Thank you for your excellent job-:) I have a question when i read the code about function transform You say, given the images and image_embedding, and the return is Predictions:Concept predictions for each image But when i read the code of transform, the output is not the concept prediction for each image. can you explain it ?Thank you very much!

I tried the demo code and it worked for a small sample, tried to feed it more images and I got this error KeyError: '[-1] not found in axis'

dependencies: concept=='0.2.1' pandas=1.4.0

/home/<username>/anaconda3/envs/rd38/lib/python3.8/site-packages/torchvision/transforms/transforms.py:332: UserWarning: Argument 'interpolation' of type int is deprecated since 0.13 and will be removed in 0.15. Please use InterpolationMode enum.
  warnings.warn(
100%|███████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:21<00:00,  1.06s/it]
---------------------------------------------------------------------------
KeyError                                  Traceback (most recent call last)
Input In [30], in <cell line: 3>()
      1 from concept import ConceptModel
      2 concept_model = ConceptModel()
----> 3 concepts = concept_model.fit_transform(img_names[3500:6000])

File ~/anaconda3/envs/rd38/lib/python3.8/site-packages/concept/_model.py:124, in ConceptModel.fit_transform(self, images, docs, image_names, image_embeddings)
    122 # Reduce dimensionality and cluster images into concepts
    123 reduced_embeddings = self._reduce_dimensionality(image_embeddings)
--> 124 predictions = self._cluster_embeddings(reduced_embeddings)
    126 # Extract representative images through exemplars
    127 representative_images = self._extract_exemplars(image_names)

File ~/anaconda3/envs/rd38/lib/python3.8/site-packages/concept/_model.py:261, in ConceptModel._cluster_embeddings(self, embeddings)
    257 self.cluster_labels = sorted(list(set(self.hdbscan_model.labels_)))
    258 predicted_clusters = list(self.hdbscan_model.labels_)
    260 self.frequency = (
--> 261     pd.DataFrame({"Cluster": predicted_clusters, "Count": predicted_clusters})
    262       .groupby("Cluster")
    263       .count()
    264       .drop(-1)
    265       .sort_values("Count", ascending=False)
    266 )
    267 return predicted_clusters

File ~/anaconda3/envs/rd38/lib/python3.8/site-packages/pandas/util/_decorators.py:311, in deprecate_nonkeyword_arguments.<locals>.decorate.<locals>.wrapper(*args, **kwargs)
    305 if len(args) > num_allow_args:
    306     warnings.warn(
    307         msg.format(arguments=arguments),
    308         FutureWarning,
    309         stacklevel=stacklevel,
    310     )
--> 311 return func(*args, **kwargs)

File ~/anaconda3/envs/rd38/lib/python3.8/site-packages/pandas/core/frame.py:4956, in DataFrame.drop(self, labels, axis, index, columns, level, inplace, errors)
   4808 @deprecate_nonkeyword_arguments(version=None, allowed_args=["self", "labels"])
   4809 def drop(
   4810     self,
   (...)
   4817     errors: str = "raise",
   4818 ):
   4819     """
   4820     Drop specified labels from rows or columns.
   4821 
   (...)
   4954             weight  1.0     0.8
   4955     """
-> 4956     return super().drop(
   4957         labels=labels,
   4958         axis=axis,
   4959         index=index,
   4960         columns=columns,
   4961         level=level,
   4962         inplace=inplace,
   4963         errors=errors,
   4964     )

File ~/anaconda3/envs/rd38/lib/python3.8/site-packages/pandas/core/generic.py:4279, in NDFrame.drop(self, labels, axis, index, columns, level, inplace, errors)
   4277 for axis, labels in axes.items():
   4278     if labels is not None:
-> 4279         obj = obj._drop_axis(labels, axis, level=level, errors=errors)
   4281 if inplace:
   4282     self._update_inplace(obj)

File ~/anaconda3/envs/rd38/lib/python3.8/site-packages/pandas/core/generic.py:4323, in NDFrame._drop_axis(self, labels, axis, level, errors, consolidate, only_slice)
   4321         new_axis = axis.drop(labels, level=level, errors=errors)
   4322     else:
-> 4323         new_axis = axis.drop(labels, errors=errors)
   4324     indexer = axis.get_indexer(new_axis)
   4326 # Case for non-unique axis
   4327 else:

File ~/anaconda3/envs/rd38/lib/python3.8/site-packages/pandas/core/indexes/base.py:6644, in Index.drop(self, labels, errors)
   6642 if mask.any():
   6643     if errors != "ignore":
-> 6644         raise KeyError(f"{list(labels[mask])} not found in axis")
   6645     indexer = indexer[~mask]
   6646 return self.delete(indexer)

KeyError: '[-1] not found in axis'

Hi.

