def __init__(self):
self.data_dir = 'E:\\gitshell\\tianchi2'
# 词组
self.word_num = 0
self.dict_word = {}
self.top_k_word = 20000 # 详细计算前20000的词组
self.word_M = np.zeros((1000000, 2)) # 第一列 记录word_id 第二列 记录 概率对数
self.word_item_array = [""] * 1000000 # 每个词被哪些商品使用
self.word_word = np.zeros((3, 3))
# 需要预测的词组
self.r_word_num = 0
self.r_dict_word = {}
self.r_word_M = np.zeros((80000, 2))
self.test_item = []
# 商品
self.dict_item = {}
self.item_M = np.zeros((600000, 2), int) # item_id 类别编号
self.item_word_array = [""] * 600000
self.item_num = 0
# 类别
self.class_M = np.zeros((3000000, 2)) # 类别id 类别商品计数/ 概率对数
self.dict_class = {}
self.class_num = 0
self.class_class = np.zeros((2, 2))
# 原始人工经验
self.exp_peo = exp_of_people()
self.exp_peo.read_jingyan()
# self.matrix_item = np.zeros((10000000,3))
# 概率优化模块
self.pro_guji = Pro_estimate()
# 只考虑 最热的 6万 商品
self.item_top_k = 60000
# 原始的搭配概率
self.p_match = 0.0006 # 任意随机商品 搭配的概率
pass
def __init__(self):
self.data_dir = 'E:\\gitshell\\tianchi3'
# 词组
self.word_num = 0
self.dict_word = {}
self.top_k_word = 15000 # 详细计算前20000的词组
self.word_M = np.zeros((1000000, 2)) # 第一列 记录word_id 第二列 记录 概率对数
self.word_item_array = [""] * 1000000 # 每个词被哪些商品使用
self.word_word = np.zeros((3, 3))
# 需要预测的词组
self.r_word_num = 0
self.r_dict_word = {}
self.r_word_M = np.zeros((80000, 2))
self.test_item = []
# 商品
self.dict_item = {}
self.item_M = np.zeros((600000, 2), int) # item_id 类别编号
self.item_word_array = [""] * 600000
self.item_num = 0
# 类别
self.class_M = np.zeros((3000000, 2)) # 类别id 类别商品计数/ 概率对数
self.dict_class = {}
self.class_num = 0
self.class_class = np.zeros((2, 2))
# 原始人工经验
# self.matrix_item = np.zeros((10000000,3))
# 概率优化模块
self.pro_guji = Pro_estimate()
# 只考虑 最热的 6万 商品
self.item_top_k = 100000
# 原始的搭配概率
self.p_match = 0.0006 # 任意随机商品 搭配的概率
pass
def __init__(self):
self.data_dir = "E:\\gitshell\\tianchi3"
self.my_matrix = np.zeros((2, 2)) # 记录词到商品的概率对数
# 词组
self.word_num = 0
self.dict_word = {}
self.top_k_word = 15000 # 详细计算前20000的词组
self.word_M = np.zeros((1000000, 2)) # 第一列 记录word_id 第二列 记录 概率
self.word_item_array = [""] * 1000000 # 每个词被哪些商品使用
self.word_word = np.zeros((3, 3)) # 统一记录真实概率
# 需要预测的词组
self.r_word_num = 0
self.r_dict_word = {}
self.r_word_M = np.zeros((80000, 2)) # 第一列词 第二列词的次数
self.test_item = []
# 商品
self.dict_item = {}
self.item_M = np.zeros((600000, 2), int) # item_id 类别编号
self.item_word_array = [""] * 600000
self.item_num = 0
# 概率优化模块
self.pro_guji = Pro_estimate()
# 只考虑 最热的 6万 商品
self.item_top_k = 100000
# 原始的搭配概率
self.p_match = 0.0006 # 任意随机商品 搭配的概率
self.num_word2 = 800
pass
class most_like():
def __init__(self):
self.data_dir = 'E:\\gitshell\\tianchi3'
# 词组
self.word_num = 0
self.dict_word = {}
self.top_k_word = 15000 # 详细计算前20000的词组
self.word_M = np.zeros((1000000, 2)) # 第一列 记录word_id 第二列 记录 概率对数
self.word_item_array = [""] * 1000000 # 每个词被哪些商品使用
self.word_word = np.zeros((3, 3))
# 需要预测的词组
self.r_word_num = 0
self.r_dict_word = {}
self.r_word_M = np.zeros((80000, 2))
self.test_item = []
# 商品
self.dict_item = {}
self.item_M = np.zeros((600000, 2), int) # item_id 类别编号
self.item_word_array = [""] * 600000
self.item_num = 0
# 类别
self.class_M = np.zeros((3000000, 2)) # 类别id 类别商品计数/ 概率对数
self.dict_class = {}
self.class_num = 0
self.class_class = np.zeros((2, 2))
# 原始人工经验
# self.matrix_item = np.zeros((10000000,3))
# 概率优化模块
self.pro_guji = Pro_estimate()
# 只考虑 最热的 6万 商品
self.item_top_k = 100000
# 原始的搭配概率
self.p_match = 0.0006 # 任意随机商品 搭配的概率
pass
def read_txt(self, filename="dim_items.txt"):
# 读取商品的类别信息表
r_path = os.path.join(self.data_dir, filename)
r_stream = open(r_path, 'r')
self.item_num = 0
for line_i in r_stream:
if self.item_num % 100000 == 0:
print self.item_num, time.time()
# 录入商品
my_str = line_i.strip('\n').split(" ")
self.dict_item[int(my_str[0])] = self.item_num
self.item_M[self.item_num, :] = [my_str[0], int(my_str[1])]
self.item_word_array[self.item_num] = my_str[2]
self.item_num += 1
# 录入不同的词组
my_str2 = my_str[2].split(',')
for x_word in my_str2:
try:
word_id = int(x_word)
except:
continue
word_ind = self.dict_word.get(word_id, -1)
if word_ind == -1:
self.dict_word[word_id] = self.word_num
self.word_M[self.word_num, :] = [word_id, 1]
# self.word_item_array[self.word_num] = my_str[0] # 商品
self.word_num += 1
else:
self.word_M[word_ind, 1] += 1
# self.word_item_array[word_ind] += ',' + my_str[0] # 商品
# 录入分类信息
class_id = int(my_str[1])
class_ind = self.dict_class.get(class_id, -1)
if class_ind == -1:
self.dict_class[class_id] = self.class_num
self.class_M[self.class_num, :] = [class_id, 1]
self.class_num += 1
else:
self.class_M[class_ind, 1] += 1
self.class_M = self.class_M[0:self.class_num, :]
self.word_M = self.word_M[0:self.word_num, :]
self.item_M = self.item_M[0:self.item_num, :]
self.item_word_array = self.item_word_array[0:self.item_num]
# self.word_item_array = self.word_item_array[0:self.word_num]
# 根据热度排行对词进行重新排序
order = np.argsort(-self.word_M[:, 1])
self.word_M = self.word_M[order, :]
# temp_a = self.word_item_array
# for x in xrange(0, len(order)):
# self.word_item_array[x] = temp_a[order[x]]
for x in xrange(0, self.word_num):
self.dict_word[int(self.word_M[x, 0])] = x
r_stream.close()
# # 转化word_M 第2 列 为概率对数：
sum_word_num = sum(self.word_M[:, 1])
self.word_M[:, 1] = np.log(self.word_M[:, 1] / sum_word_num)
# 转化word_M 第2 列 为概率对数：
sum_class_num = sum(self.class_M[:, 1])
self.class_M[:, 1] = np.log(self.class_M[:, 1] / sum_class_num)
def result_word(self, file_name='test_set.txt'):
# 只对需要预测的商品进行计算 找出需要计算的词汇
file_name = os.path.join(self.data_dir, file_name)
r_stream = open(file_name, 'r')
for line_i in r_stream:
item_id = int(line_i.strip().split('\t')[-1])
self.test_item.append(item_id)
item_ind = self.dict_item.get(item_id, -1)
if item_ind == -1:
continue
word_array = self.item_word_array[item_ind].split(',')
for word_id in word_array:
word_id = int(word_id)
word_ind = self.r_dict_word.get(word_id, -1)
if word_ind == -1:
self.r_dict_word[word_id] = self.r_word_num
self.r_word_M[self.r_word_num, :] = [word_id, 1]
self.r_word_num += 1
else:
self.r_word_M[word_ind, 1] += 1
r_stream.close()
order = np.argsort(-self.r_word_M[0:self.r_word_num, 1])
self.r_word_M = self.r_word_M[order, :]
i_record = self.r_word_num
for i in xrange(0, self.r_word_num):
if self.r_word_M[i, 1] == 1:
i_record = i
print i_record
break
self.r_word_num = i_record
self.r_word_M = self.r_word_M[0:i_record, :]
# 统计 类类 关系
def my_tongji2(self):
# 统计过程由 sql sever 完成后存为class_class.txt 这里直接读取 # 检查完毕
r_path = os.path.join(self.data_dir, "class_class.txt")
r_stream = open(r_path, 'r')
self.class_class = np.zeros((self.class_num, self.class_num))
for line in r_stream:
my_str = line.strip().split('\t')
class_ind1 = self.dict_class[int(my_str[0])]
class_ind2 = self.dict_class[int(my_str[1])]
