restructuring of handler for variables of interest by allaffa · Pull Request #392 · ORNL/HydraGNN

allaffa · 2025-11-04T17:17:49Z

Restructure Variables of Interest Configuration System

This commit introduces a new self-documenting feature configuration format
for Variables_of_interest while maintaining full backward compatibility.

Key Changes:

New self-documenting JSON format with explicit roles
Automatic feature parsing and validation
Removed ~200+ lines of redundant code
Added comprehensive validation utilities

Core Components:

feature_config.py (NEW)
config_utils.py (MODIFIED)

Examples Migrated: 16
Test Configs Migrated: 5

Testing:
✅ All validation tests passed
✅ Legacy format backward compatibility verified
✅ Code formatted with black
✅ No breaking changes

Related: #392

This commit introduces a new self-documenting feature configuration format for Variables_of_interest while maintaining full backward compatibility. Key Changes: - New self-documenting JSON format with explicit roles (input/output/both) - Automatic feature parsing and validation in update_config() - Removed ~200+ lines of redundant code across examples - Added comprehensive validation utilities Core Components: - hydragnn/utils/input_config_parsing/feature_config.py (NEW, 450+ lines) * parse_feature_config(): Handles both new and legacy formats * validate_feature_config(): Validates config against actual data * update_var_config_with_features(): Ensures backward compatibility * print_feature_summary(): Human-readable feature summary - hydragnn/utils/input_config_parsing/config_utils.py (MODIFIED) * Integrated automatic feature parsing at start of update_config() Examples Migrated (16 total): - open_materials_2024, multidataset, omat24, open_catalyst_2020 - ani1_x, alexandria, transition1x, qcml, qm7x, open_catalyst_2022 - mptrj, multidataset_deepspeed, multibranch, multibranch_hpo - dftb_uv_spectrum, open_molecules_2025 Test Configs Migrated (5 total): - ci.json, ci_multihead.json, ci_vectoroutput.json - ci_equivariant.json, ci_conv_head.json Documentation: - USER_MANUAL.md updated with comprehensive guide - FEATURE_REFACTORING_PLAN.md, MIGRATION_GUIDE.md - INTEGRATION_COMPLETE.md, MIGRATION_COMPLETE.md - VALIDATION_REPORT.md Testing: ✅ All validation tests passed (12/12 configs) ✅ Legacy format backward compatibility verified ✅ Code formatted with black==21.5b1 ✅ No breaking changes Benefits: - Simpler, self-documenting configuration - Automatic validation catches errors at config load time - Single source of truth in JSON configs - Better debugging with clear error messages - Full backward compatibility maintained Related: ORNL#392

- Remove unused var_config variable from qm9.py and zinc.py - Add required feature configuration fields to JSON files (node_feature_names, node_feature_dims, graph_feature_names, graph_feature_dims) - Fixes ValueError: Config must contain either 'node_features'/'graph_features' or 'node_feature_names'/'graph_feature_names' - All example tests now passing (qm9, md17, zinc, LennardJones)

- Replace legacy format (separate lists) with new structured format - Use node_features and graph_features dictionaries with explicit roles - Makes configuration more self-documenting and maintainable - New format: features defined with dim and role (input/output) - All tests passing with new format

- Add update_var_config_with_features call at start of run_training to populate legacy keys - Add redundant call in dataset_loading_and_splitting for safety - Fix ci_multihead.json role assignments: only x has role 'both', x2/x3 are 'output' only - Migrate qm9.json, md17.json, zinc.json to new self-documenting format - Remove unused var_config variables from qm9.py and zinc.py These changes fix KeyError: 'input_node_features' in 110+ tests and MACE multihead test failures. All 68 graph tests now passing.

- Import update_var_config_with_features in test_model_loadpred.py - Call it before get_log_name_config to populate input_node_features - Call it again after loading saved config to ensure backward compatibility - Fixes KeyError: 'input_node_features' in model load/predict test

RylieWeaver

@allaffa

I agree that something like this could be very helpful to new users getting acclimated to HydraGNN. I noted some changes for documentation, but did not find any objectionable things that would cause errors.

- New tests for edge cases and multi-dimensional node features - Update pytest.ini to allow both test_ and pytest_ function prefixes - Black formatting applied to new test script

….py for proper test imports

allaffa · 2025-11-06T15:15:50Z

    return errors


+def validate_node_feature_columns(data_x, node_feature_dims, column_indices=None):


@RylieWeaver

I added this function to handle the edge case when (possibly) data.x contains more columns than the ones extracted. If you do not specify column_index for each feature, the number of columns in data.x must exactly match the sum of the dimensions of the node features specified in your configuration JSON, and the columns must be contiguous and in the order given. If you do specify column_index for each feature, data.x can have extra columns, and only the specified columns will be used for each feature.

