def plot_surface(gr, ax, keys, imshow=False):
# from https://github.com/russelljjarvis/neuronunit/blob/dev/neuronunit/unit_test/progress_report_4thJuly.ipynb
# Not rendered https://github.com/russelljjarvis/neuronunit/blob/dev/neuronunit/unit_test/progress_report_.ipynb
gr = [g for g in gr if type(g.dtc) is not type(None)]
gr = [g for g in gr if type(g.dtc.scores) is not type(None)]
ax.cla()
gr_ = []
index = 0
for i, g in enumerate(gr):
if type(g.dtc) is not type(None):
gr_.append(g)
else:
index = i
xx = np.array([p.dtc.attrs[str(keys[0])] for p in gr])
yy = np.array([p.dtc.attrs[str(keys[1])] for p in gr])
zz = np.array([np.sum(list(p.dtc.scores.values())) for p in gr])
dim = len(xx)
N = int(np.sqrt(len(xx)))
X = xx.reshape((N, N))
Y = yy.reshape((N, N))
Z = zz.reshape((N, N))
if imshow == False:
ax.pcolormesh(X, Y, Z, edgecolors='black')
else:
import seaborn as sns
sns.set()
ax = sns.heatmap(Z)
ax.set_title(' {0} vs {1} '.format(keys[0], keys[1]))
return ax
def plot_scatter(hof, ax, keys):
z = np.array([np.sum(list(p.dtc.scores.values())) for p in hof])
x = np.array([p.dtc.attrs[str(keys[0])] for p in hof])
if len(keys) != 1:
y = np.array([p.dtc.attrs[str(keys[1])] for p in hof])
ax.cla()
ax.set_title(' {0} vs {1} '.format(keys[0], keys[1]))
ax.scatter(x, y, c=y, s=125) #, cmap='gray')
return ax
def plot_line(gr, ax, key):
ax.cla()
ax.set_title(' {0} vs score'.format(key[0]))
z = np.array([np.sum(list(p.dtc.scores.values())) for p in gr])
x = np.array([p.dtc.attrs[key[0]] for p in gr])
ax.plot(x, z)
ax.set_xlim(np.min(x), np.max(x))
ax.set_ylim(np.min(z), np.max(z))
return ax
def plot_scatter(history, ax, keys, constant):
pop = [v for v in history.genealogy_history.values()]
z = np.array([p.dtc.get_ss() for p in pop])
x = np.array([p.dtc.attrs[str(keys[0])] for p in pop])
y = np.array([p.dtc.attrs[str(keys[1])] for p in pop])
ax.cla()
ax.set_title('held constant: ' + str(constant))
ax.scatter(x, y, c=y, s=125) #, cmap='gray')
ax.set_xlabel('free: ' + str(keys[0]))
ax.set_ylabel('free: ' + str(keys[1]))
return ax
def plot_surface(gr, ax, keys, imshow=False):
# from
# https://github.com/russelljjarvis/neuronunit/blob/dev/neuronunit/unit_test/progress_report_4thJuly.ipynb
# Not rendered
# https://github.com/russelljjarvis/neuronunit/blob/dev/neuronunit/unit_test/progress_report_.ipynb
gr = [g for g in gr if type(g.dtc) is not type(None)]
gr = [g for g in gr if type(g.dtc.scores) is not type(None)]
ax.cla()
#gr = [ g
gr_ = []
index = 0
for i, g in enumerate(gr):
if type(g.dtc) is not type(None):
gr_.append(g)
else:
index = i
z = [np.sum(list(p.dtc.scores.values())) for p in gr]
x = [p.dtc.attrs[str(keys[0])] for p in gr]
y = [p.dtc.attrs[str(keys[1])] for p in gr]
# impute missings
if len(x) != 100:
delta = 100 - len(x)
for i in range(0, delta):
x.append(np.mean(x))
y.append(np.mean(y))
z.append(np.mean(z))
xx = np.array(x)
yy = np.array(y)
zz = np.array(z)
dim = len(xx)
N = int(np.sqrt(len(xx)))
X = xx.reshape((N, N))
Y = yy.reshape((N, N))
Z = zz.reshape((N, N))
if imshow == False:
ax.pcolormesh(X, Y, Z, edgecolors='black')
else:
import seaborn as sns
sns.set()
ax = sns.heatmap(Z)
#ax.imshow(Z)
#ax.pcolormesh(xi, yi, zi, edgecolors='black')
ax.set_title(' {0} vs {1} '.format(keys[0], keys[1]))
return ax
def plot_line_ss(gr, ax, free, hof, constant):
ax.cla()
ax.set_title(' {0} vs score'.format(free))
z = np.array([p.dtc.get_ss() for p in gr])
print(str(free))
print(free)
x = np.array([p.dtc.attrs[str(free)] for p in gr])
y = hof[0].dtc.attrs[free]
i = hof[0].dtc.get_ss()
#ax.hold(True)
ax.scatter(x, z)
ax.scatter(y, i)
ax.plot(x, z)
ax.set_xlabel(str(key[0]))
ax.set_ylabel(str('Sum of Errors'))
return ax
def plot_surface(gr, ax, keys, constant, imshow=True):
# from https://github.com/russelljjarvis/neuronunit/blob/dev/neuronunit/unit_test/progress_report_4thJuly.ipynb
# Not rendered https://github.com/russelljjarvis/neuronunit/blob/dev/neuronunit/unit_test/progress_report_.ipynb
gr = [g for g in gr if type(g.dtc) is not type(None)]
gr = [g for g in gr if type(g.dtc.scores) is not type(None)]
ax.cla()
gr_ = []
index = 0
for i, g in enumerate(gr):
if type(g.dtc) is not type(None):
gr_.append(g)
else:
index = i
xx = np.array([p.dtc.attrs[str(keys[0])] for p in gr])
yy = np.array([p.dtc.attrs[str(keys[1])] for p in gr])
zz = np.array([p.dtc.get_ss() for p in gr])
dim = len(xx)
N = int(np.sqrt(len(xx)))
X = xx.reshape((N, N))
Y = yy.reshape((N, N))
Z = zz.reshape((N, N))
if imshow == True:
img = ax.pcolormesh(X, Y, Z, edgecolors='black')
#ax.colorbar()
else:
import seaborn as sns
sns.set()
current_palette = sns.color_palette()
sns.palplot(current_palette)
#df = pd.DataFrame(Z, columns=xx)
img = sns.heatmap(Z) #,cm=current_palette)
#ax.colorbar()
ax.set_title(' {0} vs {1} '.format(keys[0], keys[1]))
return ax, img
def pre_run(tests, opt_keys):
