def sparse_data_example():
"""
cluster sparse data points.
"""
ndata = 20000
sizes = np.array(npr.randint(5, 9, size=(ndata, )), dtype=np.uint64)
indices_s = []
for i in range(ndata):
new_indices = random.sample(xrange(10), sizes[i])
new_indices.sort()
indices_s.extend(new_indices)
indices_s = np.array(indices_s, dtype=np.uint64)
data = npr.randn(sizes.sum())
z = pyzentas.pyzen(K=100,
max_rounds=10000,
max_itok=15.001,
seed=npr.randint(1011),
with_tests=False)
do_refinement = True
refinement_algorithm = "yinyang"
rf_max_rounds = 3000
rf_max_time = 10000.
X = z.spa(sizes, indices_s, data, do_refinement, refinement_algorithm,
rf_max_rounds, rf_max_time)
def mnist_example():
"""
Showing how do_vdimap can help.
"""
import mnist
reload(mnist)
ndata = int(1e3)
X = mnist.read_MNIST(dataset="original", ndata=ndata) / 1000.
dimension = X[0].shape[-1]
npr.seed(1000)
z = pyzentas.pyzen(K=100,
metric='l2',
energy='quadratic',
exponent_coeff=0,
max_itok=10000,
max_rounds=20,
seed=1011,
nthreads=1,
init="kmeans++-5",
with_tests=False,
patient=False)
do_vdimap = True
tangerine = z.den(X, do_vdimap, do_refinement=True)
def tests():
"""
run some tests to confirm the algorithms are working correctly.
"""
#Test 1
seed = 107
npr.seed(seed)
data = 10 * npr.rand(25, 2)
K = 7
z = pyzentas.pyzen(K=K,
metric='l2',
algorithm="clarans",
level=3,
energy='quadratic',
exponent_coeff=0,
max_time=2,
max_rounds=30,
seed=1011,
nthreads=1,
with_tests=True,
patient=False,
init="uniform",
do_balance_labels=False,
rooted=False)
tangerine = z.den(data, False, False)
#Test 2
z = pyzentas.pyzen(init=np.arange(K),
K=K,
algorithm="voronoi",
level=0,
metric='l2',
energy='identity',
exponent_coeff=0,
max_time=1,
max_rounds=40,
seed=1011,
nthreads=1,
with_tests=True,
patient=False)
tangerine = z.den(data, True)
def dense_data_example():
"""
cluster dense data points.
"""
import random
npr.seed(1011)
random.seed(1011)
K = int(1e2)
ndata = int(7e3)
dimension = 5
data = np.array(1 + npr.randn(ndata, dimension), dtype=np.float64)
seed = 1011
z = pyzentas.pyzen(init="kmeans++~1",
K=K,
metric='l2',
energy='quadratic',
exponent_coeff=0,
max_rounds=20,
max_time=1.,
max_itok=3.0,
seed=seed,
nthreads=1,
patient=False,
with_tests=False,
algorithm="clarans",
level=3)
do_vdimap = False
do_refinement = True
refinement_algorithm = "yinyang"
rf_max_rounds = 3000
rf_max_time = 10000.
tangerine = z.den(data, do_vdimap, do_refinement, refinement_algorithm,
rf_max_rounds, rf_max_time)
run_eakmeans = False
if run_eakmeans:
sys.path.append(datapaths.datapaths["eaklibdir"])
import kmeans
indices = random.sample(xrange(ndata), K)
indices = np.array(indices, dtype=np.uint64)
indices.sort()
X = kmeans.get_clustering(X=data,
n_clusters=K,
init=indices,
verbose=1,
n_threads=1,
algorithm="yin-sn")
def from_file_example():
"""
read lists of words from files
and cluster using Normalised Levenshtein.
"""
root = "../data/"
filenames_list = [root + "words1.txt", root + "words2.txt"]
outfilename = root + "output1.txt"
#the costs of indels and switches
costfilename = root + "costs.txt"
z = pyzentas.pyzen(K=5,
metric='normalised levenshtein',
max_proposals=1000,
energy='cubic',
seed=npr.randint(1000))
tangerine = z.txt_seq(filenames_list, outfilename + "N", costfilename)
def generated_sequence_example():
"""
generate random sequences of chars/ints and cluster using levenshtein
"""
ndata = 2000
sizes = np.array(npr.randint(10, 30, size=ndata), dtype=np.uint64)
#the values of the sequences
data = []
usechars = False
for i in range(sizes.sum()):
if npr.rand() < 0.25:
data.append('A' if usechars else 0)
elif npr.rand() < 0.5:
data.append('C' if usechars else 1)
elif npr.rand() < 0.75:
data.append('G' if usechars else 2)
else:
data.append('T' if usechars else 3)
data = np.array(data, dtype='c' if usechars else np.int32)
#The cost of mutating chars/ints (a 4x4 matrix)
cost_switch = np.array(
[[0., 10, 8, 11], [10, 0., 10, 9], [8, 10, 0, 10], [11, 9, 10, 0.]],
dtype=np.float64)
#The cost of inserting or deleting a char/int
cost_indel = np.array([10, 11, 10, 9], dtype=np.float64)
z = pyzentas.pyzen(K=400,
metric='levenshtein',
max_proposals=100000,
max_rounds=2,
energy='quadratic',
seed=npr.randint(1000),
nthreads=1,
init="kmeans++-2")
tangerine = z.seq(sizes=sizes,
values=data,
cost_indel=cost_indel,
cost_switch=cost_switch)
def get_results():
"""
cluster dense data points.
