utils/interp_space_utils.py

import sys

import pandas as pd
import numpy as np
import math
from collections import Counter, defaultdict
from typing import List, Any
from sklearn.feature_extraction.text import TfidfVectorizer
import os
import pickle
import hashlib
import json
from gram2vec import vectorizer
from openai import OpenAI
from openai.lib._pydantic import to_strict_json_schema
from pydantic import BaseModel
from pydantic import ValidationError
import time
from utils.llm_feat_utils import generate_feature_spans_cached
from collections import Counter
import numpy as np
from sklearn.metrics.pairwise import cosine_similarity, euclidean_distances
from sklearn.decomposition import PCA

CACHE_DIR = "datasets/embeddings_cache"
ZOOM_CACHE = "datasets/zoom_cache/features_cache.json"
REGION_CACHE = "datasets/region_cache/regions_cache.pkl"
os.makedirs(CACHE_DIR, exist_ok=True)
os.makedirs(os.path.dirname(ZOOM_CACHE), exist_ok=True)
os.makedirs(os.path.dirname(REGION_CACHE), exist_ok=True)
# Bump this whenever there is a change etc...
CACHE_VERSION = 1

class style_analysis_schema(BaseModel):
    features: list[str]
    spans: dict[str, dict[str, list[str]]]

class FeatureIdentificationSchema(BaseModel):
    features: list[str]

class SpanExtractionSchema(BaseModel):
    spans: dict[str, dict[str, list[str]]]  # {author_name: {feature: [spans]}}



def compute_g2v_features(clustered_authors_df: pd.DataFrame, task_authors_df: pd.DataFrame=None, text_clm='fullText') -> pd.DataFrame:
    """
    Computes gram2vec feature vectors for each author and adds them to the DataFrame.
    This effectively creates a mapping from each author to their vector.
    """
    if task_authors_df is not None:
        print (f"concatenating task authors and background corpus authors")
        print(f"Number of task authors: {len(task_authors_df)}")
        print(f"task authors author_ids: {task_authors_df.authorID.tolist()}")
        print(f"task authors -->")
        print(task_authors_df)
        print(f"Number of background corpus authors: {len(clustered_authors_df)}")
        clustered_authors_df = pd.concat([task_authors_df, clustered_authors_df])
        print(f"Number of authors after concatenation: {len(clustered_authors_df)}")

    # Gather the input texts (preserves list-of-strings if any)
    #texts = background_corpus_df[text_clm].fillna("").tolist()
    author_texts = ['\n\n'.join(x) for x in clustered_authors_df.fullText.tolist()]

    print(f"Number of author_texts: {len(author_texts)}")

    # Create a reproducible JSON serialization of the texts
    serialized = json.dumps({
        "col": text_clm,
        "texts": author_texts
    }, sort_keys=True, ensure_ascii=False)

    # Compute MD5 hash
    digest = hashlib.md5(serialized.encode("utf-8")).hexdigest()
    cache_path = os.path.join(CACHE_DIR, f"{digest}.pkl")

    # If cache hit, load and return
    if os.path.exists(cache_path):
        print(f"Cache hit...")
        with open(cache_path, "rb") as f:
            clustered_authors_df = pickle.load(f)
    
    else: # Else compute and cache
        g2v_feats_df = vectorizer.from_documents(author_texts, batch_size=8)

        print(f"Number of g2v features: {len(g2v_feats_df)}")
        print(f"Number of clustered_authors_df.authorID.tolist(): {len(clustered_authors_df.authorID.tolist())}")
        print(f"Number of g2v_feats_df.to_numpy().tolist(): {len(g2v_feats_df.to_numpy().tolist())}")

        ids = clustered_authors_df.authorID.tolist()
        counter = Counter(ids)
        duplicates = [k for k, v in counter.items() if v > 1]

        print(f"Duplicate authorIDs: {duplicates}")
        print(f"Number of duplicates: {len(ids) - len(set(ids))}")

        author_to_g2v_feats = {x[0]: x[1] for x in zip(clustered_authors_df.authorID.tolist(), g2v_feats_df.to_numpy().tolist())}

        print(f"Number of authors with g2v features: {len(author_to_g2v_feats)}")

        # apply normalization
        vector_std  = np.std(list(author_to_g2v_feats.values()), axis=0)
        vector_mean = np.mean(list(author_to_g2v_feats.values()), axis=0)
        vector_std[vector_std == 0] = 1.0
        author_to_g2v_feats_z_normalized = {x[0]: (x[1] - vector_mean) / vector_std for x in author_to_g2v_feats.items()}

        print(f"Number of authors with g2v features normalized: {len(author_to_g2v_feats_z_normalized)}")
        print(f" len of clustered authors df: {len(clustered_authors_df)}")
        

