| Title: | Run Predictions Inside the Database |
| Version: | 0.5.1 |
| Description: | It parses a fitted 'R' model object, and returns a formula in 'Tidy Eval' code that calculates the predictions. It works with several databases back-ends because it leverages 'dplyr' and 'dbplyr' for the final 'SQL' translation of the algorithm. It currently supports lm(), glm(), randomForest(), ranger(), earth(), xgb.Booster.complete(), cubist(), and ctree() models. |
| License: | MIT + file LICENSE |
| URL: | https://tidypredict.tidymodels.org, https://github.com/tidymodels/tidypredict |
| BugReports: | https://github.com/tidymodels/tidypredict/issues |
| Depends: | R (≥ 3.6) |
| Imports: | cli, dplyr (≥ 0.7), generics, knitr, purrr, rlang (≥ 1.1.1), tibble, tidyr |
| Suggests: | covr, Cubist, DBI, dbplyr, earth (≥ 5.1.2), methods, mlbench, modeldata, nycflights13, parsnip, partykit, randomForest, ranger, rmarkdown, RSQLite, testthat (≥ 3.2.0), xgboost, yaml |
| VignetteBuilder: | knitr |
| Config/Needs/website: | tidyverse/tidytemplate |
| Config/testthat/edition: | 3 |
| Encoding: | UTF-8 |
| RoxygenNote: | 7.3.2 |
| NeedsCompilation: | no |
| Packaged: | 2024-12-18 19:23:00 UTC; emilhvitfeldt |
| Author: | Emil Hvitfeldt [aut, cre], Edgar Ruiz [aut], Max Kuhn [aut] |
| Maintainer: | Emil Hvitfeldt <emil.hvitfeldt@posit.co> |
| Repository: | CRAN |
| Date/Publication: | 2024-12-19 08:50:02 UTC |
tidypredict: Run Predictions Inside the Database
Description
It parses a fitted 'R' model object, and returns a formula in 'Tidy Eval' code that calculates the predictions. It works with several databases back-ends because it leverages 'dplyr' and 'dbplyr' for the final 'SQL' translation of the algorithm. It currently supports lm(), glm(), randomForest(), ranger(), earth(), xgb.Booster.complete(), cubist(), and ctree() models.
Author(s)
Maintainer: Emil Hvitfeldt emil.hvitfeldt@posit.co
Authors:
Edgar Ruiz edgar@posit.co
Max Kuhn max@posit.co
See Also
Useful links:
Report bugs at https://github.com/tidymodels/tidypredict/issues
Extract classprob trees for partykit models
Description
For use in orbital package.
Usage
.extract_partykit_classprob(model)
Extract processed xgboost trees
Description
For use in orbital package.
Usage
.extract_xgb_trees(model)
Checks that the formula can be parsed
Description
Uses an S3 method to check that a given formula can be parsed based on its class. It currently scans for contrasts that are not supported and in-line functions. (e.g: lm(wt ~ as.factor(am))). Since this function is meant for function interaction, as opposed to human interaction, a successful check is silent.
Usage
acceptable_formula(model)
Arguments
model |
An R model object |
Examples
model <- lm(mpg ~ wt, mtcars)
acceptable_formula(model)
Prepares parsed model object
Description
Prepares parsed model object
Usage
as_parsed_model(x)
Arguments
x |
A parsed model object |
Knit print method for test predictions results
Description
Knit print method for test predictions results
Usage
## S3 method for class 'tidypredict_test'
knit_print(x, ...)
Converts an R model object into a table.
Description
It parses a fitted R model's structure and extracts the components needed to create a dplyr formula for prediction. The function also creates a data frame using a specific format so that other functions in the future can also pass parsed tables to a given formula creating function.
Usage
parse_model(model)
Arguments
model |
An R model object. |
Examples
library(dplyr)
df <- mutate(mtcars, cyl = paste0("cyl", cyl))
model <- lm(mpg ~ wt + cyl * disp, offset = am, data = df)
parse_model(model)
print method for test predictions results
Description
print method for test predictions results
Usage
## S3 method for class 'tidypredict_test'
print(x, ...)
