{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Miniaturowy przykład notatnika Jupyter do projektu ZUM z użyciem języka Python"
      ],
      "metadata": {
        "id": "uWO8GBphz8r8"
      }
    },
    {
      "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": "iWEro61OMReE"
      }
    },
    {
      "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": "S0bAPzgEQLEP"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TuCXV5IaxkVi",
        "outputId": "0d4aa84c-3edf-4c8d-9942-744f9af40ec4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2023-03-29 08:32:44--  http://elektron.elka.pw.edu.pl/~pcichosz/zum/projekt/census-income-train.csv\n",
            "Resolving elektron.elka.pw.edu.pl (elektron.elka.pw.edu.pl)... 194.29.160.103\n",
            "Connecting to elektron.elka.pw.edu.pl (elektron.elka.pw.edu.pl)|194.29.160.103|:80... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 3518606 (3.4M) [text/csv]\n",
            "Saving to: ‘census-income-train.csv’\n",
            "\n",
            "census-income-train 100%[===================>]   3.36M   775KB/s    in 5.1s    \n",
            "\n",
            "2023-03-29 08:32:49 (669 KB/s) - ‘census-income-train.csv’ saved [3518606/3518606]\n",
            "\n",
            "--2023-03-29 08:32:50--  http://elektron.elka.pw.edu.pl/~pcichosz/zum/projekt/census-income-test.csv\n",
            "Resolving elektron.elka.pw.edu.pl (elektron.elka.pw.edu.pl)... 194.29.160.103\n",
            "Connecting to elektron.elka.pw.edu.pl (elektron.elka.pw.edu.pl)|194.29.160.103|:80... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 1759072 (1.7M) [text/csv]\n",
            "Saving to: ‘census-income-test.csv’\n",
            "\n",
            "census-income-test. 100%[===================>]   1.68M   503KB/s    in 3.4s    \n",
            "\n",
            "2023-03-29 08:32:53 (503 KB/s) - ‘census-income-test.csv’ saved [1759072/1759072]\n",
            "\n"
          ]
        }
      ],
      "source": [
        "!wget -nc elektron.elka.pw.edu.pl/~pcichosz/zum/projekt/census-income-train.csv\n",
        "!wget -nc elektron.elka.pw.edu.pl/~pcichosz/zum/projekt/census-income-test.csv"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Pobieranie dodatkowych plików źródłowych:**"
      ],
      "metadata": {
        "id": "_bDx6nfkQTTs"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!wget -nc elektron.elka.pw.edu.pl/~pcichosz/zum/projekt/plot_roc.py"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pnrPFkCJPdGg",
        "outputId": "9bccf77b-0b29-42ed-e391-4c50550442f2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2023-03-29 08:32:53--  http://elektron.elka.pw.edu.pl/~pcichosz/zum/projekt/plot_roc.py\n",
            "Resolving elektron.elka.pw.edu.pl (elektron.elka.pw.edu.pl)... 194.29.160.103\n",
            "Connecting to elektron.elka.pw.edu.pl (elektron.elka.pw.edu.pl)|194.29.160.103|:80... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 429 [text/x-python]\n",
            "Saving to: ‘plot_roc.py’\n",
            "\n",
            "plot_roc.py         100%[===================>]     429  --.-KB/s    in 0s      \n",
            "\n",
            "2023-03-29 08:32:54 (62.7 MB/s) - ‘plot_roc.py’ saved [429/429]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Eksperymenty..."
      ],
      "metadata": {
        "id": "J01C4VuMgJQM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd"
      ],
      "metadata": {
        "id": "013yQzUd9Peu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cin_train = pd.read_csv('census-income-train.csv')\n",
        "cin_test = pd.read_csv('census-income-test.csv')"
      ],
      "metadata": {
        "id": "Hn1E9Ovh9T4B"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.preprocessing import OneHotEncoder\n",
        "from sklearn.compose import make_column_transformer, make_column_selector\n",
        "from sklearn.pipeline import make_pipeline"
      ],
      "metadata": {
        "id": "qI-s_LkX7aSM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "rf = make_pipeline(make_column_transformer((OneHotEncoder(handle_unknown='ignore'),\n",
        "                                            make_column_selector(dtype_exclude='number')),\n",
        "                                           remainder = 'passthrough'),\n",
        "                   RandomForestClassifier())\n",
        "rf.fit(cin_train.drop('income', axis=1), cin_train['income'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 191
        },
        "outputId": "88fd1885-a97e-4bbf-e5be-3b2a63632be1",
        "id": "udd2G8bzc143"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Pipeline(steps=[('columntransformer',\n",
              "                 ColumnTransformer(remainder='passthrough',\n",
              "                                   transformers=[('onehotencoder',\n",
              "                                                  OneHotEncoder(handle_unknown='ignore'),\n",
              "                                                  <sklearn.compose._column_transformer.make_column_selector object at 0x7f0dd5103d90>)])),\n",
              "                ('randomforestclassifier', RandomForestClassifier())])"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;columntransformer&#x27;,\n",
              "                 ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n",
              "                                   transformers=[(&#x27;onehotencoder&#x27;,\n",
              "                                                  OneHotEncoder(handle_unknown=&#x27;ignore&#x27;),\n",
              "                                                  &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7f0dd5103d90&gt;)])),\n",
              "                (&#x27;randomforestclassifier&#x27;, RandomForestClassifier())])</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 sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;columntransformer&#x27;,\n",
              "                 ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n",
              "                                   transformers=[(&#x27;onehotencoder&#x27;,\n",
              "                                                  OneHotEncoder(handle_unknown=&#x27;ignore&#x27;),\n",
              "                                                  &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7f0dd5103d90&gt;)])),\n",
              "                (&#x27;randomforestclassifier&#x27;, RandomForestClassifier())])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">columntransformer: ColumnTransformer</label><div class=\"sk-toggleable__content\"><pre>ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n",
              "                  transformers=[(&#x27;onehotencoder&#x27;,\n",
              "                                 OneHotEncoder(handle_unknown=&#x27;ignore&#x27;),\n",
              "                                 &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7f0dd5103d90&gt;)])</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">onehotencoder</label><div class=\"sk-toggleable__content\"><pre>&lt;sklearn.compose._column_transformer.make_column_selector object at 0x7f0dd5103d90&gt;</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OneHotEncoder</label><div class=\"sk-toggleable__content\"><pre>OneHotEncoder(handle_unknown=&#x27;ignore&#x27;)</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">remainder</label><div class=\"sk-toggleable__content\"><pre>[&#x27;age&#x27;, &#x27;fnlwgt&#x27;, &#x27;education-num&#x27;, &#x27;capital-gain&#x27;, &#x27;capital-loss&#x27;, &#x27;hours-per-week&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">passthrough</label><div class=\"sk-toggleable__content\"><pre>passthrough</pre></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier()</pre></div></div></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cin_prob = rf.predict_proba(cin_test)[:,1]"
      ],
      "metadata": {
        "id": "qLiKcgkXI3XO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from plot_roc import plot_roc"
      ],
      "metadata": {
        "id": "cusUz2N5Q320"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plot_roc(cin_test['income'].to_numpy(), cin_prob)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "id": "_4IURdNwJAeU",
        "outputId": "ee6cbe57-aad1-46c0-ed4a-f69132f154c6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}