def k_centers(X, n_clusters=8, metric='rmsd', random_state=None):
"""K-Centers clustering
Cluster a vector or Trajectory dataset using a simple heuristic to minimize
the maximum distance from any data point to its assigned cluster center.
The runtime of this algorithm is O(kN), where k is the number of
clusters and N is the size of the dataset, making it one of the least
expensive clustering algorithms available.
Parameters
----------
n_clusters : int, optional, default: 8
The number of clusters to form as well as the number of
centroids to generate.
metric : {"euclidean", "sqeuclidean", "cityblock", "chebyshev", "canberra",
"braycurtis", "hamming", "jaccard", "cityblock", "rmsd"}
The distance metric to use. metric = "rmsd" requires that sequences
passed to ``fit()`` be ```md.Trajectory```; other distance metrics
require ``np.ndarray``s.
random_state : integer or numpy.RandomState, optional
The generator used to initialize the centers. If an integer is
given, it fixes the seed. Defaults to the global numpy random
number generator.
References
----------
.. [1] Gonzalez, Teofilo F. "Clustering to minimize the maximum
intercluster distance." Theor. Comput. Sci. 38 (1985): 293-306.
.. [2] Beauchamp, Kyle A., et al. "MSMBuilder2: modeling conformational
dynamics on the picosecond to millisecond scale." J. Chem. Theory.
Comput. 7.10 (2011): 3412-3419.
Attributes
----------
cluster_centers_ : array, [n_clusters, n_features] or md.Trajectory
Coordinates of cluster centers
labels_ : array, [n_samples,]
The label of each point is an integer in [0, n_clusters).
"""
n_samples = len(X)
if random_state is -1:
seed = check_random_state(None).randint(0, n_samples)
else:
seed = random_state
print "seed=", seed
cluster_centers_ = []
cluster_centers_.append(seed) #seed = random
distances_ = pairwise_distances(X, index=seed, metric=metric)
labels_ = np.zeros(len(X), dtype=np.int32)
for i in xrange(1, n_clusters):
MaxIndex = np.argmax(distances_)
cluster_centers_.append(MaxIndex)
#set the furthest point from existing center as a new center
if distances_[ MaxIndex ] < 0:
break
new_distance_list = pairwise_distances(X, index=MaxIndex, metric=metric)
updated_indices = np.where(new_distance_list < distances_)[0]
distances_[ updated_indices ] = new_distance_list[ updated_indices ]
labels_[ updated_indices ] = i
return cluster_centers_, labels_
def k_centers_assign(X,
centers=None,
n_clusters=8,
metric='rmsd',
random_state=None):
"""K-Centers clustering
Cluster a vector or Trajectory dataset using a simple heuristic to minimize
the maximum distance from any data point to its assigned cluster center.
The runtime of this algorithm is O(kN), where k is the number of
clusters and N is the size of the dataset, making it one of the least
expensive clustering algorithms available.
Parameters
----------
n_clusters : int, optional, default: 8
The number of clusters to form as well as the number of
centroids to generate.
metric : {"euclidean", "sqeuclidean", "cityblock", "chebyshev", "canberra",
"braycurtis", "hamming", "jaccard", "cityblock", "rmsd"}
The distance metric to use. metric = "rmsd" requires that sequences
passed to ``fit()`` be ```md.Trajectory```; other distance metrics
require ``np.ndarray``s.
random_state : integer or numpy.RandomState, optional
The generator used to initialize the centers. If an integer is
given, it fixes the seed. Defaults to the global numpy random
number generator.
References
----------
.. [1] Gonzalez, Teofilo F. "Clustering to minimize the maximum
intercluster distance." Theor. Comput. Sci. 38 (1985): 293-306.
.. [2] Beauchamp, Kyle A., et al. "MSMBuilder2: modeling conformational
dynamics on the picosecond to millisecond scale." J. Chem. Theory.
Comput. 7.10 (2011): 3412-3419.
Attributes
----------
cluster_centers_ : array, [n_clusters, n_features] or md.Trajectory
Coordinates of cluster centers
labels_ : array, [n_samples,]
The label of each point is an integer in [0, n_clusters).
"""
n_samples = len(X)
if centers is None:
print("No Cluster Centers found!")
n_centers = len(centers)
print("N_Centers:", n_centers)
print("N_samples:", n_samples)
labels_ = np.zeros(n_samples, dtype=np.int32)
#distances_ = np.zeros(n_centers, dtype=np.float32)
for i in range(0, n_samples):
distances_ = pairwise_distances(X=centers, Y=X, index=i, metric=metric)
#distances_ = md.rmsd(centers, X, i, parallel=True, precentered=True)
cluster_num = np.argmin(distances_)
labels_[i] = cluster_num
return labels_
def k_centers_assign(X, centers=None, n_clusters=8, metric='rmsd', random_state=None):
"""K-Centers clustering
Cluster a vector or Trajectory dataset using a simple heuristic to minimize
the maximum distance from any data point to its assigned cluster center.
The runtime of this algorithm is O(kN), where k is the number of
clusters and N is the size of the dataset, making it one of the least
expensive clustering algorithms available.
