zeugma package¶
Submodules¶
zeugma.conf module¶
Created on the 05/01/18 @author: Nicolas Thiebaut @email: nkthiebaut@gmail.com
zeugma.embeddings module¶
-
class
zeugma.embeddings.
EmbeddingTransformer
(model: str = 'glove', aggregation: str = 'average')[source]¶ Bases:
sklearn.base.BaseEstimator
,sklearn.base.TransformerMixin
Text vectorizer class: load pre-trained embeddings and transform texts into vectors.
-
fit
(x: Iterable[Iterable[T_co]], y: Iterable[T_co] = None) → sklearn.base.BaseEstimator[source]¶ Has to define fit method to conform scikit-learn Transformer definition and integrate a sklearn.Pipeline object
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zeugma.keras_transformers module¶
Created on the 02/05/2018 @author: Nicolas Thiebaut @email: nicolas@visage.jobs
-
class
zeugma.keras_transformers.
Padder
(max_length=500)[source]¶ Bases:
sklearn.base.BaseEstimator
,sklearn.base.TransformerMixin
Pad and crop uneven lists to the same length. Only the end of lists longer than the max_length attribute are kept, and lists shorter than max_length are left-padded with zeros
Variables: - max_length (int) – sizes of sequences after padding
- max_index (int) – maximum index known by the Padder, if a higher index is met during transform it is transformed to a 0
-
class
zeugma.keras_transformers.
TextsToSequences
(**kwargs)[source]¶ Bases:
sphinx.ext.autodoc.importer._MockObject
,sklearn.base.BaseEstimator
,sklearn.base.TransformerMixin
Sklearn transformer to convert texts to indices list
Example
>>> from zeugma import TextsToSequences >>> sequencer = TextsToSequences() >>> sequencer.fit_transform(["the cute cat", "the dog"]) [[1, 2, 3], [1, 4]]
zeugma.logger module¶
zeugma.texttransformers module¶
-
class
zeugma.texttransformers.
ItemSelector
(key)[source]¶ Bases:
sklearn.base.BaseEstimator
,sklearn.base.TransformerMixin
For data grouped by feature, select subset of data at a provided key.
The data is expected to be stored in a 2D data structure, where the first index is over features and the second is over samples.
Parameters: key (hashable, required) – The key corresponding to the desired value in a mappable.
-
class
zeugma.texttransformers.
Namer
(key)[source]¶ Bases:
sklearn.base.BaseEstimator
,sklearn.base.TransformerMixin
Return a single-entry dictionary with key given by the attribute ‘key’ and value is the input data
Parameters: key (hashable, required) – The key corresponding to the output name.
Module contents¶
Created on the 05/01/18 @author: Nicolas Thiebaut @email: nkthiebaut@gmail.com