cuml_sklearn_colab_demo.ipynb

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  "metadata": {
    "colab": {
      "provenance": [],
      "gpuType": "T4",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/SujataSaurabh/myPython/blob/master/cuml_sklearn_colab_demo.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Getting Started with cuML's accelerator mode (cuml.accel)\n"
      ],
      "metadata": {
        "id": "ZhTWAPo7X5-I"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "cuML is a Python GPU library for accelerating machine learning models using a scikit-learn-like API.\n",
        "\n",
        "cuML now has an accelerator mode (cuml.accel) which allows you to bring accelerated computing to existing workflows with zero code changes required. In addition to scikit-learn, cuml.accel also provides acceleration to algorithms found in umap-learn (UMAP) and hdbscan (HDBSCAN).\n",
        "\n",
        "This notebook is a brief introduction to cuml.accel."
      ],
      "metadata": {
        "id": "BinKvOgMYOCp"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# ⚠️ Verify your setup"
      ],
      "metadata": {
        "id": "ylUzZjM-mRMH"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "First, we'll verfiy that we are running on an NVIDIA GPU:"
      ],
      "metadata": {
        "id": "e_AtDRMEZQd-"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OKWtiMvJAS60",
        "outputId": "73a26452-b097-4e3d-8a63-033d0a247736"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Wed Mar 19 17:30:32 2025       \n",
            "+-----------------------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 550.54.15              Driver Version: 550.54.15      CUDA Version: 12.4     |\n",
            "|-----------------------------------------+------------------------+----------------------+\n",
            "| GPU  Name                 Persistence-M | Bus-Id          Disp.A | Volatile Uncorr. ECC |\n",
            "| Fan  Temp   Perf          Pwr:Usage/Cap |           Memory-Usage | GPU-Util  Compute M. |\n",
            "|                                         |                        |               MIG M. |\n",
            "|=========================================+========================+======================|\n",
            "|   0  Tesla T4                       Off |   00000000:00:04.0 Off |                    0 |\n",
            "| N/A   44C    P8              9W /   70W |       0MiB /  15360MiB |      0%      Default |\n",
            "|                                         |                        |                  N/A |\n",
            "+-----------------------------------------+------------------------+----------------------+\n",
            "                                                                                         \n",
            "+-----------------------------------------------------------------------------------------+\n",
            "| Processes:                                                                              |\n",
            "|  GPU   GI   CI        PID   Type   Process name                              GPU Memory |\n",
            "|        ID   ID                                                               Usage      |\n",
            "|=========================================================================================|\n",
            "|  No running processes found                                                             |\n",
            "+-----------------------------------------------------------------------------------------+\n"
          ]
        }
      ],
      "source": [
        "!nvidia-smi  # this should display information about available GPUs"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "With classical machine learning, there is a wide range of interesting problems we can explore. In this tutorial we'll examine 3 of the more popular use cases: classification, clustering, and dimensionality reduction."
      ],
      "metadata": {
        "id": "qC3fevZecnns"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Classification"
      ],
      "metadata": {
        "id": "M37-8qsDa2Pe"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's load a dataset and see how we can use scikit-learn to classify that data.  For this example we'll use the Coverage Type dataset, which contains a number of features that can be used to predict forest cover type, such as elevation, aspect, slope, and soil-type.\n",
        "\n",
        "More information on this dataset can be found at https://archive.ics.uci.edu/dataset/31/covertype."
      ],
      "metadata": {
        "id": "vT5RNLwdce-O"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import classification_report, accuracy_score"
      ],
      "metadata": {
        "id": "PKt2Lje5lYQw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "url = \"https://archive.ics.uci.edu/ml/machine-learning-databases/covtype/covtype.data.gz\"\n",
        "\n",
        "# Column names for the dataset (from UCI Covertype description)\n",
        "columns = ['Elevation', 'Aspect', 'Slope', 'Horizontal_Distance_To_Hydrology', 'Vertical_Distance_To_Hydrology',\n",
        "           'Horizontal_Distance_To_Roadways', 'Hillshade_9am', 'Hillshade_Noon', 'Hillshade_3pm',\n",
        "           'Horizontal_Distance_To_Fire_Points', 'Wilderness_Area1', 'Wilderness_Area2', 'Wilderness_Area3',\n",
        "           'Wilderness_Area4', 'Soil_Type1', 'Soil_Type2', 'Soil_Type3', 'Soil_Type4', 'Soil_Type5', 'Soil_Type6',\n",
        "           'Soil_Type7', 'Soil_Type8', 'Soil_Type9', 'Soil_Type10', 'Soil_Type11', 'Soil_Type12', 'Soil_Type13',\n",
        "           'Soil_Type14', 'Soil_Type15', 'Soil_Type16', 'Soil_Type17', 'Soil_Type18', 'Soil_Type19', 'Soil_Type20',\n",
        "           'Soil_Type21', 'Soil_Type22', 'Soil_Type23', 'Soil_Type24', 'Soil_Type25', 'Soil_Type26', 'Soil_Type27',\n",
        "           'Soil_Type28', 'Soil_Type29', 'Soil_Type30', 'Soil_Type31', 'Soil_Type32', 'Soil_Type33', 'Soil_Type34',\n",
        "           'Soil_Type35', 'Soil_Type36', 'Soil_Type37', 'Soil_Type38', 'Soil_Type39', 'Soil_Type40', 'Cover_Type']\n",
        "\n",
        "data = pd.read_csv(url, header=None)\n",
        "data.columns=columns"
      ],
      "metadata": {
        "id": "rLHSPlLnv-1y"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "53P_F5oHmh9F",
        "outputId": "da81ae3e-9725-4a99-d11e-e65f57a99d73"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(581012, 55)"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Next, we'll separate out the classification variable (Cover_Type) from the rest of the data. This is what we will aim to predict with our classification model. We can also split our dataset into training and test data using the scikit-learn train_test_split function."
