Merge branch 'master' of https://github.com/shaharazulay/unicorn

.travis.yml

@@ -6,16 +6,13 @@ python:
 install:
   - pip install codecov
   - pip install coverage
-  # magellan
-  - cd magellan
   - pip install -r requirements.txt
   - python setup.py install
-  - cd ..
 
 script: 
-  # magellan
-  - cd magellan
   - python setup.py test
+  # magellan coverage
+  - cd magellan
   - coverage run test/_test.py
 
 after_success:

docs/README.rst

@@ -8,16 +8,13 @@ Unicorn
 Shahar Azulay, Tali Raveh, Ariel Hanemann, Yossi Cohen
 
 
-|Travis|_ |Codecov|_ |Python27|_ |Python35|_ |License|_
+|Travis|_ |Python27|_ |Python35|_ |License|_
 
 .. |License| image:: https://img.shields.io/badge/license-BSD--3--Clause-brightgreen.svg
 .. _License: https://github.com/shaharazulay/unicorn/blob/master/LICENSE
 
 .. |Travis| image:: https://travis-ci.org/shaharazulay/unicorn.svg?branch=master
 .. _Travis: https://travis-ci.org/shaharazulay/unicorn
-
-.. |Codecov| image:: https://codecov.io/gh/shaharazulay/unicorn/branch/master/graph/badge.svg
-.. _Codecov: https://codecov.io/gh/shaharazulay/unicorn
 
 .. |Python27| image:: https://img.shields.io/badge/python-2.7-blue.svg
 .. _Python27:

docs/index.rst

@@ -9,8 +9,7 @@ Contents
    self
 
 .. toctree::
-    :maxdepth: 3
-
-    overview
-    magellan_link
+   :maxdepth: 4
 
+   overview
+   MageLlan <magellan_link>

docs/magellan_link.rst

@@ -1 +1 @@
-.. include:: ../magellan/docs/index.rst
+.. include:: ../magellan/docs/overview.rst

magellan/docs/overview.rst

@@ -1,69 +1,21 @@
 .. _adapting:
 
-The Stacking Ensemble
+MageLlan
 ===================
 
 Background
 -----
 
-Stacking (sometimes called stacked generalization or bagging) is an ensemble meta-algorithm that attempts to improve a model's
-predictive power by harnessing multiple models (perferably different in nature) to a unified pipeline.
+<TBD>
 
-The Stacking method is a very general name that is sometimes used to describe different methods to crete the unfied pipeline.
-Here, we focus on a Stacking ensemble which uses the multiple models predict the target, while unifing them using a 
-meta-level regressor - which learns how to annotate proper weights to the predictions of the models under it.
 
-A simpler type of Stacking might have been to average the predictions of the different models (similar to Random Forest, 
-but perhaps without the limitation of a single-type model).
-
-In true Stacking the "stacker" or the meta-level regressor can also perform learning, where models which are proven to be
-less efficient in predicting the data are provided lower weight in the final prediction.
-
-.. image:: _static/figure_001.jpg
-
-*[1] high-level description of the stacking ensemble*
-
-
-
-Getting it Wrong
+The Method
 -----
 
-The major problem in creating a proper Stacking ensemble is getting it right.
-The wrong way to perform stacking would be to
-
-1. **Train** the first level models over the target.
-
-2. Get the first level models predictions over the inputs.
-
-3. **Train** the meta-level Stacker over the predictions of the first level models.
+<TBD>
 
-Why would that be the wrong way to go?
 
-**Overfitting**
-
-Our meta-level regressor would be exposed to severe overfitting from one of the first level models.
-For example, if one of five first level models would be highly overfitted to the target, practically "storing"
-the y target it is showns in train time for test time.
-The meta-level model, trained over the same target would see this model as excellent - predicting the target y 
-with impressive accuracy almost everytime.
-
-This will result in a hight weight to this model, making the entire pipeline useless in test time.
-
-
-The Solution
+Results
 -----
 
-The solution is never using the train abilities of the first level model - but using their abilities in test.
-What does it mean? it means the meta-level model would never be exposed to a y_hat generated by any first level
-model where the actual target sample representing this y_hat in the data was given to that model in training.
-
-Each model will deliever its predictions in a "cross_val_predict" manner (in sklearn terms). If it's a great model,
-it will demonstrate great generalization skills making its test-time predictions valuable to the meta-level regressor.
-If it's a highly overfitted model - the test-time predictions it will hand down the line will be showns for their true
-abilities, causing it to recieve a low weight.
-
-How do we achieve that? internal cross validation.
-
-.. image:: _static/figure_002.jpg
-
-*[1] achienving stacking ensemble using internal cross-validation*
+<TBD>