def main(predictions_dir,
basename,
task_type,
metric,
limit,
output_dir,
ensemble_size=None):
watch = autosklearn.util.stopwatch.StopWatch()
watch.start_task("ensemble_builder")
used_time = 0
time_iter = 0
index_run = 0
current_num_models = 0
logging.basicConfig(filename=os.path.join(predictions_dir, "ensemble.log"),
level=logging.DEBUG)
while used_time < limit:
logging.debug("Time left: %f" % (limit - used_time))
logging.debug("Time last iteration: %f" % time_iter)
# Load the true labels of the validation data
true_labels = np.load(
os.path.join(predictions_dir, "true_labels_ensemble.npy"))
# Load the predictions from the models
all_predictions_train = []
dir_ensemble = os.path.join(predictions_dir, "predictions_ensemble/")
dir_valid = os.path.join(predictions_dir, "predictions_valid/")
dir_test = os.path.join(predictions_dir, "predictions_test/")
if not os.path.isdir(dir_ensemble) or not os.path.isdir(dir_valid) or \
not os.path.isdir(dir_test):
logging.debug("Prediction directory does not exist")
time.sleep(2)
used_time = watch.wall_elapsed("ensemble_builder")
continue
dir_ensemble_list = sorted(os.listdir(dir_ensemble))
dir_valid_list = sorted(os.listdir(dir_valid))
dir_test_list = sorted(os.listdir(dir_test))
if len(dir_ensemble_list) == 0:
logging.debug("Directories are empty")
time.sleep(2)
used_time = watch.wall_elapsed("ensemble_builder")
continue
if len(dir_ensemble_list) != len(dir_valid_list):
logging.debug("Directories are inconsistent")
time.sleep(2)
used_time = watch.wall_elapsed("ensemble_builder")
continue
if len(dir_ensemble_list) != len(dir_test_list):
logging.debug("Directories are inconsistent")
time.sleep(2)
used_time = watch.wall_elapsed("ensemble_builder")
continue
if len(dir_ensemble_list) <= current_num_models:
logging.debug("Nothing has changed since the last time")
time.sleep(2)
used_time = watch.wall_elapsed("ensemble_builder")
continue
watch.start_task("ensemble_iter_" + str(index_run))
# Binary mask where True indicates that the corresponding will be excluded from the ensemble
exclude_mask = []
if ensemble_size is not None:
# Keeps track of the single scores of each model in our ensemble
scores_nbest = []
# The indices of the model that are currently in our ensemble
indices_nbest = []
model_idx = 0
for f in dir_ensemble_list:
predictions = np.load(os.path.join(dir_ensemble, f))
score = evaluator.calculate_score(true_labels, predictions,
task_type, metric,
predictions.shape[1])
if ensemble_size is not None:
if score <= 0.001:
exclude_mask.append(True)
logging.error("Model only predicts at random: " + f +
" has score: " + str(score))
# If we have less model in our ensemble than ensemble_size add the current model if it is better than random
elif len(scores_nbest) < ensemble_size:
scores_nbest.append(score)
indices_nbest.append(model_idx)
exclude_mask.append(False)
else:
# Take the worst performing model in our ensemble so far
idx = np.argmin(np.array([scores_nbest]))
# If the current model is better than the worst model in our ensemble replace it by the current model
if (scores_nbest[idx] < score):
logging.debug(
"Worst model in our ensemble: %d with score %f will be replaced by model %d with score %f"
% (idx, scores_nbest[idx], model_idx, score))
scores_nbest[idx] = score
# Exclude the old model
exclude_mask[int(indices_nbest[idx])] = True
indices_nbest[idx] = model_idx
exclude_mask.append(False)
# Otherwise exclude the current model from the ensemble
else:
exclude_mask.append(True)
else:
# Load all predictions that are better than random
if score <= 0.001:
exclude_mask.append(True)
logging.error("Model only predicts at random: " + f +
" has score: " + str(score))
else:
exclude_mask.append(False)
all_predictions_train.append(predictions)
model_idx += 1
print exclude_mask
all_predictions_valid = []
for i, f in enumerate(dir_valid_list):
predictions = np.load(os.path.join(dir_valid, f))
if not exclude_mask[i]:
all_predictions_valid.append(predictions)
all_predictions_test = []
for i, f in enumerate(dir_test_list):
predictions = np.load(os.path.join(dir_test, f))
if not exclude_mask[i]:
all_predictions_test.append(predictions)
if len(all_predictions_train) == len(all_predictions_test) == len(
all_predictions_valid) == 0:
logging.error("All models do just random guessing")
time.sleep(2)
continue
if len(all_predictions_train) == 1:
logging.debug("Only one model so far we just copy its predictions")
Y_valid = all_predictions_valid[0]
Y_test = all_predictions_test[0]
else:
try:
# Compute the weights for the ensemble
# Use equally initialized weights
n_models = len(all_predictions_train)
init_weights = np.ones([n_models]) / n_models
weights = weighted_ensemble(np.array(all_predictions_train),
true_labels, task_type, metric,
init_weights)
except (ValueError):
logging.error("Caught ValueError!")
