mirror of
https://github.com/gsi-upm/senpy
synced 2024-12-22 13:08:13 +00:00
Add basic evaluation and fix installation
* Merge branch '44-add-basic-evaluation-with-gsitk' * Refactor requirements (add extra-requirements) * Skip evaluation tests in Py2 * Fix installation with PIP * Implement the evaluation service inside the Senpy API * Connect Plugins to GSITK's evaluation module * Add an evaluation method inside the Senpy Context * Add the evaluation models and schemas * Add Evaluation to the Playground, with a table view * Add evaluation tests
This commit is contained in:
J. Fernando Sánchez
commit
55db97cf62
@ -20,8 +20,8 @@ ONBUILD WORKDIR /senpy-plugins/
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WORKDIR /usr/src/app
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COPY test-requirements.txt requirements.txt /usr/src/app/
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RUN pip install --no-cache-dir --use-wheel -r test-requirements.txt -r requirements.txt
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COPY test-requirements.txt requirements.txt extra-requirements.txt /usr/src/app/
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RUN pip install --no-cache-dir -r test-requirements.txt -r requirements.txt -r extra-requirements.txt
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COPY . /usr/src/app/
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RUN pip install --no-cache-dir --no-index --no-deps --editable .
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@ -1,8 +1,11 @@
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What is Senpy?
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--------------
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Web services can get really complex: data validation, user interaction, formatting, logging., etc.
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The figure below summarizes the typical features in an analysis service.
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Senpy is a framework for text analysis using Linked Data. There are three main applications of Senpy so far: sentiment and emotion analysis, user profiling and entity recoginition. Annotations and Services are compliant with NIF (NLP Interchange Format).
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Senpy aims at providing a framework where analysis modules can be integrated easily as plugins, and providing a core functionality for managing tasks such as data validation, user interaction, formatting, logging, translation to linked data, etc.
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The figure below summarizes the typical features in a text analysis service.
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Senpy implements all the common blocks, so developers can focus on what really matters: great analysis algorithms that solve real problems.
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.. image:: senpy-framework.png
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@ -1,8 +1,24 @@
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Vocabularies and model
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======================
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The model used in Senpy is based on the following vocabularies:
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The model used in Senpy is based on NIF 2.0 [1], which defines a semantic format and API for improving interoperability among natural language processing services.
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* Marl, a vocabulary designed to annotate and describe subjetive opinions expressed on the web or in information systems.
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* Onyx, which is built one the same principles as Marl to annotate and describe emotions, and provides interoperability with Emotion Markup Language.
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* NIF 2.0, which defines a semantic format and APO for improving interoperability among natural language processing services
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Senpy has been applied to sentiment and emotion analysis services using the following vocabularies:
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* Marl [2,6], a vocabulary designed to annotate and describe subjetive opinions expressed on the web or in information systems.
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* Onyx [3,5], which is built one the same principles as Marl to annotate and describe emotions, and provides interoperability with Emotion Markup Language.
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An overview of the vocabularies and their use can be found in [4].
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[1] Guidelines for developing NIF-based NLP services, Final Community Group Report 22 December 2015 Available at: https://www.w3.org/2015/09/bpmlod-reports/nif-based-nlp-webservices/
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[2] Marl Ontology Specification, available at http://www.gsi.dit.upm.es/ontologies/marl/
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[3] Onyx Ontology Specification, available at http://www.gsi.dit.upm.es/ontologies/onyx/
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[4] Iglesias, C. A., Sánchez-Rada, J. F., Vulcu, G., & Buitelaar, P. (2017). Linked Data Models for Sentiment and Emotion Analysis in Social Networks. In Sentiment Analysis in Social Networks (pp. 49-69).
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[5] Sánchez-Rada, J. F., & Iglesias, C. A. (2016). Onyx: A linked data approach to emotion representation. Information Processing & Management, 52(1), 99-114.
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[6] Westerski, A., Iglesias Fernandez, C. A., & Tapia Rico, F. (2011). Linked opinions: Describing sentiments on the structured web of data.
