type": "code",
-     "checksum": "65be7168bedb4f6dc2f19e2138bab232",
+     "checksum": "1879bf78f30a1ec22499eec5f975803d",
     "grade": false,
     "grade_id": "cell-6e904d692b5facad",
     "locked": false,
@@ -398,7 +413,7 @@
   },
   "outputs": [],
   "source": [
-    "%%sparql http://fuseki.gsi.upm.es/sitc/\n",
+    "%%sparql $fuseki\n",
    "\n",
    "SELECT ?entity ?prop\n",
    "WHERE {\n",
@@ -454,7 +469,7 @@
   "metadata": {},
   "outputs": [],
   "source": [
-    "%%sparql http://fuseki.gsi.upm.es/sitc/\n",
+    "%%sparql $fuseki\n",
    "\n",
    "SELECT ?type\n",
    "WHERE {\n",
@@ -479,7 +494,7 @@
   "metadata": {},
   "outputs": [],
   "source": [
-    "%%sparql http://fuseki.gsi.upm.es/sitc/\n",
+    "%%sparql $fuseki\n",
    "\n",
    "SELECT DISTINCT ?type\n",
    "WHERE {\n",
@@ -522,7 +537,7 @@
    "deletable": false,
    "nbgrader": {
     "cell_type": "code",
-     "checksum": "35563ff455c7e8b1c91f61db97b2011b",
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     "grade": false,
     "grade_id": "cell-e615f9a77c4bc9a5",
     "locked": false,
@@ -532,7 +547,7 @@
   },
   "outputs": [],
   "source": [
-    "%%sparql http://fuseki.gsi.upm.es/sitc/\n",
+    "%%sparql $fuseki\n",
    "\n",
    "SELECT DISTINCT ?property\n",
    "WHERE {\n",
@@ -585,7 +600,7 @@
   "metadata": {},
   "outputs": [],
   "source": [
-    "%%sparql http://fuseki.gsi.upm.es/sitc/\n",
+    "%%sparql $fuseki\n",
    "\n",
    "PREFIX s: <http://learningsparql.com/ns/schema/>\n",
    "PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>\n",
@@ -655,7 +670,7 @@
    "deletable": false,
    "nbgrader": {
     "cell_type": "code",
-     "checksum": "069811507dbac4b86dc5d3adc82ba4ec",
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     "grade": false,
     "grade_id": "cell-0223a51f609edcf9",
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@@ -665,7 +680,7 @@
   },
   "outputs": [],
   "source": [
-    "%%sparql http://fuseki.gsi.upm.es/sitc/\n",
+    "%%sparql $fuseki\n",
    "\n",
    "PREFIX s: <http://learningsparql.com/ns/schema/>\n",
    "PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>\n",
@@ -725,7 +740,7 @@
    "deletable": false,
    "nbgrader": {
     "cell_type": "code",
-     "checksum": "b68a279085a1ed087f5e474a6602299e",
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@@ -735,7 +750,7 @@
   },
   "outputs": [],
   "source": [
-    "%%sparql http://fuseki.gsi.upm.es/sitc/\n",
+    "%%sparql $fuseki\n",
    "\n",
    "PREFIX s: <http://learningsparql.com/ns/schema/>\n",
    "PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>\n",
@@ -812,7 +827,7 @@
    "deletable": false,
    "nbgrader": {
     "cell_type": "code",
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@@ -822,7 +837,7 @@
   },
   "outputs": [],
   "source": [
-    "%%sparql http://fuseki.gsi.upm.es/sitc/\n",
+    "%%sparql $fuseki\n",
    "\n",
    "PREFIX s: <http://learningsparql.com/ns/schema/>\n",
    "PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>\n",
@@ -891,7 +906,7 @@
    "deletable": false,
    "nbgrader": {
     "cell_type": "code",
-     "checksum": "8fb253675d2e8510e2c6780b960721e5",
