diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml new file mode 100644 index 0000000..541d546 --- /dev/null +++ b/.github/workflows/ci.yml @@ -0,0 +1,99 @@ +name: CI + +on: + workflow_dispatch: + pull_request: + push: + branches: + - main + - develop + release: + types: + - published + +concurrency: + group: ${{ github.workflow }}-${{ github.ref }} + cancel-in-progress: true + +env: + FORCE_COLOR: 3 + +jobs: + pre-commit: + name: Format + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v4 + with: + fetch-depth: 0 + - uses: actions/setup-python@v4 + with: + python-version: "3.x" + - uses: pre-commit/action@v3.0.0 + with: + extra_args: --hook-stage manual --all-files + + # checks: + # name: Check Python ${{ matrix.python-version }} on ${{ matrix.runs-on }} + # runs-on: ${{ matrix.runs-on }} + # needs: [pre-commit] + # strategy: + # fail-fast: false + # matrix: + # python-version: ["3.8"] + # runs-on: [ubuntu-latest, macos-latest] + + # steps: + # - uses: actions/checkout@v4 + # with: + # fetch-depth: 0 + + # - uses: actions/setup-python@v4 + # with: + # python-version: ${{ matrix.python-version }} + + # - name: Install torch + # run: pip install torch==1.9.0 torchvision==0.10.0 torchaudio==0.9.0 + + # - name: Debug installed packages + # run: pip list + + # - name: Debug Python environment + # run: python -V + + # - name: Install signatory + # run: | + # pip install signatory==1.2.6.1.9.0 --no-cache-dir --force-reinstall + + # - name: Install package + # run: python -m pip install .[test] + + # - name: Test package + # run: python -m pytest -ra --cov=nlpsig + + dist: + name: Distribution build + runs-on: ubuntu-latest + needs: [pre-commit] + + steps: + - uses: actions/checkout@v4 + with: + fetch-depth: 0 + + - name: Build sdist and wheel + run: pipx run build + + - uses: actions/upload-artifact@v3 + with: + path: dist + + - name: Check products + run: pipx run twine check dist/* + + - uses: pypa/gh-action-pypi-publish@v1.8.10 + if: github.event_name == 'release' && github.event.action == 'published' + with: + # Remember to generate this and set it in "GitHub Secrets" + user: __token__ + password: ${{ secrets.PYPI_API_TOKEN }} diff --git a/.gitignore b/.gitignore index 616bb5c..e640039 100644 --- a/.gitignore +++ b/.gitignore @@ -1,6 +1,167 @@ -*.DS_Store +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# C extensions +*.so + +# Distribution / packaging +.Python +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +share/python-wheels/ +*.egg-info/ +.installed.cfg +*.egg +MANIFEST + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.nox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +*.py,cover +.hypothesis/ +.pytest_cache/ +cover/ + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py +db.sqlite3 +db.sqlite3-journal + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ + +# PyBuilder +.pybuilder/ +target/ + +# Jupyter Notebook +.ipynb_checkpoints + +# IPython +profile_default/ +ipython_config.py + +# pyenv +# For a library or package, you might want to ignore these files since the code is +# intended to run in multiple environments; otherwise, check them in: +# .python-version + +# pipenv +# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. +# However, in case of collaboration, if having platform-specific dependencies or dependencies +# having no cross-platform support, pipenv may install dependencies that don't work, or not +# install all needed dependencies. +#Pipfile.lock + +# PEP 582; used by e.g. github.com/David-OConnor/pyflow +__pypackages__/ + +# Celery stuff +celerybeat-schedule +celerybeat.pid + +# SageMath parsed files +*.sage.py + +# Environments +.env +.venv +env/ +venv/ +ENV/ +env.bak/ +venv.bak/ + +# Spyder project settings +.spyderproject +.spyproject + +# Rope project settings +.ropeproject + +# mkdocs documentation +/site + +# mypy +.mypy_cache/ +.dmypy.json +dmypy.json + +# Pyre type checker +.pyre/ + +# pytype static type analyzer +.pytype/ + +# Cython debug symbols +cython_debug/ + +# setuptools_scm +src/*/_version.py + + +# ruff +.ruff_cache/ + +# OS specific stuff +.DS_Store +.DS_Store? +._* +.Spotlight-V100 +.Trashes +ehthumbs.db +Thumbs.db + +# Common editor files +*~ +*.swp + +.vscode/ + +# Miscellaneous *.ipynb_checkpoints/ **__pycache__ *.npy *.pkl -paths.py \ No newline at end of file +paths.py diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index cbc9b24..ae4190d 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -44,12 +44,6 @@ repos: - id: blacken-docs additional_dependencies: [black==23.1.0] - - repo: https://github.com/charliermarsh/ruff-pre-commit - rev: "v0.0.260" - hooks: - - id: ruff - args: ["--fix", "--show-fixes"] - - repo: https://github.com/shellcheck-py/shellcheck-py rev: "v0.9.0.2" hooks: diff --git a/.readthedocs.yml b/.readthedocs.yml new file mode 100644 index 0000000..268f4d8 --- /dev/null +++ b/.readthedocs.yml @@ -0,0 +1,23 @@ +# .readthedocs.yml +# Read the Docs configuration file +# See https://docs.readthedocs.io/en/stable/config-file/v2.html for details + +# Required +version: 2 + +# Set the version of Python and other tools you might need +build: + os: ubuntu-22.04 + tools: + python: "3.8" + +# Build documentation in the docs/ directory with Sphinx +sphinx: + configuration: docs/conf.py + +python: + install: + - method: pip + path: . + extra_requirements: + - docs diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md new file mode 100644 index 0000000..52a93a9 --- /dev/null +++ b/CONTRIBUTING.md @@ -0,0 +1,96 @@ +# Quick development + +The fastest way to start with development is to use nox. If you don't have nox, +you can use `pipx run nox` to run it without installing, or `pipx install nox`. +If you don't have pipx (pip for applications), then you can install with with +`pip install pipx` (the only case were installing an application with regular +pip is reasonable). If you use macOS, then pipx and nox are both in brew, use +`brew install pipx nox`. + +To use, run `nox`. This will lint and test using every installed version of +Python on your system, skipping ones that are not installed. You can also run +specific jobs: + +```console +$ nox -s lint # Lint only +$ nox -s tests # Python tests +$ nox -s docs -- serve # Build and serve the docs +$ nox -s build # Make an SDist and wheel +``` + +Nox handles everything for you, including setting up an temporary virtual +environment for each run. + +# Setting up a development environment manually + +You can set up a development environment by running: + +```bash +python3 -m venv .venv +source ./.venv/bin/activate +pip install -v -e .[dev] +``` + +If you have the +[Python Launcher for Unix](https://github.com/brettcannon/python-launcher), you +can instead do: + +```bash +py -m venv .venv +py -m install -v -e .[dev] +``` + +# Post setup + +You should prepare pre-commit, which will help you by checking that commits pass +required checks: + +```bash +pip install pre-commit # or brew install pre-commit on macOS +pre-commit install # Will install a pre-commit hook into the git repo +``` + +You can also/alternatively run `pre-commit run` (changes only) or +`pre-commit run --all-files` to check even without installing the hook. + +# Testing + +Use pytest to run the unit checks: + +```bash +pytest +``` + +# Coverage + +Use pytest-cov to generate coverage reports: + +```bash +pytest --cov=nlpsig +``` + +# Building docs + +You can build the docs using: + +```bash +nox -s docs +``` + +You can see a preview with: + +```bash +nox -s docs -- serve +``` + +# Pre-commit + +This project uses pre-commit for all style checking. While you can run it with +nox, this is such an important tool that it deserves to be installed on its own. +Install pre-commit and run: + +```bash +pre-commit run -a +``` + +to check all files. diff --git a/LICENSE b/LICENSE new file mode 100644 index 0000000..e7995db --- /dev/null +++ b/LICENSE @@ -0,0 +1,28 @@ +BSD 3-Clause License + +Copyright (c) Talia Tseriotou, Ryan Chan. All rights reserved. + +Redistribution and use in source and binary forms, with or without +modification, are permitted provided that the following conditions are met: + +* Redistributions of source code must retain the above copyright notice, this + list of conditions and the following disclaimer. + +* Redistributions in binary form must reproduce the above copyright notice, + this list of conditions and the following disclaimer in the documentation + and/or other materials provided with the distribution. + +* Neither the name of the vector package developers nor the names of its + contributors may be used to endorse or promote products derived from + this software without specific prior written permission. + +THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" +AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE +IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE +DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE +FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL +DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR +SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER +CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, +OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE +OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. diff --git a/README.md b/README.md index 52cc5d1..4341dc1 100644 --- a/README.md +++ b/README.md @@ -1,28 +1,232 @@ -# nlpsig-networks +# SigNetworks: Sequential Path Signature Networks -

-Model architecture -

+SigNetworks (`sig-networks`) is a PyTorch package for training and evaluating +neural networks for longitudinal NLP classification tasks. `sig-networks` is a +library that applies models first developed in +[Sequential Path Signature Networks for Personalised Longitudinal Language Modeling](https://aclanthology.org/2023.findings-acl.310/) +by Tseriotou et al. (2023) which presented a novel extension of neural +sequential models using the notion of path signatures from rough path theory. -Work in progress repo. +## Installation -First create an environment and install -[`nlpsig`](https://github.com/datasig-ac-uk/nlpsig), and then install this -package afterwards: +SigNetworks is available on PyPI and can be installed with pip: +```bash +pip install sig_networks ``` -git clone git@github.com:datasig-ac-uk/nlpsig.git -git clone git@github.com:ttseriotou/nlpsig-networks.git -cd nlpsig -conda env create --name nlpsig-networks python=3.8 -conda activate nlpsig-networks -pip install -e . -cd ../nlpsig-networks -pip install -e . + +Note that currently `sig_networks` only supports Python 3.8 since it relies on +the [`signatory`](https://github.com/patrick-kidger/signatory) library. However, +it is possible to install `signatory` with more recent Python and PyTorch +versions if you install it from source. See the installation guide in the +[signatory documentation](https://signatory.readthedocs.io/en/latest/pages/usage/installation.html) +for more details. + +### Signatory/Torch + +SigNetworks depends on the +[`patrick-kidger/signatory`](https://github.com/patrick-kidger/signatory) +library for differentiable computation of path signatures/log-signatures in +PyTorch. Please see the +[signatory documentation](https://signatory.readthedocs.io/en/latest/) for +installation instructions of the signatory library. + +A common `signatory` installation issue is that the installation requires that +you already have PyTorch installed. In this case, you can try the following: + +```bash +# install PyTorch +pip install torch==1.9.0 +# install signatory +pip install signatory==1.2.6.1.9.0 +# install sig_networks +pip install sig_networks +``` + +If you encounter any issues with the installation of `signatory`, please see the +FAQs in the +[signatory documentation](https://signatory.readthedocs.io/en/latest/pages/miscellaneous/faq.html). + +## Repo structure + +The key parts of the libary are found in [`src/`](src/): + +- [`src/sig_networks/`](src/sig_networks/) contains the source code for the + models and includes PyTorch modules for the various components of the models + (see below for more usage details) +- [`src/scripts/`](src/scripts/) contains some helper scripts for training and + evaluating the models + +## Usage + +The key components in the _signature-window_ models presented in (see +[Sequential Path Signature Networks for Personalised Longitudinal Language Modeling](https://aclanthology.org/2023.findings-acl.310/) +for full details) are written as PyTorch modules which can be used in a modular +fashion. The key components are: + +- The Signature Window Network Units (SWNUs): + [`sig_networks.SWNU`](src/sig_networks/swnu.py) + - There also exists the SWLSTM module which does not include the + 1D-convolution layer at the start of the SWNU: + [`sig_networks.SWLSTM`](src/sig_networks/swnu.py) +- The Signature Window (Multihead-)Attention Units (SWMHAUs): + [`sig_networks.SWMHAU`](src/sig_networks/swmhau.py) + - As with the SWNU, there also exists the SWMHA module which does not include + the 1D-convolution layer at the start of the SWMHAU: + [`sig_networks.SWMHA`](src/sig_networks/swmhau.py) +- The SWNU-Network model: + [`sig_networks.SWNUNetwork`](src/sig_networks/swnu_network.py) +- The SWMHAU-Network model: + [`sig_networks.SWMHAUNetwork`](src/sig_networks/swmhau_network.py) +- The SeqSigNet model: + [`sig_networks.SeqSigNet`](src/sig_networks/seqsignet_bilstm.py) +- The SeqSigNet-Attention-Encoder model: + [`sig_networks.SeqSigNetAttentionEncoder`](src/sig_networks/seqsignet_attention_encoder.py) +- The SeqSigNet-Attention-BiLSTM model: + [`sig_networks.SeqSigNetAttentionBiLSTM`](src/sig_networks/seqsignet_attention_bilstm.py) + +### `nlpsig`: Preparing the data + +The functionality to prepare the data for the SW-models and constructing paths +and inputs are found in the [`nlpsig`](https://github.com/datasig-ac-uk/nlpsig) +library which can be easily installed using `pip` and comes as a dependency in +`sig-networks`. + +Paths can be constructed using the +[`nlpsig.PrepareData`](https://nlpsig.readthedocs.io/en/latest/data_preparation.html) +class. Furthermore, there is functionality within the +[`nlpsig.TextEncoder`](https://nlpsig.readthedocs.io/en/latest/encode_text.html#nlpsig.encode_text.TextEncoder) +and +[`nlpsig.SentenceEncoder`](https://nlpsig.readthedocs.io/en/latest/encode_text.html#nlpsig.encode_text.SentenceEncoder) +classes to obtain embeddings using transformers to be used as the channels in +the paths. Since we want to take path signatures within the SW-models, we need +to ensure that the number of channels in the path are low enough that we can +take the path signatures efficiently. To enable this, there are also a number of +dimensionality reduction methods in the `nlpsig` library - see +[`nlpsig.DimReduce`](https://nlpsig.readthedocs.io/en/latest/dimensionality_reduction.html). + +For full details, see the +[`nlpsig` GitHub repo](https://github.com/datasig-ac-uk/nlpsig) and there are +examples of using the library in the [`examples/`](examples/) directory. + +Note that for obtaining inputs to the SWNU-/SWMHA-Networks and the SeqSigNet +family models, there are helper functions in the scripts (see e.g. +`obtain_SWNUNetwork_input` in +[`src/scripts/swnu_network_functions.py`](src/scripts/swnu_network_functions.py) +and `obtain_SeqSigNet_input` in +[`src/scripts/seqsignet_functions.py`](src/scripts/seqsignet_functions.py)). +There is also examples run-throughs in the [`examples/`](examples/) directory: + +- [Training a SWNU-Network model for Anno-MI client-talk-type prediction](examples/AnnoMI/anno_mi-client-swnu-example.ipynb) +- [Training a SeqSigNet model for Anno-MI client-talk-type prediction](examples/AnnoMI/anno_mi-client-seqsignet-example.ipynb) + +### Using the SWNU and SWMHAU modules + +The Signature Window units (SWNU and SWMHAU) accept a batch of streams and +returns a batch of feature representations. For example: + +```python +from sig_networks.swnu import SWNU +import torch + +# initialise a SWNU object +swnu = SWNU( + input_channels=10, + hidden_dim=5, + log_signature=False, + sig_depth=3, + pooling="signature", + BiLSTM=True, +) + +# create a three-dimensional tensor of batched streams +# shape [batch, length, channels] where channels = 10 +streams = torch.randn(2, 20, 10) + +# pass the streams through the SWNU +features = swnu(streams) + +# features is a two-dimensional tensor of shape [batch, signature_channels] +features.shape ``` -If you want to install a development version, use `pip install -e .