@allaffa

Would you envision calling this before or after the atom features are extracted with extract_atom_features() in hydragnn/preprocess/graph_samples_checks_and_updates.py? Currently I only see it in tests and an import.

@RylieWeaver
Thank you for your thorough revision!
I added a new commit where update_predicted_values now uses this check.

@allaffa

Nice! I think this will work, but consider putting it inside update_atom_features.

The reason being this check must be called before the node inputs are extracted and assigned to data.x, otherwise it will be checking for all the node features when only the node inputs are present. This has been done successfully so far because update_predicted_values is prior to update_atom_features everywhere I can find them both (serialized_dataloader, adiosdataset, distdataset, pickledataset), but it may be more robust to put it directly beforehand in the same function.

…ure Black formatting

…mn_index rules

- Remove obsolete dataset names (CuAu_32atoms, FePt_32atoms, FeSi_1024atoms) - Update configuration examples to use modern node_features/graph_features approach - Replace legacy input_node_features/output_index documentation with modern approach - Add comprehensive list of example datasets from examples/ directory - Fix typos (HydgraGNN -> HydraGNN, plane -> plans) - Add clearer references to USER_MANUAL.md throughout - Reorganize configuration section for better clarity - Update supported MPNN architectures list - Note that legacy config format is still supported but not recommended

- Remove fixed-width integer formatting (:2d) from dim and index values - This ensures output like 'dim=1' instead of 'dim= 1' - Fixes test_print_feature_summary assertion that expects exact 'dim=1' tokens

RylieWeaver · 2025-11-07T00:56:04Z

    return errors


+def validate_node_feature_columns(data_x, node_feature_dims, column_indices=None):


@allaffa

Would you envision calling this before or after the atom features are extracted with extract_atom_features() in hydragnn/preprocess/graph_samples_checks_and_updates.py? Currently I only see it in tests and an import.

RylieWeaver · 2025-11-07T01:16:00Z

+
+            # Track input features
+            role = feat_config.get("role", "input")
+            if role in ["input", "both"]:


@allaffa

Is there a scenario where we would want a feature to be both an input and an output?

@RylieWeaver
Yes. There could be self-supervised learning (autoregressive) tasks where the same feature can be used as input and output. But I do also acknowledge that currently HydraGNN does not allow this use because update_predicted_values specifically extracts target nodal labels from data.x and moves them into data.y. For now, I removed this option in this commit

RylieWeaver · 2025-11-07T01:17:55Z

+            result["graph_feature_names"].append(feat_name)
+            result["graph_feature_dims"].append(feat_config["dim"])
+
+            # Graph features are typically outputs (unless role explicitly set)


It may be beneficial to remove 'typically' here. I don't think there are any ways for a graph feature to serve as an input with how the architecture is currently set up.

@RylieWeaver
Thanks. I adjusted the comment in this commit

RylieWeaver · 2025-11-07T01:54:59Z

+                result["output_dim"].append(feat_config["dim"])
+                result["type"].append(feat_config.get("output_type", "node"))
+
+            node_idx += 1


On a second look I think there may be something to fix here. I am not fully sure, so I will lay out my complete thinking so you can check it:

Assume we are using the automatically created config as you have added here
(1) Because of the for loop above, node_idx is appended to result["input_node_features"] for every node feature given in the config. So, for n provided features, then result["input_node_features"] = [0, 1, ..., n-1] no matter the dimensions of each of those node features.
(2) Later, in hydragnn/preprocess/serialized_dataset_loader.py, we have self.input_node_features = config["NeuralNetwork"]["Variables_of_interest"]["input_node_features"] --> update_atom_features(self.input_node_features, data)
(3) Then, in hydragnn/preprocess/graph_samples_check_and_updates.py, we have

def update_atom_features(atom_features: [AtomFeatures], data: Data): feature_indices = [i for i in atom_features] data.x = data.x[:, feature_indices]

In summary, I think this might be extracting only n features for n input node features, regardless of their respective dimensionalities (not what we want).

@RylieWeaver
And you are correct here too.
I tried to address this situation in this commit

…ut at the same time

…t features

allaffa · 2025-11-21T14:58:29Z

@RylieWeaver
Thank you for feedback in your previous reviews.
I addressed the need to jump over multiple columns Indices when node features are multi-dimensional.
cf0de42
The CI test are failing for small fluctuations of the error.

