Metadata-Version: 2.4
Name: glpbench
Version: 0.1.0a1
Summary: Evaluation pipelines for transductive and semi-inductive graph link prediction
Author: Kaan Yorgancioglu
License:                                  Apache License
                                   Version 2.0, January 2004
                                http://www.apache.org/licenses/
        
           TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
        
           1. Definitions.
        
              "License" shall mean the terms and conditions for use, reproduction,
              and distribution as defined by Sections 1 through 9 of this document.
        
              "Licensor" shall mean the copyright owner or entity authorized by
              the copyright owner that is granting the License.
        
              "Legal Entity" shall mean the union of the acting entity and all
              other entities that control, are controlled by, or are under common
              control with that entity. For the purposes of this definition,
              "control" means (i) the power, direct or indirect, to cause the
              direction or management of such entity, whether by contract or
              otherwise, or (ii) ownership of fifty percent (50%) or more of the
              outstanding shares, or (iii) beneficial ownership of such entity.
        
              "You" (or "Your") shall mean an individual or Legal Entity
              exercising permissions granted by this License.
        
              "Source" form shall mean the preferred form for making modifications,
              including but not limited to software source code, documentation
              source, and configuration files.
        
              "Object" form shall mean any form resulting from mechanical
              transformation or translation of a Source form, including but
              not limited to compiled object code, generated documentation,
              and conversions to other media types.
        
              "Work" shall mean the work of authorship, whether in Source or
              Object form, made available under the License, as indicated by a
              copyright notice that is included in or attached to the work
              (an example is provided in the Appendix below).
        
              "Derivative Works" shall mean any work, whether in Source or Object
              form, that is based on (or derived from) the Work and for which the
              editorial revisions, annotations, elaborations, or other modifications
              represent, as a whole, an original work of authorship. For the purposes
              of this License, Derivative Works shall not include works that remain
              separable from, or merely link (or bind by name) to the interfaces of,
              the Work and Derivative Works thereof.
        
              "Contribution" shall mean any work of authorship, including
              the original version of the Work and any modifications or additions
              to that Work or Derivative Works thereof, that is intentionally
              submitted to Licensor for inclusion in the Work by the copyright owner
              or by an individual or Legal Entity authorized to submit on behalf of
              the copyright owner. For the purposes of this definition, "submitted"
              means any form of electronic, verbal, or written communication sent
              to the Licensor or its representatives, including but not limited to
              communication on electronic mailing lists, source code control systems,
              and issue tracking systems that are managed by, or on behalf of, the
              Licensor for the purpose of discussing and improving the Work, but
              excluding communication that is conspicuously marked or otherwise
              designated in writing by the copyright owner as "Not a Contribution."
        
              "Contributor" shall mean Licensor and any individual or Legal Entity
              on behalf of whom a Contribution has been received by Licensor and
              subsequently incorporated within the Work.
        
           2. Grant of Copyright License. Subject to the terms and conditions of
              this License, each Contributor hereby grants to You a perpetual,
              worldwide, non-exclusive, no-charge, royalty-free, irrevocable
              copyright license to reproduce, prepare Derivative Works of,
              publicly display, publicly perform, sublicense, and distribute the
              Work and such Derivative Works in Source or Object form.
        
           3. Grant of Patent License. Subject to the terms and conditions of
              this License, each Contributor hereby grants to You a perpetual,
              worldwide, non-exclusive, no-charge, royalty-free, irrevocable
              (except as stated in this section) patent license to make, have made,
              use, offer to sell, sell, import, and otherwise transfer the Work,
              where such license applies only to those patent claims licensable
              by such Contributor that are necessarily infringed by their
              Contribution(s) alone or by combination of their Contribution(s)
              with the Work to which such Contribution(s) was submitted. If You
              institute patent litigation against any entity (including a
              cross-claim or counterclaim in a lawsuit) alleging that the Work
              or a Contribution incorporated within the Work constitutes direct
              or contributory patent infringement, then any patent licenses
              granted to You under this License for that Work shall terminate
              as of the date such litigation is filed.
        
