kaggle赛题链接Home Depot Product Search Relevance，这个题目关键点就是特征提取，给的数据需要观察处理，提交的成绩，排在前10%
第一类特征(词汇语意)
- 可以用Levenshtein.ratio函数来评估两个英文单词相似度，
- 使用nltk工具，nltk.corpus 中 wordnet来判断两个词语意相似度,本文对原文进行wordnet判断，提取词干后，词发生很大变化，词干提取后主要用来计算tidf,wordvec
- 如果以上两个相似度都很低，还要查看属性文件中是否有匹配单词（只发现一个训练集是三分，但是与title、description十分不匹配，但是与属性文档中一个项匹配）
- 如果以上都不匹配，至少发现四个案例是这样，搜索的产品型号，需要使用google搜索（网络请求），用搜索到的第一个内容再来判断相似度
第二类特征 词向量（gensim中wod2vec）
- 用word2vec训练维基百科英文语料，来衡量两个词汇相关性
- 用word2vec将product_title与product_description合起来作为语料训练得到词向量
第三类特征 tidf
读取数据
import pandas as pd
# 读取数据
df_train = pd.read_csv('/Users/tangqinglong/Desktop/Scikit-learn/Depot/train.csv', encoding='ISO-8859-1')
df_test = pd.read_csv('/Users/tangqinglong/Desktop/Scikit-learn/Depot/test.csv', encoding='ISO-8859-1')
df_pro = pd.read_csv('/Users/tangqinglong/Desktop/Scikit-learn/Depot/product_descriptions.csv', encoding='ISO-8859-1')
df_attr = pd.read_csv('/Users/tangqinglong/Desktop/Scikit-learn/Depot/attributes.csv', encoding='ISO-8859-1')
设置pandas显示，这样可以全文显示，方便查看
pd.set_option('display.max_colwidth',1000)
竖合，将test数据追加到train下方，并且忽略index
df_all = pd.concat((df_train, df_test), axis=0,ignore_index=True)
将产品描述信息作为一列加入到总表中
df_all = pd.merge(df_all, df_pro, how='left', on='product_uid')
看一下打分基本分布情况
import copy
a = copy.deepcopy(df_train['relevance'].values)
a.sort()
import matplotlib.pyplot as plt
print type(a)
plt.plot(a)
plt.show()
df_y = pd.DataFrame(df_train['relevance'])
df_y['num'] = 1
df_y_g = df_y['num'].groupby(df_y['relevance'])
df_y_g.sum()
输出
relevance
1.00 2105
1.25 4
1.33 3006
1.50 5
1.67 6780
1.75 9
2.00 11730
2.25 11
2.33 16060
2.50 19
2.67 15202
2.75 11
3.00 19125
Name: num, dtype: int64
可以看到评分从1分到3分，隔0.33或者0.34一个档次，其中1.25，1.50等等样本太少，可以忽略不计，分成7类，可以把回归问题转为分类问题来考虑，先按照回归问题处理
- 建立停用词字典
dic_stopwords = dict(zip(stopwords.words('english'),xrange(len(stopwords.words('english')))))
- 文本预处理
from nltk import SnowballStemmer
from nltk.corpus import stopwords
import re
import Levenshtein
stemmer = SnowballStemmer('english')
