Cluster experiment 2


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 [7]:
%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 [1]:
from clustering_capitals import create_cluster_dataset, NewspaperArchive
DBFILE = "1745-55.db"
n = NewspaperArchive(textareas="/datastore/burneytextareas")
a = n.get_areas(newspaper="B0574REMEMBRA", year = "1748", month = "03", day = "05")
pg1a1 = a['0001']['001'][0]

print(len(pg1a1['lines']), len(pg1a1['line_widths'][:len(pg1a1['lines'])-1]))
print(pg1a1['line_widths'][:len(pg1a1['lines'])-1][-1])


Loading /datastore/burneytextareas/B0574REMEMBRA/1748.json from disc
35 34
85

In [2]:
# Get/create the dataset:

ds = create_cluster_dataset(n, daterange = [1745, 1755], dbfile = DBFILE) # , refresh = True)


Loading from db cache '1745-55.db'
254863 vectors loaded.

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


In [3]:
data, transform, id_list = ds
print(data)

print(transform.get_feature_names())


  (0, 0)	95.7142857143
  (0, 1)	0.288040949279
  (0, 2)	619.0
  (0, 3)	-1012.875
  (0, 4)	0.625
  (0, 6)	50889.984375
  (0, 8)	240.375
  (0, 9)	58688.109375
  (1, 0)	103.583333333
  (1, 1)	1.11418511066
  (1, 2)	2215.0
  (1, 3)	-1502.92
  (1, 4)	0.48
  (1, 6)	132023.4304
  (1, 8)	125.64
  (1, 9)	131.7536
  (2, 0)	106.5
  (2, 1)	51.25
  (2, 2)	615.0
  (2, 3)	-1554.14285714
  (2, 4)	0.142857142857
  (2, 6)	559.836734694
  (2, 8)	8.14285714286
  (2, 9)	11.5510204082
  (3, 0)	225.571428571
  :	:
  (254859, 1)	0.0190523969205
  (254859, 2)	5051.0
  (254859, 3)	-61.9523809524
  (254859, 4)	0.511904761905
  (254859, 5)	0.178571428571
  (254859, 6)	64127.1070011
  (254859, 8)	236.488095238
  (254859, 9)	8172.23582766
  (254860, 0)	58.9220779221
  (254860, 1)	0.0188308475295
  (254860, 2)	5328.0
  (254860, 3)	-243.705128205
  (254860, 4)	0.628205128205
  (254860, 5)	0.0512820512821
  (254860, 6)	32272.780572
  (254860, 8)	156.961538462
  (254860, 9)	12922.8745891
  (254861, 0)	1563.0
  (254861, 1)	1.183539911e-05
  (254861, 2)	154.0
  (254861, 3)	345.0
  (254861, 4)	0.5
  (254861, 6)	555927.5
  (254861, 8)	983.0
  (254861, 9)	119520.5
['ave_lsp', 'density', 'ltcount', 'redge_x2ave', 'st_caps', 'st_nums', '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_capitals import ClusterDB
db = ClusterDB(DBFILE)
item = dict(db.vecidtoitem(id_list[1]))
print(item)
print(transform.inverse_transform(data[1]))

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)

vector = db.vector(id_list[1])

print(dict(vector))

mask = {'ave_lsp': 1.0, 'density':1.0, 'ltcount':0.0, 'redge_x2ave':0.0, 'st_caps':1.0, 
        'st_nums':1.0, 'x1_var1':1.0, 'x1_var2':0.0, 'x1ave_ledge':0.0, 'x2_var1':1.0, 'x2_var2':0.0}

m_vec = transform.transform(mask)

print(m_vec)


{'month': '06', 'article': '001', 'vector_id': 2, 'page': '0001', 'day': '27', 'id': 2, 'newspaper': 'B1308WHITEHAL', 'year': '1747', 'block_number': '1'}
[{'density': 1.114185110663984, 'st_caps': 0.47999999999999998, 'redge_x2ave': -1502.9200000000001, 'x1ave_ledge': 125.63999999999999, 'ave_lsp': 103.58333333333333, 'ltcount': 2215.0, 'x2_var1': 131.75360000000003, 'x1_var1': 132023.43040000004}]
{'titleContinues': '', 'title': 'WHITEHALL JUNE 27 1747 THIS AFTERNOON A MESSENGER ARRIVED', 'placeOfPublication': 'LONDON', 'titleAbbreviation': 'B1308WHITEHAL', 'earliest_issue': None, 'datesOfPublication': '1747', 'id': 1160, 'typeOfPublication': 'NEWSPAPER', 'last_issue': None, 'titleContinuedBy': ''}
{'issueNumber': None, 'title_id': 1160, 'volumeNumber': None, 'ESTC': 'N69936', 'id': 185047, 'printedDate': 'JUNE 27 1747', 'normalisedDate': '1747-06-27', 'pageCount': '7'}
{'month': '06', 'filepath': '/cygdrive/w/APEX/B1308WHITEHAL/1747/06/27/service', 'issue_id': 185047, 'number_of_articles': 4, 'title_id': 1160, 'day': '27', 'id': 185047, 'year': '1747'}
{'st_caps': 0.48, 'x1ave_ledge': 125.63999999999999, 'id': 2, 'x1_var1': 132023.43040000004, 'density': 1.114185110663984, 'redge_x2ave': -1502.92, 'ave_lsp': 103.58333333333333, 'x1_var2': 0.0, 'x2_var2': 0.0, 'ltcount': 2215, 'x2_var1': 131.75360000000003, 'st_nums': 0.0}
  (0, 0)	1.0
  (0, 1)	1.0
  (0, 2)	0.0
  (0, 3)	0.0
  (0, 4)	1.0
  (0, 5)	1.0
  (0, 6)	1.0
  (0, 7)	0.0
  (0, 8)	0.0
  (0, 9)	1.0
  (0, 10)	0.0

