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

Dockerfile.template

@@ -20,8 +20,8 @@ ONBUILD WORKDIR /senpy-plugins/
 
 
 WORKDIR /usr/src/app
-COPY test-requirements.txt requirements.txt /usr/src/app/
-RUN pip install --no-cache-dir --use-wheel -r test-requirements.txt -r requirements.txt
+COPY test-requirements.txt requirements.txt extra-requirements.txt /usr/src/app/
+RUN pip install --no-cache-dir -r test-requirements.txt -r requirements.txt -r extra-requirements.txt
 COPY . /usr/src/app/
 RUN pip install --no-cache-dir --no-index --no-deps --editable .
 

docs/senpy.rst

 What is Senpy?
 --------------
 
-Web services can get really complex: data validation, user interaction, formatting, logging., etc. 
-The figure below summarizes the typical features in an analysis service.
+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).
+
+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. 
+
+The figure below summarizes the typical features in a text analysis service.
 Senpy implements all the common blocks, so developers can focus on what really matters: great analysis algorithms that solve real problems.
 
 .. image:: senpy-framework.png

docs/vocabularies.rst

 Vocabularies and model
 ======================
 
-The model used in Senpy is based on the following vocabularies:
+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.
 
-* Marl, a vocabulary designed to annotate and describe subjetive opinions expressed on the web or in information systems.
-* Onyx, which is built one the same principles as Marl to annotate and describe emotions, and provides interoperability with Emotion Markup Language.
-* NIF 2.0, which defines a semantic format and APO for improving interoperability among natural language processing services
+Senpy has been applied to sentiment and emotion analysis services using the following vocabularies:
+
+* Marl [2,6], a vocabulary designed to annotate and describe subjetive opinions expressed on the web or in information systems.
+* Onyx [3,5], which is built one the same principles as Marl to annotate and describe emotions, and provides interoperability with Emotion Markup Language.
+
+An overview of the vocabularies and their use can be found in [4].
+
+
+[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/
+
+[2] Marl Ontology Specification, available at http://www.gsi.dit.upm.es/ontologies/marl/
+
+[3] Onyx Ontology Specification, available at http://www.gsi.dit.upm.es/ontologies/onyx/
+
+[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).
+
+[5] Sánchez-Rada, J. F., & Iglesias, C. A. (2016). Onyx: A linked data approach to emotion representation. Information Processing & Management, 52(1), 99-114.
+
+[6] Westerski, A., Iglesias Fernandez, C. A., & Tapia Rico, F. (2011). Linked opinions: Describing sentiments on the structured web of data.

example-plugins/basic_box_plugin.py

@@ -18,7 +18,7 @@ class BasicBox(SentimentBox):
         'default': 'marl:Neutral'
     }
 
-    def predict(self, input):
+    def predict_one(self, input):
         output = basic.get_polarity(input)
         return self.mappings.get(output, self.mappings['default'])
 

example-plugins/basic_plugin.py

@@ -18,7 +18,7 @@ class Basic(MappingMixin, SentimentBox):
         'default': 'marl:Neutral'
     }
 
-    def predict(self, input):
+    def predict_one(self, input):
         return basic.get_polarity(input)
 
     test_cases = [{

example-plugins/sklearn/pipeline_plugin.py

@@ -18,7 +18,7 @@ class PipelineSentiment(MappingMixin, SentimentBox):
         -1: 'marl:Negative'
     }
 
-    def predict(self, input):
+    def predict_one(self, input):
         return pipeline.predict([input, ])[0]
 
     test_cases = [

extra-requirements.txt

+gsitk

senpy/api.py

@@ -53,6 +53,21 @@ API_PARAMS = {
     }
 }
 
+EVAL_PARAMS = {
+    "algorithm": {
+        "aliases": ["plug", "p", "plugins", "algorithms", 'algo', 'a', 'plugin'],
+        "description": "Plugins to be evaluated",
+        "required": True,
+        "help": "See activated plugins in /plugins"
+    },
+    "dataset": {
+        "aliases": ["datasets", "data", "d"],
+        "description": "Datasets to be evaluated",
+        "required": True,
+        "help": "See avalaible datasets in /datasets"
+    }
+}
+
 PLUGINS_PARAMS = {
     "plugin_type": {
         "@id": "pluginType",

senpy/blueprints.py

@@ -19,7 +19,7 @@ Blueprints for Senpy
 """
 from flask import (Blueprint, request, current_app, render_template, url_for,
                    jsonify)
-from .models import Error, Response, Help, Plugins, read_schema
+from .models import Error, Response, Help, Plugins, read_schema, Datasets
 from . import api
 from .version import __version__
 from functools import wraps
@@ -133,6 +133,17 @@ def api_root():
     req = api.parse_call(request.parameters)
     return current_app.senpy.analyse(req)
 
