From cc5f45cb22687995354bf571c6d8bb1a23388b0f Mon Sep 17 00:00:00 2001 From: Nasty Date: Thu, 18 Apr 2024 18:04:53 +0300 Subject: [PATCH 1/8] Add fintoc scripts (work in progress) --- .flake8 | 9 +- .pre-commit-config.yaml | 2 +- .../fintoc_structure_extractor.py | 23 + .../fintoc_feature_extractor.py | 245 +++++++ .../paired_feature_extractor.py | 71 ++ .../fintoc_classifier.py | 213 ++++++ scripts/fintoc2022/__init__.py | 0 scripts/fintoc2022/benchmark_fintoc.py | 0 scripts/fintoc2022/dataset_loader.py | 128 ++++ scripts/fintoc2022/metric.py | 624 ++++++++++++++++++ scripts/fintoc2022/toc_extractor.py | 93 +++ scripts/fintoc2022/train_fintoc_classifier.py | 0 scripts/fintoc2022/utils.py | 43 ++ 13 files changed, 1447 insertions(+), 4 deletions(-) create mode 100644 dedoc/structure_extractors/concrete_structure_extractors/fintoc_structure_extractor.py create mode 100644 dedoc/structure_extractors/feature_extractors/fintoc_feature_extractor.py create mode 100755 dedoc/structure_extractors/feature_extractors/paired_feature_extractor.py create mode 100755 dedoc/structure_extractors/line_type_classifiers/fintoc_classifier.py create mode 100644 scripts/fintoc2022/__init__.py create mode 100644 scripts/fintoc2022/benchmark_fintoc.py create mode 100755 scripts/fintoc2022/dataset_loader.py create mode 100755 scripts/fintoc2022/metric.py create mode 100755 scripts/fintoc2022/toc_extractor.py create mode 100644 scripts/fintoc2022/train_fintoc_classifier.py create mode 100755 scripts/fintoc2022/utils.py diff --git a/.flake8 b/.flake8 index d7afb7d1..555b4381 100644 --- a/.flake8 +++ b/.flake8 @@ -16,12 +16,15 @@ exclude = resources, venv, build, - dedoc.egg-info - docs/_build + dedoc.egg-info, + docs/_build, + scripts/fintoc2022/metric.py # ANN101 - type annotations for self +# T201 - prints found +# JS101 - Multi-line container not broken after opening character ignore = ANN101 per-file-ignores = scripts/*:T201 - scripts/benchmark_pdf_performance*:JS101,T201 + scripts/benchmark_pdf_performance*:JS101 diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 76ee04b4..0f439368 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -3,7 +3,7 @@ repos: rev: 5.0.4 hooks: - id: flake8 - exclude: \.github|.*__init__\.py|resources|docs|venv|build|dedoc\.egg-info + exclude: \.github|.*__init__\.py|resources|docs|venv|build|dedoc\.egg-info|scripts/fintoc2022/metric.py args: - "--config=.flake8" additional_dependencies: [ diff --git a/dedoc/structure_extractors/concrete_structure_extractors/fintoc_structure_extractor.py b/dedoc/structure_extractors/concrete_structure_extractors/fintoc_structure_extractor.py new file mode 100644 index 00000000..517fe13f --- /dev/null +++ b/dedoc/structure_extractors/concrete_structure_extractors/fintoc_structure_extractor.py @@ -0,0 +1,23 @@ +from typing import Optional + +from dedoc.data_structures import UnstructuredDocument +from dedoc.structure_extractors import AbstractStructureExtractor + + +class FintocStructureExtractor(AbstractStructureExtractor): + """ + This class is an implementation of the TOC extractor for the `FinTOC 2022 Shared task`_. + The code is a modification of the winner's solution (ISP RAS team). + + You can find the description of this type of structure in the section :ref:`fintoc_structure`. + """ + document_type = "fintoc" + + def extract(self, document: UnstructuredDocument, parameters: Optional[dict] = None) -> UnstructuredDocument: + """ + + To get the information about the method's parameters look at the documentation of the class \ + :class:`~dedoc.structure_extractors.AbstractStructureExtractor`. + """ + + return document diff --git a/dedoc/structure_extractors/feature_extractors/fintoc_feature_extractor.py b/dedoc/structure_extractors/feature_extractors/fintoc_feature_extractor.py new file mode 100644 index 00000000..b5719f44 --- /dev/null +++ b/dedoc/structure_extractors/feature_extractors/fintoc_feature_extractor.py @@ -0,0 +1,245 @@ +import gzip +import json +import os +import pickle +import re +import zipfile +from collections import defaultdict +from typing import Dict, Iterator, List, Optional, Tuple + +import pandas as pd +import wget +from Levenshtein._levenshtein import ratio +from tqdm import tqdm + +from dedoc.data_structures.line_with_meta import LineWithMeta +from dedoc.structure_extractors.feature_extractors.abstract_extractor import AbstractFeatureExtractor +from dedoc.structure_extractors.feature_extractors.list_features.list_features_extractor import ListFeaturesExtractor +from dedoc.structure_extractors.feature_extractors.list_features.prefix.any_letter_prefix import AnyLetterPrefix +from dedoc.structure_extractors.feature_extractors.list_features.prefix.bracket_prefix import BracketPrefix +from dedoc.structure_extractors.feature_extractors.list_features.prefix.bracket_roman_prefix import BracketRomanPrefix +from dedoc.structure_extractors.feature_extractors.list_features.prefix.bullet_prefix import BulletPrefix +from dedoc.structure_extractors.feature_extractors.list_features.prefix.dotted_prefix import DottedPrefix +from dedoc.structure_extractors.feature_extractors.list_features.prefix.empty_prefix import EmptyPrefix +from dedoc.structure_extractors.feature_extractors.list_features.prefix.letter_prefix import LetterPrefix +from dedoc.structure_extractors.feature_extractors.list_features.prefix.roman_prefix import RomanPrefix +from dedoc.structure_extractors.feature_extractors.paired_feature_extractor import PairedFeatureExtractor +from dedoc.structure_extractors.feature_extractors.toc_feature_extractor import TOCFeatureExtractor +from dedoc.structure_extractors.feature_extractors.utils_feature_extractor import normalization_by_min_max +from dedoc.structure_extractors.hierarchy_level_builders.utils_reg import regexps_year +from dedoc.utils.utils import flatten + + +class FintocFeatureExtractor(AbstractFeatureExtractor): + + def __init__(self, tocs: dict): + self.paired_feature_extractor = PairedFeatureExtractor() + self.prefix_list = [BulletPrefix, AnyLetterPrefix, LetterPrefix, BracketPrefix, BracketRomanPrefix, DottedPrefix, RomanPrefix] + self.list_feature_extractors = [ + ListFeaturesExtractor(window_size=10, prefix_list=self.prefix_list), + ListFeaturesExtractor(window_size=25, prefix_list=self.prefix_list), + ListFeaturesExtractor(window_size=100, prefix_list=self.prefix_list) + ] + self.prefix2number = {prefix.name: i for i, prefix in enumerate(self.prefix_list, start=1)} + self.prefix2number[EmptyPrefix.name] = 0 + self.tocs = tocs + + def parameters(self) -> dict: + return {} + + def fit(self, documents: List[List[LineWithMeta]], y: Optional[List[str]] = None) -> "AbstractFeatureExtractor": + return self + + def transform(self, documents: List[List[LineWithMeta]], y: Optional[List[str]] = None) -> pd.DataFrame: + assert len(documents) > 0 + result_matrix = pd.concat([self.__process_document(document) for document in tqdm(documents)], ignore_index=True) + result_matrix = pd.concat([result_matrix, self.paired_feature_extractor.transform(documents)], axis=1) + features = sorted(result_matrix.columns) + result_matrix = result_matrix[features].astype(float) + result_matrix["text"] = [line.line for line in flatten(documents)] + features.append("text") + return result_matrix[features] + + def __process_document(self, lines: List[LineWithMeta]) -> pd.DataFrame: + features_df = pd.DataFrame(self.__look_at_prev_line(document=lines, n=1)) + features_df["line_relative_length"] = self.__get_line_relative_length(lines) + + list_features = pd.concat([f_e.one_document(lines)[1] for f_e in self.list_feature_extractors], axis=1) + + page_ids = [line.metadata.page_id for line in lines] + start_page, finish_page = (min(page_ids), max(page_ids)) if page_ids else (0, 0) + + total_lines = len(lines) + one_line_features_dict = defaultdict(list) + for line_id, line in enumerate(lines): + for item in self.__one_line_features(line, total_lines, start_page=start_page, finish_page=finish_page): + feature_name, feature = item[0], item[1] + one_line_features_dict[feature_name].append(feature) + + one_line_features_df = pd.DataFrame(one_line_features_dict) + one_line_features_df["font_size"] = self._normalize_features(one_line_features_df.font_size) + + one_line_features_df = self.prev_next_line_features(one_line_features_df, 3, 3) + result_matrix = pd.concat([one_line_features_df, features_df, list_features], axis=1) + return result_matrix + + def __look_at_prev_line(self, document: List[LineWithMeta], n: int = 1) -> Dict[str, List]: + """ + Look at previous line and compare with current line + + :param document: list of lines + :param n: previous line number to look + :return: dict of features + """ + res = defaultdict(list) + for line_id, line in enumerate(document): + if line_id >= n: + prev_line = document[line_id - n] + is_prev_line_ends = prev_line.line.endswith(('.', ';')) + res["prev_line_ends"].append(1 if is_prev_line_ends else 0) + res["prev_ends_with_colon"].append(prev_line.line.endswith(":")) + res["prev_is_space"].append(prev_line.line.lower().isspace()) + else: + res["prev_line_ends"].append(0) + res["prev_ends_with_colon"].append(0) + res["prev_is_space"].append(0) + return res + + def __get_line_relative_length(self, lines: List[LineWithMeta]) -> List[float]: + max_len = max([len(line.line) for line in lines]) + relative_lengths = [len(line.line) / max_len for line in lines] + return relative_lengths + + def __one_line_features(self, line: LineWithMeta, total_lines: int, start_page: int, finish_page: int) -> Iterator[Tuple[str, int]]: + yield "normalized_page_id", normalization_by_min_max(line.metadata.page_id, min_v=start_page, max_v=finish_page) + yield "indentation", self._get_indentation(line) + yield "spacing", self._get_spacing(line) + yield "bold", self._get_bold(line) + yield "italic", self._get_italic(line) + yield from self._get_color(line) + yield "font_size", self._get_size(line) + + yield "line_id", normalization_by_min_max(line.metadata.line_id, min_v=0, max_v=total_lines) + yield "num_year_regexp", len(regexps_year.findall(line.line)) + yield "endswith_dot", line.line.endswith(".") + yield "endswith_semicolon", line.line.endswith(";") + yield "endswith_colon", line.line.endswith(":") + yield "endswith_comma", line.line.endswith(",") + yield "startswith_bracket", line.line.strip().startswith(('(', '{')) + + bracket_cnt = 0 + for char in line.line: + if char == '(': + bracket_cnt += 1 + elif char == ')': + bracket_cnt = max(0, bracket_cnt - 1) + yield "bracket_num", bracket_cnt + + probable_toc_title = re.sub(r"[\s:]", "", line.line).lower() + yield "is_toc_title", probable_toc_title in TOCFeatureExtractor.titles + yield from self.__find_in_toc(line) + + line_length = len(line.line) + 1 + yield "supper_percent", sum((1 for letter in line.line if letter.isupper())) / line_length + yield "letter_percent", sum((1 for letter in line.line if letter.isalpha())) / line_length + yield "number_percent", sum((1 for letter in line.line if letter.isnumeric())) / line_length + yield "words_number", len(line.line.split()) + + def __find_in_toc(self, line: LineWithMeta) -> Iterator[Tuple[str, int]]: + if not hasattr(line, "group"): + yield "is_toc", 0 + yield "in_toc", 0 + yield "toc_exists", 0 + else: + toc = self.tocs.get(line.group, []) + is_toc = 0 + in_toc = 0 + toc_exists = int(len(toc) > 0) + line_text = line.line.lower().strip() + for item in toc: + if ratio(line_text, item["text"].lower()) > 0.8: + is_toc = 0 if line.metadata.page_id + 1 == int(item["page"]) else 1 + in_toc = 1 if line.metadata.page_id + 1 == int(item["page"]) else 0 + break + yield "is_toc", is_toc + yield "in_toc", in_toc + yield "toc_exists", toc_exists + + +def handle_file(file: str, dir_out: str, extractor: AbstractFeatureExtractor): + file_name = os.path.split(file)[-1].split(".")[0] + with gzip.open(file) as f_in: + lines = pickle.load(file=f_in) + df = lines2dataframe(lines, extractor) + df.to_csv(os.path.join(dir_out, file_name + "_df.csv.gz"), index=False) + df.to_pickle(os.path.join(dir_out, file_name + "_df.pkl.gz")) + + +def lines2dataframe(lines: List[LineWithLabel], extractor: AbstractFeatureExtractor) -> pd.DataFrame: + assert(len(lines) > 0) + lines2docs = [] + current_document = None + reg_empty_string = re.compile(r"^\s*\n$") + special_unicode_symbols = [u"\uf0b7", u"\uf0d8", u"\uf084", u"\uf0a7", u"\uf0f0", u"\x83"] + + lines = [line for line in lines if not reg_empty_string.match(line.line)] + for line in lines: + for ch in special_unicode_symbols: + line.set_line(line.line.replace(ch, "")) + if line.group == current_document: + lines2docs[-1].append(line) + else: + current_document = line.group + lines2docs.append([line]) + df = extractor.transform(lines2docs) + + df["label"] = [int(line.label) for line in lines] + df["group"] = [line.group for line in lines] + df["uid"] = [line.uid for line in lines] + df["page_id"] = [line.metadata.page_id for line in lines] + return df + + +def main(dir_out: str, train: bool): + os.makedirs(dir_out, exist_ok=True) + + root = "/tmp/.fintoc/train" if train else "/tmp/.fintoc/test" + lines_dir = os.path.join(root, "lines") + if train: + lines_url = "https://at.ispras.ru/owncloud/index.php/s/yvYn491d6Du8ZuV/download" # train + else: + lines_url = "https://at.ispras.ru/owncloud/index.php/s/h3TdYfQipiVAxpE/download" # test + + toc_dir = os.path.join(root, "toc") + if train: + toc_url = "https://at.ispras.ru/owncloud/index.php/s/0VJbQWrD11R98Sy/download" # train + else: + toc_url = "https://at.ispras.ru/owncloud/index.php/s/GCoZitUsfCLPLVI/download" # test + + if not os.path.isdir(root): + os.makedirs(root) + + if not os.path.isdir(lines_dir): + archive = os.path.join(root, "lines.zip") + wget.download(lines_url, archive) + with zipfile.ZipFile(archive, 'r') as zip_ref: + zip_ref.extractall(root) + + if not os.path.isdir(toc_dir): + archive = os.path.join(root, "toc.zip") + wget.download(toc_url, archive) + with zipfile.ZipFile(archive, 'r') as zip_ref: + zip_ref.extractall(root) + + for lang in tqdm(["en", "fr", "sp"]): + lines_file = os.path.join(lines_dir, f"lines_{lang}_txt_layer.pkg.gz") + tocs_file = os.path.join(toc_dir, f"{lang}_toc.json") + with open(tocs_file) as f: + tocs = json.load(f) + extractor = FintocFeatureExtractor(tocs) + handle_file(file=lines_file, extractor=extractor, dir_out=dir_out) + + +if __name__ == '__main__': + stage = "test" + main(dir_out=f"/home/nasty/fintoc2022/{stage}/pandas", train=stage == "train") diff --git a/dedoc/structure_extractors/feature_extractors/paired_feature_extractor.py b/dedoc/structure_extractors/feature_extractors/paired_feature_extractor.py new file mode 100755 index 00000000..cafb4c10 --- /dev/null +++ b/dedoc/structure_extractors/feature_extractors/paired_feature_extractor.py @@ -0,0 +1,71 @@ +import json +from typing import List, Optional + +import numpy as np +import pandas as pd +from scipy.stats._multivariate import method + +from dedoc.data_structures.concrete_annotations.bbox_annotation import BBoxAnnotation +from dedoc.data_structures.concrete_annotations.size_annotation import SizeAnnotation +from dedoc.data_structures.line_with_meta import LineWithMeta +from dedoc.structure_extractors.feature_extractors.abstract_extractor import AbstractFeatureExtractor +from dedoc.utils.utils import flatten + + +class PairedFeatureExtractor(AbstractFeatureExtractor): + + def parameters(self) -> dict: + return {} + + def fit(self, documents: List[List[LineWithMeta]], y: Optional[List[str]] = None) -> "AbstractFeatureExtractor": + return self + + def transform(self, documents: List[List[LineWithMeta]], y: Optional[List[str]] = None) -> pd.DataFrame: + df = pd.DataFrame() + df["raw_depth_size"] = list(flatten([self._handle_one_document(document, self.__get_size) for document in documents])) + df["raw_depth_indentation"] = list(flatten([self._handle_one_document(document, self._get_indentation) for document in documents])) + return df + + def _handle_one_document(self, document: List[LineWithMeta], get_feature: method) -> List[int]: + if len(document) == 0: + return [] + if len(document) == 1: + return [0] + + features = [get_feature(line) for line in document] + std = np.std(features) + result = [] + stack = [] + + for line in document: + while len(stack) > 0 and self.__compare_lines(stack[-1], line, get_feature, std) <= 0: # noqa + stack.pop() + result.append(len(stack)) + stack.append(line) + + return result + + def __get_size(self, line: LineWithMeta) -> float: + annotations = line.annotations + size_annotation = [annotation for annotation in annotations if annotation.name == SizeAnnotation.name] + if len(size_annotation) > 0: + return float(size_annotation[0].value) + + bbox_annotation = [annotation for annotation in annotations if annotation.name == BBoxAnnotation.name] + if len(bbox_annotation) > 0: + bbox = json.loads(bbox_annotation[0].value) + return bbox["height"] + + return 0 + + def __compare_lines(self, first_line: LineWithMeta, second_line: LineWithMeta, get_feature: method, threshold: float = 0) -> int: + first_feature = get_feature(first_line) + second_feature = get_feature(second_line) + + if first_feature > second_feature + threshold: + return 1 + + if second_feature > first_feature + threshold: + return -1 + + return 0 diff --git a/dedoc/structure_extractors/line_type_classifiers/fintoc_classifier.py b/dedoc/structure_extractors/line_type_classifiers/fintoc_classifier.py new file mode 100755 index 00000000..1e0b1541 --- /dev/null +++ b/dedoc/structure_extractors/line_type_classifiers/fintoc_classifier.py @@ -0,0 +1,213 @@ +import gzip +import json +import os +import pickle +import shutil +from statistics import mean +from typing import List, Optional, Union + +import pandas as pd +import xgbfir +import xgboost as xgb +from sklearn.model_selection import GroupKFold +from tqdm import tqdm + +from dedoc.structure_extractors.feature_extractors.fintoc_feature_extractor import FintocFeatureExtractor +from train_dataset.data_structures.line_with_label import LineWithLabel + + +class FintocClassifier: + + def __init__(self, + tocs_path: str, + save_path: str, + binary_classifier_params: Optional[dict] = None, + target_classifier_params: Optional[dict] = None, + load_trained: bool = False, + lang: str = "en"): + self.save_path = save_path + self.lang = lang + if load_trained: + with gzip.open(os.path.join(self.save_path, f"binary_classifier_{self.lang}.pkg.gz"), "rb") as input_file: + self.binary_classifier = pickle.load(file=input_file) + with gzip.open(os.path.join(self.save_path, f"target_classifier_{self.lang}.pkg.gz"), "rb") as input_file: + self.target_classifier = pickle.load(file=input_file) + else: + assert(binary_classifier_params is not None and target_classifier_params is not None) + self.binary_classifier = xgb.XGBClassifier(**binary_classifier_params) + self.target_classifier = xgb.XGBClassifier(**target_classifier_params) + with open(tocs_path) as f: + tocs = json.load(f) + self.features_extractor = FintocFeatureExtractor(tocs) + + def fit(self, data: Union[pd.DataFrame, List[LineWithLabel]], + cross_val: bool = True, + save: bool = False, + gt_dir: Optional[str] = None, + n_splits: int = 3) -> None: + if isinstance(data, pd.DataFrame): + features_df = data + else: + features_df = lines2dataframe(data, self.features_extractor) + print("Features shape: {}".format(features_df.shape)) + results = None + + if cross_val: + assert(gt_dir is not None) + results = self.evaluate_fintoc_metric(features_df=features_df, gt_dir=gt_dir, n_splits=n_splits) + + if not save: + return + + features_names = self.__get_features_names(features_df) + self.binary_classifier.fit(features_df[features_names], features_df.label != -1) + self.target_classifier.fit(features_df[features_names][features_df.label != -1], + features_df.label[features_df.label != -1]) + self._save(features_names, results) + + def _save(self, features_names: list, scores: Optional[dict]) -> None: + os.makedirs(self.save_path, exist_ok=True) + if scores is not None: + with open(os.path.join(self.save_path, f"scores_{self.lang}.json"), "w") as f: + json.dump(scores, f) + print("Scores were saved in {}".format(os.path.join(self.save_path, f"scores_{self.lang}.json"))) + + with gzip.open(os.path.join(self.save_path, F"binary_classifier_{self.lang}.pkg.gz"), "wb") as output_file: + pickle.dump(self.binary_classifier, output_file) + with gzip.open(os.path.join(self.save_path, f"target_classifier_{self.lang}.pkg.gz"), "wb") as output_file: + pickle.dump(self.target_classifier, output_file) + print("Classifiers were saved in {} directory".format(self.save_path)) + + xgbfir.saveXgbFI(self.binary_classifier, feature_names=features_names, + OutputXlsxFile=os.path.join(self.save_path, f"feature_importances_binary_{self.lang}.xlsx")) + xgbfir.saveXgbFI(self.target_classifier, feature_names=features_names, + OutputXlsxFile=os.path.join(self.save_path, f"feature_importances_target_{self.lang}.xlsx")) + print("Features importances were saved in {} directory".format(self.save_path)) + + def predict(self, data: Union[pd.DataFrame, List[LineWithLabel]]) -> dict: + """ + param lines: list of documents lines, label isn't known or dataframe with lines features + :return: dict with TOC of the documents in the required format + """ + if isinstance(data, pd.DataFrame): + features_df = data + else: + features_df = lines2dataframe(data, self.features_extractor) + features_names = self.__get_features_names(features_df) + binary_predictions = self.binary_classifier.predict(features_df[features_names]) + features_df["label"] = binary_predictions + target_predictions = self.target_classifier.predict(features_df[features_names][features_df.label]) + result_dict = create_json_result(features_df[features_df.label], target_predictions) + return result_dict + + def evaluate_fintoc_metric(self, + features_df: pd.DataFrame, + gt_dir: str, + n_splits: int = 3) -> dict: + + features_names = self.