Welcome to my paper recommendation project! This App is here to simplify your search for relevant scientific and academic papers. Our intelligent recommendation system, powered by
Machine Learning and natural language processing, analyzes keywords, abstracts, titles, authors, and more to provide personalized suggestions based on your interests. Say goodbye to information overload and let us guide you towards new horizons in your quest for knowledge.
+ """
+ subjects = """
+ Our model is trained to recommend papers in various domains, including:
+ - Mathematics
+ - Statistics
+ - Electrical Engineering
+ - Quantitative Biology
+ - Economics
+
+ Say goodbye to information overload and let us guide you towards **new horizons** in your quest for knowledge. Join us and discover a streamlined way to **explore, learn, and stay ahead** in your field. Welcome aboard!
+ """
+ st.markdown(welcome_text, unsafe_allow_html=True)
+ st.markdown(subjects)
+ st.divider()
+
+
+ with st.container():
+ examples = """
+ ### Examples of prompts
+ - "Can you recommend papers that explore the application of deep learning in computer vision for object detection, image segmentation, and video analysis?"
+ - "Can you recommend papers that explore the use of deep reinforcement learning for autonomous driving, including perception, planning, and control?"
+ - "Could you provide papers on image and video compression algorithms based on the latest video coding standards, such as HEVC and AV1?"
+ - "Can you suggest recent papers on behavioral economics that investigate the role of emotions and biases in decision-making under uncertainty, particularly in the context of financial markets?"
+
+ """
+ st.markdown(examples)
+ st.divider()
+
+
+ with st.spinner('The model binaries are unziping ...'):
+ zip_file_path = "models/GrammarGuru.zip"
+ unzip_file(zip_file_path)
+
+ with st.spinner('The model binaries are loading, please wait...'):
+
+ df = load_papers_corpus("models/GrammarGuru/data/GrammarGuru.parquet.gzip")
+ dictionary = load_dict("models/GrammarGuru/dictionaries/GrammarGuru.dict")
+ model = load_model("models/GrammarGuru/tdidf/GrammarGuru.model")
+ matrix = load_sparse_matrix("models/GrammarGuru/similarities_matrix/GrammarGuru")
+ st.success('Models Loaded, yei!', icon="🚀")
+
+ st.markdown("#### Generate Recommendations")
+ # recs_number = st.slider("Enter the number of papers you need", min_value=1, max_value=10, value=3)
+ query = st.text_input("Enter the description of the Paper you need (the more descriptive, the better)", value="")
+
+ if query != "":
+ cleaned_prompt = gensim_tokenizer(query)
+
+ with st.spinner('Generating Recommendations ... '):
+ results_df = get_recomendations_metadata(query=query, df=df, n=3, dictionary=dictionary, index=matrix, tfidf_model=model)
+
+ ids = results_df['id'].to_list()
+ titles = results_df['title'].to_list()
+ authors = results_df['authors'].to_list()
+ categories = results_df['categories'].to_list()
+ abstracts = results_df['abstract'].to_list()
+ release_date = results_df['update_date'].to_list()
+
+ results = list(zip(ids, titles, authors, categories, abstracts, release_date))
+
+ st.write("Your top 3 papers:")
+ for result in results:
+ with st.container():
+ col1, col2 = st.columns([1,3])
+
+ with col1:
+ st.markdown(f"**Title:**")
+ st.markdown(f"**Author:**")
+ st.markdown(f"**Categories:**")
+ st.markdown(f"**release_date:**")
+ st.markdown(f"**Abstract:**")
+
+
+ with col2:
+ st.write(f"Title: {result[1]}")
+ st.write(f"Author: {result[2]}")
+ st.write(f"Categories: {result[3]}")
+ st.write(f"release_date: {result[5]}")
+ st.write(f"Abstract: {result[4]}")
+ st.markdown(f"""[Paper Link](https://arxiv.org/abs/{result[0]})""")
+ st.divider()
+ st.balloons()
+
+ else:
+ st.write("Please enter your prompt :)")
+
+
+
+
+
+elif app_mode == "About this Project":
+ intro_text = """
+ Welcome to my paper recommendation project! This application aims to simplify and speed up the process of finding relevant scientific and academic papers. It utilizes Machine Learning techniques and natural language processing to provide an effective solution for students, researchers, and general users.
+
+ ### Key Features
+
+ - **Intelligent Recommendation System:** The application uses advanced algorithms to analyze keywords, abstracts, titles, authors, and other metadata associated with each paper.
+ - **Efficient Discovery Process:** By leveraging machine learning, the system identifies and suggests the most relevant papers based on the user's interests and areas of study.
+ - **Comprehensive Analysis:** The recommendation system performs an exhaustive analysis of various aspects of each paper to ensure accurate and targeted recommendations.
+ - **Time-saving Solution:** Instead of manually searching through vast amounts of information, users can rely on this application to streamline the paper discovery process.
+
+ ### Available Models
+
+ - SemanticSherlock: trained on 100% of the data
+ - LanguageLiberator: trained on 75% of the data
+ - TextualTango: trained on 50% of the data
+ - GrammarGuru: trained on 25% of the data **(Deployed Version)**
+
+ **Note:** Due to resource limitations on the free tier of Streamlit, only the GrammarGuru version of the model is available for deployment.
+
+
+ ### Benefits
+
+ - **Saves Time and Effort:** With the application's intelligent algorithms, users can avoid the challenges and time-consuming nature of searching for papers on their own.
+ - **Increased Relevance:** By considering keywords, abstracts, titles, authors, and other metadata, the recommendation system provides users with highly relevant paper suggestions.
+ - **Tailored to User Interests:** The system takes into account each user's interests and areas of study, ensuring that the recommended papers align with their specific needs.
+ - **Accessible to All Users:** Whether you are a student, researcher, or general user, this application is designed to cater to a wide range of users' needs.
+
+ ### Get Started
+
+ Explore, discover, and reach new horizons in your search for knowledge with our paper recommendation application. Simplify your journey to finding relevant papers and stay ahead in your field.
+
+ Take a look to this proyect in my [GitHub Repo](https://github.com/LewisPons/arxiv-paper-recommender-system)
+ """
+
+
+
+ st.markdown(intro_text)
+
+
+
+elif app_mode == "About Me":
+ st.title('About Me')
+ mkdn = """
+
Hey there! I'm Luis Morales, a passionate data professional with a background in Actuarial Sciences and expertise in Data Engineering and Machine Learning. I love diving into complex data projects and helping organizations unlock the power of their data. From designing robust data pipelines to building powerful ML models, I enjoy the thrill of turning raw data into actionable insights. With my coding skills in Python and R, I'm always up for tackling challenging projects and learning new technologies along the way.
+ Thank you for taking the time to learn a little bit about me!
+ """
+ st.markdown(mkdn, unsafe_allow_html=True)
+ st.success("Feel free to contact me here 👇 ")
+
+ col1,col2,col3,col4 = st.columns((2,1,2,1))
+ col1.markdown('* [LinkedIn](https://www.linkedin.com/in/luis-morales-ponce/)')
+ col1.markdown('* [GitHub](https://github.com/LewisPons)')
+ image2 = Image.open('reports/figures/profile.jpeg')
+ st.image(image2, width=400)
+
+
diff --git a/src/data/.gitkeep b/src/data/.gitkeep
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/src/data/__init__.py b/src/data/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/src/data/make_dataset.py b/src/data/make_dataset.py
new file mode 100644
index 0000000000000000000000000000000000000000..96b377a23fe907de1ac11a553b5648155d491285
--- /dev/null
+++ b/src/data/make_dataset.py
@@ -0,0 +1,30 @@
+# -*- coding: utf-8 -*-
+import click
+import logging
+from pathlib import Path
+from dotenv import find_dotenv, load_dotenv
+
+
+@click.command()
+@click.argument('input_filepath', type=click.Path(exists=True))
+@click.argument('output_filepath', type=click.Path())
+def main(input_filepath, output_filepath):
+ """ Runs data processing scripts to turn raw data from (../raw) into
+ cleaned data ready to be analyzed (saved in ../processed).
+ """
+ logger = logging.getLogger(__name__)
+ logger.info('making final data set from raw data')
+
+
+if __name__ == '__main__':
+ log_fmt = '%(asctime)s - %(name)s - %(levelname)s - %(message)s'
+ logging.basicConfig(level=logging.INFO, format=log_fmt)
+
+ # not used in this stub but often useful for finding various files
+ project_dir = Path(__file__).resolve().parents[2]
+
+ # find .env automagically by walking up directories until it's found, then
+ # load up the .env entries as environment variables
+ load_dotenv(find_dotenv())
+
+ main()
diff --git a/src/data/transform_raw_data.py b/src/data/transform_raw_data.py
new file mode 100644
index 0000000000000000000000000000000000000000..8cbbc4fa6bb0768b9408428dd3899d8bbe366d79
--- /dev/null
+++ b/src/data/transform_raw_data.py
@@ -0,0 +1,53 @@
+"""
+ This is a utility script for use in sagemaker
+"""
+
+import json
+import pandas as pd
+import pyarrow as pa
+import pyarrow.parquet as pq
+import os
+from tqdm import tqdm
+
+# File paths
+json_file_path = "/home/studio-lab-user/arxiv-paper-recommender-system/arxiv-metadata-oai-snapshot.json"
+parquet_file_path = "/home/studio-lab-user/arxiv-paper-recommender-system/data/processed/arxiv_papers_raw.parquet.gzip"
+
+# Batch size
+batch_size = 10000
+
+# Create the parent directory if it doesn't exist
+parent_dir = os.path.dirname(parquet_file_path)
+os.makedirs(parent_dir, exist_ok=True)
+
+# Open the JSON file
+with open(json_file_path, 'r') as file:
+ # Initialize an empty list to store the data
+ arxiv_data = []
+ processed_count = 0
+
+ # Iterate over each line in the file
+ for line in tqdm(file):
+ # Load the JSON data from each line and append it to the arxiv_data list
+ arxiv_data.append(json.loads(line))
+
+ processed_count += 1
+
+ # Process a batch of data
+ if processed_count % batch_size == 0:
+ df = pd.DataFrame.from_records(arxiv_data)
+ # Convert the batch to parquet and append it to the file
+ # df.to_parquet(parquet_file_path, compression='gzip', engine='pyarrow', index=False, append=True)
+ # Create a parquet table from your dataframe
+ table = pa.Table.from_pandas(df)
+
+ # Write direct to your parquet file
+ pq.write_to_dataset(table , root_path=parquet_file_path)
+ arxiv_data = []
+
+ # Process the remaining data (if any)
+ if arxiv_data:
+ df = pd.DataFrame.from_records(arxiv_data)
+ # Convert the remaining batch to parquet and append it to the file
+ # df.to_parquet(parquet_file_path, compression='gzip', engine='pyarrow', index=False, append=True)
+ pq.write_to_dataset(parquet_file_path , root_path=parquet_file_path)
diff --git a/src/features/.gitkeep b/src/features/.gitkeep
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/src/features/__init__.py b/src/features/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/src/features/build_features.py b/src/features/build_features.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/src/models/.gitkeep b/src/models/.gitkeep
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/src/models/__init__.py b/src/models/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/src/models/__pycache__/__init__.cpython-39.pyc b/src/models/__pycache__/__init__.cpython-39.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..0ae9242994172911649f9c58133ab699ba18015b
Binary files /dev/null and b/src/models/__pycache__/__init__.cpython-39.pyc differ
diff --git a/src/models/configs.yaml b/src/models/configs.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..8838d0bc0d77a7b6373d65ceeea80db4f0057520
--- /dev/null
+++ b/src/models/configs.yaml
@@ -0,0 +1,18 @@
+
+GensimConfig:
+ Large:
+ ModelName: SemanticSherlock
+ DataSetFracSplit: null
+ RandomSeedSplit: null
+ SubLarge:
+ ModelName: LanguageLiberator
+ DataSetFracSplit: 0.75
+ RandomSeedSplit: 98765
+ Medium:
+ ModelName: TextualTango
+ DataSetFracSplit: 0.5
+ RandomSeedSplit: 42355
+ Small:
+ ModelName: GrammarGuru
+ DataSetFracSplit: 0.25
+ RandomSeedSplit: 76438
diff --git a/src/models/gensim_tfidf.ipynb b/src/models/gensim_tfidf.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..edc17a80c358d8b8d2106efd897b9680935121d5
--- /dev/null
+++ b/src/models/gensim_tfidf.ipynb
@@ -0,0 +1,862 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "2023-07-07 17:13:01.457105: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
+ "To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
+ ]
+ }
+ ],
+ "source": [
+ "import pandas as pd\n",
+ "import gensim\n",
+ "import pprint\n",
+ "from gensim import corpora\n",
+ "from gensim.utils import simple_preprocess\n",
+ "from gensim.models import TfidfModel\n",
+ "from gensim.parsing import strip_tags, strip_numeric, \\\n",
+ " strip_multiple_whitespaces, stem_text, strip_punctuation, \\\n",
+ " remove_stopwords, preprocess_string\n",
+ "import re\n",
+ "import os\n",
+ "\n",
+ "from typing import List\n",
+ "import spacy"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "transform_to_lower = lambda s: s.lower()\n",
+ "remove_single_char = lambda s: re.sub(r'\\s+\\w{1}\\s+', '', s)\n",
+ "\n",
+ "cleaning_filters = [\n",
+ " strip_tags,\n",
+ " strip_numeric,\n",
+ " strip_punctuation, \n",
+ " strip_multiple_whitespaces, \n",
+ " transform_to_lower,\n",
+ " remove_stopwords,\n",
+ " remove_single_char\n",
+ "]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 95,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df = pd.read_parquet(\"/Users/luis.morales/Desktop/arxiv-paper-recommender/data/processed/reduced_arxiv_papers.parquet.gzip\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 94,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "638707"
+ ]
+ },
+ "execution_count": 94,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "int(df.shape[0] * 0.75) "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 73,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df = pd.read_parquet(\"/Users/luis.morales/Desktop/arxiv-paper-recommender/data/processed/reduced_arxiv_papers.parquet.gzip\").sample().reset_index(drop=True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 74,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "
\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " id | \n",
+ " submitter | \n",
+ " authors | \n",
+ " title | \n",
+ " comments | \n",
+ " journal-ref | \n",
+ " doi | \n",
+ " report-no | \n",
+ " categories | \n",
+ " license | \n",
+ " abstract | \n",
+ " versions | \n",
+ " update_date | \n",
+ " authors_parsed | \n",
+ " cleaned_abstracts | \n",
+ " len_abstract | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " | 0 | \n",
+ " 2007.00905 | \n",
+ " Song Qingheng | \n",
+ " Qingheng Song, Yong Zeng, Jie Xu, and Shi Jin | \n",
+ " A Survey of Prototype and Experiment for UAV C... | \n",
+ " 24 pages, 6 figures | \n",
+ " None | \n",
+ " None | \n",
+ " None | \n",
+ " cs.IT eess.SP math.IT | \n",
+ " http://creativecommons.org/licenses/by-nc-sa/4.0/ | \n",
+ " Unmanned aerial vehicle (UAV) communications... | \n",
+ " [{'created': 'Thu, 2 Jul 2020 06:26:20 GMT', '... | \n",
+ " 2020-07-03 | \n",
+ " [[Song, Qingheng, ], [Zeng, Yong, ], [Xu, Jie,... | \n",
+ " unmanned aerial vehicle uav communication a... | \n",
+ " 865 | \n",
+ "
\n",
+ " \n",
+ " | 1 | \n",
+ " 2102.04209 | \n",
+ " Michael Stuart | \n",
+ " Michael T. Stuart and Markus Kneer | \n",
+ " Guilty Artificial Minds | \n",
+ " 20 pages, 4 figures, 1 table | \n",
+ " None | \n",
+ " None | \n",
+ " None | \n",
+ " cs.CY cs.AI cs.HC | \n",
+ " http://creativecommons.org/licenses/by/4.0/ | \n",
+ " The concepts of blameworthiness and wrongnes... | \n",
+ " [{'created': 'Sun, 24 Jan 2021 21:37:35 GMT', ... | \n",
+ " 2021-02-09 | \n",
+ " [[Stuart, Michael T., ], [Kneer, Markus, ]] | \n",
+ " concept blameworthiness wrongness fundament... | \n",
+ " 739 | \n",
+ "
\n",
+ " \n",
+ " | 2 | \n",
+ " 1201.5796 | \n",
+ " Denis Jerome | \n",
+ " Denis Jerome | \n",
+ " Organic Superconductors: when correlations and... | \n",
+ " 41 pages, 21 figures to be published in Journa... | \n",
+ " None | \n",
+ " 10.1007/s10948-012-1475-7 | \n",
+ " None | \n",
+ " cond-mat.supr-con | \n",
+ " http://arxiv.org/licenses/nonexclusive-distrib... | \n",
+ " This survey provides a brief account for the... | \n",
+ " [{'created': 'Fri, 27 Jan 2012 15:24:46 GMT', ... | \n",
+ " 2012-02-21 | \n",
+ " [[Jerome, Denis, ]] | \n",
+ " survey provide brief account start organic ... | \n",
+ " 649 | \n",
+ "
\n",
+ " \n",
+ " | 3 | \n",
+ " 1511.03076 | \n",
+ " Emma Platts Miss | \n",
+ " George F.R. Ellis, Emma Platts, David Sloan an... | \n",
+ " Current observations with a decaying cosmologi... | \n",
+ " 23 pages, 11 figures | \n",
+ " None | \n",
+ " 10.1088/1475-7516/2016/04/026 | \n",
+ " None | \n",
+ " astro-ph.CO gr-qc hep-th | \n",
+ " http://arxiv.org/licenses/nonexclusive-distrib... | \n",
+ " We use the phase plane analysis technique of... | \n",
+ " [{'created': 'Tue, 10 Nov 2015 12:08:23 GMT', ... | \n",
+ " 2016-04-27 | \n",
+ " [[Ellis, George F. R., ], [Platts, Emma, ], [S... | \n",
+ " use phase plane analysis technique madsen e... | \n",
+ " 554 | \n",
+ "
\n",
+ " \n",
+ " | 4 | \n",
+ " 1710.02954 | \n",
+ " Kirk Bansak | \n",
+ " Kirk Bansak | \n",
+ " Estimating Causal Moderation Effects with Rand... | \n",
+ " Forthcoming, Journal of the Royal Statistical ... | \n",
+ " None | \n",
+ " None | \n",
+ " None | \n",
+ " stat.ME | \n",
+ " http://arxiv.org/licenses/nonexclusive-distrib... | \n",
+ " Researchers are often interested in analyzin... | \n",
+ " [{'created': 'Mon, 9 Oct 2017 06:34:01 GMT', '... | \n",
+ " 2020-08-25 | \n",
+ " [[Bansak, Kirk, ]] | \n",
+ " researcher interested analyze conditional t... | \n",
+ " 799 | \n",
+ "
\n",
+ " \n",
+ " | ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ "
\n",
+ " \n",
+ " | 851605 | \n",
+ " 1301.0707 | \n",
+ " Sebastian Klein | \n",
+ " Sebastian Klein | \n",
+ " Chow groups of tensor triangulated categories | \n",
+ " 40 pages. The presentation of the article has ... | \n",
+ " None | \n",
+ " None | \n",
+ " None | \n",
+ " math.AG math.CT math.RT | \n",
+ " http://arxiv.org/licenses/nonexclusive-distrib... | \n",
+ " We recall P. Balmer's definition of tensor t... | \n",
+ " [{'created': 'Fri, 4 Jan 2013 11:06:40 GMT', '... | \n",
+ " 2015-10-02 | \n",
+ " [[Klein, Sebastian, ]] | \n",
+ " recall p. balmer definition tensor triangul... | \n",
+ " 787 | \n",
+ "
\n",
+ " \n",
+ " | 851606 | \n",
+ " 1707.00341 | \n",
+ " Giorgos Anastasiou | \n",
+ " Giorgos Anastasiou, Rodrigo Olea, David Rivera... | \n",
+ " Noether-Wald energy in Critical Gravity | \n",
+ " 7 pages, no figures, Final version for PLB | \n",
+ " None | \n",
+ " 10.1016/j.physletb.2018.11.021 | \n",
+ " None | \n",
+ " hep-th gr-qc | \n",
+ " http://arxiv.org/licenses/nonexclusive-distrib... | \n",
+ " Criticality represents a specific point in t... | \n",
+ " [{'created': 'Sun, 2 Jul 2017 19:52:32 GMT', '... | \n",
+ " 2018-11-21 | \n",
+ " [[Anastasiou, Giorgos, ], [Olea, Rodrigo, ], [... | \n",
+ " criticality represent specific point parame... | \n",
+ " 631 | \n",
+ "
\n",
+ " \n",
+ " | 851607 | \n",
+ " 1610.08526 | \n",
+ " Blagoje Oblak | \n",
+ " Blagoje Oblak | \n",
+ " BMS Particles in Three Dimensions | \n",
+ " 437 pages (including index), 33 figures. Appen... | \n",
+ " None | \n",
+ " 10.1007/978-3-319-61878-4 | \n",
+ " None | \n",
+ " hep-th gr-qc math-ph math.GR math.MP math.RT | \n",
+ " http://arxiv.org/licenses/nonexclusive-distrib... | \n",
+ " This thesis is devoted to the group-theoreti... | \n",
+ " [{'created': 'Wed, 26 Oct 2016 20:00:16 GMT', ... | \n",
+ " 2018-01-29 | \n",
+ " [[Oblak, Blagoje, ]] | \n",
+ " thesis devoted group theoretic aspect dimen... | \n",
+ " 542 | \n",
+ "
\n",
+ " \n",
+ " | 851608 | \n",
+ " 1211.6629 | \n",
+ " Philippe Joyez | \n",
+ " Philippe Joyez | \n",
+ " Self-consistent dynamics of a Josephson juncti... | \n",
+ " 7 pages, 1 figure | \n",
+ " None | \n",
+ " 10.1103/PhysRevLett.110.217003 | \n",
+ " None | \n",
+ " cond-mat.supr-con cond-mat.mes-hall | \n",
+ " http://arxiv.org/licenses/nonexclusive-distrib... | \n",
+ " We derive microscopically the dynamics assoc... | \n",
+ " [{'created': 'Tue, 27 Nov 2012 17:29:04 GMT', ... | \n",
+ " 2013-05-29 | \n",
+ " [[Joyez, Philippe, ]] | \n",
+ " derive microscopically dynamic associate d.... | \n",
+ " 558 | \n",
+ "
\n",
+ " \n",
+ " | 851609 | \n",
+ " 0705.2878 | \n",
+ " Benoit Perthame | \n",
+ " Benoit Perthame (DMA), Panagiotis E. Souganidis | \n",
+ " Asymmetric potentials and motor effect: a larg... | \n",
+ " None | \n",
+ " None | \n",
+ " None | \n",
+ " None | \n",
+ " math.AP | \n",
+ " None | \n",
+ " We provide a mathematical analysis of appear... | \n",
+ " [{'created': 'Sun, 20 May 2007 17:43:39 GMT', ... | \n",
+ " 2007-05-23 | \n",
+ " [[Perthame, Benoit, , DMA], [Souganidis, Panag... | \n",
+ " provide mathematical analysis appearance co... | \n",
+ " 518 | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
851610 rows × 16 columns
\n",
+ "
"
+ ],
+ "text/plain": [
+ " id submitter \n",
+ "0 2007.00905 Song Qingheng \\\n",
+ "1 2102.04209 Michael Stuart \n",
+ "2 1201.5796 Denis Jerome \n",
+ "3 1511.03076 Emma Platts Miss \n",
+ "4 1710.02954 Kirk Bansak \n",
+ "... ... ... \n",
+ "851605 1301.0707 Sebastian Klein \n",
+ "851606 1707.00341 Giorgos Anastasiou \n",
+ "851607 1610.08526 Blagoje Oblak \n",
+ "851608 1211.6629 Philippe Joyez \n",
+ "851609 0705.2878 Benoit Perthame \n",
+ "\n",
+ " authors \n",
+ "0 Qingheng Song, Yong Zeng, Jie Xu, and Shi Jin \\\n",
+ "1 Michael T. Stuart and Markus Kneer \n",
+ "2 Denis Jerome \n",
+ "3 George F.R. Ellis, Emma Platts, David Sloan an... \n",
+ "4 Kirk Bansak \n",
+ "... ... \n",
+ "851605 Sebastian Klein \n",
+ "851606 Giorgos Anastasiou, Rodrigo Olea, David Rivera... \n",
+ "851607 Blagoje Oblak \n",
+ "851608 Philippe Joyez \n",
+ "851609 Benoit Perthame (DMA), Panagiotis E. Souganidis \n",
+ "\n",
+ " title \n",
+ "0 A Survey of Prototype and Experiment for UAV C... \\\n",
+ "1 Guilty Artificial Minds \n",
+ "2 Organic Superconductors: when correlations and... \n",
+ "3 Current observations with a decaying cosmologi... \n",
+ "4 Estimating Causal Moderation Effects with Rand... \n",
+ "... ... \n",
+ "851605 Chow groups of tensor triangulated categories \n",
+ "851606 Noether-Wald energy in Critical Gravity \n",
+ "851607 BMS Particles in Three Dimensions \n",
+ "851608 Self-consistent dynamics of a Josephson juncti... \n",
+ "851609 Asymmetric potentials and motor effect: a larg... \n",
+ "\n",
+ " comments journal-ref \n",
+ "0 24 pages, 6 figures None \\\n",
+ "1 20 pages, 4 figures, 1 table None \n",
+ "2 41 pages, 21 figures to be published in Journa... None \n",
+ "3 23 pages, 11 figures None \n",
+ "4 Forthcoming, Journal of the Royal Statistical ... None \n",
+ "... ... ... \n",
+ "851605 40 pages. The presentation of the article has ... None \n",
+ "851606 7 pages, no figures, Final version for PLB None \n",
+ "851607 437 pages (including index), 33 figures. Appen... None \n",
+ "851608 7 pages, 1 figure None \n",
+ "851609 None None \n",
+ "\n",
+ " doi report-no \n",
+ "0 None None \\\n",
+ "1 None None \n",
+ "2 10.1007/s10948-012-1475-7 None \n",
+ "3 10.1088/1475-7516/2016/04/026 None \n",
+ "4 None None \n",
+ "... ... ... \n",
+ "851605 None None \n",
+ "851606 10.1016/j.physletb.2018.11.021 None \n",
+ "851607 10.1007/978-3-319-61878-4 None \n",
+ "851608 10.1103/PhysRevLett.110.217003 None \n",
+ "851609 None None \n",
+ "\n",
+ " categories \n",
+ "0 cs.IT eess.SP math.IT \\\n",
+ "1 cs.CY cs.AI cs.HC \n",
+ "2 cond-mat.supr-con \n",
+ "3 astro-ph.CO gr-qc hep-th \n",
+ "4 stat.ME \n",
+ "... ... \n",
+ "851605 math.AG math.CT math.RT \n",
+ "851606 hep-th gr-qc \n",
+ "851607 hep-th gr-qc math-ph math.GR math.MP math.RT \n",
+ "851608 cond-mat.supr-con cond-mat.mes-hall \n",
+ "851609 math.AP \n",
+ "\n",
+ " license \n",
+ "0 http://creativecommons.org/licenses/by-nc-sa/4.0/ \\\n",
+ "1 http://creativecommons.org/licenses/by/4.0/ \n",
+ "2 http://arxiv.org/licenses/nonexclusive-distrib... \n",
+ "3 http://arxiv.org/licenses/nonexclusive-distrib... \n",
+ "4 http://arxiv.org/licenses/nonexclusive-distrib... \n",
+ "... ... \n",
+ "851605 http://arxiv.org/licenses/nonexclusive-distrib... \n",
+ "851606 http://arxiv.org/licenses/nonexclusive-distrib... \n",
+ "851607 http://arxiv.org/licenses/nonexclusive-distrib... \n",
