├── .gitignore
├── Arabic Image Captioning.ipynb
├── README.md
├── data
    ├── .gitignore
    ├── Flickr8k_text
    │   ├── CrowdFlowerAnnotations.txt
    │   ├── ExpertAnnotations.txt
    │   ├── Flickr8k.arabic.full.txt
    │   ├── Flickr8k.arabic.txt
    │   ├── Flickr8k.lemma.token.txt
    │   ├── Flickr8k.token.txt
    │   ├── Flickr_8k.devImages.txt
    │   ├── Flickr_8k.testImages.txt
    │   ├── Flickr_8k.trainImages.txt
    │   └── readme.txt
    └── README.md
├── images
    ├── image_captioning_example.png
    ├── seq2seq-image-captioning-arabic-2.png
    └── seq2seq-image-captioning-arabic.png
├── requirements.txt
└── resources
    ├── .gitignore
    ├── README.md
    ├── arabic_preprocessing.py
    ├── bleu.py
    └── bleu_scorer.py


/.gitignore:
--------------------------------------------------------------------------------
  1 | # Byte-compiled / optimized / DLL files
  2 | __pycache__/
  3 | *.py[cod]
  4 | *$py.class
  5 | 
  6 | # C extensions
  7 | *.so
  8 | 
  9 | # Distribution / packaging
 10 | .Python
 11 | build/
 12 | develop-eggs/
 13 | dist/
 14 | downloads/
 15 | eggs/
 16 | .eggs/
 17 | lib/
 18 | lib64/
 19 | parts/
 20 | sdist/
 21 | var/
 22 | wheels/
 23 | *.egg-info/
 24 | .installed.cfg
 25 | *.egg
 26 | MANIFEST
 27 | 
 28 | # PyInstaller
 29 | #  Usually these files are written by a python script from a template
 30 | #  before PyInstaller builds the exe, so as to inject date/other infos into it.
 31 | *.manifest
 32 | *.spec
 33 | 
 34 | # Installer logs
 35 | pip-log.txt
 36 | pip-delete-this-directory.txt
 37 | 
 38 | # Unit test / coverage reports
 39 | htmlcov/
 40 | .tox/
 41 | .coverage
 42 | .coverage.*
 43 | .cache
 44 | nosetests.xml
 45 | coverage.xml
 46 | *.cover
 47 | .hypothesis/
 48 | .pytest_cache/
 49 | 
 50 | # Translations
 51 | *.mo
 52 | *.pot
 53 | 
 54 | # Django stuff:
 55 | *.log
 56 | local_settings.py
 57 | db.sqlite3
 58 | 
 59 | # Flask stuff:
 60 | instance/
 61 | .webassets-cache
 62 | 
 63 | # Scrapy stuff:
 64 | .scrapy
 65 | 
 66 | # Sphinx documentation
 67 | docs/_build/
 68 | 
 69 | # PyBuilder
 70 | target/
 71 | 
 72 | # Jupyter Notebook
 73 | .ipynb_checkpoints
 74 | 
 75 | # pyenv
 76 | .python-version
 77 | 
 78 | # celery beat schedule file
 79 | celerybeat-schedule
 80 | 
 81 | # SageMath parsed files
 82 | *.sage.py
 83 | 
 84 | # Environments
 85 | .env
 86 | .venv
 87 | env/
 88 | venv/
 89 | ENV/
 90 | env.bak/
 91 | venv.bak/
 92 | 
 93 | # Spyder project settings
 94 | .spyderproject
 95 | .spyproject
 96 | 
 97 | # Rope project settings
 98 | .ropeproject
 99 | 
100 | # mkdocs documentation
101 | /site
102 | 
103 | # mypy
104 | .mypy_cache/
105 | 


--------------------------------------------------------------------------------
/README.md:
--------------------------------------------------------------------------------
 1 | # Arabic-Image-Captioning
 2 | Generate Arabic captions for images using Deep Learning
 3 | 
 4 | Paper: [here](https://www.insticc.org/Primoris/Resources/PaperPdf.ashx?idPaper=88812)
 5 | 
 6 | Dataset: [./data/Flickr8k_text/Flickr8k.arabic.full.txt](./data/Flickr8k_text/Flickr8k.arabic.full.txt)
 7 | 
 8 | Presentation: [here](https://drive.google.com/file/d/1vt1LcE_0xEFHpQ95bLWLNWqrwZ9AbTS8/view?usp=sharing)
 9 | 
10 | ## Introduction
11 | Image Captioning refers to the art of describing the content of an image by computers. It is a well-known problem in CV and NLP. It has many applications, such as improved information retrieval, early childhood education, for visually impaired persons, for social media, and so on. Although remarkable work has been accomplished recently for English, and due to the lack of large and publicly available dataset, the progress on Arabic Image Captioning is still lagging. Therefore, we developed our own dataset based on Flickr8K. It can be found under **data/Flickr8k_text/** folder, named **Flickr8k.arabic.full.txt**. It has the exact same format as the original Flickr8K.
12 | 
13 | ## Model
14 | Inspired by recent advances in neural machine translation, the sequence-to-sequence encoder-decoder approach was adopted here.
15 | ![seq2seq-image-captioning-arabic](https://user-images.githubusercontent.com/9033365/50055387-e3ab9980-0156-11e9-859f-dce71314777a.png)
16 | For mode details, please check the paper.
17 | 
18 | ## Results
19 | Good example:
20 | ![good_examples](https://user-images.githubusercontent.com/9033365/50055400-181f5580-0157-11e9-8a00-1d7af672b49f.png)
21 | <br /> <br />
22 | Bad example:
23 | ![bad_examples](https://user-images.githubusercontent.com/9033365/50055408-2a998f00-0157-11e9-9d63-2b40e46a78f7.png)
24 | 
25 | Results show that developing language specific datasets and end-to-end models outperforms translating English generated captions to a destination language as the later may accumulate both models errors: image captioning and translation.
26 | ![6](https://user-images.githubusercontent.com/9033365/76162680-71f3ad00-6159-11ea-9b19-e8957435336b.PNG)
27 | 
28 | ## Installation
29 | 1. Clone or download this repository.
30 | 
31 | 2. Make sure python 3.x is installed on your PC. To check if and which version is installed, run the following command:
32 | ```
33 | python -V
34 | ```
35 | If this results an error, this means that python isn’t installed on your PC! please download and install it from [the original website](https://www.python.org/)
36 | 
37 | 3. (optional) it is recommended that you create a python virtual environment and install the necessary libraries in it to prevent versions collisions:
38 | ```
39 | python -m venv AIC
40 | ```
41 | where AIC is the environment name. Once you’ve created a virtual environment, you may activate it.
42 | ```
43 | AIC\Scripts\activate.bat
44 | ```
45 | 
46 | 4. Install required libraries from the provided file (**requirements.txt**):
47 | ```
48 | pip install -r requirements.txt
49 | ```
50 | Make sure you provide the correct path of **requirements.txt**
51 | 
52 | 5. Open jupyter notebook:
53 | ```
54 | AIC\Scripts\jupyter-notebook.exe
55 | ```
56 | then navigate to and open [Arabic Image Captioning.ipynb](./Arabic%20Image%20Captioning.ipynb)
57 | 
58 | 
59 | ## Citation
60 | Please cite [this paper](https://www.insticc.org/Primoris/Resources/PaperPdf.ashx?idPaper=88812):
61 | 
62 | ```
63 | @conference{visapp20,
64 | author={Obeida ElJundi. and Mohamad Dhaybi. and Kotaiba Mokadam. and Hazem Hajj. and Daniel Asmar.},
65 | title={Resources and End-to-End Neural Network Models for Arabic Image Captioning},
66 | booktitle={Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5: VISAPP,},
67 | year={2020},
68 | pages={233-241},
69 | publisher={SciTePress},
70 | organization={INSTICC},
71 | doi={10.5220/0008881202330241},
72 | isbn={978-989-758-402-2},
73 | }
74 | ```
75 | 
76 | ## Contact information
77 | For help, issues, or personal communication related to this work, please contact Obeida ElJundi: [oae15@mail.aub.edu](mailto:oae15@mail.aub.edu)
78 | 


