smart correlation selector closes #115 (#199)

* first draft smart corr

* add smart corr to init

* final edits

* add tests

* fix mypy error

* finishes docs and docstrings

* change wording fit, transform methods

* fix typo in docs

* rebase master and unifies params with other selectors

* fix test

Author: solegalli

docs/index.rst

@@ -77,7 +77,7 @@ Feature-engine features in the following resources
 ---------------------------------------------------
 
 - `Website <https://www.trainindata.com/feature-engine>`_.
-- `Feature Engineering for Machine Learning <https://www.udemy.com/feature-engineering-for-machine-learning/?couponCode=FEATENGREPO>`_, Online Course .
+- `Feature Engineering for Machine Learning <https://www.udemy.com/feature-engineering-for-machine-learning/?couponCode=FEATENGREPO>`_, Online Course.
 - `Python Feature Engineering Cookbook <https://www.packtpub.com/data/python-feature-engineering-cookbook>`_.
 - `Feature-engine: A new open-source Python package for feature engineering <https://www.trainindatablog.com/feature-engine-a-new-open-source-python-package-for-feature-engineering/>`_.
 - `Practical Code Implementations of Feature Engineering for Machine Learning with Python <https://www.trainindatablog.com/practical-code-implementations-of-feature-engineering-for-machine-learning-with-python/>`_.
@@ -90,23 +90,6 @@ En Español:
 More resources in the **Learning Resources** sections on the navigation panel on the
 left.
 
-Contributing
-------------
-
-Interested in contributing to Feature-engine? That is great news!
-
-Feature-engine is a welcoming and inclusive project and it would be great to have you
-on board. We follow the
-`Python Software Foundation Code of Conduct <http://www.python.org/psf/codeofconduct/>`_.
-
-Regardless of your skill level you can help us. We appreciate bug reports, user testing,
-feature requests, bug fixes, addition of tests, product enhancements, and documentation
-improvements. We also appreciate blogs about Feature-engine. If you happen to have one,
-let us know!
-
-For more details on how to contribute check the contributing page. Click on the
-"Contributing" page on the left of this page.
-
 
 Feature-engine's Transformers
 -----------------------------
@@ -173,6 +156,7 @@ Feature Selection:
 - :doc:`selection/DropConstantFeatures`: drops constant and quasi-constant variables from a dataframe
 - :doc:`selection/DropDuplicateFeatures`: drops duplicated variables from a dataframe
 - :doc:`selection/DropCorrelatedFeatures`: drops correlated variables from a dataframe
+- :doc:`selection/SmartCorrelatedSelection`: selects best feature from correlated group
 - :doc:`selection/SelectByShuffling`: selects features by evaluating model performance after feature shuffling
 - :doc:`selection/SelectBySingleFeaturePerformance`: selects features based on their performance on univariate estimators
 - :doc:`selection/SelectByTargetMeanPerformance`: selects features based on target mean encoding performance
@@ -199,6 +183,23 @@ Check if there's already an open `issue <https://github.com/solegalli/feature_en
 on the topic. If not, open a new `issue <https://github.com/solegalli/feature_engine/issues/>`_
 with your bug report, suggestion or new feature request.
 
+Contributing
+------------
+
+Interested in contributing to Feature-engine? That is great news!
+
+Feature-engine is a welcoming and inclusive project and it would be great to have you
+on board. We follow the
+`Python Software Foundation Code of Conduct <http://www.python.org/psf/codeofconduct/>`_.
+
+Regardless of your skill level you can help us. We appreciate bug reports, user testing,
+feature requests, bug fixes, addition of tests, product enhancements, and documentation
+improvements. We also appreciate blogs about Feature-engine. If you happen to have one,
+let us know!
+
+For more details on how to contribute check the contributing page. Click on the
+"Contributing" link on the left of this page.
+
 
 Open Source
 -----------
@@ -211,6 +212,7 @@ The `issues <https://github.com/solegalli/feature_engine/issues/>`_ and
 `pull requests <https://github.com/solegalli/feature_engine/pulls>`_ are tracked there.
 
 
+
 .. toctree::
    :maxdepth: 1
    :caption: Table of Contents

docs/selection/SmartCorrelatedSelection.rst

@@ -4,9 +4,80 @@ SmartCorrelatedSelection
 API Reference
 -------------
 
-Coming soon
+.. autoclass:: feature_engine.selection.SmartCorrelatedSelection
+    :members:
 
 Example
 -------
 
-Coming soon
+.. code:: python
+
+    import pandas as pd
+    from sklearn.datasets import make_classification
+    from feature_engine.selection import SmartCorrelatedSelection
+
+    # make dataframe with some correlated variables
+    def make_data():
+        X, y = make_classification(n_samples=1000,
+                                   n_features=12,
+                                   n_redundant=4,
+                                   n_clusters_per_class=1,
+                                   weights=[0.50],
+                                   class_sep=2,
+                                   random_state=1)
+
+        # trasform arrays into pandas df and series
+        colnames = ['var_'+str(i) for i in range(12)]
+        X = pd.DataFrame(X, columns=colnames)
+        return X
+
+
+    X = make_data()
+
+
+    # set up the selector
+    tr = SmartCorrelatedSelection(
+        variables=None,
+        method="pearson",
+        threshold=0.8,
+        missing_values="raise",
+        selection_method="variance",
+        estimator=None,
+    )
+
+    Xt = tr.fit_transform(X)
+
+    tr.correlated_feature_sets_
+
+
+.. code:: python
+
+    [{'var_0', 'var_8'}, {'var_4', 'var_6', 'var_7', 'var_9'}]
+
+..  code:: python
+
+    tr.selected_features_
+
+.. code:: python
+
+   ['var_1', 'var_2', 'var_3', 'var_5', 'var_10', 'var_11', 'var_8', 'var_7']
+
+.. code:: python
+
+    print(print(Xt.head()))
+
+.. code:: python
+
+          var_1     var_2     var_3     var_5    var_10    var_11     var_8  \
+    0 -2.376400 -0.247208  1.210290  0.091527  2.070526 -1.989335  2.070483
+    1  1.969326 -0.126894  0.034598 -0.186802  1.184820 -1.309524  2.421477
+    2  1.499174  0.334123 -2.233844 -0.313881 -0.066448 -0.852703  2.263546
+    3  0.075341  1.627132  0.943132 -0.468041  0.713558  0.484649  2.792500
+    4  0.372213  0.338141  0.951526  0.729005  0.398790 -0.186530  2.186741
+
+          var_7
+    0 -2.230170
+    1 -1.447490
+    2 -2.240741
+    3 -3.534861
+    4 -2.053965

feature_engine/selection/__init__.py

@@ -5,6 +5,7 @@
 from .drop_constant_features import DropConstantFeatures
 from .drop_duplicate_features import DropDuplicateFeatures
 from .drop_correlated_features import DropCorrelatedFeatures
+from .smart_correlation_selection import SmartCorrelatedSelection
 from .shuffle_features import SelectByShuffling
 from .single_feature_performance import SelectBySingleFeaturePerformance
 from .recursive_feature_addition import RecursiveFeatureAddition
@@ -16,6 +17,7 @@
     "DropConstantFeatures",
     "DropDuplicateFeatures",
     "DropCorrelatedFeatures",
+    "SmartCorrelatedSelection",
     "SelectByShuffling",
     "SelectBySingleFeaturePerformance",
     "RecursiveFeatureAddition",