def compute_distortion(cluster_list):
""" takes a list of clusters and uses cluster_error to compute its distortion """
# use compute_distortion to compute the distortions of the two clusterings in questions 5 and 6
num_clusters = 9
num_iterations = 5
hier_list = []
kmeans_list = []
# load data table
data_table = load_data_table(DATA_111_URL)
hier_list = cpf.hierarchical_clustering(cluster_list, num_clusters)
hier_error = 0
for cluster in hier_list:
hier_error += cluster.cluster_error(data_table)
kmeans_list = cpf.kmeans_clustering(cluster_list, num_clusters,
num_iterations)
kmeans_error = 0
for cluster in kmeans_list:
kmeans_error += cluster.cluster_error(data_table)
print "\n\n\n\n-------- -------- Results -------- --------"
print "Number of clusters: %r" % num_clusters
print "hierarchical_clustering error: ", hier_error
print "kmeans_clustering error: ", kmeans_error
print "----- ----- ----- ----- ----- ----- -----"
return [hier_error, kmeans_error]
def compute_distortion(cluster_list, data_table, num_clusters):
""" takes a list of clusters and uses cluster_error to compute its distortion """
# use compute_distortion to compute the distortions of the two clusterings in questions 5 and 6
num_iterations = 5
hier_list = []
kmeans_list = []
# print "\n\nCluster list created. Passing list to hierarchical_clustering ..."
hier_list = cpf.hierarchical_clustering(cluster_list, num_clusters)
# print "Computing distortion on ", len(hier_list), "hierarchical clusters"
hier_error = 0
for cluster in hier_list:
hier_error += cluster.cluster_error(data_table)
# print "\n\nPassing list to kmeans_clustering ..."
kmeans_list = cpf.kmeans_clustering(cluster_list, num_clusters,
num_iterations)
# print "Computing distortion on ", len(kmeans_list), "k-means clusters"
kmeans_error = 0
for cluster in kmeans_list:
kmeans_error += cluster.cluster_error(data_table)
# print "\n\n\n\n-------- -------- Results -------- --------"
# print "Number of clusters: %r" % num_clusters
# print "hierarchical_clustering error: ", hier_error
# print "kmeans_clustering error: ", kmeans_error
# print "----- ----- ----- ----- ----- ----- -----"
return [hier_error, kmeans_error]
def run_question(number, data_set):
"""
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
"""
global DESKTOP
print "Loading data table ..."
data_table = load_data_table(data_set)
print "Data table loaded. Creating clusters ..."
singleton_list = []
# set correct number of clusters
if number in [2, 3]:
num_clusters = 15
elif number in [5, 6]:
num_clusters = 9
print "\nQuestion number: ", number
print "Number of clusters to be calculated: ", num_clusters
# parse data_table into cluster objects
for line in data_table:
singleton_list.append(
alg_cluster.Cluster(set([line[0]]), line[1], line[2], line[3],
line[4]))
print "\nCluster list created. Passing list to hierarchical_clustering ..."
# calculate clusters
if number == 0:
cluster_list = sequential_clustering(singleton_list, 15)
print "Displaying", len(cluster_list), "sequential clusters"
elif number in [2, 5]:
cluster_list = cpf.hierarchical_clustering(singleton_list,
num_clusters)
print "Displaying", len(cluster_list), "hierarchical clusters"
elif number in [3, 6]:
cluster_list = cpf.kmeans_clustering(singleton_list, num_clusters, 5)
print "Displaying", len(cluster_list), "k-means clusters"
else:
"Please pass a valid number to run_question. Valid options are 0, 2, 3, 5, or 6."
# 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 run_question(number, data_set):
"""
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
"""
global DESKTOP
print "Loading data table ..."
data_table = load_data_table(data_set)
print "Data table loaded. Creating clusters ..."
singleton_list = []
for line in data_table:
singleton_list.append(
alg_cluster.Cluster(set([line[0]]), line[1], line[2], line[3],
line[4]))
print "Cluster list created. Passing list to hierarchical_clustering ..."
if number == 0:
cluster_list = sequential_clustering(singleton_list, 15)
print "Displaying", len(cluster_list), "sequential clusters"
elif number in [2, 5]:
cluster_list = cpf.hierarchical_clustering(singleton_list, 9)
print "Displaying", len(cluster_list), "hierarchical clusters"
elif number in [3, 6]:
cluster_list = cpf.kmeans_clustering(singleton_list, 9, 5)
print "Displaying", len(cluster_list), "k-means clusters"
else:
"Please pass a valid number to run_question."