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/data/.gitignore:
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1 | Flicker8k_Dataset/
2 | 


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1001 | 


--------------------------------------------------------------------------------
/data/Flickr8k_text/readme.txt:
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 1 | If you use this corpus / data:
 2 | 
 3 | Please cite: M. Hodosh, P. Young and J. Hockenmaier (2013) "Framing Image Description as a Ranking Task: Data, Models and Evaluation Metrics", Journal of Artifical Intellegence Research, Volume 47, pages 853-899
 4 | http://www.jair.org/papers/paper3994.html
 5 | 
 6 | 
 7 | Captions, Dataset Splits, and Human Annotations :
 8 | 
 9 | 
10 | Flickr8k.token.txt - the raw captions of the Flickr8k Dataset . The first column is the ID of the caption which is "image address # caption number"
11 | 
12 | Flickr8k.lemma.txt - the lemmatized version of the above captions 
13 | 
14 | Flickr_8k.trainImages.txt - The training images used in our experiments
15 | Flickr_8k.devImages.txt - The development/validation images used in our experiments
16 | Flickr_8k.testImages.txt - The test images used in our experiments
17 | 
18 | 
19 | ExpertAnnotations.txt is the expert judgments.  The first two columns are the image and caption IDs.  Caption IDs are <image file name>#<0-4>.  The next three columns are the expert judgments for that image-caption pair.  Scores range from 1 to 4, with a 1 indicating that the caption does not describe the image at all, a 2 indicating the caption describes minor aspects of the image but does not describe the image, a 3 indicating that the caption almost describes the image with minor mistakes, and a 4 indicating that the caption describes the image.
20 | 
21 | 
22 | CrowdFlowerAnnotations.txt contains the CrowdFlower judgments.  The first two columns are the image and caption IDs.  The third column is the percent of Yeses, the fourth column is the total number of Yeses, the fifth column is the total number of Noes.  A Yes means that the caption describes the image (possibly with minor mistakes), while a No means that the caption does not describe the image.  Each image-caption pair has a minimum of three judgments, but some may have more.
23 | 


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/data/README.md:
--------------------------------------------------------------------------------
1 | Please download **Flickr8k_Dataset.zip** from here: http://nlp.cs.illinois.edu/HockenmaierGroup/Framing_Image_Description/Flickr8k_Dataset.zip
2 | <br />
3 | Extract **Flickr8k_Dataset.zip** and place **Flicker8k_Dataset** folder here
4 | 


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/images/image_captioning_example.png:
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https://raw.githubusercontent.com/aub-mind/Arabic-Image-Captioning/2fcffc746faa35c85c18f14228f4aba22c1fa51b/images/image_captioning_example.png


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/images/seq2seq-image-captioning-arabic-2.png:
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https://raw.githubusercontent.com/aub-mind/Arabic-Image-Captioning/2fcffc746faa35c85c18f14228f4aba22c1fa51b/images/seq2seq-image-captioning-arabic-2.png


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/images/seq2seq-image-captioning-arabic.png:
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https://raw.githubusercontent.com/aub-mind/Arabic-Image-Captioning/2fcffc746faa35c85c18f14228f4aba22c1fa51b/images/seq2seq-image-captioning-arabic.png


