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refactor(Config): use a Config object instead of dictionary of dictionaries! (#184)

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* refactor(Config): use a Config object instead of dictionary of dictionaries!

* fix(Config): use default_ensemble_config

* fix(Portfolio): fixed portfolio construction
This commit is contained in:
Mark Aron Szulyovszky
2022-01-23 18:37:43 +01:00
committed by GitHub
parent 5c4a5b0cf1
commit e80fffdb65
19 changed files with 223 additions and 196 deletions
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config/preprocess.py
+13 -17
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@@ -1,16 +1,19 @@
from config.config import Config, RawConfig
from utils.helpers import flatten
from feature_extractors.feature_extractor_presets import presets as feature_extractor_presets
from models.model_map import get_model_map
from data_loader.collections import data_collections
def preprocess_config(model_config:dict, training_config:dict, data_config:dict) -> tuple[dict, dict, dict]:
model_config = __preprocess_model_config(model_config)
data_config = __preprocess_feature_extractors_config(data_config)
data_config = __preprocess_data_collections_config(data_config)
validate_config(model_config, training_config, data_config)
return model_config, training_config, data_config
def preprocess_config(raw_config: RawConfig) -> Config:
config_dict = vars(raw_config)
config_dict = __preprocess_model_config(config_dict)
config_dict = __preprocess_feature_extractors_config(config_dict)
config_dict = __preprocess_data_collections_config(config_dict)
config = Config(**config_dict)
validate_config(config)
return config
def __preprocess_feature_extractors_config(data_dict: dict) -> dict:
data_dict = data_dict.copy()
@@ -42,18 +45,11 @@ def __preprocess_data_collections_config(data_dict: dict) -> dict:
return data_dict
def validate_config(model_config:dict, training_config:dict, data_config:dict):
def validate_config(config: Config):
# We need to make sure there's only one output from the pipeline
# If level-2 model is there, we need more than one level-1 models to train
if len(model_config["meta_labeling_models"]) > 1: assert len(model_config["primary_models"]) > 0
if len(config.meta_labeling_models) > 1: assert len(config.primary_models) > 0
# If there's no level-2 model, we need to have only one level-1 model
if len(model_config["meta_labeling_models"]) == 0: assert len(model_config["primary_models"]) == 1
if len(config.meta_labeling_models) == 0: assert len(config.primary_models) == 1
def get_model_name(model_config:dict) -> str:
if len(model_config["meta_labeling_models"]) > 0:
return model_config["meta_labeling_models"][0][0]
elif len(model_config["primary_models"]) == 1:
return model_config["primary_models"][0][0]
else:
raise Exception("No model name found")