{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Usupervised classification of philosophical genres\n",
"\n",
"This notebook is a part of work being done for the [Trace of Theory project](https://github.com/htrc/ACS-TT), a collaboration between researchers of [NovelTM](http://novel-tm.ca/) and the HathiTrust Research Center ([HTRC](https://www.hathitrust.org/)).\n",
"\n",
"Here, we'll use unsupervised techniques to identify clusters of similar texts within a corpus of about 3,200 philosophical texts. These texts were previously identified in the HathiTrust public domain corpus using a list of philosophical keywords. The idea now is to look for something like philsophical \"genres\" within this subcorpus of philosophical texts and to compare the computational results to human labels. Our features will mix word-count data with measures of form and with textual metadata, so that we're examining not just subject matter, but also style and (minimal) context.\n",
"\n",
"The work below is almost exclusively about methods. There's not a lot of analysis, and the notebook ends with suggestions for things to try, rather than conclusions about philosophical genre.\n",
"\n",
"## Roadmap\n",
"\n",
"* Download feature data for the 3,200 philosophical texts from the HathiTrust Research Center\n",
"* Parse feature data\n",
"* Calculate other features derived from the same sources\n",
"* Reduce the dimensions of the feature space in order to have some hope of clustering the texts\n",
"* Perform [_k_-means](https://en.wikipedia.org/wiki/K-means_clustering) and [DBSCAN](https://en.wikipedia.org/wiki/DBSCAN) clustering on the dimension-reduced features\n",
"* Visualize the clustering output alongside the human labels; use both static plots (via Matplotlib/[Seaborn](http://stanford.edu/~mwaskom/software/seaborn/)) and interactives (via [Bokeh](http://bokeh.pydata.org/en/latest/))\n",
"\n",
"\n",
"## Imports"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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BokehJS successfully loaded.\n",
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"metadata": {},
"output_type": "display_data"
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"source": [
"import warnings\n",
"warnings.filterwarnings('ignore')\n",
"\n",
"# Data libraries\n",
"import pandas as pd\n",
"from nltk.corpus.reader import PlaintextCorpusReader\n",
"from nltk.corpus import cmudict\n",
"import nltk\n",
"from collections import defaultdict\n",
"import bz2\n",
"import json\n",
"import os\n",
"import subprocess\n",
"\n",
"# Machine learning and math libraries\n",
"from sklearn.feature_extraction.text import TfidfVectorizer\n",
"from sklearn.decomposition import PCA\n",
"from sklearn.preprocessing import StandardScaler\n",
"from sklearn.cluster import KMeans\n",
"from sklearn.cluster import DBSCAN\n",
"import numpy as np\n",
"\n",
"# Plotting libraries\n",
"import matplotlib.pyplot as plt\n",
"import matplotlib.cm as cm\n",
"import seaborn as sns # Note that seaborn >= 0.6.0 is required for some plots\n",
"from bokeh.charts import Scatter, output_notebook, show\n",
"from bokeh.models import HoverTool, ColumnDataSource\n",
"from bokeh.plotting import figure\n",
"from bokeh.palettes import Spectral5, Spectral6, Spectral7\n",
"\n",
"# Set up plotting\n",
"sns.set_context('talk')\n",
"sns.set_style('darkgrid')\n",
"plt.figure(figsize=(8, 6))\n",
"basecolor = 'steelblue'\n",
"% matplotlib inline\n",
"output_notebook()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Get input files\n",
"\n",
"In previous work, Geoffrey Rockwell and others used a [list of philosophical keywords](https://github.com/htrc/ACS-TT/blob/master/data/philosophy/keywords.txt) to identify texts from the HTRC corpus that were likely to be philosophical. We use the resulting list of [3,231 philosophical texts](https://github.com/htrc/ACS-TT/blob/master/data/philosophy/over1%20freqsort%20test.csv). From those we just want basic metadata and HTIDs in order to download the corresponding [HTRC derived feature](https://sharc.hathitrust.org/features) files. Note that we're using a 'clean,' UTF-8 encoded version of the list of texts; the original list causes problems down the line."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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" uc2.ark:/13960/t4jm2cj7x | \n",
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]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"philo_list = pd.read_csv('../../data/philosophy/over1 freqsort test clean.csv')\n",
"philo_list = philo_list[['Discipline', 'ID', 'Title', 'Author', 'Year', 'Relative Freq']]\n",
"philo_list.columns = ['label', 'htid', 'title', 'auth', 'year', 'freq']\n",
"philo_list.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In the data, 'freq' is the relative frequency of philosophical keywords. The field for 'disc' (here renamed to 'label') is a human-applied label for the type of philosophy in each volume. The labels are distributed like so:"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
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{
"data": {
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"execution_count": 3,
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"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.countplot(x='label', data=philo_list)"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": false
},
"source": [
"OK, so really only about 25% of the 'philosophical' texts are labeled philosophy proper. We'll bear this in mind. Now we want to download the HTRC derived feature files for those 3,231 volumes. To do that, we'll generate a list with full pairtree file paths, then use `rsync` to bring them over to local storage. See the [HTRC documentation](https://sharc.hathitrust.org/features#downloads) for details concerning their storage structure.\n",
