搜索广告 - 不平衡数据 Imbalanced Data

【IJCAI-2018】搜索广告 - 不平衡数据 Imbalanced Data

我并不擅长做比赛，也不擅长构造特征，也不擅长调参数，也没有服务器可以并行。大家的baseline都比我的模型要好。在这里写这篇文章，主要是想跟大家分享下我对数据的理解，以及我思考的一个大概框架，希望对大家能有那么一点点启发或者帮助。

 

像我这种无经验无战绩无队友，特征只会弄个dummy variable，降维只会PCA，模型只会LR，SVM，调参只会CV，ensemble只会求平均的人，每次在比赛里的存在感就是增大分母，当我看到大家在论坛分享自己的baseline的时候，真的是好高兴好兴奋，然后又看到大家在构造各种神奇的特征，模型的logloss居然有提高，真的是很佩服。由于我既没有聪明的头脑也没有足够的细致，于是就“拿来主义”至上，将论坛里看到的baseline copy下来，在电脑上跑了一下。哇，好牛，第一把就0.084，与排名最前的0.080，只差0.004，我这是要冲击leaderboard 的节奏啊~兴奋之余干劲更大了，各种吭哧吭哧搜模型，吭哧吭哧敲代码，感觉自带BGM，走路生风~反正就是那种全世界都属于我的感觉~但是~~当我想看看哪些预测为1的时候，我惊呆了，no one! 合着我1万8的test data，用模型预测出来之后，竟然没有一个1，一个都没有！

然后这就是问题了。可能聪明的大家早就知道了CTR的数据不平衡问题，但是愚钝如我啊，我竟然没有发现！

所以吐槽完了~

 

对于不平衡数据 Imbalanced Data，像这里的CTR里面的二分类预测，应该怎么处理呢？

正负样本比例严重不平衡的情况，比例达到了50:1，如果直接在此基础上做预测，对于样本量较小的类的召回率会极低。

 

因为传统的学习方法以降低总体分类精度为目标，将所有样本一视同仁，同等对待，造成了分类器在多数类的分类精度较高而在少数类的分类精度很低。例如ctr正负样本50:1的例子，算法就算全部预测为另一样本，准确率也会达到98%(50/51)，因此传统的学习算法在不平衡数据集中具有较大的局限性。传统的学习算法的预测结果就是favor the majority, 因为the minority 本身数量少，又本同等对待，因此miss the minority 的代价极小，所以结果就是favor the majority。

 

解决方法主要分为两个方面。

第一种方案主要从数据的角度出发，主要方法为抽样，既然我们的样本是不平衡的，那么可以通过某种策略进行抽样，从而让我们的数据相对均衡一些；resampling 方法包括 over-, under-, combination. over- is increasing # of minority, under- is decreasing # of majority.

 

第二种方案从算法的角度出发，考虑不同误分类情况代价的差异性对算法进行优化，使得我们的算法在不平衡数据下也能有较好的效果。改写cost function by giving large cost of misclassifying the minority labels. 

PS： 附件中有基于logloss , AUC 的对比的python代码，可以运行，不会memory error.

 

# -*- coding: utf-8 -*-
"""
Created on Wed Apr 4 10:53:58 2018
@author : HaiyanJiang
@email : jianghaiyan.cn@gmail.com

 

what does the doc do?
some ideas of improving the accuracy of imbalanced data classification.
data characteristics:
imbalanced data.
the models:
model_baseline : lgb
model_baseline2 : another lgb
model_baseline3 : bagging

 

Other Notes:
除了基本特征外，还包括了'用户'在当前小时内和当天的点击量统计特征，以及当前所在的小时。
'context_day', 'context_hour',
'user_query_day', 'user_query_hour', 'user_query_day_hour',
non_feat = [
'instance_id', 'user_id', 'context_id', 'item_category_list',
'item_property_list', 'predict_category_property',
'context_timestamp', 'TagTime', 'context_day'
]

 

"""

 

import time
import pandas as pd
import lightgbm as lgb
from sklearn.metrics import log_loss

 

import numpy as np
import itertools
import matplotlib.pyplot as plt
from sklearn.metrics import confusion_matrix
from sklearn.metrics import auc, roc_curve
from scipy import interp

