{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# The Hottest Topics in Machine Learning\n", "> Neural Information Processing Systems (NIPS) is one of the top machine learning conferences in the world where groundbreaking work is published. In this Project, you will analyze a large collection of NIPS research papers from the past decade to discover the latest trends in machine learning. The techniques used here to handle large amounts of data can be applied to other text datasets as well. This is the Result of Project \"The Hottest Topics in Machine Learning\", via datacamp.\n", "\n", "- toc: true \n", "- badges: true\n", "- comments: true\n", "- author: Chanseok Kang\n", "- categories: [Python, Datacamp, Data_Science, Machine_Learning]\n", "- image: images/nips_wordcloud.png" ] }, { "cell_type": "markdown", "metadata": { "dc": { "key": "4" }, "deletable": false, "editable": false, "run_control": { "frozen": true }, "tags": [ "context" ] }, "source": [ "## 1. Loading the NIPS papers\n", "
The NIPS conference (Neural Information Processing Systems) is one of the most prestigious yearly events in the machine learning community. At each NIPS conference, a large number of research papers are published. Over 50,000 PDF files were automatically downloaded and processed to obtain a dataset on various machine learning techniques. These NIPS papers are stored in datasets/papers.csv
. The CSV file contains information on the different NIPS papers that were published from 1987 until 2017 (30 years!). These papers discuss a wide variety of topics in machine learning, from neural networks to optimization methods and many more.\n",
"
First, we will explore the CSV file to determine what type of data we can use for the analysis and how it is structured. A research paper typically consists of a title, an abstract and the main text. Other data such as figures and tables were not extracted from the PDF files. Each paper discusses a novel technique or improvement. In this analysis, we will focus on analyzing these papers with natural language processing methods.
" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "dc": { "key": "4" }, "tags": [ "sample_code" ] }, "outputs": [ { "data": { "text/html": [ "\n", " | id | \n", "year | \n", "title | \n", "event_type | \n", "pdf_name | \n", "abstract | \n", "paper_text | \n", "
---|---|---|---|---|---|---|---|
0 | \n", "1 | \n", "1987 | \n", "Self-Organization of Associative Database and ... | \n", "NaN | \n", "1-self-organization-of-associative-database-an... | \n", "Abstract Missing | \n", "767\\n\\nSELF-ORGANIZATION OF ASSOCIATIVE DATABA... | \n", "
1 | \n", "10 | \n", "1987 | \n", "A Mean Field Theory of Layer IV of Visual Cort... | \n", "NaN | \n", "10-a-mean-field-theory-of-layer-iv-of-visual-c... | \n", "Abstract Missing | \n", "683\\n\\nA MEAN FIELD THEORY OF LAYER IV OF VISU... | \n", "
2 | \n", "100 | \n", "1988 | \n", "Storing Covariance by the Associative Long-Ter... | \n", "NaN | \n", "100-storing-covariance-by-the-associative-long... | \n", "Abstract Missing | \n", "394\\n\\nSTORING COVARIANCE BY THE ASSOCIATIVE\\n... | \n", "
3 | \n", "1000 | \n", "1994 | \n", "Bayesian Query Construction for Neural Network... | \n", "NaN | \n", "1000-bayesian-query-construction-for-neural-ne... | \n", "Abstract Missing | \n", "Bayesian Query Construction for Neural\\nNetwor... | \n", "
4 | \n", "1001 | \n", "1994 | \n", "Neural Network Ensembles, Cross Validation, an... | \n", "NaN | \n", "1001-neural-network-ensembles-cross-validation... | \n", "Abstract Missing | \n", "Neural Network Ensembles, Cross\\nValidation, a... | \n", "
For the analysis of the papers, we are only interested in the text data associated with the paper as well as the year the paper was published in.
\n", "We will analyze this text data using natural language processing. Since the file contains some metadata such as id's and filenames, it is necessary to remove all the columns that do not contain useful text information.
" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "dc": { "key": "11" }, "tags": [ "sample_code" ] }, "outputs": [ { "data": { "text/html": [ "\n", " | year | \n", "title | \n", "abstract | \n", "paper_text | \n", "
---|---|---|---|---|
0 | \n", "1987 | \n", "Self-Organization of Associative Database and ... | \n", "Abstract Missing | \n", "767\\n\\nSELF-ORGANIZATION OF ASSOCIATIVE DATABA... | \n", "
1 | \n", "1987 | \n", "A Mean Field Theory of Layer IV of Visual Cort... | \n", "Abstract Missing | \n", "683\\n\\nA MEAN FIELD THEORY OF LAYER IV OF VISU... | \n", "
2 | \n", "1988 | \n", "Storing Covariance by the Associative Long-Ter... | \n", "Abstract Missing | \n", "394\\n\\nSTORING COVARIANCE BY THE ASSOCIATIVE\\n... | \n", "
3 | \n", "1994 | \n", "Bayesian Query Construction for Neural Network... | \n", "Abstract Missing | \n", "Bayesian Query Construction for Neural\\nNetwor... | \n", "
4 | \n", "1994 | \n", "Neural Network Ensembles, Cross Validation, an... | \n", "Abstract Missing | \n", "Neural Network Ensembles, Cross\\nValidation, a... | \n", "
In order to understand how the machine learning field has recently exploded in popularity, we will begin by visualizing the number of publications per year.
