def row_to_sample(row, column_info, model_type="wide_n_deep"):
wide_tensor = get_wide_tensor(row, column_info)
deep_tensor = JTensor.from_ndarray(get_deep_tensor(row, column_info))
label = row[column_info.label]
model_type = model_type.lower()
if model_type == "wide_n_deep":
feature = [wide_tensor, deep_tensor]
elif model_type == "wide":
feature = wide_tensor
elif model_type == "deep":
feature = deep_tensor
else:
raise TypeError("Unsupported model_type: %s" % model_type)
return Sample.from_jtensor(feature, label)
def row_to_sample(row, column_info, model_type="wide_n_deep"):
wide_tensor = get_wide_tensor(row, column_info)
deep_tensor = JTensor.from_ndarray(get_deep_tensor(row, column_info))
label = row[column_info.label]
model_type = model_type.lower()
if model_type == "wide_n_deep":
feature = [wide_tensor, deep_tensor]
elif model_type == "wide":
feature = wide_tensor
elif model_type == "deep":
feature = deep_tensor
else:
raise TypeError("Unsupported model_type: %s" % model_type)
return Sample.from_jtensor(feature, label)
def row_to_sample(row, column_info, model_type="wide_n_deep"):
"""
convert a row to sample 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: TensorSample as input for WideAndDeep model
"""
wide_tensor = get_wide_tensor(row, column_info)
deep_tensor = get_deep_tensors(row, column_info)
deep_tensors = [JTensor.from_ndarray(ele) for ele in deep_tensor]
label = row[column_info.label]
model_type = model_type.lower()
if model_type == "wide_n_deep":
feature = [wide_tensor] + deep_tensors
elif model_type == "wide":
feature = wide_tensor
elif model_type == "deep":
feature = deep_tensors
else:
raise TypeError("Unsupported model_type: %s" % model_type)
return Sample.from_jtensor(feature, label)