def main():
'''
Main function for PAM
'''
if len(sys.argv) != 3:
print('Error: invalid number of parameters')
return(1)
# Get the parameters
filePath = sys.argv[1]
k = int(sys.argv[2])
if debugEnabled == True:
print('filePath: ', filePath)
print('k: ', k)
# Run PAM for europe.txt
data = importData(filePath)
if debugEnabled == True:
for i in range(10):
print('data=', data[i])
# Add timing here
startTime = time.time()
best_cost, best_choice, best_medoids = kmedoids(data, k)
endTime = time.time()
print('best_time: ', endTime - startTime)
print('best_cost: ', best_cost)
print('best_choice: ', best_choice)
print('best_medoids: ', best_medoids)
def main():
'''
Main function for PAM
'''
if len(sys.argv) != 3:
print('Error: invalid number of parameters')
return (1)
# Get the parameters
filePath = sys.argv[1]
k = int(sys.argv[2])
if debugEnabled == True:
print('filePath: ', filePath)
print('k: ', k)
# Run PAM for europe.txt
data = importData(filePath)
if debugEnabled == True:
number = len(data)
if number > 10:
number = 10
for i in range(number):
print('data=', data[i])
# Add timing here
startTime = time.time()
best_cost, best_choice, best_medoids = kmedoids(data, k)
endTime = time.time()
print('best_time: ', endTime - startTime)
print('best_cost: ', best_cost)
print('best_choice: ', best_choice)
print('best_medoids: ', best_medoids)
def main():
'''
Main function for Parallele PAM
'''
if len(sys.argv) != 4:
print('Error: invalid number of parameters')
return (1)
# Get the parameters
filePath = sys.argv[1]
k = int(sys.argv[2])
t = int(sys.argv[3])
if debugEnabled == True:
print('filePath: ', filePath)
print('k: ', k)
print('t: ', t)
# Run PAM for europe.txt
data = util.importData(filePath)
if debugEnabled == True:
for i in range(10):
print('data=', data[i])
# Check the timing
startTime = time.time()
best_cost, best_choice, best_medoids = kmedoids_parallel(data, k, t)
endTime = time.time()
print('best_time: ', endTime - startTime)
print('best_cost: ', best_cost)
print('best_choice: ', best_choice)
print('best_medoids: ', best_medoids)
def main():
'''
Main function for Clara
'''
print "sys.argv: ", sys.argv
print "len(sys.argv): ", len(sys.argv)
#if len(sys.argv) not in [4,6,7,8]:
# print('Error: invalid number of parameters. Your parameters should be: \n path_to_node_names k cost_type [path_to_distance_matrix [path_to_similarity_matrix] path_to_edge_matrix_(affinity)]')
# return(1)
if len(sys.argv) == 4 and sys.argv[3] == 3:
print('Error: cost based on distance/similarity matrix without the matrix specified')
return(1)
# Get the parameters
try:
splittedSim = sys.argv[8]
except:
splittedSim = False
print "no value for splittedSim parameter specified, so it is set to False"
filePath = sys.argv[1]
k = int(sys.argv[2])
COST = int(sys.argv[3])
if len(sys.argv) >= 6:
if COST == 3:
distDictPath = sys.argv[4]
affinityPath = sys.argv[5]
print "distDictPath: ", distDictPath
print "affinityPath: ", affinityPath
elif COST == 4:
distDictPath = sys.argv[4]
simDictPath = sys.argv[5]
affinityPath = sys.argv[6]
try:
acceleration = int(sys.argv[7])
print "acceleration degree: ", acceleration
except: acceleration = 0
print "distDictPath: ", distDictPath
print "simDictPath: ", simDictPath
print "affinityPath: ", affinityPath
else:
print "Error: I dunno if you pass 'similarity' (COST=4) or you pass 'distance matrix' (COST=3)"
return(1)
#affinityPath = sys.argv[5]
if debugEnabled == True:
print 'filePath: ', filePath
print 'k: ', k
print "Cost Function: ", COST
# Run Clara
if COST in [3,4]:
affinitiesOur = importData(affinityPath, ifjson=1)
print "\n affinities imported"
data = importData(filePath, ifjson=1)
distDictOur = importData(distDictPath, ifjson=1)
#distDictOur = None
print "\n pairwise distances imported"
if COST == 4:
#simDictOur = importData(simDictPath, ifjson=1)
if not splittedSim:
simDictOur = {}
