class PLSRegressionImpl():
def __init__(self,
n_components=2,
scale=True,
max_iter=500,
tol=1e-06,
copy=True):
self._hyperparams = {
'n_components': n_components,
'scale': scale,
'max_iter': max_iter,
'tol': tol,
'copy': copy
}
def fit(self, X, y=None):
self._sklearn_model = SKLModel(**self._hyperparams)
if (y is not None):
self._sklearn_model.fit(X, y)
else:
self._sklearn_model.fit(X)
return self
def transform(self, X):
return self._sklearn_model.transform(X)
def predict(self, X):
return self._sklearn_model.predict(X)
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
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
