code.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# DATA EXPLORATION & ANALYSIS FOR AGROFOOD_CO2_EMISSIONS DATASET"
      ],
      "metadata": {
        "id": "WslVnnB74Zit"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#importing the necessary libraries\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from datetime import timedelta\n",
        "from sklearn.model_selection import train_test_split\n",
        "import plotly.graph_objects as go\n",
        "from sklearn.preprocessing import MinMaxScaler, LabelEncoder, StandardScaler\n",
        "from sklearn.ensemble import RandomForestRegressor, AdaBoostRegressor, GradientBoostingRegressor\n",
        "from xgboost import XGBRegressor\n",
        "from sklearn.metrics import mean_absolute_error,mean_squared_error, r2_score\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")\n",
        "from sklearn.neighbors import LocalOutlierFactor\n",
        "from sklearn.naive_bayes import GaussianNB\n",
        "from sklearn.linear_model import Lasso, Ridge, LinearRegression\n",
        "from sklearn.model_selection import KFold\n",
        "from sklearn.svm import SVR\n",
        "import seaborn as sns\n",
        "from sklearn.decomposition import PCA\n",
        "from sklearn.ensemble import IsolationForest\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "from tensorflow.keras import layers\n",
        "from sklearn.metrics import accuracy_score\n",
        "import torch.utils.data as data_utils\n",
        "from sklearn.cluster import KMeans\n"
      ],
      "metadata": {
        "id": "ZXWhbDW14kKo"
      },
      "execution_count": 170,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Data Analysis"
      ],
      "metadata": {
        "id": "fAPxRn22O-OV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv('/content/drive/MyDrive/Agrofood_co2_emission.csv')\n",
        "df.dropna(inplace=True)"
      ],
      "metadata": {
        "id": "8e5zZUiRUmxV"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(20, 16))\n",
        "sns.heatmap(df.corr(), annot=True, cmap='coolwarm', center=0)\n",
        "plt.title(\"Visualising the CO2 Emissions Dataset using Correlation Heatmap\")\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "UvDHROgTpMx_",
        "outputId": "1b45d0a1-b45a-4eae-fd05-80a0d59b976e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x1600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "correlation_matrix = df.corr()\n",
        "threshold = 0.99\n",
        "\n",
        "pairs = []\n",
        "length = len(correlation_matrix.columns);\n",
        "for i in range(length):\n",
        "    for j in range(i):\n",
        "        if round(abs(correlation_matrix.iloc[i, j]), 2) >= threshold:\n",
        "            pairs.append((correlation_matrix.columns[i], correlation_matrix.columns[j]))\n",
        "\n",
        "figure_size = (15, 15)\n",
        "for pair in pairs:\n",
        "    sns.pairplot(data=df, vars=pair, height=3)\n",
        "    plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "0f08ruLlpWJE",
        "outputId": "31c4c2b7-fc25-489c-a1e3-8b54917dfa5e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x600 with 6 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x600 with 6 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Model"
      ],
      "metadata": {
        "id": "HWQCFj0PPE1f"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class Model:\n",
        "\n",
        "  def __init__(self, model, parameters, kf):\n",
        "    self.rs = model\n",
        "    self.parameters = parameters\n",
        "    self.df = pd.read_csv('/content/drive/MyDrive/Agrofood_co2_emission.csv')\n",
        "    self.df.dropna(inplace=True)\n",
        "    self.kf = kf\n",
        "\n",
        "  def outlier_removal(self, X, y):\n",
        "\n",
        "    # Fit the model on the training data\n",
        "    outliers_train = self.parameters['outlier_detector'].fit_predict(X)\n",
        "\n",
        "    # Identify and filter out outliers in the training set\n",
        "    mask_train = outliers_train != -1\n",
        "    X = X[mask_train]\n",
        "    y = y[mask_train]\n",
        "\n",
        "    # Print the number of outliers in the training set\n",
        "    number_outliers_train = len(outliers_train[outliers_train == -1])\n",
        "    print(\"Number of outliers in the training set:\", number_outliers_train)\n",
        "    return X, y\n",
        "\n",
        "  def preprocessing(self):\n",
        "    correlation_matrix = self.df.corr()\n",
        "    threshold = 0.99\n",
        "    correlated_features = set()\n",
        "\n",
        "    for i in range(len(correlation_matrix.columns)):\n",
        "        for j in range(i):\n",
        "            if round(abs(correlation_matrix.iloc[i, j]), 2) >= threshold:\n",
        "                colname = correlation_matrix.columns[i]\n",
        "                correlated_features.add(colname)\n",
        "\n",
        "    if(self.parameters[\"num_PCA_comp\"]==-1):\n",
        "      print(\"Number of Highly correlated features removes: \", len(correlated_features))\n",
        "      print(\"Features Removed: \", correlated_features)\n",
        "      print(\"\")\n",
        "\n",
        "    self.df.drop(columns=correlated_features, inplace=True)\n",
        "    correlation_matrix = self.df.corr()\n",
        "\n",
        "    self.X = self.df.iloc[:,0:-1]\n",
        "    self.Y = self.df.iloc[:,-1]\n",
        "\n",
        "    if(self.parameters[\"encoding\"]==\"OneHot\"):\n",
        "      self.X = pd.get_dummies(self.X, columns=self.X.select_dtypes(include=['object']).columns)\n",
        "    elif(self.parameters[\"encoding\"]==\"Label\"):\n",
        "      self.X[\"Area\"] = LabelEncoder().fit_transform(self.X[\"Area\"])\n",
        "\n",
        "  def training_validation(self):\n",
        "    self.preprocessing()\n",
        "    self.X = self.X.to_numpy()\n",
        "    self.Y = self.Y.to_numpy()\n",
        "    self.mae_train, self.rmse_train, self.r2_train = [], [], []\n",
        "    self.mae_val, self.rmse_val, self.r2_val = [], [], []\n",
        "\n",
        "    for i, (train_index, test_index) in enumerate(self.kf.split(self.X)):\n",
        "\n",
        "      X_train = np.array([self.X[i] for i in train_index])\n",
        "      Y_train = np.array([self.Y[i] for i in train_index])\n",
        "\n",
        "      if(self.parameters['outlier_detector']!=None):\n",
        "        X_train, Y_train = self.outlier_removal(X_train, Y_train)\n",
        "\n",
        "      X_val = np.array([self.X[i] for i in test_index])\n",
        "      Y_val = np.array([self.Y[i] for i in test_index])\n",
        "\n",
        "      if(self.parameters['additional_feature']):\n",
        "        kmeans = KMeans(n_clusters=3, random_state=42)\n",
        "        kmeans.fit(X_train)\n",
        "        cluster_id_train = kmeans.predict(X_train)\n",
        "        cluster_id_val = kmeans.predict(X_val)\n",
        "        X_train = np.concatenate((X_train, np.array([cluster_id_train]).T), axis=1)\n",
        "        X_val = np.concatenate((X_val, np.array([cluster_id_val]).T), axis=1)\n",
        "\n",
        "      ss=StandardScaler()\n",
        "      X_train = ss.fit_transform(X_train)\n",
        "      X_val = ss.transform(X_val)\n",
        "\n",
        "      if(self.parameters[\"num_PCA_comp\"]!=-1):\n",
        "        pca = PCA()\n",
        "        X_train = pca.fit_transform(X_train)\n",
        "        X_val = pca.transform(X_val)\n",
        "\n",
        "\n",
        "      self.rs.fit(X_train, Y_train)\n",
        "      Y_train_pred = self.rs.predict(X_train)\n",
        "      Y_val_pred = self.rs.predict(X_val)\n",
        "\n",
        "      self.mae_val.append(mean_absolute_error(Y_val, Y_val_pred))\n",
        "      self.mae_train.append(mean_absolute_error(Y_train, Y_train_pred))\n",
        "\n",
        "      self.rmse_val.append(np.sqrt(mean_squared_error(Y_val, Y_val_pred)))\n",
        "      self.rmse_train.append(np.sqrt(mean_squared_error(Y_train, Y_train_pred)))\n",
        "\n",
        "      self.r2_val.append(r2_score(Y_val, Y_val_pred))\n",
        "      self.r2_train.append(r2_score(Y_train, Y_train_pred))\n",
        "\n",
        "    if(self.parameters[\"num_PCA_comp\"]==-1):\n",
        "      print(\"Train mean absolute error: \", np.average(self.mae_train))\n",
        "      print(\"Train root mean squared error: \", np.average(self.rmse_train))\n",
        "      print(\"Train r2-score:\", np.average(self.r2_train))\n",
        "      print(\"\")\n",
        "      print(\"Validation mean absolute error: \", np.average(self.mae_val))\n",
        "      print(\"Validation root mean squared error: \", np.average(self.rmse_val))\n",
        "      print(\"Validation r2-score:\", np.average(self.r2_val))\n",
        "      print(\"\")\n",
        "    else:\n",
        "      return np.average(self.mae_train), np.average(self.rmse_train), np.average(self.r2_train), np.average(self.mae_val), np.average(self.rmse_val), np.average(self.r2_val)"
      ],
      "metadata": {
        "id": "SI_v1MGh9-Nv"
      },
      "execution_count": 184,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sZmiV4VS9gT5",
        "outputId": "6ac48b04-9a30-4744-a3ad-e29b287d9e31"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "num_folds = 5"
      ],
      "metadata": {
        "id": "-4DS_BmiJxxF"
      },
      "execution_count": 162,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "kf = KFold(n_splits=num_folds, random_state=42, shuffle=True)"
      ],
      "metadata": {
        "id": "UaZXaakXJwfg"
      },
      "execution_count": 163,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## One Hot Encoding, No PCA"
      ],
      "metadata": {
        "id": "0XHB3h1JUPKn"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Random Forest Regressor"
      ],
      "metadata": {
        "id": "kWnM7VttPPFv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = Model(model = RandomForestRegressor(), parameters = {\"encoding\": \"OneHot\", \"num_PCA_comp\": -1}, kf = kf)"
      ],
      "metadata": {
        "id": "va4oQqg-JNdk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.training_validation()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ApnPUacsLFwg",
        "outputId": "d3e9104a-555a-437c-f48f-4d7042d592d4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of Highly correlated features removes:  2\n",
            "Features Removed:  {'IPPU', 'Total Population - Female'}\n",
            "\n",
            "Train mean absolute error:  0.10287041146008265\n",
            "Train root mean squared error:  0.14160392856782425\n",
            "Train r2-score: 0.9404860067867963\n",
            "\n",
            "Validation mean absolute error:  0.27553908309697783\n",
            "Validation root mean squared error:  0.373812683361251\n",
            "Validation r2-score: 0.5844977903433669\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Linear Regression"
      ],
      "metadata": {
        "id": "VLVc4_h6KbTR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = Model(model = LinearRegression(), parameters = {\"encoding\": \"OneHot\", \"num_PCA_comp\": -1}, kf = kf)"
      ],
      "metadata": {
        "id": "TKSB1mNcKegN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.training_validation()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JzptQNEOEXdr",
        "outputId": "86890760-ea0a-4a84-dbdd-a83d725f810a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of Highly correlated features removes:  2\n",
            "Features Removed:  {'IPPU', 'Total Population - Female'}\n",
            "\n",
            "Train mean absolute error:  0.2950927547697523\n",
            "Train root mean squared error:  0.3998556434486456\n",
            "Train r2-score: 0.5254390712802296\n",
            "\n",
            "Validation mean absolute error:  232259863.91941524\n",
            "Validation root mean squared error:  4952865265.146614\n",
            "Validation r2-score: -1.7709187045609544e+20\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Gradient Boosting Regressor"
      ],
      "metadata": {
        "id": "kci05blGKh9T"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = Model(model = GradientBoostingRegressor(), parameters = {\"encoding\": \"OneHot\", \"num_PCA_comp\": -1}, kf = kf)"
      ],
      "metadata": {
        "id": "l_s-raFgKnvO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.training_validation()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yQRl-EImEskL",
        "outputId": "12f3e276-c3d3-4b18-bddb-181a12ae4767"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of Highly correlated features removes:  2\n",
            "Features Removed:  {'IPPU', 'Total Population - Female'}\n",
            "\n",
            "Train mean absolute error:  0.2728104826906141\n",
            "Train root mean squared error:  0.36495467404161086\n",
            "Train r2-score: 0.60466841279981\n",
            "\n",
            "Validation mean absolute error:  0.2972425587279775\n",
            "Validation root mean squared error:  0.39860999464426106\n",
            "Validation r2-score: 0.5274789841083053\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Adaboost Regressor"
      ],
      "metadata": {
        "id": "vvB6HXy5KrRv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = Model(model = AdaBoostRegressor(), parameters = {\"encoding\": \"OneHot\", \"num_PCA_comp\": -1}, kf = kf)"
      ],
      "metadata": {
        "id": "VKYOHfqUKxBo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.training_validation()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Djt09bwoFDrd",
        "outputId": "67478080-5738-48be-9839-a9f4c6e93493"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of Highly correlated features removes:  2\n",
            "Features Removed:  {'IPPU', 'Total Population - Female'}\n",
            "\n",
            "Train mean absolute error:  0.3453297827066076\n",
            "Train root mean squared error:  0.4393316993019708\n",
            "Train r2-score: 0.4270921046064844\n",
            "\n",
            "Validation mean absolute error:  0.35217749597048725\n",
            "Validation root mean squared error:  0.4566825149109758\n",
            "Validation r2-score: 0.3797962259817684\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### XGB Regressor"
      ],
      "metadata": {
        "id": "kWjQlo2bK4bu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = Model(model = XGBRegressor(), parameters = {\"encoding\": \"OneHot\", \"num_PCA_comp\": -1}, kf = kf)"
      ],
      "metadata": {
        "id": "tgkuCpcsKyZw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.training_validation()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nQiCAg8mFKbI",
        "outputId": "e88e84a2-b99a-4f48-c2d8-adf36b3d46d7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of Highly correlated features removes:  2\n",
            "Features Removed:  {'IPPU', 'Total Population - Female'}\n",
            "\n",
            "Train mean absolute error:  0.10916482868573203\n",
            "Train root mean squared error:  0.14488503152712146\n",
            "Train r2-score: 0.9376481305328934\n",
            "\n",
            "Validation mean absolute error:  0.27711783891769803\n",
            "Validation root mean squared error:  0.37425664830055466\n",
            "Validation r2-score: 0.5834190618025235\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Lasso Regressor"
      ],
      "metadata": {
        "id": "tVSQdLpFK7Hf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Best Alpha found is 0.0001\n",
        "alphas = [0.0, 0.0001, 0.001, 0.01, 0.1, 1.0]"
      ],
      "metadata": {
        "id": "1r9Wox03GZUG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for alpha in alphas:\n",
        "  model = Model(model = Lasso(alpha=alpha), parameters = {\"encoding\": \"OneHot\", \"num_PCA_comp\": -1}, kf = kf)\n",
        "  print(\"Alpha: \", alpha)\n",
        "  model.training_validation()\n",
        "  print(\"\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KmmEO171K0hi",
        "outputId": "da38826b-d595-4869-b4d7-d2bd90044fd2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Alpha:  0.0\n",
            "Number of Highly correlated features removes:  2\n",
            "Features Removed:  {'IPPU', 'Total Population - Female'}\n",
            "\n",
            "Train mean absolute error:  0.2951277859972585\n",
            "Train root mean squared error:  0.4000984413172426\n",
            "Train r2-score: 0.5248619942045201\n",
            "\n",
            "Validation mean absolute error:  0.30919683103283296\n",
            "Validation root mean squared error:  0.4189132755993882\n",
            "Validation r2-score: 0.4778115625164583\n",
            "\n",
            "\n",
            "Alpha:  0.0001\n",
            "Number of Highly correlated features removes:  2\n",
            "Features Removed:  {'IPPU', 'Total Population - Female'}\n",
            "\n",
            "Train mean absolute error:  0.294969284805219\n",
            "Train root mean squared error:  0.40019814528855147\n",
            "Train r2-score: 0.5246250482907234\n",
            "\n",
            "Validation mean absolute error:  0.3088952358368837\n",
            "Validation root mean squared error:  0.41883209269541605\n",
            "Validation r2-score: 0.4780155157489677\n",
            "\n",
            "\n",
            "Alpha:  0.001\n",
            "Number of Highly correlated features removes:  2\n",
            "Features Removed:  {'IPPU', 'Total Population - Female'}\n",
            "\n",
            "Train mean absolute error:  0.29644936974311237\n",
            "Train root mean squared error:  0.4025880010142894\n",
            "Train r2-score: 0.5189323888029018\n",
            "\n",
            "Validation mean absolute error:  0.3092554848361665\n",
            "Validation root mean squared error:  0.4199712109194701\n",
            "Validation r2-score: 0.4752184051999354\n",
            "\n",
            "\n",
            "Alpha:  0.01\n",
            "Number of Highly correlated features removes:  2\n",
            "Features Removed:  {'IPPU', 'Total Population - Female'}\n",
            "\n",
            "Train mean absolute error:  0.3108528468514924\n",
            "Train root mean squared error:  0.41720773676491907\n",
            "Train r2-score: 0.4833602937274966\n",
            "\n",
            "Validation mean absolute error:  0.31931932172115174\n",