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@ -18,7 +18,7 @@ class BasicBox(SentimentBox):
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'default': 'marl:Neutral'
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}
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def predict(self, input):
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def predict_one(self, input):
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output = basic.get_polarity(input)
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return self.mappings.get(output, self.mappings['default'])
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@ -18,7 +18,7 @@ class Basic(MappingMixin, SentimentBox):
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'default': 'marl:Neutral'
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}
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def predict(self, input):
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def predict_one(self, input):
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return basic.get_polarity(input)
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test_cases = [{
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@ -18,7 +18,7 @@ class PipelineSentiment(MappingMixin, SentimentBox):
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-1: 'marl:Negative'
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}
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def predict(self, input):
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def predict_one(self, input):
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return pipeline.predict([input, ])[0]
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test_cases = [
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1
extra-requirements.txt
Normal file
1
extra-requirements.txt
Normal file
@ -0,0 +1 @@
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gsitk
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15
senpy/api.py
15
senpy/api.py
@ -53,6 +53,21 @@ API_PARAMS = {
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}
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}
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EVAL_PARAMS = {
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"algorithm": {
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"aliases": ["plug", "p", "plugins", "algorithms", 'algo', 'a', 'plugin'],
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"description": "Plugins to be evaluated",
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"required": True,
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"help": "See activated plugins in /plugins"
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},
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"dataset": {
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"aliases": ["datasets", "data", "d"],
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"description": "Datasets to be evaluated",
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"required": True,
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"help": "See avalaible datasets in /datasets"
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}
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}
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PLUGINS_PARAMS = {
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"plugin_type": {
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"@id": "pluginType",
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@ -19,7 +19,7 @@ Blueprints for Senpy
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"""
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from flask import (Blueprint, request, current_app, render_template, url_for,
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jsonify)
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from .models import Error, Response, Help, Plugins, read_schema
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from .models import Error, Response, Help, Plugins, read_schema, Datasets
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from . import api
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from .version import __version__
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from functools import wraps
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@ -133,6 +133,17 @@ def api_root():
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req = api.parse_call(request.parameters)
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return current_app.senpy.analyse(req)
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@api_blueprint.route('/evaluate/', methods=['POST', 'GET'])
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@basic_api
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def evaluate():
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if request.parameters['help']:
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dic = dict(api.EVAL_PARAMS)
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response = Help(parameters=dic)
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return response
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else:
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params = api.parse_params(request.parameters, api.EVAL_PARAMS)
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response = current_app.senpy.evaluate(params)
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return response
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@api_blueprint.route('/plugins/', methods=['POST', 'GET'])
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@basic_api
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@ -150,3 +161,12 @@ def plugins():
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def plugin(plugin=None):
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sp = current_app.senpy
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return sp.get_plugin(plugin)
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@api_blueprint.route('/datasets/', methods=['POST','GET'])
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@basic_api
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def datasets():
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sp = current_app.senpy
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datasets = sp.datasets
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dic = Datasets(datasets = list(datasets.values()))
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return dic
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@ -12,10 +12,17 @@ class Client(object):
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def analyse(self, input, method='GET', **kwargs):
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return self.request('/', method=method, input=input, **kwargs)
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def evaluate(self, input, method='GET', **kwargs):
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return self.request('/evaluate', method = method, input=input, **kwargs)
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def plugins(self, *args, **kwargs):
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resp = self.request(path='/plugins').plugins
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return {p.name: p for p in resp}
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def datasets(self):
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resp = self.request(path='/datasets').datasets
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return {d.name: d for d in resp}
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def request(self, path=None, method='GET', **params):
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url = '{}{}'.format(self.endpoint, path)
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response = requests.request(method=method, url=url, params=params)
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@ -6,8 +6,8 @@ from future import standard_library
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standard_library.install_aliases()
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from . import plugins, api
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from .plugins import Plugin
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from .models import Error
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from .plugins import Plugin, evaluate
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from .models import Error, AggregatedEvaluation
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from .blueprints import api_blueprint, demo_blueprint, ns_blueprint
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from threading import Thread
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@ -17,12 +17,19 @@ import copy
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import errno
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import logging
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logger = logging.getLogger(__name__)
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try:
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from gsitk.datasets.datasets import DatasetManager
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GSITK_AVAILABLE = True
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except ImportError:
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logger.warn('GSITK is not installed. Some functions will be unavailable.')
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GSITK_AVAILABLE = False
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class Senpy(object):
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""" Default Senpy extension for Flask """
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def __init__(self,
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app=None,
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plugin_folder=".",
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@ -181,6 +188,55 @@ class Senpy(object):
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results.analysis = [i['plugin'].id for i in results.analysis]
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return results
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def _get_datasets(self, request):
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if not self.datasets:
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raise Error(
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status=404,
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message=("No datasets found."
