ray.data.preprocessors.OneHotEncoder#

class ray.data.preprocessors.OneHotEncoder(columns: List[str], *, max_categories: Dict[str, int] | None = None)[source]#

Bases: Preprocessor

One-hot encode categorical data.

This preprocessor creates a column named {column}_{category} for each unique {category} in {column}. The value of a column is 1 if the category matches and 0 otherwise.

If you encode an infrequent category (see max_categories) or a category that isn’t in the fitted dataset, then the category is encoded as all 0s.

Columns must contain hashable objects or lists of hashable objects.

Note

Lists are treated as categories. If you want to encode individual list elements, use MultiHotEncoder.

Example

>>> import pandas as pd
>>> import ray
>>> from ray.data.preprocessors import OneHotEncoder
>>>
>>> df = pd.DataFrame({"color": ["red", "green", "red", "red", "blue", "green"]})
>>> ds = ray.data.from_pandas(df)  
>>> encoder = OneHotEncoder(columns=["color"])
>>> encoder.fit_transform(ds).to_pandas()  
   color_blue  color_green  color_red
0           0            0          1
1           0            1          0
2           0            0          1
3           0            0          1
4           1            0          0
5           0            1          0

If you one-hot encode a value that isn’t in the fitted dataset, then the value is encoded with zeros.

>>> df = pd.DataFrame({"color": ["yellow"]})
>>> batch = ray.data.from_pandas(df)  
>>> encoder.transform(batch).to_pandas()  
   color_blue  color_green  color_red
0           0            0          0

Likewise, if you one-hot encode an infrequent value, then the value is encoded with zeros.

>>> encoder = OneHotEncoder(columns=["color"], max_categories={"color": 2})
>>> encoder.fit_transform(ds).to_pandas()  
   color_red  color_green
0          1            0
1          0            1
2          1            0
3          1            0
4          0            0
5          0            1
Parameters:
  • columns – The columns to separately encode.

  • max_categories – The maximum number of features to create for each column. If a value isn’t specified for a column, then a feature is created for every category in that column.

See also

MultiHotEncoder

If you want to encode individual list elements, use MultiHotEncoder.

OrdinalEncoder

If your categories are ordered, you may want to use OrdinalEncoder.

PublicAPI (alpha): This API is in alpha and may change before becoming stable.

Methods

deserialize

Load the original preprocessor serialized via self.serialize().

fit

Fit this Preprocessor to the Dataset.

fit_transform

Fit this Preprocessor to the Dataset and then transform the Dataset.

preferred_batch_format

Batch format hint for upstream producers to try yielding best block format.

serialize

Return this preprocessor serialized as a string.

transform

Transform the given dataset.

transform_batch

Transform a single batch of data.

transform_stats

Return Dataset stats for the most recent transform call, if any.