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feat: create oracle similar to collaborator, add hyperparam to xgb #178

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nubs01
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@nubs01 nubs01 commented Jan 11, 2023

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@phamhoangtuan phamhoangtuan merged commit 948597d into feat/add_xgboost_model Jan 11, 2023
@phamhoangtuan phamhoangtuan deleted the feat/refactor_oracle_and_xgboost branch January 11, 2023 06:55
hiro-v pushed a commit that referenced this pull request Feb 23, 2023
…ge_with_k1st (#186)

* feat: add user code to user Oracle

* refactor: create OracleModel class, split students and ensemble to models and modelers files

* fix: add condition to build_model func in modeler

* fix: update modeler build_model function and model predict function

* fix: update persist and load function of OracleModel

* fix: fix bug version is overlapped, remove unused Oracle class

* refactor: change param name to be clearer, split persist and load function to more smaller functions to be clearer and easier to read

* fix: rename ensemble to ensembler, add Oracle as backward compability of OracleModel, add func to gen train data for ensembler

* fix: fix FuzzyModel process rules function

* fix: update modeler build_model func comment, update test_oracle UT file

* feat: add evaluation func to modeler

* fix: use logger from loguru, not logging; comment out UT for ts oracle, update test case for fuzzy model

* feat: add ipython notebook to run Oracle with iris data

* feat: add ipython notebook to run oracle with azure iot data

* fix: update tutorial to use new Oracle code

* fix: remove unused lib in TimeSeriesOracle

* feat: add loguru to pyproject

* feat: create multi_model wrapper and kCollaborator (#173)

* feat: create multi_model wrapper and kCollaborator

* fix: type annotations

* fix: debug collaborator & multi model

* feat: add compelete user code for collaborator and multi model

* fix: change OracleModel to Oracle

* feat: add xgboost model wrapper

* fix: remove tensorflow lib

* fix: change TimeSeriesOracleModel to TimeSeriesOracle

* fix: update Model and Modeler code for XGBoost based on Roshan code

* fix: change x to X in k-collaborator, reformat python code

* feat: add Logical OR Ensemble model and modeler

* feat: create oracle similar to collaborator, add hyperparam ingestion for xgboost modeler (#178)

* fix: update var type in xgboost modeler

* fix: add name attribute to XGBoost model

* fix: add name attribute to FuzzyModel, fix approach to create dataframe from list of series in MultiModel

* fix: fix input data in k-collaborator, fix build_model of ensemble

* fix: init ensemble class

* Feat/improved models for build pipeline (#179)

* feat: add some useful ensemble model/modelers

* feat: fix xgboost regression building and create classifier

* feat: abstract data_key and output_key for all predicitive models

* feat: allow collaborator ensemble modeling with explicit labelled_data field

* feat: allow passed hyperparams to override grid search in xgboost

* fix: fix typo, add name to Model classes, change xgboost xgrid search result to int

* fix: add name property to Model, not Modeler

* fix: get student train data

* fix: k-colab can deal with prepared_data is None then only produce fuzzy model

* fix: deal with K-Oracle without prepared data then return only teacher fuzzy model

* fix: return empty df if no df to concat

* fix: add teacher model to Oracle

* fix: XGBoost if no result key then get first column of y_train data as result_key

* fix: remove xgb grid search for hyperparam

* fix: add input_features to stats when building model

* fix: add input_features as columns of prepared_data X_train

* fix: deal with None prepared_data in k-colab when saving input_features stats, deal with None ensemble in Oracle

* fix: order input_features when persist multimodel

* fix: revert student_models.py

* fix: fix NA value when concat list of Series

* fix: convert numpy.ndarray to pandas.DataFrame when running LogisticRegression and RandomForest models predict

* fix: set default value for xgb

* fix: add support parallel running when building k-colab and k-oracle

* feat: Refactor XGB Model

* fix: log

* fix: evaluate func

* refactor: Random forest model

* feat: update random forest student model

* refactor: Logistic regression model

* fix(multi-model): fix logic to append pred result

---------

Co-authored-by: Roshan Nanu <roshan.nanu@gmail.com>
Co-authored-by: Hung Vo <vphungkhtn@gmail.com>
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