        # Add the vectors as a new column of the DataFrame.
        clustered_authors_df['g2v_vector'] = [{x[1]: x[0] for x in zip(val, g2v_feats_df.columns.tolist())} 
                                            for val in author_to_g2v_feats_z_normalized.values()]
        
        with open(cache_path, "wb") as f:
            pickle.dump(clustered_authors_df, f)
    
    if task_authors_df is not None:
        task_authors_df = clustered_authors_df[clustered_authors_df.authorID.isin(task_authors_df.authorID.tolist())]
        clustered_authors_df = clustered_authors_df[~clustered_authors_df.authorID.isin(task_authors_df.authorID.tolist())]


    return clustered_authors_df['g2v_vector'].tolist(), task_authors_df['g2v_vector'].tolist()


def get_task_authors_from_background_df(background_df):
    task_authors_df = background_df[background_df.authorID.isin(["Q_author", "a0_author", "a1_author", "a2_author"])]
    return task_authors_df

def instance_to_df(instance, predicted_author=None, ground_truth_author=None):
    #create a dataframe of the task authors
    task_authos_df  = pd.DataFrame([
        {'authorID': 'Mystery author', 'fullText': instance['Q_fullText'], 'predicted': None, 'ground_truth': None},
        {'authorID': 'Candidate Author 1', 'fullText': instance['a0_fullText'], 'predicted': int(predicted_author) == 0 if predicted_author is not None else None, 'ground_truth': int(ground_truth_author) == 0 if ground_truth_author is not None else None},
        {'authorID': 'Candidate Author 2', 'fullText': instance['a1_fullText'], 'predicted': int(predicted_author) == 1 if predicted_author is not None else None, 'ground_truth': int(ground_truth_author) == 1 if ground_truth_author is not None else None},
        {'authorID': 'Candidate Author 3', 'fullText': instance['a2_fullText'], 'predicted': int(predicted_author) == 2 if predicted_author is not None else None, 'ground_truth': int(ground_truth_author) == 2 if ground_truth_author is not None else None}
                    
    ])

    if type(instance['Q_fullText']) == list:
        task_authos_df = task_authos_df.groupby('authorID').agg({'fullText': lambda x: list(x)}).reset_index()

    return task_authos_df


def generate_style_embedding(background_corpus_df: pd.DataFrame, text_clm: str, model_name: str, dimensionality_reduction: bool = True, dimensions: int = 100) -> pd.DataFrame:
    """
    Generates style embeddings for documents in a background corpus using a specified model.
    If a row in `text_clm` contains a list of strings, the final embedding for that row
    is the average of the embeddings of all strings in the list.

    Args:
        background_corpus_df (pd.DataFrame): DataFrame containing the corpus.
        text_clm (str): Name of the column containing the text data (either string or list of strings).
        model_name (str): Name of the model to use for generating embeddings.

    Returns:
        pd.DataFrame: The input DataFrame with a new column for style embeddings.
    """
    from sentence_transformers import SentenceTransformer
    import torch

    if model_name not in [
        'gabrielloiseau/LUAR-MUD-sentence-transformers',
        'gabrielloiseau/LUAR-CRUD-sentence-transformers',
        'miladalsh/light-luar',
        'AnnaWegmann/Style-Embedding',

    ]:
        print('Model is not supported')
        return background_corpus_df
    
    print(f"Generating style embeddings using {model_name} on column '{text_clm}'...")

    print(background_corpus_df.fullText.tolist()[:10])
    model = SentenceTransformer(model_name)
    embedding_dim = model.get_sentence_embedding_dimension()

    # Heuristic to check if the column contains lists of strings by checking the first valid item.
    # This assumes the column is homogenous.
    is_list_column = False
    if not background_corpus_df.empty:
        # Get the first non-NaN value to inspect its type
        series_no_na = background_corpus_df[text_clm].dropna()
        if not series_no_na.empty:
            first_valid_item = series_no_na.iloc[0]
            if isinstance(first_valid_item, list):
                is_list_column = True

    if is_list_column:
        # Flatten all texts into a single list for batch processing
        texts_to_encode = []
        row_lengths = []
        for text_list in background_corpus_df[text_clm]:
            # Ensure we handle None, empty lists, or other non-list types gracefully
            if isinstance(text_list, list) and text_list:
                texts_to_encode.extend(text_list)
                row_lengths.append(len(text_list))
            else:
                row_lengths.append(0)

        if texts_to_encode:
            all_embeddings = model.encode(texts_to_encode, convert_to_tensor=True, show_progress_bar=True)
        else:
            all_embeddings = torch.empty((0, embedding_dim), device=model.device)

        # Reconstruct and average embeddings for each row
        final_embeddings = []
        current_pos = 0
        for length in row_lengths:
            if length > 0:
                row_embeddings = all_embeddings[current_pos:current_pos + length]
                avg_embedding = torch.mean(row_embeddings, dim=0)
                final_embeddings.append(avg_embedding.cpu().numpy())
                current_pos += length
            else:
                final_embeddings.append(np.zeros(embedding_dim))
    else:
        # Column contains single strings
        texts = background_corpus_df[text_clm].fillna("").tolist()
        # convert_to_tensor=False is faster if we just need numpy arrays
        embeddings = model.encode(texts, show_progress_bar=True)
        final_embeddings = list(embeddings)

    # Apply PCA over the embeddings to reduce the dimentionality 
    if dimensionality_reduction:
        if len(final_embeddings) > 0 and len(final_embeddings[0]) > dimensions: # Only apply PCA if embeddings exist and dim > dimensions
            pca = PCA(n_components=dimensions)
            final_embeddings = pca.fit_transform(final_embeddings)

    return list(final_embeddings)

# ── wrapper with caching ───────────────────────────────────────
def cached_generate_style_embedding(background_corpus_df: pd.DataFrame,
                                    text_clm: str,
                                    model_name: str,
                                    task_authors_df: pd.DataFrame = None) -> pd.DataFrame:
    """
    Wraps `generate_style_embedding`, caching its output in pickle files
    keyed by an MD5 of (model_name + text list). If the cache exists,
    loads and returns it instead of recomputing.
    """

    if task_authors_df is not None:
        print (f"concatenating task authors and background corpus authors")
        print(f"Number of task authors: {len(task_authors_df)}")
        print(f"task authors author_ids: {task_authors_df.authorID.tolist()}")
        print(f"Number of background corpus authors: {len(background_corpus_df)}")
        background_corpus_df = pd.concat([task_authors_df, background_corpus_df])
        print(f"Number of authors after concatenation: {len(background_corpus_df)}")

