.. _tnad_latent_label:

Tensor Network for Anomaly Detection in Latent Space of Proton-Proton Collision Events at the LHC
===================================================================================================

Implementation of the anomaly detection pipeline from the paper: `10.1088/2632-2153/ae0243 <https://iopscience.iop.org/article/10.1088/2632-2153/ae0243>`__.

Install ``tn4ml`` directly from GitHub:

.. code-block:: bash

   git clone https://github.com/bsc-quantic/tn4ml.git

Install an additional package for data handling:

*   ``h5py``

(optional) Download dataset
---------------------------

The dimensionality of the dataset is reduced by passing it through autoencoder. If you are interested more in the autoencoder's architecture, please refer to `[*] <https://www.nature.com/articles/s42005-024-01811-6>`__.

Reduced dataset can be downloaded from `Zenodo <https://zenodo.org/record/7673769>`__:

`record/7673769 <https://zenodo.org/record/7673769>`__

Description of filenames:

*   ``latentrep_QCD_sig.h5``: train dataset (QCD - background)
*   ``latentrep_QCD_sig_testclustering.h5``: test dataset (QCD - background)
*   ``latentrep_RSGraviton_WW_NA_35.h5``: test dataset (Signal $\mathrm{NA \ G \rightarrow WW}$)

Or it can be downloaded directly in the ``pipeline.py``.

Run training and evaluation pipeline
------------------------------------

Data Parameters

*   ``save_dir`` (str): Path to directory for saving results (``default = "results/"``)
*   ``load_dir`` (str): Path to directory for loading the data
*   ``feature_range`` (list): Feature range for scaling (``default = [0, 1]``)
*   ``seed`` (int): Seed for random number generator
*   ``standardization`` (str): Standardization of data (``"yes"`` or ``"no"``, ``default = "yes"``)
*   ``minmax`` (str): Minmax scaling of data (``"yes"`` or ``"no"``, ``default = "yes"``)
*   ``embedding`` (str): Embedding type for input data (e.g., ``"legendre_4"``, ``"fourier_2"``, ``"hermite_3"``)
*   ``test_size`` (int): Number of samples for testing
*   ``train_size`` (int): Number of samples for training
*   ``signal_name`` (str): Name of signal dataset (``default = "RSGraviton_WW_NA_35"``)
*   ``latent (int)``: Latent space dimension

MPS Parameters

*   ``bond_dim`` (int): Bond dimension of MPS (``default = 5``)
*   ``initializer`` (str): Type of MPS initialization
*   ``shape_method`` (str): Method for distributing bond dimensions (``default = "even"``)

Training Parameters

*   ``lr`` (float): Learning rate (``default = 1e-3``)
*   ``min_delta`` (float): Minimum improvement required for early stopping (``default = 0``)
*   ``patience (int)``: Number of epochs with no improvement before early stopping (``default = 20``)
*   ``epochs (int)``: Maximum number of training epochs (``default = 100``)
*   ``batch_size`` (int): Number of samples per training batch (``default = 32``)
*   ``run`` (int): Number of training repetitions with different seeds

.. code-block:: python

   python pipeline.py -save_dir results/ \
                      -load_dir QML_paper_data \
                      -feature_range 0 1 \
                      -minmax yes \
                      -embedding laguerre_2 \
                      -test_size 5000 \
                      -train_size 10000 \
                      -bond_dim 8 \
                      -initializer unitary \
                      -lr 0.001 \
                      -patience 25 \
                      -epochs 100 \
                      -batch_size 128 \
                      -run 1 \
                      -latent 4

Run evaluation only
-------------------

Data Parameters

*   ``save_dir`` (str): Path to directory for saving results (``default = "results/"``)
*   ``load_dir`` (str): Path to directory for loading the data
*   ``feature_range`` (list): Feature range for scaling (``default = [0, 1]``)
*   ``seed`` (int): Seed for random number generator
*   ``standardization`` (str): Standardization of data (``"yes"`` or ``"no"``, ``default = "yes"``)
*   ``minmax`` (str): Minmax scaling of data (``"yes"`` or ``"no"``, ``default = "yes"``)
*   ``embedding`` (str): Embedding type for input data (e.g., ``"legendre_4"``, ``"fourier_2"``, ``"hermite_3"``)
*   ``test_size`` (int): Number of samples for testing
*   ``signal_name`` (str): Name of signal dataset (``default = "RSGraviton_WW_NA_35"``, ``options: "RSGraviton_WW_BR_15", "AtoHZ_to_ZZZ_35"``)
*   ``latent (int)``: Latent space dimension

MPS Parameters

*   ``bond_dim`` (int): Bond dimension of MPS (``default = 5``)
*   ``initializer`` (str): Type of MPS initialization

Training Parameters

*   ``batch_size`` (int): Number of samples per training batch (``default = 32``)
*   ``run`` (int): Number of training repetitions with different seeds

.. code-block:: python

   python evaluation.py -save_dir results/ \
                      -load_dir QML_paper_data \
                      -feature_range 0 1 \
                      -minmax yes \
                      -embedding laguerre_2 \
                      -test_size 5000 \
                      -bond_dim 8 \
                      -initializer unitary \
                      -batch_size 128 \
                      -run 1 \
                      -latent 4

**Additional Example Scripts**
------------------------------

Below are the Python scripts used in this example. You can view their contents directly on this page, or access them on GitHub.

*   `evaluation.py`: :download:`evaluation.py`
    `View on GitHub <https://github.com/bsc-quantic/tn4ml/blob/main/docs/source/examples/tnad_latent/evaluation.py>`__

    .. literalinclude:: evaluation.py
       :language: python
       :linenos:
       :caption: evaluation.py

*   `pipeline.py`:
    *   View in browser: :download:`pipeline.py`
    *   `View on GitHub <https://github.com/bsc-quantic/tn4ml/blob/main/docs/source/examples/tnad_latent/pipeline.py>`__

    .. literalinclude:: pipeline.py
       :language: python
       :linenos:
       :caption: pipeline.py

*   `plot.py`:
    *   View in browser: :download:`plot.py`
    *   `View on GitHub <https://github.com/bsc-quantic/tn4ml/blob/main/docs/source/examples/tnad_latent/plot.py>`__

    .. literalinclude:: plot.py
       :language: python
       :linenos:
       :caption: plot.py

*   `utils.py`:
    *   View in browser: :download:`utils.py`
    *   `View on GitHub <https://github.com/bsc-quantic/tn4ml/blob/main/docs/source/examples/tnad_latent/utils.py>`__

    .. literalinclude:: utils.py
       :language: python
       :linenos:
       :caption: utils.py