kaggle练习

kaggle练习

import  numpy as np
import pandas as pd
semble import RandomForestRegressor,BaggingRegressor
from nltk.stem.snowball import SnowballStemmer
del_selection import cross_val_score
import matplotlib.pyplot as plt'''
1、数据导入
'''
#导入训练集和测试集
df_train = pd.read_csv('G:/KNNtest/NLP/all/train.csv',encoding='ISO-8859-1')
df_test = pd.read_csv('G:/KNNtest/NLP/all/test.csv',encoding='ISO-8859-1')
#产品介绍导入
df_disc = pd.read_csv('G:/KNNtest/NLP/all/product_descriptions.csv',encoding='ISO-8859-1')#由于不需要做太多复杂处理，且已经拿到测试集和训练集，因此直接将测试集和训练集合并，一起进行文本预处理
df_all = pd.concat((df_train,df_test),axis=0,ignore_index=True)
#将产品介绍也导入进来,根据product_uid
df_all = pd.merge(df_all,df_disc,how='left',on='product_uid')
# _csv('G:/KNNtest/NLP/all/mergeData.csv')'''
2、文本预处理：我们这里遇到的文本预处理比较简单，因为最主要的就是看关键词是否会被包含。
所以我们统一化我们的文本内容，以达到任何term在我们的数据集中只有一种表达式的效果
'''
stemmer = SnowballStemmer('english')    #词干提取def str_stemmer(s):return "".join([stemmer.stem(word) for word in s.lower().split()])def str_common_word(str1,str2):return sum(int(str2.find(word)>=0) for word in str1.split())#为了计算『关键词』的有效性，我们可以naive地直接看『出现了多少次』#把所有文本进行词干提取（词形统一化）
df_all['search_term'] = df_all['search_term'].map(lambda x:str_stemmer(x))
df_all['product_title'] = df_all['product_title'].map(lambda x:str_stemmer(x))
df_all['product_description'] = df_all['product_description'].map(lambda x:str_stemmer(x))'''
3、自制文本特征
'''
df_all['len_of_query'] = df_all['search_term'].map(lambda x:len(x.split())).astype(np.int64)    #关键词长度
#题标中有多少关键词重合
df_all['commons_in_title'] = df_all.apply(lambda x:str_common_word(x['search_term'],x['product_title']), axis=1)
#描述中有多少关键词重合
df_all['commons_in_desc'] = df_all.apply(lambda x:str_common_word(x['search_term'],x['product_description']), axis=1)#将不能被机器学习模型处理的column drop掉
df_all = df_all.drop(['search_term','product_title','product_description'],axis = 1)'''
4、重塑训练集和测试集
'''
#分开训练集和测试集
df_train = df_all.loc[df_train.index]
df_test = df_all.loc[df_test.index]train_ids = df_train['id']
test_ids = df_test['id']y_train = df_train['relevance'].values      #获取y_train
X_train = df_train.drop(['id','relevance'],axis=1).values
X_test = df_test.drop(['id','relevance'],axis=1).values'''
建立模型使用CV确保结果的正确性，并调试不同的alpha值
'''
params = [1,3,5,6,7,8,9,10]
test_scores = []
for param in params:clf = RandomForestRegressor(n_estimators=30,max_depth=param)test_score = np.sqrt(-cross_val_score(clf,X_train,y_train,cv=5,scoring='neg_mean_squared_error'))test_scores.an(test_score))plt.plot(params,test_scores)
plt.title("Param VS CV Error")
plt.show()'''
获取结果
'''
rf = RandomForestRegressor(n_estimators=30,max_depth=7)
rf.fit(X_train,y_train)
y_pred = rf.predict(X_test)
pd.DataFrame({"id":test_ids,"relevance":y_pred}).to_csv('G:/KNNtest/NLP/all/submission.csv')