autogen/setup.py

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import setuptools
import os
here = os.path.abspath(os.path.dirname(__file__))
with open("README.md", "r", encoding="UTF-8") as fh:
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long_description = fh.read()
# Get the code version
version = {}
with open(os.path.join(here, "flaml/version.py")) as fp:
exec(fp.read(), version)
__version__ = version["__version__"]
install_requires = [
"NumPy>=1.17.0rc1",
]
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setuptools.setup(
name="FLAML",
version=__version__,
author="Microsoft Corporation",
author_email="hpo@microsoft.com",
description="A fast library for automated machine learning and tuning",
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long_description=long_description,
long_description_content_type="text/markdown",
url="https://github.com/microsoft/FLAML",
packages=setuptools.find_packages(include=["flaml*"]),
package_data={
"flaml.default": ["*/*.json"],
},
include_package_data=True,
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install_requires=install_requires,
extras_require={
"automl": [
"lightgbm>=2.3.1",
"xgboost>=0.90",
"scipy>=1.4.1",
"pandas>=1.1.4",
"scikit-learn>=0.24",
],
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"notebook": [
"jupyter",
],
"spark": [
Support spark dataframe as input dataset and spark models as estimators (#934) * add basic support to Spark dataframe add support to SynapseML LightGBM model update to pyspark>=3.2.0 to leverage pandas_on_Spark API * clean code, add TODOs * add sample_train_data for pyspark.pandas dataframe, fix bugs * improve some functions, fix bugs * fix dict change size during iteration * update model predict * update LightGBM model, update test * update SynapseML LightGBM params * update synapseML and tests * update TODOs * Added support to roc_auc for spark models * Added support to score of spark estimator * Added test for automl score of spark estimator * Added cv support to pyspark.pandas dataframe * Update test, fix bugs * Added tests * Updated docs, tests, added a notebook * Fix bugs in non-spark env * Fix bugs and improve tests * Fix uninstall pyspark * Fix tests error * Fix java.lang.OutOfMemoryError: Java heap space * Fix test_performance * Update test_sparkml to test_0sparkml to use the expected spark conf * Remove unnecessary widgets in notebook * Fix iloc java.lang.StackOverflowError * fix pre-commit * Added params check for spark dataframes * Refactor code for train_test_split to a function * Update train_test_split_pyspark * Refactor if-else, remove unnecessary code * Remove y from predict, remove mem control from n_iter compute * Update workflow * Improve _split_pyspark * Fix test failure of too short training time * Fix typos, improve docstrings * Fix index errors of pandas_on_spark, add spark loss metric * Fix typo of ndcgAtK * Update NDCG metrics and tests * Remove unuseful logger * Use cache and count to ensure consistent indexes * refactor for merge maain * fix errors of refactor * Updated SparkLightGBMEstimator and cache * Updated config2params * Remove unused import * Fix unknown parameters * Update default_estimator_list * Add unit tests for spark metrics
2023-03-26 03:59:46 +08:00
"pyspark>=3.2.0",
"joblibspark>=0.5.0",
"joblib<1.3.0", # temp solution for joblib 1.3.0 issue, no need once https://github.com/joblib/joblib-spark/pull/48 is merged
],
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"test": [
"lightgbm>=2.3.1",
"xgboost>=0.90",
"scipy>=1.4.1",
"pandas>=1.1.4",
"scikit-learn>=0.24",
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"thop",
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"pytest>=6.1.1",
"coverage>=5.3",
"pre-commit",
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"torch",
"torchvision",
"catboost>=0.26,<1.2",
"rgf-python",
"optuna==2.8.0",
"openml",
"statsmodels>=0.12.2",
"psutil==5.8.0",
"dataclasses",
"transformers[torch]==4.26",
"datasets",
"nltk",
"rouge_score",
"hcrystalball==0.1.10",
