PEP8 and examples resolution

Author: Guillaume Lemaitre

doc/api.rst

@@ -19,11 +19,12 @@ Classes
 
 .. autosummary::
    :toctree: generated/
-   
+
    under_sampling.ClusterCentroids
    under_sampling.CondensedNearestNeighbour
    under_sampling.EditedNearestNeighbours
    under_sampling.RepeatedEditedNearestNeighbours
+   under_sampling.AllKNN
    under_sampling.InstanceHardnessThreshold
    under_sampling.NearMiss
    under_sampling.NeighbourhoodCleaningRule
@@ -47,7 +48,7 @@ Classes
 
 .. autosummary::
    :toctree: generated/
-   
+
    over_sampling.ADASYN
    over_sampling.RandomOverSampler
    over_sampling.SMOTE
@@ -68,7 +69,7 @@ Classes
 
 .. autosummary::
    :toctree: generated/
-   
+
    combine.SMOTEENN
    combine.SMOTETomek
 
@@ -88,7 +89,7 @@ Classes
 
 .. autosummary::
    :toctree: generated/
-   
+
    ensemble.BalanceCascade
    ensemble.EasyEnsemble
 
@@ -108,14 +109,14 @@ Classes
 -------
 .. autosummary::
    :toctree: generated/
-   
+
    pipeline.Pipeline
 
 Functions
 ---------
 .. autosummary::
    :toctree: generated/
-   
+
    pipeline.make_pipeline
 
 
@@ -134,5 +135,5 @@ Functions
 ---------
 .. autosummary::
    :toctree: generated/
-   
+
    datasets.make_imbalance

examples/combine/plot_smote_enn.py

@@ -7,23 +7,22 @@
 
 """
 
-print(__doc__)
-
 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
 
+print(__doc__)
+
 sns.set()
 
 # Define some color for the plotting
 almost_black = '#262626'
 palette = sns.color_palette()
 
 
-
 # Generate the dataset
 X, y = make_classification(n_classes=2, class_sep=2, weights=[0.1, 0.9],
                            n_informative=3, n_redundant=1, flip_y=0,

examples/combine/plot_smote_tomek.py

@@ -7,23 +7,22 @@
 
 """
 
-print(__doc__)
-
 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
 
+print(__doc__)
+
 sns.set()
 
 # Define some color for the plotting
 almost_black = '#262626'
 palette = sns.color_palette()
 
 
-
 # Generate the dataset
 X, y = make_classification(n_classes=2, class_sep=2, weights=[0.1, 0.9],
                            n_informative=3, n_redundant=1, flip_y=0,

examples/datasets/plot_make_imbalance.py

@@ -7,22 +7,21 @@
 
 """
 
-print(__doc__)
-
 import matplotlib.pyplot as plt
 import seaborn as sns
 from sklearn.datasets import make_moons
 
 from imblearn.datasets import make_imbalance
 
+print(__doc__)
+
 sns.set()
 
 # Define some color for the plotting
 almost_black = '#262626'
 palette = sns.color_palette()
 
 
-
 # Generate the dataset
 X, y = make_moons(n_samples=200, shuffle=True, noise=0.5, random_state=10)
 
@@ -32,11 +31,11 @@
 axs = [a for ax in axs for a in ax]
 
 axs[0].scatter(X[y == 0, 0], X[y == 0, 1], label="Class #0",
-            alpha=0.5, edgecolor=almost_black, facecolor=palette[0],
-            linewidth=0.15)
+               alpha=0.5, edgecolor=almost_black, facecolor=palette[0],
+               linewidth=0.15)
 axs[0].scatter(X[y == 1, 0], X[y == 1, 1], label="Class #1",
-           alpha=0.5, edgecolor=almost_black, facecolor=palette[2],
-           linewidth=0.15)
+               alpha=0.5, edgecolor=almost_black, facecolor=palette[2],
+               linewidth=0.15)
 axs[0].set_title('Original set')
 
 ratios = [0.9, 0.75, 0.5, 0.25, 0.1]
@@ -46,8 +45,8 @@
     X_, y_ = make_imbalance(X, y, ratio=ratio, min_c_=1)
 
     ax.scatter(X_[y_ == 0, 0], X_[y_ == 0, 1], label="Class #0",
-                alpha=0.5, edgecolor=almost_black, facecolor=palette[0],
-                linewidth=0.15)
+               alpha=0.5, edgecolor=almost_black, facecolor=palette[0],
+               linewidth=0.15)
     ax.scatter(X_[y_ == 1, 0], X_[y_ == 1, 1], label="Class #1",
                alpha=0.5, edgecolor=almost_black, facecolor=palette[2],
                linewidth=0.15)

examples/ensemble/plot_balance_cascade.py

@@ -7,8 +7,6 @@
 
 """
 
