def plot_components_PLSR(event):
run = PLSR.run
if not run.ui['reg_type'] == 'PLSR':
return
"""Function for making plots of the latent variables from PLSR. """
ui = run.ui
ui['fig_per_row'] = int(run.frame.buttons['fig_per_row'].get())
ui['max_plots'] = int(run.frame.buttons['max_plots'].get())
common_variables = run.common_variables
reg_module = run.last_reg_module
latent_variables = np.swapaxes(reg_module.x_weights_, 0, 1)
#print("this is working")last_reg_module
#print(latent_variables.shape)
ui = run.ui
wavenum = run.last_complete_case.wavenumbers
if ui['save_check_var']:
tempax = common_variables.tempax
tempfig = common_variables.tempfig
for i, latent_variable in enumerate(latent_variables):
ax = fns.add_axis(common_variables.fig, ui['fig_per_row'],
ui['max_plots'])
yax_label = 'Latent variable ' + str(i + 1)
PLSRsave.plot_component(ax, ui, wavenum, yax_label, latent_variable)
run.draw()
if ui['save_check_var']:
tempax.cla()
PLSRsave.plot_component(tempax, ui, wavenum, yax_label,
latent_variable)
plotFileName = run.filename + '/PLSR latent variable ' + str(i + 1)
tempfig.savefig(
plotFileName.replace('.', 'p') + ui['file_extension'])
return
def run(self):
self.fig.clf()
ax = fns.add_axis(self.fig, 1)
numPulses = 100
#self.average_pulses=[]
self.ms = [255]
for i, n in enumerate(self.ms):
average_2_pulses = averageN(self.pulseIntensity, n)
self.plot_me(ax, numPulses)
if self.frame.save_check_var.get():
tempfig = self.frame.hidden_figure
tempfig.set_size_inches(4 * 1.2, 3 * 1.2)
tempfig.set_dpi(300)
tempfig.clf()
tempax = tempfig.add_subplot(1, 1, 1)
tempfig.subplots_adjust(bottom=0.17,
left=0.16,
right=0.97,
top=0.97)
self.plot_me(tempax, numPulses)
filename = self.frame.name_field_string.get()
tempfig.savefig(filename + '.png')
tempfig.savefig(filename + '.svg')
#ax2=fns.add_axis(self.fig,1)
#average_2_pulses=averageN(self.pulseIntensity,8)
#ax2.scatter(range(len(average_2_pulses)),average_2_pulses)
#if self.ui['save_check']:
# self.fig.savefig(self.ui['save_filename']+'.png')
return
def run(self):
self.fig.clf()
ax = fns.add_axis(self.fig, 1)
StartWL = 1200
EndWL = 925
minscanlength = np.inf
for scan in self.scans:
if len(scan) < minscanlength:
minscanlength = len(scan)
for i, scan in enumerate(self.scans):
self.scans[i] = scan[0:minscanlength]
n = 255
self.scans[i] = averageN(self.scans[i], n)
self.scans = np.array(self.scans)
self.averagescans = np.average(self.scans, axis=0)
for i, scan in enumerate(self.scans):
self.scans[i] = scan / self.averagescans
for i, scan in enumerate(self.scans):
if i > 0:
s = signal_alignment.chisqr_align(self.scans[0],
scan, [0, 20000],
init=0,
bound=50)
print(s)
self.scans[i] = signal_alignment.shift(scan, s, mode='nearest')
#StartWL=1200
#EndWL=925
#self.wavenumbers=StartWL+(EndWL-StartWL)*np.arange(minscanlength)/minscanlength
StartWL = 1200
EndWL = 925
self.wavenumbers = StartWL + (
EndWL - StartWL) * np.arange(minscanlength) / minscanlength
numPulses = 1000
step = 100
self.ms = [1, 2, 5, 10]
self.averaged_scans = []
for i, m in enumerate(self.ms):
self.averaged_scans.append(copy.deepcopy(self.scans))
self.averaged_scans[-1] = averageM(self.averaged_scans[-1], m)
self.plot_me(ax, step, numPulses, EndWL, StartWL)
if self.frame.save_check_var.get():
tempfig = self.frame.hidden_figure
tempfig.set_size_inches(4 * 1.2, 3 * 1.2)
tempfig.set_dpi(300)
tempfig.clf()
tempax = tempfig.add_subplot(1, 1, 1)
tempfig.subplots_adjust(bottom=0.17,
left=0.16,
right=0.97,
top=0.97)
self.plot_me(tempax, step, numPulses, EndWL, StartWL)
filename = self.frame.name_field_string.get()
tempfig.savefig(filename + '.png')
tempfig.savefig(filename + '.svg')
return
def get_or_make_absorbance_ax(run): ui=run.ui ui['fig_per_row']=int(run.frame.buttons['fig_per_row'].get()) ui['max_plots']=int(run.frame.buttons['max_plots'].get()) wavenumbers=run.original_wavenumbers common_variables=run.common_variables fig=common_variables.fig for ax in fig.axes: if hasattr(ax,'plot_type') and ax.plot_type=='absorbance': return ax ax=fns.add_axis(fig,ui['fig_per_row'],ui['max_plots']) datapoints=common_variables.datapoints PlotAbsorbance(ax,fig,datapoints,ui,wavenumbers,common_variables.original_T.X,common_variables.original_V.X) ax.plot_type='absorbance' return ax
def plot_feature_importance(event):
run = PLSR.run
if not run.ui['reg_type'] == 'Tree':
return
"""Function for plotting the feature importance of features in the Random
Forest Regressor. Feature importance is shown in a plot overlaying the
data plot. The feature importance is also saved in a separate plot if the
"Save" option is selected."""
