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auxilary_function_compute_features_voting_deputies_prediction.py
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"""
Created on Thu Oct 22 10:25:03 2020
@author: Marina e Nicolo
"""
def compute_features_voting_deputies_prediction(df_parlamentari,df_votes,moving_wind,offset_window,random_indexes_fedeli):
import pandas as pd
import numpy as np
from queries_parlamento import queries_parlamento
from IPython import get_ipython
from tqdm import tqdm
import matplotlib.pyplot as plt
from datetime import datetime, timedelta
import re
from datetime import datetime
def converti_str_in_array(rr):
res=[0.,0.,0.,0.,0.,0.,0.,0.]
if(pd.isnull(rr)==0):
rr=rr.replace('[','')
rr=rr.replace(']','')
rr = rr.split(", ")
res[0]+=int(rr[0])
res[1]+=int(rr[1])
res[2]+=int(rr[2])
res[3]+=int(rr[3])
res[4]+=int(rr[4])
res[5]+=int(rr[5])
res[6]+=int(rr[6])
res[7]+=int(rr[7])
return np.asarray(res)
def aggiusta_df_votes_per_colonne(df_votes,indice_riga,indici_colonne,parl_considerato):
riga_considerata = df_votes.loc[indice_riga]
res_n=[[0.,0.,0.,0.,0.,0.,0.,0.]]
for indic in indici_colonne:
rr = riga_considerata[indic]
if(pd.isnull(rr)==0):
rr=converti_str_in_array(rr)
res_n.append(rr)
del res_n[0]
if(len(res_n)>0):
normalizzazione=0
risultato_toto=[0,0,0,0,0,0,0,0]
for i in range(len(res_n)):
normalizzazione+=(res_n[i][0]+res_n[i][1])
risultato_toto[0]+=res_n[i][0]
risultato_toto[1]+=res_n[i][1]
risultato_toto[2]+=res_n[i][2]
risultato_toto[3]+=res_n[i][3]
risultato_toto[4]+=res_n[i][4]
risultato_toto[5]+=res_n[i][5]
risultato_toto[6]+=res_n[i][6]
risultato_toto[7]+=res_n[i][7]
risultato_toto/=normalizzazione
else:
risultato_toto=-1
return risultato_toto
###########################################################################
# per prima cosa dividiamo quelli che hanno cambiato gruppo parlamentare da quelli fedelissimi invece
df_MP_single_group = df_parlamentari[df_parlamentari.Ngruppi <= 1]
df_MP_pluri_group = df_parlamentari[df_parlamentari.Ngruppi > 1]
temp=df_votes.columns
temp=temp[4:len(temp)]
data2=[0]
for alpha in range(len(temp)):
temp=temp[alpha]
temp=temp.replace(' 00:00:00','')
temp = datetime.strptime(temp, '%Y-%m-%d').date()
data2.append(temp)
del data2[0]
date_colonne=data2
result_MPs_pluri_party=[]
alpha_iterazione=0
result_MPs_single_party=[]
index_across_single_group = 0
for index, p in df_MP_pluri_group.iterrows():
alpha_iterazione+=1
numero_gruppi=p.Ngruppi
vect=p.Gruppi
# extract dates of beginning and enf of memebership of the MPs to 'numero_gruppi'
data=[0]
for extract in range(numero_gruppi+1):
match = re.search(r'\d{2}.\d{2}.\d{4}', vect)
date = datetime.strptime(match.group(), '%d.%m.%Y').date()
vect=vect.replace(match.group(),'')
data.append(date)
del data[0]
data_temp=data.copy()
for porco in range(len(data)):
data_temp[porco]=min(data)
min_index = data. index(min(data))
del data[min_index]
data=data_temp
data_inizio_tutto = data[0]
del data[0]
del data[-1]
# in data is saved the date of the party switch
###########################################################################
# select the MP in question
i_parlamentare_vettore = df_votes.index[(df_votes['cognome'] == p['cognome'])].values
if(np.size(i_parlamentare_vettore)>1):
i_parlamentare = (df_votes.index[(df_votes['nome'] == p['nome']) & (df_votes['cognome'] == p['cognome'])]).values[0]
