ModelosII.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aahT7BukQITm"
      },
      "source": [
        "Creamos los modelos por tipo de cliente"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "id": "QwiHBAb1QITr"
      },
      "outputs": [],
      "source": [
        "# Tratamiento de datos\n",
        "# ------------------------------------------------------------------------------\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "\n",
        "# Gráficos\n",
        "# ------------------------------------------------------------------------------\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "# Modelado y evaluación\n",
        "# ------------------------------------------------------------------------------\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.tree import DecisionTreeRegressor\n",
        "from sklearn import tree\n",
        "from sklearn.metrics import r2_score, mean_squared_error, mean_absolute_error\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "from sklearn.ensemble import RandomForestRegressor \n",
        "\n",
        "# Configuración warnings\n",
        "# ------------------------------------------------------------------------------\n",
        "import warnings\n",
        "warnings.filterwarnings('once') "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
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        "id": "lnyq8Uz6Q_Kz",
        "outputId": "6e608006-9bed-4d36-9548-9f1f3cd047e6"
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              "      Upload widget is only available when the cell has been executed in the\n",
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              "      <script>// Copyright 2017 Google LLC\n",
              "//\n",
              "// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
              "// you may not use this file except in compliance with the License.\n",
              "// You may obtain a copy of the License at\n",
              "//\n",
              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
              "//\n",
              "// Unless required by applicable law or agreed to in writing, software\n",
              "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
              "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
              "// See the License for the specific language governing permissions and\n",
              "// limitations under the License.\n",
              "\n",
              "/**\n",
              " * @fileoverview Helpers for google.colab Python module.\n",
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              "(function(scope) {\n",
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              "  cancel.remove();\n",
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              "  // Disable the input element since further picks are not allowed.\n",
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              "\n",
              "  if (!files) {\n",
              "    return {\n",
              "      response: {\n",
              "        action: 'complete',\n",
              "      }\n",
              "    };\n",
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              "\n",
              "  for (const file of files) {\n",
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              "    li.append(span(\n",
              "        `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n",
              "        `last modified: ${\n",
              "            file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n",
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              "\n",
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              "    do {\n",
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              "\n",
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              "\n",
              "scope.google = scope.google || {};\n",
              "scope.google.colab = scope.google.colab || {};\n",
              "scope.google.colab._files = {\n",
              "  _uploadFiles,\n",
              "  _uploadFilesContinue,\n",
              "};\n",
              "})(self);\n",
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        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Saving casual_encoded.csv to casual_encoded.csv\n"
          ]
        }
      ],
      "source": [
        "from google.colab import files\n",
        "uploaded = files.upload() "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
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        "id": "cVgPNCISQITy",
        "outputId": "8bb40c92-6f91-471e-d41a-f709c9c9cdd3"
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              "      <th>dteday</th>\n",
              "      <th>temp_es</th>\n",
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              "      <th>dia_sem_encoded</th>\n",
              "      <th>laborables_0</th>\n",
              "      <th>laborables_1</th>\n",
              "      <th>weathersit_encoded</th>\n",
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            "text/plain": [
              "       dteday   temp_es  windspeed_es  casual_es  dia_sem_encoded  \\\n",
              "0  01-01-2018 -0.827613     -0.387833  -0.755455                4   \n",
              "1  02-01-2018 -0.722069      0.748899  -1.046996                3   \n",
              "2  03-01-2018 -1.635432      0.745931  -1.063031                1   \n",
              "3  04-01-2018 -1.615560     -0.389769  -1.080523                1   \n",
              "4  05-01-2018 -1.468226     -0.046477  -1.118424                0   \n",
              "\n",
