{"id":1080,"date":"2021-05-26T14:41:05","date_gmt":"2021-05-26T05:41:05","guid":{"rendered":"https:\/\/technologyagency.net\/wordpress\/?p=1080"},"modified":"2021-07-05T00:28:58","modified_gmt":"2021-07-04T15:28:58","slug":"recommendation-system%e3%82%92%e8%80%83%e3%81%88%e3%82%8b3","status":"publish","type":"post","link":"https:\/\/technologyagency.net\/wordpress\/2021\/05\/26\/recommendation-system%e3%82%92%e8%80%83%e3%81%88%e3%82%8b3\/","title":{"rendered":"Recommendation System\u3092\u8003\u3048\u308b \u305d\u306e\uff13"},"content":{"rendered":"\n<p> <a href=\"https:\/\/technologyagency.net\/wordpress\/2020\/09\/16\/recommendation-system%e3%82%92%e8%80%83%e3%81%88%e3%82%8b\/\">Recommendation System\u3092\u8003\u3048\u308b\uff12<\/a>\u306e\u7d9a\u304d\u3002 <\/p>\n\n\n\n<h2 class=\"wp-block-heading\">\uff11\uff0e\u3084\u308a\u305f\u3044\u3053\u3068<\/h2>\n\n\n\n<p>\u6295\u7a3f\u3055\u308c\u3066\u9593\u3082\u306a\u3044\u5c0f\u8aac\u306f\u30e6\u30fc\u30b6\u30fc\u306b\u3088\u308b\u8a55\u4fa1\u304c\u3055\u308c\u3066\u3044\u306a\u3044\u5834\u5408\u304c\u591a\u3044\u305f\u3081\u3001\u521d\u671f\u306e\u8a55\u4fa1\u30dd\u30a4\u30f3\u30c8\u306f\u305d\u306e\u5c0f\u8aac\u306e\u672c\u6765\u306e\u304a\u3082\u3057\u308d\u3055\u3092\u53cd\u6620\u3057\u3066\u3044\u306a\u3044\u3068\u601d\u308f\u308c\u308b\u3002<br>\u5c0f\u8aac\u306e\u5185\u5bb9\u3092\u6a5f\u68b0\u5b66\u7fd2\u3067\u8a55\u4fa1\u3057\u3066\u3001\u4eca\u5f8c\u4ed8\u304f\u3067\u3042\u308d\u3046\u8a55\u4fa1\u30dd\u30a4\u30f3\u30c8\u3092\u4e88\u6e2c\u3059\u308b\u3002<br>\u6700\u7d42\u7684\u306b\u306f\u65b0\u898f\u6295\u7a3f\u3055\u308c\u305f\u5c0f\u8aac\u3001\u3082\u3057\u304f\u306f\u8a55\u4fa1\u53d7\u4ed8\u8a2d\u5b9aoff\u306b\u306a\u3063\u3066\u3044\u308b\u5c0f\u8aac\u304b\u3089\u8a55\u4fa1\u30dd\u30a4\u30f3\u30c8\u304c\u9ad8\u304f\u306a\u308b\u3068\u4e88\u60f3\u3055\u308c\u308b\u5c0f\u8aac\u3092\u62bd\u51fa\u3059\u308b\u30d7\u30ed\u30b0\u30e9\u30e0\u3092\u4f5c\u6210\u3059\u308b\u3002<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">\uff12\uff0e\u5c0f\u8aac\u306e\u30b9\u30af\u30ec\u30a4\u30d4\u30f3\u30b0<\/h2>\n\n\n\n<p>\u3053\u308c\u307e\u3067\u306e\u65b9\u6cd5\u306f\u7d2f\u7a4d\u30e9\u30f3\u30ad\u30f3\u30b0\u304b\u3089\u5c0f\u8aac\u3092\u30c0\u30a6\u30f3\u30ed\u30fc\u30c9\u3059\u308b\u305f\u3081\u3001Top300\u307e\u3067\u3057\u304b\u5c0f\u8aac\u3092\u53c2\u7167\u51fa\u6765\u306a\u304b\u3063\u305f\u3002\u305d\u306e\u305f\u3081\u3001\u5e38\u306b\u30c7\u30fc\u30bf\u304c\u4e0d\u8db3\u3057\u3066\u3044\u305f\u3002\u4eca\u56de\u306f\u65b9\u6cd5\u3092\u5909\u3048\u3066\u5c0f\u8aac\u691c\u7d22\u753b\u9762\u304b\u3089\u5c0f\u8aac\u3092\u53c2\u7167\u3059\u308b\u305f\u3081\u306eURL\u4e00\u89a7\u3092\u4f5c\u6210\u3059\u308b\u3002<\/p>\n\n\n\n<p>\u307e\u305a\u306f\u300c\u5c0f\u8aac\u3092\u8aad\u3082\u3046\uff01\u300d\u306e\u691c\u7d22\u753b\u