      ],
      "metadata": {
        "id": "7Mz-yThWmlqg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X, y = data.drop('Cover_Type', axis=1), data['Cover_Type']\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)"
      ],
      "metadata": {
        "id": "heVSKrDLxdN3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now that we have our dataset split, we're ready to run a model. To start, we will just run the model using the sklearn library with a starting max depth of 5 and all of the features. Note that we can set n_jobs=-1 to utilize all available CPU cores for fitting the trees -- this will ensure we get the best performance possible on our system's CPU.  "
      ],
      "metadata": {
        "id": "OVl1oBxxm44k"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%%time\n",
        "\n",
        "clf = RandomForestClassifier(n_estimators=100, max_depth=5, max_features=1.0, n_jobs=-1)\n",
        "clf.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 115
        },
        "id": "T0Y2HUgykyLY",
        "outputId": "5977bcbc-9c5e-45bd-9714-3d4814c22e72"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "CPU times: user 3min 57s, sys: 1.29 s, total: 3min 58s\n",
            "Wall time: 2min 29s\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier(max_depth=5, max_features=1.0, n_jobs=-1)"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {\n",
              "  /* Definition of color scheme common for light and dark mode */\n",
              "  --sklearn-color-text: #000;\n",
              "  --sklearn-color-text-muted: #666;\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: flex;\n",
              "  width: 100%;\n",
              "  margin-bottom: 0;\n",
              "  padding: 0.5em;\n",
              "  box-sizing: border-box;\n",
              "  text-align: center;\n",
              "  align-items: start;\n",
              "  justify-content: space-between;\n",
              "  gap: 0.5em;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 label.sk-toggleable__label .caption {\n",
              "  font-size: 0.6rem;\n",
              "  font-weight: lighter;\n",
              "  color: var(--sklearn-color-text-muted);\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: 0.5em;\n",
              "  text-align: center;\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>RandomForestClassifier(max_depth=5, max_features=1.0, n_jobs=-1)</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\"><div><div>RandomForestClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.ensemble.RandomForestClassifier.html\">?<span>Documentation for RandomForestClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestClassifier(max_depth=5, max_features=1.0, n_jobs=-1)</pre></div> </div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "In about 2 minutes, we were able to fit our tree model using scikit-learn. This is not bad! Let's use the model we just trained to predict coverage types in our test dataset and take a look at the accuracy of our model."
      ],
      "metadata": {
        "id": "_B7glDK8nWMQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = clf.predict(X_test)\n",
        "accuracy_score(y_test, y_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SLP-K7qynwWg",
        "outputId": "d9239568-ca10-48e8-e6bb-4436d5e028a4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7043707993769524"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We can also print out a full classification report to better understand how we predicted different Coverage_Type categories."
      ],
      "metadata": {
        "id": "UAFu5rDwn6VK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(classification_report(y_test, y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AO3owg5PnwYW",
        "outputId": "3dea9a3b-b08d-4206-952d-dcd30b7fa20d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           1       0.68      0.68      0.68     42446\n",
            "           2       0.73      0.78      0.76     56686\n",
            "           3       0.63      0.86      0.73      7172\n",
            "           4       0.58      0.39      0.47       541\n",
            "           5       0.94      0.01      0.02      1897\n",
            "           6       0.72      0.03      0.06      3423\n",
            "           7       0.72      0.48      0.58      4038\n",
            "\n",
            "    accuracy                           0.70    116203\n",
            "   macro avg       0.72      0.46      0.47    116203\n",
            "weighted avg       0.71      0.70      0.69    116203\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "With scikit-learn, we built a model that was able to be trained in just a couple minutes. From the accuracy report, we can see that we predicted the correct class around 70% of the time, which is not bad but could certainly be improved.\n",
        "\n",
        "Often we want to run several different random forest models in order to optimize our hyperparameters. For example, we may want to increase the number of estimators, or modify the maximum depth of our tree. When running dozens or hundreds of different hyperparameter combinations, things start to become quite slow and iteration takes a lot longer.\n",
        "\n",
        "We provide some sample code utilizing GridSearchCV below to show what this process might look like. All of these combinations would take a LONG time to run if we spend 2 minutes fitting each model."