used_time = watch.wall_elapsed("ensemble_builder")
continue
except:
logging.error("Caught error!")
used_time = watch.wall_elapsed("ensemble_builder")
continue
# Compute the ensemble predictions for the valid data
Y_valid = ensemble_prediction(np.array(all_predictions_valid),
weights)
# Compute the ensemble predictions for the test data
Y_test = ensemble_prediction(np.array(all_predictions_test),
weights)
# Save predictions for valid and test data set
filename_test = os.path.join(
output_dir,
basename + '_valid_' + str(index_run).zfill(3) + '.predict')
data_util.save_predictions(
os.path.join(predictions_dir, filename_test), Y_valid)
filename_test = os.path.join(
output_dir,
basename + '_test_' + str(index_run).zfill(3) + '.predict')
data_util.save_predictions(
os.path.join(predictions_dir, filename_test), Y_test)
current_num_models = len(dir_ensemble_list)
watch.stop_task("ensemble_iter_" + str(index_run))
time_iter = watch.get_wall_dur("ensemble_iter_" + str(index_run))
used_time = watch.wall_elapsed("ensemble_builder")
index_run += 1
return
def main(predictions_dir, basename, task_type, metric, limit, output_dir,
ensemble_size=None, seed=1, indices_output_dir="."):
watch = StopWatch()
watch.start_task("ensemble_builder")
task_type = STRING_TO_TASK_TYPES[task_type]
used_time = 0
time_iter = 0
index_run = 0
current_num_models = 0
logging.basicConfig(filename=os.path.join(predictions_dir, "ensemble_%d.log" % seed), level=logging.DEBUG)
while used_time < limit:
logging.debug("Time left: %f", limit - used_time)
logging.debug("Time last iteration: %f", time_iter)
# Load the true labels of the validation data
true_labels = np.load(os.path.join(predictions_dir, "true_labels_ensemble.npy"))
# Load the predictions from the models
dir_ensemble = os.path.join(predictions_dir,
"predictions_ensemble_%s/" % seed)
dir_valid = os.path.join(predictions_dir,
"predictions_valid_%s/" % seed)
dir_test = os.path.join(predictions_dir,
"predictions_test_%s/" % seed)
paths_ = [dir_ensemble, dir_valid, dir_test]
exists = [os.path.isdir(dir_) for dir_ in paths_]
if not exists[0]: #all(exists):
logging.debug("Prediction directory %s does not exist!" %
dir_ensemble)
time.sleep(2)
used_time = watch.wall_elapsed("ensemble_builder")
continue
dir_ensemble_list = sorted(os.listdir(dir_ensemble))
dir_valid_list = sorted(os.listdir(dir_valid)) if exists[1] else []
dir_test_list = sorted(os.listdir(dir_test)) if exists[2] else []
if len(dir_ensemble_list) == 0:
logging.debug("Directories are empty")
time.sleep(2)
used_time = watch.wall_elapsed("ensemble_builder")
continue
if len(dir_ensemble_list) <= current_num_models:
logging.debug("Nothing has changed since the last time")
time.sleep(2)
used_time = watch.wall_elapsed("ensemble_builder")
continue
watch.start_task("ensemble_iter_" + str(index_run))
# List of num_runs (which are in the filename) which will be included
# later
include_num_runs = []
re_num_run = re.compile(r'_([0-9]*)\.npy$')
if ensemble_size is not None:
# Keeps track of the single scores of each model in our ensemble
scores_nbest = []
# The indices of the model that are currently in our ensemble
indices_nbest = []
# The names of the models
model_names = []
# The num run of the models
num_runs = []
model_names_to_scores = dict()
model_idx = 0
for model_name in dir_ensemble_list:
predictions = np.load(os.path.join(dir_ensemble, model_name))
score = evaluator.calculate_score(true_labels, predictions,
task_type, metric,
predictions.shape[1])
model_names_to_scores[model_name] = score
