isort

examples/boston_housing.py

@@ -1,16 +1,14 @@
 #!/usr/bin/env python
 
-from sklearn.datasets import load_boston
-from sklearn.preprocessing import StandardScaler
-from sklearn.preprocessing import scale
-from sklearn.model_selection import train_test_split
-from sklearn.metrics import mean_squared_error
 import matplotlib.pyplot as plt
 import numpy as np
+from sklearn.datasets import load_boston
+from sklearn.metrics import mean_squared_error
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import StandardScaler, scale
 
 from lassonet import LassoNetRegressor
 
-
 dataset = load_boston()
 X = dataset.data
 y = dataset.target

examples/boston_housing_group.py

@@ -1,16 +1,14 @@
 #!/usr/bin/env python
 
-from sklearn.datasets import load_boston
-from sklearn.preprocessing import StandardScaler
-from sklearn.preprocessing import scale
-from sklearn.model_selection import train_test_split
-from sklearn.metrics import mean_squared_error
 import matplotlib.pyplot as plt
 import numpy as np
+from sklearn.datasets import load_boston
+from sklearn.metrics import mean_squared_error
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import StandardScaler, scale
 
 from lassonet import LassoNetRegressor
 
-
 dataset = load_boston()
 X = dataset.data
 y = dataset.target

examples/cox_experiments.py

@@ -8,22 +8,18 @@
 
 from pathlib import Path
 from time import time
-from matplotlib import pyplot as plt
+
 import numpy as np
 import pandas as pd
-from tqdm import tqdm
-
-from sklearn import preprocessing
-from sklearn.model_selection import train_test_split, StratifiedKFold
-
 import sksurv.datasets
-
-from lassonet import LassoNetCoxRegressorCV
-
 from joblib import Parallel, delayed
+from matplotlib import pyplot as plt
+from sklearn import preprocessing
+from sklearn.model_selection import StratifiedKFold, train_test_split
+from tqdm import tqdm
 from tqdm_joblib import tqdm_joblib
 
-from lassonet import plot_cv
+from lassonet import LassoNetCoxRegressorCV, plot_cv
 from lassonet.utils import confidence_interval
 
 DATA_PATH = Path(__file__).parent / "data"

examples/cox_regression.py

@@ -1,9 +1,9 @@
 #!/usr/bin/env python
 from pathlib import Path
 
+import matplotlib.pyplot as plt
 import numpy as np
 from sklearn.model_selection import train_test_split
-import matplotlib.pyplot as plt
 
 from lassonet import LassoNetCoxRegressor, plot_path
 

examples/diabetes.py

@@ -1,15 +1,13 @@
 #!/usr/bin/env python
 
-from sklearn.datasets import load_diabetes
-from sklearn.preprocessing import StandardScaler
-from sklearn.preprocessing import scale
-from sklearn.model_selection import train_test_split
 import matplotlib.pyplot as plt
 import numpy as np
+from sklearn.datasets import load_diabetes
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import StandardScaler, scale
 
 from lassonet import LassoNetRegressor, plot_path
 
-
 dataset = load_diabetes()
 X = dataset.data
 y = dataset.target

examples/friedman.py

@@ -1,9 +1,8 @@
+import matplotlib.pyplot as plt
 import numpy as np
-from sklearn.preprocessing import StandardScaler
-from sklearn.model_selection import train_test_split
 from sklearn.metrics import r2_score
-import matplotlib.pyplot as plt
-
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import StandardScaler
 
 from lassonet import LassoNetRegressor, plot_path
 

examples/friedman/main.py

@@ -1,8 +1,7 @@
+import matplotlib.pyplot as plt
 import numpy as np
-from sklearn.preprocessing import StandardScaler
 from sklearn.metrics import r2_score
-import matplotlib.pyplot as plt
-
+from sklearn.preprocessing import StandardScaler
 
 from lassonet import LassoNetRegressor, plot_path
 

examples/generated.py

@@ -1,10 +1,9 @@
 from functools import partial
 
 import numpy as np
-from sklearn.preprocessing import StandardScaler
-from sklearn.model_selection import train_test_split
 from sklearn.metrics import r2_score
-
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import StandardScaler
 
 from lassonet import LassoNetRegressor, plot_path
 

examples/miceprotein.py

@@ -8,15 +8,14 @@
 Each feature is the expression level of one protein.
 """
 
+import matplotlib.pyplot as plt
 from sklearn.datasets import fetch_openml
 from sklearn.impute import SimpleImputer
-from sklearn.preprocessing import StandardScaler, LabelEncoder
 from sklearn.model_selection import train_test_split
-import matplotlib.pyplot as plt
+from sklearn.preprocessing import LabelEncoder, StandardScaler
 