` instead in -the above. +The SWMHAU is similar to the SWNU, but rather than having an LSTM to process the +signature streams, we have a multihead-attention layer. For example: + +```python +from sig_networks.swmhau import SWMHAU +import torch + +# initialise a SWMHAU object +swmhau = SWMHAU( + input_channels=10, + output_channels=5, + log_signature=False, + sig_depth=3, + num_heads=5, + num_layers=1, + dropout_rate=0.1, + pooling="signature", +) + +# create a three-dimensional tensor of batched streams +# shape [batch, length, channels] where channels = 10 +streams = torch.randn(2, 20, 10) + +# pass the streams through the SWMHAU +features = swmhau(streams) + +# features is a two-dimensional tensor of shape [batch, signature_channels] +features.shape +``` + +Note in the above, we used the `pooling="signature"` option. This means that at +the end of the SWNU/SWMHAU, we will take a final signature of the streams to get +a fixed-length feature representation for each item in the batch. There are +other options such as taking the final LSTM hidden state for SWNU (set +`pooling="lstm"`), or using a CLS pooling for SWMHAU (set `pooling="cls"`). +There is another option where `pooling=None` which means that the SWNU/SWMHAU +where no pooling is applied at the end of the SWNU/SWMHAU and the output is a +three-dimensional tensor of shape `[batch, length, hidden_dim]`. + +### Using the network models + +The library also has the SWNU-Network and SeqSigNet models as introduced in +[Sequential Path Signature Networks for Personalised Longitudinal Language Modeling](https://aclanthology.org/2023.findings-acl.310/). + +Since then, there have been developments of other models which utilise the SWNUs +and SWMHAUs discussed above. Each of these models are avaliable as PyTorch +modules which can be initialised and trained in the usual way. + +For SWNU-Network and SWMHAU-Network models, they expect two inputs: + +1. `path`: a batch of streams of shape `[batch, length, channels]` - these get + processed by the SWNU/SWMHAU +2. `features`: a batch of features of shape `[batch, features]` - these get + concatenated with the output of the SWNU/SWMHAU to be fed into a FFN layer + +For SeqSigNet models (e.g. SeqSigNet, SeqSigNet-Attention-Encoder, +SeqSigNet-Attention-BiLSTM), they also expect two inputs but the path is +slightly different: + +1. `path`: a batch of streams of shape `[batch, units, length, channels]` - each + of the units for each batch will get processed by the SWNU/SWMHAU. + Afterwards, there is a global network to process the outputs of the + SWNU/SWMHAU in order to pool the outputs into a single fixed-length feature + represenation for the history. The global network can either be a BiLSTM (in + the case of SeqSigNet and SeqSigNet-Attention-BiLSTM) or a Transformer + Encoder (in the case of SeqSigNet-Attention-Encoder). +2. `features`: a batch of features of shape `[batch, features]` - these get + concatenated with the output of the global network (either BiLSTM or a + Transformer Encoder) that processes the outputs of SWNU and SWMHAU to be fed + into a FFN layer + +### Example experiments + +In the [`examples/`](examples/) directory, there are some example experiments +using the library to comapre the SW-models with other baseline models such as a +simple FFN, a BiLSTM model on the sentence-transformer representations and using +a pre-trained Transformer model for classification. ## Pre-commit and linters diff --git a/docs/authors.rst b/docs/authors.rst new file mode 100644 index 0000000..1a678b1 --- /dev/null +++ b/docs/authors.rst @@ -0,0 +1,8 @@ +Authors / collaborators +======================= + +``sig-networks`` is a library that applies models first developed in `Sequential Path Signature Networks for Personalised Longitudinal Language Modeling `_ by Tseriotou et al. (2023) which presented a novel extension of neural sequential models using the notion of path signatures from rough path theory. The library is developed in collaboration with the `Research Engineering Team `_ at `The Alan Turing Institute `_, and was originally written by `Talia Tseriotou `_ and `Ryan Chan `_. + +We also thank `Kasra Hosseini `_ for his early contributions to the library, and to `Adam Tsakalidis `_, `Maria Likata `_, and `Terry Lyons `_ for their valuable guidance and advice. + +For an up-to-date list of collaborators to the library, please see the `GitHub repo `_ for this package. diff --git a/docs/baseline_models.rst b/docs/baseline_models.rst new file mode 100644 index 0000000..2050d58 --- /dev/null +++ b/docs/baseline_models.rst @@ -0,0 +1,12 @@ +Baseline Models +=============== + +.. automodule:: sig_networks.ffn_baseline + :members: + :undoc-members: + :show-inheritance: + +.. automodule:: sig_networks.lstm_baseline + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/conf.py b/docs/conf.py new file mode 100644 index 0000000..1972f53 --- /dev/null +++ b/docs/conf.py @@ -0,0 +1,61 @@ +# Configuration file for the Sphinx documentation builder. +# +# This file only contains a selection of the most common options. For a full +# list see the documentation: +# https://www.sphinx-doc.org/en/master/usage/configuration.html + +from __future__ import annotations + +# Warning: do not change the path here. To use autodoc, you need to install the +# package first. + +# -- Project information ----------------------------------------------------- + +project = "sig-networks" +copyright = "2023, Talia Tseriotou" +author = "Talia Tseriotou, Ryan Chan" + + +# -- General configuration --------------------------------------------------- + +# Add any Sphinx extension module names here, as strings. They can be +# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom +# ones. +extensions = [ + "myst_parser", + "sphinx.ext.autodoc", + "sphinx.ext.mathjax", + "sphinx.ext.napoleon", + "sphinx_copybutton", +] + +# Add any paths that contain templates here, relative to this directory. +templates_path = [] + +# Include both markdown and rst files +source_suffix = [".rst", ".md"] + +# List of patterns, relative to source directory, that match files and +# directories to ignore when looking for source files. +# This pattern also affects html_static_path and html_extra_path. +exclude_patterns = ["_build", "**.ipynb_checkpoints", "Thumbs.db", ".DS_Store", ".env"] + + +# -- Options for HTML output ------------------------------------------------- + +# The theme to use for HTML and HTML Help pages. See the documentation for +# a list of builtin themes. +# +html_theme = "furo" + +# Add any paths that contain custom static files (such as style sheets) here, +# relative to this directory. They are copied after the builtin static files, +# so a file named "default.css" will overwrite the builtin "default.css". +html_static_path: list[str] = [] + + +# -- Extension configuration ------------------------------------------------- +myst_enable_extensions = [ + "colon_fence", + "deflist", +] diff --git a/docs/index.rst b/docs/index.rst new file mode 100644 index 0000000..b990ede --- /dev/null +++ b/docs/index.rst @@ -0,0 +1,36 @@ + +The ``sig-networks`` Python package +====================================== + +``sig-networks`` +------------------- + +The ``sig-networks`` library provides functionality for training and evaluating neural networks for longitudinal NLP classification tasks. + +.. toctree:: + :maxdepth: 2 + :titlesonly: + :caption: API + :glob: + + baseline_models.rst + swnu.rst + sw_attention.rst + seqsignet_models.rst + +.. toctree:: + :maxdepth: 2 + :titlesonly: + :caption: About + :glob: + + authors.rst + + + +Indices and tables +================== + +* :ref:`genindex` +* :ref:`modindex` +* :ref:`search` diff --git a/docs/seqsignet_models.rst b/docs/seqsignet_models.rst new file mode 100644 index 0000000..4751359 --- /dev/null +++ b/docs/seqsignet_models.rst @@ -0,0 +1,17 @@ +SeqSigNet Models +================ + +.. automodule:: sig_networks.seqsignet_bilstm + :members: + :undoc-members: + :show-inheritance: + +.. automodule:: sig_networks.seqsignet_attention_encoder + :members: + :undoc-members: + :show-inheritance: + +.. automodule:: sig_networks.seqsignet_attention_bilstm + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/sw_attention.rst b/docs/sw_attention.rst new file mode 100644 index 0000000..69d44e6 --- /dev/null +++ b/docs/sw_attention.rst @@ -0,0 +1,12 @@ +Signature Window Attention Units +================================ + +.. automodule:: sig_networks.swmhau + :members: + :undoc-members: + :show-inheritance: + +.. automodule:: sig_networks.swmhau_network + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/swnu.rst b/docs/swnu.rst new file mode 100644 index 0000000..26823c6 --- /dev/null +++ b/docs/swnu.rst @@ -0,0 +1,12 @@ +Signature Window Network Units +============================== + +.. automodule:: sig_networks.swnu + :members: + :undoc-members: + :show-inheritance: + +.. automodule:: sig_networks.swnu_network + :members: + :undoc-members: + :show-inheritance: diff --git a/notebooks/AnnoMI/anno_mi-client-baseline-bert.py b/notebooks/AnnoMI/anno_mi-client-baseline-bert.py index 83da6b2..092d301 100644 --- a/notebooks/AnnoMI/anno_mi-client-baseline-bert.py +++ b/notebooks/AnnoMI/anno_mi-client-baseline-bert.py @@ -1,20 +1,22 @@ -import numpy as np -import pickle +from __future__ import annotations + import os +import pickle + +import numpy as np import torch import transformers - -from nlpsig_networks.scripts.fine_tune_bert_classification import ( - fine_tune_transformer_average_seed, -) - from load_anno_mi import ( anno_mi, - output_dim_client, client_index, client_transcript_id, - label_to_id_client, id_to_label_client, + label_to_id_client, + output_dim_client, +) + +from sig_networks.scripts.fine_tune_bert_classification import ( + fine_tune_transformer_average_seed, ) # set to only report critical errors to avoid excessing logging @@ -65,31 +67,26 @@ gamma = 2 for loss in ["focal", "cross_entropy"]: if loss == "focal": - results_output=f"{output_dir}/bert_classifier_focal.csv", + results_output = (f"{output_dir}/bert_classifier_focal.csv",) else: - results_output=f"{output_dir}/bert_classifier_ce.csv" - + results_output = f"{output_dir}/bert_classifier_ce.csv" + bert_classifier, best_bert_classifier, _, __ = fine_tune_transformer_average_seed( loss=loss, gamma=gamma, results_output=results_output, **kwargs, ) - + print(f"F1: {best_bert_classifier['f1'].mean()}") - print( - f"Precision: {best_bert_classifier['precision'].mean()}" - ) + print(f"Precision: {best_bert_classifier['precision'].mean()}") print(f"Recall: {best_bert_classifier['recall'].mean()}") + print(f"F1 scores: {np.stack(best_bert_classifier['f1_scores']).mean(axis=0)}") print( - "F1 scores: " - f"{np.stack(best_bert_classifier['f1_scores']).mean(axis=0)}" - ) - print( - "Precision scores: " - f"{np.stack(best_bert_classifier['precision_scores']).mean(axis=0)}" + "Precision scores: " + f"{np.stack(best_bert_classifier['precision_scores']).mean(axis=0)}" ) print( - "Recall scores: " - f"{np.stack(best_bert_classifier['recall_scores']).mean(axis=0)}" + "Recall scores: " + f"{np.stack(best_bert_classifier['recall_scores']).mean(axis=0)}" ) diff --git a/notebooks/AnnoMI/anno_mi-client-baseline-bilstm.py b/notebooks/AnnoMI/anno_mi-client-baseline-bilstm.py index 5364652..16c193f 100644 --- a/notebooks/AnnoMI/anno_mi-client-baseline-bilstm.py +++ b/notebooks/AnnoMI/anno_mi-client-baseline-bilstm.py @@ -1,16 +1,20 @@ -import numpy as np -import pickle +from __future__ import annotations + import os +import pickle + +import numpy as np import torch -from nlpsig_networks.scripts.lstm_baseline_functions import ( - lstm_hyperparameter_search, -) from load_anno_mi import ( anno_mi, - y_data_client, - output_dim_client, client_index, client_transcript_id, + output_dim_client, + y_data_client, +) + +from sig_networks.scripts.lstm_baseline_functions import ( + lstm_hyperparameter_search, ) seed = 2023 diff --git a/notebooks/AnnoMI/anno_mi-client-baseline-ffn-history.py b/notebooks/AnnoMI/anno_mi-client-baseline-ffn-history.py index d737ca3..e0632bf 100644 --- a/notebooks/AnnoMI/anno_mi-client-baseline-ffn-history.py +++ b/notebooks/AnnoMI/anno_mi-client-baseline-ffn-history.py @@ -1,16 +1,20 @@ -import numpy as np -import pickle +from __future__ import annotations + import os +import pickle + +import numpy as np import torch -from nlpsig_networks.scripts.ffn_baseline_functions import ( - histories_baseline_hyperparameter_search, -) from load_anno_mi import ( anno_mi, - y_data_client, - output_dim_client, client_index, client_transcript_id, + output_dim_client, + y_data_client, +) + +from sig_networks.scripts.ffn_baseline_functions import ( + histories_baseline_hyperparameter_search, ) seed = 2023 @@ -75,14 +79,9 @@ ) print(f"F1: {best_ffn_mean_history_kfold['f1'].mean()}") -print( - f"Precision: {best_ffn_mean_history_kfold['precision'].mean()}" -) +print(f"Precision: {best_ffn_mean_history_kfold['precision'].mean()}") print(f"Recall: {best_ffn_mean_history_kfold['recall'].mean()}") -print( - "F1 scores: " - f"{np.stack(best_ffn_mean_history_kfold['f1_scores']).mean(axis=0)}" -) +print(f"F1 scores: {np.stack(best_ffn_mean_history_kfold['f1_scores']).mean(axis=0)}") print( "Precision scores: " f"{np.stack(best_ffn_mean_history_kfold['precision_scores']).mean(axis=0)}" diff --git a/notebooks/AnnoMI/anno_mi-client-baseline-ffn.py b/notebooks/AnnoMI/anno_mi-client-baseline-ffn.py index 4ee8cb6..62b56c9 100644 --- a/notebooks/AnnoMI/anno_mi-client-baseline-ffn.py +++ b/notebooks/AnnoMI/anno_mi-client-baseline-ffn.py @@ -1,15 +1,19 @@ -import numpy as np -import pickle +from __future__ import annotations + import os +import pickle + +import numpy as np import torch -from nlpsig_networks.scripts.ffn_baseline_functions import ( - ffn_hyperparameter_search, -) from load_anno_mi import ( - y_data_client, - output_dim_client, client_index, client_transcript_id, + output_dim_client, + y_data_client, +) + +from sig_networks.scripts.ffn_baseline_functions import ( + ffn_hyperparameter_search, ) seed = 2023 @@ -64,14 +68,9 @@ ) print(f"F1: {best_ffn_current_kfold['f1'].mean()}") -print( - f"Precision: {best_ffn_current_kfold['precision'].mean()}" -) +print(f"Precision: {best_ffn_current_kfold['precision'].mean()}") print(f"Recall: {best_ffn_current_kfold['recall'].mean()}") -print( - "F1 scores: " - f"{np.stack(best_ffn_current_kfold['f1_scores']).mean(axis=0)}" -) +print("F1 scores: " f"{np.stack(best_ffn_current_kfold['f1_scores']).mean(axis=0)}") print( "Precision scores: " f"{np.stack(best_ffn_current_kfold['precision_scores']).mean(axis=0)}" diff --git a/notebooks/AnnoMI/anno_mi-client-seqsignet-attention-bilstm-script.py b/notebooks/AnnoMI/anno_mi-client-seqsignet-attention-bilstm-script.py index 4f5ab93..1e55d2d 100644 --- a/notebooks/AnnoMI/anno_mi-client-seqsignet-attention-bilstm-script.py +++ b/notebooks/AnnoMI/anno_mi-client-seqsignet-attention-bilstm-script.py @@ -1,16 +1,20 @@ -import numpy as np -import pickle +from __future__ import annotations + import os +import pickle + +import numpy as np import torch -from nlpsig_networks.scripts.seqsignet_attention_bilstm_functions import ( - seqsignet_attention_bilstm_hyperparameter_search, -) from load_anno_mi import ( anno_mi, - y_data_client, - output_dim_client, client_index, client_transcript_id, + output_dim_client, + y_data_client, +) + +from sig_networks.scripts.seqsignet_attention_bilstm_functions import ( + seqsignet_attention_bilstm_hyperparameter_search, ) seed = 2023 @@ -104,9 +108,7 @@ ) print(f"F1: {best_seqsignet_network_umap_kfold['f1'].mean()}") - print( - f"Precision: {best_seqsignet_network_umap_kfold['precision'].mean()}" - ) + print(f"Precision: {best_seqsignet_network_umap_kfold['precision'].mean()}") print(f"Recall: {best_seqsignet_network_umap_kfold['recall'].mean()}") print( "F1 scores: " diff --git a/notebooks/AnnoMI/anno_mi-client-seqsignet-attention-encoder-script.py b/notebooks/AnnoMI/anno_mi-client-seqsignet-attention-encoder-script.py index 30c1a05..0a03267 100644 --- a/notebooks/AnnoMI/anno_mi-client-seqsignet-attention-encoder-script.py +++ b/notebooks/AnnoMI/anno_mi-client-seqsignet-attention-encoder-script.py @@ -1,16 +1,20 @@ -import numpy as np -import pickle +from __future__ import annotations + import os +import pickle + +import numpy as np import torch -from nlpsig_networks.scripts.seqsignet_attention_encoder_functions import ( - seqsignet_attention_encoder_hyperparameter_search, -) from load_anno_mi import ( anno_mi, - y_data_client, - output_dim_client, client_index, client_transcript_id, + output_dim_client, + y_data_client, +) + +from sig_networks.scripts.seqsignet_attention_encoder_functions import ( + seqsignet_attention_encoder_hyperparameter_search, ) seed = 2023 diff --git a/notebooks/AnnoMI/anno_mi-client-seqsignet-script.py b/notebooks/AnnoMI/anno_mi-client-seqsignet-script.py index 38f2e53..4288f16 100644 --- a/notebooks/AnnoMI/anno_mi-client-seqsignet-script.py +++ b/notebooks/AnnoMI/anno_mi-client-seqsignet-script.py @@ -1,16 +1,20 @@ -import numpy as np -import pickle +from __future__ import annotations + import os +import pickle + +import numpy as np import torch -from nlpsig_networks.scripts.seqsignet_functions import seqsignet_hyperparameter_search from load_anno_mi import ( anno_mi, - y_data_client, - output_dim_client, client_index, client_transcript_id, + output_dim_client, + y_data_client, ) +from sig_networks.scripts.seqsignet_functions import seqsignet_hyperparameter_search + seed = 2023 # set device diff --git a/notebooks/AnnoMI/anno_mi-client-swmhau-script.py b/notebooks/AnnoMI/anno_mi-client-swmhau-script.py index 6b88c05..96e6921 100644 --- a/notebooks/AnnoMI/anno_mi-client-swmhau-script.py +++ b/notebooks/AnnoMI/anno_mi-client-swmhau-script.py @@ -1,16 +1,20 @@ -import numpy as np -import pickle +from __future__ import annotations + import os +import pickle + +import numpy as np import torch -from nlpsig_networks.scripts.swmhau_network_functions import ( - swmhau_network_hyperparameter_search, -) from load_anno_mi import ( anno_mi, - y_data_client, - output_dim_client, client_index, client_transcript_id, + output_dim_client, + y_data_client, +) + +from sig_networks.scripts.swmhau_network_functions import ( + swmhau_network_hyperparameter_search, ) seed = 2023 diff --git a/notebooks/AnnoMI/anno_mi-client-swnu-script.py b/notebooks/AnnoMI/anno_mi-client-swnu-script.py index 1cb4425..e65d8d3 100644 --- a/notebooks/AnnoMI/anno_mi-client-swnu-script.py +++ b/notebooks/AnnoMI/anno_mi-client-swnu-script.py @@ -1,16 +1,20 @@ -import numpy as np -import pickle +from __future__ import annotations + import os +import pickle + +import numpy as np import torch -from nlpsig_networks.scripts.swnu_network_functions import ( - swnu_network_hyperparameter_search, -) from load_anno_mi import ( anno_mi, - y_data_client, - output_dim_client, client_index, client_transcript_id, + output_dim_client, + y_data_client, +) + +from sig_networks.scripts.swnu_network_functions import ( + swnu_network_hyperparameter_search, ) seed = 2023 diff --git a/notebooks/AnnoMI/client_talk_type_output/ffn_current_focal_2_kfold_best_model.csv b/notebooks/AnnoMI/client_talk_type_output/ffn_current_focal_2_kfold_best_model.csv index 16cef9a..e5935e5 100644 --- a/notebooks/AnnoMI/client_talk_type_output/ffn_current_focal_2_kfold_best_model.csv +++ b/notebooks/AnnoMI/client_talk_type_output/ffn_current_focal_2_kfold_best_model.csv @@ -1,4 +1,4 @@ ,loss,accuracy,f1,f1_scores,precision,precision_scores,recall,recall_scores,valid_loss,valid_accuracy,valid_f1,valid_f1_scores,valid_precision,valid_precision_scores,valid_recall,valid_recall_scores,hidden_dim,dropout_rate,learning_rate,seed,loss_function,gamma,k_fold,n_splits,batch_size 0,,0.6402298808097839,0.518987928780223,[0.76754191 0.44606164 0.34336023],0.5131149305011911,[0.78799878 0.46352313 0.28782288],0.534480944335035,[0.7481203 0.42986799 0.42545455],,0.6575144529342651,0.5473814347074913,[0.78115157 0.46930134 0.39169139],0.5454692032240676,[0.78125 0.50996933 0.34518828],0.5561228606669005,[0.78105316 0.43464052 0.4526749 ],"(512, 512)",0.1,0.001,1,focal,2,True,5,64 0,,0.6229885220527649,0.5063565905105594,[0.75261428 0.41402619 0.3524293 ],0.49859894367141694,[0.77843016 0.42424242 0.29312425],0.5248547885442102,[0.72845575 0.40429043 0.44181818],,0.6631342172622681,0.5574780764400712,[0.78449375 0.4919713 0.39596918],0.5527498325638676,[0.79421637 0.51539012 0.34864301],0.5679187998930711,[0.7750063 0.47058824 0.45816187],"(512, 512)",0.1,0.001,12,focal,2,True,5,64 -0,,0.6406130194664001,0.5096616660227287,[0.77081222 