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c:T3043,/*@jsxRuntime automatic @jsxImportSource react*/
const {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];
const {useMDXComponents: _provideComponents} = arguments[0];
function _createMdxContent(props) {
  const _components = Object.assign({
    h3: "h3",
    p: "p",
    a: "a",
    img: "img",
    pre: "pre",
    code: "code",
    span: "span"
  }, _provideComponents(), props.components);
  return _jsxs(_Fragment, {
    children: [_jsx(_components.h3, {
      children: "BERTと説明可能性"
    }), "\n", _jsx(_components.p, {
      children: "分析者としてお客さんに説明する際、結果だけでなく、なぜその結果になったのかという分析が求められることがよくあります。"
    }), "\n", _jsx(_components.p, {
      children: "しかし、BERTのようなDeep Learning手法では、一般になぜこの結果になったのかを出すことは難しいです。"
    }), "\n", _jsxs(_components.p, {
      children: ["BERTとは?という方は", _jsx(_components.a, {
        href: "blog29.html",
        children: "以前のブログ"
      }), "を見てください。"]
    }), "\n", _jsx(_components.p, {
      children: "BERTでは一応Attentionの可視化によって説明できなくはないですが、 Attentionはたくさんあってどれをみて良いかわからない、 ポジ、ネガどちらに効いているのかわからないなど、解釈としてはあまり適していないと私は考えています。"
    }), "\n", _jsx(_components.p, {
      children: "一方、近年ちょくちょく話題になる「shap」という機械学習のモデルを解釈する手法があります。"
    }), "\n", _jsx(_components.p, {
      children: "shapについては、以下のブログがわかりやすかったので貼っておきます。"
    }), "\n", _jsx(_components.p, {
      children: _jsx(_components.a, {
        href: "https://dropout009.hatenablog.com/entry/2019/11/20/091450",
        children: "https://dropout009.hatenablog.com/entry/2019/11/20/091450"
      })
    }), "\n", _jsx(_components.p, {
      children: "tree系で使用しているcaseをよく見ますが、真価を発揮するのはブラックボックスになりがちなdeep learning系では？と思い、 BERTとshapを使って、ポジネガ分析の説明可能性を検証してみました。"
    }), "\n", _jsx(_components.h3, {
      children: "BERTとshapの準備"
    }), "\n", _jsx(_components.p, {
      children: "今回動かしたコードはgithubに置いてありますので、適宜参考にしてください。"
    }), "\n", _jsx(_components.p, {
      children: _jsx(_components.a, {
        href: "https://github.com/Shingo425/NLP/blob/main/src/BERT_shap.ipynb",
        children: "githubのコード"
      })
    }), "\n", _jsx(_components.p, {
      children: "もし重くて開けない、画像が切れているなどありましたら、以下のリポジトリをダウンロードして、「BERT_shap.html」をみてください。"
    }), "\n", _jsx(_components.p, {
      children: _jsx(_components.a, {
        href: "https://github.com/Shingo425/NLP",
        children: "githubのリポジトリ"
      })
    }), "\n", _jsxs(_components.p, {
      children: ["ポジネガ分析のモデルとしては、", _jsx(_components.a, {
        href: "blog30.html",
        children: "以前のブログ"
      }), "のデータを少し変えたものを使用しました。"]
    }), "\n", _jsx(_components.p, {
      children: "例の如く、yahoo locoのデータを用いています。"
    }), "\n", _jsx(_components.p, {
      children: _jsx(_components.a, {
        href: "https://developer.yahoo.co.jp/sitemap/",
        children: _jsx(_components.img, {
          src: "https://s.yimg.jp/images/yjdn/yjdn_attbtn2_105_17.gif",
          alt: "Webサービス by Yahoo! JAPAN",
          title: "Webサービス by Yahoo! JAPAN"
        })
      })
    }), "\n", _jsx(_components.p, {
      children: "shapのbase valueが極端な値を取らないように、ポジネガそれぞれ2000件ずつをtrain_dataとしました。"
    }), "\n", _jsx(_components.h3, {
      children: "BERTにおけるshap"
    }), "\n", _jsx(_components.p, {
      children: "shapを使うためのコードを解説します。"
