def test_tokenization(self):
expected, sample_docs, objects = get_test_documents()
calculated = {}
analyser = TextAnalyser()
id = 0
for s in sample_docs:
d = analyser.add_document(s)
calculated[str(id)] = d
id += 1
self.assertEqual(expected, calculated)
def test_tokenization(self):
expected, sample_docs, objects = get_test_documents()
calculated = {}
analyser = TextAnalyser()
id=0
for s in sample_docs:
d = analyser.add_document(s)
calculated[str(id)] = d
id+=1
self.assertEqual(expected, calculated)
'''
Created on 26 Jan 2012
@author: george
'''
import unittest, numpy
from analysis.clustering.nmf import NMFClusterer
from tests.test_document import get_test_documents
expected, sample_docs_raw, samples = get_test_documents()
nmfc = NMFClusterer(ngram=1)
nmfc.add_documents(samples)
class Test(unittest.TestCase):
def test_nmf_cluster(self):
nmfc.run(seed='random_vcol',
method='nmf',
rank=2,
max_iter=65,
display_N_tokens=6,
display_N_documents=3)
nmfc.dump_clusters_to_file("nmf_with_samples")
if __name__ == "__main__":
unittest.main()
'''
Created on 26 Jan 2012
@author: george
'''
import unittest, numpy
from analysis.clustering.kmeans import OrangeKmeansClusterer
from tests.test_document import get_test_documents
###########################################
# GLOBALS #
###########################################
ignore, ignore, samples = get_test_documents()
oc = OrangeKmeansClusterer(k=2)
for sample in samples:
oc.add_document(sample)
class Test(unittest.TestCase):
def test_orange_cluster_term_document_matrix(self):
oc.construct_term_doc_matrix()
calculated = oc.td_matrix
expected = numpy.array([[ 0.31388923, 0.11584717, 0, 0, 0, 0, 0.47083384],
[ 0, 0.13515504, 0.3662041, 0, 0.3662041, 0, 0 ],
[ 0, 0, 0, 0.54930614, 0, 0.549306140, 0 ]])
self.assertEqual(expected.all(), calculated.all())
def test_orange_save_matrix_to_tab_file(self):
oc.construct_term_doc_matrix()
oc.save_table("sample_table_orange")
'''
Created on 26 Jan 2012
@author: george
'''
import unittest, numpy
from analysis.clustering.kmeans import OrangeKmeansClusterer
from tests.test_document import get_test_documents
###########################################
# GLOBALS #
###########################################
ignore, ignore, samples = get_test_documents()
oc = OrangeKmeansClusterer(k=2)
for sample in samples:
oc.add_document(sample)
class Test(unittest.TestCase):
def test_orange_cluster_term_document_matrix(self):
oc.construct_term_doc_matrix()
calculated = oc.td_matrix
expected = numpy.array(
[[0.31388923, 0.11584717, 0, 0, 0, 0, 0.47083384],
[0, 0.13515504, 0.3662041, 0, 0.3662041, 0, 0],
[0, 0, 0, 0.54930614, 0, 0.549306140, 0]])
self.assertEqual(expected.all(), calculated.all())
def test_orange_save_matrix_to_tab_file(self):
oc.construct_term_doc_matrix()
'''
Created on 26 Jan 2012
@author: george
'''
import unittest, numpy
from analysis.clustering.online import OnlineClusterer
from analysis.clustering.structures import OnlineCluster
from tests.test_document import get_test_documents, get_orange_clustering_test_data
###########################################
# GLOBALS #
###########################################
expected, sample_docs_raw, samples = get_test_documents()
class Test(unittest.TestCase):
def test_sample_doc_clustering_with_online(self):
oc = OnlineClusterer(N=2, window=3)
samples = get_orange_clustering_test_data()
for document in samples:
index = oc.add_document(document)
oc.cluster(document)
expected = [0, 0, 0, 1, 1, 1]
for cluster in oc.clusters:
print cluster.document_dict
def test_cluster_term_document_matrix(self):
oc = OnlineClusterer(N=2, window=3)
for document in samples:
index = oc.add_document(document)
oc.cluster(document)
