if i == 0:
X_train = Xtrain
y_train = ytrain
X_test = Xtest
y_test = ytest
np.append(X_train, Xtrain, axis=0)
np.append(y_train, ytrain, axis=0)
np.append(X_test, Xtest, axis=0)
np.append(y_test, ytest, axis=0)
for j in range(len(lm)):
lin1 = difflearn.train(X_train,
y_train,
RegType='L1',
RegCoef=lm[j],
maxiter=5000,
tolerance=0.00001)
lin2 = difflearn.train(X_train,
y_train,
RegType='L2',
RegCoef=lm[j],
maxiter=5000)
DL = [lin1, lin2]
for k in range(len(DL)):
er[k, j] = mean_squared_error(y_test, DL[k].predict(X_test))
for l in range(maxncoef):
coef[k, j, l] = DL[k].coef_[l]
numcoefl1[j] = DL[0].sparse_coef_.getnnz()
def runML(setting):
version = 1
loadname = [makesavename(i, version) for i in IC]
S1 = PdfSolver()
fuk = []
fu = []
kmean = []
gridvars = []
ICparams = []
for i in range(len(IC)):
fuki, fui, kmeani, gridvarsi, ICparamsi = S1.loadSolution(loadname[i])
fuk.append(fuki)
fu.append(fui)
kmean.append(kmeani)
uu, kk, xx, tt = gridvarsi
muk, sigk, mink, maxk, sigu, a, b = ICparamsi
grid = PdfGrid()
grid.setGrid(xx, tt, uu, kk)
grid.printDetails()
lmnum = 40
lmmin = 0.0000001
lmmax = 0.00005
lm = np.linspace(lmmin, lmmax, lmnum)
options = ['linear', '2ndorder']
if setting == 'sepIC':
for opt in options:
# Get number of maximum number of coefficients: maxncoef
difflearn = PDElearn(fuk[0],
grid,
kmean[0],
fu=fu[0],
trainratio=0.8,
debug=False)
featurelist, labels, featurenames = difflearn.makeFeatures(
option=opt)
#pdb.set_trace()
maxncoef = len(featurenames) - 1
print('#################### %s ########################' % (opt))
DL = []
X = []
y = []
error = []
regopts = 2
er = np.zeros((len(IC), regopts, len(lm)))
coef = np.zeros((len(IC), regopts, len(lm), maxncoef))
numcoefl1 = np.zeros((len(IC), len(lm)))
for i in range(len(IC)):
print('---- Initial Condition ----')
print('u0: ' + IC[i]['u0'])
print('fu0: ' + IC[i]['fu0'])
print('fk: ' + IC[i]['fk'])
print('---- ----- ----')
difflearn = PDElearn(fuk[i],
grid,
kmean[i],
fu=fu[i],
trainratio=0.8,
debug=False)
featurelist, labels, featurenames = difflearn.makeFeatures(
option=opt)
Xtrain, ytrain, Xtest, ytest = difflearn.makeTTsets(
featurelist, labels, shuffle=False)
for j in range(len(lm)):
lin1 = difflearn.train(Xtrain,
ytrain,
RegType='L1',
RegCoef=lm[j],
maxiter=5000,
tolerance=0.00001)
lin2 = difflearn.train(Xtrain,
ytrain,
RegType='L2',
RegCoef=lm[j],
maxiter=5000)
DL = [lin1, lin2]
for k in range(len(DL)):
er[i, k,
j] = mean_squared_error(ytest, DL[k].predict(Xtest))
#pdb.set_trace()
for l in range(maxncoef):
coef[i, k, j, l] = DL[k].coef_[l]
numcoefl1[i, j] = DL[0].sparse_coef_.getnnz()
## Plotting
# Error as a function of lm
fig = plt.figure()
leg = []
for i in range(len(IC)):
plt.plot(lm, np.reshape(er[i, 0, :], (len(lm), )))
leg.append(makesavename(IC[i], 1))
figname = setting + ' reg coefficients L%d reg, %s' % (1, opt)
plt.xlabel('Regularization Coefficient')
plt.ylabel('Error')
plt.title(figname)
plt.legend(leg)
plt.savefig(FIGFILE + figname + '.pdf')
# Sparsity as a function of lm
fig = plt.figure()
leg = []
for i in range(len(IC)):
plt.plot(lm, numcoefl1[i])
leg.append(makesavename(IC[i], 1))
figname = setting + ' LinearIC Sparsity in L%d reg, %s' % (1, opt)
