def setup(self, argv):
self.set_parameters(argv[1:])
print self.set1, self.set2
print self.__dict__.items()
self.data = make_d.read_data(open(argv[0]))
self.data = make_d.assign_classes(self.data)
self.data = make_d.prepare_data(self.data)
print self.data.keys(), [len(v) for v in self.data.values()]
pass
def main(argv):
global C_RANGE
global GAMMA_RANGE
global SET1
global SET2
i = 0
param_grid = {}
results = []
sum_acc = 0
init(argv[1:])
print SET1, SET2
# return None
fn = argv[0]
dataset = make_d.read_data(open(fn))
dataset = make_d.assign_classes(dataset)
data = make_d.prepare_data(dataset)
print data.keys(), [len(v) for v in data.values()]
param = svm.svm_parameter('-b 1')
if KERNEL_TYPE == 'LINEAR':
param.kernel_type = svm.LINEAR
GAMMA_RANGE = 1, 0, -2
else:
param.kernel_type = svm.RBF
cvfunc = leave_one_out
n_cv = None
use_sets = not SET1 is None and not SET2 is None
outfile = os.path.basename(fn)
outfile = outfile.replace('.fasta', '')
outfile = outfile.replace('.fas', '')
if use_sets:
outfile = ''.join(map(str, map(int, SET1))) + 'vs' + ''.join(map(str, map(int, SET2)))
log_name = '%s-%s-%i-%s.csv' % (TIMESTAMP,
KERNEL_TYPE,
int(RANDOMIZE_DATA),
outfile)
logfile = open(log_name, 'w')
while i < N_RUNS:
sys.stdout.write('%i ' % i)
sys.stdout.flush()
if use_sets:
""" TODO: If set sizes are reasonably large, do not use the complete smallest set. """
set1 = dict([item for item in data.items()
if item[0] in SET1])
set2 = dict([item for item in data.items()
if item[0] in SET2])
set1 = make_d.make_set(set1, training_fraction=1.0)
set2 = make_d.make_set(set2, training_fraction=1.0)
new_sets = {1.0: set1[1], -1.0: set2[1]}
sets = make_d.make_set(new_sets, training_fraction=0.75)
else:
sets = make_d.make_set(data, training_fraction=0.75)
train_y, train_x, test_y, test_x = sets
if RANDOMIZE_DATA:
random.shuffle(train_y)
random.shuffle(test_y)
pass
print [len(x) for x in sets]
def main(argv):
global C_RANGE
global GAMMA_RANGE
global SET1
global SET2
i = 0
param_grid = {}
results = []
sum_acc = 0
init(argv[1:])
print SET1, SET2
fn = argv[0]
dataset = make_d.read_data(open(fn))
dataset = make_d.assign_classes(dataset)
data = make_d.prepare_data(dataset)
print data.keys(), [len(v) for v in data.values()]
param = svm.svm_parameter('-b 1')
if KERNEL_TYPE == 'LINEAR':
param.kernel_type = svm.LINEAR
GAMMA_RANGE = 1, 0, -2
else:
param.kernel_type = svm.RBF
cvfunc = svmfun.leave_one_out
n_cv = None
limit_sets = not SET1 is None and not SET2 is None
outfile = os.path.basename(fn)
outfile = outfile.replace('.fasta', '')
outfile = outfile.replace('.fas', '')
if limit_sets:
outfile = ''.join(map(str, map(int, SET1))) + 'vs'
outfile += ''.join(map(str, map(int, SET2)))
log_name = '%s-%s-%i-%s.csv' % (TIMESTAMP,
KERNEL_TYPE,
int(RANDOMIZE_DATA),
outfile)
logfile = open(log_name, 'w')
while i < N_RUNS:
sys.stdout.write('%i ' % i)
sys.stdout.flush()
if limit_sets:
new_sets = make_d.merge_multiclasses(data, SET1, SET2)
sets = make_d.make_set(new_sets, training_fraction=0.75)
else:
sets = make_d.make_set(data, training_fraction=0.75)
train_y, train_x, test_y, test_x = sets
if RANDOMIZE_DATA:
random.shuffle(train_y)
random.shuffle(test_y)
pass
print [len(x) for x in sets]
def main(argv):
global C_RANGE
global GAMMA_RANGE
global SET1
global SET2
i = 0
param_grid = {}
results = []
sum_acc = 0
init(argv[2:])
print SET1, SET2
param = svm.svm_parameter("-b 1")
if KERNEL_TYPE == "LINEAR":
param.kernel_type = svm.LINEAR
GAMMA_RANGE = 1, 0, -2
else:
param.kernel_type = svm.RBF
cvfunc = leave_one_out
n_cv = None
use_sets = not SET1 is None and not SET2 is None
fn = argv[0]
dataset = make_d.read_data(open(fn))
data = make_d.assign_classes(dataset)
data = [(d[0], d[1][1:]) for d in data]
data = make_d.prepare_data(data)
""" Next line is just for testing. """
data = {1.0: data[1.0], 0.0: data[0.0]}
print data.keys(), [len(v) for v in data.values()]
testdata = make_d.read_data(open(argv[1]))
testset = make_d.assign_classes(testdata)
testset = [(d[0], d[1][1:]) for d in testset]
testset = make_d.prepare_data(testset)
precursor = {}
for k, v in testdata.items():
v = v[1:]
precursor[v] = precursor.get(v, []) + [int(k.split("_")[-1])]
print precursor
outfile = os.path.basename(fn)
outfile = outfile.replace(".fasta", "")
outfile = outfile.replace(".fas", "")
if use_sets:
outfile = "".join(map(str, map(int, SET1))) + "vs" + "".join(map(str, map(int, SET2)))
log_name = "%s-%s-%i-%s.csv" % (TIMESTAMP, KERNEL_TYPE, int(RANDOMIZE_DATA), outfile)
logfile = open(log_name, "w")
""" Prepare test set (precursor fragments). """
testset[-1.0] = copy.deepcopy(testset[0.0])
del testset[0.0]
testset = make_d.make_set(testset, balanced_set=False, training_fraction=1.0)
""" 'Training' and 'Test' sets flipped """
test_y, test_x = testset[:2]
encoded_x = [make_d.encode(x, make_d.encode_dic) for x in test_x]
# logfile.write(',%s\n' % ','.join(map(str, map(int, test_y))))
""" Train and predict """
row = [0.0 for x in test_x]
while i < N_RUNS:
sys.stdout.write("%i " % i)
sys.stdout.flush()
set1 = dict([item for item in data.items() if item[0] == 1.0])
set2 = dict([item for item in data.items() if item[0] == 0.0])
set1 = make_d.make_set(set1, training_fraction=1.0)
set2 = make_d.make_set(set2, training_fraction=1.0)
new_sets = {1.0: set1[1], -1.0: set2[1]}
sets = make_d.make_set(new_sets, training_fraction=1.0)
train_y, train_x, dummy_y, dummy_x = sets
print [len(x) for x in sets]
