OrdinalEncoder¶

Ordinal encoder.

This transformer maps each feature to integers. It can useful when a feature has string values (i.e. categorical variables).

Parameters¶

• categories

  Type → dict | None

  Default → None

  Categories (unique values) per feature: None : Determine categories automatically from the training data. dict of dicts : Expected categories for each feature. The outer dict maps each feature to its inner dict. The inner dict maps each category to its code. The used categories can be found in the values attribute.

• unknown_value

  Type → int | None

  Default → 0

  The value to use for unknown categories seen during transform_one. Unknown categories will be mapped to an integer once they are seen during learn_one. This value can be set to None in order to categories to None if they've never been seen before.

• none_value

  Type → int

  Default → -1

  The value to encode None with.

Attributes¶

• values

  A dict of dicts. The outer dict maps each feature to its inner dict. The inner dict maps each category to its code.

Examples¶

from river import preprocessing

X = [
    {"country": "France", "place": "Taco Bell"},
    {"country": None, "place": None},
    {"country": "Sweden", "place": "Burger King"},
    {"country": "France", "place": "Burger King"},
    {"country": "Russia", "place": "Starbucks"},
    {"country": "Russia", "place": "Starbucks"},
    {"country": "Sweden", "place": "Taco Bell"},
    {"country": None, "place": None},
]

encoder = preprocessing.OrdinalEncoder()
for x in X:
    print(encoder.transform_one(x))
    encoder.learn_one(x)

{'country': 0, 'place': 0}
{'country': -1, 'place': -1}
{'country': 0, 'place': 0}
{'country': 1, 'place': 2}
{'country': 0, 'place': 0}
{'country': 3, 'place': 3}
{'country': 2, 'place': 1}
{'country': -1, 'place': -1}

Like in scikit-learn, you can also specify the expected categories manually. This is handy when you want to constrain category encoding space to e.g. top 20% most popular category values you've picked in advance.

categories = {'country': {'France': 1},
              'place': {'Burger King': 2, 'Starbucks': 3}}
encoder = preprocessing.OrdinalEncoder(categories=categories)
for x in X:
    print(encoder.transform_one(x))
    encoder.learn_one(x)

{'country': 1, 'place': 0}
{'country': -1, 'place': -1}
{'country': 0, 'place': 2}
{'country': 1, 'place': 2}
{'country': 0, 'place': 3}
{'country': 0, 'place': 3}
{'country': 0, 'place': 0}
{'country': -1, 'place': -1}

import pandas as pd
xb1 = pd.DataFrame(X[0:4], index=[0, 1, 2, 3])
xb2 = pd.DataFrame(X[4:8], index=[4, 5, 6, 7])

encoder = preprocessing.OrdinalEncoder()
encoder.transform_many(xb1)

   country  place
0        0      0
1       -1     -1
2        0      0
3        0      0

encoder.learn_many(xb1)
encoder.transform_many(xb2)

   country  place
4        0      0
5        0      0
6        2      1
7       -1     -1

Methods¶