Parameters
----------
n_clusters : int, optional, default: 8
The number of clusters to form as well as the number of
centroids to generate.
metric : {"euclidean", "sqeuclidean", "cityblock", "chebyshev", "canberra",
"braycurtis", "hamming", "jaccard", "cityblock", "rmsd"}
The distance metric to use. metric = "rmsd" requires that sequences
passed to ``fit()`` be ```md.Trajectory```; other distance metrics
require ``np.ndarray``s.
random_state : integer or numpy.RandomState, optional
The generator used to initialize the centers. If an integer is
given, it fixes the seed. Defaults to the global numpy random
number generator.
References
----------
.. [1] Gonzalez, Teofilo F. "Clustering to minimize the maximum
intercluster distance." Theor. Comput. Sci. 38 (1985): 293-306.
.. [2] Beauchamp, Kyle A., et al. "MSMBuilder2: modeling conformational
dynamics on the picosecond to millisecond scale." J. Chem. Theory.
Comput. 7.10 (2011): 3412-3419.
Attributes
----------
cluster_centers_ : array, [n_clusters, n_features] or md.Trajectory
Coordinates of cluster centers
labels_ : array, [n_samples,]
The label of each point is an integer in [0, n_clusters).
"""
n_samples = len(X)
if centers is None:
print "No Cluster Centers found!"
n_centers = len(centers)
print "N_Centers:", n_centers
print "N_samples:", n_samples
labels_ = np.zeros(n_samples, dtype=np.int32)
#distances_ = np.zeros(n_centers, dtype=np.float32)
for i in xrange(0, n_samples):
distances_ = pairwise_distances(X=centers, Y=X, index=i, metric=metric)
#distances_ = md.rmsd(centers, X, i, parallel=True, precentered=True)
cluster_num = np.argmin(distances_)
labels_[ i ] = cluster_num
return labels_
def run_knn(X, n_neighbors=100, n_samples=1000, metric='rmsd', algorithm='vp_tree'):
# X = check_array(X, accept_sparse='csr')
#print "Calculating pairwise ", metric, " distances of ", n_samples, " samples..."
t0 = time.time()
if metric is "rmsd":
samples = random.sample(X, n_samples)
whole_samples= reduce(operator.add, (samples[i] for i in xrange(len(samples))))
else:
whole_samples = random.sample(X, n_samples)
sample_dist_metric = pairwise_distances( whole_samples, whole_samples, metric=metric )
t1 = time.time()
#print "time:", t1-t0,
#print "Done."
# Calculate neighborhood for all samples. This leaves the original point
# in, which needs to be considered later
#print "Calculating knn..."
t0 = time.time()
if metric is 'rmsd':
shape_x = np.shape(X.xyz)
knn = knnn.vp_tree_parallel( np.reshape(X.xyz, (shape_x[0] * shape_x[1] * shape_x[2])), shape_x[1] * 3, "rmsd_serial" )
distances_, indices = knn.query( np.linspace(0, len(X.xyz)-1, len(X.xyz), dtype='int'), n_neighbors )
else:
if algorithm is 'vp_tree':
shape_x = np.shape(X)
#print "shape_x:", shape_x
knn = knnn.vp_tree_parallel( np.reshape(X, (shape_x[0] * shape_x[1])), shape_x[1], "euclidean_serial" )
distances_, indices = knn.query( np.linspace(0, len(X)-1, len(X), dtype='int'), n_neighbors )
else:
neighbors_model = NearestNeighbors(n_neighbors=n_neighbors, algorithm=algorithm, metric=metric)
neighbors_model.fit(X)
distances_, indices = neighbors_model.kneighbors(X, n_neighbors=n_neighbors, return_distance=True)
t1 = time.time()
#print "time:", t1-t0,
#print "Done."
# Calculate distance between sample, and find dc
# np.savetxt("./sample_dist_metric.txt", sample_dist_metric, fmt="%f")
#np.savetxt("./distances_.txt", distances_, fmt="%f")
#np.savetxt("./indices.txt", indices, fmt="%d")
return sample_dist_metric, distances_, indices
def main():
cli = argparse.ArgumentParser()
cli.add_argument(
'-t',
'--trajListFns',
default='trajlist',
help='List of trajectory files to read in, separated by spaces.')
cli.add_argument(
'-a',
'--atomListFns',
default='atom_indices',
help='List of atom index files to read in, separated by spaces.')
cli.add_argument('-g',
'--topology',
default='native.pdb',
help='topology file.')
