{
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "1005944835_0.34864291594519803"
      },
      "execution_count": null,
      "source": [
        "%%bash\npip install torch==1.13.1"
      ],
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1005944835_0.3903753858877983"
      },
      "execution_count": null,
      "source": [
        "import torch\nprint(torch. __version__)"
      ],
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1005944835_0.5185274770795416"
      },
      "execution_count": null,
      "source": [
        "import torch\nimport torch.nn as nn\nimport torch.nn.functional as f"
      ],
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1005944835_0.4635300909217901"
      },
      "execution_count": 1,
      "source": [
        "from Notebook.DSNotebook.NotebookExecutor import NotebookExecutor\nnb = NotebookExecutor()\ndf_Iris = nb.get_data('11121675159938577', '@SYS.USERID', 'True',{},[])\ndf_Iris"
      ],
      "outputs": [
        {
          "data": {
            "text/plain": [
              "     sepal_length  sepal_width  petal_length  petal_width    species\n0             5.1          2.0           1.0          0.2     setosa\n1             4.9          2.2           1.1          0.2     setosa\n2             4.7          2.2           1.2          0.2     setosa\n3             4.6          2.2           1.2          0.2     setosa\n4             5.0          2.3           1.3          0.2     setosa\n..            ...          ...           ...          ...        ...\n145           6.7          3.9           6.4          2.3  virginica\n146           6.3          4.0           6.6          1.9  virginica\n147           6.5          4.1           6.7          2.0  virginica\n148           6.2          4.2           6.7          2.3  virginica\n149           5.9          4.4           6.9          1.8  virginica\n\n[150 rows x 5 columns]"
            ],
            "text/html": [
              "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>sepal_length</th>\n      <th>sepal_width</th>\n      <th>petal_length</th>\n      <th>petal_width</th>\n      <th>species</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>5.1</td>\n      <td>2.0</td>\n      <td>1.0</td>\n      <td>0.2</td>\n      <td>setosa</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>4.9</td>\n      <td>2.2</td>\n      <td>1.1</td>\n      <td>0.2</td>\n      <td>setosa</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>4.7</td>\n      <td>2.2</td>\n      <td>1.2</td>\n      <td>0.2</td>\n      <td>setosa</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4.6</td>\n      <td>2.2</td>\n      <td>1.2</td>\n      <td>0.2</td>\n      <td>setosa</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>5.0</td>\n      <td>2.3</td>\n      <td>1.3</td>\n      <td>0.2</td>\n      <td>setosa</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>145</th>\n      <td>6.7</td>\n      <td>3.9</td>\n      <td>6.4</td>\n      <td>2.3</td>\n      <td>virginica</td>\n    </tr>\n    <tr>\n      <th>146</th>\n      <td>6.3</td>\n      <td>4.0</td>\n      <td>6.6</td>\n      <td>1.9</td>\n      <td>virginica</td>\n    </tr>\n    <tr>\n      <th>147</th>\n      <td>6.5</td>\n      <td>4.1</td>\n      <td>6.7</td>\n      <td>2.0</td>\n      <td>virginica</td>\n    </tr>\n    <tr>\n      <th>148</th>\n      <td>6.2</td>\n      <td>4.2</td>\n      <td>6.7</td>\n      <td>2.3</td>\n      <td>virginica</td>\n    </tr>\n    <tr>\n      <th>149</th>\n      <td>5.9</td>\n      <td>4.4</td>\n      <td>6.9</td>\n      <td>1.8</td>\n      <td>virginica</td>\n    </tr>\n  </tbody>\n</table>\n<p>150 rows × 5 columns</p>\n</div>"
            ]
          },
          "metadata": {},
          "execution_count": 2,
          "output_type": "execute_result"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1005944835_0.6494884794028646"
      },
      "execution_count": 2,
      "source": [
        "import torch\nimport torch.nn as nn\nimport torch.nn.functional as f\nimport pandas as pd\n\ndef func2():\n    \n    from Notebook.DSNotebook.NotebookExecutor import NotebookExecutor\n    nb = NotebookExecutor()\n    df_Iris = nb.get_data('11121675159938577', '@SYS.USERID', 'True',{},[])\n\n    df = df_Iris.copy()\n\n    from sklearn import preprocessing\n    le = preprocessing.LabelEncoder()\n    df['species'] = le.fit_transform(df.species.values);\n\n    X = df.drop('species', axis=1)\n    y = df['species']\n\n    X_v = X.values\n    y_v = y.values\n\n    from sklearn.model_selection import train_test_split\n    X_train, X_test, y_train, y_test = train_test_split(X_v,y_v,test_size=0.2,random_state=33)\n\n    X_train_t = torch.FloatTensor(X_train)\n    X_test_t = torch.FloatTensor(X_test)\n\n    # in pytorch if you use cross entropy loss we don't need to do one-hot encoding for multiclass scenario\n    y_train_t = torch.LongTensor(y_train)\n    y_test_t = torch.LongTensor(y_test)\n\n    from Notebook.DSNotebook.NotebookExecutor import NotebookExecutor\n    nb = NotebookExecutor()\n    loaded_model = nb.load_saved_model('11121675747735683')\n\n    data_list = []\n    with torch.no_grad():\n        for i, data in enumerate(loaded_model(X_test_t)):\n            data_list.append(data.argmax().item())\n\n    data_series = pd.Series(data_list) \n    X_test_d = pd.DataFrame(X_test, columns=X.columns)\n    pred = pd.concat([X_test_d, data_series], axis=1)\n    pred.rename({0: 'predictions'}, axis=1, inplace=True)\n\n    return pred"
      ],
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1005944835_0.7591795775284809"
      },
      "execution_count": 3,
      "source": [
        "func2()"
      ],
      "outputs": [
        {
          "data": {
            "text/plain": [
              "    sepal_length  sepal_width  petal_length  petal_width  predictions\n0            5.7          3.2           4.8          1.3            1\n1            6.7          3.0           4.0          1.4            1\n2            4.7          2.7           1.5          0.2            0\n3            6.5          2.9           3.5          1.5            1\n4            6.1          3.6           5.8          1.4            2\n..           ...          ...           ...          ...          ...\n25           6.2          3.5           5.6          1.8            2\n26           6.1          3.1           4.7          1.4            1\n27           6.1          3.0           4.2          1.3            1\n28           6.5          4.1           6.7          2.0            2\n29           5.9          4.4           6.9          1.8            2\n\n[30 rows x 5 columns]"
            ],
            "text/html": [
              "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>sepal_length</th>\n      <th>sepal_width</th>\n      <th>petal_length</th>\n      <th>petal_width</th>\n      <th>predictions</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>5.7</td>\n      <td>3.2</td>\n      <td>4.8</td>\n      <td>1.3</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>6.7</td>\n      <td>3.0</td>\n      <td>4.0</td>\n      <td>1.4</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>4.7</td>\n      <td>2.7</td>\n      <td>1.5</td>\n      <td>0.2</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>6.5</td>\n      <td>2.9</td>\n      <td>3.5</td>\n      <td>1.5</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>6.1</td>\n      <td>3.6</td>\n      <td>5.8</td>\n      <td>1.4</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>25</th>\n      <td>6.2</td>\n      <td>3.5</td>\n      <td>5.6</td>\n      <td>1.8</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>26</th>\n      <td>6.1</td>\n      <td>3.1</td>\n      <td>4.7</td>\n      <td>1.4</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>27</th>\n      <td>6.1</td>\n      <td>3.0</td>\n      <td>4.2</td>\n      <td>1.3</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>28</th>\n      <td>6.5</td>\n      <td>4.1</td>\n      <td>6.7</td>\n      <td>2.0</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>29</th>\n      <td>5.9</td>\n      <td>4.4</td>\n      <td>6.9</td>\n      <td>1.8</td>\n      <td>2</td>\n    </tr>\n  </tbody>\n</table>\n<p>30 rows × 5 columns</p>\n</div>"
            ]
          },
          "metadata": {},
          "execution_count": 4,
          "output_type": "execute_result"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1005944835_0.511567765595818"
      },
      "execution_count": null,
      "source": [
        "from Notebook.DSNotebook.NotebookExecutor import NotebookExecutor\nnb = NotebookExecutor()\nloaded_model = nb.load_saved_model('11121675747735683')"
      ],
      "outputs": []
    }
  ],
  "metadata": {},
  "nbformat": 4,
  "nbformat_minor": 2
}