View source: R/HVTMSMoptimization.R
| HVTMSMoptimization | R Documentation |
This function runs multiple iterations/experiments over the dataset across different cell counts, and computes the MAE. If a given cell configuration not results in problematic states, the baseline simulation is used; otherwise, the simulation proceeds with problematic state handling. This process helps identify the best-performing model (lowest MAE). In essence, it performs trainHVT → scoreHVT → transition probability estimation on the training data, followed by msm simulation on the ex-post (test) dataset. Note: This is not applicable for ex-ante analysis.
HVTMSMoptimization(
entire_dataset,
expost_forecasting,
time_column,
ncell_range = 3:5,
k_range = 2:9,
nn_range = 2:7,
num_simulations = 10,
mae_metric,
hvt_params = list(depth = 1, quant.err = 0.2, normalize = TRUE, distance_metric =
"L1_Norm", error_metric = "max", dim_reduction_method = "sammon"),
parallel = TRUE,
verbose = TRUE,
parallel_strategy = "multisession",
max_workers = NULL
)
entire_dataset |
Dataframe. Train dataset for model training |
expost_forecasting |
Dataframe. Test dataset for ex-post forecasting |
time_column |
Character. Name of the time column |
ncell_range |
Numeric vector. Range of cells to run experiments (default 3:5) |
k_range |
Numeric vector. Range of clusters to run experiments (default: 2:9) |
nn_range |
Numeric vector. Range of nearest neighbors to run experiments (default: 2:7) |
num_simulations |
Integer. Number of simulations (default: 10) |
mae_metric |
Character. MAE calculation method(s): "mean", "median", "mode", or "all" (default: "median") |
hvt_params |
List. Set of parameters for Model Training (refer trainHVT) |
parallel |
Character. Whether to use parallel processing (default: TRUE) |
verbose |
Character. Whether to print progress information (default: TRUE) |
parallel_strategy |
Character. Parallel processing strategy: "multisession", "multicore", etc. (default: "multisession") |
max_workers |
Maximum number of parallel workers (default: NULL for auto-detect) |
List containing optimization results for each MAE metric:
[[successful_results]] |
All successful parameter combinations |
[[nclust_best_results]] |
Best combination for each cell |
[[overall_best]] |
Overall best parameter combination |
[[all_results]] |
All attempted combinations with status |
Vishwavani <vishwavani@mu-sigma.com>, Nithya <nithya.sn@mu-sigma.com>
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