def Evaluate_Result(homedir='.',
tProb_=None,
lag_time=1,
microstate_mapping_=None,
MacroAssignments_=None,
name=None):
MacroMSM = MarkovStateModel(lag_time=lag_time)
MacroMSM.fit(MacroAssignments_)
Macro_tProb_ = MacroMSM.tProb_
Macro_tCount_ = MacroMSM.tCount_
#plot_matrix(labels=None, tProb_=tProb_, name=name)
#Calculate metastablilty
print "Calculating Metastablilty and Modularity..."
metastability = Macro_tProb_.diagonal().sum()
metastability /= len(Macro_tProb_)
print "Metastability:", metastability
#Calculate modularity
micro_tProb_ = tProb_
degree = micro_tProb_.sum(axis=1) #row sum of tProb_ matrix
total_degree = degree.sum()
modularity = 0.0
len_mapping = len(microstate_mapping_)
for i in xrange(len_mapping):
state_i = microstate_mapping_[i]
for j in xrange(len_mapping):
state_j = microstate_mapping_[j]
if state_i == state_j:
modularity += micro_tProb_[
i, j] - degree[i] * degree[j] / total_degree
modularity /= total_degree
print "Modularity:", modularity
Macro_tCountDir = homedir + "/" + name + "Macro_tCounts.mtx"
Macro_tProbDir = homedir + "/" + name + "Macro_tProb.mtx"
Metastability_Modularity_Dir = homedir + "/" + name + "Metastability_Modularity.txt"
np.savetxt(Metastability_Modularity_Dir, [metastability, modularity],
fmt="%lf")
scipy.io.mmwrite(Macro_tCountDir,
scipy.sparse.csr_matrix(Macro_tCount_),
field='integer')
scipy.io.mmwrite(Macro_tProbDir, scipy.sparse.csr_matrix(Macro_tProb_))
#Plot tProb matrix
plot_matrix(tProb_=Macro_tProb_, name=name)
def Evaluate_Result(homedir='.', tProb_=None, lag_time=1, microstate_mapping_=None, MacroAssignments_=None, name=None):
MacroMSM = MarkovStateModel(lag_time=lag_time)
MacroMSM.fit(MacroAssignments_)
Macro_tProb_ = MacroMSM.tProb_
Macro_tCount_ = MacroMSM.tCount_
#plot_matrix(labels=None, tProb_=tProb_, name=name)
#Calculate metastablilty
print "Calculating Metastablilty and Modularity..."
metastability = Macro_tProb_.diagonal().sum()
metastability /= len(Macro_tProb_)
print "Metastability:", metastability
#Calculate modularity
micro_tProb_ = tProb_
degree = micro_tProb_.sum(axis=1) #row sum of tProb_ matrix
total_degree = degree.sum()
modularity = 0.0
len_mapping = len(microstate_mapping_)
for i in xrange(len_mapping):
state_i = microstate_mapping_[i]
for j in xrange(len_mapping):
state_j = microstate_mapping_[j]
if state_i == state_j:
modularity += micro_tProb_[i, j] - degree[i]*degree[j]/total_degree
modularity /= total_degree
print "Modularity:", modularity
Macro_tCountDir = homedir + "/" + name + "Macro_tCounts.mtx"
Macro_tProbDir = homedir + "/" + name + "Macro_tProb.mtx"
Metastability_Modularity_Dir = homedir + "/" + name + "Metastability_Modularity.txt"
np.savetxt(Metastability_Modularity_Dir, [metastability, modularity], fmt="%lf")
scipy.io.mmwrite(Macro_tCountDir, scipy.sparse.csr_matrix(Macro_tCount_), field='integer')
scipy.io.mmwrite(Macro_tProbDir, scipy.sparse.csr_matrix(Macro_tProb_))
#Plot tProb matrix
plot_matrix(tProb_=Macro_tProb_, name=name)
import os,sys
import numpy as np
import scipy.io
HK_DataMiner_Path = os.path.relpath(os.pardir)
#HK_DataMiner_Path = os.path.abspath("/home/stephen/Dropbox/projects/work-2015.5/HK_DataMiner/")
sys.path.append(HK_DataMiner_Path)
from utils import plot_matrix, plot_block_matrix
from msm import MarkovStateModel
import argparse
cli = argparse.ArgumentParser()
cli.add_argument('-c', '--assignments_dir', type=str)
cli.add_argument('-p', '--tProb', type=str, default=None)
args = cli.parse_args()
assignments_dir = args.assignments_dir
tProb_dir = args.tProb
labels = np.loadtxt(assignments_dir, dtype=np.int32)
if tProb_dir is not None:
tProb_ = scipy.io.mmread(tProb_dir)
#plot_matrix(labels=labels, tProb_=tProb_, name='Matrix')
plot_block_matrix(labels=labels, tProb_=tProb_, name='BlockMatrix')
else:
MSM = MarkovStateModel(lag_time=1)
MSM.fit(labels)
tProb_ = MSM.tProb_
#plot_matrix(labels=None, tProb_=tProb_, name='Matrix')
plot_block_matrix(labels=labels, tProb_=tProb_, name='BlockMatrix')