0.41295167 0.34522111],0.5115455869756562,[0.77181792 0.47878128 0.28403756],0.5242818139535179,[0.76980914 0.3630363 0.44 ],,0.6640976071357727,0.5468888875022938,[0.79088771 0.45092838 0.39885057],0.5524170732756318,[0.7775073 0.53651939 0.34322453],0.5565400371396844,[0.80473671 0.38888889 0.47599451],"(512, 512)",0.1,0.001,123,focal,2,True,5,64 \ No newline at end of file +0,,0.6406130194664001,0.5096616660227287,[0.77081222 0.41295167 0.34522111],0.5115455869756562,[0.77181792 0.47878128 0.28403756],0.5242818139535179,[0.76980914 0.3630363 0.44 ],,0.6640976071357727,0.5468888875022938,[0.79088771 0.45092838 0.39885057],0.5524170732756318,[0.7775073 0.53651939 0.34322453],0.5565400371396844,[0.80473671 0.38888889 0.47599451],"(512, 512)",0.1,0.001,123,focal,2,True,5,64 diff --git a/notebooks/AnnoMI/client_talk_type_output/lstm_history_11_focal_2_kfold_best_model.csv b/notebooks/AnnoMI/client_talk_type_output/lstm_history_11_focal_2_kfold_best_model.csv index 7d1e85c..bec8616 100644 --- a/notebooks/AnnoMI/client_talk_type_output/lstm_history_11_focal_2_kfold_best_model.csv +++ b/notebooks/AnnoMI/client_talk_type_output/lstm_history_11_focal_2_kfold_best_model.csv @@ -1,4 +1,4 @@ ,loss,accuracy,f1,f1_scores,precision,precision_scores,recall,recall_scores,valid_loss,valid_accuracy,valid_f1,valid_f1_scores,valid_precision,valid_precision_scores,valid_recall,valid_recall_scores,num_layers,bidirectional,hidden_dim,dropout_rate,learning_rate,seed,loss_function,gamma,k_fold,n_splits,batch_size 0,,0.6379310488700867,0.5256797956726735,[0.75807416 0.45041486 0.36855037],0.5174319011572314,[0.78482972 0.43214556 0.33532042],0.5374902151869323,[0.73308271 0.47029703 0.40909091],,0.6901091933250427,0.593650607458289,[0.79871134 0.54642745 0.43581303],0.5878228582820997,[0.81746241 0.52701882 0.41898734],0.6007227033474569,[0.78080121 0.56732026 0.45404664],1,True,300,0.1,0.0005,1,focal,2,True,5,64 0,,0.6245210766792297,0.5160405336922796,[0.74690799 0.43505976 0.36615385],0.5061868528186951,[0.78058008 0.42064715 0.31733333],0.5330810478387341,[0.71602082 0.45049505 0.43272727],,0.6796724200248718,0.5892014363908511,[0.78868814 0.5440806 0.43483557],0.5789532080062053,[0.82093213 0.52490887 0.39101862],0.6044330489397405,[0.75888133 0.56470588 0.48971193],1,True,300,0.1,0.0005,12,focal,2,True,5,64 -0,,0.6093869805335999,0.512373581596779,[0.73194423 0.451341 0.35383552],0.5017814694134902,[0.79863248 0.42131617 0.28539576],0.5423210447129154,[0.67553499 0.4859736 0.46545455],,0.6687540411949158,0.5868382641499507,[0.77656859 0.5515737 0.43237251],0.5739280772923682,[0.83189407 0.52709946 0.3627907 ],0.6138513018376075,[0.72814311 0.57843137 0.53497942],1,True,300,0.1,0.0005,123,focal,2,True,5,64 \ No newline at end of file +0,,0.6093869805335999,0.512373581596779,[0.73194423 0.451341 0.35383552],0.5017814694134902,[0.79863248 0.42131617 0.28539576],0.5423210447129154,[0.67553499 0.4859736 0.46545455],,0.6687540411949158,0.5868382641499507,[0.77656859 0.5515737 0.43237251],0.5739280772923682,[0.83189407 0.52709946 0.3627907 ],0.6138513018376075,[0.72814311 0.57843137 0.53497942],1,True,300,0.1,0.0005,123,focal,2,True,5,64 diff --git a/notebooks/Reddit_MoC/load_redditmoc.py b/notebooks/Reddit_MoC/load_redditmoc.py index 242ac4d..14c27ed 100644 --- a/notebooks/Reddit_MoC/load_redditmoc.py +++ b/notebooks/Reddit_MoC/load_redditmoc.py @@ -1,8 +1,8 @@ -import pandas as pd +import pandas as pd import os import datetime import torch -import paths +import paths ################################################## ########## Load in data ########################## @@ -11,27 +11,29 @@ df = pd.read_pickle(reddit_fname) dict3 = {} -dict3['0'] = '0' -dict3['E'] = '1' -dict3['IE'] = '1' -dict3['S'] = '2' -dict3['IS'] = '2' +dict3["0"] = "0" +dict3["E"] = "1" +dict3["IE"] = "1" +dict3["S"] = "2" +dict3["IS"] = "2" -#GET THE FLAT LABELS +# GET THE FLAT LABELS df = df.replace({"label_3": dict3}) -df['label'] = df['label_3'] +df["label"] = df["label_3"] -#GET TRAIN/DEV/TEST SET -df['set'] = None -df.loc[(df.train_or_test =='test'),'set'] = 'test' -df.loc[(df.train_or_test !='test') & (df.fold !=0),'set'] = 'train' -df.loc[(df.fold ==0),'set'] = 'dev' +# GET TRAIN/DEV/TEST SET +df["set"] = None +df.loc[(df.train_or_test == "test"), "set"] = "test" +df.loc[(df.train_or_test != "test") & (df.fold != 0), "set"] = "train" +df.loc[(df.fold == 0), "set"] = "dev" -#KEEP SPECIFIC COLUMNS AND RESET INDEX -df = df[['user_id', 'timeline_id', 'postid', 'content','label', 'datetime', 'set']].reset_index(drop=True) +# KEEP SPECIFIC COLUMNS AND RESET INDEX +df = df[ + ["user_id", "timeline_id", "postid", "content", "label", "datetime", "set"] +].reset_index(drop=True) ################################################## ########## Dimensions and Y labels ############### ################################################## -output_dim = len(df['label'].unique()) -y_data = torch.tensor(df['label'].astype(float).values, dtype=torch.int64) \ No newline at end of file +output_dim = len(df["label"].unique()) +y_data = torch.tensor(df["label"].astype(float).values, dtype=torch.int64) diff --git a/notebooks/Reddit_MoC/load_sbert-embeddings.py b/notebooks/Reddit_MoC/load_sbert-embeddings.py index 68b4475..151b91f 100644 --- a/notebooks/Reddit_MoC/load_sbert-embeddings.py +++ b/notebooks/Reddit_MoC/load_sbert-embeddings.py @@ -3,9 +3,9 @@ import os import paths -#read embeddings -emb_sbert_filename= paths.embeddings_fname -with open(emb_sbert_filename, 'rb') as f: +# read embeddings +emb_sbert_filename = paths.embeddings_fname +with open(emb_sbert_filename, "rb") as f: sbert_embeddings = pickle.load(f) -sbert_embeddings = torch.tensor(sbert_embeddings) \ No newline at end of file +sbert_embeddings = torch.tensor(sbert_embeddings) diff --git a/notebooks/Reddit_MoC/redditmoc-baseline-bert.ipynb b/notebooks/Reddit_MoC/redditmoc-baseline-bert.ipynb index 9bcbbca..4e2ca85 100644 --- a/notebooks/Reddit_MoC/redditmoc-baseline-bert.ipynb +++ b/notebooks/Reddit_MoC/redditmoc-baseline-bert.ipynb @@ -131,13 +131,15 @@ "outputs": [], "source": [ "num_epochs = 10\n", - "seeds = [12] #[1, 12, 123]\n", + "seeds = [12] # [1, 12, 123]\n", "loss = \"focal\"\n", "gamma = 2\n", "validation_metric = \"f1\"\n", - "split_indices = (df[df['set']=='train'].index, \n", - " df[df['set']=='dev'].index, \n", - " df[df['set']=='test'].index)" + "split_indices = (\n", + " df[df[\"set\"] == \"train\"].index,\n", + " df[df[\"set\"] == \"dev\"].index,\n", + " df[df[\"set\"] == \"test\"].index,\n", + ")" ] }, { @@ -177,10 +179,10 @@ " split_indices=split_indices,\n", " k_fold=False,\n", " validation_metric=validation_metric,\n", - " results_output=None, #f\"{output_dir}/bert_classifier.csv\",\n", + " results_output=None, # f\"{output_dir}/bert_classifier.csv\",\n", " device=device,\n", - " verbose=False\n", - ")\n" + " verbose=False,\n", + ")" ] }, { diff --git a/notebooks/Reddit_MoC/redditmoc-baseline-bilstm.ipynb b/notebooks/Reddit_MoC/redditmoc-baseline-bilstm.ipynb index f20c8b5..69f51ae 100644 --- a/notebooks/Reddit_MoC/redditmoc-baseline-bilstm.ipynb +++ b/notebooks/Reddit_MoC/redditmoc-baseline-bilstm.ipynb @@ -44,8 +44,7 @@ "metadata": {}, "outputs": [], "source": [ - "from nlpsig_networks.scripts.lstm_baseline_functions import (\n", - " lstm_hyperparameter_search)" + "from nlpsig_networks.scripts.lstm_baseline_functions import lstm_hyperparameter_search" ] }, { @@ -128,9 +127,11 @@ "gamma = 2\n", "validation_metric = \"f1\"\n", "patience = 5\n", - "split_indices = (df[df['set']=='train'].index, \n", - " df[df['set']=='dev'].index, \n", - " df[df['set']=='test'].index)" + "split_indices = (\n", + " df[df[\"set\"] == \"train\"].index,\n", + " df[df[\"set\"] == \"dev\"].index,\n", + " df[df[\"set\"] == \"test\"].index,\n", + ")" ] }, { @@ -391,13 +392,13 @@ " gamma=gamma,\n", " device=device,\n", " path_indices=None,\n", - " split_ids= None,\n", - " split_indices = split_indices,\n", + " split_ids=None,\n", + " split_indices=split_indices,\n", " k_fold=False,\n", " patience=patience,\n", " validation_metric=validation_metric,\n", " results_output=f\"{output_dir}/lstm_history_{size}_focal_{gamma}.csv\",\n", - " verbose=False\n", + " verbose=False,\n", ")" ] }, diff --git a/notebooks/Reddit_MoC/redditmoc-baseline-ffn.ipynb b/notebooks/Reddit_MoC/redditmoc-baseline-ffn.ipynb index 0a287ba..206e6ec 100644 --- a/notebooks/Reddit_MoC/redditmoc-baseline-ffn.ipynb +++ b/notebooks/Reddit_MoC/redditmoc-baseline-ffn.ipynb @@ -98,9 +98,9 @@ "source": [ "num_epochs = 100\n", "batch = 32\n", - "hidden_dim_sizes = [[32,32]]#[[32,32], [64,64], [128,128], [256,256]]\n", - "dropout_rates = [0.2] #[0.5, 0.2, 0.1]\n", - "learning_rates = [1e-4]#[1e-3, 1e-4, 5e-4]\n", + "hidden_dim_sizes = [[32, 32]] # [[32,32], [64,64], [128,128], [256,256]]\n", + "dropout_rates = [0.2] # [0.5, 0.2, 0.1]\n", + "learning_rates = [1e-4] # [1e-3, 1e-4, 5e-4]\n", "seeds = [1, 12, 123]\n", "loss = \"focal\"\n", "gamma = 2\n", @@ -156,7 +156,7 @@ } ], "source": [ - "ffn_current, best_ffn_current, _, __ = ffn_hyperparameter_search( \n", + "ffn_current, best_ffn_current, _, __ = ffn_hyperparameter_search(\n", " num_epochs=num_epochs,\n", " x_data=sbert_embeddings,\n", " y_data=y_data,\n", @@ -170,12 +170,16 @@ " device=device,\n", " batch_size=batch,\n", " data_split_seed=123,\n", - " split_ids= None, #torch.tensor(df_rumours['timeline_id'].astype(int)),\n", - " split_indices = (df[df['set']=='train'].index, df[df['set']=='dev'].index, df[df['set']=='test'].index),\n", + " split_ids=None, # torch.tensor(df_rumours['timeline_id'].astype(int)),\n", + " split_indices=(\n", + " df[df[\"set\"] == \"train\"].index,\n", + " df[df[\"set\"] == \"dev\"].index,\n", + " df[df[\"set\"] == \"test\"].index,\n", + " ),\n", " k_fold=False,\n", " validation_metric=validation_metric,\n", " results_output=None,\n", - " verbose=False\n", + " verbose=False,\n", ")" ] }, diff --git a/notebooks/Reddit_MoC/redditmoc-swmhau-masked.ipynb b/notebooks/Reddit_MoC/redditmoc-swmhau-masked.ipynb index 4cf36f2..5a1fa69 100644 --- a/notebooks/Reddit_MoC/redditmoc-swmhau-masked.ipynb +++ b/notebooks/Reddit_MoC/redditmoc-swmhau-masked.ipynb @@ -45,7 +45,7 @@ "outputs": [], "source": [ "from nlpsig_networks.scripts.swmhau_network_functions import (\n", - " swmhau_network_hyperparameter_search\n", + " swmhau_network_hyperparameter_search,\n", ")" ] }, @@ -118,8 +118,8 @@ "metadata": {}, "outputs": [], "source": [ - "features = [\"time_encoding\"]#[\"time_encoding\", \"timeline_index\"]\n", - "standardise_method = [\"z_score\"]#[\"z_score\", None]\n", + "features = [\"time_encoding\"] # [\"time_encoding\", \"timeline_index\"]\n", + "standardise_method = [\"z_score\"] # [\"z_score\", None]\n", "num_features = len(features)\n", "add_time_in_path = False" ] @@ -132,11 +132,11 @@ "source": [ "num_epochs = 100\n", "embedding_dim = 384\n", - "dimensions = [15] # [50, 15]\n", + "dimensions = [15] # [50, 15]\n", "# define swmhau parameters: (output_channels, sig_depth, num_heads)\n", "swmhau_parameters = [(12, 3, 10), (8, 4, 6), (8, 4, 12)]\n", "num_layers = [1]\n", - "ffn_hidden_dim_sizes = [[256,256],[512,512]]\n", + "ffn_hidden_dim_sizes = [[256, 256], [512, 512]]\n", "dropout_rates = [0.5, 0.1]\n", "learning_rates = [1e-3, 1e-4, 5e-4]\n", "seeds = [1, 12, 123]\n", @@ -144,9 +144,11 @@ "gamma = 2\n", "validation_metric = \"f1\"\n", "patience = 5\n", - "split_indices = (df[df['set']=='train'].index, \n", - " df[df['set']=='dev'].index, \n", - " df[df['set']=='test'].index)" + "split_indices = (\n", + " df[df[\"set\"] == \"train\"].index,\n", + " df[df[\"set\"] == \"dev\"].index,\n", + " df[df[\"set\"] == \"test\"].index,\n", + ")" ] }, { @@ -624,7 +626,12 @@ ], "source": [ "size = 20\n", - "swmhau_network_umap, best_swmhau_network_umap, _, __ = swmhau_network_hyperparameter_search(\n", + "(\n", + " swmhau_network_umap,\n", + " best_swmhau_network_umap,\n", + " _,\n", + " __,\n", + ") = swmhau_network_hyperparameter_search(\n", " num_epochs=num_epochs,\n", " df=df,\n", " id_column=\"timeline_id\",\n", @@ -654,7 +661,7 @@ " patience=patience,\n", " validation_metric=validation_metric,\n", " results_output=f\"{output_dir}/swmhau_network_umap_focal_{gamma}_{size}.csv\",\n", - " verbose=False\n", + " verbose=False,\n", ")" ] }, @@ -1255,7 +1262,7 @@ } ], "source": [ - "best_swmhau_network_umap['f1'].mean()" + "best_swmhau_network_umap[\"f1\"].mean()" ] }, { diff --git a/notebooks/Rumours/load_rumours.py b/notebooks/Rumours/load_rumours.py index 7c64819..bbfa6ab 100644 --- a/notebooks/Rumours/load_rumours.py +++ b/notebooks/Rumours/load_rumours.py @@ -169,9 +169,7 @@ def time_fraction(x): ########## Dimensions and Y labels ############### ################################################## y_data = df_rumours["label"] -label_to_id = { - str(y_data.unique()[i]): i for i in range(len(y_data.unique())) -} +label_to_id = {str(y_data.unique()[i]): i for i in range(len(y_data.unique()))} id_to_label = {v: k for k, v in label_to_id.items()} output_dim = len(label_to_id.keys()) diff --git a/notebooks/Rumours/rumours-baseline-bert.py b/notebooks/Rumours/rumours-baseline-bert.py index 40a706d..67ccafa 100644 --- a/notebooks/Rumours/rumours-baseline-bert.py +++ b/notebooks/Rumours/rumours-baseline-bert.py @@ -1,14 +1,16 @@ -import numpy as np +from __future__ import annotations + import os + +import numpy as np import torch import transformers +from load_rumours import df_rumours, id_to_label, label_to_id, output_dim, split_ids -from nlpsig_networks.scripts.fine_tune_bert_classification import ( +from sig_networks.scripts.fine_tune_bert_classification import ( fine_tune_transformer_average_seed, ) -from load_rumours import df_rumours, output_dim, split_ids, label_to_id, id_to_label - # set to only report critical errors to avoid excessing logging transformers.utils.logging.set_verbosity(50) @@ -52,31 +54,26 @@ gamma = 2 for loss in ["focal", "cross_entropy"]: if loss == "focal": - results_output=f"{output_dir}/bert_classifier_focal.csv", + results_output = (f"{output_dir}/bert_classifier_focal.csv",) else: - results_output=f"{output_dir}/bert_classifier_ce.csv" - + results_output = f"{output_dir}/bert_classifier_ce.csv" + bert_classifier, best_bert_classifier, _, __ = fine_tune_transformer_average_seed( loss=loss, gamma=gamma, results_output=results_output, **kwargs, ) - + print(f"F1: {best_bert_classifier['f1'].mean()}") - print( - f"Precision: {best_bert_classifier['precision'].mean()}" - ) + print(f"Precision: {best_bert_classifier['precision'].mean()}") print(f"Recall: {best_bert_classifier['recall'].mean()}") + print("F1 scores: " f"{np.stack(best_bert_classifier['f1_scores']).mean(axis=0)}") print( - "F1 scores: " - f"{np.stack(best_bert_classifier['f1_scores']).mean(axis=0)}" - ) - print( - "Precision scores: " - f"{np.stack(best_bert_classifier['precision_scores']).mean(axis=0)}" + "Precision scores: " + f"{np.stack(best_bert_classifier['precision_scores']).mean(axis=0)}" ) print( - "Recall scores: " - f"{np.stack(best_bert_classifier['recall_scores']).mean(axis=0)}" + "Recall scores: " + f"{np.stack(best_bert_classifier['recall_scores']).mean(axis=0)}" ) diff --git a/notebooks/Rumours/rumours-baseline-bilstm.py b/notebooks/Rumours/rumours-baseline-bilstm.py index ec42b45..5bcb9c2 100644 --- a/notebooks/Rumours/rumours-baseline-bilstm.py +++ b/notebooks/Rumours/rumours-baseline-bilstm.py @@ -1,11 +1,15 @@ -import numpy as np +from __future__ import annotations + import os + +import numpy as np import torch -from nlpsig_networks.scripts.lstm_baseline_functions import ( +from load_rumours import df_rumours, output_dim, split_ids, y_data +from load_sbert_embeddings import sbert_embeddings + +from sig_networks.scripts.lstm_baseline_functions import ( lstm_hyperparameter_search, ) -from load_sbert_embeddings import sbert_embeddings -from load_rumours import df_rumours, y_data, output_dim, split_ids seed = 2023 diff --git a/notebooks/Rumours/rumours-baseline-ffn-history.py b/notebooks/Rumours/rumours-baseline-ffn-history.py index 63fbb68..b9de756 100644 --- a/notebooks/Rumours/rumours-baseline-ffn-history.py +++ b/notebooks/Rumours/rumours-baseline-ffn-history.py @@ -1,11 +1,15 @@ -import numpy as np +from __future__ import annotations + import os + +import numpy as np import torch -from nlpsig_networks.scripts.ffn_baseline_functions import ( +from load_rumours import df_rumours, output_dim, split_ids, y_data +from load_sbert_embeddings import sbert_embeddings + +from sig_networks.scripts.ffn_baseline_functions import ( histories_baseline_hyperparameter_search, ) -from load_sbert_embeddings import sbert_embeddings -from load_rumours import df_rumours, y_data, output_dim, split_ids seed = 2023 @@ -64,14 +68,9 @@ ) print(f"F1: {best_ffn_mean_history_kfold['f1'].mean()}") -print( - f"Precision: {best_ffn_mean_history_kfold['precision'].mean()}" -) +print(f"Precision: {best_ffn_mean_history_kfold['precision'].mean()}") print(f"Recall: {best_ffn_mean_history_kfold['recall'].mean()}") -print( - "F1 scores: " - f"{np.stack(best_ffn_mean_history_kfold['f1_scores']).mean(axis=0)}" -) +print(f"F1 scores: {np.stack(best_ffn_mean_history_kfold['f1_scores']).mean(axis=0)}") print( "Precision scores: " f"{np.stack(best_ffn_mean_history_kfold['precision_scores']).mean(axis=0)}" diff --git a/notebooks/Rumours/rumours-baseline-ffn.py b/notebooks/Rumours/rumours-baseline-ffn.py index 56bd700..c7a8656 100644 --- a/notebooks/Rumours/rumours-baseline-ffn.py +++ b/notebooks/Rumours/rumours-baseline-ffn.py @@ -1,12 +1,15 @@ -import numpy as np +from __future__ import annotations import os + +import numpy as np import torch -from nlpsig_networks.scripts.ffn_baseline_functions import ( +from load_rumours import output_dim, split_ids, y_data +from load_sbert_embeddings import sbert_embeddings + +from sig_networks.scripts.ffn_baseline_functions import ( ffn_hyperparameter_search, ) -from load_sbert_embeddings import sbert_embeddings -from load_rumours import df_rumours, y_data, output_dim, split_ids seed = 2023 @@ -56,14 +59,9 @@ ) print(f"F1: {best_ffn_current_kfold['f1'].mean()}") -print( - f"Precision: {best_ffn_current_kfold['precision'].mean()}" -) +print(f"Precision: {best_ffn_current_kfold['precision'].mean()}") print(f"Recall: {best_ffn_current_kfold['recall'].mean()}") -print( - "F1 scores: " - f"{np.stack(best_ffn_current_kfold['f1_scores']).mean(axis=0)}" -) +print("F1 scores: " f"{np.stack(best_ffn_current_kfold['f1_scores']).mean(axis=0)}") print( "Precision scores: " f"{np.stack(best_ffn_current_kfold['precision_scores']).mean(axis=0)}" diff --git