results handling (dd24d8c5) · Commits · 钟慕尧 / tvgl

.gitignore

+2 −2

Original line number	Diff line number	Diff line
		*.pyc
		*~
		drafts/*
		results/*
		network_results/*
		results/*.csv
		network_results/*.csv
		matlab/*

DataHandler.py

+33 −27

Original line number	Diff line number	Diff line
		@@ -164,47 +164,53 @@ class DataHandler(object):
		f.write("\n")
		f.write("\n\n")

		def write_results(self, datafile, alg_type, alg):
		def write_results(self, datafile, alg_type, alg, splitter=','):
		run_time = datetime.datetime.now()
		results_name = "results/%s_di%sbl%sob%sla%sbe%s_%s.txt" % (
		alg_type, alg.dimension, alg.blocks, alg.obs, alg.lambd,
		alg.beta, run_time.strftime("%Y%m%d%H%M%S"))
		results_name = "results/%s_di%sbl%sob%sla%sbe%s_%s.csv" % (
		alg_type, alg.dimension, alg.blocks, alg.obs, int(alg.lambd),
		int(alg.beta), run_time.strftime("%Y%m%d%H%M%S"))
		with open(results_name, "w") as f:
		f.write("# Information\n")
		f.write("Run datetime: %s\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("Total edges: %s\n" % alg.real_edges)
		f.write("Total edgeless: %s\n" % alg.real_edgeless)
		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("Total edges, %s\n" % alg.real_edges)
		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)
		f.write("Correct positive edges: %s\n" % alg.correct_positives)
		f.write("All positives: %s\n" % alg.all_positives)
		f.write("F1 Score: {0:.3f}\n"
		f.write("Algorithm run time, %s seconds\n" % alg.run_time)
		f.write("Iterations to complete, %s\n\n" % alg.iteration)
		f.write("Correct positive edges, %s\n" % alg.correct_positives)
		f.write("All positives, %s\n" % alg.all_positives)
		f.write("F1 Score, {0:.3f}\n"
		.format(alg.f1score))
		try:
		f.write("Temporal deviations ratio (max/mean): {0:.3f}\n"
		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"
		f.write("Temporal deviations ratio (max/mean), %s\n"
		% alg.dev_ratio)
		f.write("Temporal deviations: ")
		f.write("Temporal deviations ")
		for dev in alg.deviations:
		try:
		f.write("{0:.3f} ".format(dev))
		f.write(",{0:.3f}".format(dev))
		except ValueError:
		f.write("%s" % dev)
		f.write(",%s" % dev)
		f.write("\nNormalized Temporal deviations ")
		for dev in alg.norm_deviations:
		try:
		f.write(",{0:.3f}".format(dev))
		except ValueError:
		f.write(",%s" % dev)
		f.write("\n")

network_results/readme.txt

0 → 100644

+1 −0

Original line number	Diff line number	Diff line
		Results from real data are saved into this folder.

results/dynamic_di20bl50ob20la22be0_20170907001012.txt

deleted100644 → 0

+0 −24

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

results/parallel_di20bl50ob20la7be50_20170907000908.txt

deleted100644 → 0

+0 −24

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

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