__get_features_names(features_df) + results_path = os.path.join(self.save_path, "results") + os.makedirs(results_path, exist_ok=True) + + kf = GroupKFold(n_splits=n_splits) + + result_scores = {"td_scores": [], "toc_scores": []} + for i, (train_index, val_index) in tqdm(enumerate(kf.split(features_df, groups=features_df.group)), + total=n_splits): + df_train = features_df.loc[train_index] + df_val = features_df.loc[val_index] + self.binary_classifier.fit(df_train[features_names], df_train.label != -1) + self.target_classifier.fit( + df_train[features_names][df_train.label != -1], df_train.label[df_train.label != -1]) + result_dict = self.predict(df_val) + + tmpdir = "/tmp/fintoc/eval" + if os.path.isdir(tmpdir): + shutil.rmtree(tmpdir) + os.makedirs(tmpdir) + tmp_gt_dir, predictions_dir = os.path.join(tmpdir, "groundtruth"), os.path.join(tmpdir, "predictions") + os.makedirs(tmp_gt_dir) + os.makedirs(predictions_dir) + + for doc_name, result in result_dict.items(): + gt_doc_name = doc_name + ".pdf.fintoc4.json" + if gt_doc_name not in os.listdir(gt_dir): + print(f"\n{gt_doc_name} not found in groundtruth") + continue + with open(os.path.join(predictions_dir, gt_doc_name), "w") as json_file: + json.dump(result, json_file, indent=2) + shutil.copy(os.path.join(gt_dir, gt_doc_name), os.path.join(tmp_gt_dir, gt_doc_name)) + score(tmp_gt_dir, predictions_dir) + shutil.rmtree(tmpdir) + + path_scores = os.path.join(results_path, str(i)) + os.makedirs(path_scores, exist_ok=True) + for file_name in ['td.log', 'toc.log', 'td_report.csv', 'toc_report.csv']: + shutil.move(file_name, os.path.join(path_scores, file_name)) + f1, inex_f1 = get_values_from_csv(path_scores) + result_scores["td_scores"].append(f1) + result_scores["toc_scores"].append(inex_f1) + print(f"it {i}:\ntd {result_scores['td_scores'][-1]}\ntoc {result_scores['toc_scores'][-1]}") + result_scores["td_mean"] = mean(result_scores["td_scores"]) + result_scores["toc_mean"] = mean(result_scores["toc_scores"]) + return result_scores + + def __get_features_names(self, features_df: pd.DataFrame) -> list: + features_names = [col for col in features_df.columns if col not in ("text", "label", "group", "uid")] + return features_names + + +def train_classifier(train_dir: str) -> None: + clf_params = { + "en_binary": dict(random_state=42, learning_rate=0.25, max_depth=5, n_estimators=400, + colsample_bynode=0.8, colsample_bytree=0.5, tree_method="hist"), + "fr_binary": dict(random_state=42, learning_rate=0.1, max_depth=5, n_estimators=800, + colsample_bynode=0.5, colsample_bytree=0.8, tree_method="approx"), + "sp_binary": dict(random_state=42, learning_rate=0.25, max_depth=4, n_estimators=600, + colsample_bynode=0.5, colsample_bytree=0.5, tree_method="approx"), + "en_target": dict(random_state=42, learning_rate=0.07, max_depth=4, n_estimators=800, + colsample_bynode=1, colsample_bytree=1, tree_method="hist"), + "fr_target": dict(random_state=42, learning_rate=0.4, max_depth=5, n_estimators=800, + colsample_bynode=1, colsample_bytree=0.5, tree_method="exact"), + "sp_target": dict(random_state=42, learning_rate=0.25, max_depth=3, n_estimators=600, + colsample_bynode=0.5, colsample_bytree=1, tree_method="hist") + } + for lang in ("en", "fr", "sp"): + pandas_path = os.path.join(train_dir, "pandas", f"lines_{lang}_txt_layer_df.csv.gz") + cls = FintocClassifier(binary_classifier_params=clf_params[f"{lang}_binary"], + target_classifier_params=clf_params[f"{lang}_target"], + tocs_path=os.path.join(train_dir, "toc", f"{lang}_toc.json"), + save_path="resources", + load_trained=False, + lang=lang) + features_df = pd.read_csv(pandas_path, index_col=False) + cls.fit(data=features_df, + cross_val=True, + save=True, + gt_dir=os.path.join(train_dir, "data", lang, "annots")) + + +def get_results(test_dir: str) -> None: + for lang in ("en", "fr", "sp"): + pandas_path = os.path.join(test_dir, "pandas", f"lines_{lang}_txt_layer_df.csv.gz") + cls = FintocClassifier(tocs_path=os.path.join(test_dir, "toc", f"{lang}_toc.json"), + save_path="resources", + load_trained=True, + lang=lang) + features_df = pd.read_csv(pandas_path, index_col=False) + result_dict = cls.predict(features_df) + results_dir = os.path.join(test_dir, "results", lang) + os.makedirs(results_dir, exist_ok=True) + for doc_name, result in result_dict.items(): + json_doc_name = doc_name + ".pdf.fintoc4.json" + with open(os.path.join(results_dir, json_doc_name), "w") as json_file: + json.dump(result, json_file, indent=2) + + +if __name__ == "__main__": + train = False + fintoc_dir = "/home/nasty/fintoc2022" + if train: + train_classifier(os.path.join(fintoc_dir, "train")) + else: + get_results(os.path.join(fintoc_dir, "test")) diff --git a/scripts/fintoc2022/__init__.py b/scripts/fintoc2022/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/scripts/fintoc2022/benchmark_fintoc.py b/scripts/fintoc2022/benchmark_fintoc.py new file mode 100644 index 00000000..e69de29b diff --git a/scripts/fintoc2022/dataset_loader.py b/scripts/fintoc2022/dataset_loader.py new file mode 100755 index 00000000..1a271b41 --- /dev/null +++ b/scripts/fintoc2022/dataset_loader.py @@ -0,0 +1,128 @@ +import gzip +import json +import logging +import os +import pickle +import shutil +import tempfile +import zipfile +from collections import Counter, defaultdict +from typing import List + +import wget +from Levenshtein._levenshtein import ratio + +from dedoc.config import get_config +from dedoc.readers import PdfTabbyReader, PdfTxtlayerReader +from dedoc.utils.utils import flatten +from train_dataset.data_structures.line_with_label import LineWithLabel + + +class FintocLineWithLabelExtractor: + """ + Create LineWithLabel from documents and their annotations + """ + def __init__(self): + self.readers = { + "tabby": PdfTabbyReader(), + "txt_layer": PdfTxtlayerReader() + } + + def get_lines(self, file_name: str, file_path: str, annotation_path: str, reader_name: str) -> List[LineWithLabel]: + """ + Extract lines from PDF document, create labels for lines from annotation file given by FinTOC. + Annotations are matched to lines using Levenshtein distance (threshold=0.8). + + :param file_name: name of the file (PDF, json) + :param file_path: path to the PDF document + :param annotation_path: path to the JSON file with annotations + :param reader_name: ("tabby", "txt_layer") - type of PDF reader used for lines extraction + :return: document in form of list of lines with labels + """ + document = self.readers[reader_name].read(file_path, parameters={"need_header_footer_analysis": "True"}) + + annotations = defaultdict(list) + with open(annotation_path) as annotations_file: + for annotation in json.load(annotations_file): + annotations[annotation["page"] - 1].append(annotation) + + result = [] + for line in document.lines: + annotations_page = [(ratio(line.line, annotation["text"]), annotation) for annotation in annotations[line.metadata.page_id]] + best_annotation = max(annotations_page, key=lambda t: t[0], default=(0, {})) + depth = best_annotation[1]["depth"] if len(annotations_page) > 0 and best_annotation[0] > 0.8 else "-1" + result.append(LineWithLabel(line=line.line, metadata=line.metadata, annotations=line.annotations, label=depth, group=file_name, uid=line.uid)) + + return sorted(result, key=lambda l: (l.metadata.page_id, l.metadata.line_id)) + + +class FintocDatasetLoader: + """ + Class for downloading data from the cloud, distributing lines into document groups and sorting them. + Returns data in form of document lines with their labels. + """ + def __init__(self, dataset_dir: str, logger: logging.Logger) -> None: + """ + :param dataset_dir: path to the directory where to store downloaded dataset + :param logger: logger for logging details of dataset loading + """ + self.dataset_dir = dataset_dir + self.logger = logger + self.config = get_config() + self.data_url = "https://at.ispras.ru/owncloud/index.php/s/EZfm71WimN2h7rC/download" + self.line_extractor = FintocLineWithLabelExtractor() + + def get_data(self, language: str, reader_name: str, use_cache: bool = True) -> List[List[LineWithLabel]]: + """ + Download data from a cloud at `self.data_url` and sort document lines. + + :param language: ("en", "fr", "sp") - language group + :param reader_name: ("tabby", "txt_layer") - type of reader for lines extraction from PDF + :param use_cache: whether to use cached data (if dataset is already downloaded) or download it anyway + :return: list of documents, which are lists of lines with labels of the training dataset + """ + archive_path = os.path.join(self.dataset_dir, "dataset.zip") + if not os.path.isfile(archive_path): + os.makedirs(self.dataset_dir, exist_ok=True) + self.logger.info("Start download dataset") + wget.download(self.data_url, archive_path) + self.logger.info(f"Finish download dataset to {archive_path}") + + pkl_path = os.path.join(self.dataset_dir, f"lines_{language}_{reader_name}.pkl.gz") + + if os.path.isfile(pkl_path) and use_cache: + with gzip.open(pkl_path) as input_file: + lines = pickle.load(input_file) + self.logger.info("Data were loaded from the local disk") + return lines + + with zipfile.ZipFile(archive_path, "r") as zip_ref: + zip_ref.extractall(self.dataset_dir) + data_dir = os.path.join(self.dataset_dir, "data", language) + pdf_dir = os.path.join(data_dir, "pdf") + annotations_dir = os.path.join(data_dir, "annots") + pdf_files = {pdf_file[:-len(".pdf")]: os.path.join(pdf_dir, pdf_file) for pdf_file in os.listdir(pdf_dir) if pdf_file.endswith(".pdf")} + annotations_files = {ann_file[:-len(".pdf.fintoc4.json")]: os.path.join(annotations_dir, ann_file) + for ann_file in os.listdir(annotations_dir) if ann_file.endswith(".json")} + assert set(pdf_files) == set(annotations_files) + + result = [] + with tempfile.TemporaryDirectory() as tmp_dir: + for file_name in pdf_files: + pdf_tmp_path = os.path.join(tmp_dir, file_name) + ".pdf" + shutil.copy(pdf_files[file_name], pdf_tmp_path) + try: + document = self.line_extractor.get_lines( + file_name=file_name, + file_path=pdf_tmp_path, + annotation_path=annotations_files[file_name], + reader_name=reader_name + ) + result.append(document) + except Exception as e: + self.logger.warning(f"Failed to read {file_name} by {reader_name}, error: {e}") + + with gzip.open(pkl_path, "wb") as out: + pickle.dump(obj=result, file=out) + self.logger.info(Counter([line.label for line in flatten(result)])) + return result diff --git a/scripts/fintoc2022/metric.py b/scripts/fintoc2022/metric.py new file mode 100755 index 00000000..eb82fd4a --- /dev/null +++ b/scripts/fintoc2022/metric.py @@ -0,0 +1,624 @@ +""" +This is a python3 script that rewrites the score function used in Book Structure +Extraction Competition @ ICDAR2013 +(https://www.cs.helsinki.fi/u/doucet/papers/ICDAR2013.pdf). +It uses a classic levenshtein distance implemented by +https://pypi.org/project/python-Levenshtein/ instead of a customized levenshtein +distance. +It is used to score participants in FinTOC2020 shared task. + +------ +INSTALL +------ +pip install python-Levenshtein + +------ +USAGE +------ +python metric.fintoc2.py--gt_folder --submission_folder + + and are paths to folders containing JSON files: +[ + { + "text": String, # text of the TOC item/entry + "id": Int # identifer of the item/entry corresponding to its order in the TOC + "depth": Int # hierarchical level of the item + "page": Int # the (physical) page number where the item appears + } + +] +""" + +import argparse +import csv +import json +import logging +import os +from abc import ABC, abstractmethod +from glob import glob +from operator import itemgetter + +import Levenshtein +import numpy as np + +JSON_EXTENSION = ".fintoc4.json" +VERBOSE = True +STRING_THRESHOLD = 0.85 + + +class TOCJson: + def __init__(self, json_file): + self.parse(json_file) + + def parse(self, json_file): + with open(json_file, "r", encoding="utf-8") as infile: + content = json.load(infile) + self.entries = [] + for dict_entry in content: + self.entries.append(Title(dict_entry["text"], dict_entry["page"], dict_entry["id"], dict_entry["depth"])) + + +class Title: + def __init__(self, text, page_nb, id_, depth): + self.text = text + self.page_nb = page_nb + self.id_ = id_ + self.depth = depth + self.matched = False + + def __repr__(self): + return f"page={self.page_nb} title={repr(self.text)}" + + def compare_page_nb(self, entry): + if isinstance(entry.page_nb, str): + entry.page_nb = int(entry.page_nb) + if self.page_nb == entry.page_nb: + return 0 + if self.page_nb > entry.page_nb: + return 1 + return -1 + + def compare_depth(self, entry): + if str(self.depth) == entry.depth: + return 0 + if str(self.depth) > entry.depth: + return 1 + return -1 + + +class ICDARMetric(ABC): + + def __init__(self): + self.correct = 0 + self.added = 0 + self.missed = 0 + self.mismatch = 0 + self.p_per_doc = {} + self.r_per_doc = {} + self.f_per_doc = {} + self.title_acc_per_doc = {} + + def compute_prf(self): + self.compute_p() + self.compute_r() + try: + self.f_score = 2 * self.prec * self.reca / (self.prec + self.reca) + except ZeroDivisionError: + self.f_score = 0 + return self.prec, self.reca, self.f_score + + def compute_p(self): + try: + self.prec = self.correct / (self.correct + self.added + self.mismatch) + except ZeroDivisionError: + self.prec = 0 + + def compute_r(self): + try: + self.reca = self.correct / (self.correct + self.missed + self.mismatch) + except ZeroDivisionError: + self.reca = 0 + + @abstractmethod + def initialize_stats(self): + self.correct = 0 + self.added = 0 + self.missed = 0 + self.mismatch = 0 + self.prec = 0.0 + self.reca = 0.0 + self.f_score = 0.0 + self.title_acc = 0.0 + + @abstractmethod + def get_title_acc(self, *args): + pass + + def format_float_percent(self, float_nb): + return "%.1f" % (100 * float_nb) + + def format_res(self): + out = ["%6s" % self.format_float_percent(self.prec)] + out.append("%6s" % self.format_float_percent(self.reca)) + out.append("%6s" % self.format_float_percent(self.f_score)) + out.append("%6s" % self.format_float_percent(self.title_acc)) + return out + + def compute_avg_p(self): + return np.mean(list(self.p_per_doc.values())) + + def compute_std_p(self): + return np.std(list(self.p_per_doc.values())) + + def compute_avg_r(self): + return np.mean(list(self.r_per_doc.values())) + + def compute_std_r(self): + return np.std(list(self.r_per_doc.values())) + + def compute_avg_f(self): + return np.mean(list(self.f_per_doc.values())) + + def compute_std_f(self): + return np.std(list(self.f_per_doc.values())) + + def compute_avg_title_acc(self): + return np.mean(list(self.title_acc_per_doc.values())) + + def compute_std_title_acc(self): + return np.std(list(self.title_acc_per_doc.values())) + + +class InexMetric(ICDARMetric): + + def __init__(self): + super().__init__() + self.level_correct = 0 + self.level_acc = 0.0 + self.level_acc_per_doc = {} + + def initialize_stats(self): + super().initialize_stats() + self.level_correct = 0 + self.level_acc = 0.0 + + def get_level_acc(self, nb_valid_links): + try: + self.level_acc = self.level_correct / nb_valid_links + except ZeroDivisionError: + self.level_acc = 0.0 + return self.level_acc + + def get_title_acc(self, nb_valid_links): + try: + self.title_acc = self.correct / nb_valid_links + except ZeroDivisionError: + self.title_acc = 0.0 + return self.title_acc + + def format_res(self): + out = super().format_res() + out.append("%6s" % self.format_float_percent(self.level_acc)) + return out + + def compute_avg_level_acc(self): + return np.mean(list(self.level_acc_per_doc.values())) + + def compute_std_level_acc(self): + return np.std(list(self.level_acc_per_doc.values())) + + +class XeroxMetric(ICDARMetric): + + def __init__(self): + super().__init__() + self.text_sim = 0 + + def initialize_stats(self): + super().initialize_stats() + self.text_sim = 0 + + def get_title_acc(self): + try: + self.title_acc = self.text_sim / float(self.correct) + except ZeroDivisionError: + self.title_acc = 0.0 + return self.title_acc + + +class Stats: + + def __init__(self): + self.ok_per_doc = {} + self.pbttl_per_doc = {} + self.pblvl_per_doc = {} + self.err_per_doc = {} + self.miss_per_doc = {} + + def compute_sum_ok(self): + return sum(list(self.ok_per_doc.values())) + + def compute_sum_pbttl(self): + return sum(list(self.pbttl_per_doc.values())) + + def compute_sum_pblvl(self): + return sum(list(self.pblvl_per_doc.values())) + + def compute_sum_err(self): + return sum(list(self.err_per_doc.values())) + + def compute_sum_miss(self): + return sum(list(self.miss_per_doc.values())) + + +class Writer: + + def __init__(self): + self.toc_rows = self.format_icdar_heading() + self.td_rows = self.format_td_heading() + + @classmethod + def format_icdar_heading(self): + out = [ + "Doc", "Xrx-P", "Xrx-R", "Xrx-F1", "Xrx-Title acc", "Inex08-P", "Inex08-R", + "Inex08-F1", "Inex08-Title acc", "Inex08-Level acc", "Ok", "PbTtl", + "PbLvl", "Err", "Miss", "book id" + ] + return [out] + + @classmethod + def format_td_heading(self): + out = ["Doc", "Prec", "Rec", "F1", "Book id"] + return [out] + + def dump_all(self): + self.dump_toc() + self.dump_td() + + def dump_toc(self): + with open("toc_report.csv", "w", encoding="utf-8") as outfile: + writer = csv.writer(outfile, dialect=csv.excel, delimiter="\t") + writer.writerows(self.toc_rows) + + def dump_td(self): + with open("td_report.csv", "w", encoding="utf-8") as outfile: + writer = csv.writer(outfile, dialect=csv.excel, delimiter="\t") + writer.writerows(self.td_rows) + + +def score_title_detection(toc1, toc2, log): + correct = 0 + for entry1 in toc1.entries: + res = find_matching_entry(entry1.text, toc2) + if res is not None: + index, match_score = res + matched_text = toc2.entries[index].text + msg = "Gt title %s is matched to %s (score=%.4g)" % (repr(entry1.text), repr(matched_text), match_score) + log.info(msg) + entry1.matched = True + toc2.entries[index].matched = True + correct += 1 + else: + log.info(f"Gt title {repr(entry1.text)} is not matched to any submission title") + for entry in toc2.entries: + if not entry.matched: + log.info(f"{entry} in submission not matched") + added = len([entry for entry in toc2.entries if not entry.matched]) + missed = len([entry for entry in toc1.entries if not entry.matched]) + log.info("nb of added titles: %i", added) + log.info("nb of missed titles: %i", missed) + log.info("nb of correct titles: %i", correct) + # return score + try: + prec = correct / (correct + added) + except ZeroDivisionError: + prec = 0.0 + try: + reca = correct / (correct + missed) + except ZeroDivisionError: + reca = 0.0 + try: + f1_score = 2 * prec * reca / (prec + reca) + except ZeroDivisionError: + f1_score = 0.0 + return prec, reca, f1_score + + +def find_matching_entry(text, toc): + if len(toc.entries) == 0: + return None + similarities = [] + for entry in toc.entries: + if not entry.matched: + similarities.append(Levenshtein.ratio(text, entry.text)) + else: + similarities.append(0) + index, match_score = max(enumerate(similarities), key=itemgetter(1)) + if match_score > STRING_THRESHOLD: + return index, match_score + return None + + +def update_icdar_stats(toc1, toc2, inex_metric, xerox_metric, log): + i1, i2 = 0, 0 + if len(toc1.entries) > 0 and len(toc2.entries) > 0: + entry1 = toc1.entries[i1] + entry2 = toc2.entries[i2] + while True: + link_result = entry1.compare_page_nb(entry2) + try: + if link_result == 0: + xerox_metric.correct += 1 + text_similarity = Levenshtein.ratio(entry1.text, entry2.text) + xerox_metric.text_sim += text_similarity + if text_similarity > STRING_THRESHOLD: + inex_metric.correct += 1 + else: + if VERBOSE: + log.info(f"TITLE ERROR: {entry1} <--> {repr(entry2.text)}") + inex_metric.mismatch += 1 + depth_result = entry1.compare_depth(entry2) + if depth_result == 0: + inex_metric.level_correct += 1 + i1 += 1 + i2 += 1 + entry1 = toc1.entries[i1] + entry2 = toc2.entries[i2] + elif link_result < 0: + inex_metric.missed += 1 + xerox_metric.missed += 1 + if VERBOSE: + log.info(f"MISS: {entry1}") + i1 += 1 + entry1 = toc1.entries[i1] + else: + inex_metric.added += 1 + xerox_metric.added += 1 + if VERBOSE: + log.info(f"ADDED: {entry2}") + i2 += 1 + entry2 = toc2.entries[i2] + except IndexError: + break + # take into account remaining entries in gt + while i1 < len(toc1.entries): + if VERBOSE: + entry1 = toc1.entries[i1] + log.info(f"MISS: {entry1}") + i1 += 1 + inex_metric.missed += 1 + xerox_metric.missed += 1 + # take into account remaining entries in submission + while i2 < len(toc2.entries): + if VERBOSE: + entry2 = toc2.entries[i2] + log.info(f"ADDED: {entry2}") + i2 += 1 + inex_metric.added += 1 + xerox_metric.added += 1 + + +def score(folder1, folder2): + def get_docnames(folder, ext): + out = [] + for ele in ls(folder, ext): + out.append(basename(ele, ext)) + return out + + docnames1 = get_docnames(folder1, JSON_EXTENSION) + docnames2 = get_docnames(folder2, JSON_EXTENSION) + docnames = list(set(docnames1) & set(docnames2)) + n_missing_docs = len([ele for ele in docnames1 if ele not in docnames2]) + n_added_docs = len([ele for ele in docnames2 if ele not in docnames1]) + writer = Writer() + doc_id = 0 + # TOC generation metrics + inex = InexMetric() + xerox = XeroxMetric() + count = Stats() + # Title detection metrics + td_prec = dict(zip(docnames, [None] * len(docnames))) + td_reca = dict(zip(docnames, [None] * len(docnames))) + td_f1 = dict(zip(docnames, [None] * len(docnames))) + # loggers + toc_logger = get_logger("toc", "toc.log") + td_logger = get_logger("td", "td.log") + for json1 in ls(folder1, JSON_EXTENSION): + xerox.initialize_stats() + inex.initialize_stats() + toc1 = TOCJson(json1) + docname = basename(json1, JSON_EXTENSION) + if VERBOSE: + toc_logger.info(f"\n\nCOMPARING {docname}") + td_logger.info(f"\n\nCOMPARING {docname}") + json2 = os.path.join(folder2, docname + JSON_EXTENSION) + if not os.path.isfile(json2): + toc_logger.info(f"{docname} missing from submission") + td_logger.info(f"{docname} missing from submission") + else: + # Title detection + toc2 = TOCJson(json2) + td_prec[docname], td_reca[docname], td_f1[docname] = score_title_detection(toc1, toc2, td_logger) + writer.td_rows.append([doc_id, td_prec[docname], td_reca[docname], td_f1[docname], docname]) + # TOC generation + update_icdar_stats(toc1, toc2, inex, xerox, toc_logger) + # compute stats + count.ok_per_doc[docname] = xerox.correct + count.pbttl_per_doc[docname] = xerox.correct - inex.correct + count.pblvl_per_doc[docname] = xerox.correct - inex.level_correct + count.err_per_doc[docname] = xerox.added + count.miss_per_doc[docname] = xerox.missed + # compute Xerox score + xerox.compute_prf() + xerox.p_per_doc[docname] = xerox.prec + xerox.r_per_doc[docname] = xerox.reca + xerox.f_per_doc[docname] = xerox.f_score + xerox.title_acc_per_doc[docname] = xerox.get_title_acc() + # compute Inex score + inex.compute_prf() + inex.p_per_doc[docname] = inex.prec + inex.r_per_doc[docname] = inex.reca + inex.f_per_doc[docname] = inex.f_score + inex.title_acc_per_doc[docname] = inex.get_title_acc(xerox.correct) + inex.level_acc_per_doc[docname] = inex.get_level_acc(xerox.correct) + # result row + writer.toc_rows.append(get_row_result(doc_id, docname, xerox, inex)) + doc_id += 1 + # get avg and std scores + writer.toc_rows.append(get_avg_row(xerox, inex, count)) + writer.toc_rows.append(get_std_row(xerox, inex)) + writer.td_rows.append(get_avg_row(td_prec, td_reca, td_f1)) + writer.td_rows.append(get_std_row(td_prec, td_reca, td_f1)) + # get stats about missing and added docs + writer.toc_rows.append( + [f"Done: {len(docnames)} comparisons for {len(docnames1)} in groundtruth and {len(docnames2)} in submission"]) + if n_missing_docs: + writer.toc_rows.append([f"{n_missing_docs} docs missing from submission"]) + if n_added_docs: + writer.toc_rows.append([f"{n_added_docs} additional docs in submission (ignored)"]) + # dump + writer.dump_all() + + +def get_row_result(doc_id, doc, xerox, inex): + out = ["%4s " % doc_id] + out.extend(xerox.format_res()) + out.extend(inex.format_res()) + out.append("%7s" % xerox.correct) + out.append("%7s" % (xerox.correct - inex.correct)) + out.append("%7s" % (xerox.correct - inex.level_correct)) + out.append("%7s" % xerox.added) + out.append("%7s" % xerox.missed) + out.append("%s" % doc) + return out + + +""" +https://medium.com/practo-engineering/function-overloading-in-python-94a8b10d1e08 +""" +registry = {} + + +class MultiMethod(object): + def __init__(self, name): + self.name = name + self.typemap = {} + + def __call__(self, *args): + types = tuple(arg.