def test_encode_pointwise_features(self):
with tf.Graph().as_default():
# Batch size = 2, tf.Example input format.
features = {
"query_length":
tf.convert_to_tensor(
value=[[1], [1]]), # Repeated context feature.
"utility":
tf.convert_to_tensor(value=[[1.0], [0.0]]),
"unigrams":
tf.SparseTensor(indices=[[0, 0], [1, 0]],
values=["ranking", "regression"],
dense_shape=[2, 1])
}
context_feature_columns = {
"query_length":
tf.feature_column.numeric_column("query_length",
shape=(1, ),
default_value=0,
dtype=tf.int64)
}
example_feature_columns = {
"utility":
tf.feature_column.numeric_column("utility",
shape=(1, ),
default_value=0.0,
dtype=tf.float32),
"unigrams":
tf.feature_column.embedding_column(
feature_column.categorical_column_with_vocabulary_list(
"unigrams",
vocabulary_list=[
"ranking", "regression", "classification",
"ordinal"
]),
dimension=10)
}
(context_features,
example_features) = feature_lib.encode_pointwise_features(
features,
context_feature_columns=context_feature_columns,
example_feature_columns=example_feature_columns)
self.assertAllEqual(["query_length"], sorted(context_features))
self.assertAllEqual(["unigrams", "utility"],
sorted(example_features))
# Unigrams dense tensor has shape: [batch_size=2, list_size=1, dim=10].
self.assertAllEqual(
[2, 1, 10], example_features["unigrams"].get_shape().as_list())
with tf.compat.v1.Session() as sess:
sess.run(tf.compat.v1.global_variables_initializer())
sess.run(tf.compat.v1.tables_initializer())
context_features, example_features = sess.run(
[context_features, example_features])
self.assertAllEqual([[1], [1]],
context_features["query_length"])
# Utility tensor has shape: [batch_size=2, list_size=1, 1].
self.assertAllEqual([[[1.0]], [[0.0]]],
example_features["utility"])
def _transform_fn(self, features, mode):
"""Defines the transform fn."""
if self._transform_function is not None:
return self._transform_function(features=features, mode=mode)
if mode == tf.estimator.ModeKeys.PREDICT:
return feature.encode_pointwise_features(
features=features,
context_feature_columns=self._context_feature_columns,
example_feature_columns=self._example_feature_columns,
mode=mode,
scope="transform_layer")
else:
return feature.encode_listwise_features(
features=features,
context_feature_columns=self._context_feature_columns,
example_feature_columns=self._example_feature_columns,
mode=mode,
scope="transform_layer")