Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Jump to bottom

Handle ColumnSchema target in serialization of SequenceTransform #1214

Merged
merged 4 commits into from
Nov 2, 2023
Merged

Handle ColumnSchema target in serialization of SequenceTransform #1214

Changes from all commits
Commits
Show all changes
4 commits
Select commit Hold shift + click to select a range
08f2e69
Handle ColumnSchema `target` in serialization of `SequenceTransform`
oliverholworthy Oct 4, 2023
e9dc9fc
Merge branch 'main' into save-sequence-transform-with-schema-target
rnyak Oct 17, 2023
86ad222
Merge branch 'main' into save-sequence-transform-with-schema-target
rnyak Oct 31, 2023
735e091
Merge branch 'main' into save-sequence-transform-with-schema-target
rnyak Nov 1, 2023
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
9 changes: 8 additions & 1 deletion merlin/models/tf/transforms/sequence.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -184,7 +184,10 @@ def compute_output_shape(self, input_shape):
def get_config(self):
"""Returns the config of the layer as a Python dictionary."""
config = super().get_config()
config["target"] = self.target
target = self.target
if isinstance(target, ColumnSchema):
target = schema_utils.schema_to_tensorflow_metadata_json(Schema([target]))
config["target"] = target

return config

Expand All @@ -193,6 +196,10 @@ def from_config(cls, config):
"""Creates layer from its config. Returning the instance."""
config = tf_utils.maybe_deserialize_keras_objects(config, ["pre", "post", "aggregation"])
config["schema"] = schema_utils.tensorflow_metadata_json_to_schema(config["schema"])
if config["target"].startswith("{"): # we have a schema
config["target"] = [
col for col in schema_utils.tensorflow_metadata_json_to_schema(config["target"])
][0]
schema = config.pop("schema")
target = config.pop("target")
return cls(schema, target, **config)
Expand Down
Loading