fix(3-1): 更新 HDFS 地址
- 将 HDFS 地址从 "hdfs:///user/root/output/inverted_index" 修改为 "hdfs://master:9000/user/root/output/inverted_index" - 这个修改可能是为了适应不同的 HDFS集群配置，确保数据保存到正确的地址
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3-1.py
149
3-1.py
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# coding=utf-8
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# -*- coding: utf-8 -*-
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"""
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实验步骤3：利用 TF-IDF 加权提升文本相似性计算准确性
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1. 读取 Amazon_small.csv 和 Google_small.csv 数据，
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提取文档ID和文本（标题、描述、制造商）的组合；
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2. 对文本进行分词，构建语料库（格式：(doc_id, [token列表])）；
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3. 计算 TF（词频）：统计每个词在文档中的出现次数除以该文档总词数；
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4. 计算 IDF（逆文档频率）：IDF(t)= N / n(t)，其中 N 为文档总数，n(t) 为包含词 t 的文档数；
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5. 计算 TF-IDF：每个词的 TF-IDF = TF * IDF；
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6. 输出格式：((doc_id, term), tfidf_value)
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注意：确保 HDFS 上已上传以下文件：
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- Amazon_small.csv
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- Google_small.csv
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- 若代码中需要停用词过滤，可以自行调整或扩展（本示例未特别去除停用词）
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"""
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from pyspark import SparkContext
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import csv
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import re
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# Python 3.5 没有 f-string，使用 format
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def tokenize(text):
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# 分词并保留英文、数字
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return re.findall(r'\w+', text.lower())
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sc = SparkContext(appName="TFIDF_Analysis")
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# 请根据实际情况修改 HDFS 主机及端口，如 "hdfs://localhost:9000"
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amazon_path = "hdfs://master:9000/user/root/Amazon_small.csv"
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google_path = "hdfs://master:9000/user/root/Google_small.csv"
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def load_stopwords(sc):
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try:
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return set(sc.textFile("hdfs://master:9000/user/root/stopwords.txt").collect())
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except:
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# fallback to local
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with open("stopwords.txt", "r") as f:
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return set([line.strip() for line in f])
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def parse_csv_line(line):
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# 使用 csv.reader 兼容逗号分隔含引号的数据
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reader = csv.reader([line])
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return next(reader)
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"""
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解析 CSV 行（假设字段用 '","' 分隔，且首尾有引号）
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返回 (doc_id, text)；text 为标题、描述、制造商字段拼接后的字符串
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"""
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line = line.strip()
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if not line:
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return None
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# 去除首尾引号，然后按","分隔
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# 注意：此处简单处理，要求 CSV 文件中没有嵌入额外的引号
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parts = line.strip('"').split('","')
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if len(parts) < 4:
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return None
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doc_id = parts[0].strip()
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# 将标题、描述、制造商合并
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text = "{} {} {}".format(parts[1].strip(), parts[2].strip(), parts[3].strip())
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return (doc_id, text)
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def extract_info(line, source):
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try:
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fields = parse_csv_line(line)
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if source == "google":
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# Google: id, name, description, manufacturer...
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pid = fields[0].strip()
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text = "{} {} {}".format(fields[1], fields[2], fields[3])
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else:
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# Amazon: id, title, description, manufacturer...