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((11), dtype=bool)
# remember: ['ave_lsp', 'density', 'ltcount', 'redge_x2ave', 'st_caps', 
#            'st_nums', 'x1_var1', 'x1_var2', 'x1ave_ledge', 'x2_var1', 'x2_var2']
mask[[0,1,2,3,4,5,7,8,10]] = 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], marker = ".", s = [2] * len(marray), linewidths=[0.0] * len(marray))
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 [ ]:
# Build the clustering and show the individual clusters as best we can:
from sklearn.cluster import KMeans

cl_mask = np.ones((11), dtype=bool)
# remember: ['ave_lsp', 'density', 'ltcount', 'redge_x2ave', 'st_caps', 
#            'st_nums', 'x1_var1', 'x1_var2', 'x1ave_ledge', 'x2_var1', 'x2_var2']

# so, we should cluster on ave_lsp, density, st_caps, st_nums, x1_var1, x2_var1:
cl_mask[[2,3,7,8,10]] = False

cl_marray = npdata[:,cl_mask]

estimator = KMeans(n_clusters=12)

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

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

def highlight(label, labels):
    for l in labels.astype(np.float):
        if l != label:
            yield 2
        else:
            yield 4

# plot graphs of ave_lsp vs x2_var1?
for label in set(labels):
    print("Cluster: {0} - x1_var1 vs x2_var2".format(label))
    plt.scatter(cl_marray[:,4], cl_marray[:,5], c=list(isol(label, labels)), marker = ".", 
                s = list(highlight(label, labels)), linewidths=[0.0] * len(marray))
    plt.show()


Cluster: 0 - x1_var1 vs x2_var2

It looks like cluster 4 and perhaps cluster 11 are ones that should contain more complete poems than the rest if our assumptions are correct. Clump with very low x1 (lefthand edge) variance, but high x2 (right hand side).

What do the other aspects of 4 and 11 look like?


In [9]:
# plot graphs of ave_lsp vs x2_var1?
for label in [4,11]:
    print("Cluster: {0} - ave_lp vs density".format(label))
    plt.scatter(cl_marray[labels == label,0], cl_marray[labels == label,1], marker = ".", linewidths=1)
    plt.show()
    print("Cluster: {0} - st_caps vs st_num".format(label))
    plt.scatter(cl_marray[labels == label,2], cl_marray[labels == label,3], marker = ".", linewidths=1)
    plt.show()
    print("Cluster: {0} - x1_var1 vs x2_var2".format(label))
    plt.scatter(cl_marray[labels == label,4], cl_marray[labels == label,5], marker = ".", linewidths=1)
    plt.show()


Cluster: 4 - ave_lp vs density
Cluster: 4 - st_caps vs st_num
Cluster: 4 - x1_var1 vs x2_var2
Cluster: 11 - ave_lp vs density
Cluster: 11 - st_caps vs st_num
Cluster: 11 - x1_var1 vs x2_var2

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


In [11]:
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("exp2_cluster{0}.csv".format(label), "w") as cfn:
        fields = ["title", "titleAbbreviation", "year", "month", "day", 
              "issueNumber", "printedDate", "page", "article", "block_number", "filepath", "st_caps", "st_nums", "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