    # Gather the input texts (preserves list-of-strings if any)
    texts = background_corpus_df[text_clm].fillna("").tolist()

    # Create a reproducible JSON serialization of the texts
    serialized = json.dumps({
        "model": model_name,
        "col": text_clm,
        "texts": texts
    }, sort_keys=True, ensure_ascii=False)

    # Compute MD5 hash
    digest = hashlib.md5(serialized.encode("utf-8")).hexdigest()
    cache_path = os.path.join(CACHE_DIR, f"{digest}.pkl")

    # If cache hit, load and return
    if os.path.exists(cache_path):
        print(f"Cache hit for {model_name} on column '{text_clm}'")
        print(cache_path)
        with open(cache_path, "rb") as f:
            background_corpus_df = pickle.load(f)

    else:
        # Otherwise, compute, cache, and return
        print(f"Computing embeddings for {model_name} on column '{text_clm}', saving to {cache_path}")
        task_and_background_embeddings = generate_style_embedding(background_corpus_df, text_clm, model_name, dimensionality_reduction=False)
        # Create a clean column name from the model name
        col_name = f'{model_name.split("/")[-1]}_style_embedding'
        background_corpus_df[col_name] = task_and_background_embeddings

        with open(cache_path, "wb") as f:
            pickle.dump(background_corpus_df, f)
    
    if task_authors_df is not None:
        task_authors_df = background_corpus_df[background_corpus_df.authorID.isin(task_authors_df.authorID.tolist())]
        background_corpus_df = background_corpus_df[~background_corpus_df.authorID.isin(task_authors_df.authorID.tolist())]

    return background_corpus_df, task_authors_df

def get_style_feats_distribution(documentIDs, style_feats_dict):
    style_feats = []
    for documentId in documentIDs:
        if documentId not in document_to_style_feats:
            #print(documentId)
            continue

        style_feats+= document_to_style_feats[documentId]

    tfidf = [style_feats.count(key) * val for key, val in style_feats_dict.items()]

    return tfidf

def get_cluster_top_feats(style_feats_distribution, style_feats_list, top_k=5):
    sorted_feats = np.argsort(style_feats_distribution)[::-1]
    top_feats = [style_feats_list[x] for x in sorted_feats[:top_k] if style_feats_distribution[x] > 0]
    return top_feats

def compute_clusters_style_representation(
    background_corpus_df: pd.DataFrame,
    cluster_ids: List[Any],
    other_cluster_ids: List[Any],
    features_clm_name: str,
    cluster_label_clm_name: str = 'cluster_label',
    top_n: int = 10
) -> List[str]:
    """
    Given a DataFrame with document IDs, cluster IDs, and feature lists,
    return the top N features that are most important in the specified `cluster_ids`
    while having low importance in `other_cluster_ids`.
    Importance is determined by TF-IDF scores. The final score for a feature is
    (summed TF-IDF in `cluster_ids`) - (summed TF-IDF in `other_cluster_ids`).

    Parameters:
    - background_corpus_df: pd.DataFrame. Must contain the columns specified by
                            `cluster_label_clm_name` and `features_clm_name`.
                            The column `features_clm_name` should contain lists of strings (features).
    - cluster_ids: List of cluster IDs for which to find representative features (target clusters).
    - other_cluster_ids: List of cluster IDs whose features should be down-weighted.
                         Features prominent in these clusters will have their scores reduced.
                         Pass an empty list or None if no contrastive clusters are needed.
    - features_clm_name: The name of the column in `background_corpus_df` that
                         contains the list of features for each document.
    - cluster_label_clm_name: The name of the column in `background_corpus_df`
                              that contains the cluster labels. Defaults to 'cluster_label'.
    - top_n: Number of top features to return.
    Returns:
    - List[str]: A list of feature names. These are up to `top_n` features
                 ranked by their adjusted TF-IDF scores (score in `cluster_ids`
                 minus score in `other_cluster_ids`). Only features with a final
                 adjusted score > 0 are included.
    """

    assert background_corpus_df[features_clm_name].apply(
        lambda x: isinstance(x, list) and all(isinstance(feat, str) for feat in x)
    ).all(), f"Column '{features_clm_name}' must contain lists of strings."

    # Compute TF-IDF on the entire corpus
    vectorizer = TfidfVectorizer(
        tokenizer=lambda x: x,
        preprocessor=lambda x: x,
        token_pattern=None  # Disable default token pattern, treat items in list as tokens
    )
    tfidf_matrix = vectorizer.fit_transform(background_corpus_df[features_clm_name])
    feature_names = vectorizer.get_feature_names_out()

    # Get boolean mask for documents in selected clusters
    selected_mask = background_corpus_df[cluster_label_clm_name].isin(cluster_ids).to_numpy()

    if not selected_mask.any():
        return [] # No documents found for the given cluster_ids

    # Subset the TF-IDF matrix using the boolean mask
    selected_tfidf = tfidf_matrix[selected_mask]

    # Sum TF-IDF scores across documents for each feature in the target clusters
    target_feature_scores_sum = selected_tfidf.sum(axis=0).A1  # Convert to 1D array

