mirror of
https://github.com/Cian-H/symbolic_nn_tests.git
synced 2025-12-22 14:11:59 +00:00
First working attempt at semantic loss for expt2
This commit is contained in:
1
.gitignore
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.gitignore
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@@ -164,6 +164,7 @@ cython_debug/
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.jj
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scratchpad.ipynb
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datasets/
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explore/
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lightning_logs/
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logs/
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wandb/
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1
.mailmap
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1
.mailmap
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@@ -0,0 +1 @@
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Cian Hughes <cian.hughes@dcu.ie> <chughes000@gmail.com>
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345
poetry.lock
generated
345
poetry.lock
generated
@@ -412,6 +412,28 @@ jinxed = {version = ">=1.1.0", markers = "platform_system == \"Windows\""}
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six = ">=1.9.0"
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wcwidth = ">=0.1.4"
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[[package]]
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name = "bokeh"
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version = "3.4.1"
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description = "Interactive plots and applications in the browser from Python"
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optional = false
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||||
python-versions = ">=3.9"
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files = [
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{file = "bokeh-3.4.1-py3-none-any.whl", hash = "sha256:1e3c502a0a8205338fc74dadbfa321f8a0965441b39501e36796a47b4017b642"},
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{file = "bokeh-3.4.1.tar.gz", hash = "sha256:d824961e4265367b0750ce58b07e564ad0b83ca64b335521cd3421e9b9f10d89"},
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]
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[package.dependencies]
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contourpy = ">=1.2"
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Jinja2 = ">=2.9"
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numpy = ">=1.16"
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packaging = ">=16.8"
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pandas = ">=1.2"
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pillow = ">=7.1.0"
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PyYAML = ">=3.10"
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tornado = ">=6.2"
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xyzservices = ">=2021.09.1"
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[[package]]
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name = "bpython"
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version = "0.24"
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@@ -636,6 +658,25 @@ files = [
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{file = "colorama-0.4.6.tar.gz", hash = "sha256:08695f5cb7ed6e0531a20572697297273c47b8cae5a63ffc6d6ed5c201be6e44"},
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]
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[[package]]
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name = "colorcet"
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version = "3.1.0"
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description = "Collection of perceptually uniform colormaps"
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optional = false
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python-versions = ">=3.7"
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files = [
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{file = "colorcet-3.1.0-py3-none-any.whl", hash = "sha256:2a7d59cc8d0f7938eeedd08aad3152b5319b4ba3bcb7a612398cc17a384cb296"},
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{file = "colorcet-3.1.0.tar.gz", hash = "sha256:2921b3cd81a2288aaf2d63dbc0ce3c26dcd882e8c389cc505d6886bf7aa9a4eb"},
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]
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[package.extras]
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all = ["colorcet[doc]", "colorcet[examples]", "colorcet[tests-extra]", "colorcet[tests]"]
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doc = ["colorcet[examples]", "nbsite (>=0.8.4)", "sphinx-copybutton"]
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examples = ["bokeh", "holoviews", "matplotlib", "numpy"]
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tests = ["packaging", "pre-commit", "pytest (>=2.8.5)", "pytest-cov"]
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tests-examples = ["colorcet[examples]", "nbval"]
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tests-extra = ["colorcet[tests]", "pytest-mpl"]
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[[package]]
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name = "colorlog"
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version = "6.8.2"
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@@ -1392,6 +1433,43 @@ files = [
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{file = "h11-0.14.0.tar.gz", hash = "sha256:8f19fbbe99e72420ff35c00b27a34cb9937e902a8b810e2c88300c6f0a3b699d"},
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]
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[[package]]
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name = "holoviews"
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version = "1.18.3"
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description = "Stop plotting your data - annotate your data and let it visualize itself."
