First working attempt at semantic loss for expt2

.gitignore

@@ -164,6 +164,7 @@ cython_debug/
 .jj
 scratchpad.ipynb
 datasets/
+explore/
 lightning_logs/
 logs/
 wandb/

.mailmap

@@ -0,0 +1 @@
+Cian Hughes <cian.hughes@dcu.ie> <chughes000@gmail.com>

poetry.lock

@@ -412,6 +412,28 @@ jinxed = {version = ">=1.1.0", markers = "platform_system == \"Windows\""}
 six = ">=1.9.0"
 wcwidth = ">=0.1.4"
 
+[[package]]
+name = "bokeh"
+version = "3.4.1"
+description = "Interactive plots and applications in the browser from Python"
+optional = false
+python-versions = ">=3.9"
+files = [
+    {file = "bokeh-3.4.1-py3-none-any.whl", hash = "sha256:1e3c502a0a8205338fc74dadbfa321f8a0965441b39501e36796a47b4017b642"},
+    {file = "bokeh-3.4.1.tar.gz", hash = "sha256:d824961e4265367b0750ce58b07e564ad0b83ca64b335521cd3421e9b9f10d89"},
+]
+
+[package.dependencies]
+contourpy = ">=1.2"
+Jinja2 = ">=2.9"
+numpy = ">=1.16"
+packaging = ">=16.8"
+pandas = ">=1.2"
+pillow = ">=7.1.0"
+PyYAML = ">=3.10"
+tornado = ">=6.2"
+xyzservices = ">=2021.09.1"
+
 [[package]]
 name = "bpython"
 version = "0.24"
@@ -636,6 +658,25 @@ files = [
     {file = "colorama-0.4.6.tar.gz", hash = "sha256:08695f5cb7ed6e0531a20572697297273c47b8cae5a63ffc6d6ed5c201be6e44"},
 ]
 
+[[package]]
+name = "colorcet"
+version = "3.1.0"
+description = "Collection of perceptually uniform colormaps"
+optional = false
+python-versions = ">=3.7"
+files = [
+    {file = "colorcet-3.1.0-py3-none-any.whl", hash = "sha256:2a7d59cc8d0f7938eeedd08aad3152b5319b4ba3bcb7a612398cc17a384cb296"},
+    {file = "colorcet-3.1.0.tar.gz", hash = "sha256:2921b3cd81a2288aaf2d63dbc0ce3c26dcd882e8c389cc505d6886bf7aa9a4eb"},
+]
+
+[package.extras]
+all = ["colorcet[doc]", "colorcet[examples]", "colorcet[tests-extra]", "colorcet[tests]"]
+doc = ["colorcet[examples]", "nbsite (>=0.8.4)", "sphinx-copybutton"]
+examples = ["bokeh", "holoviews", "matplotlib", "numpy"]
+tests = ["packaging", "pre-commit", "pytest (>=2.8.5)", "pytest-cov"]
+tests-examples = ["colorcet[examples]", "nbval"]
+tests-extra = ["colorcet[tests]", "pytest-mpl"]
+
 [[package]]
 name = "colorlog"
 version = "6.8.2"
@@ -1392,6 +1433,43 @@ files = [
     {file = "h11-0.14.0.tar.gz", hash = "sha256:8f19fbbe99e72420ff35c00b27a34cb9937e902a8b810e2c88300c6f0a3b699d"},
 ]
 
