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ReasonRank: Google's PageRank for Arguments

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Myklob edited this page Apr 22, 2024 · 3 revisions

Traditional web search results rely on Google's PageRank algorithm, which measures the number and quality of links to a website to determine its relevance and importance. However, this method does not directly assess the strength and validity of the arguments presented within the content itself. ReasonRank, an adaptation of Google's PageRank Algorithm, addresses this limitation by evaluating the strength and validity of individual arguments within a pro/con forum.

ReasonRank adjusts the algorithm to consider the quantity and quality of reasons to agree or disagree, along with their corresponding sub-arguments. This allows for more persuasive arguments to be assigned greater importance, similar to how PageRank assesses the quality of links by the number and quality of links to their sub-links.

ReasonRank can evaluate specialized pro-con arguments that address whether an argument would necessarily strengthen or weaken the conclusion, as well as if an argument is verified, logically sound, or significant.

Using ReasonRank in a pro/con forum would be an effective way to evaluate the strength and impact of individual arguments, ensuring evaluations are objective, transparent, and reliable. To further enhance the process, user feedback (votes) can be incorporated to refine the scores over time and ensure the strongest arguments rise to the top.

ReasonRank, combined with user feedback and open discussion, can revolutionize the way we evaluate arguments and make decisions by providing a more direct assessment of argument quality and relevance.

Variables Needed to Program ReasonRank

The reason_rank function takes the following arguments:

  1. M_pro and M_con: The adjacency matrices for pro and con arguments, where M[i, j] represents the link from argument j to argument i. The adjacency matrix is a fundamental concept in graph theory, which is also used in Google's PageRank algorithm.

  2. M_linkage_pro and M_linkage_con: The adjacency matrices for argument-to-conclusion linkage, where M_linkage_pro[i, j] and M_linkage_con[i, j] represent the link from linkage argument j to pro or con argument i, respectively. These matrices help determine how strongly each argument is connected to the overall conclusion.

  3. uniqueness_scores_pro and uniqueness_scores_con: Vectors containing the uniqueness scores for pro and con arguments.

  4. initial_scores_pro and initial_scores_con: Vectors containing the initial scores for pro and con arguments.

  5. num_iterations: The number of iterations for the algorithm to run (default is 100, but we can have a separate pro/con argument that it should be different and track its score).

  6. d: The damping factor, a float value between 0 and 1 (default is 0.85).

  7. N_pro and N_con: The number of main pro and con arguments.

  8. v_pro and v_con: Vectors containing the pro and con argument scores at a specific iteration.

  9. M_hat_pro, M_hat_con, M_hat_linkage_pro, and M_hat_linkage_con: The modified adjacency matrices for pro, con, and linkage arguments, which include the damping factor.

  10. adjusted_v_pro and adjusted_v_con: The final adjusted pro and con argument scores, considering the linkage and uniqueness scores.

Code

Sample Code:

import numpy as np
from sklearn.feature_extraction.text import TfidfVectorizer
import dask.array as da
import spacy
import logging
from typing import Dict, List, Optional

logging.basicConfig(filename='reason_rank.log', level=logging.INFO, format='%(asctime)s: %(levelname)s: %(message)s')

nlp = spacy.load("en_core_web_lg")

def reason_rank(M_pro: np.ndarray, M_con: np.ndarray, initial_scores: Dict[str, np.ndarray],
                argument_texts: Dict[str, List[str]], num_iterations: int = 100,
                feedback_data: Optional[Dict[str, np.ndarray]] = None, damping_factor: float = 0.85) -> Dict[str, np.ndarray]:
    """
    Use advanced feedback integration, NLP techniques, and modular function design to calculate reason rank scores.

def reason_rank(args, update_strategy, feedback_data, damping_factor):
    """
    Calculate and update the scores for pro and con arguments based on their connections, initial scores, and user feedback.
    