"seqeval",
"pytorch-forecasting>=0.9.0,<=0.10.1",
"mlflow",
Support spark dataframe as input dataset and spark models as estimators (#934) * add basic support to Spark dataframe add support to SynapseML LightGBM model update to pyspark>=3.2.0 to leverage pandas_on_Spark API * clean code, add TODOs * add sample_train_data for pyspark.pandas dataframe, fix bugs * improve some functions, fix bugs * fix dict change size during iteration * update model predict * update LightGBM model, update test * update SynapseML LightGBM params * update synapseML and tests * update TODOs * Added support to roc_auc for spark models * Added support to score of spark estimator * Added test for automl score of spark estimator * Added cv support to pyspark.pandas dataframe * Update test, fix bugs * Added tests * Updated docs, tests, added a notebook * Fix bugs in non-spark env * Fix bugs and improve tests * Fix uninstall pyspark * Fix tests error * Fix java.lang.OutOfMemoryError: Java heap space * Fix test_performance * Update test_sparkml to test_0sparkml to use the expected spark conf * Remove unnecessary widgets in notebook * Fix iloc java.lang.StackOverflowError * fix pre-commit * Added params check for spark dataframes * Refactor code for train_test_split to a function * Update train_test_split_pyspark * Refactor if-else, remove unnecessary code * Remove y from predict, remove mem control from n_iter compute * Update workflow * Improve _split_pyspark * Fix test failure of too short training time * Fix typos, improve docstrings * Fix index errors of pandas_on_spark, add spark loss metric * Fix typo of ndcgAtK * Update NDCG metrics and tests * Remove unuseful logger * Use cache and count to ensure consistent indexes * refactor for merge maain * fix errors of refactor * Updated SparkLightGBMEstimator and cache * Updated config2params * Remove unused import * Fix unknown parameters * Update default_estimator_list * Add unit tests for spark metrics
2023-03-26 03:59:46 +08:00
"pyspark>=3.2.0",
"joblibspark>=0.5.0",
"nbconvert",
"nbformat",
"ipykernel",
"pytorch-lightning<1.9.1", # test_forecast_panel
Factor out time series-related functionality into a time series Task object (#989) * Refactor into automl subpackage Moved some of the packages into an automl subpackage to tidy before the task-based refactor. This is in response to discussions with the group and a comment on the first task-based PR. Only changes here are moving subpackages and modules into the new automl, fixing imports to work with this structure and fixing some dependencies in setup.py. * Fix doc building post automl subpackage refactor * Fix broken links in website post automl subpackage refactor * Fix broken links in website post automl subpackage refactor * Remove vw from test deps as this is breaking the build * Move default back to the top-level I'd moved this to automl as that's where it's used internally, but had missed that this is actually part of the public interface so makes sense to live where it was. * Re-add top level modules with deprecation warnings flaml.data, flaml.ml and flaml.model are re-added to the top level, being re-exported from flaml.automl for backwards compatability. Adding a deprecation warning so that we can have a planned removal later. * Fix model.py line-endings * WIP * WIP - Notes below Got to the point where the methods from AutoML are pulled to GenericTask. Started removing private markers and removing the passing of automl to these methods. Done with decide_split_type, started on prepare_data. Need to do the others after * Re-add generic_task * Most of the merge done, test_forecast_automl fit succeeds, fails at predict() * Remaining fixes - test_forecast.py passes * Comment out holidays-related code as it's not currently used * Further holidays cleanup * Fix imports in a test * tidy up validate_data in time series task * Test fixes * Fix tests: add Task.