Merge pull request #39 from INGEOTEC/develop

Version - 2.0

.travis.yml

@@ -35,9 +35,9 @@ script:
   - which python
   - python --version
   - which coverage
-  - if [ $python = 3.5 ]; then nosetests --verbose -exe b4msa; fi
-  - if [ $python = 3.7 ]; then nosetests --verbose -exe b4msa; fi
-  - if [ $python = 3.6 ]; then coverage run setup.py nosetests; fi
+  - if [ $python = 3.5 ]; then nosetests --verbose -exe b4msa --with-doctest; fi
+  - if [ $python = 3.7 ]; then nosetests --verbose -exe b4msa --with-doctest; fi
+  - if [ $python = 3.6 ]; then coverage run setup.py nosetests --with-doctest; fi
 
 after_success:
   - if [ $python = 3.6 ]; then coveralls || echo "Coveralls upload failed"; fi

README.md

@@ -17,6 +17,8 @@ B4MSA is a Python Sentiment Analysis Classifier for Twitter-like short texts. It
 
 It is written in Python making use of [NTLK](http://www.nltk.org/) and [scikit-learn](http://scikit-learn.org/) to create simple but effective sentiment classifiers.
 
+The documentation is on [readthedocs](https://b4msa.readthedocs.io).
+
 ## Citing B4MSA ##
 
 If you find B4MSA useful for any academic/scientific purpose, we would appreciate citations to the following journal paper: 

README.rst

@@ -1,291 +1,34 @@
-|Build Status|
-
-|Coverage Status|
-
-A Baseline for Multilingual Sentiment Analysis (B4MSA)
+A Baseline for Multilingual Sentiment Analysis (b4msa)
 ======================================================
 