      ],
      "metadata": {
        "id": "FRdxW-MpoJAt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\"\"\"\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "\n",
        "# Define the parameter grid to search over\n",
        "param_grid = {\n",
        "    'n_estimators': [50, 100, 200],\n",
        "    'max_depth': [None, 10, 20, 30],\n",
        "    'min_samples_split': [2, 5, 10],\n",
        "    'min_samples_leaf': [1, 2, 4],\n",
        "    'max_features': ['auto', 'sqrt', 'log2'],\n",
        "    'bootstrap': [True, False]\n",
        "}\n",
        "\n",
        "grid_search = GridSearchCV(estimator=clf, param_grid=param_grid, cv=5)\n",
        "grid_search.fit(X_train, y_train)\n",
        "\"\"\""
      ],
      "metadata": {
        "id": "4qFknPIWpXcs"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now let's load cuml.accel and try running the same code again to see what kind of acceleration we can get."
      ],
      "metadata": {
        "id": "5-BtB1RdqKp0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%load_ext cuml.accel"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "U31QyneR6BXJ",
        "outputId": "feebde4b-66bf-45e6-feaa-fd3f1bd9bc57"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[2025-03-10 20:51:44.205] [CUML] [info] cuML: Installed accelerator for sklearn.\n",
            "[2025-03-10 20:51:55.508] [CUML] [info] cuML: Installed accelerator for umap.\n",
            "[2025-03-10 20:51:55.521] [CUML] [info] cuML: Installed accelerator for hdbscan.\n",
            "[2025-03-10 20:51:55.521] [CUML] [info] cuML: Successfully initialized accelerator.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "After loading the IPython magic, we need to import the sklearn estimators we wish to use again."
      ],
      "metadata": {
        "id": "TNLZVjtxqTmc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import RandomForestClassifier"
      ],
      "metadata": {
        "id": "MVWHm7H9qS2T"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "%%time\n",
        "\n",
        "clf = RandomForestClassifier(n_estimators=100, max_depth=5, max_features=1.0, n_jobs=-1)\n",
        "clf.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 115
        },
        "id": "8v0QxJXrViiR",
        "outputId": "5510c2ad-5ea7-4cb7-c3b0-0527d836fc6b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "CPU times: user 3.48 s, sys: 2.3 s, total: 5.78 s\n",
            "Wall time: 3.17 s\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier(max_depth=5, max_features=1.0, n_jobs=-1)"
            ],
            "text/html": [
              "<style>#sk-container-id-2 {\n",
              "  /* Definition of color scheme common for light and dark mode */\n",
              "  --sklearn-color-text: #000;\n",
              "  --sklearn-color-text-muted: #666;\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: flex;\n",
              "  width: 100%;\n",
              "  margin-bottom: 0;\n",
              "  padding: 0.5em;\n",
              "  box-sizing: border-box;\n",
              "  text-align: center;\n",
              "  align-items: start;\n",
              "  justify-content: space-between;\n",
              "  gap: 0.5em;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 label.sk-toggleable__label .caption {\n",
              "  font-size: 0.6rem;\n",
              "  font-weight: lighter;\n",
              "  color: var(--sklearn-color-text-muted);\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: 0.5em;\n",
              "  text-align: center;\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>RandomForestClassifier(max_depth=5, max_features=1.0, n_jobs=-1)</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\"><div><div>RandomForestClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.ensemble.RandomForestClassifier.html\">?<span>Documentation for RandomForestClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestClassifier(max_depth=5, max_features=1.0, n_jobs=-1)</pre></div> </div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "That was much faster! Using cuML we're able to train this random forest model in just seconds instead of minutes. One thing to note is that cuML's implementation of RandomForestClassifier doesn't utilize the `n_jobs` parameter like scikit-learn, but we still accept it which makes it easier to use this accelerator with zero code changes.\n",
        "\n",
        "Let's take a look at the same accuracy score and classification report to compare the model's performance."
      ],
      "metadata": {
        "id": "fqJC2jtmqpva"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = clf.predict(X_test)\n",
        "cr = classification_report(y_test, y_pred)\n",
        "print(cr)"
      ],
      "metadata": {
        "id": "eVre6kav6iaS",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2c240c5f-b7c2-4eac-98b5-aee48bd4dcc7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           1       0.68      0.69      0.68     42446\n",
            "           2       0.73      0.78      0.75     56686\n",
            "           3       0.64      0.86      0.73      7172\n",
            "           4       0.66      0.44      0.53       541\n",
            "           5       0.53      0.05      0.09      1897\n",
            "           6       0.73      0.03      0.06      3423\n",
            "           7       0.73      0.48      0.58      4038\n",
            "\n",
            "    accuracy                           0.70    116203\n",
            "   macro avg       0.67      0.48      0.49    116203\n",
            "weighted avg       0.70      0.70      0.69    116203\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Out of the box, the model performed about the same as the scikit-learn implementation. Because this model ran so much faster, we can quickly iterate on the hyperparameter configuration and find a model that performs better with excellent speedups."
