Machine-Learning-foR-Psychologists/py/README.qmd at main · mattansb/Machine-Learning-foR-Psychologists · GitHub

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---
format: gfm
execute:
  echo: false
  warning: false
---

```{r}
#| include: false
library(tidyverse)
library(glue)

extract_modules <- function(fl) {
  if (length(fl) == 1) {
    txt <- read.delim(fl, header = FALSE)[[1]]

    mods <- str_extract_all(
      string = txt,
      pattern = "(?<=^(import|from)\\s)[a-zA-Z0-9_]{1,}"
    )

    mods <- unlist(mods)

    dir_py_files <- dirname(fl) |>
      list.files(pattern = "\\.py$", full.names = TRUE) |>
      basename() |>
      str_remove(pattern = "\\.py$")

    mods <- setdiff(mods, dir_py_files)
  } else if (length(fl) > 1) {
    mods <- sapply(fl, extract_modules)
  }

  mods |>
    unlist(recursive = TRUE) |>
    unique()
}
```

# Machine Learning foR Psychologists

[![](https://img.shields.io/badge/Open%20Educational%20Resources-Compatable-brightgreen)](https://creativecommons.org/about/program-areas/education-oer/)
[![](https://img.shields.io/badge/CC-BY--NC%204.0-lightgray)](http://creativecommons.org/licenses/by-nc/4.0/)
![](https://img.shields.io/badge/Languages-Python-blue)

<sub>*Last updated `r Sys.Date()`.*</sub>

This folder contains Python translations of the R tutorials in the main repo.

## Setup

You will need:

1. A fresh installation of [**Python**](https://www.python.org/downloads/) (preferably version 3.13 or above).
2. [Positron IDE](https://positron.posit.co/download.html) (optional, but recommended).
3. The following modules, listed by lesson:

```{r}
py_files <- list.files(pattern = ".py$", recursive = TRUE, full.names = TRUE)

py_files <- py_files[str_detect(py_files, "^\\./\\d{2}")]

py_lesson_names <- str_extract(py_files, pattern = "(?<=(/)).{1,}(?=(/))")

py_list <- split(py_files, py_lesson_names)

py_mdls <- lapply(py_list, extract_modules)

py_df <- tibble(
  Lesson = names(py_mdls),
  Modules = py_mdls
)

py_df |>
  mutate(
    Lesson = glue("[{Lesson}]({Lesson}//)"),
    Modules = sapply(Modules, \(x) glue('`{x}`') |> glue_collapse(sep = ", ")),
  ) |>
  knitr::kable()
```

<details>
<summary><i>Installing Python Modules</i></summary>

You can install all the Python modules used by saving a `requirements.txt` file:

```{r}
reticulate::use_python(
  "C:/Users/user/AppData/Local/Programs/Python/Python313/python.exe",
  required = TRUE
)

imp <- reticulate::import("importlib.metadata")

py_version <- possibly(imp$version, otherwise = "")

py_mdls <- py_mdls |>
  unlist(recursive = TRUE) |>
  unique() |>
  sort()


py_mdl_info <- tibble(
  module = case_match(
    py_mdls,
    "sklearn" ~ "scikit-learn",
    "imblearn" ~ "imbalanced-learn",
    .default = py_mdls
  ),
  version = sapply(module, py_version)
) |>
  arrange(module)

py_mdl_info |>
  glue_data(
    "{module}{ifelse(nzchar(py_mdl_info$version), '>=', '')}{version}"
  ) |>
  glue_collapse(sep = "\n") |>
  cat()
```

And then running

```
pip install -r requirements.txt
```

</details>