              "   laborables_0  laborables_1  weathersit_encoded  month_encoded  year_0  \\\n",
              "0             1             0                   2              1       1   \n",
              "1             0             1                   2              1       1   \n",
              "2             0             1                   3              1       1   \n",
              "3             0             1                   3              1       1   \n",
              "4             0             1                   3              1       1   \n",
              "\n",
              "   year_1  \n",
              "0       0  \n",
              "1       0  \n",
              "2       0  \n",
              "3       0  \n",
              "4       0  "
            ]
          },
          "execution_count": 29,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_casual = pd.read_csv(\"casual_encoded.csv\", index_col = 0)\n",
        "df_casual.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
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          "height": 74
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        "id": "u3qTquk_ROwk",
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              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
              "//\n",
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              "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
              "// See the License for the specific language governing permissions and\n",
              "// limitations under the License.\n",
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              "\n",
              "// Max number of bytes which will be uploaded at a time.\n",
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              "\n",
              "  const next = steps.next(outputElement.lastPromiseValue);\n",
              "  return Promise.resolve(next.value.promise).then((value) => {\n",
              "    // Cache the last promise value to make it available to the next\n",
              "    // step of the generator.\n",
              "    outputElement.lastPromiseValue = value;\n",
              "    return next.value.response;\n",
              "  });\n",
              "}\n",
              "\n",
              "/**\n",
              " * Generator function which is called between each async step of the upload\n",
              " * process.\n",
              " * @param {string} inputId Element ID of the input file picker element.\n",
              " * @param {string} outputId Element ID of the output display.\n",
              " * @return {!Iterable<!Object>} Iterable of next steps.\n",
              " */\n",
              "function* uploadFilesStep(inputId, outputId) {\n",
              "  const inputElement = document.getElementById(inputId);\n",
              "  inputElement.disabled = false;\n",
              "\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  outputElement.innerHTML = '';\n",
              "\n",
              "  const pickedPromise = new Promise((resolve) => {\n",
              "    inputElement.addEventListener('change', (e) => {\n",
              "      resolve(e.target.files);\n",
              "    });\n",
              "  });\n",
              "\n",
              "  const cancel = document.createElement('button');\n",
              "  inputElement.parentElement.appendChild(cancel);\n",
              "  cancel.textContent = 'Cancel upload';\n",
              "  const cancelPromise = new Promise((resolve) => {\n",
              "    cancel.onclick = () => {\n",
              "      resolve(null);\n",
              "    };\n",
              "  });\n",
              "\n",
              "  // Wait for the user to pick the files.\n",
              "  const files = yield {\n",
              "    promise: Promise.race([pickedPromise, cancelPromise]),\n",
              "    response: {\n",
              "      action: 'starting',\n",
              "    }\n",
              "  };\n",
              "\n",
              "  cancel.remove();\n",
              "\n",
              "  // Disable the input element since further picks are not allowed.\n",
              "  inputElement.disabled = true;\n",
              "\n",
              "  if (!files) {\n",
              "    return {\n",
              "      response: {\n",
              "        action: 'complete',\n",
              "      }\n",
              "    };\n",
              "  }\n",
              "\n",
              "  for (const file of files) {\n",
              "    const li = document.createElement('li');\n",
              "    li.append(span(file.name, {fontWeight: 'bold'}));\n",
              "    li.append(span(\n",
              "        `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n",
              "        `last modified: ${\n",
              "            file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n",
              "                                    'n/a'} - `));\n",
              "    const percent = span('0% done');\n",
              "    li.appendChild(percent);\n",
              "\n",
              "    outputElement.appendChild(li);\n",
              "\n",
              "    const fileDataPromise = new Promise((resolve) => {\n",
              "      const reader = new FileReader();\n",
              "      reader.onload = (e) => {\n",
              "        resolve(e.target.result);\n",
              "      };\n",
              "      reader.readAsArrayBuffer(file);\n",
              "    });\n",
              "    // Wait for the data to be ready.\n",
              "    let fileData = yield {\n",
              "      promise: fileDataPromise,\n",
              "      response: {\n",
              "        action: 'continue',\n",