plt.xlabel('Regularization Coefficient')
plt.ylabel('Sparsity: Number of Coeffients')
plt.title(figname)
plt.legend(leg)
plt.savefig(FIGFILE + figname + '.pdf')
# All Coefficients values as a function of lm
for j in range(len(IC)):
fig = plt.figure()
leg = []
for i in range(len(featurenames) - 1):
plt.plot(lm, np.reshape(coef[j, 0, :, i], (len(lm), )))
leg.append(featurenames[i + 1])
figname = setting + ' Linear features L%d reg, %s' % (1, opt)
plt.xlabel('Regularization Coefficient')
plt.ylabel('Coefficient Values')
plt.title(figname)
plt.legend(leg)
plt.savefig(FIGFILE + figname)
plt.show()
if setting == 'lumpIC':
for opt in options:
# Get number of maximum number of coefficients: maxncoef
difflearn = PDElearn(fuk[0],
grid,
kmean[0],
fu=fu[0],
trainratio=0.8,
debug=False)
featurelist, labels, featurenames = difflearn.makeFeatures(
option=opt)
#pdb.set_trace()
maxncoef = len(featurenames) - 1
print('#################### %s ########################' % (opt))
DL = []
X = []
y = []
error = []
regopts = 2
er = np.zeros((regopts, len(lm)))
coef = np.zeros((regopts, len(lm), maxncoef))
numcoefl1 = np.zeros((len(lm), ))
for i in range(len(IC)):
difflearn = PDElearn(fuk[i],
grid,
kmean[i],
fu=fu[i],
trainratio=0.8,
debug=False)
featurelist, labels, featurenames = difflearn.makeFeatures(
option=opt)
Xtrain, ytrain, Xtest, ytest = difflearn.makeTTsets(
featurelist, labels, shuffle=False)
if i == 0:
X_train = Xtrain
y_train = ytrain
X_test = Xtest
y_test = ytest
np.append(X_train, Xtrain, axis=0)
np.append(y_train, ytrain, axis=0)
np.append(X_test, Xtest, axis=0)
np.append(y_test, ytest, axis=0)
for j in range(len(lm)):
lin1 = difflearn.train(X_train,
y_train,
RegType='L1',
RegCoef=lm[j],
maxiter=5000,
tolerance=0.00001)
lin2 = difflearn.train(X_train,
y_train,
RegType='L2',
RegCoef=lm[j],
maxiter=5000)
DL = [lin1, lin2]
for k in range(len(DL)):
er[k, j] = mean_squared_error(y_test,
DL[k].predict(X_test))
for l in range(maxncoef):
coef[k, j, l] = DL[k].coef_[l]
numcoefl1[j] = DL[0].sparse_coef_.getnnz()
## Plotting
# Error as a function of lm
fig = plt.figure()
leg = []
plt.plot(lm, er[0, :])
figname = setting + ' reg coefficients L%d reg, %s' % (1, opt)
plt.xlabel('Regularization Coefficient')
plt.ylabel('Error')
plt.title(figname)
plt.savefig(FIGFILE + figname + '.pdf')
# Sparsity as a function of lm
fig = plt.figure()
leg = []
plt.plot(lm, numcoefl1)
figname = setting + ' LinearIC Sparsity in L%d reg, %s' % (1, opt)
plt.xlabel('Regularization Coefficient')
plt.ylabel('Sparsity: Number of Coeffients')
plt.title(figname)
plt.savefig(FIGFILE + figname + '.pdf')
# All Coefficients values as a function of lm
fig = plt.figure()
leg = []
for i in range(len(featurenames) - 1):
plt.plot(lm, np.reshape(coef[0, :, i], (len(lm), )))
leg.append(featurenames[i + 1])
figname = setting + ' LinearIC Linear features L%d reg, %s' % (1,
opt)
plt.xlabel('Regularization Coefficient')
plt.ylabel('Coefficient Values')
plt.title(figname)
plt.legend(leg)
plt.savefig(FIGFILE + figname + '.pdf')
plt.show()
def runML(setting):
Tinc = 10
Tmin = 0.3
Tmax = 0.9
T = np.linspace(Tmin, Tmax, Tinc)
version = 1
