Bucle_genético_PyGAD.ipynb

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      "cell_type": "markdown",
      "source": [
        "# Librerías"
      ],
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      "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",
        "plt.style.use('fivethirtyeight')\n",
        "plt.rcParams['lines.linewidth'] = 1.5\n",
        "import matplotlib.ticker as ticker\n",
        "from matplotlib import style\n",
        "%matplotlib inline\n",
        "\n",
        "# Modelado y Forecasting\n",
        "# ==============================================================================\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.linear_model import Lasso\n",
        "from sklearn.ensemble import RandomForestRegressor\n",
        "from sklearn.metrics import mean_squared_error\n",
        "from sklearn.metrics import mean_absolute_error\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.pipeline import make_pipeline\n",
        "\n",
        "%pip install skforecast\n",
        "from skforecast.ForecasterAutoreg import ForecasterAutoreg\n",
        "from skforecast.ForecasterAutoregCustom import ForecasterAutoregCustom\n",
        "from skforecast.ForecasterAutoregDirect import ForecasterAutoregDirect\n",
        "from skforecast.model_selection import grid_search_forecaster\n",
        "from skforecast.model_selection import backtesting_forecaster\n",
        "from skforecast.utils import save_forecaster\n",
        "from skforecast.utils import load_forecaster\n",
        "\n",
        "# Algoritmo genético\n",
        "# ==============================================================================\n",
        "%pip install pygad\n",
        "import pygad as pg\n",
        "\n",
        "# Configuración warnings\n",
        "# ==============================================================================\n",
        "import warnings\n",
        "# warnings.filterwarnings('ignore')\n",
        "\n",
        "# Google Drive\n",
        "# ==============================================================================\n",
        "#from google.colab import drive\n",
        "#drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Datasets"
      ],
      "metadata": {
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      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Generación de energía solar en España\n",
        "\n",
        "---\n",
        "Código: SOL\n",
        "\n",
        "Frecuencia temporal: 1 Hora"
      ],
      "metadata": {
        "id": "JbKTWDd80wLA"
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    {
      "cell_type": "code",
      "source": [
        "# Descarga de datos\n",
        "# ==============================================================================\n",
        "url = 'https://www.dropbox.com/s/axfbmqdcpquqqun/dataset_solar_1h.csv?dl=1'\n",
        "datos = pd.read_csv(url, sep=',', names=['fecha', 'y'], usecols=['fecha', 'y'], header=None)"
      ],
      "metadata": {
        "id": "ggbKHb-SDzNs"
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      "execution_count": null,
      "outputs": []
    },
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      "cell_type": "code",
      "source": [
        "# Preparación del dato\n",
        "# ==============================================================================\n",
        "datos['fecha'] = pd.to_datetime(datos['fecha'], format='%Y/%m/%d %H:%M:%S')\n",
        "datos = datos.set_index('fecha')\n",
        "datos = datos.rename(columns={'x': 'y'})\n",
        "datos = datos.asfreq('H')\n",
        "datos = datos.sort_index()\n",
        "datos.head()"
      ],
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              "                             y\n",
              "fecha                         \n",
              "2014-01-01 00:00:00  24.000000\n",
              "2014-01-01 01:00:00  24.000000\n",
              "2014-01-01 02:00:00  23.833333\n",
              "2014-01-01 03:00:00  24.000000\n",
              "2014-01-01 04:00:00  23.166667"
            ],
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              "      <th>2014-01-01 00:00:00</th>\n",
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              "      <th>2014-01-01 02:00:00</th>\n",
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              "      <th>2014-01-01 03:00:00</th>\n",
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              "      <th>2014-01-01 04:00:00</th>\n",
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    {
      "cell_type": "code",
      "source": [
        "print(f'Número de filas con missing values: {datos.isnull().any(axis=1).mean()}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
