support for real data (5f4872f1) · Commits · 钟慕尧 / tvgl

DataHandler.py

+61 −1

Original line number	Diff line number	Diff line
		@@ -91,6 +91,65 @@ class DataHandler(object):
		line = line[1:]
		new_file.write("%s\n" % line)

		def write_network_results(self, datafile, alg_type, alg, splitter=","):
		run_time = datetime.datetime.now()
		results_name = "network_results/%s_di%sbl%sob%sla%sbe%s_%s.csv" % (
		alg_type, alg.dimension, alg.blocks, alg.obs, alg.lambd,
		alg.beta, run_time.strftime("%Y%m%d%H%M%S"))
		""" Read features """
		with open(datafile, "r") as f:
		for i, line in enumerate(f):
		if i == 0:
		feats = line.strip().split(splitter)[1:]
		break
		features = {}
		for i, feat in enumerate(feats):
		features[i] = feat
		""" Write Results """
		with open(results_name, "w") as f:
		f.write("# Information\n")
		f.write("Run datetime, %s\n" %
		run_time.strftime("%Y-%m-%d %H:%M:%S"))
		f.write("Data file, %s\n" % datafile)
		f.write("Algorithm type, %s\n" % alg.__class__.__name__)
		f.write("Penalty function, %s\n" % alg.penalty_function)
		f.write("Data dimension, %s\n" % alg.dimension)
		f.write("Blocks, %s\n" % alg.blocks)
		f.write("Observations in a block, %s\n" % alg.obs)
		f.write("Rho, %s\n" % alg.rho)
		f.write("Beta, %s\n" % alg.beta)
		f.write("Lambda, %s\n" % alg.lambd)
		f.write("Processes used, %s\n" % alg.processes)
		f.write("\n")
		f.write("# Results\n")
		f.write("Algorithm run time, %s seconds\n" % alg.run_time)
		f.write("Iterations to complete, %s\n\n" % alg.iteration)
		try:
		f.write("Temporal deviations ratio (max/mean), {0:.3f}\n"
		.format(alg.dev_ratio))
		except ValueError:
		f.write("Temporal deviations ratio (max/mean), %s\n"
		% alg.dev_ratio)
		f.write("Temporal deviations ")
		for dev in alg.deviations:
		try:
		f.write(",{0:.3f}".format(dev))
		except ValueError:
		f.write(",%s" % dev)
		""" Write networks """
		f.write("\n\n#Networks:\n\n")
		for k in range(alg.blocks):
		f.write("Block %s\n " % k)
		for feat in feats:
		f.write("," + feat)
		f.write("\n")
		for i in range(alg.dimension):
		f.write(features[i])
		for j in range(alg.dimension):
		f.write("," + str(alg.thetas[k][i, j]))
		f.write("\n")
		f.write("\n\n")

		def write_results(self, datafile, alg_type, alg):
		run_time = datetime.datetime.now()
		results_name = "results/%s_di%sbl%sob%sla%sbe%s_%s.txt" % (
		@@ -111,7 +170,8 @@ class DataHandler(object):
		f.write("Lambda: %s\n" % alg.lambd)
		f.write("Processes used: %s\n" % alg.processes)
		f.write("Total edges: %s\n" % alg.real_edges)
		f.write("Total edgeless: %s\n\n" % alg.real_edgeless)
		f.write("Total edgeless: %s\n" % alg.real_edgeless)
		f.write("\n")
		f.write("# Results\n")
		f.write("Algorithm run time: %s seconds\n" % alg.run_time)
		f.write("Iterations to complete: %s\n\n" % alg.iteration)

TVGL.py

+13 −4

Original line number	Diff line number	Diff line
		@@ -35,7 +35,7 @@ class TVGL(object):
		self.e = 1e-5
		self.roundup = 1

		def read_data(self, filename, comment="#", splitter=","):
		def read_data(self, filename, comment="#", splitter=",", datecolumn=True):
		with open(filename, "r") as f:
		comment_count = 0
		for i, line in enumerate(f):
		@@ -43,6 +43,9 @@ class TVGL(object):
		comment_count += 1
		else:
		if self.dimension is None:
		if datecolumn:
		self.dimension = len(line.split(splitter)) - 1
		else:
		self.dimension = len(line.split(splitter))
		datasamples = i + 1 - comment_count
		print "Total data samples: %s" % datasamples
		@@ -58,8 +61,14 @@ class TVGL(object):
		if i == 1:
		self.generate_real_thetas(line, splitter)
		continue
		if datecolumn:
		lst.append([float(x)
		for x in np.array(line.strip().
		split(splitter)[1:])])
		else:
		lst.append([float(x)
		for x in np.array(line.strip().split(splitter))])
		for x in np.array(line.strip().
		split(splitter))])
		count += 1
		if count == self.obs:
		datablck = np.array(lst)

run.py

+12 −8

Original line number	Diff line number	Diff line
		@@ -9,6 +9,7 @@ from StaticGL import StaticGL


		if __name__ == "__main__" and len(sys.argv) > 1:
		synthetic = False
		datahandler = DataHandler()
		start_time = time.time()
		algo_type = sys.argv[1]
		@@ -42,6 +43,7 @@ if __name__ == "__main__" and len(sys.argv) > 1:
		algorithm.temporal_deviations()
		print "Temp deviations: "
		print algorithm.deviations
		if synthetic:
		algorithm.correct_edges()
		print "Total Edges: %s" % algorithm.real_edges
		print "Correct Edges: %s" % algorithm.correct_positives
		@@ -50,5 +52,7 @@ if __name__ == "__main__" and len(sys.argv) > 1:
		print "False Edges: %s" % false_edges
		print "F1 Score: %s" % algorithm.f1score
		datahandler.write_results(filename, algo_type, algorithm)
		else:
		datahandler.write_network_results(filename, algo_type, algorithm)
		print "Algorithm run time: %s seconds" % (algorithm.run_time)
		print "Execution time: %s seconds" % (time.time() - start_time)

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