+ "851608 http://arxiv.org/licenses/nonexclusive-distrib... \n",
+ "851609 None \n",
+ "\n",
+ " abstract \n",
+ "0 Unmanned aerial vehicle (UAV) communications... \\\n",
+ "1 The concepts of blameworthiness and wrongnes... \n",
+ "2 This survey provides a brief account for the... \n",
+ "3 We use the phase plane analysis technique of... \n",
+ "4 Researchers are often interested in analyzin... \n",
+ "... ... \n",
+ "851605 We recall P. Balmer's definition of tensor t... \n",
+ "851606 Criticality represents a specific point in t... \n",
+ "851607 This thesis is devoted to the group-theoreti... \n",
+ "851608 We derive microscopically the dynamics assoc... \n",
+ "851609 We provide a mathematical analysis of appear... \n",
+ "\n",
+ " versions update_date \n",
+ "0 [{'created': 'Thu, 2 Jul 2020 06:26:20 GMT', '... 2020-07-03 \\\n",
+ "1 [{'created': 'Sun, 24 Jan 2021 21:37:35 GMT', ... 2021-02-09 \n",
+ "2 [{'created': 'Fri, 27 Jan 2012 15:24:46 GMT', ... 2012-02-21 \n",
+ "3 [{'created': 'Tue, 10 Nov 2015 12:08:23 GMT', ... 2016-04-27 \n",
+ "4 [{'created': 'Mon, 9 Oct 2017 06:34:01 GMT', '... 2020-08-25 \n",
+ "... ... ... \n",
+ "851605 [{'created': 'Fri, 4 Jan 2013 11:06:40 GMT', '... 2015-10-02 \n",
+ "851606 [{'created': 'Sun, 2 Jul 2017 19:52:32 GMT', '... 2018-11-21 \n",
+ "851607 [{'created': 'Wed, 26 Oct 2016 20:00:16 GMT', ... 2018-01-29 \n",
+ "851608 [{'created': 'Tue, 27 Nov 2012 17:29:04 GMT', ... 2013-05-29 \n",
+ "851609 [{'created': 'Sun, 20 May 2007 17:43:39 GMT', ... 2007-05-23 \n",
+ "\n",
+ " authors_parsed \n",
+ "0 [[Song, Qingheng, ], [Zeng, Yong, ], [Xu, Jie,... \\\n",
+ "1 [[Stuart, Michael T., ], [Kneer, Markus, ]] \n",
+ "2 [[Jerome, Denis, ]] \n",
+ "3 [[Ellis, George F. R., ], [Platts, Emma, ], [S... \n",
+ "4 [[Bansak, Kirk, ]] \n",
+ "... ... \n",
+ "851605 [[Klein, Sebastian, ]] \n",
+ "851606 [[Anastasiou, Giorgos, ], [Olea, Rodrigo, ], [... \n",
+ "851607 [[Oblak, Blagoje, ]] \n",
+ "851608 [[Joyez, Philippe, ]] \n",
+ "851609 [[Perthame, Benoit, , DMA], [Souganidis, Panag... \n",
+ "\n",
+ " cleaned_abstracts len_abstract \n",
+ "0 unmanned aerial vehicle uav communication a... 865 \n",
+ "1 concept blameworthiness wrongness fundament... 739 \n",
+ "2 survey provide brief account start organic ... 649 \n",
+ "3 use phase plane analysis technique madsen e... 554 \n",
+ "4 researcher interested analyze conditional t... 799 \n",
+ "... ... ... \n",
+ "851605 recall p. balmer definition tensor triangul... 787 \n",
+ "851606 criticality represent specific point parame... 631 \n",
+ "851607 thesis devoted group theoretic aspect dimen... 542 \n",
+ "851608 derive microscopically dynamic associate d.... 558 \n",
+ "851609 provide mathematical analysis appearance co... 518 \n",
+ "\n",
+ "[851610 rows x 16 columns]"
+ ]
+ },
+ "execution_count": 74,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 75,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "corpus = df['cleaned_abstracts'].to_list()\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 76,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def gensim_tokenizer(docs: List[str]):\n",
+ " tokenized_docs = list()\n",
+ " for doc in docs:\n",
+ " processed_words = preprocess_string(doc, cleaning_filters)\n",
+ " tokenized_docs.append(processed_words)\n",
+ " \n",
+ " return tokenized_docs\n",
+ "\n",
+ "tokenized_corpus = gensim_tokenizer(corpus)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 77,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "851610"
+ ]
+ },
+ "execution_count": 77,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "len(tokenized_corpus)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 55,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def cleaning_pipe(document):\n",
+ " # Invoking gensim.parsing.preprocess_string method with set of filters\n",
+ " processed_words = preprocess_string(document, cleaning_filters)\n",
+ " return processed_words"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 78,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "/Users/luis.morales/Desktop/arxiv-paper-recommender/models\n",
+ "/Users/luis.morales/Desktop/arxiv-paper-recommender\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "False"
+ ]
+ },
+ "execution_count": 78,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "def validate_if_dictionary_exists(dictionaty_name: str) -> bool:\n",
+ " dicts_folder = \"models/nlp_dictionaries\"\n",
+ " current_dir = os.getcwd()\n",
+ " parent_dir = os.path.dirname(current_dir)\n",
+ " dict_path = f\"{parent_dir}/{dicts_folder}/{dictionaty_name}\"\n",
+ " print(current_dir)\n",
+ " print(parent_dir)\n",
+ " return os.path.isfile(dict_path)\n",
+ " \n",
+ "\n",
+ "validate_if_dictionary_exists('30ktokens.dict') "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 79,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def get_gensim_dictionary(tokenized_docs: List[str], dict_name: str = \"corpus\", save_dict: bool = False):\n",
+ " \"\"\"\n",
+ " Create dictionary of words in preprocessed corpus and saves the dict object\n",
+ " \"\"\"\n",
+ " dictionary = corpora.Dictionary(tokenized_docs)\n",
+ " if save_dict: \n",
+ " dict_lenght = len(tokenized_corpus)\n",
+ " parent_folder = \"/Users/luis.morales/Desktop/arxiv-paper-recommender/models/dictionaries\"\n",
+ " #if validate_if_dictionary_exists('30ktokens.dict'):\n",
+ " dictionary.save(f'{parent_folder}/{dict_name}.dict')\n",
+ " \n",
+ " return dictionary\n",
+ "\n",
+ "dictionary = get_gensim_dictionary(tokenized_docs=tokenized_corpus, dict_name=\"TextualTango\", save_dict=True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 31,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# def get_gensim_dictionary(tokenized_docs: List[str], dict_name: str = \"corpus\", save_dict: bool = False):\n",
+ "# \"\"\"\n",
+ "# Create dictionary of words in preprocessed corpus and saves the dict object\n",
+ "# \"\"\"\n",
+ "# dictionary = corpora.Dictionary(tokenized_docs)\n",
+ "# if save_dict: \n",
+ "# dict_lenght = len(tokenized_corpus)\n",
+ "# parent_folder = \"/Users/luis.morales/Desktop/arxiv-paper-recommender/models/nlp_dictionaries\"\n",
+ "# if validate_if_dictionary_exists('30ktokens.dict'):\n",
+ "# dictionary.save(f'{parent_folder}/{dict_name}.dict')\n",
+ " \n",
+ "# return dictionary\n",
+ "\n",
+ "# dictionary = get_gensim_dictionary(tokenized_docs=tokenized_corpus, dict_name=\"300Ktokens\", save_dict=True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 80,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "BoW_corpus = [dictionary.doc2bow(doc, allow_update=True) for doc in tokenized_corpus]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 81,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "tfidf_model = TfidfModel(BoW_corpus)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 82,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "tfidf_model.save(\"/Users/luis.morales/Desktop/arxiv-paper-recommender/models/tfidf/TextualTango.model\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Test model"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 83,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# index the tfidf vector of corpus as sparse matrix\n",
+ "from gensim import similarities\n",
+ "index = similarities.SparseMatrixSimilarity(tfidf_model[BoW_corpus], num_features=len(dictionary))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 84,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "index.save(\"/Users/luis.morales/Desktop/arxiv-paper-recommender/models/similarities_matrix/TextualTangoSimilarities/TextualTango\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 67,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def get_closest_n(query, n):\n",
+ " '''get the top matching docs as per cosine similarity\n",
+ " between tfidf vector of query and all docs'''\n",
+ " query_document = cleaning_pipe(query)\n",
+ " query_bow = dictionary.doc2bow(query_document)\n",
+ " sims = index[tfidf_model[query_bow]]\n",
+ " top_idx = sims.argsort()[-1*n:][::-1]\n",
+ " return top_idx"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 85,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def get_recomendations_metadata(query: str, n: int, df: pd.DataFrame):\n",
+ " recommendations_idxs = get_closest_n(query, n)\n",
+ " return df.iloc[recommendations_idxs].reset_index(drop=True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 91,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "User Request ---- : \n",
+ " Which papers discuss the use of statistical models and Bayesian inference for uncertainty quantification and risk assessment in engineering systems?\n",
+ "User Request ---- : \n",
+ " \n",
+ "Title: A framework for benchmarking uncertainty in deep regression\n",
+ "Abstract: We propose a framework for the assessment of uncertainty quantification in\n",
+ "deep regression. The framework is based on regression problems where the\n",
+ "regression function is a linear combination of nonlinear functions. Basically,\n",
+ "any level of complexity can be realized through the choice of the nonlinear\n",
+ "functions and the dimensionality of their domain. Results of an uncertainty\n",
+ "quantification for deep regression are compared against those obtained by a\n",
+ "statistical reference method. The reference method utilizes knowledge of the\n",
+ "underlying nonlinear functions and is based on a Bayesian linear regression\n",
+ "using a reference prior. Reliability of uncertainty quantification is assessed\n",
+ "in terms of coverage probabilities, and accuracy through the size of calculated\n",
+ "uncertainties. We illustrate the proposed framework by applying it to current\n",
+ "approaches for uncertainty quantification in deep regression. The flexibility,\n",
+ "together with the availability of a reference solution, makes the framework\n",
+ "suitable for defining benchmark sets for uncertainty quantification.\n",
+ "\n",
+ "\n",
+ "--------------------------\n",
+ "User Request ---- : \n",
+ " Which papers discuss the use of statistical models and Bayesian inference for uncertainty quantification and risk assessment in engineering systems?\n",
+ "User Request ---- : \n",
+ " \n",
+ "Title: Generative Parameter Sampler For Scalable Uncertainty Quantification\n",
+ "Abstract: Uncertainty quantification has been a core of the statistical machine\n",
+ "learning, but its computational bottleneck has been a serious challenge for\n",
+ "both Bayesians and frequentists. We propose a model-based framework in\n",
+ "quantifying uncertainty, called predictive-matching Generative Parameter\n",
+ "Sampler (GPS). This procedure considers an Uncertainty Quantification (UQ)\n",
+ "distribution on the targeted parameter, which matches the corresponding\n",
+ "predictive distribution to the observed data. This framework adopts a\n",
+ "hierarchical modeling perspective such that each observation is modeled by an\n",
+ "individual parameter. This individual parameterization permits the resulting\n",
+ "inference to be computationally scalable and robust to outliers. Our approach\n",
+ "is illustrated for linear models, Poisson processes, and deep neural networks\n",
+ "for classification. The results show that the GPS is successful in providing\n",
+ "uncertainty quantification as well as additional flexibility beyond what is\n",
+ "allowed by classical statistical procedures under the postulated statistical\n",
+ "models.\n",
+ "\n",
+ "\n",
+ "--------------------------\n",
+ "User Request ---- : \n",
+ " Which papers discuss the use of statistical models and Bayesian inference for uncertainty quantification and risk assessment in engineering systems?\n",
+ "User Request ---- : \n",
+ " \n",
+ "Title: Recent Advances in Uncertainty Quantification Methods for Engineering\n",
+ " Problems\n",
+ "Abstract: In the last few decades, uncertainty quantification (UQ) methods have been\n",
+ "used widely to ensure the robustness of engineering designs. This chapter aims\n",
+ "to detail recent advances in popular uncertainty quantification methods used in\n",
+ "engineering applications. This chapter describes the two most popular\n",
+ "meta-modeling methods for uncertainty quantification suitable for engineering\n",
+ "applications (Polynomial Chaos Method and Gaussian Process). Further, the UQ\n",
+ "methods are applied to an engineering test problem under multiple\n",
+ "uncertainties. The test problem considered here is a supersonic nozzle under\n",
+ "operational uncertainties. For the deterministic solution, an open-source\n",
+ "computational fluid dynamics (CFD) solver SU2 is used. The UQ methods are\n",
+ "developed in Matlab and are further combined with SU2 for the uncertainty and\n",
+ "sensitivity estimates. The results are presented in terms of the mean and\n",
+ "standard deviation of the output quantities.\n",
+ "\n",
+ "\n",
+ "--------------------------\n"
+ ]
+ }
+ ],
+ "source": [
+ "_input = \"Which papers discuss the use of statistical models and Bayesian inference for uncertainty quantification and risk assessment in engineering systems?\"\n",
+ "results_df = get_recomendations_metadata(query=_input, df=df, n=3)\n",
+ "\n",
+ "\n",
+ "for abstract in list(zip(results_df['abstract'].to_list(), results_df['title'].to_list())):\n",
+ " print(f\"User Request ---- : \\n {_input}\")\n",
+ " print(f\"User Request ---- : \\n \")\n",
+ " print(f\"Title: {abstract[1]}\")\n",
+ " print(f\"Abstract: {abstract[0]}\\n\")\n",
+ " print(f\"--------------------------\")"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3.11.4 ('arxiv-env': venv)",
+ "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": "3.11.4"
+ },
+ "orig_nbformat": 4,
+ "vscode": {
+ "interpreter": {
+ "hash": "aae17c2ae2f38cc6f211be9b71a2aa280701d8462782cbc1f67caa83a1603363"
+ }
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/src/models/gensim_vect_v2.py b/src/models/gensim_vect_v2.py
new file mode 100644
index 0000000000000000000000000000000000000000..4144b6dc4d2929c8470cd85d1f8c7fc09d914ad1
--- /dev/null
+++ b/src/models/gensim_vect_v2.py
@@ -0,0 +1,91 @@
+import pandas as pd
+from gensim import corpora
+from gensim import similarities
+from gensim.models import TfidfModel
+from gensim.parsing import strip_tags, strip_numeric, \
+ strip_multiple_whitespaces, stem_text, strip_punctuation, \
+ remove_stopwords, preprocess_string
+import re
+
+from typing import List
+from utils.constants import TEST_INPUTS
+import argparse
+from random import choice
+
+transform_to_lower = lambda s: s.lower()
+remove_single_char = lambda s: re.sub(r'\s+\w{1}\s+', '', s)
+
+class PaperRecommender:
+ def __init__(self,
+ num_samples=3000,
+ corpus_dictionary_path="30Ktokens",
+ arxiv_dataset_path="/Users/luis.morales/Desktop/arxiv-paper-recommender/data/processed/reduced_arxiv_papers.parquet.gzip",
+ save_dict=False,
+ query=""):
+ self.num_samples = num_samples
+ self.corpus_dictionary_path = corpus_dictionary_path
+ self.arxiv_dataset_path = arxiv_dataset_path
+ self.save_dict = save_dict
+ self.query = query
+ self.cleaning_filters = [
+ strip_tags,
+ strip_numeric,
+ strip_punctuation,
+ strip_multiple_whitespaces,
+ transform_to_lower,
+ remove_stopwords,
+ remove_single_char
+ ]
+ self.dictionary = None
+ self.index = None
+ self.tfidf_model = None
+ self.df = None
+
+ def gensim_tokenizer(self, docs: List[str]):
+ tokenized_docs = list()
+ for doc in docs:
+ processed_words = preprocess_string(doc, self.cleaning_filters)
+ tokenized_docs.append(processed_words)
+ return tokenized_docs
+
+ def cleaning_pipe(self, document):
+ processed_words = preprocess_string(document, self.cleaning_filters)
+ return processed_words
+
+ def get_gensim_dictionary(self, tokenized_docs: List[str], dict_name: str = "corpus"):
+ dictionary = corpora.Dictionary(tokenized_docs)
+ if self.save_dict:
+ parent_folder = "/Users/luis.morales/Desktop/arxiv-paper-recommender/models/nlp_dictionaries"
+ dictionary.save(f'{parent_folder}/{dict_name}.dict')
+ return dictionary
+
+ def get_closest_n(self, query: str, n: int):
+ query_document = self.cleaning_pipe(query)
+ query_bow = self.dictionary.doc2bow(query_document)
+ sims = self.index[self.tfidf_model[query_bow]]
+ top_idx = sims.argsort()[-1 * n:][::-1]
+ return top_idx
+
+ def get_recommendations_metadata(self, query: str, n: int):
+ recommendations_idxs = self.get_closest_n(query, n)
+ recommendations_metadata = self.df.iloc[recommendations_idxs]
+ recommendations_metadata = recommendations_metadata.reset_index(drop=True)
+ return recommendations_metadata
+
+ def run_recommender(self):
+ if self.num_samples is None:
+ self.df = pd.read_parquet(self.arxiv_dataset_path)
+
+ self.df = pd.read_parquet(self.arxiv_dataset_path).sample(self.num_samples).reset_index(drop=True)
+ corpus = self.df['cleaned_abstracts'].to_list()
+
+ tokenized_corpus = self.gensim_tokenizer(corpus)
+ self.dictionary = self.get_gensim_dictionary(tokenized_docs=tokenized_corpus, dict_name=self.corpus_dictionary_path)
+
+ BoW_corpus = [self.dictionary.doc2bow(doc, allow_update=True) for doc in tokenized_corpus]
+
+ self.tfidf_model = TfidfModel(BoW_corpus)
+ self.index = similarities.SparseMatrixSimilarity(self.tfidf_model[BoW_corpus], num_features=len(self.dictionary))
+ if self.query is None:
+ self.query = choice(TEST_INPUTS)
+ return self.results
diff --git a/src/models/gensim_vectorizer.py b/src/models/gensim_vectorizer.py
new file mode 100644
index 0000000000000000000000000000000000000000..5540097b8d63afc2d11139ef44e105c5b1eb6396
--- /dev/null
+++ b/src/models/gensim_vectorizer.py
@@ -0,0 +1,194 @@
+import pandas as pd
+from gensim import corpora
+from gensim import similarities
+from gensim.models import TfidfModel
+from gensim.parsing import strip_tags, strip_numeric, \
+ strip_multiple_whitespaces, stem_text, strip_punctuation, \
+ remove_stopwords, preprocess_string
+import re
+
+from typing import List
+from utils.constants import TEST_INPUTS
+import argparse
+from random import choice
+
+import sys
+
+
+
+SAMPLES = 3000
+CORPUS_DICTIONARY_PATH="30Ktokens"
+ARXIV_DATASR_PATH = "/Users/luis.morales/Desktop/arxiv-paper-recommender/data/processed/reduced_arxiv_papers.parquet.gzip"
+SAVE_DICT = False
+QUERY = ""
+
+transform_to_lower = lambda s: s.lower()
+remove_single_char = lambda s: re.sub(r'\s+\w{1}\s+', '', s)
+
+cleaning_filters = [
+ strip_tags,
+ strip_numeric,
+ strip_punctuation,
+ strip_multiple_whitespaces,
+ transform_to_lower,
+ remove_stopwords,
+ remove_single_char
+]
+
+def gensim_tokenizer(docs: List[str]):
+ """
+ Tokenizes a list of strings using a series of cleaning filters.
+
+ Args:
+ docs (List[str]): A list of strings to be tokenized.
+
+ Returns:
+ List[List[str]]: A list of tokenized documents, where each document is represented as a list of tokens.
+ """
+ tokenized_docs = list()
+ for doc in docs:
+ processed_words = preprocess_string(doc, cleaning_filters)
+ tokenized_docs.append(processed_words)
+
+ return tokenized_docs
+
+
+def cleaning_pipe(document):
+ """
+ Applies a series of cleaning steps to a document.
+
+ Args:
+ document (str): The document to be cleaned.
+
+ Returns:
+ list: A list of processed words after applying the cleaning filters.
+ """
+ # Invoking gensim.parsing.preprocess_string method with set of filters
+ processed_words = preprocess_string(document, cleaning_filters)
+ return processed_words
+
+
+def get_gensim_dictionary(tokenized_docs: List[str], dict_name: str = "corpus", save_dict: bool = False):
+ """
+ Create dictionary of words in preprocessed corpus and saves the dict object
+ """
+ dictionary = corpora.Dictionary(tokenized_docs)
+ if save_dict:
+ parent_folder = "/Users/luis.morales/Desktop/arxiv-paper-recommender/models/nlp_dictionaries"
+ dictionary.save(f'{parent_folder}/{dict_name}.dict')
+ return dictionary
+
+
+def get_closest_n(query: str, n: int):
+ '''
+ Retrieves the top matching documents as per cosine similarity
+ between the TF-IDF vector of the query and all documents.
+
+ Args:
+ query (str): The query string to find matching documents.
+ n (int): The number of closest documents to retrieve.
+
+ Returns:
+ numpy.ndarray: An array of indices representing the top matching documents.
+ '''
+ # Clean the query document using cleaning_pipe function
+ query_document = cleaning_pipe(query)
+
+ # Convert the query document to bag-of-words representation
+ query_bow = dictionary.doc2bow(query_document)
+
+ # Calculate similarity scores between the query and all documents using TF-IDF model
+ sims = index[tfidf_model[query_bow]]
+
+ # Get the indices of the top n closest documents based on similarity scores
+ top_idx = sims.argsort()[-1 * n:][::-1]
+
+ return top_idx
+
+
+def get_recomendations_metadata(query: str, df: pd.DataFrame, n: int):
+ '''
+ Retrieves metadata recommendations based on a query using cosine similarity.
+
+ Args:
+ query (str): The query string for which recommendations are sought.
+ n (int): The number of recommendations to retrieve.
+ df (pd.DataFrame): The DataFrame containing metadata information.
+
+ Returns:
+ pd.DataFrame: A DataFrame containing the recommended metadata, reset with a new index.
+ '''
+ # Get the indices of the closest matching documents based on the query
+ recommendations_idxs = get_closest_n(query, n)
+
+ # Retrieve the recommended metadata rows from the DataFrame based on the indices
+ recommendations_metadata = df.iloc[recommendations_idxs]
+
+ # Reset the index of the recommended metadata DataFrame
+ recommendations_metadata = recommendations_metadata.reset_index(drop=True)
+
+ return recommendations_metadata
+
+if __name__ == "__main__":
+ """
+ Example:
+ python script.py --samples 3000 --corpus_dictionary_path "30Ktokens.dict" --arxiv_datasr_path "/Users/luis.morales/Desktop/arxiv-paper-recommender/data/processed/reduced_arxiv_papers.parquet.gzip" --save_dict --query "your query here"
+
+ """
+ # Define and parse command-line arguments
+ parser = argparse.ArgumentParser(description='ArXiv Paper Recommender CLI')
+ parser.add_argument('--samples', default=30000, type=int, help='Number of samples to consider')
+ parser.add_argument('--corpus_dictionary_path', default=None ,type=str, help='Path to the corpus dictionary')
+ parser.add_argument('--save_dict', default=False, help='Flag to save the dictionary')
+ parser.add_argument('--arxiv_dataset_path',
+ default="/Users/luis.morales/Desktop/arxiv-paper-recommender/data/processed/reduced_arxiv_papers.parquet.gzip",
+ type=str, help='Path to the ARXIV parquet source')
+ parser.add_argument('--query', default=None, type=str, help='User query')
+ args = parser.parse_args()
+
+ num_samples = args.samples
+ corpus_dictionary_path = args.corpus_dictionary_path
+ arxiv_dataset_path = args.arxiv_dataset_path
+ save_dict = args.save_dict
+ query = args.query
+
+ print("Parameters:")
+ print(f"num_samples: {num_samples}, type: {type(num_samples)}")
+ print(f"corpus_dictionary_path: {corpus_dictionary_path}, type: {type(corpus_dictionary_path)}")
+ print(f"arxiv_dataset_path: {arxiv_dataset_path}, type: {type(arxiv_dataset_path)}")
+ print(f"save_dict: {save_dict}, type: {type(save_dict)}")
+ print(f"query: {query}, type: {type(query)}")
+
+
+ if num_samples is None:
+ df = pd.read_parquet(arxiv_dataset_path)
+ df = pd.read_parquet(arxiv_dataset_path).sample(num_samples).reset_index(drop=True)
+
+
+ corpus = df['cleaned_abstracts'].to_list()
+ tokenized_corpus = gensim_tokenizer(corpus)
+
+ dictionary = get_gensim_dictionary(
+ tokenized_docs=tokenized_corpus,
+ dict_name=corpus_dictionary_path,
+ save_dict=save_dict
+ )
+
+ BoW_corpus = [dictionary.doc2bow(doc, allow_update=True) for doc in tokenized_corpus]
+
+ tfidf_model = TfidfModel(BoW_corpus)
+
+ index = similarities.SparseMatrixSimilarity(tfidf_model[BoW_corpus], num_features=len(dictionary))
+
+ if query is None:
+ query = choice(TEST_INPUTS)
+
+ results_df = get_recomendations_metadata(query=query, df=df, n=3)
+
+
+ for abstract in list(zip(results_df['abstract'].to_list(), results_df['title'].to_list())):
+ print(f"User Request ---- : \n {query}")
+ print(f"User Request ---- : \n ")
+ print(f"Title: {abstract[1]}")
+ print(f"Abstract: {abstract[0]}\n")
+ print(f"--------------------------")
\ No newline at end of file
diff --git a/src/models/inferencePaper.py b/src/models/inferencePaper.py
new file mode 100644
index 0000000000000000000000000000000000000000..a37bfd5a406e0cfa525c8e42994b8f01bb40999a
--- /dev/null
+++ b/src/models/inferencePaper.py
@@ -0,0 +1,66 @@
+"""
+This script contains the logic for streamlit to generate inferences.
