Tpot

- Refine the logic of checking the type of an operator.

- Fix a bug causing point mutation function does not work properly with using `template` option
- Add a new built configuration called "TPOT cuML" which TPOT will search over a restricted configuration using the GPU-accelerated estimators in [RAPIDS cuML](https://github.com/rapidsai/cuml) and [DMLC XGBoost](https://github.com/dmlc/xgboost). **This configuration requires an NVIDIA Pascal architecture or better GPU with [compute capability 6.0+](https://developer.nvidia.com/cuda-gpus), and that the library cuML is installed.**
- Add string path support for log/log_file parameter
- Fix a bug in version 0.11.5 causing no update in stdout after each generation
- Fix minor bugs


v0.11.1-resAdj
- Development branch based on TPOT 0.11.1 for adjusting covariate without data leakage.

- Make `Pytorch` as an optional dependency
- Refine installation documentation

- 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

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

- 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