    # Initialize adjusted scores with target scores
    adjusted_feature_scores = target_feature_scores_sum.copy()

    # If other_cluster_ids are provided and not empty, subtract their TF-IDF sums
    if other_cluster_ids: # Checks if the list is not None and not empty
        other_selected_mask = background_corpus_df[cluster_label_clm_name].isin(other_cluster_ids).to_numpy()

        if other_selected_mask.any():
            other_selected_tfidf = tfidf_matrix[other_selected_mask]
            contrast_feature_scores_sum = other_selected_tfidf.sum(axis=0).A1
            
            # Element-wise subtraction; assumes feature_names aligns for both sums
            adjusted_feature_scores -= contrast_feature_scores_sum

    # Map scores to feature names
    feature_score_dict = dict(zip(feature_names, adjusted_feature_scores))
    # Sort features by score
    sorted_features = sorted(feature_score_dict.items(), key=lambda item: item[1], reverse=True)

    # Return the names of the top_n features that have a score > 0
    top_features = [feature for feature, score in sorted_features if score > 0][:top_n]

    return top_features

def compute_clusters_style_representation_2(
        background_corpus_df: pd.DataFrame,
        cluster_ids: List[Any],
        cluster_label_clm_name: str = 'cluster_label',
        max_num_feats: int = 5,
        max_num_documents_per_author=3,
        max_num_authors=5):
    """
    Call openAI to analyze the common writing style features of the given list of texts
    """
    client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))

    background_corpus_df['fullText'] = background_corpus_df['fullText'].map(lambda x: '\n\n'.join(x[:max_num_documents_per_author]) if isinstance(x, list) else x)
    background_corpus_df = background_corpus_df[background_corpus_df[cluster_label_clm_name].isin(cluster_ids)]
    
    author_texts = background_corpus_df['fullText'].tolist()[:max_num_authors]
    author_texts = "\n\n".join(["""Author {}:\n""".format(i+1) + text for i, text in enumerate(author_texts)])
    author_names = background_corpus_df[cluster_label_clm_name].tolist()[:max_num_authors]
    print(f"Number of authors: {len(background_corpus_df)}")
    print(author_names)
    print(author_texts)
    print(f"Number of authors: {len(author_names)}")
    print(f"Number of authors: {len(author_texts)}")
    
    prompt = f"""First identify a list of {max_num_feats} writing style features that are common between the given texts. Second for every author text and style feature, extract all spans that represent the feature. Output for every author all style features with their spans.
    Author Texts:
    \"\"\"{author_texts}\"\"\"
    """

    # Compute MD5 hash
    digest = hashlib.md5(prompt.encode("utf-8")).hexdigest()
    cache_path = os.path.join(CACHE_DIR, f"{digest}.pkl")

    # If cache hit, load and return
    if os.path.exists(cache_path):
        print(f"Loading authors writing style from cache ...")
        with open(cache_path, "rb") as f:
            parsed_response = pickle.load(f)
    
    else: # Else compute and cache

        response = client.chat.completions.create(
            model="gpt-4o-mini",
            messages=[
                {"role":"assistant","content":"You are a forensic linguistic who knows how to analyze similarites in writing styles."},
                {"role":"user","content":prompt}],
            response_format={"type": "json_schema", "json_schema": {"name": "style_analysis_schema", "schema":  to_strict_json_schema(style_analysis_schema)}}
        )

        parsed_response = json.loads(response.choices[0].message.content)

        with open(cache_path, "wb") as f:
            pickle.dump(parsed_response, f)

    return parsed_response 

def generate_cache_key(author_names: List[str], max_num_feats: int) -> str:
    """Generate a unique cache key based on author names and max features"""
    # Sort author names to ensure consistent key regardless of order
    sorted_authors = sorted(author_names)
    key_data = {
        "authors": sorted_authors,
        "max_num_feats": max_num_feats
    }
    key_string = json.dumps(key_data, sort_keys=True)
    return hashlib.md5(key_string.encode()).hexdigest()

def identify_style_features(author_texts: list[str], author_names: list[str], max_num_feats: int = 5) -> list[str]:
    cache_key = None
    if author_names:
        cache_key = generate_cache_key(author_names, max_num_feats)

        if os.path.exists(ZOOM_CACHE):
            with open(ZOOM_CACHE, 'r') as f:
                cache = json.load(f)
        else:
            cache = {}

        if cache_key in cache:
            print(f"\nCache hit! Using cached features for authors: {author_names}")
            return cache[cache_key]["features"]
        else:
            print(f"Cache miss. Computing features for authors: {author_names}")
    
    client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))
    prompt = f"""Identify {max_num_feats} writing style features that are commonly between the authors texts.
    Author Texts:

    {author_texts}
    """
    def _make_call():
        response = client.chat.completions.create(
            model="gpt-4o-mini",
            messages=[
                {"role": "assistant", "content": "You are a forensic linguist specializing in writing styles."},
                {"role": "user", "content": prompt}
            ],
            response_format={
                "type": "json_schema",
                "json_schema": {
                    "name": "FeatureIdentificationSchema",
                    "schema": to_strict_json_schema(FeatureIdentificationSchema)
                }
            }
        )
        return json.loads(response.choices[0].message.content)

    features = retry_call(_make_call, FeatureIdentificationSchema).features

    if cache_key and author_names:
        cache[cache_key] = {
            "features": features
        }
        # save_cache(cache)
        with open(ZOOM_CACHE, 'w') as f:
            json.dump(cache, f, indent=2)

        print(f"Cached features for authors: {author_names}")

def retry_call(call_fn, schema_class, max_attempts=3, wait_sec=2):
    for attempt in range(max_attempts):
        try:
            result = call_fn()
            # Validate against schema
            validated = schema_class(**result)
            return validated
        except (ValidationError, KeyError, json.JSONDecodeError) as e:
            print(f"Attempt {attempt + 1} failed with error: {e}")
            time.sleep(wait_sec)
    raise RuntimeError("All retry attempts failed for OpenAI call.")

