hw1.ipynb

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        "<a href=\"https://colab.research.google.com/github/wei-jun7/Project-MI-and-AI/blob/main/hw1.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "source": [
        "Task 1 (10 points): Describe a machine learning problem that you would like to solve using\n",
        "Logistic Regression. Clearly state why Logistic regression is the best choice for solving this\n",
        "problem"
      ],
      "metadata": {
        "id": "d161X7t8tlOS"
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      "source": [
        "The Machine learing problem I want to solove is apple Apple Quality which it is a binary classification. There have 7 feature to affect the quality of the apple, such as size, Weight, Sweetness and so on. The most important thing is  the Quality is the binary with it is 0 or 1. 1 for good and 0 for bad. As the logistic regression the result require between 0 to 1 which our data is best fit on it. Moreover, it goes beyond mere classification by estimating the likelihood of each category, offering a nuanced view of results, especially useful for apples that don't clearly fall into one group. Additionally, the model's interpretability is a key strength. It clearly indicates which characteristics most significantly impact apple quality, making logistic regression not only effective but also insightful for this classification challenge.\n",
        "\n",
        "\n",
        "The dataset I use is coming from https://www.kaggle.com/datasets/nelgiriyewithana/apple-quality\n",
        "\n"
      ],
      "metadata": {
        "id": "WAZQcgj3tpaA"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Task 2 (20 points): Pick a publicly available dataset (except The Titanic Dataset) that you will\n",
        "use to solve this problem. You must provide a link to the dataset and perform the necessary\n",
        "Exploratory Data Analysis (EDA). Clearly demonstrate the steps you follow for your EDA with a\n",
        "justification of why these were required. For example, if the dataset has lot of missing values,\n",
        "then why did you use a specific technique when handling missing data. This task may include\n",
        "data visualization"
      ],
      "metadata": {
        "id": "xx10dBTf_QWc"
      }
    },
    {
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "data = pd.read_csv('/content/apple_quality.csv')\n",
        "\n"
      ],
      "cell_type": "code",
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        "id": "c5bB3OSdD4xS"
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      "execution_count": 2,
      "outputs": []
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      "cell_type": "markdown",
      "source": [
        "The first step is read the data into the vector array which I will use the pandas to do it."
      ],
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        "id": "e0uY8bHgNlua"
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    {
      "cell_type": "code",
      "source": [
        "\n",
        "data.isna().sum()  # As we can see the last one is empty so we should remove it.\n",
        "print(data.shape)\n",
        "row = data.iloc[4000]\n",
        "print(row)\n",
        "data = data.iloc[:4000]\n",
        "print(data.shape)\n",
        "\n",
        "data = data.iloc[:4000,:]\n",
        "data.describe()\n",
        "\n"
      ],
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      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(4001, 9)\n",
            "A_id                                         NaN\n",
            "Size                                         NaN\n",
            "Weight                                       NaN\n",
            "Sweetness                                    NaN\n",
            "Crunchiness                                  NaN\n",
            "Juiciness                                    NaN\n",
            "Ripeness                                     NaN\n",
            "Acidity        Created_by_Nidula_Elgiriyewithana\n",
            "Quality                                      NaN\n",
            "Name: 4000, dtype: object\n",
            "(4000, 9)\n"
          ]
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          "output_type": "execute_result",
          "data": {
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              "              A_id         Size       Weight    Sweetness  Crunchiness  \\\n",
              "count  4000.000000  4000.000000  4000.000000  4000.000000  4000.000000   \n",
              "mean   1999.500000    -0.503015    -0.989547    -0.470479     0.985478   \n",
              "std    1154.844867     1.928059     1.602507     1.943441     1.402757   \n",
              "min       0.000000    -7.151703    -7.149848    -6.894485    -6.055058   \n",
              "25%     999.750000    -1.816765    -2.011770    -1.738425     0.062764   \n",
              "50%    1999.500000    -0.513703    -0.984736    -0.504758     0.998249   \n",
              "75%    2999.250000     0.805526     0.030976     0.801922     1.894234   \n",
              "max    3999.000000     6.406367     5.790714     6.374916     7.619852   \n",
              "\n",
              "         Juiciness     Ripeness  \n",
              "count  4000.000000  4000.000000  \n",
              "mean      0.512118     0.498277  \n",
              "std       1.930286     1.874427  \n",
              "min      -5.961897    -5.864599  \n",
