{"id":46489,"date":"2023-07-09T07:38:23","date_gmt":"2023-05-26T00:45:54","guid":{"rendered":"https:\/\/www.silicloud.com\/zh\/blog\/46489-2\/"},"modified":"2024-04-30T12:41:43","modified_gmt":"2024-04-30T04:41:43","slug":"46489-2","status":"publish","type":"post","link":"https:\/\/www.silicloud.com\/zh\/blog\/46489-2\/","title":{"rendered":""},"content":{"rendered":"

JupyterLab \u3067\u5206\u6790\u3057\u305f\u7d50\u679c\u3092\u5831\u544a\u3059\u308b\u6642\u3001\u8cc7\u6599\u3068\u3057\u3066 ipynb \u3082\u3057\u304f\u306f\u305d\u308c\u3092 HTML \u7b49\u306b\u5909\u63db\u3057\u305f\u3082\u306e\u3092\u4f7f\u3063\u3066\u826f\u3044\u306e\u3067\u3042\u308c\u3070\u3001\u4e00\u756a\u30e9\u30af\u3060\u3002\u305b\u3063\u304b\u304f Markdown \u3060\u3063\u3066\u66f8\u3051\u308b\u3053\u3068\u3060\u3057\u3002<\/p>\n

\u3067\u3082\u307e\u3041\u307b\u3068\u3093\u3069\u306e\u5834\u5408\u304c PowerPoint \u306a\u308a Google Slide \u306a\u308a\u3092\u4f5c\u308b\u3053\u3068\u306b\u306a\u308b\u3093\u3058\u3083\u306a\u304b\u308d\u3046\u304b\u3002<\/p>\n

\u305d\u306e\u6642\u306b\u4e00\u756a\u30a4\u30e4\u306a\u306e\u304c\u3001\u8868\u3092\u66f8\u304d\u5199\u3059\u4e8b\u306d\u3002\u3053\u308c\u306b\u5c3d\u304d\u308b\u3002<\/p>\n

\u5024\u3092\u9593\u9055\u3063\u3066\u306f\u3044\u3051\u306a\u3044\u3057\u3001\u9069\u5207\u306a\u6841\u3067\u56db\u6368\u4e94\u5165\u3057\u305f\u308a\u3001\u96a3\u306e\u30bb\u30eb\u3068\u898b\u9593\u9055\u3048\u305f\u308a\u3002<\/p>\n

\u3042\u308b\u6642\u30d2\u30e9\u30e1\u3044\u305f\u3002\u3053\u308c\u3001\u753b\u50cf\u306b\u3057\u3066\u30b3\u30d4\u30da\u3057\u3061\u3083\u3048\u3070\u3044\u3044\u3093\u3058\u3083\u306d\uff1f<\/p>\n

\u6e96\u5099<\/h1>\n

\u5fc5\u8981\u306a\u3082\u306e<\/p>\n

pandas<\/p>\n

\u5f53\u305f\u308a\u524d<\/p>\n

tabulate<\/p>\n

pandas.DataFrame \u3092 HTML \u5909\u63db\u3059\u308b\u3068\u304d\u306b\u6697\u306b\u5fc5\u8981<\/p>\n