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@@ -901,7 +916,7 @@
   },
   "outputs": [],
   "source": [
-    "%%sparql http://fuseki.gsi.upm.es/sitc/\n",
+    "%%sparql $fuseki\n",
    "\n",
    "PREFIX s: <http://learningsparql.com/ns/schema/>\n",
    "PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>\n",
@@ -971,7 +986,7 @@
    "deletable": false,
    "nbgrader": {
     "cell_type": "code",
-     "checksum": "c7b6620f5ba28b482197ab693cb7142a",
+     "checksum": "390eea10127829419f4026f292d907ad",
     "grade": false,
     "grade_id": "cell-e89d08031e30b299",
     "locked": false,
@@ -981,7 +996,7 @@
   },
   "outputs": [],
   "source": [
-    "%%sparql http://fuseki.gsi.upm.es/sitc/\n",
+    "%%sparql $fuseki\n",
    "\n",
    "PREFIX s: <http://learningsparql.com/ns/schema/>\n",
    "PREFIX m: <http://learningsparql.com/ns/musician/>\n",
@@ -1039,7 +1054,7 @@
    "\n",
    "Once results are grouped, they can be aggregated using any aggregation function, such as `COUNT`.\n",
    "\n",
-    "Using `GROUP BY` and `COUNT`, get the count of songs in which Ringo Starr has played each of the instruments:"
+    "Using `GROUP BY` and `COUNT`, get the count of songs that use each instrument:"
   ]
  },
  {
@@ -1049,7 +1064,7 @@
    "deletable": false,
    "nbgrader": {
     "cell_type": "code",
-     "checksum": "7556bacb20c1fbd059dec165c982908d",
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     "grade": false,
     "grade_id": "cell-1429e4eb5400dbc7",
     "locked": false,
@@ -1059,7 +1074,7 @@
   },
   "outputs": [],
   "source": [
-    "%%sparql http://fuseki.gsi.upm.es/sitc/\n",
+    "%%sparql $fuseki\n",
    "\n",
    "PREFIX s: <http://learningsparql.com/ns/schema/>\n",
    "PREFIX m: <http://learningsparql.com/ns/musician/>\n",
@@ -1123,7 +1138,7 @@
   "metadata": {},
   "outputs": [],
   "source": [
-    "%%sparql http://fuseki.gsi.upm.es/sitc/\n",
+    "%%sparql $fuseki\n",
    "\n",
    "PREFIX s: <http://learningsparql.com/ns/schema/>\n",
    "PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>\n",
@@ -1156,7 +1171,7 @@
    "deletable": false,
    "nbgrader": {
     "cell_type": "code",
-     "checksum": "3139d9b7e620266946ffe1ae0cf67581",
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     "grade": false,
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     "locked": false,
@@ -1166,7 +1181,7 @@
   },
   "outputs": [],
   "source": [
-    "%%sparql http://fuseki.gsi.upm.es/sitc/\n",
+    "%%sparql $fuseki\n",
    "\n",
    "PREFIX s: <http://learningsparql.com/ns/schema/>\n",
    "PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>\n",
@@ -1228,7 +1243,7 @@
   "metadata": {},
   "outputs": [],
   "source": [
-    "%%sparql http://fuseki.gsi.upm.es/sitc/\n",
+    "%%sparql $fuseki\n",
    "\n",
    "PREFIX s: <http://learningsparql.com/ns/schema/>\n",
    "PREFIX i: <http://learningsparql.com/ns/instrument/>\n",
@@ -1263,7 +1278,7 @@
   "metadata": {},
   "outputs": [],
   "source": [
-    "%%sparql http://fuseki.gsi.upm.es/sitc/\n",
+    "%%sparql $fuseki\n",
    "\n",
    "PREFIX s: <http://learningsparql.com/ns/schema/>\n",
