def next_action(self):
self.read_messages()
# Start timing
self.time_checkpoint = time.clock()
# Fit learner
self.fit(self.train_idx, self.feature_subset)
# Make predictions on held out set and evaluate
predictions = self.predict('X_train', self.test_idx, self.feature_subset)
truth = self.test_truth
score = libscores.eval_metric(metric=self.data_info['eval_metric'],
truth=truth,
predictions=predictions,
task=self.data_info['task'])
self.score_times.append(time.clock() - self.time_checkpoint + self.time_before_checkpoint)
self.score_values.append(score)
# Send score and time to parent
self.send_to_parent(dict(subject='score', sender=self.name,
time=self.score_times[-1],
score=self.score_values[-1]))
# Save total time taken
# TODO - this is ignoring the time taken to make valid and test predictions
self.time_before_checkpoint += time.clock() - self.time_checkpoint
# Now make predictions on valid, test and held out sets
# FIXME - send all of this data at the same time to prevent gotchas
if 'X_valid' in self.data:
predictions = self.predict('X_valid', 'all', self.feature_subset)
tmp_filename = util.random_temp_file_name('.npy')
np.save(tmp_filename, predictions)
self.valid_prediction_files.append(tmp_filename)
self.valid_prediction_times.append(self.time_before_checkpoint)
self.send_to_parent(dict(subject='predictions', sender=self.name, partition='valid',
time=self.valid_prediction_times[-1],
filename=self.valid_prediction_files[-1]))
if 'X_test' in self.data:
predictions = self.predict('X_test', 'all', self.feature_subset)
tmp_filename = util.random_temp_file_name('.npy')
np.save(tmp_filename, predictions)
self.test_prediction_files.append(tmp_filename)
self.test_prediction_times.append(self.time_before_checkpoint)
self.send_to_parent(dict(subject='predictions', sender=self.name, partition='test',
time=self.test_prediction_times[-1],
filename=self.test_prediction_files[-1]))
predictions = self.predict('X_train', self.test_idx, self.feature_subset)
tmp_filename = util.random_temp_file_name('.npy')
np.save(tmp_filename, predictions)
self.held_out_prediction_files.append(tmp_filename)
self.held_out_prediction_times.append(self.time_before_checkpoint)
self.send_to_parent(dict(subject='predictions', sender=self.name, partition='held out',
idx=self.test_idx,
time=self.held_out_prediction_times[-1],
filename=self.held_out_prediction_files[-1]))
# And I'm spent
raise TerminationEx
def next_action(self):
# Check mail
self.read_messages()
# Collect up scores and predictions - even if paused, children may still be finishing tasks
min_n_scores = min(len(scores) for scores in self.child_score_values.itervalues())
while len(self.score_values) < min_n_scores:
n = len(self.score_values)
num_scores = 0
sum_scores = 0
for child_scores in self.child_score_values.itervalues():
# noinspection PyUnresolvedReferences
if not np.isnan(child_scores[n]):
num_scores += 1
sum_scores += child_scores[n]
score = sum_scores / num_scores
# score = sum(scores[n] for scores in self.child_score_values.itervalues()) /\
# len(self.child_score_values)
maxtime = max(times[n] for times in self.child_score_times.itervalues())
self.score_values.append(score)
self.score_times.append(maxtime)
self.send_to_parent(dict(subject='score', sender=self.name,
time=self.score_times[-1],
score=self.score_values[-1]))
# FIXME - send all of this data at the same time to prevent gotchas
min_n_valid = min(len(times) for times in self.child_valid_prediction_times.itervalues())
while len(self.valid_prediction_times) < min_n_valid:
n = len(self.valid_prediction_times)
predictions = None
for child_name in self.child_score_times.iterkeys():
filename = self.child_valid_prediction_files[child_name][n]
child_predictions = np.load(filename)
os.remove(filename)
