annotate home/ubuntu/lefse_to_export/lefse.py @ 1:db64b6287cd6 draft

Modified datatypes

import os,sys,math,pickle
import random as lrand
import rpy2.robjects as robjects
import argparse
import numpy
#import svmutil

def init(): 
	lrand.seed(1982)
	robjects.r('library(splines)')
	robjects.r('library(stats4)')
	robjects.r('library(survival)')
	robjects.r('library(mvtnorm)')
	robjects.r('library(modeltools)')
	robjects.r('library(coin)')
	robjects.r('library(MASS)')

def get_class_means(class_sl,feats):
	means = {}
	clk = class_sl.keys()
	for fk,f in feats.items():
		means[fk] = [numpy.mean((f[class_sl[k][0]:class_sl[k][1]])) for k in clk] 
	return clk,means 
	
def save_res(res,filename): 
	with open(filename, 'w') as out:
		for k,v in res['cls_means'].items():
			out.write(k+"\t"+str(math.log(max(max(v),1.0),10.0))+"\t")
			if k in res['lda_res_th']:
				for i,vv in enumerate(v):
					if vv == max(v):
						out.write(str(res['cls_means_kord'][i])+"\t")
						break
				out.write(str(res['lda_res'][k])) 
			else: out.write("\t")
			out.write( "\t" + res['wilcox_res'][k]+"\n")

def load_data(input_file, nnorm = False):
	with open(input_file, 'rb') as inputf:
		inp = pickle.load(inputf)
	if nnorm: return inp['feats'],inp['cls'],inp['class_sl'],inp['subclass_sl'],inp['class_hierarchy'],inp['norm']  
	else: return inp['feats'],inp['cls'],inp['class_sl'],inp['subclass_sl'],inp['class_hierarchy']

def load_res(input_file):
	with open(input_file, 'rb') as inputf:	
		inp = pickle.load(inputf)
	return inp['res'],inp['params'],inp['class_sl'],inp['subclass_sl']		


def test_kw_r(cls,feats,p,factors):
	robjects.globalenv["y"] = robjects.FloatVector(feats)
	for i,f in enumerate(factors):
		robjects.globalenv['x'+str(i+1)] = robjects.FactorVector(robjects.StrVector(cls[f]))
	fo = "y~x1"
    #for i,f in enumerate(factors[1:]):
    #	if f == "subclass" and len(set(cls[f])) <= len(set(cls["class"])): continue
    #	if len(set(cls[f])) == len(cls[f]): continue
    #	fo += "+x"+str(i+2)
	kw_res = robjects.r('kruskal.test('+fo+',)$p.value')
	return float(tuple(kw_res)[0]) < p, float(tuple(kw_res)[0])