 

from sklearn.ensemble import BaggingClassifier
from imblearn.ensemble import BalancedBaggingClassifier

 


def read_bigcsv(filename, **kw):
with open(filename) as rf:
reader = pd.read_csv(rf, **kw, iterator=True)
chunkSize = 100000
chunks = []
while True:
try:
chunk = reader.get_chunk(chunkSize)
chunks.append(chunk)
except StopIteration:
print("Iteration is stopped.")
break
df = pd.concat(chunks, axis=0, join='outer', ignore_index=True)
return df

 


def timestamp2datetime(value):
value = time.localtime(value)
dt = time.strftime('%Y-%m-%d %H:%M:%S', value)
return dt

 


'''
from matplotlib import pyplot as plt
tt = data['context_timestamp']
plt.plot(tt)
# 可以看出时间是没有排好的,有一定的错位。如果做成online的模型，一定要将时间排好。
# aa = data[data['user_id']==24779788309075]
aa = data_train[data_train.duplicated(subset=None, keep='first')]
bb = data_train[data_train.duplicated(subset=None, keep='last')]
cc = data_train[data_train.duplicated(subset=None, keep=False)]

 

a2 = pd.DataFrame(train_id)[pd.DataFrame(train_id).duplicated(keep=False)]
b2 = train_id[train_id.duplicated(keep='last')]
c2 = train_id[train_id.duplicated(keep=False)]

 

c2 = data_train[data_train.duplicated(subset=None, keep=False)]

 

经验证, 'instance_id'有重复
a3 = Xdata[Xdata['instance_id']==1037061371711078396]
'''

 


def convert_timestamp(data):
'''
1. convert timestamp to datetime.
2. no sort, no reindex.
data.duplicated(subset=None, keep='first')
TagTime from-to is ('2018-09-18 00:00:01', '2018-09-24 23:59:47')
'user_query_day', 'user_query_day_hour', 'hour',
np.corrcoef(data['user_query_day'], data['user_query_hour'])
np.corrcoef(data['user_query_hour'], data['user_query_day_hour'])
np.corrcoef(data['user_query_day'], data['user_query_day_hour'])
'''
data['TagTime'] = data['context_timestamp'].apply(timestamp2datetime)
# data['TagTime'][0], data['TagTime'][len(data) - 1]
# x = data['TagTime'][len(data) - 1]
data['context_day'] = data['TagTime'].apply(lambda x: int(x[8:10]))
data['context_hour'] = data['TagTime'].apply(lambda x: int(x[11:13]))
query_day = data.groupby(['user_id', 'context_day']).size(
).reset_index().rename(columns={0: 'user_query_day'})
data = pd.merge(data, query_day, 'left', on=['user_id', 'context_day'])
query_hour = data.groupby(['user_id', 'context_hour']).size(
).reset_index().rename(columns={0: 'user_query_hour'})
data = pd.merge(data, query_hour, 'left', on=['user_id', 'context_hour'])
query_day_hour = data.groupby(
by=['user_id', 'context_day', 'context_hour']).size(
).reset_index().rename(columns={0: 'user_query_day_hour'})
data = pd.merge(data, query_day_hour, 'left',
on=['user_id', 'context_day', 'context_hour'])
return data

 


def plot_confusion_matrix(cm, classes, normalize=False,
title='Confusion matrix',
cmap=plt.cm.Blues):
"""
This function prints and plots the confusion matrix.
Normalization can be applied by setting 'normalize=True'.
"""
if normalize:
cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
print("Normalized confusion matrix")
else:
print('Confusion matrix, without normalization')
print(cm)
plt.imshow(cm, interpolation='nearest', cmap=cmap)
plt.title(title)
plt.colorbar()
tick_marks = np.arange(len(classes))
plt.xticks(tick_marks, classes, rotation=45)
plt.yticks(tick_marks, classes)
fmt = '.2f' if normalize else 'd'
thresh = cm.max() / 2.
for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):
plt.text(j, i, format(cm[i, j], fmt),
horizontalalignment="center",
color="white" if cm[i, j] > thresh else "black")
plt.tight_layout()
plt.ylabel('True label')
plt.xlabel('Predicted label')

