import numpy as np
import pandas as pd
from pandas import DataFrame
from pathlib import Path

from pandas.core.series import Series

from utils import printTime
from model import Model

class DepressionHealthData:

  def __init__(self) -> None:
      self.raw_health_data = self.__read_health_dataframe()

  def __read_health_dataframe(self) -> dict[str, DataFrame]:
    return {path.stem[0:2]:pd.read_csv(path, encoding="latin-1") for path in Path("./healthdata/").glob("*.csv")}

  @printTime
  def __play_healthdata(self):
    print("Read Health Data")
    #qu = reduce(lambda left, right: pd.merge(left, right, how='inner', on=['SEQN']), health_datas.values()) # Merge All
    # self.df = self.raw_health_data["qu"]
    self.df = self.raw_health_data["qu"].merge(self.raw_health_data["de"], how='left', on=['SEQN']).merge(self.raw_health_data["me"], how='left', on=['SEQN']) # Only Merge Questionnaire and Demographics
    print(self.df.head())
    print("Merged Data")

  @printTime
  def __evaluate_depression_status(self):
    print("Evaluating Depression Status")
    sum = self.get_depression_status()
    self.df.insert(1, 'SUM', sum)
    self.df = self.df[self.df['SUM'] >= 0]
    print(self.df.head())
    print("Evaluated Depression Status, inserted SUM column")

  @printTime
  def get_depression_status(self, classify = False, depression_level = 5) -> Series:
    dpq = self.df.loc[:,['SEQN','DPQ010',	'DPQ020',	'DPQ030',	'DPQ040',	'DPQ050',	'DPQ060',	'DPQ070',	'DPQ080',	'DPQ090', "DPQ100"]].copy()
    dpq = dpq.dropna(thresh = 9) # drop when > 9 NaN
    a = dpq.loc[:, ['DPQ010', 'DPQ020',	'DPQ030',	'DPQ040',	'DPQ050',	'DPQ060',	'DPQ070',	'DPQ080',	'DPQ090', "DPQ100"]]
    #a[a > 3] = 0 # 将大于 3 的DPQ值转为 0
    sum = a.sum(axis = 1)
    if classify:
      sum[sum <= depression_level] = 0
      sum[sum > depression_level] = 1
    return sum


  @printTime
  def prepare_data(self):
    self.__play_healthdata()
    self.__evaluate_depression_status()
    #self.clean_data()
    
  
  @printTime
  def clean_data(self):
    self.df = self.df.drop(['SEQN','DPQ010', 'DPQ020',	'DPQ030',	'DPQ040',	'DPQ050',	'DPQ060',	'DPQ070',	'DPQ080',	'DPQ090', "DPQ100"], axis=1)
    self.df = self.df.select_dtypes(exclude=['object'])
    self.df = self.df.fillna(0)
  
  def get_model(self) -> Model:
    m = Model(self.df)
    m.init_data_set()
    return m

f61d7258a29116a23fc3dca4e614ad40a537d5fd
<mask token>


class DepressionHealthData:
    <mask token>
    <mask token>
    <mask token>

    @printTime
    def __evaluate_depression_status(self):
        print('Evaluating Depression Status')
        sum = self.get_depression_status()
        self.df.insert(1, 'SUM', sum)
        self.df = self.df[self.df['SUM'] >= 0]
        print(self.df.head())
        print('Evaluated Depression Status, inserted SUM column')
    <mask token>

    @printTime
    def prepare_data(self):
        self.__play_healthdata()
        self.__evaluate_depression_status()

    @printTime
    def clean_data(self):
        self.df = self.df.drop(['SEQN', 'DPQ010', 'DPQ020', 'DPQ030',
            'DPQ040', 'DPQ050', 'DPQ060', 'DPQ070', 'DPQ080', 'DPQ090',
            'DPQ100'], axis=1)
        self.df = self.df.select_dtypes(exclude=['object'])
        self.df = self.df.fillna(0)
    <mask token>

<mask token>


class DepressionHealthData:

    def __init__(self) ->None:
        self.raw_health_data = self.__read_health_dataframe()

    def __read_health_dataframe(self) ->dict[str, DataFrame]:
        return {path.stem[0:2]: pd.read_csv(path, encoding='latin-1') for
            path in Path('./healthdata/').glob('*.csv')}
    <mask token>

    @printTime
    def __evaluate_depression_status(self):
        print('Evaluating Depression Status')
        sum = self.get_depression_status()
        self.df.insert(1, 'SUM', sum)
        self.df = self.df[self.df['SUM'] >= 0]
        print(self.df.head())
        print('Evaluated Depression Status, inserted SUM column')
    <mask token>

    @printTime
    def prepare_data(self):
        self.__play_healthdata()
        self.__evaluate_depression_status()

    @printTime
    def clean_data(self):
        self.df = self.df.drop(['SEQN', 'DPQ010', 'DPQ020', 'DPQ030',
            'DPQ040', 'DPQ050', 'DPQ060', 'DPQ070', 'DPQ080', 'DPQ090',
            'DPQ100'], axis=1)
        self.df = self.df.select_dtypes(exclude=['object'])
        self.df = self.df.fillna(0)
    <mask token>

<mask token>


class DepressionHealthData:

    def __init__(self) ->None:
        self.raw_health_data = self.__read_health_dataframe()

    def __read_health_dataframe(self) ->dict[str, DataFrame]:
        return {path.stem[0:2]: pd.read_csv(path, encoding='latin-1') for
            path in Path('./healthdata/').glob('*.csv')}
    <mask token>

