Documentation for `get_Data_formatted_for_ml_and_best.m` Function

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Purpose

The function get_Data_formatted_for_ml_and_best.m is designed to retrieve and preprocess data for machine learning (ML) models from a given SQLite database or XPT file. It performs several tasks such as fetching study IDs, retrieving study metadata, calculating liver toxicity scores, and tuning hyperparameters for ML models. The final output is a list containing processed data ready for machine learning and the best model.

Input Parameters

Parameter	Description	Type	Default Value
path_db	Path to the SQLite database or XPT file location.	character	None
rat_studies	Flag to filter for rat studies.	logical	FALSE
studyid_metadata	Optional metadata for study IDs. If NULL, will be generated.	data.frame	NULL
fake_study	Flag to use fake study data.	logical	FALSE
use_xpt_file	Flag to indicate whether to use an XPT file instead of SQLite database.	logical	FALSE
Round	Flag to round liver toxicity scores.	logical	FALSE
Impute	Flag to impute missing values in the dataset.	logical	FALSE
reps	Number of repetitions for cross-validation.	integer	None
holdback	Fraction of data to hold back for validation.	numeric	None
Undersample	Flag to undersample the majority class.	logical	FALSE
hyperparameter_tuning	Flag to perform hyperparameter tuning for the model.	logical	FALSE
error_correction_method	Method to handle error correction. Must be one of ‘Flip’, ‘Prune’, or ‘None’.	character	None

Output

The function returns a list with the following elements:

• Data: A data frame containing the preprocessed data ready for machine learning.
• best.m: The best machine learning model after hyperparameter tuning, if applicable.

Key Steps

1. Fetch Study IDs:
   • If use_xpt_file is TRUE, it retrieves study IDs from directories within the specified path.
   • If use_xpt_file is FALSE and fake_study is TRUE, the function connects to an SQLite database and retrieves the study IDs from the ‘dm’ table.
   • If fake_study is FALSE, it fetches repeat-dose and parallel study IDs from the database.
2. Process Study Metadata:
   • If studyid_metadata is not provided, it generates metadata by selecting unique study IDs and assigning random “Target_Organ” values (either “Liver” or “not_Liver”).
3. Calculate Liver Toxicity Scores:
   • The function calculates liver toxicity scores using the get_liver_om_lb_mi_tox_score_list function.
4. Harmonize Scores:
   • The calculated liver toxicity scores are harmonized using the get_col_harmonized_scores_df function, optionally rounding them based on the Round parameter.
5. Machine Learning Data Preparation:
   • The function prepares the data for machine learning and performs hyperparameter tuning (if hyperparameter_tuning is TRUE) using the get_ml_data_and_tuned_hyperparameters function.
6. Return Processed Data and Best Model:
   • The final output consists of the processed data and the best machine learning model (best.m).

Example Usage

```r result <- get_Data_formatted_for_ml_and_best.m( path_db = “path/to/database.db”, rat_studies = TRUE, reps = 5, holdback = 0.2, error_correction_method = “Flip” )

Access the processed data and the best model

processed_data <- result$Data best_model <- result$best.m