content stringlengths 255 17.2k |
|---|
model('en', if_exists='ignore')
pretrained_model = fasttext.load_model('cc.en.300.bin')
os.chdir(cwd)
self.profiler.set_params(text_process__vectorizer__external_model = pretrained_model)
self.profiler.set_params(text_process__vectorizer__external_model_type = 'binary')
"""
return code.replace('\\n', '\\n'+... |
+= ' output = {"status":"FAIL","message":str(e).strip(\\'"\\')}'
self.predictionFile += '\\n'
self.predictionFile += ' return json.dumps(output)'
self.predictionFile += '\\n'
self.predictionFile += ' |
")'
self.predictionFile += '\\n'
self.predictionFile += 'import json'
self.predictionFile += '\\n'
self.predictionFile += 'import os'
self.predictionFile += '\\n'
self.predictionFile += 'import sys'
self.predictionFile += '\\n'
self.predictio... |
path.join(deploy_path,'aion_opdrift.py')
f = open(filename, "wb")
f.write(str(self.predictionFile).encode('utf8'))
f.close()
def create_model_service(self,deploy_path,serviceName,problemType):
filedata = """
from flask import Flask, jsonify, request
from flask_restful im... |
'.db'
db_file = str(location/self.database_name)
self.conn = sqlite3.connect(db_file)
self.cursor = self.conn.cursor()
self.tables = []
def table_exists(self, name):
if name in self.tables:
return True
elif name:
query = f"SELECT nam... |
file += '\\n'
if(len(features) != 0) and model_type != 'BM25':
if model_type.lower()!='anomaly_detection' and model.lower() != 'autoencoder':
self.selectorfile += ' df = df['+str(features)+']'
self.selectorfile += '\\n'
self.selectorfile += ' return(df)'
filenam... |
loss = tf.keras.losses.mae(reconstructed,X)\\n"
self.modelfile += ' max_threshold = np.mean(predict_loss) + 2*np.std(predict_loss)\\n'
self.modelfile += ' min_threshold = np.mean(predict_loss) - 2*np.std(predict_loss)\\n'
self.modelfile += ' prediction_df = pd.DataFrame()\\n'
... |
== 'fbprophet':
self.modelfile += ' sessonal_freq="'+str(sessonal_freq)+'"'
self.modelfile += '\\n'
self.modelfile += ' ts_prophet_future = self.model.make_future_dataframe(periods=in |
case_name'],self.params['usecase_ver'], self.name)
def create_idrift(self):
pass
def create_odrift(self):
pass
def create_utils_folder(self):
common.create_util_folder(self.deploy_path)
<s><s> """
/**
* =====================================================================... |
positives + K.epsilon())'
self.modelfile += '\\n';
self.modelfile += ' return precision'
self.modelfile += '\\n';
if(scoreParam.lower() == 'f1_score'):
self.modelfile += 'def f1_m(y_true, y_pred):'
self.modelfile += '\\n';
... |
,datetimeFeature=''):
filename = str(Path(deploy_path)/'script'/'inputprofiler.py')
if 'profiler' in config:
if model_type == 'BM25':
code = self.profiler_code(model_type,model,['tokenize'],features, text_features,config['profiler']['word2num_features'])
elif mode... |
-> Profiler File Location :'+filename)
f = open(filename, "w",encoding="utf-8")
f.write(str(self.profilerfile))
f.close()
def isEnglish(self, s):
try:
s.encode(encoding='utf-8').decode('ascii')
exce |
== 'classification' or model_type.lower() == 'regression' or model_type.lower() == 'timeseriesforecasting': #task 11997
predictionObj.create_drift_file(deploy_path,features,targetFeature,model_type)
if model_type.lower() = |
.params['features']['text_feat']:
obj.create_text_drift_file(self.deploy_path,self.params['features']['text_feat'],self.params['features']['target_feat'],self.name)
else:
obj.create_drift_file(self.deploy_path,self.params['features']['input_feat'],self.params['features']['target_feat'],s... |
_vect', 'behave_vect', 'behind_vect', 'bein_vect', 'believe_vect', 'bell_vect', 'belly_vect', 'belovd_vect', 'best_vect', 'bet_vect', 'better_vect', 'beyond_vect', 'bf_vect', 'bid_vect', 'bids_vect', 'big_vect', 'bigger_vect', 'biggest_vect', 'bill_vect', 'billed_vect', 'billion_vect', 'bills_vect', 'bin_vect', 'biola_... |
ing_vect', 'expensive_vect', 'experience_vect', 'expired_vect', 'expires_vect', 'explain_vect', 'explicit_vect', 'explosive_vect', 'express_vect', 'extra_vect', 'eye_vect', 'eyes_vect', 'fa_vect', 'fab_vect', 'face_vect', 'facebook_vect', 'fact_vect', 'faggy_vect', 'failed_vect', 'fair_vect', 'faith_vect', 'fall_vect',... |
lo_vect', 'loads_vect', 'loan_vect', 'loans_vect', 'local_vect', 'locations_vect', 'lock_vect', 'log_vect', 'login_vect', 'logo_vect', 'logopic_vect', 'lol_vect', 'london_vect', 'lonely_vect', 'long_vect', 'longer_vect', 'look_vect', 'lookatme_vect', 'looked_vect', 'lookin_vect', 'looking_vect', 'looks_vect', 'lor_vect... |
vect', 'receive_vect', 'received_vect', 'receiving_vect', 'recent_vect', 'recently_vect', 'recession_vect', 'record_vect', 'records_vect', 'recovery_vect', 'red_vect', 'ref_vect', 'reference_vect', 'reg_vect', 'regards_vect', 'register_vect', 'registered_vect', 'regret_vect', 'regular_vect', 'relation_vect', 'relax_vec... |
ct', 'trying_vect', 'ts_vect', 'tscs_vect', 'tscs087147403231winawk_vect', 'tt_vect', 'ttyl_vect', 'tues_vect', 'tuesday_vect', 'tuition_vect', 'turn_vect', 'turning_vect', 'turns_vect', 'tv_vect', 'twelve_vect', 'twice_vect', 'two_vect', 'txt_vect', 'txtauction_vect', 'txtin_vect', 'txting_vect', 'txtno_vect', 'txts_v... |
ct', 'asked_vect', 'askin_vect', 'asking_vect', 'asks_vect', 'asleep_vect', 'ass_vect', 'assume_vect', 'ate_vect', 'atlanta_vect', 'atlast_vect', 'atm_vect', 'attached_vect', 'attempt_vect', 'attend_vect', 'auction_vect', 'august_vect', 'aunt_vect', 'aunty_vect', 'auto_vect', 'av_vect', 'available_vect', 'avatar_vect',... |
ec2a_vect', 'ee_vect', 'eek_vect', 'eerie_vect', 'effects_vect', 'eg_vect', 'egg_vect', 'eggs_vect', 'eh_vect', 'eight_vect', 'either_vect', 'ela_vect', 'electricity_vect', 'else_vect', 'elsewhere_vect', 'em_vect', 'email_vect', 'embarassed_vect', 'empty_vect', 'end_vect', 'ended_vect', 'ending_vect', 'ends_vect', 'ene... |
ct', 'laid_vect', 'land_vect', 'landline_vect', 'langport_vect', 'language_vect', 'laptop_vect', 'lar_vect', 'largest_vect', 'last_vect', 'late_vect', 'later_vect', 'latest_vect', 'latr_vect', 'laugh_vect', 'laughing_vect', 'law_vect', 'lazy_vect', 'ldn_vect', 'ldnw15h_vect', 'le_vect', 'lead_vect', 'learn_vect', 'leas... |
'project_vect', 'prolly_vect', 'promise_vect', 'promises_vect', 'promo_vect', 'proof_vect', 'properly_vect', 'prospects_vect', 'provided_vect', 'ps_vect', 'ptbo_vect', 'pub_vect', 'pull_vect', 'purchase_vect', 'purity_vect', 'purpose_vect', 'push_vect', 'pushes_vect', 'pussy_vect', 'put_vect', 'puttin_vect', 'putting_v... |
vect', 'thurs_vect', 'thursday_vect', 'tick_vect', 'ticket_vect', 'tickets_vect', 'tight_vect', 'tihs_vect', 'til_vect', 'till_vect', 'time_vect', 'times_vect', 'timing_vect', 'tired_vect', 'tirunelvali_vect', 'tirupur_vect', 'tissco_vect', 'tkts_vect', 'tm_vect', 'tming_vect', 'tmobile_vect', 'tmr_vect', 'tncs_vect', ... |
dockerdata+='COPY install.py install.py'
dockerdata+='\\n'
dockerdata+='COPY run_modelService.py run_modelService.py'