+Example:
+python3 ./src/models/paperRecommender.py --modelsize GrammarGuru
+"""
+import argparse
+from random import choice
+from utils.constants import user_requests_tests, TEST_INPUTS
+from utils.mlutilities import get_recomendations_metadata,load_arxiv_parquet, load_model, load_dict, load_sparse_matrix
+import time
+
+args = parser.parse_args()
+
+
+query = args.query
+model_name = args.modelname
+n = args.n
+
+if args.query is None:
+ query = choice(TEST_INPUTS)
+
+if model_name is None:
+ raise Exception('Please Select a model name to use: ["SemanticSherlock", "LanguageLiberator", "TextualTango", "GrammarGuru"]')
+
+start = time.time()
+parent_folder = f"/Users/luis.morales/Desktop/arxiv-paper-recommender/models/{model_name}"
+
+parquet_file = f"{parent_folder}/data/{model_name}.parquet.gzip"
+dictionary = f"{parent_folder}/dictionaries/{model_name}.dict"
+model = f"{parent_folder}/tdidf/{model_name}.model"
+sparse_matrix = f"{parent_folder}/similarities_matrix/{model_name}"
+
+
+df = load_arxiv_parquet(parquet_file)
+dict_corpus = load_dict(dictionary)
+similarities = load_sparse_matrix(sparse_matrix)
+tfidf_model = load_model(model)
+
+
+results_df = get_recomendations_metadata(query=query, df=df, n=n, dictionary=dict_corpus, index=similarities, tfidf_model=tfidf_model)
+
+results = list(zip(
+ results_df['title'].to_list(),
+ results_df['authors'].to_list(),
+ results_df['categories'].to_list(),
+ results_df['abstract'].to_list()
+ )
+)
+
+for abstract in results:
+ print(f"User Request ---- : \n {query}")
+ time.sleep(0.3)
+ print(f"--------------------------")
+ print(f"Title: {abstract[0]}")
+ time.sleep(0.3)
+ print(f"Author: {abstract[1]}")
+ time.sleep(0.3)
+ print(f"Categories: {abstract[2]}\n")
+ time.sleep(0.3)
+ print(f"Abstract: {abstract[3]}\n")
+ print(f"--------------------------")
+ time.sleep(1)
+
+end = time.time()
+total_time = end - start
+print(f"-------------- Execution Time: {total_time}")
\ No newline at end of file
diff --git a/src/models/paperRecommender.py b/src/models/paperRecommender.py
new file mode 100644
index 0000000000000000000000000000000000000000..ed306c93fc61dd3baa54b6104ba3998ebcee2a57
--- /dev/null
+++ b/src/models/paperRecommender.py
@@ -0,0 +1,78 @@
+"""
+This script is for testing in the CLI the paper Recommender
+ Example:
+ python3 ./src/models/paperRecommender.py --modelsize GrammarGuru
+"""
+import argparse
+from random import choice
+from utils.constants import user_requests_tests, TEST_INPUTS
+from utils.mlutilities import get_recomendations_metadata,load_arxiv_parquet, load_model, load_dict, load_sparse_matrix, gensim_tokenizer
+import time
+
+
+
+if __name__ == "__main__":
+
+ # Define and parse command-line arguments
+ parser = argparse.ArgumentParser(description='ArXiv Paper Recommender CLI')
+ parser.add_argument('--query', default=None, type=str, help='User query')
+ parser.add_argument('--n', default=3, type=int, help='Number of Recommendations to retieve')
+ parser.add_argument('--modelname', choices=["SemanticSherlock", "LanguageLiberator", "TextualTango", "GrammarGuru"], default=None, type=str, help='Model Name')
+
+ args = parser.parse_args()
+
+
+ query = args.query
+ model_name = args.modelname
+ n = args.n
+
+ if args.query is None:
+ query = choice(TEST_INPUTS)
+
+ query = gensim_tokenizer(query)
+
+ if model_name is None:
+ raise Exception('Please Select a model name to use: ["SemanticSherlock", "LanguageLiberator", "TextualTango", "GrammarGuru"]')
+
+ start = time.time()
+ parent_folder = f"/Users/luis.morales/Desktop/arxiv-paper-recommender/models/{model_name}"
+
+ parquet_file = f"{parent_folder}/data/{model_name}.parquet.gzip"
+ dictionary = f"{parent_folder}/dictionaries/{model_name}.dict"
+ model = f"{parent_folder}/tdidf/{model_name}.model"
+ sparse_matrix = f"{parent_folder}/similarities_matrix/{model_name}"
+
+
+ df = load_arxiv_parquet(parquet_file)
+ dict_corpus = load_dict(dictionary)
+ similarities = load_sparse_matrix(sparse_matrix)
+ tfidf_model = load_model(model)
+
+
+ results_df = get_recomendations_metadata(query=query, df=df, n=n, dictionary=dict_corpus, index=similarities, tfidf_model=tfidf_model)
+
+ results = list(zip(
+ results_df['title'].to_list(),
+ results_df['authors'].to_list(),
+ results_df['categories'].to_list(),
+ results_df['abstract'].to_list()
+ )
+ )
+
+ for abstract in results:
+ print(f"User Request ---- : \n {query}")
+ time.sleep(0.3)
+ print(f"--------------------------")
+ print(f"Title: {abstract[0]}")
+ time.sleep(0.3)
+ print(f"Author: {abstract[1]}")
+ time.sleep(0.3)
+ print(f"Categories: {abstract[2]}\n")
+ time.sleep(0.3)
+ print(f"Abstract: {abstract[3]}\n")
+ print(f"--------------------------")
+ time.sleep(1)
+
+ end = time.time()
+ total_time = end - start
+ print(f"-------------- Execution Time: {total_time}")
\ No newline at end of file
diff --git a/src/models/predict_model.py b/src/models/predict_model.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/src/models/tdidf.py b/src/models/tdidf.py
new file mode 100644
index 0000000000000000000000000000000000000000..82eeb12f1785d1b1538f19e01f4d5c61a5bc173e
--- /dev/null
+++ b/src/models/tdidf.py
@@ -0,0 +1,33 @@
+import pandas as pd
+from sklearn.pipeline import Pipeline
+from sklearn.feature_selection import ColumnSelector
+from sklearn.feature_extraction.text import TfidfVectorizer
+
+from utils.utilities import *
+import sys
+from pprint import pprint
+
+CONFIG_FILE_PATH = "/Users/luis.morales/Desktop/arxiv-paper-recommender/models/configs.yaml"
+config = read_yaml_config(CONFIG_FILE_PATH)
+pprint(config)
+
+@execution_time
+def train_tfidf():
+ df = pd.read_parquet("/Users/luis.morales/Desktop/arxiv-paper-recommender/data/processed/arxiv_papers.parquet.gzip") \
+ .sample(500000) \
+ .reset_index(drop=True)
+
+
+ vectorizer = TfidfVectorizer(**config["models"]["tfidf"]["tfidf_deffault"])
+ pprint(config["models"]["tfidf"]["tfidf_deffault"])
+ sys.exit()
+
+ vectors = vectorizer.fit_transform(df['cleaned_abstracts'])
+
+ tfidf_df = pd.DataFrame(vectors.toarray(), columns=[i for i in vectorizer.get_feature_names_out()])
+
+
+ tfidf_df.to_parquet("/Users/luis.morales/Desktop/arxiv-paper-recommender/data/processed/reduced_arxiv_tfidf.parquet.gzip")
+
+train_tfidf()
+
diff --git a/src/models/tf-idf.ipynb b/src/models/tf-idf.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..905ea1d5bb7344998e54706266ab836dc1c2e309
--- /dev/null
+++ b/src/models/tf-idf.ipynb
@@ -0,0 +1,1529 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "2023-07-08 08:44:29.719980: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
+ "To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
+ ]
+ }
+ ],
+ "source": [
+ "import pandas as pd\n",
+ "from sklearn.feature_extraction.text import TfidfVectorizer\n",
+ "from sklearn.metrics.pairwise import cosine_similarity\n",
+ "from sklearn.pipeline import Pipeline\n",
+ "import spacy\n",
+ "\n",
+ "from typing import Union\n",
+ "from collections import Counter\n",
+ "import sys"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [
+ {
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+ "df = pd.read_parquet(\"/Users/luis.morales/Desktop/arxiv-paper-recommender/data/processed/reduced_arxiv_papers.parquet.gzip\").sample(frac=None)\n",
+ "df"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
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+ " [[Lou, Jizhong, ], [Qin, Shaojin, ], [Chen, Ch... | \n",
+ " study spin-1/2 antiferromagnetic heisenberg... | \n",
+ " 856 | \n",
+ "
\n",
+ " \n",
+ " | 2 | \n",
+ " 0708.0723 | \n",
+ " Salvatore Capozziello | \n",
+ " S. Capozziello, A. Stabile, A. Troisi | \n",
+ " The Newtonian Limit of F(R) gravity | \n",
+ " 16 pages | \n",
+ " Phys.Rev.D76:104019,2007 | \n",
+ " 10.1103/PhysRevD.76.104019 | \n",
+ " None | \n",
+ " gr-qc astro-ph | \n",
+ " None | \n",
+ " A general analytic procedure is developed to... | \n",
+ " [{'created': 'Mon, 6 Aug 2007 08:46:02 GMT', '... | \n",
+ " 2008-11-26 | \n",
+ " [[Capozziello, S., ], [Stabile, A., ], [Troisi... | \n",
+ " general analytic procedure develop deal new... | \n",
+ " 821 | \n",
+ "
\n",
+ " \n",
+ " | 3 | \n",
+ " 1312.3113 | \n",
+ " Dmitry Shcherbakov M.Sc. | \n",
+ " Dmitry Shcherbakov, Matthias Ehrhardt, Michael... | \n",
+ " Force-Gradient Nested Multirate Methods for Ha... | \n",
+ " 16 pages 3 figures, submitted to Applied Numer... | \n",
+ " None | \n",
+ " None | \n",
+ " None | \n",
+ " math.NA math-ph math.MP quant-ph | \n",
+ " http://arxiv.org/licenses/nonexclusive-distrib... | \n",
+ " Force-gradient decomposition methods are use... | \n",
+ " [{'created': 'Wed, 11 Dec 2013 11:08:03 GMT', ... | \n",
+ " 2013-12-12 | \n",
+ " [[Shcherbakov, Dmitry, ], [Ehrhardt, Matthias,... | \n",
+ " force gradient decomposition method improve... | \n",
+ " 535 | \n",
+ "
\n",
+ " \n",
+ " | 4 | \n",
+ " 1607.01234 | \n",
+ " Yadao Solanke | \n",
+ " Y. S. Solanke, D. D.Pawar and V. J. Dagwal | \n",
+ " Role of the constant deceleration parameter in... | \n",
+ " None | \n",
+ " None | \n",
+ " None | \n",
+ " None | \n",
+ " gr-qc | \n",
+ " http://arxiv.org/licenses/nonexclusive-distrib... | \n",
+ " The main purpose of the present paper is to ... | \n",
+ " [{'created': 'Tue, 5 Jul 2016 12:58:00 GMT', '... | \n",
+ " 2020-03-06 | \n",
+ " [[Solanke, Y. S., ], [Pawar, D. D., ], [Dagwal... | \n",
+ " main purpose present paper investigate lrs ... | \n",
+ " 557 | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " id submitter \n",
+ "0 cond-mat/0509555 Ricardo Brito \\\n",
+ "1 cond-mat/0204489 Jizhong Lou \n",
+ "2 0708.0723 Salvatore Capozziello \n",
+ "3 1312.3113 Dmitry Shcherbakov M.Sc. \n",
+ "4 1607.01234 Yadao Solanke \n",
+ "\n",
+ " authors \n",
+ "0 M.H.Ernst and R. Brito \\\n",
+ "1 Jizhong Lou, Shaojin Qin, Changfeng Chen, Zhao... \n",
+ "2 S. Capozziello, A. Stabile, A. Troisi \n",
+ "3 Dmitry Shcherbakov, Matthias Ehrhardt, Michael... \n",
+ "4 Y. S. Solanke, D. D.Pawar and V. J. Dagwal \n",
+ "\n",
+ " title \n",
+ "0 Generalized Green-Kubo formulas for fluids wit... \\\n",
+ "1 Field-induced gap in the spin-1/2 antiferromag... \n",
+ "2 The Newtonian Limit of F(R) gravity \n",
+ "3 Force-Gradient Nested Multirate Methods for Ha... \n",
+ "4 Role of the constant deceleration parameter in... \n",
+ "\n",
+ " comments \n",
+ "0 14 pages, no figures. Version 2: expanded Intr... \\\n",
+ "1 8 pages, 6 figures \n",
+ "2 16 pages \n",
+ "3 16 pages 3 figures, submitted to Applied Numer... \n",
+ "4 None \n",
+ "\n",
+ " journal-ref doi report-no \n",
+ "0 Phys. Rev. E 72 (2006) 061102 10.1103/PhysRevE.72.061102 None \\\n",
+ "1 Phys. Rev. B 65, 064420 (2002) 10.1103/PhysRevB.65.064420 None \n",
+ "2 Phys.Rev.D76:104019,2007 10.1103/PhysRevD.76.104019 None \n",
+ "3 None None None \n",
+ "4 None None None \n",
+ "\n",
+ " categories \n",
+ "0 cond-mat.stat-mech \\\n",
+ "1 cond-mat.str-el \n",
+ "2 gr-qc astro-ph \n",
+ "3 math.NA math-ph math.MP quant-ph \n",
+ "4 gr-qc \n",
+ "\n",
+ " license \n",
+ "0 None \\\n",
+ "1 None \n",
+ "2 None \n",
+ "3 http://arxiv.org/licenses/nonexclusive-distrib... \n",
+ "4 http://arxiv.org/licenses/nonexclusive-distrib... \n",
+ "\n",
+ " abstract \n",
+ "0 We present a generalization of the Green-Kub... \\\n",
+ "1 We study the spin-1/2 antiferromagnetic Heis... \n",
+ "2 A general analytic procedure is developed to... \n",
+ "3 Force-gradient decomposition methods are use... \n",
+ "4 The main purpose of the present paper is to ... \n",
+ "\n",
+ " versions update_date \n",
+ "0 [{'created': 'Wed, 21 Sep 2005 17:18:42 GMT', ... 2007-05-23 \\\n",
+ "1 [{'created': 'Tue, 23 Apr 2002 14:28:46 GMT', ... 2009-11-07 \n",
+ "2 [{'created': 'Mon, 6 Aug 2007 08:46:02 GMT', '... 2008-11-26 \n",
+ "3 [{'created': 'Wed, 11 Dec 2013 11:08:03 GMT', ... 2013-12-12 \n",
+ "4 [{'created': 'Tue, 5 Jul 2016 12:58:00 GMT', '... 2020-03-06 \n",
+ "\n",
+ " authors_parsed \n",
+ "0 [[Ernst, M. H., ], [Brito, R., ]] \\\n",
+ "1 [[Lou, Jizhong, ], [Qin, Shaojin, ], [Chen, Ch... \n",
+ "2 [[Capozziello, S., ], [Stabile, A., ], [Troisi... \n",
+ "3 [[Shcherbakov, Dmitry, ], [Ehrhardt, Matthias,... \n",
+ "4 [[Solanke, Y. S., ], [Pawar, D. D., ], [Dagwal... \n",
+ "\n",
+ " cleaned_abstracts len_abstract \n",
+ "0 present generalization green kubo expressio... 814 \n",
+ "1 study spin-1/2 antiferromagnetic heisenberg... 856 \n",
+ "2 general analytic procedure develop deal new... 821 \n",
+ "3 force gradient decomposition method improve... 535 \n",
+ "4 main purpose present paper investigate lrs ... 557 "
+ ]
+ },
+ "execution_count": 4,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df.head()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Read Tokens"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def read_list_from_file(file_path: str):\n",
+ " \"\"\"\n",
+ " Reads a text file containing a list and converts it back to a Python list\n",
+ " Args:\n",
+ " file_path (str): Path to the text file\n",
+ " Returns:\n",
+ " list: The Python list read from the file.\n",
+ " \"\"\"\n",
+ " try:\n",
+ " with open(file_path, 'r') as file:\n",
+ " # Read the contents of the file\n",
+ " file_contents = file.read()\n",
+ " lines = file_contents.split('\\n')\n",
+ " plist = [item for item in lines if item]\n",
+ "\n",
+ " return plist\n",
+ " except FileNotFoundError:\n",
+ " raise FileNotFoundError(\"The specified file cannot be found.\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# tokens_path = \"../data/processed/corpus_tokens.txt\"\n",
+ "# tokens = read_list_from_file(tokens_path)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# tokens_set = set(tokens)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# len(tokens_set)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# count_unique_words = Counter(tokens)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# most_common_words = count_unique_words.most_common(int(len(tokens_set) * 0.7))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# dict_of_words = [pair[0] for pair in most_common_words]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# count_unique_words.most_common()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Continue"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "vectorizer = TfidfVectorizer()\n",
+ "vectors = vectorizer.fit_transform(df['cleaned_abstracts'])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "tfidf_features = [i for i in vectorizer.get_feature_names_out()]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "tfidf_vectors = vectors.toarray()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "tfidf_df = pd.DataFrame(tfidf_vectors, columns=tfidf_features)\n",
+ "# "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [
+ {
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+ "full_df = pd.concat([df, tfidf_df], axis=1)"
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+ " | 29995 | \n",
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+ " Xi Dong | \n",
+ " Ahmed Almheiri, Xi Dong, Daniel Harlow | \n",
+ " Bulk Locality and Quantum Error Correction in ... | \n",
+ " 30 pages plus appendices, 10 figures. v2: sect... | \n",
+ " JHEP 1504:163,2015 | \n",
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+ " 12 pages | \n",
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+ " id submitter \n",
+ "0 1109.5512 Keita Owari \\\n",
+ "1 cs/0403015 Ichiro Adachi \n",
+ "2 1706.05858 Zheng Xie \n",
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+ "\n",
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+ "0 Keita Owari \\\n",
+ "1 Ichiro Adachi, Taisuke Hibino, Luc Hinz, Ryosu... \n",
+ "2 Zheng Xie, Miao Li, Jianping Li, Xiaojun Duan,... \n",
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+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
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+ "full_df"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def preprocessAbstracts(doc: Union[pd.Series, str], nlp = spacy.load('en_core_web_sm')):\n",
+ " \"\"\"\n",
+ " TODO: Optimize NLP Object to only obtain stopwords, lemmas, and tokenize docs.\n",
+ " \n",
+ " Cleans and processes the input documents by performing various text cleaning operations.\n",
+ "\n",
+ " Args:\n",
+ " doc (pd.Series): The documents to be cleaned, passed in a Series object.\n",
+ " stemming (bool, optional): Specifies whether stemming should be applied. Defaults to False.\n",
+ "\n",
+ " Returns:\n",
+ " str: The cleaned and processed document as a single string.\n",
+ " \"\"\"\n",
+ " doc = doc.lower()\n",
+ " doc = nlp(doc)\n",
+ " tokens = [tokens.lower_ for tokens in doc]\n",
+ " tokens = [tokens for tokens in doc if (tokens.is_stop == False)]\n",
+ " tokens = [tokens for tokens in tokens if (tokens.is_punct == False)]\n",
+ " final_token = [token.lemma_ for token in tokens]\n",
+ " \n",
+ " return [\" \".join(final_token)]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "abstract = \"A fully differential calculation in perturbative quantum chromodynamics is presented for the production of massive photon pairs at hadron colliders. All next-to-leading order perturbative contributions from quark-antiquark, gluon-(anti)quark, and gluon-gluon subprocesses are included, as well as all-orders resummation of initial-state gluon radiation valid at next-to-next-to-leading logarithmic accuracy. The region of phase space is specified in which the calculation is most reliable. Good agreement is demonstrated with data from the Fermilab Tevatron, and predictions are made for more detailed tests with CDF and DO data. Predictions are shown for distributions of diphoton pairs produced at the energy of the Large Hadron Collider (LHC). Distributions of the diphoton pairs from the decay of a Higgs boson are contrasted with those produced from QCD processes at the LHC, showing that enhanced sensitivity to the signal can be obtained with judicious selection of events. \"\n",
+ "\n",
+ "abstract_preprocessed = preprocessAbstracts(abstract)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([[0., 0., 0., ..., 0., 0., 0.]])"
