Implement support for string representation of classification targets

[ww-jermaine]

This feature branch implements the support for string representation of classification targets.

This should allow running the example:

from sklearn.datasets import fetch_openml
from sklearn.model_selection import train_test_split

dataset = fetch_openml(name="wine", version=7, as_frame=True)

X = dataset.data.values.astype(float)
y = dataset.target.values

feature_names = dataset.feature_names

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=2, stratify=y)

X_prop_train, X_cal, y_prop_train, y_cal = train_test_split(X_train, y_train,
                                                            test_size=0.25)
from sklearn.ensemble import RandomForestClassifier

rf = RandomForestClassifier(n_jobs=-1)

rf.fit(X_prop_train, y_prop_train)

from calibrated_explanations import CalibratedExplainer, __version__
print(__version__)

explainer = CalibratedExplainer(rf, X_cal, y_cal, feature_names=feature_names)

factual_explanations = explainer.explain_factual(X_test)

[tuvelofstrom]

Excellent, could you add one or two test(s) in test_classification using your implementation? You can copy the binary_ce and multiclass_ce with string targets to ensure plots etc support the implementation.