def test_BIC(self):
"""BIC loss function"""
# -2*Likelihood == BIC for a sample size of 1
m = self.FakePointModel(dt=.02)
sol = m.solve()
s = ddm.Sample(aa([.02]), aa([]))
expected = -np.log(np.sum(sol.corr)/m.dt)
assert np.isclose(ddm.models.LossBIC(sample=s, dt=m.dt, T_dur=m.T_dur, nparams=1, samplesize=1).loss(m),
2*ddm.models.LossLikelihood(sample=s, dt=m.dt, T_dur=m.T_dur).loss(m))
def test_LossSquaredError(self):
"""Squared error loss function"""
# Should be zero for empty sample when all undecided
m = self.FakeUndecidedModel()
s = ddm.Sample(aa([]), aa([]), undecided=1)
assert ddm.models.LossSquaredError(sample=s, dt=m.dt, T_dur=m.T_dur).loss(m) == 0
# Can also be determined precisely for the point model
m = self.FakePointModel()
sol = m.solve()
err = ddm.models.LossSquaredError(sample=s, dt=m.dt, T_dur=m.T_dur).loss(m)
assert np.isclose(err, np.sum(sol.corr)**2 + np.sum(sol.err)**2)
def test_ICArbitrary(self):
"""Arbitrary starting conditions from a distribution"""
# Make sure we get out the same distribution we put in
m = ddm.Model()
unif = ddm.models.ICUniform()
unif_a = ddm.models.ICArbitrary(unif.get_IC(m.x_domain({})))
assert np.all(unif.get_IC(m.x_domain({})) == unif_a.get_IC(m.x_domain({})))
point = ddm.models.ICPointSourceCenter()
point_a = ddm.models.ICArbitrary(point.get_IC(m.x_domain({})))
assert np.all(point.get_IC(m.x_domain({})) == point_a.get_IC(m.x_domain({})))
# Make sure the distribution integrates to 1
fails(lambda : ddm.models.ICArbitrary(aa([.1, .1, 0, 0, 0])))
fails(lambda : ddm.models.ICArbitrary(aa([0, .6, .6, 0])))
assert ddm.models.ICArbitrary(aa([1]))
def arrange_metric_windows(self):
"""
Determine the grid placement of metric figure windows and assign figure numbers.
Should only be called once during the object's creation.
"""
#see if there are any other figures open so as not to overwrite their
#contents
figures=[manager.canvas.figure \
for manager in matplotlib._pylab_helpers.Gcf.get_all_fig_managers()]
int_fig_offset = max(0,len(figures) - 1)
for metric in self.ls_metrics:
if metric.plot_enabled:
#make row offset adjustment
metric.figure_location += self.grid_size[0] * self.row_offset
metric.w_size = self.resolutions[self.desktop] / self.grid_size
int_row_offset = int((metric.figure_location + .1) / self.grid_size[0])
int_col_offset = int(mod(metric.figure_location, self.grid_size[0]))
metric.w_coords = aa([int_col_offset * metric.w_size[0] + \
self.desktop * self.resolutions[self.desktop][0], \
int_row_offset * metric.w_size[1]])
#creating the Metrics' figure windows
metric.figure_number = int_fig_offset + metric.figure_location
metric.figure = plt.figure(metric.figure_number)
wm = plt.get_current_fig_manager()
wm.window.wm_geometry(str(metric.w_size[0]) + "x" + \
str(metric.w_size[1]) + "+" + \
str(metric.w_coords[0]) + "+" + \
str(metric.w_coords[1]))
def test_LossLikelihood(self):
"""Likelihood loss function"""
# We can calculate likelihood for this simple case
m = self.FakePointModel(dt=.02)
sol = m.solve()
s = ddm.Sample(aa([.02]), aa([]))
expected = -np.log(np.sum(sol.corr)/m.dt)
assert np.isclose(expected, ddm.models.LossLikelihood(sample=s, dt=m.dt, T_dur=m.T_dur).loss(m))