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/requirements.txt:
--------------------------------------------------------------------------------
  1 | absl-py==0.6.1
  2 | arabic-reshaper==2.0.14
  3 | astor==0.7.1
  4 | Babel==2.6.0
  5 | backcall==0.1.0
  6 | beautifulsoup4==4.6.3
  7 | bleach==3.0.2
  8 | boto==2.49.0
  9 | boto3==1.9.48
 10 | botocore==1.12.48
 11 | bz2file==0.98
 12 | cachetools==3.0.0
 13 | certifi==2018.10.15
 14 | chardet==3.0.4
 15 | colorama==0.4.0
 16 | configparser==3.5.0
 17 | cycler==0.10.0
 18 | dateutils==0.6.6
 19 | decorator==4.3.0
 20 | defusedxml==0.5.0
 21 | docutils==0.14
 22 | entrypoints==0.2.3
 23 | et-xmlfile==1.0.1
 24 | future==0.17.1
 25 | gast==0.2.0
 26 | gensim==3.6.0
 27 | google-api-core==1.5.2
 28 | google-api-python-client==1.7.4
 29 | google-auth==1.6.1
 30 | google-auth-httplib2==0.0.3
 31 | google-cloud-core==0.28.1
 32 | google-cloud-translate==1.3.1
 33 | googleapis-common-protos==1.5.5
 34 | googletrans==2.3.0
 35 | gp-python-client==1.0.0
 36 | grpcio==1.16.1
 37 | h5py==2.8.0
 38 | httplib2==0.12.0
 39 | idna==2.7
 40 | ipykernel==5.1.0
 41 | ipython==7.1.1
 42 | ipython-genutils==0.2.0
 43 | ipywidgets==7.4.2
 44 | jdcal==1.4
 45 | jedi==0.13.1
 46 | Jinja2==2.10
 47 | jmespath==0.9.3
 48 | jsonschema==2.6.0
 49 | jupyter==1.0.0
 50 | jupyter-client==5.2.3
 51 | jupyter-console==6.0.0
 52 | jupyter-contrib-core==0.3.3
 53 | jupyter-contrib-nbextensions==0.5.0
 54 | jupyter-core==4.4.0
 55 | jupyter-highlight-selected-word==0.2.0
 56 | jupyter-latex-envs==1.4.4
 57 | jupyter-nbextensions-configurator==0.4.0
 58 | Keras==2.2.4
 59 | Keras-Applications==1.0.6
 60 | Keras-Preprocessing==1.0.5
 61 | keras-tqdm==2.0.1
 62 | kiwisolver==1.0.1
 63 | langdetect==1.0.7
 64 | lxml==4.2.5
 65 | Markdown==3.0.1
 66 | MarkupSafe==1.1.0
 67 | matplotlib==3.0.2
 68 | mistune==0.8.4
 69 | nbconvert==5.4.0
 70 | nbformat==4.4.0
 71 | nltk==3.4
 72 | notebook==5.7.2
 73 | numpy==1.15.4
 74 | openpyxl==2.5.11
 75 | pandas==0.23.4
 76 | pandocfilters==1.4.2
 77 | parso==0.3.1
 78 | pickleshare==0.7.5
 79 | Pillow==5.3.0
 80 | prometheus-client==0.4.2
 81 | prompt-toolkit==2.0.7
 82 | protobuf==3.6.1
 83 | py-translator==1.8.9
 84 | pyasn1==0.4.4
 85 | pyasn1-modules==0.2.2
 86 | Pygments==2.2.0
 87 | pyparsing==2.3.0
 88 | python-bidi==0.4.0
 89 | python-dateutil==2.7.5
 90 | python-slugify==1.2.6
 91 | pytz==2018.7
 92 | pywinpty==0.5.4
 93 | PyYAML==3.13
 94 | pyzmq==17.1.2
 95 | qtconsole==4.4.3
 96 | requests==2.20.1
 97 | rsa==4.0
 98 | s3transfer==0.1.13
 99 | scapy==2.4.0
100 | scikit-learn==0.20.0
101 | scipy==1.1.0
102 | seaborn==0.9.0
103 | selenium==3.141.0
104 | Send2Trash==1.5.0
105 | singledispatch==3.4.0.3
106 | six==1.11.0
107 | smart-open==1.7.1
108 | tensorboard==1.12.0
109 | tensorflow-gpu==1.12.0
110 | termcolor==1.1.0
111 | terminado==0.8.1
112 | testpath==0.4.2
113 | tornado==5.1.1
114 | tqdm==4.28.1
115 | traitlets==4.3.2
116 | Unidecode==1.0.23
117 | uritemplate==3.0.0
118 | urllib3==1.24.1
119 | wcwidth==0.1.7
120 | webencodings==0.5.1
121 | Werkzeug==0.14.1
122 | widgetsnbextension==3.4.2
123 | xlrd==1.1.0
124 | 


--------------------------------------------------------------------------------
/resources/.gitignore:
--------------------------------------------------------------------------------
  1 | # Byte-compiled / optimized / DLL files
  2 | __pycache__/
  3 | *.py[cod]
  4 | *$py.class
  5 | 
  6 | # C extensions
  7 | *.so
  8 | 
  9 | # Distribution / packaging
 10 | .Python
 11 | build/
 12 | develop-eggs/
 13 | dist/
 14 | downloads/
 15 | eggs/
 16 | .eggs/
 17 | lib/
 18 | lib64/
 19 | parts/
 20 | sdist/
 21 | var/
 22 | wheels/
 23 | *.egg-info/
 24 | .installed.cfg
 25 | *.egg
 26 | MANIFEST
 27 | 
 28 | # PyInstaller
 29 | #  Usually these files are written by a python script from a template
 30 | #  before PyInstaller builds the exe, so as to inject date/other infos into it.
 31 | *.manifest
 32 | *.spec
 33 | 
 34 | # Installer logs
 35 | pip-log.txt
 36 | pip-delete-this-directory.txt
 37 | 
 38 | # Unit test / coverage reports
 39 | htmlcov/
 40 | .tox/
 41 | .coverage
 42 | .coverage.*
 43 | .cache
 44 | nosetests.xml
 45 | coverage.xml
 46 | *.cover
 47 | .hypothesis/
 48 | .pytest_cache/
 49 | 
 50 | # Translations
 51 | *.mo
 52 | *.pot
 53 | 
 54 | # Django stuff:
 55 | *.log
 56 | local_settings.py
 57 | db.sqlite3
 58 | 
 59 | # Flask stuff:
 60 | instance/
 61 | .webassets-cache
 62 | 
 63 | # Scrapy stuff:
 64 | .scrapy
 65 | 
 66 | # Sphinx documentation
 67 | docs/_build/
 68 | 
 69 | # PyBuilder
 70 | target/
 71 | 
 72 | # Jupyter Notebook
 73 | .ipynb_checkpoints
 74 | 
 75 | # pyenv
 76 | .python-version
 77 | 
 78 | # celery beat schedule file
 79 | celerybeat-schedule
 80 | 
 81 | # SageMath parsed files
 82 | *.sage.py
 83 | 
 84 | # Environments
 85 | .env
 86 | .venv
 87 | env/
 88 | venv/
 89 | ENV/
 90 | env.bak/
 91 | venv.bak/
 92 | 
 93 | # Spyder project settings
 94 | .spyderproject
 95 | .spyproject
 96 | 
 97 | # Rope project settings
 98 | .ropeproject
 99 | 
100 | # mkdocs documentation
101 | /site
102 | 
103 | # mypy
104 | .mypy_cache/
105 | 
106 | 
107 | 
108 | Flickr8K_features.pkl
109 | vgg16_weights_tf_dim_ordering_tf_kernels.h5


