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1 import os,sys,math,pickle
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2 import random as lrand
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3 import rpy2.robjects as robjects
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4 import argparse
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5 import numpy
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6 #import svmutil
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7
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8 def init():
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9 lrand.seed(1982)
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10 robjects.r('library(splines)')
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11 robjects.r('library(stats4)')
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12 robjects.r('library(survival)')
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13 robjects.r('library(mvtnorm)')
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14 robjects.r('library(modeltools)')
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15 robjects.r('library(coin)')
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16 robjects.r('library(MASS)')
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17
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18 def get_class_means(class_sl,feats):
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19 means = {}
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20 clk = class_sl.keys()
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21 for fk,f in feats.items():
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22 means[fk] = [numpy.mean((f[class_sl[k][0]:class_sl[k][1]])) for k in clk]
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23 return clk,means
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24
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25 def save_res(res,filename):
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26 with open(filename, 'w') as out:
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27 for k,v in res['cls_means'].items():
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28 out.write(k+"\t"+str(math.log(max(max(v),1.0),10.0))+"\t")
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29 if k in res['lda_res_th']:
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30 for i,vv in enumerate(v):
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31 if vv == max(v):
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32 out.write(str(res['cls_means_kord'][i])+"\t")
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33 break
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34 out.write(str(res['lda_res'][k]))
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35 else: out.write("\t")
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36 out.write( "\t" + res['wilcox_res'][k]+"\n")
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37
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38 def load_data(input_file, nnorm = False):
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39 with open(input_file, 'rb') as inputf:
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40 inp = pickle.load(inputf)
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41 if nnorm: return inp['feats'],inp['cls'],inp['class_sl'],inp['subclass_sl'],inp['class_hierarchy'],inp['norm']
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42 else: return inp['feats'],inp['cls'],inp['class_sl'],inp['subclass_sl'],inp['class_hierarchy']
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43
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44 def load_res(input_file):
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45 with open(input_file, 'rb') as inputf:
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46 inp = pickle.load(inputf)
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47 return inp['res'],inp['params'],inp['class_sl'],inp['subclass_sl']
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48
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49
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50 def test_kw_r(cls,feats,p,factors):
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51 robjects.globalenv["y"] = robjects.FloatVector(feats)
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52 for i,f in enumerate(factors):
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53 robjects.globalenv['x'+str(i+1)] = robjects.FactorVector(robjects.StrVector(cls[f]))
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54 fo = "y~x1"
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2
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55 #for i,f in enumerate(factors[1:]):
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56 # if f == "subclass" and len(set(cls[f])) <= len(set(cls["class"])): continue
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57 # if len(set(cls[f])) == len(cls[f]): continue
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58 # fo += "+x"+str(i+2)
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59 kw_res = robjects.r('kruskal.test('+fo+',)$p.value')
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60 return float(tuple(kw_res)[0]) < p, float(tuple(kw_res)[0])
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61
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62 def test_rep_wilcoxon_r(sl,cl_hie,feats,th,multiclass_strat,mul_cor,fn,min_c,comp_only_same_subcl,curv=False):
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63 comp_all_sub = not comp_only_same_subcl
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64 tot_ok = 0
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65 alpha_mtc = th
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66 all_diff = []
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67 for pair in [(x,y) for x in cl_hie.keys() for y in cl_hie.keys() if x < y]:
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68 dir_cmp = "not_set" #
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69 l_subcl1, l_subcl2 = (len(cl_hie[pair[0]]), len(cl_hie[pair[1]]))
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70 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)
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71 ok = 0
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72 curv_sign = 0
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73 first = True
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74 for i,k1 in enumerate(cl_hie[pair[0]]):
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75 br = False
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76 for j,k2 in enumerate(cl_hie[pair[1]]):
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77 if not comp_all_sub and k1[len(pair[0]):] != k2[len(pair[1]):]:
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78 ok += 1
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79 continue
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80 cl1 = feats[sl[k1][0]:sl[k1][1]]
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81 cl2 = feats[sl[k2][0]:sl[k2][1]]
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82 med_comp = False
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83 if len(cl1) < min_c or len(cl2) < min_c:
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84 med_comp = True
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85 sx,sy = numpy.median(cl1),numpy.median(cl2)
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86 if cl1[0] == cl2[0] and len(set(cl1)) == 1 and len(set(cl2)) == 1:
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87 tres, first = False, False
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88 elif not med_comp:
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89 robjects.globalenv["x"] = robjects.FloatVector(cl1+cl2)
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90 robjects.globalenv["y"] = robjects.FactorVector(robjects.StrVector(["a" for a in cl1]+["b" for b in cl2]))
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91 pv = float(robjects.r('pvalue(wilcox_test(x~y,data=data.frame(x,y)))')[0])
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92 tres = pv < alpha_mtc*2.0
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93 if first:
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94 first = False
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95 if not curv and ( med_comp or tres ):
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96 dir_cmp = sx < sy
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2
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97 #if sx == sy: br = True
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98 elif curv:
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99 dir_cmp = None
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100 if med_comp or tres:
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101 curv_sign += 1
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102 dir_cmp = sx < sy
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103 else: br = True
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104 elif not curv and med_comp:
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105 if ((sx < sy) != dir_cmp or sx == sy): br = True
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106 elif curv:
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107 if tres and dir_cmp == None:
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108 curv_sign += 1
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109 dir_cmp = sx < sy
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110 if tres and dir_cmp != (sx < sy):
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111 br = True
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112 curv_sign = -1
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113 elif not tres or (sx < sy) != dir_cmp or sx == sy: br = True
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114 if br: break
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115 ok += 1
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116 if br: break
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117 if curv: diff = curv_sign > 0
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118 else: diff = (ok == len(cl_hie[pair[1]])*len(cl_hie[pair[0]])) # or (not comp_all_sub and dir_cmp != "not_set")
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119 if diff: tot_ok += 1
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120 if not diff and multiclass_strat: return False
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121 if diff and not multiclass_strat: all_diff.append(pair)
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122 if not multiclass_strat:
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123 tot_k = len(cl_hie.keys())
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124 for k in cl_hie.keys():
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125 nk = 0
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126 for a in all_diff:
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127 if k in a: nk += 1
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128 if nk == tot_k-1: return True
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129 return False
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130 return True
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131
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132
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133
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134 def contast_within_classes_or_few_per_class(feats,inds,min_cl,ncl):
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135 ff = zip(*[v for n,v in feats.items() if n != 'class'])
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136 cols = [ff[i] for i in inds]
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137 cls = [feats['class'][i] for i in inds]
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138 if len(set(cls)) < ncl:
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139 return True
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140 for c in set(cls):
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141 if cls.count(c) < min_cl:
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142 return True
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143 cols_cl = [x for i,x in enumerate(cols) if cls[i] == c]
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144 for i,col in enumerate(zip(*cols_cl)):
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145 if (len(set(col)) <= min_cl and min_cl > 1) or (min_cl == 1 and len(set(col)) <= 1):
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146 return True
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147 return False
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148
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149 def test_lda_r(cls,feats,cl_sl,boots,fract_sample,lda_th,tol_min,nlogs):
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150 fk = feats.keys()
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151 means = dict([(k,[]) for k in feats.keys()])
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152 feats['class'] = list(cls['class'])
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153 clss = list(set(feats['class']))
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154 for uu,k in enumerate(fk):
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155 if k == 'class': continue
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156 ff = [(feats['class'][i],v) for i,v in enumerate(feats[k])]
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157 for c in clss:
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158 if len(set([float(v[1]) for v in ff if v[0] == c])) > max(float(feats['class'].count(c))*0.5,4): continue
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159 for i,v in enumerate(feats[k]):
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160 if feats['class'][i] == c:
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161 feats[k][i] = math.fabs(feats[k][i] + lrand.normalvariate(0.0,max(feats[k][i]*0.05,0.01)))
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162 rdict = {}
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163 for a,b in feats.items():
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164 if a == 'class' or a == 'subclass' or a == 'subject':
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165 rdict[a] = robjects.StrVector(b)
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166 else: rdict[a] = robjects.FloatVector(b)