num = int(my_str[2])
self.class_class[class_ind1, class_ind2] += num
r_stream.close()
row_sum = self.class_class.sum(1) # 按照行求和
# all_num = 6 # 商品总数
# self.class_class[class_ind1, class_ind2] 存储 id1 类别 后面搭配 id2 类别的概率
w_path1 = open(os.path.join(self.data_dir, "class_class1.txt"), 'w')
w_path2 = open(os.path.join(self.data_dir, "ceshi_class_class2.txt"), 'w') # 记录中间结果 用于测试
# w_path3 = open(os.path.join(self.data_dir, "o_class_class3.txt"), 'w')
for ind1 in xrange(0, self.class_num):
p_pre = np.exp(self.class_M[ind1, 1]) # 原假设： ind2 发生的概率
# w_path3.writelines(str(p_pre) + '\t') # 记录原假设
for ind2 in xrange(0, self.class_num):
p_pre = np.exp(self.class_M[ind2, 1]) # 原假设： ind2 发生的概率
if ind2 == self.class_num - 1:
w_path1.writelines(str(self.class_class[ind1, ind2]) + '\n')
else:
w_path1.writelines(str(self.class_class[ind1, ind2]) + '\t')
a = int(self.class_class[ind1, ind2])
self.class_class[ind1, ind2] = self.pro_guji.get_pro_r(p_pre,
self.class_class[ind1, ind2],
row_sum[ind1]) # ind1 条件下 ind2 的概率
if ind1 == 1:
w_path2.writelines(str(p_pre) + '\t' + str(self.class_class[ind1, ind2])+
'\t' +str(a) +'\t'+ str(row_sum[ind1]) + '\n')
w_path1.close()
w_path2.close()
# w_path3.close()
# 统计词词关系 new 基于sql sever 处理过的文件开始统计 简化代码
def my_tongji3(self):
# split_ss = self.r_word_num
temp_array = np.zeros((self.r_word_num + 1, self.top_k_word + 1))
p_remain = sum(np.exp(self.word_M[self.top_k_word:, 1])) # 残余项原始概率
i_file = 0
file_name = "word_word_pro"
r_path = os.path.join(self.data_dir, "learn_wordstr_wordstr0.txt")
r_stream = open(r_path, 'r')
for wor_str in r_stream:
my_str = wor_str.strip().split('\t')
if my_str[0] == '' or my_str[1] == '':
continue
word_p = my_str[0].split(',')
word_s = my_str[1].split(',')
num = int(my_str[2])
for word_id1 in word_p:
word_ind1 = self.r_dict_word.get(int(word_id1), -1) # 行号
if word_ind1 == -1:
continue # 非统计对象
word_ind1 = min(word_ind1, self.r_word_num)
for word_id2 in word_s:
word_ind2 = self.dict_word.get(int(word_id2), -1)
if word_ind2 == -1:
continue # 非录入词
word_ind2 = min(word_ind2, self.top_k_word)
temp_array[word_ind1, word_ind2] += num # word_ind 指示的词发生后其关联商品 为 含word_ind2的词 次数+1
r_stream.close()
# 求概率
temp_array_sum = temp_array.sum(1) # 按照行进行求和
(row_num, col_num) = temp_array.shape
o_stream = open(os.path.join(self.data_dir, "ceshi_tongji3.txt"), 'w')
for i_col in xrange(0, col_num):
if i_col % 200 == 0:
print i_col, time.time()
if i_col == self.top_k_word:
p_pre = p_remain
else:
p_pre = np.exp(self.word_M[i_col, 1])
for i_row in xrange(0, row_num):
a = temp_array[i_row, i_col]
temp_array[i_row, i_col] = \
self.pro_guji.get_pro_r(p_pre,
temp_array[i_row, i_col],
temp_array_sum[i_row]) # p n m
if i_row == 1:
o_stream.write(str(p_pre)+'\t'+str(temp_array[i_row, i_col])
+'\t'+str(a) +'\t'+str(temp_array_sum[i_row])+'\n')
# 静态存储
o_stream.close()
w_file = os.path.join(self.data_dir, file_name + str(i_file) + '.txt')
w_stream = open(w_file, 'w')
for i_row in xrange(0, row_num):
my_str = ''
for i_col in xrange(0, col_num - 1):
my_str += str(math.log(temp_array[i_row, i_col])) + ','
my_str += str(math.log(temp_array[i_row, col_num - 1])) + '\n'
w_stream.writelines(my_str)
w_stream.close()
self.word_word = temp_array
# 读取之前计算的词词关系
def read_word_word(self):
self.word_word = np.zeros((self.r_word_num + 1, self.top_k_word + 1))
o_stream = open(os.path.join(self.data_dir, "word_word_pro0.txt"), 'r')
i_line = 0
for line in o_stream:
my_str = line.strip().split(',')
for x in xrange(0, self.top_k_word + 1):
self.word_word[i_line, x] = math.exp(float(my_str[x])) # 真概率
i_line += 1
o_stream.close()
if i_line == (self.r_word_num + 1):
print time.time(), "good"
# 根据热度重组商品矩阵
def read_item_hot(self, write=True):
path = os.path.join(self.data_dir, 'my_item_hot.txt')
nums_array = np.array([0] * self.item_num)
r_stream = open(path, 'r')
for line_i in r_stream:
my_str = line_i.strip().split('\t')
item_id = int(my_str[0])
nums = int(my_str[-1])
item_ind = self.dict_item[item_id]
nums_array[item_ind] = nums
r_stream.close()
a = np.argsort(-nums_array) # 降序排列
self.item_M = self.item_M[a, :]
for x in xrange(0, self.item_num):
self.dict_item[int(self.item_M[x, 0])] = x
# 记录商品热度
if write:
w_stream = open(os.path.join(self.data_dir, 'my_item_hot.txt'), 'w')
nums_array = nums_array[a]
for x in xrange(0, self.item_num):
w_stream.writelines(str(int(self.item_M[x, 0])) + '\t' + str(nums_array[x]) + '\n')
w_stream.close()
# 仅仅计算出所有结果前6万商品的搭配结果
# 加入了 原始的搭配概率 0.0006，在 搭配中由于这一值大家相同，所以无关紧要，但在与python2中计算的概率融合时会有影响。
# 这次会计算出前6万的搭配概率，供 my_python2 做 进一步筛查
def da_pei2(self):
file_name = os.path.join(self.data_dir, 'fm_submissions2_tag_m.txt')
file_name2 = os.path.join(self.data_dir, 'fm_submissions2_tag_c.txt') # class 意见
file_name1 = os.path.join(self.data_dir, 'fm_submissions2_tag_w.txt') # word 意见
w_stream = open(file_name, 'w')
w_stream1 = open(file_name1, 'w')
w_stream2 = open(file_name2, 'w')
iii = -1
t_b = time.time()
for item_id in self.test_item:
iii += 1
t_n = time.time()
if iii % 100 == 0 or t_n>t_b+100:
t_b = t_n
print t_n, iii
item_ind = self.dict_item[item_id]
word_str = self.item_word_array[item_ind]
class_id = self.item_M[item_ind, 1] # 类别编号
class_ind = self.dict_class[class_id] # 类别索引
# item_id == self.class_M[item_ind,0]
temp_result_array = np.zeros((self.item_num, 2)) # 第一列记录词组的意见，第二列记录类别的意见 概率乘 化作 加
class_pro = np.log(self.class_class[class_ind, :]) # 搭配时 该商品类别到各个类别的概率
class_pro2 = self.class_M[:, 1] # 不搭配时 该商品类别到各个类别的概率对数
temp_word_pro = np.array([0.0] * (self.top_k_word + 1)) # 该商品词组发生后各个词组的概率
word_num = 0
word_str_array = word_str.split(',')
# 获得该商品后 其他商品的输出概率
# temp_word_pro = self.word_M[:, 1] # 原本的每个词的概率
for word_id in word_str_array:
try:
word_id2i = int(word_id)
except:
continue
word_ind1 = self.r_dict_word.get(word_id2i, -1)
if word_ind1 == -1:
continue # 非统计对象
word_ind1 = min(word_ind1, self.r_word_num)
temp_word_pro += np.log(self.word_word[word_ind1, :]) # word_word 记录的是 真实概率
word_num += 1
temp_word_pro2 = self.word_M[:, 1] # 不搭配 意见
for item_ind in xrange(0, self.item_top_k):
word_str = self.item_word_array[item_ind]
class_id = self.item_M[item_ind, 1]
class_ind00 = self.dict_class[int(class_id)]
temp_result_array[item_ind, 1] = class_pro[class_ind00] - class_pro2[class_ind00] # 其exp 为搭配是 不搭配发生的倍数
if word_str == "":
continue
word_str = word_str.split(',')
word_num2 = 0
for word_id2 in word_str:
word_ind2 = self.dict_word.get(int(word_id2), -1)
if word_ind2 == -1:
continue
word_ind2 = min(word_ind2, self.top_k_word)
temp_result_array[item_ind, 0] += temp_word_pro[word_ind2] - temp_word_pro2[word_ind2]
word_num2 += 1
# temp_result_array[item_ind, 0] *= (1.0 / word_num2)
temp_result_array[item_ind, 0] *= 1