html2image<\/p>\n

HTML \u51fa\u529b\u3057\u305f\u8868\u3092\u753b\u50cf\u306b\u3059\u308b<\/p>\n

Pillow<\/p>\n

\u753b\u50cf\u306e\u8abf\u6574<\/p>\n

matplotlib<\/p>\n

\u753b\u50cf\u306e\u8868\u793a<\/p>\n

jupyterlab<\/p>\n

JupyterLab \u3067\u3084\u308b\u304b\u3089\u306d<\/p>\n

\u95a2\u6570\u306e\u5b9f\u88c5<\/h1>\n
import<\/span> os<\/span>\r\n\r\n%<\/span>matplotlib<\/span> inline<\/span>\r\nimport<\/span> matplotlib.pyplot<\/span> as<\/span> plt<\/span>\r\nimport<\/span> pandas<\/span> as<\/span> pd<\/span>\r\nfrom<\/span> html2image<\/span> import<\/span> Html2Image<\/span>\r\nfrom<\/span> PIL<\/span> import<\/span> Image<\/span>\r\n\r\npd<\/span>.<\/span>set_option<\/span>(<\/span>\"<\/span>display.float_format<\/span>\"<\/span>,<\/span> \"<\/span>{:.4f}<\/span>\"<\/span>.<\/span>format<\/span>)<\/span>\r\n\r\n\r\ndef<\/span> show_table<\/span>(<\/span>\r\n    table<\/span>:<\/span> pd<\/span>.<\/span>DataFrame<\/span>,<\/span>\r\n    *<\/span>,<\/span>\r\n    index<\/span>:<\/span> bool<\/span> |<\/span> None<\/span> =<\/span> True<\/span>,<\/span>\r\n    font_size<\/span>:<\/span> str<\/span> |<\/span> None<\/span> =<\/span> \"<\/span>18px<\/span>\"<\/span>,<\/span>\r\n    dpi<\/span>:<\/span> float<\/span> |<\/span> None<\/span> =<\/span> 0.8<\/span>,<\/span>\r\n)<\/span> -><\/span> None<\/span>:<\/span>\r\n    \"\"\"<\/span>displays Table as an image.\r\n\r\n    Args:\r\n        table (pandas.DataFrame): the Table.\r\n        index (:obj:`bool`, optional): print index (default: True)\r\n        font_size (:obj:`str`, optional): font size (default: <\/span>\"<\/span>18px<\/span>\"<\/span>)\r\n        dpi (:obj:`float`, optional): DPI (default: 0.8)\r\n    <\/span>\"\"\"<\/span>\r\n    css<\/span> =<\/span> f<\/span>\"\"\"<\/span>\r\ntable.dataframe {{\r\n    border: none;\r\n    border-collapse: collapse;\r\n    border-spacing: 0;\r\n    color: black;\r\n    font-family: Noto Sans JP;\r\n    font-size: <\/span>{<\/span>font_size<\/span>}<\/span>;\r\n    table-layout: fixed;\r\n}}\r\ntable.dataframe thead {{\r\n    border-bottom: 1px solid black;\r\n    vertical-align: bottom;\r\n}}\r\ntable.dataframe tr, table.dataframe th, table.dataframe td {{\r\n    text-align: right;\r\n    vertical-align: middle;\r\n    padding: 0.5em 0.5em;\r\n    line-height: normal;\r\n    white-space: nowrap;\r\n    max-width: none;\r\n    border: none;\r\n}}\r\ntable.dataframe th {{\r\n    font-weight: bold;\r\n}}\r\ntable.dataframe tbody tr:nth-child(even) {{\r\n    background: #f5f5f5;\r\n}}\r\n<\/span>\"\"\"<\/span>\r\n    path<\/span>:<\/span> str<\/span> =<\/span> \"<\/span>_show_table_temp.png<\/span>\"<\/span>\r\n    hti<\/span>:<\/span> Html2Image<\/span> =<\/span> Html2Image<\/span>(<\/span>disable_logging<\/span>=<\/span>True<\/span>)<\/span>\r\n    _<\/span> =<\/span> hti<\/span>.<\/span>screenshot<\/span>(<\/span>\r\n        html_str<\/span>=<\/span>table<\/span>.<\/span>to_html<\/span>(<\/span>index<\/span>=<\/span>index<\/span>),<\/span>\r\n        css_str<\/span>=<\/span>css<\/span>,<\/span>\r\n        save_as<\/span>=<\/span>path<\/span>,<\/span>\r\n    )<\/span>\r\n    img<\/span>:<\/span> Image<\/span> =<\/span> Image<\/span>.<\/span>open<\/span>(<\/span>path<\/span>)<\/span>\r\n    img<\/span> =<\/span> img<\/span>.<\/span>crop<\/span>(<\/span>img<\/span>.<\/span>getbbox<\/span>())<\/span>\r\n    fig<\/span>,<\/span> ax<\/span> =<\/span> plt<\/span>.<\/span>subplots<\/span>(<\/span>\r\n        nrows<\/span>=<\/span>1<\/span>,<\/span> ncols<\/span>=<\/span>1<\/span>,<\/span> figsize<\/span>=<\/span>img<\/span>.