--------------------------------------------------------------------------------
/resources/README.md:
--------------------------------------------------------------------------------
1 | Please download VGG16 weights from here: https://github.com/fchollet/deep-learning-models/releases/download/v0.1/vgg16_weights_tf_dim_ordering_tf_kernels.h5
2 | <br />
3 | and place them here..
4 | <br /><br /><br /><br />
5 | Please download **Flickr8K_features.pkl** from here: https://drive.google.com/file/d/1hAbpX0EQvOWk6EnSHsSKoqP3Ncm167GB/view?usp=sharing
6 | <br />
7 | and place it here..
8 | <br />
9 | or you can create your own **Flickr8K_features.pkl** by running the **Transfer Learning** section in the notebook..


--------------------------------------------------------------------------------
/resources/arabic_preprocessing.py:
--------------------------------------------------------------------------------
  1 | 
  2 | # coding: utf-8
  3 | 
  4 | # # Preprocessing Arabic Text
  5 | 
  6 | # ### this module aims to preprocess arabic text in order to be efficiently used for Natural Language Processing & Machine Learning
  7 | # ### Preprocessing includes:
  8 | # - remove punctuations & diacritics
  9 | # - normalize (or standarize) Hamza & Ha2
 10 | # - remove repeating characters
 11 | # - remove english characters
 12 | # - remove one-character words
 13 | 
 14 | # ### import necessary libraries
 15 | 
 16 | # In[1]:
 17 | 
 18 | 
 19 | import re
 20 | #import nltk  # if nltk is being used for the first time, you may need to download some resources: nltk.download()
 21 | #from nltk.stem.arlstem import ARLSTem  # requires minimum nltk version of 3.2.5
 22 | import string
 23 | 
 24 | 
 25 | # ### Defining Arabic preprocessing class
 26 | 
 27 | # In[2]:
 28 | 
 29 | 
 30 | class Arabic_preprocessing:
 31 |     
 32 |     
 33 |     def __init__(self):
 34 |         
 35 |         #preparing punctuations list
 36 |         arabic_punctuations = '''`÷×؛<>_()*&^%][ـ،/:"؟.,'{}~¦+|!”…“–ـ'''
 37 |         english_punctuations = string.punctuation
 38 |         self.all_punctuations = set(arabic_punctuations + english_punctuations)
 39 | 
 40 |         # initializing the stemmer
 41 |         #self.stemmer = ARLSTem()  # requires minimum NLTK version of 3.2.5
 42 |         
 43 |         self.arabic_diacritics = re.compile("""
 44 |                                          ّ    | # Tashdid
 45 |                                          َ    | # Fatha
 46 |                                          ً    | # Tanwin Fath
 47 |                                          ُ    | # Damma
 48 |                                          ٌ    | # Tanwin Damm
 49 |                                          ِ    | # Kasra
 50 |                                          ٍ    | # Tanwin Kasr
 51 |                                          ْ    | # Sukun
 52 |                                          ـ     # Tatwil/Kashida
 53 | 
 54 |                                      """, re.VERBOSE)
 55 | 
 56 |         
 57 |     def normalize_arabic(self, text):
 58 |         text = re.sub("[إأآاٱ]", "ا", text)
 59 |         text = re.sub("ى", "ي", text)
 60 |         #text = re.sub("ؤ", "ء", text)
 61 |         #text = re.sub("ئ", "ء", text)
 62 |         text = re.sub("ة", "ه", text)  # replace ta2 marboota by ha2
 63 |         text = re.sub("گ", "ك", text)
 64 |         text = re.sub("\u0640", '', text)  # remove tatweel
 65 |         return text
 66 | 
 67 | 
 68 |     def remove_punctuations(self, text):
 69 |         return ''.join([c for c in text if c not in self.all_punctuations]) #remove punctuations
 70 | 
 71 | 
 72 |     def remove_diacritics(self, text):
 73 |         text = re.sub(self.arabic_diacritics, '', text)
 74 |         return text
 75 | 
 76 | 
 77 |     def remove_repeating_char(self, text):
 78 |         return re.sub(r'(.)\1+', r'\1', text)
 79 | 
 80 | 
 81 |     def remove_mention(self, text):
 82 |         return re.sub(r'@\S+', '', text)
 83 | 
 84 |     def hashtag_match(self, match_object):
 85 |         return match_object.group(1).replace('_', ' ')
 86 | 
 87 |     def normalize_hashtag(self, text):
 88 |         return re.sub(r'#(\S+)', self.hashtag_match, text)
 89 | 
 90 |     def emojis_match(self, match_object):
 91 |         return ' ' + ' '.join(list(match_object.group(1))) + ' '
 92 | 
 93 |     def separate_emojis(self, text):
 94 |         emojis_unicode = r'([\U0001F600-\U0001F64F\U00002000-\U00003000]+)'
 95 |         return re.sub(emojis_unicode, self.emojis_match, text)
 96 | 
 97 |     def replace_emojis(self, text):
 98 |         new_text = ""
 99 |         for l in text:
100 |             new_text += self.emojis_lexicon_dict[l] if l in self.emojis_lexicon_dict.keys() else l
101 |         return new_text
102 | 
103 |     def remove_english_characters(self, text):
104 |         return re.sub(r'[a-zA-Z]+', '', text)
105 |     
106 |     def clean_stop_words(self):
107 |         # normalize, and remove diacritics from, stop words to increase posibility of matching with normalized data
108 |         self.stop_words = [self.remove_diacritics(self.normalize_arabic(word)) for word in self.stop_words]
109 |     
110 |     def preprocess_arabic_text(self, text):
111 |         #self.clean_stop_words()
112 |         #text = text.replace('\\n', ' ').replace('\n', ' ')
113 |         #text = self.remove_mention(text)
114 |         #text = self.normalize_hashtag(text)
115 |         text = self.remove_punctuations(text)
116 |         text = self.remove_diacritics(text)
117 |         text = self.normalize_arabic(text)
118 |         #text = self.separate_emojis(text)
119 |         #if replace_emojis: text = self.replace_emojis(text)
120 |         #text = self.remove_repeating_char(text)
121 |         text = self.remove_english_characters(text)
122 |         #words = nltk.word_tokenize(text)
123 |         #words = [word for word in words if word not in self.stop_words]
124 |         #if stem: words = [self.stemmer.stem(word) for word in words]
125 |         text = ' '.join([w for w in text.split() if len(w)>1 and w.isalpha()]) #remove one-character & numeric words
126 |         return text
127 | 
128 | 