+ ]
+ },
+ "execution_count": 18,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "_test = vectorizer.transform(abstract_preprocessed).toarray()\n",
+ "_test"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# def music_recomendation(dataset: pd.DataFrame, song_df: pd.DataFrame):\n",
+ "# numerical_features_dataset = dataset[SONG_FEATURES]\n",
+ "# numerical_features_song = song_df[SONG_FEATURES]\n",
+ " \n",
+ "# similarity = cosine_similarity(numerical_features_dataset, numerical_features_song)\n",
+ "# similarity_df = pd.DataFrame(similarity, columns=['cosine_similarity'])\n",
+ " \n",
+ "# results = pd.concat([dataset, similarity_df], axis=1)\n",
+ " \n",
+ "# return results.sort_values('cosine_similarity', ascending=False).reset_index(drop=True).head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 62,
+ "metadata": {},
+ "outputs": [
+ {
+ "ename": "KeyboardInterrupt",
+ "evalue": "",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)",
+ "Cell \u001b[0;32mIn[62], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mtfidf_df\u001b[49m\u001b[43m[\u001b[49m\u001b[43mtfidf_features\u001b[49m\u001b[43m]\u001b[49m\u001b[38;5;241m.\u001b[39mto_array()\n",
+ "File \u001b[0;32m~/Desktop/arxiv-paper-recommender/arxiv-env/lib/python3.11/site-packages/pandas/core/frame.py:3773\u001b[0m, in \u001b[0;36mDataFrame.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 3770\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mgetattr\u001b[39m(indexer, \u001b[39m\"\u001b[39m\u001b[39mdtype\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39mNone\u001b[39;00m) \u001b[39m==\u001b[39m \u001b[39mbool\u001b[39m:\n\u001b[1;32m 3771\u001b[0m indexer \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39mwhere(indexer)[\u001b[39m0\u001b[39m]\n\u001b[0;32m-> 3773\u001b[0m data \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_take_with_is_copy(indexer, axis\u001b[39m=\u001b[39;49m\u001b[39m1\u001b[39;49m)\n\u001b[1;32m 3775\u001b[0m \u001b[39mif\u001b[39;00m is_single_key:\n\u001b[1;32m 3776\u001b[0m \u001b[39m# What does looking for a single key in a non-unique index return?\u001b[39;00m\n\u001b[1;32m 3777\u001b[0m \u001b[39m# The behavior is inconsistent. It returns a Series, except when\u001b[39;00m\n\u001b[1;32m 3778\u001b[0m \u001b[39m# - the key itself is repeated (test on data.shape, #9519), or\u001b[39;00m\n\u001b[1;32m 3779\u001b[0m \u001b[39m# - we have a MultiIndex on columns (test on self.columns, #21309)\u001b[39;00m\n\u001b[1;32m 3780\u001b[0m \u001b[39mif\u001b[39;00m data\u001b[39m.\u001b[39mshape[\u001b[39m1\u001b[39m] \u001b[39m==\u001b[39m \u001b[39m1\u001b[39m \u001b[39mand\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39misinstance\u001b[39m(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mcolumns, MultiIndex):\n\u001b[1;32m 3781\u001b[0m \u001b[39m# GH#26490 using data[key] can cause RecursionError\u001b[39;00m\n",
+ "File \u001b[0;32m~/Desktop/arxiv-paper-recommender/arxiv-env/lib/python3.11/site-packages/pandas/core/generic.py:3948\u001b[0m, in \u001b[0;36mNDFrame._take_with_is_copy\u001b[0;34m(self, indices, axis)\u001b[0m\n\u001b[1;32m 3940\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m_take_with_is_copy\u001b[39m(\u001b[39mself\u001b[39m: NDFrameT, indices, axis: Axis \u001b[39m=\u001b[39m \u001b[39m0\u001b[39m) \u001b[39m-\u001b[39m\u001b[39m>\u001b[39m NDFrameT:\n\u001b[1;32m 3941\u001b[0m \u001b[39m \u001b[39m\u001b[39m\"\"\"\u001b[39;00m\n\u001b[1;32m 3942\u001b[0m \u001b[39m Internal version of the `take` method that sets the `_is_copy`\u001b[39;00m\n\u001b[1;32m 3943\u001b[0m \u001b[39m attribute to keep track of the parent dataframe (using in indexing\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 3946\u001b[0m \u001b[39m See the docstring of `take` for full explanation of the parameters.\u001b[39;00m\n\u001b[1;32m 3947\u001b[0m \u001b[39m \"\"\"\u001b[39;00m\n\u001b[0;32m-> 3948\u001b[0m result \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_take(indices\u001b[39m=\u001b[39;49mindices, axis\u001b[39m=\u001b[39;49maxis)\n\u001b[1;32m 3949\u001b[0m \u001b[39m# Maybe set copy if we didn't actually change the index.\u001b[39;00m\n\u001b[1;32m 3950\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m result\u001b[39m.\u001b[39m_get_axis(axis)\u001b[39m.\u001b[39mequals(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_get_axis(axis)):\n",
+ "File \u001b[0;32m~/Desktop/arxiv-paper-recommender/arxiv-env/lib/python3.11/site-packages/pandas/core/generic.py:3932\u001b[0m, in \u001b[0;36mNDFrame._take\u001b[0;34m(self, indices, axis, convert_indices)\u001b[0m\n\u001b[1;32m 3924\u001b[0m \u001b[39mif\u001b[39;00m (\n\u001b[1;32m 3925\u001b[0m axis \u001b[39m==\u001b[39m \u001b[39m0\u001b[39m\n\u001b[1;32m 3926\u001b[0m \u001b[39mand\u001b[39;00m indices\u001b[39m.\u001b[39mndim \u001b[39m==\u001b[39m \u001b[39m1\u001b[39m\n\u001b[1;32m 3927\u001b[0m \u001b[39mand\u001b[39;00m using_copy_on_write()\n\u001b[1;32m 3928\u001b[0m \u001b[39mand\u001b[39;00m is_range_indexer(indices, \u001b[39mlen\u001b[39m(\u001b[39mself\u001b[39m))\n\u001b[1;32m 3929\u001b[0m ):\n\u001b[1;32m 3930\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mcopy(deep\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m)\n\u001b[0;32m-> 3932\u001b[0m new_data \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_mgr\u001b[39m.\u001b[39;49mtake(\n\u001b[1;32m 3933\u001b[0m indices,\n\u001b[1;32m 3934\u001b[0m axis\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_get_block_manager_axis(axis),\n\u001b[1;32m 3935\u001b[0m verify\u001b[39m=\u001b[39;49m\u001b[39mTrue\u001b[39;49;00m,\n\u001b[1;32m 3936\u001b[0m convert_indices\u001b[39m=\u001b[39;49mconvert_indices,\n\u001b[1;32m 3937\u001b[0m )\n\u001b[1;32m 3938\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_constructor(new_data)\u001b[39m.\u001b[39m__finalize__(\u001b[39mself\u001b[39m, method\u001b[39m=\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mtake\u001b[39m\u001b[39m\"\u001b[39m)\n",
+ "File \u001b[0;32m~/Desktop/arxiv-paper-recommender/arxiv-env/lib/python3.11/site-packages/pandas/core/internals/managers.py:963\u001b[0m, in \u001b[0;36mBaseBlockManager.take\u001b[0;34m(self, indexer, axis, verify, convert_indices)\u001b[0m\n\u001b[1;32m 960\u001b[0m indexer \u001b[39m=\u001b[39m maybe_convert_indices(indexer, n, verify\u001b[39m=\u001b[39mverify)\n\u001b[1;32m 962\u001b[0m new_labels \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39maxes[axis]\u001b[39m.\u001b[39mtake(indexer)\n\u001b[0;32m--> 963\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mreindex_indexer(\n\u001b[1;32m 964\u001b[0m new_axis\u001b[39m=\u001b[39;49mnew_labels,\n\u001b[1;32m 965\u001b[0m indexer\u001b[39m=\u001b[39;49mindexer,\n\u001b[1;32m 966\u001b[0m axis\u001b[39m=\u001b[39;49maxis,\n\u001b[1;32m 967\u001b[0m allow_dups\u001b[39m=\u001b[39;49m\u001b[39mTrue\u001b[39;49;00m,\n\u001b[1;32m 968\u001b[0m copy\u001b[39m=\u001b[39;49m\u001b[39mNone\u001b[39;49;00m,\n\u001b[1;32m 969\u001b[0m )\n",
+ "File \u001b[0;32m~/Desktop/arxiv-paper-recommender/arxiv-env/lib/python3.11/site-packages/pandas/core/internals/managers.py:740\u001b[0m, in \u001b[0;36mBaseBlockManager.reindex_indexer\u001b[0;34m(self, new_axis, indexer, axis, fill_value, allow_dups, copy, only_slice, use_na_proxy)\u001b[0m\n\u001b[1;32m 737\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mIndexError\u001b[39;00m(\u001b[39m\"\u001b[39m\u001b[39mRequested axis not found in manager\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[1;32m 739\u001b[0m \u001b[39mif\u001b[39;00m axis \u001b[39m==\u001b[39m \u001b[39m0\u001b[39m:\n\u001b[0;32m--> 740\u001b[0m new_blocks \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_slice_take_blocks_ax0(\n\u001b[1;32m 741\u001b[0m indexer,\n\u001b[1;32m 742\u001b[0m fill_value\u001b[39m=\u001b[39;49mfill_value,\n\u001b[1;32m 743\u001b[0m only_slice\u001b[39m=\u001b[39;49monly_slice,\n\u001b[1;32m 744\u001b[0m use_na_proxy\u001b[39m=\u001b[39;49muse_na_proxy,\n\u001b[1;32m 745\u001b[0m )\n\u001b[1;32m 746\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m 747\u001b[0m new_blocks \u001b[39m=\u001b[39m [\n\u001b[1;32m 748\u001b[0m blk\u001b[39m.\u001b[39mtake_nd(\n\u001b[1;32m 749\u001b[0m indexer,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 755\u001b[0m \u001b[39mfor\u001b[39;00m blk \u001b[39min\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mblocks\n\u001b[1;32m 756\u001b[0m ]\n",
+ "File \u001b[0;32m~/Desktop/arxiv-paper-recommender/arxiv-env/lib/python3.11/site-packages/pandas/core/internals/managers.py:898\u001b[0m, in \u001b[0;36mBaseBlockManager._slice_take_blocks_ax0\u001b[0;34m(self, slice_or_indexer, fill_value, only_slice, use_na_proxy)\u001b[0m\n\u001b[1;32m 896\u001b[0m blocks\u001b[39m.\u001b[39mappend(nb)\n\u001b[1;32m 897\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m--> 898\u001b[0m nb \u001b[39m=\u001b[39m blk\u001b[39m.\u001b[39;49mtake_nd(taker, axis\u001b[39m=\u001b[39;49m\u001b[39m0\u001b[39;49m, new_mgr_locs\u001b[39m=\u001b[39;49mmgr_locs)\n\u001b[1;32m 899\u001b[0m blocks\u001b[39m.\u001b[39mappend(nb)\n\u001b[1;32m 901\u001b[0m \u001b[39mreturn\u001b[39;00m blocks\n",
+ "File \u001b[0;32m~/Desktop/arxiv-paper-recommender/arxiv-env/lib/python3.11/site-packages/pandas/core/internals/blocks.py:945\u001b[0m, in \u001b[0;36mBlock.take_nd\u001b[0;34m(self, indexer, axis, new_mgr_locs, fill_value)\u001b[0m\n\u001b[1;32m 942\u001b[0m allow_fill \u001b[39m=\u001b[39m \u001b[39mTrue\u001b[39;00m\n\u001b[1;32m 944\u001b[0m \u001b[39m# Note: algos.take_nd has upcast logic similar to coerce_to_target_dtype\u001b[39;00m\n\u001b[0;32m--> 945\u001b[0m new_values \u001b[39m=\u001b[39m algos\u001b[39m.\u001b[39;49mtake_nd(\n\u001b[1;32m 946\u001b[0m values, indexer, axis\u001b[39m=\u001b[39;49maxis, allow_fill\u001b[39m=\u001b[39;49mallow_fill, fill_value\u001b[39m=\u001b[39;49mfill_value\n\u001b[1;32m 947\u001b[0m )\n\u001b[1;32m 949\u001b[0m \u001b[39m# Called from three places in managers, all of which satisfy\u001b[39;00m\n\u001b[1;32m 950\u001b[0m \u001b[39m# these assertions\u001b[39;00m\n\u001b[1;32m 951\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39misinstance\u001b[39m(\u001b[39mself\u001b[39m, ExtensionBlock):\n\u001b[1;32m 952\u001b[0m \u001b[39m# NB: in this case, the 'axis' kwarg will be ignored in the\u001b[39;00m\n\u001b[1;32m 953\u001b[0m \u001b[39m# algos.take_nd call above.\u001b[39;00m\n",
+ "File \u001b[0;32m~/Desktop/arxiv-paper-recommender/arxiv-env/lib/python3.11/site-packages/pandas/core/array_algos/take.py:117\u001b[0m, in \u001b[0;36mtake_nd\u001b[0;34m(arr, indexer, axis, fill_value, allow_fill)\u001b[0m\n\u001b[1;32m 114\u001b[0m \u001b[39mreturn\u001b[39;00m arr\u001b[39m.\u001b[39mtake(indexer, fill_value\u001b[39m=\u001b[39mfill_value, allow_fill\u001b[39m=\u001b[39mallow_fill)\n\u001b[1;32m 116\u001b[0m arr \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39masarray(arr)\n\u001b[0;32m--> 117\u001b[0m \u001b[39mreturn\u001b[39;00m _take_nd_ndarray(arr, indexer, axis, fill_value, allow_fill)\n",
+ "File \u001b[0;32m~/Desktop/arxiv-paper-recommender/arxiv-env/lib/python3.11/site-packages/pandas/core/array_algos/take.py:162\u001b[0m, in \u001b[0;36m_take_nd_ndarray\u001b[0;34m(arr, indexer, axis, fill_value, allow_fill)\u001b[0m\n\u001b[1;32m 157\u001b[0m out \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39mempty(out_shape, dtype\u001b[39m=\u001b[39mdtype)\n\u001b[1;32m 159\u001b[0m func \u001b[39m=\u001b[39m _get_take_nd_function(\n\u001b[1;32m 160\u001b[0m arr\u001b[39m.\u001b[39mndim, arr\u001b[39m.\u001b[39mdtype, out\u001b[39m.\u001b[39mdtype, axis\u001b[39m=\u001b[39maxis, mask_info\u001b[39m=\u001b[39mmask_info\n\u001b[1;32m 161\u001b[0m )\n\u001b[0;32m--> 162\u001b[0m func(arr, indexer, out, fill_value)\n\u001b[1;32m 164\u001b[0m \u001b[39mif\u001b[39;00m flip_order:\n\u001b[1;32m 165\u001b[0m out \u001b[39m=\u001b[39m out\u001b[39m.\u001b[39mT\n",
+ "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
+ ]
+ }
+ ],
+ "source": [
+ "tfidf_df[tfidf_features].to_array()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 59,
+ "metadata": {},
+ "outputs": [
+ {
+ "ename": "KeyError",
+ "evalue": "\"['00', '000', '0000002', '0000008', '0000010', '0000013', '000002', '00001', '00001887227319', '00002', '000085', '0001', '0002', '000201', '00023', '00026', '0003', '000301c', '0003184', '00034', '00036h', '0004', '0004016', '0004203', '0004222', '0005', '00057', '0007', '00079248', '0008', '00080', '00084', '0009', '000times', '001', '0010', '00105', '0011', '00116', '0012', '0012170', '00124', '0013', '00133', '00136', '0014', '00148', '0016257', '0017', '0019', '00197686', '002', '0021', '00226', '0023', '0024', '0029', '002coo_3', '003', '0031', '0033', '0035', '0038', '004', '00411', '0044', '0047', '0049', '00491', '004r', '005', '0050', '00513v1', '0052', '0055', '0056', '0057', '0059', '006', '0060', '00619', '0062', '0068', '00680', '007', '00713', '0073', '00759', '0076', '00788', '008', '0087', '008_', '009', '0093', '00938', '0094', '0095', '00_m', '01', '010', '010102', '010143', '010222', '0106554', '0107770', '010824', '0109', '011', '011013', '011032', '0115', '011745', '011902', '012', '01202', '012105', '01218', '0123', '0125', '0127', '012903', '012957', '013', '013002', '013003', '013021', '013104', '013110', '013133', '013415', '013607', '0138', '014', '014031', '014412', '0144235x', '014504', '014512', '014522', '0149', '014_', '015', '015102', '015203', '01579', '015902', '016', '016101', '016402', '01655', '01656', '017', '018', '01858', '0188', '0189', '019', '01a', '02', '020', '020101', '020103', '0201268', '0201285', '020184', '020303', '020403', '020407', '02041', '02056', '0206012', '0206102', '0206184', '0207', '0208', '021', '021007', '021013', '0211428', '0218', '0219', '022', '022118', '0222', '02274', '0229', '023', '0235', '0239', '024', '024012', '024018', '024020', '024507', '024903', '025', '025001', '025005', '025303', '025ba2cu3o', '026', '02636', '0264', '02650v1', '026801', '026804', '026808', '0269', '027', '027002', '027203', '02746', '027903', '028', '029', '02900', '02927', '02929', '0295', '02962', '02966', '02k', '03', '030', '0301036', '0301099', '0301118', '030329', '030402', '0304129', '0305', '0306189', '0306198', '0306630', '0307164', '0308111', '03082', '031', '031028', '031088', '031106', '031203', '0312041', '0312160', '0312244', '03150', '031_', '032', '0321', '032105', '032112', '032133', '032501', '033', '033134', '0332', '033351', '0334', '033602', '03385', '033906', '034', '034102', '0347', '03473', '035', '035127', '035202', '035444', '036', '036104', '036109', '03646', '036805', '037', '037113', '037s', '038', '03817', '039', '03905', '03d1gt', '03x', '04', '040', '0403', '0403063', '0403528', '04036', '040405', '0405052', '0405282', '040812', '0409209', '041328', '0416', '041903', '042', '042101', '042201', '042210', '042325', '0426', '0429', '042903', '043', '0432', '043333', '043503', '043616', '043_', '044', '044013', '044025', '044047', '04404v1', '044102', '0444', '045', '045001', '0453', '0454', '045503', '045702', '046', '04644', '04698', '047009', '0473', '04768', '048', '049', '0496243', '04cuo4', '04o', '05', '050', '0501398', '0502058', '0504', '0505537', '0506', '05060', '0507', '0508088', '0508270', '0509089', '0509130', '051', '0511', '051103', '0512058', '0512471', '0513', '05155', '051907', '052', '052105', '052133', '052301', '0525', '0527', '052701', '053', '053005', '0538', '053824', '054', '054003', '054606', '05464', '05478', '055', '055108', '05568', '056', '0560', '056101', '05634', '056402', '05648', '0565600', '0565615', '056702', '05697', '057', '058', '0581', '05893', '05920', '05_', '05b2', '05o3', '05ro', '05um', '06', '060', '0601371', '060218', '0603130', '0604075', '0605386', '0605587', '0606005', '0606760', '0611024', '061904', '062', '062104', '0622', '062309', '062326', '062604', '0630', '063130', '063414', '063419', '063607', '063b03', '064', '064024', '0649', '065', '065127', '065301', '0656', '06581', '06586', '066', '066201', '066402', '066409', '066612', '0668', '067', '068', '0682', '06848', '06863', 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'095302', '0954', '096', '0962', '0964', '097', '0976', '098', '099', '0992486', '0_', '0_0', '0_1', '0_2', '0_3', '0_b', '0_d', '0_had', '0_i', '0_ih', '0_iz', '0_j', '0_k', '0_l', '0_s', '0_u', '0_z', '0a', '0b', '0bar', '0c', '0d', '0deg', '0e18', '0ev', '0f_', '0f_1', '0fb', '0g_', '0ge40', '0h', '0i', '0k', '0mpa', '0ms', '0n', '0nutation', '0o', '0pi', '0pt', '0tcoke4keyc', '0th', '0um', '0v', '0w', '0x', '0x10', '10', '100', '1000', '10000', '100000', '10000000', '10000k', '1000c', '1000k', '1000nm', '1000pc', '1000sec', '1003', '100546', '1006', '100703', '1008', '1009', '100base', '100c', '100cst', '100h', '100k', '100kpc', '100mbps', '100ms', '100ns', '100pm', '100s', '100um', '100yr', '101', '1010', '1011', '101101', '1012', '1013', '1015', '1016', '10165', '10168', '1019', '101901', '101955', '101star', '102', '1020', '10200', '10216', '10224', '1022w', '1023', '10239', '1024', '10240', '1026', '1029', '102pd', '103', '1030', '10300', '103095', '1031', '10319', '103295n', '1032a', '1035', '1037', '1038', '10384', '1039', '103987', '104', '10400', '104102', '1042', '10420', '1043', '10430', '1044', '10465', '1047', '1048', '105', '1050', '105001', '105013', '1052', '1053', '1055', '10580', '105888', '1059', '106', '10610', '106102', '10610u', '1064', '106402', '1065', '10650', '1067', '1068', '106823', '107', '107146', '1073', '10747', '10753', '10758', '107602', '1078', '10782', '10790', '108', '1080', '1080ti', '1081', '1083', '10830', '1085', '10860', '10864', '108877', '109', '1090', '10909', '10917', '10934', '1099', '10a', '10b', '10base', '10be', '10c', '10d', '10db', '10deg', '10e', '10e11', '10e12', '10e13', '10e27', '10e6', '10eev', '10ev', '10gbps', '10ghz', '10hour', '10hz', '10k', '10khz', '10m', '10mjy', '10mpc', '10n', '10ne20', '10pb', '10pc', '10ppb', '10pt', '10pt4', '10sb0', '10t', '10th', '10um', '10wcm', '10x', '10x10cm2', '10x5', '11', '110', '1100', '11000', '1100mhz', '11020', '1103', '11036', '1106', '1109', '111', '1111', '11115', '1112', '11120', '111301', '1115', '11158', '1116', '11169', '111805', '1119', '112', '1120', '11200', '1122', '11237', '1125', '11252', '1127', '1128', '1128569561140', '1128575290808', '113', '1132', '113206', '11365', '114', '1140', '11429', '114501', '1148', '1149', '115', '1150', '11500', '115001', '1151', '11513', '115226', '115413', '115421', '115567', '1159', '115pc', '116', '11615', '116235', '116401', '11678', '1168', '11690', '117', '1170', '1171', '1175', '1176', '1178', '118', '118101', '1182', '1184', '1185', '1185k', '1186', '1187', '1189', '119', '1190', '11903', '1192', '1193', '1196', '11984', '1199', '11_', '11ad', '11af', '11be', '11d', '11m_', '11msun', '11n', '11n_c', '11th', '11x', '12', '120', '1200', '120000', '1200k', '1201', '1202', '1203', '1204', '120402', '1205', '120504', '1206', '1207', '1208', '120c', '120deg', '120kms', '120ks', '120mk', '121', '1210', '1211', '121102', '1212', '1219', '12192', '121a', '122', '12200', '1221', '1222', '1223', '1224', '1225', '122563', '1227', '122910', '123', '1231', '1232', '1234', '123441', '1235', '1237', '1239', '124', '1240', '124036', '124057', '1241', '124112', '1243', '1246', '125', '1250', '12500', '1250n', '125110', '125116', '1252', '12560k', '1257', '125723', '1259', '125gev', '125s', '125te', '125th', '126', '1260', '1261', '1264', '12673', '127', '1270', '127203', '1275', '1276', '128', '1281', '1285', '12894', '128x128', '129', '1292', '1295', '1296', '129xe', '12b', '12c', '12c14n', '12c15n', '12cao', '12ch3d', '12cn', '12co', '12coo2', '12e', '12gyr', '12h', '12helix', '12k', '12mx0', '12s', '12th', '12x', '12x12', '13', '130', '1300', '1301', '130201', '13029', '1303', '1304', '130427a', '1305', '1306', '1308', '13085', '1309', '130sb', '130te', '131', '1310', '1311', '1311010', '1312', '131231a', '131366n', '1315', '1316', '131sb', '132', '1320', '13258', '1328', '132sb', '132sn', '133', '1333', '133cs', '133te', '134', '1343', '134439', '134440', '13450k', '134705', 