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      "outputs": [
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          "name": "stdout",
          "text": [
            "Número de filas con missing values: 0.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Verificar que un índice temporal está completo\n",
        "# ==============================================================================\n",
        "(datos.index == pd.date_range(\n",
        "                    start = datos.index.min(),\n",
        "                    end   = datos.index.max(),\n",
        "                    freq  = datos.index.freq)\n",
        ").all()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Voq4ewUWBO6c",
        "outputId": "688137ff-d040-4989-d61a-e6e3793cb43e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "True"
            ]
          },
          "metadata": {},
          "execution_count": 93
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Completar huecos en un índice temporal\n",
        "# ==============================================================================\n",
        "# datos.asfreq(freq='60min', fill_value=np.nan)"
      ],
      "metadata": {
        "id": "FvsTGfnPBVSM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Generación de energía eólica en España\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "Código: EOLO\n",
        "\n",
        "Frecuencia temporal: 10 mins"
      ],
      "metadata": {
        "id": "bs22D0rN08qt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Descarga de datos\n",
        "# ==============================================================================\n",
        "url = 'https://www.dropbox.com/s/2o68sl7q6dcv156/data_no_timestamp.csv?dl=1'\n",
        "datos = pd.read_csv(url, sep=',', names=['fecha', 'y'], usecols=['fecha', 'y'], header=None)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "v9qQZLIt1E8R",
        "outputId": "eb4d54f1-1189-4b19-a02a-012fa23408aa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/IPython/core/interactiveshell.py:3326: DtypeWarning: Columns (1) have mixed types.Specify dtype option on import or set low_memory=False.\n",
            "  exec(code_obj, self.user_global_ns, self.user_ns)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Preparación del dato\n",
        "# ==============================================================================\n",
        "datos['fecha'] = pd.to_datetime(datos['fecha'], format='%Y-%m-%dT%H:%M:%S.%f', errors = 'coerce')\n",
        "datos = datos.set_index('fecha')\n",
        "datos = datos.rename(columns={'x': 'y'})\n",
        "datos = datos.asfreq('10min')\n",
        "datos = datos.sort_index()\n",
        "datos = datos.astype({'y': 'float'})\n",
        "datos.head()"
      ],
      "metadata": {
        "id": "9qOOC6k21Got"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(f'Número de filas con missing values: {datos.isnull().any(axis=1).mean()}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bqkurxz01JpN",
        "outputId": "be6bc475-1ee9-4275-f23d-2c0fa2935b12"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Número de filas con missing values: 0.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Verificar que un índice temporal está completo\n",
        "# ==============================================================================\n",
        "(datos.index == pd.date_range(\n",
        "                    start = datos.index.min(),\n",
        "                    end   = datos.index.max(),\n",
        "                    freq  = datos.index.freq)\n",
        ").all()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "msS12qxU1LO9",
        "outputId": "f04deebe-ba99-4241-d31e-381ff5c4f45a"
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      "outputs": [
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          "output_type": "execute_result",
          "data": {
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              "True"
            ]
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          "metadata": {},
          "execution_count": 36
        }
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    {
      "cell_type": "code",
      "source": [
        "# Completar huecos en un índice temporal\n",
        "# ==============================================================================\n",
        "# bool_series = datos['y'].isnull()\n",
        "# datos[bool_series]\n",
        "\n",
        "# datos.asfreq(freq='10min', fill_value=np.nan)"
      ],
      "metadata": {
        "id": "B4xQf2Vk1Mtd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Temperatura de San Francisco\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "Código: TSF\n",