cli.add_argument('-o',
'--homedir',
help='Home dir.',
default=".",
type=str)
cli.add_argument('-e',
'--iext',
help='''The file extension of input trajectory
files. Must be a filetype that mdtraj.load() can recognize.''',
default="xtc",
type=str)
cli.add_argument('-n',
'--n_clusters',
help='''n_clusters.''',
default=100,
type=int)
cli.add_argument('-m',
'--n_macro_states',
help='''n_macro_states.''',
default=6,
type=int)
cli.add_argument('-s', '--stride', help='stride.', default=None, type=int)
args = cli.parse_args()
trajlistname = args.trajListFns
atom_indicesname = args.atomListFns
trajext = args.iext
File_TOP = args.topology
homedir = args.homedir
n_clusters = args.n_clusters
n_macro_states = args.n_macro_states
stride = args.stride
# ===========================================================================
# Reading Trajs from XTC files
#print "stride:", stride
#trajreader = XTCReader(trajlistname, atom_indicesname, homedir, trajext, File_TOP, nSubSample=stride)
#trajs = trajreader.trajs
#print(trajs)
#traj_len = trajreader.traj_len
#np.savetxt("./traj_len.txt", traj_len, fmt="%d")
if os.path.isfile(
"./phi_angles.txt") and os.path.isfile("./psi_angles.txt") is True:
phi_angles = np.loadtxt("./phi_angles.txt", dtype=np.float32)
psi_angles = np.loadtxt("./psi_angles.txt", dtype=np.float32)
else:
#phi_angles, psi_angles = trajreader.get_phipsi(trajs, psi=[6, 8, 14, 16], phi=[4, 6, 8, 14])
phi_angles, psi_angles = trajreader.get_phipsi(trajs,
psi=[5, 7, 13, 15],
phi=[3, 5, 7, 13])
np.savetxt("./phi_angles.txt", phi_angles, fmt="%f")
np.savetxt("./psi_angles.txt", psi_angles, fmt="%f")
phi_psi = np.column_stack((phi_angles, psi_angles))
n_samples = 1000
percent = 0.9
import random
whole_samples = random.sample(list(phi_psi), n_samples)
#print whole_samples
from metrics.pairwise import pairwise_distances
sample_dist_metric = pairwise_distances(whole_samples,
whole_samples,
metric='euclidean')
print(sample_dist_metric.shape)
sample_dist = []
for i in range(0, n_samples):
for j in range(i + 1, n_samples):
sample_dist.append(sample_dist_metric[i, j])
sorted_sample_dist = np.sort(sample_dist)
print("Len of samples:", len(sorted_sample_dist),
np.max(sorted_sample_dist), np.min(sorted_sample_dist))
eps_list = []
len_samples = len(sorted_sample_dist)
for percent in [0.05, 0.025, 0.008]: #,0.005, 0.003,
# 0.002, 0.001, 0.0008, 0.0005, 0.0003, 0.0002, 0.0001, 0.00005]:
percent /= 10.0
index = int(round(len_samples * percent))
if index == len_samples:
index -= 1
dc = sorted_sample_dist[index]
#print index, sorted_sample_dist[index]
eps_list.append(dc)
print(eps_list)
# from sklearn.neighbors import NearestNeighbors
# print len(phi_psi)
# neighborhoods_model = NearestNeighbors(n_neighbors=len(phi_psi), algorithm='kd_tree')
# neighborhoods_model.fit(phi_psi)
# #distances, indices = neighborhoods_model.kneighbors(phi_psi)
# distances, indices = neighborhoods_model.kneighbors(phi_psi, 5)
# print distances
#print phi_psi
# ===========================================================================
# do Clustering using MR -DBSCAN method
clustering_name = "mr-dbscan_iter_"
potential = True
# potential = False
#eps = eps_list[0]
eps = 9.376904
min_samples = 1
len_frames = len(phi_psi)
print("Total frames:", len_frames)
print("Running first calculation")
db = DBSCAN(eps=eps, min_samples=min_samples,
algorithm='kd_tree').fit(phi_psi)
core_samples_mask = np.zeros_like(db.labels_, dtype=bool)
core_samples_mask[db.core_sample_indices_] = True
old_assignments = db.labels_
n_microstates = len(
set(old_assignments)) - (1 if -1 in old_assignments else 0)
print('Estimated number of clusters: %d' % n_microstates)
# Calculating percentage of each states
frame_bincount = np.bincount(
old_assignments[old_assignments >= 0]) #remove outliers
frame_freq_index_sorted = np.argsort(
frame_bincount)[::-1] # descending arg sort
frame_freq_percent_sorted = frame_bincount[
frame_freq_index_sorted] / np.float32(len_frames)
print(frame_freq_percent_sorted[0:10])
print(frame_freq_index_sorted[0:10])
old_frame_freq_percent_sorted = frame_freq_percent_sorted
old_frame_freq_index_sorted = frame_freq_index_sorted
iter_name = clustering_name + '0' + '_eps_' + str(
eps) + '_min_samples_' + str(min_samples) + '_n_states_' + str(
n_microstates)
plot_cluster(labels=old_assignments,
phi_angles=phi_angles,
