def main(args):
# get the expected file from the input file
dirname = os.path.dirname(args.i)
filename = os.path.splitext(os.path.basename(args.i))[0]
expected_file = os.path.join(os.path.normpath(dirname), f'{filename}_expected.csv')
expected = None
if os.path.exists(expected_file):
with open(expected_file, 'r') as f:
reader = csv.reader(f, quoting=csv.QUOTE_NONNUMERIC)
expected = list(reader)
else:
expected = []
expected = np.array(expected)
points = np.genfromtxt(args.i, delimiter=',')
# Kneedle classic window
knees_00 = kneedle_classic(points, args, args.sw)
cm = evaluation.cm(points, knees_00, expected)
mcc00 = evaluation.mcc(cm)
# Kneedle classic all
knees_01 = kneedle_classic(points, args, -1)
cm = evaluation.cm(points, knees_01, expected)
mcc01 = evaluation.mcc(cm)
# Novel Kneedle
knees_02 = kneedle_novel(points, args)
if len(knees_02) > 0:
cm = evaluation.cm(points, knees_02, expected)
mcc02 = evaluation.mcc(cm)
else:
mcc02 = 0.0
logger.info(f'{mcc00:10.2E} {mcc01:10.2E} {mcc02:10.2E}')
# Plot knees
x = points[:,0]
y = points[:,1]
_, (ax1, ax2, ax3) = plt.subplots(1, 3)
ax1.plot(x, y)
ax1.plot(x[knees_00], y[knees_00], 'r+')
ax2.plot(x, y)
ax2.plot(x[knees_01], y[knees_01], 'r+')
ax3.plot(x, y)
if len(knees_02) > 0:
ax3.plot(x[knees_02], y[knees_02], 'r+')
plt.show()
def test_mcc_2(self):
points = np.array([[0,0], [1,1], [2,2]])
knees = np.array([0])
expected = np.array([[1,1], [2,2]])
cm = evaluation.cm(points, knees, expected)
result = evaluation.mcc(cm)
desired = -1.0
self.assertAlmostEqual(result, desired)
def main(args):
path = os.path.expanduser(args.p)
if args.tr is Trace.all:
files = [f for f in os.listdir(path) if re.match(r'w[0-9]*-(lru|arc)\.csv', f)]
elif args.tr is Trace.arc:
files = [f for f in os.listdir(path) if re.match(r'w[0-9]*-arc\.csv', f)]
else:
files = [f for f in os.listdir(path) if re.match(r'w[0-9]*-lru\.csv', f)]
scores = []
for i in tqdm(range(len(files))):
points = np.genfromtxt(f'{path}{files[i]}', delimiter=',')
# open expected file
dirname = os.path.dirname(f'{path}{files[i]}')
filename = os.path.splitext(os.path.basename(files[i]))[0]
expected_file = os.path.join(os.path.normpath(dirname), f'{filename}_expected.csv')
expected = None
if os.path.exists(expected_file):
with open(expected_file, 'r') as f:
reader = csv.reader(f, quoting=csv.QUOTE_NONNUMERIC)
expected = list(reader)
else:
expected = []
expected = np.array(expected)
# get original x_max and y_ranges
x_max = [max(x) for x in zip(*points)][0]
y_range = [[max(y),min(y)] for y in zip(*points)][1]
# run rdp
reduced, removed = rdp.rdp(points, t=args.r, cost=args.c, distance=args.d)
points_reduced = points[reduced]
## Knee detection code ##
knees = zmethod.knees(points_reduced, dx=args.x, dy=args.y, dz=args.z, x_max=x_max, y_range=y_range)
knees = knees[knees>0]
knees = rdp.mapping(knees, reduced, removed)
if len(knees) > 0:
cm = evaluation.cm(points, knees, expected)
mcc = evaluation.mcc(cm)
else:
mcc = 0.0
scores.append(mcc)
# output the results
dirname = os.path.expanduser(args.p)
output = os.path.join(os.path.normpath(dirname), f'eval_rdp_metric_output.csv')
with open(output, 'w') as f:
writer = csv.writer(f)
for s in scores:
writer.writerow([s])
def main(args):
