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| { | ||
| "cells": [ | ||
| { | ||
| "cell_type": "markdown", | ||
| "id": "blond-letter", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "# CLIP Notebook\n", | ||
| "This is a jupyter notebook file to execute [CLIP](ontolearn.concept_learner.CLIP) and generate predictive results. We recommend you to see the [concept learners](../docs/usage/06_concept_learners.md) guide before continuing with the execution.\n", | ||
| "Also if you have not done it already, from the main directory \"Ontolearn\", run the commands for Datasets mentioned [here](https://ontolearn-docs-dice-group.netlify.app/usage/02_installation#download-external-files) to download the datasets." | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 1, | ||
| "id": "japanese-ivory", | ||
| "metadata": { | ||
| "tags": [] | ||
| }, | ||
| "outputs": [ | ||
| { | ||
| "name": "stderr", | ||
| "output_type": "stream", | ||
| "text": [ | ||
| "\n", | ||
| "Warning: SQLite3 version 3.40.0 and 3.41.2 have huge performance regressions; please install version 3.41.1 or 3.42!\n", | ||
| "\n" | ||
| ] | ||
| } | ||
| ], | ||
| "source": [ | ||
| "import json\n", | ||
| "from ontolearn.knowledge_base import KnowledgeBase\n", | ||
| "from ontolearn.concept_learner import CLIP\n", | ||
| "from ontolearn.refinement_operators import ExpressRefinement\n", | ||
| "from ontolearn.learning_problem import PosNegLPStandard\n", | ||
| "from owlapy.model import OWLNamedIndividual, IRI\n", | ||
| "from ontolearn.utils import setup_logging\n" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "id": "pending-coast", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "Open `uncle_lp.json` where we have stored the learning problem for the concept of 'Uncle' and the path to the 'family' ontology." | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 2, | ||
| "id": "beginning-syntax", | ||
| "metadata": { | ||
| "tags": [] | ||
| }, | ||
| "outputs": [], | ||
| "source": [ | ||
| "with open('uncle_lp.json') as json_file:\n", | ||
| " settings = json.load(json_file)" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "id": "humanitarian-heating", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "Create an instance of the class `KnowledeBase` by using the path that is stored in `settings`." | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 3, | ||
| "id": "caroline-indiana", | ||
| "metadata": { | ||
| "tags": [] | ||
| }, | ||
| "outputs": [], | ||
| "source": [ | ||
| "kb = KnowledgeBase(path=settings['data_path'])" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "id": "lucky-activation", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "Retreive the IRIs of the positive and negative examples of Uncle from `settings` and create an instance of `PosNegLPStandard`. (more info about this [here](../docs/usage/06_concept_learners.md#configure-the-learning-problem))" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 4, | ||
| "id": "processed-patrick", | ||
| "metadata": { | ||
| "tags": [] | ||
| }, | ||
| "outputs": [], | ||
| "source": [ | ||
| "examples = settings['Uncle']\n", | ||
| "p = set(examples['positive_examples'])\n", | ||
| "n = set(examples['negative_examples'])\n", | ||
| "typed_pos = set(map(OWLNamedIndividual, map(IRI.create, p)))\n", | ||
| "typed_neg = set(map(OWLNamedIndividual, map(IRI.create, n)))\n", | ||