            "Validation root mean squared error:  0.42979972062780625\n",
            "Validation r2-score: 0.4506888889974089\n",
            "\n",
            "\n",
            "Alpha:  0.1\n",
            "Number of Highly correlated features removes:  2\n",
            "Features Removed:  {'IPPU', 'Total Population - Female'}\n",
            "\n",
            "Train mean absolute error:  0.376858380135614\n",
            "Train root mean squared error:  0.49702724556002736\n",
            "Train r2-score: 0.26680049569357106\n",
            "\n",
            "Validation mean absolute error:  0.376990405926304\n",
            "Validation root mean squared error:  0.49724724900882605\n",
            "Validation r2-score: 0.2649686012028739\n",
            "\n",
            "\n",
            "Alpha:  1.0\n",
            "Number of Highly correlated features removes:  2\n",
            "Features Removed:  {'IPPU', 'Total Population - Female'}\n",
            "\n",
            "Train mean absolute error:  0.4504353825496491\n",
            "Train root mean squared error:  0.5804602097794778\n",
            "Train r2-score: 0.0\n",
            "\n",
            "Validation mean absolute error:  0.4505614703671757\n",
            "Validation root mean squared error:  0.5806324837299349\n",
            "Validation r2-score: -0.002216171492962227\n",
            "\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Ridge Regressor"
      ],
      "metadata": {
        "id": "oqXilqfIK-GY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Best Alpha 1.0\n",
        "alphas = [0.0, 0.0001, 0.001, 0.01, 0.1, 1.0]"
      ],
      "metadata": {
        "id": "kxaYxsXxHIKF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for alpha in alphas:\n",
        "  model = Model(model = Ridge(alpha=alpha), parameters = {\"encoding\": \"OneHot\", \"num_PCA_comp\": -1}, kf = kf)\n",
        "  print(\"Alpha: \", alpha)\n",
        "  model.training_validation()\n",
        "  print(\"\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-H9oCb_GK3LZ",
        "outputId": "fda4783d-a2cb-4340-b1b6-94b358eac476"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Alpha:  0.0\n",
            "Number of Highly correlated features removes:  2\n",
            "Features Removed:  {'IPPU', 'Total Population - Female'}\n",
            "\n",
            "Train mean absolute error:  0.2949476921374198\n",
            "Train root mean squared error:  0.399718856488112\n",
            "Train r2-score: 0.5257627907122122\n",
            "\n",
            "Validation mean absolute error:  674690638.1928765\n",
            "Validation root mean squared error:  14390236215.845821\n",
            "Validation r2-score: -2.132566697598497e+21\n",
            "\n",
            "\n",
            "Alpha:  0.0001\n",
            "Number of Highly correlated features removes:  2\n",
            "Features Removed:  {'IPPU', 'Total Population - Female'}\n",
            "\n",
            "Train mean absolute error:  0.29495060668687445\n",
            "Train root mean squared error:  0.3997183305844969\n",
            "Train r2-score: 0.5257640413031642\n",
            "\n",
            "Validation mean absolute error:  0.3095805551721883\n",
            "Validation root mean squared error:  0.418832426063226\n",
            "Validation r2-score: 0.4780088648329631\n",
            "\n",
            "\n",
            "Alpha:  0.001\n",
            "Number of Highly correlated features removes:  2\n",
            "Features Removed:  {'IPPU', 'Total Population - Female'}\n",
            "\n",
            "Train mean absolute error:  0.2949451108895986\n",
            "Train root mean squared error:  0.39971852048552853\n",
            "Train r2-score: 0.5257635908368443\n",
            "\n",
            "Validation mean absolute error:  0.3095627633759621\n",
            "Validation root mean squared error:  0.41882611529920777\n",
            "Validation r2-score: 0.47802471141552544\n",
            "\n",
            "\n",
            "Alpha:  0.01\n",
            "Number of Highly correlated features removes:  2\n",
            "Features Removed:  {'IPPU', 'Total Population - Female'}\n",
            "\n",
            "Train mean absolute error:  0.29491237966403216\n",
            "Train root mean squared error:  0.39972980224935695\n",
            "Train r2-score: 0.5257368238729642\n",
            "\n",
            "Validation mean absolute error:  0.3094410262396992\n",
            "Validation root mean squared error:  0.4187894174157429\n",
            "Validation r2-score: 0.47811673711706193\n",
            "\n",
            "\n",
            "Alpha:  0.1\n",
            "Number of Highly correlated features removes:  2\n",
            "Features Removed:  {'IPPU', 'Total Population - Female'}\n",
            "\n",
            "Train mean absolute error:  0.29488698494194493\n",
            "Train root mean squared error:  0.39982685600893836\n",
            "Train r2-score: 0.5255064214796548\n",
            "\n",
            "Validation mean absolute error:  0.3092427780938226\n",
            "Validation root mean squared error:  0.41876842349026855\n",
            "Validation r2-score: 0.47816887182129825\n",
            "\n",
            "\n",
            "Alpha:  1.0\n",
            "Number of Highly correlated features removes:  2\n",
            "Features Removed:  {'IPPU', 'Total Population - Female'}\n",
            "\n",
            "Train mean absolute error:  0.29484287265905235\n",
            "Train root mean squared error:  0.39993591300299486\n",
            "Train r2-score: 0.5252475309189976\n",
            "\n",
            "Validation mean absolute error:  0.3089492939469742\n",
            "Validation root mean squared error:  0.4187287286640716\n",
            "Validation r2-score: 0.47826788042327373\n",
            "\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Support Vector Machine"
      ],
      "metadata": {
        "id": "6E8_oFOcLLVQ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### RBF kernel"
      ],
      "metadata": {
        "id": "fwvnYD5WYOls"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = Model(model = SVR(kernel='rbf'), parameters = {\"encoding\": \"OneHot\", \"num_PCA_comp\": -1}, kf = kf)"
      ],
      "metadata": {
        "id": "xlCPU4nVKtks"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.training_validation()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uo1-ZAItKzsm",
        "outputId": "030e8012-d4e4-426f-88a6-5ed5f4ac54fd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of Highly correlated features removes:  2\n",
            "Features Removed:  {'IPPU', 'Total Population - Female'}\n",
            "\n",
            "Train mean absolute error:  0.2843938906100245\n",
            "Train root mean squared error:  0.39708073191154303\n",
            "Train r2-score: 0.5319924123983014\n",
            "\n",
            "Validation mean absolute error:  0.30732937101046\n",
            "Validation root mean squared error:  0.4210538797445473\n",
            "Validation r2-score: 0.4724346431298181\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### Linear kernel"
      ],
      "metadata": {
        "id": "26lDtzr5YVuH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = Model(model = SVR(kernel='linear'), parameters = {\"encoding\": \"OneHot\", \"num_PCA_comp\": -1}, kf = kf)"
      ],
      "metadata": {
        "id": "Mtnb6K_mK8F_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.training_validation()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "90TDGglmYp44",
        "outputId": "5e67392a-5172-4eab-9044-d4bc1f682d98"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of Highly correlated features removes:  2\n",
            "Features Removed:  {'IPPU', 'Total Population - Female'}\n",
            "\n",
            "Train mean absolute error:  0.29256213430108147\n",
            "Train root mean squared error:  0.4042210803642064\n",
            "Train r2-score: 0.5150240724173237\n",
            "\n",
            "Validation mean absolute error:  0.3107138263430083\n",
            "Validation root mean squared error:  0.4224501515245084\n",
            "Validation r2-score: 0.4690411891271259\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### Poly Kernel"
      ],
      "metadata": {
        "id": "S1ooMqvEYuI6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = Model(model = SVR(kernel='poly'), parameters = {\"encoding\": \"OneHot\", \"num_PCA_comp\": -1}, kf = kf)"
      ],
      "metadata": {
        "id": "tKeyxpjBLDaq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.training_validation()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BrKZD0okLHnX",
        "outputId": "d31287a1-d5f0-401a-f54a-6ae5ce1948ca"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of Highly correlated features removes:  2\n",
            "Features Removed:  {'IPPU', 'Total Population - Female'}\n",
            "\n",
            "Train mean absolute error:  0.3166739404118014\n",
            "Train root mean squared error:  0.44111258379130386\n",
            "Train r2-score: 0.42246076329764365\n",
            "\n",
            "Validation mean absolute error:  0.34430941236322743\n",
            "Validation root mean squared error:  0.469899275220409\n",
            "Validation r2-score: 0.3431926429694059\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### Sigmoid Kernel"
      ],
      "metadata": {
        "id": "Nq5iDmpPYzxO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = Model(model = SVR(kernel='sigmoid'), parameters = {\"encoding\": \"OneHot\", \"num_PCA_comp\": -1}, kf = kf)"
      ],
      "metadata": {
        "id": "J2K9DlNAY3Vi"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.training_validation()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "n6iczI1MLTYu",
        "outputId": "84e9e229-3d48-4f8a-c89c-8fca5fde3483"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of Highly correlated features removes:  2\n",
            "Features Removed:  {'IPPU', 'Total Population - Female'}\n",
            "\n",
            "Train mean absolute error:  1.217530884707107\n",
            "Train root mean squared error:  5.6825994693742174\n",
            "Train r2-score: -95.38572974814818\n",
            "\n",
            "Validation mean absolute error:  1.2963698325654822\n",
            "Validation root mean squared error:  5.77105769668231\n",
            "Validation r2-score: -107.04901909313367\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Label Encoding, No PCA"
      ],
      "metadata": {
        "id": "hhp9-hMCOm2c"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Random Forest Regressor"
      ],
      "metadata": {
        "id": "T77aCYBDKSEh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = Model(model = RandomForestRegressor(), parameters = {\"encoding\": \"Label\", \"num_PCA_comp\": -1}, kf = kf)"
      ],
      "metadata": {
        "id": "CbumTfVmPxdX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.training_validation()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "044a9e63-de09-4d80-ac63-2c0832a796b4",
        "id": "EXr6JObVPxdY"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of Highly correlated features removes:  2\n",
            "Features Removed:  {'IPPU', 'Total Population - Female'}\n",
            "\n",
            "Train mean absolute error:  0.10306235846965808\n",
            "Train root mean squared error:  0.14143789890486907\n",
            "Train r2-score: 0.940622892150947\n",
            "\n",
            "Validation mean absolute error:  0.27693968828487664\n",
            "Validation root mean squared error:  0.37449995310141426\n",
            "Validation r2-score: 0.5829710157898156\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Linear Regression"
      ],
      "metadata": {
        "id": "ca1dYJI-P6_F"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = Model(model = LinearRegression(), parameters = {\"encoding\": \"Label\", \"num_PCA_comp\": -1}, kf = kf)"
      ],
      "metadata": {
        "id": "io9BQtEFP6_G"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.training_validation()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8ef212d0-d1a0-4d45-8dfd-8f6e589c31e8",
        "id": "ZzH183c-P6_G"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of Highly correlated features removes:  2\n",
            "Features Removed:  {'IPPU', 'Total Population - Female'}\n",
            "\n",
            "Train mean absolute error:  0.356842185840889\n",
            "Train root mean squared error:  0.47175705576218735\n",
            "Train r2-score: 0.33945659356476676\n",
            "\n",
            "Validation mean absolute error:  0.35924682267481567\n",
            "Validation root mean squared error:  0.47456864241237834\n",
            "Validation r2-score: 0.3302657896981212\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Gradient Boosting Regressor"
      ],
      "metadata": {
        "id": "FbtUNMKJP6_G"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = Model(model = GradientBoostingRegressor(), parameters = {\"encoding\": \"Label\", \"num_PCA_comp\": -1}, kf = kf)"
      ],
      "metadata": {
        "id": "9fABgVklP6_G"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.training_validation()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d17773ac-d888-4490-d572-0614735af8be",
        "id": "jxVG1HO8P6_H"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of Highly correlated features removes:  2\n",
            "Features Removed:  {'IPPU', 'Total Population - Female'}\n",
            "\n",
            "Train mean absolute error:  0.265932201024483\n",
            "Train root mean squared error:  0.3565914812197465\n",
            "Train r2-score: 0.6225908289755405\n",
            "\n",
            "Validation mean absolute error:  0.2934705336378613\n",
            "Validation root mean squared error:  0.3949198449631355\n",
            "Validation r2-score: 0.5362513955588828\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Adaboost Regressor"
      ],
      "metadata": {
        "id": "ArRCIcaSQNDQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = Model(model = AdaBoostRegressor(), parameters = {\"encoding\": \"Label\", \"num_PCA_comp\": -1}, kf = kf)"
      ],
      "metadata": {
        "id": "wtWOfaQHQNDR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.training_validation()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "50fb7314-b2e4-4ac4-b9a7-389bba054a51",
        "id": "f6TzMo0fQNDR"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of Highly correlated features removes:  2\n",
            "Features Removed:  {'IPPU', 'Total Population - Female'}\n",
            "\n",
            "Train mean absolute error:  0.3432290438505749\n",
            "Train root mean squared error:  0.4355422522700253\n",
            "Train r2-score: 0.43690743850169483\n",
            "\n",
            "Validation mean absolute error:  0.3504450239279043\n",
            "Validation root mean squared error:  0.4534074893528957\n",
            "Validation r2-score: 0.38877634295726576\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### XGB Regressor"
      ],
      "metadata": {
        "id": "2i3lztfBQNDR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = Model(model = XGBRegressor(), parameters = {\"encoding\": \"Label\", \"num_PCA_comp\": -1}, kf = kf)"
      ],
      "metadata": {
        "id": "ggv8K1P4QNDR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.training_validation()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0236d9c4-07ea-4ef8-b7ac-ed5ab2d96da5",
        "id": "7dWdlO8GQNDS"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of Highly correlated features removes:  2\n",
            "Features Removed:  {'IPPU', 'Total Population - Female'}\n",
            "\n",
            "Train mean absolute error:  0.0884727107669114\n",
            "Train root mean squared error:  0.11699745907209773\n",
            "Train r2-score: 0.959330646723729\n",
            "\n",
            "Validation mean absolute error:  0.2776655601822541\n",
            "Validation root mean squared error:  0.3735054388924413\n",
            "Validation r2-score: 0.5851113323643502\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Lasso Regressor"
      ],
      "metadata": {
        "id": "6Mcv7W16QNDS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Best Alpha 0.0001\n",
        "alphas = [0.0, 0.0001, 0.001, 0.01, 0.1, 1.0]"
      ],
      "metadata": {
        "id": "AaAqHSLqQNDS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for alpha in alphas:\n",
        "  model = Model(model = Lasso(alpha=alpha), parameters = {\"encoding\": \"Label\", \"num_PCA_comp\": -1}, kf = kf)\n",
        "  print(\"Alpha: \", alpha)\n",
        "  model.training_validation()\n",
        "  print(\"\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3d960165-aa99-48f5-ed30-5d1b2ea66198",
        "id": "EegfaqjYQNDS"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Alpha:  0.0\n",
            "Number of Highly correlated features removes:  2\n",
            "Features Removed:  {'IPPU', 'Total Population - Female'}\n",
            "\n",
            "Train mean absolute error:  0.35678381694629446\n",
            "Train root mean squared error:  0.4719530913015757\n",
            "Train r2-score: 0.3389066016585591\n",
            "\n",
            "Validation mean absolute error:  0.35905394124759604\n",
            "Validation root mean squared error:  0.47471754421411916\n",
            "Validation r2-score: 0.3298283261662693\n",
            "\n",
            "\n",
            "Alpha:  0.0001\n",
            "Number of Highly correlated features removes:  2\n",
            "Features Removed:  {'IPPU', 'Total Population - Female'}\n",
            "\n",
            "Train mean absolute error:  0.3566704443185035\n",
            "Train root mean squared error:  0.472013738558839\n",
            "Train r2-score: 0.3387367536966014\n",
            "\n",
            "Validation mean absolute error:  0.35881080844171953\n",
            "Validation root mean squared error:  0.47470346170074\n",
            "Validation r2-score: 0.3298751659807391\n",
            "\n",
            "\n",
            "Alpha:  0.001\n",
            "Number of Highly correlated features removes:  2\n",
            "Features Removed:  {'IPPU', 'Total Population - Female'}\n",
            "\n",
            "Train mean absolute error:  0.35676628662233156\n",
            "Train root mean squared error:  0.47274054808854366\n",
