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# Hierarchical Depression Symptom Classifier

This repository contains the implementation of the model used in [Towards Automatic Text-Based Estimation of Depression through Symptom Prediction](https://www.researchsquare.com/article/rs-2096670/v1).

Before you can run the model, you have to obtain the DAIC-WOZ corpus. Please, refer to the **Availability of data and materials** section for the instructions.

## Getting started

### Data

To make it easy for you to get started with GitLab, here's a list of recommended next steps.

Once you have the data, it has to be organized in the following structure:

Already a pro? Just edit this README.md and make it your own. Want to make it easy? [Use the template at the bottom](#editing-this-readme)!

    .

    ├── ...

    ├── data

    │   ├── transcripts

    │   │   ├── 301_TRANSCRIPT.csv

    │   │   ├── 302_TRANSCRIPT.csv 

    │   │   └── ...

    │   ├── train_split_Depression_AVEC2017.csv

    │   ├── dev_split_Depression_AVEC2017.csv

    │   └── test_split_Depression_AVEC2017.csv

    └── ...

## Add your files

After, you have to run all the cells of the `TranscriptsToCONLLU.ipynb` and `prepare_data.py`.

- [ ] [Create](https://docs.gitlab.com/ee/user/project/repository/web_editor.html#create-a-file) or [upload](https://docs.gitlab.com/ee/user/project/repository/web_editor.html#upload-a-file) files

- [ ] [Add files using the command line](https://docs.gitlab.com/ee/gitlab-basics/add-file.html#add-a-file-using-the-command-line) or push an existing Git repository with the following command:

### Training the model

To train the model, run the following command:

```

cd existing_repo

git remote add origin https://git.unicaen.fr/kirill.milintsevich/hierarchical-depression-symptom-classifier.git

git branch -M master

git push -uf origin master

python ./train_bert_dist.py --conf_file config_bert.ini --seed 0 --chunking_method lines --data_oversampling none --loss_averaging none --attention_type hierarchical --pooling mean --bidirectional --batch_size 8 --num_iters 200 --num_classes 8 --multilabel --binary_only --no_regularization_loss --save_every_epoch

```

## Integrate with your tools

- [ ] [Set up project integrations](https://git.unicaen.fr/kirill.milintsevich/hierarchical-depression-symptom-classifier/-/settings/integrations)

## Collaborate with your team

- [ ] [Invite team members and collaborators](https://docs.gitlab.com/ee/user/project/members/)

- [ ] [Create a new merge request](https://docs.gitlab.com/ee/user/project/merge_requests/creating_merge_requests.html)

- [ ] [Automatically close issues from merge requests](https://docs.gitlab.com/ee/user/project/issues/managing_issues.html#closing-issues-automatically)

- [ ] [Enable merge request approvals](https://docs.gitlab.com/ee/user/project/merge_requests/approvals/)

- [ ] [Automatically merge when pipeline succeeds](https://docs.gitlab.com/ee/user/project/merge_requests/merge_when_pipeline_succeeds.html)

## Test and Deploy

Use the built-in continuous integration in GitLab.

- [ ] [Get started with GitLab CI/CD](https://docs.gitlab.com/ee/ci/quick_start/index.html)

- [ ] [Analyze your code for known vulnerabilities with Static Application Security Testing(SAST)](https://docs.gitlab.com/ee/user/application_security/sast/)

- [ ] [Deploy to Kubernetes, Amazon EC2, or Amazon ECS using Auto Deploy](https://docs.gitlab.com/ee/topics/autodevops/requirements.html)

- [ ] [Use pull-based deployments for improved Kubernetes management](https://docs.gitlab.com/ee/user/clusters/agent/)

- [ ] [Set up protected environments](https://docs.gitlab.com/ee/ci/environments/protected_environments.html)

***

# Editing this README

When you're ready to make this README your own, just edit this file and use the handy template below (or feel free to structure it however you want - this is just a starting point!). Thank you to [makeareadme.com](https://www.makeareadme.com/) for this template.

## Suggestions for a good README

Every project is different, so consider which of these sections apply to yours. The sections used in the template are suggestions for most open source projects. Also keep in mind that while a README can be too long and detailed, too long is better than too short. If you think your README is too long, consider utilizing another form of documentation rather than cutting out information.

## Name

Choose a self-explaining name for your project.

## Description

Let people know what your project can do specifically. Provide context and add a link to any reference visitors might be unfamiliar with. A list of Features or a Background subsection can also be added here. If there are alternatives to your project, this is a good place to list differentiating factors.

## Badges

On some READMEs, you may see small images that convey metadata, such as whether or not all the tests are passing for the project. You can use Shields to add some to your README. Many services also have instructions for adding a badge.

