Feature Request: `Implement Threshold-Consistent Margin Loss for Open-World Deep Metric Learning in TF-GNN`

[imitation-alpha]

I propose adding the Threshold-Consistent Margin Loss (TCM) function to the TF-GNN library. TCM is a novel loss function specifically designed for open-world deep metric learning, which has shown significant improvements in handling unseen classes and imbalanced data compared to traditional loss functions.

Motivation:

Open-world scenarios: Many real-world applications involve open-world scenarios where new classes can emerge over time. TCM is well-suited for these challenges.
Improved performance: TCM has demonstrated superior performance in terms of accuracy and robustness compared to other loss functions in open-world settings.
Community benefit: Incorporating TCM into TF-GNN will benefit the broader machine learning community by providing a powerful tool for addressing open-world problems.
Implementation details:

Function definition: Implement the TCM loss function as a TensorFlow operation.
Hyperparameters: Allow users to configure TCM hyperparameters (e.g., margin, temperature) to fine-tune the loss.
Integration: Integrate TCM with existing TF-GNN components for seamless usage.
Documentation: Provide clear documentation and examples to guide users in using TCM effectively.
Additional notes:

Consider providing pre-trained models or transfer learning options to accelerate development.
Explore opportunities for optimization and performance improvements.

By incorporating TCM into TF-GNN, we can significantly enhance the library's capabilities for open-world deep metric learning and empower researchers and developers to tackle challenging real-world problems.

Paper

[ricor07]

Hello, I'd be glad to contribute to this issue

[Vamsi995]

Hey @imitation-alpha, can I work on this issue? or is this already implemented?

[imitation-alpha]

Hey @imitation-alpha, can I work on this issue? or is this already implemented?

Hi @Vamsi995 @ricor07 ,
I am not working on this issue. Please go ahead and work on that!
thank you for the contribution

[Vamsi995]

Hi @imitation-alpha, thank you for letting me know. Ill get started on this, will let you know if I have any questions.

[Vamsi995]

Hey @imitation-alpha, could you guide me where to get started for this. How exactly should I implement this as a tensorflow operation? Most of the loss functions in the models are being used from the keras library.

[imitation-alpha]

Hey @imitation-alpha, could you guide me where to get started for this. How exactly should I implement this as a tensorflow operation? Most of the loss functions in the models are being used from the keras library.

I would suggest you look into the contrastive_losses class and trying to implement the TCM Loss under the ContrastiveLoss class link. Some similar example like DeepGraphInfomaxTask

I suggestion use llm (e.g. gemini) to brainstorm the step to do that
NotebookLM suggested plan

Here's a plan for implementing Threshold-Consistent Margin (TCM) Loss within the TF-GNN framework, drawing from the provided sources and keeping in mind your goal of applying it to open-world deep metric learning.

**Understanding the Context**

*   **Deep Metric Learning (DML) and its challenges**: DML aims to learn embeddings where similar items are close and dissimilar ones are far apart. However, standard DML losses can lead to inconsistent representation structures across different classes, requiring varying thresholds for optimal performance. This is particularly problematic in open-world scenarios.
*   **Threshold Consistency**: The core idea of TCM loss is to address this "threshold inconsistency" by promoting uniform representation structures across classes, allowing a single threshold to work well across diverse data distributions.
*   **TCM Loss Mechanism**: TCM loss penalizes hard positive and hard negative pairs near decision boundaries defined by cosine margins. This encourages compact intra-class and separated inter-class embeddings.

**Implementation Plan**

1.  **Custom Task Creation:**
    *   You'll need to create a custom `Task` class that inherits from `tfgnn.runner.Task`. This class will manage the data preprocessing, prediction, loss calculation, and metrics for your DML task.
    *   Since the provided sources already include several contrastive loss tasks (e.g., `BarlowTwinsTask`, `ContrastiveLossTask`, `DeepGraphInfomaxTask`, `TripletLossTask`, and `VicRegTask`), you can consider inheriting from `ContrastiveLossTask` or implementing your own from `Task`. The benefit of inheriting from `ContrastiveLossTask` is that it already has support for preprocessing and contrasting positive and negative `GraphTensor`s.

2.  **Preprocessing**:
    *   The `preprocess` method in your custom `Task` will be responsible for preparing the input `GraphTensor`s and generating the necessary labels.
    *   For DML, this might involve creating pairs or triplets of `GraphTensor`s, where pairs/triplets consist of an anchor, positive, and optionally negative examples. The specific pairing or triplet creation will depend on the training strategy you are using.
    *   If you are using the `ContrastiveLossTask` as a base class, the default implementation expects pairs of positive and negative `GraphTensor`s.
    *   Consider how your data is organized. Is it a single graph, or multiple subgraphs? You may need to perform sampling (see "Data Sampling Considerations") to create these pairs/triplets.
    *   You will likely need to use some kind of readout mechanism to obtain node or graph embeddings.  `tfgnn.keras.layers.StructuredReadout` is recommended for extracting features from specific nodes. You might need to add an auxiliary node set for the readout using `tfgnn.keras.layers.AddReadoutFromFirstNode`.

3.  **Model Prediction**:
    *   The `predict` method will take the output of the base GNN model and apply any necessary readout layers. The output will be an embedding used for calculating loss.
    *   This method may need to apply a readout head for contrastive losses. There are also examples of linear heads for classification.

