def q10():
nodes_list = {viz.DATA_111_URL:111, viz.DATA_290_URL:290, viz.DATA_896_URL:896}
url_list = [viz.DATA_111_URL, viz.DATA_290_URL, viz.DATA_896_URL]
kmeans_dict = dict()
hierarchical_dict = dict()
for url in url_list:
data_table = viz.load_data_table(url)
singleton_list = []
for line in data_table:
singleton_list.append(alg_cluster.Cluster(set([line[0]]), line[1], line[2], line[3], line[4]))
kmeans_dict[url] = list()
hierarchical_dict[url] = list()
cluster_range = range(6, 20 + 1)
for cluster_count in cluster_range:
#kmeans
cluster_list = alg_project3.kmeans_clustering(singleton_list, cluster_count, 5)
kmeans_error = compute_distortion(cluster_list, data_table)
kmeans_dict[url].append(kmeans_error)
#hierarchical
count = 20
while count >= 6:
alg_project3.hierarchical_clustering(singleton_list, count)
hierarchical_error = compute_distortion(singleton_list, data_table)
hierarchical_dict[url].insert(0, hierarchical_error)
count -= 1
for url in url_list:
plt.title('Distortion for hierarchical and k-means clustering for '+str(nodes_list[url])+' points')
plt.xlabel('Number of clusters')
plt.ylabel('Distortion')
line1, = plt.plot(cluster_range, kmeans_dict[url],'g')
line2, = plt.plot(cluster_range, hierarchical_dict[url],'b')
plt.legend((line1, line2), ('kmeans clustering', 'hierarchical clustering'))
plt.show()
#q2()
#q2()
#q3()
#q5()
#q6()
#q7()
#q10()
def q10():
nodes_list = {
viz.DATA_111_URL: 111,
viz.DATA_290_URL: 290,
viz.DATA_896_URL: 896
}
url_list = [viz.DATA_111_URL, viz.DATA_290_URL, viz.DATA_896_URL]
kmeans_dict = dict()
hierarchical_dict = dict()
for url in url_list:
data_table = viz.load_data_table(url)
singleton_list = []
for line in data_table:
singleton_list.append(
alg_cluster.Cluster(set([line[0]]), line[1], line[2], line[3],
line[4]))
kmeans_dict[url] = list()
hierarchical_dict[url] = list()
cluster_range = range(6, 20 + 1)
for cluster_count in cluster_range:
#kmeans
cluster_list = alg_project3.kmeans_clustering(
singleton_list, cluster_count, 5)
kmeans_error = compute_distortion(cluster_list, data_table)
kmeans_dict[url].append(kmeans_error)
#hierarchical
count = 20
while count >= 6:
alg_project3.hierarchical_clustering(singleton_list, count)
hierarchical_error = compute_distortion(singleton_list, data_table)
hierarchical_dict[url].insert(0, hierarchical_error)
count -= 1
for url in url_list:
plt.title('Distortion for hierarchical and k-means clustering for ' +
str(nodes_list[url]) + ' points')
plt.xlabel('Number of clusters')
plt.ylabel('Distortion')
line1, = plt.plot(cluster_range, kmeans_dict[url], 'g')
line2, = plt.plot(cluster_range, hierarchical_dict[url], 'b')
plt.legend((line1, line2),
('kmeans clustering', 'hierarchical clustering'))
plt.show()
def run_example():
"""
Load a data table, compute a list of clusters and
plot a list of clusters
Set DESKTOP = True/False to use either matplotlib or simplegui
"""
#data_table = load_data_table(DATA_3108_URL)
#data_table = load_data_from_file("unifiedCancerData_290.csv")
#data_table = load_data_from_file("unifiedCancerData_896.csv")
#data_table = load_data_from_file("unifiedCancerData_3108.csv")
data_table = load_data_from_file("unifiedCancerData_111.csv")
singleton_list = gen_singleton_list(data_table)
#print alg_project3.fast_closest_pair(singleton_list)
#print alg_project3.slow_closest_pairs(singleton_list)
#cluster_list = sequential_clustering(singleton_list, 15)
#print "Displaying", len(cluster_list), "sequential clusters"
cluster_list = alg_project3.kmeans_clustering(singleton_list, 9, 5)