 


def data_baseline():
filename = '../round1_ijcai_18_data/round1_ijcai_18_train_20180301.txt'
data = read_bigcsv(filename, sep=' ')
# data = pd.read_csv(filename, sep=' ')
data.drop_duplicates(inplace=True)
data.reset_index(drop=True, inplace=True) # very important
data = convert_timestamp(data)
train = data.loc[data['context_day'] < 24] # 18,19,20,21,22,23,24
test = data.loc[data['context_day'] == 24] # 暂时先使用第24天作为验证集
features = [
'item_id', 'item_brand_id', 'item_city_id', 'item_price_level',
'item_sales_level', 'item_collected_level', 'item_pv_level',
'user_gender_id', 'user_age_level', 'user_occupation_id',
'user_star_level', 'context_page_id', 'shop_id',
'shop_review_num_level', 'shop_review_positive_rate',
'shop_star_level', 'shop_score_service',
'shop_score_delivery', 'shop_score_description',
'user_query_day', 'user_query_day_hour', 'context_hour',
]
x_train = train[features]
x_test = test[features]
y_train = train['is_trade']
y_test = test['is_trade']
return x_train, x_test, y_train, y_test
# x_train, x_test, y_train, y_test = data_baseline()

 


def model_baseline(x_train, y_train, x_test, y_test):
cat_names = [
'item_price_level',
'item_sales_level',
'item_collected_level',
'item_pv_level',
'user_gender_id',
'user_age_level',
'user_occupation_id',
'user_star_level',
'context_page_id',
'shop_review_num_level',
'shop_star_level',
]
print("begin train...")
kw_lgb = dict(num_leaves=63, max_depth=7, n_estimators=80, random_state=6,)
clf = lgb.LGBMClassifier(**kw_lgb)
clf.fit(x_train, y_train, categorical_feature=cat_names,)
prob = clf.predict_proba(x_test,)[:, 1]
predict_score = [float('%.2f' % x) for x in prob]
loss_val = log_loss(y_test, predict_score)
# print(loss_val) # 0.0848226750637
fpr, tpr, thresholds = roc_curve(y_test, predict_score)
mean_fpr = np.linspace(0, 1, 100)
mean_tpr = interp(mean_fpr, fpr, tpr)
x_auc = auc(fpr, tpr)
fig = plt.figure('fig1')
ax = fig.add_subplot(1, 1, 1)
name = 'base_lgb'
plt.plot(mean_fpr, mean_tpr, linestyle='--',
label='{} (area = %0.2f, logloss = %0.2f)'.format(name) %
(x_auc, loss_val), lw=2)
y_pred = clf.predict(x_test)
cm1 = plt.figure()
cm = confusion_matrix(y_test, y_pred)
plot_confusion_matrix(cm, classes=[0, 1], title='Confusion matrix base1')
# add weighted according to the labels
clf = lgb.LGBMClassifier(**kw_lgb)
clf.fit(x_train, y_train,
sample_weight=[1 if y == 1 else 0.02 for y in y_train],
categorical_feature=cat_names)
prob = clf.predict_proba(x_test,)[:, 1]
predict_score = [float('%.2f' % x) for x in prob]
loss_val = log_loss(y_test, predict_score)
fpr, tpr, thresholds = roc_curve(y_test, predict_score)
mean_fpr = np.linspace(0, 1, 100)
mean_tpr = interp(mean_fpr, fpr, tpr)
x_auc = auc(fpr, tpr)
name = 'base_lgb_weighted'
plt.figure('fig1') # 选择图
plt.plot(
mean_fpr, mean_tpr, linestyle='--',
label='{} (area = %0.2f, logloss = %0.2f)'.format(name) %
(x_auc, loss_val), lw=2)
y_pred = clf.predict(x_test)
cm2 = plt.figure()
cm = confusion_matrix(y_test, y_pred)
plot_confusion_matrix(cm, classes=[0, 1],
title='Confusion matrix basemodle')
plt.figure('fig1') # 选择图
plt.plot([0, 1], [0, 1], linestyle='--', lw=2, color='k', label='Luck')
# make nice plotting
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
ax.get_xaxis().tick_bottom()
ax.get_yaxis().tick_left()
ax.spines['left'].set_position(('outward', 10))
ax.spines['bottom'].set_position(('outward', 10))
plt.xlim([0, 1])
plt.ylim([0, 1])
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.title('Receiver Operating Characteristic')
plt.legend(loc="lower right")
plt.show()
return cm1, cm2, fig