<\/span>size<\/span>,<\/span> dpi<\/span>=<\/span>dpi<\/span>,<\/span> layout<\/span>=<\/span>\"<\/span>tight<\/span>\"<\/span>\r\n    )<\/span>\r\n    ax<\/span>.<\/span>imshow<\/span>(<\/span>img<\/span>)<\/span>\r\n    ax<\/span>.<\/span>axis<\/span>(<\/span>\"<\/span>off<\/span>\"<\/span>)<\/span>\r\n    plt<\/span>.<\/span>show<\/span>(<\/span>fig<\/span>)<\/span>\r\n    plt<\/span>.<\/span>clf<\/span>()<\/span>\r\n    plt<\/span>.<\/span>close<\/span>()<\/span>\r\n    os<\/span>.<\/span>remove<\/span>(<\/span>path<\/span>)<\/span>\r\n    return<\/span>\r\n<\/code><\/pre>\n
CSS \u3092\u30d9\u30bf\u66f8\u304d\u3057\u3066\u308b\u304b\u3089\u9577\u3063\u305f\u3089\u3057\u3044\u95a2\u6570\u306b\u898b\u3048\u308b\u3051\u3069\u3001\u4e2d\u8eab\u306f\u305d\u308c\u307b\u3069\u3067\u3082\u306a\u3044\u3002<\/p>\n
    \n
  • \n
      \u307e\u305a\u91cd\u8981\u306a\u306e\u304c\u3001pandas.set() \u3067 display.float_format \u3092\u6307\u5b9a\u3057\u3066\u3044\u308b\u3053\u3068<\/ul>\n<\/li>\n<\/ul>\n
      \u3053\u308c\u306b\u3088\u3063\u3066\u8868\u793a\u3059\u308b\u969b\u306b\u81ea\u52d5\u7684\u306b\u56db\u6368\u4e94\u5165\u3057\u3066\u5c0f\u6570\u306e\u6841\u6570\u304c\u63c3\u3046<\/p>\n
      Html2Image.screenshot() \u306f HTML \u30c6\u30ad\u30b9\u30c8\u3092 Web \u30d6\u30e9\u30a6\u30b6\u3067\u8868\u793a\u3057\u3066\u3001\u305d\u306e\u753b\u9762\u306e\u30b9\u30af\u30ea\u30fc\u30f3\u30b7\u30e7\u30c3\u30c8\u3092\u64ae\u3063\u3066\u753b\u50cf\u306b\u3059\u308b\u3082\u306e\uff08\u3063\u307d\u3044\uff09
      \npandas.DataFrame \u3092 to_html() \u3067 HTML \u306b\u3057\u3066\u3001\u3053\u3044\u3064\u306b\u6e21\u3059
      \n\u3053\u306e\u6642\u306b\u3001Jupyter\uff08iPython\uff09\u3068\u540c\u3058\u69d8\u306b\u8868\u793a\u3059\u308b\u3053\u3068\u3092\u72d9\u3063\u305f\u305f\u3081\u3001CSS \u304c\u9577\u304f\u306a\u3063\u305f
      \n\u751f\u6210\u3055\u308c\u308b\u753b\u50cf\u306f\u3001\u9069\u5f53\u306a\u30d5\u30a1\u30a4\u30eb\u540d\u3067\u4e00\u6642\u7684\u306b\u4fdd\u5b58\u3059\u308b\u3053\u3068\u306b\u3059\u308b
      \n\u3053\u306e\u753b\u50cf\u306f\u3001\u975e\u5e38\u306b\u5927\u304d\u304f\u3066\u4f59\u767d\u304c\u591a\u3059\u304e\u308b\uff08\u305f\u3076\u3093\u30c7\u30a3\u30b9\u30d7\u30ec\u30a4\u306e\u30b5\u30a4\u30ba\uff09
      \n\u305d\u3053\u3067\u4fdd\u5b58\u3057\u305f\u753b\u50cf\u3092 Pillow \u3067\u8aad\u307f\u8fbc\u307f\u3001getbbox() \u3067 BoundingBox\uff08\u4f55\u304b\u304c\u5199\u3063\u3066\u3044\u308b\u77e9\u5f62\u9818\u57df\uff09\u3092\u53d6\u5f97\u3057\u3001\u305d\u3053\u3060\u3051\u3092 crop() \u3067\u5207\u308a\u629c\u304f
      \n\u305d\u3046\u3057\u305f\u753b\u50cf\u3092\u307e\u305f\u30d5\u30a1\u30a4\u30eb\u306b\u4fdd\u5b58\u3057\u3066\u3082\u3044\u3044\u3093\u3060\u3051\u3069\u3001\u3044\u3061\u3044\u3061\u30d5\u30a1\u30a4\u30eb\u540d\u3092\u8003\u3048\u308b\u306e\u304c\u9762\u5012\u304f\u3055\u3044\u306e\u3067\u3001JupyterLab \u306b\u8cbc\u308a\u4ed8\u3051\u308b\u3053\u3068\u306b\u3059\u308b<\/p>\n
      \u666e\u901a\u306b pandas.DataFrame \u3092\u8868\u793a\u3059\u308b\u306e\u3068\u540c\u3058\u611f\u3058\u3067\u4f7f\u3048\u308b\u3088\u3046\u306b<\/p>\n
      \u8868\u793a\u3057\u305f\u3089\u3001\u4e00\u6642\u7684\u306b\u4fdd\u5b58\u3057\u305f\u30d5\u30a1\u30a4\u30eb\u3092\u6d88\u3059<\/p>\n
      \u30ab\u30b9\u30bf\u30de\u30a4\u30ba\u3067\u304d\u308b\u3053\u3068<\/p>\n
        \n
      • \n
