def opener(option_correl, chrom, number_of_samples_ar):
comb = "_".join([config_variables.dict_option[el] for el in option_correl])
kappa_0, mu_0, alpha_0, Beta_0 = config_variables.kappa_0, config_variables.mu_0, config_variables.alpha_0, config_variables.Beta_0
save_to_folder = os.getcwd() + "/MOG_results_/"
parameter_multiple_chains = {}
for chain_number, number_of_samples in zip(chains, number_of_samples_ar):
name = 'MOG_distance_emprirical_mu_trace_of_c_{0}_{1}_{2}_{3}_{4}_{5}_{6}_{7}'.format(kappa_0, mu_0, alpha_0, Beta_0, chrom, comb, number_of_samples, chain_number)
name = save_to_folder + name
print name
parameter_multiple_chains[chain_number] = iter_loadtxt.iter_loadtxt(name, ",", dtype = int, skiprows=min(burn_in_start[index_opt]))
n_ = parameter_multiple_chains[1].shape[0]
j_ = parameter_multiple_chains[1].shape[1]
m_ = len(chains)
tensor_parameter_multiple_chains = np.zeros((m_, n_, j_))
for chain_number in chains: tensor_parameter_multiple_chains[chain_number-1] = parameter_multiple_chains[chain_number]
return tensor_parameter_multiple_chains
def loads_MoG_results(chrom):
comb = "_".join([config_variables.dict_option[el] for el in option_correl])
kappa_0, mu_0, alpha_0, Beta_0 = config_variables.kappa_0, config_variables.mu_0, config_variables.alpha_0, config_variables.Beta_0
name = 'cluster_trace_of_c_distance_{0}_{1}_{2}_{3}_{4}_{5}_{6}'.format(kappa_0, mu_0, alpha_0, Beta_0, chrom, comb, number_of_samples)
name = os.getcwd() + "/MOG_results_/" + name
import iter_loadtxt
_c_trace_raw = iter_loadtxt.iter_loadtxt(name, ",", dtype = int) # saves memory
num_of_promoters = len(config_variables.dict_chrom_pro_survived[chrom])
promoters_fixed_labels = np.zeros((len(_c_trace_raw), num_of_promoters),dtype = int)
promoters_fixed_labels[:] = np.arange(num_of_promoters, dtype = int)
_c_trace_distance = np.c_[promoters_fixed_labels, _c_trace_raw]
return _c_trace_distance
def loads_MoG_results(chrom, name):
import iter_loadtxt
_c_trace_raw = iter_loadtxt.iter_loadtxt(name, ",", dtype=int) # saves memory
if mode_of_sampler == "dirichlet_MOG":
_c_trace = _c_trace_raw
else:
num_of_promoters = len(config_variables.dict_chrom_pro_survived[chrom])
promoters_fixed_labels = np.zeros((len(_c_trace_raw), num_of_promoters), dtype=int)
promoters_fixed_labels[:] = np.arange(num_of_promoters, dtype=int)
_c_trace = np.c_[promoters_fixed_labels, _c_trace_raw]
return _c_trace
def main():
# Begin -- User defined options --
default_path_1 = os.getcwd()
os.chdir(default_path_1)
default_path = os.path.normpath(os.path.join(os.getcwd(), ".."))
challengeFolder = default_path + filesep
# challengeFolder = default_path + '\\'
dataDirectory = challengeFolder +'data'
submissionDirectory = challengeFolder + 'results' # Where ready-to-submit result files are found
modelDirectory = challengeFolder + 'models' # Where trained predictive models end up
file_count = 1
networkIdNames = []
#networkIdNames.append('normal-1') # IMPORTANT: these are the base names of your data
#networkIdNames.append('normal-2')
#networkIdNames.append('iNet1_Size100_CC01inh')
#networkIdNames.append('iNet1_Size100_CC02inh')
#networkIdNames.append('iNet1_Size100_CC03inh')
#networkIdNames.append('iNet1_Size100_CC04inh')
#networkIdNames.append('iNet1_Size100_CC05inh')
#networkIdNames.append('iNet1_Size100_CC06inh')
networkIdNames.append('mockvalid') # You can run the code as is with these options
networkIdNames.append('mocktest') # To generate a challenge submission, substitute mockvalid and mocktest for valid and test
# that are the challenge datasets.
scoringMethods = [];
scoringMethods.append('pearsonsCorrelation'); # We provide 3 "basic" examples of scoring methods. You need to write your own.
