OneHotEncoder

One-hot encoding.

This transformer will encode every feature it is provided with. If a list or set is provided, this transformer will encode every entry in the list/set. You can apply it to a subset of features by composing it with compose.Select or compose.SelectType.

Parameters

• drop_zeros

  Default → False

  Whether or not 0s should be made explicit or not.

• drop_first

  Default → False

  Whether to get k - 1 dummies out of k categorical levels by removing the first key. This is useful in some statistical models where perfectly collinear features cause problems.

Examples

Let us first create an example dataset.

from pprint import pprint
import random
import string

random.seed(42)
alphabet = list(string.ascii_lowercase)
X = [
    {
        'c1': random.choice(alphabet),
        'c2': random.choice(alphabet),
    }
    for _ in range(4)
]
pprint(X)

[{'c1': 'u', 'c2': 'd'},
    {'c1': 'a', 'c2': 'x'},
    {'c1': 'i', 'c2': 'h'},
    {'c1': 'h', 'c2': 'e'}]

e can now apply one-hot encoding. All the provided are one-hot encoded, there is therefore no need to specify which features to encode.

from river import preprocessing

oh = preprocessing.OneHotEncoder()
for x in X[:2]:
    oh = oh.learn_one(x)
    pprint(oh.transform_one(x))

{'c1_u': 1, 'c2_d': 1}
{'c1_a': 1, 'c1_u': 0, 'c2_d': 0, 'c2_x': 1}

The drop_zeros parameter can be set to True if you don't want the past features to be included in the output. Otherwise, all the past features will be included in the output.

oh = preprocessing.OneHotEncoder(drop_zeros=True)
for x in X:
    oh = oh.learn_one(x)
    pprint(oh.transform_one(x))

{'c1_u': 1, 'c2_d': 1}
{'c1_a': 1, 'c2_x': 1}
{'c1_i': 1, 'c2_h': 1}
{'c1_h': 1, 'c2_e': 1}

You can encode only k - 1 features out of k by setting drop_first to True.

oh = preprocessing.OneHotEncoder(drop_first=True, drop_zeros=True)
for x in X:
    oh = oh.learn_one(x)
    pprint(oh.transform_one(x))

{'c2_d': 1}
{'c2_x': 1}
{'c2_h': 1}
{'c2_e': 1}

A subset of the features can be one-hot encoded by piping a compose.Select into the OneHotEncoder.

from river import compose

pp = compose.Select('c1') | preprocessing.OneHotEncoder()

for x in X:
    pp = pp.learn_one(x)
    pprint(pp.transform_one(x))

{'c1_u': 1}
{'c1_a': 1, 'c1_u': 0}
{'c1_a': 0, 'c1_i': 1, 'c1_u': 0}
{'c1_a': 0, 'c1_h': 1, 'c1_i': 0, 'c1_u': 0}

You can preserve the c2 feature by using a union:

pp = compose.Select('c1') | preprocessing.OneHotEncoder()
pp += compose.Select('c2')

for x in X:
    pp = pp.learn_one(x)
    pprint(pp.transform_one(x))

{'c1_u': 1, 'c2': 'd'}
{'c1_a': 1, 'c1_u': 0, 'c2': 'x'}
{'c1_a': 0, 'c1_i': 1, 'c1_u': 0, 'c2': 'h'}
{'c1_a': 0, 'c1_h': 1, 'c1_i': 0, 'c1_u': 0, 'c2': 'e'}

Similar to the above examples, we can also pass values as a list. This will one-hot encode all of the entries individually.

X = [{'c1': ['u', 'a'], 'c2': ['d']},
    {'c1': ['a', 'b'], 'c2': ['x']},
    {'c1': ['i'], 'c2': ['h', 'z']},
    {'c1': ['h', 'b'], 'c2': ['e']}]

oh = preprocessing.OneHotEncoder(drop_zeros=True)
for x in X:
    oh = oh.learn_one(x)
    pprint(oh.transform_one(x))

{'c1_a': 1, 'c1_u': 1, 'c2_d': 1}
{'c1_a': 1, 'c1_b': 1, 'c2_x': 1}
{'c1_i': 1, 'c2_h': 1, 'c2_z': 1}
{'c1_b': 1, 'c1_h': 1, 'c2_e': 1}

Processing mini-batches is also possible.

from pprint import pprint
import random
import string

random.seed(42)
alphabet = list(string.ascii_lowercase)
X = pd.DataFrame(
    {
        'c1': random.choice(alphabet),
        'c2': random.choice(alphabet),
    }
    for _ in range(3)
)
X

  c1 c2
0  u  d
1  a  x
2  i  h

oh = preprocessing.OneHotEncoder(drop_zeros=True)
df = oh.transform_many(X)
df.sort_index(axis="columns")

   c1_a  c1_i  c1_u  c2_d  c2_h  c2_x
0     0     0     1     1     0     0
1     1     0     0     0     0     1
2     0     1     0     0     1     0

oh = preprocessing.OneHotEncoder(drop_zeros=True, drop_first=True)
df = oh.transform_many(X)
df.sort_index(axis="columns")

   c1_i  c1_u  c2_d  c2_h  c2_x
0     0     1     1     0     0
1     0     0     0     0     1
2     1     0     0     1     0

Here's an example where the zeros are kept:

oh = preprocessing.OneHotEncoder(drop_zeros=False)
X_init = pd.DataFrame([{"c1": "Oranges", "c2": "Apples"}])
oh = oh.learn_many(X_init)
oh = oh.learn_many(X)

df = oh.transform_many(X)
df.sort_index(axis="columns")

   c1_Oranges  c1_a  c1_i  c1_u  c2_Apples  c2_d  c2_h  c2_x
0           0     0     0     1          0     1     0     0
1           0     1     0     0          0     0     0     1
2           0     0     1     0          0     0     1     0

df.dtypes.sort_index()

c1_Oranges    Sparse[uint8, 0]
c1_a          Sparse[uint8, 0]
c1_i          Sparse[uint8, 0]
c1_u          Sparse[uint8, 0]
c2_Apples     Sparse[uint8, 0]
c2_d          Sparse[uint8, 0]
c2_h          Sparse[uint8, 0]
c2_x          Sparse[uint8, 0]
dtype: object

Methods

learn_many

Update with a mini-batch of features.

A lot of transformers don't actually have to do anything during the learn_many step because they are stateless. For this reason the default behavior of this function is to do nothing. Transformers that however do something during the learn_many can override this method.

Parameters

• X — 'pd.DataFrame'

Returns

Transformer: self

learn_one

Update with a set of features x.

A lot of transformers don't actually have to do anything during the learn_one step because they are stateless. For this reason the default behavior of this function is to do nothing. Transformers that however do something during the learn_one can override this method.

Parameters

• x — 'dict'

Returns

Transformer: self

transform_many

Transform a mini-batch of features.

Parameters

• X — 'pd.DataFrame'

Returns

pd.DataFrame: A new DataFrame.

transform_one

Transform a set of features x.

Parameters

• x — 'dict'
• y — defaults to None

Returns

dict: The transformed values.