-print(__doc__)
-
 import matplotlib.pyplot as plt
 import numpy as np
 import seaborn as sns
@@ -17,14 +15,15 @@
 
 from imblearn.ensemble import BalanceCascade
 
+print(__doc__)
+
 sns.set()
 
 # Define some color for the plotting
 almost_black = '#262626'
 palette = sns.color_palette()
 
 
-
 # Generate the dataset
 X, y = make_classification(n_classes=2, class_sep=2, weights=[0.1, 0.9],
                            n_informative=3, n_redundant=1, flip_y=0,

examples/ensemble/plot_easy_ensemble.py

@@ -7,8 +7,6 @@
 
 """
 
-print(__doc__)
-
 import matplotlib.pyplot as plt
 import numpy as np
 import seaborn as sns
@@ -17,14 +15,15 @@
 
 from imblearn.ensemble import EasyEnsemble
 
+print(__doc__)
+
 sns.set()
 
 # Define some color for the plotting
 almost_black = '#262626'
 palette = sns.color_palette()
 
 
-
 # Generate the dataset
 X, y = make_classification(n_classes=2, class_sep=2, weights=[0.1, 0.9],
                            n_informative=3, n_redundant=1, flip_y=0,

examples/over-sampling/plot_adasyn.py

@@ -8,23 +8,22 @@
 
 """
 
-print(__doc__)
-
 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
 
+print(__doc__)
+
 sns.set()
 
 # Define some color for the plotting
 almost_black = '#262626'
 palette = sns.color_palette()
 
 
-
 # Generate the dataset
 X, y = make_classification(n_classes=2, class_sep=2, weights=[0.1, 0.9],
                            n_informative=3, n_redundant=1, flip_y=0,

examples/over-sampling/plot_over_sampling_benchmark_3nn.py

@@ -1,14 +1,15 @@
 """
-=============================================================
+==========================================================
 Benchmark over-sampling methods in a face regognition task
-=============================================================
-In this face recognition example two faces are used from the LFW 
-(Faces in the Wild) dataset. Several implemented over-sampling 
-methods are used in conjunction with a 3NN classifier in order 
-to examine the improvement of the classifier's output quality 
+==========================================================
+
+In this face recognition example two faces are used from the LFW
+(Faces in the Wild) dataset. Several implemented over-sampling
+methods are used in conjunction with a 3NN classifier in order
+to examine the improvement of the classifier's output quality
 by using an over-sampler.
+
 """
-print(__doc__)
 
 import matplotlib.pyplot as plt
 import numpy as np
@@ -20,6 +21,8 @@
 from imblearn.over_sampling import ADASYN, SMOTE, RandomOverSampler
 from imblearn.pipeline import make_pipeline
 
+print(__doc__)
+
 LW = 2
 RANDOM_STATE = 42
 

examples/over-sampling/plot_random_over_sampling.py

@@ -7,23 +7,22 @@
 
 """
 
-print(__doc__)
-
 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
 
+print(__doc__)
+
 sns.set()
 
 # Define some color for the plotting
 almost_black = '#262626'
 palette = sns.color_palette()
 
 
-
 # Generate the dataset
 X, y = make_classification(n_classes=2, class_sep=2, weights=[0.1, 0.9],
                            n_informative=3, n_redundant=1, flip_y=0,

examples/over-sampling/plot_smote.py

@@ -7,23 +7,22 @@
 
 """
 
-print(__doc__)
-
 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
 
+print(__doc__)
+
 sns.set()
 
 # Define some color for the plotting
 almost_black = '#262626'
 palette = sns.color_palette()
 
 
-
 # Generate the dataset
 X, y = make_classification(n_classes=2, class_sep=2, weights=[0.1, 0.9],
                            n_informative=3, n_redundant=1, flip_y=0,