          \u5217\u540d\u3092\u8868\u793a\u3059\u308b\u304b\u5426\u304b\u306f\u5f15\u6570\u3067\u8a2d\u5b9a\u3067\u304d\u308b\u3088\u3046\u306b\u3057\u305f<\/ul>\n<\/li>\n<\/ul>\n
           <\/p>\n
            \n
          • \n
              \u30d5\u30a9\u30f3\u30c8\u30b5\u30a4\u30ba\u30fbDPI \u3082\u5f15\u6570\u3067\u5909\u66f4\u3067\u304d\u308b\u3088\u3046\u306b\u3057\u305f<\/ul>\n<\/li>\n<\/ul>\n
              \u3068\u308a\u3042\u3048\u305a iPython \u3068\u540c\u3058\u611f\u3058\u306b\u306a\u308b\u3088\u3046\u306b\u3057\u305f\u3051\u3069\u3001\u304a\u597d\u307f\u3067<\/p>\n
              \u30d5\u30a9\u30f3\u30c8\u306f\u6c7a\u3081\u6253\u3061\u3067 Noto Sans JP \u3092\u4f7f\u3063\u3066\u3044\u308b\u3051\u3069\u3001\u3053\u308c\u3082\u304a\u597d\u307f\u3067<\/p>\n
              \u95a2\u6570\u540d\u306f\u30fb\u30fb\u30fb\u79c1\u306f\u30cd\u30fc\u30df\u30f3\u30b0\u30bb\u30f3\u30b9\u304c\u7121\u3044\u306e\u3067\u30fb\u30fb\u30fb<\/p>\n
              \u4f7f\u3063\u3066\u307f\u308b<\/h1>\n
              \u30b5\u30f3\u30d7\u30eb\u3068\u3057\u3066\u3001\u9069\u5f53\u306b iris \u3067\u3082\u4e88\u6e2c\u3057\u3066\u307f\u308b\u3053\u3068\u306b\u3059\u308b\u3002<\/p>\n
              \u307e\u305a\u306f\u30c7\u30fc\u30bf\u3092\u8aad\u307f\u8fbc\u3093\u3067\u3001\u7279\u5fb4\u91cf\u306e\u5206\u5e03\u3092\u898b\u3066\u307f\u3088\u3046\u3002<\/p>\n
              from<\/span> sklearn.datasets<\/span> import<\/span> load_iris<\/span>\r\nfrom<\/span> sklearn.utils<\/span> import<\/span> Bunch<\/span>\r\n\r\niris<\/span>:<\/span> Bunch<\/span> =<\/span> load_iris<\/span>()<\/span>\r\nshow_table<\/span>(<\/span>\r\n    pd<\/span>.<\/span>DataFrame<\/span>(<\/span>\r\n        iris<\/span>.<\/span>data<\/span>,<\/span>\r\n        columns<\/span>=<\/span>iris<\/span>.<\/span>feature_names<\/span>,<\/span>\r\n    ).<\/span>describe<\/span>(),<\/span>\r\n)<\/span>\r\n<\/code><\/pre>\n
              \"describe.png\"<\/div>\n
              \u6b21\u306b\u3001\u3044\u3064\u3082\u306e\u3088\u3046\u306b\u5b66\u7fd2\u3059\u308b\u3002\u9069\u5f53\u306b LinearSVC \u3067\u3082\u4f7f\u3048\u3070\u3044\u3044\u304b\u3002<\/p>\n
              from<\/span> typing<\/span> import<\/span> Any<\/span>\r\nimport<\/span> numpy<\/span> as<\/span> np<\/span>\r\nfrom<\/span> scipy.stats<\/span> import<\/span> expon<\/span>\r\nfrom<\/span> sklearn.preprocessing<\/span> import<\/span> StandardScaler<\/span>\r\nfrom<\/span> sklearn.model_selection<\/span> import<\/span> (<\/span>\r\n    train_test_split<\/span>,<\/span>\r\n    RandomizedSearchCV<\/span>,<\/span>\r\n    StratifiedKFold<\/span>,<\/span>\r\n)<\/span>\r\nfrom<\/span> sklearn.svm<\/span> import<\/span> LinearSVC<\/span>\r\n\r\n(<\/span>\r\n    x_train<\/span>,<\/span>\r\n    x_test<\/span>,<\/span>\r\n    y_train<\/span>,<\/span>\r\n    y_test<\/span>,<\/span>\r\n)<\/span> =<\/span> train_test_split<\/span>(<\/span>\r\n    iris<\/span>.<\/span>data<\/span>,<\/span>\r\n    iris<\/span>.<\/span>target<\/span>,<\/span>\r\n    test_size<\/span>=<\/span>0.3<\/span>,<\/span>\r\n    shuffle<\/span>=<\/span>True<\/span>,<\/span>\r\n    stratify<\/span>=<\/span>iris<\/span>.<\/span>target<\/span>,<\/span>\r\n    random_state<\/span>=<\/span>12<\/span>,<\/span>\r\n)<\/span>\r\nsvc_params<\/span>:<\/span> dict<\/span>[<\/span>str<\/span>,<\/span> Any<\/span>]<\/span> =<\/span> {<\/span>\r\n    \"<\/span>penalty<\/span>\"<\/span>:<\/span> [<\/span>\"<\/span>l1<\/span>\"<\/span>,<\/span> \"<\/span>l2<\/span>\"<\/span>],<\/span>\r\n    \"<\/span>C<\/span>\"<\/span>:<\/span> expon<\/span>(),<\/span>\r\n    \"<\/span>class_weight<\/span>\"<\/span>:<\/span> [<\/span>None<\/span>,<\/span> \"<\/span>balanced<\/span>\"<\/span>],<\/span>\r\n}<\/span>\r\nss<\/span>:<\/span> StandardScaler<\/span> =<\/span> StandardScaler<\/span>().