def run(dataset: Dataset, config: TaskConfig):
log.info("\n**** Constant predictor (sklearn dummy) ****\n")
save_metadata(config, version=sklearn.__version__)
is_classification = config.type == 'classification'
predictor = DummyClassifier(
strategy='prior') if is_classification else DummyRegressor(
strategy='median')
encode = config.framework_params[
'encode'] if 'encode' in config.framework_params else False
X_train = dataset.train.X_enc if encode else dataset.train.X
y_train = dataset.train.y_enc if encode else dataset.train.y
X_test = dataset.test.X_enc if encode else dataset.test.X
y_test = dataset.test.y_enc if encode else dataset.test.y
with Timer() as training:
predictor.fit(X_train, y_train)
with Timer() as predict:
predictions = predictor.predict(X_test)
probabilities = predictor.predict_proba(
X_test) if is_classification else None
save_predictions(dataset=dataset,
output_file=config.output_predictions_file,
probabilities=probabilities,
predictions=predictions,
truth=y_test,
target_is_encoded=encode)
return dict(models_count=1,
training_duration=training.duration,
predict_duration=predict.duration)
def run(dataset: Dataset, config: TaskConfig):
log.info(f"\n**** Decision Tree [sklearn v{sklearn.__version__}] ****\n")
is_classification = config.type == 'classification'
X_train, X_test = impute_array(*unsparsify(dataset.train.X_enc, dataset.test.X_enc, fmt='array'))
y_train, y_test = unsparsify(dataset.train.y_enc, dataset.test.y_enc, fmt='array')
estimator = DecisionTreeClassifier if is_classification else DecisionTreeRegressor
predictor = estimator(random_state=config.seed, **config.framework_params)
with Timer() as training:
predictor.fit(X_train, y_train)
with Timer() as predict:
predictions = predictor.predict(X_test)
probabilities = predictor.predict_proba(X_test) if is_classification else None
save_predictions(dataset=dataset,
output_file=config.output_predictions_file,
probabilities=probabilities,
predictions=predictions,
truth=y_test,
target_is_encoded=is_classification)
return dict(
models_count=1,
training_duration=training.duration,
predict_duration=predict.duration
)
def run(dataset: Dataset, config: TaskConfig):
#TODO: use rpy2 instead? not necessary here though as the call is very simple
log.info("\n**** Autoxgboost (R) ****\n")
is_classification = config.type == 'classification'
if not is_classification:
raise ValueError('Regression is not supported.')
here = dir_of(__file__)
with Timer() as training:
run_cmd(
r"""Rscript --vanilla -e "source('{script}'); run('{train}', '{test}', target.index = {target_index}, '{output}', {cores}, time.budget = {time_budget})" """
.format(script=os.path.join(here, 'exec.R'),
train=dataset.train.path,
test=dataset.test.path,
target_index=dataset.target.index + 1,
output=config.output_predictions_file,
cores=config.cores,
time_budget=config.max_runtime_seconds),
_live_output_=True)
log.info("Predictions saved to %s", config.output_predictions_file)
return dict(training_duration=training.duration)
def run(dataset: Dataset, config: TaskConfig):
log.info(
f"\n**** Gradient Boosting [sklearn v{sklearn.__version__}] ****\n")
save_metadata(config, version=sklearn.__version__)
is_classification = config.type == 'classification'
X_train, X_test = impute(dataset.train.X_enc, dataset.test.X_enc)
y_train, y_test = dataset.train.y, dataset.test.y
estimator = GradientBoostingClassifier if is_classification else GradientBoostingRegressor
predictor = estimator(random_state=config.seed, **config.framework_params)
with Timer() as training:
predictor.fit(X_train, y_train)
predictions = predictor.predict(X_test)
probabilities = predictor.predict_proba(
X_test) if is_classification else None
save_predictions(dataset=dataset,
output_file=config.output_predictions_file,
probabilities=probabilities,
predictions=predictions,
truth=y_test)
return dict(models_count=1, training_duration=training.duration)
def run(dataset: Dataset, config: TaskConfig):
log.info("\n**** dabl AnyClassifier ****\n")
is_classification = config.type == 'classification'
print(dataset.train.X)
X_train, X_test = pd.DataFrame(dataset.train.X).astype(str), pd.DataFrame(dataset.test.X).astype(str)
y_train, y_test = pd.Series(dataset.train.y), pd.Series(dataset.test.y)
estimator = AnyClassifier if is_classification else None
predictor = estimator(**config.framework_params)
with Timer() as training:
predictor.fit(X_train, y_train)
predictions = predictor.predict(X_test)
probabilities = predictor.predict_proba(X_test) if is_classification else None
save_predictions_to_file(dataset=dataset,
output_file=config.output_predictions_file,
probabilities=probabilities,
predictions=predictions,
truth=y_test)
return dict(
models_count=1,
training_duration=training.duration
)
def run(dataset: Dataset, config: TaskConfig):
log.info("****TabNet****")
save_metadata(config)
is_classification = config.type == 'classification'
X_train, X_test = dataset.train.X, dataset.test.X
X_train, X_test = impute(X_train, X_test)
X = np.concatenate((X_train, X_test), axis=0)
enc = OrdinalEncoder()
enc.fit(X)
X_train = enc.transform(X_train)
X_test = enc.transform(X_test)
y_train, y_test = dataset.train.y, dataset.test.y
estimator = TabNetClassifier if is_classification else TabNetRegressor
predictor = estimator() # you can change hyperparameters
if not is_classification:
y_train = np.reshape(y_train.astype(np.float32), (-1, 1))
y_test = np.reshape(y_test.astype(np.float32), (-1, 1))
with Timer() as training:
predictor.fit(X_train,
y_train,
eval_set=[(X_train, y_train), (X_test, y_test)])
with Timer() as predict:
predictions = predictor.predict(X_test)
probabilities = predictor.predict_proba(
X_test) if is_classification else None
save_predictions(dataset=dataset,
output_file=config.output_predictions_file,
probabilities=probabilities,
predictions=predictions,
truth=y_test)
return dict(models_count=1,
training_duration=training.duration,
predict_duration=predict.duration)
def run(dataset: Dataset, config: TaskConfig):
log.info("\n**** Oboe ****\n")
is_classification = config.type == 'classification'
if not is_classification:
# regression currently fails (as of 26.02.2019: still under development state by oboe team)
raise ValueError('Regression is not yet supported (under development).')