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/resources/bleu.py:
--------------------------------------------------------------------------------
  1 | #!/usr/bin/env python
  2 | # 
  3 | # File Name : bleu.py
  4 | #
  5 | # Description : Wrapper for BLEU scorer.
  6 | #
  7 | # Creation Date : 06-01-2015
  8 | # Last Modified : Thu 19 Mar 2015 09:13:28 PM PDT
  9 | # Authors : Hao Fang <hfang@uw.edu> and Tsung-Yi Lin <tl483@cornell.edu>
 10 | 
 11 | #from bleu_scorer import BleuScorer
 12 | 
 13 | 
 14 | 
 15 | 
 16 | 
 17 | 
 18 | '''Provides:
 19 | cook_refs(refs, n=4): Transform a list of reference sentences as strings into a form usable by cook_test().
 20 | cook_test(test, refs, n=4): Transform a test sentence as a string (together with the cooked reference sentences) into a form usable by score_cooked().
 21 | '''
 22 | 
 23 | import copy
 24 | import sys, math, re
 25 | from collections import defaultdict
 26 | 
 27 | def precook(s, n=4, out=False):
 28 |     """Takes a string as input and returns an object that can be given to
 29 |     either cook_refs or cook_test. This is optional: cook_refs and cook_test
 30 |     can take string arguments as well."""
 31 |     words = s.split()
 32 |     counts = defaultdict(int)
 33 |     for k in range(1,n+1):
 34 |         for i in range(len(words)-k+1):
 35 |             ngram = tuple(words[i:i+k])
 36 |             counts[ngram] += 1
 37 |     return (len(words), counts)
 38 | 
 39 | def cook_refs(refs, eff=None, n=4): ## lhuang: oracle will call with "average"
 40 |     '''Takes a list of reference sentences for a single segment
 41 |     and returns an object that encapsulates everything that BLEU
 42 |     needs to know about them.'''
 43 | 
 44 |     reflen = []
 45 |     maxcounts = {}
 46 |     for ref in refs:
 47 |         rl, counts = precook(ref, n)
 48 |         reflen.append(rl)
 49 |         for (ngram,count) in counts.items():
 50 |             maxcounts[ngram] = max(maxcounts.get(ngram,0), count)
 51 | 
 52 |     # Calculate effective reference sentence length.
 53 |     if eff == "shortest":
 54 |         reflen = min(reflen)
 55 |     elif eff == "average":
 56 |         reflen = float(sum(reflen))/len(reflen)
 57 | 
 58 |     ## lhuang: N.B.: leave reflen computaiton to the very end!!
 59 |     
 60 |     ## lhuang: N.B.: in case of "closest", keep a list of reflens!! (bad design)
 61 | 
 62 |     return (reflen, maxcounts)
 63 | 
 64 | def cook_test(test, s, eff=None, n=4):
 65 |     '''Takes a test sentence and returns an object that
 66 |     encapsulates everything that BLEU needs to know about it.'''
 67 | 
 68 |     reflen, refmaxcounts = s
 69 | 
 70 |     testlen, counts = precook(test, n, True)
 71 | 
 72 |     result = {}
 73 | 
 74 |     # Calculate effective reference sentence length.
 75 |     
 76 |     if eff == "closest":
 77 |         result["reflen"] = min((abs(l-testlen), l) for l in reflen)[1]
 78 |     else: ## i.e., "average" or "shortest" or None
 79 |         result["reflen"] = reflen
 80 | 
 81 |     result["testlen"] = testlen
 82 | 
 83 |     result["guess"] = [max(0,testlen-k+1) for k in range(1,n+1)]
 84 | 
 85 |     result['correct'] = [0]*n
 86 |     for (ngram, count) in counts.items():
 87 |         result["correct"][len(ngram)-1] += min(refmaxcounts.get(ngram,0), count)
 88 | 
 89 |     return result
 90 | 
 91 | class BleuScorer(object):
 92 |     """Bleu scorer.
 93 |     """
 94 | 
 95 |     __slots__ = "n", "crefs", "ctest", "_score", "_ratio", "_testlen", "_reflen", "special_reflen"
 96 |     # special_reflen is used in oracle (proportional effective ref len for a node).
 97 | 
 98 |     def copy(self):
 99 |         ''' copy the refs.'''
100 |         new = BleuScorer(n=self.n)
101 |         new.ctest = copy.copy(self.ctest)
102 |         new.crefs = copy.copy(self.crefs)
103 |         new._score = None
104 |         return new
105 | 
106 |     def __init__(self, test=None, refs=None, n=4, special_reflen=None):
107 |         ''' singular instance '''
108 | 
109 |         self.n = n
110 |         self.crefs = []
111 |         self.ctest = []
112 |         self.cook_append(test, refs)
113 |         self.special_reflen = special_reflen
114 | 
115 |     def cook_append(self, test, refs):
116 |         '''called by constructor and __iadd__ to avoid creating new instances.'''
117 |         
118 |         if refs is not None:
119 |             self.crefs.append(cook_refs(refs))
120 |             if test is not None:
121 |                 cooked_test = cook_test(test, self.crefs[-1])
122 |                 self.ctest.append(cooked_test) ## N.B.: -1
123 |             else:
124 |                 self.ctest.append(None) # lens of crefs and ctest have to match
125 | 
126 |         self._score = None ## need to recompute
127 | 
128 |     def ratio(self, option=None):
129 |         self.compute_score(option=option)
130 |         return self._ratio
131 | 
132 |     def score_ratio(self, option=None):
133 |         '''return (bleu, len_ratio) pair'''
134 |         return (self.fscore(option=option), self.ratio(option=option))
135 | 
136 |     def score_ratio_str(self, option=None):
137 |         return "%.4f (%.2f)" % self.score_ratio(option)
138 | 
139 |     def reflen(self, option=None):
140 |         self.compute_score(option=option)
141 |         return self._reflen
142 | 
143 |     def testlen(self, option=None):
144 |         self.compute_score(option=option)
145 |         return self._testlen        
146 | 
147 |     def retest(self, new_test):
148 |         if type(new_test) is str:
149 |             new_test = [new_test]
150 |         assert len(new_test) == len(self.crefs), new_test
151 |         self.ctest = []
152 |         for t, rs in zip(new_test, self.crefs):
153 |             self.ctest.append(cook_test(t, rs))
154 |         self._score = None