<\/span>fit<\/span>(<\/span>x_train<\/span>)<\/span>\r\nestimator<\/span>:<\/span> LinearSVC<\/span> =<\/span> LinearSVC<\/span>(<\/span>\r\n    multi_class<\/span>=<\/span>\"<\/span>ovr<\/span>\"<\/span>,<\/span>\r\n    dual<\/span>=<\/span>\"<\/span>auto<\/span>\"<\/span>,<\/span>\r\n    fit_intercept<\/span>=<\/span>True<\/span>,<\/span>\r\n    max_iter<\/span>=<\/span>10000<\/span>,<\/span>\r\n    random_state<\/span>=<\/span>12<\/span>,<\/span>\r\n)<\/span>\r\nskf<\/span>:<\/span> StratifiedKFold<\/span> =<\/span> StratifiedKFold<\/span>(<\/span>\r\n    n_splits<\/span>=<\/span>5<\/span>,<\/span>\r\n    shuffle<\/span>=<\/span>True<\/span>,<\/span>\r\n    random_state<\/span>=<\/span>12<\/span>,<\/span>\r\n)<\/span>\r\nrcv<\/span>:<\/span> RandomizedSearchCV<\/span> =<\/span> RandomizedSearchCV<\/span>(<\/span>\r\n    estimator<\/span>,<\/span>\r\n    svc_params<\/span>,<\/span>\r\n    n_iter<\/span>=<\/span>1000<\/span>,<\/span>\r\n    scoring<\/span>=<\/span>\"<\/span>f1_macro<\/span>\"<\/span>,<\/span>\r\n    n_jobs<\/span>=-<\/span>1<\/span>,<\/span>\r\n    refit<\/span>=<\/span>True<\/span>,<\/span>\r\n    cv<\/span>=<\/span>skf<\/span>,<\/span>\r\n    random_state<\/span>=<\/span>12<\/span>,<\/span>\r\n).<\/span>fit<\/span>(<\/span>\r\n    ss<\/span>.<\/span>transform<\/span>(<\/span>x_train<\/span>),<\/span>\r\n    y<\/span>=<\/span>y_train<\/span>,<\/span>\r\n)<\/span>\r\npred_test<\/span>:<\/span> np<\/span>.<\/span>ndarray<\/span> =<\/span> rcv<\/span>.<\/span>best_estimator_<\/span>.<\/span>predict<\/span>(<\/span>\r\n    ss<\/span>.<\/span>transform<\/span>(<\/span>x_test<\/span>),<\/span>\r\n)<\/span>\r\n<\/code><\/pre>\n
              CrossValidation \u3067\u5f97\u3089\u308c\u305f Hyper Parameter \u306e\u5024\u3084 score \u3092\u898b\u3066\u307f\u3088\u3046\u3002<\/p>\n
              show_table<\/span>(<\/span>\r\n    pd<\/span>.<\/span>DataFrame<\/span>(<\/span>\r\n        [<\/span>\r\n            dict<\/span>(<\/span>\r\n                rcv<\/span>.<\/span>best_params_<\/span>,<\/span>\r\n                **<\/span>{<\/span>\"<\/span>metric<\/span>\"<\/span>:<\/span> \"<\/span>f1_macro<\/span>\"<\/span>,<\/span> \"<\/span>score<\/span>\"<\/span>:<\/span> rcv<\/span>.<\/span>best_score_<\/span>}<\/span>\r\n            )<\/span>\r\n        ]<\/span>\r\n    ),<\/span>\r\n    index<\/span>=<\/span>False<\/span>,<\/span>\r\n)<\/span>\r\n<\/code><\/pre>\n
              \"params.png\"<\/div>\n
              \u30c6\u30b9\u30c8\u30c7\u30fc\u30bf\u306b\u304a\u3051\u308b metrics \u3092\u898b\u3066\u307f\u3088\u3046\u3002<\/p>\n
              from<\/span> sklearn.metrics<\/span> import<\/span> classification_report<\/span>\r\n\r\nshow_table<\/span>(<\/span>\r\n    pd<\/span>.<\/span>DataFrame<\/span>(<\/span>\r\n        classification_report<\/span>(<\/span>\r\n            y_test<\/span>,<\/span>\r\n            pred_test<\/span>,<\/span>\r\n            target_names<\/span>=<\/span>iris<\/span>.<\/span>target_names<\/span>,<\/span>\r\n            output_dict<\/span>=<\/span>True<\/span>,<\/span>\r\n            zero_division<\/span>=<\/span>0<\/span>,<\/span>\r\n        )<\/span>\r\n    )<\/span>\r\n    .<\/span>iloc<\/span>[:<\/span>3<\/span>,<\/span> :<\/span> iris<\/span>.<\/span>target_names<\/span>.<\/span>shape<\/span>[<\/span>0<\/span>]]<\/span>\r\n    .<\/span>T<\/span>\r\n)<\/span>\r\n<\/code><\/pre>\n