a/notebooks/Rumours/rumours-seqsignet-attention-bilstm-script.py b/notebooks/Rumours/rumours-seqsignet-attention-bilstm-script.py index 9130185..0bea94a 100644 --- a/notebooks/Rumours/rumours-seqsignet-attention-bilstm-script.py +++ b/notebooks/Rumours/rumours-seqsignet-attention-bilstm-script.py @@ -1,11 +1,15 @@ -import numpy as np +from __future__ import annotations + import os + +import numpy as np import torch -from nlpsig_networks.scripts.seqsignet_attention_bilstm_functions import ( +from load_rumours import df_rumours, output_dim, split_ids, y_data +from load_sbert_embeddings import sbert_embeddings + +from sig_networks.scripts.seqsignet_attention_bilstm_functions import ( seqsignet_attention_bilstm_hyperparameter_search, ) -from load_sbert_embeddings import sbert_embeddings -from load_rumours import df_rumours, y_data, output_dim, split_ids seed = 2023 @@ -93,9 +97,7 @@ ) print(f"F1: {best_seqsignet_network_umap_kfold['f1'].mean()}") - print( - f"Precision: {best_seqsignet_network_umap_kfold['precision'].mean()}" - ) + print(f"Precision: {best_seqsignet_network_umap_kfold['precision'].mean()}") print(f"Recall: {best_seqsignet_network_umap_kfold['recall'].mean()}") print( "F1 scores: " diff --git a/notebooks/Rumours/rumours-seqsignet-attention-encoder-script.py b/notebooks/Rumours/rumours-seqsignet-attention-encoder-script.py index 5cc4b58..0d0f23c 100644 --- a/notebooks/Rumours/rumours-seqsignet-attention-encoder-script.py +++ b/notebooks/Rumours/rumours-seqsignet-attention-encoder-script.py @@ -1,11 +1,15 @@ -import numpy as np +from __future__ import annotations + import os + +import numpy as np import torch -from nlpsig_networks.scripts.seqsignet_attention_encoder_functions import ( +from load_rumours import df_rumours, output_dim, split_ids, y_data +from load_sbert_embeddings import sbert_embeddings + +from sig_networks.scripts.seqsignet_attention_encoder_functions import ( seqsignet_attention_encoder_hyperparameter_search, ) -from load_sbert_embeddings import sbert_embeddings -from load_rumours import df_rumours, y_data, output_dim, split_ids seed = 2023 diff --git a/notebooks/Rumours/rumours-seqsignet-script.py b/notebooks/Rumours/rumours-seqsignet-script.py index 80fefb3..1361faa 100644 --- a/notebooks/Rumours/rumours-seqsignet-script.py +++ b/notebooks/Rumours/rumours-seqsignet-script.py @@ -1,9 +1,13 @@ -import numpy as np +from __future__ import annotations + import os + +import numpy as np import torch -from nlpsig_networks.scripts.seqsignet_functions import seqsignet_hyperparameter_search +from load_rumours import df_rumours, output_dim, split_ids, y_data from load_sbert_embeddings import sbert_embeddings -from load_rumours import df_rumours, y_data, output_dim, split_ids + +from sig_networks.scripts.seqsignet_functions import seqsignet_hyperparameter_search seed = 2023 diff --git a/notebooks/Rumours/rumours-swmhau-script.py b/notebooks/Rumours/rumours-swmhau-script.py index 31e3465..8ca41c4 100644 --- a/notebooks/Rumours/rumours-swmhau-script.py +++ b/notebooks/Rumours/rumours-swmhau-script.py @@ -1,11 +1,15 @@ -import numpy as np +from __future__ import annotations + import os + +import numpy as np import torch -from nlpsig_networks.scripts.swmhau_network_functions import ( +from load_rumours import df_rumours, output_dim, split_ids, y_data +from load_sbert_embeddings import sbert_embeddings + +from sig_networks.scripts.swmhau_network_functions import ( swmhau_network_hyperparameter_search, ) -from load_sbert_embeddings import sbert_embeddings -from load_rumours import df_rumours, y_data, output_dim, split_ids seed = 2023 diff --git a/notebooks/Rumours/rumours-swnu-script.py b/notebooks/Rumours/rumours-swnu-script.py index 4715dce..00cfe33 100644 --- a/notebooks/Rumours/rumours-swnu-script.py +++ b/notebooks/Rumours/rumours-swnu-script.py @@ -1,11 +1,15 @@ -import numpy as np +from __future__ import annotations + import os + +import numpy as np import torch -from nlpsig_networks.scripts.swnu_network_functions import ( +from load_rumours import df_rumours, output_dim, split_ids, y_data +from load_sbert_embeddings import sbert_embeddings + +from sig_networks.scripts.swnu_network_functions import ( swnu_network_hyperparameter_search, ) -from load_sbert_embeddings import sbert_embeddings -from load_rumours import df_rumours, y_data, output_dim, split_ids seed = 2023 diff --git a/notebooks/Stance/load_stance.py b/notebooks/Stance/load_stance.py index 91d534d..4e85b8b 100644 --- a/notebooks/Stance/load_stance.py +++ b/notebooks/Stance/load_stance.py @@ -18,6 +18,7 @@ ########## Conversion to Timeline#### ############ ################################################## + # Convert conversation thread to linear timeline: we use timestamps # of each post in the twitter thread to obtain a chronologically ordered list. def tree2timeline(conversation): @@ -43,6 +44,7 @@ def tree2timeline(conversation): timeline.extend(sorted_replies) return timeline + stance_timelines = {"dev": [], "train": [], "test": []} count_threads = 0 @@ -92,18 +94,18 @@ def time_fraction(x): ################################################## ########## Label Mapping ######################### ################################################## -#labels in numbers +# labels in numbers dictl = {} -dictl['support'] = 0 -dictl['deny'] = 1 -dictl['comment'] = 2 -dictl['query'] = 3 +dictl["support"] = 0 +dictl["deny"] = 1 +dictl["comment"] = 2 +dictl["query"] = 3 -#Get the numerical labels +# Get the numerical labels df = df.replace({"label": dictl}) ################################################## ########## Dimensions and Y labels ############### ################################################## output_dim = len(df["label"].unique()) -y_data = torch.tensor(df["label"].astype(float).values, dtype=torch.int64) \ No newline at end of file +y_data = torch.tensor(df["label"].astype(float).values, dtype=torch.int64) diff --git a/notebooks/Stance/stance-baseline-ffn-history.ipynb b/notebooks/Stance/stance-baseline-ffn-history.ipynb index 86b94a6..917909a 100644 --- a/notebooks/Stance/stance-baseline-ffn-history.ipynb +++ b/notebooks/Stance/stance-baseline-ffn-history.ipynb @@ -25,7 +25,7 @@ "\n", "# set device\n", "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n", - "device='cuda:2'" + "device = \"cuda:2\"" ] }, { diff --git a/notebooks/Talklife_MoC/load_sbert-embeddings.py b/notebooks/Talklife_MoC/load_sbert-embeddings.py index 68b4475..151b91f 100644 --- a/notebooks/Talklife_MoC/load_sbert-embeddings.py +++ b/notebooks/Talklife_MoC/load_sbert-embeddings.py @@ -3,9 +3,9 @@ import os import paths -#read embeddings -emb_sbert_filename= paths.embeddings_fname -with open(emb_sbert_filename, 'rb') as f: +# read embeddings +emb_sbert_filename = paths.embeddings_fname +with open(emb_sbert_filename, "rb") as f: sbert_embeddings = pickle.load(f) -sbert_embeddings = torch.tensor(sbert_embeddings) \ No newline at end of file +sbert_embeddings = torch.tensor(sbert_embeddings) diff --git a/notebooks/Talklife_MoC/load_talklifemoc.py b/notebooks/Talklife_MoC/load_talklifemoc.py index 8770d2d..5d05e3a 100644 --- a/notebooks/Talklife_MoC/load_talklifemoc.py +++ b/notebooks/Talklife_MoC/load_talklifemoc.py @@ -1,43 +1,47 @@ -import pandas as pd +import pandas as pd import sys import pickle import torch import paths -sys.path.append("..")# Adds higher directory to python modules path +sys.path.append("..") # Adds higher directory to python modules path ################################################## ########## Load in data ########################## ################################################## -#TalkLifeDataset = data_handler.TalkLifeDataset() -#df = TalkLifeDataset.return_annotated_timelines(load_from_pickle=True) +# TalkLifeDataset = data_handler.TalkLifeDataset() +# df = TalkLifeDataset.return_annotated_timelines(load_from_pickle=True) df = pd.read_pickle(paths.data_fname) -df = df[df['content']!='nan'] +df = df[df["content"] != "nan"] -#labels in numbers +# labels in numbers dictl = {} -dictl['0'] = '0' -dictl['IE'] = '1' -dictl['IEP'] = '1' -dictl['ISB'] = '2' -dictl['IS'] = '2'# +dictl["0"] = "0" +dictl["IE"] = "1" +dictl["IEP"] = "1" +dictl["ISB"] = "2" +dictl["IS"] = "2" # -#GET THE FLAT LABELS +# GET THE FLAT LABELS df = df.replace({"label": dictl}) -#read pickle of folds +# read pickle of folds folds_fname = paths.folds_fname -with open(folds_fname, 'rb') as f: +with open(folds_fname, "rb") as f: folds = pickle.load(f) -#obtain columns for train/dev/test for each fold +# obtain columns for train/dev/test for each fold for fold in folds.keys(): - df['fold'+str(fold)] = df['timeline_id'].map(lambda x: 'train' if x in folds[fold]['train'] else ('dev' if x in folds[fold]['dev'] else 'test')) + df["fold" + str(fold)] = df["timeline_id"].map( + lambda x: "train" + if x in folds[fold]["train"] + else ("dev" if x in folds[fold]["dev"] else "test") + ) -#rest index +# rest index df = df.reset_index(drop=True) ################################################## ########## Dimensions and Y labels ############### ################################################## -output_dim = len(df['label'].unique()) -y_data = torch.tensor(df['label'].astype(float).values, dtype=torch.int64) \ No newline at end of file +output_dim = len(df["label"].unique()) +y_data = torch.tensor(df["label"].astype(float).values, dtype=torch.int64) diff --git a/notebooks/Talklife_MoC/talklifemoc-baseline-bert.ipynb b/notebooks/Talklife_MoC/talklifemoc-baseline-bert.ipynb index 817325b..c8fd1d9 100644 --- a/notebooks/Talklife_MoC/talklifemoc-baseline-bert.ipynb +++ b/notebooks/Talklife_MoC/talklifemoc-baseline-bert.ipynb @@ -131,11 +131,17 @@ "metadata": {}, "outputs": [], "source": [ - "#create indices for kfold\n", - "fold_col_names = [c for c in df.columns if 'fold' in c ]\n", + "# create indices for kfold\n", + "fold_col_names = [c for c in df.columns if \"fold\" in c]\n", "fold_list = []\n", "for foldc in fold_col_names:\n", - " fold_list.append((df[df[foldc]=='train'].index, df[df[foldc]=='dev'].index, df[df[foldc]=='test'].index))\n", + " fold_list.append(\n", + " (\n", + " df[df[foldc] == \"train\"].index,\n", + " df[df[foldc] == \"dev\"].index,\n", + " df[df[foldc] == \"test\"].index,\n", + " )\n", + " )\n", "fold_list = tuple(fold_list)" ] }, @@ -219,7 +225,7 @@ " seeds=seeds,\n", " loss=loss,\n", " gamma=gamma,\n", - " split_ids=None, \n", + " split_ids=None,\n", " split_indices=fold_list,\n", " k_fold=True,\n", " validation_metric=validation_metric,\n", diff --git a/notebooks/Talklife_MoC/talklifemoc-baseline-bilstm-w11.ipynb b/notebooks/Talklife_MoC/talklifemoc-baseline-bilstm-w11.ipynb index 9dc888b..ef88d15 100644 --- a/notebooks/Talklife_MoC/talklifemoc-baseline-bilstm-w11.ipynb +++ b/notebooks/Talklife_MoC/talklifemoc-baseline-bilstm-w11.ipynb @@ -23,7 +23,7 @@ "\n", "# set device\n", "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n", - "device = 'cuda:1'" + "device = \"cuda:1\"" ] }, { @@ -124,11 +124,17 @@ "metadata": {}, "outputs": [], "source": [ - "#create indices for kfold\n", - "fold_col_names = [c for c in df.columns if 'fold' in c ]\n", + "# create indices for kfold\n", + "fold_col_names = [c for c in df.columns if \"fold\" in c]\n", "fold_list = []\n", "for foldc in fold_col_names:\n", - " fold_list.append((df[df[foldc]=='train'].index, df[df[foldc]=='dev'].index, df[df[foldc]=='test'].index))\n", + " fold_list.append(\n", + " (\n", + " df[df[foldc] == \"train\"].index,\n", + " df[df[foldc] == \"dev\"].index,\n", + " df[df[foldc] == \"test\"].index,\n", + " )\n", + " )\n", "fold_list = tuple(fold_list)" ] }, @@ -394,13 +400,13 @@ " loss=loss,\n", " gamma=gamma,\n", " device=device,\n", - " split_ids=None, \n", + " split_ids=None,\n", " split_indices=fold_list,\n", " k_fold=True,\n", " validation_metric=validation_metric,\n", " results_output=f\"{output_dir}/lstm_history_{size}_focal_{gamma}_kfold.csv\",\n", " verbose=False,\n", - ") " + ")" ] }, { @@ -1450,13 +1456,25 @@ } ], "source": [ - "best_bilstm_history_11[['f1', 'f1_scores', 'precision', \n", - " 'recall', 'valid_f1',\n", - " 'valid_f1_scores', 'valid_precision', \n", - " 'valid_recall', \n", - " 'hidden_dim', 'dropout_rate', 'learning_rate', 'seed',\n", - " 'loss_function', 'k_fold', \n", - " 'batch_size']]" + "best_bilstm_history_11[\n", + " [\n", + " \"f1\",\n", + " \"f1_scores\",\n", + " \"precision\",\n", + " \"recall\",\n", + " \"valid_f1\",\n", + " \"valid_f1_scores\",\n", + " \"valid_precision\",\n", + " \"valid_recall\",\n", + " \"hidden_dim\",\n", + " \"dropout_rate\",\n", + " \"learning_rate\",\n", + " \"seed\",\n", + " \"loss_function\",\n", + " \"k_fold\",\n", + " \"batch_size\",\n", + " ]\n", + "]" ] }, { diff --git a/notebooks/Talklife_MoC/talklifemoc-baseline-bilstm-w20.ipynb b/notebooks/Talklife_MoC/talklifemoc-baseline-bilstm-w20.ipynb index a1c5966..d82a880 100644 --- a/notebooks/Talklife_MoC/talklifemoc-baseline-bilstm-w20.ipynb +++ b/notebooks/Talklife_MoC/talklifemoc-baseline-bilstm-w20.ipynb @@ -23,7 +23,7 @@ "\n", "# set device\n", "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n", - "device = 'cuda:1'" + "device = \"cuda:1\"" ] }, { @@ -124,11 +124,17 @@ "metadata": {}, "outputs": [], "source": [ - "#create indices for kfold\n", - "fold_col_names = [c for c in df.columns if 'fold' in c ]\n", + "# create indices for kfold\n", + "fold_col_names = [c for c in df.columns if \"fold\" in c]\n", "fold_list = []\n", "for foldc in fold_col_names:\n", - " fold_list.append((df[df[foldc]=='train'].index, df[df[foldc]=='dev'].index, df[df[foldc]=='test'].index))\n", + " fold_list.append(\n", + " (\n", + " df[df[foldc] == \"train\"].index,\n", + " df[df[foldc] == \"dev\"].index,\n", + " df[df[foldc] == \"test\"].index,\n", + " )\n", + " )\n", "fold_list = tuple(fold_list)" ] }, @@ -394,13 +400,13 @@ " loss=loss,\n", " gamma=gamma,\n", " device=device,\n", - " split_ids=None, \n", + " split_ids=None,\n", " split_indices=fold_list,\n", " k_fold=True,\n", " validation_metric=validation_metric,\n", " results_output=f\"{output_dir}/lstm_history_{size}_focal_{gamma}_kfold.csv\",\n", " verbose=False,\n", - ") " + ")" ] }, { @@ -1450,13 +1456,25 @@ } ], "source": [ - "best_bilstm_history_20[['f1', 'f1_scores', 'precision', \n", - " 'recall', 'valid_f1',\n", - " 'valid_f1_scores', 'valid_precision', \n", - " 'valid_recall', \n", - " 'hidden_dim', 'dropout_rate', 'learning_rate', 'seed',\n", - " 'loss_function', 'k_fold', \n", - " 'batch_size']]" + "best_bilstm_history_20[\n", + " [\n", + " \"f1\",\n", + " \"f1_scores\",\n", + " \"precision\",\n", + " \"recall\",\n", + " \"valid_f1\",\n", + " \"valid_f1_scores\",\n", + " \"valid_precision\",\n", + " \"valid_recall\",\n", + " \"hidden_dim\",\n", + " \"dropout_rate\",\n", + " \"learning_rate\",\n", + " \"seed\",\n", + " \"loss_function\",\n", + " \"k_fold\",\n", + " \"batch_size\",\n", + " ]\n", + "]" ] }, { diff --git a/notebooks/Talklife_MoC/talklifemoc-baseline-bilstm-w35.ipynb b/notebooks/Talklife_MoC/talklifemoc-baseline-bilstm-w35.ipynb index 338693c..2440d24 100644 --- a/notebooks/Talklife_MoC/talklifemoc-baseline-bilstm-w35.ipynb +++ b/notebooks/Talklife_MoC/talklifemoc-baseline-bilstm-w35.ipynb @@ -23,7 +23,7 @@ "\n", "# set device\n", "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n", - "device = 'cuda:0'" + "device = \"cuda:0\"" ] }, { @@ -124,11 +124,17 @@ "metadata": {}, "outputs": [], "source": [ - "#create indices for kfold\n", - "fold_col_names = [c for c in df.columns if 'fold' in c ]\n", + "# create indices for kfold\n", + "fold_col_names = [c for c in df.columns if \"fold\" in c]\n", "fold_list = []\n", "for foldc in fold_col_names:\n", - " fold_list.append((df[df[foldc]=='train'].index, df[df[foldc]=='dev'].index, df[df[foldc]=='test'].index))\n", + " fold_list.append(\n", + " (\n", + " df[df[foldc] == \"train\"].index,\n", + " df[df[foldc] == \"dev\"].index,\n", + " df[df[foldc] == \"test\"].index,\n", + " )\n", + " )\n", "fold_list = tuple(fold_list)" ] }, @@ -394,13 +400,13 @@ " loss=loss,\n", " gamma=gamma,\n", " device=device,\n", - " split_ids=None, \n", + " split_ids=None,\n", " split_indices=fold_list,\n", " k_fold=True,\n", " validation_metric=validation_metric,\n", " results_output=f\"{output_dir}/lstm_history_{size}_focal_{gamma}_kfold.csv\",\n", " verbose=False,\n", - ") " + ")" ] }, { @@ -1450,13 +1456,25 @@ } ], "source": [ - "best_bilstm_history_35[['f1', 'f1_scores', 'precision', \n", - " 'recall', 'valid_f1',\n", - " 'valid_f1_scores', 'valid_precision', \n", - " 'valid_recall', \n", - " 'hidden_dim', 'dropout_rate', 'learning_rate', 'seed',\n", - " 'loss_function', 'k_fold', \n", - " 'batch_size']]" + "best_bilstm_history_35[\n", + " [\n", + " \"f1\",\n", + " \"f1_scores\",\n", + " \"precision\",\n", + " \"recall\",\n", + " \"valid_f1\",\n", + " \"valid_f1_scores\",\n", + " \"valid_precision\",\n", + " \"valid_recall\",\n", + " \"hidden_dim\",\n", + " \"dropout_rate\",\n", + " \"learning_rate\",\n", + " \"seed\",\n", + " \"loss_function\",\n", + " \"k_fold\",\n", + " \"batch_size\",\n", + " ]\n", + "]" ] }, { diff --git a/notebooks/Talklife_MoC/talklifemoc-baseline-bilstm-w5.ipynb b/notebooks/Talklife_MoC/talklifemoc-baseline-bilstm-w5.ipynb index b5261a2..5c0424e 100644 --- a/notebooks/Talklife_MoC/talklifemoc-baseline-bilstm-w5.ipynb +++ b/notebooks/Talklife_MoC/talklifemoc-baseline-bilstm-w5.ipynb @@ -136,11 +136,17 @@ "metadata": {}, "outputs": [], "source": [ - "#create indices for kfold\n", - "fold_col_names = [c for c in df.columns if 'fold' in c ]\n", + "# create indices for kfold\n", + "fold_col_names = [c for c in df.columns if \"fold\" in c]\n", "fold_list = []\n", "for foldc in fold_col_names:\n", - " fold_list.append((df[df[foldc]=='train'].index, df[df[foldc]=='dev'].index, df[df[foldc]=='test'].index))\n", + " fold_list.append(\n", + " (\n", + " df[df[foldc] == \"train\"].index,\n", + " df[df[foldc] == \"dev\"].index,\n", + " df[df[foldc] == \"test\"].index,\n", + " )\n", + " )\n", "fold_list = tuple(fold_list)" ] }, @@ -406,13 +412,13 @@ " loss=loss,\n", " gamma=gamma,\n", " device=device,\n", - " split_ids=None, \n", + " split_ids=None,\n", " split_indices=fold_list,\n", " k_fold=True,\n", " validation_metric=validation_metric,\n", " results_output=f\"{output_dir}/lstm_history_{size}_focal_{gamma}_kfold.csv\",\n", " verbose=False,\n", - ") " + ")" ] }, { @@ -1462,13 +1468,25 @@ } ], "source": [ - "best_bilstm_history_5[['f1', 'f1_scores', 'precision', \n", - " 'recall', 'valid_f1',\n", - " 'valid_f1_scores', 'valid_precision', \n", - " 'valid_recall', \n", - " 'hidden_dim', 'dropout_rate', 'learning_rate', 