Observations.ipynb

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      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "%matplotlib inline\n",
        "sns.set_style(\"darkgrid\")\n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')"
      ],
      "execution_count": null,
      "outputs": []
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      "source": [
        "df_singleLSTMv1 = pd.read_csv('/content/drive/MyDrive/data/sel1_observation.csv', index_col='trial')\n",
        "df_singleLSTMv2 = pd.read_csv('/content/drive/MyDrive/data/sel2_observation.csv', index_col='trial')\n",
        "df_stackedLSTMv1 = pd.read_csv('/content/drive/MyDrive/data/sdl1_observation.csv', index_col='trial')\n",
        "df_stackedLSTMv2 = pd.read_csv('/content/drive/MyDrive/data/sdl2_observation.csv', index_col='trial')"
      ],
      "execution_count": null,
      "outputs": []
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      "source": [
        "df_singleLSTMv1.head()"
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              "      optimizer  units  epochs  batch_size  dropout_rate  learning_rate  \\\n",
              "trial                                                                     \n",
              "1           SGD   2450     150         128      0.455343       0.005748   \n",
              "2       Adagrad   1900    3050          32      0.393865       0.001704   \n",
              "3       RMSprop   2500     750          32      0.488702       0.054738   \n",
              "4       RMSprop    700    4700         256      0.596639       0.001470   \n",
              "5       Adagrad    400     700         512      0.393315       0.020313   \n",
              "\n",
              "            loss  \n",
              "trial             \n",
              "1      66.150764  \n",
              "2       6.330302  \n",
              "3      11.116813  \n",
              "4       6.985736  \n",
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              "      <th>4</th>\n",
              "      <td>RMSprop</td>\n",
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              "      <th>5</th>\n",
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              "      <td>400</td>\n",
              "      <td>700</td>\n",
              "      <td>512</td>\n",
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            "text/plain": [
              "      optimizer  units  epochs  batch_size  dropout_rate  learning_rate  \\\n",
              "trial                                                                     \n",
              "1         Nadam   1150     650          64      0.425399       0.013856   \n",
              "2           SGD    800    3650         256      0.599323       0.003561   \n",
              "3        Adamax    550     150         512      0.155783       0.006360   \n",
              "4           SGD    850    4500          64      0.113502       0.321504   \n",
              "5         Nadam   2700    3700         512      0.286647       0.002063   \n",
              "\n",
              "            loss  \n",
              "trial             \n",
              "1       8.000573  \n",
              "2      15.017938  \n",
              "3       9.951816  \n",
              "4       6.722417  \n",
              "5      14.451367  "
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              "      <th>1</th>\n",
              "      <td>Nadam</td>\n",
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              "      <th>4</th>\n",
              "      <td>SGD</td>\n",
              "      <td>850</td>\n",
              "      <td>4500</td>\n",
              "      <td>64</td>\n",
              "      <td>0.113502</td>\n",
              "      <td>0.321504</td>\n",
              "      <td>6.722417</td>\n",
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              "      <th>5</th>\n",
              "      <td>Nadam</td>\n",
              "      <td>2700</td>\n",
              "      <td>3700</td>\n",
              "      <td>512</td>\n",
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              "      optimizer  units_1  dropout_rate_1  units_2  dropout_rate_2  units_3  \\\n",
              "trial                                                                        \n",
              "1         Nadam     1700        0.547128     2400        0.225553     2400   \n",
              "2         Nadam     3600        0.449154     3850        0.129923     2450   \n",
              "3       Adagrad     3400        0.487318     1950        0.284208     1800   \n",
              "4       Adagrad      750        0.557559     2600        0.472017      650   \n",
              "5        Adamax     2500        0.149514      900        0.374907     1950   \n",
              "\n",
              "       dropout_rate_3  units_4  dropout_rate_4  epochs  batch_size  \\\n",
              "trial                                                                \n",
              "1            0.120854     3500        0.122884    2750         128   \n",
              "2            0.287952     3050        0.157726    2300          32   \n",
              "3            0.444930     2550        0.571218    3500         256   \n",
              "4            0.495224     2700        0.319140     950          64   \n",
