def predict(self,x):
#x is a list of data frames to feed into each submodel.
#This allows different normalizations to be used with each submodel
predictions=[]
for i,k in enumerate(self.submodels):
xtemp=x[i].xs('wvl',axis=1,level=0,drop_level=False)
xtemp,mean_vect=meancenter(xtemp,'wvl',previous_mean=self.mean_vects[i])
predictions.append(k.predict(xtemp['wvl']))
return predictions
def predict(self, x):
#x is a list of data frames to feed into each submodel.
#This allows different normalizations to be used with each submodel
predictions = []
for i, k in enumerate(self.submodels):
xtemp = x[i].xs('wvl', axis=1, level=0, drop_level=False)
xtemp, mean_vect = meancenter(xtemp,
'wvl',
previous_mean=self.mean_vects[i])
predictions.append(k.predict(xtemp['wvl']))
return predictions
def fit(self, trainsets, ranges, ncs, ycol, figpath=None):
self.ranges = ranges
self.ncs = ncs
self.ycol = ycol
submodels = []
mean_vects = []
for i, rangei in enumerate(ranges):
data_tmp = within_range.within_range(trainsets[i], rangei, ycol)
x = data_tmp.xs('wvl', axis=1, level=0, drop_level=False)
y = data_tmp['meta'][ycol]
x_centered, x_mean_vect = meancenter(
x, 'wvl') #mean center training data
pls = PLSRegression(n_components=ncs[i], scale=False)
pls.fit(x, y)
submodels.append(pls)
mean_vects.append(x_mean_vect)
if figpath is not None:
#calculate spectral residuals
E = x_centered - np.dot(pls.x_scores_,
pls.x_loadings_.transpose())
Q_res = np.dot(E, E.transpose()).diagonal()
#calculate leverage
T = pls.x_scores_
leverage = np.diag(
T @ np.linalg.inv(T.transpose() @ T) @ T.transpose())
plot.figure()
plot.scatter(leverage, Q_res, color='r', edgecolor='k')
plot.title(ycol + ' (' + str(rangei[0]) + '-' +
str(rangei[1]) + ')')
plot.xlabel('Leverage')
plot.ylabel('Q')
plot.ylim([0, 1.1 * np.max(Q_res)])
plot.xlim([0, 1.1 * np.max(leverage)])
plot.savefig(figpath + '/' + ycol + '_' + str(rangei[0]) +
'-' + str(rangei[1]) + 'Qres_vs_Leverage.png',
dpi=600)
self.leverage = leverage
self.Q_res = Q_res
self.submodels = submodels
self.mean_vects = mean_vects
def fit(self,trainsets,ranges,ncs,ycol,figpath=None):
self.ranges=ranges
self.ncs=ncs
self.ycol=ycol
submodels=[]
mean_vects=[]
for i,rangei in enumerate(ranges):
data_tmp=within_range.within_range(trainsets[i],rangei,ycol)
x=data_tmp.xs('wvl',axis=1,level=0,drop_level=False)
y=data_tmp['meta'][ycol]
x_centered,x_mean_vect=meancenter(x,'wvl') #mean center training data
pls=PLSRegression(n_components=ncs[i],scale=False)
pls.fit(x,y)
submodels.append(pls)
mean_vects.append(x_mean_vect)
if figpath is not None:
#calculate spectral residuals
E=x_centered-np.dot(pls.x_scores_,pls.x_loadings_.transpose())
Q_res=np.dot(E,E.transpose()).diagonal()
#calculate leverage
T=pls.x_scores_
leverage=np.diag([email protected](T.transpose()@T)@T.transpose())
plot.figure()
plot.scatter(leverage,Q_res,color='r',edgecolor='k')
plot.title(ycol+' ('+str(rangei[0])+'-'+str(rangei[1])+')')
plot.xlabel('Leverage')
plot.ylabel('Q')
plot.ylim([0,1.1*np.max(Q_res)])
plot.xlim([0,1.1*np.max(leverage)])
