Merge pull request #69 from ealcobaca/online-doc-fix

Online doc fix

docs/source/about.rst

@@ -10,10 +10,15 @@ You can find the contributors of this package here_.
 .. _here: https://github.com/ealcobaca/pymfe/graphs/contributors
 
 
-Citing pymfe
------------------------
+Citing PyMFE
+------------
 
-If you use pymfe in a scientific publication, we would appreciate
+If you use PyMFE in a scientific publication, we would appreciate
 citations to the following paper::
 
   None
+
+Extra information
+-----------------
+See the `README <https://github.com/ealcobaca/pymfe/blob/master/README.md>`_
+file from GitHub for extra information.

docs/source/api.rst

@@ -3,7 +3,7 @@
 #################
 API Documentation
 #################
-This is the full API documentation of the `pymfe` toolbox.
+This is the full API documentation of the PyMFE package.
 
 .. _mfe_ref:
 
@@ -59,7 +59,7 @@ This is the full API documentation of the `pymfe` toolbox.
 .. _info_theory_ref:
 
 :mod:`pymfe.info_theory`: Information theory Meta-features
-================================================================
+==========================================================
 
 .. automodule:: pymfe.info_theory
    :no-members:
@@ -126,8 +126,25 @@ This is the full API documentation of the `pymfe` toolbox.
 
 .. _clustering_ref:
 
+:mod:`pymfe.clustering`: Clustering Meta-features
+=================================================
+
+.. automodule:: pymfe.clustering
+   :no-members:
+   :no-inherited-members:
+
+.. currentmodule:: pymfe
+
+.. autosummary::
+   :toctree: generated/
+
+   clustering.MFEClustering
+
+
+.. _concept_ref:
+
 :mod:`pymfe.concept`: Concept Meta-features
-===================================================
+===========================================
 
 .. automodule:: pymfe.concept
    :no-members:
@@ -144,7 +161,7 @@ This is the full API documentation of the `pymfe` toolbox.
 .. _itemset_ref:
 
 :mod:`pymfe.itemset`: Itemset Meta-features
-===================================================
+===========================================
 
 .. automodule:: pymfe.itemset
    :no-members:

docs/source/index.rst

@@ -1,9 +1,9 @@
-.. pymfe documentation master file, created by
+.. PyMFE documentation master file, created by
    sphinx-quickstart on Mon Apr 22 10:19:46 2019.
    You can adapt this file completely to your liking, but it should at least
    contain the root `toctree` directive.
 
-Welcome to pymfe's documentation!
+Welcome to PyMFE's documentation!
 =================================
 
 .. toctree::
@@ -17,15 +17,15 @@ Welcome to pymfe's documentation!
 .. toctree::
    :maxdepth: 2
    :hidden:
-   :caption: Documentation
+   :caption: API Documentation
 
    auto_pages/meta_features_description
    api
 
 .. toctree::
    :maxdepth: 2
    :hidden:
-   :caption: Tutorial - Examples
+   :caption: Tutorial and Examples
 
    auto_examples/index
 
@@ -50,25 +50,22 @@ Information to install, test, and contribute to the package.
 
 `API Documentation <api.html>`_
 -------------------------------
-The exact API of all functions and classes, as given in the
-doctring. The API documents expected types and allowed features for
-all functions, and all parameters available for the meta-feature extraction.
+In this section, we document expected types and allowed features for all
+functions, and all parameters available for the meta-feature extraction.
 
 
 `Examples <auto_examples/index.html>`_
 --------------------------------------
-A set of examples illustrating the use of the different meta-features.
+A set of examples illustrating the use of PyMFE package. You will learn in this
+section how PyMFE works, patter, tips, and more. 
 
 
-`What's new <new.html>`_
+`What's new ? <new.html>`_
 ------------------------------
-Log of the pymfe history.
+Log of the PyMFE history.
 