print "Displaying", len(cluster_list), "k-means clusters"
print "compute_distortion : ", alg_project3.compute_distortion(
cluster_list, data_table)
cluster_list = alg_project3.hierarchical_clustering(singleton_list, 9)
print "Displaying", len(cluster_list), "hierarchical clusters"
print "compute_distortion : ", alg_project3.compute_distortion(
cluster_list, data_table)
def run_example():
"""
Load a data table, compute a list of clusters and
plot a list of clusters
Set DESKTOP = True/False to use either matplotlib or simplegui
"""
data_table = load_data_table(DATA_111_URL)
singleton_list = []
for line in data_table:
singleton_list.append(
alg_cluster.Cluster(set([line[0]]), line[1], line[2], line[3],
line[4]))
cluster_list = sequential_clustering(singleton_list, 15)
print("Displaying", len(cluster_list), "sequential clusters")
cluster_list = alg_project3.hierarchical_clustering(singleton_list, 9)
#print "Displaying", len(cluster_list), "hierarchical clusters"
#cluster_list = alg_project3.kmeans_clustering(singleton_list, 9, 5)
#print "Displaying", len(cluster_list), "k-means clusters"
# draw the clusters using matplotlib or simplegui
if DESKTOP:
#alg_clusters_matplotlib.plot_clusters(data_table, cluster_list, False)
alg_clusters_matplotlib.plot_clusters(data_table, cluster_list,
True) #add cluster centers
else:
alg_clusters_simplegui.PlotClusters(
data_table,
cluster_list) # use toggle in GUI to add cluster centers
def q2(): data_table = viz.load_data_table(viz.DATA_3108_URL) singleton_list = [] for line in data_table: singleton_list.append(alg_cluster.Cluster(set([line[0]]), line[1], line[2], line[3], line[4])) cluster_list = alg_project3.hierarchical_clustering(singleton_list, 15) alg_clusters_matplotlib.plot_clusters(data_table, cluster_list, True)
def run_example():
"""
Load a data table, compute a list of clusters and
plot a list of clusters
Set DESKTOP = True/False to use either matplotlib or simplegui
"""
#data_table = load_data_table(DATA_3108_URL)
#data_table = load_data_from_file("unifiedCancerData_290.csv")
#data_table = load_data_from_file("unifiedCancerData_896.csv")
#data_table = load_data_from_file("unifiedCancerData_3108.csv")
data_table = load_data_from_file("unifiedCancerData_111.csv")
singleton_list = gen_singleton_list(data_table)
#print alg_project3.fast_closest_pair(singleton_list)
#print alg_project3.slow_closest_pairs(singleton_list)
#cluster_list = sequential_clustering(singleton_list, 15)
#print "Displaying", len(cluster_list), "sequential clusters"
cluster_list = alg_project3.kmeans_clustering(singleton_list, 9, 5)
print "Displaying", len(cluster_list), "k-means clusters"
print "compute_distortion : ", alg_project3.compute_distortion(cluster_list, data_table)
cluster_list = alg_project3.hierarchical_clustering(singleton_list, 9)
print "Displaying", len(cluster_list), "hierarchical clusters"
print "compute_distortion : ", alg_project3.compute_distortion(cluster_list, data_table)
def run_example():
"""
Load a data table, compute a list of clusters and
plot a list of clusters
Set DESKTOP = True/False to use either matplotlib or simplegui
"""
data_table = load_data_table(DATA_111_URL)
singleton_list = []
for line in data_table:
singleton_list.append(alg_cluster.Cluster(set([line[0]]), line[1], line[2], line[3], line[4]))
cluster_list = sequential_clustering(singleton_list, 15)
print "Displaying", len(cluster_list), "sequential clusters"
cluster_list = alg_project3.hierarchical_clustering(singleton_list, 9)
