def main(denoise = True):
#allMatrix, xtrain, xval, xtest,lenList,accList = getData()
diction = [('ind_empleado', 5), ('pais_residencia', 6066), ('sexo', 3), ('ind_nuevo', 2), ('indrel', 2), ('indrel_1mes', 4), ('tiprel_1mes', 4), ('indresi', 2), ('indext', 2), ('conyuemp', 3), ('canal_entrada', 158), ('indfall', 2), ('cod_prov', 53), ('ind_actividad_cliente', 2), ('segmento', 4), ('antiguedad_binned', 10), ('age_binned', 6066), ('renta_binned', 10)]
lenList = []
for tuppl in diction:
val = tuppl[1]
lenList.append(val)
accList = []
for i in range(len(lenList)):
if i ==0:
accList.append(lenList[i])
else:
accList.append(accList[i-1]+lenList[i])
with h5py.File('need_lncran_gene_micrna_go.h5', 'r') as hf:
xtrain = hf['infor'][:]
with h5py.File('need_lncran_disease_tr.h5', 'r') as hf:
rating_mat = hf['rating'][:]
W1,W2,b1,b2,c1,c2 = auto.initialization(INPUT_LAYER,HIDDEN_UNIT1,HIDDEN_UNIT2,mu,sigma)
#define user and item matrices
u=np.random.rand(rating_mat.shape[0],l)
v=np.random.rand(rating_mat.shape[1],l)
'''
with h5py.File('u_30_40bi_40+100_10_auto.h5', 'r') as hf:
u = hf['u'][:]
with h5py.File('v_30_40bi_40+100_10_auto.h5', 'r') as hf:
v = hf['v'][:]
with h5py.File('W1_30_40bi_40+100_10.h5', 'r') as hf:
W1 = hf['W1'][:]
with h5py.File('b1_30_40bi_40+100_10.h5', 'r') as hf:
b1 = hf['b1'][:]
with h5py.File('c1_30_40bi_40+100_10.h5', 'r') as hf:
c1 = hf['c1'][:]
with h5py.File('W2_30_40bi_40+100_10.h5', 'r') as hf:
W2 = hf['W2'][:]
with h5py.File('b2_30_40bi_40+100_10.h5', 'r') as hf:
b2 = hf['b2'][:]
with h5py.File('c2_30_40bi_40+100_10.h5', 'r') as hf:
c2 = hf['c2'][:]
'''
#define preference and confidence matrices
p=np.zeros(rating_mat.shape)
p[rating_mat>0]=1
c=np.zeros(rating_mat.shape)
c=1+alpha*rating_mat
iteration=1
print('start')
for iterate in range(iteration):
for i in range(rating_mat.shape[0]):
c_diag=np.diag(c[i,:])
temp_u=np.dot(np.dot(p[i,:],c_diag),v)
u[i,:]=np.dot(temp_u,np.linalg.pinv(l2_u*np.identity(l)+np.dot(np.dot(v.T,c_diag),v))) #identity(l)单位矩阵
print('u complete')
#update v
for j in range(rating_mat.shape[1]):
#print(j)
c_diag=np.diag(c[:,j])
temp_v=np.dot(np.dot(p[:,j],c_diag),u)
v[j,:]=np.dot(temp_v,np.linalg.pinv(l2_v*np.identity(l)+np.dot(np.dot(u.T,c_diag),u)))
print('v complete')
print(np.linalg.norm(p-np.dot(u,v.T)))
W1,W2,b1,b2,c1,c2 = auto.autoEncoder(ratio_l,ratio_u,batch,W1,W2,xtrain,u,b1,b2,c1,c2,accList,EPOCH_NUM,LEARNING_RATE,denoise = True)
hidden = auto.getoutPut(W1,W2,b1,b2,xtrain,accList)
u=hidden
print(np.linalg.norm(p-np.dot(u,v.T)))
with h5py.File('u_40_lncdis_40+100_auto.h5', 'w') as hf:
hf.create_dataset("u", data=u)
with h5py.File('v_40_lncdis_40+100_auto.h5', 'w') as hf:
hf.create_dataset("v", data=v)
with h5py.File('W1_30_40bi_40+100_10.h5', 'w') as hf:
hf.create_dataset("W1", data=W1)
with h5py.File('b1_30_40bi_40+100_10.h5', 'w') as hf:
hf.create_dataset("b1", data=b1)
with h5py.File('c1_30_40bi_40+100_10.h5', 'w') as hf:
hf.create_dataset("c1", data=c1)
with h5py.File('W2_30_40bi_40+100_10.h5', 'w') as hf:
hf.create_dataset("W2", data=W2)
with h5py.File('b2_30_40bi_40+100_10.h5', 'w') as hf:
hf.create_dataset("b2", data=b2)
with h5py.File('c2_30_40bi_40+100_10.h5', 'w') as hf:
hf.create_dataset("c2", data=c2)
#making_graph(trainLoss, valiLoss, testLoss)
#
# hidden = pd.DataFrame(hidden.T)
# prediction = pd.DataFrame(prediction.T)
# hidden.to_csv('hidden.csv')
# prediction.to_csv('prediction.csv')
return hidden
def main(denoise=True):
#allMatrix, xtrain, xval, xtest,lenList,accList = getData()
diction = [('ind_empleado', 5), ('pais_residencia', 24), ('sexo', 3),
('ind_nuevo', 2), ('indrel', 2), ('indrel_1mes', 4),
('tiprel_1mes', 4), ('indresi', 2), ('indext', 2),
('conyuemp', 3), ('canal_entrada', 158), ('indfall', 2),
('cod_prov', 53), ('ind_actividad_cliente', 2), ('segmento', 4),
('antiguedad_binned', 10), ('age_binned', 24),
('renta_binned', 10)]
lenList = []
for tuppl in diction:
val = tuppl[1]
lenList.append(val)
accList = []
for i in range(len(lenList)):
if i == 0:
accList.append(lenList[i])
else:
accList.append(accList[i - 1] + lenList[i])
#read user infor
with h5py.File('user_infor.h5', 'r') as hf:
xtrain = hf['infor'][:]
#read rating matrix
with h5py.File('rating_tr_numpy.h5', 'r') as hf:
rating_mat = hf['rating'][:]
W1, b1, c1 = auto.initialization(INPUT_LAYER, [HIDDEN_UNIT1], mu, sigma)
#define user and item matrices
u = np.random.rand(rating_mat.shape[0], l)
v = np.random.rand(rating_mat.shape[1], l)
'''
with h5py.File('u_40_mono_40+100_try_auto.h5', 'r') as hf:
u = hf['u'][:]
with h5py.File('v_40_mono_40+100_try_auto.h5', 'r') as hf:
v = hf['v'][:]
with h5py.File('W1_40_mono_40+100_try.h5', 'r') as hf:
W1 = hf['W1'][:]
with h5py.File('b1_40_mono_40+100_try.h5', 'r') as hf:
b1 = hf['b1'][:]
with h5py.File('c1_40_mono_40+100_try.h5', 'r') as hf:
c1 = hf['c1'][:]
'''
#define preference and confidence matrices
p = np.zeros(rating_mat.shape)
p[rating_mat > 0] = 1
c = np.zeros(rating_mat.shape)
c = 1 + alpha * rating_mat
iteration = 100
print('start')
for iterate in range(iteration):
#update u
start = time.time()
v2 = v.T.dot(v)
for i in range(rating_mat.shape[0]):
nonzero_list = np.nonzero(p[i, :])[0]
v_nonzero = v[nonzero_list, :]
c_diag_nonzero = np.diag(c[i, nonzero_list] - 1)
temp_u = np.dot(c[i, nonzero_list], v_nonzero)
u[i, :] = np.dot(
temp_u,
np.linalg.pinv(
l2_u * np.identity(l) +
np.dot(np.dot(v_nonzero.T, c_diag_nonzero), v_nonzero) +
v2))
print('u complete')
end = time.time()
print(end - start)
#update v
start = time.time()
u2 = u.T.dot(u)
for j in range(rating_mat.shape[1]):
#print(j)
nonzero_list = np.nonzero(p[:, j])[0]
c_diag_nonzero = np.diag(c[nonzero_list, j] - 1)
u_nonzero = u[nonzero_list, :]
temp_v = np.dot(c[nonzero_list, j], u_nonzero)
v[j, :] = np.dot(
temp_v,
np.linalg.pinv(
l2_v * np.identity(l) +
np.dot(np.dot(u_nonzero.T, c_diag_nonzero), u_nonzero) +
u2))
print('v complete')
end = time.time()
print(end - start)
print(np.linalg.norm(p - np.dot(u, v.T)))
W1, b1, c1 = auto.autoEncoder_mono(ratio_l,
ratio_u,
batch,
W1,
xtrain,
u,
b1,
c1,
accList,
EPOCH_NUM,
LEARNING_RATE,
l1,
denoise=True)
hidden = auto.getoutPut_mono(W1, b1, xtrain, accList)
u = hidden
print(np.linalg.norm(p - np.dot(u, v.T)))