else:
i_parlamentare = df_votes.index[(df_votes['cognome'] == p['cognome'])].values[0]
inizio_xvii_leg = datetime(2013, 4, 9)
inizio_xvii_leg=inizio_xvii_leg.date()
inizio_zona = data[0]-timedelta(moving_wind)-timedelta(offset_window)
fine_zona = inizio_zona + timedelta(moving_wind)
if(fine_zona>inizio_xvii_leg):
da=0
indici_colonne=[0]
for pd1 in range(len(date_colonne)):
if(date_colonne[pd1]>inizio_zona):
if(date_colonne[pd1]<=fine_zona):
indici_colonne.append(pd1)
del indici_colonne[0]
indici_colonne=np.asarray(indici_colonne)+4
output_final=aggiusta_df_votes_per_colonne(df_votes,i_parlamentare,indici_colonne,p)
if(type(output_final)==np.ndarray):
output_non_timevarying = df_parlamentari.loc[i_parlamentare]['categoriaRegione_id']
output_non_timevarying1 = df_parlamentari.loc[i_parlamentare]['categoriaRegione_collegio_id']
output_non_timevarying2 = df_parlamentari.loc[i_parlamentare]['categoriaIstruzione']
output_non_timevarying3 = df_parlamentari.loc[i_parlamentare]['sesso_id']
output_non_timevarying4 = df_parlamentari.loc[i_parlamentare]['eta']
output_non_timevarying5 = df_parlamentari.loc[i_parlamentare]['numeroMandati']
output_final = np.append(output_final,output_non_timevarying)
output_final = np.append(output_final,output_non_timevarying1)
output_final = np.append(output_final,output_non_timevarying2)
output_final = np.append(output_final,output_non_timevarying3)
output_final = np.append(output_final,output_non_timevarying4)
output_final = np.append(output_final,output_non_timevarying5)
result_MPs_pluri_party.append(output_final)
if(type(output_final)==np.ndarray):
output_final_fedeli=-1
while(type(output_final_fedeli)!=np.ndarray):
p_fedele = df_MP_single_group.iloc[random_indexes_fedeli[index_across_single_group]]
index_across_single_group += 1
i_parlamentare = df_votes.index[(df_votes['cognome'] == p_fedele['cognome'])].values
if(np.size(i_parlamentare)>1):
i_parlamentare = (df_votes.index[(df_votes['nome'] == p_fedele['nome']) & (df_votes['cognome'] == p_fedele['cognome'])]).values[0]
else:
i_parlamentare = df_votes.index[(df_votes['cognome'] == p_fedele['cognome'])].values[0]
output_final_fedeli=aggiusta_df_votes_per_colonne(df_votes,i_parlamentare,indici_colonne,p_fedele)
if(type(output_final_fedeli)==np.ndarray):
output_non_timevarying = df_parlamentari.loc[i_parlamentare]['categoriaRegione_id']
output_non_timevarying1 = df_parlamentari.loc[i_parlamentare]['categoriaRegione_collegio_id']
output_non_timevarying2 = df_parlamentari.loc[i_parlamentare]['categoriaIstruzione']
output_non_timevarying3 = df_parlamentari.loc[i_parlamentare]['sesso_id']
output_non_timevarying4 = df_parlamentari.loc[i_parlamentare]['eta']
output_non_timevarying5 = df_parlamentari.loc[i_parlamentare]['numeroMandati']
output_final_fedeli = np.append(output_final_fedeli,output_non_timevarying)
output_final_fedeli = np.append(output_final_fedeli,output_non_timevarying1)
output_final_fedeli = np.append(output_final_fedeli,output_non_timevarying2)
output_final_fedeli = np.append(output_final_fedeli,output_non_timevarying3)
output_final_fedeli = np.append(output_final_fedeli,output_non_timevarying4)
output_final_fedeli = np.append(output_final_fedeli,output_non_timevarying5)
result_MPs_single_party.append(output_final_fedeli)
return result_MPs_pluri_party, result_MPs_single_party