              "      }\n",
              "    };\n",
              "\n",
              "    // Use a chunked sending to avoid message size limits. See b/62115660.\n",
              "    let position = 0;\n",
              "    do {\n",
              "      const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n",
              "      const chunk = new Uint8Array(fileData, position, length);\n",
              "      position += length;\n",
              "\n",
              "      const base64 = btoa(String.fromCharCode.apply(null, chunk));\n",
              "      yield {\n",
              "        response: {\n",
              "          action: 'append',\n",
              "          file: file.name,\n",
              "          data: base64,\n",
              "        },\n",
              "      };\n",
              "\n",
              "      let percentDone = fileData.byteLength === 0 ?\n",
              "          100 :\n",
              "          Math.round((position / fileData.byteLength) * 100);\n",
              "      percent.textContent = `${percentDone}% done`;\n",
              "\n",
              "    } while (position < fileData.byteLength);\n",
              "  }\n",
              "\n",
              "  // All done.\n",
              "  yield {\n",
              "    response: {\n",
              "      action: 'complete',\n",
              "    }\n",
              "  };\n",
              "}\n",
              "\n",
              "scope.google = scope.google || {};\n",
              "scope.google.colab = scope.google.colab || {};\n",
              "scope.google.colab._files = {\n",
              "  _uploadFiles,\n",
              "  _uploadFilesContinue,\n",
              "};\n",
              "})(self);\n",
              "</script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Saving registered_encoded.csv to registered_encoded.csv\n"
          ]
        }
      ],
      "source": [
        "from google.colab import files\n",
        "uploaded = files.upload() "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "hovatWDQQIT0",
        "outputId": "f49ac40a-8c64-4679-fa47-36cc2a3f310c"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-229cff7c-d605-4e15-b691-ffd7c15c8aca\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\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>dteday</th>\n",
              "      <th>temp_es</th>\n",
              "      <th>windspeed_es</th>\n",
              "      <th>registered_es</th>\n",
              "      <th>dia_sem_encoded</th>\n",
              "      <th>laborables_0</th>\n",
              "      <th>laborables_1</th>\n",
              "      <th>weathersit_encoded</th>\n",
              "      <th>month_encoded</th>\n",
              "      <th>year_0</th>\n",
              "      <th>year_1</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>01-01-2018</td>\n",
              "      <td>-0.827613</td>\n",
              "      <td>-0.387833</td>\n",
              "      <td>-1.927745</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>02-01-2018</td>\n",
              "      <td>-0.722069</td>\n",
              "      <td>0.748899</td>\n",
              "      <td>-1.917480</td>\n",
              "      <td>1</td>\n",
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              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>03-01-2018</td>\n",
              "      <td>-1.635432</td>\n",
              "      <td>0.745931</td>\n",
              "      <td>-1.558846</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>04-01-2018</td>\n",
              "      <td>-1.615560</td>\n",
              "      <td>-0.389769</td>\n",
              "      <td>-1.414494</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>05-01-2018</td>\n",
              "      <td>-1.468226</td>\n",
              "      <td>-0.046477</td>\n",
              "      <td>-1.373434</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
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              "</table>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-229cff7c-d605-4e15-b691-ffd7c15c8aca')\"\n",
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              "    [theme=dark] .colab-df-convert:hover {\n",
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              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-229cff7c-d605-4e15-b691-ffd7c15c8aca button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-229cff7c-d605-4e15-b691-ffd7c15c8aca');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "        }\n",
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              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "       dteday   temp_es  windspeed_es  registered_es  dia_sem_encoded  \\\n",
              "0  01-01-2018 -0.827613     -0.387833      -1.927745                1   \n",
              "1  02-01-2018 -0.722069      0.748899      -1.917480                1   \n",
              "2  03-01-2018 -1.635432      0.745931      -1.558846                2   \n",
              "3  04-01-2018 -1.615560     -0.389769      -1.414494                3   \n",
              "4  05-01-2018 -1.468226     -0.046477      -1.373434                3   \n",
              "\n",
              "   laborables_0  laborables_1  weathersit_encoded  month_encoded  year_0  \\\n",
              "0             1             0                   2              1       1   \n",
              "1             0             1                   2              1       1   \n",
              "2             0             1                   3              1       1   \n",
              "3             0             1                   3              1       1   \n",
