Bug with dabl.explain()

I've tried dabl with my own data but I replicated the "quickstart guide".

In particular I have
```simple_clf = dabl.SimpleClassifier(random_state = 1234)
X_train = train_df_clean
y_train = train_df.is_match
simple_clf.fit(X_train, y_train)
```
which had the following results:
```
Running DummyClassifier(strategy='prior')
accuracy: 0.993 average_precision: 0.007 roc_auc: 0.500 recall_macro: 0.500 f1_macro: 0.498
=== new best DummyClassifier(strategy='prior') (using recall_macro):
accuracy: 0.993 average_precision: 0.007 roc_auc: 0.500 recall_macro: 0.500 f1_macro: 0.498

Running GaussianNB()
accuracy: 1.000 average_precision: 1.000 roc_auc: 1.000 recall_macro: 1.000 f1_macro: 1.000
=== new best GaussianNB() (using recall_macro):
accuracy: 1.000 average_precision: 1.000 roc_auc: 1.000 recall_macro: 1.000 f1_macro: 1.000

Running MultinomialNB()
accuracy: 1.000 average_precision: 1.000 roc_auc: 1.000 recall_macro: 1.000 f1_macro: 1.000
Running DecisionTreeClassifier(class_weight='balanced', max_depth=1)
accuracy: 1.000 average_precision: 1.000 roc_auc: 1.000 recall_macro: 1.000 f1_macro: 1.000
Running DecisionTreeClassifier(class_weight='balanced', max_depth=5)
accuracy: 1.000 average_precision: 1.000 roc_auc: 1.000 recall_macro: 1.000 f1_macro: 1.000
Running DecisionTreeClassifier(class_weight='balanced', min_impurity_decrease=0.01)
accuracy: 1.000 average_precision: 1.000 roc_auc: 1.000 recall_macro: 1.000 f1_macro: 1.000
Running LogisticRegression(C=0.1, class_weight='balanced', max_iter=1000)
accuracy: 1.000 average_precision: 1.000 roc_auc: 1.000 recall_macro: 1.000 f1_macro: 0.997
Running LogisticRegression(class_weight='balanced', max_iter=1000)
accuracy: 1.000 average_precision: 1.000 roc_auc: 1.000 recall_macro: 1.000 f1_macro: 1.000

Best model:
GaussianNB()
Best Scores:
accuracy: 1.000 average_precision: 1.000 roc_auc: 1.000 recall_macro: 1.000 f1_macro: 1.000
SimpleClassifier(random_state=1234, refit=True, shuffle=True, type_hints=None,
verbose=1)
```

This is able to make predictions without issue, but when I tried running:
`dabl.explain(simple_clf)`
I got the following warning:
```

TypeErrorTraceback (most recent call last)
/usr/local/lib/python3.7/site-packages/dabl/explain.py in _extract_inner_estimator(estimator, feature_names)
235 feature_names = inner_estimator.steps[0][1].get_feature_names(
--> 236 feature_names)
237 except TypeError:

TypeError: get_feature_names() takes 1 positional argument but 2 were given

During handling of the above exception, another exception occurred:

ValueErrorTraceback (most recent call last)
in
----> 1 dabl.explain(simple_clf)

/usr/local/lib/python3.7/site-packages/dabl/explain.py in explain(estimator, X_val, y_val, target_col, feature_names, n_top_features)
123
124 inner_estimator, inner_feature_names = _extract_inner_estimator(
--> 125 estimator, feature_names)
126
127 if X_val is not None:

/usr/local/lib/python3.7/site-packages/dabl/explain.py in _extract_inner_estimator(estimator, feature_names)
236 feature_names)
237 except TypeError:
--> 238 feature_names = inner_estimator.steps[0][1].get_feature_names()
239
240 # now we have input feature names for the final step

/usr/local/lib/python3.7/site-packages/dabl/preprocessing.py in get_feature_names(self)
607 # FIXME that is really strange?!
608 ohe_cols = self.columns_[self.columns_.map(cols)]
--> 609 feature_names.extend(ohe.get_feature_names(ohe_cols))
610 elif name == "remainder":
611 assert trans == "drop"

/usr/local/lib/python3.7/site-packages/sklearn/preprocessing/_encoders.py in get_feature_names(self, input_features)
530 "input_features should have length equal to number of "
531 "features ({}), got {}".format(len(self.categories_),
--> 532 len(input_features)))
533
534 feature_names = []

ValueError: input_features should have length equal to number of features (16), got 14
```

My `train_df_clean` has the following structure:
| |continuous | dirty_float | low_card_int | categorical | date | free_string | useless |
|---|---|---|---|---|---|---|---|
| `id` | False | False | False | False | False | True | False |
| `wt_ratio` | True | False | False | False | False | False | False |
| `cat_a` | False | False | False | False | False | True | False |
| `cat_b` | False | False | False | False | False | True | False |
| `cat_c` | False | False | False | False | False | True | False |
| `cat_d` | False | False | False | False | False | True | False |
| `cat_e`| False | False | False | True | False | False | False |
| `cat_f `| False | False | False | True | False | False | False |
| `cat_g` | False | False | False | True | False | False | False |
| `alt_id` | False | False | False | False | False | True | False |
| `type_1` | False | False | False | False | False | True | False |
| `type_2` | False | False | False | False | False | True | False |
| `class` | False | False | False | True | False | False | False |
| `subclass_1`| False | False | False | True | False | False | False |
| `subclass_2` | False | False | False | False | False | True | False |
| `in_cap` | False | False | False | False | False | True | False |
| `tax_1` | False | False | False | True | False | False | False |
| `tax_2` | False | False | False | True | False | False | False |
| `col`| False | False | False | False | False | True | False |
| `cap_loc` | False | False | False | True | False | False | False |
| 'grp_1` | False | False | False | True | False | False | False |
| `grp_2` | False | False | False | False | False | True | False |
| `grp_3` | False | False | False | False | False | True | False |
| `temp`| False | False | False | False | False | True | False |
| `notes_1` | False | False | False | False | False | True | False |
| `notes_2` | False | False | False | False | False | True | False |
| `meas_1` | True | False | False | False | False | False | False |
| `meas_2` | True | False | False | False | False | False | False |
| `meas_3` | True | False | False | False | False | False | False |
| `meas_4` | False | False | False | True | False | False | False |
| `len` | True | False | False | False | False | False | False |
| `loc_area1` | False | False | False | True | False | False | False |
| `loc_area2` | False | False | False | True | False | False | False |
| `loc_area3` | False | False | False | True | False | False | False |
| `loc_area4` | False | False | False | True | False | False | False |

And I can see that I have 14 categories which seems to align with the count in the error, but I would have assumed that the GaussianNB would also use the continuous fields.

Thank you in advance!