-B4MSA is a Python Sentiment Analysis Classifier for Twitter-like short
-texts. It can be used to create a first approximation to a sentiment
-classifier on any given language. It is almost language-independent, but
-it can take advantage of the particularities of a language.
-
-It is written in Python making use of `NTLK <http://www.nltk.org/>`__,
-`scikit-learn <http://scikit-learn.org/>`__ and
-`gensim <https://radimrehurek.com/gensim/>`__ to create simple but
-effective sentiment classifiers.
-
-Performance
-===========
-
-In order to know the performance of B4MSA, it was decided to compare
-B4MSA against different commercial sentiment analyzers such as:
-`Google <http://cloud.google.com>`__ `HP
-Haven <https://www.havenondemand.com>`__, and
-`Sentiment140 <http://sentiment140.com>`__. This comparison was
-performed on **July 28, 2016**.
-
-The performance measures used in the comparison are taken from three
-different sentiment analysis competitions: TASS, SemEval, and SENTIPOLC.
-These measures are the accuracy, F1-positive-negative, and
-F0F1-positive-negative.
-
-F1-positive-negative is defined as the average of the F1 obtained from
-the positive class and F1 of the negative class.
-
-F0F1-positive-negative is defined as the average between
-F0-positive-negative and F1-positive-negative. F0-positive-negative is
-defined as the average of the F0 of the positive class and F0 of the
-negative class; F1-positive-negative is defined similarly. For more
-information regarding this measure we refer the reader to the overview
-of `SENTIPOLC
-challenge <http://www.fileli.unipi.it/projects/clic/proceedings/Proceedings-EVALITA-2014.pdf>`__
-
-SENTIPOLC 2014 (Italian)
-------------------------
-
-+------------------------------------------------+--------------+------------------------+--------------------------+
-| Algorithm                                      | Accuracy     | F1-positive-negative   | F0F1-positive-negative   |
-+================================================+==============+========================+==========================+
-| `HP Haven <https://www.havenondemand.com>`__   | 0.4693       | 0.3921                 | 0.4788                   |
-+------------------------------------------------+--------------+------------------------+--------------------------+
-| B4MSA                                          | **0.6072**   | **0.6041**             | **0.6354**               |
-+------------------------------------------------+--------------+------------------------+--------------------------+
-
-TASS 2015-small (Spanish)
--------------------------
-
-+------------------------------------------------+--------------+------------------------+--------------------------+
-| Algorithm                                      | Accuracy     | F1-positive-negative   | F0F1-positive-negative   |
-+================================================+==============+========================+==========================+
-| `HP Haven <https://www.havenondemand.com>`__   | 0.5350       | 0.4564                 | 0.5775                   |
-+------------------------------------------------+--------------+------------------------+--------------------------+
-| `Sentiment140 <http://sentiment140.com>`__     | 0.0590       | 0.1001                 | 0.3225                   |
-+------------------------------------------------+--------------+------------------------+--------------------------+
-| B4MSA                                          | **0.6330**   | **0.6949**             | **0.6529**               |
-+------------------------------------------------+--------------+------------------------+--------------------------+
-
-SemEval 2015 (English)
-----------------------
-
-+------------------------------------------------+--------------+------------------------+--------------------------+
-| Algorithm                                      | Accuracy     | F1-positive-negative   | F0F1-positive-negative   |
-+================================================+==============+========================+==========================+
-| `HP Haven <https://www.havenondemand.com>`__   | 0.6241       | **0.5724**             | 0.6120                   |
-+------------------------------------------------+--------------+------------------------+--------------------------+
-| `Google <http://cloud.google.com>`__           | 0.4218       | 0.4967                 | 0.4624                   |
-+------------------------------------------------+--------------+------------------------+--------------------------+
-| `Sentiment140 <http://sentiment140.com>`__     | 0.5553       | 0.4790                 | 0.5368                   |
-+------------------------------------------------+--------------+------------------------+--------------------------+
-| B4MSA                                          | **0.6281**   | 0.5281                 | **0.6578**               |
-+------------------------------------------------+--------------+------------------------+--------------------------+
-
-SemEval 2016 (English)
-----------------------
-
-+------------------------------------------------+--------------+------------------------+--------------------------+
-| Algorithm                                      | Accuracy     | F1-positive-negative   | F0F1-positive-negative   |
-+================================================+==============+========================+==========================+
-| `HP Haven <https://www.havenondemand.com>`__   | **0.5986**   | **0.5173**             | **0.5337**               |
-+------------------------------------------------+--------------+------------------------+--------------------------+