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'1900', '1901', '1902', '1903', '190344', '1904', '1905', '1906', '1908', '1909', '191', '1910', '1911', '1912', '1913', '1915', '1916', '1917', '1918', '1919', '192', '1920', '19200', '1920s', '192105', '1922', '1923', '1924', '1925', '1926', '1928', '193', '1930', '1930s', '1931', '1932', '1933', '1934', '1935', '1936', '1937', '1938', '1939', '193901', '194', '1940', '19400', '1941', '1943', '1944', '1945', '1946', '1947', '1948', '1949', '195', '1950', '1951', '195117', '1952', '1953', '1954', '1955', '1956', '1957', '1958', '1959', '196', '1960', '19603', '1960ie', '1961', '1962', '1963', '1964', '1965', '1966', '1967', '1968', '1969', '196944', '197', '1970', '1971', '1972', '1973', '1974', '1975', '1976', '1977', '1978', '1979', '197au', '198', '1980', '1980s', '1981', '1982', '1983', '1984', '1985', '1986', '1987', '1987a', '1988', '1989', '1989a', '198x', '199', '1990', '1990ie', '1990jcoph', '1990s', '1991', '1992', '1993', '1993j', '1994', '1995', '1995n', '1996', '1996b', '1997', '1997ef', '1998', '1998a', '1998bw', '1999', '1999by', '1999da', '1999dk', '1999gp', '199hg', '19b', '19charge', '19f', '19hz', '19k', '19m_', '19th', '19yb0', '1_', '1_0', '1_1', '1_10', '1_11', '1_2', '1_d', '1_et', '1_f', '1_k', '1_loc', '1_n', '1_nis', '1_r', '1_t', '1_v', '1_xl', '1a', '1and', '1au', '1b', '1b_1', '1bmmrelax', '1brelax', '1c', '1cr', '1cz', '1d', '1d2', '1d_2', '1day', '1db', '1dbpp', '1deg', '1deg27', '1e', '1e0657', '1e10', '1e13', '1e14', '1e1547', '1e16', '1e161348', '1e1740', '1e18', '1e19', '1e20', '1e21', '1e4', '1e41', '1e49', '1e5', '1e7', '1eev', '1es', '1ev', '1f1', '1f3', '1f7', '1g', '1g4', '1gbps', '1gev', '1go', '1gsps', '1gyr', '1h', '1h0707', '1jby', '1k', '1l2s', '1la_0', '1lc', '1m', '1mhz', '1mjy', '1mk', '1mo', '1mole', '1ms', '1msun', '1n', '1p', '1p1', '1p_', '1p_1', '1pc', '1pi', '1pn', '1q', '1ql', '1re', '1rsb', '1rxs', '1s', '1s0', '1s2s', '1s3d', '1s3p', '1s_', '1s_0', '1sax', '1sigma', '1st', '1t', '1tev', '1u', '1ufa', '1ufas', '1um', '1v', '1vs1', '1x1', '1x10', '1x104', '1x2', '1x4', '1xb', '1xs', '1yr', '1z_', '1zr', '20', '200', '2000', '20000', '2000bk', '2000ce', '2000k', '2000q', '2000s', '2000x', '2001', '2001a', '2002', '2002a', '2002ap', '2002b', '2002er', '2002fk', '2002hh', '2002rm', '2003', '2003bg', '2003cg', '2003du', '2003fk', '2003gd', '2003ie', '2003kf', '2004', '200404', '200405', '2004a', '2004b', '2004et', '2005', '2006', '200603', '2006a', '2006aj', '2006c', '2006jc', '2006ov', '2007', '2007gr', '2007or10', '2008', '2008d', '2008mq', '2009', '2009bb', '2009jf', '200au', '200gev', '200k', '200pc', '200um', '200x', '201', '2010', '2011', '201104', '2011dh', '2011fe', '2011nk', '2012', '20121102a', '2012b', '2012cg', '2012qut', '2013', '2013a', '2013b', '2013kna', '2014', '2015', '2015hga', '2015wwa', '2016', '201626', '2016a', '2016prf', '2016som', '2016xxf', '2017', '2017a', '2017gfo', '2017jlg', '2018', '201889', '2018a', '2018b', '2019', '2019kaq', '201hg', '202', '2020', '20201124', '2020b', '2021', '2021a', '2021b', '2021yja', '2022', '2023', '2024', '2025', '2028', '2029', '203', '2030', '203001', '2030s', '2032', '2035', '203602', '2039', '203km', '204', '2040', '2048', '20483', '204907', '205', '2050', '2056', '206', '2068', '206at', '207', '2070', '2071', '207208', '207401', '2076', '2078', '207po', '208', '2080', '2081b', '208pb', '209', '2094', '209458', '209458b', '2099', '209bi', '209rn', '20b', '20b2', '20gev', '20h', '20k', '20khz', '20mev', '20ne', '20newsgroups', '20ppb', '20th', '20x', '20x20', '21', '210', '2100', '2104', '2105', '2106', '2107', '2108', '210k', '211', '2110', '2111', '2112', '211211a', '2114', '212', '2120', '21207022', '212505', '212po', '213', '2135', '214', '214104', '214109', '214420', '214bi', '214pb', '215', '2150', '2152', '2153', '2154', '2155', '215d', '216', '2160', '2166', '216k', '217', '217206', '2174', '2175', '2178', '218', '218fr', '219', '2190', '219188', '21f_', '21k', '21n', '21st', '21te2', '21w', '22', '220', '2200', '2201', '2202', '220408b', '2205', '220502', '2206', '2207', '220k', '221', '2210504', '2212', '2213', '2218', '222', '2221', '2223', '222rn', '223', '2230', '223002', '223201', '2237', '2239', '223ra', '224', '2240', '2246', '225', '2250', '2251', '2255', '2257', '2259', '225hf0', '225mo0', '225ta0', '225w0', '226', '2260', '22600', '2261', '2264', '2264d', '2265', '226ac', '226ra', '227', '2271', '227601', '228', '2281', '228th', '229', '22920', '22940', '2295', '2296', '229b', '229m', '229th', '22cm', '22fdx', '22k', '22na', '22r', '22x', '23', '230', '2300', '2301', '230401', '2306', '230601', '23098', '230k', '231', '2310', '2312', '2316a', '2317', '2319', '231pa', '232', '2326', '232u', '233', '2336', '233809', '233u', '234', '2344', '2344411', '2349', '234u', '235', '2350', '235001', '2352', '235205', '235u', '236', '2360', '2366', '236b', '237', '2376', '237x', '238', '2380', '2389', '238pube', '238u', '239', '2390', '2391', '2392', '23fm', '23n', '23rd', '24', '240', '2400', '2400100', '240401', '240402', '240503', '2408', '240u', '241', '241101', '241102', '241406', '2417', '241ambe', '242', '2420', '2422', '243', '2430', '2437', '244', '244111', '2446aj', '2446ak', '2446al', '245', '2450', '245124', '2451917', '245302', '2454605', '2456', '2456074', '2457412', '245a', '246', '2460', '2463', '246401', '2466', '2467', '247', '2470', '2477', '248', '2480', '249', '2496', '249bk', '24a', '24b', '24ga0', '24h', '24k', '24mg', '24microns', '24myr', '24na', '24th', '25', '250', '2500', '25000', '2500k', '2501', '250504', '250k', '250r', '250v', '250x', '251', '2516', '252', '253', '2531', '2532', '2534', '2536', '254', '2547', '255', '2550', '255001', '2554', '25546', '2555', '256', '2560', '2562', '256802', '257', '257003', '2577', '258', '258701', '259', '2595', '25al', '25at', '25ca0', '25k', '25kpa', '25mag', '25mg', '25s', '25th', '25x', '26', '260', '2600', '2600mhz', '260411', '260601', '260_', '261', '2615', '2619', '262', '2620050', '26262', '2628', '2629', '263', '2632', '264', '2643', '2648', '265', '2650', '2654', '265ms', '266', '2669', '2669v1', '267', '267004', '2677', '268', '2683', '2688', '268k', '269', '269d', '26al', '26b', '26d', '26deg2', '26k', '26mg', '26th', '27', '270', '27000', '2700cm', '2702', '270k', '271', '2713', '2718', '272', '2724', '273', '2732', '273pb', '274', '2741', '2742', '2743', '2744', '2748', '275', '27500', '27500k', '2754', '2755', '2756', '276', '277', '277906', '278', '2782', '2784', '2787', '279', '2796', '2798', '27al', '27mn', '27mo', '27th', '27x', '28', '280', '2800', '2800_', '2806', '2808', '281', '2816', '282', '2825', '283', '2834', '2838', '284', '2841', '2844', '284423', '285', '285bp', '286', '2860', '287', '288', '2880', '2883', '289', '28k', '28p', '28si', '28siresonant', '28th', '28x', '29', '290', '2900', '2904', '2909', '291', '2910', '2915', '2919', '292', '2927', '292747', '292917', '293', '294', '2940', '2942', '2943', '294344', '295', '2950', '2958', '296', '297', '2976', '298', '2988', '299', '2996', '29c', '29e', '29si', '29sic2', '29th', '29um', '2_', '2_0', '2_1', '2_2', '2_f', '2_i', '2_k', '2_m', '2_n', '2_q', '2_s', '2_t', '2_tw', '2_w', '2_x', '2a', '2a1', '2abc', '2ad', '2alpha', '2as2', '2b', '2ba2cu3o', '2c', '2c_1', '2c_2', '2c_3', '2c_5', '2ca1', '2ch', '2ch3cooh', '2chx_3', '2ck', '2clo4', '2cr0', '2cuo4', '2d', '2d2', '2d_', '2d_1', '2d_1d_2', '2d_vae_uda_for_3d_sythesis', '2db', '2dch', '2de', '2deg', '2degs', '2dels', '2delta', '2des', '2df', '2dfgrs', '2dhg', '2dp', '2dti', '2dv_g', '2dx', '2e', '2e15', '2e2nc', '2e47', '2e_0', '2ec', '2ef', '2ev', '2ex', '2exptime', '2f', '2f1', '2f_1', '2f_3', '2f_c', '2fb', '2fgl', '2fm', '2g', '2g_0', '2g_n', '2gamma', '2ghz', '2gm', '2h', '2h1p', '2h_2o', '2h_c2', '2hdm', '2hdms', '2hf0', '2i', '2i3', '2j', '2jx', '2k', '2k_f', '2kh', '2kx4k', '2l', '2l1s', '2l_', '2l_e', '2lac', '2log_2', '2ls', '2m', '2m0850', '2m0850b', '2m_', '2m_b', '2m_c', '2m_sun', '2m_w', '2m_z', '2ma', '2mass', '2mdm', '2mhg', '2micron', '2mn', '2mno3', '2mo0', '2ms', '2msun', '2mv', '2n', '2n_c', '2n_f', '2nb0', '2nd', '2neb', '2nee', '2ng', '2nn', '2o3', '2obs', '2omega', '2p', '2p2h', '2p_f', '2p_j', '2p_t', '2pc', '2pf6', '2pi', '2pn', '2ppt', '2pr', '2pt', '2q', '2r', '2rbzn', '2reo4', '2rqaw', '2s', '2s2p', '2s_', '2s_1', '2sb', '2sc', '2sf', '2sigma', '2sls', '2sqrt', '2t', '2ta0', '2ta_0', '2tau', '2tc', '2theta_', '2u', '2u_a', '2uu_', '2uu_x', '2v', '2vt', '2w', '2w0', '2x', '2x1', '2x10', '2x12h2o', '2x2', '2x3', '2x4', '2x4aqh2bmb', '2x_n', '2xcy_3', '2xmmp', '2y', '2z', '2zr0', '30', '300', '3000', '30000', '3000k', '3000mpc', '3002', '3003', '300au', '300gpa', '300h', '300hz', '300k', '300mpc', '300x300', '300x30x2', '301', '3012', '302', '3021', '303', '3031', '3032', '304', '3045', '305', '3050', '3054', '3055', '306', '3062', '307', '3072', '3073', '3079', '30795', '308', '3080', '309', '3096', '309h', '30b', '30db', '30deg', '30hz', '30k', '30m', '30mk', '30ms', '30p2o5', '30pc', '30s', '30sic2', '30ujy', '30x', '30x30', '30zn64', '31', '310', '3100', '3109', '310f', '310ghz', '311', '3110', '3114', '3116', '312', '3129', '312g', '313', '31347', '314', '315', '316', '3161', '3162', '3168', '317', '318', '3185', '3187', '319', '3195', '31k', '31mjy', '31o', '31th', '31x', '32', '320', '3200', '3200k', '3201', '3202', '3205', '3208', '320h', '320ms', '321', '3226', '3227', '323', '324', '3240', '325', '326', '327', '32768', '328', '3283', '329', '3296', '32k', '33', '330', '3300', '330mhz', '331', '332', '3327', '333', '3338', '3339', '334', '3344', '3348837', '335', '3358', '336', '3367', '3368', '337', '3371', '3372', '3375', '33761', '3379', '338', '3387', '3388', '339', '3390', '33_', '33k', '33mno3', '33pc', '34', '340', '34004', '341', '3412', '342', '3420', '3421', '343', '344', '3447', '345', '34506', '3456', '3457', '3459', '346', '347', '34700', '3476', '3478', '348', '3480', '348000', '34808', '3482', '3486', '3488', '349', '3492', '34970', '34b', '34ev', '34k', '34mm', '34th', '34w', '34x1018', '35', '350', '3500', '35000', '3500kms', '3509', '350micron', '350mw', '351', '3514950', '351724', '35187', '352', '3525', '3526', '352619', '3527', '3543', '3546', '355', '3559m_', '356', '35600', '35610879', '357', '3574', '358', '359', '35a', '35b', '35cl', '35coo2', '35ev', '35gev', '35k', '35msun', '35s', '35th', '35x', '36', '360', '3600', '3603', '360isod', '361', '362', '36216', '3624', '3627', '36278', '363', '36351', '364', '36491', '365', '3654', '3656', '3658', '366', '3665', '3669', '367', '3677', '3686', '369', '36900', '36b', '36kr86', '36mers', '36x', '37', '370', '3700', '37000', '3706', '371', '3710', '3718', '3725', '3726', '3727', '3734', '374', '375', '376', '377', '3770', '3784', '3787', '37x', '38', '380', '3800', '380651', '3808', '381', '3819', '382', '3820', '3824v4', '3825', '3827', '383', '384', '3840', '3840x2160', '385', '3850', '386', '387', '3872', '3875', '3879', '3880', '3884', '38deg', '38mk', '39', '390', '3900', '3906', '3911', '3915', '3916', '3918', '392', '393', '394', '3940', '3946', '3947', '395', '396', '3966', '397', '3973', '3974', '398', '3985', '399', '39mag', '39x', '3_', '3_1', '3_s', '3_x', '3a', '3an', '3b', '3beta', '3bf', '3bp', '3bw', '3c', '3c186', '3c273', '3c286', '3c294', '3c58', '3ca', '3ca1', '3cg', '3cl', '3clpro', '3cm', '3cnf', '3cr', '3d', '3d0', '3d1', '3d2', '3d5', '3d_1', '3db', '3dcertify', '3dcnn', '3def', '3deg', '3dircadb', '3dof', '3dpeople', '3dpw', '3dresnet18', '3drt', '3dvar', '3dx2', '3dyz', '3e', '3e14', '3e22', '3e25', '3e6', '3ef', '3eg', '3ev', '3f', '3f3', '3f_2', '3fb', '3fgl', '3fhl', '3g', '3gpp', '3h', '3h_', '3hdm', '3he', '3i', '3i1o', '3j', '3k', '3khz', '3ks', '3ll', '3m', '3m_b', '3mev', '3mgy', '3mhz', '3micron', '3mm', '3mno3', '3n', '3nb2', '3nu', '3o_', '3p', '3p0', '3p1', '3p_0', '3p_1', '3p_2', '3p_j', '3pi', '3pn', '3q', '3q_1q_5', '3r', '3r_', '3rd', '3ro', '3s', '3s1', '3s3p', '3s_1', '3sat', '3sigma', '3sr1', '3t_3', '3t_c', '3term', '3u_', '3x', '3x10', '3x2', '3x3', '3x48', '3x64', '3xmm', '3xrp', '3xs', '3xs5', '3y', '3z', '3z2', '40', '400', '4000', '4003309', '400331', '40051', '400k', '400km', '400ng', '400pc', '401', '401214', '40176', '402', '4020', '4025', '4026', '403', '4038', '404', '4040', '4047ab', '405', '4051', '4056', '406', '40613', '4072', '4078', '408', '409', '409552', '4096', '40_', '40ar', '40c', '40ca', '40db', '40deg', '40k', '40k87rb', '40k_2', '40kk', '40nm', '40pc', '40x', '41', '410', '4100', '4100ab', '41076', '4108', '410b', '411', '4112', '41177', '4118x', '412', '4123', '413', '4131', '414', '4140', '415', '41501', '4153ab', '415x', '416', '4160', '417', '418', '419', '41979', '419k', '41k', '42', '420', '420k', '421', '4211', '4214', '422', '4220', '4224', '4227', '423', '4232', '4236', '424', '4247', '425', '426', '4260', '4261', '4263', '4266', '427', '4270', '4274', '4276', '4278', '428', '4284', '429', '4291', '4294', '42d', '42k', '42um', '42x', '43', '430', '4300', '4305', '4306', '431', '43160', '432', '4320', '4325', '433', '4332', '434', '4342', '4348', '434gops', '435', '4350', '436', '4360', '4363', '437', '4370', '43771963', '438', '4380', '43806', '4381', '4388', '439', '4394', '4395', '43k', '43mn', '43x', '44', '440', '4412', '4414', '4415', '4419ev', '441n', '442', '443', '4430', '4438', '444', '4442', '44447', '44497', '445', '4450', '4459', '447', '4470', '4471', '4472', '4479b', '448', '4485', '4486a', '449', '4490', '44986', '44ev', '45', '450', '4500', '451', '4510', '452', '453', '4533', '45364b', '454', '4550', '456', '4568', '4579', '458', '4580', '4587', '4590', '45ev', '45mn', '45mno3', '45p', '46', '460', '4600', '461', '4616', '465', '4658', '4660', '467', '4672', '468', '4686', '4691', '4696', '46b', '46ca', '46s', '47', '470', '4700', '4707', '471', '4715', '472', '47205', '4721', '4724', '4726', '473', '4730', '4731', '4732', '4740', '4750', '4750858', '4754', '476', '4763', '477', '4771', '478', '4780', '4785', '4789', '479', '4796a', '48', '480', '4800', '4808', '481', '482', '483', '48369689570172', '484', '4843j', '485', '4850', '4859', '486', '487', '4877', '489', '48au', '48c', '48ca', '48g', '49', '490', '4900', '4904', '491', '492', '493', '4937', '494', '4945', '495', '4950', '4955', '495611', '4959', '496', '4964', '497', '4988', '499', '4993', '49933', '4995', '4997', '4_', '4_2', '4_d', '4_s', '4a', '4b', '4c', '4ce0', '4chan', '4cm', '4d', '4db', '4de1', '4dvar', '4e', '4e21', '4em', '4ev', '4f', '4f_', '4fb', '4fe2as2', '4fgl', '4g', '4g_n', '4gamma', '4ghz', '4h', '4h2o', '4hb', '4he', '4he_n', '4i_', '4j', '4k', '4k_f', '4kev', '4l', '4lac', '4lqs', '4m', '4m_', '4m_c', '4m_solar', '4mcf', '4minute', '4mno3', '4most', '4ms', '4msun', '4n', '4n_e', '4n_f', '4nls', '4on', '4open', '4p', '4p_', '4pi', '4q', '4r_', '4r_s', '4re', '4s', '4s5', '4s6', '4s_', '4sigma', '4sin', '4t', '4t1', '4t2', '4t_', '4te7', '4th', '4u', '4u1538', '4u1700', '4u_t', '4v', '4wm', '4x', '4x10', '4x2', '4x4', '4z', '50', '500', '5000', '5000cm', '5000k', '5000th', '5007', '5007a', '500k', '500kev', '500pc', '501', '502', '502x', '5039', '504', '5040', '504a', '505', '5053', '5055', '506', '5065', '508', '5085', '509', '50a', '50au', '50fps', '50hz', '50k', '50kda', '50kw', '50m_', '50mo', '50n', '50pc', '50snf2', '50states10k', '50th', '50x50', '51', '510', '5100', '5100a', '5105v1', '5106', '5109', '511', '511ex', '512', '5128', '512x512', '513', '513353', '5135', '514', '515', '5151', '516', '5160', '51629', '5167', '517', '5178', '518', '5181', '518304551833', '51891', '519', '51d', '52', '520', '5201', '5209', '521', '5216', '5218', '522', '523', '5235', '523504', '5237', '524', '5240', '525', '525150', '5253', '5255', '528', '5281', '529', '52b', '52d', '52g', '52li0', '52m', '53', '530', '5300', '5303', '531', '53143', '531906', '532', '53206', '533', '534', '535', '535x10', '536', '538', '5380', '53a', '54', '540', '5400', '5408', 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'zirconia', 'zirconium', 'zirnbauer', 'zisman', 'zitt', 'zitterbewegung', 'ziv', 'zj', 'zjj', 'zju', 'zju3dv', 'zjuicsr', 'zjzj', 'zk', 'zklaim', 'zkp', 'zkps', 'zl_il_j', 'zlfh2015', 'zm', 'zmp', 'zn', 'zn0', 'zn1', 'zn_', 'znajek', 'znal', 'znc', 'zncu', 'zncu3', 'znfe2o4', 'zng', 'zniak', 'zniro', 'zno', 'znpt', 'zns', 'znsb', 'znse', 'znt', 'znte', 'zntpp', 'znwo4', 'zo', 'zodiacal', 'zograf', 'zohar', 'zoller', 'zolotare', 'zolotareff', 'zolotarev', 'zolpidem', 'zombie', 'zonal', 'zonally', 'zone', 'zong3', 'zonotope', 'zoo', 'zoologici', 'zoom', 'zooming', 'zooniverse', 'zorich', 'zotyev', 'zp', 'zpf', 'zpl', 'zps', 'zpt', 'zq', 'zr', 'zr0', 'zr4', 'zr_', 'zrb12', 'zrb2', 'zrc', 'zrncl', 'zrnial', 'zrp', 'zrpt', 'zrs', 'zrsis', 'zrsise', 'zrsite', 'zrsix', 'zrte', 'zrw2o8', 'zs', 'zschau', 'zsl', 'zsm', 'zsol', 'zsun', 'zt', 'ztd', 'ztf', 'ztf18abvkwla', 'zuazua', 'zubaran', 'zubarev', 'zuber', 'zuckerman', 'zudilin', 'zuily', 'zukowski', 'zulu', 'zumbr', 'zumbrun', 'zumino', 'zureick', 'zurek', 'zurich', 'zurieck', 'zvi', 'zw', 'zw1', 'zwam', 'zwanzig', 'zwanziger', 'zweger', 'zweig', 'zwick', 'zwicky', 'zwiebach', 'zx', 'zxt', 'zy', 'zygmund', 'zygodontomys', 'zyme', 'zynq', 'zyuzin', 'zyx', 'zyzfeaso1', 'zz', 'zz_3', 'zz_7', 'zzh', 'zzz'] not in index\"",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)",
+ "Cell \u001b[0;32mIn[59], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m similarity \u001b[38;5;241m=\u001b[39m cosine_similarity(\u001b[43mdf\u001b[49m\u001b[43m[\u001b[49m\u001b[43mtfidf_features\u001b[49m\u001b[43m]\u001b[49m, _test)\n",
+ "File \u001b[0;32m~/Desktop/arxiv-paper-recommender/arxiv-env/lib/python3.11/site-packages/pandas/core/frame.py:3767\u001b[0m, in \u001b[0;36mDataFrame.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 3765\u001b[0m \u001b[39mif\u001b[39;00m is_iterator(key):\n\u001b[1;32m 3766\u001b[0m key \u001b[39m=\u001b[39m \u001b[39mlist\u001b[39m(key)\n\u001b[0;32m-> 3767\u001b[0m indexer \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mcolumns\u001b[39m.\u001b[39;49m_get_indexer_strict(key, \u001b[39m\"\u001b[39;49m\u001b[39mcolumns\u001b[39;49m\u001b[39m\"\u001b[39;49m)[\u001b[39m1\u001b[39m]\n\u001b[1;32m 3769\u001b[0m \u001b[39m# take() does not accept boolean indexers\u001b[39;00m\n\u001b[1;32m 3770\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mgetattr\u001b[39m(indexer, \u001b[39m\"\u001b[39m\u001b[39mdtype\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39mNone\u001b[39;00m) \u001b[39m==\u001b[39m \u001b[39mbool\u001b[39m:\n",
+ "File \u001b[0;32m~/Desktop/arxiv-paper-recommender/arxiv-env/lib/python3.11/site-packages/pandas/core/indexes/base.py:5876\u001b[0m, in \u001b[0;36mIndex._get_indexer_strict\u001b[0;34m(self, key, axis_name)\u001b[0m\n\u001b[1;32m 5873\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m 5874\u001b[0m keyarr, indexer, new_indexer \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_reindex_non_unique(keyarr)\n\u001b[0;32m-> 5876\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_raise_if_missing(keyarr, indexer, axis_name)\n\u001b[1;32m 5878\u001b[0m keyarr \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mtake(indexer)\n\u001b[1;32m 5879\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39misinstance\u001b[39m(key, Index):\n\u001b[1;32m 5880\u001b[0m \u001b[39m# GH 42790 - Preserve name from an Index\u001b[39;00m\n",
+ "File \u001b[0;32m~/Desktop/arxiv-paper-recommender/arxiv-env/lib/python3.11/site-packages/pandas/core/indexes/base.py:5938\u001b[0m, in \u001b[0;36mIndex._raise_if_missing\u001b[0;34m(self, key, indexer, axis_name)\u001b[0m\n\u001b[1;32m 5935\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mKeyError\u001b[39;00m(\u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mNone of [\u001b[39m\u001b[39m{\u001b[39;00mkey\u001b[39m}\u001b[39;00m\u001b[39m] are in the [\u001b[39m\u001b[39m{\u001b[39;00maxis_name\u001b[39m}\u001b[39;00m\u001b[39m]\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[1;32m 5937\u001b[0m not_found \u001b[39m=\u001b[39m \u001b[39mlist\u001b[39m(ensure_index(key)[missing_mask\u001b[39m.\u001b[39mnonzero()[\u001b[39m0\u001b[39m]]\u001b[39m.\u001b[39munique())\n\u001b[0;32m-> 5938\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mKeyError\u001b[39;00m(\u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39m{\u001b[39;00mnot_found\u001b[39m}\u001b[39;00m\u001b[39m not in index\u001b[39m\u001b[39m\"\u001b[39m)\n",