feature_importance = run.last_reg_module.regr.feature_importances_
common_variables = run.common_variables
wavenum = run.last_complete_case.wavenumbers
ui = run.ui
ui['fig_per_row'] = int(run.frame.buttons['fig_per_row'].get())
ui['max_plots'] = int(run.frame.buttons['max_plots'].get())
ax = PLSRsave.get_or_make_absorbance_ax(run)
PLSRsave.add_feature_importance_twinx(ax, common_variables, ui, xax,
feature_importance)
ax = fns.add_axis(common_variables.fig, ui['fig_per_row'], ui['max_plots'])
PLSRsave.plot_feature_importance(ax, common_variables, ui, xax,
feature_importance)
if ui['save_check_var']:
tempax = common_variables.tempax
tempfig = common_variables.tempfig
common_variables.tempfig.subplots_adjust(bottom=0.13,
left=0.15,
right=0.85,
top=0.97)
PLSRsave.PlotAbsorbance(tempax, tempfig,
run.last_complete_case.active_wavenumers, ui,
wavenumbers, common_variables.original_T.X,
common_variables.original_V.X)
twinx = PLSRsave.add_feature_importance_twinx(tempax, common_variables,
run.ui, xax,
feature_importance)
plotFileName = run.filename + '/transmissionFullAndFeatureImportance'
tempfig.savefig(plotFileName.replace('.', 'p') + ui['file_extension'])
tempax.cla()
twinx.remove()
common_variables.tempfig.subplots_adjust(bottom=0.13,
left=0.15,
right=0.97,
top=0.97)
PLSRsave.plot_feature_importance(tempax, common_variables, ui, xax,
feature_importance)
plotFileName = run.filename + '/FeatureImportance'
tempfig.savefig(plotFileName.replace('.', 'p') + ui['file_extension'])
tempax.cla()
run.draw()
def plot_node_correlations(event):
run = PLSR.run
if not run.ui['reg_type'] == 'NeuralNet':
return
print(run.complete_cases[-1].keywords['NN_type'])
if run.complete_cases[-1].keywords['NN_type'] == 'Convolutional':
return stability_selection(event)
return
if not hasattr(run, 'last_Xval_case'):
print('Not done running')
return
ui = run.ui
ui['fig_per_row'] = int(run.frame.buttons['fig_per_row'].get())
ui['max_plots'] = int(run.frame.buttons['max_plots'].get())
ax = fns.add_axis(run.common_variables.fig, ui['fig_per_row'],
ui['max_plots'])
ax.plot_type = 'NN node map'
V = run.last_Xval_case.V
if len(V.X) == 0:
V = run.last_Xval_case.T
transformedDataset = run.last_reg_module.scaler.transform(V.X)
values = run.last_reg_module.neural_net.get_values(transformedDataset)
run.NNvalues = values
y_midpoint = run.last_reg_module.neural_net.layer_size / 2
X = []
Y = []
corr = []
y_rot = np.rot90(np.atleast_2d(V.Y), -1)
corr_param = run.last_reg_module.neural_net.y_scaler.transform(
y_rot).reshape(-1)
for j in range((len(values) + 2) // 3):
layer = values[j * 3]
shape = layer.shape
corr.append([])
for i in range(shape[1]):
slope, intercept, r_value, p_value, std_err = scipy.stats.linregress(
layer[:, i], corr_param)
corr[-1].append(r_value**2)
X.append(j)
Y.append(i)
run.corr = corr
flat_corr = [item for sublist in corr for item in sublist]
sc = ax.scatter(X, Y, c=flat_corr, cmap='viridis')
cbar = run.fig.colorbar(sc, ax=ax)
cbar.set_label(r'r$^2$')
run.draw()
def do_stability_selection(self,run):
positively_changed_weights=self.stability_selection_pass(run.last_Xval_case.T)
for i in range(100-1):
positively_changed_weights+=self.stability_selection_pass(run.last_Xval_case.T)
print(str(i+1)+' of 100')
run.draw()
ui=run.ui
ui['fig_per_row']=int(run.frame.buttons['fig_per_row'].get())
ui['max_plots']=int(run.frame.buttons['max_plots'].get())
import fns
ax=fns.add_axis(run.common_variables.fig,ui['fig_per_row'],ui['max_plots'])
positively_changed_weights=positively_changed_weights.reshape((-1,self.layer_size))
wavenum=run.last_complete_case.wavenumbers
dl=(len(wavenum)-len(positively_changed_weights))//2
wavenum=wavenum[dl:-dl]
for i,a in enumerate(positively_changed_weights[0]):
ax.plot(wavenum,positively_changed_weights[:,i])#,label=i)
ax.invert_xaxis()
#ax.legend()
#ax.plot(positively_changed_weights)
run.draw()
def run_wrapper_case(self):
fig = self.fig
locations = self.locations
frame = self.frame
ui = self.ui
eprint('running')
self.fig = fig
fig.clf()
self.frame = frame
# get variables from buttons
common_variables = types.SimpleNamespace()
common_variables.draw = self.draw
self.common_variables = common_variables
common_variables.keyword_lists = {}
PLSRregressionMethods.get_relevant_keywords(common_variables, ui)
ui['multiprocessing'] = 1 - (ui['no_multiprocessing'])