psi_angles=psi_angles,
name=iter_name,
potential=potential)
n_iterations = len(eps_list)
print("n_iterations:", n_iterations)
eps_list = [9.376904, 3.3741567, 0.87675905]
min_samples_list = [1, 20, 20]
#min_samples_list = [50, 30, 20, 15, 10, 8, 5, 2]
n_min_samples = len(min_samples_list)
#eps_list = [3.0, 2.0, 1.0, 0.8, 0.5]
#min_samples_list = [3, 3, 3, 3, 3, 2, 2]
r #esults = np.zeros((n_min_samples,n_iterations,len_frames), dtype=np.int32)
results = np.zeros((n_iterations, len_frames), dtype=np.int32)
for i in range(0, n_iterations):
#for j in range(0, n_min_samples):
eps = eps_list[i]
min_samples = min_samples_list[i]
db = DBSCAN(eps=eps, min_samples=min_samples).fit(phi_psi)
'''
core_samples_mask = np.zeros_like(db.labels_, dtype=bool)
core_samples_mask[db.core_sample_indices_] = True
new_assignments = db.labels_
if i < 7:
remove_outliers = True
else:
remove_outliers = False
assignments = merge_assignments(new_assignments, old_assignments, remove_outliers=remove_outliers)
n_microstates = len(set(assignments)) - (1 if -1 in assignments else 0)
# Calculating percentage of each states
frame_bincount = np.bincount(assignments[assignments >= 0]) # remove outliers
frame_freq_index_sorted = np.argsort(frame_bincount)[::-1] # descending arg sort
frame_freq_percent_sorted = frame_bincount[frame_freq_index_sorted] / np.float32(len_frames)
frame_freq_percent_sorted = frame_freq_percent_sorted[0:10]
frame_freq_index_sorted = frame_freq_index_sorted[0:10]
print frame_freq_percent_sorted
print frame_freq_index_sorted
old_frame_freq_index_sorted = []
for j in xrange(0, 10):
index = np.argwhere(assignments==frame_freq_index_sorted[j])[0]
old_frame_freq_index_sorted.append(old_assignments[index][0])
print old_frame_freq_index_sorted
'''
core_samples_mask = np.zeros_like(db.labels_, dtype=bool)
core_samples_mask[db.core_sample_indices_] = True
assignments = db.labels_
n_microstates = len(set(assignments)) - (1 if -1 in assignments else 0)
#results[j,i, :]= np.array(assignments)
results[i, :] = np.array(assignments)
print("Iter:", i, "Running MR-DBSCAN at eps:", eps, 'min_sampes:',
min_samples, 'Estimated number of clusters:', n_microstates)
#print('Estimated number of clusters: %d' % n_microstates)
iter_name = clustering_name + str(i) + '_eps_' + str(
eps) + '_min_samples_' + str(min_samples) + '_n_states_' + str(
n_microstates)
plot_cluster(labels=assignments,
phi_angles=phi_angles,
psi_angles=psi_angles,
name=iter_name,
potential=potential)
#old_assignments = assignments
print(results)
np.save("results.npy", results)
#np.savetxt("results.csv", results, fmt="%d", delimiter=",")
np.savetxt("eps_list.txt", eps_list, fmt="%f", delimiter=",")
np.savetxt("min_samples_list.txt",
min_samples_list,
fmt="%d",
delimiter=",")
def k_centers(X, n_clusters=8, metric='rmsd', random_state=None):
"""K-Centers clustering
Cluster a vector or Trajectory dataset using a simple heuristic to minimize
the maximum distance from any data point to its assigned cluster center.
The runtime of this algorithm is O(kN), where k is the number of
clusters and N is the size of the dataset, making it one of the least
expensive clustering algorithms available.
Parameters
----------
n_clusters : int, optional, default: 8
The number of clusters to form as well as the number of
centroids to generate.
metric : {"euclidean", "sqeuclidean", "cityblock", "chebyshev", "canberra",
"braycurtis", "hamming", "jaccard", "cityblock", "rmsd"}
The distance metric to use. metric = "rmsd" requires that sequences
passed to ``fit()`` be ```md.Trajectory```; other distance metrics
require ``np.ndarray``s.
random_state : integer or numpy.RandomState, optional
The generator used to initialize the centers. If an integer is
given, it fixes the seed. Defaults to the global numpy random
number generator.
References
----------
.. [1] Gonzalez, Teofilo F. "Clustering to minimize the maximum
intercluster distance." Theor. Comput. Sci. 38 (1985): 293-306.
.. [2] Beauchamp, Kyle A., et al. "MSMBuilder2: modeling conformational
dynamics on the picosecond to millisecond scale." J. Chem. Theory.
Comput. 7.10 (2011): 3412-3419.
Attributes
----------
cluster_centers_ : array, [n_clusters, n_features] or md.Trajectory
Coordinates of cluster centers
labels_ : array, [n_samples,]
The label of each point is an integer in [0, n_clusters).