           4. Redistribution. You may reproduce and distribute copies of the
              Work or Derivative Works thereof in any medium, with or without
              modifications, and in Source or Object form, provided that You
              meet the following conditions:
        
              (a) You must give any other recipients of the Work or
                  Derivative Works a copy of this License; and
        
              (b) You must cause any modified files to carry prominent notices
                  stating that You changed the files; and
        
              (c) You must retain, in the Source form of any Derivative Works
                  that You distribute, all copyright, patent, trademark, and
                  attribution notices from the Source form of the Work,
                  excluding those notices that do not pertain to any part of
                  the Derivative Works; and
        
              (d) If the Work includes a "NOTICE" text file as part of its
                  distribution, then any Derivative Works that You distribute must
                  include a readable copy of the attribution notices contained
                  within such NOTICE file, excluding those notices that do not
                  pertain to any part of the Derivative Works, in at least one
                  of the following places: within a NOTICE text file distributed
                  as part of the Derivative Works; within the Source form or
                  documentation, if provided along with the Derivative Works; or,
                  within a display generated by the Derivative Works, if and
                  wherever such third-party notices normally appear. The contents
                  of the NOTICE file are for informational purposes only and
                  do not modify the License. You may add Your own attribution
                  notices within Derivative Works that You distribute, alongside
                  or as an addendum to the NOTICE text from the Work, provided
                  that such additional attribution notices cannot be construed
                  as modifying the License.
        
              You may add Your own copyright statement to Your modifications and
              may provide additional or different license terms and conditions
              for use, reproduction, or distribution of Your modifications, or
              for any such Derivative Works as a whole, provided Your use,
              reproduction, and distribution of the Work otherwise complies with
              the conditions stated in this License.
        
           5. Submission of Contributions. Unless You explicitly state otherwise,
              any Contribution intentionally submitted for inclusion in the Work
              by You to the Licensor shall be under the terms and conditions of
              this License, without any additional terms or conditions.
              Notwithstanding the above, nothing herein shall supersede or modify
              the terms of any separate license agreement you may have executed
              with Licensor regarding such Contributions.
        
           6. Trademarks. This License does not grant permission to use the trade
              names, trademarks, service marks, or product names of the Licensor,
              except as required for reasonable and customary use in describing the
              origin of the Work and reproducing the content of the NOTICE file.
        
           7. Disclaimer of Warranty. Unless required by applicable law or
              agreed to in writing, Licensor provides the Work (and each
              Contributor provides its Contributions) on an "AS IS" BASIS,
              WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
              implied, including, without limitation, any warranties or conditions
              of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A
              PARTICULAR PURPOSE. You are solely responsible for determining the
              appropriateness of using or redistributing the Work and assume any
              risks associated with Your exercise of permissions under this License.
        
           8. Limitation of Liability. In no event and under no legal theory,
              whether in tort (including negligence), contract, or otherwise,
              unless required by applicable law (such as deliberate and grossly
              negligent acts) or agreed to in writing, shall any Contributor be
              liable to You for damages, including any direct, indirect, special,
              incidental, or consequential damages of any character arising as a
              result of this License or out of the use or inability to use the
              Work (including but not limited to damages for loss of goodwill,
              work stoppage, computer failure or malfunction, or any and all
              other commercial damages or losses), even if such Contributor
              has been advised of the possibility of such damages.
        
           9. Accepting Warranty or Additional Liability. While redistributing
              the Work or Derivative Works thereof, You may choose to offer,
              and charge a fee for, acceptance of support, warranty, indemnity,
              or other liability obligations and/or rights consistent with this
              License. However, in accepting such obligations, You may act only
              on Your own behalf and on Your sole responsibility, not on behalf
              of any other Contributor, and only if You agree to indemnify,
              defend, and hold each Contributor harmless for any liability
              incurred by, or claims asserted against, such Contributor by reason
              of your accepting any such warranty or additional liability.
        
