def annotations():
callback = request.GET.get('callback')
documentId = request.GET.get('document_id')
#classifier_name = request.GET.get('classifier_name')
classifier_dump = request.GET.get('classifier')
questionId = request.GET.get('question_id')
collection_id = request.GET.get('collection_id')
range = request.GET.get('range')
meh = StringIO(classifier_dump)
buff = meh.getvalue()
clf = pickle.loads(buff)
features_train, labels_train = wtf.getArrays(collection_id)
print("params :")
#print("classifier_name" + classifier_name)
print("classifier_dump" + classifier_dump)
print("question_id" + question_id)
print("collection_id" + collection_id)
print("range" + range)
print("document_id" + document_id)
preditions = clf.predict(features_train)
data = preds_to_send(questionId, documentId, preditions[0], range)
put.send("http://localhost:8080/dataprocessing/rest-api/annotations", data)
return '{0}({1})'.format(callback, {'a': 1, 'b': 2})
def annotations():
callback = request.GET.get('callback')
documentId = request.GET.get('document_id')
#classifier_name = request.GET.get('classifier_name')
classifier_dump = request.GET.get('classifier')
questionId = request.GET.get('question_id')
collection_id = request.GET.get('collection_id')
range = request.GET.get('range')
meh = StringIO(classifier_dump)
buff = meh.getvalue()
clf = pickle.loads(buff)
features_train, labels_train = wtf.getArrays(collection_id)
print("params :")
#print("classifier_name" + classifier_name)
print("classifier_dump" + classifier_dump)
print("question_id" + question_id)
print("collection_id" + collection_id)
print("range" + range)
print("document_id" + document_id)
preditions = clf.predict(features_train)
data = preds_to_send(questionId,documentId,preditions[0],range)
put.send("http://localhost:8080/dataprocessing/rest-api/annotations", data)
return '{0}({1})'.format(callback, {'a':1, 'b':2})
def clientThread(clientSocket, address):
doc_type = ''
while True:
msg = recvall(clientSocket)
msg_data = msg.split('\r\n\r\n'.encode())
headers = msg_data[0]
try:
header = headers.decode()
except UnicodeDecodeError:
pass
try:
pieces = header.split('\n')
except AttributeError:
pass
if len(msg) != 0:
if len(pieces) > 0:
request = pieces[0].split(' ')
#GET request
if request[0] == 'GET':
for line in pieces:
header = line.split(':')
if header[0] == 'Accept-Encoding':
encoding = header[1].strip()
break
else:
encoding = ''
if request[1].find('.png') == -1 and request[1].find(
'.jpg') == -1:
response_html = GET.get(pieces, address)
try:
if ((encoding == 'zlib' or encoding == 'gzip')
and response_html.find('404') < 0
and response_html.find('406') < 0):
clientSocket.sendall(response_html)
else:
clientSocket.sendall(response_html.encode())
except (TypeError, BrokenPipeError):
try:
clientSocket.sendall(response_html)
except TypeError:
clientSocket.sendall(response_html.encode())
else:
try:
response_html, response_image = GET.get(
pieces, address)
try:
clientSocket.sendall(response_html.encode() +
response_image)
except BrokenPipeError:
pass
except ValueError:
response_html = GET.get(pieces, address)
try:
clientSocket.sendall(response_html.encode())
except BrokenPipeError:
pass
#HEAD request
elif request[0] == 'HEAD':
response_html = HEAD.head(pieces, address)
clientSocket.sendall(response_html.encode())
#DELETE request
elif request[0] == 'DELETE':
if request[1].find('.png') == -1 and request[1].find(
'.jpg') == -1:
response_html = DELETE.delete(pieces, address)
try:
clientSocket.sendall(response_html.encode())
except BrokenPipeError:
pass
else:
try:
response_html, response_image = DELETE.delete(
pieces, address)
try:
clientSocket.sendall(response_html.encode() +
response_image)
except BrokenPipeError:
pass
except ValueError:
response_html = DELETE.delete(pieces, address)
try:
clientSocket.sendall(response_html.encode())
except BrokenPipeError:
pass
elif request[0] == 'PUT':
for line in pieces:
header = line.split(':')
if header[0] == 'Content-Type':
doc_type = header[1]
break
if doc_type.find('html') >= 0 or doc_type.find('plain') >= 0:
payload = msg_data[1].decode()
response_html = PUT.put(pieces, address, payload)
try:
clientSocket.sendall(response_html.encode())
except BrokenPipeError:
pass
else:
try:
payload = msg_data[1]
except IndexError:
pass
try:
response_html, response_image = PUT.put(
pieces, address, payload)
try:
clientSocket.sendall(response_html.encode() +
response_image)
except BrokenPipeError:
pass
except ValueError:
response_html = PUT.put(pieces, address, '')
try:
clientSocket.sendall(response_html.encode())
except BrokenPipeError:
pass
elif request[0] == 'POST':
payload = msg_data[1].decode()
response_html = POST.post(pieces, address, payload)
try:
clientSocket.sendall(response_html.encode())
except BrokenPipeError:
pass
try:
clientSocket.shutdown(socket.SHUT_RDWR)
except OSError:
#Transport endpoint not connected
pass
else:
clientSocket.close()
remove(clientSocket)
break
def train():
callback = request.GET.get('callback')
classifier_name = request.GET.get('classifier_name')
classifier_id = request.GET.get('classifier_id')
user_id = request.GET.get('user_id')
classifier_type = request.GET.get('classifier_type')
classifier_params = request.GET.get('classifier_params')
cross_validation_type = request.GET.get('cross_validation_type')
cross_validation_params = request.GET.get('cross_validation_params')
result_test_classifiers_id = request.GET.get('result_test_classifiers_id')
collection_id = request.GET.get('collection_id')
vectorized_document_collection_id = request.GET.get(
'vectorized_document_collection_id')
train_size = request.GET.get('train_size')
#data = classifier_to_send(user_id, classifier_name, classifier_params, "", "", 1)
#post.send("http://localhost:8080/dataprocessing/rest-api/classifiers/",data)
print("Params :")
print(classifier_name)
print(classifier_type)
print(classifier_params)
print(cross_validation_type)
print(cross_validation_params)
print(collection_id)
print(train_size)
clf = classifier.configure_classifier(classifier_type, classifier_params)
#features_train, labels_train = makeTerrainData(n_points=200)
features_train, labels_train, features_test, labels_test = makeTerrainData(
)
#print(features_train)
if (cross_validation_type == 'None'):
cross_validation_type = None
cv = classifier.configure_cross_validation(cross_validation_type,
cross_validation_params,
n=len(features_train))
print("features_train :")
print(len(features_train))
print("labels_train :")
print(len(labels_train))
clf.fit(features_train, labels_train)
fig = classifier.train(clf,
train_sizes=np.linspace(.1, 1.0, train_size),
cv=cv,
params=" ",
features=features_train,
labels=labels_train)
imgdata = StringIO()
fig.savefig(imgdata, format='svg')
imgdata.seek(0) # rewind the data
svg_dta = imgdata.getvalue() # this is svg data
import pickle
s = pickle.dumps(clf)
print("classifier dump:")
#print(s)
data = classifier_to_send(
user_id=user_id,
name=classifier_name,
vectorizedDocumentCollectionId=vectorized_document_collection_id,
parameter=classifier_params,
learningCurve=svg_dta,
content=s,
flag=1)
put.send("http://localhost:8080/dataprocessing/rest-api/classifiers/",
classifier_id, data)
pred = clf.predict(features_train)
from sklearn.metrics import accuracy_score
acc = accuracy_score(labels_train, pred)
precision = precision_score(labels_train, pred)
recall = recall_score(labels_train, pred)
print("result_test_classifiers_id: " + result_test_classifiers_id)
data = test_data_to_send(
id_result_test_classifier=result_test_classifiers_id,
user_id=user_id,
classifierId=classifier_id,
vectorizedDocumentCollectionId=vectorized_document_collection_id,
parameter=" ",
precision=precision,
accuracy=acc,
recall=recall)
print(data)
put.send(
"http://localhost:8080/dataprocessing/rest-api/resultTestClassifiers/",
result_test_classifiers_id, data)