def extract_all_spans(authors_df: pd.DataFrame, features: list[str], cluster_label_clm_name: str = 'authorID') -> dict[str, dict[str, list[str]]]:
    """
    For each author, use `generate_feature_spans_cached` to get feature->span mappings.
    Returns a dict: {author_name: {feature: [spans]}}
    """
    client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))

    spans_by_author = {}

    for _, row in authors_df.iterrows():
        author_name = str(row[cluster_label_clm_name])
        print(author_name)
        role = f"{author_name}"
        full_text = row['fullText']
        spans = generate_feature_spans_cached(client, full_text, features, role)
        spans_by_author[author_name] = spans

    return spans_by_author

def compute_clusters_style_representation_3(
    background_corpus_df: pd.DataFrame,
    cluster_ids: List[Any],
    cluster_label_clm_name: str = 'authorID',
    max_num_feats: int = 20,
    max_num_documents_per_author=1,
    max_num_authors=10,
    max_authors_for_span_extraction=4
    ):

    print(f"Computing style representation for visible clusters: {len(cluster_ids)}")
    # STEP 1: Identify features on 5 visible authors
    background_corpus_df['fullText'] = background_corpus_df['fullText'].map(lambda x: '\n\n'.join(x[:max_num_documents_per_author]) if isinstance(x, list) else x)
    background_corpus_df_feat_id = background_corpus_df[background_corpus_df[cluster_label_clm_name].isin(cluster_ids)]
    
    author_texts = background_corpus_df_feat_id['fullText'].tolist()[:max_num_authors]
    author_texts = "\n\n".join(["""Author {}:\n""".format(i+1) + text for i, text in enumerate(author_texts)])
    author_names = background_corpus_df_feat_id[cluster_label_clm_name].tolist()[:max_num_authors]
    print(f"Number of authors: {len(background_corpus_df_feat_id)}")
    print(author_names)
    features = identify_style_features(author_texts, author_names, max_num_feats=max_num_feats)

    # STEP 2: Prepare author pool for span extraction
    span_df = background_corpus_df.iloc[:max_authors_for_span_extraction]
    author_names = span_df[cluster_label_clm_name].tolist()[:max_authors_for_span_extraction]
    print(f"Number of authors for span detection : {len(span_df)}")
    print(author_names)
    spans_by_author = extract_all_spans(span_df, features, cluster_label_clm_name)
    
    # Filter out features that are not present in any of the authors
    filtered_spans_by_author = {x[0] : x[1] for x in spans_by_author.items() if x[0] in {'Mystery author', 'Candidate Author 1', 'Candidate Author 2', 'Candidate Author 3'}.intersection(set(cluster_ids))}
    print(filtered_spans_by_author.keys())
    filtered_spans_by_author = [set([f[0] for f in   x[1].items() if len(f[1]) > 0]) for x in filtered_spans_by_author.items()]

    filtered_set_of_features =  filtered_spans_by_author[0] # all features that appear in all the sets in the filtered_Spans_by_authors list
    for x in filtered_spans_by_author[1:]:
        filtered_set_of_features = filtered_set_of_features.intersection(x)

    print('filtered set of features: ', filtered_set_of_features)

    return {
        "features": list(filtered_set_of_features),
        "spans": spans_by_author
    }

def compute_clusters_g2v_representation(
    background_corpus_df: pd.DataFrame,
    author_ids: List[Any],
    other_author_ids: List[Any],
    features_clm_name: str,
    top_n: int = 10,
    mode: str = "contrastive",
    sharedness_method: str = "mean_minus_alpha_std",
    alpha: float = 0.5
) -> List[tuple]:  # Changed return type to List[tuple] to include scores


    selected_mask = background_corpus_df['authorID'].isin(author_ids).to_numpy()

    if not selected_mask.any():
        return [] # No documents found for the given cluster_ids

    selected_feats = background_corpus_df[selected_mask][features_clm_name].tolist()
    all_g2v_feats  = list(selected_feats[0].keys())

    # If the user requested a sharedness-based score, compute it and return top-N.
    if mode == "sharedness":
        selected_matrix = np.array([list(x.values()) for x in selected_feats], dtype=float)

        if sharedness_method == "mean":
            scores = selected_matrix.mean(axis=0)
        elif sharedness_method in ("mean_minus_alpha_std", "mean-std", "mean_minus_std"):
            means = selected_matrix.mean(axis=0)
            stds  = selected_matrix.std(axis=0)
            scores = means - float(alpha) * stds
        elif sharedness_method == "min":
            scores = selected_matrix.min(axis=0)
        else:
            # Default fallback to mean-minus-alpha*std if unknown method
            means = selected_matrix.mean(axis=0)
            stds  = selected_matrix.std(axis=0)
            scores = means - float(alpha) * stds

        # Rank and return with scores
        feature_scores = [(feat, score) for feat, score in zip(all_g2v_feats, scores) if score > 0]
        feature_scores.sort(key=lambda x: x[1], reverse=True)
        return feature_scores[:top_n]  # Return tuples instead of just features