__class__ for arg in args) + function = self.typemap.get(types) + if function is None: + raise TypeError("no match") + return function(*args) + + def register(self, types, function): + self.typemap[types] = function + + +def overload(*types): + def register(function): + name = function.__name__ + mm = registry.get(name) + if mm is None: + mm = registry[name] = MultiMethod(name) + mm.register(types, function) + return mm + + return register + + +""" +https://medium.com/practo-engineering/function-overloading-in-python-94a8b10d1e08 +""" + + +@overload(XeroxMetric, InexMetric, Stats) +def get_avg_row(xerox, inex, count): + out = [] + out.append("%4s " % "AVG") + # xerox + out.append("%6s" % xerox.format_float_percent(xerox.compute_avg_p())) + out.append("%6s" % xerox.format_float_percent(xerox.compute_avg_r())) + out.append("%6s" % xerox.format_float_percent(xerox.compute_avg_f())) + out.append("%6s" % xerox.format_float_percent(xerox.compute_avg_title_acc())) + # inex + out.append("%6s" % inex.format_float_percent(inex.compute_avg_p())) + out.append("%6s" % inex.format_float_percent(inex.compute_avg_r())) + out.append("%6s" % inex.format_float_percent(inex.compute_avg_f())) + out.append("%6s" % inex.format_float_percent(inex.compute_avg_title_acc())) + out.append("%6s" % inex.format_float_percent(inex.compute_avg_level_acc())) + # count stats + out.append("%7s" % (count.compute_sum_ok())) + out.append("%7s" % (count.compute_sum_pbttl())) + out.append("%7s" % (count.compute_sum_pblvl())) + out.append("%7s" % (count.compute_sum_err())) + out.append("%7s" % (count.compute_sum_miss())) + return out + + +@overload(XeroxMetric, InexMetric) +def get_std_row(xerox, inex): + out = ["%4s " % "sdev"] + # xerox + out.append("%6s" % xerox.format_float_percent(xerox.compute_std_p())) + out.append("%6s" % xerox.format_float_percent(xerox.compute_std_r())) + out.append("%6s" % xerox.format_float_percent(xerox.compute_std_f())) + out.append("%6s" % xerox.format_float_percent(xerox.compute_std_title_acc())) + # inex + out.append("%6s" % inex.format_float_percent(inex.compute_std_p())) + out.append("%6s" % inex.format_float_percent(inex.compute_std_r())) + out.append("%6s" % inex.format_float_percent(inex.compute_std_f())) + out.append("%6s" % inex.format_float_percent(inex.compute_std_title_acc())) + out.append("%6s" % inex.format_float_percent(inex.compute_std_level_acc())) + return out + + +@overload(dict, dict, dict) +def get_avg_row(td_prec, td_reca, td_f1): + return [ + "AVG", + np.mean(list(td_prec.values())), + np.mean(list(td_reca.values())), + np.mean(list(td_f1.values())) + ] + + +@overload(dict, dict, dict) +def get_std_row(td_prec, td_reca, td_f1): + return [ + "stdev", + np.std(list(td_prec.values())), + np.std(list(td_reca.values())), + np.std(list(td_f1.values())) + ] + + +def get_logger(name, path_to_log, level=logging.INFO): + handler = logging.FileHandler(path_to_log, mode="w") + formatter = logging.Formatter("%(message)s") + handler.setFormatter(formatter) + logger = logging.getLogger(name) + logger.setLevel(level) + logger.addHandler(handler) + return logger + + +def basename(path, ext): + return os.path.basename(path).split(ext)[0] + + +def ls(folder, ext): + pattern = os.path.join(folder, "*" + ext) + return glob(pattern) + + +if __name__ == "__main__": + PARSER = argparse.ArgumentParser(description="This is the scoring script used for FincTOC2021. It outputs two csv " + "reports, one for title detection, and another for toc generation. It also logs " + "information in two separate log files.") + PARSER.add_argument("--gt_folder", required=True, type=str, + help="path to folder containing groundtruth files (one groundtruth file in json format per document") + PARSER.add_argument("--submission_folder", required=True, type=str, + help="path to folder containing submission files (one submission file in json format per document") + ARGS = PARSER.parse_args() + score(ARGS.gt_folder, ARGS.submission_folder) diff --git a/scripts/fintoc2022/toc_extractor.py b/scripts/fintoc2022/toc_extractor.py new file mode 100755 index 00000000..6c7eb826 --- /dev/null +++ b/scripts/fintoc2022/toc_extractor.py @@ -0,0 +1,93 @@ +import json +import os +import re +import shutil +import subprocess +import tempfile +import zipfile +from typing import List + +import wget +from PyPDF2 import PdfFileReader, PdfFileWriter +from dedoc.data_structures.line_with_meta import LineWithMeta +from tqdm import tqdm + +from config import _config as config +from doc_reader.line_type_classifier.feature_extractor.toc_feature_extractor import TOCFeatureExtractor +from doc_reader.readers.scanned_reader.pdftxtlayer_reader.pdf_with_text_reader import PdfWithTextReader + +toc_item = re.compile(r'"([^"]+)" (\d+)') +reader = PdfWithTextReader(config=config) + + +def get_one_columns_lines(path: str) -> List[LineWithMeta]: + if path.startswith("file://"): + path = path[len("file://"):] + with tempfile.TemporaryDirectory() as tmpdir: + path_tmp = os.path.join(tmpdir, os.path.basename(path)) + pdf_reader = PdfFileReader(path) + writer = PdfFileWriter() + for page_id in range(0, min(9, pdf_reader.getNumPages())): + writer.addPage(pdf_reader.getPage(page_id)) + with open(path_tmp, 'wb') as write_file: + writer.write(write_file) + return reader.read(path=path_tmp, document_type=None, parameters={"is_one_column_document": True, + "need_header_footer_analysis": "True"}).lines + + +def get_automatic_toc(path: str) -> List[dict]: + result = [] + cmd = "pdftocio -p {}".format(path) + with os.popen(cmd) as out: + toc = out.readlines() + if toc: + for line in toc: + match = toc_item.match(line.strip()) + if match: + result.append({"text": match.group(1), "page": match.group(2)}) + return result + + +def main(dir_out: str): + toc_extractor = TOCFeatureExtractor() + os.makedirs(dir_out, exist_ok=True) + data_url = "https://at.ispras.ru/owncloud/index.php/s/EZfm71WimN2h7rC/download" + print("use 'pip install -U pdf.tocgen' to install tool for automatic toc extraction") + subprocess.run("pip install -U pdf.tocgen".split(" ")) + + root = "/tmp/.fintoc/" + if os.path.isdir(root): + shutil.rmtree(root) + os.makedirs(root) + archive = os.path.join(root, "dataset.zip") + wget.download(data_url, archive) + with zipfile.ZipFile(archive, 'r') as zip_ref: + zip_ref.extractall(root) + data_dir = os.path.join(root, "data") + + for lang in ("en", "fr", "sp"): + pdf_dir = os.path.join(data_dir, lang, "pdf") + lang_dir_out = os.path.join(dir_out, lang) + if os.path.isdir(lang_dir_out): + shutil.rmtree(lang_dir_out) + os.makedirs(lang_dir_out) + + tocs = {} + for file in tqdm(os.listdir(pdf_dir)): + if not file.endswith(".pdf"): + continue + path = os.path.join(pdf_dir, file) + toc = get_automatic_toc(path) + if len(toc) == 0: + lines = get_one_columns_lines(path) + toc = toc_extractor.get_toc(lines) + doc_name = file[: -len(".pdf")] + tocs[doc_name] = toc + with open(os.path.join(lang_dir_out, f"{doc_name}_toc.json"), "w") as out: + json.dump(obj=toc, fp=out, indent=4, ensure_ascii=False) + + with open(os.path.join(dir_out, f"{lang}_toc.json"), "w") as out: + json.dump(tocs, out) + + +main(dir_out="/home/nasty/fintoc2022/toc") diff --git a/scripts/fintoc2022/train_fintoc_classifier.py b/scripts/fintoc2022/train_fintoc_classifier.py new file mode 100644 index 00000000..e69de29b diff --git a/scripts/fintoc2022/utils.py b/scripts/fintoc2022/utils.py new file mode 100755 index 00000000..9a4ab457 --- /dev/null +++ b/scripts/fintoc2022/utils.py @@ -0,0 +1,43 @@ +import os +from collections import defaultdict +from typing import List, Tuple + +import pandas as pd + + +def create_json_result(data: pd.DataFrame, predictions: List[int]) -> dict: + """ + Creates dictionary with TOCs for each document: {"doc_name": TOC}. + TOC is a following list of dictionaries: + [ + { + "text": String, # text of the TOC item/entry + "id": Int # identifier of the item/entry corresponding to its order in the TOC + "depth": Int # hierarchical level of the item + "page": Int # the (physical) page number where the item appears + } + ] + """ + uid2line = {item[1].uid: {"text": item[1].text.strip() if isinstance(item[1].text, str) else "", + "page": item[1].page_id + 1, + "group": item[1].group} for item in data.iterrows()} + result = defaultdict(list) + assert(data.shape[0] == len(predictions)) + for i, (line_uid, prediction) in enumerate(zip(data.uid, predictions)): + line = uid2line[line_uid] + if line["text"] == "": + continue + # TODO crop text lines containing colon + result[line["group"]].append({"id": i, "depth": str(prediction), + "text": line["text"], "page": line["page"]}) + return result + + +def get_values_from_csv(dir_path: str) -> Tuple[float, float]: + td_name = "td_report.csv" + toc_name = "toc_report.csv" + td_df = pd.read_csv(os.path.join(dir_path, td_name), delimiter="\t") + toc_df = pd.read_csv(os.path.join(dir_path, toc_name), delimiter="\t") + f1 = td_df[td_df["Doc"] == "AVG"]["F1"].item() + inex_f1 = toc_df[toc_df["Doc"] == " AVG "]["Inex08-F1"].item() + return f1, inex_f1 From be31fca00756ba6362c5469b2acdea54308741ea Mon Sep 17 00:00:00 2001 From: Nasty Date: Tue, 23 Apr 2024 17:13:01 +0300 Subject: [PATCH 2/8] Big refactoring of fintoc scripts --- dedoc/readers/pdf_reader/pdf_base_reader.py | 2 +- .../fintoc_structure_extractor.py | 95 ++++++- .../fintoc_feature_extractor.py | 136 ++------- .../toc_feature_extractor.py | 6 +- .../fintoc_classifier.py | 269 +++++------------- requirements.txt | 1 + scripts/benchmark_pdf_performance.py | 2 +- scripts/fintoc2022/benchmark_fintoc.py | 0 scripts/fintoc2022/dataset_loader.py | 31 +- scripts/fintoc2022/toc_extractor.py | 93 ------ scripts/fintoc2022/train_fintoc_classifier.py | 45 +++ scripts/fintoc2022/trainer.py | 173 +++++++++++ scripts/fintoc2022/utils.py | 15 +- 13 files changed, 433 insertions(+), 435 deletions(-) delete mode 100644 scripts/fintoc2022/benchmark_fintoc.py delete mode 100755 scripts/fintoc2022/toc_extractor.py create mode 100644 scripts/fintoc2022/trainer.py diff --git a/dedoc/readers/pdf_reader/pdf_base_reader.py b/dedoc/readers/pdf_reader/pdf_base_reader.py index 8372fb92..688d82c7 100644 --- a/dedoc/readers/pdf_reader/pdf_base_reader.py +++ b/dedoc/readers/pdf_reader/pdf_base_reader.py @@ -53,7 +53,7 @@ class PdfBaseReader(BaseReader): def __init__(self, *, config: Optional[dict] = None) -> None: super().__init__(config=config) - self.config["n_jobs"] = config.get("n_jobs", 1) + self.config["n_jobs"] = self.config.get("n_jobs", 1) self.table_recognizer = TableRecognizer(config=self.config) self.metadata_extractor = LineMetadataExtractor(config=self.config) self.attachment_extractor = PDFAttachmentsExtractor(config=self.config) diff --git a/dedoc/structure_extractors/concrete_structure_extractors/fintoc_structure_extractor.py b/dedoc/structure_extractors/concrete_structure_extractors/fintoc_structure_extractor.py index 517fe13f..3d69ccad 100644 --- a/dedoc/structure_extractors/concrete_structure_extractors/fintoc_structure_extractor.py +++ b/dedoc/structure_extractors/concrete_structure_extractors/fintoc_structure_extractor.py @@ -1,7 +1,17 @@ -from typing import Optional +import os +import re +import tempfile +from typing import Dict, List, Optional, Tuple, Union -from dedoc.data_structures import UnstructuredDocument +import pandas as pd +from PyPDF2 import PdfFileReader, PdfFileWriter + +from dedoc.data_structures import HierarchyLevel, LineWithMeta, UnstructuredDocument +from dedoc.readers import PdfTxtlayerReader from dedoc.structure_extractors import AbstractStructureExtractor +from dedoc.structure_extractors.feature_extractors.fintoc_feature_extractor import FintocFeatureExtractor +from dedoc.structure_extractors.feature_extractors.toc_feature_extractor import TOCFeatureExtractor +from dedoc.structure_extractors.line_type_classifiers.fintoc_classifier import FintocClassifier class FintocStructureExtractor(AbstractStructureExtractor): @@ -13,11 +23,90 @@ class FintocStructureExtractor(AbstractStructureExtractor): """ document_type = "fintoc" - def extract(self, document: UnstructuredDocument, parameters: Optional[dict] = None) -> UnstructuredDocument: + def __init__(self, *, config: Optional[dict] = None) -> None: + super().__init__(config=config) + self.pdf_reader = PdfTxtlayerReader(config=self.config) + self.toc_extractor = TOCFeatureExtractor() + self.features_extractor = FintocFeatureExtractor() + self.languages = ("en", "fr", "sp") + self.classifiers = {language: FintocClassifier(language=language) for language in self.languages} + self.toc_item_regexp = re.compile(r'"([^"]+)" (\d+)') + self.empty_string_regexp = re.compile(r"^\s*\n$") + + def extract(self, document: UnstructuredDocument, parameters: Optional[dict] = None, file_path: Optional[str] = None) -> UnstructuredDocument: """ To get the information about the method's parameters look at the documentation of the class \ :class:`~dedoc.structure_extractors.AbstractStructureExtractor`. """ + parameters = {} if parameters is None else parameters + language = parameters.get("language", "en") + if language not in self.languages: + raise ValueError(f"Language {language} is not supported by this extractor. Supported languages: {self.languages}") + + features, documents = self.get_features(documents_dict={file_path: document.lines}) + predictions = self.classifiers[language].predict(features) + lines: List[LineWithMeta] = documents[0] + assert len(lines) == len(predictions) + + for line, prediction in zip(lines, predictions): + if prediction > 0: + line.metadata.hierarchy_level = HierarchyLevel(level_1=1, level_2=prediction, line_type=HierarchyLevel.header, can_be_multiline=True) + else: + line.metadata.hierarchy_level = HierarchyLevel.create_raw_text() + document.lines = lines return document + + def get_features(self, documents_dict: Dict[str, List[LineWithMeta]]) -> Tuple[pd.DataFrame, List[List[LineWithMeta]]]: + toc_lines, documents = [], [] + for file_path, document_lines in documents_dict.items(): + toc_lines.append(self.__get_toc(file_path=file_path)) + documents.append(self.__filter_lines(document_lines)) + features = self.features_extractor.transform(documents=documents, toc_lines=toc_lines) + return features, documents + + def __filter_lines(self, lines: List[LineWithMeta]) -> List[LineWithMeta]: + special_unicode_symbols = [u"\uf0b7", u"\uf0d8", u"\uf084", u"\uf0a7", u"\uf0f0", u"\x83"] + + lines = [line for line in lines if not self.empty_string_regexp.match(line.line)] + for line in lines: + for ch in special_unicode_symbols: + line.set_line(line.line.replace(ch, "")) + + return lines + + def __get_toc(self, file_path: Optional[str]) -> Optional[List[Dict[str, Union[LineWithMeta, str]]]]: + if file_path is None: + return + + toc = self.__get_automatic_toc(path=file_path) + if len(toc) > 0: + return toc + + pdf_reader = PdfFileReader(file_path) + writer = PdfFileWriter() + + with tempfile.TemporaryDirectory() as tmpdir: + tmp_path = os.path.join(tmpdir, os.path.basename(file_path)) + for page_id in range(0, min(9, pdf_reader.getNumPages())): + writer.addPage(pdf_reader.getPage(page_id)) + with open(tmp_path, "wb") as write_file: + writer.write(write_file) + lines = self.pdf_reader.read(file_path=tmp_path, parameters={"is_one_column_document": "True", "need_header_footer_analysis": "True"}).lines + + return self.toc_extractor.get_toc(lines) + + def __get_automatic_toc(self, path: str) -> List[Dict[str, Union[LineWithMeta, str]]]: + result = [] + with os.popen(f"pdftocio -p {path}") as out: + toc = out.readlines() + if len(toc) == 0: + return result + + for line in toc: + match = self.toc_item_regexp.match(line.strip()) + if match: + result.append({"line": LineWithMeta(match.group(1)), "page": match.group(2)}) + + return result diff --git a/dedoc/structure_extractors/feature_extractors/fintoc_feature_extractor.py b/dedoc/structure_extractors/feature_extractors/fintoc_feature_extractor.py index b5719f44..79ca919e 100644 --- a/dedoc/structure_extractors/feature_extractors/fintoc_feature_extractor.py +++ b/dedoc/structure_extractors/feature_extractors/fintoc_feature_extractor.py @@ -1,16 +1,9 @@ -import gzip -import json -import os -import pickle import re -import zipfile from collections import defaultdict from typing import Dict, Iterator, List, Optional, Tuple import pandas as pd -import wget from Levenshtein._levenshtein import ratio -from tqdm import tqdm from dedoc.data_structures.line_with_meta import LineWithMeta from dedoc.structure_extractors.feature_extractors.abstract_extractor import AbstractFeatureExtractor @@ -27,12 +20,11 @@ from dedoc.structure_extractors.feature_extractors.toc_feature_extractor import TOCFeatureExtractor from dedoc.structure_extractors.feature_extractors.utils_feature_extractor import normalization_by_min_max from dedoc.structure_extractors.hierarchy_level_builders.utils_reg import regexps_year -from dedoc.utils.utils import flatten class FintocFeatureExtractor(AbstractFeatureExtractor): - def __init__(self, tocs: dict): + def __init__(self) -> None: self.paired_feature_extractor = PairedFeatureExtractor() self.prefix_list = [BulletPrefix, AnyLetterPrefix, LetterPrefix, BracketPrefix, BracketRomanPrefix, DottedPrefix, RomanPrefix] self.list_feature_extractors = [ @@ -42,7 +34,6 @@ def __init__(self, tocs: dict): ] self.prefix2number = {prefix.name: i for i, prefix in enumerate(self.prefix_list, start=1)} self.prefix2number[EmptyPrefix.name] = 0 - self.tocs = tocs def parameters(self) -> dict: return {} @@ -50,17 +41,15 @@ def parameters(self) -> dict: def fit(self, documents: List[List[LineWithMeta]], y: Optional[List[str]] = None) -> "AbstractFeatureExtractor": return self - def transform(self, documents: List[List[LineWithMeta]], y: Optional[List[str]] = None) -> pd.DataFrame: + def transform(self, documents: List[List[LineWithMeta]], y: Optional[List[str]] = None, toc_lines: Optional[List[List[dict]]] = None) -> pd.DataFrame: assert len(documents) > 0 - result_matrix = pd.concat([self.__process_document(document) for document in tqdm(documents)], ignore_index=True) + result_matrix = pd.concat([self.__process_document(document, d_toc_lines) for document, d_toc_lines in zip(documents, toc_lines)], ignore_index=True) result_matrix = pd.concat([result_matrix, self.paired_feature_extractor.transform(documents)], axis=1) features = sorted(result_matrix.columns) result_matrix = result_matrix[features].astype(float) - result_matrix["text"] = [line.line for line in flatten(documents)] - features.append("text") return result_matrix[features] - def __process_document(self, lines: List[LineWithMeta]) -> pd.DataFrame: + def __process_document(self, lines: List[LineWithMeta], toc: Optional[list] = None) -> pd.DataFrame: features_df = pd.DataFrame(self.__look_at_prev_line(document=lines, n=1)) features_df["line_relative_length"] = self.__get_line_relative_length(lines) @@ -71,8 +60,8 @@ def __process_document(self, lines: List[LineWithMeta]) -> pd.DataFrame: total_lines = len(lines) one_line_features_dict = defaultdict(list) - for line_id, line in enumerate(lines): - for item in self.__one_line_features(line, total_lines, start_page=start_page, finish_page=finish_page): + for line in lines: + for item in self.