              "5            0.265092     3150        0.436872    4100         256   \n",
              "\n",
              "       learning_rate       loss  \n",
              "trial                            \n",
              "1           0.001558  17.899176  \n",
              "2           0.006648  53.008534  \n",
              "3           0.080419  74.436131  \n",
              "4           0.021409  74.104248  \n",
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              "      <th></th>\n",
              "      <th>optimizer</th>\n",
              "      <th>units_1</th>\n",
              "      <th>dropout_rate_1</th>\n",
              "      <th>units_2</th>\n",
              "      <th>dropout_rate_2</th>\n",
              "      <th>units_3</th>\n",
              "      <th>dropout_rate_3</th>\n",
              "      <th>units_4</th>\n",
              "      <th>dropout_rate_4</th>\n",
              "      <th>epochs</th>\n",
              "      <th>batch_size</th>\n",
              "      <th>learning_rate</th>\n",
              "      <th>loss</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>trial</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Nadam</td>\n",
              "      <td>1700</td>\n",
              "      <td>0.547128</td>\n",
              "      <td>2400</td>\n",
              "      <td>0.225553</td>\n",
              "      <td>2400</td>\n",
              "      <td>0.120854</td>\n",
              "      <td>3500</td>\n",
              "      <td>0.122884</td>\n",
              "      <td>2750</td>\n",
              "      <td>128</td>\n",
              "      <td>0.001558</td>\n",
              "      <td>17.899176</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Nadam</td>\n",
              "      <td>3600</td>\n",
              "      <td>0.449154</td>\n",
              "      <td>3850</td>\n",
              "      <td>0.129923</td>\n",
              "      <td>2450</td>\n",
              "      <td>0.287952</td>\n",
              "      <td>3050</td>\n",
              "      <td>0.157726</td>\n",
              "      <td>2300</td>\n",
              "      <td>32</td>\n",
              "      <td>0.006648</td>\n",
              "      <td>53.008534</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Adagrad</td>\n",
              "      <td>3400</td>\n",
              "      <td>0.487318</td>\n",
              "      <td>1950</td>\n",
              "      <td>0.284208</td>\n",
              "      <td>1800</td>\n",
              "      <td>0.444930</td>\n",
              "      <td>2550</td>\n",
              "      <td>0.571218</td>\n",
              "      <td>3500</td>\n",
              "      <td>256</td>\n",
              "      <td>0.080419</td>\n",
              "      <td>74.436131</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Adagrad</td>\n",
              "      <td>750</td>\n",
              "      <td>0.557559</td>\n",
              "      <td>2600</td>\n",
              "      <td>0.472017</td>\n",
              "      <td>650</td>\n",
              "      <td>0.495224</td>\n",
              "      <td>2700</td>\n",
              "      <td>0.319140</td>\n",
              "      <td>950</td>\n",
              "      <td>64</td>\n",
              "      <td>0.021409</td>\n",
              "      <td>74.104248</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Adamax</td>\n",
              "      <td>2500</td>\n",
              "      <td>0.149514</td>\n",
              "      <td>900</td>\n",
              "      <td>0.374907</td>\n",
              "      <td>1950</td>\n",
              "      <td>0.265092</td>\n",
              "      <td>3150</td>\n",
              "      <td>0.436872</td>\n",
              "      <td>4100</td>\n",
              "      <td>256</td>\n",
              "      <td>0.002054</td>\n",
              "      <td>7.638906</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "      </button>\n",
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              "\n",
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-96f62ef8-a23b-4f96-9136-1669deafbb6e');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "id": "0P42VW3rVimG",
        "outputId": "2fa83f09-00cc-46d8-be2b-7d05f23fc300"
      },
      "source": [
        "df_stackedLSTMv2.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      optimizer  units_1  dropout_rate_1  units_2  dropout_rate_2  units_3  \\\n",
              "trial                                                                        \n",
              "1        Adamax      350        0.333622     3900        0.438655     3350   \n",
              "2         Nadam     1550        0.443789     1300        0.358592     2350   \n",
              "3        Adamax     2550        0.380375     1600        0.142205      600   \n",
              "4        Adamax      450        0.575113     3150        0.301692     3750   \n",
              "5       RMSprop     2750        0.538624     2850        0.103476     3100   \n",
              "\n",
              "       dropout_rate_3  units_4  dropout_rate_4  epochs  batch_size  \\\n",
              "trial                                                                \n",
              "1            0.140808     3400        0.427832    3050         512   \n",
              "2            0.231559     3200        0.191327    4950          32   \n",
