Machine_Learning_with_RAPIDS_(Random_Forest).ipynb

{
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  "metadata": {
    "colab": {
      "name": "Machine Learning with RAPIDS (Random Forest).ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/srivatsan88/YouTubeLI/blob/master/Machine_Learning_with_RAPIDS_(Random_Forest).ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tm4mqdNMJRGd",
        "colab_type": "code",
        "outputId": "617dad92-5334-431d-e73e-c498b5cb2568",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 326
        }
      },
      "source": [
        "!nvidia-smi"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Sat Jan 11 18:33:30 2020       \n",
            "+-----------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 440.44       Driver Version: 418.67       CUDA Version: 10.1     |\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",
            "|===============================+======================+======================|\n",
            "|   0  Tesla P100-PCIE...  Off  | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   34C    P0    27W / 250W |      0MiB / 16280MiB |      0%      Default |\n",
            "+-------------------------------+----------------------+----------------------+\n",
            "                                                                               \n",
            "+-----------------------------------------------------------------------------+\n",
            "| Processes:                                                       GPU Memory |\n",
            "|  GPU       PID   Type   Process name                             Usage      |\n",
            "|=============================================================================|\n",
            "|  No running processes found                                                 |\n",
            "+-----------------------------------------------------------------------------+\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oiQRzo0zGrCK",
        "colab_type": "code",
        "outputId": "b1c945e1-8975-4272-e560-e54fdbc34713",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        }
      },
      "source": [
        "#This install will ask for 2 prompts\n",
        "#Start with 0.11 and then subsequent prompt say Y (only for 0.11). If it hangs in solving dependency for more than 2 min than kill,\n",
        "#restart with 0.10 install\n",
        "#This script for 0.10 and 0.11 has some bugs so follow subsequent steps\n",
        "!wget -nc https://github.com/rapidsai/notebooks-extended/raw/master/utils/rapids-colab.sh\n",
        "!bash rapids-colab.sh\n",
        "\n",
        "import sys, os\n",
        "\n",
        "dist_package_index = sys.path.index(\"/usr/local/lib/python3.6/dist-packages\")\n",
        "sys.path = sys.path[:dist_package_index] + [\"/usr/local/lib/python3.6/site-packages\"] + sys.path[dist_package_index:]\n",
        "sys.path\n",
        "if os.path.exists('update_pyarrow.py'): ## This file only exists if you're using RAPIDS version 0.11 or higher\n",
        "  exec(open(\"update_pyarrow.py\").read(), globals())\n",
        "\n",
        "sys.path.append('/usr/local/lib/python3.6/site-packages/')"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "File ‘rapids-colab.sh’ already there; not retrieving.\n",
            "\n",
            "PLEASE READ\n",
            "********************************************************************************************************\n",
            "Colab v0.11+ Migration Notice:\n",
            " \n",
            "There has been a NECESSARY Colab script code change for VERSION 0.11+ that MAY REQUIRE an update how you install RAPIDS into Colab!  \n",
            "Not all Colab notebooks are updated (like personal Colabs) and while the script will install RAPIDS correctly, \n",
            "a neccessary script to update pyarrow to v0.15.x to be compatible with RAPIDS v0.11+ may not run, and your RAPIDS instance\n",
            "will BREAK\n",
            " \n",
            "Please enter in the box your desired RAPIDS version (ex: '0.10' or '0.11', between 0.9 to 0.11, without the quotes) and hit Enter. \n",
            "0.10\n",
            "You may not have to change anything.  All versions of our script should work with this version of Colab\n",
            "File ‘env-check.py’ already there; not retrieving.\n",
            "\n",
            "Checking for GPU type:\n",
            "***********************************************************************\n",
            "Woo! Your instance has the right kind of GPU, a 'Tesla P100-PCIE-16GB'!\n",
            "***********************************************************************\n",
            "\n",
            "\n",
            "*********************************************\n",
            "Your Google Colab instance is RAPIDS ready!\n",
            "*********************************************\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Zc7bhhbDxCps",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 108
        },
        "outputId": "d5459cda-55f3-4062-c753-8c34463d2f65"
      },
      "source": [
        "#only for 0.10\n",
        "!conda install -y --prefix /usr/local -c rapidsai/label/xgboost -c rapidsai -c nvidia -c conda-forge dask-cudf xgboost"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting package metadata (current_repodata.json): - \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\bdone\n",
