Adding stats

prmu.py

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+# -*- coding: utf-8 -*-
+
+import sys
+import json
+import spacy
+
+from nltk.stem.snowball import SnowballStemmer as Stemmer
+
+nlp = spacy.load("en_core_web_sm")
+
+# 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 = (
+    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
+
+
+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"
+
+
+if __name__ == '__main__':
+
+    data = []
+
+    # read the dataset
+    with open(sys.argv[1], 'r') as f:
+        # loop through the documents
+        for line in f:
+            doc = json.loads(line.strip())
+
+            title_spacy = nlp(doc['title'])
+            abstract_spacy = nlp(doc['abstract'])
+
+            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]
+
+            keyphrases_stems = []
+            for keyphrase in doc['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]
+                keyphrases_stems.append(keyphrase_stems)
+
+            prmu = [find_pmru(title_stems, abstract_stems, kp) for kp in keyphrases_stems]
+            doc['prmu'] = prmu
+
+            data.append(json.dumps(doc))
+            print(doc['id'])
+
+    # write the json
+    with open(sys.argv[2], 'w') as o:
+        o.write("\n".join(data))

stats.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "eba2ee81",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "09e8150022c94f569f19b76663ffb89f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Downloading builder script:   0%|          | 0.00/7.79k [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "No config specified, defaulting to: kp_times/raw\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Downloading and preparing dataset kp_times/raw to /Users/boudin-f/.cache/huggingface/datasets/taln-ls2n___kp_times/raw/1.1.0/81f75cd972e595c55ef8cc865e898b0bc01ce7d220287a246b566b7417f07274...\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5f40668afdd0428eb9bec18770b4bf3e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Downloading data files:   0%|          | 0/3 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2f17b141c71d4f03ac58df7b4d1133cd",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Extracting data files:   0%|          | 0/3 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Generating train split: 0 examples [00:00, ? examples/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Generating test split: 0 examples [00:00, ? examples/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Generating validation split: 0 examples [00:00, ? examples/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Dataset kp_times downloaded and prepared to /Users/boudin-f/.cache/huggingface/datasets/taln-ls2n___kp_times/raw/1.1.0/81f75cd972e595c55ef8cc865e898b0bc01ce7d220287a246b566b7417f07274. Subsequent calls will reuse this data.\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "716568658d6749da8a0926dcb1fb384e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/3 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from datasets import load_dataset\n",
+    "\n",
+    "dataset = load_dataset('taln-ls2n/kptimes')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "4ba72244",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "cb61d182cdc14ea7868eb258413ca117",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/259923 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "abf721e69b3f46079166a1986514b5bf",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/10000 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "827aab4cd5414084adf841e52d4899c4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/20000 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "# keyphrases: 5.03\n",
+      "% P: 46.64\n",
+      "% R: 15.11\n",
+      "% M: 28.89\n",
+      "% U: 9.36\n"
+     ]
+    }
+   ],
+   "source": [
+    "from tqdm.notebook import tqdm\n",
+    "\n",
+    "P, R, M, U, nb_kps = [], [], [], [], []\n",
+    "\n",
+    "for split in ['train', 'validation', 'test']:\n",
+    "    \n",
+    "    for sample in tqdm(dataset[split]):\n",
+    "        nb_kps.append(len(sample[\"keyphrases\"]))\n",
+    "        P.append(sample[\"prmu\"].count(\"P\") / nb_kps[-1])\n",
+    "        R.append(sample[\"prmu\"].count(\"R\") / nb_kps[-1])\n",
+    "        M.append(sample[\"prmu\"].count(\"M\") / nb_kps[-1])\n",
+    "        U.append(sample[\"prmu\"].count(\"U\") / nb_kps[-1])\n",
+    "        \n",
+    "print(\"# keyphrases: {:.2f}\".format(sum(nb_kps)/len(nb_kps)))\n",
+    "print(\"% P: {:.2f}\".format(sum(P)/len(P)*100))\n",
+    "print(\"% R: {:.2f}\".format(sum(R)/len(R)*100))\n",
+    "print(\"% M: {:.2f}\".format(sum(M)/len(M)*100))\n",
+    "print(\"% U: {:.2f}\".format(sum(U)/len(U)*100))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "52dda817",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import spacy\n",
+    "\n",
+    "nlp = spacy.load(\"en_core_web_sm\")\n",
+    "\n",
+    "# https://spacy.io/usage/linguistic-features#native-tokenizer-additions\n",
+    "\n",
+    "from spacy.lang.char_classes import ALPHA, ALPHA_LOWER, ALPHA_UPPER\n",
+    "from spacy.lang.char_classes import CONCAT_QUOTES, LIST_ELLIPSES, LIST_ICONS\n",
+    "from spacy.util import compile_infix_regex\n",
+    "\n",
+    "# Modify tokenizer infix patterns\n",
+    "infixes = (\n",
+    "    LIST_ELLIPSES\n",
+    "    + LIST_ICONS\n",
+    "    + [\n",
+    "        r\"(?<=[0-9])[+\\-\\*^](?=[0-9-])\",\n",
+    "        r\"(?<=[{al}{q}])\\.(?=[{au}{q}])\".format(\n",
+    "            al=ALPHA_LOWER, au=ALPHA_UPPER, q=CONCAT_QUOTES\n",
+    "        ),\n",
+    "        r\"(?<=[{a}]),(?=[{a}])\".format(a=ALPHA),\n",
+    "        # ✅ Commented out regex that splits on hyphens between letters:\n",
+    "        # r\"(?<=[{a}])(?:{h})(?=[{a}])\".format(a=ALPHA, h=HYPHENS),\n",
+    "        r\"(?<=[{a}0-9])[:<>=/](?=[{a}])\".format(a=ALPHA),\n",
+    "    ]\n",
+    ")\n",
+    "\n",
+    "infix_re = compile_infix_regex(infixes)\n",
+    "nlp.tokenizer.infix_finditer = infix_re.finditer"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "047ab1cc",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "45f4357088854088870320517821adc4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/259923 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "doc_len = []\n",
+    "for split in ['train', 'validation', 'test']:\n",
+    "    for sample in tqdm(dataset[split]):\n",
+    "        doc_len.append(len(nlp(sample[\"title\"])) + len(nlp(sample[\"abstract\"])))\n",
+    "print(\"avg doc len: {:.1f}\".format(sum(doc_len)/len(doc_len)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0d55f0f0",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}