-| `Google <http://cloud.google.com>`__           | 0.3952       | 0.4728                 | 0.4303                   |
-+------------------------------------------------+--------------+------------------------+--------------------------+
-| `Sentiment140 <http://sentiment140.com>`__     | 0.5562       | 0.4135                 | 0.4560                   |
-+------------------------------------------------+--------------+------------------------+--------------------------+
-| B4MSA                                          | 0.5106       | 0.4082                 | 0.4866                   |
-+------------------------------------------------+--------------+------------------------+--------------------------+
-
-Note regarding `Google <http://cloud.google.com>`__:
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-`Google <http://cloud.google.com>`__'s sentiment analysis algorithm does
-not provide classes instead the service returns a continuous value
-between -1 and 1; however, in the api documentation it was mentioned the
-following rule:
-
-.. code:: python
-
-    def get_class(pol):
-        if pol < -0.75:
-            return 'negative'
-        elif pol > 0.25:
-            return 'positive'
-        return 'neutral'
-
-Installing B4MSA
-================
-
-B4MSA can be installed using ``pip``
-
-.. code:: bash
-
-    pip install b4msa
-
-or cloning the `b4msa <https://github.com/INGEOTEC/b4msa>`__ repository
-from github, e.g.,
-
-.. code:: bash
-
-    git clone https://github.com/INGEOTEC/b4msa.git
-
-Predict a training set using B4MSA
-==================================
-
-Suppose you have a workload of classified tweets ``tweets.json.gz`` to
-model your problem, let us assume that b4msa is already installed, then
-the stratisfied k-fold can be computed as follows:
-
-.. code:: bash
-
-    b4msa-params -k5 -s24 -n24 tweets.json.gz -o tweets.json
-
-the parameters means for:
-
--  ``-k5`` five folds
--  ``-s48`` b4msa optimizes model's parameters for you, and ``-s48``
-   specifies that the parameter space should be sampled in 48 points and
-   it simply get the best among them
--  ``-n24`` let us specify the number of workds to be launch, it is a
-   good idea to set ``-s`` as a multiply of ``-n``.
--  ``-o tweets.json`` specifies the file to store the configurations
-   found by the parameter selection process, in best first order; a
-   number of metrics are given, but it is in descending order by
-   ``_score``
-
-The ``tweets.json`` looks like (for a four-classes problem)
-
-::
-
-    [
-      {
-        "_accuracy": 0.7773561997268175,
-        "_macro_f1": 0.5703751933361809,
-        "_score": 0.5703751933361809,
-        "_time": 36.73965764045715,
-        "_weighted_f1": 0.7467834129359526,
-        "del_dup1": false,
-        "lc": true,
-        "num_option": "group",
-        "strip_diac": true,
-        "token_list": [
-          1,
-          2,
-          3,
-          6
-        ],
-        "url_option": "none",
-        "usr_option": "group"
-      },
-    ...
-
-each entry specifies a configuration, please check the code (a manual is
-coming soon) to learn about each parameter. Since first configurations
-show how best/good setups are composed, it is possible to learn
-something about your dataset making some analysis on these setups.
-
-There exist other useful flags like:
-
--  ``-H`` makes b4msa to perform last hill climbing search for the
-   parameter selection, in many cases, this will produce much better
-   configurations (never worst, guaranteed)
--  ``--lang spanish|english|german|italian`` it specifies the language
-   of the dataset, it allows b4msa to use language dependent techniques
-   to the parameter selection procedure; currently, only ``spanish`` is
-   supported.
-
-.. code:: bash
-
-    b4msa-params -H -k5 -s48 -n24 tweets.json.gz -o tweets-spanish.json --lang spanish
-
-The ``tweets-spanish.json`` file looks as follows:
-
-::
-
-    [
-      {
-        "_accuracy": 0.7750796782516315,
-        "_macro_f1": 0.5736270120411987,
-        "_score": 0.5736270120411987,
-        "_time": 36.68731508255005,
-        "_weighted_f1": 0.7472079134492694,
-        "del_dup1": true,
-        "lc": true,
-        "negation": false,
-        "num_option": "group",
-        "stemming": true,
-        "stopwords": "delete",
-        "strip_diac": true,
-        "token_list": [
-          1,
-          2,
-          3,
-          5
-        ],
-        "url_option": "delete",
-        "usr_option": "none"
-      },
-    ...
-
-Here we can see that ``negation``, ``stemming`` and ``stopwords``
-parameters were considered.
-
-Using the models to create a sentiment classifier
--------------------------------------------------
-
-Testing a sentiment classifier against a workload
--------------------------------------------------
-
-Minimum requirements
-====================
-
-In the modeling stage, the minimum requirements are dependent on the
-knowledge database being processed. Make sure you have enough memory for
-it. Take into account that b4msa can take advantage of multicore
-architectures using the ``multiprocessing`` module of python, this means
-that the memory requirements are multiplied by the number of processes
-you run.
-
-It is recomended to use as many cores as you have to obtain good results
-in short running times.
-
-On the training and testing stages only one core is used and there is no
-extra memory needs; however, no multicore support is provided for these
-stages.
-
-Installing dependencies
-=======================
-
-Let us download python (from conda distribution), install it, and
-include python in the PATH.
-
-.. code:: bash
+.. image:: https://travis-ci.org/INGEOTEC/b4msa.svg?branch=master
+	:target: https://travis-ci.org/INGEOTEC/b4msa   
 