            "Solving environment: - \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\bdone\n",
            "\n",
            "# All requested packages already installed.\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XACkVY_qxq6B",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "8b4ce982-bf1d-476e-ae47-9ff82188ead1"
      },
      "source": [
        "!ls /usr/lib/libxgboost.so"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "ls: cannot access '/usr/lib/libxgboost.so': No such file or directory\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dtRD94MSs8BY",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!cp /usr/local/lib/libxgboost.so /usr/lib"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8tRX3zJ3nO6A",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import cudf"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wYsohgWlDB8X",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from __future__ import print_function\n",
        "import sys,tempfile, urllib, os\n",
        "import pandas as pd\n",
        "from sklearn.model_selection import train_test_split\n",
        "import numpy as np"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZtuVC35u3qTL",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.datasets import fetch_openml\n",
        "covtyp = fetch_openml(name='covertype', version=4)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3OR4SZNm65qf",
        "colab_type": "code",
        "outputId": "9c51379e-9293-4064-da91-febaa0ea95ec",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "covtyp.data.shape"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(581012, 54)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QhW9jLy_7mHv",
        "colab_type": "code",
        "outputId": "36ed789d-f215-46fa-9d0c-be7a74b06ca2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "np.unique(covtyp.target)"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['1', '2', '3', '4', '5', '6', '7'], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0eSauT6En37r",
        "colab_type": "code",
        "outputId": "17d69f86-a2e2-4b53-815f-333c78b5dfca",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 326
        }
      },
      "source": [
        "!nvidia-smi"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Sat Jan 11 18:49:11 2020       \n",
            "+-----------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 440.44       Driver Version: 418.67       CUDA Version: 10.1     |\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",
            "|===============================+======================+======================|\n",
            "|   0  Tesla P100-PCIE...  Off  | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   33C    P0    26W / 250W |      0MiB / 16280MiB |      0%      Default |\n",
            "+-------------------------------+----------------------+----------------------+\n",
            "                                                                               \n",
            "+-----------------------------------------------------------------------------+\n",
            "| Processes:                                                       GPU Memory |\n",
            "|  GPU       PID   Type   Process name                             Usage      |\n",
            "|=============================================================================|\n",
            "|  No running processes found                                                 |\n",
            "+-----------------------------------------------------------------------------+\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2kdUEoVk8uQ6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "cov_df = pd.DataFrame(data= np.c_[covtyp['data'], covtyp['target']],\n",
        "                     columns= covtyp['feature_names'] + ['target'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8TB9_2CqOsPP",
        "colab_type": "code",
        "outputId": "a8537cf9-8eba-4b1d-9eaa-d1454091a7cd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "cov_df.memory_usage(index=True).sum()"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "255645408"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cwaOT3fCDVR8",
        "colab_type": "code",
        "outputId": "fdd6e9a5-4132-431f-d50e-e8cfd6443b61",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 218
        }
      },
      "source": [
        "cov_df.head()"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2785</td>\n",
              "      <td>155</td>\n",
              "      <td>18</td>\n",
              "      <td>242</td>\n",
              "      <td>118</td>\n",
              "      <td>3090</td>\n",
              "      <td>238</td>\n",
              "      <td>238</td>\n",
              "      <td>122</td>\n",
              "      <td>6211</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2595</td>\n",
              "      <td>45</td>\n",
              "      <td>2</td>\n",
              "      <td>153</td>\n",
              "      <td>-1</td>\n",
              "      <td>391</td>\n",
              "      <td>220</td>\n",
              "      <td>234</td>\n",
              "      <td>150</td>\n",
              "      <td>6172</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  Elevation Aspect Slope  ... Soil_Type39 Soil_Type40 target\n",
              "0      2596     51     3  ...           0           0      5\n",
              "1      2590     56     2  ...           0           0      5\n",
