Tpot

- 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

- **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`](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.SGDClassifier.html) and [`SGDRegressor`](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.SGDRegressor.html) into TPOT default configs.
- Documentation has been updated.
- Fix minor bugs.

- **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

- 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.

- Add a new `template` option to specify a desired structure for machine learning pipeline in TPOT. Check [TPOT API](https://epistasislab.github.io/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](https://www.biorxiv.org/content/10.1101/502484v1.article-info) 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.

- 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