#print "Displaying", len(cluster_list), "hierarchical clusters"
#cluster_list = alg_project3.kmeans_clustering(singleton_list, 9, 5)
#print "Displaying", len(cluster_list), "k-means clusters"
# draw the clusters using matplotlib or simplegui
if DESKTOP:
#alg_clusters_matplotlib.plot_clusters(data_table, cluster_list, False)
alg_clusters_matplotlib.plot_clusters(data_table, cluster_list, True) #add cluster centers
else:
alg_clusters_simplegui.PlotClusters(data_table, cluster_list) # use toggle in GUI to add cluster centers
def test_hierarchical24():
"""
Test for hierarchical clustering
Note that hierarchical_clustering mutates cluster_list
"""
# load small data table
print
print "Testing hierarchical_clustering on 24 county set"
data_24_table = load_data_table(DATA_24_URL)
# test data of the form [size of output cluster, sets of county tuples]
hierdata_24 = [[23, set([('11001', '51013'), ('01073',), ('06059',), ('06037',), ('06029',), ('06071',), ('06075',), ('08031',), ('24510',), ('34013',), ('34039',), ('34017',), ('36061',), ('36005',), ('36047',), ('36059',), ('36081',), ('41051',), ('41067',), ('51840',), ('51760',), ('55079',), ('54009',)])],
[22, set([('11001', '51013'), ('36047', '36081'), ('01073',), ('06059',), ('06037',), ('06029',), ('06071',), ('06075',), ('08031',), ('24510',), ('34013',), ('34039',), ('34017',), ('36061',), ('36005',), ('36059',), ('41051',), ('41067',), ('51840',), ('51760',), ('55079',), ('54009',)])],
[21, set([('11001', '51013'), ('36005', '36061'), ('36047', '36081'), ('01073',), ('06059',), ('06037',), ('06029',), ('06071',), ('06075',), ('08031',), ('24510',), ('34013',), ('34039',), ('34017',), ('36059',), ('41051',), ('41067',), ('51840',), ('51760',), ('55079',), ('54009',)])],
[20, set([('11001', '51013'), ('36005', '36061'), ('36047', '36081'), ('01073',), ('06059',), ('06037',), ('06029',), ('06071',), ('06075',), ('08031',), ('24510',), ('34039',), ('34013', '34017'), ('36059',), ('41051',), ('41067',), ('51840',), ('51760',), ('55079',), ('54009',)])],
[19, set([('34013', '34017', '34039'), ('11001', '51013'), ('36005', '36061'), ('36047', '36081'), ('01073',), ('06059',), ('06037',), ('06029',), ('06071',), ('06075',), ('08031',), ('24510',), ('36059',), ('41051',), ('41067',), ('51840',), ('51760',), ('55079',), ('54009',)])],
[18, set([('34013', '34017', '34039'), ('11001', '51013'), ('01073',), ('06059',), ('06037',), ('06029',), ('06071',), ('06075',), ('08031',), ('24510',), ('36059',), ('36005', '36047', '36061', '36081'), ('41051',), ('41067',), ('51840',), ('51760',), ('55079',), ('54009',)])],
[17, set([('11001', '51013'), ('01073',), ('06059',), ('06037',), ('06029',), ('06071',), ('06075',), ('08031',), ('24510',), ('36059',), ('34013', '34017', '34039', '36005', '36047', '36061', '36081'), ('41051',), ('41067',), ('51840',), ('51760',), ('55079',), ('54009',)])],
[16, set([('11001', '51013'), ('01073',), ('06059',), ('06037',), ('06029',), ('06071',), ('06075',), ('08031',), ('24510',), ('34013', '34017', '34039', '36005', '36047', '36059', '36061', '36081'), ('41051',), ('41067',), ('51840',), ('51760',), ('55079',), ('54009',)])],
[15, set([('11001', '51013'), ('01073',), ('06059',), ('06037',), ('06029',), ('06071',), ('06075',), ('08031',), ('24510',), ('34013', '34017', '34039', '36005', '36047', '36059', '36061', '36081'), ('41051', '41067'), ('51840',), ('51760',), ('55079',), ('54009',)])],