plot.savefig(figpath+'/'+ycol+'_'+str(rangei[0])+'-'+str(rangei[1])+'Qres_vs_Leverage.png',dpi=600)
self.leverage=leverage
self.Q_res=Q_res
self.submodels=submodels
self.mean_vects=mean_vects
def pls_cv(Train,Test=None,nc=20,nfolds=5,ycol='SiO2',doplot=True,outpath='.',plotfile='pls_cv.png'):
#create empty arrays for the RMSE values
pls_rmsecv=np.empty(nc)
pls_rmsec=np.empty(nc)
#If there is a test set provided, create the RMSEP array to hold test set errors
if Test is not None:
pls_rmsep=np.empty(nc)
#loop through each number of components
for i in range(1,nc+1):
print('nc='+str(i))
Train[('meta',ycol+'_cv_PLS_nc'+str(i))]=0 #create a column to hold the PLS cross validation results for this nc
Train[('meta',ycol+'_PLS_nc'+str(i))]=0 #create a column to hold the PLS training set results for this nc
if Test is not None:
Test[('meta',ycol+'_PLS_nc'+str(i))]=0 #create a column to hold the PLS test set results for this nc
#Do the cross validation
cv_iterator=LeaveOneLabelOut(Train[('meta','Folds')]) #create the iterator for cross validation within the training data
for train,holdout in cv_iterator: #Iterate through each of the folds in the training set
cv_train=Train.iloc[train]
cv_holdout=Train.iloc[holdout]
#Do PLS for this number of components
cv_train_centered,cv_train_mean_vect=meancenter(cv_train) #mean center training data
cv_holdout_centered,cv_holdout_mean_vect=meancenter(cv_holdout,previous_mean=cv_train_mean_vect) #apply same mean centering to holdout data
pls=PLSRegression(n_components=i,scale=False)
pls.fit(cv_train_centered['wvl'],cv_train_centered['meta'][ycol])
y_pred_holdout=pls.predict(cv_holdout_centered['wvl'])
Train.set_value(Train.index[holdout],('meta',ycol+'_cv_PLS_nc'+str(i)),y_pred_holdout)
pls_rmsecv[i-1]=np.sqrt(np.mean(np.subtract(Train[('meta',ycol)],Train[('meta',ycol+'_cv_PLS_nc'+str(i))])**2,axis=0))
#Do train and test set PLS predictions for this number of components
Train_centered,Train_mean_vect=meancenter(Train)
pls=PLSRegression(n_components=i,scale=False)
pls.fit(Train_centered['wvl'],Train_centered['meta'][ycol])
y_pred=pls.predict(Train_centered['wvl'])
Train.set_value(Train.index,('meta',ycol+'_PLS_nc'+str(i)),y_pred)
pls_rmsec[i-1]=np.sqrt(np.mean(np.subtract(Train[('meta',ycol)],Train[('meta',ycol+'_PLS_nc'+str(i))])**2,axis=0))
if Test is not None:
Test_centered,Train_mean_vect=meancenter(Test,previous_mean=Train_mean_vect)
y_pred=pls.predict(Test_centered['wvl'])
Test.set_value(Test.index,('meta',ycol+'_PLS_nc'+str(i)),y_pred)
pls_rmsep[i-1]=np.sqrt(np.mean(np.subtract(Test[('meta',ycol)],Test[('meta',ycol+'_PLS_nc'+str(i))])**2,axis=0))
if doplot==True:
plot.figure()
plot.title(ycol)
plot.xlabel('# of components')
plot.ylabel(ycol+' RMSE (wt.%)')
plot.plot(range(1,nc+1),pls_rmsecv,label='RMSECV',color='r')
plot.plot(range(1,nc+1),pls_rmsec,label='RMSEC',color='b')
if Test is not None:
plot.plot(range(1,nc+1),pls_rmsep,label='RMSEP',color='g')
plot.legend(loc=0,fontsize=6)
plot.savefig(outpath+'/'+plotfile,dpi=600)
rmses={'RMSEC':pls_rmsec,'RMSECV':pls_rmsecv}
if Test is not None:
rmses['RMSEP']=pls_rmsep
return rmses
