Prototype Implementation - GOEA

Prototype Implementation - GOEA

Author: tkv29

Dockerfile

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+# base image
+FROM ubuntu:latest
+
+# set environment variables
+ENV PYTHONUNBUFFERED=1
+
+# set working directory
+ENV DockerHOME=/home/app/GOEA
+RUN mkdir -p $DockerHOME 
+WORKDIR $DockerHOME
+
+# copy source files
+COPY . $DockerHOME 
+
+# expose port
+EXPOSE 8000
+
+# install dependencies
+RUN apt-get update && apt-get install -y python3 python3-pip
+RUN pip install --break-system-packages --no-cache-dir -r requirements.txt
+
+# start server  
+CMD ["python3", "GOEA/manage.py", "runserver", "0.0.0.0:8000"]

GOEA/GOEA/__init__.py

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+"""
+ASGI config for GOEA project.
+
+It exposes the ASGI callable as a module-level variable named ``application``.
+
+For more information on this file, see
+https://docs.djangoproject.com/en/5.0/howto/deployment/asgi/
+"""
+
+import os
+
+from django.core.asgi import get_asgi_application
+
+os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'GOEA.settings')
+
+application = get_asgi_application()

GOEA/GOEA/__pycache__/__init__.cpython-310.pyc

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+# Ignore everything in this directory
+*
+# Except this file
+!.gitignore

GOEA/GOEA/__pycache__/forms.cpython-310.pyc

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+from django import forms
+from GOEA.models import UploadedFiles
+
+class UploadFilesForm(forms.ModelForm):
+    class Meta:
+        model = UploadedFiles
+        fields = ['xes_file', 'owl_file', 'custom_ontology_used']
+
+class APIKeyForm(forms.Form):
+    key = forms.CharField(label='OpenAI API Key', max_length = 100)