dockerdata+='\\n'
dockerdata+='COPY AIX-0.1-py3-none-any.whl AIX-0.1-py3-none-any.whl'
dockerdata+='\\n'
dockerdata+='COPY Drift-0.1-py3-none-any.whl Drift-0.1-py3-none-an... |
, 'pytransform'), os.path.join(dir, 'pytransform'))
except Exception as error_obj:
print("Exception in file ", error_obj)
shutil.rmtree(secure_path)
except Exception as error_obj:
print("Exception in dir ", error_obj)
def start_Obfuscate(path):
project_path = path
subdirs = [dI for dI in os.listdir(... |
):
imported_modules = [
{'module': 'numpy', 'mod_from': None, 'mod_as': 'np'},
{'module': 'pandas', 'mod_from': None, 'mod_as': 'pd'},
]
importer = importModule()
utility.import_modules(importer, imported_modules)
code = """
class inputpr... |
Path(__file__).parent/"data")/"trainingdata.csv")
dictDiffCount = {self.params['training']['dictDiffCount']}
target_features = "{self.params['features']['target_feat']}"
columns = target_features.split(',')
pred = pd.DataFrame(index=range(0,len(predictions)),columns=columns)
for ... |
kwargs)
if not as_proba:
return data[self.labels]
else:
return predproba_dict
def predict(data):
try:
if os.path.splitext(data)[1] == ".tsv":
df=pd.read_csv(data,encoding='utf-8',sep='\\t')
elif os.path.splitext(data)[1] == ".csv":
d... |
communication using aws boto3 lib
# s3_client = boto3.client('ecr',aws_access_key_id=AWS_ACCESS_KEY_ID,aws_secret_access_key=AWS_SECRET_ACCESS_KEY,aws_session_token=AWS_SESSION_TOKEN,region_name=region)
# s3 = boto3.resource('ecr', aws_access_key_id=AWS_ACCESS_KEY_ID, aws_secret_access_key= AWS_SECRET_A... |
flow_root_dir))
# self.deployModel2sagemaker(mlflowtosagemakerPushImageName,tag_id,mlflowtosagemakerdeployModeluri)
try:
if (deploy_status):
self.deployModel2sagemaker(mlflowtosagemakerPushImageName,tag_... |
aws_access_key_id,
aws_secret_access_key=aws_secret_key)
response = ec2.describe_instance_status(InstanceIds=[instance_id],IncludeAllInstances=True)
if response['InstanceStatuses'][0]['InstanceState']['Name'] == 'running':
ip = response['Reservations'][0]['Instances'][0]['PublicIp... |
_output
outputStr = save_output(config_data['basic']['deployLocation'],config_data['basic']['modelName'],config_data['basic']['modelVersion'],outputStr)
print(outputStr)
if "Tuning completed Successfully" in log_data:
update_sqllite_data(modelid,'status','Success')
... |
['basic']['modelVersion'])
from appbe.compute import readComputeConfig
cloud_infra = readComputeConfig()
currentDirectory = os.path.dirname(os.path.abspath(__file__))
filetimestamp = str(int(time.time()))
instance_name = config_data['basic']['modelName']+'-'+str(config_data['basic']['modelVersion'])... |
:{output}\\n")
return output
else:
output_json = {"status":"FAIL","message":'Failed to initialize the instance',"LogFile":''}
output = json.dumps(output_json)
log.info("Status:-|... Failed to initialize the instance")
print(f"\\naion_le... |
instanceid = instanceid
else:
raise ValueError("Either provide 'image name' or 'instance id'")
self.credentialsJson = self.cloud_infra['gcpCredentials']['gcpCredentials']
self.projectID = self.cloud_infra['gcpCredentials']['projectID']
self.zone = self.ami_details['regionName... |
Type,temperature, max_token, response));
self.conn.commit()
<s> import paramiko
from pathlib import Path
import logging
import json
import os
import sys
import pandas as pd
import time
import timeit
import re
running_state_code = 16
stopped_state_code = 80