        "\n",
        "Frecuencia temporal: 1 hora"
      ],
      "metadata": {
        "id": "pQokZyV2xXld"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Descarga de datos\n",
        "# ==============================================================================\n",
        "url = 'https://www.dropbox.com/s/60vw4wp6770iqbu/dataset_tsf.csv?dl=1'\n",
        "datos = pd.read_csv(url, sep=',', names=['y'], usecols=['y'], header=None)"
      ],
      "metadata": {
        "id": "fAnE1Jg_yJMc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Preparación del dato\n",
        "# ==============================================================================\n",
        "datos['fecha'] = pd.date_range(start='1/11/2012 13:00:00', periods=len(datos), freq='H')\n",
        "datos = datos.set_index('fecha')\n",
        "datos = datos.rename(columns={'x': 'y'})\n",
        "datos = datos.asfreq('H')\n",
        "datos = datos.sort_index()\n",
        "datos = datos.astype({'y': 'float'})\n",
        "datos.head()"
      ],
      "metadata": {
        "id": "hj2FXjJQym6E"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(f'Número de filas con missing values: {datos.isnull().any(axis=1).mean()}')"
      ],
      "metadata": {
        "id": "ugEOJ5WRyowV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Verificar que un índice temporal está completo\n",
        "# ==============================================================================\n",
        "(datos.index == pd.date_range(\n",
        "                    start = datos.index.min(),\n",
        "                    end   = datos.index.max(),\n",
        "                    freq  = datos.index.freq)\n",
        ").all()"
      ],
      "metadata": {
        "id": "g-IGGc_wyrGD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Completar huecos en un índice temporal\n",
        "# ==============================================================================\n",
        "# bool_series = datos['y'].isnull()\n",
        "# datos[bool_series]\n",
        "\n",
        "# datos.asfreq(freq='H', fill_value=np.nan)"
      ],
      "metadata": {
        "id": "tfHsIKccysgc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Inicialización y estratégia de ventana"
      ],
      "metadata": {
        "id": "YWS_2gRK8DU0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "lags = 48\n",
        "steps = 1"
      ],
      "metadata": {
        "id": "SBpNY0B40PZY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Separación datos train-test\n",
        "# ==============================================================================\n",
        "datos_train = datos[:-steps]\n",
        "datos_test  = datos[-steps:]\n",
        "\n",
        "print(f\"Fechas train : {datos_train.index.min()} --- {datos_train.index.max()}  (n={len(datos_train)})\")\n",
        "print(f\"Fechas test  : {datos_test.index.min()} --- {datos_test.index.max()}  (n={len(datos_test)})\")\n",
        "\n",
        "colors = plt.get_cmap('Set2', 10)\n",
        "\n",
        "fig, ax = plt.subplots(1,1,figsize=(15, 5))\n",
        "ax.plot(datos_train['y'], 'o-', c=colors(0), markersize=3.5, label='train')\n",
        "ax.plot(datos_test['y'], 'x-', c=colors(0), markersize=3.5, label='test')\n",
        "ax.legend();"
      ],
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          "height": 351
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      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fechas train : 2014-01-01 00:00:00 --- 2020-12-31 23:40:00  (n=368207)\n",
            "Fechas test  : 2020-12-31 23:50:00 --- 2020-12-31 23:50:00  (n=1)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Predicción caso base"
      ],
      "metadata": {
        "id": "R-65TcfcJ79c"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## ForecasterAutoreg"
      ],
      "metadata": {
        "id": "yL95k6PKIy90"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Crear y entrenar forecaster\n",
        "# ==============================================================================\n",
        "forecaster = ForecasterAutoreg(\n",
        "                regressor = RandomForestRegressor(random_state=123),\n",
        "                lags = lags\n",
        "             )\n",
        "\n",
        "forecaster.fit(datos_train['y'])\n",
        "forecaster"
      ],
      "metadata": {
        "id": "S3w3_K8fszDW",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "626a4b7f-7f27-434d-bea8-1306347eb817"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "================= \n",
              "ForecasterAutoreg \n",
              "================= \n",
              "Regressor: RandomForestRegressor(random_state=123) \n",