# get the expected file from the input file
dirname = os.path.dirname(args.i)
filename = os.path.splitext(os.path.basename(args.i))[0]
expected_file = os.path.join(os.path.normpath(dirname), f'{filename}_expected.csv')
expected = None
if os.path.exists(expected_file):
with open(expected_file, 'r') as f:
reader = csv.reader(f, quoting=csv.QUOTE_NONNUMERIC)
expected = list(reader)
else:
expected = []
expected = np.array(expected)
points = np.genfromtxt(args.i, delimiter=',')
## Knee detection code ##
reduced, removed = rdp.rdp(points, args.r)
points_reduced = points[reduced]
knees = np.arange(1, len(reduced))
t_k = pp.filter_worst_knees(points_reduced, knees)
t_k = pp.filter_corner_knees(points_reduced, t_k, t=args.c)
filtered_knees = pp.filter_clusters(points_reduced, t_k, clustering.average_linkage, args.t, args.k)
##########################################################################################
# add even points
if args.a:
knees = pp.add_points_even(points, reduced, filtered_knees, removed)
else:
knees = rdp.mapping(filtered_knees, reduced, removed)
rmspe_k = evaluation.rmspe(points, knees, expected, evaluation.Strategy.knees)
rmspe_e = evaluation.rmspe(points, knees, expected, evaluation.Strategy.expected)
cm = evaluation.cm(points, knees, expected, t = 0.01)
mcc = evaluation.mcc(cm)
logger.info(f'RMSE(knees) RMSE(exp) MCC')
logger.info(f'-------------------------------------------')
logger.info(f'{rmspe_k:10.2E} {rmspe_e:10.2E} {mcc:10.2E}')
# store outpout
if args.o:
dirname = os.path.dirname(args.i)
filename = os.path.splitext(os.path.basename(args.i))[0]
output = os.path.join(os.path.normpath(dirname), f'{filename}_output.csv')
dataset = points[knees]
with open(output, 'w') as f:
writer = csv.writer(f)
writer.writerows(dataset)
# display result
if args.g:
x = points[:, 0]
y = points[:, 1]
plt.plot(x, y)
plt.plot(x[knees], y[knees], 'r+')
plt.show()
def main(args):
# get the expected file from the input file
dirname = os.path.dirname(args.i)
filename = os.path.splitext(os.path.basename(args.i))[0]
expected_file = os.path.join(os.path.normpath(dirname),
f'{filename}_expected.csv')
expected = None
if os.path.exists(expected_file):
with open(expected_file, 'r') as f:
reader = csv.reader(f, quoting=csv.QUOTE_NONNUMERIC)
expected = list(reader)
else:
expected = []
expected = np.array(expected)
points = np.genfromtxt(args.i, delimiter=',')
# get original x_max and y_ranges
x_max = [max(x) for x in zip(*points)][0]
y_range = [[max(y), min(y)] for y in zip(*points)][1]
# run rdp
reduced, removed = rdp.rdp(points, args.r)
points_reduced = points[reduced]
## Knee detection code ##
knees = zmethod.knees(points_reduced,
dx=args.x,
dy=args.y,
dz=args.z,
x_max=x_max,
y_range=y_range)
knees = knees[knees > 0]
##########################
# add even points
if args.a:
knees = pp.add_points_even(points, reduced, knees, removed)
else:
knees = rdp.mapping(knees, reduced, removed)
rmspe_k = evaluation.rmspe(points, knees, expected,
evaluation.Strategy.knees)
rmspe_e = evaluation.rmspe(points, knees, expected,
evaluation.Strategy.expected)
cm = evaluation.cm(points, knees, expected, t=0.01)
mcc = evaluation.mcc(cm)
logger.info(f'RMSE(knees) RMSE(exp) MCC')
logger.info(f'-------------------------------------------')