      ],
      "metadata": {
        "id": "8xkoz247VsIX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%%time\n",
        "\n",
        "clf = RandomForestClassifier(n_estimators=100, max_depth=30, max_features=1.0, n_jobs=-1)\n",
        "clf.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 115
        },
        "id": "mfZamg7FVoPe",
        "outputId": "19d44cd9-9863-495b-ac8d-067118fd9c22"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "CPU times: user 17.2 s, sys: 12.4 s, total: 29.6 s\n",
            "Wall time: 16.7 s\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier(max_depth=30, max_features=1.0, n_jobs=-1)"
            ],
            "text/html": [
              "<style>#sk-container-id-3 {\n",
              "  /* Definition of color scheme common for light and dark mode */\n",
              "  --sklearn-color-text: #000;\n",
              "  --sklearn-color-text-muted: #666;\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-3 {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 pre {\n",
              "  padding: 0;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 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-3 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-3 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-3 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-3 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-3 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-3 div.sk-parallel-item {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-parallel-item:first-child::after {\n",
              "  align-self: flex-end;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-parallel-item:last-child::after {\n",
              "  align-self: flex-start;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-parallel-item:only-child::after {\n",
              "  width: 0;\n",
              "}\n",
              "\n",
              "/* Serial-specific style estimator block */\n",
              "\n",
              "#sk-container-id-3 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-3 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-3 label.sk-toggleable__label {\n",
              "  cursor: pointer;\n",
              "  display: flex;\n",
              "  width: 100%;\n",
              "  margin-bottom: 0;\n",
              "  padding: 0.5em;\n",
              "  box-sizing: border-box;\n",
              "  text-align: center;\n",
              "  align-items: start;\n",
              "  justify-content: space-between;\n",
              "  gap: 0.5em;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 label.sk-toggleable__label .caption {\n",
              "  font-size: 0.6rem;\n",
              "  font-weight: lighter;\n",
              "  color: var(--sklearn-color-text-muted);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 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-3 label.sk-toggleable__label-arrow:hover:before {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "/* Toggleable content - dropdown */\n",
              "\n",
              "#sk-container-id-3 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-3 div.sk-toggleable__content.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 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-3 div.sk-toggleable__content.fitted pre {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 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-3 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-3 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-3 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-3 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-3 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-3 div.sk-label label.sk-toggleable__label,\n",
              "#sk-container-id-3 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-3 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-3 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-3 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-3 div.sk-label-container {\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "/* Estimator-specific */\n",
              "#sk-container-id-3 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-3 div.sk-estimator.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "/* on hover */\n",
              "#sk-container-id-3 div.sk-estimator:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 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: 0.5em;\n",
              "  text-align: center;\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-3 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-3 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-3 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-3 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-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(max_depth=30, max_features=1.0, n_jobs=-1)</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-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>RandomForestClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.ensemble.RandomForestClassifier.html\">?<span>Documentation for RandomForestClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestClassifier(max_depth=30, max_features=1.0, n_jobs=-1)</pre></div> </div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = clf.predict(X_test)\n",
        "print(classification_report(y_test, y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "E97LObEYVocu",
        "outputId": "e560b1a6-1b12-4cb5-bc66-09b3530af692"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           1       0.97      0.96      0.96     42446\n",
            "           2       0.97      0.98      0.97     56686\n",
            "           3       0.96      0.96      0.96      7172\n",
            "           4       0.90      0.89      0.89       541\n",
            "           5       0.93      0.85      0.89      1897\n",
            "           6       0.93      0.93      0.93      3423\n",
            "           7       0.97      0.96      0.96      4038\n",
            "\n",
            "    accuracy                           0.96    116203\n",
            "   macro avg       0.95      0.93      0.94    116203\n",
            "weighted avg       0.96      0.96      0.96    116203\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "With a model that runs in just seconds, we can perform hyperparameter optimization using a method like the grid search shown above, and have results in just minutes instead of hours."
