STARK/dependency-parsetree.py

#!/usr/bin/env python
# Copyright 2019 CJVT
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import argparse
import configparser
import copy
import csv
import hashlib
import os
import pickle
import re
import string
import time
from multiprocessing import Pool
from pathlib import Path
import gzip
import sys
import pyconll
from Tree import Tree
from generic import get_collocabilities, create_output_string_form, create_output_string_deprel, create_output_string_lemma, create_output_string_upos, create_output_string_xpos, create_output_string_feats
sys.setrecursionlimit(25000)

def save_zipped_pickle(obj, filename, protocol=-1):
    with gzip.open(filename, 'wb') as f:
        pickle.dump(obj, f, protocol)

def load_zipped_pickle(filename):
    with gzip.open(filename, 'rb') as f:
        loaded_object = pickle.load(f)
        return loaded_object

def decode_query(orig_query, dependency_type, feats_detailed_list):
    new_query = False

    # if command in bracelets remove them and treat command as new query
    if orig_query[0] == '(' and orig_query[-1] == ')':
        new_query = True
        orig_query = orig_query[1:-1]

    if dependency_type != '':
        decoded_query = {'deprel': dependency_type}
    else:
        decoded_query = {}

    if orig_query == '_':
        return decoded_query
    # if no spaces in query then this is query node and do this otherwise further split query
    elif len(orig_query.split(' ')) == 1:
        orig_query_split_parts = orig_query.split(' ')[0].split('&')
        for orig_query_split_part in orig_query_split_parts:
            orig_query_split = orig_query_split_part.split('=', 1)
            if len(orig_query_split) > 1:
                if orig_query_split[0] == 'L':
                    decoded_query['lemma'] = orig_query_split[1]
                elif orig_query_split[0] == 'upos':
                    decoded_query['upos'] = orig_query_split[1]
                elif orig_query_split[0] == 'xpos':
                    decoded_query['xpos'] = orig_query_split[1]
                elif orig_query_split[0] == 'form':
                    decoded_query['form'] = orig_query_split[1]
                elif orig_query_split[0] == 'feats':
                    decoded_query['feats'] = orig_query_split[1]
                elif orig_query_split[0] in feats_detailed_list:
                    decoded_query['feats_detailed'] = {}
                    decoded_query['feats_detailed'][orig_query_split[0]] = orig_query_split[1]
                    return decoded_query
                elif not new_query:
                    raise Exception('Not supported yet!')
                else:
                    print('???')
            elif not new_query:
                decoded_query['form'] = orig_query_split_part
        return decoded_query

    # split over spaces if not inside braces
    all_orders = re.split(r"\s+(?=[^()]*(?:\(|$))", orig_query)

    node_actions = all_orders[::2]
    priority_actions = all_orders[1::2]
    priority_actions_beginnings = [a[0] for a in priority_actions]

    # find root index
    try:
        root_index = priority_actions_beginnings.index('>')
    except ValueError:
        root_index = len(priority_actions)

    children = []
    root = None
    for i, node_action in enumerate(node_actions):
        if i < root_index:
            children.append(decode_query(node_action, priority_actions[i][1:], feats_detailed_list))
        elif i > root_index:
            children.append(decode_query(node_action, priority_actions[i - 1][1:], feats_detailed_list))
        else:
            root = decode_query(node_action, dependency_type, feats_detailed_list)
    if children:
        root["children"] = children
    return root


def create_trees(input_path, internal_saves, feats_detailed_dict={}, save=True):
    hash_object = hashlib.sha1(input_path.encode('utf-8'))
    hex_dig = hash_object.hexdigest()
    trees_read_outputfile = os.path.join(internal_saves, hex_dig)
    print(Path(input_path).name)
    if not os.path.exists(trees_read_outputfile) or not save:

        train = pyconll.load_from_file(input_path)

        form_dict, lemma_dict, upos_dict, xpos_dict, deprel_dict, feats_dict = {}, {}, {}, {}, {}, {}

        all_trees = []
        corpus_size = 0

        for sentence in train:
            root = None
            token_nodes = []
            for token in sentence:
                if not token.id.isdigit():
                    continue

