def add_metric(dataset, analysis, force_import=False, *args, **kwargs):
analysis = Analysis.objects.get(dataset__name=dataset, name=analysis)
for attr in analysis.dataset.attribute_set.all():
for val in attr.value_set.all():
metric_name = 'Token Count for %s: %s' % (attr.name, val.value)
print metric_name
try:
metric = TopicMetric.objects.get(name=metric_name,
analysis=analysis)
if not force_import:
raise RuntimeError('%s is already in the database for this '
'analysis!' % metric_name)
except TopicMetric.DoesNotExist:
metric = TopicMetric(name=metric_name, analysis=analysis)
metric.save()
topics = analysis.topics.all()
docs = [d.id for d in analysis.dataset.documents.filter(
attributevaluedocument__attribute=attr,
attributevaluedocument__value=val)]
for topic in topics:
count = 0
for dt in topic.documenttopic_set.filter(document__id__in=docs):
count += dt.count
tmv = TopicMetricValue(topic=topic, metric=metric, value=count)
tmv.save()
def add_metric(dataset, analysis, force_import=False, *args, **kwargs):
analysis = Analysis.objects.get(dataset__name=dataset, name=analysis)
for attr in analysis.dataset.attribute_set.all():
for val in attr.value_set.all():
metric_name = 'Document Entropy for %s: %s' % (attr.name,
val.value)
print metric_name
try:
metric = TopicMetric.objects.get(name=metric_name,
analysis=analysis)
if not force_import:
raise RuntimeError(
'%s is already in the database for this '
'analysis!' % metric_name)
except TopicMetric.DoesNotExist:
metric = TopicMetric(name=metric_name, analysis=analysis)
metric.save()
topics = analysis.topics.all()
docs = [
d.id for d in analysis.dataset.documents.filter(
attributevaluedocument__attribute=attr,
attributevaluedocument__value=val)
]
for topic in topics:
ent = 0
for dt in topic.documenttopic_set.filter(
document__id__in=docs):
prob = dt.count / topic.total_count
ent -= prob * (log(prob) / log(2))
tmv = TopicMetricValue(topic=topic, metric=metric, value=ent)
tmv.save()
def add_metric(dataset, analysis, force_import=False, *args, **kwargs):
analysis = Analysis.objects.get(dataset__name=dataset, name=analysis)
try:
metric = TopicMetric.objects.get(name='Percent Tokens Positive '
'Sentiment', analysis=analysis)
if not force_import:
raise RuntimeError('Sentiment is already in the database '
'for this analysis!')
except TopicMetric.DoesNotExist:
metric = TopicMetric(name='Percent Tokens Positive Sentiment',
analysis=analysis)
metric.save()
# call stuff to classify documents and get sentiment information, as in
# parse_dependencies.py
data_root = analysis.dataset.dataset_dir
topics = analysis.topics.all()
for topic in topics:
positive = 0;
negative = 0;
for docTopic in topic.documenttopic_set.all():
filename = data_root + '/' + docTopic.document.filename
print topic, filename
sentiment = float(sentiment_document(filename))
print 'sentiment returned:', sentiment
if sentiment == 1 :
positive += docTopic.count
print '%d/%d' % (positive, topic.total_count)
# compute aggregate information for topic
topicSentiment = float(positive)/float(topic.total_count)
tmv = TopicMetricValue(topic=topic, metric=metric, value=topicSentiment)
tmv.save()
def add_metric(dataset, analysis, force_import=False, *args, **kwargs):
analysis = Analysis.objects.get(dataset__name=dataset, name=analysis)
try:
metric = TopicMetric.objects.get(name=metric_name, analysis=analysis)
if not force_import:
raise RuntimeError('%s is already in the database for this '
'analysis!' % metric_name)
except TopicMetric.DoesNotExist:
metric = TopicMetric(name=metric_name, analysis=analysis)
metric.save()