if predictions is None:
predictions = child_predictions
else:
predictions += child_predictions
predictions /= len(self.child_score_times)
tmp_filename = util.random_temp_file_name('.npy')
np.save(tmp_filename, predictions)
maxtime = max(times[n] for times in self.child_valid_prediction_times.itervalues())
self.valid_prediction_files.append(tmp_filename)
self.valid_prediction_times.append(maxtime)
self.send_to_parent(dict(subject='predictions', sender=self.name, partition='valid',
time=self.valid_prediction_times[-1],
filename=self.valid_prediction_files[-1]))
min_n_test = min(len(times) for times in self.child_test_prediction_times.itervalues())
while len(self.test_prediction_times) < min_n_test:
n = len(self.test_prediction_times)
predictions = None
for child_name in self.child_score_times.iterkeys():
filename = self.child_test_prediction_files[child_name][n]
child_predictions = np.load(filename)
os.remove(filename)
if predictions is None:
predictions = child_predictions
else:
predictions += child_predictions
predictions /= len(self.child_score_times)
tmp_filename = util.random_temp_file_name('.npy')
np.save(tmp_filename, predictions)
maxtime = max(times[n] for times in self.child_test_prediction_times.itervalues())
self.test_prediction_files.append(tmp_filename)
self.test_prediction_times.append(maxtime)
self.send_to_parent(dict(subject='predictions', sender=self.name, partition='test',
time=self.test_prediction_times[-1],
filename=self.test_prediction_files[-1]))
min_n_held_out = min(len(times) for times in self.child_held_out_prediction_times.itervalues())
while len(self.held_out_prediction_times) < min_n_held_out:
n = len(self.held_out_prediction_times)
# FIXME - get rid of if else here
if self.data_info['task'] == 'multiclass.classification':
predictions = np.zeros(self.data['Y_train_1_of_k'].shape)
# print('Prediction shape')
# print(predictions.shape)
else:
predictions = np.zeros(self.data['Y_train'].shape)
for child_name in self.child_score_times.iterkeys():
filename = self.child_held_out_prediction_files[child_name][n]
child_predictions = np.load(filename)
os.remove(filename)
predictions[self.child_held_out_idx[child_name]] = child_predictions
# print('Combined predictions')
# print(predictions[0])
tmp_filename = util.random_temp_file_name('.npy')
np.save(tmp_filename, predictions)
maxtime = max(times[n] for times in self.child_held_out_prediction_times.itervalues())
self.held_out_prediction_files.append(tmp_filename)
self.held_out_prediction_times.append(maxtime)
self.send_to_parent(dict(subject='predictions', sender=self.name, partition='held out',
time=self.held_out_prediction_times[-1],
filename=self.held_out_prediction_files[-1]))
# Check to see if all children have terminated - if so, terminate this agent
# immortal child dying is failure
# mortal child dying without sending results is failure
# any child failure should kill parent
if self.immortal_offspring is True and len(self.conns_from_children) != len(self.child_states):
logger.error("%s: Immortal child has died. Dying of grief", self.name)
raise TerminationEx
elif self.immortal_offspring is False:
dead_kids = [x for x in self.child_states if x not in self.conns_from_children]
for dk in dead_kids:
if len(self.child_test_prediction_files[dk]) == 0:
logger.error("%s: Mortal child %s has died without sending results", self.name, dk)
raise TerminationEx
if len(self.conns_from_children) == 0:
logger.info("%s: No children remaining. Terminating.", self.name)
raise TerminationEx
def next_action(self):
# Read messages
self.read_messages()
# Start timing
self.time_checkpoint = time.clock()
predict_time = self.time_quantum / self.n_samples
scores_so_far = 0
# Increase estimators and learn
while time.clock() - self.time_checkpoint < self.time_quantum:
# Read messages - maybe compute quantum has changed?
self.get_parent_inbox()
self.read_messages()
# Do learning
self.learner.n_estimators += self.n_estimators_quantum
start_time = time.clock()
self.fit(self.train_idx, self.feature_subset)
time_taken = time.clock() - start_time
if global_data.exp['slowdown_factor'] > 1:
util.waste_cpu_time(time_taken * (global_data.exp['slowdown_factor'] - 1))
if time.clock() - self.time_checkpoint > predict_time:
predictions = self.predict('X_train', self.test_idx, self.feature_subset)
truth = self.test_truth
score = libscores.eval_metric(metric=self.data_info['eval_metric'],
truth=truth,
predictions=predictions,
task=self.data_info['task'])
self.score_times.append(time.clock() - self.time_checkpoint + self.time_before_checkpoint)
self.score_values.append(score)
# Send score and time to parent
self.send_to_parent(dict(subject='score', sender=self.name,
time=self.score_times[-1],
score=self.score_values[-1]))
scores_so_far += 1
# Next time at which to make a prediction
if self.n_samples > scores_so_far:
predict_time = time.clock() - self.time_checkpoint + \
(self.time_quantum - (time.clock() - self.time_checkpoint)) / \
(self.n_samples - scores_so_far)
else:
break
# Save total time taken
# TODO - this is ignoring the time taken to make valid and test predictions
self.time_before_checkpoint += time.clock() - self.time_checkpoint
# Now make predictions
# FIXME - send all of this data at the same time to prevent gotchas
if 'X_valid' in self.data:
predictions = self.predict('X_valid', 'all', self.feature_subset)
tmp_filename = util.random_temp_file_name('.npy')
np.save(tmp_filename, predictions)
self.valid_prediction_files.append(tmp_filename)
self.valid_prediction_times.append(self.time_before_checkpoint)
self.send_to_parent(dict(subject='predictions', sender=self.name, partition='valid',
time=self.valid_prediction_times[-1],
filename=self.valid_prediction_files[-1]))
if 'X_test' in self.data:
predictions = self.predict('X_test', 'all', self.feature_subset)
tmp_filename = util.random_temp_file_name('.npy')
np.save(tmp_filename, predictions)
self.test_prediction_files.append(tmp_filename)
self.test_prediction_times.append(self.time_before_checkpoint)
self.send_to_parent(dict(subject='predictions', sender=self.name, partition='test',
time=self.test_prediction_times[-1],
filename=self.test_prediction_files[-1]))
predictions = self.predict('X_train', self.test_idx, self.feature_subset)
# print('Held out')
# print(predictions[0])
tmp_filename = util.random_temp_file_name('.npy')
np.save(tmp_filename, predictions)
self.held_out_prediction_files.append(tmp_filename)
self.held_out_prediction_times.append(self.time_before_checkpoint)
self.send_to_parent(dict(subject='predictions', sender=self.name, partition='held out',
idx=self.test_idx,
time=self.held_out_prediction_times[-1],
filename=self.held_out_prediction_files[-1]))
if self.run_one_iteration:
self.pause()
def next_action(self):
# Check mail
self.read_messages()
# Collect up scores and predictions - even if paused, children may still be finishing tasks
min_n_scores = min(
len(scores) for scores in self.child_score_values.itervalues())
while len(self.score_values) < min_n_scores:
n = len(self.score_values)
num_scores = 0
sum_scores = 0
for child_scores in self.child_score_values.itervalues():
# noinspection PyUnresolvedReferences
if not np.isnan(child_scores[n]):
num_scores += 1
sum_scores += child_scores[n]
score = sum_scores / num_scores
# score = sum(scores[n] for scores in self.child_score_values.itervalues()) /\
# len(self.child_score_values)
maxtime = max(times[n]
for times in self.child_score_times.itervalues())
self.score_values.append(score)
self.score_times.append(maxtime)
self.send_to_parent(
dict(subject='score',
sender=self.name,
time=self.score_times[-1],
score=self.score_values[-1]))
# FIXME - send all of this data at the same time to prevent gotchas
min_n_valid = min(
len(times)
for times in self.child_valid_prediction_times.itervalues())
while len(self.valid_prediction_times) < min_n_valid:
n = len(self.valid_prediction_times)
predictions = None
for child_name in self.child_score_times.iterkeys():
filename = self.child_valid_prediction_files[child_name][n]
child_predictions = np.load(filename)
os.remove(filename)
if predictions is None:
predictions = child_predictions
else:
predictions += child_predictions
predictions /= len(self.child_score_times)
tmp_filename = util.random_temp_file_name('.npy')
np.save(tmp_filename, predictions)
maxtime = max(
times[n]
for times in self.child_valid_prediction_times.itervalues())
self.valid_prediction_files.append(tmp_filename)
self.valid_prediction_times.append(maxtime)
self.send_to_parent(
dict(subject='predictions',
sender=self.name,
partition='valid',
time=self.valid_prediction_times[-1],
filename=self.valid_prediction_files[-1]))
min_n_test = min(
len(times)
for times in self.child_test_prediction_times.itervalues())
while len(self.test_prediction_times) < min_n_test:
n = len(self.test_prediction_times)
predictions = None
for child_name in self.child_score_times.iterkeys():
filename = self.child_test_prediction_files[child_name][n]
child_predictions = np.load(filename)
os.remove(filename)
if predictions is None:
predictions = child_predictions
else:
predictions += child_predictions
predictions /= len(self.child_score_times)
tmp_filename = util.random_temp_file_name('.npy')
np.save(tmp_filename, predictions)
maxtime = max(
times[n]
for times in self.child_test_prediction_times.itervalues())
self.test_prediction_files.append(tmp_filename)
self.test_prediction_times.append(maxtime)
self.send_to_parent(
dict(subject='predictions',
sender=self.name,
partition='test',
time=self.test_prediction_times[-1],
filename=self.test_prediction_files[-1]))
min_n_held_out = min(
len(times)
for times in self.child_held_out_prediction_times.itervalues())
while len(self.held_out_prediction_times) < min_n_held_out:
n = len(self.held_out_prediction_times)
# FIXME - get rid of if else here
if self.data_info['task'] == 'multiclass.classification':
predictions = np.zeros(self.data['Y_train_1_of_k'].shape)
# print('Prediction shape')
# print(predictions.shape)
else:
predictions = np.zeros(self.data['Y_train'].shape)
for child_name in self.child_score_times.iterkeys():
filename = self.child_held_out_prediction_files[child_name][n]
child_predictions = np.load(filename)
os.remove(filename)
predictions[
self.child_held_out_idx[child_name]] = child_predictions
# print('Combined predictions')
# print(predictions[0])
tmp_filename = util.random_temp_file_name('.npy')
np.save(tmp_filename, predictions)
maxtime = max(
times[n]
for times in self.child_held_out_prediction_times.itervalues())
self.held_out_prediction_files.append(tmp_filename)
self.held_out_prediction_times.append(maxtime)
self.send_to_parent(
dict(subject='predictions',
sender=self.name,
partition='held out',
time=self.held_out_prediction_times[-1],
filename=self.held_out_prediction_files[-1]))
# Check to see if all children have terminated - if so, terminate this agent
# immortal child dying is failure
# mortal child dying without sending results is failure
# any child failure should kill parent
if self.immortal_offspring is True and len(
self.conns_from_children) != len(self.child_states):
logger.error("%s: Immortal child has died. Dying of grief",
self.name)
raise TerminationEx
elif self.immortal_offspring is False:
dead_kids = [
x for x in self.child_states
if x not in self.conns_from_children
]
for dk in dead_kids:
if len(self.child_test_prediction_files[dk]) == 0:
logger.error(
"%s: Mortal child %s has died without sending results",
self.name, dk)
raise TerminationEx
if len(self.conns_from_children) == 0:
logger.info("%s: No children remaining. Terminating.",
self.name)
raise TerminationEx
def next_action(self):
self.read_messages()
# Start timing
self.time_checkpoint = time.clock()
# Fit learner
self.fit(self.train_idx, self.feature_subset)
# Make predictions on held out set and evaluate
predictions = self.predict('X_train', self.test_idx,
self.feature_subset)
truth = self.test_truth
score = libscores.eval_metric(metric=self.data_info['eval_metric'],
truth=truth,
predictions=predictions,
task=self.data_info['task'])
self.score_times.append(time.clock() - self.time_checkpoint +
self.time_before_checkpoint)
self.score_values.append(score)
# Send score and time to parent
self.send_to_parent(
dict(subject='score',
sender=self.name,
time=self.score_times[-1],
score=self.score_values[-1]))
# Save total time taken
# TODO - this is ignoring the time taken to make valid and test predictions
self.time_before_checkpoint += time.clock() - self.time_checkpoint
# Now make predictions on valid, test and held out sets
# FIXME - send all of this data at the same time to prevent gotchas
if 'X_valid' in self.data:
predictions = self.predict('X_valid', 'all', self.feature_subset)
tmp_filename = util.random_temp_file_name('.npy')
np.save(tmp_filename, predictions)
self.valid_prediction_files.append(tmp_filename)
self.valid_prediction_times.append(self.time_before_checkpoint)
self.send_to_parent(
dict(subject='predictions',
sender=self.name,
partition='valid',
time=self.valid_prediction_times[-1],
filename=self.valid_prediction_files[-1]))
if 'X_test' in self.data:
predictions = self.predict('X_test', 'all', self.feature_subset)
tmp_filename = util.random_temp_file_name('.npy')
np.save(tmp_filename, predictions)
self.test_prediction_files.append(tmp_filename)
self.test_prediction_times.append(self.time_before_checkpoint)
self.send_to_parent(
dict(subject='predictions',
sender=self.name,
partition='test',
time=self.test_prediction_times[-1],
filename=self.test_prediction_files[-1]))
predictions = self.predict('X_train', self.test_idx,
self.feature_subset)
tmp_filename = util.random_temp_file_name('.npy')
np.save(tmp_filename, predictions)
self.held_out_prediction_files.append(tmp_filename)
self.held_out_prediction_times.append(self.time_before_checkpoint)
self.send_to_parent(
dict(subject='predictions',
sender=self.name,
partition='held out',
idx=self.test_idx,
time=self.held_out_prediction_times[-1],
filename=self.held_out_prediction_files[-1]))
# And I'm spent
raise TerminationEx
def next_action(self):
# Read messages
self.read_messages()
# Start timing
self.time_checkpoint = time.clock()
predict_time = self.time_quantum / self.n_samples
scores_so_far = 0
# Increase estimators and learn
while time.clock() - self.time_checkpoint < self.time_quantum:
# Read messages - maybe compute quantum has changed?
self.get_parent_inbox()
self.read_messages()
# Do learning
self.learner.n_estimators += self.n_estimators_quantum
start_time = time.clock()
self.fit(self.train_idx, self.feature_subset)
time_taken = time.clock() - start_time
if global_data.exp['slowdown_factor'] > 1:
util.waste_cpu_time(time_taken *
(global_data.exp['slowdown_factor'] - 1))
if time.clock() - self.time_checkpoint > predict_time:
predictions = self.predict('X_train', self.test_idx,
self.feature_subset)
truth = self.test_truth
score = libscores.eval_metric(
metric=self.data_info['eval_metric'],
truth=truth,
predictions=predictions,
task=self.data_info['task'])
self.score_times.append(time.clock() - self.time_checkpoint +
self.time_before_checkpoint)
self.score_values.append(score)
# Send score and time to parent
self.send_to_parent(
dict(subject='score',
sender=self.name,
time=self.score_times[-1],
score=self.score_values[-1]))
scores_so_far += 1
# Next time at which to make a prediction
if self.n_samples > scores_so_far:
predict_time = time.clock() - self.time_checkpoint + \
(self.time_quantum - (time.clock() - self.time_checkpoint)) / \
(self.n_samples - scores_so_far)
else:
break
# Save total time taken
# TODO - this is ignoring the time taken to make valid and test predictions
self.time_before_checkpoint += time.clock() - self.time_checkpoint
# Now make predictions
# FIXME - send all of this data at the same time to prevent gotchas
if 'X_valid' in self.data:
predictions = self.predict('X_valid', 'all', self.feature_subset)
tmp_filename = util.random_temp_file_name('.npy')
np.save(tmp_filename, predictions)
self.valid_prediction_files.append(tmp_filename)
self.valid_prediction_times.append(self.time_before_checkpoint)
self.send_to_parent(
dict(subject='predictions',
sender=self.name,
partition='valid',
time=self.valid_prediction_times[-1],
filename=self.valid_prediction_files[-1]))
if 'X_test' in self.data:
predictions = self.predict('X_test', 'all', self.feature_subset)
tmp_filename = util.random_temp_file_name('.npy')
np.save(tmp_filename, predictions)
self.test_prediction_files.append(tmp_filename)
self.test_prediction_times.append(self.time_before_checkpoint)
self.send_to_parent(
dict(subject='predictions',
sender=self.name,
partition='test',
time=self.test_prediction_times[-1],
filename=self.test_prediction_files[-1]))
predictions = self.predict('X_train', self.test_idx,
self.feature_subset)
# print('Held out')
# print(predictions[0])
tmp_filename = util.random_temp_file_name('.npy')
np.save(tmp_filename, predictions)
self.held_out_prediction_files.append(tmp_filename)
self.held_out_prediction_times.append(self.time_before_checkpoint)
self.send_to_parent(
dict(subject='predictions',
sender=self.name,
partition='held out',
idx=self.test_idx,
time=self.held_out_prediction_times[-1],
filename=self.held_out_prediction_files[-1]))
if self.run_one_iteration:
self.pause()