9762\u306eURL\u3092\u751f\u6210\u3057\u3001\u691c\u7d22\u7d50\u679c\u4e00\u89a7\u3092\u53d6\u5f97\u3059\u308b\u3002\u691c\u7d22\u7d50\u679c\u4e00\u89a7\u306f1\u30da\u30fc\u30b8\u3042\u305f\u308a20\u30ea\u30f3\u30af\u3042\u308a\u3001\u6700\u5927100\u30da\u30fc\u30b8\u307e\u3067\u9061\u3063\u3066\u8868\u793a\u3059\u308b\u4e8b\u304c\u3067\u304d\u308b\u3002\u305d\u306e\u53d6\u5f97\u3057\u305f\u7d50\u679c\u3092\u30c6\u30ad\u30b9\u30c8\u30d5\u30a1\u30a4\u30eb\u306b\u66f8\u304d\u51fa\u3057\u3066\u304a\u304f\u3002<\/p>\n\n\n<div class=\"wp-block-syntaxhighlighter-code \"><pre class=\"brush: python; title: ; notranslate\" title=\"\">\nimport requests\nimport socket\n\nfrom bs4 import BeautifulSoup\nfrom pathlib import Path\nfrom urllib.error import HTTPError, URLError\nfrom time import sleep\nfrom tqdm import tqdm\n\nREQUEST_HEADERS = {\n    &#039;User-Agent&#039;:\n    &#039;Mozilla\/5.0 (Windows NT 10.0; WOW64; Trident\/7.0; rv:11.0) like Gecko&#039;\n  }\nREQUEST_TIMEOUT = 20\n\nSEARCH_URL = &#039;https:\/\/yomou.syosetu.com\/search.php&#039;\nNOVEL_SEARCH_RESULT_PATH = str(Path.home()) + &#039;\/vscode\/syosetu\/data\/search_novels.txt&#039;\n\n# \u5c0f\u8aac\u3092\u691c\u7d22\u3057\u3066\u30c0\u30a6\u30f3\u30ed\u30fc\u30c9\ndef novel_search_dler(max_search_page=100, max_search_result=20):\n  with open(NOVEL_SEARCH_RESULT_PATH, &#039;w&#039;) as f:\n    for page in tqdm(range(max_search_page)):\n      #search_params = {&#039;word&#039;: &#039;&#039;, &#039;type&#039;: &#039;re&#039;, &#039;order_former&#039;: &#039;search&#039;, &#039;order&#039;: &#039;new&#039;, &#039;notnizi&#039;: &#039;1&#039;, &#039;minlen&#039;: &#039;5000&#039;, &#039;min_globalpoint&#039;: &#039;1000&#039;, &#039;p&#039;: page + 1}\n      search_params = {&#039;word&#039;: &#039;&#039;, &#039;type&#039;: &#039;re&#039;, &#039;order_former&#039;: &#039;search&#039;, &#039;order&#039;: &#039;hyoka&#039;, &#039;notnizi&#039;: &#039;1&#039;, &#039;minlen&#039;: &#039;5000&#039;, &#039;min_globalpoint&#039;: &#039;1000&#039;, &#039;p&#039;: page + 1}\n      r = requests.get(SEARCH_URL, headers=REQUEST_HEADERS, timeout=REQUEST_TIMEOUT, params=search_params)\n      r.encoding = r.apparent_encoding\n      soup = BeautifulSoup(r.text, &#039;html.parser&#039;)\n      sleep(1)\n\n      search_result = soup.find_all(&#039;div&#039;, class_=&#039;searchkekka_box&#039;)\n\n      for r_count in range(max_search_result):\n        a_list = search_result&#x5B;r_count].find_all(&#039;a&#039;, limit=3)\n        url = a_list&#x5B;0].get(&#039;href&#039;)\n        info_url = a_list&#x5B;2].get(&#039;href&#039;)\n        title = a_list&#x5B;0].get_text()\n        # \u5c0f\u8aac\u306eURL\u3001\u5c0f\u8aac\u60c5\u5831\u306eURL\u3068\u5c0f\u8aac\u306e\u30bf\u30a4\u30c8\u30eb\u3092\u8a2d\u5b9a\n        