diff --git a/.github/workflows/python-main.yml b/.github/workflows/python-main.yml index 8543156..ad52e2f 100644 --- a/.github/workflows/python-main.yml +++ b/.github/workflows/python-main.yml @@ -13,7 +13,7 @@ jobs: strategy: fail-fast: false matrix: - python-version: ['3.8'] + python-version: ['3.9'] steps: - uses: actions/checkout@v3 - name: Set up Python ${{ matrix.python-version }} @@ -21,7 +21,7 @@ jobs: with: python-version: ${{ matrix.python-version }} - name: Install tox and any other packages - run: pip install tox + run: pip install tox tox-uv - name: Run tox # Run tox using tox.ini run: tox -c tox.ini diff --git a/.github/workflows/python-pull-request.yml b/.github/workflows/python-pull-request.yml index d9ffc6a..c9a046e 100644 --- a/.github/workflows/python-pull-request.yml +++ b/.github/workflows/python-pull-request.yml @@ -14,7 +14,7 @@ jobs: strategy: fail-fast: false matrix: - python-version: ['3.8'] + python-version: ['3.9'] steps: - uses: actions/checkout@v3 - name: Set up Python ${{ matrix.python-version }} @@ -22,7 +22,7 @@ jobs: with: python-version: ${{ matrix.python-version }} - name: Install tox and any other packages - run: pip install tox + run: pip install tox tox-uv - name: Run tox # Run tox using tox.ini run: tox -c tox.ini diff --git a/Makefile b/Makefile index 6cec10d..08ed07f 100644 --- a/Makefile +++ b/Makefile @@ -1,13 +1,13 @@ -pip-compile: requirements.in test-requirements.in nb-requirements.in dev-requirements.in ray-env-requirements.in rendering-requirements.in - pip-compile --no-emit-index-url --no-emit-options --no-emit-find-links requirements.in - pip-compile --no-emit-index-url --no-emit-options --no-emit-find-links test-requirements.in - pip-compile --no-emit-index-url --no-emit-options --no-emit-find-links nb-requirements.in - pip-compile --no-emit-index-url --no-emit-options --no-emit-find-links dev-requirements.in - pip-compile --no-emit-index-url --no-emit-options --no-emit-find-links ray-env-requirements.in --unsafe-package ray - pip-compile --no-emit-index-url --no-emit-options --no-emit-find-links rendering-requirements.in +pip-compile: requirements.in test-requirements.in nb-requirements.in dev-requirements.in ray-env-requirements.in rendering-requirements.txt + uv pip compile --no-emit-index-url --no-emit-find-links requirements.in -o requirements.txt + uv pip compile --no-emit-index-url --no-emit-find-links test-requirements.in -o test-requirements.txt + uv pip compile --no-emit-index-url --no-emit-find-links nb-requirements.in -o nb-requirements.txt + uv pip compile --no-emit-index-url --no-emit-find-links dev-requirements.in -o dev-requirements.txt + uv pip compile --no-emit-index-url --no-emit-find-links ray-env-requirements.in -o ray-env-requirements.txt --unsafe-package ray + uv pip compile --no-emit-index-url --no-emit-find-links rendering-requirements.in -o rendering-requirements.txt pip-install: pip-compile - pip install -r dev-requirements.txt -e . + uv pip install -r dev-requirements.txt -e . format: ruff format src tests --line-length 88 diff --git a/README.md b/README.md index f77cc69..85ed403 100644 --- a/README.md +++ b/README.md @@ -32,8 +32,8 @@ For specific snapshots of code submitted to conferences: ## Dev Env First, make sure the following python development tools are installed: - - pip - - pip-tools(==7.3.0) + - [uv](https://docs.astral.sh/uv/getting-started/installation/) + - [ruff](https://docs.astral.sh/ruff/installation/) Then, in a virtual environment, run pip-compile and install: @@ -44,6 +44,12 @@ $ make pip-install These should install all the requirements dependencies for development. +For building, install tox and tox-uv + +``` +$ pip install tox tox-uv +``` + ## Dependencies The dependecy files map to a purpose as follows: @@ -54,4 +60,4 @@ The dependecy files map to a purpose as follows: - [rendering-requirements.in](rendering-requirements.in): for environments can be rendered in a graphical interface, with OpenGL. - [ray-env-requirements.in](ray-env-requirements.in): for ray in a cluster environment. During compilation with `pip-compile`, it's best to exclude the version of ray (see [Makefile](Makefile)). -All requirements files are compiled using `pip-compile`. \ No newline at end of file +All requirements files are compiled using `uv`. \ No newline at end of file diff --git a/dev-requirements.txt b/dev-requirements.txt index ac69c5a..490e08c 100644 --- a/dev-requirements.txt +++ b/dev-requirements.txt @@ -1,9 +1,5 @@ -# -# This file is autogenerated by pip-compile with Python 3.9 -# by the following command: -# -# pip-compile --no-emit-find-links --no-emit-index-url --no-emit-options dev-requirements.in -# +# This file was autogenerated by uv via the following command: +# uv pip compile --no-emit-index-url --no-emit-find-links dev-requirements.in -o dev-requirements.txt absl-py==2.0.0 # via # -r nb-requirements.txt @@ -17,13 +13,11 @@ aiohttp==3.8.4 # -r requirements.txt # -r test-requirements.txt # aiohttp-cors - # ray aiohttp-cors==0.7.0 # via # -r nb-requirements.txt # -r requirements.txt # -r test-requirements.txt - # ray aiosignal==1.3.1 # via # -r nb-requirements.txt @@ -31,16 +25,14 @@ aiosignal==1.3.1 # -r test-requirements.txt # aiohttp # ray -anyio==3.6.2 +anyio==4.4.0 # via # -r nb-requirements.txt # jupyter-server -argon2-cffi==21.3.0 +argon2-cffi==23.1.0 # via # -r nb-requirements.txt # jupyter-server - # nbclassic - # notebook argon2-cffi-bindings==21.2.0 # via # -r nb-requirements.txt @@ -49,7 +41,7 @@ arrow==1.3.0 # via # -r nb-requirements.txt # isoduration -asttokens==2.2.1 +asttokens==2.4.1 # via # -r nb-requirements.txt # stack-data @@ -77,28 +69,18 @@ attrs==22.2.0 # aiohttp # hypothesis # jsonschema -babel==2.15.0 +babel==2.16.0 # via # -r nb-requirements.txt # jupyterlab-server -backcall==0.2.0 - # via - # -r nb-requirements.txt - # ipython -beautifulsoup4==4.11.2 +beautifulsoup4==4.12.3 # via # -r nb-requirements.txt # nbconvert -bleach==6.0.0 +bleach==6.1.0 # via # -r nb-requirements.txt # nbconvert -blessed==1.20.0 - # via - # -r nb-requirements.txt - # -r requirements.txt - # -r test-requirements.txt - # gpustat bumpversion==0.5.3 # via -r dev-requirements.in cachetools==5.3.0 @@ -113,7 +95,7 @@ certifi==2022.12.7 # -r requirements.txt # -r test-requirements.txt # requests -cffi==1.15.1 +cffi==1.17.1 # via # -r nb-requirements.txt # argon2-cffi-bindings @@ -124,6 +106,12 @@ charset-normalizer==3.1.0 # -r test-requirements.txt # aiohttp # requests +clarabel==0.9.0 + # via + # -r nb-requirements.txt + # -r requirements.txt + # -r test-requirements.txt + # cvxpy click==8.0.4 # via # -r nb-requirements.txt @@ -141,24 +129,29 @@ colorful==0.5.5 # -r nb-requirements.txt # -r requirements.txt # -r test-requirements.txt - # ray -comm==0.1.2 +comm==0.2.2 # via # -r nb-requirements.txt # ipykernel -contourpy==1.0.7 + # ipywidgets +contourpy==1.3.0 # via # -r nb-requirements.txt # matplotlib -coverage[toml]==7.3.2 +coverage==7.3.2 # via # -r test-requirements.txt # pytest-cov -cycler==0.11.0 +cvxpy==1.5.3 + # via + # -r nb-requirements.txt + # -r requirements.txt + # -r test-requirements.txt +cycler==0.12.1 # via # -r nb-requirements.txt # matplotlib -debugpy==1.6.6 +debugpy==1.8.5 # via # -r nb-requirements.txt # ipykernel @@ -176,12 +169,21 @@ distlib==0.3.6 # -r requirements.txt # -r test-requirements.txt # virtualenv +ecos==2.0.14 + # via + # -r nb-requirements.txt + # -r requirements.txt + # -r test-requirements.txt + # cvxpy exceptiongroup==1.2.0 # via + # -r nb-requirements.txt # -r test-requirements.txt + # anyio # hypothesis + # ipython # pytest -executing==1.2.0 +executing==2.1.0 # via # -r nb-requirements.txt # stack-data @@ -191,7 +193,7 @@ farama-notifications==0.0.4 # -r requirements.txt # -r test-requirements.txt # gymnasium -fastjsonschema==2.16.3 +fastjsonschema==2.20.0 # via # -r nb-requirements.txt # nbformat @@ -208,7 +210,7 @@ flatbuffers==23.5.26 # -r requirements.txt # -r test-requirements.txt # tensorflow -fonttools==4.39.0 +fonttools==4.53.1 # via # -r nb-requirements.txt # matplotlib @@ -229,7 +231,6 @@ fsspec==2024.2.0 # -r nb-requirements.txt # -r requirements.txt # -r test-requirements.txt - # ray gast==0.4.0 # via # -r nb-requirements.txt @@ -268,18 +269,11 @@ googleapis-common-protos==1.58.0 # -r requirements.txt # -r test-requirements.txt # google-api-core -gpustat==1.0.0 - # via - # -r nb-requirements.txt - # -r requirements.txt - # -r test-requirements.txt - # ray grpcio==1.51.3 # via # -r nb-requirements.txt # -r requirements.txt # -r test-requirements.txt - # ray # tensorboard # tensorflow gymnasium==0.28.1 @@ -317,7 +311,7 @@ importlib-metadata==6.0.0 # jupyterlab-server # markdown # nbconvert -importlib-resources==5.12.0 +importlib-resources==6.4.5 # via # -r nb-requirements.txt # matplotlib @@ -325,21 +319,17 @@ iniconfig==2.0.0 # via # -r test-requirements.txt # pytest -ipykernel==6.21.3 +ipykernel==6.29.5 # via # -r nb-requirements.txt # jupyterlab - # nbclassic - # notebook -ipython==8.11.0 +ipython==8.18.1 # via # -r nb-requirements.txt # ipykernel -ipython-genutils==0.2.0 - # via - # -r nb-requirements.txt - # nbclassic - # notebook + # ipywidgets +ipywidgets==8.1.5 + # via -r nb-requirements.txt isoduration==20.11.0 # via # -r nb-requirements.txt @@ -350,28 +340,29 @@ jax-jumpy==1.0.0 # -r requirements.txt # -r test-requirements.txt # gymnasium -jedi==0.18.2 +jedi==0.19.1 # via # -r nb-requirements.txt # ipython -jinja2==3.1.2 +jinja2==3.1.4 # via # -r nb-requirements.txt + # -r requirements.txt + # -r test-requirements.txt # jupyter-server # jupyterlab # jupyterlab-server - # nbclassic + # memray # nbconvert - # notebook json5==0.9.25 # via # -r nb-requirements.txt # jupyterlab-server -jsonpointer==2.4 +jsonpointer==3.0.0 # via # -r nb-requirements.txt # jsonschema -jsonschema[format-nongpl]==4.17.3 +jsonschema==4.17.3 # via # -r nb-requirements.txt # -r requirements.txt @@ -380,19 +371,17 @@ jsonschema[format-nongpl]==4.17.3 # jupyterlab-server # nbformat # ray -jupyter-client==8.0.3 +jupyter-client==8.6.2 # via # -r nb-requirements.txt # ipykernel # jupyter-server - # nbclassic # nbclient - # notebook jupyter-contrib-core==0.4.2 # via # -r nb-requirements.txt # jupyter-nbextensions-configurator -jupyter-core==5.2.0 +jupyter-core==5.7.2 # via # -r nb-requirements.txt # ipykernel @@ -401,11 +390,9 @@ jupyter-core==5.2.0 # jupyter-nbextensions-configurator # jupyter-server # jupyterlab - # nbclassic # nbclient # nbconvert # nbformat - # notebook jupyter-events==0.6.3 # via # -r nb-requirements.txt @@ -418,22 +405,24 @@ jupyter-nbextensions-configurator==0.6.3 # via -r nb-requirements.txt jupyter-resource-usage==0.7.2 # via -r nb-requirements.txt -jupyter-server==2.7.0 +jupyter-server==2.10.0 # via # -r nb-requirements.txt # jupyter-lsp # jupyter-resource-usage # jupyterlab # jupyterlab-server - # nbclassic + # notebook # notebook-shim jupyter-server-terminals==0.5.3 # via # -r nb-requirements.txt # jupyter-server jupyterlab==4.0.13 - # via -r nb-requirements.txt -jupyterlab-pygments==0.2.2 + # via + # -r nb-requirements.txt + # notebook +jupyterlab-pygments==0.3.0 # via # -r nb-requirements.txt # nbconvert @@ -441,13 +430,18 @@ jupyterlab-server==2.24.0 # via # -r nb-requirements.txt # jupyterlab + # notebook +jupyterlab-widgets==3.0.13 + # via + # -r nb-requirements.txt + # ipywidgets keras==2.13.1 # via # -r nb-requirements.txt # -r requirements.txt # -r test-requirements.txt # tensorflow -kiwisolver==1.4.4 +kiwisolver==1.4.7 # via # -r nb-requirements.txt # matplotlib @@ -457,12 +451,26 @@ libclang==16.0.6 # -r requirements.txt # -r test-requirements.txt # tensorflow +linkify-it-py==2.0.3 + # via + # -r nb-requirements.txt + # -r requirements.txt + # -r test-requirements.txt + # markdown-it-py markdown==3.5.1 # via # -r nb-requirements.txt # -r requirements.txt # -r test-requirements.txt # tensorboard +markdown-it-py==3.0.0 + # via + # -r nb-requirements.txt + # -r requirements.txt + # -r test-requirements.txt + # mdit-py-plugins + # rich + # textual markupsafe==2.1.3 # via # -r nb-requirements.txt @@ -471,16 +479,33 @@ markupsafe==2.1.3 # jinja2 # nbconvert # werkzeug -matplotlib==3.7.1 +matplotlib==3.9.2 # via # -r nb-requirements.txt # seaborn -matplotlib-inline==0.1.6 +matplotlib-inline==0.1.7 # via # -r nb-requirements.txt # ipykernel # ipython -mistune==2.0.5 +mdit-py-plugins==0.4.2 + # via + # -r nb-requirements.txt + # -r requirements.txt + # -r test-requirements.txt + # markdown-it-py +mdurl==0.1.2 + # via + # -r nb-requirements.txt + # -r requirements.txt + # -r test-requirements.txt + # markdown-it-py +memray==1.14.0 + # via + # -r nb-requirements.txt + # -r requirements.txt + # -r test-requirements.txt +mistune==3.0.2 # via # -r nb-requirements.txt # nbconvert @@ -497,71 +522,60 @@ multidict==6.0.4 # -r test-requirements.txt # aiohttp # yarl -nbclassic==0.5.3 - # via - # -r nb-requirements.txt - # notebook -nbclient==0.7.2 +nbclient==0.10.0 # via # -r nb-requirements.txt # nbconvert -nbconvert==7.2.10 +nbconvert==7.16.4 # via # -r nb-requirements.txt # jupyter-server - # nbclassic - # notebook -nbformat==5.7.3 +nbformat==5.10.4 # via # -r nb-requirements.txt # jupyter-server - # nbclassic # nbclient # nbconvert - # notebook -nest-asyncio==1.5.6 +nest-asyncio==1.6.0 # via # -r nb-requirements.txt # ipykernel - # nbclassic - # notebook -notebook==6.5.4 +notebook==7.0.8 # via # -r nb-requirements.txt # jupyter-contrib-core # jupyter-nbextensions-configurator -notebook-shim==0.2.2 +notebook-shim==0.2.4 # via # -r nb-requirements.txt # jupyterlab - # nbclassic + # notebook numpy==1.23.5 # via # -r nb-requirements.txt # -r requirements.txt # -r test-requirements.txt + # clarabel # contourpy + # cvxpy + # ecos # gymnasium # h5py # jax-jumpy # matplotlib # opt-einsum + # osqp # pandas # patsy # pyarrow - # ray + # qdldl # rlplg # scipy + # scs # seaborn # statsmodels # tensorboard # tensorflow -nvidia-ml-py==11.495.46 - # via - # -r nb-requirements.txt - # -r requirements.txt - # -r test-requirements.txt - # gpustat oauthlib==3.2.2 # via # -r nb-requirements.txt @@ -573,7 +587,6 @@ opencensus==0.11.2 # -r nb-requirements.txt # -r requirements.txt # -r test-requirements.txt - # ray opencensus-context==0.1.3 # via # -r nb-requirements.txt @@ -586,6 +599,12 @@ opt-einsum==3.3.0 # -r requirements.txt # -r test-requirements.txt # tensorflow +osqp==0.6.7.post1 + # via + # -r nb-requirements.txt + # -r requirements.txt + # -r test-requirements.txt + # cvxpy overrides==7.7.0 # via # -r nb-requirements.txt @@ -610,14 +629,13 @@ pandas==2.0.3 # -r nb-requirements.txt # -r requirements.txt # -r test-requirements.txt - # ray # seaborn # statsmodels -pandocfilters==1.5.0 +pandocfilters==1.5.1 # via # -r nb-requirements.txt # nbconvert -parso==0.8.3 +parso==0.8.4 # via # -r nb-requirements.txt # jedi @@ -625,11 +643,7 @@ patsy==0.5.6 # via # -r nb-requirements.txt # statsmodels -pexpect==4.8.0 - # via - # -r nb-requirements.txt - # ipython -pickleshare==0.7.5 +pexpect==4.9.0 # via # -r nb-requirements.txt # ipython @@ -658,10 +672,7 @@ prometheus-client==0.13.1 # -r test-requirements.txt # jupyter-resource-usage # jupyter-server - # nbclassic - # notebook - # ray -prompt-toolkit==3.0.38 +prompt-toolkit==3.0.47 # via # -r nb-requirements.txt # ipython @@ -675,12 +686,9 @@ protobuf==4.25.1 # ray # tensorboard # tensorflow -psutil==5.9.4 +psutil==5.9.8 # via # -r nb-requirements.txt - # -r requirements.txt - # -r test-requirements.txt - # gpustat # ipykernel # jupyter-resource-usage ptyprocess==0.7.0 @@ -688,7 +696,7 @@ ptyprocess==0.7.0 # -r nb-requirements.txt # pexpect # terminado -pure-eval==0.2.2 +pure-eval==0.2.3 # via # -r nb-requirements.txt # stack-data @@ -697,13 +705,11 @@ py-spy==0.3.14 # -r nb-requirements.txt # -r requirements.txt # -r test-requirements.txt - # ray pyarrow==15.0.0 # via # -r nb-requirements.txt # -r requirements.txt # -r test-requirements.txt - # ray pyasn1==0.4.8 # via # -r nb-requirements.txt @@ -717,7 +723,7 @@ pyasn1-modules==0.2.8 # -r requirements.txt # -r test-requirements.txt # google-auth -pycparser==2.21 +pycparser==2.22 # via # -r nb-requirements.txt # cffi @@ -726,13 +732,15 @@ pydantic==1.10.6 # -r nb-requirements.txt # -r requirements.txt # -r test-requirements.txt - # ray -pygments==2.14.0 +pygments==2.18.0 # via # -r nb-requirements.txt + # -r requirements.txt + # -r test-requirements.txt # ipython # nbconvert -pyparsing==3.0.9 + # rich +pyparsing==3.1.4 # via # -r nb-requirements.txt # matplotlib @@ -775,16 +783,20 @@ pyyaml==6.0 # jupyter-events # jupyter-nbextensions-configurator # ray -pyzmq==25.0.1 +pyzmq==26.2.0 # via # -r nb-requirements.txt # ipykernel # jupyter-client # jupyter-resource-usage # jupyter-server - # nbclassic - # notebook -ray[data,default]==2.9.3 +qdldl==0.1.7.post4 + # via + # -r nb-requirements.txt + # -r requirements.txt + # -r test-requirements.txt + # osqp +ray==2.23.0 # via # -r nb-requirements.txt # -r requirements.txt @@ -815,7 +827,14 @@ rfc3986-validator==0.1.1 # -r nb-requirements.txt # jsonschema # jupyter-events -rlplg @ git+https://github.com/guidj/rlplg.git@v0.19.10 +rich==13.8.1 + # via + # -r nb-requirements.txt + # -r requirements.txt + # -r test-requirements.txt + # memray + # textual +rlplg @ git+https://github.com/guidj/rlplg.git@93d819cab2eeefdd9bbd0ec7acb5d1dd0e5d1bed # via # -r nb-requirements.txt # -r requirements.txt @@ -833,16 +852,34 @@ scipy==1.10.1 # -r nb-requirements.txt # -r requirements.txt # -r test-requirements.txt + # clarabel + # cvxpy + # ecos + # osqp + # qdldl # rlplg + # scs # statsmodels +scs==3.2.7 + # via + # -r nb-requirements.txt + # -r requirements.txt + # -r test-requirements.txt + # cvxpy seaborn==0.13.1 # via -r nb-requirements.txt -send2trash==1.8.0 +send2trash==1.8.3 # via # -r nb-requirements.txt # jupyter-server - # nbclassic - # notebook +setuptools==74.1.2 + # via + # -r nb-requirements.txt + # -r requirements.txt + # -r test-requirements.txt + # jupyter-contrib-core + # tensorboard + # tensorflow six==1.16.0 # via # -r nb-requirements.txt @@ -851,10 +888,8 @@ six==1.16.0 # asttokens # astunparse # bleach - # blessed # google-auth # google-pasta - # gpustat # patsy # python-dateutil # rfc3339-validator @@ -864,8 +899,7 @@ smart-open==6.3.0 # -r nb-requirements.txt # -r requirements.txt # -r test-requirements.txt - # ray -sniffio==1.3.0 +sniffio==1.3.1 # via # -r nb-requirements.txt # anyio @@ -873,11 +907,11 @@ sortedcontainers==2.4.0 # via # -r test-requirements.txt # hypothesis -soupsieve==2.4 +soupsieve==2.6 # via # -r nb-requirements.txt # beautifulsoup4 -stack-data==0.6.2 +stack-data==0.6.3 # via # -r nb-requirements.txt # ipython @@ -918,14 +952,18 @@ termcolor==2.3.0 # -r requirements.txt # -r test-requirements.txt # tensorflow -terminado==0.17.1 +terminado==0.18.1 # via # -r nb-requirements.txt # jupyter-server # jupyter-server-terminals - # nbclassic - # notebook -tinycss2==1.2.1 +textual==0.78.0 + # via + # -r nb-requirements.txt + # -r requirements.txt + # -r test-requirements.txt + # memray +tinycss2==1.3.0 # via # -r nb-requirements.txt # nbconvert @@ -936,7 +974,7 @@ tomli==2.0.1 # coverage # jupyterlab # pytest -tornado==6.2 +tornado==6.4.1 # via # -r nb-requirements.txt # ipykernel @@ -945,15 +983,17 @@ tornado==6.2 # jupyter-nbextensions-configurator # jupyter-server # jupyterlab - # nbclassic # notebook # terminado -traitlets==5.9.0 +tqdm==4.66.5 + # via -r nb-requirements.txt +traitlets==5.14.3 # via # -r nb-requirements.txt # comm # ipykernel # ipython + # ipywidgets # jupyter-client # jupyter-contrib-core # jupyter-core @@ -962,12 +1002,10 @@ traitlets==5.9.0 # jupyter-server # jupyterlab # matplotlib-inline - # nbclassic # nbclient # nbconvert # nbformat - # notebook -types-python-dateutil==2.8.19.14 +types-python-dateutil==2.9.0.20240906 # via # -r nb-requirements.txt # arrow @@ -976,16 +1014,25 @@ typing-extensions==4.5.0 # -r nb-requirements.txt # -r requirements.txt # -r test-requirements.txt + # anyio # async-lru # gymnasium + # ipython # pydantic # tensorflow + # textual tzdata==2024.1 # via # -r nb-requirements.txt # -r requirements.txt # -r test-requirements.txt # pandas +uc-micro-py==1.0.3 + # via + # -r nb-requirements.txt + # -r requirements.txt + # -r test-requirements.txt + # linkify-it-py uri-template==1.3.0 # via # -r nb-requirements.txt @@ -1001,15 +1048,11 @@ virtualenv==20.21.0 # -r nb-requirements.txt # -r requirements.txt # -r test-requirements.txt - # ray -wcwidth==0.2.6 +wcwidth==0.2.13 # via # -r nb-requirements.txt - # -r requirements.txt - # -r test-requirements.txt - # blessed # prompt-toolkit -webcolors==1.13 +webcolors==24.8.0 # via # -r nb-requirements.txt # jsonschema @@ -1018,7 +1061,7 @@ webencodings==0.5.1 # -r nb-requirements.txt # bleach # tinycss2 -websocket-client==1.5.1 +websocket-client==1.8.0 # via # -r nb-requirements.txt # jupyter-server @@ -1035,6 +1078,10 @@ wheel==0.42.0 # -r test-requirements.txt # astunparse # tensorboard +widgetsnbextension==4.0.13 + # via + # -r nb-requirements.txt + # ipywidgets wrapt==1.16.0 # via # -r nb-requirements.txt @@ -1054,6 +1101,3 @@ zipp==3.15.0 # -r test-requirements.txt # importlib-metadata # importlib-resources - -# The following packages are considered to be unsafe in a requirements file: -# setuptools diff --git a/experiments/policycontrol/experiments.csv b/experiments/policycontrol/experiments.csv index 2357d97..da87ddc 100644 --- a/experiments/policycontrol/experiments.csv +++ b/experiments/policycontrol/experiments.csv @@ -1,159 +1,159 @@ "algorithm","policy_type","traj_mapping_method","reward_period","drop_truncated_feedback_episodes","discount_factor","learning_rate","epsilon","algorithm_args" -"sarsa","single-step","identity-mapper","1",false,1.0,0.1,0.2,"{}" -"sarsa","single-step","identity-mapper","1",false,0.99,0.1,0.2,"{}" -"sarsa","single-step","daaf-trajectory-mapper","2",false,1.0,0.1,0.2,"{}" -"sarsa","single-step","daaf-trajectory-mapper","4",false,1.0,0.1,0.2,"{}" -"sarsa","single-step","daaf-trajectory-mapper","6",false,1.0,0.1,0.2,"{}" -"sarsa","single-step","daaf-trajectory-mapper","8",false,1.0,0.1,0.2,"{}" -"sarsa","single-step","daaf-trajectory-mapper","16",false,1.0,0.1,0.2,"{}" -"sarsa","single-step","daaf-trajectory-mapper","2",false,0.99,0.1,0.2,"{}" -"sarsa","single-step","daaf-trajectory-mapper","4",false,0.99,0.1,0.2,"{}" -"sarsa","single-step","daaf-trajectory-mapper","6",false,0.99,0.1,0.2,"{}" -"sarsa","single-step","daaf-trajectory-mapper","8",false,0.99,0.1,0.2,"{}" -"sarsa","single-step","daaf-trajectory-mapper","16",false,0.99,0.1,0.2,"{}" -"sarsa","single-step","daaf-impute-missing-reward-mapper","2",false,1.0,0.1,0.2,"{}" -"sarsa","single-step","daaf-impute-missing-reward-mapper","2",false,0.99,0.1,0.2,"{}" -"sarsa","single-step","daaf-impute-missing-reward-mapper","4",false,1.0,0.1,0.2,"{}" -"sarsa","single-step","daaf-impute-missing-reward-mapper","4",false,0.99,0.1,0.2,"{}" -"sarsa","single-step","daaf-impute-missing-reward-mapper","6",false,1.0,0.1,0.2,"{}" -"sarsa","single-step","daaf-impute-missing-reward-mapper","6",false,0.99,0.1,0.2,"{}" -"sarsa","single-step","daaf-impute-missing-reward-mapper","8",false,1.0,0.1,0.2,"{}" -"sarsa","single-step","daaf-impute-missing-reward-mapper","8",false,0.99,0.1,0.2,"{}" -"sarsa","single-step","daaf-impute-missing-reward-mapper","16",false,1.0,0.1,0.2,"{}" -"sarsa","single-step","daaf-impute-missing-reward-mapper","16",false,0.99,0.1,0.2,"{}" -"sarsa","options","daaf-mdp-with-options-mapper","2",false,1.0,0.1,0.2,"{}" -"sarsa","options","daaf-mdp-with-options-mapper","2",false,0.99,0.1,0.2,"{}" -"sarsa","options","daaf-mdp-with-options-mapper","4",false,1.0,0.1,0.2,"{}" -"sarsa","options","daaf-mdp-with-options-mapper","4",false,0.99,0.1,0.2,"{}" -"sarsa","options","daaf-mdp-with-options-mapper","6",false,1.0,0.1,0.2,"{}" -"sarsa","options","daaf-mdp-with-options-mapper","6",false,0.99,0.1,0.2,"{}" -"sarsa","single-step","daaf-lsq-reward-attribution-mapper","2",false,1.0,0.1,0.2,"{}" -"sarsa","single-step","daaf-lsq-reward-attribution-mapper","2",false,0.99,0.1,0.2,"{}" -"sarsa","single-step","daaf-lsq-reward-attribution-mapper","4",false,1.0,0.1,0.2,"{}" -"sarsa","single-step","daaf-lsq-reward-attribution-mapper","4",false,0.99,0.1,0.2,"{}" -"sarsa","single-step","daaf-lsq-reward-attribution-mapper","6",false,1.0,0.1,0.2,"{}" -"sarsa","single-step","daaf-lsq-reward-attribution-mapper","6",false,0.99,0.1,0.2,"{}" -"sarsa","single-step","daaf-lsq-reward-attribution-mapper","8",false,1.0,0.1,0.2,"{}" -"sarsa","single-step","daaf-lsq-reward-attribution-mapper","8",false,0.99,0.1,0.2,"{}" -"sarsa","single-step","daaf-lsq-reward-attribution-mapper","16",false,1.0,0.1,0.2,"{}" -"sarsa","single-step","daaf-lsq-reward-attribution-mapper","16",false,0.99,0.1,0.2,"{}" -"q-learning","single-step","identity-mapper","1",false,1.0,0.1,0.2,"{}" -"q-learning","single-step","identity-mapper","1",false,0.99,0.1,0.2,"{}" -"q-learning","single-step","daaf-trajectory-mapper","2",false,1.0,0.1,0.2,"{}" -"q-learning","single-step","daaf-trajectory-mapper","4",false,1.0,0.1,0.2,"{}" -"q-learning","single-step","daaf-trajectory-mapper","6",false,1.0,0.1,0.2,"{}" -"q-learning","single-step","daaf-trajectory-mapper","8",false,1.0,0.1,0.2,"{}" -"q-learning","single-step","daaf-trajectory-mapper","16",false,1.0,0.1,0.2,"{}" -"q-learning","single-step","daaf-trajectory-mapper","2",false,0.99,0.1,0.2,"{}" -"q-learning","single-step","daaf-trajectory-mapper","4",false,0.99,0.1,0.2,"{}" -"q-learning","single-step","daaf-trajectory-mapper","6",false,0.99,0.1,0.2,"{}" -"q-learning","single-step","daaf-trajectory-mapper","8",false,0.99,0.1,0.2,"{}" -"q-learning","single-step","daaf-trajectory-mapper","16",false,0.99,0.1,0.2,"{}" -"q-learning","single-step","daaf-impute-missing-reward-mapper","2",false,1.0,0.1,0.2,"{}" -"q-learning","single-step","daaf-impute-missing-reward-mapper","2",false,0.99,0.1,0.2,"{}" -"q-learning","single-step","daaf-impute-missing-reward-mapper","4",false,1.0,0.1,0.2,"{}" -"q-learning","single-step","daaf-impute-missing-reward-mapper","4",false,0.99,0.1,0.2,"{}" -"q-learning","single-step","daaf-impute-missing-reward-mapper","6",false,1.0,0.1,0.2,"{}" -"q-learning","single-step","daaf-impute-missing-reward-mapper","6",false,0.99,0.1,0.2,"{}" -"q-learning","single-step","daaf-impute-missing-reward-mapper","8",false,1.0,0.1,0.2,"{}" -"q-learning","single-step","daaf-impute-missing-reward-mapper","8",false,0.99,0.1,0.2,"{}" -"q-learning","single-step","daaf-impute-missing-reward-mapper","16",false,1.0,0.1,0.2,"{}" -"q-learning","single-step","daaf-impute-missing-reward-mapper","16",false,0.99,0.1,0.2,"{}" -"q-learning","options","daaf-mdp-with-options-mapper","2",false,1.0,0.1,0.2,"{}" -"q-learning","options","daaf-mdp-with-options-mapper","2",false,0.99,0.1,0.2,"{}" -"q-learning","options","daaf-mdp-with-options-mapper","4",false,1.0,0.1,0.2,"{}" -"q-learning","options","daaf-mdp-with-options-mapper","4",false,0.99,0.1,0.2,"{}" -"q-learning","options","daaf-mdp-with-options-mapper","6",false,1.0,0.1,0.2,"{}" -"q-learning","options","daaf-mdp-with-options-mapper","6",false,0.99,0.1,0.2,"{}" -"q-learning","single-step","daaf-lsq-reward-attribution-mapper","2",false,1.0,0.1,0.2,"{}" -"q-learning","single-step","daaf-lsq-reward-attribution-mapper","2",false,0.99,0.1,0.2,"{}" -"q-learning","single-step","daaf-lsq-reward-attribution-mapper","4",false,1.0,0.1,0.2,"{}" -"q-learning","single-step","daaf-lsq-reward-attribution-mapper","4",false,0.99,0.1,0.2,"{}" -"q-learning","single-step","daaf-lsq-reward-attribution-mapper","6",false,1.0,0.1,0.2,"{}" -"q-learning","single-step","daaf-lsq-reward-attribution-mapper","6",false,0.99,0.1,0.2,"{}" -"q-learning","single-step","daaf-lsq-reward-attribution-mapper","8",false,1.0,0.1,0.2,"{}" -"q-learning","single-step","daaf-lsq-reward-attribution-mapper","8",false,0.99,0.1,0.2,"{}" -"q-learning","single-step","daaf-lsq-reward-attribution-mapper","16",false,1.0,0.1,0.2,"{}" -"q-learning","single-step","daaf-lsq-reward-attribution-mapper","16",false,0.99,0.1,0.2,"{}" -"nstep-sarsa","single-step","identity-mapper","1",false,1.0,0.1,0.2,"{""nstep"":1}" -"nstep-sarsa","single-step","identity-mapper","1",false,0.99,0.1,0.2,"{""nstep"":1}" -"nstep-sarsa","single-step","identity-mapper","1",false,1.0,0.1,0.2,"{""nstep"":2}" -"nstep-sarsa","single-step","identity-mapper","1",false,0.99,0.1,0.2,"{""nstep"":2}" -"nstep-sarsa","single-step","identity-mapper","1",false,1.0,0.1,0.2,"{""nstep"":4}" -"nstep-sarsa","single-step","identity-mapper","1",false,0.99,0.1,0.2,"{""nstep"":4}" -"nstep-sarsa","single-step","identity-mapper","1",false,1.0,0.1,0.2,"{""nstep"":6}" -"nstep-sarsa","single-step","identity-mapper","1",false,0.99,0.1,0.2,"{""nstep"":6}" -"nstep-sarsa","single-step","identity-mapper","1",false,1.0,0.1,0.2,"{""nstep"":8}" -"nstep-sarsa","single-step","identity-mapper","1",false,0.99,0.1,0.2,"{""nstep"":8}" -"nstep-sarsa","single-step","daaf-impute-missing-reward-mapper","2",false,1.0,0.1,0.2,"{""nstep"":2}" -"nstep-sarsa","single-step","daaf-impute-missing-reward-mapper","2",false,0.99,0.1,0.2,"{""nstep"":2}" -"nstep-sarsa","single-step","daaf-impute-missing-reward-mapper","2",false,1.0,0.1,0.2,"{""nstep"":4}" -"nstep-sarsa","single-step","daaf-impute-missing-reward-mapper","2",false,0.99,0.1,0.2,"{""nstep"":4}" -"nstep-sarsa","single-step","daaf-impute-missing-reward-mapper","2",false,1.0,0.1,0.2,"{""nstep"":6}" -"nstep-sarsa","single-step","daaf-impute-missing-reward-mapper","2",false,0.99,0.1,0.2,"{""nstep"":6}" -"nstep-sarsa","single-step","daaf-impute-missing-reward-mapper","2",false,1.0,0.1,0.2,"{""nstep"":8}" -"nstep-sarsa","single-step","daaf-impute-missing-reward-mapper","2",false,0.99,0.1,0.2,"{""nstep"":8}" 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This file is autogenerated by pip-compile with Python 3.9 -# by the following command: -# -# pip-compile --no-emit-find-links --no-emit-index-url --no-emit-options nb-requirements.in -# +# This file was autogenerated by uv via the following command: +# uv pip compile --no-emit-index-url --no-emit-find-links nb-requirements.in -o nb-requirements.txt absl-py==2.0.0 # via # -r requirements.txt @@ -13,28 +9,22 @@ aiohttp==3.8.4 # via # -r requirements.txt # aiohttp-cors - # ray aiohttp-cors==0.7.0 - # via - # -r requirements.txt - # ray + # via -r requirements.txt aiosignal==1.3.1 # via # -r requirements.txt # aiohttp # ray -anyio==3.6.2 +anyio==4.4.0 + # via jupyter-server +argon2-cffi==23.1.0 # via jupyter-server -argon2-cffi==21.3.0 - # via - # jupyter-server - # nbclassic - # notebook argon2-cffi-bindings==21.2.0 # via argon2-cffi arrow==1.3.0 # via isoduration -asttokens==2.2.1 +asttokens==2.4.1 # via stack-data astunparse==1.6.3 # via @@ -51,18 +41,12 @@ attrs==22.2.0 # -r requirements.txt # aiohttp # jsonschema -babel==2.15.0 +babel==2.16.0 # via jupyterlab-server -backcall==0.2.0 - # via ipython -beautifulsoup4==4.11.2 +beautifulsoup4==4.12.3 # via nbconvert -bleach==6.0.0 +bleach==6.1.0 # via nbconvert -blessed==1.20.0 - # via - # -r requirements.txt - # gpustat cachetools==5.3.0 # via # -r requirements.txt @@ -71,13 +55,17 @@ certifi==2022.12.7 # via # -r requirements.txt # requests -cffi==1.15.1 +cffi==1.17.1 # via argon2-cffi-bindings charset-normalizer==3.1.0 # via # -r requirements.txt # aiohttp # requests +clarabel==0.9.0 + # via + # -r requirements.txt + # cvxpy click==8.0.4 # via # -r requirements.txt @@ -87,16 +75,18 @@ cloudpickle==2.2.1 # -r requirements.txt # gymnasium colorful==0.5.5 + # via -r requirements.txt +comm==0.2.2 # via - # -r requirements.txt - # ray -comm==0.1.2 - # via ipykernel -contourpy==1.0.7 + # ipykernel + # ipywidgets +contourpy==1.3.0 # via matplotlib -cycler==0.11.0 +cvxpy==1.5.3 + # via -r requirements.txt +cycler==0.12.1 # via matplotlib -debugpy==1.6.6 +debugpy==1.8.5 # via ipykernel decorator==5.1.1 # via ipython @@ -106,13 +96,22 @@ distlib==0.3.6 # via # -r requirements.txt # virtualenv -executing==1.2.0 +ecos==2.0.14 + # via + # -r requirements.txt + # cvxpy +exceptiongroup==1.2.0 + # via + # -r nb-requirements.in + # anyio + # ipython +executing==2.1.0 # via stack-data farama-notifications==0.0.4 # via # -r requirements.txt # gymnasium -fastjsonschema==2.16.3 +fastjsonschema==2.20.0 # via nbformat filelock==3.9.1 # via @@ -123,7 +122,7 @@ flatbuffers==23.5.26 # via # -r requirements.txt # tensorflow -fonttools==4.39.0 +fonttools==4.53.1 # via matplotlib fqdn==1.5.1 # via jsonschema @@ -134,9 +133,7 @@ frozenlist==1.3.3 # aiosignal # ray fsspec==2024.2.0 - # via - # -r requirements.txt - # ray + # via -r requirements.txt gast==0.4.0 # via # -r requirements.txt @@ -163,14 +160,9 @@ googleapis-common-protos==1.58.0 # via # -r requirements.txt # google-api-core -gpustat==1.0.0 - # via - # -r requirements.txt - # ray grpcio==1.51.3 # via # -r requirements.txt - # ray # tensorboard # tensorflow gymnasium==0.28.1 @@ -198,56 +190,51 @@ importlib-metadata==6.0.0 # jupyterlab-server # markdown # nbconvert -importlib-resources==5.12.0 +importlib-resources==6.4.5 # via matplotlib -ipykernel==6.21.3 - # via - # jupyterlab - # nbclassic - # notebook -ipython==8.11.0 - # via ipykernel -ipython-genutils==0.2.0 +ipykernel==6.29.5 + # via jupyterlab +ipython==8.18.1 # via - # nbclassic - # notebook + # ipykernel + # ipywidgets +ipywidgets==8.1.5 + # via -r nb-requirements.in isoduration==20.11.0 # via jsonschema jax-jumpy==1.0.0 # via # -r requirements.txt # gymnasium -jedi==0.18.2 +jedi==0.19.1 # via ipython -jinja2==3.1.2 +jinja2==3.1.4 # via + # -r requirements.txt # jupyter-server # jupyterlab # jupyterlab-server - # nbclassic + # memray # nbconvert - # notebook json5==0.9.25 # via jupyterlab-server -jsonpointer==2.4 +jsonpointer==3.0.0 # via jsonschema -jsonschema[format-nongpl]==4.17.3 +jsonschema==4.17.3 # via # -r requirements.txt # jupyter-events # jupyterlab-server # nbformat # ray -jupyter-client==8.0.3 +jupyter-client==8.6.2 # via # ipykernel # jupyter-server - # nbclassic # nbclient - # notebook jupyter-contrib-core==0.4.2 # via jupyter-nbextensions-configurator -jupyter-core==5.2.0 +jupyter-core==5.7.2 # via # ipykernel # jupyter-client @@ -255,11 +242,9 @@ jupyter-core==5.2.0 # jupyter-nbextensions-configurator # jupyter-server # jupyterlab - # nbclassic # nbclient # nbconvert # nbformat - # notebook jupyter-events==0.6.3 # via jupyter-server jupyter-lsp==2.2.5 @@ -268,52 +253,78 @@ jupyter-nbextensions-configurator==0.6.3 # via -r nb-requirements.in jupyter-resource-usage==0.7.2 # via -r nb-requirements.in -jupyter-server==2.7.0 +jupyter-server==2.10.0 # via # -r nb-requirements.in # jupyter-lsp # jupyter-resource-usage # jupyterlab # jupyterlab-server - # nbclassic + # notebook # notebook-shim jupyter-server-terminals==0.5.3 # via jupyter-server jupyterlab==4.0.13 - # via -r nb-requirements.in -jupyterlab-pygments==0.2.2 + # via + # -r nb-requirements.in + # notebook +jupyterlab-pygments==0.3.0 # via nbconvert jupyterlab-server==2.24.0 - # via jupyterlab + # via + # jupyterlab + # notebook +jupyterlab-widgets==3.0.13 + # via ipywidgets keras==2.13.1 # via # -r requirements.txt # tensorflow -kiwisolver==1.4.4 +kiwisolver==1.4.7 # via matplotlib libclang==16.0.6 # via # -r requirements.txt # tensorflow +linkify-it-py==2.0.3 + # via + # -r requirements.txt + # markdown-it-py markdown==3.5.1 # via # -r requirements.txt # tensorboard +markdown-it-py==3.0.0 + # via + # -r requirements.txt + # mdit-py-plugins + # rich + # textual markupsafe==2.1.3 # via # -r requirements.txt # jinja2 # nbconvert # werkzeug -matplotlib==3.7.1 +matplotlib==3.9.2 # via # -r nb-requirements.in # seaborn -matplotlib-inline==0.1.6 +matplotlib-inline==0.1.7 # via # ipykernel # ipython -mistune==2.0.5 +mdit-py-plugins==0.4.2 + # via + # -r requirements.txt + # markdown-it-py +mdurl==0.1.2 + # via + # -r requirements.txt + # markdown-it-py +memray==1.14.0 + # via -r requirements.txt +mistune==3.0.2 # via nbconvert msgpack==1.0.5 # via @@ -324,66 +335,55 @@ multidict==6.0.4 # -r requirements.txt # aiohttp # yarl -nbclassic==0.5.3 - # via notebook -nbclient==0.7.2 +nbclient==0.10.0 # via nbconvert -nbconvert==7.2.10 - # via - # jupyter-server - # nbclassic - # notebook -nbformat==5.7.3 +nbconvert==7.16.4 + # via jupyter-server +nbformat==5.10.4 # via # jupyter-server - # nbclassic # nbclient # nbconvert - # notebook -nest-asyncio==1.5.6 - # via - # ipykernel - # nbclassic - # notebook -notebook==6.5.4 +nest-asyncio==1.6.0 + # via ipykernel +notebook==7.0.8 # via # jupyter-contrib-core # jupyter-nbextensions-configurator -notebook-shim==0.2.2 +notebook-shim==0.2.4 # via # jupyterlab - # nbclassic + # notebook numpy==1.23.5 # via # -r requirements.txt + # clarabel # contourpy + # cvxpy + # ecos # gymnasium # h5py # jax-jumpy # matplotlib # opt-einsum + # osqp # pandas # patsy # pyarrow - # ray + # qdldl # rlplg # scipy + # scs # seaborn # statsmodels # tensorboard # tensorflow -nvidia-ml-py==11.495.46 - # via - # -r requirements.txt - # gpustat oauthlib==3.2.2 # via # -r requirements.txt # requests-oauthlib opencensus==0.11.2 - # via - # -r requirements.txt - # ray + # via -r requirements.txt opencensus-context==0.1.3 # via # -r requirements.txt @@ -392,6 +392,10 @@ opt-einsum==3.3.0 # via # -r requirements.txt # tensorflow +osqp==0.6.7.post1 + # via + # -r requirements.txt + # cvxpy overrides==7.7.0 # via jupyter-server packaging==23.2 @@ -409,18 +413,15 @@ packaging==23.2 pandas==2.0.3 # via # -r requirements.txt - # ray # seaborn # statsmodels -pandocfilters==1.5.0 +pandocfilters==1.5.1 # via nbconvert -parso==0.8.3 +parso==0.8.4 # via jedi patsy==0.5.6 # via statsmodels -pexpect==4.8.0 - # via ipython -pickleshare==0.7.5 +pexpect==4.9.0 # via ipython pillow==9.4.0 # via @@ -437,10 +438,7 @@ prometheus-client==0.13.1 # -r requirements.txt # jupyter-resource-usage # jupyter-server - # nbclassic - # notebook - # ray -prompt-toolkit==3.0.38 +prompt-toolkit==3.0.47 # via ipython protobuf==4.25.1 # via @@ -450,26 +448,20 @@ protobuf==4.25.1 # ray # tensorboard # tensorflow -psutil==5.9.4 +psutil==5.9.8 # via - # -r requirements.txt - # gpustat # ipykernel # jupyter-resource-usage ptyprocess==0.7.0 # via # pexpect # terminado -pure-eval==0.2.2 +pure-eval==0.2.3 # via stack-data py-spy==0.3.14 - # via - # -r requirements.txt - # ray + # via -r requirements.txt pyarrow==15.0.0 - # via - # -r requirements.txt - # ray + # via -r requirements.txt pyasn1==0.4.8 # via # -r requirements.txt @@ -479,17 +471,17 @@ pyasn1-modules==0.2.8 # via # -r requirements.txt # google-auth -pycparser==2.21 +pycparser==2.22 # via cffi pydantic==1.10.6 + # via -r requirements.txt +pygments==2.18.0 # via # -r requirements.txt - # ray -pygments==2.14.0 - # via # ipython # nbconvert -pyparsing==3.0.9 + # rich +pyparsing==3.1.4 # via matplotlib pyrsistent==0.19.3 # via @@ -514,15 +506,17 @@ pyyaml==6.0 # jupyter-events # jupyter-nbextensions-configurator # ray -pyzmq==25.0.1 +pyzmq==26.2.0 # via # ipykernel # jupyter-client # jupyter-resource-usage # jupyter-server - # nbclassic - # notebook -ray[data,default]==2.9.3 +qdldl==0.1.7.post4 + # via + # -r requirements.txt + # osqp +ray==2.23.0 # via -r requirements.txt requests==2.28.2 # via @@ -544,7 +538,12 @@ rfc3986-validator==0.1.1 # via # jsonschema # jupyter-events -rlplg @ git+https://github.com/guidj/rlplg.git@v0.19.10 +rich==13.8.1 + # via + # -r requirements.txt + # memray + # textual +rlplg @ git+https://github.com/guidj/rlplg.git@93d819cab2eeefdd9bbd0ec7acb5d1dd0e5d1bed # via -r requirements.txt rsa==4.9 # via @@ -553,38 +552,47 @@ rsa==4.9 scipy==1.10.1 # via # -r requirements.txt + # clarabel + # cvxpy + # ecos + # osqp + # qdldl # rlplg + # scs # statsmodels +scs==3.2.7 + # via + # -r requirements.txt + # cvxpy seaborn==0.13.1 # via -r nb-requirements.in -send2trash==1.8.0 +send2trash==1.8.3 + # via jupyter-server +setuptools==74.1.2 # via - # jupyter-server - # nbclassic - # notebook + # -r requirements.txt + # jupyter-contrib-core + # tensorboard + # tensorflow six==1.16.0 # via # -r requirements.txt # asttokens # astunparse # bleach - # blessed # google-auth # google-pasta - # gpustat # patsy # python-dateutil # rfc3339-validator # tensorflow smart-open==6.3.0 - # via - # -r requirements.txt - # ray -sniffio==1.3.0 + # via -r requirements.txt +sniffio==1.3.1 # via anyio -soupsieve==2.4 +soupsieve==2.6 # via beautifulsoup4 -stack-data==0.6.2 +stack-data==0.6.3 # via ipython statsmodels==0.14.1 # via -r nb-requirements.in @@ -610,17 +618,19 @@ termcolor==2.3.0 # via # -r requirements.txt # tensorflow -terminado==0.17.1 +terminado==0.18.1 # via # jupyter-server # jupyter-server-terminals - # nbclassic - # notebook -tinycss2==1.2.1 +textual==0.78.0 + # via + # -r requirements.txt + # memray +tinycss2==1.3.0 # via nbconvert tomli==2.0.1 # via jupyterlab -tornado==6.2 +tornado==6.4.1 # via # ipykernel # jupyter-client @@ -628,14 +638,16 @@ tornado==6.2 # jupyter-nbextensions-configurator # jupyter-server # jupyterlab - # nbclassic # notebook # terminado -traitlets==5.9.0 +tqdm==4.66.5 + # via -r nb-requirements.in +traitlets==5.14.3 # via # comm # ipykernel # ipython + # ipywidgets # jupyter-client # jupyter-contrib-core # jupyter-core @@ -644,24 +656,29 @@ traitlets==5.9.0 # jupyter-server # jupyterlab # matplotlib-inline - # nbclassic # nbclient # nbconvert # nbformat - # notebook -types-python-dateutil==2.8.19.14 +types-python-dateutil==2.9.0.20240906 # via arrow typing-extensions==4.5.0 # via # -r requirements.txt + # anyio # async-lru # gymnasium + # ipython # pydantic # tensorflow + # textual tzdata==2024.1 # via # -r requirements.txt # pandas +uc-micro-py==1.0.3 + # via + # -r requirements.txt + # linkify-it-py uri-template==1.3.0 # via jsonschema urllib3==1.26.15 @@ -669,21 +686,16 @@ urllib3==1.26.15 # -r requirements.txt # requests virtualenv==20.21.0 - # via - # -r requirements.txt - # ray -wcwidth==0.2.6 - # via - # -r requirements.txt - # blessed - # prompt-toolkit -webcolors==1.13 + # via -r requirements.txt +wcwidth==0.2.13 + # via prompt-toolkit +webcolors==24.8.0 # via jsonschema webencodings==0.5.1 # via # bleach # tinycss2 -websocket-client==1.5.1 +websocket-client==1.8.0 # via jupyter-server werkzeug==3.0.1 # via @@ -694,6 +706,8 @@ wheel==0.42.0 # -r requirements.txt # astunparse # tensorboard +widgetsnbextension==4.0.13 + # via ipywidgets wrapt==1.16.0 # via # -r requirements.txt @@ -707,6 +721,3 @@ zipp==3.15.0 # -r requirements.txt # importlib-metadata # importlib-resources - -# The following packages are considered to be unsafe in a requirements file: -# setuptools diff --git a/notebooks/daaf_analyses/onpolicy_eval/exp-policyeval-v3-s0-debug.ipynb b/notebooks/daaf_analyses/onpolicy_eval/exp-policyeval-v3-s0-debug.ipynb index 8221a63..b48be63 100644 --- a/notebooks/daaf_analyses/onpolicy_eval/exp-policyeval-v3-s0-debug.ipynb +++ b/notebooks/daaf_analyses/onpolicy_eval/exp-policyeval-v3-s0-debug.ipynb @@ -44,11 +44,11 @@ "name": "stderr", "output_type": "stream", "text": [ - "2024-05-09 14:25:53.518099: I tensorflow/tsl/cuda/cudart_stub.cc:28] Could not find cuda drivers on your machine, GPU will not be used.