"""
n_samples = len(X)
if random_state is -1:
seed = check_random_state(None).randint(0, n_samples)
else:
seed = random_state
print("seed=", seed)
cluster_centers_ = []
cluster_centers_.append(seed) #seed = random
distances_ = pairwise_distances(X, index=seed, metric=metric)
labels_ = np.zeros(len(X), dtype=np.int32)
for i in range(1, n_clusters):
MaxIndex = np.argmax(distances_)
cluster_centers_.append(MaxIndex)
#set the furthest point from existing center as a new center
if distances_[MaxIndex] < 0:
break
new_distance_list = pairwise_distances(X,
index=MaxIndex,
metric=metric)
updated_indices = np.where(new_distance_list < distances_)[0]
distances_[updated_indices] = new_distance_list[updated_indices]
labels_[updated_indices] = i
return cluster_centers_, labels_
def main():
cli = argparse.ArgumentParser()
cli.add_argument('-e', '--eps', help='eps', default=1, type=float)
cli.add_argument('-m',
'--min_samples',
help='min_samples',
default=5,
type=int)
cli.add_argument('-l', '--nlist', help='nlist', default=1000, type=int)
cli.add_argument('-p', '--nprobe', help='nprob', default=10, type=int)
# Download example dataset
from msmbuilder.example_datasets import AlanineDipeptide
ala2 = AlanineDipeptide(verbose=False)
xyz = ala2.get().trajectories
print(ala2.description())
#xyz = [t[::10] for t in xyz]
print("{} trajectories".format(len(xyz)))
# msmbuilder does not keep track of units! You must keep track of your
# data's timestep
to_ns = 0.5
print("with length {} ns".format(set(len(x) * to_ns for x in xyz)))
from msmbuilder.featurizer import DihedralFeaturizer
featurizer = DihedralFeaturizer(types=['phi', 'psi'])
diheds = featurizer.fit_transform(xyz)
print(xyz[0].xyz.shape)
print(diheds[0].shape)
from msmbuilder.preprocessing import RobustScaler
scaler = RobustScaler()
scaled_diheds = scaler.fit_transform(diheds)
print(diheds[0].shape)
print(scaled_diheds[0].shape)
from msmbuilder.decomposition import tICA
tica_model = tICA(lag_time=2, n_components=2)
# fit and transform can be done in seperate steps:
tica_model.fit(diheds)
tica_trajs = tica_model.transform(diheds)
featurizer = DihedralFeaturizer(types=['phi', 'psi'], sincos=False)
diheds = featurizer.fit_transform(xyz)
print(diheds[0].shape)
print(tica_trajs[0].shape)
# ===========================================================================
#if os.path.isfile("./phi_angles.txt") and os.path.isfile("./psi_angles.txt") is True:
# phi_angles = np.loadtxt("./phi_angles.txt", dtype=np.float32)
# psi_angles = np.loadtxt("./psi_angles.txt", dtype=np.float32)
#X = np.column_stack((phi_angles, psi_angles))
#print(X.shape)
phi_angles = np.degrees(diheds[0][:, 0])
psi_angles = np.degrees(diheds[0][:, 1])
print(phi_angles)
X = tica_trajs[0].astype(np.float32)
#rint(X)
n_size = X.shape[0]
dimension = X.shape[1]
#print(X.shape)
# ===========================================================================
args = cli.parse_args()
eps = args.eps # eps
min_samples = args.min_samples # min_samples
nlist = args.nlist
nprobe = args.nprobe
IVFFlat = True
print('n_size = %d,\t dimension = %d,\t eps = %f, min_samples = %d' %
(n_size, dimension, eps, min_samples))
n_samples = 1000
percent = 0.9
import random
whole_samples = random.sample(list(X), n_samples)
#print whole_samples
from metrics.pairwise import pairwise_distances
sample_dist_metric = pairwise_distances(whole_samples,
whole_samples,
metric='l2')
print(sample_dist_metric.shape)
sample_dist = []
for i in range(0, n_samples):
for j in range(i + 1, n_samples):
sample_dist.append(sample_dist_metric[i, j])
sorted_sample_dist = np.sort(sample_dist)
print("Len of samples:", len(sorted_sample_dist),
np.max(sorted_sample_dist), np.min(sorted_sample_dist))
eps_list = []
len_samples = len(sorted_sample_dist)
for percent in [0.30, 0.20, 0.10]: #,0.005, 0.003,
# 0.002, 0.001, 0.0008, 0.0005, 0.0003, 0.0002, 0.0001, 0.00005]:
#percent /= 10.0
index = int(round(len_samples * percent))
if index == len_samples:
index -= 1
dc = sorted_sample_dist[index]
#print index, sorted_sample_dist[index]
eps_list.append(dc)
print(eps_list)
#print X
# ===========================================================================
# do Clustering using MR -DBSCAN method
clustering_name = "mr-dbscan_iter_"
#potential = True
remove_outliers = False
potential = False
eps = eps_list[0]