"\n",
"Note that HTIDs need to be convernted to filesystem-safe versions by converting instances of ':' to '+' and '/' to '+'."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"
\n",
" \n",
" \n",
" | \n",
" label | \n",
" title | \n",
" auth | \n",
" year | \n",
" freq | \n",
" htid | \n",
" file | \n",
"
\n",
" \n",
" \n",
" \n",
" 0 | \n",
" theo | \n",
" Will higher of God and free will of life made ... | \n",
" Comstock, William Charles, 1847-1924. | \n",
" 1914 | \n",
" 0.262600 | \n",
" uc2.ark:/13960/t4jm2cj7x | \n",
" uc2.ark+=13960=t4jm2cj7x | \n",
"
\n",
" \n",
" 1 | \n",
" theo | \n",
" Man, the life free, by the authors of \"Thought... | \n",
" Comstock, William Charles, 1847-1924. | \n",
" 1916 | \n",
" 0.257248 | \n",
" uc2.ark:/13960/t6j105c55 | \n",
" uc2.ark+=13960=t6j105c55 | \n",
"
\n",
" \n",
" 2 | \n",
" theo | \n",
" Thought for help, from those who know men's ne... | \n",
" Comstock, William Charles, 1847-1924. | \n",
" 1913 | \n",
" 0.218047 | \n",
" uc2.ark:/13960/t2f76gd3p | \n",
" uc2.ark+=13960=t2f76gd3p | \n",
"
\n",
" \n",
" 3 | \n",
" ed | \n",
" The metaphysics of education. [By] Arthur C Fl... | \n",
" Fleshman, Arthur Cary. | \n",
" 1914 | \n",
" 0.174253 | \n",
" loc.ark:/13960/t97664b0x | \n",
" loc.ark+=13960=t97664b0x | \n",
"
\n",
" \n",
" 4 | \n",
" ed | \n",
" Syllabus of a course on the philosophy of educ... | \n",
" MacVannel, John Angus, 1871-1915. | \n",
" 1904 | \n",
" 0.169895 | \n",
" uc2.ark:/13960/t18k7739n | \n",
" uc2.ark+=13960=t18k7739n | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" label title \\\n",
"0 theo Will higher of God and free will of life made ... \n",
"1 theo Man, the life free, by the authors of \"Thought... \n",
"2 theo Thought for help, from those who know men's ne... \n",
"3 ed The metaphysics of education. [By] Arthur C Fl... \n",
"4 ed Syllabus of a course on the philosophy of educ... \n",
"\n",
" auth year freq \\\n",
"0 Comstock, William Charles, 1847-1924. 1914 0.262600 \n",
"1 Comstock, William Charles, 1847-1924. 1916 0.257248 \n",
"2 Comstock, William Charles, 1847-1924. 1913 0.218047 \n",
"3 Fleshman, Arthur Cary. 1914 0.174253 \n",
"4 MacVannel, John Angus, 1871-1915. 1904 0.169895 \n",
"\n",
" htid file \n",
"0 uc2.ark:/13960/t4jm2cj7x uc2.ark+=13960=t4jm2cj7x \n",
"1 uc2.ark:/13960/t6j105c55 uc2.ark+=13960=t6j105c55 \n",
"2 uc2.ark:/13960/t2f76gd3p uc2.ark+=13960=t2f76gd3p \n",
"3 loc.ark:/13960/t97664b0x loc.ark+=13960=t97664b0x \n",
"4 uc2.ark:/13960/t18k7739n uc2.ark+=13960=t18k7739n "
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Convert HTIDs to filesystem-safe versions for rsync\n",
"philo_list['file'] = philo_list['htid'].str.replace(':', '+')\n",
"philo_list['file'] = philo_list['file'].str.replace('/', '=')\n",
"philo_list = philo_list[['label', 'title', 'auth', 'year', 'freq', 'htid', 'file']]\n",
"philo_list.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now parse the file-safe HTIDs into pairtree paths (as used by the HTRC). In a different world, I suppose we'd use a pairtree library rather than handling this from scratch ..."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# A function to convert HTIDs to pairtree paths\n",
"def htid2ptpath(htid, prefix='basic', suffix='.basic.json.bz2'):\n",
" '''A function to convert HathiTrust file IDs to pairtree paths.\n",
" Takes an input HTID, an optional path prefix to be prepended to the\n",
" generated path, and an optional suffix (like '.basic.json.bz2').\n",
" Returns a string representing the HTRC pairtree path.'''\n",
" inst, ptid = htid.split(sep='.', maxsplit=1)\n",
" pt_segments = (len(ptid)//2) + 1\n",
" path = prefix + '/' + inst + '/' + 'pairtree_root' + '/'\n",
" for i in range(pt_segments):\n",
" try:\n",
" path += ptid[i*2:(i+1)*2] + '/'\n",
" except:\n",
" path += ptid[i*2:i*2+1] + '/'\n",
" return path[0:-1] + ptid + '/' + inst + '.' + ptid + suffix"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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" 0.257248 | \n",
" uc2.ark:/13960/t6j105c55 | \n",
" uc2.ark+=13960=t6j105c55 | \n",
" basic/uc2/pairtree_root/ar/k+/=1/39/60/=t/6j/1... | \n",
"
\n",
" \n",
" 2 | \n",
" theo | \n",
" Thought for help, from those who know men's ne... | \n",
" Comstock, William Charles, 1847-1924. | \n",
" 1913 | \n",
" 0.218047 | \n",
" uc2.ark:/13960/t2f76gd3p | \n",
" uc2.ark+=13960=t2f76gd3p | \n",
" basic/uc2/pairtree_root/ar/k+/=1/39/60/=t/2f/7... | \n",
"
\n",
" \n",
" 3 | \n",
" ed | \n",
" The metaphysics of education. [By] Arthur C Fl... | \n",
" Fleshman, Arthur Cary. | \n",
" 1914 | \n",
" 0.174253 | \n",
" loc.ark:/13960/t97664b0x | \n",
" loc.ark+=13960=t97664b0x | \n",
" basic/loc/pairtree_root/ar/k+/=1/39/60/=t/97/6... | \n",
"
\n",
" \n",
" 4 | \n",
" ed | \n",
" Syllabus of a course on the philosophy of educ... | \n",
" MacVannel, John Angus, 1871-1915. | \n",
" 1904 | \n",
" 0.169895 | \n",
" uc2.ark:/13960/t18k7739n | \n",
" uc2.ark+=13960=t18k7739n | \n",
" basic/uc2/pairtree_root/ar/k+/=1/39/60/=t/18/k... | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" label title \\\n",
"0 theo Will higher of God and free will of life made ... \n",
"1 theo Man, the life free, by the authors of \"Thought... \n",
"2 theo Thought for help, from those who know men's ne... \n",
"3 ed The metaphysics of education. [By] Arthur C Fl... \n",
"4 ed Syllabus of a course on the philosophy of educ... \n",
"\n",
" auth year freq \\\n",
"0 Comstock, William Charles, 1847-1924. 1914 0.262600 \n",
"1 Comstock, William Charles, 1847-1924. 1916 0.257248 \n",
