Change the package name

glemaitre · Jun 26, 2016 · b6e015e · b6e015e
1 parent 1c30fbe
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Showing 203 changed files with 109 additions and 105 deletions.
diff --git a/.coveragerc b/.coveragerc
@@ -2,8 +2,8 @@
 
 [run]
 branch = True
-source = unbalanced_dataset
-include = */unbalanced_dataset/*
+source = imblearn
+include = */imblearn/*
 omit =
     */setup.py
 

diff --git a/.travis.yml b/.travis.yml
@@ -23,7 +23,7 @@ env:
   global:
     # Directory where tests are run from
     - TEST_DIR=/tmp/test_dir
-    - MODULE=unbalanced_dataset
+    - MODULE=imblearn
     - OMP_NUM_THREADS=4
     - OPENBLAS_NUM_THREADS=4
   matrix:

diff --git a/Makefile b/Makefile
@@ -17,13 +17,13 @@ clean:
 	rm -rf examples/.ipynb_checkpoints
 
 test:
-	$(NOSETESTS) -s -v unbalanced_dataset
+	$(NOSETESTS) -s -v imblearn
 
 # doctest:
-# 	$(PYTHON) -c "import unbalanced_dataset, sys, io; sys.exit(unbalanced_dataset.doctest_verbose())"
+# 	$(PYTHON) -c "import imblearn, sys, io; sys.exit(imblearn.doctest_verbose())"
 
 coverage:
-	$(NOSETESTS) unbalanced_dataset -s -v --with-coverage --cover-package=unbalanced_dataset
+	$(NOSETESTS) imblearn -s -v --with-coverage --cover-package=imblearn
 
 html:
 	conda install -y sphinx sphinx_rtd_theme numpydoc

diff --git a/README.md b/README.md
@@ -1,7 +1,7 @@
-UnbalancedDataset
+imbalanced-learn
 =================
 
-UnbalancedDataset is a python package offering a number of re-sampling techniques commonly used in datasets showing strong between-class imbalance.
+imbalanced-learn is a python package offering a number of re-sampling techniques commonly used in datasets showing strong between-class imbalance.
 It is compatible with [scikit-learn](http://scikit-learn.org/stable/) and has been submitted to be part of [scikit-learn-contrib](https://github.com/scikit-learn-contrib) projects.
 
 [![Code Health](https://landscape.io/github/glemaitre/UnbalancedDataset/master/landscape.svg?style=flat)](https://landscape.io/github/glemaitre/UnbalancedDataset/master)
@@ -19,15 +19,15 @@ Installation
 
 ### Dependencies
 
-UnbalancedDataset is tested to work under Python 2.7 and Python 3.5.
+imbalanced-learn is tested to work under Python 2.7 and Python 3.5.
 
 * scipy(>=0.17.0)
 * numpy(>=1.10.4)
 * scikit-learn(>=0.17.1)
 
 ### Installation
 
-UnbalancedDataset is not currently available on the PyPi's reporitories, 
+imbalanced-learn is not currently available on the PyPi's reporitories, 
 however you can install it via `pip`:
 
     pip install git+https://github.com/fmfn/UnbalancedDataset
@@ -60,7 +60,7 @@ Re-sampling techniques are divided in two categories:
 	3. Combining over- and under-sampling.
 	4. Create ensemble balanced sets.
 
-Bellow is a list of the methods currently implemented in this module.
+Below is a list of the methods currently implemented in this module.
 
 * Under-sampling
     1. Random majority under-sampling with replacement

diff --git a/doc/api.rst b/doc/api.rst
@@ -1,21 +1,21 @@
-#################
-API Documentation
-#################
+######################
+`imbalanced-learn` API
+######################
 
-This is the full API documentation of the `unbalanced_dataset` toolbox.
+This is the full API documentation of the `imbalanced-learn` toolbox.
 