save_check_var = frame.save_check_var.get()
ui['save_check_var'] = save_check_var
filename = frame.name_field_string.get()
self.filename = filename
#prepare figures for display (set correct number of axes, each pointing to the next axis)
######################### if crossval and moving window -> stop ###########
if ui['is_validation'] == 'X-val on training' and ui[
'regression_wavelength_selection'] == 'Moving window':
print("Use of x-validation with moving window is not supported")
return
######################### if RMSEP and no validation -> stop ##############
if ui['is_validation'] == 'Training' and ui['RMS_type'] == 'RMSEP':
print("Unable to calculate RMSEP with only training set")
return
#################### if RMSEP and RMSEC and no validation -> only RMSEP ###
if ui['is_validation'] == 'Training':
ui['RMS_type'] = 'RMSEC'
if ui['RMS_type'] == 'Default':
ui['RMS_type'] = 'RMSEC'
else:
if ui['RMS_type'] == 'Default':
ui['RMS_type'] = 'RMSEP'
common_variables.frame = frame
common_variables.fig = fig
################################################################################################
######################### Load data as training or validation ##################################
################################################################################################
T = types.SimpleNamespace()
V = types.SimpleNamespace()
if len(frame.training_files) == 0:
print('training set required')
return
#load training set
T.X, T.Y, common_variables.trainingfiles, self.wavenumbers, self.regressionCurControlTypes = PLSR_file_import.get_files(
frame.training_files, ui['max_range'])
self.original_wavenumbers = self.wavenumbers
for i, contrltytpe in enumerate(self.regressionCurControlTypes):
frame.button_handles['cur_col'][i]["text"] = contrltytpe
if ui['is_validation'] == 'Training' or ui[
'is_validation'] == 'X-val on training': # if training or crossval -> deselect validation
frame.nav.deselect()
#frame.nav.clear_color('color3')
#frame.validation_files=frame.nav.get_paths_of_selected_items()
V.X = np.array([]) # set empty validation set
V.Y = np.array([])
elif ui['is_validation'] == 'Training and Validation':
if len(frame.validation_files) == 0:
print(
'training and validation set, but no validation set in in put'
)
return
#load validation set
V.X, V.Y, common_variables.validationfiles, _, _2 = PLSR_file_import.get_files(
frame.validation_files, ui['max_range'])
common_variables.original_T = copy.deepcopy(T)
common_variables.original_V = copy.deepcopy(V)
################################################################################################
################################## load reference spectra #######################################
################################################################################################
if ui['reference_spectra'] == '':
self.reference_spectra = None
else:
try:
temp, _1, _2, _3, _4 = PLSR_file_import.get_files(
[ui['reference_spectra']], np.inf)
if len(temp) > 0:
print(
'first reference spectra in list selected for reference spectra selected as reference spectra'
)
self.reference_spectra = np.array(temp[0])
except Exception as e:
self.reference_spectra = None
print(e)
print('error importing referece spectra -> ignoring')
if ui['background_spectra'] == '':
self.background_spectra = None
else:
try:
temp, _1, _2, _3, _4 = PLSR_file_import.get_files(
[ui['background_spectra']], np.inf)
if len(temp) > 0:
print(
'first background spectra in list selected for reference spectra selected as reference spectra'
)
self.background_spectra = np.array(temp[0])
except Exception as e:
self.background_spectra = None
print(e)
print('error importing referece spectra -> ignoring')
################################################################################################
################# set up folder, save log and temporary figure for saving ######################
################################################################################################
if save_check_var:
if not os.path.exists(filename):
os.makedirs(filename)
PLSRsave.SaveLogFile(filename, ui, common_variables)
common_variables.tempfig, common_variables.tempax = PLSRsave.make_tempfig(
ui, frame)
################################################################################################