           END OF TERMS AND CONDITIONS
        
           APPENDIX: How to apply the Apache License to your work.
        
              To apply the Apache License to your work, attach the following
              boilerplate notice, with the fields enclosed by brackets "[]"
              replaced with your own identifying information. (Don't include
              the brackets!)  The text should be enclosed in the appropriate
              comment syntax for the file format. We also recommend that a
              file or class name and description of purpose be included on the
              same "printed page" as the copyright notice for easier
              identification within third-party archives.
        
           Copyright [yyyy] [name of copyright owner]
        
           Licensed under the Apache License, Version 2.0 (the "License");
           you may not use this file except in compliance with the License.
           You may obtain a copy of the License at
        
               http://www.apache.org/licenses/LICENSE-2.0
        
           Unless required by applicable law or agreed to in writing, software
           distributed under the License is distributed on an "AS IS" BASIS,
           WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
           See the License for the specific language governing permissions and
           limitations under the License.
        
Project-URL: Repository, https://github.com/yorgancioglukaan/glpbench
Project-URL: Issues, https://github.com/yorgancioglukaan/glpbench/issues
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3 :: Only
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Python :: 3.13
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Requires-Python: >=3.10
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: numpy>=1.24
Requires-Dist: PyYAML>=6.0
Requires-Dist: scikit-learn>=1.2
Requires-Dist: torch>=2.1
Requires-Dist: torch-geometric>=2.5
Requires-Dist: tqdm>=4.60
Dynamic: license-file

# GLPBench

GLPBench is a reproducible evaluation pipeline for transductive and
semi-inductive graph link prediction. It includes matched and leakage-audited
data splits, benchmark-dataset loading, YAML benchmark grids, validation-only
hyperparameter search, result logging, and reference model implementations.

This is an alpha release. Its public API and configuration schema may change
before the first stable release.

Install GLPBench from PyPI with:

```bash
python -m pip install glpbench
```

Run `glpbench --help` to list the available experiment, benchmark, search, and
audit commands.

## Contents

- [Asymmetric Graph Autoencoder](#asymmetric-graph-autoencoder)
- [Experiment CLI](#experiment-cli)
- [Benchmark grids](#benchmark-grids)
- [Hyperparameter search](#hyperparameter-search)
- [Python API](#python-api)
  - [Dataset loading](#dataset-loading)
  - [Feature preprocessing and graph splits](#feature-preprocessing-and-graph-splits)
  - [Link-prediction models](#link-prediction-models)
  - [Result logging](#result-logging)
- [License](#license)

## Asymmetric Graph Autoencoder

Standard graph autoencoders score a candidate edge using graph-derived latent representations for both endpoints. This is problematic in the semi-inductive setting, where one endpoint is a new query node whose attributes are available but whose edges were not observed during training.

The Asymmetric Graph Autoencoder (AGAE) instead combines a message-passing representation of the observed source node with a feature-derived representation of the target node. Given latent source representations $Z \in \mathbb{R}^{n \times d}$ and reduced node features $X_{\mathrm{reduced}} \in \mathbb{R}^{n \times d}$, its decoder reconstructs link scores as

$$
\hat{A} = Z X_{\mathrm{reduced}}^{T}.
$$

The feature reducer is fitted only on training-visible nodes and can transform unseen query nodes without retraining the graph encoder. The framework evaluates this decoder under matched transductive and semi-inductive protocols alongside inner-product, MLP-decoder, variational, DEAL, and feature-only baselines.

## Experiment CLI

Run an experiment with a supported dataset:

```bash
glpbench experiment cora out/results.csv
```

When `--model-type` is omitted, the script runs every registered architecture
on the same splits. The eight current architectures produce sixteen result
rows under one run ID after evaluation on both test settings. The
feature-cosine baseline is evaluated directly without training.