Thank you very much for creating this. It is an absolutely brilliant idea. Once we have created the model, how do we save the model and use it for any new data that comes in?

I was reproducing the same Colab notebook in the ReadME without any change: https://colab.research.google.com/drive/1XHwQPT2itZXu1HayvGoj60-xAXxg9mqe?usp=sharing#scrollTo=VcgGxrLH-AU9

While importing the library from concept import ConceptModel, this error appears:

TypeError: init() got an unexpected keyword argument 'cachedir'

Apparently it stems from hdbscan module as cachedir was removed from joblib.Memory. https://github.com/joblib/joblib/blame/3fb7fbde772e10415f879e0cb7e5d986fede8460/joblib/memory.py#L910

Hi there,

I am trying to run Concept on a very small dataset of images (10 images in jpg) but while I can run it on the sample you provided (Colab) I get the following error with my dataset. Any idea what might be the issue?

Aside from this specific issue, this is an amazing work!

Extract the textual representation not through cosine similarity of embeddings but by generating a set of words for each image and running c-TF-IDF over the clusters of words.

Code for English:

from concept import ConceptModel
concept_model = ConceptModel()
concepts = concept_model.fit_transform(images, docs)
# Works correctly!

Guide suggests "Use Concept(embedding_model="clip-ViT-B-32-multilingual-v1") to select a model that supports 50+ languages.":

from concept import Concept
# ImportError: cannot import name 'Concept' from 'concept' --> I guess you mean to import ConceptModel

Importing ConceptModel:

from concept import ConceptModel
concept_model = ConceptModel(embedding_model="clip-ViT-B-32-multilingual-v1")
concepts = concept_model.fit_transform(images, docs)
# TypeError: 'JpegImageFile' object is not subscriptable

Hi, thanks for your awesome libraries.

Just a short question: In this line:

https://github.com/MaartenGr/Concept/blob/d270607d6ea4d789a42d54880ab4a0c977bb69ce/concept/_model.py#L304

you're casting the numpy int64s to integers, presumably so they can be used as indexes? In any case, the cluster keys remain np.int64. This means the whole dict cannot be serialized (as json doesn't know how to handle numpy data types).

My suggestion would be to int() the keys as well to make this a bit less perplexing. But I'm not sure if you rely on the indexes being np.int64 in some other place?

Running a Concept example on OS S Monterey 12.3.1 ...Transformers/Image_utils #143: return (image - mean) / std

image is (4,224,224) mean is (3,) std is (3,)

Python 3.8.13 
% pip show tensorflow_macos
WARNING: Ignoring invalid distribution -umpy (/Users/davidlaxer/tensorflow-metal/lib/python3.8/site-packages)
Name: tensorflow-macos
Version: 2.8.0
Summary: TensorFlow is an open source machine learning framework for everyone.
Home-page: https://www.tensorflow.org/
Author: Google Inc.
Author-email: [email protected]
License: Apache 2.0
Location: /Users/davidlaxer/tensorflow-metal/lib/python3.8/site-packages
Requires: absl-py, astunparse, flatbuffers, gast, google-pasta, grpcio, h5py, keras, keras-preprocessing, libclang, numpy, opt-einsum, protobuf, setuptools, six, tensorboard, termcolor, tf-estimator-nightly, typing-extensions, wrapt
Required-by: 

pip show sentence_transformers
WARNING: Ignoring invalid distribution -umpy (/Users/davidlaxer/tensorflow-metal/lib/python3.8/site-packages)
Name: sentence-transformers
Version: 2.1.0
Summary: Sentence Embeddings using BERT / RoBERTa / XLM-R
Home-page: https://github.com/UKPLab/sentence-transformers
Author: Nils Reimers
Author-email: [email protected]
License: Apache License 2.0
Location: /Users/davidlaxer/tensorflow-metal/lib/python3.8/site-packages
Requires: huggingface-hub, nltk, numpy, scikit-learn, scipy, sentencepiece, tokenizers, torch, torchvision, tqdm, transformers
Required-by: bertopic, concept