      ],
      "metadata": {
        "id": "DmcwmqN4q4cv"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# CPU Fallback\n",
        "\n"
      ],
      "metadata": {
        "id": "9IXkjC6MLZoj"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "There are some algorithms and functionality from scikit-learn, UMAP, and HDBSCAN that are *not* implemented in cuML. For cases where the underlying functionality is not supported on GPU, the cuML accelerator will gracefully fall back and execute on the CPU instead.\n"
      ],
      "metadata": {
        "id": "9zSIAHaKn7oa"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.neighbors import KernelDensity\n",
        "import numpy as np\n",
        "\n",
        "X = np.concatenate((np.random.normal(0, 1, 10000),\n",
        "                    np.random.normal(5, 1, 10000)))[:, np.newaxis]\n"
      ],
      "metadata": {
        "id": "qtB_3HlFn-wx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "kde = KernelDensity(kernel='gaussian', bandwidth=0.5)\n",
        "kde.fit(X)\n"
      ],
      "metadata": {
        "id": "GYmEAsvXn-yv",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 80
        },
        "outputId": "9e868e87-514c-4910-d19a-50cc4a67ad7d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KernelDensity(bandwidth=0.5)"
            ],
            "text/html": [
              "<style>#sk-container-id-4 {\n",
              "  /* Definition of color scheme common for light and dark mode */\n",
              "  --sklearn-color-text: #000;\n",
              "  --sklearn-color-text-muted: #666;\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-4 {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "#sk-container-id-4 pre {\n",
              "  padding: 0;\n",
              "}\n",
              "\n",
              "#sk-container-id-4 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-4 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-4 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-4 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-4 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-4 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-4 div.sk-parallel-item {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-parallel-item:first-child::after {\n",
              "  align-self: flex-end;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-parallel-item:last-child::after {\n",
              "  align-self: flex-start;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-parallel-item:only-child::after {\n",
              "  width: 0;\n",
              "}\n",
              "\n",
              "/* Serial-specific style estimator block */\n",
              "\n",
              "#sk-container-id-4 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-4 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-4 label.sk-toggleable__label {\n",
              "  cursor: pointer;\n",
              "  display: flex;\n",
              "  width: 100%;\n",
              "  margin-bottom: 0;\n",
              "  padding: 0.5em;\n",
              "  box-sizing: border-box;\n",
              "  text-align: center;\n",
              "  align-items: start;\n",
              "  justify-content: space-between;\n",
              "  gap: 0.5em;\n",
              "}\n",
              "\n",
              "#sk-container-id-4 label.sk-toggleable__label .caption {\n",
              "  font-size: 0.6rem;\n",
              "  font-weight: lighter;\n",
              "  color: var(--sklearn-color-text-muted);\n",
              "}\n",
              "\n",
              "#sk-container-id-4 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-4 label.sk-toggleable__label-arrow:hover:before {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "/* Toggleable content - dropdown */\n",
              "\n",
              "#sk-container-id-4 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-4 div.sk-toggleable__content.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-4 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-4 div.sk-toggleable__content.fitted pre {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-4 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-4 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-4 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-4 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-4 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-4 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-4 div.sk-label label.sk-toggleable__label,\n",
              "#sk-container-id-4 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-4 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-4 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-4 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-4 div.sk-label-container {\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "/* Estimator-specific */\n",
              "#sk-container-id-4 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-4 div.sk-estimator.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "/* on hover */\n",
              "#sk-container-id-4 div.sk-estimator:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-4 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: 0.5em;\n",
              "  text-align: center;\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-4 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-4 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-4 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-4 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-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KernelDensity(bandwidth=0.5)</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-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>KernelDensity</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.neighbors.KernelDensity.html\">?<span>Documentation for KernelDensity</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>KernelDensity(bandwidth=0.5)</pre></div> </div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(kde.score_samples(X))"
      ],
      "metadata": {
        "id": "rBB16r0in-1F",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5a5cfef5-ea8e-4240-e866-4138b7f700b1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[-1.73977133 -1.96377755 -1.85124204 ... -1.7259015  -1.87922778\n",
            " -1.9905058 ]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Tco36A_pLi_m"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Next, let's restart the kernel to unload the accelerator extension and observe the same performance comparisons on a few other algorithms."
      ],
      "metadata": {
        "id": "OWcDT_9Cm6Mk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "get_ipython().kernel.do_shutdown(restart=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Y3aZxpgPDgKd",
        "outputId": "3a064364-f781-4052-a314-39ca96e80640"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'status': 'ok', 'restart': True}"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We'll now take a look at a clustering example using HDBSCAN.\n"
      ],
      "metadata": {
        "id": "9Ql7UCNUW1AO"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Clustering"
      ],
      "metadata": {
        "id": "pdjvwlZ-DaoW"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Clustering is an important data science workflow because it helps uncover hidden patterns and structures within data without requiring labeled outcomes. In practice, with high dimensional data it can be difficult to discern whether the clusters we've chosen are good or not. One way to determine the quality of our clustering is with sklearn's [silhouette score](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.silhouette_score.html#sklearn.metrics.silhouette_score), which we'll examine shortly.\n",
        "\n",
        "HDBSCAN is a popular density-based clustering algorithm that is highly flexible. We'll load a toy sklearn dataset to illustrate how HDBSCAN can be accelerated with cuml.accel."
