getting prmu and reordering present keyphrases

.gitattributes

@@ -35,3 +35,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.mp3 filter=lfs diff=lfs merge=lfs -text
 *.ogg filter=lfs diff=lfs merge=lfs -text
 *.wav filter=lfs diff=lfs merge=lfs -text
+data.jsonl filter=lfs diff=lfs merge=lfs -text

data.jsonl

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:a03b0b004ab77c9ada8e4fcc6912d023ef85905cf80b2415a1bc032fdcfdf888
-size 40569656
+oid sha256:56e6b432c7c37f32067d8df68eddeff90eca027448d9b80038d5d2ea7f685065
+size 40486751

prmu.py

@@ -0,0 +1,131 @@
+#!/usr/bin/env python
+# 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
+
+# In[2]:
+
+print("LOADING DATASET")
+dataset = load_dataset("json", data_files={"test":"data.jsonl"})
+
+
+
+# In[3]:
+
+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
+
+
+
+# Modify tokenizer infix patterns
+infixes = (
+    LIST_ELLIPSES
+    + LIST_ICONS
+    + [
+        r"(?<=[0-9])[+\-\*^](?=[0-9-])",
+        r"(?<=[{al}{q}])\.(?=[{au}{q}])".format(
+            al=ALPHA_LOWER, au=ALPHA_UPPER, q=CONCAT_QUOTES
+        ),
+        r"(?<=[{a}]),(?=[{a}])".format(a=ALPHA),
+        # ✅ Commented out regex that splits on hyphens between letters:
+        # r"(?<=[{a}])(?:{h})(?=[{a}])".format(a=ALPHA, h=HYPHENS),
+        r"(?<=[{a}0-9])[:<>=/](?=[{a}])".format(a=ALPHA),
+    ]
+)
+
+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."""
+
+    # if kp is present
+    if contains(tok_kp, tok_title) or contains(tok_kp, tok_text):
+        return "P"
+
+    # if kp is considered as absent
+    else:
+
+        # find present and absent words
+        present_words = [w for w in tok_kp if w in tok_title or w in tok_text]
+
+        # if "all" words are present
+        if len(present_words) == len(tok_kp):
+            return "R"
+        # if "some" words are present
+        elif len(present_words) > 0:
+            return "M"
+        # 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'])
+
+    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]
+
+    prmu = [find_pmru(title_stems, abstract_stems, kp) for kp in dataset["tokenized_keyphrases"]]
+
+    dataset['prmu'] = prmu
+
+    return dataset
+
+
+# In[6]:
+
+
+print("TOKENIZATION")
+dataset = dataset.map(tokenize,num_proc=sys.argv[1])
+
+print("GETTING PRMU")
+dataset = dataset.map(prmu_dataset,num_proc=sys.argv[1])
+
+dataset["test"].to_json("data.jsonl")

pubmed.py

@@ -63,9 +63,10 @@ class Pubmed(datasets.GeneratorBasedBuilder):
             features = datasets.Features(
                 {
                     "id": datasets.Value("string"),
+                    "title": datasets.Value("string"),
                     "text": datasets.Value("string"),
                     "keyphrases": datasets.features.Sequence(datasets.Value("string")),
-                    "stemmed_keyphrases": datasets.features.Sequence(datasets.Value("string"))
+                    "prmu": datasets.features.Sequence(datasets.Value("string"))
                 }
             )
         return datasets.DatasetInfo(
@@ -116,8 +117,9 @@ class Pubmed(datasets.GeneratorBasedBuilder):
                 # Yields examples as (key, example) tuples
                 yield key, {
                     "id": data["id"],
+                    "title": data["title"],
                     "text": data["text"],
                     "keyphrases":  data["keyphrases"],
-                    "stemmed_keyphrases": data["stemmed_keyphrases"]
+                    "prmu": data["prmu"]
                 }