% pip show transformers
WARNING: Ignoring invalid distribution -umpy (/Users/davidlaxer/tensorflow-metal/lib/python3.8/site-packages)
Name: transformers
Version: 4.11.3
Summary: State-of-the-art Natural Language Processing for TensorFlow 2.0 and PyTorch
Home-page: https://github.com/huggingface/transformers
Author: Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Sam Shleifer, Patrick von Platen, Sylvain Gugger, Suraj Patil, Stas Bekman, Google AI Language Team Authors, Open AI team Authors, Facebook AI Authors, Carnegie Mellon University Authors
Author-email: [email protected]
License: Apache
Location: /Users/davidlaxer/tensorflow-metal/lib/python3.8/site-packages
Requires: filelock, huggingface-hub, numpy, packaging, pyyaml, regex, requests, sacremoses, tokenizers, tqdm
Required-by: sentence-transformers

Here's the code:

import os
import glob
import zipfile
from tqdm import tqdm
from sentence_transformers import util

# 25k images from Unsplash
img_folder = 'photos/'
if not os.path.exists(img_folder) or len(os.listdir(img_folder)) == 0:
    os.makedirs(img_folder, exist_ok=True)

    photo_filename = 'unsplash-25k-photos.zip'
    if not os.path.exists(photo_filename):  # Download dataset if does not exist
        util.http_get('http://sbert.net/datasets/' + photo_filename, photo_filename)

    # Extract all images
    with zipfile.ZipFile(photo_filename, 'r') as zf:
        for member in tqdm(zf.infolist(), desc='Extracting'):
            zf.extract(member, img_folder)
img_names = list(glob.glob('photos/*.jpg'))

from concept import ConceptModel
concept_model = ConceptModel()
concepts = concept_model.fit_transform(img_names)

B/s]
  0%|                                                   | 0/196 [00:00<?, ?it/s]/Users/davidlaxer/tensorflow-metal/lib/python3.8/site-packages/transformers/feature_extraction_utils.py:158: UserWarning: Creating a tensor from a list of numpy.ndarrays is extremely slow. Please consider converting the list to a single numpy.ndarray with numpy.array() before converting to a tensor. (Triggered internally at  ../torch/csrc/utils/tensor_new.cpp:201.)
  tensor = as_tensor(value)
  5%|█▉                                         | 9/196 [02:21<48:54, 15.69s/it]
---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
Input In [2], in <cell line: 3>()
      1 from concept import ConceptModel
      2 concept_model = ConceptModel()
----> 3 concepts = concept_model.fit_transform(img_names)

File ~/Concept/concept/_model.py:120, in ConceptModel.fit_transform(self, images, docs, image_names, image_embeddings)
    118 # Calculate image embeddings if not already generated
    119 if image_embeddings is None:
--> 120     image_embeddings = self._embed_images(images)
    122 # Reduce dimensionality and cluster images into concepts
    123 reduced_embeddings = self._reduce_dimensionality(image_embeddings)

File ~/Concept/concept/_model.py:224, in ConceptModel._embed_images(self, images)
    221 end_index = (i * batch_size) + batch_size
    223 images_to_embed = [Image.open(filepath) for filepath in images[start_index:end_index]]
--> 224 img_emb = self.embedding_model.encode(images_to_embed, show_progress_bar=False)
    225 embeddings.extend(img_emb.tolist())
    227 # Close images

File ~/tensorflow-metal/lib/python3.8/site-packages/sentence_transformers/SentenceTransformer.py:153, in SentenceTransformer.encode(self, sentences, batch_size, show_progress_bar, output_value, convert_to_numpy, convert_to_tensor, device, normalize_embeddings)
    151 for start_index in trange(0, len(sentences), batch_size, desc="Batches", disable=not show_progress_bar):
    152     sentences_batch = sentences_sorted[start_index:start_index+batch_size]
--> 153     features = self.tokenize(sentences_batch)
    154     features = batch_to_device(features, device)
    156     with torch.no_grad():

File ~/tensorflow-metal/lib/python3.8/site-packages/sentence_transformers/SentenceTransformer.py:311, in SentenceTransformer.tokenize(self, texts)
    307 def tokenize(self, texts: Union[List[str], List[Dict], List[Tuple[str, str]]]):
    308     """
    309     Tokenizes the texts
    310     """
--> 311     return self._first_module().tokenize(texts)

