In [1]:
from pymongo import MongoClient
In [3]:
client=MongoClient()
client=MongoClient('mongodb://localhost:29017/')
db=client['eventData']
sen=db.documents_english
In [4]:
from nltk.tokenize import RegexpTokenizer
from stop_words import get_stop_words
from nltk.stem.porter import PorterStemmer
from gensim import corpora, models
import gensim
In [5]:
tokenizer = RegexpTokenizer(r'\w+')
# create English stop words list
en_stop = get_stop_words('en')
# Create p_stemmer of class PorterStemmer
p_stemmer = PorterStemmer()
since sen.find(0 cursor run the order never change so we can get the docId attached to the training result in this way.
In [8]:
%%time
texts = []
docIds=[]
actuallyTrained=0;
temp=0;
for i in sen.find():
if temp<1000:
temp=temp+1
try:
raw = ''.join(i['document']).lower()
tokens = tokenizer.tokenize(raw)
stopped_tokens = [i for i in tokens if not i in en_stop]
stemmed_tokens = [p_stemmer.stem(i) for i in stopped_tokens]
texts.append(stemmed_tokens)
docIds.append(i['_id'])
actuallyTrained=actuallyTrained+1
except:
pass
else:
break
print(actuallyTrained)
1000
CPU times: user 3.46 s, sys: 20 ms, total: 3.48 s
Wall time: 3.49 s
In [9]:
%%time
dictionary = corpora.Dictionary(texts)
CPU times: user 156 ms, sys: 0 ns, total: 156 ms
Wall time: 154 ms
In [10]:
%%time
corpus = [dictionary.doc2bow(text) for text in texts]
CPU times: user 204 ms, sys: 0 ns, total: 204 ms
Wall time: 203 ms
In [11]:
%%time
ldamodel = gensim.models.ldamodel.LdaModel(corpus, num_topics=20, id2word = dictionary, passes=1)
CPU times: user 17.1 s, sys: 2.09 s, total: 19.2 s
Wall time: 16.9 s
In [12]:
actuallyTrained
Out[12]:
1000
In [13]:
%%time
#the dim is the same number of topics
dim=20
result=[]
for i in range(0,actuallyTrained):
feature=[]
previousindex=0
for item in ldamodel[corpus[i]]:
index=item[0]
#print(index)
for beforeindex in range(previousindex,index):
feature.append(0)
feature.append(item[1])
previousindex=index+1
while (len(feature)<dim):
feature.append(0); #add in 0 at the end
result.append(feature)
CPU times: user 1.97 s, sys: 1.44 s, total: 3.41 s
Wall time: 1.81 s
In [14]:
from sklearn.cluster import KMeans
import numpy as np
In [15]:
%%time
kmeanstest=np.array(result)
CPU times: user 4 ms, sys: 0 ns, total: 4 ms
Wall time: 1.82 ms
In [16]:
%%time
kmeans = KMeans(n_clusters=20, random_state=0).fit(kmeanstest)
CPU times: user 536 ms, sys: 3.66 s, total: 4.2 s
Wall time: 305 ms
In [17]:
kmeans.labels_.size
Out[17]:
1000
In [18]:
len(docIds)
Out[18]:
1000
In [19]:
#and before building the dictionary test if the size of docIds and cluster result dimensions are the same.