num_run = int(re_num_run.search(model_name).group(1))
if ensemble_size is not None:
if score <= 0.001:
# include_num_runs.append(True)
logging.error("Model only predicts at random: " + model_name + " has score: " + str(score))
# If we have less models in our ensemble than ensemble_size add the current model if it is better than random
elif len(scores_nbest) < ensemble_size:
scores_nbest.append(score)
indices_nbest.append(model_idx)
include_num_runs.append(num_run)
model_names.append(model_name)
num_runs.append(num_run)
else:
# Take the worst performing model in our ensemble so far
idx = np.argmin(np.array([scores_nbest]))
# If the current model is better than the worst model in our ensemble replace it by the current model
if(scores_nbest[idx] < score):
logging.debug("Worst model in our ensemble: %s with "
"score %f will be replaced by model %s "
"with score %f",
model_names[idx], scores_nbest[idx],
model_name, score)
# Exclude the old model
del scores_nbest[idx]
scores_nbest.append(score)
del include_num_runs[idx]
del indices_nbest[idx]
indices_nbest.append(model_idx)
include_num_runs.append(num_run)
del model_names[idx]
model_names.append(model_name)
del num_runs[idx]
num_runs.append(num_run)
# Otherwise exclude the current model from the ensemble
else:
#include_num_runs.append(True)
pass
else:
# Load all predictions that are better than random
if score <= 0.001:
#include_num_runs.append(True)
logging.error("Model only predicts at random: " + model_name + " has score: " + str(score))
else:
include_num_runs.append(num_run)
model_idx += 1
indices_to_model_names = dict()
indices_to_run_num = dict()
for i, model_name in enumerate(dir_ensemble_list):
num_run = int(re_num_run.search(model_name).group(1))
if num_run in include_num_runs:
num_indices = len(indices_to_model_names)
indices_to_model_names[num_indices] = model_name
indices_to_run_num[num_indices] = num_run
#logging.info("Indices to model names:")
#logging.info(indices_to_model_names)
#for i, item in enumerate(sorted(model_names_to_scores.items(),
# key=lambda t: t[1])):
# logging.info("%d: %s", i, item)
include_num_runs = set(include_num_runs)
all_predictions_train = []
for i, model_name in enumerate(dir_ensemble_list):
num_run = int(re_num_run.search(model_name).group(1))
if num_run in include_num_runs:
predictions = np.load(os.path.join(dir_ensemble, model_name))
all_predictions_train.append(predictions)
all_predictions_valid = []
for i, model_name in enumerate(dir_valid_list):
num_run = int(re_num_run.search(model_name).group(1))
if num_run in include_num_runs:
predictions = np.load(os.path.join(dir_valid, model_name))
all_predictions_valid.append(predictions)
all_predictions_test = []
for i, model_name in enumerate(dir_test_list):
num_run = int(re_num_run.search(model_name).group(1))
if num_run in include_num_runs:
predictions = np.load(os.path.join(dir_test, model_name))
all_predictions_test.append(predictions)
if len(all_predictions_train) == len(all_predictions_test) == len(all_predictions_valid) == 0:
logging.error("All models do just random guessing")
time.sleep(2)
continue
elif len(all_predictions_train) == 1:
logging.debug("Only one model so far we just copy its predictions")
ensemble_members_run_numbers = {0: 1.0}
# Output the score
logging.info("Training performance: %f" % np.max(
model_names_to_scores.values()))
else:
try:
indices, trajectory = ensemble_selection(
np.array(all_predictions_train), true_labels,
ensemble_size, task_type, metric)
logging.info("Trajectory and indices!")