"2 Comstock, William Charles, 1847-1924. 1913 0.218047 \n",
"3 Fleshman, Arthur Cary. 1914 0.174253 \n",
"4 MacVannel, John Angus, 1871-1915. 1904 0.169895 \n",
"\n",
" htid file \\\n",
"0 uc2.ark:/13960/t4jm2cj7x uc2.ark+=13960=t4jm2cj7x \n",
"1 uc2.ark:/13960/t6j105c55 uc2.ark+=13960=t6j105c55 \n",
"2 uc2.ark:/13960/t2f76gd3p uc2.ark+=13960=t2f76gd3p \n",
"3 loc.ark:/13960/t97664b0x loc.ark+=13960=t97664b0x \n",
"4 uc2.ark:/13960/t18k7739n uc2.ark+=13960=t18k7739n \n",
"\n",
" path \n",
"0 basic/uc2/pairtree_root/ar/k+/=1/39/60/=t/4j/m... \n",
"1 basic/uc2/pairtree_root/ar/k+/=1/39/60/=t/6j/1... \n",
"2 basic/uc2/pairtree_root/ar/k+/=1/39/60/=t/2f/7... \n",
"3 basic/loc/pairtree_root/ar/k+/=1/39/60/=t/97/6... \n",
"4 basic/uc2/pairtree_root/ar/k+/=1/39/60/=t/18/k... "
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"philo_list['path'] = philo_list['file'].apply(htid2ptpath)\n",
"philo_list.head()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Write out list of feature files for use by rsync\n",
"philo_list['path'].to_csv('../../data/philosophy/3k_paths.csv', index=False)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Call `rsync` to retrieve the feature files from the HTRC. Note that a few files (32 out of 3,231) will fail. One could examine these by printing the `failures` list."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [],
"source": [
"# Call rsync to download the feature files\n",
"data_dir = os.path.expanduser('~/Documents/Code/ACS-TT/data/philosophy')\n",
"rsync_list = os.path.join(data_dir, '3k_paths.csv')\n",
"rsync_dir = os.path.join(data_dir, '3k_features')\n",
"\n",
"failures = []\n",
"\n",
"try:\n",
" subprocess.check_output(['rsync', '-av', '--files-from', rsync_list, 'data.sharc.hathitrust.org::pd-features', rsync_dir],\n",
" stderr=subprocess.STDOUT, universal_newlines=True, )\n",
"except subprocess.CalledProcessError as e:\n",
" failures.append(e.output)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If you examine the list of `failures`, you can see that some of the feature files are missing. But not many. We'll deal with this later by excluding the missing files.\n",
"\n",
"## Extract features from texts\n",
"\n",
"We'd like to use Peter Organisciak's [htrc-feature-reader](https://github.com/organisciak/htrc-feature-reader) library to extract data from the feature files, but it isn't working at the moment. Switch to that library in the future, since it's easier than the hand-coding below.\n",
"\n",
"In the end, we want to extract and/or compute the following features for each volume:\n",
"\n",
"* TF-IDF scores for the top, say, 5,000 words. Then reduce dimensionality for these features via PCA\n",
"* Length of volume in words\n",
"* Date of publication\n",
"* Reading level score, which requires knowing average sentence length in words and average syllables per word\n",
"* Diction score, as measured by proportion of tokens that entered the English language before 1150. Higher values here correspond to \"lower\" diction (i.e., more Germanic, less Latinate).\n",
"* Fraction of tokens that are proper nouns (how character-driven is the text?)\n",
"* Fraction of tokens that are verbs (how action-oriented is the text?)\n",
"\n",
"The idea isn't that these are the only -- or even necessarily the correct -- features to use. But they combine content analysis with formal and metadata features is a way that may be illustrative.\n",
"\n",
"First, make a pass over all the files, eliminating from the data frame any that did not download from HTRC. This isn't super efficient, but it's easier than keeping track while we're also calculating features in the next step."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"3199"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Get full local paths to feature files\n",
"paths = [os.path.join(rsync_dir, i) for i in philo_list['path']]\n",
"\n",
"good = []\n",
"for i in paths:\n",
" if os.path.isfile(i):\n",
" good.append(True)\n",
" else:\n",
" good.append(False)\n",
"good = pd.Series(good)\n",
"philo_list = philo_list.drop(good[good == False].index)\n",
"philo_list['htid'].count()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Note that we've gone from 3231 entries to 3199; there were 32 feature files that failed to download from HTRC. That's good enough for now."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Pre-1150 words: 2211\n",
"Post-1150 words: 7324\n"
]
}
],
"source": [
"# Define functions to look up word era (for diction scores) and syllables per word\n",
"\n",
"# Syllables per word\n",
"d = cmudict.dict()\n",
"def nsyl(word):\n",
" '''Takes a string. Returns the number fo syllables in that string.'''\n",
" return [len(list(y for y in x if y[-1].isdigit())) for x in d[word.lower()]]\n",
"\n",
"# Word era. Etymolgy data from Ted Underwood and Jordan Sellers.\n",
"etym = pd.read_csv('../../data/philosophy/Etymologies.txt', sep='\\t', names=['word', 'date', 'count'])\n",
"pre = etym[(etym['date'] > 1) & (etym['date'] < 1150)]['word']\n",
"post = etym[etym['date'] >= 1150]['word']\n",
"pre = set(pre)\n",
"post = set(post)\n",
"print('Pre-1150 words: ', len(pre))\n",
"print('Post-1150 words:', len(post))\n",
"\n",
"def wordEra(word, prelist, postlist):\n",
" '''\n",
" Takes string 'word' and lists of pre- and post-era words. \n",
" Returns 0 if word in prelist, 1 if word in postlist, else raises error.'''\n",
" if word in prelist:\n",
" return 0\n",
" elif word in postlist:\n",
" return 1\n",
" else:\n",
" raise ValueError(word, 'in neither list')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Define a function that calculates the desired volume-level features from an HTRC compressed feature file."
