Random_Forest_Classification.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Random Forest Classification.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "NHsxjXJcSSr7",
        "colab_type": "code",
        "outputId": "221de69e-618e-419a-fd98-4ac1311b8b51",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 125
        }
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive/')"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&response_type=code&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly\n",
            "\n",
            "Enter your authorization code:\n",
            "··········\n",
            "Mounted at /content/drive/\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "suUgE_EWSkJH",
        "colab_type": "code",
        "outputId": "21c4e562-26a7-484a-a9e5-6c1aedd87451",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "cd \"/content/drive/My Drive/ML\""
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/drive/My Drive/ML\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5XwotHKhSrOS",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from pathlib import Path\n",
        "\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.impute import SimpleImputer\n",
        "from sklearn.compose import ColumnTransformer\n",
        "\n",
        "from sklearn.pipeline import Pipeline\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import f1_score\n",
        "from sklearn.utils import resample\n",
        "from sklearn.model_selection import cross_val_score\n",
        "\n",
        "RANDOM_SEED = 8    # Set a random seed for reproducibility!\n",
        "pd.set_option(\"display.max_columns\", 100)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tH0_nHHE7o9v",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "DATA_PATH = Path.cwd().parent / \"data\" / \"final\" / \"public\"\n",
        "\n",
        "train_df = pd.read_csv(\n",
        "    DATA_PATH / \"/content/drive/My Drive/ML/train.csv\", \n",
        "    index_col=\"tripid\"\n",
        ")\n",
        "test_features_df = pd.read_csv(\n",
        "    DATA_PATH / \"/content/drive/My Drive/ML/test.csv\", \n",
        "    index_col=\"tripid\"\n",
        ")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S6Swy-Kg7N11",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "labels_df = train_df[['label']]\n",
        "features_df = train_df.drop('label', axis=1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_R073VaVAVqE",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "labels_df=labels_df.replace(\"correct\", 1)\n",
        "labels_df=labels_df.replace(\"incorrect\", 0)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LQd3tdDxWVnn",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "numeric_cols = features_df.columns[features_df.dtypes != \"object\"].values"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1E7stb9CWjjI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "numeric_preprocessing_steps = Pipeline(steps=[\n",
        "    ('imputer', SimpleImputer(strategy='median')),\n",
        "    ('standard_scaler', StandardScaler())])\n",
        "\n",
        "preprocessor = ColumnTransformer(\n",
        "    transformers = [\n",
        "        (\"numeric\", numeric_preprocessing_steps, numeric_cols)\n",
        "    ],\n",
        "    remainder = \"drop\"\n",
        ")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T5VhODrPWl_W",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "estimator = RandomForestClassifier(n_estimators=10000)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bIFFdgdeWolO",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "full_pipeline = Pipeline([\n",
        "    (\"preprocessor\", preprocessor),\n",
        "    (\"estimators\", estimator),\n",
        "])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cDZr5E8HWw_9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X_train, X_eval, y_train, y_eval = train_test_split(\n",
        "    features_df,\n",
        "    labels_df,\n",
        "    test_size=0.33,\n",
        "    shuffle=True,\n",
        "    stratify=labels_df,\n",
        "    random_state=RANDOM_SEED\n",
        ")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0kyaGehvXSI_",
        "colab_type": "code",
        "outputId": "d2bff960-863d-4efa-eb38-3c6c2df473b4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        }
      },
      "source": [
        "%%time\n",
        "\n",
        "# Train model\n",
        "full_pipeline.fit(X_train, y_train.values.ravel())\n",
        "\n",
        "None   # don't print out the whole pipeline representation"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "CPU times: user 6min, sys: 505 ms, total: 6min\n",
            "Wall time: 6min 1s\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bkS3XIz92EAY",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Predict on evaluation set\n",
        "preds = full_pipeline.predict(X_eval)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sbq8l-t-X6Gy",
        "colab_type": "code",
        "outputId": "b017e36a-4f10-4f74-8037-0cd7ba248da0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 245
        }
      },
      "source": [
        "y_preds = pd.DataFrame(\n",
        "    {\n",
        "        \"label\": preds,\n",
        "    },\n",
        "    index = y_eval.index\n",
        ")\n",
        "print(\"y_preds.shape:\", y_preds.shape)\n",
        "y_preds.head()"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "y_preds.shape: (5669, 1)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
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              "\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>label</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>tripid</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>200328361</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>191127057</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>200136070</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>195614910</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>191488838</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           label\n",
              "tripid          \n",
              "200328361      1\n",
              "191127057      1\n",
              "200136070      1\n",
              "195614910      1\n",
              "191488838      1"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JZoJhQ_uYHG0",
        "colab_type": "code",
        "outputId": "6ac2ba08-d09e-4239-8d77-1532bfb44311",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "f1_score(y_eval, y_preds, average='macro')"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7967226047045324"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XzW18o9lhS_x",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "outputId": "381abaae-0c78-420b-d5d5-98ffa528891b"
      },
      "source": [
        "# retrain on full dataset\n",
        "%%time \n",
        "\n",
        "full_pipeline.fit(features_df, labels_df.values.ravel())\n",
        "\n",
        "None   # So we don't print out the whole pipeline representation"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "CPU times: user 9min 39s, sys: 427 ms, total: 9min 39s\n",
            "Wall time: 9min 40s\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NSrik_r9hsxD",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "preds1 = full_pipeline.predict(test_features_df)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1cZIp7k0h2FW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "submission_df = pd.read_csv(DATA_PATH / \"/content/drive/My Drive/ML/sample_submission.csv\", \n",
        "                            index_col=\"tripid\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UvPw8rxoiCQl",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Make sure we have the rows in the same order\n",
        "np.testing.assert_array_equal(test_features_df.index.values, \n",
        "                              submission_df.index.values)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TEwjq-7kpJeL",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Save predictions to submission data frame\n",
        "submission_df[\"prediction\"] = preds1"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BLdRLhxmiRxv",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "submission_df.to_csv('my_submission.csv', index=True)"
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}