def load_dataset(organism):
from ml_data import SequenceNucsData, SequenceDinucLabelsProperties
global max_features
global maxlen
print('Load organism: {}'.format(organism))
npath, ppath = './fasta/{}_neg.fa'.format(
organism), './fasta/{}_pos.fa'.format(organism)
print(npath, ppath)
k = 2
max_features = 4**k
samples = SequenceNucsData(npath, ppath, k=k)
# k = 4
# max_features = 4**k
# samples = SequenceNucsData(npath, ppath, k=1)
X, y = samples.getX(), samples.getY()
# X = X.reshape(-1, 38, 79, 1).astype('float32')
X = X.astype('int32')
# Cut width
X = X[:, (199 - 10):(199 + 31)]
y = y.astype('int32')
print('Input Shapes\nX: {} | y: {}'.format(X.shape, y.shape))
maxlen = X.shape[1]
print('>>> maxlen {}'.format(maxlen))
return X, y
def load_dataset(organism):
from ml_data import SequenceNucsData
print('Load organism: {}'.format(organism))
npath, ppath = './fasta/{}_neg.fa'.format(
organism), './fasta/{}_pos.fa'.format(organism)
print(npath, ppath)
samples = SequenceNucsData(npath, ppath, k=k)
X, y = samples.getX(), samples.getY()
# X = X.reshape(-1, 38, 79, 1).astype('float32')
X = X.astype('int32')
y = y.astype('int32')
print('Input Shapes\nX: {} | y: {}'.format(X.shape, y.shape))
maxlen = X.shape[1]
return X, y
def load_dataset(organism):
from ml_data import SequenceNucsData, SimpleHistData
global max_features
global maxlen
print('Load organism: {}'.format(organism))
npath, ppath = './fasta/{}_neg.fa'.format(
organism), './fasta/{}_pos.fa'.format(organism)
print(npath, ppath)
ini = 59
k = 1
max_features = 4**k
samples = SequenceNucsData(npath, ppath, k=k)
samples2 = SimpleHistData(npath, ppath, k=3, upto=True)
X, y = samples.getX(), samples.getY()
X = np.hstack((X, samples2.getX()))
# X = X.reshape(-1, 38, 79, 1).astype('float32')
np.random.seed(int(time.time()))
mask = np.array(np.random.randint(2, size=X.shape[1]) + 1, dtype=bool)
# mask = np.array(np.zeros(X.shape[1]) , dtype=bool)
# mask[59]=1
# mask[60]=1
# mask[61]=1
# mask[49]=1
# mask[50]=1
# mask[51]=1
# mask[24]=1
# mask[25]=1
# mask[26]=1
mask[ini] = 1
print(mask)
X = X[:, mask]
X = X.astype('int32')
# ini = 199
# X = X[:, (ini-30):(ini+11)]
y = y.astype('int32')
print('Input Shapes\nX: {} | y: {}'.format(X.shape, y.shape))
maxlen = X.shape[1]
return X, y
def load_dataset(organism, coding_type='onehot', k=1):
from ml_data import SequenceNucsData, SequenceNucHotvector, SequenceMotifHot
print('Load organism: {}'.format(organism))
npath, ppath = './fasta/{}_neg.fa'.format(
organism), './fasta/{}_pos.fa'.format(organism)
print(npath, ppath)
if coding_type == 'onehot':
samples = SequenceNucHotvector(npath, ppath)
elif coding_type == 'embedding':
k = 1
samples = SequenceNucsData(npath, ppath, k=k)
X, y = samples.getX(), samples.getY()
X, y = X.astype('int32'), y.astype('int32')
# X = X.reshape(-1, 38, 79, 1).astype('float32')
# ini = 59
# # ini = 199
# X = X[:, (ini-30):(ini+11)]
print('Input Shapes\nX: {} | y: {}'.format(X.shape, y.shape))
return X, y, X.shape[1:]
numpy.random.seed(7)
earlyStopping=EarlyStopping(monitor='val_loss', patience=30, verbose=0, mode='auto')
reduceLR = ReduceLROnPlateau(monitor='val_loss', factor=0.01, patience=5, verbose=0, mode='auto', epsilon=0.001, cooldown=0, min_lr=0)
#npath = "fasta/Bacillus_non_prom.fa"
#ppath = "fasta/Bacillus_prom.fa"
#npath = "fasta/Arabidopsis_non_prom_big.fa"
#ppath = "fasta/Arabidopsis_non_tata.fa"
npath = "fasta/Ecoli_non_prom.fa"
ppath = "fasta/Ecoli_prom.fa"
mldata = SequenceNucsData(npath, ppath, k=2)
mldata2 = SequenceNucsData(npath, ppath, k=3)
X = mldata.getX()
X2 = mldata2.getX()
newCol = numpy.array([-1 for _ in range(X2.shape[0])])
X2 = numpy.column_stack([ X2, newCol ])
Y = mldata.getY()
print 'X1: {}'.format(X.shape)
print 'X2: {}'.format(X2.shape)
print 'Y: {}'.format(Y.shape)
toRemove = False
if toRemove:
def load_partition(train_index, test_index, X, y):
x_train = X[train_index, :]
y_train = y[train_index]
x_test = X[test_index, :]
y_test = y[test_index]
return (x_train, y_train), (x_test, y_test)
organism = 'Bacillus'
npath, ppath = './fasta/{}_neg.fa'.format(
organism), './fasta/{}_pos.fa'.format(organism)
k = 1
data = SequenceNucsData(npath, ppath, k=k)
X = data.getX()
y = data.getY()
kf = StratifiedShuffleSplit(n_splits=5, random_state=34267)
kf.get_n_splits(X, y)
partition = 0
for train_index, test_index in kf.split(X, y):
partition += 1
(x_train,
y_train), (x_test, y_test) = load_partition(train_index, test_index, X, y)
y_train = np.expand_dims(y_train, axis=1)
y_test = np.expand_dims(y_test, axis=1)
verbose=0,
mode='auto')
reduceLR = ReduceLROnPlateau(monitor='val_loss',
factor=0.1,
patience=5,
verbose=0,
mode='auto',
epsilon=0.001,
cooldown=0,
min_lr=0)
npath = "fasta/Bacillus_non_prom.fa"
ppath = "fasta/Bacillus_prom.fa"
#mldata = SequenceNucsData(npath, ppath, k=3)
mldata = SimpleHistData(npath, ppath, k=4)
mldata2 = SequenceNucsData(npath, ppath, k=3)
mldata3 = DinucAutoCovarData(npath, ppath)
X = mldata.getX()
Y = mldata.getY()
X2 = mldata2.getX()
X3 = mldata3.getX()
kf = StratifiedKFold(n_splits=10, shuffle=True, random_state=1234)
kf.get_n_splits(X, Y)
cvscores = []
for train_index, test_index in kf.split(X, Y):
X_train = X[train_index, :]