    # Contrastive mode (default): compute target mean and subtract contrast mean
    all_g2v_values = np.array([list(x.values()) for x in selected_feats]).mean(axis=0)

    # If an explicit contrast set is provided, use it; otherwise use everyone outside selection
    if other_author_ids:
        explicit_mask = background_corpus_df['authorID'].isin(other_author_ids).to_numpy()
        # Ensure contrast set is disjoint from the selected set
        contrast_mask = np.logical_and(explicit_mask, ~selected_mask)
    else:
        contrast_mask = ~selected_mask

    other_selected_feats = background_corpus_df[contrast_mask][features_clm_name].tolist()
    if len(other_selected_feats) > 0:
        all_g2v_other_values = np.array([list(x.values()) for x in other_selected_feats]).mean(axis=0)
    else:
        # No contrast docs → treat contrast mean as zeros
        all_g2v_other_values = np.zeros_like(all_g2v_values)

    final_g2v_feats_values = all_g2v_values - all_g2v_other_values

    # Compute z-scores for normalization
    # Get population statistics from all features (both selected and contrast)
    all_feats = background_corpus_df[features_clm_name].tolist()
    population_matrix = np.array([list(x.values()) for x in all_feats])
    population_mean = population_matrix.mean(axis=0)
    population_std = population_matrix.std(axis=0)
    
    # Avoid division by zero
    population_std = np.where(population_std == 0, 1, population_std)
    
    # Calculate z-scores for the contrastive values
    z_scores = (final_g2v_feats_values - population_mean) / population_std

    # Keep only features that have a positive contrastive score
    top_g2v_feats = sorted(
        [(feat, val, z_score) for feat, val, z_score in zip(all_g2v_feats, final_g2v_feats_values, z_scores) if val > 0],
        key=lambda x: -x[1]  # Sort by contrastive score
    )
    
    # Filter in only features that are present in selected_authors
    selected_authors = {'Mystery author', 'Candidate Author 1', 'Candidate Author 2', 'Candidate Author 3'}.intersection(set(author_ids))
    
    # DEBUG: Print what we're actually working with
    print(f"[DEBUG] author_ids parameter: {author_ids}")
    print(f"[DEBUG] Hardcoded selected_authors set: {{'Mystery author', 'Candidate Author 1', 'Candidate Author 2', 'Candidate Author 3'}}")
    print(f"[DEBUG] Intersection result: {selected_authors}")
    print(f"[DEBUG] Is selected_authors empty? {len(selected_authors) == 0}")
    
    # Filter in only features that are present in selected_authors
    selected_authors_g2v_data = background_corpus_df[background_corpus_df['authorID'].isin(selected_authors)][features_clm_name].tolist()
    
    print(f"[DEBUG] selected_authors_g2v_data length: {len(selected_authors_g2v_data)}")
    print(f"[DEBUG] selected_authors_g2v_data content: {selected_authors_g2v_data}")

    # Get the actual text documents for the selected authors to verify feature presence
    selected_authors_docs = background_corpus_df[background_corpus_df['authorID'].isin(selected_authors)]['fullText'].tolist()
    print(f"[DEBUG] Found {len(selected_authors_docs)} documents for selected authors")
    
    # Import find_feature_spans for text-based feature verification
    try:
        from gram2vec.feature_locator import find_feature_spans
        print("[DEBUG] Successfully imported find_feature_spans")
    except ImportError:
        print("[WARNING] Could not import find_feature_spans, falling back to vector-based filtering")
        find_feature_spans = None

    filtered_features = []
    for feature, score, z_score in top_g2v_feats:
        # DEBUG: Print what we're checking for this feature
        print(f"[DEBUG] Checking feature: {feature}")
        print(f"[DEBUG] Feature score: {score}, z_score: {z_score}")
        
        # Check if the feature has a non-zero value in all of the selected authors
        feature_presence = []
        for i, author_g2v_feats in enumerate(selected_authors_g2v_data):
            feature_value = author_g2v_feats.get(feature, 0)
            feature_presence.append(feature_value)
            print(f"[DEBUG] Author {i} has feature '{feature}' = {feature_value}")
        
        print(f"[DEBUG] All feature values: {feature_presence}")
        print(f"[DEBUG] All values > 0? {[v > 0 for v in feature_presence]}")
        print(f"[DEBUG] All values > 0? {all(v > 0 for v in feature_presence)}")
        
        # First check: feature must be present in Gram2Vec vectors
        vector_present = all(author_g2v_feats.get(feature, 0) > 0 for author_g2v_feats in selected_authors_g2v_data)
        
        # Second check: feature must be present in actual text documents
        text_present = True
        if find_feature_spans and selected_authors_docs:
            try:
                # Check if feature appears in at least one document from each selected author
                for i, doc in enumerate(selected_authors_docs):
                    if isinstance(doc, list):
                        doc_text = '\n\n'.join(doc)
                    else:
                        doc_text = str(doc)
                    
                    spans = find_feature_spans(doc_text, feature)
                    if not spans:  # No spans found in this document
                        print(f"[DEBUG] ✗ Feature '{feature}' not found in document {i} of selected author")
                        text_present = False
                        break
                    else:
                        print(f"[DEBUG] ✓ Feature '{feature}' found in document {i} with {len(spans)} spans")
            except Exception as e:
                print(f"[WARNING] Error checking text presence for feature '{feature}': {e}")
                # Fall back to vector-based filtering if text checking fails
                text_present = vector_present
        