 .. _under_sampling_ref:
 
 Under-sampling methods
 ======================
 
-.. automodule:: unbalanced_dataset.under_sampling
+.. automodule:: imblearn.under_sampling
     :no-members:
     :no-inherited-members:
 
 Classes
 -------
-.. currentmodule:: unbalanced_dataset
+.. currentmodule:: imblearn
 
 .. autosummary::
    :toctree: generated/
@@ -37,13 +37,13 @@ Classes
 Over-sampling methods
 =====================
 
-.. automodule:: unbalanced_dataset.over_sampling
+.. automodule:: imblearn.over_sampling
     :no-members:
     :no-inherited-members:
 
 Classes
 -------
-.. currentmodule:: unbalanced_dataset
+.. currentmodule:: imblearn
 
 .. autosummary::
    :toctree: generated/
@@ -57,13 +57,13 @@ Classes
 Combination of over- and under-sampling methods
 ===============================================
 
-.. automodule:: unbalanced_dataset.combine
+.. automodule:: imblearn.combine
     :no-members:
     :no-inherited-members:
 
 Classes
 -------
-.. currentmodule:: unbalanced_dataset
+.. currentmodule:: imblearn
 
 .. autosummary::
    :toctree: generated/
@@ -77,13 +77,13 @@ Classes
 Ensemble methods
 ================
 
-.. automodule:: unbalanced_dataset.ensemble
+.. automodule:: imblearn.ensemble
     :no-members:
     :no-inherited-members:
 
 Classes
 -------
-.. currentmodule:: unbalanced_dataset
+.. currentmodule:: imblearn
 
 .. autosummary::
    :toctree: generated/
@@ -97,17 +97,21 @@ Classes
 Pipeline
 ========
 
-.. automodule:: unbalanced_dataset.pipeline
+.. automodule:: imblearn.pipeline
     :no-members:
     :no-inherited-members:
 
-.. currentmodule:: unbalanced_dataset
+.. currentmodule:: imblearn
 
+Classes
+-------
 .. autosummary::
    :toctree: generated/
 
    pipeline.Pipeline
 
+Functions
+---------
 .. autosummary::
    :toctree: generated/
 

diff --git a/doc/conf.py b/doc/conf.py
@@ -79,7 +79,7 @@
 master_doc = 'index'
 
 # General information about the project.
-project = u'unbalanced_dataset'
+project = u'imbalanced-learn'
 copyright = u'2016, Guillaume Lemaitre, Fernando Nogueira'
 
 # The version info for the project you're documenting, acts as replacement for
@@ -89,7 +89,7 @@
 # The short X.Y version.
 version = '0.1'
 # The full version, including alpha/beta/rc tags.
-release = '0.1.dev0'
+release = '0.1'
 
 # The language for content autogenerated by Sphinx. Refer to documentation
 # for a list of supported languages.

diff --git a/doc/index.rst b/doc/index.rst
@@ -3,9 +3,9 @@
    You can adapt this file completely to your liking, but it should at least
    contain the root `toctree` directive.
 
-############################################
-Welcome to unbalanced_dataset focumentation!
-############################################
+##########################################
+Welcome to imbalanced-learn documentation!
+##########################################
 
 Contents:
 =========

diff --git a/doc/install.rst b/doc/install.rst
@@ -5,7 +5,7 @@ Getting Started
 Install
 =======
 
-The install of ``UnbalancedDataset`` is almost straightforward. You need to clone it from GitHub_::
+The install of ``imbalanced-learn`` is almost straightforward. You need to clone it from GitHub_::
 
   $ git clone https://github.com/fmfn/UnbalancedDataset.git
   $ python setup.py install

diff --git a/examples/README.txt b/examples/README.txt
@@ -3,4 +3,4 @@
 General examples
 ----------------
 
-General-purpose and introductory examples for the unbalanced_dataset.
+General-purpose and introductory examples for the `imbalanced-learn` toolbox.
diff --git a/examples/combine/plot_smote_enn.py b/examples/combine/plot_smote_enn.py
@@ -20,7 +20,7 @@
 from sklearn.datasets import make_classification
 from sklearn.decomposition import PCA
 
-from unbalanced_dataset.combine import SMOTEENN
+from imblearn.combine import SMOTEENN
 
 # Generate the dataset
 X, y = make_classification(n_classes=2, class_sep=2, weights=[0.1, 0.9],

diff --git a/examples/combine/plot_smote_tomek.py b/examples/combine/plot_smote_tomek.py
@@ -20,7 +20,7 @@
 from sklearn.datasets import make_classification
 from sklearn.decomposition import PCA
 
-from unbalanced_dataset.combine import SMOTETomek
+from imblearn.combine import SMOTETomek
 
 # Generate the dataset
 X, y = make_classification(n_classes=2, class_sep=2, weights=[0.1, 0.9],

diff --git a/examples/ensemble/plot_balance_cascade.py b/examples/ensemble/plot_balance_cascade.py
@@ -21,7 +21,7 @@
 from sklearn.datasets import make_classification
 from sklearn.decomposition import PCA
 