a = temp_result_array[:, 0] + temp_result_array[:, 1] # 类别的意见， 加上词的意见 a元素 中存储的是
pro_a = self.p_match * np.exp(a) / (self.p_match * np.exp(a) + (1 - self.p_match) * 1) # 得到各个商品 的概率
# my_str00 = ""
w_stream.writelines(str(item_id) + '\t')
w_stream1.writelines(str(item_id) + '\t')
w_stream2.writelines(str(item_id) + '\t')
for item_ind in xrange(0, self.item_top_k):
if item_ind != (self.item_top_k - 1):
w_stream.writelines(str(round(pro_a[item_ind], 9)) + '\t')
w_stream1.writelines(str(round(np.exp(temp_result_array[item_ind, 0]), 9)) + '\t')
w_stream2.writelines(str(round(np.exp(temp_result_array[item_ind, 1]), 9)) + '\t')
else:
w_stream.writelines(str(round(pro_a[item_ind], 9)) + '\n')
w_stream1.writelines(str(round(np.exp(temp_result_array[item_ind, 0]), 9)) + '\n')
w_stream2.writelines(str(round(np.exp(temp_result_array[item_ind, 1]), 9)) + '\n')
w_stream.close()
class most_like():
def __init__(self):
self.data_dir = 'E:\\gitshell\\tianchi2'
# 词组
self.word_num = 0
self.dict_word = {}
self.top_k_word = 20000 # 详细计算前20000的词组
self.word_M = np.zeros((1000000, 2)) # 第一列 记录word_id 第二列 记录 概率对数
self.word_item_array = [""] * 1000000 # 每个词被哪些商品使用
self.word_word = np.zeros((3, 3))
# 需要预测的词组
self.r_word_num = 0
self.r_dict_word = {}
self.r_word_M = np.zeros((80000, 2))
self.test_item = []
# 商品
self.dict_item = {}
self.item_M = np.zeros((600000, 2), int) # item_id 类别编号
self.item_word_array = [""] * 600000
self.item_num = 0
# 类别
self.class_M = np.zeros((3000000, 2)) # 类别id 类别商品计数/ 概率对数
self.dict_class = {}
self.class_num = 0
self.class_class = np.zeros((2, 2))
# 原始人工经验
self.exp_peo = exp_of_people()
self.exp_peo.read_jingyan()
# self.matrix_item = np.zeros((10000000,3))
# 概率优化模块
self.pro_guji = Pro_estimate()
# 只考虑 最热的 6万 商品
self.item_top_k = 60000
# 原始的搭配概率
self.p_match = 0.0006 # 任意随机商品 搭配的概率
pass
def read_txt(self, filename="dim_items.txt"):
r_path = os.path.join(self.data_dir, filename)
r_stream = open(r_path, 'r')
self.item_num = 0
for line_i in r_stream:
if self.item_num % 100000 == 0:
print self.item_num, time.time()
# 录入商品
my_str = line_i.strip('\n').split(" ")
self.dict_item[int(my_str[0])] = self.item_num
self.item_M[self.item_num, :] = [my_str[0], int(my_str[1])]
self.item_word_array[self.item_num] = my_str[2]
self.item_num += 1
# 录入不同的词组
my_str2 = my_str[2].split(',')
for x_word in my_str2:
try:
word_id = int(x_word)
except:
continue
word_ind = self.dict_word.get(word_id, -1)
if word_ind == -1:
self.dict_word[word_id] = self.word_num
self.word_M[self.word_num, :] = [word_id, 1]
# self.word_item_array[self.word_num] = my_str[0] # 商品
self.word_num += 1
else:
self.word_M[word_ind, 1] += 1
# self.word_item_array[word_ind] += ',' + my_str[0] # 商品
# 录入分类信息
class_id = int(my_str[1])
class_ind = self.dict_class.get(class_id, -1)
if class_ind == -1:
self.dict_class[class_id] = self.class_num
self.class_M[self.class_num, :] = [class_id, 1]
self.class_num += 1
else:
self.class_M[class_ind, 1] += 1
self.class_M = self.class_M[0:self.class_num, :]
self.word_M = self.word_M[0:self.word_num, :]
self.item_M = self.item_M[0:self.item_num, :]
self.item_word_array = self.item_word_array[0:self.item_num]
# self.word_item_array = self.word_item_array[0:self.word_num]
# 根据热度排行对词进行重新排序
order = np.argsort(-self.word_M[:, 1])
self.word_M = self.word_M[order, :]
# temp_a = self.word_item_array
# for x in xrange(0, len(order)):
# self.word_item_array[x] = temp_a[order[x]]
for x in xrange(0, self.word_num):
self.dict_word[int(self.word_M[x, 0])] = x
r_stream.close()
# # 转化word_M 第2 列 为概率对数：
sum_word_num = sum(self.word_M[:, 1])
self.word_M[:, 1] = np.log(self.word_M[:, 1] / sum_word_num)
# 转化word_M 第2 列 为概率对数：
sum_class_num = sum(self.class_M[:, 1])
self.class_M[:, 1] = np.log(self.class_M[:, 1] / sum_class_num)
def result_word(self, file_name='test_items2.txt'):
# 找出需要计算的词汇
file_name = os.path.join(self.data_dir, file_name)
r_stream = open(file_name, 'r')
for line_i in r_stream:
item_id = int(line_i.strip())
self.test_item.append(item_id)
item_ind = self.dict_item.get(item_id, -1)
if item_ind == -1:
continue
word_array = self.item_word_array[item_ind].split(',')
for word_id in word_array:
word_id = int(word_id)
word_ind = self.r_dict_word.get(word_id, -1)
if word_ind == -1:
self.r_dict_word[word_id] = self.r_word_num
self.r_word_M[self.r_word_num, :] = [word_id, 1]
self.r_word_num += 1
else:
self.r_word_M[word_ind, 1] += 1
r_stream.close()
order = np.argsort(-self.r_word_M[0:self.r_word_num, 1])
self.r_word_M = self.r_word_M[order, :]
i_record = self.r_word_num
for i in xrange(0, self.r_word_num):
if self.r_word_M[i, 1] == 1:
i_record = i
break
self.r_word_num = i_record
self.r_word_M = self.r_word_M[0:i_record, :]
# 返回一个词的所有 商品
def get_item_array(self, word_id):
word_ind = self.dict_word.get(word_id, -1)
item_array = []
if word_ind == -1:
return item_array
else:
item_str = self.word_item_array[word_ind].split(',')
for item in item_str:
item_array.append(int(item))
return item_array
# 统计词词关系
def my_tongji1(self):
split_ss = self.r_word_num
temp_array = np.zeros((split_ss, self.top_k_word + 1))
p_remain = sum(self.word_M[self.top_k_word:, 1]) # 残余项原始概率
i_file = 0
file_name = "word_word_pro"
# word_ind = 0
for word_ind in xrange(0, self.r_word_num):
word_id = int(self.r_word_M[word_ind, 0])
item_array = self.get_item_array(word_id)
for item_id in item_array:
item_array2 = self.exp_peo.associated_items(int(item_id)) # 关联商品
if len(item_array2) == 0:
continue
for item_id2 in item_array2:
item_ind = self.dict_item.get(item_id2, -1)
if item_ind != -1:
word_str = self.item_word_array[item_ind] # 所关联商品的分词组
if word_str == '':
continue
word_array = word_str.split(',')
for word_id2 in word_array:
word_ind2 = min(self.dict_word[int(word_id2)], self.top_k_word)
temp_array[word_ind, word_ind2] += 1 # word_ind 指示的词发生后其关联商品 为 含word_ind2的词 次数+1
if word_ind > 0 and ((word_ind % split_ss == 0) | (word_ind == self.r_word_num - 1)):
temp_array_sum = temp_array.sum(1) # 按照行进行求和
(row_num, col_num) = temp_array.shape
for i_col in xrange(0, col_num):
if i_col % 200 == 0:
print i_col, time.time()
if i_col == self.top_k_word:
p_pre = p_remain
else:
p_pre = self.word_M[i_col, 1]
for i_row in xrange(0, row_num):
temp_array[i_row, i_col] = \
self.pro_guji.get_pro_r(p_pre,
temp_array[i_row, i_col],
temp_array_sum[i_row]) # p n m
# 静态存储
i_file = math.floor(word_ind / split_ss)
w_file = os.path.join(self.data_dir, file_name + str(i_file) + '.txt')
w_stream = open(w_file, 'w')
for i_row in xrange(0, row_num):
my_str = ''
for i_col in xrange(0, col_num - 1):
my_str += str(math.log(temp_array[i_row, i_col])) + ','
my_str += str(math.log(temp_array[i_row, col_num - 1])) + '\n'
w_stream.writelines(my_str)
w_stream.close()
pass
# 统计 类类 关系
def my_tongji2(self):
