def plot_map(self, nmap=0, colbar='yes', min_m=-100, max_m=-100):
"""
Plot a map created during the training process,
:param int nmap: Number of created map, default is 0
:param float min_m: Lower limit for coloring the cells, -100 uses min value
:param float max_m: Upper limit for coloring the cells, -100 uses max value
:param str colbar: Include a colorbar ('yes','no')
"""
path_map = self.Pars.path_output_maps
fileM = self.Pars.somfilename + '_%04d.npy' % nmap
utils_mlz.printpz('Plotting ', fileM)
M = np.load(path_map + fileM)
M = M.item()
M.plot_map(colbar=colbar, min_m=min_m, max_m=max_m)
def plot_tree(self, ntree=0, save_files='no', fileroot='TPZ', path=''):
"""
Plot a tree created during the training process,
uses the Graphviz package (dot and neato)
:param int ntree: Number of created tree, default is 0
:param str save_files: Saves the created files from Graphviz (the .png and the .dot files) 'yes'/'no'
:param str fileroot: root name for saved files
:param str path: path name for output files
"""
path_tree = self.Pars.path_output_trees
fileT = self.Pars.treefilename + '_%04d.npy' % ntree
utils_mlz.printpz('Plotting ', fileT)
T = np.load(path_tree + fileT)
T = T.item()
if T.dict_dim == 'all':
dd = {}
for i in range(len(self.Pars.att)):
dd[self.Pars.att[i]] = {'ind': i}
T.dict_dim = dd
T.plot_tree(save_png=save_files, fileout=fileroot, path=path)
def plot_importance(self, result_id=0, Nzb=10, list_att=''):
"""
Plot ranking of importance of attributes used during the training/testing process
.. note ::
The key `OobError` and `VarImportance` in :ref:`input-file` must be set to 'yes' to compute these quantities
:param int results_id: Result id number as the output on the results folder, default 0
:param int Nzb: Number of redshift bins
"""
filenum = str(result_id)
froot = self.Pars.path_results + self.Pars.finalfilename + '_oob'
zs, za, zb, oa, ob, ea, eb = np.loadtxt(froot + '.' + filenum + '.mlz',
unpack=True)
fdz = lambda x, y: (abs(x - y)) / (1. + y)
O_all = utils_mlz.bias(zs,
zb,
'OOB_all',
self.Pars.minz,
self.Pars.maxz,
1,
mode=0,
d_z=fdz,
verb=False)
ki = np.arange(len(self.Pars.att))
Im0 = np.zeros(len(ki))
Im1 = np.zeros(len(ki))
keys = []
utils_mlz.printpz("Importance")
utils_mlz.printpz("----------")
for k in range(len(self.Pars.att)):
kk = self.Pars.att[k]
keys.append(kk)
filev = froot + '_' + kk + '.' + filenum + '.mlz'
zs, za, zb, oa, ob, ea, eb = np.loadtxt(filev, unpack=True)
O_temp = utils_mlz.bias(zs,
zb,
'OOB_temp',
self.Pars.minz,
self.Pars.maxz,
1,
mode=0,
d_z=fdz,
verb=False)
Im0[k] = O_temp.mean / O_all.mean
Im1[k] = O_temp.sigma / O_all.sigma
del O_temp
imp = '%.5f' % Im1[k]
utils_mlz.printpz(kk, ' ', imp)
fimportance = froot + '.' + filenum + '.importance'
FF = open(fimportance, 'w')
linew = ','.join(keys)
linew = '#' + linew + '\n'
FF.write(linew)
for i in range(len(ki)):
linew = '%.8f\n' % Im0[i]
FF.write(linew)
FF.close()
utils_mlz.printpz()
utils_mlz.printpz("Importance file saved in: ", fimportance)
Im = (Im0 + Im1) / 2.
sk = np.argsort(Im0)
sk = sk[::-1]
fig, ax = plt.subplots()
ax.plot(ki, Im0[sk], 'bo-', label='bias')
ax.plot(ki, Im1[sk], 'go-', label='sigma')
ax.plot(ki, Im[sk], 'ro-', label='avg')
ax.set_xlabel('Importance', fontsize=16)
ax.set_ylabel('Attributes', fontsize=16)
plt.legend(loc=0)
ax.set_xticks(ki)
ax.set_xticklabels(np.array(keys)[sk],
rotation=40,
ha='right',
fontsize=12)
ax.set_xlim(-1, len(ki))
##
zs, za, zb, oa, ob, ea, eb = np.loadtxt(froot + '.' + filenum + '.mlz',
unpack=True)
O_all = utils_mlz.bias(zs,
zb,
'OOB_all',
self.Pars.minz,
self.Pars.maxz,
Nzb,
mode=1,
d_z=fdz,
verb=False)
ki = np.arange(len(self.Pars.att))
Im0 = np.zeros((Nzb, len(ki)))
Im1 = np.zeros((Nzb, len(ki)))
keys = []
for k in range(len(self.Pars.att)):
kk = self.Pars.att[k]
keys.append(kk)
filev = froot + '_' + kk + '.' + filenum + '.mlz'
zs, za, zb, oa, ob, ea, eb = np.loadtxt(filev, unpack=True)
O_temp = utils_mlz.bias(zs,
zb,
'OOB_temp',
self.Pars.minz,
self.Pars.maxz,
Nzb,
mode=1,
d_z=fdz,
verb=False)
Im0[:, k] = O_temp.mean / O_all.mean
Im1[:, k] = O_temp.sigma / O_all.sigma
del O_temp
fig2, ax2 = plt.subplots()
if list_att == '':
ax2.plot(O_all.bins, (Im0[:, sk[0]] + Im1[:, sk[0]]) / 2,
'bo-',
label=np.array(keys)[sk[0]])
ax2.plot(O_all.bins, (Im0[:, sk[1]] + Im1[:, sk[1]]) / 2,
'go-',
label=np.array(keys)[sk[1]])
ax2.plot(O_all.bins, (Im0[:, sk[-1]] + Im1[:, sk[-1]]) / 2,
'ro-',
label=np.array(keys)[sk[-1]])
else:
for katt in list_att:
wk = np.where(np.array(keys) == katt)[0]
ax2.plot(O_all.bins, (Im0[:, wk[0]] + Im1[:, wk[0]]) / 2,
'o-',
label=np.array(keys)[wk[0]])
ax2.set_xlabel('redshift', fontsize=16)
ax2.set_ylabel('Importance', fontsize=16)
plt.legend(loc=0)
plt.show()