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" Please verify DatasetManager"))
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datasets_name = request.parameters.get('dataset', None).split(',')
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for dataset in datasets_name:
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if dataset not in self.datasets:
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logger.debug(("The dataset '{}' is not valid\n"
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"Valid datasets: {}").format(dataset,
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self.datasets.keys()))
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raise Error(
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status=404,
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message="The dataset '{}' is not valid".format(dataset))
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dm = DatasetManager()
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datasets = dm.prepare_datasets(datasets_name)
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return datasets
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@property
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def datasets(self):
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if not GSITK_AVAILABLE:
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raise Exception('GSITK is not available. Install it to use this function.')
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self._dataset_list = {}
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dm = DatasetManager()
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for item in dm.get_datasets():
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for key in item:
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if key in self._dataset_list:
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continue
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properties = item[key]
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properties['@id'] = key
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self._dataset_list[key] = properties
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return self._dataset_list
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def evaluate(self, params):
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if not GSITK_AVAILABLE:
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raise Exception('GSITK is not available. Install it to use this function.')
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logger.debug("evaluating request: {}".format(params))
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results = AggregatedEvaluation()
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results.parameters = params
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datasets = self._get_datasets(results)
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plugins = self._get_plugins(results)
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for eval in evaluate(plugins, datasets):
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results.evaluations.append(eval)
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if 'with_parameters' not in results.parameters:
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del results.parameters
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logger.debug("Returning evaluation result: {}".format(results))
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return results
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def _conversion_candidates(self, fromModel, toModel):
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candidates = self.plugins(plugin_type='emotionConversionPlugin')
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for candidate in candidates:
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@ -335,5 +335,11 @@ for i in [
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'results',
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'sentimentPlugin',
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'suggestion',
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'aggregatedEvaluation',
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'evaluation',
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'metric',
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'dataset',
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'datasets',
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]:
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_add_class_from_schema(i)
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@ -19,11 +19,22 @@ import importlib
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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:
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from gsitk.evaluation.evaluation import Evaluation as Eval
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from sklearn.pipeline import Pipeline
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GSITK_AVAILABLE = True
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except ImportError:
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logger.warn('GSITK is not installed. Some functions will be unavailable.')
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GSITK_AVAILABLE = False
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class PluginMeta(models.BaseMeta):
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_classes = {}
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@ -251,7 +262,7 @@ class Box(AnalysisPlugin):
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.. code-block::
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entry --> input() --> predict() --> output() --> entry'
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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
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@ -267,15 +278,33 @@ class Box(AnalysisPlugin):
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'''Transforms the results of the black box into an entry'''
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return output
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def predict(self, input):
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def predict_one(self, input):
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raise NotImplementedError('You should define the behavior of this plugin')
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def analyse_entries(self, entries, params):
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for entry in entries:
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input = self.input(entry=entry, params=params)
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results = self.predict(input=input)
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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):
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return self
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def transform(self, X):
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return np.array([self.predict_one(x) for x in X])
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def predict(self, X):
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return self.transform(X)
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def fit_transform(self, X, y):
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self.fit(X, y)
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return self.transform(X)
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def as_pipe(self):
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pipe = Pipeline([('plugin', self)])
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pipe.name = self.name
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return pipe
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class TextBox(Box):
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'''A black box plugin that takes only text as input'''
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@ -438,7 +467,7 @@ def install_deps(*plugins):
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for info in plugins:
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requirements = info.get('requirements', [])
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if requirements:
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pip_args = [sys.executable, '-m', 'pip', 'install', '--use-wheel']
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pip_args = [sys.executable, '-m', 'pip', 'install']
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for req in requirements:
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pip_args.append(req)
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logger.info('Installing requirements: ' + str(requirements))
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@ -560,3 +589,50 @@ def _from_loaded_module(module, info=None, **kwargs):
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yield cls(info=info, **kwargs)
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for instance in _instances_in_module(module):
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yield instance
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def evaluate(plugins, datasets, **kwargs):
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if not GSITK_AVAILABLE:
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raise Exception('GSITK is not available. Install it to use this function.')
