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import numpy as np
import pandas as pd
import torch
import dgl
from fainress_component import disparate_impact_remover, reweighting, sample
import fastText
def ali_RHGN_pre_process(df, df_user, df_click, df_item, sens_attr, label, special_case, debaising_approach=None):
# load and clean data
if debaising_approach != None:
# special case == csv data
if special_case == True:
df_user.rename(columns={'userid':'uid', 'final_gender_code':'gender','age_level':'age', 'pvalue_level':'buy', 'occupation':'student', 'new_user_class_level ':'city'}, inplace=True)
df_user.dropna(inplace=True)
df_user = apply_bin_age(df_user)
df_user = apply_bin_buy(df_user)
df_user = apply_bin_gender(df_user)
if debaising_approach == 'disparate_impact_remover':
df_user = disparate_impact_remover(df_user, sens_attr, label)
elif debaising_approach == 'reweighting':
df_user = reweighting(df_user, sens_attr, label)
elif debaising_approach == 'sample':
df_user = sample(df_user, sens_attr, label)
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)
elif debaising_approach == 'reweighting':
df = reweighting(df, sens_attr, label)
elif debaising_approach == 'sample':
df = sample(df, sens_attr, label)
df_user, df_item, df_click = divide_data_2(df)
df_user.rename(columns={'userid':'uid', 'pvalue_level':'buy', 'occupation':'student', 'new_user_class_level':'city'}, inplace=True)
df_user.dropna(inplace=True)
df_user = apply_bin_buy(df_user)
else:
# df_user = label
if special_case == False:
df_user, df_item, df_click = divide_data(df)
df_user.rename(columns={'userid':'uid', 'final_gender_code':'gender','age_level':'age', 'pvalue_level':'buy', 'occupation':'student', 'new_user_class_level ':'city'}, inplace=True)
df_user.dropna(inplace=True)
df_user = apply_bin_age(df_user)
df_user = apply_bin_buy(df_user)
# df_item = pid_cid
if special_case == False:
df_item.dropna(axis=0, subset=['cate_id', 'campaign_id', 'brand'], inplace=True)
df_item.rename(columns={'adgroup_id':'pid', 'cate_id':'cid'}, inplace=True)
df_click.rename(columns={'user':'uid', 'adgroup_id':'pid'}, inplace=True)
df_click = df_click[df_click['clk']>0]
df_click.drop('clk', axis=1, inplace=True)
df_click = df_click[df_click["uid"].isin(df_user["uid"])]
df_click = df_click[df_click["pid"].isin(df_item["pid"])]
df_click.drop_duplicates(inplace=True)
# Filter and Process
# Before filtering
users = set(df_click.uid.tolist())
items = set(df_click.pid.tolist())
print('User before filtering {} and items before filtering {}'.format(len(users), len(items)))
df_click, uid_activity, pid_popularity = filter_triplets(df_click, 'uid', 'pid', min_uc=0, min_sc=2) # min_sc>=2
#sparsity = 1. * df_click.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%%)" %
# (df_click.shape[0], uid_activity.shape[0], pid_popularity.shape[0], sparsity * 100))
# After filtering
users = set(df_click.uid.tolist())
items = set(df_click.pid.tolist())
print('Users after filtering {} and items after filtering {}'.format(len(users), len(items)))
df_user = df_user[df_user['uid'].isin(users)]
df_item = df_item[df_item['pid'].isin(items)]
df_user.reset_index(drop=True, inplace=True)
df_item.reset_index(drop=True, inplace=True)
# save??
# Re-process
df_user = df_user.astype({'uid': 'str'}, copy=False)
df_item = df_item.astype({'pid': 'str', 'cid': 'str', 'campaign_id': 'str', 'brand': 'str'}, copy=False)
df_click = df_click.astype({'uid': 'str', 'pid': 'str'}, copy=False)
# Build a dictionary and remove duplicate items
if special_case == True and debaising_approach == 'reweighting' or debaising_approach == 'disparate_impact_remover':
df_user['uid'] = df_user['uid'].astype(float).astype(int).astype(str)
user_dic = {k: v for v, k in enumerate(df_user.uid)}
cate_dic = {k: v for v, k in enumerate(df_item.cid.drop_duplicates())}
campaign_dic = {k: v for v, k in enumerate(df_item.campaign_id.drop_duplicates())}
brand_dic = {k: v for v, k in enumerate(df_item.brand.drop_duplicates())}
item_dic = {}
c1, c2, c3=[],[],[]
for i in range(len(df_item)):
k=df_item.at[i,'pid']
v=i
item_dic[k]=v
c1.append(cate_dic[df_item.at[i,'cid']])
c2.append(campaign_dic[df_item.at[i,'campaign_id']])
c3.append(brand_dic[df_item.at[i,'brand']])
print(min(c1), min(c2), min(c3))
print(len(cate_dic), len(campaign_dic), len(brand_dic))
df_click=df_click[df_click['pid'].isin(item_dic)]
df_click=df_click[df_click['uid'].isin(user_dic)]
df_click.reset_index(drop=True, inplace=True)
# Generate graph
G, cid1_feature, cid2_feature, cid3_feature, user_label = generate_graph(df_user, df_item, df_click, user_dic, item_dic, cate_dic, campaign_dic, brand_dic, c1, c2, c3)
