senpy/tests/test_plugins.py

#!/usr/local/bin/python
# -*- coding: utf-8 -*-
#
#    Copyright 2014 Grupo de Sistemas Inteligentes (GSI) DIT, UPM
#
#    Licensed under the Apache License, Version 2.0 (the "License");
#    you may not use this file except in compliance with the License.
#    You may obtain a copy of the License at
#
#        http://www.apache.org/licenses/LICENSE-2.0
#
#    Unless required by applicable law or agreed to in writing, software
#    distributed under the License is distributed on an "AS IS" BASIS,
#    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.
#


import os
import pickle
import shutil
import tempfile

from unittest import TestCase, skipIf
from senpy.models import Results, Entry, EmotionSet, Emotion, Plugins
from senpy import plugins
from senpy.plugins.postprocessing.emotion.centroids import CentroidConversion
from senpy.gsitk_compat import GSITK_AVAILABLE
from senpy import config

import pandas as pd

ROOT = os.path.join(os.path.dirname(__file__), '..')

class ShelfDummyPlugin(plugins.SentimentPlugin, plugins.ShelfMixin):
    '''Dummy plugin for tests.'''
    name = 'Shelf'
    version = 0
    author = 'the senpy community'

    def activate(self, *args, **kwargs):
        if 'counter' not in self.sh:
            self.sh['counter'] = 0
        self.save()

    def deactivate(self, *args, **kwargs):
        self.save()

    def analyse(self, *args, **kwargs):
        self.sh['counter'] = self.sh['counter'] + 1
        e = Entry()
        e.nif__isString = self.sh['counter']
        r = Results()
        r.entries.append(e)
        return r


class PluginsTest(TestCase):
    def tearDown(self):
        if os.path.exists(self.shelf_dir):
            shutil.rmtree(self.shelf_dir)
        if os.path.isfile(self.shelf_file):
            os.remove(self.shelf_file)

    def setUp(self):
        self.shelf_dir = tempfile.mkdtemp()
        self.shelf_file = os.path.join(self.shelf_dir, "shelf")

    def test_serialize(self):
        '''A plugin should be serializable and de-serializable'''
        dummy = ShelfDummyPlugin()
        dummy.serialize()

    def test_jsonld(self):
        '''A plugin should be serializable and de-serializable'''
        dummy = ShelfDummyPlugin()
        dummy.jsonld()

    def test_shelf_file(self):
        a = ShelfDummyPlugin(
            info={'name': 'default_shelve_file',
                  'description': 'Dummy plugin for tests',
                  'version': 'test'})
        a.activate()
        assert os.path.isfile(a.shelf_file)
        os.remove(a.shelf_file)

    def test_plugin_filter(self):
        ps = Plugins()
        for i in (plugins.SentimentPlugin,
                  plugins.EmotionPlugin,
                  plugins.Analyser):
            p = i(name='Plugin_{}'.format(i.__name__),
                  description='TEST',
                  version=0,
                  author='NOBODY')
            ps.plugins.append(p)
        assert len(ps.plugins) == 3
        cases = [('AnalysisPlugin', 3),
                 ('SentimentPlugin', 1),
                 ('EmotionPlugin', 1)]

        for name, num in cases:
            res = list(plugins.pfilter(ps.plugins, plugin_type=name))
            assert len(res) == num

    def test_shelf(self):
        ''' A shelf is created and the value is stored '''
        newfile = self.shelf_file + "new"
        a = ShelfDummyPlugin(info={
            'name': 'shelve',
            'description': 'Shelf plugin for tests',
            'version': 'test',
            'shelf_file': newfile
        })
        assert a.sh == {}
        a.activate()
        assert a.sh == {'counter': 0}
        assert a.shelf_file == newfile

        a.sh['a'] = 'fromA'
        assert a.sh['a'] == 'fromA'

        a.save()

        sh = pickle.load(open(newfile, 'rb'))

        assert sh['a'] == 'fromA'

    def test_dummy_shelf(self):
        with open(self.shelf_file, 'wb') as f:
            pickle.dump({'counter': 99}, f)
        a = ShelfDummyPlugin(info={
            'name': 'DummyShelf',
            'description': 'Dummy plugin for tests',
            'shelf_file': self.shelf_file,
            'version': 'test'
        })
        a.activate()

