def generateDataFrameSingleWordCountBy(self, mFun, word):
agglomeratedMessages = ConvStats._getMessagesBy(
mFun, self.conversation.messages)
df = self._generateDataFrameAgglomeratedStatsBy(
mFun, agglomeratedMessages)
wOcc1 = [
(d,
statsUtil.getWordsCount(
list(
filter(lambda m: m.sender == self.conversation.sender1,
a)))) for d, a in agglomeratedMessages.items()
]
wOcc2 = [
(d,
statsUtil.getWordsCount(
list(
filter(lambda m: m.sender == self.conversation.sender2,
a)))) for d, a in agglomeratedMessages.items()
]
s1Count = [
count[word] if word in count else (by, 0) for (by, count) in wOcc1
]
s2Count = [
count[word] if word in count else (by, 0) for (by, count) in wOcc2
]
df[self.conversation.sender1 + '_count'] = np.array(s1Count)
df[self.conversation.sender2 + '_count'] = np.array(s2Count)
df['totCount'] = df[self.conversation.sender1 +
'_count'] + df[self.conversation.sender2 +
'_count']
return df
def _getWordsUsedJustByStats(sender1Messages, sender2Messages):
wordsSaidBySender1 = statsUtil.getWordsCount(sender1Messages).keys()
wordsSaidBySender2 = statsUtil.getWordsCount(sender2Messages).keys()
wordsSaidByBoth = set(wordsSaidBySender1).intersection(wordsSaidBySender2)
wordsSaidJustByS1 = set(wordsSaidBySender1).difference(wordsSaidBySender2)
wordsSaidJustByS2 = set(wordsSaidBySender2).difference(wordsSaidBySender1)
return wordsSaidByBoth, wordsSaidJustByS1, wordsSaidJustByS2
def _getWordsUsedJustByStats(sender1Messages, sender2Messages):
wordsSaidBySender1 = statsUtil.getWordsCount(sender1Messages).keys()
wordsSaidBySender2 = statsUtil.getWordsCount(sender2Messages).keys()
wordsSaidByBoth = set(wordsSaidBySender1).intersection(
wordsSaidBySender2)
wordsSaidJustByS1 = set(wordsSaidBySender1).difference(
wordsSaidBySender2)
wordsSaidJustByS2 = set(wordsSaidBySender2).difference(
wordsSaidBySender1)
return wordsSaidByBoth, wordsSaidJustByS1, wordsSaidJustByS2
def _getWordsCountStats(messages, limit=0):
wCount = statsUtil.getWordsCount(messages)
if limit == 0:
return wCount.most_common()
else:
return wCount.most_common(limit)
def _getWordsCountStats(messages, limit=0):
wCount = statsUtil.getWordsCount(messages)
if limit == 0:
return wCount.most_common()
else:
return wCount.most_common(limit)
def _generateWordCountStatsBy(self, groupByColumns=[], word=None):
fun = lambda x: tuple(sorted(
statsUtil.getWordsCount(" ".join(x)).items(), key=lambda y: y[1], reverse=True))
label = 'wordCount'
countId = 'word'
results = self._generateCountStatsBy(fun, label, countId, groupByColumns, word)
return results
def generateDataFrameSingleWordCountBy(self, mFun, word):
agglomeratedMessages = ConvStats._getMessagesBy(mFun, self.conversation.messages)
df = self._generateDataFrameAgglomeratedStatsBy(mFun, agglomeratedMessages)
wOcc1 = [
(d, statsUtil.getWordsCount(list(filter(lambda m: m.sender == self.conversation.sender1, a))))
for d, a in agglomeratedMessages.items()
]
wOcc2 = [
(d, statsUtil.getWordsCount(list(filter(lambda m: m.sender == self.conversation.sender2, a))))
for d, a in agglomeratedMessages.items()
]
s1Count = [count[word] if word in count else (by, 0) for (by, count) in wOcc1]
s2Count = [count[word] if word in count else (by, 0) for (by, count) in wOcc2]
df[self.conversation.sender1 + "_count"] = np.array(s1Count)
df[self.conversation.sender2 + "_count"] = np.array(s2Count)
df["totCount"] = df[self.conversation.sender1 + "_count"] + df[self.conversation.sender2 + "_count"]
return df
