diff --git a/.github/workflows/python-package.yml b/.github/workflows/python-package.yml new file mode 100644 index 0000000..e42a70e --- /dev/null +++ b/.github/workflows/python-package.yml @@ -0,0 +1,46 @@ +# This workflow will install Python dependencies, run tests and lint with a variety of Python versions +# For more information see: https://docs.github.com/en/actions/automating-builds-and-tests/building-and-testing-python + +name: Python package + +on: + push: + branches: [ "main" ] + pull_request: + branches: [ "main" ] + +jobs: + build: + + runs-on: ubuntu-latest + strategy: + fail-fast: false + matrix: + python-version: ["3.9", "3.10", "3.11"] + poetry-version: [2.1.1] + + steps: + - uses: actions/checkout@v4 + - name: Set up Python ${{ matrix.python-version }} + uses: actions/setup-python@v5 + with: + python-version: ${{ matrix.python-version }} + - name: Install Poetry ${{ matrix.poetry-version }} + uses: abatilo/actions-poetry@v4.0.0 + with: + poetry-version: ${{ matrix.poetry-version }} + - name: Install dependencies + run: | + python -m pip install --upgrade pip + python -m pip install flake8 pytest + poetry run pip install --upgrade setuptools wheel + poetry install + - name: Lint with flake8 + run: | + # stop the build if there are Python syntax errors or undefined names + flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics + # exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide + flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics + - name: Test with pytest + run: | + poetry run poe test diff --git a/docs/notebooks/examples.ipynb b/docs/notebooks/examples.ipynb index e6f820a..e0fba31 100644 --- a/docs/notebooks/examples.ipynb +++ b/docs/notebooks/examples.ipynb @@ -1,606 +1,561 @@ { - "cells": [ - { - "cell_type": "code", - "source": [ - "from google.colab import drive\n", - "drive.mount('/content/drive')" - ], - "metadata": { - "id": "1ydj18zZEzs-" - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "pip install '/content/drive/MyDrive/pywhy-llm'" - ], - "metadata": { - "collapsed": true, - "id": "c52A4DNmE2WQ" - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "pip install guidance" - ], - "metadata": { - "collapsed": true, - "id": "oE9HxS8LFNnm" - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "pip install python-dotenv" - ], - "metadata": { - "id": "cf0nNdMMFRXK" - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "dGRmzUIWEyLR" - }, - "outputs": [], - "source": [ - "from dotenv import load_dotenv\n", - "import os\n", - "import guidance\n", - "from guidance import models\n", - "\n", - "load_dotenv()\n", - "\n", - "os.environ[\"OPENAI_API_KEY\"] = ''" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "21IoxDY7EyLT" - }, - "outputs": [], - "source": [ - "from typing import Dict, Tuple, List\n", - "\n", - "sea_ice_variables = [\n", - " \"geopotential_heights\",\n", - " \"relative_humidity\",\n", - " \"sea_level_pressure\",\n", - " \"zonal_wind_at_10_meters\",\n", - " \"meridional_wind_at_10_meters\",\n", - " \"sensible_plus_latent_heat_flux\",\n", - " \"total_precipitation\",\n", - " \"total_cloud_cover\",\n", - " \"total_cloud_water_path\",\n", - " \"surface_net_shortwave_flux\",\n", - " \"surface_net_longwave_flux\",\n", - " \"northern_hemisphere_sea_ice_extent\",\n", - "]\n", - "\n", - "treatment = \"surface_net_longwave_flux\"\n", - "outcome = \"northern_hemisphere_sea_ice_extent\"\n", - "\n", - "# ground truth confounders to the relationship between surface_net_longwave_flux and northern_hemisphere_sea_ice_extent\n", - "sea_ice_confounders = [\"total_precipitation\"]\n", - "\n", - "sea_ice_relationships: List[Tuple[str, str]] = [\n", - " (\"surface_net_longwave_flux\", \"northern_hemisphere_sea_ice_extent\"),\n", - "\n", - " (\"geopotential_heights\", \"surface_net_longwave_flux\"),\n", - " (\"geopotential_heights\", \"relative_humidity\"),\n", - " (\"geopotential_heights\", \"sea_level_pressure\"),\n", - "\n", - " (\"relative_humidity\", \"total_cloud_cover\"),\n", - " (\"relative_humidity\", \"total_cloud_water_path\"),\n", - " (\"relative_humidity\", \"total_precipitation\"),\n", - " (\"relative_humidity\", \"surface_net_longwave_flux\"),\n", - "\n", - " (\"sea_level_pressure\", \"relative_humidity\"),\n", - " (\"sea_level_pressure\", \"geopotential_heights\"),\n", - " (\"sea_level_pressure\", \"zonal_wind_at_10_meters\"),\n", - " (\"sea_level_pressure\", \"northern_hemisphere_sea_ice_extent\"),\n", - " (\"sea_level_pressure\", \"sensible_plus_latent_heat_flux\"),\n", - " (\"sea_level_pressure\", \"meridional_wind_at_10_meters\"),\n", - "\n", - " (\"zonal_wind_at_10_meters\", \"northern_hemisphere_sea_ice_extent\"),\n", - " (\"zonal_wind_at_10_meters\", \"sensible_plus_latent_heat_flux\"),\n", - "\n", - " (\"meridional_wind_at_10_meters\", \"northern_hemisphere_sea_ice_extent\"),\n", - " (\"meridional_wind_at_10_meters\", \"sensible_plus_latent_heat_flux\"),\n", - "\n", - " (\"sensible_plus_latent_heat_flux\", \"northern_hemisphere_sea_ice_extent\"),\n", - " (\"sensible_plus_latent_heat_flux\", \"sea_level_pressure\"),\n", - " (\"sensible_plus_latent_heat_flux\", \"zonal_wind_at_10_meters\"),\n", - " (\"sensible_plus_latent_heat_flux\", \"meridional_wind_at_10_meters\"),\n", - " (\"sensible_plus_latent_heat_flux\", \"total_precipitation\"),\n", - " (\"sensible_plus_latent_heat_flux\", \"total_cloud_cover\"),\n", - " (\"sensible_plus_latent_heat_flux\", \"total_cloud_water_path\"),\n", - "\n", - " (\"total_precipitation\", \"northern_hemisphere_sea_ice_extent\"),\n", - " (\"total_precipitation\", \"relative_humidity\"),\n", - " (\"total_precipitation\", \"sensible_plus_latent_heat_flux\"),\n", - " (\"total_precipitation\", \"surface_net_longwave_flux\"),\n", - " (\"total_precipitation\", \"total_cloud_cover\"),\n", - " (\"total_precipitation\", \"total_cloud_water_path\"),\n", - "\n", - " (\"total_cloud_water_path\", \"total_precipitation\"),\n", - " (\"total_cloud_water_path\", \"sensible_plus_latent_heat_flux\"),\n", - " (\"total_cloud_water_path