# 统计结果由 sql sever 完成后存为txt 这里直接读取 # 检查完毕
r_path = os.path.join(self.data_dir, "class_class.txt")
r_stream = open(r_path, 'r')
self.class_class = np.zeros((self.class_num, self.class_num))
for line in r_stream:
my_str = line.strip().split('\t')
class_ind1 = self.dict_class[int(my_str[0])]
class_ind2 = self.dict_class[int(my_str[1])]
num = int(my_str[2])
self.class_class[class_ind1, class_ind2] += num
r_stream.close()
row_sum = self.class_class.sum(1) # 按照行求和
# all_num = 6 # 商品总数
# self.class_class[class_ind1, class_ind2] 存储 id1 类别 后面搭配 id2 类别的概率
w_path1 = open(os.path.join(self.data_dir, "class_class1.txt"), 'w')
w_path2 = open(os.path.join(self.data_dir, "class_class2.txt"), 'w')
w_path3 = open(os.path.join(self.data_dir, "class_class3.txt"), 'w')
for ind1 in xrange(0, self.class_num):
p_pre = np.exp(self.class_M[ind1, 1]) # 原假设： ind2 发生的概率
w_path3.writelines(str(p_pre) + '\t')
for ind2 in xrange(0, self.class_num):
p_pre = np.exp(self.class_M[ind2, 1]) # 原假设： ind2 发生的概率
if ind2 == self.class_num - 1:
w_path1.writelines(str(self.class_class[ind1, ind2]) + '\n')
else:
w_path1.writelines(str(self.class_class[ind1, ind2]) + '\t')
self.class_class[ind1, ind2] = self.pro_guji.get_pro_r(p_pre,
self.class_class[ind1, ind2],
row_sum[ind1]) # ind1 条件下 ind2 的概率
if ind2 == self.class_num - 1:
w_path2.writelines(str(self.class_class[ind1, ind2]) + '\n')
else:
w_path2.writelines(str(self.class_class[ind1, ind2]) + '\t')
w_path1.close()
w_path2.close()
w_path3.close()
# 统计词词关系 new 基于sql sever 处理过的文件开始统计 简化代码
def my_tongji3(self):
split_ss = self.r_word_num
temp_array = np.zeros((self.r_word_num + 1, self.top_k_word + 1))
p_remain = sum(np.exp(self.word_M[self.top_k_word:, 1])) # 残余项原始概率
i_file = 0
file_name = "word_word_pro"
r_path = os.path.join(self.data_dir, "wordstr_wordstr.txt")
r_stream = open(r_path, 'r')
for wor_str in r_stream:
my_str = wor_str.strip().split('\t')
if my_str[0] == '' or my_str[1] == '':
continue
word_p = my_str[0].split(',')
word_s = my_str[1].split(',')
num = int(my_str[2])
for word_id1 in word_p:
word_ind1 = self.r_dict_word.get(int(word_id1), -1) # 行号
if word_ind1 == -1:
continue # 非统计对象
word_ind1 = min(word_ind1, self.r_word_num)
for word_id2 in word_s:
word_ind2 = self.dict_word.get(int(word_id2), -1)
if word_ind2 == -1:
continue # 非录入词
word_ind2 = min(word_ind2, self.top_k_word)
temp_array[word_ind1, word_ind2] += num # word_ind 指示的词发生后其关联商品 为 含word_ind2的词 次数+1
r_stream.close()
# 求概率
temp_array_sum = temp_array.sum(1) # 按照行进行求和
(row_num, col_num) = temp_array.shape
for i_col in xrange(0, col_num):
if i_col % 200 == 0:
print i_col, time.time()
if i_col == self.top_k_word:
p_pre = p_remain
else:
p_pre = np.exp(self.word_M[i_col, 1])
for i_row in xrange(0, row_num):
temp_array[i_row, i_col] = \
self.pro_guji.get_pro_r(p_pre,
temp_array[i_row, i_col],
temp_array_sum[i_row]) # p n m
# 静态存储
w_file = os.path.join(self.data_dir, file_name + str(i_file) + '.txt')
w_stream = open(w_file, 'w')
for i_row in xrange(0, row_num):
my_str = ''
for i_col in xrange(0, col_num - 1):
my_str += str(math.log(temp_array[i_row, i_col])) + ','
my_str += str(math.log(temp_array[i_row, col_num - 1])) + '\n'
w_stream.writelines(my_str)
w_stream.close()
self.word_word = temp_array
# 读取之前计算的词词关系
def read_word_word(self):
self.word_word = np.zeros((self.r_word_num + 1, self.top_k_word + 1))
o_stream = open(os.path.join(self.data_dir, "word_word_pro0.txt"), 'r')
i_line = 0
for line in o_stream:
my_str = line.strip().split(',')
for x in xrange(0, self.top_k_word + 1):
self.word_word[i_line, x] = math.exp(float(my_str[x])) # 真概率
i_line += 1
o_stream.close()
if i_line == (self.r_word_num + 1):
print time.time(), "good"
# 根据热度重组商品矩阵
def read_item_hot(self, write=True):
path = os.path.join(self.data_dir, 'dim_items2.txt')
nums_array = np.array([0] * self.item_num)
r_stream = open(path, 'r')
for line_i in r_stream:
my_str = line_i.strip().split(' ')
item_id = int(my_str[0])
nums = int(my_str[3])
item_ind = self.dict_item[item_id]
nums_array[item_ind] = nums
r_stream.close()
a = np.argsort(-nums_array)
self.item_M = self.item_M[a, :]
for x in xrange(0, self.item_num):
self.dict_item[int(self.item_M[x, 0])] = x
# 记录商品热度
if write:
w_stream = open(os.path.join(self.data_dir, 'my_item_hot.txt'), 'w')
nums_array = nums_array[a]
for x in xrange(0, self.item_num):
w_stream.writelines(str(int(self.item_M[x, 0])) + '\t' + str(nums_array[x]) + '\n')
w_stream.close()
# 搭配算法 主进程
def da_pei(self):
file_name = os.path.join(self.data_dir, 'fm_submissions2_tag.txt')
w_stream = open(file_name, 'w')
iii = -1
for item_id in self.test_item:
iii += 1
if iii % 100 == 0:
print time.time(), iii
item_ind = self.dict_item[item_id]
word_str = self.item_word_array[item_ind]
class_id = self.item_M[item_ind, 1] # 类别编号
class_ind = self.dict_class[class_id] # 类别索引
# item_id == self.class_M[item_ind,0]
temp_result_array = np.zeros((self.item_num, 2)) # 第一列记录词组的意见，第二列记录类别的意见 概率乘 化作 加
class_pro = np.log(self.class_class[class_ind, :]) # 搭配时 该商品类别到各个类别的概率
class_pro2 = self.class_M[:, 1] # 不搭配时 该商品类别到各个类别的概率
temp_word_pro = np.array([0.0] * (self.top_k_word + 1)) # 该商品词组到各个词组的概率
word_num = 0
word_str_array = word_str.split(',')
# 获得该商品后 其他商品的输出概率
for word_id in word_str_array:
try:
word_id2i = int(word_id)
except:
continue
word_ind1 = self.r_dict_word.get(word_id2i, -1)
if word_ind1 == -1:
continue # 非统计对象
word_ind1 = min(word_ind1, self.r_word_num)
temp_word_pro += np.log(self.word_word[word_ind1, :]) # word_word 记录的是 真实概率
word_num += 1
if word_num == 0:
temp_word_pro = self.word_M[:, 1]
else:
temp_word_pro *= (1.0 / word_num) # 搭配 平均词意见
temp_word_pro2 = self.word_M[:, 1] # 不搭配 意见
for item_ind in xrange(0, self.item_top_k):
word_str = self.item_word_array[item_ind]
class_id = self.item_M[item_ind, 1]
class_ind00 = self.dict_class[int(class_id)]
temp_result_array[item_ind, 1] = class_pro[class_ind00] - class_pro2[class_ind00] # 其exp 为搭配是 不搭配的倍数
if word_str == "":
continue
word_str = word_str.split(',')
word_num2 = 0
for word_id2 in word_str:
word_ind2 = self.dict_word.get(int(word_id2), -1)
if word_ind2 == -1:
continue
word_ind2 = min(word_ind2, self.top_k_word)
temp_result_array[item_ind, 0] += temp_word_pro[word_ind2] - temp_word_pro2[word_ind2]
word_num2 += 1
if word_num2 == 0:
temp_result_array[item_ind, 0] = 0
else:
temp_result_array[item_ind, 0] *= (1.0 / word_num2)
a = temp_result_array[:, 0] + temp_result_array[:, 1] # 类别的意见， 加上词的意见
my_order = np.argsort(-a) # 降序排序 并输出
# 找出前6百个 按照热度进行排名 将 前 200 个 写入文件
top_top_k = 400 # 重要参数
temp_rrr = np.zeros((top_top_k, 2))
for i in xrange(0, top_top_k):
temp_rrr[i, :] = [self.item_M[my_order[i], 0], my_order[i]]
my_order = np.argsort(temp_rrr[:, 1]) # 按照序号排名 即热度
result_str = str(item_id) + ' ' + str(int(temp_rrr[my_order[0], 0]))
