def evaluation():
uniquemodelid = get_request_value('uniqueid')
modeldir = storage.get_model_dir('dirhash')
minscore = get_request_value('minscore', pattern='[^0-9.$]')
maxdeviation = get_request_value('maxdeviation', pattern='[^0-9.$]')
niterations = get_request_value('niterations', pattern='[^0-9$]')
datasetpath = get_file_path(storage.get_localbasedir(), 'dataset')
trainedmodeldirhash = get_request_value('trainedmodeldirhash',
exception=False)
if trainedmodeldirhash is not False:
# The trained model dir in the server is namespaced by uniquemodelid
# and the trainedmodeldirhash which determines where should the results
# be stored.
trainedmodeldir = storage.get_model_dir('trainedmodeldirhash',
fetch_model=True)
else:
trainedmodeldir = False
classifier = estimator.Classifier(uniquemodelid, modeldir, datasetpath)
result = classifier.evaluate_dataset(datasetpath, float(minscore),
float(maxdeviation), int(niterations),
trainedmodeldir)
return json.dumps(result)
def prediction():
uniquemodelid = get_request_value('uniqueid')
modeldir = storage.get_model_dir('dirhash')
datasetpath = get_file_path(storage.get_localbasedir(), 'dataset')
classifier = estimator.Classifier(uniquemodelid, modeldir, datasetpath)
result = classifier.predict_dataset(datasetpath)
return json.dumps(result)
def import_model():
uniquemodelid = get_request_value('uniqueid')
modeldir = storage.get_model_dir('dirhash')
importzippath = get_file_path(storage.get_localbasedir(), 'importzip')
with zipfile.ZipFile(importzippath, 'r') as zipobject:
importtempdir = tempfile.TemporaryDirectory()
zipobject.extractall(importtempdir.name)
classifier = estimator.Classifier(uniquemodelid, modeldir)
classifier.import_classifier(importtempdir.name)
return 'Ok', 200
