def setup():
db = Database()
db.createMatchTable()
employeePath = '../data/employees.csv'
employerPath = '../data/employers.csv'
model = pickle.load(open('/data/model.pickle', 'rb'))
employeeData = pd.read_csv(employeePath, sep=',')
employeeData['merge'] = 1
employerDataReader = pd.read_csv(employerPath, sep=',', chunksize=1)
for employerData in employerDataReader:
employerData['merge'] = 1
df = pd.merge(employerData, employeeData, on='merge')
del df['merge']
attrs = set(df.columns.values)
ignoredAttrs = set([
'id_x', 'id_y', 'nameFirst_x', 'nameFirst_y', 'nameLast_x',
'nameLast_y', 'origin_x', 'origin_y'
])
inputAttrs = list(attrs - ignoredAttrs)
inputsMap = DataFrameMapper([(inputAttrs, None)])
inputSamples = inputsMap.fit_transform(df)
#print(df)
result = pd.DataFrame(
data={
'id_x': df['id_x'],
'id_y': df['id_y'],
'score': model.predict(inputSamples)
})
id = 0
result.sort_values(by=['score'], ascending=False, inplace=True)
result = result.head(5)
for index, row in result.iterrows():
id += 1
print(row)
print(row[0])
print(row[1])
print(row[2])
db.insertMatchTable(id, row[0], row[1], row[2])
inputAttrs = list(attrs - ignoredAttrs)
inputsMap = DataFrameMapper([
(inputAttrs, None)
])
inputSamples = inputsMap.fit_transform(df)
print(inputSamples)
result = pd.DataFrame(data={
'id_x': df['id_x'],
'id_y': df['id_y'],
'score': model.predict(inputSamples)
});
id = 0
result.sort_values(by=['score'], ascending=False, inplace=True);
result = result.head(5)
for index, row in result.iterrows():
id += 1
print(row)
print(row[0])
print(row[1])
print(row[2])
db.insertMatchTable(id, row[0], row[1], row[2])