\n", - "2024-05-09 14:25:53.601199: I tensorflow/tsl/cuda/cudart_stub.cc:28] Could not find cuda drivers on your machine, GPU will not be used.\n", - "2024-05-09 14:25:53.602788: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n", + "2024-10-22 20:10:43.322322: I tensorflow/tsl/cuda/cudart_stub.cc:28] Could not find cuda drivers on your machine, GPU will not be used.\n", + "2024-10-22 20:10:43.570276: I tensorflow/tsl/cuda/cudart_stub.cc:28] Could not find cuda drivers on your machine, GPU will not be used.\n", + "2024-10-22 20:10:43.574880: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n", "To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n", - "2024-05-09 14:25:55.277181: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n" + "2024-10-22 20:10:47.471641: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n" ] } ], @@ -102,8 +102,7 @@ }, "outputs": [], "source": [ - "# files = tf.io.gfile.glob(f\"{pathlib.Path.home()}/fs/daaf/exp/evaljob/agg/1713705374/1713706816/logs/*.parquet\")\n", - "files = tf.io.gfile.glob(f\"{pathlib.Path.home()}/fs/daaf/exp/evaljob/agg/1715263005/1715264399/logs/*.parquet\")" + "files = tf.io.gfile.glob(f\"{pathlib.Path.home()}/fs/daaf/exp/evaljob/agg/1726821587/1726836104/logs/*.parquet\")" ] }, { @@ -113,45 +112,23 @@ "metadata": { "tags": [] }, - "outputs": [], - "source": [ - "def read_data(files):\n", - " def parse_state_values(sv):\n", - " return np.reshape(\n", - " sv[\"data\"], newshape=sv[\"shape\"]\n", - " )\n", - "\n", - " with ray.init() as context:\n", - " ds_logs = ray.data.read_parquet(files)\n", - " df_logs = ds_logs.to_pandas()\n", - " df_logs[\"state_values\"] = df_logs[\"state_values\"].apply(parse_state_values)\n", - " return df_logs" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "0a64b5b3-7f77-4651-ad49-fb47ad711ab6", - "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "2024-05-09 14:26:01,096\tINFO worker.py:1715 -- Started a local Ray instance. View the dashboard at \u001b[1m\u001b[32mhttp://127.0.0.1:8265 \u001b[39m\u001b[22m\n", - "/home/guilherme/.pyenv/versions/3.8.15/envs/rl_daaf_py38/lib/python3.8/site-packages/ray/data/datasource/parquet_datasource.py:242: FutureWarning: Passing 'use_legacy_dataset' is deprecated as of pyarrow 15.0.0 and will be removed in a future version.\n", - " pq_ds = pq.ParquetDataset(\n" + "2024-10-22 20:10:54,958\tINFO worker.py:1740 -- Started a local Ray instance. View the dashboard at \u001b[1m\u001b[32m127.0.0.1:8265 \u001b[39m\u001b[22m\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "1765603bc1134e0ea4f3f7292f02c3b3", "version_major": 2, "version_minor": 0 }, "text/plain": [ - "Metadata Fetch Progress 0: 0%| | 0/8 [00:00exp_id\n", " meta\n", " state_values\n", + " method\n", " \n", " \n", " \n", " \n", " 0\n", - " 190\n", - " 1715263005-63036c13-RedGreenSeq\n", - " {'algorithm': 'nstep-td', 'algorithm_args': '{...\n", - " [[-19.80916204739628, -17.15846900836637, -15....\n", + " 990\n", + " 1726821587-21b840ef-ABCSeq\n", + " {'algorithm': 'one-step-td', 'algorithm_args':...\n", + " [[-62.38473431795828, -57.893917238232035, -48...\n", + " OP/OT\n", " \n", " \n", " 1\n", - " 190\n", - " 1715263005-62c63001-RedGreenSeq\n", - " {'algorithm': 'one-step-td', 'algorithm_args':...\n", - " [[-43.92046183252249, -50.5326225834207, -46.3...\n", + " 990\n", + " 1726821587-2254cc65-RedGreenSeq\n", + " {'algorithm': 'nstep-td', 'algorithm_args': '{...\n", + " [[-25.86085290433757, -20.250514343849584, -16...\n", + " PP/nTD-SU\n", " \n", " \n", " 2\n", - " 190\n", - " 1715263005-610000df-IceWorld\n", - " {'algorithm': 'first-visit-mc', 'algorithm_arg...\n", - " [[-14.827999747368427, -11.283347368421053, -7...\n", + " 990\n", + " 1726821587-04f7bf9a-ABCSeq\n", + " {'algorithm': 'nstep-td', 'algorithm_args': '{...\n", + " [[-53.60551642990765, -51.4935817493364, -41.9...\n", + " PP/LEAST\n", " \n", " \n", " 3\n", - " 190\n", - " 1715263005-620823ad-IceWorld\n", - " {'algorithm': 'one-step-td', 'algorithm_args':...\n", - " [[-3.4963652196459276, -3.329828339930383, -3....\n", + " 990\n", + " 1726821587-0507c0a7-IceWorld\n", + " {'algorithm': 'nstep-td', 'algorithm_args': '{...\n", + " [[-35.681611557425725, -35.04424679205454, -35...\n", + " PP/FR\n", " \n", " \n", " 4\n", - " 190\n", - " 1715263005-621fa01e-IceWorld\n", - " {'algorithm': 'nstep-td', 'algorithm_args': '{...\n", - " [[-24.19032434155747, -20.160954699992576, -13...\n", + " 990\n", + " 1726821587-15400276-RedGreenSeq\n", + " {'algorithm': 'one-step-td', 'algorithm_args':...\n", + " [[-19.99905073604775, -17.97932123818952, -15....\n", + " PP/LEAST\n", " \n", " \n", " ...\n", @@ -273,91 +248,97 @@ " ...\n", " ...\n", " ...\n", + " ...\n", " \n", " \n", - " 35195\n", - " 320\n", - " 1715263005-fe765c36-ABCSeq\n", - " {'algorithm': 'one-step-td', 'algorithm_args':...\n", - " [[-60.63560866795348, -54.81887338278657, -49....\n", + " 191995\n", + " 2070\n", + " 1726821587-e6fee995-IceWorld\n", + " {'algorithm': 'first-visit-mc', 'algorithm_arg...\n", + " [[-74.13333333333331, -74.73787599754469, -72....\n", + " PP/LEAST\n", " \n", " \n", - " 35196\n", - " 320\n", - " 1715263005-fe035497-TowerOfHanoi\n", - " {'algorithm': 'nstep-td', 'algorithm_args': '{...\n", - " [[-585.2524318287763, -447.4177079856222, -392...\n", + " 191996\n", + " 2070\n", + " 1726821587-e6466ca2-IceWorld\n", + " {'algorithm': 'one-step-td', 'algorithm_args':...\n", + " [[-139.34779864785156, -136.62189048544238, -1...\n", + " PP/DMR\n", " \n", " \n", - " 35197\n", - " 320\n", - " 1715263005-fdfb0ab0-FrozenLake-v1\n", - " {'algorithm': 'one-step-td', 'algorithm_args':...\n", - " [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...\n", + " 191997\n", + " 2070\n", + " 1726821587-e608c03d-TowerOfHanoi\n", + " {'algorithm': 'first-visit-mc', 'algorithm_arg...\n", + " [[-97.2945799153621, -93.4193206879249, -89.01...\n", + " PP/LEAST\n", " \n", " \n", - " 35198\n", - " 320\n", - " 1715263005-fe4244b9-RedGreenSeq\n", - " {'algorithm': 'nstep-td', 'algorithm_args': '{...\n", - " [[-22.268445336471185, -20.09385533420391, -15...\n", + " 191998\n", + " 2070\n", + " 1726821587-fd16e07c-TowerOfHanoi\n", + " {'algorithm': 'one-step-td', 'algorithm_args':...\n", + " [[-2093.2026275281783, -1795.0428249931097, -1...\n", + " OP/OT\n", " \n", " \n", - " 35199\n", - " 320\n", - " 1715263005-ff4a7b3a-GridWorld\n", - " {'algorithm': 'nstep-td', 'algorithm_args': '{...\n", - " [[-112626.75179711408, -112630.30079229665, -1...\n", + " 191999\n", + " 2070\n", + " 1726821587-fd001225-TowerOfHanoi\n", + " {'algorithm': 'one-step-td', 'algorithm_args':...\n", + " [[-590.0846928476416, -558.3777259512275, -516...\n", + " PP/DMR\n", " \n", " \n", "\n", - "

35200 rows × 4 columns

\n", + "

192000 rows × 5 columns

\n", "" ], "text/plain": [ - " episode exp_id \\\n", - "0 190 1715263005-63036c13-RedGreenSeq \n", - "1 190 1715263005-62c63001-RedGreenSeq \n", - "2 190 1715263005-610000df-IceWorld \n", - "3 190 1715263005-620823ad-IceWorld \n", - "4 190 1715263005-621fa01e-IceWorld \n", - "... ... ... \n", - "35195 320 1715263005-fe765c36-ABCSeq \n", - "35196 320 1715263005-fe035497-TowerOfHanoi \n", - "35197 320 1715263005-fdfb0ab0-FrozenLake-v1 \n", - "35198 320 1715263005-fe4244b9-RedGreenSeq \n", - "35199 320 1715263005-ff4a7b3a-GridWorld \n", + " episode exp_id \\\n", + "0 990 1726821587-21b840ef-ABCSeq \n", + "1 990 1726821587-2254cc65-RedGreenSeq \n", + "2 990 1726821587-04f7bf9a-ABCSeq \n", + "3 990 1726821587-0507c0a7-IceWorld \n", + "4 990 1726821587-15400276-RedGreenSeq \n", + "... ... ... \n", + "191995 2070 1726821587-e6fee995-IceWorld \n", + "191996 2070 1726821587-e6466ca2-IceWorld \n", + "191997 2070 1726821587-e608c03d-TowerOfHanoi \n", + "191998 2070 1726821587-fd16e07c-TowerOfHanoi \n", + "191999 2070 1726821587-fd001225-TowerOfHanoi \n", "\n", - " meta \\\n", - "0 {'algorithm': 'nstep-td', 'algorithm_args': '{... \n", - "1 {'algorithm': 'one-step-td', 'algorithm_args':... \n", - "2 {'algorithm': 'first-visit-mc', 'algorithm_arg... \n", - "3 {'algorithm': 'one-step-td', 'algorithm_args':... \n", - "4 {'algorithm': 'nstep-td', 'algorithm_args': '{... \n", - "... ... \n", - "35195 {'algorithm': 'one-step-td', 'algorithm_args':... \n", - "35196 {'algorithm': 'nstep-td', 'algorithm_args': '{... \n", - "35197 {'algorithm': 'one-step-td', 'algorithm_args':... \n", - "35198 {'algorithm': 'nstep-td', 'algorithm_args': '{... \n", - "35199 {'algorithm': 'nstep-td', 'algorithm_args': '{... \n", + " meta \\\n", + "0 {'algorithm': 'one-step-td', 'algorithm_args':... \n", + "1 {'algorithm': 'nstep-td', 'algorithm_args': '{... \n", + "2 {'algorithm': 'nstep-td', 'algorithm_args': '{... \n", + "3 {'algorithm': 'nstep-td', 'algorithm_args': '{... \n", + "4 {'algorithm': 'one-step-td', 'algorithm_args':... \n", + "... ... \n", + "191995 {'algorithm': 'first-visit-mc', 'algorithm_arg... \n", + "191996 {'algorithm': 'one-step-td', 'algorithm_args':... \n", + "191997 {'algorithm': 'first-visit-mc', 'algorithm_arg... \n", + "191998 {'algorithm': 'one-step-td', 'algorithm_args':... \n", + "191999 {'algorithm': 'one-step-td', 'algorithm_args':... \n", "\n", - " state_values \n", - "0 [[-19.80916204739628, -17.15846900836637, -15.... \n", - "1 [[-43.92046183252249, -50.5326225834207, -46.3... \n", - "2 [[-14.827999747368427, -11.283347368421053, -7... \n", - "3 [[-3.4963652196459276, -3.329828339930383, -3.... \n", - "4 [[-24.19032434155747, -20.160954699992576, -13... \n", - "... ... \n", - "35195 [[-60.63560866795348, -54.81887338278657, -49.... \n", - "35196 [[-585.2524318287763, -447.4177079856222, -392... \n", - "35197 [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,... \n", - "35198 [[-22.268445336471185, -20.09385533420391, -15... \n", - "35199 [[-112626.75179711408, -112630.30079229665, -1... \n", + " state_values method \n", + "0 [[-62.38473431795828, -57.893917238232035, -48... OP/OT \n", + "1 [[-25.86085290433757, -20.250514343849584, -16... PP/nTD-SU \n", + "2 [[-53.60551642990765, -51.4935817493364, -41.9... PP/LEAST \n", + "3 [[-35.681611557425725, -35.04424679205454, -35... PP/FR \n", + "4 [[-19.99905073604775, -17.97932123818952, -15.... PP/LEAST \n", + "... ... ... \n", + "191995 [[-74.13333333333331, -74.73787599754469, -72.... PP/LEAST \n", + "191996 [[-139.34779864785156, -136.62189048544238, -1... PP/DMR \n", + "191997 [[-97.2945799153621, -93.4193206879249, -89.01... PP/LEAST \n", + "191998 [[-2093.2026275281783, -1795.0428249931097, -1... OP/OT \n", + "191999 [[-590.0846928476416, -558.3777259512275, -516... PP/DMR \n", "\n", - "[35200 rows x 4 columns]" + "[192000 rows x 5 columns]" ] }, - "execution_count": 10, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -368,33 +349,33 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 10, "id": "4f0a9fd6-4f63-491c-96bc-8b61db780c15", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "{'algorithm': 'nstep-td',\n", + "{'algorithm': 'one-step-td',\n", " 'algorithm_args': '{}',\n", - " 'discount_factor': 0.99,\n", + " 'discount_factor': 1.0,\n", " 'drop_truncated_feedback_episodes': False,\n", - " 'dyna_prog_state_values': array([-21.2446288 , -18.85810242, -16.39925704, -13.8659012 ,\n", - " -11.25577699, -8.56655811, -5.79584775, -2.94117647,\n", - " 0. ]),\n", - " 'env': {'args': '{\"cure\": [\"red\", \"green\", \"wait\", \"green\", \"red\", \"red\", \"green\", \"wait\"]}',\n", - " 'level': 'NNLHYJFTC5ENMMDZWRNQ37B6VVDXQ7WHB5EJOPXYZFLMJEZOYLTSLB4ID4WHQG57XQPNUHGZCFDCWHYGXWSBW7FBWYRZGAGBW4J7MEQ=',\n", - " 'name': 'RedGreenSeq'},\n", + " 'dyna_prog_state_values': array([-48.99999993, -41.99999999, -35. , -28. ,\n", + " -21. , -14. , -7. , 0. ]),\n", + " 'env': {'args': '{\"length\": 7, \"distance_penalty\": false}',\n", + " 'level': 'n=7',\n", + " 'name': 'ABCSeq',\n", + " '_level': '7'},\n", " 'epsilon': 0.0,\n", - " 'exp_id': '1715263005-63036c13-RedGreenSeq',\n", + " 'exp_id': '1726821587-21b840ef-ABCSeq',\n", " 'learning_rate': 0.1,\n", - " 'policy_type': 'single-step',\n", - " 'reward_period': 1,\n", - " 'run_id': 0,\n", - " 'traj_mapping_method': 'identity-mapper'}" + " 'policy_type': 'OP',\n", + " 'reward_period': 4,\n", + " 'run_id': 1,\n", + " 'traj_mapping_method': 'OT'}" ] }, - "execution_count": 11, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -405,31 +386,57 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 11, "id": "88a3d394-6efe-4078-bb42-7968518f84e3", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([[-19.80916205, -17.15846901, -15.07822161, -13.10162062,\n", - " -11.11065543, -7.89008745, -5.64204705, -3.77681442,\n", - " 0. ],\n", - " [-20.80130241, -18.15388695, -15.95066797, -12.58330711,\n", - " -11.19311473, -7.55809078, -5.55615529, -2.39728152,\n", - " 0. ],\n", - " [-20.09659903, -18.01323126, -15.60486008, -13.13494021,\n", - " -9.75580219, -7.6048606 , -5.73190235, -2.44412537,\n", - " 0. ],\n", - " [-18.75516043, -15.96808788, -14.15271232, -12.3543663 ,\n", - " -9.64362078, -7.67929014, -4.77736684, -2.09508469,\n", - " 0. ],\n", - " [-19.13063067, -17.23350318, -14.73423066, -12.40770957,\n", - " -9.70660169, -7.28868384, -5.00166949, -2.04525222,\n", - " 0. ]])" + "array([array([-62.38473432, -57.89391724, -48.40994031, -41.93988829,\n", + " -36.82012224, -33.00358336, -33.35413741, 0. ]),\n", + " array([-70.21600194, -64.05622633, -59.23307837, -54.43573262,\n", + " -48.10336585, -37.65105584, -39.33444675, 0. ]),\n", + " array([-59.31433658, -53.6538727 , -50.5297967 , -44.29168706,\n", + " -39.00348638, -33.22616021, -34.11424269, 0. ]),\n", + " array([-64.4761011 , -55.82896246, -46.97910587, -39.72116391,\n", + " -32.28467785, -25.51095978, -20.26832619, 0. ]),\n", + " array([-69.59847322, -61.45655243, -54.78226728, -44.16563635,\n", + " -36.41240245, -27.19048432, -18.00215805, 0. ]),\n", + " array([-61.54400074, -53.34351252, -46.98489461, -43.8664305 ,\n", + " -38.36243059, -28.31403682, -21.84773079, 0. ]),\n", + " array([-59.78711475, -51.53655656, -46.78929235, -41.40451792,\n", + " -37.24107973, -33.87089113, -26.02184843, 0. ]),\n", + " array([-59.53803928, -53.6398948 , -48.93794001, -40.44610007,\n", + " -35.91181892, -28.89896686, -22.22427463, 0. ]),\n", + " array([-65.42240892, -59.45936588, -52.5658377 , -45.07079332,\n", + " -41.3818037 , -37.27629655, -33.92328829, 0. ]),\n", + " array([-68.31657327, -64.55343529, -60.79172138, -52.26920948,\n", + " -46.65858095, -34.59051215, -27.12679202, 0. ]),\n", + " array([-66.04777687, -59.77373016, -52.67904634, -43.42434074,\n", + " -38.21979578, -33.00999479, -28.59073861, 0. ]),\n", + " array([-68.34315818, -61.06738001, -58.05289359, -50.87478519,\n", + " -40.25280686, -36.79997692, -23.27329403, 0. ]),\n", + " array([-65.46381065, -59.60573407, -52.65617267, -50.40200058,\n", + " -39.1041481 , -36.25872015, -30.14125955, 0. ]),\n", + " array([-69.43674113, -66.91938263, -58.59263317, -48.75698361,\n", + " -42.91883335, -31.77018949, -28.96906258, 0. ]),\n", + " array([-68.81135842, -63.57972422, -54.29431291, -45.76332261,\n", + " -41.23253474, -31.83075389, -24.66734394, 0. ]),\n", + " array([-73.97143748, -65.80372165, -57.73843701, -49.10862825,\n", + " -42.7424336 , -31.97280061, -27.78977434, 0. ]),\n", + " array([-64.02734503, -59.36834589, -52.88777528, -47.8488008 ,\n", + " -39.12020938, -30.00429947, -19.03085421, 0. ]),\n", + " array([-61.35066975, -53.28254387, -46.41961134, -42.342734 ,\n", + " -37.6979335 , -30.06854272, -23.55119613, 0. ]),\n", + " array([-59.31065636, -52.71621682, -48.55534776, -40.9161892 ,\n", + " -35.05792463, -29.54701469, -27.17912862, 0. ]),\n", + " array([-61.9275178 , -56.78906942, -50.84300074, -42.76652845,\n", + " -36.13238298, -30.5997187 , -20.79458891, 0. ])],\n", + " dtype=object)" ] }, - "execution_count": 12, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -440,7 +447,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 12, "id": "c5097387-244d-40b2-89b6-f631867eb280", "metadata": {}, "outputs": [], @@ -490,7 +497,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 13, "id": "ec0f89b6-3cc5-49ed-a2dd-55792bbbc895", "metadata": {}, "outputs": [], @@ -498,32 +505,39 @@ "def plots_value_changes(df_problem: pd.DataFrame):\n", " # plot per state and traj mapper\n", " rows = []\n", - " for row in df_problem.to_dict(\"records\"):\n", - " state_values = row[\"state_values\"]\n", + " df_ref = copy.deepcopy(df_problem)\n", + " \n", + " for row in df_ref.to_dict(\"records\"):\n", + " state_values = np.stack(row[\"state_values\"])\n", " # shape: (runs x states)\n", - " for run in range(state_values.shape[0]):\n", - " for state in range(state_values.shape[1]):\n", - " new_row = copy.deepcopy(row)\n", - " del new_row[\"state_values\"]\n", - " new_row[\"run\"] = run\n", - " new_row[\"state\"] = state\n", - " new_row[\"state_value\"] = row[\"state_values\"][run, state]\n", - " rows.append(new_row)\n", - " df_methods = pd.DataFrame(rows)\n", + " num_runs, _ = state_values.shape\n", + " # new_row = copy.deepcopy(row)\n", + " for run in range(num_runs):\n", + " rows.extend(\n", + " [\n", + " {**row, \"run\": run, \"state\": state, \"state_value\": value}\n", + " for state, value in enumerate(state_values[run])\n", + " ]\n", + " )\n", "\n", + " df_methods = pd.DataFrame(rows)\n", " ref_row = copy.deepcopy(df_methods.iloc[0]).to_dict()\n", " episodes = df_methods[\"episode\"].unique()\n", " runs = df_methods[\"run\"].unique()\n", + " \n", " for episode in episodes:\n", " for run in runs:\n", - " for state in range(len(ref_row[\"dyna_prog_state_values\"])):\n", - " ref_row[\"episode\"] = episode\n", - " ref_row[\"run\"] = run\n", - " ref_row[\"state\"] = state\n", - " ref_row[\"state_value\"] = ref_row[\"dyna_prog_state_values\"][state]\n", - " ref_row[\"traj_mapping_method\"] = \"dynamic-programming\"\n", - " rows.append(copy.deepcopy(ref_row))\n", + " rows.extend(\n", + " [\n", + " {**ref_row, \"episode\": episode, \"run\": run, \n", + " \"state\": state, \"state_value\": value,\n", + " \"traj_mapping_method\": \"dynamic-programming\"}\n", + " for state, value in enumerate(ref_row[\"dyna_prog_state_values\"])\n", + " ]\n", + " )\n", + " \n", " df_plot = pd.DataFrame(rows)\n", + "\n", " palette = sns.color_palette(\"tab10\")\n", " return sns.relplot(\n", " data=df_plot,\n", @@ -544,13 +558,13 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 14, "id": "a6134de5-95c9-435d-a57b-5fb7ef7bab8a", "metadata": {}, "outputs": [], "source": [ "df_abc_td = isolate_problem(\n", - " df_logs, problem=\"ABCSeq\", level=\"7\",\n", + " df_logs, problem=\"ABCSeq\", level=\"n=7\",\n", " algo=\"one-step-td\",\n", " reward_period=4,\n", " gamma=1.0\n", @@ -559,29 +573,8 @@ }, { "cell_type": "code", - "execution_count": 16, - "id": "58542734-fa6b-4c2b-a3e9-2a51a9ccc2e0", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(250, 14)" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_abc_td.