pattern_replace_pair_list = [
(r"<.+?>", r""),
# html codes
(r" ", r" "),
(r"&", r"&"),
(r"'", r"'"),
(r"/>/Agt/>", r""),
(r"</a<gt/", r""),
(r"gt/>", r""),
(r"/>", r""),
(r"<br", r""),
# do not remove [".", "/", "-", "%"] as they are useful in numbers, e.g., 1.97, 1-1/2, 10%, etc.
(r"[ &<>)(_,;:!?\+^~@#\$\*]+", r" "),
(r"'s\\b", r""),
(r"[']+", r""),
# 将DeckOver这样次分开，字母与字符英文连在一起的也分开
#(r'([A-Z][a-z]+|[a-z]+|\d+)', r'\1 '),
(r'(\d?)([a-zA-Z]+)', r'\1 \2 '),
#(r'(/d+)', r' \1 '),
(r'([A-Z][a-z]+)', r' \1 '),
]
dic = {1:'one', 2:'two', 3:'three', 4:'four', 5:'five',6:'six', 7:'seven', 8:'eight', 9:'night',0:'zero'}
def dashrep(matchobj):
if len(matchobj.group())==1:
return dic[int(matchobj.group())]
else:
return matchobj.group()
# 小写 去除标点符号，停用词
def transform(text):
for pattern, replace in pattern_replace_pair_list:
try:
text = re.sub(pattern, replace, text)
except:
pass
#text = re.sub(r'[\d]+', dashrep, text)
text = re.sub(r"\s+", " ", text).strip()
return ' '.join([word for word in text.lower().split() if word not in dic_stopwords])
#word_list = "Package stopwords is already up-to-date".split(" ")
#filtered_words = [word for word in word_list if word not in stopwords.words('english')]
# 词干提取
def str_stemmer(s):
# 不进行词干提取，只是变小写
if isinstance(s, float):
s = unicode(s)
return ' '.join([stemmer.stem(word) for word in s.split()])
# str2 中有多少单词在str1中
def str_common_word(str1, str2):
return sum(int(str2.find(word)>=0) for word in str1.split())
def str_notcommon_word(str1, str2):
return sum(int(str2.find(word)==-1) for word in str1.split())
# str1:title,str2:pro,str3:desc
# 功能：判断标题中有几个单词共同出现在str2,str3中
def str_common_desc_pro_word(str1, str2, str3):
return sum(int(str2.find(word)>=0 and str3.find(word)>=0) for word in str1.split())
def word_vs_word_ratio(str1, str2):
ratio = 0
count = 0
for word1 in str1.split():
for word2 in str2.split():
ratio = Levenshtein.ratio(word1, word2)+ratio
count+=1
return ratio/max(count,1)
def search_vs_word_ratio(str_search, str_des):
ratio = 0
if len(str_search) ==0:
return 0
for word in str_des:
ratio = max(Levenshtein.ratio(str_search, word), ratio)
return ratio
import nltk
import Levenshtein
def similarity(word1, word2):
from nltk.corpus import wordnet as wn
word_1 = wn.synsets(word1)
word_2 = wn.synsets(word2)
sl = 0.
for el1 in word_1:
for el2 in word_2:
val = el1.path_similarity(el2)
if val is not None:
sl = max(val,sl)
if sl > 0.8:
break
return sl
def similarity_sentences(str1, str2):
sl, count, Ntotal, Nmatch = 0., 0., 0., 0.
for word1 in nltk.pos_tag(str1.split()):
score = 0
for word2 in nltk.pos_tag(str2.split()):
score = max(Levenshtein.ratio(word1[0],word2[0]),score)
if score < 0.75:
if word1[1][0]==word2[1][0]:
score = max(similarity(word1[0], word2[0]),score)
#print score, word1, word2
if score < 0.75:
continue
sl += score
count += 1
break
if score > 0.7:
Nmatch += 1
if count == 0:
return [0., 0.]
return [sl/count,Nmatch/max(len(str1.split()),1)]
- 去除标点符号，变小写，去停用词
%time df_all['search_term_transform'] = df_all['search_term'].map(lambda x:transform(x))
%time df_all['product_title_transform'] = df_all['product_title'].map(lambda x:transform(x))
%time df_all['pro_des_trans'] = df_all['product_description'].map(lambda x:transform(x))
- 词干提取
%time df_all['search_term_transform_stem'] = df_all['search_term_transform'].map(lambda x:str_stemmer(x))
