def question_one():
"""
Function for answering first question
"""
xvals = range(2, 200)
slow_yvals = []
fast_yvals = []
for num in xvals:
cluster_list = gen_random_clusters(num)
initial = time.time()
answer = prj3.slow_closest_pairs(cluster_list)
final = time.time()
slow_yvals.append(final - initial)
for num in xvals:
cluster_list = gen_random_clusters(num)
initial = time.time()
answer = prj3.fast_closest_pair(cluster_list)
final = time.time()
fast_yvals.append(final - initial)
slow_line = plt.plot(xvals, slow_yvals, color='r', label="Slow Closest Pair")
fast_line = plt.plot(xvals, fast_yvals, color='b', label="Fast Closest Pair")
plt.legend(loc=2)
plt.title("Efficiency of Slow and Fast Closest Pairs Algorithms")
plt.xlabel("Number of Clusters")
plt.ylabel("Run Times in Milliseconds")
plt.show()
def test_clustering(data_set, method):
'''
Test the distortion of a data_set under clustering method.
Input: a tuple (data_table, cluster_list) and a clustering algorithm
Output: a list of distortion values
'''
# number of clusters to form
num_cluster_list = np.arange(6, 21, 1)
# init list to store distortion
distortion_list = []
# Loop over cluster sizes list
for num_cluster in num_cluster_list:
# make a copy of the cluster_list
cluster_list = [cluster.copy() for cluster in data_set[1]]
if method == 'K-Means':
# perfrom kmeans
cluster_list = project.kmeans_clustering(cluster_list, num_cluster,
10)
else:
# perform hier clustering
project.hierarchical_clustering(cluster_list, num_cluster)
# calculate and save distortion
distortion_list.append(
compute_distortion_data_set(data_set[0], cluster_list))
return distortion_list
def compute_running_times():
'''
Computes the running time for slow_closest_pair() and fast_closest_pair()
for clusters of size 2 to 200.
Input: NONE
Output: a pandas df
'''
# Disables garbage collection
gc.disable()
# Init cluster sizes
num_clusters_list = np.arange(2, 201, 1)
# Init list variables for storing run times
slow_runtimes = []
fast_runtimes = []
# loop over cluster sizes
for num_clusters in num_clusters_list:
# Generate a cluster_list of size num_clusters
clusters_list = gen_random_clusters(num_clusters)
# Sort the cluster_list
clusters_list.sort(key=lambda cluster: cluster.horiz_center())
# Gets current time in seconds
start_time = time.clock()
# Perform slow clustering
dummy_result = project.slow_closest_pair(clusters_list)
# Calculate runtime and store value
slow_runtimes.append(time.clock() - start_time)
# Gets current time in seconds
start_time = time.clock()
# Perform fast clustering
dummy_result = project.fast_closest_pair(clusters_list)
# Calculate runtime and store value
fast_runtimes.append(time.clock() - start_time)
# create df from results
return pd.DataFrame(
dict({
'slow': slow_runtimes,
'fast': fast_runtimes,
'num clusters': num_clusters_list
}))
def clustering(algo_used, num_clusters, num_iter = 5):
"""
Uses specified algorithm to cluster data
input: int for specified algorithm, data_table
output: cluster_list
"""
singleton_list = []
for line in data_table:
singleton_list.append(alg_cluster.Cluster(set([line[0]]), line[1], line[2], line[3], line[4]))
if algo_used == 1:
cluster_list = sequential_clustering(singleton_list, num_clusters)
print "Displaying", len(cluster_list), "sequential clusters"
elif algo_used == 2:
cluster_list = prj3.hierarchical_clustering(singleton_list, num_clusters)
print "Displaying", len(cluster_list), "hierarchical clusters"
elif algo_used == 3:
cluster_list = prj3.kmeans_clustering(singleton_list, num_clusters, num_iter)
print "Displaying", len(cluster_list), "k-means clusters"
return cluster_list
def cluster_data():
'''
Load a data table, compute a list of clusters and
Output: a tuple of two list of clusters (hierarchical, kmeans)
'''
DIRECTORY = "http://commondatastorage.googleapis.com/codeskulptor-assets/"
DATA_111_URL = DIRECTORY + "data_clustering/unifiedCancerData_111.csv"
DATA_290_URL = DIRECTORY + "data_clustering/unifiedCancerData_290.csv"
data_table = viz.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]))
singleton_list_copy = [cluster.copy() for cluster in singleton_list]
return (project.hierarchical_clustering(singleton_list, 9),
project.kmeans_clustering(singleton_list_copy, 9, 5))
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]))
def compute_distortion(cluster_list):
distortion = 0
for clus in cluster_list:
error = clus.cluster_error(data_table)
distortion += error
return distortion
hierarchical_distortion = []
kmeans_distortion = []
# cluster_list = sequential_clustering(singleton_list, 15)
# print "Displaying", len(cluster_list), "sequential clusters"
for num_final_clusters in range(6, 21):
hierarchical_cluster_list = alg_project3_solution.hierarchical_clustering(singleton_list, num_final_clusters)
print "Displaying", len(hierarchical_cluster_list), "hierarchical clusters"
hierarchical_distortion.append(compute_distortion(hierarchical_cluster_list))
kmeans_cluster_list = alg_project3_solution.kmeans_clustering(singleton_list, num_final_clusters, 5)
print "Displaying", len(kmeans_cluster_list), "k-means clusters"
kmeans_distortion.append(compute_distortion(kmeans_cluster_list))
plt.plot(range(6, 21), hierarchical_distortion, 'g', lw = 2, label = "hierarchical distortion")
plt.plot(range(6, 21), kmeans_distortion, 'r', lw = 2, label = "kmeans distortion")
plt.legend(loc = 'upper left')
plt.xlabel('Number of final clusters')
plt.xlabel('Number of final clusters')
plt.ylabel('Distortion')
plt.title('Comparison of distortion between two clustering methods \n based on 111 county data set')
plt.grid()
plt.savefig('Comparison of distortion (111)')
def plot_Q6():
"""
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
"""
DIRECTORY = "http://commondatastorage.googleapis.com/codeskulptor-assets/"
DATA_111_URL = DIRECTORY + "data_clustering/unifiedCancerData_111.csv"
data_table = viz.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 = project.kmeans_clustering(singleton_list, 9, 5)
print "Displaying", len(cluster_list), "hierarchical clusters"
# draw the clusters using matplotlib
alg_clusters_matplotlib.plot_clusters(data_table, cluster_list, False)
Divide and Conquer Method and Clustering
Closest Pairs and Clustering Algorithms
Test File
"""
import alg_cluster
import Project_3 as prj3
slow_dist = True
fast_dist = True
if slow_dist == True:
print "-------Testing Slow closest pairs first-------"
print "\nTest 1.."