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# A function to do all the heavy lifting.\n",
"def get_feature_data_raw(filename):\n",
" '''Takes the name of an HTRC extracted feature file. Assumes file is compressed with bz2.\n",
" Returns a bunch of features:\n",
" htid, # The HathiTrust ID of the volume\n",
" words, # Count of word tokens (total)\n",
" reading_level, # Calculated reading level score\n",
" pre_frac, # Fraction of recognized tokens that entered English before 1150\n",
" verb_frac, # Fraction of tokens that are verbs (any tense)\n",
" np_frac, # Fraction of tokens that are proper nouns\n",
" text_blob # Bag of words from the volume as a whole (for later processing)\n",
" '''\n",
" with bz2.open(filename, 'rt', encoding='utf-8') as b:\n",
" data = json.load(b)\n",
" \n",
" words = 0\n",
" sentences = 0\n",
" syllables = 0\n",
" syl_words = 0\n",
" pre1150 = 0\n",
" post1150 = 0\n",
" proper_nouns = 0\n",
" verbs = 0\n",
" vol_tokens = defaultdict(int)\n",
" nnp_set = ('NNP', 'NNPS') # Tags for proper nouns\n",
" vb_set = ('VB', 'VBD', 'VBG', 'VBN', 'VBP', 'VBZ') # Tags for verbs\n",
" ignore_set = ('.', 'CD') # Token types to ignore (punctuation and numbers)\n",
" text_blob = ''\n",
" \n",
" for page in data['features']['pages']: # page is a dict of page-level data\n",
" words += int(page['body']['tokenCount'])\n",
" sentences += int(page['body']['sentenceCount'])\n",
" for token in page['body']['tokenPosCount']: # token is a word in page body\n",
" for tag in page['body']['tokenPosCount'][token].keys():\n",
" if tag in nnp_set:\n",
" proper_nouns += int(page['body']['tokenPosCount'][token][tag])\n",
" if tag in vb_set:\n",
" verbs += int(page['body']['tokenPosCount'][token][tag])\n",
" if tag not in ignore_set:\n",
" vol_tokens[token.lower()] += int(page['body']['tokenPosCount'][token][tag])\n",
" \n",
" for token in vol_tokens:\n",
" occurs = vol_tokens[token]\n",
" try:\n",
" era = wordEra(token, pre, post) # Look up word era\n",
" if era:\n",
" post1150 += occurs\n",
" else:\n",
" pre1150 += occurs\n",
" except:\n",
" pass\n",
" try:\n",
" syl = nsyl(token)[0] # Look up syllables in this word\n",
" syllables += syl*occurs\n",
" syl_words += occurs\n",
" except:\n",
" pass\n",
" text_blob += (token + ' ')*occurs\n",
"\n",
" # Calculate values to return\n",
" reading_level = 0.39*(words/sentences) + 11.8*(syllables/syl_words) - 15.59\n",
" pre_frac = pre1150/(pre1150+post1150)\n",
" verb_frac = verbs/words\n",
" np_frac = proper_nouns/words\n",
" htid = data['id'].replace(':', '+').replace('/', '=')\n",
" \n",
" return (htid, words, reading_level, pre_frac, verb_frac, np_frac, text_blob)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Extract derived feature data from HTRC files\n",
"This is slow; it takes about half an hour on my laptop."
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"100 files complete\n",
"200 files complete\n",
"300 files complete\n",
"400 files complete\n",
"500 files complete\n",
"600 files complete\n",
"700 files complete\n",
"800 files complete\n",
"900 files complete\n",
"1000 files complete\n",
"1100 files complete\n",
"1200 files complete\n",
"1300 files complete\n",
"1400 files complete\n",
"1500 files complete\n",
"1600 files complete\n",
"1700 files complete\n",
"1800 files complete\n",
"1900 files complete\n",
"2000 files complete\n",
"2100 files complete\n",
"2200 files complete\n",
"2300 files complete\n",
"2400 files complete\n",
"2500 files complete\n",
"2600 files complete\n",
"2700 files complete\n",
"2800 files complete\n",
"2900 files complete\n",
"3000 files complete\n",
"3100 files complete\n",
"Total files processed: 3199\n"
]
}
],
"source": [
"# Iterate over feature files to calculate derived features\n",
"# New list of paths, now excluding the feature files that did not download\n",
"paths = [os.path.join(rsync_dir, i) for i in philo_list['path']]\n",
"\n",
"processed = 0\n",
"word_counts = []\n",
"reading_levels = []\n",
"pre_fractions = []\n",
"verb_fractions = []\n",
"np_fractions = []\n",
"text_blob_dir = '../../data/philosophy/3k_text_blobs' # Directory to store bags of words\n",
"\n",
"if not os.path.exists(text_blob_dir):\n",
" os.makedirs(text_blob_dir)\n",
"\n",
"# Iterate over all the feature files and store results. Slow (tens of minutes).\n",
"for fn in paths:\n",
" htid, wc, rl, pf, vf, nf, blob = get_feature_data_raw(fn)\n",
" word_counts.append(wc)\n",
" reading_levels.append(rl)\n",
" pre_fractions.append(pf)\n",
" verb_fractions.append(vf)\n",
" np_fractions.append(nf)\n",
" f = open(os.path.join(text_blob_dir, htid), 'w')\n",
" f.write(blob)\n",
" f.close()\n",
" processed += 1\n",
" if (processed)%100 == 0:\n",
" print(processed, 'files complete')\n",
"\n",
"print('Total files processed:', processed)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Add newly calculated features to data frame\n",
"philo_list['words'] = pd.Series(word_counts)\n",
"philo_list['read'] = pd.Series(reading_levels)\n",
"philo_list['pre'] = pd.Series(pre_fractions)\n",
"philo_list['verb'] = pd.Series(verb_fractions)\n",
"philo_list['nounp'] = pd.Series(np_fractions)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Calculating features above is slow work; write to csv to avoid rerunning in future.\n",
"philo_list.to_csv('../../data/philosophy/3k_df_mid.csv', sep='\\t')"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 13min 30s, sys: 24.4 s, total: 13min 54s\n",
"Wall time: 14min 15s\n"
]
}
],
"source": [
"# Vectorize TF-IDF data for the texts. Slow (minutes).\n",
"blob_dir = '../../data/philosophy/3k_text_blobs/'\n",