X_train, X_test = impute(dataset.train.X_enc, dataset.test.X_enc)
y_train, y_test = dataset.train.y_enc, dataset.test.y_enc
training_params = {k: v for k, v in config.framework_params.items() if not k.startswith('_')}
n_cores = config.framework_params.get('_n_cores', config.cores)
log.info('Running oboe with a maximum time of {}s on {} cores.'.format(config.max_runtime_seconds, n_cores))
log.warning('We completely ignore the advice to optimize towards metric: {}.'.format(config.metric))
aml = AutoLearner(p_type='classification' if is_classification else 'regression',
n_cores=n_cores,
runtime_limit=config.max_runtime_seconds,
**training_params)
aml_models = lambda: [aml.ensemble, *aml.ensemble.base_learners] if len(aml.ensemble.base_learners) > 0 else []
with Timer() as training:
try:
aml.fit(X_train, y_train)
except IndexError as e:
if len(aml_models()) == 0: # incorrect handling of some IndexError in oboe if ensemble is empty
raise NoResultError("Oboe could not produce any model in the requested time.") from e
raise e
predictions = aml.predict(X_test).reshape(len(X_test))
if is_classification:
target_values_enc = dataset.target.label_encoder.transform(dataset.target.values)
probabilities = Encoder('one-hot', target=False, encoded_type=float).fit(target_values_enc).transform(predictions)
else:
probabilities = None
save_predictions_to_file(dataset=dataset,
output_file=config.output_predictions_file,
probabilities=probabilities,
predictions=predictions,
truth=y_test,
target_is_encoded=True)
return dict(
models_count=len(aml_models()),
training_duration=training.duration
)
def run_random_forest(dataset, config, tuner, log):
is_classification = config.type == 'classification'
X_train, X_test = impute(dataset.train.X, dataset.test.X)
y_train, y_test = dataset.train.y, dataset.test.y
estimator = RandomForestClassifier if is_classification else RandomForestRegressor
best_score, best_params, best_model = None, None, None
score_higher_better = True
tuner.update_search_space(SEARCH_SPACE)
start_time = time.time()
while True:
try:
param_idx, cur_params = tuner.generate_parameters()
cur_model = estimator(random_state=config.seed, **cur_params)
# Here score is the output of score() from the estimator
cur_score = cross_val_score(cur_model, X_train, y_train)
cur_score = sum(cur_score) / float(len(cur_score))
if best_score is None or (score_higher_better
and cur_score > best_score) or (
not score_higher_better
and cur_score < best_score):
best_score, best_params, best_model = cur_score, cur_params, cur_model
log.info("Trial {}: \n{}\nScore: {}\n".format(
param_idx, cur_params, cur_score))
tuner.receive_trial_result(param_idx, cur_params, cur_score)
current_time = time.time()
elapsed_time = current_time - start_time
if elapsed_time > config.max_runtime_seconds:
break
except:
break
# This line is required to fully terminate some advisors
tuner.handle_terminate()
log.info("Tuning done, the best parameters are:\n{}\n".format(best_params))
# retrain on the whole dataset
with Timer() as training:
best_model.fit(X_train, y_train)
predictions = best_model.predict(X_test)
probabilities = best_model.predict_proba(
X_test) if is_classification else None
return probabilities, predictions, training, y_test
def run(dataset: Dataset, config: TaskConfig):
log.info("\n**** Random Forest (sklearn) ****\n")
is_classification = config.type == 'classification'
X_train, X_test = impute(dataset.train.X_enc, dataset.test.X_enc)
y_train, y_test = dataset.train.y_enc, dataset.test.y_enc
training_params = {
k: v
for k, v in config.framework_params.items() if not k.startswith('_')
}
n_jobs = config.framework_params.get(
'_n_jobs', config.cores
) # useful to disable multicore, regardless of the dataset config
log.info(
"Running RandomForest with a maximum time of {}s on {} cores.".format(
config.max_runtime_seconds, n_jobs))
log.warning(
"We completely ignore the requirement to stay within the time limit.")
log.warning(
"We completely ignore the advice to optimize towards metric: {}.".
format(config.metric))
estimator = RandomForestClassifier if is_classification else RandomForestRegressor
rf = estimator(n_jobs=n_jobs, random_state=config.seed, **training_params)
with Timer() as training:
rf.fit(X_train, y_train)
predictions = rf.predict(X_test)
probabilities = rf.predict_proba(X_test) if is_classification else None
save_predictions_to_file(dataset=dataset,
output_file=config.output_predictions_file,
probabilities=probabilities,
predictions=predictions,
truth=y_test,
target_is_encoded=True)
return dict(models_count=len(rf), training_duration=training.duration)
def run(dataset: Dataset, config: TaskConfig):
log.info("\n**** Decision Tree (sklearn) ****\n")
is_classification = config.type == 'classification'
X_train, X_test = impute(dataset.train.X_enc, dataset.test.X_enc)
y_train, y_test = dataset.train.y, dataset.test.y
estimator = DecisionTreeClassifier if is_classification else DecisionTreeRegressor
predictor = estimator(random_state=config.seed, **config.framework_params)
with Timer() as training:
predictor.fit(X_train, y_train)
predictions = predictor.predict(X_test)
probabilities = predictor.predict_proba(
X_test) if is_classification else None
save_predictions_to_file(dataset=dataset,
output_file=config.output_predictions_file,
probabilities=probabilities,
predictions=predictions,
truth=y_test)
return dict(models_count=1, training_duration=training.duration)
def run_in_venv(caller_file,
script_file: str,
*args,
input_data: Union[dict, ns],
dataset: Dataset,
config: TaskConfig,
process_results=None,
python_exec=None):
here = dir_of(caller_file)
venv_bin_path = os.path.join(here, 'venv', 'bin')
if python_exec is None: # use local virtual env by default
python_exec = os.path.join(venv_bin_path, 'python -W ignore')
script_path = os.path.join(here, script_file)
cmd = f"{python_exec} {script_path}"
input_data = ns.from_dict(input_data)
with TemporaryDirectory() as tmpdir:
def make_path(k, v, parents=None):
if isinstance(v, np.ndarray):
path = os.path.join(tmpdir, '.'.join(parents + [k, 'npy']))
