02 - Modelos y analisis.ipynb

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      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/JohanSH7/PROYECTO_SBERBANK_RUSSIAN_HOUSING_MARKET/blob/main/02%20-%20Modelos%20y%20analisis.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-mCd6yNBYd6T"
      },
      "source": [
        "# **Importamos las Bibliotecas**\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!wget --no-cache -O init.py -q https://raw.githubusercontent.com/rramosp/ai4eng.v1/main/content/init.py\n",
        "import init; init.init(force_download=False); init.get_weblink()\n",
        "! pip install kneed"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 128
        },
        "id": "fbwV9nf8fAA_",
        "outputId": "4c4a22c1-9fda-40a2-b019-165e7eb21a47"
      },
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              "<script src=\"https://apis.google.com/js/client:platform.js?onload=google_button_start\"></script>\n",
              "    "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting kneed\n",
            "  Downloading kneed-0.8.5-py3-none-any.whl (10 kB)\n",
            "Requirement already satisfied: numpy>=1.14.2 in /usr/local/lib/python3.10/dist-packages (from kneed) (1.23.5)\n",
            "Requirement already satisfied: scipy>=1.0.0 in /usr/local/lib/python3.10/dist-packages (from kneed) (1.11.3)\n",
            "Installing collected packages: kneed\n",
            "Successfully installed kneed-0.8.5\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 102,
      "metadata": {
        "id": "88GPOqTIVEbJ"
      },
      "outputs": [],
      "source": [
        "#Bibliotecas y librerias necesarias\n",
        "from sklearn.neighbors import NearestNeighbors\n",
        "from sklearn import cluster, mixture\n",
        "from kneed import KneeLocator\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "from local.lib import calhousing as ch\n",
        "import matplotlib.pyplot as plt\n",
        "import gdown\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.svm import SVR\n",
        "from sklearn.ensemble import RandomForestRegressor\n",
        "from sklearn.model_selection import train_test_split, ShuffleSplit, cross_val_score, learning_curve\n",
        "from sklearn.metrics import median_absolute_error, r2_score, mean_squared_log_error, log_loss, make_scorer\n",
        "from sklearn.preprocessing import MultiLabelBinarizer, StandardScaler\n",
        "from sklearn.multioutput import MultiOutputClassifier\n",
        "from sklearn.decomposition import PCA\n",
        "from progressbar import progressbar as pbar\n",
        "from sklearn.tree import DecisionTreeRegressor"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Mqm_PPmbYttX"
      },
      "source": [
        "# **Preparamos los datos**\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 112,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rwZeG4TdY0fD",
        "outputId": "ca4e6191-fdbb-4ab6-9c26-6b2ecf112f9c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Downloading...\n",
            "From: https://drive.google.com/uc?id=1SDuXb0zIWnj2ZDPOuXjj-fXcghwbVeb4\n",
            "To: /content/train_final.csv\n",
            "100%|██████████| 57.9M/57.9M [00:00<00:00, 267MB/s]\n",
            "Downloading...\n",
            "From: https://drive.google.com/uc?id=1QnfSJJC1l84fjeeHgl3Fffe0orrLPHj-\n",
            "To: /content/test_final.csv\n",
            "100%|██████████| 14.4M/14.4M [00:00<00:00, 222MB/s]\n"
          ]
        }
      ],
      "source": [
        "# Enlaces directos a los archivos en Google Drive\n",
        "urls = {\n",
        "    'train_final': 'https://drive.google.com/uc?id=1SDuXb0zIWnj2ZDPOuXjj-fXcghwbVeb4',\n",
        "    'test_final': 'https://drive.google.com/uc?id=1QnfSJJC1l84fjeeHgl3Fffe0orrLPHj-',\n",
        "}\n",
        "\n",
        "# Descargar y leer cada archivo\n",
        "dfs = {}\n",
        "for name, url in urls.items():\n",
        "    output = f\"{name}.csv\"\n",
        "    gdown.download(url, output, quiet=False)\n",
        "    dfs[name] = pd.read_csv(output)\n",
        "\n",
        "# Ahora las siguientes variables contienen los DataFrames correspondientes a cada archivo\n",
        "DF = dfs['train_final']\n",
        "Test = dfs['test_final']\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Elegimos unos cuantos registros puesto que al entrenar los modelos el tiempo de ejecucion es demasiado extenso"
