def main(): # load the data f_train = 'train.dat' f_test = 'test.dat' ## convert train data datSet_total= [] n_train = 0 with open(f_train,'rb') as csvfile: data = csv.reader(csvfile, delimiter=",") for i_row, row in enumerate(data): if i_row == 0: header = row # first row is header row else: datSet_total.append(row[1:]) # remove row indices n_train += 1 with open(f_test,'rb') as csvfile: data = csv.reader(csvfile, delimiter=",") for i_row, row in enumerate(data): # assume same header, thus skip first row if i_row>0: datSet_total.append(row[1:]) # remove row indices datSet_total= np.array(datSet_total) dataX = datSet_total[:,:-1] # data datay = datSet_total[:,-1] # labels datay = datay.reshape((len(datay),1)) dtype = 'int' dataX = cat2bool(dataX, categorical,dtype) print dataX[:10,:] fname_train = 'train_NN.dat' fname_test = 'test_NN.dat' print dataX.shape print datay.shape data_train = np.hstack((dataX[:n_train,:],datay[:n_train])) data_test = np.hstack((dataX[n_train:,:],datay[n_train:])) print data_train.shape print data_test.shape dataset2file(data_train,[],fname_train) dataset2file(data_test,[],fname_test)
def main():
# load the data
f_train = 'train.dat'
f_test = 'test.dat'
## convert train data
datSet_total = []
n_train = 0
with open(f_train, 'rb') as csvfile:
data = csv.reader(csvfile, delimiter=",")
for i_row, row in enumerate(data):
if i_row == 0:
header = row # first row is header row
else:
datSet_total.append(row[1:]) # remove row indices
n_train += 1
with open(f_test, 'rb') as csvfile:
data = csv.reader(csvfile, delimiter=",")
for i_row, row in enumerate(data):
# assume same header, thus skip first row
if i_row > 0:
datSet_total.append(row[1:]) # remove row indices
datSet_total = np.array(datSet_total)
dataX = datSet_total[:, :-1] # data
datay = datSet_total[:, -1] # labels
datay = datay.reshape((len(datay), 1))
dtype = 'int'
dataX = cat2bool(dataX, categorical, dtype)
print dataX[:10, :]
fname_train = 'train_NN.dat'
fname_test = 'test_NN.dat'
print dataX.shape
print datay.shape
data_train = np.hstack((dataX[:n_train, :], datay[:n_train]))
data_test = np.hstack((dataX[n_train:, :], datay[n_train:]))
print data_train.shape
print data_test.shape
dataset2file(data_train, [], fname_train)
dataset2file(data_test, [], fname_test)
def main():
## Load data
f_train = 'train.csv'
f_test = 'test.csv'
datSet_total = []
with open(f_train,'rb') as csvfile:
data = csv.reader(csvfile, delimiter=",")
for row in data:
datSet_total.append(row)
n_train = len(datSet_total)
n_feat = len(datSet_total[0])-1 # last column contains labels
with open(f_test,'rb') as csvfile:
data = csv.reader(csvfile, delimiter=",")
for row in data:
row.append('?') # ? stands for unknown label
datSet_total.append(row)
print 'Features: %d'%n_feat
# Find uniqe values per column
n_data = len(datSet_total)
print 'n_data: %d'%n_data
print 'n_train: %d'%n_train
print 'n_test: %d'%(n_data-n_train)
# construct remapping for number to char via ascii
remap = range(97,122+1) # 'a' - 'z'
tmp_map = range(65,90+1) # 'A' - 'Z'
remap.extend(tmp_map)
datSet_total = np.array(datSet_total)
datSet_total = normalizedata(datSet_total,categorical)
#max_n = 10
n_levels = []
for i in range(n_feat):
if categorical[i]:
print 'Feature %d is categorical'%i
else:
print 'Feature %d is continuous'%i
unique_vals = []
for d in range(n_data):
#if d>max_n:
# break
elem_cur = datSet_total[d][i]
if elem_cur == 'NaN' or elem_cur == 'nan':
datSet_total[d][i] = 'NA' # use ?
#datSet_total[d][i] = '0' # use ? (perform after normalization)
continue
elif categorical[i]:
if elem_cur not in unique_vals:
unique_vals.append(elem_cur)
index = unique_vals.index(elem_cur)
if index > len(remap)-1:
sys.exit('Too many unique values')
datSet_total[d][i] = chr(remap[index])
elem_new = datSet_total[d][i]
n_levels.append([len(unique_vals)])
#print '%s -> %s'%(elem_cur, elem_new)
## Split data again in test and train data
datSet_train = datSet_total[:n_train]
datSet_test = datSet_total[n_train:]
for i_Set, Set in enumerate([datSet_train, datSet_test]):
if i_Set == 0:
fname = f_train
Set = changelabels(Set,('0','1'),('-','+'))
else:
fname = f_test
# create output files
basename = os.path.splitext(fname)[0]
fname_out = basename+'.dat'
dataset2file(Set,[],fname_out)