def plot_errors_classweights_C(classweights, fourclutest, fourclutrain, Clist):
false_discovery_rate, true_positive_rate = supe.pre_plotROC(fourclutest)
false_discovery_train_rate, true_positive_train_rate = supe.pre_plotROC(fourclutrain)
weightzero = np.zeros(len(classweights))
weightone = np.zeros(len(classweights))
#Extract the weights
for i in np.arange(len(classweights)):
weightzero[i] = classweights[i][0]
weightone[i] = classweights[i][1]
fig = plt.figure()
#axes1 = fig1.add_axes([0.1,0.1,0.8,0.8])
axes1 = fig.add_subplot(221)
axes1.set_xlabel('log(C*weight 0)')
axes1.set_ylabel('log(C*weight 1)')
axes1.set_title('Test set FDR')
axes1.hold(True)
axes2 = fig.add_subplot(222)
axes2.set_xlabel('log(C*weight 0)')
axes2.set_ylabel('log(C*weight 1)')
axes2.set_title('Test set 1-TPR')
axes2.hold(True)
axes3 = fig.add_subplot(223)
axes3.set_xlabel('log(C*weight 0)')
axes3.set_ylabel('log(C*weight 1)')
axes3.set_title('Training set FDR')
axes3.hold(True)
axes4 = fig.add_subplot(224)
axes4.set_xlabel('log(C*weight 0)')
axes4.set_ylabel('log(C*weight 1)')
axes4.set_title('Training set 1-TPR')
axes4.hold(True)
numCvalues = len(Clist)
#Number of values of C,
# fourclu should have shape (numCvalues, len(classweights), numspikes)
cm = plt.cm.get_cmap('RdYlBu')
sizeadjust = 100
for c, cval in enumerate(Clist):
sc1 = axes1.scatter(np.log(cval*weightzero[:]),np.log(cval*weightone[:]),marker = 's',s=sizeadjust, c=false_discovery_rate[c,:] , cmap=cm)
sc1.set_clim(vmin=0,vmax=1)
fig.colorbar(sc1, ax=axes1, shrink=0.9)
sc2 = axes2.scatter(np.log(cval*weightzero[:]),np.log(cval*weightone[:]),marker = 's',s=sizeadjust, c=(1-true_positive_rate[c,:]) , cmap=cm)
#plt.colorbar(sc2)
sc2.set_clim(vmin=0,vmax=1)
fig.colorbar(sc2, ax=axes2, shrink=0.9)
sc3 = axes3.scatter(np.log(cval*weightzero[:]),np.log(cval*weightone[:]),marker = 's',s=sizeadjust, c=false_discovery_train_rate[c,:] , cmap=cm)
#plt.colorbar(sc3)
sc3.set_clim(vmin=0,vmax=1)
fig.colorbar(sc3, ax=axes3, shrink=0.9)
sc4 = axes4.scatter(np.log(cval*weightzero[:]),np.log(cval*weightone[:]), marker = 's',s=sizeadjust,c=(1-true_positive_train_rate[c,:]) , cmap=cm)
sc4.set_clim(vmin=0,vmax=1)
fig.colorbar(sc4, ax=axes4, shrink=0.9)
#plt.colorbar(sc4)
plt.show()
def plot_errors_classweights_C(classweights, fourclu, Clist):
false_discovery_rate, true_positive_rate = supe.pre_plotROC(fourclu)
#false_discovery_train_rate, true_positive_train_rate = supe.pre_plotROC(fourclutest)
weightzero = np.zeros(len(classweights))
weightone = np.zeros(len(classweights))
#Extract the weights
for i in np.arange(len(classweights)):
weightzero[i] = classweights[i][0]
weightone[i] = classweights[i][1]
fig1 = plt.figure()
axes1 = fig1.add_axes([0.1,0.1,0.8,0.8])
axes1.set_xlabel('log(C*weight 0)')
axes1.set_ylabel('log(C*weight 1)')
axes1.hold(True)
numCvalues = len(Clist)
#Number of values of C,
# fourclu should have shape (numCvalues, len(classweights), numspikes)
cm = plt.cm.get_cmap('RdYlBu')
for c, cval in enumerate(Clist):
sc = axes1.scatter(np.log(cval*weightzero[:]),np.log(cval*weightone[:]), c=false_discovery_rate[c,:] , cmap=cm)
plt.colorbar(sc)
plt.show()
return axes1
def make_one_list(fourclulist):
false_discovery_rate = []
true_positive_rate = []
for s, tclu in enumerate(fourclulist):
fdr, tpr = supe.pre_plotROC(tclu)
for c in np.arange(fdr.shape[0]):
false_discovery_rate.append(fdr)
true_positive_rate.append(tpr)
return false_discovery_rate, true_positive_rate
def plotROCcomplete(graphpath,fourclu,KKconflistsquared):
false_discovery_rate, true_positive_rate = supe.pre_plotROC(fourclu)
fig1 = plt.figure(1)
axes1 = fig1.add_axes([0.1,0.1,0.8,0.8])
#Area on page not axes
#dreadful notation, but we are stuck with it!