+@api_blueprint.route('/evaluate/', methods=['POST', 'GET'])
+@basic_api
+def evaluate():
+    if request.parameters['help']:
+        dic = dict(api.EVAL_PARAMS)
+        response = Help(parameters=dic)
+        return response
+    else:
+        params = api.parse_params(request.parameters, api.EVAL_PARAMS)
+        response = current_app.senpy.evaluate(params)
+        return response
 
 @api_blueprint.route('/plugins/', methods=['POST', 'GET'])
 @basic_api
@@ -150,3 +161,12 @@ def plugins():
 def plugin(plugin=None):
     sp = current_app.senpy
     return sp.get_plugin(plugin)
+
+
+@api_blueprint.route('/datasets/', methods=['POST','GET'])
+@basic_api
+def datasets():
+    sp = current_app.senpy
+    datasets = sp.datasets
+    dic = Datasets(datasets = list(datasets.values()))
+    return dic
\ No newline at end of file

senpy/client.py

@@ -12,10 +12,17 @@ class Client(object):
     def analyse(self, input, method='GET', **kwargs):
         return self.request('/', method=method, input=input, **kwargs)
 
+    def evaluate(self, input, method='GET', **kwargs):
+        return self.request('/evaluate', method = method, input=input, **kwargs)
+
     def plugins(self, *args, **kwargs):
         resp = self.request(path='/plugins').plugins
         return {p.name: p for p in resp}
 
+    def datasets(self):
+        resp = self.request(path='/datasets').datasets
+        return {d.name: d for d in resp}
+
     def request(self, path=None, method='GET', **params):
         url = '{}{}'.format(self.endpoint, path)
         response = requests.request(method=method, url=url, params=params)

senpy/extensions.py

@@ -6,8 +6,8 @@ from future import standard_library
 standard_library.install_aliases()
 
 from . import plugins, api
-from .plugins import Plugin
-from .models import Error
+from .plugins import Plugin, evaluate
+from .models import Error, AggregatedEvaluation
 from .blueprints import api_blueprint, demo_blueprint, ns_blueprint
 
 from threading import Thread
@@ -17,12 +17,19 @@ import copy
 import errno
 import logging
 
+
 logger = logging.getLogger(__name__)
 
+try:
+    from gsitk.datasets.datasets import DatasetManager
+    GSITK_AVAILABLE = True
+except ImportError:
+    logger.warn('GSITK is not installed. Some functions will be unavailable.')
+    GSITK_AVAILABLE = False
+
 
 class Senpy(object):
     """ Default Senpy extension for Flask """
-
     def __init__(self,
                  app=None,
                  plugin_folder=".",
@@ -181,6 +188,55 @@ class Senpy(object):
         results.analysis = [i['plugin'].id for i in results.analysis]
         return results
 
+    def _get_datasets(self, request):
+        if not self.datasets:
+            raise Error(
+                status=404,
+                message=("No datasets found."
+                         " Please verify DatasetManager"))
+        datasets_name = request.parameters.get('dataset', None).split(',')
+        for dataset in datasets_name:
+            if dataset not in self.datasets:
+                logger.debug(("The dataset '{}' is not valid\n"
+                              "Valid datasets: {}").format(dataset,
+                                                           self.datasets.keys()))
+                raise Error(
+                    status=404,
+                    message="The dataset '{}' is not valid".format(dataset))
+        dm = DatasetManager()
+        datasets = dm.prepare_datasets(datasets_name)
+        return datasets
+
+    @property
+    def datasets(self):
+        if not GSITK_AVAILABLE:
+            raise Exception('GSITK is not available. Install it to use this function.')
+        self._dataset_list = {}
+        dm = DatasetManager()
+        for item in dm.get_datasets():
+            for key in item:
+                if key in self._dataset_list:
+                    continue
+                properties = item[key]
+                properties['@id'] = key
+                self._dataset_list[key] = properties
+        return self._dataset_list
+
+    def evaluate(self, params):
+        if not GSITK_AVAILABLE:
+            raise Exception('GSITK is not available. Install it to use this function.')
+        logger.debug("evaluating request: {}".format(params))
+        results = AggregatedEvaluation()
+        results.parameters = params
+        datasets = self._get_datasets(results)
+        plugins = self._get_plugins(results)
+        for eval in evaluate(plugins, datasets):
+            results.evaluations.append(eval)
+        if 'with_parameters' not in results.parameters:
+            del results.parameters
+        logger.debug("Returning evaluation result: {}".format(results))
+        return results
+
     def _conversion_candidates(self, fromModel, toModel):
         candidates = self.plugins(plugin_type='emotionConversionPlugin')
         for candidate in candidates:

senpy/models.py

@@ -335,5 +335,11 @@ for i in [
         'results',
         'sentimentPlugin',
         'suggestion',
+        'aggregatedEvaluation',
+        'evaluation',
+        'metric',
+        'dataset',
+        'datasets',
+
 ]:
     _add_class_from_schema(i)

senpy/plugins/__init__.py

@@ -19,11 +19,22 @@ import importlib
 import yaml
 import threading
 
+import numpy as np
+
 from .. import models, utils
 from .. import api
 
+
 logger = logging.getLogger(__name__)
 