logger.info(f'{rmspe_k:10.2E} {rmspe_e:10.2E} {mcc:10.2E}')
# store outpout
if args.o:
dirname = os.path.dirname(args.i)
filename = os.path.splitext(os.path.basename(args.i))[0]
output = os.path.join(os.path.normpath(dirname),
f'{filename}_output.csv')
dataset = points[knees]
with open(output, 'w') as f:
writer = csv.writer(f)
writer.writerows(dataset)
# display result
if args.g:
x = points[:, 0]
y = points[:, 1]
plt.plot(x, y)
plt.plot(x[knees], y[knees], 'r+')
plt.show()
def main(args):
# get the expected file from the input file
dirname = os.path.dirname(args.i)
filename = os.path.splitext(os.path.basename(args.i))[0]
expected_file = os.path.join(os.path.normpath(dirname), f'{filename}_expected.csv')
expected = None
if os.path.exists(expected_file):
with open(expected_file, 'r') as f:
reader = csv.reader(f, quoting=csv.QUOTE_NONNUMERIC)
expected = list(reader)
else:
expected = []
expected = np.array(expected)
points = np.genfromtxt(args.i, delimiter=',')
## Knee detection code ##
# For all possible sliding windows
left = 0
right = 0
knees = []
while right < len(points):
if args.sw == -1:
right = len(points)
else:
right = min(left+args.sw, len(points))
logger.info(f'[{left}, {right}]')
window_points = points[left:right+1]
if args.d:
window_knees = kneedle.knees(window_points, args.t, args.cd, args.cc, args.s, debug=True)
dd = window_knees['dd']
x = dd[:, 0]
y = dd[:, 1]
peaks = window_knees['peaks']
window_knees = window_knees['knees']
plt.plot(x, y)
plt.plot(x[peaks], y[peaks], 'ro')
plt.plot(x[window_knees], y[window_knees], 'yx')
plt.show()
else:
window_knees = kneedle.knees(window_points, args.t, args.cd, args.cc, args.s, debug=False)
window_knees += left
left = left + args.so
knees.extend(window_knees.tolist())
knees = np.unique(np.array(knees))
#knees = np.array(knees)
#t_k = pp.filter_worst_knees(points_reduced, knees)
#t_k = pp.filter_corner_knees(points_reduced, t_k, t=args.c)
#filtered_knees = pp.filter_clustring(points_reduced, t_k, clustering.average_linkage, args.t, knee_ranking.ClusterRanking.left)
##########################################################################################
# add even points
#if args.a:
# knees = pp.add_points_even(points, points_reduced, filtered_knees, points_removed, 0.009, 0.009)
#else:
# knees = rdp.mapping(filtered_knees, points_reduced, points_removed)
nk = len(knees)
if nk > 0:
rmspe_k = evaluation.rmspe(points, knees, expected, evaluation.Strategy.knees)
rmspe_e = evaluation.rmspe(points, knees, expected, evaluation.Strategy.expected)
cm = evaluation.cm(points, knees, expected, t = 0.01)
mcc = evaluation.mcc(cm)
else:
rmspe_k = 999
rmspe_e = 999
mcc = -1
logger.info(f'RMSE(knees) RMSE(exp) MCC N_Knees')
logger.info(f'-------------------------------------------')
logger.info(f'{rmspe_k:10.2E} {rmspe_e:10.2E} {mcc:10.2E} {nk}')
# store outpout
if args.o:
dirname = os.path.dirname(args.i)
filename = os.path.splitext(os.path.basename(args.i))[0]
output = os.path.join(os.path.normpath(dirname), f'{filename}_output.csv')
dataset = points[knees]
with open(output, 'w') as f:
writer = csv.writer(f)
writer.writerows(dataset)
# display result
if args.g:
x = points[:, 0]
y = points[:, 1]
plt.plot(x, y)
plt.plot(x[knees], y[knees], 'r+')
plt.show()