AUTOENCODER3.ipynb

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            "Requirement already satisfied: ggplot in /usr/local/lib/python3.7/dist-packages (0.11.5)\n",
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            "Requirement already satisfied: pandas in /usr/local/lib/python3.7/dist-packages (from ggplot) (1.3.5)\n",
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            "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from ggplot) (1.21.5)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.7/dist-packages (from ggplot) (1.4.1)\n",
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            "Requirement already satisfied: statsmodels in /usr/local/lib/python3.7/dist-packages (from ggplot) (0.10.2)\n",
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            "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->ggplot) (2.8.2)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from kiwisolver>=1.0.1->matplotlib->ggplot) (3.10.0.2)\n",
            "Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.7/dist-packages (from pandas->ggplot) (2018.9)\n"
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      "source": [
        "import pandas as pd\n",
        "otutable = pd.read_csv('/content/drive/MyDrive/abundance-Soil-non-saline.tsv', sep = '\\t', index_col=0)"
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              "      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-ac926cb3-85ec-4b69-9615-f61850cee813 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-ac926cb3-85ec-4b69-9615-f61850cee813');\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": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import math\n",
        "import pandas as pd\n",
        "import tensorflow as tf\n",
        "import matplotlib.pyplot as plt\n",
        "from tensorflow.keras import Model\n",
        "from tensorflow.keras import Sequential\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from tensorflow.keras.layers import Dense, Dropout\n",
        "from sklearn.model_selection import train_test_split\n",
        "from tensorflow.keras.losses import MeanSquaredLogarithmicError"
      ],
      "metadata": {
        "id": "1mKlbIdliWo8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class AutoEncoders(Model):\n",
        "\n",
        "  def __init__(self, output_units):\n",
        "\n",
        "    super().__init__()\n",
        "    self.encoder = Sequential(\n",
        "        [\n",
        "          Dense(129, activation=\"relu\"),\n",
        "          #Dense(64, activation=\"relu\"),\n",
        "          #Dense(32, activation=\"relu\"),\n",
        "          #Dense(16, activation = \"relu\"),\n",
        "          #Dense(8, activation = \"relu\"),\n",
        "          #Dense(4, activation = \"relu\"),\n",
        "          Dense(2, activation = \"relu\")\n",
        "        ]\n",
        "    )\n",
        "\n",
        "    self.decoder = Sequential(\n",
        "        [\n",
        "          Dense(2, activation = \"relu\"),\n",
        "          #Dense(4, activation = \"relu\"),\n",
        "          #Dense(8, activation=\"relu\"),\n",
        "          #Dense(16, activation=\"relu\"),\n",
        "          #Dense(32, activation = \"relu\"),\n",
        "          #Dense(64, activation = \"relu\"),\n",
        "          Dense(129, activation = \"relu\"),\n",
        "          Dense(output_units, activation=\"sigmoid\")\n",
        "        ]\n",
        "    )\n",
        "\n",
        "  def call(self, inputs):\n",
        "\n",
        "    encoded = self.encoder(inputs)\n",
        "    decoded = self.decoder(encoded)\n",
        "    return decoded\n",
        "  \n",
        "auto_encoder = AutoEncoders(len(otu.columns))\n",
        "\n",
        "auto_encoder.compile(\n",
        "    loss='mae',\n",
        "    metrics=['mae'],\n",
        "    optimizer='adam'\n",
        ")\n",
        "\n",
        "history = auto_encoder.fit(\n",
        "    otu, \n",
        "    otu, \n",
        "    epochs=4, \n",
        "    batch_size=32, \n",
        "    #validation_data=(otu, otu)\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8brzyeemiWrG",
        "outputId": "0a5fd64e-c202-4085-d070-8948875fcc05"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/4\n",
            "103/103 [==============================] - 1s 2ms/step - loss: 11.5868 - mae: 11.5868\n",
            "Epoch 2/4\n",
            "103/103 [==============================] - 0s 2ms/step - loss: 11.5290 - mae: 11.5290\n",
            "Epoch 3/4\n",
            "103/103 [==============================] - 0s 2ms/step - loss: 11.5278 - mae: 11.5278\n",
            "Epoch 4/4\n",
            "103/103 [==============================] - 0s 2ms/step - loss: 11.5278 - mae: 11.5278\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "encoder_layer = auto_encoder.get_layer('sequential')\n",
        "reduced_df = pd.DataFrame(encoder_layer.predict(otu))\n",
        "reduced_df = reduced_df.add_prefix('S_')"
      ],
      "metadata": {
        "id": "QUFUdQCBiWtP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "reduced_df.insert(0, 'Col', range(0,len(reduced_df)))"
      ],
      "metadata": {
        "id": "IA4OtjrwywSl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "reduced_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "Aliu60T76qVv",
        "outputId": "0a368cfa-cd29-43f4-be1b-0e3ac1cc8b52"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Col          S_0          S_1\n",
              "0        0   156.641983   199.405655\n",
              "1        1    20.405941    95.613785\n",
              "2        2    68.314705   142.001770\n",
              "3        3  2647.833008  7186.339844\n",
              "4        4   148.970154   224.589111\n",
              "...    ...          ...          ...\n",
              "3271  3271  1071.689209  1647.051514\n",
              "3272  3272    95.516029   867.421448\n",
              "3273  3273    18.088142   195.302124\n",
              "3274  3274  2271.273193  4183.951172\n",
              "3275  3275    26.670519    67.021034\n",
              "\n",
              "[3276 rows x 3 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-0af25f77-d9b9-4679-8026-b28b65ff4ee3\">\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>Col</th>\n",
              "      <th>S_0</th>\n",
              "      <th>S_1</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>156.641983</td>\n",
              "      <td>199.405655</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>20.405941</td>\n",
              "      <td>95.613785</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>68.314705</td>\n",
              "      <td>142.001770</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>2647.833008</td>\n",
              "      <td>7186.339844</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>148.970154</td>\n",
              "      <td>224.589111</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>3271</th>\n",
              "      <td>3271</td>\n",
              "      <td>1071.689209</td>\n",
              "      <td>1647.051514</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3272</th>\n",
              "      <td>3272</td>\n",
              "      <td>95.516029</td>\n",
              "      <td>867.421448</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3273</th>\n",
              "      <td>3273</td>\n",
              "      <td>18.088142</td>\n",
              "      <td>195.302124</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3274</th>\n",
              "      <td>3274</td>\n",
              "      <td>2271.273193</td>\n",
              "      <td>4183.951172</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3275</th>\n",
              "      <td>3275</td>\n",
              "      <td>26.670519</td>\n",
              "      <td>67.021034</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>3276 rows × 3 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0af25f77-d9b9-4679-8026-b28b65ff4ee3')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
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              "  </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",
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              "      border-radius: 50%;\n",
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              "\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-0af25f77-d9b9-4679-8026-b28b65ff4ee3 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-0af25f77-d9b9-4679-8026-b28b65ff4ee3');\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",
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              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "col = pd.to_numeric(reduced_df['Col'])"
      ],
      "metadata": {
        "id": "fbaRV6jU2blT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn\n",
        "seaborn.relplot(data=reduced_df, x='S_0', y='S_1', hue='Col', hue_order=reduced_df['Col'], aspect=1.61)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 370
        },
        "id": "RJ6RhkQj3Yn9",
        "outputId": "092e1dfa-8d8c-445a-fb19-5f7d48b73f2c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640.85x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "seaborn.relplot(data=reduced_df, x='S_0', y='S_1', hue='Col', hue_order=reduced_df['Col'], aspect = 4)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        },
        "id": "rH3iE7fIAurF",
        "outputId": "f57d1a8e-cb7a-407a-90c0-77f40d0611a6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1501.25x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from ggplot import *\n",
        "import rpy2\n",
        "ggplot(aes(x = 'S_0', y = 'S_1', color = 'Col'), data = reduced_df) + geom_point()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 584
        },
        "id": "WwPk_VTf4CTZ",
        "outputId": "92180bcf-ac1e-4796-cce9-d7341b092049"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
            "  import pandas.util.testing as tm\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 792x576 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<ggplot: (8747501393109)>"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ax = reduced_df.plot.scatter(x = 'S_0', y = 'S_1', color = col)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 280
        },
        "id": "U8PbM-cc09zo",
        "outputId": "8fbd3b76-0c62-4dd9-c021-0e91dcc14683"
      },
      "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": [
        "#PLOT SCATTER\n",
        "ax = reduced_df.plot.scatter(x = 'S_0', y = 'S_1')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 280
        },
        "id": "18QoCqJEzCZh",
        "outputId": "cae52358-9322-48c4-eef1-2559c33e0c34"
      },
      "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": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.rcParams[\"figure.figsize\"] = [7.50, 3.50]\n",
        "plt.rcParams[\"figure.autolayout\"] = True\n",
        "reduced_df.plot()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 261
        },
        "id": "hqkwCsKaijNH",
        "outputId": "d721078c-31c6-44fd-fc90-6fd53a6ae5a4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 540x252 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import MinMaxScaler\n",
        "\n",
        "def scale_dataset(x_train):\n",
        "  standard_scaler = MinMaxScaler()\n",
        "  x_train_scaled = pd.DataFrame(standard_scaler.fit_transform(x_train), columns = x_train.columns)\n",
        "  return x_train_scaled"
      ],
      "metadata": {
        "id": "Yc-TwQV_qHb-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "otu_scaled = scale_dataset(otu)"
      ],
      "metadata": {
        "id": "Iam0KdRiqHgV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "otu_scaled"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "LmCBjE2VqNal",
        "outputId": "69c0792f-6eac-48e6-d686-5d01a3e3e908"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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    {
      "cell_type": "code",
      "source": [
        "class AutoEncoders(Model):\n",
        "\n",
        "  def __init__(self, output_units):\n",
        "\n",
        "    super().__init__()\n",
        "    self.encoder = Sequential(\n",
        "        [\n",
        "          Dense(3276, activation=\"relu\"),\n",
        "          Dense(2000, activation = \"relu\"),\n",
        "          Dense(1000, activation=\"relu\"),\n",
        "          Dense(512, activation = \"relu\"),\n",
        "          Dense(256, activation=\"relu\"),\n",
        "          Dense(128, activation = \"relu\"),\n",
        "          Dense(10, activation = \"relu\")\n",
        "        ]\n",
        "    )\n",
        "\n",
        "    self.decoder = Sequential(\n",
        "        [\n",
        "          Dense(10, activation=\"relu\"),\n",
        "          Dense(128, activation=\"relu\"),\n",
        "          Dense(256, activation = \"relu\"),\n",
        "          Dense(512, activation = \"relu\"),\n",
        "          Dense(1000, activation = \"relu\"),\n",
        "          Dense(2000, activation = \"relu\"),\n",
        "          Dense(3276, activation = \"relu\"),\n",
        "          Dense(output_units, activation=\"sigmoid\")\n",
        "        ]\n",
        "    )\n",
        "\n",
        "  def call(self, inputs):\n",
        "\n",
        "    encoded = self.encoder(inputs)\n",
        "    decoded = self.decoder(encoded)\n",
        "    return decoded\n",
        "  \n",
        "auto_encoder = AutoEncoders(len(otu_scaled.columns))\n",
        "\n",
        "auto_encoder.compile(\n",
        "    loss='mae',\n",
        "    metrics=['mae'],\n",
        "    optimizer='adam'\n",
        ")\n",
        "\n",
        "history = auto_encoder.fit(\n",
        "    otu_scaled, \n",
        "    otu_scaled, \n",
        "    epochs=30, \n",
        "    batch_size=1, \n",
        "    #validation_data=(otu, otu)\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jffRFW_imVdJ",
        "outputId": "8539cebf-f0d8-42b8-d76e-94a7185cfab9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/30\n",
            "129/129 [==============================] - 22s 161ms/step - loss: 0.0582 - mae: 0.0582\n",
            "Epoch 2/30\n",
            "129/129 [==============================] - 21s 163ms/step - loss: 0.0376 - mae: 0.0376\n",
            "Epoch 3/30\n",
            "129/129 [==============================] - 22s 167ms/step - loss: 0.0376 - mae: 0.0376\n",
            "Epoch 4/30\n",
            "129/129 [==============================] - 21s 161ms/step - loss: 0.0376 - mae: 0.0376\n",
            "Epoch 5/30\n",
            "129/129 [==============================] - 23s 178ms/step - loss: 0.0376 - mae: 0.0376\n",
            "Epoch 6/30\n",
            "129/129 [==============================] - 21s 165ms/step - loss: 0.0376 - mae: 0.0376\n",
            "Epoch 7/30\n",
            "129/129 [==============================] - 21s 166ms/step - loss: 0.0376 - mae: 0.0376\n",
            "Epoch 8/30\n",
            "129/129 [==============================] - 21s 166ms/step - loss: 0.0376 - mae: 0.0376\n",
            "Epoch 9/30\n",
            "129/129 [==============================] - 21s 165ms/step - loss: 0.0376 - mae: 0.0376\n",
            "Epoch 10/30\n",
            "129/129 [==============================] - 21s 164ms/step - loss: 0.0376 - mae: 0.0376\n",
            "Epoch 11/30\n",
            "129/129 [==============================] - 21s 164ms/step - loss: 0.0376 - mae: 0.0376\n",
            "Epoch 12/30\n",
            "129/129 [==============================] - 21s 163ms/step - loss: 0.0376 - mae: 0.0376\n",
            "Epoch 13/30\n",
            "129/129 [==============================] - 21s 166ms/step - loss: 0.0376 - mae: 0.0376\n",
            "Epoch 14/30\n",
            "129/129 [==============================] - 21s 165ms/step - loss: 0.0376 - mae: 0.0376\n",
            "Epoch 15/30\n",
            "129/129 [==============================] - 21s 164ms/step - loss: 0.0376 - mae: 0.0376\n",
            "Epoch 16/30\n",
            "129/129 [==============================] - 21s 164ms/step - loss: 0.0376 - mae: 0.0376\n",
            "Epoch 17/30\n",
            "129/129 [==============================] - 21s 163ms/step - loss: 0.0376 - mae: 0.0376\n",
            "Epoch 18/30\n",
            "129/129 [==============================] - 21s 163ms/step - loss: 0.0376 - mae: 0.0376\n",
            "Epoch 19/30\n",
            "129/129 [==============================] - 21s 165ms/step - loss: 0.0376 - mae: 0.0376\n",
            "Epoch 20/30\n",
            "129/129 [==============================] - 21s 163ms/step - loss: 0.0376 - mae: 0.0376\n",
            "Epoch 21/30\n",
            "129/129 [==============================] - 21s 164ms/step - loss: 0.0376 - mae: 0.0376\n",
            "Epoch 22/30\n",
            "129/129 [==============================] - 21s 166ms/step - loss: 0.0376 - mae: 0.0376\n",
            "Epoch 23/30\n",
            "129/129 [==============================] - 21s 163ms/step - loss: 0.0376 - mae: 0.0376\n",
            "Epoch 24/30\n",
            "129/129 [==============================] - 21s 165ms/step - loss: 0.0376 - mae: 0.0376\n",
            "Epoch 25/30\n",
            "129/129 [==============================] - 21s 165ms/step - loss: 0.0376 - mae: 0.0376\n",
            "Epoch 26/30\n",
            "129/129 [==============================] - 21s 164ms/step - loss: 0.0376 - mae: 0.0376\n",
            "Epoch 27/30\n",
            "129/129 [==============================] - 21s 166ms/step - loss: 0.0376 - mae: 0.0376\n",
            "Epoch 28/30\n",
            "129/129 [==============================] - 21s 165ms/step - loss: 0.0376 - mae: 0.0376\n",
            "Epoch 29/30\n",
            "129/129 [==============================] - 21s 164ms/step - loss: 0.0376 - mae: 0.0376\n",
            "Epoch 30/30\n",
            "129/129 [==============================] - 21s 164ms/step - loss: 0.0376 - mae: 0.0376\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "encoder_layer = auto_encoder.get_layer('sequential_10')\n",
        "reduced_df = pd.DataFrame(encoder_layer.predict(otu_scaled))\n",
        "reduced_df = reduced_df.add_prefix('otu_')"
      ],
      "metadata": {
        "id": "tSAB_8PHm1NM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "reduced_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "9apKBkb1m2IS",
        "outputId": "a919d99e-2f4a-4b6b-8fe0-bf85579bc92c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "          otu_0  otu_1        otu_2        otu_3        otu_4  otu_5  \\\n",
              "0    157.511841    0.0  1132.981689  1507.757080   278.641602    0.0   \n",
              "1    150.291183    0.0  1081.487061  1439.250610   265.978668    0.0   \n",
              "2    334.736053    0.0  2408.198975  3204.772949   592.225830    0.0   \n",
              "3     36.135941    0.0   260.102661   346.151428    63.977768    0.0   \n",
              "4    160.074799    0.0  1151.812866  1532.822144   283.234955    0.0   \n",
              "..          ...    ...          ...          ...          ...    ...   \n",
              "124  474.322571    0.0  3410.299072  4538.240723   838.521790    0.0   \n",
              "125   54.894436    0.0   394.897156   525.512451    97.104187    0.0   \n",
              "126  344.533630    0.0  2479.140381  3299.145020   609.612061    0.0   \n",
              "127  567.537415    0.0  4083.321045  5434.057129  1004.059204    0.0   \n",
              "128  271.239838    0.0  1951.463623  2597.128418   479.890869    0.0   \n",
              "\n",
              "           otu_6        otu_7        otu_8        otu_9  \n",
              "0     613.757751   510.269867  1122.866089   755.306030  \n",
              "1     585.893494   487.071686  1071.758179   720.980713  \n",
              "2    1304.630371  1084.604492  2386.527832  1605.508301  \n",
              "3     140.904694   117.142250   257.754303   173.408112  \n",
              "4     624.016418   518.766479  1141.405640   767.887878  \n",
              "..           ...          ...          ...          ...  \n",
              "124  1847.246826  1535.975586  3379.743652  2273.508789  \n",
              "125   213.922226   177.849152   391.348938   263.265167  \n",
              "126  1342.985229  1116.550781  2456.673096  1652.738892  \n",
              "127  2212.049561  1839.154297  4046.351318  2722.337402  \n",
              "128  1057.220337   878.952209  1933.920654  1301.007812  \n",
              "\n",
              "[129 rows x 10 columns]"
            ],
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              "<p>129 rows × 10 columns</p>\n",
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              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.rcParams[\"figure.figsize\"] = [7.50, 3.50]\n",
        "plt.rcParams[\"figure.autolayout\"] = True\n",
        "reduced_df.plot()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 261
        },
        "id": "nc2fqHJv2gN4",
        "outputId": "fead2432-93de-4597-e09f-003aa23c44a4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 540x252 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}