    "PREFIX i: <http://learningsparql.com/ns/instrument/>\n",
@@ -1297,7 +1312,7 @@
    "deletable": false,
    "nbgrader": {
     "cell_type": "code",
-     "checksum": "3bc508872193750d57d07efbf334c212",
+     "checksum": "513916421ec8451b8a10a6923fde775b",
     "grade": false,
     "grade_id": "cell-dcd68c45c1608a28",
     "locked": false,
@@ -1307,7 +1322,7 @@
   },
   "outputs": [],
   "source": [
-    "%%sparql http://fuseki.gsi.upm.es/sitc/\n",
+    "%%sparql $fuseki\n",
    "\n",
    "PREFIX s: <http://learningsparql.com/ns/schema/>\n",
    "PREFIX i: <http://learningsparql.com/ns/instrument/>\n",
@@ -1381,7 +1396,7 @@
    "deletable": false,
    "nbgrader": {
     "cell_type": "code",
-     "checksum": "300df0a3cf9729dd4814b3153b2fedb4",
+     "checksum": "7b6c7de8beb6cd78b99fbc8eaa1b2c87",
     "grade": false,
     "grade_id": "cell-0c7cc924a13d792a",
     "locked": false,
@@ -1391,7 +1406,7 @@
   },
   "outputs": [],
   "source": [
-    "%%sparql http://fuseki.gsi.upm.es/sitc/\n",
+    "%%sparql $fuseki\n",
    "\n",
    "PREFIX s: <http://learningsparql.com/ns/schema/>\n",
    "PREFIX i: <http://learningsparql.com/ns/instrument/>\n",
@@ -1456,7 +1471,7 @@
    "deletable": false,
    "nbgrader": {
     "cell_type": "code",
-     "checksum": "e4e898c8a16b8aa5865dfde2f6e68ec6",
+     "checksum": "f4b8b1601e9cd1c05464ebad8e6836a6",
     "grade": false,
     "grade_id": "cell-d750b6d64c6aa0a7",
     "locked": false,
@@ -1466,7 +1481,7 @@
   },
   "outputs": [],
   "source": [
-    "%%sparql http://fuseki.gsi.upm.es/sitc/\n",
+    "%%sparql $fuseki\n",
    "\n",
    "PREFIX  rdfs:  <http://www.w3.org/2000/01/rdf-schema#> \n",
    "PREFIX s: <http://learningsparql.com/ns/schema/>\n",
@@ -1521,7 +1536,7 @@
    "deletable": false,
    "nbgrader": {
     "cell_type": "code",
-     "checksum": "fade6ab714376e0eabfa595dd6bd6a8b",
+     "checksum": "b3a570ae42656d907a1ce60f199fdbec",
     "grade": false,
     "grade_id": "cell-2f5aa516f8191787",
     "locked": false,
@@ -1531,7 +1546,7 @@
   },
   "outputs": [],
   "source": [
-    "%%sparql http://fuseki.gsi.upm.es/sitc/\n",
+    "%%sparql $fuseki\n",
    "\n",
    "PREFIX  rdfs:  <http://www.w3.org/2000/01/rdf-schema#> \n",
    "PREFIX s: <http://learningsparql.com/ns/schema/>\n",
@@ -1577,7 +1592,7 @@
   "cell_type": "markdown",
   "metadata": {},
   "source": [
-    "### Which songs had Ringo in drums OR Lennon in lead vocals? (UNION)"
+    "### Which songs had Ringo in dums OR Lennon in lead vocals? (UNION)"
   ]
  },
  {
@@ -1612,7 +1627,7 @@
    "deletable": false,
    "nbgrader": {
     "cell_type": "code",
-     "checksum": "09262d81449c498c37e4b9d9b1dcdfed",
+     "checksum": "4c1d0eaf45f7e69233bb998f5dfb9a48",
     "grade": false,
     "grade_id": "cell-d3a742bd87d9c793",
     "locked": false,
@@ -1622,7 +1637,7 @@
   },
   "outputs": [],
   "source": [
-    "%%sparql http://fuseki.gsi.upm.es/sitc/\n",
+    "%%sparql $fuseki\n",
    "\n",
    "PREFIX  rdfs:  <http://www.w3.org/2000/01/rdf-schema#> \n",
    "PREFIX s: <http://learningsparql.com/ns/schema/>\n",
@@ -1643,7 +1658,7 @@
    "editable": false,
    "nbgrader": {
     "cell_type": "code",