conn = sqlite3.connect(kwargs['counts'])
c = conn.cursor()
c.execute("select words from total_counts")
for row in c:
total_words = float(row[0])
c.execute("select cooccurrences from total_counts")
for row in c:
total_cooccurrences = float(row[0])
topics = analysis.topics.all()
for topic in topics:
topicwords = topic.topicword_set.filter(
word__ngram=False).order_by('-count')
# We just grab the first ten words - there's probably a better way to
# do this
words = [tw.word.type for tw in topicwords[:10]]
total_pmi = 0
for w1 in words:
for w2 in words:
if w1 == w2: continue
total_pmi += compute_pmi(w1, w2, c, total_words,
total_cooccurrences)
average_pmi = total_pmi / (len(words)**2)
tmv = TopicMetricValue(topic=topic, metric=metric, value=average_pmi)
tmv.save()
def add_metric(dataset, analysis, force_import=False, *args, **kwargs):
analysis = Analysis.objects.get(dataset__name=dataset, name=analysis)
try:
metric = TopicMetric.objects.get(name=metric_name, analysis=analysis)
if not force_import:
raise RuntimeError('%s is already in the database for this '
'analysis!' % metric_name)
except TopicMetric.DoesNotExist:
metric = TopicMetric(name=metric_name, analysis=analysis)
metric.save()
conn = sqlite3.connect(kwargs['counts'])
c = conn.cursor()
c.execute("select words from total_counts")
for row in c:
total_words = float(row[0])
c.execute("select cooccurrences from total_counts")
for row in c:
total_cooccurrences = float(row[0])
topics = analysis.topics.all()
for topic in topics:
topicwords = topic.topicword_set.filter(
word__ngram=False).order_by('-count')
# We just grab the first ten words - there's probably a better way to
# do this
words = [tw.word.type for tw in topicwords[:10]]
total_pmi = 0
for w1 in words:
for w2 in words:
if w1 == w2: continue
total_pmi += compute_pmi(w1, w2, c, total_words,
total_cooccurrences)
average_pmi = total_pmi / (len(words)**2)
tmv = TopicMetricValue(topic=topic, metric=metric, value=average_pmi)
tmv.save()
def add_metric(dataset, analysis, force_import=False):
analysis = Analysis.objects.get(dataset__name=dataset, name=analysis)
try:
metric = TopicMetric.objects.get(name=metric_name,
analysis=analysis)
if not force_import:
raise RuntimeError('Number of tokens is already in the database '
'for this analysis!')
except TopicMetric.DoesNotExist:
metric = TopicMetric(name='Number of tokens', analysis=analysis)
metric.save()
topics = analysis.topics.all()
for topic in topics:
tmv = TopicMetricValue(topic=topic, metric=metric,
value=topic.tokens.count())
tmv.save()
def add_metric(dataset, analysis, force_import=False, *args, **kwargs):
analysis = Analysis.objects.get(dataset__name=dataset, name=analysis)
try:
metric = TopicMetric.objects.get(name=metric_name, analysis=analysis)
if not force_import:
raise RuntimeError('Word Entropy is already in the database '
'for this analysis!')
except TopicMetric.DoesNotExist:
metric = TopicMetric(name='Word Entropy', analysis=analysis)
metric.save()
topics = analysis.topic_set.all()
for topic in topics:
entropy = 0
for tw in topic.topicword_set.all():
prob = tw.count / topic.total_count
entropy -= prob * (log(prob) / log(2))
tmv = TopicMetricValue(topic=topic, metric=metric, value=entropy)
tmv.save()
transaction.commit()
def add_metric(dataset, analysis, force_import=False, *args, **kwargs):
# try:
analysis = Analysis.objects.get(dataset__name=dataset, name=analysis)
try:
metric = TopicMetric.objects.get(name=metric_name,
analysis=analysis)
if not force_import:
raise RuntimeError('Number of types is already in the database '
'for this analysis!')
