def clusterDocument(title, abstract, tokenizer, token2indexMap, tfidf_model, lsi_model, gmm_model):
tokens = tokenize(title + " " + abstract, tokenizer)
tokenCounts = groupAndCount(tokens)
matrix = build_csr_matrix(listOfMaps=[tokenCounts], token2IndexMap=token2indexMap)
transformedMatrix = lsi_model.transform(tfidf_model.transform(matrix))
prediction = gmm_model.predict(transformedMatrix)[0]
return prediction
def clusterDocument(title, abstract, tokenizer, token2indexMap, tfidf_model,
lsi_model, gmm_model):
tokens = tokenize(title + " " + abstract, tokenizer)
tokenCounts = groupAndCount(tokens)
matrix = build_csr_matrix(listOfMaps=[tokenCounts],
token2IndexMap=token2indexMap)
transformedMatrix = lsi_model.transform(tfidf_model.transform(matrix))
prediction = gmm_model.predict(transformedMatrix)[0]
return prediction
mat = load_csr_matrix("derived_data/tfidf_theorem_tdm_grouped_by_docs.npz")
train_mat = mat[train_doc_ind, :]
train_labels = itemgetter(*train_doc_ind)(ordered_document_labels)
svd = TruncatedSVD(n_components=1000)
svd.fit(train_mat)
test_mat = mat[test_doc_ind, :]
test_labels = itemgetter(*test_doc_ind)(ordered_document_labels)
clf = svm.LinearSVC()
clf.fit(svd.transform(train_mat), train_labels)
# eval results
predictions = clf.predict(svd.transform(test_mat)).tolist()
def f(pred_label_pair):
if pred_label_pair[0] == 1 and pred_label_pair[1] == 1:
return "tp"
elif pred_label_pair[0] == 1 and pred_label_pair[1] == 0:
return "fp"
elif pred_label_pair[0] == 0 and pred_label_pair[1] == 1:
return "fn"
else:
return "tn"
cats = map(lambda x: f(x), zip(predictions, test_labels))
print groupAndCount(cats)
mat = load_csr_matrix("derived_data/tfidf_theorem_tdm_grouped_by_docs.npz")
train_mat = mat[train_doc_ind, :]
train_labels = itemgetter(*train_doc_ind)(ordered_document_labels)
svd = TruncatedSVD(n_components=1000)
svd.fit(train_mat)
test_mat = mat[test_doc_ind, :]
test_labels = itemgetter(*test_doc_ind)(ordered_document_labels)
clf = svm.LinearSVC()
clf.fit(svd.transform(train_mat), train_labels)
# eval results
predictions = clf.predict(svd.transform(test_mat)).tolist()
def f(pred_label_pair):
if pred_label_pair[0] == 1 and pred_label_pair[1] == 1:
return "tp"
elif pred_label_pair[0] == 1 and pred_label_pair[1] == 0:
return "fp"
elif pred_label_pair[0] == 0 and pred_label_pair[1] == 1:
return "fn"
else:
return "tn"
cats = map(lambda x: f(x), zip(predictions, test_labels))
print groupAndCount(cats)