# We did not implement GTE (see the Matlab and C++ code for that).
#scoringMethods.append('crossCorrelation');
#scoringMethods.append('randomScore');
#scoringMethods.append('computeGranger');
#scoringMethods.append('information-geometry-causal-inference');
#scoringMethods.append('mutualinformation');
modelName = 'sample_model' # Name of the model used by the trainedPredictor scoring method
concatenateScores = 0; # 1 if all scores from various networks are appended to the same file (for each method)
# End -- User defined options --
# Initializations
if not os.path.exists(submissionDirectory):
os.makedirs(submissionDirectory)
the_date = datetime.now()
timestr = time.strftime("%Y%m%d-%H%M%S")
extension='.txt';
# logfile = submissionDirectory + '\\' + 'logfile.txt'
logfile = submissionDirectory + filesep + 'logfile.txt'
flog=open(logfile, 'a');
print('==========================================================\n')
print('\n ChaLearn connectomics challenge, sample code version '+sys.version+'\n')
print(' Date and time started: ' + the_date.strftime("%d/%m/%Y %H:%M:%S")+'\n')
print(' Saving AUC results in ' + logfile+'\n')
print('==========================================================\n\n')
start = time.time()
metNum=len(scoringMethods);
netNum=len(networkIdNames);
scores = numpy.empty((netNum,metNum))
for j in range(0,metNum):
scoringMethod = scoringMethods[j];
# scoreFile = submissionDirectory + '\\' + scoringMethod + '_' +"_".join(str(x) for x in networkIdNames )+timestr+'.csv';
target = submissionDirectory + filesep + scoringMethod + '_' +"_".join(str(x) for x in networkIdNames )+timestr+'_kaggle_ready.csv';
tf = open(target, 'a')
scoreFile = submissionDirectory + filesep + scoringMethod + '_' +"_".join(str(x) for x in networkIdNames )+timestr+'.csv';
# Loop over all networks you want to process
for i in range(0,netNum):
networkId = networkIdNames[i];
print('***'+ scoringMethod +' on '+networkId+' ***\n\n' );
# Load the Fluorescence signal
# fluorescenceFile = dataDirectory + '\\'+ 'fluorescence_'+networkId+extension;
fluorescenceFile = dataDirectory + filesep+ 'fluorescence_'+networkId+extension;
#F = loadtxt(fluorescenceFile,delimiter=",")
F = iter_loadtxt(fluorescenceFile)
# Compute the scores for all pairs of neurons i, j
tic = time.clock()
print('Computing scores with ' + scoringMethod + '\n')
if scoringMethod == 'trainedPredictor':
# arg = modelDirectory + '\\' + modelName + '.mat'
arg = modelDirectory + filesep + modelName + '.mat'
else:
arg = 'false'
scores = funcdict[scoringMethod](F, arg)
#scores = numpy.corrcoef(F, rowvar = 0)