# And for the uniform case we can assert equivalence
m = self.FakeUniformModel()
s1 = ddm.Sample(aa([.02, .05, .07, .12]), aa([.33, .21]))
s2 = ddm.Sample(aa([.13, .1, .02]), aa([.66, .15, .89]))
assert np.isclose(ddm.models.LossLikelihood(sample=s1, dt=m.dt, T_dur=m.T_dur).loss(m),
ddm.models.LossLikelihood(sample=s2, dt=m.dt, T_dur=m.T_dur).loss(m))
def setUp(self):
self.samps = {
# Empty sample
"empty":
ddm.Sample(aa([]), aa([]), 0),
# Simple sample
"simple":
ddm.Sample(aa([1, 2]), aa([.5, .7]), 0),
# Sample with conditions
"conds":
ddm.Sample(aa([1, 2, 3]), aa([]), 0, cond1=(aa([1, 1,
2]), aa([]))),
# Sample with conditions and explicitly showing undecided
"condsexp":
ddm.Sample(aa([1, 2, 3]),
aa([]),
0,
cond1=(aa([1, 1, 2]), aa([]), aa([]))),
# Sample with undecided
"undec":
ddm.Sample(aa([1, 2]), aa([.5, .7]), 2),
# Sample with undecided and conditions
"undeccond":
ddm.Sample(aa([1, 2, 3]),
aa([]),
3,
cond1=(aa([1, 1, 2]), aa([]), aa([2, 2, 1]))),
# For the adding test
"adda":
ddm.Sample(aa([1]),
aa([2, 4]),
3,
cond1=(aa(["a"]), aa(["a", "b"]), aa(["a", "b", "b"]))),
"addb":
ddm.Sample(aa([1.5, 2, 1]),
aa([]),
1,
cond1=(aa(["b", "b", "c"]), aa([]), aa(["d"]))),
# Two conditions
"two":
ddm.Sample(aa([1]),
aa([2]),
1,
conda=(aa(["a"]), aa(["b"]), aa(["a"])),
condb=(aa([1]), aa([2]), aa([2]))),
}
def __init__(self, ps_parameters, str_section):
"""Class constructor for Results.
"""
super(Results,self).__init__(ps_parameters,str_section)
self.ls_metric_names = self.get_val('metrics',False)
if self.ls_metric_names.__class__.__name__ == 'str':
self.ls_metric_names = [self.ls_metric_names]
self.ls_metrics = [sf.create_section(ps_parameters, self.ls_metric_names[i]) \
for i in arange(len(self.ls_metric_names))]
self.ls_metrics_csv = [self.ls_metrics[i]
for i in arange(len(self.ls_metrics))
if self.ls_metrics[i].has_csv]
self.ls_metrics_no_csv = [self.ls_metrics[i]
for i in arange(len(self.ls_metrics))
if not self.ls_metrics[i].has_csv]
self.grid_size = aa([self.get_val('figuregridwidth',True), \
self.get_val('figuregridheight',True)], dtype = np.int)
self.desktop = self.get_val('desktop',True)
self.row_offset = self.get_val('rowoffset',True)
self.int_overlap = max(5,self.get_val('overlap',True))
self.save_interval = self.get_val('saveinterval',True)
self.output_directory = self.get_val('outputdirectory',False)
#default to the configuration file's filename as a prefix
self.output_filename = self.get_val('outputfilename',False,
default_value=self.get_params_fname(False))
self.overwrite_results = self.get_val('overwriteresults',True)
self.zeros = self.get_val('zeros', True, DEFAULT_ZEROS)
self.display_enabled = not self.get_val('disablefigures',True)
self.monitor_count=0
self.resolutions=tuple()
#get screen info
if self.display_enabled:
#try to find a display
screen = os.popen("xrandr -q -d :0").readlines()
if len(screen)>0:
ls_res=re.findall(' connected [0-9]*x[0-9]*', ''.join(screen))
ls_res=re.findall('[0-9]*x[0-9]*', ''.join(ls_res))
#the ordering of these assumes the screens are in the same order
#if this is not the case, simply rearrange your screens ;-)
