[MRG] DOC: Fix spelling in documentation (#432)

Author: gnsiva

.gitignore

@@ -42,6 +42,7 @@ htmlcov/
 nosetests.xml
 coverage.xml
 *,cover
+.pytest_cache/
 
 # Translations
 *.mo
@@ -66,4 +67,7 @@ target/
 *.sln
 *.pyproj
 *.suo
-*.vs
+*.vs
+
+# PyCharm
+.idea/

imblearn/combine/smote_enn.py

@@ -50,7 +50,7 @@ class SMOTEENN(SamplerMixin):
     -----
     The method is presented in [1]_.
 
-    Supports mutli-class resampling. Refer to SMOTE and ENN regarding the
+    Supports multi-class resampling. Refer to SMOTE and ENN regarding the
     scheme which used.
 
     See :ref:`sphx_glr_auto_examples_combine_plot_smote_enn.py` and

imblearn/combine/smote_tomek.py

@@ -57,7 +57,7 @@ class SMOTETomek(SamplerMixin):
     -----
     The methos is presented in [1]_.
 
-    Supports mutli-class resampling. Refer to SMOTE and TomekLinks regarding
+    Supports multi-class resampling. Refer to SMOTE and TomekLinks regarding
     the scheme which used.
 
     See :ref:`sphx_glr_auto_examples_combine_plot_smote_tomek.py` and

imblearn/ensemble/balance_cascade.py

@@ -63,7 +63,7 @@ class BalanceCascade(BaseEnsembleSampler):
     -----
     The method is described in [1]_.
 
-    Supports mutli-class resampling. A one-vs.-rest scheme is used as
+    Supports multi-class resampling. A one-vs.-rest scheme is used as
     originally proposed in [1]_.
 
     See :ref:`sphx_glr_auto_examples_ensemble_plot_balance_cascade.py`.

imblearn/ensemble/easy_ensemble.py

@@ -53,7 +53,7 @@ class EasyEnsemble(BaseEnsembleSampler):
     -----
     The method is described in [1]_.
 
-    Supports mutli-class resampling by sampling each class independently.
+    Supports multi-class resampling by sampling each class independently.
 
     See :ref:`sphx_glr_auto_examples_ensemble_plot_easy_ensemble.py`.
 

imblearn/over_sampling/adasyn.py

@@ -50,7 +50,7 @@ class ADASYN(BaseOverSampler):
     -----
     The implementation is based on [1]_.
 
-    Supports mutli-class resampling. A one-vs.-rest scheme is used.
+    Supports multi-class resampling. A one-vs.-rest scheme is used.
 
     See
     :ref:`sphx_glr_auto_examples_applications_plot_over_sampling_benchmark_lfw.py`,

imblearn/over_sampling/random_over_sampler.py

@@ -39,7 +39,7 @@ class RandomOverSampler(BaseOverSampler):
 
     Notes
     -----
-    Supports mutli-class resampling by sampling each class independently.
+    Supports multi-class resampling by sampling each class independently.
 
     See
     :ref:`sphx_glr_auto_examples_over-sampling_plot_comparison_over_sampling.py`,

imblearn/over_sampling/smote.py

@@ -77,7 +77,7 @@ class SMOTE(BaseOverSampler):
     -----
     See the original papers: [1]_, [2]_, [3]_ for more details.
 
-    Supports mutli-class resampling. A one-vs.-rest scheme is used as
+    Supports multi-class resampling. A one-vs.-rest scheme is used as
     originally proposed in [1]_.
 
     See

imblearn/under_sampling/prototype_generation/cluster_centroids.py

@@ -69,7 +69,7 @@ class ClusterCentroids(BaseUnderSampler):
 
     Notes
     -----
-    Supports mutli-class resampling by sampling each class independently.
+    Supports multi-class resampling by sampling each class independently.
 
     See :ref:`sphx_glr_auto_examples_under-sampling_plot_cluster_centroids.py`.
 

imblearn/under_sampling/prototype_selection/condensed_nearest_neighbour.py

@@ -62,7 +62,7 @@ class CondensedNearestNeighbour(BaseCleaningSampler):
     -----
     The method is based on [1]_.
 