 


def model_baseline3(x_train, y_train, x_test, y_test):
bagging = BaggingClassifier(random_state=0)
balanced_bagging = BalancedBaggingClassifier(random_state=0)
bagging.fit(x_train, y_train)
balanced_bagging.fit(x_train, y_train)
prob = bagging.predict_proba(x_test)[:, 1]
predict_score = [float('%.2f' % x) for x in prob]
loss_val = log_loss(y_test, predict_score)
y_pred = [1 if x > 0.5 else 0 for x in predict_score]
fpr, tpr, thresholds = roc_curve(y_test, predict_score)
mean_fpr = np.linspace(0, 1, 100)
mean_tpr = interp(mean_fpr, fpr, tpr)
x_auc = auc(fpr, tpr)
fig = plt.figure('Bagging')
ax = fig.add_subplot(1, 1, 1)
name = 'base_Bagging'
plt.plot(mean_fpr, mean_tpr, linestyle='--',
label='{} (area = %0.2f, logloss = %0.2f)'.format(name) %
(x_auc, loss_val), lw=2)
y_pred_bagging = bagging.predict(x_test)
cm_bagging = confusion_matrix(y_test, y_pred_bagging)
cm1 = plt.figure()
plot_confusion_matrix(cm_bagging,
classes=[0, 1],
title='Confusion matrix of BaggingClassifier')
# balanced_bagging
prob = balanced_bagging.predict_proba(x_test)[:, 1]
predict_score = [float('%.2f' % x) for x in prob]
loss_val = log_loss(y_test, predict_score)
fpr, tpr, thresholds = roc_curve(y_test, predict_score)
mean_fpr = np.linspace(0, 1, 100)
mean_tpr = interp(mean_fpr, fpr, tpr)
x_auc = auc(fpr, tpr)
plt.figure('Bagging') # 选择图
name = 'base_Balanced_Bagging'
plt.plot(
mean_fpr, mean_tpr, linestyle='--',
label='{} (area = %0.2f, logloss = %0.2f)'.format(name) %
(x_auc, loss_val), lw=2)
y_pred_balanced_bagging = balanced_bagging.predict(x_test)
cm_balanced_bagging = confusion_matrix(y_test, y_pred_balanced_bagging)
cm2 = plt.figure()
plot_confusion_matrix(cm_balanced_bagging,
classes=[0, 1],
title='Confusion matrix of BalancedBagging')
plt.figure('Bagging') # 选择图
plt.plot([0, 1], [0, 1], linestyle='--', lw=2, color='k', label='Luck')
# make nice plotting
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
ax.get_xaxis().tick_bottom()
ax.get_yaxis().tick_left()
ax.spines['left'].set_position(('outward', 10))
ax.spines['bottom'].set_position(('outward', 10))
plt.xlim([0, 1])
plt.ylim([0, 1])
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.title('Receiver Operating Characteristic')
plt.legend(loc="lower right")
plt.show()
return cm1, cm2, fig

 