            "Train r2-score: 0.3366987368918836\n",
            "\n",
            "Validation mean absolute error:  0.35860146306217483\n",
            "Validation root mean squared error:  0.47515054064291967\n",
            "Validation r2-score: 0.3286185725969535\n",
            "\n",
            "\n",
            "Alpha:  0.01\n",
            "Number of Highly correlated features removes:  2\n",
            "Features Removed:  {'IPPU', 'Total Population - Female'}\n",
            "\n",
            "Train mean absolute error:  0.36164055510165555\n",
            "Train root mean squared error:  0.4788240056231129\n",
            "Train r2-score: 0.319519425496988\n",
            "\n",
            "Validation mean absolute error:  0.3622376654426639\n",
            "Validation root mean squared error:  0.47956654518752845\n",
            "Validation r2-score: 0.31617984417188716\n",
            "\n",
            "\n",
            "Alpha:  0.1\n",
            "Number of Highly correlated features removes:  2\n",
            "Features Removed:  {'IPPU', 'Total Population - Female'}\n",
            "\n",
            "Train mean absolute error:  0.376858380135614\n",
            "Train root mean squared error:  0.49702724556002736\n",
            "Train r2-score: 0.26680049569357106\n",
            "\n",
            "Validation mean absolute error:  0.376990405926304\n",
            "Validation root mean squared error:  0.49724724900882605\n",
            "Validation r2-score: 0.2649686012028739\n",
            "\n",
            "\n",
            "Alpha:  1.0\n",
            "Number of Highly correlated features removes:  2\n",
            "Features Removed:  {'IPPU', 'Total Population - Female'}\n",
            "\n",
            "Train mean absolute error:  0.4504353825496491\n",
            "Train root mean squared error:  0.5804602097794778\n",
            "Train r2-score: 0.0\n",
            "\n",
            "Validation mean absolute error:  0.4505614703671757\n",
            "Validation root mean squared error:  0.5806324837299349\n",
            "Validation r2-score: -0.002216171492962227\n",
            "\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Ridge Regressor"
      ],
      "metadata": {
        "id": "0oqvIFBqQNDS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Best Alpha 0.1\n",
        "alphas = [0.0, 0.0001, 0.001, 0.01, 0.1, 1.0]"
      ],
      "metadata": {
        "id": "4hdb8XVJQNDS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for alpha in alphas:\n",
        "  model = Model(model = Ridge(alpha=alpha), parameters = {\"encoding\": \"Label\", \"num_PCA_comp\": -1}, kf = kf)\n",
        "  print(\"Alpha: \", alpha)\n",
        "  model.training_validation()\n",
        "  print(\"\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0cd98297-efbe-46a2-edd9-55dd2869ec88",
        "id": "QgWb6baFQNDT"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Alpha:  0.0\n",
            "Number of Highly correlated features removes:  2\n",
            "Features Removed:  {'IPPU', 'Total Population - Female'}\n",
            "\n",
            "Train mean absolute error:  0.35684218584088956\n",
            "Train root mean squared error:  0.47175705576218735\n",
            "Train r2-score: 0.3394565935647669\n",
            "\n",
            "Validation mean absolute error:  0.35924682267481745\n",
            "Validation root mean squared error:  0.4745686424123795\n",
            "Validation r2-score: 0.33026578969811815\n",
            "\n",
            "\n",
            "Alpha:  0.0001\n",
            "Number of Highly correlated features removes:  2\n",
            "Features Removed:  {'IPPU', 'Total Population - Female'}\n",
            "\n",
            "Train mean absolute error:  0.3568421103639974\n",
            "Train root mean squared error:  0.4717570557802763\n",
            "Train r2-score: 0.3394565935140245\n",
            "\n",
            "Validation mean absolute error:  0.3592467103688805\n",
            "Validation root mean squared error:  0.47456862576445447\n",
            "Validation r2-score: 0.3302658310596673\n",
            "\n",
            "\n",
            "Alpha:  0.001\n",
            "Number of Highly correlated features removes:  2\n",
            "Features Removed:  {'IPPU', 'Total Population - Female'}\n",
            "\n",
            "Train mean absolute error:  0.3568414329142921\n",
            "Train root mean squared error:  0.4717570575617941\n",
            "Train r2-score: 0.33945658851659866\n",
            "\n",
            "Validation mean absolute error:  0.3592457021935084\n",
            "Validation root mean squared error:  0.4745684777926199\n",
            "Validation r2-score: 0.3302661982463563\n",
            "\n",
            "\n",
            "Alpha:  0.01\n",
            "Number of Highly correlated features removes:  2\n",
            "Features Removed:  {'IPPU', 'Total Population - Female'}\n",
            "\n",
            "Train mean absolute error:  0.35683524955292506\n",
            "Train root mean squared error:  0.47175722681513055\n",
            "Train r2-score: 0.33945611373255413\n",
            "\n",
            "Validation mean absolute error:  0.35923588486891267\n",
            "Validation root mean squared error:  0.47456717299112317\n",
            "Validation r2-score: 0.3302693937939779\n",
            "\n",
            "\n",
            "Alpha:  0.1\n",
            "Number of Highly correlated features removes:  2\n",
            "Features Removed:  {'IPPU', 'Total Population - Female'}\n",
            "\n",
            "Train mean absolute error:  0.3567959240814542\n",
            "Train root mean squared error:  0.4717680116061261\n",
            "Train r2-score: 0.3394258589257294\n",
            "\n",
            "Validation mean absolute error:  0.35916127309069823\n",
            "Validation root mean squared error:  0.47456523671287804\n",
            "Validation r2-score: 0.3302713021625148\n",
            "\n",
            "\n",
            "Alpha:  1.0\n",
            "Number of Highly correlated features removes:  2\n",
            "Features Removed:  {'IPPU', 'Total Population - Female'}\n",
            "\n",
            "Train mean absolute error:  0.35671507324393426\n",
            "Train root mean squared error:  0.4718869593307121\n",
            "Train r2-score: 0.3390921485515392\n",
            "\n",
            "Validation mean absolute error:  0.3589569534662317\n",
            "Validation root mean squared error:  0.47462767095928654\n",
            "Validation r2-score: 0.33008943494739623\n",
            "\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Support Vector Machine"
      ],
      "metadata": {
        "id": "nO4S_o4pQNDT"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### RBF kernel"
      ],
      "metadata": {
        "id": "clDALBoCQNDT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = Model(model = SVR(kernel='rbf'), parameters = {\"encoding\": \"Label\", \"num_PCA_comp\": -1}, kf = kf)"
      ],
      "metadata": {
        "id": "7Hml4S9BQNDT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.training_validation()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1159e978-7632-4f5e-99ef-26d116564b90",
        "id": "psDkP9R3QNDT"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of Highly correlated features removes:  2\n",
            "Features Removed:  {'IPPU', 'Total Population - Female'}\n",
            "\n",
            "Train mean absolute error:  0.33045025204597994\n",
            "Train root mean squared error:  0.4464998907914727\n",
            "Train r2-score: 0.4082765412485022\n",
            "\n",
            "Validation mean absolute error:  0.34271788169274175\n",
            "Validation root mean squared error:  0.45686348571190827\n",
            "Validation r2-score: 0.37912291580208735\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### Linear kernel"
      ],
      "metadata": {
        "id": "xjjNE9IhQNDT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = Model(model = SVR(kernel='linear'), parameters = {\"encoding\": \"Label\", \"num_PCA_comp\": -1}, kf = kf)"
      ],
      "metadata": {
        "id": "MZis5LiWQNDU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.training_validation()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fadc288f-d540-4797-c60c-ba405f398315",
        "id": "1-IDVoHeQNDU"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of Highly correlated features removes:  2\n",
            "Features Removed:  {'IPPU', 'Total Population - Female'}\n",
            "\n",
            "Train mean absolute error:  0.3547747543201372\n",
            "Train root mean squared error:  0.4746343063285865\n",
            "Train r2-score: 0.33137501873184616\n",
            "\n",
            "Validation mean absolute error:  0.3582660925413852\n",
            "Validation root mean squared error:  0.47834296214971445\n",
            "Validation r2-score: 0.3196248111835711\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### Poly Kernel"
      ],
      "metadata": {
        "id": "Rp833nX1QNDU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = Model(model = SVR(kernel='poly'), parameters = {\"encoding\": \"Label\", \"num_PCA_comp\": -1}, kf = kf)"
      ],
      "metadata": {
        "id": "ahxCQFn4QNDU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.training_validation()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "80ac27fb-3464-454c-a0d2-4f62d1ee242c",
        "id": "AXs-XH06QNDU"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of Highly correlated features removes:  2\n",
            "Features Removed:  {'IPPU', 'Total Population - Female'}\n",
            "\n",
            "Train mean absolute error:  0.3578715433025387\n",
            "Train root mean squared error:  0.48237478392264616\n",
            "Train r2-score: 0.3094038240868038\n",
            "\n",
            "Validation mean absolute error:  0.4031995979333768\n",
            "Validation root mean squared error:  1.1393744175825304\n",
            "Validation r2-score: -7.049532619352698\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### Sigmoid Kernel"
      ],
      "metadata": {
        "id": "hwYuS-BPQNDU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = Model(model = SVR(kernel='sigmoid'), parameters = {\"encoding\": \"Label\", \"num_PCA_comp\": -1}, kf = kf)"
      ],
      "metadata": {
        "id": "VaZ7Ck4RQNDU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.training_validation()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a07b95f2-ca44-4ec2-9a91-0fe02ab60f43",
        "id": "vR_z7jzXQNDU"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of Highly correlated features removes:  2\n",
            "Features Removed:  {'IPPU', 'Total Population - Female'}\n",
            "\n",
            "Train mean absolute error:  22.13490560285293\n",
            "Train root mean squared error:  76.70595042230335\n",
            "Train r2-score: -17463.758938824416\n",
            "\n",
            "Validation mean absolute error:  22.380068371064333\n",
            "Validation root mean squared error:  76.8255447066315\n",
            "Validation r2-score: -18086.22501228758\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "ZEvBzsodazRx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## One Hot Encoding With PCA"
      ],
      "metadata": {
        "id": "Clf6cR_ka7jT"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Random Forest Regressor"
      ],
      "metadata": {
        "id": "vdsXEXXAa7jd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = Model(model = RandomForestRegressor(), parameters = {\"encoding\": \"OneHot\", \"num_PCA_comp\": -1}, kf = kf)"
      ],
      "metadata": {
        "id": "KeyYZ9waa7je"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.training_validation()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "898fc1ed-c9c3-4402-a722-1c4023ecdb56",
        "id": "lCbGdA48a7je"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of Highly correlated features removes:  2\n",
            "Features Removed:  {'IPPU', 'Total Population - Female'}\n",
            "\n",
            "Train mean absolute error:  0.10243935859360628\n",
            "Train root mean squared error:  0.14049002428345148\n",
            "Train r2-score: 0.9414144078630716\n",
            "\n",
            "Validation mean absolute error:  0.27570388555071235\n",
            "Validation root mean squared error:  0.3736381085608825\n",
            "Validation r2-score: 0.5848675143995041\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6APlzmWlcAgO",
        "outputId": "d40ccdf6-51e9-4344-d605-7d9e1ebc7939"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(4546, 183)"
            ]
          },
          "metadata": {},
          "execution_count": 166
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Linear Regression"
      ],
      "metadata": {
        "id": "GqNW8uBsa7jf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = Model(model = LinearRegression(), parameters = {\"encoding\": \"OneHot\", \"num_PCA_comp\": -1}, kf = kf)"
      ],
      "metadata": {
        "id": "1hfKuZHua7jf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.training_validation()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "86890760-ea0a-4a84-dbdd-a83d725f810a",
        "id": "-6csZB6na7jf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of Highly correlated features removes:  2\n",
            "Features Removed:  {'IPPU', 'Total Population - Female'}\n",
            "\n",
            "Train mean absolute error:  0.2950927547697523\n",
            "Train root mean squared error:  0.3998556434486456\n",
            "Train r2-score: 0.5254390712802296\n",
            "\n",
            "Validation mean absolute error:  232259863.91941524\n",
            "Validation root mean squared error:  4952865265.146614\n",
            "Validation r2-score: -1.7709187045609544e+20\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Gradient Boosting Regressor"
      ],
      "metadata": {
        "id": "Se9Johtza7jg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = Model(model = GradientBoostingRegressor(), parameters = {\"encoding\": \"OneHot\", \"num_PCA_comp\": -1}, kf = kf)"
      ],
      "metadata": {
        "id": "Hqk6my-ya7jg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.training_validation()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "12f3e276-c3d3-4b18-bddb-181a12ae4767",
        "id": "_ODNLxgAa7jg"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of Highly correlated features removes:  2\n",
            "Features Removed:  {'IPPU', 'Total Population - Female'}\n",
            "\n",
            "Train mean absolute error:  0.2728104826906141\n",
            "Train root mean squared error:  0.36495467404161086\n",
            "Train r2-score: 0.60466841279981\n",
            "\n",
            "Validation mean absolute error:  0.2972425587279775\n",
            "Validation root mean squared error:  0.39860999464426106\n",
            "Validation r2-score: 0.5274789841083053\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Adaboost Regressor"
      ],
      "metadata": {
        "id": "AAsQ9J16a7jg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = Model(model = AdaBoostRegressor(), parameters = {\"encoding\": \"OneHot\", \"num_PCA_comp\": -1}, kf = kf)"
      ],
      "metadata": {
        "id": "9opiMYX4a7jg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.training_validation()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "67478080-5738-48be-9839-a9f4c6e93493",
        "id": "tNYM6CQBa7jg"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of Highly correlated features removes:  2\n",
            "Features Removed:  {'IPPU', 'Total Population - Female'}\n",
            "\n",
            "Train mean absolute error:  0.3453297827066076\n",
            "Train root mean squared error:  0.4393316993019708\n",
            "Train r2-score: 0.4270921046064844\n",
            "\n",
            "Validation mean absolute error:  0.35217749597048725\n",
            "Validation root mean squared error:  0.4566825149109758\n",
            "Validation r2-score: 0.3797962259817684\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### XGB Regressor"
      ],
      "metadata": {
        "id": "PqSr_4TIa7jg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = Model(model = XGBRegressor(), parameters = {\"encoding\": \"OneHot\", \"num_PCA_comp\": -1}, kf = kf)"
      ],
      "metadata": {
        "id": "gmnx-ykja7jh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.training_validation()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e88e84a2-b99a-4f48-c2d8-adf36b3d46d7",
        "id": "A3cJST6Pa7jh"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of Highly correlated features removes:  2\n",
            "Features Removed:  {'IPPU', 'Total Population - Female'}\n",
            "\n",
            "Train mean absolute error:  0.10916482868573203\n",
            "Train root mean squared error:  0.14488503152712146\n",
            "Train r2-score: 0.9376481305328934\n",
            "\n",
            "Validation mean absolute error:  0.27711783891769803\n",
            "Validation root mean squared error:  0.37425664830055466\n",
            "Validation r2-score: 0.5834190618025235\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Lasso Regressor"
      ],
      "metadata": {
        "id": "xOM0SPZqa7jh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Best Alpha found is 0.0001\n",
        "alphas = [0.0, 0.0001, 0.001, 0.01, 0.1, 1.0]"
      ],
      "metadata": {
        "id": "eb5cq0U_a7jh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for alpha in alphas:\n",
        "  model = Model(model = Lasso(alpha=alpha), parameters = {\"encoding\": \"OneHot\", \"num_PCA_comp\": -1}, kf = kf)\n",
        "  print(\"Alpha: \", alpha)\n",
        "  model.training_validation()\n",
        "  print(\"\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "da38826b-d595-4869-b4d7-d2bd90044fd2",
        "id": "2zateOAOa7ji"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Alpha:  0.0\n",
            "Number of Highly correlated features removes:  2\n",
            "Features Removed:  {'IPPU', 'Total Population - Female'}\n",
            "\n",
            "Train mean absolute error:  0.2951277859972585\n",
            "Train root mean squared error:  0.4000984413172426\n",
            "Train r2-score: 0.5248619942045201\n",
            "\n",
            "Validation mean absolute error:  0.30919683103283296\n",
