ML_Project.ipynb

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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "ML_Project.ipynb",
      "version": "0.3.2",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "metadata": {
        "id": "ghLD8P3MjARb",
        "colab_type": "code",
        "outputId": "acc032ee-15e6-4c98-9a83-304cc0bfbf42",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        }
      },
      "cell_type": "code",
      "source": [
        "!pip install kaggle\n",
        "\n",
        "# to access kaggle datasets"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: kaggle in /usr/local/lib/python3.6/dist-packages (1.5.3)\n",
            "Requirement already satisfied: urllib3<1.25,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from kaggle) (1.22)\n",
            "Requirement already satisfied: six>=1.10 in /usr/local/lib/python3.6/dist-packages (from kaggle) (1.11.0)\n",
            "Requirement already satisfied: certifi in /usr/local/lib/python3.6/dist-packages (from kaggle) (2019.3.9)\n",
            "Requirement already satisfied: python-dateutil in /usr/local/lib/python3.6/dist-packages (from kaggle) (2.5.3)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.6/dist-packages (from kaggle) (2.18.4)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.6/dist-packages (from kaggle) (4.28.1)\n",
            "Requirement already satisfied: python-slugify in /usr/local/lib/python3.6/dist-packages (from kaggle) (3.0.2)\n",
            "Requirement already satisfied: idna<2.7,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests->kaggle) (2.6)\n",
            "Requirement already satisfied: chardet<3.1.0,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests->kaggle) (3.0.4)\n",
            "Requirement already satisfied: text-unidecode==1.2 in /usr/local/lib/python3.6/dist-packages (from python-slugify->kaggle) (1.2)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "myEzfOSujRoz",
        "colab_type": "code",
        "outputId": "5a1db6df-4f19-45c2-8330-e9aefd7443b9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 327
        }
      },
      "cell_type": "code",
      "source": [
        "!pip install numpy==1.15.0\n",
        "\n",
        "\n",
        "#math ops"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting numpy==1.15.0\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/88/29/f4c845648ed23264e986cdc5fbab5f8eace1be5e62144ef69ccc7189461d/numpy-1.15.0-cp36-cp36m-manylinux1_x86_64.whl (13.9MB)\n",
            "\u001b[K    100% |████████████████████████████████| 13.9MB 2.4MB/s \n",
            "\u001b[31mfeaturetools 0.4.1 has requirement pandas>=0.23.0, but you'll have pandas 0.22.0 which is incompatible.\u001b[0m\n",
            "\u001b[31mdatascience 0.10.6 has requirement folium==0.2.1, but you'll have folium 0.8.3 which is incompatible.\u001b[0m\n",
            "\u001b[31malbumentations 0.1.12 has requirement imgaug<0.2.7,>=0.2.5, but you'll have imgaug 0.2.8 which is incompatible.\u001b[0m\n",
            "\u001b[?25hInstalling collected packages: numpy\n",
            "  Found existing installation: numpy 1.14.6\n",
            "    Uninstalling numpy-1.14.6:\n",
            "      Successfully uninstalled numpy-1.14.6\n",
            "Successfully installed numpy-1.15.0\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "numpy"
                ]
              }
            }
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "y5QMjf8mjdAG",
        "colab_type": "code",
        "outputId": "7445704a-da99-4e01-d1a5-1ea4f6912699",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        }
      },
      "cell_type": "code",
      "source": [
        "!pip install catboost\n",
        "\n",
        "# machine learning"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting catboost\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/3e/62/b442e8d747e8a34ac8a981f7a4ff717c1f887aedb42c3f670660bda41af5/catboost-0.13.1-cp36-none-manylinux1_x86_64.whl (60.1MB)\n",
            "\u001b[K    100% |████████████████████████████████| 60.1MB 504kB/s \n",
            "\u001b[?25hRequirement already satisfied: enum34 in /usr/local/lib/python3.6/dist-packages (from catboost) (1.1.6)\n",
            "Requirement already satisfied: numpy>=1.11.1 in /usr/local/lib/python3.6/dist-packages (from catboost) (1.15.0)\n",
            "Requirement already satisfied: pandas>=0.19.1 in /usr/local/lib/python3.6/dist-packages (from catboost) (0.22.0)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from catboost) (1.11.0)\n",
            "Requirement already satisfied: python-dateutil>=2 in /usr/local/lib/python3.6/dist-packages (from pandas>=0.19.1->catboost) (2.5.3)\n",
            "Requirement already satisfied: pytz>=2011k in /usr/local/lib/python3.6/dist-packages (from pandas>=0.19.1->catboost) (2018.9)\n",
            "Installing collected packages: catboost\n",
            "Successfully installed catboost-0.13.1\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "vKIqBWivjqEc",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "#data preprocessing\n",
        "\n",
        "import pandas as pd\n",
        "\n",
        "# math ops\n",
        "\n",
        "import numpy as np\n",
        "\n",
        "#machine learning\n",
        "\n",
        "from catboost import CatBoostRegressor, Pool\n",
        "\n",
        "# data scaling\n",
        "\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "\n",
        "# hyper parameter optimisation\n",
        "\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "\n",
        "# supprt vector machine model\n",
