Add sklearn

* Add sklearn example
* Fix test_case
* Add SenpyClientUse docs

a.k.a. The wise men edition

docs/SenpyClientUse.rst

@@ -0,0 +1,106 @@
+
+Client
+======
+
+Demo Endpoint
+-------------
+
+Import Client and send a request
+
+.. code:: python
+
+    from senpy.client import Client
+    
+    c = Client('http://latest.senpy.cluster.gsi.dit.upm.es/api')
+    r = c.analyse('I like Pizza', algorithm='sentiment140')
+
+Print response
+
+.. code:: python
+
+    for entry in r.entries:
+          print('{} -> {}'.format(entry['text'], entry['sentiments'][0]['marl:hasPolarity']))
+
+
+.. parsed-literal::
+
+    I like Pizza -> marl:Positive
+
+
+Obtain a list of available plugins
+
+.. code:: python
+
+    for plugin in c.request('/plugins')['plugins']:
+        print(plugin['name'])
+
+
+.. parsed-literal::
+
+    emoRand
+    rand
+    sentiment140
+
+
+Local Endpoint
+--------------
+
+Run a docker container with Senpy image and default plugins
+
+.. code::
+
+    docker run -ti --name 'SenpyEndpoint' -d -p 5000:5000 gsiupm/senpy:0.8.6 --host 0.0.0.0 --default-plugins
+
+
+.. parsed-literal::
+
+    a0157cd98057072388bfebeed78a830da7cf0a796f4f1a3fd9188f9f2e5fe562
+
+
+Import client and send a request to localhost
+
+.. code:: python
+
+    c_local = Client('http://127.0.0.1:5000/api')
+    r = c_local.analyse('Hello world', algorithm='sentiment140')
+
+Print response
+
+.. code:: python
+
+    for entry in r.entries:
+          print('{} -> {}'.format(entry['text'], entry['sentiments'][0]['marl:hasPolarity']))
+
+
+.. parsed-literal::
+
+    Hello world -> marl:Neutral
+
+
+Obtain a list of available plugins deployed locally
+
+.. code:: python
+
+    c_local.plugins().keys()
+
+
+.. parsed-literal::
+
+    rand
+    sentiment140
+    emoRand
+
+
+Stop the docker container
+
+.. code:: python
+
+    !docker stop SenpyEndpoint
+    !docker rm SenpyEndpoint
+
+
+.. parsed-literal::
+
+    SenpyEndpoint
+    SenpyEndpoint
+

example-plugins/dummy_required_plugin.py

@@ -22,7 +22,7 @@ class DummyRequired(AnalysisPlugin):
         'entry': {
             'nif:isString': 'Hello',
         },
-        'expected': None
+        'should_fail': True
     }, {
         'entry': {
             'nif:isString': 'Hello',

example-plugins/sklearn/mydata.py

@@ -0,0 +1,33 @@
+'''
+Create a dummy dataset.
+Messages with a happy emoticon are labelled positive
+Messages with a sad emoticon are labelled negative
+'''
+import random
+
+dataset = []
+
+vocabulary = ['hello', 'world', 'senpy', 'cool', 'goodbye', 'random', 'text']
+
+emojimap = {
+    1: [':)', ],
+    -1: [':(', ]
+}
+
+
+for tag, values in emojimap.items():
+    for i in range(1000):
+        msg = ''
+        for j in range(3):
+            msg += random.choice(vocabulary)
+            msg += " "
+        msg += random.choice(values)
+        dataset.append([msg, tag])
+
+
+text = []
+labels = []
+
+for i in dataset:
+    text.append(i[0])
+    labels.append(i[1])