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "b3c3d094-13f1-4dc7-8019-87aac0736529", + "execution_count": 15, + "id": "cc8a59d2-4c01-421b-86b5-9da84b3f354f", "metadata": {}, "outputs": [ { @@ -608,6 +601,7 @@ " episode\n", " exp_id\n", " state_values\n", + " method\n", " algorithm\n", " algorithm_args\n", " discount_factor\n", @@ -623,10 +617,11 @@ " \n", " \n", " \n", - " 85\n", + " 126\n", " 0\n", - " 1715263005-0fceea2e-ABCSeq\n", - " [[0.0, -0.4, -2.4, 0.0, -0.4, -2.2, 0.0, 0.0],...\n", + " 1726821587-1ea78f43-ABCSeq\n", + " [[-0.36, -0.36, -1.08, -1.08, 0.0, -0.4, -0.36...\n", + " PP/LEAST\n", " one-step-td\n", " {}\n", " 1.0\n", @@ -634,16 +629,17 @@ " [-48.99999993362666, -41.99999998952205, -34.9...\n", " 0.0\n", " 0.1\n", - " single-step\n", + " PP\n", " 4\n", - " 3\n", - " daaf-trajectory-mapper\n", + " 0\n", + " LEAST\n", " \n", " \n", - " 84\n", + " 124\n", " 0\n", - " 1715263005-21b6c410-ABCSeq\n", - " [[-1.44, -0.36, -1.08, -1.84, -0.4, -0.72, -0....\n", + " 1726821587-21b840ef-ABCSeq\n", + " [[-0.4, 0.0, -0.4, -0.4, -1.12, -0.4, -1.6, 0....\n", + " OP/OT\n", " one-step-td\n", " {}\n", " 1.0\n", @@ -651,16 +647,17 @@ " [-48.99999993362666, -41.99999998952205, -34.9...\n", " 0.0\n", " 0.1\n", - " single-step\n", - " 4\n", + " OP\n", " 4\n", - " daaf-lsq-reward-attribution-mapper\n", + " 0\n", + " OT\n", " \n", " \n", - " 86\n", + " 125\n", " 0\n", - " 1715263005-a0875722-ABCSeq\n", - " [[-0.4, -1.12, 0.0, -1.12, -1.12, 0.0, -0.8, 0...\n", + " 1726821587-2c44d7d4-ABCSeq\n", + " [[0.0, -0.76, -1.08, 0.0, -1.44, -0.36, -1.480...\n", + " PP/IMR\n", " one-step-td\n", " {}\n", " 1.0\n", @@ -668,16 +665,17 @@ " [-48.99999993362666, -41.99999998952205, -34.9...\n", " 0.0\n", " 0.1\n", - " options\n", + " PP\n", " 4\n", - " 0\n", - " daaf-mdp-with-options-mapper\n", + " 7\n", + " IMR\n", " \n", " \n", - " 221\n", + " 127\n", " 0\n", - " 1715263005-bc83cde9-ABCSeq\n", - " [[-0.19, -0.9999999999999999, -0.82, -0.1, -0....\n", + " 1726821587-73d209a3-ABCSeq\n", + " [[0.0, -0.76, 0.0, -0.8, -0.4, -0.4, -0.4, 0.0...\n", + " PP/DMR\n", " one-step-td\n", " {}\n", " 1.0\n", @@ -685,16 +683,17 @@ " [-48.99999993362666, -41.99999998952205, -34.9...\n", " 0.0\n", " 0.1\n", - " single-step\n", - " 1\n", - " 2\n", - " identity-mapper\n", + " PP\n", + " 4\n", + " 7\n", + " DMR\n", " \n", " \n", - " 87\n", - " 0\n", - " 1715263005-deb09c2d-ABCSeq\n", - " [[-0.36, -0.72, -2.52, -1.44, -1.12, -1.08, -1...\n", + " 630\n", + " 10\n", + " 1726821587-1ea78f43-ABCSeq\n", + " [[-5.32313996368, -6.18825221716, -6.524410501...\n", + " PP/LEAST\n", " one-step-td\n", " {}\n", " 1.0\n", @@ -702,10 +701,10 @@ " [-48.99999993362666, -41.99999998952205, -34.9...\n", " 0.0\n", " 0.1\n", - " single-step\n", + " PP\n", " 4\n", - " 4\n", - " daaf-impute-missing-reward-mapper\n", + " 5\n", + " LEAST\n", " \n", " \n", " ...\n", @@ -723,12 +722,14 @@ " ...\n", " ...\n", " ...\n", + " ...\n", " \n", " \n", - " 73\n", - " 490\n", - " 1715263005-0fceea2e-ABCSeq\n", - " [[-184.2275547627881, -168.00976950574454, -14...\n", + " 227\n", + " 2480\n", + " 1726821587-73d209a3-ABCSeq\n", + " [[-182.56377883692443, -155.14354656423944, -1...\n", + " PP/DMR\n", " one-step-td\n", " {}\n", " 1.0\n", @@ -736,16 +737,17 @@ " [-48.99999993362666, -41.99999998952205, -34.9...\n", " 0.0\n", " 0.1\n", - " single-step\n", + " PP\n", " 4\n", - " 0\n", - " daaf-trajectory-mapper\n", + " 13\n", + " DMR\n", " \n", " \n", - " 72\n", - " 490\n", - " 1715263005-21b6c410-ABCSeq\n", - " [[-44.32532817580987, -36.053428897770765, -28...\n", + " 922\n", + " 2490\n", + " 1726821587-1ea78f43-ABCSeq\n", + " [[-45.99478030246791, -38.75632803206125, -31....\n", + " PP/LEAST\n", " one-step-td\n", " {}\n", " 1.0\n", @@ -753,16 +755,17 @@ " [-48.99999993362666, -41.99999998952205, -34.9...\n", " 0.0\n", " 0.1\n", - " single-step\n", + " PP\n", " 4\n", - " 0\n", - " daaf-lsq-reward-attribution-mapper\n", + " 9\n", + " LEAST\n", " \n", " \n", - " 74\n", - " 490\n", - " 1715263005-a0875722-ABCSeq\n", - " [[-68.52090593819865, -64.48037932133991, -54....\n", + " 920\n", + " 2490\n", + " 1726821587-21b840ef-ABCSeq\n", + " [[-66.92386938335389, -60.87563408620904, -57....\n", + " OP/OT\n", " one-step-td\n", " {}\n", " 1.0\n", @@ -770,16 +773,17 @@ " [-48.99999993362666, -41.99999998952205, -34.9...\n", " 0.0\n", " 0.1\n", - " options\n", + " OP\n", " 4\n", - " 4\n", - " daaf-mdp-with-options-mapper\n", + " 9\n", + " OT\n", " \n", " \n", - " 218\n", - " 490\n", - " 1715263005-bc83cde9-ABCSeq\n", - " [[-44.05348390668802, -39.67905217710317, -34....\n", + " 921\n", + " 2490\n", + " 1726821587-2c44d7d4-ABCSeq\n", + " [[-45.47058454401428, -40.75236931873213, -30....\n", + " PP/IMR\n", " one-step-td\n", " {}\n", " 1.0\n", @@ -787,16 +791,17 @@ " [-48.99999993362666, -41.99999998952205, -34.9...\n", " 0.0\n", " 0.1\n", - " single-step\n", - " 1\n", + " PP\n", " 4\n", - " identity-mapper\n", + " 7\n", + " IMR\n", " \n", " \n", - " 75\n", - " 490\n", - " 1715263005-deb09c2d-ABCSeq\n", - " [[-41.98748866631166, -39.082314732788504, -30...\n", + " 923\n", + " 2490\n", + " 1726821587-73d209a3-ABCSeq\n", + " [[-198.9943684873033, -194.72431378660377, -12...\n", + " PP/DMR\n", " one-step-td\n", " {}\n", " 1.0\n", @@ -804,99 +809,86 @@ " [-48.99999993362666, -41.99999998952205, -34.9...\n", " 0.0\n", " 0.1\n", - " single-step\n", + " PP\n", " 4\n", - " 3\n", - " daaf-impute-missing-reward-mapper\n", + " 7\n", + " DMR\n", " \n", " \n", "\n", - "

250 rows × 14 columns

\n", + "

1000 rows × 15 columns

\n", "" ], "text/plain": [ " episode exp_id \\\n", - "85 0 1715263005-0fceea2e-ABCSeq \n", - "84 0 1715263005-21b6c410-ABCSeq \n", - "86 0 1715263005-a0875722-ABCSeq \n", - "221 0 1715263005-bc83cde9-ABCSeq \n", - "87 0 1715263005-deb09c2d-ABCSeq \n", + "126 0 1726821587-1ea78f43-ABCSeq \n", + "124 0 1726821587-21b840ef-ABCSeq \n", + "125 0 1726821587-2c44d7d4-ABCSeq \n", + "127 0 1726821587-73d209a3-ABCSeq \n", + "630 10 1726821587-1ea78f43-ABCSeq \n", ".. ... ... \n", - "73 490 1715263005-0fceea2e-ABCSeq \n", - "72 490 1715263005-21b6c410-ABCSeq \n", - "74 490 1715263005-a0875722-ABCSeq \n", - "218 490 1715263005-bc83cde9-ABCSeq \n", - "75 490 1715263005-deb09c2d-ABCSeq \n", + "227 2480 1726821587-73d209a3-ABCSeq \n", + "922 2490 1726821587-1ea78f43-ABCSeq \n", + "920 2490 1726821587-21b840ef-ABCSeq \n", + "921 2490 1726821587-2c44d7d4-ABCSeq \n", + "923 2490 1726821587-73d209a3-ABCSeq \n", "\n", - " state_values algorithm \\\n", - "85 [[0.0, -0.4, -2.4, 0.0, -0.4, -2.2, 0.0, 0.0],... one-step-td \n", - "84 [[-1.44, -0.36, -1.08, -1.84, -0.4, -0.72, -0.... one-step-td \n", - "86 [[-0.4, -1.12, 0.0, -1.12, -1.12, 0.0, -0.8, 0... one-step-td \n", - "221 [[-0.19, -0.9999999999999999, -0.82, -0.1, -0.... one-step-td \n", - "87 [[-0.36, -0.72, -2.52, -1.44, -1.12, -1.08, -1... one-step-td \n", - ".. ... ... \n", - "73 [[-184.2275547627881, -168.00976950574454, -14... one-step-td \n", - "72 [[-44.32532817580987, -36.053428897770765, -28... one-step-td \n", - "74 [[-68.52090593819865, -64.48037932133991, -54.... one-step-td \n", - "218 [[-44.05348390668802, -39.67905217710317, -34.... one-step-td \n", - "75 [[-41.98748866631166, -39.082314732788504, -30... one-step-td \n", + " state_values method algorithm \\\n", + "126 [[-0.36, -0.36, -1.08, -1.08, 0.0, -0.4, -0.36... PP/LEAST one-step-td \n", + "124 [[-0.4, 0.0, -0.4, -0.4, -1.12, -0.4, -1.6, 0.... OP/OT one-step-td \n", + "125 [[0.0, -0.76, -1.08, 0.0, -1.44, -0.36, -1.480... PP/IMR one-step-td \n", + "127 [[0.0, -0.76, 0.0, -0.8, -0.4, -0.4, -0.4, 0.0... PP/DMR one-step-td \n", + "630 [[-5.32313996368, -6.18825221716, -6.524410501... PP/LEAST one-step-td \n", + ".. ... ... ... \n", + "227 [[-182.56377883692443, -155.14354656423944, -1... PP/DMR one-step-td \n", + "922 [[-45.99478030246791, -38.75632803206125, -31.... PP/LEAST one-step-td \n", + "920 [[-66.92386938335389, -60.87563408620904, -57.... OP/OT one-step-td \n", + "921 [[-45.47058454401428, -40.75236931873213, -30.... PP/IMR one-step-td \n", + "923 [[-198.9943684873033, -194.72431378660377, -12... PP/DMR one-step-td \n", "\n", " algorithm_args discount_factor drop_truncated_feedback_episodes \\\n", - "85 {} 1.0 False \n", - "84 {} 1.0 False \n", - "86 {} 1.0 False \n", - "221 {} 1.0 False \n", - "87 {} 1.0 False \n", + "126 {} 1.0 False \n", + "124 {} 1.0 False \n", + "125 {} 1.0 False \n", + "127 {} 1.0 False \n", + "630 {} 1.0 False \n", ".. ... ... ... \n", - "73 {} 1.0 False \n", - "72 {} 1.0 False \n", - "74 {} 1.0 False \n", - "218 {} 1.0 False \n", - "75 {} 1.0 False \n", + "227 {} 1.0 False \n", + "922 {} 1.0 False \n", + "920 {} 1.0 False \n", + "921 {} 1.0 False \n", + "923 {} 1.0 False \n", "\n", " dyna_prog_state_values epsilon \\\n", - "85 [-48.99999993362666, -41.99999998952205, -34.9... 0.0 \n", - "84 [-48.99999993362666, -41.99999998952205, -34.9... 0.0 \n", - "86 [-48.99999993362666, -41.99999998952205, -34.9... 0.0 \n", - "221 [-48.99999993362666, -41.99999998952205, -34.9... 0.0 \n", - "87 [-48.99999993362666, -41.99999998952205, -34.9... 0.0 \n", + "126 [-48.99999993362666, -41.99999998952205, -34.9... 0.0 \n", + "124 [-48.99999993362666, -41.99999998952205, -34.9... 0.0 \n", + "125 [-48.99999993362666, -41.99999998952205, -34.9... 0.0 \n", + "127 [-48.99999993362666, -41.99999998952205, -34.9... 0.0 \n", + "630 [-48.99999993362666, -41.99999998952205, -34.9... 0.0 \n", ".. ... ... \n", - "73 [-48.99999993362666, -41.99999998952205, -34.9... 0.0 \n", - "72 [-48.99999993362666, -41.99999998952205, -34.9... 0.0 \n", - "74 [-48.99999993362666, -41.99999998952205, -34.9... 0.0 \n", - "218 [-48.99999993362666, -41.99999998952205, -34.9... 0.0 \n", - "75 [-48.99999993362666, -41.99999998952205, -34.9... 0.0 \n", - "\n", - " learning_rate policy_type reward_period run_id \\\n", - "85 0.1 single-step 4 3 \n", - "84 0.1 single-step 4 4 \n", - "86 0.1 options 4 0 \n", - "221 0.1 single-step 1 2 \n", - "87 0.1 single-step 4 4 \n", - ".. ... ... ... ... \n", - "73 0.1 single-step 4 0 \n", - "72 0.1 single-step 4 0 \n", - "74 0.1 options 4 4 \n", - "218 0.1 single-step 1 4 \n", - "75 0.1 single-step 4 3 \n", + "227 [-48.99999993362666, -41.99999998952205, -34.9... 0.0 \n", + "922 [-48.99999993362666, -41.99999998952205, -34.9... 0.0 \n", + "920 [-48.99999993362666, -41.99999998952205, -34.9... 0.0 \n", + "921 [-48.99999993362666, -41.99999998952205, -34.9... 0.0 \n", + "923 [-48.99999993362666, -41.99999998952205, -34.9... 0.0 \n", "\n", - " traj_mapping_method \n", - "85 daaf-trajectory-mapper \n", - "84 daaf-lsq-reward-attribution-mapper \n", - "86 daaf-mdp-with-options-mapper \n", - "221 identity-mapper \n", - "87 daaf-impute-missing-reward-mapper \n", - ".. ... \n", - "73 daaf-trajectory-mapper \n", - "72 daaf-lsq-reward-attribution-mapper \n", - "74 daaf-mdp-with-options-mapper \n", - "218 identity-mapper \n", - "75 daaf-impute-missing-reward-mapper \n", + " learning_rate policy_type reward_period run_id traj_mapping_method \n", + "126 0.1 PP 4 0 LEAST \n", + "124 0.1 OP 4 0 OT \n", + "125 0.1 PP 4 7 IMR \n", + "127 0.1 PP 4 7 DMR \n", + "630 0.1 PP 4 5 LEAST \n", + ".. ... ... ... ... ... \n", + "227 0.1 PP 4 13 DMR \n", + "922 0.1 PP 4 9 LEAST \n", + "920 0.1 OP 4 9 OT \n", + "921 0.1 PP 4 7 IMR \n", + "923 0.1 PP 4 7 DMR \n", "\n", - "[250 rows x 14 columns]" + "[1000 rows x 15 columns]" ] }, - "execution_count": 17, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } @@ -907,170 +899,942 @@ }, { "cell_type": "code", - "execution_count": 18, - "id": "f44eb0de-3f33-4202-93b9-5e98bb5fca12", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array(['daaf-trajectory-mapper', 'daaf-lsq-reward-attribution-mapper',\n", - " 'daaf-mdp-with-options-mapper', 'identity-mapper',\n", - " 'daaf-impute-missing-reward-mapper'], dtype=object)" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_abc_td[\"traj_mapping_method\"].unique()" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "9bafca96-a251-436e-8b77-024eba96fc53", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(5, 8)" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_abc_td[\"state_values\"].iloc[0].shape" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "6564c1d0-2573-4c2e-afce-b9a75827ec45", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_3266377/3725396169.py:31: UserWarning: The palette list has more values (10) than needed (6), which may not be intended.\n", - " return sns.relplot(\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plots_value_changes(df_abc_td)" - ] - }, - { - "cell_type": "markdown", - "id": "a49f0cbf-c120-4d08-96e1-ed8b2c668769", - "metadata": {}, - "source": [ - "## Monte Carlo" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "4958f05d-67c9-441f-9eff-6cd37717fb99", - "metadata": {}, - "outputs": [], - "source": [ - "df_abc_mc = isolate_problem(\n", - " df_logs, problem=\"ABCSeq\", level=\"7\",\n", - " algo=\"first-visit-mc\",\n", - " reward_period=4,\n", - " gamma=1.0\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "id": "6317b523-ccac-4435-a21e-23cad48ac96a", + "execution_count": 16, + "id": "58542734-fa6b-4c2b-a3e9-2a51a9ccc2e0", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array(['daaf-impute-missing-reward-mapper', 'daaf-trajectory-mapper',\n", - " 'identity-mapper', 'daaf-mdp-with-options-mapper',\n", - " 'daaf-lsq-reward-attribution-mapper'], dtype=object)" + "(1000, 15)" ] }, - "execution_count": 22, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "df_abc_mc[\"traj_mapping_method\"].unique()" + "df_abc_td.shape" ] }, { "cell_type": "code", - "execution_count": 23, - "id": "67ff8052-7972-4bcb-808b-7c48b0138b4e", + "execution_count": 17, + "id": "b3c3d094-13f1-4dc7-8019-87aac0736529", "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_3266377/3725396169.py:31: UserWarning: The palette list has more values (10) than needed (6), which may not be intended.\n", - " return sns.relplot(\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - }, { "data": { - "image/png": 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" + ], + "text/plain": [ + " episode exp_id \\\n", + "126 0 1726821587-1ea78f43-ABCSeq \n", + "124 0 1726821587-21b840ef-ABCSeq \n", + "125 0 1726821587-2c44d7d4-ABCSeq \n", + "127 0 1726821587-73d209a3-ABCSeq \n", + "630 10 1726821587-1ea78f43-ABCSeq \n", + ".. ... ... \n", + "227 2480 1726821587-73d209a3-ABCSeq \n", + "922 2490 1726821587-1ea78f43-ABCSeq \n", + "920 2490 1726821587-21b840ef-ABCSeq \n", + "921 2490 1726821587-2c44d7d4-ABCSeq \n", + "923 2490 1726821587-73d209a3-ABCSeq \n", + "\n", + " state_values method algorithm \\\n", + "126 [[-0.36, -0.36, -1.08, -1.08, 0.0, -0.4, -0.36... PP/LEAST one-step-td \n", + "124 [[-0.4, 0.0, -0.4, -0.4, -1.12, -0.4, -1.6, 0.... OP/OT one-step-td \n", + "125 [[0.0, -0.76, -1.08, 0.0, -1.44, -0.36, -1.480... PP/IMR one-step-td \n", + "127 [[0.0, -0.76, 0.0, -0.8, -0.4, -0.4, -0.4, 0.0... PP/DMR one-step-td \n", + "630 [[-5.32313996368, -6.18825221716, -6.524410501... PP/LEAST one-step-td \n", + ".. ... ... ... \n", + "227 [[-182.56377883692443, -155.14354656423944, -1... PP/DMR one-step-td \n", + "922 [[-45.99478030246791, -38.75632803206125, -31.... PP/LEAST one-step-td \n", + "920 [[-66.92386938335389, -60.87563408620904, -57.... OP/OT one-step-td \n", + "921 [[-45.47058454401428, -40.75236931873213, -30.... PP/IMR one-step-td \n", + "923 [[-198.9943684873033, -194.72431378660377, -12... PP/DMR one-step-td \n", + "\n", + " algorithm_args discount_factor drop_truncated_feedback_episodes \\\n", + "126 {} 1.0 False \n", + "124 {} 1.0 False \n", + "125 {} 1.0 False \n", + "127 {} 1.0 False \n", + "630 {} 1.0 False \n", + ".. ... ... ... \n", + "227 {} 1.0 False \n", + "922 {} 1.0 False \n", + "920 {} 1.0 False \n", + "921 {} 1.0 False \n", + "923 {} 1.0 False \n", + "\n", + " dyna_prog_state_values epsilon \\\n", + "126 [-48.99999993362666, -41.99999998952205, -34.9... 0.0 \n", + "124 [-48.99999993362666, -41.99999998952205, -34.9... 0.0 \n", + "125 [-48.99999993362666, -41.99999998952205, -34.9... 0.0 \n", + "127 [-48.99999993362666, -41.99999998952205, -34.9... 0.0 \n", + "630 [-48.99999993362666, -41.99999998952205, -34.9... 0.0 \n", + ".. ... ... \n", + "227 [-48.99999993362666, -41.99999998952205, -34.9... 0.0 \n", + "922 [-48.99999993362666, -41.99999998952205, -34.9... 0.0 \n", + "920 [-48.99999993362666, -41.99999998952205, -34.9... 0.0 \n", + "921 [-48.99999993362666, -41.99999998952205, -34.9... 0.0 \n", + "923 [-48.99999993362666, -41.99999998952205, -34.9... 0.0 \n", + "\n", + " learning_rate policy_type reward_period run_id traj_mapping_method \n", + "126 0.1 PP 4 0 LEAST \n", + "124 0.1 OP 4 0 OT \n", + "125 0.1 PP 4 7 IMR \n", + "127 0.1 PP 4 7 DMR \n", + "630 0.1 PP 4 5 LEAST \n", + ".. ... ... ... ... ... \n", + "227 0.1 PP 4 13 DMR \n", + "922 0.1 PP 4 9 LEAST \n", + "920 0.1 OP 4 9 OT \n", + "921 0.1 PP 4 7 IMR \n", + "923 0.1 PP 4 7 DMR \n", + "\n", + "[1000 rows x 15 columns]" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_abc_td" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "f44eb0de-3f33-4202-93b9-5e98bb5fca12", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['LEAST', 'OT', 'IMR', 'DMR'], dtype=object)" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_abc_td[\"traj_mapping_method\"].unique()" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "9bafca96-a251-436e-8b77-024eba96fc53", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(20,)" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_abc_td[\"state_values\"].iloc[0].shape" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "6564c1d0-2573-4c2e-afce-b9a75827ec45", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_3114566/2457586796.py:38: UserWarning: The palette list has more values (10) than needed (5), which may not be intended.\n", + " return sns.relplot(\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plots_value_changes(df_abc_td)" + ] + }, + { + "cell_type": "markdown", + "id": "a49f0cbf-c120-4d08-96e1-ed8b2c668769", + "metadata": {}, + "source": [ + "## Monte Carlo" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "4958f05d-67c9-441f-9eff-6cd37717fb99", + "metadata": {}, + "outputs": [], + "source": [ + "df_abc_mc = isolate_problem(\n", + " df_logs, problem=\"ABCSeq\", level=\"n=7\",\n", + " algo=\"first-visit-mc\",\n", + " reward_period=4,\n", + " gamma=1.0\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "6317b523-ccac-4435-a21e-23cad48ac96a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['DMR', 'LEAST', 'IMR', 'OT'], dtype=object)" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_abc_mc[\"traj_mapping_method\"].unique()" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "67ff8052-7972-4bcb-808b-7c48b0138b4e", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_3114566/2457586796.py:38: UserWarning: The palette list has more values (10) than needed (5), which may not be intended.\n", + " return sns.relplot(\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plots_value_changes(df_abc_mc)" + ] + }, + { + "cell_type": "markdown", + "id": "3a07d7a5-1e00-4873-928f-74f20b66b0f5", + "metadata": {}, + "source": [ + "## Full Rewards" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "5737ec07-f812-4d6b-bbde-60dd72bf2408", + "metadata": {}, + "outputs": [], + "source": [ + "df_fr = isolate_problem(\n", + " df_logs, problem=\"ABCSeq\", level=\"n=7\",\n", + " algo=\"one-step-td\",\n", + " reward_period=1,\n", + " gamma=1.0\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "5fa7b13c-1c92-49fc-a2a1-0ee4ab28416d", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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episodeexp_idstate_valuesmethodalgorithmalgorithm_argsdiscount_factordrop_truncated_feedback_episodesdyna_prog_state_valuesepsilonlearning_ratepolicy_typereward_periodrun_idtraj_mapping_method
3101726821587-74818508-ABCSeq[[-0.37000000000000005, -0.1, -0.3700000000000...PP/FRone-step-td{}1.0False[-48.99999993362666, -41.99999998952205, -34.9...0.00.1PP10FR
157101726821587-74818508-ABCSeq[[-8.10481282220499, -8.441783053979991, -7.75...PP/FRone-step-td{}1.0False[-48.99999993362666, -41.99999998952205, -34.9...0.00.1PP15FR
243201726821587-74818508-ABCSeq[[-13.263838468341735, -15.149045449201608, -1...PP/FRone-step-td{}1.0False[-48.99999993362666, -41.99999998952205, -34.9...0.00.1PP13FR
172301726821587-74818508-ABCSeq[[-21.37771271226704, -20.82920026250384, -19....PP/FRone-step-td{}1.0False[-48.99999993362666, -41.99999998952205, -34.9...0.00.1PP113FR
91401726821587-74818508-ABCSeq[[-27.699128593575917, -25.629846225256244, -2...PP/FRone-step-td{}1.0False[-48.99999993362666, -41.99999998952205, -34.9...0.00.1PP15FR
................................................
