def rundist_2loaders(arg): l1, l2, nc = arg m = Data().fit(l1(), l2(), debug_ftsel=False, quiet=True, pca=20, titles=("3", "6"), make_even=True) labels = p.gmm_2(*m.dx, nc=nc, cov='full') labels2 = p.gmm_2(*m.dx, nc=10, cov='full') a, b = Q.rari_score(*labels, *m.dx) c, d = Q.rari_score(*labels2, *m.dx) return a, c
def samplenum(arg): loader, samp = arg t = time.time() m = Data().fit(*loader(seed=None, subsample=samp), debug_ftsel=False, quiet=True, pca=20, titles=("3", "6"), make_even=True) #labels = p.gmm_2(*m.dx,nc=nc,cov='full') #a,b = Q.rari_score(*labels, *m.dx) t1 = time.time() - t a = Q.rari_score(*p.gmm_2(*m.dx, nc=15, cov='full'), *m.dx)[0] t2 = time.time() - t b = Q.rari_score(*p.gmm_2(*m.dx, nc=15, cov='tied'), *m.dx)[0] return a, b, t1, t2
def rundist(arg): loader, nc, scale = arg m = Data().fit(*loader(), debug_ftsel=False, scale=scale, maxgenes=800, quiet=True, pca=20, titles=("3", "6"), make_even=True) clust = lambda ncc: Q.rari_score(*p.gmm_2(*m.dx, nc=ncc, cov='full'), *m.dx )[0] return [clust(ncc) for ncc in nc]
def get_noise_run(args): loader, pool, cluster = args adat = loader() noiserange = range(0, 110, 10) if type(adat.X) != csr_matrix: adat.X = csr_matrix(adat.X) # i could use mp in get_noise_data i think rdydata = noise.get_noise_data(adat, noiserange, title="magic", poolsize=pool, cluster=cluster) r = [Q.rari_score(*m.labels, *m.dx) for m in rdydata] return r
def normal(arg): l1, l2, pca, scale = arg m = Data().fit(l1(), l2(), debug_ftsel=False, quiet=True, scale=scale, pca=pca, dimensions=20, titles=("3", "6"), make_even=True) #labels = p.gmm_2(*m.dx,nc=nc,cov='full') #a,b = Q.rari_score(*labels, *m.dx) nc = [15] return [ Q.rari_score(*p.gmm_2(*m.dx, nc=NC, cov='full'), *m.dx)[0] for NC in nc ]
def get_noise_run_moar(args): loader, cluster, level, metrics = args if level == 0: return [1] * len(cluster) * len( metrics) # should be as long a a normal return adat = loader() if type(adat.X) != csr_matrix: adat.X = csr_matrix(adat.X) # todo: also loop over cluster algos m = noise.get_noise_single(adat, level) r = [ Q.rari_score(*c(*m.dx), *m.dx, metric=metric) for c in cluster for metric in metrics ] return r
dimensions=umapdim, ft_combine = lambda x,y: x or y, umap_n_neighbors=10, # used in example maxmean= 4, minmean = 0.02, corrcoef=False, mitochondria = "mt-", pp='linear', debug_ftsel=False, scale=False, quiet = True, titles = ("3","6"), make_even=True) ### get numberz :) getnumber = lambda y,x: q.rari_score(*y,*x)[0] def clustandscore(d): t = h.cluster_ab(*d.dx, nc =9, cov = 'tied' ) f = h.cluster_ab(*d.dx, nc =9, cov = 'full' ) #print ("asdasd",t,f,d.dx) t= getnumber(t,d.dx) f= getnumber(f,d.dx) #print(f"{t} {f}") return t,f def get_data_mp(x): return clustandscore(getdata(*x))