min_samples = 1
len_frames = len(X)
print("Total frames:", len_frames)
print("Running first calculation")
db = Faiss_DBSCAN(eps=eps,
min_samples=min_samples,
nlist=nlist,
nprobe=nprobe,
metric="l2",
GPU=False,
IVFFlat=IVFFlat)
db.fit(X)
core_samples_mask = np.zeros_like(db.labels_, dtype=bool)
core_samples_mask[db.core_sample_indices_] = True
old_assignments = db.labels_
n_microstates = len(
set(old_assignments)) - (1 if -1 in old_assignments else 0)
print('Estimated number of clusters: %d' % n_microstates)
# Calculating percentage of each states
frame_bincount = np.bincount(
old_assignments[old_assignments >= 0]) #remove outliers
frame_freq_index_sorted = np.argsort(
frame_bincount)[::-1] # descending arg sort
frame_freq_percent_sorted = frame_bincount[
frame_freq_index_sorted] / np.float32(len_frames)
print(frame_freq_percent_sorted[0:10])
print(frame_freq_index_sorted[0:10])
old_frame_freq_percent_sorted = frame_freq_percent_sorted
old_frame_freq_index_sorted = frame_freq_index_sorted
n_microstates = len(
set(old_assignments)) - (1 if -1 in old_assignments else 0)
print('Estimated number of clusters: %d' % n_microstates)
iter_name = clustering_name + '0' + '_eps_' + str(
eps) + '_min_samples_' + str(min_samples) + '_n_states_' + str(
n_microstates)
plot_cluster(labels=old_assignments,
phi_angles=phi_angles,
psi_angles=psi_angles,
name=iter_name,
potential=potential)
n_iterations = len(eps_list)
print("n_iterations:", n_iterations)
min_samples_list = [50, 30, 10]
#min_samples_list = [50, 30, 20, 15, 10, 8, 5, 2]
n_min_samples = len(min_samples_list)
#eps_list = [3.0, 2.0, 1.0, 0.8, 0.5]
#min_samples_list = [3, 3, 3, 3, 3, 2, 2]
results = np.zeros((n_min_samples, n_iterations, len_frames),
dtype=np.int32)
for i in range(1, n_iterations):
eps = eps_list[i]
min_samples = min_samples_list[i]
db = Faiss_DBSCAN(eps=eps,
min_samples=min_samples,
nlist=nlist,
nprobe=nprobe,
metric="l2",
GPU=False,
IVFFlat=IVFFlat).fit(X)
core_samples_mask = np.zeros_like(db.labels_, dtype=bool)
core_samples_mask[db.core_sample_indices_] = True
new_assignments = db.labels_
if i is n_iterations - 1:
remove_outliers = True
#else:
# remove_outliers = False
assignments = merge_assignments(new_assignments,
old_assignments,
remove_outliers=remove_outliers)
n_microstates = len(set(assignments)) - (1 if -1 in assignments else 0)
#results[j,i, :]= np.array(assignments)
print("Iter:", i, "Running MR-DBSCAN at eps:", eps, 'min_sampes:',
min_samples, 'Estimated number of clusters:', n_microstates)
#print('Estimated number of clusters: %d' % n_microstates)
iter_name = clustering_name + str(i) + '_eps_' + str(
eps) + '_min_samples_' + str(min_samples) + '_n_states_' + str(
n_microstates)
plot_cluster(labels=assignments,
phi_angles=phi_angles,
psi_angles=psi_angles,
name=iter_name,
potential=potential)
#old_assignments = assignments
#print(results)
#np.save("results.npy", results)
#np.savetxt("results.csv", results, fmt="%d", delimiter=",")
np.savetxt("eps_list.txt", eps_list, fmt="%f", delimiter=",")
np.savetxt("min_samples_list.txt",
min_samples_list,
fmt="%d",
delimiter=",")
def main():
cli = argparse.ArgumentParser()
cli.add_argument('-e', '--eps', help='eps', default=1, type=float)
cli.add_argument('-m', '--min_samples', help='min_samples', default=5, type=int)
cli.add_argument('-l', '--nlist', help='nlist', default=1000, type=int)
cli.add_argument('-p', '--nprobe', help='nprob', default=10, type=int)
# ===========================================================================
if os.path.isfile("./phi_angles.txt") and os.path.isfile("./psi_angles.txt") is True:
phi_angles = np.loadtxt("./phi_angles.txt", dtype=np.float32)
psi_angles = np.loadtxt("./psi_angles.txt", dtype=np.float32)
X = np.column_stack((phi_angles, psi_angles))
print(X.shape)
n_size = X.shape[0]
dimension = X.shape[1]
# ===========================================================================
args = cli.parse_args()
eps = args.eps # eps
min_samples = args.min_samples # min_samples
nlist = args.nlist
nprobe = args.nprobe
IVFFlat = True
print('n_size = %d,\t dimension = %d,\t eps = %f, min_samples = %d' % (n_size, dimension, eps, min_samples))
n_samples = 1000
percent = 0.9
import random
whole_samples = random.sample(list(X), n_samples)
#print whole_samples
from metrics.pairwise import pairwise_distances