      ],
      "metadata": {
        "id": "psAoQWMBQGUV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Para el conjunto de datos DF\n",
        "X = DF.sample(n=10000, random_state=42)  # Elige un valor apropiado para random_state"
      ],
      "metadata": {
        "id": "gFw1Db2kQXGw"
      },
      "execution_count": 113,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Separamos la variable a predecir**"
      ],
      "metadata": {
        "id": "jI6nnHy9spJ9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "y = X['price_doc']\n",
        "x = X.drop(['price_doc'], axis=1)"
      ],
      "metadata": {
        "id": "yMrr7Qn6sooY"
      },
      "execution_count": 114,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(x.shape, y.shape, Test.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "prVTpxEfvnMp",
        "outputId": "e674131f-7f5d-4759-e49c-ed2d698194b6"
      },
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(10000, 450) (10000,) (7662, 450)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Modelo Supervisado**"
      ],
      "metadata": {
        "id": "qR_lO9YEPcDC"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Definimos la metrica de desempeño"
      ],
      "metadata": {
        "id": "fGm3CO6TWuqO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def rmlse(estimator, X, y):\n",
        "    preds = estimator.predict(X)\n",
        "    relative_errors = np.log1p(preds) - np.log1p(y)\n",
        "    squared_errors = relative_errors**2\n",
        "    mean_squared_error = np.mean(squared_errors)\n",
        "    rmlse = np.sqrt(mean_squared_error)\n",
        "    return rmlse"
      ],
      "metadata": {
        "id": "i97V7_6uWs4s"
      },
      "execution_count": 85,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Separamos los datos en train y validacion"
      ],
      "metadata": {
        "id": "8DhMqvEtW1_J"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "Xtr, Xts, ytr, yts = train_test_split(x,y, test_size=0.3)\n",
        "print (Xtr.shape, ytr.shape, Xts.shape, yts.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZPv48Qp-8WGp",
        "outputId": "dd44e719-eda1-4fcb-feb6-8ab7787bd78a"
      },
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(7000, 450) (7000,) (3000, 450) (3000,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Entrenamos los modelos a evaluar (RandomForest, SuportVectorMachine, DecisionTree)"
      ],
      "metadata": {
        "id": "TY8ggNxVXMuq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "rf = RandomForestRegressor(n_estimators=5, max_depth=10)\n",
        "rf.fit(Xtr,ytr)\n",
        "\n",
        "svr = SVR(gamma=2)\n",
        "svr.fit(Xtr, ytr)\n",
        "\n",
        "dtr = DecisionTreeRegressor(max_depth=5)\n",
        "dtr.fit(Xtr, ytr)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "iFUWBMLoXUcJ",
        "outputId": "e0f33aa4-3edf-4e8c-aff9-8fcf36d78d1b"
      },
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeRegressor(max_depth=5)"
            ],
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            ]
          },
          "metadata": {},
          "execution_count": 77
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Evaluamos con nuestra metrica de desempeño"
      ],
      "metadata": {
        "id": "B0lgmB4wXiQd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(rmlse(rf, Xtr, ytr), rmlse(rf, Xts, yts))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0yxKng9hXhWE",
        "outputId": "8655f829-f2ce-491c-a222-2999d4645648"
      },
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.39867161832679193 0.4765729068615031\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(rmlse(svr, Xtr, ytr), rmlse(svr, Xts, yts))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aEjRTmaNbnI_",
        "outputId": "21ed3220-b4dd-44ea-c45d-a135959c8046"
      },
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.6051424857685843 0.5900018695540759\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(rmlse(dtr, Xtr, ytr), rmlse(dtr, Xts, yts))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zh0uMvy0bop0",
        "outputId": "06ec4d1f-82ff-45a4-d840-97f64491702d"
      },
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.4896266179969588 0.49224113337258274\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Evaluamos las metricas con cross-validation."