with open(simDictPath, 'r') as S:
for line in S:
simDictOur.update(json.loads(line))
else:
simDictOur = load_splitted_sim(simDictPath, coresLoaded)
print "\n pairwise similarities imported\n"
else:
data = importData(filePath)
if debugEnabled == True:
for i in range(10):
print('example_data=', data[i])
# Add timing
startTime = time.time()
if COST == 3:
best_cost, best_choice, best_medoids, cost_list, isolates = clara(
data, k, COST, distDictClara=distDictOur, simDictClara={},
affinities=affinitiesOur, saveAllResults=False, acceleration=acceleration,
take_all_nodes=takeAllNodes)
elif COST == 4:
best_cost, best_choice, best_medoids, cost_list, isolates = clara(
data, k, COST, distDictClara=distDictOur, simDictClara=simDictOur,
affinities=affinitiesOur, saveAllResults=False, acceleration=acceleration,
take_all_nodes=takeAllNodes)
else:
best_cost, best_choice, best_medoids, cost_list, isolates = clara(
data, k, COST, saveAllResults=False,
take_all_nodes=takeAllNodes)
endTime = time.time()
mod = None
harmonic_centrality = None
'''
#don't delete!!!!!!!!!!!!~~~~~
# Compute modularity and display it
startMod = time.time()
if COST in [3,4]:
mod = modularity(data, COST=COST, distDict=distDictOur,
edgeDict=affinitiesOur, medoids=best_medoids)
else:
print "no modularity for this regime, sorry"
pass
endMod = time.time()
# comment up to this ~~~~~~~~~~
startCentr = time.time()
harmonic_centrality = intra_cluster_centrality(data, COST=COST,
distDict=distDictOur, medoids=best_medoids)
endCentr = time.time()
'''
# Save the result
fordump = [best_cost, best_choice, best_medoids, mod, harmonic_centrality, isolates]
json_filename = "clara_json_version_" + str(int(time.time())/10)[3:] + "_one" #time.strftime("%d %b %Y %H:%M:%S", time.gmtime())
json.dump(fordump, open(json_filename, "w"))
# Print the result
diff = (endTime - startTime)
best_cluster_length = []
for i in best_choice:
best_cluster_length.append(len(best_medoids[i]))
print '\n\n'
print 'best_time: ', diff
#print 'best_modularity_time: ', endMod - startMod
#print 'best_centrality_time: ', endCentr - startCentr
#print 'best_modularity: ', mod
print 'best_cost: ', best_cost
print 'best_choice: ', best_choice
print 'best_cluster_lengths: ', best_cluster_length
print 'clustered_nodes: ', sum(best_cluster_length)
#print 'all_nodes: ', len(data)
print 'isolates: ', len(isolates), isolates
print '\n\n'
print 'best_medoids: ', best_medoids
print '\n\n'
def main():
'''
Main function for Clara
'''
print "sys.argv: ", sys.argv
print "len(sys.argv): ", len(sys.argv)
#if len(sys.argv) not in [4,6,7,8]:
# print('Error: invalid number of parameters. Your parameters should be: \n path_to_node_names k cost_type [path_to_distance_matrix [path_to_similarity_matrix] path_to_edge_matrix_(affinity)]')
# return(1)
if len(sys.argv) == 4 and sys.argv[3] == 3:
print(
'Error: cost based on distance/similarity matrix without the matrix specified'
)
return (1)
# Get the parameters
try:
splittedSim = sys.argv[8]
except:
splittedSim = False
print "no value for splittedSim parameter specified, so it is set to False"
filePath = sys.argv[1]
k = int(sys.argv[2])
COST = int(sys.argv[3])
if len(sys.argv) >= 6:
if COST == 3:
distDictPath = sys.argv[4]
affinityPath = sys.argv[5]
print "distDictPath: ", distDictPath
print "affinityPath: ", affinityPath
elif COST == 4:
distDictPath = sys.argv[4]
simDictPath = sys.argv[5]
affinityPath = sys.argv[6]
try:
acceleration = int(sys.argv[7])
print "acceleration degree: ", acceleration
except:
acceleration = 0
print "distDictPath: ", distDictPath
print "simDictPath: ", simDictPath
print "affinityPath: ", affinityPath
else:
print "Error: I dunno if you pass 'similarity' (COST=4) or you pass 'distance matrix' (COST=3)"
return (1)
#affinityPath = sys.argv[5]
if debugEnabled == True:
print 'filePath: ', filePath
print 'k: ', k
print "Cost Function: ", COST
