Refactor black_check.py for improved readability and consistency

day1/day1_practice.ipynb

@@ -1,5 +1,15 @@
 {
   "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/Yozefu0315/lecture-ai-engineering/blob/master/day1/day1_practice.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {
@@ -59,11 +69,27 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "AIXMavdDEP8U"
-      },
-      "outputs": [],
+      "execution_count": 1,
+      "metadata": {
+        "id": "AIXMavdDEP8U",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "7e13451d-eda6-45a1-b5e8-c6ac451744bb"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Cloning into 'lecture-ai-engineering'...\n",
+            "remote: Enumerating objects: 52, done.\u001b[K\n",
+            "remote: Total 52 (delta 0), reused 0 (delta 0), pack-reused 52 (from 1)\u001b[K\n",
+            "Receiving objects: 100% (52/52), 83.21 KiB | 1.63 MiB/s, done.\n",
+            "Resolving deltas: 100% (9/9), done.\n"
+          ]
+        }
+      ],
       "source": [
         "!git clone https://github.com/matsuolab/lecture-ai-engineering.git"
       ]
@@ -115,11 +141,34 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "bvEowFfg5lrq"
-      },
-      "outputs": [],
+      "execution_count": 3,
+      "metadata": {
+        "id": "bvEowFfg5lrq",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "1b087006-8b84-4440-adb5-2c61806b0aa1"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Requirement already satisfied: python-dotenv in /usr/local/lib/python3.11/dist-packages (1.1.0)\n",
+            "/content/lecture-ai-engineering/day1\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "True"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 3
+        }
+      ],
       "source": [
         "!pip install python-dotenv\n",
         "from dotenv import load_dotenv, find_dotenv\n",
@@ -141,11 +190,23 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "S28XgOm0ELSM"
-      },
-      "outputs": [],
+      "execution_count": 4,
+      "metadata": {
+        "id": "S28XgOm0ELSM",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "659a410f-8310-4d80-b243-7ac1a8c32e4a"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "/content/lecture-ai-engineering/day1/01_streamlit_UI\n"
+          ]
+        }
+      ],
       "source": [
         "%cd /content/lecture-ai-engineering/day1/01_streamlit_UI"
       ]
@@ -161,7 +222,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 5,
       "metadata": {
         "id": "nBe41LFiELSN"
       },
@@ -182,11 +243,23 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "aYw1q0iXELSN"
-      },
-      "outputs": [],
+      "execution_count": 6,
+      "metadata": {
+        "id": "aYw1q0iXELSN",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "32f147d4-b8e6-4d4b-f1bf-efecd367dde6"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Authtoken saved to configuration file: /root/.config/ngrok/ngrok.yml\n"
+          ]
+        }
+      ],
       "source": [
         "!ngrok authtoken $$NGROK_TOKEN"
       ]
@@ -202,11 +275,35 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "f-E7ucR6ELSN"
-      },
-      "outputs": [],
+      "execution_count": 8,
+      "metadata": {
+        "id": "f-E7ucR6ELSN",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "e2ed84b0-95fd-4fb8-ad1e-7134c279b7d8"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "公開URL: https://03dc-34-172-63-208.ngrok-free.app\n",
+            "\n",
+            "Collecting usage statistics. To deactivate, set browser.gatherUsageStats to false.\n",
+            "\u001b[0m\n",
+            "\u001b[0m\n",
+            "\u001b[34m\u001b[1m  You can now view your Streamlit app in your browser.\u001b[0m\n",
+            "\u001b[0m\n",
+            "\u001b[34m  Local URL: \u001b[0m\u001b[1mhttp://localhost:8501\u001b[0m\n",
+            "\u001b[34m  Network URL: \u001b[0m\u001b[1mhttp://172.28.0.12:8501\u001b[0m\n",
+            "\u001b[34m  External URL: \u001b[0m\u001b[1mhttp://34.172.63.208:8501\u001b[0m\n",
+            "\u001b[0m\n",
+            "\u001b[34m  Stopping...\u001b[0m\n",
+            "^C\n"
+          ]
+        }
+      ],
       "source": [
         "from pyngrok import ngrok\n",
         "\n",
@@ -243,7 +340,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 9,
       "metadata": {
         "id": "8Ek9QgahELSO"
       },
@@ -391,7 +488,9 @@
     },
     {
       "cell_type": "markdown",
-      "metadata": {},
+      "metadata": {
+        "id": "jWDYLk2xAlC4"
+      },
       "source": [
         "アプリケーションの機能としては、チャット機能や履歴閲覧があります。\n",
         "\n",
@@ -555,7 +654,8 @@
   ],
   "metadata": {
     "colab": {
-      "provenance": []
+      "provenance": [],
+      "include_colab_link": true
     },
     "kernelspec": {
       "display_name": "Python 3",
@@ -567,4 +667,4 @@
   },
   "nbformat": 4,
   "nbformat_minor": 0
-}
+}

day5/演習1/main.py

@@ -51,6 +51,21 @@ def train_and_evaluate(
     accuracy = accuracy_score(y_test, predictions)
     return model, accuracy
 
+# 推論精度と推論時間を評価
+def evaluate_inference_performance(model, X_test, y_test):
+    start_time = time.time()
+    predictions = model.predict(X_test)
+    end_time = time.time()
+
+    total_time = end_time - start_time
+    avg_time_per_sample = total_time / len(X_test)
+
+    accuracy = accuracy_score(y_test, predictions)
+
+    print("\n=== 推論性能評価 ===")
+    print(f"精度（accuracy）: {accuracy:.4f}")
+    print(f"全体推論時間: {total_time:.4f}秒")
+    print(f"1件あたり平均推論時間: {avg_time_per_sample * 1000:.4f} ms")
 
 # モデル保存
 def log_model(model, accuracy, params):
@@ -112,6 +127,8 @@ def log_model(model, accuracy, params):
         random_state=model_random_state,
     )
 
+    evaluate_inference_performance(model, X_test, y_test)  # ← ★ 追加呼び出し
+
     # モデル保存
     log_model(model, accuracy, params)
 

day5/演習2/black_check.py

@@ -1,7 +1,18 @@
+def say_hello(name):
+    print("Hello," + name + "!")  # greet
+
+
+def say_hello(name):
+    print("Hello," + name + "!")  # greet
+
+
+def add(a, b):
+    return a + b
+
+
+def add(a, b):
+    return a + b
+
 
-def say_hello(name):print("Hello,"+name+"!")   # greet
-def say_hello(name):print("Hello,"     +    name   +"!")   # greet
-def add( a,b):return a+b
-def add( a ,     b    ):return a+b
 def add(a, b):
-    return a+b
+    return a + b