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What is the logic of the extra columns in Tensorflow categorical encoding?

I am following the official Tensorflow tutorial for preprocessing layers, and I am not sure I get why I end up getting these extra columns after the categorical encoding. [UPDATE 2024: the default behavior has now changed, so the current tutorial no longer gives the exact results I show below]

Here is a stripped-down minimal reproducible example (including the data):

import numpy as np
import pandas as pd
import tensorflow as tf
from tensorflow.keras import layers
from tensorflow.keras.layers.experimental import preprocessing
import pathlib

dataset_url = 'http://storage.googleapis.com/download.tensorflow.org/data/petfinder-mini.zip'
csv_file = 'datasets/petfinder-mini/petfinder-mini.csv'
tf.keras.utils.get_file('petfinder_mini.zip', dataset_url, extract=True, cache_dir='.')
df = pd.read_csv(csv_file)

# In the original dataset "4" indicates the pet was not adopted.
df['target'] = np.where(df['AdoptionSpeed']==4, 0, 1)
# Drop un-used columns.
df = df.drop(columns=['AdoptionSpeed', 'Description'])

# A utility method to create a tf.data dataset from a Pandas Dataframe
def df_to_dataset(dataframe, shuffle=True, batch_size=32):
  dataframe = dataframe.copy()
  labels = dataframe.pop('target')
  ds = tf.data.Dataset.from_tensor_slices((dict(dataframe), labels))
  if shuffle:
    ds = ds.shuffle(buffer_size=len(dataframe))
  ds = ds.batch(batch_size)
  ds = ds.prefetch(batch_size)
  return ds

batch_size = 5
ds = df_to_dataset(df, batch_size=batch_size)
[(train_features, label_batch)] = ds.take(1)

def get_category_encoding_layer(name, dataset, dtype, max_tokens=None):
  # Create a StringLookup layer which will turn strings into integer indices
  if dtype == 'string':
    index = preprocessing.StringLookup(max_tokens=max_tokens)
  else:
    index = preprocessing.IntegerLookup(max_values=max_tokens)

  # Prepare a Dataset that only yields our feature
  feature_ds = dataset.map(lambda x, y: x[name])

  # Learn the set of possible values and assign them a fixed integer index.
  index.adapt(feature_ds)

  # Create a Discretization for our integer indices.
  encoder = preprocessing.CategoryEncoding(max_tokens=index.vocab_size())
  #encoder = preprocessing.CategoryEncoding(max_tokens=2)

  # Prepare a Dataset that only yields our feature.
  feature_ds = feature_ds.map(index)

  # Learn the space of possible indices.
  encoder.adapt(feature_ds)

  # Apply one-hot encoding to our indices. The lambda function captures the
  # layer so we can use them, or include them in the functional model later.
  return lambda feature: encoder(index(feature))

So, after running

type_col = train_features['Type']
layer = get_category_encoding_layer('Type', ds, 'string')
layer(type_col)

I get a result as:

<tf.Tensor: shape=(5, 4), dtype=float32, numpy=
array([[0., 0., 1., 0.],
       [0., 0., 1., 0.],
       [0., 0., 0., 1.],
       [0., 0., 0., 1.],
       [0., 0., 0., 1.]], dtype=float32)>

similar to what is shown in the tutorial indeed.

Notice that this is a binary classification problem (Cat/Dog):

np.unique(type_col)
# array([b'Cat', b'Dog'], dtype=object)

So, what is the logic of the 2 extra columns after the categorical encoding shown in the result above? What do they represent, and why they are 2 (and not, say, 1, or 3, or more)?

(I am perfectly aware that, should I wish for a simple one-hot encoding, I could simply use to_categorical(), but this is not the question here)

Solution

  • As already implied in the question, categorical encoding is somewhat richer than simple one-hot encoding. To see what these two columns represent it suffices to add a diagnostic print somewhere inside the get_category_encoding_layer() function:

    print(index.get_vocabulary())

    Then the result of the last commands will be:

    ['', '[UNK]', 'Dog', 'Cat']
<tf.Tensor: shape=(5, 4), dtype=float32, numpy=
array([[0., 0., 1., 0.],
       [0., 0., 1., 0.],
       [0., 0., 0., 1.],
       [0., 0., 0., 1.],
       [0., 0., 0., 1.]], dtype=float32)>

    The hint should hopefully be clear: the extra two columns here represent the empty value '' and unknown ones '[UNK]', respectively, which could be present in future (unseen) data.

    This is actually determined from the default arguments, not of CategoryEncoding, but of the preceding StringLookup; from the docs:

    mask_token=''
oov_token='[UNK]'

    You can end up with a somewhat more tight encoding (only 1 extra column instead of 2) by asking for oov_token='' instead of oov_token='[UNK]'; replace the call to StringLookup in the get_category_encoding_layer() function with

        index = preprocessing.StringLookup(oov_token='',mask_token=None, max_tokens=max_tokens)

    after which, the result will be:

    ['', 'Dog', 'Cat']
<tf.Tensor: shape=(5, 3), dtype=float32, numpy=
array([[0., 1., 0.],
       [0., 1., 0.],
       [0., 0., 1.],
       [0., 0., 1.],
       [0., 0., 1.]], dtype=float32)>

    i.e. with only 3 columns (without a dedicated one for '[UNK]'). AFAIK, this is the lowest you can go - attempting to set both mask_token and oov_token to None will result to an error.