        "from sklearn.svm import NuSVR, SVR\n",
        "\n",
        "#kernel ridge model\n",
        "from sklearn.kernel_ridge import KernelRidge\n",
        "\n",
        "# data visualization\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "44o86faCkwZM",
        "colab_type": "code",
        "outputId": "f6dbe4c3-6506-4949-cd40-10e9184de161",
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
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              "ok": true,
              "headers": [
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              "status": 200,
              "status_text": "OK"
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 93
        }
      },
      "cell_type": "code",
      "source": [
        "# Step 2 Importing data from kaggle\n",
        "# collect kaggle data\n",
        "\n",
        "#colab file access feature\n",
        "from google.colab import files\n",
        "\n",
        "# retrieve uploaded files\n",
        "uploaded = files.upload()\n",
        "\n",
        "# then move kaggle.json into the folder where the api expects to fin\n",
        "!mkdir -p ~/.kaggle/ && mv kaggle.json ~/.kaggle/ && chmod 600 ~/kaggle/kaggle.json"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-c82bd06f-6340-4e30-8d4d-cc664f275d1e\" name=\"files[]\" multiple disabled />\n",
              "     <output id=\"result-c82bd06f-6340-4e30-8d4d-cc664f275d1e\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Saving kaggle.json to kaggle.json\n",
            "chmod: cannot access '/root/kaggle/kaggle.json': No such file or directory\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "YOYsVjSzmD1H",
        "colab_type": "code",
        "outputId": "22a7992f-bda8-4cbe-f079-0d8e6144809d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 456
        }
      },
      "cell_type": "code",
      "source": [
        "# list of kaggle comps\n",
        "!kaggle competitions list"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Warning: Your Kaggle API key is readable by other users on this system! To fix this, you can run 'chmod 600 /root/.kaggle/kaggle.json'\n",
            "ref                                                deadline             category            reward  teamCount  userHasEntered  \n",
            "-------------------------------------------------  -------------------  ---------------  ---------  ---------  --------------  \n",
            "digit-recognizer                                   2030-01-01 00:00:00  Getting Started  Knowledge       2645           False  \n",
            "titanic                                            2030-01-01 00:00:00  Getting Started  Knowledge      10989           False  \n",
            "house-prices-advanced-regression-techniques        2030-01-01 00:00:00  Getting Started  Knowledge       4407           False  \n",
            "imagenet-object-localization-challenge             2029-12-31 07:00:00  Research         Knowledge         34           False  \n",
            "competitive-data-science-predict-future-sales      2019-12-31 23:59:00  Playground           Kudos       2679           False  \n",
            "two-sigma-financial-news                           2019-07-15 23:59:00  Featured          $100,000       2927           False  \n",
            "aerial-cactus-identification                       2019-07-08 23:59:00  Playground       Knowledge        256           False  \n",
            "jigsaw-unintended-bias-in-toxicity-classification  2019-06-26 23:59:00  Featured           $65,000        464           False  \n",
            "inaturalist-2019-fgvc6                             2019-06-10 23:59:00  Research             Kudos         29           False  \n",
            "freesound-audio-tagging-2019                       2019-06-10 11:59:00  Research            $5,000         73           False  \n",
            "iwildcam-2019-fgvc6                                2019-06-07 23:59:00  Playground           Kudos         65           False  \n",
            "imet-2019-fgvc6                                    2019-06-04 23:59:00  Research             Kudos        145           False  \n",
            "LANL-Earthquake-Prediction                         2019-06-03 23:59:00  Research           $50,000       2349            True  \n",
            "tmdb-box-office-prediction                         2019-05-30 23:59:00  Playground       Knowledge        610           False  \n",
            "dont-overfit-ii                                    2019-05-07 23:59:00  Playground            Swag       1501           False  \n",
            "ciphertext-challenge-ii                            2019-04-25 23:59:00  Playground            Swag         29           False  \n",
            "data-science-for-good-careervillage                2019-04-23 23:59:00  Analytics          $15,000          0           False  \n",
            "gendered-pronoun-resolution                        2019-04-22 23:59:00  Research           $25,000        643           False  \n",
            "petfinder-adoption-prediction                      2019-04-18 23:59:00  Featured           $25,000       2010           False  \n",
            "career-con-2019                                    2019-04-11 23:59:00  Recruitment           Swag       1323           False  \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "4HhkhzCBmOPq",
        "colab_type": "code",
        "outputId": "c97c1ec2-41a1-4dca-bb94-0c536a6c0b15",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 219
        }
      },
      "cell_type": "code",
      "source": [
        "# download earthquake data\n",