f.write(&#039;{0}\\t{1}\\t{2}\\n&#039;.format(url, info_url, title))\n        print(&#039;url: {0} info_url: {1} title: {2}&#039;.format(url, info_url, title))\n\nnovel_search_dler()\n<\/pre><\/div>\n\n\n<p>\u7d9a\u3044\u3066\u5148\u7a0b\u4f5c\u6210\u3057\u305f\u691c\u7d22\u7d50\u679c\u3092\u9806\u756a\u306b\u8aad\u307f\u8fbc\u307f\u3001\u5c0f\u8aac\u306e\u5185\u5bb9\u3068\u8a55\u4fa1\u30dd\u30a4\u30f3\u30c8\u3092\u53d6\u5f97\u3059\u308b\u3002\u203bmorphologicalAnalysis\u306e\u30b3\u30fc\u30c9\u306f<a href=\"https:\/\/technologyagency.net\/wordpress\/2021\/04\/12\/mecab%e8%a8%ad%e5%ae%9a\/\">mecab\u8a2d\u5b9a<\/a>\u306e\u30da\u30fc\u30b8\u306b\u8a18\u8f09\u3002<\/p>\n\n\n<div class=\"wp-block-syntaxhighlighter-code \"><pre class=\"brush: python; title: ; notranslate\" title=\"\">\nimport morphologicalAnalysis as ma\n\nimport requests\nimport socket\n\nimport pandas as pd\n\nfrom bs4 import BeautifulSoup\nfrom pathlib import Path\nfrom urllib.error import HTTPError, URLError\nfrom time import sleep\nfrom tqdm import tqdm\n\nREQUEST_HEADERS = {\n    &#039;User-Agent&#039;:\n    &#039;Mozilla\/5.0 (Windows NT 10.0; WOW64; Trident\/7.0; rv:11.0) like Gecko&#039;\n  }\nREQUEST_TIMEOUT = 60\n\nSCRAP_WORD = 5000\nSCRAP_CHAPTER = 40\n\nCOL_NAMES = &#x5B;&#039;URL&#039;, &#039;INFO_URL&#039;, &#039;TITLE&#039;]\nNOVEL_SEARCH_RESULT_PATH = str(Path.home()) + &#039;\/vscode\/syosetu\/data\/search_novels.txt&#039;\nNOVEL_SEARCH_RESULT_DATA_PATH = str(Path.home()) + &#039;\/vscode\/syosetu\/data\/search_novel_datas.txt&#039;\n\n# \u5c0f\u8aac\u60c5\u5831\u3092\u30c0\u30a6\u30f3\u30ed\u30fc\u30c9\ndef novel_info_dler(url):\n  KEYWORD = 2\n  EVALUATION = 6\n\n  r = requests.get(url, headers=REQUEST_HEADERS, timeout=REQUEST_TIMEOUT)\n  r.encoding = r.apparent_encoding\n  soup = BeautifulSoup(r.text, &#039;html.parser&#039;)\n  sleep(1)\n\n  keyword = soup.find(id=&#039;noveltable1&#039;).find_all(&#039;td&#039;)&#x5B;KEYWORD].get_text(strip=True)\n  evaluation = soup.find(id=&#039;noveltable2&#039;).find_all(&#039;td&#039;)&#x5B;EVALUATION].get_text(strip=True).replace(&#039;pt&#039;, &#039;&#039;)\n  return keyword,evaluation\n\n# \u672c\u6587\u3092\u30c0\u30a6\u30f3\u30ed\u30fc\u30c9\ndef novel_text_dler(url):\n  r = requests.get(url, headers=REQUEST_HEADERS, timeout=REQUEST_TIMEOUT)\n  r.encoding = r.apparent_encoding\n  soup = BeautifulSoup(r.text, &#039;html.parser&#039;)\n  sleep(1)\n\n  novel_text = &#039;&#039;\n  # \u7ae0\u306e\u30bf\u30a4\u30c8\u30eb\u306b\u300c\u8a2d\u5b9a\u300d\u3001\u300c\u767b\u5834\u4eba\u7269\u300d\u304c\u542b\u307e\u308c\u308b\u5834\u5408\u3001\u623b\u308a\u5024\u306f&#039;&#039;\u3068\u3059\u308b\n  subtitle = soup.find(&#039;p&#039;, class_=&#039;novel_subtitle&#039;)\n  if subtitle is None or &#039;\u8a2d\u5b9a&#039; in subtitle or &#039;\u767b\u5834\u4eba\u7269&#039; in subtitle:\n    return