[14, set([('01073',), ('06059',), ('06037',), ('06029',), ('06071',), ('06075',), ('08031',), ('34013', '34017', '34039', '36005', '36047', '36059', '36061', '36081'), ('41051', '41067'), ('51840',), ('51760',), ('55079',), ('54009',), ('11001', '24510', '51013')])],
[13, set([('06037', '06059'), ('01073',), ('06029',), ('06071',), ('06075',), ('08031',), ('34013', '34017', '34039', '36005', '36047', '36059', '36061', '36081'), ('41051', '41067'), ('51840',), ('51760',), ('55079',), ('54009',), ('11001', '24510', '51013')])],
[12, set([('06037', '06059'), ('01073',), ('06029',), ('06071',), ('06075',), ('08031',), ('34013', '34017', '34039', '36005', '36047', '36059', '36061', '36081'), ('41051', '41067'), ('51760',), ('55079',), ('54009',), ('11001', '24510', '51013', '51840')])],
[11, set([('06029', '06037', '06059'), ('01073',), ('06071',), ('06075',), ('08031',), ('34013', '34017', '34039', '36005', '36047', '36059', '36061', '36081'), ('41051', '41067'), ('51760',), ('55079',), ('54009',), ('11001', '24510', '51013', '51840')])],
[10, set([('06029', '06037', '06059'), ('01073',), ('06071',), ('06075',), ('08031',), ('34013', '34017', '34039', '36005', '36047', '36059', '36061', '36081'), ('41051', '41067'), ('55079',), ('54009',), ('11001', '24510', '51013', '51760', '51840')])],
[9, set([('01073',), ('06029', '06037', '06059', '06071'), ('06075',), ('08031',), ('34013', '34017', '34039', '36005', '36047', '36059', '36061', '36081'), ('41051', '41067'), ('55079',), ('54009',), ('11001', '24510', '51013', '51760', '51840')])],
[8, set([('01073',), ('06029', '06037', '06059', '06071'), ('06075',), ('08031',), ('41051', '41067'), ('55079',), ('54009',), ('11001', '24510', '34013', '34017', '34039', '36005', '36047', '36059', '36061', '36081', '51013', '51760', '51840')])],
[7, set([('01073',), ('06029', '06037', '06059', '06071'), ('06075',), ('08031',), ('41051', '41067'), ('55079',), ('11001', '24510', '34013', '34017', '34039', '36005', '36047', '36059', '36061', '36081', '51013', '51760', '51840', '54009')])],
[6, set([('06029', '06037', '06059', '06071', '06075'), ('01073',), ('08031',), ('41051', '41067'), ('55079',), ('11001', '24510', '34013', '34017', '34039', '36005', '36047', '36059', '36061', '36081', '51013', '51760', '51840', '54009')])],
[5, set([('06029', '06037', '06059', '06071', '06075'), ('08031',), ('41051', '41067'), ('01073', '55079'), ('11001', '24510', '34013', '34017', '34039', '36005', '36047', '36059', '36061', '36081', '51013', '51760', '51840', '54009')])],
[4, set([('06029', '06037', '06059', '06071', '06075'), ('01073', '11001', '24510', '34013', '34017', '34039', '36005', '36047', '36059', '36061', '36081', '51013', '51760', '51840', '54009', '55079'), ('08031',), ('41051', '41067')])],
[3, set([('06029', '06037', '06059', '06071', '06075', '41051', '41067'), ('01073', '11001', '24510', '34013', '34017', '34039', '36005', '36047', '36059', '36061', '36081', '51013', '51760', '51840', '54009', '55079'), ('08031',)])],
[2, set([('01073', '11001', '24510', '34013', '34017', '34039', '36005', '36047', '36059', '36061', '36081', '51013', '51760', '51840', '54009', '55079'), ('06029', '06037', '06059', '06071', '06075', '08031', '41051', '41067')])],
]
suite = poc_simpletest.TestSuite()
for num_clusters, expected_county_tuple in hierdata_24:
# build initial list of clusters for each test since mutation is allowed
cluster_list = []
for idx in range(len(data_24_table)):
line = data_24_table[idx]
cluster_list.append(alg_cluster.Cluster(set([line[0]]), line[1], line[2], line[3], line[4]))