        "!kaggle competitions download -c LANL-Earthquake-Prediction"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Warning: Your Kaggle API key is readable by other users on this system! To fix this, you can run 'chmod 600 /root/.kaggle/kaggle.json'\n",
            "Downloading sample_submission.csv to /content\n",
            "\r  0% 0.00/33.3k [00:00<?, ?B/s]\n",
            "100% 33.3k/33.3k [00:00<00:00, 13.4MB/s]\n",
            "Downloading test.zip to /content\n",
            " 94% 228M/242M [00:01<00:00, 157MB/s]\n",
            "100% 242M/242M [00:01<00:00, 177MB/s]\n",
            "Downloading train.csv.zip to /content\n",
            "100% 2.02G/2.03G [00:42<00:00, 19.9MB/s]\n",
            "100% 2.03G/2.03G [00:42<00:00, 51.4MB/s]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "bD1Hy8sumkLY",
        "colab_type": "code",
        "outputId": "f9ca57e2-0780-46d5-dd40-3f88a8da8c13",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 72
        }
      },
      "cell_type": "code",
      "source": [
        "#unzip training data for usage\n",
        "\n",
        "!unzip train.csv.zip \n",
        "!ls"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Archive:  train.csv.zip\n",
            "  inflating: train.csv               \n",
            "sample_data  sample_submission.csv  test.zip  train.csv  train.csv.zip\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "j7h5YQDjnCGB",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Extract training data into a dataframe\n",
        "train = pd.read_csv('train.csv', nrows=6000000, dtype={'acoustic_data' : np.int16, 'time_to_failure' : np.float64})"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "DhFyOPLFndpM",
        "colab_type": "code",
        "outputId": "0dbd1f23-692c-4608-a2b7-af6c33ea894d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        }
      },
      "cell_type": "code",
      "source": [
        "train.head(10)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>acoustic_data</th>\n",
              "      <th>time_to_failure</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>12</td>\n",
              "      <td>1.4691</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>6</td>\n",
              "      <td>1.4691</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>8</td>\n",
              "      <td>1.4691</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>5</td>\n",
              "      <td>1.4691</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>8</td>\n",
              "      <td>1.4691</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>8</td>\n",
              "      <td>1.4691</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>9</td>\n",
              "      <td>1.4691</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>7</td>\n",
              "      <td>1.4691</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>-5</td>\n",
              "      <td>1.4691</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>3</td>\n",
              "      <td>1.4691</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   acoustic_data  time_to_failure\n",
              "0             12           1.4691\n",
              "1              6           1.4691\n",
              "2              8           1.4691\n",
              "3              5           1.4691\n",
              "4              8           1.4691\n",
              "5              8           1.4691\n",
              "6              9           1.4691\n",
              "7              7           1.4691\n",
              "8             -5           1.4691\n",
              "9              3           1.4691"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "metadata": {
        "id": "ULgQEsc2nwd8",
        "colab_type": "code",
        "outputId": "2c8c8e3f-7b8f-4a32-c8fb-a2d4155e7b2e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 498
        }
      },
      "cell_type": "code",
      "source": [
        "# visulaize the sample dataset\n",
        "\n",
        "train_ad_sample_df = train['acoustic_data'].values[::100]\n",
        "train_ttf_sample_df = train['time_to_failure'].values[::100]\n",
        "\n",
        "#plotting based on both features\n",
        "\n",
        "def plot_acc_ttf_data(train_ad_sample_df, train_ttf_sample_df, title=\"Acoustic data and time to failure: 1% sampled data\"):\n",
        "  fig, ax1 = plt.subplots(figsize = (12, 8))\n",
        "  plt.title(title)\n",
        "  \n",
        "  plt.plot(train_ad_sample_df,color = 'r')\n",
        "  ax1.set_ylabel('acoustic_data', color = 'r')\n",
        "  plt.legend(['acoustic_data'], loc = (0.01, 0.95))\n",
        "  ax2 = ax1.twinx()\n",
        "  \n",
        "  \n",
        "  plt.plot(train_ttf_sample_df,color = 'b')\n",
        "  ax2.set_ylabel('time_to_failure', color = 'b')\n",
        "  plt.legend(['time_to_failure'], loc = (0.01, 0.9))\n",
        "  \n",
        "  plt.grid(True)\n",
        "  \n",
        "plot_acc_ttf_data(train_ad_sample_df, train_ttf_sample_df)\n",
        "del train_ad_sample_df\n",
        "del train_ttf_sample_df"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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wD7dv38rVNoaEBGPp0lXQ6/U4evQQli1bjaVLV+HYscMIDg7O1bqIiIiI6PkU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\nxLBRTAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 864x576 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "g7SbTRcBr3l5",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# feature engineering\n"
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}