if vector_keys.match(k):
v = v.reshape(-1, 1)
np.save(path, v, allow_pickle=True)
return k, path
return k, v
ds = ns.walk(input_data, make_path)
dataset.release()
config.result_dir = tmpdir
config.result_file = mktemp(dir=tmpdir)
params = json_dumps(dict(dataset=ds, config=config), style='compact')
with Timer() as proc_timer:
output, err = run_cmd(
cmd,
*args,
_input_str_=params,
_live_output_=True,
_error_level_=logging.DEBUG,
_env_=dict(PATH=os.pathsep.join(
[venv_bin_path, os.environ['PATH']]),
PYTHONPATH=os.pathsep.join([
rconfig().root_dir,
]),
AMLB_PATH=os.path.join(rconfig().root_dir, "amlb")),
)
res = ns(lambda: None)
if os.path.exists(config.result_file):
res = json_load(config.result_file, as_namespace=True)
log.debug("Result from subprocess:\n%s", res)
if not res:
raise NoResultError(f"Process crashed:\n{err}")
if res.error_message is not None:
raise NoResultError(res.error_message)
for name in ['predictions', 'truth', 'probabilities']:
res[name] = np.load(
res[name],
allow_pickle=True) if res[name] is not None else None
if callable(process_results):
res = process_results(res)
if res.output_file:
save_predictions(
dataset=dataset,
output_file=res.output_file,
predictions=res.predictions.reshape(-1)
if res.predictions is not None else None,
truth=res.truth.reshape(-1) if res.truth is not None else None,
probabilities=res.probabilities,
probabilities_labels=res.probabilities_labels,
target_is_encoded=res.target_is_encoded)
return dict(models_count=res.models_count
if res.models_count is not None else 1,
training_duration=res.training_duration if
res.training_duration is not None else proc_timer.duration,
predict_duration=res.predict_duration,
**res.others.__dict__)
def run(dataset: Dataset, config: TaskConfig):
log.info(f"\n**** AutoWEKA [v{config.framework_version}]****\n")
save_metadata(config)
is_classification = config.type == 'classification'
if not is_classification:
raise ValueError('Regression is not supported.')
# Mapping of benchmark metrics to Weka metrics
metrics_mapping = dict(acc='errorRate',
auc='areaUnderROC',
logloss='kBInformation')
metric = metrics_mapping[
config.metric] if config.metric in metrics_mapping else None
if metric is None:
raise ValueError("Performance metric {} not supported.".format(
config.metric))
train_file = dataset.train.path
test_file = dataset.test.path
# Weka to requires target as the last attribute
if dataset.target.index != len(dataset.predictors):
train_file = reorder_dataset(dataset.train.path,
target_src=dataset.target.index)
test_file = reorder_dataset(dataset.test.path,
target_src=dataset.target.index)
training_params = {
k: v
for k, v in config.framework_params.items() if not k.startswith('_')
}
parallelRuns = config.framework_params.get('_parallelRuns', config.cores)
memLimit = config.framework_params.get('_memLimit', 'auto')
if memLimit == 'auto':
memLimit = max(
min(config.max_mem_size_mb,
math.ceil(config.max_mem_size_mb / parallelRuns)),
1024) # AutoWEKA default memLimit
log.info("Using %sMB memory per run on %s parallel runs.", memLimit,
parallelRuns)
f = split_path(config.output_predictions_file)
f.extension = '.weka_pred.csv'
weka_file = path_from_split(f)
cmd_root = "java -cp {here}/lib/autoweka/autoweka.jar weka.classifiers.meta.AutoWEKAClassifier ".format(
here=dir_of(__file__))
cmd_params = dict(
t='"{}"'.format(train_file),
T='"{}"'.format(test_file),
memLimit=memLimit,
classifications=
'"weka.classifiers.evaluation.output.prediction.CSV -distribution -file \\\"{}\\\""'
.format(weka_file),
timeLimit=int(config.max_runtime_seconds / 60),
parallelRuns=parallelRuns,
metric=metric,
seed=config.seed % (1 << 16), # weka accepts only int16 as seeds
**training_params)
cmd = cmd_root + ' '.join(
["-{} {}".format(k, v) for k, v in cmd_params.items()])
with Timer() as training:
run_cmd(cmd, _live_output_=True)
# if target values are not sorted alphabetically in the ARFF file, then class probabilities are returned in the original order
# interestingly, other frameworks seem to always sort the target values first
# that's why we need to specify the probabilities labels here: sorting+formatting is done in saving function
probabilities_labels = dataset.target.values
if not os.path.exists(weka_file):
raise NoResultError("AutoWEKA failed producing any prediction.")
with open(weka_file, 'r') as weka_file:
probabilities = []
predictions = []
truth = []
for line in weka_file.readlines()[1:-1]:
inst, actual, predicted, error, *distribution = line.split(',')
pred_probabilities = [
pred_probability.replace('*', '').replace('\n', '')
for pred_probability in distribution
]
_, pred = predicted.split(':')
_, tru = actual.split(':')
probabilities.append(pred_probabilities)
predictions.append(pred)
truth.append(tru)
save_predictions(dataset=dataset,
output_file=config.output_predictions_file,
probabilities=probabilities,
predictions=predictions,
truth=truth,
probabilities_labels=probabilities_labels)
return dict(training_duration=training.duration)
def run_in_venv(caller_file,
script_file: str,
*args,
input_data: Union[dict, ns],
dataset: Dataset,
config: TaskConfig,
process_results=None,
python_exec=None):
here = dir_of(caller_file)
if python_exec is None: # use local virtual env by default
python_exec = os.path.join(here, 'venv/bin/python -W ignore')
script_path = os.path.join(here, script_file)
cmd = f"{python_exec} {script_path}"
input_data = ns.from_dict(input_data)
with TmpDir() as tmpdir:
def make_path(k, v, parents=None):
if isinstance(v, np.ndarray):
path = os.path.join(tmpdir, '.'.join(parents + [k, 'npy']))
if vector_keys.match(k):
v = v.reshape(-1, 1)
np.save(path, v, allow_pickle=True)
return k, path
return k, v
ds = ns.walk(input_data, make_path)
dataset.release()
config.result_token = str(uuid.uuid1())
config.result_dir = tmpdir
params = json_dumps(dict(dataset=ds, config=config), style='compact')
with Timer() as proc_timer:
output, err = run_cmd(cmd,
*args,
_input_str_=params,
_live_output_=True,
_env_=dict(PYTHONPATH=os.pathsep.join([
rconfig().root_dir,
os.path.join(rconfig().root_dir, "amlb"),
])))