              "Lags: [ 1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24\n",
              " 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48] \n",
              "Transformer for y: None \n",
              "Transformer for exog: None \n",
              "Window size: 48 \n",
              "Weight function included: False \n",
              "Exogenous included: False \n",
              "Type of exogenous variable: None \n",
              "Exogenous variables names: None \n",
              "Training range: [Timestamp('2014-01-01 00:00:00'), Timestamp('2020-12-31 23:40:00')] \n",
              "Training index type: DatetimeIndex \n",
              "Training index frequency: 10T \n",
              "Regressor parameters: {'bootstrap': True, 'ccp_alpha': 0.0, 'criterion': 'squared_error', 'max_depth': None, 'max_features': 'auto', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': None, 'oob_score': False, 'random_state': 123, 'verbose': 0, 'warm_start': False} \n",
              "Creation date: 2022-12-12 09:15:47 \n",
              "Last fit date: 2022-12-12 09:50:19 \n",
              "Skforecast version: 0.6.0 \n",
              "Python version: 3.8.16 "
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "window = forecaster.create_train_X_y(datos_train['y'])\n",
        "window[0]"
      ],
      "metadata": {
        "id": "mA4sMkWaDWf0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 846
        },
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              "                      lag_1   lag_2   lag_3   lag_4   lag_5   lag_6   lag_7  \\\n",
              "fecha                                                                         \n",
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              "...                     ...     ...     ...     ...     ...     ...     ...   \n",
              "2020-12-31 23:00:00  7887.0  8032.0  8044.0  8164.0  8040.0  7877.0  8033.0   \n",
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              "\n",
              "                      lag_8   lag_9  lag_10  ...   lag_39   lag_40   lag_41  \\\n",
              "fecha                                        ...                              \n",
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              "...                     ...     ...     ...  ...      ...      ...      ...   \n",
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              "\n",
              "                      lag_42   lag_43   lag_44   lag_45   lag_46   lag_47  \\\n",
              "fecha                                                                       \n",
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              "...                      ...      ...      ...      ...      ...      ...   \n",
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              "2020-12-31 23:40:00   9990.0  10096.0  10050.0  10109.0  10129.0  10207.0   \n",
              "\n",
              "                      lag_48  \n",
              "fecha                         \n",
              "2014-01-01 08:00:00  11128.0  \n",
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              "...                      ...  \n",
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              "\n",
              "[368159 rows x 48 columns]"
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              "      <th>2014-01-01 08:30:00</th>\n",
              "      <td>9904.0</td>\n",
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              "      <th>2020-12-31 23:10:00</th>\n",
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              "      <td>9990.0</td>\n",
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              "      <td>8099.0</td>\n",
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              "      <td>9791.0</td>\n",
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              "      <td>10276.0</td>\n",
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              "      <th>2020-12-31 23:30:00</th>\n",
              "      <td>7434.0</td>\n",
              "      <td>7577.0</td>\n",
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              "      <td>7887.0</td>\n",
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              "      <td>7877.0</td>\n",
              "      <td>8033.0</td>\n",
              "      <td>...</td>\n",
              "      <td>9659.0</td>\n",
              "      <td>9791.0</td>\n",
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              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-f6883314-2e1e-42ab-967d-1eb1ed425c13 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-f6883314-2e1e-42ab-967d-1eb1ed425c13');\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",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Predicciones\n",