        assert a.shelf_file == self.shelf_file
        res1 = a.analyse(input=1)
        assert res1.entries[0].nif__isString == 100
        a.deactivate()
        del a

        with open(self.shelf_file, 'rb') as f:
            sh = pickle.load(f)
            assert sh['counter'] == 100

    def test_corrupt_shelf(self):
        ''' Reusing the values of a previous shelf '''

        emptyfile = os.path.join(self.shelf_dir, "emptyfile")
        invalidfile = os.path.join(self.shelf_dir, "invalid_file")
        with open(emptyfile, 'w+b'), open(invalidfile, 'w+b') as inf:
            inf.write(b'ohno')

        files = {emptyfile: ['empty file', (EOFError, IndexError)],
                 invalidfile: ['invalid file', (pickle.UnpicklingError, IndexError)]}

        for fn in files:
            with open(fn, 'rb') as f:
                msg, error = files[fn]
                a = ShelfDummyPlugin(info={
                    'name': 'test_corrupt_shelf_{}'.format(msg),
                    'description': 'Dummy plugin for tests',
                    'version': 'test',
                    'shelf_file': f.name
                })
                assert os.path.isfile(a.shelf_file)
                print('Shelf file: %s' % a.shelf_file)
                with self.assertRaises(error):
                    a.sh['a'] = 'fromA'
                    a.save()
                del a._sh
                assert os.path.isfile(a.shelf_file)
                a.force_shelf = True
                a.sh['a'] = 'fromA'
                a.save()
                b = pickle.load(f)
                assert b['a'] == 'fromA'

    def test_reuse_shelf(self):
        ''' Reusing the values of a previous shelf '''
        a = ShelfDummyPlugin(info={
            'name': 'shelve',
            'description': 'Dummy plugin for tests',
            'version': 'test',
            'shelf_file': self.shelf_file
        })
        a.activate()
        print('Shelf file: %s' % a.shelf_file)
        a.sh['a'] = 'fromA'
        a.save()

        b = ShelfDummyPlugin(info={
            'name': 'shelve',
            'description': 'Dummy plugin for tests',
            'version': 'test',
            'shelf_file': self.shelf_file
        })
        b.activate()
        assert b.sh['a'] == 'fromA'
        b.sh['a'] = 'fromB'
        assert b.sh['a'] == 'fromB'

    def test_extra_params(self):
        ''' Should be able to set extra parameters'''
        a = ShelfDummyPlugin(info={
            'name': 'shelve',
            'description': 'Dummy shelf plugin for tests',
            'version': 'test',
            'shelf_file': self.shelf_file,
            'extra_params': {
                'example': {
                    'aliases': ['example', 'ex'],
                    'required': True,
                    'default': 'nonsense'
                }
            }
        })
        assert 'example' in a.extra_params

    def test_box(self):

        class MyBox(plugins.Box):
            ''' Vague description'''

            author = 'me'
            version = 0

            def to_features(self, entry, **kwargs):
                return entry.text.split()

            def predict_one(self, features, **kwargs):
                return ['SIGN' in features]

            def to_entry(self, features, entry, **kwargs):
                print('Features for to_entry:', features)
                if features[0]:
                    entry.myAnnotation = 'DETECTED'
                return entry

            test_cases = [
                {
                    'input': "nothing here",
                    'expected': {'myAnnotation': 'DETECTED'},
                    'should_fail': True
                }, {
                    'input': "SIGN",
                    'expected': {'myAnnotation': 'DETECTED'}
                }]

        MyBox().test()

    def test_sentimentbox(self):

        class SentimentBox(plugins.SentimentBox):
            ''' Vague description'''

            author = 'me'
            version = 0

            def predict_one(self, features, **kwargs):
                text = ' '.join(features)
                if ':)' in text:
                    return [1, 0, 0]
                return [0, 0, 1]

            test_cases = [
                {
                    'input': 'a happy face :)',
                    'polarity': 'marl:Positive'
                }, {
                    'input': "Nothing",
                    'polarity': 'marl:Negative'
                }]