out = io.StringIO(output)
res = ns()
for line in out:
li = line.rstrip()
if li == config.result_token:
res = json_loads(out.readline(), as_namespace=True)
break
if res.error_message is not None:
raise NoResultError(res.error_message)
for name in ['predictions', 'truth', 'probabilities']:
res[name] = np.load(
res[name],
allow_pickle=True) if res[name] is not None else None
log.debug("Result from subprocess:\n%s", res)
if callable(process_results):
res = process_results(res)
save_predictions_to_file(
dataset=dataset,
output_file=res.output_file,
predictions=res.predictions.reshape(-1)
if res.predictions is not None else None,
truth=res.truth.reshape(-1) if res.truth is not None else None,
probabilities=res.probabilities,
target_is_encoded=res.target_is_encoded)
return dict(models_count=res.models_count
if res.models_count is not None else 1,
training_duration=res.training_duration if
res.training_duration is not None else proc_timer.duration)
def run(dataset: Dataset, config: TaskConfig):
log.info("\n**** H2O AutoML ****\n")
# Mapping of benchmark metrics to H2O metrics
metrics_mapping = dict(acc='mean_per_class_error',
auc='AUC',
logloss='logloss',
mae='mae',
mse='mse',
rmse='rmse',
rmsle='rmsle')
sort_metric = metrics_mapping[
config.metric] if config.metric in metrics_mapping else None
if sort_metric is None:
# TODO: Figure out if we are going to blindly pass metrics through, or if we use a strict mapping
log.warning("Performance metric %s not supported, defaulting to AUTO.",
config.metric)
try:
training_params = {
k: v
for k, v in config.framework_params.items()
if not k.startswith('_')
}
nthreads = config.framework_params.get('_nthreads', config.cores)
log.info("Starting H2O cluster with %s cores, %sMB memory.", nthreads,
config.max_mem_size_mb)
h2o.init(nthreads=nthreads,
min_mem_size=str(config.max_mem_size_mb) + "M",
max_mem_size=str(config.max_mem_size_mb) + "M",
log_dir=os.path.join(config.output_dir, 'logs', config.name,
str(config.fold)))
# Load train as an H2O Frame, but test as a Pandas DataFrame
log.debug("Loading train data from %s.", dataset.train.path)
train = h2o.import_file(dataset.train.path)
# train.impute(method='mean')
log.debug("Loading test data from %s.", dataset.test.path)
test = h2o.import_file(dataset.test.path)
# test.impute(method='mean')
log.info("Running model on task %s, fold %s.", config.name,
config.fold)
log.debug(
"Running H2O AutoML with a maximum time of %ss on %s core(s), optimizing %s.",
config.max_runtime_seconds, config.cores, sort_metric)
aml = H2OAutoML(max_runtime_secs=config.max_runtime_seconds,
sort_metric=sort_metric,
seed=config.seed,
**training_params)
with Timer() as training:
aml.train(y=dataset.target.index, training_frame=train)
if not aml.leader:
raise NoResultError(
"H2O could not produce any model in the requested time.")
lb = aml.leaderboard.as_data_frame()
log.debug("Leaderboard:\n%s", lb.to_string())
lbf = split_path(config.output_predictions_file)
lbf.extension = '.leaderboard.csv'
lbf = path_from_split(lbf)
write_csv(lb, lbf)
h2o_preds = aml.predict(test).as_data_frame(use_pandas=False)
preds = to_data_frame(h2o_preds[1:], columns=h2o_preds[0])
y_pred = preds.iloc[:, 0]
h2o_truth = test[:,
dataset.target.index].as_data_frame(use_pandas=False,
header=False)
y_truth = to_data_frame(h2o_truth)
predictions = y_pred.values
probabilities = preds.iloc[:, 1:].values
truth = y_truth.values
save_predictions_to_file(dataset=dataset,
output_file=config.output_predictions_file,
probabilities=probabilities,
predictions=predictions,
truth=truth)
return dict(models_count=len(aml.leaderboard),
training_duration=training.duration)
finally:
if h2o.connection():
h2o.remove_all()
h2o.connection().close()
if h2o.connection().local_server:
h2o.connection().local_server.shutdown()
def run_random_forest(dataset, config, tuner, log):
"""
Using the given tuner, tune a random forest within the given time constraint.
This function uses cross validation score as the feedback score to the tuner.
The search space on which tuners search on is defined above empirically as a global variable.
"""
limit_type, trial_limit = config.framework_params['limit_type'], None
if limit_type == 'ntrials':
trial_limit = int(config.framework_params['trial_limit'])
X_train, X_test = preprocess_random_forest(dataset, log)
y_train, y_test = dataset.train.y, dataset.test.y
is_classification = config.type == 'classification'
estimator = RandomForestClassifier if is_classification else RandomForestRegressor
best_score, best_params, best_model = None, None, None
score_higher_better = True
tuner.update_search_space(SEARCH_SPACE)
start_time = time.time()
trial_count = 0
intermediate_scores = []
intermediate_best_scores = [] # should be monotonically increasing
while True:
try:
trial_count += 1
param_idx, cur_params = tuner.generate_parameters()
train_params = cur_params.copy()
if 'TRIAL_BUDGET' in cur_params:
train_params.pop('TRIAL_BUDGET')
if cur_params['max_leaf_nodes'] == 0:
train_params.pop('max_leaf_nodes')
if cur_params['max_depth'] == 0:
train_params.pop('max_depth')
log.info("Trial {}: \n{}\n".format(param_idx, cur_params))
cur_model = estimator(random_state=config.seed, **train_params)
# Here score is the output of score() from the estimator
cur_score = cross_val_score(cur_model, X_train, y_train)
cur_score = sum(cur_score) / float(len(cur_score))
if np.isnan(cur_score):
cur_score = 0
log.info("Score: {}\n".format(cur_score))
if best_score is None or (score_higher_better and cur_score > best_score) or (not score_higher_better and cur_score < best_score):
best_score, best_params, best_model = cur_score, cur_params, cur_model
intermediate_scores.append(cur_score)
intermediate_best_scores.append(best_score)
tuner.receive_trial_result(param_idx, cur_params, cur_score)
if limit_type == 'time':
current_time = time.time()
elapsed_time = current_time - start_time
if elapsed_time >= config.max_runtime_seconds:
break
elif limit_type == 'ntrials':
if trial_count >= trial_limit:
break
except:
break
# This line is required to fully terminate some advisors
tuner.handle_terminate()
