How can I make a colored Confusion Matrix (heatmap)? Or, show the full matrix?

Question

I'm plotting a confusion matrix showing predicted vs actual of a total of 26 classes (26 letters of the alphabet).

My code is as follows:

y_pred = np.argmax(predictions, axis=1) # Transform predictions into 1-D array with label number

pd.DataFrame(confusion_matrix(y_test, y_pred), 
             columns=["pA", "pB", "pC", "pD", "pE", "pF", "pG", "pH", "pI", "pJ", "pK", "pL", "pM", "pN", "pO", "pP", "pQ", "pR", "pS", "pT", "pU", "pV", "pW", "pX", "pY", "pZ"],
             index=["aA", "aB", "aC", "aD", "aE", "aF", "aG", "aH", "aI", "aJ", "aK", "aL", "aM", "aN", "aO", "aP", "aQ", "aR", "aS", "aT", "aU", "aV", "aW", "aX", "aY", "aZ"])

My Output Looks like so:

The question is, how can I either display the entire rows/columns (without the... ) OR to make it more visually pleasing and easy to see, how can I change this to the colored version, I'm ok even if it doesn't show the numbers.

Thanks for taking the time to go over and help me with this, cheers!

Answer 1

you can treat the dataframe as an image:

import matplotlib.pyplot as plt

df = pd.DataFrame(confusion_matrix(y_test, y_pred), 
         columns=["pA", "pB", "pC", "pD", "pE", "pF", "pG", "pH", "pI", "pJ", "pK", "pL", "pM", "pN", "pO", "pP", "pQ", "pR", "pS", "pT", "pU", "pV", "pW", "pX", "pY", "pZ"],
         index=["aA", "aB", "aC", "aD", "aE", "aF", "aG", "aH", "aI", "aJ", "aK", "aL", "aM", "aN", "aO", "aP", "aQ", "aR", "aS", "aT", "aU", "aV", "aW", "aX", "aY", "aZ"])

plt.imshow(df[:])

edit:

you can add values using plt.annotate:

cols = ["pA", "pB", "pC", "pD", "pE", "pF", "pG", "pH", "pI", "pJ", "pK", "pL", "pM", "pN", "pO", "pP", "pQ", "pR", "pS", "pT", "pU", "pV", "pW", "pX", "pY", "pZ"]
ix=["aA", "aB", "aC", "aD", "aE", "aF", "aG", "aH", "aI", "aJ", "aK", "aL", "aM", "aN", "aO", "aP", "aQ", "aR", "aS", "aT", "aU", "aV", "aW", "aX", "aY", "aZ"]
data = np.random.randint(0,10,(len(cols), len(ix)))
df = pd.DataFrame(data=data, 
         columns=cols,
         index=ix)

plt.figure(figsize=(10,10))
plt.imshow(df[:])

for r, (i, row) in enumerate(df.iterrows()):
    for c, entry in enumerate(row):
        plt.annotate(entry, xy=(c-0.3,r+.1), fontsize=8) # using annotate with offset for xy, to allow better positioning