## Visuals

Depending on what you are making, it can be a good idea to include screenshots or even a video (you'll frequently see GIFs rather than actual videos). Tools like ttygif can help, but check out Asciinema for a more sophisticated method.

## Installation

Within a particular ecosystem, there may be a common way of installing things, such as using Yarn, NuGet, or Homebrew. However, consider the possibility that whoever is reading your README is a novice and would like more guidance. Listing specific steps helps remove ambiguity and gets people to using your project as quickly as possible. If it only runs in a specific context like a particular programming language version or operating system or has dependencies that have to be installed manually, also add a Requirements subsection.

## Usage

Use examples liberally, and show the expected output if you can. It's helpful to have inline the smallest example of usage that you can demonstrate, while providing links to more sophisticated examples if they are too long to reasonably include in the README.

## Support

Tell people where they can go to for help. It can be any combination of an issue tracker, a chat room, an email address, etc.

## Roadmap

If you have ideas for releases in the future, it is a good idea to list them in the README.

## Contributing

State if you are open to contributions and what your requirements are for accepting them.

For people who want to make changes to your project, it's helpful to have some documentation on how to get started. Perhaps there is a script that they should run or some environment variables that they need to set. Make these steps explicit. These instructions could also be useful to your future self.

You can also document commands to lint the code or run tests. These steps help to ensure high code quality and reduce the likelihood that the changes inadvertently break something. Having instructions for running tests is especially helpful if it requires external setup, such as starting a Selenium server for testing in a browser.

## Authors and acknowledgment

Show your appreciation to those who have contributed to the project.

## License

For open source projects, say how it is licensed.

### Testing the model

## Project status

If you have run out of energy or time for your project, put a note at the top of the README saying that development has slowed down or stopped completely. Someone may choose to fork your project or volunteer to step in as a maintainer or owner, allowing your project to keep going. You can also make an explicit request for maintainers.

Refer to `test_model.py` for more information.

 No newline at end of file

%% Cell type:code id: tags:

``` python

import stanza

from pathlib import Path

from collections import namedtuple, Counter

import csv

import conllu

from conllu.models import TokenList, Token

import logging

import re

```

%% Cell type:code id: tags:

``` python

logger_format = f'%(asctime)s %(levelname)s: %(message)s'

logging.basicConfig(level=logging.DEBUG, format=logger_format)

```

%% Cell type:code id: tags:

``` python

transcript_dir = Path('data/transcripts')

save_dir = Path('data/transcripts/conllu')

ellie_line = re.compile('(.+)\s\((.+)\)')

abbreviation = re.compile('[a-z](?:_[a-z])+')

def transcript_name(idx):

    return f'{idx}_TRANSCRIPT.csv'

sent = {0: 'negative', 1: 'neutral', 2: 'positive'}

```

%% Cell type:code id: tags:

``` python

def normalize_abbreviations(line):

    abbreviation = re.compile('[a-z](?:_[a-z])+')

    abbs_text = abbreviation.findall(line)

    abbs_new = [a.replace('_', '').upper() for a in abbs_text]

    for a, b in zip(abbs_text, abbs_new):

        line = line.replace(a, b)

    return line

```

%% Cell type:code id: tags:

``` python

TranscriptLine = namedtuple('TranscriptLine', ['actor', 'text'])

```

%% Cell type:code id: tags:

``` python

def read_transcript(path):

    transcript = []

    reader = csv.reader(open(path, encoding='utf-8'), delimiter='\t')

    next(reader)

    for line in reader:

        if len(line) == 4:

            ellie_match = ellie_line.match(line[3])

            if line[2] == 'Ellie' and ellie_match:

                transcript.append(TranscriptLine(line[2], (ellie_match[1], normalize_abbreviations(ellie_match[2]))))

            else:

                transcript.append(TranscriptLine(line[2], normalize_abbreviations(line[3])))

    return transcript

```

%% Cell type:code id: tags:

``` python

transcripts = {int(path.name.split('_')[0]): read_transcript(path) for path in transcript_dir.iterdir() if path.suffix == '.csv'}

```

%% Cell type:code id: tags:

``` python

ellie_lines = Counter()

abbreviations = Counter()

for idx, transcript in transcripts.items():

    for line in transcript:

        if isinstance(line.text, tuple):

            ellie_lines[line.text] += 1

            abbs = abbreviation.findall(line.text[1])

            if abbs:

                abbreviations.update(abbs)

        elif isinstance(line.text, str):

            abbs = abbreviation.findall(line.text)

            if abbs:

                abbreviations.update(abbs)

```

%% Cell type:code id: tags:

``` python

nlp = stanza.Pipeline('en', tokenize_no_ssplit=True, processors='tokenize', verbose=True)

