def get_wide_tensor(row, column_info):
"""
convert a row to tensor given column feature information of a WideAndDeep model
:param row: Row of userId, itemId, features and label
:param column_info: ColumnFeatureInfo specify information of different features
:return: an array of tensors as input for wide part of a WideAndDeep model
"""
wide_columns = column_info.wide_base_cols + column_info.wide_cross_cols
wide_dims = column_info.wide_base_dims + column_info.wide_cross_dims
wide_length = len(wide_columns)
acc = 0
indices = []
for i in range(0, wide_length):
index = row[wide_columns[i]]
if i == 0:
res = index
else:
acc += wide_dims[i - 1]
res = acc + index
indices.append(res)
values = np.array([i + 1 for i in indices])
shape = np.array([sum(wide_dims)])
return JTensor.sparse(values, np.array(indices), shape)
def get_wide_tensor(row, column_info):
wide_columns = column_info.wide_base_cols + column_info.wide_cross_cols
wide_dims = column_info.wide_base_dims + column_info.wide_cross_dims
wide_length = len(wide_columns)
acc = 0
indices = []
for i in range(0, wide_length):
index = row[wide_columns[i]]
if i == 0:
res = index
else:
acc += wide_dims[i-1]
res = acc + index
indices.append(res)
values = np.array([i + 1 for i in indices])
shape = np.array([sum(wide_dims)])
return JTensor.sparse(values, np.array(indices), shape)
def get_wide_tensor(row, column_info):
wide_columns = column_info.wide_base_cols + column_info.wide_cross_cols
wide_dims = column_info.wide_base_dims + column_info.wide_cross_dims
wide_length = len(wide_columns)
acc = 0
indices = []
for i in range(0, wide_length):
index = row[wide_columns[i]]
if i == 0:
res = index
else:
acc += wide_dims[i-1]
res = acc + index
indices.append(res)
values = np.array([i + 1 for i in indices])
shape = np.array([sum(wide_dims)])
return JTensor.sparse(values, np.array(indices), shape)