logging.info(trajectory)
logging.info(indices)
except ValueError as e:
logging.error("Caught ValueError: " + str(e))
used_time = watch.wall_elapsed("ensemble_builder")
continue
except Exception as e:
logging.error("Caught error! %s", e.message)
used_time = watch.wall_elapsed("ensemble_builder")
continue
# Output the score
logging.info("Training performance: %f" % trajectory[-1])
# Print the ensemble members:
ensemble_members_run_numbers = dict()
ensemble_members = Counter(indices).most_common()
ensemble_members_string = "Ensemble members:\n"
logging.info(ensemble_members)
for ensemble_member in ensemble_members:
weight = float(ensemble_member[1]) / len(indices)
ensemble_members_string += \
(" %s; weight: %10f; performance: %10f\n" %
(indices_to_model_names[ensemble_member[0]],
weight,
model_names_to_scores[indices_to_model_names[ensemble_member[0]]]))
ensemble_members_run_numbers[indices_to_run_num[
ensemble_member[0]]] = weight
logging.info(ensemble_members_string)
# Save the ensemble indices for later use!
filename_indices = os.path.join(indices_output_dir,
str(index_run).zfill(5) + ".indices")
logging.info(ensemble_members_run_numbers)
with open(filename_indices, "w") as fh:
pickle.dump(ensemble_members_run_numbers, fh)
# Save predictions for valid and test data set
if len(dir_valid_list) == len(dir_ensemble_list):
ensemble_predictions_valid = np.mean(
all_predictions_valid[indices.astype(int)], axis=0)
filename_test = os.path.join(output_dir, basename + '_valid_' + str(index_run).zfill(3) + '.predict')
data_util.save_predictions(os.path.join(predictions_dir, filename_test),
ensemble_predictions_valid)
else:
logging.info("Could not find as many validation set predictions "
"as ensemble predictions!.")
if len(dir_test_list) == len(dir_ensemble_list):
ensemble_predictions_test = np.mean(
all_predictions_test[indices.astype(int)], axis=0)
filename_test = os.path.join(output_dir, basename + '_test_' + str(index_run).zfill(3) + '.predict')
data_util.save_predictions(os.path.join(predictions_dir, filename_test),
ensemble_predictions_test)
else:
logging.info("Could not find as many test set predictions as "
"ensemble predictions!")
current_num_models = len(dir_ensemble_list)
watch.stop_task("ensemble_iter_" + str(index_run))
time_iter = watch.get_wall_dur("ensemble_iter_" + str(index_run))
used_time = watch.wall_elapsed("ensemble_builder")
index_run += 1
return
def main(predictions_dir,
basename,
task_type,
metric,
limit,
output_dir,
ensemble_size=None,
seed=1,
indices_output_dir="."):
watch = StopWatch()
watch.start_task("ensemble_builder")
task_type = STRING_TO_TASK_TYPES[task_type]
used_time = 0
time_iter = 0
index_run = 0
current_num_models = 0
logging.basicConfig(filename=os.path.join(predictions_dir,
"ensemble_%d.log" % seed),
level=logging.DEBUG)
while used_time < limit:
logging.debug("Time left: %f", limit - used_time)
logging.debug("Time last iteration: %f", time_iter)
# Load the true labels of the validation data
true_labels = np.load(
os.path.join(predictions_dir, "true_labels_ensemble.npy"))
# Load the predictions from the models
dir_ensemble = os.path.join(predictions_dir,
"predictions_ensemble_%s/" % seed)
dir_valid = os.path.join(predictions_dir,
"predictions_valid_%s/" % seed)
dir_test = os.path.join(predictions_dir, "predictions_test_%s/" % seed)
paths_ = [dir_ensemble, dir_valid, dir_test]
exists = [os.path.isdir(dir_) for dir_ in paths_]
if not exists[0]: #all(exists):
logging.debug("Prediction directory %s does not exist!" %
dir_ensemble)
time.sleep(2)
used_time = watch.wall_elapsed("ensemble_builder")
continue
dir_ensemble_list = sorted(os.listdir(dir_ensemble))
dir_valid_list = sorted(os.listdir(dir_valid)) if exists[1] else []
dir_test_list = sorted(os.listdir(dir_test)) if exists[2] else []
if len(dir_ensemble_list) == 0:
logging.debug("Directories are empty")
time.sleep(2)
used_time = watch.wall_elapsed("ensemble_builder")
continue
if len(dir_ensemble_list) <= current_num_models:
logging.debug("Nothing has changed since the last time")
time.sleep(2)
used_time = watch.wall_elapsed("ensemble_builder")
continue
watch.start_task("ensemble_iter_" + str(index_run))