File ~/tensorflow-metal/lib/python3.8/site-packages/sentence_transformers/models/CLIPModel.py:71, in CLIPModel.tokenize(self, texts)
     68 if len(images) == 0:
     69     images = None
---> 71 inputs = self.processor(text=texts_values, images=images, return_tensors="pt", padding=True)
     72 inputs['image_text_info'] = image_text_info
     73 return inputs

File ~/tensorflow-metal/lib/python3.8/site-packages/transformers/models/clip/processing_clip.py:148, in CLIPProcessor.__call__(self, text, images, return_tensors, **kwargs)
    145     encoding = self.tokenizer(text, return_tensors=return_tensors, **kwargs)
    147 if images is not None:
--> 148     image_features = self.feature_extractor(images, return_tensors=return_tensors, **kwargs)
    150 if text is not None and images is not None:
    151     encoding["pixel_values"] = image_features.pixel_values

File ~/tensorflow-metal/lib/python3.8/site-packages/transformers/models/clip/feature_extraction_clip.py:150, in CLIPFeatureExtractor.__call__(self, images, return_tensors, **kwargs)
    148     images = [self.center_crop(image, self.crop_size) for image in images]
    149 if self.do_normalize:
--> 150     images = [self.normalize(image=image, mean=self.image_mean, std=self.image_std) for image in images]
    152 # return as BatchFeature
    153 data = {"pixel_values": images}

File ~/tensorflow-metal/lib/python3.8/site-packages/transformers/models/clip/feature_extraction_clip.py:150, in <listcomp>(.0)
    148     images = [self.center_crop(image, self.crop_size) for image in images]
    149 if self.do_normalize:
--> 150     images = [self.normalize(image=image, mean=self.image_mean, std=self.image_std) for image in images]
    152 # return as BatchFeature
    153 data = {"pixel_values": images}

File ~/tensorflow-metal/lib/python3.8/site-packages/transformers/image_utils.py:143, in ImageFeatureExtractionMixin.normalize(self, image, mean, std)
    141     return (image - mean[:, None, None]) / std[:, None, None]
    142 else:
--> 143     return (image - mean) / std

ValueError: operands could not be broadcast together with shapes (4,224,224) (3,) 

The exception is in the normalize() function ... I believe in the 9th Pil image:

What do recommend setting max_open_files to?

images = [Image.open("photos/"+filepath) for filepath in tqdm(img_names[:5000])]
image_names = img_names[:5000]
image_embeddings = img_embeddings[:5000]

54%|███████████████████▍                | 2693/5000 [00:00<00:00, 13545.87it/s]
---------------------------------------------------------------------------
OSError                                   Traceback (most recent call last)
Input In [4], in <cell line: 1>()
----> 1 images = [Image.open("photos/"+filepath) for filepath in tqdm(img_names[:5000])]
      2 image_names = img_names[:5000]
      3 image_embeddings = img_embeddings[:5000]

Input In [4], in <listcomp>(.0)
----> 1 images = [Image.open("photos/"+filepath) for filepath in tqdm(img_names[:5000])]
      2 image_names = img_names[:5000]
      3 image_embeddings = img_embeddings[:5000]

File ~/tensorflow-metal/lib/python3.8/site-packages/PIL/Image.py:2968, in open(fp, mode, formats)
   2965     filename = fp
   2967 if filename:
-> 2968     fp = builtins.open(filename, "rb")
   2969     exclusive_fp = True
   2971 try:

OSError: [Errno 24] Too many open files: 'photos/icnZ2R8PcDs.jpg'

% ulimit -a
-t: cpu time (seconds)              unlimited
-f: file size (blocks)              unlimited
-d: data seg size (kbytes)          unlimited
-s: stack size (kbytes)             8192
-c: core file size (blocks)         0
-v: address space (kbytes)          unlimited
-l: locked-in-memory size (kbytes)  unlimited
-u: processes                       11136
-n: file descriptors                8192
(base) [email protected]_64-apple-darwin13 notebooks % 

Hello,

Thank you for sharing you great work. I'd like to have a better understanding of the "fit_transform" function.

How do you intend to use the parameter "image_names" ? For instance, i'd like to classify facebook posts. Does it means that I can pass posts messages with images embeddings to improve topics results ? Can you share any example of code using this parameter ?

Is it possible to return top keywords describing each topic ? As far as I understand your code 'fit_transform' returns only the list of topic predictions.

Thank you very much