example-plugins/sklearn/mypipeline.py

@@ -0,0 +1,30 @@
+from sklearn.pipeline import Pipeline
+from sklearn.feature_extraction.text import CountVectorizer
+from sklearn.model_selection import train_test_split
+
+from mydata import text, labels
+
+X_train, X_test, y_train, y_test = train_test_split(text, labels, test_size=0.12, random_state=42)
+
+from sklearn.naive_bayes import MultinomialNB
+
+
+count_vec = CountVectorizer(tokenizer=lambda x: x.split())
+clf3 = MultinomialNB()
+pipeline = Pipeline([('cv', count_vec),
+                    ('clf', clf3)])
+
+pipeline.fit(X_train, y_train)
+print('Feature names: {}'.format(count_vec.get_feature_names()))
+print('Class count: {}'.format(clf3.class_count_))
+
+
+if __name__ == '__main__':
+    print('--Results--')
+    tests = [
+        (['The sentiment for senpy should be positive :)', ], 1),
+        (['The sentiment for anything else should be negative :()', ], -1)
+    ]
+    for features, expected in tests:
+        result = pipeline.predict(features)
+        print('Input: {}\nExpected: {}\nGot: {}'.format(features[0], expected, result))

example-plugins/sklearn/pipeline_plugin.py

@@ -0,0 +1,37 @@
+from senpy import SentimentBox, MappingMixin, easy_test
+
+from mypipeline import pipeline
+
+
+class PipelineSentiment(MappingMixin, SentimentBox):
+    '''
+    This is a pipeline plugin that wraps a classifier defined in another module
+    (mypipeline).
+    '''
+    author = '@balkian'
+    version = 0.1
+    maxPolarityValue = 1
+    minPolarityValue = -1
+
+    mappings = {
+        1: 'marl:Positive',
+        -1: 'marl:Negative'
+    }
+
+    def box(self, input, *args, **kwargs):
+        return pipeline.predict([input, ])[0]
+
+    test_cases = [
+        {
+            'input': 'The sentiment for senpy should be positive :)',
+            'polarity': 'marl:Positive'
+        },
+        {
+            'input': 'The sentiment for senpy should be negative :(',
+            'polarity': 'marl:Negative'
+        }
+    ]
+
+
+if __name__ == '__main__':
+    easy_test()

senpy/plugins/__init__.py

@@ -114,6 +114,7 @@ class Plugin(with_metaclass(PluginMeta, models.Plugin)):
         for case in test_cases:
             try:
                 self.test_case(case)
+                logger.debug('Test case passed:\n{}'.format(pprint.pformat(case)))
             except Exception as ex:
                 logger.warn('Test case failed:\n{}'.format(pprint.pformat(case)))
                 raise
@@ -121,7 +122,7 @@ class Plugin(with_metaclass(PluginMeta, models.Plugin)):
     def test_case(self, case):
         entry = models.Entry(case['entry'])
         given_parameters = case.get('params', case.get('parameters', {}))
-        expected = case['expected']
+        expected = case.get('expected', None)
         should_fail = case.get('should_fail', False)
         try:
             params = api.parse_params(given_parameters, self.extra_params)
@@ -135,6 +136,7 @@ class Plugin(with_metaclass(PluginMeta, models.Plugin)):
         except models.Error:
             if should_fail:
                 return
+            raise
         assert not should_fail
 
     def open(self, fpath, *args, **kwargs):
@@ -213,8 +215,8 @@ class SentimentPlugin(Analysis, models.SentimentPlugin):
     maxPolarityValue = 1
 
     def test_case(self, case):
-        expected = case.get('expected', {})
         if 'polarity' in case:
+            expected = case.get('expected', {})
             s = models.Sentiment(_auto_id=False)
             s.marl__hasPolarity = case['polarity']
             if 'sentiments' not in expected:
@@ -320,6 +322,14 @@ class EmotionBox(TextBox, EmotionPlugin):
 
 class MappingMixin(object):
 
+    @property
+    def mappings(self):
+        return self._mappings
+
+    @mappings.setter
+    def mappings(self, value):
+        self._mappings = value
+
     def output(self, output, entry, params):
         output = self.mappings.get(output,
                                    self.mappings.get('default', output))

senpy/utils.py

@@ -76,6 +76,7 @@ def easy_test(plugin_list=None):
         plugin_list = plugins.from_module(__main__)
     for plug in plugin_list:
         plug.test()
+        logger.info('The tests for {} passed!'.format(plug.name))
     logger.info('All tests passed!')