+[[package]]
+name = "holoviews"
+version = "1.18.3"
+description = "Stop plotting your data - annotate your data and let it visualize itself."
+optional = false
+python-versions = ">=3.9"
+files = [
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+    {file = "holoviews-1.18.3.tar.gz", hash = "sha256:578e30e89d72754f97a83ebe08198fec8e87cc7e49b25b9f31ec393f939ca500"},
+]
+
+[package.dependencies]
+colorcet = "*"
+numpy = ">=1.0"
+packaging = "*"
+pandas = ">=0.20.0"
+panel = ">=1.0"
+param = ">=1.12.0,<3.0"
+pyviz-comms = ">=0.7.4"
+
+[package.extras]
+all = ["bokeh (>=3.1)", "cftime", "codecov", "contourpy", "cudf", "dash (>=1.16)", "dask", "datashader (>=0.11.1)", "ffmpeg", "graphviz", "ibis-framework", "ipython (>=5.4.0)", "matplotlib (>=3)", "myst-nb (<1)", "nbconvert", "nbsite (>=0.8.4,<0.9.0)", "nbval", "netcdf4", "networkx", "notebook", "notebook (>=7.0)", "pillow", "playwright", "plotly (>=4.0)", "pooch", "pre-commit", "pyarrow", "pytest", "pytest-cov", "pytest-github-actions-annotate-failures", "pytest-playwright", "pytest-rerunfailures", "pytest-xdist", "ruff", "scikit-image", "scipy", "scipy (>=1.10)", "selenium", "shapely", "spatialpandas", "streamz (>=0.5.0)", "xarray (>=0.10.4)"]
+build = ["param (>=1.7.0)", "pyct (>=0.4.4)", "setuptools (>=30.3.0)"]
+doc = ["bokeh (>=3.1)", "cftime", "dash (>=1.16)", "dask", "datashader (>=0.11.1)", "ffmpeg", "graphviz", "ipython (>=5.4.0)", "matplotlib (>=3)", "myst-nb (<1)", "nbsite (>=0.8.4,<0.9.0)", "netcdf4", "networkx", "notebook", "notebook (>=7.0)", "pillow", "plotly (>=4.0)", "pooch", "pyarrow", "scikit-image", "scipy", "selenium", "shapely", "streamz (>=0.5.0)", "xarray (>=0.10.4)"]
+examples = ["bokeh (>=3.1)", "cftime", "dash (>=1.16)", "dask", "datashader (>=0.11.1)", "ffmpeg", "ipython (>=5.4.0)", "matplotlib (>=3)", "netcdf4", "networkx", "notebook", "notebook (>=7.0)", "pillow", "plotly (>=4.0)", "pooch", "pyarrow", "scikit-image", "scipy", "shapely", "streamz (>=0.5.0)", "xarray (>=0.10.4)"]
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+lint = ["pre-commit", "ruff"]
+notebook = ["ipython (>=5.4.0)", "notebook"]
+recommended = ["bokeh (>=3.1)", "ipython (>=5.4.0)", "matplotlib (>=3)", "notebook"]
+tests = ["bokeh (>=3.1)", "cftime", "contourpy", "dash (>=1.16)", "dask", "datashader (>=0.11.1)", "ffmpeg", "ibis-framework", "ipython (>=5.4.0)", "matplotlib (>=3)", "nbconvert", "networkx", "pillow", "plotly (>=4.0)", "pytest", "pytest-cov", "pytest-rerunfailures", "pytest-xdist", "scipy (>=1.10)", "selenium", "shapely", "spatialpandas", "xarray (>=0.10.4)"]
+tests-ci = ["codecov", "pytest-github-actions-annotate-failures"]
+tests-core = ["bokeh (>=3.1)", "contourpy", "ipython (>=5.4.0)", "matplotlib (>=3)", "nbconvert", "pillow", "plotly (>=4.0)", "pytest", "pytest-cov", "pytest-rerunfailures", "pytest-xdist"]
+tests-gpu = ["bokeh (>=3.1)", "cftime", "contourpy", "cudf", "dash (>=1.16)", "dask", "datashader (>=0.11.1)", "ffmpeg", "ibis-framework", "ipython (>=5.4.0)", "matplotlib (>=3)", "nbconvert", "networkx", "pillow", "plotly (>=4.0)", "pytest", "pytest-cov", "pytest-rerunfailures", "pytest-xdist", "scipy (>=1.10)", "selenium", "shapely", "spatialpandas", "xarray (>=0.10.4)"]
+tests-nb = ["nbval"]
+ui = ["playwright", "pytest-playwright"]
+unit-tests = ["bokeh (>=3.1)", "cftime", "contourpy", "dash (>=1.16)", "dask", "datashader (>=0.11.1)", "ffmpeg", "ibis-framework", "ipython (>=5.4.0)", "matplotlib (>=3)", "nbconvert", "netcdf4", "networkx", "notebook", "notebook (>=7.0)", "pillow", "plotly (>=4.0)", "pooch", "pre-commit", "pyarrow", "pytest", "pytest-cov", "pytest-rerunfailures", "pytest-xdist", "ruff", "scikit-image", "scipy", "scipy (>=1.10)", "selenium", "shapely", "spatialpandas", "streamz (>=0.5.0)", "xarray (>=0.10.4)"]
+
 [[package]]
 name = "httpcore"
 version = "1.0.5"
@@ -1437,6 +1515,41 @@ cli = ["click (==8.*)", "pygments (==2.*)", "rich (>=10,<14)"]
 http2 = ["h2 (>=3,<5)"]
 socks = ["socksio (==1.*)"]
 