for i in xrange(1, 200):
result_str += ',' + str(int(temp_rrr[my_order[i], 0]))
w_stream.writelines(result_str + '\n')
pass
# 仅仅计算出所有结果前6万商品的搭配结果
# 加入了 原始的搭配概率 0.006，在 搭配中由于这一直大家相同，所以无关紧要。
# 这次会计算出前6万的搭配概率，供 my_python2 做 进一步筛查
def da_pei2(self):
file_name = os.path.join(self.data_dir, 'fm_submissions2_tag_m.txt')
w_stream = open(file_name, 'w')
iii = -1
for item_id in self.test_item:
iii += 1
if iii % 100 == 0:
print time.time(), iii
item_ind = self.dict_item[item_id]
word_str = self.item_word_array[item_ind]
class_id = self.item_M[item_ind, 1] # 类别编号
class_ind = self.dict_class[class_id] # 类别索引
# item_id == self.class_M[item_ind,0]
temp_result_array = np.zeros((self.item_num, 2)) # 第一列记录词组的意见，第二列记录类别的意见 概率乘 化作 加
class_pro = np.log(self.class_class[class_ind, :]) # 搭配时 该商品类别到各个类别的概率
class_pro2 = self.class_M[:, 1] # 不搭配时 该商品类别到各个类别的概率对数
temp_word_pro = np.array([0.0] * (self.top_k_word + 1)) # 该商品词组发生后各个词组的概率
word_num = 0
word_str_array = word_str.split(',')
# 获得该商品后 其他商品的输出概率
for word_id in word_str_array:
try:
word_id2i = int(word_id)
except:
continue
word_ind1 = self.r_dict_word.get(word_id2i, -1)
if word_ind1 == -1:
continue # 非统计对象
word_ind1 = min(word_ind1, self.r_word_num)
temp_word_pro += np.log(self.word_word[word_ind1, :]) # word_word 记录的是 真实概率
word_num += 1
if word_num == 0:
temp_word_pro = self.word_M[:, 1]
else:
temp_word_pro *= (1.0 / word_num) # 搭配 平均词意见
temp_word_pro2 = self.word_M[:, 1] # 不搭配 意见
for item_ind in xrange(0, self.item_top_k):
word_str = self.item_word_array[item_ind]
class_id = self.item_M[item_ind, 1]
class_ind00 = self.dict_class[int(class_id)]
temp_result_array[item_ind, 1] = class_pro[class_ind00] - class_pro2[class_ind00] # 其exp 为搭配是 不搭配发生的倍数
if word_str == "":
continue
word_str = word_str.split(',')
word_num2 = 0
for word_id2 in word_str:
word_ind2 = self.dict_word.get(int(word_id2), -1)
if word_ind2 == -1:
continue
word_ind2 = min(word_ind2, self.top_k_word)
temp_result_array[item_ind, 0] += temp_word_pro[word_ind2] - temp_word_pro2[word_ind2]
word_num2 += 1
if word_num2 == 0:
temp_result_array[item_ind, 0] = 0
else:
temp_result_array[item_ind, 0] *= (1.0 / word_num2)
a = temp_result_array[:, 0] + temp_result_array[:, 1] # 类别的意见， 加上词的意见 a元素 中存储的是
pro_a = self.p_match * np.exp(a) / (self.p_match * np.exp(a) + (1 - self.p_match) * 1) # 得到各个商品 的概率
# my_str00 = ""
w_stream.writelines(str(item_id) + '\t')
for item_ind in xrange(0, self.item_top_k):
if item_ind != (self.item_top_k - 1):
w_stream.writelines(str(round(pro_a[item_ind], 9)) + '\t')
else:
w_stream.writelines(str(round(pro_a[item_ind], 9)) + '\n')
w_stream.close()
class most_like():
def __init__(self):
self.data_dir = 'E:\\gitshell\\tianchi2'
# 词组
self.word_num = 0
self.dict_word = {}
self.top_k_word = 20000 # 详细计算前20000的词组
self.word_M = np.zeros((1000000, 2)) # 第一列 记录word_id 第二列 记录 概率对数
self.word_item_array = [""] * 1000000 # 每个词被哪些商品使用
self.word_word = np.zeros((3, 3))
# 需要预测的词组
self.r_word_num = 0
self.r_dict_word = {}
self.r_word_M = np.zeros((80000, 2))
self.test_item = []
# 商品
self.dict_item = {}
self.item_M = np.zeros((600000, 2), int) # item_id 类别编号
self.item_word_array = [""] * 600000
self.item_num = 0
# 类别
self.class_M = np.zeros((3000000, 2)) # 类别id 类别商品计数/ 概率对数
self.dict_class = {}
self.class_num = 0
self.class_class = np.zeros((2, 2))
# 原始人工经验
self.exp_peo = exp_of_people()
self.exp_peo.read_jingyan()
# self.matrix_item = np.zeros((10000000,3))
# 概率优化模块
self.pro_guji = Pro_estimate()
# 只考虑 最热的 6万 商品
self.item_top_k = 60000
# 原始的搭配概率
self.p_match = 0.0006 # 任意随机商品 搭配的概率
pass
def read_txt(self, filename="dim_items.txt"):
r_path = os.path.join(self.data_dir, filename)
r_stream = open(r_path, 'r')
self.item_num = 0
for line_i in r_stream:
if self.item_num % 100000 == 0:
print self.item_num, time.time()
# 录入商品
my_str = line_i.strip('\n').split(" ")
self.dict_item[int(my_str[0])] = self.item_num
self.item_M[self.item_num, :] = [my_str[0], int(my_str[1])]
self.item_word_array[self.item_num] = my_str[2]
self.item_num += 1
# 录入不同的词组
my_str2 = my_str[2].split(',')
for x_word in my_str2:
try:
word_id = int(x_word)
except:
continue
word_ind = self.dict_word.get(word_id, -1)
if word_ind == -1:
self.dict_word[word_id] = self.word_num
self.word_M[self.word_num, :] = [word_id, 1]
# self.word_item_array[self.word_num] = my_str[0] # 商品
self.word_num += 1
else:
self.word_M[word_ind, 1] += 1
# self.word_item_array[word_ind] += ',' + my_str[0] # 商品
# 录入分类信息
class_id = int(my_str[1])
class_ind = self.dict_class.get(class_id, -1)
if class_ind == -1:
self.dict_class[class_id] = self.class_num
self.class_M[self.class_num, :] = [class_id, 1]
self.class_num += 1
else:
self.class_M[class_ind, 1] += 1
self.class_M = self.class_M[0:self.class_num, :]
self.word_M = self.word_M[0:self.word_num, :]
self.item_M = self.item_M[0:self.item_num, :]
self.item_word_array = self.item_word_array[0:self.item_num]
# self.word_item_array = self.word_item_array[0:self.word_num]
# 根据热度排行对词进行重新排序
order = np.argsort(-self.word_M[:, 1])
self.word_M = self.word_M[order, :]
# temp_a = self.word_item_array
# for x in xrange(0, len(order)):
# self.word_item_array[x] = temp_a[order[x]]
for x in xrange(0, self.word_num):
self.dict_word[int(self.word_M[x, 0])] = x
r_stream.close()
# # 转化word_M 第2 列 为概率对数：
sum_word_num = sum(self.word_M[:, 1])
self.word_M[:, 1] = np.log(self.word_M[:, 1] / sum_word_num)
# 转化word_M 第2 列 为概率对数：
sum_class_num = sum(self.class_M[:, 1])
self.class_M[:, 1] = np.log(self.class_M[:, 1] / sum_class_num)
def result_word(self, file_name='test_items2.txt'):
# 找出需要计算的词汇
file_name = os.path.join(self.data_dir, file_name)
r_stream = open(file_name, 'r')
for line_i in r_stream:
item_id = int(line_i.strip())
self.test_item.append(item_id)
item_ind = self.dict_item.get(item_id, -1)
if item_ind == -1:
continue
word_array = self.item_word_array[item_ind].split(',')
for word_id in word_array:
word_id = int(word_id)
word_ind = self.r_dict_word.get(word_id, -1)
if word_ind == -1:
self.r_dict_word[word_id] = self.r_word_num
self.r_word_M[self.r_word_num, :] = [word_id, 1]
self.r_word_num += 1
else:
self.r_word_M[word_ind, 1] += 1
r_stream.close()
order = np.argsort(-self.r_word_M[0:self.r_word_num, 1])
self.r_word_M = self.r_word_M[order, :]
i_record = self.r_word_num
for i in xrange(0, self.r_word_num):
if self.r_word_M[i, 1] == 1:
i_record = i
break
self.r_word_num = i_record
self.r_word_M = self.r_word_M[0:i_record, :]
# 返回一个词的所有 商品
def get_item_array(self, word_id):
word_ind = self.dict_word.get(word_id, -1)
item_array = []
if word_ind == -1:
return item_array
else:
item_str = self.word_item_array[word_ind].split(',')
for item in item_str:
item_array.append(int(item))
return item_array
