In JohnGavin/ccxt: ccxt Analysis

TODO

• Use db and pins together
• pins
• https://blog.sellorm.com/2022/06/25/5-tips-for-using-pins-with-r/
• db
• https://jthomasmock.github.io/arrow-dplyr/#/
  • for compressed csv
• duckdb for sql database
  • https://arrow.apache.org/docs/r/reference/to_duckdb.html
  • arrow::to_duckdb() treat on-disk data as a duckdb db!

https://rstudio.github.io/vetiver-r/articles/model-card.html

library(vetiver)
library(pins)
model_board <- board_temp()

cars_lm <- lm(mpg ~ ., data = mtcars)
v <- vetiver_model(cars_lm, "cars_linear")
options(rlib_message_verbosity = 'quiet')
vetiver_pin_write(model_board, v)

library(tidyverse)
library(vetiver)
library(pins)
library(yardstick)
knitr::opts_chunk$set(echo = FALSE)

# create a board manually
#   https://blog.sellorm.com/2022/06/25/5-tips-for-using-pins-with-r/
description <- "The data was extracted from the 1974 Motor Trend US magazine, and comprises fuel consumption and 10 aspects of automobile design and performance for 32 automobiles (1973–74 models)."
metadata <- list(owner       = "sellorm",
  deptartment = "R&D",
  r.version.string = version$version.string,
  url = "https://blog.sellorm.com")
# package versions? / computer hostname 
# data status, eg.not-validated
# data infoeg. location / demographic / project related info

b_tmp <- board_temp() # v board_local() 
b_tmp %>% pin_write(x = mtcars,
  name  = "motor_trend_cars",
  title = "Motor Trend Car Road Tests",
  metadata = metadata,
  description = description) %>% 
  # use httr’s with_verbose() for more connection details
  httr::with_verbose() ->
  brd_lcl
pin_meta(board = brd_lcl)
# Get the pin
b_tmp %>% pin_list()
nm <- b_tmp %>% pin_list() %>% head(1) 
b_tmp %>% 
  pin_read(nm) %>% 
  head()
# Get its metadata
b_tmp %>% pin_meta(nm)
# Get path to underlying data
b_tmp %>% pin_download(nm)

v <- vetiver_pin_read(params$board, params$name, version = params$version)
v_meta <- pin_meta(params$board, params$name)
theme_set(theme_light())

A model card provides brief, transparent, responsible reporting for a trained machine learning model.

Model details

• Developed by PERSON AND/OR TEAM
• r cli::pluralize("{v$description} using {ncol(v$ptype)} feature{?s}")
• More details about how model was developed and what it is predicting
• More details on feature engineering and/or data preprocessing for model
• Version r v$metadata$version of this model was published at r v_meta$created
• Citation and/or license details for the model
• If you have questions about this model, please contact [e-mail]

Intended use

• The primary intended uses of this model are ...
• The primary intended users of this model are ...
• Some use cases are out of scope for this model, such as ...

Important aspects/factors

• Aspects or factors (demographic, environmental, technical) that are relevant to the context of this model are ...
• In evaluating this model, we examined aspects such as ...

Metrics

• The metrics used to evaluate this model are ...
• These metrics are computed via ...
• We chose these metrics because ...

Training data & evaluation data

• The training dataset for this model was ...
• The training dataset for this model has the "prototype" or signature:

```r glimpse(v$ptype)

- The evaluation dataset used in this model card is ...
- We chose this evaluation data because ...

```r
## EVALUATION DATA:

data(Sacramento, package = "modeldata")

## consider using a package like skimr or DataExplorer for automated 
## presentation of evaluation data characteristics

Quantitative analyses {.tabset}

## compute predictions for your evaluation data
## `handler_startup` is designed to get the R process ready to make predictions
suppressPackageStartupMessages(handler_startup(v))
preds <- augment(v, Sacramento)

Overall model performance

preds %>%
  metrics(price, .pred)

Disaggregated model performance

preds %>%
  group_by(type) %>%
  metrics(price, .pred)

Visualize model performance

preds %>%
  ggplot(aes(price, .pred, color = type)) +
  geom_abline(slope = 1, lty = 2, color = "gray60", size = 1.2) +
  geom_point(alpha = 0.5, show.legend = FALSE) +
  facet_wrap(vars(type))

Make a custom plot

preds %>%
  mutate(.resid = price - .pred) %>%
  ggplot(aes(longitude, latitude, color = .resid)) +
  geom_point(alpha = 0.8) +
  scale_color_gradient2() +
  coord_fixed()

Ethical considerations

• We considered ...

Caveats & recommendations

• This model does ...
• This model does not ...
• We recommend ...

JohnGavin/ccxt documentation built on Sept. 3, 2022, 5:53 a.m.