+[[package]]
+name = "hvplot"
+version = "0.10.0"
+description = "A high-level plotting API for the PyData ecosystem built on HoloViews."
+optional = false
+python-versions = ">=3.8"
+files = [
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+    {file = "hvplot-0.10.0.tar.gz", hash = "sha256:e87486a95bfe151ab52ef163a5e93d9cbd043992cf0b755ccadd2bf36fedd376"},
+]
+
+[package.dependencies]
+bokeh = ">=1.0.0"
+colorcet = ">=2"
+holoviews = ">=1.11.0"
+numpy = ">=1.15"
+packaging = "*"
+pandas = "*"
+panel = ">=0.11.0"
+param = ">=1.12.0,<3.0"
+
+[package.extras]
+dev-extras = ["setuptools-scm (>=6)"]
+doc = ["hvplot[examples]", "nbsite (>=0.8.4)", "sphinxext-rediraffe"]
+examples = ["dask[dataframe] (>=2021.3.0)", "datashader (>=0.6.5)", "fugue[sql]", "geodatasets (>=2023.12.0)", "hvplot[fugue-sql]", "ibis-framework[duckdb]", "intake (>=0.6.5,<2.0.0)", "intake-parquet (>=0.2.3)", "intake-xarray (>=0.5.0)", "ipywidgets", "matplotlib", "networkx (>=2.6.3)", "notebook (>=5.4)", "numba (>=0.51.0)", "pillow (>=8.2.0)", "plotly", "polars", "pooch (>=1.6.0)", "s3fs (>=2022.1.0)", "scikit-image (>=0.17.2)", "scipy (>=1.5.3)", "selenium (>=3.141.0)", "streamz (>=0.3.0)", "xarray (>=0.18.2)", "xyzservices (>=2022.9.0)"]
+examples-tests = ["hvplot[examples]", "hvplot[tests-nb]"]
+fugue-sql = ["fugue-sql-antlr (>=0.2.0)", "jinja2", "qpd (>=0.4.4)", "sqlglot"]
+geo = ["cartopy", "fiona", "geopandas", "geoviews (>=1.9.0)", "pyproj", "rasterio", "rioxarray", "spatialpandas (>=0.4.3)"]
+graphviz = ["pygraphviz"]
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+hvdev-geo = ["geoviews (>=0.0.1a1)"]
+tests = ["fugue[sql]", "hvplot[fugue-sql]", "hvplot[tests-core]", "ibis-framework[duckdb]", "polars"]
+tests-core = ["dask[dataframe]", "ipywidgets", "matplotlib", "parameterized", "plotly", "pooch", "pre-commit", "pytest", "pytest-cov", "ruff", "scipy", "xarray"]
+tests-nb = ["nbval", "pytest-xdist"]
+
 [[package]]
 name = "idna"
 version = "3.7"
@@ -3001,6 +3114,78 @@ files = [
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+description = "Powerful data structures for data analysis, time series, and statistics"
+optional = false
+python-versions = ">=3.9"
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+spss = ["pyreadstat (>=1.2.0)"]
+sql-other = ["SQLAlchemy (>=2.0.0)", "adbc-driver-postgresql (>=0.8.0)", "adbc-driver-sqlite (>=0.8.0)"]
+test = ["hypothesis (>=6.46.1)", "pytest (>=7.3.2)", "pytest-xdist (>=2.2.0)"]
+xml = ["lxml (>=4.9.2)"]
+
 [[package]]
 name = "pandocfilters"
 version = "1.5.1"
@@ -3012,6 +3197,64 @@ files = [
     {file = "pandocfilters-1.5.1.tar.gz", hash = "sha256:002b4a555ee4ebc03f8b66307e287fa492e4a77b4ea14d3f934328297bb4939e"},
 ]
 