self.resolutions=tuple([np.array(res.split('x'),'uint16') for res in ls_res])
self.monitor_count = len(self.resolutions)
self.desktop=np.mod(self.desktop,self.monitor_count)
self.arrange_metric_windows() #figure out the coordinates
else: #turn display off
self.monitor_count=0
self.display_enabled = False
#multiple viewport/desktop support not enabled, so wrapping the desktop to the range
#of available monitors
#create a folder in the output directory with the current minute's time stamp
if self.output_directory=='':
print ('Not writing results to file no output dir specified')
return None
st = '/' + self.output_filename
self.output_directory += st + '/'
if not os.path.exists(self.output_directory):
os.makedirs(self.output_directory)
if not self.overwrite_results:
#old timestamping method, keep in case this is deemed better in the future
# st += '/' + datetime.datetime.fromtimestamp(time.time()).strftime('%Y-%m-%d_%H-%M-%S')
#get the count of the current numbered directories
files_enumerated = enumerate(os.walk(self.output_directory))
files,dir_info=files_enumerated.next()
results_count_string=str(len(dir_info[1])).zfill(self.zeros)
self.output_directory += '/' + results_count_string + '/'
if not os.path.exists(self.output_directory):
os.makedirs(self.output_directory)
#save the parameters to this folder as ini, and write as csv
self.ps_parameters.write(self.output_directory + self.output_filename + '_config.ini')
self.ps_parameters.write_csv(self.output_directory + self.output_filename + '_config.' + DEFAULT_CSV_EXT)
def setUp(self):
# You need some gymnastics to get numpy to accept an array of
# same-length tuples
_tuple_same_length = np.empty(3, dtype=object)
_tuple_same_length[:] = [(3, 1, 2), (3, 3, 3), (5, 4, 3)]
self.samps = {
# Empty sample
"empty":
ddm.Sample(aa([]), aa([]), 0),
# Simple sample
"simple":
ddm.Sample(aa([1, 2]), aa([.5, .7]), 0),
# Sample with conditions
"conds":
ddm.Sample(aa([1, 2, 3]), aa([]), 0, cond1=(aa([1, 1,
2]), aa([]))),
# Sample with conditions as strings
"condsstr":
ddm.Sample(aa([1, 2, 3]),
aa([]),
0,
cond1=(aa(["x", "yy", "z z z"]), aa([]))),
# Sample with conditions as tuples
"condstuple":
ddm.Sample(aa([1, 2, 3]),
aa([]),
0,
cond1=(aa([(3, 1, 2), (5, 5), (1, 1, 1, 1, 1)],
dtype=object), aa([]))),
# Sample with conditions as tuples which are the same length
"condstuplesame":
ddm.Sample(aa([1, 2, 3]),
aa([]),
0,
cond1=(_tuple_same_length, aa([]))),
# Sample with conditions and explicitly showing undecided
"condsexp":
ddm.Sample(aa([1, 2, 3]),
aa([]),
0,
cond1=(aa([1, 1, 2]), aa([]), aa([]))),
# Sample with undecided
"undec":
ddm.Sample(aa([1, 2]), aa([.5, .7]), 2),
# Sample with undecided and conditions
"undeccond":
ddm.Sample(aa([1, 2, 3]),
aa([]),
3,
cond1=(aa([1, 1, 2]), aa([]), aa([2, 2, 1]))),
# For the adding test
"adda":
ddm.Sample(aa([1]),
aa([2, 4]),
3,
cond1=(aa(["a"]), aa(["a", "b"]), aa(["a", "b", "b"]))),
"addb":
ddm.Sample(aa([1.5, 2, 1]),
aa([]),
1,
cond1=(aa(["b", "b", "c"]), aa([]), aa(["d"]))),
# Two conditions
"two":
ddm.Sample(aa([1]),
aa([2]),
1,
conda=(aa(["a"]), aa(["b"]), aa(["a"])),
condb=(aa([1]), aa([2]), aa([2]))),
}