              "3            0.219512     3500        0.260868    4200          32   \n",
              "4            0.113502     1650        0.544925    1350         128   \n",
              "5            0.170243     2750        0.536542    3400         512   \n",
              "\n",
              "       learning_rate        loss  \n",
              "trial                             \n",
              "1           0.005467   12.159953  \n",
              "2           0.005424   36.003931  \n",
              "3           0.002290    8.001553  \n",
              "4           0.082733  938.447022  \n",
              "5           0.003233   15.602328  "
            ],
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>optimizer</th>\n",
              "      <th>units_1</th>\n",
              "      <th>dropout_rate_1</th>\n",
              "      <th>units_2</th>\n",
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              "      <th>units_4</th>\n",
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              "      <th>batch_size</th>\n",
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              "    <tr>\n",
              "      <th>trial</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
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              "      <th></th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Adamax</td>\n",
              "      <td>350</td>\n",
              "      <td>0.333622</td>\n",
              "      <td>3900</td>\n",
              "      <td>0.438655</td>\n",
              "      <td>3350</td>\n",
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              "      <td>12.159953</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Nadam</td>\n",
              "      <td>1550</td>\n",
              "      <td>0.443789</td>\n",
              "      <td>1300</td>\n",
              "      <td>0.358592</td>\n",
              "      <td>2350</td>\n",
              "      <td>0.231559</td>\n",
              "      <td>3200</td>\n",
              "      <td>0.191327</td>\n",
              "      <td>4950</td>\n",
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              "      <td>0.005424</td>\n",
              "      <td>36.003931</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Adamax</td>\n",
              "      <td>2550</td>\n",
              "      <td>0.380375</td>\n",
              "      <td>1600</td>\n",
              "      <td>0.142205</td>\n",
              "      <td>600</td>\n",
              "      <td>0.219512</td>\n",
              "      <td>3500</td>\n",
              "      <td>0.260868</td>\n",
              "      <td>4200</td>\n",
              "      <td>32</td>\n",
              "      <td>0.002290</td>\n",
              "      <td>8.001553</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Adamax</td>\n",
              "      <td>450</td>\n",
              "      <td>0.575113</td>\n",
              "      <td>3150</td>\n",
              "      <td>0.301692</td>\n",
              "      <td>3750</td>\n",
              "      <td>0.113502</td>\n",
              "      <td>1650</td>\n",
              "      <td>0.544925</td>\n",
              "      <td>1350</td>\n",
              "      <td>128</td>\n",
              "      <td>0.082733</td>\n",
              "      <td>938.447022</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>RMSprop</td>\n",
              "      <td>2750</td>\n",
              "      <td>0.538624</td>\n",
              "      <td>2850</td>\n",
              "      <td>0.103476</td>\n",
              "      <td>3100</td>\n",
              "      <td>0.170243</td>\n",
              "      <td>2750</td>\n",
              "      <td>0.536542</td>\n",
              "      <td>3400</td>\n",
              "      <td>512</td>\n",
              "      <td>0.003233</td>\n",
              "      <td>15.602328</td>\n",
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              "\n",
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              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-8279a85f-fd35-4b93-bfe6-236b40e372a3');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          element.appendChild(docLink);\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uyQrIsgIX07W"
      },
      "source": [
        "# Exclude losses from observations which are above 15\n",
        "limit = 15\n",
        "df_singleLSTMv1 = df_singleLSTMv1[df_singleLSTMv1.loss <= limit]\n",
        "df_singleLSTMv2 = df_singleLSTMv2[df_singleLSTMv2.loss <= limit]\n",
        "df_stackedLSTMv1 = df_stackedLSTMv1[df_stackedLSTMv1.loss <= limit]\n",
        "df_stackedLSTMv2 = df_stackedLSTMv2[df_stackedLSTMv2.loss <= limit]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "JsqPT-ONvj0a",
        "outputId": "ec61792c-1569-42b5-ab6b-af9458a3650e"
      },
      "source": [
        "df_singleLSTMv1.describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             units       epochs  batch_size  dropout_rate  learning_rate  \\\n",
              "count    43.000000    43.000000   43.000000     43.000000      43.000000   \n",
              "mean   2287.209302  2594.186047  127.255814      0.276151       0.121215   \n",
              "std    1099.842685  1083.875833  140.960665      0.144666       0.161581   \n",
              "min     250.000000   400.000000   16.000000      0.100304       0.001006   \n",
