Is it possible to use `plot_decision_regions` function with model fit on a Pandas DataFrame?

question originally posted at: https://stackoverflow.com/q/49167961/1870832
reproduced below...

With plot_decision_regions, X and y have to be numpy arrays, and clf needs to have a predict() method. My problem is that in my case, the model clf object I would like to visualize has already been fitted on a Pandas DataFrame...

import numpy as np
import pandas as pd
import xgboost as xgb

import matplotlib
matplotlib.use('Agg')
from mlxtend.plotting import plot_decision_regions
import matplotlib.pyplot as plt


# Create arbitrary dataset for example
df = pd.DataFrame({'Planned_End': np.random.uniform(low=-5, high=5, size=50),
                   'Actual_End':  np.random.uniform(low=-1, high=1, size=50),
                   'Late':        np.random.random_integers(low=0,  high=2, size=50)}
)

# Fit a Classifier to the data
# This classifier is fit on the data as a Pandas DataFrame
X = df[['Planned_End', 'Actual_End']]
y = df['Late']

clf = xgb.XGBClassifier()
clf.fit(X, y)

So now when I try to use plot_decision_regions passing X/y as numpy arrays...

# Plot Decision Region using mlxtend's awesome plotting function
plot_decision_regions(X=X.values,
                      y=y.values,
                      clf=clf,
                      legend=2)

I (understandably) get an error that the model can't find the column names of the dataset it was trained on

ValueError: feature_names mismatch: ['Planned_End', 'Actual_End'] ['f0', 'f1']
expected Planned_End, Actual_End in input data
training data did not have the following fields: f1, f0

In my actual case, it would be a big deal to avoid training our model on Pandas DataFrames. Is there a way to still produce decision_regions plots for a classifier trained on a Pandas DataFrame?

Start at the plot_decision_regions entry point and reproduce the issue with the provided Pandas DataFrame, NumPy arrays, and XGBClassifier example. Trace how the plotting function passes features to clf.predict; done means a classifier trained on the DataFrame can produce the decision-regions plot without the feature-name mismatch.