def run(self, dataset, time_limit):
"""
Run the automatic statistician.
"""
histories = [ModelHistory.new(model, dataset, self.bo, self.gp,
self.perf_sample_std, self.runtime_sample_std)
for model in models.list_classification_models()]
# do multi-armed bandit for N iterations
print ", ".join(["iters", "time_left", "model", "hp", "perf", "runtime"])
try:
time_left = time_limit
iters = 0
while time_left > 0:
selected = self.select(histories, time_left)
hp, perf, runtime = selected.run()
print ", ".join(str(t) for t in [iters, time_left, selected.model.__name__, float(hp), perf, runtime])
time_left -= runtime
iters += 1
except KeyboardInterrupt:
print "Caught keyboard interrupt, finishing early..."
# plot resulting beliefs
for m in histories:
m.plot()
def run(self, dataset, time_limit):
"""
Run the automatic statistician.
"""
histories = [
ModelHistory.new(model, dataset, self.bo, self.gp, self.perf_sample_std, self.runtime_sample_std)
for model in models.list_classification_models()
]
# do multi-armed bandit for N iterations
print ", ".join(["iters", "time_left", "model", "hp", "perf", "runtime"])
try:
time_left = time_limit
iters = 0
while time_left > 0:
selected = self.select(histories, time_left)
hp, perf, runtime = selected.run()
print ", ".join(str(t) for t in [iters, time_left, selected.model.__name__, float(hp), perf, runtime])
time_left -= runtime
iters += 1
except KeyboardInterrupt:
print "Caught keyboard interrupt, finishing early..."
# plot resulting beliefs
for m in histories:
m.plot()
def __init__(self):
self.models = models.list_classification_models()
def __init__(self):
self.models = models.list_classification_models()