      ],
      "metadata": {
        "id": "evWI85Oqbs2o"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "z = cross_val_score(rf, x, y, cv = ShuffleSplit(n_splits=10, test_size=0.3), scoring=rmlse)\n",
        "print (z)\n",
        "print (\"test score  %.3f (±%.4f)\"%(np.mean(z), np.std(z)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "s-DNsJXWcDrp",
        "outputId": "26cd0b71-6389-412f-c12f-2bd632f7d6ad"
      },
      "execution_count": 86,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0.50537727 0.48823242 0.46170753 0.4930086  0.45705083 0.46481507\n",
            " 0.48069792 0.49249753 0.48419297 0.49262655]\n",
            "test score  0.482 (±0.0151)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "El siguiente codigo es el analisis para el modelo SVR, no se pone en bloque de codigo, por tiempo de ejecucion.\n",
        "\n",
        "z = cross_val_score(svr, x, y, cv = ShuffleSplit(n_splits=5, test_size=0.3), scoring=rmlse)\n",
        "print (z)\n",
        "print (\"test score  %.3f (±%.4f)\"%(np.mean(z), np.std(z)))\n",
        "*****\n",
        "[0.60756651 0.5879302  0.59375958 0.61269421 0.59700581]\n",
        "\n",
        "test score  0.600 (±0.0091)"
      ],
      "metadata": {
        "id": "xQheRgNwdm5H"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "z = cross_val_score(dtr, x, y, cv = ShuffleSplit(n_splits=10, test_size=0.3), scoring=rmlse)\n",
        "print (z)\n",
        "print (\"test score  %.3f (±%.4f)\"%(np.mean(z), np.std(z)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kCJPJPz6c_Eb",
        "outputId": "d12fad57-fc87-4f04-e399-06356d1eb662"
      },
      "execution_count": 88,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0.48947823 0.48791437 0.51439166 0.48740191 0.5252822  0.5143389\n",
            " 0.51425814 0.49133538 0.48656893 0.48637485]\n",
            "test score  0.500 (±0.0145)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Curvas de aprendizaje\n",
        "\n",
        "**Nota:** Duran aproximadamente 1 minuto en ejecucion."
      ],
      "metadata": {
        "id": "UA-8XaOKebur"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "cv = ShuffleSplit(n_splits=10, test_size=.3)\n",
        "ch.plot_learning_curve(rf, rf.__class__.__name__, x, y,\n",
        "                       cv=cv, scoring=rmlse, ylim=(0,0.8))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "akpw_LT0ec5d",
        "outputId": "e79db9d6-379d-4bd5-e540-852068db63e5"
      },
      "execution_count": 97,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Nota:** El siguiente modelo tardo 10 minutos en ejecucion."
      ],
      "metadata": {
        "id": "XkInr1kqk9_D"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "cv = ShuffleSplit(n_splits=5, test_size=.3)\n",
        "ch.plot_learning_curve(svr, svr.__class__.__name__, x, y,\n",
        "                       cv=cv, scoring=rmlse, ylim=(0,0.8))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "yZlfMGgiiakp",
        "outputId": "6cc10098-e792-4da5-aa3f-266fe2a662ee"
      },
      "execution_count": 98,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cv = ShuffleSplit(n_splits=10, test_size=.3)\n",
        "ch.plot_learning_curve(dtr, dtr.__class__.__name__, x, y,\n",
        "                       cv=cv, scoring=rmlse, ylim=(0,0.8))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "ZmVlSXrCijyp",
        "outputId": "28e81e1f-4065-4325-e396-7c6ed8c8732d"
      },
      "execution_count": 99,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Modelos No Supervisados**"
      ],
      "metadata": {
        "id": "Kl3sXnZpl1Dh"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eT1ciPahZaYI"
      },
      "source": [
        "## **Analisamos la cantidad de componentes optima para el proyecto**\n",
        "\n",
        "Debido a la alta dimensionalidad, es necesario aplicar una tecnica de reduccion de dimensionalidad y de esta forma mejorar la eficiencia en cuanto a tiempo de ejecucion."