# Note: these scoring methods do not make use of available neuron
# positions (in their 2-D layout simulating neuron cultures).
toc = time.clock()
#Write the scores to the submission directory, ready to be submitted
# resuFile = submissionDirectory + '\\'+scoringMethod+'_'+networkId+'_'+timestr+'.csv';
resuFile = submissionDirectory + filesep+scoringMethod+'_'+networkId+'_'+timestr+'.csv';
if ~concatenateScores:
scoreFile = resuFile
print 'Writing ' + scoreFile + ' \n'
if scoringMethod == 'randomScore':
scores_dense = scores.toarray()
else:
scores_dense = scores
writeNetworkScoresInCSV(scoreFile, scores_dense, networkId)
if file_count == 1:
tf.write(open(scoreFile).read())
file_count = file_count + 1
else:
src1 = open(scoreFile)
src1.readline()
tf.write(src1.read())
src1.close()
# If we have the network architecture... compute/plot the ROC curve:
# (network architecture provided only for training data to the participants)
# networkFile = dataDirectory +'\\'+'network_' +networkId +extension;
networkFile = dataDirectory + filesep + 'network_' + networkId + extension
if os.path.exists(networkFile):
print'Computing ROC with using network ' + networkFile + '\n'
network = readNetworkScores(networkFile);
if scoringMethod == 'randomScore':
scores_dense = scores.toarray()
else:
scores_dense = scores
pred = reshapeScores(scores_dense)
true = reshapeNetwork(network)
###############################################
fpr, tpr, thresholds = metrics.roc_curve(true,pred)
print('\n==> AUC = '+ str(metrics.auc(fpr,tpr))+'\n');
resuFile = scoringMethod+'_'+networkId+'_'+timestr+'.png'
fullpath = os.path.join(submissionDirectory , resuFile)
plotROC(fpr,tpr,fullpath)
flog.write(the_date.strftime("%d/%m/%Y %H:%M:%S")+'\t'+scoringMethod+'\t'+networkId+'\t'+'%.4f\n' % ((metrics.auc(fpr, tpr))))
#close all open files
end = time.time()
#print networkId
print end - start
print the_date.strftime("%d/%m/%Y %H:%M:%S")+' Challenge solved.' +'\n'
toc = time.clock();
flog.close()
tf.close()
def main():
# Begin -- User defined options --
default_path_1 = os.getcwd()
os.chdir(default_path_1)
default_path = os.path.normpath(os.path.join(os.getcwd(), ".."))
challengeFolder = default_path + filesep
# challengeFolder = default_path + '\\'
dataDirectory = challengeFolder + 'data'
submissionDirectory = challengeFolder + 'results' # Where ready-to-submit result files are found
modelDirectory = challengeFolder + 'models' # Where trained predictive models end up
file_count = 1
networkIdNames = []
#networkIdNames.append('normal-1') # IMPORTANT: these are the base names of your data
#networkIdNames.append('normal-2')
#networkIdNames.append('iNet1_Size100_CC01inh')
#networkIdNames.append('iNet1_Size100_CC02inh')
#networkIdNames.append('iNet1_Size100_CC03inh')
#networkIdNames.append('iNet1_Size100_CC04inh')
#networkIdNames.append('iNet1_Size100_CC05inh')
#networkIdNames.append('iNet1_Size100_CC06inh')
networkIdNames.append(
'mockvalid') # You can run the code as is with these options
networkIdNames.append(
'mocktest'
) # To generate a challenge submission, substitute mockvalid and mocktest for valid and test
# that are the challenge datasets.
scoringMethods = []
scoringMethods.append('pearsonsCorrelation')
