Commit 1087692d authored by J. Fernando Sánchez's avatar J. Fernando Sánchez

Add sklearn

* Add sklearn example
* Fix test_case
* Add SenpyClientUse docs

a.k.a. The wise men edition
parent 3e2b8bae
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
......@@ -22,7 +22,7 @@ class DummyRequired(AnalysisPlugin):
'entry': {
'nif:isString': 'Hello',
},
'expected': None
'should_fail': True
}, {
'entry': {
'nif:isString': 'Hello',
......
'''
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])
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))
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()
......@@ -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))
......
......@@ -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!')
......
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