#prompt_command = '/home/aion/AION/llm/sbin/run_inf... |
})
agg_method = get_one_true_option(aggregation['type'])
self.config['aggregation'] = {}
self.config['aggregation']['enabled'] = agg_method in VALID_AGGREGATION_METHODS
self.config['aggregation']['method'] = agg_method
granularity = aggregation.get('granularity',{})
granu... |
frame = dataframe.groupby([pd.Grouper(freq=frequency),groupbyfeatures]).min()
dataframe = dataframe.rename("groupby_value")
dataframe = dataframe.to_frame()
dataframe = dataframe.reset_index()
'''
return dataframe,grouperbyjson
def readDf(self,dataF,featureList,targetColumn):
dataDf = dataF[featur... |
from text.textProfiler import set_pretrained_model
set_pretrained_model(pipe)
conversion_method = self.get_conversion_method()
process_data = pipe.fit_transform(self.data, y=self.target)
# save for testing
if DEBUG_ENABLED:
... |
== numFillDict.get(col, '') and en == normalizationDict.get(col,''):
self.num_fill_method_dict[f][en].append(col)
if not self.num_fill_method_dict[f][en] :
del self.num_fill_method_di |
if colm in self.cat_feature:
if method.lower() in cs.supported_method['categoryEncoding']:
if 'catEncoder' not in self.process_method.keys():
self.process_method['catEncoder'] = {}
if method == 'na' and self.process_method['catEncoder'].get(colm, ... |
ugmentedImages(self, df):
removeDf = df[df['AugmentedImage'] == True]['loc'].unique().tolist()
#df[df['imageAugmentationOriginalImage'] != True][loocationField].apply(lambda x: Path(x).unlink())
for file in removeDf:
if file:
Path(file).unlink()
def augment(self,... |
(self,folderlocation,folderdetails,deployLocation):
try:
dataset_directory = Path(folderlocation)
dataset_csv_file = dataset_directory/folderdetails['label_csv_file_name']
tfrecord_directory = Path(deployLocation)/'Video_TFRecord'
from savp import PreprocessSAVP
import csv
... |
", "do not")
text = text.replace("wasn't", "was not")
text = text.replace("weren't", "were not")
text = text.replace("doesn't", "does not")
text = text.replace("'s", " is")
text = text.replace("'re", " are")
text = text.replace("'m", " am")
text = text.replace("'d", " would")
text = text.replace... |
_version,'modelmonitoring',dataset_addr)
docker_images['DataIngestion'] = 'dataingestion'+'_'+model_name.lower()+':'+model_version
dataset_addr = os.path.join(mlaac__code_path,'DataIngestion')
createDockerImage(model_name,model_version,'dataingestion',dataset_addr)
transformer_addr = os.path.join(mlaac__code_path,'... |
({'Status':'SUCCESS'})
if __name__ == '__main__':
try:
if shutil.which('git') is None:
raise ValueError("git is not installed on this system")
parser = argparse.ArgumentParser()
parser.add_argument('-c', '--config', help='Config file l... |
Features(self):
try:
if(self.basic['trainingFeatures']):
modFeatures = self.basic['trainingFeatures']
modFeatures = modFeatures.split(",")
modFeatures = list(map(str.strip, modFeatures))
modFeatures = ",".join([modf for modf in modFeatures])
return(modFeatures)
else:
return('NA')
excep... |
'].lower()!='labelencoding':
return False
return True
class timeseries():
def __init__(self,config):
self.config=config
#task 11997
if self.config['basic']['analysisType']['timeSeriesForecasting'].lower()=='true':
self.problemType = 'timeSeriesForecasting'
elif self.config['basic']['analysisType']['ti... |
filer"] = config['advance']['profiler']
self.code_config["feature_selector"]= self.__get_feature_selector(config['advance']['selector'])