except TopicMetric.DoesNotExist:
metric = TopicMetric(name='Number of types', analysis=analysis)
metric.save()
topics = analysis.topics.all()
for topic in topics:
# types = WordType.objects.filter(tokens__topics__contains=topic).all()
types = set(x[0] for x in topic.tokens.values_list('type__type'))
tmv = TopicMetricValue(topic=topic, metric=metric,
value=len(types))
tmv.save()
def add_metric(dataset, analysis, force_import=False, *args, **kwargs):
analysis = Analysis.objects.get(dataset__name=dataset, name=analysis)
for attr in analysis.dataset.attribute_set.all():
name = 'Value Entropy for Attribute %s' % attr.name
try:
metric = TopicMetric.objects.get(name=name, analysis=analysis)
if not force_import:
raise RuntimeError('%s is already in the database for this '
'analysis!' % name)
except TopicMetric.DoesNotExist:
metric = TopicMetric(name=name, analysis=analysis)
metric.save()
topics = analysis.topics.all()
for topic in topics:
entropy = 0
for avt in topic.attributevaluetopic_set.filter(attribute=attr):
prob = avt.count / topic.total_count
entropy -= prob * (log(prob) / log(2))
tmv = TopicMetricValue(topic=topic, metric=metric, value=entropy)
tmv.save()
def add_metric(dataset, analysis, force_import=False):
analysis = Analysis.objects.get(dataset__name=dataset, name=analysis)
try:
## TODO: add more intelligent checking; check for any TopicMetricValues
metric = TopicMetric.objects.get(name=metric_name, analysis=analysis)
if not force_import:
raise RuntimeError('Word Entropy is already in the database '
'for this analysis!')
except TopicMetric.DoesNotExist:
metric = TopicMetric(name='Word Entropy', analysis=analysis)
metric.save()
topics = analysis.topics.all()
for topic in topics:
total_count = float(topic.tokens.count())
topictokencounts = topic.tokens.values('type__type').annotate(count=Count('type__type'))
entropy = 0
for tw in topictokencounts:
prob = float(tw['count']) / total_count
entropy -= prob * (log(prob) / log(2))
tmv = TopicMetricValue(topic=topic, metric=metric, value=entropy)
tmv.save()
def add_metric(dataset, analysis, force_import=False, *args, **kwargs):
# try:
analysis = Analysis.objects.get(dataset__name=dataset, name=analysis)
try:
metric = TopicMetric.objects.get(name=metric_name,
analysis=analysis)
if not force_import:
raise RuntimeError('%s is already in the database for this '
'analysis!' % metric_name)
except TopicMetric.DoesNotExist:
metric = TopicMetric(name=metric_name, analysis=analysis)
metric.save()
topics = analysis.topics.all()
for topic in topics:
total_count = float(topic.tokens.count())
entropy = 0
doctopic_counts = topic.tokens.values('document__id').annotate(count=Count('document__id'))
for dt in doctopic_counts:
prob = float(dt['count']) / total_count
entropy -= prob * (log(prob) / log(2))
tmv = TopicMetricValue(topic=topic, metric=metric, value=entropy)
tmv.save()
def add_metric(dataset, analysis, force_import=False, *args, **kwargs):
analysis = Analysis.objects.get(dataset__name=dataset, name=analysis)
try:
metric = TopicMetric.objects.get(name=metric_name, analysis=analysis)
if not force_import:
raise RuntimeError('%s is already in the database for this '
'analysis!' % metric_name)
except TopicMetric.DoesNotExist:
metric = TopicMetric(name=metric_name, analysis=analysis)
metric.save()
if 'state_file' not in kwargs:
raise RuntimeError('I need a state file for this metric!')
state_file = open(kwargs['state_file'])
# this is specific to mallet state files!
_ = state_file.readline()
alpha_vector = state_file.readline()
alphas = alpha_vector.split(': ')[1].split()
for number, alpha in enumerate(alphas):
topic = analysis.topic_set.get(number=number)
tmv = TopicMetricValue(topic=topic, metric=metric,
value=float(alpha))
tmv.save()
transaction.commit()