      ],
      "metadata": {
        "id": "70sdcTIdfzkK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import hdbscan\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.datasets import make_blobs\n",
        "from sklearn.metrics import silhouette_score"
      ],
      "metadata": {
        "id": "oIlRPHZ1DZ7q"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "N = 20000\n",
        "K = 100\n",
        "\n",
        "X, y = make_blobs(\n",
        "    n_samples=N,\n",
        "    n_features=K,\n",
        "    centers=5,\n",
        "    cluster_std=[3,1,2,1.5,0.5],\n",
        "    random_state=42\n",
        ")\n",
        "\n",
        "plt.scatter(X[:, 0], X[:, 1], c=y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "RqXkNWRjDZ9n",
        "outputId": "57435e6c-a303-4531-d64b-87ba14cce410"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7f0cc1dc5450>"
            ]
          },
          "metadata": {},
          "execution_count": 6
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "clusterer = hdbscan.HDBSCAN()\n",
        "%time clusterer.fit(X)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 184
        },
        "id": "536jWwBWg1Ou",
        "outputId": "c589e021-e14e-4848-d8b5-2302c00d4ef7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.11/dist-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.11/dist-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "CPU times: user 46.1 s, sys: 128 ms, total: 46.3 s\n",
            "Wall time: 46.7 s\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "HDBSCAN()"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {\n",
              "  /* Definition of color scheme common for light and dark mode */\n",
              "  --sklearn-color-text: #000;\n",
              "  --sklearn-color-text-muted: #666;\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: flex;\n",
              "  width: 100%;\n",
              "  margin-bottom: 0;\n",
              "  padding: 0.5em;\n",
              "  box-sizing: border-box;\n",
              "  text-align: center;\n",
              "  align-items: start;\n",
              "  justify-content: space-between;\n",
              "  gap: 0.5em;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 label.sk-toggleable__label .caption {\n",
              "  font-size: 0.6rem;\n",
              "  font-weight: lighter;\n",
              "  color: var(--sklearn-color-text-muted);\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: 0.5em;\n",
              "  text-align: center;\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>HDBSCAN()</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\"><div><div>HDBSCAN</div></div><div><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>HDBSCAN()</pre></div> </div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(silhouette_score(X, clusterer.labels_))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wrAzGgqrxHtY",
        "outputId": "05493c84-d6cf-469a-e533-db36558c76ec"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.7352694189002873\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%load_ext cuml.accel"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XyU6JqsHhG6X",
        "outputId": "925752ea-0d68-425a-b085-14feef3371c0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[2025-03-10 20:36:10.574] [CUML] [info] cuML: Installed accelerator for sklearn.\n",
            "[2025-03-10 20:36:21.844] [CUML] [info] cuML: Installed accelerator for umap.\n",
            "[2025-03-10 20:36:21.845] [CUML] [info] cuML: Installed accelerator for hdbscan.\n",
            "[2025-03-10 20:36:21.845] [CUML] [info] cuML: Successfully initialized accelerator.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import hdbscan"
      ],
      "metadata": {
        "id": "vuwUVZeihJWh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "clusterer = hdbscan.HDBSCAN()\n",
        "%time clusterer.fit(X)\n"
      ],
      "metadata": {
        "id": "BfDHJoiSDZ_9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 115
        },
        "outputId": "8e7f9de5-e545-4051-e1a7-698b734577cf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "CPU times: user 632 ms, sys: 185 ms, total: 816 ms\n",
            "Wall time: 1.03 s\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "HDBSCAN()"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {\n",
              "  /* Definition of color scheme common for light and dark mode */\n",
              "  --sklearn-color-text: #000;\n",
              "  --sklearn-color-text-muted: #666;\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: flex;\n",
              "  width: 100%;\n",
              "  margin-bottom: 0;\n",
              "  padding: 0.5em;\n",
              "  box-sizing: border-box;\n",
              "  text-align: center;\n",
              "  align-items: start;\n",
              "  justify-content: space-between;\n",
              "  gap: 0.5em;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 label.sk-toggleable__label .caption {\n",
              "  font-size: 0.6rem;\n",
              "  font-weight: lighter;\n",
              "  color: var(--sklearn-color-text-muted);\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: 0.5em;\n",
              "  text-align: center;\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>HDBSCAN()</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\"><div><div>HDBSCAN</div></div><div><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>HDBSCAN()</pre></div> </div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Here we go from around 45 seconds to 1 second to fit the clustering model! This is a massive speed-up we got just from loading the `cuml.accel` extension."
      ],
      "metadata": {
        "id": "B_tOMHd8y5ai"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(silhouette_score(X, clusterer.labels_))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zZNpIYCqkk_8",
        "outputId": "61451ac0-e21c-4b85-e718-0fbad3128bef"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.7226569165160748\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "It's important to note that on real-world datasets, the silhouette score produced by the GPU and CPU implementations of HDBSCAN will often have slight differences. The cuML implementation of HDBSCAN should provide equivalent results, but it is normal for the actual clusters to vary slightly when dealing with complex datasets."
      ],
      "metadata": {
        "id": "91a7zkUIoE5d"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "smJAD1d2H00T"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Lastly, let's take a look at how we can use cuml's accelerator mode for a third popular machine learning task -- dimensionality reduction. We'll restart the kernel to unload the extension yet again.\n",
        "\n",
        "Keep in mind that we don't normally need to restart the kernel when using `cuml.accel`, we just do it for the sake of showing the speed-ups in this demo. In practice, you'd just load the accelerator one time up front and be set."