"corpus = PlaintextCorpusReader(blob_dir, '\\w.*', encoding='latin-1')\n",
"vectorizer = TfidfVectorizer(use_idf=True,\n",
" stop_words=nltk.corpus.stopwords.words('english'),\n",
" max_features=5000,\n",
" decode_error='ignore')\n",
"sorted_fileids = [fileid for fileid in sorted(corpus.fileids())]\n",
"%time tfidf_data = vectorizer.fit_transform([corpus.raw(fileid) for fileid in sorted_fileids])"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(3199, 5000)"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# 3199 documents x 5000 terms\n",
"tfidf_data.shape"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 1min 38s, sys: 2.39 s, total: 1min 40s\n",
"Wall time: 55 s\n"
]
}
],
"source": [
"# Run PCA on tfidf data to reduce dimensionality\n",
"components = 10 # Number of principal components to fit\n",
"pca = PCA(n_components=components)\n",
"\n",
"%time tfidf_pca = pca.fit_transform(tfidf_data.toarray())"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(10, 5000)\n",
"Cumulative variance explained: 0.0651931023337 , PC 1\n",
"Cumulative variance explained: 0.115006561991 , PC 2\n",
"Cumulative variance explained: 0.147654216865 , PC 3\n",
"Cumulative variance explained: 0.176846409273 , PC 4\n",
"Cumulative variance explained: 0.197697797643 , PC 5\n",
"Cumulative variance explained: 0.217592191473 , PC 6\n",
"Cumulative variance explained: 0.233724163117 , PC 7\n",
"Cumulative variance explained: 0.247953777425 , PC 8\n",
"Cumulative variance explained: 0.260202397093 , PC 9\n",
"Cumulative variance explained: 0.271562051642 , PC 10\n"
]
}
],
"source": [
"# Get loadings for later use\n",
"pca_loadings = pca.components_\n",
"print(pca_loadings.shape)\n",
"\n",
"# How much variance do we capture?\n",
"for i in range(1,components+1):\n",
" pctvar = sum(pca.explained_variance_ratio_[0:i])\n",
" print(\"Cumulative variance explained:\", pctvar, \", PC\", i)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"OK, we capture 27.2% of the observed variance in 5,000 tfidf terms with these 10 components. Is this good? Well, on one hand, we lose almost 3/4 of the underlying word-frequency information. On the other hand, we've reduced our dimensionality by 99.8% (from 5,000 dimensions to 10) and still retained more than a quarter of the info, so ..."
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false
},
"outputs": [
{
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"loc.ark+=13960=t01z4ks70 0.195769 -0.247471 -0.192854 0.026626 0.022854 \n",
"loc.ark+=13960=t02z20g9f 0.168137 -0.198680 -0.202354 -0.042993 -0.058926 \n",
"loc.ark+=13960=t03x8r043 0.159177 -0.048058 -0.048808 0.275640 -0.009422 \n",
"\n",
" PC5 PC6 PC7 PC8 PC9 \n",
"file \n",
"loc.ark+=13960=t0000w06f 0.084271 -0.203856 0.065482 -0.053169 0.053021 \n",
"loc.ark+=13960=t00z7sr8b 0.161549 -0.019496 -0.034174 0.034889 0.002782 \n",
"loc.ark+=13960=t01z4ks70 -0.019168 0.172995 0.162775 0.112626 -0.023345 \n",
"loc.ark+=13960=t02z20g9f 0.044049 0.124556 0.089082 0.052765 0.006583 \n",
"loc.ark+=13960=t03x8r043 -0.104153 -0.018228 -0.096360 -0.093210 -0.010319 "
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Convert dimension-reduced tfidf matrix to pandas dataframe, indexed by file name\n",
"col_labels = ['PC'+str(i) for i in range(10)]\n",
"tfidf_df = pd.DataFrame(tfidf_pca, index=sorted_fileids, columns=col_labels)\n",
"tfidf_df.index.rename('file', inplace=True)\n",
"tfidf_df.head()"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Create a new data frame from original philo_list with escaped htid \n",
"# as index (to match the index on tfidf data)\n",
"philo = philo_list.set_index('file')\n",
"philo.sort_index(inplace=True)"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Merge the two data frames\n",
"philo = philo.merge(tfidf_df, left_index=True, right_index=True)"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Drop the volumes for which we lack one or more pieces of data\n",
"philo = philo.dropna()"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Write out the data for later reuse\n",
"philo.to_csv('../../data/philosophy/3k_df_fin.csv', sep='\\t')\n",
"pca_loadings_df = pd.DataFrame(pca_loadings)\n",
"pca_loadings_df.to_csv('../../data/philosophy/3k_loadings.csv', sep='\\t')"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Uncomment the line below to read in the saved data frame, in case you skipped everything above\n",
"philo = pd.read_csv('../../data/philosophy/3k_df_fin.csv', sep='\\t', index_col='file')"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Index: 3165 entries, loc.ark+=13960=t0000w06f to yale.39002098631758\n",
"Data columns (total 22 columns):\n",
"label 3165 non-null object\n",
"title 3165 non-null object\n",
"auth 3165 non-null object\n",
"year 3165 non-null float64\n",
"freq 3165 non-null float64\n",
"htid 3165 non-null object\n",
"path 3165 non-null object\n",
"words 3165 non-null float64\n",
"read 3165 non-null float64\n",
"pre 3165 non-null float64\n",
"verb 3165 non-null float64\n",
"nounp 3165 non-null float64\n",
"PC0 3165 non-null float64\n",
"PC1 3165 non-null float64\n",
"PC2 3165 non-null float64\n",
"PC3 3165 non-null float64\n",
"PC4 3165 non-null float64\n",
"PC5 3165 non-null float64\n",
"PC6 3165 non-null float64\n",
"PC7 3165 non-null float64\n",
"PC8 3165 non-null float64\n",
"PC9 3165 non-null float64\n",
"dtypes: float64(17), object(5)\n",
"memory usage: 568.7+ KB\n"
]
}
],
"source": [
"# Some quick summary info about the final, complete data frame \n",
"philo.info()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Some quick vis for reference\n",
"\n",
"Plot histograms of corpus data."
