{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZZKpEgCwWRHO"
      },
      "source": [
        "[![github-badge](https://img.shields.io/badge/GitHub-sidchaini/DistClassiPyTutorial-blue)](https://github.com/sidchaini/DistClassiPyTutorial)\n",
        "[![colab-button](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/sidchaini/DistClassiPyTutorial/blob/main/tutorial.ipynb)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2InkCSL4WRHQ"
      },
      "source": [
        "# Leveraging Distance Metrics for Better Machine Learning"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "editable": true,
        "tags": [],
        "id": "jq02wZ59WRHR"
      },
      "source": [
        "**Siddharth Chaini, 6th January, 2025**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2025-01-06T09:28:24.796939Z",
          "iopub.status.busy": "2025-01-06T09:28:24.794895Z",
          "iopub.status.idle": "2025-01-06T09:28:24.813726Z",
          "shell.execute_reply": "2025-01-06T09:28:24.812563Z",
          "shell.execute_reply.started": "2025-01-06T09:28:24.796033Z"
        },
        "id": "N-8EeHfwWRHR"
      },
      "source": [
        "(Special thanks to Federica Bianco, Ashish Mahabal and Ajit Kembhavi!)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "editable": true,
        "tags": [],
        "id": "2yVQjkQOWRHR"
      },
      "source": [
        "This hands-on session is largely based on and derived from the work described in [Chaini et. al 2024](https://arxiv.org/abs/2403.12120). It will go over:\n",
        "1. What are distance metrics?\n",
        "2. Where are they used in machine learning?\n",
        "3. DistClassiPy\n",
        "    - Demo on a real astronomical dataset!"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "J9z1KniSWRHR"
      },
      "source": [
        "---"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "editable": true,
        "tags": [],
        "id": "ERwNzTmDWRHR"
      },
      "source": [
        "### 0. Prerequisites"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "editable": true,
        "tags": [],
        "id": "g1ykMMBcWRHS"
      },
      "source": [
        "Let us first install DistClassiPy from PyPI. I am installing 0.2.1, the latest as of 2025-01-05."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "editable": true,
        "execution": {
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          "iopub.status.idle": "2025-01-05T21:10:49.126600Z",
          "shell.execute_reply": "2025-01-05T21:10:49.125943Z",
          "shell.execute_reply.started": "2025-01-05T21:10:45.698991Z"
        },
        "id": "ZZ5CAUOfHwO4",
        "outputId": "d49967a0-e20f-497b-b5f4-bfb4e138d628",
        "scrolled": true,
        "tags": []
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Collecting distclassipy==0.2.1\n",
            "  Downloading distclassipy-0.2.1-py3-none-any.whl.metadata (47 kB)\n",
            "Requirement already satisfied: joblib>=1.3.2 in /Users/sidchaini/miniconda3/lib/python3.12/site-packages (from distclassipy==0.2.1) (1.4.2)\n",
            "Requirement already satisfied: numpy>=1.25.2 in /Users/sidchaini/miniconda3/lib/python3.12/site-packages (from distclassipy==0.2.1) (1.26.4)\n",
            "Requirement already satisfied: pandas>=2.0.3 in /Users/sidchaini/miniconda3/lib/python3.12/site-packages (from distclassipy==0.2.1) (2.2.3)\n",
            "Requirement already satisfied: scikit-learn>=1.2.2 in /Users/sidchaini/miniconda3/lib/python3.12/site-packages (from distclassipy==0.2.1) (1.5.1)\n",
            "Requirement already satisfied: python-dateutil>=2.8.2 in /Users/sidchaini/miniconda3/lib/python3.12/site-packages (from pandas>=2.0.3->distclassipy==0.2.1) (2.9.0.post0)\n",
            "Requirement already satisfied: pytz>=2020.1 in /Users/sidchaini/miniconda3/lib/python3.12/site-packages (from pandas>=2.0.3->distclassipy==0.2.1) (2024.1)\n",
            "Requirement already satisfied: tzdata>=2022.7 in /Users/sidchaini/miniconda3/lib/python3.12/site-packages (from pandas>=2.0.3->distclassipy==0.2.1) (2024.1)\n",
            "Requirement already satisfied: scipy>=1.6.0 in /Users/sidchaini/miniconda3/lib/python3.12/site-packages (from scikit-learn>=1.2.2->distclassipy==0.2.1) (1.14.1)\n",
            "Requirement already satisfied: threadpoolctl>=3.1.0 in /Users/sidchaini/miniconda3/lib/python3.12/site-packages (from scikit-learn>=1.2.2->distclassipy==0.2.1) (3.5.0)\n",
            "Requirement already satisfied: six>=1.5 in /Users/sidchaini/miniconda3/lib/python3.12/site-packages (from python-dateutil>=2.8.2->pandas>=2.0.3->distclassipy==0.2.1) (1.16.0)\n",
            "Downloading distclassipy-0.2.1-py3-none-any.whl (43 kB)\n",
            "Installing collected packages: distclassipy\n",
            "  Attempting uninstall: distclassipy\n",
            "    Found existing installation: distclassipy 0.2.2\n",
            "    Uninstalling distclassipy-0.2.2:\n",
            "      Successfully uninstalled distclassipy-0.2.2\n",
            "Successfully installed distclassipy-0.2.1\n"
          ]
        }
      ],
      "source": [
        "!pip install distclassipy==0.2.1 # latest as of 2025-01-05."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "editable": true,
        "execution": {
          "iopub.execute_input": "2025-01-05T21:10:49.127735Z",
          "iopub.status.busy": "2025-01-05T21:10:49.127579Z",
          "iopub.status.idle": "2025-01-05T21:11:02.412041Z",
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          "shell.execute_reply.started": "2025-01-05T21:10:49.127714Z"
        },
        "tags": [],
        "id": "4e7gX34IWRHT"
      },
      "outputs": [],
      "source": [
        "%%capture\n",
        "!wget https://github.com/sidchaini/DistClassiPyTutorial/archive/refs/heads/main.zip\n",
        "!unzip main.zip\n",
        "!mv DistClassiPyTutorial-main/* .\n",
        "!rm -rf main.zip DistClassiPyTutorial-main"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2025-01-06T05:17:00.923240Z",
          "iopub.status.busy": "2025-01-06T05:17:00.922572Z",
          "iopub.status.idle": "2025-01-06T05:17:01.945476Z",
          "shell.execute_reply": "2025-01-06T05:17:01.945051Z",
          "shell.execute_reply.started": "2025-01-06T05:17:00.923211Z"
        },
        "id": "9GC3xlkMQu-Z",
        "outputId": "cd50adee-2904-48a9-8602-147c607b479c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "distclassipy version 0.2.1\n"
          ]
        }
      ],
      "source": [
        "import numpy as np\n",
        "\n",
        "seed = 0\n",
        "np.random.seed(seed)\n",
        "import pandas as pd\n",
        "import distclassipy as dcpy\n",
        "import utils\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import accuracy_score, f1_score\n",
        "print(f\"distclassipy version {dcpy.__version__}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qM8py5zyWRHT"
      },
      "source": [
        "---"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zQR651l0WRHU"
      },
      "source": [
        "### 1. What are distance metrics?\n",
        "\n",
        "**Definition**: A distance is a quantity that tells us how similar two objects are. It follows the axioms:\n",
        "1. *Identity of indiscernibles*: $$d(x, y)=0 \\iff x=y $$\n",
        "2. *Symmetry*: $$d(x, y)=d(y, x)$$\n",
        "3. *Triangle inequality*: $$d(x, y)\\leq d(x, z) + d(z, y)$$"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wa2yS_8GWRHU"
      },
      "source": [
        "---"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dXEtOYkhWRHU"
      },
      "source": [
        "**Small exercise**: Which of the following is a distance metric, and which is not? Why?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2025-01-06T05:17:04.870456Z",
          "iopub.status.busy": "2025-01-06T05:17:04.869441Z",
          "iopub.status.idle": "2025-01-06T05:17:04.878326Z",
          "shell.execute_reply": "2025-01-06T05:17:04.876845Z",
          "shell.execute_reply.started": "2025-01-06T05:17:04.870422Z"
        },
        "id": "CNqPVgBAWRHU"
      },
      "outputs": [],
      "source": [
        "def custom_fn1(x, y):\n",
        "    return np.sum(np.abs(x - y))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2025-01-06T05:17:05.164069Z",
          "iopub.status.busy": "2025-01-06T05:17:05.163452Z",
          "iopub.status.idle": "2025-01-06T05:17:05.172463Z",
          "shell.execute_reply": "2025-01-06T05:17:05.170093Z",
          "shell.execute_reply.started": "2025-01-06T05:17:05.164040Z"
        },
        "id": "7MJhDqCMWRHU"
      },
      "outputs": [],
      "source": [
        "def custom_fn2(x, y):\n",
        "    return (1 + np.sum(np.abs(x - y)))**2"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2025-01-06T05:17:05.330215Z",
          "iopub.status.busy": "2025-01-06T05:17:05.329838Z",
          "iopub.status.idle": "2025-01-06T05:17:05.339992Z",
          "shell.execute_reply": "2025-01-06T05:17:05.339146Z",
          "shell.execute_reply.started": "2025-01-06T05:17:05.330191Z"
        },
        "id": "Y4JxyoGzWRHU",
        "outputId": "b18a44f7-90ca-45d9-d422-9476d28281e2"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "1"
            ]
          },
          "execution_count": 6,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "p1 = np.array([1, 2, 3])\n",
        "\n",
        "custom_fn2(p1, p1) # fails distance metric defn as nonzero"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2025-01-06T05:17:06.497451Z",
          "iopub.status.busy": "2025-01-06T05:17:06.496267Z",
          "iopub.status.idle": "2025-01-06T05:17:06.505625Z",
          "shell.execute_reply": "2025-01-06T05:17:06.504520Z",
          "shell.execute_reply.started": "2025-01-06T05:17:06.497420Z"
        },
        "id": "xGZQY0cnWRHU",
        "outputId": "36b869c7-2fbe-4da8-f2f1-37929f7eaec2"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "DCPY has 43 metrics\n",
            "['euclidean', 'braycurtis', 'canberra', 'cityblock', 'chebyshev', 'clark', 'correlation', 'cosine', 'hellinger', 'jaccard', 'lorentzian', 'marylandbridge', 'meehl', 'motyka', 'soergel', 'wave_hedges', 'kulczynski', 'add_chisq', 'acc', 'chebyshev_min', 'czekanowski', 'dice', 'divergence', 'google', 'gower', 'jeffreys', 'jensenshannon_divergence', 'jensen_difference', 'kumarjohnson', 'matusita', 'minkowski', 'penroseshape', 'prob_chisq', 'ruzicka', 'sorensen', 'squared_chisq', 'squaredchord', 'squared_euclidean', 'taneja', 'tanimoto', 'topsoe', 'vicis_symmetric_chisq', 'vicis_wave_hedges']\n"
          ]
        }
      ],
      "source": [
        "print(f\"DCPY has {len(dcpy._ALL_METRICS)} metrics\")\n",
        "print(dcpy._ALL_METRICS)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZsrP3uqOWRHU"
      },
      "source": [
        "**Visualizing 2D distance metric spaces**: We can plot the locus of a central point (*e.g.,*$(5,5)$) in a given two dimensional space. The locus appear as contour lines, which can illustrate geometry of the space when plotted in Euclidean space."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2025-01-06T05:17:07.166762Z",
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        },
        "id": "o1vxViLYWRHV",
        "outputId": "3fc4dbbf-fd14-47c6-93d6-b5bbdd3ef139"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 400x400 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "utils.visualize_distance(\"euclidean\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ViQ4acOOWRHV"
      },
      "source": [
        "---"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9JwDUgnTWRHV"
      },
      "source": [
        "### 2. Distances in Machine Learning\n",
        "\n",
        "Distance metrics power different ML tasks:\n",
        "\n",
        "- **Clustering**: Distance metrics help group similar data points (e.g., K-Means, Hierarchical Clustering).\n",
        "- **Dimensionality Reduction**: They preserve data structure in fewer dimensions (e.g., PCA, t-SNE).\n",
        "- **Classification**: They determine proximity for decision-making (e.g., K-Nearest Neighbors, SVM, **DistClassiPy**)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2025-01-06T05:17:07.761698Z",
          "iopub.status.busy": "2025-01-06T05:17:07.760956Z",
          "iopub.status.idle": "2025-01-06T05:17:07.774577Z",
          "shell.execute_reply": "2025-01-06T05:17:07.772715Z",
          "shell.execute_reply.started": "2025-01-06T05:17:07.761647Z"
        },
        "id": "5hZ6nMlnWRHV",
        "outputId": "5369d855-2a3a-40c3-fca1-a391060e395f"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<video src=\"https://sidchaini.github.io/videos/distclassipy.mp4\" controls  width=\"480\"  height=\"240\">\n",
              "      Your browser does not support the <code>video</code> element.\n",
              "    </video>"
            ],
            "text/plain": [
              "<IPython.core.display.Video object>"
            ]
          },
          "execution_count": 9,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from IPython.display import Video\n",
        "Video(\n",
        "    \"https://sidchaini.github.io/videos/distclassipy.mp4\",\n",
        "    width=480, height=240\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QTKkblTGWRHV"
      },
      "source": [
        "---"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "S1V0_AGWWRHV"
      },
      "source": [
        "### 3. DistClassiPy for ZTF Light Curve Classification\n",
        "\n",
        "For this example, we will be using data from \"The ZTF Source Classification Project: III. A Catalog of Variable Sources\" through which they have made available on Zenodo.\n",
        "\n",
        "[![zenodo-badge](https://zenodo.org/badge/DOI/10.5281/zenodo.14155156.svg)](https://zenodo.org/records/14155156)\n",
        "\n",
        "I downloaded and downsampled them to choose 4000 objects from 4 classes of variable stars:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2025-01-06T05:17:08.243027Z",
          "iopub.status.busy": "2025-01-06T05:17:08.242360Z",
          "iopub.status.idle": "2025-01-06T05:17:08.497724Z",
          "shell.execute_reply": "2025-01-06T05:17:08.497449Z",
          "shell.execute_reply.started": "2025-01-06T05:17:08.242995Z"
        },
        "id": "ZA7xra5vWRHV"
      },
      "outputs": [],
      "source": [
        "features = pd.read_csv(\"data/ztfscope_features.csv\", index_col=0)\n",
        "labels = pd.read_csv(\"data/ztfscope_labels.csv\", index_col=0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2025-01-06T05:17:08.498485Z",
          "iopub.status.busy": "2025-01-06T05:17:08.498395Z",
          "iopub.status.idle": "2025-01-06T05:17:08.507993Z",
          "shell.execute_reply": "2025-01-06T05:17:08.507501Z",
          "shell.execute_reply.started": "2025-01-06T05:17:08.498477Z"
        },
        "id": "V9ibyhFMWRHV",
        "outputId": "2c06483b-a63e-4b9f-d6cd-e98f4900d9e8"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "class\n",
              "CEP      1000\n",
              "DSCT     1000\n",
              "RR       1000\n",
              "RRc      1000\n",
              "Name: count, dtype: int64"
            ]
          },
          "execution_count": 11,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "labels.value_counts()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RjpNSN0nWRHV"
      },
      "source": [
        "For the sake of simplicity, let us focus on three features from the complete ZTF SCoPE features (refer to [Healy et al. 2024](https://arxiv.org/abs/2312.00143) for more details):\n",
        "- ```inv_vonneumannratio```: Inverse of von Neumann ratio ([von Neumann 1941](https://projecteuclid.org/journals/annals-of-mathematical-statistics/volume-12/issue-4/Distribution-of-the-Ratio-of-the-Mean-Square-Successive-Difference/10.1214/aoms/1177731677.full), [1942](https://projecteuclid.org/journals/annals-of-mathematical-statistics/volume-13/issue-1/A-Further-Remark-Concerning-the-Distribution-of-the-Ratio-of/10.1214/aoms/1177731645.full)), which is the ratio of correlated variance and variance - it detects non-randomness, and a high value implies periodic behaviour.\n",
        "- ```norm_peak_to_peak_amp```: Normalized peak-to-peak amplitude [(Sokolovsky et al. 2009)](https://arxiv.org/abs/0901.1064) - it tells us about the source brightness.\n",
        "- ```stetson_k```: Stetson K coefficient ([Stetson 1996](https://iopscience.iop.org/article/10.1086/133808/meta?casa_token=EMo0hxKqIkUAAAAA:b8y8ONGzEQAJq2WJfrCASQt_FMw7HX_h7i-VChDbTYc1ShDkEih4I2Sm184VFLTS1UpDbATGN8GPmTY4YXRG87jP2Q)) is related to the observed scatter - it tells us about the light curve shape."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2025-01-06T05:17:08.742560Z",
          "iopub.status.busy": "2025-01-06T05:17:08.741885Z",
          "iopub.status.idle": "2025-01-06T05:17:08.751749Z",
          "shell.execute_reply": "2025-01-06T05:17:08.750840Z",
          "shell.execute_reply.started": "2025-01-06T05:17:08.742529Z"
        },
        "id": "_IMkBjj0WRHW"
      },
      "outputs": [],
      "source": [
        "feature_names = [\"inv_vonneumannratio\", \"norm_peak_to_peak_amp\", \"stetson_k\"]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2025-01-06T05:17:08.888060Z",
          "iopub.status.busy": "2025-01-06T05:17:08.887695Z",
          "iopub.status.idle": "2025-01-06T05:17:10.202860Z",
          "shell.execute_reply": "2025-01-06T05:17:10.202550Z",
          "shell.execute_reply.started": "2025-01-06T05:17:08.888038Z"
        },
        "id": "W0FSa0SZWRHW",
        "outputId": "7a308aa6-c18d-46c2-9899-eba8f700376d"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 837.75x750 with 12 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "df = features.loc[:, feature_names]\n",
        "df[\"class\"] = labels[\"class\"]\n",
        "sns.pairplot(df, hue=\"class\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2025-01-06T05:17:10.203909Z",
          "iopub.status.busy": "2025-01-06T05:17:10.203795Z",
          "iopub.status.idle": "2025-01-06T05:17:10.206257Z",
          "shell.execute_reply": "2025-01-06T05:17:10.205813Z",
          "shell.execute_reply.started": "2025-01-06T05:17:10.203899Z"
        },
        "id": "InEhiy5KWRHW"
      },
      "outputs": [],
      "source": [
        "X = features.loc[:, feature_names].to_numpy()\n",
        "y = labels.to_numpy().ravel()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2025-01-06T05:17:10.208244Z",
          "iopub.status.busy": "2025-01-06T05:17:10.207582Z",
          "iopub.status.idle": "2025-01-06T05:17:10.213326Z",
          "shell.execute_reply": "2025-01-06T05:17:10.212864Z",
          "shell.execute_reply.started": "2025-01-06T05:17:10.208209Z"
        },
        "id": "zwBi0a6DWRHW"
      },
      "outputs": [],
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(\n",
        "    X, y, test_size=0.25, random_state=seed\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2025-01-06T05:17:10.214653Z",
          "iopub.status.busy": "2025-01-06T05:17:10.214476Z",
          "iopub.status.idle": "2025-01-06T05:17:10.227234Z",
          "shell.execute_reply": "2025-01-06T05:17:10.226781Z",
          "shell.execute_reply.started": "2025-01-06T05:17:10.214641Z"
        },
        "id": "d64v35oAWRHW",
        "outputId": "0fc89ff7-d56b-4937-a8ba-ab23cd28d646"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<style>#sk-container-id-1 {\n",
              "  /* Definition of color scheme common for light and dark mode */\n",
              "  --sklearn-color-text: black;\n",
              "  --sklearn-color-line: gray;\n",
              "  /* Definition of color scheme for unfitted estimators */\n",
              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
              "  --sklearn-color-unfitted-level-3: chocolate;\n",
              "  /* Definition of color scheme for fitted estimators */\n",
              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
              "\n",
              "  /* Specific color for light theme */\n",
              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-icon: #696969;\n",
              "\n",
              "  @media (prefers-color-scheme: dark) {\n",
              "    /* Redefinition of color scheme for dark theme */\n",
              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-icon: #878787;\n",
              "  }\n",
              "}\n",
              "\n",
              "#sk-container-id-1 {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 pre {\n",
              "  padding: 0;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 input.sk-hidden--visually {\n",
              "  border: 0;\n",
              "  clip: rect(1px 1px 1px 1px);\n",
              "  clip: rect(1px, 1px, 1px, 1px);\n",
              "  height: 1px;\n",
              "  margin: -1px;\n",
              "  overflow: hidden;\n",
              "  padding: 0;\n",
              "  position: absolute;\n",
              "  width: 1px;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-dashed-wrapped {\n",
              "  border: 1px dashed var(--sklearn-color-line);\n",
              "  margin: 0 0.4em 0.5em 0.4em;\n",
              "  box-sizing: border-box;\n",
              "  padding-bottom: 0.4em;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-container {\n",
              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
              "     so we also need the `!important` here to be able to override the\n",
              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
              "  display: inline-block !important;\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-text-repr-fallback {\n",
              "  display: none;\n",
              "}\n",
              "\n",
              "div.sk-parallel-item,\n",
              "div.sk-serial,\n",
              "div.sk-item {\n",
              "  /* draw centered vertical line to link estimators */\n",
              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
              "  background-size: 2px 100%;\n",
              "  background-repeat: no-repeat;\n",
              "  background-position: center center;\n",
              "}\n",
              "\n",
              "/* Parallel-specific style estimator block */\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item::after {\n",
              "  content: \"\";\n",
              "  width: 100%;\n",
              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
              "  flex-grow: 1;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel {\n",
              "  display: flex;\n",
              "  align-items: stretch;\n",
              "  justify-content: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
              "  align-self: flex-end;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
              "  align-self: flex-start;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
              "  width: 0;\n",
              "}\n",
              "\n",
              "/* Serial-specific style estimator block */\n",
              "\n",
              "#sk-container-id-1 div.sk-serial {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "  align-items: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  padding-right: 1em;\n",
              "  padding-left: 1em;\n",
              "}\n",
              "\n",
              "\n",
              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
              "clickable and can be expanded/collapsed.\n",
              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
              "*/\n",
              "\n",
              "/* Pipeline and ColumnTransformer style (default) */\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable {\n",
              "  /* Default theme specific background. It is overwritten whether we have a\n",
              "  specific estimator or a Pipeline/ColumnTransformer */\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "/* Toggleable label */\n",
              "#sk-container-id-1 label.sk-toggleable__label {\n",
              "  cursor: pointer;\n",
              "  display: block;\n",
              "  width: 100%;\n",
              "  margin-bottom: 0;\n",
              "  padding: 0.5em;\n",
              "  box-sizing: border-box;\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
              "  /* Arrow on the left of the label */\n",
              "  content: \"▸\";\n",
              "  float: left;\n",
              "  margin-right: 0.25em;\n",
              "  color: var(--sklearn-color-icon);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "/* Toggleable content - dropdown */\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content {\n",
              "  max-height: 0;\n",
              "  max-width: 0;\n",
              "  overflow: hidden;\n",
              "  text-align: left;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content pre {\n",
              "  margin: 0.2em;\n",
              "  border-radius: 0.25em;\n",
              "  color: var(--sklearn-color-text);\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
              "  /* Expand drop-down */\n",
              "  max-height: 200px;\n",
              "  max-width: 100%;\n",
              "  overflow: auto;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
              "  content: \"▾\";\n",
              "}\n",
              "\n",
              "/* Pipeline/ColumnTransformer-specific style */\n",
              "\n",
              "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator-specific style */\n",
              "\n",
              "/* Colorize estimator box */\n",
              "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
              "#sk-container-id-1 div.sk-label label {\n",
              "  /* The background is the default theme color */\n",
              "  color: var(--sklearn-color-text-on-default-background);\n",
              "}\n",
              "\n",
              "/* On hover, darken the color of the background */\n",
              "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "/* Label box, darken color on hover, fitted */\n",
              "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator label */\n",
              "\n",
              "#sk-container-id-1 div.sk-label label {\n",
              "  font-family: monospace;\n",
              "  font-weight: bold;\n",
              "  display: inline-block;\n",
              "  line-height: 1.2em;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-label-container {\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "/* Estimator-specific */\n",
              "#sk-container-id-1 div.sk-estimator {\n",
              "  font-family: monospace;\n",
              "  border: 1px dotted var(--sklearn-color-border-box);\n",
              "  border-radius: 0.25em;\n",
              "  box-sizing: border-box;\n",
              "  margin-bottom: 0.5em;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-estimator.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "/* on hover */\n",
              "#sk-container-id-1 div.sk-estimator:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
              "\n",
              "/* Common style for \"i\" and \"?\" */\n",
              "\n",
              ".sk-estimator-doc-link,\n",
              "a:link.sk-estimator-doc-link,\n",
              "a:visited.sk-estimator-doc-link {\n",
              "  float: right;\n",
              "  font-size: smaller;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1em;\n",
              "  height: 1em;\n",
              "  width: 1em;\n",
              "  text-decoration: none !important;\n",
              "  margin-left: 1ex;\n",
              "  /* unfitted */\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted,\n",
              "a:link.sk-estimator-doc-link.fitted,\n",
              "a:visited.sk-estimator-doc-link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "/* Span, style for the box shown on hovering the info icon */\n",
              ".sk-estimator-doc-link span {\n",
              "  display: none;\n",
              "  z-index: 9999;\n",
              "  position: relative;\n",
              "  font-weight: normal;\n",
              "  right: .2ex;\n",
              "  padding: .5ex;\n",
              "  margin: .5ex;\n",
              "  width: min-content;\n",
              "  min-width: 20ex;\n",
              "  max-width: 50ex;\n",
              "  color: var(--sklearn-color-text);\n",
              "  box-shadow: 2pt 2pt 4pt #999;\n",
              "  /* unfitted */\n",
              "  background: var(--sklearn-color-unfitted-level-0);\n",
              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted span {\n",
              "  /* fitted */\n",
              "  background: var(--sklearn-color-fitted-level-0);\n",
              "  border: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link:hover span {\n",
              "  display: block;\n",
              "}\n",
              "\n",
              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
              "\n",
              "#sk-container-id-1 a.estimator_doc_link {\n",
              "  float: right;\n",
              "  font-size: 1rem;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1rem;\n",
              "  height: 1rem;\n",
              "  width: 1rem;\n",
              "  text-decoration: none;\n",
              "  /* unfitted */\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "#sk-container-id-1 a.estimator_doc_link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DistanceMetricClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;DistanceMetricClassifier<span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>DistanceMetricClassifier()</pre></div> </div></div></div></div>"
            ],
            "text/plain": [
              "DistanceMetricClassifier()"
            ]
          },
          "execution_count": 16,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "clf = dcpy.DistanceMetricClassifier()\n",
        "clf.fit(X_train, y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2025-01-06T05:17:10.229170Z",
          "iopub.status.busy": "2025-01-06T05:17:10.228700Z",
          "iopub.status.idle": "2025-01-06T05:17:10.236239Z",
          "shell.execute_reply": "2025-01-06T05:17:10.235671Z",
          "shell.execute_reply.started": "2025-01-06T05:17:10.229153Z"
        },
        "id": "KIGGBLlUWRHW"
      },
      "outputs": [],
      "source": [
        "y_pred = clf.predict_and_analyse(X_test, metric=\"euclidean\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2025-01-06T05:17:10.237513Z",
          "iopub.status.busy": "2025-01-06T05:17:10.237156Z",
          "iopub.status.idle": "2025-01-06T05:17:10.250397Z",
          "shell.execute_reply": "2025-01-06T05:17:10.249400Z",
          "shell.execute_reply.started": "2025-01-06T05:17:10.237498Z"
        },
        "id": "VnHYjvygWRHW",
        "outputId": "22f4e860-a1db-4d94-bcb4-850ec6b032a3"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy = 0.642\n",
            "F1 = 0.635\n"
          ]
        }
      ],
      "source": [
        "acc = accuracy_score(y_true=y_test, y_pred=y_pred)\n",
        "f1 = f1_score(y_true=y_test, y_pred=y_pred, average=\"macro\")\n",
        "\n",
        "print(f\"Accuracy = {acc:.3f}\")\n",
        "print(f\"F1 = {f1:.3f}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2025-01-06T05:17:10.252447Z",
          "iopub.status.busy": "2025-01-06T05:17:10.252172Z",
          "iopub.status.idle": "2025-01-06T05:17:10.263682Z",
          "shell.execute_reply": "2025-01-06T05:17:10.263214Z",
          "shell.execute_reply.started": "2025-01-06T05:17:10.252433Z"
        },
        "id": "S4OzUI3_WRHW",
        "outputId": "0ee3ee58-3ef0-4565-c210-5003d67ff35e"
      },
      "outputs": [
        {
          "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>CEP_dist</th>\n",
              "      <th>DSCT_dist</th>\n",
              "      <th>RR_dist</th>\n",
              "      <th>RRc_dist</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.805759</td>\n",
              "      <td>2.641208</td>\n",
              "      <td>0.824424</td>\n",
              "      <td>2.848626</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1.220526</td>\n",
              "      <td>1.423540</td>\n",
              "      <td>2.151157</td>\n",
              "      <td>1.164521</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1.325282</td>\n",
              "      <td>3.792195</td>\n",
              "      <td>1.503853</td>\n",
              "      <td>4.076885</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1.064865</td>\n",
              "      <td>8.376741</td>\n",
              "      <td>1.781160</td>\n",
              "      <td>1.323827</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.480929</td>\n",
              "      <td>2.229321</td>\n",
              "      <td>0.915641</td>\n",
              "      <td>2.055988</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>995</th>\n",
              "      <td>1.015133</td>\n",
              "      <td>3.548696</td>\n",
              "      <td>1.743593</td>\n",
              "      <td>0.106400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>996</th>\n",
              "      <td>0.957050</td>\n",
              "      <td>10.627296</td>\n",
              "      <td>1.705001</td>\n",
              "      <td>1.205451</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>997</th>\n",
              "      <td>0.810418</td>\n",
              "      <td>14.319456</td>\n",
              "      <td>1.574726</td>\n",
              "      <td>1.767178</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>998</th>\n",
              "      <td>1.023541</td>\n",
              "      <td>2.556731</td>\n",
              "      <td>1.699193</td>\n",
              "      <td>0.937611</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>999</th>\n",
              "      <td>1.452081</td>\n",
              "      <td>1.219772</td>\n",
              "      <td>2.336256</td>\n",
              "      <td>2.059953</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1000 rows × 4 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     CEP_dist  DSCT_dist   RR_dist  RRc_dist\n",
              "0    0.805759   2.641208  0.824424  2.848626\n",
              "1    1.220526   1.423540  2.151157  1.164521\n",
              "2    1.325282   3.792195  1.503853  4.076885\n",
              "3    1.064865   8.376741  1.781160  1.323827\n",
              "4    0.480929   2.229321  0.915641  2.055988\n",
              "..        ...        ...       ...       ...\n",
              "995  1.015133   3.548696  1.743593  0.106400\n",
              "996  0.957050  10.627296  1.705001  1.205451\n",
              "997  0.810418  14.319456  1.574726  1.767178\n",
              "998  1.023541   2.556731  1.699193  0.937611\n",
              "999  1.452081   1.219772  2.336256  2.059953\n",
              "\n",
              "[1000 rows x 4 columns]"
            ]
          },
          "execution_count": 19,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "clf.centroid_dist_df_"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Z0jhGuM7WRHW"
      },
      "source": [
        "---"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JWf3o-oAWRHW"
      },
      "source": [
        "#### Using multiple distance metrics together!\n",
        "\n",
        "We can combine multiple distance metrics together!"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6bx5ZGEhWRHj"
      },
      "source": [
        "**Case 1**: Keeping the same set of features, vary the distance metric."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2025-01-06T05:17:10.538316Z",
          "iopub.status.busy": "2025-01-06T05:17:10.537450Z",
          "iopub.status.idle": "2025-01-06T05:17:11.407606Z",
          "shell.execute_reply": "2025-01-06T05:17:11.407333Z",
          "shell.execute_reply.started": "2025-01-06T05:17:10.538266Z"
        },
        "id": "h8KNCrROWRHj",
        "outputId": "e782ae42-f3aa-4817-e907-8db4be70b51f"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<style>#sk-container-id-2 {\n",
              "  /* Definition of color scheme common for light and dark mode */\n",
              "  --sklearn-color-text: black;\n",
              "  --sklearn-color-line: gray;\n",
              "  /* Definition of color scheme for unfitted estimators */\n",
              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
              "  --sklearn-color-unfitted-level-3: chocolate;\n",
              "  /* Definition of color scheme for fitted estimators */\n",
              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
              "\n",
              "  /* Specific color for light theme */\n",
              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-icon: #696969;\n",
              "\n",
              "  @media (prefers-color-scheme: dark) {\n",
              "    /* Redefinition of color scheme for dark theme */\n",
              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-icon: #878787;\n",
              "  }\n",
              "}\n",
              "\n",
              "#sk-container-id-2 {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 pre {\n",
              "  padding: 0;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 input.sk-hidden--visually {\n",
              "  border: 0;\n",
              "  clip: rect(1px 1px 1px 1px);\n",
              "  clip: rect(1px, 1px, 1px, 1px);\n",
              "  height: 1px;\n",
              "  margin: -1px;\n",
              "  overflow: hidden;\n",
              "  padding: 0;\n",
              "  position: absolute;\n",
              "  width: 1px;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-dashed-wrapped {\n",
              "  border: 1px dashed var(--sklearn-color-line);\n",
              "  margin: 0 0.4em 0.5em 0.4em;\n",
              "  box-sizing: border-box;\n",
              "  padding-bottom: 0.4em;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-container {\n",
              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
              "     so we also need the `!important` here to be able to override the\n",
              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
              "  display: inline-block !important;\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-text-repr-fallback {\n",
              "  display: none;\n",
              "}\n",
              "\n",
              "div.sk-parallel-item,\n",
              "div.sk-serial,\n",
              "div.sk-item {\n",
              "  /* draw centered vertical line to link estimators */\n",
              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
              "  background-size: 2px 100%;\n",
              "  background-repeat: no-repeat;\n",
              "  background-position: center center;\n",
              "}\n",
              "\n",
              "/* Parallel-specific style estimator block */\n",
              "\n",
              "#sk-container-id-2 div.sk-parallel-item::after {\n",
              "  content: \"\";\n",
              "  width: 100%;\n",
              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
              "  flex-grow: 1;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-parallel {\n",
              "  display: flex;\n",
              "  align-items: stretch;\n",
              "  justify-content: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-parallel-item {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
              "  align-self: flex-end;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
              "  align-self: flex-start;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
              "  width: 0;\n",
              "}\n",
              "\n",
              "/* Serial-specific style estimator block */\n",
              "\n",
              "#sk-container-id-2 div.sk-serial {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "  align-items: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  padding-right: 1em;\n",
              "  padding-left: 1em;\n",
              "}\n",
              "\n",
              "\n",
              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
              "clickable and can be expanded/collapsed.\n",
              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
              "*/\n",
              "\n",
              "/* Pipeline and ColumnTransformer style (default) */\n",
              "\n",
              "#sk-container-id-2 div.sk-toggleable {\n",
              "  /* Default theme specific background. It is overwritten whether we have a\n",
              "  specific estimator or a Pipeline/ColumnTransformer */\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "/* Toggleable label */\n",
              "#sk-container-id-2 label.sk-toggleable__label {\n",
              "  cursor: pointer;\n",
              "  display: block;\n",
              "  width: 100%;\n",
              "  margin-bottom: 0;\n",
              "  padding: 0.5em;\n",
              "  box-sizing: border-box;\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
              "  /* Arrow on the left of the label */\n",
              "  content: \"▸\";\n",
              "  float: left;\n",
              "  margin-right: 0.25em;\n",
              "  color: var(--sklearn-color-icon);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "/* Toggleable content - dropdown */\n",
              "\n",
              "#sk-container-id-2 div.sk-toggleable__content {\n",
              "  max-height: 0;\n",
              "  max-width: 0;\n",
              "  overflow: hidden;\n",
              "  text-align: left;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-toggleable__content pre {\n",
              "  margin: 0.2em;\n",
              "  border-radius: 0.25em;\n",
              "  color: var(--sklearn-color-text);\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
              "  /* Expand drop-down */\n",
              "  max-height: 200px;\n",
              "  max-width: 100%;\n",
              "  overflow: auto;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
              "  content: \"▾\";\n",
              "}\n",
              "\n",
              "/* Pipeline/ColumnTransformer-specific style */\n",
              "\n",
              "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator-specific style */\n",
              "\n",
              "/* Colorize estimator box */\n",
              "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
              "#sk-container-id-2 div.sk-label label {\n",
              "  /* The background is the default theme color */\n",
              "  color: var(--sklearn-color-text-on-default-background);\n",
              "}\n",
              "\n",
              "/* On hover, darken the color of the background */\n",
              "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "/* Label box, darken color on hover, fitted */\n",
              "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator label */\n",
              "\n",
              "#sk-container-id-2 div.sk-label label {\n",
              "  font-family: monospace;\n",
              "  font-weight: bold;\n",
              "  display: inline-block;\n",
              "  line-height: 1.2em;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-label-container {\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "/* Estimator-specific */\n",
              "#sk-container-id-2 div.sk-estimator {\n",
              "  font-family: monospace;\n",
              "  border: 1px dotted var(--sklearn-color-border-box);\n",
              "  border-radius: 0.25em;\n",
              "  box-sizing: border-box;\n",
              "  margin-bottom: 0.5em;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-estimator.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "/* on hover */\n",
              "#sk-container-id-2 div.sk-estimator:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
              "\n",
              "/* Common style for \"i\" and \"?\" */\n",
              "\n",
              ".sk-estimator-doc-link,\n",
              "a:link.sk-estimator-doc-link,\n",
              "a:visited.sk-estimator-doc-link {\n",
              "  float: right;\n",
              "  font-size: smaller;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1em;\n",
              "  height: 1em;\n",
              "  width: 1em;\n",
              "  text-decoration: none !important;\n",
              "  margin-left: 1ex;\n",
              "  /* unfitted */\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted,\n",
              "a:link.sk-estimator-doc-link.fitted,\n",
              "a:visited.sk-estimator-doc-link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "/* Span, style for the box shown on hovering the info icon */\n",
              ".sk-estimator-doc-link span {\n",
              "  display: none;\n",
              "  z-index: 9999;\n",
              "  position: relative;\n",
              "  font-weight: normal;\n",
              "  right: .2ex;\n",
              "  padding: .5ex;\n",
              "  margin: .5ex;\n",
              "  width: min-content;\n",
              "  min-width: 20ex;\n",
              "  max-width: 50ex;\n",
              "  color: var(--sklearn-color-text);\n",
              "  box-shadow: 2pt 2pt 4pt #999;\n",
              "  /* unfitted */\n",
              "  background: var(--sklearn-color-unfitted-level-0);\n",
              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted span {\n",
              "  /* fitted */\n",
              "  background: var(--sklearn-color-fitted-level-0);\n",
              "  border: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link:hover span {\n",
              "  display: block;\n",
              "}\n",
              "\n",
              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
              "\n",
              "#sk-container-id-2 a.estimator_doc_link {\n",
              "  float: right;\n",
              "  font-size: 1rem;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1rem;\n",
              "  height: 1rem;\n",
              "  width: 1rem;\n",
              "  text-decoration: none;\n",
              "  /* unfitted */\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "#sk-container-id-2 a.estimator_doc_link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>EnsembleDistanceClassifier(feat_idx=0, random_state=0)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;EnsembleDistanceClassifier<span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>EnsembleDistanceClassifier(feat_idx=0, random_state=0)</pre></div> </div></div></div></div>"
            ],
            "text/plain": [
              "EnsembleDistanceClassifier(feat_idx=0, random_state=0)"
            ]
          },
          "execution_count": 20,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "ensemble_clf = dcpy.EnsembleDistanceClassifier(feat_idx=0, random_state=seed)\n",
        "ensemble_clf.fit(X_train, y_train, n_quantiles=4)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2025-01-06T05:17:11.408503Z",
          "iopub.status.busy": "2025-01-06T05:17:11.408404Z",
          "iopub.status.idle": "2025-01-06T05:17:11.443517Z",
          "shell.execute_reply": "2025-01-06T05:17:11.443232Z",
          "shell.execute_reply.started": "2025-01-06T05:17:11.408495Z"
        },
        "id": "ielDglv8WRHk"
      },
      "outputs": [],
      "source": [
        "y_pred_ensemble = ensemble_clf.predict(X_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2025-01-06T05:17:11.444237Z",
          "iopub.status.busy": "2025-01-06T05:17:11.444121Z",
          "iopub.status.idle": "2025-01-06T05:17:11.450591Z",
          "shell.execute_reply": "2025-01-06T05:17:11.450344Z",
          "shell.execute_reply.started": "2025-01-06T05:17:11.444228Z"
        },
        "id": "ZnKwB-ADWRHk",
        "outputId": "90a8a713-91c3-40a1-fa63-efbefe46869d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy = 0.785\n",
            "F1 = 0.784\n"
          ]
        }
      ],
      "source": [
        "acc = accuracy_score(y_true=y_test, y_pred=y_pred_ensemble)\n",
        "f1 = f1_score(y_true=y_test, y_pred=y_pred_ensemble, average=\"macro\")\n",
        "\n",
        "print(f\"Accuracy = {acc:.3f}\")\n",
        "print(f\"F1 = {f1:.3f}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2025-01-06T05:17:11.451606Z",
          "iopub.status.busy": "2025-01-06T05:17:11.451505Z",
          "iopub.status.idle": "2025-01-06T05:17:11.454520Z",
          "shell.execute_reply": "2025-01-06T05:17:11.454276Z",
          "shell.execute_reply.started": "2025-01-06T05:17:11.451597Z"
        },
        "id": "3eCy5AwfWRHk",
        "outputId": "9cef1265-84ec-45dd-930e-93ab1d413df1"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Quantile 1                 canberra\n",
              "Quantile 2                 jeffreys\n",
              "Quantile 3              wave_hedges\n",
              "Quantile 4    vicis_symmetric_chisq\n",
              "dtype: object"
            ]
          },
          "execution_count": 23,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "ensemble_clf.best_metrics_per_quantile_"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2025-01-06T05:17:11.454986Z",
          "iopub.status.busy": "2025-01-06T05:17:11.454861Z",
          "iopub.status.idle": "2025-01-06T05:17:11.461259Z",
          "shell.execute_reply": "2025-01-06T05:17:11.461014Z",
          "shell.execute_reply.started": "2025-01-06T05:17:11.454977Z"
        },
        "id": "bWv4RXrbWRHk",
        "outputId": "f2171997-3b7b-4f40-b923-83e46c8cf840"
      },
      "outputs": [
        {
          "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>Quantile 1</th>\n",
              "      <th>Quantile 2</th>\n",
              "      <th>Quantile 3</th>\n",
              "      <th>Quantile 4</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>euclidean</th>\n",
              "      <td>62.765957</td>\n",
              "      <td>48.404255</td>\n",
              "      <td>48.663102</td>\n",
              "      <td>79.679144</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>braycurtis</th>\n",
              "      <td>56.382979</td>\n",
              "      <td>65.425532</td>\n",
              "      <td>66.310160</td>\n",
              "      <td>79.679144</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>canberra</th>\n",
              "      <td>89.893617</td>\n",
              "      <td>72.872340</td>\n",
              "      <td>70.588235</td>\n",
              "      <td>79.679144</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>cityblock</th>\n",
              "      <td>60.638298</td>\n",
              "      <td>48.936170</td>\n",
              "      <td>51.871658</td>\n",
              "      <td>79.679144</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>chebyshev</th>\n",
              "      <td>63.829787</td>\n",
              "      <td>51.063830</td>\n",
              "      <td>50.267380</td>\n",
              "      <td>79.679144</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>clark</th>\n",
              "      <td>89.893617</td>\n",
              "      <td>72.340426</td>\n",
              "      <td>71.122995</td>\n",
              "      <td>81.818182</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>correlation</th>\n",
              "      <td>29.787234</td>\n",
              "      <td>37.765957</td>\n",
              "      <td>52.941176</td>\n",
              "      <td>76.470588</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>cosine</th>\n",
              "      <td>54.255319</td>\n",
              "      <td>57.446809</td>\n",
              "      <td>60.962567</td>\n",
              "      <td>79.679144</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>hellinger</th>\n",
              "      <td>80.319149</td>\n",
              "      <td>72.340426</td>\n",
              "      <td>71.122995</td>\n",
              "      <td>79.679144</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>jaccard</th>\n",
              "      <td>55.851064</td>\n",
              "      <td>63.829787</td>\n",
              "      <td>66.310160</td>\n",
              "      <td>80.213904</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>lorentzian</th>\n",
              "      <td>62.234043</td>\n",
              "      <td>49.468085</td>\n",
              "      <td>52.406417</td>\n",
              "      <td>79.679144</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>marylandbridge</th>\n",
              "      <td>24.468085</td>\n",
              "      <td>30.851064</td>\n",
              "      <td>38.502674</td>\n",
              "      <td>75.935829</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>meehl</th>\n",
              "      <td>49.468085</td>\n",
              "      <td>41.489362</td>\n",
              "      <td>55.614973</td>\n",
              "      <td>79.144385</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>wave_hedges</th>\n",
              "      <td>88.829787</td>\n",
              "      <td>71.808511</td>\n",
              "      <td>72.192513</td>\n",
              "      <td>74.866310</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>add_chisq</th>\n",
              "      <td>88.297872</td>\n",
              "      <td>74.468085</td>\n",
              "      <td>70.588235</td>\n",
              "      <td>79.679144</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>acc</th>\n",
              "      <td>63.297872</td>\n",
              "      <td>48.936170</td>\n",
              "      <td>51.336898</td>\n",
              "      <td>79.679144</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>chebyshev_min</th>\n",
              "      <td>60.638298</td>\n",
              "      <td>42.021277</td>\n",
              "      <td>43.850267</td>\n",
              "      <td>55.614973</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>dice</th>\n",
              "      <td>2.659574</td>\n",
              "      <td>30.319149</td>\n",
              "      <td>60.427807</td>\n",
              "      <td>29.411765</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>google</th>\n",
              "      <td>69.148936</td>\n",
              "      <td>65.425532</td>\n",
              "      <td>63.101604</td>\n",
              "      <td>76.470588</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>jeffreys</th>\n",
              "      <td>83.510638</td>\n",
              "      <td>75.000000</td>\n",
              "      <td>71.122995</td>\n",
              "      <td>79.679144</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>jensenshannon_divergence</th>\n",
              "      <td>78.723404</td>\n",
              "      <td>71.276596</td>\n",
              "      <td>71.122995</td>\n",
              "      <td>79.679144</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>kumarjohnson</th>\n",
              "      <td>89.361702</td>\n",
              "      <td>75.000000</td>\n",
              "      <td>70.588235</td>\n",
              "      <td>79.679144</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>penroseshape</th>\n",
              "      <td>40.425532</td>\n",
              "      <td>49.468085</td>\n",
              "      <td>58.288770</td>\n",
              "      <td>79.679144</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>prob_chisq</th>\n",
              "      <td>75.531915</td>\n",
              "      <td>69.680851</td>\n",
              "      <td>71.122995</td>\n",
              "      <td>79.679144</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>taneja</th>\n",
              "      <td>86.702128</td>\n",
              "      <td>75.000000</td>\n",
              "      <td>71.122995</td>\n",
              "      <td>79.679144</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>vicis_symmetric_chisq</th>\n",
              "      <td>89.893617</td>\n",
              "      <td>69.148936</td>\n",
              "      <td>71.657754</td>\n",
              "      <td>82.352941</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>vicis_wave_hedges</th>\n",
              "      <td>89.893617</td>\n",
              "      <td>71.808511</td>\n",
              "      <td>71.657754</td>\n",
              "      <td>82.352941</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                          Quantile 1  Quantile 2  Quantile 3  Quantile 4\n",
              "euclidean                  62.765957   48.404255   48.663102   79.679144\n",
              "braycurtis                 56.382979   65.425532   66.310160   79.679144\n",
              "canberra                   89.893617   72.872340   70.588235   79.679144\n",
              "cityblock                  60.638298   48.936170   51.871658   79.679144\n",
              "chebyshev                  63.829787   51.063830   50.267380   79.679144\n",
              "clark                      89.893617   72.340426   71.122995   81.818182\n",
              "correlation                29.787234   37.765957   52.941176   76.470588\n",
              "cosine                     54.255319   57.446809   60.962567   79.679144\n",
              "hellinger                  80.319149   72.340426   71.122995   79.679144\n",
              "jaccard                    55.851064   63.829787   66.310160   80.213904\n",
              "lorentzian                 62.234043   49.468085   52.406417   79.679144\n",
              "marylandbridge             24.468085   30.851064   38.502674   75.935829\n",
              "meehl                      49.468085   41.489362   55.614973   79.144385\n",
              "wave_hedges                88.829787   71.808511   72.192513   74.866310\n",
              "add_chisq                  88.297872   74.468085   70.588235   79.679144\n",
              "acc                        63.297872   48.936170   51.336898   79.679144\n",
              "chebyshev_min              60.638298   42.021277   43.850267   55.614973\n",
              "dice                        2.659574   30.319149   60.427807   29.411765\n",
              "google                     69.148936   65.425532   63.101604   76.470588\n",
              "jeffreys                   83.510638   75.000000   71.122995   79.679144\n",
              "jensenshannon_divergence   78.723404   71.276596   71.122995   79.679144\n",
              "kumarjohnson               89.361702   75.000000   70.588235   79.679144\n",
              "penroseshape               40.425532   49.468085   58.288770   79.679144\n",
              "prob_chisq                 75.531915   69.680851   71.122995   79.679144\n",
              "taneja                     86.702128   75.000000   71.122995   79.679144\n",
              "vicis_symmetric_chisq      89.893617   69.148936   71.657754   82.352941\n",
              "vicis_wave_hedges          89.893617   71.808511   71.657754   82.352941"
            ]
          },
          "execution_count": 24,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "ensemble_clf.quantile_scores_df_.drop_duplicates()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2025-01-06T05:17:11.533864Z",
          "iopub.status.busy": "2025-01-06T05:17:11.533682Z",
          "iopub.status.idle": "2025-01-06T05:17:11.672123Z",
          "shell.execute_reply": "2025-01-06T05:17:11.671645Z",
          "shell.execute_reply.started": "2025-01-06T05:17:11.533855Z"
        },
        "id": "bxolYPaCWRHk",
        "outputId": "82e3fa5d-d1f6-4273-d450-721e157b588d"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "sns.heatmap(\n",
        "    ensemble_clf.quantile_scores_df_.drop_duplicates(), annot=True, cmap=\"Blues\"\n",
        ")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QjNk6SujWRHk"
      },
      "source": [
        "The performance improves, but not by a lot.\n",
        "\n",
        "But what if we also allowed each metric to work with different features?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2jKNgWM3WRHk"
      },
      "source": [
        "---"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ykn4GwRXWRHk"
      },
      "source": [
        "**Case 2**: Varying the features AND the distance metric."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7QdR3YzGWRHk"
      },
      "source": [
        "From our work, we found:\n",
        "- ```We can select a distance metric that works best based on the object of interest!```"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2025-01-06T05:17:12.727060Z",
          "iopub.status.busy": "2025-01-06T05:17:12.726440Z",
          "iopub.status.idle": "2025-01-06T05:17:12.733847Z",
          "shell.execute_reply": "2025-01-06T05:17:12.732561Z",
          "shell.execute_reply.started": "2025-01-06T05:17:12.727018Z"
        },
        "id": "gVU5mollWRHk",
        "outputId": "0a402254-068d-4311-aed5-96e996dc818d"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<img src=\"https://arxiv.org/html/2403.12120v2/x31.png\" width=\"480\"/>"
            ],
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "execution_count": 26,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# @title\n",
        "from IPython.display import Image\n",
        "Image(url=\"https://arxiv.org/html/2403.12120v2/x31.png\",width=480)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-BdGlcd8WRHk"
      },
      "source": [
        "Performance improvement here is much more significant!\n",
        "\n",
        "If you are interested in more details:\n",
        "\n",
        "[![arxiv-badge](https://img.shields.io/badge/arXiv-2403.12120-red)](https://arxiv.org/abs/2403.12120)\n",
        "[![github-badge](https://img.shields.io/badge/GitHub-sidchaini/LightCurveDistanceClassification-blue)](https://github.com/sidchaini/LightCurveDistanceClassification)"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.12.4"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}