              \"metrics.png\"<\/div>\n
              \u6700\u5f8c\u306b\u3001\u30c6\u30b9\u30c8\u30c7\u30fc\u30bf\u306e confusion matrix \u3092\u898b\u3066\u307f\u3088\u3046\u3002<\/p>\n
              from<\/span> sklearn.metrics<\/span> import<\/span> confusion_matrix<\/span>\r\n\r\nmatrix<\/span>:<\/span> pd<\/span>.<\/span>DataFrame<\/span> =<\/span> pd<\/span>.<\/span>DataFrame<\/span>(<\/span>\r\n    confusion_matrix<\/span>(<\/span>y_test<\/span>,<\/span> pred_test<\/span>),<\/span>\r\n    index<\/span>=<\/span>iris<\/span>.<\/span>target_names<\/span>,<\/span>\r\n    columns<\/span>=<\/span>iris<\/span>.<\/span>target_names<\/span>,<\/span>\r\n)<\/span>\r\nmatrix<\/span> =<\/span> pd<\/span>.<\/span>concat<\/span>(<\/span>\r\n    [<\/span>\r\n        matrix<\/span>,<\/span>\r\n        pd<\/span>.<\/span>Series<\/span>(<\/span>matrix<\/span>.<\/span>sum<\/span>(<\/span>axis<\/span>=<\/span>\"<\/span>columns<\/span>\"<\/span>),<\/span> name<\/span>=<\/span>\"<\/span>\u5b9f\u969b\u5408\u8a08<\/span>\"<\/span>),<\/span>\r\n    ],<\/span>\r\n    axis<\/span>=<\/span>\"<\/span>columns<\/span>\"<\/span>,<\/span>\r\n)<\/span>\r\nmatrix<\/span> =<\/span> pd<\/span>.<\/span>concat<\/span>(<\/span>\r\n    [<\/span>\r\n        matrix<\/span>,<\/span>\r\n        pd<\/span>.<\/span>Series<\/span>(<\/span>matrix<\/span>.<\/span>sum<\/span>(<\/span>axis<\/span>=<\/span>\"<\/span>index<\/span>\"<\/span>),<\/span> name<\/span>=<\/span>\"<\/span>\u4e88\u6e2c\u5408\u8a08<\/span>\"<\/span>).<\/span>to_frame<\/span>().<\/span>T<\/span>,<\/span>\r\n    ],<\/span>\r\n    axis<\/span>=<\/span>\"<\/span>index<\/span>\"<\/span>,<\/span>\r\n)<\/span>\r\nshow_table<\/span>(<\/span>matrix<\/span>)<\/span>\r\n<\/code><\/pre>\n
              \"confusion.png\"<\/div>\n
              \u7dba\u9e97\u306b\u51fa\u529b\u3055\u308c\u3066\u3044\u308b\u306d\uff01\uff08\u3053\u308c\u304c\u8a00\u3044\u305f\u304b\u3063\u305f\u3060\u3051\uff09<\/p>\n
              JupyterLab \u306e\u30c6\u30f3\u30d7\u30ec\u30fc\u30c8\u3092\u4f7f\u3046<\/h1>\n
              \u4f5c\u3063\u3066\u307f\u3066\u601d\u3063\u305f\u3002<\/p>\n
                \n
              • \n
                  \u3053\u3093\u306a\u9577\u3063\u305f\u3089\u3057\u3044\u95a2\u6570\u3001\u899a\u3048\u3066\u304a\u304d\u305f\u304f\u306a\u3044\u3057\u3001\u3069\u3063\u304b\u304b\u3089\u30b3\u30d4\u30da\u3059\u308b\u306e\u3082\u30a4\u30e4\u3060<\/ul>\n<\/li>\n<\/ul>\n
                   <\/p>\n
                    \u304b\u3068\u3044\u3063\u3066\u30d1\u30c3\u30b1\u30fc\u30b8\u5316\u3059\u308b\u306e\u3082\u30e1\u30f3\u30c9\u30a4<\/ul>\n
                    \u305d\u3093\u306a\u3068\u304d\u306b jupyterlab-templates \u3067\u3059\u3088\u3002<\/p>\n
                    \u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u65b9\u6cd5\u306f\u4e0a\u8a18\u30ea\u30f3\u30af\u306b\u8f09\u3063\u3066\u3044\u308b\u304b\u3089\u3044\u3044\u3068\u3057\u3066\u3001\u8a2d\u5b9a\u306b\u3061\u3087\u3063\u3068\u30af\u30bb\u304c\u3042\u308b\u3002<\/p>\n
                    \u307e\u305a\u306f JupyterLab \u306e config path \u3092\u78ba\u8a8d\u3059\u308b\u3002<\/p>\n
                    pipx \u306b JupyterLab \u3092\u5165\u308c\u3066\u3044\u308b\u4eba\u306f\u3001pipx run –spec jupyterlab jupyter –config-dir\uff08\u666e\u901a\u306b\u4eee\u60f3\u74b0\u5883\u7b49\u306b\u5165\u308c\u3066\u3044\u308b\u4eba\u306f jupyter –config-dir\uff09\u306e\u7d50\u679c\u304c config path\u3002<\/p>\n