# List of num_runs (which are in the filename) which will be included
# later
include_num_runs = []
re_num_run = re.compile(r'_([0-9]*)\.npy$')
if ensemble_size is not None:
# Keeps track of the single scores of each model in our ensemble
scores_nbest = []
# The indices of the model that are currently in our ensemble
indices_nbest = []
# The names of the models
model_names = []
# The num run of the models
num_runs = []
model_names_to_scores = dict()
model_idx = 0
for model_name in dir_ensemble_list:
predictions = np.load(os.path.join(dir_ensemble, model_name))
score = evaluator.calculate_score(true_labels, predictions,
task_type, metric,
predictions.shape[1])
model_names_to_scores[model_name] = score
num_run = int(re_num_run.search(model_name).group(1))
if ensemble_size is not None:
if score <= 0.001:
# include_num_runs.append(True)
logging.error("Model only predicts at random: " +
model_name + " has score: " + str(score))
# If we have less models in our ensemble than ensemble_size add the current model if it is better than random
elif len(scores_nbest) < ensemble_size:
scores_nbest.append(score)
indices_nbest.append(model_idx)
include_num_runs.append(num_run)
model_names.append(model_name)
num_runs.append(num_run)
else:
# Take the worst performing model in our ensemble so far
idx = np.argmin(np.array([scores_nbest]))
# If the current model is better than the worst model in our ensemble replace it by the current model
if (scores_nbest[idx] < score):
logging.debug(
"Worst model in our ensemble: %s with "
"score %f will be replaced by model %s "
"with score %f", model_names[idx],
scores_nbest[idx], model_name, score)
# Exclude the old model
del scores_nbest[idx]
scores_nbest.append(score)
del include_num_runs[idx]
del indices_nbest[idx]
indices_nbest.append(model_idx)
include_num_runs.append(num_run)
del model_names[idx]
model_names.append(model_name)
del num_runs[idx]
num_runs.append(num_run)
# Otherwise exclude the current model from the ensemble
else:
#include_num_runs.append(True)
pass
else:
# Load all predictions that are better than random
if score <= 0.001:
#include_num_runs.append(True)
logging.error("Model only predicts at random: " +
model_name + " has score: " + str(score))
else:
include_num_runs.append(num_run)
model_idx += 1
indices_to_model_names = dict()
indices_to_run_num = dict()
for i, model_name in enumerate(dir_ensemble_list):
num_run = int(re_num_run.search(model_name).group(1))
if num_run in include_num_runs:
num_indices = len(indices_to_model_names)
indices_to_model_names[num_indices] = model_name
indices_to_run_num[num_indices] = num_run
#logging.info("Indices to model names:")
#logging.info(indices_to_model_names)
#for i, item in enumerate(sorted(model_names_to_scores.items(),
# key=lambda t: t[1])):
# logging.info("%d: %s", i, item)
include_num_runs = set(include_num_runs)
all_predictions_train = []
for i, model_name in enumerate(dir_ensemble_list):
num_run = int(re_num_run.search(model_name).group(1))
if num_run in include_num_runs:
predictions = np.load(os.path.join(dir_ensemble, model_name))
all_predictions_train.append(predictions)
all_predictions_valid = []
for i, model_name in enumerate(dir_valid_list):
num_run = int(re_num_run.search(model_name).group(1))
if num_run in include_num_runs:
predictions = np.load(os.path.join(dir_valid, model_name))
all_predictions_valid.append(predictions)
all_predictions_test = []
for i, model_name in enumerate(dir_test_list):
num_run = int(re_num_run.search(model_name).group(1))
if num_run in include_num_runs:
predictions = np.load(os.path.join(dir_test, model_name))
all_predictions_test.append(predictions)
if len(all_predictions_train) == len(all_predictions_test) == len(
all_predictions_valid) == 0:
logging.error("All models do just random guessing")
time.sleep(2)
continue
elif len(all_predictions_train) == 1:
logging.debug("Only one model so far we just copy its predictions")
ensemble_members_run_numbers = {0: 1.0}
# Output the score
logging.info("Training performance: %f" %
np.max(model_names_to_scores.values()))
else:
try:
indices, trajectory = ensemble_selection(
np.array(all_predictions_train), true_labels,
ensemble_size, task_type, metric)
logging.info("Trajectory and indices!")
