diff --git a/README.md b/README.md
index 4664778..f4fd37b 100644
--- a/README.md
+++ b/README.md
@@ -1,60 +1,156 @@
-# github-dagger-workflow-project
+# ITU BDS SDSE'24 - Project
-
- 
-
+This project is part of the Software Development and Software Engineering course at ITU. The original project description can be found [here](https://github.com/lasselundstenjensen/itu-sdse-project).
-A short description of the project.
+In this project we were tasked with restructuring a Python monolith using the concepts we have learned throughout the course. This project contains a [Dagger workflow](https://github.com/PLtier/github-dagger-workflow-project/blob/main/pipeline.go) and a [GitHub workflow](https://github.com/PLtier/github-dagger-workflow-project/blob/main/.github/workflows/test_action.yml).
-## Project Organization
+
+
+## Project Structure
```
-├── LICENSE <- Open-source license if one is chosen
-├── Makefile <- Makefile with convenience commands like `make data` or `make train`
-├── README.md <- The top-level README for developers using this project.
-├── data
-│ ├── external <- Data from third party sources.
-│ ├── interim <- Intermediate data that has been transformed.
-│ ├── processed <- The final, canonical data sets for modeling.
-│ └── raw <- The original, immutable data dump.
+├── README.md <- Project description and how to run the code
+│
+├── .github/workflows <- GitHub Action workflows
+│ │
+│ ├── tag_version.yml <- Workflow for creating version tags
+│ │
+│ └── log_and_test_action.yml <- Workflow that automatically trains and tests model
+│
+├── pipeline_deps
+│ │
+│ └── requirements.txt <- Dependencies for the pipeline
+│
+├── CODEOWNERS <- Defines codeowners for the repository
+│
+├── go.mod <- Go file that defines the module and required dependencies
+│
+├── go.sum <- Go file that ensures continuity and integrity of dependencies
│
-├── docs <- A default mkdocs project; see www.mkdocs.org for details
+├── pipeline.go <- Dagger workflow written in Go
│
-├── models <- Trained and serialized models, model predictions, or model summaries
+├── pyproject.toml <- Project metadata and configuration
│
-├── 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`.
+├── .pre-commit-config.yaml <- Checks quality of code before commits
│
-├── pyproject.toml <- Project configuration file with package metadata for
-│ github-dagger-workflow-project and configuration for tools like black
+├── Makefile.venv <- Library for managing venv via makefile
│
-├── references <- Data dictionaries, manuals, and all other explanatory materials.
+├── Makefile <- Project related scripts
│
-├── reports <- Generated analysis as HTML, PDF, LaTeX, etc.
-│ └── figures <- Generated graphics and figures to be used in reporting
+├── references <- Documentation and extra resources
│
-├── requirements.txt <- The requirements file for reproducing the analysis environment, e.g.
-│ generated with `pip freeze > requirements.txt`
+├── requirements.txt <- Python dependencies need for the project
│
-├── setup.cfg <- Configuration file for flake8
+├── tests
+│ │
+│ └── verify_artifacts.py <- Tests to check if all artifacts are copied correctly
│
-└── github_dagger_workflow_project <- Source code for use in this project.
+└── github_dagger_workflow_project <- Source code for the project
│
- ├── __init__.py <- Makes github_dagger_workflow_project a Python module
+ ├── __init__.py <- Marks the directory as a Python package
│
- ├── config.py <- Store useful variables and configuration
+ ├── 01_data_transformations.py <- Script for data preprocessing and transformation
│
- ├── dataset.py <- Scripts to download or generate data
+ ├── 02_model_training.py <- Script for training the models
│
- ├── features.py <- Code to create features for modeling
+ ├── 03_model_selection.py <- Script for selecting the best perfoming model
│
- ├── modeling
- │ ├── __init__.py
- │ ├── predict.py <- Code to run model inference with trained models
- │ └── train.py <- Code to train models
+ ├── 04_prod_model.py <- Script for comparing new best model and production model
│
- └── plots.py <- Code to create visualizations
+ ├── 05_model_deployment.py <- Script for deploying model
+ │
+ ├── config.py <- Constants and paths used in the pipeline's scripts
+ │
+ ├── pipeline_utils.py <- Encapsulated code from the .py monolith.
+ │
+ ├── artifacts
+ │ │
+ │ └── raw_data.csv.dvc <- Metadata tracked by DVC for data file
+ │
+ └── utils.py <- Helper functions extracted from the .py monolith
+```
+
+# How to run the code
+
+## Artifact creation
+
+The workflow can be triggered either on pull requests to `main` or manually.
+
+It can be triggered manually [here](https://github.com/PLtier/github-dagger-workflow-project/actions/workflows/log_and_test_action.yml) by pressing `Run workflow` on the `main` branch, then refresh the page and the triggered workflow will appear. After all the jobs have been run, the model artifact can be found on the summary page of the run of the first job. We also store other artifacts for convenience.