'''
sens_attr = 'age'
predict_attr = 'gender'
label_number = 100
seed = 42
sens_number = 512
labels = user_label[predict_attr].values
import random
random.seed(seed)
label_idx = np.where(labels>=0)[0]
random.shuffle(label_idx)
idx_train = label_idx[:min(int(0.5 * len(label_idx)),label_number)]
idx_test = label_idx[label_number:]
idx_val = idx_test
sens = user_label[sens_attr].values
sens_idx = set(np.where(sens >= 0)[0])
idx_test = np.asarray(list(sens_idx & set(idx_test)))
sens = torch.FloatTensor(sens)
idx_sens_train = list(sens_idx - set(idx_val) - set(idx_test))
random.seed(seed)
random.shuffle(idx_sens_train)
idx_sens_train = torch.LongTensor(idx_sens_train[:sens_number])
idx_train = torch.LongTensor(idx_train)
idx_val = torch.LongTensor(idx_val)
idx_test = torch.LongTensor(idx_test)
'''
return G, cid1_feature, cid2_feature, cid3_feature # use this graph for the input of the model (see RHGN repo for details)
#return G, cid1_feature, cid2_feature, cid3_feature, idx_sens_train, idx_train, sens
def divide_data(df):
# divide data into 3 (df_user, df_item, df_click)
df_user = df[['userid', 'final_gender_code', 'age_level', '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 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 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 apply_bin_age(df_user):
df_user['bin_age'] = df_user['age']
df_user['bin_age'] = df_user['bin_age'].replace(1,0)
df_user['bin_age'] = df_user['bin_age'].replace(2,0)
df_user['bin_age'] = df_user['bin_age'].replace(3,0)
df_user['bin_age'] = df_user['bin_age'].replace(4,1)
df_user['bin_age'] = df_user['bin_age'].replace(5,1)
df_user['bin_age'] = df_user['bin_age'].replace(6,1)
return df_user
def apply_bin_buy(df_user):
df_user['bin_buy'] = df_user['buy']
df_user['bin_buy'] = df_user['bin_buy'].replace(3.0,2.0)
df_user['bin_buy'] = df_user['bin_buy'].astype('int64')
return df_user
def apply_bin_gender(df_user):
df_user['bin_gender'] = df_user['gender']
df_user['bin_gender'] = df_user['bin_gender'].replace(2,0)
return df_user
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 generate_graph(df_user, df_item, df_click, user_dic, item_dic, cate_dic, campaign_dic, brand_dic, c1, c2, c3):
click_user = [user_dic[user] for user in df_click.uid]
click_item = [item_dic[item] for item in df_click.pid]
data_dict = {
("user", "click", "item"): (torch.tensor(click_user), torch.tensor(click_item)),
("item", "click_by", "user"): (torch.tensor(click_item), torch.tensor(click_user))
}
G = dgl.heterograph(data_dict)
# process with fastext model
# Todo install fasttext in the repo
# Todo test this in Jupyter (not tested)
#model = fasttext.load_model('../fastText/cc.zh.200.bin')
model = fasttext.load_model('../cc.zh.200.bin')
temp1 = {k: model.get_sentence_vector(v) for v,k in cate_dic.items()}
cid1_feature = torch.tensor([temp1[k] for _, k in cate_dic.items()])
temp2 = {k: model.get_sentence_vector(v) for v, k in campaign_dic.items()}
cid2_feature = torch.tensor([temp2[k] for _, k in campaign_dic.items()])
temp3 = {k: model.get_sentence_vector(v) for v, k in brand_dic.items()}
cid3_feature = torch.tensor([temp3[k] for _, k in brand_dic.items()])
uid2id = {num: i for i, num in enumerate(df_user['uid'])}
df_user = col_map(df_user, 'uid', uid2id)
user_label = label_map(df_user, df_user.columns[1:])
# Pass the label into "label"
label_gender = user_label.gender
label_age = user_label.bin_age
label_buy = user_label.buy
label_student = user_label.student
label_city = user_label.city
label_bin_buy = user_label.bin_buy
G.nodes['user'].data['bin_gender'] = torch.tensor(label_gender[:G.number_of_nodes('user')])
G.nodes['user'].data['bin_age'] = torch.tensor(label_age[:G.number_of_nodes('user')])
G.nodes['user'].data['buy'] = torch.tensor(label_buy[:G.number_of_nodes('user')])
G.nodes['user'].data['student'] = torch.tensor(label_student[:G.number_of_nodes('user')])
G.nodes['user'].data['city'] = torch.tensor(label_city[:G.number_of_nodes('user')])
G.nodes['user'].data['bin_buy'] = torch.tensor(label_bin_buy[:G.number_of_nodes('user')])
G.nodes['item'].data['cid1'] = torch.tensor(c1[:G.number_of_nodes('item')])
G.nodes['item'].data['cid2'] = torch.tensor(c2[:G.number_of_nodes('item')])
G.nodes['item'].data['cid3'] = torch.tensor(c3[:G.number_of_nodes('item')])
print(G)
print(cid1_feature.shape,)
print(cid2_feature.shape,)
print(cid3_feature.shape,)
return G, cid1_feature, cid2_feature, cid3_feature, user_label
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