The structure of a discipline condensates itself in texts. Of those texts, classics, on the one hand and encyclopedias on the other have an important role as they are frequently used as teaching resources. Investigating their structure is therefore of quite some interest.
In this notebook we will have a look at the Stanford Encyclopedia of Philosophy, a formidable resource that contains, at the time of writing ~ 1600 articles.
To learn how it represents the structure of philosophy, I used some techniques borrowed from machine learning. If you are interested in the details, I have put the code below.
The basic idea is simple. Every article is represented in a bag of words model, which means that all the words in it are taken out of their context and the number of their occurences is counted. These wordcounts can now be used to calculate a a similarity-metric, called cosine similarity, between all texts. Texts that use the same words are similar, those that do not, are not.
These similarities can now be flattened down (or embedded) into a two-dimensional space using a pretty new and very useful algorithm called umap. We do this to get a nice visualization of the groups in our data, as we can see above. Then we use a clustering method called hdbscan to color the points that form the groups with the highest density, and plot everything with plotly. Points that were not asssigned a cluster are left light-grey.
We can clearly make out sensible groups. In red on the right side, we find a large cluster of classical history of philosophy. On the far left of the graph we find a cluster of articles on logic, colored green. There are also some smaller clusters, like philosophy of religion at (x=15,y=14), colored dark blue, feminism at (16, 18.5) or Chinese & Indian philosophy (18,19). And at (16,18) we have the large field of political philosophy. But there is a lot more to explore: hover your mouse over the points to see the titles of the articles, or click-and-drag to select a window to zoom in.College Weeden Pro White 3 Jersey Combat Brandon Stitched Cowboys
And here as promised is the code. We start by importing some stuff:
College Seminoles Jersey Jameis 5 Stitched Winston Red Limited
import pandas as pd import numpy as np from random importBlackout Jalen Limited Jersey Crimson Tide College Hurts 2 Stitched randint import datetime %matplotlib inline import seaborn as sns import matplotlib.pyplot as plt College 27 Stitched Maroon Chippewas Brown Jersey #For Tables: from IPython.display import display pd.set_option('display.max_columns', 500) #For R (ggplot2) %load_ext rpy2.ipython from sklearn.feature_extraction.text import TfidfVectorizer,TfidfTransformer,CountVectorizer from sklearn import datasets fromAuthentic Joel College Basketball 21 Jayhawks Jersey Embiid Stitched White glob College 27 Stitched Maroon Chippewas Brown Jersey import glob from sklearn.preprocessing importPerine Jersey Sooners White Samaje New 32 College Xii Stitched LabelEncoder from sklearn.datasets import load_files College 27 Stitched Maroon Chippewas Brown Jersey from scipy College 27 Stitched Maroon Chippewas Brown Jersey import sparse
Now, let us load the textual data:
texts = load_files("./trainingdata", description=None, #categories=categories, load_content=College 27 Stitched Maroon Chippewas Brown Jersey True, encoding='utf-8', shuffle=False)#, random_state=42)
count_vect = CountVectorizer(stop_words="english",ngram_range=(1,2), binary=True, min_df = 10, max_df = 1000) X = count_vect.fit_transform(texts.data) # tfidf_transformer = TfidfTransformer() College 27 Stitched Maroon Chippewas Brown Jersey # X = tfidf_transformer.fit_transform(X)
Embed with umap:
import umap embedding = umap.UMAP(n_neighbors=5,#small => local, large => global: 5-50 min_dist=College 27 Stitched Maroon Chippewas Brown Jersey 0.001, #small => local, large => global: 0.001-0.5 metric='cosine').fit_transform(X) embedding = pd.DataFrame(embedding) embedding.columns = ['x','y'] plt.scatter(embedding['x'], embedding['y'], color='grey') embedding["example"] =texts.target_names
Cluster with hdbscan:
import hdbscan clusterer = hdbscan.HDBSCAN(min_cluster_size=25,min_samples=15,gen_min_span_tree=True) clusterer.fit(embedding[["x","y"]]) XCLUST = clusterer.labels_ clusternum = len(set( clusterer.labels_))-1 #samples.append(clusternum) dfclust = pd.DataFrame(XCLUST) dfclust.columns = ['cluster'] print(clusternum) embeddingC = pd.concat([embedding,dfclust], axis=1, join_axes=[embedding.index]) # display(embeddingC)
College 27 Stitched Maroon Chippewas Brown Jersey And produce the plotly-graph you can see at the top:
%%R -i embeddingC #-o myPal means <- aggregate(embedding[,c("x","y")], list(embeddingC$cluster), median) means <- data.frame(means) n=nrow(means) means <- means[-1,] #Make the colors: mycolors <- c("#293757","#568D4B","#D5BB56","#D26A1B",College 27 Stitched Maroon Chippewas Brown Jersey "#A41D1A") #Gene Davis # mycolors <- c("#c03728","#919c4c","#fd8f24","#f5c04a","#e68c7c","#00666b","#142948","#6f5438") pal <- colorRampPalette(sample(mycolors)) s <- n-1 myGray <- c('#95a5a6') myNewColors <- sample(pal(s)) myPal <- append(myGray,myNewColors) library(plotly) p <- plot_ly( type = 'scatter', mode='markers', x=embeddingC$x, y=embeddingC$y, color=as.factor(embeddingC$cluster),colors=myPal, text=embedding$example, hoverinfo="text" , marker=list( size=8, opacity=College 27 Stitched Maroon Chippewas Brown Jersey 0.4)) %>% layout( margin = list(l = 50, r = 50, b = College 27 Stitched Maroon Chippewas Brown Jersey 50, t = 80, pad = 4), #font = t, title = 'Stanford Encyclopedia - umap embedded
...based on the code by McInnes, Healy (2018)', xaxis = list(title = 'umap-x', zerolinecolor = toRGB("lightgray")), yaxis = list(title = 'umap-y', zerolinecolor = toRGB(College 27 Stitched Maroon Chippewas Brown Jersey "lightgray")))%>% config(displayModeBar = F) htmlwidgets::saveWidget(as_widget(p),selfcontained = TRUE, "graph.html")
McInnes L, Healy J. Accelerated Hierarchical Density Based Clustering In: 2017 IEEE International Conference on Data Mining Workshops (ICDMW), IEEE, pp 33-42. 2017
McInnes, L, Healy, J, UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction, ArXiv e-prints 1802.03426, 2018