def train_and_swap(fn, old, tmp, verbose=False):
if os.path.exists(fn):
old_model = model.BaoRegression(have_cache_data=True)
old_model.load(fn)
else:
old_model = None
new_model = train_and_save_model(tmp, verbose=verbose)
max_retries = 5
current_retry = 1
while not reg_blocker.should_replace_model(old_model, new_model):
if current_retry >= max_retries == 0:
print("Could not train model with better regression profile.")
return
print("New model rejected when compared with old model. " +
"Trying to retrain with emphasis on regressions.")
print("Retry #", current_retry)
new_model = train_and_save_model(tmp,
verbose=verbose,
emphasize_experiments=current_retry)
current_retry += 1
if os.path.exists(fn):
shutil.rmtree(old, ignore_errors=True)
os.rename(fn, old)
os.rename(tmp, fn)
def load_model(self, fp):
try:
new_model = model.BaoRegression(have_cache_data=True)
new_model.load(fp)
if reg_blocker.should_replace_model(self.__current_model,
new_model):
self.__current_model = new_model
print("Accepted new model.")
else:
print("Rejecting load of new model due to regresison profile.")
except Exception as e:
print("Failed to load Bao model from", fp, "Exception:",
sys.exc_info()[0])
raise e
def train_and_save_model(fn, verbose=True, emphasize_experiments=0):
all_experience = storage.experience()
for _ in range(emphasize_experiments):
all_experience.extend(storage.experiment_experience())
x = [i[0] for i in all_experience]
y = [i[1] for i in all_experience]
if not all_experience:
raise BaoTrainingException(
"Cannot train a Bao model with no experience")
if len(all_experience) < 20:
print(
"Warning: trying to train a Bao model with fewer than 20 datapoints."
)
reg = model.BaoRegression(have_cache_data=True, verbose=verbose)
reg.fit(x, y)
reg.save(fn)
return reg
all_experience.extend(storage.experiment_experience())
x = [i[0] for i in all_experience]
y = [i[1] for i in all_experience]
if not all_experience:
raise BaoTrainingException(
"Cannot train a Bao model with no experience")
if len(all_experience) < 20:
print(
"Warning: trying to train a Bao model with fewer than 20 datapoints."
)
reg = model.BaoRegression(have_cache_data=True, verbose=verbose)
reg.fit(x, y)
reg.save(fn)
return reg
if __name__ == "__main__":
import sys
if len(sys.argv) != 2:
print("Usage: train.py MODEL_FILE")
exit(-1)
train_and_save_model(sys.argv[1])
print("Model saved, attempting load...")
reg = model.BaoRegression(have_cache_data=True)
reg.load(sys.argv[1])