def run(ff, prior, regularization_strength):
predictions, measurements, uncertainties = experiment_loader.load(ff, stride=ALA3.cross_val_stride)
if prior == "maxent":
model_factory = lambda predictions, measurements, uncertainties: belt.MaxEntBELT(predictions, measurements, uncertainties, regularization_strength)
elif prior == "dirichlet":
model_factory = lambda predictions, measurements, uncertainties: belt.DirichletBELT(predictions, measurements, uncertainties, regularization_strength)
elif prior == "MVN":
precision = np.cov(predictions.values.T)
model_factory = lambda predictions, measurements, uncertainties: belt.MVNBELT(predictions, measurements, uncertainties, regularization_strength, precision=precision)
bootstrap_index_list = np.array_split(np.arange(len(predictions)), ALA3.kfold)
train_chi, test_chi = belt.cross_validated_mcmc(predictions.values, measurements.values, uncertainties.values, model_factory, bootstrap_index_list, ALA3.num_samples, thin=ALA3.thin)
print regularization_strength, train_chi.mean(), test_chi.mean()
F = open(ALA3.cross_val_filename, 'a')
F.write("%s,%s,%f,%d,%d,%f,%f \n"% (ff, prior, regularization_strength, ALA3.cross_val_stride, ALA3.num_samples, train_chi.mean(), test_chi.mean()))
F.close()
import numpy as np
from fitensemble import belt
belt.ne.set_num_threads(1)
kfold = 2
thin = 10
n_samples = 200000
measurements = np.array([0.0])
uncertainties = np.array([1.0])
n_predictions_list = np.array([100, 1000, 10000, 100000])
regularization_strength_list = np.array([0.75, 1.0, 1.5, 2.0, 2.5])
results = np.zeros((len(n_predictions_list), len(regularization_strength_list), 2))
for i, n_predictions in enumerate(n_predictions_list):
predictions = np.random.normal(1.0, 1.0, size=(n_predictions, 1))
bootstrap_index_list = np.array_split(np.arange(len(predictions)), kfold)
for j, regularization_strength in enumerate(regularization_strength_list):
model_factory = lambda predictions, measurements, uncertainties: belt.MaxEntBELT(
predictions, measurements, uncertainties, regularization_strength
)
train_chi, test_chi = belt.cross_validated_mcmc(
predictions, measurements, uncertainties, model_factory, bootstrap_index_list, n_samples, thin=thin
)
print regularization_strength, train_chi.mean(), test_chi.mean()
results[i, j] = train_chi.mean(), test_chi.mean()
