Spaces:
Runtime error
Runtime error
File size: 12,726 Bytes
d2a8669 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 |
import pandas as pd
import numpy as np
import torch
import dgl
import fastText
from fainress_component import disparate_impact_remover, reweighting, sample
def tec_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:
if special_case == True:
df_user.dropna(inplace=True)
age_dic = {'11~15':0, '16~20':0, '21~25':0, '26~30':1, '31~35':1, '36~40':2, '41~45':2, '46~50':3, '51~55':3, '56~60':4, '61~65':4, '66~70':4, '71~':4}
df_user[["age_range"]] = df_user[["age_range"]].applymap(lambda x:age_dic[x])
df_user.rename(columns={"user_id":"uid", "age_range":"age"}, inplace=True)
# binarize age
df_user = apply_bin_age(df_user)
#df_extra = df[['cid1_name', 'cid2_name ', 'cid3_name']].copy()
#df.drop(columns=["cid1_name", "cid2_name ", "cid3_name", "item_name", "seg_name"], inplace=True)
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.dropna(inplace=True)
age_dic = {'11~15':0, '16~20':0, '21~25':0, '26~30':1, '31~35':1, '36~40':2, '41~45':2, '46~50':3, '51~55':3, '56~60':4, '61~65':4, '66~70':4, '71~':4}
df[["age_range"]] = df[["age_range"]].applymap(lambda x:age_dic[x])
df.rename(columns={"user_id":"uid", "age_range":"age"}, inplace=True)
df = apply_bin_age(df)
df_extra = df[['cid1_name', 'cid2_name ', 'cid3_name']].copy()
df.drop(columns=["cid1_name", "cid2_name ", "cid3_name", "item_name", "seg_name"], inplace=True)
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_data2(df)
else:
if special_case == False:
print('special case is False')
df_user, df_item, df_click = divide_data(df)
# df_user process
df_user.dropna(inplace=True)
age_dic = {'11~15':0, '16~20':0, '21~25':0, '26~30':1, '31~35':1, '36~40':2, '41~45':2, '46~50':3, '51~55':3, '56~60':4, '61~65':4, '66~70':4, '71~':4}
df_user[["age_range"]] = df_user[["age_range"]].applymap(lambda x:age_dic[x])
df_user.rename(columns={"user_id":"uid", "age_range":"age"}, inplace=True)
# binarize age
df_user = apply_bin_age(df_user)
# df_item process
df_item.dropna(inplace=True)
df_item.rename(columns={"item_id":"pid", "brand_code":"brand"}, inplace=True)
df_item.reset_index(drop=True, inplace=True)
df_item = df_item.sample(frac=0.15, random_state=11)
df_item.reset_index(drop=True, inplace=True)
# df_click process
df_click.dropna(inplace=True)
df_click.rename(columns={"user_id":"uid", "item_id":"pid"}, inplace=True)
df_click.reset_index(drop=True, inplace=True)
df_click = df_click.sample(frac=0.15, random_state=11)
df_click.reset_index(drop=True, 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)
df_click.reset_index(drop=True, inplace=True)
# 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)
sparsity = 1. * df_click.shape[0] / (uid_activity.shape[0] * pid_popularity.shape[0])
print("After filtering, there are %d interaction 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)))
# Process
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)
df_user = df_user.astype({"uid": "str"}, copy=False)
df_item = df_item.astype({'pid': 'str', 'cid1': 'str', 'cid2': 'str', 'cid3': 'str', 'brand': 'str'}, copy=False)
df_click = df_click.astype({'uid': 'str', 'pid': 'str'}, copy=False)
if debaising_approach != None and special_case == True:
df_user.uid = df_user.uid.astype(float).astype(int) # works
df_user.uid = df_user.uid.astype(str)
# Build a dictionary and remove duplicate items
if debaising_approach != None and special_case == False:
user_dic = {k: v for v,k in enumerate(df_user.uid)}
cid1_dic = {k: v for v,k in enumerate(df_extra.cid1_name.drop_duplicates())}
cid2_dic = {k: v for v,k in enumerate(df_extra['cid2_name'].drop_duplicates())}
cid3_dic = {k: v for v,k in enumerate(df_extra.cid3_name.drop_duplicates())}
brand_dic = {k: v for v, k in enumerate(df_item.brand.drop_duplicates())}
else:
user_dic = {k: v for v,k in enumerate(df_user.uid)}
cid1_dic = {k: v for v, k in enumerate(df_item.cid1_name.drop_duplicates())}