axes1.hold(True)
colours = ['r', 'g', 'b']
axes1.set_xlim([0,1])
axes1.set_ylim([0,1])
#plt.xlim([0,1])
#plt.ylim([0,1])
axes1.set_xlabel('False discovery rate')
axes1.set_ylabel('True positive rate')
fig1.suptitle(' %s '%(graphpath), fontsize=14, fontweight='bold')
#embed()
for i in np.arange(fourclu.shape[0]):
axes1.scatter(false_discovery_rate[i,:],true_positive_rate[i,:],marker = 'x', color = 'c')
ratepoints = zip(false_discovery_rate[i,:],true_positive_rate[i,:])
hull = ConvexHull(ratepoints)
#simplexlist = [simplex for simplex in hull.simplices]
#print simplexlist
for simplex in hull.simplices:
for data in itertools.combinations(simplex.coords,2):
data = np.array(data)
print data
print 'next!'
axes1.plot(data[:,0],data[:,1],color = 'c')
#print vertex_index
#xes = ratepoints[vertex_index,0]
#print xes
#yes = ratepoints[vertex_index,1]
#axes1.plot(ratepoints[vertex_index,0],ratepoints[vertex_index,1],'k-')
#plt.show()
#plt.savefig('%s_%g.pdf'%(graphpath,i))
errorlist = []
for k in np.arange(len(KKconflistsquared)):
errors = Error_rates_KK(KKconflistsquared[k])
false_discovery_rateKK = errors[4]
true_positive_rateKK = errors[5]
axes1.scatter(false_discovery_rateKK,true_positive_rateKK, color = colours[np.mod(k,len(colours))])
#embed()
errorlist.append(errors)
fig1.savefig('%s'%(graphpath))
#plt.figure(2)
plt.show()
#fig2 = plt.figure(2)
#axes2 = fig2.add_axes([0.1,0.1,0.8,0.8])
#return ratepoints
#return xes, yes,
return errorlist, hull, ratepoints, false_discovery_rate, true_positive_rate
def plotROCcomplete_simple(graphpath,fourclu,KKconflistsquared):
false_discovery_rate, true_positive_rate = supe.pre_plotROC(fourclu)
fig1 = plt.figure(1)
axes1 = fig1.add_axes([0.1,0.1,0.8,0.8])
#Area on page not axes
#dreadful notation, but we are stuck with it!
axes1.hold(True)
colours = ['r', 'g', 'b']
axes1.set_xlim([0,1])
axes1.set_ylim([0,1])
#plt.xlim([0,1])
#plt.ylim([0,1])
axes1.set_xlabel('False discovery rate')
axes1.set_ylabel('True positive rate')
fig1.suptitle(' %s '%(graphpath), fontsize=14, fontweight='bold')
for i in np.arange(fourclu.shape[0]):
axes1.scatter(false_discovery_rate[i,:],true_positive_rate[i,:],marker = 'x', color = 'c')
#Simple plot - No convex hull
for k in np.arange(len(KKconflistsquared)):
errors = Error_rates_KK(KKconflistsquared[k])
false_discovery_rateKK = errors[4]
true_positive_rateKK = errors[5]
axes1.scatter(false_discovery_rateKK,true_positive_rateKK, color = colours[np.mod(k,len(colours))])
plt.show()
fig1.savefig('%s'%(graphpath))
#plt.figure(2)
#fig2 = plt.figure(2)
#axes2 = fig2.add_axes([0.1,0.1,0.8,0.8])
#return ratepoints
#return xes, yes,
return false_discovery_rate, true_positive_rate