'seed',\n", - " 'loss_function', 'k_fold', \n", - " 'batch_size']]" + "best_bilstm_history_5[\n", + " [\n", + " \"f1\",\n", + " \"f1_scores\",\n", + " \"precision\",\n", + " \"recall\",\n", + " \"valid_f1\",\n", + " \"valid_f1_scores\",\n", + " \"valid_precision\",\n", + " \"valid_recall\",\n", + " \"hidden_dim\",\n", + " \"dropout_rate\",\n", + " \"learning_rate\",\n", + " \"seed\",\n", + " \"loss_function\",\n", + " \"k_fold\",\n", + " \"batch_size\",\n", + " ]\n", + "]" ] }, { diff --git a/notebooks/Talklife_MoC/talklifemoc-baseline-ffn-history.ipynb b/notebooks/Talklife_MoC/talklifemoc-baseline-ffn-history.ipynb index b7d1c49..e6c7a1d 100644 --- a/notebooks/Talklife_MoC/talklifemoc-baseline-ffn-history.ipynb +++ b/notebooks/Talklife_MoC/talklifemoc-baseline-ffn-history.ipynb @@ -125,11 +125,17 @@ "metadata": {}, "outputs": [], "source": [ - "#create indices for kfold\n", - "fold_col_names = [c for c in df.columns if 'fold' in c ]\n", + "# create indices for kfold\n", + "fold_col_names = [c for c in df.columns if \"fold\" in c]\n", "fold_list = []\n", "for foldc in fold_col_names:\n", - " fold_list.append((df[df[foldc]=='train'].index, df[df[foldc]=='dev'].index, df[df[foldc]=='test'].index))\n", + " fold_list.append(\n", + " (\n", + " df[df[foldc] == \"train\"].index,\n", + " df[df[foldc] == \"dev\"].index,\n", + " df[df[foldc] == \"test\"].index,\n", + " )\n", + " )\n", "fold_list = tuple(fold_list)" ] }, @@ -405,13 +411,13 @@ " loss=loss,\n", " gamma=gamma,\n", " device=device,\n", - " split_ids=None, \n", + " split_ids=None,\n", " split_indices=fold_list,\n", " k_fold=True,\n", " patience=patience,\n", " validation_metric=validation_metric,\n", - " results_output= f\"{output_dir}/ffn_mean_history_focal_{gamma}.csv\",\n", - " verbose=False\n", + " results_output=f\"{output_dir}/ffn_mean_history_focal_{gamma}.csv\",\n", + " verbose=False,\n", ")" ] }, @@ -1125,13 +1131,25 @@ } ], "source": [ - "best_ffn_mean_history[['f1', 'f1_scores', 'precision', \n", - " 'recall', 'valid_f1',\n", - " 'valid_f1_scores', 'valid_precision', \n", - " 'valid_recall', \n", - " 'hidden_dim', 'dropout_rate', 'learning_rate', 'seed',\n", - " 'loss_function', 'k_fold', \n", - " 'batch_size']]" + "best_ffn_mean_history[\n", + " [\n", + " \"f1\",\n", + " \"f1_scores\",\n", + " \"precision\",\n", + " \"recall\",\n", + " \"valid_f1\",\n", + " \"valid_f1_scores\",\n", + " \"valid_precision\",\n", + " \"valid_recall\",\n", + " \"hidden_dim\",\n", + " \"dropout_rate\",\n", + " \"learning_rate\",\n", + " \"seed\",\n", + " \"loss_function\",\n", + " \"k_fold\",\n", + " \"batch_size\",\n", + " ]\n", + "]" ] }, { diff --git a/notebooks/Talklife_MoC/talklifemoc-baseline-ffn.ipynb b/notebooks/Talklife_MoC/talklifemoc-baseline-ffn.ipynb index 8834234..3ead992 100644 --- a/notebooks/Talklife_MoC/talklifemoc-baseline-ffn.ipynb +++ b/notebooks/Talklife_MoC/talklifemoc-baseline-ffn.ipynb @@ -120,7 +120,7 @@ "outputs": [], "source": [ "num_epochs = 100\n", - "hidden_dim_sizes = [[64,64],[128,128],[256,256],[512, 512]]\n", + "hidden_dim_sizes = [[64, 64], [128, 128], [256, 256], [512, 512]]\n", "dropout_rates = [0.1, 0.2]\n", "learning_rates = [1e-3, 1e-4, 5e-4]\n", "seeds = [1, 12, 123]\n", @@ -136,11 +136,17 @@ "metadata": {}, "outputs": [], "source": [ - "#create indices for kfold\n", - "fold_col_names = [c for c in df.columns if 'fold' in c ]\n", + "# create indices for kfold\n", + "fold_col_names = [c for c in df.columns if \"fold\" in c]\n", "fold_list = []\n", "for foldc in fold_col_names:\n", - " fold_list.append((df[df[foldc]=='train'].index, df[df[foldc]=='dev'].index, df[df[foldc]=='test'].index))\n", + " fold_list.append(\n", + " (\n", + " df[df[foldc] == \"train\"].index,\n", + " df[df[foldc] == \"dev\"].index,\n", + " df[df[foldc] == \"test\"].index,\n", + " )\n", + " )\n", "fold_list = tuple(fold_list)" ] }, @@ -341,7 +347,7 @@ } ], "source": [ - "ffn_current, best_ffn_current, _, __ = ffn_hyperparameter_search( \n", + "ffn_current, best_ffn_current, _, __ = ffn_hyperparameter_search(\n", " num_epochs=num_epochs,\n", " x_data=sbert_embeddings,\n", " y_data=y_data,\n", @@ -353,13 +359,13 @@ " loss=loss,\n", " gamma=gamma,\n", " device=device,\n", - " split_ids=None, \n", + " split_ids=None,\n", " split_indices=fold_list,\n", " k_fold=True,\n", " patience=patience,\n", " validation_metric=validation_metric,\n", - " results_output= f\"{output_dir}/ffn_current_focal_{gamma}.csv\",\n", - " verbose=False\n", + " results_output=f\"{output_dir}/ffn_current_focal_{gamma}.csv\",\n", + " verbose=False,\n", ")" ] }, @@ -668,13 +674,25 @@ } ], "source": [ - "best_ffn_current[['f1', 'f1_scores', 'precision', \n", - " 'recall', 'valid_f1',\n", - " 'valid_f1_scores', 'valid_precision', \n", - " 'valid_recall', \n", - " 'hidden_dim', 'dropout_rate', 'learning_rate', 'seed',\n", - " 'loss_function', 'k_fold', \n", - " 'batch_size']]" + "best_ffn_current[\n", + " [\n", + " \"f1\",\n", + " \"f1_scores\",\n", + " \"precision\",\n", + " \"recall\",\n", + " \"valid_f1\",\n", + " \"valid_f1_scores\",\n", + " \"valid_precision\",\n", + " \"valid_recall\",\n", + " \"hidden_dim\",\n", + " \"dropout_rate\",\n", + " \"learning_rate\",\n", + " \"seed\",\n", + " \"loss_function\",\n", + " \"k_fold\",\n", + " \"batch_size\",\n", + " ]\n", + "]" ] }, { diff --git a/notebooks/Talklife_MoC/talklifemoc-swmhau-w11.ipynb b/notebooks/Talklife_MoC/talklifemoc-swmhau-w11.ipynb index 2034592..4f9f73d 100644 --- a/notebooks/Talklife_MoC/talklifemoc-swmhau-w11.ipynb +++ b/notebooks/Talklife_MoC/talklifemoc-swmhau-w11.ipynb @@ -33,7 +33,7 @@ "outputs": [], "source": [ "from nlpsig_networks.scripts.swmhau_network_functions import (\n", - " swmhau_network_hyperparameter_search\n", + " swmhau_network_hyperparameter_search,\n", ")" ] }, @@ -118,11 +118,17 @@ "metadata": {}, "outputs": [], "source": [ - "#create indices for kfold\n", - "fold_col_names = [c for c in df.columns if 'fold' in c ]\n", + "# create indices for kfold\n", + "fold_col_names = [c for c in df.columns if \"fold\" in c]\n", "fold_list = []\n", "for foldc in fold_col_names:\n", - " fold_list.append((df[df[foldc]=='train'].index, df[df[foldc]=='dev'].index, df[df[foldc]=='test'].index))\n", + " fold_list.append(\n", + " (\n", + " df[df[foldc] == \"train\"].index,\n", + " df[df[foldc] == \"dev\"].index,\n", + " df[df[foldc] == \"test\"].index,\n", + " )\n", + " )\n", "fold_list = tuple(fold_list)" ] }, @@ -134,13 +140,13 @@ "source": [ "num_epochs = 100\n", "embedding_dim = 384\n", - "dimensions = [15] \n", + "dimensions = [15]\n", "dimreduction_method = [\"umap\"]\n", "# define swmhau parameters: (output_channels, sig_depth, num_heads)\n", "swmhau_parameters = [(12, 3, 10), (8, 4, 6)]\n", "num_layers = [1]\n", - "ffn_hidden_dim_sizes = [[256,256],[512,512]]\n", - "dropout_rates = [0.1, 0.2] \n", + "ffn_hidden_dim_sizes = [[256, 256], [512, 512]]\n", + "dropout_rates = [0.1, 0.2]\n", "learning_rates = [1e-3, 1e-4, 5e-4]\n", "seeds = [1, 12, 123]\n", "loss = \"focal\"\n", @@ -512,7 +518,12 @@ ], "source": [ "size = 11\n", - "swmhau_network_umap_11, best_swmhau_network_umap_11, _, __ = swmhau_network_hyperparameter_search(\n", + "(\n", + " swmhau_network_umap_11,\n", + " best_swmhau_network_umap_11,\n", + " _,\n", + " __,\n", + ") = swmhau_network_hyperparameter_search(\n", " num_epochs=num_epochs,\n", " df=df,\n", " id_column=\"timeline_id\",\n", @@ -537,13 +548,13 @@ " standardise_method=standardise_method,\n", " include_features_in_path=include_features_in_path,\n", " include_features_in_input=include_features_in_input,\n", - " split_ids=None, \n", + " split_ids=None,\n", " split_indices=fold_list,\n", " k_fold=True,\n", " patience=patience,\n", " validation_metric=validation_metric,\n", " results_output=f\"{output_dir}/swmhau_network_umap_focal_{gamma}_{size}.csv\",\n", - " verbose=False\n", + " verbose=False,\n", ")" ] }, @@ -553,7 +564,9 @@ "metadata": {}, "outputs": [], "source": [ - "best_swmhau_network_umap_11 = pd.read_csv('talklife_moc_output/swmhau_network_umap_focal_2_11_best_model.csv')" + "best_swmhau_network_umap_11 = pd.read_csv(\n", + " \"talklife_moc_output/swmhau_network_umap_focal_2_11_best_model.csv\"\n", + ")" ] }, { @@ -711,14 +724,30 @@ } ], "source": [ - "best_swmhau_network_umap_11[['f1', 'f1_scores', 'precision', \n", - " 'recall', 'valid_f1',\n", - " 'valid_f1_scores', 'valid_precision', \n", - " 'valid_recall', 'sig_depth',\n", - " 'num_heads', \n", - " 'ffn_hidden_dim', 'dropout_rate', 'learning_rate', 'seed',\n", - " 'loss_function', 'k_fold', 'augmentation_type',\n", - " 'hidden_dim_aug', 'comb_method', 'batch_size']]" + "best_swmhau_network_umap_11[\n", + " [\n", + " \"f1\",\n", + " \"f1_scores\",\n", + " \"precision\",\n", + " \"recall\",\n", + " \"valid_f1\",\n", + " \"valid_f1_scores\",\n", + " \"valid_precision\",\n", + " \"valid_recall\",\n", + " \"sig_depth\",\n", + " \"num_heads\",\n", + " \"ffn_hidden_dim\",\n", + " \"dropout_rate\",\n", + " \"learning_rate\",\n", + " \"seed\",\n", + " \"loss_function\",\n", + " \"k_fold\",\n", + " \"augmentation_type\",\n", + " \"hidden_dim_aug\",\n", + " \"comb_method\",\n", + " \"batch_size\",\n", + " ]\n", + "]" ] }, { @@ -787,9 +816,28 @@ "metadata": {}, "outputs": [], "source": [ - "best_swmhau_network_umap_11['f1_scores'] = best_swmhau_network_umap_11['f1_scores'].map(lambda x: [float(idx.replace(\" \", \"\")) for idx in x.replace(\"[\", \"\").replace(\"]\", \"\").replace(\" 0\", \",0\").split(',')])\n", - "best_swmhau_network_umap_11['precision_scores'] = best_swmhau_network_umap_11['precision_scores'].map(lambda x: [float(idx.replace(\" \", \"\")) for idx in x.replace(\"[\", \"\").replace(\"]\", \"\").replace(\" 0\", \",0\").split(',')])\n", - "best_swmhau_network_umap_11['recall_scores'] = best_swmhau_network_umap_11['recall_scores'].map(lambda x: [float(idx.replace(\" \", \"\")) for idx in x.replace(\"[\", \"\").replace(\"]\", \"\").replace(\" 0\", \",0\").split(',')])" + "best_swmhau_network_umap_11[\"f1_scores\"] = best_swmhau_network_umap_11[\"f1_scores\"].map(\n", + " lambda x: [\n", + " float(idx.replace(\" \", \"\"))\n", + " for idx in x.replace(\"[\", \"\").replace(\"]\", \"\").replace(\" 0\", \",0\").split(\",\")\n", + " ]\n", + ")\n", + "best_swmhau_network_umap_11[\"precision_scores\"] = best_swmhau_network_umap_11[\n", + " \"precision_scores\"\n", + "].map(\n", + " lambda x: [\n", + " float(idx.replace(\" \", \"\"))\n", + " for idx in x.replace(\"[\", \"\").replace(\"]\", \"\").replace(\" 0\", \",0\").split(\",\")\n", + " ]\n", + ")\n", + "best_swmhau_network_umap_11[\"recall_scores\"] = best_swmhau_network_umap_11[\n", + " \"recall_scores\"\n", + "].map(\n", + " lambda x: [\n", + " float(idx.replace(\" \", \"\"))\n", + " for idx in x.replace(\"[\", \"\").replace(\"]\", \"\").replace(\" 0\", \",0\").split(\",\")\n", + " ]\n", + ")" ] }, { diff --git a/notebooks/Talklife_MoC/talklifemoc-swmhau-w20.ipynb b/notebooks/Talklife_MoC/talklifemoc-swmhau-w20.ipynb index cfce3bd..83c9903 100644 --- a/notebooks/Talklife_MoC/talklifemoc-swmhau-w20.ipynb +++ b/notebooks/Talklife_MoC/talklifemoc-swmhau-w20.ipynb @@ -33,7 +33,7 @@ "outputs": [], "source": [ "from nlpsig_networks.scripts.swmhau_network_functions import (\n", - " swmhau_network_hyperparameter_search\n", + " swmhau_network_hyperparameter_search,\n", ")" ] }, @@ -118,11 +118,17 @@ "metadata": {}, "outputs": [], "source": [ - "#create indices for kfold\n", - "fold_col_names = [c for c in df.columns if 'fold' in c ]\n", + "# create indices for kfold\n", + "fold_col_names = [c for c in df.columns if \"fold\" in c]\n", "fold_list = []\n", "for foldc in fold_col_names:\n", - " fold_list.append((df[df[foldc]=='train'].index, df[df[foldc]=='dev'].index, df[df[foldc]=='test'].index))\n", + " fold_list.append(\n", + " (\n", + " df[df[foldc] == \"train\"].index,\n", + " df[df[foldc] == \"dev\"].index,\n", + " df[df[foldc] == \"test\"].index,\n", + " )\n", + " )\n", "fold_list = tuple(fold_list)" ] }, @@ -134,13 +140,13 @@ "source": [ "num_epochs = 100\n", "embedding_dim = 384\n", - "dimensions = [15] \n", + "dimensions = [15]\n", "dimreduction_method = [\"umap\"]\n", "# define swmhau parameters: (output_channels, sig_depth, num_heads)\n", "swmhau_parameters = [(12, 3, 10), (8, 4, 6)]\n", "num_layers = [1]\n", - "ffn_hidden_dim_sizes = [[256,256],[512,512]]\n", - "dropout_rates = [0.1, 0.2] \n", + "ffn_hidden_dim_sizes = [[256, 256], [512, 512]]\n", + "dropout_rates = [0.1, 0.2]\n", "learning_rates = [1e-3, 1e-4, 5e-4]\n", "seeds = [1, 12, 123]\n", "loss = \"focal\"\n", @@ -512,7 +518,12 @@ ], "source": [ "size = 20\n", - "swmhau_network_umap_20, best_swmhau_network_umap_20, _, __ = swmhau_network_hyperparameter_search(\n", + "(\n", + " swmhau_network_umap_20,\n", + " best_swmhau_network_umap_20,\n", + " _,\n", + " __,\n", + ") = swmhau_network_hyperparameter_search(\n", " num_epochs=num_epochs,\n", " df=df,\n", " id_column=\"timeline_id\",\n", @@ -537,13 +548,13 @@ " standardise_method=standardise_method,\n", " include_features_in_path=include_features_in_path,\n", " include_features_in_input=include_features_in_input,\n", - " split_ids=None, \n", + " split_ids=None,\n", " split_indices=fold_list,\n", " k_fold=True,\n", " patience=patience,\n", " validation_metric=validation_metric,\n", " results_output=f\"{output_dir}/swmhau_network_umap_focal_{gamma}_{size}.csv\",\n", - " verbose=False\n", + " verbose=False,\n", ")" ] }, @@ -702,14 +713,30 @@ } ], "source": [ - "best_swmhau_network_umap_20[['f1', 'f1_scores', 'precision', \n", - " 'recall', 'valid_f1',\n", - " 'valid_f1_scores', 'valid_precision', \n", - " 'valid_recall', 'sig_depth',\n", - " 'num_heads', \n", - " 'ffn_hidden_dim', 'dropout_rate', 'learning_rate', 'seed',\n", - " 'loss_function', 'k_fold', 'augmentation_type',\n", - " 'hidden_dim_aug', 'comb_method', 'batch_size']]" + "best_swmhau_network_umap_20[\n", + " [\n", + " \"f1\",\n", + " \"f1_scores\",\n", + " \"precision\",\n", + " \"recall\",\n", + " \"valid_f1\",\n", + " \"valid_f1_scores\",\n", + " \"valid_precision\",\n", + " \"valid_recall\",\n", + " \"sig_depth\",\n", + " \"num_heads\",\n", + " \"ffn_hidden_dim\",\n", + " \"dropout_rate\",\n", + " \"learning_rate\",\n", + " \"seed\",\n", + " \"loss_function\",\n", + " \"k_fold\",\n", + " \"augmentation_type\",\n", + " \"hidden_dim_aug\",\n", + " \"comb_method\",\n", + " \"batch_size\",\n", + " ]\n", + "]" ] }, { diff --git a/notebooks/Talklife_MoC/talklifemoc-swmhau-w5.ipynb b/notebooks/Talklife_MoC/talklifemoc-swmhau-w5.ipynb index 00d6c34..1a7f4d2 100644 --- a/notebooks/Talklife_MoC/talklifemoc-swmhau-w5.ipynb +++ b/notebooks/Talklife_MoC/talklifemoc-swmhau-w5.ipynb @@ -24,7 +24,7 @@ "\n", "# set device\n", "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n", - "device = 'cuda:1'" + "device = \"cuda:1\"" ] }, { @@ -34,7 +34,7 @@ "outputs": [], "source": [ "from nlpsig_networks.scripts.swmhau_network_functions import (\n", - " swmhau_network_hyperparameter_search\n", + " swmhau_network_hyperparameter_search,\n", ")" ] }, @@ -119,11 +119,17 @@ "metadata": {}, "outputs": [], "source": [ - "#create indices for kfold\n", - "fold_col_names = [c for c in df.columns if 'fold' in c ]\n", + "# create indices for kfold\n", + "fold_col_names = [c for c in df.columns if \"fold\" in c]\n", "fold_list = []\n", "for foldc in fold_col_names:\n", - " fold_list.append((df[df[foldc]=='train'].index, df[df[foldc]=='dev'].index, df[df[foldc]=='test'].index))\n", + " fold_list.append(\n", + " (\n", + " df[df[foldc] == \"train\"].index,\n", + " df[df[foldc] == \"dev\"].index,\n", + " df[df[foldc] == \"test\"].index,\n", + " )\n", + " )\n", "fold_list = tuple(fold_list)" ] }, @@ -135,13 +141,13 @@ "source": [ "num_epochs = 100\n", "embedding_dim = 384\n", - "dimensions = [15] \n", + "dimensions = [15]\n", "dimreduction_method = [\"umap\"]\n", "# define swmhau parameters: (output_channels, sig_depth, num_heads)\n", "swmhau_parameters = [(12, 3, 10), (8, 4, 6)]\n", "num_layers = [1]\n", - "ffn_hidden_dim_sizes = [[256,256],[512,512]]\n", - "dropout_rates = [0.1, 0.2] \n", + "ffn_hidden_dim_sizes = [[256, 256], [512, 512]]\n", + "dropout_rates = [0.1, 0.2]\n", "learning_rates = [1e-3, 1e-4, 5e-4]\n", "seeds = [1, 12, 123]\n", "loss = \"focal\"\n", @@ -513,7 +519,12 @@ ], "source": [ "size = 5\n", - "swmhau_network_umap_5, best_swmhau_network_umap_5, _, __ = swmhau_network_hyperparameter_search(\n", + "(\n", + " swmhau_network_umap_5,\n", + " best_swmhau_network_umap_5,\n", + " _,\n", + " __,\n", + ") = swmhau_network_hyperparameter_search(\n", " num_epochs=num_epochs,\n", " df=df,\n", " id_column=\"timeline_id\",\n", @@ -538,13 +549,13 @@ " standardise_method=standardise_method,\n", " include_features_in_path=include_features_in_path,\n", " include_features_in_input=include_features_in_input,\n", - " split_ids=None, \n", + " split_ids=None,\n", " split_indices=fold_list,\n", " k_fold=True,\n", " patience=patience,\n", " validation_metric=validation_metric,\n", " results_output=f\"{output_dir}/swmhau_network_umap_focal_{gamma}_{size}.csv\",\n", - " verbose=False\n", + " verbose=False,\n", ")" ] }, @@ -703,14 +714,30 @@ } ], "source": [ - "best_swmhau_network_umap_5[['f1', 'f1_scores', 'precision', \n", - " 'recall', 'valid_f1',\n", - " 'valid_f1_scores', 'valid_precision', \n", - " 'valid_recall', 'sig_depth',\n", - " 'num_heads', \n", - " 'ffn_hidden_dim', 'dropout_rate', 'learning_rate', 'seed',\n", - " 'loss_function', 'k_fold', 'augmentation_type',\n", - " 'hidden_dim_aug', 'comb_method', 'batch_size']]" + "best_swmhau_network_umap_5[\n", + " [\n", + " \"f1\",\n", + " \"f1_scores\",\n", + " \"precision\",\n", + " \"recall\",\n", + " \"valid_f1\",\n", + " \"valid_f1_scores\",\n", + " \"valid_precision\",\n", + " \"valid_recall\",\n", + " \"sig_depth\",\n", + " \"num_heads\",\n", + " \"ffn_hidden_dim\",\n", + " \"dropout_rate\",\n", + " \"learning_rate\",\n", + " \"seed\",\n", + " \"loss_function\",\n", + " \"k_fold\",\n", + " \"augmentation_type\",\n", + " \"hidden_dim_aug\",\n", + " \"comb_method\",\n", + " \"batch_size\",\n", + " ]\n", + "]" ] }, { diff --git a/notebooks/Talklife_MoC/talklifemoc-swnu-bilstm-35.ipynb b/notebooks/Talklife_MoC/talklifemoc-swnu-bilstm-35.ipynb index e864a2a..3603aef 100644 --- a/notebooks/Talklife_MoC/talklifemoc-swnu-bilstm-35.ipynb +++ b/notebooks/Talklife_MoC/talklifemoc-swnu-bilstm-35.ipynb @@ -116,11 +116,17 @@ "metadata": {}, "outputs": [], "source": [ - "#create indices for kfold\n", - "fold_col_names = [c for c in df.columns if 'fold' in c ]\n", + "# create indices for kfold\n", + "fold_col_names = [c for c in df.columns if \"fold\" in