            "Validation root mean squared error:  0.4189132755993882\n",
            "Validation r2-score: 0.4778115625164583\n",
            "\n",
            "\n",
            "Alpha:  0.0001\n",
            "Number of Highly correlated features removes:  2\n",
            "Features Removed:  {'IPPU', 'Total Population - Female'}\n",
            "\n",
            "Train mean absolute error:  0.294969284805219\n",
            "Train root mean squared error:  0.40019814528855147\n",
            "Train r2-score: 0.5246250482907234\n",
            "\n",
            "Validation mean absolute error:  0.3088952358368837\n",
            "Validation root mean squared error:  0.41883209269541605\n",
            "Validation r2-score: 0.4780155157489677\n",
            "\n",
            "\n",
            "Alpha:  0.001\n",
            "Number of Highly correlated features removes:  2\n",
            "Features Removed:  {'IPPU', 'Total Population - Female'}\n",
            "\n",
            "Train mean absolute error:  0.29644936974311237\n",
            "Train root mean squared error:  0.4025880010142894\n",
            "Train r2-score: 0.5189323888029018\n",
            "\n",
            "Validation mean absolute error:  0.3092554848361665\n",
            "Validation root mean squared error:  0.4199712109194701\n",
            "Validation r2-score: 0.4752184051999354\n",
            "\n",
            "\n",
            "Alpha:  0.01\n",
            "Number of Highly correlated features removes:  2\n",
            "Features Removed:  {'IPPU', 'Total Population - Female'}\n",
            "\n",
            "Train mean absolute error:  0.3108528468514924\n",
            "Train root mean squared error:  0.41720773676491907\n",
            "Train r2-score: 0.4833602937274966\n",
            "\n",
            "Validation mean absolute error:  0.31931932172115174\n",
            "Validation root mean squared error:  0.42979972062780625\n",
            "Validation r2-score: 0.4506888889974089\n",
            "\n",
            "\n",
            "Alpha:  0.1\n",
            "Number of Highly correlated features removes:  2\n",
            "Features Removed:  {'IPPU', 'Total Population - Female'}\n",
            "\n",
            "Train mean absolute error:  0.376858380135614\n",
            "Train root mean squared error:  0.49702724556002736\n",
            "Train r2-score: 0.26680049569357106\n",
            "\n",
            "Validation mean absolute error:  0.376990405926304\n",
            "Validation root mean squared error:  0.49724724900882605\n",
            "Validation r2-score: 0.2649686012028739\n",
            "\n",
            "\n",
            "Alpha:  1.0\n",
            "Number of Highly correlated features removes:  2\n",
            "Features Removed:  {'IPPU', 'Total Population - Female'}\n",
            "\n",
            "Train mean absolute error:  0.4504353825496491\n",
            "Train root mean squared error:  0.5804602097794778\n",
            "Train r2-score: 0.0\n",
            "\n",
            "Validation mean absolute error:  0.4505614703671757\n",
            "Validation root mean squared error:  0.5806324837299349\n",
            "Validation r2-score: -0.002216171492962227\n",
            "\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Ridge Regressor"
      ],
      "metadata": {
        "id": "86Tc9Nyya7ji"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Best Alpha 1.0\n",
        "alphas = [0.0, 0.0001, 0.001, 0.01, 0.1, 1.0]"
      ],
      "metadata": {
        "id": "4ghygfd8a7ji"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for alpha in alphas:\n",
        "  model = Model(model = Ridge(alpha=alpha), parameters = {\"encoding\": \"OneHot\", \"num_PCA_comp\": -1}, kf = kf)\n",
        "  print(\"Alpha: \", alpha)\n",
        "  model.training_validation()\n",
        "  print(\"\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fda4783d-a2cb-4340-b1b6-94b358eac476",
        "id": "Xvq6AeZra7ji"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Alpha:  0.0\n",
            "Number of Highly correlated features removes:  2\n",
            "Features Removed:  {'IPPU', 'Total Population - Female'}\n",
            "\n",
            "Train mean absolute error:  0.2949476921374198\n",
            "Train root mean squared error:  0.399718856488112\n",
            "Train r2-score: 0.5257627907122122\n",
            "\n",
            "Validation mean absolute error:  674690638.1928765\n",
            "Validation root mean squared error:  14390236215.845821\n",
            "Validation r2-score: -2.132566697598497e+21\n",
            "\n",
            "\n",
            "Alpha:  0.0001\n",
            "Number of Highly correlated features removes:  2\n",
            "Features Removed:  {'IPPU', 'Total Population - Female'}\n",
            "\n",
            "Train mean absolute error:  0.29495060668687445\n",
            "Train root mean squared error:  0.3997183305844969\n",
            "Train r2-score: 0.5257640413031642\n",
            "\n",
            "Validation mean absolute error:  0.3095805551721883\n",
            "Validation root mean squared error:  0.418832426063226\n",
            "Validation r2-score: 0.4780088648329631\n",
            "\n",
            "\n",
            "Alpha:  0.001\n",
            "Number of Highly correlated features removes:  2\n",
            "Features Removed:  {'IPPU', 'Total Population - Female'}\n",
            "\n",
            "Train mean absolute error:  0.2949451108895986\n",
            "Train root mean squared error:  0.39971852048552853\n",
            "Train r2-score: 0.5257635908368443\n",
            "\n",
            "Validation mean absolute error:  0.3095627633759621\n",
            "Validation root mean squared error:  0.41882611529920777\n",
            "Validation r2-score: 0.47802471141552544\n",
            "\n",
            "\n",
            "Alpha:  0.01\n",
            "Number of Highly correlated features removes:  2\n",
            "Features Removed:  {'IPPU', 'Total Population - Female'}\n",
            "\n",
            "Train mean absolute error:  0.29491237966403216\n",
            "Train root mean squared error:  0.39972980224935695\n",
            "Train r2-score: 0.5257368238729642\n",
            "\n",
            "Validation mean absolute error:  0.3094410262396992\n",
            "Validation root mean squared error:  0.4187894174157429\n",
            "Validation r2-score: 0.47811673711706193\n",
            "\n",
            "\n",
            "Alpha:  0.1\n",
            "Number of Highly correlated features removes:  2\n",
            "Features Removed:  {'IPPU', 'Total Population - Female'}\n",
            "\n",
            "Train mean absolute error:  0.29488698494194493\n",
            "Train root mean squared error:  0.39982685600893836\n",
            "Train r2-score: 0.5255064214796548\n",
            "\n",
            "Validation mean absolute error:  0.3092427780938226\n",
            "Validation root mean squared error:  0.41876842349026855\n",
            "Validation r2-score: 0.47816887182129825\n",
            "\n",
            "\n",
            "Alpha:  1.0\n",
            "Number of Highly correlated features removes:  2\n",
            "Features Removed:  {'IPPU', 'Total Population - Female'}\n",
            "\n",
            "Train mean absolute error:  0.29484287265905235\n",
            "Train root mean squared error:  0.39993591300299486\n",
            "Train r2-score: 0.5252475309189976\n",
            "\n",
            "Validation mean absolute error:  0.3089492939469742\n",
            "Validation root mean squared error:  0.4187287286640716\n",
            "Validation r2-score: 0.47826788042327373\n",
            "\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Support Vector Machine"
      ],
      "metadata": {
        "id": "pTcMccUDa7jj"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### RBF kernel"
      ],
      "metadata": {
        "id": "9dTWCpWHa7jj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = Model(model = SVR(kernel='rbf'), parameters = {\"encoding\": \"OneHot\", \"num_PCA_comp\": -1}, kf = kf)"
      ],
      "metadata": {
        "id": "m-oonVeSa7jj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.training_validation()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "030e8012-d4e4-426f-88a6-5ed5f4ac54fd",
        "id": "z3fDP-Gda7jj"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of Highly correlated features removes:  2\n",
            "Features Removed:  {'IPPU', 'Total Population - Female'}\n",
            "\n",
            "Train mean absolute error:  0.2843938906100245\n",
            "Train root mean squared error:  0.39708073191154303\n",
            "Train r2-score: 0.5319924123983014\n",
            "\n",
            "Validation mean absolute error:  0.30732937101046\n",
            "Validation root mean squared error:  0.4210538797445473\n",
            "Validation r2-score: 0.4724346431298181\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### Linear kernel"
      ],
      "metadata": {
        "id": "BtRNMP1Ua7jj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = Model(model = SVR(kernel='linear'), parameters = {\"encoding\": \"OneHot\", \"num_PCA_comp\": -1}, kf = kf)"
      ],
      "metadata": {
        "id": "5mtKIQpja7jj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.training_validation()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5e67392a-5172-4eab-9044-d4bc1f682d98",
        "id": "G0TxK_gMa7jj"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of Highly correlated features removes:  2\n",
            "Features Removed:  {'IPPU', 'Total Population - Female'}\n",
            "\n",
            "Train mean absolute error:  0.29256213430108147\n",
            "Train root mean squared error:  0.4042210803642064\n",
            "Train r2-score: 0.5150240724173237\n",
            "\n",
            "Validation mean absolute error:  0.3107138263430083\n",
            "Validation root mean squared error:  0.4224501515245084\n",
            "Validation r2-score: 0.4690411891271259\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### Poly Kernel"
      ],
      "metadata": {
        "id": "4tRQHUILa7jj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = Model(model = SVR(kernel='poly'), parameters = {\"encoding\": \"OneHot\", \"num_PCA_comp\": -1}, kf = kf)"
      ],
      "metadata": {
        "id": "PLoh7mwYa7jj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.training_validation()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d31287a1-d5f0-401a-f54a-6ae5ce1948ca",
        "id": "7fIxawJZa7jk"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of Highly correlated features removes:  2\n",
            "Features Removed:  {'IPPU', 'Total Population - Female'}\n",
            "\n",
            "Train mean absolute error:  0.3166739404118014\n",
            "Train root mean squared error:  0.44111258379130386\n",
            "Train r2-score: 0.42246076329764365\n",
            "\n",
            "Validation mean absolute error:  0.34430941236322743\n",
            "Validation root mean squared error:  0.469899275220409\n",
            "Validation r2-score: 0.3431926429694059\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### Sigmoid Kernel"
      ],
      "metadata": {
        "id": "3kuf2m0da7jk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = Model(model = SVR(kernel='sigmoid'), parameters = {\"encoding\": \"OneHot\", \"num_PCA_comp\": -1}, kf = kf)"
      ],
      "metadata": {
        "id": "aboSYUPwa7jk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.training_validation()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "84e9e229-3d48-4f8a-c89c-8fca5fde3483",
        "id": "u8KQdi6Xa7jk"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of Highly correlated features removes:  2\n",
            "Features Removed:  {'IPPU', 'Total Population - Female'}\n",
            "\n",
            "Train mean absolute error:  1.217530884707107\n",
            "Train root mean squared error:  5.6825994693742174\n",
            "Train r2-score: -95.38572974814818\n",
            "\n",
            "Validation mean absolute error:  1.2963698325654822\n",
            "Validation root mean squared error:  5.77105769668231\n",
            "Validation r2-score: -107.04901909313367\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "---\n",
        "\n"
      ],
      "metadata": {
        "id": "z9uQsE_e7VIB"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Artificial Neural Network"
      ],
      "metadata": {
        "id": "sjQJjr6EiKvx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "import torch.nn as nn"
      ],
      "metadata": {
        "id": "hcDefwq6vSGq"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class Network1(nn.Module):\n",
        "  def __init__(self, input_shape):\n",
        "    super().__init__();\n",
        "    self.model = nn.Sequential(\n",
        "        nn.Linear(in_features=input_shape, out_features=64),\n",
        "        nn.ReLU(),\n",
        "        nn.Linear(in_features=64, out_features=32),\n",
        "        nn.ReLU(),\n",
        "        nn.Linear(in_features=32, out_features=8),\n",
        "        nn.ReLU(),\n",
        "        nn.Linear(in_features=8, out_features=1)\n",
        "    )\n",
        "\n",
        "  def forward(self, x):\n",
        "    return self.model(x)"
      ],
      "metadata": {
        "id": "J3DfnR3G2mkg"
      },
      "execution_count": 140,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class Network2(nn.Module):\n",
        "  def __init__(self, input_shape):\n",
        "    super().__init__();\n",
        "    self.model = nn.Sequential(\n",
        "        nn.Linear(in_features=input_shape, out_features=256),\n",
        "        nn.ReLU(),\n",
        "        nn.Linear(in_features=256, out_features=64),\n",
        "        nn.ReLU(),\n",
        "        nn.Linear(in_features=64, out_features=16),\n",
        "        nn.ReLU(),\n",
        "        nn.Linear(in_features=16, out_features=1)\n",
        "    )\n",
        "\n",
        "  def forward(self, x):\n",
        "    return self.model(x)"
      ],
      "metadata": {
        "id": "NPmRgVezs_u8"
      },
      "execution_count": 141,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class ANN:\n",
        "\n",
        "  def __init__(self, parameters):\n",
        "    self.parameters = parameters\n",
        "    self.df = pd.read_csv('/content/drive/MyDrive/Agrofood_co2_emission.csv')\n",
        "    self.df.dropna(inplace=True)\n",
        "\n",
        "  def preprocessing(self):\n",
        "    correlation_matrix = self.df.corr()\n",
        "    threshold = 0.99\n",
        "    correlated_features = set()\n",
        "\n",
        "    for i in range(len(correlation_matrix.columns)):\n",
        "        for j in range(i):\n",
        "            if round(abs(correlation_matrix.iloc[i, j]), 2) >= threshold:\n",
        "                colname = correlation_matrix.columns[i]\n",
        "                correlated_features.add(colname)\n",
        "\n",
        "    if(self.parameters[\"num_PCA_comp\"]==-1):\n",
        "      print(\"Number of Highly correlated features removes: \", len(correlated_features))\n",
        "      print(\"Features Removed: \", correlated_features)\n",
        "      print(\"\")\n",
        "\n",
        "    self.df.drop(columns=correlated_features, inplace=True)\n",
        "    correlation_matrix = self.df.corr()\n",
        "\n",
        "    self.X = self.df.iloc[:,0:-1]\n",
        "    self.Y = self.df.iloc[:,-1]\n",
        "\n",
        "    if(self.parameters[\"encoding\"]==\"OneHot\"):\n",
        "      self.X = pd.get_dummies(self.X, columns=self.X.select_dtypes(include=['object']).columns)\n",
        "    elif(self.parameters[\"encoding\"]==\"Label\"):\n",
        "      self.X[\"Area\"] = LabelEncoder().fit_transform(self.X[\"Area\"])\n",
        "\n",
        "\n",
        "  def training_validation(self):\n",
        "    self.preprocessing()\n",
        "    self.X = self.X.to_numpy()\n",
        "    self.Y = self.Y.to_numpy()\n",
        "    self.mae_train, self.rmse_train, self.r2_train = [], [], []\n",
        "    self.mae_val, self.rmse_val, self.r2_val = [], [], []\n",
        "    X_train, X_val, Y_train, Y_val = train_test_split(self.X, self.Y, test_size=0.2, random_state=42)\n",
        "\n",
        "    ss=StandardScaler()\n",
        "    X_train = ss.fit_transform(X_train)\n",
        "    X_val = ss.transform(X_val)\n",
        "\n",
        "    if(self.parameters[\"num_PCA_comp\"]!=-1):\n",
        "      pca = PCA()\n",
        "      X_train = pca.fit_transform(X_train)\n",
        "      X_val = pca.transform(X_val)\n",
        "\n",
        "    X_train, X_val, Y_train, Y_val = torch.from_numpy(X_train).to(torch.float32), torch.from_numpy(X_val).to(torch.float32), torch.from_numpy(Y_train).to(torch.float32), torch.from_numpy(Y_val).to(torch.float32)\n",
        "    print(X_train.shape[1])\n",
        "    if(self.parameters['encoding']=='Label'):\n",
        "      model = Network1(X_train.shape[1])\n",
        "    elif(self.parameters['encoding']=='OneHot'):\n",
        "      model = Network2(X_train.shape[1])\n",
        "    loss_fn = nn.MSELoss()\n",
        "    optimizer = torch.optim.SGD(params=model.parameters(), lr=0.0001)\n",
        "\n",
        "    for epoch in range(self.parameters['iterations']):\n",
        "      train_set = data_utils.TensorDataset(X_train, Y_train)\n",
        "      train_loader = data_utils.DataLoader(train_set, batch_size=self.parameters[\"batch_size\"], shuffle=True)\n",
        "\n",
        "      model.train()\n",
        "      for batch_idx, (x, y) in enumerate(train_loader):\n",
        "\n",
        "        y_pred = model(x)\n",
        "        loss = loss_fn(y, y_pred)\n",
        "\n",
        "        optimizer.zero_grad()\n",
        "\n",
        "        loss.backward()\n",
        "\n",
        "        optimizer.step()\n",
        "\n",
        "      model.eval()\n",
        "      with torch.inference_mode():\n",
        "        y_pred_train = model(X_train)\n",
        "        y_pred_val = model(X_val)\n",
        "        self.mae_val.append(mean_absolute_error(Y_val, y_pred_val))\n",
        "        self.mae_train.append(mean_absolute_error(Y_train, y_pred_train))\n",
        "\n",
        "        self.rmse_val.append(np.sqrt(mean_squared_error(Y_val, y_pred_val)))\n",
        "        self.rmse_train.append(np.sqrt(mean_squared_error(Y_train, y_pred_train)))\n",
        "\n",