                # TODO check if 5th place is always there for feats
                feats = token._fields[5]
                node = Tree(int(token.id), token.form, token.lemma, token.upos, token.xpos, token.deprel, feats, token.feats, form_dict,
                            lemma_dict, upos_dict, xpos_dict, deprel_dict, feats_dict, feats_detailed_dict, token.head)
                token_nodes.append(node)
                if token.deprel == 'root':
                    root = node

                corpus_size += 1

            for token_id, token in enumerate(token_nodes):
                if isinstance(token.parent, int) or token.parent == '':
                    root = None
                    print('No parent: ' + sentence.id)
                    break
                if int(token.parent) == 0:
                    token.set_parent(None)
                else:
                    parent_id = int(token.parent) - 1
                    if token_nodes[parent_id].children_split == -1 and token_id > parent_id:
                        token_nodes[parent_id].children_split = len(token_nodes[parent_id].children)
                    token_nodes[parent_id].add_child(token)
                    token.set_parent(token_nodes[parent_id])

            for token in token_nodes:
                if token.children_split == -1:
                    token.children_split = len(token.children)

            if root == None:
                print('No root: ' + sentence.id)
                continue
            all_trees.append(root)

        if save:
            save_zipped_pickle((all_trees, form_dict, lemma_dict, upos_dict, xpos_dict, deprel_dict, corpus_size, feats_detailed_dict), trees_read_outputfile, protocol=2)
    else:
        print('Reading trees:')
        print('Completed')
        all_trees, form_dict, lemma_dict, upos_dict, xpos_dict, deprel_dict, corpus_size, feats_detailed_dict = load_zipped_pickle(trees_read_outputfile)

    return all_trees, form_dict, lemma_dict, upos_dict, xpos_dict, deprel_dict, corpus_size, feats_detailed_dict

def printable_answers(query):
    # all_orders = re.findall(r"(?:[^ ()]|\([^]*\))+", query)
    all_orders = re.split(r"\s+(?=[^()]*(?:\(|$))", query)

    # all_orders = orig_query.split()
    node_actions = all_orders[::2]
    # priority_actions = all_orders[1::2]

    if len(node_actions) > 1:
        res = []
        # for node_action in node_actions[:-1]:
        #     res.extend(printable_answers(node_action[1:-1]))
        # res.extend([node_actions[-1]])
        for node_action in node_actions:
            # if command in bracelets remove them and treat command as new query
            # TODO FIX BRACELETS IN A BETTER WAY
            if not node_action:
                res.extend(['('])
            elif node_action[0] == '(' and node_action[-1] == ')':
                res.extend(printable_answers(node_action[1:-1]))
            else:
                res.extend([node_action])
        return res
    else:
        return [query]


def tree_calculations(input_data):
    tree, query_tree, create_output_string_funct, filters = input_data
    _, subtrees = tree.get_subtrees(query_tree, [], create_output_string_funct, filters)
    return subtrees

def get_unigrams(input_data):
    tree, query_tree, create_output_string_funct, filters = input_data
    unigrams = tree.get_unigrams(create_output_string_funct, filters)
    return unigrams


def tree_calculations_chunks(input_data):
    trees, query_tree, create_output_string_funct, filters = input_data

    result_dict = {}
    for tree in trees:
        _, subtrees = tree.get_subtrees(query_tree, [], create_output_string_funct, filters)

        for query_results in subtrees:
            for r in query_results:
                if r in result_dict:
                    result_dict[r] += 1
                else:
                    result_dict[r] = 1
    return result_dict


def add_node(tree):
    if 'children' in tree:
        tree['children'].append({})
    else:
        tree['children'] = [{}]