def model_baseline2(x_train, y_train, x_test, y_test):
params = {
'task': 'train',
'boosting_type': 'gbdt',
'objective': 'multiclass',
'num_class': 2,
'verbose': 0,
'metric': 'logloss',
'max_bin': 255,
'max_depth': 7,
'learning_rate': 0.3,
'nthread': 4,
'n_estimators': 85,
'num_leaves': 63,
'feature_fraction': 0.8,
'num_boost_round': 160,
}
lgb_train = lgb.Dataset(x_train, label=y_train)
lgb_eval = lgb.Dataset(x_test, label=y_test, reference=lgb_train)
print("begin train...")
bst = lgb.train(params, lgb_train, valid_sets=lgb_eval)
prob = bst.predict(x_test)[:, 1]
predict_score = [float('%.2f' % x) for x in prob]
loss_val = log_loss(y_test, predict_score)
y_pred = [1 if x > 0.5 else 0 for x in predict_score]
fpr, tpr, thresholds = roc_curve(y_test, predict_score)
x_auc = auc(fpr, tpr)
mean_fpr = np.linspace(0, 1, 100)
mean_tpr = interp(mean_fpr, fpr, tpr)
fig = plt.figure('weighted')
ax = fig.add_subplot(1, 1, 1)
name = 'base_lgb'
plt.plot(mean_fpr, mean_tpr, linestyle='--',
label='{} (area = %0.2f, logloss = %0.2f)'.format(name) %
(x_auc, loss_val), lw=2)
cm1 = plt.figure()
cm = confusion_matrix(y_test, y_pred)
plot_confusion_matrix(cm, classes=[0, 1],
title='Confusion matrix basemodle')
# add weighted according to the labels
lgb_train = lgb.Dataset(
x_train, label=y_train,
weight=[1 if y == 1 else 0.02 for y in y_train])
lgb_eval = lgb.Dataset(
x_test, label=y_test, reference=lgb_train,
weight=[1 if y == 1 else 0.02 for y in y_test])
bst = lgb.train(params, lgb_train, valid_sets=lgb_eval)
prob = bst.predict(x_test)[:, 1]
predict_score = [float('%.2f' % x) for x in prob]
loss_val = log_loss(y_test, predict_score)
y_pred = [1 if x > 0.5 else 0 for x in predict_score]
fpr, tpr, thresholds = roc_curve(y_test, predict_score)
mean_fpr = np.linspace(0, 1, 100)
mean_tpr = interp(mean_fpr, fpr, tpr)
x_auc = auc(fpr, tpr)
plt.figure('weighted') # 选择图
name = 'base_lgb_weighted'
plt.plot(
mean_fpr, mean_tpr, linestyle='--',
label='{} (area = %0.2f, logloss = %0.2f)'.format(name) %
(x_auc, loss_val), lw=2)
cm2 = plt.figure()
cm = confusion_matrix(y_test, y_pred)
plot_confusion_matrix(cm, classes=[0, 1],
title='Confusion matrix basemodle')
plt.figure('weighted') # 选择图
plt.plot([0, 1], [0, 1], linestyle='--', lw=2, color='k', label='Luck')
# make nice plotting
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
ax.get_xaxis().tick_bottom()
ax.get_yaxis().tick_left()
ax.spines['left'].set_position(('outward', 10))
ax.spines['bottom'].set_position(('outward', 10))
plt.xlim([0, 1])
plt.ylim([0, 1])
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.title('Receiver Operating Characteristic')
plt.legend(loc="lower right")
plt.show()
return cm1, cm2, fig

 


'''
1. logloss VS AUC
虽然 baseline 的 logloss= 0.0819, 确实很小，但是从 Confusion matrix 看出，
模型倾向于将所有的数据都分成多的那个，加了weight 之后稍好一点？
Though the logloss is 0.0819, which is a very small value.
Confusion matrix shows y_pred all 0, which feavors the majority classes.

 

AUC 只有 0.64~0.67.
AUC如此小，按理来说不应该啊，但是为什么呢？
因为数据的label 极度不平衡，1 的比例大概只有 2%. 50:1.
AUC 对不平衡数据的分类性能测试更友好，用AUC去选特征，可能结果更好哦。
这里只提供一个大概的思考改进点。
2. handling with imbalanced data:
1. resampling, over- or under-,
over- is increasing # of minority, under- is decreasing # of majority.
2. revalue the loss function by giving large loss of misclassifying the
minority labels.
'''

 


if __name__ == "__main__":
x_train, x_test, y_train, y_test = data_baseline()
cm11, cm12, fig1 = model_baseline(x_train, y_train, x_test, y_test)
cm21, cm22, fig2 = model_baseline2(x_train, y_train, x_test, y_test)
cm31, cm32, fig3 = model_baseline3(x_train, y_train, x_test, y_test)

 

fig1.savefig('./base_lgb_weighted.jpg', format='jpg')
cm11.savefig('./Confusion matrix1.jpg', format='jpg')
cm12.savefig('./Confusion matrix2.jpg', format='jpg')