stepik_ml_contest_features.ipynb

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    "colab": {
      "name": "stepik_ml_contest_features.ipynb",
      "provenance": [],
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      "authorship_tag": "ABX9TyM6LIo2+TxooPPmmcbKLKt7",
      "include_colab_link": true
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      "name": "python3",
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Jiablero/notebooks/blob/master/stepik_ml_contest_features.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fhuMRlk9bLQs",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bfFifymvbWp6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# results_example = pd.read_csv('https://stepik.org/media/attachments/course/4852/submission_example.csv')\n",
        "submissions = pd.read_csv('https://stepik.org/media/attachments/course/4852/submission_data_test.csv')\n",
        "events = pd.read_csv('https://stepik.org/media/attachments/course/4852/events_data_test.csv')\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YWKk2pXKQM_r",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# полные варианты таблиц из https://stepik.org/lesson/222124/step/3?unit=195045\n",
        "submissions_full = pd.read_csv('https://stepik.org/media/attachments/course/4852/submissions_data_train.zip')\n",
        "events_full = pd.read_csv('https://stepik.org/media/attachments/course/4852/event_data_train.zip')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WrdWXwNRRKpb",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# первоначальный вариант обработки данных\n",
        "def process_users_data(submissions, events):\n",
        "  score_by_user = events[events.action == 'passed'].groupby('user_id').agg({'action': 'count'}).rename({'action': 'score'}, axis = 1).reset_index()\n",
        "  submissions = pd.get_dummies(submissions)\n",
        "  attempts_by_user = submissions.groupby('user_id').agg({'submission_status_correct': 'sum', 'submission_status_wrong': 'sum'}) \\\n",
        "  .rename({'submission_status_correct': 'correct', 'submission_status_wrong': 'wrong'}, axis = 1).reset_index()\n",
        "  user_scores = score_by_user.merge(attempts_by_user, how='inner')\n",
        "  return user_scores"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "my0yaw99SQ44",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "user_scores = process_users_data(submissions, events)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4bdA9oBap7Cx",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "user_scores['passed'] = user_scores.score.map(lambda x: 1 if x >= 40 else 0)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B6Lsl4oRq_kK",
        "colab_type": "code",
        "outputId": "6d77c4c0-4c8d-4910-8a32-6d82dc93ba56",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        }
      },
      "source": [
        "user_scores"
      ],
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>user_id</th>\n",
              "      <th>score</th>\n",
              "      <th>correct</th>\n",
              "      <th>wrong</th>\n",
              "      <th>passed</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>12</td>\n",
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              "      <td>29</td>\n",
              "      <td>36</td>\n",
              "      <td>1</td>\n",
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              "      <th>4</th>\n",
              "      <td>35</td>\n",
              "      <td>30</td>\n",
              "      <td>7</td>\n",
              "      <td>35</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
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              "      <th>2781</th>\n",
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              "      <td>43</td>\n",
              "      <td>16</td>\n",
              "      <td>7</td>\n",
              "      <td>1</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "<p>2786 rows × 5 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      user_id  score  correct  wrong  passed\n",
              "0          12      9        1      0       0\n",
              "1          13     70       29     36       1\n",
              "2          15      1       10     30       0\n",
              "3          21     68       24    103       1\n",
              "4          35     30        7     35       0\n",
              "...       ...    ...      ...    ...     ...\n",
              "2781    26775    103       46    160       1\n",
              "2782    26780     43       16      7       1\n",
              "2783    26785      4        3      1       0\n",
              "2784    26796      4        2      3       0\n",
              "2785    26799      6        2      0       0\n",
              "\n",
              "[2786 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