        # Feature must pass BOTH checks
        if vector_present and text_present:
            filtered_features.append((feature, score, z_score))
            print(f"[DEBUG] ✓ Feature '{feature}' PASSED both vector and text checks")
        else:
            if not vector_present:
                print(f"[DEBUG] ✗ Feature '{feature}' FAILED vector check")
            if not text_present:
                print(f"[DEBUG] ✗ Feature '{feature}' FAILED text check")
            print(f"[DEBUG] ✗ Feature '{feature}' FAILED the filter")


    print('Filtered G2V features: ', [(f[0], f[2]) for f in filtered_features])  # Print feature names and z-scores
    
    return filtered_features[:top_n]  # Return tuples with z-scores

def generate_interpretable_space_representation(interp_space_path, styles_df_path, feat_clm, output_clm, num_feats=5):
    
    styles_df = pd.read_csv(styles_df_path)[[feat_clm, "documentID"]]

    # A dictionary of style features and their IDF
    style_feats_agg_df = styles_df.groupby(feat_clm).agg({'documentID': lambda x : len(list(x))}).reset_index()
    style_feats_agg_df['document_freq'] = style_feats_agg_df.documentID
    style_to_feats_dfreq = {x[0]: math.log(styles_df.documentID.nunique()/x[1]) for x in zip(style_feats_agg_df[feat_clm].tolist(), style_feats_agg_df.document_freq.tolist())}
    
    # A list of style features we work with
    style_feats_list = style_feats_agg_df[feat_clm].tolist()
    print('Number of style feats ', len(style_feats_list))
    
    # A list of documents and what list of style features each has
    doc_style_agg_df     = styles_df.groupby('documentID').agg({feat_clm: lambda x : list(x)}).reset_index()
    document_to_feats_dict = {x[0]: x[1] for x in zip(doc_style_agg_df.documentID.tolist(), doc_style_agg_df[feat_clm].tolist())}
    
    

    # Load the clustering information
    df = pd.read_pickle(interp_space_path)
    df = df[df.cluster_label != -1]
    # A cluster to list of documents
    clusterd_df = df.groupby('cluster_label').agg({
        'documentID': lambda x: [d_id for doc_ids in x for d_id in doc_ids]
    }).reset_index()
    
    # Filter-in only documents that has a style description
    clusterd_df['documentID'] = clusterd_df.documentID.apply(lambda documentIDs: [documentID for documentID in documentIDs if documentID in document_to_feats_dict])
    # Map from cluster label to list of features through the document information
    clusterd_df[feat_clm] = clusterd_df.documentID.apply(lambda doc_ids: [f for d_id in doc_ids for f in document_to_feats_dict[d_id]])

    def compute_tfidf(row):
        style_counts = Counter(row[feat_clm])
        total_num_styles = sum(style_counts.values())
        #print(style_counts, total_num_styles)
        style_distribution = {
            style: math.log(1+count) * style_to_feats_dfreq[style] if style in style_to_feats_dfreq else 0 for style, count in style_counts.items()
        } #TF-IDF
        
        return style_distribution

    def create_tfidf_rep(tfidf_dist, num_feats):
        style_feats = sorted(tfidf_dist.items(), key=lambda x: -x[1])
        top_k_feats = [x[0] for x in style_feats[:num_feats] if str(x[0]) != 'nan']
        return top_k_feats

    clusterd_df[output_clm +'_dist'] = clusterd_df.apply(lambda row: compute_tfidf(row), axis=1)
    clusterd_df[output_clm]         = clusterd_df[output_clm +'_dist'].apply(lambda dist: create_tfidf_rep(dist, num_feats))

        
    return clusterd_df

def compute_predicted_author(task_authors_df: pd.DataFrame, col_name: str) -> int:
    """
    Computes the predicted author based on the style features.
    """
    print("Computing predicted author using LUAR-MUD-style embeddings...")

    # Extract LUAR embeddings from task authors dataframe
    mystery_embedding = np.array(task_authors_df.iloc[0][col_name]).reshape(1, -1)
    candidate_embeddings = np.array([
        task_authors_df.iloc[1][col_name],
        task_authors_df.iloc[2][col_name],
        task_authors_df.iloc[3][col_name]
    ])

    # Compute cosine similarities
    similarities = cosine_similarity(mystery_embedding, candidate_embeddings)[0]
    predicted_author = int(np.argmax(similarities))
    print(f"Predicted author is Candidate {predicted_author + 1}")

    return predicted_author


def compute_precomputed_regions(bg_proj, bg_ids, q_proj, c_proj, model_name, n_neighbors=7):
    """
    Compute precomputed regions for mystery author and candidates.
    
    Args:
        bg_proj: (N,2) numpy array with 2D coordinates of background authors
        bg_ids: list of N author IDs for background authors
        q_proj: (1,2) numpy array with mystery author coordinates 
        c_proj: (3,2) numpy array with candidate author coordinates
        n_neighbors: number of closest neighbors to include in each region
    
    Returns:
        dict: mapping region names to bounding boxes and author lists
    """
    print("Computing sugested regions for zoom...")
    key = f"{hashlib.md5((model_name + str(q_proj.tolist()) + str(c_proj.tolist()) + str(n_neighbors)).encode()).hexdigest()}"
    
    if os.path.exists(REGION_CACHE):
        with open(REGION_CACHE, 'rb') as f:
            cache = pickle.load(f)
    else:
        cache = {}
    if key in cache:
        print(f"\nCache hit! Using cached regions.")
        return cache[key]
    else:
        print(f"Cache miss. Computing regions.")
    
