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import numpy as np
import pandas as pd
import scipy.sparse as sp
import time
import os
from fainress_component import disparate_impact_remover, reweighting, sample
def ali_CatGCN_pre_processing(df, label, uid_pid, pid_cid, sens_attr, label_pred, special_case, debaising_approach=None):
#print(df.columns.tolist())
print('')
if debaising_approach != None:
if special_case == True:
label.rename(columns={'userid':'uid', 'final_gender_code':'gender','age_level':'age', 'pvalue_level':'buy', 'occupation':'student', 'new_user_class_level ':'city'}, inplace=True)
label.dropna(inplace=True)
label['gender'] = label['gender'].replace(1, 0)
label['gender'] = label['gender'].replace(2, 1)
label = apply_bin_age(label)
label = apply_bin_buy(label)
if debaising_approach == 'disparate_impact_remover':
label = disparate_impact_remover(label, sens_attr, label_pred)
elif debaising_approach == 'reweighting':
label = reweighting(label, sens_attr, label_pred)
elif debaising_approach == 'sample':
label = sample(label, sens_attr, label_pred)
if debaising_approach == 'sample':
label = label.reset_index()
label = label.drop(['index'], axis=1)
label = label.drop_duplicates()
else:
df.rename(columns={'final_gender_code': 'gender', 'age_level':'age'}, inplace=True)
df = apply_bin_age(df)
df['gender'] = df['gender'].replace(1, 0)
df['gender'] = df['gender'].replace(2, 1)
if debaising_approach == 'disparate_impact_remover':
df = disparate_impact_remover(df, sens_attr, label_pred)
elif debaising_approach == 'reweighting':
df = reweighting(df, sens_attr, label_pred)
elif debaising_approach == 'sample':
df = sample(df, sens_attr, label_pred)
if debaising_approach == 'sample':
df = df.reset_index()
df = df.drop(['index'], axis=1)
df = df.drop_duplicates()
label, pid_cid, uid_pid = divide_data_2(df)
label.rename(columns={'userid':'uid', 'pvalue_level':'buy', 'occupation':'student', 'new_user_class_level':'city'}, inplace=True)
label.dropna(inplace=True)
label = apply_bin_buy(label)
else:
# load ana clean data
if special_case == False:
label, pid_cid, uid_pid = divide_data(df)
label.rename(columns={'userid':'uid', 'final_gender_code':'gender','age_level':'age', 'pvalue_level':'buy', 'occupation':'student', 'new_user_class_level ':'city'}, inplace=True)
label.dropna(inplace=True)
label = apply_bin_age(label)
label = apply_bin_buy(label)
#pid_cid
pid_cid.rename(columns={'adgroup_id':'pid','cate_id':'cid'}, inplace=True)
#uid_pid
if special_case == True:
uid_pid.rename(columns={'user':'uid','adgroup_id':'pid'}, inplace=True)
else:
uid_pid.rename(columns={'userid':'uid','adgroup_id':'pid'}, inplace=True)
uid_pid = uid_pid[uid_pid['clk']>0]
uid_pid.drop('clk', axis=1, inplace=True)
uid_pid = uid_pid[uid_pid['uid'].isin(label['uid'])]
uid_pid = uid_pid[uid_pid['pid'].isin(pid_cid['pid'])]
uid_pid.drop_duplicates(inplace=True)
# Filter and process
# Filter uid_pid (item_interactions >= 2)
uid_pid, uid_activity, pid_popularity = filter_triplets(uid_pid, 'uid', 'pid', min_uc=0, min_sc=2) # min_sc>=2
sparsity = 1. * uid_pid.shape[0] / (uid_activity.shape[0] * pid_popularity.shape[0])
print("After filtering, there are %d interacton events from %d users and %d items (sparsity: %.4f%%)" %
(uid_pid.shape[0], uid_activity.shape[0], pid_popularity.shape[0], sparsity * 100))
# create uid_cid
uid_pid_cid = pd.merge(uid_pid, pid_cid, how='inner', on='pid')
raw_uid_cid = uid_pid_cid.drop('pid', axis=1, inplace=False)
