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);