Use the Banks example corpus¶
Load TF¶
We (down)load the corpus.
from tf.app import use
A = use("annotation/banks", hoist=globals())
F.letters.freqList()
For the node types we can get info by calling this:
C.levels.data
It means that chapters are 49.5 words long on average, and that the chapter nodes are 101 and 102.
And you see that we have 99 words.
Add to the banks corpus¶
We are going to make a relationship between each pair of words, and we annotate each related pair with how similar they are.
We measure the similarity by looking at the distinct letters in each word (lowercase), and computing the percentage of how many letters they have in common with respect to how many letters they jointly have.
This will become a symmetric edge feature. Symmetric means, that if a and b are similar, then b and a as well, with the same similarity.
We only store one copy of each symmetric pair of edges.
We can then use
E.sim.b(node)
to find all nodes that are parallel to node.
If words do not have letters in common, their similarity is 0, and we do not make an edge.
Preparation¶
We pre-compute all letter sets for all words.
def makeSet(w):
return set(F.letters.v(w).lower())
words = {}
for w in F.otype.s("word"):
words[w] = makeSet(w)
nWords = len(words)
print(f"{nWords} words")
def sim(wSet, vSet):
return int(round(100 * len(wSet & vSet) / len(wSet | vSet)))
def computeSim():
similarity = {}
wordNodes = sorted(words.keys())
nWords = len(wordNodes)
nComparisons = nWords * (nWords - 1) // 2
print(f"{nComparisons} comparisons to make")
TF.indent(reset=True)
co = 0
si = 0
stop = False
for i in range(nWords):
nodeI = wordNodes[i]
wordI = words[nodeI]
for j in range(i + 1, nWords):
nodeJ = wordNodes[j]
wordJ = words[nodeJ]
s = sim(wordI, wordJ)
co += 1
if s:
similarity[(nodeI, nodeJ)] = sim(wordI, wordJ)
si += 1
if stop:
break
TF.info(f"{co:>4} comparisons and {si:>4} similarities")
return similarity
similarity = computeSim()
print(min(similarity.values()))
print(max(similarity.values()))
eq = [x for x in similarity.items() if x[1] >= 100]
neq = [x for x in similarity.items() if x[1] <= 50]
print(eq[0])
print(neq[0])
print(len(eq))
print(len(neq))
print(eq[0][0][0], F.letters.v(eq[0][0][0]))
print(eq[0][0][1], F.letters.v(eq[0][0][1]))
print(neq[0][0][0], F.letters.v(neq[0][0][0]))
print(neq[0][0][1], F.letters.v(neq[0][0][1]))
Add parallels to the TF dataset¶
We now add this information to the Banks dataset as an edge feature.
import os
GH_BASE = os.path.expanduser("~/github")
path = f"{A.context.org}/{A.context.repo}/sim/tf"
location = f"{GH_BASE}/{path}"
module = A.context.version
metaData = {
"": {
"name": "Banks (similar words)",
"converters": "Dirk Roorda",
"sourceUrl": "https://nbviewer.jupyter.org/github/annotation/tutorials/blob/master/text-fabric/use.ipynb",
"version": "0.2",
},
"sim": {
"valueType": "int",
"edgeValues": True,
"description": "similarity between words, as a percentage of the common material wrt the combined material",
},
}
simData = {}
for ((f, t), d) in similarity.items():
simData.setdefault(f, {})[t] = d
A.api.TF.save(
edgeFeatures=dict(sim=simData), metaData=metaData, location=location, module=module
)