              "4             0             1                   3              1       1   \n",
              "\n",
              "   year_1  \n",
              "0       0  \n",
              "1       0  \n",
              "2       0  \n",
              "3       0  \n",
              "4       0  "
            ]
          },
          "execution_count": 30,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_registered = pd.read_csv(\"registered_encoded.csv\", index_col = 0)\n",
        "df_registered.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "id": "nSTtDQs2QIT2"
      },
      "outputs": [],
      "source": [
        "X = df_casual.drop([\"casual_es\", \"dteday\"], axis = 1)\n",
        "y = df_casual[\"casual_es\"]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "id": "wZNu86r_QIT3"
      },
      "outputs": [],
      "source": [
        "x_train, x_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 42)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "P6VlXGgAQIT4",
        "outputId": "fec9063d-e8c5-4d04-b657-87139059237d"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "DecisionTreeRegressor(random_state=0)"
            ]
          },
          "execution_count": 10,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "arbol = DecisionTreeRegressor(random_state =0)\n",
        "\n",
        "arbol.fit(x_train, y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "s1_AuDJdQSlu"
      },
      "outputs": [],
      "source": [
        "#fig = plt.figure(figsize = (10,6))\n",
        "#tree.plot_tree(arbol, feature_names = x_train.columns, filled = True)\n",
        "#plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WEM58VJPQZvc",
        "outputId": "f659e5f0-5518-4873-d2bb-f89392e20e47"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "3.0"
            ]
          },
          "execution_count": 12,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "max_features = np.sqrt(len(x_train.columns))\n",
        "max_features"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2PrhtvucQePw",
        "outputId": "3833515e-320b-4a82-edd3-d903f09ef4c2"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "19\n"
          ]
        }
      ],
      "source": [
        "print(arbol.tree_.max_depth)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "RpbA5zCEQhvi"
      },
      "outputs": [],
      "source": [
        "param = {\"max_depth\": [4,5,6,7,8,9,10,12, 13, 14, 15], # teniendo en cuenta que teníamos overfitting tendremos que reducir la profundidad del modelo, la nuestra anterior era de 17. Bajaremos mucho este valor ya que teníamos un overfitting muy claro\n",
        "        \"max_features\": [1,2,3],# calculamos en celdas anteriores, probaremos a hacer el modelo como una variable, 2, 3 y 4. Ponemos como límite el 4 ya que es el resultado de la raiz cuadrada. \n",
        "        # estos dos hiperparámetros son más difíciles de definir, pero usualmente se suelen elegir los siguientes valores\n",
        "        \"min_samples_split\": [10, 50, 100],\n",
        "        \"min_samples_leaf\": [10,50,100],\n",
        "        \"random_state\":[0]} "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {
        "id": "RtrxB6TwQr7j"
      },
      "outputs": [],
      "source": [
        "gs = GridSearchCV(\n",
        "            estimator=DecisionTreeRegressor(), # tipo de modelo que queremos hacer\n",
        "            param_grid= param, # que hiperparámetros queremos que testee\n",
        "            cv=10, # crossvalidation que aprendimos en la lección de regresión lineal intro. \n",
        "            verbose=-1, # para que no nos printee ningún mensaje en pantalla\n",
        "            return_train_score = True, # para que nos devuelva el valor de las métricas de set de datos de entrenamiento\n",
        "            scoring=\"neg_mean_squared_error\") # la métrica que queremos que nos devuelva"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UbG6fmT-v7vj",
        "outputId": "03edd91e-b307-4084-fd93-112242fb5e3a"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "GridSearchCV(cv=10, estimator=DecisionTreeRegressor(),\n",
              "             param_grid={'max_depth': [4, 5, 6, 7, 8, 9, 10, 12],\n",
              "                         'max_features': [1, 2, 3],\n",
              "                         'min_samples_leaf': [10, 50, 100],\n",
              "                         'min_samples_split': [10, 50, 100],\n",
              "                         'random_state': [0]},\n",
              "             return_train_score=True, scoring='neg_mean_squared_error',\n",
              "             verbose=-1)"
            ]
          },
          "execution_count": 36,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "gs.fit(x_train, y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "L2qPg9KVv7vt",
        "outputId": "392ac33b-6c03-4283-8e70-decc3341487d"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "DecisionTreeRegressor(max_depth=5, max_features=3, min_samples_leaf=10,\n",
              "                      min_samples_split=10, random_state=0)"
            ]
          },
          "execution_count": 37,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "mejor_modelo = gs.best_estimator_\n",
        "mejor_modelo"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "metadata": {
        "id": "trJHJvslv7vw"
      },
      "outputs": [],
      "source": [
        "y_pred_test_dt2 = mejor_modelo.predict(x_test)\n",
        "y_pred_train_dt2 = mejor_modelo.predict(x_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "id": "O46Gt8Hjv7v0"
      },
      "outputs": [],
      "source": [
        "def metricas(y_test, y_train, y_test_pred, y_train_pred, tipo_modelo):\n",
        "    \n",
        "    \n",
