xaif-eval

xaif_eval

Overview

xaif is a Python library for working with Argument Interchange Format (AIF), primarily designed to facilitate the development, manipulation, and evaluation of argumentat structures. This package provides essential utilities to validate, traverse, and manipulate AIF-compliant JSON structures, enabling users to effectively work with complex argumentation data.

xAIF Format

Here is an example of the xAIF JSON format:

{
  "AIF": {
    "nodes": [],
    "edges": [],
    "schemefulfillments": [],
    "descriptorfulfillments": [],
    "participants": [],
    "locutions": []
  },
  "text": "",
  "dialog": true,
  "OVA": {
    "firstname": "",
    "surname": "",
    "url": "",
    "nodes": [],
    "edges": []
  }
}

## Features

- Validate AIF JSON structures: Ensure the validity and integrity of your AIF data.
- Manage argument components: Add and manipulate various components of an argumentation framework, including relations, nodes, and edges.
- Export data in CSV format: Generate tabular representations of argument components with their respective relation types.


## Installation

You can install the `xaif_eval` package via pip:

```sh
pip install xaif_eval

Usage

Importing the Library

from xaif_eval import AIF

Example

from xaif_eval import AIF

# Sample xAIF JSON 
aif= {
  "AIF": {
    "descriptorfulfillments": null,
    "edges": [
      {
        "edgeID": 0,
        "fromID": 0,
        "toID": 4
      },
      {
        "edgeID": 1,
        "fromID": 4,
        "toID": 3
      },
      {
        "edgeID": 2,
        "fromID": 1,
        "toID": 6
      },
      {
        "edgeID": 3,
        "fromID": 6,
        "toID": 5
      },
      {
        "edgeID": 4,
        "fromID": 2,
        "toID": 8
      },
      {
        "edgeID": 5,
        "fromID": 8,
        "toID": 7
      },
      {
        "edgeID": 6,
        "fromID": 3,
        "toID": 9
      },
      {
        "edgeID": 7,
        "fromID": 9,
        "toID": 7
      }
    ],
    "locutions": [
      {
        "nodeID": 0,
        "personID": 0
      },
      {
        "nodeID": 1,
        "personID": 1
      },
      {
        "nodeID": 2,
        "personID": 2
      }
    ],
    "nodes": [
      {
        "nodeID": 0,
        "text": "disagreements between party members are entirely to be expected.",
        "type": "L"
      },
      {
        "nodeID": 1,
        "text": "the SNP has disagreements.",
        "type": "L"
      },
      {
        "nodeID": 2,
        "text": "it's not uncommon for there to be disagreements between party members.",
        "type": "L"
      },
      {
        "nodeID": 3,
        "text": "disagreements between party members are entirely to be expected.",
        "type": "I"
      },
      {
        "nodeID": 4,
        "text": "Default Illocuting",
        "type": "YA"
      },
      {
        "nodeID": 5,
        "text": "the SNP has disagreements.",
        "type": "I"
      },
      {
        "nodeID": 6,
        "text": "Default Illocuting",
        "type": "YA"
      },
      {
        "nodeID": 7,
        "text": "it's not uncommon for there to be disagreements between party members.",
        "type": "I"
      },
      {
        "nodeID": 8,
        "text": "Default Illocuting",
        "type": "YA"
      },
      {
        "nodeID": 9,
        "text": "Default Inference",
        "type": "RA"
      }
    ],
    "participants": [
      {
        "firstname": "Speaker",
        "participantID": 0,
        "surname": "1"
      },
      {
        "firstname": "Speaker",
        "participantID": 1,
        "surname": "2"
      }
    ],
    "schemefulfillments": null
  },
  "dialog": true,
  "ova": [],
  "text": {
    "txt": " Speaker 1 <span class=\"highlighted\" id=\"0\">disagreements between party members are entirely to be expected.</span>.<br><br> Speaker 2 <span class=\"highlighted\" id=\"1\">the SNP has disagreements.</span>.<br><br> Speaker 1 <span class=\"highlighted\" id=\"2\">it's not uncommon for there to be disagreements between party members. </span>.<br><br>"
  }
}


# Initialize the AIF object with xAIF data (AIF data structure provided as input)
aif = AIF(xaif_data)

# Alternatively, initialize the AIF object with raw text data (you can use text directly instead of a full xAIF structure)
# Here, "text_data" is a simple string representing the text you wish to analyze
aif = AIF("here is the text.")

# Adding components to the AIF object: 
# The add_component method takes the component type (e.g., "locution"), 
# followed by component specific information like ID or associated data (e.g., text or related components).


# 1. Adding a locution component (a piece of spoken or written text associated with a participant)
locution_text = "another text"
first_speaker = "First Speaker"
aif.add_component("locution", locution_text, first_speaker)

# 2. Adding another locution component for the second speaker
second_speaker_text = "the third text. fourth text"
second_speaker = "Second Speaker"
aif.add_component("locution", second_speaker_text, second_speaker)

# 3. Adding segments: Argument units created by segmenting existing locutions into elementary discourse units.
locution_id = 2
segments = ["the third text.", "fourth text"]
aif.add_component("segment", locution_id, segments)

# 4. Adding information nodes (proposition) for existing locution
locution_id = 3
proposition_text_1 = "the third text."
aif.add_component("proposition", locution_id, proposition_text_1)

# 5. Adding another proposition
locution_id = 4
proposition_text_2 = "fourth text."
aif.add_component("proposition", locution_id, proposition_text_2)

# 6. Adding an argument relation
argument_relation_type = "RA"
iNode_id_1 = 5
iNode_id_2 = 7
aif.add_component("argument_relation", argument_relation_type, iNode_id_1, iNode_id_2)

# Print the generated xAIF data (the argumentation framework after the components are added)
# "aif.xaif" contains the final xAIF representation of all the components added to the object
print(aif.xaif)


# Generate a dataframe from the xAIF data. It returns pairs of components along with the relations between them.
# It takes the type of relations needed as an argument.

print(aif.get_csv("argument-relation"))  # For generating argument relations in tabular format as a CSV file.

Documentation

The full documentation is available at xaif_eval Documentation.

Contributing

Contributions are welcome! Please visit the Contributing Guidelines for more information.

Issues

If you encounter any problems, please file an issue at the Issue Tracker.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Authors

• DEBELA - [e-mail]

Acknowledgments

• Thanks to all contributors and users for their feedback and support.