logging.info(trajectory)
logging.info(indices)
except ValueError as e:
logging.error("Caught ValueError: " + str(e))
used_time = watch.wall_elapsed("ensemble_builder")
continue
except Exception as e:
logging.error("Caught error! %s", e.message)
used_time = watch.wall_elapsed("ensemble_builder")
continue
# Output the score
logging.info("Training performance: %f" % trajectory[-1])
# Print the ensemble members:
ensemble_members_run_numbers = dict()
ensemble_members = Counter(indices).most_common()
ensemble_members_string = "Ensemble members:\n"
logging.info(ensemble_members)
for ensemble_member in ensemble_members:
weight = float(ensemble_member[1]) / len(indices)
ensemble_members_string += \
(" %s; weight: %10f; performance: %10f\n" %
(indices_to_model_names[ensemble_member[0]],
weight,
model_names_to_scores[indices_to_model_names[ensemble_member[0]]]))
ensemble_members_run_numbers[indices_to_run_num[
ensemble_member[0]]] = weight
logging.info(ensemble_members_string)
# Save the ensemble indices for later use!
filename_indices = os.path.join(indices_output_dir,
str(index_run).zfill(5) + ".indices")
logging.info(ensemble_members_run_numbers)
with open(filename_indices, "w") as fh:
pickle.dump(ensemble_members_run_numbers, fh)
# Save predictions for valid and test data set
if len(dir_valid_list) == len(dir_ensemble_list):
ensemble_predictions_valid = np.mean(
all_predictions_valid[indices.astype(int)], axis=0)
filename_test = os.path.join(
output_dir,
basename + '_valid_' + str(index_run).zfill(3) + '.predict')
data_util.save_predictions(
os.path.join(predictions_dir, filename_test),
ensemble_predictions_valid)
else:
logging.info("Could not find as many validation set predictions "
"as ensemble predictions!.")
if len(dir_test_list) == len(dir_ensemble_list):
ensemble_predictions_test = np.mean(
all_predictions_test[indices.astype(int)], axis=0)
filename_test = os.path.join(
output_dir,
basename + '_test_' + str(index_run).zfill(3) + '.predict')
data_util.save_predictions(
os.path.join(predictions_dir, filename_test),
ensemble_predictions_test)
else:
logging.info("Could not find as many test set predictions as "
"ensemble predictions!")
current_num_models = len(dir_ensemble_list)
watch.stop_task("ensemble_iter_" + str(index_run))
time_iter = watch.get_wall_dur("ensemble_iter_" + str(index_run))
used_time = watch.wall_elapsed("ensemble_builder")
index_run += 1
return
def main(predictions_dir, basename, task_type, metric, limit, output_dir, ensemble_size=None):
watch = autosklearn.util.stopwatch.StopWatch()
watch.start_task("ensemble_builder")
used_time = 0
time_iter = 0
index_run = 0
current_num_models = 0
logging.basicConfig(filename=os.path.join(predictions_dir, "ensemble.log"), level=logging.DEBUG)
while used_time < limit:
logging.debug("Time left: %f" % (limit - used_time))
logging.debug("Time last iteration: %f" % time_iter)
# Load the true labels of the validation data
true_labels = np.load(os.path.join(predictions_dir, "true_labels_ensemble.npy"))
# Load the predictions from the models
all_predictions_train = []
dir_ensemble = os.path.join(predictions_dir, "predictions_ensemble/")
dir_valid = os.path.join(predictions_dir, "predictions_valid/")
dir_test = os.path.join(predictions_dir, "predictions_test/")
if not os.path.isdir(dir_ensemble) or not os.path.isdir(dir_valid) or \
not os.path.isdir(dir_test):
logging.debug("Prediction directory does not exist")
time.sleep(2)
used_time = watch.wall_elapsed("ensemble_builder")
continue
dir_ensemble_list = sorted(os.listdir(dir_ensemble))
dir_valid_list = sorted(os.listdir(dir_valid))
dir_test_list = sorted(os.listdir(dir_test))
if len(dir_ensemble_list) == 0:
logging.debug("Directories are empty")
time.sleep(2)
used_time = watch.wall_elapsed("ensemble_builder")
continue
if len(dir_ensemble_list) != len(dir_valid_list):
logging.debug("Directories are inconsistent")
time.sleep(2)
used_time = watch.wall_elapsed("ensemble_builder")
continue
if len(dir_ensemble_list) != len(dir_test_list):
logging.debug("Directories are inconsistent")
time.sleep(2)
used_time = watch.wall_elapsed("ensemble_builder")
continue
