simpler prmu script

prmu.py

@@ -1,37 +1,18 @@
-#!/usr/bin/env python
-# coding: utf-8
+# -*- coding: utf-8 -*-
 
-# In[1]:
-
-
-from this import d
-from datasets import load_dataset, load_from_disk
-import spacy
-import re
-# from spacy.lang.en import English
-from spacy.tokenizer import _get_regex_pattern
-from spacy.lang.char_classes import ALPHA, ALPHA_LOWER, ALPHA_UPPER
-from spacy.lang.char_classes import CONCAT_QUOTES, LIST_ELLIPSES, LIST_ICONS
-from spacy.util import compile_infix_regex
-from nltk.stem.snowball import SnowballStemmer as Stemmer
-import numpy as np
 import sys
+import json
+import spacy
 
-# In[2]:
-
-print("LOADING DATASET")
-dataset = load_dataset("json", data_files={"test":"data.jsonl"})
-
-
-
-# In[3]:
+from nltk.stem.snowball import SnowballStemmer as Stemmer
 
 nlp = spacy.load("en_core_web_sm")
-re_token_match = _get_regex_pattern(nlp.Defaults.token_match)
-re_token_match = f"({re_token_match}|\w+-\w+)"
-nlp.tokenizer.token_match = re.compile(re_token_match).match
 
+# https://spacy.io/usage/linguistic-features#native-tokenizer-additions
 
+from spacy.lang.char_classes import ALPHA, ALPHA_LOWER, ALPHA_UPPER
+from spacy.lang.char_classes import CONCAT_QUOTES, LIST_ELLIPSES, LIST_ICONS
+from spacy.util import compile_infix_regex
 
 # Modify tokenizer infix patterns
 infixes = (
@@ -53,15 +34,10 @@ infix_re = compile_infix_regex(infixes)
 nlp.tokenizer.infix_finditer = infix_re.finditer
 
 
-# In[5]:
-
-
 def contains(subseq, inseq):
     return any(inseq[pos:pos + len(subseq)] == subseq for pos in range(0, len(inseq) - len(subseq) + 1))
 
 
-   
-
 def find_pmru(tok_title, tok_text, tok_kp):
     """Find PRMU category of a given keyphrase."""
 
@@ -84,48 +60,41 @@ def find_pmru(tok_title, tok_text, tok_kp):
         # if "no" words are present
         else:
             return "U"
-    return prmu
 
-def tokenize(dataset):
-    keyphrases_stems= []
-    for keyphrase in dataset["keyphrases"]:
-        keyphrase_spacy = nlp(keyphrase)
-        keyphrase_tokens = [token.text for token in keyphrase_spacy]
-        keyphrase_stems = [Stemmer('porter').stem(w.lower()) for w in keyphrase_tokens]
-        keyphrase_stems = " ".join(keyphrase_stems)
-        keyphrases_stems.append(keyphrase_stems)
-        
-    dataset["tokenized_keyphrases"] = keyphrases_stems
-    return dataset
 
-"""
-Function that tokenizes the dataset (title, text and keyphrases)
-and runs the prmu algorithm.
-"""
-def prmu_dataset(dataset):
-    title_spacy = nlp(dataset['title'])
-    abstract_spacy = nlp(dataset['text'])
+if __name__ == '__main__':
 
-    title_tokens = [token.text for token in title_spacy]
-    abstract_tokens = [token.text for token in abstract_spacy]
+    data = []
 
-    title_stems = [Stemmer('porter').stem(w.lower()) for w in title_tokens]
-    abstract_stems = [Stemmer('porter').stem(w.lower()) for w in abstract_tokens]
+    # read the dataset
+    with open(sys.argv[1], 'r') as f:
+        # loop through the documents
+        for line in f:
+            doc = json.loads(line.strip())
 
-    prmu = [find_pmru(title_stems, abstract_stems, kp) for kp in dataset["tokenized_keyphrases"]]
+            print(doc['id'])
 
-    dataset['prmu'] = prmu
+            title_spacy = nlp(doc['title'])
+            abstract_spacy = nlp(doc['abstract'])
 
-    return dataset
+            title_tokens = [token.text for token in title_spacy]
+            abstract_tokens = [token.text for token in abstract_spacy]
 
+            title_stems = [Stemmer('porter').stem(w.lower()) for w in title_tokens]
+            abstract_stems = [Stemmer('porter').stem(w.lower()) for w in abstract_tokens]
 
-# In[6]:
+            keyphrases_stems = []
+            for keyphrase in doc['keyphrases']:
+                keyphrases_stems.append(keyphrase.split())
 
+            prmu = [find_pmru(title_stems, abstract_stems, kp) for kp in keyphrases_stems]
 
-print("TOKENIZATION")
-dataset = dataset.map(tokenize,num_proc=sys.argv[1])
+            if doc['prmu'] != prmu:
+                print("PRMU categories are not identical!")
 
-print("GETTING PRMU")
-dataset = dataset.map(prmu_dataset,num_proc=sys.argv[1])
+            doc['prmu'] = prmu
+            data.append(json.dumps(doc))
 
-dataset["test"].to_json("data.jsonl")
+    # write the json
+    with open(sys.argv[2], 'w') as o:
+        o.write("\n".join(data))