      ],
      "metadata": {
        "id": "k6AvkVh7QMlw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "get_ipython().kernel.do_shutdown(restart=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JmVN96HplXQb",
        "outputId": "334e84bf-19a9-4b3d-9581-4e4394235dc5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'status': 'ok', 'restart': True}"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Dimensionality Reduction"
      ],
      "metadata": {
        "id": "93qD18LqDiOj"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "UMAP is a popular dimensionality reduction technique that is used for both data visualization and as preprocessing for downstream modeling due to its ability to balance preserving both local and global structure of high-dimensional data. To learn more about how it works, visit the [UMAP documentation](https://umap-learn.readthedocs.io/en/latest/).\n",
        "\n",
        "To explore how cuML can accelerate UMAP, let's load in another dataset from UCI. We'll use the Human Activity Recognition (HAR) dataset, which was created from recordings of 30 subjects performing activities of daily living (ADL) while carrying a waist-mounted smartphone with embedded inertial sensors."
      ],
      "metadata": {
        "id": "n0mwvzC0rdq4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!wget https://archive.ics.uci.edu/ml/machine-learning-databases/00240/UCI%20HAR%20Dataset.zip -O /tmp/HAR_data.zip"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JOeW2dt9D2IZ",
        "outputId": "3f687e7c-8652-4b8e-bfc3-43cd1b291531"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2025-03-10 20:39:18--  https://archive.ics.uci.edu/ml/machine-learning-databases/00240/UCI%20HAR%20Dataset.zip\n",
            "Resolving archive.ics.uci.edu (archive.ics.uci.edu)... 128.195.10.252\n",
            "Connecting to archive.ics.uci.edu (archive.ics.uci.edu)|128.195.10.252|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: unspecified\n",
            "Saving to: ‘/tmp/HAR_data.zip’\n",
            "\n",
            "/tmp/HAR_data.zip       [                 <=>]  58.17M  17.0MB/s    in 4.4s    \n",
            "\n",
            "2025-03-10 20:39:24 (13.3 MB/s) - ‘/tmp/HAR_data.zip’ saved [60999314]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!unzip /tmp/HAR_data.zip -d /tmp/HAR_data/"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TR78I9VuEGlu",
        "outputId": "aad52a74-9c40-4be5-dc30-d6cda2b0f971"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Archive:  /tmp/HAR_data.zip\n",
            "replace /tmp/HAR_data/UCI HAR Dataset/.DS_Store? [y]es, [n]o, [A]ll, [N]one, [r]ename: "
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "X_train = pd.read_csv(\"/tmp/HAR_data/UCI HAR Dataset/train/X_train.txt\", sep=\"\\s+\", header=None)\n",
        "y_train = pd.read_csv(\"/tmp/HAR_data/UCI HAR Dataset/train/y_train.txt\", sep=\"\\s+\", header=None)\n",
        "X_test = pd.read_csv(\"/tmp/HAR_data/UCI HAR Dataset/test/X_test.txt\", sep=\"\\s+\", header=None)\n",
        "y_test = pd.read_csv(\"/tmp/HAR_data/UCI HAR Dataset/test/y_test.txt\", sep=\"\\s+\", header=None)\n",
        "labels = pd.read_csv(\"/tmp/HAR_data/UCI HAR Dataset/activity_labels.txt\", sep=\"\\s+\", header=None)"
      ],
      "metadata": {
        "id": "QjGw0WDwCu2_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oesYc97pQ1vr",
        "outputId": "e7dca260-5abb-4f27-d61e-e336955b5271"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(7352, 561)"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's take a look at the activity labels to better understand the data we're working with. We can see that the sensors have grouped activities into 6 different classes."
      ],
      "metadata": {
        "id": "F5Cle6mGOH7S"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "labels"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "id": "22YYMRvXOUUz",
        "outputId": "b15af6d7-8c52-474e-f102-f573dd7f2d2e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   0                   1\n",
              "0  1             WALKING\n",
              "1  2    WALKING_UPSTAIRS\n",
              "2  3  WALKING_DOWNSTAIRS\n",
              "3  4             SITTING\n",
              "4  5            STANDING\n",
              "5  6              LAYING"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-67573893-4f0a-46e1-94ed-1e1a2b5d9b8f\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>0</th>\n",
              "      <th>1</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>WALKING</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>WALKING_UPSTAIRS</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>WALKING_DOWNSTAIRS</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>SITTING</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>STANDING</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>6</td>\n",
              "      <td>LAYING</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-67573893-4f0a-46e1-94ed-1e1a2b5d9b8f')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "labels",
              "summary": "{\n  \"name\": \"labels\",\n  \"rows\": 6,\n  \"fields\": [\n    {\n      \"column\": 0,\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 1,\n        \"max\": 6,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          1,\n          2,\n          6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": 1,\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          \"WALKING\",\n          \"WALKING_UPSTAIRS\",\n          \"LAYING\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "\n",
        "# Scale the data before applying UMAP\n",
        "scaler = StandardScaler()\n",
        "X_train_scaled = scaler.fit_transform(X_train)\n"
      ],
      "metadata": {
        "id": "8rA7SsEpCu5M"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's run UMAP with some basic parameters and explore a lower-dimensionality projection of this dataset."
