Fixes issue #20

• [x] Adds progress bar to all text and image embedders.
• [x] Tests for SentenceEncoder.
• [ ] Use perfplot for progress bar?
• [ ] Can we ensure fast NumPy vectorization while using a progress bar?

The device argument in SentenceEncoder is not defined as an attribute. This leads to bugs when using it with sklearn. I encountered attribute errors when trying to print out a Pipeline representation that has SentenceEncoder as a component.

Should be easy to fix by just adding self.device in SentenceEncoder.__init__. We can consider adding tests for text encoders so we can catch these errors beforehand.

The scikit-learn development docs make it clear every argument should be defined as an attribute:

every keyword argument accepted by init should correspond to an attribute on the instance. Scikit-learn relies on this to find the relevant attributes to set on an estimator when doing model selection.

Error message: AttributeError: 'SentenceEncoder' object has no attribute 'device'.

Reproduction: Python 3.8 with embetter = "^0.2.2"

se = SentenceEncoder()
repr(se)

Fix:

Add self.device on SentenceEncoder

class SentenceEncoder(EmbetterBase):
    .
    .
    def __init__(self, name="all-MiniLM-L6-v2", device=None):
        if not device:
            device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
        self.device = device
        self.name = name
        self.tfm = SBERT(name, device=self.device)

This approach could work pretty well as an implementation: https://danielmuellerkomorowska.com/2020/06/17/analyzing-image-histograms-with-scikit-image/

To do something similar to what is explained here: https://www.pinecone.io/learn/color-histograms/

Hi,

Do you think it would be a good idea to add support for static word embeddings (word2vec, glove, etc.)? The embedder would need:

• A filename to a local embedding file (e.g., glove.6b.100d.txt)
• Either a callable tokenizer or regex string (i.e., the way sci-kit learn's TfIdfVectorizer splits words).
• A (name of a) pooling function (e.g., "mean", "max", "sum").

The second and third parameters could easily have sensible defaults, of course. If you think it's a good idea, I can do the PR somewhere next week.

Stéphan

I think it would be a nice addition to add an embedder that can easily vectorize text through SpaCy. I already have an implementation class for this and would be happy to contribute it here.

SpaCy Docs on vector: https://spacy.io/api/doc#vector

Example code for single string:

import spacy
nlp = spacy.load("en_core_web_sm")
doc = nlp("This here text")
doc.vector

I would be happy to implement get_feature_names_out for all the Embetter objects. I will implement them by just adding a new method (without a Mixin).

I was playing a bit with the library and found out that the TimmEncoder returns 1000-dimensional vectors for all the models I selected. That is caused by returning the state of the last FC classification layer and the fact all of the models were trained on ImageNet with 1000 classes. In practice, it's typically replaced with identity.

Are there any reasons for returning the state of that last layer as an embedding? I'd be happy to submit a PR fixing that.

https://keras.io/api/applications/

https://www.tensorflow.org/api_docs/python/tf/keras/applications/mobilenet_v2/MobileNetV2 https://www.tensorflow.org/api_docs/python/tf/keras/applications/xception/Xception

text_emb_pipeline = make_pipeline(
  ColumnGrabber("text"),
  SentenceEncoder('all-MiniLM-L6-v2')
)

# This pipeline can also be trained to make predictions, using
# the embedded features. 
text_clf_pipeline = make_pipeline(
  text_emb_pipeline,
  LogisticRegression()
)

dataf = pd.DataFrame({
  "text": ["positive sentiment", "super negative"],
  "label_col": ["pos", "neg"]
})

X = text_emb_pipeline.fit_transform(dataf, dataf['label_col'])
text_clf_pipeline.fit(dataf, dataf['label_col'])

This code gives this error: 'SentenceEncoder' object has no attribute 'device'