+[[package]]
+name = "panel"
+version = "1.4.4"
+description = "The powerful data exploration & web app framework for Python."
+optional = false
+python-versions = ">=3.9"
+files = [
+    {file = "panel-1.4.4-py3-none-any.whl", hash = "sha256:b49bb9676567b0c0730bf69348c057247080811aec56364dd4fcfba80e5e09a0"},
+    {file = "panel-1.4.4.tar.gz", hash = "sha256:659e9fc5b495e6519c5d07e8148fa5eeed9bc648356ec83fc299381ba5a726ef"},
+]
+
+[package.dependencies]
+bleach = "*"
+bokeh = ">=3.4.0,<3.5.0"
+linkify-it-py = "*"
+markdown = "*"
+markdown-it-py = "*"
+mdit-py-plugins = "*"
+pandas = ">=1.2"
+param = ">=2.1.0,<3.0"
+pyviz-comms = ">=2.0.0"
+requests = "*"
+tqdm = ">=4.48.0"
+typing-extensions = "*"
+xyzservices = ">=2021.09.1"
+
+[package.extras]
+all = ["aiohttp", "altair", "anywidget", "channels", "croniter", "dask-expr", "datashader", "diskcache", "django (<4)", "fastparquet", "flake8", "folium", "graphviz", "holoviews (>=1.16.0)", "hvplot", "ipyleaflet", "ipympl", "ipython (>=7.0)", "ipyvolume", "ipyvuetify", "ipywidgets", "ipywidgets-bokeh", "jupyter-bokeh (>=3.0.7)", "jupyter-server", "jupyterlab", "lxml", "matplotlib", "nbsite (>=0.8.4)", "nbval", "networkx (>=2.5)", "numba (<0.58)", "numpy", "pandas (<2.1.0)", "pandas (>=1.3)", "parameterized", "pillow", "playwright", "plotly", "plotly (>=4.0)", "pre-commit", "psutil", "pydeck", "pygraphviz", "pyinstrument (>=4.0)", "pytest", "pytest-asyncio", "pytest-cov", "pytest-playwright", "pytest-rerunfailures", "pytest-xdist", "python-graphviz", "pyvista", "reacton", "scikit-image", "scikit-learn", "scipy", "seaborn", "streamz", "textual", "tomli", "twine", "vega-datasets", "vtk", "watchfiles", "xarray", "xgboost"]
+all-pip = ["aiohttp", "altair", "anywidget", "channels", "croniter", "dask-expr", "datashader", "diskcache", "django (<4)", "fastparquet", "flake8", "folium", "graphviz", "holoviews (>=1.16.0)", "hvplot", "ipyleaflet", "ipympl", "ipython (>=7.0)", "ipyvolume", "ipyvuetify", "ipywidgets", "ipywidgets-bokeh", "jupyter-bokeh (>=3.0.7)", "jupyter-server", "jupyterlab", "lxml", "matplotlib", "nbsite (>=0.8.4)", "nbval", "networkx (>=2.5)", "numba (<0.58)", "numpy", "pandas (<2.1.0)", "pandas (>=1.3)", "parameterized", "pillow", "playwright", "plotly", "plotly (>=4.0)", "pre-commit", "psutil", "pydeck", "pyinstrument (>=4.0)", "pytest", "pytest-asyncio", "pytest-cov", "pytest-playwright", "pytest-rerunfailures", "pytest-xdist", "pyvista", "reacton", "scikit-image", "scikit-learn", "scipy", "seaborn", "streamz", "textual", "tomli", "twine", "vega-datasets", "vtk", "watchfiles", "xarray", "xgboost"]
+build = ["bleach", "bokeh (>=3.4.0,<3.5.0)", "cryptography (<39)", "markdown", "packaging", "param (>=2.0.0)", "pyviz-comms (>=2.0.0)", "requests", "setuptools (>=42)", "tqdm (>=4.48.0)", "urllib3 (<2.0)"]
+doc = ["holoviews (>=1.16.0)", "jupyterlab", "lxml", "matplotlib", "nbsite (>=0.8.4)", "pandas (<2.1.0)", "pillow", "plotly"]
+examples = ["aiohttp", "altair", "channels", "croniter", "dask-expr", "datashader", "django (<4)", "fastparquet", "folium", "graphviz", "holoviews (>=1.16.0)", "hvplot", "ipyleaflet", "ipympl", "ipyvolume", "ipyvuetify", "ipywidgets", "ipywidgets-bokeh", "jupyter-bokeh (>=3.0.7)", "networkx (>=2.5)", "plotly (>=4.0)", "pydeck", "pygraphviz", "pyinstrument (>=4.0)", "python-graphviz", "pyvista", "reacton", "scikit-image", "scikit-learn", "seaborn", "streamz", "textual", "vega-datasets", "vtk", "xarray", "xgboost"]
+recommended = ["holoviews (>=1.16.0)", "jupyterlab", "matplotlib", "pillow", "plotly"]
+tests = ["altair", "anywidget", "diskcache", "flake8", "folium", "holoviews (>=1.16.0)", "ipympl", "ipython (>=7.0)", "ipyvuetify", "ipywidgets-bokeh", "nbval", "numba (<0.58)", "numpy", "pandas (>=1.3)", "parameterized", "pre-commit", "psutil", "pytest", "pytest-asyncio", "pytest-cov", "pytest-rerunfailures", "pytest-xdist", "reacton", "scipy", "textual", "twine", "watchfiles"]
+tests-core = ["altair", "anywidget", "diskcache", "flake8", "folium", "holoviews (>=1.16.0)", "ipython (>=7.0)", "nbval", "numpy", "pandas (>=1.3)", "parameterized", "pre-commit", "psutil", "pytest", "pytest-asyncio", "pytest-cov", "pytest-rerunfailures", "pytest-xdist", "scipy", "textual", "watchfiles"]
+ui = ["jupyter-server", "playwright", "pytest-playwright", "tomli"]
+
+[[package]]
+name = "param"
+version = "2.1.0"
+description = "Make your Python code clearer and more reliable by declaring Parameters."
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "param-2.1.0-py3-none-any.whl", hash = "sha256:f31d3745d227347d29b5868c4e4e3077df07463889b91d3bb28e634fde211e1c"},
+    {file = "param-2.1.0.tar.gz", hash = "sha256:a7b30b08b547e2b78b02aeba6ed34e3c6a638f8e4824a76a96ffa2d7cf57e71f"},
+]
+
+[package.extras]
+all = ["param[doc]", "param[lint]", "param[tests-full]"]
+doc = ["nbsite (==0.8.4)", "param[examples]", "sphinx-remove-toctrees"]
+examples = ["aiohttp", "pandas", "panel"]
+lint = ["flake8", "pre-commit"]
+tests = ["coverage[toml]", "pytest", "pytest-asyncio"]
+tests-deser = ["odfpy", "openpyxl", "pyarrow", "tables", "xlrd"]
+tests-examples = ["nbval", "param[examples]", "pytest (<8.1)", "pytest-asyncio", "pytest-xdist"]
+tests-full = ["cloudpickle", "gmpy", "ipython", "jsonschema", "nest-asyncio", "numpy", "pandas", "param[tests-deser]", "param[tests-examples]", "param[tests]"]
+
 [[package]]
 name = "parso"
 version = "0.8.4"
@@ -3310,6 +3553,54 @@ files = [
 [package.extras]
 tests = ["pytest"]
 