############################## calculate window ranges #########################################
################################################################################################
common_variables.datapoints = np.arange(len(self.wavenumbers))
#common_variables.datapointlists=[common_variables.datapoints]# declare this for get_or_make_absorbance_ax
#common_variables.datapoints, common_variables.datapointlists=PLSRpreprocessing.GetDatapoints(self.wavenumbers, ui)
################################################################################################
################################### save unprocessed spectra ###################################
################################################################################################
if ui['plot_spectra_before_preprocessing']:
eprint('plot abs')
if ui['save_check_var']:
PLSRsave.PlotAbsorbance(common_variables.tempax,
common_variables.tempfig,
common_variables.datapoints, ui,
self.wavenumbers, T.X, V.X)
plotFileName = filename + '/SpectraPrePreprocessing'
common_variables.tempfig.savefig(
plotFileName.replace('.', 'p') + ui['file_extension'])
common_variables.tempax.cla()
ax = PLSRsave.get_or_make_absorbance_ax(self)
self.draw()
################################################################################################
################################### make pychem input file #####################################
################################################################################################
if int(ui['make_pyChem_input_file']):
if ui['is_validation'] == 'Training and Validation':
PLSRsave.writePyChemFile(T.X, T.Y, validation,
validationtruevalues)
else:
PLSRsave.writePyChemFile(T.X, T.Y, [], [])
################################################################################################
################## set current control and remove data higher than maxrange ####################
################################################################################################
datasets = [T]
if ui['is_validation'] == 'Training and Validation':
datasets.append(V)
for E in datasets:
keepsamples = []
for i, _ in enumerate(E.Y):
if not E.Y[i, ui['cur_col']] > ui['max_range']:
keepsamples.append(i)
E.X = E.X[keepsamples, :]
E.Y = E.Y[keepsamples, ui['cur_col']]
ui['cur_control_string'] = self.regressionCurControlTypes[
ui['cur_col']]
PLSRpreprocessing.do_preprocessing(self, T, V)
if ui['plot_fourier']:
if hasattr(T, 'X_fft'):
ax = fns.add_axis(fig, ui['fig_per_row'], ui['max_plots'])
PLSRsave.plot_fourier(ax, fig, T, V, ui)
self.complete_cases = []
for _ in [1]: # is a loop so that you can use 'break'
for i, dercase in enumerate(self.preprocessed_cases):
#need to set data range in case of derrivative, rerunn in all cases anyways
datapoints = PLSRpreprocessing.GetDatapoints(
dercase.wavenumbers, ui)
#common_variables.datapoints=datapoints
#common_variables.datapointlists=datapointlists
if ui['plot_spectra_after_preprocessing']:
ax = fns.add_axis(fig, ui['fig_per_row'], ui['max_plots'])
PLSRsave.PlotAbsorbance(ax,
fig,
datapoints,
ui,
dercase.wavenumbers,
dercase.T.X,
dercase.V.X,
dercase=dercase)
self.draw()
if ui['save_check_var']:
PLSRsave.PlotAbsorbance(common_variables.tempax,
common_variables.tempfig,
datapoints,
ui,
dercase.wavenumbers,
dercase.T.X,
dercase.V.X,
dercase=dercase)
plotFileName = dercase.folder + '/SpectraPostPreprocessing'
common_variables.tempfig.savefig(
plotFileName.replace('.', 'p') +
ui['file_extension'])
common_variables.tempax.cla()
for E in [dercase.T, dercase.V]:
if len(E.Y) > 0:
E.X = E.X[:, datapoints]
dercase.wavenumbers = dercase.wavenumbers[datapoints]
#create complete cases for all pemutations of keyword values in keyword_lists
for keyword_case in PLSRregressionMethods.generate_keyword_cases(
common_variables.keyword_lists):
self.complete_cases.append(types.SimpleNamespace())
self.complete_cases[-1].wavenumbers = dercase.wavenumbers
self.complete_cases[-1].folder = dercase.folder
self.complete_cases[-1].sg_config = dercase.sg_config
self.complete_cases[-1].derrivative = dercase.derrivative
self.complete_cases[-1].T = dercase.T
self.complete_cases[-1].V = dercase.V
self.complete_cases[
-1].preprocessing_done = dercase.preprocessing_done
self.complete_cases[-1].keywords = keyword_case
