feat: create multi_model wrapper and kCollaborator #173
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| modelers: list['h1st.MLModeler']=[], | ||
| ensemble_modeler: 'h1st.Modeler' = RuleBasedModeler(MajorityVotingEnsemble), | ||
| models: list['h1st.PredictiveModel']=None, | ||
| inject_x_in_ensembler: bool=False) -> 'BaseOracle': |
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this one mean build_model return a string value?
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No it returns a kCollaboratorModel. Forgot to fix type annotation. Corrected now, but also strings instead of importing the actual class are accept methods of type annotating.
| super().__init__() | ||
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| def build_model(self, prepared_data: dict, | ||
| modelers: list['h1st.MLModeler']=[], |
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this one should be
List[MLModeler]
with
from typing import List
from h1st import MLModeler
hiro-v
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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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