log.info("Tuning done, the best parameters are:\n{}\n".format(best_params))
# retrain on the whole dataset
with Timer() as training:
best_model.fit(X_train, y_train)
predictions = best_model.predict(X_test)
probabilities = best_model.predict_proba(X_test) if is_classification else None
return probabilities, predictions, training, y_test, intermediate_scores, intermediate_best_scores
def run(dataset: Dataset, config: TaskConfig):
log.info("\n**** H2O AutoML ****\n")
# Mapping of benchmark metrics to H2O metrics
metrics_mapping = dict(acc='mean_per_class_error',
auc='AUC',
logloss='logloss',
mae='mae',
mse='mse',
r2='r2',
rmse='rmse',
rmsle='rmsle')
sort_metric = metrics_mapping[
config.metric] if config.metric in metrics_mapping else None
if sort_metric is None:
# TODO: Figure out if we are going to blindly pass metrics through, or if we use a strict mapping
log.warning("Performance metric %s not supported, defaulting to AUTO.",
config.metric)
try:
training_params = {
k: v
for k, v in config.framework_params.items()
if not k.startswith('_')
}
nthreads = config.framework_params.get('_nthreads', config.cores)
jvm_memory = str(
round(config.max_mem_size_mb * 2 /
3)) + "M" # leaving 1/3rd of available memory for XGBoost
log.info("Starting H2O cluster with %s cores, %s memory.", nthreads,
jvm_memory)
max_port_range = 49151
min_port_range = 1024
rnd_port = os.getpid() % (max_port_range -
min_port_range) + min_port_range
port = config.framework_params.get('_port', rnd_port)
h2o.init(
nthreads=nthreads,
port=port,
min_mem_size=jvm_memory,
max_mem_size=jvm_memory,
strict_version_check=config.framework_params.get(
'_strict_version_check', True)
# log_dir=os.path.join(config.output_dir, 'logs', config.name, str(config.fold))
)
# Load train as an H2O Frame, but test as a Pandas DataFrame
log.debug("Loading train data from %s.", dataset.train.path)
train = h2o.import_file(dataset.train.path,
destination_frame=frame_name('train', config))
# train.impute(method='mean')
log.debug("Loading test data from %s.", dataset.test.path)
test = h2o.import_file(dataset.test.path,
destination_frame=frame_name('test', config))
# test.impute(method='mean')
log.info("Running model on task %s, fold %s.", config.name,
config.fold)
log.debug(
"Running H2O AutoML with a maximum time of %ss on %s core(s), optimizing %s.",
config.max_runtime_seconds, config.cores, sort_metric)
aml = H2OAutoML(
max_runtime_secs=config.max_runtime_seconds,
max_runtime_secs_per_model=round(
config.max_runtime_seconds /
2), # to prevent timeout on ensembles
sort_metric=sort_metric,
seed=config.seed,
**training_params)
monitor = (
BackendMemoryMonitoring(
frequency_seconds=rconfig().monitoring.frequency_seconds,
check_on_exit=True,
verbosity=rconfig().monitoring.verbosity)
if config.framework_params.get('_monitor_backend', False)
# else contextlib.nullcontext # Py 3.7+ only
else contextlib.contextmanager(iter)([0]))
with Timer() as training:
with monitor:
aml.train(y=dataset.target.index, training_frame=train)
if not aml.leader:
raise NoResultError(
"H2O could not produce any model in the requested time.")
save_predictions(aml, test, dataset=dataset, config=config)
save_artifacts(aml, dataset=dataset, config=config)
return dict(models_count=len(aml.leaderboard),
training_duration=training.duration)
finally:
if h2o.connection():
# h2o.remove_all()
h2o.connection().close()
if h2o.connection().local_server:
h2o.connection().local_server.shutdown()
def run(dataset: Dataset, config: TaskConfig):
log.info(
"\n**** Random Forest (sklearn) Tuned with NNI EvolutionTuner ****\n")
is_classification = config.type == 'classification'
X_train, X_test = impute(dataset.train.X, dataset.test.X)
y_train, y_test = dataset.train.y, dataset.test.y
estimator = RandomForestClassifier if is_classification else RandomForestRegressor
# model = estimator(random_state=config.seed, **config.framework_params)
best_score, best_params, best_model = None, None, None
score_higher_better = True
log.info(
"Tuning hyperparameters with NNI EvolutionTuner with a maximum time of {}s\n"
.format(config.max_runtime_seconds))
tuner = EvolutionTuner()
tuner.update_search_space(SEARCH_SPACE)
start_time = time.time()
param_idx = 0
while True:
try:
cur_params = tuner.generate_parameters(param_idx)
cur_model = estimator(random_state=config.seed,
**cur_params,
**config.framework_params)
# Here score is the output of score() from the estimator
cur_score = cross_val_score(cur_model, X_train, y_train)
cur_score = sum(cur_score) / float(len(cur_score))
if best_score is None or (score_higher_better
and cur_score > best_score) or (
not score_higher_better
and cur_score < best_score):
best_score, best_params, best_model = cur_score, cur_params, cur_model
log.info("Trial {}: \n{}\nScore: {}\n".format(
param_idx, cur_params, cur_score))
tuner.receive_trial_result(param_idx, cur_params, cur_score)
param_idx += 1
current_time = time.time()
elapsed_time = current_time - start_time
if elapsed_time > config.max_runtime_seconds:
break
except:
break
log.info("Tuning done, the best parameters are:\n{}\n".format(best_params))
# retrain on the whole dataset
with Timer() as training:
best_model.fit(X_train, y_train)
predictions = best_model.predict(X_test)
probabilities = best_model.predict_proba(
X_test) if is_classification else None
save_predictions_to_file(dataset=dataset,
output_file=config.output_predictions_file,
probabilities=probabilities,
predictions=predictions,
truth=y_test)
return dict(models_count=1, training_duration=training.duration)
def run(dataset: Dataset, config: TaskConfig):
log.info("\n**** Hyperopt-sklearn ****\n")
is_classification = config.type == 'classification'
default = lambda: 0
metrics_to_loss_mapping = dict(
acc=(default, False), # lambda y, pred: 1.0 - accuracy_score(y, pred)
auc=(lambda y, pred: 1.0 - roc_auc_score(y, pred), False),
f1=(lambda y, pred: 1.0 - f1_score(y, pred), False),
# logloss=(log_loss, True),
mae=(mean_absolute_error, False),
mse=(mean_squared_error, False),
msle=(mean_squared_log_error, False),
