olpy.preprocessing.LabelEncoder
- class olpy.preprocessing.LabelEncoder(positive_label=1)[source]
Bases:
objectEncodes an output vector to match the specifications.
Given that online learning algorithms usually work on output vectors with entries (-1, 1), this function performs this action for the user.
- positive_label
The number in the output field that represents the positive label. The value passed should be different than -1. Defaults to 1.
- Type
int, optional
- labels
represents the labels that are present in the dataset. This can be used at prediction time.
- Type
Methods
Fits the output vector y.
Fits and transforms the data.
Transforms the data.
- fit(y)[source]
Fits the output vector y.
This method parses the parsed value and sets the necessary values to transform it later.
- Parameters
y (
listor numpy.ndarray) – the data to be transformed.- Returns
the current instance.
- Return type
self
- Raises
ValueError – if the number of labels is different than 2.
AssertionError – if the positive label is not found in the labels.
- fit_transform(y, return_labels=True)[source]
Fits and transforms the data.
Combines the actions of fit and transform methods.
- Parameters
- Returns
numpy.ndarray if return_labels is True else None
- Raises
ValueError – if the number of labels is different than 2.
AssertionError – if the positive label is not found in the labels.
NotFittedError if the encoder was not already fitted. –
- transform(return_labels=True)[source]
Transforms the data.
Based on the information collected while fitting, this fuction returns the transformed labels that can be used directly for training.
- Parameters
return_labels (bool, optional) – whether the labels should be returned or not. Default True.
- Returns
numpy.ndarray if return_labels is True else None
- Raises
NotFittedError if the encoder was not already fitted. –