# 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 compute_distortion_plot_helper(cluster_list, data_table, num_clusters):
""" takes a list of clusters and uses cluster_error to compute its distortion """
# use compute_distortion to compute the distortions of the two clusterings in questions 5 and 6
num_iterations = 5
hier_list = []
kmeans_list = []
hier_list = cpf.hierarchical_clustering(cluster_list, num_clusters)
hier_error = 0
for cluster in hier_list:
hier_error += cluster.cluster_error(data_table)
kmeans_list = cpf.kmeans_clustering(cluster_list, num_clusters,
num_iterations)
kmeans_error = 0
for cluster in kmeans_list:
kmeans_error += cluster.cluster_error(data_table)
# print "\n\n\n\n-------- -------- Results -------- --------"
# print "Number of clusters: %r" % num_clusters
# print "hierarchical_clustering error: ", hier_error
# print "kmeans_clustering error: ", kmeans_error
# print "----- ----- ----- ----- ----- ----- -----"
return [hier_error, kmeans_error]
def test_kmeans():
"""
Test for k-means clustering
kmeans_clustering should not mutate cluster_list, but make a new copy of each test anyways
"""
# load small data table
print
print "\n\nTesting kmeans_clustering on 24 county set"
data_24_table = load_data_table(DATA_24_URL)
kmeansdata_24 = [[
15, 1,
set([('34017', '36061'), ('06037', ), ('06059', ), ('36047', ),
('36081', ), ('06071', '08031'), ('36059', ), ('36005', ),
('55079', ), ('34013', '34039'), ('06075', ), ('01073', ),
('06029', ), ('41051', '41067'),
('11001', '24510', '51013', '51760', '51840', '54009')])
],
[
15, 3,
set([('34017', '36061'), ('06037', '06059'),
('06071', ), ('36047', ), ('36081', ),
('08031', ), ('36059', ), ('36005', ),
('55079', ), ('34013', '34039'), ('06075', ),
('01073', ), ('06029', ), ('41051', '41067'),
('11001', '24510', '51013', '51760', '51840',
'54009')])
],
[
15, 5,
set([('34017', '36061'), ('06037', '06059'),
('06071', ), ('36047', ), ('36081', ),
('08031', ), ('36059', ), ('36005', ),
('55079', ), ('34013', '34039'), ('06075', ),
('01073', ), ('06029', ), ('41051', '41067'),
('11001', '24510', '51013', '51760', '51840',
'54009')])
],
[
10, 1,
set([('34017', '36061'), ('06029', '06037', '06075'),
('11001', '24510', '34013', '34039', '51013',
'51760', '51840', '54009'), ('06059', ),
('36047', ), ('36081', ),
('06071', '08031', '41051', '41067'),
('36059', ), ('36005', ), ('01073', '55079')])
],
[
10, 3,
set([('34013', '34017', '36061'),
('06029', '06037', '06075'),
('08031', '41051', '41067'), ('06059', '06071'),
('34039', '36047'), ('36081', ), ('36059', ),
('36005', ), ('01073', '55079'),
('11001', '24510', '51013', '51760', '51840',
'54009')])
],
[
10, 5,
set([('34013', '34017', '36061'),
('06029', '06037', '06075'),
('08031', '41051', '41067'), ('06059', '06071'),
('34039', '36047'), ('36081', ), ('36059', ),
('36005', ), ('01073', '55079'),
('11001', '24510', '51013', '51760', '51840',
'54009')])
],
[
5, 1,
set([('06029', '06037', '06075'),
('01073', '11001', '24510', '34013', '34017',
'34039', '36047', '51013', '51760', '51840',
'54009', '55079'), ('06059', ),
('36005', '36059', '36061', '36081'),
('06071', '08031', '41051', '41067')])
],
[
5, 3,
set([('06029', '06037', '06075'),
('11001', '24510', '34013', '34017', '34039',
'36005', '36047', '36059', '36061', '36081',
'51013'), ('08031', '41051', '41067'),
('06059', '06071'),
('01073', '51760', '51840', '54009', '55079')])
],
[
5, 5,
set([('06029', '06037', '06075'),
('08031', '41051', '41067'), ('06059', '06071'),
('01073', '55079'),
('11001', '24510', '34013', '34017', '34039',
'36005', '36047', '36059', '36061', '36081',
'51013', '51760', '51840', '54009')])
]]
suite = poc_simpletest.TestSuite()
for num_clusters, num_iterations, expected_county_tuple in kmeansdata_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.kmeans_clustering(cluster_list,
num_clusters,
num_iterations)
student_county_tuple = set_of_county_tuples(student_clustering)
# Prepare test
error_message = "\n\nTesting kmeans_custering on 24 county table, \nnum_clusters = " + str(
num_clusters)
error_message += " num_iterations = " + str(num_iterations)
error_message += "\n\nStudent county tuples: " + str(
student_county_tuple)
error_message += "\n\nExpected county tuples: " + str(
expected_county_tuple)
suite.run_test(student_county_tuple == expected_county_tuple, True,
error_message)
print "\n\n"
suite.report_results()