155 | 
156 |         return self
157 | 
158 |     def rescore(self, new_test):
159 |         ''' replace test(s) with new test(s), and returns the new score.'''
160 |         
161 |         return self.retest(new_test).compute_score()
162 | 
163 |     def size(self):
164 |         assert len(self.crefs) == len(self.ctest), "refs/test mismatch! %d<>%d" % (len(self.crefs), len(self.ctest))
165 |         return len(self.crefs)
166 | 
167 |     def __iadd__(self, other):
168 |         '''add an instance (e.g., from another sentence).'''
169 | 
170 |         if type(other) is tuple:
171 |             ## avoid creating new BleuScorer instances
172 |             self.cook_append(other[0], other[1])
173 |         else:
174 |             assert self.compatible(other), "incompatible BLEUs."
175 |             self.ctest.extend(other.ctest)
176 |             self.crefs.extend(other.crefs)
177 |             self._score = None ## need to recompute
178 | 
179 |         return self        
180 | 
181 |     def compatible(self, other):
182 |         return isinstance(other, BleuScorer) and self.n == other.n
183 | 
184 |     def single_reflen(self, option="average"):
185 |         return self._single_reflen(self.crefs[0][0], option)
186 | 
187 |     def _single_reflen(self, reflens, option=None, testlen=None):
188 |         
189 |         if option == "shortest":
190 |             reflen = min(reflens)
191 |         elif option == "average":
192 |             reflen = float(sum(reflens))/len(reflens)
193 |         elif option == "closest":
194 |             reflen = min((abs(l-testlen), l) for l in reflens)[1]
195 |         else:
196 |             assert False, "unsupported reflen option %s" % option
197 | 
198 |         return reflen
199 | 
200 |     def recompute_score(self, option=None, verbose=0):
201 |         self._score = None
202 |         return self.compute_score(option, verbose)
203 |         
204 |     def compute_score(self, option=None, verbose=0):
205 |         n = self.n
206 |         small = 1e-9
207 |         tiny = 1e-15 ## so that if guess is 0 still return 0
208 |         bleu_list = [[] for _ in range(n)]
209 | 
210 |         if self._score is not None:
211 |             return self._score
212 | 
213 |         if option is None:
214 |             option = "average" if len(self.crefs) == 1 else "closest"
215 | 
216 |         self._testlen = 0
217 |         self._reflen = 0
218 |         totalcomps = {'testlen':0, 'reflen':0, 'guess':[0]*n, 'correct':[0]*n}
219 | 
220 |         # for each sentence
221 |         for comps in self.ctest:            
222 |             testlen = comps['testlen']
223 |             self._testlen += testlen
224 | 
225 |             if self.special_reflen is None: ## need computation
226 |                 reflen = self._single_reflen(comps['reflen'], option, testlen)
227 |             else:
228 |                 reflen = self.special_reflen
229 | 
230 |             self._reflen += reflen
231 |                 
232 |             for key in ['guess','correct']:
233 |                 for k in range(n):
234 |                     totalcomps[key][k] += comps[key][k]
235 | 
236 |             # append per image bleu score
237 |             bleu = 1.
238 |             for k in range(n):
239 |                 bleu *= (float(comps['correct'][k]) + tiny) \
240 |                         /(float(comps['guess'][k]) + small) 
241 |                 bleu_list[k].append(bleu ** (1./(k+1)))
242 |             ratio = (testlen + tiny) / (reflen + small) ## N.B.: avoid zero division
243 |             if ratio < 1:
244 |                 for k in range(n):
245 |                     bleu_list[k][-1] *= math.exp(1 - 1/ratio)
246 | 
247 |             if verbose > 1:
248 |                 print(comps, reflen)
249 | 
250 |         totalcomps['reflen'] = self._reflen
251 |         totalcomps['testlen'] = self._testlen
252 | 
253 |         bleus = []
254 |         bleu = 1.
255 |         for k in range(n):
256 |             bleu *= float(totalcomps['correct'][k] + tiny) \
257 |                     / (totalcomps['guess'][k] + small)
258 |             bleus.append(bleu ** (1./(k+1)))
259 |         ratio = (self._testlen + tiny) / (self._reflen + small) ## N.B.: avoid zero division
260 |         if ratio < 1:
261 |             for k in range(n):
262 |                 bleus[k] *= math.exp(1 - 1/ratio)
263 | 
264 |         if verbose > 0:
265 |             print(totalcomps)
266 |             print("ratio:", ratio)
267 | 
268 |         self._score = bleus
269 |         return self._score, bleu_list
270 | 
271 | 
272 | 
273 | 
274 | 
275 | 
276 | 
277 | 
278 | 
279 | 
280 | 
281 | 
282 | 
283 | 
284 | 
285 | 
286 | class Bleu:
287 |     def __init__(self, n=4):
288 |         # default compute Blue score up to 4
289 |         self._n = n
290 |         self._hypo_for_image = {}
291 |         self.ref_for_image = {}
292 | 
293 |     def compute_score(self, gts, res):
294 | 
295 |         assert(gts.keys() == res.keys())
296 |         imgIds = gts.keys()
297 | 
298 |         bleu_scorer = BleuScorer(n=self._n)
299 |         for id in imgIds:
300 |             hypo = res[id]
301 |             ref = gts[id]
302 | 
303 |             # Sanity check.
304 |             assert(type(hypo) is list)
305 |             assert(len(hypo) == 1)
306 |             assert(type(ref) is list)
307 |             assert(len(ref) >= 1)
308 | 
309 |             bleu_scorer += (hypo[0], ref)
310 | 
311 |         #score, scores = bleu_scorer.compute_score(option='shortest')
312 |         score, scores = bleu_scorer.compute_score(option='closest', verbose=1)
313 |         #score, scores = bleu_scorer.compute_score(option='average', verbose=1)
314 | 
315 |         # return (bleu, bleu_info)
316 |         return score, scores
317 | 
318 |     def method(self):
319 |         return "Bleu"
320 | 