+[[package]]
+name = "pyarrow"
+version = "16.1.0"
+description = "Python library for Apache Arrow"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "pyarrow-16.1.0-cp310-cp310-macosx_10_15_x86_64.whl", hash = "sha256:17e23b9a65a70cc733d8b738baa6ad3722298fa0c81d88f63ff94bf25eaa77b9"},
+    {file = "pyarrow-16.1.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:4740cc41e2ba5d641071d0ab5e9ef9b5e6e8c7611351a5cb7c1d175eaf43674a"},
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+    {file = "pyarrow-16.1.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:f68f409e7b283c085f2da014f9ef81e885d90dcd733bd648cfba3ef265961848"},
+    {file = "pyarrow-16.1.0-cp310-cp310-manylinux_2_28_aarch64.whl", hash = "sha256:a8914cd176f448e09746037b0c6b3a9d7688cef451ec5735094055116857580c"},
+    {file = "pyarrow-16.1.0-cp310-cp310-manylinux_2_28_x86_64.whl", hash = "sha256:48be160782c0556156d91adbdd5a4a7e719f8d407cb46ae3bb4eaee09b3111bd"},
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+    {file = "pyarrow-16.1.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:ddfe389a08ea374972bd4065d5f25d14e36b43ebc22fc75f7b951f24378bf0b5"},
+    {file = "pyarrow-16.1.0-cp39-cp39-manylinux_2_28_aarch64.whl", hash = "sha256:3b20bd67c94b3a2ea0a749d2a5712fc845a69cb5d52e78e6449bbd295611f3aa"},
+    {file = "pyarrow-16.1.0-cp39-cp39-manylinux_2_28_x86_64.whl", hash = "sha256:ba8ac20693c0bb0bf4b238751d4409e62852004a8cf031c73b0e0962b03e45e3"},
+    {file = "pyarrow-16.1.0-cp39-cp39-win_amd64.whl", hash = "sha256:31a1851751433d89a986616015841977e0a188662fcffd1a5677453f1df2de0a"},
+    {file = "pyarrow-16.1.0.tar.gz", hash = "sha256:15fbb22ea96d11f0b5768504a3f961edab25eaf4197c341720c4a387f6c60315"},
+]
+
+[package.dependencies]
+numpy = ">=1.16.6"
+
 [[package]]
 name = "pycparser"
 version = "2.22"
@@ -3444,6 +3735,36 @@ extra = ["bitsandbytes (==0.41.0)", "hydra-core (>=1.0.5)", "jsonargparse[signat
 strategies = ["deepspeed (>=0.8.2,<=0.9.3)"]
 test = ["cloudpickle (>=1.3)", "coverage (==7.3.1)", "fastapi", "onnx (>=0.14.0)", "onnxruntime (>=0.15.0)", "pandas (>1.0)", "psutil (<5.9.6)", "pytest (==7.4.0)", "pytest-cov (==4.1.0)", "pytest-random-order (==1.1.0)", "pytest-rerunfailures (==12.0)", "pytest-timeout (==2.1.0)", "scikit-learn (>0.22.1)", "tensorboard (>=2.9.1)", "uvicorn"]
 