sample_dist_metric = pairwise_distances(whole_samples, whole_samples, metric='l2')
print(sample_dist_metric.shape)
sample_dist = []
for i in range(0, n_samples):
for j in range(i+1, n_samples):
sample_dist.append(sample_dist_metric[i, j])
sorted_sample_dist = np.sort(sample_dist)
print("Len of samples:", len(sorted_sample_dist), np.max(sorted_sample_dist), np.min(sorted_sample_dist))
eps_list = []
len_samples = len(sorted_sample_dist)
for percent in [0.20, 0.05, 0.020 ]: #,0.005, 0.003,
# 0.002, 0.001, 0.0008, 0.0005, 0.0003, 0.0002, 0.0001, 0.00005]:
#percent /= 10.0
index = int(round(len_samples*percent))
if index == len_samples:
index -= 1
dc = sorted_sample_dist[index]
#print index, sorted_sample_dist[index]
eps_list.append(dc)
print(eps_list)
#print X
# ===========================================================================
# do Clustering using MR -DBSCAN method
clustering_name = "mr-dbscan_iter_"
potential = True
remove_outliers = False
# potential = False
eps = eps_list[0]
min_samples = 1
len_frames = len(X)
print("Total frames:", len_frames)
print("Running first calculation")
db = Faiss_DBSCAN(eps=eps, min_samples=min_samples, nlist=nlist, nprobe=nprobe, metric="l2", GPU=False, IVFFlat=IVFFlat)
db.fit(X)
core_samples_mask = np.zeros_like(db.labels_, dtype=bool)
core_samples_mask[db.core_sample_indices_] = True
old_assignments = db.labels_
n_microstates = len(set(old_assignments)) - (1 if -1 in old_assignments else 0)
print('Estimated number of clusters: %d' % n_microstates)
# Calculating percentage of each states
frame_bincount = np.bincount(old_assignments[old_assignments>=0]) #remove outliers
frame_freq_index_sorted = np.argsort(frame_bincount)[::-1] # descending arg sort
frame_freq_percent_sorted = frame_bincount[frame_freq_index_sorted]/np.float32(len_frames)
print(frame_freq_percent_sorted[0:10])
print(frame_freq_index_sorted[0:10])
old_frame_freq_percent_sorted = frame_freq_percent_sorted
old_frame_freq_index_sorted = frame_freq_index_sorted
iter_name = clustering_name + '0' + '_eps_' + str(eps) + '_min_samples_' + str(min_samples) + '_n_states_' + str(n_microstates)
plot_cluster(labels=old_assignments, phi_angles=phi_angles, psi_angles=psi_angles, name=iter_name, potential=potential)
n_iterations = len(eps_list)
print("n_iterations:", n_iterations)
min_samples_list = [10, 20, 20]
#min_samples_list = [50, 30, 20, 15, 10, 8, 5, 2]
n_min_samples = len(min_samples_list)
#eps_list = [3.0, 2.0, 1.0, 0.8, 0.5]
#min_samples_list = [3, 3, 3, 3, 3, 2, 2]
results = np.zeros((n_min_samples,n_iterations,len_frames), dtype=np.int32)
for i in range(1, n_iterations):
eps = eps_list[i]
min_samples = min_samples_list[i]
db = Faiss_DBSCAN(eps=eps, min_samples=min_samples, nlist=nlist, nprobe=nprobe, metric="l2", GPU=False, IVFFlat=IVFFlat).fit(X)
core_samples_mask = np.zeros_like(db.labels_, dtype=bool)
core_samples_mask[db.core_sample_indices_] = True
new_assignments = db.labels_
if i is n_iterations - 1:
remove_outliers = True
#else:
# remove_outliers = False
assignments = merge_assignments(new_assignments, old_assignments, remove_outliers=remove_outliers)
n_microstates = len(set(assignments)) - (1 if -1 in assignments else 0)
#results[j,i, :]= np.array(assignments)
print("Iter:", i, "Running MR-DBSCAN at eps:", eps, 'min_sampes:', min_samples, 'Estimated number of clusters:', n_microstates)
#print('Estimated number of clusters: %d' % n_microstates)
iter_name = clustering_name + str(i) + '_eps_' + str(eps) + '_min_samples_' + str(min_samples) + '_n_states_' + str(n_microstates)
plot_cluster(labels=assignments, phi_angles=phi_angles, psi_angles=psi_angles, name=iter_name, potential=potential)
#old_assignments = assignments
#print(results)
#np.save("results.npy", results)
#np.savetxt("results.csv", results, fmt="%d", delimiter=",")
np.savetxt("eps_list.txt", eps_list, fmt="%f", delimiter=",")
np.savetxt("min_samples_list.txt", min_samples_list, fmt="%d", delimiter=",")
def main():
cli = argparse.ArgumentParser()
cli.add_argument(
'-t',
'--trajListFns',
default='trajlist',
help='List of trajectory files to read in, separated by spaces.')
cli.add_argument(
'-a',
'--atomListFns',
default='atom_indices',
help='List of atom index files to read in, separated by spaces.')
cli.add_argument('-g',
'--topology',
default='native.pdb',
help='topology file.')
