def xy_xval(start,stop,data,rows,f,z,k,m,check,abcd):
rmax = len(rows)
test = []
hypotheses = {}
temp = ""
acc = 0.0
for r in range(start,stop):
d = rows[r]
test.append(d)
bef = "__bef"
makeTable(colname[z],bef)
for r in range(0,rmax):
d = rows[r]
addRow(d,bef)
for ts in test:
# print ">>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>"
ind = data[z].index(ts)#index of test row in data[z]
l = "__aft"+str(test.index(ts))
dafter = xy_proj(bef,data,ind,z,check)
makeTable(colname[z],l)
for r in range(0,len(dafter)):
d = dafter[r]
addRow(d,l)
if check ==True: tableprint(l) #print each leaf table
hypotheses = hypbuild(data,l)
where = klassAt(l)
total = 0.0
for h in hypotheses:
total += len(data[h])
want = ts[where]
got = xy_nb(ts,data,hypotheses,total,l,k,m,check)
#check what we are expecting and getting
if check == True: print "want:",want,"got:",got
if want == got: acc+=1.0
if check == True: sys.exit() #exit after a round
abcd.keep(want,got)
def xy_dt(z, args, pdffile=False):
zlst = xy_proj(z, data, args)
if args['distprune']:
zlst = tshortener.distance_pruner(zlst)
clustered_data = regroup(zlst, "data")
clustered_target = regroup(zlst, "target")
#Convert lists to np.ndarrays
clustered_target = np.asarray(np.float_(clustered_target))
cd_temp = []
for row in clustered_data:
row_temp = []
for item in row:
row_temp.append(np.float_(item))
cd_temp.append(row_temp)
clustered_data = np.asarray(cd_temp)
#Build Classifier
clf = tree.DecisionTreeClassifier(min_samples_leaf=args['l'],
criterion=args['c']) #,max_depth=5)
clf = clf.fit(clustered_data, clustered_target)
if pdffile:
from sklearn.externals.six import StringIO
with open(z + "iris.dot", 'w') as f:
f = tree.export_graphviz(clf, out_file=f, feature_names=colname[z])
if args['dtreeprune']:
branches = tshortener.prune_similar(clf,
args,
colname[zlst[1]],
delete_more=True,
prune=True)
print "\n", "#" * 25, "New Tree", "#" * 25
branches = xy_dt0(clf, colname[zlst[1]])
return zlst, branches
def xy_projed():
csvfile = open('../data/'+argv[1]+'.csv','r')
readCsv(csvfile,argv[2])
#tableprint(argv[2])
k = xy_proj(argv[2],data,4,argv[2],False)
