def add_condition(self,feature,condition,threshold): from globfile import args,buckets if args['d']: self.features.append(feature) self.conditions.append(condition) buck_fea = [ i for i in buckets.keys() if feature == i[1:]][0] if condition: if int(threshold) in buckets[buck_fea].hi.keys(): self.thresholds.append(buckets[buck_fea].hi[int(threshold)]) else: self.thresholds.append(buckets[buck_fea].hi[0]) else: if int(threshold) in buckets[buck_fea].lo.keys(): self.thresholds.append(buckets[buck_fea].lo[int(threshold)]) else: self.thresholds.append(buckets[buck_fea].lo[0]) else: self.features.append(feature) self.conditions.append(condition) self.thresholds.append(threshold)
def add_condition(self, feature, condition, threshold): from globfile import args, buckets if args['d']: self.features.append(feature) self.conditions.append(condition) buck_fea = [i for i in buckets.keys() if feature == i[1:]][0] if condition: if int(threshold) in buckets[buck_fea].hi.keys(): self.thresholds.append( buckets[buck_fea].hi[int(threshold)]) else: self.thresholds.append(buckets[buck_fea].hi[0]) else: if int(threshold) in buckets[buck_fea].lo.keys(): self.thresholds.append( buckets[buck_fea].lo[int(threshold)]) else: self.thresholds.append(buckets[buck_fea].lo[0]) else: self.features.append(feature) self.conditions.append(condition) self.thresholds.append(threshold)
def tshortener(z,zlst,colname,data,dep,indep,patt=1.0,discretize=True): #The infogain techniques of pruning columns and discretization class Bucket: #class for each column with splitted pairs of data def __init__(self,name): self.pairs = [] #unsorted row pairs self.name = name self.wsum = 0 self.dinds = {} #sorted split indexs self.lo = {} self.hi = {} def addpairs(self,pairs): self.pairs.append(pairs) def addwsum(self,wsum): self.wsum = wsum def __repr__(self): s = 'n: '+str(self.name)+":" s += ' l: '+str(len(self.pairs)) s += ' e: '+str(self.wsum)+'\n' return s from globfile import buckets outcols = [] for key,value in buckets.items(): buckets[key] = None for Z in zlst[1:]: for c in indep[Z]: if c == 'C_id': continue if c not in buckets.keys(): buckets[c] = Bucket(c) elif buckets[c] is None: buckets[c] = Bucket(c) ind = colname[Z].index(c) cind = colname[Z].index('C_id') for r in data[Z]: buckets[c].addpairs((r[ind],str(r[cind]))) reader.removeTable(Z) buckets = weighted_entropies(buckets) vals = buckets.values()[:] vals.sort(key=lambda x: x.wsum,reverse=False) for i in range(0,int(len(vals)*patt)): outcols.append(vals[i].name) zshort = 'shortenedz' outcols = [i for i in colname[z] if i in outcols] print outcols,"#infogained" #Convert outcols to discrete attributes if discretize: outcols = [c[1:] for c in outcols] print outcols,"#discretized" reader.makeTable(outcols+dep[z],zshort) for r in data[z]: temp = [] for i,c in enumerate(colname[z]): if discretize: if c[1:] in outcols or c in dep[z]: temp.append(r[i]) else: if c in outcols+dep[z]: temp.append(r[i]) reader.addRow(temp,zshort) if discretize: discretizer(zshort,buckets) for Z in zlst: reader.removeTable(Z) #discretizer(zshort,buckets) return zshort
def tshortener(z, zlst, colname, data, dep, indep, patt=1.0, discretize=True): #The infogain techniques of pruning columns and discretization class Bucket: #class for each column with splitted pairs of data def __init__(self, name): self.pairs = [] #unsorted row pairs self.name = name self.wsum = 0 self.dinds = {} #sorted split indexs self.lo = {} self.hi = {} def addpairs(self, pairs): self.pairs.append(pairs) def addwsum(self, wsum): self.wsum = wsum def __repr__(self): s = 'n: ' + str(self.name) + ":" s += ' l: ' + str(len(self.pairs)) s += ' e: ' + str(self.wsum) + '\n' return s from globfile import buckets outcols = [] for key, value in buckets.items(): buckets[key] = None for Z in zlst[1:]: for c in indep[Z]: if c == 'C_id': continue if c not in buckets.keys(): buckets[c] = Bucket(c) elif buckets[c] is None: buckets[c] = Bucket(c) ind = colname[Z].index(c) cind = colname[Z].index('C_id') for r in data[Z]: buckets[c].addpairs((r[ind], str(r[cind]))) reader.removeTable(Z) buckets = weighted_entropies(buckets) vals = buckets.values()[:] vals.sort(key=lambda x: x.wsum, reverse=False) for i in range(0, int(len(vals) * patt)): outcols.append(vals[i].name) zshort = 'shortenedz' outcols = [i for i in colname[z] if i in outcols] print outcols, "#infogained" #Convert outcols to discrete attributes if discretize: outcols = [c[1:] for c in outcols] print outcols, "#discretized" reader.makeTable(outcols + dep[z], zshort) for r in data[z]: temp = [] for i, c in enumerate(colname[z]): if discretize: if c[1:] in outcols or c in dep[z]: temp.append(r[i]) else: if c in outcols + dep[z]: temp.append(r[i]) reader.addRow(temp, zshort) if discretize: discretizer(zshort, buckets) for Z in zlst: reader.removeTable(Z) #discretizer(zshort,buckets) return zshort