# walk over all nodes in tree and add a node to each possible node
def tree_grow(orig_tree):
    new_trees = []
    new_tree = copy.deepcopy(orig_tree)
    add_node(new_tree)
    new_trees.append(new_tree)
    if 'children' in orig_tree:
        children = []
        for child_tree in orig_tree['children']:
            children.append(tree_grow(child_tree))
        for i, child in enumerate(children):
            for child_res in child:
                new_tree = copy.deepcopy(orig_tree)
                new_tree['children'][i] = child_res
                new_trees.append(new_tree)

    return new_trees


def compare_trees(tree1, tree2):
    if tree1 == {} and tree2 == {}:
        return True

    if 'children' not in tree1 or 'children' not in tree2 or len(tree1['children']) != len(tree2['children']):
        return False

    children2_connections = []

    for child1_i, child1 in enumerate(tree1['children']):
        child_duplicated = False
        for child2_i, child2 in enumerate(tree2['children']):
            if child2_i in children2_connections:
                pass
            if compare_trees(child1, child2):
                children2_connections.append(child2_i)
                child_duplicated = True
                break
        if not child_duplicated:
            return False

    return True

def create_ngrams_query_trees(n, trees):
    for i in range(n - 1):
        new_trees = []
        for tree in trees:
            # append new_tree only if it is not already inside
            for new_tree in tree_grow(tree):
                duplicate = False
                for confirmed_new_tree in new_trees:
                    if compare_trees(new_tree, confirmed_new_tree):
                        duplicate = True
                        break
                if not duplicate:
                    new_trees.append(new_tree)

        trees = new_trees
    return trees

def count_trees(cpu_cores, all_trees, query_tree, create_output_string_functs, filters, unigrams_dict, result_dict):
    with Pool(cpu_cores) as p:
        if cpu_cores > 1:
            all_unigrams = p.map(get_unigrams, [(tree, query_tree, create_output_string_functs, filters) for tree in all_trees])
            for unigrams in all_unigrams:
                for unigram in unigrams:
                    if unigram in unigrams_dict:
                        unigrams_dict[unigram] += 1
                    else:
                        unigrams_dict[unigram] = 1

            all_subtrees = p.map(tree_calculations, [(tree, query_tree, create_output_string_functs, filters) for tree in all_trees])

            for tree_i, subtrees in enumerate(all_subtrees):

                for query_results in subtrees:
                    for r in query_results:
                        if filters['node_order']:
                            key = r.get_key() + r.order
                        else:
                            key = r.get_key()
                        if key in result_dict:
                            result_dict[key]['number'] += 1
                        else:
                            result_dict[key] = {'object': r, 'number': 1}

        # 3.65 s (1 core)
        else:
            for tree_i, tree in enumerate(all_trees):
                input_data = (tree, query_tree, create_output_string_functs, filters)
                if filters['association_measures']:
                    unigrams = get_unigrams(input_data)
                    for unigram in unigrams:
                        if unigram in unigrams_dict:
                            unigrams_dict[unigram] += 1
                        else:
                            unigrams_dict[unigram] = 1

                subtrees = tree_calculations(input_data)
                for query_results in subtrees:
                    for r in query_results:
                        if filters['node_order']:
                            key = r.get_key() + r.order
                        else:
                            key = r.get_key()
                        if key in result_dict:
                            result_dict[key]['number'] += 1
                        else:
                            result_dict[key] = {'object': r, 'number': 1}

def read_filters(config, feats_detailed_list):
    tree_size_range = config.get('settings', 'tree_size', fallback='0').split('-')
    tree_size_range = [int(r) for r in tree_size_range]

    if tree_size_range[0] > 1:
        if len(tree_size_range) == 1:
            query_tree = create_ngrams_query_trees(tree_size_range[0], [{}])
        elif len(tree_size_range) == 2:
            query_tree = []
            for i in range(tree_size_range[0], tree_size_range[1] + 1):
                query_tree.extend(create_ngrams_query_trees(i, [{}]))
    else:
        query_tree = [decode_query('(' + config.get('settings', 'query') + ')', '', feats_detailed_list)]