        "# ==============================================================================\n",
        "predicciones = forecaster.predict(steps=steps)\n",
        "predicciones.head(5)"
      ],
      "metadata": {
        "id": "yaReh991J-iT",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ff69f805-1b6f-4389-d23d-5a719d195bc0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2020-12-31 23:50:00    7129.78\n",
              "Freq: 10T, Name: pred, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Error test\n",
        "# ==============================================================================\n",
        "error_mse = mean_squared_error(\n",
        "                y_true = datos_test['y'],\n",
        "                y_pred = predicciones\n",
        "            )\n",
        "\n",
        "error_mae = mean_absolute_error(datos_test['y'], predicciones)\n",
        "\n",
        "print(f\"Error de test (mse): {error_mse}\")\n",
        "print(f\"Error de test (mae): {error_mae}\")"
      ],
      "metadata": {
        "id": "iTZG514OBibk",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0009b102-7e3e-4bbb-909d-690482721f56"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Error de test (mse): 450.2884000000108\n",
            "Error de test (mae): 21.220000000000255\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Ajuste de hiperparámetros"
      ],
      "metadata": {
        "id": "doA2Zj5DBugN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Grid search de hiperparámetros\n",
        "# ==============================================================================\n",
        "forecaster = ForecasterAutoreg(\n",
        "                regressor = RandomForestRegressor(random_state=123),\n",
        "                lags      = lags # Este valor será remplazado en el grid search\n",
        "             )\n",
        "\n",
        "# Lags utilizados como predictores\n",
        "lags_grid = [24]\n",
        "\n",
        "# Hiperparámetros del regresor\n",
        "param_grid = {'n_estimators': [100, 500],\n",
        "              'max_depth': [3, 5, 10]}\n",
        "\n",
        "resultados_grid = grid_search_forecaster(\n",
        "                        forecaster         = forecaster,\n",
        "                        y                  = datos_train['y'],\n",
        "                        param_grid         = param_grid,\n",
        "                        lags_grid          = lags_grid,\n",
        "                        steps              = steps,\n",
        "                        refit              = False,\n",
        "                        metric             = 'mean_squared_error',\n",
        "                        initial_train_size = int(len(datos_train)*0.5),\n",
        "                        fixed_train_size   = False,\n",
        "                        return_best        = True,\n",
        "                        verbose            = False\n",
        "                  )"
      ],
      "metadata": {
        "id": "KePECuGsBvtD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Resultados Grid Search\n",
        "# ==============================================================================\n",
        "resultados_grid"
      ],
      "metadata": {
        "id": "Bbo7CcNoByUD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Predicción final"
      ],
      "metadata": {
        "id": "8-xn5yoWBzUs"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Crear y entrenar forecaster con mejores hiperparámetros\n",
        "# ==============================================================================\n",
        "regressor = RandomForestRegressor(max_depth=10, n_estimators=500, random_state=123)\n",
        "forecaster = ForecasterAutoreg(\n",
        "                regressor = regressor,\n",
        "                lags      = lags\n",
        "             )\n",
        "\n",
        "forecaster.fit(y=datos_train['y'])"
      ],
      "metadata": {
        "id": "izgeGjq9B2al"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Predicciones\n",
        "# ==============================================================================\n",
        "predicciones = forecaster.predict(steps=steps)\n",
        "predicciones.head(5)"
      ],
      "metadata": {
        "id": "eswr9cRhB4XE",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "004a6860-585a-4ada-d0cd-b77dbebbed35"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2022-02-25    39.097241\n",
              "Freq: H, Name: pred, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Gráfico\n",
        "# ==============================================================================\n",