self.code_config["feature_reducer"]= self.__get_feature_reducer(config['advance']['selector'])
self.code_config["corr_threshold"]= float(config['advance']['sel... |
_algorithm.keys():
if conf_algorithm[key] == 'True':
if self.mlmodels != '':
self.mlmodels += ','
self.mlmodels += key
self.mllearner = False
self.dllearner = False
i... |
mllearner_config['modelParams']['classifierModelParams']['Dueling Deep Q Network'] = self.advance['rllearner_config']['modelParams']['classifierModelParams']['Dueling Deep Q Network']
mllearner_config['modelParams']['regressorModelParams']['Deep Q Network'] = self.advance['rllearner_config']['modelParams']['regr... |
mentation'].get('Enable', "False")
keepAugImages = self.advance['ImageAugmentation'].get('KeepAugmentedImages', "False")
if enable == "True":
operations = {}
operations.update(self.advance['ImageAugmentation'].get('Noise', {}))
operations.update(self.advance['ImageAugmentation'].get('Transformation', ... |
elif delimiter.lower() == 'space' or delimiter.lower() == ' ':
delimiter = ' '
elif delimiter.lower() == 'other':
if 'other' in csv_setting:
delimiter = csv_setting['other']
else:
delimiter = ','
elif delimiter == '':
delimiter = ','
else:
delimiter = ','
... |
_hazard()
#cph.predict_expectation()
#cph.predict_log_partial_hazard()
#cph.predict_median()
#cph.predict_partial_hazard()
#cph.predict_percentile()
#cph.predict_survival_function()
#cph.predict_hazard()
#cph.score()
... |
model.predict(X_train)
score = objClf.get_score(scoreParam,y_test, predictm)
self.log.info("Autokeras struct data regression metrics: \\n")
return modelName,nas_reg,score
def nasMain(self,scoreParam):
modelName = ""
nasclf=None
nas_reg=None
#text_reg_m... |
ED_TASKS:
raise KeyError("Unknown task {}, available tasks are {}".format(task, list(SUPPORTED_TASKS.keys())))
targeted_task = SUPPORTED_TASKS[task]
task_class = targeted_task["impl"]
# Use default model/config/tokenizer for the task if no model is provided
if model is None:
model = ta... |
Slk1+IUO2E49Hy8i9dym5FUaBRyTRH6R+
GTF1kcpd+1QinIZDMIdsmAc95Y8pTufxY30QxCkOhVASitSQWHS/IiWQHmsTJwdr
38lqZnQQloOt/iPlhcavbxu/yKFzwBmp+nM+ErDTnCBh6EGCGrw1xWF30T2IBpmp
WwMEoqZsFV69RzwQAw39KG1KCxi5uscrB62YPgUdlT2b4Yaa90egQhGLLVdnKvhP
ORiGT9omCH90Dkm1oMMQ0Y2JBLezgXa/bunSqtTBxEwzlwUAX2JJcanFYrzKy2OL
xzwNRlWUXilZ4R/1RHAgUdNyKb... |
zgXa/bunSqtTBxEwzlwUAX2JJcanFYrzKy2OLxzwN
RlWUXilZ4R/1RHAgUdNyKbYxZqc24MApoQIDAQAB
-----END RSA PUBLIC KEY-----
'''
pubkey = rsa.PublicKey.load_pkcs1(pkeydata)
encrypted_message = rsa.encrypt(msg.encode(), pubkey)
encrypted_message = binascii.hexlify(encrypted_message).decode()
return(encrypted_message)... |
'{"Anomaly":"Error","Remarks":"'+str(Int)+'"}'
resp=resp+"\\n"
resp=resp.encode()
self.wfile.write(resp)
elif None != re.search('/AION/pattern_anomaly_settings', self.path):
ctype, pdict = cgi.parse_header(self.headers.get('content-type'))
if ctype == 'application/json':
length = int(self.header... |
now = datetime.now() # current date and time
date_time = now.strftime("%m/%d/%Y, %H:%M:%S")
data = {'usecase':model,'status':status,'Msg':Msg,'RecordTime':date_time,'version':version}
data = pd.DataFrame(data, index=[0])
sqlite_dbObj.write(data,'monitoring')... |
black; border-collapse: collapse;}</style>"""
msg+='<body>\\n'
msg+='<h2>Model Metrices - Deployed Version '+str(version)+'</h2>'
msg+='<br/>\\n'
msg+='<table style="width:80%">\\n'
msg+="""<tr>
<th>Model</th>
<th>Version</th>
<th>ScoreType</th>
<th>Score</th>
</tr
"""