-from lassonet import LassoNetClassifier, plot_path
+from lassonet import LassoNetClassifier, plot_cv, plot_path
 from lassonet.interfaces import LassoNetClassifierCV
-from lassonet import plot_cv
 
 X, y = fetch_openml(name="miceprotein", return_X_y=True)
 # Fill missing values with the mean

examples/mnist_ae.py

@@ -1,7 +1,8 @@
+import matplotlib.pyplot as plt
 from sklearn.datasets import fetch_openml
-from lassonet import LassoNetAutoEncoder
 from sklearn.metrics import mean_squared_error
-import matplotlib.pyplot as plt
+
+from lassonet import LassoNetAutoEncoder
 
 X, y = fetch_openml(name="mnist_784", return_X_y=True)
 filter = y == "3"

examples/mnist_classif.py

@@ -1,8 +1,8 @@
+import matplotlib.pyplot as plt
 from sklearn.datasets import fetch_openml
-from sklearn.preprocessing import LabelEncoder
-from sklearn.model_selection import train_test_split
 from sklearn.metrics import accuracy_score
-import matplotlib.pyplot as plt
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import LabelEncoder
 
 from lassonet import LassoNetClassifier
 

examples/mnist_reconstruction.py

@@ -1,7 +1,8 @@
+import matplotlib.pyplot as plt
 from sklearn.datasets import fetch_openml
-from lassonet import LassoNetRegressor
 from sklearn.metrics import mean_squared_error
-import matplotlib.pyplot as plt
+
+from lassonet import LassoNetRegressor
 
 X, y = fetch_openml(name="mnist_784", return_X_y=True)
 filter = y == "3"

experiments/data_utils.py

@@ -1,17 +1,15 @@
 import pickle
+from collections import defaultdict
 from os.path import join
 from pathlib import Path
-from sklearn.model_selection import train_test_split
-from collections import defaultdict
+
 import numpy as np
+from sklearn.model_selection import train_test_split
 from sklearn.preprocessing import MinMaxScaler
 
-
 # The code to load some of these datasets is reproduced from
 # https://github.com/mfbalin/Concrete-Autoencoders/blob/master/experiments/generate_comparison_figures.py
 
-import numpy as np
-from sklearn.preprocessing import MinMaxScaler
 
 
 def load_mice(one_hot=False):
@@ -181,9 +179,10 @@ def load_epileptic():
     return (X[:8000], Y[:8000]), (X[8000:], Y[8000:])
 
 
-from PIL import Image
 import os
 
+from PIL import Image
+
 
 def load_coil():
     samples = []
@@ -294,6 +293,7 @@ def load_mnist_two_digits(digit1, digit2):
 
 
 import os
+
 from sklearn.preprocessing import MinMaxScaler
 
 

lassonet/__init__.py

@@ -1,13 +1,13 @@
 # flake8: noqa
-from .model import LassoNet
-from .prox import prox
 from .interfaces import (
     LassoNetClassifier,
-    LassoNetRegressor,
-    LassoNetCoxRegressor,
     LassoNetClassifierCV,
-    LassoNetRegressorCV,
+    LassoNetCoxRegressor,
     LassoNetCoxRegressorCV,
+    LassoNetRegressor,
+    LassoNetRegressorCV,
     lassonet_path,
 )
-from .plot import plot_path, plot_cv
+from .model import LassoNet
+from .plot import plot_cv, plot_path
+from .prox import prox

lassonet/model.py

@@ -4,7 +4,7 @@
 from torch import nn
 from torch.nn import functional as F
 
-from .prox import inplace_prox, inplace_group_prox, prox
+from .prox import inplace_group_prox, inplace_prox, prox
 
 
 class LassoNet(nn.Module):

lassonet/plot.py

@@ -1,6 +1,6 @@
 import matplotlib.pyplot as plt
-from .interfaces import BaseLassoNetCV
 
+from .interfaces import BaseLassoNetCV
 from .utils import confidence_interval, eval_on_path
 
 

lassonet/r.py

@@ -1,11 +1,10 @@
 from dataclasses import asdict
-import torch
-from .interfaces import (
-    lassonet_path as _lassonet_path,
-    LassoNetClassifier,
-    LassoNetRegressor,
-)
+
 import numpy as np
+import torch
+
+from .interfaces import LassoNetClassifier, LassoNetRegressor
+from .interfaces import lassonet_path as _lassonet_path
 
 
 def make_writable(x):

tests/test_interface.py

@@ -1,7 +1,7 @@
-from lassonet import LassoNetRegressor, LassoNetClassifier
-
 from sklearn.datasets import load_diabetes, load_digits
 
+from lassonet import LassoNetClassifier, LassoNetRegressor
+
 
 def test_regressor():
     X, y = load_diabetes(return_X_y=True)