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# Escala los datos para tener media cero y varianza unitaria\n",
        "scaler = StandardScaler()\n",
        "x_scaled = scaler.fit_transform(x)\n",
        "\n",
        "# Encuentra el número óptimo de componentes\n",
        "pca = PCA()\n",
        "pca.fit(x_scaled)\n",
        "\n",
        "# Calcula la varianza acumulativa explicada\n",
        "cumulative_variance = np.cumsum(pca.explained_variance_ratio_)\n",
        "\n",
        "# Imprime el porcentaje de varianza acumulativa para cada componente\n",
        "for i in range(0, len(cumulative_variance), 20):\n",
        "    print(f'Número de Componentes: {i + 1}, Varianza Acumulativa Explicada: {cumulative_variance[i] * 100:.2f}%')\n",
        "\n",
        "# Grafica la varianza acumulativa explicada\n",
        "plt.plot(cumulative_variance)\n",
        "plt.xlabel('Número de Componentes Principales')\n",
        "plt.ylabel('Varianza Acumulativa Explicada')\n",
        "plt.title('Varianza Acumulativa Explicada por Componentes Principales')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 896
        },
        "id": "zNsik3dF9akI",
        "outputId": "6e7d376b-277a-4e19-d53c-acfb9f5ed40f"
      },
      "execution_count": 106,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Número de Componentes: 1, Varianza Acumulativa Explicada: 21.25%\n",
            "Número de Componentes: 21, Varianza Acumulativa Explicada: 58.30%\n",
            "Número de Componentes: 41, Varianza Acumulativa Explicada: 67.91%\n",
            "Número de Componentes: 61, Varianza Acumulativa Explicada: 74.27%\n",
            "Número de Componentes: 81, Varianza Acumulativa Explicada: 79.42%\n",
            "Número de Componentes: 101, Varianza Acumulativa Explicada: 84.13%\n",
            "Número de Componentes: 121, Varianza Acumulativa Explicada: 88.68%\n",
            "Número de Componentes: 141, Varianza Acumulativa Explicada: 93.18%\n",
            "Número de Componentes: 161, Varianza Acumulativa Explicada: 96.77%\n",
            "Número de Componentes: 181, Varianza Acumulativa Explicada: 98.50%\n",
            "Número de Componentes: 201, Varianza Acumulativa Explicada: 99.26%\n",
            "Número de Componentes: 221, Varianza Acumulativa Explicada: 99.64%\n",
            "Número de Componentes: 241, Varianza Acumulativa Explicada: 99.82%\n",
            "Número de Componentes: 261, Varianza Acumulativa Explicada: 99.91%\n",
            "Número de Componentes: 281, Varianza Acumulativa Explicada: 99.96%\n",
            "Número de Componentes: 301, Varianza Acumulativa Explicada: 99.98%\n",
            "Número de Componentes: 321, Varianza Acumulativa Explicada: 100.00%\n",
            "Número de Componentes: 341, Varianza Acumulativa Explicada: 100.00%\n",
            "Número de Componentes: 361, Varianza Acumulativa Explicada: 100.00%\n",
            "Número de Componentes: 381, Varianza Acumulativa Explicada: 100.00%\n",
            "Número de Componentes: 401, Varianza Acumulativa Explicada: 100.00%\n",
            "Número de Componentes: 421, Varianza Acumulativa Explicada: 100.00%\n",
            "Número de Componentes: 441, Varianza Acumulativa Explicada: 100.00%\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "PCA - Random Forest"
      ],
      "metadata": {
        "id": "0UQcaa0erLZG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Número de componentes para PCA\n",
        "n_components = 101  # Puedes cambiar este valor al número específico que necesitas\n",
        "\n",