-     "checksum": "11061e79ec06ccb3a9c496319a528366",
+     "checksum": "d583b30a1e00960df3a4411b6854c8c8",
     "grade": true,
     "grade_id": "cell-409402df0e801d09",
     "locked": true,
@@ -1654,7 +1669,7 @@
   },
   "outputs": [],
   "source": [
-    "assert len(solution()['tuples']) == 209"
+    "assert len(solution()['tuples']) == 246"
   ]
  },
  {
@@ -1695,7 +1710,7 @@
    "deletable": false,
    "nbgrader": {
     "cell_type": "code",
-     "checksum": "9ddd2d1f50f841b889bfd29b175d06da",
+     "checksum": "161fd1c6ed06206d4661e4e6c3e255c7",
     "grade": false,
     "grade_id": "cell-9d1ec854eb530235",
     "locked": false,
@@ -1705,7 +1720,7 @@
   },
   "outputs": [],
   "source": [
-    "%%sparql http://fuseki.gsi.upm.es/sitc/\n",
+    "%%sparql $fuseki\n",
    "\n",
    "PREFIX  rdfs:  <http://www.w3.org/2000/01/rdf-schema#> \n",
    "\n",
@@ -1789,7 +1804,7 @@
    "deletable": false,
    "nbgrader": {
     "cell_type": "code",
-     "checksum": "d18e8b6e1d32aed395a533febb29fcb5",
+     "checksum": "16d79b02f510bbfffcb2cc36af159081",
     "grade": false,
     "grade_id": "cell-7ea1f5154cdd8324",
     "locked": false,
@@ -1799,7 +1814,7 @@
   },
   "outputs": [],
   "source": [
-    "%%sparql http://fuseki.gsi.upm.es/sitc/\n",
+    "%%sparql $fuseki\n",
    "PREFIX  rdfs:  <http://www.w3.org/2000/01/rdf-schema#> \n",
    "PREFIX s: <http://learningsparql.com/ns/schema/>\n",
    "PREFIX i: <http://learningsparql.com/ns/instrument/>\n",
@@ -1836,7 +1851,7 @@
    "deletable": false,
    "nbgrader": {
     "cell_type": "code",
-     "checksum": "f926fa3a3568d122454a12312859cda1",
+     "checksum": "65cece835581895b44257668948a5130",
     "grade": false,
     "grade_id": "cell-b6bee887a1b1fc60",
     "locked": false,
@@ -1846,7 +1861,7 @@
   },
   "outputs": [],
   "source": [
-    "%%sparql http://fuseki.gsi.upm.es/sitc/\n",
+    "%%sparql $fuseki\n",
    "PREFIX  rdfs:  <http://www.w3.org/2000/01/rdf-schema#> \n",
    "PREFIX s: <http://learningsparql.com/ns/schema/>\n",
    "PREFIX i: <http://learningsparql.com/ns/instrument/>\n",
@@ -1898,7 +1913,7 @@
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
-   "version": "3.8.10"
+   "version": "3.12.2"
  },
  "toc": {
   "base_numbering": 1,
--- a/lod/created-dataset.jpg
+++ b/lod/created-dataset.jpg
--- a/lod/docker-run-jena-fuseki.jpg
+++ b/lod/docker-run-jena-fuseki.jpg
--- a/lod/fuseki-running.jpg
+++ b/lod/fuseki-running.jpg
--- a/lod/new-dataset.jpg
+++ b/lod/new-dataset.jpg
--- a/lod/query-results.jpg
+++ b/lod/query-results.jpg
--- a/lod/query-ui.jpg
+++ b/lod/query-ui.jpg
--- a/lod/upload-data.jpg
+++ b/lod/upload-data.jpg
--- a/ml1/2_3_1_Advanced_Visualisation.ipynb
+++ b/ml1/2_3_1_Advanced_Visualisation.ipynb
@@ -330,7 +330,7 @@
    "# Saving the resulting axes as ax each time causes the resulting plot to be shown\n",
    "# on top of the previous axes\n",
    "ax = sns.boxplot(x=\"species\", y=\"petal length (cm)\", data=iris_df)\n",
-    "ax = sns.stripplot(x=\"species\", y=\"petal length (cm)\", data=iris_df, jitter=True, edgecolor=\"gray\")"
+    "ax = sns.stripplot(x=\"species\", y=\"petal length (cm)\", data=iris_df, jitter=True, edgecolor=\"auto\")"