__one_line_features(line, total_lines, start_page=start_page, finish_page=finish_page, toc=toc): feature_name, feature = item[0], item[1] one_line_features_dict[feature_name].append(feature) @@ -81,6 +70,7 @@ def __process_document(self, lines: List[LineWithMeta]) -> pd.DataFrame: one_line_features_df = self.prev_next_line_features(one_line_features_df, 3, 3) result_matrix = pd.concat([one_line_features_df, features_df, list_features], axis=1) + result_matrix["page_id"] = [line.metadata.page_id for line in lines] return result_matrix def __look_at_prev_line(self, document: List[LineWithMeta], n: int = 1) -> Dict[str, List]: @@ -92,10 +82,10 @@ def __look_at_prev_line(self, document: List[LineWithMeta], n: int = 1) -> Dict[ :return: dict of features """ res = defaultdict(list) - for line_id, line in enumerate(document): + for line_id, _ in enumerate(document): if line_id >= n: prev_line = document[line_id - n] - is_prev_line_ends = prev_line.line.endswith(('.', ';')) + is_prev_line_ends = prev_line.line.endswith((".", ";")) res["prev_line_ends"].append(1 if is_prev_line_ends else 0) res["prev_ends_with_colon"].append(prev_line.line.endswith(":")) res["prev_is_space"].append(prev_line.line.lower().isspace()) @@ -110,7 +100,7 @@ def __get_line_relative_length(self, lines: List[LineWithMeta]) -> List[float]: relative_lengths = [len(line.line) / max_len for line in lines] return relative_lengths - def __one_line_features(self, line: LineWithMeta, total_lines: int, start_page: int, finish_page: int) -> Iterator[Tuple[str, int]]: + def __one_line_features(self, line: LineWithMeta, total_lines: int, start_page: int, finish_page: int, toc: Optional[list]) -> Iterator[tuple]: yield "normalized_page_id", normalization_by_min_max(line.metadata.page_id, min_v=start_page, max_v=finish_page) yield "indentation", self._get_indentation(line) yield "spacing", self._get_spacing(line) @@ -125,19 +115,19 @@ def __one_line_features(self, line: LineWithMeta, total_lines: int, start_page: yield "endswith_semicolon", line.line.endswith(";") yield "endswith_colon", line.line.endswith(":") yield "endswith_comma", line.line.endswith(",") - yield "startswith_bracket", line.line.strip().startswith(('(', '{')) + yield "startswith_bracket", line.line.strip().startswith(("(", "{")) bracket_cnt = 0 for char in line.line: - if char == '(': + if char == "(": bracket_cnt += 1 - elif char == ')': + elif char == ")": bracket_cnt = max(0, bracket_cnt - 1) yield "bracket_num", bracket_cnt probable_toc_title = re.sub(r"[\s:]", "", line.line).lower() yield "is_toc_title", probable_toc_title in TOCFeatureExtractor.titles - yield from self.__find_in_toc(line) + yield from self.__find_in_toc(line, toc) line_length = len(line.line) + 1 yield "supper_percent", sum((1 for letter in line.line if letter.isupper())) / line_length @@ -145,101 +135,25 @@ def __one_line_features(self, line: LineWithMeta, total_lines: int, start_page: yield "number_percent", sum((1 for letter in line.line if letter.isnumeric())) / line_length yield "words_number", len(line.line.split()) - def __find_in_toc(self, line: LineWithMeta) -> Iterator[Tuple[str, int]]: - if not hasattr(line, "group"): + def __find_in_toc(self, line: LineWithMeta, toc: Optional[List[dict]]) -> Iterator[Tuple[str, int]]: + if toc is None: yield "is_toc", 0 yield "in_toc", 0 yield "toc_exists", 0 else: - toc = self.tocs.get(line.group, []) - is_toc = 0 - in_toc = 0 - toc_exists = int(len(toc) > 0) + is_toc, in_toc, toc_exists = 0, 0, int(len(toc) > 0) line_text = line.line.lower().strip() for item in toc: - if ratio(line_text, item["text"].lower()) > 0.8: + if ratio(line_text, item["line"].line.lower()) < 0.8: + continue + # toc entry found + try: is_toc = 0 if line.metadata.page_id + 1 == int(item["page"]) else 1 in_toc = 1 if line.metadata.page_id + 1 == int(item["page"]) else 0 - break + except TypeError: + pass + break + yield "is_toc", is_toc yield "in_toc", in_toc yield "toc_exists", toc_exists - - -def handle_file(file: str, dir_out: str, extractor: AbstractFeatureExtractor): - file_name = os.path.split(file)[-1].split(".")[0] - with gzip.open(file) as f_in: - lines = pickle.load(file=f_in) - df = lines2dataframe(lines, extractor) - df.to_csv(os.path.join(dir_out, file_name + "_df.csv.gz"), index=False) - df.to_pickle(os.path.join(dir_out, file_name + "_df.pkl.gz")) - - -def lines2dataframe(lines: List[LineWithLabel], extractor: AbstractFeatureExtractor) -> pd.DataFrame: - assert(len(lines) > 0) - lines2docs = [] - current_document = None - reg_empty_string = re.compile(r"^\s*\n$") - special_unicode_symbols = [u"\uf0b7", u"\uf0d8", u"\uf084", u"\uf0a7", u"\uf0f0", u"\x83"] - - lines = [line for line in lines if not reg_empty_string.match(line.line)] - for line in lines: - for ch in special_unicode_symbols: - line.set_line(line.line.replace(ch, "")) - if line.group == current_document: - lines2docs[-1].append(line) - else: - current_document = line.group - lines2docs.append([line]) - df = extractor.transform(lines2docs) - - df["label"] = [int(line.label) for line in lines] - df["group"] = [line.group for line in lines] - df["uid"] = [line.uid for line in lines] - df["page_id"] = [line.metadata.page_id for line in lines] - return df - - -def main(dir_out: str, train: bool): - os.makedirs(dir_out, exist_ok=True) - - root = "/tmp/.fintoc/train" if train else "/tmp/.fintoc/test" - lines_dir = os.path.join(root, "lines") - if train: - lines_url = "https://at.ispras.ru/owncloud/index.php/s/yvYn491d6Du8ZuV/download" # train - else: - lines_url = "https://at.ispras.ru/owncloud/index.php/s/h3TdYfQipiVAxpE/download" # test - - toc_dir = os.path.join(root, "toc") - if train: - toc_url = "https://at.ispras.ru/owncloud/index.php/s/0VJbQWrD11R98Sy/download" # train - else: - toc_url = "https://at.ispras.ru/owncloud/index.php/s/GCoZitUsfCLPLVI/download" # test - - if not os.path.isdir(root): - os.makedirs(root) - - if not os.path.isdir(lines_dir): - archive = os.path.join(root, "lines.zip") - wget.download(lines_url, archive) - with zipfile.ZipFile(archive, 'r') as zip_ref: - zip_ref.extractall(root) - - if not os.path.isdir(toc_dir): - archive = os.path.join(root, "toc.zip") - wget.download(toc_url, archive) - with zipfile.ZipFile(archive, 'r') as zip_ref: - zip_ref.extractall(root) - - for lang in tqdm(["en", "fr", "sp"]): - lines_file = os.path.join(lines_dir, f"lines_{lang}_txt_layer.pkg.gz") - tocs_file = os.path.join(toc_dir, f"{lang}_toc.json") - with open(tocs_file) as f: - tocs = json.load(f) - extractor = FintocFeatureExtractor(tocs) - handle_file(file=lines_file, extractor=extractor, dir_out=dir_out) - - -if __name__ == '__main__': - stage = "test" - main(dir_out=f"/home/nasty/fintoc2022/{stage}/pandas", train=stage == "train") diff --git a/dedoc/structure_extractors/feature_extractors/toc_feature_extractor.py b/dedoc/structure_extractors/feature_extractors/toc_feature_extractor.py index 28fab042..a0000e0a 100644 --- a/dedoc/structure_extractors/feature_extractors/toc_feature_extractor.py +++ b/dedoc/structure_extractors/feature_extractors/toc_feature_extractor.py @@ -1,5 +1,5 @@ import re -from typing import List, Optional, Tuple, Union +from typing import Dict, List, Optional, Tuple, Union import numpy as np from Levenshtein._levenshtein import ratio @@ -17,11 +17,11 @@ class TOCFeatureExtractor: "indice", "índice", "contenidos", "tabladecontenido" # spanish ) - def get_toc(self, document: List[LineWithMeta]) -> List[dict]: + def get_toc(self, document: List[LineWithMeta]) -> List[Dict[str, Union[LineWithMeta, str]]]: """ Finds the table of contents in the given document Returns: - list of dictionaries with toc item and page number where it is located: {"line", "page"} + list of dictionaries with toc item (LineWithMeta) and page number where it is located: {"line", "page"} """ corrected_lines, marks = self.__get_probable_toc(document) diff --git a/dedoc/structure_extractors/line_type_classifiers/fintoc_classifier.py b/dedoc/structure_extractors/line_type_classifiers/fintoc_classifier.py index 1e0b1541..0c761ee0 100755 --- a/dedoc/structure_extractors/line_type_classifiers/fintoc_classifier.py +++ b/dedoc/structure_extractors/line_type_classifiers/fintoc_classifier.py @@ -1,213 +1,80 @@ import gzip -import json +import logging import os import pickle -import shutil -from statistics import mean -from typing import List, Optional, Union +from typing import List, Optional +import numpy as np import pandas as pd import xgbfir -import xgboost as xgb -from sklearn.model_selection import GroupKFold -from tqdm import tqdm - -from dedoc.structure_extractors.feature_extractors.fintoc_feature_extractor import FintocFeatureExtractor -from train_dataset.data_structures.line_with_label import LineWithLabel +from xgboost import XGBClassifier class FintocClassifier: - - def __init__(self, - tocs_path: str, - save_path: str, - binary_classifier_params: Optional[dict] = None, - target_classifier_params: Optional[dict] = None, - load_trained: bool = False, - lang: str = "en"): - self.save_path = save_path - self.lang = lang - if load_trained: - with gzip.open(os.path.join(self.save_path, f"binary_classifier_{self.lang}.pkg.gz"), "rb") as input_file: - self.binary_classifier = pickle.load(file=input_file) - with gzip.open(os.path.join(self.save_path, f"target_classifier_{self.lang}.pkg.gz"), "rb") as input_file: - self.target_classifier = pickle.load(file=input_file) - else: - assert(binary_classifier_params is not None and target_classifier_params is not None) - self.binary_classifier = xgb.XGBClassifier(**binary_classifier_params) - self.target_classifier = xgb.XGBClassifier(**target_classifier_params) - with open(tocs_path) as f: - tocs = json.load(f) - self.features_extractor = FintocFeatureExtractor(tocs) - - def fit(self, data: Union[pd.DataFrame, List[LineWithLabel]], - cross_val: bool = True, - save: bool = False, - gt_dir: Optional[str] = None, - n_splits: int = 3) -> None: - if isinstance(data, pd.DataFrame): - features_df = data - else: - features_df = lines2dataframe(data, self.features_extractor) - print("Features shape: {}".format(features_df.shape)) - results = None - - if cross_val: - assert(gt_dir is not None) - results = self.evaluate_fintoc_metric(features_df=features_df, gt_dir=gt_dir, n_splits=n_splits) - - if not save: - return - - features_names = self.__get_features_names(features_df) - self.binary_classifier.fit(features_df[features_names], features_df.label != -1) - self.target_classifier.fit(features_df[features_names][features_df.label != -1], - features_df.label[features_df.label != -1]) - self._save(features_names, results) - - def _save(self, features_names: list, scores: Optional[dict]) -> None: - os.makedirs(self.save_path, exist_ok=True) - if scores is not None: - with open(os.path.join(self.save_path, f"scores_{self.lang}.json"), "w") as f: - json.dump(scores, f) - print("Scores were saved in {}".format(os.path.join(self.save_path, f"scores_{self.lang}.json"))) - - with gzip.open(os.path.join(self.save_path, F"binary_classifier_{self.lang}.pkg.gz"), "wb") as output_file: - pickle.dump(self.binary_classifier, output_file) - with gzip.open(os.path.join(self.save_path, f"target_classifier_{self.lang}.pkg.gz"), "wb") as output_file: - pickle.dump(self.target_classifier, output_file) - print("Classifiers were saved in {} directory".format(self.save_path)) - - xgbfir.saveXgbFI(self.binary_classifier, feature_names=features_names, - OutputXlsxFile=os.path.join(self.save_path, f"feature_importances_binary_{self.lang}.xlsx")) - xgbfir.saveXgbFI(self.target_classifier, feature_names=features_names, - OutputXlsxFile=os.path.join(self.save_path, f"feature_importances_target_{self.lang}.xlsx")) - print("Features importances were saved in {} directory".format(self.save_path)) - - def predict(self, data: Union[pd.DataFrame, List[LineWithLabel]]) -> dict: + """ + Classifier of financial documents for the FinTOC 2022 Shared task (https://wp.lancs.ac.uk/cfie/fintoc2022/). + Lines are classified in two stages: + 1. Binary classification title/not title (title detection task) + 2. Classification of title lines into title depth classes (1-6) (TOC generation task) + + More important lines have a lesser depth. + As a result: + 1. For non-title lines, classifier returns -1. + 2. For title lines, classifier returns their depth (from 1 to 6). + """ + + def __init__(self, language: str, weights_dir_path: Optional[str] = None) -> None: """ - param lines: list of documents lines, label isn't known or dataframe with lines features - :return: dict with TOC of the documents in the required format + :param language: language of data ("en", "fr", "sp") + :param weights_dir_path: path to directory with trained models weights """ - if isinstance(data, pd.DataFrame): - features_df = data - else: - features_df = lines2dataframe(data, self.features_extractor) - features_names = self.__get_features_names(features_df) - binary_predictions = self.binary_classifier.predict(features_df[features_names]) - features_df["label"] = binary_predictions - target_predictions = self.target_classifier.predict(features_df[features_names][features_df.label]) - result_dict = create_json_result(features_df[features_df.label], target_predictions) - return result_dict - - def evaluate_fintoc_metric(self, - features_df: pd.DataFrame, - gt_dir: str, - n_splits: int = 3) -> dict: - - features_names = self.__get_features_names(features_df) - results_path = os.path.join(self.save_path, "results") - os.makedirs(results_path, exist_ok=True) - - kf = GroupKFold(n_splits=n_splits) + self.weights_dir_path = weights_dir_path + self.language = language + self.classifiers = {"binary": None, "target": None} - result_scores = {"td_scores": [], "toc_scores": []} - for i, (train_index, val_index) in tqdm(enumerate(kf.split(features_df, groups=features_df.group)), - total=n_splits): - df_train = features_df.loc[train_index] - df_val = features_df.loc[val_index] - self.binary_classifier.fit(df_train[features_names], df_train.label != -1) - self.target_classifier.fit( - df_train[features_names][df_train.label != -1], df_train.label[df_train.label != -1]) - result_dict = self.predict(df_val) - - tmpdir = "/tmp/fintoc/eval" - if os.path.isdir(tmpdir): - shutil.rmtree(tmpdir) - os.makedirs(tmpdir) - tmp_gt_dir, predictions_dir = os.path.join(tmpdir, "groundtruth"), os.path.join(tmpdir, "predictions") - os.makedirs(tmp_gt_dir) - os.makedirs(predictions_dir) - - for doc_name, result in result_dict.items(): - gt_doc_name = doc_name + ".pdf.fintoc4.json" - if gt_doc_name not in os.listdir(gt_dir): - print(f"\n{gt_doc_name} not found in groundtruth") - continue - with open(os.path.join(predictions_dir, gt_doc_name), "w") as json_file: - json.dump(result, json_file, indent=2) - shutil.copy(os.path.join(gt_dir, gt_doc_name), os.path.join(tmp_gt_dir, gt_doc_name)) - score(tmp_gt_dir, predictions_dir) - shutil.rmtree(tmpdir) - - path_scores = os.path.join(results_path, str(i)) - os.makedirs(path_scores, exist_ok=True) - for file_name in ['td.log', 'toc.log', 'td_report.csv', 'toc_report.csv']: - shutil.move(file_name, os.path.join(path_scores, file_name)) - f1, inex_f1 = get_values_from_csv(path_scores) - result_scores["td_scores"].append(f1) - result_scores["toc_scores"].append(inex_f1) - print(f"it {i}:\ntd {result_scores['td_scores'][-1]}\ntoc {result_scores['toc_scores'][-1]}") - result_scores["td_mean"] = mean(result_scores["td_scores"]) - result_scores["toc_mean"] = mean(result_scores["toc_scores"]) - return result_scores - - def __get_features_names(self, features_df: pd.DataFrame) -> list: - features_names = [col for col in features_df.columns if col not in ("text", "label", "group", "uid")] - return features_names - - -def train_classifier(train_dir: str) -> None: - clf_params = { - "en_binary": dict(random_state=42, learning_rate=0.25, max_depth=5, n_estimators=400, - colsample_bynode=0.8, colsample_bytree=0.5, tree_method="hist"), - "fr_binary": dict(random_state=42, learning_rate=0.1, max_depth=5, n_estimators=800, - colsample_bynode=0.5, colsample_bytree=0.8, tree_method="approx"), - "sp_binary": dict(random_state=42, learning_rate=0.25, max_depth=4, n_estimators=600, - colsample_bynode=0.5, colsample_bytree=0.5, tree_method="approx"), - "en_target": dict(random_state=42, learning_rate=0.07, max_depth=4, n_estimators=800, - colsample_bynode=1, colsample_bytree=1, tree_method="hist"), - "fr_target": dict(random_state=42, learning_rate=0.4, max_depth=5, n_estimators=800, - colsample_bynode=1, colsample_bytree=0.5, tree_method="exact"), - "sp_target": dict(random_state=42, learning_rate=0.25, max_depth=3, n_estimators=600, - colsample_bynode=0.5, colsample_bytree=1, tree_method="hist") - } - for lang in ("en", "fr", "sp"): - pandas_path = os.path.join(train_dir, "pandas", f"lines_{lang}_txt_layer_df.csv.gz") - cls = FintocClassifier(binary_classifier_params=clf_params[f"{lang}_binary"], - target_classifier_params=clf_params[f"{lang}_target"], - tocs_path=os.path.join(train_dir, "toc", f"{lang}_toc.json"), - save_path="resources", - load_trained=False, - lang=lang) - features_df = pd.read_csv(pandas_path, index_col=False) - cls.fit(data=features_df, - cross_val=True, - save=True, - gt_dir=os.path.join(train_dir, "data", lang, "annots")) - - -def get_results(test_dir: str) -> None: - for lang in ("en", "fr", "sp"): - pandas_path = os.path.join(test_dir, "pandas", f"lines_{lang}_txt_layer_df.csv.gz") - cls = FintocClassifier(tocs_path=os.path.join(test_dir, "toc", f"{lang}_toc.json"), - save_path="resources", - load_trained=True, - lang=lang) - features_df = pd.read_csv(pandas_path, index_col=False) - result_dict = cls.predict(features_df) - results_dir = os.path.join(test_dir, "results", lang) - os.makedirs(results_dir, exist_ok=True) - for doc_name, result in result_dict.items(): - json_doc_name = doc_name + ".pdf.fintoc4.json" - with open(os.path.join(results_dir, json_doc_name), "w") as json_file: - json.dump(result, json_file, indent=2) - - -if __name__ == "__main__": - train = False - fintoc_dir = "/home/nasty/fintoc2022" - if train: - train_classifier(os.path.join(fintoc_dir, "train")) - else: - get_results(os.path.join(fintoc_dir, "test")) + def predict(self, features: pd.DataFrame) -> List[int]: + """ + Two-staged classification: title/not title and depth classification for titles. + For non-title lines, classifier returns -1, for title lines, classifier returns their depth (from 1 to 6). + """ + binary_predictions = self.binary_classifier.predict(features) + # binary_predictions = [True, False, ...], target predictions are predicted only for True items + target_predictions = self.target_classifier.predict(features[binary_predictions]) + result = np.ones_like(binary_predictions) * -1 + result[binary_predictions] = target_predictions + # return list [1, 2, 3, -1, -1, ...], where positive values mean headers depth, -1 mean non-header lines + return list(result) + + def fit(self, binary_classifier_parameters: dict, target_classifier_parameters: dict, features: pd.DataFrame, features_names: list[str]) -> None: + self.classifiers["binary"] = XGBClassifier(**binary_classifier_parameters) + self.classifiers["target"] = XGBClassifier(**target_classifier_parameters) + self.binary_classifier.fit(features[features_names], features.label != -1) + self.target_classifier.fit(features[features_names][features.label != -1], features.label[features.label != -1]) + + def save(self, classifiers_dir_path: str, features_importances_dir_path: str, logger: logging.Logger, features_names: List[str]) -> None: + os.makedirs(classifiers_dir_path, exist_ok=True) + for classifier_type in ("binary", "target"): + with gzip.open(os.path.join(classifiers_dir_path, f"{classifier_type}_classifier_{self.language}.pkg.gz"), "wb") as output_file: + pickle.dump(self.classifiers[classifier_type], output_file) + logger.info(f"Classifiers were saved in {classifiers_dir_path} directory") + + os.makedirs(features_importances_dir_path, exist_ok=True) + for classifier_type in ("binary", "target"): + xgbfir.saveXgbFI(self.classifiers[classifier_type], feature_names=features_names, + OutputXlsxFile=os.path.join(features_importances_dir_path, f"feature_importances_{classifier_type}_{self.language}.xlsx")) + logger.info(f"Features importances were saved in {features_importances_dir_path} directory") + + @property + def binary_classifier(self) -> XGBClassifier: + return self.__lazy_load_weights("binary") + + @property + def target_classifier(self) -> XGBClassifier: + return self.