terga1_fit: Fit a Model Using an Evolutionary Algorithm with Controlled...
| terga1_fit | R Documentation |
Fit a Model Using an Evolutionary Algorithm with Controlled Parameters
Description
This function fits a model to the data using an evolutionary algorithm, allowing for controlled sparsity, fitness evaluation, and multiple evolution strategies. The function iteratively evolves a population of models, optimizing for accuracy or correlation, and terminates based on specified conditions.
Usage
terga1_fit(X, y, clf)
Arguments
X |
A matrix or data frame of feature values, where each row represents a feature and each column represents a sample. |
y |
A response vector or target variable for supervised learning. The
length of |
clf |
A classifier object containing parameters for fitting, including: - 'params': A list of parameters for fitting, such as 'language', 'objective', 'evalToFit', 'sparsity', 'size_pop', 'nb_generations', 'maxTime', and 'evolver'. - 'feature.cor': A matrix of feature correlations for pre-selection of features based on correlation. - 'functions': A list of functions to be used within the fitting process, such as 'evolve', 'mutate', and 'cross'. |
Details
The function performs the following steps:
1. **Parameter Initialization**: Sets parameters based on the specified 'language' and adjusts 'evalToFit' based on the 'objective'. 2. **Feature Pre-selection**: Optionally restricts 'X' to the most significant features based on 'clf$feature.cor' if 'max.nb.features' is specified. 3. **Population Initialization**: Initializes a population of models based on 'in_pop' (if available) or generates a new population with random individuals. 4. **Evolutionary Process**: - Iteratively evolves the population over generations, evaluating and ranking individuals by fitness. - Applies selection, crossover, and mutation strategies to generate new models. - Stores the best models by sparsity level for each generation. 5. **Termination**: Stops when the maximum number of generations or specified execution time ('maxTime') is reached.
The function returns a collection of the best models, sorted by fitness, and allows for plotting evolution if 'plot' is set to 'TRUE'.
Value
A list representing a model collection of the best individuals from the final population, each evaluated by fitness and sparsity level.
Examples
## Not run:
X <- matrix(rnorm(1000), nrow = 10) # Random features
y <- sample(c(0, 1), 100, replace = TRUE) # Random binary response
clf <- list(
params = list(language = "bin", objective = "accuracy", evalToFit = "accuracy_", size_pop = 20,
nb_generations = 50, maxTime = 3600, evolver = "v2m"),
feature.cor = data.frame(p = runif(10)),
functions = list(evolver = evolve2m)
)
best_models <- terga1_fit(X, y, clf)
print(best_models)
## End(Not run)