   ]
  },
  {
@@ -348,7 +348,7 @@
   "source": [
    "# A violin plot combines the benefits of the previous two plots and simplifies them\n",
    "# Denser regions of the data are fatter, and sparser thinner in a violin plot\n",
-    "sns.violinplot(x=\"species\", y=\"petal length (cm)\", data=iris_df, size=6)"
+    "sns.violinplot(x=\"species\", y=\"petal length (cm)\", data=iris_df)"
   ]
  },
  {
--- a/ml1/2_5_0_Machine_Learning.ipynb
+++ b/ml1/2_5_0_Machine_Learning.ipynb
@@ -72,7 +72,7 @@
    "Machine learning algorithms are programs that learn a model from a dataset to make predictions or learn structures to organize the data.\n",
    "\n",
    "In scikit-learn, machine learning algorithms take as input a *numpy* array (n_samples, n_features), where\n",
-    "* **n_samples**: number of samples. Each sample  is an item to process (i.e., classify). A sample can be a document, a picture, a sound, a video, a row in a database or CSV file, or whatever you can describe with a fixed set of quantitative traits.\n",
+    "* **n_samples**: number of samples. Each sample  is an item to be processed (i.e., classified). A sample can be a document, a picture, a sound, a video, a row in a database or CSV file, or whatever you can describe with a fixed set of quantitative traits.\n",
    "* **n_features**: The number of features or distinct traits that can be used to describe each item quantitatively.\n",
    "\n",
    "The number of features should be defined in advance. A specific type of feature set is high-dimensional (e.g., millions of features), but most values are zero for a given sample. Using (numpy) arrays, all those zero values would also take up memory. For this reason, these feature sets are often represented with sparse matrices (scipy.sparse) instead of (numpy) arrays.\n",
@@ -112,7 +112,7 @@
   "metadata": {},
   "source": [
    "In *unsupervised machine learning models*, the machine learning model algorithm takes as input the feature vectors. It produces a predictive model that is used to fit its parameters to summarize the best regularities found in the data.\n",
-    "![](files/images/plot_ML_flow_chart_3.png)"
+    "![](./images/plot_ML_flow_chart_3.png)"
   ]
  },
  {
@@ -140,7 +140,7 @@
    "    * **model.fit_transform()**: Some estimators implement this method, which performs a fit and a transform on the same input data.\n",
    "\n",
    "\n",
-    "![](files/images/plot_ML_flow_chart_2.png)"
+    "![](./images/plot_ML_flow_chart_2.png)"
   ]
  },
  {
--- a/ml1/util_ds.py
+++ b/ml1/util_ds.py
@@ -53,10 +53,10 @@ import matplotlib.pyplot as plt

 from sklearn.datasets import load_iris
 from sklearn.tree import DecisionTreeClassifier
+from sklearn.inspection import DecisionBoundaryDisplay

 def plot_tree_iris():
    """
-
    Taken fromhttp://scikit-learn.org/stable/auto_examples/tree/plot_iris.html
    """
    # Parameters
@@ -67,11 +67,11 @@ def plot_tree_iris():
    # Load data
    iris = load_iris()

-    for pairidx, pair in enumerate([[0, 1], [0, 2], [0, 3], 
-                                [1, 2], [1, 3], [2, 3]]):