def test_rep_wilcoxon_r(sl,cl_hie,feats,th,multiclass_strat,mul_cor,fn,min_c,comp_only_same_subcl,curv=False):
	comp_all_sub = not comp_only_same_subcl
	tot_ok =  0
	alpha_mtc = th
	all_diff = []
	for pair in [(x,y) for x in cl_hie.keys() for y in cl_hie.keys() if x < y]:
		dir_cmp = "not_set" #
		l_subcl1, l_subcl2 = (len(cl_hie[pair[0]]), len(cl_hie[pair[1]]))
		if mul_cor != 0: alpha_mtc = th*l_subcl1*l_subcl2 if mul_cor == 2 else 1.0-math.pow(1.0-th,l_subcl1*l_subcl2)
		ok = 0
		curv_sign = 0
		first = True
		for i,k1 in enumerate(cl_hie[pair[0]]):
			br = False
			for j,k2 in enumerate(cl_hie[pair[1]]):
				if not comp_all_sub and k1[len(pair[0]):] != k2[len(pair[1]):]: 
					ok += 1	
					continue 
				cl1 = feats[sl[k1][0]:sl[k1][1]]
				cl2 = feats[sl[k2][0]:sl[k2][1]]
				med_comp = False
				if len(cl1) < min_c or len(cl2) < min_c: 
					med_comp = True
				sx,sy = numpy.median(cl1),numpy.median(cl2)
				if cl1[0] == cl2[0] and len(set(cl1)) == 1 and  len(set(cl2)) == 1: 
					tres, first = False, False
				elif not med_comp:
					robjects.globalenv["x"] = robjects.FloatVector(cl1+cl2)
					robjects.globalenv["y"] = robjects.FactorVector(robjects.StrVector(["a" for a in cl1]+["b" for b in cl2]))	
					pv = float(robjects.r('pvalue(wilcox_test(x~y,data=data.frame(x,y)))')[0])
					tres = pv < alpha_mtc*2.0
				if first:
					first = False
					if not curv and ( med_comp or tres ): 
						dir_cmp = sx < sy
                        #if sx == sy: br = True
					elif curv: 
						dir_cmp = None
						if med_comp or tres: 
							curv_sign += 1
							dir_cmp = sx < sy
					else: br = True
				elif not curv and med_comp:
					if ((sx < sy) != dir_cmp or sx == sy): br = True
				elif curv:
					if tres and dir_cmp == None:
						curv_sign += 1
						dir_cmp = sx < sy
					if tres and dir_cmp != (sx < sy):
						br = True
						curv_sign = -1
				elif not tres or (sx < sy) != dir_cmp or sx == sy: br = True
				if br: break
				ok += 1
			if br: break
		if curv: diff = curv_sign > 0
		else: diff = (ok == len(cl_hie[pair[1]])*len(cl_hie[pair[0]])) # or (not comp_all_sub and dir_cmp != "not_set") 
		if diff: tot_ok += 1
		if not diff and multiclass_strat: return False
		if diff and not multiclass_strat: all_diff.append(pair)
	if not multiclass_strat:
		tot_k = len(cl_hie.keys())
		for k in cl_hie.keys():
			nk = 0
			for a in all_diff:
				if k in a: nk += 1
			if nk == tot_k-1: return True
		return False
	return True 



def contast_within_classes_or_few_per_class(feats,inds,min_cl,ncl):
	ff = zip(*[v for n,v in feats.items() if n != 'class'])
	cols = [ff[i] for i in inds]
	cls = [feats['class'][i] for i in inds]
	if len(set(cls)) < ncl:
		return True
	for c in set(cls):
		if cls.count(c) < min_cl:
			return True
		cols_cl = [x for i,x in enumerate(cols) if cls[i] == c]
		for i,col in enumerate(zip(*cols_cl)):
			if (len(set(col)) <= min_cl and min_cl > 1) or (min_cl == 1 and len(set(col)) <= 1):
				return True
	return False 

def test_lda_r(cls,feats,cl_sl,boots,fract_sample,lda_th,tol_min,nlogs):
        fk = feats.keys()
        means = dict([(k,[]) for k in feats.keys()])
        feats['class'] = list(cls['class'])
        clss = list(set(feats['class']))
        for uu,k in enumerate(fk):
                if k == 'class': continue
                ff = [(feats['class'][i],v) for i,v in enumerate(feats[k])]
                for c in clss:
                        if len(set([float(v[1]) for v in ff if v[0] == c])) > max(float(feats['class'].count(c))*0.5,4): continue
                        for i,v in enumerate(feats[k]):
                                if feats['class'][i] == c:
                                        feats[k][i] = math.fabs(feats[k][i] + lrand.normalvariate(0.0,max(feats[k][i]*0.05,0.01)))
        rdict = {}
        for a,b in feats.items():
                if a == 'class' or a == 'subclass' or a == 'subject':
                        rdict[a] = robjects.StrVector(b)
                else: rdict[a] = robjects.FloatVector(b)
        robjects.globalenv["d"] = robjects.DataFrame(rdict)
        lfk = len(feats[fk[0]])
        rfk = int(float(len(feats[fk[0]]))*fract_sample)
        f = "class ~ "+fk[0]
        for k in fk[1:]: f += " + " + k.strip()
        ncl = len(set(cls['class']))
        min_cl = int(float(min([cls['class'].count(c) for c in set(cls['class'])]))*fract_sample*fract_sample*0.5) 
        min_cl = max(min_cl,1) 
        pairs = [(a,b) for a in set(cls['class']) for b in set(cls['class']) if a > b]