if ui['reg_type'] == 'None':
break
for case in self.complete_cases:
case.XvalRMSEs = []
case.XvalCorrClass = []
common_variables.keywords = case.keywords
#GeneticAlgorithm(ui,T,V,datapoints,components)
if ui['regression_wavelength_selection'] == 'No wavelength selection':
active_wavenumers = np.ones(len(case.wavenumbers),
dtype=bool)
else:
# report to user regarding split module
if self.ui['WS_loss_type'] == 'X-validation on training':
if self.ui['WS_cross_val_N'] == 1 and self.ui[
'WS_cross_val_max_cases'] == -1:
print('Using sklearn.LeaveOneOut on ' +
str(len(case.T.Y)) +
' measurements. Maxcases set to ' +
str(len(case.T.Y)))
else:
if self.ui['WS_cross_val_max_cases'] == -1:
print(
'WS_cross_val_max_cases set to -1, GA_cross_val_N not set to 1. Setting GAcross_val_max_cases to default (20)'
)
self.ui['WS_cross_val_max_cases'] = 20
if ui['regression_wavelength_selection'] == 'Genetic Algorithm':
GAobject = PLSRGeneticAlgorithm.GeneticAlgorithm(
common_variables, ui, case)
active_wavenumers = GAobject.run(
fns.add_axis(common_variables.fig,
ui['fig_per_row'], ui['max_plots']),
case.wavenumbers, case.folder, self.draw)
elif ui['regression_wavelength_selection'] == 'Moving Window':
active_wavenumers = PLSRwavelengthSelection.MW(
case, ui, common_variables)
elif ui['regression_wavelength_selection'] == 'Sequential Feature Selector':
FSobject = PLSRsequential_feature_selectors.sequentialFeatureSelector(
common_variables, ui, case, self.draw)
active_wavenumers = FSobject.run()
Xval_cases = crossval(
case.T, case.V, ui, case
) # returns [T],[V] if not crossva, otherwise makes cases from validation dataset
for Xval_case in Xval_cases:
# ui.datapoints=runGeneticAlgorithm(dercase[0],dercase[1],dercase[2],dercase[3],dercase[4],dercase[5],dercase[6],dercase[7])
#def MW(T,V,wavenumbers, folder,ui,sg_config,curDerivative,supressplot):
if ui['save_check_var'] and not ui['do_not_save_plots']:
active_wavenumbers_file = case.folder + ui[
'reg_type'] + PLSRsave.get_unique_keywords_formatted(
common_variables.keyword_lists,
case.keywords).replace(
'.', 'p') + 'active_wavenumers.dpb'
PLSRsave.save_active_wavenumbers(
active_wavenumbers_file, case.wavenumbers,
active_wavenumers)
case.active_wavenumers = active_wavenumers
self.draw()
self.last_reg_module, RMSe = run_reg_module(
Xval_case,
case,
ui,
common_variables,
active_wavenumers,
self.filename + '/results_table',
keywords={})
self.draw()
self.last_complete_case = case
self.last_Xval_case = Xval_case
if Xval_case.supressplot == 0:
if ui['is_validation'] == 'X-val on training':
#if ui['RMS_type']=='Combined RMSEP+RMSEC':
# print('RMSEC+RMSEP = '+PLSRsave.custom_round(case.xvalRMSE,3)+' '+ui['unit'])
if not 'classifier_type' in case.keywords:
case.xvalRMSE = np.sqrt(
np.sum(np.array(case.XvalRMSEs)**2) /
len(case.XvalRMSEs))
if ui['RMS_type'] == 'RMSEC':
print('RMSEC = ' + PLSRsave.custom_round(
case.xvalRMSE, 3) + ' ' + ui['unit'])
elif ui['RMS_type'] == 'RMSEP':
print('RMSEP = ' + PLSRsave.custom_round(
case.xvalRMSE, 3) + ' ' + ui['unit'])
else:
print(case.XvalCorrClass)
case.xvalCorrClas = np.average(
case.XvalCorrClass)
print(case.xvalCorrClas)
if ui['RMS_type'] == 'RMSEC':
print('x-val corr classifed training = ' +
str(round(case.xvalCorrClas *
100, 3)) + ' %')
elif ui['RMS_type'] == 'RMSEP':
print(
'x-val corr classifed prediction = ' +
str(round(case.xvalCorrClas *
100, 3)) + ' %')
case.XvalRMSEs = []
eprint('done')
elif ui['coeff_det_type'] == 'R':
coeff_det = Xval_case.R_not_squared
if reg_module.type == 'classifier': #'classifier_type' in keywords:
frac_cor_lab = PLSRclassifiers.get_correct_categorized(
case.X_val_Y[-1], case.X_val_pred[-1])
case.XvalCorrClass.append(frac_cor_lab)
else:
frac_cor_lab = -1
#plot
if not supressplot:
if not ui['do_not_save_plots']:
PLSRsave.plot_regression(Xval_case,
case,
ui,
fns.add_axis(common_variables.fig,
ui['fig_per_row'],
ui['max_plots']),
keywords,
RMSe,
coeff_det,
frac_cor_lab=frac_cor_lab)
if ui['save_check_var']:
if not ui['do_not_save_plots']:
PLSRsave.plot_regression(Xval_case,
case,
ui,
common_variables.tempax,
keywords,
RMSe,
coeff_det,
frac_cor_lab=frac_cor_lab)
def plot_components_PCR(event):
run = PLSR.run
if not run.ui['reg_type'] == 'PCR':
return
"""Function for making plots of the principal components from PCR."""
