The prediction function associated with the fnn model allowing for users to quickly get scalar or functional outputs.
fnn.predict( model, func_cov, scalar_cov = NULL, basis_choice = c("fourier"), num_basis = c(7), domain_range = list(c(0, 1)), covariate_scaling = T, raw_data = F )
| model | A keras model as outputted by |
|---|---|
| func_cov | The form of this depends on whether the |
| scalar_cov | A matrix contained the multivariate information associated with the data set. This is all of your
non-longitudinal data. Must be the same covariates as input into
|
| basis_choice | A vector of size k (the number of functional covariates) with either "fourier" or "bspline" as the inputs.
This is the choice for the basis functions used for the functional weight expansion. If you only specify one, with k > 1,
then the argument will repeat that choice for all k functional covariates. Should be the same choices as input into
|
| num_basis | A vector of size k defining the number of basis functions to be used in the basis expansion. Must be odd
for |
| domain_range | List of size k. Each element of the list is a 2-dimensional vector containing the upper and lower
bounds of the k-th functional weight. Must be the same covariates as input into |
| covariate_scaling | If True, then data will be internally scaled before model development. |
| raw_data | If True, then user does not need to create functional observations beforehand. The function will internally take care of that pre-processing. |
The following is returned:
Predictions -- A vector of scalar predictions or a matrix of basis coefficients for functional responses.
No additional details for now.
# First, we do an example with a scalar response: # loading data tecator = FNN::tecator # libraries library(fda) # define the time points on which the functional predictor is observed. timepts = tecator$absorp.fdata$argvals # define the fourier basis nbasis = 29 spline_basis = create.fourier.basis(tecator$absorp.fdata$rangeval, nbasis) # convert the functional predictor into a fda object and getting deriv tecator_fd = Data2fd(timepts, t(tecator$absorp.fdata$data), spline_basis) tecator_deriv = deriv.fd(tecator_fd) tecator_deriv2 = deriv.fd(tecator_deriv) # Non functional covariate tecator_scalar = data.frame(water = tecator$y$Water) # Response tecator_resp = tecator$y$Fat # Getting data into right format tecator_data = array(dim = c(nbasis, length(tecator_resp), 3)) tecator_data[,,1] = tecator_fd$coefs tecator_data[,,2] = tecator_deriv$coefs tecator_data[,,3] = tecator_deriv2$coefs # Splitting into test and train for third FNN ind = 1:165 tec_data_train <- array(dim = c(nbasis, length(ind), 3)) tec_data_test <- array(dim = c(nbasis, nrow(tecator$absorp.fdata$data) - length(ind), 3)) tec_data_train = tecator_data[, ind, ] tec_data_test = tecator_data[, -ind, ] tecResp_train = tecator_resp[ind] tecResp_test = tecator_resp[-ind] scalar_train = data.frame(tecator_scalar[ind,1]) scalar_test = data.frame(tecator_scalar[-ind,1]) # Setting up network tecator_fnn = fnn.fit(resp = tecResp_train, func_cov = tec_data_train, scalar_cov = scalar_train, basis_choice = c("fourier", "fourier", "fourier"), num_basis = c(5, 5, 7), hidden_layers = 4, neurons_per_layer = c(64, 64, 64, 64), activations_in_layers = c("relu", "relu", "relu", "linear"), domain_range = list(c(850, 1050), c(850, 1050), c(850, 1050)), epochs = 300, learn_rate = 0.002) # Predicting pred_tec = fnn.predict(tecator_fnn, tec_data_test, scalar_cov = scalar_test, basis_choice = c("fourier", "fourier", "fourier"), num_basis = c(5, 5, 7), domain_range = list(c(850, 1050), c(850, 1050), c(850, 1050))) # Now an example with functional responses # libraries library(fda) # Loading data data("daily") # Creating functional data temp_data = array(dim = c(65, 35, 1)) tempbasis65 = create.fourier.basis(c(0,365), 65) tempbasis7 = create.bspline.basis(c(0,365), 7, norder = 4) timepts = seq(1, 365, 1) temp_fd = Data2fd(timepts, daily$tempav, tempbasis65) prec_fd = Data2fd(timepts, daily$precav, tempbasis7) prec_fd$coefs = scale(prec_fd$coefs) # Data set up temp_data[,,1] = temp_fd$coefs resp_mat = prec_fd$coefs # Non functional covariate weather_scalar = data.frame(total_prec = apply(daily$precav, 2, sum)) # Splitting into test and train ind = 1:30 nbasis = 65 weather_data_train <- array(dim = c(nbasis, length(ind), 1)) weather_data_test <- array(dim = c(nbasis, ncol(daily$tempav) - length(ind), 1)) weather_data_train[,,1] = temp_data[, ind, ] weather_data_test[,,1] = temp_data[, -ind, ] scalar_train = data.frame(weather_scalar[ind,1]) scalar_test = data.frame(weather_scalar[-ind,1]) resp_train = t(resp_mat[,ind]) resp_test = t(resp_mat[,-ind]) # Running model weather_func_fnn <- fnn.fit(resp = resp_train, func_cov = weather_data_train, scalar_cov = scalar_train, basis_choice = c("bspline"), num_basis = c(7), hidden_layers = 2, neurons_per_layer = c(1024, 1024), activations_in_layers = c("sigmoid", "linear"), domain_range = list(c(1, 365)), epochs = 300, learn_rate = 0.01, func_resp_method = 1) # Getting Predictions predictions = fnn.predict(weather_func_fnn, weather_data_test, scalar_cov = scalar_test, basis_choice = c("bspline"), num_basis = c(7), domain_range = list(c(1, 365))) # Looking at predictions predictions # Classification Prediction # Loading data tecator = FNN::tecator # Making classification bins tecator_resp = as.factor(ifelse(tecator$y$Fat > 25, 1, 0)) # Non functional covariate tecator_scalar = data.frame(water = tecator$y$Water) # Splitting data ind = sample(1:length(tecator_resp), round(0.75*length(tecator_resp))) train_y = tecator_resp[ind] test_y = tecator_resp[-ind] train_x = tecator$absorp.fdata$data[ind,] test_x = tecator$absorp.fdata$data[-ind,] scalar_train = data.frame(tecator_scalar[ind,1]) scalar_test = data.frame(tecator_scalar[-ind,1]) # Making list element to pass in func_covs_train = list(train_x) func_covs_test = list(test_x) # Now running model fit_class = fnn.fit(resp = train_y, func_cov = func_covs_train, scalar_cov = scalar_train, hidden_layers = 6, neurons_per_layer = c(24, 24, 24, 24, 24, 58), activations_in_layers = c("relu", "relu", "relu", "relu", "relu", "linear"), domain_range = list(c(850, 1050)), learn_rate = 0.001, epochs = 100, raw_data = T, early_stopping = T) # Running prediction predict_class = fnn.predict(fit_class, func_cov = func_covs_test, scalar_cov = scalar_test, domain_range = list(c(850, 1050)), raw_data = T) # Rounding predictions (they are probabilities) rounded_preds = ifelse(round(predict_class)[,2] == 1, 1, 0) # Confusion matrix confusionMatrix(as.factor(rounded_preds), as.factor(test_y))