def test_group_results():
results = test_utilities.load_eng_trans_data()
experiments, outcomes = results
# test indices
groups = {'set1':np.arange(0,11),
'set2':np.arange(11,25),
'set3':np.arange(25,experiments.shape[0])}
groups = group_results(experiments, outcomes,
group_by='index',
grouping_specifiers=groups.values(),
grouping_labels= groups.keys())
total_data = 0
for value in groups.values():
total_data += value[0].shape[0]
print(experiments.shape[0], total_data)
# test continuous parameter type
array = experiments['average planning and construction period T1']
grouping_specifiers = make_continuous_grouping_specifiers(array, nr_of_groups=5)
groups = group_results(experiments, outcomes,
group_by='average planning and construction period T1',
grouping_specifiers=grouping_specifiers,
grouping_labels = [str(entry) for entry in grouping_specifiers])
total_data = 0
for value in groups.values():
total_data += value[0].shape[0]
print(experiments.shape[0], total_data)
# test integer type
array = experiments['seed PR T1']
grouping_specifiers = make_continuous_grouping_specifiers(array, nr_of_groups=10)
groups = group_results(experiments, outcomes,
group_by='seed PR T1',
grouping_specifiers=grouping_specifiers,
grouping_labels = [str(entry) for entry in grouping_specifiers])
total_data = 0
for value in groups.values():
total_data += value[0].shape[0]
print(experiments.shape[0], total_data)
# test categorical type
grouping_specifiers = set(experiments["policy"])
groups = group_results(experiments, outcomes,
group_by='policy',
grouping_specifiers=grouping_specifiers,
grouping_labels = [str(entry) for entry in grouping_specifiers])
total_data = 0
for value in groups.values():
total_data += value[0].shape[0]
print(experiments.shape[0], total_data)
def test_group_results():
results = load_results(r'./../data/eng_trans_100.cPickle', zipped=False)
experiments, outcomes = results
# test indices
grouping_specifiers = {'set1':np.arange(0,11),
'set2':np.arange(11,25),
'set3':np.arange(25,experiments.shape[0])}
groups = group_results(experiments, outcomes,
group_by='index',
grouping_specifiers=grouping_specifiers)
total_data = 0
for value in groups.values():
total_data += value[0].shape[0]
print experiments.shape[0], total_data
# test continuous parameter type
array = experiments['average planning and construction period T1']
grouping_specifiers = make_continuous_grouping_specifiers(array, nr_of_groups=5)
groups = group_results(experiments, outcomes,
group_by='average planning and construction period T1',
grouping_specifiers=grouping_specifiers)
total_data = 0
for value in groups.values():
total_data += value[0].shape[0]
print experiments.shape[0], total_data
# test integer type
array = experiments['seed PR T1']
grouping_specifiers = make_continuous_grouping_specifiers(array, nr_of_groups=10)
groups = group_results(experiments, outcomes,
group_by='seed PR T1',
grouping_specifiers=grouping_specifiers)
total_data = 0
for value in groups.values():
total_data += value[0].shape[0]
print experiments.shape[0], total_data
# test categorical type
grouping_specifiers = set(experiments["policy"])
groups = group_results(experiments, outcomes,
group_by='policy',
grouping_specifiers=grouping_specifiers)
total_data = 0
for value in groups.values():
total_data += value[0].shape[0]
print experiments.shape[0], total_data
def test_make_continuous_grouping_specifiers():
array = np.random.randint(1,100, size=(1000,))
categories = make_continuous_grouping_specifiers(array, nr_of_groups=10)
for entry in categories:
print(repr(entry))
print(np.min(array), np.max(array))
def test_make_continuous_grouping_specifiers():
array = np.random.randint(1, 100, size=(1000, ))
categories = make_continuous_grouping_specifiers(array, nr_of_groups=10)
for entry in categories:
print(repr(entry))
print(np.min(array), np.max(array))
def envelopes3d_group_by(results,
outcome,
groupBy = 'policy',
discretesize = None,
logSpace=False,
ymin = None,
ymax = None):
'''
Function for making 3d envelopes. In contrast to the envelopes in
:mod:`graphs`, this version shows the density for every time step, instead
of only for the end state. Note that this function makes an envelope for
only 1 outcome. This envelopes will group the results based on the
specified uncertainty. The user can supply a discretesize function
to control the grouping in case of parameterUncertainties. This function
will make a separate envelope for each group.
:param results: The return from :meth:`run experiments`.
:param outcome: Specify the name of outcome of interest for which you want to make
the 3d envelopes.
