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Apr 5, 2018
Merged

switch to python 3 #27

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0b9ec5e
switching to python3
scoutsaachi Nov 14, 2017
e7dcd29
Merge remote-tracking branch 'upstream/master'
scoutsaachi Mar 2, 2018
1aedeab
Merge remote-tracking branch 'upstream/master'
scoutsaachi Mar 8, 2018
bc7ce09
lasting git commit issues
scoutsaachi Mar 22, 2018
cf92833
Merge branch 'master' of https://github.com/davidhallac/TICC
scoutsaachi Apr 5, 2018
ebf6238
change to python3
scoutsaachi Apr 5, 2018
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83 changes: 42 additions & 41 deletions TICC_solver.py
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Original file line number Diff line number Diff line change
@@ -1,6 +1,5 @@
import numpy as np
import math, time, collections, os, errno, sys, code, random
import __builtin__ as bt
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
Expand Down Expand Up @@ -45,15 +44,15 @@ def solve(window_size=10, number_of_clusters=5, lambda_parameter=11e-2,
lam_sparse = lambda_parameter # sparsity parameter
num_clusters = number_of_clusters # Number of clusters
cluster_reassignment = 20 # number of points to reassign to a 0 cluster
print "lam_sparse", lam_sparse
print "switch_penalty", switch_penalty
print "num_cluster", num_clusters
print "num stacked", num_stacked
print("lam_sparse", lam_sparse)
print("switch_penalty", switch_penalty)
print("num_cluster", num_clusters)
print("num stacked", num_stacked)

######### Get Data into proper format
Data = np.loadtxt(input_file, delimiter= ",")
(m,n) = Data.shape # m: num of observations, n: size of observation vector
print "completed getting the data"
print("completed getting the data")

############
##The basic folder to be created
Expand All @@ -68,12 +67,12 @@ def solve(window_size=10, number_of_clusters=5, lambda_parameter=11e-2,
###-------INITIALIZATION----------
# Train test split
training_indices = getTrainTestSplit(m, num_blocks, num_stacked) #indices of the training samples
num_train_points = bt.len(training_indices)
num_train_points = len(training_indices)
num_test_points = m - num_train_points
##Stack the training data
complete_D_train = np.zeros([num_train_points, num_stacked*n])
for i in xrange(num_train_points):
for k in xrange(num_stacked):
for i in range(num_train_points):
for k in range(num_stacked):
if i+k < num_train_points:
idx_k = training_indices[i+k]
complete_D_train[i][k*n:(k+1)*n] = Data[idx_k][0:n]
Expand All @@ -100,25 +99,26 @@ def solve(window_size=10, number_of_clusters=5, lambda_parameter=11e-2,

# PERFORM TRAINING ITERATIONS
pool=Pool(processes=num_proc)
for iters in xrange(maxIters):
print "\n\n\nITERATION ###", iters
for iters in range(maxIters):
print("\n\n\nITERATION ###", iters)
##Get the train and test points
train_clusters = collections.defaultdict(list) # {cluster: [point indices]}
for point, cluster in enumerate(clustered_points):
train_clusters[cluster].append(point)

len_train_clusters = {k: len(train_clusters[k]) for k in xrange(num_clusters)}
len_train_clusters = {k: len(train_clusters[k]) for k in range(num_clusters)}

# train_clusters holds the indices in complete_D_train
# for each of the clusters
optRes = [None for i in xrange(num_clusters)]
for cluster in xrange(num_clusters):
optRes = [None for i in range(num_clusters)]
clusterValues = []
for cluster in range(num_clusters):
cluster_length = len_train_clusters[cluster]
if cluster_length != 0:
size_blocks = n
indices = train_clusters[cluster]
D_train = np.zeros([cluster_length,num_stacked*n])
for i in xrange(cluster_length):
for i in range(cluster_length):
point = indices[i]
D_train[i,:] = complete_D_train[point,:]

Expand All @@ -132,15 +132,16 @@ def solve(window_size=10, number_of_clusters=5, lambda_parameter=11e-2,

rho = 1
solver = ADMMSolver(lamb, num_stacked, size_blocks, 1, S)
clusterValues.append(solver(1000, 1e-6, 1e-6, False))
# apply to process pool
optRes[cluster] = pool.apply_async(solver, (1000, 1e-6, 1e-6, False,))


for cluster in xrange(num_clusters):
for cluster in range(num_clusters):
if optRes[cluster] == None:
continue
val = optRes[cluster].get()
print "OPTIMIZATION for Cluster #", cluster,"DONE!!!"
print("OPTIMIZATION for Cluster #", cluster,"DONE!!!")
#THIS IS THE SOLUTION
S_est = upperToFull(val, 0)
X2 = S_est
Expand All @@ -152,16 +153,16 @@ def solve(window_size=10, number_of_clusters=5, lambda_parameter=11e-2,
computed_covariance[num_clusters,cluster] = cov_out
train_cluster_inverse[cluster] = X2

for cluster in xrange(num_clusters):
print "length of the cluster ", cluster,"------>", len_train_clusters[cluster]
for cluster in range(num_clusters):
print("length of the cluster ", cluster,"------>", len_train_clusters[cluster])

