3.2_Pycaret_Anomaly_Detection_End_to_End.ipynb

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        "Help is available on : https://www.youtube.com/watch?v=Rgr_3iXORmo"
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      "source": [
        "# install pycaret\n",
        "! pip install pycaret\n",
        "\n",
        "# pip install pycaret[full]"
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            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/b2/11/583f4eac99d802c79af9217e1eff56027742a69e6c866b295cce6a5a8fc2/Boruta-0.3-py3-none-any.whl (56kB)\n",
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            "Requirement already satisfied: networkx>=2.4 in /usr/local/lib/python3.7/dist-packages (from visions[type_image_path]==0.6.0->pandas-profiling>=2.8.0->pycaret) (2.5.1)\n",
            "Collecting imagehash; extra == \"type_image_path\"\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/8e/18/9dbb772b5ef73a3069c66bb5bf29b9fb4dd57af0d5790c781c3f559bcca6/ImageHash-4.2.0-py2.py3-none-any.whl (295kB)\n",
            "\u001b[K     |████████████████████████████████| 296kB 38.8MB/s \n",
            "\u001b[?25hRequirement already satisfied: typing-extensions>=3.6.4; python_version < \"3.8\" in /usr/local/lib/python3.7/dist-packages (from importlib-metadata>=0.20; python_version < \"3.8\"->catalogue<1.1.0,>=0.0.7->spacy<2.4.0->pycaret) (3.7.4.3)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata>=0.20; python_version < \"3.8\"->catalogue<1.1.0,>=0.0.7->spacy<2.4.0->pycaret) (3.4.1)\n",
            "Collecting smmap<5,>=3.0.1\n",
            "  Downloading https://files.pythonhosted.org/packages/68/ee/d540eb5e5996eb81c26ceffac6ee49041d473bc5125f2aa995cf51ec1cf1/smmap-4.0.0-py2.py3-none-any.whl\n",
            "Requirement already satisfied: pyzmq>=13 in /usr/local/lib/python3.7/dist-packages (from jupyter-client->ipykernel>=4.5.1->ipywidgets->pycaret) (22.0.3)\n",
            "Requirement already satisfied: terminado>=0.8.1 in /usr/local/lib/python3.7/dist-packages (from notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets->pycaret) (0.9.4)\n",
            "Requirement already satisfied: Send2Trash in /usr/local/lib/python3.7/dist-packages (from notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets->pycaret) (1.5.0)\n",
            "Requirement already satisfied: nbconvert in /usr/local/lib/python3.7/dist-packages (from notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets->pycaret) (5.6.1)\n",
            "Requirement already satisfied: PyWavelets in /usr/local/lib/python3.7/dist-packages (from imagehash; extra == \"type_image_path\"->visions[type_image_path]==0.6.0->pandas-profiling>=2.8.0->pycaret) (1.1.1)\n",
            "Requirement already satisfied: mistune<2,>=0.8.1 in /usr/local/lib/python3.7/dist-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets->pycaret) (0.8.4)\n",
            "Requirement already satisfied: pandocfilters>=1.4.1 in /usr/local/lib/python3.7/dist-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets->pycaret) (1.4.3)\n",
            "Requirement already satisfied: bleach in /usr/local/lib/python3.7/dist-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets->pycaret) (3.3.0)\n",
            "Requirement already satisfied: testpath in /usr/local/lib/python3.7/dist-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets->pycaret) (0.4.4)\n",
            "Requirement already satisfied: defusedxml in /usr/local/lib/python3.7/dist-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets->pycaret) (0.7.1)\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.7/dist-packages (from bleach->nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets->pycaret) (20.9)\n",
            "Requirement already satisfied: webencodings in /usr/local/lib/python3.7/dist-packages (from bleach->nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets->pycaret) (0.5.1)\n",
            "Building wheels for collected packages: pyLDAvis\n",
            "  Building wheel for pyLDAvis (PEP 517) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for pyLDAvis: filename=pyLDAvis-3.3.1-cp37-none-any.whl size=136870 sha256=a2c53ceadf3c7753e30c8451b8f131f250384be1e63b9c900928b4c13dfbe665\n",
            "  Stored in directory: /root/.cache/pip/wheels/a0/9c/fc/c6e00689d35c82cf96a8adc70edfe7ba7904374fdac3240ac2\n",
            "Successfully built pyLDAvis\n",
            "Building wheels for collected packages: umap-learn, pyod, pynndescent, prometheus-flask-exporter, alembic, databricks-cli, htmlmin, phik\n",
            "  Building wheel for umap-learn (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for umap-learn: filename=umap_learn-0.5.1-cp37-none-any.whl size=76569 sha256=201429ad57371bdfae0fa243ffa59f8e8c55e76b875907e3a3af90c43630d658\n",
            "  Stored in directory: /root/.cache/pip/wheels/ad/df/d5/a3691296ff779f25cd1cf415a3af954b987fb53111e3392cf4\n",
            "  Building wheel for pyod (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for pyod: filename=pyod-0.8.8-cp37-none-any.whl size=116967 sha256=22d67814a4d30c2f75588b6623ac6918eafee0273367237b3372cc22a56dbcc5\n",
            "  Stored in directory: /root/.cache/pip/wheels/54/dd/36/a12fa930455b7c38902f2dbf737175421ad30436e47ac5b24d\n",
            "  Building wheel for pynndescent (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for pynndescent: filename=pynndescent-0.5.2-cp37-none-any.whl size=51351 sha256=e87d9bd92077d8892131f4b5d74bf5fc5a8470905972e4fced444dc1072cc7ce\n",
            "  Stored in directory: /root/.cache/pip/wheels/ba/52/4e/4c28d04d144a28f89e2575fb63628df6e6d49b56c5ddd0c74e\n",
            "  Building wheel for prometheus-flask-exporter (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for prometheus-flask-exporter: filename=prometheus_flask_exporter-0.18.1-cp37-none-any.whl size=17159 sha256=5d30b2c39ae8b49a3365e18682d9eab79e64736f0ba994f5d156cec849919961\n",
            "  Stored in directory: /root/.cache/pip/wheels/b4/1f/b8/66bd9bc3a9d6c6987ff6c4dfeb6f1fe97b5a0e5ed5849c0437\n",
            "  Building wheel for alembic (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for alembic: filename=alembic-1.4.1-py2.py3-none-any.whl size=158155 sha256=30eb48aefcc1d03b815598ddbc79b5c1a560f02e1c2a7afb2036beaee1bd8f5e\n",
            "  Stored in directory: /root/.cache/pip/wheels/84/07/f7/12f7370ca47a66030c2edeedcc23dec26ea0ac22dcb4c4a0f3\n",
            "  Building wheel for databricks-cli (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for databricks-cli: filename=databricks_cli-0.14.3-cp37-none-any.whl size=100557 sha256=0edcf6769e231f70b3c00da7ce931cd256fa2a834dcabde4ceda9fd34e7098cd\n",
            "  Stored in directory: /root/.cache/pip/wheels/5b/24/f3/34d8e3964dac4ba849d844273c49a679111b00d5799ebb934a\n",
            "  Building wheel for htmlmin (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for htmlmin: filename=htmlmin-0.1.12-cp37-none-any.whl size=27085 sha256=b6c04c44c7deca34d52bfe78169f4c1d529af82eb1bbf322c10570a3d4857484\n",
            "  Stored in directory: /root/.cache/pip/wheels/43/07/ac/7c5a9d708d65247ac1f94066cf1db075540b85716c30255459\n",
            "  Building wheel for phik (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for phik: filename=phik-0.11.2-cp37-none-any.whl size=1107413 sha256=8f0567ae72ec756727aa54edb4a5004f27dfad268be43dbdd2625653a14d9394\n",
            "  Stored in directory: /root/.cache/pip/wheels/c0/a3/b0/f27b1cfe32ea131a3715169132ff6d85653789e80e966c3bf6\n",
            "Successfully built umap-learn pyod pynndescent prometheus-flask-exporter alembic databricks-cli htmlmin phik\n",
            "\u001b[31mERROR: pandas-profiling 2.11.0 has requirement requests>=2.24.0, but you'll have requests 2.23.0 which is incompatible.\u001b[0m\n",
            "\u001b[31mERROR: pandas-profiling 2.11.0 has requirement tqdm>=4.48.2, but you'll have tqdm 4.41.1 which is incompatible.\u001b[0m\n",
            "\u001b[31mERROR: pyldavis 3.3.1 has requirement numpy>=1.20.0, but you'll have numpy 1.19.5 which is incompatible.\u001b[0m\n",
            "\u001b[31mERROR: pyldavis 3.3.1 has requirement pandas>=1.2.0, but you'll have pandas 1.1.5 which is incompatible.\u001b[0m\n",
            "\u001b[31mERROR: phik 0.11.2 has requirement scipy>=1.5.2, but you'll have scipy 1.4.1 which is incompatible.\u001b[0m\n",
            "Installing collected packages: threadpoolctl, scikit-learn, scikit-plot, pynndescent, umap-learn, kmodes, yellowbrick, websocket-client, docker, smmap, gitdb, gitpython, querystring-parser, prometheus-flask-exporter, Mako, python-editor, alembic, gunicorn, databricks-cli, mlflow, lightgbm, funcy, pyLDAvis, Boruta, imbalanced-learn, mlxtend, pyod, htmlmin, confuse, phik, tangled-up-in-unicode, imagehash, visions, pandas-profiling, pycaret\n",
            "  Found existing installation: scikit-learn 0.22.2.post1\n",
            "    Uninstalling scikit-learn-0.22.2.post1:\n",
            "      Successfully uninstalled scikit-learn-0.22.2.post1\n",
            "  Found existing installation: yellowbrick 0.9.1\n",
            "    Uninstalling yellowbrick-0.9.1:\n",
            "      Successfully uninstalled yellowbrick-0.9.1\n",
            "  Found existing installation: lightgbm 2.2.3\n",
            "    Uninstalling lightgbm-2.2.3:\n",
            "      Successfully uninstalled lightgbm-2.2.3\n",
            "  Found existing installation: imbalanced-learn 0.4.3\n",
            "    Uninstalling imbalanced-learn-0.4.3:\n",