ui = run.ui
ui['fig_per_row'] = int(run.frame.buttons['fig_per_row'].get())
ui['max_plots'] = int(run.frame.buttons['max_plots'].get())
common_variables = run.common_variables
reg_module = run.last_reg_module
components = reg_module.pca.components_[:reg_module.components]
#print(components)
ui = run.ui
wavenum = run.last_complete_case.wavenumbers
if ui['save_check_var']:
tempax = common_variables.tempax
tempfig = common_variables.tempfig
for i, component in enumerate(components):
ax = fns.add_axis(common_variables.fig, ui['fig_per_row'],
ui['max_plots'])
yax_label = 'Component ' + str(i + 1)
PLSRsave.plot_component(ax, ui, wavenum, yax_label, component)
run.draw()
if ui['save_check_var']:
tempax.cla()
PLSRsave.plot_component(tempax, ui, wavenum, yax_label, component)
plotFileName = run.filename + '/PCR component ' + str(i + 1)
tempfig.savefig(
plotFileName.replace('.', 'p') + ui['file_extension'])
linreg_coef = reg_module.linreg.coef_
linreg_coef = linreg_coef / sum(linreg_coef)
ax = fns.add_axis(common_variables.fig, ui['fig_per_row'], ui['max_plots'])
PLSRsave.plot_component_weights(ax, ui, linreg_coef)
if ui['save_check_var']:
tempax.cla()
PLSRsave.plot_component_weights(tempax, ui, linreg_coef)
plotFileName = run.filename + '/PCR Weights'
tempfig.savefig(plotFileName.replace('.', 'p') + ui['file_extension'])
run.draw()
ax = fns.add_axis(common_variables.fig, ui['fig_per_row'], ui['max_plots'])
product = np.dot(np.transpose(components), linreg_coef)
yax_label = r'Comps$\cdot$weights'
PLSRsave.plot_component(ax, ui, wavenum, yax_label, product)
if ui['save_check_var']:
tempax.cla()
PLSRsave.plot_component(tempax, ui, wavenum, yax_label, product)
plotFileName = run.filename + '/PCR components times weights'
tempfig.savefig(plotFileName.replace('.', 'p') + ui['file_extension'])
run.draw()
ax = PLSRsave.get_or_make_absorbance_ax(run)
PLSRsave.plot_component_weights_twinx(ax, ui, wavenum, yax_label, product)
if ui['save_check_var']:
tempax = common_variables.tempax
tempfig = common_variables.tempfig
common_variables.tempfig.subplots_adjust(bottom=0.13,
left=0.15,
right=0.85,
top=0.97)
PLSRsave.PlotAbsorbance(tempax, tempfig,
run.last_complete_case.active_wavenumers, ui,
run.last_complete_case.wavenumbers,
common_variables.original_T.X,
common_variables.original_V.X)
twinx = PLSRsave.plot_component_weights_twinx(tempax, ui, wavenum,
yax_label, product)
plotFileName = run.filename + '/transmission and PCR components times weights '
tempfig.savefig(plotFileName.replace('.', 'p') + ui['file_extension'])
tempax.cla()
twinx.remove()
common_variables.tempfig.subplots_adjust(bottom=0.13,
left=0.15,
right=0.97,
top=0.97)
run.draw()
return
def plot_node_activation_vector(event):
run = PLSR.run
if not run.ui['reg_type'] == 'NeuralNet':
return
ui = run.ui
layer = int(event.xdata + 0.5)
node = int(event.ydata + 0.5)
try:
r2 = run.corr[layer][node]
print('node at x', layer, 'y', node, 'r**2', r2)
except:
print('no node at x', layer, 'y', node)
return
ui['fig_per_row'] = int(run.frame.buttons['fig_per_row'].get())
ui['max_plots'] = int(run.frame.buttons['max_plots'].get())
ax = fns.add_axis(run.common_variables.fig, ui['fig_per_row'],
ui['max_plots'])
weights = run.last_reg_module.neural_net.get_weights()
V = run.last_Xval_case.V
if len(V.X) == 0:
V = run.last_Xval_case.T
data = run.last_reg_module.scaler.transform(V.X)
input_dim = data.shape[1]
num_inputs = data.shape[0]
print(data.shape)
sensitivity = [[]]
for i in range(input_dim):
sensitivity[-1].append(np.zeros(data.shape))
sensitivity[-1][-1][:, i] = 1
activation = run.last_reg_module.neural_net.activation
for ll in range((len(weights) + 2) // 3):
sensitivity.append([])
for i in range(input_dim):
sensitivity[-1].append(sensitivity[-2][i] @ weights[ll * 3][0][:])