                    \u3053\u308c\u304c ~\/.jupyter \u3060\u3063\u305f\u3082\u306e\u3068\u3059\u308b\u3002\uff08\u4e0a\u8a18\u7d50\u679c\u306e\u8868\u793a\u306f\u30d5\u30eb\u30d1\u30b9\u3060\u308d\u3046\u3051\u3069\uff09<\/p>\n
                    \u6b21\u306b ~\/.jupyter\/jupyter_lab_config.py \u3092\u4ee5\u4e0b\u306e\u3088\u3046\u306b\u4f5c\u6210\/\u7de8\u96c6\u3059\u308b\u3002<\/p>\n
                    import<\/span> os<\/span>\r\n\r\nc<\/span> =<\/span> get_config<\/span>()<\/span>  # noqa\r\n<\/span>c<\/span>.<\/span>JupyterLabTemplates<\/span>.<\/span>include_default<\/span> =<\/span> False<\/span>\r\nc<\/span>.<\/span>JupyterLabTemplates<\/span>.<\/span>include_core_paths<\/span> =<\/span> False<\/span>\r\nc<\/span>.<\/span>JupyterLabTemplates<\/span>.<\/span>template_dirs<\/span> =<\/span> [<\/span>os<\/span>.<\/span>path<\/span>.<\/span>expanduser<\/span>(<\/span>\"<\/span>~\/.jupyter\/templates<\/span>\"<\/span>)]<\/span>\r\n<\/code><\/pre>\n
                    include_default \u3068 include_core_paths \u3092 False \u306b\u3057\u3066\u3044\u308b\u306e\u306f\u3001jupyterlab-templates \u306b\u5165\u3063\u3066\u3044\u308b\u30c7\u30d5\u30a9\u30eb\u30c8\u306e\u30c6\u30f3\u30d7\u30ec\u30fc\u30c8\u3092\u8aad\u307f\u8fbc\u307e\u306a\u3044\u3088\u3046\u306b\u3059\u308b\u8a2d\u5b9a\u3002<\/p>\n
                    \u305d\u3057\u3066 template_dirs \u3092 ~\/.jupyter\/templates \u306b\u8a2d\u5b9a\u3057\u3066\u3044\u308b\u3002<\/p>\n
                    \u306a\u306e\u3067\u6b21\u306f\u305d\u306e\u30c7\u30a3\u30ec\u30af\u30c8\u30ea\u3092\u4f5c\u308a\u30c6\u30f3\u30d7\u30ec\u30fc\u30c8\u3068\u306a\u308b ipynb \u3092\u5165\u308c\u308b\u306e\u3060\u304c\u3001\u3053\u3053\u3067\u6ce8\u610f\u3002<\/p>\n
                    template_dirs \u306b\u8a2d\u5b9a\u3057\u305f\u30c7\u30a3\u30ec\u30af\u30c8\u30ea\uff08\u4e0a\u8a18\u306e\u4f8b\u3067\u306f ~\/.jupyter\/templates\uff09\u306b\u3001\u3055\u3089\u306b\u30c7\u30a3\u30ec\u30af\u30c8\u30ea\u3092\u6398\u308a\u3001\u305d\u306e\u4e0b\u306b\u30c6\u30f3\u30d7\u30ec\u30fc\u30c8\u3068\u306a\u308b ipynb \u3092\u5165\u308c\u306a\u3044\u3068\u30c0\u30e1\u3002<\/p>\n
                    \u8981\u3059\u308b\u306b\u3001\u3053\u3046\u3044\u3046\u3053\u3068\u3092\u3057\u306a\u304d\u3083\u3044\u3051\u306a\u3044\u3002<\/p>\n
                    mkdir<\/span> -p<\/span> ~\/.jupyter\/templates\/jupyterlab_templates\r\ncp <\/span>template.ipynb ~\/.jupyter\/templates\/jupyterlab_templates\/.\r\n<\/code><\/pre>\n
                    \u3053\u308c\u3001\u8a2d\u5b9a\u3059\u308b\u305f\u3073\u306b\u6bce\u56de\u5fd8\u308c\u3066\u6570\u6642\u9593\u306f\u7126\u308b\u3002<\/p>\n
                    \u4e00\u7dd2\u306b\u30c6\u30f3\u30d7\u30ec\u30fc\u30c8\u3057\u305f\u3044\u8a2d\u5b9a<\/h1>\n
                    \u305b\u3063\u304b\u304f\u30c6\u30f3\u30d7\u30ec\u30fc\u30c8\u3092\u4f5c\u308b\u306e\u3067\u3001\u4ed6\u306b\u3082\u5165\u308c\u308b\u3079\u304d\u3082\u306e\u306f\u5165\u308c\u3066\u3057\u307e\u3044\u305f\u3044\u3002<\/p>\n
                    \u305d\u3053\u3067\u304a\u85a6\u3081\u3057\u305f\u3044\u306e\u306f\u300cJupyterLab \u306e Notebook\u3001code cell \u306e\u884c\u9593\u3092\u72ed\u304f\u3059\u308b\u300d\u8a2d\u5b9a\u3002<\/p>\n
                    \u3053\u308c\u3001JupyterLab \u306e\u3069\u3053\u306e\u8a2d\u5b9a\u3092\u3044\u3058\u3063\u3066\u3082\u3001\u3069\u3053\u306e CSS \u3092\u3044\u3058\u3063\u3066\u3082\u72ed\u304f\u306a\u3089\u306a\u3044\u3002<\/p>\n