if len(dir_ensemble_list) <= current_num_models:
logging.debug("Nothing has changed since the last time")
time.sleep(2)
used_time = watch.wall_elapsed("ensemble_builder")
continue
watch.start_task("ensemble_iter_" + str(index_run))
# Binary mask where True indicates that the corresponding will be excluded from the ensemble
exclude_mask = []
if ensemble_size is not None:
# Keeps track of the single scores of each model in our ensemble
scores_nbest = []
# The indices of the model that are currently in our ensemble
indices_nbest = []
model_idx = 0
for f in dir_ensemble_list:
predictions = np.load(os.path.join(dir_ensemble, f))
score = evaluator.calculate_score(true_labels, predictions,
task_type, metric, predictions.shape[1])
if ensemble_size is not None:
if score <= 0.001:
exclude_mask.append(True)
logging.error("Model only predicts at random: " + f + " has score: " + str(score))
# If we have less model in our ensemble than ensemble_size add the current model if it is better than random
elif len(scores_nbest) < ensemble_size:
scores_nbest.append(score)
indices_nbest.append(model_idx)
exclude_mask.append(False)
else:
# Take the worst performing model in our ensemble so far
idx = np.argmin(np.array([scores_nbest]))
# If the current model is better than the worst model in our ensemble replace it by the current model
if(scores_nbest[idx] < score):
logging.debug("Worst model in our ensemble: %d with score %f will be replaced by model %d with score %f" % (idx, scores_nbest[idx], model_idx, score))
scores_nbest[idx] = score
# Exclude the old model
exclude_mask[int(indices_nbest[idx])] = True
indices_nbest[idx] = model_idx
exclude_mask.append(False)
# Otherwise exclude the current model from the ensemble
else:
exclude_mask.append(True)
else:
# Load all predictions that are better than random
if score <= 0.001:
exclude_mask.append(True)
logging.error("Model only predicts at random: " + f + " has score: " + str(score))
else:
exclude_mask.append(False)
all_predictions_train.append(predictions)
model_idx += 1
print exclude_mask
all_predictions_valid = []
for i, f in enumerate(dir_valid_list):
predictions = np.load(os.path.join(dir_valid, f))
if not exclude_mask[i]:
all_predictions_valid.append(predictions)
all_predictions_test = []
for i, f in enumerate(dir_test_list):
predictions = np.load(os.path.join(dir_test, f))
if not exclude_mask[i]:
all_predictions_test.append(predictions)
if len(all_predictions_train) == len(all_predictions_test) == len(all_predictions_valid) == 0:
logging.error("All models do just random guessing")
time.sleep(2)
continue
if len(all_predictions_train) == 1:
logging.debug("Only one model so far we just copy its predictions")
Y_valid = all_predictions_valid[0]
Y_test = all_predictions_test[0]
else:
try:
# Compute the weights for the ensemble
# Use equally initialized weights
n_models = len(all_predictions_train)
init_weights = np.ones([n_models]) / n_models
weights = weighted_ensemble(np.array(all_predictions_train),
true_labels, task_type, metric, init_weights)
except (ValueError):
logging.error("Caught ValueError!")
used_time = watch.wall_elapsed("ensemble_builder")
continue
except:
logging.error("Caught error!")
used_time = watch.wall_elapsed("ensemble_builder")
continue
# Compute the ensemble predictions for the valid data
Y_valid = ensemble_prediction(np.array(all_predictions_valid), weights)
# Compute the ensemble predictions for the test data
Y_test = ensemble_prediction(np.array(all_predictions_test), weights)
# Save predictions for valid and test data set
filename_test = os.path.join(output_dir, basename + '_valid_' + str(index_run).zfill(3) + '.predict')
data_util.save_predictions(os.path.join(predictions_dir, filename_test), Y_valid)
filename_test = os.path.join(output_dir, basename + '_test_' + str(index_run).zfill(3) + '.predict')
data_util.save_predictions(os.path.join(predictions_dir, filename_test), Y_test)
current_num_models = len(dir_ensemble_list)
watch.stop_task("ensemble_iter_" + str(index_run))
time_iter = watch.get_wall_dur("ensemble_iter_" + str(index_run))
used_time = watch.wall_elapsed("ensemble_builder")
index_run += 1
return