def __init__(self,
model,
holdout,
batch_size=30,
iters_per_spin=10,
validation_period=10):
self.problem = rr.BanditValueProblem(model=model)
self.tr_base = adel.BasicDataset()
self.val_base = adel.BasicDataset()
self.holdout = adel.HoldoutWrapper(training=self.tr_base,
holdout=self.val_base,
**holdout)
self.tr_sampler = adel.DatasetSampler(base=self.tr_base,
method='uniform')
self.tr_sampled = adel.LabeledDatasetTranslator(base=self.tr_sampler)
self.tr_data = adel.LabeledDatasetTranslator(base=self.tr_base)
self.val_data = adel.LabeledDatasetTranslator(base=self.val_base)
self.reporter = adel.LabeledDatasetTranslator(base=self.holdout)
self.iters_per_spin = iters_per_spin
self.val_period = validation_period
self.batch_size = batch_size
self.spin_counter = 0
self.error_plotter = rr.LineSeriesPlotter(
title='Value error over time %s' % model.scope,
xlabel='Spin iter',
ylabel='Mean squared loss')
self.value_plotter = rr.LineSeriesPlotter(title='Values %s' %
model.scope,
xlabel='Spin iter',
ylabel='Value')
def __init__(self,
problem,
holdout,
batch_size=30,
iters_per_spin=10,
validation_period=10):
self.problem = problem
self.train_base = adel.BasicDataset()
self.val_base = adel.BasicDataset()
self.holdout = adel.HoldoutWrapper(training=self.train_base,
holdout=self.val_base,
**holdout)
self.train_sampler = adel.DatasetSampler(base=self.train_base,
method='uniform')
self.train_sampled = adel.LabeledDatasetTranslator(
base=self.train_sampler)
self.train_data = adel.LabeledDatasetTranslator(base=self.train_base)
self.val_data = adel.LabeledDatasetTranslator(base=self.val_base)
self.reporter = adel.LabeledDatasetTranslator(base=self.holdout)
self.iters_per_spin = iters_per_spin
self.val_period = validation_period
self.batch_size = batch_size
self.spin_counter = 0
self.error_plotter = LineSeriesPlotter(
title='Value error over time %s' % self.scope,
xlabel='Spin iter',
ylabel=self.problem.loss_type)
self.value_plotter = ScatterPlotter(title='Value parities %s' %
self.scope,
xlabel='Target value',
ylabel='Estimated value')
self.plottables = [self.error_plotter, self.value_plotter]
if self.problem.model.using_image:
# TODO HACK
self.filters = self.problem.params[0]
n_filters = int(self.filters.shape[-1])
self.filter_plotter = FilterPlotter(n_filters)
self.plottables.append(self.filter_plotter)
def __init__(self,
classifier,
holdout,
optimizer,
visualize=True,
vis_res=10):
self.classifier = classifier
self.optimizer = optim.parse_optimizers(**optimizer)
self.training_base = adel.BasicDataset()
self.tuning_base = adel.BasicDataset()
self.tuning_holdout = adel.HoldoutWrapper(training=self.training_base,
holdout=self.tuning_base,
**holdout)
self.report_binary = adel.BinaryDatasetTranslator(self.tuning_holdout)
self.training_binary = adel.BinaryDatasetTranslator(self.training_base)
self.tuning_binary = adel.BinaryDatasetTranslator(self.tuning_base)
self.validation_base = adel.BasicDataset()
self.validation_binary = adel.BinaryDatasetTranslator(
self.validation_base)
self.update_counter = 0
self.visualize = visualize
self.vis_res = vis_res
if self.visualize:
# TODO Put scope in name
self.heat_plotter = ImagePlotter(vmin=0,
vmax=1,
title='Classifier')
self.point_plotter = LineSeriesPlotter(other=self.heat_plotter)
self.error_plotter = LineSeriesPlotter(
title='Logistic losses over time',
xlabel='Spin iter',
ylabel='Logistic loss')
self.roc_plotter = LineSeriesPlotter(title='ROC',
xlabel='False positive rate',
ylabel='True positive rate')