__str__ * Fix tests: test for ray.ObjectRef * Hotwire TS_Sklearn wrapper to fix test fail * Attempt at test fix * Fix test where val_pred_y is a list * Attempt to fix remaining tests * Push to retrigger tests * Push to retrigger tests * Push to retrigger tests * Push to retrigger tests * Remove plots from automl/test_forecast * Remove unused data size field from Task * Fix import for CLASSIFICATION in notebook * Monkey patch TFT to avoid plotting, to fix tests on MacOS * Monkey patch TFT to avoid plotting v2, to fix tests on MacOS * Monkey patch TFT to avoid plotting v2, to fix tests on MacOS * Fix circular import * remove redundant code in task.py post-merge * Fix test: set svd_solver="full" in PCA * Update flaml/automl/data.py Co-authored-by: Chi Wang <wang.chi@microsoft.com> * Fix review comments * Fix task -> str in custom learner constructor * Remove unused CLASSIFICATION imports * Hotwire TS_Sklearn wrapper to fix test fail by setting optimizer_for_horizon == False * Revert changes to the automl_classification and pin FLAML version * Fix imports in reverted notebook * Fix FLAML version in automl notebooks * Fix ml.py line endings * Fix CLASSIFICATION task import in automl_classification notebook * Uncomment pip install in notebook and revert import Not convinced this will work because of installing an older version of the package into the environment in which we're running the tests, but let's see. * Revert c6a5dd1a0 * Fix get_classification_objective import in suggest.py * Remove hcrystallball docs reference in TS_Sklearn * Merge markharley:extract-task-class-from-automl into this * Fix import, remove smooth.py * Fix dependencies to fix TFT fail on Windows Python 3.8 and 3.9 * Add tensorboardX dependency to fix TFT fail on Windows Python 3.8 and 3.9 * Set pytorch-lightning==1.9.0 to fix TFT fail on Windows Python 3.8 and 3.9 * Set pytorch-lightning==1.9.0 to fix TFT fail on Windows Python 3.8 and 3.9 * Disable PCA reduction of lagged features for now, to fix svd convervence fail * Merge flaml/main into time_series_task * Attempt to fix formatting * Attempt to fix formatting * tentatively implement holt-winters-no covariates * fix forecast method, clean class * checking external regressors too * update test forecast * remove duplicated test file, re-add sarimax, search space cleanup * Update flaml/automl/model.py removed links. Most important one probably was: https://robjhyndman.com/hyndsight/ets-regressors/ Co-authored-by: Chi Wang <wang.chi@microsoft.com> * prevent short series * add docs * First attempt at merging Holt-Winters * Linter fix * Add holt-winters to TimeSeriesTask.estimators * Fix spark test fail * Attempt to fix another spark test fail * Attempt to fix another spark test fail * Change Black max line length to 127 * Change Black max line length to 120 * Add logging for ARIMA params, clean up time series models inheritance * Add more logging for missing ARIMA params * Remove a meaningless test causing a fail, add stricter check on ARIMA params * Fix a bug in HoltWinters * A pointless change to hopefully trigger the on and off KeyError in ARIMA.fit() * Fix formatting * Attempt to fix formatting * Attempt to fix formatting * Attempt to fix formatting * Attempt to fix formatting * Add type annotations to _train_with_config() in state.py * Add type annotations to prepare_sample_train_data() in state.py * Add docstring for time_col argument of AutoML.fit() * Address @sonichi's comments on PR * Fix formatting * Fix formatting * Reduce test time budget * Reduce test time budget * Increase time budget for the test to pass * Remove redundant imports * Remove more redundant imports * Minor fixes of points raised by Qingyun * Try to fix pandas import fail * Try to fix pandas import fail, again * Try to fix pandas import fail, again * Try to fix pandas import fail, again * Try to fix pandas import fail, again * Try to fix pandas import fail, again * Try to fix pandas import fail, again * Try to fix pandas import fail, again * Try to fix pandas import fail, again * Try to fix pandas import fail, again * Try to fix pandas import fail, again * Formatting