def _demo_cv():
dim_x = 2
num_ch = 2
num_x_p = 2000
num_x_n = 500
x_p = 2 * np.random.randn(num_ch, num_x_p, dim_x)
x_n = 4 + np.random.randn(num_ch, num_x_n, dim_x)
y_p = [1] * num_x_p
y_n = [0] * num_x_n
x = np.concatenate((x_p, x_n), 1)
y = np.concatenate(np.asarray([y_p, y_n]), 0)
permutation = np.random.permutation(x.shape[1])
x = x[:, permutation, :]
y = y[permutation]
arg_0 = test_cv(x, y)
rda = RegularizedDiscriminantAnalysis()
pca = DummyDimReduction()
pipeline = Pipeline()
pipeline.add(pca)
pipeline.add(rda)
arg_1 = cross_validation(x, y, pipeline)
print('Cross Validation Flows!')
return 0
def test_cv(x, y):
rda = RegularizedDiscriminantAnalysis()
pca = DummyDimReduction()
pipeline = Pipeline()
pipeline.add(pca)
pipeline.add(rda)
arg = cross_validation(x, y, pipeline)
return arg
def train_pca_rda_kde_model(x, y, k_folds=10):
""" Trains the Cw-PCA RDA KDE model given the input data and labels with
cross validation and returns the model
Args:
x(ndarray[float]): C x N x k data array
y(ndarray[int]): N x 1 observation (class) array
N is number of samples k is dimensionality of features
C is number of channels
k_folds(int): Number of cross validation folds
Return:
model(pipeline): trained likelihood model
"""
# Pipeline is the model. It can be populated manually
rda = RegularizedDiscriminantAnalysis()
pca = ChannelWisePrincipalComponentAnalysis(var_tol=.1 ** 5,
num_ch=x.shape[0])
model = Pipeline()
model.add(pca)
model.add(rda)
# Cross validate
arg_cv = cross_validation(x, y, model=model, k_folds=k_folds)
# Get the AUC before the regularization
tmp, sc_cv, y_cv = cost_cross_validation_auc(model, 1, x, y, arg_cv,
k_folds=10, split='uniform')
auc_init = -tmp
# Start Cross validation
lam = arg_cv[0]
gam = arg_cv[1]
log.debug('Optimized val [gam:{} \ lam:{}]'.format(lam, gam))
model.pipeline[1].lam = lam
model.pipeline[1].gam = gam
tmp, sc_cv, y_cv = cost_cross_validation_auc(model, 1, x, y, arg_cv,
k_folds=10, split='uniform')
auc_cv = -tmp
# After finding cross validation scores do one more round to learn the final RDA model
model.fit(x, y)
# Insert the density estimates to the model and train using the cross validated
# scores to avoid over fitting. Observe that these scores are not obtained using
# the final model
bandwidth = 1.06 * min(
np.std(sc_cv), iqr(sc_cv) / 1.34) * np.power(x.shape[0], -0.2)
model.add(KernelDensityEstimate(bandwidth=bandwidth))
model.pipeline[-1].fit(sc_cv, y_cv)
# Report AUC
log.debug('AUC-i: {}, AUC-cv: {}'.format(auc_init, auc_cv))
return model, auc_cv