        "        self.r2_val.append(r2_score(Y_val, y_pred_val))\n",
        "        self.r2_train.append(r2_score(Y_train, y_pred_train))\n",
        "        print(f\"Epoch: {epoch} Training MAE: {self.mae_train[-1]} RMSE: {self.rmse_train[-1]} R2: {self.r2_train[-1]}\")\n",
        "        print(f\"Epoch: {epoch} Validation MAE: {self.mae_val[-1]} RMSE: {self.rmse_val[-1]} R2: {self.r2_val[-1]}\")\n",
        "        print(\"\")"
      ],
      "metadata": {
        "id": "b42XN-l7fvQz"
      },
      "execution_count": 147,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ann = ANN(parameters = {\"encoding\": \"Label\", \"num_PCA_comp\": -1, \"batch_size\": 1, 'iterations': 500})\n",
        "ann.training_validation()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uho6nF8UfvGm",
        "outputId": "eace1dd5-3ded-4def-d16a-191117642ccb"
      },
      "execution_count": 153,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of Highly correlated features removes:  2\n",
            "Features Removed:  {'Total Population - Female', 'IPPU'}\n",
            "\n",
            "28\n",
            "Epoch: 0 Training MAE: 0.5627920031547546 RMSE: 0.7236998677253723 R2: -0.5619817890887253\n",
            "Epoch: 0 Validation MAE: 0.5561773180961609 RMSE: 0.7172669172286987 R2: -0.4986792205818771\n",
            "\n",
            "Epoch: 1 Training MAE: 0.46330058574676514 RMSE: 0.6066235303878784 R2: -0.09748237218988454\n",
            "Epoch: 1 Validation MAE: 0.46585479378700256 RMSE: 0.6070759296417236 R2: -0.07357679844347342\n",
            "\n",
            "Epoch: 2 Training MAE: 0.4488278329372406 RMSE: 0.5831611156463623 R2: -0.014229217687795837\n",
            "Epoch: 2 Validation MAE: 0.4540059268474579 RMSE: 0.5871197581291199 R2: -0.004154361093790859\n",
            "\n",
            "Epoch: 3 Training MAE: 0.4465959966182709 RMSE: 0.578048586845398 R2: 0.0034761165603451882\n",
            "Epoch: 3 Validation MAE: 0.45290786027908325 RMSE: 0.5835399627685547 R2: 0.00805332740753828\n",
            "\n",
            "Epoch: 4 Training MAE: 0.4451742172241211 RMSE: 0.5756090879440308 R2: 0.011869514776930212\n",
            "Epoch: 4 Validation MAE: 0.45203161239624023 RMSE: 0.581745982170105 R2: 0.014143003150354772\n",
            "\n",
            "Epoch: 5 Training MAE: 0.4434628486633301 RMSE: 0.5733339190483093 R2: 0.01966536299108501\n",
            "Epoch: 5 Validation MAE: 0.4507196247577667 RMSE: 0.5798572301864624 R2: 0.02053413017638639\n",
            "\n",
            "Epoch: 6 Training MAE: 0.4413362145423889 RMSE: 0.5707734823226929 R2: 0.028402142099586913\n",
            "Epoch: 6 Validation MAE: 0.44887930154800415 RMSE: 0.5775934457778931 R2: 0.028167179985657098\n",
            "\n",
            "Epoch: 7 Training MAE: 0.4379121661186218 RMSE: 0.5667941570281982 R2: 0.04190258116912693\n",
            "Epoch: 7 Validation MAE: 0.44579923152923584 RMSE: 0.5739173889160156 R2: 0.04049794806916285\n",
            "\n",
            "Epoch: 8 Training MAE: 0.4338304400444031 RMSE: 0.5623069405555725 R2: 0.057012555952099975\n",
            "Epoch: 8 Validation MAE: 0.44198769330978394 RMSE: 0.5695788860321045 R2: 0.05494983758880345\n",
            "\n",
            "Epoch: 9 Training MAE: 0.43001866340637207 RMSE: 0.5578551292419434 R2: 0.071884905495599\n",
            "Epoch: 9 Validation MAE: 0.4385094940662384 RMSE: 0.5653873682022095 R2: 0.06880767107789465\n",
            "\n",
            "Epoch: 10 Training MAE: 0.4262065589427948 RMSE: 0.5529616475105286 R2: 0.08809613546009021\n",
            "Epoch: 10 Validation MAE: 0.4352908730506897 RMSE: 0.5610032677650452 R2: 0.08319314130997912\n",
            "\n",
            "Epoch: 11 Training MAE: 0.42115798592567444 RMSE: 0.5473983883857727 R2: 0.10635279846262347\n",
            "Epoch: 11 Validation MAE: 0.4306456744670868 RMSE: 0.5556640028953552 R2: 0.10056124467885508\n",
            "\n",
            "Epoch: 12 Training MAE: 0.41582849621772766 RMSE: 0.5411736965179443 R2: 0.12656148213915253\n",
            "Epoch: 12 Validation MAE: 0.42601636052131653 RMSE: 0.5498635172843933 R2: 0.11924148180490135\n",
            "\n",
            "Epoch: 13 Training MAE: 0.4097074568271637 RMSE: 0.5342943668365479 R2: 0.1486263716435876\n",
            "Epoch: 13 Validation MAE: 0.42063644528388977 RMSE: 0.5433662533760071 R2: 0.13993258034657263\n",
            "\n",
            "Epoch: 14 Training MAE: 0.4035961925983429 RMSE: 0.5268508195877075 R2: 0.17218312153590254\n",
            "Epoch: 14 Validation MAE: 0.4152895212173462 RMSE: 0.536579966545105 R2: 0.16128184151960323\n",
            "\n",
            "Epoch: 15 Training MAE: 0.3967008888721466 RMSE: 0.5190874934196472 R2: 0.19639968653624806\n",
            "Epoch: 15 Validation MAE: 0.4088202118873596 RMSE: 0.5292627811431885 R2: 0.18400064152314044\n",
            "\n",
            "Epoch: 16 Training MAE: 0.39069733023643494 RMSE: 0.5114738941192627 R2: 0.21980003538427484\n",
            "Epoch: 16 Validation MAE: 0.4032730460166931 RMSE: 0.5224128365516663 R2: 0.20498608240881522\n",
            "\n",
            "Epoch: 17 Training MAE: 0.3840583860874176 RMSE: 0.5043745040893555 R2: 0.24130834460422135\n",
            "Epoch: 17 Validation MAE: 0.3972082734107971 RMSE: 0.5156282782554626 R2: 0.22550163644729893\n",
            "\n",
            "Epoch: 18 Training MAE: 0.3787022531032562 RMSE: 0.4983290731906891 R2: 0.25938676916730996\n",
            "Epoch: 18 Validation MAE: 0.3924506604671478 RMSE: 0.5100875496864319 R2: 0.24205705734124527\n",
            "\n",
            "Epoch: 19 Training MAE: 0.37494349479675293 RMSE: 0.4933187663555145 R2: 0.2742044950176722\n",
            "Epoch: 19 Validation MAE: 0.38910335302352905 RMSE: 0.5057910680770874 R2: 0.2547715961660014\n",
            "\n",
            "Epoch: 20 Training MAE: 0.3722456395626068 RMSE: 0.48944664001464844 R2: 0.28555346867246156\n",
            "Epoch: 20 Validation MAE: 0.3866872191429138 RMSE: 0.5025169849395752 R2: 0.264388333616121\n",
            "\n",
            "Epoch: 21 Training MAE: 0.36874276399612427 RMSE: 0.48622387647628784 R2: 0.294931136435131\n",
            "Epoch: 21 Validation MAE: 0.3834128677845001 RMSE: 0.49919286370277405 R2: 0.2740883088260784\n",
            "\n",
            "Epoch: 22 Training MAE: 0.3659972846508026 RMSE: 0.4838899075984955 R2: 0.3016837229910132\n",
            "Epoch: 22 Validation MAE: 0.3808189034461975 RMSE: 0.4967321455478668 R2: 0.2812271835836244\n",
            "\n",
            "Epoch: 23 Training MAE: 0.3647923469543457 RMSE: 0.48194238543510437 R2: 0.3072934835157397\n",
            "Epoch: 23 Validation MAE: 0.37954476475715637 RMSE: 0.4951830804347992 R2: 0.28570317348682384\n",
            "\n",
            "Epoch: 24 Training MAE: 0.3638767898082733 RMSE: 0.4804804027080536 R2: 0.3114897782510523\n",
            "Epoch: 24 Validation MAE: 0.37864044308662415 RMSE: 0.4940812289714813 R2: 0.2888785812526472\n",
            "\n",
            "Epoch: 25 Training MAE: 0.36334672570228577 RMSE: 0.47938433289527893 R2: 0.3146275252831324\n",
            "Epoch: 25 Validation MAE: 0.37803590297698975 RMSE: 0.4933091998100281 R2: 0.29109903443864305\n",
            "\n",
            "Epoch: 26 Training MAE: 0.36235323548316956 RMSE: 0.4783778488636017 R2: 0.31750237279102556\n",
            "Epoch: 26 Validation MAE: 0.3770976960659027 RMSE: 0.49235349893569946 R2: 0.29384318299668344\n",
            "\n",
            "Epoch: 27 Training MAE: 0.3619416356086731 RMSE: 0.4775436818599701 R2: 0.3198804804745544\n",
            "Epoch: 27 Validation MAE: 0.37673601508140564 RMSE: 0.4917963743209839 R2: 0.29544041009432775\n",
            "\n",
            "Epoch: 28 Training MAE: 0.3606894612312317 RMSE: 0.47669312357902527 R2: 0.3223010983272472\n",
            "Epoch: 28 Validation MAE: 0.3756076693534851 RMSE: 0.49079856276512146 R2: 0.29829648394443564\n",
            "\n",
            "Epoch: 29 Training MAE: 0.3610367476940155 RMSE: 0.47613823413848877 R2: 0.32387792537208115\n",
            "Epoch: 29 Validation MAE: 0.3760194182395935 RMSE: 0.49075958132743835 R2: 0.29840790488370905\n",
            "\n",
            "Epoch: 30 Training MAE: 0.35982728004455566 RMSE: 0.4754045903682709 R2: 0.32595986691020606\n",
            "Epoch: 30 Validation MAE: 0.37494519352912903 RMSE: 0.48977479338645935 R2: 0.30122083438401015\n",
            "\n",
            "Epoch: 31 Training MAE: 0.35909783840179443 RMSE: 0.47480034828186035 R2: 0.3276721640429817\n",
            "Epoch: 31 Validation MAE: 0.37433284521102905 RMSE: 0.48914825916290283 R2: 0.30300748904440644\n",
            "\n",
            "Epoch: 32 Training MAE: 0.3581334948539734 RMSE: 0.47438740730285645 R2: 0.3288412037855948\n",
            "Epoch: 32 Validation MAE: 0.37342119216918945 RMSE: 0.4884323179721832 R2: 0.30504625949514685\n",
            "\n",
            "Epoch: 33 Training MAE: 0.3578352630138397 RMSE: 0.4738164246082306 R2: 0.33045576072941163\n",
            "Epoch: 33 Validation MAE: 0.37327152490615845 RMSE: 0.4880128800868988 R2: 0.30623933020391203\n",
            "\n",
            "Epoch: 34 Training MAE: 0.3575402796268463 RMSE: 0.47331172227859497 R2: 0.3318813905582708\n",
            "Epoch: 34 Validation MAE: 0.3731103241443634 RMSE: 0.48766404390335083 R2: 0.30723080505979905\n",
            "\n",
            "Epoch: 35 Training MAE: 0.35710322856903076 RMSE: 0.4728754162788391 R2: 0.33311268030607644\n",
            "Epoch: 35 Validation MAE: 0.3727545440196991 RMSE: 0.4872751533985138 R2: 0.30833523067144075\n",
            "\n",
            "Epoch: 36 Training MAE: 0.3564363718032837 RMSE: 0.4726342260837555 R2: 0.33379274380645096\n",
            "Epoch: 36 Validation MAE: 0.37212029099464417 RMSE: 0.4867546260356903 R2: 0.30981216693912417\n",
            "\n",
            "Epoch: 37 Training MAE: 0.35632458329200745 RMSE: 0.4719851315021515 R2: 0.33562136405809706\n",
            "Epoch: 37 Validation MAE: 0.3721553683280945 RMSE: 0.4864346385002136 R2: 0.3107193831415076\n",
            "\n",
            "Epoch: 38 Training MAE: 0.35608887672424316 RMSE: 0.4715234935283661 R2: 0.33692034653923486\n",
            "Epoch: 38 Validation MAE: 0.3720160126686096 RMSE: 0.486131876707077 R2: 0.31157708848861654\n",
            "\n",
            "Epoch: 39 Training MAE: 0.3561059534549713 RMSE: 0.47109413146972656 R2: 0.33812747884580874\n",
            "Epoch: 39 Validation MAE: 0.37208110094070435 RMSE: 0.48595476150512695 R2: 0.31207869364335006\n",
            "\n",
            "Epoch: 40 Training MAE: 0.35566842555999756 RMSE: 0.4706847071647644 R2: 0.33927735776286727\n",
            "Epoch: 40 Validation MAE: 0.3717169761657715 RMSE: 0.4855446517467499 R2: 0.31323924937983405\n",
            "\n",
            "Epoch: 41 Training MAE: 0.35562750697135925 RMSE: 0.47032350301742554 R2: 0.3402910464502087\n",
            "Epoch: 41 Validation MAE: 0.37185347080230713 RMSE: 0.48551687598228455 R2: 0.31331785599658435\n",
            "\n",
            "Epoch: 42 Training MAE: 0.3549051880836487 RMSE: 0.4699261486530304 R2: 0.3414053130416621\n",
            "Epoch: 42 Validation MAE: 0.37114471197128296 RMSE: 0.48487135767936707 R2: 0.3151425591504279\n",
            "\n",
            "Epoch: 43 Training MAE: 0.35471388697624207 RMSE: 0.46955162286758423 R2: 0.3424546909032812\n",
            "Epoch: 43 Validation MAE: 0.3710595667362213 RMSE: 0.48467111587524414 R2: 0.315708206460226\n",
            "\n",
            "Epoch: 44 Training MAE: 0.35449570417404175 RMSE: 0.4691970646381378 R2: 0.3434472820373693\n",
            "Epoch: 44 Validation MAE: 0.37088072299957275 RMSE: 0.48442453145980835 R2: 0.31640420401005565\n",
            "\n",
            "Epoch: 45 Training MAE: 0.3540087938308716 RMSE: 0.46883857250213623 R2: 0.3444502241456564\n",
            "Epoch: 45 Validation MAE: 0.37044987082481384 RMSE: 0.4839923679828644 R2: 0.31762341663709304\n",
            "\n",
            "Epoch: 46 Training MAE: 0.3544297516345978 RMSE: 0.46862325072288513 R2: 0.34505216947276895\n",
            "Epoch: 46 Validation MAE: 0.3709182143211365 RMSE: 0.4842647612094879 R2: 0.316855080438021\n",
            "\n",
            "Epoch: 47 Training MAE: 0.35353320837020874 RMSE: 0.4681563377380371 R2: 0.3463566806862217\n",
            "Epoch: 47 Validation MAE: 0.37001195549964905 RMSE: 0.48346975445747375 R2: 0.31909621688164236\n",
            "\n",
            "Epoch: 48 Training MAE: 0.35317426919937134 RMSE: 0.46782490611076355 R2: 0.3472818406366821\n",
            "Epoch: 48 Validation MAE: 0.36967626214027405 RMSE: 0.48311200737953186 R2: 0.3201035501538694\n",
            "\n",
            "Epoch: 49 Training MAE: 0.3530920147895813 RMSE: 0.4675087332725525 R2: 0.3481637980182505\n",
            "Epoch: 49 Validation MAE: 0.36968761682510376 RMSE: 0.48301470279693604 R2: 0.32037746286366353\n",
            "\n",
            "Epoch: 50 Training MAE: 0.35275205969810486 RMSE: 0.46717992424964905 R2: 0.3490803722237663\n",
            "Epoch: 50 Validation MAE: 0.3693546652793884 RMSE: 0.48267513513565063 R2: 0.3213326916240703\n",
            "\n",
            "Epoch: 51 Training MAE: 0.3527565002441406 RMSE: 0.46692314743995667 R2: 0.3497957282171772\n",
            "Epoch: 51 Validation MAE: 0.36940157413482666 RMSE: 0.482643723487854 R2: 0.32142100645304683\n",
            "\n",
            "Epoch: 52 Training MAE: 0.3520512878894806 RMSE: 0.4665604829788208 R2: 0.3508053312355933\n",
            "Epoch: 52 Validation MAE: 0.36857113242149353 RMSE: 0.4819357097148895 R2: 0.3234104911501461\n",
            "\n",
            "Epoch: 53 Training MAE: 0.3517700135707855 RMSE: 0.46626198291778564 R2: 0.3516358185468341\n",
            "Epoch: 53 Validation MAE: 0.3682668209075928 RMSE: 0.48162534832954407 R2: 0.32428154650869945\n",
            "\n",
            "Epoch: 54 Training MAE: 0.35189417004585266 RMSE: 0.4660249352455139 R2: 0.3522948924169572\n",
            "Epoch: 54 Validation MAE: 0.36848634481430054 RMSE: 0.48169806599617004 R2: 0.32407749192266966\n",
            "\n",
            "Epoch: 55 Training MAE: 0.35147637128829956 RMSE: 0.4657024145126343 R2: 0.35319116953625374\n",
            "Epoch: 55 Validation MAE: 0.3680214285850525 RMSE: 0.48125895857810974 R2: 0.32530924492536395\n",
            "\n",
            "Epoch: 56 Training MAE: 0.35086727142333984 RMSE: 0.4654760956764221 R2: 0.3538196032217573\n",
            "Epoch: 56 Validation MAE: 0.36719340085983276 RMSE: 0.48061349987983704 R2: 0.3271178341463933\n",
            "\n",
            "Epoch: 57 Training MAE: 0.3510855734348297 RMSE: 0.46517327427864075 R2: 0.3546601066007653\n",
            "Epoch: 57 Validation MAE: 0.3676353693008423 RMSE: 0.48082730174064636 R2: 0.32651901613938583\n",
            "\n",
            "Epoch: 58 Training MAE: 0.35034075379371643 RMSE: 0.4650548994541168 R2: 0.35498851940470655\n",
            "Epoch: 58 Validation MAE: 0.36653220653533936 RMSE: 0.4800227880477905 R2: 0.32877083848333477\n",
            "\n",
            "Epoch: 59 Training MAE: 0.35037919878959656 RMSE: 0.4646225869655609 R2: 0.3561872041639861\n",
            "Epoch: 59 Validation MAE: 0.36672869324684143 RMSE: 0.48006314039230347 R2: 0.3286580075344757\n",
            "\n",
            "Epoch: 60 Training MAE: 0.35017749667167664 RMSE: 0.4643349349498749 R2: 0.35698404636937153\n",
            "Epoch: 60 Validation MAE: 0.36657944321632385 RMSE: 0.4798744022846222 R2: 0.3291858223105334\n",
            "\n",
            "Epoch: 61 Training MAE: 0.3503153622150421 RMSE: 0.46410343050956726 R2: 0.35762511869988334\n",
            "Epoch: 61 Validation MAE: 0.36682048439979553 RMSE: 0.47999048233032227 R2: 0.32886116784782193\n",
            "\n",
            "Epoch: 62 Training MAE: 0.3496684730052948 RMSE: 0.46399232745170593 R2: 0.3579325891274804\n",
            "Epoch: 62 Validation MAE: 0.3658396303653717 RMSE: 0.47929391264915466 R2: 0.3308077446490457\n",
            "\n",
            "Epoch: 63 Training MAE: 0.3496553897857666 RMSE: 0.46362966299057007 R2: 0.35893587681714434\n",
            "Epoch: 63 Validation MAE: 0.3659258782863617 RMSE: 0.4793025851249695 R2: 0.33078350426076264\n",
            "\n",
            "Epoch: 64 Training MAE: 0.3499653935432434 RMSE: 0.46345850825309753 R2: 0.3594091415853644\n",
            "Epoch: 64 Validation MAE: 0.3664959967136383 RMSE: 0.4796231985092163 R2: 0.32988785651843844\n",
            "\n",
            "Epoch: 65 Training MAE: 0.34935981035232544 RMSE: 0.4630914330482483 R2: 0.3604234493039322\n",
            "Epoch: 65 Validation MAE: 0.36574023962020874 RMSE: 0.47898730635643005 R2: 0.33166361387063337\n",
            "\n",
            "Epoch: 66 Training MAE: 0.349257230758667 RMSE: 0.4628910422325134 R2: 0.3609769159482339\n",
            "Epoch: 66 Validation MAE: 0.36568698287010193 RMSE: 0.4789011776447296 R2: 0.3319038749692127\n",
            "\n",
            "Epoch: 67 Training MAE: 0.34940534830093384 RMSE: 0.4627877473831177 R2: 0.3612620289673619\n",
            "Epoch: 67 Validation MAE: 0.36591237783432007 RMSE: 0.47906070947647095 R2: 0.33145872759865747\n",
            "\n",
            "Epoch: 68 Training MAE: 0.34873250126838684 RMSE: 0.4624151587486267 R2: 0.36229009016259783\n",
            "Epoch: 68 Validation MAE: 0.3650999367237091 RMSE: 0.4783892333507538 R2: 0.3333315548046911\n",
            "\n",
            "Epoch: 69 Training MAE: 0.3483639061450958 RMSE: 0.462243914604187 R2: 0.362762309853941\n",
            "Epoch: 69 Validation MAE: 0.36449652910232544 RMSE: 0.4779358506202698 R2: 0.3345946026710016\n",
            "\n",
            "Epoch: 70 Training MAE: 0.3482506275177002 RMSE: 0.4619766175746918 R2: 0.3634991192415582\n",
            "Epoch: 70 Validation MAE: 0.36446014046669006 RMSE: 0.4778676927089691 R2: 0.3347844079853515\n",
            "\n",
            "Epoch: 71 Training MAE: 0.34813588857650757 RMSE: 0.46175962686538696 R2: 0.36409693943581745\n",
            "Epoch: 71 Validation MAE: 0.3643897473812103 RMSE: 0.47780609130859375 R2: 0.33495587703003094\n",
            "\n",
            "Epoch: 72 Training MAE: 0.34790199995040894 RMSE: 0.4615381956100464 R2: 0.36470664421385346\n",
            "Epoch: 72 Validation MAE: 0.3641586899757385 RMSE: 0.4775705635547638 R2: 0.3356112889478329\n",
            "\n",
            "Epoch: 73 Training MAE: 0.34767216444015503 RMSE: 0.46135804057121277 R2: 0.3652025276999584\n",
            "Epoch: 73 Validation MAE: 0.36386609077453613 RMSE: 0.47733139991760254 R2: 0.33627671869232845\n",
            "\n",
