__init__.py 19.4 KB
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from future import standard_library
standard_library.install_aliases()
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from future.utils import with_metaclass
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import os.path
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import os
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import re
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import pickle
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import logging
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import copy
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import pprint
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import inspect
import sys
import subprocess
import importlib
import yaml
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import threading
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import numpy as np

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from .. import models, utils
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from .. import api
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logger = logging.getLogger(__name__)

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try:
    from gsitk.evaluation.evaluation import Evaluation as Eval
    from sklearn.pipeline import Pipeline
    GSITK_AVAILABLE = True
except ImportError:
    logger.warn('GSITK is not installed. Some functions will be unavailable.')
    GSITK_AVAILABLE = False

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class PluginMeta(models.BaseMeta):
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    _classes = {}
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    def __new__(mcs, name, bases, attrs, **kwargs):
        plugin_type = []
        if hasattr(bases[0], 'plugin_type'):
            plugin_type += bases[0].plugin_type
        plugin_type.append(name)
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        alias = attrs.get('name', name)
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        attrs['plugin_type'] = plugin_type
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        attrs['name'] = alias
        if 'description' not in attrs:
            doc = attrs.get('__doc__', None)
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            if doc:
                attrs['description'] = doc
            else:
                logger.warn(('Plugin {} does not have a description. '
                             'Please, add a short summary to help other developers').format(name))
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        cls = super(PluginMeta, mcs).__new__(mcs, name, bases, attrs)
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        if alias in mcs._classes:
            if os.environ.get('SENPY_TESTING', ""):
                raise Exception(('The type of plugin {} already exists. '
                                'Please, choose a different name').format(name))
            else:
                logger.warn('Overloading plugin class: {}'.format(alias))
        mcs._classes[alias] = cls
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        return cls

    @classmethod
    def for_type(cls, ptype):
        return cls._classes[ptype]
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class Plugin(with_metaclass(PluginMeta, models.Plugin)):
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    '''
    Base class for all plugins in senpy.
    A plugin must provide at least these attributes:

        - version
        - description (or docstring)
        - author

    Additionally, they may provide a URL (url) of a repository or website.

    '''
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    def __init__(self, info=None, data_folder=None, **kwargs):
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        """
        Provides a canonical name for plugins and serves as base for other
        kinds of plugins.
        """
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        logger.debug("Initialising {}".format(info))
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        super(Plugin, self).__init__(**kwargs)
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        if info:
            self.update(info)
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        self.validate()
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        self.id = 'plugins/{}_{}'.format(self['name'], self['version'])
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        self.is_activated = False
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        self._lock = threading.Lock()
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        self.data_folder = data_folder or os.getcwd()
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    def validate(self):
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        missing = []
        for x in ['name', 'description', 'version']:
            if x not in self:
                missing.append(x)
        if missing:
            raise models.Error('Missing configuration parameters: {}'.format(missing))
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    def get_folder(self):
        return os.path.dirname(inspect.getfile(self.__class__))

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    def activate(self):
        pass

    def deactivate(self):
        pass

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    def test(self, test_cases=None):
        if not test_cases:
            if not hasattr(self, 'test_cases'):
                raise AttributeError(('Plugin {} [{}] does not have any defined '
                                      'test cases').format(self.id,
                                                           inspect.getfile(self.__class__)))
            test_cases = self.test_cases
        for case in test_cases:
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            try:
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                self.test_case(case)
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                logger.debug('Test case passed:\n{}'.format(pprint.pformat(case)))
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            except Exception as ex:
                logger.warn('Test case failed:\n{}'.format(pprint.pformat(case)))
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                raise

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    def test_case(self, case):
        entry = models.Entry(case['entry'])
        given_parameters = case.get('params', case.get('parameters', {}))
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        expected = case.get('expected', None)
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        should_fail = case.get('should_fail', False)
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        try:
            params = api.parse_params(given_parameters, self.extra_params)
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            res = list(self.analyse_entries([entry, ], params))

            if not isinstance(expected, list):
                expected = [expected]
            utils.check_template(res, expected)
            for r in res:
                r.validate()
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        except models.Error:
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            if should_fail:
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                return
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            raise
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        assert not should_fail
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    def open(self, fpath, *args, **kwargs):
        if not os.path.isabs(fpath):
            fpath = os.path.join(self.data_folder, fpath)
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        return open(fpath, *args, **kwargs)

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    def serve(self, debug=True, **kwargs):
        utils.easy(plugin_list=[self, ], plugin_folder=None, debug=debug, **kwargs)
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# For backwards compatibility
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SenpyPlugin = Plugin