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ev = Eval(tuples=None,
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datasets=datasets,
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pipelines=[plugin.as_pipe() for plugin in plugins])
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ev.evaluate()
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results = ev.results
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evaluations = evaluations_to_JSONLD(results, **kwargs)
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return evaluations
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def evaluations_to_JSONLD(results, flatten=False):
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'''
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Map the evaluation results to a JSONLD scheme
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'''
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evaluations = list()
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metric_names = ['accuracy', 'precision_macro', 'recall_macro',
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'f1_macro', 'f1_weighted', 'f1_micro', 'f1_macro']
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for index, row in results.iterrows():
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evaluation = models.Evaluation()
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if row.get('CV', True):
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evaluation['@type'] = ['StaticCV', 'Evaluation']
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evaluation.evaluatesOn = row['Dataset']
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evaluation.evaluates = row['Model']
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i = 0
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if flatten:
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metric = models.Metric()
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for name in metric_names:
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metric[name] = row[name]
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evaluation.metrics.append(metric)
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else:
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# We should probably discontinue this representation
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for name in metric_names:
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metric = models.Metric()
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metric['@id'] = 'Metric' + str(i)
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metric['@type'] = name.capitalize()
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metric.value = row[name]
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evaluation.metrics.append(metric)
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i += 1
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evaluations.append(evaluation)
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return evaluations
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|
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38
senpy/schemas/aggregatedEvaluation.json
Normal file
38
senpy/schemas/aggregatedEvaluation.json
Normal file
@ -0,0 +1,38 @@
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{
|
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"$schema": "http://json-schema.org/draft-04/schema#",
|
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"allOf": [
|
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{"$ref": "response.json"},
|
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{
|
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"title": "AggregatedEvaluation",
|
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"description": "The results of the evaluation",
|
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"type": "object",
|
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"properties": {
|
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"@context": {
|
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"$ref": "context.json"
|
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},
|
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"@type": {
|
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"default": "AggregatedEvaluation"
|
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},
|
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"@id": {
|
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"description": "ID of the aggregated evaluation",
|
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"type": "string"