__lazy_load_weights("target") + + def __lazy_load_weights(self, classifier_type: str) -> XGBClassifier: + if self.classifiers[classifier_type] is None: + with gzip.open(os.path.join(self.weights_dir_path, f"{classifier_type}_classifier_{self.language}.pkg.gz"), "rb") as input_file: + self.classifiers[classifier_type] = pickle.load(file=input_file) + + return self.classifiers[classifier_type] diff --git a/requirements.txt b/requirements.txt index 30469034..10af796e 100644 --- a/requirements.txt +++ b/requirements.txt @@ -12,6 +12,7 @@ olefile~=0.46 opencv-python>=4.5.5.64,<4.6.0 orjson>=3.8.11,<=3.9.5 pandas>=1.4.1,<=1.9.0 +pdf.tocgen>=1.3.0,<=1.3.4 pdf2image==1.10.0 #1.14.0 - there are converting artifacts '№' != '№\n\x0c' pdfminer.six==20211012 piexif==1.1.3 diff --git a/scripts/benchmark_pdf_performance.py b/scripts/benchmark_pdf_performance.py index c3fa48af..c5701034 100644 --- a/scripts/benchmark_pdf_performance.py +++ b/scripts/benchmark_pdf_performance.py @@ -95,7 +95,7 @@ def main() -> None: assert args.loops > 0, "The number of repetitions of testing one file must be positive" - print(f'Run pdf performance benchmark with next pdf options: {", ".join(args.pdf_options)}') + print(f'Run pdf performance benchmark with next pdf options: {", ".join(args.pdf_options)}') # noqa configs = [{}] if args.parameters: diff --git a/scripts/fintoc2022/benchmark_fintoc.py b/scripts/fintoc2022/benchmark_fintoc.py deleted file mode 100644 index e69de29b..00000000 diff --git a/scripts/fintoc2022/dataset_loader.py b/scripts/fintoc2022/dataset_loader.py index 1a271b41..f2beb730 100755 --- a/scripts/fintoc2022/dataset_loader.py +++ b/scripts/fintoc2022/dataset_loader.py @@ -7,14 +7,13 @@ import tempfile import zipfile from collections import Counter, defaultdict -from typing import List +from typing import Dict, List import wget from Levenshtein._levenshtein import ratio from dedoc.config import get_config from dedoc.readers import PdfTabbyReader, PdfTxtlayerReader -from dedoc.utils.utils import flatten from train_dataset.data_structures.line_with_label import LineWithLabel @@ -22,11 +21,9 @@ class FintocLineWithLabelExtractor: """ Create LineWithLabel from documents and their annotations """ - def __init__(self): - self.readers = { - "tabby": PdfTabbyReader(), - "txt_layer": PdfTxtlayerReader() - } + + def __init__(self) -> None: + self.readers = {"tabby": PdfTabbyReader(), "txt_layer": PdfTxtlayerReader()} def get_lines(self, file_name: str, file_path: str, annotation_path: str, reader_name: str) -> List[LineWithLabel]: """ @@ -51,9 +48,9 @@ def get_lines(self, file_name: str, file_path: str, annotation_path: str, reader annotations_page = [(ratio(line.line, annotation["text"]), annotation) for annotation in annotations[line.metadata.page_id]] best_annotation = max(annotations_page, key=lambda t: t[0], default=(0, {})) depth = best_annotation[1]["depth"] if len(annotations_page) > 0 and best_annotation[0] > 0.8 else "-1" - result.append(LineWithLabel(line=line.line, metadata=line.metadata, annotations=line.annotations, label=depth, group=file_name, uid=line.uid)) + result.append(LineWithLabel(line=line.line, metadata=line.metadata, annotations=line.annotations, label=str(depth), group=file_name, uid=line.uid)) - return sorted(result, key=lambda l: (l.metadata.page_id, l.metadata.line_id)) + return sorted(result, key=lambda x: (x.metadata.page_id, x.metadata.line_id)) class FintocDatasetLoader: @@ -72,14 +69,14 @@ def __init__(self, dataset_dir: str, logger: logging.Logger) -> None: self.data_url = "https://at.ispras.ru/owncloud/index.php/s/EZfm71WimN2h7rC/download" self.line_extractor = FintocLineWithLabelExtractor() - def get_data(self, language: str, reader_name: str, use_cache: bool = True) -> List[List[LineWithLabel]]: + def get_data(self, language: str, reader_name: str, use_cache: bool = True) -> Dict[str, List[LineWithLabel]]: """ Download data from a cloud at `self.data_url` and sort document lines. :param language: ("en", "fr", "sp") - language group :param reader_name: ("tabby", "txt_layer") - type of reader for lines extraction from PDF :param use_cache: whether to use cached data (if dataset is already downloaded) or download it anyway - :return: list of documents, which are lists of lines with labels of the training dataset + :return: dict of documents {document path: document}, where document is a list of lines with labels of the training dataset """ archive_path = os.path.join(self.dataset_dir, "dataset.zip") if not os.path.isfile(archive_path): @@ -102,11 +99,13 @@ def get_data(self, language: str, reader_name: str, use_cache: bool = True) -> L pdf_dir = os.path.join(data_dir, "pdf") annotations_dir = os.path.join(data_dir, "annots") pdf_files = {pdf_file[:-len(".pdf")]: os.path.join(pdf_dir, pdf_file) for pdf_file in os.listdir(pdf_dir) if pdf_file.endswith(".pdf")} - annotations_files = {ann_file[:-len(".pdf.fintoc4.json")]: os.path.join(annotations_dir, ann_file) - for ann_file in os.listdir(annotations_dir) if ann_file.endswith(".json")} + annotations_files = { + ann_file[:-len(".pdf.fintoc4.json")]: os.path.join(annotations_dir, ann_file) + for ann_file in os.listdir(annotations_dir) if ann_file.endswith(".json") + } assert set(pdf_files) == set(annotations_files) - result = [] + result = {} with tempfile.TemporaryDirectory() as tmp_dir: for file_name in pdf_files: pdf_tmp_path = os.path.join(tmp_dir, file_name) + ".pdf" @@ -118,11 +117,11 @@ def get_data(self, language: str, reader_name: str, use_cache: bool = True) -> L annotation_path=annotations_files[file_name], reader_name=reader_name ) - result.append(document) + result[pdf_files[file_name]] = document except Exception as e: self.logger.warning(f"Failed to read {file_name} by {reader_name}, error: {e}") with gzip.open(pkl_path, "wb") as out: pickle.dump(obj=result, file=out) - self.logger.info(Counter([line.label for line in flatten(result)])) + self.logger.info(Counter([line.label for document in result.values() for line in document])) return result diff --git a/scripts/fintoc2022/toc_extractor.py b/scripts/fintoc2022/toc_extractor.py deleted file mode 100755 index 6c7eb826..00000000 --- a/scripts/fintoc2022/toc_extractor.py +++ /dev/null @@ -1,93 +0,0 @@ -import json -import os -import re -import shutil -import subprocess -import tempfile -import zipfile -from typing import List - -import wget -from PyPDF2 import PdfFileReader, PdfFileWriter -from dedoc.data_structures.line_with_meta import LineWithMeta -from tqdm import tqdm - -from config import _config as config -from doc_reader.line_type_classifier.feature_extractor.toc_feature_extractor import TOCFeatureExtractor -from doc_reader.readers.scanned_reader.pdftxtlayer_reader.pdf_with_text_reader import PdfWithTextReader - -toc_item = re.compile(r'"([^"]+)" (\d+)') -reader = PdfWithTextReader(config=config) - - -def get_one_columns_lines(path: str) -> List[LineWithMeta]: - if path.startswith("file://"): - path = path[len("file://"):] - with tempfile.TemporaryDirectory() as tmpdir: - path_tmp = os.path.join(tmpdir, os.path.basename(path)) - pdf_reader = PdfFileReader(path) - writer = PdfFileWriter() - for page_id in range(0, min(9, pdf_reader.getNumPages())): - writer.addPage(pdf_reader.getPage(page_id)) - with open(path_tmp, 'wb') as write_file: - writer.write(write_file) - return reader.read(path=path_tmp, document_type=None, parameters={"is_one_column_document": True, - "need_header_footer_analysis": "True"}).lines - - -def get_automatic_toc(path: str) -> List[dict]: - result = [] - cmd = "pdftocio -p {}".format(path) - with os.popen(cmd) as out: - toc = out.readlines() - if toc: - for line in toc: - match = toc_item.match(line.strip()) - if match: - result.append({"text": match.group(1), "page": match.group(2)}) - return result - - -def main(dir_out: str): - toc_extractor = TOCFeatureExtractor() - os.makedirs(dir_out, exist_ok=True) - data_url = "https://at.ispras.ru/owncloud/index.php/s/EZfm71WimN2h7rC/download" - print("use 'pip install -U pdf.tocgen' to install tool for automatic toc extraction") - subprocess.run("pip install -U pdf.tocgen".split(" ")) - - root = "/tmp/.fintoc/" - if os.path.isdir(root): - shutil.rmtree(root) - os.makedirs(root) - archive = os.path.join(root, "dataset.zip") - wget.download(data_url, archive) - with zipfile.ZipFile(archive, 'r') as zip_ref: - zip_ref.extractall(root) - data_dir = os.path.join(root, "data") - - for lang in ("en", "fr", "sp"): - pdf_dir = os.path.join(data_dir, lang, "pdf") - lang_dir_out = os.path.join(dir_out, lang) - if os.path.isdir(lang_dir_out): - shutil.rmtree(lang_dir_out) - os.makedirs(lang_dir_out) - - tocs = {} - for file in tqdm(os.listdir(pdf_dir)): - if not file.endswith(".pdf"): - continue - path = os.path.join(pdf_dir, file) - toc = get_automatic_toc(path) - if len(toc) == 0: - lines = get_one_columns_lines(path) - toc = toc_extractor.get_toc(lines) - doc_name = file[: -len(".pdf")] - tocs[doc_name] = toc - with open(os.path.join(lang_dir_out, f"{doc_name}_toc.json"), "w") as out: - json.dump(obj=toc, fp=out, indent=4, ensure_ascii=False) - - with open(os.path.join(dir_out, f"{lang}_toc.json"), "w") as out: - json.dump(tocs, out) - - -main(dir_out="/home/nasty/fintoc2022/toc") diff --git a/scripts/fintoc2022/train_fintoc_classifier.py b/scripts/fintoc2022/train_fintoc_classifier.py index e69de29b..8758f570 100644 --- a/scripts/fintoc2022/train_fintoc_classifier.py +++ b/scripts/fintoc2022/train_fintoc_classifier.py @@ -0,0 +1,45 @@ +""" +Training script for the FinTOC 2022 Shared task (https://wp.lancs.ac.uk/cfie/fintoc2022/). +The code is a modification of the winner's solution (ISP RAS team). +""" +import argparse +import logging +import os + +from scripts.fintoc2022.trainer import FintocTrainer + +clf_params = { + "en_binary": dict(random_state=42, learning_rate=0.25, max_depth=5, n_estimators=400, colsample_bynode=0.8, colsample_bytree=0.5, tree_method="hist"), + "fr_binary": dict(random_state=42, learning_rate=0.1, max_depth=5, n_estimators=800, colsample_bynode=0.5, colsample_bytree=0.8, tree_method="approx"), + "sp_binary": dict(random_state=42, learning_rate=0.25, max_depth=4, n_estimators=600, colsample_bynode=0.5, colsample_bytree=0.5, tree_method="approx"), + "en_target": dict(random_state=42, learning_rate=0.07, max_depth=4, n_estimators=800, colsample_bynode=1, colsample_bytree=1, tree_method="hist"), + "fr_target": dict(random_state=42, learning_rate=0.4, max_depth=5, n_estimators=800, colsample_bynode=1, colsample_bytree=0.5, tree_method="exact"), + "sp_target": dict(random_state=42, learning_rate=0.25, max_depth=3, n_estimators=600, colsample_bynode=0.5, colsample_bytree=1, tree_method="hist") +} + + +if __name__ == "__main__": + base_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), "..", "..", "resources", "fintoc2022")) + os.makedirs(base_dir, exist_ok=True) + + parser = argparse.ArgumentParser() + parser.add_argument("--language", choices=["en", "fr", "sp"], help="Language of training data", default="en") + parser.add_argument("--reader", choices=["tabby", "txt_layer"], help="Type of PDF reader used for lines extraction", default="tabby") + parser.add_argument("--cross_val", type=bool, help="Whether to do a cross-validation", default=True) + parser.add_argument("--n_splits", type=int, help="Number of splits for cross-validation", default=3) + args = parser.parse_args() + + trainer = FintocTrainer( + data_url="https://at.ispras.ru/owncloud/index.php/s/EZfm71WimN2h7rC/download", + logger=logging.getLogger(), + language=args.language, + n_splits=args.n_splits, + classifiers_dir_path=os.path.join(base_dir, "classifiers"), + scores_dir_path=os.path.join(base_dir, "scores"), + features_importances_dir_path=os.path.join(base_dir, "features_importances"), + tmp_dir="/tmp/fintoc/", # path where dataset and predicted jsons will be saved + binary_classifier_parameters=clf_params[f"{args.language}_binary"], + target_classifier_parameters=clf_params[f"{args.language}_target"] + ) + + trainer.fit(reader_name=args.reader, cross_val=args.cross_val) diff --git a/scripts/fintoc2022/trainer.py b/scripts/fintoc2022/trainer.py new file mode 100644 index 00000000..662c8374 --- /dev/null +++ b/scripts/fintoc2022/trainer.py @@ -0,0 +1,173 @@ +import hashlib +import json +import logging +import os +import shutil +from statistics import mean +from typing import Optional + +import pandas as pd +from sklearn.model_selection import GroupKFold +from tqdm import tqdm + +from dedoc.structure_extractors.concrete_structure_extractors.fintoc_structure_extractor import FintocStructureExtractor +from dedoc.structure_extractors.feature_extractors.fintoc_feature_extractor import FintocFeatureExtractor +from dedoc.structure_extractors.line_type_classifiers.fintoc_classifier import FintocClassifier +from dedoc.utils.utils import flatten +from scripts.fintoc2022.dataset_loader import FintocDatasetLoader +from scripts.fintoc2022.metric import score +from scripts.fintoc2022.utils import create_json_result, get_values_from_csv + + +class FintocTrainer: + """ + Class to train and evaluate classifiers for the FinTOC 2022 Shared task (https://wp.lancs.ac.uk/cfie/fintoc2022/). + The code is a modification of the winner's solution (ISP RAS team). + """ + def __init__(self, + data_url: str, + logger: logging.Logger, + language: str, + classifiers_dir_path: str, + scores_dir_path: str, + features_importances_dir_path: str, + tmp_dir: str, + binary_classifier_parameters: dict = None, + target_classifier_parameters: dict = None, + n_splits: int = 3) -> None: + """ + :param data_url: url to download training data for FintocDatasetLoader + :param logger: logger for logging details of classifier training + :param language: language of data ("en", "fr", "sp") + :param classifiers_dir_path: path to the directory where to save trained classifiers + :param scores_dir_path: path to the directory where to save final scores during cross-validation + :param features_importances_dir_path: path to the directory where to save XLSX files with information about most important features for classifiers + :param tmp_dir: path to temporary directory for saving the dataset and output json files with predictions + :param binary_classifier_parameters: parameters to pass to xgboost.XGBClassifier for classification header/non-header + :param target_classifier_parameters: parameters to pass to xgboost.XGBClassifier for lines depth classification + :param n_splits: number of splits for cross-validation + """ + self.logger = logger + self.language = language + self.feature_extractor = FintocFeatureExtractor() + self.structure_extractor = FintocStructureExtractor() + + self.binary_classifier_parameters = {} if binary_classifier_parameters is None else binary_classifier_parameters + self.target_classifier_parameters = {} if target_classifier_parameters is None else target_classifier_parameters + self.classifier = FintocClassifier(language=self.language) + + self.tmp_dir = tmp_dir + os.makedirs(self.tmp_dir, exist_ok=True) + self.scores_dir_path = scores_dir_path + self.features_importances_dir_path = features_importances_dir_path + self.classifiers_dir_path = classifiers_dir_path + + self.data_url = data_url + url_hash = hashlib.md5(self.data_url.encode()).hexdigest() + self.dataset_dir = os.path.join(self.tmp_dir, f"dataset_{url_hash}") + self.data_loader = FintocDatasetLoader(dataset_dir=self.dataset_dir, logger=logger) + + self.n_splits = n_splits + self.additional_features_fields = ("line", "label", "group", "uid") + + def fit(self, reader_name: str, cross_val: bool = True, use_cache: bool = True) -> None: + """ + 1 - Load data by `self.data_url` if needed, extract lines from PDF by chosen reader by `reader_name` if needed (FintocDatasetLoader). + 2 - Extract a feature matrix for extracted document lines (FintocFeatureExtractor). + 3 - Do a cross-validation if needed. + 4 - Train resulting classifiers (binary, target) and save them to `self.classifiers_dir_path` (FintocClassifier). + + :param reader_name: ("tabby", "txt_layer") - type of reader for lines extraction from PDF + :param cross_val: whether to do cross-validation or not + :param use_cache: whether to use cached extracted lines as training data + """ + # obtain training data + self.logger.info("Get data for training and evaluation") + data = self.data_loader.get_data(language=self.language, reader_name=reader_name, use_cache=use_cache) + + # create feature matrix + self.logger.info("Create a feature matrix") + features, documents = self.structure_extractor.get_features(documents_dict=data) + self.logger.info(f"Features shape: {features.shape}") + for feature_field in self.additional_features_fields: + features[feature_field] = [getattr(line, feature_field) for line in flatten(documents)] + features["label"] = features["label"].astype(int) + features_names = self.__get_features_names(features) + + # cross-validation using fintoc metric + gt_dir = os.path.join(self.dataset_dir, "data", self.language, "annots") + scores = self.__cross_validate(features=features, gt_dir=gt_dir) if cross_val else None + + # train resulting classifiers on all data + self.logger.info("Train resulting classifiers") + self.classifier.fit(self.binary_classifier_parameters, self.target_classifier_parameters, features=features, features_names=features_names) + self.__save(features_names=features_names, scores=scores) + + def __get_features_names(self, features_df: pd.DataFrame) -> list: + features_names = [col for col in features_df.columns if col not in self.additional_features_fields] + return features_names + + def __cross_validate(self, features: pd.DataFrame, gt_dir: str) -> dict: + self.logger.info("Start cross-validation") + features_names = self.__get_features_names(features) + results_path = os.path.join(self.scores_dir_path, "cross_val_results", self.language) + os.makedirs(results_path, exist_ok=True) + + kf = GroupKFold(n_splits=self.n_splits) + json_results_dir = os.path.join(self.tmp_dir, "json_results", self.language) + + result_scores = {"td_scores": [], "toc_scores": []} + for i, (train_index, val_index) in tqdm(enumerate(kf.split(features, groups=features.group)), total=self.n_splits): + df_train = features.loc[train_index] + df_val = features.loc[val_index] + self.classifier.fit(self.binary_classifier_parameters, self.target_classifier_parameters, features=df_train, features_names=features_names) + predicted_classes = self.classifier.predict(df_val[features_names]) + result_dict = create_json_result(df_val, predicted_classes) + + if os.path.isdir(json_results_dir): + shutil.rmtree(json_results_dir) + os.makedirs(json_results_dir) + + tmp_gt_dir, predictions_dir = os.path.join(json_results_dir, "groundtruth"), os.path.join(self.tmp_dir, "predictions") + os.makedirs(tmp_gt_dir) + os.makedirs(predictions_dir) + + for doc_name, result in result_dict.items(): + gt_doc_name = doc_name + ".pdf.fintoc4.json" + if gt_doc_name not in os.listdir(gt_dir): + self.logger.warning(f"{gt_doc_name} is not found in groundtruth") + continue + with open(os.path.join(predictions_dir, gt_doc_name), "w") as json_file: + json.dump(result, json_file, indent=2) + shutil.copy(os.path.join(gt_dir, gt_doc_name), os.path.join(tmp_gt_dir, gt_doc_name)) + score(tmp_gt_dir, predictions_dir) + + path_scores = os.path.join(results_path, str(i)) + os.makedirs(path_scores, exist_ok=True) + for file_name in ["td.log", "toc.log", "td_report.csv", "toc_report.csv"]: + shutil.move(file_name, os.path.join(path_scores, file_name)) + + f1, inex_f1 = get_values_from_csv(path_scores) + result_scores["td_scores"].append(f1) + result_scores["toc_scores"].append(inex_f1) + self.logger.info(f'Iteration {i}:\ntd={result_scores["td_scores"][-1]}\ntoc={result_scores["toc_scores"][-1]}') + + result_scores["td_mean"] = mean(result_scores["td_scores"]) + result_scores["toc_mean"] = mean(result_scores["toc_scores"]) + return result_scores + + def __save(self, features_names: list[str], scores: Optional[dict]) -> None: + + if scores is not None: + os.makedirs(self.scores_dir_path, exist_ok=True) + scores_path = os.path.join(self.scores_dir_path, f"scores_{self.language}.json") + with open(scores_path, "w") as f: + json.dump(scores, f) + self.logger.info(f"Scores were saved in {scores_path}") + + self.classifier.save( + classifiers_dir_path=self.classifiers_dir_path, + features_importances_dir_path=self.features_importances_dir_path, + features_names=features_names, + logger=self.logger + ) diff --git a/scripts/fintoc2022/utils.py b/scripts/fintoc2022/utils.py index 9a4ab457..e5a9f018 100755 --- a/scripts/fintoc2022/utils.py +++ b/scripts/fintoc2022/utils.py @@ -18,18 +18,21 @@ def create_json_result(data: pd.DataFrame, predictions: List[int]) -> dict: } ] """ - uid2line = {item[1].uid: {"text": item[1].text.strip() if isinstance(item[1].text, str) else "", - "page": item[1].page_id + 1, - "group": item[1].group} for item in data.iterrows()} + uid2line = { + item[1].uid: { + "text": item[1].line.strip() if isinstance(item[1].line, str) else "", + "page": item[1].page_id + 1, + "group": item[1].group + } for item in data.iterrows() + } result = defaultdict(list) - assert(data.shape[0] == len(predictions)) + assert data.shape[0] == len(predictions) for i, (line_uid, prediction) in enumerate(zip(data.uid, predictions)): line = uid2line[line_uid] if line["text"] == "": continue # TODO crop text lines containing colon - result[line["group"]].append({"id": i, "depth": str(prediction), - "text": line["text"], "page": line["page"]}) + result[line["group"]].append({"id": i, "depth": str(prediction), "text": line["text"], "page": line["page"]}) return result From 3d472143374d24330d0016b1c2a17901f9b95bb5 Mon Sep 17 00:00:00 2001 From: Nasty Date: Wed, 24 Apr 2024 16:09:47 +0300 Subject: [PATCH 3/8] Fix some bugs and write documentation --- dedoc/api/api_args.py | 2 +- dedoc/api/web/index.html | 1 + dedoc/manager_config.py | 9 +++-- dedoc/structure_extractors/__init__.py | 5 ++- .../fintoc_structure_extractor.py | 39 ++++++++++++++----- .../fintoc_classifier.py | 6 +-- docs/source/dedoc_api_usage/api.rst | 3 +- docs/source/index.rst | 2 + docs/source/modules/structure_extractors.rst | 6 +++ docs/source/parameters/structure_type.rst | 12 +++--- docs/source/structure_types/fintoc.rst | 5 +++ scripts/fintoc2022/metric.py | 2 +- scripts/fintoc2022/train_fintoc_classifier.py | 3 +- scripts/fintoc2022/trainer.py | 21 +++++----- scripts/fintoc2022/utils.py | 4 +- 15 files changed, 82 insertions(+), 38 deletions(-) create mode 100644 docs/source/structure_types/fintoc.rst diff --git a/dedoc/api/api_args.py b/dedoc/api/api_args.py index 1c260b37..f85da673 100644 --- a/dedoc/api/api_args.py +++ b/dedoc/api/api_args.py @@ -7,7 +7,7 @@ @dataclass class QueryParameters: # type of document structure parsing - document_type: str = Form("", enum=["", "law", "tz", "diploma"], description="Document domain") + document_type: str = Form("", enum=["", "law", "tz", "diploma", "article", "fintoc"], description="Document domain") structure_type: str = Form("tree", enum=["linear", "tree"], description="Output structure type") return_format: str = Form("json", enum=["json", "html", "plain_text", "tree", "collapsed_tree", "ujson", "pretty_json"], description="Response representation, most types (except json) are used for debug purposes only") diff --git a/dedoc/api/web/index.html b/dedoc/api/web/index.html index 5ca05cec..e8b15f68 100644 --- a/dedoc/api/web/index.html +++ b/dedoc/api/web/index.html @@ -38,6 +38,7 @@

Type of document structure parsing

+ document_type