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class Analysis(Plugin):
    '''
    A subclass of Plugin that analyses text and provides an annotation.
    '''
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    def analyse(self, *args, **kwargs):
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        raise NotImplementedError(
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            'Your plugin should implement either analyse or analyse_entry')
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    def analyse_entry(self, entry, parameters):
        """ An implemented plugin should override this method.
        This base method is here to adapt old style plugins which only
        implement the *analyse* function.
        Note that this method may yield an annotated entry or a list of
        entries (e.g. in a tokenizer)
        """
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        text = entry['nif:isString']
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        params = copy.copy(parameters)
        params['input'] = text
        results = self.analyse(**params)
        for i in results.entries:
            yield i
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    def analyse_entries(self, entries, parameters):
        for entry in entries:
            logger.debug('Analysing entry with plugin {}: {}'.format(self, entry))
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            results = self.analyse_entry(entry, parameters)
            if inspect.isgenerator(results):
                for result in results:
                    yield result
            else:
                yield results

    def test_case(self, case):
        if 'entry' not in case and 'input' in case:
            entry = models.Entry(_auto_id=False)
            entry.nif__isString = case['input']
            case['entry'] = entry
        super(Analysis, self).test_case(case)
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AnalysisPlugin = Analysis


class Conversion(Plugin):
    '''
    A subclass of Plugins that convert between different annotation models.
    e.g. a conversion of emotion models, or normalization of sentiment values.
    '''
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    pass
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ConversionPlugin = Conversion


class SentimentPlugin(Analysis, models.SentimentPlugin):
    '''
    Sentiment plugins provide sentiment annotation (using Marl)
    '''
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    minPolarityValue = 0
    maxPolarityValue = 1
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    def test_case(self, case):
        if 'polarity' in case:
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            expected = case.get('expected', {})
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            s = models.Sentiment(_auto_id=False)
            s.marl__hasPolarity = case['polarity']
            if 'sentiments' not in expected:
                expected['sentiments'] = []
            expected['sentiments'].append(s)
            case['expected'] = expected
        super(SentimentPlugin, self).test_case(case)


class EmotionPlugin(Analysis, models.EmotionPlugin):
    '''
    Emotion plugins provide emotion annotation (using Onyx)
    '''
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    minEmotionValue = 0
    maxEmotionValue = 1
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class EmotionConversion(Conversion):
    '''
    A subclass of Conversion that converts emotion annotations using different models
    '''
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    pass
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EmotionConversionPlugin = EmotionConversion


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class Box(AnalysisPlugin):
    '''
    Black box plugins delegate analysis to a function.
    The flow is like so:

    .. code-block::

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                   entry --> input() --> predict_one() --> output() --> entry'
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    In other words: their ``input`` method convers a query (entry and a set of parameters) into
    the input to the box method. The ``output`` method convers the results given by the box into
    an entry that senpy can handle.
    '''

    def input(self, entry, params=None):
        '''Transforms a query (entry+param) into an input for the black box'''
        return entry

    def output(self, output, entry=None, params=None):
        '''Transforms the results of the black box into an entry'''
        return output

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    def predict_one(self, input):
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        raise NotImplementedError('You should define the behavior of this plugin')

    def analyse_entries(self, entries, params):
        for entry in entries:
            input = self.input(entry=entry, params=params)
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            results = self.predict_one(input=input)
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            yield self.output(output=results, entry=entry, params=params)

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    def fit(self, X=None, y=None):
        return self

    def transform(self, X):
        return np.array([self.predict_one(x) for x in X])

    def predict(self, X):
        return self.transform(X)

    def fit_transform(self, X, y):
        self.fit(X, y)
        return self.transform(X)

    def as_pipe(self):
        pipe = Pipeline([('plugin', self)])
        pipe.name = self.name
        return pipe

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class TextBox(Box):
    '''A black box plugin that takes only text as input'''

    def input(self, entry, params):
        entry = super(TextBox, self).input(entry, params)
        return entry['nif:isString']


class SentimentBox(TextBox, SentimentPlugin):
    '''
    A box plugin where the output is only a polarity label or a tuple (polarity, polarityValue)
    '''

    def output(self, output, entry, **kwargs):
        s = models.Sentiment()
        try:
            label, value = output
        except ValueError:
            label, value = output, None
        s.prov(self)
        s.polarity = label
        if value is not None:
            s.polarityValue = value
        entry.sentiments.append(s)
        return entry


class EmotionBox(TextBox, EmotionPlugin):
    '''
    A box plugin where the output is only an a tuple of emotion labels
    '''

    def output(self, output, entry, **kwargs):
        if not isinstance(output, list):
            output = [output]
        s = models.EmotionSet()
        entry.emotions.append(s)
        for label in output:
            e = models.Emotion(onyx__hasEmotionCategory=label)
            s.append(e)
        return entry


class MappingMixin(object):