        "fig, ax = plt.subplots(figsize=(9, 4))\n",
        "datos_train['y'].plot(ax=ax, label='train')\n",
        "datos_test['y'].plot(ax=ax, label='test')\n",
        "predicciones.plot(ax=ax, label='predicciones')\n",
        "ax.legend();"
      ],
      "metadata": {
        "id": "4r-PC5EaB5e8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Error de test\n",
        "# ==============================================================================\n",
        "error_mse = mean_squared_error(\n",
        "                y_true = datos_test['y'],\n",
        "                y_pred = predicciones\n",
        "            )\n",
        "\n",
        "error_mae = mean_absolute_error(datos_test['y'], predicciones)\n",
        "\n",
        "print(f\"Error de test (mse): {error_mse}\")\n",
        "print(f\"Error de test (mae): {error_mae}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JSSnPX-tB6wU",
        "outputId": "e5a291c8-8ed8-4a67-f33c-1ecff4ec548b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Error de test (mse): 34.84256749245909\n",
            "Error de test (mae): 5.902759311750657\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Algoritmo genético"
      ],
      "metadata": {
        "id": "IDjfr0rnNRSD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "function_inputs = datos_train['y'][:48]\n",
        "desired_output = datos_test['y'][0]"
      ],
      "metadata": {
        "id": "cGW3AU68NYTV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def fitness_func(solution, solution_idx):\n",
        "    output = np.sum(solution*function_inputs)\n",
        "    fitness = 1.0 / np.abs(output - desired_output)\n",
        "    return fitness"
      ],
      "metadata": {
        "id": "tLuISlWUNZ69"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "fitness_function = fitness_func\n",
        "\n",
        "num_generations = 50\n",
        "num_parents_mating = 4\n",
        "\n",
        "sol_per_pop = 8\n",
        "num_genes = len(function_inputs)\n",
        "\n",
        "init_range_low = -2\n",
        "init_range_high = 5\n",
        "\n",
        "parent_selection_type = \"sss\"\n",
        "keep_parents = 1\n",
        "\n",
        "crossover_type = \"single_point\"\n",
        "\n",
        "mutation_type = \"random\"\n",
        "mutation_percent_genes = 10"
      ],
      "metadata": {
        "id": "aAVjBdqYNbI7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ga_instance = pg.GA(num_generations=num_generations,\n",
        "                       num_parents_mating=num_parents_mating,\n",
        "                       fitness_func=fitness_function,\n",
        "                       sol_per_pop=sol_per_pop,\n",
        "                       num_genes=num_genes,\n",
        "                       init_range_low=init_range_low,\n",
        "                       init_range_high=init_range_high,\n",
        "                       parent_selection_type=parent_selection_type,\n",
        "                       keep_parents=keep_parents,\n",
        "                       crossover_type=crossover_type,\n",
        "                       mutation_type=mutation_type,\n",
        "                       mutation_percent_genes=mutation_percent_genes)"
      ],
      "metadata": {
        "id": "mN_ALfZQNc2E"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ga_instance.run()"
      ],
      "metadata": {
        "id": "B8xPvApMNdNV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "solution, solution_fitness, solution_idx = ga_instance.best_solution()\n",
        "print(\"Parameters of the best solution : {solution}\".format(solution=solution))\n",
        "print(\"Fitness value of the best solution = {solution_fitness}\".format(solution_fitness=solution_fitness))\n",
        "\n",
        "prediction = np.sum(np.array(function_inputs)*solution)\n",
        "print(\"Predicted output based on the best solution : {prediction}\".format(prediction=prediction))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_qfDTF0tNfNT",
        "outputId": "00eb9d31-bd43-43c6-ce39-b18512eff5bf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Parameters of the best solution : [-3.12490887  1.56340335  0.99667457 -0.4330145  -0.36318699 -1.745986\n",
            " -0.94163263  3.01198829  4.54733298 -1.67807689  3.91066719 -2.54098127\n",
            " -1.78645156 -0.08483592 -1.96101106  0.61088098 -2.07683298 -2.95719514\n",
            " -0.06125509  0.71621025  1.98014302 -0.44296854  3.30862819  0.99910378\n",
            " -2.90023211 -0.67983943  3.37496525  1.46218416 -3.73728743 -1.44351556\n",
            " -2.5176048   1.72589926  3.75344506 -2.10173709 -0.28365075  2.29263356\n",