r2=(default, False), # lambda y, pred: 1.0 - r2_score(y, pred)
)
loss_fn, continuous_loss_fn = metrics_to_loss_mapping[
config.metric] if config.metric in metrics_to_loss_mapping else (None,
False)
if loss_fn is None:
log.warning("Performance metric %s not supported: defaulting to %s.",
config.metric, 'accuracy' if is_classification else 'r2')
if loss_fn is default:
loss_fn = None
training_params = {
k: v
for k, v in config.framework_params.items() if not k.startswith('_')
}
log.warning("Ignoring cores constraint of %s cores.", config.cores)
log.info(
"Running hyperopt-sklearn with a maximum time of %ss on %s cores, optimizing %s.",
config.max_runtime_seconds, 'all', config.metric)
X_train, X_test = impute(dataset.train.X_enc, dataset.test.X_enc)
y_train, y_test = dataset.train.y_enc, dataset.test.y_enc
if is_classification:
classifier = any_classifier('clf')
regressor = None
else:
classifier = None
regressor = any_regressor('rgr')
estimator = HyperoptEstimator(classifier=classifier,
regressor=regressor,
algo=tpe.suggest,
loss_fn=loss_fn,
continuous_loss_fn=continuous_loss_fn,
trial_timeout=config.max_runtime_seconds,
seed=config.seed,
**training_params)
with InterruptTimeout(config.max_runtime_seconds * 4 / 3,
sig=signal.SIGQUIT):
with InterruptTimeout(config.max_runtime_seconds,
before_interrupt=ft.partial(
kill_proc_tree,
timeout=5,
include_parent=False)):
with Timer() as training:
estimator.fit(X_train, y_train)
predictions = estimator.predict(X_test)
if is_classification:
target_values_enc = dataset.target.label_encoder.transform(
dataset.target.values)
probabilities = Encoder(
'one-hot', target=False,
encoded_type=float).fit(target_values_enc).transform(predictions)
else:
probabilities = None
save_predictions_to_file(dataset=dataset,
output_file=config.output_predictions_file,
probabilities=probabilities,
predictions=predictions,
truth=y_test,
target_is_encoded=True)
return dict(models_count=len(estimator.trials),
training_duration=training.duration)
def run(dataset: Dataset, config: TaskConfig):
log.info("\n**** TPOT ****\n")
is_classification = config.type == 'classification'
# Mapping of benchmark metrics to TPOT metrics
metrics_mapping = dict(acc='accuracy',
auc='roc_auc',
f1='f1',
logloss='neg_log_loss',
mae='neg_mean_absolute_error',
mse='neg_mean_squared_error',
msle='neg_mean_squared_log_error',
r2='r2')
scoring_metric = metrics_mapping[
config.metric] if config.metric in metrics_mapping else None
if scoring_metric is None:
raise ValueError("Performance metric {} not supported.".format(
config.metric))
X_train, X_test = impute(dataset.train.X_enc, dataset.test.X_enc)
y_train, y_test = dataset.train.y_enc, dataset.test.y_enc
training_params = {
k: v
for k, v in config.framework_params.items() if not k.startswith('_')
}
n_jobs = config.framework_params.get(
'_n_jobs', config.cores
) # useful to disable multicore, regardless of the dataset config
log.info(
'Running TPOT with a maximum time of %ss on %s cores, optimizing %s.',
config.max_runtime_seconds, n_jobs, scoring_metric)
runtime_min = (config.max_runtime_seconds / 60)
estimator = TPOTClassifier if is_classification else TPOTRegressor
tpot = estimator(n_jobs=n_jobs,
max_time_mins=runtime_min,
scoring=scoring_metric,
random_state=config.seed,
**training_params)
with Timer() as training:
tpot.fit(X_train, y_train)
log.info('Predicting on the test set.')
predictions = tpot.predict(X_test)
try:
probabilities = tpot.predict_proba(
X_test) if is_classification else None
except RuntimeError:
# TPOT throws a RuntimeError if the optimized pipeline does not support `predict_proba`.
target_values_enc = dataset.target.label_encoder.transform(
dataset.target.values)
probabilities = Encoder(
'one-hot', target=False,
encoded_type=float).fit(target_values_enc).transform(predictions)
save_predictions_to_file(dataset=dataset,
output_file=config.output_predictions_file,
probabilities=probabilities,
predictions=predictions,
truth=y_test,
target_is_encoded=is_classification)
save_artifacts(tpot, config)
return dict(models_count=len(tpot.evaluated_individuals_),
training_duration=training.duration)
def run(dataset: Dataset, config: TaskConfig):
log.info("\n**** H2O AutoML ****\n")
# Mapping of benchmark metrics to H2O metrics
metrics_mapping = dict(acc='mean_per_class_error',
auc='AUC',
logloss='logloss',
mae='mae',
mse='mse',
rmse='rmse',
rmsle='rmsle')
sort_metric = metrics_mapping[
config.metric] if config.metric in metrics_mapping else None
if sort_metric is None:
# TODO: Figure out if we are going to blindly pass metrics through, or if we use a strict mapping
log.warning("Performance metric %s not supported, defaulting to AUTO.",
config.metric)
try:
training_params = {
k: v
for k, v in config.framework_params.items()
if not k.startswith('_')
}
nthreads = config.framework_params.get('_nthreads', config.cores)
log.info("Starting H2O cluster with %s cores, %sMB memory.", nthreads,
config.max_mem_size_mb)
h2o.init(
nthreads=nthreads,
min_mem_size=str(config.max_mem_size_mb) + "M",
max_mem_size=str(config.max_mem_size_mb) + "M",
# log_dir=os.path.join(config.output_dir, 'logs', config.name, str(config.fold))
)
# Load train as an H2O Frame, but test as a Pandas DataFrame
log.debug("Loading train data from %s.", dataset.train.path)
train = h2o.import_file(dataset.train.path,
destination_frame=frame_name('train', config))
# train.impute(method='mean')
log.debug("Loading test data from %s.", dataset.test.path)
test = h2o.import_file(dataset.test.path,
destination_frame=frame_name('test', config))
# test.impute(method='mean')
log.info("Running model on task %s, fold %s.", config.name,
config.fold)
log.debug(
"Running H2O AutoML with a maximum time of %ss on %s core(s), optimizing %s.",
config.max_runtime_seconds, config.cores, sort_metric)
aml = H2OAutoML(max_runtime_secs=config.max_runtime_seconds,
sort_metric=sort_metric,
seed=config.seed,
**training_params)
with Timer() as training:
aml.train(y=dataset.target.index, training_frame=train)
if not aml.leader:
raise NoResultError(
"H2O could not produce any model in the requested time.")