    Parameters:
    - args (dict): A collection of arguments and their properties, including:
        - M_pro (np.array): A matrix capturing the connections and strengths between pro arguments. A non-zero entry (i, j) represents a link from pro argument i to pro argument j, with the value indicating the connection's strength.
        - M_con (np.array): Similarly, this matrix represents the connections between con arguments. A non-zero entry (i, j) indicates a link from con argument i to con argument j, where the value represents the connection's strength.
        - initial_scores (dict): A dictionary containing the initial scores for both pro and con arguments. Each argument's unique identifier maps to its initial score.
        - argument_texts (dict): A dictionary containing the text for each pro and con argument, with each argument's unique identifier serving as the key.

    - update_strategy (str): Specifies the method for updating argument scores. Options include:
        - "iterations": Uses a fixed number of iterations to update scores (current implementation).
        - "periodic_reevaluation": Allows for dynamic updates based on new data or user interactions (planned for future implementation).

    - feedback_data (dict): Optional data that may include upvote/downvote counts or sentiment analysis scores for arguments. This data can inform score updates, potentially weighted by factors like user credibility or recency of feedback.

    - damping_factor (float): A parameter between 0 and 1 that moderates the influence of past scores on current scores. A higher value prioritizes score stability over time, while a lower value allows scores to more rapidly reflect recent changes in argument connections or user feedback.

    Returns:
    - argument_scores (dict): A dictionary containing updated scores for both pro and con arguments related to a specific topic or belief. Scores can be accessed using the unique identifiers for arguments.
    """

    try:
        uniqueness_scores = compute_uniqueness_scores(argument_texts)
        feedback_scores = integrate_feedback(feedback_data) if feedback_data else {'pro': np.ones_like(initial_scores['pro']), 'con': np.ones_like(initial_scores['con'])}
        scores = {'pro': initial_scores['pro'].copy(), 'con': initial_scores['con'].copy()}

        for _ in range(num_iterations):
            scores = propagate_scores(M_pro, M_con, scores, uniqueness_scores, feedback_scores, damping_factor)

        final_scores = apply_domain_specific_enhancements(scores, argument_texts)
        return final_scores
    except Exception as e:
        logging.exception(f"An error occurred in reason_rank: {str(e)}")
        raise

def propagate_scores(M_pro: np.ndarray, M_con: np.ndarray, scores: Dict[str, np.ndarray],
                     uniqueness_scores: Dict[str, np.ndarray], feedback_scores: Dict[str, np.ndarray],
                     damping_factor: float) -> Dict[str, np.ndarray]:
    """
    Performs parallel score propagation utilizing Dask for efficiency.
    """
    updated_scores = {}
    for arg_type, M in [('pro', M_pro), ('con', M_con)]:
        dask_M = da.from_array(M, chunks=(1000, 1000))
        dask_scores = da.from_array(scores[arg_type] * uniqueness_scores[arg_type] * feedback_scores[arg_type], chunks=(1000,))
        updated_scores[arg_type] = da.dot(dask_M, dask_scores).compute() * damping_factor
    return updated_scores

def compute_uniqueness_scores(argument_texts: Dict[str, List[str]]) -> Dict[str, np.ndarray]:
    """
    Calculates uniqueness scores for arguments based on TF-IDF vectorization.
    """
    all_texts = argument_texts['pro'] + argument_texts['con']
    vectorizer = TfidfVectorizer().fit(all_texts)
    uniqueness_scores = {}
    for arg_type in ['pro', 'con']:
        tfidf_matrix = vectorizer.transform(argument_texts[arg_type])
        uniqueness_scores[arg_type] = 1 - tfidf_matrix.toarray().max(axis=1)
    return uniqueness_scores

def integrate_feedback(feedback_data: Dict[str, np.ndarray]) -> Dict[str, np.ndarray]:
    """
    Integrates feedback into scores using predefined models or heuristics.
    """
    feedback_scores = {}
    for arg_type in ['pro', 'con']:
        if arg_type in feedback_data:
            feedback_scores[arg_type] = np.mean(feedback_data[arg_type], axis=0)
        else:
            feedback_scores[arg_type] = np.ones(len(feedback_data[arg_type]))
    return feedback_scores