            "Epoch: 74 Training MAE: 0.3476305902004242 RMSE: 0.461092472076416 R2: 0.36593313517986126\n",
            "Epoch: 74 Validation MAE: 0.36397039890289307 RMSE: 0.4773322343826294 R2: 0.3362743794043227\n",
            "\n",
            "Epoch: 75 Training MAE: 0.3478614091873169 RMSE: 0.4609563648700714 R2: 0.3663073619283752\n",
            "Epoch: 75 Validation MAE: 0.36431190371513367 RMSE: 0.47762903571128845 R2: 0.33544866521689454\n",
            "\n",
            "Epoch: 76 Training MAE: 0.3473816215991974 RMSE: 0.4607867896556854 R2: 0.36677357916660713\n",
            "Epoch: 76 Validation MAE: 0.36354947090148926 RMSE: 0.4770434498786926 R2: 0.3370771745878728\n",
            "\n",
            "Epoch: 77 Training MAE: 0.34728145599365234 RMSE: 0.4604882001876831 R2: 0.367593914093847\n",
            "Epoch: 77 Validation MAE: 0.3636453151702881 RMSE: 0.4770229458808899 R2: 0.33713415955248605\n",
            "\n",
            "Epoch: 78 Training MAE: 0.3474278748035431 RMSE: 0.4603728950023651 R2: 0.36791059956156646\n",
            "Epoch: 78 Validation MAE: 0.36384883522987366 RMSE: 0.4771997332572937 R2: 0.33664277227475725\n",
            "\n",
            "Epoch: 79 Training MAE: 0.3466563820838928 RMSE: 0.46009254455566406 R2: 0.3686802447065045\n",
            "Epoch: 79 Validation MAE: 0.3628823757171631 RMSE: 0.47633546590805054 R2: 0.3390434368937726\n",
            "\n",
            "Epoch: 80 Training MAE: 0.34653979539871216 RMSE: 0.45988956093788147 R2: 0.3692371231612168\n",
            "Epoch: 80 Validation MAE: 0.36273878812789917 RMSE: 0.47620680928230286 R2: 0.33940031909912605\n",
            "\n",
            "Epoch: 81 Training MAE: 0.3462076187133789 RMSE: 0.4597599506378174 R2: 0.36959263334911285\n",
            "Epoch: 81 Validation MAE: 0.3623117208480835 RMSE: 0.4758191704750061 R2: 0.3404754279188501\n",
            "\n",
            "Epoch: 82 Training MAE: 0.34615838527679443 RMSE: 0.45948001742362976 R2: 0.3703600649028619\n",
            "Epoch: 82 Validation MAE: 0.36234137415885925 RMSE: 0.47584986686706543 R2: 0.3403903871691861\n",
            "\n",
            "Epoch: 83 Training MAE: 0.3460158109664917 RMSE: 0.45930635929107666 R2: 0.37083593224932376\n",
            "Epoch: 83 Validation MAE: 0.3621859848499298 RMSE: 0.47572076320648193 R2: 0.3407481384963821\n",
            "\n",
            "Epoch: 84 Training MAE: 0.34582459926605225 RMSE: 0.45908626914024353 R2: 0.3714387716595974\n",
            "Epoch: 84 Validation MAE: 0.3619922697544098 RMSE: 0.4755205810070038 R2: 0.34130291894970954\n",
            "\n",
            "Epoch: 85 Training MAE: 0.34585806727409363 RMSE: 0.45887476205825806 R2: 0.3720177545588823\n",
            "Epoch: 85 Validation MAE: 0.36207443475723267 RMSE: 0.47560495138168335 R2: 0.34106911345551394\n",
            "\n",
            "Epoch: 86 Training MAE: 0.3454701602458954 RMSE: 0.4587114751338959 R2: 0.37246456117600335\n",
            "Epoch: 86 Validation MAE: 0.36161109805107117 RMSE: 0.4751780331134796 R2: 0.3422516119082034\n",
            "\n",
            "Epoch: 87 Training MAE: 0.34665820002555847 RMSE: 0.4589465856552124 R2: 0.37182109226769067\n",
            "Epoch: 87 Validation MAE: 0.3629230260848999 RMSE: 0.4764500558376312 R2: 0.3387253126123203\n",
            "\n",
            "Epoch: 88 Training MAE: 0.3453332781791687 RMSE: 0.4582940340042114 R2: 0.37360627236852617\n",
            "Epoch: 88 Validation MAE: 0.36148151755332947 RMSE: 0.47509387135505676 R2: 0.3424845751171379\n",
            "\n",
            "Epoch: 89 Training MAE: 0.3449341058731079 RMSE: 0.458238422870636 R2: 0.37375825065789303\n",
            "Epoch: 89 Validation MAE: 0.3609282374382019 RMSE: 0.4745710790157318 R2: 0.3439308059241942\n",
            "\n",
            "Epoch: 90 Training MAE: 0.3448449373245239 RMSE: 0.4580262005329132 R2: 0.37433813115976744\n",
            "Epoch: 90 Validation MAE: 0.36084356904029846 RMSE: 0.4744997024536133 R2: 0.3441281580712636\n",
            "\n",
            "Epoch: 91 Training MAE: 0.34468603134155273 RMSE: 0.4578344523906708 R2: 0.37486187538134097\n",
            "Epoch: 91 Validation MAE: 0.3607167601585388 RMSE: 0.4744012653827667 R2: 0.3444001910852419\n",
            "\n",
            "Epoch: 92 Training MAE: 0.3445934057235718 RMSE: 0.4575645923614502 R2: 0.3755986293206406\n",
            "Epoch: 92 Validation MAE: 0.36065083742141724 RMSE: 0.4743211567401886 R2: 0.34462169422012945\n",
            "\n",
            "Epoch: 93 Training MAE: 0.3447268009185791 RMSE: 0.45738303661346436 R2: 0.3760940319933386\n",
            "Epoch: 93 Validation MAE: 0.3608841300010681 RMSE: 0.47454434633255005 R2: 0.34400478098229337\n",
            "\n",
            "Epoch: 94 Training MAE: 0.3443681597709656 RMSE: 0.45719558000564575 R2: 0.37660525751739715\n",
            "Epoch: 94 Validation MAE: 0.3605084717273712 RMSE: 0.47419798374176025 R2: 0.34496191221161776\n",
            "\n",
            "Epoch: 95 Training MAE: 0.3443176746368408 RMSE: 0.4570186734199524 R2: 0.3770877140459765\n",
            "Epoch: 95 Validation MAE: 0.360429972410202 RMSE: 0.4741151034832001 R2: 0.34519095055713855\n",
            "\n",
            "Epoch: 96 Training MAE: 0.3438771367073059 RMSE: 0.45691820979118347 R2: 0.3773615051433129\n",
            "Epoch: 96 Validation MAE: 0.35981547832489014 RMSE: 0.4735642671585083 R2: 0.3467116289892407\n",
            "\n",
            "Epoch: 97 Training MAE: 0.3437608778476715 RMSE: 0.4566366672515869 R2: 0.37812863898351423\n",
            "Epoch: 97 Validation MAE: 0.3596835434436798 RMSE: 0.4734465181827545 R2: 0.3470364454762941\n",
            "\n",
            "Epoch: 98 Training MAE: 0.3437960147857666 RMSE: 0.4564269781112671 R2: 0.37869967720048303\n",
            "Epoch: 98 Validation MAE: 0.3597427010536194 RMSE: 0.4734863340854645 R2: 0.34692659713750607\n",
            "\n",
            "Epoch: 99 Training MAE: 0.34381338953971863 RMSE: 0.4562637209892273 R2: 0.37914396467354483\n",
            "Epoch: 99 Validation MAE: 0.35982802510261536 RMSE: 0.4735642671585083 R2: 0.3467115870624933\n",
            "\n",
            "Epoch: 100 Training MAE: 0.3439856767654419 RMSE: 0.45618075132369995 R2: 0.37936978247686504\n",
            "Epoch: 100 Validation MAE: 0.36006516218185425 RMSE: 0.47381719946861267 R2: 0.3460135369215017\n",
            "\n",
            "Epoch: 101 Training MAE: 0.34371280670166016 RMSE: 0.4559464156627655 R2: 0.3800072345097688\n",
            "Epoch: 101 Validation MAE: 0.3597768247127533 RMSE: 0.4735480546951294 R2: 0.34675635777831837\n",
            "\n",
            "Epoch: 102 Training MAE: 0.3433704078197479 RMSE: 0.4557237923145294 R2: 0.38061248567490924\n",
            "Epoch: 102 Validation MAE: 0.3593719005584717 RMSE: 0.4731753170490265 R2: 0.34778424487353166\n",
            "\n",
            "Epoch: 103 Training MAE: 0.34324076771736145 RMSE: 0.4555460512638092 R2: 0.3810955804939756\n",
            "Epoch: 103 Validation MAE: 0.35926344990730286 RMSE: 0.47306835651397705 R2: 0.34807913687092895\n",
            "\n",
            "Epoch: 104 Training MAE: 0.34343239665031433 RMSE: 0.4555248022079468 R2: 0.38115335097322667\n",
            "Epoch: 104 Validation MAE: 0.3594777286052704 RMSE: 0.4732781946659088 R2: 0.3475006140307736\n",
            "\n",
            "Epoch: 105 Training MAE: 0.34264686703681946 RMSE: 0.45518913865089417 R2: 0.38206502595563496\n",
            "Epoch: 105 Validation MAE: 0.3586142659187317 RMSE: 0.47247204184532166 R2: 0.3497215161921996\n",
            "\n",
            "Epoch: 106 Training MAE: 0.34311559796333313 RMSE: 0.45516249537467957 R2: 0.3821373441168697\n",
            "Epoch: 106 Validation MAE: 0.35921475291252136 RMSE: 0.4730122983455658 R2: 0.3482336021858037\n",
            "\n",
            "Epoch: 107 Training MAE: 0.3424525558948517 RMSE: 0.45480871200561523 R2: 0.38309744262557177\n",
            "Epoch: 107 Validation MAE: 0.35839247703552246 RMSE: 0.47226375341415405 R2: 0.35029482493656794\n",
            "\n",
            "Epoch: 108 Training MAE: 0.3421895503997803 RMSE: 0.45465248823165894 R2: 0.3835211896556229\n",
            "Epoch: 108 Validation MAE: 0.35807138681411743 RMSE: 0.47197654843330383 R2: 0.3510847961352169\n",
            "\n",
            "Epoch: 109 Training MAE: 0.3424890637397766 RMSE: 0.454572468996048 R2: 0.3837381284093221\n",
            "Epoch: 109 Validation MAE: 0.358502596616745 RMSE: 0.4723640978336334 R2: 0.3500187157528394\n",
            "\n",
            "Epoch: 110 Training MAE: 0.34214669466018677 RMSE: 0.45429539680480957 R2: 0.3844891797022767\n",
            "Epoch: 110 Validation MAE: 0.3582012355327606 RMSE: 0.4720517694950104 R2: 0.3508779817566984\n",
            "\n",
            "Epoch: 111 Training MAE: 0.3419051170349121 RMSE: 0.4541372060775757 R2: 0.3849177177257118\n",
            "Epoch: 111 Validation MAE: 0.35793817043304443 RMSE: 0.4718261659145355 R2: 0.35149826365877923\n",
            "\n",
            "Epoch: 112 Training MAE: 0.3424731492996216 RMSE: 0.4541770815849304 R2: 0.38480968599115484\n",
            "Epoch: 112 Validation MAE: 0.3587324917316437 RMSE: 0.47253137826919556 R2: 0.3495582661968817\n",
            "\n",
            "Epoch: 113 Training MAE: 0.3415670394897461 RMSE: 0.45384910702705383 R2: 0.3856979849135932\n",
            "Epoch: 113 Validation MAE: 0.35757574439048767 RMSE: 0.471466064453125 R2: 0.35248777883844395\n",
            "\n",
            "Epoch: 114 Training MAE: 0.3414084315299988 RMSE: 0.45362886786460876 R2: 0.38629396902256596\n",
            "Epoch: 114 Validation MAE: 0.35745951533317566 RMSE: 0.4713444411754608 R2: 0.35282181014420244\n",
            "\n",
            "Epoch: 115 Training MAE: 0.34145355224609375 RMSE: 0.4534837007522583 R2: 0.3866867777885048\n",
            "Epoch: 115 Validation MAE: 0.35757705569267273 RMSE: 0.4714549779891968 R2: 0.3525182499116102\n",
            "\n",
            "Epoch: 116 Training MAE: 0.34233447909355164 RMSE: 0.4538373649120331 R2: 0.38572972320291476\n",
            "Epoch: 116 Validation MAE: 0.35853472352027893 RMSE: 0.472400426864624 R2: 0.3499187401926306\n",
            "\n",
            "Epoch: 117 Training MAE: 0.3409460783004761 RMSE: 0.453157514333725 R2: 0.3875687181673887\n",
            "Epoch: 117 Validation MAE: 0.35694339871406555 RMSE: 0.47082772850990295 R2: 0.3542399236455409\n",
            "\n",
            "Epoch: 118 Training MAE: 0.34123069047927856 RMSE: 0.4530770778656006 R2: 0.38778611187167067\n",
            "Epoch: 118 Validation MAE: 0.35742563009262085 RMSE: 0.4712398946285248 R2: 0.3531089490106486\n",
            "\n",
            "Epoch: 119 Training MAE: 0.3406387269496918 RMSE: 0.45317885279655457 R2: 0.387510994311024\n",
            "Epoch: 119 Validation MAE: 0.35648205876350403 RMSE: 0.47043129801750183 R2: 0.35532697556559445\n",
            "\n",
            "Epoch: 120 Training MAE: 0.3410554528236389 RMSE: 0.4527619481086731 R2: 0.3886373730548902\n",
            "Epoch: 120 Validation MAE: 0.35729357600212097 RMSE: 0.47110748291015625 R2: 0.353472362135301\n",
            "\n",
            "Epoch: 121 Training MAE: 0.34053534269332886 RMSE: 0.45252081751823425 R2: 0.3892884381584526\n",
            "Epoch: 121 Validation MAE: 0.3565799593925476 RMSE: 0.47047489881515503 R2: 0.35520748795981394\n",
            "\n",
            "Epoch: 122 Training MAE: 0.3404689431190491 RMSE: 0.4524247646331787 R2: 0.38954766802724583\n",
            "Epoch: 122 Validation MAE: 0.35651105642318726 RMSE: 0.4704117178916931 R2: 0.3553806401596491\n",
            "\n",
            "Epoch: 123 Training MAE: 0.34075289964675903 RMSE: 0.4523254334926605 R2: 0.3898157091714932\n",
            "Epoch: 123 Validation MAE: 0.35701093077659607 RMSE: 0.4708518981933594 R2: 0.3541736261781244\n",
            "\n",
            "Epoch: 124 Training MAE: 0.34030818939208984 RMSE: 0.4520952105522156 R2: 0.39043664646624043\n",
            "Epoch: 124 Validation MAE: 0.3564659357070923 RMSE: 0.4703017771244049 R2: 0.35568190849152936\n",
            "\n",
            "Epoch: 125 Training MAE: 0.33992093801498413 RMSE: 0.4518773853778839 R2: 0.39102391558870186\n",
            "Epoch: 125 Validation MAE: 0.3560585677623749 RMSE: 0.4698295593261719 R2: 0.3569751588765384\n",
            "\n",
            "Epoch: 126 Training MAE: 0.3398144841194153 RMSE: 0.45173773169517517 R2: 0.39140023814971237\n",
            "Epoch: 126 Validation MAE: 0.35589954257011414 RMSE: 0.4696575999259949 R2: 0.3574457472424688\n",
            "\n",
            "Epoch: 127 Training MAE: 0.3397812247276306 RMSE: 0.45179808139801025 R2: 0.39123766217662437\n",
            "Epoch: 127 Validation MAE: 0.35579395294189453 RMSE: 0.4695776104927063 R2: 0.3576646063354768\n",
            "\n",
            "Epoch: 128 Training MAE: 0.3393882215023041 RMSE: 0.4515663981437683 R2: 0.39186184576770966\n",
            "Epoch: 128 Validation MAE: 0.35537222027778625 RMSE: 0.46922317147254944 R2: 0.3586339152450688\n",
            "\n",
            "Epoch: 129 Training MAE: 0.3399525582790375 RMSE: 0.4513687491416931 R2: 0.39239405648303927\n",
            "Epoch: 129 Validation MAE: 0.35633426904678345 RMSE: 0.47007516026496887 R2: 0.35630270347624615\n",
            "\n",
            "Epoch: 130 Training MAE: 0.33929014205932617 RMSE: 0.45108696818351746 R2: 0.39315244894987345\n",
            "Epoch: 130 Validation MAE: 0.35542240738868713 RMSE: 0.4692927598953247 R2: 0.3584436209919498\n",
            "\n",
            "Epoch: 131 Training MAE: 0.33898988366127014 RMSE: 0.45108234882354736 R2: 0.3931648673968837\n",
            "Epoch: 131 Validation MAE: 0.35493090748786926 RMSE: 0.46883782744407654 R2: 0.35968688854560726\n",
            "\n",
            "Epoch: 132 Training MAE: 0.3392944633960724 RMSE: 0.4508109390735626 R2: 0.3938948736888175\n",
            "Epoch: 132 Validation MAE: 0.3555378317832947 RMSE: 0.46932652592658997 R2: 0.35835129033868784\n",
            "\n",
            "Epoch: 133 Training MAE: 0.33870208263397217 RMSE: 0.45069316029548645 R2: 0.3942116184722394\n",
            "Epoch: 133 Validation MAE: 0.3547346293926239 RMSE: 0.4685901403427124 R2: 0.3603632215252024\n",
            "\n",
            "Epoch: 134 Training MAE: 0.33868399262428284 RMSE: 0.4504558742046356 R2: 0.394849331417779\n",
            "Epoch: 134 Validation MAE: 0.354869544506073 RMSE: 0.46866750717163086 R2: 0.3601520349831\n",
            "\n",
            "Epoch: 135 Training MAE: 0.3385492265224457 RMSE: 0.45029839873313904 R2: 0.395272353179258\n",
            "Epoch: 135 Validation MAE: 0.3547629415988922 RMSE: 0.4685758054256439 R2: 0.36040244029483215\n",
            "\n",
            "Epoch: 136 Training MAE: 0.33841586112976074 RMSE: 0.45016801357269287 R2: 0.3956224892742035\n",
            "Epoch: 136 Validation MAE: 0.35460254549980164 RMSE: 0.4684077501296997 R2: 0.3608610707774407\n",
            "\n",
            "Epoch: 137 Training MAE: 0.3383302390575409 RMSE: 0.44997310638427734 R2: 0.39614574732792807\n",
            "Epoch: 137 Validation MAE: 0.3545413613319397 RMSE: 0.4683729112148285 R2: 0.36095615097513845\n",
            "\n",
            "Epoch: 138 Training MAE: 0.3389268219470978 RMSE: 0.45016616582870483 R2: 0.3956275452669059\n",
            "Epoch: 138 Validation MAE: 0.3553614020347595 RMSE: 0.4692534804344177 R2: 0.35855096549904875\n",
            "\n",
            "Epoch: 139 Training MAE: 0.3381631672382355 RMSE: 0.4496627151966095 R2: 0.3969784903575764\n",
            "Epoch: 139 Validation MAE: 0.3544473648071289 RMSE: 0.4682683050632477 R2: 0.3612416335326174\n",
            "\n",
            "Epoch: 140 Training MAE: 0.3380182683467865 RMSE: 0.4495379328727722 R2: 0.3973131993205412\n",
            "Epoch: 140 Validation MAE: 0.3541988134384155 RMSE: 0.4680207073688507 R2: 0.3619169173545834\n",
            "\n",
            "Epoch: 141 Training MAE: 0.33766329288482666 RMSE: 0.44954121112823486 R2: 0.3973043594067933\n",
            "Epoch: 141 Validation MAE: 0.3536796271800995 RMSE: 0.4675886332988739 R2: 0.36309452657760843\n",
            "\n",
            "Epoch: 142 Training MAE: 0.3378069996833801 RMSE: 0.44923099875450134 R2: 0.39813589381080317\n",
            "Epoch: 142 Validation MAE: 0.3541039824485779 RMSE: 0.4679899513721466 R2: 0.3620007881273022\n",
            "\n",
            "Epoch: 143 Training MAE: 0.33754441142082214 RMSE: 0.4490702152252197 R2: 0.39856669678663403\n",
            "Epoch: 143 Validation MAE: 0.35382112860679626 RMSE: 0.46766898036003113 R2: 0.36287563964674496\n",
            "\n",
            "Epoch: 144 Training MAE: 0.3373073637485504 RMSE: 0.44898006319999695 R2: 0.3988080730918805\n",
            "Epoch: 144 Validation MAE: 0.35342705249786377 RMSE: 0.46731430292129517 R2: 0.3638415809097889\n",
            "\n",
            "Epoch: 145 Training MAE: 0.337618350982666 RMSE: 0.44887906312942505 R2: 0.3990785444110624\n",
            "Epoch: 145 Validation MAE: 0.3537771999835968 RMSE: 0.46774613857269287 R2: 0.3626653704204469\n",
            "\n",
            "Epoch: 146 Training MAE: 0.33728814125061035 RMSE: 0.4486287534236908 R2: 0.3997485525171631\n",
            "Epoch: 146 Validation MAE: 0.35347631573677063 RMSE: 0.46739453077316284 R2: 0.36362318027219254\n",
            "\n",
            "Epoch: 147 Training MAE: 0.33717024326324463 RMSE: 0.4488566517829895 R2: 0.39913851948165113\n",
            "Epoch: 147 Validation MAE: 0.3530691862106323 RMSE: 0.466884583234787 R2: 0.36501101663215263\n",
            "\n",
            "Epoch: 148 Training MAE: 0.33719196915626526 RMSE: 0.44833213090896606 R2: 0.400541999279141\n",
            "Epoch: 148 Validation MAE: 0.3534775674343109 RMSE: 0.4672352969646454 R2: 0.3640566387519928\n",
            "\n",
            "Epoch: 149 Training MAE: 0.33686402440071106 RMSE: 0.4481634497642517 R2: 0.40099302468429854\n",
            "Epoch: 149 Validation MAE: 0.3529798686504364 RMSE: 0.46687352657318115 R2: 0.3650411604893956\n",
            "\n",
            "Epoch: 150 Training MAE: 0.33676350116729736 RMSE: 0.448025107383728 R2: 0.40136276090993905\n",
            "Epoch: 150 Validation MAE: 0.35293087363243103 RMSE: 0.46683093905448914 R2: 0.3651569398537482\n",
            "\n",
            "Epoch: 151 Training MAE: 0.3365907669067383 RMSE: 0.4478973150253296 R2: 0.40170424281146266\n",
            "Epoch: 151 Validation MAE: 0.3526293933391571 RMSE: 0.46641597151756287 R2: 0.36628511312564993\n",
            "\n",
            "Epoch: 152 Training MAE: 0.33707356452941895 RMSE: 0.4479336738586426 R2: 0.40160712578775437\n",