# compute student answer
student_clustering = student.hierarchical_clustering(cluster_list, num_clusters)
student_county_tuple = set_of_county_tuples(student_clustering)
# Prepare test
error_message = "Testing hierarchical_clustering on 24 county table, num_clusters = " + str(num_clusters)
error_message += "\nStudent county tuples: " + str(student_county_tuple)
error_message += "\nExpected county tuples: " + str(expected_county_tuple)
suite.run_test(student_county_tuple == expected_county_tuple, True, error_message)
suite.report_results()
def q2():
data_table = viz.load_data_table(viz.DATA_3108_URL)
singleton_list = []
for line in data_table:
singleton_list.append(
alg_cluster.Cluster(set([line[0]]), line[1], line[2], line[3],
line[4]))
cluster_list = alg_project3.hierarchical_clustering(singleton_list, 15)
alg_clusters_matplotlib.plot_clusters(data_table, cluster_list, True)
def q7(): data_table = viz.load_data_table(viz.DATA_111_URL) singleton_list = [] for line in data_table: singleton_list.append(alg_cluster.Cluster(set([line[0]]), line[1], line[2], line[3], line[4])) cluster_list = alg_project3.kmeans_clustering(singleton_list, 9, 5) error2 = compute_distortion(cluster_list, data_table) cluster_list = alg_project3.hierarchical_clustering(singleton_list, 9) error1 = compute_distortion(cluster_list, data_table) print 'hierarchical clustering',error1 print 'kmeans clustering', error2
def q7():
data_table = viz.load_data_table(viz.DATA_111_URL)
singleton_list = []
for line in data_table:
singleton_list.append(
alg_cluster.Cluster(set([line[0]]), line[1], line[2], line[3],
line[4]))
cluster_list = alg_project3.kmeans_clustering(singleton_list, 9, 5)
error2 = compute_distortion(cluster_list, data_table)
cluster_list = alg_project3.hierarchical_clustering(singleton_list, 9)
error1 = compute_distortion(cluster_list, data_table)
print('hierarchical clustering', error1)
print('kmeans clustering', error2)
def run_example():
"""
Load a data table, compute a list of clusters and
plot a list of clusters
Set DESKTOP = True/False to use either matplotlib or simplegui
"""
data_table = load_data_table(DATA_896_URL)
singleton_list = []
for line in data_table:
singleton_list.append(alg_cluster.Cluster(set([line[0]]), line[1], line[2], line[3], line[4]))
#cluster_list = sequential_clustering(singleton_list, 15)
#print "Displaying", len(cluster_list), "sequential clusters"
k_dis = []
h_dis = []
for cluster_num in xrange(6, 21):
cluster_list = alg_project3.hierarchical_clustering([c.copy() for c in singleton_list], cluster_num)
print "Displaying", len(cluster_list), "hierarchical clusters"
h_dis.append(alg_app3.compute_distortion(cluster_list, data_table))
cluster_list = alg_project3.kmeans_clustering([c.copy() for c in singleton_list], cluster_num, 5)
print "Displaying", len(cluster_list), "k-means clusters"
k_dis.append(alg_app3.compute_distortion(cluster_list, data_table))
xvals = range(6, 21)
plt.plot(xvals, k_dis, '-b', label='kmeans_clustering (5 iteration)')
plt.plot(xvals, h_dis, '-r', label='hierarchical_clustering')
plt.legend(loc='upper right')
plt.xlabel("number of clusters")
plt.ylabel("distortion")
plt.title("Distortion of 2 clustering methods: 896 counties")
plt.show()
# draw the clusters using matplotlib or simplegui
"""
def test_hierarchical24():
"""
Test for hierarchical clustering
Note that hierarchical_clustering mutates cluster_list
"""
# load small data table
print
print "Testing hierarchical_clustering on 24 county set"
data_24_table = load_data_table(DATA_24_URL)