              "25%      -0.801286    -0.771677  \n",
              "50%       0.534219     0.503445  \n",
              "75%       1.835976     1.766212  \n",
              "max       7.364403     7.237837  "
            ],
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              "      <th>A_id</th>\n",
              "      <th>Size</th>\n",
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              "      <th>Sweetness</th>\n",
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              "      <th>count</th>\n",
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              "      <td>4000.000000</td>\n",
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              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>1999.500000</td>\n",
              "      <td>-0.503015</td>\n",
              "      <td>-0.989547</td>\n",
              "      <td>-0.470479</td>\n",
              "      <td>0.985478</td>\n",
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              "      <td>0.498277</td>\n",
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              "      <th>std</th>\n",
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              "      <td>1.928059</td>\n",
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              "      <td>1.943441</td>\n",
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              "      <td>1.874427</td>\n",
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              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>-7.151703</td>\n",
              "      <td>-7.149848</td>\n",
              "      <td>-6.894485</td>\n",
              "      <td>-6.055058</td>\n",
              "      <td>-5.961897</td>\n",
              "      <td>-5.864599</td>\n",
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              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>999.750000</td>\n",
              "      <td>-1.816765</td>\n",
              "      <td>-2.011770</td>\n",
              "      <td>-1.738425</td>\n",
              "      <td>0.062764</td>\n",
              "      <td>-0.801286</td>\n",
              "      <td>-0.771677</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>1999.500000</td>\n",
              "      <td>-0.513703</td>\n",
              "      <td>-0.984736</td>\n",
              "      <td>-0.504758</td>\n",
              "      <td>0.998249</td>\n",
              "      <td>0.534219</td>\n",
              "      <td>0.503445</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>2999.250000</td>\n",
              "      <td>0.805526</td>\n",
              "      <td>0.030976</td>\n",
              "      <td>0.801922</td>\n",
              "      <td>1.894234</td>\n",
              "      <td>1.835976</td>\n",
              "      <td>1.766212</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>3999.000000</td>\n",
              "      <td>6.406367</td>\n",
              "      <td>5.790714</td>\n",
              "      <td>6.374916</td>\n",
              "      <td>7.619852</td>\n",
              "      <td>7.364403</td>\n",
              "      <td>7.237837</td>\n",
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          "metadata": {},
          "execution_count": 3
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The Second step find any NAN data in the dataset I found out the last row is use for author name with we should delete this row which affect the model accuracy. I use the the iloc to remove it and use the describe() function to see summary of each feature."
      ],
      "metadata": {
        "id": "eAP3Q9ZcOKTS"
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    {
      "cell_type": "code",
      "source": [
        "print(data.columns)\n",
        "data['Quality'] = np.where(data['Quality'] == 'good',1,0)\n",
        "data = data.drop(labels='A_id',axis=1)# as we know the id number should not be the model with with affect the model accuracy\n",
        "type(data['Acidity'][0])\n",
        "data['Acidity']=data['Acidity'].astype(float)#it origin is str but we should change to float with let it show in the graph\n",
        "fig, axs=plt.subplots(nrows=2,ncols=4,figsize=(20,8))\n",
        "axs = axs.flatten()\n",
        "for i, column in enumerate(data.columns):\n",
        "    sns.histplot(data[column], kde=True, ax=axs[i])\n",
        "    axs[i].set_title(f'Distplot of {column}')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 579
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        "id": "44ADIqx6GatE",
        "outputId": "efe3319e-45c9-45ea-a2fc-c10a60a96035"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Index(['A_id', 'Size', 'Weight', 'Sweetness', 'Crunchiness', 'Juiciness',\n",
            "       'Ripeness', 'Acidity', 'Quality'],\n",
            "      dtype='object')\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x800 with 8 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "I remove the A_id with it indicate each sample in our dataset but it is not import with we have the row to indicate the each sample. Next, I creat the each feature to see the disturbution. As we can see most all the feature expect the quality have the normal disturbution. It is a good sign with indicate our model can handel the target value with good quality and bad quality. Also, I notice that in the quality column, I tranfer good to 1 and bad to 0 which it is help us to the do the binary regression."