print prj3.slow_closest_pairs([alg_cluster.Cluster(set([]), 0, 0, 1, 0), alg_cluster.Cluster(set([]), 1, 0, 1, 0)])
print "Expected: set([(1.0, 0, 1)])"
print "\nTest 2.."
print prj3.slow_closest_pairs([alg_cluster.Cluster(set([]), 0, 0, 1, 0), alg_cluster.Cluster(set([]), 0, 1, 1, 0), alg_cluster.Cluster(set([]), 0, 2, 1, 0)])
print "Expected: set([(1.0, 0, 1), (1.0, 1, 2)])"
if fast_dist == True:
print "\n" + "-------Testing fast closest pairs-------"
print "\nTest 1..."
print prj3.fast_closest_pair([alg_cluster.Cluster(set([]), 0, 0, 1, 0), alg_cluster.Cluster(set([]), 1, 0, 1, 0)])
print "\nTest 2..."
print prj3.fast_closest_pair([alg_cluster.Cluster(set([]), 0, 0, 1, 0), alg_cluster.Cluster(set([]), 1, 0, 1, 0), alg_cluster.Cluster(set([]), 2, 0, 1, 0), alg_cluster.Cluster(set([]), 3, 0, 1, 0), alg_cluster.Cluster(set([]), 4, 0, 1, 0), alg_cluster.Cluster(set([]), 5, 0, 1, 0), alg_cluster.Cluster(set([]), 6, 0, 1, 0), alg_cluster.Cluster(set([]), 7, 0, 1, 0), alg_cluster.Cluster(set([]), 8, 0, 1, 0), alg_cluster.Cluster(set([]), 9, 0, 1, 0), alg_cluster.Cluster(set([]), 10, 0, 1, 0), alg_cluster.Cluster(set([]), 11, 0, 1, 0), alg_cluster.Cluster(set([]), 12, 0, 1, 0), alg_cluster.Cluster(set([]), 13, 0, 1, 0), alg_cluster.Cluster(set([]), 14, 0, 1, 0), alg_cluster.Cluster(set([]), 15, 0, 1, 0), alg_cluster.Cluster(set([]), 16, 0, 1, 0), alg_cluster.Cluster(set([]), 17, 0, 1, 0), alg_cluster.Cluster(set([]), 18, 0, 1, 0), alg_cluster.Cluster(set([]), 19, 0, 1, 0)])
print "Expected: one of the tuples in set([(1.0, 9, 10), (1.0, 2, 3), (1.0, 15, 16), (1.0, 11, 12), (1.0, 13, 14), (1.0, 16, 17), (1.0, 14, 15), (1.0, 12, 13), (1.0, 4, 5), (1.0, 18, 19), (1.0, 3, 4), (1.0, 8, 9), (1.0, 17, 18), (1.0, 6, 7), (1.0, 7, 8), (1.0, 5, 6), (1.0, 10, 11), (1.0, 0, 1), (1.0, 1, 2)])"
print "\nTest 3..."
print prj3.fast_closest_pair([alg_cluster.Cluster(set([]), 90.9548590217, -17.089022585, 1, 0), alg_cluster.Cluster(set([]), 90.2536656675, -70.5911544718, 1, 0), alg_cluster.Cluster(set([]), -57.5872347006, 99.7124028905, 1, 0), alg_cluster.Cluster(set([]), -15.9338519877, 5.91547495626, 1, 0), alg_cluster.Cluster(set([]), 19.1869055492, -28.0681513017, 1, 0), alg_cluster.Cluster(set([]), -23.0752410653, -42.1353490324, 1, 0), alg_cluster.Cluster(set([]), -65.1732261872, 19.675582646, 1, 0), alg_cluster.Cluster(set([]), 99.7789872101, -11.2619165604, 1, 0), alg_cluster.Cluster(set([]), -43.3699854405, -94.7349852817, 1, 0), alg_cluster.Cluster(set([]), 48.2281912402, -53.3441788034, 1, 0)])
print "Expected: one of the tuples in set([(10.5745166749, 0, 7)])"
