updates

scripts/lpls/run_smoker.py

@@ -32,7 +32,7 @@ print "SAM done"
 qq = rpy.r('qobj<-qvalue(sam.out@p.value)')
 qvals = asarray(qq['qvalues'])
 # cut off
-co = 0.001
+co = 0.1
 index = where(qvals<0.01)[0]
 
 # Subset data
@@ -51,7 +51,7 @@ terms = list(terms)
 rpy.r.library("GOSim")
 # Go-term similarity matrix
 methods = ("JiangConrath","Resnik","Lin","CoutoEnriched","CoutoJiangConrath","CoutoResnik","CoutoLin")
-meth = methods[0]
+meth = methods[2]
 print "Term-term similarity matrix (method = %s)" %meth
 if meth=="CoutoEnriched":
     rpy.r('setEnrichmentFactors(alpha=0.1,beta=0.5)')
@@ -100,7 +100,6 @@ T, W, P, Q, U, L, K, B, b0, evx, evy, evz = nipals_lpls(Xr,Y,Z, a_max, alpha)
 dx,Rx,ssx= correlation_loadings(Xr, T, P)
 dx,Ry,ssx= correlation_loadings(Y, T, Q)
 cadx,Rz,ssx= correlation_loadings(Z.T, K, L)
-
 # Prediction error
 rmsep , yhat, class_error = cv_lpls(Xr, Y, Z, a_max, alpha=alpha)
 
@@ -112,11 +111,19 @@ tsqz = cx_stats.hotelling(Wz,L[:,:aopt])
 
 ## plots ##
 figure(1) #rmsep
-#bar()
+bar_w = .2
+bar_col = 'rgb'*5
+m = Y.shape[1]
+for a in range(m):
+    bar(arange(a_max)+a*bar_w+.1, rmsep[:,a], width=bar_w, color=bar_col[a])
+ylim([rmsep.min()-.05, rmsep.max()+.05])
+
 figure(2) # Hypoid correlations
+title('RMSEP')
 plot_corrloads(Rz, pc1=0, pc2=1, s=tsqz/10.0, c='b', zorder=5, expvar=evz, ax=None)
 ax = gca()
-plot_corrloads(Ry, pc1=0, pc2=1, s=150, c='g', zorder=5, expvar=evy, ax=ax)
+ylabels = DY.get_identifiers('_cat', sorted=True)
+plot_corrloads(Ry, pc1=0, pc2=1, s=150, c='g', zorder=5, expvar=evy, ax=ax,labels=ylabels)
 
 figure(3)
 subplot(221)