        "    resultados = {'MAE': [mean_absolute_error(y_test, y_test_pred), mean_absolute_error(y_train, y_train_pred)],\n",
        "                'MSE': [mean_squared_error(y_test, y_test_pred), mean_squared_error(y_train, y_train_pred)],\n",
        "                'RMSE': [np.sqrt(mean_squared_error(y_test, y_test_pred)), np.sqrt(mean_squared_error(y_train, y_train_pred))],\n",
        "                'R2':  [r2_score(y_test, y_test_pred), r2_score(y_train, y_train_pred)],\n",
        "                 \"set\": [\"test\", \"train\"]}\n",
        "    df = pd.DataFrame(resultados)\n",
        "    df[\"modelo\"] = tipo_modelo\n",
        "    return df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "5oM9FuBMv7v6",
        "outputId": "2c22d846-eab4-44c9-f213-eb3edf56ee70"
      },
      "outputs": [
        {
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>MAE</th>\n",
              "      <th>MSE</th>\n",
              "      <th>RMSE</th>\n",
              "      <th>R2</th>\n",
              "      <th>set</th>\n",
              "      <th>modelo</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.380847</td>\n",
              "      <td>0.326443</td>\n",
              "      <td>0.571352</td>\n",
              "      <td>0.680226</td>\n",
              "      <td>test</td>\n",
              "      <td>Decision tree II</td>\n",
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              "      <td>0.345174</td>\n",
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              "      <td>0.518978</td>\n",
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              "      <td>train</td>\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-2f22f83b-d088-413e-9834-6062c07d49bc');\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "        MAE       MSE      RMSE        R2    set            modelo\n",
              "0  0.380847  0.326443  0.571352  0.680226   test  Decision tree II\n",
              "1  0.345174  0.269338  0.518978  0.728990  train  Decision tree II"
            ]
          },
          "execution_count": 40,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "dt_results2 = metricas(y_test, y_train, y_pred_test_dt2, y_pred_train_dt2, \"Decision tree II\")\n",
        "dt_results2"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 41,
      "metadata": {
        "id": "8zExZG3QyxzX"
      },
      "outputs": [],
      "source": [
        "param2 = {\"max_depth\": 5,\n",
        "        \"max_features\": 3,\n",
        "        \"min_samples_split\":10,\n",
        "        \"min_samples_leaf\": 10,\n",
        "        \"random_state\":0} "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Dmev_cCN0HXC"
      },
      "outputs": [],
      "source": [
        "forest = \"max_depth\": 5,\n",
        "        \"max_features\": 3,\n",
        "        \"min_samples_split\":10,\n",
        "        \"min_samples_leaf\": 10,\n",
        "        \"random_state\":0"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "id": "x89-VSThz9oM"
      },
      "outputs": [],
      "source": [
        "gs_rf = GridSearchCV(\n",
        "            estimator=RandomForestRegressor(), # tipo de modelo que queremos hacer\n",
        "            param_grid= param, # que hiperparámetros queremos que testee\n",
        "            cv=10, # crossvalidation que aprendimos en la lección de regresión lineal intro. \n",
        "            verbose=-1, # para que no nos printee ningún mensaje en pantalla\n",
        "            return_train_score = True, # para que nos devuelva el valor de las métricas de set de datos de entrenamiento\n",
        "            scoring=\"neg_mean_squared_error\") # la métrica que queremos que nos devuelva"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MoD-tcofzkjg",
        "outputId": "7372424a-d601-4d5e-fbdf-cf5039be88a9"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "GridSearchCV(cv=10, estimator=RandomForestRegressor(),\n",
              "             param_grid={'max_depth': [4, 5, 6, 7, 8, 9, 10, 12, 13, 14, 15],\n",
              "                         'max_features': [1, 2, 3],\n",
              "                         'min_samples_leaf': [10, 50, 100],\n",
              "                         'min_samples_split': [10, 50, 100],\n",
              "                         'random_state': [0]},\n",
              "             return_train_score=True, scoring='neg_mean_squared_error',\n",
              "             verbose=-1)"
            ]
          },
          "execution_count": 12,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "gs_rf.fit(x_train, y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4b0HkW6l0khl",
        "outputId": "14438bd2-752d-4975-e039-8d28b2ab9ca7"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "RandomForestRegressor(max_depth=6, max_features=3, min_samples_leaf=10,\n",
              "                      min_samples_split=10, random_state=0)"
            ]
          },
          "execution_count": 13,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "bosque = gs_rf.best_estimator_\n",
        "bosque"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "cBWLtNxj0ltV"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "HFX1pcLZzZaW"
      },
      "outputs": [],
      "source": [
        "# Le añadimos max_depths mayores y nos volvió a tomar la max_depth = 6, las métricas que devuelve son las mismas\n",
        "y_pred_test_rf = bosque.predict(x_test)\n",
        "y_pred_train_rf = bosque.predict(x_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "lRMQth2L02I0",
        "outputId": "cc5a1824-e577-4cfb-8cb0-7d61170c1bb7"
      },
      "outputs": [
        {
          "data": {