```

%% Output

    2022-11-04 17:08:35 INFO: Checking for updates to resources.json in case models have been updated.  Note: this behavior can be turned off with download_method=None or download_method=DownloadMethod.REUSE_RESOURCES

    2022-11-04 17:08:35,869 INFO: Checking for updates to resources.json in case models have been updated.  Note: this behavior can be turned off with download_method=None or download_method=DownloadMethod.REUSE_RESOURCES

    2022-11-04 17:08:35,873 DEBUG: Starting new HTTPS connection (1): raw.githubusercontent.com:443

    2022-11-04 17:08:35,939 DEBUG: https://raw.githubusercontent.com:443 "GET /stanfordnlp/stanza-resources/main/resources_1.4.1.json HTTP/1.1" 200 28918

    2022-11-04 17:08:35 INFO: Loading these models for language: en (English):

    ========================

    | Processor | Package  |

    ------------------------

    | tokenize  | combined |

    ========================

    2022-11-04 17:08:35,955 INFO: Loading these models for language: en (English):

    ========================

    | Processor | Package  |

    ------------------------

    | tokenize  | combined |

    ========================

    2022-11-04 17:08:35 INFO: Use device: cpu

    2022-11-04 17:08:35,955 INFO: Use device: cpu

    2022-11-04 17:08:35 INFO: Loading: tokenize

    2022-11-04 17:08:35,956 INFO: Loading: tokenize

    2022-11-04 17:08:35 INFO: Done loading processors!

    2022-11-04 17:08:35,959 INFO: Done loading processors!

%% Cell type:code id: tags:

``` python

def normalize_sentence_dict(sentence_dict):

    DEFAULT_FIELDS = ('id', 'form', 'lemma', 'upos', 'xpos', 'feats', 'head', 'deprel', 'deps', 'misc')

    token_dict = {field: '_' for field in DEFAULT_FIELDS}

    new_sentence = []

    for token in sentence_dict:

        for key, value in token.items():

            if key == 'text':

                key = 'form'

            token_dict[key] = value

        new_sentence.append(token_dict)

        token_dict = {field: '_' for field in DEFAULT_FIELDS}

    return new_sentence

```

%% Cell type:code id: tags:

``` python

if not save_dir.exists():

    save_dir.mkdir()

docs = []

actors_list = []

ellie_codes_list = []

ids = []

logging.info(f"[Reading transcripts...]")

for idx, transcript in transcripts.items():

    doc = '\n\n'.join([t.text if not isinstance(t.text, tuple) else t.text[1] for t in transcript])

    actor = [t.actor for t in transcript]

    ellie_code = [t.text[0] if isinstance(t.text, tuple) else None for t in transcript]

    docs.append(doc)

    actors_list.append(actor)

    ellie_codes_list.append(ellie_code)

    ids.append(idx)

logging.info("[Done reading!]")

logging.info("[Processing the transcripts with Stanza...]")

in_docs = [stanza.Document([], text=d) for d in docs]

stanza_docs = nlp(in_docs)

logging.info("[Done processing!]")

logging.info("[Writting CoNLL-U files...]")

for doc, actors, ellie_codes, idx in zip(stanza_docs, actors_list, ellie_codes_list, ids):

    c = []

    for actor, sentence, ellie_code in zip(actors, doc.sentences, ellie_codes):

        sentence_dict = sentence.to_dict()

        meta = {

            'text': sentence.text,

            'actor': actor,

        }

        if ellie_code:

            meta['ellie_code'] = ellie_code

        c.append(TokenList(normalize_sentence_dict(sentence_dict), metadata=meta))

    save_name = Path(f'{idx}_TRANSCRIPT.conllu')

    with open(save_dir / save_name, 'w', encoding='utf-8') as f:

        f.write(''.join([tokens.serialize() for tokens in c]))

logging.info("[Done!]")

```

%% Output

    2022-11-04 17:19:48,858 INFO: [Reading transcripts...]

    2022-11-04 17:19:48,870 INFO: [Done reading!]

    2022-11-04 17:19:48,871 INFO: [Processing the transcripts with Stanza...]

    2022-11-04 17:20:20,360 INFO: [Done processing!]

    2022-11-04 17:20:20,361 INFO: [Writting CoNLL-U files...]

    2022-11-04 17:20:22,703 INFO: [Done!]

%% Cell type:code id: tags:

``` python

```

[Defaults]

bert_model = sentence-transformers/all-distilroberta-v1

transcript_dir = data/transcripts/conllu

train_labels = data/train_split_Depression_AVEC2017.csv

dev_labels = data/dev_split_Depression_AVEC2017.csv

pretrained_path = external_resources/pretrained.pt

line2type_path = data/ellie_lines_labeled.tsv

seed = 0

save_dir = saved_models

encoder_hidden_dim = 300

encoder_num_layers = 1

batch_size = 8

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