fixes * More formatting fixes * Added test that loops over TS models to ensure coverage * Fix formatting issues * Fix more formatting issues * Fix random fail in check * Put back in tests for ARIMA predict without fit * Put back in tests for lgbm * Update test/test_model.py cover dedup * Match target length to X length in missing test --------- Co-authored-by: Mark Harley <mark.harley@transferwise.com> Co-authored-by: Mark Harley <mharley.code@gmail.com> Co-authored-by: Qingyun Wu <qingyun.wu@psu.edu> Co-authored-by: Chi Wang <wang.chi@microsoft.com> Co-authored-by: Andrea W <a.ruggerini@ammagamma.com> Co-authored-by: Andrea Ruggerini <nescio.adv@gmail.com> Co-authored-by: Egor Kraev <Egor.Kraev@tw.com> Co-authored-by: Li Jiang <bnujli@gmail.com>
2023-06-19 12:20:32 +01:00
"tensorboardX==2.6", # test_forecast_panel
"requests<2.29.0", # https://github.com/docker/docker-py/issues/3113
"packaging",
"pydantic==1.10.9",
"sympy",
"wolframalpha",
"joblib<1.3.0", # temp solution for joblib 1.3.0 issue, no need once https://github.com/joblib/joblib-spark/pull/48 is merged
],
"catboost": ["catboost>=0.26"],
"blendsearch": [
"optuna==2.8.0",
"packaging",
],
"ray": [
"ray[tune]~=1.13",
],
"azureml": [
"azureml-mlflow",
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],
"nni": [
"nni",
],
Add ChaCha (#92) * pickle the AutoML object * get best model per estimator * test deberta * stateless API * pickle the AutoML object * get best model per estimator * test deberta * stateless API * prevent divide by zero * test roberta * BlendSearchTuner * sync * version number * update gitignore * delta time * reindex columns when dropping int-indexed columns * add seed * add seed in Args * merge * init upload of ChaCha * remove redundancy * add back catboost * improve AutoVW API * set min_resource_lease in VWOnlineTrial * docstr * rename * docstr * add docstr * improve API and documentation * fix name * docstr * naming * remove max_resource in scheduler * add TODO in flow2 * remove redundancy in rearcher * add input type * adapt code from ray.tune * move files * naming * documentation * fix import error * fix format issues * remove cb in worse than test * improve _generate_all_comb * remove ray tune * naming * VowpalWabbitTrial * import error * import error * merge test code * scheduler import * fix import * remove * import, minor bug and version * Float or Categorical * fix default * add test_autovw.py * add vowpalwabbit and openml * lint * reorg * lint * indent * add autovw notebook * update notebook * update log msg and autovw notebook * update autovw notebook * update autovw notebook * add available strings for model_select_policy * string for metric * Update vw format in flaml/onlineml/trial.py Co-authored-by: olgavrou <olgavrou@gmail.com> * make init_config optional * add _setup_trial_runner and update notebook * space Co-authored-by: Chi Wang (MSR) <chiw@microsoft.com> Co-authored-by: Chi Wang <wang.chi@microsoft.com> Co-authored-by: Qingyun Wu <qiw@microsoft.com> Co-authored-by: olgavrou <olgavrou@gmail.com>
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"vw": [
"vowpalwabbit>=8.10.0, <9.0.0",
"scikit-learn",
],
"hf": [
"transformers[torch]==4.26",
"datasets",
"nltk",
"rouge_score",
"seqeval",
],
"nlp": [ # for backward compatibility; hf is the new option name
"transformers[torch]==4.26",
"datasets",
"nltk",
"rouge_score",
"seqeval",
],
"ts_forecast": [
"holidays<0.14", # to prevent installation error for prophet
"prophet>=1.0.1",
"statsmodels>=0.12.2",
"hcrystalball==0.1.10",
],
"forecast": [
"holidays<0.14", # to prevent installation error for prophet
"prophet>=1.0.1",
"statsmodels>=0.12.2",
"hcrystalball==0.1.10",