novel_text\n\n  honbun = soup.find_all(&#039;div&#039;, class_=&#039;novel_view&#039;)\n\n  for text in honbun:\n    novel_text += text.text.replace(&#039;\\n&#039;, &#039; &#039;) # \u6539\u884c\u30b3\u30fc\u30c9\u3092\u30b9\u30da\u30fc\u30b9\u306b\u5909\u63db\n  return novel_text\n\n# \u5c0f\u8aac\u306e\u5404\u8a71\u3092\u30c0\u30a6\u30f3\u30ed\u30fc\u30c9\ndef novel_chapter_dler(url, scrap_word=SCRAP_WORD, scrap_chapter=SCRAP_CHAPTER):\n  chapter = 0\n  word_count = 0\n  total_words = &#039;&#039;\n\n  while word_count &lt; scrap_word and chapter &lt; scrap_chapter:\n    # \u5358\u8a9e\u304cscrap_word\u672a\u6e80\u304b\u3064\u3001scrap_chapter\u8a71\u4ee5\u4e0b\u306e\u9593\u7e70\u308a\u8fd4\u3057\u53d6\u5f97\u3059\u308b\n    try:\n      words = novel_text_dler(&#039;{0}{1}\/&#039;.format(url, chapter + 1))\n    except (HTTPError, URLError) as e:\n      print(e)\n      break\n    except socket.timeout as e:\n      print(e)\n      continue\n    else:\n      #print(&#039;chapter:{0}&#039;.format(chapter + 1))\n      word_count += len(ma.analysis(words).split())\n      total_words += words\n    chapter += 1\n  return total_words\n\n# \u5c0f\u8aac\u306e\u691c\u7d22\u7d50\u679c\u3092\u3082\u3068\u306b\u30c0\u30a6\u30f3\u30ed\u30fc\u30c9\ndef novel_search_data_dler():\n  df = pd.read_table(NOVEL_SEARCH_RESULT_PATH, header=None, names=COL_NAMES)\n\n  with open(NOVEL_SEARCH_RESULT_DATA_PATH, &#039;w&#039;) as f:\n    for index,item in tqdm(df.iterrows()):\n      url = item&#x5B;COL_NAMES&#x5B;0]]\n      info_url = item&#x5B;COL_NAMES&#x5B;1]]\n      title = item&#x5B;COL_NAMES&#x5B;2]]\n      keyword,evaluation = novel_info_dler(info_url)\n      # URL\u3001\u5c0f\u8aac\u306e\u30bf\u30a4\u30c8\u30eb\u3001\u30ad\u30fc\u30ef\u30fc\u30c9\u3068\u8a55\u4fa1\u30dd\u30a4\u30f3\u30c8\u3092\u8a2d\u5b9a\n      f.write(&#039;{0}\\t{1}\\t{2}\\t{3}\\t&#039;.format(url, title, keyword, evaluation))\n      #print(&#039;url:{0} title:{1} keywords:{2} evaluation:{3}&#039;.format(url, title, keyword, evaluation))\n      # \u5c0f\u8aac\u306e\u5404\u8a71\u3092\u8a2d\u5b9a\n      f.write(novel_chapter_dler(url))\n      f.write(&#039;\\n&#039;)\n\nnovel_search_data_dler()\n<\/pre><\/div>\n\n\n<p>\u30b5\u30fc\u30d0\u30fc\u306b\u8ca0\u8377\u3092\u304b\u3051\u306a\u3044\u3088\u3046\u306b\u3059\u308b\u305f\u3081\u3001\u30da\u30fc\u30b8\u306e\u53d6\u5f97\u306b1\u79d2\u306e\u30a4\u30f3\u30bf\u30fc\u30d0\u30eb\u3092\u304a\u3044\u3066\u3044\u308b\u305f\u3081\u3001\u5168\u3066\u306e\u53d6\u5f97\u306b\u306f7\u6642\u9593\u307b\u3069\u304b\u304b\u308b\u3002<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">\uff13\uff0e\u56de\u5e30\u5206\u6790<\/h2>\n\n\n\n<p>\u826f\u3044\u7d50\u679c\u306f\u5f97\u3089\u308c\u3066\u3044\u306a\u3044\u304c\u3001\u305b\u3063\u304b\u304f\u306a\u306e\u3067\u56de\u5e30\u5206\u6790\u3059\u308b\u3068\u3053\u308d\u307e\u3067\u306e\u30bd\u30fc\u30b9\u30b3\u30fc\u30c9\u3092\u516c\u958b\u3059\u308b\u3002\u8a55\u4fa1\u30dd\u30a4\u30f3\u30c8\u3092\uff15\u6bb5\u968e\u304f\u3089\u3044\u306b\u5206\u3051\u3066\u5206\u985e\u3057\u305f\u307b\u3046\u304c\u826f\u3044\u7d50\u679c\u306b\u306a\u308b\u3068\u601d\u3046\u3002<\/p>\n\n\n\n<h5 