# update old computed covariance
old_computed_covariance = computed_covariance
print "UPDATED THE OLD COVARIANCE"
print("UPDATED THE OLD COVARIANCE")

inv_cov_dict = {} # cluster to inv_cov
log_det_dict = {} # cluster to log_det
for cluster in xrange(num_clusters):
for cluster in range(num_clusters):
cov_matrix = computed_covariance[num_clusters,cluster][0:(num_blocks-1)*n,0:(num_blocks-1)*n]
inv_cov_matrix = np.linalg.inv(cov_matrix)
log_det_cov = np.log(np.linalg.det(cov_matrix))# log(det(sigma2|1))
Expand All @@ -170,12 +171,12 @@ def solve(window_size=10, number_of_clusters=5, lambda_parameter=11e-2,

# -----------------------SMOOTHENING
# For each point compute the LLE
print "beginning the smoothening ALGORITHM"
print("beginning the smoothening ALGORITHM")

LLE_all_points_clusters = np.zeros([bt.len(clustered_points),num_clusters])
for point in xrange(bt.len(clustered_points)):
LLE_all_points_clusters = np.zeros([len(clustered_points),num_clusters])
for point in range(len(clustered_points)):
if point + num_stacked-1 < complete_D_train.shape[0]:
for cluster in xrange(num_clusters):
for cluster in range(num_clusters):
cluster_mean = cluster_mean_info[num_clusters,cluster]
cluster_mean_stacked = cluster_mean_stacked_info[num_clusters,cluster]
x = complete_D_train[point,:] - cluster_mean_stacked[0:(num_blocks-1)*n]
Expand All @@ -188,19 +189,19 @@ def solve(window_size=10, number_of_clusters=5, lambda_parameter=11e-2,
clustered_points = updateClusters(LLE_all_points_clusters,switch_penalty = switch_penalty)

if iters != 0:
cluster_norms = [(np.linalg.norm(old_computed_covariance[num_clusters,i]), i) for i in xrange(num_clusters)]
cluster_norms = [(np.linalg.norm(old_computed_covariance[num_clusters,i]), i) for i in range(num_clusters)]
norms_sorted = sorted(cluster_norms,reverse = True)
# clusters that are not 0 as sorted by norm
valid_clusters = [cp[1] for cp in norms_sorted if len_train_clusters[cp[1]] != 0]

# Add a point to the empty clusters
# assuming more non empty clusters than empty ones
counter = 0
for cluster in xrange(num_clusters):
for cluster in range(num_clusters):
if len_train_clusters[cluster] == 0:
cluster_selected = valid_clusters[counter] # a cluster that is not len 0
counter = (counter+1) % len(valid_clusters)
print "cluster that is zero is:", cluster, "selected cluster instead is:", cluster_selected
print("cluster that is zero is:", cluster, "selected cluster instead is:", cluster_selected)
start_point = np.random.choice(train_clusters[cluster_selected]) # random point number from that cluster
for i in range(0, cluster_reassignment):
# put cluster_reassignment points from point_num in this cluster
Expand All @@ -213,16 +214,16 @@ def solve(window_size=10, number_of_clusters=5, lambda_parameter=11e-2,
cluster_mean_info[num_clusters,cluster] = complete_D_train[point_to_move,:][(num_stacked-1)*n:num_stacked*n]


for cluster in xrange(num_clusters):
print "length of cluster #", cluster, "-------->", sum([x== cluster for x in clustered_points])
for cluster in range(num_clusters):
print("length of cluster #", cluster, "-------->", sum([x== cluster for x in clustered_points]))

##Save a figure of segmentation
plt.figure()
plt.plot(training_indices[0:bt.len(clustered_points)],clustered_points,color = "r")#,marker = ".",s =100)
plt.plot(training_indices[0:len(clustered_points)],clustered_points,color = "r")#,marker = ".",s =100)
plt.ylim((-0.5,num_clusters + 0.5))
if write_out_file: plt.savefig(str_NULL + "TRAINING_EM_lam_sparse="+str(lam_sparse) + "switch_penalty = " + str(switch_penalty) + ".jpg")
plt.close("all")
print "Done writing the figure"
print("Done writing the figure")

true_confusion_matrix = compute_confusion_matrix(num_clusters,clustered_points,training_indices)