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167 robjects.globalenv["d"] = robjects.DataFrame(rdict)
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168 lfk = len(feats[fk[0]])
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169 rfk = int(float(len(feats[fk[0]]))*fract_sample)
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170 f = "class ~ "+fk[0]
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171 for k in fk[1:]: f += " + " + k.strip()
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172 ncl = len(set(cls['class']))
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173 min_cl = int(float(min([cls['class'].count(c) for c in set(cls['class'])]))*fract_sample*fract_sample*0.5)
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174 min_cl = max(min_cl,1)
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175 pairs = [(a,b) for a in set(cls['class']) for b in set(cls['class']) if a > b]
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176
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177 for k in fk:
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178 for i in range(boots):
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179 means[k].append([])
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180 for i in range(boots):
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181 for rtmp in range(1000):
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182 rand_s = [lrand.randint(0,lfk-1) for v in range(rfk)]
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183 if not contast_within_classes_or_few_per_class(feats,rand_s,min_cl,ncl): break
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184 rand_s = [r+1 for r in rand_s]
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185 means[k][i] = []
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186 for p in pairs:
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187 robjects.globalenv["rand_s"] = robjects.IntVector(rand_s)
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188 robjects.globalenv["sub_d"] = robjects.r('d[rand_s,]')
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189 z = robjects.r('z <- suppressWarnings(lda(as.formula('+f+'),data=sub_d,tol='+str(tol_min)+'))')
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190 robjects.r('w <- z$scaling[,1]')
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191 robjects.r('w.unit <- w/sqrt(sum(w^2))')
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192 robjects.r('ss <- sub_d[,-match("class",colnames(sub_d))]')
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193 if 'subclass' in feats:
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194 robjects.r('ss <- ss[,-match("subclass",colnames(ss))]')
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195 if 'subject' in feats:
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196 robjects.r('ss <- ss[,-match("subject",colnames(ss))]')
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197 robjects.r('xy.matrix <- as.matrix(ss)')
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198 robjects.r('LD <- xy.matrix%*%w.unit')
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199 robjects.r('effect.size <- abs(mean(LD[sub_d[,"class"]=="'+p[0]+'"]) - mean(LD[sub_d[,"class"]=="'+p[1]+'"]))')
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200 scal = robjects.r('wfinal <- w.unit * effect.size')
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201 rres = robjects.r('mm <- z$means')
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202 rowns = list(rres.rownames)
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203 lenc = len(list(rres.colnames))
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204 coeff = [abs(float(v)) if not math.isnan(float(v)) else 0.0 for v in scal]
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205 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]]])
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206 for j,k in enumerate(fk):
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207 gm = abs(res[p[0]][j] - res[p[1]][j])
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208 means[k][i].append((gm+coeff[j])*0.5)
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209 res = {}
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210 for k in fk:
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211 m = max([numpy.mean([means[k][kk][p] for kk in range(boots)]) for p in range(len(pairs))])
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212 res[k] = math.copysign(1.0,m)*math.log(1.0+math.fabs(m),10)
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213 return res,dict([(k,x) for k,x in res.items() if math.fabs(x) > lda_th])
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214
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215
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216 def test_svm(cls,feats,cl_sl,boots,fract_sample,lda_th,tol_min,nsvm):
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217 return NULL
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218 """
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219 fk = feats.keys()
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220 clss = list(set(cls['class']))
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221 y = [clss.index(c)*2-1 for c in list(cls['class'])]
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222 xx = [feats[f] for f in fk]
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223 if nsvm:
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224 maxs = [max(v) for v in xx]
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225 mins = [min(v) for v in xx]
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226 x = [ dict([(i+1,(v-mins[i])/(maxs[i]-mins[i])) for i,v in enumerate(f)]) for f in zip(*xx)]
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227 else: x = [ dict([(i+1,v) for i,v in enumerate(f)]) for f in zip(*xx)]
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228
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229 lfk = len(feats[fk[0]])
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230 rfk = int(float(len(feats[fk[0]]))*fract_sample)
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231 mm = []
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232
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233 best_c = svmutil.svm_ms(y, x, [pow(2.0,i) for i in range(-5,10)],'-t 0 -q')
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234 for i in range(boots):
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235 rand_s = [lrand.randint(0,lfk-1) for v in range(rfk)]
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236 r = svmutil.svm_w([y[yi] for yi in rand_s], [x[xi] for xi in rand_s], best_c,'-t 0 -q')
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237 mm.append(r[:len(fk)])
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238 m = [numpy.mean(v) for v in zip(*mm)]
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239 res = dict([(v,m[i]) for i,v in enumerate(fk)])
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240 return res,dict([(k,x) for k,x in res.items() if math.fabs(x) > lda_th])
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241 """
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