The Course is a Sphinx project made of :
-
Jupyter notebooks
*/*.ipynbfiles. -
Python files using sphinx-gallery
*/*.pyfiles. -
ReStructuredText or Markdown files.
All notebooks and python files are converted into rst format and then assembled together using sphinx.
Directories and main files: introduction/ └── introduction_python_for_datascience.rst
python_lang/
├── python_lang.py
└── python_lang_solutions.py
data_manipulation/
├── data_numpy.py
├── data_numpy_solutions.py
├── data_pandas.py
├── data_pandas_solutions.py
└── data_visualization.ipynb
numerical_methods/
├── optim_gradient_descent.ipynb
├── numerical_differentiation_integration.ipynb
├── symbolic_maths.ipynb
├── symbolic_maths.rst
├── time_series.ipynb
└── time_series.rst
statistics/
├── stat_univ.ipynb
├── stat_univ_solutions.ipynb
├── stat_multiv.ipynb
├── stat_multiv_solutions.py
├── lmm
│ └── lmm.ipynb
└── stat_montecarlo.ipynb
ml_unsupervised/
├── clustering.ipynb
├── introduction_to_ml.rst
├── linear_dimensionality_reduction.ipynb
├── linear_dimensionality_reduction_solutions.ipynb
├── manifold_learning.ipynb
└── manifold_learning_solutions.ipynb
ml_supervised/
├── overfitting.ipynb
├── ensemble_learning.py
├── kernel_svm.py
├── linear_classification.ipynb
└── linear_regression.ipynb
deep_learning/
├── dl_backprop_numpy-pytorch-sklearn.ipynb
├── dl_mlp_pytorch.ipynb
├── dl_cnn_cifar10_pytorch.ipynb
└── dl_cnn-pretraining_pytorch.rst
Clone the repository
```
git clone https://github.com/duchesnay/pystatsml.git
cd pystatsml
```
Using Anaconda
~~~~~~~~~~~~~~
Install [Anaconda](https://www.anaconda.com) with python >= 3.
Standard user (student) should install the required data analysis packages.
Create and activate the `pystatsml_student` environment:
```
conda env create -f environment_student.yml
conda activate pystatsml_student
```
Usinf Pixi
~~~~~~~~~~
Install [Pixi](https://pixi.sh/latest/)
Linux & macOS
```
curl -fsSL https://pixi.sh/install.sh | bash
```
Windows
```
iwr -useb https://pixi.sh/install.ps1 | iex
```
Install dependencies contained in pixi.toml file (within the project directory)
```
pixi install
```
Activate an environment (within the project directory)
```
pixi shell
```
What’s in the environment?
```
pixi list
```
Deactivating an environment
```
exit
```
Installation for teachers: to build the documents
-------------------------------------------------
Expert users (teachers) who need to build (pdf, html, etc.) the course should install additional packages including:
- p
5DDA
andoc
- [sphinx-gallery](https://sphinx-gallery.readthedocs.io)
- [nbstripout](https://github.com/kynan/nbstripout)
Create and activate the ``pystatsml_teacher`` environment:
```
conda env create -f environment_teacher.yml
conda activate pystatsml_teacher
```
Build the documents.
Configure your git repository with `nbstripout`: a pre-commit hook for users who don't want to track notebooks' outputs in git.
```
nbstripout --install
```
Optional: install LaTeX to generate pdf. For Linux debian like:
```
sudo apt-get install latexmk texlive-latex-extra
```
After pulling the repository execute Jupyter notebooks (outputs are expected to be removed before git submission):
```
make exe
```
Build the pdf file (requires LaTeX):
```
make pdf
```
Build the html files:
```
make html
```
Clean everything:
```
make clean
```
Optional to generate Microsoft docx. Use [docxbuilder](https://docxbuilder.readthedocs.io/en/latest/docxbuilder.html):
```
make docx
```
Contributing
------------
Cross-references
~~~~~~~~~~~~~~~~
Add the `ref:` prefix to your references.
Defining references label:
- Markdown file (add `#` before the label)
```
### Demonstration of Negative Log-Likelihood (NLL) {#ref:demonstration-nll}
```
Cross-referencing:
- Jupyter Notebook
```
[Demonstration of Negative Log-Likelihood (NLL)](ref:demonstration-nll)
```