raw_uid_cid.drop_duplicates(inplace=True)
# Filter uid_cid (cid_interactions >= 2 is optional)
uid_cid, uid_activity, cid_popularity = filter_triplets(raw_uid_cid, 'uid', 'cid', min_uc=0, min_sc=2) # min_sc>=2
sparsity = 1. * uid_cid.shape[0] / (uid_activity.shape[0] * cid_popularity.shape[0])
print("After filtering, there are %d interacton events from %d users and %d items (sparsity: %.4f%%)" %
(uid_cid.shape[0], uid_activity.shape[0], cid_popularity.shape[0], sparsity * 100))
# create uid_uid
uid_pid = uid_pid[uid_pid['uid'].isin(uid_cid['uid'])]
uid_pid_1 = uid_pid[['uid','pid']].copy()
uid_pid_1.rename(columns={'uid':'uid1'}, inplace=True)
uid_pid_2 = uid_pid[['uid','pid']].copy()
uid_pid_2.rename(columns={'uid':'uid2'}, inplace=True)
uid_pid_uid = pd.merge(uid_pid_1, uid_pid_2, how='inner', on='pid')
uid_uid = uid_pid_uid.drop('pid', axis=1, inplace=False)
uid_uid.drop_duplicates(inplace=True)
del uid_pid_1, uid_pid_2, uid_pid_uid
# Map
user_label = label[label['uid'].isin(uid_cid['uid'])]
uid2id = {num: i for i, num in enumerate(user_label['uid'])}
cid2id = {num: i for i, num in enumerate(pd.unique(uid_cid['cid']))}
user_label = col_map(user_label, 'uid', uid2id)
user_label = label_map(user_label, user_label.columns[1:])
# create user_edge (uid - uid)
user_edge = uid_uid[uid_uid['uid1'].isin(uid_cid['uid'])]
user_edge = user_edge[user_edge['uid2'].isin(uid_cid['uid'])]
user_edge = col_map(user_edge, 'uid1', uid2id)
user_edge = col_map(user_edge, 'uid2', uid2id)
# create user_field (uid - cid)
user_field = col_map(uid_cid, 'uid', uid2id)
user_field = col_map(user_field, 'cid', cid2id)
if debaising_approach == 'disparate_impact_remover':
user_field = user_field.reset_index()
user_field = user_field.drop(['uid'], axis=1)
user_field = user_field.rename(columns={"index": "uid"})
user_field['uid'] = user_field['uid'].astype(str).astype(int)
# save ?
save_path = './'
user_edge.to_csv(os.path.join(save_path, 'user_edge.csv'), index=False)
user_field.to_csv(os.path.join(save_path, 'user_field.csv'), index=False)
user_label.to_csv(os.path.join(save_path, 'user_labels.csv'), index=False)
print('user_label columns', user_label.columns.tolist())
user_label[['uid','buy']].to_csv(os.path.join(save_path, 'user_buy.csv'), index=False)
# create the user_buy variable for the return of the function
user_buy = user_label[['uid','buy']]
user_label[['uid','city']].to_csv(os.path.join(save_path, 'user_city.csv'), index=False)
user_label[['uid','bin_age']].to_csv(os.path.join(save_path, 'user_age.csv'), index=False)
user_label[['uid','gender']].to_csv(os.path.join(save_path, 'user_gender.csv'), index=False)
user_gender = user_label[['uid', 'gender']]
user_label[['uid','student']].to_csv(os.path.join(save_path, 'user_student.csv'), index=False)
user_label[['uid','bin_age']].to_csv(os.path.join(save_path, 'user_bin_age.csv'), index=False)
user_label[['uid','bin_buy']].to_csv(os.path.join(save_path, 'user_bin_buy.csv'), index=False)
# re_process
NUM_FIELD = 10
#np.random_seed(42)
# load user_field.csv
user_field = field_reader(os.path.join(save_path, 'user_field.csv'))
print("Shapes of user with field:", user_field.shape)
print("Number of user with field:", np.count_nonzero(np.sum(user_field, axis=1)))
neighs = get_neighs(user_field)
if debaising_approach == 'disparate_impact_remover':
neighs = [x for x in neighs if x.size != 0]
sample_neighs = []
for i in range(len(neighs)):