+[[package]]
+name = "pytz"
+version = "2024.1"
+description = "World timezone definitions, modern and historical"
+optional = false
+python-versions = "*"
+files = [
+    {file = "pytz-2024.1-py2.py3-none-any.whl", hash = "sha256:328171f4e3623139da4983451950b28e95ac706e13f3f2630a879749e7a8b319"},
+    {file = "pytz-2024.1.tar.gz", hash = "sha256:2a29735ea9c18baf14b448846bde5a48030ed267578472d8955cd0e7443a9812"},
+]
+
+[[package]]
+name = "pyviz-comms"
+version = "3.0.2"
+description = "A JupyterLab extension for rendering HoloViz content."
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "pyviz_comms-3.0.2-py3-none-any.whl", hash = "sha256:31541b976a21b7738557c3ea23bd8e44e94e736b9ed269570dcc28db4449d7e3"},
+    {file = "pyviz_comms-3.0.2.tar.gz", hash = "sha256:3167df932656416c4bd711205dad47e986a3ebae1f316258ddc26f9e01513ef7"},
+]
+
+[package.dependencies]
+param = "*"
+
+[package.extras]
+all = ["pyviz-comms[build]", "pyviz-comms[tests]"]
+build = ["jupyterlab (>=4.0,<5.0)", "keyring", "rfc3986", "setuptools (>=40.8.0)", "twine"]
+tests = ["flake8", "pytest"]
+
 [[package]]
 name = "pywin32"
 version = "306"
@@ -4823,6 +5144,17 @@ files = [
 mypy-extensions = ">=0.3.0"
 typing-extensions = ">=3.7.4"
 
+[[package]]
+name = "tzdata"
+version = "2024.1"
+description = "Provider of IANA time zone data"
+optional = false
+python-versions = ">=2"
+files = [
+    {file = "tzdata-2024.1-py2.py3-none-any.whl", hash = "sha256:9068bc196136463f5245e51efda838afa15aaeca9903f49050dfa2679db4d252"},
+    {file = "tzdata-2024.1.tar.gz", hash = "sha256:2674120f8d891909751c38abcdfd386ac0a5a1127954fbc332af6b5ceae07efd"},
+]
+
 [[package]]
 name = "uc-micro-py"
 version = "1.0.3"
@@ -5009,6 +5341,17 @@ files = [
     {file = "widgetsnbextension-4.0.10.tar.gz", hash = "sha256:64196c5ff3b9a9183a8e699a4227fb0b7002f252c814098e66c4d1cd0644688f"},
 ]
 
