def _adaptiveAggregation(V, n, yIntervals, weightF, param, freq):
'''Apply adaptive aggregation algorithm to the given vocabulary.
Algorithm 2 from paper.
'''
# Initialize returned parameters
finalVocabs = SortedDict()
periodGroups = SortedDict()
# Select weighting function
f = _selectWeightingFunction(weightF, param)
# Iterate over time frames
for t in _arrangeIntervals(V, yIntervals, freq):
mu_t = getRangeMiddle(t[0], t[-1])
V_prime = SortedDict({tx: V[tx] for tx in t})
score = defaultdict(float)
for years_v, words_v in V_prime.iteritems():
mu_v = getRangeMiddle(years_v)
fvt = f(mu_v, mu_t)
for word, score_wv in words_v:
score[word] += fvt * score_wv
# Top n terms w sorted by score_w
scoreList = [(k, v) for k, v in score.iteritems()]
scoreList = sorted(scoreList, key=lambda pair: pair[1], reverse=True)
topN = scoreList[:n]
finalVocabs[str(int(mu_t))] = topN
periodGroups[str(int(mu_t))] = t
return finalVocabs, periodGroups
def _adaptiveAggregation(V, n, yIntervals, weightF, param, freq):
'''Apply adaptive aggregation algorithm to the given vocabulary.
Algorithm 2 from paper.
'''
# Initialize returned parameters
finalVocabs = SortedDict()
periodGroups = SortedDict()
# Select weighting function
f = _selectWeightingFunction(weightF, param)
# Iterate over time frames
for t in _arrangeIntervals(V, yIntervals, freq):
mu_t = getRangeMiddle(t[0], t[-1])
V_prime = SortedDict({tx: V[tx] for tx in t})
score = defaultdict(float)
for years_v, words_v in V_prime.iteritems():
mu_v = getRangeMiddle(years_v)
fvt = f(mu_v, mu_t)
for word, score_wv in words_v:
score[word] += fvt * score_wv
# Top n terms w sorted by score_w
scoreList = [(k, v) for k, v in score.iteritems()]
scoreList = sorted(scoreList, key=lambda pair: pair[1], reverse=True)
topN = scoreList[:n]
finalVocabs[str(int(mu_t))] = topN
periodGroups[str(int(mu_t))] = t
return finalVocabs, periodGroups
def doSpaceEmbedding(monitor, results, aggMetadata):
'''Create 2D word embedding from given set of results'''
embeddedResults = SortedDict()
wordsT0 = None
locsT0 = None
for label,r in results.iteritems():
model = monitor._models[label]
wordsT1 = [ w for w,_ in r ]
dists = _getPairwiseDistances(wordsT1, model)
locsT1 = _getMDSEmbedding(dists)
if wordsT0 is not None:
T = _findTransform(wordsT0, locsT0, wordsT1, locsT1)
locsT1 = locsT1.dot(T)
locsT1 = _normalizeCloud(locsT1)
wordsT0 = wordsT1
locsT0 = locsT1
str_label = str(int(getRangeMiddle(label)))
embeddedResults[str_label] = [ wordLocationAsDict(wordsT1[i],locsT1[i,:]) for i in range(len(wordsT1)) ]
# Aggregation step (more like throwing away some years)
embeddedResultsAgg = { year: embeddedResults[year] for year in aggMetadata }
embeddedResultsAgg = SortedDict(embeddedResultsAgg)
return embeddedResultsAgg
def doSpaceEmbedding(monitor, results, aggMetadata):
'''Create 2D word embedding from given set of results'''
embeddedResults = SortedDict()
wordsT0 = None
locsT0 = None
for label, r in results.iteritems():
model = monitor._models[label]
wordsT1 = [w for w, _ in r]
dists = _getPairwiseDistances(wordsT1, model)
locsT1 = _getMDSEmbedding(dists)
if wordsT0 is not None:
T = _findTransform(wordsT0, locsT0, wordsT1, locsT1)
locsT1 = locsT1.dot(T)
locsT1 = _normalizeCloud(locsT1)
wordsT0 = wordsT1
locsT0 = locsT1
str_label = str(int(getRangeMiddle(label)))
embeddedResults[str_label] = [
wordLocationAsDict(wordsT1[i], locsT1[i, :])
for i in range(len(wordsT1))
]
# Aggregation step (more like throwing away some years)
embeddedResultsAgg = {year: embeddedResults[year] for year in aggMetadata}
embeddedResultsAgg = SortedDict(embeddedResultsAgg)
return embeddedResultsAgg
