def main(args):
# define clustering methods
cmethod = {Clustering.single: clustering.single_linkage, Clustering.complete: clustering.complete_linkage, Clustering.average: clustering.average_linkage}
# 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 = []
points = np.genfromtxt(args.i, delimiter=',')
## Knee detection code ##
points_reduced, points_removed = rdp.rdp(points, args.r)
knees = kneedle.auto_knees(points_reduced)
t_k = pp.filter_worst_knees(points_reduced, knees)
t_k = pp.filter_corner_knees(points_reduced, t_k)
filtered_knees = pp.filter_clustring(points_reduced, t_k, cmethod[args.c], args.t, args.m)
if args.a:
knees = pp.add_points_even(points, points_reduced, filtered_knees, points_removed)
else:
knees = rdp.mapping(filtered_knees, points_reduced, points_removed)
##########################
"""
def kneedle_novel(points, args):
reduced, removed = rdp.rdp(points, args.r)
points_reduced = points[reduced]
knees = kneedle.auto_knees(points_reduced, p=kneedle.PeakDetection.All)
knees = pp.filter_worst_knees(points_reduced, knees)
knees = pp.filter_corner_knees(points_reduced, knees, t=args.c)
knees = pp.filter_clustring(points_reduced, knees,
clustering.average_linkage, args.t, args.k)
knees = rdp.mapping(knees, reduced, removed)
return knees
def postprocessing(points,
knees,
c=Clustering.average,
t=0.1,
m=ClusterRanking.left):
knees = filter_worst_knees(points, knees)
knees = filter_corner_knees(points, knees)
cmethod = {
Clustering.single: clustering.single_linkage,
Clustering.complete: clustering.complete_linkage,
Clustering.average: clustering.average_linkage
}
current_knees = filter_clustring(points, knees, cmethod[c], t, m)
return current_knees
def kneedle_novel(points, args):
reduced, removed = rdp.rdp(points, args.r)
points_reduced = points[reduced]
knees = kneedle.auto_knees(points_reduced, p=kneedle.PeakDetection.All)
#x = points_reduced[:, 0]
#y = points_reduced[:, 1]
#plt.plot(x, y)
#plt.plot(x[knees], y[knees], 'r+')
#plt.show()
knees = pp.filter_worst_knees(points_reduced, knees)
knees = pp.filter_corner_knees(points_reduced, knees, t=args.c)
knees = pp.filter_clusters(points_reduced, knees, clustering.average_linkage, args.t, args.k)
knees = rdp.mapping(knees, reduced, removed)
return knees
def postprocessing(points, knees, args):
logger.info('Post Processing')
#logger.info('Knees: %s', knees)
logger.info('Initial #Knees: %s', len(knees))
wknees = filter_worst_knees(points, knees)
#plot_knees(plt, points, knees, 'Worst Knees')
logger.info('After Worst #Knees: %s', len(knees))
knees = filter_corner_knees(points, wknees)
#plot_knees(plt, points, knees, 'Corner Knees')
#diff = np.setdiff1d(wknees, knees)
#plot_knees(plt, points, diff, 'Diff Knees')
logger.info('After Corner #Knees: %s', len(knees))
#logger.info('Worst Knees: %s', len(knees))
cmethod = {Clustering.single: clustering.single_linkage, Clustering.complete:
clustering.complete_linkage, Clustering.average: clustering.average_linkage}
current_knees = filter_clustring(points, knees, cmethod[args.c], args.t, args.m)
logger.info('Clustering Knees: %s', len(current_knees))
return current_knees
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=',')
rs = [0.75, 0.80, 0.85, 0.90, 0.95]
ts = [0.01, 0.02, 0.03, 0.04, 0.05]
evaluations = []
for r in rs:
## Knee detection code ##
points_reduced, points_removed = rdp.rdp(points, r)
knees = np.arange(1, len(points_reduced))
t_k = pp.filter_worst_knees(points_reduced, knees)
t_k = pp.filter_corner_knees(points_reduced, t_k)
for t in ts:
## Clustering ##
filtered_knees = pp.filter_clustring(points_reduced, t_k,
clustering.average_linkage, t,
ClusterRanking.left)
final_knees = pp.add_points_even(points, points_reduced,
filtered_knees, points_removed)
## Evaluation ##
error_rmspe = evaluation.rmspe(points, final_knees, expected,
evaluation.Strategy.knees)
error_rmspe_exp = evaluation.rmspe(points, final_knees, expected,
evaluation.Strategy.expected)
_, _, _, _, cost_trace = evaluation.accuracy_trace(
points, final_knees)
_, _, _, _, cost_knee = evaluation.accuracy_knee(
points, final_knees)
evaluations.append(
[error_rmspe, error_rmspe_exp, cost_trace, cost_knee])
## Compute the Correlation ##
evaluations = np.array(evaluations)
rho = np.corrcoef(evaluations.T)
rmspe_rmspe_exp = rho[0, 1]
rmspe_cost_trace = rho[0, 2]
rmspe_cost_knee = rho[0, 3]
rmspe_exp_cost_trace = rho[1, 2]
rmspe_exp_cost_knee = rho[1, 3]
cost_trace_cost_knee = rho[2, 3]
#logger.info(f'{rho}')
logger.info(
f'{rmspe_rmspe_exp}, {rmspe_cost_trace}, {rmspe_cost_knee}, {rmspe_exp_cost_trace}, {rmspe_exp_cost_knee}, {cost_trace_cost_knee}'
)
def test_filter_corner_knees_00(self):
points = np.array([[1,3], [2,3], [3,3], [4,2.5], [5,2], [6,1.5], [7, 1]])
knees = np.array([1,2,3])
result = pp.filter_corner_knees(points, knees, .5)
desired = np.array([1,3])
np.testing.assert_array_equal(result, desired)
def test_filter_corner_knees_01(self):
points = np.array([[33.0, 0.25715391], [4.29000000e+02, 2.49621243e-01], [4.62000000e+02, 1.72661497e-01]])
knees = np.array([1])
result = pp.filter_corner_knees(points, knees, .5)
desired = np.array([])
np.testing.assert_array_equal(result, desired)
