5 模型设计
#!/usr/bin/env python
# -*- coding: UTF-8 -*-
import sys
import pandas as pd
import numpy as np
import xgboost as xgb
from sklearn.model_selection import train_test_split
import operator
from matplotlib import pylab as plt
from datetime import datetime
import time
from sklearn.model_selection import GridSearchCV
data = pd.read_csv(\'train_set.csv\')
data.head()
Index([\'user_id\', \'sku_id\', \'cate\', \'action_before_3_1.0_x\',
\'action_before_3_2.0_x\', \'action_before_3_3.0_x\',
\'action_before_3_4.0_x\', \'action_before_3_5.0_x\',
\'action_before_3_6.0_x\', \'action_before_3_1.0_y\',
...
\'cate_action_5_mean\', \'cate_action_6_mean\', \'has_bad_comment\',
\'bad_comment_rate\', \'comment_num_0\', \'comment_num_1\', \'comment_num_2\',
\'comment_num_3\', \'comment_num_4\', \'label\'],
dtype=\'object\', length=251)
data_x = data.loc[:,data.columns != \'label\']
data_y = data.loc[:,data.columns == \'label\']
x_train, x_test, y_train, y_test = train_test_split(data_x,data_y,test_size = 0.2, random_state = 0)
(2924, 250)
x_val = x_test.iloc[:1500,:]
y_val = y_test.iloc[:1500,:]
x_test = x_test.iloc[1500:,:]
y_test = y_test.iloc[1500:,:]
print (x_val.shape)
print (x_test.shape)
(1500, 250)
(1424, 250)
del x_train[\'user_id\']
del x_train[\'sku_id\']
del x_val[\'user_id\']
del x_val[\'sku_id\']
x_train.head()
dtrain = xgb.DMatrix(x_train, label=y_train)
dvalid = xgb.DMatrix(x_val, label=y_val)
param = {\'n_estimators\': 4000, \'max_depth\': 3, \'min_child_weight\': 5, \'gamma\': 0, \'subsample\': 1.0,
\'colsample_bytree\': 0.8, \'scale_pos_weight\':10, \'eta\': 0.1, \'silent\': 1, \'objective\': \'binary:logistic\',
\'eval_metric\':\'auc\'}
num_round = param[\'n_estimators\']
plst = param.items()
evallist = [(dtrain, \'train\'), (dvalid, \'eval\')]
bst = xgb.train(plst, dtrain, num_round, evallist, early_stopping_rounds=10)
bst.save_model(\'bst.model\')
{\'best_iteration\': \'198\', \'best_msg\': \'[198]\ttrain-auc:0.989114\teval-auc:0.97177\', \'best_score\': \'0.97177\'}
def create_feature_map(features):
outfile = open(r\'xgb.fmap\', \'w\')
i = 0
for feat in features:
outfile.write(\'{0}\t{1}\tq\n\'.format(i, feat))
i = i + 1
outfile.close()
features = list(x_train.columns[:])
create_feature_map(features)
def feature_importance(bst_xgb):
importance = bst_xgb.get_fscore(fmap=r\'xgb.fmap\')
importance = sorted(importance.items(), key=operator.itemgetter(1), reverse=True)
df = pd.DataFrame(importance, columns=[\'feature\', \'fscore\'])
df[\'fscore\'] = df[\'fscore\'] / df[\'fscore\'].sum()
file_name = \'feature_importance_\' + str(datetime.now().date())[5:] + \'.csv\'
df.to_csv(file_name)
feature_importance(bst)
fi = pd.read_csv(\'feature_importance_10-24.csv\')
fi.sort_values("fscore", inplace=True, ascending=False)
fi.head()
users = x_test[[\'user_id\', \'sku_id\', \'cate\']].copy()
del x_test[\'user_id\']
del x_test[\'sku_id\']
x_test_DMatrix = xgb.DMatrix(x_test)
y_pred = bst.predict(x_test_DMatrix, ntree_limit=bst.best_ntree_limit)
x_test[\'pred_label\'] = y_pred
x_test.head()
def label(column):
if column[\'pred_label\'] > 0.5:
#rint (\'yes\')
column[\'pred_label\'] = 1
else:
column[\'pred_label\'] = 0
return column
x_test = x_test.apply(label,axis = 1)
x_test.head()
x_test[\'true_label\'] = y_test
x_test.head()
x_test[\'user_id\'] = users[\'user_id\']
x_test[\'sku_id\'] = users[\'sku_id\']
x_test.head()
# 所有购买用户
all_user_set = x_test[x_test[\'true_label\']==1][\'user_id\'].unique()
print (len(all_user_set))
# 所有预测购买的用户
all_user_test_set = x_test[x_test[\'pred_label\'] == 1][\'user_id\'].unique()
print (len(all_user_test_set))
all_user_test_item_pair = x_test[x_test[\'pred_label\'] == 1][\'user_id\'].map(str) + \'-\' + x_test[x_test[\'pred_label\'] == 1][\'sku_id\'].map(str)
all_user_test_item_pair = np.array(all_user_test_item_pair)
print (len(all_user_test_item_pair))
126
224
243
pos, neg = 0,0
for user_id in all_user_test_set:
if user_id in all_user_set:
pos += 1
else:
neg += 1
all_user_acc = 1.0 * pos / ( pos + neg)
all_user_recall = 1.0 * pos / len(all_user_set)
print (\'所有用户中预测购买用户的准确率为 \' + str(all_user_acc))
print (\'所有用户中预测购买用户的召回率\' + str(all_user_recall))
所有用户中预测购买用户的准确率为 0.5357142857142857
所有用户中预测购买用户的召回率0.9523809523809523
#所有实际商品对
all_user_item_pair = x_test[x_test[\'true_label\']==1][\'user_id\'].map(str) + \'-\' + x_test[x_test[\'true_label\']==1][\'sku_id\'].map(str)
all_user_item_pair = np.array(all_user_item_pair)
#print (len(all_user_item_pair))
#print(all_user_item_pair)
pos, neg = 0, 0
for user_item_pair in all_user_test_item_pair:
#print (user_item_pair)
if user_item_pair in all_user_item_pair:
pos += 1
else:
neg += 1
all_item_acc = 1.0 * pos / ( pos + neg)
all_item_recall = 1.0 * pos / len(all_user_item_pair)
print (\'所有用户中预测购买商品的准确率为 \' + str(all_item_acc))
print (\'所有用户中预测购买商品的召回率\' + str(all_item_recall))
F11 = 6.0 * all_user_recall * all_user_acc / (5.0 * all_user_recall + all_user_acc)
F12 = 5.0 * all_item_acc * all_item_recall / (2.0 * all_item_recall + 3 * all_item_acc)
score = 0.4 * F11 + 0.6 * F12
print (\'F11=\' + str(F11))
print (\'F12=\' + str(F12))
print (\'score=\' + str(score))
所有用户中预测购买商品的准确率为 0.5679012345679012
所有用户中预测购买商品的召回率0.9583333333333334
F11=0.5778491171749598
F12=0.7516339869281046
score=0.6821200390268466