    }), "\n", _jsx(_components.p, {
      children: "shapの実装には以下のブログを参考にしました。"
    }), "\n", _jsx(_components.p, {
      children: _jsx(_components.a, {
        href: "https://qiita.com/m__k/items/87cf3e4acf414408bfed",
        children: "https://qiita.com/m__k/items/87cf3e4acf414408bfed"
      })
    }), "\n", _jsx(_components.p, {
      children: "まずは、shapのインスタンスを作成します。"
    }), "\n", _jsx(_components.p, {
      children: "modelにはテキストを入力して予測値を出力する関数を指定します。"
    }), "\n", _jsx(_components.p, {
      children: "maskerにはmodelに使用したBERTのtokenizerを指定します。"
    }), "\n", _jsx(_components.pre, {
      children: _jsxs(_components.code, {
        className: "hljs language-python",
        children: [_jsx(_components.span, {
          className: "hljs-keyword",
          children: "import"
        }), " shap\n\n", _jsx(_components.span, {
          className: "hljs-keyword",
          children: "def"
        }), " ", _jsx(_components.span, {
          className: "hljs-title function_",
          children: "f"
        }), "(", _jsx(_components.span, {
          className: "hljs-params",
          children: "X_test"
        }), "): \n    pred_probas = [] \n    test_ds = ReviewDataset(texts=X_test) \n    test_dl = torch.utils.data.DataLoader(test_ds, batch_size=", _jsx(_components.span, {
          className: "hljs-number",
          children: "4"
        }), ", shuffle=", _jsx(_components.span, {
          className: "hljs-literal",
          children: "False"
        }), ")\n\n    ", _jsx(_components.span, {
          className: "hljs-keyword",
          children: "for"
        }), " input_ids, segment_ids, attention_masks ", _jsx(_components.span, {
          className: "hljs-keyword",
          children: "in"
        }), " test_dl:\n        ", _jsx(_components.span, {
          className: "hljs-keyword",
          children: "if"
        }), " cuda: \n            input_ids, segment_ids, attention_masks = input_ids.cuda(), segment_ids.cuda(), attention_masks.cuda() \n        outputs = model(input_ids = input_ids) \n        pred_probas.append(outputs.softmax(dim=", _jsx(_components.span, {
          className: "hljs-number",
          children: "1"
        }), ")[:,", _jsx(_components.span, {
          className: "hljs-number",
          children: "1"
        }), "].detach().cpu().numpy())\n    ", _jsx(_components.span, {
          className: "hljs-keyword",
          children: "return"
        }), " np.concatenate(pred_probas) \n\n", _jsx(_components.span, {
          className: "hljs-comment",
          children: "# SHAP値を計算するインスタンスを生成する "
        }), "\nexplainer = shap.Explainer(model=f, masker=tokenizer)\n"]
      })
    }), "\n", _jsx(_components.h3, {
      children: "shap valueの可視化1"
    }), "\n", _jsx(_components.p, {
      children: "実際に、shap valueを可視化してみます。"
    }), "\n", _jsx(_components.pre, {
      children: _jsxs(_components.code, {
        className: "hljs language-python",
        children: ["X_test = [", _jsx(_components.span, {
          className: "hljs-string",
          children: "\"今日のランチは最高だった！\""
        }), ",", _jsx(_components.span, {
          className: "hljs-string",
          children: "\"今日のランチは微妙だった\""