+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
+
 
 class PluginMeta(models.BaseMeta):
     _classes = {}
@@ -251,7 +262,7 @@ class Box(AnalysisPlugin):
 
     .. code-block::
 
-                   entry --> input() --> predict() --> output() --> entry'
+                   entry --> input() --> predict_one() --> output() --> entry'
 
 
     In other words: their ``input`` method convers a query (entry and a set of parameters) into
@@ -267,15 +278,33 @@ class Box(AnalysisPlugin):
         '''Transforms the results of the black box into an entry'''
         return output
 
-    def predict(self, input):
+    def predict_one(self, input):
         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)
-            results = self.predict(input=input)
+            results = self.predict_one(input=input)
             yield self.output(output=results, entry=entry, params=params)
 
+    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
+
 
 class TextBox(Box):
     '''A black box plugin that takes only text as input'''
@@ -438,7 +467,7 @@ def install_deps(*plugins):
     for info in plugins:
         requirements = info.get('requirements', [])
         if requirements:
-            pip_args = [sys.executable, '-m', 'pip', 'install', '--use-wheel']
+            pip_args = [sys.executable, '-m', 'pip', 'install']
             for req in requirements:
                 pip_args.append(req)
             logger.info('Installing requirements: ' + str(requirements))
@@ -560,3 +589,50 @@ def _from_loaded_module(module, info=None, **kwargs):
         yield cls(info=info, **kwargs)
     for instance in _instances_in_module(module):
         yield instance
+
+
+def evaluate(plugins, datasets, **kwargs):
+    if not GSITK_AVAILABLE:
+        raise Exception('GSITK is not available. Install it to use this function.')
+
+    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

senpy/schemas/aggregatedEvaluation.json

+{
+  "$schema": "http://json-schema.org/draft-04/schema#",
+  "allOf": [
+    {"$ref": "response.json"},
+    {
+      "title": "AggregatedEvaluation",
+      "description": "The results of the evaluation",
+      "type": "object",
+      "properties": {
+        "@context": {
+          "$ref": "context.json"
+        },
+        "@type": {
+          "default": "AggregatedEvaluation"
+        },
+        "@id": {
+          "description": "ID of the aggregated evaluation",
+          "type": "string"
+        },
+        "evaluations": {
+          "default": [],
+          "type": "array",
+          "items": {
+            "anyOf": [
+              {
+                "$ref": "evaluation.json"
+              },{
+                "type": "string"
+              }
+            ]
+          }
+        }
+
+      },
+      "required": ["@id", "evaluations"]
+    }
+  ]
+}

senpy/schemas/dataset.json

+{
+  "$schema": "http://json-schema.org/draft-04/schema#",
+  "name": "Dataset",
+  "properties": {
+    "@id": {
+      "type": "string"
+    },
+    "name": {
+      "type": "string"
+    },
+    "compression": {
+      "type": "string"
+    },
+    "expected_bytes": {
+      "type": "int"
+    },
+    "filename": {
+      "description": "Name of the dataset",
+      "type": "string"
+    },
+    "url": {
+      "description": "Classifier or plugin evaluated",
+      "type": "string"
+    },
+    "stats": {        
+    }
+  },
+  "required": ["@id"]
+}

senpy/schemas/datasets.json

+{
+  "$schema": "http://json-schema.org/draft-04/schema#",
+  "allOf": [
+    {"$ref": "response.json"},
+    {
+     "required": ["datasets"],
+      "properties": {
+        "datasets": {
+          "type": "array",
+          "default": [],
+          "items": {
+            "$ref": "dataset.json"
+          }
+        }
+      }
+    }
+  ]
+}

senpy/schemas/definitions.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"
   }
 }

senpy/schemas/evaluation.json

+{
+  "$schema": "http://json-schema.org/draft-04/schema#",
+  "name": "Evalation",
+  "properties": {
+    "@id": {
+      "type": "string"
+    },
+    "@type": {
+      "type": "array",
+      "default": "Evaluation"
+
+    },
+    "metrics": {
+      "type": "array",
+      "items": {"$ref": "metric.json" },
+      "default": []
+    },
+    "evaluatesOn": {
+      "description": "Name of the dataset evaluated ",
+      "type": "string"
+    },
+    "evaluates": {
+      "description": "Classifier or plugin evaluated",
+      "type": "string"
+    }
+  },
+  "required": ["@id", "metrics"]
+}

senpy/schemas/metric.json

+{
+  "$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"]
+}

senpy/static/js/main.js

@@ -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();
 
     $