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              "      <th></th>\n",
              "      <th>MAE</th>\n",
              "      <th>MSE</th>\n",
              "      <th>RMSE</th>\n",
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              "      <th>0</th>\n",
              "      <td>0.324401</td>\n",
              "      <td>0.216808</td>\n",
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              "      <td>0.787621</td>\n",
              "      <td>test</td>\n",
              "      <td>Random Forest I</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.333230</td>\n",
              "      <td>0.239996</td>\n",
              "      <td>0.489894</td>\n",
              "      <td>0.758515</td>\n",
              "      <td>train</td>\n",
              "      <td>Random Forest I</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7fe20339-3b20-420e-92ba-093e66884ad2')\"\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
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              "\n",
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              "      background-color: #434B5C;\n",
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              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-7fe20339-3b20-420e-92ba-093e66884ad2 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-7fe20339-3b20-420e-92ba-093e66884ad2');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "        MAE       MSE      RMSE        R2    set           modelo\n",
              "0  0.324401  0.216808  0.465626  0.787621   test  Random Forest I\n",
              "1  0.333230  0.239996  0.489894  0.758515  train  Random Forest I"
            ]
          },
          "execution_count": 15,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "dt_results3 = metricas(y_test, y_train, y_pred_test_rf, y_pred_train_rf, \"Random Forest I\")\n",
        "dt_results3"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bpIKKHabYJXn"
      },
      "source": [
        "Como nos salieron el mismo best estimator y las mismas métricas, vamos a probar a retirar del modelo alguna variable."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ySJOmQ8XXwO9"
      },
      "outputs": [],
      "source": [
        "# Vimos en los boxplots que en los usuarios casuales no había mucha diferencia entre las medianas según si el día era laborable o no."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 54,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "oVib9S212Kt6",
        "outputId": "1bcaac09-5b88-4cd3-c4af-0b616e56591e"
      },
      "outputs": [
        {
          "data": {
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              "      <th></th>\n",
              "      <th>temp_es</th>\n",
              "      <th>windspeed_es</th>\n",
              "      <th>dia_sem_encoded</th>\n",
              "      <th>laborables_0</th>\n",
              "      <th>laborables_1</th>\n",
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              "      <td>-0.827613</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-1.635432</td>\n",
              "      <td>0.745931</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
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              "      <td>3</td>\n",
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              "      <td>1</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-1.615560</td>\n",
              "      <td>-0.389769</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>-1.468226</td>\n",
              "      <td>-0.046477</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "    temp_es  windspeed_es  dia_sem_encoded  laborables_0  laborables_1  \\\n",
              "0 -0.827613     -0.387833                4             1             0   \n",
              "1 -0.722069      0.748899                3             0             1   \n",
              "2 -1.635432      0.745931                1             0             1   \n",
              "3 -1.615560     -0.389769                1             0             1   \n",
              "4 -1.468226     -0.046477                0             0             1   \n",
              "\n",
              "   weathersit_encoded  month_encoded  year_0  year_1  \n",
              "0                   2              1       1       0  \n",
              "1                   2              1       1       0  \n",
              "2                   3              1       1       0  \n",
              "3                   3              1       1       0  \n",
              "4                   3              1       1       0  "
            ]
          },
          "execution_count": 54,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "X.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {
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          "height": 112
        },
        "id": "yLEIJg1a2Cp-",
        "outputId": "c62cd8c8-2a48-45c6-f15b-afd71213c8fa"
      },
      "outputs": [
        {
          "data": {
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            "text/plain": [
              "      temp_es  windspeed_es  dia_sem_encoded  weathersit_encoded  \\\n",
              "468 -0.003234      0.005358                2                   3   \n",
              "148  0.939567      0.302416                3                   3   \n",
              "\n",
              "     month_encoded  year_0  year_1  \n",
              "468              5       0       1  \n",
              "148              6       1       0  "
            ]
          },
          "execution_count": 35,
          "metadata": {},
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      "source": [
        "# Retiramos las columnas de laborables\n",