        "# Aplicar PCA\n",
        "pca = PCA(n_components=n_components)\n",
        "X_pca = pca.fit_transform(x)\n",
        "\n",
        "# Dividir los datos en conjuntos de entrenamiento y prueba\n",
        "X_train, X_test, y_train, y_test = train_test_split(X_pca, y, test_size=0.3, random_state=42)\n",
        "\n",
        "# Definir el modelo Random Forest\n",
        "regressor = RandomForestRegressor( max_depth=5)\n",
        "\n",
        "# Entrenar el modelo\n",
        "regressor.fit(X_train, y_train)\n",
        "\n",
        "# Realizar predicciones en el conjunto de prueba\n",
        "y_pred = regressor.predict(X_test)\n",
        "\n",
        "# Calcular el RMSLE\n",
        "rmsle = np.sqrt(mean_squared_log_error(y_test, y_pred))\n",
        "\n",
        "# Imprimir el resultado\n",
        "print(f\"RMSLE con {n_components} componente(s): {rmsle}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "y-Dal4u_sTZ7",
        "outputId": "cb22df22-e378-4449-8744-7a8ef2afa8de"
      },
      "execution_count": 118,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "RMSLE con 101 componente(s): 0.5237447190090955\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "K-Means Clustering - Regresión Lineal y Random Forest"
      ],
      "metadata": {
        "id": "mRRTdlOa0heT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.cluster import KMeans\n",
        "from sklearn.ensemble import RandomForestRegressor\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import mean_squared_log_error\n",
        "import numpy as np\n",
        "\n",
        "# Número de clústeres (ajústalo según tus necesidades)\n",
        "n_clusters = 3\n",
        "\n",
        "# Aplicar K-Means Clustering\n",
        "kmeans = KMeans(n_clusters=n_clusters, random_state=42)\n",
        "clusters = kmeans.fit_predict(x)\n",
        "\n",
        "# Agregar la asignación de clústeres como una nueva característica\n",
        "X_clustered = np.column_stack((X, clusters))\n",
        "\n",
        "# Dividir los datos en conjuntos de entrenamiento y prueba\n",
        "X_train, X_test, y_train, y_test = train_test_split(X_clustered, y, test_size=0.3, random_state=42)\n",
        "\n",
        "# Modelo supervisado - Regresión Lineal\n",
        "regressor_lr = LinearRegression()\n",
        "regressor_lr.fit(X_train, y_train)\n",
        "y_pred_lr = regressor_lr.predict(X_test)\n",
        "\n",
        "# Calcular RMSLE\n",
        "rmsle_lr = np.sqrt(mean_squared_log_error(y_test, y_pred_lr))\n",
        "print(f\"RMSLE con K-Means + Regresión Lineal: {rmsle_lr}\")\n",
        "\n",
        "# Modelo supervisado - Random Forest\n",
        "regressor_rf = RandomForestRegressor(n_estimators=100, max_depth=None, min_samples_split=2, random_state=42)\n",
        "regressor_rf.fit(X_train, y_train)\n",
        "y_pred_rf = regressor_rf.predict(X_test)\n",
        "\n",
        "# Calcular RMSLE\n",
        "rmsle_rf = np.sqrt(mean_squared_log_error(y_test, y_pred_rf))\n",
        "print(f\"RMSLE con K-Means + Random Forest: {rmsle_rf}\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CDDFqYa80ag6",
        "outputId": "ca5590a4-efc7-4487-a53e-75fa2e0eb6b6"
      },
      "execution_count": 119,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "RMSLE con K-Means + Regresión Lineal: 3.589237313069348e-15\n",
            "RMSLE con K-Means + Random Forest: 0.009861334444173355\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Curvas de aprendizaje"
      ],
      "metadata": {