+[[package]]
+name = "xyzservices"
+version = "2024.4.0"
+description = "Source of XYZ tiles providers"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "xyzservices-2024.4.0-py3-none-any.whl", hash = "sha256:b83e48c5b776c9969fffcfff57b03d02b1b1cd6607a9d9c4e7f568b01ef47f4c"},
+    {file = "xyzservices-2024.4.0.tar.gz", hash = "sha256:6a04f11487a6fb77d92a98984cd107fbd9157fd5e65f929add9c3d6e604ee88c"},
+]
+
 [[package]]
 name = "yarl"
 version = "1.9.4"
@@ -5115,4 +5458,4 @@ multidict = ">=4.0"
 [metadata]
 lock-version = "2.0"
 python-versions = "^3.11"
-content-hash = "74e84f457ce411d0612153d658f0e43f92f140297fcc1812501b74e7757fedc3"
+content-hash = "f607472660b04b7f6f5d49a4561730f788a46f0d1e0176322e872111b00481cd"

pyproject.toml

@@ -28,6 +28,8 @@ polars = "^0.20.28"
 jupyter = "^1.0.0"
 safetensors = "^0.4.3"
 alive-progress = "^3.1.5"
+hvplot = "^0.10.0"
+pyarrow = "^16.1.0"
 
 
 [build-system]

symbolic_nn_tests/experiment2/model.py

@@ -40,12 +40,12 @@ class Model(nn.Module):
         x0, x1 = x
         y0 = self.encode_x0(x0)
         y1 = self.encode_x1(x1)
-        x = torch.cat([y0, y1], dim=1)
+        y = torch.cat([y0, y1], dim=1)
-        y = self.ff(x)
+        y = self.ff(y)
         if self.return_module_y:
-            return y, y0, y1
+            return x, (y, y0, y1)
         else:
-            return y
+            return x, y
 
 
 # This is just a quick, lazy way to ensure all models are trained on the same dataset
@@ -59,7 +59,95 @@ def get_singleton_dataset():
     )
 
 
-def main(loss_func=nn.functional.smooth_l1_loss, logger=None, **kwargs):
+def smooth_l1_loss(out, y):
+    _, y_pred = out
+    return nn.functional.smooth_l1_loss(y_pred, y)
+
+
+def sech(x):
+    return torch.reciprocal(torch.cosh(x))
+
+
+def linear_fit(x, y):
+    mean_x = torch.mean(x)
+    mean_y = torch.mean(y)
+    cov_xy = torch.mean(x * y) - (mean_x * mean_y)
+    var_x = torch.mean(x * x) - (mean_x * mean_x)
+    m = cov_xy / var_x
+    c = mean_y - (m * mean_x)
+    return m, c
+
+
+def line(x, m, c):
+    return (m * x) + c
+
+
+def linear_residuals(x, y, m, c):
+    return y - line(x, m, c)
+
+
+def semantic_loss(x, y_pred, w, a):
+    m, c = linear_fit(x, y_pred)
+    residuals = linear_residuals(x, y_pred, m, c)
+    scaled_residuals = residuals * sech(w * x)
+    slope_penalty = torch.nn.functional.softmax(a * m, dim=0)
+    loss = torch.mean(scaled_residuals**2) + torch.mean(slope_penalty)
+    return loss
+
+
+def loss(out, y):
+    x, y_pred = out
+    x0, x1 = x
+
+    # Here, we want to make semantic use of the differential electronegativity of the molecule
+    # so start by calculating that
+    mean_electronegativities = torch.tensor(
+        [i[:, 3].mean() for i in x0], dtype=torch.float32
+    ).to(y_pred.device)
+    diff_electronegativity = (
+        torch.tensor(
+            [
+                (i[:, 3] - mean).abs().sum()
+                for i, mean in zip(x0, mean_electronegativities)
+            ],
+            dtype=torch.float32,
+        )
+        * 4.0
+    ).to(y_pred.device)
+
+    # Then, we need to get a linear best fit on that. Our semantic info is based on a graph of
+    # En (y) vs differential electronegativity on the x vs y axes, so y_pred is y here
+    m, c = linear_fit(diff_electronegativity, y_pred)
+
+    # To start with, we want to calculate a penalty based on deviation from a linear relationship
+    # Scaling is being based on 1/sech(w*r) as this increases multiplier as deviation grows.
+    # `w` was selected based on noting that the residual spread before eneg scaling was about 25;
+    # enegs were normalised as x/4, so we want to incentivize a spread of about 25/4~=6, and w=0.2
+    # causes the penalty function to cross 2 at just over 6. Yes, that's a bit arbitrary but we're
+    # just steering the model not applying hard constraints to it shold be fine.
+    residual_penalty = (
+        (
+            linear_residuals(diff_electronegativity, y_pred, m, c)
+            / sech(0.2 * diff_electronegativity)
+        )
+        .abs()
+        .float()
+        .mean()
+    )
+
+    # We also need to calculate a penalty that incentivizes a positive slope. For this, im using softmax
+    # to scale the slope as it will penalise negative slopes while not just creating a reward hack for
+    # maximizing slope. The softmax function approximates 1 from about 5 onwards, so if we multiply m by
+    # 500, then our penalty should be almost minimised for any slope above 0.01 and maximised below 0.01.
+    # This should suffice for incentivizing the model to favour positive slopes.
+    slope_penalty = (torch.nn.functional.softmax(-m * 500.0) + 1).mean()
+
+    # Finally, let's get a smooth L1 loss and scale it based on these penalty functions
+    return nn.functional.smooth_l1_loss(y_pred, y) * residual_penalty * slope_penalty
+
+
+# def main(loss_func=smooth_l1_loss, logger=None, **kwargs):
+def main(loss_func=loss, logger=None, **kwargs):
     import lightning as L
 