            "Epoch: 152 Validation MAE: 0.35332000255584717 RMSE: 0.4672059118747711 R2: 0.36413673070746944\n",
            "\n",
            "Epoch: 153 Training MAE: 0.3367546498775482 RMSE: 0.4476315677165985 R2: 0.40241401441035307\n",
            "Epoch: 153 Validation MAE: 0.35305389761924744 RMSE: 0.4669336974620819 R2: 0.36487743805677597\n",
            "\n",
            "Epoch: 154 Training MAE: 0.33691641688346863 RMSE: 0.44766318798065186 R2: 0.40232959908314325\n",
            "Epoch: 154 Validation MAE: 0.35318315029144287 RMSE: 0.4671561121940613 R2: 0.36427226275131275\n",
            "\n",
            "Epoch: 155 Training MAE: 0.3362981379032135 RMSE: 0.4472602903842926 R2: 0.4034049603752209\n",
            "Epoch: 155 Validation MAE: 0.3524583876132965 RMSE: 0.46637868881225586 R2: 0.3663864550465231\n",
            "\n",
            "Epoch: 156 Training MAE: 0.33614134788513184 RMSE: 0.4471961259841919 R2: 0.4035759742617634\n",
            "Epoch: 156 Validation MAE: 0.35216376185417175 RMSE: 0.46603500843048096 R2: 0.36731991389076546\n",
            "\n",
            "Epoch: 157 Training MAE: 0.3361785113811493 RMSE: 0.4470510184764862 R2: 0.4039630293042201\n",
            "Epoch: 157 Validation MAE: 0.35224586725234985 RMSE: 0.46623650193214417 R2: 0.36677265783752155\n",
            "\n",
            "Epoch: 158 Training MAE: 0.33614581823349 RMSE: 0.4469044506549835 R2: 0.40435380706401525\n",
            "Epoch: 158 Validation MAE: 0.35243964195251465 RMSE: 0.4662339389324188 R2: 0.36677960794702125\n",
            "\n",
            "Epoch: 159 Training MAE: 0.33565741777420044 RMSE: 0.4466727077960968 R2: 0.4049713743957565\n",
            "Epoch: 159 Validation MAE: 0.3516528904438019 RMSE: 0.465492844581604 R2: 0.36879109445163716\n",
            "\n",
            "Epoch: 160 Training MAE: 0.33589282631874084 RMSE: 0.44663742184638977 R2: 0.4050654412179838\n",
            "Epoch: 160 Validation MAE: 0.3520446717739105 RMSE: 0.46595561504364014 R2: 0.3675354654833637\n",
            "\n",
            "Epoch: 161 Training MAE: 0.33541354537010193 RMSE: 0.44637420773506165 R2: 0.40576643248996114\n",
            "Epoch: 161 Validation MAE: 0.35143163800239563 RMSE: 0.4652569890022278 R2: 0.3694306145545665\n",
            "\n",
            "Epoch: 162 Training MAE: 0.3353303074836731 RMSE: 0.446301132440567 R2: 0.4059609128575401\n",
            "Epoch: 162 Validation MAE: 0.35123056173324585 RMSE: 0.46506568789482117 R2: 0.36994897350419054\n",
            "\n",
            "Epoch: 163 Training MAE: 0.33508285880088806 RMSE: 0.4463039040565491 R2: 0.40595355926761034\n",
            "Epoch: 163 Validation MAE: 0.3508872091770172 RMSE: 0.46481630206108093 R2: 0.3706244751508607\n",
            "\n",
            "Epoch: 164 Training MAE: 0.3352035582065582 RMSE: 0.4459403157234192 R2: 0.40692109095833684\n",
            "Epoch: 164 Validation MAE: 0.35129648447036743 RMSE: 0.4652095139026642 R2: 0.3695591900176589\n",
            "\n",
            "Epoch: 165 Training MAE: 0.33620917797088623 RMSE: 0.4463062882423401 R2: 0.4059472430351897\n",
            "Epoch: 165 Validation MAE: 0.3526000678539276 RMSE: 0.46640339493751526 R2: 0.36631929113232686\n",
            "\n",
            "Epoch: 166 Training MAE: 0.33489546179771423 RMSE: 0.44579124450683594 R2: 0.4073175029583873\n",
            "Epoch: 166 Validation MAE: 0.3506868779659271 RMSE: 0.46444830298423767 R2: 0.3716207227357501\n",
            "\n",
            "Epoch: 167 Training MAE: 0.3346327841281891 RMSE: 0.4456654489040375 R2: 0.4076519652817935\n",
            "Epoch: 167 Validation MAE: 0.3502369523048401 RMSE: 0.46420755982398987 R2: 0.3722719404383379\n",
            "\n",
            "Epoch: 168 Training MAE: 0.33455222845077515 RMSE: 0.4456469416618347 R2: 0.40770118643894493\n",
            "Epoch: 168 Validation MAE: 0.35036832094192505 RMSE: 0.46424946188926697 R2: 0.3721586490452854\n",
            "\n",
            "Epoch: 169 Training MAE: 0.3346981704235077 RMSE: 0.4453713297843933 R2: 0.4084335664282228\n",
            "Epoch: 169 Validation MAE: 0.35064369440078735 RMSE: 0.46440884470939636 R2: 0.3717274856780767\n",
            "\n",
            "Epoch: 170 Training MAE: 0.33479318022727966 RMSE: 0.44518688321113586 R2: 0.40892345883875747\n",
            "Epoch: 170 Validation MAE: 0.3509165346622467 RMSE: 0.46484678983688354 R2: 0.3705420192727128\n",
            "\n",
            "Epoch: 171 Training MAE: 0.3354983627796173 RMSE: 0.44559967517852783 R2: 0.40782677511489573\n",
            "Epoch: 171 Validation MAE: 0.35172557830810547 RMSE: 0.46576058864593506 R2: 0.3680647737484358\n",
            "\n",
            "Epoch: 172 Training MAE: 0.3346843123435974 RMSE: 0.44493597745895386 R2: 0.4095895695845393\n",
            "Epoch: 172 Validation MAE: 0.3508661687374115 RMSE: 0.4647431969642639 R2: 0.37082253124221387\n",
            "\n",
            "Epoch: 173 Training MAE: 0.3340867757797241 RMSE: 0.4450639486312866 R2: 0.4092498268416166\n",
            "Epoch: 173 Validation MAE: 0.34982404112815857 RMSE: 0.4636008143424988 R2: 0.3739118254262759\n",
            "\n",
            "Epoch: 174 Training MAE: 0.33462968468666077 RMSE: 0.4447546601295471 R2: 0.41007056139402454\n",
            "Epoch: 174 Validation MAE: 0.3508739471435547 RMSE: 0.4646475911140442 R2: 0.3710813687873754\n",
            "\n",
            "Epoch: 175 Training MAE: 0.3344228267669678 RMSE: 0.44457462430000305 R2: 0.41054806352118567\n",
            "Epoch: 175 Validation MAE: 0.3504612147808075 RMSE: 0.4644269049167633 R2: 0.37167865084754736\n",
            "\n",
            "Epoch: 176 Training MAE: 0.33402276039123535 RMSE: 0.44430357217788696 R2: 0.41126662938980285\n",
            "Epoch: 176 Validation MAE: 0.3500930964946747 RMSE: 0.4639117121696472 R2: 0.37307181916856347\n",
            "\n",
            "Epoch: 177 Training MAE: 0.3345356285572052 RMSE: 0.4445951581001282 R2: 0.4104937150471991\n",
            "Epoch: 177 Validation MAE: 0.35028311610221863 RMSE: 0.4643436372280121 R2: 0.3719039243834653\n",
            "\n",
            "Epoch: 178 Training MAE: 0.33363205194473267 RMSE: 0.444114089012146 R2: 0.41176872249196816\n",
            "Epoch: 178 Validation MAE: 0.34910550713539124 RMSE: 0.46318507194519043 R2: 0.3750342259327144\n",
            "\n",
            "Epoch: 179 Training MAE: 0.3337092995643616 RMSE: 0.4438844621181488 R2: 0.4123769034817817\n",
            "Epoch: 179 Validation MAE: 0.34987571835517883 RMSE: 0.4636407494544983 R2: 0.37380403027897846\n",
            "\n",
            "Epoch: 180 Training MAE: 0.33349624276161194 RMSE: 0.44373568892478943 R2: 0.4127707173493199\n",
            "Epoch: 180 Validation MAE: 0.3494817316532135 RMSE: 0.4632110297679901 R2: 0.37496421110831857\n",
            "\n",
            "Epoch: 181 Training MAE: 0.33358660340309143 RMSE: 0.4436553418636322 R2: 0.4129833391309361\n",
            "Epoch: 181 Validation MAE: 0.3497766852378845 RMSE: 0.4634134769439697 R2: 0.3744176849927384\n",
            "\n",
            "Epoch: 182 Training MAE: 0.3333827555179596 RMSE: 0.44349271059036255 R2: 0.4134135594674798\n",
            "Epoch: 182 Validation MAE: 0.3495378792285919 RMSE: 0.4631699025630951 R2: 0.3750752368056989\n",
            "\n",
            "Epoch: 183 Training MAE: 0.33303648233413696 RMSE: 0.4435464143753052 R2: 0.41327156319744074\n",
            "Epoch: 183 Validation MAE: 0.3487570285797119 RMSE: 0.4624524414539337 R2: 0.3770097668494513\n",
            "\n",
            "Epoch: 184 Training MAE: 0.33300167322158813 RMSE: 0.4432407021522522 R2: 0.4140800657899033\n",
            "Epoch: 184 Validation MAE: 0.3486556112766266 RMSE: 0.4624433219432831 R2: 0.37703428699995745\n",
            "\n",
            "Epoch: 185 Training MAE: 0.33306482434272766 RMSE: 0.44339853525161743 R2: 0.41366269218009666\n",
            "Epoch: 185 Validation MAE: 0.34824520349502563 RMSE: 0.462162047624588 R2: 0.3777918999581842\n",
            "\n",
            "Epoch: 186 Training MAE: 0.33275139331817627 RMSE: 0.44302085041999817 R2: 0.4146610983654603\n",
            "Epoch: 186 Validation MAE: 0.3485778868198395 RMSE: 0.46236899495124817 R2: 0.3772345314717539\n",
            "\n",
            "Epoch: 187 Training MAE: 0.33328914642333984 RMSE: 0.443034827709198 R2: 0.4146242440060375\n",
            "Epoch: 187 Validation MAE: 0.3493451178073883 RMSE: 0.4631127417087555 R2: 0.37522940880373057\n",
            "\n",
            "Epoch: 188 Training MAE: 0.3331170380115509 RMSE: 0.4428471326828003 R2: 0.41512015126051094\n",
            "Epoch: 188 Validation MAE: 0.34918972849845886 RMSE: 0.46257591247558594 R2: 0.3766769925802147\n",
            "\n",
            "Epoch: 189 Training MAE: 0.332518994808197 RMSE: 0.4426717460155487 R2: 0.4155832417153492\n",
            "Epoch: 189 Validation MAE: 0.34840714931488037 RMSE: 0.4618256092071533 R2: 0.3786975495658359\n",
            "\n",
            "Epoch: 190 Training MAE: 0.3326047956943512 RMSE: 0.4424940347671509 R2: 0.4160524420700026\n",
            "Epoch: 190 Validation MAE: 0.348700612783432 RMSE: 0.46221446990966797 R2: 0.37765069142264296\n",
            "\n",
            "Epoch: 191 Training MAE: 0.33233606815338135 RMSE: 0.44258925318717957 R2: 0.41580107083331097\n",
            "Epoch: 191 Validation MAE: 0.34776484966278076 RMSE: 0.46146252751350403 R2: 0.3796740221599535\n",
            "\n",
            "Epoch: 192 Training MAE: 0.33231648802757263 RMSE: 0.44251298904418945 R2: 0.41600245896656585\n",
            "Epoch: 192 Validation MAE: 0.3478811979293823 RMSE: 0.46142759919166565 R2: 0.37976789066366146\n",
            "\n",
            "Epoch: 193 Training MAE: 0.33292481303215027 RMSE: 0.44241952896118164 R2: 0.41624903705832506\n",
            "Epoch: 193 Validation MAE: 0.34887513518333435 RMSE: 0.4626619517803192 R2: 0.37644514806595575\n",
            "\n",
            "Epoch: 194 Training MAE: 0.33209100365638733 RMSE: 0.4420057535171509 R2: 0.41734046333396235\n",
            "Epoch: 194 Validation MAE: 0.34797346591949463 RMSE: 0.46142885088920593 R2: 0.3797644846565664\n",
            "\n",
            "Epoch: 195 Training MAE: 0.3320033848285675 RMSE: 0.4418559670448303 R2: 0.41773535684488106\n",
            "Epoch: 195 Validation MAE: 0.3480547368526459 RMSE: 0.46143144369125366 R2: 0.37975760659665303\n",
            "\n",
            "Epoch: 196 Training MAE: 0.33196693658828735 RMSE: 0.44184431433677673 R2: 0.41776604078733637\n",
            "Epoch: 196 Validation MAE: 0.34787464141845703 RMSE: 0.4612496793270111 R2: 0.38024614116639444\n",
            "\n",
            "Epoch: 197 Training MAE: 0.33212780952453613 RMSE: 0.4416724741458893 R2: 0.4182188339097782\n",
            "Epoch: 197 Validation MAE: 0.3481302857398987 RMSE: 0.4616783857345581 R2: 0.3790935595448486\n",
            "\n",
            "Epoch: 198 Training MAE: 0.3334139883518219 RMSE: 0.4425058364868164 R2: 0.41602129449445246\n",
            "Epoch: 198 Validation MAE: 0.3496024012565613 RMSE: 0.4632086753845215 R2: 0.37497058585623544\n",
            "\n",
            "Epoch: 199 Training MAE: 0.33171361684799194 RMSE: 0.4413927495479584 R2: 0.41895545748074325\n",
            "Epoch: 199 Validation MAE: 0.3477477729320526 RMSE: 0.4610477387905121 R2: 0.38078865931445294\n",
            "\n",
            "Epoch: 200 Training MAE: 0.33153295516967773 RMSE: 0.44129517674446106 R2: 0.41921233596840424\n",
            "Epoch: 200 Validation MAE: 0.34739065170288086 RMSE: 0.4606589376926422 R2: 0.3818325605946795\n",
            "\n",
            "Epoch: 201 Training MAE: 0.3320625424385071 RMSE: 0.4413560628890991 R2: 0.41905203364719457\n",
            "Epoch: 201 Validation MAE: 0.348248690366745 RMSE: 0.4617161452770233 R2: 0.3789919599113768\n",
            "\n",
            "Epoch: 202 Training MAE: 0.3319188356399536 RMSE: 0.4412209987640381 R2: 0.41940757091574987\n",
            "Epoch: 202 Validation MAE: 0.34778961539268494 RMSE: 0.46139031648635864 R2: 0.3798680872285294\n",
            "\n",
            "Epoch: 203 Training MAE: 0.33204248547554016 RMSE: 0.44121667742729187 R2: 0.4194189881609447\n",
            "Epoch: 203 Validation MAE: 0.3483918309211731 RMSE: 0.46178504824638367 R2: 0.3788065718454201\n",
            "\n",
            "Epoch: 204 Training MAE: 0.3313140273094177 RMSE: 0.4407942593097687 R2: 0.4205300952462485\n",
            "Epoch: 204 Validation MAE: 0.3473483622074127 RMSE: 0.46075549721717834 R2: 0.38157336325641\n",
            "\n",
            "Epoch: 205 Training MAE: 0.33124685287475586 RMSE: 0.44102030992507935 R2: 0.4199356277064775\n",
            "Epoch: 205 Validation MAE: 0.34696057438850403 RMSE: 0.460199773311615 R2: 0.3830643300257772\n",
            "\n",
            "Epoch: 206 Training MAE: 0.33106955885887146 RMSE: 0.4405544698238373 R2: 0.4211603321041839\n",
            "Epoch: 206 Validation MAE: 0.3469696342945099 RMSE: 0.46012604236602783 R2: 0.38326195617965264\n",
            "\n",
            "Epoch: 207 Training MAE: 0.3312094509601593 RMSE: 0.4412168562412262 R2: 0.4194184409312155\n",
            "Epoch: 207 Validation MAE: 0.34670886397361755 RMSE: 0.45988893508911133 R2: 0.38389735660603086\n",
            "\n",
            "Epoch: 208 Training MAE: 0.3312775790691376 RMSE: 0.44045165181159973 R2: 0.42143056944512625\n",
            "Epoch: 208 Validation MAE: 0.34717676043510437 RMSE: 0.46040844917297363 R2: 0.3825046503807227\n",
            "\n",
            "Epoch: 209 Training MAE: 0.3309324085712433 RMSE: 0.44021302461624146 R2: 0.4220573535300971\n",
            "Epoch: 209 Validation MAE: 0.3469778895378113 RMSE: 0.45999592542648315 R2: 0.3836107647005714\n",
            "\n",
            "Epoch: 210 Training MAE: 0.3308503031730652 RMSE: 0.4401024878025055 R2: 0.42234751499628653\n",
            "Epoch: 210 Validation MAE: 0.3466927111148834 RMSE: 0.45977357029914856 R2: 0.38420644359225564\n",
            "\n",
            "Epoch: 211 Training MAE: 0.3307202160358429 RMSE: 0.44002875685691833 R2: 0.4225410220871887\n",
            "Epoch: 211 Validation MAE: 0.346235066652298 RMSE: 0.4594118297100067 R2: 0.3851750469228111\n",
            "\n",
            "Epoch: 212 Training MAE: 0.330482542514801 RMSE: 0.440014123916626 R2: 0.42257941488171\n",
            "Epoch: 212 Validation MAE: 0.34617725014686584 RMSE: 0.45907339453697205 R2: 0.38608062269207544\n",
            "\n",
            "Epoch: 213 Training MAE: 0.33040952682495117 RMSE: 0.4399397075176239 R2: 0.42277475616350213\n",
            "Epoch: 213 Validation MAE: 0.3462405800819397 RMSE: 0.4590771198272705 R2: 0.3860706669990118\n",
            "\n",
            "Epoch: 214 Training MAE: 0.33029305934906006 RMSE: 0.43991217017173767 R2: 0.4228470328680337\n",
            "Epoch: 214 Validation MAE: 0.3459441661834717 RMSE: 0.458843857049942 R2: 0.3866944182640879\n",
            "\n",
            "Epoch: 215 Training MAE: 0.33035147190093994 RMSE: 0.4395352602005005 R2: 0.4238356130639448\n",
            "Epoch: 215 Validation MAE: 0.3466353416442871 RMSE: 0.4595583379268646 R2: 0.38478288410109474\n",
            "\n",
            "Epoch: 216 Training MAE: 0.3304733633995056 RMSE: 0.43949711322784424 R2: 0.4239355744626806\n",
            "Epoch: 216 Validation MAE: 0.3467031717300415 RMSE: 0.4595958888530731 R2: 0.384682404806568\n",
            "\n",
            "Epoch: 217 Training MAE: 0.33012285828590393 RMSE: 0.43929368257522583 R2: 0.4244687282834746\n",
            "Epoch: 217 Validation MAE: 0.34615278244018555 RMSE: 0.45910683274269104 R2: 0.3859911840097867\n",
            "\n",
            "Epoch: 218 Training MAE: 0.33010390400886536 RMSE: 0.43929097056388855 R2: 0.4244758045250221\n",
            "Epoch: 218 Validation MAE: 0.34570372104644775 RMSE: 0.4588161110877991 R2: 0.38676851604527773\n",
            "\n",
            "Epoch: 219 Training MAE: 0.3299258351325989 RMSE: 0.43905723094940186 R2: 0.4250881487143523\n",
            "Epoch: 219 Validation MAE: 0.34603506326675415 RMSE: 0.4589203894138336 R2: 0.3864897907333823\n",
            "\n",
            "Epoch: 220 Training MAE: 0.32991793751716614 RMSE: 0.4390067756175995 R2: 0.4252203178653049\n",
            "Epoch: 220 Validation MAE: 0.3459879457950592 RMSE: 0.4586957097053528 R2: 0.3870903165082\n",
            "\n",
            "Epoch: 221 Training MAE: 0.3299292027950287 RMSE: 0.43887022137641907 R2: 0.42557779085588865\n",
            "Epoch: 221 Validation MAE: 0.3458445072174072 RMSE: 0.4587787389755249 R2: 0.3868684772703721\n",
            "\n",
            "Epoch: 222 Training MAE: 0.3295936584472656 RMSE: 0.43893539905548096 R2: 0.4254071464663982\n",
            "Epoch: 222 Validation MAE: 0.3453732132911682 RMSE: 0.4581016004085541 R2: 0.38867705136081876\n",
            "\n",
            "Epoch: 223 Training MAE: 0.3295218348503113 RMSE: 0.43878960609436035 R2: 0.42578875282080564\n",
            "Epoch: 223 Validation MAE: 0.3451351523399353 RMSE: 0.4578365683555603 R2: 0.38938417150468974\n",
            "\n",
            "Epoch: 224 Training MAE: 0.32955244183540344 RMSE: 0.43866971135139465 R2: 0.4261025533325936\n",
            "Epoch: 224 Validation MAE: 0.3454242944717407 RMSE: 0.45794108510017395 R2: 0.38910538452643995\n",
            "\n",
            "Epoch: 225 Training MAE: 0.32951250672340393 RMSE: 0.43878060579299927 R2: 0.42581231291615873\n",
            "Epoch: 225 Validation MAE: 0.3455045819282532 RMSE: 0.4580664336681366 R2: 0.3887708954104844\n",
            "\n",
            "Epoch: 226 Training MAE: 0.32940325140953064 RMSE: 0.43846407532691956 R2: 0.4266404503539746\n",
            "Epoch: 226 Validation MAE: 0.3447684943675995 RMSE: 0.45763951539993286 R2: 0.3899096749305446\n",
            "\n",
            "Epoch: 227 Training MAE: 0.3295540511608124 RMSE: 0.43881726264953613 R2: 0.42571646729611723\n",
            "Epoch: 227 Validation MAE: 0.3453889489173889 RMSE: 0.45786312222480774 R2: 0.38931339363817996\n",
            "\n",
            "Epoch: 228 Training MAE: 0.3292596638202667 RMSE: 0.4381503164768219 R2: 0.4274607186561379\n",
            "Epoch: 228 Validation MAE: 0.34553202986717224 RMSE: 0.45810002088546753 R2: 0.38868128041902816\n",
            "\n",
            "Epoch: 229 Training MAE: 0.3301860988140106 RMSE: 0.4386146366596222 R2: 0.42624660581648666\n",
            "Epoch: 229 Validation MAE: 0.3466652035713196 RMSE: 0.45952776074409485 R2: 0.38486470398780404\n",
            "\n",
            "Epoch: 230 Training MAE: 0.33001288771629333 RMSE: 0.43838703632354736 R2: 0.4268419460426096\n",
