todataset.py

import os
import os.path as path
import unittest
import pandas as pd

from tokenizers             import Regex, NormalizedString
from tokenizers.normalizers import Sequence, NFKC, Replace, BertNormalizer, Strip

from datasets               import Dataset

class CustomNormalizer:
    def __init__(self):
        self.normalization = Sequence([
            NFKC(),
            Replace("[",    ""), # les annotations sont tellement inconsistantes pour les [] et le () que
            Replace("(",    ""), # que je pense qu'il vaut mieux tout virer et ls considérer tous pareil
            Replace("]",    ""),
            Replace(")",    ""),
            Replace("\r\n", ""),
            Replace(Regex(r"\s{2,}"), " "),
            BertNormalizer(clean_text=True, handle_chinese_chars=True, strip_accents=True, lowercase=True),
            Strip()
        ])

    def normalize(self, text: NormalizedString):
        return self.normalization.normalize(text)
    
    def normalize_str(self, sequence: str):
        return self.normalization.normalize_str(sequence)


class TestNormalizer(unittest.TestCase):
    def test_it_normalizes_in_place(self):
        norm     = CustomNormalizer()
        actual   = NormalizedString("coucou[[[xavier")
        norm.normalize(actual)
        expected = "coucouxavier"
        self.assertEqual(actual.normalized, expected)

    def test_it_removes_opening_bracket(self):
        norm     = CustomNormalizer()
        actual   = norm.normalize_str("coucou[[[xavier")
        expected = "coucouxavier"
        self.assertEqual(actual, expected)
    
    def test_it_removes_closing_bracket(self):
        norm     = CustomNormalizer()
        actual   = norm.normalize_str("coucou]]]xavier")
        expected = "coucouxavier"
        self.assertEqual(actual, expected)
    
    def test_it_removes_both_brackets(self):
        norm     = CustomNormalizer()
        actual   = norm.normalize_str("coucou[[[-]]]xavier")
        expected = "coucou-xavier"
        self.assertEqual(actual, expected)
    
    def test_it_lowertcases_text(self):
        norm     = CustomNormalizer()
        actual   = norm.normalize_str("CouCou Xavier")
        expected = "coucou xavier"
        self.assertEqual(actual, expected)

    def test_it_replaces_long_sequences_of_whitespaces(self):
        norm     = CustomNormalizer()
        actual   = norm.normalize_str("coucou    xavier")
        expected = "coucou xavier"
        self.assertEqual(actual, expected)
        #
        actual   = norm.normalize_str("coucou  xavier")
        expected = "coucou xavier"
        self.assertEqual(actual, expected)
        #
        actual   = norm.normalize_str("coucou \txavier")
        expected = "coucou xavier"
        self.assertEqual(actual, expected)
    
    def test_it_normalizes_funky_chars(self):
        norm     = CustomNormalizer()
        actual   = norm.normalize_str("coucou xavier")
        expected = "coucou xavier"
        self.assertEqual(actual, expected)
        #
        actual   = norm.normalize_str("coucou  xavier")
        expected = "coucou xavier"
        self.assertEqual(actual, expected)
        #
        actual   = norm.normalize_str("coucou \txavier")
        expected = "coucou xavier"
        self.assertEqual(actual, expected)


def file_to_pandas(project, fname):
    norm    = CustomNormalizer()
    file_id = path.basename(fname)
    page_id = 0
    line_id = 0
    df_dict = {
        "project": [],
        "file_id": [],
        "page_id": [],
        "line_id": [],
        "text"   : []
    }
    #
    with open(fname, encoding="utf8") as f:
        for line in f.readlines():
            line = NormalizedString(line)
            norm.normalize(line)
            line = line.normalized
            #
            if not line: # emptystring is seen as false
                pass
            elif line == "###":
                page_id += 1
            else:
                line_id += 1
                df_dict["project"].append(project)
                df_dict["file_id"].append(file_id)
                df_dict["page_id"].append(page_id)
                df_dict["line_id"].append(line_id)
                df_dict["text"]   .append(line)
    #
    return pd.DataFrame.from_dict(df_dict)

def text(df):
    return "\n".join(df["text"].to_list())

def documents_text(df: pd.DataFrame):
    '''
    This function returns a dataframe equivalent to the original 'df'. However, the returned dataframe
    loses the page and line information so as to retrieve the complete text of each documents pertaining
    to the dataset
    
    # Params
    - df is a pandas dataframe
    '''
    groups = df.groupby(["project", "file_id"])
    data   = [(project, file_id, text(groups.get_group((project, file_id)).sort_values(["page_id", "line_id"]))) for (project, file_id),_v in groups ]
    return pd.DataFrame.from_records(data, columns=["project", "file_id", "text"])

def pages_text(df: pd.DataFrame):
    '''
    This function returns a dataframe equivalent to the original 'df'. However, the returned dataframe
    loses the line information so as to retrieve the complete text of each page of processed data.s
    
    # Params
    - df is a pandas dataframe
    '''
    groups = df.groupby(["project", "file_id", "page_id"])
    data   = [(project, file_id, page_id, text(groups.get_group((project, file_id, page_id)).sort_values("line_id"))) for (project, file_id, page_id),_v in groups ]
    return pd.DataFrame.from_records(data, columns=["project", "file_id","page_id", "text"])

def prepare(df: pd.DataFrame, config_name: str):
    ds     = Dataset.from_pandas(df)
    splits = ds.train_test_split(train_size=0.8, shuffle=True)
    train  = splits["train"]
    test   = splits["test"]
    splits = train.train_test_split(train_size=0.8)
    train  = splits["train"]
    valid  = splits["test"]
    train.to_csv("data/{config}/train.csv".format(config=config_name))
    test .to_csv("data/{config}/test.csv".format(config=config_name))
    valid.to_csv("data/{config}/valid.csv".format(config=config_name))

if __name__ == '__main__':
    pth= r'C:\Users\xavier.gillard\Documents\ARKEY\data'
    prj= "pardons"
    df1 = pd.concat([file_to_pandas(prj, path.join(pth, f)) for f in os.listdir(path=pth)], axis=0, ignore_index=True)

    pth = r'C:\Users\xavier.gillard\Documents\REPO\vonnis'
    prj = "sentences"
    df2 = pd.concat([file_to_pandas(prj, path.join(pth, f)) for f in os.listdir(path=pth)], axis=0, ignore_index=True)

    df = pd.concat([df1, df2], axis=0, ignore_index=True)
    df.to_csv("data/full_corpus.csv", index=False)

    #df = pd.read_csv("data/full_corpus.csv")
    lines = df[["project","file_id","page_id","line_id","text"]]
    pages = pages_text(df)[["project","file_id","page_id","text"]]
    docs  = documents_text(df)[["project","file_id","text"]]
    
    prepare(lines, "line_by_line")
    prepare(pages, "page_by_page")
    prepare(docs,  "doc_by_doc")