Tpot

- **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](http://jaquesgrobler.github.io/online-sklearn-build/modules/generated/sklearn.metrics.Scorer.html).

- 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.

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

* We have added an "early stopping" option for stopping the optimization process if no improvement is made within a set number of generations. Look up the `early_stop` parameter to access this functionality.

* TPOT now reduces the number of duplicated pipelines between generations, which saves you time during the optimization process.

* TPOT now supports custom scoring functions via the command-line mode.

* We have added a new optional argument, `periodic_checkpoint_folder`, that allows TPOT to periodically save the best pipeline so far to a local folder during optimization process.

* TPOT no longer uses `sklearn.externals.joblib` when `n_jobs=1` to avoid the potential freezing issue [that scikit-learn suffers from](http://scikit-learn.org/stable/faq.html#why-do-i-sometime-get-a-crash-freeze-with-n-jobs-1-under-osx-or-linux).

* We have added `pandas` as a dependency to read input datasets instead of `numpy.recfromcsv`. NumPy's `recfromcsv` function is unable to parse datasets with complex data types.

* Fixed a bug that `DEFAULT` in the parameter(s) of nested estimator raises `KeyError` when exporting pipelines.

* Fixed a bug related to setting `random_state` in nested estimators. The issue would happen with pipeline with `SelectFromModel` (`ExtraTreesClassifier` as nested estimator) or `StackingEstimator` if nested estimator has `random_state` parameter.

* Fixed a bug in the missing value imputation function in TPOT to impute along columns instead rows.

* Refined input checking for sparse matrices in TPOT.

* **TPOT now detects whether there are missing values in your dataset** and replaces them with the median value of the column.

* TPOT now allows you to set a `group` parameter in the `fit` function so you can use the [GroupKFold](http://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GroupKFold.html) cross-validation strategy.

* 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.

* **TPOT now has more [built-in configurations](http://rhiever.github.io/tpot/using/#built-in-tpot-configurations)**, including TPOT MDR and TPOT light, for both classification and regression problems.

* `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](http://rhiever.github.io/tpot/api/).

* Oh, **TPOT now has [thorough API documentation](http://rhiever.github.io/tpot/api/)**. 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.

* Refined input checking in TPOT.

* Removed Python 2 uncompliant code.

TPOT 0.7 is now out, featuring multiprocessing support for Linux and macOS, customizable operator configurations, and more.

* **TPOT now has multiprocessing support (Linux and macOS only).** TPOT allows you to use multiple processes for accelerating pipeline optimization in TPOT with the `n_jobs` parameter in both TPOTClassifier and TPOTRegressor.

* TPOT now allows you to **customize the operators and parameters explored during the optimization process.** TPOT allows you to customize the list of operators and parameters in optimization process of TPOT with the `config_dict` parameter. The format of this customized dictionary can be found in the [online documentation](/using/tpot-with-code).

* 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.

* We tweaked TPOT's underlying evolutionary optimization algorithm to work even better, including using the [mu+lambda algorithm](http://deap.readthedocs.io/en/master/api/algo.html#deap.algorithms.eaMuPlusLambda). This algorithm gives you more control of how many pipelines are generated every iteration with the `offspring_size` parameter.

* Fixed a reproducibility issue where setting `random_seed` didn't necessarily result in the same results every time. This bug was present since version 0.6.

* Refined the default operators and parameters in TPOT, so TPOT 0.7 should work even better than 0.6.

* TPOT now supports sample weights in the fitness function if some if your samples are more important to classify correctly than others. The sample weights option works the same as in scikit-learn, e.g., `tpot.fit(x_train, y_train, sample_weights=sample_weights)`.

* The default scoring metric in TPOT has been changed from balanced accuracy to accuracy, the same default metric for classification algorithms in scikit-learn. Balanced accuracy can still be used by setting `scoring='balanced_accuracy'` when creating a TPOT instance.

- **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](http://rhiever.github.io/tpot//using/#tpot-on-the-command-line) 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.
- Added a new operator that performs feature selection using [ExtraTrees](http://scikit-learn.org/stable/modules/ensemble.html#extremely-randomized-trees) feature importance scores.
- **[XGBoost](https://github.com/dmlc/xgboost) has been added as an optional dependency to TPOT.** If you have XGBoost installed, TPOT will automatically detect your installation and use the `XGBoostClassifier` and `XGBoostRegressor` in its pipelines.
- TPOT now offers a verbosity level of 3 ("science mode"), which outputs the entire Pareto front instead of only the current best score. This feature may be useful for users looking to make a trade-off between pipeline complexity and score.

- Major refactor: Each operator is defined in a separate class file. Hooray for easier-to-maintain code!
- TPOT now **exports directly to scikit-learn Pipelines** instead of hacky code.
- Internal representation of individuals now uses scikit-learn pipelines.
- Parameters for each operator have been optimized so TPOT spends less time exploring useless parameters.
- We have removed pandas as a dependency and instead use numpy matrices to store the data.
- TPOT now uses **k-fold cross-validation** when evaluating pipelines, with a default k = 3. This k parameter can be tuned when creating a new TPOT instance.
- Improved **scoring function support**: Even though TPOT uses balanced accuracy by default, you can now have TPOT use [any of the scoring functions](http://scikit-learn.org/stable/modules/model_evaluation.html#common-cases-predefined-values) that `cross_val_score` supports.
- Added the scikit-learn [Normalizer](http://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.Normalizer.html) preprocessor.
- Minor text fixes.