with h5py.File('u_40_mono_40+100_try_auto.h5', 'w') as hf:
hf.create_dataset("u", data=u)
with h5py.File('v_40_mono_40+100_try_auto.h5', 'w') as hf:
hf.create_dataset("v", data=v)
with h5py.File('W1_40_mono_40+100_try.h5', 'w') as hf:
hf.create_dataset("W1", data=W1)
with h5py.File('b1_40_mono_40+100_try.h5', 'w') as hf:
hf.create_dataset("b1", data=b1)
with h5py.File('c1_40_mono_40+100_try.h5', 'w') as hf:
hf.create_dataset("c1", data=c1)
with h5py.File('h_40_mono_40+100_auto_try.h5', 'w') as hf:
hf.create_dataset("hidden", data=hidden)
#making_graph(trainLoss, valiLoss, testLoss)
#
# hidden = pd.DataFrame(hidden.T)
# prediction = pd.DataFrame(prediction.T)
# hidden.to_csv('hidden.csv')
# prediction.to_csv('prediction.csv')
return hidden
def main(denoise=True):
#allMatrix, xtrain, xval, xtest,lenList,accList = getData()
diction = [('ind_empleado', 5), ('pais_residencia', 24), ('sexo', 3),
('ind_nuevo', 2), ('indrel', 2), ('indrel_1mes', 4),
('tiprel_1mes', 4), ('indresi', 2), ('indext', 2),
('conyuemp', 3), ('canal_entrada', 158), ('indfall', 2),
('cod_prov', 53), ('ind_actividad_cliente', 2), ('segmento', 4),
('antiguedad_binned', 10), ('age_binned', 24),
('renta_binned', 10)]
lenList = []
for tuppl in diction:
val = tuppl[1]
lenList.append(val)
accList = []
for i in range(len(lenList)):
if i == 0:
accList.append(lenList[i])
else:
accList.append(accList[i - 1] + lenList[i])
with h5py.File('user_infor.h5', 'r') as hf:
xtrain = hf['infor'][:]
#read rating matrix
with h5py.File('rating_tr_numpy.h5', 'r') as hf:
rating_mat = hf['rating'][:]
W1, W2, b1, b2, c1, c2 = auto.initialization(INPUT_LAYER, HIDDEN_UNIT1,
HIDDEN_UNIT2, mu, sigma)
u = np.random.rand(rating_mat.shape[0], l)
v = np.random.rand(rating_mat.shape[1], l)
p = np.zeros(rating_mat.shape)
p[rating_mat > 0] = 1
c = np.zeros(rating_mat.shape)
c = 1 + alpha * rating_mat #confidence matrices
iteration = 30
print('start')
for iterate in range(iteration):
for i in range(rating_mat.shape[0]):
c_diag = np.diag(c[i, :])
temp_u = np.dot(np.dot(p[i, :], c_diag), v)
u[i, :] = np.dot(
temp_u,
np.linalg.pinv(l2_u * np.identity(l) +
np.dot(np.dot(v.T, c_diag), v)))
print('u complete')
for j in range(rating_mat.shape[1]):
#print(j)
c_diag = np.diag(c[:, j])
temp_v = np.dot(np.dot(p[:, j], c_diag), u)
v[j, :] = np.dot(
temp_v,
np.linalg.pinv(l2_v * np.identity(l) +
np.dot(np.dot(u.T, c_diag), u)))
print('v complete')
print(np.linalg.norm(p - np.dot(u, v.T)))
W1, b1, c1 = auto.autoEncoder_mono(ratio_l,
ratio_u,
batch,
W1,
xtrain,
u,
b1,
c1,
accList,
EPOCH_NUM,
LEARNING_RATE,
denoise=True)
hidden = auto.getoutPut_mono(W1, b1, xtrain, accList)
u = hidden
print(np.linalg.norm(p - np.dot(u, v.T)))
with h5py.File('u_40_mono_40+100_auto.h5', 'w') as hf:
hf.create_dataset("u", data=u)
with h5py.File('v_40_mono_40+100_auto.h5', 'w') as hf:
hf.create_dataset("v", data=v)
with h5py.File('W1_40_mono_40+100.h5', 'w') as hf:
hf.create_dataset("W1", data=W1)
with h5py.File('b1_40_mono_40+100.h5', 'w') as hf:
hf.create_dataset("b1", data=b1)
with h5py.File('c1_40_mono_40+100.h5', 'w') as hf:
hf.create_dataset("c1", data=c1)
return hidden