class=\"wp-block-heading\">\u30c7\u30fc\u30bf\u3092\u8aad\u307f\u8fbc\u3080<\/h5>\n\n\n\n<p>Doc2Vec\u30c7\u30fc\u30bf\u3092\u8aad\u307f\u8fbc\u307f\u3001\u914d\u5217\u306b\u5909\u63db\u3059\u308b\u3002<\/p>\n\n\n<div class=\"wp-block-syntaxhighlighter-code \"><pre class=\"brush: python; title: ; notranslate\" title=\"\">\nfrom gensim.models.doc2vec import Doc2Vec\nfrom pathlib import Path\n\nMODEL_DATA_PATH = str(Path.home()) + &#039;\/vscode\/syosetu\/data\/search_novel_doc2vec_100.model&#039;\n\nm = Doc2Vec.load(MODEL_DATA_PATH)\nvectors_list = &#x5B;m.dv&#x5B;n] for n in range(len(m.dv))]\n<\/pre><\/div>\n\n\n<p>\u30c7\u30fc\u30bf\u51e6\u7406\u3057\u3084\u3059\u3044\u3088\u3046\u306b\u3001\u5fc5\u8981\u306a\u60c5\u5831\u3092Pandas\u306b\u307e\u3068\u3081\u308b\u3002<\/p>\n\n\n<div class=\"wp-block-syntaxhighlighter-code \"><pre class=\"brush: python; title: ; notranslate\" title=\"\">\nimport pandas as pd\nimport numpy as np\n\nMODEL_TITLES_CSV_PATH = str(Path.home()) + &#039;\/vscode\/syosetu\/data\/search_novel_titles.csv&#039;\n\ndf = pd.read_csv(MODEL_TITLES_CSV_PATH, thousands=&#039;,&#039;)\ndf = df.drop(columns=&#x5B;&#039;URL&#039;])\ndf&#x5B;&#039;VECTORS&#039;] = vectors_list\n<\/pre><\/div>\n\n\n<p>Doc2Vec\u306e\u30c7\u30fc\u30bf\u3092\u305d\u306e\u307e\u307e\u4f7f\u3046\u3068\u6b21\u5143\u6570\u304c\u5927\u304d\u3059\u304e\u308b\u306e\u3067\u3001PCA\u3092\u4f7f\u3063\u3066\u6b21\u5143\u5727\u7e2e\u3059\u308b\u3002<\/p>\n\n\n<div class=\"wp-block-syntaxhighlighter-code \"><pre class=\"brush: python; title: ; notranslate\" title=\"\">\nfrom sklearn.decomposition import PCA\n\ndef dimension_reduction(data, pca_dimension=20):\n  pca_data = data.copy()\n  pca = PCA(n_components=pca_dimension)\n  vector = np.array(&#x5B;np.array(v) for v in pca_data&#x5B;&#039;VECTORS&#039;]])\n  pca_vectors = pca.fit_transform(vector)\n  pca_data&#x5B;&#039;PCA_VECTORS&#039;] = &#x5B;v for v in pca_vectors]\n\n  return pca_data\n\ndf = dimension_reduction(data=df)\ndf.info()\n\n&lt;class &#039;pandas.core.frame.DataFrame&#039;&gt;\nRangeIndex: 2000 entries, 0 to 1999\nData columns (total 5 columns):\n #   Column       Non-Null Count  Dtype \n---  ------       --------------  ----- \n 0   TITLE        2000 non-null   object\n 1   KEYWORDS     2000 non-null   object\n 2   EVALUATION   2000 non-null   object\n 3   VECTORS      2000 non-null   object\n 4   PCA_VECTORS  2000 non-null   object\ndtypes: object(5)\nmemory usage: 78.2+ KB\n<\/pre><\/div>\n\n\n<p>\u4e0d\u8981\u306a\u6587\u5b57\u3092\u53d6\u308a\u9664\u304d\u3001\u6570\u5024\u306b\u5909\u63db\u3059\u308b\u3002<\/p>\n\n\n<div class=\"wp-block-syntaxhighlighter-code \"><pre class=\"brush: python; title: ; notranslate\" title=\"\">\ndf&#x5B;&#039;EVALUATION&#039;] = df&#x5B;&#039;EVALUATION&#039;].str.replace(&#039;,&#039;, &#039;&#039;)\ndf&#x5B;&#039;EVALUATION&#039;] = df&#x5B;&#039;EVALUATION&#039;].str.replace(&#039;\u8a55\u4fa1\u53d7\u4ed8\u505c\u6b62\u4e2d&#039;, &#039;&#039;).astype(float)\n\ndf&#x5B;&#039;EVALUATION&#039;].describe()\n\ncount      2000.000000\nmean      32735.061500\nstd       33540.729876\nmin        4019.000000\n25%       14118.000000\n50%       21271.000000\n75%       38219.250000\nmax      323929.000000\nName: EVALUATION, dtype: float64\n<\/pre><\/div>\n\n\n<p>\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0\u30c7\u30fc\u30bf\u3068\u30c6\u30b9\u30c8\u30c7\u30fc\u30bf\u306b\u5206\u3051\u308b\u3002<\/p>\n\n\n<div class=\"wp-block-syntaxhighlighter-code \"><pre class=\"brush: python; title: ; notranslate\" title=\"\">\nfrom sklearn.model_selection import train_test_split\n\nX = pd.DataFrame(df&#x5B;&#039;PCA_VECTORS&#039;].tolist(), index=df.index)\nX.columns = &#x5B;f&#039;No{i+1}&#039; for i in range(len(X.columns))]\ny = df&#x5B;&#039;EVALUATION&#039;]\n\nX_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)\n<\/pre><\/div>\n\n\n<h5 class=\"wp-block-heading\">\u30c7\u30fc\u30bf\u3092\u78ba\u8a8d\u3059\u308b<\/h5>\n\n\n\n<p>\u5fc5\u8981\u306a\u30e9\u30a4\u30d6\u30e9\u30ea\u3092\u30a4\u30f3\u30dd\u30fc\u30c8\u3057\u3066\u304a\u304f\u3002<\/p>\n\n\n<div class=\"wp-block-syntaxhighlighter-code \"><pre class=\"brush: python; title: ; notranslate\" title=\"\">\nfrom scipy import stats\nfrom scipy.stats import norm, skew # for some statistics\n\n# visualization\nimport matplotlib.pyplot as plt\n%matplotlib inline\n\nimport seaborn as sns\ncolor = sns.color_palette()\nsns.set_style(&#039;darkgrid&#039;)\n\nimport warnings\ndef ignore_warn(*args, **kwargs):\n  pass\nwarnings.warn = ignore_warn # Ignore annoying warning (from sklearn and seaborn)\n<\/pre><\/div>\n\n\n<p>\u8a55\u4fa1\u30dd\u30a4\u30f3\u30c8\u306e\u5206\u5e03\u3092\u53ef\u8996\u5316\u3059\u308b\u3002<\/p>\n\n\n<div class=\"wp-block-syntaxhighlighter-code \"><pre class=\"brush: python; title: ; notranslate\" title=\"\">\nsns.distplot(y_train, fit=norm)\n\n# Get the fitted parameters used by the function\n(mu, sigma) = norm.fit(y_train)\nprint( &#039;\\n mu = {:.2f} and sigma = {:.2f}\\n&#039;.format(mu, sigma))\n\n# Now plot the distribution\nplt.legend(&#x5B;&#039;Normal dist. ($\\mu=$ {:.2f} and $\\sigma=$ {:.2f} )&#039;.format(mu, sigma)],\n            loc=&#039;best&#039;)\nplt.ylabel(&#039;Frequency&#039;)\nplt.title(&#039;Evaluation distribution&#039;)\n\n# Get also the QQ-plot\nfig = plt.figure()\nres = stats.probplot(y_train, plot=plt)\nplt.show()\n<\/pre><\/div>\n\n\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"530\" height=\"370\" src=\"https:\/\/technologyagency.net\/wordpress\/wp-content\/uploads\/2021\/05\/evaluation.png\" alt=\"\" class=\"wp-image-1109\" srcset=\"https:\/\/technologyagency.net\/wordpress\/wp-content\/uploads\/2021\/05\/evaluation.png 530w, https:\/\/technologyagency.net\/wordpress\/wp-content\/uploads\/2021\/05\/evaluation-300x209.png 300w\" sizes=\"auto, (max-width: 530px) 85vw, 530px\" \/><\/figure>\n\n\n\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"547\" height=\"370\" src=\"https:\/\/technologyagency.net\/wordpress\/wp-content\/uploads\/2021\/06\/evaluation2.png\" alt=\"\" class=\"wp-image-1110\" srcset=\"https:\/\/technologyagency.net\/wordpress\/wp-content\/uploads\/2021\/06\/evaluation2.png 547w, https:\/\/technologyagency.net\/wordpress\/wp-content\/uploads\/2021\/06\/evaluation2-300x203.png 300w\" sizes=\"auto, (max-width: 547px) 85vw, 547px\" \/><\/figure>\n\n\n\n<p>\u3060\u3044\u3076\u6b6a\u3093\u3060\u5206\u5e03\u3092\u3057\u3066\u3044\u308b\u306e\u3067\u3001\u5bfe\u6570\u5909\u63db\u3092\u884c\u3046\u3002<\/p>\n\n\n<div class=\"wp-block-syntaxhighlighter-code \"><pre class=\"brush: python; title: ; notranslate\" title=\"\">\n# We use the numpy fuction log1p which applies log(1+x) to all elements of the column\ny_train = np.log1p(y_train)\n\n# Check the new distribution \nsns.distplot(y_train, fit=norm)\n\n# Get the fitted parameters used by the function\n(mu, sigma) = norm.fit(y_train)\nprint( &#039;\\n mu = {:.2f} and sigma = {:.2f}\\n&#039;.format(mu, sigma))\n\n# Now plot the distribution\nplt.legend(&#x5B;&#039;Normal dist. ($\\mu=$ {:.2f} and $\\sigma=$ {:.2f} )&#039;.format(mu, sigma)],\n            loc=&#039;best&#039;)\nplt.ylabel(&#039;Frequency&#039;)\nplt.title(&#039;Evaluation distribution&#039;)\n\n# Get also the QQ-plot\nfig = plt.figure()\nres = stats.probplot(y_train, plot=plt)\nplt.show()\n<\/pre><\/div>\n\n\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"514\" height=\"370\" src=\"https:\/\/technologyagency.net\/wordpress\/wp-content\/uploads\/2021\/06\/evaluation3.png\" alt=\"\" class=\"wp-image-1114\" srcset=\"https:\/\/technologyagency.net\/wordpress\/wp-content\/uploads\/2021\/06\/evaluation3.png 514w, https:\/\/technologyagency.net\/wordpress\/wp-content\/uploads\/2021\/06\/evaluation3-300x216.png 300w\" sizes=\"auto, (max-width: 514px) 85vw, 514px\" \/><\/figure>\n\n\n\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"510\" height=\"370\" src=\"https:\/\/technologyagency.net\/wordpress\/wp-content\/uploads\/2021\/06\/evaluation4.png\" alt=\"\" class=\"wp-image-1115\" srcset=\"https:\/\/technologyagency.net\/wordpress\/wp-content\/uploads\/2021\/06\/evaluation4.png 