def mlr_show( clf, RMv, yEv, disp = True, graph = True):
yEv_calc = clf.predict( RMv)
if len( np.shape(yEv)) == 2 and len( np.shape(yEv_calc)) == 1:
yEv_calc = np.mat( yEv_calc).T
r_sqr, RMSE = kchem.estimate_accuracy( yEv, yEv_calc, disp = disp)
if graph:
plt.figure()
ms_sz = max(min( 4000 / yEv.shape[0], 8), 1)
plt.plot( yEv.tolist(), yEv_calc.tolist(), '.', ms = ms_sz)
ax = plt.gca()
lims = [
np.min([ax.get_xlim(), ax.get_ylim()]), # min of both axes
np.max([ax.get_xlim(), ax.get_ylim()]), # max of both axes
]
# now plot both limits against eachother
#ax.plot(lims, lims, 'k-', alpha=0.75, zorder=0)
ax.plot(lims, lims, '-', color = 'pink')
plt.xlabel('Experiment')
plt.ylabel('Prediction')
plt.title( '$r^2$ = {0:.2e}, RMSE = {1:.2e}'.format( r_sqr, RMSE))
plt.show()
return r_sqr, RMSE
def cv_show( yEv, yEv_calc, disp = True, graph = True, grid_std = None):
# if the output is a vector and the original is a metrix,
# the output is translated to a matrix.
if len( np.shape(yEv_calc)) == 1:
yEv_calc = np.mat( yEv_calc).T
if len( np.shape(yEv)) == 1:
yEv = np.mat( yEv).T
r_sqr, RMSE = kchem.estimate_accuracy( yEv, yEv_calc, disp = disp)
if graph:
#plt.scatter( yEv.tolist(), yEv_calc.tolist())
plt.figure()
ms_sz = max(min( 4000 / yEv.shape[0], 8), 1)
plt.plot( yEv.tolist(), yEv_calc.tolist(), '.', ms = ms_sz) # Change ms
ax = plt.gca()
lims = [
np.min([ax.get_xlim(), ax.get_ylim()]), # min of both axes
np.max([ax.get_xlim(), ax.get_ylim()]), # max of both axes
]
# now plot both limits against eachother
#ax.plot(lims, lims, 'k-', alpha=0.75, zorder=0)
ax.plot(lims, lims, '-', color = 'pink')
plt.xlabel('Experiment')
plt.ylabel('Prediction')
if grid_std:
plt.title( '($r^2$, std) = ({0:.2e}, {1:.2e}), RMSE = {2:.2e}'.format( r_sqr, grid_std, RMSE))
else:
plt.title( '$r^2$ = {0:.2e}, RMSE = {1:.2e}'.format( r_sqr, RMSE))
plt.show()
return r_sqr, RMSE
def mlr_show(clf, RMv, yEv, disp=True, graph=True):
yEv_calc = clf.predict(RMv)
if len(np.shape(yEv)) == 2 and len(np.shape(yEv_calc)) == 1:
yEv_calc = np.mat(yEv_calc).T
r_sqr, RMSE = kchem.estimate_accuracy(yEv, yEv_calc, disp=disp)
if graph:
plt.figure()
ms_sz = max(min(4000 / yEv.shape[0], 8), 1)
plt.plot(yEv.tolist(), yEv_calc.tolist(), '.', ms=ms_sz)
ax = plt.gca()
lims = [
np.min([ax.get_xlim(), ax.get_ylim()]), # min of both axes
np.max([ax.get_xlim(), ax.get_ylim()]), # max of both axes
]
# now plot both limits against eachother
#ax.plot(lims, lims, 'k-', alpha=0.75, zorder=0)
ax.plot(lims, lims, '-', color='pink')
plt.xlabel('Experiment')
plt.ylabel('Prediction')
plt.title('$r^2$ = {0:.2e}, RMSE = {1:.2e}'.format(r_sqr, RMSE))
plt.show()
return r_sqr, RMSE
def cv_show(yEv, yEv_calc, disp=True, graph=True, grid_std=None):
# if the output is a vector and the original is a metrix,
# the output is translated to a matrix.
if len(np.shape(yEv_calc)) == 1:
yEv_calc = np.mat(yEv_calc).T
if len(np.shape(yEv)) == 1:
yEv = np.mat(yEv).T
r_sqr, RMSE = kchem.estimate_accuracy(yEv, yEv_calc, disp=disp)
if graph:
#plt.scatter( yEv.tolist(), yEv_calc.tolist())
plt.figure()
ms_sz = max(min(4000 / yEv.shape[0], 8), 1)
plt.plot(yEv.tolist(), yEv_calc.tolist(), '.', ms=ms_sz) # Change ms
ax = plt.gca()
lims = [
np.min([ax.get_xlim(), ax.get_ylim()]), # min of both axes
np.max([ax.get_xlim(), ax.get_ylim()]), # max of both axes
]
# now plot both limits against eachother
#ax.plot(lims, lims, 'k-', alpha=0.75, zorder=0)
ax.plot(lims, lims, '-', color='pink')
plt.xlabel('Experiment')
plt.ylabel('Prediction')
if grid_std:
plt.title(
'($r^2$, std) = ({0:.2e}, {1:.2e}), RMSE = {2:.2e}'.format(
r_sqr, grid_std, RMSE))
else:
plt.title('$r^2$ = {0:.2e}, RMSE = {1:.2e}'.format(r_sqr, RMSE))
plt.show()
return r_sqr, RMSE
def _regress_show_r0( yEv, yEv_calc, disp = True, graph = True, plt_title = None):
# if the output is a vector and the original is a metrix,
# the output is translated to a matrix.
if len( np.shape(yEv)) == 2 and len( np.shape(yEv_calc)) == 1:
yEv_calc = np.mat( yEv_calc).T
r_sqr, RMSE = kchem.estimate_accuracy( yEv, yEv_calc, disp = disp)
if graph:
plt.scatter( yEv.tolist(), yEv_calc.tolist())
ax = plt.gca()
lims = [
np.min([ax.get_xlim(), ax.get_ylim()]), # min of both axes
np.max([ax.get_xlim(), ax.get_ylim()]), # max of both axes
]
# now plot both limits against eachother
#ax.plot(lims, lims, 'k-', alpha=0.75, zorder=0)
ax.plot(lims, lims, '-', color = 'pink')
plt.xlabel('Target')
plt.ylabel('Prediction')
if plt_title:
plt.title( plt_title)
plt.show()
return r_sqr, RMSE
def _regress_show_r0(yEv, yEv_calc, disp=True, graph=True, plt_title=None):
# if the output is a vector and the original is a metrix,
# the output is translated to a matrix.
if len(np.shape(yEv)) == 2 and len(np.shape(yEv_calc)) == 1:
yEv_calc = np.mat(yEv_calc).T
r_sqr, RMSE = kchem.estimate_accuracy(yEv, yEv_calc, disp=disp)
if graph:
plt.scatter(yEv.tolist(), yEv_calc.tolist())
ax = plt.gca()
lims = [
np.min([ax.get_xlim(), ax.get_ylim()]), # min of both axes
np.max([ax.get_xlim(), ax.get_ylim()]), # max of both axes
]
# now plot both limits against eachother
#ax.plot(lims, lims, 'k-', alpha=0.75, zorder=0)
ax.plot(lims, lims, '-', color='pink')
plt.xlabel('Target')
plt.ylabel('Prediction')
if plt_title:
plt.title(plt_title)
plt.show()
return r_sqr, RMSE