\", \"relative_humidity\"),\n", - " (\"total_cloud_water_path\", \"surface_net_longwave_flux\"),\n", - " (\"total_cloud_water_path\", \"surface_net_shortwave_flux\"),\n", - "\n", - " (\"total_cloud_cover\", \"total_precipitation\"),\n", - " (\"total_cloud_cover\", \"sensible_plus_latent_heat_flux\"),\n", - " (\"total_cloud_cover\", \"relative_humidity\"),\n", - " (\"total_cloud_cover\", \"surface_net_longwave_flux\"),\n", - " (\"total_cloud_cover\", \"surface_net_shortwave_flux\"),\n", - "\n", - " (\"surface_net_shortwave_flux\", \"northern_hemisphere_sea_ice_extent\"),\n", - "\n", - " (\"northern_hemisphere_sea_ice_extent\", \"sea_level_pressure\"),\n", - " (\"northern_hemisphere_sea_ice_extent\", \"zonal_wind_at_10_meters\"),\n", - " (\"northern_hemisphere_sea_ice_extent\", \"meridional_wind_at_10_meters\"),\n", - " (\"northern_hemisphere_sea_ice_extent\", \"sensible_plus_latent_heat_flux\"),\n", - " (\"northern_hemisphere_sea_ice_extent\", \"surface_net_shortwave_flux\"),\n", - " (\"northern_hemisphere_sea_ice_extent\", \"surface_net_longwave_flux\"),\n", - "]" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "awn-H8kaEyLU" - }, - "source": [ - "## Helpers" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "M3RolVDYEyLV" - }, - "source": [ - "Model type - the type of LLM used\n", - "By default it's set to completions models" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "3GSYRbQVEyLV" - }, - "source": [ - "Relationship strategy - is the type of request made to the LLM (request parent, child, pairwise relationship)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "HOUhJZbcEyLV" - }, - "outputs": [], - "source": [ - "from pywhyllm import ModelType, RelationshipStrategy\n", - "model_type = ModelType.Completion\n", - "relationship_strategy = RelationshipStrategy.Parent" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "bvsTJhlbEyLV" - }, - "source": [ - "## Model" - ] - }, - { - "cell_type": "code", - "source": [ - "from pywhyllm.suggesters.model_suggester import ModelSuggester\n", - "m = ModelSuggester('gpt-4')" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "pEdp6Lwpksi2", - "outputId": "5a1e1683-6e19-41ee-bb1e-1c42a86416aa" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "stream", - "name": "stderr", - "text": [ - "WARNING:guidance.models._model:gpustat is not installed, run `pip install gpustat` to collect GPU stats.\n" - ] - } - ] - }, - { - "cell_type": "code", - "source": [ - "domain_expertises = m.suggest_domain_expertises(\"causal mechanisms\", sea_ice_variables)" - ], - "metadata": { - "id": "mIRwbAZ_kd4q" - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "domain_expertises" - ], - "metadata": { - "id": "hBhGgry1k-EG" - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "domain_experts = m.suggest_domain_experts(\"causal mechanisms\", sea_ice_variables)" - ], - "metadata": { - "id": "0eHUZVSGliJx" - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "domain_experts" - ], - "metadata": { - "id": "XenD7Zz7ll4P" - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "parents = m.suggest_parents(\"causal mechanisms\", \"meterology\", \"relative_humidity\", sea_ice_variables)" - ], - "metadata": { - "id": "_-cEK7j9ATAO" - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "parents" - ], - "metadata": { - "id": "fPq3fnxKAuLk" - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "children = m.suggest_children(\"causal mechanisms\", \"meterology\", \"relative_humidity\", sea_ice_variables)" - ], - "metadata": { - "id": "U18jzHQzJYmZ" - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "children" - ], - "metadata": { - "id": "nw8OOCBSOpAP" - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "pairwise_relationship = m.suggest_pairwise_relationship(\"meterology\", \"total_precipitation\", \"relative_humidity\")" - ], - "metadata": { - "id": "wWGjSa_cby5j" - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "pairwise_relationship" - ], - "metadata": { - "id": "qeqcF4Wwd5xm" - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "confounders = m.suggest_confounders(treatment, outcome, sea_ice_variables, domain_expertises,\"causal mechanisms\")" - ], - "metadata": { - "id": "5WkPttsK-_rz" - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "confounders" - ], - "metadata": { - "id": "sW0Hlk-u-V0v" - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "9jAA5UxmEyLV" - }, - "outputs": [], - "source": [ - "\"\"\"returns a dictionary with the how many times that edge was suggested\"\"\"\n", - "model_edges = m.suggest_relationships(treatment, outcome, sea_ice_variables, domain_expertises, \"causal mechanisms\", None, RelationshipStrategy.Pairwise)" - ] - }, - { - "cell_type": "code", - "source": [ - "model_edges" - ], - "metadata": { - "id": "7j7fYC1yiwna" - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "ZXAOVBjWEyLW" - }, - "source": [ - "## Identifier" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "OtdU46dQEyLW" - }, - "outputs": [], - "source": [ - "from pywhyllm.suggesters.identification_suggester import IdentificationSuggester\n", - "i = IdentificationSuggester('gpt-4')" - ] - }, - { - "cell_type": "code", - "source": [ - "\"\"\"calls modeler suggest_confounders in the background\"\"\"\n", - "backdoor = i.suggest_backdoor(treatment, outcome, sea_ice_variables, domain_expertises, \"causal mechanisms\")" - ], - "metadata": { - "id": "ldAYLVc1EW88" - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "backdoor" - ], - "metadata": { - "id": "0Isz1yq-kBVU" - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "\"\"\"suggests instrumental variables\"\"\"\n", - "ivs = i.suggest_ivs(treatment, outcome, sea_ice_variables, domain_expertises)" - ], - "metadata": { - "id": "c9Su67G5kTKP" - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "ivs" - ], - "metadata": { - "id": "e5CaH4lckbQl" - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "4lsrPKj7EyLW" - }, - "source": [ - "## Validator" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "69rLl6dtEyLW" - }, - "outputs": [], - "source": [ - "from pywhyllm.suggesters.validation_suggester import ValidationSuggester\n", - "v = ValidationSuggester('gpt-4')" - ] - }, - { - "cell_type": "code", - "source": [ - "latent_confounders = v.suggest_latent_confounders(treatment, outcome, domain_expertises)" - ], - "metadata": { - "id": "DU9ql-uTz4tE" - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "latent_confounders" - ], - "metadata": { - "id": "X1TptA_P0FQZ" - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "negative_controls = v.suggest_negative_controls(treatment, outcome, sea_ice_variables, domain_expertises)" - ], - "metadata": { - "id": "AIj3GD8J0GaJ" - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "negative_controls" - ], - "metadata": { - "id": "ZJwgwJLP2pON" - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "critique = v.request_pairwise_critique(domain_expertises[0], \"total_precipitation\", \"relative_humidity\")" - ], - "metadata": { - "id": "xfTpbzuPpC4f" - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "critique" - ], - "metadata": { - "id": "saykqPTQsco2" - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "parent=RelationshipStrategy.Parent\n", - "child=RelationshipStrategy.Child\n", - "pairwise=RelationshipStrategy.Pairwise" - ], - "metadata": { - "id": "9CJgSelu3L-s" - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "critique = v.critique_graph(sea_ice_variables, model_edges, domain_expertises, \"causal mechanisms\", None, pairwise)" - ], - "metadata": { - "id": "ES6P8_st3NXU" - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "critique" - ], - "metadata": { - "id": "JrlTj-QmykJL" - }, - "execution_count": null, - "outputs": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": ".venv", - "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": "3.11.4" - }, - "orig_nbformat": 4, + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "cf0nNdMMFRXK" + }, + "outputs": [], + "source": [ + "!pip install python-dotenv" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "dGRmzUIWEyLR" + }, + "outputs": [], + "source": [ + "from dotenv import load_dotenv\n", + "import os\n", + "import guidance\n", + "from guidance import models\n", + "\n", + "load_dotenv()\n", + "\n", + "os.environ[\"OPENAI_API_KEY\"] = '' # specify your key here" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "21IoxDY7EyLT" + }, + "outputs": [], + "source": [ + "from typing import Dict, Tuple, List\n", + "\n", + "sea_ice_variables = [\n", + " \"geopotential_heights\",\n", + " \"relative_humidity\",\n", + " \"sea_level_pressure\",\n", + " \"zonal_wind_at_10_meters\",\n", + " \"meridional_wind_at_10_meters\",\n", + " \"sensible_plus_latent_heat_flux\",\n", + " \"total_precipitation\",\n", + " \"total_cloud_cover\",\n", + " \"total_cloud_water_path\",\n", + " \"surface_net_shortwave_flux\",\n", + " \"surface_net_longwave_flux\",\n", + " \"northern_hemisphere_sea_ice_extent\",\n", + "]\n", + "\n", + "treatment = \"surface_net_longwave_flux\"\n", + "outcome = \"northern_hemisphere_sea_ice_extent\"\n", + "\n", + "# ground truth confounders to the relationship between surface_net_longwave_flux and northern_hemisphere_sea_ice_extent\n", + "sea_ice_confounders = [\"total_precipitation\"]\n", + "\n", + "sea_ice_relationships: List[Tuple[str, str]] = [\n", + " (\"surface_net_longwave_flux\", \"northern_hemisphere_sea_ice_extent\"),\n", + "\n", + " (\"geopotential_heights\", \"surface_net_longwave_flux\"),\n", + " (\"geopotential_heights\", \"relative_humidity\"),\n", + " (\"geopotential_heights\", \"sea_level_pressure\"),\n", + "\n", + " (\"relative_humidity\", \"total_cloud_cover\"),\n", + " (\"relative_humidity\", \"total_cloud_water_path\"),\n", + " (\"relative_humidity\", \"total_precipitation\"),\n", + " (\"relative_humidity\", \"surface_net_longwave_flux\"),\n", + "\n", + " (\"sea_level_pressure\", \"relative_humidity\"),\n", + " (\"sea_level_pressure\", \"geopotential_heights\"),\n", + " (\"sea_level_pressure\", \"zonal_wind_at_10_meters\"),\n", + " (\"sea_level_pressure\", \"northern_hemisphere_sea_ice_extent\"),\n", + " (\"sea_level_pressure\", \"sensible_plus_latent_heat_flux\"),\n", + " (\"sea_level_pressure\", \"meridional_wind_at_10_meters\"),\n", + "\n", + " (\"zonal_wind_at_10_meters\", \"northern_hemisphere_sea_ice_extent\"),\n", + " (\"zonal_wind_at_10_meters\", \"sensible_plus_latent_heat_flux\"),\n", + "\n", + " (\"meridional_wind_at_10_meters\", \"northern_hemisphere_sea_ice_extent\"),\n", + " (\"meridional_wind_at_10_meters\", \"sensible_plus_latent_heat_flux\"),\n", + "\n", + " (\"sensible_plus_latent_heat_flux\", \"northern_hemisphere_sea_ice_extent\"),\n", + " (\"sensible_plus_latent_heat_flux\", \"sea_level_pressure\"),\n", + " (\"sensible_plus_latent_heat_flux\", \"zonal_wind_at_10_meters\"),\n", + " (\"sensible_plus_latent_heat_flux\", \"meridional_wind_at_10_meters\"),\n", + " (\"sensible_plus_latent_heat_flux\", \"total_precipitation\"),\n", + " (\"sensible_plus_latent_heat_flux\", \"total_cloud_cover\"),\n", + " (\"sensible_plus_latent_heat_flux\", \"total_cloud_water_path\"),\n", + "\n", + " (\"total_precipitation\", \"northern_hemisphere_sea_ice_extent\"),\n", + " (\"total_precipitation\", \"relative_humidity\"),\n", + " (\"total_precipitation\", \"sensible_plus_latent_heat_flux\"),\n", + " (\"total_precipitation\", \"surface_net_longwave_flux\"),\n", + " (\"total_precipitation\", \"total_cloud_cover\"),\n", + " (\"total_precipitation\", \"total_cloud_water_path\"),\n", + "\n", + " (\"total_cloud_water_path\", \"total_precipitation\"),\n", + " (\"total_cloud_water_path\", \"sensible_plus_latent_heat_flux\"),\n", + " (\"total_cloud_water_path\", \"relative_humidity\"),\n", + " (\"total_cloud_water_path\", \"surface_net_longwave_flux\"),\n", + " (\"total_cloud_water_path\", \"surface_net_shortwave_flux\"),\n", + "\n", + " (\"total_cloud_cover\", \"total_precipitation\"),\n", + " (\"total_cloud_cover\", \"sensible_plus_latent_heat_flux\"),\n", + " (\"total_cloud_cover\", \"relative_humidity\"),\n", + " (\"total_cloud_cover\", \"surface_net_longwave_flux\"),\n", + " (\"total_cloud_cover\", \"surface_net_shortwave_flux\"),\n", + "\n", + " (\"surface_net_shortwave_flux\", \"northern_hemisphere_sea_ice_extent\"),\n", + "\n", + " (\"northern_hemisphere_sea_ice_extent\", \"sea_level_pressure\"),\n", + " (\"northern_hemisphere_sea_ice_extent\", \"zonal_wind_at_10_meters\"),\n", + " (\"northern_hemisphere_sea_ice_extent\", \"meridional_wind_at_10_meters\"),\n", + " (\"northern_hemisphere_sea_ice_extent\", \"sensible_plus_latent_heat_flux\"),\n", + " (\"northern_hemisphere_sea_ice_extent\", \"surface_net_shortwave_flux\"),\n", + " (\"northern_hemisphere_sea_ice_extent\", \"surface_net_longwave_flux\"),\n", + "]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "awn-H8kaEyLU" + }, + "source": [ + "## Helpers" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "M3RolVDYEyLV" + }, + "source": [ + "Model type - the type of LLM used\n", + "By default it's set to completions models" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "3GSYRbQVEyLV" + }, + "source": [ + "Relationship strategy - is the type of request made to the LLM (request parent, child, pairwise relationship)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "HOUhJZbcEyLV" + }, + "outputs": [], + "source": [ + "from pywhyllm import ModelType, RelationshipStrategy\n", + "model_type = ModelType.Completion\n", + "relationship_strategy = RelationshipStrategy.Parent" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "bvsTJhlbEyLV" + }, + "source": [ + "## Model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { "colab": { - "provenance": [] - } + "base_uri": "https://localhost:8080/" + }, + "id": "pEdp6Lwpksi2", + "outputId": "5a1e1683-6e19-41ee-bb1e-1c42a86416aa" + }, + "outputs": [], + "source": [ + "from pywhyllm.suggesters.model_suggester import ModelSuggester\n", + "m = ModelSuggester('gpt-4')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "mIRwbAZ_kd4q" + }, + "outputs": [], + "source": [ + "domain_expertises = m.suggest_domain_expertises(\"causal mechanisms\", sea_ice_variables)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "hBhGgry1k-EG" + }, + "outputs": [], + "source": [ + "domain_expertises" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "0eHUZVSGliJx" + }, + "outputs": [], + "source": [ + "domain_experts = m.suggest_domain_experts(\"causal mechanisms\", sea_ice_variables)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "XenD7Zz7ll4P" + }, + "outputs": [], + "source": [ + "domain_experts" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "_-cEK7j9ATAO" + }, + "outputs": [], + "source": [ + "parents = m.suggest_parents(\"causal mechanisms\", \"meterology\", \"relative_humidity\", sea_ice_variables)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "fPq3fnxKAuLk" + }, + "outputs": [], + "source": [ + "parents" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "U18jzHQzJYmZ" + }, + "outputs": [], + "source": [ + "children = m.suggest_children(\"causal mechanisms\", \"meterology\", \"relative_humidity\", sea_ice_variables)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "nw8OOCBSOpAP" + }, + "outputs": [], + "source": [ + "children" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "wWGjSa_cby5j" + }, + "outputs": [], + "source": [ + "pairwise_relationship = m.suggest_pairwise_relationship(\"meterology\", \"total_precipitation\", \"relative_humidity\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "qeqcF4Wwd5xm" + }, + "outputs": [], + "source": [ + "pairwise_relationship" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "5WkPttsK-_rz" + }, + "outputs": [], + "source": [ + "confounders = m.suggest_confounders(treatment, outcome, sea_ice_variables, domain_expertises,\"causal mechanisms\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "sW0Hlk-u-V0v" + }, + "outputs": [], + "source": [ + "confounders" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "9jAA5UxmEyLV" + }, + "outputs": [], + "source": [ + "\"\"\"returns a dictionary with the how many times that edge was suggested\"\"\"\n", + "model_edges = m.suggest_relationships(treatment, outcome, sea_ice_variables, domain_expertises, \"causal mechanisms\", None, RelationshipStrategy.Pairwise)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "7j7fYC1yiwna" + }, + "outputs": [], + "source": [ + "model_edges" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ZXAOVBjWEyLW" + }, + "source": [ + "## Identifier" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "OtdU46dQEyLW" + }, + "outputs": [], + "source": [ + "from pywhyllm.suggesters.identification_suggester import IdentificationSuggester\n", + "i = IdentificationSuggester('gpt-4')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "ldAYLVc1EW88" + }, + "outputs": [], + "source": [ + "\"\"\"calls modeler suggest_confounders in the background\"\"\"\n", + "backdoor = i.suggest_backdoor(treatment, outcome, sea_ice_variables, domain_expertises, \"causal mechanisms\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "0Isz1yq-kBVU" + }, + "outputs": [], + "source": [ + "backdoor" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "c9Su67G5kTKP" + }, + "outputs": [], + "source": [ + "\"\"\"suggests instrumental variables\"\"\"\n", + "ivs = i.suggest_ivs(treatment, outcome, sea_ice_variables, domain_expertises)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "e5CaH4lckbQl" + }, + "outputs": [], + "source": [ + "ivs" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "4lsrPKj7EyLW" + }, + "source": [ + "## Validator" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "69rLl6dtEyLW" + }, + "outputs": [], + "source": [ + "from pywhyllm.suggesters.validation_suggester import ValidationSuggester\n", + "v = ValidationSuggester('gpt-4')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "DU9ql-uTz4tE" + }, + "outputs": [], + "source": [ + "latent_confounders = v.suggest_latent_confounders(treatment, outcome, domain_expertises)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "X1TptA_P0FQZ" + }, + "outputs": [], + "source": [ + "latent_confounders" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "AIj3GD8J0GaJ" + }, + "outputs": [], + "source": [ + "negative_controls = v.suggest_negative_controls(treatment, outcome, sea_ice_variables, domain_expertises)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "ZJwgwJLP2pON" + }, + "outputs": [], + "source": [ + "negative_controls" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "xfTpbzuPpC4f" + }, + "outputs": [], + "source": [ + "critique = v.request_pairwise_critique(domain_expertises[0], \"total_precipitation\", \"relative_humidity\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "saykqPTQsco2" + }, + "outputs": [], + "source": [ + "critique" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "9CJgSelu3L-s" + }, + "outputs": [], + "source": [ + "parent=RelationshipStrategy.Parent\n", + "child=RelationshipStrategy.Child\n", + "pairwise=RelationshipStrategy.Pairwise" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "ES6P8_st3NXU" + }, + "outputs": [], + "source": [ + "critique = v.critique_graph(sea_ice_variables, model_edges, domain_expertises, \"causal mechanisms\", None, pairwise)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "JrlTj-QmykJL" + }, + "outputs": [], + "source": [ + "critique" + ] + } + ], + "metadata": { + "colab": { + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" }, - "nbformat": 4, - "nbformat_minor": 0 -} \ No newline at end of file + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.5" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/docs/notebooks/walkthrough.ipynb b/docs/notebooks/walkthrough.ipynb index 7fd435d..b9f132a 100644 --- a/docs/notebooks/walkthrough.ipynb +++ b/docs/notebooks/walkthrough.ipynb @@ -9,149 +9,7 @@ }, { "cell_type": "code", - "execution_count": 1, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "JwUzWIdy4DjA", - "outputId": "75a3e037-1486-4a9d-c250-9c04a4f2a461" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Mounted at /content/drive\n" - ] - } - ], - "source": [ - "from google.colab import drive\n", - "drive.mount('/content/drive')" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "collapsed": true, - "id": "IFXQk0x3J5YO", - "jupyter": { - "outputs_hidden": true - }, - "outputId": "39514ba6-a88d-4f38-dccd-17ed2b008c04" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Collecting guidance\n", - " Downloading guidance-0.2.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (2.8 kB)\n", - "Collecting diskcache (from 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pywhyllm\n", - " Building wheel for pywhyllm (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n", - " Created wheel for pywhyllm: filename=pywhyllm-0.0.0-py3-none-any.whl size=21041 sha256=ef0b2f23191cef49878a29dd4fd81fe3c00af38084153ee416b632f3a9c17569\n", - " Stored in directory: /root/.cache/pip/wheels/40/a9/c3/60f411076164ff75e8433d2123b746f808206a62a9a3a3fa03\n", - "Successfully built pywhyllm\n", - "Installing collected packages: pywhyllm\n", - "Successfully installed pywhyllm-0.0.0\n" - ] - } - ], - "source": [ - "pip install '/content/drive/MyDrive/pywhy-llm'" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "pn7WLx36DpTg", - "outputId": "8413ae67-ab22-4954-d671-4ddd4cc22381" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Collecting gpustat\n", - " Downloading gpustat-1.1.1.tar.gz (98 kB)\n", - "\u001b[?25l 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sha256=56886eeb00c620277ad48366e6f60b747bd7cd9ebb3da078c06e8b1eb030a6a6\n", - " Stored in directory: /root/.cache/pip/wheels/c9/2b/d9/a0b77d6e8623ce6b5c73813af455a3ace394abfc2e8aef7ed6\n", - "Successfully built gpustat\n", - "Installing collected packages: nvidia-ml-py, blessed, gpustat\n", - "Successfully installed blessed-1.20.0 gpustat-1.1.1 nvidia-ml-py-12.570.86\n" - ] - } - ], + "outputs": [], "source": [ - "pip install gpustat" + " !pip install python-dotenv" ] }, { @@ -316,16 +37,12 @@ "\n", "load_dotenv()\n", "\n", - "from getpass import getpass\n", - "\n", - "OPENAI_API_KEY = getpass()\n", - "\n", - "os.environ[\"OPENAI_API_KEY\"] = OPENAI_API_KEY" + "os.environ[\"OPENAI_API_KEY\"] = '' # specify your key here" ] }, { "cell_type": "code", - "execution_count": 24, + "execution_count": null, "metadata": { "id": "GtLY4ihkJ5YR" }, @@ -346,7 +63,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -354,22 +71,14 @@ "id": "H1Y4XeqdJ5YS", "outputId": "cf4356dc-f57d-4892-d473-fdb64964c089" }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The most likely cause-and-effect relationship is C . This is because ice cream sales and shark attacks are not directly related to each other. They are both correlated with a third variable, which is likely to be the weather. For example, during the summer months, people are more likely to buy ice cream and go swimming in the ocean, which could potentially increase the risk of shark attacks. However, one does not cause the other.\n" - ] - } - ], + "outputs": [], "source": [ "result = modeler.suggest_pairwise_relationship(\"ice cream sales\", \"shark attacks\")\n" ] }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -377,27 +86,14 @@ "id": "oYIJWcLTpf6K", "outputId": "3fa333d5-f327-4345-f36f-ba59f737adeb" }, - "outputs": [ - { - "data": { - "text/plain": [ - "[None,\n", - " None,\n", - " 'The most likely cause-and-effect relationship is C . This is because ice cream sales and shark attacks are not directly related to each other. They are both correlated with a third variable, which is likely to be the weather. For example, during the summer months, people are more likely to buy ice cream and go swimming in the ocean, which could potentially increase the risk of shark attacks. However, one does not cause the other.']" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "result" ] }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -405,15 +101,7 @@ "id": "Zoveb0KGJ5YS", "outputId": "41de089c-28bb-4074-ffa5-139a2e7c22cc" }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "neither causes the other\n" - ] - } - ], + "outputs": [], "source": [ "# doesn't work\n", "if result[0] is not None:\n", @@ -433,7 +121,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -441,40 +129,7 @@ "id": "j59zNdp2J5YT", "outputId": "d1163791-f626-4045-c681-202111a1bfc4" }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "1/3.0: Querying for relationship between ice cream sales and temperature\n", - "The cause-and-effect relationship is more likely to be B. Temperature causes ice cream sales. This is because when the temperature is high (during summer, for example), people are more likely to buy ice cream to cool down. On the other hand, buying ice cream does not have an effect on the temperature. \n", - "\n", - " B \n", - "\ttemperature causes ice cream sales\n", - "2/3.0: Querying for relationship between ice cream sales and cavities\n", - "The cause-and-effect relationship between ice cream sales and cavities is not straightforward. While it's true that consuming too much ice cream, which is high in sugar, can contribute to the development of cavities, it's not the sales of ice cream that directly cause cavities. It's the consumption and poor dental hygiene that does. On the other hand, cavities do not cause ice cream sales. People with cavities might choose to eat ice cream because it's soft and easy to eat, but cavities themselves do not increase ice cream sales. Therefore, neither ice cream sales nor cavities cause each other directly. \n", - "\n", - " C \n", - "\tNo relationship found between ice cream sales and cavities\n", - "3/3.0: Querying for relationship between temperature and cavities\n", - "Cavities are caused by factors such as poor oral hygiene, frequent snacking on sugary foods and drinks, and not getting enough fluoride. Temperature, on the other hand, does not cause cavities. Similarly, cavities do not cause temperature. Temperature is a measure of heat or coldness and is not influenced by dental health. Therefore, the most likely cause-and-effect relationship is C. neither temperature nor cavities cause each other.\n", - "\n", - "C\n", - "\tNo relationship found between temperature and cavities\n" - ] - }, - { - "data": { - "text/plain": [ - "{('temperature',\n", - " 'ice cream sales'): 'The cause-and-effect relationship is more likely to be B. Temperature causes ice cream sales. This is because when the temperature is high (during summer, for example), people are more likely to buy ice cream to cool down. On the other hand, buying ice cream does not have an effect on the temperature. \\n\\n B '}" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "variables = [\"ice cream sales\", \"temperature\", \"cavities\"]\n", "results = modeler.suggest_relationships(variables)\n", @@ -484,7 +139,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -492,19 +147,7 @@ "id": "Q_ndDUhlB1Ai", "outputId": "03220e84-263c-4546-abb6-65cbcf4d2500" }, - "outputs": [ - { - "data": { - "text/plain": [ - "{('temperature',\n", - " 'ice cream sales'): 'The cause-and-effect relationship is more likely to be B. Temperature causes ice cream sales. This is because when the temperature is high (during summer, for example), people are more likely to buy ice cream to cool down. On the other hand, buying ice cream does not have an effect on the temperature. \\n\\n B '}" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "results" ] @@ -520,7 +163,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -528,15 +171,7 @@ "id": "07CvHj5sJ5YT", "outputId": "0abd593a-31fb-4e07-88d4-2318555424c9" }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['Beach attendance', 'Season of the year', 'Water temperature', 'Availability of ice cream', 'Shark population', 'Public holidays', 'Leisure time', 'Tourist season', 'Swimming habits', 'Marketing campaigns for ice cream']\n" - ] - } - ], + "outputs": [], "source": [ "variables = [\"ice cream sales\", \"temperature\", \"cavities\"]\n", "latents = modeler.suggest_confounders(variables, \"ice cream sales\", \"shark attacks\")\n", @@ -546,7 +181,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -554,15 +189,7 @@ "id": "X6AjKq8JJ5YT", "outputId": "3de57801-d8ea-4789-95c6-915b52b0d849" }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['Physical activity level', 'Smoking status', 'Alcohol consumption', 'Genetic predisposition', 'Stress levels', 'Chronic diseases', 'Medication use', 'Socioeconomic status']\n" - ] - } - ], + "outputs": [], "source": [ "latents = modeler.suggest_confounders([\"weight\", \"diet\", \"age\"], \"vitamin c\", \"cardiovascular health\")\n", "\n", @@ -589,7 +216,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": null, "metadata": { "id": "MNL9qLCNJ5YT" }, @@ -601,7 +228,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -609,18 +236,7 @@ "id": "DYRimHThJ5YT", "outputId": "fed3fa60-c224-448e-e93d-9369d6bd45e3" }, - "outputs": [ - { - "data": { - "text/plain": [ - "['cigarette taxes']" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "variables = [\"cigarette taxes\", \"rain\", \"car sales\", \"property taxes\", \"heart attacks\"]\n", "ivs = identifier.suggest_iv(variables, \"smoking\", \"birth weight\")\n", @@ -639,7 +255,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -647,15 +263,7 @@ "id": "xuhj4TEKJ5YU", "outputId": "1fc51fa1-68ec-41b4-9ff4-6a3c849710c6" }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[\"'Age'\", \"'Sex'\", \"'HbA1c'\", \"'HDL'\", \"'LDL'\", \"'eGFR'\", \"'Prior MI'\", \"'Prior Stroke or TIA'\", \"'Prior Heart Failure'\", \"'Cardiovascular medication'\", \"'T2DM medication'\", \"'Insulin'\", \"'Morbid obesity'\"]\n" - ] - } - ], + "outputs": [], "source": [ "variables = [\"Age\", \"Sex\", \"HbA1c\", \"HDL\", \"LDL\", \"eGFR\", \"Prior MI\",\n", " \"Prior Stroke or TIA\", \"Prior Heart Failure\", \"Cardiovascular medication\",\n", @@ -679,7 +287,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -687,15 +295,7 @@ "id": "yer720-5J5YU", "outputId": "f385428f-8e09-42a9-977b-430a4985000d" }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[\"'HbA1c'\", \"'T2DM medication'\", \"'Insulin'\", \"'Cardiovascular medication'\", \"'Prior MI'\", \"'Prior Stroke or TIA'\", \"'Prior Heart Failure'\", \"'First occurrence of Nonfatal myocardial infarction, nonfatal stroke, death from all cause'\"]\n" - ] - } - ], + "outputs": [], "source": [ "frontdoors = identifier.suggest_frontdoor(variables,\n", " treatment=\"semaglutide treatment\", outcome = \"cardiovascular health\")\n", @@ -725,7 +325,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.10" + "version": "3.9.5" } }, "nbformat": 4, diff --git a/poetry.lock b/poetry.lock index b23165c..a1261d9 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,114 +1,4 @@ -# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand. - 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"sha256:3893a00122eafde6894c59914446a512f728a0c1a45f9bb9b63721b6bacf0b4a"}, @@ -1356,7 +1108,7 @@ zipp = {version = ">=3.1.0", markers = "python_version < \"3.10\""} [package.extras] docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (<7.2.5)", "sphinx (>=3.5)", "sphinx-lint"] -testing = ["pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-mypy (>=0.9.1)", "pytest-ruff", "zipp (>=3.17)"] +testing = ["pytest (>=6)", "pytest-black (>=0.3.7) ; platform_python_implementation != \"PyPy\"", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-mypy (>=0.9.1) ; platform_python_implementation != \"PyPy\"", "pytest-ruff", "zipp (>=3.17)"] [[package]] name = "iniconfig" @@ -1364,6 +1116,7 @@ version = "2.0.0" description = "brain-dead simple config-ini parsing" optional = false python-versions = ">=3.7" +groups = ["dev"] files = [ {file = 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= "6.0.1" description = "A Python utility / library to sort Python imports." optional = false -python-versions = ">=3.8.0" +python-versions = ">=3.9.0" +groups = ["dev"] files = [ - {file = "isort-5.13.1-py3-none-any.whl", hash = "sha256:56a51732c25f94ca96f6721be206dd96a95f42950502eb26c1015d333bc6edb7"}, - {file = "isort-5.13.1.tar.gz", hash = "sha256:aaed790b463e8703fb1eddb831dfa8e8616bacde2c083bd557ef73c8189b7263"}, + {file = "isort-6.0.1-py3-none-any.whl", hash = "sha256:2dc5d7f65c9678d94c88dfc29161a320eec67328bc97aad576874cb4be1e9615"}, + {file = "isort-6.0.1.tar.gz", hash = "sha256:1cb5df28dfbc742e490c5e41bad6da41b805b0a8be7bc93cd0fb2a8a890ac450"}, ] +[package.extras] +colors = ["colorama"] +plugins = ["setuptools"] + [[package]] name = "jedi" version = "0.19.1" description = "An autocompletion tool for Python that can be used for text editors." optional = false python-versions = ">=3.6" +groups = ["main", "dev", "docs"] files = [ {file = "jedi-0.19.1-py2.py3-none-any.whl", hash = "sha256:e983c654fe5c02867aef4cdfce5a2fbb4a50adc0af145f70504238f18ef5e7e0"}, {file = "jedi-0.19.1.tar.gz", hash = "sha256:cf0496f3651bc65d7174ac1b7d043eff454892c708a87d1b683e57b569927ffd"}, @@ -1512,6 +1275,7 @@ version = "3.1.2" description = "A very fast and expressive template engine." optional = false python-versions = ">=3.7" +groups = ["dev", "docs"] files = [ {file = "Jinja2-3.1.2-py3-none-any.whl", hash = "sha256:6088930bfe239f0e6710546ab9c19c9ef35e29792895fed6e6e31a023a182a61"}, {file = "Jinja2-3.1.2.tar.gz", hash = "sha256:31351a702a408a9e7595a8fc6150fc3f43bb6bf7e319770cbc0db9df9437e852"}, @@ -1529,6 +1293,7 @@ version = "0.9.14" description = "A Python implementation of the JSON5 data format." optional = false python-versions = "*" +groups = ["dev"] files = [ {file = "json5-0.9.14-py2.py3-none-any.whl", hash = "sha256:740c7f1b9e584a468dbb2939d8d458db3427f2c93ae2139d05f47e453eae964f"}, {file = "json5-0.9.14.tar.gz", hash = "sha256:9ed66c3a6ca3510a976a9ef9b8c0787de24802724ab1860bc0153c7fdd589b02"}, @@ -1543,6 +1308,7 @@ version = "2.4" description = "Identify specific nodes in a JSON document (RFC 6901)" optional = false python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*, !=3.5.*, !=3.6.*" +groups = ["dev"] files = [ {file = "jsonpointer-2.4-py2.py3-none-any.whl", hash = "sha256:15d51bba20eea3165644553647711d150376234112651b4f1811022aecad7d7a"}, {file = "jsonpointer-2.4.tar.gz", hash = "sha256:585cee82b70211fa9e6043b7bb89db6e1aa49524340dde8ad6b63206ea689d88"}, @@ -1554,6 +1320,7 @@ version = "4.20.0" description = "An implementation of JSON Schema validation for Python" optional = false python-versions = ">=3.8" +groups = ["dev", "docs"] files = [ {file = "jsonschema-4.20.0-py3-none-any.whl", hash = "sha256:ed6231f0429ecf966f5bc8dfef245998220549cbbcf140f913b7464c52c3b6b3"}, {file = "jsonschema-4.20.0.tar.gz", hash = "sha256:4f614fd46d8d61258610998997743ec5492a648b33cf478c1ddc23ed4598a5fa"}, @@ -1563,11 +1330,9 @@ files = [ attrs = ">=22.2.0" fqdn = {version = "*", optional = true, markers = "extra == \"format-nongpl\""} idna = {version = "*", optional = true, markers = "extra == \"format-nongpl\""} -importlib-resources = {version = ">=1.4.0", markers = "python_version < \"3.9\""} isoduration = {version = "*", optional = true, markers = "extra == \"format-nongpl\""} jsonpointer = {version = ">1.13", optional = true, markers = "extra == \"format-nongpl\""} jsonschema-specifications = ">=2023.03.6" -pkgutil-resolve-name = {version = ">=1.3.10", markers = "python_version < \"3.9\""} referencing = ">=0.28.4" rfc3339-validator = {version = "*", optional = true, markers = "extra == \"format-nongpl\""} rfc3986-validator = {version = ">0.1.0", optional = true, markers = "extra == \"format-nongpl\""} @@ -1585,34 +1350,34 @@ version = "2023.11.2" description = "The JSON Schema meta-schemas and vocabularies, exposed as a Registry" optional = false python-versions = ">=3.8" +groups = ["dev", "docs"] files = [ {file = "jsonschema_specifications-2023.11.2-py3-none-any.whl", hash = "sha256:e74ba7c0a65e8cb49dc26837d6cfe576557084a8b423ed16a420984228104f93"}, {file = "jsonschema_specifications-2023.11.2.tar.gz", hash = "sha256:9472fc4fea474cd74bea4a2b190daeccb5a9e4db2ea80efcf7a1b582fc9a81b8"}, ] [package.dependencies] -importlib-resources = {version = ">=1.4.0", markers = "python_version < \"3.9\""} referencing = ">=0.31.0" [[package]] name = "jupyter" -version = "1.0.0" +version = "1.1.1" description = "Jupyter metapackage. Install all the Jupyter components in one go." optional = false python-versions = "*" +groups = ["dev"] files = [ - {file = "jupyter-1.0.0-py2.py3-none-any.whl", hash = "sha256:5b290f93b98ffbc21c0c7e749f054b3267782166d72fa5e3ed1ed4eaf34a2b78"}, - {file = "jupyter-1.0.0.tar.gz", hash = "sha256:d9dc4b3318f310e34c82951ea5d6683f67bed7def4b259fafbfe4f1beb1d8e5f"}, - {file = "jupyter-1.0.0.zip", hash = "sha256:3e1f86076bbb7c8c207829390305a2b1fe836d471ed54be66a3b8c41e7f46cc7"}, + {file = "jupyter-1.1.1-py2.py3-none-any.whl", hash = "sha256:7a59533c22af65439b24bbe60373a4e95af8f16ac65a6c00820ad378e3f7cc83"}, + {file = "jupyter-1.1.1.tar.gz", hash = "sha256:d55467bceabdea49d7e3624af7e33d59c37fff53ed3a350e1ac957bed731de7a"}, ] [package.dependencies] ipykernel = "*" ipywidgets = "*" jupyter-console = "*" +jupyterlab = "*" nbconvert = "*" notebook = "*" -qtconsole = "*" [[package]] name = "jupyter-client" @@ -1620,6 +1385,7 @@ version = "8.6.0" description = "Jupyter protocol implementation and client libraries" optional = false python-versions = ">=3.8" +groups = ["dev", "docs"] files = [ {file = "jupyter_client-8.6.0-py3-none-any.whl", hash = "sha256:909c474dbe62582ae62b758bca86d6518c85234bdee2d908c778db6d72f39d99"}, {file = "jupyter_client-8.6.0.tar.gz", hash = "sha256:0642244bb83b4764ae60d07e010e15f0e2d275ec4e918a8f7b80fbbef3ca60c7"}, @@ -1635,7 +1401,7 @@ traitlets = ">=5.3" [package.extras] docs = ["ipykernel", "myst-parser", "pydata-sphinx-theme", "sphinx (>=4)", "sphinx-autodoc-typehints", "sphinxcontrib-github-alt", "sphinxcontrib-spelling"] -test = ["coverage", "ipykernel (>=6.14)", "mypy", "paramiko", "pre-commit", "pytest", "pytest-cov", "pytest-jupyter[client] (>=0.4.1)", "pytest-timeout"] +test = ["coverage", "ipykernel (>=6.14)", "mypy", "paramiko ; sys_platform == \"win32\"", "pre-commit", "pytest", "pytest-cov", "pytest-jupyter[client] (>=0.4.1)", "pytest-timeout"] [[package]] name = "jupyter-console" @@ -1643,6 +1409,7 @@ version = "6.6.3" description = "Jupyter terminal console" optional = false python-versions = ">=3.7" +groups = ["dev"] files = [ {file = "jupyter_console-6.6.3-py3-none-any.whl", hash = "sha256:309d33409fcc92ffdad25f0bcdf9a4a9daa61b6f341177570fdac03de5352485"}, {file = "jupyter_console-6.6.3.tar.gz", hash = "sha256:566a4bf31c87adbfadf22cdf846e3069b59a71ed5da71d6ba4d8aaad14a53539"}, @@ -1667,6 +1434,7 @@ version = "5.5.0" description = "Jupyter core package. A base package on which Jupyter projects rely." optional = false python-versions = ">=3.8" +groups = ["dev", "docs"] files = [ {file = "jupyter_core-5.5.0-py3-none-any.whl", hash = "sha256:e11e02cd8ae0a9de5c6c44abf5727df9f2581055afe00b22183f621ba3585805"}, {file = "jupyter_core-5.5.0.tar.gz", hash = "sha256:880b86053bf298a8724994f95e99b99130659022a4f7f45f563084b6223861d3"}, @@ -1687,6 +1455,7 @@ version = "0.9.0" description = "Jupyter Event System library" optional = false python-versions = ">=3.8" +groups = ["dev"] files = [ {file = "jupyter_events-0.9.0-py3-none-any.whl", hash = "sha256:d853b3c10273ff9bc8bb8b30076d65e2c9685579db736873de6c2232dde148bf"}, {file = "jupyter_events-0.9.0.tar.gz", hash = "sha256:81ad2e4bc710881ec274d31c6c50669d71bbaa5dd9d01e600b56faa85700d399"}, @@ -1712,6 +1481,7 @@ version = "2.2.1" description = "Multi-Language Server WebSocket proxy for Jupyter Notebook/Lab server" optional = false python-versions = ">=3.8" +groups = ["dev"] files = [ {file = 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platform_python_implementation != \"PyPy\"", "pytest-ruff"] [metadata] -lock-version = "2.0" -python-versions = ">=3.8.1,<3.13" -content-hash = "eb487712a6b267301a3c7a13eadfcd36f4d4e27c70a5fbec5cd5343f83de6e96" +lock-version = "2.1" +python-versions = ">=3.9,<3.13" +content-hash = "58ef489c09c78a269c155e14550911105811823410eedccc8b7cdf628d95d208" diff --git a/pyproject.toml b/pyproject.toml index e885eb4..32223aa 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -15,7 +15,6 @@ repository = "https://github.com/py-why/pywhy-llm" classifiers = [ 'Development Status :: 4 - Beta', 'License :: OSI Approved :: MIT License', - 'Programming Language :: Python :: 3.8', 'Programming Language :: Python :: 3.9', 'Programming Language :: Python :: 3.10', 'Programming Language :: Python :: 3.11', @@ -34,7 +33,7 @@ include = ['docs', 'tests', 'CONTRIBUTING.md', 'GOVERNANCE.md', 'MAINTAINERS.md' readme = 'README.md' [build-system] -requires = ["poetry-core>=1.0.0"] +requires = ["poetry-core>=2.1.1"] build-backend = "poetry.core.masonry.api" [tool.poetry-dynamic-versioning] @@ -50,22 +49,22 @@ files = ["pywhyllm/__init__.py"] # Dependency compatibility notes: # [tool.poetry.dependencies] -python = ">=3.8.1,<3.13" -networkx = ">=2.8.5" +python = ">=3.9,<3.13" +# to support python 3.9 with networkx +networkx = "<=3.2.1" guidance = ">=0.2" [tool.poetry.group.dev.dependencies] -poethepoet = "^0.16.0" -flake8 = "^4.0.1" -black = { version = "^22.6.0", extras = ["jupyter"] } -isort = "^5.10.1" -pytest = "^7.1.2" -pytest-cov = "^3.0.0" -pytest-split = "^0.8.0" -nbformat = "^5.4.0" -jupyter = "^1.0.0" -flaky = "^3.7.0" -mypy = "^0.971" +poethepoet = "^0.33.0" +flake8 = "^7.1.2" +black = { version = "^25.1.0", extras = ["jupyter"] } +isort = "^6.0.1" +pytest = "^7.4.3" +pytest-cov = "^6.0.0" +pytest-split = "^0.10.0" +nbformat = "^5.10.4" +jupyter = "^1.1.1" +mypy = "^1.15.0" [tool.poetry.group.docs] optional = true diff --git a/pywhyllm/tests/test_notebooks.py b/pywhyllm/tests/test_notebooks.py index 93a2b0c..dfa56a4 100644 --- a/pywhyllm/tests/test_notebooks.py +++ b/pywhyllm/tests/test_notebooks.py @@ -13,6 +13,9 @@ } ignore_notebooks = [ + # notebooks that require openai key to run + "examples.ipynb", + "walkthrough.ipynb" ] # Adding the root folder to the python path so that jupyter notebooks