For trainable architectures, each result row records
`training_time_seconds`, the total wall-clock time spent across all requested
training epochs and validation-based checkpoint selection. Every architecture
also records its exact `trainable_parameter_count`. Reduced-feature models
record `reducer_fit_time_seconds` separately; other models leave that field
empty. Paired transductive and inductive rows share these values because they
use the same fitted reducer and trained model. Non-trainable baselines leave
training time empty and report zero trainable parameters.

Add `--verbose` to show dataset preparation, architecture progress, epoch progress, and live loss components:

```bash
glpbench experiment cora out/results.csv --verbose
```

Common options include:

```bash
glpbench experiment citeseer out/results.csv \
  --conv-layer-type GATv2Conv \
  --gatv2-kwargs '{"heads": 2, "concat": false, "dropout": 0.1}' \
  --hidden-channels 128 \
  --out-channels 64 \
  --reduction-method pca \
  --decoder-hidden-channels 64 \
  --decoder-dropout 0.2 \
  --kl-weight 1.0 \
  --learning-rate 0.01 \
  --lr-scheduler reduce-on-plateau \
  --lr-scheduler-kwargs \
    '{"factor": 0.5, "patience": 25, "threshold": 0.001, "minimum_learning_rate": 0.00001}' \
  --weight-decay 0.0 \
  --query-size 10 \
  --validation-query-size 10 \
  --train-ratio 0.8 \
  --epochs 100 \
  --selection-metric roc_auc \
  --early-stopping-patience 20 \
  --early-stopping-min-delta 0.001 \
  --seed 42 \
  --device cpu
```

Use `--query-fraction` instead of `--query-size` to select a fraction of
eligible, non-isolated nodes. The two options are mutually exclusive; when
both are omitted, the CLI selects 10 query nodes.

Validation uses a disjoint set of cold-start query nodes. Its size defaults to
the resolved test-query size; use `--validation-query-size` or
`--validation-query-fraction` to override it. Both query populations are
excluded from feature-reducer fitting and from the training topology.

Trainable models run for the full requested epoch budget. After every epoch,
the model is evaluated on the semi-inductive validation split, and the best
checkpoint is restored before either test split is evaluated. Checkpoints use
ROC-AUC by default; `--selection-metric average_precision` selects by average
precision instead.

GATv2 constructor options can be supplied as a JSON object with
`--gatv2-kwargs`. Result rows retain these options in `extra_fields` alongside
the reducer, learning rate, device, query-selection configuration, and any
architecture-specific decoder, VGAE, DEAL, or regularization settings.

Early stopping is disabled unless `--early-stopping-patience` is supplied.
When enabled, training stops after that many consecutive epochs without an
improvement greater than `--early-stopping-min-delta`. The configured epoch
count remains the maximum budget, and the best checkpoint is restored in all
cases. Logged training time includes training, validation, and checkpoint
selection.

Learning-rate scheduling is disabled by default. Use `--lr-scheduler` with
`reduce-on-plateau` or `cosine-annealing`; scheduler parameters are supplied
as a JSON object through `--lr-scheduler-kwargs`. Plateau scheduling uses the
same validation metric as checkpoint selection, while cosine annealing follows
the fixed maximum epoch budget. Result rows log the scheduler configuration
and final learning rate.

Repeat `--seed` to run paired repetitions in one invocation:

```bash
glpbench experiment cora out/results.csv --seed 1 --seed 2 --seed 3
```

Alternatively, `--num-seeds 10` runs seeds 0 through 9. All rows from the
invocation share a `run_id`; repetitions of the same model and hyperparameter
configuration share a stable `configuration_id`. Data splits and evaluation
negatives are generated once per seed and reused across architectures.

Available model types are `symmetric`, `asymmetric`, `mlp`,
`asymmetric-mlp`, `variational`, `deal`, `feature-cosine`, and `feature-mlp`.
`feature-mlp` is a trainable, graph-independent baseline that applies the
symmetric MLP decoder directly to pairs of input node features. Repeat
`--model-type` to choose several explicitly:

```bash
glpbench experiment cora \
  --model-type mlp \
  --model-type variational
```

Supported datasets are Cora, CiteSeer, PubMed, Amazon Photo, Amazon Computers, Coauthor CS, DBLP, WikiCS, and Deezer Europe. Supported convolution layers are `GCNConv`, `GraphConv`, `SAGEConv`, and `GATv2Conv`. Run `glpbench experiment --help` for the complete option list. Newly registered architectures will automatically become available to the CLI.