	for k in fk:	
		for i in range(boots):
			means[k].append([])	
        for i in range(boots):
                for rtmp in range(1000):
                        rand_s = [lrand.randint(0,lfk-1) for v in range(rfk)]
                        if not contast_within_classes_or_few_per_class(feats,rand_s,min_cl,ncl): break
                rand_s = [r+1 for r in rand_s]
		means[k][i] = []
		for p in pairs:
	        	robjects.globalenv["rand_s"] = robjects.IntVector(rand_s)
                	robjects.globalenv["sub_d"] = robjects.r('d[rand_s,]')
                	z = robjects.r('z <- suppressWarnings(lda(as.formula('+f+'),data=sub_d,tol='+str(tol_min)+'))')
			robjects.r('w <- z$scaling[,1]')
			robjects.r('w.unit <- w/sqrt(sum(w^2))')
			robjects.r('ss <- sub_d[,-match("class",colnames(sub_d))]')
			if 'subclass' in feats:
				robjects.r('ss <- ss[,-match("subclass",colnames(ss))]')
			if 'subject' in feats:
				robjects.r('ss <- ss[,-match("subject",colnames(ss))]')
			robjects.r('xy.matrix <- as.matrix(ss)')
			robjects.r('LD <- xy.matrix%*%w.unit')
			robjects.r('effect.size <- abs(mean(LD[sub_d[,"class"]=="'+p[0]+'"]) - mean(LD[sub_d[,"class"]=="'+p[1]+'"]))')
			scal = robjects.r('wfinal <- w.unit * effect.size')
			rres = robjects.r('mm <- z$means')
			rowns = list(rres.rownames)
			lenc = len(list(rres.colnames))
			coeff = [abs(float(v)) if not math.isnan(float(v)) else 0.0 for v in scal]
                	res = dict([(pp,[float(ff) for ff in rres.rx(pp,True)] if pp in rowns else [0.0]*lenc ) for pp in [p[0],p[1]]])
			for j,k in enumerate(fk):
				gm = abs(res[p[0]][j] - res[p[1]][j])
                        	means[k][i].append((gm+coeff[j])*0.5)
        res = {}
        for k in fk:
		m = max([numpy.mean([means[k][kk][p] for kk in range(boots)]) for p in range(len(pairs))])
                res[k] = math.copysign(1.0,m)*math.log(1.0+math.fabs(m),10)
        return res,dict([(k,x) for k,x in res.items() if math.fabs(x) > lda_th])


def test_svm(cls,feats,cl_sl,boots,fract_sample,lda_th,tol_min,nsvm):
	return NULL
"""
	fk = feats.keys()
	clss = list(set(cls['class']))
	y = [clss.index(c)*2-1 for c in list(cls['class'])]
	xx = [feats[f] for f in fk]
	if nsvm: 
		maxs = [max(v) for v in xx]
		mins = [min(v) for v in xx]
		x = [ dict([(i+1,(v-mins[i])/(maxs[i]-mins[i])) for i,v in enumerate(f)]) for f in zip(*xx)]
	else: x = [ dict([(i+1,v) for i,v in enumerate(f)]) for f in zip(*xx)]
	
	lfk = len(feats[fk[0]])
	rfk = int(float(len(feats[fk[0]]))*fract_sample)
	mm = []

	best_c = svmutil.svm_ms(y, x, [pow(2.0,i) for i in range(-5,10)],'-t 0 -q')
	for i in range(boots):
		rand_s = [lrand.randint(0,lfk-1) for v in range(rfk)]
		r = svmutil.svm_w([y[yi] for yi in rand_s], [x[xi] for xi in rand_s], best_c,'-t 0 -q')
		mm.append(r[:len(fk)])
	m = [numpy.mean(v) for v in zip(*mm)]
	res = dict([(v,m[i]) for i,v in enumerate(fk)])
	return res,dict([(k,x) for k,x in res.items() if math.fabs(x) > lda_th])
"""