            "  1.9057329  -0.30222329 -2.62331426 -0.64667414  1.64821854  0.14020174\n",
            "  0.04952852 -2.40881175  2.95651599 -1.7214395  -0.90338934  2.10833557]\n",
            "Fitness value of the best solution = 0.008095032574281827\n",
            "Predicted output based on the best solution : 7027.46745200544\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Bucle de genético"
      ],
      "metadata": {
        "id": "cMdLx-9e_A27"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Preparativos\n",
        "# ==============================================================================\n",
        "from collections import defaultdict\n",
        "results = defaultdict(lambda: {})\n",
        "\n",
        "# Descarga de datos\n",
        "# ==============================================================================\n",
        "url = 'https://www.dropbox.com/s/2o68sl7q6dcv156/data_no_timestamp.csv?dl=1'\n",
        "datos = pd.read_csv(url, sep=',', names=['fecha', 'y'], usecols=['fecha', 'y'], header=None)\n",
        "\n",
        "# Preparación del dato\n",
        "# ==============================================================================\n",
        "datos['fecha'] = pd.to_datetime(datos['fecha'], format='%Y-%m-%dT%H:%M:%S.%f', errors = 'coerce')\n",
        "datos = datos.set_index('fecha')\n",
        "datos = datos.rename(columns={'x': 'y'})\n",
        "datos = datos.asfreq('10min')\n",
        "datos = datos.astype({'y': 'float'})\n",
        "datos = datos.sort_index()\n",
        "\n",
        "for i in range(0, len(datos) - 49):\n",
        "  # Separación datos train-test\n",
        "  # ==============================================================================\n",
        "  function_inputs = datos['y'][i:i+48]\n",
        "  desired_output  = datos['y'][i+48:i+49]\n",
        "\n",
        "  # Función fitness\n",
        "  # ==============================================================================\n",
        "  def fitness_func(solution, solution_idx):\n",
        "    output = np.sum(solution*function_inputs)\n",
        "    fitness = 1.0 / np.abs(output - desired_output)\n",
        "    return fitness[0]\n",
        "\n",
        "  # Crear y entrenar\n",
        "  # ==============================================================================\n",
        "  fitness_function = fitness_func\n",
        "\n",
        "  num_generations = 50\n",
        "  num_parents_mating = 4\n",
        "\n",
        "  sol_per_pop = 8\n",
        "  num_genes = len(function_inputs)\n",
        "\n",
        "  init_range_low = -2\n",
        "  init_range_high = 5\n",
        "\n",
        "  parent_selection_type = \"sss\"\n",
        "  keep_parents = 1\n",
        "\n",
        "  crossover_type = \"single_point\"\n",
        "\n",
        "  mutation_type = \"random\"\n",
        "  mutation_percent_genes = 10\n",
        "\n",
        "  ga_instance = pg.GA(num_generations=num_generations,\n",
        "                       num_parents_mating=num_parents_mating,\n",
        "                       fitness_func=fitness_function,\n",
        "                       sol_per_pop=sol_per_pop,\n",
        "                       num_genes=num_genes,\n",
        "                       init_range_low=init_range_low,\n",
        "                       init_range_high=init_range_high,\n",
        "                       parent_selection_type=parent_selection_type,\n",
        "                       keep_parents=keep_parents,\n",
        "                       crossover_type=crossover_type,\n",
        "                       mutation_type=mutation_type,\n",
        "                       mutation_percent_genes=mutation_percent_genes)\n",
        "\n",
        "  # Predicciones\n",
        "  # ==============================================================================\n",
        "  ga_instance.run()\n",
        "\n",
        "  solution, solution_fitness, solution_idx = ga_instance.best_solution()\n",
        "  #print(\"Parameters of the best solution : {solution}\".format(solution=solution))\n",
        "  #print(\"Fitness value of the best solution = {solution_fitness}\".format(solution_fitness=solution_fitness))\n",
        "\n",
        "  prediction = np.sum(np.array(function_inputs)*solution)\n",
        "  #print(\"Predicted output based on the best solution : {prediction}\".format(prediction=prediction))\n",
        "\n",
        "  # Error test\n",
        "  # ==============================================================================\n",
        "  error_mse = mean_squared_error(\n",
        "                  y_true = desired_output,\n",
        "                  y_pred = [prediction]\n",
        "              )\n",
        "\n",