+ "\u001b[0;31mKeyError\u001b[0m: \"['00', '000', '0000002', '0000008', '0000010', '0000013', '000002', '00001', '00001887227319', '00002', '000085', '0001', '0002', '000201', '00023', '00026', '0003', '000301c', '0003184', '00034', '00036h', '0004', '0004016', '0004203', '0004222', '0005', '00057', '0007', '00079248', '0008', '00080', '00084', '0009', '000times', '001', '0010', '00105', '0011', '00116', '0012', '0012170', '00124', '0013', '00133', '00136', '0014', '00148', '0016257', '0017', '0019', '00197686', '002', '0021', '00226', '0023', '0024', '0029', '002coo_3', '003', '0031', '0033', '0035', '0038', '004', '00411', '0044', '0047', '0049', '00491', '004r', '005', '0050', '00513v1', '0052', '0055', '0056', '0057', '0059', '006', '0060', '00619', '0062', '0068', '00680', '007', '00713', '0073', '00759', '0076', '00788', '008', '0087', '008_', '009', '0093', '00938', '0094', '0095', '00_m', '01', '010', '010102', '010143', '010222', '0106554', '0107770', '010824', '0109', '011', '011013', '011032', '0115', '011745', '011902', '012', '01202', '012105', '01218', '0123', '0125', '0127', '012903', '012957', '013', '013002', '013003', '013021', '013104', '013110', '013133', '013415', '013607', '0138', '014', '014031', '014412', '0144235x', '014504', '014512', '014522', '0149', '014_', '015', '015102', '015203', '01579', '015902', '016', '016101', '016402', '01655', '01656', '017', '018', '01858', '0188', '0189', '019', '01a', '02', '020', '020101', '020103', '0201268', '0201285', '020184', '020303', '020403', '020407', '02041', '02056', '0206012', '0206102', '0206184', '0207', '0208', '021', '021007', '021013', '0211428', '0218', '0219', '022', '022118', '0222', '02274', '0229', '023', '0235', '0239', '024', '024012', '024018', '024020', '024507', '024903', '025', '025001', '025005', '025303', '025ba2cu3o', '026', '02636', '0264', '02650v1', '026801', '026804', '026808', '0269', '027', '027002', '027203', '02746', '027903', '028', '029', '02900', '02927', '02929', '0295', '02962', '02966', '02k', '03', '030', '0301036', '0301099', '0301118', '030329', '030402', '0304129', '0305', '0306189', '0306198', '0306630', '0307164', '0308111', '03082', '031', '031028', '031088', '031106', '031203', '0312041', '0312160', '0312244', '03150', '031_', '032', '0321', '032105', '032112', '032133', '032501', '033', '033134', '0332', '033351', '0334', '033602', '03385', '033906', '034', '034102', '0347', '03473', '035', '035127', '035202', '035444', '036', '036104', '036109', '03646', '036805', '037', '037113', '037s', '038', '03817', '039', '03905', '03d1gt', '03x', '04', '040', '0403', '0403063', '0403528', '04036', '040405', '0405052', '0405282', '040812', '0409209', '041328', '0416', '041903', '042', '042101', '042201', '042210', '042325', '0426', '0429', '042903', '043', '0432', '043333', '043503', '043616', '043_', '044', '044013', '044025', '044047', '04404v1', '044102', '0444', '045', '045001', '0453', '0454', '045503', '045702', '046', '04644', '04698', '047009', '0473', '04768', '048', '049', '0496243', '04cuo4', '04o', '05', '050', '0501398', '0502058', '0504', '0505537', '0506', '05060', '0507', '0508088', '0508270', '0509089', '0509130', '051', '0511', '051103', '0512058', '0512471', '0513', '05155', '051907', '052', '052105', '052133', '052301', '0525', '0527', '052701', '053', '053005', '0538', '053824', '054', '054003', '054606', '05464', '05478', '055', '055108', '05568', '056', '0560', '056101', '05634', '056402', '05648', '0565600', '0565615', '056702', '05697', '057', '058', '0581', '05893', '05920', '05_', '05b2', '05o3', '05ro', '05um', '06', '060', '0601371', '060218', '0603130', '0604075', '0605386', '0605587', '0606005', '0606760', '0611024', '061904', '062', '062104', '0622', '062309', '062326', '062604', '0630', '063130', '063414', '063419', '063607', '063b03', '064', '064024', '0649', '065', '065127', '065301', '0656', '06581', '06586', '066', '066201', '066402', '066409', '066612', '0668', '067', '068', '0682', '06848', '06863', '069', '06944', '06d35', '06f35', '06fm', '06x', '07', '070', '0703150v1', '0704', '070404', '0707', '0708', '0710', '0711', '0712', '071227', '071301', '07135', '071442', '0716', '07161', '072', '0721', '0728', '072gpus', '073', '073005', '0732', '07334', '0736', '0737', '07396', '074019', '07455v2', '074711', '075', '075122', '075125', '0755', '076', '07635', '076406', '07664', '077', '077001', '077002', '07719', '07929', '08', '080', '080109', '0802', '0803', '080319b', '0805', '080514b', '0806', '0808', '080916c', '081', '0810', '081024b', '0811', '0812', '081301', '082', '0823', '08250', '082501', '0827', '0828', '083', '0832', '08339', '084', '0840', '084502', '085', '0851', '085102', '085113', '086', '086802', '0869', '087', '088', '089', '0894', '08_', '08x', '09', '0901', '0902', '0904', '090426', '0906', '0907', '0908', '09082v3', '090926b', '09092v2', '091', '0911', '0912', '0913', '091442', '092', '093', '093001', '093439', '09387', '094', '0943', '094411', '095', '095301', '095302', '0954', '096', '0962', '0964', '097', '0976', '098', '099', '0992486', '0_', '0_0', '0_1', '0_2', '0_3', '0_b', '0_d', '0_had', '0_i', '0_ih', '0_iz', '0_j', '0_k', '0_l', '0_s', '0_u', '0_z', '0a', '0b', '0bar', '0c', '0d', '0deg', '0e18', '0ev', '0f_', '0f_1', '0fb', '0g_', '0ge40', '0h', '0i', '0k', '0mpa', '0ms', '0n', '0nutation', '0o', '0pi', '0pt', '0tcoke4keyc', '0th', '0um', '0v', '0w', '0x', '0x10', '10', '100', '1000', '10000', '100000', '10000000', '10000k', '1000c', '1000k', '1000nm', '1000pc', '1000sec', '1003', '100546', '1006', '100703', '1008', '1009', '100base', '100c', '100cst', '100h', '100k', '100kpc', '100mbps', '100ms', '100ns', '100pm', '100s', '100um', '100yr', '101', '1010', '1011', '101101', '1012', '1013', '1015', '1016', '10165', '10168', '1019', '101901', '101955', '101star', '102', '1020', '10200', '10216', '10224', '1022w', '1023', '10239', '1024', '10240', '1026', '1029', '102pd', '103', '1030', '10300', '103095', '1031', '10319', '103295n', '1032a', '1035', '1037', '1038', '10384', '1039', '103987', '104', '10400', '104102', '1042', '10420', '1043', '10430', '1044', '10465', '1047', '1048', '105', '1050', '105001', '105013', '1052', '1053', '1055', '10580', '105888', '1059', '106', '10610', '106102', '10610u', '1064', '106402', '1065', '10650', '1067', '1068', '106823', '107', '107146', '1073', '10747', '10753', '10758', '107602', '1078', '10782', '10790', '108', '1080', '1080ti', '1081', '1083', '10830', '1085', '10860', '10864', '108877', '109', '1090', '10909', '10917', '10934', '1099', '10a', '10b', '10base', '10be', '10c', '10d', '10db', '10deg', '10e', '10e11', '10e12', '10e13', '10e27', '10e6', '10eev', '10ev', '10gbps', '10ghz', '10hour', '10hz', '10k', '10khz', '10m', '10mjy', '10mpc', '10n', '10ne20', '10pb', '10pc', '10ppb', '10pt', '10pt4', '10sb0', '10t', '10th', '10um', '10wcm', '10x', '10x10cm2', '10x5', '11', '110', '1100', '11000', '1100mhz', '11020', '1103', '11036', '1106', '1109', '111', '1111', '11115', '1112', '11120', '111301', '1115', '11158', '1116', '11169', '111805', '1119', '112', '1120', '11200', '1122', '11237', '1125', '11252', '1127', '1128', '1128569561140', '1128575290808', '113', '1132', '113206', '11365', '114', '1140', '11429', '114501', '1148', '1149', '115', '1150', '11500', '115001', '1151', '11513', '115226', '115413', '115421', '115567', '1159', '115pc', '116', '11615', '116235', '116401', '11678', '1168', '11690', '117', '1170', '1171', '1175', '1176', '1178', '118', '118101', '1182', '1184', '1185', '1185k', '1186', '1187', '1189', '119', '1190', '11903', '1192', '1193', '1196', '11984', '1199', '11_', '11ad', '11af', '11be', '11d', '11m_', '11msun', '11n', '11n_c', '11th', '11x', '12', '120', '1200', '120000', '1200k', '1201', '1202', '1203', '1204', '120402', '1205', '120504', '1206', '1207', '1208', '120c', '120deg', '120kms', '120ks', '120mk', '121', '1210', '1211', '121102', '1212', '1219', '12192', '121a', '122', '12200', '1221', '1222', '1223', '1224', '1225', '122563', '1227', '122910', '123', '1231', '1232', '1234', '123441', '1235', '1237', '1239', '124', '1240', '124036', '124057', '1241', '124112', '1243', '1246', '125', '1250', '12500', '1250n', '125110', '125116', '1252', '12560k', '1257', '125723', '1259', '125gev', '125s', '125te', '125th', '126', '1260', '1261', '1264', '12673', '127', '1270', '127203', '1275', '1276', '128', '1281', '1285', '12894', '128x128', '129', '1292', '1295', '1296', '129xe', '12b', '12c', '12c14n', '12c15n', '12cao', '12ch3d', '12cn', '12co', '12coo2', '12e', '12gyr', '12h', '12helix', '12k', '12mx0', '12s', '12th', '12x', '12x12', '13', '130', '1300', '1301', '130201', '13029', '1303', '1304', '130427a', '1305', '1306', '1308', '13085', '1309', '130sb', '130te', '131', '1310', '1311', '1311010', '1312', '131231a', '131366n', '1315', '1316', '131sb', '132', '1320', '13258', '1328', '132sb', '132sn', '133', '1333', '133cs', '133te', '134', '1343', '134439', '134440', '13450k', '134705', '13472i', '1349', '134901', '134ce', '134sn', '135', '1350', '1351', '1351672', '1352', '135693', '135701', '135930', '136', '1361', '1364', '136472', '1368', '136931', '137', '1370', '1374', '13754', '137cs', '138', '1380', '13800', '1383', '1385', '1387', '138baf', '139', '1390', '1391', '1394', '13945', '1396', '139664', '139822n', '1399', '139la', '13a', '13au', '13b', '13c', '13c14n', '13cch', '13ch4', '13cl', '13cn', '13co', '13e', '13h', '13k', '13m_1m_2', '13th', '13x', '14', '140', '1400', '1402', '140283', '1403', '1404', '1405', '140504', '1406', '1407', '1408', '1409', '140au', '140k', '140x140', '141', '1410', '1411', '1412', '1414', '1415', '1416', '142', '1420', '1423', '1426', '1426h', '1427h', '142857', '142857142857', '1429', '143', '1430', '143201', '14324', '143907', '143eu', '144', '1440', '144412', '144448', '144501', '144515', '1446', '145', '1450', '1456', '1459', '145k', '146', '1466', '147', '1472', '1475', '14771', '147926', '148', '1480', '1482', '148330', '14858', '1486', '14862', '149', '1498', '14c', '14h', '14hours', '14i', '14k', '14m_', '14n', '14nh3', '14th', '15', '150', '1500', '15000', '1501', '15011', '1502', '15043', '1505', '1506', '150601', '1507', '1508', '1509', '150ev', '150k', '150pc', '151', '1510', '151030', '1511', '1511a', '1511b', '1512', '1514', '15153', '1516', '1518', '151eu', '152', '1520', '1525', '152690', '1527', '152gops', '153', '1530', '1531', '1533', '1535', '15350', '15351', '1536', '153603', '1537', '1538', '15388', '1539a', '154', '1540', '15400', '1543', '1544', '1546', '1549', '154902', '15494', '155', '1550', '155006', '1553', '1555', '156', '1560', '156101', '1566', '1567', '1569', '157', '157001', '157087', '1572', '1576', '157pc', '158', '1580', '1581', '15890', '158a', '158agev', '159', '15929', '1593', '15c', '15db', '15mk', '15mm', '15mv', '15n', '15o', '15th', '15x', '16', '160', '1600', '1601', '1602', '1603', '1604', '160401', '1605', '160529', '1606', '1608', '1609', '160deg', '160um', '161', '1610', '161108', '1612', '1613', '1614', '161405', '1615', '1617', '161796', '161b', '161gev', '162', '1620', '16249', '162520', '1625b', '1627', '16293', '163', '16307', '1635', '1636', '16384', '163899', '164', '1640', '164204', '1647', '165', '1650', '16500', '1650019', '1650ti', '1651', '165134', '1654', '16547', '1658', '166', '166102', '1665', '1667', '166870', '1669', '167', '16709', '167203', '1673', '1675', '1679', '168', '1680', '168001', '1684', '1685', '168625', '168k', '169', '1690', '1697', '169tm', '16_', '16a', '16b', '16hz', '16k', '16m', '16mev', '16o', '16o2', '16pc', '16se0', '16th', '16x', '16x12', '17', '170', '1700', '1701', '1702', '1703', '1704', '1705', '1706', '1707', '1708', '170817', '170817a', '1709', '170922a', '170x', '171', '1710', '171019', '171421', '171433', '1715', '17156', '1719', '171a', '171yb', '172', '1720', '1723', '1726', '1728', '172gev', '173', '1730', '1737', '174', '1740', '1741', '1744', '1746', '175', '1750', '1750016', '1756', '175640', '1758', '175b', '175gev', '176', '1761', '176mw', '177', '1775', '178', '1780', '1785', '179', '17909', '179hf', '17a', '17h', '17kt', '17mn', '17muas', '17th', '18', '180', '1800', '18000', '1801', '1802', '1802081', '1803', '1804', '180402', '180409', '1805', '180503', '180505', '180506', '180520r', '1806', '1807', '1808', '180916', '180916b', '180k', '180w', '181', '1810', '1811', '1812', '1814', '18162', '1818', '181ta', '182', '1820', '1822', '1825', '183', '1834', '18354', '1836', '1837', '183gev', '183w', '184', '1840', '1841', '184421', '1845', '184982', '185', '1850', '1850040', '18510', '18540', '1855', '18555', '1856', '1858', '1859', '186', '1860', '1861', '1863', '1864', '186406', '186w', '187', '1870', '187123b', '1874', '1875', '1877', '1879', '187re', '188', '1881', '1883', '1884', '1885', '1886', '1887', '188774', '1888', '1889', '189', '1892', '1893', '1895', '18ar40', '18b', '18ev', '18f', '18h32m20s', '18k', '18m_', '18o', '18r', '18th', '19', '190', '1900', '1901', '1902', '1903', '190344', '1904', '1905', '1906', '1908', '1909', '191', '1910', '1911', '1912', '1913', '1915', '1916', '1917', '1918', '1919', '192', '1920', '19200', '1920s', '192105', '1922', '1923', '1924', '1925', '1926', '1928', '193', '1930', '1930s', '1931', '1932', '1933', '1934', '1935', '1936', '1937', '1938', '1939', '193901', '194', '1940', '19400', '1941', '1943', '1944', '1945', '1946', '1947', '1948', '1949', '195', '1950', '1951', '195117', '1952', '1953', '1954', '1955', '1956', '1957', '1958', '1959', '196', '1960', '19603', '1960ie', '1961', '1962', '1963', '1964', '1965', '1966', '1967', '1968', '1969', '196944', '197', '1970', '1971', '1972', '1973', '1974', '1975', '1976', '1977', '1978', '1979', '197au', '198', '1980', '1980s', '1981', '1982', '1983', '1984', '1985', '1986', '1987', '1987a', '1988', '1989', '1989a', '198x', '199', '1990', '1990ie', '1990jcoph', '1990s', '1991', '1992', '1993', '1993j', '1994', '1995', '1995n', '1996', '1996b', '1997', '1997ef', '1998', '1998a', '1998bw', '1999', '1999by', '1999da', '1999dk', '1999gp', '199hg', '19b', '19charge', '19f', '19hz', '19k', '19m_', '19th', '19yb0', '1_', '1_0', '1_1', '1_10', '1_11', '1_2', '1_d', '1_et', '1_f', '1_k', '1_loc', '1_n', '1_nis', '1_r', '1_t', '1_v', '1_xl', '1a', '1and', '1au', '1b', '1b_1', '1bmmrelax', '1brelax', '1c', '1cr', '1cz', '1d', '1d2', '1d_2', '1day', '1db', '1dbpp', '1deg', '1deg27', '1e', '1e0657', '1e10', '1e13', '1e14', '1e1547', '1e16', '1e161348', '1e1740', '1e18', '1e19', '1e20', '1e21', '1e4', '1e41', '1e49', '1e5', '1e7', '1eev', '1es', '1ev', '1f1', '1f3', '1f7', '1g', '1g4', '1gbps', '1gev', '1go', '1gsps', '1gyr', '1h', '1h0707', '1jby', '1k', '1l2s', '1la_0', '1lc', '1m', '1mhz', '1mjy', '1mk', '1mo', '1mole', '1ms', '1msun', '1n', '1p', '1p1', '1p_', '1p_1', '1pc', '1pi', '1pn', '1q', '1ql', '1re', '1rsb', '1rxs', '1s', '1s0', '1s2s', '1s3d', '1s3p', '1s_', '1s_0', '1sax', '1sigma', '1st', '1t', '1tev', '1u', '1ufa', '1ufas', '1um', '1v', '1vs1', '1x1', '1x10', '1x104', '1x2', '1x4', '1xb', '1xs', '1yr', '1z_', '1zr', '20', '200', '2000', '20000', '2000bk', '2000ce', '2000k', '2000q', '2000s', '2000x', '2001', '2001a', '2002', '2002a', '2002ap', '2002b', '2002er', '2002fk', '2002hh', '2002rm', '2003', '2003bg', '2003cg', '2003du', '2003fk', '2003gd', '2003ie', '2003kf', '2004', '200404', '200405', '2004a', '2004b', '2004et', '2005', '2006', '200603', '2006a', '2006aj', '2006c', '2006jc', '2006ov', '2007', '2007gr', '2007or10', '2008', '2008d', '2008mq', '2009', '2009bb', '2009jf', '200au', '200gev', '200k', '200pc', '200um', '200x', '201', '2010', '2011', '201104', '2011dh', '2011fe', '2011nk', '2012', '20121102a', '2012b', '2012cg', '2012qut', '2013', '2013a', '2013b', '2013kna', '2014', '2015', '2015hga', '2015wwa', '2016', '201626', '2016a', '2016prf', '2016som', '2016xxf', '2017', '2017a', '2017gfo', '2017jlg', '2018', '201889', '2018a', '2018b', '2019', '2019kaq', '201hg', '202', '2020', '20201124', '2020b', '2021', '2021a', '2021b', '2021yja', '2022', '2023', '2024', '2025', '2028', '2029', '203', '2030', '203001', '2030s', '2032', '2035', '203602', '2039', '203km', '204', '2040', '2048', '20483', '204907', '205', '2050', '2056', '206', '2068', '206at', '207', '2070', '2071', '207208', '207401', '2076', '2078', '207po', '208', '2080', '2081b', '208pb', '209', '2094', '209458', '209458b', '2099', '209bi', '209rn', '20b', '20b2', '20gev', '20h', '20k', '20khz', '20mev', '20ne', '20newsgroups', '20ppb', '20th', '20x', '20x20', '21', '210', '2100', '2104', '2105', '2106', '2107', '2108', '210k', '211', '2110', '2111', '2112', '211211a', '2114', '212', '2120', '21207022', '212505', '212po', '213', '2135', '214', '214104', '214109', '214420', '214bi', '214pb', '215', '2150', '2152', '2153', '2154', '2155', '215d', '216', '2160', '2166', '216k', '217', '217206', '2174', '2175', '2178', '218', '218fr', '219', '2190', '219188', '21f_', '21k', '21n', '21st', '21te2', '21w', '22', '220', '2200', '2201', '2202', '220408b', '2205', '220502', '2206', '2207', '220k', '221', '2210504', '2212', '2213', '2218', '222', '2221', '2223', '222rn', '223', '2230', '223002', '223201', '2237', '2239', '223ra', '224', '2240', '2246', '225', '2250', '2251', '2255', '2257', '2259', '225hf0', '225mo0', '225ta0', '225w0', '226', '2260', '22600', '2261', '2264', '2264d', '2265', '226ac', '226ra', '227', '2271', '227601', '228', '2281', '228th', '229', '22920', '22940', '2295', '2296', '229b', '229m', '229th', '22cm', '22fdx', '22k', '22na', '22r', '22x', '23', '230', '2300', '2301', '230401', '2306', '230601', '23098', '230k', '231', '2310', '2312', '2316a', '2317', '2319', '231pa', '232', '2326', '232u', '233', '2336', '233809', '233u', '234', '2344', '2344411', '2349', '234u', '235', '2350', '235001', '2352', '235205', '235u', '236', '2360', '2366', '236b', '237', '2376', '237x', '238', '2380', '2389', '238pube', '238u', '239', '2390', '2391', '2392', '23fm', '23n', '23rd', '24', '240', '2400', '2400100', '240401', '240402', '240503', '2408', '240u', '241', '241101', '241102', '241406', '2417', '241ambe', '242', '2420', '2422', '243', '2430', '2437', '244', '244111', '2446aj', '2446ak', '2446al', '245', '2450', '245124', '2451917', '245302', '2454605', '2456', '2456074', '2457412', '245a', '246', '2460', '2463', '246401', '2466', '2467', '247', '2470', '2477', '248', '2480', '249', '2496', '249bk', '24a', '24b', '24ga0', '24h', '24k', '24mg', '24microns', '24myr', '24na', '24th', '25', '250', '2500', '25000', '2500k', '2501', '250504', '250k', '250r', '250v', '250x', '251', '2516', '252', '253', '2531', '2532', '2534', '2536', '254', '2547', '255', '2550', '255001', '2554', '25546', '2555', '256', '2560', '2562', '256802', '257', '257003', '2577', '258', '258701', '259', '2595', '25al', '25at', '25ca0', '25k', '25kpa', '25mag', '25mg', '25s', '25th', '25x', '26', '260', '2600', '2600mhz', '260411', '260601', '260_', '261', '2615', '2619', '262', '2620050', '26262', '2628', '2629', '263', '2632', '264', '2643', '2648', '265', '2650', '2654', '265ms', '266', '2669', '2669v1', '267', '267004', '2677', '268', '2683', '2688', '268k', '269', '269d', '26al', '26b', '26d', '26deg2', '26k', '26mg', '26th', '27', '270', '27000', '2700cm', '2702', '270k', '271', '2713', '2718', '272', '2724', '273', '2732', '273pb', '274', '2741', '2742', '2743', '2744', '2748', '275', '27500', '27500k', '2754', '2755', '2756', '276', '277', '277906', '278', '2782', '2784', '2787', '279', '2796', '2798', '27al', '27mn', '27mo', '27th', '27x', '28', '280', '2800', '2800_', '2806', '2808', '281', '2816', '282', '2825', '283', '2834', '2838', '284', '2841', '2844', '284423', '285', '285bp', '286', '2860', '287', '288', '2880', '2883', '289', '28k', '28p', '28si', '28siresonant', '28th', '28x', '29', '290', '2900', '2904', '2909', '291', '2910', '2915', '2919', '292', '2927', '292747', '292917', '293', '294', '2940', '2942', '2943', '294344', '295', '2950', '2958', '296', '297', '2976', '298', '2988', '299', '2996', '29c', '29e', '29si', '29sic2', '29th', '29um', '2_', '2_0', '2_1', '2_2', '2_f', '2_i', '2_k', '2_m', '2_n', '2_q', '2_s', '2_t', '2_tw', '2_w', '2_x', '2a', '2a1', '2abc', '2ad', '2alpha', '2as2', '2b', '2ba2cu3o', '2c', '2c_1', '2c_2', '2c_3', '2c_5', '2ca1', '2ch', '2ch3cooh', '2chx_3', '2ck', '2clo4', '2cr0', '2cuo4', '2d', '2d2', '2d_', '2d_1', '2d_1d_2', '2d_vae_uda_for_3d_sythesis', '2db', '2dch', '2de', '2deg', '2degs', '2dels', '2delta', '2des', '2df', '2dfgrs', '2dhg', '2dp', '2dti', '2dv_g', '2dx', '2e', '2e15', '2e2nc', '2e47', '2e_0', '2ec', '2ef', '2ev', '2ex', '2exptime', '2f', '2f1', '2f_1', '2f_3', '2f_c', '2fb', '2fgl', '2fm', '2g', '2g_0', '2g_n', '2gamma', '2ghz', '2gm', '2h', '2h1p', '2h_2o', '2h_c2', '2hdm', '2hdms', '2hf0', '2i', '2i3', '2j', '2jx', '2k', '2k_f', '2kh', '2kx4k', '2l', '2l1s', '2l_', '2l_e', '2lac', '2log_2', '2ls', '2m', '2m0850', '2m0850b', '2m_', '2m_b', '2m_c', '2m_sun', '2m_w', '2m_z', '2ma', '2mass', '2mdm', '2mhg', '2micron', '2mn', '2mno3', '2mo0', '2ms', '2msun', '2mv', '2n', '2n_c', '2n_f', '2nb0', '2nd', '2neb', '2nee', '2ng', '2nn', '2o3', '2obs', '2omega', '2p', '2p2h', '2p_f', '2p_j', '2p_t', '2pc', '2pf6', '2pi', '2pn', '2ppt', '2pr', '2pt', '2q', '2r', '2rbzn', '2reo4', '2rqaw', '2s', '2s2p', '2s_', '2s_1', '2sb', '2sc', '2sf', '2sigma', '2sls', '2sqrt', '2t', '2ta0', '2ta_0', '2tau', '2tc', '2theta_', '2u', '2u_a', '2uu_', '2uu_x', '2v', '2vt', '2w', '2w0', '2x', '2x1', '2x10', '2x12h2o', '2x2', '2x3', '2x4', '2x4aqh2bmb', '2x_n', '2xcy_3', '2xmmp', '2y', '2z', '2zr0', '30', '300', '3000', '30000', '3000k', '3000mpc', '3002', '3003', '300au', '300gpa', '300h', '300hz', '300k', '300mpc', '300x300', '300x30x2', '301', '3012', '302', '3021', '303', '3031', '3032', '304', '3045', '305', '3050', '3054', '3055', '306', '3062', '307', '3072', '3073', '3079', '30795', '308', '3080', '309', '3096', '309h', '30b', '30db', '30deg', '30hz', '30k', '30m', '30mk', '30ms', '30p2o5', '30pc', '30s', '30sic2', '30ujy', '30x', '30x30', '30zn64', '31', '310', '3100', '3109', '310f', '310ghz', '311', '3110', '3114', '3116', '312', '3129', '312g', '313', '31347', '314', '315', '316', '3161', '3162', '3168', '317', '318', '3185', '3187', '319', '3195', '31k', '31mjy', '31o', '31th', '31x', '32', '320', '3200', '3200k', '3201', '3202', '3205', '3208', '320h', '320ms', '321', '3226', '3227', '323', '324', '3240', '325', '326', '327', '32768', '328', '3283', '329', '3296', '32k', '33', '330', '3300', '330mhz', '331', '332', '3327', '333', '3338', '3339', '334', '3344', '3348837', '335', '3358', '336', '3367', '3368', '337', '3371', '3372', '3375', '33761', '3379', '338', '3387', '3388', '339', '3390', '33_', '33k', '33mno3', '33pc', '34', '340', '34004', '341', '3412', '342', '3420', '3421', '343', '344', '3447', '345', '34506', '3456', '3457', '3459', '346', '347', '34700', '3476', '3478', '348', '3480', '348000', '34808', '3482', '3486', '3488', '349', '3492', '34970', '34b', '34ev', '34k', '34mm', '34th', '34w', '34x1018', '35', '350', '3500', '35000', '3500kms', '3509', '350micron', '350mw', '351', '3514950', '351724', '35187', '352', '3525', '3526', '352619', '3527', '3543', '3546', '355', '3559m_', '356', '35600', '35610879', '357', '3574', '358', '359', '35a', '35b', '35cl', '35coo2', '35ev', '35gev', '35k', '35msun', '35s', '35th', '35x', '36', '360', '3600', '3603', '360isod', '361', '362', '36216', '3624', '3627', '36278', '363', '36351', '364', '36491', '365', '3654', '3656', '3658', '366', '3665', '3669', '367', '3677', '3686', '369', '36900', '36b', '36kr86', '36mers', '36x', '37', '370', '3700', '37000', '3706', '371', '3710', '3718', '3725', '3726', '3727', '3734', '374', '375', '376', '377', '3770', '3784', '3787', '37x', '38', '380', '3800', '380651', '3808', '381', '3819', '382', '3820', '3824v4', '3825', '3827', '383', '384', '3840', '3840x2160', '385', '3850', '386', '387', '3872', '3875', '3879', '3880', '3884', '38deg', '38mk', '39', '390', '3900', '3906', '3911', '3915', '3916', '3918', '392', '393', '394', '3940', '3946', '3947