              "2      2804    139     9  ...           0           0      2\n",
              "3      2785    155    18  ...           0           0      2\n",
              "4      2595     45     2  ...           0           0      5\n",
              "\n",
              "[5 rows x 55 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oOgRjV_ULCtl",
        "colab_type": "code",
        "outputId": "5d4bffa1-4798-45ef-ebb5-8862dc386432",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        }
      },
      "source": [
        "print (\"Rows     : \" ,cov_df.shape[0])\n",
        "print (\"Columns  : \" ,cov_df.shape[1])"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Rows     :  581012\n",
            "Columns  :  55\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nCjmIZE4K8iW",
        "colab_type": "code",
        "outputId": "628c897b-57f2-4d18-967a-b4b157f525f4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 163
        }
      },
      "source": [
        "cov_df.target.value_counts()"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2    283301\n",
              "1    211840\n",
              "3     35754\n",
              "7     20510\n",
              "6     17367\n",
              "5      9493\n",
              "4      2747\n",
              "Name: target, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9G_kjG8QSd4Y",
        "colab_type": "code",
        "outputId": "dfd87004-1c0f-44ea-b7f4-8f2d33a0e956",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "cov_df.dtypes"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Elevation                             object\n",
              "Aspect                                object\n",
              "Slope                                 object\n",
              "Horizontal_Distance_To_Hydrology      object\n",
              "Vertical_Distance_To_Hydrology        object\n",
              "Horizontal_Distance_To_Roadways       object\n",
              "Hillshade_9am                         object\n",
              "Hillshade_Noon                        object\n",
              "Hillshade_3pm                         object\n",
              "Horizontal_Distance_To_Fire_Points    object\n",
              "Wilderness_Area1                      object\n",
              "Wilderness_Area2                      object\n",
              "Wilderness_Area3                      object\n",
              "Wilderness_Area4                      object\n",
              "Soil_Type1                            object\n",
              "Soil_Type2                            object\n",
              "Soil_Type3                            object\n",
              "Soil_Type4                            object\n",
              "Soil_Type5                            object\n",
              "Soil_Type6                            object\n",
              "Soil_Type7                            object\n",
              "Soil_Type8                            object\n",
              "Soil_Type9                            object\n",
              "Soil_Type10                           object\n",
              "Soil_Type11                           object\n",
              "Soil_Type12                           object\n",
              "Soil_Type13                           object\n",
              "Soil_Type14                           object\n",
              "Soil_Type15                           object\n",
              "Soil_Type16                           object\n",
              "Soil_Type17                           object\n",
              "Soil_Type18                           object\n",
              "Soil_Type19                           object\n",
              "Soil_Type20                           object\n",
              "Soil_Type21                           object\n",
              "Soil_Type22                           object\n",
              "Soil_Type23                           object\n",
              "Soil_Type24                           object\n",
              "Soil_Type25                           object\n",
              "Soil_Type26                           object\n",
              "Soil_Type27                           object\n",
              "Soil_Type28                           object\n",
              "Soil_Type29                           object\n",
              "Soil_Type30                           object\n",
              "Soil_Type31                           object\n",
              "Soil_Type32                           object\n",
              "Soil_Type33                           object\n",
              "Soil_Type34                           object\n",
              "Soil_Type35                           object\n",
              "Soil_Type36                           object\n",
              "Soil_Type37                           object\n",
              "Soil_Type38                           object\n",
              "Soil_Type39                           object\n",
              "Soil_Type40                           object\n",
              "target                                object\n",
              "dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QjdZdC0_aY4r",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "for cols in cov_df.columns:\n",
        "  cov_df[cols] = pd.to_numeric(cov_df[cols], downcast='float')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FCUjxIRvgL6a",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "cov_df['target'] = cov_df['target'].astype(np.int32)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9bAi7NZYBBiP",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "cov_df['target'] = cov_df['target']-1"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "nUYeVzKa9q9V",