# 统计词词关系
def my_tongji1(self):
split_ss = self.r_word_num
temp_array = np.zeros((split_ss, self.top_k_word + 1))
p_remain = sum(self.word_M[self.top_k_word:, 1]) # 残余项原始概率
i_file = 0
file_name = "word_word_pro"
# word_ind = 0
for word_ind in xrange(0, self.r_word_num):
word_id = int(self.r_word_M[word_ind, 0])
item_array = self.get_item_array(word_id)
for item_id in item_array:
item_array2 = self.exp_peo.associated_items(
int(item_id)) # 关联商品
if len(item_array2) == 0:
continue
for item_id2 in item_array2:
item_ind = self.dict_item.get(item_id2, -1)
if item_ind != -1:
word_str = self.item_word_array[item_ind] # 所关联商品的分词组
if word_str == '':
continue
word_array = word_str.split(',')
for word_id2 in word_array:
word_ind2 = min(self.dict_word[int(word_id2)],
self.top_k_word)
temp_array[
word_ind,
word_ind2] += 1 # word_ind 指示的词发生后其关联商品 为 含word_ind2的词 次数+1
if word_ind > 0 and ((word_ind % split_ss == 0) |
(word_ind == self.r_word_num - 1)):
temp_array_sum = temp_array.sum(1) # 按照行进行求和
(row_num, col_num) = temp_array.shape
for i_col in xrange(0, col_num):
if i_col % 200 == 0:
print i_col, time.time()
if i_col == self.top_k_word:
p_pre = p_remain
else:
p_pre = self.word_M[i_col, 1]
for i_row in xrange(0, row_num):
temp_array[i_row, i_col] = \
self.pro_guji.get_pro_r(p_pre,
temp_array[i_row, i_col],
temp_array_sum[i_row]) # p n m
# 静态存储
i_file = math.floor(word_ind / split_ss)
w_file = os.path.join(self.data_dir,
file_name + str(i_file) + '.txt')
w_stream = open(w_file, 'w')
for i_row in xrange(0, row_num):
my_str = ''
for i_col in xrange(0, col_num - 1):
my_str += str(math.log(temp_array[i_row, i_col])) + ','
my_str += str(math.log(temp_array[i_row,
col_num - 1])) + '\n'
w_stream.writelines(my_str)
w_stream.close()
pass
# 统计 类类 关系
def my_tongji2(self):
# 统计结果由 sql sever 完成后存为txt 这里直接读取 # 检查完毕
r_path = os.path.join(self.data_dir, "class_class.txt")
r_stream = open(r_path, 'r')
self.class_class = np.zeros((self.class_num, self.class_num))
for line in r_stream:
my_str = line.strip().split('\t')
class_ind1 = self.dict_class[int(my_str[0])]
class_ind2 = self.dict_class[int(my_str[1])]
num = int(my_str[2])
self.class_class[class_ind1, class_ind2] += num
r_stream.close()
row_sum = self.class_class.sum(1) # 按照行求和
# all_num = 6 # 商品总数
# self.class_class[class_ind1, class_ind2] 存储 id1 类别 后面搭配 id2 类别的概率
w_path1 = open(os.path.join(self.data_dir, "class_class1.txt"), 'w')
w_path2 = open(os.path.join(self.data_dir, "class_class2.txt"), 'w')
w_path3 = open(os.path.join(self.data_dir, "class_class3.txt"), 'w')
for ind1 in xrange(0, self.class_num):
p_pre = np.exp(self.class_M[ind1, 1]) # 原假设： ind2 发生的概率
w_path3.writelines(str(p_pre) + '\t')
for ind2 in xrange(0, self.class_num):
p_pre = np.exp(self.class_M[ind2, 1]) # 原假设： ind2 发生的概率
if ind2 == self.class_num - 1:
w_path1.writelines(
str(self.class_class[ind1, ind2]) + '\n')
else:
w_path1.writelines(
str(self.class_class[ind1, ind2]) + '\t')
self.class_class[ind1, ind2] = self.pro_guji.get_pro_r(
p_pre, self.class_class[ind1, ind2],
row_sum[ind1]) # ind1 条件下 ind2 的概率
if ind2 == self.class_num - 1:
w_path2.writelines(
str(self.class_class[ind1, ind2]) + '\n')
else:
w_path2.writelines(
str(self.class_class[ind1, ind2]) + '\t')
w_path1.close()
w_path2.close()
w_path3.close()
# 统计词词关系 new 基于sql sever 处理过的文件开始统计 简化代码
def my_tongji3(self):
split_ss = self.r_word_num
temp_array = np.zeros((self.r_word_num + 1, self.top_k_word + 1))
p_remain = sum(np.exp(self.word_M[self.top_k_word:, 1])) # 残余项原始概率
i_file = 0
file_name = "word_word_pro"
r_path = os.path.join(self.data_dir, "wordstr_wordstr.txt")
r_stream = open(r_path, 'r')
for wor_str in r_stream:
my_str = wor_str.strip().split('\t')
if my_str[0] == '' or my_str[1] == '':
continue
word_p = my_str[0].split(',')
word_s = my_str[1].split(',')
num = int(my_str[2])
for word_id1 in word_p:
word_ind1 = self.r_dict_word.get(int(word_id1), -1) # 行号
if word_ind1 == -1:
continue # 非统计对象
word_ind1 = min(word_ind1, self.r_word_num)
for word_id2 in word_s:
word_ind2 = self.dict_word.get(int(word_id2), -1)
if word_ind2 == -1:
continue # 非录入词
word_ind2 = min(word_ind2, self.top_k_word)
temp_array[
word_ind1,
word_ind2] += num # word_ind 指示的词发生后其关联商品 为 含word_ind2的词 次数+1
r_stream.close()
# 求概率
temp_array_sum = temp_array.sum(1) # 按照行进行求和
(row_num, col_num) = temp_array.shape
for i_col in xrange(0, col_num):
if i_col % 200 == 0:
print i_col, time.time()
if i_col == self.top_k_word:
p_pre = p_remain
else:
p_pre = np.exp(self.word_M[i_col, 1])
for i_row in xrange(0, row_num):
temp_array[i_row, i_col] = \
self.pro_guji.get_pro_r(p_pre,
temp_array[i_row, i_col],
temp_array_sum[i_row]) # p n m
# 静态存储
w_file = os.path.join(self.data_dir, file_name + str(i_file) + '.txt')
w_stream = open(w_file, 'w')
for i_row in xrange(0, row_num):
my_str = ''
for i_col in xrange(0, col_num - 1):
my_str += str(math.log(temp_array[i_row, i_col])) + ','
my_str += str(math.log(temp_array[i_row, col_num - 1])) + '\n'
w_stream.writelines(my_str)
w_stream.close()
self.word_word = temp_array
# 读取之前计算的词词关系
def read_word_word(self):
self.word_word = np.zeros((self.r_word_num + 1, self.top_k_word + 1))
o_stream = open(os.path.join(self.data_dir, "word_word_pro0.txt"), 'r')
i_line = 0
for line in o_stream:
my_str = line.strip().split(',')
for x in xrange(0, self.top_k_word + 1):
self.word_word[i_line, x] = math.exp(float(my_str[x])) # 真概率
i_line += 1
o_stream.close()
if i_line == (self.r_word_num + 1):
print time.time(), "good"
# 根据热度重组商品矩阵
def read_item_hot(self, write=True):
path = os.path.join(self.data_dir, 'dim_items2.txt')
nums_array = np.array([0] * self.item_num)
r_stream = open(path, 'r')
for line_i in r_stream:
my_str = line_i.strip().split(' ')
item_id = int(my_str[0])
nums = int(my_str[3])
item_ind = self.dict_item[item_id]
nums_array[item_ind] = nums
r_stream.close()
a = np.argsort(-nums_array)
self.item_M = self.item_M[a, :]
for x in xrange(0, self.item_num):
self.dict_item[int(self.item_M[x, 0])] = x
# 记录商品热度
if write:
w_stream = open(os.path.join(self.data_dir, 'my_item_hot.txt'),
'w')
nums_array = nums_array[a]
for x in xrange(0, self.item_num):
w_stream.writelines(
str(int(self.item_M[x, 0])) + '\t' + str(nums_array[x]) +
'\n')
w_stream.close()
# 搭配算法 主进程
def da_pei(self):
file_name = os.path.join(self.data_dir, 'fm_submissions2_tag.txt')
w_stream = open(file_name, 'w')
iii = -1
for item_id in self.test_item:
iii += 1
if iii % 100 == 0:
print time.time(), iii
item_ind = self.dict_item[item_id]
word_str = self.item_word_array[item_ind]
class_id = self.item_M[item_ind, 1] # 类别编号
class_ind = self.dict_class[class_id] # 类别索引
# item_id == self.class_M[item_ind,0]
temp_result_array = np.zeros(
(self.item_num, 2)) # 第一列记录词组的意见，第二列记录类别的意见 概率乘 化作 加