Saving label 0
Tackling line 0 - saved 0 lines for this label
Tackling line 1000 - saved 26 lines for this label
Tackling line 2000 - saved 48 lines for this label
Tackling line 3000 - saved 75 lines for this label
Tackling line 4000 - saved 96 lines for this label
Tackling line 5000 - saved 122 lines for this label
Tackling line 6000 - saved 161 lines for this label
Tackling line 7000 - saved 190 lines for this label
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Tackling line 9000 - saved 267 lines for this label
Tackling line 10000 - saved 298 lines for this label
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Tackling line 14000 - saved 420 lines for this label
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Tackling line 17000 - saved 510 lines for this label
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Tackling line 19000 - saved 551 lines for this label
Tackling line 20000 - saved 577 lines for this label
Tackling line 21000 - saved 606 lines for this label
Tackling line 22000 - saved 632 lines for this label
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Tackling line 24000 - saved 674 lines for this label
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Tackling line 38000 - saved 1014 lines for this label
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Tackling line 50000 - saved 1339 lines for this label
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Saving label 1
Tackling line 0 - saved 0 lines for this label
Tackling line 1000 - saved 5 lines for this label
Tackling line 2000 - saved 8 lines for this label
Tackling line 3000 - saved 13 lines for this label
Tackling line 4000 - saved 13 lines for this label
Tackling line 5000 - saved 13 lines for this label
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Tackling line 8000 - saved 15 lines for this label
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Tackling line 10000 - saved 25 lines for this label
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Tackling line 20000 - saved 79 lines for this label
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Tackling line 62000 - saved 201 lines for this label
Tackling line 63000 - saved 204 lines for this label
Tackling line 64000 - saved 213 lines for this label
Tackling line 65000 - saved 216 lines for this label
Saving label 2
Tackling line 0 - saved 0 lines for this label
Tackling line 1000 - saved 79 lines for this label
Tackling line 2000 - saved 164 lines for this label
Tackling line 3000 - saved 238 lines for this label
Tackling line 4000 - saved 276 lines for this label
Tackling line 5000 - saved 301 lines for this label
Tackling line 6000 - saved 349 lines for this label
Tackling line 7000 - saved 391 lines for this label
Tackling line 8000 - saved 441 lines for this label
Tackling line 9000 - saved 527 lines for this label
Tackling line 10000 - saved 607 lines for this label
Tackling line 11000 - saved 672 lines for this label
Tackling line 12000 - saved 758 lines for this label
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Tackling line 14000 - saved 934 lines for this label
Tackling line 15000 - saved 974 lines for this label
Tackling line 16000 - saved 1017 lines for this label
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Tackling line 18000 - saved 1092 lines for this label
Tackling line 19000 - saved 1152 lines for this label
Tackling line 20000 - saved 1191 lines for this label
Tackling line 21000 - saved 1222 lines for this label
Tackling line 22000 - saved 1251 lines for this label
Tackling line 23000 - saved 1276 lines for this label
Tackling line 24000 - saved 1311 lines for this label
Tackling line 25000 - saved 1339 lines for this label
Tackling line 26000 - saved 1396 lines for this label
Tackling line 27000 - saved 1425 lines for this label
Tackling line 28000 - saved 1468 lines for this label
Tackling line 29000 - saved 1560 lines for this label
Tackling line 30000 - saved 1590 lines for this label
Tackling line 31000 - saved 1696 lines for this label
Tackling line 32000 - saved 1976 lines for this label
Tackling line 33000 - saved 2256 lines for this label
Tackling line 34000 - saved 2504 lines for this label
Tackling line 35000 - saved 2627 lines for this label
Tackling line 36000 - saved 2730 lines for this label
Tackling line 37000 - saved 2847 lines for this label
Tackling line 38000 - saved 2865 lines for this label
Tackling line 39000 - saved 2877 lines for this label
Tackling line 40000 - saved 2947 lines for this label
Tackling line 41000 - saved 3018 lines for this label
Tackling line 42000 - saved 3060 lines for this label
Tackling line 43000 - saved 3154 lines for this label
Tackling line 44000 - saved 3418 lines for this label
Tackling line 45000 - saved 3571 lines for this label
Tackling line 46000 - saved 3636 lines for this label
Tackling line 47000 - saved 3755 lines for this label
Tackling line 48000 - saved 3920 lines for this label
Tackling line 49000 - saved 4065 lines for this label
Tackling line 50000 - saved 4211 lines for this label
Tackling line 51000 - saved 4282 lines for this label
Tackling line 52000 - saved 4335 lines for this label
Tackling line 53000 - saved 4370 lines for this label
Tackling line 54000 - saved 4492 lines for this label
Tackling line 55000 - saved 4630 lines for this label
Tackling line 56000 - saved 4733 lines for this label
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Tackling line 58000 - saved 4908 lines for this label
Tackling line 59000 - saved 5022 lines for this label
Tackling line 60000 - saved 5100 lines for this label
Tackling line 61000 - saved 5191 lines for this label
Tackling line 62000 - saved 5300 lines for this label
Tackling line 63000 - saved 5386 lines for this label
Tackling line 64000 - saved 5487 lines for this label
Tackling line 65000 - saved 5608 lines for this label
Saving label 3
Tackling line 0 - saved 0 lines for this label
Tackling line 1000 - saved 0 lines for this label
Tackling line 2000 - saved 2 lines for this label
Tackling line 3000 - saved 3 lines for this label
Tackling line 4000 - saved 3 lines for this label
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Tackling line 8000 - saved 4 lines for this label
Tackling line 9000 - saved 5 lines for this label
Tackling line 10000 - saved 8 lines for this label
Tackling line 11000 - saved 9 lines for this label
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Tackling line 20000 - saved 15 lines for this label
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Tackling line 57000 - saved 38 lines for this label
Tackling line 58000 - saved 38 lines for this label
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Saving label 4
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Saving label 5
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Saving label 6
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Saving label 7
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Saving label 8
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Saving label 9
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Saving label 10
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Saving label 11
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Tackling line 63000 - saved 99 lines for this label
Tackling line 64000 - saved 106 lines for this label
Tackling line 65000 - saved 107 lines for this label

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