Objects exported from other packages
Description
These objects are imported from other packages. Follow the links below to see their documentation.
- generics
Tidy the parsed model results
Description
Tidy the parsed model results
Usage
## S3 method for class 'pm_regression'
tidy(x, ...)
Arguments
x |
A parsed_model object |
... |
Reserved for future use |
Returns a Tidy Eval formula to calculate fitted values
Description
It parses a model or uses an already parsed model to return a Tidy Eval formula that can then be used inside a dplyr command.
Usage
tidypredict_fit(model)
Arguments
model |
An R model or a list with a parsed model. |
Examples
model <- lm(mpg ~ wt + cyl * disp, offset = am, data = mtcars)
tidypredict_fit(model)
Returns a Tidy Eval formula to calculate prediction interval.
Description
It parses a model or uses an already parsed model to return a Tidy Eval formula that can then be used inside a dplyr command.
Usage
tidypredict_interval(model, interval = 0.95)
Arguments
model |
An R model or a list with a parsed model |
interval |
The prediction interval, defaults to 0.95 |
Details
The result still has to be added to and subtracted from the fit to obtain the upper and lower bound respectively.
Examples
model <- lm(mpg ~ wt + cyl * disp, offset = am, data = mtcars)
tidypredict_interval(model)
Returns a SQL query with formula to calculate fitted values
Description
Returns a SQL query with formula to calculate fitted values
Usage
tidypredict_sql(model, con)
Arguments
model |
An R model or a list with a parsed model |
con |
Database connection object. It is used to select the correct SQL translation syntax. |
Examples
library(dbplyr)
model <- lm(mpg ~ wt + am + cyl, data = mtcars)
tidypredict_sql(model, simulate_dbi())
Returns a SQL query with formula to calculate predicted interval
Description
Returns a SQL query with formula to calculate predicted interval
Usage
tidypredict_sql_interval(model, con, interval = 0.95)
Arguments
model |
An R model or a tibble with a parsed model |
con |
Database connection object. It is used to select the correct SQL translation syntax. |
interval |
The prediction interval, defaults to 0.95 |
Examples
library(dbplyr)
model <- lm(mpg ~ wt + am + cyl, data = mtcars)
tidypredict_sql_interval(model, simulate_dbi())
Tests base predict function against tidypredict
Description
Compares the results of predict() and tidypredict_to_column() functions.
Usage
tidypredict_test(
model,
df = model$model,
threshold = 1e-12,
include_intervals = FALSE,
max_rows = NULL,
xg_df = NULL
)
Arguments
model |
An R model or a list with a parsed model. It currently supports lm(), glm() and randomForest() models. |
df |
A data frame that contains all of the needed fields to run the prediction. It defaults to the "model" data frame object inside the model object. |
threshold |
The number that a given result difference, between predict() and tidypredict_to_column() should not exceed. For continuous predictions, the default value is 0.000000000001 (1e-12), and for categorical predictions, the default value is 0. |
include_intervals |
Switch to indicate if the prediction intervals should be included in the test. It defaults to FALSE. |
max_rows |
The number of rows in the object passed in the df argument. Highly recommended for large data sets. |
xg_df |
A xgb.DMatrix object, required only for XGBoost models. It defaults to NULL recommended for large data sets. |
Examples
model <- lm(mpg ~ wt + cyl * disp, offset = am, data = mtcars)
tidypredict_test(model)
Adds the prediction columns to a piped command set.
Description
Adds a new column with the results from tidypredict_fit() to a piped command set. If add_interval is set to TRUE, it will add two additional columns- one for the lower and another for the upper prediction interval bounds.
Usage
tidypredict_to_column(
df,
model,
add_interval = FALSE,
interval = 0.95,
vars = c("fit", "upper", "lower")
)
Arguments
df |
A data.frame or tibble |
model |
An R model or a parsed model inside a data frame |
add_interval |
Switch that indicates if the prediction interval columns should be added. Defaults to FALSE |
interval |
The prediction interval, defaults to 0.95. Ignored if add_interval is set to FALSE |
vars |
The name of the variables that this function will produce. Defaults to "fit", "upper", and "lower". |