4.  **TCM Loss Implementation:**
    *   The core of your implementation lies in the `losses` method. You'll need to define a custom loss function that calculates the TCM loss as described in the paper.
    *   **TCM Loss Calculation**:
        *   Calculate cosine similarities between embeddings in your batch.
        *   Identify hard positive and hard negative pairs based on the cosine similarities and the defined margins `m+` and `m-`.
        *   Penalize these hard pairs by adding a weighted penalty to the overall loss.  The weights `λ+` and `λ−` are hyper-parameters. You will also need to incorporate your base loss, as shown in Figure 4 of the paper. You could use the mean squared error as a starting point for your base loss, since there are many examples of this in the sources. The paper recommends an additive method of combining the base loss with TCM regularization loss.
    *   You will need to implement the pseudocode provided in Algorithm 2.
    *   Ensure your custom loss function is compatible with `tf.keras.losses` interface, such as `MeanSquaredError`.  This will allow seamless integration into the TF-GNN training pipeline.

5.  **Metrics**:
    *   The `metrics` method in your `Task` should return relevant metrics for evaluating performance.
    *   For DML, this might include metrics like accuracy, false acceptance rate (FAR), false rejection rate (FRR), or the Operating-Point-Inconsistency-Score (OPIS) proposed in the paper.
    *   Consider whether you want to calculate metrics across all the different classes within each batch, or if you want to calculate the metrics per class.

6.  **Integration with TF-GNN Runner**:
    *   The `tfgnn.runner` provides a high-level API for training GNNs and is recommended as a starting point.
    *   You will need to define a model function (`model_fn`) that produces the base GNN model, and use the runner's `run` function to define the training loop, plugging in your custom `Task`, and the `model_fn`.
    *   You can configure the `Trainer` to use different strategies (e.g., `TPUStrategy` or `ParameterServerStrategy`) and other parameters.

7.  **Data Sampling Considerations**:
    *   The library supports reading streams of `tf.train.Example` proto messages.
    *   You can create these `tf.train.Example` proto messages by:
        *   Creating eager instances of `GraphTensor`.
        *   Calling `tensorflow_gnn.write_example()`.
        *   Serializing the `tf.train.Example` message to a file.
    *   For large graphs, you will need to implement your own code that samples neighborhoods around nodes or edges of interest.
    *   There are tools in TF-GNN that can assist you with sampling, like `SamplingSpecBuilder`, the in-memory sampler, and functions for sampling subgraphs around edges for link prediction.
    *   You'll likely need to define a `tfgnn.GraphSchema` that describes the structure of your input graphs and use a `tfgnn.sampler.SamplingSpec` to define how you want to sample the graphs.
    *   You can use the `tfgnn.sampler.create_link_sampling_model_from_spec` or `tfgnn.sampler.create_sampling_model_from_spec` to build the sampling model based on the `SamplingSpec`.
    *   You may need to use a custom edge sampler using the `interfaces.OutgoingEdgesSampler` interface.

8.  **Padding and Batching**:
    *   TF-GNN provides options for padding the `GraphTensor`s to ensure they have consistent shapes within a batch. This is particularly important when training on TPUs.
    *   `FitOrSkipPadding` and `TightPadding` are available options.  `tfgnn.learn_fit_or_skip_size_constraints` is a utility function that can be used to find appropriate size constraints.  You can also use `tfgnn.find_tight_size_constraints` to find size constraints. You will need to configure your `GraphTensorPadding` using one of these classes.
     * You can also use `tfgnn.dynamic_batch()` which replaces the usual `Dataset.batch()` method and batches as many consecutive graphs as will fit the constraints.
    *   Be sure to use `tfgnn.GraphTensor.merge_batch_to_components` before passing your graphs to the GNN for training.

9.  **Validation and Testing**:
    *   Set up a validation dataset and appropriate validation metrics (as in step 5) to monitor the model performance during training.
    *   Ensure that your validation dataset can be handled by your specified padding mechanism, particularly if using the `TPUStrategy`.
    *   Pay attention to the requirement that if training on TPUs, all tensors within a `GraphTensor` must have statically known shapes.

10. **Model Saving**:
    *   You can save your model as a SavedModel using `tf.keras.Model.export()`, which is the recommended approach for deployment. The guide on model saving may provide more information if needed.

**Key TF-GNN Concepts to Use:**

*   **GraphTensor:** The core data structure for representing graph data.
*   **GraphSchema**: Defines the structure of your graph data, including node sets, edge sets, and features.
*   **Task**:  Represents a learning objective and defines preprocessing, prediction, and loss/metric calculation.
*   **Runner**: High-level API for training GNNs.
*   **StructuredReadout**: Mechanism for extracting features from specific nodes or edges.
*   **Padding:** Essential to ensure graphs in a batch have compatible dimensions.

**Additional Considerations**

*   **Hyperparameter Tuning**: You'll need to tune the hyperparameters of the TCM loss, such as the margins (`m+`, `m-`) and the weights (`λ+`, `λ−`), as well as your base loss, and the learning rate of the model.
*   **Reproducibility**: Set random seeds to ensure the reproducibility of results.
*   **Large-Scale Training**: If you are using very large graphs, pay special attention to memory usage and potentially consider distributed training with TPUs or parameter servers.
*   **Validation**: Implement a robust validation strategy for evaluation.
*   **Graph Updates**: The `GraphUpdate` layer and its pieces are described in source.
*   **Model Templates**: The model templates `mt_albis` and `vanilla_mpnn` offer a starting point for building your GNN model..
*   **Integrated Gradients**:  The TF-GNN runner has support for integrated gradients which may be useful for your analysis.

This detailed plan provides a solid foundation for implementing TCM Loss in TF-GNN. Please ask if you have more specific questions.

[Vamsi995]

Thanks for this @imitation-alpha, I will go through this and get back to you if I have any doubts.