', '395', '396', '3966', '397', '3973', '3974', '398', '3985', '399', '39mag', '39x', '3_', '3_1', '3_s', '3_x', '3a', '3an', '3b', '3beta', '3bf', '3bp', '3bw', '3c', '3c186', '3c273', '3c286', '3c294', '3c58', '3ca', '3ca1', '3cg', '3cl', '3clpro', '3cm', '3cnf', '3cr', '3d', '3d0', '3d1', '3d2', '3d5', '3d_1', '3db', '3dcertify', '3dcnn', '3def', '3deg', '3dircadb', '3dof', '3dpeople', '3dpw', '3dresnet18', '3drt', '3dvar', '3dx2', '3dyz', '3e', '3e14', '3e22', '3e25', '3e6', '3ef', '3eg', '3ev', '3f', '3f3', '3f_2', '3fb', '3fgl', '3fhl', '3g', '3gpp', '3h', '3h_', '3hdm', '3he', '3i', '3i1o', '3j', '3k', '3khz', '3ks', '3ll', '3m', '3m_b', '3mev', '3mgy', '3mhz', '3micron', '3mm', '3mno3', '3n', '3nb2', '3nu', '3o_', '3p', '3p0', '3p1', '3p_0', '3p_1', '3p_2', '3p_j', '3pi', '3pn', '3q', '3q_1q_5', '3r', '3r_', '3rd', '3ro', '3s', '3s1', '3s3p', '3s_1', '3sat', '3sigma', '3sr1', '3t_3', '3t_c', '3term', '3u_', '3x', '3x10', '3x2', '3x3', '3x48', '3x64', '3xmm', '3xrp', '3xs', '3xs5', '3y', '3z', '3z2', '40', '400', '4000', '4003309', '400331', '40051', '400k', '400km', '400ng', '400pc', '401', '401214', '40176', '402', '4020', '4025', '4026', '403', '4038', '404', '4040', '4047ab', '405', '4051', '4056', '406', '40613', '4072', '4078', '408', '409', '409552', '4096', '40_', '40ar', '40c', '40ca', '40db', '40deg', '40k', '40k87rb', '40k_2', '40kk', '40nm', '40pc', '40x', '41', '410', '4100', '4100ab', '41076', '4108', '410b', '411', '4112', '41177', '4118x', '412', '4123', '413', '4131', '414', '4140', '415', '41501', '4153ab', '415x', '416', '4160', '417', '418', '419', '41979', '419k', '41k', '42', '420', '420k', '421', '4211', '4214', '422', '4220', '4224', '4227', '423', '4232', '4236', '424', '4247', '425', '426', '4260', '4261', '4263', '4266', '427', '4270', '4274', '4276', '4278', '428', '4284', '429', '4291', '4294', '42d', '42k', '42um', '42x', '43', '430', '4300', '4305', '4306', '431', '43160', '432', '4320', '4325', '433', '4332', '434', '4342', '4348', '434gops', '435', '4350', '436', '4360', '4363', '437', '4370', '43771963', '438', '4380', '43806', '4381', '4388', '439', '4394', '4395', '43k', '43mn', '43x', '44', '440', '4412', '4414', '4415', '4419ev', '441n', '442', '443', '4430', '4438', '444', '4442', '44447', '44497', '445', '4450', '4459', '447', '4470', '4471', '4472', '4479b', '448', '4485', '4486a', '449', '4490', '44986', '44ev', '45', '450', '4500', '451', '4510', '452', '453', '4533', '45364b', '454', '4550', '456', '4568', '4579', '458', '4580', '4587', '4590', '45ev', '45mn', '45mno3', '45p', '46', '460', '4600', '461', '4616', '465', '4658', '4660', '467', '4672', '468', '4686', '4691', '4696', '46b', '46ca', '46s', '47', '470', '4700', '4707', '471', '4715', '472', '47205', '4721', '4724', '4726', '473', '4730', '4731', '4732', '4740', '4750', '4750858', '4754', '476', '4763', '477', '4771', '478', '4780', '4785', '4789', '479', '4796a', '48', '480', '4800', '4808', '481', '482', '483', '48369689570172', '484', '4843j', '485', '4850', '4859', '486', '487', '4877', '489', '48au', '48c', '48ca', '48g', '49', '490', '4900', '4904', '491', '492', '493', '4937', '494', '4945', '495', '4950', '4955', '495611', '4959', '496', '4964', '497', '4988', '499', '4993', '49933', '4995', '4997', '4_', '4_2', '4_d', '4_s', '4a', '4b', '4c', '4ce0', '4chan', '4cm', '4d', '4db', '4de1', '4dvar', '4e', '4e21', '4em', '4ev', '4f', '4f_', '4fb', '4fe2as2', '4fgl', '4g', '4g_n', '4gamma', '4ghz', '4h', '4h2o', '4hb', '4he', '4he_n', '4i_', '4j', '4k', '4k_f', '4kev', '4l', '4lac', '4lqs', '4m', '4m_', '4m_c', '4m_solar', '4mcf', '4minute', '4mno3', '4most', '4ms', '4msun', '4n', '4n_e', '4n_f', '4nls', '4on', '4open', '4p', '4p_', '4pi', '4q', '4r_', '4r_s', '4re', '4s', '4s5', '4s6', '4s_', '4sigma', '4sin', '4t', '4t1', '4t2', '4t_', '4te7', '4th', '4u', '4u1538', '4u1700', '4u_t', '4v', '4wm', '4x', '4x10', '4x2', '4x4', '4z', '50', '500', '5000', '5000cm', '5000k', '5000th', '5007', '5007a', '500k', '500kev', '500pc', '501', '502', '502x', '5039', '504', '5040', '504a', '505', '5053', '5055', '506', '5065', '508', '5085', '509', '50a', '50au', '50fps', '50hz', '50k', '50kda', '50kw', '50m_', '50mo', '50n', '50pc', '50snf2', '50states10k', '50th', '50x50', '51', '510', '5100', '5100a', '5105v1', '5106', '5109', '511', '511ex', '512', '5128', '512x512', '513', '513353', '5135', '514', '515', '5151', '516', '5160', '51629', '5167', '517', '5178', '518', '5181', '518304551833', '51891', '519', '51d', '52', '520', '5201', '5209', '521', '5216', '5218', '522', '523', '5235', '523504', '5237', '524', '5240', '525', '525150', '5253', '5255', '528', '5281', '529', '52b', '52d', '52g', '52li0', '52m', '53', '530', '5300', '5303', '531', '53143', '531906', '532', '53206', '533', '534', '535', '535x10', '536', '538', '5380', '53a', '54', '540', '5400', '5408', '540b', '541', '5410', '5417', '542', '543', '5436', '544', '5445', '545', '546', '5466', '547', '547403', '54757', '547k', '548', '549', '5493', '54ms', '55', '550', '5500', '55000', '55000k', '5503', '5504035', '550_', '551', '5510', '553', '554', '5548', '555', '5550', '5557', '556', '5568', '557', '55761', '558', '55915', '5595', '5598', '559e', '55c', '55ev', '55gpa', '55ks', '55sr0', '55x', '56', '560', '5600', '561', '56126', '561a', '561b', '562', '563', '5630', '564', '565', '5653', '566', '569', '569b', '56b', '56ni', '56x', '57', '570', '5714', '572', '5721', '573', '5747', '575', '5750', '5751', '575218', '576', '576x384', '577', '5772', '5777', '578', '5780', '579', '5797', '57_', '57fe', '57m', '58', '580', '5800', '581', '5819', '582', '583', '5830', '5837', '584', '5840', '5846', '5847', '585', '5850', '585059', '5858', '586', '587', '588', '589', '58900', '5897v1', '58gev', '58m_', '58pb', '59', '590', '5900', '5907', '5916', '592', '5925', '5927', '593', '5934', '594', '595', '5950', '596', '5968', '5970', '598', '599', '59a', '59co', '59d', '5_', '5_g', '5a', '5a_b', '5al0', '5arcsec', '5b', '5baco_4o_7', '5bis2', '5c', '5ca_0', '5coo2', '5d', '5d_', '5db', '5deg', '5e', '5e10', '5e15', '5e16', '5e20', '5e30', '5e39', '5e40', '5f', '5f0', '5fb', '5fbis2', '5fe_0', '5feal', '5ff', '5g', '5ghz', '5gpa', '5he', '5hz', '5iii', '5j', '5k', '5kg', '5kv', '5la0', '5m', '5m_', '5mev', '5micron', '5millisecond', '5mjy', '5mm', '5mm2', '5mno4', '5mo', '5ms', '5msec', '5n', '5nm', '5o2', '5oe', '5p', '5pev', '5pn', '5q', '5re0', '5ro', '5s', '5s5p', '5se0', '5sigma', '5sns', '5sr1', '5t_fwhm', '5te0', '5th', '5the', '5tm', '5tudr', '5ujy', '5um', '5v', '5v0', '5w1', '5x', '5x10', '5x5', '5yr', '5zr0', '60', '600', '6000', '60000', '6000x', '6004', '600k', '600sec', '600x', '601', '603', '6033', '604', '60446053', '605', '60532b', '606', '6061', '607', '6073', '60776', '608', '6082', '6087', '609', '6093', '6099', '60co', '60ghz', '60k', '60ks', '60ma', '60mev', '60mk', '60ns', '60th', '61', '610', '610d', '611', '612', '6127', '613', '6133', '6141', '6144', '6145', '6147', '6148', '6149', '61499', '6150', '616', '617', '6172', '618', '6183', '619', '6192', '6196', '62', '620', '6203', '621', '62185', '6227', '623', '6231', '624', '6240', '6248', '625', '6250', '627', '628', '6280', '6289', '629', '62d', '63', '630', '6302', '6304', '631', '632231', '632232', '633', '6332', '6334', '634', '6341', '636', '6361', '6362', '6368', '637', '6374', '6376', '6379', '6388', '639', '63cu', '63k', '64', '640', '6400', '6408', '640ms', '641', '6416', '642', '643', '644', '6440', '6441', '645', '647', '647n', '64ghz', '64o', '64x', '64x64', '64zn', '65', '650', '6500', '651', '6513', '6517', '6522', '652854', '653', '6534', '654', '6541', '6543', '655', '6557', '656', '6579', '657x10', '658', '65803', '6583', '6584', '6584a', '659', '65al10co25', '65cu', '65la_0', '65mj', '66', '660', '661', '6610', '6612', '6614', '662', '6620', '6629', '664', '665', '6653', '6661', '667', '6673', '668', '6688', '669', '6696', '67', '670', '6700', '6703a', '6707', '6708', '670bp', '6715', '6717', '672', '673', '6731', '6738', '674', '675', '6752', '678', '6791', '6792', '67km', '67p', '67sr0', '68', '680', '681', '6822', '683', '684', '685', '6859', '686', '6866', '687', '688', '6888', '689', '69', '690', '6900', '691', '6913', '693', '694', '6946', '6951', '6958', '696_', '697', '698', '6994', '69msun', '6_', '6_u', '6a', '6arc', '6c', '6cl2', '6d', '6degree', '6df', '6dfgs', '6dof', '6e', '6e20', '6e46', '6e47', '6ev', '6g', '6g_', '6ga1', '6gev', '6h', '6he', '6hr', '6hz', '6j', '6k', '6k0', '6li', '6li40k', '6lif', '6lowpan', '6m', '6m_', '6msec', '6mus', '6n', '6p', '6p_', '6p_z', '6q', '6r', '6s', '6s5d', '6s6p', '6se0', '6sn0', '6so4', '6sr0', '6sr2rucu2o10', '6th', '6u', '6um', '6uu_x', '6v', '6x', '6x10', '6x3', '6y', '70', '700', '7000', '700k', '700pc', '701', '7012', '702', '7020', '7023298', '7027', '7043512', '705', '706', '707', '7078', '708', '709', '70a', '70k', '70kpc', '70kt', '70th', '70um', '70x', '70x70', '70zn', '71', '710', '7100', '711', '713', '713k', '714', '715', '7159', '716', '717', '718ab', '719', '72', '720', '7200', '72086', '721', '722', '7229', '723', '724', '7243', '7249', '725', '726352', '72651', '727', '728', '729', '72900', '72m_', '72x', '73', '730', '732', '7332', '734', '735', '735459', '736', '737', '738', '738989', '73gev', '73mev', '74', '740', '7409', '7414', '74156', '7419', '7421', '743176', '745', '7466262', '747', '7476', '747sp', '74b20', '74d10', '74l1', '74x', '75', '750', '7500', '750000', '751', '753', '7539', '754', '755', '7558', '756', '757', '758', '759', '75c', '75cr0', '75k', '75mno3', '75mo', '76', '760', '7608', '763', '7631', '76382', '7639', '764', '765', '7651', '7658', '766', '7666', '7674', '768', '769', '76as', '76b', '76ge', '76x', '77', '770', '7700', '7709', '771', '772', '773', '77361', '774', '775', '776', '777', '778', '7789', '7793', '77a', '77db', '77k', '77x', '78', '780', '781', '782', '783', '784', '785', '785k', '786', '787', '7871531', '788', '78802', '7888', '7888399', '789', '7892', '78a', '78msec', '78x', '79', '790', '7900', '791kg', '792', '793', '793584140', '794', '795', '797', '7970', '7970740', '798', '7994', '79n', '7_ce_x_cuo_4', '7a', '7ab', '7al', '7al0', '7b', '7be', '7c', '7ca0', '7ce0', '7coo2', '7d', '7dof', '7e', '7e13', '7e30', '7ev', '7eyongxiangfan', '7f_1', '7fe2as2', '7ghz', '7h', '7h24m3s', '7k', '7li', '7lif', '7ms', '7p', '7rad', '7s', '7se0', '7tev', '7th', '7um', '7w', '7x', '7x10', '7yr', '7zip', '80', '800', '8000', '80000', '800k', '800mgy', '800mhz', '800pc', '801', '80188', '801k', '802', '803', '80370', '804', '806', '807', '808', '809', '80b', '80ghz', '80k', '80li0', '80n', '80st', '80x', '81', '810', '8103', '811', '8113', '8125', '813', '8131', '814', '8143', '8144', '815', '817', '8174', '8192', '81b12', '81k', '82', '820', '8211', '8219268', '822', '8225', '823', '825', '826', 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'x_0x_1', 'x_1', 'x_1x_2', 'x_2', 'x_2x_3', 'x_2y_1y_3', 'x_3', 'x_3x_1', 'x_7', 'x_a', 'x_b', 'x_c', 'x_cr', 'x_d', 'x_f', 'x_g', 'x_ge', 'x_i', 'x_is', 'x_j', 'x_k', 'x_l', 'x_la_0', 'x_m', 'x_n', 'x_p', 'x_q', 'x_r', 'x_s', 'x_t', 'x_u', 'x_v', 'x_x', 'x_y', 'x_z', 'xa', 'xa_1', 'xadc', 'xafs', 'xai', 'xair', 'xaisuite', 'xal', 'xalp', 'xalxm2', 'xane', 'xanes', 'xas', 'xavi', 'xavier', 'xb', 'xb6', 'xbart', 'xbs', 'xc', 'xc5', 'xcat', 'xcaxcomno6', 'xception', 'xcexcuo4', 'xciii', 'xcox', 'xcoxnige', 'xcrxs', 'xcuxb2', 'xcx', 'xdiag', 'xdinss', 'xdr', 'xdrs', 'xdx', 'xe', 'xe_54', 'xenon', 'xenon1', 'xenon10', 'xenon100', 'xenonnt', 'xeon', 'xer2o3', 'xes', 'xeus', 'xeuxse', 'xf', 'xf_', 'xf_3', 'xfct', 'xfel', 'xfeyse2', 'xfitter', 'xfx', 'xfxfeas', 'xg', 'xg_2', 'xga', 'xgastaldi', 'xgaxn', 'xgaxo3', 'xgboost', 'xgd', 'xh', 'xh1', 'xi', 'xi_', 'xi_0', 'xi_1', 'xi_2', 'xi_4', 'xi_b', 'xi_c', 'xi_e', 'xi_g', 'xi_i', 'xi_j', 'xi_k', 'xi_l', 'xi_n', 'xi_q', 'xi_t', 'xi_x', 'xi_y', 'xia', 'xiang', 'xiao', 'xiaomi', 'xib', 'xib_l', 'xie', 'xiii', 'xilinx', 'xinshuoweng', 'xis', 'xitaso', 'xiv', 'xix', 'xj', 'xk', 'xl', 'xlens', 'xlf', 'xlixb2', 'xlm', 'xlnet', 'xlsr', 'xlssc', 'xlssj022403', 'xm', 'xmax', 'xmcd', 'xmed', 'xml', 'xmld', 'xmls', 'xmm', 'xmmu', 'xmn1', 'xmnxas', 'xmr', 'xn', 'xna0', 'xndxp5o14', 'xnnpack', 'xnor', 'xnystrace', 'xo', 'xor', 'xorgens', 'xoy', 'xp', 'xpath', 'xpcs', 'xpd', 'xpoly', 'xpot', 'xps', 'xps3', 'xpt', 'xptxas', 'xpw', 'xpx', 'xr', 'xray', 'xrb', 'xrd', 'xrf', 'xrfs', 'xrightarrow', 'xrism', 'xrm', 'xrootd', 'xrp', 'xrpl', 'xrt', 'xrux', 'xs', 'xs_', 'xsbx', 'xsda', 'xse', 'xse2', 'xsec', 'xsede', 'xsh', 'xshooter', 'xsm', 'xspace', 'xsph', 'xsrxal2o4', 'xsrxcuo4', 'xss', 'xst', 'xstar', 'xstm', 'xsu', 'xsu_l', 'xsum', 'xsx', 'xt', 'xt2', 'xt_i', 'xte', 'xtf', 'xti', 'xtion', 'xtrace', 'xtraction', 'xtree', 'xty', 'xtyr_mih3ou', 'xu', 'xu_x', 'xue', 'xueyq2019', 'xurllc', 'xuv', 'xva', 'xvi', 'xview2', 'xvii', 'xw_', 'xx', 'xxs', 'xxt', 'xxv', 'xxx', 'xxxi', 'xxxiii', 'xxxx', 'xxz', 'xy', 'xym', 'xyt', 'xyz', 'xz', 'xz2', 'y0', 'y1', 'y1ba2cu3ox', 'y2', 'y25', 'y2mo2o7', 'y2sio5', 'y3', 'y3fe5o12', 'y3luh24', 'y4s', 'y_', 'y_0', 'y_1', 'y_2', 'y_2y_3', 'y_3', 'y_4', 'y_b', 'y_cut', 'y_g', 'y_h', 'y_i', 'y_is', 'y_j', 'y_k', 'y_m', 'y_n', 'y_p', 'y_pi', 'y_s', 'y_t', 'y_x', 'ya', 'yaacov', 'yafet', 'yaff', 'yag', 'yahoo', 'yakonov', 'yakovenko', 'yakovlev', 'yakubovich', 'yale', 'yaleb', 'yalig', 'yalo3', 'yamabe', 'yamada', 'yamagishi', 'yamaguchi', 'yamamoto', 'yamanouchi', 'yamashita', 'yamawaki', 'yamnet', 'yamnitsky', 'yan', 'yanagida', 'yanase', 'yanet', 'yang', 'yang2011generalize', 'yangangwang', 'yangbaje', 'yangian', 'yangians', 'yankielowicz', 'yann', 'yannakaki', 'yano', 'yano10', 'yano13', 'yao', 'yap', 'yardstick', 'yarkovsky', 'yaroslavsky', 'yas8', 'yate', 'yau', 'yau1', 'yaus', 'yaw', 'yazadjiev', 'yazadjiev2014', 'yb', 'yb171', 'yb2ni12as7', 'yb2ni12p7', 'yb2ni12pn7', 'yb3', 'yba', 'yba2c3o7', 'yba2cu3o6', 'yba2cu3o7', 'yba2cu3ox', 'yba2cu4o8', 'yba_2cu_3o_', 'yba_2cu_3o_x', 'yba_2cu_4o_8', 'ybacuo', 'ybagge', 'ybal', 'ybalb_4', 'ybamn2o6', 'ybarancan', 'ybb6', 'ybc', 'ybco', 'ybco6', 'ybcu', 'ybir', 'ybj', 'ybmggao', 'ybni', 'ybrh', 'ybrh2si2', 'ybrh_2si_2', 'ycb', 'ydx', 'ye', 'year', 'yearly', 'years', 'yeast', 'yee', 'yefsah', 'yeh', 'yehia', 'yehuda', 'yehudayoff', 'yeld', 'yellow', 'yelp', 'yennie', 'yerevan', 'yersinia', 'yes', 'yeti', 'yetter', 'yeu20a', 'yeu20b', 'yevick', 'yfe2', 'yfe_2zn_', 'yfitter', 'yfs', 'yh2o', 'yi', 'yield', 'yielded', 'yielding', 'yields', 'yig', 'yigehy3_jco', 'yilmaz', 'yin', 'yip', 'yiyunya', 'yj', 'yjtw0syo', 'yk', 'ylay', 'ylhz', 'ylmethyl', 'ylu3h24', 'yluh12', 'ylw', 'ym', 'ym_', 'ym_2', 'ymc', 'ymn6sn6', 'ynamic', 'yngvason', 'yni_', 'yni_2b_2c', 'ynicki', 'ynot', 'yohkoh', 'yoke', 'yolo', 'yolov4', 'yolov5', 'yolox', 'yomdin', 'yoneda', 'yonetoku', 'yoneya', 'yong', 'yor', 'york', 'yorke', 'yornoise', 'yoshida', 'yosida', 'yossef', 'youbot', 'youdescribe', 'youm', 'young', 'younger', 'youngminpil', 'youngseng', 'younicon', 'yourprivacyprotector', 'youth', 'youthful', 'youtu', 'youtube', 'yquilibrium', 'yr', 'yrare', 'yrast', 'yreo', 'yrh', 'yrs', 'ys', 'ys_', 'yse2', 'yserentant', 'ysf', 'yso', 'ysos', 'ysr', 'ysu', 'yt', 'ytex', 'ytio3', 'ytterbium', 'yttrium', 'yu', 'yuan', 'yue', 'yugeten', 'yukawa', 'yule', 'yung', 'yunnan', 'yuri', 'yurke', 'yusipov', 'yuster', 'yutong', 'yv', 'yvo_3', 'yvon', 'yx', 'yy', 'yz', 'z0', 'z1', 'z11', 'z2', 'z24', 'z2fsl', 'z2xz2', 'z4', 'z6', 'z8', 'z_', 'z_0', 'z_1', 'z_2', 'z_2w', 'z_3', 'z_4', 'z_5', 'z_6', 'z_7', 'z_8', 'z_a', 'z_b', 'z_c', 'z_eff', 'z_g', 'z_i', 'z_j', 'z_k', 'z_l', 'z_m', 'z_n', 'z_nn_n', 'z_p', 'z_peak', 'z_phot', 'z_r', 'z_s', 'z_t', 'z_v', 'z_x', 'z_y', 'za', 'zab', 'zabrocki', 'zaca', 'zacharias', 'zachariasen', 'zachary', 'zachila', 'zade', 'zadeh', 'zag', 'zagging', 'zagi', 'zagier', 'zagreb', 'zahar', 'zaharescu', 'zaharko', 'zahorski', 'zaitsev', 'zak', 'zakai', 'zakharov', 'zaks', 'zakynthinou', 'zaldarriaga', 'zalesskii', 'zalgall', 'zalgaller', 'zam', 'zamalloa2014', 'zamboni', 'zame', 'zamolodchikov', 'zamora', 'zampieri', 'zanelli', 'zanello', 'zang', 'zank', 'zapatrin', 'zaragoza', 'zaremba', 'zarembo', 'zariphopoulou', 'zariski', 'zaslow', 'zassenhaus', 'zatsepin', 'zayas', 'zaytsev', 'zb', 'zbb', 'zbb_bar', 'zbc', 'zbcp', 'zblan', 'zbw', 'zc', 'zcbf', 'zcdp', 'zcomp', 'zd', 'zdd', 'zdf', 'zdhnarsil', 'zdi', 'zdock', 'zdock2', 'zds', 'ze', 'zealand', 'zealot', 'zebra', 'zebrafish', 'zebu', 'zeckendorf', 'zee', 'zeek', 'zeeman', 'zeev', 'zehnder', 'zeilberger', 'zeilinger', 'zeitgeist', 'zeitlin', 'zeitouni', 'zeitschr', 'zel', 'zelda', 'zelditch', 'zeldovich', 'zelevinsky', 'zelinsky', 'zelmanov', 'zemach', 'zemax', 'zemblanitous', 'zemblanity', 'zemmour', 'zen', 'zener', 'zeng', 'zenith', 'zenner', 'zeno', 'zenodo', 'zenon', 'zeolite', 'zeor', 'zepam', 'zepf', 'zeptosecond', 'zer', 'zerbe', 'zeriahi', 'zerilli', 'zermelo', 'zernike', 'zero', 'zeroed', 'zeroing', 'zeroise', 'zeroline', 'zeromode', 'zeromq', 'zeropoint', 'zeroprompt', 'zeros', 'zeroshot', 'zeroth', 'zers', 'zeta', 'zeta_', 'zeta_1', 'zeta_g', 'zeta_i', 'zeta_k', 'zeta_m', 'zeta_n', 'zeta_p', 'zetlin', 'zeus', 'zev', 'zf', 'zfc', 'zfcw', 'zff', 'zgirski', 'zgr', 'zgrs', 'zh', 'zhabotinsky', 'zhai', 'zhan', 'zhang', 'zhang2005', 'zhang4', 'zhang_differentiable_2021', 'zhao', 'zhaus', 'zhdanov', 'zhejiang', 'zheng', 'zhh', 'zhidenko', 'zhihu', 'zhikov', 'zhiqiangshen', 'zhitnitski', 'zhnr', 'zhnrs', 'zhong', 'zhou', 'zhs', 'zhu', 'zhuang', 'zhukovskii', 'ziadi', 'ziegler', 'ziesche2016bernoulli', 'zig', 'zigsaw', 'zigzag', 'zika', 'zilch', 'zimm', 'zimmer', 'zimmermann', 'zinbiel', 'zinc', 'zincblende', 'zindon', 'zineau', 'zinger', 'zink', 'zinkophilic', 'zinkophobic', 'zinn', 'zinoviev', 'zintl', 'zip', 'zipf', 'zipoy', 'zipper', 'zippered', 'zipperposition', 'zippin', 'zirbel', 'zircaloy', 'zirconate', 'zirconia', 'zirconium', 'zirnbauer', 'zisman', 'zitt', 'zitterbewegung', 'ziv', 'zj', 'zjj', 'zju', 'zju3dv', 'zjuicsr', 'zjzj', 'zk', 'zklaim', 'zkp', 'zkps', 'zl_il_j', 'zlfh2015', 'zm', 'zmp', 'zn', 'zn0', 'zn1', 'zn_', 'znajek', 'znal', 'znc', 'zncu', 'zncu3', 'znfe2o4', 'zng', 'zniak', 'zniro', 'zno', 'znpt', 'zns', 'znsb', 'znse', 'znt', 'znte', 'zntpp', 'znwo4', 'zo', 'zodiacal', 'zograf', 'zohar', 'zoller', 'zolotare', 'zolotareff', 'zolotarev', 'zolpidem', 'zombie', 'zonal', 'zonally', 'zone', 'zong3', 'zonotope', 'zoo', 'zoologici', 'zoom', 'zooming', 'zooniverse', 'zorich', 'zotyev', 'zp', 'zpf', 'zpl', 'zps', 'zpt', 'zq', 'zr', 'zr0', 'zr4', 'zr_', 'zrb12', 'zrb2', 'zrc', 'zrncl', 'zrnial', 'zrp', 'zrpt', 'zrs', 'zrsis', 'zrsise', 'zrsite', 'zrsix', 'zrte', 'zrw2o8', 'zs', 'zschau', 'zsl', 'zsm', 'zsol', 'zsun', 'zt', 'ztd', 'ztf', 'ztf18abvkwla', 'zuazua', 'zubaran', 'zubarev', 'zuber', 'zuckerman', 'zudilin', 'zuily', 'zukowski', 'zulu', 'zumbr', 'zumbrun', 'zumino', 'zureick', 'zurek', 'zurich', 'zurieck', 'zvi', 'zw', 'zw1', 'zwam', 'zwanzig', 'zwanziger', 'zweger', 'zweig', 'zwick', 'zwicky', 'zwiebach', 'zx', 'zxt', 'zy', 'zygmund', 'zygodontomys', 'zyme', 'zynq', 'zyuzin', 'zyx', 'zyzfeaso1', 'zz', 'zz_3', 'zz_7', 'zzh', 'zzz'] not in index\""
+ ]
+ }
+ ],
+ "source": [
+ "similarity = cosine_similarity(tfidf_vectors, _test)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def inference(tfidf_df: pd.DataFrame, abstract: str):\n",
+ " processed_abstract = preprocessAbstracts(abstract)\n",
+ " abstract_vector = vectorizer.fit_transform(processed_abstract)\n",
+ " \n",
+ " \n",
+ " return \"\""
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3.11.4 ('arxiv-env': venv)",
+ "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": "3.11.4"
+ },
+ "orig_nbformat": 4,
+ "vscode": {
+ "interpreter": {
+ "hash": "aae17c2ae2f38cc6f211be9b71a2aa280701d8462782cbc1f67caa83a1603363"
+ }
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/src/models/train_recommender.py b/src/models/train_recommender.py
new file mode 100644
index 0000000000000000000000000000000000000000..d86317d85321567bf1d382c9f75a45597c886c42
--- /dev/null
+++ b/src/models/train_recommender.py
@@ -0,0 +1,102 @@
+import pandas as pd
+from gensim.similarities import SparseMatrixSimilarity
+import argparse
+import logging
+import time
+
+from utils.utilities import read_yaml_config, validate_and_create_subfolders
+from utils.mlutilities import *
+
+import logging
+import sys
+
+logging.basicConfig(
+ level=logging.INFO,
+ format="%(asctime)s [%(levelname)s] %(message)s",
+ handlers=[
+ logging.FileHandler("debug.log"),
+ logging.StreamHandler(sys.stdout)
+ ]
+)
+
+
+model_configurations = read_yaml_config("/Users/luis.morales/Desktop/arxiv-paper-recommender/src/models/configs.yaml")
+
+
+if __name__ == "__main__":
+ """
+ Example:
+ python3 ./src/models/train_recommender.py --modelsize Medium
+
+ """
+ # Define and parse command-line arguments
+ parser = argparse.ArgumentParser(description='ArXiv Paper Recommender CLI')
+ parser.add_argument('--modelsize',choices=["Large", "SubLarge", "Medium", "Small"], default=None, type=str, help='Model Size')
+
+ args = parser.parse_args()
+ model_size = args.modelsize
+ start = time.time()
+
+
+ if model_size is None:
+ raise Exception("The `modelsize` flag was no passed to the CLI.")
+
+
+ model_config = model_configurations["GensimConfig"][model_size]
+ model_name = model_configurations["GensimConfig"][model_size]["ModelName"]
+ dataset_fraq_split = model_configurations["GensimConfig"][model_size]["DataSetFracSplit"]
+ random_seed = model_configurations["GensimConfig"][model_size]["RandomSeedSplit"]
+ logging.info(f"Started training of {model_name} Model.")