      ],
      "metadata": {
        "id": "bdTiZCa5O0-V"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data['Quality'] = np.where(data['Quality'] == 'good',1,0)\n",
        "data_cor = data.corr()\n",
        "plt.figure(figsize=(8,8))\n",
        "cmap_rb = sns.color_palette(\"Reds\", 10)\n",
        "sns.heatmap(data_cor,cmap=cmap_rb,annot=True)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 752
        },
        "id": "puIAX-fsL9om",
        "outputId": "d74920d6-6266-4c17-bb23-70a0d577d411"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x800 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "I creat the one heat map to see the each feature relationship with we can see teh the strongest postive relationship is size and acidity which mean that the size bigger the acidity stronger. The strongest negtive realtionis is size and sweetness which bigger size less sweetness. In overall, each feature is not have strong relationship which it better to use the logistic regression to find out the relationship between each feature."
      ],
      "metadata": {
        "id": "xnyB-gdCQLPG"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Task 3 (25 points): Implement, Logistic Regression in this step. Clearly write your cost function\n",
        "and derivatives before implementing gradient descent. Do not use any built-in packages for this\n",
        "step. You can use the vectorization techniques demonstrated in class. Implement any 2 variants\n",
        "of gradient descent in their original form. (Refer to the research paper discussed in class).\n"
      ],
      "metadata": {
        "id": "aqLZxxdLRZyv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "\n",
        "def sigmoid(z):#active function\n",
        "\n",
        "\n",
        "    return 1 / (1 + np.exp(-z))\n",
        "\n",
        "def compute_cost(X, y, theta):\n",
        "    m = y.size\n",
        "    h = sigmoid(X.dot(theta))\n",
        "    cost = -1/m * (np.dot(y, np.log(h)) + np.dot(1 - y, np.log(1 - h)))# cross Entropy loss\n",
        "    return cost\n",
        "\n",
        "def batch_gradient_descent(X, y, theta, alpha, iterations):\n",
        "    m = y.size\n",
        "    cost_history = np.zeros(iterations)\n",
        "    for i in range(iterations):\n",
        "        h = sigmoid(X.dot(theta))\n",
        "        gradient = (1/m) * X.T.dot(h - y)\n",
        "        theta -= alpha * gradient\n",
        "        cost_history[i] = compute_cost(X, y, theta)\n",
        "\n",
        "    return theta, cost_history\n",
        "\n",
        "\n",
        "# Data preprocessing\n",
        "\n",
        "dataset = data.to_numpy()\n",
        "\n",
        "\n",
        "X = dataset[:, :-1]\n",
        "y = dataset[:, -1]\n",
        "split_size = int(0.8 * len(X))\n",
        "X_train, X_test = X[:split_size], X[split_size:]\n",
        "y_train, y_test = y[:split_size], y[split_size:]\n",
        "\n",
        "\n",
        "# Convert to numpy arrays and reshape if necessary\n",
        "X_train = np.array(X_train)\n",
        "y_train = np.array(y_train)\n",
        "X_test = np.array(X_test)\n",
        "y_test = np.array(y_test)\n",
        "\n",
        "# Add an intercept term to X_train and X_test\n",
        "X_train = np.column_stack((np.ones(X_train.shape[0]), X_train))\n",
        "X_test = np.column_stack((np.ones(X_test.shape[0]), X_test))\n",
        "\n",
        "# Initialize parameters\n",
        "theta = np.zeros(X_train.shape[1])\n",
        "alpha = 0.001\n",
        "iterations = 1000\n",
        "\n",
        "# Train the model\n",
        "theta_final, cost_history = batch_gradient_descent(X_train, y_train, theta, alpha, iterations)\n",
        "\n",
        "# Output the final parameters and cost history\n",
        "print(\"Final theta:\", theta_final)\n",
