content stringlengths 255 17.2k |
|---|
computeinfrastructure = compute.readComputeConfig()
context['computeinfrastructure'] = computeinfrastructure
context['version'] = AION_VERSION
return render(request, 'advancedconfig.html', context)
def updateRunConfig(_trainingTime, _filesize, _features, _modelname, _problem_type):
returnVal = '... |
Min"]))) + "," +
str(round(float(label["xMax"]))) + "," +
str(round(float(label["yMin"]))) + "," +
str(round(float(label["yMax"]))) + "," +
str(round(float(label["height"]))) + "," +
str(round(float(label["width"]))) + "... |
_gcs_bucket(),
'currentstate': request.session['currentstate'], 'finalstate': request.session['finalstate'],'azurestorage':get_azureStorage(),
'selected': 'modeltraning','computeinfrastructure':computeinfrastructure,'datatype':request.session['datatype'],
... |
_type)#bugid 12513
# Set the HTTP header for sending to browser
filename = p.usecaseid+'.log'
response['Content-Disposition'] = "attachment; filename=%s" % filename
return response
else:
response = HttpResponse('File Not Found')#bugid 12513... |
session['UseCaseName']
ModelVersion = request.session['ModelVersion']
ModelStatus = request.session['ModelStatus']
import requests
setting_url = service_url.read_service_url_params(request)
usecasename = request.session['usecaseid'].replace(" ", "_")
setting_url = setting_url+'pattern_anomaly_se... |
data})
'''
usecase = usecasedetails.objects.all()
models = Existusecases.objects.filter(Status='SUCCESS')
selected_use_case,ModelVersion,ModelStatus = getusercasestatus(request)
if len(usecase) > 0:
nouc = usecasedetails.objects.latest('id')
nouc = (n... |
ails,Existusecases))
def createpackagedocker(request, id,version):
try:
context = installPackage.createPackagePackage(request,id,version,usecasedetails,Existusecases)
context['version'] = AION_VERSION
return render(request, 'usecases.html',context)
except Exception as e:
re... |
ifier
@csrf_exempt
def upload_and_read_file_data(request):
file_path, file_ext = handle_uploaded_file(path=DATA_FILE_PATH, file=request.FILES['uploaded_file'])
file_delim = request.POST.get("file_delim")
textqualifier = request.POST.get("qualifier")
delimiters = request.POST.get("delimiters")
... |
i].replace("'", '')
tempFeatureUsedInTraining[i] = tempFeatureUsedInTraining[i].lstrip()
tempFeatureUsedInTraining[i] = tempFeatureUsedInTraining[i].rstrip()
finalFeatures.append(tempFeatureUsedInTraining[i])
featureUsedInTraining = finalFeatures
#print("trainingDataPath--... |
n", ""))
metricvalues = metric_values
text = [eval(x) for x in generations]
gen = [x[0]['generated_text'].split('\\n')[1:] for x in text]
Generations = [' '.join(x) for x in gen]
resultoutput = eval(output['data']['resultoutput'])[0]
... |
(request):
from appbe import compute
from appbe.pages import get_usecase_page
try:
compute.updateToComputeSettings('AWS')
time.sleep(2)
#print(1)
request.session['IsRetraining'] = 'No'
status,context,action = get_usecase_page(request,usecasedetails,Exist... |
settings
usecasetab = settings()
from appbe import compute
computeinfrastructure = compute.readComputeConfig()
from appfe.modelTraining.models import Existusecases
clusteringModels = Existusecases.objects.filter(Status='SUCCESS',ProblemType='unsupervised').order_by('-id')
selected_use_case = req... |
'BulkImage'
dataFile = request.session['csvfullpath']
csvfilename = request.session['csvfullpath']
labelfileexists = False
dflabels = pd.DataFrame()
context = {'tab': 'upload', 'file': dataFile, 'csvfilename': csvfilename,'type':type,'csvgenerated': True,'se... |
UseCaseName']
ModelVersion = request.session['ModelVersion']
ModelStatus = request.session['ModelStatus']
request.session['currentstate'] = 0
request.session['finalstate'] = 0
request.session['datatype'] = 'Normal'
from appbe.aion_config import get_edafeatures
No... |
columns = des1.columns.to_list()
curr_columns.remove('Features Type')
insert_i = curr_columns.index('Features')+1
curr_columns.insert(insert_i,'Features Type')
des1 = des1[curr_columns]
des1.to_excel(excel_writer, sheet_name='Data Overview',startrow=0, startcol=0,index=False)
## Hopkins value ad... |
Text== "":
context = {'originalText': originalText,'returnedText': "No Input given"}
print("returned due to None")
return render(request, "textsummarization.html",context)
KeyWords=str(request.GET.get('userUpdatedKeyword'))