    # set filters
    node_types = config.get('settings', 'node_type').split('+')
    create_output_string_functs = []
    for node_type in node_types:
        assert node_type in ['deprel', 'lemma', 'upos', 'xpos', 'form', 'feats'], '"node_type" is not set up correctly'
        cpu_cores = config.getint('settings', 'cpu_cores')
        if node_type == 'deprel':
            create_output_string_funct = create_output_string_deprel
        elif node_type == 'lemma':
            create_output_string_funct = create_output_string_lemma
        elif node_type == 'upos':
            create_output_string_funct = create_output_string_upos
        elif node_type == 'xpos':
            create_output_string_funct = create_output_string_xpos
        elif node_type == 'feats':
            create_output_string_funct = create_output_string_feats
        else:
            create_output_string_funct = create_output_string_form
        create_output_string_functs.append(create_output_string_funct)

    filters = {}
    filters['internal_saves'] = config.get('settings', 'internal_saves')
    filters['input'] = config.get('settings', 'input')
    filters['node_order'] = config.get('settings', 'node_order') == 'fixed'
    # filters['caching'] = config.getboolean('settings', 'caching')
    filters['dependency_type'] = config.get('settings', 'dependency_type') == 'labeled'
    if config.has_option('settings', 'label_whitelist'):
        filters['label_whitelist'] = config.get('settings', 'label_whitelist').split('|')
    else:
        filters['label_whitelist'] = []

    if config.has_option('settings', 'root_whitelist'):
        # test
        filters['root_whitelist'] = []

        for option in config.get('settings', 'root_whitelist').split('|'):
            attribute_dict = {}
            for attribute in option.split('&'):
                value = attribute.split('=')
                attribute_dict[value[0]] = value[1]
            filters['root_whitelist'].append(attribute_dict)
    else:
        filters['root_whitelist'] = []

    filters['complete_tree_type'] = config.get('settings', 'tree_type') == 'complete'
    filters['association_measures'] = config.getboolean('settings', 'association_measures')
    filters['nodes_number'] = config.getboolean('settings', 'nodes_number')
    filters['frequency_threshold'] = config.getfloat('settings', 'frequency_threshold', fallback=0)
    filters['lines_threshold'] = config.getint('settings', 'lines_threshold', fallback=0)
    filters['print_root'] = config.getboolean('settings', 'print_root')

    return filters, query_tree, create_output_string_functs, cpu_cores, tree_size_range, node_types

def main():
    parser = argparse.ArgumentParser()

    ## Required parameters
    parser.add_argument("--config_file",
                        default=None,
                        type=str,
                        required=True,
                        help="The input config file.")
    args = parser.parse_args()

    config = configparser.ConfigParser()
    config.read(args.config_file)

    internal_saves = config.get('settings', 'internal_saves')
    input_path = config.get('settings', 'input')

    if os.path.isdir(input_path):

        checkpoint_path = Path(internal_saves, 'checkpoint.pkl')
        continuation_processing = config.getboolean('settings', 'continuation_processing', fallback=False)

        if not checkpoint_path.exists() or not continuation_processing:
            already_processed = set()
            result_dict = {}
            unigrams_dict = {}
            corpus_size = 0
            feats_detailed_list = {}
            if checkpoint_path.exists():
                os.remove(checkpoint_path)
        else:
            already_processed, result_dict, unigrams_dict, corpus_size, feats_detailed_list = load_zipped_pickle(
                checkpoint_path)

        for path in sorted(os.listdir(input_path)):
            path_obj = Path(input_path, path)
            pathlist = path_obj.glob('**/*.conllu')
            if path_obj.name in already_processed:
                continue
            start_exe_time = time.time()
            for path in sorted(pathlist):
                # because path is object not string
                path_str = str(path)

