Clustering experiment #1

Plan:

Derive a vector for left and righthand alignment variance for each contiguous block of text in each article (newspaper has pages, which hold articles, which consist of blocks).
Create a k=8 kmeans clustering using data from all newspapers from 1745 to 1756.
Visualise the clustering
Given the set of found poems, see into which clusters the poems get assigned.
Report on the spread of these and if a cluster is found which just has poems, report on all of the references within that cluster.



In [1]:

    
%matplotlib inline

# Load this library to make the graphs interactive for smaller samples
#import mpld3
#mpld3.enable_notebook()

# Turns out, multiple interactive scattergraphs with 170,000+ points each is a bit too much for a browser
# Who knew?!



In [2]:

    
# Get the dataset:
from clustering import create_cluster_dataset, NewspaperArchive
DBFILE = "1745_1756_no_drift.db"
n = NewspaperArchive()
ds = create_cluster_dataset(n, daterange = [1745, 1756], dbfile = DBFILE)









    



Loading from db cache '1745_1756_no_drift.db'
170339 vectors loaded.

What do these 'vectors' look like? What do the columns refer to?



In [3]:

    
data, transform, id_list = ds
print(data.toarray())

print(transform.get_feature_names())









    



[[  4.06902985e+00   4.36200000e+03  -6.78854839e+02 ...,   5.26129032e+01
    2.28537981e+00   0.00000000e+00]
 [  9.25095785e+00   4.82900000e+03  -9.04459016e+02 ...,   2.97377049e+01
    5.52700887e+00   0.00000000e+00]
 [  1.68010517e+00   3.83400000e+03  -8.20021739e+02 ...,   4.95434783e+01
    7.56499527e+02   0.00000000e+00]
 ..., 
 [  1.81267474e+00   5.83500000e+03  -9.39681159e+02 ...,   4.17246377e+01
    1.73811384e+02   0.00000000e+00]
 [  3.74772727e-01   3.29800000e+03  -7.68963636e+02 ...,   4.71454545e+01
    7.99259504e+01   0.00000000e+00]
 [  1.52893411e-04   2.38000000e+02   8.50000000e+01 ...,   1.69420000e+03
    2.85616000e+04   0.00000000e+00]]
['density', 'ltcount', 'redge_x2ave', 'x1_var1', 'x1_var2', 'x1ave_ledge', 'x2_var1', 'x2_var2']

Going from a vector back to the metadata reference:

By keeping an 'id_list', we can look up the identifier for any vector in the list from the database we've made for this clustering attemp. This lets us look up what the reference for that is, and where we can find it:



In [4]:

    
from clustering import ClusterDB
db = ClusterDB(DBFILE)
item = dict(db.vecidtoitem(id_list[-1]))
print(item)

from burney_data import BurneyDB
bdb = BurneyDB("burney.db")

titlemd = bdb.get_title_row(titleAbbreviation=item['newspaper'])
entry = bdb.get_entry_row(year=item['year'], month=item['month'], day=item['day'], title_id= titlemd['id'])
issue = bdb.get_issue_row(id=entry['issue_id'])

print(titlemd)
print(issue)
print(entry)









    



{'block_number': '7', 'year': '1746', 'day': '27', 'newspaper': 'B0477OLENCOJO', 'month': '09', 'page': '0004', 'vector_id': 223713, 'article': '006', 'id': 223713}
{'earliest_issue': None, 'titleContinuedBy': 'OLD ENGLAND OR THE BROADBOTTOM JOURNAL', 'datesOfPublication': '1743-1746', 'id': 405, 'titleContinues': '', 'typeOfPublication': 'NEWSPAPER', 'titleAbbreviation': 'B0477OLENCOJO', 'last_issue': None, 'title': 'OLD ENGLAND OR THE CONSTITUTIONAL JOURNAL', 'placeOfPublication': 'LONDON'}
{'volumeNumber': None, 'title_id': 405, 'id': 115004, 'pageCount': '4', 'ESTC': 'P1753', 'printedDate': 'SATURDAY SEPTEMBER 27 1746', 'normalisedDate': '1746-09-27', 'issueNumber': '178'}
{'number_of_articles': 6, 'title_id': 405, 'year': '1746', 'id': 115004, 'month': '09', 'filepath': '/cygdrive/w/APEX/B0477OLENCOJO/1746/09/27/service', 'issue_id': 115004, 'day': '27'}



In [5]:

    
import numpy as np
from matplotlib import pyplot as plt

# Mask off leaving just the left and right variance columns
npdata = data.toarray()
mask = np.ones((8), dtype=bool)
mask[[0,1,2,4,5,7]] = False

marray = npdata[:,mask]

x1 vs x2 varience?