|
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},
|
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"evaluations": {
|
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"default": [],
|
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"type": "array",
|
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"items": {
|
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"anyOf": [
|
||||
{
|
||||
"$ref": "evaluation.json"
|
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},{
|
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"type": "string"
|
||||
}
|
||||
]
|
||||
}
|
||||
}
|
||||
|
||||
},
|
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"required": ["@id", "evaluations"]
|
||||
}
|
||||
]
|
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}
|
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29
senpy/schemas/dataset.json
Normal file
29
senpy/schemas/dataset.json
Normal file
@ -0,0 +1,29 @@
|
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{
|
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"$schema": "http://json-schema.org/draft-04/schema#",
|
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"name": "Dataset",
|
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"properties": {
|
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"@id": {
|
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"type": "string"
|
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},
|
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"name": {
|
||||
"type": "string"
|
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},
|
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"compression": {
|
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"type": "string"
|
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},
|
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"expected_bytes": {
|
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"type": "int"
|
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},
|
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"filename": {
|
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"description": "Name of the dataset",
|
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"type": "string"
|
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},
|
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"url": {
|
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"description": "Classifier or plugin evaluated",
|
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"type": "string"
|
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},
|
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"stats": {
|
||||
}
|
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},
|
||||
"required": ["@id"]
|
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}
|
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18
senpy/schemas/datasets.json
Normal file
18
senpy/schemas/datasets.json
Normal file
@ -0,0 +1,18 @@
|
||||
{
|
||||
"$schema": "http://json-schema.org/draft-04/schema#",
|
||||
"allOf": [
|
||||
{"$ref": "response.json"},
|
||||
{
|
||||
"required": ["datasets"],
|
||||
"properties": {
|
||||
"datasets": {
|
||||
"type": "array",
|
||||
"default": [],
|
||||
"items": {
|
||||
"$ref": "dataset.json"
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
]
|
||||
}
|
||||
@ -41,5 +41,20 @@
|
||||
},
|
||||
"Response": {
|
||||
"$ref": "response.json"
|
||||
},
|
||||
"AggregatedEvaluation": {
|
||||
"$ref": "aggregatedEvaluation.json"
|
||||
},
|
||||
"Evaluation": {
|
||||
"$ref": "evaluation.json"
|
||||
},
|
||||
"Metric": {
|
||||
"$ref": "metric.json"
|
||||
},
|
||||
"Dataset": {
|
||||
"$ref": "dataset.json"
|
||||
},
|
||||
"Datasets": {
|
||||
"$ref": "datasets.json"
|
||||
}
|
||||
}
|
||||
|
||||
28
senpy/schemas/evaluation.json
Normal file
28
senpy/schemas/evaluation.json
Normal file
@ -0,0 +1,28 @@
|
||||
{
|
||||
"$schema": "http://json-schema.org/draft-04/schema#",
|
||||
"name": "Evalation",
|
||||
"properties": {
|
||||
"@id": {
|
||||
"type": "string"
|
||||
},
|
||||
"@type": {
|
||||
"type": "array",
|
||||
"default": "Evaluation"
|
||||
|
||||
},
|
||||
"metrics": {
|
||||
"type": "array",
|
||||
"items": {"$ref": "metric.json" },
|
||||
"default": []
|
||||
},
|
||||
"evaluatesOn": {
|
||||
"description": "Name of the dataset evaluated ",
|
||||
"type": "string"
|
||||
},
|
||||
"evaluates": {
|
||||
"description": "Classifier or plugin evaluated",
|
||||
"type": "string"
|
||||
}
|
||||
},
|
||||
"required": ["@id", "metrics"]
|
||||
}
|
||||
24
senpy/schemas/metric.json
Normal file
24
senpy/schemas/metric.json