                (all_trees, form_dict, lemma_dict, upos_dict, xpos_dict, deprel_dict, sub_corpus_size,
                 feats_detailed_list) = create_trees(path_str, internal_saves, feats_detailed_dict=feats_detailed_list, save=False)

                corpus_size += sub_corpus_size

                filters, query_tree, create_output_string_functs, cpu_cores, tree_size_range, node_types = read_filters(
                    config, feats_detailed_list)


                count_trees(cpu_cores, all_trees, query_tree, create_output_string_functs, filters, unigrams_dict,
                            result_dict)

            already_processed.add(path_obj.name)

            # 15.26
            print("Execution time:")
            print("--- %s seconds ---" % (time.time() - start_exe_time))
            save_zipped_pickle(
                (already_processed, result_dict, unigrams_dict, corpus_size, feats_detailed_list),
                checkpoint_path, protocol=2)




    else:
    # 261 - 9 grams
    # 647 - 10 grams
    # 1622 - 11 grams
    # 4126 - 12 grams
    # 10598 - 13 grams
        (all_trees, form_dict, lemma_dict, upos_dict, xpos_dict, deprel_dict, corpus_size,
         feats_detailed_list) = create_trees(input_path, internal_saves)

        result_dict = {}
        unigrams_dict = {}

        filters, query_tree, create_output_string_functs, cpu_cores, tree_size_range, node_types = read_filters(config, feats_detailed_list)

        start_exe_time = time.time()
        count_trees(cpu_cores, all_trees, query_tree, create_output_string_functs, filters, unigrams_dict, result_dict)

        print("Execution time:")
        print("--- %s seconds ---" % (time.time() - start_exe_time))
    sorted_list = sorted(result_dict.items(), key=lambda x: x[1]['number'], reverse=True)

    with open(config.get('settings', 'output'), "w", newline="", encoding="utf-8") as f:
        # header - use every second space as a split
        writer = csv.writer(f, delimiter='\t')
        if tree_size_range[-1]:
            len_words = tree_size_range[-1]
        else:
            len_words = int(len(config.get('settings', 'query').split(" "))/2 + 1)
        header = ["Structure"] + ["Node " + string.ascii_uppercase[i] + "-" + node_type for i in range(len_words) for node_type in node_types] + ['Absolute frequency']
        header += ['Relative frequency']
        if filters['node_order']:
            header += ['Order']
            header += ['Free structure']
        if filters['nodes_number']:
            header += ['Number of nodes']
        if filters['print_root']:
            header += ['Root node']
        if filters['association_measures']:
            header += ['MI', 'MI3', 'Dice', 'logDice', 't-score', 'simple-LL']
        writer.writerow(header)

        if filters['lines_threshold']:
            sorted_list = sorted_list[:filters['lines_threshold']]

        # body
        for k, v in sorted_list:
            v['object'].get_array()
            relative_frequency = v['number'] * 1000000.0 / corpus_size
            if filters['frequency_threshold'] and filters['frequency_threshold'] > v['number']:
                break
            words_only = [word_att for word in v['object'].array for word_att in word] + ['' for i in range((tree_size_range[-1] - len(v['object'].array)) * len(v['object'].array[0]))]
            row = [v['object'].get_key()[1:-1]] + words_only + [str(v['number'])]
            row += ['%.4f' % relative_frequency]
            if filters['node_order']:
                row += [v['object'].order]
                row += [v['object'].get_key_sorted()[1:-1]]
            if filters['nodes_number']:
                row += ['%d' % len(v['object'].array)]
            if filters['print_root']:
                row += [v['object'].node.name]
            if filters['association_measures']:
                row += get_collocabilities(v, unigrams_dict, corpus_size)
            writer.writerow(row)

    return "Done"


if __name__ == "__main__":
    start_time = time.time()
    main()
    print("Total:")
    print("--- %s seconds ---" % (time.time() - start_time))