| from datasets import Dataset, load_dataset, disable_progress_bars, concatenate_datasets
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|
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| from tqdm import tqdm
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|
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| from page_mix import *
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|
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| import re
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| disable_progress_bars()
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|
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| def update_processed_docs(ds,document,msg):
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|
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| if ds["problem"] == "no":
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| old_p = ""
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| else:
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| old_p = ds["problem"]
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|
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| if ds["filename"] == document:
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| ds.update({"problem":old_p + msg})
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| else:
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| pass
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| return ds
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|
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| def empty_docs(documents):
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| ds=[]
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| for i in documents:
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| ds.append({"filename":i,"above_ref_line":0.0,"processed":False,"problem":"empty"})
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| ds = Dataset.from_list(ds)
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|
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| return ds
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|
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|
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| def main():
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| quality_list = []
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| meta_data = load_dataset("./repr_meta",split="train")
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|
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| ref_docs = Dataset.from_parquet("good_qual_10k_counts.parquet")
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| reference_line = np.median(ref_docs["text_rel_a_words"])
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| year_list = sorted(list(set(meta_data["published"])))
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|
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| for year in tqdm(year_list,desc="YEARS "):
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| year_meta_data = meta_data.filter(lambda ds,year:ds["published"] == year, num_proc= 10, fn_kwargs={"year":year})
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|
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| document_list = sorted(list(set(year_meta_data["file_name"])))
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|
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| for document in tqdm(document_list,
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| leave=False,
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| desc="DOCS "):
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| doc_meta_data = year_meta_data.filter(lambda ds,document:ds["file_name"] == document, fn_kwargs={"document":document})
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|
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| rp = compare_to_ref_line(doc_meta_data,
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| ref_val=reference_line,
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| column_to_compare="page_text_rel_a_words")
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|
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| quality_list.append({"filename":document,"above_ref_line":rp,"processed":True,"problem":"no"})
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| quality_ds = Dataset.from_list(quality_list)
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|
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| with open("./repr_meta.log") as f:
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| lines = f.readlines()
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|
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| re_pat= r"ERROR:.*pdf"
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|
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| epe = [i for i in lines if "EmptyDocumentError" in i]
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| nme = [i for i in lines if "NoMixError" in i]
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| lpe = [i for i in lines if "LowProportionError" in i]
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| small_doc = [i for i in lines if "small_doc:True" in i]
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| nme_fns = [re.findall(re_pat,i)[0].replace("ERROR: ", "") for i in nme]
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| epe_fns = [re.findall(re_pat,i)[0].replace("ERROR: ", "") for i in epe]
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| lpe_fns = [re.findall(re_pat,i)[0].replace("ERROR: ", "") for i in lpe]
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| small_doc_fns = [re.findall(re_pat,i)[0].replace("ERROR: ", "") for i in small_doc]
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| for i in tqdm(small_doc_fns,leave=False,desc="small_docs"):
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| quality_ds = quality_ds.map(update_processed_docs,fn_kwargs={"document":i,"msg":"small_doc;"})
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|
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| for i in tqdm(nme_fns,leave=False,desc="no_mix"):
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| quality_ds = quality_ds.map(update_processed_docs,fn_kwargs={"document":i,"msg":"no_mix;"})
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|
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| for i in tqdm(lpe_fns,leave=False,desc="low_prop"):
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| quality_ds = quality_ds.map(update_processed_docs,fn_kwargs={"document":i,"msg":"low_prop;"})
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|
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| empty_document = empty_docs(epe_fns)
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|
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| quality_ds = concatenate_datasets([quality_ds,empty_document])
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| quality_ds.to_json("../ocr_metadata.json")
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|
|
| if __name__ == "__main__":
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| main() |