c]\n", "fold_list = []\n", "for foldc in fold_col_names:\n", - " fold_list.append((df[df[foldc]=='train'].index, df[df[foldc]=='dev'].index, df[df[foldc]=='test'].index))\n", + " fold_list.append(\n", + " (\n", + " df[df[foldc] == \"train\"].index,\n", + " df[df[foldc] == \"dev\"].index,\n", + " df[df[foldc] == \"test\"].index,\n", + " )\n", + " )\n", "fold_list = tuple(fold_list)" ] }, @@ -132,7 +138,7 @@ "source": [ "num_epochs = 100\n", "embedding_dim = 384\n", - "dimensions = [15] \n", + "dimensions = [15]\n", "dimreduction_method = [\"umap\"]\n", "swnu_hidden_dim_sizes_and_sig_depths = [([12], 3), ([10], 4)]\n", "lstm_hidden_dim_sizes = [384]\n", @@ -567,7 +573,7 @@ " best_seqsignet_network_umap_35,\n", " _,\n", " __,\n", - ") = seqsignet_hyperparameter_search( \n", + ") = seqsignet_hyperparameter_search(\n", " num_epochs=num_epochs,\n", " df=df,\n", " id_column=\"timeline_id\",\n", @@ -595,13 +601,13 @@ " standardise_method=standardise_method,\n", " include_features_in_path=include_features_in_path,\n", " include_features_in_input=include_features_in_input,\n", - " split_ids=None, \n", + " split_ids=None,\n", " split_indices=fold_list,\n", " k_fold=True,\n", " patience=patience,\n", " validation_metric=validation_metric,\n", " results_output=f\"{output_dir}/seqsignet_umap_focal_{gamma}_{size}_kfold.csv\",\n", - " verbose=False\n", + " verbose=False,\n", ")" ] }, @@ -620,12 +626,30 @@ "metadata": {}, "outputs": [], "source": [ - "best_seqsignet_network_umap_35[['f1', 'f1_scores', 'precision', \n", - " 'recall', 'valid_f1',\n", - " 'valid_f1_scores', 'valid_precision', \n", - " 'valid_recall', 'features','standardise_method' ,'output_channels','lstm_hidden_dim',\n", - " 'dimensions','swnu_hidden_dim','sig_depth','ffn_hidden_dim', 'dropout_rate', 'learning_rate', 'seed',\n", - " 'batch_size']]" + "best_seqsignet_network_umap_35[\n", + " [\n", + " \"f1\",\n", + " \"f1_scores\",\n", + " \"precision\",\n", + " \"recall\",\n", + " \"valid_f1\",\n", + " \"valid_f1_scores\",\n", + " \"valid_precision\",\n", + " \"valid_recall\",\n", + " \"features\",\n", + " \"standardise_method\",\n", + " \"output_channels\",\n", + " \"lstm_hidden_dim\",\n", + " \"dimensions\",\n", + " \"swnu_hidden_dim\",\n", + " \"sig_depth\",\n", + " \"ffn_hidden_dim\",\n", + " \"dropout_rate\",\n", + " \"learning_rate\",\n", + " \"seed\",\n", + " \"batch_size\",\n", + " ]\n", + "]" ] }, { diff --git a/notebooks/Talklife_MoC/talklifemoc-swnu-bilstm.ipynb b/notebooks/Talklife_MoC/talklifemoc-swnu-bilstm.ipynb index ed57fde..5c461b5 100644 --- a/notebooks/Talklife_MoC/talklifemoc-swnu-bilstm.ipynb +++ b/notebooks/Talklife_MoC/talklifemoc-swnu-bilstm.ipynb @@ -24,7 +24,7 @@ "\n", "# set device\n", "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n", - "device='cuda:1'" + "device = \"cuda:1\"" ] }, { @@ -117,11 +117,17 @@ "metadata": {}, "outputs": [], "source": [ - "#create indices for kfold\n", - "fold_col_names = [c for c in df.columns if 'fold' in c ]\n", + "# create indices for kfold\n", + "fold_col_names = [c for c in df.columns if \"fold\" in c]\n", "fold_list = []\n", "for foldc in fold_col_names:\n", - " fold_list.append((df[df[foldc]=='train'].index, df[df[foldc]=='dev'].index, df[df[foldc]=='test'].index))\n", + " fold_list.append(\n", + " (\n", + " df[df[foldc] == \"train\"].index,\n", + " df[df[foldc] == \"dev\"].index,\n", + " df[df[foldc] == \"test\"].index,\n", + " )\n", + " )\n", "fold_list = tuple(fold_list)" ] }, @@ -133,7 +139,7 @@ "source": [ "num_epochs = 100\n", "embedding_dim = 384\n", - "dimensions = [15] \n", + "dimensions = [15]\n", "dimreduction_method = [\"umap\"]\n", "swnu_hidden_dim_sizes_and_sig_depths = [([12], 3), ([10], 4)]\n", "lstm_hidden_dim_sizes = [384]\n", @@ -570,7 +576,7 @@ " best_seqsignet_network_umap_20,\n", " _,\n", " __,\n", - ") = seqsignet_hyperparameter_search( \n", + ") = seqsignet_hyperparameter_search(\n", " num_epochs=num_epochs,\n", " df=df,\n", " id_column=\"timeline_id\",\n", @@ -598,13 +604,13 @@ " standardise_method=standardise_method,\n", " include_features_in_path=include_features_in_path,\n", " include_features_in_input=include_features_in_input,\n", - " split_ids=None, \n", + " split_ids=None,\n", " split_indices=fold_list,\n", " k_fold=True,\n", " patience=patience,\n", " validation_metric=validation_metric,\n", " results_output=f\"{output_dir}/seqsignet_umap_focal_{gamma}_{size}_kfold.csv\",\n", - " verbose=False\n", + " verbose=False,\n", ")" ] }, @@ -1178,12 +1184,30 @@ } ], "source": [ - "best_seqsignet_network_umap_20[['f1', 'f1_scores', 'precision', \n", - " 'recall', 'valid_f1',\n", - " 'valid_f1_scores', 'valid_precision', \n", - " 'valid_recall', 'features','standardise_method' ,'output_channels','lstm_hidden_dim',\n", - " 'dimensions','swnu_hidden_dim','sig_depth','ffn_hidden_dim', 'dropout_rate', 'learning_rate', 'seed',\n", - " 'batch_size']]" + "best_seqsignet_network_umap_20[\n", + " [\n", + " \"f1\",\n", + " \"f1_scores\",\n", + " \"precision\",\n", + " \"recall\",\n", + " \"valid_f1\",\n", + " \"valid_f1_scores\",\n", + " \"valid_precision\",\n", + " \"valid_recall\",\n", + " \"features\",\n", + " \"standardise_method\",\n", + " \"output_channels\",\n", + " \"lstm_hidden_dim\",\n", + " \"dimensions\",\n", + " \"swnu_hidden_dim\",\n", + " \"sig_depth\",\n", + " \"ffn_hidden_dim\",\n", + " \"dropout_rate\",\n", + " \"learning_rate\",\n", + " \"seed\",\n", + " \"batch_size\",\n", + " ]\n", + "]" ] }, { diff --git a/notebooks/Talklife_MoC/talklifemoc-swnu-w11.ipynb b/notebooks/Talklife_MoC/talklifemoc-swnu-w11.ipynb index 502bba2..7cca10a 100644 --- a/notebooks/Talklife_MoC/talklifemoc-swnu-w11.ipynb +++ b/notebooks/Talklife_MoC/talklifemoc-swnu-w11.ipynb @@ -24,7 +24,7 @@ "\n", "# set device\n", "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n", - "device = 'cuda:1'" + "device = \"cuda:1\"" ] }, { @@ -120,11 +120,17 @@ "metadata": {}, "outputs": [], "source": [ - "#create indices for kfold\n", - "fold_col_names = [c for c in df.columns if 'fold' in c ]\n", + "# create indices for kfold\n", + "fold_col_names = [c for c in df.columns if \"fold\" in c]\n", "fold_list = []\n", "for foldc in fold_col_names:\n", - " fold_list.append((df[df[foldc]=='train'].index, df[df[foldc]=='dev'].index, df[df[foldc]=='test'].index))\n", + " fold_list.append(\n", + " (\n", + " df[df[foldc] == \"train\"].index,\n", + " df[df[foldc] == \"dev\"].index,\n", + " df[df[foldc] == \"test\"].index,\n", + " )\n", + " )\n", "fold_list = tuple(fold_list)" ] }, @@ -540,7 +546,8 @@ ], "source": [ "size = 11\n", - "(swnu_network_umap_11,\n", + "(\n", + " swnu_network_umap_11,\n", " best_swnu_network_umap_11,\n", " _,\n", " __,\n", @@ -569,13 +576,13 @@ " standardise_method=standardise_method,\n", " include_features_in_path=include_features_in_path,\n", " include_features_in_input=include_features_in_input,\n", - " split_ids=None, \n", + " split_ids=None,\n", " split_indices=fold_list,\n", " k_fold=True,\n", " patience=patience,\n", " validation_metric=validation_metric,\n", " results_output=f\"{output_dir}/swnu_network_umap_focal_{gamma}_{size}_kfold.csv\",\n", - " verbose=False\n", + " verbose=False,\n", ")" ] }, @@ -2164,13 +2171,28 @@ } ], "source": [ - "best_swnu_network_umap_11[['f1', 'f1_scores', 'precision', \n", - " 'recall', 'valid_f1',\n", - " 'valid_f1_scores', 'valid_precision', \n", - " 'valid_recall', \n", - " 'dimensions','swnu_hidden_dim','sig_depth','ffn_hidden_dim', 'dropout_rate', 'learning_rate', 'seed',\n", - " 'loss_function', 'k_fold', \n", - " 'batch_size']]" + "best_swnu_network_umap_11[\n", + " [\n", + " \"f1\",\n", + " \"f1_scores\",\n", + " \"precision\",\n", + " \"recall\",\n", + " \"valid_f1\",\n", + " \"valid_f1_scores\",\n", + " \"valid_precision\",\n", + " \"valid_recall\",\n", + " \"dimensions\",\n", + " \"swnu_hidden_dim\",\n", + " \"sig_depth\",\n", + " \"ffn_hidden_dim\",\n", + " \"dropout_rate\",\n", + " \"learning_rate\",\n", + " \"seed\",\n", + " \"loss_function\",\n", + " \"k_fold\",\n", + " \"batch_size\",\n", + " ]\n", + "]" ] }, { diff --git a/notebooks/Talklife_MoC/talklifemoc-swnu-w20.ipynb b/notebooks/Talklife_MoC/talklifemoc-swnu-w20.ipynb index 3cba6fc..df649fb 100644 --- a/notebooks/Talklife_MoC/talklifemoc-swnu-w20.ipynb +++ b/notebooks/Talklife_MoC/talklifemoc-swnu-w20.ipynb @@ -119,11 +119,17 @@ "metadata": {}, "outputs": [], "source": [ - "#create indices for kfold\n", - "fold_col_names = [c for c in df.columns if 'fold' in c ]\n", + "# create indices for kfold\n", + "fold_col_names = [c for c in df.columns if \"fold\" in c]\n", "fold_list = []\n", "for foldc in fold_col_names:\n", - " fold_list.append((df[df[foldc]=='train'].index, df[df[foldc]=='dev'].index, df[df[foldc]=='test'].index))\n", + " fold_list.append(\n", + " (\n", + " df[df[foldc] == \"train\"].index,\n", + " df[df[foldc] == \"dev\"].index,\n", + " df[df[foldc] == \"test\"].index,\n", + " )\n", + " )\n", "fold_list = tuple(fold_list)" ] }, @@ -539,7 +545,8 @@ ], "source": [ "size = 20\n", - "(swnu_network_umap_20,\n", + "(\n", + " swnu_network_umap_20,\n", " best_swnu_network_umap_20,\n", " _,\n", " __,\n", @@ -568,13 +575,13 @@ " standardise_method=standardise_method,\n", " include_features_in_path=include_features_in_path,\n", " include_features_in_input=include_features_in_input,\n", - " split_ids=None, \n", + " split_ids=None,\n", " split_indices=fold_list,\n", " k_fold=True,\n", " patience=patience,\n", " validation_metric=validation_metric,\n", " results_output=f\"{output_dir}/swnu_network_umap_focal_{gamma}_{size}_kfold.csv\",\n", - " verbose=False\n", + " verbose=False,\n", ")" ] }, @@ -2163,13 +2170,28 @@ } ], "source": [ - "best_swnu_network_umap_20[['f1', 'f1_scores', 'precision', \n", - " 'recall', 'valid_f1',\n", - " 'valid_f1_scores', 'valid_precision', \n", - " 'valid_recall', \n", - " 'dimensions','swnu_hidden_dim','sig_depth','ffn_hidden_dim', 'dropout_rate', 'learning_rate', 'seed',\n", - " 'loss_function', 'k_fold', \n", - " 'batch_size']]" + "best_swnu_network_umap_20[\n", + " [\n", + " \"f1\",\n", + " \"f1_scores\",\n", + " \"precision\",\n", + " \"recall\",\n", + " \"valid_f1\",\n", + " \"valid_f1_scores\",\n", + " \"valid_precision\",\n", + " \"valid_recall\",\n", + " \"dimensions\",\n", + " \"swnu_hidden_dim\",\n", + " \"sig_depth\",\n", + " \"ffn_hidden_dim\",\n", + " \"dropout_rate\",\n", + " \"learning_rate\",\n", + " \"seed\",\n", + " \"loss_function\",\n", + " \"k_fold\",\n", + " \"batch_size\",\n", + " ]\n", + "]" ] }, { diff --git a/notebooks/Talklife_MoC/talklifemoc-swnu-w5.ipynb b/notebooks/Talklife_MoC/talklifemoc-swnu-w5.ipynb index 8db4486..a93ce03 100644 --- a/notebooks/Talklife_MoC/talklifemoc-swnu-w5.ipynb +++ b/notebooks/Talklife_MoC/talklifemoc-swnu-w5.ipynb @@ -24,7 +24,7 @@ "\n", "# set device\n", "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n", - "device = 'cuda:2'" + "device = \"cuda:2\"" ] }, { @@ -120,11 +120,17 @@ "metadata": {}, "outputs": [], "source": [ - "#create indices for kfold\n", - "fold_col_names = [c for c in df.columns if 'fold' in c ]\n", + "# create indices for kfold\n", + "fold_col_names = [c for c in df.columns if \"fold\" in c]\n", "fold_list = []\n", "for foldc in fold_col_names:\n", - " fold_list.append((df[df[foldc]=='train'].index, df[df[foldc]=='dev'].index, df[df[foldc]=='test'].index))\n", + " fold_list.append(\n", + " (\n", + " df[df[foldc] == \"train\"].index,\n", + " df[df[foldc] == \"dev\"].index,\n", + " df[df[foldc] == \"test\"].index,\n", + " )\n", + " )\n", "fold_list = tuple(fold_list)" ] }, @@ -540,7 +546,8 @@ ], "source": [ "size = 5\n", - "(swnu_network_umap_5,\n", + "(\n", + " swnu_network_umap_5,\n", " best_swnu_network_umap_5,\n", " _,\n", " __,\n", @@ -569,13 +576,13 @@ " standardise_method=standardise_method,\n", " include_features_in_path=include_features_in_path,\n", " include_features_in_input=include_features_in_input,\n", - " split_ids=None, \n", + " split_ids=None,\n", " split_indices=fold_list,\n", " k_fold=True,\n", " patience=patience,\n", " validation_metric=validation_metric,\n", " results_output=f\"{output_dir}/swnu_network_umap_focal_{gamma}_{size}_kfold.csv\",\n", - " verbose=False\n", + " verbose=False,\n", ")" ] }, @@ -2164,13 +2171,28 @@ } ], "source": [ - "best_swnu_network_umap_5[['f1', 'f1_scores', 'precision', \n", - " 'recall', 'valid_f1',\n", - " 'valid_f1_scores', 'valid_precision', \n", - " 'valid_recall', \n", - " 'dimensions','swnu_hidden_dim','sig_depth','ffn_hidden_dim', 'dropout_rate', 'learning_rate', 'seed',\n", - " 'loss_function', 'k_fold', \n", - " 'batch_size']]" + "best_swnu_network_umap_5[\n", + " [\n", + " \"f1\",\n", + " \"f1_scores\",\n", + " \"precision\",\n", + " \"recall\",\n", + " \"valid_f1\",\n", + " \"valid_f1_scores\",\n", + " \"valid_precision\",\n", + " \"valid_recall\",\n", + " \"dimensions\",\n", + " \"swnu_hidden_dim\",\n", + " \"sig_depth\",\n", + " \"ffn_hidden_dim\",\n", + " \"dropout_rate\",\n", + " \"learning_rate\",\n", + " \"seed\",\n", + " \"loss_function\",\n", + " \"k_fold\",\n", + " \"batch_size\",\n", + " ]\n", + "]" ] }, { diff --git a/notebooks/feed-forward-mnist.ipynb b/notebooks/feed-forward-mnist.ipynb index 83ea293..4fc8547 100644 --- a/notebooks/feed-forward-mnist.ipynb +++ b/notebooks/feed-forward-mnist.ipynb @@ -14,10 +14,10 @@ "import matplotlib.pyplot as plt\n", "from torch.optim.lr_scheduler import ReduceLROnPlateau, StepLR\n", "\n", - "import nlpsig_networks\n", - "from nlpsig_networks.ffn import FeedforwardNeuralNetModel\n", - "from nlpsig_networks.focal_loss import FocalLoss\n", - "from nlpsig_networks.pytorch_utils import training_pytorch, testing_pytorch\n", + "import sig_networks\n", + "from sig_networks.ffn import FeedforwardNeuralNetModel\n", + "from sig_networks.focal_loss import FocalLoss\n", + "from sig_networkstorch_utils import training_pytorch, testing_pytorch\n", "\n", "seed = 2023" ] diff --git a/notebooks/results-readout.py b/notebooks/results-readout.py index 46e5ce0..ab4c438 100644 --- a/notebooks/results-readout.py +++ b/notebooks/results-readout.py @@ -6,6 +6,7 @@ sizes = [5, 11, 20, 35, 80] seqsignet_sizes = [(3, 5, 3), (3, 5, 6), (3, 5, 11), (3, 5, 26)] + def readout_results_from_csv(csv_filename: str, model_name: str, digits: int): try: results_df = pd.read_csv(csv_filename) @@ -15,18 +16,37 @@ def readout_results_from_csv(csv_filename: str, model_name: str, digits: int): print(f"Precision: {round(results_df['precision'].mean(), digits)}") print(f"Recall: {round(results_df['recall'].mean(), digits)}") # print individual class F1, precision and recall scores averaged - f1_scores_stacked = np.stack(results_df['f1_scores'].apply(lambda x: list(np.fromstring(x[1:-1], sep=' ')))) - print(f"F1 scores: {[round(x, digits) for x in f1_scores_stacked.mean(axis=0)]}") - precision_scores_stacked = np.stack(results_df['precision_scores'].apply(lambda x: list(np.fromstring(x[1:-1], sep=' ')))) - print(f"Precision scores: {[round(x, digits) for x in precision_scores_stacked.mean(axis=0)]}") - recall_scores_stacked = np.stack(results_df['recall_scores'].apply(lambda x: list(np.fromstring(x[1:-1], sep=' ')))) - print(f"Recall scores: {[round(x, digits) for x in recall_scores_stacked.mean(axis=0)]}") + f1_scores_stacked = np.stack( + results_df["f1_scores"].apply( + lambda x: list(np.fromstring(x[1:-1], sep=" ")) + ) + ) + print( + f"F1 scores: {[round(x, digits) for x in f1_scores_stacked.mean(axis=0)]}" + ) + precision_scores_stacked = np.stack( + results_df["precision_scores"].apply( + lambda x: list(np.fromstring(x[1:-1], sep=" ")) + ) + ) + print( + f"Precision scores: {[round(x, digits) for x in precision_scores_stacked.mean(axis=0)]}" + ) + recall_scores_stacked = np.stack( + results_df["recall_scores"].apply( + lambda x: list(np.fromstring(x[1:-1], sep=" ")) + ) + ) + print( + f"Recall scores: {[round(x, digits) for x in recall_scores_stacked.mean(axis=0)]}" + ) print("\n") except: print(f"Error reading {csv_filename}") + def main(): - # parse command line arguments + # parse command line arguments parser = argparse.ArgumentParser() parser.add_argument( "--results-dir", @@ -47,14 +67,18 @@ def main(): gamma = 2 sizes = [5, 11, 20, 35] seqsignet_sizes = [(3, 5, 3), (3, 5, 6), (3, 5, 11)] - + # readout FFN file = f"{args.results_dir}/ffn_current_focal_{gamma}_kfold_best_model.csv" readout_results_from_csv(file, model_name="FFN with current", digits=args.digits) # readout FFN with history concatenation file = f"{args.results_dir}/ffn_mean_history_focal_{gamma}_kfold_best_model.csv" - readout_results_from_csv(file, model_name="FFN with mean history concatenated with current", digits=args.digits) + readout_results_from_csv( + file, + model_name="FFN with mean history concatenated with current", + digits=args.digits, + ) # readout BERT (focal loss) file = f"{args.results_dir}/bert_classifier_focal.csv" @@ -62,40 +86,57 @@ def main(): # readout BERT (ce) file = f"{args.results_dir}/bert_classifier_ce.csv" - readout_results_from_csv(file, model_name="BERT (cross-entropy)", digits=args.digits) + readout_results_from_csv( + file, model_name="BERT (cross-entropy)", digits=args.digits + ) # readout LSTM for size in sizes: - file = f"{args.results_dir}/lstm_history_{size}_focal_{gamma}_kfold_best_model.csv" - readout_results_from_csv(file, model_name=f"BiLSTM (size={size})", digits=args.digits) - + file = ( + f"{args.results_dir}/lstm_history_{size}_focal_{gamma}_kfold_best_model.csv" + ) + readout_results_from_csv( + file, model_name=f"BiLSTM (size={size})", digits=args.digits + ) + # readout SWNU-Network for size in sizes: file = f"{args.results_dir}/swnu_network_umap_focal_{gamma}_{size}_kfold_best_model.csv" - readout_results_from_csv(file, model_name=f"SWNU-Network (size={size})", digits=args.digits) + readout_results_from_csv( + file, model_name=f"SWNU-Network (size={size})", digits=args.digits + ) # readout SWMHAU-Network for size in sizes: file = f"{args.results_dir}/swmhau_network_umap_focal_{gamma}_{size}_kfold_best_model.csv" - readout_results_from_csv(file, model_name=f"SWMHAU-Network (size={size})", digits=args.digits) + readout_results_from_csv( + file, model_name=f"SWMHAU-Network (size={size})", digits=args.digits + ) # readout SeqSigNet for shift, window_size, n in seqsignet_sizes: file = f"{args.results_dir}/seqsignet_umap_focal_{gamma}_{shift}_{window_size}_{n}_kfold_best_model.csv" k = shift * n + (window_size - shift) - readout_results_from_csv(file, model_name=f"SeqSigNet (size={k})", digits=args.digits) + readout_results_from_csv( + file, model_name=f"SeqSigNet (size={k})", digits=args.digits + ) # readout SeqSigNetAttentionBiLSTM for shift, window_size, n in seqsignet_sizes: file = f"{args.results_dir}/seqsignet_attention_bilstm_umap_focal_{gamma}_{shift}_{window_size}_{n}_kfold_best_model.csv" k = shift * n + (window_size - shift) - readout_results_from_csv(file, model_name=f"SeqSigNetAttentionBiLSTM (size={k})", digits=args.digits) - + readout_results_from_csv( + file, model_name=f"SeqSigNetAttentionBiLSTM (size={k})", digits=args.digits + ) + # readout SeqSigNetAttentionEncoder for shift, window_size, n in seqsignet_sizes: file = f"{args.results_dir}/seqsignet_attention_encoder_umap_focal_{gamma}_{shift}_{window_size}_{n}_kfold_best_model.csv" k = shift * n + (window_size - shift) - readout_results_from_csv(file, model_name=f"SeqSigNetAttentionEncoder (size={k})", digits=args.digits) + readout_results_from_csv( + file, model_name=f"SeqSigNetAttentionEncoder (size={k})", digits=args.digits + ) + if __name__ == "__main__": - main() \ No newline at end of file + main() diff --git a/noxfile.py b/noxfile.py new file mode 100644 index 0000000..55a3580 --- /dev/null +++ b/noxfile.py @@ -0,0 +1,91 @@ +from __future__ import annotations + +import argparse +import shutil +from pathlib import Path + +import nox + +DIR = Path(__file__).parent.resolve() + +nox.options.sessions = ["lint", "tests"] + + +@nox.session(python=["3.8"]) +def lint(session: nox.Session) -> None: + """ + Run the linter. + """ + session.install("pre-commit") + session.run("pre-commit", "run", "--all-files", *session.posargs) + + +@nox.session(python=["3.8"]) +def tests(session: nox.Session) -> None: + """ + Run the unit and regular tests. + """ + session.install(".