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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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transformations/base.py
+4 -4
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@@ -3,14 +3,16 @@ from typing import Literal, Optional, Union
from abc import ABC, abstractmethod
import pandas as pd
class Transformation(ABC):
class Transformation(ABC):
@abstractmethod
def fit(self, X: pd.DataFrame, y: Optional[pd.Series]) -> None:
raise NotImplementedError
@abstractmethod
def fit_transform(self, X: pd.DataFrame, y: Optional[pd.Series] = None) -> pd.DataFrame:
def fit_transform(
self, X: pd.DataFrame, y: Optional[pd.Series] = None
) -> pd.DataFrame:
raise NotImplementedError
@abstractmethod
@@ -24,5 +26,3 @@ class Transformation(ABC):
@abstractmethod
def get_name(self) -> str:
raise NotImplementedError
transformations/pca.py
+13 -11
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@@ -5,6 +5,7 @@ from copy import deepcopy
from sklearn.decomposition import PCA
import pandas as pd
class PCATransformation(Transformation):
pca: PCA
@@ -14,16 +15,23 @@ class PCATransformation(Transformation):
self.sliding_window_size = sliding_window_size
def fit(self, X: pd.DataFrame, y: Optional[pd.Series] = None) -> None:
self.pca = PCA(n_components = min(int(len(X.columns) * self.ratio_components_to_keep), self.sliding_window_size))
self.pca = PCA(
n_components=min(
int(len(X.columns) * self.ratio_components_to_keep),
self.sliding_window_size,
)
)
self.pca.fit(X, y)
def fit_transform(self, X: pd.DataFrame, y: Optional[pd.Series] = None) -> pd.DataFrame:
def fit_transform(
self, X: pd.DataFrame, y: Optional[pd.Series] = None
) -> pd.DataFrame:
self.fit(X, y)
return self.transform(X)
def transform(self, X: pd.DataFrame) -> pd.DataFrame:
X = pd.DataFrame(self.pca.transform(X), index = X.index)
X.columns = ['PCA_' + str(i) for i in range(1, len(X.columns)+1)]
X = pd.DataFrame(self.pca.transform(X), index=X.index)
X.columns = ["PCA_" + str(i) for i in range(1, len(X.columns) + 1)]
return X
def clone(self) -> PCATransformation:
@@ -31,9 +39,3 @@ class PCATransformation(Transformation):
def get_name(self) -> str:
return "PCA"
transformations/rfe.py
+13 -13
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@@ -6,37 +6,37 @@ from sklearn.feature_selection import RFE
import pandas as pd
from models.sklearn import SKLearnModel
class RFETransformation(Transformation):
rfe: RFE
n_feature_to_select: int
def __init__(self, n_feature_to_select: int, model: SKLearnModel, step = 0.1):
def __init__(self, n_feature_to_select: int, model: SKLearnModel, step=0.1):
self.n_feature_to_keep = n_feature_to_select
self.model = model
self.rfe = RFE(model, n_features_to_select= n_feature_to_select, step=step)
self.rfe = RFE(model, n_features_to_select=n_feature_to_select, step=step)
def fit(self, X: pd.DataFrame, y: Optional[pd.Series] = None) -> None:
if self.rfe is None: return
if self.rfe is None:
return
self.rfe.fit(X, y)
def fit_transform(self, X: pd.DataFrame, y: Optional[pd.Series] = None) -> pd.DataFrame:
if self.rfe is None: return X
def fit_transform(
self, X: pd.DataFrame, y: Optional[pd.Series] = None
) -> pd.DataFrame:
if self.rfe is None:
return X
self.fit(X, y)
return self.transform(X)
def transform(self, X: pd.DataFrame) -> pd.DataFrame:
if self.rfe is None: return X
return pd.DataFrame(X[X.columns[self.rfe.support_]], index= X.index)
if self.rfe is None:
return X
return pd.DataFrame(X[X.columns[self.rfe.support_]], index=X.index)
def clone(self) -> RFETransformation:
return deepcopy(self)
def get_name(self) -> str:
return "RFE"
transformations/scaler.py
+7 -6
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@@ -2,14 +2,15 @@ from sklearn.preprocessing import MinMaxScaler, Normalizer, StandardScaler
from .sklearn import SKLearnTransformation
from typing import Literal
ScalerTypes = Literal['normalize', 'minmax', 'standardize']
ScalerTypes = Literal["normalize", "minmax", "standardize"]
def get_scaler(type: ScalerTypes) -> SKLearnTransformation:
if type == 'normalize':
if type == "normalize":
return SKLearnTransformation(Normalizer())
elif type == 'minmax':
return SKLearnTransformation(MinMaxScaler(feature_range= (-1, 1)))
elif type == 'standardize':
elif type == "minmax":
return SKLearnTransformation(MinMaxScaler(feature_range=(-1, 1)))
elif type == "standardize":
return SKLearnTransformation(StandardScaler())
else:
raise Exception("Scaler type not supported")
raise Exception("Scaler type not supported")
transformations/sklearn.py
+5 -8
View File
@@ -4,6 +4,7 @@ from typing import Literal, Optional, Union
from sklearn.base import clone, BaseEstimator
import pandas as pd
class SKLearnTransformation(Transformation):
transformer: BaseEstimator
@@ -13,22 +14,18 @@ class SKLearnTransformation(Transformation):
def fit(self, X: pd.DataFrame, y: Optional[pd.Series] = None) -> None:
self.transformer.fit(X, y)
def fit_transform(self, X: pd.DataFrame, y: Optional[pd.Series]) -> pd.DataFrame:
self.fit(X, y)
return self.transform(X)
def transform(self, X: pd.DataFrame) -> pd.DataFrame:
return pd.DataFrame(self.transformer.transform(X), index = X.index, columns = X.columns)
return pd.DataFrame(
self.transformer.transform(X), index=X.index, columns=X.columns
)
def clone(self) -> SKLearnTransformation:
return SKLearnTransformation(clone(self.transformer))
def get_name(self) -> str:
return self.transformer.__class__.__name__