-from unbalanced_dataset.ensemble import BalanceCascade
+from imblearn.ensemble import BalanceCascade
 
 # Generate the dataset
 X, y = make_classification(n_classes=2, class_sep=2, weights=[0.1, 0.9],

diff --git a/examples/ensemble/plot_easy_ensemble.py b/examples/ensemble/plot_easy_ensemble.py
@@ -21,7 +21,7 @@
 from sklearn.datasets import make_classification
 from sklearn.decomposition import PCA
 
-from unbalanced_dataset.ensemble import EasyEnsemble
+from imblearn.ensemble import EasyEnsemble
 
 # Generate the dataset
 X, y = make_classification(n_classes=2, class_sep=2, weights=[0.1, 0.9],

diff --git a/examples/over-sampling/plot_random_over_sampling.py b/examples/over-sampling/plot_random_over_sampling.py
@@ -20,7 +20,7 @@
 from sklearn.datasets import make_classification
 from sklearn.decomposition import PCA
 
-from unbalanced_dataset.over_sampling import RandomOverSampler
+from imblearn.over_sampling import RandomOverSampler
 
 # Generate the dataset
 X, y = make_classification(n_classes=2, class_sep=2, weights=[0.1, 0.9],

diff --git a/examples/over-sampling/plot_smote.py b/examples/over-sampling/plot_smote.py
@@ -20,7 +20,7 @@
 from sklearn.datasets import make_classification
 from sklearn.decomposition import PCA
 
-from unbalanced_dataset.over_sampling import SMOTE
+from imblearn.over_sampling import SMOTE
 
 # Generate the dataset
 X, y = make_classification(n_classes=2, class_sep=2, weights=[0.1, 0.9],

diff --git a/examples/over-sampling/plot_smote_bordeline_1.py b/examples/over-sampling/plot_smote_bordeline_1.py
@@ -20,7 +20,7 @@
 from sklearn.datasets import make_classification
 from sklearn.decomposition import PCA
 
-from unbalanced_dataset.over_sampling import SMOTE
+from imblearn.over_sampling import SMOTE
 
 # Generate the dataset
 X, y = make_classification(n_classes=2, class_sep=2, weights=[0.1, 0.9],

diff --git a/examples/over-sampling/plot_smote_bordeline_2.py b/examples/over-sampling/plot_smote_bordeline_2.py
@@ -20,7 +20,7 @@
 from sklearn.datasets import make_classification
 from sklearn.decomposition import PCA
 
-from unbalanced_dataset.over_sampling import SMOTE
+from imblearn.over_sampling import SMOTE
 
 # Generate the dataset
 X, y = make_classification(n_classes=2, class_sep=2, weights=[0.1, 0.9],

diff --git a/examples/over-sampling/plot_smote_svm.py b/examples/over-sampling/plot_smote_svm.py
@@ -20,7 +20,7 @@
 from sklearn.datasets import make_classification
 from sklearn.decomposition import PCA
 
-from unbalanced_dataset.over_sampling import SMOTE
+from imblearn.over_sampling import SMOTE
 
 # Generate the dataset
 X, y = make_classification(n_classes=2, class_sep=2, weights=[0.1, 0.9],

diff --git a/examples/pipeline/plot_pipeline_classification.py b/examples/pipeline/plot_pipeline_classification.py
@@ -17,9 +17,9 @@
 from sklearn.metrics import classification_report
 
 
-from unbalanced_dataset.pipeline import make_pipeline
-from unbalanced_dataset.under_sampling import EditedNearestNeighbours, \
-    RepeatedEditedNearestNeighbours
+from imblearn.pipeline import make_pipeline
+from imblearn.under_sampling import EditedNearestNeighbours
+from imblearn.under_sampling import RepeatedEditedNearestNeighbours
 
 # Generate the dataset
 X, y = make_classification(n_classes=2, class_sep=1.25, weights=[0.3, 0.7],

diff --git a/examples/under-sampling/plot_cluster_centroids.py b/examples/under-sampling/plot_cluster_centroids.py
@@ -20,7 +20,7 @@
 from sklearn.datasets import make_classification
 from sklearn.decomposition import PCA
 