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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2tEySOpch0mBwySHs/5TnTHm/qY5i+vv7WLRoIQ899Aijo2PMm1f2Njj6kTTXDJEZUDqK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\nYII=\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"words_fig = plt.hist(list(philo['words']), color=basecolor, bins=np.linspace(0,300000,24))\n",
"plt.title('Word counts')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Hmm ... that's a bit worrying. What's with all the short books? Take a closer look ..."
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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3f2ugtqkczOxA4C533y163QAsBWaSYP0NhX26h7rcD3iacGuc9D56hbv/IJqv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\nrkJggg==\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"words_fig2 = plt.hist(list(philo['words']), color=basecolor, bins=np.linspace(0,20000,20))\n",
"plt.title('Word counts (low values)')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Meh, guess there are just a lot of pamphlets in the dataset. Moving on."
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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ulEr8TvTbrLxhbzpz9zXAWcBVhG6k3wEudPdngZlAA7AO+DFwhbuviGZdBCwl9Cx6jnDd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\nIsD/B2tS2OKAWQTOAAAAAElFTkSuQmCC\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"read_fig = plt.hist(list(philo['read']), color=basecolor, bins=np.linspace(5,20,15))\n",
"plt.title('Reading levels')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Reading levels more or less normally distributed. These are not easy-reading books."
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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rBr4BLAAWAa8B2oBOM1tC6GV0MaHHEcBaYKmZrQeGgGWEbqmJ5XIjDA9P7g85rx5jsXnz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\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"pre_fig = plt.hist(list(philo['pre']), color=basecolor, bins=np.linspace(0.2,0.7,20))\n",
"plt.title('Fraction pre-1150')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Relatively normal distribution of diction scores."
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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y8CQwGzgBuM/Mtrn7JjNbTpRE5ofi3wSuAG5IpeUiIlJTSS8xnQhsLZ5hZm3AImCluw+7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\ngg==\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"verb_fig = plt.hist(list(philo['verb']), color=basecolor, bins=np.linspace(0.06,0.18,20))\n",
"plt.title('Verb fraction')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Ditto verb fractions."
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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gBuBwoBO4xN2vr3Z/IiJSH1k0Uk8GLnD3jUaLM7M7gFeBccA+wP1m9rS7P5bB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\nkkgBQkREEilAiIhIov8B3q3UnwhrMfUAAAAASUVORK5CYII=\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"nounp_fig = plt.hist(list(philo['nounp']), color=basecolor, bins=np.linspace(0,0.1,20))\n",
"plt.title('Proper noun fraction')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Not at all normal distribution of proper nouns; long tail into high values."
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"year_fig = plt.hist(list(philo['year']), color=basecolor, bins=np.linspace(1825,1925,20))\n",
"plt.title('Year')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Books skew heavily toward recent publication. All data is drawn from the HathiTrust public domain corpus, so there's nothing after 1923, hence the wall at 1920."
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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B3wamuvsjwHRgNLAc+Clwvrs/HHedDcwDFgCPAw8As3J/BSIiUheZfijn7ncAd6SsXw2c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\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"freq_fig = plt.hist(list(philo['freq']), color=basecolor, bins=np.linspace(0.09,0.18,20))\n",
"plt.title('Philo keyword frequency')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Looks like there was an explicit cutoff in the keyword-based selection process at philosophical word frequency = 0.10. Result is the clipped high side of what I presume is an underlying normal distribution of these terms in the full corpus. OK, no problem."