"""
if always_run_from_scratch == False and os.path.exists(datapaths.datapaths["comparing_levels_fn"]):
X = np.load(datapaths.datapaths["comparing_levels_fn"])
results = {}
for l in levels:
results[l] = X[()][l]
else:
results = {}
for level in levels:
z = pyzentas.pyzen(init = "uniform", K = K, metric = 'l2', energy = 'quadratic', exponent_coeff = 0, max_rounds = 20000, max_time = 60, seed = seed, nthreads = 4, patient = True, with_tests = False, algorithm = "clarans", level = level, capture_output = True)
tangerine = z.den(data, do_vdimap = False, do_refinement = False)
results[level] = pyzentas.get_processed_output(tangerine['output'])
return results
def capture_example():
"""
extracting statistics from runs, with plots
"""
import matplotlib.pyplot as pl
K = int(1e3)
ndata = int(1e4)
dimension = 3
#so that distance to nearest ~ 1
centers = K**(1. / dimension) * npr.rand(K, dimension)
indices = npr.randint(K, size=(ndata, ))
data = centers[indices] + 0.5 * npr.randn(ndata, dimension)
pl.clf()
pl.ion()
for max_itok in [x / 2. for x in range(10)]:
for init in [
"kmeans++-5"
]: #["kmeans++-10", "uniform"]:# "kmeans++", "afk-mc2-10", "afk-mc2-100", ]:
print "with ", init, max_itok, "."
z = pyzentas.pyzen(K=K,
capture_output=True,
init=init,
max_itok=max_itok,
seed=1011,
level=3)
tangerine = z.den(data, do_vdimap=False, do_refinement=True)
oo = pyzentas.get_processed_output(tangerine['output'])
pl.plot(oo['tT'],
oo['mE'],
label=init + "[" + str(max_itok) + "]",
linestyle='none',
marker='.')
#pl.xscale('log')
pl.xlabel("time [ms]")
pl.ylabel("mean energy")
def afk_mc2_subopt_mode():
"""
We present an example where afk-mc^2 requires extremely
long chain lengths to match k-means++. There are K centers,
K-1 are uniformly distributed in [0,1] x [0,1], and the
K'th center is at (10,10). Data points are drawn as follows:
(1) select one of the K centers at random,
(2) add N(0,sigma) to it, where sigma is small (see below).
Essentially, afk-mc^2 reduces to k-mc^2
(see Bachem 2016 for algorithms).
"""
#################
# Generate data #
#################
K = 200
ndata = K * 50
dimension = 2
centers = npr.rand(K, 2)
centers[-1] = [10, 10]
sigma = 1e-5
labels = npr.randint(K, size=(ndata, ))
data = centers[labels] + sigma * npr.randn(ndata, 2)
data /= sigma
################
# Run kmeans++ #
################
E_kmeanspp = []
print "kmeans++ energies: ",
for i in range(5):
z = pyzentas.pyzen(K=K,
metric='l2',
energy='quadratic',
seed=npr.randint(1000),
max_rounds=0,
init="kmeans++-1",
capture_output=True)
tangerine = z.den(data)
E_kmeanspp.append(
tangerine['output'].split("R=0")[1].split("mE=")[1].split()[0])
print E_kmeanspp[-1], " ",
print "\nwith afk-mc2:"
###############
# Run afk-mc2 #
###############
E_afkmc2 = []
l_afkmc2 = []
for i in range(30):
chain_length = i**2
z = pyzentas.pyzen(K=K,
metric='l2',
energy='quadratic',
seed=npr.randint(1000),
max_rounds=0,
init="afk-mc2-%d" % (chain_length, ),
capture_output=True)
tangerine = z.den(data)
E_afkmc2.append(
tangerine['output'].split("R=0")[1].split("mE=")[1].split()[0])
l_afkmc2.append(chain_length)
print "chain length: ", l_afkmc2[-1], "\tenergy: ", E_afkmc2[-1]
return {
"E_kmeanspp": E_kmeanspp,
"E_afkmc2": E_afkmc2,
"l_afkmc2": l_afkmc2
}
def go(X, K, withskl, witheak, withzen):
"""
X : data
K : number of clusters
withskl, witheak, withzen : bools indicating whether to run with em.