+The testing is automatically run afterwards to let the user check if it was of a quality.
+Artifacts are stored for 90 days.
+
+## Local development / Running
+
+### Environment installation
+
+For local running you need:
+
+- `docker` (Server): >= 4.36
+- `dagger` >= 0.14
+
+For local development you need as well:
+
+- `go` - 1.23.3 is currently used.
+- `git` >= 2.39
+- `python` >= 3.11
+- `make` >= 3.81 (lower should work too)
+
+Then run:
+
+```shell
+make setup
+.venv\Scripts\activate # for windows
+source .venv/bin/activate # for linux/macos
+```
+
+Additionally, It installs `pre-commit` which takes care of formatting and linting before commits for go and python.
+
+### Running the code:
+
+#### Run scripts on the host machine
+
+For that you can run scripts sequentially in the github_dagger_workflow_project.
+
+> Beware: all artifacts will be appended to your repo dir!
+
+#### Run in a container
+
+The command will run the `dagger` pipeline. In the end, **only** final artifacts will be appended to
+
+```shell
+make container_run
+```
+
+#### Local testing
+
+Perhaps most useful. It will not append any of the container-produced files to the host machine, but it will run a test script **which will ensure that all important artifacts are indeed logged**
+
+```shell
+make test
```
----
+> Beware: it will not test the model on the inference test!
+
+## Inference testing
+
+The same workflow which generates artifacts automatically runs the inference testing. Also, the artifacts testing and the inference test is carried out after every PR (and subsequent commits) to `main`
+
+## Maintaining code quality
+
+- We used `pre-commit` to lint and format, as stated above. We use `ruff`, `ruff format`, `gofmt` and `govet`. We check for PEP8 warnings and errors.
+- `main` branch-protection (with github repo settings)
+ - PR is required before merging
+ - at least one approval is needed. We automatically assign reviewers with `CODEOWNERS` file.
+ - we required status checks to be passed for both of our jobs i.e. `Train and Upload Model` and `Unit Test Model Artifacts`. The test checks explicitly whether all artifacts have been generated and if the model passes inference test. Jobs are automatically triggered on merge.
+- We maintained a clear goals via `Issues` and often quite verbose reviews.
+- we used 90% of time semantic commits
+
+## Code releases
+
+On every push to main a new tag is released with the current time it was published.
+See current tags: [Tags](https://github.com/PLtier/github-dagger-workflow-project/tags)
+
+# Code decisions and reflections
+
+> This is not the part of the documentation: you can read about a few (hard) decisions we have made on [Reflections](./references/project_reflections.md)
diff --git a/references/diagrams.excalidraw b/references/diagrams.excalidraw
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diff --git a/references/problem_specification.md b/references/problem_specification.md
deleted file mode 100644
index f2a3a22..0000000
--- a/references/problem_specification.md
+++ /dev/null
@@ -1,77 +0,0 @@
-# ITU BDS SDSE'24 - Project
-
-## Task
-
-Based on the input provided (see below), fork the repository and restructure the code to adhere to the concepts and ideas you have seen throughout the course. The diagram below provides a detailed overview of the structure that the solution is expected to follow.
-
-
-
-For the exam submission, we expect you to submit a pdf containing:
-- the list of members of the group
-- the link to the github.com private repository hosting your solution. You will need to invite the three of us as collaborators: lasselundstenjensen, Jeppe-T-K, paolotell.
-
-The repository linked in the submission should contain:
-
-- A README.md file that describes the project
-- GitHub automation workflow
-- Dagger workflow (in Go)
-- All history
-
-
-## Inputs
-
-You are given the following material:
-- Python monolith (see `notebooks` folder)
-- Raw input data (see `notebooks/artifacts` folder)
-- GitHub action to test model inference (see [`model-validator`](https://github.com/lasselundstenjensen/itu-sdse-project-model-validator) action)
-
-## Outputs
-
-- Your GitHub repository (including all history)
- - A README.md file that describes the project
- - GitHub automation workflow
- - Dagger workflow (in Go)
-- Model artifact produced by GitHub workflow and named 'model'
-
-> **NOTE:**
-> The Dagger workflow can be run locally or inside the GitHub workflow—both are viable options during development.
->
-> The Dagger workflow can run locally and can also be made to produce outputs locally during development. But when wrapping the Dagger workflow in a GitHub workflow, the output is instead stored inside the GitHub runner (i.e. a virtual machine).
->
-> Use the publicly available [`actions/upload-artifact`](https://github.com/actions/upload-artifact) to store the model artifact in the GitHub worklow pipeline.