For example, run the Amazon Photo benchmark with:

```bash
glpbench experiment amazon-photo out/results.csv --verbose
```

## Benchmark grids

`benchmark.py` expands a versioned YAML configuration and calls the Python
experiment API sequentially. Start from
[`configs/example_benchmark.yaml`](configs/example_benchmark.yaml), then inspect
the expansion without training:

```bash
glpbench benchmark configs/example_benchmark.yaml --dry-run
```

Run the grid with:

```bash
glpbench benchmark configs/example_benchmark.yaml --verbose
```

Verbose benchmark output uses one overall model–seed progress bar and one
temporary epoch bar for the current run. Use `--verbose-details` instead when
diagnosing an experiment and you need the standalone seed, architecture, and
stage messages.

All newly written rows share one benchmark `run_id`. Individual failures are
appended to the configured JSONL error file while later jobs continue. Use
`--fail-fast` to stop on the first error, or `--resume` to read completed
`(configuration_id, random_seed)` pairs from the result CSV and avoid duplicate
rows. Dataset, model, convolution, reducer, regularization, seed, and common
training dimensions are validated before execution.

A version-2 benchmark can import the dataset-specific architecture settings
written by `search.py`. The preliminary held-out benchmark uses the pilot
selections with new evaluation seeds:

```bash
glpbench benchmark configs/preliminary_benchmark.yaml --dry-run
glpbench benchmark configs/preliminary_benchmark.yaml --verbose
```

The selected-configuration document must contain every requested
dataset–architecture pair. Search fields for preprocessing, AGAE
regularization, MLP decoders, VGAE, and DEAL are translated to the experiment
API and validated before execution.

## Hyperparameter search

`search.py` performs validation-only model selection and never evaluates either
test split. The pilot specification is
[`configs/pilot_search.yaml`](configs/pilot_search.yaml). Inspect its
deterministic candidate expansion without loading data or training:

```bash
glpbench search configs/pilot_search.yaml --dry-run
```

Run or resume the CPU pilot with:

```bash
glpbench search configs/pilot_search.yaml --verbose
glpbench search configs/pilot_search.yaml --resume --verbose
```

Successful candidate–seed runs are written to a search-history CSV. Selection
requires all configured tuning seeds and uses mean semi-inductive validation
ROC-AUC, then average precision and trainable parameter count as tie-breakers.
The runner also writes an aggregate CSV, a JSONL failure log, and a YAML file
containing the selected resolved configuration for each dataset–architecture
pair.

## Python API

### Dataset loading

`datasets.load_dataset()` loads a supported benchmark, optionally normalizes its features, moves it to a device, and attaches dataset metadata:

```python
from datasets import load_dataset

data = load_dataset("cora", root="data", device="cpu")
```

`datasets.available_datasets()` returns the normalized names accepted by the loader.

### Feature preprocessing and graph splits

`utils.calculate_reduced_features()` fits a registered feature reducer and stores the transformed features in `data.x_reduced`. Pass a node mask to fit without exposing inductive query features:

```python
import torch

from utils import (
    calculate_reduced_features,
    generate_comparable_splits,
    select_query_nodes,
)

query = select_query_nodes(data, query_size=3, seed=42)
fit_mask = torch.ones(data.num_nodes, dtype=torch.bool)
fit_mask[query] = False
calculate_reduced_features(data, dim=64, fit_nodes=fit_mask)
train, val, test_transductive, test_inductive = generate_comparable_splits(
    data,
    query=query,
    train_ratio=0.8,
)
```