        "id": "GJxO7Ato1R0G"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.decomposition import PCA\n",
        "from sklearn.ensemble import RandomForestRegressor\n",
        "from sklearn.cluster import KMeans\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.model_selection import learning_curve, train_test_split\n",
        "\n",
        "# Función para visualizar curvas de aprendizaje\n",
        "def plot_learning_curve(estimator, title, X, y, ylim=None, cv=None, n_jobs=None, train_sizes=np.linspace(.1, 1.0, 5)):\n",
        "    plt.figure()\n",
        "    plt.title(title)\n",
        "    if ylim is not None:\n",
        "        plt.ylim(*ylim)\n",
        "    plt.xlabel(\"Training examples\")\n",
        "    plt.ylabel(\"Score\")\n",
        "\n",
        "    train_sizes, train_scores, test_scores = learning_curve(\n",
        "        estimator, X, y, cv=cv, n_jobs=n_jobs, train_sizes=train_sizes, scoring='neg_mean_squared_log_error')\n",
        "\n",
        "    train_scores_mean = np.mean(train_scores, axis=1)\n",
        "    train_scores_std = np.std(train_scores, axis=1)\n",
        "    test_scores_mean = np.mean(test_scores, axis=1)\n",
        "    test_scores_std = np.std(test_scores, axis=1)\n",
        "\n",
        "    plt.grid()\n",
        "\n",
        "    plt.fill_between(train_sizes, train_scores_mean - train_scores_std,\n",
        "                     train_scores_mean + train_scores_std, alpha=0.1,\n",
        "                     color=\"r\")\n",
        "    plt.fill_between(train_sizes, test_scores_mean - test_scores_std,\n",
        "                     test_scores_mean + test_scores_std, alpha=0.1, color=\"g\")\n",
        "    plt.plot(train_sizes, train_scores_mean, 'o-', color=\"r\", label=\"Training score\")\n",
        "    plt.plot(train_sizes, test_scores_mean, 'o-', color=\"g\", label=\"Cross-validation score\")\n",
        "\n",
        "    plt.legend(loc=\"best\")\n",
        "    return plt\n",
        "\n",
        "# PCA + Random Forest\n",
        "n_components = 101  # Ajusta según tus necesidades\n",
        "pca = PCA(n_components=n_components)\n",
        "X_pca = pca.fit_transform(x)\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X_pca, y, test_size=0.3, random_state=42)\n",
        "\n",
        "estimator_pca_rf = RandomForestRegressor(max_depth=5)\n",
        "\n",
        "title = \"Curva de Aprendizaje PCA + Random Forest\"\n",
        "plot_learning_curve(estimator_pca_rf, title, X_train, y_train, ylim=(0, 0.5), cv=5, n_jobs=-1)\n",
        "\n",
        "# K-Means + Regresión Lineal\n",
        "n_clusters = 3  # Ajusta según tus necesidades\n",
        "kmeans = KMeans(n_clusters=n_clusters, random_state=42)\n",
        "clusters = kmeans.fit_predict(X)\n",
        "\n",
        "X_clustered = np.column_stack((X, clusters))\n",
        "X_train, X_test, y_train, y_test = train_test_split(X_clustered, y, test_size=0.3, random_state=42)\n",
        "\n",
        "estimator_kmeans_lr = LinearRegression()\n",
        "\n",
        "title = \"Curva de Aprendizaje K-Means + Regresión Lineal\"\n",
        "plot_learning_curve(estimator_kmeans_lr, title, X_train, y_train, ylim=(0, 0.5), cv=2)\n",
        "\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 927
        },
        "id": "5aJxub5_1RXj",
        "outputId": "5d92d7d4-0039-46a8-d80e-1e785d870e92"
      },
      "execution_count": 121,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyN/M/mPRyxs2sSWctaCt4Gq",
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}