            "      Successfully uninstalled imbalanced-learn-0.4.3\n",
            "  Found existing installation: mlxtend 0.14.0\n",
            "    Uninstalling mlxtend-0.14.0:\n",
            "      Successfully uninstalled mlxtend-0.14.0\n",
            "  Found existing installation: pandas-profiling 1.4.1\n",
            "    Uninstalling pandas-profiling-1.4.1:\n",
            "      Successfully uninstalled pandas-profiling-1.4.1\n",
            "Successfully installed Boruta-0.3 Mako-1.1.4 alembic-1.4.1 confuse-1.4.0 databricks-cli-0.14.3 docker-5.0.0 funcy-1.15 gitdb-4.0.7 gitpython-3.1.14 gunicorn-20.1.0 htmlmin-0.1.12 imagehash-4.2.0 imbalanced-learn-0.7.0 kmodes-0.11.0 lightgbm-3.2.1 mlflow-1.16.0 mlxtend-0.18.0 pandas-profiling-2.11.0 phik-0.11.2 prometheus-flask-exporter-0.18.1 pyLDAvis-3.3.1 pycaret-2.3.1 pynndescent-0.5.2 pyod-0.8.8 python-editor-1.0.4 querystring-parser-1.2.4 scikit-learn-0.23.2 scikit-plot-0.3.7 smmap-4.0.0 tangled-up-in-unicode-0.0.7 threadpoolctl-2.1.0 umap-learn-0.5.1 visions-0.6.0 websocket-client-0.58.0 yellowbrick-1.3.post1\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bv442ZXZNPqB",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "5b722e47-5ad0-454b-beb2-8baf54eb93ac"
      },
      "source": [
        "from pycaret.utils import version\n",
        "version()"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'2.3.1'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f4qqnA1HNPqD",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 224
        },
        "outputId": "f1d4f479-7722-468b-9913-a19f68b450da"
      },
      "source": [
        "from pycaret.datasets import get_data\n",
        "data = get_data('anomaly')"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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              "       Col1      Col2      Col3  ...      Col8      Col9     Col10\n",
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              "\n",
              "[5 rows x 10 columns]"
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          },
          "metadata": {
            "tags": []
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      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dfLdmVQuNPqF",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 359
        },
        "outputId": "6425e1a3-8e48-4a30-9978-88a5745a9e6a"
      },
      "source": [
        "data.describe().transpose()"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th>std</th>\n",
              "      <th>min</th>\n",
              "      <th>25%</th>\n",
              "      <th>50%</th>\n",
              "      <th>75%</th>\n",
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              "      <td>1000.0</td>\n",
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              "      <th>Col6</th>\n",
              "      <td>1000.0</td>\n",
              "      <td>0.514636</td>\n",
              "      <td>0.317470</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.142587</td>\n",
              "      <td>0.537953</td>\n",
              "      <td>0.856512</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Col7</th>\n",
              "      <td>1000.0</td>\n",
              "      <td>0.508270</td>\n",
              "      <td>0.278483</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.246021</td>\n",
              "      <td>0.465679</td>\n",
              "      <td>0.804935</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Col8</th>\n",
              "      <td>1000.0</td>\n",
              "      <td>0.457541</td>\n",
              "      <td>0.220129</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.245539</td>\n",
              "      <td>0.515619</td>\n",
              "      <td>0.626757</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Col9</th>\n",
              "      <td>1000.0</td>\n",
              "      <td>0.477685</td>\n",
              "      <td>0.241432</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.291452</td>\n",
              "      <td>0.387753</td>\n",
              "      <td>0.723674</td>\n",
              "      <td>0.988732</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Col10</th>\n",
              "      <td>1000.0</td>\n",
              "      <td>0.495760</td>\n",
              "      <td>0.211677</td>\n",
              "      <td>0.014495</td>\n",
              "      <td>0.329904</td>\n",
              "      <td>0.488891</td>\n",
              "      <td>0.659528</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        count      mean       std  ...       50%       75%       max\n",
              "Col1   1000.0  0.491362  0.259138  ...  0.492070  0.694192  0.994431\n",
              "Col2   1000.0  0.490200  0.251931  ...  0.488656  0.686531  1.000000\n",
              "Col3   1000.0  0.509077  0.256606  ...  0.510077  0.686914  1.000000\n",
              "Col4   1000.0  0.497362  0.263562  ...  0.497537  0.731949  1.000000\n",
              "Col5   1000.0  0.586120  0.334658  ...  0.782019  0.847956  1.000000\n",
              "Col6   1000.0  0.514636  0.317470  ...  0.537953  0.856512  1.000000\n",
              "Col7   1000.0  0.508270  0.278483  ...  0.465679  0.804935  1.000000\n",
              "Col8   1000.0  0.457541  0.220129  ...  0.515619  0.626757  1.000000\n",
              "Col9   1000.0  0.477685  0.241432  ...  0.387753  0.723674  0.988732\n",
              "Col10  1000.0  0.495760  0.211677  ...  0.488891  0.659528  1.000000\n",
              "\n",
              "[10 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7Xr31v9FNPqH",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ed60c4bb-e836-45a4-ea87-992e830842a1"
      },
      "source": [
        "type(data)"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "pandas.core.frame.DataFrame"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L_6WNZanNPqJ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3d19c543-f09c-4727-82c5-0f6c5d59f455"
      },
      "source": [
        "data.shape"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1000, 10)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cOe2pzYhNPqK",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
          "referenced_widgets": [
            "4dde79d4aaf84d1bae1d3d9f0078eca3",
            "0a451ab890d4446ab21fbcac18f57516",
            "8c2625ae41ec451e8bcf67db62311d64",
            "2bc3c839ebff4b7aa163eaf2d95060f7",
            "ab63338590bb42a4808968ae71d43db6",
            "5d9e25db79d0483a8b6010ef525770c8"
          ]
        },
        "outputId": "9cfe0bf7-17cb-4002-c952-89bfc7eb2458"
      },
      "source": [
        "from pycaret.anomaly import *\n",
        "s = setup(data, session_id = 123, log_experiment = True, experiment_name = 'anomaly-demo')"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\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>Description</th>\n",
              "      <th>Value</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>session_id</td>\n",
              "      <td>123</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Original Data</td>\n",
              "      <td>(1000, 10)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Missing Values</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Numeric Features</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Categorical Features</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Ordinal Features</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>High Cardinality Features</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>High Cardinality Method</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Transformed Data</td>\n",
              "      <td>(1000, 10)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>CPU Jobs</td>\n",
              "      <td>-1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>Use GPU</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>Log Experiment</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>Experiment Name</td>\n",
              "      <td>anomaly-demo</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>USI</td>\n",
              "      <td>c782</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>Imputation Type</td>\n",
              "      <td>simple</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>Iterative Imputation Iteration</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>Numeric Imputer</td>\n",
              "      <td>mean</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>Iterative Imputation Numeric Model</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>Categorical Imputer</td>\n",
              "      <td>mode</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>Iterative Imputation Categorical Model</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>Unknown Categoricals Handling</td>\n",