# test data of the form [size of output cluster, sets of county tuples]
hierdata_24 = [
[
23,
set([('11001', '51013'), ('01073', ), ('06059', ), ('06037', ),
('06029', ), ('06071', ), ('06075', ), ('08031', ),
('24510', ), ('34013', ), ('34039', ), ('34017', ),
('36061', ), ('36005', ), ('36047', ), ('36059', ),
('36081', ), ('41051', ), ('41067', ), ('51840', ),
('51760', ), ('55079', ), ('54009', )])
],
[
22,
set([('11001', '51013'), ('36047', '36081'), ('01073', ),
('06059', ), ('06037', ), ('06029', ), ('06071', ),
('06075', ), ('08031', ), ('24510', ), ('34013', ),
('34039', ), ('34017', ), ('36061', ), ('36005', ),
('36059', ), ('41051', ), ('41067', ), ('51840', ),
('51760', ), ('55079', ), ('54009', )])
],
[
21,
set([('11001', '51013'), ('36005', '36061'), ('36047', '36081'),
('01073', ), ('06059', ), ('06037', ), ('06029', ),
('06071', ), ('06075', ), ('08031', ), ('24510', ),
('34013', ), ('34039', ), ('34017', ), ('36059', ),
('41051', ), ('41067', ), ('51840', ), ('51760', ),
('55079', ), ('54009', )])
],
[
20,
set([('11001', '51013'), ('36005', '36061'), ('36047', '36081'),
('01073', ), ('06059', ), ('06037', ), ('06029', ),
('06071', ),
('06075', ), ('08031', ), ('24510', ), ('34039', ),
('34013', '34017'), ('36059', ), ('41051', ), ('41067', ),
('51840', ), ('51760', ), ('55079', ), ('54009', )])
],
[
19,
set([('34013', '34017', '34039'), ('11001', '51013'),
('36005', '36061'), ('36047', '36081'), ('01073', ),
('06059', ), ('06037', ), ('06029', ), ('06071', ),
('06075', ), ('08031', ), ('24510', ), ('36059', ),
('41051', ), ('41067', ), ('51840', ), ('51760', ),
('55079', ), ('54009', )])
],
[
18,
set([('34013', '34017', '34039'), ('11001', '51013'), ('01073', ),
('06059', ), ('06037', ), ('06029', ), ('06071', ),
('06075', ), ('08031', ), ('24510', ), ('36059', ),
('36005', '36047', '36061',
'36081'), ('41051', ), ('41067', ), ('51840', ), ('51760', ),
('55079', ), ('54009', )])
],
[
17,
set([('11001', '51013'), ('01073', ), ('06059', ), ('06037', ),
('06029', ), ('06071', ), ('06075', ), ('08031', ),
('24510', ), ('36059', ),
('34013', '34017', '34039', '36005', '36047', '36061',
'36081'), ('41051', ), ('41067', ), ('51840', ), ('51760', ),
('55079', ), ('54009', )])
],
[
16,
set([('11001', '51013'), ('01073', ), ('06059', ), ('06037', ),
('06029', ), ('06071', ), ('06075', ), ('08031', ),
('24510', ),
('34013', '34017', '34039', '36005', '36047', '36059',
'36061', '36081'), ('41051', ), ('41067', ), ('51840', ),
('51760', ), ('55079', ), ('54009', )])
],
[
15,
set([('11001', '51013'), ('01073', ), ('06059', ), ('06037', ),
('06029', ), ('06071', ), ('06075', ), ('08031', ),
('24510', ),
('34013', '34017', '34039', '36005', '36047', '36059',
'36061', '36081'), ('41051', '41067'), ('51840', ),
('51760', ), ('55079', ), ('54009', )])
],
[
14,
set([('01073', ), ('06059', ), ('06037', ), ('06029', ),
('06071', ), ('06075', ), ('08031', ),
('34013', '34017', '34039', '36005', '36047', '36059',
'36061', '36081'), ('41051', '41067'), ('51840', ),
('51760', ), ('55079', ), ('54009', ),
('11001', '24510', '51013')])
],
[
13,
set([('06037', '06059'), ('01073', ), ('06029', ), ('06071', ),
('06075', ), ('08031', ),
('34013', '34017', '34039', '36005', '36047', '36059',
'36061', '36081'), ('41051', '41067'), ('51840', ),
('51760', ), ('55079', ), ('54009', ),
('11001', '24510', '51013')])
],
[
12,
set([('06037', '06059'), ('01073', ), ('06029', ), ('06071', ),
('06075', ), ('08031', ),
('34013', '34017', '34039', '36005',