try:
assert(len(docIds)==kmeans.labels_.size)
dictionary_cocId_topicClusterItBelongs={}
for i in range(0,actuallyTrained):
dictionary_cocId_topicClusterItBelongs.update({docIds[i]:kmeans.labels_[i]})
except:
print("the docIds size is different from the topic # cluster size")
In [20]:
dictionary_cocId_topicClusterItBelongs
Out[20]:
{'56ef2cdf172ab8077b9975de': 4,
'56ef2cdf172ab8077b9975e4': 16,
'56ef2cdf172ab8077b9975e0': 8,
'56ef2c88172ab806913ca82b': 11,
'56ef2c89172ab806943ca85b': 9,
'56ef2cdd172ab8077b99758f': 1,
'56ef2c88172ab806923ca847': 0,
'56ef2cde172ab8077a9975c3': 4,
'56ef2c87172ab806903ca800': 4,
'56ef2cdf172ab807789975f7': 14,
'56ef2c87172ab806923ca81a': 1,
'56ef2cde172ab807789975c0': 11,
'56ef2c87172ab806933ca80a': 1,
'56ef2cdd172ab80779997595': 3,
'56ef2cdf172ab8077a997606': 0,
'56ef2c87172ab806933ca80b': 9,
'56ef2c88172ab806913ca83e': 4,
'56ef2cde172ab807799975db': 6,
'56ef2c87172ab806933ca80d': 16,
'56ef2c88172ab806943ca84a': 1,
'56ef2c89172ab806903ca8a0': 6,
'56ef2c8a172ab806903ca8b4': 19,
'56ef2c87172ab806913ca81d': 8,
'56ef2c88172ab806943ca84f': 3,
'56ef2cde172ab8077a9975c1': 18,
'56ef2cde172ab807789975da': 0,
'56ef2c88172ab806943ca823': 19,
'56ef2c88172ab806903ca85f': 0,
'56ef2c87172ab806913ca81b': 1,
'56ef2cde172ab807799975cd': 4,
'56ef2c89172ab806923ca869': 4,
'56ef2cdd172ab80779997598': 1,
'56ef2c88172ab806943ca845': 9,
'56ef2c87172ab806903ca819': 6,
'56ef2cdf172ab8077c9975d6': 1,
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'56ef2c89172ab806923ca860': 18,
'56ef2c87172ab806923ca819': 4,
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'56ef2c88172ab806913ca83d': 15,
'56ef2cdf172ab8077a9975e4': 12,
'56ef2c89172ab806903ca896': 3,
'56ef2c88172ab806903ca863': 11,
'56ef2c89172ab806903ca882': 0,
'56ef2c88172ab806903ca82d': 1,
'56ef2cdf172ab80779997615': 3,
'56ef2c87172ab806933ca810': 9,
'56ef2c88172ab806913ca82e': 0,
'56ef2c88172ab806903ca836': 13,
'56ef2cde172ab807789975c4': 13,
'56ef2cde172ab8077c9975a0': 6,
'56ef2cde172ab807799975e1': 3,
'56ef2c8a172ab806923ca880': 15,
'56ef2cdf172ab8077c9975ee': 4,
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'56ef2cde172ab8077a9975b6': 0,
'56ef2c87172ab806943ca80a': 4,
'56ef2cde172ab8077a9975d7': 0,
'56ef2cde172ab8077b9975ce': 6,
'56ef2cde172ab807799975c3': 14,
'56ef2c89172ab806903ca898': 1,
'56ef2cdf172ab8077a9975dd': 18,
'56ef2c87172ab806943ca81f': 2,
'56ef2c88172ab806943ca833': 1,
'56ef2c89172ab806903ca897': 15,
'56ef2cdf172ab8077c9975f0': 8,
'56ef2c87172ab806923ca810': 6,
'56ef2c86172ab806913ca7f9': 19,
'56ef2c88172ab806933ca822': 6,
'56ef2c86172ab806923ca7fa': 6,
'56ef2cde172ab8077c9975c7': 8,
'56ef2cdf172ab8077c9975ec': 5,
'56ef2cde172ab8077c9975c4': 17,
'56ef2cdd172ab8077a9975a2': 1,
'56ef2c88172ab806943ca840': 3,
'56ef2c88172ab806913ca833': 8,
'56ef2cde172ab807789975d6': 4,
'56ef2c89172ab806923ca864': 9,
'56ef2c87172ab806913ca810': 18,
'56ef2c89172ab806923ca85c': 5,
'56ef2cdd172ab80778997591': 3,
'56ef2c88172ab806903ca84b': 6,
'56ef2cdf172ab80778997606': 13,
'56ef2cde172ab8077a9975d1': 19,
'56ef2c88172ab806903ca825': 0,
'56ef2cdd172ab8077a99759e': 3,
'56ef2c87172ab806933ca814': 1,
'56ef2c89172ab806923ca855': 4,
'56ef2c89172ab806903ca87a': 12,
'56ef2cdd172ab80778997597': 5,
'56ef2cde172ab8077c9975b9': 0,
'56ef2cde172ab807789975d2': 1,
'56ef2cde172ab8077c9975ca': 15,
'56ef2cde172ab807789975b4': 6,
'56ef2c87172ab806923ca813': 12,
'56ef2cde172ab807789975a1': 19,
'56ef2cde172ab8077c9975a1': 17,
'56ef2cde172ab8077b9975a2': 4,
'56ef2c8a172ab806903ca8c2': 11,
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'56ef2cde172ab807799975d4': 8,
'56ef2c88172ab806943ca82b': 10,
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'56ef2cde172ab807799975d1': 5,
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'56ef2c87172ab806923ca811': 5,
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'56ef2cde172ab807789975c1': 10,
