def do_regression_predict(self, datakey, modelkey, predictname):
try:
prediction = self.models[modelkey].predict(self.data[datakey].df[self.model_xvars[modelkey]])
self.data[datakey].df[predictname] = prediction
pass
except Exception as e:
error_print(e)
def del_layout(self):
# Deleting a whole lotta lists... >_<
try:
del_qwidget_(self.greyed_modules[-1])
del self.greyed_modules[-1]
self._list.del_module()
except:
error_print("Cannot delete")
def removenull(self,datakey,colname):
try:
print(self.data[datakey].df.shape)
self.data[datakey] = spectral_data(self.data[datakey].df.ix[-self.data[datakey].df[colname].isnull()])
print(self.data[datakey].df.shape)
except Exception as e:
error_print(e)
def get_data(self, filename, keyname):
try:
print('Loading data file: ' + str(filename))
self.data[keyname] = spectral_data(pd.read_csv(filename, header=[0, 1]))
self.datakeys.append(keyname)
pass
except Exception as e:
error_print('Problem reading data: {}'.format(e))
def do_cv_train(self, datakey, xvars, yvars, yrange, method, params):
try:
cv_obj=cv.cv(params)
self.data[datakey].df,self.cv_results=cv_obj.do_cv(self.data[datakey].df,xcols=xvars,ycol=yvars,yrange=yrange,method=method)
self.data['CV Results']=self.cv_results
except Exception as e:
error_print(e)
def do_norm(self, datakey, ranges):
print("{}".format(ranges))
try:
print(self.data[datakey].df.columns.levels[0])
self.data[datakey].norm(ranges)
print(self.data[datakey].df.columns.levels[0])
print("Normalization has been applied to the ranges: " + str(ranges))
except Exception as e:
error_print(e)
def run(self):
# TODO this function will take all the enumerated functions and parameters and run them
try:
for i in range(len(self.greyed_modules)):
r_list = self._list.pull()
print(r_list)
getattr(self, r_list[2])(*r_list[3], **r_list[4])
self.greyed_modules[0].setDisabled(True)
del self.greyed_modules[0]
self.taskFinished.emit()
except Exception as e:
error_print(e)
self.taskFinished.emit()
def do_regression_train(self, datakey, xvars, yvars, yrange, method, params, ransacparams, modelkey=None):
try:
if modelkey is None:
modelkey = method + '-' + str(yvars) + ' (' + str(yrange[0]) + '-' + str(yrange([1]) + ') ')
self.models[modelkey] = regression.regression([method], [yrange], [params], i=0,
ransacparams=[ransacparams])
self.modelkeys.append(modelkey)
x = self.data[datakey].df[xvars]
y = self.data[datakey].df[yvars]
x = np.array(x)
y = np.array(y)
ymask = np.squeeze((y > yrange[0]) & (y < yrange[1]))
y = y[ymask]
x = x[ymask, :]
self.models[modelkey].fit(x, y)
self.model_xvars[modelkey] = xvars
self.model_yvars[modelkey] = yvars
print('foo')
except Exception as e:
error_print(e)
def do_plot(self, datakey,
xvar, yvar,
figfile=None, xrange=None,
yrange=None, xtitle='Reference (wt.%)',
ytitle='Prediction (wt.%)', title=None,
lbl=None, one_to_one=False,
dpi=1000, color=None,
annot_mask=None,
cmap=None, colortitle='', figname=None, masklabel='',
marker='o', linestyle='None'
):
try:
x = self.data[datakey].df[xvar]
y = self.data[datakey].df[yvar]
except:
x = self.data[datakey][xvar]
y = self.data[datakey][yvar]
try:
loadfig = self.figs[figname]
except:
loadfig = None
# outpath=self.outpath
try:
# Alpha is missing, fix this!
outpath = self.outpath
self.figs[figname] = make_plot(x, y, outpath, figfile, xrange=xrange, yrange=yrange, xtitle=xtitle,
ytitle=ytitle, title=title,
lbl=lbl, one_to_one=one_to_one, dpi=dpi, color=color,
annot_mask=annot_mask, cmap=cmap,
colortitle=colortitle, loadfig=loadfig,marker=marker,linestyle=linestyle)
except Exception as e:
error_print(e)
# dealing with the a possibly missing outpath
outpath = './'
self.figs[figname] = make_plot(x, y, outpath, figfile, xrange=xrange, yrange=yrange, xtitle=xtitle,
ytitle=ytitle, title=title,
lbl=lbl, one_to_one=one_to_one, dpi=dpi, color=color,
annot_mask=annot_mask, cmap=cmap,
colortitle=colortitle, loadfig=loadfig,marker=marker,linestyle=linestyle)
def do_ica_jade(self, datakey, nc, col, load_fit=None, corrcols=None):
try:
self.data[datakey].ica_jade(col, nc=nc, load_fit=load_fit, corrcols=corrcols)
except Exception as e:
error_print(e)
def do_ica(self, datakey, nc, col, load_fit=None):
try:
self.data[datakey].ica(col, nc=nc, load_fit=load_fit)
except Exception as e:
error_print(e)
def do_pca(self, datakey, nc, col, load_fit=None):
print(self.data[datakey].df.columns.levels[0])
try:
self.data[datakey].pca(col, nc=nc, load_fit=load_fit)
except Exception as e:
error_print(e)
def do_dim_red(self,datakey,method,params,method_kws={},col='wvl',load_fit=None,dim_red_key=None):
try:
self.dim_reds[dim_red_key]=self.data[datakey].dim_red(col, method, params, method_kws, load_fit=load_fit)
self.dim_red_keys.append(dim_red_key)
except Exception as e:
error_print(e)
def do_interp(self, datakey_to_interp, datakey_ref):
print(self.data[datakey_ref].df.columns.levels[0])
try:
self.data[datakey_to_interp].interp(self.data[datakey_ref].df['wvl'].columns)
except Exception as e:
error_print(e)
def do_mask(self, datakey, maskfile):
try:
self.data[datakey].mask(maskfile)
print("Mask applied")
except Exception as e:
error_print(e)
def set_file_outpath(self, outpath):
try:
self.outpath = outpath
print("Output path folder has been set to "+outpath)
except Exception as e:
error_print(e)