"""
indices_init = np.arange(K, dtype=np.uint64)
C_init = X[indices_init]
results = {}
if withskl == True:
results["skl"] = {}
from sklearn.cluster import KMeans
# run until convergence, initialise with scikit-learn's special version of k-means++ (see zentas wiki entry for discussion).
sklc = KMeans(n_clusters=K,
init="k-means++",
max_iter=100000000,
tol=1e-20,
verbose=0,
n_init=1)
tsk0 = time.time()
sklc.fit(X)
tsk1 = time.time()
sklacc = np.sum(
np.min(np.sum((np.expand_dims(X, axis=1) -
np.expand_dims(sklc.cluster_centers_, axis=0))**2,
axis=2),
axis=1)) / X.shape[0]
results["skl"]["t"] = tsk1 - tsk0
results["skl"]["mse"] = sklacc
if witheak:
results["eak"] = {}
sys.path.append(datapaths.datapath["eaklibdir"])
import kmeans
teak0 = time.time()
eak = kmeans.get_clustering(X,
K,
verbose=1,
init="kmeans++",
n_threads=4)
teak1 = time.time()
results["eak"]["t"] = teak1 - teak0
results["eak"]['mse'] = eak["mse"]
if withzen:
results["zen"] = {}
# run with zentas. pipeline here is (1) kmeans++ (2) clarans (3) lloyd.
z = pyzentas.pyzen(K=K,
metric='l2',
energy='quadratic',
max_itok=10.0,
max_time=5.0,
max_proposals=K**2,
seed=npr.randint(1000),
patient=True,
nthreads=4,
init="kmeans++-4",
with_tests=False,
capture_output=True,
rooted=False)
tzen0 = time.time()
tangerine = z.den(X,
do_vdimap=True,
do_refinement=True,
rf_max_rounds=10000000)
tzen1 = time.time()
results["zen"]["t"] = tzen0 - tzen1
results["zen"]["out"] = pyzentas.get_processed_output(
tangerine['output'])
results["zen"]['mse'] = results["zen"]["out"]["mE"][-1]
return results
X = npr.randn(N, dim)
results = {}
for K in [int(x) for x in 2**np.linspace(np.log2(40), np.log2(2500), 40)]:
results[K] = {}
max_itoks = [0, 1, 2, 4]
for max_itok in max_itoks:
results[K][max_itok] = {}
z = pyzentas.pyzen(K=K,
metric='l2',
energy='quadratic',
max_itok=max_itok,
max_time=100000,
max_proposals=K**2,
seed=1011,
patient=True,
nthreads=4,
init="kmeans++-4",
with_tests=False,
capture_output=True,
rooted=False)
tzen0 = time.time()
tangerine = z.den(X,
do_vdimap=True,
do_refinement=True,
rf_max_rounds=10000000)
tzen1 = time.time()
results[K][max_itok]["t"] = tzen0 - tzen1
results[K][max_itok]["out"] = pyzentas.get_processed_output(
def go(X, K, alg, seed, kmpp_greedy):
"""
One run.
X : data
K : number of clusters
"""
results = {}
if "kmpp-cla" in alg:
max_itok = float(alg.split("-")[-1])
z = pyzentas.pyzen(K=K,
metric='l2',
energy='quadratic',
max_itok=max_itok,
max_time=500.0,
max_proposals=K**2,
seed=seed,
patient=True,
nthreads=4,
init="kmeans++-6",
with_tests=False,
capture_output=True,
rooted=False)
elif alg == "kmpp":
if (kmpp_greedy > 1):
init = "kmeans++~%d" % (kmpp_greedy)
else:
init = "kmeans++-6"
z = pyzentas.pyzen(K=K,
metric='l2',
energy='quadratic',
max_itok=0.0,
max_time=5.0,
max_proposals=K**2,
seed=seed,
patient=True,
nthreads=4,
init=init,
with_tests=False,
capture_output=True,
rooted=False)
else:
raise RuntimeError("unrecognised alg in go")
tzen0 = time.time()
if X.shape[1] > 15:
rf_alg = "yinyang"
do_vdimap = True
else:
rf_alg = "exponion"
do_vdimap = False
tangerine = z.den(X,
do_vdimap=do_vdimap,
do_refinement=True,
rf_alg=rf_alg,
rf_max_rounds=100)
tzen1 = time.time()
results["t"] = tzen1 - tzen0
results["out"] = pyzentas.get_processed_output(tangerine['output'])
results['mse'] = results["out"]["mE"][-1]
return results