              "25%    1475.000000  1850.000000   32.000000      0.148506       0.006820   \n",
              "50%    2250.000000  2550.000000  128.000000      0.234450       0.057598   \n",
              "75%    3300.000000  3375.000000  128.000000      0.393590       0.175370   \n",
              "max    3950.000000  4700.000000  512.000000      0.596639       0.746457   \n",
              "\n",
              "            loss  \n",
              "count  43.000000  \n",
              "mean    6.907337  \n",
              "std     1.347834  \n",
              "min     6.230225  \n",
              "25%     6.258791  \n",
              "50%     6.330302  \n",
              "75%     6.859737  \n",
              "max    11.862604  "
            ],
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              "      <th></th>\n",
              "      <th>units</th>\n",
              "      <th>epochs</th>\n",
              "      <th>batch_size</th>\n",
              "      <th>dropout_rate</th>\n",
              "      <th>learning_rate</th>\n",
              "      <th>loss</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>43.000000</td>\n",
              "      <td>43.000000</td>\n",
              "      <td>43.000000</td>\n",
              "      <td>43.000000</td>\n",
              "      <td>43.000000</td>\n",
              "      <td>43.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>2287.209302</td>\n",
              "      <td>2594.186047</td>\n",
              "      <td>127.255814</td>\n",
              "      <td>0.276151</td>\n",
              "      <td>0.121215</td>\n",
              "      <td>6.907337</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>1099.842685</td>\n",
              "      <td>1083.875833</td>\n",
              "      <td>140.960665</td>\n",
              "      <td>0.144666</td>\n",
              "      <td>0.161581</td>\n",
              "      <td>1.347834</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>250.000000</td>\n",
              "      <td>400.000000</td>\n",
              "      <td>16.000000</td>\n",
              "      <td>0.100304</td>\n",
              "      <td>0.001006</td>\n",
              "      <td>6.230225</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>1475.000000</td>\n",
              "      <td>1850.000000</td>\n",
              "      <td>32.000000</td>\n",
              "      <td>0.148506</td>\n",
              "      <td>0.006820</td>\n",
              "      <td>6.258791</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>2250.000000</td>\n",
              "      <td>2550.000000</td>\n",
              "      <td>128.000000</td>\n",
              "      <td>0.234450</td>\n",
              "      <td>0.057598</td>\n",
              "      <td>6.330302</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>3300.000000</td>\n",
              "      <td>3375.000000</td>\n",
              "      <td>128.000000</td>\n",
              "      <td>0.393590</td>\n",
              "      <td>0.175370</td>\n",
              "      <td>6.859737</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>3950.000000</td>\n",
              "      <td>4700.000000</td>\n",
              "      <td>512.000000</td>\n",
              "      <td>0.596639</td>\n",
              "      <td>0.746457</td>\n",
              "      <td>11.862604</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "          document.querySelector('#df-f190e393-e6ed-4daa-a1fe-4d7e96b5d215 button.colab-df-convert');\n",
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              "          const element = document.querySelector('#df-f190e393-e6ed-4daa-a1fe-4d7e96b5d215');\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 287
        },
        "id": "sCcZG0oKjW0F",
        "outputId": "75558061-6468-44f2-9b80-c38886e2f49c"
      },
      "source": [
        "df_singleLSTMv1.boxplot(column=['loss'], by=['optimizer'])\n",
        "plt.title('')\n",
        "plt.xlabel('')\n",
        "plt.ylabel('loss', fontsize=12)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "kn8zYv60wh3A",
        "outputId": "2711c6c9-159b-4726-a82b-a90cb428f2ac"
      },
      "source": [
        "df_singleLSTMv2.describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             units       epochs  batch_size  dropout_rate  learning_rate  \\\n",
              "count    44.000000    44.000000   44.000000     44.000000      44.000000   \n",
              "mean   2467.045455  1752.272727  135.272727      0.405405       0.106817   \n",
              "std     901.674359  1254.524581  163.804246      0.115335       0.151130   \n",
              "min     250.000000   100.000000   16.000000      0.113502       0.001273   \n",
              "25%    2025.000000   887.500000   32.000000      0.348833       0.019440   \n",
              "50%    2650.000000  1450.000000   64.000000      0.428084       0.052520   \n",
              "75%    3062.500000  2200.000000  128.000000      0.488363       0.132111   \n",
              "max    4000.000000  5000.000000  512.000000      0.596413       0.799883   \n",
              "\n",
              "            loss  \n",
              "count  44.000000  \n",
              "mean    7.799691  \n",
              "std     2.220086  \n",
              "min     6.184800  \n",
              "25%     6.345242  \n",