@erdemcaliskan
any feedback from you would also be highly appreciated.

…ormatting

Back-compat new feature parsing

allaffa requested review from RylieWeaver, jychoi-hpc, kshitij-v-mehta, messerlyra and pzhanggit November 4, 2025 17:17

allaffa assigned allaffa and Copilot Nov 4, 2025

allaffa added the enhancement New feature or request label Nov 4, 2025

allaffa force-pushed the variables_of_interest_restructured branch from b3c0668 to cc0114f Compare November 4, 2025 17:19

black formatting fixed

808ca81

allaffa changed the title ~~restructuring of handler for variables of interest completed~~ restructuring of handler for variables of interest Nov 4, 2025

OpenEquivariance Integration and others added 16 commits November 4, 2025 12:25

removed power_grid_simple example

2e0175a

Delete .vscode/launch.json

a797660

Delete activate_env.sh

c4de823

removed omat24 folder

a0079ae

Delete examples/open_materials_2024/omat24_energy_new_format.json

8b8e7d5

Delete examples/power_grid/FIXES_COMPLETE.md

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Delete examples/power_grid/PINN_FIXES_SUMMARY.md

33790c1

Delete examples/power_grid/final_verification.sh

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Delete examples/power_grid/power_grid_no_masking.json

7046942

Delete examples/power_grid/training_output.log

f0bfd92

Delete requirements.txt

4a8026c

Reformat code with black==21.5b1

29f668b

allaffa requested a review from erdemcaliskan November 5, 2025 18:07

RylieWeaver requested changes Nov 5, 2025

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Comment thread USER_MANUAL.md Outdated

Comment thread USER_MANUAL.md Outdated

Comment thread hydragnn/utils/input_config_parsing/feature_config.py

rylie comments addressed

d774bbf

allaffa requested a review from RylieWeaver November 5, 2025 22:53

RylieWeaver approved these changes Nov 5, 2025

View reviewed changes

OpenEquivariance Integration added 3 commits November 6, 2025 09:52

Add tests for validate_node_feature_columns and fix pytest discovery

1f854ce

- New tests for edge cases and multi-dimensional node features - Update pytest.ini to allow both test_ and pytest_ function prefixes - Black formatting applied to new test script

Export validate_node_feature_columns in input_config_parsing/__init__…

0651f66

….py for proper test imports

Add validate_node_feature_columns function to feature_config.py

3824522

allaffa commented Nov 6, 2025

View reviewed changes

allaffa requested a review from RylieWeaver November 6, 2025 15:16

OpenEquivariance Integration added 4 commits November 6, 2025 10:50

Clarify column matching rules in documentation and code comments; ens…

b20cd51

…ure Black formatting

Update README.md for consistency with strict column matching and colu…

e958385

…mn_index rules

Fix print_feature_summary formatting to remove extra padding

1301159

- Remove fixed-width integer formatting (:2d) from dim and index values - This ensures output like 'dim=1' instead of 'dim= 1' - Fixes test_print_feature_summary assertion that expects exact 'dim=1' tokens

RylieWeaver reviewed Nov 7, 2025

View reviewed changes

allaffa force-pushed the variables_of_interest_restructured branch from cdf66d2 to 1301159 Compare November 7, 2025 19:17

allaffa marked this pull request as draft November 10, 2025 18:28

removed bith as option for szame feature ot be used as input and outp…

5b07b53

…ut at the same time

allaffa force-pushed the variables_of_interest_restructured branch from 52b9029 to 5b07b53 Compare November 19, 2025 13:23

OpenEquivariance Integration added 6 commits November 19, 2025 17:50

JSON files for tests in test_graph fixed

5eba422

input_node_features fixed to handle multi-dimensional node features

7c96985

update_atom_features corrected to handle multi-dimensional nodal inpu…

cf0de42

…t features

black formatting fixed

fa7346b

removed launch.json

3b9cd1e

removed unnecessary outout_type from Variables of Interest section

5837f94

allaffa requested a review from RylieWeaver November 21, 2025 14:55

RylieWeaver and others added 2 commits December 1, 2025 16:39

Draft of back-compat new feature parsing. Next commit will be black f…

04a18f5

…ormatting

Merge pull request #51 from RylieWeaver/back_compat_input_node_features

aed8d77

Back-compat new feature parsing

		return errors


		def validate_node_feature_columns(data_x, node_feature_dims, column_indices=None):

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