[test]") + session.run("pytest", *session.posargs) + + +@nox.session(python=["3.8"]) +def coverage(session: nox.Session) -> None: + """ + Run tests and compute coverage. + """ + + session.posargs.append("--cov=nlpsig") + tests(session) + + +@nox.session(python=["3.8"]) +def docs(session: nox.Session) -> None: + """ + Build the docs. Pass "--serve" to serve. + """ + + parser = argparse.ArgumentParser() + parser.add_argument("--serve", action="store_true", help="Serve after building") + args = parser.parse_args(session.posargs) + + session.install(".[docs]") + session.chdir("docs") + session.run("sphinx-build", "-M", "html", ".", "_build") + + if args.serve: + print("Launching docs at http://localhost:8000/ - use Ctrl-C to quit") + session.run("python", "-m", "http.server", "8000", "-d", "_build/html") + + +@nox.session(python=["3.8"]) +def build_api_docs(session: nox.Session) -> None: + """ + Build (regenerate) API docs. + """ + + session.install("sphinx") + session.chdir("docs") + session.run( + "sphinx-apidoc", + "-o", + "api/", + "--no-toc", + "--force", + "--module-first", + "../src/nlpsig", + ) + + +@nox.session(python=["3.8"]) +def build(session: nox.Session) -> None: + """ + Build an SDist and wheel. + """ + + build_p = DIR.joinpath("build") + if build_p.exists(): + shutil.rmtree(build_p) + + session.install("build") + session.run("python", "-m", "build") diff --git a/poetry.lock b/poetry.lock deleted file mode 100644 index 22c679f..0000000 --- a/poetry.lock +++ /dev/null @@ -1,1077 +0,0 @@ -# This file is automatically @generated by Poetry and should not be changed by hand. - -[[package]] -name = "accelerate" -version = "0.20.1" -description = "Accelerate" -category = "main" -optional = false -python-versions = ">=3.7.0" -files = [ - {file = "accelerate-0.20.1-py3-none-any.whl", hash = "sha256:bbc1f06879e724079a4e564691dae4021591531fd5558864fa4a644d70bede88"}, - {file = "accelerate-0.20.1.tar.gz", hash = "sha256:5d26bfdad7e31f4e479839c6afc2bdac4e153cfc3a06d52d9af17461e3c72121"}, -] - -[package.dependencies] -numpy = ">=1.17" -packaging = ">=20.0" -psutil = "*" -pyyaml = "*" -torch = ">=1.6.0" - -[package.extras] -dev = ["black (>=23.1,<24.0)", "datasets", "deepspeed", "evaluate", "hf-doc-builder (>=0.3.0)", "parameterized", "pytest", "pytest-subtests", "pytest-xdist", "rich", "ruff (>=0.0.241)", "scikit-learn", "scipy", "tqdm", "transformers", "urllib3 (<2.0.0)"] -quality = ["black (>=23.1,<24.0)", "hf-doc-builder (>=0.3.0)", "ruff (>=0.0.241)", "urllib3 (<2.0.0)"] -rich = ["rich"] -sagemaker = ["sagemaker"] -test-dev = ["datasets", "deepspeed", "evaluate", "scikit-learn", "scipy", "tqdm", "transformers"] -test-prod = ["parameterized", "pytest", "pytest-subtests", "pytest-xdist"] -test-trackers = ["comet-ml", "tensorboard", "wandb"] -testing = ["datasets", "deepspeed", "evaluate", "parameterized", "pytest", "pytest-subtests", "pytest-xdist", "scikit-learn", "scipy", "tqdm", "transformers"] - -[[package]] -name = "certifi" -version = "2023.7.22" -description = "Python package for providing Mozilla's CA Bundle." -category = "main" -optional = false -python-versions = ">=3.6" -files = [ - {file = "certifi-2023.7.22-py3-none-any.whl", hash = "sha256:92d6037539857d8206b8f6ae472e8b77db8058fec5937a1ef3f54304089edbb9"}, - {file = "certifi-2023.7.22.tar.gz", hash = "sha256:539cc1d13202e33ca466e88b2807e29f4c13049d6d87031a3c110744495cb082"}, -] - -[[package]] -name = "charset-normalizer" -version = "3.2.0" -description = "The Real First Universal Charset Detector. 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-[tool.poetry] -name = "nlpsig_networks" -version = "0.1.0" -description = "" +[build-system] +requires = ["hatchling"] +build-backend = "hatchling.build" + + +[project] +name = "sig_networks" authors = [ - "Ryan Chan ", - "Talia Tseriotou ", - "Kasra Hosseini " + { name = "Ryan Chan", email = "rchan@turing.ac.uk" }, + { name = "Talia Tseriotou", email = "t.tseriotou@qmul.ac.uk" }, + { name = "Kasra Hosseini", email = "khosseini@turing.ac.uk" }, ] +description = "Neural networks for longitudinal NLP classification tasks." readme = "README.md" -packages = [{include = "nlpsig_networks"}] - -[tool.poetry.dependencies] -python = ">=3.8, <3.9" -nlpsig = "^0.2.0" -torch = "1.9.0" -torchvision = "0.10.0" -signatory = "1.2.6.1.9.0" -scikit-learn = "^1.2.2" -pandas = "^1.5.3" -accelerate = "0.20.1" -transformers = "4.30.2" +requires-python = ">=3.8, <3.9" +classifiers = [ + "Development Status :: 1 - Planning", + "Intended Audience :: Science/Research", + "Intended Audience :: Developers", + "License :: OSI Approved :: BSD License", + "Operating System :: OS Independent", + "Programming Language :: Python", + "Programming Language :: Python :: 3", + "Programming Language :: Python :: 3 :: Only", + "Programming Language :: Python :: 3.8", + "Programming Language :: Python :: 3.9", + "Programming Language :: Python :: 3.10", + "Programming Language :: Python :: 3.11", + "Topic :: Scientific/Engineering", +] +dynamic = ["version"] +dependencies = [ + "torch == 1.9.0", + "torchvision == 0.10.0", + "signatory == 1.2.6.1.9.0", + "tdqm >= 0.0.1", + "pandas >= 1.5.3", + "umap-learn >= 0.5.3", + "scikit-learn >= 1.2.2", + "datasets >= 2.6.1", + "distinctipy >= 1.2.2", + "evaluate >= 0.4.0", + "accelerate == 0.20.1", + "transformers == 4.30.2" +] -[build-system] -requires = ["poetry-core"] -build-backend = "poetry.core.masonry.api" +[project.optional-dependencies] +test = [ + "pytest >=6", + "pytest-cov >=3", +] +dev = [ + "pytest >=6", + "pytest-cov >=3", +] +docs = [ + "sphinx>=4.0", + "myst_parser>=0.13", + "sphinx-book-theme>=0.1.0", + "sphinx_copybutton", + "furo", +] + +[project.urls] +Homepage = "https://github.com/datasig-ac-uk/nlpsig" +"Bug Tracker" = "https://github.com/datasig-ac-uk/nlpsig/issues" +Discussions = "https://github.com/datasig-ac-uk/nlpsig/discussions" +Changelog = "https://github.com/datasig-ac-uk/nlpsig/releases" +[tool.hatch] +version.path = "src/sig_networks/__init__.py" +envs.default.dependencies = [ + "pytest", + "pytest-cov", +] + + +[tool.pytest.ini_options] +minversion = "6.0" +addopts = ["-ra", "--showlocals", "--strict-markers", "--strict-config"] +xfail_strict = true +filterwarnings = [ + "error", + "ignore::UserWarning", + "ignore:Tensorflow not installed; ParametricUMAP will be unavailable:ImportWarning", # umap + "ignore:pkg_resources is deprecated as an API:DeprecationWarning", # umap + "ignore:Deprecated call to *:DeprecationWarning", + "ignore:numba.core.errors.NumbaDeprecationWarning", # umap using numba + "ignore:numba.core.errors.NumbaPendingDeprecationWarning", # umap using numba +] +log_cli_level = "INFO" +testpaths = [ + "tests", +] + + +[tool.mypy] +files = "src" +python_version = "3.8" +# warn_unused_configs = true +# strict = true +# show_error_codes = true +# enable_error_code = ["ignore-without-code", "redundant-expr", "truthy-bool"] +# warn_unreachable = true + + +[tool.ruff.per-file-ignores] +"tests/**" = ["T20"] +"noxfile.py" = ["T20"] + + +[tool.pylint] +py-version = "3.8" +ignore-paths= ["src/nlpsig/_version.py"] +reports.output-format = "colorized" +similarities.ignore-imports = "yes" +messages_control.disable = [ + "design", + "fixme", + "line-too-long", + "missing-module-docstring", + "wrong-import-position", +] diff --git a/src/sig_networks/__init__.py b/src/sig_networks/__init__.py new file mode 100644 index 0000000..f11ec6a --- /dev/null +++ b/src/sig_networks/__init__.py @@ -0,0 +1,3 @@ +from __future__ import annotations + +__version__ = "0.1.0" diff --git a/src/sig_networks/_compat/__init__.py b/src/sig_networks/_compat/__init__.py new file mode 100644 index 0000000..9d48db4 --- /dev/null +++ b/src/sig_networks/_compat/__init__.py @@ -0,0 +1 @@ +from __future__ import annotations diff --git a/src/sig_networks/_compat/typing.py b/src/sig_networks/_compat/typing.py new file mode 100644 index 0000000..9c94ee1 --- /dev/null +++ b/src/sig_networks/_compat/typing.py @@ -0,0 +1,14 @@ +from __future__ import annotations + +import sys + +if sys.version_info < (3, 8): + from typing import Literal, Protocol, runtime_checkable +else: + from typing import Literal, Protocol, runtime_checkable + +__all__ = ["Protocol", "runtime_checkable", "Literal"] + + +def __dir__() -> list[str]: + return __all__ diff --git a/nlpsig_networks/feature_concatenation.py b/src/sig_networks/feature_concatenation.py similarity index 99% rename from nlpsig_networks/feature_concatenation.py rename to src/sig_networks/feature_concatenation.py index e7395a9..e2dd2dc 100644 --- a/nlpsig_networks/feature_concatenation.py +++ b/src/sig_networks/feature_concatenation.py @@ -42,7 +42,7 @@ def __init__( - scaled_concatenation: concatenation of single value scaled path signature and embedding vector """ - super(FeatureConcatenation, self).__init__() + super().__init__() if comb_method not in [ "concatenation", diff --git a/nlpsig_networks/ffn_baseline.py b/src/sig_networks/ffn_baseline.py similarity index 97% rename from nlpsig_networks/ffn_baseline.py rename to src/sig_networks/ffn_baseline.py index 9303b7c..18a3d83 100644 --- a/nlpsig_networks/ffn_baseline.py +++ b/src/sig_networks/ffn_baseline.py @@ -32,7 +32,7 @@ def __init__( dropout_rate : float Probability of dropout. """ - super(FeedforwardNeuralNetModel, self).__init__() + super().__init__() if type(hidden_dim) == int: hidden_dim = [hidden_dim] diff --git a/nlpsig_networks/focal_loss.py b/src/sig_networks/focal_loss.py similarity index 95% rename from nlpsig_networks/focal_loss.py rename to src/sig_networks/focal_loss.py index 97f69e6..8427845 100644 --- a/nlpsig_networks/focal_loss.py +++ b/src/sig_networks/focal_loss.py @@ -1,5 +1,6 @@ +from __future__ import annotations + import math -from typing import List, Optional, Union import numpy as np import torch @@ -18,10 +19,10 @@ class FocalLoss(nn.Module): def __init__( self, gamma: float = 0.0, - alpha: Optional[Union[float, list]] = None, + alpha: float | list | None = None, size_average: bool = True, ): - super(FocalLoss, self).__init__() + super().__init__() self.gamma = gamma self.alpha = alpha if isinstance(alpha, (float, int)): @@ -73,9 +74,9 @@ def __init__( gamma: float, beta: float, no_of_classes: int, - samples_per_cls: Optional[List] = None, + samples_per_cls: list | None = None, ): - super(ClassBalanced_FocalLoss, self).__init__() + super().__init__() self.gamma = gamma self.beta = beta self.no_of_classes = no_of_classes diff --git a/nlpsig_networks/huggingface_loader.py b/src/sig_networks/huggingface_loader.py similarity index 97% rename from nlpsig_networks/huggingface_loader.py rename to src/sig_networks/huggingface_loader.py index 3f0c9e3..cd77fcf 100644 --- a/nlpsig_networks/huggingface_loader.py +++ b/src/sig_networks/huggingface_loader.py @@ -1,5 +1,6 @@ +from __future__ import annotations + from random import choice, randrange -from typing import List import pandas as pd from datasets import load_dataset @@ -86,7 +87,7 @@ def default_preprocess_newspop(self) -> None: self.dataset_df = dataset_df.replace(encode_labels) print("[INFO] preprocessed dataframe can be accessed: .dataset_df") - def _list_default_datetimes(self) -> List[str]: + def _list_default_datetimes(self) -> list[str]: list_datetimes = [ "2015-01-01 00:00:00", "2015-01-01 00:12:00", diff --git a/nlpsig_networks/lstm_baseline.py b/src/sig_networks/lstm_baseline.py similarity index 98% rename from nlpsig_networks/lstm_baseline.py rename to src/sig_networks/lstm_baseline.py index 864ddaa..14a8c2b 100644 --- a/nlpsig_networks/lstm_baseline.py +++ b/src/sig_networks/lstm_baseline.py @@ -37,7 +37,7 @@ def __init__( dropout_rate : float Probability of dropout. """ - super(LSTMModel, self).__init__() + super().__init__() self.hidden_dim = hidden_dim self.bidirectional = bidirectional diff --git a/nlpsig_networks/pytorch_utils.py b/src/sig_networks/pytorch_utils.py similarity index 96% rename from nlpsig_networks/pytorch_utils.py rename to src/sig_networks/pytorch_utils.py index de5495e..d368a0e 100644 --- a/nlpsig_networks/pytorch_utils.py +++ b/src/sig_networks/pytorch_utils.py @@ -2,7 +2,7 @@ import datetime import os -from typing import Optional +from uuid import uuid4 import numpy as np import pandas as pd @@ -14,9 +14,8 @@ from torch.optim.optimizer import Optimizer from torch.utils.data.dataloader import DataLoader from tqdm.auto import tqdm -from uuid import uuid4 -from nlpsig_networks.focal_loss import ClassBalanced_FocalLoss, FocalLoss +from sig_networks.focal_loss import ClassBalanced_FocalLoss, FocalLoss def _get_timestamp(add_runid: bool = True) -> str: @@ -370,16 +369,15 @@ def validation_pytorch( # compute macro recall score recall = sum(recall_scores) / len(recall_scores) - if verbose: - if epoch % verbose_epoch == 0: - print( - f"[Validation] || Epoch: {epoch+1} || " - f"Loss: {total_loss / len(valid_loader)} || " - f"Accuracy: {accuracy} || " - f"F1-score: {f1} || " - f"Precision: {precision} ||" - f"Recall: {recall}" - ) + if verbose and epoch % verbose_epoch == 0: + print( + f"[Validation] || Epoch: {epoch+1} || " + f"Loss: {total_loss / len(valid_loader)} || " + f"Accuracy: {accuracy} || " + f"F1-score: {f1} || " + f"Precision: {precision} ||" + f"Recall: {recall}" + ) return { "loss": total_loss / len(valid_loader), @@ -399,15 +397,15 @@ def training_pytorch( criterion: nn.Module, optimizer: Optimizer, num_epochs: int, - scheduler: Optional[_LRScheduler] = None, - valid_loader: Optional[DataLoader] = None, - seed: Optional[int] = 42, + scheduler: _LRScheduler | None = None, + valid_loader: DataLoader | None = None, + seed: int | None = 42, return_best: bool = False, save_best: bool = False, output: str = f"best_model_{_get_timestamp()}.pkl", early_stopping: bool = False, validation_metric: str = "loss", - patience: Optional[int] = 10, + patience: int | None = 10, device: str | None = None, verbose: bool = False, verbose_epoch: int = 100, @@ -475,10 +473,7 @@ def training_pytorch( ) # model train (& validation) per epoch - if verbose: - epochs_loop = tqdm(range(num_epochs)) - else: - epochs_loop = range(num_epochs) + epochs_loop = tqdm(range(num_epochs)) if verbose else range(num_epochs) for epoch in epochs_loop: # iterate through the training dataloader @@ -496,13 +491,11 @@ def training_pytorch( optimizer.step() # show training progress - if verbose: - if epoch % verbose_epoch == 0: - print("-" * 50) - print( - f"[Train] | Epoch: {epoch+1}/{num_epochs} || " - + f"Loss: {loss.item()}" - ) + if verbose and epoch % verbose_epoch == 0: + print("-" * 50) + print( + f"[Train] | Epoch: {epoch+1}/{num_epochs} || " + f"Loss: {loss.item()}" + ) if isinstance(valid_loader, DataLoader): # compute loss, accuracy and F1 on validation set @@ -559,12 +552,11 @@ def training_pytorch( if save_best | return_best: checkpoint = torch.load(f=output) model.load_state_dict(checkpoint["model_state_dict"]) - if save_best: - if verbose: - print( - f"Returning the best model which occurred at " - f"epoch {checkpoint['epoch']}" - ) + if save_best and verbose: + print( + f"Returning the best model which occurred at " + f"epoch {checkpoint['epoch']}" + ) if return_best: if not save_best: os.remove(output) @@ -684,10 +676,10 @@ def KFold_pytorch( num_epochs: int, batch_size: int = 64, return_metric_for_each_fold: bool = False, - seed: Optional[int] = 42, + seed: int | None = 42, return_best: bool = False, early_stopping: bool = False, - patience: Optional[int] = 10, + patience: int | None = 10, device: str | None = None, verbose: bool = False, verbose_epoch: int = 100, diff --git a/nlpsig_networks/__init__.py b/src/sig_networks/scripts/__init__.py similarity index 100% rename from nlpsig_networks/__init__.py rename to src/sig_networks/scripts/__init__.py diff --git a/nlpsig_networks/scripts/ffn_baseline_functions.py b/src/sig_networks/scripts/ffn_baseline_functions.py similarity index 99% rename from nlpsig_networks/scripts/ffn_baseline_functions.py rename to src/sig_networks/scripts/ffn_baseline_functions.py index b8ee5d7..e70d2bb 100644 --- a/nlpsig_networks/scripts/ffn_baseline_functions.py +++ b/src/sig_networks/scripts/ffn_baseline_functions.py @@ -10,9 +10,9 @@ import torch from tqdm.auto import tqdm -from nlpsig_networks.ffn_baseline import FeedforwardNeuralNetModel -from nlpsig_networks.pytorch_utils import SaveBestModel, _get_timestamp, set_seed -from nlpsig_networks.scripts.implement_model import implement_model +from sig_networks.ffn_baseline import FeedforwardNeuralNetModel +from sig_networks.pytorch_utils import SaveBestModel, _get_timestamp, set_seed +from sig_networks.scripts.implement_model import implement_model def implement_ffn( diff --git a/nlpsig_networks/scripts/fine_tune_bert_classification.py b/src/sig_networks/scripts/fine_tune_bert_classification.py similarity index 99% rename from nlpsig_networks/scripts/fine_tune_bert_classification.py rename to src/sig_networks/scripts/fine_tune_bert_classification.py index ec2251e..d08890e 