 
-`About pymfe <about.html>`_
+`About PyMFE <about.html>`_
 --------------------------------------
-Just to know about history of pymfe.
-
-
-See the `README <https://github.com/ealcobaca/pymfe/blob/master/README.md>`_
-for more information.
+If you would like to know more about this project, how to cite it, and the
+contributors, see this section.

docs/source/install.rst

@@ -4,19 +4,21 @@ Install
 Requirements
 =============
 
-The pymfe package requires the following dependencies:
+The PyMFE package requires the following dependencies:
 
 * numpy
 * scipy
-* scikit-learn
+* sklearn
 * pandas
 * patsy
+* statsmodels
+* texttable
 
 
 Install
 =======
 
-The pymfe is available on the `PyPi <https://pypi.org/project/pymfe/>`_. You can install it via `pip` as follow::
+The PyMFE is available on the `PyPi <https://pypi.org/project/pymfe/>`_. You can install it via `pip` as follow::
 
   pip install -U pymfe
 
@@ -39,8 +41,10 @@ Test and coverage
 
 You want to test/test-coverage the code before to install::
 
+  $ make install-dev
   $ make test-cov
 
 Or::
 
+  $ make install-dev
   $ pytest --cov=pymfe/ tests/

docs/source/new.rst

@@ -1,5 +1,39 @@
-What is new on pymfe package?
+What is new on PyMFE package?
 #############################
+The PyMFE releases are available in PyPI_ and GitHub_.
+
+.. _PyPI: https://pypi.org/project/pymfe/
+.. _GitHub: https://github.com/ealcobaca/pymfe/releases
+
+
+Version 0.2.0
+-------------
+* New meta-feature groups
+
+  * Complexity
+
+  * Itemset
+
+  * Concept
+
+* New feature in MFE to list meta-feature description and references
+
+* Dev class update
+
+* Integration, system tests, tests updates
+
+* Old module reviews
+
+* Docstring improvement
+
+* Online documentation improvement
+
+* Clustering group updated
+
+* Landmarking group updated
+
+* Statistical group updated
+
 
 Version 0.1.1
 -------------
@@ -13,14 +47,14 @@ Version 0.1.1
 
 * Modified 'verbosity' (from 'extract' method) argument type from boolean to
   integer. Now the user can choose the desired level of verbosity.
-  Verbosity = 1 means that a progress bar will be shown during the metafeature
+  Verbosity = 1 means that a progress bar will be shown during the meta-feature
   extraction process. Verbosity = 2 maintains all the previous verbose messages
   (i.e., it logs every "extract" step) plus additional information about the
   current percentage of progress done so far.
 
 
-Version 0.1.0
--------------
+Version 0.1.0 (Available on PyPI)
+---------------------------------
 * Meta-feature groups available
 
    * Relative landmarking
@@ -35,7 +69,7 @@ Version 0.1.0
 
    * Now you can list available groups
 
-   * Now you can list available metafeatures
+   * Now you can list available meta-features
 
 * Documentation
 
@@ -45,16 +79,17 @@ Version 0.1.0
 
 * Bugs
 
-   * Problems in parse categoric metafeatures solved
+   * Problems in parse categoric meta-features solved
 
    * Categorization of attributes with constant values solved
 
 * Test
 
    * Several new tests added
 
-Version 0.0.3
--------------
+
+Version 0.0.3 (Available on PyPI)
+---------------------------------
 * Documentation improvement
 
 * Setup improvement
@@ -74,3 +109,4 @@ Initial Release
 
   * Landmarking
 
+

docs/source/using.rst

@@ -1,12 +1,12 @@
-Using pymfe
+Using PyMFE
 ###########
-
-Extracting metafeatures with pymfe is easy.
-
+Extracting metafeatures with PyMFE is easy.                                     
+
 The parameters are the measures, the group of measures and the summarization
-functions to be extracted. The default parameter is extract all common
-measures. The fit function can be called by passing the X and y. The extract
-function is used to extract the related measures. See this example::
+functions to be extracted. The default behavior is to extract all default
+measures, which is. The ``fit`` function can be called by passing the ``X``
+and ``y``. The ``extract`` function is used to extract the related measures.
+See this example::
 
