Merge pull request #163 from chkoar/roi

[MRG + 1] Reorganize imports

Author: dvro

doc/conf.py

@@ -12,8 +12,8 @@
 # All configuration values have a default; values that are commented out
 # serve to show the default.
 
-import sys
 import os
+import sys
 
 import sphinx_rtd_theme
 

examples/combine/plot_smote_enn.py

@@ -11,16 +11,18 @@
 
 import matplotlib.pyplot as plt
 import seaborn as sns
+from sklearn.datasets import make_classification
+from sklearn.decomposition import PCA
+
+from imblearn.combine import SMOTEENN
+
 sns.set()
 
 # Define some color for the plotting
 almost_black = '#262626'
 palette = sns.color_palette()
 
-from sklearn.datasets import make_classification
-from sklearn.decomposition import PCA
 
-from imblearn.combine import SMOTEENN
 
 # Generate the dataset
 X, y = make_classification(n_classes=2, class_sep=2, weights=[0.1, 0.9],

examples/combine/plot_smote_tomek.py

@@ -11,16 +11,18 @@
 
 import matplotlib.pyplot as plt
 import seaborn as sns
+from sklearn.datasets import make_classification
+from sklearn.decomposition import PCA
+
+from imblearn.combine import SMOTETomek
+
 sns.set()
 
 # Define some color for the plotting
 almost_black = '#262626'
 palette = sns.color_palette()
 
-from sklearn.datasets import make_classification
-from sklearn.decomposition import PCA
 
-from imblearn.combine import SMOTETomek
 
 # Generate the dataset
 X, y = make_classification(n_classes=2, class_sep=2, weights=[0.1, 0.9],

examples/datasets/plot_make_imbalance.py

@@ -11,14 +11,16 @@
 
 import matplotlib.pyplot as plt
 import seaborn as sns
+from sklearn.datasets import make_moons
+
+from imblearn.datasets import make_imbalance
+
 sns.set()
 
 # Define some color for the plotting
 almost_black = '#262626'
 palette = sns.color_palette()
 
-from sklearn.datasets import make_moons
-from imblearn.datasets import make_imbalance
 
 
 # Generate the dataset

examples/ensemble/plot_balance_cascade.py

@@ -9,19 +9,21 @@
 
 print(__doc__)
 
-import numpy as np
 import matplotlib.pyplot as plt
+import numpy as np
 import seaborn as sns
+from sklearn.datasets import make_classification
+from sklearn.decomposition import PCA
+
+from imblearn.ensemble import BalanceCascade
+
 sns.set()
 
 # Define some color for the plotting
 almost_black = '#262626'
 palette = sns.color_palette()
 
-from sklearn.datasets import make_classification
-from sklearn.decomposition import PCA
 
-from imblearn.ensemble import BalanceCascade
 
 # Generate the dataset
 X, y = make_classification(n_classes=2, class_sep=2, weights=[0.1, 0.9],

examples/ensemble/plot_easy_ensemble.py

@@ -9,19 +9,21 @@
 
 print(__doc__)
 
-import numpy as np
 import matplotlib.pyplot as plt
+import numpy as np
 import seaborn as sns
+from sklearn.datasets import make_classification
+from sklearn.decomposition import PCA
+
+from imblearn.ensemble import EasyEnsemble
+
 sns.set()
 
 # Define some color for the plotting
 almost_black = '#262626'
 palette = sns.color_palette()
 
-from sklearn.datasets import make_classification
-from sklearn.decomposition import PCA
 
-from imblearn.ensemble import EasyEnsemble
 
 # Generate the dataset
 X, y = make_classification(n_classes=2, class_sep=2, weights=[0.1, 0.9],

examples/over-sampling/plot_adasyn.py

@@ -12,16 +12,18 @@
 
 import matplotlib.pyplot as plt
 import seaborn as sns
+from sklearn.datasets import make_classification
+from sklearn.decomposition import PCA
+
+from imblearn.over_sampling import ADASYN
+
 sns.set()
 
 # Define some color for the plotting
 almost_black = '#262626'
 palette = sns.color_palette()
 
-from sklearn.datasets import make_classification
-from sklearn.decomposition import PCA
 
