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chore(Linter): reformatted code with black (#211)

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* chore(Linter): reformatted code with black

* Create black.yaml
This commit is contained in:
Mark Aron Szulyovszky
2022-02-17 19:22:17 +01:00
committed by GitHub
parent f3fee4a4e1
commit 8dd2d88740
101 changed files with 2596 additions and 2320 deletions
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models/model_map.py
+52 -20
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@@ -3,49 +3,81 @@ from sklearnex.ensemble import RandomForestClassifier
from sklearnex.ensemble import RandomForestRegressor
from .base import Model
default_feature_selector_classification = SKLearnModel(RandomForestClassifier(n_jobs=-1, max_depth=20, random_state=1))
default_feature_selector_classification = SKLearnModel(
RandomForestClassifier(n_jobs=-1, max_depth=20, random_state=1)
)
def get_model(model_name: str) -> Model:
def set_name(model: Model) -> Model:
model.name = model_name
return model
if model_name == 'LogisticRegression_two_class':
if model_name == "LogisticRegression_two_class":
from sklearn.linear_model import LogisticRegression
return set_name(SKLearnModel(LogisticRegression(C=10, random_state=1, solver='liblinear', max_iter=1000)))
elif model_name == 'LogisticRegression_three_class':
return set_name(
SKLearnModel(
LogisticRegression(
C=10, random_state=1, solver="liblinear", max_iter=1000
)
)
)
elif model_name == "LogisticRegression_three_class":
from sklearnex.linear_model import LogisticRegression as LogisticRegression_EX
return set_name(SKLearnModel(LogisticRegression_EX(C=10, random_state=1, max_iter=1000, n_jobs=-1)))
elif model_name == 'LDA':
return set_name(
SKLearnModel(
LogisticRegression_EX(C=10, random_state=1, max_iter=1000, n_jobs=-1)
)
)
elif model_name == "LDA":
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
return set_name(SKLearnModel(LinearDiscriminantAnalysis()))
elif model_name == 'KNN':
elif model_name == "KNN":
from sklearn.neighbors import KNeighborsClassifier
return set_name(SKLearnModel(KNeighborsClassifier()))
elif model_name == 'CART':
elif model_name == "CART":
from sklearn.tree import DecisionTreeClassifier
return set_name(SKLearnModel(DecisionTreeClassifier(max_depth=15, random_state=1)))
elif model_name == 'NB':
return set_name(
SKLearnModel(DecisionTreeClassifier(max_depth=15, random_state=1))
)
elif model_name == "NB":
from sklearn.naive_bayes import GaussianNB
return set_name(SKLearnModel(GaussianNB()))
elif model_name == 'AB':
elif model_name == "AB":
from sklearn.ensemble import AdaBoostClassifier
return set_name(SKLearnModel(AdaBoostClassifier(n_estimators=15)))
elif model_name == 'RFC':
return set_name(SKLearnModel(RandomForestClassifier(n_jobs=-1, max_depth=20, random_state=1)))
elif model_name == 'SVC':
elif model_name == "RFC":
return set_name(
SKLearnModel(
RandomForestClassifier(n_jobs=-1, max_depth=20, random_state=1)
)
)
elif model_name == "SVC":
from sklearn.svm import SVC
return set_name(SKLearnModel(SVC(kernel='rbf', C=1e3, probability=True, random_state=1)))
return set_name(
SKLearnModel(SVC(kernel="rbf", C=1e3, probability=True, random_state=1))
)
# elif model_name == 'XGB_two_class':
# from xgboost import XGBClassifier
# from models.xgboost import XGBoostModel
# return set_name(XGBoostModel(XGBClassifier(n_jobs=-1, max_depth = 20, random_state=1, objective='binary:logistic', use_label_encoder= False, eval_metric='mlogloss')))
elif model_name == 'LGBM':
elif model_name == "LGBM":
from lightgbm import LGBMClassifier
return set_name(SKLearnModel(LGBMClassifier(n_jobs=-1, max_depth=20, random_state=1)))
elif model_name == 'StaticMom':
return set_name(
SKLearnModel(LGBMClassifier(n_jobs=-1, max_depth=20, random_state=1))
)
elif model_name == "StaticMom":
from models.momentum import StaticMomentumModel
return set_name(StaticMomentumModel(allow_short=True))
else:
raise Exception(f'Model {model_name} not found')
raise Exception(f"Model {model_name} not found")