-from unbalanced_dataset.under_sampling import ClusterCentroids
+from imblearn.under_sampling import ClusterCentroids
 
 # Generate the dataset
 X, y = make_classification(n_classes=2, class_sep=2, weights=[0.1, 0.9],

diff --git a/examples/under-sampling/plot_condensed_nearest_neighbour.py b/examples/under-sampling/plot_condensed_nearest_neighbour.py
@@ -20,7 +20,7 @@
 from sklearn.datasets import make_classification
 from sklearn.decomposition import PCA
 
-from unbalanced_dataset.under_sampling import CondensedNearestNeighbour
+from imblearn.under_sampling import CondensedNearestNeighbour
 
 # Generate the dataset
 X, y = make_classification(n_classes=2, class_sep=2, weights=[0.1, 0.9],

diff --git a/examples/under-sampling/plot_edited_nearest_neighbours.py b/examples/under-sampling/plot_edited_nearest_neighbours.py
@@ -20,7 +20,7 @@
 from sklearn.datasets import make_classification
 from sklearn.decomposition import PCA
 
-from unbalanced_dataset.under_sampling import EditedNearestNeighbours
+from imblearn.under_sampling import EditedNearestNeighbours
 
 # Generate the dataset
 X, y = make_classification(n_classes=2, class_sep=2, weights=[0.1, 0.9],

diff --git a/examples/under-sampling/plot_instance_hardness_threshold.py b/examples/under-sampling/plot_instance_hardness_threshold.py
@@ -20,7 +20,7 @@
 from sklearn.datasets import make_classification
 from sklearn.decomposition import PCA
 
-from unbalanced_dataset.under_sampling import InstanceHardnessThreshold
+from imblearn.under_sampling import InstanceHardnessThreshold
 
 # Generate the dataset
 X, y = make_classification(n_classes=2, class_sep=1., weights=[0.05, 0.95],

diff --git a/examples/under-sampling/plot_nearmiss_1.py b/examples/under-sampling/plot_nearmiss_1.py
@@ -20,7 +20,7 @@
 from sklearn.datasets import make_classification
 from sklearn.decomposition import PCA
 
-from unbalanced_dataset.under_sampling import NearMiss
+from imblearn.under_sampling import NearMiss
 
 # Generate the dataset
 X, y = make_classification(n_classes=2, class_sep=2, weights=[0.1, 0.9],

diff --git a/examples/under-sampling/plot_nearmiss_2.py b/examples/under-sampling/plot_nearmiss_2.py
@@ -20,7 +20,7 @@
 from sklearn.datasets import make_classification
 from sklearn.decomposition import PCA
 
-from unbalanced_dataset.under_sampling import NearMiss
+from imblearn.under_sampling import NearMiss
 
 # Generate the dataset
 X, y = make_classification(n_classes=2, class_sep=2, weights=[0.1, 0.9],

diff --git a/examples/under-sampling/plot_nearmiss_3.py b/examples/under-sampling/plot_nearmiss_3.py
@@ -20,7 +20,7 @@
 from sklearn.datasets import make_classification
 from sklearn.decomposition import PCA
 
-from unbalanced_dataset.under_sampling import NearMiss
+from imblearn.under_sampling import NearMiss
 
 # Generate the dataset
 X, y = make_classification(n_classes=2, class_sep=2, weights=[0.1, 0.9],

diff --git a/examples/under-sampling/plot_neighbourhood_cleaning_rule.py b/examples/under-sampling/plot_neighbourhood_cleaning_rule.py
@@ -20,7 +20,7 @@
 from sklearn.datasets import make_classification
 from sklearn.decomposition import PCA
 
-from unbalanced_dataset.under_sampling import NeighbourhoodCleaningRule
+from imblearn.under_sampling import NeighbourhoodCleaningRule
 
 # Generate the dataset
 X, y = make_classification(n_classes=2, class_sep=2, weights=[0.1, 0.9],

diff --git a/examples/under-sampling/plot_one_sided_selection.py b/examples/under-sampling/plot_one_sided_selection.py
@@ -20,7 +20,7 @@
 from sklearn.datasets import make_classification
 from sklearn.decomposition import PCA
 
-from unbalanced_dataset.under_sampling import OneSidedSelection
+from imblearn.under_sampling import OneSidedSelection
 
 # Generate the dataset
 X, y = make_classification(n_classes=2, class_sep=2, weights=[0.1, 0.9],