%time df_all['product_title_transform_stem'] = df_all['product_title_transform'].map(lambda x:str_stemmer(x))
%time df_all['pro_des_trans_stem'] = df_all['pro_des_trans'].map(lambda x:str_stemmer(x))
df_all['all_texts_transform'] = df_all['product_title_transform'] + ' . ' + df_all['pro_des_trans']
df_all['all_texts_trans_stemm'] = df_all['product_title_transform_stem'] + ' . ' + df_all['pro_des_trans_stem']
from joblib import Parallel, delayed
def func_similarity(df):
sea_ter_tit = df.apply(lambda temp:similarity_sentences(temp['search_term_transform'],temp['product_title_transform']), axis=1).values[0]
sea_ter_des = df.apply(lambda temp:similarity_sentences(temp['search_term_transform'],temp['pro_des_trans']), axis=1).values[0]
sea_ter_all = df.apply(lambda temp:similarity_sentences(temp['search_term_transform'],temp['all_texts_transform']), axis=1).values[0]
df.loc[:, 'sea_ter_tit'] = sea_ter_tit[0]
df.loc[:, 'sea_ter_tit_ratio'] = sea_ter_tit[1]
df.loc[:, 'sea_ter_des'] = sea_ter_des[0]
df.loc[:, 'sea_ter_des_ratio'] = sea_ter_des[1]
df.loc[:, 'sea_ter_all'] = sea_ter_all[0]
df.loc[:, 'sea_ter_all_ratio'] = sea_ter_all[1]
return df
def apply_parallel(df_grouped, func):
"""利用 Parallel 和 delayed 函数实现并行运算"""
results = Parallel(n_jobs=-1)(delayed(func)(group) for name, group in df_grouped)
return pd.concat(results)
df_model = pd.DataFrame({})
使用多进程计算特征
df_grouped =df_all.groupby(df_all.index)
%time df_model = apply_parallel(df_grouped, func_similarity)[['sea_ter_tit', \
'sea_ter_tit_ratio',\
'sea_ter_des',\
'sea_ter_des_ratio',\
'sea_ter_all',\
'sea_ter_all_ratio',\
'product_uid']]
import numpy as np
df_model['len_search_term']=df_all['search_term_transform_stem'].map(
lambda x:len(x.split())).astype(np.int64)
#【新特征1】: search_term 和 product_title比较
df_model['dist_in_title'] = df_all.apply(lambda x:word_vs_word_ratio((x['search_term_transform_stem']),x['product_title_transform_stem']), axis=1)
df_model['dist_in_title1'] = df_all.apply(lambda x:search_vs_word_ratio((x['search_term_transform_stem']),x['product_title_transform_stem']), axis=1)
#【新特征2】: search_term 和 product_description比较
df_model['dist_in_desc'] = df_all.apply(lambda x:word_vs_word_ratio((x['search_term_transform_stem']),x['pro_des_trans_stem']), axis=1)
df_model['dist_in_desc1'] = df_all.apply(lambda x:search_vs_word_ratio((x['search_term_transform_stem']),x['pro_des_trans_stem']), axis=1)
df_model['len_of_query']=df_all['search_term_transform_stem'].map(
lambda x:len(x.split())).astype(np.int64)
df_model['len_search'] = df_all['search_term_transform_stem'].map(lambda x:len(x))
# 产品标题中有多少关键词重合
df_model['commons_in_title']=df_all.apply(
lambda x:str_common_word(
x['search_term_transform_stem'],x['product_title_transform_stem']),axis=1)
# 描述中有多少关键词重合
%time df_model['commons_in_desc'] = df_all.apply(lambda x:str_common_word(x['search_term_transform_stem'], x['pro_des_trans_stem']), axis=1)
%time df_model['common_in_desc_pro']=df_all.apply(lambda x: str_common_desc_pro_word(x['search_term_transform_stem'],x['pro_des_trans_stem'],x['product_title_transform_stem']), axis=1)
df_model['nn_word_in_search'] = df_all['search_term_transform_stem'].map(nn_word_numbers_In_Search)
df_model['queryvstitle'] = df_model.apply(lambda x: float(x['commons_in_title'])/max((x['len_of_query']),1), axis=1)
df_model['product_uid'] =df_all['product_uid']