    regions = {}
    
    # All points for distance calculation (mystery + candidates + background)
    all_points = np.vstack([q_proj.reshape(1, -1), c_proj, bg_proj])
    all_ids = ['mystery'] + [f'candidate_{i}' for i in range(3)] + bg_ids
    
    def get_region_around_point(center_point, region_name, include_points=None):
        """Get region around a specific point"""
        # Ensure center_point is 2D for euclidean_distances
        if center_point.ndim == 1:
            center_point = center_point.reshape(1, -1)
        
        # Calculate distances from center point to all background authors
        distances = euclidean_distances(center_point, bg_proj)[0]
        
        # Get indices of closest neighbors
        closest_indices = np.argsort(distances)[:n_neighbors]
        closest_authors = [bg_ids[i] for i in closest_indices]
        closest_points = bg_proj[closest_indices]
        
        # Include the center point in the region
        # region_points = np.vstack([center_point.reshape(1, -1), closest_points])
        if include_points is not None:
            region_points = include_points.copy()
            # Add center point and closest background authors
            region_points = np.vstack([region_points, center_point, closest_points])
        else:
            # Standard case - just center point and neighbors
            region_points = np.vstack([center_point, closest_points])
        
        # Calculate bounding box with some padding
        x_min, x_max = region_points[:, 0].min(), region_points[:, 0].max()
        y_min, y_max = region_points[:, 1].min(), region_points[:, 1].max()
        
        # Add padding (10% of range)
        x_padding = (x_max - x_min) * 0.1
        y_padding = (y_max - y_min) * 0.1
        
        bbox = {
            'xaxis': [x_min - x_padding, x_max + x_padding],
            'yaxis': [y_min - y_padding, y_max + y_padding]
        }
        
        return {
            'bbox': bbox,
            'authors': closest_authors,
            'center_point': center_point,
            'description': f"Region around {region_name} ({len(closest_authors)} closest authors)"
        }
    
    def get_region_between_points(point1, point2, name1, name2):
        """Get region around the midpoint between two points"""
        midpoint = (point1 + point2) / 2
        region_name = f"{name1} & {name2}"
        # Include both original points in the region
        include_points = np.vstack([point1.reshape(1, -1), point2.reshape(1, -1)])
        return get_region_around_point(midpoint, region_name, include_points=include_points)

    # Region 1: Around mystery author only
    regions["Mystery Author Neighborhood"] = get_region_around_point(
        q_proj, "Mystery Author"
    )
    
    # Regions 2-4: Around each candidate
    for i in range(3):
        regions[f"Candidate {i+1} Neighborhood"] = get_region_around_point(
            c_proj[i], f"Candidate {i+1}"
        )
    
    # Regions 5-7: Between mystery and each candidate  
    for i in range(3):
        region_name = f"Mystery & Candidate {i+1}"
        regions[region_name] = get_region_between_points(
            q_proj, c_proj[i], "Mystery", f"Candidate {i+1}"
        )
    
    # Regions 8-10: Between candidate pairs
    candidate_pairs = [(0, 1), (0, 2), (1, 2)]
    for i, (c1, c2) in enumerate(candidate_pairs):
        region_name = f"Candidate {c1+1} & Candidate {c2+1}"
        regions[region_name] = get_region_between_points(
            c_proj[c1], c_proj[c2], f"Candidate {c1+1}", f"Candidate {c2+1}"
        )
    
    # Regions 11-12: Around predicted and ground truth (if different)
    # This would need predicted_author and ground_truth_author indices
    # For now, we'll create generic regions
    
    # Region 11: Centroid of all task authors (mystery + 3 candidates)
    task_centroid = np.mean(np.vstack([q_proj, c_proj]), axis=0)
    regions["All Task Authors Centroid"] = get_region_around_point(
        task_centroid, "All Task Authors"
    )
    
    def serialize_numpy_dtypes(obj):
        if isinstance(obj, np.ndarray):
            return obj.tolist()
        elif isinstance(obj, (np.float32, np.float64)):
            return float(obj)
        elif isinstance(obj, (np.int32, np.int64)):
            return int(obj)
        elif isinstance(obj, dict):
            return {key: serialize_numpy_dtypes(value) for key, value in obj.items()}
        elif isinstance(obj, list):
            return [serialize_numpy_dtypes(item) for item in obj]
        else:
            return obj

    serializable_regions = serialize_numpy_dtypes(regions)
    response = json.dumps(serializable_regions, default=str)
    cache[key] = response
    with open(REGION_CACHE, 'wb') as f: 
        pickle.dump(cache, f)

    return response

if __name__ == "__main__":
    background_corpus = pd.read_pickle('../datasets/luar_interp_space_cluster_19/train_authors.pkl')
    print(background_corpus.columns)
    print(background_corpus[['authorID', 'fullText', 'cluster_label']].head())
    # # Example: Find features for clusters [2,3,4] that are NOT prominent in cluster [1]
    # feats = compute_clusters_style_representation(
    #     background_corpus_df=background_corpus,
    #     cluster_ids=['00005a5c-5c06-3a36-37f9-53c6422a31d8',],
    #     other_cluster_ids=[], # Pass the contrastive cluster IDs here
    #     cluster_label_clm_name='authorID',
    #     features_clm_name='final_attribute_name'
    # )
    # print(feats)
    generate_style_embedding(background_corpus, 'fullText', 'AnnaWegmann/Style-Embedding')
    print(background_corpus.columns)