class_pro = np.log(
self.class_class[class_ind, :]) # 搭配时 该商品类别到各个类别的概率
class_pro2 = self.class_M[:, 1] # 不搭配时 该商品类别到各个类别的概率
temp_word_pro = np.array([0.0] *
(self.top_k_word + 1)) # 该商品词组到各个词组的概率
word_num = 0
word_str_array = word_str.split(',')
# 获得该商品后 其他商品的输出概率
for word_id in word_str_array:
try:
word_id2i = int(word_id)
except:
continue
word_ind1 = self.r_dict_word.get(word_id2i, -1)
if word_ind1 == -1:
continue # 非统计对象
word_ind1 = min(word_ind1, self.r_word_num)
temp_word_pro += np.log(
self.word_word[word_ind1, :]) # word_word 记录的是 真实概率
word_num += 1
if word_num == 0:
temp_word_pro = self.word_M[:, 1]
else:
temp_word_pro *= (1.0 / word_num) # 搭配 平均词意见
temp_word_pro2 = self.word_M[:, 1] # 不搭配 意见
for item_ind in xrange(0, self.item_top_k):
word_str = self.item_word_array[item_ind]
class_id = self.item_M[item_ind, 1]
class_ind00 = self.dict_class[int(class_id)]
temp_result_array[item_ind,
1] = class_pro[class_ind00] - class_pro2[
class_ind00] # 其exp 为搭配是 不搭配的倍数
if word_str == "":
continue
word_str = word_str.split(',')
word_num2 = 0
for word_id2 in word_str:
word_ind2 = self.dict_word.get(int(word_id2), -1)
if word_ind2 == -1:
continue
word_ind2 = min(word_ind2, self.top_k_word)
temp_result_array[item_ind, 0] += temp_word_pro[
word_ind2] - temp_word_pro2[word_ind2]
word_num2 += 1
if word_num2 == 0:
temp_result_array[item_ind, 0] = 0
else:
temp_result_array[item_ind, 0] *= (1.0 / word_num2)
a = temp_result_array[:, 0] + temp_result_array[:,
1] # 类别的意见， 加上词的意见
my_order = np.argsort(-a) # 降序排序 并输出
# 找出前6百个 按照热度进行排名 将 前 200 个 写入文件
top_top_k = 400 # 重要参数
temp_rrr = np.zeros((top_top_k, 2))
for i in xrange(0, top_top_k):
temp_rrr[i, :] = [self.item_M[my_order[i], 0], my_order[i]]
my_order = np.argsort(temp_rrr[:, 1]) # 按照序号排名 即热度
result_str = str(item_id) + ' ' + str(int(temp_rrr[my_order[0],
0]))
for i in xrange(1, 200):
result_str += ',' + str(int(temp_rrr[my_order[i], 0]))
w_stream.writelines(result_str + '\n')
pass
# 仅仅计算出所有结果前6万商品的搭配结果
# 加入了 原始的搭配概率 0.006，在 搭配中由于这一直大家相同，所以无关紧要。
# 这次会计算出前6万的搭配概率，供 my_python2 做 进一步筛查
def da_pei2(self):
file_name = os.path.join(self.data_dir, 'fm_submissions2_tag_m.txt')
w_stream = open(file_name, 'w')
iii = -1
for item_id in self.test_item:
iii += 1
if iii % 100 == 0:
print time.time(), iii
item_ind = self.dict_item[item_id]
word_str = self.item_word_array[item_ind]
class_id = self.item_M[item_ind, 1] # 类别编号
class_ind = self.dict_class[class_id] # 类别索引
# item_id == self.class_M[item_ind,0]
temp_result_array = np.zeros(
(self.item_num, 2)) # 第一列记录词组的意见，第二列记录类别的意见 概率乘 化作 加
class_pro = np.log(
self.class_class[class_ind, :]) # 搭配时 该商品类别到各个类别的概率
class_pro2 = self.class_M[:, 1] # 不搭配时 该商品类别到各个类别的概率对数
temp_word_pro = np.array([0.0] *
(self.top_k_word + 1)) # 该商品词组发生后各个词组的概率
word_num = 0
word_str_array = word_str.split(',')
# 获得该商品后 其他商品的输出概率
for word_id in word_str_array:
try:
word_id2i = int(word_id)
except:
continue
word_ind1 = self.r_dict_word.get(word_id2i, -1)
if word_ind1 == -1:
continue # 非统计对象
word_ind1 = min(word_ind1, self.r_word_num)
temp_word_pro += np.log(
self.word_word[word_ind1, :]) # word_word 记录的是 真实概率
word_num += 1
if word_num == 0:
temp_word_pro = self.word_M[:, 1]
else:
temp_word_pro *= (1.0 / word_num) # 搭配 平均词意见
temp_word_pro2 = self.word_M[:, 1] # 不搭配 意见
for item_ind in xrange(0, self.item_top_k):
word_str = self.item_word_array[item_ind]
class_id = self.item_M[item_ind, 1]
class_ind00 = self.dict_class[int(class_id)]
temp_result_array[item_ind,
1] = class_pro[class_ind00] - class_pro2[
class_ind00] # 其exp 为搭配是 不搭配发生的倍数
if word_str == "":
continue
word_str = word_str.split(',')
word_num2 = 0
for word_id2 in word_str:
word_ind2 = self.dict_word.get(int(word_id2), -1)
if word_ind2 == -1:
continue
word_ind2 = min(word_ind2, self.top_k_word)
temp_result_array[item_ind, 0] += temp_word_pro[
word_ind2] - temp_word_pro2[word_ind2]
word_num2 += 1
if word_num2 == 0:
temp_result_array[item_ind, 0] = 0
else:
temp_result_array[item_ind, 0] *= (1.0 / word_num2)
a = temp_result_array[:,
0] + temp_result_array[:,
1] # 类别的意见， 加上词的意见 a元素 中存储的是
pro_a = self.p_match * np.exp(a) / (self.p_match * np.exp(a) +
(1 - self.p_match) * 1
) # 得到各个商品 的概率
# my_str00 = ""
w_stream.writelines(str(item_id) + '\t')
for item_ind in xrange(0, self.item_top_k):
if item_ind != (self.item_top_k - 1):
w_stream.writelines(str(round(pro_a[item_ind], 9)) + '\t')
else:
w_stream.writelines(str(round(pro_a[item_ind], 9)) + '\n')
w_stream.close()
class WordOpinion:
def __init__(self):
self.data_dir = "E:\\gitshell\\tianchi3"
self.my_matrix = np.zeros((2, 2)) # 记录词到商品的概率对数
# 词组
self.word_num = 0
self.dict_word = {}
self.top_k_word = 15000 # 详细计算前20000的词组
self.word_M = np.zeros((1000000, 2)) # 第一列 记录word_id 第二列 记录 概率
self.word_item_array = [""] * 1000000 # 每个词被哪些商品使用
self.word_word = np.zeros((3, 3)) # 统一记录真实概率
# 需要预测的词组
self.r_word_num = 0
self.r_dict_word = {}
self.r_word_M = np.zeros((80000, 2)) # 第一列词 第二列词的次数
self.test_item = []
# 商品
self.dict_item = {}
self.item_M = np.zeros((600000, 2), int) # item_id 类别编号
self.item_word_array = [""] * 600000
self.item_num = 0
# 概率优化模块
self.pro_guji = Pro_estimate()
# 只考虑 最热的 6万 商品
self.item_top_k = 100000
# 原始的搭配概率
self.p_match = 0.0006 # 任意随机商品 搭配的概率
self.num_word2 = 800
pass
def read_txt(self, filename="dim_items.txt"):
# 读取商品的类别信息表
r_path = os.path.join(self.data_dir, filename)
r_stream = open(r_path, "r")
self.item_num = 0
for line_i in r_stream:
if self.item_num % 100000 == 0:
print self.item_num, time.time()
# 录入商品
my_str = line_i.strip("\n").split(" ")
self.dict_item[int(my_str[0])] = self.item_num
self.item_M[self.item_num, :] = [my_str[0], int(my_str[1])]
self.item_word_array[self.item_num] = my_str[2]
self.item_num += 1
# 录入不同的词组
my_str2 = my_str[2].split(",")
for x_word in my_str2:
try:
word_id = int(x_word)
except:
continue
word_ind = self.dict_word.get(word_id, -1)
if word_ind == -1:
self.dict_word[word_id] = self.word_num
self.word_M[self.word_num, :] = [word_id, 1]
# self.word_item_array[self.word_num] = my_str[0] # 商品
self.word_num += 1
else:
self.word_M[word_ind, 1] += 1
# self.word_item_array[word_ind] += ',' + my_str[0] # 商品
self.word_M = self.word_M[0 : self.word_num, :]
self.item_M = self.item_M[0 : self.item_num, :]
self.item_word_array = self.item_word_array[0 : self.item_num]
# self.word_item_array = self.word_item_array[0:self.word_num]
# 根据热度排行对词进行重新排序
order = np.argsort(-self.word_M[:, 1])
self.word_M = self.word_M[order, :]
# temp_a = self.word_item_array
# for x in xrange(0, len(order)):
# self.word_item_array[x] = temp_a[order[x]]
for x in xrange(0, self.word_num):
self.dict_word[int(self.word_M[x, 0])] = x
r_stream.close()
# # 转化word_M 第2 列 为概率对数：
sum_word_num = sum(self.word_M[:, 1])
self.word_M[:, 1] = self.word_M[:, 1] / sum_word_num
