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)