{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "ir",
      "display_name": "R"
    },
    "language_info": {
      "name": "R"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Miniaturowy przykład notatnika Jupyter do projektu ZUM z użyciem języka R"
      ],
      "metadata": {
        "id": "ljOMXw7Gzcoc"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Jest to przykład demonstrujący **wyłącznie** pobieranie plików z danymi oraz dodatkowych plików źródłowych z kodem, nie należy go traktować jako wzorca realizacji i dokumentowania projektu."
      ],
      "metadata": {
        "id": "PSF4gYhKNI0c"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Pobieranie plików z danymi (oryginalne źródło [UCI](https://archive.ics.uci.edu/ml/datasets/census+income), tutaj używana wersja przetworzona do formatu CSV):**"
      ],
      "metadata": {
        "id": "XOtM7KUWfaig"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "37k9piuyyZdj"
      },
      "outputs": [],
      "source": [
        "download.file(\"elektron.elka.pw.edu.pl/~pcichosz/zum/projekt/census-income-train.csv\",\n",
        "              \"census_income_train.csv\")\n",
        "download.file(\"elektron.elka.pw.edu.pl/~pcichosz/zum/projekt/census-income-test.csv\",\n",
        "              \"census_income_test.csv\")"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Pobieranie dodatkowych plików źródłowych:**"
      ],
      "metadata": {
        "id": "E5zQvpcFffs0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "download.file(\"elektron.elka.pw.edu.pl/~pcichosz/zum/projekt/plot_roc.R\",\n",
        "              \"plot_roc.R\")"
      ],
      "metadata": {
        "id": "y41Zy13gfYwc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Eksperymenty..."
      ],
      "metadata": {
        "id": "QGG4koUigBHm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "cin.train <- read.csv(\"census_income_train.csv\", stringsAsFactors=TRUE)\n",
        "cin.test <- read.csv(\"census_income_test.csv\", stringsAsFactors=TRUE)"
      ],
      "metadata": {
        "id": "VtqTLakuXJxr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cin_top_countries <- names(which(prop.table(table(cin.train$native.country))>=0.01))\n",
        "cin.train$native.country <- as.factor(ifelse(cin.train$native.country %in% cin_top_countries,\n",
        "                                             as.character(cin.train$native.country), \"OTHER\"))\n",
        "cin.test$native.country <- as.factor(ifelse(cin.test$native.country %in% cin_top_countries,\n",
        "                                             as.character(cin.test$native.country), \"OTHER\"))"
      ],
      "metadata": {
        "id": "LjrW9bZsbT1V"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "if (!\"randomForest\" %in% .packages(all.available=TRUE))\n",
        "  install.packages(\"randomForest\")\n",
        "library(randomForest)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "P_DWI3kZXT3S",
        "outputId": "86e5eba1-cf1a-4857-9902-c58ccbfe038a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "randomForest 4.7-1.1\n",
            "\n",
            "Type rfNews() to see new features/changes/bug fixes.\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cin.rf = randomForest(income~., cin.train)"
      ],
      "metadata": {
        "id": "FgYLIkLtYVvv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cin.prob = predict(cin.rf, cin.test, type=\"prob\")[,2]"
      ],
      "metadata": {
        "id": "lF5YabkrZcrm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "source(\"plot_roc.R\")"
      ],
      "metadata": {
        "id": "cSFwIc5JdE9o"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plot.roc(cin.test$income, cin.prob)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 437
        },
        "id": "CbQbBBs0ec3M",
        "outputId": "e74cc15d-1e84-4b16-f71a-9b163c34a9c1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "plot without title"
            ],
            "image/png": 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uizz3hmDgAsjmIHWFBRkZYu1aef6osv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\nXV1tkVUtWbKkrKzs+eeft8ja0Fzvv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}