def train():
classfier_name = request.forms.get('classfier_name')
classfier_type = request.forms.get('classfier_type')
classfier_params = request.forms.get('classfier_params')
cross_validation_type = request.forms.get('cross_validation_type')
learning_curve_params = request.forms.get('learning_curve_params')
train_size = request.forms.get('train_size')
clf = classifier.configure_classifier(classfier_type,classfier_params)
cv = classifier.configure_cross_validation(cross_validation_type,classfier_params)
features_train, labels_train = wtf.getArrays()
clf, train_sizes, train_scores, test_scores = classifier.train(clf,
train_sizes = np.linspace(.1, 1.0,train_size),
cv = cv,
params = " ",
features = features_train,
labels = labels_train )
data = classfier_to_send(classfier_name, clf, train_sizes, train_scores, test_scores)
post.send("http://naos-software.com/dataprocessing/rest-api","/classifiers","",data)
return data
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 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})