:param groupBy: The uncertainty to group by. (default=policy)
:param discretesize: a discretesize function to control the grouping in case of parameterUncertainties
:param logSpace: Boolean, if true, the log of the input data is used
:param ymin: If provided, lower bound for the KDE, if not, ymin = np.min(results.get(outcome))
:param ymax: If provided, lower bound for the KDE, if not, ymax = np.max(results.get(outcome))
'''
def f(x, y, results):
"""
function that performs the kde for each timestep
"""
x1 = x[:,0]
y1 = y[0,:]
results = np.asarray(results)
z = []
for i in range(len(list(x1))):
data = results[:, i]
try:
z1 = kde.gaussian_kde(data)
z1 = z1.evaluate(y1)
except:
z1 = np.zeros(shape=y1.shape)
z.append(z1)
z = np.asarray(z)
z = np.log(z+1)
return z
#prepare the data
experiments, results = results
#get time axis
try:
time = results.pop('TIME')[0, :]
except:
time = np.arange(0, results.values()[0].shape[1])
def make_logical(cases, criterion, interval=False):
if interval:
return (cases[groupBy] >= criterion[0]) & (cases[groupBy] < criterion[1])
else:
return cases[groupBy]== criterion
#get the results for the specific outcome of interest
results = results.get(outcome)
#log results
if logSpace:
results = np.log(results+1)
#generate the grid
if ymin == None:
ymin = np.min(results)
info("ymin: %s" % ymin)
if ymax == None:
ymax = np.max(results)
info("ymax: %s" % ymax)
length = min(100, results.shape[1])
y = np.arange(ymin, ymax, (ymax-ymin)/length)
X, Y = np.meshgrid(time, y)
z = []
#do the preparation for grouping by
interval=False
if (experiments[groupBy].dtype == np.float32) |\
(experiments[groupBy].dtype == np.float64) |\
((experiments[groupBy].dtype == np.int) & (len(set(experiments[groupBy])) > 5)):
interval=True
if discretesize:
categories = discretesize(experiments[groupBy])
else:
categories = make_continuous_grouping_specifiers(experiments[groupBy])
else:
categories = set(experiments[groupBy])
for category in categories:
if interval:
info("calculating kde for (%s, %s)" % (category))
else:
info("calculating kde for %s" % (category))
logical = make_logical(experiments, category, interval)
Z = f(X.T,Y.T, results=results[logical])
z.append(Z)
#calculate the kde for the grid
#visualize results
fig = mlab.figure(1, bgcolor=(1, 1, 1), fgcolor=(0, 0, 0))
fig.scene.disable_render = True
for i, category in enumerate(categories):
if interval:
info("plotting (%s, %s)" % (category))
else:
info("plotting %s" % (category))
Z = z[i]
extent = (-14+i*10,-6+i*10, 0,10, 0,5)
s = mlab.mesh(X,Y, Z.T, extent=extent)
mlab.outline(s, color=(.7, .7, .7), extent=extent)
if i==0:
mlab.axes(s,
extent=extent,
xlabel = '',
ylabel = '',
zlabel = 'density',
x_axis_visibility=False,
y_axis_visibility=False,
z_axis_visibility=False)
category_name = repr(category)
mlab.text(-16+10*i, i+10, category_name, z=-2, width=0.14)
fig.scene.disable_render = False
mlab.title(outcome, line_width=0.5)
mlab.show()
def test_group_results():
results = test_utilities.load_eng_trans_data()
experiments, outcomes = results
# test indices
groups = {
'set1': np.arange(0, 11),
'set2': np.arange(11, 25),
'set3': np.arange(25, experiments.shape[0])
}
groups = group_results(experiments,
outcomes,
group_by='index',
grouping_specifiers=groups.values(),
grouping_labels=groups.keys())
total_data = 0
for value in groups.values():
total_data += value[0].shape[0]
print(experiments.shape[0], total_data)
# test continuous parameter type
array = experiments['average planning and construction period T1']
grouping_specifiers = make_continuous_grouping_specifiers(array,
nr_of_groups=5)
groups = group_results(
experiments,
outcomes,
group_by='average planning and construction period T1',
grouping_specifiers=grouping_specifiers,
grouping_labels=[str(entry) for entry in grouping_specifiers])
total_data = 0
for value in groups.values():
total_data += value[0].shape[0]
print(experiments.shape[0], total_data)
# test integer type
array = experiments['seed PR T1']
grouping_specifiers = make_continuous_grouping_specifiers(array,
nr_of_groups=10)
groups = group_results(
experiments,
outcomes,
group_by='seed PR T1',
grouping_specifiers=grouping_specifiers,
grouping_labels=[str(entry) for entry in grouping_specifiers])
total_data = 0
for value in groups.values():
total_data += value[0].shape[0]
print(experiments.shape[0], total_data)
# test categorical type
grouping_specifiers = set(experiments["policy"])
groups = group_results(
experiments,
outcomes,
group_by='policy',
grouping_specifiers=grouping_specifiers,
grouping_labels=[str(entry) for entry in grouping_specifiers])
total_data = 0
for value in groups.values():
total_data += value[0].shape[0]
print(experiments.shape[0], total_data)