'36047', '36059', '36061', '36081'), ('41051', '41067'),
('51760', ), ('55079', ), ('54009', ),
('11001', '24510', '51013', '51840')])
],
[
11,
set([('06029', '06037', '06059'), ('01073', ), ('06071', ),
('06075', ), ('08031', ),
('34013', '34017', '34039', '36005',
'36047', '36059', '36061', '36081'), ('41051', '41067'),
('51760', ), ('55079', ), ('54009', ),
('11001', '24510', '51013', '51840')])
],
[
10,
set([('06029', '06037', '06059'), ('01073', ), ('06071', ),
('06075', ), ('08031', ),
('34013', '34017', '34039', '36005', '36047', '36059',
'36061', '36081'), ('41051', '41067'), ('55079', ),
('54009', ), ('11001', '24510', '51013', '51760', '51840')])
],
[
9,
set([('01073', ), ('06029', '06037', '06059', '06071'),
('06075', ), ('08031', ),
('34013', '34017', '34039', '36005', '36047', '36059',
'36061', '36081'), ('41051', '41067'), ('55079', ),
('54009', ), ('11001', '24510', '51013', '51760', '51840')])
],
[
8,
set([
('01073', ), ('06029', '06037', '06059', '06071'), ('06075', ),
('08031', ), ('41051', '41067'), ('55079', ), ('54009', ),
('11001', '24510', '34013', '34017', '34039', '36005', '36047',
'36059', '36061', '36081', '51013', '51760', '51840')
])
],
[
7,
set([
('01073', ), ('06029', '06037', '06059', '06071'), ('06075', ),
('08031', ), ('41051', '41067'), ('55079', ),
('11001', '24510', '34013', '34017', '34039', '36005', '36047',
'36059', '36061', '36081', '51013', '51760', '51840', '54009')
])
],
[
6,
set([
('06029', '06037', '06059', '06071', '06075'), ('01073', ),
('08031', ), ('41051', '41067'), ('55079', ),
('11001', '24510', '34013', '34017', '34039', '36005', '36047',
'36059', '36061', '36081', '51013', '51760', '51840', '54009')
])
],
[
5,
set([
('06029', '06037', '06059', '06071', '06075'), ('08031', ),
('41051', '41067'), ('01073', '55079'),
('11001', '24510', '34013', '34017', '34039', '36005', '36047',
'36059', '36061', '36081', '51013', '51760', '51840', '54009')
])
],
[
4,
set([('06029', '06037', '06059', '06071', '06075'),
('01073', '11001', '24510', '34013', '34017', '34039',
'36005', '36047', '36059', '36061', '36081', '51013',
'51760', '51840', '54009', '55079'), ('08031', ),
('41051', '41067')])
],
[
3,
set([('06029', '06037', '06059', '06071', '06075', '41051',
'41067'),
('01073', '11001', '24510', '34013', '34017', '34039',
'36005', '36047', '36059', '36061', '36081', '51013',
'51760', '51840', '54009', '55079'), ('08031', )])
],
[
2,
set([('01073', '11001', '24510', '34013', '34017', '34039',
'36005', '36047', '36059', '36061', '36081', '51013',
'51760', '51840', '54009', '55079'),
('06029', '06037', '06059', '06071', '06075', '08031',
'41051', '41067')])
],
]
suite = poc_simpletest.TestSuite()
for num_clusters, expected_county_tuple in hierdata_24:
# build initial list of clusters for each test since mutation is allowed
cluster_list = []
for idx in range(len(data_24_table)):
line = data_24_table[idx]
cluster_list.append(
alg_cluster.Cluster(set([line[0]]), line[1], line[2], line[3],
line[4]))
# compute student answer
student_clustering = student.hierarchical_clustering(
cluster_list, num_clusters)
student_county_tuple = set_of_county_tuples(student_clustering)
# Prepare test
error_message = "Testing hierarchical_clustering on 24 county table, num_clusters = " + str(
num_clusters)
error_message += "\nStudent county tuples: " + str(
student_county_tuple)
error_message += "\nExpected county tuples: " + str(
expected_county_tuple)
suite.run_test(student_county_tuple == expected_county_tuple, True,
error_message)
suite.report_results()