Expand All @@ -242,27 +243,27 @@ def solve(window_size=10, number_of_clusters=5, lambda_parameter=11e-2,
correct_EM = 0
correct_GMM = 0
correct_KMeans = 0
for cluster in xrange(num_clusters):
for cluster in range(num_clusters):
matched_cluster_EM = matching_EM[cluster]
matched_cluster_GMM = matching_GMM[cluster]
matched_cluster_Kmeans = matching_Kmeans[cluster]

correct_EM += train_confusion_matrix_EM[cluster,matched_cluster_EM]
correct_GMM += train_confusion_matrix_GMM[cluster,matched_cluster_GMM]
correct_KMeans += train_confusion_matrix_kmeans[cluster, matched_cluster_Kmeans]
binary_EM = correct_EM/bt.len(clustered_points)
binary_GMM = correct_GMM/bt.len(gmm_clustered_pts)
binary_Kmeans = correct_KMeans/bt.len(kmeans_clustered_pts)
binary_EM = correct_EM/len(clustered_points)
binary_GMM = correct_GMM/len(gmm_clustered_pts)
binary_Kmeans = correct_KMeans/len(kmeans_clustered_pts)

##compute the F1 macro scores
f1_EM_tr = -1#computeF1_macro(train_confusion_matrix_EM,matching_EM,num_clusters)
f1_GMM_tr = -1#computeF1_macro(train_confusion_matrix_GMM,matching_GMM,num_clusters)
f1_kmeans_tr = -1#computeF1_macro(train_confusion_matrix_kmeans,matching_Kmeans,num_clusters)

print "\n\n\n"
print("\n\n\n")

if np.array_equal(old_clustered_points,clustered_points):
print "\n\n\n\nCONVERGED!!! BREAKING EARLY!!!"
print("\n\n\n\nCONVERGED!!! BREAKING EARLY!!!")
break
old_clustered_points = clustered_points
# end of training
Expand All @@ -279,13 +280,13 @@ def solve(window_size=10, number_of_clusters=5, lambda_parameter=11e-2,
f1_GMM_tr = -1#computeF1_macro(train_confusion_matrix_GMM,matching_GMM,num_clusters)
f1_kmeans_tr = -1#computeF1_macro(train_confusion_matrix_kmeans,matching_Kmeans,num_clusters)

print "\n\n"
print "TRAINING F1 score:", f1_EM_tr, f1_GMM_tr, f1_kmeans_tr
print("\n\n")
print("TRAINING F1 score:", f1_EM_tr, f1_GMM_tr, f1_kmeans_tr)

correct_EM = 0
correct_GMM = 0
correct_KMeans = 0
for cluster in xrange(num_clusters):
for cluster in range(num_clusters):
matched_cluster_EM = matching_EM[cluster]
matched_cluster_GMM = matching_GMM[cluster]
matched_cluster_Kmeans = matching_Kmeans[cluster]
Expand Down
4 changes: 2 additions & 2 deletions UnitTest.py
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Original file line number Diff line number Diff line change
Expand Up @@ -18,7 +18,7 @@ def test_example(self):
for i in range(8):
mrf = np.loadtxt("UnitTest_Data/cluster_"+str(i)+".txt",delimiter=',')
try:
np.testing.assert_array_almost_equal(mrf, cluster_MRFs[i])
np.testing.assert_array_almost_equal(mrf, cluster_MRFs[i], decimal=3)
except AssertionError:
#Test failed
self.assertTrue(1==0)
Expand All @@ -35,7 +35,7 @@ def test_multiExample(self):
for i in range(5):
mrf = np.loadtxt("UnitTest_Data/multiCluster_"+str(i)+".txt",delimiter=',')
try:
np.testing.assert_array_almost_equal(mrf, cluster_MRFs[i])
np.testing.assert_array_almost_equal(mrf, cluster_MRFs[i], decimal=3)
except AssertionError:
#Test failed
self.assertTrue(1==0)
Expand Down
2 changes: 1 addition & 1 deletion example.py
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Original file line number Diff line number Diff line change
Expand Up @@ -5,5 +5,5 @@
fname = "example_data.txt"
(cluster_assignment, cluster_MRFs) = TICC.solve(window_size = 1,number_of_clusters = 8, lambda_parameter = 11e-2, beta = 600, maxIters = 100, threshold = 2e-5, write_out_file = False, input_file = fname, prefix_string = "output_folder/", num_proc=1)

print cluster_assignment
print(cluster_assignment)
np.savetxt('Results.txt', cluster_assignment, fmt='%d', delimiter=',')
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