time series forecasting with panel datasets (#541) * time series forecasting with panel datasets - integrate Temporal Fusion Transformer as a learner based on pytorchforecasting Signed-off-by: Kevin Chen <chenkevin.8787@gmail.com> * update setup.py Signed-off-by: Kevin Chen <chenkevin.8787@gmail.com> * update test_forecast.py Signed-off-by: Kevin Chen <chenkevin.8787@gmail.com> * update setup.py Signed-off-by: Kevin Chen <chenkevin.8787@gmail.com> * update setup.py Signed-off-by: Kevin Chen <chenkevin.8787@gmail.com> * update model.py and test_forecast.py - remove blank lines Signed-off-by: Kevin Chen <chenkevin.8787@gmail.com> * update model.py to prevent errors Signed-off-by: Kevin Chen <chenkevin.8787@gmail.com> * update automl.py and data.py - change forecast task name - update documentation for fit() method Signed-off-by: Kevin Chen <chenkevin.8787@gmail.com> * update test_forecast.py Signed-off-by: Kevin Chen <chenkevin.8787@gmail.com> * update test_forecast.py - add performance test - use 'fit_kwargs_by_estimator' Signed-off-by: Kevin Chen <chenkevin.8787@gmail.com> * add time index function Signed-off-by: Kevin Chen <chenkevin.8787@gmail.com> * update test_forecast.py performance test Signed-off-by: Kevin Chen <chenkevin.8787@gmail.com> * update data.py Signed-off-by: Kevin Chen <chenkevin.8787@gmail.com> * update automl.py Signed-off-by: Kevin Chen <chenkevin.8787@gmail.com> * update data.py to prevent type error Signed-off-by: Kevin Chen <chenkevin.8787@gmail.com> * update setup.py Signed-off-by: Kevin Chen <chenkevin.8787@gmail.com> * update for pytorch forecasting tft on panel datasets Signed-off-by: Kevin Chen <chenkevin.8787@gmail.com> * update automl.py documentations Signed-off-by: Kevin Chen <chenkevin.8787@gmail.com> * - rename estimator - add 'gpu_per_trial' for tft estimator Signed-off-by: Kevin Chen <chenkevin.8787@gmail.com> * update test_forecast.py Signed-off-by: Kevin Chen <chenkevin.8787@gmail.com> * include ts panel forecasting as an example Signed-off-by: Kevin Chen <chenkevin.8787@gmail.com> * update model.py Signed-off-by: Kevin Chen <chenkevin.8787@gmail.com> * update documentations Signed-off-by: Kevin Chen <chenkevin.8787@gmail.com> * update automl_time_series_forecast.ipynb Signed-off-by: Kevin Chen <chenkevin.8787@gmail.com> * update documentations Signed-off-by: Kevin Chen <chenkevin.8787@gmail.com> * "weights_summary" argument deprecated and removed for pl.Trainer() Signed-off-by: Kevin Chen <chenkevin.8787@gmail.com> * update model.py tft estimator prediction method Signed-off-by: Kevin Chen <chenkevin.8787@gmail.com> * update model.py Signed-off-by: Kevin Chen <chenkevin.8787@gmail.com> * update `fit_kwargs` documentation Signed-off-by: Kevin Chen <chenkevin.8787@gmail.com> * update automl.py Signed-off-by: Kevin Chen <chenkevin.8787@gmail.com> Signed-off-by: Kevin Chen <chenkevin.8787@gmail.com> Co-authored-by: Chi Wang <wang.chi@microsoft.com>
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"pytorch-forecasting>=0.9.0",
Factor out time series-related functionality into a time series Task object (#989) * Refactor into automl subpackage Moved some of the packages into an automl subpackage to tidy before the task-based refactor. This is in response to discussions with the group and a comment on the first task-based PR. Only changes here are moving subpackages and modules into the new automl, fixing imports to work with this structure and fixing some dependencies in setup.py. * Fix doc building post automl subpackage refactor * Fix broken links in website post automl subpackage refactor * Fix broken links in website post automl subpackage refactor * Remove vw from test deps as this is breaking the build * Move default back to the top-level I'd moved this to automl as that's where it's used internally, but had missed that this is actually part of the public interface so makes sense to live where it was. * Re-add top level modules with deprecation warnings flaml.data, flaml.ml and flaml.model are re-added to the top level, being re-exported from flaml.automl for backwards compatability. Adding a deprecation warning so that we can have a planned removal later. * Fix model.py line-endings * WIP * WIP - Notes below Got to the point where the methods from AutoML are pulled to GenericTask. Started removing private markers and removing the passing of automl to these methods. Done with decide_split_type, started on prepare_data. Need to do the others after * Re-add generic_task * Most of the merge done, test_forecast_automl fit succeeds, fails at predict() * Remaining fixes - test_forecast.py passes * Comment out holidays-related code as it's not currently used * Further holidays cleanup * Fix imports in a test * tidy up validate_data in time series task * Test fixes * Fix tests: add Task.