#sensitivity[-1][-1] += weights[ll*3][1] #do not include this, we are calculating derivatives, not response
data = data @ weights[ll * 3][0][:]
data += weights[ll * 3][1] #add bias
if not ll == (len(weights) - 1) // 3:
for i in range(input_dim):
sensitivity[-1][i] = activation(sensitivity[-1][i], pivot=data)
#print(np.sum(sensitivity[-1][-1]==0))
data = activation(data)
#for i in range(data.shape[1]):
# print(np.sum(data[:,i]==0))
#sensitivity[layer][input][wavenumber,node
#sensitivity[layer+1]=np.array(sensitivity[layer+1])
#ode_sensitivity=sensitivity[layer+1][:,:,node]
run.sensitivity = sensitivity
node_sensitivity = []
for i in range(input_dim):
node_sensitivity.append(sensitivity[layer + 1][i][:, node])
node_sensitivity = np.array(node_sensitivity)
#print(len(sensitivity),sensitivity[layer+1].shape,node_sensitivity.shape)
sense_vector = (np.average(node_sensitivity, axis=1))
sense_std = (np.std(node_sensitivity, axis=1))
#for s in sensitivity[-1]:
#print(run.sense_vector[layer+1][node])
wavenum = run.last_complete_case.wavenumbers
wavenum = wavenum[run.last_complete_case.active_wavenumers]
ax.errorbar(wavenum,
sense_vector,
yerr=sense_std,
color=[1, 0, 0, 1],
ecolor=[0, 0, 1, 1])
ax.invert_xaxis()
#ax.plot(wavenum,sense_vector)
#ax=fns.add_axis(run.common_variables.fig,ui['fig_per_row'],ui['max_plots'])
#ax.plot(wavenum,sense_std)
'''ax=fns.add_axis(run.common_variables.fig,ui['fig_per_row'],ui['max_plots'])
ax.plot(wavenum,node_sensitivity,'+')
ax.invert_xaxis()
ax=fns.add_axis(run.common_variables.fig,ui['fig_per_row'],ui['max_plots'])
ax.plot(wavenum,run.last_reg_module.scaler.transform(V.X).swapaxes(0,1),'+')
ax.invert_xaxis()'''
'''ax=fns.add_axis(run.common_variables.fig,ui['fig_per_row'],ui['max_plots'])
data=run.last_reg_module.neural_net.y_scaler.inverse_transform(data)
ax.plot(V.Y,data,'o')'''
#ax.plot(sense_std)
run.draw()
#ax.plot()
#values=run.last_reg_module.neural_net.get_values(transformedDataset)
'''inv_act=run.last_reg_module.neural_net.inv_activation
T, V, trail_active_wavenumbers,
use_stored=True)
# done moving window
Wresults=Wresults+(Wresults==0)*np.max(Wresults) # set empty datapoints to max value
j,i=np.unravel_index(Wresults.argmin(), Wresults.shape)
bestVal=Wresults[j,i]
bestSize=Wsizes[i]
bestStart=j-bestSize//2
# plot MWresults
Wresults=np.array(Wresults)
# make plot
Wwindowsize,Wwavenumbers = np.meshgrid(Wsizes*abs(dw), wavenumbers)
unique_keywords=PLSRsave.get_unique_keywords_formatted(common_variables.keyword_lists,keywords)
PLSRsave.PcolorMW(Wwavenumbers,Wwindowsize,Wresults,fns.add_axis(common_variables.fig,ui['fig_per_row'],ui['max_plots']),unique_keywords[1:],ui)
if ui['save_check_var']:
tempCbar=PLSRsave.PcolorMW(Wwavenumbers,Wwindowsize,Wresults,common_variables.tempax,unique_keywords[1:],ui)
common_variables.tempfig.subplots_adjust(bottom=0.13,left=0.15, right=0.97, top=0.9)
plotFileName=folder+ui['reg_type']+unique_keywords.replace('.','p')+'_moving_window'
common_variables.tempfig.savefig(plotFileName+ui['file_extension'])
tempCbar.remove()
# set result as keywords, so that they are saved
bestEnd=bestStart+bestSize
Wwidth=wavenumbers[bestStart]-wavenumbers[bestEnd-1] #cm-1
Wcenter=0.5*(wavenumbers[bestStart]+wavenumbers[bestEnd-1]) #cm-1
keywords['MW width']=str(round(Wwidth,1))+r' cm$^{-1}$'
keywords['MW center']=str(round(Wcenter,1))+r' cm$^{-1}$'
# prepare return vector
active_wavenumers=np.zeros(len(wavenumbers), dtype=bool)
active_wavenumers[bestStart:bestEnd]=True
def run(self):
'''if self.ui['SFS type']=='Forward':
return self.forward_selection()
if self.ui['SFS type']=='Backwards':
return self.backwards_selection()
def forward_selection(self):'''
wavenumbers = self.case.wavenumbers
if 'Forward' in self.ui['SFS type']:
direction = 'Forward '
current_active_wavenumbers = np.zeros(len(wavenumbers), dtype=bool)
elif 'Backward' in self.ui['SFS type']:
direction = 'Backward'
current_active_wavenumbers = np.ones(len(wavenumbers), dtype=bool)
if self.ui['SFS_floating']:
floating = True
else:
floating = False
ax = fns.add_axis(self.common_variables.fig, self.ui['fig_per_row'],
self.ui['max_plots'])
# calculate the needed X-val splits and store them
PLSRwavelengthSelection.WS_getCrossvalSplits([0, 1],
self.T,
self.V,
self.ui,
use_stored=False)
PLSRsave.PlotChromosomes(ax,
wavenumbers, [],
self.ui,
ylabel='Iteration')
if self.ui['SFS type'] == 'Forward':
current_active_wavenumbers = np.zeros(len(wavenumbers), dtype=bool)
elif self.ui['SFS type'] == 'Backwards':
current_active_wavenumbers = np.ones(len(wavenumbers), dtype=bool)
best_historic_active = []
best_loss = []
generation = 0
while True:
#main step
if direction == 'Forward ':
trail_active_wavenumbers = self.get_trails_forward(
current_active_wavenumbers)
else: # direction=='Backward'
trail_active_wavenumbers = self.get_trails_backward(
current_active_wavenumbers)
if len(trail_active_wavenumbers) == 0:
break
trail_active_wavenumbers = cut_previous(trail_active_wavenumbers,
best_historic_active)
current_active_wavenumbers, l, out_str = self.do_pass(
trail_active_wavenumbers, generation)
print(direction + ' ' + out_str)
best_loss.append(l)
PLSRsave.PlotChromosome(ax, wavenumbers,
current_active_wavenumbers, generation)
self.draw_fun()
best_historic_active.append(copy.copy(current_active_wavenumbers))
best_historic_generation = np.argmin(best_loss)
generation += 1
if generation == self.ui['SFS_max_iterations']:
break
if floating:
while True:
if direction == 'Forward ':
if np.sum(current_active_wavenumbers) == 1:
break
else:
trail_active_wavenumbers = self.get_trails_backward(
current_active_wavenumbers
) #reverse of main loop
else: # direction=='Backward'
if np.sum(current_active_wavenumbers) == len(
current_active_wavenumbers):
break
trail_active_wavenumbers = self.get_trails_forward(
current_active_wavenumbers) #reverse of main loop
trail_active_wavenumbers = cut_previous(
trail_active_wavenumbers, best_historic_active)
if len(trail_active_wavenumbers) == 0:
break
best_trail, l, out_str = self.do_pass(
trail_active_wavenumbers, generation)
if l < best_loss[-1]:
print('Floating' + ' ' + out_str)
current_active_wavenumbers = best_trail
best_loss.append(l)
PLSRsave.PlotChromosome(ax, wavenumbers,
current_active_wavenumbers,
generation)
self.draw_fun()
best_historic_active.append(
copy.copy(current_active_wavenumbers))
best_historic_generation = np.argmin(best_loss)
generation += 1
else:
break
if generation == self.ui[
'SFS_max_iterations'] or best_historic_generation < len(
best_loss) - self.ui['SFS_num_after_min'] or np.sum(
current_active_wavenumbers
) == self.ui['SFS_target']:
break
print('best iteration ' + str(best_historic_generation + 1) +
', best ' + self.rmse_string + ' = ' +
PLSRsave.custom_round(best_loss[best_historic_generation], 2))
PLSRsave.PlotChromosome(ax,
wavenumbers,
best_historic_active[best_historic_generation],
best_historic_generation,
color=[1, 0, 0, 1])
if self.ui['save_check_var'] == 1:
PLSRsave.PlotChromosomes(self.common_variables.tempax,
wavenumbers,
best_historic_active,
self.ui,
ylabel='Iteration')
PLSRsave.PlotChromosome(
self.common_variables.tempax,
wavenumbers,
best_historic_active[best_historic_generation],
best_historic_generation,
color=[1, 0, 0, 1])
self.common_variables.tempfig.subplots_adjust(bottom=0.13,
left=0.15,
right=0.97,
top=0.9)
unique_keywords = PLSRsave.get_unique_keywords_formatted(
self.common_variables.keyword_lists, self.case.keywords)
plotFileName = case.folder + self.ui[
'reg_type'] + unique_keywords.replace('.', 'p') + 'SFS'
self.common_variables.tempfig.savefig(
plotFileName.replace('.', 'p') + self.ui['file_extension'])
return best_historic_active[best_historic_generation]
