Sklearn custom transformer to extract date features doesn't work in a Pipeline

The data I have in comes in as a string data type, from that string I want to extract some dates that are relevant at prediction time.

An example would be:

And I want it to be

etc.

from sklearn.base import BaseEstimator, TransformerMixin

class DateTransformer(BaseEstimator, TransformerMixin):
    def fit(self, X, y=None):
        return self

    def transform(Self, X, y = None):
        X.date = pd.to_datetime(X.date)
        X["year"] = X.date.dt.year
        X["month"] = X.date.dt.month
        X["day"] = X.date.dt.day
        X["dow"] = X.date.dt.dayofweek
        X["quarter"] = X.date.dt.quarter
        X = X.drop("date", axis=1)
        X = X.astype(str)
        return X

It is used in the following pipeline

naiveBaseline = Pipeline([
    ('dates', DateTransformer()),
    ('onehot', OneHotEncoder()),
    ('regression', RidgeCV())
])

When used like this the Pipeline actually works:

naiveBaseline2.fit(X_train,y_train)
naiveBaseline2.predict(X_test)

But ideally I use the following function to benchmark the performance of multiple models:

def evaluate(model, X, y, cv):
    cv_results = cross_validate(
        model,
        X,
        y,
        cv=cv,
        scoring=["neg_mean_absolute_error", "neg_root_mean_squared_error"],
    )
    mae = -cv_results["test_neg_mean_absolute_error"]
    rmse = -cv_results["test_neg_root_mean_squared_error"]
    print(
        f"Mean Absolute Error:     {mae.mean():.3f} +/- {mae.std():.3f}\n"
        f"Root Mean Squared Error: {rmse.mean():.3f} +/- {rmse.std():.3f}"
    )

If I run it through this evaluate function as such: evaluate(naiveBaseline2, X, y, TimeSeriesSplit())

I simply get NaN's for both metrics. I've been going over it for hours and really can't seem to understand what is going wrong, would any of you know?