GOEA/GOEA/__pycache__/models.cpython-310.pyc

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+# Standard Library Imports
+import networkx as nx
+
+# Third-Party Imports
+from django.conf import settings
+from pyvis.network import Network
+from rdflib import Graph, RDFS, OWL
+import pm4py
+
+# Local Imports
+from GOEA.logic import prompts as p
+from GOEA.logic import utils as u
+
+class EventAbstractor:
+    """Singleton class that abstracts events from a XES file using an ontology file."""
+
+    _instance = None
+
+    def __new__(cls, xes_path, owl_path):
+        if not cls._instance:
+            cls._instance = super().__new__(cls)
+        return cls._instance
+
+    def __init__(self, xes_path=None, owl_path=None):
+        self.xes_path = xes_path
+        self.owl_path = owl_path
+        self.data = None
+        if xes_path and owl_path:
+            self.xes_df = pm4py.read_xes(xes_path)
+            self.ontology_graph = self._read_owl_file(owl_path)
+
+    @classmethod
+    def get_instance(cls):
+        if cls._instance is None:
+            EventAbstractor()
+        return cls._instance
+
+    def get_xes_df(self):
+        return self.xes_df
+
+    def get_data(self):
+        return self.data
+
+    def _read_owl_file(self, file_path):
+        ontology_graph = Graph()
+        ontology_graph.parse(file_path)
+        return ontology_graph
+
+    def _get_subclasses(self, class_uri):
+        return list(self.ontology_graph.subjects(RDFS.subClassOf, class_uri))
+
+    def _get_class_depth(self, class_uri, depth=0):
+        subclasses = self._get_subclasses(class_uri)
+        if not subclasses:
+            return depth
+        return max(self._get_class_depth(subclass, depth + 1) for subclass in subclasses)
+
+    def get_max_depth(self):
+        root_class = OWL.Thing
+        return self._get_class_depth(root_class)
+
+    def _create_ontology_string(self, class_uri, processed_classes, selected_depth, current_depth=0, indent=""):
+        ontology_string = ""
+        if current_depth < selected_depth:
+            if class_uri not in processed_classes:
+                processed_classes.add(class_uri)
+                subclasses = self._get_subclasses(class_uri)
+                for subclass in subclasses:
+                    ontology_string += self._create_ontology_string(
+                        subclass, processed_classes, selected_depth, current_depth + 1, indent
+                    )
+        else:
+            class_label = self._get_label(class_uri)
+            ontology_string += indent + f"{current_depth}. " + class_label + "\n"
+            if class_uri not in processed_classes:
+                processed_classes.add(class_uri)
+                subclasses = self._get_subclasses(class_uri)
+                for subclass in subclasses:
+                    ontology_string += self._create_ontology_string(
+                        subclass, processed_classes, selected_depth, current_depth + 1, indent + " "
+                    )
+        return ontology_string
+
+    def create_ontology_representation(self, selected_depth):
+        root_class = OWL.Thing
+        processed_classes = set()
+        ontology_string = self._create_ontology_string(root_class, processed_classes, selected_depth)
+        return ontology_string
+
+    def _create_visualization_graph(self):
+        visualization_graph = nx.DiGraph()
+        root_class = OWL.Thing
+        self._add_nodes_recursive(visualization_graph, root_class, 0)
+        return visualization_graph
+
+    def _add_nodes_recursive(self, graph, node, depth):
+        node_label = self._get_label(node)
+        graph.add_node(node_label, depth=depth)
+
+        subclasses = list(self.ontology_graph.subjects(RDFS.subClassOf, node))
+        for subclass in subclasses:
+            subclass_label = self._get_label(subclass)
+            graph.add_edge(node_label, subclass_label)
+            self._add_nodes_recursive(graph, subclass, depth + 1)
+
+    def _get_label(self, uri):
+        label = self.ontology_graph.value(uri, RDFS.label)
+        if label is None:
+            label = uri.split("/")[-1].replace("_", " ")
+        return str(label)
+
+    def visualize_graph(self, abstraction_level):
+        visualization_graph = self._create_visualization_graph()
+        nodes_to_add = [node for node in visualization_graph.nodes() if str(node) != "owl#Thing"]
+
+        net = Network(height='800px', width='100%', bgcolor='#ffffff', font_color='black')
+        net.from_nx(visualization_graph.subgraph(nodes_to_add))
+
+        marked_nodes = {node for node, data in visualization_graph.nodes(data=True) if data['depth'] == abstraction_level}
+        descendants = {descendant for node in marked_nodes for descendant in nx.descendants(visualization_graph, node)}
+
+        for node in nodes_to_add:
+            net_node = net.get_node(node)
+            if node in marked_nodes:
+                net_node['color'] = '#FF6A00'  # Orange color for target abstraction level
+                net_node['size'] = 20
+            elif node in descendants:
+                net_node['color'] = '#0D6EFD'  # Blue for potential abstraction
+            else:
+                net_node['color'] = '#808080'  # Grey for not considered nodes
+
+        for edge in net.edges:
+            source, target = edge['from'], edge['to']
+            if (source in marked_nodes and target in descendants) or (source in descendants and target in descendants):
+                edge['color'] = '#0D6EFD'  # Blue for edges of potential abstraction
+                edge['width'] = 2.5
+
+        net.repulsion(node_distance=420, central_gravity=0.33, spring_length=110, spring_strength=0.10, damping=0.95)
+
+        html_file = net.generate_html()
+        modified_html = html_file.replace('lib/bindings/utils.js', f'{settings.STATIC_URL}js/utils.js')
+
+        return modified_html
+
+    def abstract(self, view, abstraction_level, custom_ontology_used):
+        event_log_df = self.xes_df
+
+        total_rows = len(event_log_df)
+        event_log_df["medication"] = event_log_df.apply(lambda row: self._start_extraction_medication(row, view, total_rows), axis=1)