-    wget http://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh -O miniconda.sh
-    chmod 755 miniconda.sh
-    ./miniconda.sh -b
-    export PATH=/home/$USER/miniconda3/bin:$PATH
+.. image:: https://ci.appveyor.com/api/projects/status/y8vwd9998p74hw0n/branch/master?svg=true
+	:target: https://ci.appveyor.com/project/mgraffg/b4msa/branch/master   
 
-B4MSA needs the following dependencies.
+.. image:: https://coveralls.io/repos/github/INGEOTEC/b4msa/badge.svg?branch=master
+	:target: https://coveralls.io/github/INGEOTEC/b4msa?branch=master
 
-.. code:: bash
+.. image:: https://anaconda.org/ingeotec/b4msa/badges/version.svg
+	:target: https://anaconda.org/ingeotec/b4msa   
 
-    pip install coverage
-    pip install numpy
-    pip install scipy
-    pip install scikit-learn
-    pip install gensim
-    pip install nose
-    pip install nltk
+.. image:: https://badge.fury.io/py/b4msa.svg
+	:target: https://badge.fury.io/py/b4msa
 
-For the eager people, it is recommended to install the ``tqdm`` package
+.. image:: https://readthedocs.org/projects/b4msa/badge/?version=latest		 
+        :target: https://b4msa.readthedocs.io/en/latest/?badge=latest
 
-.. code:: bash
+b4msa is multilingual framework, that can be served as a baseline for sentiment analysis
+classifiers, as well as a starting point to build new sentiment analysis
+systems.
 
-    pip install tqdm
+b4msa extends our work on creating a text classifier (see `microTC
+<http://github.com/ingeotec/microtc>`_) by incorporating different
+language dependent techniques such as:
 
-However, it is better to prepare a coffee and a sandwich :)
+* Stemming
+* Stopword
+* Negations
 
-.. |Build Status| image:: https://travis-ci.org/INGEOTEC/b4msa.svg?branch=master
-   :target: https://travis-ci.org/INGEOTEC/b4msa
-.. |Coverage Status| image:: https://coveralls.io/repos/github/INGEOTEC/b4msa/badge.svg?branch=master
-   :target: https://coveralls.io/github/INGEOTEC/b4msa?branch=master
+The documentation is on `readthedocs <https://b4msa.readthedocs.io>`_.

b4msa/__init__.py

@@ -11,4 +11,4 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-__version__ = "1.5"
+__version__ = "2.0"

b4msa/classifier.py

@@ -35,11 +35,11 @@ class SVC(object):
 
     >>> from b4msa.textmodel import TextModel
     >>> from b4msa.classifier import SVC
-    >>> X = ['buenos dias', 'catedras conacyt', 'categorizacion de texto ingeotec']
-    >>> textmodel = TextModel(X)
+    >>> corpus = ['buenos dias', 'catedras conacyt', 'categorizacion de texto ingeotec']
+    >>> textmodel = TextModel(corpus)
     >>> svc = SVC(textmodel)
-    >>> svc.fit([textmodel[x] for x in X], [1, 0, 0])
-    >>> print(svc.predict_text('hola'))
+    >>> _ = svc.fit([textmodel[x] for x in corpus], [1, 0, 0])
+    >>> svc.predict_text('hola')
     0
     """
     def __init__(self, model, **kwargs):
@@ -162,6 +162,8 @@ def tqdm(x, **kwargs):
     @classmethod
     def train_predict_pool(cls, args):
         X, y, tr, ts, textModel_params = args
+        params = TextModel.params()
+        textModel_params = {k: v for k, v in textModel_params.items() if k in params}
         t = TextModel([X[x] for x in tr], **textModel_params)
         m = cls(t).fit([t[X[x]] for x in tr], [y[x] for x in tr])
         return ts, np.array(m.predict([t[X[x]] for x in ts]))

b4msa/command_line.py

@@ -28,6 +28,11 @@
 # from b4msa.params import ParameterSelection
 
 
+def clean_params(kw):
+    params = TextModel.params()
+    return {k: v for k, v in kw.items() if k in params}
+
+
 def load_json(filename):
     if filename.endswith('.gz'):
         func = gzip.open
@@ -170,6 +175,7 @@ def main(self):
                 best = best[0]
         else:
             best = dict()
+        best = clean_params(best)
         kw = json.loads(self.data.kwargs) if self.data.kwargs is not None else dict()
         best.update(kw)
         svc = SVC.fit_from_file(self.data.training_set, best)
@@ -280,6 +286,7 @@ def main(self, args=None):
         best = load_json(self.data.params_fname)
         if isinstance(best, list):
             best = best[0]
+        best = clean_params(best)
         print(self.data.params_fname, self.data.training_set)
         corpus, labels = read_data_labels(self.data.training_set)
         le = LabelEncoder()