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optional = false
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python-versions = ">=3.9"
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files = [
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{file = "holoviews-1.18.3-py2.py3-none-any.whl", hash = "sha256:b94b96560b64a84c07e89115aaf9b226e6009684800ec84d3c88cbad122c0c46"},
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{file = "holoviews-1.18.3.tar.gz", hash = "sha256:578e30e89d72754f97a83ebe08198fec8e87cc7e49b25b9f31ec393f939ca500"},
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]
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[package.dependencies]
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colorcet = "*"
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numpy = ">=1.0"
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packaging = "*"
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pandas = ">=0.20.0"
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panel = ">=1.0"
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||||
param = ">=1.12.0,<3.0"
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||||
pyviz-comms = ">=0.7.4"
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||||
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[package.extras]
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||||
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)"]
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||||
build = ["param (>=1.7.0)", "pyct (>=0.4.4)", "setuptools (>=30.3.0)"]
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||||
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)"]
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||||
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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||||
examples-tests = ["bokeh (>=3.1)", "cftime", "dash (>=1.16)", "dask", "datashader (>=0.11.1)", "ffmpeg", "ipython (>=5.4.0)", "matplotlib (>=3)", "nbval", "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"]
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||||
notebook = ["ipython (>=5.4.0)", "notebook"]
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recommended = ["bokeh (>=3.1)", "ipython (>=5.4.0)", "matplotlib (>=3)", "notebook"]
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||||
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)"]
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tests-ci = ["codecov", "pytest-github-actions-annotate-failures"]
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||||
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)"]
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||||
tests-nb = ["nbval"]
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||||
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)"]
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[[package]]
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name = "httpcore"
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version = "1.0.5"
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@@ -1437,6 +1515,41 @@ cli = ["click (==8.*)", "pygments (==2.*)", "rich (>=10,<14)"]
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http2 = ["h2 (>=3,<5)"]
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socks = ["socksio (==1.*)"]
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[[package]]
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||||
name = "hvplot"
|
||||
version = "0.10.0"
|
||||
description = "A high-level plotting API for the PyData ecosystem built on HoloViews."
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||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "hvplot-0.10.0-py3-none-any.whl", hash = "sha256:fe90ccb48163a6a62ae5bd6b008c2cb15cbf5b276f6ad6839ef5470b1c480d16"},
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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"
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||||
|
||||
[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"]
|
||||
hvdev = ["colorcet (>=0.0.1a1)", "datashader (>=0.0.1a1)", "holoviews (>=0.0.1a1)", "panel (>=0.0.1a1)", "param (>=0.0.1a1)", "pyviz-comms (>=0.0.1a1)"]
|
||||
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 = [
|
||||
{file = "packaging-24.0.tar.gz", hash = "sha256:eb82c5e3e56209074766e6885bb04b8c38a0c015d0a30036ebe7ece34c9989e9"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "pandas"
|
||||
version = "2.2.2"
|
||||
description = "Powerful data structures for data analysis, time series, and statistics"
|
||||
optional = false
|
||||
python-versions = ">=3.9"
|
||||
files = [
|
||||
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{file = "pandas-2.2.2.tar.gz", hash = "sha256:9e79019aba43cb4fda9e4d983f8e88ca0373adbb697ae9c6c43093218de28b54"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
numpy = [
|
||||
{version = ">=1.23.2", markers = "python_version == \"3.11\""},
|
||||
{version = ">=1.26.0", markers = "python_version >= \"3.12\""},
|
||||
]
|
||||
python-dateutil = ">=2.8.2"
|
||||
pytz = ">=2020.1"
|
||||
tzdata = ">=2022.7"
|