loadname = [makesavename(i, version) for i in IC]
S1 = PdfSolver()
fuk = []
fu = []
kmean = []
gridvars = []
ICparams = []
for i in range(len(IC)):
fuki, fui, kmeani, gridvarsi, ICparamsi = S1.loadSolution(loadname[i])
fuk.append(fuki)
fu.append(fui)
kmean.append(kmeani)
uu, kk, xx, tt = gridvarsi
muk, sigk, mink, maxk, sigu, a, b = ICparamsi
grid = PdfGrid()
grid.setGrid(xx, tt, uu, kk)
grid.printDetails()
if setting == 'sepIC':
options = ['linear', '2ndorder']
for opt in options:
print('#################### %s ########################' % (opt))
DL = []
X = []
y = []
error = []
er = np.zeros((len(IC), 3, len(T)))
for i in range(len(fuk)):
print('---- Initial Condition ----')
print('u0: ' + IC[i]['u0'])
print('fu0: ' + IC[i]['fu0'])
print('fk: ' + IC[i]['fk'])
print('---- ----- ----')
for j in range(len(T)):
print('\n ###### Training %3.2f percent ###### \n ' %
(T[j]))
difflearn = PDElearn(fuk[i],
grid,
kmean[i],
fu=fu[i],
trainratio=T[j],
debug=False)
featurelist, labels, featurenames = difflearn.makeFeatures(
option=opt)
Xtrain, ytrain, Xtest, ytest = difflearn.makeTTsets(
featurelist, labels, shuffle=False)
lin0 = difflearn.train(Xtrain, ytrain, RegType='L0')
lin1 = difflearn.train(Xtrain,
ytrain,
RegType='L1',
RegCoef=0.000001,
maxiter=5000,
tolerance=0.00001)
lin2 = difflearn.train(Xtrain,
ytrain,
RegType='L2',
RegCoef=0.01,
maxiter=5000)
DL = [lin0, lin1, lin2]
for k in range(len(DL)):
# Do it for each initial condition
er[i, k,
j] = mean_squared_error(ytest, DL[k].predict(Xtest))
## Plotting
for l in range(len(DL)):
fig = plt.figure()
leg = []
for i in range(len(IC)):
plt.plot(T, np.reshape(er[i, l, :], (len(T), )))
leg.append(makesavename(IC[i], 1))
plt.xlabel('Training Time Span (\%)')
plt.ylabel('Error')
plt.title('Time Generalization for L%d reg, %s' % (l, opt))
plt.legend(leg)
plt.show()
if setting == 'lumpIC':
#### Lump initial conditions ####
opt = 'linear'
DL = []
er = np.zeros((3, len(T)))
for j in range(len(T)):
print('\n ###### Training %3.2f percent ###### \n ' % (T[j]))
for i in range(len(IC)):
difflearn = PDElearn(fuk[i],
grid,
kmean[i],
fu=fu[i],
trainratio=T[j],
debug=False)
featurelist, labels, featurenames = difflearn.makeFeatures(
option=opt)
Xtrain, ytrain, Xtest, ytest = difflearn.makeTTsets(
featurelist, labels, shuffle=False)
if i == 0:
X_train = Xtrain
y_train = ytrain
X_test = Xtest
y_test = ytest
np.append(X_train, Xtrain, axis=0)
np.append(y_train, ytrain, axis=0)
np.append(X_test, Xtest, axis=0)
np.append(y_test, ytest, axis=0)
lin0 = difflearn.train(X_train, y_train, RegType='L0')
lin1 = difflearn.train(X_train,
y_train,
RegType='L1',
RegCoef=0.00001,
maxiter=5000,
tolerance=0.00001)
lin2 = difflearn.train(X_train,
y_train,
RegType='L2',
RegCoef=0.01,
maxiter=5000)
DL = [lin0, lin1, lin2]
for k in range(len(DL)):
# Do it for each initial condition
er[k, j] = mean_squared_error(y_test, DL[k].predict(X_test))
## Plotting
for l in range(len(DL)):
fig = plt.figure()
figname = 'Time Generalization L%d reg - linear lumped IC' % (l)
plt.plot(T, er[l, :])
plt.xlabel('Training Time Span (\%)')
plt.ylabel('Error')
plt.title(figname)
fig.savefig(figname + '.pdf')
plt.show()