def get_association_rules(self):
"""associationRules getter helper"""
if self.__rule is None:
(host, port) = FrovedisServer.getServerInstance()
ldf = rpclib.get_association_rules(host, port, self.__mid)
excpt = rpclib.check_server_exception()
if excpt["status"]:
raise RuntimeError(excpt["info"])
rules = pd.DataFrame()
for each in ldf:
res = fpd.DataFrame().load_dummy(each["dfptr"], \
each["names"], each["types"])
rules = merge_and_append_rules(rules, res.to_pandas_dataframe())
self.__rule = rules.reset_index(drop=True)
return self.__rule
def freqItemsets(self):
"""freqItemsets getter"""
if self.__fis is None:
(host, port) = FrovedisServer.getServerInstance()
ldf = rpclib.get_fpgrowth_fis(host, port, self.__mid)
excpt = rpclib.check_server_exception()
if excpt["status"]:
raise RuntimeError(excpt["info"])
fis = pd.DataFrame()
for each in ldf:
res = fpd.DataFrame().load_dummy(each["dfptr"], \
each["names"], each["types"])
fis = merge_and_append_fis(fis, res.to_pandas_dataframe())
self.__fis = fis.reset_index(drop=True)
return self.__fis
def transform(self, data):
"""transform"""
f_df = self.__check_input(data)
self.generate_rules() # uses confidence provided during init()
(host, port) = FrovedisServer.getServerInstance()
pred = rpclib.fp_transform(host, port, f_df.get(), self.__mid)
excpt = rpclib.check_server_exception()
if excpt["status"]:
raise RuntimeError(excpt["info"])
res = fpd.DataFrame().load_dummy(pred["dfptr"], \
pred["names"], pred["types"])
if len(res) > 0:
pred = adjust_transform_output(
res.to_pandas(),
f_df.to_pandas())
else:
res = pd.DataFrame({'trans_id':[], 'consequent':[]})
pred = adjust_transform_output(
res,
f_df.to_pandas())
return pred
def __convert_iterable(self, data):
"""
converts an iterable to frovedis DataFrame
"""
tid = []
item = []
cur_id = 1
#print("### iterable data: ")
#print(data)
if self.encode_string_input:
data = self.__encode_input(data)
#print("### encoded iterable data: ")
#print(data)
for trans in data: # for each transaction in data
for trans_it in trans: # for each item in each transaction
tid.append(cur_id)
item.append(trans_it)
cur_id = cur_id + 1
df_t = pd.DataFrame({'trans_id': tid, 'item': item}, \
columns=['trans_id', 'item'])
#print("### converted pandas dataframe: ")
#print(df_t)
return fpd.DataFrame(df_t)
peopleDF = {
'Ename': ['Michael', 'Andy', 'Tanaka', 'Raul', 'Yuta'],
'Age': [29, 30, 27, 19, 31],
'Country': ['USA', 'England', 'Japan', 'France', 'Japan'],
'isMale': [False, False, False, False, True]
}
countryDF = {
'Ccode': [1, 2, 3, 4],
'Country': ['USA', 'England', 'Japan', 'France']
}
pdf1 = pd.DataFrame(peopleDF)
pdf2 = pd.DataFrame(countryDF)
fdf1 = fdf.DataFrame(pdf1)
fdf2 = fdf.DataFrame(pdf2)
# displaying created frovedis dataframes
fdf1.show()
fdf2.show()
# select demo
fdf1["Ename"].show() # single column
fdf1[["Ename", "Age"]].show() # multiple column
# filter demo
fdf1[fdf1.Age > 19].show()
fdf1[(fdf1.Age > 19) & (fdf1.Country == 'Japan')].show() # AND Demo
fdf1[~((fdf1.Age > 19) & (fdf1.Country == 'Japan'))].show() # NOT Demo