-from imblearn.over_sampling import ADASYN
 
 # Generate the dataset
 X, y = make_classification(n_classes=2, class_sep=2, weights=[0.1, 0.9],

examples/over-sampling/plot_random_over_sampling.py

@@ -11,16 +11,18 @@
 
 import matplotlib.pyplot as plt
 import seaborn as sns
+from sklearn.datasets import make_classification
+from sklearn.decomposition import PCA
+
+from imblearn.over_sampling import RandomOverSampler
+
 sns.set()
 
 # Define some color for the plotting
 almost_black = '#262626'
 palette = sns.color_palette()
 
-from sklearn.datasets import make_classification
-from sklearn.decomposition import PCA
 
-from imblearn.over_sampling import RandomOverSampler
 
 # Generate the dataset
 X, y = make_classification(n_classes=2, class_sep=2, weights=[0.1, 0.9],

examples/over-sampling/plot_smote.py

@@ -11,16 +11,18 @@
 
 import matplotlib.pyplot as plt
 import seaborn as sns
+from sklearn.datasets import make_classification
+from sklearn.decomposition import PCA
+
+from imblearn.over_sampling import SMOTE
+
 sns.set()
 
 # Define some color for the plotting
 almost_black = '#262626'
 palette = sns.color_palette()
 
-from sklearn.datasets import make_classification
-from sklearn.decomposition import PCA
 
-from imblearn.over_sampling import SMOTE
 
 # Generate the dataset
 X, y = make_classification(n_classes=2, class_sep=2, weights=[0.1, 0.9],

examples/over-sampling/plot_smote_bordeline_1.py

@@ -11,16 +11,18 @@
 
 import matplotlib.pyplot as plt
 import seaborn as sns
+from sklearn.datasets import make_classification
+from sklearn.decomposition import PCA
+
+from imblearn.over_sampling import SMOTE
+
 sns.set()
 
 # Define some color for the plotting
 almost_black = '#262626'
 palette = sns.color_palette()
 
-from sklearn.datasets import make_classification
-from sklearn.decomposition import PCA
 
-from imblearn.over_sampling import SMOTE
 
 # Generate the dataset
 X, y = make_classification(n_classes=2, class_sep=2, weights=[0.1, 0.9],

examples/over-sampling/plot_smote_bordeline_2.py

@@ -11,16 +11,18 @@
 
 import matplotlib.pyplot as plt
 import seaborn as sns
+from sklearn.datasets import make_classification
+from sklearn.decomposition import PCA
+
+from imblearn.over_sampling import SMOTE
+
 sns.set()
 
 # Define some color for the plotting
 almost_black = '#262626'
 palette = sns.color_palette()
 
-from sklearn.datasets import make_classification
-from sklearn.decomposition import PCA
 
-from imblearn.over_sampling import SMOTE
 
 # Generate the dataset
 X, y = make_classification(n_classes=2, class_sep=2, weights=[0.1, 0.9],

examples/over-sampling/plot_smote_svm.py

@@ -11,16 +11,18 @@
 
 import matplotlib.pyplot as plt
 import seaborn as sns
+from sklearn.datasets import make_classification
+from sklearn.decomposition import PCA
+
+from imblearn.over_sampling import SMOTE
+
 sns.set()
 
 # Define some color for the plotting
 almost_black = '#262626'
 palette = sns.color_palette()
 
-from sklearn.datasets import make_classification
-from sklearn.decomposition import PCA
 
-from imblearn.over_sampling import SMOTE
 
 # Generate the dataset
 X, y = make_classification(n_classes=2, class_sep=2, weights=[0.1, 0.9],

examples/pipeline/plot_pipeline_classification.py

@@ -13,13 +13,12 @@
 from sklearn.cross_validation import train_test_split as tts
 from sklearn.datasets import make_classification
 from sklearn.decomposition import PCA
-from sklearn.neighbors import KNeighborsClassifier as KNN
 from sklearn.metrics import classification_report
-
+from sklearn.neighbors import KNeighborsClassifier as KNN
 
 from imblearn.pipeline import make_pipeline
-from imblearn.under_sampling import EditedNearestNeighbours
-from imblearn.under_sampling import RepeatedEditedNearestNeighbours
+from imblearn.under_sampling import (EditedNearestNeighbours,
+                                     RepeatedEditedNearestNeighbours)
 