20224501726821587-74818508-ABCSeq[[-53.88400977444208, -44.773783195429914, -34...PP/FRone-step-td{}1.0False[-48.99999993362666, -41.99999998952205, -34.9...0.00.1PP114FR
22124601726821587-74818508-ABCSeq[[-43.45472354381776, -34.5372137401518, -30.6...PP/FRone-step-td{}1.0False[-48.99999993362666, -41.99999998952205, -34.9...0.00.1PP17FR
10124701726821587-74818508-ABCSeq[[-45.73286567267412, -39.93073384516813, -30....PP/FRone-step-td{}1.0False[-48.99999993362666, -41.99999998952205, -34.9...0.00.1PP15FR
5624801726821587-74818508-ABCSeq[[-45.24511475426608, -41.35581741781331, -31....PP/FRone-step-td{}1.0False[-48.99999993362666, -41.99999998952205, -34.9...0.00.1PP11FR
23024901726821587-74818508-ABCSeq[[-43.208181578307695, -34.8156996409463, -27....PP/FRone-step-td{}1.0False[-48.99999993362666, -41.99999998952205, -34.9...0.00.1PP19FR
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250 rows × 15 columns

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" + ], + "text/plain": [ + " episode exp_id \\\n", + "31 0 1726821587-74818508-ABCSeq \n", + "157 10 1726821587-74818508-ABCSeq \n", + "243 20 1726821587-74818508-ABCSeq \n", + "172 30 1726821587-74818508-ABCSeq \n", + "91 40 1726821587-74818508-ABCSeq \n", + ".. ... ... \n", + "202 2450 1726821587-74818508-ABCSeq \n", + "221 2460 1726821587-74818508-ABCSeq \n", + "101 2470 1726821587-74818508-ABCSeq \n", + "56 2480 1726821587-74818508-ABCSeq \n", + "230 2490 1726821587-74818508-ABCSeq \n", + "\n", + " state_values method algorithm \\\n", + "31 [[-0.37000000000000005, -0.1, -0.3700000000000... PP/FR one-step-td \n", + "157 [[-8.10481282220499, -8.441783053979991, -7.75... PP/FR one-step-td \n", + "243 [[-13.263838468341735, -15.149045449201608, -1... PP/FR one-step-td \n", + "172 [[-21.37771271226704, -20.82920026250384, -19.... PP/FR one-step-td \n", + "91 [[-27.699128593575917, -25.629846225256244, -2... PP/FR one-step-td \n", + ".. ... ... ... \n", + "202 [[-53.88400977444208, -44.773783195429914, -34... PP/FR one-step-td \n", + "221 [[-43.45472354381776, -34.5372137401518, -30.6... PP/FR one-step-td \n", + "101 [[-45.73286567267412, -39.93073384516813, -30.... PP/FR one-step-td \n", + "56 [[-45.24511475426608, -41.35581741781331, -31.... PP/FR one-step-td \n", + "230 [[-43.208181578307695, -34.8156996409463, -27.... PP/FR one-step-td \n", + "\n", + " algorithm_args discount_factor drop_truncated_feedback_episodes \\\n", + "31 {} 1.0 False \n", + "157 {} 1.0 False \n", + "243 {} 1.0 False \n", + "172 {} 1.0 False \n", + "91 {} 1.0 False \n", + ".. ... ... ... \n", + "202 {} 1.0 False \n", + "221 {} 1.0 False \n", + "101 {} 1.0 False \n", + "56 {} 1.0 False \n", + "230 {} 1.0 False \n", + "\n", + " dyna_prog_state_values epsilon \\\n", + "31 [-48.99999993362666, -41.99999998952205, -34.9... 0.0 \n", + "157 [-48.99999993362666, -41.99999998952205, -34.9... 0.0 \n", + "243 [-48.99999993362666, -41.99999998952205, -34.9... 0.0 \n", + "172 [-48.99999993362666, -41.99999998952205, -34.9... 0.0 \n", + "91 [-48.99999993362666, -41.99999998952205, -34.9... 0.0 \n", + ".. ... ... \n", + "202 [-48.99999993362666, -41.99999998952205, -34.9... 0.0 \n", + "221 [-48.99999993362666, -41.99999998952205, -34.9... 0.0 \n", + "101 [-48.99999993362666, -41.99999998952205, -34.9... 0.0 \n", + "56 [-48.99999993362666, -41.99999998952205, -34.9... 0.0 \n", + "230 [-48.99999993362666, -41.99999998952205, -34.9... 0.0 \n", + "\n", + " learning_rate policy_type reward_period run_id traj_mapping_method \n", + "31 0.1 PP 1 0 FR \n", + "157 0.1 PP 1 5 FR \n", + "243 0.1 PP 1 3 FR \n", + "172 0.1 PP 1 13 FR \n", + "91 0.1 PP 1 5 FR \n", + ".. ... ... ... ... ... \n", + "202 0.1 PP 1 14 FR \n", + "221 0.1 PP 1 7 FR \n", + "101 0.1 PP 1 5 FR \n", + "56 0.1 PP 1 1 FR \n", + "230 0.1 PP 1 9 FR \n", + "\n", + "[250 rows x 15 columns]" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_fr" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "86e63173-c58b-4bff-b330-c79f12c2a752", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([array([-0.37, -0.1 , -0.37, -0.37, -0.1 , -0.46, -0.37, 0. ]),\n", + " array([-0.73, -0.46, -0.28, -0.19, -0.73, -0.46, -0.19, 0. ]),\n", + " array([-0.46, -0.19, -1.54, -0.73, -0.1 , -0.46, -0.91, 0. ]),\n", + " array([-0.64, -0.46, -0.37, -0.28, -0.1 , -0.28, -0.37, 0. ]),\n", + " array([-0.1 , -0.19, -0.37, -0.37, -1.09, -1.18, -0.28, 0. ]),\n", + " array([-1.18, -0.46, -0.19, -0.28, -0.28, -1.18, -0.46, 0. ]),\n", + " array([-0.28, -0.1 , -0.19, -1.18, -0.19, -0.19, -0.1 , 0. ]),\n", + " array([-0.55, -0.37, -0.19, -0.91, -0.73, -0.19, -1.54, 0. ]),\n", + " array([-0.1 , -0.64, -0.64, -0.1 , -0.19, -0.19, -0.91, 0. ]),\n", + " array([-1.54, -0.19, -0.55, -0.1 , -0.28, -0.1 , -0.1 , 0. ]),\n", + " array([-0.19, -0.1 , -1.09, -1. , -0.19, -0.91, -0.37, 0. ]),\n", + " array([-0.28, -0.37, -0.19, -0.73, -0.37, -0.19, -0.46, 0. ]),\n", + " array([-0.28, -0.1 , -0.28, -0.37, -0.91, -1.54, -0.55, 0. ]),\n", + " array([-0.37, -0.1 , -0.19, -0.1 , -0.37, -1. , -0.1 , 0. ]),\n", + " array([-1.36, -0.46, -0.1 , -0.55, -0.19, -1.99, -0.19, 0. ]),\n", + " array([-0.19, -1.36, -0.1 , -0.46, -0.19, -0.37, -0.19, 0. ]),\n", + " array([-0.28, -0.19, -0.19, -1.09, -0.1 , -0.37, -0.28, 0. ]),\n", + " array([-0.1 , -1. , -0.55, -0.1 , -0.1 , -0.64, -1.45, 0. ]),\n", + " array([-0.37, -0.1 , -0.1 , -0.28, -0.55, -0.37, -0.28, 0. ]),\n", + " array([-0.28, -0.46, -0.73, -0.28, -1.99, -0.1 , -0.28, 0. ])],\n", + " dtype=object)" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_fr.iloc[0][\"state_values\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "8a44abaf-010e-4857-9f54-779f2d2f12e1", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_3114566/2457586796.py:38: UserWarning: The palette list has more values (10) than needed (2), which may not be intended.\n", + " return sns.relplot(\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plots_value_changes(df_fr)" ] } ], @@ -1090,7 +1854,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.15" + "version": "3.9.16" } }, "nbformat": 4, diff --git a/notebooks/daaf_analyses/reward_estimation/reward-recovery-lskv-factor-ts.ipynb b/notebooks/daaf_analyses/reward_estimation/reward-recovery-lskv-factor-ts.ipynb new file mode 100644 index 0000000..0d5ae46 --- /dev/null +++ b/notebooks/daaf_analyses/reward_estimation/reward-recovery-lskv-factor-ts.ipynb @@ -0,0 +1,582 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "import copy\n", + "import pathlib\n", + "import os.path\n", + "import json\n", + "import hashlib\n", + "import itertools\n", + "import collections\n", + "from typing import Any, Sequence, Mapping, Set" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-08-08 12:40:42.277525: I tensorflow/tsl/cuda/cudart_stub.cc:28] Could not find cuda drivers on your machine, GPU will not be used.\n", + "2024-08-08 12:40:42.352900: I tensorflow/tsl/cuda/cudart_stub.cc:28] Could not find cuda drivers on your machine, GPU will not be used.\n", + "2024-08-08 12:40:42.355094: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n", + "To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n", + "2024-08-08 12:40:44.074258: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "import tensorflow as tf" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib as mpl\n", + "from matplotlib import pyplot as plt\n", + "import seaborn as sns" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "from rlplg import envsuite, core\n", + "from daaf import estimator_metrics" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "ENVS_MAPPING = {\n", + " (\n", + " \"IceWorld\",\n", + " \"4KE3ASUFQGGUPERSDDRQAZAMA46CI2CMCJHGWJ7MRNI64JMEBETNDXFFPYWTQJF46S5BJ4NXXCHNMJSLII3ROYXI76DFOC3VAABGNVA=\",\n", + " ): {\"args\": '{\"map_name\": \"4x4\"}', \"name\": \"4x4\"},\n", + " (\"ABCSeq\", \"2\"): {\n", + " \"args\": '{\"length\": 3, \"distance_penalty\": false}',\n", + " \"name\": \"n=3\",\n", + " }, \n", + " (\"ABCSeq\", \"3\"): {\n", + " \"args\": '{\"length\": 3, \"distance_penalty\": false}',\n", + " \"name\": \"n=3\",\n", + " }, \n", + " (\"ABCSeq\", \"10\"): {\n", + " \"args\": '{\"length\": 10, \"distance_penalty\": false}',\n", + " \"name\": \"n=10\",\n", + " },\n", + " (\n", + " \"RedGreenSeq\",\n", + " \"NNLHYJFTC5ENMMDZWRNQ37B6VVDXQ7WHB5EJOPXYZFLMJEZOYLTSLB4ID4WHQG57XQPNUHGZCFDCWHYGXWSBW7FBWYRZGAGBW4J7MEQ=\",\n", + " ): {\n", + " \"args\": '{\"cure\": [\"red\", \"green\", \"wait\", \"green\", \"red\", \"red\", \"green\", \"wait\"]}',\n", + " \"name\": \"n=9\",\n", + " },\n", + " (\n", + " \"FrozenLake-v1\",\n", + " \"U75ZLQLLXYRFQE5KOJJGNVQZGQ65U5RVVN3ZV5F4UNYQVK6NGTAAU62O2DKMOEGACNNUQOSWGYYOV7LQHK7GAWG2CL3U3RZJFIEIB5I=\",\n", + " ): {\"args\": '{\"is_slippery\": false, \"map_name\": \"4x4\"}', \"name\": \"4x4\"},\n", + " (\"TowerOfHanoi\", \"4\"): {\"args\": '{\"num_disks\": 4}', \"name\": \"disks=4\"},\n", + " (\"ABCSeq\", \"7\"): {\n", + " \"args\": '{\"length\": 7, \"distance_penalty\": false}',\n", + " \"name\": \"n=7\",\n", + " },\n", + " (\n", + " \"IceWorld\",\n", + " \"JKNDNWGM45FELU53ZLLVJEPY2SFZBCX54PSACOQOFMTDUAK5VNQ4KE45QZINGYFU5GR6D7F3GJMW7EC4TAY5PHCYRN5GPGP7YNACHEI=\",\n", + " ): {\"args\": '{\"map_name\": \"8x8\"}', \"name\": \"8x8\"},\n", + " (\n", + " \"GridWorld\",\n", + " \"P3VJZBIJ7PNUOFG2SCF532NH5AQ6NOBZEZ6UZNZ7D3AU3GQZSLKURMS2SRPEUF6O65F3ETJXEFNTR3UYS73TUCIIU3YIONXHAR6WE5A=\",\n", + " ): {\n", + " \"args\": '{\"grid\": \"oooooooooooo\\\\noooooooooooo\\\\noooooooooooo\\\\nsxxxxxxxxxxg\"}',\n", + " \"name\": \"4x12\",\n", + " },\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "EXCLUDED_ENVS = set([\"FrozenLake-v1\"])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Load Data" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "PATH = str(pathlib.Path.home() / \"fs/daaf/exp/reward-recovery/1723120236-report.json\")" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "def read_data(path: str) -> pd.DataFrame:\n", + " return pd.read_json(path)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "df_raw = read_data(PATH)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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specmethodoutput
0{'name': 'ABCSeq', 'args': {'length': 3, 'dist...factor-ts{'least': [[-1.1666666667, -0.75, -1.083333333...
1{'name': 'ABCSeq', 'args': {'length': 2, 'dist...factor-ts{'least': [[-1.0, -1.0], [-1.0, -1.0]], 'ols-e...
2{'name': 'RedGreenSeq', 'args': {'cure': ['red...factor-ts{'least': [[-1.0, -1.0, -1.0], [-1.0, -1.0, -1...
3{'name': 'RedGreenSeq', 'args': {'cure': ['red...plain{'least': [[-1.0, -1.0, -1.0], [-1.0, -1.0, -1...
4{'name': 'ABCSeq', 'args': {'length': 3, 'dist...plain{'least': [[-1.0, -1.0, -1.0], [-1.0, -1.0, -1...
5{'name': 'ABCSeq', 'args': {'length': 2, 'dist...plain{'least': [[-1.0, -1.0], [-1.0, -1.0], [0.0, 0...
\n", + "
" + ], + "text/plain": [ + " spec method \\\n", + "0 {'name': 'ABCSeq', 'args': {'length': 3, 'dist... factor-ts \n", + "1 {'name': 'ABCSeq', 'args': {'length': 2, 'dist... factor-ts \n", + "2 {'name': 'RedGreenSeq', 'args': {'cure': ['red... factor-ts \n", + "3 {'name': 'RedGreenSeq', 'args': {'cure': ['red... plain \n", + "4 {'name': 'ABCSeq', 'args': {'length': 3, 'dist... plain \n", + "5 {'name': 'ABCSeq', 'args': {'length': 2, 'dist... plain \n", + "\n", + " output \n", + "0 {'least': [[-1.1666666667, -0.75, -1.083333333... \n", + "1 {'least': [[-1.0, -1.0], [-1.0, -1.0]], 'ols-e... \n", + "2 {'least': [[-1.0, -1.0, -1.0], [-1.0, -1.0, -1... \n", + "3 {'least': [[-1.0, -1.0, -1.0], [-1.0, -1.0, -1... \n", + "4 {'least': [[-1.0, -1.0, -1.0], [-1.0, -1.0, -1... \n", + "5 {'least': [[-1.0, -1.0], [-1.0, -1.0], [0.0, 0... " + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_raw" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'name': 'ABCSeq', 'args': {'length': 3, 'distance_penalty': False}}" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_raw.iloc[0][\"spec\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "def get_env_level(env_spec):\n", + " loaded_env_spec = envsuite.load(env_spec[\"name\"], **env_spec[\"args\"])\n", + " return loaded_env_spec.level" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'3'" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "get_env_level(df_raw.iloc[0][\"spec\"])" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "def proc_data(df_raw: pd.DataFrame) -> pd.DataFrame:\n", + " rows = []\n", + " for row in df_raw.to_dict(\"records\"):\n", + " new_row = copy.deepcopy(row)\n", + " \n", + " # add env id;\n", + " new_row[\"env_name\"] = new_row[\"spec\"][\"name\"]\n", + " new_row[\"env_level\"] = get_env_level(new_row[\"spec\"])\n", + " eid = (new_row[\"spec\"][\"name\"], new_row[\"env_level\"])\n", + " new_row[\"env_label\"] = ENVS_MAPPING[eid][\"name\"]\n", + " rows.append(new_row)\n", + " return pd.DataFrame(rows)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "df_results = proc_data(df_raw)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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specmethodoutputenv_nameenv_levelenv_label
0{'name': 'ABCSeq', 'args': {'length': 3, 'dist...factor-ts{'least': [[-1.1666666667, -0.75, -1.083333333...ABCSeq3n=3
1{'name': 'ABCSeq', 'args': {'length': 2, 'dist...factor-ts{'least': [[-1.0, -1.0], [-1.0, -1.0]], 'ols-e...ABCSeq2n=3
2{'name': 'RedGreenSeq', 'args': {'cure': ['red...factor-ts{'least': [[-1.0, -1.0, -1.0], [-1.0, -1.0, -1...RedGreenSeqNNLHYJFTC5ENMMDZWRNQ37B6VVDXQ7WHB5EJOPXYZFLMJE...n=9
3{'name': 'RedGreenSeq', 'args': {'cure': ['red...plain{'least': [[-1.0, -1.0, -1.0], [-1.0, -1.0, -1...RedGreenSeqNNLHYJFTC5ENMMDZWRNQ37B6VVDXQ7WHB5EJOPXYZFLMJE...n=9
4{'name': 'ABCSeq', 'args': {'length': 3, 'dist...plain{'least': [[-1.0, -1.0, -1.0], [-1.0, -1.0, -1...ABCSeq3n=3
5{'name': 'ABCSeq', 'args': {'length': 2, 'dist...plain{'least': [[-1.0, -1.0], [-1.0, -1.0], [0.0, 0...ABCSeq2n=3
\n", + "
" + ], + "text/plain": [ + " spec method \\\n", + "0 {'name': 'ABCSeq', 'args': {'length': 3, 'dist... factor-ts \n", + "1 {'name': 'ABCSeq', 'args': {'length': 2, 'dist... factor-ts \n", + "2 {'name': 'RedGreenSeq', 'args': {'cure': ['red... factor-ts \n", + "3 {'name': 'RedGreenSeq', 'args': {'cure': ['red... plain \n", + "4 {'name': 'ABCSeq', 'args': {'length': 3, 'dist... plain \n", + "5 {'name': 'ABCSeq', 'args': {'length': 2, 'dist... plain \n", + "\n", + " output env_name \\\n", + "0 {'least': [[-1.1666666667, -0.75, -1.083333333... ABCSeq \n", + "1 {'least': [[-1.0, -1.0], [-1.0, -1.0]], 'ols-e... ABCSeq \n", + "2 {'least': [[-1.0, -1.0, -1.0], [-1.0, -1.0, -1... RedGreenSeq \n", + "3 {'least': [[-1.0, -1.0, -1.0], [-1.0, -1.0, -1... RedGreenSeq \n", + "4 {'least': [[-1.0, -1.0, -1.0], [-1.0, -1.0, -1... ABCSeq \n", + "5 {'least': [[-1.0, -1.0], [-1.0, -1.0], [0.0, 0... ABCSeq \n", + "\n", + " env_level env_label \n", + "0 3 n=3 \n", + "1 2 n=3 \n", + "2 NNLHYJFTC5ENMMDZWRNQ37B6VVDXQ7WHB5EJOPXYZFLMJE... n=9 \n", + "3 NNLHYJFTC5ENMMDZWRNQ37B6VVDXQ7WHB5EJOPXYZFLMJE... n=9 \n", + "4 3 n=3 \n", + "5 2 n=3 " + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_results" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "('factor-ts',\n", + " [[1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0],\n", + " [1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0],\n", + " [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 3.0, 0.0],\n", + " [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 2.0, 0.0, 1.0],\n", + " [1.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 1.0, 0.0],\n", + " [1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0],\n", + " [1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0],\n", + " [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 4.0, 0.0],\n", + " [0.0, 1.0, 3.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]],\n", + " [-4.0, -4.0, -4.0, -3.0, -4.0, -4.0, -4.0, -4.0, -4.0],\n", + " [[-1.1666666667, -0.75, -1.0833333333],\n", + " [-1.0, -0.75, -1.0833333333],\n", + " [-1.0, -1.0, -1.0]])" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_results.iloc[0][\"method\"], df_results.iloc[0][\"output\"][\"matrix\"], df_results.iloc[0][\"output\"][\"rhs\"], df_results.iloc[0][\"output\"][\"least\"]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The factor-ts matrix above excludes terminal states." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "('plain',\n", + " [[1.0, 2.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],\n", + " [0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0],\n", + " [1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],\n", + " [0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0],\n", + " [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 2.0, 1.0, 0.0, 0.0, 0.0],\n", + " [0.0, 1.0, 3.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],\n", + " [1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0],\n", + " [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0],\n", + " [1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],\n", + " [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0],\n", + " [0.0, 0.0, 0.0, 2.0, 0.0, 2.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],\n", + " [1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 1.0]],\n", + " [-4.0, -4.0, -4.0, -4.0, -4.0, -4.0, -4.0, -3.0, -4.0, -3.0, -4.0, -3.0],\n", + " [[-1.0, -1.0, -1.0],\n", + " [-1.0, -1.0, -1.0],\n", + " [-1.0, -1.0, -1.0],\n", + " [3.7021965659999997e-16, 2.908664673e-16, -0.0]])" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_results.iloc[4][\"method\"], df_results.iloc[4][\"output\"][\"matrix\"], df_results.iloc[4][\"output\"][\"rhs\"], df_results.iloc[4][\"output\"][\"least\"]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The `factor-ts` estimate above is an example of cases where reward recovery has multiple possible solutions. Without any compensating strategies, the rewards learned in such cases can be incorrect.\n", + "\n", + "The `plain` estimate has correct values for most state-action pairs - this is because visitations to the terminal state anchor the values of non-terminal states visited before it.\n", + "Still, there are some minor errors in the values estimates of rewards for the terminal states, which can be manually corrected since we know them." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.16" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/notebooks/daaf_analyses/reward_estimation/reward_recovery_example.py b/notebooks/daaf_analyses/reward_estimation/reward_recovery_example.py index 4303f15..b23365c 100644 --- a/notebooks/daaf_analyses/reward_estimation/reward_recovery_example.py +++ b/notebooks/daaf_analyses/reward_estimation/reward_recovery_example.py @@ -7,10 +7,9 @@ import numpy as np import pandas as pd -from rlplg import envplay, envsuite -from rlplg.learning.tabular import policies - from daaf import math_ops, replay_mapper +from rlplg import core, envplay, envsuite +from rlplg.learning.tabular import policies ENV_SPECS = [ {"name": "ABCSeq", "args": {"length": 7}}, @@ -107,6 +106,7 @@ def estimate_reward( buffer_size=env_spec.mdp.env_desc.num_states * env_spec.mdp.env_desc.num_actions * BUFFER_MULT, + terminal_states=core.infer_env_terminal_states(env_spec.mdp.transition), ) policy = policies.PyRandomPolicy(num_actions=env_spec.mdp.env_desc.num_actions) # collect data @@ -188,5 +188,8 @@ def estimation_experiment(env_specs: Sequence[Mapping[str, Any]]): now = int(time.time()) df_results = estimation_experiment(env_specs=ENV_SPECS) df_results.to_json( - os.path.join(str(pathlib.Path.home()), f"fs/daaf/exp/reward-recovery/{now}-report.json"), orient="records" + os.path.join( + str(pathlib.Path.home()), f"fs/daaf/exp/reward-recovery/{now}-report.json" + ), + orient="records", ) diff --git a/notebooks/daaf_analyses/reward_estimation/reward_recovery_lskv.py b/notebooks/daaf_analyses/reward_estimation/reward_recovery_lskv.py new file mode 100644 index 0000000..2283fd1 --- /dev/null +++ b/notebooks/daaf_analyses/reward_estimation/reward_recovery_lskv.py @@ -0,0 +1,241 @@ +import concurrent.futures +import logging +import os.path +import pathlib +import time +from typing import Any, Mapping, Optional, Sequence, Tuple + +import numpy as np +import pandas as pd +from daaf import math_ops, replay_mapper +from rlplg import core, envplay, envsuite +from rlplg.learning.tabular import policies + +ENV_SPECS = [ + {"name": "ABCSeq", "args": {"length": 2, "distance_penalty": False}}, + {"name": "ABCSeq", "args": {"length": 3, "distance_penalty": False}}, + {"name": "ABCSeq", "args": {"length": 7, "distance_penalty": False}}, + {"name": "FrozenLake-v1", "args": {"is_slippery": False, "map_name": "4x4"}}, + { + "name": "GridWorld", + "args": {"grid": "oooooooooooo\noooooooooooo\noooooooooooo\nsxxxxxxxxxxg"}, + }, + { + "name": "RedGreenSeq", + "args": { + "cure": ["red", "green", "wait", "green", "red", "red", "green", "wait"] + }, + }, + {"name": "IceWorld", "args": {"map_name": "4x4"}}, + {"name": "TowerOfHanoi", "args": {"num_disks": 4}}, +] + +BUFFER_MULT = 2**10 +EST_PLAIN = "plain" +EST_FACTOR_TS = "factor-ts" +EST_PREFILL_BUFFER = "prefill-buffer" + + +def estimation_experiment(env_specs: Sequence[Mapping[str, Any]]): + rows = [] + with concurrent.futures.ProcessPoolExecutor() as executor: + future_to_spec = {} + for spec in env_specs: + for method in (EST_PLAIN, EST_FACTOR_TS): + future_to_spec[executor.submit(run_fn, spec, method)] = (spec, method) + for future in concurrent.futures.as_completed(future_to_spec): + spec, method = future_to_spec[future] + output = future.result() + rows.append({"spec": spec, "method": method, "output": output}) + return pd.DataFrame(rows) + + +def run_fn(spec: Mapping[str, Any], method: str): + if method == EST_PLAIN: + factor_terminal_states = False + prefill_buffer = False + elif method == EST_FACTOR_TS: + factor_terminal_states = True + prefill_buffer = False + # elif method == EST_PREFILL_BUFFER: + # factor_terminal_states = False + # prefill_buffer = True + else: + raise ValueError(f"Unsupported method: {method}") + + del prefill_buffer + return estimate_reward(spec=spec, factor_terminal_states=factor_terminal_states) + + +def estimate_reward( + spec: Mapping[str, Any], + accuracy: float = 1e-8, + max_episodes: int = 7500, + logging_steps: int = 100, + factor_terminal_states: bool = False, +) -> Mapping[str, np.ndarray]: + def reshape_rr(array: np.ndarray, nrows: int, ncols: int) -> np.ndarray: + return np.reshape( + array, + newshape=( + nrows, + ncols, + ), + ) + + env_spec = envsuite.load(spec["name"], **spec["args"]) + # logging.info("Env: %s, %s", env_spec.name, env_spec.level) + init_rtable = np.zeros( + shape=(env_spec.mdp.env_desc.num_states, env_spec.mdp.env_desc.num_actions), + dtype=np.float64, + ) + terminal_states = core.infer_env_terminal_states(env_spec.mdp.transition) + mapper = replay_mapper.DaafLsqRewardAttributionMapper( + num_states=env_spec.mdp.env_desc.num_states, + num_actions=env_spec.mdp.env_desc.num_actions, + reward_period=4, + state_id_fn=env_spec.discretizer.state, + action_id_fn=env_spec.discretizer.action, + init_rtable=init_rtable, + buffer_size=env_spec.mdp.env_desc.num_states + * env_spec.mdp.env_desc.num_actions + * BUFFER_MULT, + terminal_states=terminal_states, + factor_terminal_states=factor_terminal_states, + ) + policy = policies.PyRandomPolicy(num_actions=env_spec.mdp.env_desc.num_actions) + # collect data + logging.info("Collecting data for %s", spec["name"]) + episode = 1 + while True: + traj = envplay.generate_episodes( + env_spec.environment, policy=policy, num_episodes=1 + ) + for _ in mapper.apply(traj): + pass + + if ( + not mapper._estimation_buffer.is_empty + and mapper._estimation_buffer.is_full_rank + ): + break + + if episode % logging_steps == 0: + logging.info("Data collection for %s at %d episodes", spec["name"], episode) + if episode >= max_episodes: + break + episode += 1 + + # estimate rewards + yhat_lstsq: Optional[np.ndarray] = None + yhat_ols_em: Optional[np.ndarray] = None + if mapper._estimation_buffer.is_full_rank: + logging.info( + "Estimating rewards for %s, after %d episodes. Matrix shape: %s", + spec["name"], + episode, + mapper._estimation_buffer.matrix.shape, + ) + yhat_ols_em, iters = ols_em_reward_estimation( + obs_matrix=mapper._estimation_buffer.matrix, + agg_rewards=mapper._estimation_buffer.rhs, + accuracy=accuracy, + ) + yhat_ols_em = reshape_rr( + yhat_ols_em, + env_spec.mdp.env_desc.num_states + - (len(terminal_states) if factor_terminal_states else 0), + env_spec.mdp.env_desc.num_actions, + ) + logging.info("OLS ran in %d iterations for %s", iters, spec["name"]) + yhat_lstsq = lstsq_reward_estimation( + obs_matrix=mapper._estimation_buffer.matrix, + agg_rewards=mapper._estimation_buffer.rhs, + ) + yhat_lstsq = reshape_rr( + yhat_lstsq, + env_spec.mdp.env_desc.num_states + - (len(terminal_states) if factor_terminal_states else 0), + env_spec.mdp.env_desc.num_actions, + ) + else: + logging.info( + "Matrix is ill defined. Skipping reward estimation for %s: %s", + spec["name"], + spec["args"], + ) + return { + "least": yhat_lstsq, + "ols-em": yhat_ols_em, + "matrix": mapper._estimation_buffer.matrix, + "rhs": mapper._estimation_buffer.rhs, + } + + +def lstsq_reward_estimation( + obs_matrix: np.ndarray, agg_rewards: np.ndarray +) -> np.ndarray: + return math_ops.solve_least_squares( + matrix=obs_matrix, + rhs=agg_rewards, + ) + + +def ols_em_reward_estimation( + obs_matrix: np.ndarray, + agg_rewards: np.ndarray, + accuracy: float = 1e-8, + max_iters: int = 1_000_000, + stop_check_interval: int = 1000, +) -> Tuple[np.ndarray, int]: + iteration = 1 + yhat_rewards = np.random.rand(obs_matrix.shape[1]) + # multiply the cumulative reward by visits of each state action + # dim: (num obs, num states x num actions) + nomin = np.expand_dims(agg_rewards, axis=-1) * obs_matrix + qs = np.sum(obs_matrix, axis=0) + while True: + delta = np.zeros_like(yhat_rewards) + # multiply reward guess by row and sum each row's entry + # dim: num obs + denom = np.sum(yhat_rewards * obs_matrix, axis=1) + factor = np.sum(nomin / np.expand_dims(denom, 1), axis=0) + new_yhat_rewards = yhat_rewards * (factor / qs) + delta = np.maximum(delta, np.abs(yhat_rewards - new_yhat_rewards)) + if ( + iteration % stop_check_interval == 0 + and np.sum(np.isnan(new_yhat_rewards)) > 0 + ): + logging.info( + "Stopping at iteration %d/%d. `nan` values: %s", + iteration, + max_iters, + new_yhat_rewards, + ) + break + if np.alltrue(delta < accuracy) or iteration >= max_iters: + logging.info( + "Stopping at iteration %d/%d. Max error: %f", + iteration, + max_iters, + np.max(delta), + ) + break + yhat_rewards = new_yhat_rewards + iteration += 1 + return yhat_rewards, iteration + + +def main(): + now = int(time.time()) + df_results = estimation_experiment(env_specs=ENV_SPECS) + df_results.to_json( + os.path.join( + str(pathlib.Path.home()), f"fs/daaf/exp/reward-recovery/{now}-report.json" + ), + orient="records", + ) + + +if __name__ == "__main__": + main() diff --git a/ray-env-requirements.txt b/ray-env-requirements.txt index 8a90333..be70f31 100644 --- a/ray-env-requirements.txt +++ b/ray-env-requirements.txt @@ -1,9 +1,5 @@ -# -# This file is autogenerated by pip-compile with Python 3.9 -# by the following command: -# -# pip-compile --no-emit-find-links --no-emit-index-url --no-emit-options --unsafe-package=ray ray-env-requirements.in -# +# This file was autogenerated by uv via the following command: +# uv pip compile --no-emit-index-url --no-emit-find-links ray-env-requirements.in -o ray-env-requirements.txt --unsafe-package ray absl-py==2.0.0 # via # -r requirements.txt @@ -13,11 +9,8 @@ aiohttp==3.8.4 # via # -r requirements.txt # aiohttp-cors - # ray aiohttp-cors==0.7.0 - # via - # -r requirements.txt - # ray + # via -r requirements.txt aiosignal==1.3.1 # via # -r requirements.txt @@ -36,10 +29,6 @@ attrs==22.2.0 # -r requirements.txt # aiohttp # jsonschema -blessed==1.20.0 - # via - # -r requirements.txt - # gpustat cachetools==5.3.0 # via # -r requirements.txt @@ -53,6 +42,10 @@ charset-normalizer==3.1.0 # -r requirements.txt # aiohttp # requests +clarabel==0.9.0 + # via + # -r requirements.txt + # cvxpy click==8.0.4 # via # -r requirements.txt @@ -62,13 +55,17 @@ cloudpickle==2.2.1 # -r requirements.txt # gymnasium colorful==0.5.5 - # via - # -r requirements.txt - # ray + # via -r requirements.txt +cvxpy==1.5.3 + # via -r requirements.txt distlib==0.3.6 # via # -r requirements.txt # virtualenv +ecos==2.0.14 + # via + # -r requirements.txt + # cvxpy farama-notifications==0.0.4 # via # -r requirements.txt @@ -89,9 +86,7 @@ frozenlist==1.3.3 # aiosignal # ray fsspec==2024.2.0 - # via - # -r requirements.txt - # ray + # via -r requirements.txt gast==0.4.0 # via # -r requirements.txt @@ -118,14 +113,9 @@ googleapis-common-protos==1.58.0 # via # -r requirements.txt # google-api-core -gpustat==1.0.0 - # via - # -r requirements.txt - # ray grpcio==1.51.3 # via # -r requirements.txt - # ray # tensorboard # tensorflow gymnasium==0.28.1 @@ -150,6 +140,10 @@ jax-jumpy==1.0.0 # via # -r requirements.txt # gymnasium +jinja2==3.1.4 + # via + # -r requirements.txt + # memray jsonschema==4.17.3 # via # -r requirements.txt @@ -162,14 +156,35 @@ libclang==16.0.6 # via # -r requirements.txt # tensorflow +linkify-it-py==2.0.3 + # via + # -r requirements.txt + # markdown-it-py markdown==3.5.1 # via # -r requirements.txt # tensorboard +markdown-it-py==3.0.0 + # via + # -r requirements.txt + # mdit-py-plugins + # rich + # textual markupsafe==2.1.3 # via # -r requirements.txt + # jinja2 # werkzeug +mdit-py-plugins==0.4.2 + # via + # -r requirements.txt + # markdown-it-py +mdurl==0.1.2 + # via + # -r requirements.txt + # markdown-it-py +memray==1.14.0 + # via -r requirements.txt msgpack==1.0.5 # via # -r requirements.txt @@ -182,29 +197,28 @@ multidict==6.0.4 numpy==1.23.5 # via # -r requirements.txt + # clarabel + # cvxpy + # ecos # gymnasium # h5py # jax-jumpy # opt-einsum + # osqp # pandas # pyarrow - # ray + # qdldl # rlplg # scipy + # scs # tensorboard # tensorflow -nvidia-ml-py==11.495.46 - # via - # -r requirements.txt - # gpustat oauthlib==3.2.2 # via # -r requirements.txt # requests-oauthlib opencensus==0.11.2 - # via - # -r requirements.txt - # ray + # via -r requirements.txt opencensus-context==0.1.3 # via # -r requirements.txt @@ -213,15 +227,17 @@ opt-einsum==3.3.0 # via # -r requirements.txt # tensorflow +osqp==0.6.7.post1 + # via + # -r requirements.txt + # cvxpy packaging==23.2 # via # -r requirements.txt # ray # tensorflow pandas==2.0.3 - # via - # -r requirements.txt - # ray + # via -r requirements.txt pillow==9.4.0 # via # -r requirements.txt @@ -231,9 +247,7 @@ platformdirs==3.1.1 # -r requirements.txt # virtualenv prometheus-client==0.13.1 - # via - # -r requirements.txt - # ray + # via -r requirements.txt protobuf==4.25.1 # via # -r requirements.txt @@ -242,18 +256,10 @@ protobuf==4.25.1 # ray # tensorboard # tensorflow -psutil==5.9.4 - # via - # -r requirements.txt - # gpustat py-spy==0.3.14 - # via - # -r requirements.txt - # ray + # via -r requirements.txt pyarrow==15.0.0 - # via - # -r requirements.txt - # ray + # via -r requirements.txt pyasn1==0.4.8 # via # -r requirements.txt @@ -264,9 +270,11 @@ pyasn1-modules==0.2.8 # -r requirements.txt # google-auth pydantic==1.10.6 + # via -r requirements.txt +pygments==2.18.0 # via # -r requirements.txt - # ray + # rich pyrsistent==0.19.3 # via # -r requirements.txt @@ -283,6 +291,10 @@ pyyaml==6.0 # via # -r requirements.txt # ray +qdldl==0.1.7.post4 + # via + # -r requirements.txt + # osqp requests==2.28.2 # via # -r requirements.txt @@ -294,7 +306,12 @@ requests-oauthlib==1.3.1 # via # -r requirements.txt # google-auth-oauthlib -rlplg @ git+https://github.com/guidj/rlplg.git@v0.19.10 +rich==13.8.1 + # via + # -r requirements.txt + # memray + # textual +rlplg @ git+https://github.com/guidj/rlplg.git@93d819cab2eeefdd9bbd0ec7acb5d1dd0e5d1bed # via -r requirements.txt rsa==4.9 # via @@ -303,25 +320,32 @@ rsa==4.9 scipy==1.10.1 # via # -r requirements.txt + # clarabel + # cvxpy + # ecos + # osqp + # qdldl # rlplg -setuptools==69.0.3 + # scs +scs==3.2.7 + # via + # -r requirements.txt + # cvxpy +setuptools==74.1.2 # via + # -r requirements.txt # tensorboard # tensorflow six==1.16.0 # via # -r requirements.txt # astunparse - # blessed # google-auth # google-pasta - # gpustat # python-dateutil # tensorflow smart-open==6.3.0 - # via - # -r requirements.txt - # ray + # via -r requirements.txt tblib==1.7.0 # via -r ray-env-requirements.in tensorboard==2.13.0 @@ -346,28 +370,31 @@ termcolor==2.3.0 # via # -r requirements.txt # tensorflow +textual==0.78.0 + # via + # -r requirements.txt + # memray typing-extensions==4.5.0 # via # -r requirements.txt # gymnasium # pydantic # tensorflow + # textual tzdata==2024.1 # via # -r requirements.txt # pandas +uc-micro-py==1.0.3 + # via + # -r requirements.txt + # linkify-it-py urllib3==1.26.15 # via # -r requirements.txt # requests virtualenv==20.21.0 - # via - # -r requirements.txt - # ray -wcwidth==0.2.6 - # via - # -r requirements.txt - # blessed + # via -r requirements.txt werkzeug==3.0.1 # via # -r requirements.txt @@ -390,5 +417,5 @@ zipp==3.15.0 # -r requirements.txt # importlib-metadata -# The following packages are considered to be unsafe in a requirements file: +# The following packages were excluded from the output: # ray diff --git a/rendering-requirements.txt b/rendering-requirements.txt index 44f2771..60aedeb 100644 --- a/rendering-requirements.txt +++ b/rendering-requirements.txt @@ -1,8 +1,4 @@ -# -# This file is autogenerated by pip-compile with Python 3.9 -# by the following command: -# -# pip-compile --no-emit-find-links --no-emit-index-url --no-emit-options rendering-requirements.in -# +# This file was autogenerated by uv via the following command: +# uv pip compile --no-emit-index-url --no-emit-find-links rendering-requirements.in -o rendering-requirements.txt pyglet==1.5.21 # via -r rendering-requirements.in diff --git a/requirements.in b/requirements.in index 0623678..343e5fb 100644 --- a/requirements.in +++ b/requirements.in @@ -1,6 +1,7 @@ rlplg @ git+https://github.com/guidj/rlplg.git@v0.19.10 # dashboard + cluster -ray[default]==2.9.3 -ray[data]==2.9.3 +ray[default]==2.23.0 +ray[data]==2.23.0 numpy==1.23.5 tensorflow>=2.11.1 +cvxpy==1.5.3 diff --git a/requirements.txt b/requirements.txt index 9a66d8a..88d8735 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,9 +1,5 @@ -# -# This file is autogenerated by pip-compile with Python 3.9 -# by the following command: -# -# pip-compile --no-emit-find-links --no-emit-index-url --no-emit-options requirements.in -# +# This file was autogenerated by uv via the following command: +# uv pip compile --no-emit-index-url --no-emit-find-links requirements.in -o requirements.txt absl-py==2.0.0 # via # tensorboard @@ -26,8 +22,6 @@ attrs==22.2.0 # via # aiohttp # jsonschema -blessed==1.20.0 - # via gpustat cachetools==5.3.0 # via google-auth certifi==2022.12.7 @@ -36,14 +30,20 @@ charset-normalizer==3.1.0 # via # aiohttp # requests +clarabel==0.9.0 + # via cvxpy click==8.0.4 # via ray cloudpickle==2.2.1 # via gymnasium colorful==0.5.5 # via ray +cvxpy==1.5.3 + # via -r requirements.in distlib==0.3.6 # via virtualenv +ecos==2.0.14 + # via cvxpy farama-notifications==0.0.4 # via gymnasium filelock==3.9.1 @@ -74,8 +74,6 @@ google-pasta==0.2.0 # via tensorflow googleapis-common-protos==1.58.0 # via google-api-core -gpustat==1.0.0 - # via ray grpcio==1.51.3 # via # ray @@ -95,16 +93,33 @@ importlib-metadata==6.0.0 # markdown jax-jumpy==1.0.0 # via gymnasium +jinja2==3.1.4 + # via memray jsonschema==4.17.3 # via ray keras==2.13.1 # via tensorflow libclang==16.0.6 # via tensorflow +linkify-it-py==2.0.3 + # via markdown-it-py markdown==3.5.1 # via tensorboard +markdown-it-py==3.0.0 + # via + # mdit-py-plugins + # rich + # textual markupsafe==2.1.3 - # via werkzeug + # via + # jinja2 + # werkzeug +mdit-py-plugins==0.4.2 + # via markdown-it-py +mdurl==0.1.2 + # via markdown-it-py +memray==1.14.0 + # via ray msgpack==1.0.5 # via ray multidict==6.0.4 @@ -114,19 +129,23 @@ multidict==6.0.4 numpy==1.23.5 # via # -r requirements.in + # clarabel + # cvxpy + # ecos # gymnasium # h5py # jax-jumpy # opt-einsum + # osqp # pandas # pyarrow + # qdldl # ray # rlplg # scipy + # scs # tensorboard # tensorflow -nvidia-ml-py==11.495.46 - # via gpustat oauthlib==3.2.2 # via requests-oauthlib opencensus==0.11.2 @@ -135,6 +154,8 @@ opencensus-context==0.1.3 # via opencensus opt-einsum==3.3.0 # via tensorflow +osqp==0.6.7.post1 + # via cvxpy packaging==23.2 # via # ray @@ -154,8 +175,6 @@ protobuf==4.25.1 # ray # tensorboard # tensorflow -psutil==5.9.4 - # via gpustat py-spy==0.3.14 # via ray pyarrow==15.0.0 @@ -168,6 +187,8 @@ pyasn1-modules==0.2.8 # via google-auth pydantic==1.10.6 # via ray +pygments==2.18.0 + # via rich pyrsistent==0.19.3 # via jsonschema python-dateutil==2.8.2 @@ -176,7 +197,9 @@ pytz==2024.1 # via pandas pyyaml==6.0 # via ray -ray[data,default]==2.9.3 +qdldl==0.1.7.post4 + # via osqp +ray==2.23.0 # via -r requirements.in requests==2.28.2 # via @@ -186,19 +209,34 @@ requests==2.28.2 # tensorboard requests-oauthlib==1.3.1 # via google-auth-oauthlib -rlplg @ git+https://github.com/guidj/rlplg.git@v0.19.10 +rich==13.8.1 + # via + # memray + # textual +rlplg @ git+https://github.com/guidj/rlplg.git@93d819cab2eeefdd9bbd0ec7acb5d1dd0e5d1bed # via -r requirements.in rsa==4.9 # via google-auth scipy==1.10.1 - # via rlplg + # via + # clarabel + # cvxpy + # ecos + # osqp + # qdldl + # rlplg + # scs +scs==3.2.7 + # via cvxpy +setuptools==74.1.2 + # via + # tensorboard + # tensorflow six==1.16.0 # via # astunparse - # blessed # google-auth # google-pasta - # gpustat # python-dateutil # tensorflow smart-open==6.3.0 @@ -215,19 +253,22 @@ tensorflow-io-gcs-filesystem==0.34.0 # via tensorflow termcolor==2.3.0 # via tensorflow +textual==0.78.0 + # via memray typing-extensions==4.5.0 # via # gymnasium # pydantic # tensorflow + # textual tzdata==2024.1 # via pandas +uc-micro-py==1.0.3 + # via linkify-it-py urllib3==1.26.15 # via requests virtualenv==20.21.0 # via ray -wcwidth==0.2.6 - # via blessed werkzeug==3.0.1 # via tensorboard wheel==0.42.0 @@ -240,6 +281,3 @@ yarl==1.8.2 # via aiohttp zipp==3.15.0 # via importlib-metadata - -# The following packages are considered to be unsafe in a requirements file: -# setuptools diff --git a/sbin/local/reward-estjob.sh b/sbin/local/reward-estjob.sh index 3544710..6639f97 100755 --- a/sbin/local/reward-estjob.sh +++ b/sbin/local/reward-estjob.sh @@ -7,6 +7,6 @@ PARENT_DIR=$DIR/../.. TIMESTAMP=`date +%s` python $PARENT_DIR/src/daaf/rewardest/estjob.py \ --num-runs=10 \ ---max-episodes=7500 \ +--max-episodes=2500 \ --output-dir=$HOME/fs/daaf/exp/reward-estjob/logs/$TIMESTAMP \ --log-episode-frequency=10 diff --git a/setup.py b/setup.py index c70668a..8508070 100644 --- a/setup.py +++ b/setup.py @@ -55,7 +55,7 @@ def read_requirements(filename): "Operating System :: POSIX :: Linux", "Operating System :: MacOS :: MacOS X", "Programming Language :: Python", - "Programming Language :: Python :: 3.8", + "Programming Language :: Python :: 3.9", "Programming Language :: Python :: Implementation :: CPython", ] META_FILE = read(META_PATH) diff --git a/src/daaf/controlexps/control.py b/src/daaf/controlexps/control.py index 14d8dd0..b58945e 100644 --- a/src/daaf/controlexps/control.py +++ b/src/daaf/controlexps/control.py @@ -20,7 +20,7 @@ from daaf.controlexps import methods -def run_fn(experiment_task: expconfig.ExperimentTask): +def run_fn(experiment_run: expconfig.ExperimentRun): """ Entry point running on-policy evaluation for DAAF. @@ -29,15 +29,15 @@ def run_fn(experiment_task: expconfig.ExperimentTask): """ # init env and agent env_spec = task.create_env_spec( - problem=experiment_task.experiment.env_config.name, - env_args=experiment_task.experiment.env_config.args, + problem=experiment_run.experiment.env_config.name, + env_args=experiment_run.experiment.env_config.args, ) traj_mappers = task.create_trajectory_mappers( env_spec=env_spec, - reward_period=experiment_task.experiment.daaf_config.reward_period, - traj_mapping_method=experiment_task.experiment.daaf_config.traj_mapping_method, + reward_period=experiment_run.experiment.daaf_config.reward_period, + traj_mapping_method=experiment_run.experiment.daaf_config.traj_mapping_method, buffer_size_or_multiplier=(None, None), - drop_truncated_feedback_episodes=experiment_task.experiment.daaf_config.drop_truncated_feedback_episodes, + drop_truncated_feedback_episodes=experiment_run.experiment.daaf_config.drop_truncated_feedback_episodes, ) # Collect returns on underlying MDP # before other mappers change it. @@ -46,30 +46,30 @@ def run_fn(experiment_task: expconfig.ExperimentTask): logging.debug("Starting DAAF Control Experiments") results = policy_control( env_spec=env_spec, - daaf_config=experiment_task.experiment.daaf_config, - num_episodes=experiment_task.run_config.num_episodes, - learnign_args=experiment_task.experiment.learning_args, + daaf_config=experiment_run.experiment.daaf_config, + num_episodes=experiment_run.run_config.num_episodes, + learnign_args=experiment_run.experiment.learning_args, generate_steps_fn=task.create_generate_episode_fn(mappers=traj_mappers), ) env_info: Mapping[str, Any] = { "env": { "name": env_spec.name, "level": env_spec.level, - "args": json.dumps(experiment_task.experiment.env_config.args), + "args": json.dumps(experiment_run.experiment.env_config.args), }, } with utils.ExperimentLogger( - log_dir=experiment_task.run_config.output_dir, - exp_id=experiment_task.exp_id, - run_id=experiment_task.run_id, + log_dir=experiment_run.run_config.output_dir, + exp_id=experiment_run.exp_id, + run_id=experiment_run.run_id, params={ **env_info, **utils.json_from_dict( - dataclasses.asdict(experiment_task.experiment.daaf_config), + dataclasses.asdict(experiment_run.experiment.daaf_config), dict_encode_level=0, ), - **dataclasses.asdict(experiment_task.experiment.learning_args), - **experiment_task.context, + **dataclasses.asdict(experiment_run.experiment.learning_args), + **experiment_run.context, }, ) as exp_logger: state_values: Optional[np.ndarray] = None @@ -78,7 +78,7 @@ def run_fn(experiment_task: expconfig.ExperimentTask): for episode, snapshot in enumerate(results): state_values = np.max(snapshot.action_values, axis=1) state_actions = np.argmax(snapshot.action_values, axis=1) - if episode % experiment_task.run_config.log_episode_frequency == 0: + if episode % experiment_run.run_config.log_episode_frequency == 0: mean_returns = np.mean(returns_collector.traj_returns) exp_logger.log( episode=episode, @@ -95,13 +95,13 @@ def run_fn(experiment_task: expconfig.ExperimentTask): logging.debug( "\nEstimated values run %d of %s:\n%s", - experiment_task.run_id, - experiment_task.exp_id, + experiment_run.run_id, + experiment_run.exp_id, state_values, ) except Exception as err: raise RuntimeError( - f"Task {experiment_task.exp_id}, run {experiment_task.run_id} failed" + f"Task {experiment_run.exp_id}, run {experiment_run.run_id} failed" ) from err env_spec.environment.close() diff --git a/src/daaf/controlexps/controljob.py b/src/daaf/controlexps/controljob.py index 290cab5..2981ad8 100644 --- a/src/daaf/controlexps/controljob.py +++ b/src/daaf/controlexps/controljob.py @@ -82,7 +82,7 @@ def create_tasks( output_dir: str, task_prefix: str, log_episode_frequency: int, -) -> Sequence[Tuple[ray.ObjectRef, expconfig.ExperimentTask]]: +) -> Sequence[Tuple[ray.ObjectRef, expconfig.ExperimentRun]]: """ Runs numerical experiments on policy evaluation. """ @@ -97,7 +97,7 @@ def create_tasks( ) ) experiments_and_context = add_experiment_context(experiments, assets_dir=assets_dir) - experiment_tasks = tuple( + experiment_runs = tuple( expconfig.generate_tasks_from_experiments_context_and_run_config( run_config=expconfig.RunConfig( num_episodes=num_episodes, @@ -110,12 +110,12 @@ def create_tasks( ) ) # shuffle tasks to balance workload - experiment_tasks = random.sample( - experiment_tasks, - len(experiment_tasks), # type: ignore + experiment_runs = random.sample( + experiment_runs, + len(experiment_runs), # type: ignore ) experiment_batches = utils.bundle( - experiment_tasks, bundle_size=constants.DEFAULT_BATCH_SIZE + experiment_runs, bundle_size=constants.DEFAULT_BATCH_SIZE ) logging.info( "Parsed %d DAAF configs and %d environments into %d tasks", @@ -171,7 +171,7 @@ def add_experiment_context( @ray.remote def run_experiments( - experiments_batch: Sequence[expconfig.ExperimentTask], + experiments_batch: Sequence[expconfig.ExperimentRun], ) -> Sequence[str]: """ Run experiments. diff --git a/src/daaf/controlexps/results_agg_pipeline.py b/src/daaf/controlexps/results_agg_pipeline.py index bc377a0..03b1036 100644 --- a/src/daaf/controlexps/results_agg_pipeline.py +++ b/src/daaf/controlexps/results_agg_pipeline.py @@ -139,11 +139,7 @@ def parse_experiment_logs(paths: Sequence[str]) -> ray.data.Dataset: """ logs_files = [os.path.join(path, "experiment-logs.jsonl") for path in paths] ds_logs = ray.data.read_json( - logs_files, - include_paths=True, - partition_filter=ray.data.datasource.FileExtensionFilter( - file_extensions=["jsonl"] - ), + logs_files, include_paths=True, file_extensions=["jsonl"] ) return ds_logs diff --git a/src/daaf/estimator_metrics.py b/src/daaf/estimator_metrics.py index 009645d..8212b79 100644 --- a/src/daaf/estimator_metrics.py +++ b/src/daaf/estimator_metrics.py @@ -12,12 +12,12 @@ def rmse(v_pred: np.ndarray, v_true: np.ndarray, axis: int): v_pred: An array of shape [b, k] v_true: An array of shape [b, k] """ - if v_pred.shape != v_true.shape: + if np.shape(v_pred) != np.shape(v_true): raise ValueError( - f"Tensors have different shapes: {v_pred.shape} != {v_true.shape}" + f"Tensors have different shapes: {np.shape(v_pred)} != {np.shape(v_true)}" ) return np.sqrt( - np.sum(np.power(v_pred - v_true, 2.0), axis=axis) / v_pred.shape[axis] + np.sum(np.power(v_pred - v_true, 2.0), axis=axis) / np.shape(v_pred)[axis] ) @@ -27,9 +27,9 @@ def mean_absolute_error(v_pred: np.ndarray, v_true: np.ndarray, axis: int): v_pred: An array of shape [b, k] v_true: An array of shape [b, k] """ - if v_pred.shape != v_true.shape: + if np.shape(v_pred) != np.shape(v_true): raise ValueError( - f"Tensors have different shapes: {v_pred.shape} != {v_true.shape}" + f"Tensors have different shapes: {np.shape(v_pred)} != {np.shape(v_true)}" ) delta = np.abs(v_pred - v_true) return np.mean(delta, axis=axis) @@ -53,16 +53,18 @@ def cosine_distance(v_pred: np.ndarray, v_true: np.ndarray): v_pred: An array of shape [b, k] v_true: An array of shape [b, k] """ - if v_pred.shape != v_true.shape: + if np.shape(v_pred) != np.shape(v_true): raise ValueError( - f"Tensors have different shapes: {v_pred.shape} != {v_true.shape}" + f"Tensors have different shapes: {np.shape(v_pred)} != {np.shape(v_true)}" ) - if len(v_pred.shape) != 2: - raise ValueError(f"Tensors are not 2-dim: {v_pred.shape}, {v_true.shape}") + if len(np.shape(v_pred)) != 2: + raise ValueError( + f"Tensors are not 2-dim: {np.shape(v_pred)}, {np.shape(v_true)}" + ) cosines = [] - for row in range(v_pred.shape[0]): + for row in range(np.shape(v_pred)[0]): cosines.append(distance.cosine(v_pred[row], v_true[row])) return np.array(cosines, dtype=v_true.dtype) @@ -73,15 +75,17 @@ def dotproduct(v_pred: np.ndarray, v_true: np.ndarray): v_pred: An array of shape [b, k] v_true: An array of shape [b, k] """ - if v_pred.shape != v_true.shape: + if np.shape(v_pred) != np.shape(v_true): raise ValueError( - f"Tensors have different shapes: {v_pred.shape} != {v_true.shape}" + f"Tensors have different shapes: {np.shape(v_pred)} != {np.shape(v_true)}" ) - if len(v_pred.shape) != 2: - raise ValueError(f"Tensors are not 2-dim: {v_pred.shape}, {v_true.shape}") + if len(np.shape(v_pred)) != 2: + raise ValueError( + f"Tensors are not 2-dim: {np.shape(v_pred)}, {np.shape(v_true)}" + ) dps = [] - for row in range(v_pred.shape[0]): + for row in range(np.shape(v_pred)[0]): dps.append(np.dot(v_pred[row], v_true[row])) return np.array(dps, dtype=v_true.dtype) diff --git a/src/daaf/evalexps/evaljob.py b/src/daaf/evalexps/evaljob.py index 5d246d7..27c413e 100644 --- a/src/daaf/evalexps/evaljob.py +++ b/src/daaf/evalexps/evaljob.py @@ -84,7 +84,7 @@ def create_tasks( output_dir: str, task_prefix: str, log_episode_frequency: int, -) -> Sequence[Tuple[ray.ObjectRef, expconfig.ExperimentTask]]: +) -> Sequence[Tuple[ray.ObjectRef, expconfig.ExperimentRun]]: """ Runs numerical experiments on policy evaluation. """ @@ -99,7 +99,7 @@ def create_tasks( ) ) experiments_and_context = add_experiment_context(experiments, assets_dir=assets_dir) - experiment_tasks = tuple( + experiment_runs = tuple( expconfig.generate_tasks_from_experiments_context_and_run_config( run_config=expconfig.RunConfig( num_episodes=num_episodes, @@ -112,10 +112,10 @@ def create_tasks( ) ) # shuffle tasks to balance workload - experiment_tasks = random.sample(experiment_tasks, len(experiment_tasks)) # type: ignore + experiment_runs = random.sample(experiment_runs, len(experiment_runs)) # type: ignore # bundle tasks experiment_batches = utils.bundle( - experiment_tasks, bundle_size=constants.DEFAULT_BATCH_SIZE + experiment_runs, bundle_size=constants.DEFAULT_BATCH_SIZE ) logging.info( "Parsed %d DAAF configs and %d environments into %d tasks", @@ -171,20 +171,20 @@ def add_experiment_context( @ray.remote def run_experiments( - experiments_batch: Sequence[expconfig.ExperimentTask], + experiments_batch: Sequence[expconfig.ExperimentRun], ) -> Sequence[str]: """ Runs experiments. """ ids: List[str] = [] - for experiment_task in experiments_batch: - task_id = f"{experiment_task.exp_id}/{experiment_task.run_id}" + for experiment_run in experiments_batch: + task_id = f"{experiment_run.exp_id}/{experiment_run.run_id}" logging.debug( "Experiment %s starting: %s", task_id, - experiment_task, + experiment_run, ) - evaluation.run_fn(experiment_task) + evaluation.run_fn(experiment_run) ids.append(task_id) logging.debug("Experiment %s finished", task_id) return ids diff --git a/src/daaf/evalexps/evaluation.py b/src/daaf/evalexps/evaluation.py index 161d99d..fe4cce9 100644 --- a/src/daaf/evalexps/evaluation.py +++ b/src/daaf/evalexps/evaluation.py @@ -17,7 +17,7 @@ from daaf.evalexps import methods -def run_fn(experiment_task: expconfig.ExperimentTask): +def run_fn(experiment_run: expconfig.ExperimentRun): """ Entry point running on-policy evaluation for DAAF. @@ -26,50 +26,50 @@ def run_fn(experiment_task: expconfig.ExperimentTask): """ # init env and agent env_spec = task.create_env_spec( - problem=experiment_task.experiment.env_config.name, - env_args=experiment_task.experiment.env_config.args, + problem=experiment_run.experiment.env_config.name, + env_args=experiment_run.experiment.env_config.args, ) traj_mappers = task.create_trajectory_mappers( env_spec=env_spec, - reward_period=experiment_task.experiment.daaf_config.reward_period, - traj_mapping_method=experiment_task.experiment.daaf_config.traj_mapping_method, + reward_period=experiment_run.experiment.daaf_config.reward_period, + traj_mapping_method=experiment_run.experiment.daaf_config.traj_mapping_method, buffer_size_or_multiplier=(None, None), - drop_truncated_feedback_episodes=experiment_task.experiment.daaf_config.drop_truncated_feedback_episodes, + drop_truncated_feedback_episodes=experiment_run.experiment.daaf_config.drop_truncated_feedback_episodes, ) returns_collector = task.returns_collection_mapper() traj_mappers = tuple([returns_collector] + list(traj_mappers)) # Policy Eval with DAAF logging.info("Starting DAAF Evaluation Experiments") policy = create_eval_policy( - env_spec=env_spec, daaf_config=experiment_task.experiment.daaf_config + env_spec=env_spec, daaf_config=experiment_run.experiment.daaf_config ) results = evaluate_policy( policy=policy, env_spec=env_spec, - daaf_config=experiment_task.experiment.daaf_config, - num_episodes=experiment_task.run_config.num_episodes, - algorithm=experiment_task.experiment.daaf_config.algorithm, - learnign_args=experiment_task.experiment.learning_args, + daaf_config=experiment_run.experiment.daaf_config, + num_episodes=experiment_run.run_config.num_episodes, + algorithm=experiment_run.experiment.daaf_config.algorithm, + learnign_args=experiment_run.experiment.learning_args, generate_steps_fn=task.create_generate_episode_fn(mappers=traj_mappers), ) env_info: Mapping[str, Any] = { "env": { "name": env_spec.name, "level": env_spec.level, - "args": json.dumps(experiment_task.experiment.env_config.args), + "args": json.dumps(experiment_run.experiment.env_config.args), }, } with utils.ExperimentLogger( - log_dir=experiment_task.run_config.output_dir, - exp_id=experiment_task.exp_id, - run_id=experiment_task.run_id, + log_dir=experiment_run.run_config.output_dir, + exp_id=experiment_run.exp_id, + run_id=experiment_run.run_id, params={ **utils.json_from_dict( - dataclasses.asdict(experiment_task.experiment.daaf_config), + dataclasses.asdict(experiment_run.experiment.daaf_config), dict_encode_level=0, ), - **dataclasses.asdict(experiment_task.experiment.learning_args), - **experiment_task.context, + **dataclasses.asdict(experiment_run.experiment.learning_args), + **experiment_run.context, **env_info, }, ) as exp_logger: @@ -77,7 +77,7 @@ def run_fn(experiment_task: expconfig.ExperimentTask): try: for episode, snapshot in enumerate(results): state_values = snapshot.values - if episode % experiment_task.run_config.log_episode_frequency == 0: + if episode % experiment_run.run_config.log_episode_frequency == 0: mean_returns = np.mean(returns_collector.traj_returns) exp_logger.log( episode=episode, @@ -90,13 +90,13 @@ def run_fn(experiment_task: expconfig.ExperimentTask): logging.debug( "\nEstimated values run %d of %s:\n%s", - experiment_task.run_id, - experiment_task.exp_id, + experiment_run.run_id, + experiment_run.exp_id, state_values, ) except Exception as err: raise RuntimeError( - f"Task {experiment_task.exp_id}, run {experiment_task.run_id} failed" + f"Task {experiment_run.exp_id}, run {experiment_run.run_id} failed" ) from err env_spec.environment.close() @@ -161,7 +161,7 @@ def evaluate_policy( schedule=task.constant_learning_rate, ), gamma=learnign_args.discount_factor, - nstep=daaf_config.reward_period, + nstep=daaf_config.algorithm_args["nstep"], state_id_fn=env_spec.discretizer.state, initial_values=initial_state_values, generate_episode=generate_steps_fn, @@ -176,7 +176,7 @@ def evaluate_policy( schedule=task.constant_learning_rate, ), gamma=learnign_args.discount_factor, - nstep=daaf_config.reward_period, + nstep=daaf_config.algorithm_args["nstep"], state_id_fn=env_spec.discretizer.state, initial_values=initial_state_values, generate_episode=generate_steps_fn, diff --git a/src/daaf/evalexps/results_agg_pipeline.py b/src/daaf/evalexps/results_agg_pipeline.py index b05489d..28991f9 100644 --- a/src/daaf/evalexps/results_agg_pipeline.py +++ b/src/daaf/evalexps/results_agg_pipeline.py @@ -141,11 +141,7 @@ def parse_experiment_logs(paths: Sequence[str]) -> ray.data.Dataset: """ logs_files = [os.path.join(path, "experiment-logs.jsonl") for path in paths] ds_logs = ray.data.read_json( - logs_files, - include_paths=True, - partition_filter=ray.data.datasource.FileExtensionFilter( - file_extensions=["jsonl"] - ), + logs_files, include_paths=True, file_extensions=["jsonl"] ) return ds_logs @@ -240,7 +236,7 @@ def calc_state_metrics(y_preds, y_true, axis): } def apply(row): - y_preds = row["state_values"] + y_preds = np.array(row["state_values"]) y_true = np.tile(row["meta"]["dyna_prog_state_values"], reps=(len(y_preds), 1)) over_runs_then_states = calc_state_metrics( y_preds=y_preds, y_true=y_true, axis=0 diff --git a/src/daaf/expconfig.py b/src/daaf/expconfig.py index 6e39f4f..5046880 100644 --- a/src/daaf/expconfig.py +++ b/src/daaf/expconfig.py @@ -72,7 +72,7 @@ class Experiment: @dataclasses.dataclass(frozen=True) -class ExperimentTask: +class ExperimentRun: """ A single experiment task. """ @@ -156,7 +156,7 @@ def generate_tasks_from_experiments_context_and_run_config( experiments_and_context: Sequence[Tuple[Experiment, Mapping[str, Any]]], num_runs: int, task_prefix: str, -) -> Iterator[ExperimentTask]: +) -> Iterator[ExperimentRun]: """ Given a sequence of experiments, expands them to tasks. @@ -179,7 +179,7 @@ def generate_tasks_from_experiments_context_and_run_config( ] ) for idx in range(num_runs): - yield ExperimentTask( + yield ExperimentRun( exp_id=exp_id, run_id=idx, experiment=experiment, diff --git a/src/daaf/replay_mapper.py b/src/daaf/replay_mapper.py index d2830a8..4dd2e7e 100644 --- a/src/daaf/replay_mapper.py +++ b/src/daaf/replay_mapper.py @@ -9,16 +9,7 @@ import copy import dataclasses import logging -from typing import ( - Any, - Callable, - Dict, - FrozenSet, - Iterator, - Optional, - Sequence, - Set, -) +from typing import Any, Callable, Dict, FrozenSet, Iterator, Optional, Sequence, Set import numpy as np from rlplg import combinatorics, core @@ -181,6 +172,12 @@ def __init__( buffer_size: The maximum number of trajectories to keep in the buffer - each one should contain `reward_period` steps. impute_value: Value to use when rewards are missing. + terminal_states: A set of terminal states. When provided, + estimates for these states are ignored. + factor_terminal_states: Solves LEAST for non-terminal states + only when `True`. + prefill_buffer: Examples for terminal states are added + before data collection when `True`. Note: decay isn't used when summing up the rewards for K steps. """ @@ -208,6 +205,7 @@ def __init__( self.state_id_fn = state_id_fn self.action_id_fn = action_id_fn self.buffer_size = buffer_size + self.terminal_states = terminal_states self.factor_terminal_states = factor_terminal_states self.prefill_buffer = prefill_buffer self.num_updates = 0 @@ -272,32 +270,20 @@ def apply( reward_sum = 0.0 # Run estimation at the first possible moment, if self._estimation_buffer.is_full_rank: - logging.debug("Estimating rewards with Least-Squares.") try: - estimated_rewards = math_ops.solve_least_squares( + estimated_rewards = self.__estimate_rewards( matrix=self._estimation_buffer.matrix, rhs=self._estimation_buffer.rhs, + num_states=self.num_states, + num_actions=self.num_actions, + terminal_state_action_mask=self._terminal_state_action_mask, ) - # we only solved for non-terminal states - if estimated_rewards.size < ( - self.num_states * self.num_actions - ): - pos = 0 - est_rewards_ext = np.zeros( - self.num_states * self.num_actions, dtype=np.float64 - ) - ignore_factors_mask = np.reshape( - self._terminal_state_action_mask, newshape=[-1] - ) - for idx in range(len(ignore_factors_mask)): - if ignore_factors_mask[idx] == 1: - est_rewards_ext[idx] = 0.0 - else: - est_rewards_ext[idx] = estimated_rewards[pos] - pos += 1 - estimated_rewards = est_rewards_ext new_rtable = np.reshape( - estimated_rewards, + self.__zero_terminal_states( + estimated_rewards, + terminal_states=self.terminal_states, + num_actions=self.num_actions, + ), newshape=(self.num_states, self.num_actions), ) # update the reward estimates by a fraction of the delta @@ -317,6 +303,60 @@ def apply( yield dataclasses.replace(traj_step, reward=reward) + @staticmethod + def __estimate_rewards( + matrix: np.ndarray, + rhs: np.ndarray, + num_states: int, + num_actions: int, + terminal_state_action_mask: np.ndarray, + ): + logging.debug("Estimating rewards with Least-Squares.") + estimated_rewards = math_ops.solve_least_squares( + matrix=matrix, + rhs=rhs, + ) + # we only solved for non-terminal states + if estimated_rewards.size < (num_states * num_actions): + pos = 0 + est_rewards_ext = np.zeros(num_states * num_actions, dtype=np.float64) + ignore_factors_mask = np.reshape(terminal_state_action_mask, newshape=[-1]) + for idx in range(len(ignore_factors_mask)): + if ignore_factors_mask[idx] == 1: + est_rewards_ext[idx] = 0.0 + else: + est_rewards_ext[idx] = estimated_rewards[pos] + pos += 1 + estimated_rewards = est_rewards_ext + new_rtable = np.reshape( + estimated_rewards, + newshape=(num_states, num_actions), + ) + return new_rtable + + @staticmethod + def __zero_terminal_states( + rtable: np.ndarray, terminal_states: FrozenSet[int], num_actions: int + ): + """ + Assumes states are zero-indexed. + """ + new_rtable = copy.deepcopy(rtable) + ndim = np.ndim(new_rtable) + if ndim > 2: + raise ValueError(f"`rtable` must be 1D or 2D tensor. Got: {ndim}.") + + for tstate in terminal_states: + if ndim == 1: + # Examples + # nA = 2 + # 0 -> 0*2, (0+1)*2-1 -> 0, 1 + # 2 -> 2*2, (2+1)*2-1 -> 4, 5 + new_rtable[tstate * num_actions : (tstate + 1) * num_actions - 1] = 0.0 + elif ndim == 2: + new_rtable[tstate, :] = 0.0 + return new_rtable + class DaafMdpWithOptionsMapper(TrajMapper): """ @@ -527,17 +567,22 @@ def __init__( if ignore_factors_mask is not None else np.zeros(num_factors, dtype=np.int64) ) + self.nkeep_factors = self.num_factors - ( + 0 + if self.ignore_factors_mask is None + else np.sum(self.ignore_factors_mask).item() + ) self._keep_factors_mask = (self.ignore_factors_mask - 1) * -1 # `np.where` returns a tuple per dim; # Keep the first dim - self._col_mask = np.where(self._keep_factors_mask == 1)[0] + self._cols_mask = np.where(self._keep_factors_mask == 1)[0] # pre-allocate arrays - self._rows = np.zeros(shape=(buffer_size, num_factors), dtype=np.float64) + self._rows = np.zeros(shape=(buffer_size, self.nkeep_factors), dtype=np.float64) self._b = np.zeros(shape=(buffer_size,), dtype=np.float64) self._next_pos = 0 self._additions = 0 self._factors_tracker: Set[int] = set() - self._rank_flag = np.zeros(shape=self.num_factors, dtype=np.float64) + self._rank_flag = np.zeros(shape=self.nkeep_factors, dtype=np.float64) def add(self, row: np.ndarray, rhs: np.ndarray) -> None: """ @@ -553,24 +598,28 @@ def add(self, row: np.ndarray, rhs: np.ndarray) -> None: ) # Add rows for factors of interest (`keep_factors_mask`). - if np.sum(row * self._keep_factors_mask) > 0: - mask = (row > 0).astype(np.int64) - row_key = combinatorics.sequence_to_integer(space_size=2, sequence=mask) + candidate_row = row[self._cols_mask] + if np.sum(candidate_row) > 0: + mask = (candidate_row > 0).astype(np.int64) + row_factors_key = combinatorics.sequence_to_integer( + space_size=2, sequence=mask + ) # Only add distict rows - based on their mask - if row_key not in self._factors_tracker: + if row_factors_key not in self._factors_tracker: + current_row_mask = (self._rows[self._next_pos] > 0).astype(np.int64) current_row_key = combinatorics.sequence_to_integer( space_size=2, - sequence=(self._rows[self._next_pos] > 0).astype(np.int64), + sequence=current_row_mask, ) if current_row_key in self._factors_tracker: # Every row is unique, thus removing it # removes it's marker self._factors_tracker.remove(current_row_key) self._rank_flag -= self._rows[self._next_pos] - self._factors_tracker.add(row_key) + self._factors_tracker.add(row_factors_key) self._rank_flag += mask - self._rows[self._next_pos] = row + self._rows[self._next_pos] = candidate_row self._b[self._next_pos] = rhs # cycle least recent self._next_pos = (self._next_pos + 1) % self.buffer_size @@ -584,8 +633,8 @@ def matrix(self) -> np.ndarray: it returns the values available - which can be an empty array. """ if self._additions >= self.buffer_size: - return self._rows[:, self._col_mask] - return self._rows[: self._next_pos, self._col_mask] + return self._rows + return self._rows[: self._next_pos, :] @property def rhs(self) -> np.ndarray: @@ -608,9 +657,11 @@ def is_empty(self) -> bool: @property def is_full_rank(self) -> bool: - return self._additions >= self.num_factors and np.sum( - (self._rank_flag * self._keep_factors_mask) > 0 - ) == np.sum(self._keep_factors_mask) + square_or_tall = self._additions >= self.nkeep_factors + factors_rank = ( + np.sum((self._rank_flag > 0).astype(np.int64)) == self.nkeep_factors + ).item() + return square_or_tall and factors_rank class Counter: diff --git a/src/daaf/rewardest/estimation.py b/src/daaf/rewardest/estimation.py index 31a2140..2a604b1 100644 --- a/src/daaf/rewardest/estimation.py +++ b/src/daaf/rewardest/estimation.py @@ -20,14 +20,9 @@ def estimate_reward( logging_steps: int = 100, factor_terminal_states: bool = False, prefill_buffer: bool = False, - export_path: Optional[str] = None, ) -> Mapping[str, np.ndarray]: env_spec = envsuite.load(spec["name"], **spec["args"]) - terminal_states = ( - core.infer_env_terminal_states(env_spec.mdp.transition) - if factor_terminal_states - else frozenset() - ) + terminal_states = core.infer_env_terminal_states(env_spec.mdp.transition) init_rtable = np.zeros( shape=(env_spec.mdp.env_desc.num_states, env_spec.mdp.env_desc.num_actions), dtype=np.float64, @@ -48,13 +43,17 @@ def estimate_reward( ) policy = policies.PyRandomPolicy(num_actions=env_spec.mdp.env_desc.num_actions) # collect data - logging.info("Collecting data for %s", spec["name"]) + logging.info("Collecting data for %s/%s", spec["name"], spec["args"]) episode = 1 steps = 0 yhat_lstsq: Optional[np.ndarray] = None yhat_ols_em: Optional[np.ndarray] = None - meta: Dict[str, Any] = {"max_episodes": max_episodes, "est_accuracy": accuracy} - visited_states: Dict[int, int] = collections.defaultdict(int) + meta: Dict[str, Any] = { + "max_episodes": max_episodes, + "est_accuracy": accuracy, + "ols_iters": None, + } + num_visited_states_dist: Dict[int, int] = collections.defaultdict(int) while True: traj = envplay.generate_episode(env_spec.environment, policy=policy) @@ -63,7 +62,7 @@ def estimate_reward( episode_visited_states.add( env_spec.discretizer.state(traj_step.observation) ) - visited_states[len(episode_visited_states)] += 1 + num_visited_states_dist[len(episode_visited_states)] += 1 if ( not mapper._estimation_buffer.is_empty @@ -72,7 +71,12 @@ def estimate_reward( break if episode % logging_steps == 0: - logging.info("Data collection for %s at %d episodes", spec["name"], episode) + logging.info( + "Data collection for %s/%s at %d episodes", + spec["name"], + spec["args"], + episode, + ) if episode >= max_episodes: break episode += 1 @@ -81,8 +85,9 @@ def estimate_reward( # estimate rewards if mapper._estimation_buffer.is_full_rank: logging.info( - "Estimating rewards for %s, after %d episodes (%d steps). Matrix shape: %s", + "Estimating rewards for %s/%s, after %d episodes (%d steps). Matrix shape: %s", spec["name"], + spec["args"], episode, steps, mapper._estimation_buffer.matrix.shape, @@ -92,7 +97,9 @@ def estimate_reward( agg_rewards=mapper._estimation_buffer.rhs, accuracy=accuracy, ) - logging.info("OLS ran in %d iterations for %s", iters, spec["name"]) + logging.info( + "OLS ran in %d iterations for %s/%s", iters, spec["name"], spec["args"] + ) yhat_lstsq = lstsq_reward_estimation( obs_matrix=mapper._estimation_buffer.matrix, agg_rewards=mapper._estimation_buffer.rhs, @@ -118,19 +125,6 @@ def estimate_reward( spec["name"], spec["args"], ) - - if export_path: - import os.path - - for name, array in zip( - ["lhs", "rhs"], - [mapper._estimation_buffer.matrix, mapper._estimation_buffer.rhs], - ): - if not os.path.exists(export_path): - os.makedirs(export_path) - with open(os.path.join(export_path, name), "wb") as writable: - np.save(writable, array) - return { "least": yhat_lstsq, "ols_em": yhat_ols_em, @@ -138,8 +132,15 @@ def estimate_reward( "steps": steps, "full_rank": mapper._estimation_buffer.is_full_rank, "samples": mapper._estimation_buffer.matrix.shape[0], + "data": { + "lhs": mapper._estimation_buffer.matrix, + "rhs": mapper._estimation_buffer.rhs, + }, "buffer_size": mapper._estimation_buffer.buffer_size, - "episode_visited_states_count": dict(visited_states), + "episode_visited_states_count": { + "num_unique_states": list(num_visited_states_dist.keys()), + "num_episodes": list(num_visited_states_dist.values()), + }, "meta": meta, } diff --git a/src/daaf/rewardest/estjob.py b/src/daaf/rewardest/estjob.py index 5e414b1..bd9a7ee 100644 --- a/src/daaf/rewardest/estjob.py +++ b/src/daaf/rewardest/estjob.py @@ -3,26 +3,39 @@ """ import argparse +import copy import dataclasses +import itertools import json import logging +import pathlib import random +import time import uuid from typing import Any, Mapping, Optional, Sequence, Tuple +import numpy as np import ray import ray.data +from daaf import constants, utils from daaf.rewardest import estimation ENV_SPECS = [ + {"name": "ABCSeq", "args": {"length": 2, "distance_penalty": False}}, + {"name": "ABCSeq", "args": {"length": 3, "distance_penalty": False}}, {"name": "ABCSeq", "args": {"length": 7, "distance_penalty": False}}, {"name": "ABCSeq", "args": {"length": 10, "distance_penalty": False}}, {"name": "FrozenLake-v1", "args": {"is_slippery": False, "map_name": "4x4"}}, + { + "name": "GridWorld", + "args": {"grid": "ooooo\nooxoo\noxooo\nsxxxg"}, + }, { "name": "GridWorld", "args": {"grid": "oooooooooooo\noooooooooooo\noooooooooooo\nsxxxxxxxxxxg"}, }, + {"name": "RedGreenSeq", "args": {"cure": ["red", "green"]}}, { "name": "RedGreenSeq", "args": { @@ -37,8 +50,7 @@ EST_PLAIN = "plain" EST_FACTOR_TS = "factor-ts" EST_PREFILL_BUFFER = "prefill-buffer" - -AGG_REWARD_PERIODS = [2, 3, 4, 5, 6, 7, 8] +AGG_REWARD_PERIODS = [2, 3, 4, 5, 6, 7, 8, 15] EST_ACCURACY = 1e-8 @@ -59,7 +71,7 @@ class EstimationPipelineArgs: @dataclasses.dataclass(frozen=True) -class EstimationTask: +class EstimationRun: uid: str env_spec: Mapping[str, Any] run_id: int @@ -81,7 +93,7 @@ def main(args: EstimationPipelineArgs): logging.info("Ray Context: %s", context) logging.info("Ray Nodes: %s", ray.nodes()) - tasks_futures = create_tasks( + tasks_and_result_refs = create_tasks( env_specs=ENV_SPECS, agg_reward_periods=AGG_REWARD_PERIODS, num_runs=args.num_runs, @@ -92,29 +104,33 @@ def main(args: EstimationPipelineArgs): # since ray tracks objectref items # we swap the key:value - task_ref_to_spec = {future: task for task, future in tasks_futures} - results = [] - unfinished_tasks = list(task_ref_to_spec.keys()) + task_ref_to_tasks = { + result_ref: tasks for tasks, result_ref in tasks_and_result_refs + } + datasets = [] + unfinished_task_ref = list(task_ref_to_tasks.keys()) while True: - finished_tasks, unfinished_tasks = ray.wait(unfinished_tasks) - for finished_task in finished_tasks: - task = task_ref_to_spec[finished_task] - result = {"result": ray.get(finished_task)} - task_dict = dataclasses.asdict(task) - task_dict["env_spec"] = json.dumps(task_dict["env_spec"]) - entry = {**result, **task_dict} - results.append(entry) + finished_task_ref, unfinished_task_ref = ray.wait(unfinished_task_ref) + for finished_task_ref in finished_task_ref: + datasets.append(ray.get(finished_task_ref)) logging.info( "Tasks left: %d out of %d.", - len(unfinished_tasks), - len(task_ref_to_spec), + len(unfinished_task_ref), + len(task_ref_to_tasks), ) - if len(unfinished_tasks) == 0: + if len(unfinished_task_ref) == 0: break - ray.data.from_items(results).write_json(args.output_dir) + if len(datasets) > 0: + if len(datasets) > 1: + ds_head, ds_tail = datasets[0], datasets[1:] + ds_result: ray.data.Dataset = ds_head.union(*ds_tail) + else: + ds_result: ray.data.Dataset = datasets[0] + ds_output = ds_result.map(serialize) + ds_output.write_parquet(args.output_dir) def create_tasks( @@ -124,36 +140,56 @@ def create_tasks( max_episodes: int, log_episode_frequency: int, accuracy: float, -) -> Sequence[Tuple[EstimationTask, ray.ObjectRef]]: - tasks = [] +) -> Sequence[Tuple[ray.ObjectRef]]: + estimation_runs = [] futures = [] - for env_spec in env_specs: - for reward_period in agg_reward_periods: - for method in (EST_PLAIN, EST_FACTOR_TS, EST_PREFILL_BUFFER): - uid = str(uuid.uuid4()) - for run_id in range(num_runs): - task = EstimationTask( - uid=uid, - env_spec=env_spec, - reward_period=reward_period, - run_id=run_id, - accuracy=accuracy, - max_episodes=max_episodes, - log_episode_frequency=log_episode_frequency, - method=method, - ) - tasks.append(task) + methods = (EST_PLAIN, EST_FACTOR_TS, EST_PREFILL_BUFFER) + for env_spec, reward_period, method in itertools.product( + env_specs, agg_reward_periods, methods + ): + uid = str(uuid.uuid4()) + estimation_runs.extend( + [ + EstimationRun( + uid=uid, + env_spec=env_spec, + reward_period=reward_period, + run_id=run_id, + accuracy=accuracy, + max_episodes=max_episodes, + log_episode_frequency=log_episode_frequency, + method=method, + ) + for run_id in range(num_runs) + ] + ) + # shuffle to workload - random.shuffle(tasks) - for task in tasks: - futures.append((task, estimate.remote(task))) + random.shuffle(estimation_runs) + # batch tasks + estimation_run_batches = utils.bundle( + estimation_runs, bundle_size=constants.DEFAULT_BATCH_SIZE + ) + for batch in estimation_run_batches: + futures.append((batch, run_fn.remote(batch))) return futures @ray.remote -def estimate(task: EstimationTask) -> Mapping[str, Any]: +def run_fn(estimation_runs: Sequence[EstimationRun]) -> ray.data.Dataset: + results = [] + for experiment_run in estimation_runs: + estimation_run_dict = dataclasses.asdict(experiment_run) + result = estimate(experiment_run) + result = {"result": result} + entry = {**result, **estimation_run_dict} + results.append(entry) + return ray.data.from_items(results) + + +def estimate(task: EstimationRun) -> Mapping[str, Any]: """ - Runs evaluation. + Reward estimation. """ logging.info( "Task %s for %s/%d (%s) starting", @@ -191,15 +227,39 @@ def estimate(task: EstimationTask) -> Mapping[str, Any]: return result +def serialize(example: Mapping[str, Any]) -> Mapping[str, Any]: + def go(key: str, element: Any): + if key == "args" and isinstance(element, Mapping): + return json.dumps(element) + elif isinstance(element, Mapping): + return {skey: go(skey, svalue) for skey, svalue in element.items()} + elif isinstance(element, np.ndarray): + return element.flatten() + return copy.deepcopy(element) + + return {key: go(key, value) for key, value in example.items()} + + def parse_args() -> EstimationPipelineArgs: """ Parses program arguments. """ arg_parser = argparse.ArgumentParser() - arg_parser.add_argument("--num-runs", type=int, required=True) - arg_parser.add_argument("--max-episodes", type=int, required=True) - arg_parser.add_argument("--output-dir", type=str, required=True) - arg_parser.add_argument("--log-episode-frequency", type=int, required=True) + arg_parser.add_argument("--num-runs", type=int, default=3) + arg_parser.add_argument("--max-episodes", type=int, default=2500) + arg_parser.add_argument( + "--output-dir", + type=str, + default=pathlib.Path.home() + / "fs/daaf/exp/reward-estjob/logs" + / str(int(time.time())), + ) + arg_parser.add_argument("--log-episode-frequency", type=int, default=1) + + # arg_parser.add_argument("--num-runs", type=int, required=True) + # arg_parser.add_argument("--max-episodes", type=int, required=True) + # arg_parser.add_argument("--output-dir", type=str, required=True) + # arg_parser.add_argument("--log-episode-frequency", type=int, required=True) arg_parser.add_argument("--cluster-uri", type=str, default=None) known_args, unknown_args = arg_parser.parse_known_args() logging.info("Unknown args: %s", unknown_args) diff --git a/src/daaf/task.py b/src/daaf/task.py index dc2b9a3..19ee216 100644 --- a/src/daaf/task.py +++ b/src/daaf/task.py @@ -86,6 +86,7 @@ def create_trajectory_mappers( num_states=env_spec.mdp.env_desc.num_states, num_actions=env_spec.mdp.env_desc.num_actions, ), + terminal_states=core.infer_env_terminal_states(env_spec.mdp.transition), ) ) elif traj_mapping_method == constants.MDP_WITH_OPTIONS_MAPPER: diff --git a/test-requirements.txt b/test-requirements.txt index f624d30..5187225 100644 --- a/test-requirements.txt +++ b/test-requirements.txt @@ -1,9 +1,5 @@ -# -# This file is autogenerated by pip-compile with Python 3.9 -# by the following command: -# -# pip-compile --no-emit-find-links --no-emit-index-url --no-emit-options test-requirements.in -# +# This file was autogenerated by uv via the following command: +# uv pip compile --no-emit-index-url --no-emit-find-links test-requirements.in -o test-requirements.txt absl-py==2.0.0 # via # -r requirements.txt @@ -13,11 +9,8 @@ aiohttp==3.8.4 # via # -r requirements.txt # aiohttp-cors - # ray aiohttp-cors==0.7.0 - # via - # -r requirements.txt - # ray + # via -r requirements.txt aiosignal==1.3.1 # via # -r requirements.txt @@ -37,10 +30,6 @@ attrs==22.2.0 # aiohttp # hypothesis # jsonschema -blessed==1.20.0 - # via - # -r requirements.txt - # gpustat cachetools==5.3.0 # via # -r requirements.txt @@ -54,6 +43,10 @@ charset-normalizer==3.1.0 # -r requirements.txt # aiohttp # requests +clarabel==0.9.0 + # via + # -r requirements.txt + # cvxpy click==8.0.4 # via # -r requirements.txt @@ -63,17 +56,21 @@ cloudpickle==2.2.1 # -r requirements.txt # gymnasium colorful==0.5.5 - # via - # -r requirements.txt - # ray -coverage[toml]==7.3.2 + # via -r requirements.txt +coverage==7.3.2 # via # -r test-requirements.in # pytest-cov +cvxpy==1.5.3 + # via -r requirements.txt distlib==0.3.6 # via # -r requirements.txt # virtualenv +ecos==2.0.14 + # via + # -r requirements.txt + # cvxpy exceptiongroup==1.2.0 # via # hypothesis @@ -98,9 +95,7 @@ frozenlist==1.3.3 # aiosignal # ray fsspec==2024.2.0 - # via - # -r requirements.txt - # ray + # via -r requirements.txt gast==0.4.0 # via # -r requirements.txt @@ -127,14 +122,9 @@ googleapis-common-protos==1.58.0 # via # -r requirements.txt # google-api-core -gpustat==1.0.0 - # via - # -r requirements.txt - # ray grpcio==1.51.3 # via # -r requirements.txt - # ray # tensorboard # tensorflow gymnasium==0.28.1 @@ -163,6 +153,10 @@ jax-jumpy==1.0.0 # via # -r requirements.txt # gymnasium +jinja2==3.1.4 + # via + # -r requirements.txt + # memray jsonschema==4.17.3 # via # -r requirements.txt @@ -175,14 +169,35 @@ libclang==16.0.6 # via # -r requirements.txt # tensorflow +linkify-it-py==2.0.3 + # via + # -r requirements.txt + # markdown-it-py markdown==3.5.1 # via # -r requirements.txt # tensorboard +markdown-it-py==3.0.0 + # via + # -r requirements.txt + # mdit-py-plugins + # rich + # textual markupsafe==2.1.3 # via # -r requirements.txt + # jinja2 # werkzeug +mdit-py-plugins==0.4.2 + # via + # -r requirements.txt + # markdown-it-py +mdurl==0.1.2 + # via + # -r requirements.txt + # markdown-it-py +memray==1.14.0 + # via -r requirements.txt msgpack==1.0.5 # via # -r requirements.txt @@ -195,29 +210,28 @@ multidict==6.0.4 numpy==1.23.5 # via # -r requirements.txt + # clarabel + # cvxpy + # ecos # gymnasium # h5py # jax-jumpy # opt-einsum + # osqp # pandas # pyarrow - # ray + # qdldl # rlplg # scipy + # scs # tensorboard # tensorflow -nvidia-ml-py==11.495.46 - # via - # -r requirements.txt - # gpustat oauthlib==3.2.2 # via # -r requirements.txt # requests-oauthlib opencensus==0.11.2 - # via - # -r requirements.txt - # ray + # via -r requirements.txt opencensus-context==0.1.3 # via # -r requirements.txt @@ -226,6 +240,10 @@ opt-einsum==3.3.0 # via # -r requirements.txt # tensorflow +osqp==0.6.7.post1 + # via + # -r requirements.txt + # cvxpy packaging==23.2 # via # -r requirements.txt @@ -233,9 +251,7 @@ packaging==23.2 # ray # tensorflow pandas==2.0.3 - # via - # -r requirements.txt - # ray + # via -r requirements.txt pillow==9.4.0 # via # -r requirements.txt @@ -247,9 +263,7 @@ platformdirs==3.1.1 pluggy==1.0.0 # via pytest prometheus-client==0.13.1 - # via - # -r requirements.txt - # ray + # via -r requirements.txt protobuf==4.25.1 # via # -r requirements.txt @@ -258,18 +272,10 @@ protobuf==4.25.1 # ray # tensorboard # tensorflow -psutil==5.9.4 - # via - # -r requirements.txt - # gpustat py-spy==0.3.14 - # via - # -r requirements.txt - # ray + # via -r requirements.txt pyarrow==15.0.0 - # via - # -r requirements.txt - # ray + # via -r requirements.txt pyasn1==0.4.8 # via # -r requirements.txt @@ -280,9 +286,11 @@ pyasn1-modules==0.2.8 # -r requirements.txt # google-auth pydantic==1.10.6 + # via -r requirements.txt +pygments==2.18.0 # via # -r requirements.txt - # ray + # rich pyrsistent==0.19.3 # via # -r requirements.txt @@ -305,7 +313,11 @@ pyyaml==6.0 # via # -r requirements.txt # ray -ray[data,default]==2.9.3 +qdldl==0.1.7.post4 + # via + # -r requirements.txt + # osqp +ray==2.23.0 # via -r requirements.txt requests==2.28.2 # via @@ -318,7 +330,12 @@ requests-oauthlib==1.3.1 # via # -r requirements.txt # google-auth-oauthlib -rlplg @ git+https://github.com/guidj/rlplg.git@v0.19.10 +rich==13.8.1 + # via + # -r requirements.txt + # memray + # textual +rlplg @ git+https://github.com/guidj/rlplg.git@93d819cab2eeefdd9bbd0ec7acb5d1dd0e5d1bed # via -r requirements.txt rsa==4.9 # via @@ -327,21 +344,32 @@ rsa==4.9 scipy==1.10.1 # via # -r requirements.txt + # clarabel + # cvxpy + # ecos + # osqp + # qdldl # rlplg + # scs +scs==3.2.7 + # via + # -r requirements.txt + # cvxpy +setuptools==74.1.2 + # via + # -r requirements.txt + # tensorboard + # tensorflow six==1.16.0 # via # -r requirements.txt # astunparse - # blessed # google-auth # google-pasta - # gpustat # python-dateutil # tensorflow smart-open==6.3.0 - # via - # -r requirements.txt - # ray + # via -r requirements.txt sortedcontainers==2.4.0 # via hypothesis tensorboard==2.13.0 @@ -366,6 +394,10 @@ termcolor==2.3.0 # via # -r requirements.txt # tensorflow +textual==0.78.0 + # via + # -r requirements.txt + # memray tomli==2.0.1 # via # coverage @@ -376,22 +408,21 @@ typing-extensions==4.5.0 # gymnasium # pydantic # tensorflow + # textual tzdata==2024.1 # via # -r requirements.txt # pandas +uc-micro-py==1.0.3 + # via + # -r requirements.txt + # linkify-it-py urllib3==1.26.15 # via # -r requirements.txt # requests virtualenv==20.21.0 - # via - # -r requirements.txt - # ray -wcwidth==0.2.6 - # via - # -r requirements.txt - # blessed + # via -r requirements.txt werkzeug==3.0.1 # via # -r requirements.txt @@ -413,6 +444,3 @@ zipp==3.15.0 # via # -r requirements.txt # importlib-metadata - -# The following packages are considered to be unsafe in a requirements file: -# setuptools diff --git a/tests/daaf/test_replay_mapper.py b/tests/daaf/test_replay_mapper.py index d82a819..349824a 100644 --- a/tests/daaf/test_replay_mapper.py +++ b/tests/daaf/test_replay_mapper.py @@ -244,7 +244,6 @@ def test_daaf_lsq_reward_attribution_mapper_apply(): """ Initial events will have reward values from rtable. Once there are enough samples, Least Square Estimates are used instead. - The estimates are updated at `update_steps` intervals. Problem: Two states (A, B), two actions (left, right) Table: @@ -335,6 +334,119 @@ def test_daaf_lsq_reward_attribution_mapper_apply(): np.testing.assert_array_equal(output.truncated, expected.truncated) +def test_daaf_lsq_reward_attribution_mapper_apply_with_terminal_states(): + """ + Initial events will have reward values from rtable. + Once there are enough samples, Least Square Estimates are used instead. + + Problem: Three states (A, B, C), two actions (left, right) + Table: + Actions + States Left Right + A 0 1 + B 0 1 + C 0 0 + + events: (A, left, A, right) -> (0, 0), (0, 1) -> 0 + 1 = 1 + (B, left, B, right) -> (1, 0), (1, 1) -> 0 + 1 = 1 + (A, right, B, left) -> (0, 1), (1, 0) -> 1 + 0 = 1 + (A, right, B, right) -> (0, 1), (1, 1) -> 1 + 1 = 2 + (C, left, C, right) -> (2, 0), (2, 1) -> 0 + 0 = 0 + + matrix: (A, left), (A, right), (B, left), (B, right) (C,left) (C, right) + 1 1 0 0 0 0 + 0 0 1 1 0 0 + 0 1 1 0 0 0 + 0 1 0 1 0 0 + 0 0 0 0 1 1 + rhs: 1, 1, 1, 2, 0 + """ + + mapper = replay_mapper.DaafLsqRewardAttributionMapper( + num_states=3, + num_actions=2, + reward_period=2, + state_id_fn=item, + action_id_fn=item, + buffer_size=8, + init_rtable=defaults.array([-1.0, -1.0], [-1.0, -1.0], [-1.0, -1.0]), + impute_value=88, + terminal_states={ + 2, + }, + ) + + # We are simulating cumulative rewards. + # So we supply the actual rewards to the simulator to aggregate (sum). + inputs = [ + traj_step(state=0, action=0, reward=0.0, prob=0.0), + traj_step(state=0, action=1, reward=1.0, prob=1.0), + traj_step(state=1, action=0, reward=0.0, prob=0.0), + traj_step(state=1, action=1, reward=1.0, prob=1.0), + traj_step(state=2, action=0, reward=-1.0, prob=0.0), + traj_step(state=2, action=1, reward=-1.0, prob=1.0), + # after the event above, all factors are present, but we still lack rows + # to satisfy the condition m >= n + traj_step(state=0, action=1, reward=1.0, prob=1.0), + traj_step(state=1, action=0, reward=0.0, prob=0.0), + traj_step(state=0, action=1, reward=1.0, prob=1.0), + traj_step(state=1, action=1, reward=1.0, prob=1.0), + traj_step(state=1, action=0, reward=0.0, prob=0.0), + traj_step(state=2, action=1, reward=-1.0, prob=1.0), + # after the event above, m >= n + # the events will below will be emitted with estimated rewards + traj_step(state=0, action=0, reward=-7.0, prob=0.0), + traj_step(state=0, action=1, reward=-7.0, prob=1.0), + traj_step(state=1, action=0, reward=-7.0, prob=0.0), + traj_step(state=1, action=1, reward=-7.0, prob=1.0), + traj_step(state=0, action=1, reward=-7.0, prob=1.0), + traj_step(state=1, action=0, reward=-7.0, prob=0.0), + traj_step(state=0, action=1, reward=-7.0, prob=1.0), + traj_step(state=1, action=1, reward=-7.0, prob=1.0), + traj_step(state=2, action=0, reward=0.0, prob=0.0), + traj_step(state=2, action=1, reward=0.0, prob=1.0), + ] + expectactions = [ + # the events below are emitted with the impute value + # or the aggregate feedback + traj_step(state=0, action=0, reward=88, prob=0.0), + traj_step(state=0, action=1, reward=1.0, prob=1.0), + traj_step(state=1, action=0, reward=88, prob=0.0), + traj_step(state=1, action=1, reward=1.0, prob=1.0), + traj_step(state=2, action=0, reward=88, prob=0.0), + traj_step(state=2, action=1, reward=-2.0, prob=1.0), + traj_step(state=0, action=1, reward=88, prob=1.0), + traj_step(state=1, action=0, reward=1.0, prob=0.0), + traj_step(state=0, action=1, reward=88, prob=1.0), + traj_step(state=1, action=1, reward=2.0, prob=1.0), + traj_step(state=1, action=0, reward=88, prob=0.0), + traj_step(state=2, action=1, reward=-1.0, prob=1.0), + # the events below are emitted with estimated rewards + traj_step(state=0, action=0, reward=0.0, prob=0.0), + traj_step(state=0, action=1, reward=1.0, prob=1.0), + traj_step(state=1, action=0, reward=0.0, prob=0.0), + traj_step(state=1, action=1, reward=1.0, prob=1.0), + traj_step(state=0, action=1, reward=1.0, prob=1.0), + traj_step(state=1, action=0, reward=0.0, prob=0.0), + traj_step(state=0, action=1, reward=1.0, prob=1.0), + traj_step(state=1, action=1, reward=1.0, prob=1.0), + # zero'd out because 2 is passed as a terminal state + traj_step(state=2, action=0, reward=0.0, prob=0.0), + traj_step(state=2, action=1, reward=0.0, prob=1.0), + ] + + outputs = tuple(mapper.apply(inputs)) + assert len(outputs) == 22 + for output, expected in zip(outputs, expectactions): + # reward can only be approximately equal + np.testing.assert_array_equal(output.observation, expected.observation) + np.testing.assert_array_equal(output.action, expected.action) + np.testing.assert_array_equal(output.policy_info, expected.policy_info) + np.testing.assert_array_almost_equal(output.reward, expected.reward) + np.testing.assert_array_equal(output.terminated, expected.terminated) + np.testing.assert_array_equal(output.truncated, expected.truncated) + + def test_daaf_mdp_with_options_mapper_apply_given_truncated_options(): mapper = replay_mapper.DaafMdpWithOptionsMapper() inputs = [ @@ -637,36 +749,100 @@ def test_abqueuebuffer_init(): def test_abqueuebuffer(): buffer = replay_mapper.AbQueueBuffer(buffer_size=4, num_factors=3) + # First entry, added. buffer.add(np.array([1, 0, 0]), 1) assert getattr(buffer, "_factors_tracker") == set([4]) np.testing.assert_allclose(getattr(buffer, "_rank_flag"), np.array([1, 0, 0])) + assert buffer.is_empty is False + assert buffer.is_full_rank is False + np.testing.assert_allclose(buffer.matrix, np.array([[1, 0, 0]])) + # Second entry, independent, added. buffer.add(np.array([1, 0, 1]), 2) assert getattr(buffer, "_factors_tracker") == set([4, 5]) np.testing.assert_allclose(getattr(buffer, "_rank_flag"), np.array([2, 0, 1])) + assert buffer.is_empty is False + assert buffer.is_full_rank is False + np.testing.assert_allclose(buffer.matrix, np.array([[1, 0, 0], [1, 0, 1]])) + # Third entry, independent, added. + # Matrix is now full rank. buffer.add(np.array([1, 1, 1]), 3) assert getattr(buffer, "_factors_tracker") == set([4, 5, 7]) np.testing.assert_allclose(getattr(buffer, "_rank_flag"), np.array([3, 1, 2])) + assert buffer.is_empty is False + assert buffer.is_full_rank is True + np.testing.assert_allclose( + buffer.matrix, np.array([[1, 0, 0], [1, 0, 1], [1, 1, 1]]) + ) - # duplicate entry; no change + # Fourth entry, non-independent; no change buffer.add(np.array([1, 0, 1]), 4) assert getattr(buffer, "_factors_tracker") == set([4, 5, 7]) np.testing.assert_allclose(getattr(buffer, "_rank_flag"), np.array([3, 1, 2])) - + assert buffer.is_empty is False + assert buffer.is_full_rank is True np.testing.assert_allclose( buffer.matrix, np.array([[1, 0, 0], [1, 0, 1], [1, 1, 1]]) ) + # Fifth entry, indenpedent, added. buffer.add(np.array([1, 1, 0]), 5) assert getattr(buffer, "_factors_tracker") == set([4, 5, 7, 6]) np.testing.assert_allclose(getattr(buffer, "_rank_flag"), np.array([4, 2, 2])) - + assert buffer.is_empty is False + assert buffer.is_full_rank is True np.testing.assert_allclose( buffer.matrix, np.array([[1, 0, 0], [1, 0, 1], [1, 1, 1], [1, 1, 0]]) ) +def test_abqueuebuffer_ignore_factors_mask(): + buffer = replay_mapper.AbQueueBuffer( + buffer_size=4, num_factors=3, ignore_factors_mask=np.array([0, 1, 0]) + ) + + # First entry, added. + buffer.add(np.array([1, 0, 0]), 1) + assert getattr(buffer, "_factors_tracker") == set([2]) + np.testing.assert_allclose(getattr(buffer, "_rank_flag"), np.array([1, 0])) + assert buffer.is_empty is False + assert buffer.is_full_rank is False + np.testing.assert_allclose(buffer.matrix, np.array([[1, 0]])) + + # Second entry, added. + buffer.add(np.array([1, 0, 1]), 2) + assert getattr(buffer, "_factors_tracker") == set([2, 3]) + np.testing.assert_allclose(getattr(buffer, "_rank_flag"), np.array([2, 1])) + assert buffer.is_empty is False + assert buffer.is_full_rank is True + np.testing.assert_allclose(buffer.matrix, np.array([[1, 0], [1, 1]])) + + # Third entry, non-independent, ignored. + buffer.add(np.array([1, 1, 1]), 3) + assert getattr(buffer, "_factors_tracker") == set([2, 3]) + np.testing.assert_allclose(getattr(buffer, "_rank_flag"), np.array([2, 1])) + assert buffer.is_empty is False + assert buffer.is_full_rank is True + np.testing.assert_allclose(buffer.matrix, np.array([[1, 0], [1, 1]])) + + # Fourth entry, duplicate, ignored. + buffer.add(np.array([0, 1, 0]), 4) + assert getattr(buffer, "_factors_tracker") == set([2, 3]) + np.testing.assert_allclose(getattr(buffer, "_rank_flag"), np.array([2, 1])) + assert buffer.is_empty is False + assert buffer.is_full_rank is True + np.testing.assert_allclose(buffer.matrix, np.array([[1, 0], [1, 1]])) + + # Fifth entry, independent, added. + buffer.add(np.array([0, 0, 1]), 5) + assert getattr(buffer, "_factors_tracker") == set([2, 3, 1]) + np.testing.assert_allclose(getattr(buffer, "_rank_flag"), np.array([2, 2])) + assert buffer.is_empty is False + assert buffer.is_full_rank is True + np.testing.assert_allclose(buffer.matrix, np.array([[1, 0], [1, 1], [0, 1]])) + + def test_counter_init(): counter = replay_mapper.Counter() assert counter.value is None diff --git a/tox.ini b/tox.ini index a4b4a1f..03b750b 100644 --- a/tox.ini +++ b/tox.ini @@ -1,10 +1,10 @@ [tox] -envlist = py38,docs,manifest,check-formatting,lint +envlist = py39,docs,manifest,check-formatting,lint skipsdist = True usedevelop = True [testenv] -basepython = python3.8 +basepython = python3.9 deps = -r{toxinidir}/dev-requirements.txt -e . @@ -13,20 +13,20 @@ commands = [testenv:manifest] ; a safety check for source distributions -basepython = python3.8 +basepython = python3.9 deps = check-manifest skip_install = true commands = check-manifest [testenv:check-formatting] -basepython = python3.8 +basepython = python3.9 deps = ruff==0.2.2 skip_install = true commands = ruff format src/daaf tests --line-length 88 --diff --check [testenv:format] -basepython = python3.8 +basepython = python3.9 deps = ruff==0.2.2 skip_install = true commands = @@ -35,7 +35,7 @@ commands = [testenv:lint] ; feel free to add flake8 plugins (i.e. flake8-import-order), add isort, ; or use another linter of your choice -basepython = python3.8 +basepython = python3.9 deps = ruff==0.2.2 skip_install = true commands = ruff check @@ -45,7 +45,7 @@ commands = ruff check ; i.e. `(env) $ pytest`, it will still pick up this configuration. [testenv:lint-types] -basepython = python3.8 +basepython = python3.9 deps = mypy skip_install = true setenv =