||||
|
||||
[package.extras]
|
||||
all = ["PyQt5 (>=5.15.9)", "SQLAlchemy (>=2.0.0)", "adbc-driver-postgresql (>=0.8.0)", "adbc-driver-sqlite (>=0.8.0)", "beautifulsoup4 (>=4.11.2)", "bottleneck (>=1.3.6)", "dataframe-api-compat (>=0.1.7)", "fastparquet (>=2022.12.0)", "fsspec (>=2022.11.0)", "gcsfs (>=2022.11.0)", "html5lib (>=1.1)", "hypothesis (>=6.46.1)", "jinja2 (>=3.1.2)", "lxml (>=4.9.2)", "matplotlib (>=3.6.3)", "numba (>=0.56.4)", "numexpr (>=2.8.4)", "odfpy (>=1.4.1)", "openpyxl (>=3.1.0)", "pandas-gbq (>=0.19.0)", "psycopg2 (>=2.9.6)", "pyarrow (>=10.0.1)", "pymysql (>=1.0.2)", "pyreadstat (>=1.2.0)", "pytest (>=7.3.2)", "pytest-xdist (>=2.2.0)", "python-calamine (>=0.1.7)", "pyxlsb (>=1.0.10)", "qtpy (>=2.3.0)", "s3fs (>=2022.11.0)", "scipy (>=1.10.0)", "tables (>=3.8.0)", "tabulate (>=0.9.0)", "xarray (>=2022.12.0)", "xlrd (>=2.0.1)", "xlsxwriter (>=3.0.5)", "zstandard (>=0.19.0)"]
|
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aws = ["s3fs (>=2022.11.0)"]
|
||||
clipboard = ["PyQt5 (>=5.15.9)", "qtpy (>=2.3.0)"]
|
||||
compression = ["zstandard (>=0.19.0)"]
|
||||
computation = ["scipy (>=1.10.0)", "xarray (>=2022.12.0)"]
|
||||
consortium-standard = ["dataframe-api-compat (>=0.1.7)"]
|
||||
excel = ["odfpy (>=1.4.1)", "openpyxl (>=3.1.0)", "python-calamine (>=0.1.7)", "pyxlsb (>=1.0.10)", "xlrd (>=2.0.1)", "xlsxwriter (>=3.0.5)"]
|
||||
feather = ["pyarrow (>=10.0.1)"]
|
||||
fss = ["fsspec (>=2022.11.0)"]
|
||||
gcp = ["gcsfs (>=2022.11.0)", "pandas-gbq (>=0.19.0)"]
|
||||
hdf5 = ["tables (>=3.8.0)"]
|
||||
html = ["beautifulsoup4 (>=4.11.2)", "html5lib (>=1.1)", "lxml (>=4.9.2)"]
|
||||
mysql = ["SQLAlchemy (>=2.0.0)", "pymysql (>=1.0.2)"]
|
||||
output-formatting = ["jinja2 (>=3.1.2)", "tabulate (>=0.9.0)"]
|
||||
parquet = ["pyarrow (>=10.0.1)"]
|
||||
performance = ["bottleneck (>=1.3.6)", "numba (>=0.56.4)", "numexpr (>=2.8.4)"]
|
||||
plot = ["matplotlib (>=3.6.3)"]
|
||||
postgresql = ["SQLAlchemy (>=2.0.0)", "adbc-driver-postgresql (>=0.8.0)", "psycopg2 (>=2.9.6)"]
|
||||
pyarrow = ["pyarrow (>=10.0.1)"]
|
||||
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"},
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||||
{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"]
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||||
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 = [
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{file = "param-2.1.0-py3-none-any.whl", hash = "sha256:f31d3745d227347d29b5868c4e4e3077df07463889b91d3bb28e634fde211e1c"},
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||||
]
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||||
|
||||
[package.extras]
|
||||
all = ["param[doc]", "param[lint]", "param[tests-full]"]
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||||
doc = ["nbsite (==0.8.4)", "param[examples]", "sphinx-remove-toctrees"]
|
||||
examples = ["aiohttp", "pandas", "panel"]
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||||
lint = ["flake8", "pre-commit"]
|
||||
tests = ["coverage[toml]", "pytest", "pytest-asyncio"]
|
||||
tests-deser = ["odfpy", "openpyxl", "pyarrow", "tables", "xlrd"]
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||||
tests-examples = ["nbval", "param[examples]", "pytest (<8.1)", "pytest-asyncio", "pytest-xdist"]
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||||
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 = [
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||||
[package.extras]
|
||||
tests = ["pytest"]
|
||||
|
||||
[[package]]
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||||
name = "pyarrow"
|
||||
version = "16.1.0"
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||||
description = "Python library for Apache Arrow"
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||||
optional = false
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||||
python-versions = ">=3.8"
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files = [
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[package.dependencies]
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numpy = ">=1.16.6"
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[[package]]
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name = "pycparser"
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version = "2.22"
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@@ -3444,6 +3735,36 @@ extra = ["bitsandbytes (==0.41.0)", "hydra-core (>=1.0.5)", "jsonargparse[signat
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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"
|
||||
|
||||
@@ -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]
|
||||
|
||||
@@ -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 = self.ff(x)
|
||||
y = torch.cat([y0, y1], dim=1)
|
||||
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)
|
||||
|
||||
|
||||
@@ -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.
|
||||
|
||||
20
symbolic_nn_tests/experiment2/train.py
Normal file
20
symbolic_nn_tests/experiment2/train.py
Normal file
@@ -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)
|
||||
Reference in New Issue
Block a user