              "      <td>least_frequent</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>Normalize</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>Normalize Method</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>Transformation</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>Transformation Method</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>PCA</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>PCA Method</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>PCA Components</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>Ignore Low Variance</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>Combine Rare Levels</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>Rare Level Threshold</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>Numeric Binning</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>Remove Outliers</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>Outliers Threshold</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>Remove Multicollinearity</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>Multicollinearity Threshold</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>Clustering</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37</th>\n",
              "      <td>Clustering Iteration</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38</th>\n",
              "      <td>Polynomial Features</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39</th>\n",
              "      <td>Polynomial Degree</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40</th>\n",
              "      <td>Trignometry Features</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41</th>\n",
              "      <td>Polynomial Threshold</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42</th>\n",
              "      <td>Group Features</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43</th>\n",
              "      <td>Feature Selection</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44</th>\n",
              "      <td>Feature Selection Method</td>\n",
              "      <td>classic</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>45</th>\n",
              "      <td>Features Selection Threshold</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>46</th>\n",
              "      <td>Feature Interaction</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>47</th>\n",
              "      <td>Feature Ratio</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48</th>\n",
              "      <td>Interaction Threshold</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                               Description           Value\n",
              "0                               session_id             123\n",
              "1                            Original Data      (1000, 10)\n",
              "2                           Missing Values           False\n",
              "3                         Numeric Features              10\n",
              "4                     Categorical Features               0\n",
              "5                         Ordinal Features           False\n",
              "6                High Cardinality Features           False\n",
              "7                  High Cardinality Method            None\n",
              "8                         Transformed Data      (1000, 10)\n",
              "9                                 CPU Jobs              -1\n",
              "10                                 Use GPU           False\n",
              "11                          Log Experiment            True\n",
              "12                         Experiment Name    anomaly-demo\n",
              "13                                     USI            c782\n",
              "14                         Imputation Type          simple\n",
              "15          Iterative Imputation Iteration            None\n",
              "16                         Numeric Imputer            mean\n",
              "17      Iterative Imputation Numeric Model            None\n",
              "18                     Categorical Imputer            mode\n",
              "19  Iterative Imputation Categorical Model            None\n",
              "20           Unknown Categoricals Handling  least_frequent\n",
              "21                               Normalize           False\n",
              "22                        Normalize Method            None\n",
              "23                          Transformation           False\n",
              "24                   Transformation Method            None\n",
              "25                                     PCA           False\n",
              "26                              PCA Method            None\n",
              "27                          PCA Components            None\n",
              "28                     Ignore Low Variance           False\n",
              "29                     Combine Rare Levels           False\n",
              "30                    Rare Level Threshold            None\n",
              "31                         Numeric Binning           False\n",
              "32                         Remove Outliers           False\n",
              "33                      Outliers Threshold            None\n",
              "34                Remove Multicollinearity           False\n",
              "35             Multicollinearity Threshold            None\n",
              "36                              Clustering           False\n",
              "37                    Clustering Iteration            None\n",
              "38                     Polynomial Features           False\n",
              "39                       Polynomial Degree            None\n",
              "40                    Trignometry Features           False\n",
              "41                    Polynomial Threshold            None\n",
              "42                          Group Features           False\n",
              "43                       Feature Selection           False\n",
              "44                Feature Selection Method         classic\n",
              "45            Features Selection Threshold            None\n",
              "46                     Feature Interaction           False\n",
              "47                           Feature Ratio           False\n",
              "48                   Interaction Threshold            None"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CEuMeYHhNPqL",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 452
        },
        "outputId": "7bd98ecd-5598-48a9-b290-f025e729e6f3"
      },
      "source": [
        "models()"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Name</th>\n",
              "      <th>Reference</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ID</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>abod</th>\n",
              "      <td>Angle-base Outlier Detection</td>\n",
              "      <td>pyod.models.abod.ABOD</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>cluster</th>\n",
              "      <td>Clustering-Based Local Outlier</td>\n",
              "      <td>pyod.models.cblof.CBLOF</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>cof</th>\n",
              "      <td>Connectivity-Based Local Outlier</td>\n",
              "      <td>pyod.models.cof.COF</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>iforest</th>\n",
              "      <td>Isolation Forest</td>\n",
              "      <td>pyod.models.iforest.IForest</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>histogram</th>\n",
              "      <td>Histogram-based Outlier Detection</td>\n",
              "      <td>pyod.models.hbos.HBOS</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>knn</th>\n",
              "      <td>K-Nearest Neighbors Detector</td>\n",
              "      <td>pyod.models.knn.KNN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>lof</th>\n",
              "      <td>Local Outlier Factor</td>\n",
              "      <td>pyod.models.lof.LOF</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>svm</th>\n",
              "      <td>One-class SVM detector</td>\n",
              "      <td>pyod.models.ocsvm.OCSVM</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>pca</th>\n",
              "      <td>Principal Component Analysis</td>\n",
              "      <td>pyod.models.pca.PCA</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mcd</th>\n",
              "      <td>Minimum Covariance Determinant</td>\n",
              "      <td>pyod.models.mcd.MCD</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sod</th>\n",
              "      <td>Subspace Outlier Detection</td>\n",
              "      <td>pyod.models.sod.SOD</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sos</th>\n",
              "      <td>Stochastic Outlier Selection</td>\n",
              "      <td>pyod.models.sos.SOS</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                        Name                    Reference\n",
              "ID                                                                       \n",
              "abod            Angle-base Outlier Detection        pyod.models.abod.ABOD\n",
              "cluster       Clustering-Based Local Outlier      pyod.models.cblof.CBLOF\n",
              "cof         Connectivity-Based Local Outlier          pyod.models.cof.COF\n",
              "iforest                     Isolation Forest  pyod.models.iforest.IForest\n",
              "histogram  Histogram-based Outlier Detection        pyod.models.hbos.HBOS\n",
              "knn             K-Nearest Neighbors Detector          pyod.models.knn.KNN\n",
              "lof                     Local Outlier Factor          pyod.models.lof.LOF\n",
              "svm                   One-class SVM detector      pyod.models.ocsvm.OCSVM\n",
              "pca             Principal Component Analysis          pyod.models.pca.PCA\n",
              "mcd           Minimum Covariance Determinant          pyod.models.mcd.MCD\n",
              "sod               Subspace Outlier Detection          pyod.models.sod.SOD\n",
              "sos             Stochastic Outlier Selection          pyod.models.sos.SOS"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "weO9FJrPNPqM"
      },
      "source": [
        "iforest = create_model('iforest')"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bMlcg3P7NPqM",
        "colab": {
          "base_uri": "https://localhost:8080/"
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        "outputId": "7dddc25a-5d78-4d1a-86d4-4661d0e9b214"
      },
      "source": [
        "iforest"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "IForest(behaviour='new', bootstrap=False, contamination=0.05,\n",
              "    max_features=1.0, max_samples='auto', n_estimators=100, n_jobs=-1,\n",
              "    random_state=123, verbose=0)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
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      "metadata": {
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        "colab": {
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          "height": 156,
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      "source": [
        "iforest = create_model('iforest', n_estimators=200)\n",
        "iforest"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "IForest(behaviour='new', bootstrap=False, contamination=0.05,\n",
              "    max_features=1.0, max_samples='auto', n_estimators=200, n_jobs=-1,\n",
              "    random_state=123, verbose=0)"
            ]
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            "tags": []
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          "execution_count": 11
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        "outputId": "6bbca1b8-460d-40d2-e3b5-f4f6cc9160c1"
      },
      "source": [
        "type(iforest)"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "pyod.models.iforest.IForest"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
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        "outputId": "9e0629d4-c895-4255-fd57-621e1f7677f2"
      },
      "source": [
        "plot_model(iforest)"
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      "execution_count": 24,
      "outputs": [
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              "            Plotly.purge(gd);\n",
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          "metadata": {
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    {
      "cell_type": "code",
      "metadata": {
        "id": "LbBHxYVdNPqP",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "16dd6227-38f1-47e3-b88e-d412d65f5d57"
      },
      "source": [
        "save_model(iforest, model_name = 'abc')"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Transformation Pipeline and Model Succesfully Saved\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(Pipeline(memory=None,\n",
              "          steps=[('dtypes',\n",
              "                  DataTypes_Auto_infer(categorical_features=[],\n",
              "                                       display_types=True, features_todrop=[],\n",
              "                                       id_columns=[], ml_usecase='regression',\n",
              "                                       numerical_features=[],\n",
              "                                       target='UNSUPERVISED_DUMMY_TARGET',\n",
              "                                       time_features=[])),\n",
              "                 ('imputer',\n",
              "                  Simple_Imputer(categorical_strategy='most frequent',\n",
              "                                 fill_value_categorical=None,\n",
              "                                 fill_value_numerical=None...\n",
              "                 ('fix_perfect', 'passthrough'),\n",
              "                 ('clean_names', Clean_Colum_Names()),\n",
              "                 ('feature_select', 'passthrough'), ('fix_multi', 'passthrough'),\n",
              "                 ('dfs', 'passthrough'), ('pca', 'passthrough'),\n",
              "                 ['trained_model',\n",
              "                  IForest(behaviour='new', bootstrap=False, contamination=0.05,\n",
              "     max_features=1.0, max_samples='auto', n_estimators=200, n_jobs=-1,\n",
              "     random_state=123, verbose=0)]],\n",
              "          verbose=False), 'abc.pkl')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wrxwq_aANPqQ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "abd6384b-e5fd-4060-9926-54fd2a9742f3"
      },
      "source": [
        "l = load_model('abc')"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Transformation Pipeline and Model Successfully Loaded\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 241
        },
        "id": "0um2yU_XR3X_",
        "outputId": "8ea81aae-2505-43eb-90f2-767cda5f9e38"
      },
      "source": [
        "from pycaret.anomaly import load_model, predict_model\n",
        "l = load_model('abc')\n",
        "p = predict_model(l, data=data)\n",
        "p.head()"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Transformation Pipeline and Model Successfully Loaded\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Col1</th>\n",
              "      <th>Col2</th>\n",
              "      <th>Col3</th>\n",
              "      <th>Col4</th>\n",
              "      <th>Col5</th>\n",
              "      <th>Col6</th>\n",
              "      <th>Col7</th>\n",
              "      <th>Col8</th>\n",
              "      <th>Col9</th>\n",
              "      <th>Col10</th>\n",
              "      <th>Anomaly</th>\n",
              "      <th>Anomaly_Score</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.263995</td>\n",
              "      <td>0.764929</td>\n",
              "      <td>0.138424</td>\n",
              "      <td>0.935242</td>\n",
              "      <td>0.605867</td>\n",
              "      <td>0.518790</td>\n",
              "      <td>0.912225</td>\n",
              "      <td>0.608234</td>\n",
              "      <td>0.723782</td>\n",
              "      <td>0.733591</td>\n",
              "      <td>0</td>\n",
              "      <td>-0.030361</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.546092</td>\n",
              "      <td>0.653975</td>\n",
              "      <td>0.065575</td>\n",
              "      <td>0.227772</td>\n",
              "      <td>0.845269</td>\n",
              "      <td>0.837066</td>\n",
              "      <td>0.272379</td>\n",
              "      <td>0.331679</td>\n",
              "      <td>0.429297</td>\n",
              "      <td>0.367422</td>\n",
              "      <td>0</td>\n",
              "      <td>-0.078290</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.336714</td>\n",
              "      <td>0.538842</td>\n",
              "      <td>0.192801</td>\n",
              "      <td>0.553563</td>\n",
              "      <td>0.074515</td>\n",
              "      <td>0.332993</td>\n",
              "      <td>0.365792</td>\n",
              "      <td>0.861309</td>\n",
              "      <td>0.899017</td>\n",
              "      <td>0.088600</td>\n",
              "      <td>1</td>\n",
              "      <td>0.026938</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.092108</td>\n",
              "      <td>0.995017</td>\n",
              "      <td>0.014465</td>\n",
              "      <td>0.176371</td>\n",
              "      <td>0.241530</td>\n",
              "      <td>0.514724</td>\n",
              "      <td>0.562208</td>\n",
              "      <td>0.158963</td>\n",
              "      <td>0.073715</td>\n",
              "      <td>0.208463</td>\n",
              "      <td>1</td>\n",
              "      <td>0.053551</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.325261</td>\n",
              "      <td>0.805968</td>\n",
              "      <td>0.957033</td>\n",
              "      <td>0.331665</td>\n",
              "      <td>0.307923</td>\n",
              "      <td>0.355315</td>\n",
              "      <td>0.501899</td>\n",
              "      <td>0.558449</td>\n",
              "      <td>0.885169</td>\n",
              "      <td>0.182754</td>\n",
              "      <td>0</td>\n",
              "      <td>-0.015639</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       Col1      Col2      Col3  ...     Col10  Anomaly  Anomaly_Score\n",
              "0  0.263995  0.764929  0.138424  ...  0.733591        0      -0.030361\n",
              "1  0.546092  0.653975  0.065575  ...  0.367422        0      -0.078290\n",
              "2  0.336714  0.538842  0.192801  ...  0.088600        1       0.026938\n",
              "3  0.092108  0.995017  0.014465  ...  0.208463        1       0.053551\n",
              "4  0.325261  0.805968  0.957033  ...  0.182754        0      -0.015639\n",
              "\n",
              "[5 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K5p_q891NPqR"
      },
      "source": [
        "## Loading model from cloud\n",
        "# from pycaret.anomaly import load_model, predict_model\n",
        "# l = load_model('iforest-abc', platform = 'aws', authentication = .......)\n",
        "# p = predict_model(l, data=data)\n",
        "# p.head()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qrHZGAmONPqS"
      },
      "source": [
        "#p"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oa2NI-ZdNPqT",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 224
        },
        "outputId": "45bddc91-ab7d-41f1-ac41-ef97bc7c0e01"
      },
      "source": [
        "iforest = create_model('iforest', fraction = 0.05)\n",
        "results = assign_model(iforest)  #only with seen data\n",
        "results.head()"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "    .dataframe thead th {\n",
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              "</style>\n",
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              "      <th></th>\n",
              "      <th>Col1</th>\n",
              "      <th>Col2</th>\n",
              "      <th>Col3</th>\n",
              "      <th>Col4</th>\n",
              "      <th>Col5</th>\n",
              "      <th>Col6</th>\n",
              "      <th>Col7</th>\n",
              "      <th>Col8</th>\n",
              "      <th>Col9</th>\n",
              "      <th>Col10</th>\n",
              "      <th>Anomaly</th>\n",
              "      <th>Anomaly_Score</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.263995</td>\n",
              "      <td>0.764929</td>\n",
              "      <td>0.138424</td>\n",
              "      <td>0.935242</td>\n",
              "      <td>0.605867</td>\n",
              "      <td>0.518790</td>\n",
              "      <td>0.912225</td>\n",
              "      <td>0.608234</td>\n",
              "      <td>0.723782</td>\n",
              "      <td>0.733591</td>\n",
              "      <td>0</td>\n",
              "      <td>-0.035865</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.546092</td>\n",
              "      <td>0.653975</td>\n",
              "      <td>0.065575</td>\n",
              "      <td>0.227772</td>\n",
              "      <td>0.845269</td>\n",
              "      <td>0.837066</td>\n",
              "      <td>0.272379</td>\n",
              "      <td>0.331679</td>\n",
              "      <td>0.429297</td>\n",
              "      <td>0.367422</td>\n",
              "      <td>0</td>\n",
              "      <td>-0.084927</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.336714</td>\n",
              "      <td>0.538842</td>\n",
              "      <td>0.192801</td>\n",
              "      <td>0.553563</td>\n",
              "      <td>0.074515</td>\n",
              "      <td>0.332993</td>\n",
              "      <td>0.365792</td>\n",
              "      <td>0.861309</td>\n",
              "      <td>0.899017</td>\n",
              "      <td>0.088600</td>\n",
              "      <td>1</td>\n",
              "      <td>0.025356</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.092108</td>\n",
              "      <td>0.995017</td>\n",
              "      <td>0.014465</td>\n",
              "      <td>0.176371</td>\n",
              "      <td>0.241530</td>\n",
              "      <td>0.514724</td>\n",
              "      <td>0.562208</td>\n",
              "      <td>0.158963</td>\n",
              "      <td>0.073715</td>\n",
              "      <td>0.208463</td>\n",
              "      <td>1</td>\n",
              "      <td>0.042415</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.325261</td>\n",
              "      <td>0.805968</td>\n",
              "      <td>0.957033</td>\n",
              "      <td>0.331665</td>\n",
              "      <td>0.307923</td>\n",
              "      <td>0.355315</td>\n",
              "      <td>0.501899</td>\n",
              "      <td>0.558449</td>\n",
              "      <td>0.885169</td>\n",
              "      <td>0.182754</td>\n",
              "      <td>0</td>\n",
              "      <td>-0.023408</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       Col1      Col2      Col3  ...     Col10  Anomaly  Anomaly_Score\n",
              "0  0.263995  0.764929  0.138424  ...  0.733591        0      -0.035865\n",
              "1  0.546092  0.653975  0.065575  ...  0.367422        0      -0.084927\n",
              "2  0.336714  0.538842  0.192801  ...  0.088600        1       0.025356\n",
              "3  0.092108  0.995017  0.014465  ...  0.208463        1       0.042415\n",
              "4  0.325261  0.805968  0.957033  ...  0.182754        0      -0.023408\n",
              "\n",
              "[5 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 439
        },
        "id": "Gm6vSnlRQbRp",
        "outputId": "f50f0cf7-d837-4807-eb94-a3d996432f32"
      },
      "source": [
        "predict_model(iforest, data=data) #use with unseen data"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Col1</th>\n",
              "      <th>Col2</th>\n",
              "      <th>Col3</th>\n",
              "      <th>Col4</th>\n",
              "      <th>Col5</th>\n",
              "      <th>Col6</th>\n",
              "      <th>Col7</th>\n",
              "      <th>Col8</th>\n",
              "      <th>Col9</th>\n",
              "      <th>Col10</th>\n",
              "      <th>Anomaly</th>\n",
              "      <th>Anomaly_Score</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.263995</td>\n",
              "      <td>0.764929</td>\n",
              "      <td>0.138424</td>\n",
              "      <td>0.935242</td>\n",
              "      <td>0.605867</td>\n",
              "      <td>0.518790</td>\n",
              "      <td>0.912225</td>\n",
              "      <td>0.608234</td>\n",
              "      <td>0.723782</td>\n",
              "      <td>0.733591</td>\n",
              "      <td>0</td>\n",
              "      <td>-0.035865</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.546092</td>\n",
              "      <td>0.653975</td>\n",
              "      <td>0.065575</td>\n",
              "      <td>0.227772</td>\n",
              "      <td>0.845269</td>\n",
              "      <td>0.837066</td>\n",
              "      <td>0.272379</td>\n",
              "      <td>0.331679</td>\n",
              "      <td>0.429297</td>\n",
              "      <td>0.367422</td>\n",
              "      <td>0</td>\n",
              "      <td>-0.084927</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.336714</td>\n",
              "      <td>0.538842</td>\n",
              "      <td>0.192801</td>\n",
              "      <td>0.553563</td>\n",
              "      <td>0.074515</td>\n",
              "      <td>0.332993</td>\n",
              "      <td>0.365792</td>\n",
              "      <td>0.861309</td>\n",
              "      <td>0.899017</td>\n",
              "      <td>0.088600</td>\n",
              "      <td>1</td>\n",
              "      <td>0.025356</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.092108</td>\n",
              "      <td>0.995017</td>\n",
              "      <td>0.014465</td>\n",
              "      <td>0.176371</td>\n",
              "      <td>0.241530</td>\n",
              "      <td>0.514724</td>\n",
              "      <td>0.562208</td>\n",
              "      <td>0.158963</td>\n",
              "      <td>0.073715</td>\n",
              "      <td>0.208463</td>\n",
              "      <td>1</td>\n",
              "      <td>0.042415</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.325261</td>\n",
              "      <td>0.805968</td>\n",
              "      <td>0.957033</td>\n",
              "      <td>0.331665</td>\n",
              "      <td>0.307923</td>\n",
              "      <td>0.355315</td>\n",
              "      <td>0.501899</td>\n",
              "      <td>0.558449</td>\n",
              "      <td>0.885169</td>\n",
              "      <td>0.182754</td>\n",
              "      <td>0</td>\n",
              "      <td>-0.023408</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>995</th>\n",
              "      <td>0.305055</td>\n",
              "      <td>0.656837</td>\n",
              "      <td>0.331665</td>\n",
              "      <td>0.822525</td>\n",
              "      <td>0.907127</td>\n",
              "      <td>0.882276</td>\n",
              "      <td>0.855732</td>\n",
              "      <td>0.584786</td>\n",
              "      <td>0.808640</td>\n",
              "      <td>0.242762</td>\n",
              "      <td>0</td>\n",
              "      <td>-0.083981</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>996</th>\n",
              "      <td>0.812627</td>\n",
              "      <td>0.864258</td>\n",
              "      <td>0.616604</td>\n",
              "      <td>0.167966</td>\n",
              "      <td>0.811223</td>\n",
              "      <td>0.938071</td>\n",
              "      <td>0.418462</td>\n",
              "      <td>0.472306</td>\n",
              "      <td>0.348347</td>\n",
              "      <td>0.671129</td>\n",
              "      <td>0</td>\n",
              "      <td>-0.075839</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>997</th>\n",
              "      <td>0.250967</td>\n",
              "      <td>0.138627</td>\n",
              "      <td>0.919703</td>\n",
              "      <td>0.461234</td>\n",
              "      <td>0.886555</td>\n",
              "      <td>0.869888</td>\n",
              "      <td>0.800908</td>\n",
              "      <td>0.530324</td>\n",
              "      <td>0.779433</td>\n",
              "      <td>0.234952</td>\n",
              "      <td>0</td>\n",
              "      <td>-0.052903</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>998</th>\n",
              "      <td>0.502436</td>\n",
              "      <td>0.936820</td>\n",
              "      <td>0.580062</td>\n",
              "      <td>0.540773</td>\n",
              "      <td>0.151995</td>\n",
              "      <td>0.059452</td>\n",
              "      <td>0.225220</td>\n",
              "      <td>0.242755</td>\n",
              "      <td>0.279385</td>\n",
              "      <td>0.538755</td>\n",
              "      <td>0</td>\n",
              "      <td>-0.075104</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>999</th>\n",
              "      <td>0.457991</td>\n",
              "      <td>0.017755</td>\n",
              "      <td>0.714113</td>\n",
              "      <td>0.125992</td>\n",
              "      <td>0.063316</td>\n",
              "      <td>0.154739</td>\n",
              "      <td>0.922974</td>\n",
              "      <td>0.692299</td>\n",
              "      <td>0.816777</td>\n",
              "      <td>0.307592</td>\n",
              "      <td>0</td>\n",
              "      <td>-0.008665</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1000 rows × 12 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "         Col1      Col2      Col3  ...     Col10  Anomaly  Anomaly_Score\n",
              "0    0.263995  0.764929  0.138424  ...  0.733591        0      -0.035865\n",
              "1    0.546092  0.653975  0.065575  ...  0.367422        0      -0.084927\n",
              "2    0.336714  0.538842  0.192801  ...  0.088600        1       0.025356\n",
              "3    0.092108  0.995017  0.014465  ...  0.208463        1       0.042415\n",
              "4    0.325261  0.805968  0.957033  ...  0.182754        0      -0.023408\n",
              "..        ...       ...       ...  ...       ...      ...            ...\n",
              "995  0.305055  0.656837  0.331665  ...  0.242762        0      -0.083981\n",
              "996  0.812627  0.864258  0.616604  ...  0.671129        0      -0.075839\n",
              "997  0.250967  0.138627  0.919703  ...  0.234952        0      -0.052903\n",
              "998  0.502436  0.936820  0.580062  ...  0.538755        0      -0.075104\n",
              "999  0.457991  0.017755  0.714113  ...  0.307592        0      -0.008665\n",
              "\n",
              "[1000 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S39kg0AkNPqT",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 391
        },
        "outputId": "a6d6997a-2cac-4856-a461-dedb339b8d09"
      },
      "source": [
        "results['Anomaly_Score'].hist(bins=100, figsize=(10,6))"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f55bb725f90>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAk4AAAFlCAYAAAAd2medAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAeJElEQVR4nO3de3BU9fnH8U8IuRAEI5GkMJZ6GdDoqtUILVAuCUoLbS22o0BKaCu1tgoFUSFVCTi2gkCYEW0brtYRqRmj03FaxjBUnKE1Blk7TBe00c60pkhiAlRuSUjW8/vDH1sxG/bJXs9m36+/5OScPc/3u5uTj9+z+2ya4ziOAAAAEFK/RBcAAACQLAhOAAAARgQnAAAAI4ITAACAEcEJAADAqH+sT/DJJ5/o1KlTysjIUFpaWqxPBwAAEDbHcdTZ2amBAweqX7/u60sxD06nTp1SQ0NDrE8DAAAQNaNGjdKgQYO6bY95cMrIyAgUkJmZGevTRZXP55PH40l0GQmV6nPA+FN7/BJzkOrjl5iDVBv/mTNn1NDQEMgvnxfz4HT29lxmZqaysrJifbqoS8aaoy3V54Dxp/b4JeYg1ccvMQepOP6e3l7Em8MBAACMCE4AAABGBCcAAAAjghMAAIARwQkAAMCI4AQAAGBEcAIAADAiOAEAABgRnAAAAIxCdg5va2tTeXm5jhw5oo6ODt1zzz2qra3VgQMHlJubK0maN2+eJk+eHOtaAQAAEipkcNq9e7c8Ho/uuusuHTp0SHfeeaduuOEGLV68WMXFxfGoEQAAwBVCBqfp06cH/vvw4cMqKCiIaUEAAABuleY4jmPZcdasWWpqalJVVZV+97vfqaWlRZ2dncrLy9OyZcs0ZMiQoMd1dHTI5/NFtWggXGO2H+y2bW/p1QmoBADgZh6PJ+iXG5uDkyS98847WrJkiR566CHl5uaqsLBQGzduVFNTkyoqKoIeczY49VSAm3m9XhUVFSW6jITqa3OQfv9z3bb5K8t63L+vjb+3Un38EnOQ6uOXmINUG3+o3BLyU3U+n0+HDx+WJBUWFsrv92vUqFEqLCyUJJWUlKihoSHKZQMAALhPyOC0b98+bd26VZLU2tqq06dPq6KiQo2NjZKk+vp6jRw5MrZVAgAAuEDIN4fPmjVLDz/8sEpLS9Xe3q6Kigrl5ORo0aJFGjBggHJycrRy5cp41AoAAJBQIYNTdna2Kisru21/6aWXYlIQAACAW9E5HAAAwIjgBAAAYERwAgAAMCI4AQAAGBGcAAAAjAhOAAAARgQnAAAAI4ITAACAEcEJAADAiOAEAABgRHACAAAwIjgBAAAYhfySXyAVpd//3P/+sf2gJMlfWZagagAAbsGKEwAAgBHBCQAAwIjgBAAAYERwAgAAMCI4AQAAGBGcAAAAjAhOAAAARgQnAAAAIxpgAlF2TvPM/0fzTADoG1hxAgAAMCI4AQAAGBGcAAAAjAhOAAAARgQnAAAAI4ITAACAEcEJAADAiOAEAABgRANMIA5oigkAfQMrTgAAAEYEJwAAACOCEwAAgBHBCQAAwCjkm8Pb2tpUXl6uI0eOqKOjQ/fcc4+uuuoqLVmyRH6/X0OHDtWaNWuUmZkZj3oBAAASJmRw2r17tzwej+666y4dOnRId955p2688UaVlpZq2rRpWrdunWpqalRaWhqPegEAABIm5K266dOn66677pIkHT58WAUFBaqvr9eUKVMkScXFxaqrq4ttlQAAAC5g7uM0a9YsNTU1qaqqSj/60Y8Ct+by8vLU0tISswIBAADcwhycXnjhBb3zzjt68MEH5ThOYPtn//t8fD5f76tzAa/Xm+gSEq6vz4F1fNGeh2SZ12SpM5ZSfQ5SffwSc5Dq4/+skMHJ5/MpLy9Pw4YNU2Fhofx+vwYOHKj29nZlZ2erublZ+fn5IU/k8XiUlZUVlaLjxev1qqioKNFlJFSfm4PtB7ttCjo+637GcwSTDPPa557/MKT6HKT6+CXmINXG39HRcd7FnpDvcdq3b5+2bt0qSWptbdXp06c1btw41dbWSpJ27typCRMmRKlcAAAA9wq54jRr1iw9/PDDKi0tVXt7uyoqKuTxeLR06VJVV1dr+PDhmjFjRjxqBQAASKiQwSk7O1uVlZXdtj/zzDMxKQgAAMCt6BwOAABgRHACAAAwIjgBAAAYmfs4Aeg70u9/rts2f2VZAioBgOTCihMAAIARwQkAAMCI4AQAAGBEcAIAADAiOAEAABgRnAAAAIwITgAAAEYEJwAAACMaYCLpWJs3BtvP+ngAAATDihMAAIARwQkAAMCI4AQAAGBEcAIAADAiOAEAABgRnAAAAIwITgAAAEYEJwAAACMaYAKQFLwR6N7SqxNQCQC4FytOAAAARgQnAAAAI4ITAACAEcEJAADAiOAEAABgRHACAAAwIjgBAAAYEZwAAACMaICJPiFY80ZEbsz2g9L2g+ds81eWJagaAEg8VpwAAACMCE4AAABGBCcAAAAjghMAAICR6c3hq1evltfrVVdXl+6++2699tprOnDggHJzcyVJ8+bN0+TJk2NZJwAAQMKFDE5vvvmm3nvvPVVXV+vYsWO67bbb9NWvflWLFy9WcXFxPGoEAABwhZDBafTo0bruuuskSYMHD1ZbW5v8fn/MCwMAAHCbkO9xSk9PV05OjiSppqZGEydOVHp6urZt26a5c+fqvvvu09GjR2NeKAAAQKKlOY7jWHbctWuXNmzYoK1bt8rn8yk3N1eFhYXauHGjmpqaVFFREfS4jo4O+Xy+qBaN1Dbmcw0Z42Vv6dWm/az1WR8vFiKZw0TWDQDx4vF4lJWV1W276c3he/bsUVVVlTZv3qxBgwZp7NixgZ+VlJRoxYoVYRfgZl6vV0VFRYkuI6FcOQcJCk7meTDWl9B5jWAOXfd6iDFX/g7EUaqPX2IOUm38oRZ8Qt6qO3HihFavXq0NGzYEPkW3YMECNTY2SpLq6+s1cuTIKJULAADgXiFXnHbs2KFjx45p0aJFgW3f/e53tWjRIg0YMEA5OTlauXJlTIsEAABwg5DBaebMmZo5c2a37bfddltMCgIAAHArOocDAAAYEZwAAACMCE4AAABGBCcAAAAjUx8nAFL6/c912+avLEtAJb0TrG4AQHhYcQIAADAiOAEAABgRnAAAAIwITgAAAEYEJwAAACOCEwAAgBHBCQAAwIjgBAAAYEQDTABxk6xNRAHgLFacAAAAjAhOAAAARgQnAAAAI4ITAACAEcEJAADAiOAEAABgRHACAAAwIjgBAAAY0QATcUcTRABAsmLFCQAAwIjgBAAAYERwAgAAMCI4AQAAGBGcAAAAjAhOAAAARgQnAAAAI/o4wbWC9Xvq6yLtcZWKcwYA8cSKEwAAgBHBCQAAwIjgBAAAYERwAgAAMCI4AQAAGJk+Vbd69Wp5vV51dXXp7rvv1rXXXqslS5bI7/dr6NChWrNmjTIzM2NdKwAAQEKFDE5vvvmm3nvvPVVXV+vYsWO67bbbNHbsWJWWlmratGlat26dampqVFpaGo96AQAAEibkrbrRo0frySeflCQNHjxYbW1tqq+v15QpUyRJxcXFqquri22VAAAALhByxSk9PV05OTmSpJqaGk2cOFF/+ctfArfm8vLy1NLSEvJEPp8vwlITw+v1JrqEhLPOwZjtB7tt21t6dVTP4TaRNJy0HtvTfta5jbZoP1fJ8NwnQ42xlOrjl5iDVB//Z5k7h+/atUs1NTXaunWrpk6dGtjuOI7peI/Ho6ysrN5XmEBer1dFRUWJLiOhejUHQYJT0GMj2A//k6g5i+h3wvrcu0iqXwdSffwSc5Bq4+/o6DjvYo/pU3V79uxRVVWVNm3apEGDBiknJ0ft7e2SpObmZuXn50enWgAAABcLGZxOnDih1atXa8OGDcrNzZUkjRs3TrW1tZKknTt3asKECbGtEgAAwAVC3qrbsWOHjh07pkWLFgW2rVq1So888oiqq6s1fPhwzZgxI6ZFAgAAuEHI4DRz5kzNnDmz2/ZnnnkmJgUBAAC4FZ3DAQAAjAhOAAAARgQnAAAAI3MfJyCWImkkCXfiOQXQF7HiBAAAYERwAgAAMCI4AQAAGBGcAAAAjAhOAAAARgQnAAAAI4ITAACAEcEJAADAiAaYiCmaIMYOcwsA8ceKEwAAgBHBCQAAwIjgBAAAYERwAgAAMCI4AQAAGBGcAAAAjAhOAAAARgQnAAAAIxpgIqRgjRb9lWUJqAQAgMRixQkAAMCI4AQAAGBEcAIAADAiOAEAABgRnAAAAIwITgAAAEYEJwAAACOCEwAAgBENMHGOYM0ugViiwSqAZMKKEwAAgBHBCQAAwIjgBAAAYERwAgAAMDIFp4aGBt18883atm2bJKm8vFzf/va3VVZWprKyMr3++uuxrBEAAMAVQn6q7vTp03rsscc0duzYc7YvXrxYxcXFMSsMAADAbUKuOGVmZmrTpk3Kz8+PRz0AAACuFTI49e/fX9nZ2d22b9u2TXPnztV9992no0ePxqQ4AAAAN0lzHMex7PjUU0/poosu0pw5c1RXV6fc3FwVFhZq48aNampqUkVFRdDjOjo65PP5olp0XzJm+8Gg2/eWXh3nSj7VUz3A+QR7vUbyWkrU6x8AzvJ4PMrKyuq2PazO4Z99v1NJSYlWrFgRdgFu5vV6VVRUFNuT9PDHJebn7QnBCWEI+nqN4LWUsNd/EHG5DrhYqo9fYg5SbfyhFnzCakewYMECNTY2SpLq6+s1cuTI8KoDAABIIiFXnHw+n5544gkdOnRI/fv3V21trebMmaNFixZpwIABysnJ0cqVK+NRKwAAQEKFDE4ej0fPPdf9Szi//vWvx6QgAAAAt6JzOAAAgBHBCQAAwIjgBAAAYERwAgAAMCI4AQAAGBGcAAAAjAhOAAAARgQnAAAAI4ITAACAEcEJAADAiOAEAABgRHACAAAwCvklv+gb0u/v/kXN/sqyBFQChJasr9dkrRuAHStOAAAARgQnAAAAI4ITAACAEcEJAADAiOAEAABgRHACAAAwIjgBAAAYEZwAAACMaIDZBwVrwgcAACLHihMAAIARwQkAAMCI4AQAAGBEcAIAADAiOAEAABgRnAAAAIwITgAAAEYEJwAAACMaYAKIWKKarvZ0Xn9lWVQf019UFPbjAehbWHECAAAwIjgBAAAYEZwAAACMCE4AAABGBCcAAAAjU3BqaGjQzTffrG3btkmSDh8+rLKyMpWWlmrhwoU6c+ZMTIsEAABwg5DB6fTp03rsscc0duzYwLb169ertLRU27dv15e+9CXV1NTEtEgAAAA3CBmcMjMztWnTJuXn5we21dfXa8qUKZKk4uJi1dXVxa5CAAAAlwjZALN///7q3//c3dra2pSZmSlJysvLU0tLS8gT+Xy+MEtMLK/X22fPm6imhUA4evM7Yd13zPaDUT93NI91i74whkil+hyk+vg/K+LO4Y7jmPbzeDzKysqK9HRx5fV6VRTrjsE9XLgjOq/xjwGQTIL+TkT6+2P8XYnk8WJ+DYmxuFwHXS7V5yDVxt/R0XHexZ6wPlWXk5Oj9vZ2SVJzc/M5t/EAAAD6qrCC07hx41RbWytJ2rlzpyZMmBDVogAAANwo5K06n8+nJ554QocOHVL//v1VW1urtWvXqry8XNXV1Ro+fLhmzJgRj1oBAAASKmRw8ng8eu657m8ifuaZZ2JSEAAAgFvRORwAAMCI4AQAAGBEcAIAADCKuI8TAMRDbxq2BtvXX1kWzXIApChWnAAAAIwITgAAAEYEJwAAACOCEwAAgBHBCQAAwIjgBAAAYERwAgAAMCI4AQAAGNEA06Vo4AdEV28aaFqO5fcRSE2sOAEAABgRnAAAAIwITgAAAEYEJwAAACOCEwAAgBHBCQAAwIjgBAAAYERwAgAAMKIBZpKLpKkfAADoHVacAAAAjAhOAAAARgQnAAAAI4ITAACAEcEJAADAiOAEAABgRHACAAAwIjgBAAAY0QATAOLM2rjWX1lmOjbYfr05d2+OB1IdK04AAABGBCcAAAAjghMAAIARwQkAAMAorDeH19fXa+HChRo5cqQkadSoUVq2bFlUCwMAAHCbsD9VN2bMGK1fvz6atQAAALgat+oAAACM0hzHcXp7UH19vR599FGNGDFCH3/8sebPn6/x48cH3bejo0M+ny/iQvuCMdsPJroEAHG2t/Tqbtus14JoH9vT8dbz9PSY0Tw22txUC5KLx+NRVlZWt+1h3aq79NJLNX/+fE2bNk2NjY2aO3eudu7cqczMzF4X4GZer1dFRUXRe0CCE5Bygl5DjNeCqB/bw/HW85ivh5EcG21RqCXqfwuSTKqNP9SCT1i36goKCjR9+nSlpaVpxIgRuvjii9Xc3Bx2kQAAAMkgrOD0yiuvaMuWLZKklpYWHTlyRAUFBVEtDAAAwG3CulVXUlKiBx54QH/+85/V2dmpFStWnPc2HQAAQF8QVnC64IILVFVVFe1aAAAAXI12BAAAAEYEJwAAACOCEwAAgFHYX7mC80u//7lElwAArhHsmuivLEtAJYll/dsQbG6YQ3dgxQkAAMCI4AQAAGBEcAIAADAiOAEAABgRnAAAAIwITgAAAEYEJwAAACOCEwAAgBENMKOAZpcAepKo6wPXJSA2WHECAAAwIjgBAAAYEZwAAACMCE4AAABGBCcAAAAjghMAAIARwQkAAMCI4AQAAGDUpxpgBmv45q8si/k5ACAW4nW9ScbrWjyu95FyU/NTt81NMmPFCQAAwIjgBAAAYERwAgAAMCI4AQAAGBGcAAAAjAhOAAAARgQnAAAAI4ITAACAUZ9qgBlMxA3Ith+MTiEAkOSi3dDRTQ0ipeBNIgP7hvhbkKhmy5HsF4ummNbzRHu/eGLFCQAAwIjgBAAAYERwAgAAMCI4AQAAGIX95vDHH39c+/fvV1pamh566CFdd9110awLAADAdcIKTnv37tW///1vVVdX65///KceeughVVdXR7s2AAAAVwnrVl1dXZ1uvvlmSdIVV1yhjz/+WCdPnoxqYQAAAG4T1opTa2urrrnmmsC/hwwZopaWFl1wwQXd9nUcR5J05syZMEu0GzYwI+bnAAC4U0dHR7dtvfm7EMnxkZ47EYLVHOm+wcZsnZtI9oums3nlbH75vDSnp5+cx7JlyzRp0qTAqtPs2bP1+OOP67LLLuu274kTJ9TQ0NDbUwAAACTMqFGjNGjQoG7bw1pxys/PV2tra+DfH330kYYOHRp034EDB2rUqFHKyMhQWlpaOKcDAACIC8dx1NnZqYEDBwb9eVjBafz48Xrqqac0a9YsHThwQPn5+UFv00lSv379giY2AAAAN8rOzu7xZ2EFpxtvvFHXXHONZs2apbS0NC1fvjzs4gAAAJJFWO9xAgAASEV0DgcAADAiOAEAABiF/ZUryayzs1Pl5eX68MMPlZ6erpUrV+qLX/ziOft8/PHHWrx4sQYOHKj169dLkrq6uvTwww/rgw8+kN/v15IlS3TTTTeprKxMp0+fVk5OjiRp6dKl8ng8cR9Xb4Q7Bz0d9+6772rFihWSpCuvvFKPPvpovIfUK5bxv/LKK3r22WfVr18/3XHHHbr99tv129/+Vm+88YYk6ZNPPlFra6tqa2tVUlKiL3zhC0pPT5ckrV27VgUFBXEfl1W443/55Zf15JNPasSIEZKkcePG6Wc/+1nSPf9S+HPQF64D5/vKrDfeeEPr1q1Tenq6Jk6cqHvvvbfHYw4fPqwlS5bI7/dr6NChWrNmjTIzMxM1LLNwxr969Wp5vV51dXXp7rvv1tSpU1VeXq4DBw4oNzdXkjRv3jxNnjw5EUPqtd7OQX19vRYuXKiRI0dK+vSj+suWLUva10BEnBT08ssvOytWrHAcx3H27NnjLFy4sNs+CxcudH796187CxYsCGyrqalxli9f7jiO4zQ0NDjf+973HMdxnDlz5jj/+Mc/Yl94FIU7Bz0dN2fOHGf//v2O4zjO4sWLnddffz3WQ4hIqPGfOnXKmTp1qnP8+HGnra3N+eY3v+kcO3as22Ns2rTJcRzHKS4udk6ePBmf4qMg3PG/9NJLzqpVq7o9XrI9/44T/hwk+3Wgvr7e+clPfuI4juO8//77zh133HHOz6dNm+Z8+OGHjt/vd2bPnu289957PR5TXl7u7Nixw3Ecx6msrHSef/75OI4kPOGMv66uzvnxj3/sOI7jHD161Jk0aZLjOI6zdOlS57XXXotr/dEQzhy8+eab5/wtOCsZXwORSslbdXV1dbrlllskffp/zG+//Xa3fX75y1+qqKjonG233nqrfvGLX0j6tFv6f//739gXGyPhzkGw486cOaNDhw4F/o+luLhYdXV1MR5BZEKNf//+/br22ms1aNAgZWdn68Ybbzxnn66uLv3+97/XnDlz4lp3tEQ6/s9KxudfCn8Okv06cL6vzGpsbNSFF16oYcOGqV+/fpo0aZLq6up6PKa+vl5TpkyRlFzPe2/HP3r0aD355JOSpMGDB6utrU1+vz9hY4hUOHPQk2R8DUQqJYNTa2urhgwZIunTPlNpaWndvhImWF+qjIwMZWVlSZKeffZZfetb3wr8bP369fr+97+viooKtbe3x7D66Ah3DoId19raqsGDBwf2ycvLU0tLSwyrj1yo8X/259L/vlborJ07d+prX/vaOb0+li9frtmzZ2vt2rU9tup3i0jGv3fvXs2bN08/+MEPdPDgQR07dizpnn8p/DlI9utAa2urLrroosC/P/vctrS0BB1zT8e0tbUFbssk0/Pe2/Gnp6cHbsHW1NRo4sSJgdvy27Zt09y5c3Xffffp6NGjcRxJ+MKZA0l6//339dOf/lSzZ8/WX//6V0lKytdApPr8e5xefPFFvfjii+ds279//zn/7u0fueeff14HDhxQVVWVJGnu3Lm68sorNWLECC1fvlzPP/+85s2bF1nhURSLOTjfcW4LDdEY/+d//tJLL53zPp6f//znmjBhgi688ELde++9qq2t1Te+8Y0IK4+OaI7/+uuv15AhQzR58mT97W9/09KlS7V58+ZePVYixOI1kGzXgZ6E83wlw++9VW/q3rVrl2pqarR161ZJ0ne+8x3l5uaqsLBQGzdu1NNPP62KiopYlRozljm49NJLNX/+fE2bNk2NjY2aO3eudu7c2evH6Qv6fHC6/fbbdfvtt5+zrby8XC0tLbrqqqvU2dkpx3HMb2Z78cUX9dprr+k3v/mNMjI+/fLBs8v9klRSUqIdO3ZEbwBREM05yM/P73bc0KFDz7ld0dzcrPz8/KiPI1zhjD/Y1wp9+ctfliSdPn1aTU1NuuSSSwI/nzFjRuC/J06cqIaGBtcEp2iO/4orrtAVV1whSbrhhht09OhRXXTRRa5+/qXovwaS8Tpw1vm+MuvzPzv7XGZkZAQ9JicnR+3t7crOznbl8x5MOOOXpD179qiqqkqbN28OfBvG2LFjA/uWlJQEPiDhduHMQUFBgaZPny5JGjFihC6++GI1Nzcn5WsgUil5q278+PF69dVXJUm7d+/WV77yFdNxjY2NeuGFF/T0008Hluodx9EPf/hDHT9+XNKn93vPfurAzcKdg2DHZWRk6PLLL9e+ffskfXoba8KECbEpPEpCjf/666/X3//+dx0/flynTp3S22+/rZtuukmS9O677+ryyy8P7HvixAnNmzcvcJvnrbfecv1rINzxb9q0SX/84x8lSQ0NDRoyZIgyMzOT7vmXwp+DZL8OjB8/XrW1tZLU7SuzLrnkEp08eVL/+c9/1NXVpd27d2v8+PE9HjNu3LjA9mR63ns7/hMnTmj16tXasGFD4BN0krRgwQI1NjZKcvdz/nnhzMErr7yiLVu2SPr0dt6RI0dUUFCQlK+BSKVk53C/369HHnlE//rXv5SZmalVq1Zp2LBh2rhxo0aPHq3rrrsucBFsbm7WyJEjdc8996iurk5/+tOfNHz48MBjbdmyRbt27dLmzZs1YMAAFRQU6Fe/+pUGDBiQwBGGFu4cjBkzJuhx77//vioqKvTJJ5/o+uuvD7x51q1Cjf+GG27Qq6++qi1btigtLU1z5szRrbfeKkmqra3VG2+8cc6tumeffVZ/+MMflJWVpauvvlrLli1z9Zdahzv+pqYmPfjgg3IcR11dXYGPMSfb8y+FPwfr1q1L+uvA2rVrtW/fvsBXZh08eFCDBg3SLbfcorfeektr166VJE2dOjVwu/Hzx1x11VX66KOPtHTpUnV0dGj48OFauXJlYAXOzXo7/urqaj311FO67LLLAo/xxBNP6IMPPtCaNWs0YMAA5eTkaOXKlcrLy0vUsHqlt3Nw8uRJPfDAAzp+/Lg6Ozs1f/58TZo0KWlfA5FIyeAEAAAQjpS8VQcAABAOghMAAIARwQkAAMCI4AQAAGBEcAIAADAiOAEAABgRnAAAAIwITgAAAEb/BwoVWhDpyxgYAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lKEot9ebQ2Nc"
      },
      "source": [
        "For any other model, let's try for KNN model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lpDIJlW_NPqO",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3838896e-2a46-4a16-e43b-b1410ba603e7"
      },
      "source": [
        "knn = create_model('knn')\n",
        "print(knn)"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "KNN(algorithm='auto', contamination=0.05, leaf_size=30, method='largest',\n",
            "  metric='minkowski', metric_params=None, n_jobs=-1, n_neighbors=5, p=2,\n",
            "  radius=1.0)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-AzYQUedNPqV"
      },
      "source": [
        "from sklearn import set_config\n",
        "set_config(display=None)"
      ],
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Yy9aOqFYNPqW",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "27dbbe50-e3ba-4612-f59d-d0738ac20d5d"
      },
      "source": [
        "l"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Pipeline(memory=None,\n",
              "         steps=[('dtypes',\n",
              "                 DataTypes_Auto_infer(categorical_features=[],\n",
              "                                      display_types=True, features_todrop=[],\n",
              "                                      id_columns=[], ml_usecase='regression',\n",
              "                                      numerical_features=[],\n",
              "                                      target='UNSUPERVISED_DUMMY_TARGET',\n",
              "                                      time_features=[])),\n",
              "                ('imputer',\n",
              "                 Simple_Imputer(categorical_strategy='most frequent',\n",
              "                                fill_value_categorical=None,\n",
              "                                fill_value_numerical=None...\n",
              "                ('fix_perfect', 'passthrough'),\n",
              "                ('clean_names', Clean_Colum_Names()),\n",
              "                ('feature_select', 'passthrough'), ('fix_multi', 'passthrough'),\n",
              "                ('dfs', 'passthrough'), ('pca', 'passthrough'),\n",
              "                ['trained_model',\n",
              "                 IForest(behaviour='new', bootstrap=False, contamination=0.05,\n",
              "    max_features=1.0, max_samples='auto', n_estimators=200, n_jobs=-1,\n",
              "    random_state=123, verbose=0)]],\n",
              "         verbose=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 845
        },
        "id": "TkExaAAUSIrL",
        "outputId": "7eeea6ae-5401-4f7b-eeeb-ab83ceeea618"
      },
      "source": [
        "set_config(display=\"diagram\")\n",
        "l"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<style>div.sk-top-container {color: black;background-color: white;}div.sk-toggleable {background-color: white;}label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.2em 0.3em;box-sizing: border-box;text-align: center;}div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}div.sk-estimator {font-family: monospace;background-color: #f0f8ff;margin: 0.25em 0.25em;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;}div.sk-estimator:hover {background-color: #d4ebff;}div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;}div.sk-item {z-index: 1;}div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;}div.sk-parallel-item {display: flex;flex-direction: column;position: relative;background-color: white;}div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}div.sk-parallel-item:only-child::after {width: 0;}div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0.2em;box-sizing: border-box;padding-bottom: 0.1em;background-color: white;position: relative;}div.sk-label label {font-family: monospace;font-weight: bold;background-color: white;display: inline-block;line-height: 1.2em;}div.sk-label-container {position: relative;z-index: 2;text-align: center;}div.sk-container {display: inline-block;position: relative;}</style><div class=\"sk-top-container\"><div class=\"sk-container\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"55654ea0-ad97-46bb-95f8-a5267931b5ab\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"55654ea0-ad97-46bb-95f8-a5267931b5ab\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(memory=None,\n",
              "         steps=[('dtypes',\n",
              "                 DataTypes_Auto_infer(categorical_features=[],\n",
              "                                      display_types=True, features_todrop=[],\n",
              "                                      id_columns=[], ml_usecase='regression',\n",
              "                                      numerical_features=[],\n",
              "                                      target='UNSUPERVISED_DUMMY_TARGET',\n",
              "                                      time_features=[])),\n",
              "                ('imputer',\n",
              "                 Simple_Imputer(categorical_strategy='most frequent',\n",
              "                                fill_value_categorical=None,\n",
              "                                fill_value_numerical=None...\n",
              "                ('fix_perfect', 'passthrough'),\n",
              "                ('clean_names', Clean_Colum_Names()),\n",
              "                ('feature_select', 'passthrough'), ('fix_multi', 'passthrough'),\n",
              "                ('dfs', 'passthrough'), ('pca', 'passthrough'),\n",
              "                ['trained_model',\n",
              "                 IForest(behaviour='new', bootstrap=False, contamination=0.05,\n",
              "    max_features=1.0, max_samples='auto', n_estimators=200, n_jobs=-1,\n",
              "    random_state=123, verbose=0)]],\n",
              "         verbose=False)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"a87fafc2-05ee-47c5-a844-6bca5b4b8554\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"a87fafc2-05ee-47c5-a844-6bca5b4b8554\">DataTypes_Auto_infer</label><div class=\"sk-toggleable__content\"><pre>DataTypes_Auto_infer(ml_usecase='regression',\n",
              "                     target='UNSUPERVISED_DUMMY_TARGET')</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"7ecfe1aa-5b9c-48f0-a642-346b9463a36d\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"7ecfe1aa-5b9c-48f0-a642-346b9463a36d\">Simple_Imputer</label><div class=\"sk-toggleable__content\"><pre>Simple_Imputer(categorical_strategy='most frequent',\n",
              "               fill_value_categorical=None, fill_value_numerical=None,\n",
              "               numeric_strategy='mean', target_variable=None)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"6d0f3381-b40b-4781-9aff-dd771bb63624\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"6d0f3381-b40b-4781-9aff-dd771bb63624\">New_Catagorical_Levels_in_TestData</label><div class=\"sk-toggleable__content\"><pre>New_Catagorical_Levels_in_TestData(replacement_strategy='least frequent',\n",
              "                                   target='UNSUPERVISED_DUMMY_TARGET')</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"ef0aec5b-0b32-4225-8b04-cf3da422781b\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"ef0aec5b-0b32-4225-8b04-cf3da422781b\">passthrough</label><div class=\"sk-toggleable__content\"><pre>passthrough</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"bc74cad3-5b13-4830-9d97-a47ade54779e\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"bc74cad3-5b13-4830-9d97-a47ade54779e\">passthrough</label><div class=\"sk-toggleable__content\"><pre>passthrough</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"5be14d44-a38d-4049-9f61-b1d898aeb01b\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"5be14d44-a38d-4049-9f61-b1d898aeb01b\">passthrough</label><div class=\"sk-toggleable__content\"><pre>passthrough</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"0e51f365-3537-4de6-8c98-c266bf14f3e6\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"0e51f365-3537-4de6-8c98-c266bf14f3e6\">passthrough</label><div class=\"sk-toggleable__content\"><pre>passthrough</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"4b07f436-9c53-4b2c-9dea-a0f1793cdf33\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"4b07f436-9c53-4b2c-9dea-a0f1793cdf33\">New_Catagorical_Levels_in_TestData</label><div class=\"sk-toggleable__content\"><pre>New_Catagorical_Levels_in_TestData(replacement_strategy='least frequent',\n",
              "                                   target='UNSUPERVISED_DUMMY_TARGET')</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"c6bf61af-1070-4cb2-9136-36caa13fefff\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"c6bf61af-1070-4cb2-9136-36caa13fefff\">Make_Time_Features</label><div class=\"sk-toggleable__content\"><pre>Make_Time_Features(list_of_features=None,\n",
              "                   time_feature=Index([], dtype='object'))</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"1a7cc6a4-32be-4d22-8a2e-3bbc8d255adb\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"1a7cc6a4-32be-4d22-8a2e-3bbc8d255adb\">passthrough</label><div class=\"sk-toggleable__content\"><pre>passthrough</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"d7ac4d51-28b2-432e-a4c2-e968b647e0ba\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"d7ac4d51-28b2-432e-a4c2-e968b647e0ba\">passthrough</label><div class=\"sk-toggleable__content\"><pre>passthrough</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"e83cb850-7c19-43d1-b95f-4abbaa844dc0\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"e83cb850-7c19-43d1-b95f-4abbaa844dc0\">passthrough</label><div class=\"sk-toggleable__content\"><pre>passthrough</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"86ff1280-0dc9-4d8f-b6ea-7bf757a4b317\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"86ff1280-0dc9-4d8f-b6ea-7bf757a4b317\">passthrough</label><div class=\"sk-toggleable__content\"><pre>passthrough</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"3dc823b6-2055-46af-821c-ad8bfeadd854\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"3dc823b6-2055-46af-821c-ad8bfeadd854\">passthrough</label><div class=\"sk-toggleable__content\"><pre>passthrough</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"d7613459-d412-4448-8ed4-0cf56a379452\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"d7613459-d412-4448-8ed4-0cf56a379452\">passthrough</label><div class=\"sk-toggleable__content\"><pre>passthrough</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"026731da-36cf-4a2f-a51b-9a8f1e222636\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"026731da-36cf-4a2f-a51b-9a8f1e222636\">passthrough</label><div class=\"sk-toggleable__content\"><pre>passthrough</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"bcdf9eab-641f-4805-8d2c-c2cd6e0a5e81\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"bcdf9eab-641f-4805-8d2c-c2cd6e0a5e81\">Dummify</label><div class=\"sk-toggleable__content\"><pre>Dummify(target='UNSUPERVISED_DUMMY_TARGET')</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"d7fa96f4-36d4-4fe1-8462-89feabf85505\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"d7fa96f4-36d4-4fe1-8462-89feabf85505\">passthrough</label><div class=\"sk-toggleable__content\"><pre>passthrough</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"66bf9b93-7d7a-4ed8-a8ea-3e42569b0e5d\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"66bf9b93-7d7a-4ed8-a8ea-3e42569b0e5d\">Clean_Colum_Names</label><div class=\"sk-toggleable__content\"><pre>Clean_Colum_Names()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"82495e28-2849-4e17-a873-7b3a08e125f2\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"82495e28-2849-4e17-a873-7b3a08e125f2\">passthrough</label><div class=\"sk-toggleable__content\"><pre>passthrough</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"d309ec55-bb1c-47a8-94b9-d26b1cc144cf\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"d309ec55-bb1c-47a8-94b9-d26b1cc144cf\">passthrough</label><div class=\"sk-toggleable__content\"><pre>passthrough</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"34c8d9db-876f-41f4-9ee0-c41f8f553019\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"34c8d9db-876f-41f4-9ee0-c41f8f553019\">passthrough</label><div class=\"sk-toggleable__content\"><pre>passthrough</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"fde46f59-fe9c-4e0f-be21-3860d4b8afc4\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"fde46f59-fe9c-4e0f-be21-3860d4b8afc4\">passthrough</label><div class=\"sk-toggleable__content\"><pre>passthrough</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"c2be7e7b-3c5d-4e3f-aa00-62234cf33309\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"c2be7e7b-3c5d-4e3f-aa00-62234cf33309\">IForest</label><div class=\"sk-toggleable__content\"><pre>IForest(behaviour='new', bootstrap=False, contamination=0.05,\n",
              "    max_features=1.0, max_samples='auto', n_estimators=200, n_jobs=-1,\n",
              "    random_state=123, verbose=0)</pre></div></div></div></div></div></div></div>"
            ],
            "text/plain": [
              "Pipeline(memory=None,\n",
              "         steps=[('dtypes',\n",
              "                 DataTypes_Auto_infer(categorical_features=[],\n",
              "                                      display_types=True, features_todrop=[],\n",
              "                                      id_columns=[], ml_usecase='regression',\n",
              "                                      numerical_features=[],\n",
              "                                      target='UNSUPERVISED_DUMMY_TARGET',\n",
              "                                      time_features=[])),\n",
              "                ('imputer',\n",
              "                 Simple_Imputer(categorical_strategy='most frequent',\n",
              "                                fill_value_categorical=None,\n",
              "                                fill_value_numerical=None...\n",
              "                ('fix_perfect', 'passthrough'),\n",
              "                ('clean_names', Clean_Colum_Names()),\n",
              "                ('feature_select', 'passthrough'), ('fix_multi', 'passthrough'),\n",
              "                ('dfs', 'passthrough'), ('pca', 'passthrough'),\n",
              "                ['trained_model',\n",
              "                 IForest(behaviour='new', bootstrap=False, contamination=0.05,\n",
              "    max_features=1.0, max_samples='auto', n_estimators=200, n_jobs=-1,\n",
              "    random_state=123, verbose=0)]],\n",
              "         verbose=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MB0wJGTnShUm"
      },
      "source": [
        "#Deployment"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xyRQXbF8NPqX"
      },
      "source": [
        "deploy_model(iforest, 'iforest-abc', platform = 'aws', \n",
        "             authentication = {'bucket' : 'pycaret-test'})"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4sqw9L1xUCA8"
      },
      "source": [
        "----------------------"
      ]
    }
  ]
}