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#!/usr/bin/env python3

# -*- coding: utf-8 -*-

"""Word Sense Induction system for SemEval 2013, Task 11

This module performs word sense induction for a given word on a corpus and

matches a list of contexts to each. The method to achieve this is a modified

reimplementation of Véronis' Hyperlex (2004).

Example:

    The function can be called with the following command.:

        $ python3 absinth.py

    The function can be called with a list of modifiers.

Modifiers:

    '-t': Runs absinth.py on the trial path given in the config.py instead of the

        data_path.

    '-p n': Runs absinth.py with n concurrent processes (standard: 1). 

.. _Association Based Semantic Induction Tools from Heidelberg

    https://gitlab.cl.uni-heidelberg.de/zimmermann/absinth

"""

import sys

print('[A] Loading ' + sys.argv[0] + '.\n')

from nltk.corpus import stopwords

from copy import deepcopy

nlp = spacy.load('en') # standard english nlp

def read_dataset(data_path):

def read_dataset(data_path: str) -> (dict, dict):

    """Collects topics.txt and results.txt.

    Iterates over topics.txt and results.txt in the data path and converts them

    to dictionaries with the ID as key and the target word / title + snippet as

    values.

    Args:

        data_path: File path to directory containing topics.txt and results.txt.

    Returns:

        One dictionary for each file. 

    """

    results = dict()

    return results, topics

def frequencies(target_string, search_result_list):

def frequencies(target_string: str, search_result_list: list) -> (dict, dict):

    """Counts occurrences of nodes and cooccurrences.

    Iterates over the corpus (and snippets provided with the task) line by line 

        search_result_list: List of titles and snippets provided with the task.

    Returns:

        node_freq_dict: Dictionary of occurrences of every eligible token

            within every context the target occurs in.

        edge_freq_dict: Dictionary of occurrences of every eligible tuple of

            tokens within every context the target occurs in.

        Dictionary of occurrences of every eligible token within every context

            the target occurs in, dictionary of occurrences of every eligible

            tuple of tokens within every context the target occurs in.

    """

    return node_freq_dict, edge_freq_dict

def process_file(context_list, target_string, node_freq_dict, edge_freq_dict):

def process_file(context_list: list, target_string: str,

                 node_freq_dict: dict, edge_freq_dict: dict) -> (dict, dict): 

    """Updates the counts of nodes and edges for a given document and target.

    Ammends the input dictionaries with counts from each context withing the

            tokens within every context the target occurs in.

    Returns:

        node_freq_dict: Updated version of the input node dict.

        edge_freq_dict: Updated version of the input edge dict.

        Updated versions of the input node dict and input edge dict.

    """

    spaced_target_string = target_string.replace('_', ' ')

    return node_freq_dict, edge_freq_dict

def build_graph(node_freq_dict, edge_freq_dict):

def build_graph(node_freq_dict: dict, edge_freq_dict: dict) -> nx.Graph:

    """Builds undirected weighted graph from dictionaries.

    Creates graph and appends every edge and node in the parameter dictionaries,

            tokens within every context the target occurs in.

    Returns:

        cooccurrence_graph: Filtered undirected dice weighted small word 

            cooccurrence graph for a given target entity.

        Filtered undirected dice weighted small word cooccurrence graph for a 

            given target entity.

    """

    min_node_freq = config.min_node_freq

    return cooccurrence_graph

def root_hubs(graph, edge_freq_dict):

def root_hubs(graph: nx.Graph, edge_freq_dict: dict) -> list:

    """Identifies senses (root hubs) by choosing nodes with high degrees

    Selects root hubs according to the algorithm in Véronis (2004). Nodes with

        edge_freq_dict: Dictionary of weights for every tuple in our graph.

    Returns:

        hub_list: List of root hubs, i.e. strings that are selected using the

            algorithm explained above.

        List of root hubs, i.e. strings that are selected using the algorithm

            explained above.

    """

    min_neighbors = config.min_neighbors

    return hub_list

def components(graph, root_hub_list, target_string):

def components(graph: nx.Graph, root_hub_list: list, target_string: str) -> nx.Graph:

    """Builds minimum spanning tree from graph and removes singletons.

    Applies components algorithm from Véronis (2004) and removes singletons.

        target_string: Root of minimum spanning tree.

    Returns:

        minimum_spanning_tree: Minimum spanning tree with target as

            root and root hubs as direct children. Singletons removed.

        Minimum spanning tree with target as root and root hubs as direct

            children. Singletons removed.

    """

    graph_copy = deepcopy(graph)

    return minimum_spanning_tree

def score(graph, component, root_hub_list):

def score(graph: nx.Graph, component: str, root_hub_list: list) -> np.array:

    """Calculate score for a given component in a minimum spanning tree.

    First the correct root for the component is chosen. If no root hub is

        root_hub_list: List of strings of root hubs (senses) of original graph.

    Returns:

        score_array: Array with one score for the correct root hub and filled 

            with zeroes..

        Array with one score for the correct root hub and filled with zeroes.

    """

    root_hub_count = len(root_hub_list)

    return score_array

def induce(topic_name, result_list):

def induce(topic_name: str, result_list: list) -> (nx.Graph, list, dict):

    """Induces word senses for a given topic from corpus.

    Counts frequencies from corpus and search result list, builds graph from

        result_list: List of search result (context) strings.

    Returns:

        root_hub_list: List of root hub strings (senses).

        stat dict: Various statistics.

        A cooccurrence graph,

            a list of root hub strings (senses) 

            and dictionary of various statistics.

    """

    stat_dict = dict()

        return graph, root_hub_list, stat_dict

def disambiguate(graph, root_hub_list, context_list, topic_name):

def disambiguate(graph: nx.Graph, root_hub_list: list,

                 context_list: list, topic_name: str) -> dict:

    """Matches contexts to senses.

    Builds minimum spanning tree from graph.

        topic_name: String of target word, also root of MST.

    Returns:

        mapping_dict: Dictionary of root hubs (senses) as keys and context ids

            as values.

        Dictionary of root hubs (senses) as keys and context IDs as values.

    """

    #performs minimum_spanning_tree algorithm on graph

    return mapping_dict

def main(topic_id, topic_name, result_dict):

def main(topic_id: int, topic_name: str, result_dict: dict) -> None:

    """Calls induction and disambiguation functions, performs main task.

    The task is to both induce senses and match search results to them. This

        result_dict: Dictionary with topic_id as key and list of search queries

            (from results.txt) as values.

    Returns:

        None

    """

    print('[a]', 'Inducing word senses for {}.'.format(topic_name))