diff --git a/dedoc/manager_config.py b/dedoc/manager_config.py index 679db954..35815ecf 100644 --- a/dedoc/manager_config.py +++ b/dedoc/manager_config.py @@ -1,7 +1,5 @@ from typing import Optional -from dedoc.readers.article_reader.article_reader import ArticleReader - def _get_manager_config(config: dict) -> dict: """ @@ -23,6 +21,7 @@ def _get_manager_config(config: dict) -> dict: from dedoc.metadata_extractors.concrete_metadata_extractors.pdf_metadata_extractor import PdfMetadataExtractor from dedoc.metadata_extractors.metadata_extractor_composition import MetadataExtractorComposition from dedoc.readers.archive_reader.archive_reader import ArchiveReader + from dedoc.readers.article_reader.article_reader import ArticleReader from dedoc.readers.csv_reader.csv_reader import CSVReader from dedoc.readers.docx_reader.docx_reader import DocxReader from dedoc.readers.email_reader.email_reader import EmailReader @@ -41,9 +40,11 @@ def _get_manager_config(config: dict) -> dict: from dedoc.structure_constructors.concrete_structure_constructors.linear_constructor import LinearConstructor from dedoc.structure_constructors.concrete_structure_constructors.tree_constructor import TreeConstructor from dedoc.structure_constructors.structure_constructor_composition import StructureConstructorComposition + from dedoc.structure_extractors.concrete_structure_extractors.article_structure_extractor import ArticleStructureExtractor from dedoc.structure_extractors.concrete_structure_extractors.classifying_law_structure_extractor import ClassifyingLawStructureExtractor from dedoc.structure_extractors.concrete_structure_extractors.default_structure_extractor import DefaultStructureExtractor from dedoc.structure_extractors.concrete_structure_extractors.diploma_structure_extractor import DiplomaStructureExtractor + from dedoc.structure_extractors.concrete_structure_extractors.fintoc_structure_extractor import FintocStructureExtractor from dedoc.structure_extractors.concrete_structure_extractors.foiv_law_structure_extractor import FoivLawStructureExtractor from dedoc.structure_extractors.concrete_structure_extractors.law_structure_excractor import LawStructureExtractor from dedoc.structure_extractors.concrete_structure_extractors.tz_structure_extractor import TzStructureExtractor @@ -93,7 +94,9 @@ def _get_manager_config(config: dict) -> dict: DefaultStructureExtractor.document_type: DefaultStructureExtractor(config=config), DiplomaStructureExtractor.document_type: DiplomaStructureExtractor(config=config), TzStructureExtractor.document_type: TzStructureExtractor(config=config), - ClassifyingLawStructureExtractor.document_type: ClassifyingLawStructureExtractor(extractors=law_extractors, config=config) + ClassifyingLawStructureExtractor.document_type: ClassifyingLawStructureExtractor(extractors=law_extractors, config=config), + ArticleStructureExtractor.document_type: ArticleStructureExtractor(config=config), + FintocStructureExtractor.document_type: FintocStructureExtractor(config=config) } return dict( diff --git a/dedoc/structure_extractors/__init__.py b/dedoc/structure_extractors/__init__.py index 404d915c..20f6d350 100644 --- a/dedoc/structure_extractors/__init__.py +++ b/dedoc/structure_extractors/__init__.py @@ -4,11 +4,12 @@ from .concrete_structure_extractors.article_structure_extractor import ArticleStructureExtractor from .concrete_structure_extractors.classifying_law_structure_extractor import ClassifyingLawStructureExtractor from .concrete_structure_extractors.diploma_structure_extractor import DiplomaStructureExtractor +from .concrete_structure_extractors.fintoc_structure_extractor import FintocStructureExtractor from .concrete_structure_extractors.foiv_law_structure_extractor import FoivLawStructureExtractor from .concrete_structure_extractors.law_structure_excractor import LawStructureExtractor from .concrete_structure_extractors.tz_structure_extractor import TzStructureExtractor from .structure_extractor_composition import StructureExtractorComposition __all__ = ['AbstractStructureExtractor', 'AbstractLawStructureExtractor', 'ArticleStructureExtractor', 'ClassifyingLawStructureExtractor', - 'DefaultStructureExtractor', 'DiplomaStructureExtractor', 'FoivLawStructureExtractor', 'LawStructureExtractor', 'TzStructureExtractor', - 'StructureExtractorComposition'] + 'DefaultStructureExtractor', 'DiplomaStructureExtractor', 'FintocStructureExtractor', 'FoivLawStructureExtractor', 'LawStructureExtractor', + 'TzStructureExtractor', 'StructureExtractorComposition'] diff --git a/dedoc/structure_extractors/concrete_structure_extractors/fintoc_structure_extractor.py b/dedoc/structure_extractors/concrete_structure_extractors/fintoc_structure_extractor.py index 3d69ccad..e0d0c30e 100644 --- a/dedoc/structure_extractors/concrete_structure_extractors/fintoc_structure_extractor.py +++ b/dedoc/structure_extractors/concrete_structure_extractors/fintoc_structure_extractor.py @@ -7,7 +7,6 @@ from PyPDF2 import PdfFileReader, PdfFileWriter from dedoc.data_structures import HierarchyLevel, LineWithMeta, UnstructuredDocument -from dedoc.readers import PdfTxtlayerReader from dedoc.structure_extractors import AbstractStructureExtractor from dedoc.structure_extractors.feature_extractors.fintoc_feature_extractor import FintocFeatureExtractor from dedoc.structure_extractors.feature_extractors.toc_feature_extractor import TOCFeatureExtractor @@ -16,15 +15,18 @@ class FintocStructureExtractor(AbstractStructureExtractor): """ - This class is an implementation of the TOC extractor for the `FinTOC 2022 Shared task`_. + This class is an implementation of the TOC extractor for the `FinTOC 2022 Shared task `_. The code is a modification of the winner's solution (ISP RAS team). - You can find the description of this type of structure in the section :ref:`fintoc_structure`. + This structure extractor is used for English, French and Spanish financial prospects in PDF format (with a textual layer). + It is recommended to use :class:`~dedoc.readers.PdfTxtlayerReader` to obtain document lines. + You can find the more detailed description of this type of structure in the section :ref:`fintoc_structure`. """ document_type = "fintoc" def __init__(self, *, config: Optional[dict] = None) -> None: super().__init__(config=config) + from dedoc.readers import PdfTxtlayerReader # to exclude circular imports self.pdf_reader = PdfTxtlayerReader(config=self.config) self.toc_extractor = TOCFeatureExtractor() self.features_extractor = FintocFeatureExtractor() @@ -35,9 +37,22 @@ def __init__(self, *, config: Optional[dict] = None) -> None: def extract(self, document: UnstructuredDocument, parameters: Optional[dict] = None, file_path: Optional[str] = None) -> UnstructuredDocument: """ - - To get the information about the method's parameters look at the documentation of the class \ - :class:`~dedoc.structure_extractors.AbstractStructureExtractor`. + According to the `FinTOC 2022 `_ title detection task, lines are classified as titles and non-titles. + The information about titles is saved in ``line.metadata.hierarchy_level`` (:class:`~dedoc.data_structures.HierarchyLevel` class): + + - Title lines have ``HierarchyLevel.header`` type, and their depth (``HierarchyLevel.level_2``) is similar to \ + the depth of TOC item from the FinTOC 2022 TOC generation task. + - Non-title lines have ``HierarchyLevel.raw_text`` type, and their depth isn't obtained. + + :param document: document content that has been received from some of the readers (:class:`~dedoc.readers.PdfTxtlayerReader` is recommended). + :param parameters: for this structure extractor, "language" parameter is used for setting document's language, e.g. ``parameters={"language": "en"}``. \ + The following options are supported: + + * "en" - English (default); + * "fr" - French; + * "sp" - Spanish. + :param file_path: path to the file on disk. + :return: document content with added additional information about title/non-title lines and hierarchy levels of titles. """ parameters = {} if parameters is None else parameters language = parameters.get("language", "en") @@ -76,12 +91,16 @@ def __filter_lines(self, lines: List[LineWithMeta]) -> List[LineWithMeta]: return lines - def __get_toc(self, file_path: Optional[str]) -> Optional[List[Dict[str, Union[LineWithMeta, str]]]]: - if file_path is None: - return + def __get_toc(self, file_path: Optional[str]) -> List[Dict[str, Union[LineWithMeta, str]]]: + """ + Try to get TOC from PDF automatically. If TOC wasn't extracted automatically, it is extracted using regular expressions. + """ + if file_path is None or not file_path.lower().endswith(".pdf"): + return [] toc = self.__get_automatic_toc(path=file_path) if len(toc) > 0: + self.logger.info(f"Got automatic TOC from {os.path.basename(file_path)}") return toc pdf_reader = PdfFileReader(file_path) @@ -99,7 +118,7 @@ def __get_toc(self, file_path: Optional[str]) -> Optional[List[Dict[str, Union[L def __get_automatic_toc(self, path: str) -> List[Dict[str, Union[LineWithMeta, str]]]: result = [] - with os.popen(f"pdftocio -p {path}") as out: + with os.popen(f'pdftocio -p "{path}"') as out: toc = out.readlines() if len(toc) == 0: return result diff --git a/dedoc/structure_extractors/line_type_classifiers/fintoc_classifier.py b/dedoc/structure_extractors/line_type_classifiers/fintoc_classifier.py index 0c761ee0..cb5f0d5b 100755 --- a/dedoc/structure_extractors/line_type_classifiers/fintoc_classifier.py +++ b/dedoc/structure_extractors/line_type_classifiers/fintoc_classifier.py @@ -51,17 +51,17 @@ def fit(self, binary_classifier_parameters: dict, target_classifier_parameters: self.binary_classifier.fit(features[features_names], features.label != -1) self.target_classifier.fit(features[features_names][features.label != -1], features.label[features.label != -1]) - def save(self, classifiers_dir_path: str, features_importances_dir_path: str, logger: logging.Logger, features_names: List[str]) -> None: + def save(self, classifiers_dir_path: str, features_importances_dir_path: str, logger: logging.Logger, features_names: List[str], reader: str) -> None: os.makedirs(classifiers_dir_path, exist_ok=True) for classifier_type in ("binary", "target"): - with gzip.open(os.path.join(classifiers_dir_path, f"{classifier_type}_classifier_{self.language}.pkg.gz"), "wb") as output_file: + with gzip.open(os.path.join(classifiers_dir_path, f"{classifier_type}_classifier_{self.language}_{reader}.pkg.gz"), "wb") as output_file: pickle.dump(self.classifiers[classifier_type], output_file) logger.info(f"Classifiers were saved in {classifiers_dir_path} directory") os.makedirs(features_importances_dir_path, exist_ok=True) for classifier_type in ("binary", "target"): xgbfir.saveXgbFI(self.classifiers[classifier_type], feature_names=features_names, - OutputXlsxFile=os.path.join(features_importances_dir_path, f"feature_importances_{classifier_type}_{self.language}.xlsx")) + OutputXlsxFile=os.path.join(features_importances_dir_path, f"feature_importances_{classifier_type}_{self.language}_{reader}.xlsx")) logger.info(f"Features importances were saved in {features_importances_dir_path} directory") @property diff --git a/docs/source/dedoc_api_usage/api.rst b/docs/source/dedoc_api_usage/api.rst index b06b345d..8db61804 100644 --- a/docs/source/dedoc_api_usage/api.rst +++ b/docs/source/dedoc_api_usage/api.rst @@ -80,7 +80,8 @@ Api parameters description * **law** -- Russian laws (:ref:`law_structure`); * **tz** -- Russian technical specifications (:ref:`tz_structure`); * **diploma** -- Russian thesis (:ref:`diploma_structure`); - * **article** -- scientific article (:ref:`article_structure`). + * **article** -- scientific article (:ref:`article_structure`); + * **fintoc** -- English, French and Spanish financial prospects (:ref:`fintoc_structure`). This type is used for choosing a specific structure extractor (and, in some cases, a specific reader). diff --git a/docs/source/index.rst b/docs/source/index.rst index 92582bec..779a6adb 100644 --- a/docs/source/index.rst +++ b/docs/source/index.rst @@ -209,6 +209,7 @@ Currently the following domains can be handled: * Russian technical specifications (:ref:`structure description `). * Russian thesis for bachelor or master degree (:ref:`structure description `). * English scientific articles (:ref:`structure description `). + * English, French and Spanish financial prospects (:ref:`structure description `). For a document of unknown or unsupported domain there is an option to use default structure extractor (``document_type=other`` at :ref:`api_parameters`), the default document structure described :ref:`here `. @@ -257,6 +258,7 @@ For a document of unknown or unsupported domain there is an option to use defaul structure_types/tz structure_types/diploma structure_types/article + structure_types/fintoc .. toctree:: diff --git a/docs/source/modules/structure_extractors.rst b/docs/source/modules/structure_extractors.rst index 08655f06..86ded2c3 100644 --- a/docs/source/modules/structure_extractors.rst +++ b/docs/source/modules/structure_extractors.rst @@ -58,3 +58,9 @@ dedoc.structure_extractors :members: .. autoattribute:: document_type + +.. autoclass:: dedoc.structure_extractors.FintocStructureExtractor + :show-inheritance: + :members: + + .. autoattribute:: document_type diff --git a/docs/source/parameters/structure_type.rst b/docs/source/parameters/structure_type.rst index 842f6afc..dfd09803 100644 --- a/docs/source/parameters/structure_type.rst +++ b/docs/source/parameters/structure_type.rst @@ -22,16 +22,18 @@ Structure type configuring - Type of the document structure according to specific domain. If you use default manager config for :class:`~dedoc.DedocManager`, then the following options are available: - * **other** -- structure for document of any domain (:ref:`other_structure`) + * **other** -- structure for document of any domain (:ref:`other_structure`). In this case, :class:`~dedoc.structure_extractors.DefaultStructureExtractor` is used. - * **law** -- Russian laws (:ref:`law_structure`) + * **law** -- Russian laws (:ref:`law_structure`). In this case, :class:`~dedoc.structure_extractors.ClassifyingLawStructureExtractor` is used. - * **tz** -- Russian technical specifications (:ref:`tz_structure`) + * **tz** -- Russian technical specifications (:ref:`tz_structure`). In this case, :class:`~dedoc.structure_extractors.TzStructureExtractor` is used. - * **diploma** -- Russian thesis (:ref:`diploma_structure`) + * **diploma** -- Russian thesis (:ref:`diploma_structure`). In this case, :class:`~dedoc.structure_extractors.DiplomaStructureExtractor` is used. - * **article** -- scientific article (:ref:`article_structure`) + * **article** -- scientific article (:ref:`article_structure`). In this case, :class:`~dedoc.readers.ArticleReader` and :class:`~dedoc.structure_extractors.ArticleStructureExtractor` are used. + * **fintoc** -- English, French and Spanish financial prospects (:ref:`fintoc_structure`). + In this case, :class:`~dedoc.structure_extractors.FintocStructureExtractor` is used. If you use your custom configuration, look to the documentation of :class:`~dedoc.structure_extractors.StructureExtractorComposition` diff --git a/docs/source/structure_types/fintoc.rst b/docs/source/structure_types/fintoc.rst new file mode 100644 index 00000000..6c9e6e17 --- /dev/null +++ b/docs/source/structure_types/fintoc.rst @@ -0,0 +1,5 @@ +.. _fintoc_structure: + +FinTOC structure type +===================== + diff --git a/scripts/fintoc2022/metric.py b/scripts/fintoc2022/metric.py index eb82fd4a..3ab4da15 100755 --- a/scripts/fintoc2022/metric.py +++ b/scripts/fintoc2022/metric.py @@ -42,7 +42,7 @@ import numpy as np JSON_EXTENSION = ".fintoc4.json" -VERBOSE = True +VERBOSE = False STRING_THRESHOLD = 0.85 diff --git a/scripts/fintoc2022/train_fintoc_classifier.py b/scripts/fintoc2022/train_fintoc_classifier.py index 8758f570..ddee19da 100644 --- a/scripts/fintoc2022/train_fintoc_classifier.py +++ b/scripts/fintoc2022/train_fintoc_classifier.py @@ -33,6 +33,7 @@ data_url="https://at.ispras.ru/owncloud/index.php/s/EZfm71WimN2h7rC/download", logger=logging.getLogger(), language=args.language, + reader_name=args.reader, n_splits=args.n_splits, classifiers_dir_path=os.path.join(base_dir, "classifiers"), scores_dir_path=os.path.join(base_dir, "scores"), @@ -42,4 +43,4 @@ target_classifier_parameters=clf_params[f"{args.language}_target"] ) - trainer.fit(reader_name=args.reader, cross_val=args.cross_val) + trainer.fit(cross_val=args.cross_val) diff --git a/scripts/fintoc2022/trainer.py b/scripts/fintoc2022/trainer.py index 662c8374..0b0b311b 100644 --- a/scripts/fintoc2022/trainer.py +++ b/scripts/fintoc2022/trainer.py @@ -28,6 +28,7 @@ def __init__(self, data_url: str, logger: logging.Logger, language: str, + reader_name: str, classifiers_dir_path: str, scores_dir_path: str, features_importances_dir_path: str, @@ -38,7 +39,8 @@ def __init__(self, """ :param data_url: url to download training data for FintocDatasetLoader :param logger: logger for logging details of classifier training - :param language: language of data ("en", "fr", "sp") + :param language: ("en", "fr", "sp") - language of data + :param reader_name: ("tabby", "txt_layer") - type of reader for lines extraction from PDF :param classifiers_dir_path: path to the directory where to save trained classifiers :param scores_dir_path: path to the directory where to save final scores during cross-validation :param features_importances_dir_path: path to the directory where to save XLSX files with information about most important features for classifiers @@ -49,6 +51,7 @@ def __init__(self, """ self.logger = logger self.language = language + self.reader_name = reader_name self.feature_extractor = FintocFeatureExtractor() self.structure_extractor = FintocStructureExtractor() @@ -70,20 +73,19 @@ def __init__(self, self.n_splits = n_splits self.additional_features_fields = ("line", "label", "group", "uid") - def fit(self, reader_name: str, cross_val: bool = True, use_cache: bool = True) -> None: + def fit(self, cross_val: bool = True, use_cache: bool = True) -> None: """ 1 - Load data by `self.data_url` if needed, extract lines from PDF by chosen reader by `reader_name` if needed (FintocDatasetLoader). 2 - Extract a feature matrix for extracted document lines (FintocFeatureExtractor). 3 - Do a cross-validation if needed. 4 - Train resulting classifiers (binary, target) and save them to `self.classifiers_dir_path` (FintocClassifier). - :param reader_name: ("tabby", "txt_layer") - type of reader for lines extraction from PDF :param cross_val: whether to do cross-validation or not :param use_cache: whether to use cached extracted lines as training data """ # obtain training data self.logger.info("Get data for training and evaluation") - data = self.data_loader.get_data(language=self.language, reader_name=reader_name, use_cache=use_cache) + data = self.data_loader.get_data(language=self.language, reader_name=self.reader_name, use_cache=use_cache) # create feature matrix self.logger.info("Create a feature matrix") @@ -110,11 +112,11 @@ def __get_features_names(self, features_df: pd.DataFrame) -> list: def __cross_validate(self, features: pd.DataFrame, gt_dir: str) -> dict: self.logger.info("Start cross-validation") features_names = self.__get_features_names(features) - results_path = os.path.join(self.scores_dir_path, "cross_val_results", self.language) + results_path = os.path.join(self.scores_dir_path, f"cross_val_results_{self.language}_{self.reader_name}") os.makedirs(results_path, exist_ok=True) kf = GroupKFold(n_splits=self.n_splits) - json_results_dir = os.path.join(self.tmp_dir, "json_results", self.language) + json_results_dir = os.path.join(self.tmp_dir, f"json_results_{self.language}_{self.reader_name}") result_scores = {"td_scores": [], "toc_scores": []} for i, (train_index, val_index) in tqdm(enumerate(kf.split(features, groups=features.group)), total=self.n_splits): @@ -128,7 +130,7 @@ def __cross_validate(self, features: pd.DataFrame, gt_dir: str) -> dict: shutil.rmtree(json_results_dir) os.makedirs(json_results_dir) - tmp_gt_dir, predictions_dir = os.path.join(json_results_dir, "groundtruth"), os.path.join(self.tmp_dir, "predictions") + tmp_gt_dir, predictions_dir = os.path.join(json_results_dir, "groundtruth"), os.path.join(json_results_dir, "predictions") os.makedirs(tmp_gt_dir) os.makedirs(predictions_dir) @@ -160,7 +162,7 @@ def __save(self, features_names: list[str], scores: Optional[dict]) -> None: if scores is not None: os.makedirs(self.scores_dir_path, exist_ok=True) - scores_path = os.path.join(self.scores_dir_path, f"scores_{self.language}.json") + scores_path = os.path.join(self.scores_dir_path, f"scores_{self.language}_{self.reader_name}.json") with open(scores_path, "w") as f: json.dump(scores, f) self.logger.info(f"Scores were saved in {scores_path}") @@ -169,5 +171,6 @@ def __save(self, features_names: list[str], scores: Optional[dict]) -> None: classifiers_dir_path=self.classifiers_dir_path, features_importances_dir_path=self.features_importances_dir_path, features_names=features_names, - logger=self.logger + logger=self.logger, + reader=self.reader_name ) diff --git a/scripts/fintoc2022/utils.py b/scripts/fintoc2022/utils.py index e5a9f018..187f5158 100755 --- a/scripts/fintoc2022/utils.py +++ b/scripts/fintoc2022/utils.py @@ -21,7 +21,7 @@ def create_json_result(data: pd.DataFrame, predictions: List[int]) -> dict: uid2line = { item[1].uid: { "text": item[1].line.strip() if isinstance(item[1].line, str) else "", - "page": item[1].page_id + 1, + "page": int(item[1].page_id + 1), "group": item[1].group } for item in data.iterrows() } @@ -29,7 +29,7 @@ def create_json_result(data: pd.DataFrame, predictions: List[int]) -> dict: assert data.shape[0] == len(predictions) for i, (line_uid, prediction) in enumerate(zip(data.uid, predictions)): line = uid2line[line_uid] - if line["text"] == "": + if line["text"] == "" or prediction == -1: continue # TODO crop text lines containing colon result[line["group"]].append({"id": i, "depth": str(prediction), "text": line["text"], "page": line["page"]}) From 4e3822f8008bb042ffb61929745273f4ffd90a6e Mon Sep 17 00:00:00 2001 From: Nasty Date: Wed, 24 Apr 2024 17:50:57 +0300 Subject: [PATCH 4/8] Add classifiers downloading, small fixes --- dedoc/api/api_args.py | 2 +- dedoc/api/web/index.html | 4 ++- dedoc/download_models.py | 11 ++++++- .../fintoc_structure_extractor.py | 30 ++++++++++++++----- .../fintoc_classifier.py | 13 +++++++- scripts/fintoc2022/metric.py | 4 +-- scripts/fintoc2022/trainer.py | 2 +- 7 files changed, 52 insertions(+), 14 deletions(-) diff --git a/dedoc/api/api_args.py b/dedoc/api/api_args.py index f85da673..f139733f 100644 --- a/dedoc/api/api_args.py +++ b/dedoc/api/api_args.py @@ -29,7 +29,7 @@ class QueryParameters: # pdf handling pdf_with_text_layer: str = Form("auto_tabby", enum=["true", "false", "auto", "auto_tabby", "tabby"], description="Extract text from a text layer of PDF or using OCR methods for image-like documents") - language: str = Form("rus+eng", enum=["rus+eng", "rus", "eng"], description="Recognition language") + language: str = Form("rus+eng", enum=["rus+eng", "rus", "eng", "fra", "spa"], description="Recognition language") pages: str = Form(":", description='Page numbers range for reading PDF or images, "left:right" means read pages from left to right') is_one_column_document: str = Form("auto", enum=["auto", "true", "false"], description='One or multiple column document, "auto" - predict number of page columns automatically') diff --git a/dedoc/api/web/index.html b/dedoc/api/web/index.html index e8b15f68..055ef58b 100644 --- a/dedoc/api/web/index.html +++ b/dedoc/api/web/index.html @@ -38,7 +38,7 @@

