variable_selection: Variable selection

In dhopp1/nowcastLSTM: LSTMs for Nowcasting

Variable selection

Description

Pick best-performing variables for a given set of hyperparameters. All parameters before 'n_folds' identical to a base LSTM model.

Usage

variable_selection(
  data,
  target_variable,
  n_timesteps,
  fill_na_func = "mean",
  fill_ragged_edges_func = "mean",
  n_models = 1,
  train_episodes = 200,
  batch_size = 30,
  decay = 0.98,
  n_hidden = 20,
  n_layers = 2,
  dropout = 0,
  criterion = "''",
  optimizer = "''",
  optimizer_parameters = list(lr = 0.01),
  n_folds = 1,
  init_test_size = 0.2,
  pub_lags = c(),
  lags = c(),
  performance_metric = "RMSE",
  alpha = 0,
  initial_ordering = "feature_contribution",
  quiet = FALSE
)

Arguments

n_folds	how many folds for rolling fold validation to do
init_test_size	ϵ [0,1]. What proportion of the data to use for testing at the first fold
pub_lags	list of periods back each input variable is set to missing. I.e. publication lag of the variable. Leave empty to pick variables only on complete information, no synthetic vintages.
lags	simulated periods back to test when selecting variables. E.g. -2 = simulating data as it would have been 2 months before target period, 1 = 1 month after, etc. So [-2, 0, 2] will account for those vintages in model selection. Leave empty to pick variables only on complete information, no synthetic vintages.
performance_metric	performance metric to use for variable selection. Pass "RMSE" for root mean square error, "MAE" for mean absolute error, or "AICc" for correctd Akaike Information Criterion. Alternatively can pass a function that takes arguments of a pandas Series of predictions and actuals and returns a scalar. E.g. custom_function(preds, actuals).
alpha	ϵ [0,1]. 0 implies no penalization for additional regressors, 1 implies most severe penalty for additional regressors. Not used for "AICc" performance metric.
initial_ordering	ϵ ["feature_contribution", "univariate"]. How to get initial order of features to check additively. "feature_contribution" uses the feature contribution of one model, "univariate" calculates univariate models of all features and orders by performance metric. Feature contribution is about twice as fast.
quiet	whether or not to print progress

Value

A list containing the following elements:

col_names	list of best-performing column names
performance	performance metric of these variables (i.e. best performing)

dhopp1/nowcastLSTM documentation built on May 7, 2024, 9:30 a.m.