                    \u305d\u308c\u3067\u3082\u89e3\u6c7a\u7b56\u3067\u306f\u306a\u3044\u306e\u3060\u304c\u3001\u4ee5\u4e0b\u306e code cell \u3092\u5b9f\u884c\u3059\u308b\u3053\u3068\u3067\u5f37\u5236\u7684\u306b\u5909\u66f4\u3059\u308b\u3068\u3044\u3046\u3053\u3068\u3092\u3057\u3066\u3044\u308b\u3002<\/p>\n
                    %%html\r\n<!-- set height of code lines narrow --><\/span>\r\n<style><\/span>\r\ndiv<\/span>.cm-content<\/span> {<\/span> line-height<\/span>:<\/span> 1.05<\/span> !important<\/span>;<\/span> }<\/span>\r\n<\/style><\/span>\r\n<\/code><\/pre>\n
                    \u6700\u5f8c\u306b<\/h1>\n
                    \u3053\u3093\u306a\u5834\u5f53\u305f\u308a\u7684\u306a\u65b9\u6cd5\u3067\u306f\u306a\u304f\u3001\u3061\u3083\u3093\u3068 JupyterLab code cell \u306e\u884c\u9593\u3092\u72ed\u304f\u3059\u308b\u65b9\u6cd5\u3092\u3054\u5b58\u77e5\u306e\u65b9\u304c\u3044\u3089\u3063\u3057\u3083\u3063\u305f\u3089\u662f\u975e\u6559\u3048\u3066\u304f\u3060\u3055\u3044\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"
                    JupyterLab \u3067\u5206\u6790\u3057\u305f\u7d50\u679c\u3092\u5831\u544a\u3059\u308b\u6642\u3001\u8cc7\u6599\u3068\u3057\u3066 ipynb \u3082\u3057\u304f\u306f\u305d\u308c\u3092 HTML \u7b49\u306b\u5909\u63db\u3057 […]<\/p>\n","protected":false},"author":7,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[],"class_list":["post-46489","post","type-post","status-publish","format-standard","hentry","category-uncategorized"],"yoast_head":"\n- Blog - Silicon Cloud<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.silicloud.com\/zh\/blog\/46489-2\/\" \/>\n<meta property=\"og:locale\" content=\"zh_CN\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:description\" content=\"JupyterLab \u3067\u5206\u6790\u3057\u305f\u7d50\u679c\u3092\u5831\u544a\u3059\u308b\u6642\u3001\u8cc7\u6599\u3068\u3057\u3066 ipynb \u3082\u3057\u304f\u306f\u305d\u308c\u3092 HTML \u7b49\u306b\u5909\u63db\u3057 […]\" \/>\n<meta property=\"og:url\" content=\"https:\/\/www.silicloud.com\/zh\/blog\/46489-2\/\" \/>\n<meta property=\"og:site_name\" content=\"Blog - Silicon Cloud\" \/>\n<meta property=\"article:published_time\" content=\"2023-05-26T00:45:54+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2024-04-30T04:41:43+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/cdn.silicloud.com\/blog-img\/blog\/img\/657d653e37434c4406d06977\/19-0.png\" \/>\n<meta name=\"author\" content=\"\u79d1, \u9896\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"\u4f5c\u8005\" \/>\n\t<meta name=\"twitter:data1\" content=\"\u79d1, \u9896\" \/>\n\t<meta name=\"twitter:label2\" content=\"\u9884\u8ba1\u9605\u8bfb\u65f6\u95f4\" \/>\n\t<meta name=\"twitter:data2\" content=\"3 \u5206\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\/\/www.silicloud.com\/zh\/blog\/46489-2\/\",\"url\":\"https:\/\/www.silicloud.com\/zh\/blog\/46489-2\/\",\"name\":\"- Blog - Silicon Cloud\",\"isPartOf\":{\"@id\":\"https:\/\/www.silicloud.com\/zh\/blog\/#website\"},\"datePublished\":\"2023-05-26T00:45:54+00:00\",\"dateModified\":\"2024-04-30T04:41:43+00:00\",\"author\":{\"@id\":\"https:\/\/www.silicloud.com\/zh\/blog\/#\/schema\/person\/8ca01ba7f7362ad4edb7da206a12f29e\"},\"inLanguage\":\"zh-Hans\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\/\/www.silicloud.com\/zh\/blog\/46489-2\/\"]}]},{\"@type\":\"WebSite\",\"@id\":\"https:\/\/www.silicloud.com\/zh\/blog\/#website\",\"url\":\"https:\/\/www.silicloud.com\/zh\/blog\/\",\"name\":\"Blog - 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