Commit d6f4cc2d authored by Ignacio Corcuera's avatar Ignacio Corcuera
Browse files

Connecting the Plugin to the evaluation module of GSITK

parent 4af69209
......@@ -22,6 +22,9 @@ import threading
from .. import models, utils
from .. import api
from gsitk.evaluation.evaluation import Evaluation as Eval
from sklearn.pipeline import Pipeline
logger = logging.getLogger(__name__)
......@@ -320,6 +323,48 @@ class EmotionBox(TextBox, EmotionPlugin):
return entry
class EvaluationBox():
A box plugin where it is implemented the evaluation. It is necessary to have a pipeline.
def score(self, datasets):
pipelines = [self._pipeline]
ev = Eval(tuples = None,
datasets = datasets,
pipelines = pipelines)
results = ev.results
evaluations = self._evaluations_toJSONLD(results)
return evaluations
def _evaluations_toJSONLD(self, results):
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['CV'] == False:
evaluation['@type'] = ['StaticCV', 'Evaluation']
evaluation.evaluatesOn = row['Dataset']
evaluation.evaluates = row['Model']
i = 0
for name in metric_names:
metric = models.Metric()
metric['@id'] = 'Metric' + str(i)
metric['@type'] = name.capitalize()
metric.value = row[name]
return evaluations
class MappingMixin(object):
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