Available reduction methods are `pca`, `truncated-svd`, and
`sparse-random-projection`. For example:

```bash
glpbench experiment coauthor-cs out/results.csv \
  --model-type asymmetric \
  --reduction-method truncated-svd
```

`select_query_nodes()` performs deterministic automatic selection while ensuring
that every query retains at least one visible neighbor. Both split functions can
also select queries directly by accepting `query_size` or `query_fraction` with
a `seed`. `generate_comparable_splits()` creates transductive and inductive test
sets with matching message-passing graphs and equal numbers of labeled edges.
It automatically calls `audit_comparable_splits()` to enforce node-role,
topology, positive/negative leakage, orientation, and paired-test invariants.
The split protocol expects a simple undirected graph and rejects self-loops or
duplicate directed edge entries rather than silently modifying them.
`utils.inductive_split()` is available when only a cold-start split is needed.

### Link-prediction models

The model classes are:

- `GAE_Model`: inner-product symmetric graph autoencoder.
- `AGAE_Model`: asymmetric latent-to-feature decoder.
- `MLP_GAE_Model`: learned symmetric MLP edge decoder.
- `MLP_AGAE_Model`: learned MLP decoder over latent sources and feature-derived targets.
- `VGAE_Model`: variational encoder with inner-product decoding and KL regularization.
- `DEALModel`: aligned structural-identity and attribute encoders for inductive prediction.
- `FeatureCosineModel`: non-trainable cosine similarity between endpoint features.
- `FeatureMLPModel`: trainable symmetric MLP over endpoint feature interactions.

`link_prediction.LinkPrediction` provides the shared training and inference interface:

```python
from link_prediction import LinkPrediction

predictor = LinkPrediction(
    train,
    model_type="asymmetric",
    conv_layer_type="SAGEConv",
    hidden_channels=128,
    out_channels=64,
)

for _ in range(100):
    loss = predictor.train()

probabilities = predictor.test(test_inductive)
```

Configure both MLP decoders with `decoder_hidden_channels` and
`decoder_dropout`, and the variational baseline with `kl_weight`:

```python
mlp = LinkPrediction(
    train,
    model_type="mlp",
    decoder_hidden_channels=64,
    decoder_dropout=0.2,
)
asymmetric_mlp = LinkPrediction(
    train,
    model_type="asymmetric-mlp",
    decoder_hidden_channels=64,
    decoder_dropout=0.2,
)
vgae = LinkPrediction(train, model_type="variational", kl_weight=1.0)
```

AGAE supports `none`, `latent-l2`, `mse-alignment`, `cosine-alignment`, and
`graph-smoothness` regularization. Repeat `--agae-regularization` to compare
several methods on the same graph splits and negative samples:

```bash
glpbench experiment cora out/regularization.csv \
  --model-type asymmetric \
  --agae-regularization none \
  --agae-regularization latent-l2 \
  --agae-regularization cosine-alignment \
  --regularization-strength 0.001
```

Use `--weight-decay` to evaluate Adam weight decay separately or in
combination with an explicit AGAE penalty. Regularization settings are stored
in dedicated result columns.

DEAL loss weights use `theta_ss`, `theta_aa`, and `theta_sa`; inference weights use `lambda_ss`, `lambda_aa`, and `lambda_sa`. The CLI exposes these as `--deal-theta-*` and `--deal-lambda-*`, along with similarity scale and margin options.

GATv2-specific options can be passed directly or through `gatv2_kwargs`:

```python
predictor = LinkPrediction(
    train,
    conv_layer_type="GATv2Conv",
    heads=4,
    dropout=0.2,
)
```

### Result logging

`experiment_logging.ExperimentResult` validates experiment metadata and metrics. `CSVResultLogger` appends results to a stable CSV schema; rows written by the same logger share a run ID:

```python
from experiment_logging import CSVResultLogger, ExperimentResult

logger = CSVResultLogger("out/results.csv")
logger.log(ExperimentResult(...))
logger.reset_run_id()  # Start a new experiment group.
```

## License

GLPBench is distributed under the
[Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0).
