def eventHandler(event, context):
"""
Main lambda for our classification.
It invokes lambda enrich location as a first step.
Case RAW : only add lcoation and google place type if finded
Case Structured : send Purchease classification, our classif is only used if Purchease send Non-Reconnu
:param event: json input send by Purchease
:param context: non need
:return: output json to be send to Purchease
"""
print "event init :", event
d = AccessDB(event['stageVariables'])
event_bytes = json.dumps(event['body'])
function_name = event['stageVariables'][
'stage'] + '-' + "smartticket-location"
invoke_response = lambda_client.invoke(FunctionName=function_name,
InvocationType='RequestResponse',
Payload=event_bytes)
new_event = invoke_response['Payload'].read()
print "New event : ", new_event
new_event_smartt = json.loads(new_event)['smartticket']
new_event_smartt = replace_empty_string(new_event_smartt)
if new_event_smartt['extraction_type'] == 'STRUCTURED':
ll = LoadPurchease(new_event_smartt)
output_full = ll.fill_input_with_classif(new_event_smartt)
# Save into db
d.put_item_into_dynamodb(output_full)
# Clean output for send into post : del purchease_cat and rmw_cat, only stay cat
return respond('', ll.good_shape_output_for_post(output_full))
else:
return respond('', new_event)
def eventHandler(event, context):
print "event init :", event
d = AccessDB(event['stageVariables'])
event_bytes = json.dumps(event['body'])
function_name = event['stageVariables'][
'stage'] + '-' + "smartticket-location"
invoke_response = lambda_client.invoke(FunctionName=function_name,
InvocationType='RequestResponse',
Payload=event_bytes)
new_event = invoke_response['Payload'].read()
print "New event : ", new_event
new_event_smartt = json.loads(new_event)['smartticket']
new_event_smartt = replace_empty_string(new_event_smartt)
if new_event_smartt['extraction_type'] == 'STRUCTURED':
ll = LoadPurchease(new_event_smartt)
output_full = ll.fill_input_with_classif(new_event_smartt)
# Save into db
d.put_item_into_dynamodb(output_full)
# Clean output for send into post : del purchease_cat and rmw_cat, only stay cat
return respond('', ll.good_shape_output_for_post(output_full))
else:
return respond('', new_event)
import cPickle as pickle
from time import gmtime, strftime
import boto3
import pandas as pd
import re
import sklearn.metrics as m
from sklearn.model_selection import cross_val_score
from utilCsv import UnicodeWriter, Utils
from Code.Utils.utilNormalizer import Normalizer
from Code.db_access.dynamoDB import AccessDB
from tfidfClassification import Classification
# TODO : create context
a = AccessDB()
c = Classification()
n = Normalizer()
u = Utils()
# AWS Access
client = boto3.client('s3', region_name="eu-west-1")
s3 = boto3.resource('s3')
tfidfp = client.get_object(Bucket='smartticket-analytics', Key='dumpTfIdf.pkl')
tf_idf_load_from_pickle = pickle.loads(tfidfp['Body'].read())
rfp = client.get_object(Bucket='smartticket-analytics', Key='dumpRf.pkl')
rf_load_from_pickle = pickle.loads(rfp['Body'].read())
table = boto3.resource('dynamodb').Table('prod-analytics-smarttickets')
classif_result = s3.Object(bucket_name='smartticket-analytics',
key='classif_results.csv')
from Code.db_access.accessRds import AccessRDS
from Code.db_access.dynamoDB import AccessDB
from Code.db_access.dynamoDBDataPrep import dynamoDBPrep
ards = AccessRDS()
ad = AccessDB()
d = dynamoDBPrep()
def handler(event, context):
# Get dynamobd data : full info TODO replace by active flow
input_json = ad.get_item_from_dynamodb()
result_df = d.from_dynamo_to_mysql(input_json)
ards.getBonsPlansInfosRow(result_df)
handler('', '')