What is the rough shape of this data? The varience of x1 and x2 are equivalent to the left and right alignment of the text varies in a given block of text.



In [ ]:

    
plt.scatter(marray[:,0], marray[:,1])
plt.show()

Attempting K-Means

What sort of clustering algorithm to employ is actually a good question. K-means can give fairly meaningless responses if the data is of a given sort. Generally, it can be useful but cannot be used blindly.

Given the data above, it might be a good start however.



In [6]:

    
# Build the clustering and show the individual clusters as best we can:
from sklearn.cluster import KMeans

estimator = KMeans(n_clusters=8)

clusters = estimator.fit(marray)
labels = estimator.labels_

def isol(label, labels):
    for l in labels.astype(np.float):
        if l != label:
            yield "#444444"
        else:
            yield "#FF3355"

#for label in set(labels):
#    print(label)
#    plt.scatter(marray[:,0], marray[:,1], c=list(isol(label, labels)))
#    plt.show()

It looks like cluster 5 here is the one that should contain more complete poems as a block of text than the rest if our assumptions are correct.

Lets export this as a list of references to explore further - "cluster5.csv"



In [8]:

    
import csv

def get_info(item_id):
    record = dict(db.vecidtoitem(item_id))
    vect = dict(db.vector(item_id))
    titlemd = bdb.get_title_row(titleAbbreviation=record['newspaper'])
    entry = bdb.get_entry_row(year=record['year'], month=record['month'], day=record['day'], title_id= titlemd['id'])
    issue = bdb.get_issue_row(id=entry['issue_id'])
    record.update(titlemd)
    record.update(entry)
    record.update(issue)
    record.update(vect)
    return record

for label in set(labels):
    print("Saving label {0}".format(label))
    with open("exp1_cluster{0}.csv".format(label), "w") as cfn:
        fields = ["title", "titleAbbreviation", "year", "month", "day", 
              "issueNumber", "printedDate", "page", "article", "block_number", "filepath", "x1_var1", "x2_var1", "ltcount"]
        csvdoc = csv.DictWriter(cfn, fieldnames = fields)
        csvdoc.writerow(dict([(x,x) for x in fields]))
        count = 0
        for idx, vlabel in enumerate(list(labels)):
            if idx % 1000 == 0:
                print("Tackling line {0} - saved {1} lines for this label".format(idx, count))
            if vlabel == label:
                record = get_info(id_list[idx])
                csvdoc.writerow(dict([(x,record[x]) for x in fields]))
                count += 1









    



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Tackling line 48000 - saved 347 lines for this label
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Tackling line 57000 - saved 379 lines for this label
Tackling line 58000 - saved 380 lines for this label
Tackling line 59000 - saved 380 lines for this label
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Tackling line 61000 - saved 388 lines for this label
Tackling line 62000 - saved 401 lines for this label
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Tackling line 78000 - saved 504 lines for this label
Tackling line 79000 - saved 506 lines for this label
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Tackling line 81000 - saved 509 lines for this label
Tackling line 82000 - saved 510 lines for this label
Tackling line 83000 - saved 513 lines for this label
Tackling line 84000 - saved 514 lines for this label
Tackling line 85000 - saved 519 lines for this label
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Tackling line 88000 - saved 527 lines for this label
Tackling line 89000 - saved 528 lines for this label
Tackling line 90000 - saved 528 lines for this label
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Tackling line 92000 - saved 535 lines for this label
Tackling line 93000 - saved 538 lines for this label
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Tackling line 99000 - saved 564 lines for this label
Tackling line 100000 - saved 569 lines for this label
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Tackling line 102000 - saved 574 lines for this label
Tackling line 103000 - saved 576 lines for this label
Tackling line 104000 - saved 580 lines for this label
Tackling line 105000 - saved 583 lines for this label
Tackling line 106000 - saved 584 lines for this label
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Tackling line 108000 - saved 594 lines for this label
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Tackling line 110000 - saved 600 lines for this label
Tackling line 111000 - saved 601 lines for this label
Tackling line 112000 - saved 606 lines for this label
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Tackling line 115000 - saved 617 lines for this label
Tackling line 116000 - saved 628 lines for this label
Tackling line 117000 - saved 629 lines for this label
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Tackling line 127000 - saved 675 lines for this label
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Tackling line 155000 - saved 849 lines for this label
Tackling line 156000 - saved 850 lines for this label
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Saving label 6
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Tackling line 50000 - saved 1184 lines for this label
Tackling line 51000 - saved 1184 lines for this label
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Saving label 7
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