Skplumber

Now, the `ga_tune` method performs a `fit` operation on the pipeline passed to it, fitting using the best hyperparameter configuration the tuner was able to find.

The `SKPlumber.fit` method now returns a `SearchResult` named tuple which contains data about how the AutoML search went. The tuple members are:
- `time`: The time in seconds it took the fit method to complete
- `n_sample_iters`: The total number of pipelines tried in the sampling stage
- `n_tune_iters`: The total number of pipelines tried in the hyperparameter tuning stage
- `best_score`: The best score the evaluator was able to find during the AutoML search

This release simplifies the API of `SKPlumber`, making it do more under the hood. Now the only required parameters are problem type (e.g. `"classification"`), and a `budget` argument that specifies the number of seconds skplumber is allowed to search for. It and also changes the `SKPlumber` class to accept parameters to its constructor, instead of the `crank` method. It renames the `crank` method to `fit`, which only accepts the data to fit to/search for solutions for.

Two main features this release:

Hyperparameter Optimization

Hyperparameters can be optimized on the best found pipeline via the `skplumber.SKPlumber.crank(..., tune=True)` API or the on any single pipeline using the `skplumber.tuners.ga.ga_tune` method. This is accomplished via the `flexga` package and hyperparameter annotations which have been added to all machine learning primitives.

Custom Evaluation

Previously, `skplumber.SKPlumber.crank` could only do k-fold cross validation. Now, by passing in a custom evaluator e.g. `skplumber.SKPlumber.crank(..., evaluator=my_evaluator)`, any other pipeline evaluation method can be used. `skplumber` provides evaluators for k-fold cross validation, simple train/test splitting, and down-sampled train/test splitting.