smartFactory_sequential_Yumin.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c74ed87a-784a-4d96-883e-0f65fd15758d",
        "id": "edWlZSskiEhV"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "import pandas as pd\n",
        "import random\n",
        "import os\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt   \n",
        "import matplotlib.patches as mpathces \n",
        "import seaborn as sns \n",
        "\n",
        "# sklearn \n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import accuracy_score, classification_report, f1_score\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "from sklearn.ensemble import VotingClassifier\n",
        "\n",
        "# sequential model modeling \n",
        "import tensorflow as tf\n",
        "from keras import regularizers\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense,Dropout, Activation, BatchNormalization \n",
        "from keras import optimizers, metrics, callbacks\n",
        "\n",
        "# oversampling imbalanced learning \n",
        "from imblearn.over_sampling import SMOTE, BorderlineSMOTE, ADASYN, SVMSMOTE \n",
        "\n",
        "import optuna\n",
        "from optuna.samplers import TPESampler\n",
        "from optuna import Trial\n",
        "\n",
        "# GoodleDrive mount\n",
        "from google.colab import drive   \n",
        "drive.mount('/content/drive')"
      ],
      "id": "edWlZSskiEhV"
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "id": "94682a80",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "94682a80",
        "outputId": "48c899d1-d535-44a1-eef8-3430860030a7"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "import pandas as pd\n",
        "import random\n",
        "import os\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt   \n",
        "import seaborn as sns \n",
        "\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras import regularizers\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense,Dropout, Activation\n",
        "\n",
        "from sklearn.metrics import f1_score, classification_report\n",
        "from catboost import CatBoostClassifier\n",
        "\n",
        "\n",
        "import optuna\n",
        "from optuna.samplers import TPESampler\n",
        "from optuna import Trial\n",
        "\n",
        "from google.colab import drive  # GoodleDrive mount \n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "wqkW_3EbQxj1",
      "metadata": {
        "id": "wqkW_3EbQxj1"
      },
      "source": [
        "# 1.preprocessing\n",
        "1. Label Encoding: categorical values `LINE`, `PRODUCT_CODE`\n",
        "2. Missing values: fillna(0) \n",
        "3. scaling: StandardScaler"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "979289c3-9537-43af-e340-10bd15074c1a",
        "id": "FLHA36Z1iEhW"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "8/8 [==============================] - 4s 33ms/step - loss: 0.8584 - accuracy: 0.6695 - val_loss: 0.9085 - val_accuracy: 0.6333\n",
            "Epoch 2/10\n",
            "8/8 [==============================] - 0s 7ms/step - loss: 0.6826 - accuracy: 0.7364 - val_loss: 0.9080 - val_accuracy: 0.6417\n",
            "Epoch 3/10\n",
            "8/8 [==============================] - 0s 6ms/step - loss: 0.6440 - accuracy: 0.7385 - val_loss: 0.9296 - val_accuracy: 0.6500\n",
            "Epoch 4/10\n",
            "8/8 [==============================] - 0s 7ms/step - loss: 0.6474 - accuracy: 0.7636 - val_loss: 1.0604 - val_accuracy: 0.6250\n",
            "Epoch 5/10\n",
            "8/8 [==============================] - 0s 7ms/step - loss: 0.6232 - accuracy: 0.7720 - val_loss: 0.9168 - val_accuracy: 0.6250\n",
            "Epoch 6/10\n",
            "8/8 [==============================] - 0s 7ms/step - loss: 0.5536 - accuracy: 0.7971 - val_loss: 1.0447 - val_accuracy: 0.6250\n",
            "Epoch 7/10\n",
            "8/8 [==============================] - 0s 7ms/step - loss: 0.5530 - accuracy: 0.7929 - val_loss: 1.0145 - val_accuracy: 0.6250\n",
            "Epoch 8/10\n",
            "8/8 [==============================] - 0s 7ms/step - loss: 0.5615 - accuracy: 0.7845 - val_loss: 0.9686 - val_accuracy: 0.6333\n",
            "Epoch 9/10\n",
            "8/8 [==============================] - 0s 7ms/step - loss: 0.5240 - accuracy: 0.7929 - val_loss: 1.0133 - val_accuracy: 0.6250\n",
            "Epoch 10/10\n",
            "8/8 [==============================] - 0s 7ms/step - loss: 0.5401 - accuracy: 0.7887 - val_loss: 0.9969 - val_accuracy: 0.6417\n"
          ]
        }
      ],
      "source": [
        "train_df = pd.read_csv('/content/drive/MyDrive/train.csv')\n",
        "test_df = pd.read_csv('/content/drive/MyDrive/test.csv')\n",
        "submit = pd.read_csv('/content/drive/MyDrive/sample_submission.csv')\n",
        "\n",
        "train_x = train_df.drop(columns=['PRODUCT_ID', 'TIMESTAMP', 'Y_Class','Y_Quality'])\n",
        "train_y = train_df['Y_Class']\n",
        "test_x = test_df.drop(columns=['PRODUCT_ID', 'TIMESTAMP'])   \n",
        "\n",
        "# 1) qualitative to quantitative\n",
        "qual_col = ['LINE', 'PRODUCT_CODE']\n",
        "for i in qual_col:\n",
        "    le = LabelEncoder()    # TRY one-hot encoding \n",
        "    le = le.fit(train_x[i])\n",
        "    train_x[i] = le.transform(train_x[i])\n",
        "    for label in np.unique(test_x[i]): \n",
        "        if label not in le.classes_: \n",
        "            le.classes_ = np.append(le.classes_, label)\n",
        "    test_x[i] = le.transform(test_x[i]) \n",
        "\n",
        "# 2) Missing Values \n",
        "train_x = train_x.fillna(0)\n",
        "test_x = test_x.fillna(0)\n",
        "\n",
        "\n",
        "# 3) scaling: only `X_???` values (continuous)\n",
        "Xs = train_x.select_dtypes(include=float).iloc[:,1:].columns.tolist()\n",
        "scaler = StandardScaler().fit(train_x.loc[:, Xs])\n",
        "train_x.loc[:, Xs] = scaler.transform(train_x.loc[:, Xs])\n",
        "test_x.loc[:, Xs] = scaler.transform(test_x.loc[:, Xs])\n",
        "\n",
        "model = Sequential([\n",
        "        # Input layer: ReLU (decide input size according to dimension)\n",
        "        Dense(512, activation='relu', input_shape=(train_x.shape[-1],)),      \n",
        "\n",
        "        # layer2: softmax  (output layer)\n",
        "        Dense(3, activation='softmax')\n",
        "        ])\n",
        "\n",
        "\n",
        "model.compile(optimizer = optimizers.Adam(1e-4),  # decrease learning rate (default learning rate=1e-3) \n",
        "              loss='sparse_categorical_crossentropy',  # tow or more label classes (`Y_Class`: 0,1,2)\n",
        "              metrics=['accuracy'])\n",
        "\n",
        "history = model.fit(train_x, train_y,\n",
        "                    epochs=10,\n",
        "                    batch_size=64,  # batch size: 2^x && half of data size(598/2) \n",
        "                    validation_split=0.2,\n",
        "                    # callbacks=callbacks\n",
        "                    )   "
      ],
      "id": "FLHA36Z1iEhW"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "57a8de60",
        "scrolled": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "outputId": "4e115f24-88f7-40f3-dcfd-2f799186d7ab"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "10/10 [==============================] - 0s 2ms/step\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "AttributeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-4-9d962316cb57>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0msns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcountplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m;\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m \u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m310\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mAttributeError\u001b[0m: 'list' object has no attribute 'reshape'"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "preds = model.predict(test_x)\n",
        "a=[]\n",
        "for i in preds: \n",
        "    i = list(i)\n",
        "    a.append(i.index(max(i)))\n",
        "sns.countplot(x=a);  "
      ],
      "id": "57a8de60"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "b687c6a1"
      },
      "outputs": [],
      "source": [
        "submit = pd.read_csv('/content/drive/MyDrive/sample_submission.csv')\n",
        "submit['Y_Class'] = a\n",
        "\n",
        "submit.to_csv('ANN.stsc.trainx.b64-smartFactorySubmission.csv', index=False)"
      ],
      "id": "b687c6a1"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "f5a8c473",
        "outputId": "fcd7e3ac-a62a-4577-db75-1845d8d40c27"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1008x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "epochs = range(1, len(history.history[\"loss\"]) + 1)\n",
        "\n",
        "fig, axes = plt.subplots(1,2,figsize=(14,4))\n",
        "ax= axes.flatten()\n",
        "a, b = ax[0], ax[1]\n",
        "\n",
        "a.plot(epochs, history.history[\"loss\"], \"bo\", label=\"Training loss\")\n",
        "a.plot(epochs, history.history[\"val_loss\"], \"b\", label=\"Validation loss\")\n",
        "a.set_title(\"Training and validation loss\")\n",
        "a.set_xlabel(\"Epochs\")\n",
        "a.set_ylabel(\"Loss\")\n",
        "a.legend()   \n",
        "\n",
        "b.plot(epochs, history.history[\"accuracy\"], \"bo\", label=\"Training accuracy\")\n",
        "b.plot(epochs, history.history[\"val_accuracy\"], \"b\", label=\"Validation accuracy\")\n",
        "b.set_title(\"Training and validation accuracy\")\n",
        "b.set_xlabel(\"Epochs\")\n",
        "b.set_ylabel(\"Accuracy\")\n",
        "b.legend();"
      ],
      "id": "f5a8c473"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "369e67a9",
      "metadata": {
        "id": "369e67a9"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "provenance": [],
      "machine_shape": "hm",
      "toc_visible": true
    },
    "gpuClass": "premium",
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.9.7"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 5
}