     from symbolic_nn_tests.train import TrainingWrapper
@@ -72,7 +160,7 @@ def main(loss_func=nn.functional.smooth_l1_loss, logger=None, **kwargs):
     train, val, test = get_singleton_dataset()
     lmodel = TrainingWrapper(Model(), loss_func=loss_func)
     lmodel.configure_optimizers(optimizer=torch.optim.NAdam, **kwargs)
-    trainer = L.Trainer(max_epochs=20, logger=logger)
+    trainer = L.Trainer(max_epochs=10, logger=logger, num_sanity_val_steps=0)
     trainer.fit(model=lmodel, train_dataloaders=train, val_dataloaders=val)
     trainer.test(dataloaders=test)
 

symbolic_nn_tests/experiment2/semantic_loss.py

@@ -1 +1,16 @@
 import torch
+
+
+# TODO: implement semantic loss functions
+# These functions would enforce relationships we expect to be present in the results
+# if the model is performing correctly. The first ones to implement would be:
+#     - En should be proportional to molecular mass
+#          - Bigger molecule = more dof and more strain
+#     - En should be inversely proportional to the differential in electronegativity
+#          - Higher electroneg diff = more stable molecule
+#          - calc diff as `torch.sum( torch.abs( electronegativity - electronegativity.mean() ) )`
+# Best way to enforce this relationship would probably be to apply a multiplier based on
+# a normalized sigmoid curve. This would incentivize the model to ensure slope has correct sign
+# without creating a reward hack for maximizing/minimizing m and preventing exploding gradients.
+# It also allows us to avoid the assumption of linearity: we only care about the direction of
+# proportionality.

symbolic_nn_tests/experiment2/train.py

@@ -0,0 +1,20 @@
+from symbolic_nn_tests.train import TrainingWrapper
+
+
+class SemanticModuleTrainingWrapper(TrainingWrapper):
+    def __init__(self, model, *args, loss_func0, loss_func1, loss_agg, **kwargs):
+        assert len(args) == 0
+
+        super().__init__(model, **kwargs)
+        self.loss_func0 = loss_func0
+        self.loss_func1 = loss_func1
+        self.loss_agg = loss_agg
+
+    def _forward_step(self, batch, batch_idx, label=""):
+        x, y = batch
+        y_pred, y0, y1 = self.model(x)
+        loss = self.loss_func(y_pred, y)
+        loss0 = self.loss_func0(y0, x)
+        loss1 = self.loss_func1(y1, x)
+        self.log(f"{label}{'_' if label else ''}loss", loss)
+        return self.loss_agg(loss, loss0, loss1)