        SentimentBox().test()

    def test_conversion_centroids(self):
        info = {
            "name": "CentroidTest",
            "description": "Centroid test",
            "version": 0,
            "centroids": {
                "c1": {"V1": 0.5,
                       "V2": 0.5},
                "c2": {"V1": -0.5,
                       "V2": 0.5},
                "c3": {"V1": -0.5,
                       "V2": -0.5},
                "c4": {"V1": 0.5,
                       "V2": -0.5}},
            "aliases": {
                "V1": "X-dimension",
                "V2": "Y-dimension"
            },
            "centroids_direction": ["emoml:big6", "emoml:fsre-dimensions"]
        }
        c = CentroidConversion(info)
        print(c.serialize())

        es1 = EmotionSet()
        e1 = Emotion()
        e1.onyx__hasEmotionCategory = "c1"
        es1.onyx__hasEmotion.append(e1)
        res = c._forward_conversion(es1)
        assert res["X-dimension"] == 0.5
        assert res["Y-dimension"] == 0.5
        print(res)

        e2 = Emotion()
        e2.onyx__hasEmotionCategory = "c2"
        es1.onyx__hasEmotion.append(e2)
        res = c._forward_conversion(es1)
        assert res["X-dimension"] == 0
        assert res["Y-dimension"] == 1
        print(res)

        e = Emotion()
        e["X-dimension"] = -0.2
        e["Y-dimension"] = -0.3
        res = c._backwards_conversion(e)
        assert res["onyx:hasEmotionCategory"] == "c3"
        print(res)

        e = Emotion()
        e["X-dimension"] = -0.2
        e["Y-dimension"] = 0.3
        res = c._backwards_conversion(e)
        assert res["onyx:hasEmotionCategory"] == "c2"

    def test_installation(self):
        sentiment = next(plugins.from_path('senpy/plugins/sentiment/basic/sentiment_basic_plugin.py'))
        assert sentiment
        inst, missing, nltk_deps = plugins.list_dependencies(sentiment)
        assert 'punkt_tab' in nltk_deps

        emotion = next(plugins.from_path('senpy/plugins/emotion/wnaffect/emotion_wnaffect_plugin.py'))
        assert emotion
        inst, missing, nltk_deps = plugins.list_dependencies(emotion)
        assert 'averaged_perceptron_tagger_eng' in nltk_deps


    def _test_evaluation(self):
        testdata = []
        for i in range(50):
            testdata.append(["good", 1])
        for i in range(50):
            testdata.append(["bad", -1])
        dataset = pd.DataFrame(testdata, columns=['text', 'polarity'])

        class DummyPlugin(plugins.SentimentBox):
            description = 'Plugin to test evaluation'
            version = 0

            classes = ['marl:Positive', 'marl:Negative']

            def predict_one(self, features, **kwargs):
                print(features[0])
                return [0, 1]

        class SmartPlugin(plugins.SentimentBox):
            description = 'Plugin to test evaluation'
            version = 0

            classes = ['marl:Positive', 'marl:Negative']

            def predict_one(self, features, **kwargs):
                print(features[0])
                if features[0] == 'good':
                    print('positive')
                    return [1, 0]
                print('negative')
                return [0, 1]

        dpipe = DummyPlugin()
        results = plugins.evaluate(datasets={'testdata': dataset}, plugins=[dpipe], flatten=True)
        dumb_metrics = results[0].metrics[0]
        assert abs(dumb_metrics['accuracy'] - 0.5) < 0.01

        spipe = SmartPlugin()
        results = plugins.evaluate(datasets={'testdata': dataset}, plugins=[spipe], flatten=True)
        smart_metrics = results[0].metrics[0]
        assert abs(smart_metrics['accuracy'] - 1) < 0.01

    @skipIf(not GSITK_AVAILABLE, "GSITK is not available")
    def test_evaluation(self):
        self._test_evaluation()

    @skipIf(GSITK_AVAILABLE, "GSITK is available")
    def test_evaluation_unavailable(self):
        with self.assertRaises(Exception) as context:
            self._test_evaluation()
        self.assertTrue('GSITK ' in str(context.exception))


def make_mini_test(fpath):
    def mini_test(self):
        for plugin in plugins.from_path(fpath, strict=True):
            plugins.install_deps(plugin)
            plugin.test()
    return mini_test


def _add_tests():
    print(ROOT)
    for fpath in plugins.find_plugins([ROOT, ]):
        pass
        t_method = make_mini_test(fpath)
        t_method.__name__ = 'test_plugin_{}'.format(fpath)
        setattr(PluginsTest, t_method.__name__, t_method)
        del t_method


_add_tests()