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    @property
    def mappings(self):
        return self._mappings

    @mappings.setter
    def mappings(self, value):
        self._mappings = value

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    def output(self, output, entry, params):
        output = self.mappings.get(output,
                                   self.mappings.get('default', output))
        return super(MappingMixin, self).output(output=output,
                                                entry=entry,
                                                params=params)


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class ShelfMixin(object):
    @property
    def sh(self):
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        if not hasattr(self, '_sh') or self._sh is None:
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            self._sh = {}
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            if os.path.isfile(self.shelf_file):
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                try:
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                    with self.open(self.shelf_file, 'rb') as p:
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                        self._sh = pickle.load(p)
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                except (IndexError, EOFError, pickle.UnpicklingError):
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                    logger.warning('{} has a corrupted shelf file!'.format(self.id))
                    if not self.get('force_shelf', False):
                        raise
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        return self._sh

    @sh.deleter
    def sh(self):
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        if os.path.isfile(self.shelf_file):
            os.remove(self.shelf_file)
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            del self._sh
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        self.save()
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    @sh.setter
    def sh(self, value):
        self._sh = value

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    @property
    def shelf_file(self):
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        if not hasattr(self, '_shelf_file') or not self._shelf_file:
            self._shelf_file = os.path.join(self.data_folder, self.name + '.p')
        return self._shelf_file

    @shelf_file.setter
    def shelf_file(self, value):
        self._shelf_file = value
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    def save(self):
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        logger.debug('saving pickle')
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        if hasattr(self, '_sh') and self._sh is not None:
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            with self.open(self.shelf_file, 'wb') as f:
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                pickle.dump(self._sh, f)
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def pfilter(plugins, **kwargs):
    """ Filter plugins by different criteria """
    if isinstance(plugins, models.Plugins):
        plugins = plugins.plugins
    elif isinstance(plugins, dict):
        plugins = plugins.values()
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    ptype = kwargs.pop('plugin_type', Plugin)
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    logger.debug('#' * 100)
    logger.debug('ptype {}'.format(ptype))
    if ptype:
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        if isinstance(ptype, PluginMeta):
            ptype = ptype.__name__
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        try:
            ptype = ptype[0].upper() + ptype[1:]
            pclass = globals()[ptype]
            logger.debug('Class: {}'.format(pclass))
            candidates = filter(lambda x: isinstance(x, pclass),
                                plugins)
        except KeyError:
            raise models.Error('{} is not a valid type'.format(ptype))
    else:
        candidates = plugins

    logger.debug(candidates)

    def matches(plug):
        res = all(getattr(plug, k, None) == v for (k, v) in kwargs.items())
        logger.debug(
            "matching {} with {}: {}".format(plug.name, kwargs, res))
        return res

    if kwargs:
        candidates = filter(matches, candidates)
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    return candidates
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def load_module(name, root=None):
    if root:
        sys.path.append(root)
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    tmp = importlib.import_module(name)
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    if root:
        sys.path.remove(root)
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    return tmp


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def _log_subprocess_output(process):
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    for line in iter(process.stdout.readline, b''):
        logger.info('%r', line)
    for line in iter(process.stderr.readline, b''):
        logger.error('%r', line)


def install_deps(*plugins):
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    installed = False
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    for info in plugins:
        requirements = info.get('requirements', [])
        if requirements:
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            pip_args = [sys.executable, '-m', 'pip', 'install']
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            for req in requirements:
                pip_args.append(req)
            logger.info('Installing requirements: ' + str(requirements))
            process = subprocess.Popen(pip_args,
                                       stdout=subprocess.PIPE,
                                       stderr=subprocess.PIPE)
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            _log_subprocess_output(process)
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            exitcode = process.wait()
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            installed = True
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            if exitcode != 0:
                raise models.Error("Dependencies not properly installed")
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    return installed
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is_plugin_file = re.compile(r'.*\.senpy$|senpy_[a-zA-Z0-9_]+\.py$|'
                            '^(?!test_)[a-zA-Z0-9_]+_plugin.py$')
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def find_plugins(folders):
    for search_folder in folders:
        for root, dirnames, filenames in os.walk(search_folder):
            # Do not look for plugins in hidden or special folders
            dirnames[:] = [d for d in dirnames if d[0] not in ['.', '_']]
            for filename in filter(is_plugin_file.match, filenames):
                fpath = os.path.join(root, filename)
                yield fpath