    # Load a dataset
    from sklearn.datasets import load_iris
@@ -16,7 +16,7 @@ function is used to extract the related measures. See this example::
    y = data.target
    X = data.data
 
-   # Extract all measures
+   # Extract default measures
    mfe = MFE()
    mfe.fit(X, y)
    ft = mfe.extract()

examples/01_introductory_examples/README.txt

@@ -1,4 +1,4 @@
 Introductory Examples
 ---------------------
 
-Introductory examples for the pymfe package.
+Introductory examples for the PyMFE package.

examples/01_introductory_examples/plot_pymfe_default.py

@@ -1,6 +1,6 @@
 """
 Basic of meta-features extraction
-======================================
+=================================
 
 This example show how to extract meta-features using standard configuration.
 """

examples/01_introductory_examples/plot_pymfe_specific.py

@@ -1,6 +1,6 @@
 """
 Select specific measures and summaries
-===========================================
+======================================
 
 To customize the measure extraction, is necessary to use the feature
 and summary attribute. For instance, ``info-theo and`` and ``statistical``
@@ -21,7 +21,7 @@
 
 ###############################################################################
 # Select specific measures and summaries for ``statistical``
-# -------------------------------------------------------------------
+# ----------------------------------------------------------
 #
 # Extracting three information theoretical measures.
 
@@ -34,7 +34,7 @@
 
 ###############################################################################
 # Select specific measures and summaries for ``info-theo``
-# ------------------------------------------------------------------
+# --------------------------------------------------------
 #
 # Extracting two statistical measures.
 
@@ -47,7 +47,7 @@
 
 ###############################################################################
 # Select specific measures for both ``info-theo`` and ``statistical``
-# --------------------------------------------------------------------
+# -------------------------------------------------------------------
 #
 # Extracting five measures.
 

examples/01_introductory_examples/plot_summary.py

@@ -1,6 +1,6 @@
 """
 Using Summaries
-====================
+===============
 
 In this example we will explain the different ways to select summary functions.
 """
@@ -21,7 +21,7 @@
 #
 # Several meta-features generate multiple values and ``mean`` and ``sd`` are
 # the standard method to summary these values. In order to increase the
-# flexibility, the pymfe package implemented the summary (or post processing)
+# flexibility, the PyMFE package implemented the summary (or post processing)
 # methods to deal with multiple measures values. This method is able to deal
 # with descriptive statistic (resulting in a single value) or a distribution
 # (resulting in multiple values).

examples/02_advanced_examples/plot_custom_arguments.py

@@ -19,7 +19,7 @@
 # Custom Arguments
 # ----------------
 #
-# It is possible to pass custom arguments to every meta-feature using pymfe
+# It is possible to pass custom arguments to every meta-feature using PyMFE
 # extract method kwargs. The keywords must be the target meta-feature name, and
 # the value must be a dictionary in the format {argument: value}, i.e., each
 # key in the dictionary is a target argument with its respective value. In the

examples/03_miscellaneous_examples/plot_listing_metafeatures_groups.py

@@ -10,7 +10,7 @@
 from pymfe.mfe import MFE
 
 ###############################################################################
-# Print all available metafeature groups from the ``pymfe`` package.
+# Print all available metafeature groups from the PyMFE package.
 model = MFE()
 model_groups = model.valid_groups()
 print(model_groups)
@@ -21,7 +21,7 @@
 print(model_groups)
 
 ###############################################################################
-# Print all available metafeatures from some groups of the ``pymfe`` package
+# Print all available metafeatures from some groups of the PyMFE package
 # If no parameter is given (or is 'None'), then all available
 # will be returned.
 model = MFE()
@@ -43,7 +43,7 @@
 print(mtfs_subset)
 
 ###############################################################################
-# Print all available summary functions from the ``pymfe`` package
+# Print all available summary functions from the PyMFE package
 model = MFE()
 summaries = model.valid_summary()
 print(summaries)