def __init__(self, filename, tag, score, country):
print("INSIDE FUZZZZZZZZY APIIIIIIIIIIII")
self.filename = filename
self.score = score
self.tag = tag
self.country = country
self.df_cls = PandaDataFrame(filename, COLUMNS_FOR_CRM_RECORD,
DESTINATION_FOLDER)
def __init__(self, filename, country, territory):
print("INSIDE FUZZZZZZZZY")
self.filename = filename
self.country = country
self.territory = territory
self.ignore_words_cls = IgnoreWords()
self.df_cls = PandaDataFrame(filename, COLUMNS, SOURCE_FOLDER)
self.df_cls.add_columns()
self.postgres_interface_cls = PostgresInterface()
def __init__(self, filename, tag, score, country):
print ("INSIDE FUZZZZZZZZY APIIIIIIIIIIII")
self.filename = filename
self.score = score
self.tag=tag
self.country = country
self.df_cls = PandaDataFrame(filename, COLUMNS_FOR_CRM_RECORD, DESTINATION_FOLDER)
class FuzzyAPI:
def __init__(self, filename, tag, score, country):
print ("INSIDE FUZZZZZZZZY APIIIIIIIIIIII")
self.filename = filename
self.score = score
self.tag=tag
self.country = country
self.df_cls = PandaDataFrame(filename, COLUMNS_FOR_CRM_RECORD, DESTINATION_FOLDER)
def load_file(self):
companies = []
for extracted_row in self.df_cls.extract_row_generator(self.df_cls.df):
index, row = extracted_row
if row["Match Status"] < self.score:
if len(companies) <= MAXIMUM_FILES_TO_SEND_PER_REQUEST:
companies.append(parse(row))
else:
# companies, self.tag, type: record
data = {
'companies' : companies,
'tag' : self.tag,
'type' : "record",
'country' : self.country
}
data = json.dumps(data)
self.post_to_crm(data)
companies = []
return 1
def send_to_crm(self):
return self.load_file()
def parse(self, row):
return row
def post_to_crm(self, data):
print ("**************** POST *******************")
pprint.pprint(data)
r = requests.post(CRM_API_URL, data=data)
return (r.status_code,r.text)
class FuzzyAPI:
def __init__(self, filename, tag, score, country):
print("INSIDE FUZZZZZZZZY APIIIIIIIIIIII")
self.filename = filename
self.score = score
self.tag = tag
self.country = country
self.df_cls = PandaDataFrame(filename, COLUMNS_FOR_CRM_RECORD,
DESTINATION_FOLDER)
def load_file(self):
companies = []
for extracted_row in self.df_cls.extract_row_generator(self.df_cls.df):
index, row = extracted_row
if row["Match Status"] < self.score:
if len(companies) <= MAXIMUM_FILES_TO_SEND_PER_REQUEST:
companies.append(parse(row))
else:
# companies, self.tag, type: record
data = {
'companies': companies,
'tag': self.tag,
'type': "record",
'country': self.country
}
data = json.dumps(data)
self.post_to_crm(data)
companies = []
return 1
def send_to_crm(self):
return self.load_file()
def parse(self, row):
return row
def post_to_crm(self, data):
print("**************** POST *******************")
pprint.pprint(data)
r = requests.post(CRM_API_URL, data=data)
return (r.status_code, r.text)
class Fuzzy:
def __init__(self, filename, country, territory):
print("INSIDE FUZZZZZZZZY")
self.filename = filename
self.country = country
self.territory = territory
self.ignore_words_cls = IgnoreWords()
self.df_cls = PandaDataFrame(filename, COLUMNS, SOURCE_FOLDER)
self.df_cls.add_columns()
self.postgres_interface_cls = PostgresInterface()
def start(self):
print("START")
for extracted_row in self.df_cls.extract_row_generator(self.df_cls.df):
index, row = extracted_row
## Clean company_name
company_name = self.clean(row["Company Name"]).lower()
## Ignore words
company_keywords_list = self.ignore_words_cls.return_keyword_lists(
company_name)
print("*****************")
print(row["Company Name"])
print(company_keywords_list)
## Find matches in DB using keywords , country and territory
crm_results = self.postgres_interface_cls.get_record_match(
company_name, company_keywords_list, self.country,
self.territory)
print("crm_results: {}".format(crm_results))
## Fuzzy match
best_match = Match(crm_company_id="",
crm_company_name="",
crm_group_id="",
score="")
best_score = 0
best_match, best_score = self.call_fuzzy_match_generator(
best_match, best_score, row["Company Name"].lower(),
crm_results)
## Test Prints
# if best_score >= 75:
# #print ("keword_list: {} + crm_results count: {}".format(company_keywords_list,len(crm_results)))
# print("{} => best_match: {} => '{}'".format(best_score, row["Company Name"], best_match))
print("{} => best_match: {}".format(best_score, best_match))
### Generate new file
self.df_cls.update_df(index, best_match, best_score)
def start_test(self):
# print("START TEST ")
for extracted_row in self.df_cls.extract_row_generator(self.df_cls.df):
index, row = extracted_row
## Clean company_name
company_name = self.clean(row["Company Name"]).lower()
## Ignore words
company_keywords_list = self.ignore_words_cls.return_keyword_lists(
company_name)
# print ("*****************")
# print (row["Company Name"])
# print (company_keywords_list)
self.save()
return 1
def save(self):
print("START TEST ")
self.df_cls.save()
# def extract_row_generator(self,df):
# for index, row in df.iterrows():
# yield (index,row)
def clean(self, company_name):
## Clean spaces
company_name = re.sub(r'([^\s\+\&\w\.])|_+', ' ', company_name)
return re.sub(' +', ' ', company_name).strip()
def get_fuzzy_match_generator(self, crm_results, excel_company_name):
for result in crm_results:
# yield Match(crm_company_id = result[0],crm_company_name = result[1],crm_group_id = result[2], score=fuzz.weighted_6(excel_company_name, result[1], total_weight =50))
yield Match(crm_company_id=result[0],
crm_company_name=result[1],
crm_group_id=result[2],
score=fuzz.partial_ratio(excel_company_name,
result[1].lower()))
def call_fuzzy_match_generator(self, best_match, best_score, company_name,
crm_results):
for match in self.get_fuzzy_match_generator(crm_results, company_name):
if match.score >= best_score:
if match.score > best_score:
best_match = {}
best_score = match.score
best_match = match
return best_match, best_score