for idx in... |
prediction"].astype(int)
df["prediction"] = df["prediction"].astype(str)
df["prediction_label"] = df["prediction"].map(label_maping)
if df["prediction_label"].dtype == None:
df["prediction_label"] = df["prediction"]
outputjson = df.compute().to_json(orient='records')
outputjson = {"status":"SUCCESS"... |
omaly Detected - In-frequent Pattern Detected'
anomaly = True
else:
user_records['SessionID'] = data[sessionid]
user_records['Activity'] = data[activity]
user_records['probability'] = 0
user_records['probarry'] = []
user_records['chainprobability'] = 0
user_records['pre... |
show=False)
image = io.BytesIO()
plt.savefig(image, format='png', bbox_inches='tight')
image.seek(0)
string = base64.b64encode(image.read())
image3_64 = 'data:image/png;base64,' + urllib.parse.quote(string)
except Exception as inst:
print(inst)
exc_type, exc_obj, exc_tb = sys.exc_info()
fname = os.path... |
.path.join(os.path.dirname(os.path.abspath(__file__)),"model",config['saved_model']))
predict_fn = lambda x: model.predict(x)
dathPath = os.path.join(os.path.dirname(os.path.abspath(__file__)),'data','postprocesseddata.csv.gz')
dataFrame=pd.read_csv(dathPath,compression='gzip')
testdathPath = os.path.join(os.path.... |
train.min(axis=0), x_train.max(axis=0))
cf = CounterFactualProto(prdictfn,shape,cat_vars=cat_vars_ord)
explanation = cf.explain(X)
print(explanation)
def getAnchorTabularofFirstRecord(predict_fn,features,X_train,X_test,labelMap, class_percent=None):
threshold = 0.95
from alibi.explainers import AnchorTabular
ex... |
== 'recall'):
model = load_model(filename_dl,custom_objects={"recall": recall_m},compile=False)
model.compile(loss='binary_crossentropy',optimizer='Nadam', metrics=[recall_m])
elif(scoreParam.lower() == 'precision'):
model = load_model(filename_dl, |
processed "+str(self.num_records)+".-- Loss: "+str(loss)+". -- accuracy: "+str(accuracy))
logger.info("FL Client model intercept: "+str(model.intercept_))
logger.info("FL Client model coefficients: "+str(model.coef_))
self.model_save(self.model)
return loss, len(self.X_te... |
n")
server_address,model_name,problem_type,data_location,model_params,model_version,selected_feature,target_feature,train_size,num_records,wait_time,model_overwrite = dataLoad(json_file)
file_name=model_name+'_'+model_version+".log"
cwd = os.path.abspath(os.path.dirname(__file__))
log_location = os.path... |
(modelname.lower() == "decisiontreeclassifier"):
modelUsed = DecisionTreeClassifier()
return True
else:
return False
except Exception as e:
log(INFO, "set fl model name fn issue: ",e)
def get_model_parameters(model:modelUsed) -> LogRegParams:
"""Returns the paramters of a sklearn LogisticRegression m... |
outputjson = {"status":"SUCCESS","data":json.loads(outputjson)}
return(json.dumps(outputjson))
def predict(self,data):
try:
df = self.readData(data)
dfOrg = df.copy()
self.readConfig()
if len(self.configDict)!=0:
self.loadSavedModels()
df = self.profiler(df)
modeloutput = self.traine... |
profiling of New Training Data')
else:
self.log.info('Starting profiling of Testing Data')
startTime = timeit.default_timer()
df = self.dataFramePreProcess(df)
if 'num_fill' in self.configDict:
if self.configDict['num_fill'] == 'drop':
df = df.dropna(axis = 0, subset=self.allNumCols)
elif self.conf... |
exc()))
if self.updConfigDict != None:
self.saveConfig()
output = {"status":"FAIL","Msg":str(e).strip('"')}
return json.dumps(output)
if __name__ == "__main__":
incBLObj = incBatchLearner()