__str__ * Fix tests: test for ray.ObjectRef * Hotwire TS_Sklearn wrapper to fix test fail * Attempt at test fix * Fix test where val_pred_y is a list * Attempt to fix remaining tests * Push to retrigger tests * Push to retrigger tests * Push to retrigger tests * Push to retrigger tests * Remove plots from automl/test_forecast * Remove unused data size field from Task * Fix import for CLASSIFICATION in notebook * Monkey patch TFT to avoid plotting, to fix tests on MacOS * Monkey patch TFT to avoid plotting v2, to fix tests on MacOS * Monkey patch TFT to avoid plotting v2, to fix tests on MacOS * Fix circular import * remove redundant code in task.py post-merge * Fix test: set svd_solver="full" in PCA * Update flaml/automl/data.py Co-authored-by: Chi Wang <wang.chi@microsoft.com> * Fix review comments * Fix task -> str in custom learner constructor * Remove unused CLASSIFICATION imports * Hotwire TS_Sklearn wrapper to fix test fail by setting optimizer_for_horizon == False * Revert changes to the automl_classification and pin FLAML version * Fix imports in reverted notebook * Fix FLAML version in automl notebooks * Fix ml.py line endings * Fix CLASSIFICATION task import in automl_classification notebook * Uncomment pip install in notebook and revert import Not convinced this will work because of installing an older version of the package into the environment in which we're running the tests, but let's see. * Revert c6a5dd1a0 * Fix get_classification_objective import in suggest.py * Remove hcrystallball docs reference in TS_Sklearn * Merge markharley:extract-task-class-from-automl into this * Fix import, remove smooth.py * Fix dependencies to fix TFT fail on Windows Python 3.8 and 3.9 * Add tensorboardX dependency to fix TFT fail on Windows Python 3.8 and 3.9 * Set pytorch-lightning==1.9.0 to fix TFT fail on Windows Python 3.8 and 3.9 * Set pytorch-lightning==1.9.0 to fix TFT fail on Windows Python 3.8 and 3.9 * Disable PCA reduction of lagged features for now, to fix svd convervence fail * Merge flaml/main into time_series_task * Attempt to fix formatting * Attempt to fix formatting * tentatively implement holt-winters-no covariates * fix forecast method, clean class * checking external regressors too * update test forecast * remove duplicated test file, re-add sarimax, search space cleanup * Update flaml/automl/model.py removed links. Most important one probably was: https://robjhyndman.com/hyndsight/ets-regressors/ Co-authored-by: Chi Wang <wang.chi@microsoft.com> * prevent short series * add docs * First attempt at merging Holt-Winters * Linter fix * Add holt-winters to TimeSeriesTask.estimators * Fix spark test fail * Attempt to fix another spark test fail * Attempt to fix another spark test fail * Change Black max line length to 127 * Change Black max line length to 120 * Add logging for ARIMA params, clean up time series models inheritance * Add more logging for missing ARIMA params * Remove a meaningless test causing a fail, add stricter check on ARIMA params * Fix a bug in HoltWinters * A pointless change to hopefully trigger the on and off KeyError in ARIMA.fit() * Fix formatting * Attempt to fix formatting * Attempt to fix formatting * Attempt to fix formatting * Attempt to fix formatting * Add type annotations to _train_with_config() in state.py * Add type annotations to prepare_sample_train_data() in state.py * Add docstring for time_col argument of AutoML.fit() * Address @sonichi's comments on PR * Fix formatting * Fix formatting * Reduce test time budget * Reduce test time budget * Increase time