\n", "By looking at the number of published papers per year, we can understand the extent of the machine learning 'revolution'! Typically, this significant increase in popularity is attributed to the large amounts of compute power, data and improvements in algorithms.
" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "dc": { "key": "18" }, "tags": [ "sample_code" ] }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "Let's now analyze the titles of the different papers to identify machine learning trends. First, we will perform some simple preprocessing on the titles in order to make them more amenable for analysis. We will use a regular expression to remove any punctuation in the title. Then we will perform lowercasing. We'll then print the titles of the first rows before and after applying the modification.
" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "dc": { "key": "25" }, "tags": [ "sample_code" ] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0 Self-Organization of Associative Database and ...\n", "1 A Mean Field Theory of Layer IV of Visual Cort...\n", "2 Storing Covariance by the Associative Long-Ter...\n", "3 Bayesian Query Construction for Neural Network...\n", "4 Neural Network Ensembles, Cross Validation, an...\n", "Name: title, dtype: object\n" ] }, { "data": { "text/html": [ "\n", " | year | \n", "title | \n", "abstract | \n", "paper_text | \n", "title_processed | \n", "
---|---|---|---|---|---|
0 | \n", "1987 | \n", "Self-Organization of Associative Database and ... | \n", "Abstract Missing | \n", "767\\n\\nSELF-ORGANIZATION OF ASSOCIATIVE DATABA... | \n", "self-organization of associative database and ... | \n", "
1 | \n", "1987 | \n", "A Mean Field Theory of Layer IV of Visual Cort... | \n", "Abstract Missing | \n", "683\\n\\nA MEAN FIELD THEORY OF LAYER IV OF VISU... | \n", "a mean field theory of layer iv of visual cort... | \n", "
2 | \n", "1988 | \n", "Storing Covariance by the Associative Long-Ter... | \n", "Abstract Missing | \n", "394\\n\\nSTORING COVARIANCE BY THE ASSOCIATIVE\\n... | \n", "storing covariance by the associative long-ter... | \n", "
3 | \n", "1994 | \n", "Bayesian Query Construction for Neural Network... | \n", "Abstract Missing | \n", "Bayesian Query Construction for Neural\\nNetwor... | \n", "bayesian query construction for neural network... | \n", "
4 | \n", "1994 | \n", "Neural Network Ensembles, Cross Validation, an... | \n", "Abstract Missing | \n", "Neural Network Ensembles, Cross\\nValidation, a... | \n", "neural network ensembles cross validation and ... | \n", "
In order to verify whether the preprocessing happened correctly, we can make a word cloud of the titles of the research papers. This will give us a visual representation of the most common words. Visualisation is key to understanding whether we are still on the right track! In addition, it allows us to verify whether we need additional preprocessing before further analyzing the text data.
\n", "Python has a massive number of open libraries! Instead of trying to develop a method to create word clouds ourselves, we'll use Andreas Mueller's wordcloud library.
" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "dc": { "key": "32" }, "tags": [ "sample_code" ] }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "The main text analysis method that we will use is latent Dirichlet allocation (LDA). LDA is able to perform topic detection on large document sets, determining what the main 'topics' are in a large unlabeled set of texts. A 'topic' is a collection of words that tend to co-occur often. The hypothesis is that LDA might be able to clarify what the different topics in the research titles are. These topics can then be used as a starting point for further analysis.
\n", "LDA does not work directly on text data. First, it is necessary to convert the documents into a simple vector representation. This representation will then be used by LDA to determine the topics. Each entry of a 'document vector' will correspond with the number of times a word occurred in the document. In conclusion, we will convert a list of titles into a list of vectors, all with length equal to the vocabulary. For example, 'Analyzing machine learning trends with neural networks.' would be transformed into [1, 0, 1, ..., 1, 0]
.
We'll then plot the 10 most common words based on the outcome of this operation (the list of document vectors). As a check, these words should also occur in the word cloud.
" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "dc": { "key": "39" }, "tags": [ "sample_code" ] }, "outputs": [ { "data": { "image/png": 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\n", 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