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          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hcYWMbZnQxZj",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "user_scores_full = process_users_data(submissions_full, events_full)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WUpSo-QM3P51",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Рефакторинг фичаинжинеринга !!!1"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rJVAIvq-4SLT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# добываем статистику степов из submissions\n",
        "def process_steps_data(submissions):\n",
        "  steps_stat = pd.get_dummies(submissions).groupby('step_id') \\\n",
        "    .agg({'submission_status_correct': 'sum', 'submission_status_wrong': 'sum'}) \\\n",
        "    .reset_index()\n",
        "  \n",
        "  # доля правильных ответов в сумме отправленных\n",
        "  steps_stat['corrrate'] = steps_stat.submission_status_correct / (steps_stat.submission_status_correct + steps_stat.submission_status_wrong)\n",
        "\n",
        "  # популярность степов в % по отношению к самому популярному\n",
        "  steps_stat['popularity'] = steps_stat.submission_status_correct + steps_stat.submission_status_wrong\n",
        "  steps_stat.popularity = steps_stat.popularity / steps_stat.popularity.max()\n",
        "  \n",
        "  return steps_stat"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nfYlZqOaIt1R",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# добываем статистику степов из events\n",
        "def process_steps_data_ev(events):\n",
        "  steps_stat_ev = pd.get_dummies(events).groupby('step_id') \\\n",
        "  .agg({'action_discovered': 'sum', \n",
        "        'action_passed': 'sum', \n",
        "        'action_started_attempt': 'sum', \n",
        "        'action_viewed': 'sum'}).reset_index()\n",
        "\n",
        "  # доля пройденных степов по отношению к начатым\n",
        "  steps_stat_ev['corrrate_ev'] = steps_stat_ev.action_passed / steps_stat_ev.action_started_attempt\n",
        "  \n",
        "  # популярность степов в % по отношению к самому популярному\n",
        "  steps_stat_ev['popularity_ev'] = steps_stat_ev.action_viewed / steps_stat_ev.action_viewed.max()\n",
        "  \n",
        "  return steps_stat_ev"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vPcT-_VdPm1z",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "steps_stat = process_steps_data(submissions)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "waxOAM9OPy2K",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "steps_stat_ev = process_steps_data_ev(events)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0EBpOdVQP3g5",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "steps_stat_merged = steps_stat.merge(steps_stat_ev, how='inner')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z8GKBDKLBmNM",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        },
        "outputId": "029b4e6e-a781-4512-ebf3-78d8190a4f7d"
      },
      "source": [
        "# время от времени следует задавать себе вопрос: а не хуйню ли я делаю?\n",
        "steps_stat_merged[['corrrate', 'corrrate_ev', 'popularity', 'popularity_ev']].corr()"
      ],
      "execution_count": 227,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>corrrate</th>\n",
              "      <th>corrrate_ev</th>\n",
              "      <th>popularity</th>\n",
              "      <th>popularity_ev</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>corrrate</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.323288</td>\n",
              "      <td>0.169948</td>\n",
              "      <td>0.256477</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>corrrate_ev</th>\n",
              "      <td>0.323288</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.694321</td>\n",
              "      <td>0.712206</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>popularity</th>\n",
              "      <td>0.169948</td>\n",
              "      <td>0.694321</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.963518</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>popularity_ev</th>\n",
              "      <td>0.256477</td>\n",
              "      <td>0.712206</td>\n",
              "      <td>0.963518</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "               corrrate  corrrate_ev  popularity  popularity_ev\n",
              "corrrate       1.000000     0.323288    0.169948       0.256477\n",
              "corrrate_ev    0.323288     1.000000    0.694321       0.712206\n",
              "popularity     0.169948     0.694321    1.000000       0.963518\n",
              "popularity_ev  0.256477     0.712206    0.963518       1.000000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 227
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uUhOHK5GjhG6",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "outputId": "7626f3df-3784-4a00-a306-a75a36d83e02"
      },
      "source": [
        "steps_stat_merged.plot('corrrate', 'corrrate_ev')"
      ],
      "execution_count": 229,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fe04aac40f0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 229
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-Rtr_j9Qk5CM",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# хуйню...\n",
        "del(steps_stat_merged)"
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}