Normal file
@ -0,0 +1,24 @@
|
||||
{
|
||||
"$schema": "http://json-schema.org/draft-04/schema#",
|
||||
"properties": {
|
||||
"@id": {
|
||||
"type": "string"
|
||||
},
|
||||
"@type": {
|
||||
"type": "string"
|
||||
},
|
||||
"maxValue": {
|
||||
"type": "number"
|
||||
},
|
||||
"minValue": {
|
||||
"type": "number"
|
||||
},
|
||||
"value": {
|
||||
"type": "number"
|
||||
},
|
||||
"deviation": {
|
||||
"type": "number"
|
||||
}
|
||||
},
|
||||
"required": ["@id"]
|
||||
}
|
||||
@ -33,6 +33,10 @@ function get_plugins(response){
|
||||
plugins = response.plugins;
|
||||
}
|
||||
|
||||
function get_datasets(response){
|
||||
datasets = response.datasets
|
||||
}
|
||||
|
||||
function group_plugins(){
|
||||
for (r in plugins){
|
||||
ptype = plugins[r]['@type'];
|
||||
@ -77,7 +81,10 @@ function draw_plugins_selection(){
|
||||
}
|
||||
}
|
||||
html += "</optgroup>"
|
||||
document.getElementById('plugins').innerHTML = html;
|
||||
// Two elements with plugin class
|
||||
// One from the evaluate tab and another one from the analyse tab
|
||||
document.getElementsByClassName('plugin')[0].innerHTML = html;
|
||||
document.getElementsByClassName('plugin')[1].innerHTML = html;
|
||||
}
|
||||
|
||||
function draw_plugins_list(){
|
||||
@ -98,15 +105,29 @@ function draw_plugins_list(){
|
||||
}
|
||||
}
|
||||
|
||||
function draw_datasets(){
|
||||
html = "";
|
||||
repeated_html = "<input class=\"checks-datasets\" type=\"checkbox\" value=\"";
|
||||
for (dataset in datasets){
|
||||
html += repeated_html+datasets[dataset]["@id"]+"\">"+datasets[dataset]["@id"];
|
||||
html += "<br>"
|
||||
}
|
||||
document.getElementById("datasets").innerHTML = html;
|
||||
}
|
||||
|
||||
$(document).ready(function() {
|
||||
var response = JSON.parse($.ajax({type: "GET", url: "/api/plugins/" , async: false}).responseText);
|
||||
defaultPlugin= JSON.parse($.ajax({type: "GET", url: "/api/plugins/default" , async: false}).responseText);
|
||||
var response2 = JSON.parse($.ajax({type: "GET", url: "/api/datasets/" , async: false}).responseText);
|
||||
|
||||
get_plugins(response);
|
||||
get_default_parameters();
|
||||
get_datasets(response2);
|
||||
|
||||
draw_plugins_list();
|
||||
draw_plugins_selection();
|
||||
draw_parameters();
|
||||
draw_datasets();
|
||||
|
||||
$(window).on('hashchange', hashchanged);
|
||||
hashchanged();
|
||||
@ -129,7 +150,7 @@ function draw_default_parameters(){
|
||||
}
|
||||
|
||||
function draw_extra_parameters(){
|
||||
var plugin = document.getElementById("plugins").options[document.getElementById("plugins").selectedIndex].value;
|
||||
var plugin = document.getElementsByClassName('plugin')[0].options[document.getElementsByClassName('plugin')[0].selectedIndex].value;
|
||||
get_parameters();
|
||||
|
||||
var extra_params = document.getElementById("extra_params");
|
||||
@ -240,13 +261,16 @@ function add_param(key, value){
|
||||
return "&"+key+"="+value;
|
||||
}
|
||||
|
||||
|
||||
function load_JSON(){
|
||||
url = "/api";
|
||||
var container = document.getElementById('results');
|
||||
var rawcontainer = document.getElementById("jsonraw");
|
||||
rawcontainer.innerHTML = '';
|
||||
container.innerHTML = '';
|
||||
var plugin = document.getElementById("plugins").options[document.getElementById("plugins").selectedIndex].value;
|
||||
|
||||
var plugin = document.getElementsByClassName("plugin")[0].options[document.getElementsByClassName("plugin")[0].selectedIndex].value;
|
||||
|
||||
var input = encodeURIComponent(document.getElementById("input").value);
|
||||
url += "?algo="+plugin+"&i="+input
|
||||
|
||||
@ -278,3 +302,85 @@ function load_JSON(){
|
||||
// location.hash = 'raw';
|
||||
}
|
||||
}
|
||||
|
||||
function get_datasets_from_checkbox(){
|
||||
var checks = document.getElementsByClassName("checks-datasets");
|
||||
|
||||
datasets = "";
|
||||
for (var i = 0; i < checks.length; i++){
|
||||
if (checks[i].checked){
|
||||
datasets += checks[i].value + ",";
|
||||
}
|
||||
}
|
||||
datasets = datasets.slice(0, -1);
|
||||
}
|
||||
|
||||
|
||||
function create_body_metrics(evaluations){
|
||||
var new_tbody = document.createElement('tbody')
|
||||
var metric_html = ""
|
||||
for (var eval in evaluations){
|
||||
metric_html += "<tr><th>"+evaluations[eval].evaluates+"</th><th>"+evaluations[eval].evaluatesOn+"</th>";
|
||||
for (var metric in evaluations[eval].metrics){
|
||||
metric_html += "<th>"+parseFloat(evaluations[eval].metrics[metric].value.toFixed(4))+"</th>";
|
||||
}
|
||||
metric_html += "</tr>";
|
||||
}
|
||||
new_tbody.innerHTML = metric_html
|
||||
return new_tbody
|
||||
}
|
||||
