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 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://naos-software.com/dataprocessing/rest-api/resultTestClassifiers",data1)
data2 = preds_to_send(2, 2, -5, 5)
post.send("http://naos-software.com/dataprocessing/rest-api/annotations",data2)
data3 = classfier_to_send(2, 2, 1)
put.send("http://naos-software.com/dataprocessing/rest-api/classifiers/",id,data3)