def result_word(self, file_name="test_set.txt"):
# 只对需要预测的商品进行计算 找出需要计算的词汇
file_name = os.path.join(self.data_dir, file_name)
r_stream = open(file_name, "r")
for line_i in r_stream:
item_id = int(line_i.strip().split("\t")[-1])
self.test_item.append(item_id)
item_ind = self.dict_item.get(item_id, -1)
if item_ind == -1:
continue
word_array = self.item_word_array[item_ind].split(",")
for word_id in word_array:
word_id = int(word_id)
word_ind = self.r_dict_word.get(word_id, -1)
if word_ind == -1:
self.r_dict_word[word_id] = self.r_word_num
self.r_word_M[self.r_word_num, :] = [word_id, 1]
self.r_word_num += 1
else:
self.r_word_M[word_ind, 1] += 1
r_stream.close()
order = np.argsort(-self.r_word_M[0 : self.r_word_num, 1])
self.r_word_M = self.r_word_M[order, :]
i_record = self.r_word_num
for i in xrange(0, self.r_word_num):
if self.r_word_M[i, 1] == 1:
i_record = i
print i_record
break
self.r_word_num = i_record
self.r_word_M = self.r_word_M[0:i_record, :]
# 统计词词关系 new 基于sql sever 处理过的文件开始统计 简化代码
def my_tongji3(self):
# split_ss = self.r_word_num
temp_array = np.zeros((self.r_word_num + 1, self.top_k_word + 1))
p_remain = sum(self.word_M[self.top_k_word :, 1]) # 残余项原始概率
i_file = 0
file_name = "word_word_pro"
r_path = os.path.join(self.data_dir, "learn_wordstr_wordstr0.txt")
r_stream = open(r_path, "r")
for wor_str in r_stream:
my_str = wor_str.strip().split("\t")
if my_str[0] == "" or my_str[1] == "":
continue
word_p = my_str[0].split(",")
word_s = my_str[1].split(",")
num = int(my_str[2])
for word_id1 in word_p:
word_ind1 = self.r_dict_word.get(int(word_id1), -1) # 行号
if word_ind1 == -1:
continue # 非统计对象
word_ind1 = min(word_ind1, self.r_word_num)
for word_id2 in word_s:
word_ind2 = self.dict_word.get(int(word_id2), -1)
if word_ind2 == -1:
continue # 非录入词
word_ind2 = min(word_ind2, self.top_k_word)
temp_array[word_ind1, word_ind2] += num # word_ind 指示的词发生后其关联商品 为 含word_ind2的词 次数+1
r_stream.close()
for x in xrange(0, self.top_k_word):
temp_array[:, x] += self.word_M[x, 1] # 增加 一个未知事件 保证没有概率为0 的情况
temp_array[:, self.top_k_word] += sum(self.word_M[self.top_k_word :, 1])
# 求概率
temp_array_sum = temp_array.sum(1) # 按照行进行求和
(row_num, col_num) = temp_array.shape
o_stream = open(os.path.join(self.data_dir, "ceshi_tongji3.txt"), "w")
for i_col in xrange(0, col_num):
if i_col % 200 == 0:
print i_col, time.time()
if i_col == self.top_k_word:
p_pre = p_remain
else:
p_pre = self.word_M[i_col, 1]
for i_row in xrange(0, row_num):
a = temp_array[i_row, i_col]
# temp_array[i_row, i_col] = temp_array[i_row, i_col]/temp_array_sum[i_row]
temp_array[i_row, i_col] = self.pro_guji.get_pro_r(
p_pre, temp_array[i_row, i_col], temp_array_sum[i_row]
) # p n m
if i_row == 1:
o_stream.write(
str(p_pre)
+ "\t"
+ str(temp_array[i_row, i_col])
+ "\t"
+ str(a)
+ "\t"
+ str(temp_array_sum[i_row])
+ "\n"
)
# 静态存储
o_stream.close()
w_file = os.path.join(self.data_dir, file_name + str(i_file) + ".txt")
w_stream = open(w_file, "w")
for i_row in xrange(0, row_num):
my_str = ""
for i_col in xrange(0, col_num - 1):
my_str += str(math.log(temp_array[i_row, i_col])) + ","
my_str += str(math.log(temp_array[i_row, col_num - 1])) + "\n"
w_stream.writelines(my_str)
w_stream.close()
self.word_word = temp_array
# 读取之前计算的词词关系
def read_word_word(self):
self.word_word = np.zeros((self.r_word_num + 1, self.top_k_word + 1))
o_stream = open(os.path.join(self.data_dir, "word_word_pro0.txt"), "r")
i_line = 0
for line in o_stream:
my_str = line.strip().split(",")
for x in xrange(0, self.top_k_word + 1):
self.word_word[i_line, x] = math.exp(float(my_str[x])) # 真概率
i_line += 1
o_stream.close()
if i_line == (self.r_word_num + 1):
print time.time(), "good"
# 根据热度重组商品矩阵
def read_item_hot(self, write=True):
path = os.path.join(self.data_dir, "my_item_hot.txt")
nums_array = np.array([0] * self.item_num)
r_stream = open(path, "r")
for line_i in r_stream:
my_str = line_i.strip().split("\t")
item_id = int(my_str[0])
nums = int(my_str[-1])
item_ind = self.dict_item[item_id]
nums_array[item_ind] = nums
r_stream.close()
a = np.argsort(-nums_array) # 降序排列
self.item_M = self.item_M[a, :]
for x in xrange(0, self.item_num):
self.dict_item[int(self.item_M[x, 0])] = x
# 记录商品热度
if write:
w_stream = open(os.path.join(self.data_dir, "my_item_hot.txt"), "w")
nums_array = nums_array[a]
for x in xrange(0, self.item_num):
w_stream.writelines(str(int(self.item_M[x, 0])) + "\t" + str(nums_array[x]) + "\n")
w_stream.close()
# 对2000个词来 进行构造矩阵
def dapei(self):
# 每一个词后面 各个商品的发生几率
self.my_matrix = np.zeros((self.num_word2, self.item_top_k))
for word_ind1 in xrange(0, self.num_word2):
temp_word = self.r_word_M[word_ind1,]
print word_ind1
# word_id1 = int(temp_word[0])
self.my_matrix[word_ind1,] = self.get_pro_1(word_ind1, False)
# 返回某一个词对每个 商品的搭配意见
def get_pro_1(self, word_ind1, ab=True):
if word_ind1 < self.num_word2 and ab:
return self.my_matrix[word_ind1,]
else:
array0 = np.array([0.0] * (self.item_top_k))
temp_word_pro2 = np.log(self.word_M[0 : self.top_k_word + 1, 1])
temp_word_pro2[self.top_k_word] = sum(self.word_M[self.top_k_word :, 1])
temp_word_pro = np.log(self.word_word[word_ind1,])
for item_ind in xrange(0, self.item_top_k):
word_str = self.item_word_array[item_ind]
if word_str == "": # 没有任何词语
continue
word_str = word_str.split(",")
word_num2 = 0
for word_id2 in word_str:
word_ind2 = self.dict_word.get(int(word_id2), -1)
if word_ind2 == -1:
continue
word_ind2 = min(word_ind2, self.top_k_word)
array0[item_ind] += temp_word_pro[word_ind2] - temp_word_pro2[word_ind2]
word_num2 += 1
array0[item_ind] = array0[item_ind] / word_num2
return array0
# 得到某一个词的搭配商品
def get(self, item_id):
yyy = np.array([0.0] * self.item_top_k)
item_ind = self.dict_item[item_id]
word_str = self.item_word_array[item_ind]
if len(word_str) == 0:
return yyy
word_str_array = word_str.split(",")
aaa = 1
for word_id in word_str_array:
try:
word_id2i = int(word_id)
except:
continue
word_ind1 = self.r_dict_word.get(word_id2i, -1)
if word_ind1 == -1:
continue # 非统计对象
word_ind1 = min(word_ind1, self.r_word_num)
yyy += self.get_pro_1(word_ind1) # word_word 记录的是 真实概率
aaa += 1
yyy = yyy / aaa
b = max(yyy)
yyy = np.exp(yyy - b) # 最大值化为一
return yyy
def write_result(self):
file_name1 = os.path.join(self.data_dir, "fm_submissions2_tag_w.txt") # word 意见
w_stream1 = open(file_name1, "w")
iii = -1
t_b = time.time()
for item_id in self.test_item:
iii += 1
if iii % 100 == 0 or time.time() - t_b > 100:
t_b = time.time()
print iii, t_b
pro_gailv = self.get(item_id)
w_stream1.writelines(str(item_id) + "\t")
for item_ind in xrange(0, self.item_top_k - 1):
w_stream1.writelines(str(pro_gailv[item_ind]) + "\t")
w_stream1.writelines(str(pro_gailv[self.item_top_k - 1]) + "\n")
w_stream1.close()