        "colab": {}
      },
      "source": [
        "cov_df_X = cov_df.copy()\n",
        "cov_df_y =  cov_df_X.pop('target')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZsKHF1ejlkKF",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(cov_df_X, cov_df_y, train_size=0.95, test_size=0.05)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m58fCw6guyOj",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X_train_gdf = cudf.DataFrame.from_pandas(X_train)\n",
        "X_test_gdf = cudf.DataFrame.from_pandas(X_test)\n",
        "y_train_gdf = cudf.DataFrame.from_pandas(pd.DataFrame(y_train))\n",
        "y_test_gdf = cudf.DataFrame.from_pandas(pd.DataFrame(y_test))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E_oNY9Dzu9hc",
        "colab_type": "code",
        "outputId": "82a46878-d0dc-4de2-d8b9-b2280385064d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 308
        }
      },
      "source": [
        "!nvidia-smi"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Sat Jan 11 18:49:23 2020       \n",
            "+-----------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 440.44       Driver Version: 418.67       CUDA Version: 10.1     |\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",
            "|===============================+======================+======================|\n",
            "|   0  Tesla P100-PCIE...  Off  | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   33C    P0    31W / 250W |    605MiB / 16280MiB |      0%      Default |\n",
            "+-------------------------------+----------------------+----------------------+\n",
            "                                                                               \n",
            "+-----------------------------------------------------------------------------+\n",
            "| Processes:                                                       GPU Memory |\n",
            "|  GPU       PID   Type   Process name                             Usage      |\n",
            "|=============================================================================|\n",
            "+-----------------------------------------------------------------------------+\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vSGbIXcYesbO",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from cuml import RandomForestClassifier as cuRF\n",
        "import time"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9Rzbir13esot",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "cu_rf_params = {\n",
        "    'n_estimators': 250,\n",
        "    'max_depth': 8,\n",
        "    'n_streams': 1,\n",
        "    'split_algo': 0, \n",
        "    'seed': 1010\n",
        "}\n",
        "cu_rf = cuRF(**cu_rf_params)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bf38NATmfrvd",
        "colab_type": "code",
        "outputId": "aa85ba58-3a02-49a5-cc27-5754f59715ed",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "start_time = time.time()\n",
        "cu_rf.fit(X_train_gdf, y_train_gdf)\n",
        "print(\"GPU Training Time with GPU dataframe: %s seconds\" % (str(time.time() - start_time)))"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "GPU Training Time with GPU dataframe: 1.6164190769195557 seconds\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vNN_y2Kbhc8u",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "predvalue = cu_rf.predict(X_test_gdf)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7qMDiL1bp3A4",
        "colab_type": "code",
        "outputId": "de7eabb2-a81d-4f57-e17b-fdb72a1e301b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "predvalue[0]"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZY4LUyby02S_",
        "colab_type": "code",
        "outputId": "5eb23676-c43b-4c69-9ee0-df85a6cc85e9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "#cu_rf.score(X_test_gdf, y_test_gdf) --> use GPU frame if using 0.11 version of rapids\n",
        "cu_rf.score(X_test_gdf, y_test.to_numpy())"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.692162036895752"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qNKbv0nN6yrf",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "pred_out=predvalue.copy_to_host()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9eNF98go5M4w",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        " from sklearn.metrics import confusion_matrix"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nt80V2t68Ysg",
        "colab_type": "code",
        "outputId": "9901079f-59c0-4480-fb99-a9a2f777a0d3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145
        }
      },
      "source": [
        "confusion_matrix(y_test, pred_out)"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 6371,  4252,     0,     0,     0,     0,     0],\n",
              "       [ 2172, 11887,     0,     0,     0,     0,     0],\n",
              "       [    0,  1794,     0,     0,     0,     0,     0],\n",
              "       [    0,   129,     0,     0,     0,     0,     0],\n",
              "       [    0,   499,     0,     0,     0,     0,     0],\n",
              "       [    0,   901,     0,     0,     0,     0,     0],\n",
              "       [  170,   876,     0,     0,     0,     0,     0]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    }
  ]
}