output = incBLObj.updateLearning(sys.argv[1])
print("aion_learner_status:",output)
<s> '''
*
* ===================... |
key,value in start_index.items():
for k,v in value.items():
if k == 'vectorizer':
return len(v)
return 0
def update_output_features_names(self, pipe):
columns = self.output_columns
start_index = {}
index_shifter = 0
... |
licate')
def log_drop_feature(self, columns, reason):
self.log.info(f'---------- Dropping {reason} features ----------')
self.log.info(f'\\nStatus:- |... {reason} feature treatment done: {len(columns)} {reason} feature(s) found')
self.log.info(f'-------> Drop Features: {columns}')
s... |
.split(',')
self.data[itemId] = self.data[itemId].astype(np.int32)
self.data[userId] = self.data[userId].astype(np.int32)
self.data[rating] = self.data[rating].astype(np.float32)
return self.data
except Exception as inst:
self.log.info("Error: dataProfiler failed "+str(inst))... |
np.sqrt(metrics.mean_squared_error(y, y_pred))
log(INFO, "global model mean_absolute_error: %f ",mean_absolute_error)
log(INFO, "global model mean_squared_error: %f ",mean_squared_error)
log(INFO, "global model root_mean_squared_error: %f ",r... |
bias_initializer='zeros',
activation=act_func)(x)
x = tf.keras.layers.Dense(16,
kernel_initializer='he_normal',
bias_initializer='zeros',
activation=act_func)(x)
x = tf.keras.layers.Dense(8,
... |
model_name.rsplit('.', 1)
file_name=file_name[0]
file_name=file_name+".log"
try:
hm_log=os.path.normpath(os.path.join(cwd,'logs',file_name))
os.makedirs(os.path.dirname(hm_log), exist_ok=True)
except Exception as e:
print("L... |
0
cwd = os.path.abspath(os.path.dirname(__file__))
# model_name=model_name
file_name = model_name.rsplit('.', 1)
file_name=file_name[0]
file_name=file_name+".log"
try:
hm_log=os.path.normpath(os.path.join(cwd,'logs',file_name))
os.makedirs(os.path.... |
script.aion_predict import selector
from script.inputprofiler import inputprofiler
import argparse
class aion_hemulticlient:
def __init__(self):
self.confdata=None
def dataload(self,datapath):
df = pd.read_csv(datapath)
## Data preprocess in test dataset, In aion... |
, path):
loaded_model = pickle.load(open(path, 'rb'))
return loaded_model
#Generating secure key
def generate_ppboostkey(self):
try:
public_key_file = Path(__file__).parent.parent/'keys'/'public.k'
private_key_file = Path(__file__).parent.parent/'key... |
ype(np.int32)
return X_train, y_train, X_test, y_test
def get_train_test_val(X_train: np.ndarray, y_train: np.ndarray, X_test: np.ndarray, y_test: np.ndarray, min_classes: List[int],
maj_classes: List[int], imb_ratio: float = None, imb_test: bool = True, val_frac: float = 0.25,
... |
-> None: # pragma: no cover
"""Plots confusion matric of given TP, FN, FP, TN.
:param TP: True Positive
:type TP: int
:param FN: False Negative
:type FN: int
:param FP: False Positive
:type FP: int
:param TN: True Nega |
per_episode,memory_length=memory_length, collect_every=collect_every, n_step_update=n_step_update, model_path=model_save_path,log_dir=logFilePath)
model.compile_model(X_train,y_train,layers)
model.q_net.summary()
model.train(xval,yval)
network = model.get_network()
predictedytrain=network_prediction... |
_val: np.ndarray
:param save_best: Saving the best model of all validation runs based on given metric:
Choose one of: {Gmean, F1, Precision, Recall, TP, TN, FP, FN}
This improves stability since the model at the last episode is not guaranteed to be the best model.