| "lp = PosNegLPStandard(pos=typed_pos, neg=typed_neg)" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "id": "mechanical-latin", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "Create a model of [CLIP](ontolearn.concept_learner.CLIP) and fit the learning problem to the model." | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 5, | ||
| "id": "171d1aa4-6c12-42c0-b7e9-8cf2dce85ff9", | ||
| "metadata": { | ||
| "tags": [] | ||
| }, | ||
| "outputs": [], | ||
| "source": [ | ||
| "op = ExpressRefinement(knowledge_base=kb, use_inverse=False,\n", | ||
| " use_numeric_datatypes=False)" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 6, | ||
| "id": "binding-moderator", | ||
| "metadata": { | ||
| "tags": [] | ||
| }, | ||
| "outputs": [ | ||
| { | ||
| "name": "stdout", | ||
| "output_type": "stream", | ||
| "text": [ | ||
| "\n", | ||
| " Loaded length predictor!\n", | ||
| "\n", | ||
| " Loaded length predictor!\n", | ||
| "\n", | ||
| " Loaded length predictor!\n", | ||
| "\n", | ||
| " Loaded length predictor!\n", | ||
| "\n", | ||
| "***** Predicted length: 5 *****\n", | ||
| "\n", | ||
| "***** Predicted length: 5 *****\n" | ||
| ] | ||
| }, | ||
| { | ||
| "data": { | ||
| "text/plain": [ | ||
| "<ontolearn.concept_learner.CLIP at 0x7f762ae039a0>" | ||
| ] | ||
| }, | ||
| "execution_count": 6, | ||
| "metadata": {}, | ||
| "output_type": "execute_result" | ||
| } | ||
| ], | ||
| "source": [ | ||
| "model = CLIP(knowledge_base=kb, path_of_embeddings=\"../CLIPData/family/embeddings/ConEx_entity_embeddings.csv\",\n", | ||
| " refinement_operator=op, load_pretrained=True, max_runtime=200)\n", | ||
| "model.fit(lp)" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "id": "d981f2b9-3489-494e-825d-6a72ee480d4f", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "## Retrieve top 3 hypotheses and print them." | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 7, | ||
| "id": "c6a90b21-3594-441d-bed0-eb822db5f993", | ||
| "metadata": { | ||
| "tags": [] | ||
| }, | ||
| "outputs": [ | ||
| { | ||
| "name": "stdout", | ||
| "output_type": "stream", | ||
| "text": [ | ||
| "<class 'ontolearn.search.OENode'> at 0x0304774\tMale ⊓ (∀ hasParent.Grandparent)\tQuality:0.90476\tHeuristic:0.40407\tDepth:2\tH_exp:6\t|RC|:7\t|Indv.|:None\n", | ||
| "<class 'ontolearn.search.OENode'> at 0x0ca154a\tMale ⊓ (∀ hasChild.Grandchild)\tQuality:0.90476\tHeuristic:0.36919\tDepth:1\tH_exp:7\t|RC|:7\t|Indv.|:None\n", | ||
| "<class 'ontolearn.search.OENode'> at 0x2adbb89\tMale ⊓ (∀ hasChild.(¬Grandfather))\tQuality:0.88889\tHeuristic:0.39044\tDepth:3\tH_exp:6\t|RC|:0\t|Indv.|:None\n" | ||
| ] | ||
| }, | ||
| { | ||
| "data": { | ||
| "text/plain": [ | ||
| "[None, None, None]" | ||
| ] | ||
| }, | ||
| "execution_count": 7, | ||
| "metadata": {}, | ||
| "output_type": "execute_result" | ||
| } | ||
| ], | ||
| "source": [ | ||
| "hypotheses = list(model.best_hypotheses(n=3))\n", | ||
| "[print(_) for _ in hypotheses]" | ||
| ] | ||
| } | ||
| ], | ||
| "metadata": { | ||
| "kernelspec": { | ||
| "display_name": "onto", | ||
| "language": "python", | ||
| "name": "onto" | ||
| }, | ||
| "language_info": { | ||
| "codemirror_mode": { | ||
| "name": "ipython", | ||
| "version": 3 | ||
| }, | ||
| "file_extension": ".py", | ||
| "mimetype": "text/x-python", | ||
| "name": "python", | ||
| "nbconvert_exporter": "python", | ||
| "pygments_lexer": "ipython3", | ||
| "version": "3.9.18" | ||
| } | ||
| }, | ||
| "nbformat": 4, | ||
| "nbformat_minor": 5 | ||
| } |
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