TPOT v0.11.4 minor release

• Add a new built configuration "TPOT NN" which includes all operators in "Default TPOT" plus additional neural network estimators written in PyTorch (currently tpot.builtins.PytorchLRClassifier and tpot.builtins.PytorchMLPClassifier for classification tasks only)
• Refine log_file parameter's behavior

TPOT v0.11.3 minor release

• Fix a bug in TPOTRegressor in v0.11.2
• Add -log option in command line interface to save process log to a file.

TPOT v0.11.2 Minor Release

• Fix early_stop parameter does not work properly
• TPOT built-in OneHotEncoder can refit to different datasets
• Fix the issue that the attribute evaluated_individuals_ cannot record correct generation info.
• Add a new parameter log_file to output logs to a file instead of sys.stdout
• Fix some code quality issues and mistakes in documentations
• Fix minor bugs

TPOT v0.11.1 Minor Release

• Fix compatibility issue with scikit-learn v0.22
• warm_start now saves both Primitive Sets and evaluated_pipelines_ from previous runs;
• Fix the error that TPOT assign wrong fitness scores to non-evaluated pipelines (interrupted by max_min_mins or KeyboardInterrupt) ;
• Fix the bug that mutation operator cannot generate new pipeline when template is not default value and warm_start is True;
• Fix the bug that max_time_mins cannot stop optimization process when search space is limited.
• Fix a bug in exported codes when the exported pipeline is only 1 estimator
• Fix spelling mistakes in documentations
• Fix some code quality issues

Version 0.11.0

• Support for Python 3.4 and below has been officially dropped. Also support for scikit-learn 0.20 or below has been dropped.
• The support of a metric function with the signature score_func(y_true, y_pred) for scoring parameter has been dropped.
• Refine StackingEstimator for not stacking NaN/Infinity predication probabilities.
• Fix a bug that population doesn't persist even warm_start=True when max_time_mins is not default value.
• 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.
• TPOT can independently use generations and max_time_mins to limit the optimization process through using one of the parameters or both.
• .export() function will return string of exported pipeline if output filename is not specified.
• Add SGDClassifier and SGDRegressor into TPOT default configs.
• Documentation has been updated.
• Fix minor bugs.

TPOT v0.10.2 minor release

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

TPOT v0.10.1 minor release

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

TPOT 0.10.0 Release

• 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).
• 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)
• 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. It is related to the issue #846.
• Now memory parameter can create memory cache directory if it does not exist. It is related to the issue #837.
• Fix minor bugs.

TPOT 0.9.6 Minor Release

• Fix a bug causing that max_time_mins parameter doesn't work when use_dask=True in TPOT 0.9.5
• Now TPOT saves best pareto values best pareto pipeline s in checkpoint folder
• TPOT raises ImportError if operators in the TPOT configuration are not available when verbosity>2
• 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.
• Fix bugs in configuration of TPOTRegressor
• Error fixes in documentation

TPOT now supports integration with Dask for parallelization

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

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

• TPOTClassifier and TPOTRegressor now follows scikit-learn estimator API.

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

• We refined parameters in VarianceThreshold and FeatureAgglomeration.

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

• We improved documentation of TPOT.