:type save_bes... |
():
experiences, _ = next(iterator)
return self.agent.train(experiences).loss
_train = common.function(_train) # Optimalization
ts = None
policy_state = self.agent.collect_policy.get_initial_state(self.train_env.batch_size)
print('Before Collect Metrics')
... |
/3.0/topics/http/urls/
Examples:
Function views
1. Add an import: from my_app import views
2. Add a URL to urlpatterns: path('', views.home, name='home')
Class-based views
1. Add an import: from other_app.views import Home
2. Add a URL to urlpatterns: path('', Home.as_view(), name='home')
Including ... |
","msg":"File extension not supported"}),content_type="application/json")
except Exception as e:
print(e)
return HttpResponse(json.dumps({"status":"error","msg":"File upload exception"}),content_type="application/json")
def help_text(request,usecaseid,version):
hosturl =request.get_host()
... |
user_records['prevclusterid'] = -1
user_records['NoOfClusterHopping'] = 0
user_records['pageclicks'] = 1
else:
prevactivity = user_records['Activity']
user_records['Activity'] = currentactivity
... |
# Confidence_Interval_Plot = 'data:image/png;base64,' + urllib.parse.quote(string)
# PICP_Plot = uq_test['PICP Plot']
# if PICP_Plot != '':
# string = base64.b64encode(open(PICP_Plot, "rb").read())
# PIC... |
TrainOuputLocation='')
ps.save()
if(model.count() > 0):
context = {'range':range(1,101),'samplePercentage':samplePercentage, 'samplePercentval':samplePercentval, 'showRecommended':showRecommended,'featuresList': featuresList, 'tab': 'tabconfigure','data': df_json,'selected_use_case': selecte... |
:Please train the model first or launch an existing trained model')
return render(request,'businessview.html',{'selected_use_case':selected_use_case,'ModelStatus':ModelStatus,'ModelVersion':ModelVersion,'error':'Please train the model first or launch an existing trained model','selected':'visualizer','subselected':... |
.update_layout(barmode='stack',xaxis_title='Features')
bargraph = cfig.to_html(full_html=False, default_height=450,default_width=1000)
dftoprecords = dfimp.head(2)
topTwoFeatures = dfimp['labels'].tolist()
topFeaturesMsg = []
for i in range(0,len(dfimp)):
value = round(dfimp.loc[i, "values"],2)*100
v... |
],mode='markers',name='Predicted Outliers'))
fig.update_xaxes(title_text="Principal Component 1")
fig.update_yaxes(title_text="Principal Component 2")
frgraph = fig.to_html(full_html=False, default_height=400, default_width=1100)
... |
path(os.path.join(os.path.dirname(os.path.abspath(__file__)), '..', '..','aion.py'))
outputStr = subprocess.check_output([sys.executable, scriptPath,'-m','onlinetraining','-c',updatedConfigFile])
elif configSettings['basic']['distributedLearning'] == 'True':
... |
_type'] = dproblem_type
if mlmodels != '':
configSettings['basic']['mllearner'] = 'enable'
if dlmodels != '':
configSettings['basic']['dllearner'] = 'enable'
if configSettings['basic']['analysisType']['multiLabelPrediction'] == 'True':
configSettings['basic']['... |
in ['llm_document', 'llm_code']:
filesCount, filesSize = getDataFileCountAndSize(configSettingsJson['basic'])
except:
pass
if request.session['finalstate'] <= 3:
request.session['finalstate'] = 3
request.session['currentstate'] = 3
if request.sessi... |
)
# EION_SCRIPT_PATH = 'C:\\\\Project\\\\Analytics\\\\eion\\\\eion\\\\eion.py'
PYTHON_PATH = 'python.exe'
AION_VERSION = getversion()
usecasetab = settings()
#AION_VERSION
# MainPage
logg_obj = logg(LOG_LOCATION)
log = logg_obj.create_log(AION_VERSION)
def index(request):
from appbe.pages import index_page
... |
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