100644 --- a/nlpsig_networks/scripts/fine_tune_bert_classification.py +++ b/src/sig_networks/scripts/fine_tune_bert_classification.py @@ -12,16 +12,16 @@ from nlpsig import TextEncoder from nlpsig.classification_utils import DataSplits, Folds from sklearn import metrics +from tqdm.auto import tqdm from transformers import ( AutoModelForSequenceClassification, AutoTokenizer, DataCollatorWithPadding, Trainer, ) -from tqdm.auto import tqdm -from nlpsig_networks.focal_loss import FocalLoss -from nlpsig_networks.pytorch_utils import SaveBestModel, _get_timestamp, set_seed +from sig_networks.focal_loss import FocalLoss +from sig_networks.pytorch_utils import SaveBestModel, _get_timestamp, set_seed def testing_transformer( @@ -483,10 +483,10 @@ def fine_tune_transformer_for_classification( valid_recall = [] valid_recall_scores = [] - labels = torch.empty((0)) - predicted = torch.empty((0)) - valid_labels = torch.empty((0)) - valid_predicted = torch.empty((0)) + labels = torch.empty(0) + predicted = torch.empty(0) + valid_labels = torch.empty(0) + valid_predicted = torch.empty(0) for k in range(n_splits): # compute how well the model performs on this fold results_for_fold = _fine_tune_transformer_for_data_split( diff --git a/nlpsig_networks/scripts/implement_model.py b/src/sig_networks/scripts/implement_model.py similarity index 99% rename from nlpsig_networks/scripts/implement_model.py rename to src/sig_networks/scripts/implement_model.py index 5296790..c8ea405 100644 --- a/nlpsig_networks/scripts/implement_model.py +++ b/src/sig_networks/scripts/implement_model.py @@ -9,8 +9,8 @@ import torch.nn as nn from nlpsig.classification_utils import DataSplits, Folds -from nlpsig_networks.focal_loss import FocalLoss -from nlpsig_networks.pytorch_utils import ( +from sig_networks.focal_loss import FocalLoss +from sig_networks.pytorch_utils import ( KFold_pytorch, _get_timestamp, testing_pytorch, diff --git a/nlpsig_networks/scripts/lstm_baseline_functions.py b/src/sig_networks/scripts/lstm_baseline_functions.py similarity index 99% rename from nlpsig_networks/scripts/lstm_baseline_functions.py rename to src/sig_networks/scripts/lstm_baseline_functions.py index 08381bb..9aeae95 100644 --- a/nlpsig_networks/scripts/lstm_baseline_functions.py +++ b/src/sig_networks/scripts/lstm_baseline_functions.py @@ -9,9 +9,9 @@ import torch from tqdm.auto import tqdm -from nlpsig_networks.lstm_baseline import LSTMModel -from nlpsig_networks.pytorch_utils import SaveBestModel, _get_timestamp, set_seed -from nlpsig_networks.scripts.implement_model import implement_model +from sig_networks.lstm_baseline import LSTMModel +from sig_networks.pytorch_utils import SaveBestModel, _get_timestamp, set_seed +from sig_networks.scripts.implement_model import implement_model def implement_lstm( diff --git a/nlpsig_networks/scripts/seqsignet_attention_bilstm_functions.py b/src/sig_networks/scripts/seqsignet_attention_bilstm_functions.py similarity index 99% rename from nlpsig_networks/scripts/seqsignet_attention_bilstm_functions.py rename to src/sig_networks/scripts/seqsignet_attention_bilstm_functions.py index a8b5eb9..48bed76 100644 --- a/nlpsig_networks/scripts/seqsignet_attention_bilstm_functions.py +++ b/src/sig_networks/scripts/seqsignet_attention_bilstm_functions.py @@ -8,10 +8,10 @@ import torch from tqdm.auto import tqdm -from nlpsig_networks.pytorch_utils import SaveBestModel, _get_timestamp, set_seed -from nlpsig_networks.scripts.implement_model import implement_model -from nlpsig_networks.scripts.seqsignet_functions import obtain_SeqSigNet_input -from nlpsig_networks.seqsignet_attention_bilstm import SeqSigNetAttentionBiLSTM +from sig_networks.pytorch_utils import SaveBestModel, _get_timestamp, set_seed +from sig_networks.scripts.implement_model import implement_model +from sig_networks.scripts.seqsignet_functions import obtain_SeqSigNet_input +from sig_networks.seqsignet_attention_bilstm import SeqSigNetAttentionBiLSTM def implement_seqsignet_attention_bilstm( diff --git a/nlpsig_networks/scripts/seqsignet_attention_encoder_functions.py b/src/sig_networks/scripts/seqsignet_attention_encoder_functions.py similarity index 99% rename from nlpsig_networks/scripts/seqsignet_attention_encoder_functions.py rename to src/sig_networks/scripts/seqsignet_attention_encoder_functions.py index e70bcf8..d7b7a53 100644 --- a/nlpsig_networks/scripts/seqsignet_attention_encoder_functions.py +++ b/src/sig_networks/scripts/seqsignet_attention_encoder_functions.py @@ -8,10 +8,10 @@ import torch from tqdm.auto import tqdm -from nlpsig_networks.pytorch_utils import SaveBestModel, _get_timestamp, set_seed -from nlpsig_networks.scripts.implement_model import implement_model -from nlpsig_networks.scripts.seqsignet_functions import obtain_SeqSigNet_input -from nlpsig_networks.seqsignet_attention_encoder import ( +from sig_networks.pytorch_utils import SaveBestModel, _get_timestamp, set_seed +from sig_networks.scripts.implement_model import implement_model +from sig_networks.scripts.seqsignet_functions import obtain_SeqSigNet_input +from sig_networks.seqsignet_attention_encoder import ( SeqSigNetAttentionEncoder, ) diff --git a/nlpsig_networks/scripts/seqsignet_functions.py b/src/sig_networks/scripts/seqsignet_functions.py similarity index 99% rename from nlpsig_networks/scripts/seqsignet_functions.py rename to src/sig_networks/scripts/seqsignet_functions.py index b936c54..a579fc5 100644 --- a/nlpsig_networks/scripts/seqsignet_functions.py +++ b/src/sig_networks/scripts/seqsignet_functions.py @@ -9,9 +9,9 @@ import torch from tqdm.auto import tqdm -from nlpsig_networks.pytorch_utils import SaveBestModel, _get_timestamp, set_seed -from nlpsig_networks.scripts.implement_model import implement_model -from nlpsig_networks.seqsignet_bilstm import SeqSigNet +from sig_networks.pytorch_utils import SaveBestModel, _get_timestamp, set_seed +from sig_networks.scripts.implement_model import implement_model +from sig_networks.seqsignet_bilstm import SeqSigNet def obtain_SeqSigNet_input( diff --git a/nlpsig_networks/scripts/swmhau_network_functions.py b/src/sig_networks/scripts/swmhau_network_functions.py similarity index 99% rename from nlpsig_networks/scripts/swmhau_network_functions.py rename to src/sig_networks/scripts/swmhau_network_functions.py index 2356592..4018ef0 100644 --- a/nlpsig_networks/scripts/swmhau_network_functions.py +++ b/src/sig_networks/scripts/swmhau_network_functions.py @@ -8,10 +8,10 @@ import torch from tqdm.auto import tqdm -from nlpsig_networks.pytorch_utils import SaveBestModel, _get_timestamp, set_seed -from nlpsig_networks.scripts.implement_model import implement_model -from nlpsig_networks.scripts.swnu_network_functions import obtain_SWNUNetwork_input -from nlpsig_networks.swmhau_network import SWMHAUNetwork +from sig_networks.pytorch_utils import SaveBestModel, _get_timestamp, set_seed +from sig_networks.scripts.implement_model import implement_model +from sig_networks.scripts.swnu_network_functions import obtain_SWNUNetwork_input +from sig_networks.swmhau_network import SWMHAUNetwork def implement_swmhau_network( diff --git a/nlpsig_networks/scripts/swnu_network_functions.py b/src/sig_networks/scripts/swnu_network_functions.py similarity index 99% rename from nlpsig_networks/scripts/swnu_network_functions.py rename to src/sig_networks/scripts/swnu_network_functions.py index 137baf2..cf87354 100644 --- a/nlpsig_networks/scripts/swnu_network_functions.py +++ b/src/sig_networks/scripts/swnu_network_functions.py @@ -9,9 +9,9 @@ import torch from tqdm.auto import tqdm -from nlpsig_networks.pytorch_utils import SaveBestModel, _get_timestamp, set_seed -from nlpsig_networks.scripts.implement_model import implement_model -from nlpsig_networks.swnu_network import SWNUNetwork +from sig_networks.pytorch_utils import SaveBestModel, _get_timestamp, set_seed +from sig_networks.scripts.implement_model import implement_model +from sig_networks.swnu_network import SWNUNetwork def obtain_SWNUNetwork_input( @@ -864,7 +864,7 @@ def swnu_network_hyperparameter_search( None if checkpoint["extra_info"]["hidden_dim_aug"] is None else [ - tuple([checkpoint["extra_info"]["hidden_dim_aug"]]) + (checkpoint["extra_info"]["hidden_dim_aug"],) for _ in range(len(test_results.index)) ] if (type(checkpoint["extra_info"]["hidden_dim_aug"]) == int) diff --git a/nlpsig_networks/seqsignet_attention_bilstm.py b/src/sig_networks/seqsignet_attention_bilstm.py similarity index 96% rename from nlpsig_networks/seqsignet_attention_bilstm.py rename to src/sig_networks/seqsignet_attention_bilstm.py index 4bc1de1..84212c6 100644 --- a/nlpsig_networks/seqsignet_attention_bilstm.py +++ b/src/sig_networks/seqsignet_attention_bilstm.py @@ -3,9 +3,9 @@ import torch import torch.nn as nn -from nlpsig_networks.feature_concatenation import FeatureConcatenation -from nlpsig_networks.ffn_baseline import FeedforwardNeuralNetModel -from nlpsig_networks.swmhau import SWMHAU +from sig_networks.feature_concatenation import FeatureConcatenation +from sig_networks.ffn_baseline import FeedforwardNeuralNetModel +from sig_networks.swmhau import SWMHAU class SeqSigNetAttentionBiLSTM(nn.Module): @@ -98,7 +98,7 @@ def __init__( - scaled_concatenation: concatenation of single value scaled path signature and embedding vector """ - super(SeqSigNetAttentionBiLSTM, self).__init__() + super().__init__() # SWMHAU applied to the input (the unit includes the convolution layer) self.swmhau = SWMHAU( diff --git a/nlpsig_networks/seqsignet_attention_encoder.py b/src/sig_networks/seqsignet_attention_encoder.py similarity index 96% rename from nlpsig_networks/seqsignet_attention_encoder.py rename to src/sig_networks/seqsignet_attention_encoder.py index 554d77d..239f352 100644 --- a/nlpsig_networks/seqsignet_attention_encoder.py +++ b/src/sig_networks/seqsignet_attention_encoder.py @@ -3,10 +3,10 @@ import torch import torch.nn as nn -from nlpsig_networks.feature_concatenation import FeatureConcatenation -from nlpsig_networks.ffn_baseline import FeedforwardNeuralNetModel -from nlpsig_networks.swmhau import SWMHAU -from nlpsig_networks.utils import obtain_signatures_mask +from sig_networks.feature_concatenation import FeatureConcatenation +from sig_networks.ffn_baseline import FeedforwardNeuralNetModel +from sig_networks.swmhau import SWMHAU +from sig_networks.utils import obtain_signatures_mask class SeqSigNetAttentionEncoder(nn.Module): @@ -101,7 +101,7 @@ def __init__( - scaled_concatenation: concatenation of single value scaled path signature and embedding vector """ - super(SeqSigNetAttentionEncoder, self).__init__() + super().__init__() if transformer_encoder_layers < 1: raise ValueError( diff --git a/nlpsig_networks/seqsignet_bilstm.py b/src/sig_networks/seqsignet_bilstm.py similarity index 97% rename from nlpsig_networks/seqsignet_bilstm.py rename to src/sig_networks/seqsignet_bilstm.py index ca32107..47e28cf 100644 --- a/nlpsig_networks/seqsignet_bilstm.py +++ b/src/sig_networks/seqsignet_bilstm.py @@ -3,9 +3,9 @@ import torch import torch.nn as nn -from nlpsig_networks.feature_concatenation import FeatureConcatenation -from nlpsig_networks.ffn_baseline import FeedforwardNeuralNetModel -from nlpsig_networks.swnu import SWNU +from sig_networks.feature_concatenation import FeatureConcatenation +from sig_networks.ffn_baseline import FeedforwardNeuralNetModel +from sig_networks.swnu import SWNU class SeqSigNet(nn.Module): @@ -101,7 +101,7 @@ def __init__( - scaled_concatenation: concatenation of single value scaled path signature and embedding vector """ - super(SeqSigNet, self).__init__() + super().__init__() if pooling not in ["signature", "lstm"]: raise ValueError( diff --git a/nlpsig_networks/specific_classification_utils.py b/src/sig_networks/specific_classification_utils.py similarity index 98% rename from nlpsig_networks/specific_classification_utils.py rename to src/sig_networks/specific_classification_utils.py index 2eba93e..a838051 100644 --- a/nlpsig_networks/specific_classification_utils.py +++ b/src/sig_networks/specific_classification_utils.py @@ -1,3 +1,5 @@ +from __future__ import annotations + import pickle from os import listdir from os.path import isfile, join @@ -183,8 +185,8 @@ def process_model_results(model_code_name, FOLDER_results): results0 = pickle.load(fin) for my_ran_seed in results0["classifier_params"]["RANDOM_SEED_list"]: - labels_final = torch.empty((0)) - predicted_final = torch.empty((0)) + labels_final = torch.empty(0) + predicted_final = torch.empty(0) seed_files = [f for f in per_model_files if (str(my_ran_seed) + "seed") in f] for sf in seed_files: diff --git a/nlpsig_networks/swmhau.py b/src/sig_networks/swmhau.py similarity index 97% rename from nlpsig_networks/swmhau.py rename to src/sig_networks/swmhau.py index 76193c3..eed7f30 100644 --- a/nlpsig_networks/swmhau.py +++ b/src/sig_networks/swmhau.py @@ -10,8 +10,8 @@ signature_channels, ) -from nlpsig_networks.ffn_baseline import FeedforwardNeuralNetModel -from nlpsig_networks.utils import obtain_signatures_mask +from sig_networks.ffn_baseline import FeedforwardNeuralNetModel +from sig_networks.utils import obtain_signatures_mask class SWMHA(nn.Module): @@ -60,7 +60,7 @@ def __init__( Whether or not to reverse the path before passing it through the signature layers, by default False. """ - super(SWMHA, self).__init__() + super().__init__() self.signature_terms = None # check if the parameters are compatible with each other @@ -266,7 +266,7 @@ class SWMHAU(nn.Module): def __init__( self, input_channels: int, - output_channels: int | None, + output_channels: int, log_signature: bool, sig_depth: int, num_heads: int, @@ -317,7 +317,7 @@ def __init__( Passed into `Augment` class from `signatory` package if `augmentation_type='signatory'`, by default None. """ - super(SWMHAU, self).__init__() + super().__init__() self.input_channels = input_channels self.output_channels = output_channels @@ -351,7 +351,7 @@ def __init__( # alternative to convolution: using Augment from signatory self.augment = Augment( in_channels=self.input_channels, - layer_sizes=self.hidden_dim_aug + [self.output_channels], + layer_sizes=[*self.hidden_dim_aug, self.output_channels], include_original=False, include_time=False, kernel_size=3, diff --git a/nlpsig_networks/swmhau_network.py b/src/sig_networks/swmhau_network.py similarity index 96% rename from nlpsig_networks/swmhau_network.py rename to src/sig_networks/swmhau_network.py index 8890116..044d569 100644 --- a/nlpsig_networks/swmhau_network.py +++ b/src/sig_networks/swmhau_network.py @@ -3,9 +3,9 @@ import torch import torch.nn as nn -from nlpsig_networks.feature_concatenation import FeatureConcatenation -from nlpsig_networks.ffn_baseline import FeedforwardNeuralNetModel -from nlpsig_networks.swmhau import SWMHAU +from sig_networks.feature_concatenation import FeatureConcatenation +from sig_networks.ffn_baseline import FeedforwardNeuralNetModel +from sig_networks.swmhau import SWMHAU class SWMHAUNetwork(nn.Module): @@ -90,7 +90,7 @@ def __init__( - scaled_concatenation: concatenation of single value scaled path signature and embedding vector """ - super(SWMHAUNetwork, self).__init__() + super().__init__() self.swmhau = SWMHAU( input_channels=input_channels, diff --git a/nlpsig_networks/swnu.py b/src/sig_networks/swnu.py similarity index 97% rename from nlpsig_networks/swnu.py rename to src/sig_networks/swnu.py index 0ecaa85..09f9095 100644 --- a/nlpsig_networks/swnu.py +++ b/src/sig_networks/swnu.py @@ -10,7 +10,7 @@ signature_channels, ) -from nlpsig_networks.utils import obtain_signatures_mask +from sig_networks.utils import obtain_signatures_mask class SWLSTM(nn.Module): @@ -57,7 +57,7 @@ def __init__( Whether or not a birectional LSTM is used for the final SWLSTM block, by default False (unidirectional LSTM is used in this case). """ - super(SWLSTM, self).__init__() + super().__init__() # logging inputs to the class self.input_size = input_size @@ -205,10 +205,7 @@ def forward(self, x: torch.Tensor): out = self.final_signature(x) elif self.pooling == "lstm": # add element-wise the forward and backward LSTM states - if self.BiLSTM: - out = h_n[-1, :, :] + h_n[-2, :, :] - else: - out = h_n[-1, :, :] + out = h_n[-1, :, :] + h_n[-2, :, :] if self.BiLSTM else h_n[-1, :, :] # reverse sequence padding out = out[inverse_perm] else: @@ -276,7 +273,7 @@ def __init__( Passed into `Augment` class from `signatory` package if `augmentation_type='signatory'`, by default None. """ - super(SWNU, self).__init__() + super().__init__() self.input_channels = input_channels self.log_signature = log_signature @@ -316,7 +313,7 @@ def __init__( # alternative to convolution: using Augment from signatory self.augment = Augment( in_channels=self.input_channels, - layer_sizes=self.hidden_dim_aug + [self.output_channels], + layer_sizes=[*self.hidden_dim_aug, self.output_channels], include_original=False, include_time=False, kernel_size=3, diff --git a/nlpsig_networks/swnu_network.py b/src/sig_networks/swnu_network.py similarity index 96% rename from nlpsig_networks/swnu_network.py rename to src/sig_networks/swnu_network.py index 411123d..d7c15ca 100644 --- a/nlpsig_networks/swnu_network.py +++ b/src/sig_networks/swnu_network.py @@ -3,9 +3,9 @@ import torch import torch.nn as nn -from nlpsig_networks.feature_concatenation import FeatureConcatenation -from nlpsig_networks.ffn_baseline import FeedforwardNeuralNetModel -from nlpsig_networks.swnu import SWNU +from sig_networks.feature_concatenation import FeatureConcatenation +from sig_networks.ffn_baseline import FeedforwardNeuralNetModel +from sig_networks.swnu import SWNU class SWNUNetwork(nn.Module): @@ -92,7 +92,7 @@ def __init__( - scaled_concatenation: concatenation of single value scaled path signature and embedding vector """ - super(SWNUNetwork, self).__init__() + super().__init__() if pooling not in ["signature", "lstm"]: raise ValueError( diff --git a/nlpsig_networks/utils.py b/src/sig_networks/utils.py similarity index 100% rename from nlpsig_networks/utils.py rename to src/sig_networks/utils.py diff --git a/tests/test_package.py b/tests/test_package.py new file mode 100644 index 0000000..a833527 --- /dev/null +++ b/tests/test_package.py @@ -0,0 +1,23 @@ +from __future__ import annotations + +import sig_networks as m +from sig_networks._compat.typing import Protocol, runtime_checkable + + +def test_version(): + assert m.__version__ + + +@runtime_checkable +class HasQuack(Protocol): + def quack() -> str: + ... + + +class Duck: + def quack() -> str: + return "quack" + + +def test_has_typing(): + assert isinstance(Duck(), HasQuack)