'56ef2c88172ab806913ca82a': 15,
'56ef2c87172ab806923ca804': 9,
'56ef2cdf172ab8077a9975db': 9,
'56ef2c88172ab806943ca846': 8,
'56ef2cdf172ab8077c9975e6': 1,
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'56ef2cde172ab8077c9975ad': 0,
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'56ef2c89172ab806923ca85e': 0,
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'56ef2cde172ab807799975b1': 8,
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'56ef2cdf172ab8077b9975eb': 1,
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'56ef2c88172ab806923ca83f': 3,
'56ef2cde172ab8077b9975a0': 0,
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'56ef2c88172ab806933ca82a': 4,
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'56ef2cdf172ab8077c9975ed': 0,
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'56ef2cdf172ab8077b99760a': 15,
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'56ef2cdd172ab8077b99759b': 0,
'56ef2cde172ab8077a9975a4': 8,
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'56ef2c87172ab806943ca814': 18,
'56ef2c88172ab806923ca846': 15,
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'56ef2cde172ab8077a9975b5': 1,
'56ef2cde172ab8077a9975c8': 2,
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'56ef2c87172ab806933ca817': 1,
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'56ef2cde172ab8077a9975b8': 19,
'56ef2cde172ab8077c9975b6': 9,
'56ef2cdd172ab80779997591': 3,
'56ef2c88172ab806923ca824': 0,
'56ef2c89172ab806923ca86e': 5,
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'56ef2c88172ab806903ca830': 0,
'56ef2cdf172ab8077b9975f3': 6,
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'56ef2c88172ab806903ca83b': 8,
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'56ef2cdf172ab8077a9975e7': 9,
'56ef2c88172ab806943ca850': 1,
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'56ef2c88172ab806933ca823': 5,
'56ef2c89172ab806903ca88a': 6,
'56ef2c89172ab806903ca889': 6,
'56ef2cdf172ab8077b9975f8': 14,
'56ef2cde172ab807799975de': 4,
'56ef2cdf172ab8077c9975f2': 6,
'56ef2c8a172ab806923ca881': 17,
'56ef2cdf172ab8077c9975e3': 9,
'56ef2cdf172ab8077b9975f4': 8,
'56ef2c88172ab806923ca82d': 5,
'56ef2cde172ab8077b9975d1': 11,
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'56ef2c89172ab806923ca86d': 1,
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'56ef2cdf172ab8077a9975ec': 1,
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'56ef2cde172ab8077c9975a5': 3,
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'56ef2c89172ab806903ca8a1': 13,
'56ef2c87172ab806923ca802': 0,
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'56ef2c88172ab806903ca838': 8,
'56ef2c88172ab806903ca84f': 6,
'56ef2c8a172ab806903ca8ab': 3,
'56ef2cdf172ab8077b9975f1': 8,
'56ef2cde172ab8077a9975c7': 6,
'56ef2cdf172ab8077899760d': 4,
'56ef2c88172ab806943ca83c': 11,
'56ef2cdf172ab8077999760e': 0,
'56ef2c88172ab806923ca81d': 6,
'56ef2cde172ab807789975bc': 5,
'56ef2c89172ab806923ca87c': 4,
'56ef2cdf172ab8077b9975fa': 10,
'56ef2cde172ab8077a9975a8': 8,
'56ef2c89172ab806923ca85a': 1,
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'56ef2c89172ab806923ca877': 1,
'56ef2cde172ab807789975c5': 5,
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'56ef2c88172ab806913ca830': 0,
'56ef2cdf172ab8077a9975e8': 1,
'56ef2c89172ab806943ca861': 3,
'56ef2c89172ab806903ca86f': 6,
'56ef2c89172ab806903ca893': 6,
'56ef2c88172ab806933ca835': 6,
'56ef2cdf172ab8077a9975ed': 4,
'56ef2cdf172ab8077a9975e3': 3,
'56ef2cde172ab807789975af': 1,
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In [23]:
#using pickle to dump and load the data
import pickle
In [25]:
with open('traingrst_english.pkl', 'wb') as output:
pickle.dump(dictionary_cocId_topicClusterItBelongs,output)
In [ ]:
Content source: oudalab/fajita
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