+
+        event_log_df["normalized_medication"] = event_log_df.apply(lambda row: self._start_normalization_medication(row, view, total_rows), axis=1)
+        ontology_string = self.create_ontology_representation(abstraction_level)
+        total_rows = len(event_log_df)
+        event_log_df["abstracted_medication"] = event_log_df.apply(
+            lambda row: self._start_medication_abstraction(
+                row, ontology_string, abstraction_level, custom_ontology_used, view, total_rows
+            ),
+            axis=1
+        )
+
+        self.data = event_log_df
+        return event_log_df
+
+    def _start_extraction_medication(self, row, view, total_rows):
+        extracted_medication = self._extract_medication(row["activity"])
+        row_number = row.name
+        self._update_progress(view, row_number, total_rows, "Extracting Drug or Medicament of Activities")
+        return extracted_medication
+    
+    def _start_normalization_medication(self, row, view, total_rows):
+        normalized_medication = "N/A"
+        if row["medication"] != "N/A":
+            normalized_medication = self._normalize_medication(row["medication"])
+        row_number = row.name
+        self._update_progress(view, row_number, total_rows, "Normalizing Drug or Medicament of Extracted Medication")
+        return normalized_medication
+    
+    def _start_medication_abstraction(self, row, ontology_string, abstraction_level, custom_ontology_used, view, total_rows):
+        abstracted_medication = "N/A"
+        medication = row["normalized_medication"]
+        if medication != "N/A":
+            abstracted_medication = self._abstract_medication(ontology_string, medication, abstraction_level, custom_ontology_used)
+        row_number = row.name
+        self._update_progress(view, row_number, total_rows, "Abstracting Drug Medicament on Target Abstraction Level")
+        return abstracted_medication
+
+    @staticmethod
+    def _extract_medication(activity):
+        extraction_messages = p.EXTRACTION_MESSAGES[:]
+        extraction_messages.append(
+            {
+                "role": "user",
+                "content": activity,
+            }
+        )
+        extracted_medication = u.query_gpt(messages=extraction_messages)
+        return extracted_medication
+    
+    @staticmethod
+    def _normalize_medication(extracted_medication):
+        extraction_messages = p.NORMALIZATION_MESSAGES[:]
+        extraction_messages.append(
+            {
+                "role": "user",
+                "content": extracted_medication,
+            }
+        )
+        normalized_medication = u.query_gpt(messages=extraction_messages)
+        return normalized_medication
+
+    @staticmethod
+    def _abstract_medication(ontology, medication, abstraction_level, custom_ontology_used):
+        if custom_ontology_used:
+            abstraction_messages = p.CUSTOM_ABSTRACTION_MESSAGES[:]
+            abstraction_messages.extend([
+                {
+                    "role": "user",
+                    "content": (
+                        "Here the hierarchy you should use as reference: \n" + ontology +
+                        "\n Classify the medication in one of the uppermost classes on the target abstraction level. "
+                        "If it does not fit in any classes, return N/A. \n" +
+                        "The target abstraction level should be: " + "'" + str(abstraction_level) + ".'"
+                    ),
+                },
+                {
+                    "role": "user",
+                    "content": "In which category on abstraction level: " + str(abstraction_level) + " would " + medication + " fit in?",
+                }
+            ])
+        else:
+            abstraction_messages = p.ABSTRACTION_MESSAGES[:]
+            abstraction_messages.extend([
+                {
+                    "role": "user",
+                    "content": (
+                        "Here the hierarchy you should use as reference: \n" + ontology +
+                        "\n Check if the following medicine is part of the hierarchy and map them to the uppermost class on the target abstraction level. "
+                        "If the term is not part of the hierarchy, return N/A. \n" +
+                        "The target abstraction level should be: " + "'" + str(abstraction_level) + ".'"
+                    ),
+                },
+                {
+                    "role": "user",
+                    "content": "What is the uppermost class of " + medication + " which is on the level: " + str(abstraction_level) + "?",
+                }
+            ])
+
+        abstracted_medication = u.query_gpt(messages=abstraction_messages)
+        return abstracted_medication
+
+    def _update_progress(self, view, current_step, total_steps, status):
+        """Update the progress of the extraction."""
+        if view is not None:
+            percentage = round((current_step / total_steps) * 100)
+            view.request.session["progress"] = percentage
+            view.request.session["status"] = status
+            view.request.session.save()

GOEA/GOEA/__pycache__/settings.cpython-310.pyc

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+"""Module providing functions for using OpenAI function calling."""
+TOOLS = [
+    {
+        "type": "function",
+        "function": {
+            "name": "extract_medication_rows",
+            "description": "this function extracts only the relevant rows from a table which are related to medications",
+            "parameters": {
+                "type": "object",
+                "properties": {
+                    "output": {
+                        "type": "array",
+                        "items": {
+                            "type": "string",
+                            "description": "a row from the table which is related to medications",
+                        },
+                    },
+                },
+                "required": ["output"],
+            },
+        },
+    },
+]