+    for pairidx, pair in enumerate([[0, 1], [0, 2], [0, 3], [1, 2], [1, 3], [2, 3]]):
        # We only take the two corresponding features
        X = iris.data[:, pair]
        y = iris.target
+        '''

        # Shuffle
        idx = np.arange(X.shape[0])
@@ -84,34 +84,38 @@ def plot_tree_iris():
        mean = X.mean(axis=0)
        std = X.std(axis=0)
        X = (X - mean) / std
-
+	'''
        # Train
-        model = DecisionTreeClassifier(max_depth=3, random_state=1).fit(X, y)
+        clf = DecisionTreeClassifier(max_depth=3, random_state=1).fit(X, y)

        # Plot the decision boundary
-        plt.subplot(2, 3, pairidx + 1)
-
-        x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1
-        y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1
-        xx, yy = np.meshgrid(np.arange(x_min, x_max, plot_step),
-                 np.arange(y_min, y_max, plot_step))
-
-        Z = model.predict(np.c_[xx.ravel(), yy.ravel()])
-        Z = Z.reshape(xx.shape)
-        cs = plt.contourf(xx, yy, Z, cmap=plt.cm.Paired)
-
-        plt.xlabel(iris.feature_names[pair[0]])
-        plt.ylabel(iris.feature_names[pair[1]])
-        plt.axis("tight")
+        # Taken from https://scikit-learn.org/stable/auto_examples/tree/plot_iris_dtc.html
+        # Plot the decision boundary
+        ax = plt.subplot(2, 3, pairidx + 1)
+        plt.tight_layout(h_pad=0.5, w_pad=0.5, pad=2.5)
+        DecisionBoundaryDisplay.from_estimator(
+            clf,
+            X,
+            cmap=plt.cm.RdYlBu,
+            response_method="predict",
+            ax=ax,
+            xlabel=iris.feature_names[pair[0]],
+            ylabel=iris.feature_names[pair[1]],
+        )

        # Plot the training points
        for i, color in zip(range(n_classes), plot_colors):
-            idx = np.where(y == i)
-            plt.scatter(X[idx, 0], X[idx, 1], c=color, label=iris.target_names[i],
-                        cmap=plt.cm.Paired)
-
+            idx = np.asarray(y == i).nonzero()
+            plt.scatter(
+            X[idx, 0],
+            X[idx, 1],
+            c=color,
+            label=iris.target_names[i],
+            edgecolor="black",
+            s=15
+        )
    plt.axis("tight")
-
    plt.suptitle("Decision surface of a decision tree using paired features")
-    plt.legend()
+    #plt.legend()
+    plt.legend(bbox_to_anchor=(1.04, 1), loc="upper left")
    plt.show()
Author	SHA1	Message	Date
Carlos A. Iglesias	5d01f26e72	Add files via upload Updated visualize decision tree functions	2026-02-25 17:19:08 +01:00
Carlos A. Iglesias	8e177963af	Update 2_5_0_Machine_Learning.ipynb Updated image path	2026-02-25 16:32:10 +01:00
Carlos A. Iglesias	ecd53ceee5	Update 2_3_1_Advanced_Visualisation.ipynb Updated violinplot	2026-02-25 16:27:04 +01:00
Carlos A. Iglesias	99f8032d05	Update 2_3_1_Advanced_Visualisation.ipynb Updated stripplot to auto instead of gray	2026-02-25 16:21:44 +01:00
cif	470a3d692d	updated fuseki	2026-02-19 12:34:40 +01:00
cif	d66bb25245	Updated server install	2026-02-18 17:20:14 +01:00
cif	f10e6650c9	Updated server install	2026-02-18 17:18:24 +01:00
cif	c9d16bdda3	run in fuseki	2026-02-18 16:42:39 +01:00