Query: is this solution for this task?
Construct the frequency ordered and tf-idf ordered top 10 keywords from the track record keywords for each reviewer we have an ADG profile for (hopefully most of them). Save the raw output somewhere.

Determine the overlap between the reviewer and ADG keywords, regardless of where the keywords rank in each top 10.

```
def process_profile(email: str, top_10_keywords_df: set, top_10_tfidf_keywords: set,
                    reviewer_keywords: list, adg_keywords: set) -> dict:
    """
    Process a single profile using precomputed TF-IDF scores.

    :param email: Email of the profile.
    :param top_10_keywords_df: Keywords from the database for the reviewer email.
    :param top_10_tfidf_keywords: Dictionary of precomputed TF-IDF scores for all unique keywords.
    :param reviewer_keywords: Reviewer-provided keywords.
    :param adg_keywords: All ADG keywords associated with the email.
    :return: Dictionary containing processed data for the profile.
    """
    overlap_percentage_frequency = calculate_overlap(top_10_keywords_df, reviewer_keywords)
    overlap_percentage_tfidf = calculate_overlap(top_10_tfidf_keywords, reviewer_keywords)
    adg_reviewer_overlap = calculate_overlap(adg_keywords, reviewer_keywords)

    return {
        'email': email,
        'overlap_percentage_frequency': overlap_percentage_frequency,
        'overlap_percentage_tfidf': overlap_percentage_tfidf,
        'adg_reviewer_overlap': adg_reviewer_overlap,
    }

    def fetch_and_process_reviewer_keywords(args: CliArgs, reviewer_profiles: dict):
    """
    Fetch ADG profiles associated with provided emails from PostgreSQL, process the data to calculate keyword
    rankings and overlaps, and save the results to CSV files.

    :param args: CLI parsed arguments.
    :param reviewer_profiles: Dictionary of reviewer emails and keywords.
    """
    adg_profiles, keywords_df, total_documents = fetch_data_from_db(args, reviewer_profiles)
    tfidf_scores = calculate_tf_idf(adg_profiles, keywords_df, total_documents)

    output_data = []

    for email, reviewer_keywords in reviewer_profiles.items():
        email_keywords_df = {
            keyword: freq
            for keyword, freq in keywords_df.items() if keyword in reviewer_keywords
        }
        adg_keywords = set(
            next((keywords for adg_email, keywords in adg_profiles if adg_email == email), [])
        )
        top_keywords_df = {
            keyword
            for keyword in email_keywords_df.keys() if keyword in adg_keywords
        }
        email_tfidf_scores = tfidf_scores.get(email, {})
        top_keywords_tfidf = {
            keyword
            for keyword in email_tfidf_scores.keys() if keyword in adg_keywords
        }

        processed_data = process_profile(email, top_keywords_df, top_keywords_tfidf, reviewer_keywords, adg_keywords)
        output_data.append(processed_data)

    rank_evaluation = evaluate_rank_ordering(output_data)

    for data in output_data:
        email = data['email']
        data['rank_evaluation'] = rank_evaluation[email]

    save_results(args, output_data)

    conclusion = analyze_results(output_data, reviewer_profiles)
    logger.info(conclusion)

```

Subject: computer science
Intent: Other