budget for the test to pass * Remove redundant imports * Remove more redundant imports * Minor fixes of points raised by Qingyun * Try to fix pandas import fail * Try to fix pandas import fail, again * Try to fix pandas import fail, again * Try to fix pandas import fail, again * Try to fix pandas import fail, again * Try to fix pandas import fail, again * Try to fix pandas import fail, again * Try to fix pandas import fail, again * Try to fix pandas import fail, again * Try to fix pandas import fail, again * Try to fix pandas import fail, again * Formatting fixes * More formatting fixes * Added test that loops over TS models to ensure coverage * Fix formatting issues * Fix more formatting issues * Fix random fail in check * Put back in tests for ARIMA predict without fit * Put back in tests for lgbm * Update test/test_model.py cover dedup * Match target length to X length in missing test --------- Co-authored-by: Mark Harley <mark.harley@transferwise.com> Co-authored-by: Mark Harley <mharley.code@gmail.com> Co-authored-by: Qingyun Wu <qingyun.wu@psu.edu> Co-authored-by: Chi Wang <wang.chi@microsoft.com> Co-authored-by: Andrea W <a.ruggerini@ammagamma.com> Co-authored-by: Andrea Ruggerini <nescio.adv@gmail.com> Co-authored-by: Egor Kraev <Egor.Kraev@tw.com> Co-authored-by: Li Jiang <bnujli@gmail.com>
2023-06-19 12:20:32 +01:00
"pytorch-lightning==1.9.0",
"tensorboardX==2.6",
],
"benchmark": ["catboost>=0.26", "psutil==5.8.0", "xgboost==1.3.3", "pandas==1.1.4"],
"openai": ["openai==0.27.8", "diskcache"],
raise error when msg is invalid; fix docstr; improve ResponsiveAgent; update doc and packaging; capture ipython output; find code blocks with llm when regex fails. (#1154) * autogen.agent -> autogen.agentchat * bug fix in portfolio * notebook * timeout * timeout * infer lang; close #1150 * timeout * message context * context handling * add sender to generate_reply * clean up the receive function * move mathchat to contrib * contrib * last_message * Add OptiGuide: agent and notebook * Optiguide notebook: add figures and URL 1. figures and code points to remote URL 2. simplify the prompt for the interpreter, because all information is already in the chat history. * Update name: Agent -> GenericAgent * Update notebook * Rename: GenericAgent -> ResponsiveAgent * Rebase to autogen.agentchat * OptiGuide: Comment, sytle, and notebook updates * simplify optiguide * raise error when msg is invalid; fix docstr * allow return None for generate_reply() * update_system_message * test update_system_message * simplify optiguide * simplify optiguide * simplify optiguide * simplify optiguide * move test * add test and fix bug * doc update * doc update * doc update * color * optiguide * prompt * test danger case * packaging * docker * remove path in traceback * capture ipython output * simplify * find code blocks with llm * find code with llm * order * order * fix bug in context handling * print executing msg * print executing msg * test find code * test find code * disable find_code * default_auto_reply * default auto reply * remove optiguide * remove -e --------- Co-authored-by: Beibin Li <beibin79@gmail.com>
2023-07-31 19:22:30 -07:00
"autogen": ["openai==0.27.8", "diskcache", "termcolor"],
"mathchat": ["openai==0.27.8", "diskcache", "termcolor", "sympy", "pydantic==1.10.9", "wolframalpha"],
"retrievechat": [
"openai==0.27.8",
"diskcache",
"termcolor",
"chromadb",
"tiktoken",
"sentence_transformers",
],
"synapse": [
"joblibspark>=0.5.0",
"optuna==2.8.0",
"pyspark>=3.2.0",
"joblib<1.3.0", # temp solution for joblib 1.3.0 issue, no need once https://github.com/joblib/joblib-spark/pull/48 is merged
],
"autozero": ["scikit-learn", "pandas", "packaging"],
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},
classifiers=[
"Programming Language :: Python :: 3",
"License :: OSI Approved :: MIT License",
"Operating System :: OS Independent",
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],
python_requires=">=3.6",
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)