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/resources/bleu_scorer.py:
--------------------------------------------------------------------------------
  1 | #!/usr/bin/env python
  2 | 
  3 | # bleu_scorer.py
  4 | # David Chiang <chiang@isi.edu>
  5 | 
  6 | # Copyright (c) 2004-2006 University of Maryland. All rights
  7 | # reserved. Do not redistribute without permission from the
  8 | # author. Not for commercial use.
  9 | 
 10 | # Modified by: 
 11 | # Hao Fang <hfang@uw.edu>
 12 | # Tsung-Yi Lin <tl483@cornell.edu>
 13 | 
 14 | '''Provides:
 15 | cook_refs(refs, n=4): Transform a list of reference sentences as strings into a form usable by cook_test().
 16 | cook_test(test, refs, n=4): Transform a test sentence as a string (together with the cooked reference sentences) into a form usable by score_cooked().
 17 | '''
 18 | 
 19 | import copy
 20 | import sys, math, re
 21 | from collections import defaultdict
 22 | 
 23 | def precook(s, n=4, out=False):
 24 |     """Takes a string as input and returns an object that can be given to
 25 |     either cook_refs or cook_test. This is optional: cook_refs and cook_test
 26 |     can take string arguments as well."""
 27 |     words = s.split()
 28 |     counts = defaultdict(int)
 29 |     for k in range(1,n+1):
 30 |         for i in range(len(words)-k+1):
 31 |             ngram = tuple(words[i:i+k])
 32 |             counts[ngram] += 1
 33 |     return (len(words), counts)
 34 | 
 35 | def cook_refs(refs, eff=None, n=4): ## lhuang: oracle will call with "average"
 36 |     '''Takes a list of reference sentences for a single segment
 37 |     and returns an object that encapsulates everything that BLEU
 38 |     needs to know about them.'''
 39 | 
 40 |     reflen = []
 41 |     maxcounts = {}
 42 |     for ref in refs:
 43 |         rl, counts = precook(ref, n)
 44 |         reflen.append(rl)
 45 |         for (ngram,count) in counts.items():
 46 |             maxcounts[ngram] = max(maxcounts.get(ngram,0), count)
 47 | 
 48 |     # Calculate effective reference sentence length.
 49 |     if eff == "shortest":
 50 |         reflen = min(reflen)
 51 |     elif eff == "average":
 52 |         reflen = float(sum(reflen))/len(reflen)
 53 | 
 54 |     ## lhuang: N.B.: leave reflen computaiton to the very end!!
 55 |     
 56 |     ## lhuang: N.B.: in case of "closest", keep a list of reflens!! (bad design)
 57 | 
 58 |     return (reflen, maxcounts)
 59 | 
 60 | def cook_test(test, s, eff=None, n=4):
 61 |     '''Takes a test sentence and returns an object that
 62 |     encapsulates everything that BLEU needs to know about it.'''
 63 | 
 64 |     reflen, refmaxcounts = s
 65 | 
 66 |     testlen, counts = precook(test, n, True)
 67 | 
 68 |     result = {}
 69 | 
 70 |     # Calculate effective reference sentence length.
 71 |     
 72 |     if eff == "closest":
 73 |         result["reflen"] = min((abs(l-testlen), l) for l in reflen)[1]
 74 |     else: ## i.e., "average" or "shortest" or None
 75 |         result["reflen"] = reflen
 76 | 
 77 |     result["testlen"] = testlen
 78 | 
 79 |     result["guess"] = [max(0,testlen-k+1) for k in range(1,n+1)]
 80 | 
 81 |     result['correct'] = [0]*n
 82 |     for (ngram, count) in counts.items():
 83 |         result["correct"][len(ngram)-1] += min(refmaxcounts.get(ngram,0), count)
 84 | 
 85 |     return result
 86 | 
 87 | class BleuScorer(object):
 88 |     """Bleu scorer.
 89 |     """
 90 | 
 91 |     __slots__ = "n", "crefs", "ctest", "_score", "_ratio", "_testlen", "_reflen", "special_reflen"
 92 |     # special_reflen is used in oracle (proportional effective ref len for a node).
 93 | 
 94 |     def copy(self):
 95 |         ''' copy the refs.'''
 96 |         new = BleuScorer(n=self.n)
 97 |         new.ctest = copy.copy(self.ctest)
 98 |         new.crefs = copy.copy(self.crefs)
 99 |         new._score = None
100 |         return new
101 | 
102 |     def __init__(self, test=None, refs=None, n=4, special_reflen=None):
103 |         ''' singular instance '''
104 | 
105 |         self.n = n
106 |         self.crefs = []
107 |         self.ctest = []
108 |         self.cook_append(test, refs)
109 |         self.special_reflen = special_reflen
110 | 
111 |     def cook_append(self, test, refs):
112 |         '''called by constructor and __iadd__ to avoid creating new instances.'''
113 |         
114 |         if refs is not None:
115 |             self.crefs.append(cook_refs(refs))
116 |             if test is not None:
117 |                 cooked_test = cook_test(test, self.crefs[-1])
118 |                 self.ctest.append(cooked_test) ## N.B.: -1
119 |             else:
120 |                 self.ctest.append(None) # lens of crefs and ctest have to match
121 | 
122 |         self._score = None ## need to recompute
123 | 
124 |     def ratio(self, option=None):
125 |         self.compute_score(option=option)
126 |         return self._ratio
127 | 
128 |     def score_ratio(self, option=None):
129 |         '''return (bleu, len_ratio) pair'''
130 |         return (self.fscore(option=option), self.ratio(option=option))
131 | 
132 |     def score_ratio_str(self, option=None):
133 |         return "%.4f (%.2f)" % self.score_ratio(option)
134 | 
135 |     def reflen(self, option=None):
136 |         self.compute_score(option=option)
137 |         return self._reflen
138 | 
139 |     def testlen(self, option=None):
140 |         self.compute_score(option=option)
141 |         return self._testlen        
142 | 
143 |     def retest(self, new_test):
144 |         if type(new_test) is str:
145 |             new_test = [new_test]
146 |         assert len(new_test) == len(self.crefs), new_test
147 |         self.ctest = []
148 |         for t, rs in zip(new_test, self.crefs):
149 |             self.ctest.append(cook_test(t, rs))
150 |         self._score = None
151 | 
152 |         return self
153 | 
154 |     def rescore(self, new_test):
155 |         ''' replace test(s) with new test(s), and returns the new score.'''
156 |         
157 |         return self.retest(new_test).compute_score()
158 | 
159 |     def size(self):
160 |         assert len(self.crefs) == len(self.ctest), "refs/test mismatch! %d<>%d" % (len(self.crefs), len(self.ctest))
161 |         return len(self.crefs)
162 | 
163 |     def __iadd__(self, other):
164 |         '''add an instance (e.g., from another sentence).'''
165 | 
166 |         if type(other) is tuple:
167 |             ## avoid creating new BleuScorer instances
168 |             self.cook_append(other[0], other[1])
169 |         else:
170 |             assert self.compatible(other), "incompatible BLEUs."
171 |             self.ctest.extend(other.ctest)
172 |             self.crefs.extend(other.crefs)
173 |             self._score = None ## need to recompute
174 | 
175 |         return self        
176 | 
177 |     def compatible(self, other):
178 |         return isinstance(other, BleuScorer) and self.n == other.n
179 | 
180 |     def single_reflen(self, option="average"):
181 |         return self._single_reflen(self.crefs[0][0], option)
182 | 
183 |     def _single_reflen(self, reflens, option=None, testlen=None):
184 |         
185 |         if option == "shortest":
186 |             reflen = min(reflens)
187 |         elif option == "average":
188 |             reflen = float(sum(reflens))/len(reflens)
189 |         elif option == "closest":
190 |             reflen = min((abs(l-testlen), l) for l in reflens)[1]
191 |         else:
192 |             assert False, "unsupported reflen option %s" % option
193 | 
194 |         return reflen
195 | 
196 |     def recompute_score(self, option=None, verbose=0):
197 |         self._score = None
198 |         return self.compute_score(option, verbose)
199 |         
200 |     def compute_score(self, option=None, verbose=0):
201 |         n = self.n
202 |         small = 1e-9
203 |         tiny = 1e-15 ## so that if guess is 0 still return 0
204 |         bleu_list = [[] for _ in range(n)]
205 | 
206 |         if self._score is not None:
207 |             return self._score
208 | 
209 |         if option is None:
210 |             option = "average" if len(self.crefs) == 1 else "closest"
211 | 
212 |         self._testlen = 0
213 |         self._reflen = 0
214 |         totalcomps = {'testlen':0, 'reflen':0, 'guess':[0]*n, 'correct':[0]*n}
215 | 
216 |         # for each sentence
217 |         for comps in self.ctest:            
218 |             testlen = comps['testlen']
219 |             self._testlen += testlen
220 | 
221 |             if self.special_reflen is None: ## need computation
222 |                 reflen = self._single_reflen(comps['reflen'], option, testlen)
223 |             else:
224 |                 reflen = self.special_reflen
225 | 
226 |             self._reflen += reflen
227 |                 
228 |             for key in ['guess','correct']:
229 |                 for k in range(n):
230 |                     totalcomps[key][k] += comps[key][k]
231 | 
232 |             # append per image bleu score
233 |             bleu = 1.
234 |             for k in range(n):
235 |                 bleu *= (float(comps['correct'][k]) + tiny) \
236 |                         /(float(comps['guess'][k]) + small) 
237 |                 bleu_list[k].append(bleu ** (1./(k+1)))
238 |             ratio = (testlen + tiny) / (reflen + small) ## N.B.: avoid zero division
239 |             if ratio < 1:
240 |                 for k in range(n):
241 |                     bleu_list[k][-1] *= math.exp(1 - 1/ratio)
242 | 
243 |             if verbose > 1:
244 |                 print(comps, reflen)
245 | 
246 |         totalcomps['reflen'] = self._reflen
247 |         totalcomps['testlen'] = self._testlen
248 | 
249 |         bleus = []
250 |         bleu = 1.
251 |         for k in range(n):
252 |             bleu *= float(totalcomps['correct'][k] + tiny) \
253 |                     / (totalcomps['guess'][k] + small)
254 |             bleus.append(bleu ** (1./(k+1)))
255 |         ratio = (self._testlen + tiny) / (self._reflen + small) ## N.B.: avoid zero division
256 |         if ratio < 1:
257 |             for k in range(n):
258 |                 bleus[k] *= math.exp(1 - 1/ratio)
259 | 
260 |         if verbose > 0:
261 |             print(totalcomps)
262 |             print("ratio:", ratio)
263 | 
264 |         self._score = bleus
265 |         return self._score, bleu_list
266 | 


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