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refactor logging in api composer
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@gkirgizov
gkirgizov committed Sep 20, 2022
1 parent c19d905 commit 019cdca
Showing 1 changed file with 16 additions and 17 deletions.
33 changes: 16 additions & 17 deletions fedot/api/api_utils/api_composer.py
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Original file line number Diff line number Diff line change
Expand Up @@ -13,7 +13,7 @@
from fedot.core.composer.gp_composer.specific_operators import boosting_mutation, parameter_change_mutation
from fedot.core.constants import DEFAULT_TUNING_ITERATIONS_NUMBER
from fedot.core.data.data import InputData
from fedot.core.log import LoggerAdapter, default_log
from fedot.core.log import default_log
from fedot.core.optimisers.gp_comp.evaluation import determine_n_jobs
from fedot.core.optimisers.gp_comp.gp_params import GPGraphOptimizerParameters
from fedot.core.optimisers.gp_comp.operators.inheritance import GeneticSchemeTypesEnum
Expand All @@ -32,6 +32,7 @@
class ApiComposer:

def __init__(self, problem: str):
self.log = default_log(self)
self.metrics = ApiMetrics(problem)
self.pipelines_cache: Optional[OperationsCache] = None
self.preprocessing_cache: Optional[PreprocessingCache] = None
Expand Down Expand Up @@ -173,7 +174,6 @@ def _get_default_mutations(task_type: TaskTypesEnum) -> Sequence[MutationTypesEn
def compose_fedot_model(self, api_params: dict, composer_params: dict, tuning_params: dict) \
-> Tuple[Pipeline, Sequence[Pipeline], OptHistory]:
""" Function for composing FEDOT pipeline model """
log: LoggerAdapter = default_log(self)
task: Task = api_params['task']
train_data = api_params['train_data']
timeout = api_params['timeout']
Expand All @@ -193,7 +193,7 @@ def compose_fedot_model(self, api_params: dict, composer_params: dict, tuning_pa
assumption_handler.fit_assumption_and_check_correctness(initial_assumption[0],
pipelines_cache=self.pipelines_cache,
preprocessing_cache=self.preprocessing_cache)
log.info(f'Initial pipeline was fitted for {self.timer.assumption_fit_spend_time.total_seconds()} sec.')
self.log.info(f'Initial pipeline was fitted for {self.timer.assumption_fit_spend_time.total_seconds()} sec.')

n_jobs = determine_n_jobs(api_params['n_jobs'])
self.preset_name = assumption_handler.propose_preset(preset, self.timer, n_jobs=n_jobs)
Expand All @@ -204,26 +204,25 @@ def compose_fedot_model(self, api_params: dict, composer_params: dict, tuning_pa
self.preset_name)
metric_functions = self.obtain_metric(task, composer_params['metric'])

log.info(f"AutoML configured."
f" Parameters tuning: {with_tuning}"
f" Time limit: {timeout} min"
f" Set of candidate models: {available_operations}")
self.log.info(f"AutoML configured."
f" Parameters tuning: {with_tuning}"
f" Time limit: {timeout} min"
f" Set of candidate models: {available_operations}")

best_pipeline, best_pipeline_candidates, gp_composer = self.compose_pipeline(task, train_data,
fitted_assumption,
metric_functions,
composer_requirements,
composer_params, log)
composer_params)
if with_tuning:
best_pipeline = self.tune_final_pipeline(task, train_data,
metric_functions[0],
composer_requirements,
best_pipeline,
log)
best_pipeline)
# enforce memory cleaning
gc.collect()

log.info('Model generation finished')
self.log.info('Model generation finished')
return best_pipeline, best_pipeline_candidates, gp_composer.history

def compose_pipeline(self, task: Task,
Expand All @@ -232,7 +231,7 @@ def compose_pipeline(self, task: Task,
metric_functions: Sequence[MetricsEnum],
composer_requirements: PipelineComposerRequirements,
composer_params: dict,
log: LoggerAdapter) -> Tuple[Pipeline, List[Pipeline], GPComposer]:
) -> Tuple[Pipeline, List[Pipeline], GPComposer]:

multi_objective = len(metric_functions) > 1
optimizer_params = ApiComposer._init_optimizer_parameters(composer_params,
Expand All @@ -253,18 +252,18 @@ def compose_pipeline(self, task: Task,
if self.timer.have_time_for_composing(composer_params['pop_size'], n_jobs):
# Launch pipeline structure composition
with self.timer.launch_composing():
log.info('Pipeline composition started.')
self.log.info('Pipeline composition started.')
best_pipelines = gp_composer.compose_pipeline(data=train_data)
best_pipeline_candidates = gp_composer.best_models
else:
# Use initial pipeline as final solution
log.info(f'Timeout is too small for composing and is skipped '
f'because fit_time is {self.timer.assumption_fit_spend_time.total_seconds()} sec.')
self.log.info(f'Timeout is too small for composing and is skipped '
f'because fit_time is {self.timer.assumption_fit_spend_time.total_seconds()} sec.')
best_pipelines = fitted_assumption
best_pipeline_candidates = [fitted_assumption]

for pipeline in best_pipeline_candidates:
pipeline.log = log
pipeline.log = self.log
best_pipeline = best_pipelines[0] if isinstance(best_pipelines, Sequence) else best_pipelines
return best_pipeline, best_pipeline_candidates, gp_composer

Expand All @@ -273,7 +272,7 @@ def tune_final_pipeline(self, task: Task,
metric_function: Optional[MetricType],
composer_requirements: PipelineComposerRequirements,
pipeline_gp_composed: Pipeline,
log: LoggerAdapter) -> Pipeline:
) -> Pipeline:
""" Launch tuning procedure for obtained pipeline by composer """
timeout_for_tuning = abs(self.timer.determine_resources_for_tuning()) / 60
tuner = TunerBuilder(task) \
Expand Down

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