save_predictions(aml, test, dataset=dataset, config=config)
save_artifacts(aml, dataset=dataset, config=config)
return dict(models_count=len(aml.leaderboard),
training_duration=training.duration)
finally:
if h2o.connection():
h2o.remove_all()
h2o.connection().close()
if h2o.connection().local_server:
h2o.connection().local_server.shutdown()
def run(dataset: Dataset, config: TaskConfig):
log.info("\n**** AutoSklearn ****\n")
warnings.simplefilter(action='ignore', category=FutureWarning)
warnings.simplefilter(action='ignore', category=DeprecationWarning)
is_classification = config.type == 'classification'
# Mapping of benchmark metrics to autosklearn metrics
metrics_mapping = dict(acc=metrics.accuracy,
auc=metrics.roc_auc,
f1=metrics.f1,
logloss=metrics.log_loss,
mae=metrics.mean_absolute_error,
mse=metrics.mean_squared_error,
r2=metrics.r2)
perf_metric = metrics_mapping[
config.metric] if config.metric in metrics_mapping else None
if perf_metric is None:
# TODO: figure out if we are going to blindly pass metrics through, or if we use a strict mapping
log.warning("Performance metric %s not supported.", config.metric)
# Set resources based on datasize
log.info(
"Running auto-sklearn with a maximum time of %ss on %s cores with %sMB, optimizing %s.",
config.max_runtime_seconds, config.cores, config.max_mem_size_mb,
perf_metric)
log.info("Environment: %s", os.environ)
X_train = dataset.train.X_enc
y_train = dataset.train.y_enc
# log.info("finite=%s", np.isfinite(X_train))
predictors_type = [
'Categorical' if p.is_categorical() else 'Numerical'
for p in dataset.predictors
]
training_params = {
k: v
for k, v in config.framework_params.items() if not k.startswith('_')
}
n_jobs = config.framework_params.get('_n_jobs', config.cores)
ml_memory_limit = config.framework_params.get('_ml_memory_limit', 'auto')
ensemble_memory_limit = config.framework_params.get(
'_ensemble_memory_limit', 'auto')
# when memory is large enough, we should have:
# (cores - 1) * ml_memory_limit_mb + ensemble_memory_limit_mb = config.max_mem_size_mb
total_memory_mb = system_memory_mb().total
if ml_memory_limit == 'auto':
ml_memory_limit = max(
min(config.max_mem_size_mb, math.ceil(total_memory_mb / n_jobs)),
3072) # 3072 is autosklearn defaults
if ensemble_memory_limit == 'auto':
ensemble_memory_limit = max(
math.ceil(ml_memory_limit -
(total_memory_mb - config.max_mem_size_mb)),
math.ceil(ml_memory_limit / 3), # default proportions
1024) # 1024 is autosklearn defaults
log.info(
"Using %sMB memory per ML job and %sMB for ensemble job on a total of %s jobs.",
ml_memory_limit, ensemble_memory_limit, n_jobs)
log.warning(
"Using meta-learned initialization, which might be bad (leakage).")
# TODO: do we need to set per_run_time_limit too?
estimator = AutoSklearnClassifier if is_classification else AutoSklearnRegressor
auto_sklearn = estimator(
time_left_for_this_task=config.max_runtime_seconds,
n_jobs=n_jobs,
ml_memory_limit=ml_memory_limit,
ensemble_memory_limit=ensemble_memory_limit,
seed=config.seed,
**training_params)
with Timer() as training:
auto_sklearn.fit(X_train,
y_train,
metric=perf_metric,
feat_type=predictors_type)
# Convert output to strings for classification
log.info("Predicting on the test set.")
X_test = dataset.test.X_enc
y_test = dataset.test.y_enc
predictions = auto_sklearn.predict(X_test)
probabilities = auto_sklearn.predict_proba(
X_test) if is_classification else None
save_predictions_to_file(dataset=dataset,
output_file=config.output_predictions_file,
probabilities=probabilities,
predictions=predictions,
truth=y_test,
target_is_encoded=True)
save_artifacts(auto_sklearn, config)
return dict(models_count=len(auto_sklearn.get_models_with_weights()),
training_duration=training.duration)
def run(dataset: Dataset, config: TaskConfig):
log.info(f"\n**** MLNet [v{config.framework_version}] ****\n")
avaible_task_list = ['classification', 'regression']
if config.type not in avaible_task_list:
raise ValueError(f'{config.type} is not supported.')
dir_path = os.path.dirname(os.path.realpath(__file__))
DOTNET_INSTALL_DIR = os.path.join(dir_path, 'lib')
os.environ['DOTNET_ROOT'] = DOTNET_INSTALL_DIR
os.environ['MLNetCLIEnablePredict'] = 'True'
os.environ['MLNET_MAX_THREAD'] = str(config.cores)
mlnet = os.path.join(DOTNET_INSTALL_DIR, 'mlnet')
train_time_in_seconds = config.max_runtime_seconds
sub_command = config.type
# set up MODELBUILDER_AUTOML
MODELBUILDER_AUTOML = config.framework_params.get('automl_type', 'NNI')
os.environ['MODELBUILDER_AUTOML'] = MODELBUILDER_AUTOML
artifacts = config.framework_params.get('_save_artifacts', [])
tmpdir = tempfile.mkdtemp()
tmp_output_folder = os.path.join(tmpdir, str(config.fold))
output_dir = output_subdir(
'models',
config=config) if 'models' in artifacts else tmp_output_folder
log_dir = output_subdir(
'logs', config=config) if 'logs' in artifacts else tmp_output_folder
log_path = os.path.join(log_dir, 'log.txt')
try:
label = dataset.target.index
train_dataset_path = dataset.train.data_path('csv')
test_dataset_path = dataset.test.data_path('csv')
log.info(f'train dataset: {train_dataset_path}')
log.info(f'test dataset: {test_dataset_path}')
cmd = (
f"{mlnet} {sub_command}"
f" --dataset {train_dataset_path} --test-dataset {test_dataset_path} --train-time {train_time_in_seconds}"
f" --label-col {label} --output {os.path.dirname(output_dir)} --name {config.fold}"
f" --verbosity q --log-file-path {log_path}")
with Timer() as training:
run_cmd(cmd)
train_result_json = os.path.join(output_dir,
'{}.mbconfig'.format(config.fold))
if not os.path.exists(train_result_json):
raise NoResultError("MLNet failed producing any prediction.")