contextOfText=str(request.GET.get('userUpdatedContext'))
doc... |
ategorical":
catfeature.append(feat_conf['feature'])
output={'targetfeature':targetfeature,'trainingfeature':trainingfeature,'catfeature':catfeature,'problemType':problemType}
return HttpResponse(json.dumps(output))
def fairnesmetrics(request): #Richard--Task-13581
from appbe.pages import us... |
try:
inputFieldsDict = df.to_dict(orient='index')[0]
except:
inputFieldsDict = pd.Series(0, index =inputFeaturesList).to_dict()
else:
inputFi... |
htmlPath = decoded_data['htmlPath']
if 'Message' in data:
Msg = []
Msg.append(data['Message'])
else:
Msg = data['Affected Columns']
log.info('Drift : ' + str(selected_use_case) + ' : ' + str(ModelVersion) + ' : ' + '0' + 'sec... |
POST['datafile']
if(os.path.isfile(models)):
modelformat = models.rsplit('.', 1)[1]
if(os.path.isfile(models) and os.path.exists(datafile)) and modelformat.lower()=='onnx':
inputDataType = datafile.rsplit('.', 1)[1]
if inputDataTyp... |
usecasedetails',
name='Description',
field=models.CharField(max_length=200),
),
migrations.AlterField(
model_name='usecasedetails',
name='UsecaseName',
field=models.CharField(max_length=50),
),
]
<s> # Generated by Django 3.0.8 on 2... |
_app.views import Home
2. Add a URL to urlpatterns: path('', Home.as_view(), name='home')
Including another URLconf
1. Import the include() function: from django.urls import include, path
2. Add a URL to urlpatterns: path('blog/', include('blog.urls'))
"""
from django.contrib import admin
from django.urls... |
^get_tables_fields_list', upload_views.get_tables_fields_list, name='get_tables_fields_list'),
re_path(r'^validate_query', upload_views.validate_query, name='validate_query'),
re_path(r'^trigger_DAG', views.trigger_DAG, name = 'trigger_DAG'),
# The home page
path('dataoperations', views.dataoperations, ... |
_feature
#Added libs from MLTest
import sys
import time
from sklearn.metrics import confusion_matrix
from pathlib import Path
import logging
# import json
class aionUQ:
# def __init__(self,uqdf,targetFeature,xtrain,ytrain,xtest,ytest,uqconfig_base,uqconfig_meta,deployLocation,saved_model):
def __init__(self,df,dfp,... |
l=None
if (model_name == "SVC"):
from sklearn.calibration import CalibratedClassifierCV
basemodel=SVC(**model_params)
calibrated_mdl = CalibratedClassifierCV(basemodel,method='sigmoid',cv=3)
calibrated_mdl.fit(X_train, y_train)
basepredict = calibrated_mdl.predict(X_test)
predprob_base = cali... |
test_size=0.3, random_state=0)
# modelName = ""
self.log.info('<!------------- Inside BlackBox MetaModel Regression process. ---------------> ')
try:
from uq360.algorithms.blackbox_metamodel import BlackboxMetamodelRegression
import pandas as pd
base_modelname=__class__.__name__
base_config = self... |
.metrics import accuracy_score
from sklearn.metrics import precision_score
from sklearn.metrics import recall_score
from sklearn.metrics import f1_score
from sklearn.metrics import roc_curve
from sklearn.metrics import r2_score,mean_squared_error, explained_variance_score,mean_absolute_error
from uq360.metrics import p... |
rain,self.xtest,self.ytrain,self.ytest
uq_scoring_param='accuracy'
basemodel=None
if (model_name == "GradientBoostingClassifier"):
basemodel=GradientBoostingClassifier
elif (model_name == "SGDClassifier"):
basemodel=SGDClassifier
elif (model_name == "GaussianNB"):
basemodel=GaussianNB
elif (model_n... |
base
meta_config = self.uqconfig_base
model_name=self.basemodel.__class__.__name__
model_params=self.basemodel.get_params()
# #print("model_params['criterion']: \\n",model_params['criterion'])
key = 'criterion'
#if key in model_params:
try:
#if model_params.has_key(key):
if key in model_par... |
[2], 'count')).reset_index()
self.log.info('-------> Top 10 most rated Items:')
self.log.info(df_eda.sort_values(by='number_of_ratings', ascending=False).head(10))
matrix = data.pivot_table(index=selectedColumns[1], columns=selectedColumns[0], values=selectedColumns[2])
relative_file = os.path.join(outputfile... |
log.info('-------> Test Data ROC AUC Score : '+str(rocauc_sco))
matrix = '"Accuracy":'+str(acc_sco)+',"ROC AUC":'+str(rocauc_sco)
prediction = model.predict([train_sentence1, train_sentence2])
prediction = np.where(prediction > similarityIndex,1,0)
train_roca... |
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