ML7331 Environments

Create Environment

In Bash (Use mamba or conda depending on your system setup)

mamba create -n ml7331 python=3.9 r-base=4.4.1
mamba activate ml7331
mamba install ipykernel
python -m ipykernel install --user --name ml7331 --display-name "Python (ml7331)"
# (restart VSCode or source ~/.bashrc)
mamba activate ml7331
mamba install tzlocal
mamba install plotly 
pip install rpy2
pip install seaborn
pip install jupyter
pip install matplotlib
pip install scipy
pip install scikit-learn
pip install pillow
pip install tensorflow
pip install notebook
pip install folium
pip install geopandas
pip install PyGithub
pip install github3.py
pip install python-gitlab
pip check

In R

.libPaths(c("C:/Users/jessi/mambaforge/envs/ml7331/Lib/R/library", .libPaths())) 
install.packages("reticulate")
install.packages('arules')
install.packages('arulesViz')
install.packages('mlbench')
install.packages("tidyverse")
install.packages("caret")
install.packages("mlr")
install.packages("xgboost")

Verify Installs:

library(arules)
library(arulesViz)
library(mlbench)
library(reticulate)

Save the Environment:

mamba env export -n ml7331 > ml7331_environment.yml
# If updated:
mamba env export -n ml7331 > ml7331_environment_updated.yml
conda clean --all

In R Studio:

library(reticulate)
use_condaenv("ml7331", required = TRUE)

Run a Simple Python Command:

py_run_string("x = 10")
py_run_string("y = x + 5")
py$x  # Access the Python variable in R
py$y  # Access another Python variable in R

Install an R Package Using Mamba:

system("mamba install -c conda-forge r-ggplot2")

Install numpy Using Mamba:

system("mamba install -c conda-forge numpy")

Alternatively Use PIP:

# Install numpy using pip
py_install("numpy", method = "pip")

Verify Installs:

Check TensorFlow:

tensorflow <- import("tensorflow")
print(tensorflow$__version__)

Check Keras (if needed):

keras <- import("keras")
print(keras$__version__)

Check Installed Packages:

system("mamba list")
system("pip list")

In R Markdown, Notebook, or Quarto:

x = 10
y = x + 5
print(y)

Notes:

• rpy2 is used to run R within Python (install with pip).
• reticulate is used to run Python within R (install within R).

Additional Setup Notes for August 20, 2024

Set Python Path in R Studio:

library(reticulate)
use_python("C:/Users/jessi/mambaforge/envs/ml7331/python.exe", required = TRUE)

Configure Quarto to Use the Correct Python Environment:

• Bash:

  export QUARTO_PYTHON="C:/Users/jessi/mambaforge/envs/ml7331/python.exe"

• Command Prompt:

  set QUARTO_PYTHON="C:/Users/jessi/mambaforge/envs/ml7331/python.exe"

• R:

  file.edit("~/.Renviron")

• TinyTeX Installation:

  tinytex::install_tinytex(bundle = 'TinyTeX-2')

Check Versions and Paths:

• Get R Path:

  R.home()

• Get Python Version:

  python --version

Example Python Code to Set R_HOME in Python:

import os  

# Set R_HOME to the full directory path  
os.environ['R_HOME'] = r'C:\Program Files\R\R-4.4.1'  

import rpy2.robjects as ro  
# Test R integration  
print(ro.r('R.version.string'))

Install Specific Package Versions:

mamba install \
    numpy=1.23.5 \
    pandas=2.2.2 \
    scikit-learn=1.5.1 \
    matplotlib=3.9.1 \
    seaborn=0.13.2 \
    tensorflow=2.10.0 \
    jupyterlab=4.2.4
mamba install \
    scipy=1.13.1 \
    pytorch=2.3.1 \
    torch-geometric=2.5.3 \
    transformers=4.37.2

GitHub Integration Example Code:

• Using PyGithub:

```python
#from github import Github  

#g = Github("your_personal_access_token")  

#user = g.get_user("username")  

#for repo in user.get_repos():  
    #print(repo.name)
```

• Using GitLab:

```python
#import gitlab  

#gl = gitlab.Gitlab('https://gitlab.com', private_token='your_private_access_token')  

#projects = gl.projects.list()  
#for project in projects:  
    #print(project.name)
```

GraphLab Environment Setup:

Create and Activate GraphLab Environment:

mamba create -n graphlab-env python=2.7 r-base=3.6.1
mamba activate graphlab-env
mamba install anaconda rpy2 tzlocal plotly pillow
pip install tornado==4.5.3

In R:

.libPaths(c("C:/Users/jessi/mambaforge/envs/graphlab-env/Lib/R/library", .libPaths()))
install.packages("reticulate")
install.packages("ggplot2")

Save the Environment:

mamba env export -n graphlab-env > graphlab-env.yml

Reopen the Environment in R Studio:

library(reticulate)
use_condaenv("graphlab-env", required = TRUE)

Useful Mamba Commands:

• List Environments:

  mamba env list

• Remove Environment:

  mamba remove -n environment_name --all

• Quarto Publishing

• Netlify

• Lucid

• EigenFactor

• Airflow

• Pandoc

• posit Cloud

• Posit Cheatsheets

• Jenkins

• Jenkins Default Port Run

• Quarto

• Posit Connect Cloud

• TinyTeX

• HugeTeX

• MiKTeX

• tug/tlmgr

• CRAN Packages

• Quarto Reference

• RStudio Github io

• Quarto Editing Shortcuts

• quarto::quarto_publish_doc() 

• quarto pandoc -o template.pptx --print-default-data-file reference.pptx 
  quarto::quarto_publish_doc() 
  
  # Terminal
  quarto publish quarto-pub document.qmd
  quarto publish gh-pages document.qmd
  quarto publish netlify document.qmd
  quarto publish connect document.qmd