cid2_dic = {k: v for v, k in enumerate(df_item['cid2_name'].drop_duplicates())}
cid3_dic = {k: v for v, k in enumerate(df_item.cid3_name.drop_duplicates())}
brand_dic = {k: v for v, k in enumerate(df_item.brand.drop_duplicates())}
item_dic = {}
c1, c2, c3, brand = [], [], [], []
for i in range(len(df_item)):
k = df_item.at[i,'pid']
v = i
item_dic[k] = v
if debaising_approach != None and special_case == False:
c1.append(cid1_dic[df_extra.at[i,'cid1_name']])
c2.append(cid2_dic[df_extra.at[i,'cid2_name']])
c3.append(cid3_dic[df_extra.at[i,'cid3_name']])
brand.append(brand_dic[df_item.at[i,'brand']])
else:
c1.append(cid1_dic[df_item.at[i,'cid1_name']])
c2.append(cid2_dic[df_item.at[i,'cid2_name']])
c3.append(cid3_dic[df_item.at[i,'cid3_name']])
brand.append(brand_dic[df_item.at[i,'brand']])
if debaising_approach != None:
df_item.drop(columns=["price"], inplace=True)
else:
df_item.drop(columns=["cid1_name", "cid2_name", "cid3_name", "price", "item_name", "seg_name"], inplace=True)
#df_user['bin_age'] = df_user['bin_age'].replace(1,2)
#df_user['bin_age'] = df_user['bin_age'].replace(0,1)
#df_user['bin_age'] = df_user['bin_age'].replace(2,0)
if debaising_approach != None:
if 'bin_age' not in df_user:
df_user = df_user.join(df_user['bin_age'])
# Save?
# Generate Graph
G, cid1_feature, cid2_feature, cid3_feature, brand_feature = generate_graph(df_user,
df_item,
df_click,
user_dic,
item_dic,
cid1_dic,
cid2_dic,
cid3_dic,
brand_dic,
c1,
c2,
c3,
brand,
debaising_approach)
return G, cid1_feature, cid2_feature, cid3_feature, brand_feature # brand_feature not used (same as cid4_feature?)
def divide_data(df):
df_user = df[['user_id', 'gender', 'age_range']].copy()
df_item = df[['item_id', 'cid1', 'cid2', 'cid3', 'cid1_name', 'cid2_name', 'cid3_name','brand_code', 'price', 'item_name', 'seg_name']].copy()
df_click = df[['user_id', 'item_id']].copy()
return df_user, df_item, df_click
def divide_data2(df):
df_user = df[['uid', 'gender', 'age']].copy()
df_item = df[['item_id', 'cid1', 'cid2', 'cid3', 'brand_code', 'price']].copy()
df_click = df[['uid', 'item_id']].copy()
return df_user, df_item, df_click
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(2,1)
df_user["bin_age"] = df_user["bin_age"].replace(3,1)
df_user["bin_age"] = df_user["bin_age"].replace(4,1)
return df_user
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 generate_graph(df_user, df_item, df_click, user_dic, item_dic, cid1_dic, cid2_dic, cid3_dic, brand_dic, c1, c2, c3, brand, debaising_approach):
u = {v:k for k,v in user_dic.items()}
i = {v:k for k,v in item_dic.items()}
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)
# todo import the fasttext correctly
model = fasttext.load_model('../cc.zh.200.bin')
temp = {k: model.get_sentence_vector(v) for v, k in cid1_dic.items()}
cid1_feature = torch.tensor([temp[k] for _, k in cid1_dic.items()])
temp = {k: model.get_sentence_vector(v) for v, k in cid2_dic.items()}
cid2_feature = torch.tensor([temp[k] for _, k in cid2_dic.items()])
temp = {k: model.get_sentence_vector(v) for v, k in cid3_dic.items()}
cid3_feature = torch.tensor([temp[k] for _, k in cid3_dic.items()])
temp = {k: model.get_sentence_vector(v) for v, k in brand_dic.items()}
brand_feature = torch.tensor([temp[k] for _, k in brand_dic.items()])
# Passing labels into label
if debaising_approach == 'disparate_impact_remover' or debaising_approach == 'reweighting':
df_user['gender'] = df_user['gender'].astype(np.int64)
label_gender = df_user.gender
label_age = df_user.age
label_bin_age = df_user.bin_age
G.nodes['user'].data['gender'] = torch.tensor(label_gender[:G.number_of_nodes('user')])
G.nodes['user'].data['age'] = torch.tensor(label_age[:G.number_of_nodes('user')])
G.nodes['user'].data['bin_age'] = torch.tensor(label_bin_age[: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')])
G.nodes['item'].data['brand'] = torch.tensor(brand[:G.number_of_nodes('item')])
return G, cid1_feature, cid2_feature, cid3_feature, brand_feature |