        }), "] shap_values = explainer(X_test) shap.plots.text(shap_values)\n"]
      })
    }), "\n", _jsx(_components.p, {
      children: _jsx(_components.img, {
        src: "/images/blog32/image1.png",
        alt: ""
      })
    }), "\n", _jsx(_components.p, {
      children: "f(x)が予測値で、base valueがデータ全体の期待値です。"
    }), "\n", _jsx(_components.p, {
      children: "shapは、個々のデータについて、どの特徴量がプラス、マイナスに働いたかを表示することができます。"
    }), "\n", _jsx(_components.p, {
      children: "たとえば、「今日のランチは最高だった!」は全体の期待値よりも高めに予測をし、 「は最高」の部分がプラスに働いていると解釈できます。"
    }), "\n", _jsx(_components.p, {
      children: "反対に、「今日のランチは微妙だった」では、全体の期待値よりも低めに予測し、 「は微妙」の部分がマイナスに働いていることがわかります。"
    }), "\n", _jsx(_components.p, {
      children: "めっちゃわかりやすくないですか？自分が見たときは結構感動しました。"
    }), "\n", _jsx(_components.p, {
      children: "Deep Learningはブラックボックスになりがちだけど、 どこが効いているか、解釈を出すことができるかできないかで全然違う気がします。"
    }), "\n", _jsx(_components.h3, {
      children: "shap valueの可視化2"
    }), "\n", _jsx(_components.p, {
      children: "次に、yahoo locoのvalidationデータで面白いと思ったshapの結果を紹介します。"
    }), "\n", _jsx(_components.p, {
      children: "1. 前半部は褒めているのでプラス、後半は大量に飲んだからお金がよりかかったということでマイナスに判定されています。"
    }), "\n", _jsx(_components.p, {
      children: _jsx(_components.img, {
        src: "/images/blog32/image2.png",
        alt: ""
      })
    }), "\n", _jsx(_components.p, {
      children: "2. 前半部は美味しくないというのでマイナスですが、後半はボリュームやリッチについて書かれていてプラスに判定されていて最終的にはプラスになっています。"
    }), "\n", _jsx(_components.p, {
      children: _jsx(_components.img, {
        src: "/images/blog32/image3.png",
        alt: ""
      })
    }), "\n", _jsx(_components.p, {
      children: "githubのコードでは、shap valueの例をたくさん出しているので、 より気になる方はみてみてください！"
    }), "\n", _jsx(_components.h3, {
      children: "shap valueによる変数重要度"
    }), "\n", _jsx(_components.p, {
      children: "最後に、shap valueによる変数重要度を表示します。"
    }), "\n", _jsx(_components.pre, {
      children: _jsxs(_components.code, {
        className: "hljs language-python",
        children: ["shap.plots.bar(shap_values_Xval.mean(", _jsx(_components.span, {
          className: "hljs-number",
          children: "0"
        }), "), max_display=", _jsx(_components.span, {
          className: "hljs-number",
          children: "100"
        }), ")\n"]
      })
    }), "\n", _jsx(_components.p, {
      children: _jsx(_components.img, {
        src: "/images/blog32/image4.png",
        alt: ""
      })
    }), "\n", _jsx(_components.p, {
      children: "解釈は難しいですが、目玉、素晴、スペシャル、お気に入りなどはポジティブに影響していそうですね。"
    }), "\n", _jsx(_components.h3, {
      children: "終わりに"
    }), "\n", _jsx(_components.p, {
      children: "shapについてはまだまだ勉強不足なので、どのモデルに適用するのが妥当か、ちゃんとした解釈や、 中身などは勉強したいと思っています。"
    }), "\n", _jsx(_components.p, {
      children: "ただ、shapの結果は見るだけで結構面白いので、一度試してみるのはありかなと思っています。"
    }), "\n", _jsx(_components.p, {
      children: "みなさんもぜひshap、使ってみてください！"
    })]
  });
}
function MDXContent(props = {}) {
  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);
  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {
    children: _jsx(_createMdxContent, props)
  })) : _createMdxContent(props);
}
return {
  default: MDXContent
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9:null
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