|
||||
function evaluate_JSON(){
|
||||
|
||||
url = "/api/evaluate";
|
||||
|
||||
var container = document.getElementById('results_eval');
|
||||
var rawcontainer = document.getElementById('jsonraw_eval');
|
||||
var table = document.getElementById("eval_table");
|
||||
|
||||
rawcontainer.innerHTML = "";
|
||||
container.innerHTML = "";
|
||||
|
||||
var plugin = document.getElementsByClassName("plugin")[0].options[document.getElementsByClassName("plugin")[0].selectedIndex].value;
|
||||
|
||||
get_datasets_from_checkbox();
|
||||
|
||||
url += "?algo="+plugin+"&dataset="+datasets
|
||||
|
||||
var response = $.ajax({type: "GET", url: url , async: false, dataType: 'json'}).responseText;
|
||||
rawcontainer.innerHTML = replaceURLWithHTMLLinks(response);
|
||||
|
||||
document.getElementById("input_request_eval").innerHTML = "<a href='"+url+"'>"+url+"</a>"
|
||||
document.getElementById("evaluate-div").style.display = 'block';
|
||||
|
||||
try {
|
||||
response = JSON.parse(response);
|
||||
var options = {
|
||||
mode: 'view'
|
||||
};
|
||||
|
||||
//Control the single response results
|
||||
if (!(Array.isArray(response.evaluations))){
|
||||
response.evaluations = [response.evaluations]
|
||||
}
|
||||
|
||||
new_tbody = create_body_metrics(response.evaluations)
|
||||
table.replaceChild(new_tbody, table.lastElementChild)
|
||||
|
||||
var editor = new JSONEditor(container, options, response);
|
||||
editor.expandAll();
|
||||
// $('#results-div a[href="#viewer"]').tab('show');
|
||||
$('#evaluate-div a[href="#evaluate-table"]').click();
|
||||
// location.hash = 'raw';
|
||||
|
||||
|
||||
}
|
||||
catch(err){
|
||||
console.log("Error decoding JSON (got turtle?)");
|
||||
$('#evaluate-div a[href="#evaluate-raw"]').click();
|
||||
// location.hash = 'raw';
|
||||
}
|
||||
|
||||
|
||||
|
||||
}
|
||||
@ -32,6 +32,8 @@
|
||||
<ul class="nav nav-tabs" role="tablist">
|
||||
<li role="presentation" ><a class="active" href="#about">About</a></li>
|
||||
<li role="presentation"class="active"><a class="active" href="#test">Test it</a></li>
|
||||
<li role="presentation"><a class="active" href="#evaluate">Evaluate Plugins</a></li>
|
||||
|
||||
</ul>
|
||||
|
||||
<div class="tab-content">
|
||||
@ -54,6 +56,7 @@
|
||||
<ul>
|
||||
<li>List all available plugins: <a href="/api/plugins">/api/plugins</a></li>
|
||||
<li>Get information about the default plugin: <a href="/api/plugins/default">/api/plugins/default</a></li>
|
||||
<li>List all available datasets: <a href="/api/datasets">/api/datasets</a></li>
|
||||
<li>Download the JSON-LD context used: <a href="/api/contexts/Results.jsonld">/api/contexts/Results.jsonld</a></li>
|
||||
</ul>
|
||||
|
||||
@ -95,7 +98,7 @@ I cannot believe it!</textarea>
|
||||
</div>
|
||||
<div>
|
||||
<label>Select the plugin:</label>
|
||||
<select id="plugins" name="plugins" onchange="draw_extra_parameters()">
|
||||
<select id="plugins" name="plugins" class=plugin onchange="draw_extra_parameters()">
|
||||
</select>
|
||||
</div>
|
||||
<!-- PARAMETERS -->
|
||||
@ -151,6 +154,70 @@ I cannot believe it!</textarea>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<div class="tab-pane" id="evaluate">
|
||||
<div class="well">
|
||||
<form id="form" class="container" onsubmit="return getPlugins();" accept-charset="utf-8">
|
||||
<div>
|
||||
<label>Select the plugin:</label>
|
||||
<select id="plugins-eval" name="plugins-eval" class=plugin onchange="draw_extra_parameters()">
|
||||
</select>
|
||||
</div>
|
||||
<div>
|
||||
<label>Select the datasets:</label>
|
||||
<div id="datasets" name="datasets" >
|
||||
</select>
|
||||
</div>
|
||||
|
||||
<a id="preview" class="btn btn-lg btn-primary" onclick="evaluate_JSON()">Evaluate Plugin!</a>
|
||||
<!--<button id="visualise" name="type" type="button">Visualise!</button>-->
|
||||
</form>
|
||||
</div>
|
||||
<span id="input_request_eval"></span>
|
||||
<div id="evaluate-div">
|
||||
<ul class="nav nav-tabs" role="tablist">
|
||||
<li role="presentation" class="active"><a data-toggle="tab" class="active" href="#evaluate-viewer">Viewer</a></li>
|
||||
<li role="presentation"><a data-toggle="tab" class="active" href="#evaluate-raw">Raw</a></li>
|
||||
<li role="presentation"><a data-toggle="tab" class="active" href="#evaluate-table">Table</a></li>
|
||||
</ul>
|
||||
<div class="tab-content" id="evaluate-container">
|
||||
|
||||
<div class="tab-pane active" id="evaluate-viewer">
|
||||
<div id="content">
|
||||
<pre id="results_eval" class="results_eval"></pre>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<div class="tab-pane" id="evaluate-raw">
|
||||