# algorithmically find the the edges of parameter ranges, via a course grained
# sampling of extreme parameter values
# to find solvable instances of Izhi-model, (models with a rheobase value).
nparams = len(opt_keys)
from neuronunit.models.NeuroML2 import model_parameters as modelp
mp = copy.copy(modelp.model_params)
mp['b'] = [-0.5, 500.0]
mp['vr'] = [-100.0, 10.0]
mp['a'] = [-10, 5]
cnt = 0
fc = {} # final container
for key in opt_keys:
cnt = 0
print(key, mp)
gr = run_grid(3, tests, provided_keys=key, mp_in=mp)
line = [g.dtc.get_ss() for g in gr]
nr = {key: None}
_, range_adj, new, index = check_line(line, gr, nr, key)
while range_adj == True:
# while the sampled line is not concave (when minimas are at the edges)
# sample a point to a greater extreme
gr_ = update_deap_pop(new, tests, key)
param_line = [g.dtc.attrs[key] for g in gr]
temp = list(mp[key])
inter = None
if index == 0:
p0 = (gr_.dtc.attrs[key], gr_.dtc.get_ss())
p1 = (gr[0].dtc.attrs[key], gr[0].dtc.get_ss())
inter = interpolate(p0, p1)
print(inter)
gr.insert(index, gr_)
temp.insert(index, new)
elif index == -1:
p0 = (gr_.dtc.attrs[key], gr_.dtc.get_ss())
p1 = (gr[-1].dtc.attrs[key], gr[-1].dtc.get_ss())
inter = interpolate(p0, p1)
print(inter)
gr.append(gr_)
temp.append(gr_)
mp[key] = np.array(temp)
line = [g.dtc.get_ss() for g in gr]
_, range_adj, new, index = check_line(line, gr, mp, key)
cnt += 1
with open('temp_range.p', 'wb') as f:
pickle.dump(mp, f)
if type(inter) is not type(None):
with open('temp_inter.p', 'wb') as f:
pickle.dump([mp, inter], f)
param_line = [g.dtc.attrs[key] for g in gr]
line = [g.dtc.get_ss() for g in gr]
plt.clf()
plt.plot(param_line, line)
plt.savefig('check_' + str(key) + '.png')
fc[key] = {}
fc[key]['line'] = line
fc[key]['range'] = mp
fc[key]['cnt'] = cnt
return fc, mp
def get_justas_plot(history):
# try:
import plotly.plotly as py
from plotly.offline import download_plotlyjs, init_notebook_mode, plot #, iplot
import plotly.graph_objs as go
import cufflinks as cf
cf.go_offline()
gr = [v for v in history.genealogy_history.values()]
gr = [g for g in gr if type(g.dtc) is not type(None)]
gr = [g for g in gr if type(g.dtc.scores) is not type(None)]
keys = list(gr[0].dtc.attrs.keys())
xx = np.array([p.dtc.attrs[str(keys[0])] for p in gr])
yy = np.array([p.dtc.attrs[str(keys[1])] for p in gr])
zz = np.array([p.dtc.attrs[str(keys[2])] for p in gr])
ee = np.array([np.sum(list(p.dtc.scores.values())) for p in gr])
#pdb.set_trace()
# z_data = np.array((xx,yy,zz,ee))
list_of_dicts = []
for x, y, z, e in zip(list(xx), list(yy), list(zz), list(ee)):
list_of_dicts.append({
keys[0]: x,
keys[1]: y,
keys[2]: z,
str('error'): e
})
z_data = pd.DataFrame(list_of_dicts)
data = [go.Surface(z=z_data.as_matrix())]
layout = go.Layout(
width=1000,
height=1000,
autosize=False,
title='Sciunit Errors',
scene=dict(
xaxis=dict(
title=str(keys[0]),
#gridcolor='rgb(255, 255, 255)',
#zerolinecolor='rgb(255, 255, 255)',
#showbackground=True,
#backgroundcolor='rgb(230, 230,230)'
),
yaxis=dict(
title=str(keys[1]),
#gridcolor='rgb(255, 255, 255)',
#zerolinecolor='rgb(255, 255, 255)',
#showbackground=True,
#backgroundcolor='rgb(230, 230,230)'
),
zaxis=dict(
title=str(keys[2]),
#gridcolor='rgb(255, 255, 255)',
#zerolinecolor='rgb(255, 255, 255)',
#showbackground=True,
#backgroundcolor='rgb(230, 230,230)'
),
aspectratio=dict(x=1, y=1, z=0.7),
aspectmode='manual'),
margin=dict(l=65, r=50, b=65, t=90))
fig = go.Figure(
data=data, layout=layout
) #,xTitle=str(keys[0]),yTitle=str(keys[1]),title='SciUnitOptimization')
plot(fig, filename='sciunit-score-3d-surface.html')