cli.add_argument('-o',
'--homedir',
help='Home dir.',
default=".",
type=str)
cli.add_argument('-e',
'--iext',
help='''The file extension of input trajectory
files. Must be a filetype that mdtraj.load() can recognize.''',
default="xtc",
type=str)
cli.add_argument('-n',
'--n_clusters',
help='''n_clusters.''',
default=100,
type=int)
cli.add_argument('-m',
'--n_macro_states',
help='''n_macro_states.''',
default=6,
type=int)
cli.add_argument('-s', '--stride', help='stride.', default=None, type=int)
args = cli.parse_args()
trajlistname = args.trajListFns
atom_indicesname = args.atomListFns
trajext = args.iext
File_TOP = args.topology
homedir = args.homedir
n_clusters = args.n_clusters
n_macro_states = args.n_macro_states
stride = args.stride
# ===========================================================================
# Reading Trajs from XTC files
#print "stride:", stride
#trajreader = XTCReader(trajlistname, atom_indicesname, homedir, trajext, File_TOP, nSubSample=stride)
#trajs = trajreader.trajs
#print trajs
#traj_len = trajreader.traj_len
#np.savetxt("./traj_len.txt", traj_len, fmt="%d")
if os.path.isfile(
"./phi_angles.txt") and os.path.isfile("./psi_angles.txt") is True:
phi_angles = np.loadtxt("./phi_angles.txt", dtype=np.float32)
psi_angles = np.loadtxt("./psi_angles.txt", dtype=np.float32)
else:
phi_angles, psi_angles = trajreader.get_phipsi(trajs,
psi=[6, 8, 14, 16],
phi=[4, 6, 8, 14])
#phi_angles, psi_angles = trajreader.get_phipsi(trajs, psi=[5, 7, 13, 15], phi=[3, 5, 7, 13])
np.savetxt("./phi_angles.txt", phi_angles, fmt="%f")
np.savetxt("./psi_angles.txt", psi_angles, fmt="%f")
phi_psi = np.column_stack((phi_angles, psi_angles))
n_samples = 1000
percent = 0.9
import random
whole_samples = random.sample(phi_psi, n_samples)
#print whole_samples
from metrics.pairwise import pairwise_distances
sample_dist_metric = pairwise_distances(whole_samples,
whole_samples,
metric='rmsd')
print sample_dist_metric.shape
sample_dist = []
for i in xrange(0, n_samples):
for j in xrange(i + 1, n_samples):
sample_dist.append(sample_dist_metric[i, j])
sorted_sample_dist = np.sort(sample_dist)
print "Len of samples:", len(sorted_sample_dist), np.max(
sorted_sample_dist), np.min(sorted_sample_dist)
eps_list = []
len_samples = len(sorted_sample_dist)
for percent in [
0.40, 0.35, 0.3, 0.25, 0.2, 0.15, 0.1, 0.05, 0.025, 0.010, 0.008,
0.005, 0.003, 0.001, 0.0005, 0.0003, 0.0001, 0.00005, 0.00001
]:
percent /= 10.0
index = int(round(len_samples * percent))
if index == len_samples:
index -= 1
dc = sorted_sample_dist[index]
#print index, sorted_sample_dist[index]
eps_list.append(dc)
print eps_list
# from sklearn.neighbors import NearestNeighbors
# print len(phi_psi)
# neighborhoods_model = NearestNeighbors(n_neighbors=len(phi_psi), algorithm='kd_tree')
# neighborhoods_model.fit(phi_psi)
# #distances, indices = neighborhoods_model.kneighbors(phi_psi)
# distances, indices = neighborhoods_model.kneighbors(phi_psi, 5)
# print distances
#print phi_psi
# ===========================================================================
# do Clustering using MR -DBSCAN method
clustering_name = "mr-dbscan_iter_"
potential = True
# potential = False
eps = eps_list[0]
min_samples = 5
print "Running first calculation"
db = DBSCAN(eps=eps, min_samples=min_samples, metric='rmsd').fit(phi_psi)
core_samples_mask = np.zeros_like(db.labels_, dtype=bool)
core_samples_mask[db.core_sample_indices_] = True
old_assignments = db.labels_
n_microstates = len(
set(old_assignments)) - (1 if -1 in old_assignments else 0)
print('Estimated number of clusters: %d' % n_microstates)
iter_name = clustering_name + '0' + '_eps_' + str(
eps) + '_min_samples_' + str(min_samples) + '_n_states_' + str(
n_microstates)
plot_cluster(labels=old_assignments,
phi_angles=phi_angles,
psi_angles=psi_angles,
name=iter_name,
potential=potential)
n_iterations = len(eps_list)
print "n_iterations:", n_iterations
#eps_list = [3.0, 2.0, 1.0, 0.8, 0.5]
min_samples_list = [3, 3, 3, 3, 3, 2, 2]
for i in xrange(1, n_iterations):
eps = eps_list[i]
#min_samples = min_samples_list[i]
db = DBSCAN(eps=eps, min_samples=min_samples,
metric='rmsd').fit(phi_psi)
print "Iter:", i, "Running MR-DBSCAN at eps:", eps, 'min_sampes:', min_samples
core_samples_mask = np.zeros_like(db.labels_, dtype=bool)
core_samples_mask[db.core_sample_indices_] = True
new_assignments = db.labels_
old_assignments = merge_assignments(new_assignments, old_assignments)
n_microstates = len(
set(old_assignments)) - (1 if -1 in old_assignments else 0)
print('Estimated number of clusters: %d' % n_microstates)
iter_name = clustering_name + str(i) + '_eps_' + str(
eps) + '_min_samples_' + str(min_samples) + '_n_states_' + str(
n_microstates)
plot_cluster(labels=old_assignments,
phi_angles=phi_angles,
psi_angles=psi_angles,
name=iter_name,
potential=potential)
labels = old_assignments
print labels
n_microstates = len(set(labels)) - (1 if -1 in labels else 0)
print('Estimated number of clusters: %d' % n_microstates)
#cluster_centers_ = cluster.cluster_centers_
# plot micro states
clustering_name = "mr-dbscan_n_" + str(n_microstates)
np.savetxt("assignments_" + clustering_name + ".txt", labels, fmt="%d")
#np.savetxt("cluster_centers_"+clustering_name+".txt", cluster_centers_, fmt="%d")
plot_cluster(labels=labels,
phi_angles=phi_angles,
psi_angles=psi_angles,
name=clustering_name,
potential=potential)