def apply_domain_specific_enhancements(scores: Dict[str, np.ndarray], argument_texts: Dict[str, List[str]]) -> Dict[str, np.ndarray]:
    """
    Applies domain-specific enhancements to argument scores based on NLP analysis.
    """
    enhanced_scores = scores.copy()
    for arg_type in ['pro', 'con']:
        for i, text in enumerate(argument_texts[arg_type]):
            doc = nlp(text)
            sentiment = doc.sentiment
            entities = [(ent.text, ent.label_) for ent in doc.ents]
            # Placeholder for incorporating sentiment and entity information into scores
            enhanced_scores[arg_type][i] *= (1 + sentiment)
    return enhanced_scores

# Example usage
M_pro = np.array([[0.1, 0.2], [0.2, 0.1]])
M_con = np.array([[0.1, 0.2], [0.2, 0.1]])
initial_scores = {'pro': np.array([1, 1]), 'con': np.array([1, 1])}
argument_texts = {'pro': ["Pro argument 1", "Pro argument 2"], 'con': ["Con argument 1", "Con argument 2"]}
feedback_data = {'pro': np.array([[0.9, 1.1]]), 'con': np.array([[0.8, 1.2]])}

final_scores = reason_rank(M_pro, M_con, initial_scores, argument_texts, feedback_data=feedback_data)
print("Final Scores:", final_scores)

Here's an updated explanation that matches the latest code:

Explanation

The reason_rank function calculates reason rank scores for pro and con arguments using advanced feedback integration, NLP techniques, and modular function design.

The function takes the following inputs:

  • M_pro and M_con: Adjacency matrices for pro and con arguments.
  • initial_scores: Dictionary containing initial scores for pro and con arguments.
  • argument_texts: Dictionary containing texts for pro and con arguments.
  • num_iterations: Number of iterations for score propagation (default is 100).
  • feedback_data: Optional dictionary containing feedback matrices and expert opinions.
  • damping_factor: Damping factor for score propagation (default is 0.85).

The function performs the following steps:

  1. Computes uniqueness scores for arguments using the compute_uniqueness_scores function, which calculates the scores based on TF-IDF vectorization of the argument texts.

  2. Integrates feedback into scores using the integrate_feedback function, which uses predefined models or heuristics to incorporate feedback data. If no feedback data is provided, it defaults to ones.

  3. Initializes the scores dictionary with the initial scores for pro and con arguments.

  4. Iterates for the specified number of iterations (num_iterations):

    • Propagates scores using the propagate_scores function, which performs parallel score propagation utilizing Dask for efficiency. The scores are updated based on the adjacency matrices, uniqueness scores, feedback scores, and damping factor.

  5. Applies domain-specific enhancements to the final scores using the apply_domain_specific_enhancements function, which incorporates NLP analysis techniques such as sentiment analysis and named entity recognition.

  6. Returns the final scores for pro and con arguments.

The propagate_scores function performs parallel score propagation using Dask. It takes the adjacency matrices, scores, uniqueness scores, feedback scores, and damping factor as inputs. For each argument type (pro and con), it creates Dask arrays for the adjacency matrix and scores, computes the updated scores using matrix multiplication, and applies the damping factor.

The compute_uniqueness_scores function calculates uniqueness scores for arguments based on TF-IDF vectorization. It combines the pro and con argument texts, fits a TF-IDF vectorizer on all texts, and then transforms the pro and con arguments separately to obtain their uniqueness scores.

The integrate_feedback function integrates feedback into scores using predefined models or heuristics. It takes the feedback data as input and computes the average feedback scores for pro and con arguments. If feedback data is not available for an argument type, it defaults to ones.

The apply_domain_specific_enhancements function applies domain-specific enhancements to argument scores based on NLP analysis. It uses the spaCy library to perform sentiment analysis and named entity recognition on the argument texts. The scores are then modified based on the sentiment scores (placeholder logic).

The example usage demonstrates how to call the reason_rank function with sample input data, including adjacency matrices, initial scores, argument texts, and feedback data. The final scores for pro and con arguments are printed as the output.

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