content stringlengths 255 17.2k |
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<s> import argparse
import sys
import os
import subprocess
INSTALL = 'install'
LINUXINSTALL = 'linuxinstall'
FE_MIGRATE = 'migrateappfe'
LAUNCH_KAFKA = 'launchkafkaconsumer'
RUN_LOCAL_MLAC_PIPELINE = 'runpipelinelocal'
BUILD_MLAC_CONTAINER = 'buildmlaccontainerlocal'
CONVERT_MODEL = 'convertmodel'
START_MLFLOW = 'mlfl... |
log.handlers[:]: # remove the existing file handlers
if isinstance(hdlr,logging.FileHandler):
log.removeHandler(hdlr)
log.addHandler(filehandler)
log.setLevel(logging.INFO)
return log
class server():
def __init__(self):
self.response = None
self.features=[]... |
, preprocess_pipe, label_encoder = profilerObj.transform()
preprocess_out_columns = dataFrame.columns.tolist()
if not timeseriesStatus: #task 12627 preprocess_out_columns goes as output_columns in target folder script/input_profiler.py, It should contain the target feature also a... |
= time.time()
deeplearnerJson = config_obj.getEionDeepLearnerConfiguration()
targetColumn = targetFeature
method = deeplearnerJson['optimizationMethod']
optimizationHyperParameter = deeplearn |
_inv[:, targetColIndx]
predout = predout.reshape(len(pred_1d),1)
#y_future.append(predout)
col = targetFeature.split(",")
pred = pd.DataFrame(index=range(0,len(predout)),columns=col)
... |
)
plot.savefig(img_location,bbox_inches='tight')
sa_images.append(img_location)
p+=1
log.info('Status:-|... AION SurvivalAnalysis completed')
log.info('\\n================ SurvivalAnalysis Completed ================ ... |
status:{output}\\n")
return output
if __name__ == "__main__":
aion_train_model( sys.argv[1])
<s> '''
*
* =============================================================================
* COPYRIGHT NOTICE
* =============================================================================
* @ Copyright HCL Technologi... |
}')")
@classmethod
def load(cls, path):
""" Load MultilabelPredictor from disk `path` previously specified when creating this MultilabelPredictor. """
path = os.path.expanduser(path)
if path[-1] != os.path.sep:
path = path + os.path.sep
return load_pkl.load(path=path... |
.executable,predict_path,dataStr])
outputStr = outputStr.decode('utf-8')
outputStr = re.search(r'predictions:(.*)',str(outputStr), re.IGNORECASE).group(1)
outputStr = outputStr.strip()
resp = outputStr
elif operation.lower() == 'explain':
predict_path = os.path.join(model_path,'... |
displaymsg = self.getModelFeatures(modelSignature)
if status:
urltext = '/AION/'+modelSignature+'/features'
else:
displaymsg = json.dumps(datajson)
else:
displaymsg = json.dumps(datajson)
msg="""
URL:{url}
RequestType: POST
Content-Type=application/json
Output: {displaymsg}.
"""... |
target_folder)
def validate(config):
error = ''
if 'error' in config.keys():
error = config['error']
return error
def generate_mlac_code(config):
with open(config, 'r') as f:
config = json.load(f)
error = validate(config)
if error:
raise ValueError(error)
... |
Path)
getAlgo, getMethod = configObj.getTextSummarize()
summarize = Obj.generateSummary(text_data, getAlgo, getMethod)
output = {'status':'Success','summary':summarize}
output_json = json.dumps(output)
return(output_json)
if __name__ == "__main__":
aion_textsummary(sys.argv[1])
<s> '''
*
* ================... |
Rows,self.dfNumCols,saved_model,scoreParam,learner_type,model,featureReduction,reduction_data_file)
visualizationObj.visualizationrecommandsystem()
visualizer_mlexecutionTime=time.time() - visualizer_mlstart
log.info('-------> COMPUTING: Total Visualizer Execution Time '+str(visualizer_mlexecutionTime))
... |
iction
outputjson = df.to_json(orient='records')
outputjson = {"status":"SUCCESS","data":json.loads(outputjson)}
outputjson = json.dumps(outputjson)
#print("predictions: "+str(outputjson))
predictionStatus=True
except Exception as e:
... |
corrThresholdInput = float(statisticalConfig.get('correlationThresholdFeatures',0.50))
corrThresholdTarget = float(statisticalConfig.get('correlationThresholdTarget',0.85))
pValThresholdInput = float(statisticalConfig.get('pValueThresholdFeatures',0.05))
pValThresholdTarget = float(statisticalConfig.get('pValu... |
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