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Version 0.9

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Version 0.11.0

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  • Support for Python 3.4 and below has been officially dropped. Also support for scikit-learn 0.20 or below has been dropped.
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  • The support of a metric function with the signature score_func(y_true, y_pred) for scoring parameter has been dropped.
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  • Refine StackingEstimator for not stacking NaN/Infinity predication probabilities.
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  • Fix a bug that population doesn't persist by warm_start=True when max_time_mins is not default value.
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  • Now the random_state parameter in TPOT is used for pipeline evaluation instead of using a fixed random seed of 42 before. The set_param_recursive function has been moved to export_utils.py and it can be used in exported codes for setting random_state recursively in scikit-learn Pipeline. It is used to set random_state in fitted_pipeline_ attribute and exported pipelines.
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  • TPOT can independently use generations and max_time_mins to limit the optimization process through using one of the parameters or both.
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  • .export() function will return string of exported pipeline if output filename is not specified.
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  • Add SGDClassifier and SGDRegressor into TPOT default configs.
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  • Documentation has been updated.
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Version 0.10.2

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  • TPOT v0.10.2 is the last version to support Python 2.7 and Python 3.4.
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  • Minor updates for fixing compatibility issues with the latest version of scikit-learn (version > 0.21) and xgboost (v0.90)
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  • Default value of template parameter is changed to None instead.
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  • Fix errors in documentation
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Version 0.10.1

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  • Add data_file_path option into expert function for replacing 'PATH/TO/DATA/FILE' to customized dataset path in exported scripts. (Related issue #838)
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  • Change python version in CI tests to 3.7
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  • Add CI tests for macOS.
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Version 0.10.0

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  • Add a new template option to specify a desired structure for machine learning pipeline in TPOT. Check TPOT API (it will be updated once it is merge to master branch).
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  • Add FeatureSetSelector operator into TPOT for feature selection based on priori export knowledge. Please check our preprint paper for more details (Note: it was named DatasetSelector in 1st version paper but we will rename to FeatureSetSelector in next version of the paper)
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  • Refine n_jobs parameter to accept value below -1. For n_jobs below -1, (n_cpus + 1 + n_jobs) are used. Thus for n_jobs = -2, all CPUs but one are used.
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  • Now memory parameter can create memory cache directory if it does not exist.
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  • Fix minor bugs.
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Version 0.9.6

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  • Fix a bug causing that max_time_mins parameter doesn't work when use_dask=True in TPOT 0.9.5
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  • Now TPOT saves best pareto values best pareto pipeline s in checkpoint folder
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  • TPOT raises ImportError if operators in the TPOT configuration are not available when verbosity>2
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  • Thank @PGijsbers for the suggestions. Now TPOT can save scores of individuals already evaluated in any generation even the evaluation process of that generation is interrupted/stopped. But it is noted that, in this case, TPOT will raise this warning message: WARNING: TPOT may not provide a good pipeline if TPOT is stopped/interrupted in a early generation., because the pipelines in early generation, e.g. 1st generation, are evolved/modified very limited times via evolutionary algorithm.
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  • Fix bugs in configuration of TPOTRegressor
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  • Error fixes in documentation
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Version 0.9.5

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    TPOT now supports integration with Dask for parallelization + smart caching. Big thanks to the Dask dev team for making this happen!

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    TPOT now supports for imputation/sparse matrices into predict and predict_proba functions.

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    TPOTClassifier and TPOTRegressor now follows scikit-learn estimator API.

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    We refined scoring parameter in TPOT API for accepting Scorer object.

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    We refined parameters in VarianceThreshold and FeatureAgglomeration.

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    TPOT now supports using memory caching within a Pipeline via a optional memory parameter.

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    We improved documentation of TPOT.

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Version 0.9

  • TPOT now supports sparse matrices with a new built-in TPOT configuration, "TPOT sparse". We are using a custom OneHotEncoder implementation that supports missing values and continuous features.

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    Version 0.8

    TPOT now allows you to set a subsample ratio of the training instance with the subsample parameter. For example, setting subsample=0.5 tells TPOT to create a fixed subsample of half of the training data for the pipeline optimization process. This parameter can be useful for speeding up the pipeline optimization process, but may give less accurate performance estimates from cross-validation.

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    TPOT now has more built-in configurations, including TPOT MDR and TPOT light, for both classification and regression problems.

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    TPOT now has more built-in configurations, including TPOT MDR and TPOT light, for both classification and regression problems.

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    TPOTClassifier and TPOTRegressor now expose three useful internal attributes, fitted_pipeline_, pareto_front_fitted_pipelines_, and evaluated_individuals_. These attributes are described in the API documentation.

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    TPOTClassifier and TPOTRegressor now expose three useful internal attributes, fitted_pipeline_, pareto_front_fitted_pipelines_, and evaluated_individuals_. These attributes are described in the API documentation.

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    Oh, TPOT now has thorough API documentation. Check it out!

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    Oh, TPOT now has thorough API documentation. Check it out!

  • Fixed a reproducibility issue where setting random_seed didn't necessarily result in the same results every time. This bug was present since TPOT v0.7.

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    Version 0.7

    TPOT now has multiprocessing support. TPOT allows you to use multiple processes in parallel to accelerate the pipeline optimization process in TPOT with the n_jobs parameter.

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    TPOT now allows you to customize the operators and parameters considered during the optimization process, which can be accomplished with the new config_dict parameter. The format of this customized dictionary can be found in the online documentation, along with a list of built-in configurations.

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    TPOT now allows you to customize the operators and parameters considered during the optimization process, which can be accomplished with the new config_dict parameter. The format of this customized dictionary can be found in the online documentation, along with a list of built-in configurations.

  • TPOT now allows you to specify a time limit for evaluating a single pipeline (default limit is 5 minutes) in optimization process with the max_eval_time_mins parameter, so TPOT won't spend hours evaluating overly-complex pipelines.

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    Version 0.7

    Version 0.6

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      TPOT now supports regression problems! We have created two separate TPOTClassifier and TPOTRegressor classes to support classification and regression problems, respectively. The command-line interface also supports this feature through the -mode parameter.

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      TPOT now supports regression problems! We have created two separate TPOTClassifier and TPOTRegressor classes to support classification and regression problems, respectively. The command-line interface also supports this feature through the -mode parameter.

    • TPOT now allows you to specify a time limit for the optimization process with the max_time_mins parameter, so you don't need to guess how long TPOT will take any more to recommend a pipeline to you.

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      Version 0.1

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