df_model['queryvsdesc'] = df_model.apply(lambda x: float(x['commons_in_desc'])/max(x['len_of_query'],1), axis=1)
tidf特征
# 有了组合好的句子，可以分词了准备
# 分词：这里我们用gensim，为了更加细致的分解TFIDF的步骤动作；其实sklearn本身也有简单好用的tfidf模型
# Tokenize可以用各家或者各种方法，就是把长长的string变成list of tokens。包括NLTK，SKLEARN都有自家的解决方案
from gensim.utils import tokenize
from gensim.corpora.dictionary import Dictionary
#得到了一个很多单词的大词典
dictionary = Dictionary(list(tokenize(x, errors='ignore')) for x in df_all['all_texts_trans_stemm'].values)
print(dictionary)
#这个类所做的事情也很简单，就是扫便我们所有的语料，并且转化成简单的单词的个数计算
class MyCorpus(object):
def __iter__(self):
for x in df_all['all_texts_trans_stemm'].values:
yield dictionary.doc2bow(list(tokenize(x, errors='ignore')))
# 这里这么折腾一下，仅仅是为了内存friendly。面对大量corpus数据时，你直接存成一个list，会使得整个运行变得很慢。
# 所以我们搞成这样，一次只输出一组。但本质上依旧长得跟 [['sentence', '1'], ['sentence', '2'], ...]一样
corpus = MyCorpus()
# 有了我们标准形式的语料库，我们于是就可以init我们的TFIDFmodel了。这里做的事情，就是把已经变成BoW向量的数组，做一次TFIDF的计算。
from gensim.models.tfidfmodel import TfidfModel
tfidf = TfidfModel(corpus)
#示例:这下我们看看一个普通的句子放过来长什么样子：
tfidf[dictionary.doc2bow(list(tokenize('hello world, good morning', errors='ignore')))]
#怎么判断两个句子的相似度呢？
#这里有个trick，因为我们得到的tfidf只是『有这个字，就有这个值』，并不是一个全部值。
#也就是说，两个matrix可能size是完全不一样的。
#想用cosine计算的同学就会问了，两个matrix的size都不fix，怎么办？
#咦，这里就注意咯。他们的size其实是一样的。只是把全部是0的那部分给省略了对吧？
#于是，我们只要拿其中一个作为index。扩展开全部的matrixsize，另一个带入，就可以计算了
from gensim.similarities import MatrixSimilarity
# 先把刚刚那句话包装成一个方法
def to_tfidf(text):
res = tfidf[dictionary.doc2bow(list(tokenize(text, errors='ignore')))]
return res
# 然后，我们创造一个cosine similarity的比较方法
def cos_sim(text1, text2):
tfidf1 = to_tfidf(text1)
tfidf2 = to_tfidf(text2)
index = MatrixSimilarity([tfidf1],num_features=len(dictionary))
sim = index[tfidf2]
# 本来sim输出是一个array，我们不需要一个array来表示，
# 所以我们直接cast成一个float
return float(sim[0])
#计算搜索词语与产品title相似度
df_model['tfidf_cos_sim_in_title'] = df_all.apply(lambda x: cos_sim(x['search_term_transform_stem'], x['product_title_transform_stem']), axis=1)
#计算搜索词与产品描述description相似度
df_model['tfidf_cos_sim_in_desc'] = df_all.apply(lambda x: cos_sim(x['search_term_transform_stem'], x['pro_des_trans_stem']), axis=1)
通过Word2Vec来评判距离,搜索词与产品title，产品描述的
import nltk
#1）nltk也是自带一个强大的句子分割器。【调用工具】
tokenizer = nltk.data.load('tokenizers/punkt/english.pickle')
#2）我们先把长文本搞成list of 句子，再把句子变成list of 单词：【文本->句子】
sentences = [tokenizer.tokenize(x) for x in df_all['all_texts_trans_stemm'].values]
#3）我们把list of lists 给 flatten了。【句子 -> 扁平化flatten】
sentences = [y for x in sentences for y in x] #一共1998321个句子。
#4）我们把句子里的单词给分好。可以用刚刚Gensim的tokenizer， 也可以用nltk的word_tokenizer 【句子 -> 单词】
from nltk.tokenize import word_tokenize
w2v_corpus = [word_tokenize(x) for x in sentences]
#5） 训练我们的预料库，成为词向量 【单词 -> 训练语料库model】
from gensim.models.word2vec import Word2Vec
model = Word2Vec(w2v_corpus, size=128, window=5, min_count=5, workers=4)
- 可以得到每个单词的向量，但是每一格句子中由多个单词组成，把每个单词向量取平均,
import numpy as np
#6) 可以得到每个单词的向量，但是每一格句子中由多个单词组成，把每个单词向量取平均,
vocab = model.vocabulary
#得到任意text句子的vector（就是取平均）
def get_vector(text):
res = np.zeros([128])
count = 0
for word in word_tokenize(text):
res += model[word]
count+=1
return res/max(count,1)
- 计算两个句子的vector的相似度, 用cosine similarity,用scipy的spatial功能
from scipy import spatial
def w2v_cos_sim(text1, text2):
try:
w2v1 = get_vector(text1)
w2v2 = get_vector(text2)
sim = 1 - spatial.distance.cosine(w2v1, w2v2)
return float(sim)
except:
return float(0)
df_model['w2v_cos_sim_in_title'] = df_all.apply(lambda x: w2v_cos_sim(x['search_term_transform_stem'], x['product_title_transform_stem']), axis=1)
df_model['w2v_cos_sim_in_desc'] = df_all.apply(lambda x: w2v_cos_sim(x['search_term_transform_stem'], x['pro_des_trans_stem']), axis=1)
处理异常数据
def check_series(series):
i = 0
for el in series:
if np.isnan(el):
series[i] = 0
i+=1
return series
df_model.apply(check_series)
# 记录测试集的id
test_ids = df_test['id']
# 分离出y_train
y_train = df_train['relevance'].values
X_train = df_model.loc[df_train.index]
X_test = df_model.loc[len(df_train.index):]
from sklearn import preprocessing
scaler = preprocessing.StandardScaler().fit(X_train.values)
train = scaler.transform(X_train.values)
test = scaler.transform(X_test.values)
相关性系数
from scipy.stats import pearsonr
lable=y_train
lr = []
for i, line in enumerate(X_train.values.T):
lr.append([pearsonr(lable,line),i])
lr.sort()
print lr
xgboost
- 由于xgboost不支持logcosh,需要自定义损失函数
import xgboost as xgb
import random
import numpy as np
def huber_approx_obj(preds, dtrain):
d = dtrain.get_label() - preds #remove .get_labels() for sklearn
h = 1 #h is delta in the graphic
scale = 1 + (d / h) ** 2
scale_sqrt = np.sqrt(scale)
grad = d / scale_sqrt
hess = 1 / scale / scale_sqrt
return grad, hess
def log_cosh_obj(preds, dtrain):
x = dtrain.get_label() - preds
grad = np.tanh(-x)
hess = 1- np.tanh(x)**2
return grad, hess
def square_loss(preds, dtrain):
#x = dtrain.get_label()-preds
grad = preds - dtrain.get_label()
hess = [1]*len(grad)
return grad,hess
xgb_params = {
'eta': 0.03,
'max_depth': 6,
'gamma':2, # 在树的叶子节点下一个分区的最小损失，越大算法模型越保守 。[0:]
'subsample': 0.6,
'colsample_bytree': 0.7,
'objective': 'reg:linear',
'eval_metric': 'rmse',
'silent': 1 ,
'min_child_weight':1,
'lambda':100,
'seed':1,
'booster':'gbtree'
#'booster':'gblinear'
}
# xgboost加载数据为DMatrix对象
dtrain = xgb.DMatrix(train, y_train)
# xgboost交叉验证并输出rmse
cv_output = xgb.cv(xgb_params, dtrain, num_boost_round=3000, early_stopping_rounds=100,obj = log_cosh_obj,
verbose_eval=50,nfold=5, show_stdv=False, shuffle=True)
cv_output[['train-rmse-mean', 'test-rmse-mean']].plot()
bst = xgb.train(xgb_params, dtrain, num_boost_round=3000, early_stopping_rounds=None, obj=log_cosh_obj)
dtest = xgb.DMatrix(test)
xgb_preds = bst.predict(dtest)
xgb_preds = scale_pred(xgb_preds)
把预测值控制到1到3
def scale_pred(pred):
for i, el in enumerate(pred):
if el > 3:
pred[i] = 3
elif el < 1:
pred[i] = 1
return pred
pd.DataFrame({'id':test_ids, 'relevance':xgb_preds[:,0]}).to_csv(
'/Users/tangqinglong/Desktop/Scikit-learn/Depot/submission.csv', index=False)
keras+tf
import keras
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation
from keras.optimizers import SGD
# Generate dummy data
import numpy as np
model = Sequential()
# Dense(64) is a fully-connected layer with 64 hidden units.
# in the first layer, you must specify the expected input data shape:
# here, 20-dimensional vectors.
L=len(X_train.columns)
model.add(Dense(64, activation='relu', input_dim=L))
model.add(Dropout(0.5))
model.add(Dense(32, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(1))
sgd = SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True)
model.compile(loss='logcosh',
optimizer=sgd,
metrics=['mse'])
model.fit(train, y_train,
epochs=1000,
batch_size=256)
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