def pre_run(tests):
dim = len(hof[0].dtc.attrs.keys())
flat_iter = [(i, ki, j, kj) for i, ki in enumerate(hof[0].dtc.attrs.keys())
for j, kj in enumerate(hof[0].dtc.attrs.keys())]
cnt = 0
for i, ki, j, kj in flat_iter:
free_param = set([
ki, kj
]) # construct a small-set out of the indexed keys 2. If both keys are
# are the same, this set will only contain one index
bs = set(
hof[0].dtc.attrs.keys()
) # construct a full set out of all of the keys available, including ones not indexed here.
diff = bs.difference(
free_param) # diff is simply the key that is not indexed.
# BD is the dictionary of parameters to be held constant
# if the plot is 1D then two parameters should be held constant.
hc = {}
for d in diff:
hc[d] = hof[0].dtc.attrs[d]
if i == j:
assert len(free_param) == len(hc) - 1
assert len(hc) == len(free_param) + 1
from neuronunit.models.NeuroML2 import model_parameters as modelp
mp = copy.copy(modelp.model_params)
gr = run_grid(3,
tests,
provided_keys=free_param,
hold_constant=hc,
mp_in=mp)
# make a psuedo test, that still depends on input Parametersself.
# each test evaluates a normal PDP.
line = [np.sum(list(g.dtc.scores.values())) for g in gr]
nr = {str(list(free_param)[0]): None}
newrange, range_adj = check_line(line, gr, nr)
while range_adj == True:
mp = mp_process(newrange)
gr = run_grid(3,
tests,
provided_keys=newrange,
hold_constant=hc,
mp_in=mp)
# make a psuedo test, that still depends on input Parametersself.
# each test evaluates a normal PDP.
nr = {}
line = [np.sum(list(g.dtc.scores.values())) for g in gr]
newrange, range_adj = check_line(line, gr, newrange)
mp = mp_process(newrange)
with open('parameter_bf_ranges.p', 'wb') as f:
pickle.dump(mp, f)
import pdb
pdb.set_trace()
package = run_ga(
mp, nparams * 2, 12, tests_,
provided_keys=opt_keys) #, use_cache = True, cache_name='simple')
return package
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 grids(hof,tests):
dim = len(hof[0].dtc.attrs.keys())
flat_iter = [ (i,ki,j,kj) for i,ki in enumerate(hof[0].dtc.attrs.keys()) for j,kj in enumerate(hof[0].dtc.attrs.keys()) ]
matrix = [[0 for x in range(dim)] for y in range(dim)]
plt.clf()
fig,ax = plt.subplots(dim,dim,figsize=(10,10))
#fig1,ax1 = plt.subplots(dim,dim,figsize=(10,10))
cnt = 0
for i,ki,j,kj in flat_iter:
free_param = set([ki,kj]) # construct a small-set out of the indexed keys 2. If both keys are
# are the same, this set will only contain one index
bs = set(hof[0].dtc.attrs.keys()) # construct a full set out of all of the keys available, including ones not indexed here.
diff = bs.difference(free_param) # diff is simply the key that is not indexed.
# BD is the dictionary of parameters to be held constant
# if the plot is 1D then two parameters should be held constant.
hc = {}
for d in diff:
hc[d] = hof[0].dtc.attrs[d]
if i == j:
assert len(free_param) == len(hc) - 1
assert len(hc) == len(free_param) + 1
gr = run_grid(10,tests,provided_keys = free_param ,hold_constant = hc)
line = [ np.sum(list(g.dtc.scores.values())) for g in gr]
# make a psuedo test, that still depends on input Parametersself.
# each test evaluates a normal PDP.
matrix[i][j] = ( free_param,gr )
fp = list(copy.copy(free_param))
ax[i,j] = plot_line(gr,ax[i,j],fp)
if i >j:
assert len(free_param) == len(hc) + 1
assert len(hc) == len(free_param) - 1
# what I want to do, I want to plot grid lines not a heat map.
# I want to plot bd.attrs is cross hairs,
# I want the range of the plot shown to be bigger than than the grid lines.
gr = run_grid(10,tests,provided_keys = free_param ,hold_constant = hc)
fp = list(copy.copy(free_param))
#if len(gr) == 100:
ax[i,j] = plot_surface(gr,ax[i,j],fp,imshow=False)
matrix[i][j] = ( free_param,gr )
if i < j:
matrix[i][j] = ( free_param,gr )
fp = list(copy.copy(free_param))
if len(fp) == 2:
ax[i,j] = plot_scatter(hof,ax[i,j],fp)
plt.savefig(str('first_surfaces.png'))
return matrix
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_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 check_range(matrix,hof):
dim = np.shape(matrix)[0]
print(dim)
cnt = 0
fig,ax = plt.subplots(dim,dim,figsize=(10,10))
flat_iter = []
newrange = {}
for i,k in enumerate(matrix):
for j,r in enumerate(k):
keys = list(r[0])
gr = r[1]
print(line)
if i==j:
line = [ np.sum(list(g.dtc.scores.values())) for g in gr]
(newrange, range_adj) = check_line(line,newrange)
print(newrange,'newrange')
return (newrange, range_adj)
def evidence(matrix,hof):
dim = np.shape(matrix)[0]
print(dim)
cnt = 0
fig,ax = plt.subplots(dim,dim,figsize=(10,10))
flat_iter = []
newrange = {}
for i,k in enumerate(matrix):
for j,r in enumerate(k):
keys = list(r[0])
gr = r[1]
line = [ np.sum(list(g.dtc.scores.values())) for g in gr]
print(line)
if i==j:
keys = keys[0]
min_ = np.min(line)
print(min_,line[0],line[1],'diff?')
if line[0] == min_:
print('hit')
attrs = gr[0].dtc.attrs[keys]
remin = - np.abs(attrs)*10
remax = np.abs(gr[-1].dtc.attrs[keys])*10
nr = np.linspace(remin,remax,10)
newrange[keys] = nr
if line[-1] == min_:
print('hit')
attrs = gr[-1].dtc.attrs[keys]
remin = - np.abs(attrs)*10
remax = np.abs(gr[-1].dtc.attrs[keys])*10
nr = np.linspace(remin,remax,10)
newrange[keys] = nr
print(newrange,'newrange')
return newrange
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')