return '{0}({1})'.format(callback, {'a': 1, 'b': 2})
def preds_to_send(questionId, documentId, value, range):
data ={
"userId" : "2",
"questionId" : questionId,
"documentId" : documentId,
"value" : value,
"range" : range,
}
return data
def test_data_to_send(classifierId, vectoriziedDocumentCollectionId, parameter, precision, accuracy, recall):
data ={
"classifierId" : classifierId,
"vectoriziedDocumentCollectionId" : vectoriziedDocumentCollectionId,
"parameter" : parameter,
"precision" : precision,
"accuracy" : accuracy,
"recall" : recall
}
return data
data1 = test_data_to_send(1, 1, 1)
post.send("http://localhost:8080/dataprocessing/rest-api/resultTestClassifiers",data1)
data2 = preds_to_send(2, 2, -5, 5)
post.send("http://localhost:8080/dataprocessing/rest-api/annotations",data2)
data3 = classfier_to_send(2, 2, 1)
put.send("http://localhost:8080/dataprocessing/rest-api/classifiers/",id,data3)
def train():
callback = request.GET.get('callback')
classifier_name = request.GET.get('classifier_name')
classifier_id = request.GET.get('classifier_id')
user_id = request.GET.get('user_id')
classifier_type = request.GET.get('classifier_type')
classifier_params = request.GET.get('classifier_params')
cross_validation_type = request.GET.get('cross_validation_type')
cross_validation_params = request.GET.get('cross_validation_params')
result_test_classifiers_id = request.GET.get('result_test_classifiers_id')
collection_id = request.GET.get('collection_id')
vectorized_document_collection_id = request.GET.get('vectorized_document_collection_id')
train_size = request.GET.get('train_size')
#data = classifier_to_send(user_id, classifier_name, classifier_params, "", "", 1)
#post.send("http://localhost:8080/dataprocessing/rest-api/classifiers/",data)
print("Params :")
print(classifier_name)
print(classifier_type)
print(classifier_params)
print(cross_validation_type)
print(cross_validation_params)
print(collection_id)
print(train_size)
clf = classifier.configure_classifier(classifier_type,classifier_params)
#features_train, labels_train = makeTerrainData(n_points=200)
features_train, labels_train, features_test, labels_test = makeTerrainData()
#print(features_train)
if(cross_validation_type == 'None') :
cross_validation_type = None
cv = classifier.configure_cross_validation(cross_validation_type,cross_validation_params, n = len(features_train))
print("features_train :")
print(len(features_train))
print("labels_train :")
print(len(labels_train))
clf.fit(features_train, labels_train)
fig = classifier.train(clf,
train_sizes = np.linspace(.1, 1.0,train_size),
cv = cv,
params = " ",
features = features_train,
labels = labels_train )
imgdata = StringIO()
fig.savefig(imgdata, format='svg')
imgdata.seek(0) # rewind the data
svg_dta = imgdata.getvalue() # this is svg data
import pickle
s = pickle.dumps(clf)
print("classifier dump:")
#print(s)
data = classifier_to_send(user_id = user_id, name = classifier_name, vectorizedDocumentCollectionId= vectorized_document_collection_id, parameter = classifier_params, learningCurve = svg_dta, content = s, flag = 1)
put.send("http://localhost:8080/dataprocessing/rest-api/classifiers/",classifier_id, data)
pred = clf.predict(features_train)
from sklearn.metrics import accuracy_score
acc = accuracy_score(labels_train, pred)
precision = precision_score(labels_train, pred)
recall = recall_score(labels_train, pred)
print("result_test_classifiers_id: " + result_test_classifiers_id )
data = test_data_to_send(id_result_test_classifier = result_test_classifiers_id, user_id = user_id, classifierId = classifier_id, vectorizedDocumentCollectionId = vectorized_document_collection_id, parameter = " ", precision = precision, accuracy = acc, recall = recall)
print(data)
put.send("http://localhost:8080/dataprocessing/rest-api/resultTestClassifiers/", result_test_classifiers_id, data)
return '{0}({1})'.format(callback, {'a':1, 'b':2})