# Run Clara
if COST in [3, 4]:
affinitiesOur = importData(affinityPath, ifjson=1)
print "\n affinities imported"
data = importData(filePath, ifjson=1)
distDictOur = importData(distDictPath, ifjson=1)
#distDictOur = None
print "\n pairwise distances imported"
if COST == 4:
#simDictOur = importData(simDictPath, ifjson=1)
if not splittedSim:
simDictOur = {}
with open(simDictPath, 'r') as S:
for line in S:
simDictOur.update(json.loads(line))
else:
simDictOur = load_splitted_sim(simDictPath, coresLoaded)
print "\n pairwise similarities imported\n"
else:
data = importData(filePath)
if debugEnabled == True:
for i in range(10):
print('example_data=', data[i])
# Add timing
startTime = time.time()
if COST == 3:
best_cost, best_choice, best_medoids, cost_list, isolates = clara(
data,
k,
COST,
distDictClara=distDictOur,
simDictClara={},
affinities=affinitiesOur,
saveAllResults=False,
acceleration=acceleration,
take_all_nodes=takeAllNodes)
elif COST == 4:
best_cost, best_choice, best_medoids, cost_list, isolates = clara(
data,
k,
COST,
distDictClara=distDictOur,
simDictClara=simDictOur,
affinities=affinitiesOur,
saveAllResults=False,
acceleration=acceleration,
take_all_nodes=takeAllNodes)
else:
best_cost, best_choice, best_medoids, cost_list, isolates = clara(
data, k, COST, saveAllResults=False, take_all_nodes=takeAllNodes)
endTime = time.time()
mod = None
harmonic_centrality = None
'''
#don't delete!!!!!!!!!!!!~~~~~
# Compute modularity and display it
startMod = time.time()
if COST in [3,4]:
mod = modularity(data, COST=COST, distDict=distDictOur,
edgeDict=affinitiesOur, medoids=best_medoids)
else:
print "no modularity for this regime, sorry"
pass
endMod = time.time()
# comment up to this ~~~~~~~~~~
startCentr = time.time()
harmonic_centrality = intra_cluster_centrality(data, COST=COST,
distDict=distDictOur, medoids=best_medoids)
endCentr = time.time()
'''
# Save the result
fordump = [
best_cost, best_choice, best_medoids, mod, harmonic_centrality,
isolates
]
json_filename = "clara_json_version_" + str(
int(time.time()) /
10)[3:] + "_one" #time.strftime("%d %b %Y %H:%M:%S", time.gmtime())
json.dump(fordump, open(json_filename, "w"))
# Print the result
diff = (endTime - startTime)
best_cluster_length = []
for i in best_choice:
best_cluster_length.append(len(best_medoids[i]))
print '\n\n'
print 'best_time: ', diff
#print 'best_modularity_time: ', endMod - startMod
#print 'best_centrality_time: ', endCentr - startCentr
#print 'best_modularity: ', mod
print 'best_cost: ', best_cost
print 'best_choice: ', best_choice
print 'best_cluster_lengths: ', best_cluster_length
print 'clustered_nodes: ', sum(best_cluster_length)
#print 'all_nodes: ', len(data)
print 'isolates: ', len(isolates), isolates
print '\n\n'
print 'best_medoids: ', best_medoids
print '\n\n'
def main():
'''
Main function for PAM
'''
print sys.argv
print len(sys.argv)
if len(sys.argv) == 4 and sys.argv[3] == 3:
print 'Error: cost based on distance matrix without distance matrix specified'
return (1)
if len(sys.argv) not in [4, 5]:
print 'Error: invalid number of parameters. Your parameters should be: \n path_to_node_names k cost_type [pairwise_matrix_(distance_or_similarity)]'
return (1)
# Get the parameters
filePath = sys.argv[1]
k = int(sys.argv[2])
COST = int(sys.argv[3]
) # here it's obligatory, but in kmedoids(), optional. FIX IT
if len(sys.argv) == 5:
if COST == 4: # k-medoids based on similarity matrix (e.g Jaccard score)
simDictPath = sys.argv[4]
print simDictPath
elif COST == 3: # k-medoids based on distance matrix (e.g. Average shortest path)
distDictPath = sys.argv[4]
print distDictPath # distDictPath is not a file - it's a path to file (string)
else:
print "Error: I dunno whether you pass affinities to compute 'similarity' (COST=4) or you pass 'distance matrix' (COST=3)"
return (1)
if debugEnabled == True:
print('filePath: ', filePath)
print('k: ', k)
print('cost function number: ', COST
) # better yet, display the name, not number FIX IT
# Run PAM for europe.txt
distDictOur = {}
simDictOur = {}
if COST == 3:
data = importData(
filePath, ifjson=1
) # actually, ifjson=1 is not tantamount to direct distance method FIX IT
distDictOur = importData(distDictPath, ifjson=1)
# print "distance: ", distDict.items()[0]
elif COST == 4:
data = importData(filePath, ifjson=1)
simDictOur = importData(simDictPath, ifjson=1)
print "pairwise similarities imported"
else:
data = importData(filePath)
if debugEnabled:
for i in range(10):
print('data=', data[i])
# Add timing here
startTime = time.time()
if COST not in [3, 4]:
best_cost, best_choice, best_medoids = kmedoids(data, k, COST)
elif COST == 3:
best_cost, best_choice, best_medoids = kmedoids(data,
k,
COST,
distDictOur,
simDictKM={})
elif COST == 4:
best_cost, best_choice, best_medoids = kmedoids(data,
k,
COST,
distDictKM={},
simDictKM=simDictOur)
endTime = time.time()
# Saving the result into new file
fordump = [best_cost, best_choice, best_medoids]
json_filename = "pam_json_version " + str(
int(time.time()) / 10)[3:] # find normal time format FIX IT
json.dump(fordump, open(json_filename, "w"))
best_cluster_length = []
for i in best_choice:
try:
best_cluster_length.append(len(best_medoids[i]))
except KeyError:
best_cluster_length.append("KeyError ;)")
print 'best_time: ', (endTime - startTime)
print 'best_cost: ', best_cost
print 'best_choice: \n', best_choice
print 'best_cluster_lengths: \n', best_cluster_length
print 'best_cluster_contents: \n', best_medoids
def main():
# parse arguments
args = read_arguments()
data = []
affinities = {}
simDict = {}
if args.distDict != None:
distDict = importData(args.distDict, ifjson=1)
print "\n pairwise distances imported"
else:
distDict = {}
if not args.loadedCores:
data = importData(args.data, ifjson=1)
affinities = importData(args.edgeDict, ifjson=1)
print "\n affinities imported"
simDict = importData(args.simDict, ifjson=1)
print "\n pairwise similarities imported\n"
#~~~~~~~~~~~~ method for finding the optimal K ~~~~~~~~~~~#
if args.method == 'kopt':
if args.trials_decay: trials_decay = True
else: trials_decay = False
if args.KlastSeq:
if args.Kupper:
Klist = range(1, args.KlastSeq + 1) + args.Kupper
else:
Klist = range(1, args.KlastSeq + 1)
Klist.append(args.Kmax)
else:
Klist = range(1, args.Kmax + 1)
startTime = time.time()
Kopt, mod_lists, modStdev, modMean, modMax = ModularityProfile(
data,
Kmin=args.Kmin,
Kmax=args.Kmax,
Klist=Klist,
edgeDict=affinities,
simDict=simDict,
bagSize=args.bagSize,
trials=args.trials,
trials_decay=trials_decay)
endTime = time.time()
fordump = [Kopt, mod_lists, modStdev, modMean, modMax]
json_filename = "kopt_json_version_" + Version
json.dump(fordump, open(json_filename, "w"))
print '\n\n'
print 'saved as: ', json_filename
print 'time: ', endTime - startTime
print 'optimal K: ', Kopt
print 'modMax: ', modMax
for i in xrange(len(Klist)):
print "mod_list for k=%d is: %s" % (Klist[i], mod_lists[i])
print '\n\n'
#~~~~~~~~~~~ method for averaging clustering results ~~~~~~~~~~~#
if args.method == "cores":
print "gotcha"
result = SGJRIcores(data=data,
K=args.K,
edgeDict=affinities,
simDict=simDict,
distDict=distDict,
bagSize=args.bagSize,
trials=args.trials,
threshold=args.threshold,
loadedCores=args.loadedCores,
filepathCores=args.filepathCores,
dendroFormat=args.dendroFormat,
acceleration=int(args.acceleration))
if 0 == 0: #args.distDict != None:
clustData, commonCluster, treeStruct, segmentDict, harmCentr = result[:
-1]
mongo = result[-1]
if args.saveCommonClusters:
fordump = [
clustData, commonCluster, treeStruct, segmentDict,
harmCentr, mongo
]
else:
fordump = [
clustData, ['commonCluster_placeholder'], treeStruct,
segmentDict, harmCentr, mongo
]
else:
clustData, commonCluster, treeStruct, segmentDict = result
fordump = [clustData, commonCluster, treeStruct, segmentDict]
pkl_filename = "cores_pickled_version_" + Version
with open(pkl_filename, 'w') as f:
pickle.dump(fordump, f, pickle.HIGHEST_PROTOCOL)
print "Saved as: ", pkl_filename
def main():
'''
Main function for PAM
'''
print sys.argv
print len(sys.argv)
if len(sys.argv) == 4 and sys.argv[3] == 3:
print 'Error: cost based on distance matrix without distance matrix specified'
return (1)
if len(sys.argv) not in [4,5]:
print 'Error: invalid number of parameters. Your parameters should be: \n path_to_node_names k cost_type [pairwise_matrix_(distance_or_similarity)]'
return (1)
# Get the parameters
filePath = sys.argv[1]
k = int(sys.argv[2])
COST = int(sys.argv[3]) # here it's obligatory, but in kmedoids(), optional. FIX IT
if len(sys.argv) == 5:
if COST == 4: # k-medoids based on similarity matrix (e.g Jaccard score)
simDictPath = sys.argv[4]
print simDictPath
elif COST == 3: # k-medoids based on distance matrix (e.g. Average shortest path)
distDictPath = sys.argv[4]
print distDictPath # distDictPath is not a file - it's a path to file (string)
else:
print "Error: I dunno whether you pass affinities to compute 'similarity' (COST=4) or you pass 'distance matrix' (COST=3)"
return(1)
if debugEnabled == True:
print('filePath: ', filePath)
print('k: ', k)
print('cost function number: ', COST) # better yet, display the name, not number FIX IT
# Run PAM for europe.txt
distDictOur = {}
simDictOur = {}
if COST == 3:
data = importData(filePath, ifjson=1) # actually, ifjson=1 is not tantamount to direct distance method FIX IT
distDictOur = importData(distDictPath, ifjson=1)
# print "distance: ", distDict.items()[0]
elif COST == 4:
data = importData(filePath, ifjson=1)
simDictOur = importData(simDictPath, ifjson=1)
print "pairwise similarities imported"
else:
data = importData(filePath)
if debugEnabled:
for i in range(10):
print('data=', data[i])
# Add timing here
startTime = time.time()
if COST not in [3,4]:
best_cost, best_choice, best_medoids = kmedoids(data, k, COST)
elif COST == 3:
best_cost, best_choice, best_medoids = kmedoids(data, k, COST, distDictOur, simDictKM={})
elif COST == 4:
best_cost, best_choice, best_medoids = kmedoids(data, k, COST, distDictKM={}, simDictKM=simDictOur)
endTime = time.time()
# Saving the result into new file
fordump = [best_cost, best_choice, best_medoids]
json_filename = "pam_json_version " + str(int(time.time())/10)[3:] # find normal time format FIX IT
json.dump(fordump, open(json_filename, "w"))
best_cluster_length = []
for i in best_choice:
try:
best_cluster_length.append(len(best_medoids[i]))
except KeyError:
best_cluster_length.append("KeyError ;)")
print 'best_time: ', (endTime - startTime)
print 'best_cost: ', best_cost
print 'best_choice: \n', best_choice
print 'best_cluster_lengths: \n', best_cluster_length
print 'best_cluster_contents: \n', best_medoids
from util import importData
from util import CLASS_MAP
import pandas as pd
# Datasets for machine learning tasks
#---------------------------------------------------------------------------------------------------------------------
#Import and process raw data
df = importData("data-raw/winemag-data_first150k.csv",
censor=True,
filter=True,
processDescriptions=True)
#Keep only necessary columns
df_keep = df[['index', 'color', 'class', 'description']]
#Parameters to subset data
num_test = 20000
num_dev = 20000
num_train = df_keep.shape[0] - num_test - num_dev
assert num_train > 0
#Subset Data
data = df_keep.sample(frac=1, replace=False,
random_state=1415926).reset_index(drop=True)
data_test = data.iloc[0:num_test]
data_dev = data.iloc[num_test:(num_test + num_dev)]
data_train = data.iloc[(num_test + num_dev):(num_test + num_dev + num_train)]
#Save to disk
data_test.to_csv('data-processed/data.test', index=False)
