文件列表:

.pre-commit-config.yaml (541, 2022-11-29)
Dockerfile (403, 2022-11-29)
app.png (34188, 2022-11-29)
app.py (1536, 2022-11-29)
data (0, 2022-11-29)
data\raw (0, 2022-11-29)
libs-dev.txt (103, 2022-11-29)
libs.txt (46, 2022-11-29)
model (0, 2022-11-29)
notebooks (0, 2022-11-29)
notebooks\model.ipynb (70051, 2022-11-29)
params.yaml (365, 2022-11-29)
requirements-dev.txt (1959, 2022-11-29)
requirements.txt (303, 2022-11-29)
src (0, 2022-11-29)
src\__init__.py (0, 2022-11-29)
src\models (0, 2022-11-29)
src\models\__init__.py (0, 2022-11-29)
src\models\base_model.py (5056, 2022-11-29)
src\models\utils.py (470, 2022-11-29)
templates (0, 2022-11-29)
templates\index.html (2173, 2022-11-29)
tox.ini (70, 2022-11-29)

# Bitcoin news sentiment Flask-ML application to predict the sentiment of Bitcoin news using tf-idf and logistic regression ## Project artifacts: ------------ 1. You can find all the necessary artifacts here: [Link](https://drive.google.com/drive/folders/1VLEOCD0heLWvYuIo4nzVevtZXoaQuSG_?usp=share_link) 2. app.py designed to be capable of recreating model .pkl if 'model/' is empty 3. if 'data/raw' is empty, you'll give an error message with instructions ## Model reproducibility params: ------------ model_input: - rnd_seed: 21 - test_size: 0.20 - regularization_strength: 0.01 - class_weight: "balanced" vectorizer_input: - max_features: 4000 - ngram_range: [1,2] NOTE: you can change model params using 'params.yaml' ## Modelling pipeline: ------------ 1. src/models/base_model.py OR 2. notebooks/model.ipynb ## Local model deployment (two alternative ways): ------------ ### I. Using virtual environment: 0. Using this option, be aware that application work was tested only on Ubuntu 20.04.4 LTS, ***-bit 1. Install Python 3.8.5 using pyenv (Python version management): $ pyenv install 3.8.5 2. Create local virtual environment using pyenv (Python version management) and pyenv-virtualenv (virtual environment management): $ pyenv virtualenv 3.8.5 3. Unzip the repo locally and open a terminal in the repo's directory 4. Activate the pyenv virtual environment in the project's diroctory: $ pyenv local 5. If you work in repository through VS Code, you usually have additionally to activate virtual environment for repo using: Shift + Ctrl + P -> Python: Select Interpreteur -> choose your environment and run a terminal for the following instructions 6. To be sure that you use a relevant virtual environment use: $ pyenv version or $ pyenv versions 7. Install all the necessary deployment libraries and dependencies using: $ pip install -r requirements.txt 8. To check the list of the dependencies installed to the virtual environment use: $ pip list 9. Run the application from the repo directory using: $ python app.py 10. You can check the application work in your browser running: http://127.0.0.1:5000 11. To stop the application use Ctrl+C in your terminal 12. If you don't have pyenv as a version management, use following instructions: [Link](https://realpython.com/intro-to-pyenv/#installing-pyenv) 13. If you don't have pyenv-virtualenv as a virtual environment management, use following insturctions: [Link1](https://github.com/pyenv/pyenv-virtualenv), [Link2](https://realpython.com/intro-to-pyenv/#virtual-environments-and-pyenv) ### II. Using Docker (recommended option): 1. Unzip the repo locally and open a terminal in the repo's directory 2. (optionally) Inslall Docker if you don't have it: [Link](https://docs.docker.com/engine/install/) 3. Run the following in a terminal to build a Docker image: $ docker build -t bitcoin_sentiment . 4. Run the following in a terminal to run an application in a Docker container: $ docker run --rm -it -p 5000:5000 bitcoin_sentiment 5. You can check the application work in your browser running: http://127.0.0.1:5000 6. To stop the application use Ctrl+C in your terminal ------------ ### Finally, you'll have to see this:  ## Project Organization ------------ ├── README.md <- The top-level README with the deployment instuctions | ├── data │ └── raw <- The original, immutable data dump. │ ├── model <- Trained and serialized models, model predictions, or model summaries │ ├── notebooks <- Jupyter notebooks. Naming convention is a number (for ordering), │ the creator's initials, and a short `-` delimited description, e.g. │ `1.0-jqp-initial-data-exploration`. │ ├── references <- Data dictionaries, manuals, and all other explanatory materials. │ ├── templates <- A folder for .html templates | ├── params.yaml <- Model params and artifacts paths | ├── libs.txt <- A list of installed libraries (deployment) │ ├── libs-dev.txt <- A list of installed libraries (dev) | ├── requirements.txt <- The deployment requirements file for reproducing the analysis environment, e.g. │ generated with `pip freeze > requirements.txt` | ├── requirements-dev.txt <- The requirements file for reproducing the project (dev) | ├── Dockerfile <- A script with instructions to build the project Docker image | ├── app.py <- Flask application script | ├── src <- Source code for use in this project. │ ├── __init__.py <- Makes src a Python module │ │ │ └── models <- Scripts to train models and then use trained models to make │ │ predictions │ ├── predict_model.py │ └── train_model.py | ├── .pre-commit-config.yaml <- A script that automatically activates linters and formatters before each commit | └── tox.ini <- tox file with settings for running tox; see tox.readthedocs.io

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