def from_path(fpath, **kwargs):
    logger.debug("Loading plugin from {}".format(fpath))
    if fpath.endswith('.py'):
        # We asume root is the dir of the file, and module is the name of the file
        root = os.path.dirname(fpath)
        module = os.path.basename(fpath)[:-3]
        for instance in _from_module_name(module=module, root=root, **kwargs):
            yield instance
    else:
        info = parse_plugin_info(fpath)
        yield from_info(info, **kwargs)


def from_folder(folders, loader=from_path, **kwargs):
    plugins = []
    for fpath in find_plugins(folders):
        for plugin in loader(fpath, **kwargs):
            plugins.append(plugin)
    return plugins


def from_info(info, root=None, **kwargs):
    if any(x not in info for x in ('module',)):
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        raise ValueError('Plugin info is not valid: {}'.format(info))
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    module = info["module"]

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    if not root and '_path' in info:
        root = os.path.dirname(info['_path'])

    return one_from_module(module, root=root, info=info, **kwargs)
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def parse_plugin_info(fpath):
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    logger.debug("Parsing plugin info: {}".format(fpath))
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    with open(fpath, 'r') as f:
        info = yaml.load(f)
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    info['_path'] = fpath
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    return info
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def from_module(module, **kwargs):
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    if inspect.ismodule(module):
        res = _from_loaded_module(module, **kwargs)
    else:
        res = _from_module_name(module, **kwargs)
    for p in res:
        yield p
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def one_from_module(module, root, info, **kwargs):
    if '@type' in info:
        cls = PluginMeta.from_type(info['@type'])
        return cls(info=info, **kwargs)
    instance = next(from_module(module=module, root=root, info=info, **kwargs), None)
    if not instance:
        raise Exception("No valid plugin for: {}".format(module))
    return instance


def _classes_in_module(module):
    for _, obj in inspect.getmembers(module):
        if inspect.isclass(obj) and inspect.getmodule(obj) == module:
            logger.debug(("Found plugin class:"
                          " {}@{}").format(obj, inspect.getmodule(obj)))
            yield obj


def _instances_in_module(module):
    for _, obj in inspect.getmembers(module):
        if isinstance(obj, Plugin) and inspect.getmodule(obj) == module:
            logger.debug(("Found plugin instance:"
                          " {}@{}").format(obj, inspect.getmodule(obj)))
            yield obj


def _from_module_name(module, root, info=None, install=True, **kwargs):
    try:
        module = load_module(module, root)
    except ImportError:
        if not install or not info:
            raise
        install_deps(info)
        module = load_module(module, root)
    for plugin in _from_loaded_module(module=module, root=root, info=info, **kwargs):
        yield plugin


def _from_loaded_module(module, info=None, **kwargs):
    for cls in _classes_in_module(module):
        yield cls(info=info, **kwargs)
    for instance in _instances_in_module(module):
        yield instance
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def evaluate(plugins, datasets, **kwargs):
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    if not GSITK_AVAILABLE:
        raise Exception('GSITK is not available. Install it to use this function.')

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    ev = Eval(tuples=None,
              datasets=datasets,
              pipelines=[plugin.as_pipe() for plugin in plugins])
    ev.evaluate()
    results = ev.results
    evaluations = evaluations_to_JSONLD(results, **kwargs)
    return evaluations


def evaluations_to_JSONLD(results, flatten=False):
    '''
    Map the evaluation results to a JSONLD scheme
    '''

    evaluations = list()
    metric_names = ['accuracy', 'precision_macro', 'recall_macro',
                    'f1_macro', 'f1_weighted', 'f1_micro', 'f1_macro']

    for index, row in results.iterrows():
        evaluation = models.Evaluation()
        if row.get('CV', True):
            evaluation['@type'] = ['StaticCV', 'Evaluation']
        evaluation.evaluatesOn = row['Dataset']
        evaluation.evaluates = row['Model']
        i = 0
        if flatten:
            metric = models.Metric()
            for name in metric_names:
                metric[name] = row[name]
            evaluation.metrics.append(metric)
        else:
            # We should probably discontinue this representation
            for name in metric_names:
                metric = models.Metric()
                metric['@id'] = 'Metric' + str(i)
                metric['@type'] = name.capitalize()
                metric.value = row[name]
                evaluation.metrics.append(metric)
                i += 1
        evaluations.append(evaluation)
    return evaluations