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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rI+KQzNxY3hfA7V2eryVDQ/X4wyWpPaOjw+y//z4ttR0bG+lxmkWnlvVY0uK0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7892lIH5upcHTNB/XNJkiRJqkY/MzC2Am8BiIj3AUdTzLy4qjx/A3BhRNwO7AYuothKVRWbmmox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\nJEmSJKn2/j9O+ZJXXXIyYgAAAABJRU5ErkJggg==\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# And the TFIDF PCAs\n",
"pca_fig = plt.figure(figsize=(15,10))\n",
"for i in range(10):\n",
" component = 'PC'+str(i)\n",
" figno = i+1\n",
" subfig = pca_fig.add_subplot(4,3,figno)\n",
" subfig.hist(list(philo[component]), color=basecolor, bins=np.linspace(0,0.4,20))\n",
" subfig.set_title(component)\n",
"pca_fig.tight_layout()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"OK, all our data is in place, though it's not (yet) normalized. Time to do some clustering and visualization.\n",
"\n",
"## Clustering\n",
"\n",
"We'll try both _k_-means and DBSCAN ..."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"4.64054859857e-16 1.0\n",
"(3165, 17)\n"
]
}
],
"source": [
"# Extract data for scikit-learn and normalize\n",
"# Keep list of human labels\n",
"philo_labels = list(philo['label'])\n",
"# Drop columns not used for analysis\n",
"philo_data = philo.drop(['label', 'title', 'auth', 'htid', 'path'], axis=1)\n",
"# Convert to numpy array\n",
"philo_data = philo_data.as_matrix()\n",
"# Set up scaler\n",
"scaler = StandardScaler()\n",
"# Normalize data\n",
"philo_data = scaler.fit_transform(philo_data)\n",
"print(np.mean(philo_data), np.std(philo_data))\n",
"print(philo_data.shape)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Note that the normalized mean is zero and standard deviation is 1. There are 3165 documents, each having 17 features (10 word-based PCs and 7 form/metadata features)."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 1.1 s, sys: 12 ms, total: 1.11 s\n",
"Wall time: 443 ms\n"
]
},
{
"data": {
"text/plain": [
"KMeans(copy_x=True, init='k-means++', max_iter=300, n_clusters=7, n_init=10,\n",
" n_jobs=1, precompute_distances='auto', random_state=None, tol=0.0001,\n",
" verbose=0)"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# k-means\n",
"k = 7 # Matches number of human labels\n",
"km = KMeans(n_clusters=k)\n",
"%time km.fit(philo_data)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Note, FWIW, that _k_-means clustering is very fast. DBSCAN is slower, but not radically so. The slow steps in all this work, as you'll already have seen if you've run the code yourself, involve acquiring and wrangling the data, not working with it once it's nicely ingested."
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"2 776\n",
"0 571\n",
"1 524\n",
"5 453\n",
"3 428\n",
"4 337\n",
"6 76\n",
"Name: k_label, dtype: int64"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Examine the labels for k-means\n",
"k_labels = km.labels_.tolist()\n",
"k_labels = pd.Series(k_labels, index=philo.index, name='k_label')\n",
"k_labels.value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 396 ms, sys: 2.4 ms, total: 398 ms\n",
"Wall time: 431 ms\n"
]
},
{
"data": {
"text/plain": [
"-1 1889\n",
" 0 1162\n",
" 1 58\n",
" 4 17\n",
" 5 12\n",
" 3 12\n",
" 6 10\n",
" 2 5\n",
"Name: d_label, dtype: int64"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Same thing for DBSCAN\n",
"db = DBSCAN(eps=2.44, min_samples=10)\n",
"%time db.fit(philo_data)\n",
"d_labels = db.labels_.tolist()\n",
"d_labels = pd.Series(d_labels, index=philo.index, name='d_label')\n",
"d_labels.value_counts()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Cluster -1 in DBSCAN output means 'Other', i.e., noise. This suggests one of two things: either we don't have the right value of epsilon or our textual data is insufficiently dense in the feature space.\n",
"\n",
"Back of the envelope calculation re: density ... We're working in 17 dimensions, and we have 3,165 texts. Assume each dimension can take only binary values, which is to say that it divides the possible clustering space in half. That means we have $2^{17}$ = 131,072 logical bins into which to place the texts. So, ummm, no wonder we don't really see good clustering over just 3,165 texts. The chance of seeing even two or three texts in the same bin is pretty small.\n",
"\n",
"This is an obvious area for improvement. What would be an appropriate number of dimensions in whch to cluster c. 3,000 points? Well, we want more bins than clusters, so that we don't just end up with unrelated things being stuffed into the same bin for lack of alternatives. But we don't want so many bins that none of them has any significant number of texts. Maybe, say, 300-ish bins, for an average of 10 items per bin? $2^8$ = 256, which is in the right ballpark.\n",
"\n",
"The thing to do, then, would be to use PCA (or another dimensional reduction technique, like MDS or _t_-SNE) to bring our dimensions down to 8. Not doing this here, but the idea is exactly the same as what we did to reduce the 5,000 TF-IDF features to 10, or how we reduce the current 17 dimensions to 2 for plotting below."
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Add cluster labels to data frame for plotting\n",
"philo['k_label'] = k_labels\n",
"philo['d_label'] = d_labels"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Plots\n",
"\n",
"### Human labels\n",
"\n",
"Recall the distribution of human labels in our data. Remember that 'x' and '?' are junk or miscellaneous categories; we don't expect them to correspond to known-good philosophical subgenres."
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 39,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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U3Z81sxzwj8AiwueH3Qt80N07k69+cGb2fuAqoMXdnzGzzwLzgZe7++6Ua7sD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\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Human labels\n",
"sns.countplot(x='label', data=philo)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### k-means\n",
"\n",
"**NB.** Labels aren't correlated with human labels, so this IS NOT a plot of performance vs. humans. Don't read anything into the colors or ording of clusters."