 # Generate the dataset
 X, y = make_classification(n_classes=2, class_sep=1.25, weights=[0.3, 0.7],

examples/under-sampling/plot_allknn.py

@@ -11,18 +11,19 @@
 
 import matplotlib.pyplot as plt
 import seaborn as sns
+from sklearn.datasets import make_classification
+from sklearn.decomposition import PCA
+
+from imblearn.under_sampling import (AllKNN, EditedNearestNeighbours,
+                                     RepeatedEditedNearestNeighbours)
+
 sns.set()
 
 # Define some color for the plotting
 almost_black = '#262626'
 palette = sns.color_palette()
 
-from sklearn.datasets import make_classification
-from sklearn.decomposition import PCA
 
-from imblearn.under_sampling import EditedNearestNeighbours
-from imblearn.under_sampling import RepeatedEditedNearestNeighbours
-from imblearn.under_sampling import AllKNN
 
 # Generate the dataset
 X, y = make_classification(n_classes=2, class_sep=1.25, weights=[0.3, 0.7],

examples/under-sampling/plot_cluster_centroids.py

@@ -11,16 +11,18 @@
 
 import matplotlib.pyplot as plt
 import seaborn as sns
+from sklearn.datasets import make_classification
+from sklearn.decomposition import PCA
+
+from imblearn.under_sampling import ClusterCentroids
+
 sns.set()
 
 # Define some color for the plotting
 almost_black = '#262626'
 palette = sns.color_palette()
 
-from sklearn.datasets import make_classification
-from sklearn.decomposition import PCA
 
-from imblearn.under_sampling import ClusterCentroids
 
 # Generate the dataset
 X, y = make_classification(n_classes=2, class_sep=2, weights=[0.1, 0.9],

examples/under-sampling/plot_condensed_nearest_neighbour.py

@@ -11,16 +11,18 @@
 
 import matplotlib.pyplot as plt
 import seaborn as sns
+from sklearn.datasets import make_classification
+from sklearn.decomposition import PCA
+
+from imblearn.under_sampling import CondensedNearestNeighbour
+
 sns.set()
 
 # Define some color for the plotting
 almost_black = '#262626'
 palette = sns.color_palette()
 
-from sklearn.datasets import make_classification
-from sklearn.decomposition import PCA
 
-from imblearn.under_sampling import CondensedNearestNeighbour
 
 # Generate the dataset
 X, y = make_classification(n_classes=2, class_sep=2, weights=[0.1, 0.9],

examples/under-sampling/plot_edited_nearest_neighbours.py

@@ -11,16 +11,18 @@
 
 import matplotlib.pyplot as plt
 import seaborn as sns
+from sklearn.datasets import make_classification
+from sklearn.decomposition import PCA
+
+from imblearn.under_sampling import EditedNearestNeighbours
+
 sns.set()
 
 # Define some color for the plotting
 almost_black = '#262626'
 palette = sns.color_palette()
 
-from sklearn.datasets import make_classification
-from sklearn.decomposition import PCA
 
-from imblearn.under_sampling import EditedNearestNeighbours
 
 # Generate the dataset
 X, y = make_classification(n_classes=2, class_sep=2, weights=[0.1, 0.9],

examples/under-sampling/plot_instance_hardness_threshold.py

@@ -11,16 +11,18 @@
 
 import matplotlib.pyplot as plt
 import seaborn as sns
+from sklearn.datasets import make_classification
+from sklearn.decomposition import PCA
+
+from imblearn.under_sampling import InstanceHardnessThreshold
+
 sns.set()
 
 # Define some color for the plotting
 almost_black = '#262626'
 palette = sns.color_palette()
 
-from sklearn.datasets import make_classification
-from sklearn.decomposition import PCA
 
-from imblearn.under_sampling import InstanceHardnessThreshold
 
 # Generate the dataset
 X, y = make_classification(n_classes=2, class_sep=1., weights=[0.05, 0.95],