        "x_test = x_test.drop(['laborables_0', 'laborables_1'], axis=1)\n",
        "x_test.head(2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
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        },
        "id": "c28JdIKmZYL3",
        "outputId": "f436d9cc-c788-4e69-a258-875ef72f9c34"
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        {
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              "\n",
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              "                                                     [key], {});\n",
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              "\n",
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              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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            ],
            "text/plain": [
              "      temp_es  windspeed_es  dia_sem_encoded  weathersit_encoded  \\\n",
              "247  0.971447      0.286389                1                   2   \n",
              "335 -0.991579     -1.158085                2                   3   \n",
              "\n",
              "     month_encoded  year_0  year_1  \n",
              "247              7       1       0  \n",
              "335              3       1       0  "
            ]
          },
          "execution_count": 36,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "x_train = x_train.drop(['laborables_0', 'laborables_1'], axis=1)\n",
        "x_train.head(2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "8E9gAD18Z3x1"
      },
      "outputs": [],
      "source": [
        "# Vamos a por el modelo de Random Forest con las nuevas predictoras\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {
        "id": "swxD2T6YZ_gQ"
      },
      "outputs": [],
      "source": [
        "gs_rf2 = GridSearchCV(\n",
        "            estimator=RandomForestRegressor(), # tipo de modelo que queremos hacer\n",
        "            param_grid= param, # que hiperparámetros queremos que testee\n",
        "            cv=10, # crossvalidation que aprendimos en la lección de regresión lineal intro. \n",
        "            verbose=-1, # para que no nos printee ningún mensaje en pantalla\n",
        "            return_train_score = True, # para que nos devuelva el valor de las métricas de set de datos de entrenamiento\n",
        "            scoring=\"neg_mean_squared_error\") # la métrica que queremos que nos devuelva"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OLIhqUuxaGH5",
        "outputId": "90960193-5fbb-44ef-be15-7d18266516df"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "GridSearchCV(cv=10, estimator=RandomForestRegressor(),\n",
              "             param_grid={'max_depth': [4, 5, 6, 7, 8, 9, 10, 12, 13, 14, 15],\n",
              "                         'max_features': [1, 2, 3],\n",
              "                         'min_samples_leaf': [10, 50, 100],\n",
              "                         'min_samples_split': [10, 50, 100],\n",
              "                         'random_state': [0]},\n",
              "             return_train_score=True, scoring='neg_mean_squared_error',\n",
              "             verbose=-1)"
            ]
          },
          "execution_count": 38,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "gs_rf2.fit(x_train, y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lwfMdxWEdfZG",
        "outputId": "ac8d3371-1c96-4084-884b-ff0baceac009"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "RandomForestRegressor(max_depth=8, max_features=3, min_samples_leaf=10,\n",
              "                      min_samples_split=10, random_state=0)"
            ]
          },
          "execution_count": 39,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "bosque2 = gs_rf2.best_estimator_\n",
        "bosque2"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "metadata": {
        "id": "ClzKZjGNpZ0f"
      },
      "outputs": [],
      "source": [
        "y_pred_test_rf2 = bosque2.predict(x_test)\n",
        "y_pred_train_rf2 = bosque2.predict(x_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 41,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "ua92asQZpfHe",
        "outputId": "16353a64-82c6-498d-d3b2-8bc5b19765bf"
      },
      "outputs": [
        {
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              "      <th></th>\n",
              "      <th>MAE</th>\n",
              "      <th>MSE</th>\n",
              "      <th>RMSE</th>\n",
              "      <th>R2</th>\n",
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              "      <td>0.770566</td>\n",
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              "      <td>Random Forest II</td>\n",
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              "      <td>0.514760</td>\n",
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              "      <td>train</td>\n",
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              "                                                     [key], {});\n",
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              "        MAE       MSE      RMSE        R2    set            modelo\n",
              "0  0.339536  0.234219  0.483962  0.770566   test  Random Forest II\n",
              "1  0.348676  0.264978  0.514760  0.733377  train  Random Forest II"
            ]
          },
          "execution_count": 41,
          "metadata": {},
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        "dt_results4 = metricas(y_test, y_train, y_pred_test_rf2, y_pred_train_rf2, \"Random Forest II\")\n",
        "dt_results4"
      ]
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