Type of document structure parsing

- + document_type

@@ -138,6 +138,8 @@

PDF handling

+ + language

diff --git a/dedoc/download_models.py b/dedoc/download_models.py index 643cf30e..04bce97f 100644 --- a/dedoc/download_models.py +++ b/dedoc/download_models.py @@ -15,7 +15,8 @@ scan_orientation_efficient_net_b0="9ea283f3d346ae4fdd82463a9f60b5369a3ffb58", font_classifier="db4481ad60ab050cbb42079b64f97f9e431feb07", paragraph_classifier="00bf989876cec171c1cf9859a6b712af6445e864", - line_type_classifiers="2e498d1ec82b72c1a96ba0d25344b71402997013" + line_type_classifiers="2e498d1ec82b72c1a96ba0d25344b71402997013", + fintoc_classifiers="ec08837809f8e8da6010777b07272af705df9e1b" ) @@ -42,6 +43,14 @@ def download(resources_path: str) -> None: repo_name="line_type_classifiers", hub_name=f"{classifier_type}.pkl.gz") + fintoc_classifiers_resources_path = os.path.join(resources_path, "fintoc_classifiers") + for language in ("en",): # TODO ("en", "fr", "sp"): + for classifier_type in ("target", "binary"): + download_from_hub(out_dir=fintoc_classifiers_resources_path, + out_name=f"{classifier_type}_classifier_{language}.pkg.gz", + repo_name="fintoc_classifiers", + hub_name=f"{classifier_type}_classifier_{language}_txt_layer.pkg.gz") + if __name__ == "__main__": resources_path = get_config()["resources_path"] diff --git a/dedoc/structure_extractors/concrete_structure_extractors/fintoc_structure_extractor.py b/dedoc/structure_extractors/concrete_structure_extractors/fintoc_structure_extractor.py index e0d0c30e..591f8cc3 100644 --- a/dedoc/structure_extractors/concrete_structure_extractors/fintoc_structure_extractor.py +++ b/dedoc/structure_extractors/concrete_structure_extractors/fintoc_structure_extractor.py @@ -6,6 +6,7 @@ import pandas as pd from PyPDF2 import PdfFileReader, PdfFileWriter +from dedoc.config import get_config from dedoc.data_structures import HierarchyLevel, LineWithMeta, UnstructuredDocument from dedoc.structure_extractors import AbstractStructureExtractor from dedoc.structure_extractors.feature_extractors.fintoc_feature_extractor import FintocFeatureExtractor @@ -31,7 +32,8 @@ def __init__(self, *, config: Optional[dict] = None) -> None: self.toc_extractor = TOCFeatureExtractor() self.features_extractor = FintocFeatureExtractor() self.languages = ("en", "fr", "sp") - self.classifiers = {language: FintocClassifier(language=language) for language in self.languages} + path = os.path.join(get_config()["resources_path"], "fintoc_classifiers") + self.classifiers = {language: FintocClassifier(language=language, weights_dir_path=path) for language in self.languages} self.toc_item_regexp = re.compile(r'"([^"]+)" (\d+)') self.empty_string_regexp = re.compile(r"^\s*\n$") @@ -48,16 +50,14 @@ def extract(self, document: UnstructuredDocument, parameters: Optional[dict] = N :param parameters: for this structure extractor, "language" parameter is used for setting document's language, e.g. ``parameters={"language": "en"}``. \ The following options are supported: - * "en" - English (default); - * "fr" - French; - * "sp" - Spanish. + * "en", "eng" - English (default); + * "fr", "fra" - French; + * "sp", "spa" - Spanish. :param file_path: path to the file on disk. :return: document content with added additional information about title/non-title lines and hierarchy levels of titles. """ parameters = {} if parameters is None else parameters - language = parameters.get("language", "en") - if language not in self.languages: - raise ValueError(f"Language {language} is not supported by this extractor. Supported languages: {self.languages}") + language = self.__get_param_language(parameters=parameters) features, documents = self.get_features(documents_dict={file_path: document.lines}) predictions = self.classifiers[language].predict(features) @@ -73,6 +73,22 @@ def extract(self, document: UnstructuredDocument, parameters: Optional[dict] = N return document + def __get_param_language(self, parameters: dict) -> str: + language = parameters.get("language", "en") + + if language in ("en", "eng", "rus+eng"): + return "en" + + if language in ("fr", "fra"): + return "fr" + + if language in ("sp", "spa"): + return "sp" + + if language not in self.languages: + self.logger.warning(f"Language {language} is not supported by this extractor. Use default language (en)") + return "en" + def get_features(self, documents_dict: Dict[str, List[LineWithMeta]]) -> Tuple[pd.DataFrame, List[List[LineWithMeta]]]: toc_lines, documents = [], [] for file_path, document_lines in documents_dict.items(): diff --git a/dedoc/structure_extractors/line_type_classifiers/fintoc_classifier.py b/dedoc/structure_extractors/line_type_classifiers/fintoc_classifier.py index cb5f0d5b..79a7ed72 100755 --- a/dedoc/structure_extractors/line_type_classifiers/fintoc_classifier.py +++ b/dedoc/structure_extractors/line_type_classifiers/fintoc_classifier.py @@ -9,6 +9,8 @@ import xgbfir from xgboost import XGBClassifier +from dedoc.download_models import download_from_hub + class FintocClassifier: """ @@ -73,8 +75,17 @@ def target_classifier(self) -> XGBClassifier: return self.__lazy_load_weights("target") def __lazy_load_weights(self, classifier_type: str) -> XGBClassifier: + assert self.weights_dir_path is not None if self.classifiers[classifier_type] is None: - with gzip.open(os.path.join(self.weights_dir_path, f"{classifier_type}_classifier_{self.language}.pkg.gz"), "rb") as input_file: + file_name = f"{classifier_type}_classifier_{self.language}.pkg.gz" + classifier_path = os.path.join(self.weights_dir_path, file_name) + if not os.path.isfile(classifier_path): + download_from_hub(out_dir=self.weights_dir_path, + out_name=file_name, + repo_name="fintoc_classifiers", + hub_name=f"{classifier_type}_classifier_{self.language}_txt_layer.pkg.gz") + + with gzip.open(classifier_path, "rb") as input_file: self.classifiers[classifier_type] = pickle.load(file=input_file) return self.classifiers[classifier_type] diff --git a/scripts/fintoc2022/metric.py b/scripts/fintoc2022/metric.py index 3ab4da15..ad6695b7 100755 --- a/scripts/fintoc2022/metric.py +++ b/scripts/fintoc2022/metric.py @@ -42,7 +42,7 @@ import numpy as np JSON_EXTENSION = ".fintoc4.json" -VERBOSE = False +VERBOSE = True STRING_THRESHOLD = 0.85 @@ -593,7 +593,7 @@ def get_std_row(td_prec, td_reca, td_f1): ] -def get_logger(name, path_to_log, level=logging.INFO): +def get_logger(name, path_to_log, level=logging.ERROR): handler = logging.FileHandler(path_to_log, mode="w") formatter = logging.Formatter("%(message)s") handler.setFormatter(formatter) diff --git a/scripts/fintoc2022/trainer.py b/scripts/fintoc2022/trainer.py index 0b0b311b..88339138 100644 --- a/scripts/fintoc2022/trainer.py +++ b/scripts/fintoc2022/trainer.py @@ -164,7 +164,7 @@ def __save(self, features_names: list[str], scores: Optional[dict]) -> None: os.makedirs(self.scores_dir_path, exist_ok=True) scores_path = os.path.join(self.scores_dir_path, f"scores_{self.language}_{self.reader_name}.json") with open(scores_path, "w") as f: - json.dump(scores, f) + json.dump(scores, f, indent=2) self.logger.info(f"Scores were saved in {scores_path}") self.classifier.save( From 751e0b572145202ca3d471b5f6880df8df8cee08 Mon Sep 17 00:00:00 2001 From: Nasty Date: Thu, 25 Apr 2024 12:58:53 +0300 Subject: [PATCH 5/8] FinTOC benchmarks added and all classifiers uploaded to huggingface --- dedoc/download_models.py | 4 +- .../fintoc_classifier.py | 2 +- resources/benchmarks/fintoc_scores.html | 83 +++++++++++++++++++ scripts/fintoc2022/benchmark_fintoc.py | 43 ++++++++++ 4 files changed, 129 insertions(+), 3 deletions(-) create mode 100644 resources/benchmarks/fintoc_scores.html create mode 100644 scripts/fintoc2022/benchmark_fintoc.py diff --git a/dedoc/download_models.py b/dedoc/download_models.py index 04bce97f..b520a7df 100644 --- a/dedoc/download_models.py +++ b/dedoc/download_models.py @@ -16,7 +16,7 @@ font_classifier="db4481ad60ab050cbb42079b64f97f9e431feb07", paragraph_classifier="00bf989876cec171c1cf9859a6b712af6445e864", line_type_classifiers="2e498d1ec82b72c1a96ba0d25344b71402997013", - fintoc_classifiers="ec08837809f8e8da6010777b07272af705df9e1b" + fintoc_classifiers="42f8ada99a5da608139b078c93bebfffc5b30263" ) @@ -44,7 +44,7 @@ def download(resources_path: str) -> None: hub_name=f"{classifier_type}.pkl.gz") fintoc_classifiers_resources_path = os.path.join(resources_path, "fintoc_classifiers") - for language in ("en",): # TODO ("en", "fr", "sp"): + for language in ("en", "fr", "sp"): for classifier_type in ("target", "binary"): download_from_hub(out_dir=fintoc_classifiers_resources_path, out_name=f"{classifier_type}_classifier_{language}.pkg.gz", diff --git a/dedoc/structure_extractors/line_type_classifiers/fintoc_classifier.py b/dedoc/structure_extractors/line_type_classifiers/fintoc_classifier.py index 79a7ed72..15516c75 100755 --- a/dedoc/structure_extractors/line_type_classifiers/fintoc_classifier.py +++ b/dedoc/structure_extractors/line_type_classifiers/fintoc_classifier.py @@ -75,8 +75,8 @@ def target_classifier(self) -> XGBClassifier: return self.__lazy_load_weights("target") def __lazy_load_weights(self, classifier_type: str) -> XGBClassifier: - assert self.weights_dir_path is not None if self.classifiers[classifier_type] is None: + assert self.weights_dir_path is not None file_name = f"{classifier_type}_classifier_{self.language}.pkg.gz" classifier_path = os.path.join(self.weights_dir_path, file_name) if not os.path.isfile(classifier_path): diff --git a/resources/benchmarks/fintoc_scores.html b/resources/benchmarks/fintoc_scores.html new file mode 100644 index 00000000..50624b7d --- /dev/null +++ b/resources/benchmarks/fintoc_scores.html @@ -0,0 +1,83 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
TD 0TD 1TD 2TD meanTOC 0TOC 1TOC 2TOC mean
en_tabby0.8115220.8337980.8642390.83652056.558.064.959.800000
en_txt_layer0.8213600.8532580.8336230.83608157.862.157.859.233333
fr_tabby0.7534090.7442320.7821690.75993751.247.951.550.200000
fr_txt_layer0.7405300.7944600.7660590.76701645.652.250.149.300000
sp_tabby0.6067180.6228390.5990940.60955037.143.643.441.366667
sp_txt_layer0.6290520.6679760.4468270.58128546.448.830.741.966667
\ No newline at end of file diff --git a/scripts/fintoc2022/benchmark_fintoc.py b/scripts/fintoc2022/benchmark_fintoc.py new file mode 100644 index 00000000..62040cb5 --- /dev/null +++ b/scripts/fintoc2022/benchmark_fintoc.py @@ -0,0 +1,43 @@ +""" +Merge results for all classifiers after running training script `train_fintoc_classifier.py`. +Results are represented in a table (.html file) in benchmarks directory. + +Results are obtained from cross-validation on the training set from FinTOC 2022 Shared task (https://wp.lancs.ac.uk/cfie/fintoc2022/). +Three languages are supported: English, French and Spanish ("en", "fr", "sp"). +Two readers are used: `PdfTabbyReader` and `PdfTxtlayerReader` ("tabby", "txt_layer"). +""" +import json +import os +from collections import defaultdict + +import pandas as pd + +if __name__ == "__main__": + scores_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), "..", "..", "resources", "fintoc2022", "scores")) + benchmarks_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), "..", "..", "resources", "benchmarks")) + + assert os.path.isdir(scores_dir), "Directory with scores doesn't exist, run `train_fintoc_classifier.py` beforehand" + + all_scores_dict = defaultdict(list) + names = [] + for scores_file in sorted(os.listdir(scores_dir)): + if not scores_file.endswith(".json"): + continue + + # files are named like: scores_en_tabby.json + with open(os.path.join(scores_dir, scores_file), "r") as f: + scores_dict = json.load(f) + + names.append(scores_file[len("scores_"):-len(".json")]) + + for i, td_score in enumerate(scores_dict["td_scores"]): + all_scores_dict[f"TD {i}"].append(td_score) + all_scores_dict["TD mean"].append(scores_dict["td_mean"]) + + for i, toc_score in enumerate(scores_dict["toc_scores"]): + all_scores_dict[f"TOC {i}"].append(toc_score) + all_scores_dict["TOC mean"].append(scores_dict["toc_mean"]) + + scores_df = pd.DataFrame(all_scores_dict, index=names) + with open(os.path.join(benchmarks_dir, "fintoc_scores.html"), "w") as f: + f.write(scores_df.to_html()) From c693de1188876d5ff56821b26a4f51e362cf6658 Mon Sep 17 00:00:00 2001 From: Nasty Date: Thu, 25 Apr 2024 15:53:28 +0300 Subject: [PATCH 6/8] Added structure type documentation and tests --- .../_static/structure_examples/fintoc.pdf | Bin 0 -> 252920 bytes docs/source/structure_types/fintoc.rst | 137 ++++++++++++++++++ tests/api_tests/test_api_doctype_fintoc.py | 64 ++++++++ .../pdf_with_text_layer/prospectus_fr.pdf | Bin 0 -> 301355 bytes .../pdf_with_text_layer/prospectus_sp.pdf | Bin 0 -> 1186754 bytes 5 files changed, 201 insertions(+) create mode 100644 docs/source/_static/structure_examples/fintoc.pdf create mode 100644 tests/api_tests/test_api_doctype_fintoc.py create mode 100644 tests/data/pdf_with_text_layer/prospectus_fr.pdf create mode 100644 tests/data/pdf_with_text_layer/prospectus_sp.pdf diff --git a/docs/source/_static/structure_examples/fintoc.pdf 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zE~^O?H`8EuLM(eG4*!>ia1P6T{YyikJ`FQSfrbzSE#M_dt`e-+4mLaa{UIb2YxwQp z#1PD9MP8_q!_-owv2fZ!Kx4R?5w9I1&>6qqFgAGZ64A=+~%3!&S zN0FCAx#WOH!?{xiJQ|!h63{5YTtMOcmOuuD2*&}XT7geeI4jVFB89$7(O>9~74}CX zLf@rfgPT7Nn&7HxisPl>{EUFcV1jlGB9zOZLb*_w<@E;>hURLoigIQ37ak3U# zToO*&^5zjrft~sNQBdLLj}404{Pzo!2w7`c5a4bQ8@RIoRh#)Y2oCqnMAhrM+Dbx?lC$KXdxfa-fVEI^#5hP45{&-_VbP^|EMP^|EMK&UL}4@xb9@q*h6 z+hM}@K|>J7*H1VvxzlUH_d!DhBjAOjC<2-wAB6r$;roDGL)ag~uN43`_. +You can see the :download:`example <../_static/structure_examples/fintoc.pdf>` of the document of this structure type. + +According to the FinTOC 2022 Shared task, there were two subtasks to be solved: + + * **Title detection (TD)** -- selection from all lines of the document only those that should be included in the table of contents. + * **Table of contents (TOC) generation** -- identification nesting depths of selected titles. + +Based on these tasks, we propose the FinTOC structure type with **header** and **raw_text** node types. +The detailed description of each node type: + + * **header** -- title nodes (from the title detection task). Titles can be nested, so their depth is determined according to the + TOC generation task. **header** nodes can have other **header** nodes or **raw_text** nodes as children nodes. + * **raw_text** -- non-title nodes. Unlike forming the result of TOC generation task, + we add non-title lines in the result document tree. **raw_text** node refers to a simple document line. + It has the least importance in the document tree hierarchy, so it is situated in the leaves of the tree. + It is nested to the node corresponding the previous line with more important type. + + +The documents for the FinTOC 2022 Shared task are PDF files with a textual layer, +so it is recommended to use :class:`~dedoc.readers.PdfTxtlayerReader` or :class:`~dedoc.readers.PdfTabbyReader` for their parsing +(``pdf_with_text_layer="true"`` or ``pdf_with_text_layer="tabby"`` in the :ref:`API parameters `). + +.. note:: + + During structure extraction step, we use classifiers trained on data extracted by :class:`~dedoc.readers.PdfTxtlayerReader` -- + usage of :class:`~dedoc.readers.PdfTxtlayerReader` or ``pdf_with_text_layer="true"`` is more preferable. + +The training dataset contains English, French, and Spanish documents, so three language options are available ("en", "fr", "sp"). +It is possible to set document's language using ``language`` option in parameters (e.g., ``parameters={"language": "en"}``). + +To obtain FinTOC structure, we use our method described in `our article `_ +(winners of FinTOC 2022 Shared task!). +The results of our method for different languages and readers are given in the :ref:`table below ` +(they slightly changed since the competition finished). +The name of each experiment consists of the reader type ("tabby" -- :class:`~dedoc.readers.PdfTabbyReader`, +"txt_layer" -- :class:`~dedoc.readers.PdfTxtlayerReader`) +and the document's language ("en" -- English, "fr" -- French, "sp" -- Spanish). +As in the FinTOC 2022 Shared task, we use two metrics for results evaluation (metrics from the `article `_): +**TD** -- F1 measure for the title detection task, **TOC** -- harmonic mean of Inex F1 score and Inex level accuracy for the TOC generation task. + +.. _fintoc_results_table: + +.. list-table:: The results from 3-fold cross-validation on the FinTOC 2022 training dataset + :widths: 20 10 10 10 10 10 10 10 10 + :header-rows: 1 + + * - Name + - TD 0 + - TD 1 + - TD 2 + - TD mean + - TOC 0 + - TOC 1 + - TOC 2 + - TOC mean + * - **en_tabby** + - 0.811 + - 0.833 + - 0.864 + - **0.836** + - 56.5 + - 58.0 + - 64.9 + - **59.8** + * - **en_txt_layer** + - 0.821 + - 0.853 + - 0.833 + - **0.836** + - 57.8 + - 62.1 + - 57.8 + - **59.2** + * - **fr_tabby** + - 0.753 + - 0.744 + - 0.782 + - **0.759** + - 51.2 + - 47.9 + - 51.5 + - **50.2** + * - **fr_txt_layer** + - 0.740 + - 0.794 + - 0.766 + - **0.767** + - 45.6 + - 52.2 + - 50.1 + - **49.3** + * - **sp_tabby** + - 0.606 + - 0.622 + - 0.599 + - **0.609** + - 37.1 + - 43.6 + - 43.4 + - **41.3** + * - **sp_txt_layer** + - 0.629 + - 0.667 + - 0.446 + - **0.581** + - 46.4 + - 48.8 + - 30.7 + - **41.9** + +.. seealso:: + + Please see our article `ISPRAS@FinTOC-2022 shared task: Two-stage TOC generation model `_ + to get more information about the FinTOC 2022 Shared task and our method of solving it. + We will be grateful, if you cite our work (see citation in BibTeX format below). + +.. code-block:: RST + + @inproceedings{bogatenkova-etal-2022-ispras, + title = "{ISPRAS}@{F}in{TOC}-2022 Shared Task: Two-stage {TOC} Generation Model", + author = "Bogatenkova, Anastasiia and + Belyaeva, Oksana Vladimirovna and + Perminov, Andrew Igorevich and + Kozlov, Ilya Sergeevich", + editor = "El-Haj, Mahmoud and + Rayson, Paul and + Zmandar, Nadhem", + booktitle = "Proceedings of the 4th Financial Narrative Processing Workshop @LREC2022", + month = jun, + year = "2022", + address = "Marseille, France", + publisher = "European Language Resources Association", + url = "https://aclanthology.org/2022.fnp-1.13", + pages = "89--94" + } diff --git a/tests/api_tests/test_api_doctype_fintoc.py b/tests/api_tests/test_api_doctype_fintoc.py new file mode 100644 index 00000000..a238d476 --- /dev/null +++ b/tests/api_tests/test_api_doctype_fintoc.py @@ -0,0 +1,64 @@ +from api_tests.abstract_api_test import AbstractTestApiDocReader + + +class TestApiFintoc(AbstractTestApiDocReader): + + def test_article_en(self) -> None: + file_name = "pdf_with_text_layer/prospectus.pdf" + result = self._send_request(file_name, dict(document_type="fintoc", pdf_with_text_layer="true")) + + tree = result["content"]["structure"] + self._check_tree_sanity(tree) + + # headers + node = self._get_by_tree_path(tree, "0.1") + self.assertEqual("header", node["metadata"]["paragraph_type"]) + self.assertEqual("Prospectus", node["text"].strip()) + node = self._get_by_tree_path(tree, "0.1.2") + self.assertEqual("header", node["metadata"]["paragraph_type"]) + self.assertEqual("TABLE OF CONTENTS", node["text"].strip()) + + # raw text + node = self._get_by_tree_path(tree, "0.1.2.0") + self.assertEqual("raw_text", node["metadata"]["paragraph_type"]) + self.assertTrue(node["text"].startswith("PART I - GENERAL INFORMATION")) + + def test_article_fr(self) -> None: + file_name = "pdf_with_text_layer/prospectus_fr.pdf" + result = self._send_request(file_name, dict(document_type="fintoc", pdf_with_text_layer="true", language="fr")) + + tree = result["content"]["structure"] + self._check_tree_sanity(tree) + + # headers + node = self._get_by_tree_path(tree, "0.0") + self.assertEqual("header", node["metadata"]["paragraph_type"]) + self.assertEqual("INFORMATIONS CLES POUR L’INVESTISSEUR", node["text"].strip()) + node = self._get_by_tree_path(tree, "0.1") + self.assertEqual("header", node["metadata"]["paragraph_type"]) + self.assertEqual("Prospectus", node["text"].strip()) + + # raw text + node = self._get_by_tree_path(tree, "0.1.0") + self.assertEqual("raw_text", node["metadata"]["paragraph_type"]) + self.assertEqual("OPCVM relevant de la directive européenne 2009/65/CE", node["text"].strip()) + + def test_article_sp(self) -> None: + file_name = "pdf_with_text_layer/prospectus_sp.pdf" + result = self._send_request(file_name, dict(document_type="fintoc", pdf_with_text_layer="true", language="sp")) + + tree = result["content"]["structure"] + self._check_tree_sanity(tree) + + # headers + node = self._get_by_tree_path(tree, "0.0") + self.assertEqual("header", node["metadata"]["paragraph_type"]) + self.assertEqual("INFORME ANUAL", node["text"].strip()) + node = self._get_by_tree_path(tree, "0.0.1") + self.assertEqual("header", node["metadata"]["paragraph_type"]) + self.assertEqual("ÍNDICE.", node["text"].strip()) + + # raw text + node = self._get_by_tree_path(tree, "0.0.0") + self.assertEqual("raw_text", node["metadata"]["paragraph_type"]) + self.assertTrue(node["text"].startswith("2015")) diff --git a/tests/data/pdf_with_text_layer/prospectus_fr.pdf b/tests/data/pdf_with_text_layer/prospectus_fr.pdf new file mode 100644 index 0000000000000000000000000000000000000000..a05c517c6e749e08d1128ffecd2e86e3db9c684a GIT binary patch literal 301355 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z!x@}npcq1&VT2IQLI8^px}H!