# Non-R Models

A model that is trained in any language are able to integrate with `tidypredict`, and thus with `broom`. The requirement is that the model in that language is exported using the parse model spec. The easiest file format would be YAML.

## python example

A model that was fitted using `sklearn`’s `linear_model`. The model is based on diabetes data. Ten baseline variables, age, sex, body mass index, average blood pressure, and six blood serum measurements were obtained for each of n = 442 diabetes patients, as well as the response of interest, a quantitative measure of disease progression one year after baseline. The model’s results were converted to YAML by the same python script, I copied and pasted the top part here:

    general:
      is_glm: 0
      model: lm
      residual: 0
      sigma2: 0
      type: regression
      version: 2.0
    terms:
    - coef: 152.76430691633442
      fields:
      - col: (Intercept)
        type: ordinary
      is_intercept: 1
      label: (Intercept)

## Read in R

The YAML data can be read in R by using the `yaml` package. In this example, we have copy-pasted most of the models inside a variable called `sklearn_model`. Because `yaml` requires local YAML variables to be split by line, we use `strsplit()`.

    library(yaml)

    sklearn_model <- strsplit("general:
      is_glm: 0
      model: lm
      residual: 0
      sigma2: 0
      type: regression
      version: 2.0
    terms:
    - coef: 152.76430691633442
      fields:
      - col: (Intercept)
        type: ordinary
      is_intercept: 1
      label: (Intercept)
    - coef: 0.3034995490660432
      fields:
      - col: age
        type: ordinary
      is_intercept: 0
      label: age
    - coef: -237.63931533353403
      fields:
      - col: sex
        type: ordinary
      is_intercept: 0
      label: sex
    - coef: 510.5306054362253
      fields:
      - col: bmi
        type: ordinary
      is_intercept: 0
      label: bmi
    - coef: 327.7369804093466
      fields:
      - col: bp
        type: ordinary
      is_intercept: 0
      label: bp
    - coef: -814.1317093725387
      fields:
      - col: s1
        type: ordinary
      is_intercept: 0
      label: s1
    ", split = "\n")[[1]]

Now the model is converted to an R `list` using `yaml.load`.

    sklearn_model <- yaml.load(sklearn_model)

    str(sklearn_model, 2)
    #> List of 2
    #>  $ general:List of 6
    #>   ..$ is_glm  : int 0
    #>   ..$ model   : chr "lm"
    #>   ..$ residual: int 0
    #>   ..$ sigma2  : int 0
    #>   ..$ type    : chr "regression"
    #>   ..$ version : num 2
    #>  $ terms  :List of 6
    #>   ..$ :List of 4
    #>   ..$ :List of 4
    #>   ..$ :List of 4
    #>   ..$ :List of 4
    #>   ..$ :List of 4
    #>   ..$ :List of 4

## `tidypredict`

The `list` object needs to be recognized as a `tidypredict` parsed model. To do that, we use `as_parsed_model()`

    library(tidypredict)

    spm <- as_parsed_model(sklearn_model)

    class(spm)
    #> [1] "parsed_model"  "pm_regression" "list"

The `spm` variable now works just as any parsed model inside R. Use `tidypredict_fit()` to view the resulting formula.

    tidypredict_fit(spm)
    #> 152.764306916334 + (age * 0.303499549066043) + (sex * -237.639315333534) +
    #>     (bmi * 510.530605436225) + (bp * 327.736980409347) + (s1 *
    #>     -814.131709372539)

Now, the model can run inside a database

    tidypredict_sql(spm, dbplyr::simulate_mssql())
    #> <SQL> 152.764306916334 + (`age` * 0.303499549066043) + (`sex` * -237.639315333534) + (`bmi` * 510.530605436225) + (`bp` * 327.736980409347) + (`s1` * -814.131709372539)

## `broom`

Now that we have a `parsed_model` object, it is possible to use `broom`’s `tidy()` function. This means that we are able to integrate a totally external model, with `broom`.

    tidy(spm)
    #> # A tibble: 6 x 2
    #>   term        estimate
    #>   <chr>          <dbl>
    #> 1 (Intercept)  153.
    #> 2 age            0.303
    #> 3 sex         -238.
    #> 4 bmi          511.
    #> 5 bp           328.
    #> 6 s1          -814.