        "  error_mae = mean_absolute_error(desired_output, [prediction])\n",
        "\n",
        "  results['Iter'][i] = i\n",
        "  results['Solut'][i] = solution\n",
        "  results['Orig'][i] = desired_output[0]\n",
        "  results['Pred'][i] = prediction\n",
        "  results['MAE'][i] = error_mae\n",
        "\n",
        "  #print(\"Iteración: \", i,\", Original: \", desired_output[0],\", MAE: \", error_mae)\n",
        "  #print(\"- - - - - - - - - - - - - - - - - -\")"
      ],
      "metadata": {
        "id": "l2MDRuL8_FoD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Registro en CSV"
      ],
      "metadata": {
        "id": "8o65PqvAa1hn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import csv\n",
        "\n",
        "with open('/content/drive/MyDrive/Ejecucion1EOLO.csv', 'w') as csvfile:\n",
        "  fieldnames = ['iter', 'solut', 'orig', 'pred', 'mae']\n",
        "  writer = csv.DictWriter(csvfile, fieldnames=fieldnames)\n",
        "\n",
        "  writer.writeheader()\n",
        "  for i in range(len(results['MAE'])):\n",
        "    writer.writerow({'iter': results['Iter'][i],\n",
        "                     'solut': results['Solut'][i],\n",
        "                     'orig': results['Orig'][i],\n",
        "                     'pred': results['Pred'][i],\n",
        "                     'mae': results['MAE'][i]})"
      ],
      "metadata": {
        "id": "eJFLe2Cw5-38"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Lectura de CSV"
      ],
      "metadata": {
        "id": "Z_Lrzq_Za8C1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from collections import defaultdict\n",
        "results = defaultdict(lambda: {})\n",
        "\n",
        "# Read training set\n",
        "filename = '/content/drive/MyDrive/Investigación/CSVs PyGAD/Ejecucion3.csv'\n",
        "datos = pd.read_csv(filename, sep=',')\n",
        "datos = datos.set_index('iter')\n",
        "\n",
        "for i in range(len(datos)):\n",
        "  results['Iter'][i] = i\n",
        "  results['Solut'][i] = datos['solut'][i].split()\n",
        "  results['Orig'][i] = datos['orig'][i]\n",
        "  results['Pred'][i] = datos['pred'][i]\n",
        "  results['MAE'][i] = datos['mae'][i]"
      ],
      "metadata": {
        "id": "P1CUlWOta8tu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Operaciones"
      ],
      "metadata": {
        "id": "62gJB1p1bAI2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sum(results['MAE'].values()) / len(results['MAE'])"
      ],
      "metadata": {
        "id": "16jYWLMc5W7d"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "max(results['MAE'].values())"
      ],
      "metadata": {
        "id": "r_I90LQV7YFl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "min(results['MAE'].values())"
      ],
      "metadata": {
        "id": "W799eRjJ7a3P"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Mapa de Calor"
      ],
      "metadata": {
        "id": "t3gBFC3zh-vQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns\n",
        "\n",
        "def heatmap_generations(solutions):\n",
        "  heat2 = []\n",
        "\n",
        "  errors = []\n",
        "  for x in solutions:\n",
        "    for z in x:\n",
        "      if (z != '[' and z != ']'):\n",
        "        w = z.replace(\"[\", \"\")\n",
        "        h = w.replace(\"]\", \"\")\n",
        "        errors.append(float(h))\n",
        "\n",
        "  index_pos = np.arange(0, len(solutions))\n",
        "  index_alfa = np.arange(0, len(solutions))\n",
        "\n",
        "  data = {\n",
        "      'generacion': np.repeat(index_pos, len(solutions)),\n",
        "      'posicion': np.tile(index_alfa, len(solutions)),\n",
        "      'ventana': errors\n",
        "    }\n",
        "\n",
        "  df = pd.DataFrame(data, columns = ['generacion', 'posicion', 'ventana'])\n",
        "  df = df.pivot('generacion', 'posicion', 'ventana')\n",
        "\n",
        "  fig, ax = plt.subplots(figsize=(50,50)) \n",
        "\n",
        "  sns.heatmap(df, ax=ax)"
      ],
      "metadata": {
        "id": "3duv6PJRiAhY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "array = []\n",
        "\n",
        "for x in range(48):\n",
        "  array.append(results['Solut'][len(results['Solut']) - (48 - x)])\n",
        "\n",
        "heatmap_generations(array)"
      ],
      "metadata": {
        "id": "nCYlTXftjEJI",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "cb8248d1-ccf1-4720-a287-dcd2985db85a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 3600x3600 with 2 Axes>"
            ],
            "image/png": 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\n"
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}