+
+
+ validate_and_create_subfolders(
+ model_name=model_name
+ )
+ logging.info(f"Model Folder `{model_name}` was created successfully.")
+
+
+ if dataset_fraq_split is None:
+ df = pd.read_parquet("/Users/luis.morales/Desktop/arxiv-paper-recommender/data/processed/reduced_arxiv_papers.parquet.gzip")
+ logging.info(f"The full text Corpus was readed.")
+
+ else :
+ df = pd.read_parquet("/Users/luis.morales/Desktop/arxiv-paper-recommender/data/processed/reduced_arxiv_papers.parquet.gzip") \
+ .sample(frac=dataset_fraq_split, random_state=random_seed) \
+ .reset_index(drop=True)
+ logging.info(f"A random split of {dataset_fraq_split}% was applied on the Text Corpus ")
+ logging.info(f"Dimensions of the dataset: {df.shape}")
+
+ df.to_parquet(f"/Users/luis.morales/Desktop/arxiv-paper-recommender/models/data/{model_name}.parquet.gzip", compression='gzip')
+ logging.info(f"The Dataset used for this training was successfully saved in: `/Users/luis.morales/Desktop/arxiv-paper-recommender/models/data/{model_name}.parquet.gzip`.")
+
+
+
+ corpus = df['cleaned_abstracts'].to_list()
+ tokenized_corpus = gensim_tokenizer(corpus)
+ logging.info(f"Dictionary Learned on the {model_name} corpus dataset.")
+
+
+ dictionary = get_gensim_dictionary(tokenized_docs=tokenized_corpus, dict_name=model_name, save_dict=True)
+ logging.info("Dictionary Saved Locally.")
+
+
+ BoW_corpus = [dictionary.doc2bow(doc, allow_update=True) for doc in tokenized_corpus]
+ tfidf_model = TfidfModel(BoW_corpus)
+ logging.info(f"TD-IDF {model_name} Model was successfully trained.")
+
+
+ tfidf_model.save(f"/Users/luis.morales/Desktop/arxiv-paper-recommender/models/tfidf/{model_name}.model")
+ logging.info(f"Model: {model_name} was successfully saved.")
+
+
+ index = SparseMatrixSimilarity(tfidf_model[BoW_corpus], num_features=len(dictionary))
+ logging.info(f"The Similarities Sparse Matrix was successfully created.")
+ index.save(f"/Users/luis.morales/Desktop/arxiv-paper-recommender/models/similarities_matrix/{model_name}")
+ logging.info(f"The Similarities Matrix was successfully saved for the model: {model_name}.")
+
+ end = time.time()
+ total_time = end - start
+ logging.info(f"Full Training of {model_size} model took {total_time} secs.")
+ logging.info(f"The {model_name} Model was successfully trained! yei :)")
+
+
\ No newline at end of file
diff --git a/src/models/utils/__pycache__/constants.cpython-39.pyc b/src/models/utils/__pycache__/constants.cpython-39.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..207c7a24b1371b63d9c19b9c9bf0a4fa5fce60ba
Binary files /dev/null and b/src/models/utils/__pycache__/constants.cpython-39.pyc differ
diff --git a/src/models/utils/__pycache__/mlutilities.cpython-39.pyc b/src/models/utils/__pycache__/mlutilities.cpython-39.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..c68d7ffb0c3159edb46a78e1f236a5272bf4ec14
Binary files /dev/null and b/src/models/utils/__pycache__/mlutilities.cpython-39.pyc differ
diff --git a/src/models/utils/constants.py b/src/models/utils/constants.py
new file mode 100644
index 0000000000000000000000000000000000000000..5af79a9ecf20e2d821d0ab4cf2b7967a1766e4a2
--- /dev/null
+++ b/src/models/utils/constants.py
@@ -0,0 +1,60 @@
+
+TEST_INPUTS = [
+ "Can you recommend papers that explore the application of deep learning in computer vision for object detection, image segmentation, and video analysis?",
+ "I'm looking for recent papers that discuss the advancements in natural language processing, particularly in neural language models, sentiment analysis, and text generation.",
+ "Which papers should I read for understanding the use of reinforcement learning in robotics for tasks such as autonomous navigation, grasping and manipulation, and human-robot interaction?",
+ "Can you suggest papers that investigate the use of machine learning techniques, including neural networks, support vector machines, and random forests, for predictive modeling in financial markets?",
+ "I'm interested in reading papers on the intersection of biology and artificial intelligence, focusing on topics like bioinformatics, genomics, and drug discovery.",
+ "Which papers discuss the theory and applications of graph neural networks for tasks such as node classification, link prediction, and graph generation?",
+ "Can you recommend papers on the use of deep learning and natural language processing for automated machine translation across multiple languages?",
+ "I'm looking for papers that analyze the impact of different feature selection techniques and dimensionality reduction methods in high-dimensional datasets.",
+ "Which papers should I read for understanding the latest advancements in quantum computing, including quantum algorithms, error correction, and quantum simulation?",
+ "Can you suggest papers on the application of machine learning in personalized medicine, focusing on precision diagnostics, treatment recommendations, and patient outcome prediction?",
+ "I'm interested in reading papers on the use of machine learning and data mining techniques for anomaly detection in cybersecurity and network intrusion detection.",
+ "Which papers discuss the use of statistical models and Bayesian inference for uncertainty quantification and risk assessment in engineering systems?",
+ "Can you recommend papers that explore the use of deep reinforcement learning for autonomous driving, including perception, planning, and control?",
+ "I'm looking for papers on the analysis of social networks, studying topics such as community detection, influence analysis, and information diffusion.",
+ "Which papers should I read for understanding the principles and applications of quantum machine learning, including quantum-enhanced algorithms and quantum data processing?",
+ "Can you suggest papers on the use of machine learning and data mining in environmental science, focusing on climate modeling, ecological forecasting, and remote sensing?",
+ "I'm interested in reading papers that investigate the ethical implications of artificial intelligence, including fairness, accountability, and transparency.",
+ "Which papers discuss the use of machine learning for healthcare analytics, including disease diagnosis, patient monitoring, and treatment recommendation?",
+ "Can you recommend papers on the intersection of robotics and human-computer interaction, studying topics like social robots, assistive technologies, and collaborative robotics?"
+]
+
+user_requests_tests = {
+ 'Math': [
+ "Can you recommend recent papers on algebraic geometry that focus on the intersection theory and its applications in enumerative geometry?",
+ "I'm looking for papers on convex optimization algorithms for solving large-scale linear programming problems in transportation and logistics.",
+ "Could you suggest papers on the Langlands program and its connections to number theory and automorphic forms?",
+ "I need papers on partial differential equations with an emphasis on numerical methods for solving the Navier-Stokes equations in fluid dynamics.",
+ "Can you recommend papers on mathematical modeling of epidemics using compartmental models such as SIR and SEIR?"
+ ],
+ 'Statistics': [
+ "I'm interested in papers that compare Bayesian and frequentist approaches in the context of hierarchical modeling for analyzing longitudinal healthcare data.",
+ "Could you suggest papers on the design and analysis of experiments with factorial designs and interactions in the presence of missing data?",
+ "Can you recommend papers on forecasting methods for multivariate time series with long-term dependencies, such as long short-term memory (LSTM) networks?",
+ "I need papers on high-dimensional statistical inference, specifically focused on variable selection and regularization methods for genomic data analysis.",
+ "Could you provide papers on nonparametric kernel density estimation techniques for analyzing environmental data with spatial dependence?"
+ ],
+ 'ElectricalEngineering': [
+ "Can you suggest papers on deep learning-based speech enhancement algorithms for improving the intelligibility of noisy speech in real-world environments?",
+ "I'm interested in research on massive multiple-input multiple-output (MIMO) systems and beamforming techniques for next-generation wireless communication networks.",
+ "Could you provide papers on image and video compression algorithms based on the latest video coding standards, such as HEVC and AV1?",
+ "I'm looking for recent papers on power electronic converters and control strategies for grid-connected photovoltaic systems with maximum power point tracking (MPPT).",
+ "Can you recommend papers on low-power circuit design techniques for implementing energy-efficient Internet of Things (IoT) devices with constrained resources?"
+ ],
+ 'QuantitativeBiology': [
+ "I need papers on the analysis of single-cell RNA sequencing (scRNA-seq) data using dimensionality reduction techniques like t-SNE and UMAP.",
+ "Could you suggest papers on mathematical modeling and simulation of gene regulatory networks using ordinary differential equations (ODEs) and stochastic methods?",
+ "Can you recommend papers on population genetics and the statistical inference of demographic history using coalescent theory and phylogenetic methods?",
+ "I'm interested in research on computational neuroscience, particularly papers focusing on neural network models for understanding information processing in the visual cortex.",
+ "Could you provide papers on statistical methods for analyzing gene expression data from high-throughput sequencing technologies like RNA-seq and ChIP-seq, with a focus on differential expression analysis and pathway enrichment analysis?"
+ ],
+ 'Economics': [
+ "Can you suggest recent papers on behavioral economics that investigate the role of emotions and biases in decision-making under uncertainty, particularly in the context of financial markets?",
+ "I'm looking for papers on econometric modeling of time-varying volatility using GARCH models with applications to analyzing stock market returns.",
+ "Could you recommend papers on game theory and mechanism design applied to auction theory and market design in the field of online advertising?",
+ "I need papers on the role of institutions and governance in promoting economic growth and development, with a focus on empirical studies using cross-country panel data.",
+ "Can you provide papers on financial market modeling using stochastic volatility models, specifically focusing on estimating risk measures like Value-at-Risk (VaR) and Expected Shortfall (ES) for portfolio management?"
+ ]
+}
diff --git a/src/models/utils/gensimRecommender.ipynb b/src/models/utils/gensimRecommender.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..a5be251c0c059a9d3f4908a7dd09dd8a7d8f6202
--- /dev/null
+++ b/src/models/utils/gensimRecommender.ipynb
@@ -0,0 +1,228 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from gensim import corpora\n",
+ "from gensim.similarities import SparseMatrixSimilarity\n",
+ "from gensim.models import TfidfModel\n",
+ "import pandas as pd\n",
+ "import gensim\n",
+ "import pprint\n",
+ "from gensim import corpora\n",
+ "from gensim.utils import simple_preprocess\n",
+ "from gensim.models import TfidfModel\n",
+ "from gensim.parsing import strip_tags, strip_numeric, \\\n",
+ " strip_multiple_whitespaces, stem_text, strip_punctuation, \\\n",
+ " remove_stopwords, preprocess_string\n",
+ "import re\n",
+ "import os\n",
+ "from typing import List"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "ename": "NameError",
+ "evalue": "name 'strip_tags' is not defined",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
+ "Cell \u001b[0;32mIn[6], line 5\u001b[0m\n\u001b[1;32m 1\u001b[0m transform_to_lower \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mlambda\u001b[39;00m s: s\u001b[38;5;241m.\u001b[39mlower()\n\u001b[1;32m 2\u001b[0m remove_single_char \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mlambda\u001b[39;00m s: re\u001b[38;5;241m.\u001b[39msub(\u001b[38;5;124mr\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m\\\u001b[39m\u001b[38;5;124ms+\u001b[39m\u001b[38;5;124m\\\u001b[39m\u001b[38;5;124mw\u001b[39m\u001b[38;5;132;01m{1}\u001b[39;00m\u001b[38;5;124m\\\u001b[39m\u001b[38;5;124ms+\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m'\u001b[39m, s)\n\u001b[1;32m 4\u001b[0m cleaning_filters \u001b[38;5;241m=\u001b[39m [\n\u001b[0;32m----> 5\u001b[0m \u001b[43mstrip_tags\u001b[49m,\n\u001b[1;32m 6\u001b[0m strip_numeric,\n\u001b[1;32m 7\u001b[0m strip_punctuation, \n\u001b[1;32m 8\u001b[0m strip_multiple_whitespaces, \n\u001b[1;32m 9\u001b[0m transform_to_lower,\n\u001b[1;32m 10\u001b[0m remove_stopwords,\n\u001b[1;32m 11\u001b[0m remove_single_char\n\u001b[1;32m 12\u001b[0m ]\n\u001b[1;32m 14\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mgensim_tokenizer\u001b[39m(docs: List[\u001b[38;5;28mstr\u001b[39m]):\n\u001b[1;32m 15\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 16\u001b[0m \u001b[38;5;124;03m Tokenizes a list of strings using a series of cleaning filters.\u001b[39;00m\n\u001b[1;32m 17\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 22\u001b[0m \u001b[38;5;124;03m List[List[str]]: A list of tokenized documents, where each document is represented as a list of tokens.\u001b[39;00m\n\u001b[1;32m 23\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n",
+ "\u001b[0;31mNameError\u001b[0m: name 'strip_tags' is not defined"
+ ]
+ }
+ ],
+ "source": [
+ "\n",
+ "transform_to_lower = lambda s: s.lower()\n",
+ "remove_single_char = lambda s: re.sub(r'\\s+\\w{1}\\s+', '', s)\n",
+ "\n",
+ "cleaning_filters = [\n",
+ " strip_tags,\n",
+ " strip_numeric,\n",
+ " strip_punctuation, \n",
+ " strip_multiple_whitespaces, \n",
+ " transform_to_lower,\n",
+ " remove_stopwords,\n",
+ " remove_single_char\n",
+ "]\n",
+ "\n",
+ "def gensim_tokenizer(docs: List[str]):\n",
+ " \"\"\"\n",
+ " Tokenizes a list of strings using a series of cleaning filters.\n",
+ "\n",
+ " Args:\n",
+ " docs (List[str]): A list of strings to be tokenized.\n",
+ "\n",
+ " Returns:\n",
+ " List[List[str]]: A list of tokenized documents, where each document is represented as a list of tokens.\n",
+ " \"\"\"\n",
+ " tokenized_docs = list()\n",
+ " for doc in docs:\n",
+ " processed_words = preprocess_string(doc, cleaning_filters)\n",
+ " tokenized_docs.append(processed_words)\n",
+ " \n",
+ " return tokenized_docs\n",
+ "\n",
+ "\n",
+ "def cleaning_pipe(document):\n",
+ " \"\"\"\n",
+ " Applies a series of cleaning steps to a document.\n",
+ "\n",
+ " Args:\n",
+ " document (str): The document to be cleaned.\n",
+ "\n",
+ " Returns:\n",
+ " list: A list of processed words after applying the cleaning filters.\n",
+ " \"\"\"\n",
+ " # Invoking gensim.parsing.preprocess_string method with set of filters\n",
+ " processed_words = preprocess_string(document, cleaning_filters)\n",
+ " return processed_words\n",
+ "\n",
+ "\n",
+ "def get_gensim_dictionary(tokenized_docs: List[str], dict_name: str = \"corpus\", save_dict: bool = False):\n",
+ " \"\"\"\n",
+ " Create dictionary of words in preprocessed corpus and saves the dict object\n",
+ " \"\"\"\n",
+ " dictionary = corpora.Dictionary(tokenized_docs)\n",
+ " if save_dict: \n",
+ " parent_folder = \"/Users/luis.morales/Desktop/arxiv-paper-recommender/models/nlp_dictionaries\"\n",
+ " dictionary.save(f'{parent_folder}/{dict_name}.dict')\n",
+ " return dictionary\n",
+ "\n",
+ "\n",
+ "def get_closest_n(index_matrix: SparseMatrixSimilarity, query: str, n: int):\n",
+ " '''\n",
+ " Retrieves the top matching documents as per cosine similarity\n",
+ " between the TF-IDF vector of the query and all documents.\n",
+ "\n",
+ " Args:\n",
+ " query (str): The query string to find matching documents.\n",
+ " n (int): The number of closest documents to retrieve.\n",
+ "\n",
+ " Returns:\n",
+ " numpy.ndarray: An array of indices representing the top matching documents.\n",
+ " '''\n",
+ " # Clean the query document using cleaning_pipe function\n",
+ " query_document = cleaning_pipe(query)\n",
+ "\n",
+ " # Convert the query document to bag-of-words representation\n",
+ " query_bow = dictionary.doc2bow(query_document)\n",
+ "\n",
+ " # Calculate similarity scores between the query and all documents using TF-IDF model\n",
+ " sims = index_matrix[index_matrix[query_bow]]\n",
+ "\n",
+ " # Get the indices of the top n closest documents based on similarity scores\n",
+ " top_idx = sims.argsort()[-1 * n:][::-1]\n",
+ "\n",
+ " return top_idx\n",
+ "\n",
+ "\n",
+ "def get_recomendations_metadata(query: str, df: pd.DataFrame, n: int):\n",
+ " '''\n",
+ " Retrieves metadata recommendations based on a query using cosine similarity.\n",
+ "\n",
+ " Args:\n",
+ " query (str): The query string for which recommendations are sought.\n",
+ " n (int): The number of recommendations to retrieve.\n",
+ " df (pd.DataFrame): The DataFrame containing metadata information.\n",
+ "\n",
+ " Returns:\n",
+ " pd.DataFrame: A DataFrame containing the recommended metadata, reset with a new index.\n",
+ " '''\n",
+ " # Get the indices of the closest matching documents based on the query\n",
+ " recommendations_idxs = get_closest_n(query, n)\n",
+ " \n",
+ " # Retrieve the recommended metadata rows from the DataFrame based on the indices\n",
+ " recommendations_metadata = df.iloc[recommendations_idxs]\n",
+ " \n",
+ " # Reset the index of the recommended metadata DataFrame\n",
+ " recommendations_metadata = recommendations_metadata.reset_index(drop=True)\n",
+ " \n",
+ " return recommendations_metadata"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "corpus = corpora.Dictionary.load()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "td_idf_model = TfidfModel.load(\"/Users/luis.morales/Desktop/arxiv-paper-recommender/models/tfidf/SemanticSherlock.model\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "similarities = SparseMatrixSimilarity.load(\"/Users/luis.morales/Desktop/arxiv-paper-recommender/models/similarities_matrix/LanguageLiberatorSimilarities/LanguageLiberator\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ " query = args.query\n",
+ " \n",
+ " df = pd.read_parquet(\"/Users/luis.morales/Desktop/arxiv-paper-recommender/data/processed/reduced_arxiv_papers.parquet.gzip\")\n",
+ " \n",
+ " dict_corpus = Dictionary.load(\"/Users/luis.morales/Desktop/arxiv-paper-recommender/models/dictionaries/LanguageLiberator.dict\")\n",
+ " \n",
+ " td_idf_model = TfidfModel.load(\"/Users/luis.morales/Desktop/arxiv-paper-recommender/models/tfidf/SemanticSherlock.model\")\n",
+ " \n",
+ " similarities = SparseMatrixSimilarity.load(\"/Users/luis.morales/Desktop/arxiv-paper-recommender/models/similarities_matrix/LanguageLiberatorSimilarities/LanguageLiberator\")\n",
+ " \n",
+ " results_df = get_recomendations_metadata(query=query, df=df, n=3)\n",
+ " print(results_df.head())"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3.11.4 ('arxiv-env': venv)",
+ "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": "3.11.4"
+ },
+ "orig_nbformat": 4,
+ "vscode": {
+ "interpreter": {
+ "hash": "aae17c2ae2f38cc6f211be9b71a2aa280701d8462782cbc1f67caa83a1603363"
+ }
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/src/models/utils/mlutilities.py b/src/models/utils/mlutilities.py
new file mode 100644
index 0000000000000000000000000000000000000000..911460a166eceb45e6f7c2c7fafd189a29c6bead
--- /dev/null
+++ b/src/models/utils/mlutilities.py
@@ -0,0 +1,131 @@
+import pandas as pd
+from gensim.corpora import Dictionary
+from gensim.similarities import SparseMatrixSimilarity
+from gensim.models import TfidfModel
+from gensim.parsing import strip_tags, strip_numeric, \
+ strip_multiple_whitespaces, stem_text, strip_punctuation, \
+ remove_stopwords, preprocess_string
+
+from re import sub
+from typing import List
+from functools import cache
+
+
+transform_to_lower = lambda s: s.lower()
+remove_single_char = lambda s: sub(r'\s+\w{1}\s+', '', s)
+
+cleaning_filters = [
+ strip_tags,
+ strip_numeric,
+ strip_punctuation,
+ strip_multiple_whitespaces,
+ transform_to_lower,
+ remove_stopwords,
+ remove_single_char
+]
+
+def gensim_tokenizer(docs: List[str]):
+ """
+ Tokenizes a list of strings using a series of cleaning filters.
+
+ Args:
+ docs (List[str]): A list of strings to be tokenized.
+
+ Returns:
+ List[List[str]]: A list of tokenized documents, where each document is represented as a list of tokens.
+ """
+ tokenized_docs = list()
+ for doc in docs:
+ processed_words = preprocess_string(doc, cleaning_filters)
+ tokenized_docs.append(processed_words)
+
+ return tokenized_docs
+
+
+def cleaning_pipe(document):
+ """
+ Applies a series of cleaning steps to a document.
+
+ Args:
+ document (str): The document to be cleaned.
+
+ Returns:
+ list: A list of processed words after applying the cleaning filters.
+ """
+ # Invoking gensim.parsing.preprocess_string method with set of filters
+ processed_words = preprocess_string(document, cleaning_filters)
+ return processed_words
+
+
+def get_closest_n(dictionary: Dictionary, index: SparseMatrixSimilarity, tfidf_model : TfidfModel, query: str, n: int):
+ '''
+ Retrieves the top matching documents as per cosine similarity
+ between the TF-IDF vector of the query and all documents.
+
+ Args:
+ query (str): The query string to find matching documents.
+ n (int): The number of closest documents to retrieve.
+
+ Returns:
+ numpy.ndarray: An array of indices representing the top matching documents.
+ '''
+ # Clean the query document using cleaning_pipe function
+ query_document = cleaning_pipe(query)
+
+ # Convert the query document to bag-of-words representation
+ query_bow = dictionary.doc2bow(query_document)
+
+ # Calculate similarity scores between the query and all documents using TF-IDF model
+ sims = index[tfidf_model[query_bow]]
+
+ # Get the indices of the top n closest documents based on similarity scores
+ top_idx = sims.argsort()[-1 * n:][::-1]
+
+ return top_idx
+
+
+def get_recomendations_metadata(query: str, df: pd.DataFrame, n: int,
+ dictionary: Dictionary, index: SparseMatrixSimilarity,
+ tfidf_model : TfidfModel) -> pd.DataFrame:
+ '''
+ Retrieves metadata recommendations based on a query using cosine similarity.
+
+ Args:
+ query (str): The query string for which recommendations are sought.
+ n (int): The number of recommendations to retrieve.
+ df (pd.DataFrame): The DataFrame containing metadata information.
+
+ Returns:
+ pd.DataFrame: A DataFrame containing the recommended metadata, reset with a new index.
+ '''
+ # Get the indices of the closest matching documents based on the query
+ recommendations_idxs = get_closest_n(dictionary, index, tfidf_model, query, n)
+
+ # Retrieve the recommended metadata rows from the DataFrame based on the indices
+ recommendations_metadata = df.iloc[recommendations_idxs]
+
+ # Reset the index of the recommended metadata DataFrame
+ recommendations_metadata = recommendations_metadata.reset_index(drop=True)
+
+ return recommendations_metadata
+ # return recommendations_idxs
+
+@cache
+def load_arxiv_parquet(path: str):
+ df = pd.read_parquet(path)
+ return df
+
+@cache
+def load_dict(path: str):
+ dict_corpus = Dictionary.load(path)
+ return dict_corpus
+
+@cache
+def load_model(path: str ):
+ tfidf_model = TfidfModel.load(path)
+ return tfidf_model
+
+@cache
+def load_sparse_matrix(path: str):
+ similarities = SparseMatrixSimilarity.load(path)
+ return similarities
\ No newline at end of file
diff --git a/src/models/utils/utilities.py b/src/models/utils/utilities.py
new file mode 100644
index 0000000000000000000000000000000000000000..275731cd5e2efa3332c54c0de4b77e65d243723f
--- /dev/null
+++ b/src/models/utils/utilities.py
@@ -0,0 +1,74 @@
+import yaml
+import time
+from typing import Dict, Union
+import pandas as pd
+import spacy
+import os
+
+
+def read_yaml_config(file_path: str) -> Dict:
+ """
+ Reads a YAML configuration file and returns the loaded configuration as a dictionary.
+
+ Args:
+ file_path (str): The path to the YAML configuration file.
+ """
+ with open(file_path, 'r') as file:
+ config = yaml.safe_load(file)
+ return config
+
+
+
+def validate_and_create_subfolders(
+ model_name: str,
+ parent_subfolder: str = "/Users/luis.morales/Desktop/arxiv-paper-recommender/models"
+):
+ model_subfolders = ["data", "dictionaries", "similarities_matrix", "tdidf"]
+
+ if not os.path.exists(f"{parent_subfolder}/{model_name}"):
+ os.makedirs(f"{parent_subfolder}/{model_name}")
+ for msubfolder in model_subfolders:
+ os.makedirs(f"{parent_subfolder}/{model_name}/{msubfolder}")
+
+
+
+
+
+
+def execution_time(func):
+ """
+ Decorator that measures the execution time of a function and prints the elapsed time.
+ """
+ def wrapper(*args, **kwargs):
+ start_time = time.time()
+ result = func(*args, **kwargs)
+ end_time = time.time()
+ execution_seconds = end_time - start_time
+ print(f"Function '{func.__name__}' executed in {execution_seconds:.4f} seconds.")
+ return result
+ return wrapper
+
+
+def cleanData(doc: Union[pd.Series, str], nlp = spacy.load('en_core_web_sm')):
+ """
+ TODO: Optimize NLP Object to only obtain stopwords, lemmas, and tokenize docs.
+
+ Cleans and processes the input documents by performing various text cleaning operations.
+
+ Args:
+ doc (pd.Series): The documents to be cleaned, passed in a Series object.
+ stemming (bool, optional): Specifies whether stemming should be applied. Defaults to False.
+
+ Returns:
+ str: The cleaned and processed document as a single string.
+ """
+ doc = doc.lower()
+ doc = nlp(doc)
+ tokens = [tokens.lower_ for tokens in doc]
+ tokens = [tokens for tokens in doc if (tokens.is_stop == False)]
+ tokens = [tokens for tokens in tokens if (tokens.is_punct == False)]
+ final_token = [token.lemma_ for token in tokens]
+
+ return " ".join(final_token)
+
+
diff --git a/src/visualization/.gitkeep b/src/visualization/.gitkeep
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/src/visualization/__init__.py b/src/visualization/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/src/visualization/visualize.py b/src/visualization/visualize.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391