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Change bool to int, separate location

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This commit is contained in:
Aleksey Filippov 2023-06-06 00:33:08 +04:00
parent 5089eb4b10
commit 0eefc9fde0
1 changed files with 14 additions and 9 deletions
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23
src/main/df_loader.py
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@ -2,7 +2,6 @@ from datetime import date
import numpy as np import numpy as np
import pandas as pd import pandas as pd
from numpy import ndarray
from pandas import DataFrame from pandas import DataFrame
from src.main.constants import Constants as const from src.main.constants import Constants as const
@ -49,27 +48,33 @@ class DfLoader:
def __prepare_dataset_status(self) -> None: def __prepare_dataset_status(self) -> None:
is_univer_mask = ((self.__df['age'] >= const.university_gr_age()) | (self.__df['age'] == const.empty_age())) & \ is_univer_mask = ((self.__df['age'] >= const.university_gr_age()) | (self.__df['age'] == const.empty_age())) & \
((self.__df['universities'].str.len() > 0) | (self.__df['occupation_type'] == 'university')) ((self.__df['universities'].str.len() > 0) | (self.__df['occupation_type'] == 'university'))
self.__df['is_university'] = np.where(is_univer_mask, True, False) self.__df['is_university'] = np.where(is_univer_mask, 1, 0)
is_work_mask = ((self.__df['age'] > const.school_gr_age()) | (self.__df['age'] == const.empty_age())) & \ is_work_mask = ((self.__df['age'] > const.school_gr_age()) | (self.__df['age'] == const.empty_age())) & \
((self.__df['is_university']) | (self.__df['occupation_type'] == 'work')) | \ ((self.__df['is_university'] == 1) | (self.__df['occupation_type'] == 'work')) | \
(self.__df['age'] > const.university_gr_age()) (self.__df['age'] > const.university_gr_age())
self.__df['is_work'] = np.where(is_work_mask, True, False) self.__df['is_work'] = np.where(is_work_mask, 1, 0)
is_student_mask = ((self.__df['occupation_type'] == 'university') & is_student_mask = ((self.__df['occupation_type'] == 'university') &
((self.__df['age'] >= const.school_gr_age()) & ((self.__df['age'] >= const.school_gr_age()) &
(self.__df['age'] <= const.university_gr_age()))) (self.__df['age'] <= const.university_gr_age())))
self.__df['is_student'] = np.where(is_student_mask, True, False) self.__df['is_student'] = np.where(is_student_mask, 1, 0)
is_schoolboy_mask = ((self.__df['age'] < const.school_gr_age()) & (self.__df['age'] != const.empty_age())) | \ is_schoolboy_mask = ((self.__df['age'] < const.school_gr_age()) & (self.__df['age'] != const.empty_age())) | \
((self.__df['age'] == const.empty_age()) & (self.__df['occupation_type'] == 'school')) ((self.__df['age'] == const.empty_age()) & (self.__df['occupation_type'] == 'school'))
self.__df['is_schoolboy'] = np.where(is_schoolboy_mask, True, False) self.__df['is_schoolboy'] = np.where(is_schoolboy_mask, 1, 0)
def __prepare_dataset_location(self) -> None: def __prepare_dataset_location(self) -> None:
self.__geocache.update_geo_cache(self.__df['city'].unique().tolist()) self.__geocache.update_geo_cache(self.__df['city'].unique().tolist())
self.__df['location'] = self.__df['city'] \ self.__df['location'] = self.__df['city'] \
.apply(lambda val: '' if Utils.is_empty_str(val) else self.__geocache.get_location(val)) .apply(lambda val: '' if Utils.is_empty_str(val) else self.__geocache.get_location(val))
self.__df['location-la'] = self.__df.loc[:, 'location'] \
.apply(lambda val: 0 if Utils.is_empty_collection(val) else val[0])
self.__df['location-lo'] = self.__df.loc[:, 'location'] \
.apply(lambda val: 0 if Utils.is_empty_collection(val) else val[1])
def get_clustering_data(self) -> ndarray: def get_clustering_data(self) -> DataFrame:
columns: [] = ['location', 'sex', 'age', 'is_university', 'is_work', 'is_student', 'is_schoolboy'] columns: [] = ['location-la', 'location-lo',
return self.__df[columns].to_numpy() 'sex', 'age', 'is_university', 'is_work', 'is_student', 'is_schoolboy']
df = self.__df
return df[columns]