->
-> This model artifact can then be picked up by the [action provided](https://github.com/lasselundstenjensen/itu-sdse-project-model-validator), which will run some inference tests to ensure that the correct model was trained.
-
-
-## How will we assess
-
-Below, we provide information on how we will assess the submission clustered around several aspects. The list relates to groups of size 3; if your group is of size 4, you are expected also to work on the optional items, i.e., to use pull requests and to provide tests.
-
-#### Versioning
-
-- Use of Git (semantic commit messages, branches, branch longevity, commit frequency/size)
-- Management of data
-- Use of pull requests (OPTIONAL)
-
-#### Programming
-
-- Decomposition of Python notebook
-- Adherance to standard data science MLOps project structure
-- Presence of tests (OPTIONAL)
-
-#### Workflow automation
-
-- Presence of a workflow that trains the model
-- Presence of a workflow that tests the model
-- Structure of Dagger workflow
-- Orchestration of Dagger workflow through GitHub workflow
-
-#### Documentation (README.md)
-
-- Description of project structure
-- How to run the code and generate the model artifact
-
-
-## Questions
-
-If you have any questions about the information shared here, please feel free to post them on Learnit. Answers to private emails on this topic will also be shared on Learnit, along with the original email content, so that everyone has access to the same information.
diff --git a/references/project_reflections.md b/references/project_reflections.md
new file mode 100644
index 0000000..104e561
--- /dev/null
+++ b/references/project_reflections.md
@@ -0,0 +1,25 @@
+# Reflections
+
+Below is not the part of the documentation.
+
+## A few decisions
+
+- We have noticed a few strong signs an XGBoost was supposed to be in the pipeline. We initially included it, but finally decided on wrapping the code in such a way, that by one-liner one can start effectively compare LR with XGBoost. Please read more in: _Originally posted by @PLtier in [#3 Issues](https://github.com/PLtier/github-dagger-workflow-project/issues/3#issuecomment-2551304436)_.
+- We tried to make the code more explicit. E.g. We made `f1_score` to explicitly use `average='binary'` as there were both used `binary` and `weighted` versions.
+- LR regression: we realised that code was performing GridSearch but unfortunately it was still saving unfitted LR. We strongly think it was a bug and decided to store best_model output by GridSearch.
+- XGBoost: in the initial code `classification_report` for LR regression was computed using `test_data` whereas for Xgboost on `train_data` (!). We changed it to use `test_data`. We strongly think it was an oversight and have changed it.
+- XGBoost / LR comparison metric: there was one MLFlow comparison (model selection) using binary F1-Score and also the second one based on weighted F1-Score `model_result` . We decided to use `binary` because it was used in MLFlow comparison which we are certain intent of. (As stated above: we still output always LR).
+- We strived to encapsulate as much of the code into functions (no global variables shared except constants). This was to improve
+ - readibility
+ - better troubleshooting
+ - not polluting global namespace (so fewer bugs)
+- Imports: we sorted them and removed relative imports i.e. we don't do `import utils` in order not to confuse with an external library.
+- The code responsible for registering / transition to staging / deployment has not been deleted (except a few lines) but wrapped though not used.
+- We moved all constants in scripts to external file.
+- We realised that in MLFlow runs, LR would copy the whole artifacts folder again to its MLFlow folder, even including xgboost model. We think it might be unoptimal, but we leave it as it is as we are uncertain of the motives.
+- We keep all artifacts in the artifacts folder because there are not many of them and it allows us for easy testing them and retrieving during workflows.
+
+## What to improve upon
+
+- Take out tests out of the production code as right now we do it
+- Modularise pipeline.go. We think that now it's fine to keep helper/util function within the pipeline file as there are not many of them, but we see that we could have put them already somewhere else.
diff --git a/references/subjective_code_practices_mpj.md b/references/subjective_code_practices_mpj.md
index 326049b..329d307 100644
--- a/references/subjective_code_practices_mpj.md
+++ b/references/subjective_code_practices_mpj.md
@@ -106,17 +106,6 @@ It's easy to refactor notebook code because the ccds template makes your project
from classification_fashion_mnist.data import make_dataset
```
-### installing dependencies
-
-First of all, install `make`
-
-Due to simplicity we use `venv`.
-
-```shell
-make create_environment # creates .venv
-make requirements # fetches & install deps
-```
-
### Naming Convention for Notebooks
We use name notebooks with a scheme that looks like this:
@@ -172,5 +161,7 @@ git push -u origin main #NOT! It's blocked besides
```
### Few practices:
+
From "Accelerate", Forsgren:
-"In short and maybe- counter-intuitively - going faster and releasing more frequently actually LEDs to higher quality products."
\ No newline at end of file
+
+> "In short and maybe- counter-intuitively - going faster and releasing more frequently actually LEDs to higher quality products."