510w, https:\/\/technologyagency.net\/wordpress\/wp-content\/uploads\/2021\/06\/evaluation4-300x218.png 300w\" sizes=\"auto, (max-width: 510px) 85vw, 510px\" \/><\/figure>\n\n\n\n<h5 class=\"wp-block-heading\">\u4f5c\u6210\u3057\u305f\u30c7\u30fc\u30bf\u3092\u5143\u306b\u4e88\u6e2c\u3059\u308b<\/h5>\n\n\n\n<p>Lasso\u3067\u56de\u5e30\u5206\u6790\u3092\u5b9f\u65bd\u3059\u308b\u3002<\/p>\n\n\n<div class=\"wp-block-syntaxhighlighter-code \"><pre class=\"brush: python; title: ; notranslate\" title=\"\">\nfrom sklearn.model_selection import cross_val_score, KFold\n\nkf = KFold(5, shuffle=True, random_state=0).get_n_splits(X_train)\n\n# Validation function\ndef rmsle_cv(classifier):\n  return np.sqrt(-cross_val_score(classifier, X_train.values, y_train.values, scoring=&quot;neg_mean_squared_error&quot;, cv=kf))\n<\/pre><\/div>\n\n<div class=\"wp-block-syntaxhighlighter-code \"><pre class=\"brush: python; title: ; notranslate\" title=\"\">\nfrom sklearn.linear_model import Lasso\nfrom sklearn.pipeline import make_pipeline\nfrom sklearn.preprocessing import RobustScaler\n\nlasso = make_pipeline(RobustScaler(), Lasso(random_state=0))\n\nscore = rmsle_cv(lasso)\nprint(&quot;Lasso score: {:.4f} ({:.4f})\\n&quot;.format(score.mean(), score.std()))\n\nLasso score: 0.7408 (0.0095)\n<\/pre><\/div>\n\n\n<h2 class=\"wp-block-heading\">\uff14\uff0e\u307e\u3068\u3081<\/h2>\n\n\n\n<p>\u4eca\u56de\u306f\u3084\u308a\u305f\u3044\u3053\u3068\u304c\u51fa\u6765\u306a\u304b\u3063\u305f\u305f\u3081\u3001\u524d\u8ff0\u3057\u305f\u901a\u308a\u5206\u985e\u554f\u984c\u3067\u6539\u3081\u3066\u5206\u6790\u3057\u3066\u307f\u308b\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Recommendation System\u3092\u8003\u3048\u308b\uff12\u306e\u7d9a\u304d\u3002 \uff11\uff0e\u3084\u308a\u305f\u3044\u3053\u3068 \u6295\u7a3f\u3055\u308c\u3066\u9593\u3082\u306a\u3044\u5c0f\u8aac\u306f\u30e6\u30fc\u30b6\u30fc\u306b\u3088\u308b\u8a55\u4fa1\u304c\u3055\u308c\u3066\u3044\u306a\u3044\u5834\u5408\u304c\u591a\u3044\u305f\u3081\u3001\u521d\u671f\u306e\u8a55\u4fa1\u30dd\u30a4\u30f3\u30c8\u306f\u305d\u306e\u5c0f\u8aac\u306e\u672c\u6765\u306e\u304a\u3082\u3057\u308d\u3055\u3092\u53cd\u6620\u3057\u3066\u3044\u306a\u3044\u3068\u601d\u308f &hellip; <a href=\"https:\/\/technologyagency.net\/wordpress\/2021\/05\/26\/recommendation-system%e3%82%92%e8%80%83%e3%81%88%e3%82%8b3\/\" class=\"more-link\"><span class=\"screen-reader-text\">&#8220;Recommendation System\u3092\u8003\u3048\u308b \u305d\u306e\uff13&#8221; \u306e<\/span>\u7d9a\u304d\u3092\u8aad\u3080<\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[41],"tags":[46,51],"class_list":["post-1080","post","type-post","status-publish","format-standard","hentry","category-python","tag-46","tag-51"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.3 - 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