def prediction():
"""Returns predictions for a given dataset."""
# Missing arguments.
if len(sys.argv) < 4:
result = dict()
result['runid'] = str(int(time.time()))
result['status'] = estimator.GENERAL_ERROR
result['info'] = [
'Missing arguments, you should set:\
- The model unique identifier\
- The directory to store all generated outputs\
- The file with samples to predict\
Received: ' + ' '.join(sys.argv)
]
print(json.dumps(result))
sys.exit(result['status'])
modelid = sys.argv[1]
directory = sys.argv[2]
dataset = sys.argv[3]
# TensorFlow binary classifier - NN.
classifier = estimator.Classifier(modelid, directory, dataset)
result = classifier.predict_dataset(dataset)
print(json.dumps(result))
sys.exit(result['status'])
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 training():
"""Trains a ML classifier."""
# Missing arguments.
if len(sys.argv) < 4:
result = dict()
result['runid'] = str(int(time.time()))
result['status'] = estimator.GENERAL_ERROR
result['info'] = [
'Missing arguments, you should set:\
- The model unique identifier\
- The directory to store all generated outputs\
- The training file\
Received: ' + ' '.join(sys.argv)
]
print(json.dumps(result))
sys.exit(result['status'])
modelid = sys.argv[1]
directory = sys.argv[2]
dataset = sys.argv[3]
classifier = estimator.Classifier(modelid, directory, dataset)
result = classifier.train_dataset(dataset)
print(json.dumps(result))
sys.exit(result['status'])
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 export_classifier():
"""Exports the classifier."""
modelid = sys.argv[1]
directory = sys.argv[2]
classifier = estimator.Classifier(modelid, directory)
exportdir = classifier.export_classifier(sys.argv[3])
if exportdir:
print(exportdir)
sys.exit(0)
sys.exit(1)
def import_classifier():
"""Imports a trained classifier."""
modelid = sys.argv[1]
directory = sys.argv[2]
classifier = estimator.Classifier(modelid, directory)
classifier.import_classifier(sys.argv[3])
print('Ok')
# An exception will be thrown before if it can be imported.
print('Ok')
sys.exit(0)
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
def export():
uniquemodelid = get_request_value('uniqueid')
modeldir = storage.get_model_dir('dirhash')
# We can use a temp directory for the export data
# as we don't need to keep it forever.
tempdir = tempfile.TemporaryDirectory()
classifier = estimator.Classifier(uniquemodelid, modeldir)
exportdir = classifier.export_classifier(tempdir.name)
if exportdir is False:
return Response('There is nothing to export.', 503)
zipf = tempfile.NamedTemporaryFile()
zipdir(exportdir, zipf)
return send_file(zipf.name, mimetype='application/zip')
def evaluation():
"""Evaluates the provided dataset."""
# Missing arguments.
if len(sys.argv) < 7:
result = dict()
result['runid'] = str(int(time.time()))
result['status'] = estimator.GENERAL_ERROR
result['info'] = ['Missing arguments, you should set:\
- The model unique identifier\
- The directory to store all generated outputs\
- The training file\
- The minimum score (from 0 to 1) to consider the model as valid (defaults to 0.6)\
- The minimum deviation to accept the model as valid (defaults to 0.02)\
- The number of times the evaluation will run (defaults to 100)\
Received: ' + ' '.join(sys.argv)]
print(json.dumps(result))
sys.exit(result['status'])
modelid = sys.argv[1]
directory = sys.argv[2]
dataset = sys.argv[3]
classifier = estimator.Classifier(modelid, directory, dataset)
if len(sys.argv) > 7:
trained_model_dir = sys.argv[7]
else:
trained_model_dir = False
result = classifier.evaluate_dataset(dataset,
float(sys.argv[4]),
float(sys.argv[5]),
int(sys.argv[6]),
trained_model_dir)
print(json.dumps(result))
sys.exit(result['status'])