File size: 3,719 Bytes
c8f0f88 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 | from datasets import Dataset, load_dataset, disable_progress_bars, concatenate_datasets
from tqdm import tqdm
from page_mix import *
import re
disable_progress_bars()
def update_processed_docs(ds,document,msg):
#save current problem, if
if ds["problem"] == "no":
old_p = ""
else:
old_p = ds["problem"]
if ds["filename"] == document:
ds.update({"problem":old_p + msg})
else:
pass
return ds
def empty_docs(documents):
ds=[]
for i in documents:
ds.append({"filename":i,"above_ref_line":0.0,"processed":False,"problem":"empty"})
ds = Dataset.from_list(ds)
return ds
def main():
quality_list = []
#assemble dataset from representative pages
meta_data = load_dataset("./repr_meta",split="train")
#get reference line to from "good quality" documents
#by taking the median of its alphabetic character ratio dist
ref_docs = Dataset.from_parquet("good_qual_10k_counts.parquet")
reference_line = np.median(ref_docs["text_rel_a_words"])
#separate years
year_list = sorted(list(set(meta_data["published"])))
for year in tqdm(year_list,desc="YEARS "):
#filter each year
year_meta_data = meta_data.filter(lambda ds,year:ds["published"] == year, num_proc= 10, fn_kwargs={"year":year})
document_list = sorted(list(set(year_meta_data["file_name"])))
for document in tqdm(document_list,
leave=False,
desc="DOCS "):
#compare page alphabethic ratios to ref line
doc_meta_data = year_meta_data.filter(lambda ds,document:ds["file_name"] == document, fn_kwargs={"document":document})
rp = compare_to_ref_line(doc_meta_data,
ref_val=reference_line,
column_to_compare="page_text_rel_a_words")
#assigning for inspection
quality_list.append({"filename":document,"above_ref_line":rp,"processed":True,"problem":"no"})
#create dataset
quality_ds = Dataset.from_list(quality_list)
######
#Error docs
with open("./repr_meta.log") as f:
lines = f.readlines()
re_pat= r"ERROR:.*pdf"
epe = [i for i in lines if "EmptyDocumentError" in i]
nme = [i for i in lines if "NoMixError" in i]
lpe = [i for i in lines if "LowProportionError" in i]
small_doc = [i for i in lines if "small_doc:True" in i]
#pair the errors with filenames
nme_fns = [re.findall(re_pat,i)[0].replace("ERROR: ", "") for i in nme]
epe_fns = [re.findall(re_pat,i)[0].replace("ERROR: ", "") for i in epe]
lpe_fns = [re.findall(re_pat,i)[0].replace("ERROR: ", "") for i in lpe]
small_doc_fns = [re.findall(re_pat,i)[0].replace("ERROR: ", "") for i in small_doc]
#append vals from logs
for i in tqdm(small_doc_fns,leave=False,desc="small_docs"):
quality_ds = quality_ds.map(update_processed_docs,fn_kwargs={"document":i,"msg":"small_doc;"})
for i in tqdm(nme_fns,leave=False,desc="no_mix"):
quality_ds = quality_ds.map(update_processed_docs,fn_kwargs={"document":i,"msg":"no_mix;"})
for i in tqdm(lpe_fns,leave=False,desc="low_prop"):
quality_ds = quality_ds.map(update_processed_docs,fn_kwargs={"document":i,"msg":"low_prop;"})
empty_document = empty_docs(epe_fns)
quality_ds = concatenate_datasets([quality_ds,empty_document])
#print(epe_fns)
#save results
quality_ds.to_json("../ocr_metadata.json")
if __name__ == "__main__":
main() |