      ],
      "metadata": {
        "id": "_9ltJhG7tQt0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import umap\n",
        "umap_model = umap.UMAP(n_neighbors=15, n_components=2, random_state=42, min_dist=0.0)"
      ],
      "metadata": {
        "id": "t4sR7x3sF_9a"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "%%time\n",
        "\n",
        "# Fit UMAP model to the data\n",
        "X_train_umap = umap_model.fit_transform(X_train_scaled)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cGD49PFVlyrz",
        "outputId": "81f5a19c-d2c6-498c-c673-facc3ced964d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.11/dist-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.11/dist-packages/umap/umap_.py:1952: UserWarning: n_jobs value 1 overridden to 1 by setting random_state. Use no seed for parallelism.\n",
            "  warn(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "CPU times: user 36.6 s, sys: 1.09 s, total: 37.6 s\n",
            "Wall time: 52.2 s\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "It's often quite interesting to visualize the resulting projection of the embeddings created by UMAP. In this case, let's take a look at the now 2-dimensional dataset."
      ],
      "metadata": {
        "id": "kwJC2woSl4U9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Plot the UMAP result\n",
        "plt.figure(figsize=(10, 8))\n",
        "plt.scatter(X_train_umap[:, 0], X_train_umap[:, 1], c=y_train.values.ravel(), cmap='Spectral', s=10)\n",
        "plt.colorbar(label=\"Activity\")\n",
        "plt.title(\"UMAP projection of the UCI HAR dataset\")\n",
        "plt.xlabel(\"UMAP Component 1\")\n",
        "plt.ylabel(\"UMAP Component 2\")\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 718
        },
        "id": "PDRxaBEdF__X",
        "outputId": "2ddd2b8b-8b1a-48e4-e06b-b5ffe0245f27"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x800 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "It's interesting to see how our different categories are grouped in relation to one another.\n",
        "\n",
        "We can look at the trustworthiness score to better understand how well the structure of the original dataset was preserved by our 2D projection"
      ],
      "metadata": {
        "id": "gKFBLuE7mFrs"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.manifold import trustworthiness\n",
        "trustworthiness(X_train, X_train_umap, n_neighbors=15)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Say1EIdqMLoq",
        "outputId": "3c3f1623-8ef2-4703-9dcb-1e2ba3e148d7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9725294692634925"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "It looks like this projection is a great representation of our full dataset.\n",
        "\n",
        "Let's now run the same thing with the accelerator turned on."
      ],
      "metadata": {
        "id": "1OzvVajcm2jX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%load_ext cuml.accel"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Rarog7-LnOJG",
        "outputId": "9c601ad1-b4b8-42fb-e1fe-57b042400a01"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[2025-03-10 20:42:01.171] [CUML] [info] cuML: Installed accelerator for sklearn.\n",
            "[2025-03-10 20:42:01.172] [CUML] [info] cuML: Installed accelerator for umap.\n",
            "[2025-03-10 20:42:01.183] [CUML] [info] cuML: Installed accelerator for hdbscan.\n",
            "[2025-03-10 20:42:01.183] [CUML] [info] cuML: Successfully initialized accelerator.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import umap\n",
        "umap_model = umap.UMAP(n_neighbors=15, n_components=2, random_state=42, min_dist=0.0)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "imdGmrWvnOLP",
        "outputId": "0fbbd970-743f-4b82-8c39-87b064ff2ed8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[2025-03-10 20:42:01.192] [CUML] [info] build_algo set to brute_force_knn because random_state is given\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%%time\n",
        "\n",
        "# Fit UMAP model to the data\n",
        "X_train_umap = umap_model.fit_transform(X_train_scaled)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4yUnvHUOnONH",
        "outputId": "e296feec-39e8-40f7-ab8c-9e703d62a64d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "CPU times: user 372 ms, sys: 152 ms, total: 523 ms\n",
            "Wall time: 520 ms\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Plot the UMAP result\n",
        "plt.figure(figsize=(10, 8))\n",
        "plt.scatter(X_train_umap[:, 0], X_train_umap[:, 1], c=y_train.values.ravel(), cmap='Spectral', s=10)\n",
        "plt.colorbar(label=\"Activity\")\n",
        "plt.title(\"UMAP projection of the UCI HAR dataset\")\n",
        "plt.xlabel(\"UMAP Component 1\")\n",
        "plt.ylabel(\"UMAP Component 2\")\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 718
        },
        "id": "K1qsC-Xmndgb",
        "outputId": "439aba27-ab59-4f6d-9e09-882e1b1a48d8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x800 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Note that while the projection here is not identical to the umap-learn plot, the quality of the results are equivalent. We can run the trustworthiness score again to compare and verify this claim."
      ],
      "metadata": {
        "id": "AyFnouZInffh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.manifold import trustworthiness\n",
        "trustworthiness(X_train, X_train_umap, n_neighbors=15)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "czIK4d6WndkA",
        "outputId": "b45f3ae2-e39f-4ba3-d7f1-cb4001ab6f12"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9709069888612091"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "nHVmnyGinOO3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "For more information on getting started with `cuml.accel`, check out [RAPIDS.ai](https://rapids.ai/cuml-accel/) or the [cuML Docs](https://docs.rapids.ai/api/cuml/stable/)."
      ],
      "metadata": {
        "id": "JEAEGvqHztTI"
      }
    }
  ]
}