Adress issue #200 - Add issue and PR templates (#202)

Author: glemaitre

.github/ISSUE_TEMPLATE.md

@@ -0,0 +1,53 @@
+<!--
+If your issue is a usage question, submit it here instead:
+- The imbalanced learn gitter: https://gitter.im/scikit-learn-contrib/imbalanced-learn
+-->
+
+<!-- Instructions For Filing a Bug: https://github.com/scikit-learn-contrib/imbalanced-learn/blob/master/CONTRIBUTING.md#filing-bugs -->
+
+#### Description
+<!-- Example: Joblib Error thrown when calling fit on LatentDirichletAllocation with evaluate_every > 0-->
+
+#### Steps/Code to Reproduce
+<!--
+Example:
+```
+from sklearn.feature_extraction.text import CountVectorizer
+from sklearn.decomposition import LatentDirichletAllocation
+
+docs = ["Help I have a bug" for i in range(1000)]
+
+vectorizer = CountVectorizer(input=docs, analyzer='word')
+lda_features = vectorizer.fit_transform(docs)
+
+lda_model = LatentDirichletAllocation(
+    n_topics=10,
+    learning_method='online',
+    evaluate_every=10,
+    n_jobs=4,
+)
+model = lda_model.fit(lda_features)
+```
+If the code is too long, feel free to put it in a public gist and link
+it in the issue: https://gist.github.com
+-->
+
+#### Expected Results
+<!-- Example: No error is thrown. Please paste or describe the expected results.-->
+
+#### Actual Results
+<!-- Please paste or specifically describe the actual output or traceback. -->
+
+#### Versions
+<!--
+Please run the following snippet and paste the output below.
+import platform; print(platform.platform())
+import sys; print("Python", sys.version)
+import numpy; print("NumPy", numpy.__version__)
+import scipy; print("SciPy", scipy.__version__)
+import sklearn; print("Scikit-Learn", sklearn.__version__)
+import imblearn; print("Imbalanced-Learn", imblearn.__version__)
+-->
+
+
+<!-- Thanks for contributing! -->

.github/PULL_REQUEST_TEMPLATE.md

@@ -0,0 +1,24 @@
+<!--
+Thanks for contributing a pull request! Please ensure you have taken a look at
+the contribution guidelines: https://github.com/scikit-learn-contrib/imbalanced-learn/blob/master/CONTRIBUTING.md#contributing-pull-requests
+-->
+#### Reference Issue
+<!-- Example: Fixes #1234 -->
+
+
+#### What does this implement/fix? Explain your changes.
+
+
+#### Any other comments?
+
+
+<!--
+Please be aware that we are a loose team of volunteers so patience is
+necessary; assistance handling other issues is very welcome. We value
+all user contributions, no matter how minor they are. If we are slow to
+review, either the pull request needs some benchmarking, tinkering,
+convincing, etc. or more likely the reviewers are simply busy. In either
+case, we ask for your understanding during the review process.
+
+Thanks for contributing!
+-->

CONTRIBUTING.md

@@ -1,4 +1,3 @@
-
 Contributing code
 =================
 
@@ -7,7 +6,7 @@ This guide is adapted from (scikit-learn)[https://github.com/scikit-learn/scikit
 How to contribute
 -----------------
 
-The preferred way to contribute to imbalanced-learn is to fork the 
+The preferred way to contribute to imbalanced-learn is to fork the
 [main repository](https://github.com/scikit-learn-contrib/imbalanced-learn) on
 GitHub:
 
@@ -40,7 +39,7 @@ Finally, go to the web page of your fork of the imbalanced-learn repo,
 and click 'Pull request' to send your changes to the maintainers for
 review. This will send an email to the committers.
 
-(If any of the above seems like magic to you, then look up the 
+(If any of the above seems like magic to you, then look up the
 [Git documentation](http://git-scm.com/documentation) on the web.)
 
 Contributing Pull Requests
@@ -143,8 +142,9 @@ following rules before submitting:
    import numpy; print("NumPy", numpy.__version__)
    import scipy; print("SciPy", scipy.__version__)
    import sklearn; print("Scikit-Learn", sklearn.__version__)
+   import imblearn; print("Imbalanced-Learn", imblearn.__version__)
    ```
-   
+
 -  Please be specific about what estimators and/or functions are involved
    and the shape of the data, as appropriate; please include a
    [reproducible](http://stackoverflow.com/help/mcve) code snippet