# We provide 3 "basic" examples of scoring methods. You need to write your own.
# We did not implement GTE (see the Matlab and C++ code for that).
#scoringMethods.append('crossCorrelation');
#scoringMethods.append('randomScore');
#scoringMethods.append('computeGranger');
#scoringMethods.append('information-geometry-causal-inference');
#scoringMethods.append('mutualinformation');
modelName = 'sample_model' # Name of the model used by the trainedPredictor scoring method
concatenateScores = 0
# 1 if all scores from various networks are appended to the same file (for each method)
# End -- User defined options --
# Initializations
if not os.path.exists(submissionDirectory):
os.makedirs(submissionDirectory)
the_date = datetime.now()
timestr = time.strftime("%Y%m%d-%H%M%S")
extension = '.txt'
# logfile = submissionDirectory + '\\' + 'logfile.txt'
logfile = submissionDirectory + filesep + 'logfile.txt'
flog = open(logfile, 'a')
print('==========================================================\n')
print('\n ChaLearn connectomics challenge, sample code version ' +
sys.version + '\n')
print(' Date and time started: ' + the_date.strftime("%d/%m/%Y %H:%M:%S") +
'\n')
print(' Saving AUC results in ' + logfile + '\n')
print('==========================================================\n\n')
start = time.time()
metNum = len(scoringMethods)
netNum = len(networkIdNames)
scores = numpy.empty((netNum, metNum))
for j in range(0, metNum):
scoringMethod = scoringMethods[j]
# scoreFile = submissionDirectory + '\\' + scoringMethod + '_' +"_".join(str(x) for x in networkIdNames )+timestr+'.csv';
target = submissionDirectory + filesep + scoringMethod + '_' + "_".join(
str(x) for x in networkIdNames) + timestr + '_kaggle_ready.csv'
tf = open(target, 'a')
scoreFile = submissionDirectory + filesep + scoringMethod + '_' + "_".join(
str(x) for x in networkIdNames) + timestr + '.csv'
# Loop over all networks you want to process
for i in range(0, netNum):
networkId = networkIdNames[i]
print('***' + scoringMethod + ' on ' + networkId + ' ***\n\n')
# Load the Fluorescence signal
# fluorescenceFile = dataDirectory + '\\'+ 'fluorescence_'+networkId+extension;
fluorescenceFile = dataDirectory + filesep + 'fluorescence_' + networkId + extension
#F = loadtxt(fluorescenceFile,delimiter=",")
F = iter_loadtxt(fluorescenceFile)
# Compute the scores for all pairs of neurons i, j
tic = time.clock()
print('Computing scores with ' + scoringMethod + '\n')
if scoringMethod == 'trainedPredictor':
# arg = modelDirectory + '\\' + modelName + '.mat'
arg = modelDirectory + filesep + modelName + '.mat'
else:
arg = 'false'
scores = funcdict[scoringMethod](F, arg)
#scores = numpy.corrcoef(F, rowvar = 0)
# Note: these scoring methods do not make use of available neuron
# positions (in their 2-D layout simulating neuron cultures).
toc = time.clock()
#Write the scores to the submission directory, ready to be submitted
# resuFile = submissionDirectory + '\\'+scoringMethod+'_'+networkId+'_'+timestr+'.csv';
resuFile = submissionDirectory + filesep + scoringMethod + '_' + networkId + '_' + timestr + '.csv'
if ~concatenateScores:
scoreFile = resuFile
print 'Writing ' + scoreFile + ' \n'
if scoringMethod == 'randomScore':
scores_dense = scores.toarray()
else:
scores_dense = scores
writeNetworkScoresInCSV(scoreFile, scores_dense, networkId)
if file_count == 1:
tf.write(open(scoreFile).read())
file_count = file_count + 1
else:
src1 = open(scoreFile)
src1.readline()
tf.write(src1.read())
src1.close()
# If we have the network architecture... compute/plot the ROC curve:
# (network architecture provided only for training data to the participants)
# networkFile = dataDirectory +'\\'+'network_' +networkId +extension;
networkFile = dataDirectory + filesep + 'network_' + networkId + extension
if os.path.exists(networkFile):
print 'Computing ROC with using network ' + networkFile + '\n'
network = readNetworkScores(networkFile)
if scoringMethod == 'randomScore':
scores_dense = scores.toarray()
else:
scores_dense = scores
pred = reshapeScores(scores_dense)
true = reshapeNetwork(network)
###############################################
fpr, tpr, thresholds = metrics.roc_curve(true, pred)
print('\n==> AUC = ' + str(metrics.auc(fpr, tpr)) + '\n')
resuFile = scoringMethod + '_' + networkId + '_' + timestr + '.png'
fullpath = os.path.join(submissionDirectory, resuFile)
plotROC(fpr, tpr, fullpath)
flog.write(
the_date.strftime("%d/%m/%Y %H:%M:%S") + '\t' +
scoringMethod + '\t' + networkId + '\t' + '%.4f\n' %
((metrics.auc(fpr, tpr))))
#close all open files
end = time.time()
#print networkId
print end - start
print the_date.strftime("%d/%m/%Y %H:%M:%S") + ' Challenge solved.' + '\n'
toc = time.clock()
flog.close()
tf.close()