        "print(\"Final cost:\", cost_history[-1])\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yhinbctFUL_K",
        "outputId": "b413dd2d-6ff5-47b2-d2bc-5baaca2455ce"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Final theta: [-0.34996922  0.14967444  0.25690458  0.12871541 -0.28924534 -0.13428604\n",
            " -0.11010055 -0.02915669]\n",
            "Final cost: 0.3193179803605742\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "predictions = sigmoid(np.dot(X_test, theta_final)) >= 0.5\n",
        "accuracy = np.mean(predictions == y_test)\n",
        "print(\"Accuracy:\", accuracy)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SrYKZaOsUcee",
        "outputId": "1b314e18-6858-4ba7-c9ee-10392870799c"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.96375\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "\n",
        "def sigmoid(z):\n",
        "\n",
        "    return 1 / (1 + np.exp(-z))\n",
        "\n",
        "def compute_cost(X, y, theta):\n",
        "    m = y.size\n",
        "    h = sigmoid(X.dot(theta))\n",
        "    cost = -1/m * (np.dot(y, np.log(h)) + np.dot(1 - y, np.log(1 - h)))\n",
        "    return cost\n",
        "\n",
        "def stochastic_gradient_descent(X, y, theta, alpha, iterations):\n",
        "    m = y.size\n",
        "    cost_history = np.zeros(iterations)\n",
        "\n",
        "    for it in range(iterations):\n",
        "        # Shuffle the data for each iteration\n",
        "        indices = np.arange(m)\n",
        "        np.random.shuffle(indices)\n",
        "        X = X[indices]\n",
        "        y = y[indices]# randomly choice a point to update.\n",
        "\n",
        "        for i in range(m):\n",
        "            xi = X[i, :].reshape(1, -1)\n",
        "            yi = y[i]\n",
        "            hi = sigmoid(xi.dot(theta))\n",
        "            gradient = xi.T * (hi - yi)\n",
        "            theta -= alpha * gradient # updata each time compute the result.\n",
        "\n",
        "            #record the cost\n",
        "            cost_history[it] = compute_cost(X, y, theta)\n",
        "\n",
        "    return theta, cost_history\n",
        "\n",
        "np.random.seed(0)# make sure each time result can be repeat.\n",
        "# Data preprocessing\n",
        "dataset = data.to_numpy()\n",
        "X = dataset[:, :-1]\n",
        "y = dataset[:, -1]\n",
        "split_size = int(0.8 * len(X))\n",
        "X_train, X_test = X[:split_size], X[split_size:]\n",
        "y_train, y_test = y[:split_size], y[split_size:]\n",
        "\n",
        "\n",
        "# Convert to numpy arrays and reshape if necessary\n",
        "X_train = np.array(X_train)\n",
        "y_train = np.array(y_train)\n",
        "X_test = np.array(X_test)\n",
        "y_test = np.array(y_test)\n",
        "\n",
        "# Add an intercept term to X_train and X_test\n",
        "X_train = np.column_stack((np.ones(X_train.shape[0]), X_train))\n",
        "X_test = np.column_stack((np.ones(X_test.shape[0]), X_test))\n",
        "\n",
        "# Initialize parameters\n",
        "theta = np.zeros(X_train.shape[1])\n",
        "alpha = 0.01\n",
        "iterations = 1000\n",
        "\n",
        "# Train the model\n",
        "theta_final, cost_history = batch_gradient_descent(X_train, y_train, theta, alpha, iterations)\n",
        "\n",
        "# Output the final parameters and cost history\n",
        "print(\"Final theta:\", theta_final)\n",
        "print(\"Final cost:\", cost_history[-1])\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a9gXt5HofZ3t",
        "outputId": "a9a2bdfc-0a54-4bcd-c6b7-d2c9b5bd5352"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Final theta: [-1.50812923  0.37810347  0.46075976  0.29920637 -0.62848341 -0.2863944\n",
            " -0.14042569 -0.10457217]\n",
            "Final cost: 0.09425296851298473\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "predictions = sigmoid(np.dot(X_test, theta_final)) >= 0.5\n",