<div id="content">
|
||||
<pre id="jsonraw_eval" class="results_eval"></pre>
|
||||
</div>
|
||||
</div>
|
||||
<div class="tab-pane" id="evaluate-table">
|
||||
<table id="eval_table" class="table table-condensed">
|
||||
<thead>
|
||||
<tr>
|
||||
<th>Plugin</th>
|
||||
<th>Dataset</th>
|
||||
<th>Accuracy</th>
|
||||
<th>Precision_macro</th>
|
||||
<th>Recall_macro</th>
|
||||
<th>F1_macro</th>
|
||||
<th>F1_weighted</th>
|
||||
<th>F1_micro</th>
|
||||
<th>F1</th>
|
||||
</tr>
|
||||
</thead>
|
||||
<tbody>
|
||||
</tbody>
|
||||
</table>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<a href="http://www.gsi.dit.upm.es" target="_blank"><img class="center-block" src="static/img/gsi.png"/> </a>
|
||||
|
||||
</div>
|
||||
|
||||
27
setup.py
27
setup.py
@ -1,23 +1,20 @@
|
||||
import pip
|
||||
from setuptools import setup
|
||||
# parse_requirements() returns generator of pip.req.InstallRequirement objects
|
||||
from pip.req import parse_requirements
|
||||
|
||||
with open('senpy/VERSION') as f:
|
||||
__version__ = f.read().strip()
|
||||
assert __version__
|
||||
|
||||
try:
|
||||
install_reqs = parse_requirements(
|
||||
"requirements.txt", session=pip.download.PipSession())
|
||||
test_reqs = parse_requirements(
|
||||
"test-requirements.txt", session=pip.download.PipSession())
|
||||
except AttributeError:
|
||||
install_reqs = parse_requirements("requirements.txt")
|
||||
test_reqs = parse_requirements("test-requirements.txt")
|
||||
|
||||
install_reqs = [str(ir.req) for ir in install_reqs]
|
||||
test_reqs = [str(ir.req) for ir in test_reqs]
|
||||
def parse_requirements(filename):
|
||||
""" load requirements from a pip requirements file """
|
||||
with open(filename, 'r') as f:
|
||||
lineiter = list(line.strip() for line in f)
|
||||
return [line for line in lineiter if line and not line.startswith("#")]
|
||||
|
||||
|
||||
install_reqs = parse_requirements("requirements.txt")
|
||||
test_reqs = parse_requirements("test-requirements.txt")
|
||||
extra_reqs = parse_requirements("extra-requirements.txt")
|
||||
|
||||
|
||||
setup(
|
||||
@ -38,9 +35,7 @@ setup(
|
||||
tests_require=test_reqs,
|
||||
setup_requires=['pytest-runner', ],
|
||||
extras_require={
|
||||
'evaluation': [
|
||||
'gsitk'
|
||||
]
|
||||
'evaluation': extra_reqs
|
||||
},
|
||||
include_package_data=True,
|
||||
entry_points={
|
||||
|
||||
@ -1,15 +1,18 @@
|
||||
#!/bin/env python
|
||||
|
||||
import os
|
||||
import sys
|
||||
import pickle
|
||||
import shutil
|
||||
import tempfile
|
||||
|
||||
from unittest import TestCase
|
||||
from unittest import TestCase, skipIf
|
||||
from senpy.models import Results, Entry, EmotionSet, Emotion, Plugins
|
||||
from senpy import plugins
|
||||
from senpy.plugins.conversion.emotion.centroids import CentroidConversion
|
||||
|
||||
import pandas as pd
|
||||
|
||||
|
||||
class ShelfDummyPlugin(plugins.SentimentPlugin, plugins.ShelfMixin):
|
||||
'''Dummy plugin for tests.'''
|
||||
@ -212,7 +215,7 @@ class PluginsTest(TestCase):
|
||||
def input(self, entry, **kwargs):
|
||||
return entry.text
|
||||
|
||||
def predict(self, input):
|
||||
def predict_one(self, input):
|
||||
return 'SIGN' in input
|
||||
|
||||
def output(self, output, entry, **kwargs):
|
||||
@ -242,7 +245,7 @@ class PluginsTest(TestCase):
|
||||
|
||||
mappings = {'happy': 'marl:Positive', 'sad': 'marl:Negative'}
|
||||
|
||||
def predict(self, input, **kwargs):
|
||||
def predict_one(self, input, **kwargs):
|
||||
return 'happy' if ':)' in input else 'sad'
|
||||
|
||||
test_cases = [
|
||||
@ -309,6 +312,42 @@ class PluginsTest(TestCase):
|
||||
res = c._backwards_conversion(e)
|
||||
assert res["onyx:hasEmotionCategory"] == "c2"
|
||||
|
||||
@skipIf(sys.version_info < (3, 0),
|
||||
reason="requires Python3")
|
||||
def test_evaluation(self):
|
||||
testdata = []
|
||||
for i in range(50):
|
||||
testdata.append(["good", 1])
|
||||
for i in range(50):
|
||||
testdata.append(["bad", 0])
|
||||
dataset = pd.DataFrame(testdata, columns=['text', 'polarity'])
|
||||
|
||||
class DummyPlugin(plugins.TextBox):
|
||||
description = 'Plugin to test evaluation'
|
||||
version = 0
|
||||
|
||||
def predict_one(self, input):
|
||||
return 0
|
||||
|
||||
class SmartPlugin(plugins.TextBox):
|
||||
description = 'Plugin to test evaluation'
|
||||
version = 0
|
||||
|
||||
def predict_one(self, input):
|
||||
if input == 'good':
|
||||
return 1
|
||||
return 0
|
||||
|
||||
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
|
||||
|
||||
|
||||
def make_mini_test(fpath):
|
||||
def mini_test(self):
|
||||
|
||||
Loading…
Reference in New Issue
Block a user