sample_neighs.append(list(sample_neigh(neighs[i], NUM_FIELD)))
sample_neighs = np.array(sample_neighs)
np.save(os.path.join(save_path, 'user_field.npy'), sample_neighs)
user_field_new = sample_neighs
user_edge_path = './user_edge.csv'
user_field_new_path = './user_field.npy'
user_gender_path = './user_gender.csv'
user_label_path = './user_labels.csv'
return user_edge_path, user_field_new_path, user_gender_path, user_label_path
def divide_data(df):
# divide data into 3
label = df[['userid', 'final_gender_code', 'age_level', 'pvalue_level', 'occupation', 'new_user_class_level']].copy()
pid_cid = df[['adgroup_id', 'cate_id']].copy()
uid_pid = df[['userid', 'adgroup_id', 'clk']].copy()
return label, pid_cid, uid_pid
def divide_data_2(df):
df_user = df[{'userid', 'gender', 'bin_age', 'pvalue_level', 'occupation', 'new_user_class_level'}].copy()
df_item = df[['adgroup_id', 'cate_id', 'campaign_id', 'brand']].copy()
df_click = df[['userid', 'adgroup_id', 'clk']].copy()
return df_user, df_item, df_click
def apply_bin_age(label):
label['bin_age'] = label['age']
label['bin_age'] = label['bin_age'].replace(1,0)
label['bin_age'] = label['bin_age'].replace(2,0)
label['bin_age'] = label['bin_age'].replace(3,1)
label['bin_age'] = label['bin_age'].replace(4,0)
label['bin_age'] = label['bin_age'].replace(5,0)
label['bin_age'] = label['bin_age'].replace(6,0)
return label
def apply_bin_buy(label):
label['bin_buy'] = label['buy']
label['bin_buy'] = label['bin_buy'].replace(3.0,2.0)
return label
def get_count(tp, id):
playcount_groupbyid = tp[[id]].groupby(id, as_index=True)
count = playcount_groupbyid.size()
return count
def filter_triplets(tp, user, item, min_uc=0, min_sc=0):
# Only keep the triplets for users who clicked on at least min_uc items
if min_uc > 0:
usercount = get_count(tp, user)
tp = tp[tp[user].isin(usercount.index[usercount >= min_uc])]
# Only keep the triplets for items which were clicked on by at least min_sc users.
if min_sc > 0:
itemcount = get_count(tp, item)
tp = tp[tp[item].isin(itemcount.index[itemcount >= min_sc])]
# Update both usercount and itemcount after filtering
usercount, itemcount = get_count(tp, user), get_count(tp, item)
return tp, usercount, itemcount
def col_map(df, col, num2id):
df[[col]] = df[[col]].applymap(lambda x: num2id[x])
return df
def label_map(label_df, label_list):
for label in label_list:
label2id = {num: i for i, num in enumerate(pd.unique(label_df[label]))}
label_df = col_map(label_df, label, label2id)
return label_df
def field_reader(path):
"""
Reading the sparse field matrix stored as csv from the disk.
:param path: Path to the csv file.
:return field: csr matrix of field.
"""
user_field = pd.read_csv(path)
user_index = user_field["uid"].values.tolist()
field_index = user_field["cid"].values.tolist()
user_count = max(user_index)+1
field_count = max(field_index)+1
field_index = sp.csr_matrix((np.ones_like(user_index), (user_index, field_index)), shape=(user_count, field_count))
return field_index
def get_neighs(csr):
neighs = []
# t = time.time()
idx = np.arange(csr.shape[1])
for i in range(csr.shape[0]):
x = csr[i, :].toarray()[0] > 0
if idx[x].size > 0:
neighs.append(idx[x])
# if i % (10*1000) == 0:
# print('sec/10k:', time.time()-t)
return neighs
def sample_neigh(neigh, num_sample):
if len(neigh) >= num_sample:
sample_neigh = np.random.choice(neigh, num_sample, replace=False)
elif len(neigh) < num_sample:
sample_neigh = np.random.choice(neigh, num_sample, replace=True)
return sample_neigh |