This tutorial is about using gensim models as a part of your scikit learn workflow with the help of wrappers found at gensim.sklearn_integration.sklearn_wrapper_gensim_ldaModel
The wrapper available (as of now) are :
gensim.sklearn_integration.sklearn_wrapper_gensim_ldaModel.SklearnWrapperLdaModel),which implements gensim's LdaModel in a scikit-learn interfaceTo use LdaModel begin with importing LdaModel wrapper
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from gensim.sklearn_integration.sklearn_wrapper_gensim_ldaModel import SklearnWrapperLdaModel
Next we will create a dummy set of texts and convert it into a corpus
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from gensim.corpora import Dictionary
texts = [['complier', 'system', 'computer'],
['eulerian', 'node', 'cycle', 'graph', 'tree', 'path'],
['graph', 'flow', 'network', 'graph'],
['loading', 'computer', 'system'],
['user', 'server', 'system'],
['tree','hamiltonian'],
['graph', 'trees'],
['computer', 'kernel', 'malfunction','computer'],
['server','system','computer']]
dictionary = Dictionary(texts)
corpus = [dictionary.doc2bow(text) for text in texts]
Then to run the LdaModel on it
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model=SklearnWrapperLdaModel(num_topics=2,id2word=dictionary,iterations=20, random_state=1)
model.fit(corpus)
model.print_topics(2)
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To provide a better example of how it can be used with Sklearn, Let's use CountVectorizer method of sklearn. For this example we will use 20 Newsgroups data set. We will only use the categories rec.sport.baseball and sci.crypt and use it to generate topics.
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import numpy as np
from gensim import matutils
from gensim.models.ldamodel import LdaModel
from sklearn.datasets import fetch_20newsgroups
from sklearn.feature_extraction.text import CountVectorizer
from gensim.sklearn_integration.sklearn_wrapper_gensim_ldaModel import SklearnWrapperLdaModel
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rand = np.random.mtrand.RandomState(1) # set seed for getting same result
cats = ['rec.sport.baseball', 'sci.crypt']
data = fetch_20newsgroups(subset='train',
categories=cats,
shuffle=True)
Next, we use countvectorizer to convert the collection of text documents to a matrix of token counts.
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vec = CountVectorizer(min_df=10, stop_words='english')
X = vec.fit_transform(data.data)
vocab = vec.get_feature_names() #vocab to be converted to id2word
id2word=dict([(i, s) for i, s in enumerate(vocab)])
Next, we just need to fit X and id2word to our Lda wrapper.
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obj=SklearnWrapperLdaModel(id2word=id2word,num_topics=5,passes=20)
lda=obj.fit(X)
lda.print_topics()
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Now lets try Sklearn's logistic classifier to classify the given categories into two types.Ideally we should get postive weights when cryptography is talked about and negative when baseball is talked about.
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from sklearn import linear_model
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def print_features(clf, vocab, n=10):
''' Better printing for sorted list '''
coef = clf.coef_[0]
print 'Positive features: %s' % (' '.join(['%s:%.2f' % (vocab[j], coef[j]) for j in np.argsort(coef)[::-1][:n] if coef[j] > 0]))
print 'Negative features: %s' % (' '.join(['%s:%.2f' % (vocab[j], coef[j]) for j in np.argsort(coef)[:n] if coef[j] < 0]))
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clf=linear_model.LogisticRegression(penalty='l1', C=0.1) #l1 penalty used
clf.fit(X,data.target)
print_features(clf,vocab)
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