with open(train_result_json, 'r') as f:
json_str = f.read()
mb_config = json.loads(json_str)
model_path = os.path.join(output_dir, f"{config.fold}.zip")
output_prediction_path = os.path.join(
log_dir, "prediction.txt"
) # keeping this in log dir as it contains useful error when prediction fails
models_count = len(mb_config['RunHistory']['Trials'])
# predict
predict_cmd = (
f"{mlnet} predict --task-type {config.type}"
f" --model {model_path} --dataset {test_dataset_path} --label-col {dataset.target.name} > {output_prediction_path}"
)
with Timer() as prediction:
run_cmd(predict_cmd)
if config.type == 'classification':
prediction_df = pd.read_csv(output_prediction_path,
dtype={'PredictedLabel': 'object'})
save_predictions(
dataset=dataset,
output_file=config.output_predictions_file,
predictions=prediction_df['PredictedLabel'].values,
truth=dataset.test.y,
probabilities=prediction_df.values[:, :-1],
probabilities_labels=list(
prediction_df.columns.values[:-1]),
)
if config.type == 'regression':
prediction_df = pd.read_csv(output_prediction_path)
save_predictions(
dataset=dataset,
output_file=config.output_predictions_file,
predictions=prediction_df['Score'].values,
truth=dataset.test.y,
)
return dict(
models_count=models_count,
training_duration=training.duration,
predict_duration=prediction.duration,
)
finally:
if 'logs' in artifacts:
logs_zip = os.path.join(log_dir, "logs.zip")
zip_path(log_dir, logs_zip)
clean_dir(log_dir, filter_=lambda p: p != logs_zip)
if 'models' in artifacts:
models_zip = os.path.join(output_dir, "models.zip")
zip_path(output_dir, models_zip)
clean_dir(output_dir, filter_=lambda p: p != models_zip)
shutil.rmtree(tmpdir, ignore_errors=True)
def run(dataset: Dataset, config: TaskConfig):
log.info("\n**** Tuned Random Forest (sklearn) ****\n")
is_classification = config.type == 'classification'
training_params = {
k: v
for k, v in config.framework_params.items() if not k.startswith('_')
}
tuning_params = config.framework_params.get('_tuning', training_params)
n_jobs = config.framework_params.get(
'_n_jobs', config.cores
) # useful to disable multicore, regardless of the dataset config
# Impute any missing data (can test using -t 146606)
X_train, X_test = impute(dataset.train.X_enc, dataset.test.X_enc)
y_train, y_test = dataset.train.y_enc, dataset.test.y_enc
log.info(
"Running RandomForest with a maximum time of {}s on {} cores.".format(
config.max_runtime_seconds, n_jobs))
estimator = RandomForestClassifier if is_classification else RandomForestRegressor
metric = dict(auc='roc_auc', logloss='neg_log_loss',
acc='accuracy')[config.metric]
n_features = X_train.shape[1]
default_value = max(1, int(math.sqrt(n_features)))
below_default = pick_values_uniform(start=1,
end=default_value,
length=5 + 1)[:-1] # 5 below
above_default = pick_values_uniform(start=default_value,
end=n_features,
length=10 + 1 -
len(below_default))[1:] # 5 above
# Mix up the order of `max_features` to try, so that a fair range is tried even if we have too little time
# to try all possible values. Order: [sqrt(p), 1, p, random order for remaining values]
# max_features_to_try = below_default[1:] + above_default[:-1]
# max_features_values = ([default_value, 1, n_features]
# + random.sample(max_features_to_try, k=len(max_features_to_try)))
max_features_values = [default_value] + below_default + above_default
# Define up to how much of total time we spend 'optimizing' `max_features`.
# (the remainder if used for fitting the final model).
safety_factor = 0.85
with stopit.ThreadingTimeout(seconds=int(config.max_runtime_seconds *
safety_factor)):
log.info("Evaluating multiple values for `max_features`: %s.",
max_features_values)
max_feature_scores = []
tuning_durations = []
for i, max_features_value in enumerate(max_features_values):
log.info("[{:2d}/{:2d}] Evaluating max_features={}".format(
i + 1, len(max_features_values), max_features_value))
imputation = Imputer()
random_forest = estimator(n_jobs=n_jobs,
random_state=config.seed,
max_features=max_features_value,
**tuning_params)
pipeline = Pipeline(steps=[('preprocessing',
imputation), ('learning',
random_forest)])
with Timer() as cv_scoring:
try:
scores = cross_val_score(estimator=pipeline,
X=dataset.train.X_enc,
y=dataset.train.y_enc,
scoring=metric,
cv=5)
max_feature_scores.append(
(statistics.mean(scores), max_features_value))
except stopit.utils.TimeoutException as toe:
log.error(
"Failed CV scoring for max_features=%s : Timeout",
max_features_value)
tuning_durations.append(
(max_features_value, cv_scoring.duration))
raise toe
except Exception as e:
log.error("Failed CV scoring for max_features=%s :\n%s",
max_features_value, e)
log.debug("Exception:", exc_info=True)
tuning_durations.append((max_features_value, cv_scoring.duration))
log.info("Tuning scores:\n%s", sorted(max_feature_scores))
log.info("Tuning durations:\n%s", sorted(tuning_durations))
_, best_max_features_value = max(
max_feature_scores) if len(max_feature_scores) > 0 else (math.nan,
'auto')
log.info("Training final model with `max_features={}`.".format(
best_max_features_value))
rf = estimator(n_jobs=n_jobs,
random_state=config.seed,
max_features=best_max_features_value,
**training_params)
with Timer() as training:
rf.fit(X_train, y_train)
predictions = rf.predict(X_test)
probabilities = rf.predict_proba(X_test) if is_classification else None
save_predictions_to_file(dataset=dataset,
output_file=config.output_predictions_file,
probabilities=probabilities,
predictions=predictions,
truth=y_test,
target_is_encoded=True)
return dict(models_count=len(rf),
training_duration=training.duration +
sum(map(lambda t: t[1], tuning_durations)))
