weightsdc = []
for n in range(0, plsr.x_weights_.shape[1]):
lvd = pd.DataFrame(plsr.x_weights_[:, n:n + 1].T)
lvd.columns = input_data.columns
vars()['LV%d' % (n + 1)] = spectra(lvd, styles=styles)
#weightsdl.append("Weights on LV %s" % (n+1))
weightsdc.append("LV %s" % (n + 1))
weights.index = weightsdc
# Build scores plots for all combinations up to n
score_combinations = set([(a, b) for a in range(0, n)
for b in range(a + 1, n + 1)])
if config['plot_sample_numbers']:
label_index = 'Sample'
else:
label_index = None
for sc in score_combinations:
vars()['Scores %dv%d' % (sc[0] + 1, sc[1] + 1)] = scatterplot(
scores.iloc[:, sc], styles=styles, label_index=label_index)
weightsd = None
# Clean up
lvd = None
# Clean up
dso_pc = {}
weightsi = []
# Generate simple result figure (using pathomx libs)
from pathomx.figures import spectra, scatterplot
for n in range(0, pca.components_.shape[0]):
pcd = pd.DataFrame(weights.values[n:n + 1, :])
pcd.columns = input_data.columns
vars()['PC%d' % (n + 1)] = spectra(pcd, styles=styles)
weightsi.append("PC %d" % (n + 1))
weights.index = weightsi
if config['plot_sample_numbers']:
label_index = 'Sample'
else:
label_index = None
# Build scores plots for all combinations up to n
score_combinations = set([ (a,b) for a in range(0,n) for b in range(a+1, n+1)])
for sc in score_combinations:
vars()['Scores %dv%d' % (sc[0]+1, sc[1]+1)] = scatterplot(scores.iloc[:,sc], styles=styles, label_index=label_index)
pcd = None
# Clean up
scoresl.append( 'Latent Variable %d' % (n+1) ) #, plsr.y_weights_[0][n])
scores.columns = scoresl
weights = pd.DataFrame( plsr.x_weights_.T )
weights.columns = input_data.columns
dso_lv = {}
# Generate simple result figure (using pathomx libs)
from pathomx.figures import spectra, scatterplot
weightsdc=[]
for n in range(0, plsr.x_weights_.shape[1] ):
lvd = pd.DataFrame( plsr.x_weights_[:,n:n+1].T )
lvd.columns = input_data.columns
vars()['LV%d' % (n+1)] = spectra(lvd, styles=styles)
#weightsdl.append("Weights on LV %s" % (n+1))
weightsdc.append("LV %s" % (n+1))
weights.index = weightsdc
Scores = scatterplot(scores, styles=styles)
weightsd = None; # Clean up
lvd = None; # Clean up
Scores
ry = y.values.astype(np.float)
# Skip if zeros or containing nans
try:
slope, intercept, r_value, p_value, std_err = sp.stats.linregress(rx, ry)
except:
# Skip any that fail
pass
else:
correlations["R%d" % (n+1)] = {
'data': do,
'fit': fit,
'label': u'r²=%0.2f, p=%0.2f' % (r_value**2, p_value)
}
progress(float(n)/total_n)
do = None;
# Generate simple result figure (using pathomx libs)
from pathomx.figures import scatterplot
for k,c in correlations.items():
x_data = np.linspace(np.min(c['data'].iloc[:,0]), np.max(c['data'].iloc[:,0]), 50)
lines = [
(x_data, np.polyval(c['fit'], x_data), c['label'])
]
vars()[k] = scatterplot(c['data'], lines=lines, styles=styles);