-2}Q6Ufny>l25=(b{sJ3ALj4djB#A?H2pN$`2>B5N z$Oei5Y%~hx0-7ddpgAB&6eXcL08}8upRpo`qHsb4V+E2y;hZUD?W8EsBN!K;bg*`Ur6|k~Fa=;^glYq-rj#>azEUqm z${I_F7^OXhVsKIkdxk_Q7(YNQSUYK;#jvi?C_xbr-)NM^D5wq^WiSkiL1P#N^Nq%E zC2gXCmcaW0u!$IG#%WAMk&u?qI7U#g9|$3WdPL(C0rLphXbkE(Xd7V7&po1r1cfWv zNR)x=1^X7P_n>Pi4r3(*41wm41~Se-yaYA^<}a|3u(s1e5eAxI2RJ9VGX1I z%lQS^Xbi?4*kC;Xej=rLCJ9ujUZ9BZ9!i5vN(j%@_f1MEX9f%wgeirhO7lmdK%iki z92_^5Y$6QqIYltaF#z9`{J^48cQRC=J&gC7W}^gyKtDn0nY z2G#lC|EnIzq~1ZnV(Ir=xt9h-iND{+Y(CG<-GU_TFgwD=9 Date: Sat, 27 Apr 2024 15:12:26 +0300 Subject: [PATCH 7/8] Review fixes --- .../fintoc_structure_extractor.py | 15 ++++- .../fintoc_feature_extractor.py | 9 ++- .../paired_feature_extractor.py | 16 +++++ .../fintoc_classifier.py | 8 ++- docs/source/dedoc_api_usage/api.rst | 8 ++- docs/source/parameters/pdf_handling.rst | 7 ++- docs/source/parameters/structure_type.rst | 2 +- scripts/fintoc2022/dataset_loader.py | 57 +++++++++--------- scripts/fintoc2022/trainer.py | 10 +-- scripts/fintoc2022/utils.py | 4 +- tests/api_tests/test_api_doctype_fintoc.py | 18 +++--- tests/data/fintoc/prospectus_en.pdf | Bin 0 -> 334113 bytes .../prospectus_fr.pdf | Bin .../prospectus_sp.pdf | Bin 14 files changed, 92 insertions(+), 62 deletions(-) create mode 100644 tests/data/fintoc/prospectus_en.pdf rename tests/data/{pdf_with_text_layer => fintoc}/prospectus_fr.pdf (100%) rename tests/data/{pdf_with_text_layer => fintoc}/prospectus_sp.pdf (100%) diff --git a/dedoc/structure_extractors/concrete_structure_extractors/fintoc_structure_extractor.py b/dedoc/structure_extractors/concrete_structure_extractors/fintoc_structure_extractor.py index 591f8cc3..5cf46cc3 100644 --- a/dedoc/structure_extractors/concrete_structure_extractors/fintoc_structure_extractor.py +++ b/dedoc/structure_extractors/concrete_structure_extractors/fintoc_structure_extractor.py @@ -119,6 +119,17 @@ def __get_toc(self, file_path: Optional[str]) -> List[Dict[str, Union[LineWithMe self.logger.info(f"Got automatic TOC from {os.path.basename(file_path)}") return toc + lines = self.__read_one_column_lines(file_path) + return self.toc_extractor.get_toc(lines) + + def __read_one_column_lines(self, file_path: str) -> List[LineWithMeta]: + """ + TOC is one-columned even in two-columned documents (as a rule), so we handle TOC lines extraction separately: + 1. save first 10 pages of the document to a temporary directory; + 2. read lines from these pages in one-column mode without headers and footers. + + Later these lines will be analysed for TOC lines extraction. + """ pdf_reader = PdfFileReader(file_path) writer = PdfFileWriter() @@ -130,14 +141,12 @@ def __get_toc(self, file_path: Optional[str]) -> List[Dict[str, Union[LineWithMe writer.write(write_file) lines = self.pdf_reader.read(file_path=tmp_path, parameters={"is_one_column_document": "True", "need_header_footer_analysis": "True"}).lines - return self.toc_extractor.get_toc(lines) + return lines def __get_automatic_toc(self, path: str) -> List[Dict[str, Union[LineWithMeta, str]]]: result = [] with os.popen(f'pdftocio -p "{path}"') as out: toc = out.readlines() - if len(toc) == 0: - return result for line in toc: match = self.toc_item_regexp.match(line.strip()) diff --git a/dedoc/structure_extractors/feature_extractors/fintoc_feature_extractor.py b/dedoc/structure_extractors/feature_extractors/fintoc_feature_extractor.py index 79ca919e..82e53111 100644 --- a/dedoc/structure_extractors/feature_extractors/fintoc_feature_extractor.py +++ b/dedoc/structure_extractors/feature_extractors/fintoc_feature_extractor.py @@ -85,14 +85,13 @@ def __look_at_prev_line(self, document: List[LineWithMeta], n: int = 1) -> Dict[ for line_id, _ in enumerate(document): if line_id >= n: prev_line = document[line_id - n] - is_prev_line_ends = prev_line.line.endswith((".", ";")) - res["prev_line_ends"].append(1 if is_prev_line_ends else 0) + res["prev_line_ends"].append(prev_line.line.endswith((".", ";"))) res["prev_ends_with_colon"].append(prev_line.line.endswith(":")) res["prev_is_space"].append(prev_line.line.lower().isspace()) else: - res["prev_line_ends"].append(0) - res["prev_ends_with_colon"].append(0) - res["prev_is_space"].append(0) + res["prev_line_ends"].append(False) + res["prev_ends_with_colon"].append(False) + res["prev_is_space"].append(False) return res def __get_line_relative_length(self, lines: List[LineWithMeta]) -> List[float]: diff --git a/dedoc/structure_extractors/feature_extractors/paired_feature_extractor.py b/dedoc/structure_extractors/feature_extractors/paired_feature_extractor.py index cafb4c10..130a5560 100755 --- a/dedoc/structure_extractors/feature_extractors/paired_feature_extractor.py +++ b/dedoc/structure_extractors/feature_extractors/paired_feature_extractor.py @@ -13,6 +13,22 @@ class PairedFeatureExtractor(AbstractFeatureExtractor): + """ + This class is used as an auxiliary feature extractor to the main extractor. + It allows to add "raw" features related to the lines importance. + Based on one line property (size, indentation) it computes a raw line's depth inside the document tree. + + Example: + For lines + line1 (size=16) + line2 (size=14) + line3 (size=12) + line4 (size=12) + line5 (size=14) + line6 (size=12) + We will obtain a feature vector (raw_depth_size) + [0, 1, 2, 2, 1, 2] + """ def parameters(self) -> dict: return {} diff --git a/dedoc/structure_extractors/line_type_classifiers/fintoc_classifier.py b/dedoc/structure_extractors/line_type_classifiers/fintoc_classifier.py index 15516c75..9e00e819 100755 --- a/dedoc/structure_extractors/line_type_classifiers/fintoc_classifier.py +++ b/dedoc/structure_extractors/line_type_classifiers/fintoc_classifier.py @@ -2,7 +2,7 @@ import logging import os import pickle -from typing import List, Optional +from typing import Dict, List, Optional, Union import numpy as np import pandas as pd @@ -47,7 +47,11 @@ def predict(self, features: pd.DataFrame) -> List[int]: # return list [1, 2, 3, -1, -1, ...], where positive values mean headers depth, -1 mean non-header lines return list(result) - def fit(self, binary_classifier_parameters: dict, target_classifier_parameters: dict, features: pd.DataFrame, features_names: list[str]) -> None: + def fit(self, + binary_classifier_parameters: Dict[str, Union[int, float, str]], + target_classifier_parameters: Dict[str, Union[int, float, str]], + features: pd.DataFrame, + features_names: List[str]) -> None: self.classifiers["binary"] = XGBClassifier(**binary_classifier_parameters) self.classifiers["target"] = XGBClassifier(**target_classifier_parameters) self.binary_classifier.fit(features[features_names], features.label != -1) diff --git a/docs/source/dedoc_api_usage/api.rst b/docs/source/dedoc_api_usage/api.rst index 8db61804..ee68c29f 100644 --- a/docs/source/dedoc_api_usage/api.rst +++ b/docs/source/dedoc_api_usage/api.rst @@ -70,7 +70,7 @@ Api parameters description * - :cspan:`3` **Type of document structure parsing** * - document_type - - other, law, tz, diploma + - other, law, tz, diploma, article, fintoc - other - Type of the document structure according to specific domain. @@ -216,13 +216,15 @@ Api parameters description It is highly recommended to use this option value for any PDF document parsing. * - language - - rus, eng, rus+eng + - rus, eng, rus+eng, fra, spa - rus+eng - Language of the parsed PDF document without a textual layer. The following values are available: * **rus** -- Russian; * **eng** -- English; - * **rus+eng** -- both Russian and English. + * **rus+eng** -- both Russian and English; + * **fra** -- French (for fintoc structure type); + * **spa** -- Spanish (for fintoc structure type). * - pages - :, start:, :end, start:end diff --git a/docs/source/parameters/pdf_handling.rst b/docs/source/parameters/pdf_handling.rst index b3781b2d..60ad0096 100644 --- a/docs/source/parameters/pdf_handling.rst +++ b/docs/source/parameters/pdf_handling.rst @@ -49,16 +49,19 @@ PDF and images handling It is highly recommended to use this option value for any PDF document parsing. * - language - - rus, eng, rus+eng + - rus, eng, rus+eng, fra, spa - rus+eng - * :meth:`dedoc.DedocManager.parse` * :meth:`dedoc.readers.PdfAutoReader.read`, :meth:`dedoc.readers.PdfImageReader.read` * :meth:`dedoc.readers.ReaderComposition.read` + * :meth:`dedoc.structure_extractors.FintocStructureExtractor.extract` - Language of the document without a textual layer. The following values are available: * **rus** -- Russian; * **eng** -- English; - * **rus+eng** -- both Russian and English. + * **rus+eng** -- both Russian and English; + * **fra** -- French (for :class:`~dedoc.structure_extractors.FintocStructureExtractor`); + * **spa** -- Spanish (for :class:`~dedoc.structure_extractors.FintocStructureExtractor`). * - pages - :, start:, :end, start:end diff --git a/docs/source/parameters/structure_type.rst b/docs/source/parameters/structure_type.rst index dfd09803..09b592c2 100644 --- a/docs/source/parameters/structure_type.rst +++ b/docs/source/parameters/structure_type.rst @@ -15,7 +15,7 @@ Structure type configuring - Description * - document_type - - other, law, tz, diploma + - other, law, tz, diploma, article, fintoc - other - * :meth:`dedoc.DedocManager.parse` * :meth:`dedoc.structure_extractors.StructureExtractorComposition.extract` diff --git a/scripts/fintoc2022/dataset_loader.py b/scripts/fintoc2022/dataset_loader.py index f2beb730..9d9560e2 100755 --- a/scripts/fintoc2022/dataset_loader.py +++ b/scripts/fintoc2022/dataset_loader.py @@ -25,29 +25,29 @@ class FintocLineWithLabelExtractor: def __init__(self) -> None: self.readers = {"tabby": PdfTabbyReader(), "txt_layer": PdfTxtlayerReader()} - def get_lines(self, file_name: str, file_path: str, annotation_path: str, reader_name: str) -> List[LineWithLabel]: + def get_lines(self, file_name: str, file_path: str, gt_path: str, reader_name: str) -> List[LineWithLabel]: """ - Extract lines from PDF document, create labels for lines from annotation file given by FinTOC. - Annotations are matched to lines using Levenshtein distance (threshold=0.8). + Extract lines from PDF document, create labels for lines from the groundtruth file given by FinTOC. + Labeled lines are matched to the lines extracted by dedoc using Levenshtein distance (threshold=0.8). :param file_name: name of the file (PDF, json) :param file_path: path to the PDF document - :param annotation_path: path to the JSON file with annotations + :param gt_path: path to the groundtruth JSON file with labels :param reader_name: ("tabby", "txt_layer") - type of PDF reader used for lines extraction :return: document in form of list of lines with labels """ document = self.readers[reader_name].read(file_path, parameters={"need_header_footer_analysis": "True"}) - annotations = defaultdict(list) - with open(annotation_path) as annotations_file: - for annotation in json.load(annotations_file): - annotations[annotation["page"] - 1].append(annotation) + labeled_lines = defaultdict(list) + with open(gt_path) as gt_file: + for labeled_line in json.load(gt_file): + labeled_lines[labeled_line["page"] - 1].append(labeled_line) result = [] for line in document.lines: - annotations_page = [(ratio(line.line, annotation["text"]), annotation) for annotation in annotations[line.metadata.page_id]] - best_annotation = max(annotations_page, key=lambda t: t[0], default=(0, {})) - depth = best_annotation[1]["depth"] if len(annotations_page) > 0 and best_annotation[0] > 0.8 else "-1" + page_candidates = [(ratio(line.line, labeled_line["text"]), labeled_line) for labeled_line in labeled_lines[line.metadata.page_id]] + best_line = max(page_candidates, key=lambda t: t[0], default=(0, {})) + depth = best_line[1]["depth"] if len(page_candidates) > 0 and best_line[0] > 0.8 else "-1" result.append(LineWithLabel(line=line.line, metadata=line.metadata, annotations=line.annotations, label=str(depth), group=file_name, uid=line.uid)) return sorted(result, key=lambda x: (x.metadata.page_id, x.metadata.line_id)) @@ -89,21 +89,27 @@ def get_data(self, language: str, reader_name: str, use_cache: bool = True) -> D if os.path.isfile(pkl_path) and use_cache: with gzip.open(pkl_path) as input_file: - lines = pickle.load(input_file) - self.logger.info("Data were loaded from the local disk") - return lines + parsed_files = pickle.load(input_file) + self.logger.info(f"Data were loaded from the local disk: {len(parsed_files)} files") + return parsed_files + result = self.__read_pdf_lines(archive_path, language, reader_name) + + with gzip.open(pkl_path, "wb") as out: + pickle.dump(obj=result, file=out) + self.logger.info(Counter([line.label for document in result.values() for line in document])) + return result + + def __read_pdf_lines(self, archive_path: str, language: str, reader_name: str) -> Dict[str, List[LineWithLabel]]: with zipfile.ZipFile(archive_path, "r") as zip_ref: zip_ref.extractall(self.dataset_dir) + data_dir = os.path.join(self.dataset_dir, "data", language) pdf_dir = os.path.join(data_dir, "pdf") - annotations_dir = os.path.join(data_dir, "annots") + gt_dir = os.path.join(data_dir, "annots") pdf_files = {pdf_file[:-len(".pdf")]: os.path.join(pdf_dir, pdf_file) for pdf_file in os.listdir(pdf_dir) if pdf_file.endswith(".pdf")} - annotations_files = { - ann_file[:-len(".pdf.fintoc4.json")]: os.path.join(annotations_dir, ann_file) - for ann_file in os.listdir(annotations_dir) if ann_file.endswith(".json") - } - assert set(pdf_files) == set(annotations_files) + gt_files = {gt_file[:-len(".pdf.fintoc4.json")]: os.path.join(gt_dir, gt_file) for gt_file in os.listdir(gt_dir) if gt_file.endswith(".json")} + assert set(pdf_files) == set(gt_files) result = {} with tempfile.TemporaryDirectory() as tmp_dir: @@ -111,17 +117,8 @@ def get_data(self, language: str, reader_name: str, use_cache: bool = True) -> D pdf_tmp_path = os.path.join(tmp_dir, file_name) + ".pdf" shutil.copy(pdf_files[file_name], pdf_tmp_path) try: - document = self.line_extractor.get_lines( - file_name=file_name, - file_path=pdf_tmp_path, - annotation_path=annotations_files[file_name], - reader_name=reader_name - ) + document = self.line_extractor.get_lines(file_name=file_name, file_path=pdf_tmp_path, gt_path=gt_files[file_name], reader_name=reader_name) result[pdf_files[file_name]] = document except Exception as e: self.logger.warning(f"Failed to read {file_name} by {reader_name}, error: {e}") - - with gzip.open(pkl_path, "wb") as out: - pickle.dump(obj=result, file=out) - self.logger.info(Counter([line.label for document in result.values() for line in document])) return result diff --git a/scripts/fintoc2022/trainer.py b/scripts/fintoc2022/trainer.py index 88339138..aab3fb20 100644 --- a/scripts/fintoc2022/trainer.py +++ b/scripts/fintoc2022/trainer.py @@ -4,7 +4,7 @@ import os import shutil from statistics import mean -from typing import Optional +from typing import Dict, List, Optional, Union import pandas as pd from sklearn.model_selection import GroupKFold @@ -101,15 +101,15 @@ def fit(self, cross_val: bool = True, use_cache: bool = True) -> None: scores = self.__cross_validate(features=features, gt_dir=gt_dir) if cross_val else None # train resulting classifiers on all data - self.logger.info("Train resulting classifiers") + self.logger.info("Start training resulting classifiers on all data") self.classifier.fit(self.binary_classifier_parameters, self.target_classifier_parameters, features=features, features_names=features_names) self.__save(features_names=features_names, scores=scores) - def __get_features_names(self, features_df: pd.DataFrame) -> list: + def __get_features_names(self, features_df: pd.DataFrame) -> List[str]: features_names = [col for col in features_df.columns if col not in self.additional_features_fields] return features_names - def __cross_validate(self, features: pd.DataFrame, gt_dir: str) -> dict: + def __cross_validate(self, features: pd.DataFrame, gt_dir: str) -> Dict[str, Union[List[float], float]]: self.logger.info("Start cross-validation") features_names = self.__get_features_names(features) results_path = os.path.join(self.scores_dir_path, f"cross_val_results_{self.language}_{self.reader_name}") @@ -158,7 +158,7 @@ def __cross_validate(self, features: pd.DataFrame, gt_dir: str) -> dict: result_scores["toc_mean"] = mean(result_scores["toc_scores"]) return result_scores - def __save(self, features_names: list[str], scores: Optional[dict]) -> None: + def __save(self, features_names: List[str], scores: Optional[Dict[str, Union[List[float], float]]]) -> None: if scores is not None: os.makedirs(self.scores_dir_path, exist_ok=True) diff --git a/scripts/fintoc2022/utils.py b/scripts/fintoc2022/utils.py index 187f5158..b9354a7b 100755 --- a/scripts/fintoc2022/utils.py +++ b/scripts/fintoc2022/utils.py @@ -1,11 +1,11 @@ import os from collections import defaultdict -from typing import List, Tuple +from typing import Dict, List, Tuple, Union import pandas as pd -def create_json_result(data: pd.DataFrame, predictions: List[int]) -> dict: +def create_json_result(data: pd.DataFrame, predictions: List[int]) -> Dict[str, List[Dict[str, Union[str, int]]]]: """ Creates dictionary with TOCs for each document: {"doc_name": TOC}. TOC is a following list of dictionaries: diff --git a/tests/api_tests/test_api_doctype_fintoc.py b/tests/api_tests/test_api_doctype_fintoc.py index a238d476..7a70ca56 100644 --- a/tests/api_tests/test_api_doctype_fintoc.py +++ b/tests/api_tests/test_api_doctype_fintoc.py @@ -4,27 +4,27 @@ class TestApiFintoc(AbstractTestApiDocReader): def test_article_en(self) -> None: - file_name = "pdf_with_text_layer/prospectus.pdf" + file_name = "fintoc/prospectus_en.pdf" result = self._send_request(file_name, dict(document_type="fintoc", pdf_with_text_layer="true")) tree = result["content"]["structure"] self._check_tree_sanity(tree) # headers - node = self._get_by_tree_path(tree, "0.1") + node = self._get_by_tree_path(tree, "0.0") self.assertEqual("header", node["metadata"]["paragraph_type"]) - self.assertEqual("Prospectus", node["text"].strip()) - node = self._get_by_tree_path(tree, "0.1.2") + self.assertEqual("Key Information Document (KID)", node["text"].strip()) + node = self._get_by_tree_path(tree, "0.0.0") self.assertEqual("header", node["metadata"]["paragraph_type"]) - self.assertEqual("TABLE OF CONTENTS", node["text"].strip()) + self.assertEqual("PURPOSE", node["text"].strip()) # raw text - node = self._get_by_tree_path(tree, "0.1.2.0") + node = self._get_by_tree_path(tree, "0.0.0.0") self.assertEqual("raw_text", node["metadata"]["paragraph_type"]) - self.assertTrue(node["text"].startswith("PART I - GENERAL INFORMATION")) + self.assertTrue(node["text"].startswith("This document provides")) def test_article_fr(self) -> None: - file_name = "pdf_with_text_layer/prospectus_fr.pdf" + file_name = "fintoc/prospectus_fr.pdf" result = self._send_request(file_name, dict(document_type="fintoc", pdf_with_text_layer="true", language="fr")) tree = result["content"]["structure"] @@ -44,7 +44,7 @@ def test_article_fr(self) -> None: self.assertEqual("OPCVM relevant de la directive européenne 2009/65/CE", node["text"].strip()) def test_article_sp(self) -> None: - 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--- .../fintoc_structure_extractor.py | 26 ++----------------- 1 file changed, 2 insertions(+), 24 deletions(-) diff --git a/dedoc/structure_extractors/concrete_structure_extractors/fintoc_structure_extractor.py b/dedoc/structure_extractors/concrete_structure_extractors/fintoc_structure_extractor.py index 5cf46cc3..0d78c783 100644 --- a/dedoc/structure_extractors/concrete_structure_extractors/fintoc_structure_extractor.py +++ b/dedoc/structure_extractors/concrete_structure_extractors/fintoc_structure_extractor.py @@ -1,10 +1,8 @@ import os import re -import tempfile from typing import Dict, List, Optional, Tuple, Union import pandas as pd -from PyPDF2 import PdfFileReader, PdfFileWriter from dedoc.config import get_config from dedoc.data_structures import HierarchyLevel, LineWithMeta, UnstructuredDocument @@ -119,30 +117,10 @@ def __get_toc(self, file_path: Optional[str]) -> List[Dict[str, Union[LineWithMe self.logger.info(f"Got automatic TOC from {os.path.basename(file_path)}") return toc - lines = self.__read_one_column_lines(file_path) + parameters = {"is_one_column_document": "True", "need_header_footer_analysis": "True", "pages": ":10"} + lines = self.pdf_reader.read(file_path=file_path, parameters=parameters).lines return self.toc_extractor.get_toc(lines) - def __read_one_column_lines(self, file_path: str) -> List[LineWithMeta]: - """ - TOC is one-columned even in two-columned documents (as a rule), so we handle TOC lines extraction separately: - 1. save first 10 pages of the document to a temporary directory; - 2. read lines from these pages in one-column mode without headers and footers. - - Later these lines will be analysed for TOC lines extraction. - """ - pdf_reader = PdfFileReader(file_path) - writer = PdfFileWriter() - - with tempfile.TemporaryDirectory() as tmpdir: - tmp_path = os.path.join(tmpdir, os.path.basename(file_path)) - for page_id in range(0, min(9, pdf_reader.getNumPages())): - writer.addPage(pdf_reader.getPage(page_id)) - with open(tmp_path, "wb") as write_file: - writer.write(write_file) - lines = self.pdf_reader.read(file_path=tmp_path, parameters={"is_one_column_document": "True", "need_header_footer_analysis": "True"}).lines - - return lines - def __get_automatic_toc(self, path: str) -> List[Dict[str, Union[LineWithMeta, str]]]: result = [] with os.popen(f'pdftocio -p "{path}"') as out: