examples/elmd_densmap_cluster_colab.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/sgbaird-mwe/mat_discover/blob/main/examples/elmd_densmap_cluster_colab.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# ElMD DensMAP Clustering Visualization\n",
        "This example is to show how to plot only the ElMD/DensMAP embeddings (colored by cluster label or property of your choice) rather than create all the plots. This will produce both an interactive Plotly figure as well as a \"paper-ready\" PNG image. An Plotly `.html` file and the `.png` image are exported automatically to the `./figures/` directory, which is created relative to your current working directory if it doesn't already exist. Please note that if you are using Google Colab, the built-in storage doesn't persist, so you'll need to e.g. mount Google Drive and save to there if you want to keep the figures.\n",
        "\n",
        "To clarify some of the acronyms:\n",
        "- [Element Mover's Distance (ElMD)](https://github.com/lrcfmd/ElMD)\n",
        "- [Density-preserving Uniform Manifold Approximation and Projection (DensMAP)](https://umap-learn.readthedocs.io/en/latest/densmap_demo.html)\n",
        "\n",
        "[HDBSCAN* is used for clustering](https://umap-learn.readthedocs.io/en/latest/clustering.html)."
      ],
      "metadata": {
        "id": "sCrxGXS1QH1w"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "The basic workflow is:\n",
        "- install dependencies (⚠ WARNING: possibly need to restart the runtime after installation ⚠)\n",
        "- Load some data\n",
        "- fit Discover()\n",
        "- predict on validation data\n",
        "- plot"
      ],
      "metadata": {
        "id": "Bldodc-4SWIX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%%time\n",
        "!pip install -U pip\n",
        "!pip install -UI mat_discover #-UI not necessary when not on Colab"
      ],
      "metadata": {
        "id": "nMkMhBMDi5IE",
        "outputId": "cc313587-3dc7-4e38-b059-961f0f36a5d3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "Installing collected packages: pytz, kaleido, iniconfig, bounded-pool-executor, zipp, typing-extensions, tqdm, tomli, threadpoolctl, tenacity, six, setuptools, pyparsing, py, psutil, pillow, numpy, llvmlite, joblib, fonttools, dill, cython, cycler, colorama, attrs, scipy, python-dateutil, pqdm, plotly, packaging, kiwisolver, importlib-metadata, scikit_learn, pluggy, pandas, numba, matplotlib, seaborn, pytest, pynndescent, hdbscan, ElMD, dist_matrix, umap-learn, crabnet, composition_based_feature_vector, chem_wasserstein, mat_discover\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
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            "thinc 8.1.0 requires typing-extensions<4.2.0,>=3.7.4.1; python_version < \"3.8\", but you have typing-extensions 4.3.0 which is incompatible.\n",
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            "\u001b[0mSuccessfully installed ElMD-0.4.8 attrs-22.1.0 bounded-pool-executor-0.0.3 chem_wasserstein-1.0.11 colorama-0.4.5 composition_based_feature_vector-1.0.4 crabnet-2.0.7 cycler-0.11.0 cython-0.29.32 dill-0.3.5.1 dist_matrix-1.0.2 fonttools-4.36.0 hdbscan-0.8.28 importlib-metadata-4.12.0 iniconfig-1.1.1 joblib-1.1.0 kaleido-0.2.1 kiwisolver-1.4.4 llvmlite-0.39.0 mat_discover-2.2.7 matplotlib-3.5.3 numba-0.56.0 numpy-1.21.6 packaging-21.3 pandas-1.3.5 pillow-9.2.0 plotly-5.10.0 pluggy-1.0.0 pqdm-0.2.0 psutil-5.9.1 py-1.11.0 pynndescent-0.5.7 pyparsing-3.0.9 pytest-7.1.2 python-dateutil-2.8.2 pytz-2022.2.1 scikit_learn-1.0.2 scipy-1.7.3 seaborn-0.11.2 setuptools-65.1.0 six-1.16.0 tenacity-8.0.1 threadpoolctl-3.1.0 tomli-2.0.1 tqdm-4.64.0 typing-extensions-4.3.0 umap-learn-0.5.3 zipp-3.8.1\n",
            "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
            "\u001b[0mCPU times: user 970 ms, sys: 149 ms, total: 1.12 s\n",
            "Wall time: 2min 9s\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Setup"
      ],
      "metadata": {
        "id": "_OdwrIvnSP5v"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Imports"
      ],
      "metadata": {
        "id": "rvW0dT19SMtv"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "NXWp_LW6ixlz"
      },
      "outputs": [],
      "source": [
        "from os.path import join\n",
        "import pandas as pd\n",
        "from crabnet.data.materials_data import elasticity\n",
        "from mat_discover.mat_discover_ import Discover\n",
        "from mat_discover.utils.pareto import pareto_plot"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
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        "id": "2nedJNOdixl1",
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          "base_uri": "https://localhost:8080/",
          "height": 424
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      formula           index  target  count\n",
              "4002  Li2CuS2  (11237, 11254)    41.0      2\n",
              "334     As2O5         (5126,)    93.0      1\n",
              "1613  Co2B4Mo         (8018,)   303.0      1\n",
              "4329   LiCaF3        (10205,)    85.0      1\n",
              "792   Be2CoPt         (7684,)   207.0      1\n",
              "...       ...             ...     ...    ...\n",
              "5734    NaPt2         (2642,)   124.0      1\n",
              "5191   Mn2GaW         (7848,)   220.0      1\n",
              "5390  MnSnPd2         (6509,)   133.0      1\n",
              "860     BeNi3        (11839,)    63.0      1\n",
              "7270  ScAl2Ni          (261,)   107.0      1\n",
              "\n",
              "[8465 rows x 4 columns]"
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              "      <th>5734</th>\n",
              "      <td>NaPt2</td>\n",
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              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>5191</th>\n",
              "      <td>Mn2GaW</td>\n",
              "      <td>(7848,)</td>\n",
              "      <td>220.0</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>5390</th>\n",
              "      <td>MnSnPd2</td>\n",
              "      <td>(6509,)</td>\n",
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              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>860</th>\n",
              "      <td>BeNi3</td>\n",
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              "      <td>63.0</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>7270</th>\n",
              "      <td>ScAl2Ni</td>\n",
              "      <td>(261,)</td>\n",
              "      <td>107.0</td>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ],
      "source": [
        "# %% setup\n",
        "# set dummy to True for a quicker run --> small dataset, MDS instead of UMAP\n",
        "dummy = False\n",
        "disc = Discover(\n",
        "    dummy_run=dummy, pred_weight=0\n",
        ")  # pred_weight=0 means no target property regression\n",
        "train_df, val_df = disc.data(elasticity, fname=\"train.csv\", dummy=dummy)\n",
        "train_df  # only formula and target columns required"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "eFs0z_k6ixl2"
      },
      "outputs": [],
      "source": [
        "disc.fit(\n",
        "    train_df\n",
        ")  # fitting is quick since property regression model is skipped (`pred_weight=0`)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "C7lfqbLEixl2",
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        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "val RMSE:  0.0\n",
            "Fitting mod_petti kernel matrix\n",
            "Constructing distances\n",
            "[fit-wasserstein]\n",
            "Elapsed: 21.00964\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/umap/umap_.py:1780: UserWarning:\n",
            "\n",
            "using precomputed metric; inverse_transform will be unavailable\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[fit-UMAP]\n",
            "Elapsed: 87.10397\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/umap/umap_.py:1780: UserWarning:\n",
            "\n",
            "using precomputed metric; inverse_transform will be unavailable\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[fit-vis-UMAP]\n",
            "Elapsed: 42.08381\n",
            "\n",
            "[DensMAP]\n",
            "Elapsed: 129.18966\n",
            "\n",
            "[HDBSCAN*]\n",
            "Elapsed: 0.14847\n",
            "\n",
            "[pdf-summation]\n",
            "Elapsed: 12.271\n",
            "\n",
            "[gridded-pdf-summation]\n",
            "Elapsed: 12.42868\n",
            "\n",
            "[train-val-pdf-summation]\n",
            "Elapsed: 3.20845\n",
            "\n",
            "[nearest-neighbor-properties]\n",
            "Elapsed: 2.93471\n",
            "\n",
            "CPU times: user 2min 47s, sys: 3.1 s, total: 2min 51s\n",
            "Wall time: 2min 49s\n"
          ]
        }
      ],
      "source": [
        "%%time\n",
        "score = disc.predict(val_df, umap_random_state=42)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "3At5TC0gixl3",
        "outputId": "e3913831-f537-4a46-e3fc-67b50895cc8f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 514
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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          },
          "metadata": {}
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      ],
      "source": [
        "# Interactive scatter plot colored by clusters\n",
        "x = \"DensMAP Dim. 1\"\n",
        "y = \"DensMAP Dim. 2\"\n",
        "umap_df = pd.DataFrame(\n",
        "    {\n",
        "        x: disc.std_emb[:, 0],\n",
        "        y: disc.std_emb[:, 1],\n",
        "        \"cluster ID\": disc.labels,\n",
        "        \"formula\": disc.all_formula,\n",
        "    }\n",
        ")\n",
        "fig, _ = pareto_plot(\n",
        "    umap_df,\n",
        "    x=x,\n",
        "    y=y,\n",
        "    color=\"cluster ID\",\n",
        "    fpath=join(disc.figure_dir, \"px-umap-cluster-scatter\"),\n",
        "    pareto_front=False,\n",
        "    parity_type=None,\n",
        ")\n",
        "fig"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Example Output"
      ],
      "metadata": {
        "id": "SCRFmthpQE5B"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "![example-plotly-output](https://github.com/sparks-baird/mat_discover/blob/main/examples/figures/cluster-example-output.png?raw=1)"
      ],
      "metadata": {
        "id": "vEIwdiVbPj-h"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "(when you run this notebook, the above plot will be interactive and a Plotly `.html` file and a [matplotlibified](https://github.com/sparks-baird/mat_discover/blob/4abeea75664d291275900016e8a15d2cacd63838/mat_discover/utils/plotting.py#L250) \"paper-ready\" `.png` image will also be exported to `./figures/`)"
      ],
      "metadata": {
        "id": "tDMjDzOBPqqY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "Wy3d1G_FPrsI"
      },
      "execution_count": 6,
      "outputs": []
    }
  ],
  "metadata": {
    "language_info": {
      "name": "python"
    },
    "orig_nbformat": 4,
    "colab": {
      "name": "elmd_densmap_cluster.ipynb",
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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    "gpuClass": "standard"
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  "nbformat_minor": 0
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