-    Supports mutli-class resampling. A one-vs.-rest scheme is used when
+    Supports multi-class resampling. A one-vs.-rest scheme is used when
     sampling a class as proposed in [1]_.
 
     See

imblearn/under_sampling/prototype_selection/edited_nearest_neighbours.py

@@ -72,7 +72,7 @@ class EditedNearestNeighbours(BaseCleaningSampler):
     -----
     The method is based on [1]_.
 
-    Supports mutli-class resampling. A one-vs.-rest scheme is used when
+    Supports multi-class resampling. A one-vs.-rest scheme is used when
     sampling a class as proposed in [1]_.
 
     See
@@ -252,7 +252,7 @@ class RepeatedEditedNearestNeighbours(BaseCleaningSampler):
     The method is based on [1]_. A one-vs.-rest scheme is used when
     sampling a class as proposed in [1]_.
 
-    Supports mutli-class resampling.
+    Supports multi-class resampling.
 
     See
     :ref:`sphx_glr_auto_examples_pipeline_plot_pipeline_classification.py` and
@@ -464,7 +464,7 @@ class without early stopping.
     -----
     The method is based on [1]_.
 
-    Supports mutli-class resampling. A one-vs.-rest scheme is used when
+    Supports multi-class resampling. A one-vs.-rest scheme is used when
     sampling a class as proposed in [1]_.
 
     See :ref:`sphx_glr_auto_examples_under-sampling_plot_enn_renn_allknn.py`.

imblearn/under_sampling/prototype_selection/instance_hardness_threshold.py

@@ -65,7 +65,7 @@ class InstanceHardnessThreshold(BaseCleaningSampler):
     -----
     The method is based on [1]_.
 
-    Supports mutli-class resampling. A one-vs.-rest scheme is used when
+    Supports multi-class resampling. A one-vs.-rest scheme is used when
     sampling a class as proposed in [1]_.
 
     See

imblearn/under_sampling/prototype_selection/nearmiss.py

@@ -71,7 +71,7 @@ class NearMiss(BaseUnderSampler):
     -----
     The methods are based on [1]_.
 
-    Supports mutli-class resampling.
+    Supports multi-class resampling.
 
     See
     :ref:`sphx_glr_auto_examples_applications_plot_multi_class_under_sampling.py`

imblearn/under_sampling/prototype_selection/neighbourhood_cleaning_rule.py

@@ -72,7 +72,7 @@ class NeighbourhoodCleaningRule(BaseCleaningSampler):
     -----
     See the original paper: [1]_.
 
-    Supports mutli-class resampling. A one-vs.-rest scheme is used when
+    Supports multi-class resampling. A one-vs.-rest scheme is used when
     sampling a class as proposed in [1]_.
 
     See

imblearn/under_sampling/prototype_selection/one_sided_selection.py

@@ -58,7 +58,7 @@ class OneSidedSelection(BaseCleaningSampler):
     -----
     The method is based on [1]_.
 
-    Supports mutli-class resampling. A one-vs.-one scheme is used when sampling
+    Supports multi-class resampling. A one-vs.-one scheme is used when sampling
     a class as proposed in [1]_. For each class to be sampled, all samples of
     this class and the minority class are used during the sampling procedure.
 

imblearn/under_sampling/prototype_selection/random_under_sampler.py

@@ -45,7 +45,7 @@ class RandomUnderSampler(BaseUnderSampler):
 
     Notes
     -----
-    Supports mutli-class resampling by sampling each class independently.
+    Supports multi-class resampling by sampling each class independently.
 
     See
     :ref:`sphx_glr_auto_examples_plot_sampling_strategy_usage.py` and

imblearn/under_sampling/prototype_selection/tomek_links.py

@@ -50,7 +50,7 @@ class TomekLinks(BaseCleaningSampler):
     -----
     This method is based on [1]_.
 
-    Supports mutli-class resampling. A one-vs.-rest scheme is used as
+    Supports multi-class resampling. A one-vs.-rest scheme is used as
     originally proposed in [1]_.
 
     See