    @printTime
    def __evaluate_depression_status(self):
        print('Evaluating Depression Status')
        sum = self.get_depression_status()
        self.df.insert(1, 'SUM', sum)
        self.df = self.df[self.df['SUM'] >= 0]
        print(self.df.head())
        print('Evaluated Depression Status, inserted SUM column')
    <mask token>

    @printTime
    def prepare_data(self):
        self.__play_healthdata()
        self.__evaluate_depression_status()

    @printTime
    def clean_data(self):
        self.df = self.df.drop(['SEQN', 'DPQ010', 'DPQ020', 'DPQ030',
            'DPQ040', 'DPQ050', 'DPQ060', 'DPQ070', 'DPQ080', 'DPQ090',
            'DPQ100'], axis=1)
        self.df = self.df.select_dtypes(exclude=['object'])
        self.df = self.df.fillna(0)

    def get_model(self) ->Model:
        m = Model(self.df)
        m.init_data_set()
        return m

<mask token>


class DepressionHealthData:

    def __init__(self) ->None:
        self.raw_health_data = self.__read_health_dataframe()

    def __read_health_dataframe(self) ->dict[str, DataFrame]:
        return {path.stem[0:2]: pd.read_csv(path, encoding='latin-1') for
            path in Path('./healthdata/').glob('*.csv')}

    @printTime
    def __play_healthdata(self):
        print('Read Health Data')
        self.df = self.raw_health_data['qu'].merge(self.raw_health_data[
            'de'], how='left', on=['SEQN']).merge(self.raw_health_data['me'
            ], how='left', on=['SEQN'])
        print(self.df.head())
        print('Merged Data')

    @printTime
    def __evaluate_depression_status(self):
        print('Evaluating Depression Status')
        sum = self.get_depression_status()
        self.df.insert(1, 'SUM', sum)
        self.df = self.df[self.df['SUM'] >= 0]
        print(self.df.head())
        print('Evaluated Depression Status, inserted SUM column')
    <mask token>

    @printTime
    def prepare_data(self):
        self.__play_healthdata()
        self.__evaluate_depression_status()

    @printTime
    def clean_data(self):
        self.df = self.df.drop(['SEQN', 'DPQ010', 'DPQ020', 'DPQ030',
            'DPQ040', 'DPQ050', 'DPQ060', 'DPQ070', 'DPQ080', 'DPQ090',
            'DPQ100'], axis=1)
        self.df = self.df.select_dtypes(exclude=['object'])
        self.df = self.df.fillna(0)

    def get_model(self) ->Model:
        m = Model(self.df)
        m.init_data_set()
        return m

import numpy as np
import pandas as pd
from pandas import DataFrame
from pathlib import Path

from pandas.core.series import Series

from utils import printTime
from model import Model

class DepressionHealthData:

  def __init__(self) -> None:
      self.raw_health_data = self.__read_health_dataframe()

  def __read_health_dataframe(self) -> dict[str, DataFrame]:
    return {path.stem[0:2]:pd.read_csv(path, encoding="latin-1") for path in Path("./healthdata/").glob("*.csv")}

  @printTime
  def __play_healthdata(self):
    print("Read Health Data")
    #qu = reduce(lambda left, right: pd.merge(left, right, how='inner', on=['SEQN']), health_datas.values()) # Merge All
    # self.df = self.raw_health_data["qu"]
    self.df = self.raw_health_data["qu"].merge(self.raw_health_data["de"], how='left', on=['SEQN']).merge(self.raw_health_data["me"], how='left', on=['SEQN']) # Only Merge Questionnaire and Demographics
    print(self.df.head())
    print("Merged Data")

  @printTime
  def __evaluate_depression_status(self):
    print("Evaluating Depression Status")
    sum = self.get_depression_status()
    self.df.insert(1, 'SUM', sum)
    self.df = self.df[self.df['SUM'] >= 0]
    print(self.df.head())
    print("Evaluated Depression Status, inserted SUM column")

  @printTime
  def get_depression_status(self, classify = False, depression_level = 5) -> Series:
    dpq = self.df.loc[:,['SEQN','DPQ010',	'DPQ020',	'DPQ030',	'DPQ040',	'DPQ050',	'DPQ060',	'DPQ070',	'DPQ080',	'DPQ090', "DPQ100"]].copy()
    dpq = dpq.dropna(thresh = 9) # drop when > 9 NaN
    a = dpq.loc[:, ['DPQ010', 'DPQ020',	'DPQ030',	'DPQ040',	'DPQ050',	'DPQ060',	'DPQ070',	'DPQ080',	'DPQ090', "DPQ100"]]
    #a[a > 3] = 0 # 将大于 3 的DPQ值转为 0
    sum = a.sum(axis = 1)
    if classify:
      sum[sum <= depression_level] = 0
      sum[sum > depression_level] = 1
    return sum


  @printTime
  def prepare_data(self):
    self.__play_healthdata()
    self.__evaluate_depression_status()
    #self.clean_data()
    
  
  @printTime
  def clean_data(self):
    self.df = self.df.drop(['SEQN','DPQ010', 'DPQ020',	'DPQ030',	'DPQ040',	'DPQ050',	'DPQ060',	'DPQ070',	'DPQ080',	'DPQ090', "DPQ100"], axis=1)
    self.df = self.df.select_dtypes(exclude=['object'])
    self.df = self.df.fillna(0)
  
  def get_model(self) -> Model:
    m = Model(self.df)
    m.init_data_set()
    return m
