TF-Ranking transform data to ELWC - ExampleListWithContext form - tensorflow

Question

I have read all the guides, videos, and everything, but I have no idea how to convert my feature set to an ELWC datasheet format for TF-Rank ListWise problem. There is no description of this structure.
For example, a students profile is:
Student ID age grade math% physics% english% art% math_competit language_competit Rank
14588 16 k12 98 67 88 100 first_place very_good 5
If I have 20 students in the same class, how can I transform this data to be able to make a listwise prediction for every grade ( theoretically in every grade has 3 class with 20 students)

Answer 1

ELWC format requires 'context' and 'example features'. Example features are features that are different for every item in a query list. Context features are ones which are dependent only on the query.
Therefore, every query will have a list of features for every item in the list (the Example features), and a single list of features for Context features.
To convert to ELWC format, start by gathering all the items for a given query. The code below shows a query with two items along with some context information. Use input_pb2.ExampleListWithContext() to create an instance of a ELWC formatter. Then all you have to do is feed in the context and examples.
Save using TFRecordWriter.
from tensorflow_serving.apis import input_pb2
import tensorflow as tf
def _float_feature(value):
return tf.train.Feature(float_list=tf.train.FloatList(value=[value]))
def _int64_feature(value):
return tf.train.Feature(int64_list=tf.train.Int64List(value=[value]))
def _bytes_feature(value):
return tf.train.Feature(bytes_list=tf.train.BytesList(value=[value]))
context = {
'custom_features_1': _float_feature(1.0),
'utility': _int64_feature(1),
}
examples = [
{
'custom_features_1': _float_feature(1.0),
'custom_features_2': _float_feature(1.5),
'utility': _int64_feature(1),
},
{
'custom_features_1': _float_feature(1.0),
'custom_features_2': _float_feature(2.1),
'utility': _int64_feature(0),
}
]
def to_example(dictionary):
return tf.train.Example(features=tf.train.Features(feature=dictionary))
ELWC = input_pb2.ExampleListWithContext()
ELWC.context.CopyFrom(to_example(context))
for expl in examples:
example_features = ELWC.examples.add()
example_features.CopyFrom(to_example(expl))
print(ELWC)