XGBoost supports NaN but tpot enforces imputation

As a general rule tpot enforces imputation to match sklearn requirements for all real values in the input and output data. XGboost as a special case allows for the input of NaN values.

## Context of the issue

I am trying to optimise XGboost specifically using a data set with quite a lot of holes in it. I do not want to perform imputation as it affects the results. I looked in base.py and quickly modified the `_check_data` function to ignore NaN values and to not perform imputation but was wondering if tpot can be modified to accommodate this scenario with XGboost?

A 'no_imputation' keyword might be added to `TPOTBase .__init__` for example to prevent imputation.

**Example Edits:**
```
else:
if not self._imputed and np.any(np.isnan(features)):
self._imputed = True
features = self._impute_values(features)

try:
if target is not None:
X, y = check_X_y(features, target, accept_sparse=True, dtype=np.float64,

force_all_finite='allow-nan')

return X, y
```