round precision to 1 in networks (d8cfd339) · Commits · 钟慕尧 / tvgl

DynamicGL.py

+1 −1

Original line number	Diff line number	Diff line
		@@ -64,4 +64,4 @@ class DynamicGL(TVGL):
		p.close()
		p.join()
		self.run_time = '{0:.3g}'.format(time.time() - start_time)
		self.thetas = [np.round(theta, 3) for theta in self.thetas]
		self.thetas = [np.round(theta, self.roundup) for theta in self.thetas]

+2 −2

Original line number	Diff line number	Diff line
		@@ -89,7 +89,7 @@ def mp_parallel_tvgl((thetas, z0s, z1s, z2s, u0s, u1s, u2s,
		if prev_pipe is not None:
		prev_pipe.send(None)
		out_queue.put((final_thetas, iteration))
		print "Process %s finished" % proc_index
		#print "Process %s finished" % proc_index
		except Exception as e:
		traceback.print_exc()
		raise e
		@@ -192,4 +192,4 @@ class ParallelTVGL(TVGL):
		self.iteration = iteration
		print self.iteration
		self.run_time = '{0:.3g}'.format(time.time() - start_time)
		self.thetas = [np.round(theta, 3) for theta in self.thetas]
		self.thetas = [np.round(theta, self.roundup) for theta in self.thetas]

+2 −1

Original line number	Diff line number	Diff line
		@@ -33,6 +33,7 @@ class TVGL(object):
		self.u2s = [np.zeros((self.dimension, self.dimension))] * self.blocks
		self.nju = float(self.obs)/float(3*self.rho)
		self.e = 1e-5
		self.roundup = 1

		def read_data(self, filename, comment="#", splitter=","):
		with open(filename, "r") as f:
		@@ -165,7 +166,7 @@ class TVGL(object):
		pass

		def final_tuning(self, stopping_criteria, max_iter):
		self.thetas = [np.round(theta, 3) for theta in self.thetas]
		self.thetas = [np.round(theta, self.roundup) for theta in self.thetas]
		self.terminate_pools()
		if stopping_criteria:
		print "\nIterations to complete: %s" % self.iteration

0 → 100644

+24 −0

Original line number	Diff line number	Diff line
		# Information
		Run datetime: 2017-09-07 00:10:12
		Data file: synthetic_data/1000x20_20170823203622.csv
		Algorithm type: DynamicGL
		Penalty function: n/a
		Data dimension: 20
		Blocks: 50
		Observations in a block: 20
		Rho: 21
		Beta: 0
		Lambda: 22
		Processes used: 25
		Total edges: 1000
		Total edgeless: 8500

		# Results
		Algorithm run time: 0.555 seconds
		Iterations to complete: n/a

		Correct positive edges: 406
		All positives: 667
		F1 Score: 0.487
		Temporal deviations ratio (max/mean): 2.194
		Temporal deviations: 0.666 0.409 0.563 0.261 0.433 0.503 0.460 0.433 0.396 0.506 0.475 0.383 0.676 0.581 0.519 0.566 0.405 0.449 0.453 0.346 0.542 0.445 0.489 0.535 1.000 0.441 0.437 0.445 0.581 0.485 0.365 0.542 0.478 0.413 0.478 0.396 0.482 0.456 0.441 0.482 0.341 0.392 0.355 0.274 0.226 0.437 0.280 0.325 0.286

0 → 100644

+24 −0

Original line number	Diff line number	Diff line
		# Information
		Run datetime: 2017-09-07 00:09:08
		Data file: synthetic_data/1000x20_20170823203622.csv
		Algorithm type: ParallelTVGL
		Penalty function: Group Lasso
		Data dimension: 20
		Blocks: 50
		Observations in a block: 20
		Rho: 6.7
		Beta: 50
		Lambda: 7
		Processes used: 25
		Total edges: 1000
		Total edgeless: 8500

		# Results
		Algorithm run time: 16.2 seconds
		Iterations to complete: 1622

		Correct positive edges: 873
		All positives: 884
		F1 Score: 0.927
		Temporal deviations ratio (max/mean): 26.417
		Temporal deviations: 0.000 0.000 0.000 0.053 0.000 0.053 0.000 0.000 0.000 0.000 0.000 0.000 0.053 0.000 0.074 0.053 0.000 0.053 0.000 0.000 0.000 0.000 0.000 0.053 1.000 0.053 0.074 0.053 0.000 0.053 0.053 0.000 0.000 0.000 0.053 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.053 0.074