              "50%     6.707750  \n",
              "75%     8.143473  \n",
              "max    14.451367  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-5ca13615-5bb7-4ade-8149-4fa0931396f4\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>units</th>\n",
              "      <th>epochs</th>\n",
              "      <th>batch_size</th>\n",
              "      <th>dropout_rate</th>\n",
              "      <th>learning_rate</th>\n",
              "      <th>loss</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>44.000000</td>\n",
              "      <td>44.000000</td>\n",
              "      <td>44.000000</td>\n",
              "      <td>44.000000</td>\n",
              "      <td>44.000000</td>\n",
              "      <td>44.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>2467.045455</td>\n",
              "      <td>1752.272727</td>\n",
              "      <td>135.272727</td>\n",
              "      <td>0.405405</td>\n",
              "      <td>0.106817</td>\n",
              "      <td>7.799691</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>901.674359</td>\n",
              "      <td>1254.524581</td>\n",
              "      <td>163.804246</td>\n",
              "      <td>0.115335</td>\n",
              "      <td>0.151130</td>\n",
              "      <td>2.220086</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>250.000000</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>16.000000</td>\n",
              "      <td>0.113502</td>\n",
              "      <td>0.001273</td>\n",
              "      <td>6.184800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>2025.000000</td>\n",
              "      <td>887.500000</td>\n",
              "      <td>32.000000</td>\n",
              "      <td>0.348833</td>\n",
              "      <td>0.019440</td>\n",
              "      <td>6.345242</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>2650.000000</td>\n",
              "      <td>1450.000000</td>\n",
              "      <td>64.000000</td>\n",
              "      <td>0.428084</td>\n",
              "      <td>0.052520</td>\n",
              "      <td>6.707750</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>3062.500000</td>\n",
              "      <td>2200.000000</td>\n",
              "      <td>128.000000</td>\n",
              "      <td>0.488363</td>\n",
              "      <td>0.132111</td>\n",
              "      <td>8.143473</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>4000.000000</td>\n",
              "      <td>5000.000000</td>\n",
              "      <td>512.000000</td>\n",
              "      <td>0.596413</td>\n",
              "      <td>0.799883</td>\n",
              "      <td>14.451367</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "          const element = document.querySelector('#df-5ca13615-5bb7-4ade-8149-4fa0931396f4');\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 287
        },
        "id": "gJFsOMowV-6M",
        "outputId": "16b9438b-9a76-4068-d652-d610ee9d8258"
      },
      "source": [
        "df_singleLSTMv2.boxplot(column=['loss'], by=['optimizer'])\n",
        "plt.title('')\n",
        "plt.xlabel('')\n",
        "plt.ylabel('loss', fontsize=12)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "B1oaVal1wjJf",
        "outputId": "2a7a1e1a-43f1-4066-c87a-a85d4261f53c"
      },
      "source": [
        "df_stackedLSTMv1.describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           units_1  dropout_rate_1      units_2  dropout_rate_2      units_3  \\\n",
              "count    15.000000       15.000000    15.000000       15.000000    15.000000   \n",
              "mean   2390.000000        0.204472   703.333333        0.358314  3016.666667   \n",
              "std     643.705789        0.093074   371.034590        0.115572   525.991128   \n",
              "min    1300.000000        0.108669    50.000000        0.103006  1950.000000   \n",
              "25%    2050.000000        0.146286   425.000000        0.326419  2775.000000   \n",
              "50%    2500.000000        0.174404   700.000000        0.342210  3150.000000   \n",
              "75%    2675.000000        0.224886   900.000000        0.392183  3300.000000   \n",
              "max    3800.000000        0.478055  1350.000000        0.596250  3750.000000   \n",
              "\n",
              "       dropout_rate_3      units_4  dropout_rate_4       epochs  batch_size  \\\n",
              "count       15.000000    15.000000       15.000000    15.000000   15.000000   \n",
              "mean         0.335308  3176.666667        0.414284  4173.333333  403.200000   \n",
              "std          0.079988   671.848906        0.094606   586.109042  194.102771   \n",
              "min          0.195917  1800.000000        0.194876  2750.000000   16.000000   \n",
              "25%          0.288085  2750.000000        0.386567  3900.000000  384.000000   \n",
              "50%          0.358310  3500.000000        0.435616  4250.000000  512.000000   \n",
              "75%          0.381323  3650.000000        0.467968  4550.000000  512.000000   \n",
              "max          0.480648  3900.000000        0.538908  5000.000000  512.000000   \n",
              "\n",
              "       learning_rate       loss  \n",
              "count      15.000000  15.000000  \n",
              "mean        0.002472   7.786318  \n",
              "std         0.001552   1.321633  \n",
              "min         0.001008   6.321012  \n",
              "25%         0.001052   6.864372  \n",
              "50%         0.002082   7.301474  \n",
              "75%         0.002881   8.394687  \n",
              "max         0.005702  11.224712  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-a30aa5f8-f2ec-4665-ae90-9c4cb7df7318\">\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>units_1</th>\n",
              "      <th>dropout_rate_1</th>\n",
              "      <th>units_2</th>\n",
              "      <th>dropout_rate_2</th>\n",
              "      <th>units_3</th>\n",
              "      <th>dropout_rate_3</th>\n",
              "      <th>units_4</th>\n",
              "      <th>dropout_rate_4</th>\n",
              "      <th>epochs</th>\n",
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              "      <th>learning_rate</th>\n",
              "      <th>loss</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>15.000000</td>\n",
              "      <td>15.000000</td>\n",
              "      <td>15.000000</td>\n",
              "      <td>15.000000</td>\n",
              "      <td>15.000000</td>\n",
              "      <td>15.000000</td>\n",
              "      <td>15.000000</td>\n",
              "      <td>15.000000</td>\n",
              "      <td>15.000000</td>\n",
              "      <td>15.000000</td>\n",
              "      <td>15.000000</td>\n",
              "      <td>15.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>2390.000000</td>\n",
              "      <td>0.204472</td>\n",
              "      <td>703.333333</td>\n",
              "      <td>0.358314</td>\n",
              "      <td>3016.666667</td>\n",
              "      <td>0.335308</td>\n",
              "      <td>3176.666667</td>\n",
              "      <td>0.414284</td>\n",
              "      <td>4173.333333</td>\n",
              "      <td>403.200000</td>\n",
              "      <td>0.002472</td>\n",
              "      <td>7.786318</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>643.705789</td>\n",
              "      <td>0.093074</td>\n",
              "      <td>371.034590</td>\n",
              "      <td>0.115572</td>\n",
              "      <td>525.991128</td>\n",
              "      <td>0.079988</td>\n",
              "      <td>671.848906</td>\n",
              "      <td>0.094606</td>\n",
              "      <td>586.109042</td>\n",
              "      <td>194.102771</td>\n",
              "      <td>0.001552</td>\n",
              "      <td>1.321633</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1300.000000</td>\n",
              "      <td>0.108669</td>\n",
              "      <td>50.000000</td>\n",
              "      <td>0.103006</td>\n",
              "      <td>1950.000000</td>\n",
              "      <td>0.195917</td>\n",
              "      <td>1800.000000</td>\n",
              "      <td>0.194876</td>\n",
              "      <td>2750.000000</td>\n",
              "      <td>16.000000</td>\n",
              "      <td>0.001008</td>\n",
              "      <td>6.321012</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>2050.000000</td>\n",
              "      <td>0.146286</td>\n",
              "      <td>425.000000</td>\n",
              "      <td>0.326419</td>\n",
              "      <td>2775.000000</td>\n",
              "      <td>0.288085</td>\n",
              "      <td>2750.000000</td>\n",
              "      <td>0.386567</td>\n",
              "      <td>3900.000000</td>\n",
              "      <td>384.000000</td>\n",
              "      <td>0.001052</td>\n",
              "      <td>6.864372</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>2500.000000</td>\n",
              "      <td>0.174404</td>\n",
              "      <td>700.000000</td>\n",
              "      <td>0.342210</td>\n",
              "      <td>3150.000000</td>\n",
              "      <td>0.358310</td>\n",
              "      <td>3500.000000</td>\n",
              "      <td>0.435616</td>\n",
              "      <td>4250.000000</td>\n",
              "      <td>512.000000</td>\n",
              "      <td>0.002082</td>\n",
              "      <td>7.301474</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>2675.000000</td>\n",
              "      <td>0.224886</td>\n",
              "      <td>900.000000</td>\n",
              "      <td>0.392183</td>\n",
              "      <td>3300.000000</td>\n",
              "      <td>0.381323</td>\n",
              "      <td>3650.000000</td>\n",
              "      <td>0.467968</td>\n",
              "      <td>4550.000000</td>\n",
              "      <td>512.000000</td>\n",
              "      <td>0.002881</td>\n",
              "      <td>8.394687</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>3800.000000</td>\n",
              "      <td>0.478055</td>\n",
              "      <td>1350.000000</td>\n",
              "      <td>0.596250</td>\n",
              "      <td>3750.000000</td>\n",
              "      <td>0.480648</td>\n",
              "      <td>3900.000000</td>\n",
              "      <td>0.538908</td>\n",
              "      <td>5000.000000</td>\n",
              "      <td>512.000000</td>\n",
              "      <td>0.005702</td>\n",
              "      <td>11.224712</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a30aa5f8-f2ec-4665-ae90-9c4cb7df7318')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "    [theme=dark] .colab-df-convert {\n",
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              "\n",
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              "        const buttonEl =\n",
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              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
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              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 287
        },
        "id": "u9KClDcUj0R1",
        "outputId": "91d282ac-fe20-4ba8-894f-56d10732919e"
      },
      "source": [
        "df_stackedLSTMv1.boxplot(column=['loss'], by=['optimizer'])\n",
        "plt.title('')\n",
        "plt.xlabel('')\n",
        "plt.ylabel('loss', fontsize=12)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "cR4hM4Ovwk24",
        "outputId": "afc5af6d-07f6-4c94-dc0c-82af1a62a90b"
      },
      "source": [
        "df_stackedLSTMv2.describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           units_1  dropout_rate_1      units_2  dropout_rate_2      units_3  \\\n",
              "count    16.000000       16.000000    16.000000       16.000000    16.000000   \n",
              "mean   2915.625000        0.291658  1812.500000        0.248054  1125.000000   \n",
              "std     919.006483        0.089862   729.497544        0.128508   732.802838   \n",
              "min     350.000000        0.112058   800.000000        0.135093   100.000000   \n",
              "25%    2375.000000        0.233032  1525.000000        0.160063   737.500000   \n",
              "50%    3150.000000        0.295716  1800.000000        0.199644   925.000000   \n",
              "75%    3525.000000        0.339292  2012.500000        0.276204  1287.500000   \n",
              "max    4000.000000        0.464007  3900.000000        0.577324  3350.000000   \n",
              "\n",
              "       dropout_rate_3      units_4  dropout_rate_4       epochs  batch_size  \\\n",
              "count       16.000000    16.000000       16.000000    16.000000   16.000000   \n",
              "mean         0.412615  1078.125000        0.213845  3453.125000   99.000000   \n",
              "std          0.112342  1140.024671        0.128080   905.072511  134.275339   \n",
              "min          0.140808    50.000000        0.103343  1600.000000   16.000000   \n",
              "25%          0.384138   187.500000        0.134491  3000.000000   32.000000   \n",
              "50%          0.433601   600.000000        0.163956  3625.000000   32.000000   \n",
              "75%          0.467088  1525.000000        0.261933  4075.000000   80.000000   \n",
              "max          0.590292  3500.000000        0.574660  4500.000000  512.000000   \n",
              "\n",
              "       learning_rate       loss  \n",
              "count      16.000000  16.000000  \n",
              "mean        0.007386   8.452071  \n",
              "std         0.017954   2.422718  \n",
              "min         0.001046   6.352539  \n",
              "25%         0.001175   6.556361  \n",
              "50%         0.001901   7.846646  \n",
              "75%         0.002457   8.835074  \n",
              "max         0.072872  13.469746  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-e5b1ab6b-20fe-430e-908b-61c92a6f94a2\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>units_1</th>\n",
              "      <th>dropout_rate_1</th>\n",
              "      <th>units_2</th>\n",
              "      <th>dropout_rate_2</th>\n",
              "      <th>units_3</th>\n",
              "      <th>dropout_rate_3</th>\n",
              "      <th>units_4</th>\n",
              "      <th>dropout_rate_4</th>\n",
              "      <th>epochs</th>\n",
              "      <th>batch_size</th>\n",
              "      <th>learning_rate</th>\n",
              "      <th>loss</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>16.000000</td>\n",
              "      <td>16.000000</td>\n",
              "      <td>16.000000</td>\n",
              "      <td>16.000000</td>\n",
              "      <td>16.000000</td>\n",
              "      <td>16.000000</td>\n",
              "      <td>16.000000</td>\n",
              "      <td>16.000000</td>\n",
              "      <td>16.000000</td>\n",
              "      <td>16.000000</td>\n",
              "      <td>16.000000</td>\n",
              "      <td>16.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>2915.625000</td>\n",
              "      <td>0.291658</td>\n",
              "      <td>1812.500000</td>\n",
              "      <td>0.248054</td>\n",
              "      <td>1125.000000</td>\n",
              "      <td>0.412615</td>\n",
              "      <td>1078.125000</td>\n",
              "      <td>0.213845</td>\n",
              "      <td>3453.125000</td>\n",
              "      <td>99.000000</td>\n",
              "      <td>0.007386</td>\n",
              "      <td>8.452071</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>919.006483</td>\n",
              "      <td>0.089862</td>\n",
              "      <td>729.497544</td>\n",
              "      <td>0.128508</td>\n",
              "      <td>732.802838</td>\n",
              "      <td>0.112342</td>\n",
              "      <td>1140.024671</td>\n",
              "      <td>0.128080</td>\n",
              "      <td>905.072511</td>\n",
              "      <td>134.275339</td>\n",
              "      <td>0.017954</td>\n",
              "      <td>2.422718</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>350.000000</td>\n",
              "      <td>0.112058</td>\n",
              "      <td>800.000000</td>\n",
              "      <td>0.135093</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>0.140808</td>\n",
              "      <td>50.000000</td>\n",
              "      <td>0.103343</td>\n",
              "      <td>1600.000000</td>\n",
              "      <td>16.000000</td>\n",
              "      <td>0.001046</td>\n",
              "      <td>6.352539</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>2375.000000</td>\n",
              "      <td>0.233032</td>\n",
              "      <td>1525.000000</td>\n",
              "      <td>0.160063</td>\n",
              "      <td>737.500000</td>\n",
              "      <td>0.384138</td>\n",
              "      <td>187.500000</td>\n",
              "      <td>0.134491</td>\n",
              "      <td>3000.000000</td>\n",
              "      <td>32.000000</td>\n",
              "      <td>0.001175</td>\n",
              "      <td>6.556361</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>3150.000000</td>\n",
              "      <td>0.295716</td>\n",
              "      <td>1800.000000</td>\n",
              "      <td>0.199644</td>\n",
              "      <td>925.000000</td>\n",
              "      <td>0.433601</td>\n",
              "      <td>600.000000</td>\n",
              "      <td>0.163956</td>\n",
              "      <td>3625.000000</td>\n",
              "      <td>32.000000</td>\n",
              "      <td>0.001901</td>\n",
              "      <td>7.846646</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>3525.000000</td>\n",
              "      <td>0.339292</td>\n",
              "      <td>2012.500000</td>\n",
              "      <td>0.276204</td>\n",
              "      <td>1287.500000</td>\n",
              "      <td>0.467088</td>\n",
              "      <td>1525.000000</td>\n",
              "      <td>0.261933</td>\n",
              "      <td>4075.000000</td>\n",
              "      <td>80.000000</td>\n",
              "      <td>0.002457</td>\n",
              "      <td>8.835074</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>4000.000000</td>\n",
              "      <td>0.464007</td>\n",
              "      <td>3900.000000</td>\n",
              "      <td>0.577324</td>\n",
              "      <td>3350.000000</td>\n",
              "      <td>0.590292</td>\n",
              "      <td>3500.000000</td>\n",
              "      <td>0.574660</td>\n",
              "      <td>4500.000000</td>\n",
              "      <td>512.000000</td>\n",
              "      <td>0.072872</td>\n",
              "      <td>13.469746</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e5b1ab6b-20fe-430e-908b-61c92a6f94a2')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-e5b1ab6b-20fe-430e-908b-61c92a6f94a2 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-e5b1ab6b-20fe-430e-908b-61c92a6f94a2');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 287
        },
        "id": "Ix-PdxPVdGCG",
        "outputId": "4800c258-7857-4b7a-d16d-a8498e9b439d"
      },
      "source": [
        "df_stackedLSTMv2.boxplot(column=['loss'], by=['optimizer'])\n",
        "plt.title('')\n",
        "plt.xlabel('')\n",
        "plt.ylabel('loss', fontsize=12)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "wW38ATCjqMI_",
        "outputId": "9e233b45-94f7-4c87-8a7d-7061b332de41"
      },
      "source": [
        "# Overall model loss evaluation\n",
        "# SL: Single LSTM Model\n",
        "# ST: Stacked LSTM Model\n",
        "# V1: Version 1\n",
        "# V2: Version 2\n",
        "# Single LSTM Version 1 has least loss as compared to other models\n",
        "\n",
        "plt.boxplot([df_singleLSTMv1.loss, df_singleLSTMv2.loss, df_stackedLSTMv1.loss, df_stackedLSTMv2.loss], labels=['SL_V1', 'SL_V2', 'ST_V1', 'ST_V2'])\n",
        "plt.title('Single/Stacked LSTM models V1 & V2')\n",
        "plt.ylabel('loss', fontsize=14)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Overall model loss\n",
        "# M is Manual hyperparameter optimization\n",
        "# A is Automated hyperparameter optimization\n",
        "model_loss = {\n",
        "    'model': ['ARIMA', 'GARCH', 'LR', 'M_SL_V1', 'M_SL_V2', 'M_ST_V1', 'M_ST_V2', 'A_SL_V1', 'A_SL_V2', 'A_ST_V1', 'A_ST_V2'],\n",
        "    'loss': [9.37, 2.93, 9.40, 10.95, 10.83, 10.53, 11.94, 7.21, 7.44, 7.72, 7.31]\n",
        "    }\n",
        "df_loss = pd.DataFrame(data=model_loss)"
      ],
      "metadata": {
        "id": "XA5EhNn-iJNq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data = df_loss.loss\n",
        "labels = df_loss.model\n",
        "plt.xticks(range(len(data)), labels, rotation=90)\n",
        "plt.ylabel('Loss (RMSE)')\n",
        "plt.title('Error loss of models')\n",
        "plt.bar(range(len(data)), data) \n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 315
        },
        "id": "gYdJkj7Ek7zW",
        "outputId": "44b1a988-991a-418e-e76d-e16858f7de3e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}