        "accuracy = np.mean(predictions == y_test)\n",
        "print(\"Accuracy:\", accuracy)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oFeWozfmfsUr",
        "outputId": "ac8dbeb0-8fa8-43f8-bf87-b83d0673d484"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.9925\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "So, on the task 3, I make the batch_gradient_descent and stochastic_gradient_descent, As we can see that they heave the similar performance on the accuracy but the cost is different which the stochastice_gradient_descent is less than batch_gradient_descent. Because we know that the batch_gradient_descent is use all data point for one updata but the stochastic_gradient_descent is no, just pick a point for updata which we know it is doing faster than batch_gradient_descent."
      ],
      "metadata": {
        "id": "FPUqqbuibh_E"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Task 4 (45 points): Pick any 2 optimization algorithms that are used to optimize the ‘vanilla’\n",
        "gradient descent. Implement both. You may implement these algorithms yourself OR use a\n",
        "package. In your conclusion, compare both optimization techniques/algorithms with respect to\n",
        "the results you achieve. Also compare these results with the original implementation of\n",
        "gradient descent (Task 3 above). Describe why we should or why we should not use\n",
        "optimization algorithms for the task at hand. For comparison of these algorithms define an\n",
        "evaluation metric appropriate for your problem."
      ],
      "metadata": {
        "id": "wzQGkFFCF5o-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.optim as optim\n",
        "import numpy as np\n",
        "\n",
        "\n",
        "\n",
        "# make DataFrame transfer NumPy array\n",
        "dataset = data.to_numpy()\n",
        "X = dataset[:, :-1]\n",
        "y = dataset[:, -1]\n",
        "split_size = int(0.8 * len(X))\n",
        "X_train, X_test = X[:split_size], X[split_size:]\n",
        "y_train, y_test = y[:split_size], y[split_size:]\n",
        "\n",
        "# make NumPy tranfer to  PyTorch\n",
        "X_train_tensor = torch.tensor(X_train, dtype=torch.float32)\n",
        "y_train_tensor = torch.tensor(y_train, dtype=torch.float32).view(-1, 1)\n",
        "X_test_tensor = torch.tensor(X_test, dtype=torch.float32)\n",
        "y_test_tensor = torch.tensor(y_test, dtype=torch.float32).view(-1, 1)\n",
        "\n",
        "# define regression model\n",
        "def create_model(input_size):\n",
        "    return nn.Sequential(nn.Linear(input_size, 1), nn.Sigmoid())\n",
        "\n",
        "model_momentum = create_model(X_train.shape[1])\n",
        "model_minibatch = create_model(X_train.shape[1])\n",
        "\n",
        "# define the loss function\n",
        "criterion = nn.BCELoss()\n",
        "\n",
        "# defined two train model\n",
        "optimizer_momentum = optim.SGD(model_momentum.parameters(), lr=0.01, momentum=0.9)\n",
        "optimizer_minibatch = optim.SGD(model_minibatch.parameters(), lr=0.01)\n",
        "\n",
        "# use forward and backward to train the modlel\n",
        "def train_model(model, optimizer, X_train_tensor, y_train_tensor, epochs=100):\n",
        "    for epoch in range(epochs):\n",
        "        outputs = model(X_train_tensor)\n",
        "        loss = criterion(outputs, y_train_tensor)\n",
        "\n",
        "        optimizer.zero_grad()\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "\n",
        "        # if (epoch+1) % 10 == 0:\n",
        "        #     print(f'Epoch [{epoch+1}/{epochs}], Loss: {loss.item():.4f}')\n",
        "    return loss\n",
        "\n",
        "# calculate the accuracy function\n",
        "def calculate_accuracy(model, X_test_tensor, y_test_tensor):\n",
        "    with torch.no_grad():\n",
        "        outputs = model(X_test_tensor)\n",
        "        predicted = (outputs > 0.5).float()\n",
        "        total = y_test_tensor.size(0)\n",
        "        correct = (predicted == y_test_tensor).sum().item()\n",
        "        accuracy = 100 * correct / total\n",
        "        return accuracy\n",
        "\n",
        "# print out the result\n",
        "Momentum_loss=train_model(model_momentum, optimizer_momentum, X_train_tensor, y_train_tensor)\n",
        "print(f\"Training with Gradient Descent with Momentum loss: {Momentum_loss:.4f}\")\n",
        "accuracy_momentum = calculate_accuracy(model_momentum, X_test_tensor, y_test_tensor)\n",
        "print(f\"Accuracy with Gradient Descent with Momentum: {accuracy_momentum:.2f}%\")\n",
        "\n",
        "Min_loss = train_model(model_minibatch, optimizer_minibatch, X_train_tensor, y_train_tensor)\n",
        "print(f\"\\nTraining with Mini-batch Gradient Descent: {Min_loss:.4f} \")\n",
        "accuracy_minibatch = calculate_accuracy(model_minibatch, X_test_tensor, y_test_tensor)\n",
        "print(f\"Accuracy with Mini-batch Gradient Descent: {accuracy_minibatch:.2f}%\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qsy_yLNnfy2Q",
        "outputId": "96015e2d-6387-4741-896e-e7d511f63adb"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training with Gradient Descent with Momentum loss: 0.0862\n",
            "Accuracy with Gradient Descent with Momentum: 99.12%\n",
            "\n",
            "Training with Mini-batch Gradient Descent: 0.2632 \n",
            "Accuracy with Mini-batch Gradient Descent: 98.12%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        " As we can see that they have similar perform on the accuracy which all over the 90% however compare with the cost there have a big differetnt which  the mini-batch gradient descent and batch gradient descent have the similar cost and Gradient descent with momentum and stochastic_gradient_descent have the similar cost. In comaparsion, the Gradient descent with momentum and stochastic_gradient_descent is cost less than other two, one reason is because it not need to make the each updata with all dataset and it can move faster and cost less.\n",
        "\n",
        "\n",
        "\n",
        " The reason we use the optimization algorithms beacuse it have faster convergence, computational efficiency and stability and accuracy. In my example, the accuracy that need to updata all table, such as mini-batch gradient descent and batch gradient descent is alway less accuracy than other two, one reason for it is the noisy in the table have affect on accuracy and the cost of mini-batch gradient descent and batch gradient descent is stronger than other two.\n",
        "\n",
        " The reason we not use the optimization algorithms because Complexity and Overfitting Risk. one reason for it because each time they will pick a radom point to use for the model. it maybe reuduce the noisy but increase the overfitting. Another reason is my dataset is a small dataset and clear which differnet of real case. those model will amplify the noisy to affect to the result.\n",
        "\n",
        " In summary, while optimization algorithms can significantly improve the performance of gradient descent, the choice of algorithm should be aligned with the specific requirements and constraints of the task, such as data size, problem complexity, and available computational resources. For tasks with large datasets, Mini-batch Gradient Descent can be particularly useful, whereas Gradient Descent with Momentum might be more suitable for scenarios where a smoother convergence is desired."
      ],
      "metadata": {
        "id": "P7Tkh-gTpKCl"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "DIN1xv2nzMqP"
      },
      "execution_count": 10,
      "outputs": []
    }
  ]
}