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pid = fields[0].strip()
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text = "{} {} {}".format(fields[1], fields[2], fields[3])
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return (pid, text)
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except:
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return (None, None)
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if __name__ == "__main__":
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sc = SparkContext(appName="InvertedIndex")
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def tokenize(text):
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"""
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分词：转成小写后提取所有字母数字字符组合（词）
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"""
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return re.findall(r'\w+', text.lower())
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stopwords = load_stopwords(sc)
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# 加载数据
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google = sc.textFile("hdfs://master:9000/user/root/Google.csv")
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amazon = sc.textFile("hdfs://master:9000/user/root/Amazon.csv")
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# 读取数据文件并解析
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# 过滤掉可能的表头（假设表头 doc_id 为 "id"）
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amazon_rdd = sc.textFile(amazon_path).map(parse_csv_line) \
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.filter(lambda x: x is not None and x[0].lower() != "id")
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google_rdd = sc.textFile(google_path).map(parse_csv_line) \
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.filter(lambda x: x is not None and x[0].lower() != "id")
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# 提取内容
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google_rdd = google.map(lambda line: extract_info(line, "google")) \
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.filter(lambda x: x[0] is not None)
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# 转换为 (doc_id, [token列表])
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amazonRecToToken = amazon_rdd.map(lambda x: (x[0], tokenize(x[1])))
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googleRecToToken = google_rdd.map(lambda x: (x[0], tokenize(x[1])))
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amazon_rdd = amazon.map(lambda line: extract_info(line, "amazon")) \
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.filter(lambda x: x[0] is not None)
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# 合并语料库
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corpus = amazonRecToToken.union(googleRecToToken)
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N = corpus.count() # 语料库中的文档总数
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# 合并两数据集
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all_data = google_rdd.union(amazon_rdd)
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# --------------- 计算 TF（词频） ---------------
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# 对每个文档，生成 ((doc_id, term), 1) 对
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doc_term_pairs = corpus.flatMap(lambda x: [((x[0], term), 1) for term in x[1]])
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# 构建倒排索引
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inverted_index = all_data.flatMap(lambda x: [((word, x[0])) for word in tokenize(x[1]) if word not in stopwords]) \
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.groupByKey() \
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.mapValues(lambda ids: list(set(ids)))
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# 对同一文档中相同词求和得到每个 (doc_id, term) 的出现次数
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doc_term_counts = doc_term_pairs.reduceByKey(lambda a, b: a + b)
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# 输出（可保存到 HDFS）
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inverted_index.saveAsTextFile("hdfs://master:9000/user/root/output/inverted_index")
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# 计算每个文档的总词数 (doc_id, total_terms)
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doc_lengths = corpus.map(lambda x: (x[0], len(x[1])))
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sc.stop()
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# 为方便后续 join，先将 doc_term_counts 转换为 (doc_id, (term, count))
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doc_term_counts_mapped = doc_term_counts.map(lambda x: (x[0][0], (x[0][1], x[1])))
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# join 以获得每个文档的总词数
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tf_joined = doc_term_counts_mapped.join(doc_lengths)
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# tf_joined 格式：(doc_id, ((term, count), total_terms))
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# 计算 TF： count / total_terms，输出 ((doc_id, term), tf_value)
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tf_rdd = tf_joined.map(lambda x: ((x[0], x[1][0][0]), float(x[1][0][1]) / float(x[1][1])))
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# --------------- 计算 IDF（逆文档频率） ---------------
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# 为每个文档生成 (term, doc_id) 对，注意使用 set 去重，避免重复计数
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term_doc_pairs = corpus.flatMap(lambda x: [(term, x[0]) for term in set(x[1])])
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# 去重后统计每个 term 出现的文档数
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df_rdd = term_doc_pairs.distinct().map(lambda x: (x[0], 1)).reduceByKey(lambda a, b: a + b)
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# 计算 IDF，不取对数，使用公式：IDF(t) = N / df
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idf_rdd = df_rdd.map(lambda x: (x[0], float(N) / float(x[1])))
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# --------------- 计算 TF-IDF ---------------
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# 将 tf_rdd 以 term 为 key，方便与 idf_rdd join
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tf_rdd_by_term = tf_rdd.map(lambda x: (x[0][1], (x[0][0], x[1])))
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# join 得到 (term, ((doc_id, tf), idf))
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tfidf_joined = tf_rdd_by_term.join(idf_rdd)
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# 计算 TF-IDF： tf * idf，输出格式 ((doc_id, term), tfidf_value)
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tfidf_rdd = tfidf_joined.map(lambda x: ((x[1][0][0], x[0]), x[1][0][1] * x[1][1]))
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# --------------- 输出 TF-IDF 结果 ---------------
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output_path = "hdfs://master:9000/user/root/output/tfidf"
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tfidf_rdd.saveAsTextFile(output_path)
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# 调试时打印前 5 个 TF-IDF 结果
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for item in tfidf_rdd.take(5):
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print(item)
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sc.stop()
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