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 40,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# k-means\n",
"sns.countplot(x='k_label', data=philo)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### DBSCAN\n",
"\n",
"Ditto re: lack of correlation here. Note that cluster `-1` means \"no cluster\" or \"outlier.\""
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 41,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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/0dS8OfAg8O6I2Dkzhz5Pg5cPf/XXj+fXj2fVbWPy1FPPvTRiWbZs5egbd9CyZStZunTF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\nJKkog0WSVJTBIkkqymCRJBX1/wFOHJeSikuDkwAAAABJRU5ErkJggg==\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.countplot(x='d_label', data=philo)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Plot in PCA space to see the clusters"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 685 ms, sys: 16 ms, total: 701 ms\n",
"Wall time: 347 ms\n"
]
}
],
"source": [
"# Reduce philo_data to two dimensions for plotting\n",
"dim = 2\n",
"pca_plot = PCA(n_components=dim)\n",
"\n",
"%time philo_data_plot = pca_plot.fit_transform(philo_data)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Add the PCs to data frame for easier plotting\n",
"philo['P1'] = philo_data_plot[:,0]\n",
"philo['P2'] = philo_data_plot[:,1]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Human labels"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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UVcXHPrnNVCG8tR/w5R/aXGqFTso0CC8yj6OMydwRBGFZWF4t5Qlwa0c/uHWPVRNQMyvd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\nFuVpbmEGBoq5ebKZObyH7PwcHn9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"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"human_plot = sns.FacetGrid(philo, col='label', col_wrap = 3)\n",
"human_plot.map(plt.scatter, 'P1', 'P2', alpha=0.2)\n",
"human_plot.set(xlim=(-5,5), ylim=(-5,5))\n",
"plt.subplots_adjust(top=0.9)\n",
"human_plot.fig.suptitle('HUMAN')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This mostly shows that there's little detectable differentiation between the various subgenres of philosophy in 17-dimensional feature space.\n",
"\n",
"### Human labels in Bokeh with interactivity\n",
"\n",
"Here's the same thing, but plotted using Bokeh to give us interactivity. Note, too, that we're now leaving out 'x' and '?' types, for a bit of additional clarity.\n",
"\n",
"The first plot here uses Bokeh's high-level plotting interface, which is really easy, but doesn't allow for the complex mouse-over information that we'll add in the second plot."
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"\n"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"df = philo[(philo['label'] != '?') & (philo['label'] != 'x')]\n",
"p = Scatter(df, x='P1', y='P2', title=\"Human labels\",\n",
" xlabel=\"PC1\", ylabel=\"PC2\",\n",
" color='label', legend='top_left')\n",
"show(p)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If there's no inteactive visualization above, it's probably because you're viewing this notebook in a viewer rather than running the code live. Sorry!\n",
"\n",
"Now the same info, but using Bokeh's \"mid-level\" plotting API to add more complex mouse-over behavior, better color and alpha support, etc."
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"\n"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"df['titl'] = df['title'].str.slice(0,40) # Short title info for mouseover\n",
"\n",
"# Set up colormap\n",
"labels = df['label'].unique()\n",
"colors = Spectral5\n",
"colormap = {}\n",
"for i in range(len(labels)):\n",
" colormap[labels[i]] = colors[i]\n",
"df['color'] = df['label'].map(lambda x: colormap[x])\n",
"data = ColumnDataSource(df)\n",
"\n",
"hover = HoverTool(\n",
" tooltips=[\n",
" (\"label\", \"@label\"),\n",
" (\"author\", \"@auth\"),\n",
" (\"title\", \"@titl\"),\n",
" (\"year\", \"@year\"),\n",
" ]\n",
" )\n",
"q = figure(title=\"Human labels\",\n",
" x_axis_label=\"PC1\", y_axis_label=\"PC2\")\n",
"q.circle('P1', 'P2', source=data,\n",
" alpha=0.4, size=10, fill_color='color', line_color=None\n",
" )\n",
"q.add_tools(hover)\n",
"show(q)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Again, there should be a cool interactive vis above, including points labeled on mouseover, colored by class, and arbitrarily zoomable. If you don't see it, you'll need to download and run this notebook on your own machine."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### _k_-Means labels"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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lTjgTFha5WVowy0rivGZro8cnZY87zQL42mafOCkBCTQXno6Fe9SyLIY9H601cVKyOuiw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YAAAg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c251EJYnUrSrdg6q1esQP6blFRETkaCiZO0Ee5py2o55/JyIiIoejYdY5d9i+bfczHLtUyRNg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\n5piSOREREZE5pmROREREZI79/zA/lb/LsZHWAAAAAElFTkSuQmCC\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"k_plot = sns.FacetGrid(philo, col='k_label', col_wrap = 3)\n",
"k_plot.map(plt.scatter, 'P1', 'P2', alpha=0.2)\n",
"k_plot.set(xlim=(-5,5), ylim=(-5,5))\n",
"plt.subplots_adjust(top=0.9)\n",
"k_plot.fig.suptitle('K-MEANS')\n",
"plt.savefig('../../data/philosophy/3k_kmeans.png', dpi=300)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### DBSCAN labels"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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ysEBrSXHk0Ztn2hMIHwmtQeTC/nIhFx5UJmUUucyzhkHknnrOlpjeri8fy+XS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UpRFDXtwEFrh6br7vWInaQFnmuxPcp5+3oby7EZj2vWVwN0DdX0Fb51vfXcOqDZ7w56v3b66Qvr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\nnrnp6QlmZxfoduv7eTQlR5OyNCXHILPMzEyO7GS11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"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# DBSCAN\n",
"d_plot = sns.FacetGrid(philo, col='d_label', col_wrap = 3)\n",
"d_plot.map(plt.scatter, 'P1', 'P2', alpha=0.2)\n",
"d_plot.set(xlim=(-5,5), ylim=(-5,5))\n",
"plt.subplots_adjust(top=0.9)\n",
"d_plot.fig.suptitle('DBSCAN')\n",
"plt.savefig('../../data/philosophy/3k_dbscan.png', dpi=300)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## To do\n",
"\n",
"Things that one might try to improve these results:\n",
"\n",
"* Perform grid search on DBSCAN to find best parameters. Currently non-optimal; see few large clusters and tendency to mark many as noise. \n",
"* Dimensionality is very likely too high overall. Reduce feature space prior to clustering.\n",
"* Correlate human labels with machine labels. Maybe just by majority vote vs. human labels.\n",
"* Part of any future analytical work will involve examining the PCA loadings to see what's driving texts in certain directions.\n",
"* Extend the same analsysis to literary criticism, which is also part of the \"Trace of Theory\" project mandate."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"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": "3.4.3"
}
},
"nbformat": 4,
"nbformat_minor": 0
}