            "Epoch: 230 Validation MAE: 0.34580081701278687 RMSE: 0.45880499482154846 R2: 0.3867982807879393\n",
            "\n",
            "Epoch: 231 Training MAE: 0.32925891876220703 RMSE: 0.4378948509693146 R2: 0.42812816476610294\n",
            "Epoch: 231 Validation MAE: 0.3456648886203766 RMSE: 0.4582236111164093 R2: 0.3883513169812214\n",
            "\n",
            "Epoch: 232 Training MAE: 0.3290092349052429 RMSE: 0.43791088461875916 R2: 0.428086282881157\n",
            "Epoch: 232 Validation MAE: 0.34532102942466736 RMSE: 0.4577260911464691 R2: 0.38967880748740846\n",
            "\n",
            "Epoch: 233 Training MAE: 0.3288211524486542 RMSE: 0.4376240670681 R2: 0.42883522320094514\n",
            "Epoch: 233 Validation MAE: 0.3451571762561798 RMSE: 0.4577083885669708 R2: 0.38972608252123186\n",
            "\n",
            "Epoch: 234 Training MAE: 0.3293941020965576 RMSE: 0.4378555715084076 R2: 0.4282308100718655\n",
            "Epoch: 234 Validation MAE: 0.3457321524620056 RMSE: 0.45845621824264526 R2: 0.38773023724454314\n",
            "\n",
            "Epoch: 235 Training MAE: 0.32879436016082764 RMSE: 0.4375237822532654 R2: 0.4290969333760266\n",
            "Epoch: 235 Validation MAE: 0.3451516032218933 RMSE: 0.4577026069164276 R2: 0.38974146385648434\n",
            "\n",
            "Epoch: 236 Training MAE: 0.3286704123020172 RMSE: 0.4375019371509552 R2: 0.4291540180909875\n",
            "Epoch: 236 Validation MAE: 0.34419775009155273 RMSE: 0.456889808177948 R2: 0.3919069265244899\n",
            "\n",
            "Epoch: 237 Training MAE: 0.3287807106971741 RMSE: 0.4373134970664978 R2: 0.4296455905543479\n",
            "Epoch: 237 Validation MAE: 0.34531745314598083 RMSE: 0.4577540159225464 R2: 0.3896044597678977\n",
            "\n",
            "Epoch: 238 Training MAE: 0.32846716046333313 RMSE: 0.43723925948143005 R2: 0.4298392824730144\n",
            "Epoch: 238 Validation MAE: 0.34478551149368286 RMSE: 0.4571616053581238 R2: 0.3911832415117542\n",
            "\n",
            "Epoch: 239 Training MAE: 0.3285869061946869 RMSE: 0.43709295988082886 R2: 0.4302206968584962\n",
            "Epoch: 239 Validation MAE: 0.344388484954834 RMSE: 0.457182914018631 R2: 0.39112654232217614\n",
            "\n",
            "Epoch: 240 Training MAE: 0.3287822902202606 RMSE: 0.43710991740226746 R2: 0.4301765043594383\n",
            "Epoch: 240 Validation MAE: 0.3446942865848541 RMSE: 0.4573761522769928 R2: 0.3906116558132523\n",
            "\n",
            "Epoch: 241 Training MAE: 0.32838761806488037 RMSE: 0.43690744042396545 R2: 0.43070425650416666\n",
            "Epoch: 241 Validation MAE: 0.34496551752090454 RMSE: 0.45720911026000977 R2: 0.39105674225640963\n",
            "\n",
            "Epoch: 242 Training MAE: 0.32853230834007263 RMSE: 0.4368284344673157 R2: 0.4309102322228541\n",
            "Epoch: 242 Validation MAE: 0.34442463517189026 RMSE: 0.4570177495479584 R2: 0.3915663874138867\n",
            "\n",
            "Epoch: 243 Training MAE: 0.32818764448165894 RMSE: 0.43665894865989685 R2: 0.4313517266298903\n",
            "Epoch: 243 Validation MAE: 0.3448432683944702 RMSE: 0.45725035667419434 R2: 0.39094682200483477\n",
            "\n",
            "Epoch: 244 Training MAE: 0.3281570374965668 RMSE: 0.4367881417274475 R2: 0.43101518766627556\n",
            "Epoch: 244 Validation MAE: 0.3447437882423401 RMSE: 0.45698609948158264 R2: 0.3916506718824908\n",
            "\n",
            "Epoch: 245 Training MAE: 0.32806235551834106 RMSE: 0.43652570247650146 R2: 0.43169865682397446\n",
            "Epoch: 245 Validation MAE: 0.34481045603752136 RMSE: 0.45693621039390564 R2: 0.39178345413034366\n",
            "\n",
            "Epoch: 246 Training MAE: 0.3278239965438843 RMSE: 0.4368250370025635 R2: 0.43091904767306444\n",
            "Epoch: 246 Validation MAE: 0.34417423605918884 RMSE: 0.4563834071159363 R2: 0.39325415524477003\n",
            "\n",
            "Epoch: 247 Training MAE: 0.32816535234451294 RMSE: 0.4363915026187897 R2: 0.4320480936179927\n",
            "Epoch: 247 Validation MAE: 0.34479138255119324 RMSE: 0.45695021748542786 R2: 0.3917461369137365\n",
            "\n",
            "Epoch: 248 Training MAE: 0.3278473913669586 RMSE: 0.4361427426338196 R2: 0.4326953579755717\n",
            "Epoch: 248 Validation MAE: 0.3440626263618469 RMSE: 0.4563779830932617 R2: 0.3932686252903018\n",
            "\n",
            "Epoch: 249 Training MAE: 0.3275440037250519 RMSE: 0.43605470657348633 R2: 0.43292440358916984\n",
            "Epoch: 249 Validation MAE: 0.3439980745315552 RMSE: 0.45628368854522705 R2: 0.39351931298036413\n",
            "\n",
            "Epoch: 250 Training MAE: 0.3276292085647583 RMSE: 0.43596044182777405 R2: 0.433169590905912\n",
            "Epoch: 250 Validation MAE: 0.34380868077278137 RMSE: 0.4562860429286957 R2: 0.3935130197675857\n",
            "\n",
            "Epoch: 251 Training MAE: 0.3278310298919678 RMSE: 0.435983270406723 R2: 0.43311022903861207\n",
            "Epoch: 251 Validation MAE: 0.3445364534854889 RMSE: 0.4568006694316864 R2: 0.3921442145490851\n",
            "\n",
            "Epoch: 252 Training MAE: 0.32728317379951477 RMSE: 0.4357839524745941 R2: 0.4336284199651207\n",
            "Epoch: 252 Validation MAE: 0.3439202308654785 RMSE: 0.4560868442058563 R2: 0.3940423962670283\n",
            "\n",
            "Epoch: 253 Training MAE: 0.3274616301059723 RMSE: 0.4357236325740814 R2: 0.43378509745095095\n",
            "Epoch: 253 Validation MAE: 0.34449222683906555 RMSE: 0.4565243422985077 R2: 0.39287934408071035\n",
            "\n",
            "Epoch: 254 Training MAE: 0.3270919620990753 RMSE: 0.4357130527496338 R2: 0.4338126388404382\n",
            "Epoch: 254 Validation MAE: 0.34375131130218506 RMSE: 0.45563939213752747 R2: 0.3952308358542226\n",
            "\n",
            "Epoch: 255 Training MAE: 0.32725608348846436 RMSE: 0.43549859523773193 R2: 0.43436987392674453\n",
            "Epoch: 255 Validation MAE: 0.34365394711494446 RMSE: 0.4559624493122101 R2: 0.39437302445382605\n",
            "\n",
            "Epoch: 256 Training MAE: 0.3271709680557251 RMSE: 0.43554338812828064 R2: 0.43425353427941726\n",
            "Epoch: 256 Validation MAE: 0.3436407744884491 RMSE: 0.45591363310813904 R2: 0.39450261590101654\n",
            "\n",
            "Epoch: 257 Training MAE: 0.32752150297164917 RMSE: 0.43552806973457336 R2: 0.43429325751699643\n",
            "Epoch: 257 Validation MAE: 0.34418511390686035 RMSE: 0.456443190574646 R2: 0.393095197371511\n",
            "\n",
            "Epoch: 258 Training MAE: 0.3267918527126312 RMSE: 0.4354914426803589 R2: 0.43438849057692785\n",
            "Epoch: 258 Validation MAE: 0.3436298668384552 RMSE: 0.4555974304676056 R2: 0.39534225162731007\n",
            "\n",
            "Epoch: 259 Training MAE: 0.3267984986305237 RMSE: 0.43545398116111755 R2: 0.4344857101766564\n",
            "Epoch: 259 Validation MAE: 0.34368395805358887 RMSE: 0.45563721656799316 R2: 0.3952366415717059\n",
            "\n",
            "Epoch: 260 Training MAE: 0.3272327184677124 RMSE: 0.43516942858695984 R2: 0.43522454891689855\n",
            "Epoch: 260 Validation MAE: 0.3441225290298462 RMSE: 0.4563696086406708 R2: 0.39329088376620214\n",
            "\n",
            "Epoch: 261 Training MAE: 0.3275241255760193 RMSE: 0.4353095293045044 R2: 0.4348608440484891\n",
            "Epoch: 261 Validation MAE: 0.34460029006004333 RMSE: 0.45696455240249634 R2: 0.3917079637509403\n",
            "\n",
            "Epoch: 262 Training MAE: 0.32682809233665466 RMSE: 0.43485087156295776 R2: 0.43605118205771776\n",
            "Epoch: 262 Validation MAE: 0.34380874037742615 RMSE: 0.4559645652770996 R2: 0.3943673022954727\n",
            "\n",
            "Epoch: 263 Training MAE: 0.3267189562320709 RMSE: 0.4347304701805115 R2: 0.4363634157467634\n",
            "Epoch: 263 Validation MAE: 0.34377387166023254 RMSE: 0.4560548961162567 R2: 0.3941273552172395\n",
            "\n",
            "Epoch: 264 Training MAE: 0.3266259431838989 RMSE: 0.434992253780365 R2: 0.43568438120456576\n",
            "Epoch: 264 Validation MAE: 0.34362491965293884 RMSE: 0.45563754439353943 R2: 0.3952357977919575\n",
            "\n",
            "Epoch: 265 Training MAE: 0.3266680836677551 RMSE: 0.43462011218070984 R2: 0.43664957209637856\n",
            "Epoch: 265 Validation MAE: 0.34313884377479553 RMSE: 0.45515328645706177 R2: 0.39652052766337\n",
            "\n",
            "Epoch: 266 Training MAE: 0.3266027867794037 RMSE: 0.4353637397289276 R2: 0.4347201650635739\n",
            "Epoch: 266 Validation MAE: 0.34350115060806274 RMSE: 0.4550991654396057 R2: 0.3966640292373925\n",
            "\n",
            "Epoch: 267 Training MAE: 0.32647600769996643 RMSE: 0.43439680337905884 R2: 0.43722828683011816\n",
            "Epoch: 267 Validation MAE: 0.34372660517692566 RMSE: 0.45552825927734375 R2: 0.3955259353494174\n",
            "\n",
            "Epoch: 268 Training MAE: 0.3261883854866028 RMSE: 0.43423646688461304 R2: 0.43764365689204765\n",
            "Epoch: 268 Validation MAE: 0.34339961409568787 RMSE: 0.455255389213562 R2: 0.396249796919387\n",
            "\n",
            "Epoch: 269 Training MAE: 0.3260291516780853 RMSE: 0.43419498205184937 R2: 0.4377510309399788\n",
            "Epoch: 269 Validation MAE: 0.34330686926841736 RMSE: 0.45509159564971924 R2: 0.39668420400839177\n",
            "\n",
            "Epoch: 270 Training MAE: 0.326713502407074 RMSE: 0.4343988597393036 R2: 0.4372229241307781\n",
            "Epoch: 270 Validation MAE: 0.34412887692451477 RMSE: 0.4561305046081543 R2: 0.3939264608171328\n",
            "\n",
            "Epoch: 271 Training MAE: 0.3261302709579468 RMSE: 0.4340435266494751 R2: 0.43814332158063607\n",
            "Epoch: 271 Validation MAE: 0.34346869587898254 RMSE: 0.45518797636032104 R2: 0.39642859097182326\n",
            "\n",
            "Epoch: 272 Training MAE: 0.32786446809768677 RMSE: 0.4351331889629364 R2: 0.4353186985625932\n",
            "Epoch: 272 Validation MAE: 0.3452049195766449 RMSE: 0.4572240710258484 R2: 0.39101687042673994\n",
            "\n",
            "Epoch: 273 Training MAE: 0.3261805474758148 RMSE: 0.4343191683292389 R2: 0.43742946624369494\n",
            "Epoch: 273 Validation MAE: 0.3431718647480011 RMSE: 0.4548870921134949 R2: 0.3972262939274308\n",
            "\n",
            "Epoch: 274 Training MAE: 0.3258642852306366 RMSE: 0.4339620769023895 R2: 0.4383540812522584\n",
            "Epoch: 274 Validation MAE: 0.34298455715179443 RMSE: 0.45454108715057373 R2: 0.39814293307709425\n",
            "\n",
            "Epoch: 275 Training MAE: 0.32573139667510986 RMSE: 0.4338620603084564 R2: 0.43861304572111615\n",
            "Epoch: 275 Validation MAE: 0.34278517961502075 RMSE: 0.4543319344520569 R2: 0.3986966420754444\n",
            "\n",
            "Epoch: 276 Training MAE: 0.3259642422199249 RMSE: 0.43368566036224365 R2: 0.4390694091764492\n",
            "Epoch: 276 Validation MAE: 0.34360143542289734 RMSE: 0.4552540183067322 R2: 0.39625342474757974\n",
            "\n",
            "Epoch: 277 Training MAE: 0.3257468342781067 RMSE: 0.4339291751384735 R2: 0.43843931134777403\n",
            "Epoch: 277 Validation MAE: 0.3428313136100769 RMSE: 0.4543395936489105 R2: 0.3986763835630356\n",
            "\n",
            "Epoch: 278 Training MAE: 0.3262026607990265 RMSE: 0.4335678517818451 R2: 0.4393741318629246\n",
            "Epoch: 278 Validation MAE: 0.34284305572509766 RMSE: 0.4547417163848877 R2: 0.39761147682646325\n",
            "\n",
            "Epoch: 279 Training MAE: 0.32585054636001587 RMSE: 0.43338465690612793 R2: 0.43984770649021754\n",
            "Epoch: 279 Validation MAE: 0.34303662180900574 RMSE: 0.4548145532608032 R2: 0.3974184772716297\n",
            "\n",
            "Epoch: 280 Training MAE: 0.3255629241466522 RMSE: 0.4332531690597534 R2: 0.4401875770660113\n",
            "Epoch: 280 Validation MAE: 0.34309619665145874 RMSE: 0.4546496570110321 R2: 0.3978553625677369\n",
            "\n",
            "Epoch: 281 Training MAE: 0.3255067765712738 RMSE: 0.43360382318496704 R2: 0.4392810806322356\n",
            "Epoch: 281 Validation MAE: 0.3433658480644226 RMSE: 0.454719215631485 R2: 0.39767109028614245\n",
            "\n",
            "Epoch: 282 Training MAE: 0.32558733224868774 RMSE: 0.4334854185581207 R2: 0.4395872035599886\n",
            "Epoch: 282 Validation MAE: 0.3428719639778137 RMSE: 0.45424389839172363 R2: 0.39892963462890096\n",
            "\n",
            "Epoch: 283 Training MAE: 0.3252863585948944 RMSE: 0.43291527032852173 R2: 0.44106042584259897\n",
            "Epoch: 283 Validation MAE: 0.34278854727745056 RMSE: 0.45431458950042725 R2: 0.3987425878609857\n",
            "\n",
            "Epoch: 284 Training MAE: 0.3253309428691864 RMSE: 0.4328608512878418 R2: 0.44120093110920344\n",
            "Epoch: 284 Validation MAE: 0.3430306613445282 RMSE: 0.454603374004364 R2: 0.3979779159844008\n",
            "\n",
            "Epoch: 285 Training MAE: 0.32554829120635986 RMSE: 0.43290531635284424 R2: 0.4410862254040536\n",
            "Epoch: 285 Validation MAE: 0.3433248996734619 RMSE: 0.4549531042575836 R2: 0.3970513159385566\n",
            "\n",
            "Epoch: 286 Training MAE: 0.3253028690814972 RMSE: 0.4327448308467865 R2: 0.44150053035269976\n",
            "Epoch: 286 Validation MAE: 0.34247276186943054 RMSE: 0.4540872871875763 R2: 0.39934399172989743\n",
            "\n",
            "Epoch: 287 Training MAE: 0.32533296942710876 RMSE: 0.43274345993995667 R2: 0.4415040044773899\n",
            "Epoch: 287 Validation MAE: 0.3428594768047333 RMSE: 0.454210102558136 R2: 0.39901911372014\n",
            "\n",
            "Epoch: 288 Training MAE: 0.3248344659805298 RMSE: 0.4326428771018982 R2: 0.441763641896295\n",
            "Epoch: 288 Validation MAE: 0.34261518716812134 RMSE: 0.4539185166358948 R2: 0.3997904574146667\n",
            "\n",
            "Epoch: 289 Training MAE: 0.3254088759422302 RMSE: 0.4335823357105255 R2: 0.43933661547108527\n",
            "Epoch: 289 Validation MAE: 0.342675119638443 RMSE: 0.4536670446395874 R2: 0.40045522354849095\n",
            "\n",
            "Epoch: 290 Training MAE: 0.3248022496700287 RMSE: 0.43233662843704224 R2: 0.4425536357936819\n",
            "Epoch: 290 Validation MAE: 0.3424442410469055 RMSE: 0.45380550622940063 R2: 0.40008931993840946\n",
            "\n",
            "Epoch: 291 Training MAE: 0.3246936798095703 RMSE: 0.43228527903556824 R2: 0.44268603087868863\n",
            "Epoch: 291 Validation MAE: 0.3425421416759491 RMSE: 0.4538908898830414 R2: 0.3998634976369505\n",
            "\n",
            "Epoch: 292 Training MAE: 0.3250892758369446 RMSE: 0.4330286383628845 R2: 0.4407676747314718\n",
            "Epoch: 292 Validation MAE: 0.3426617681980133 RMSE: 0.45368462800979614 R2: 0.40040879227761694\n",
            "\n",
            "Epoch: 293 Training MAE: 0.3251795470714569 RMSE: 0.43228307366371155 R2: 0.44269178943743304\n",
            "Epoch: 293 Validation MAE: 0.34283655881881714 RMSE: 0.45432019233703613 R2: 0.39872767066220804\n",
            "\n",
            "Epoch: 294 Training MAE: 0.32468903064727783 RMSE: 0.43238410353660583 R2: 0.4424312354326839\n",
            "Epoch: 294 Validation MAE: 0.3423002362251282 RMSE: 0.4533856511116028 R2: 0.4011988400534059\n",
            "\n",
            "Epoch: 295 Training MAE: 0.3248497247695923 RMSE: 0.43205419182777405 R2: 0.4432817445508421\n",
            "Epoch: 295 Validation MAE: 0.34285032749176025 RMSE: 0.45403727889060974 R2: 0.39947634636011686\n",
            "\n",
            "Epoch: 296 Training MAE: 0.3247009813785553 RMSE: 0.43196725845336914 R2: 0.4435057059715386\n",
            "Epoch: 296 Validation MAE: 0.34269100427627563 RMSE: 0.4540877938270569 R2: 0.3993427439827798\n",
            "\n",
            "Epoch: 297 Training MAE: 0.32576602697372437 RMSE: 0.4327089786529541 R2: 0.44159300982904415\n",
            "Epoch: 297 Validation MAE: 0.34379681944847107 RMSE: 0.4553392231464386 R2: 0.396027394613399\n",
            "\n",
            "Epoch: 298 Training MAE: 0.3243359327316284 RMSE: 0.43169137835502625 R2: 0.4442163025107755\n",
            "Epoch: 298 Validation MAE: 0.3423168957233429 RMSE: 0.453395813703537 R2: 0.40117196439083536\n",
            "\n",
            "Epoch: 299 Training MAE: 0.32441246509552 RMSE: 0.4316575229167938 R2: 0.4443034897918744\n",
            "Epoch: 299 Validation MAE: 0.3422098457813263 RMSE: 0.4532749056816101 R2: 0.40149133227719047\n",
            "\n",
            "Epoch: 300 Training MAE: 0.32461780309677124 RMSE: 0.432147741317749 R2: 0.4430406332934578\n",
            "Epoch: 300 Validation MAE: 0.34191808104515076 RMSE: 0.45282915234565735 R2: 0.4026678941428401\n",
            "\n",
            "Epoch: 301 Training MAE: 0.3256600797176361 RMSE: 0.4321313798427582 R2: 0.44308279326162847\n",
            "Epoch: 301 Validation MAE: 0.34289345145225525 RMSE: 0.4542109966278076 R2: 0.3990167947235894\n",
            "\n",
            "Epoch: 302 Training MAE: 0.3240831196308136 RMSE: 0.43152305483818054 R2: 0.44464966352514745\n",
            "Epoch: 302 Validation MAE: 0.34215375781059265 RMSE: 0.45312368869781494 R2: 0.4018905436600756\n",
            "\n",
            "Epoch: 303 Training MAE: 0.3244239389896393 RMSE: 0.43141937255859375 R2: 0.4449165813850908\n",
            "Epoch: 303 Validation MAE: 0.34197404980659485 RMSE: 0.4530569612979889 R2: 0.40206670528851807\n",
            "\n",
            "Epoch: 304 Training MAE: 0.3246137797832489 RMSE: 0.4314308166503906 R2: 0.4448870604448294\n",
            "Epoch: 304 Validation MAE: 0.34188058972358704 RMSE: 0.4531986713409424 R2: 0.4016926500289516\n",
            "\n",
            "Epoch: 305 Training MAE: 0.32419779896736145 RMSE: 0.43123751878738403 R2: 0.4453843879311371\n",
            "Epoch: 305 Validation MAE: 0.34203410148620605 RMSE: 0.4533287584781647 R2: 0.4013491335984042\n",
            "\n",
            "Epoch: 306 Training MAE: 0.32377204298973083 RMSE: 0.43099725246429443 R2: 0.44600223424642993\n",
            "Epoch: 306 Validation MAE: 0.3419383764266968 RMSE: 0.45294758677482605 R2: 0.4023553856798536\n",
            "\n",
            "Epoch: 307 Training MAE: 0.32394781708717346 RMSE: 0.43092331290245056 R2: 0.44619232815158183\n",
            "Epoch: 307 Validation MAE: 0.34127283096313477 RMSE: 0.45243796706199646 R2: 0.4036994772404716\n",
            "\n",
            "Epoch: 308 Training MAE: 0.3243545889854431 RMSE: 0.43117383122444153 R2: 0.44554814925740505\n",
            "Epoch: 308 Validation MAE: 0.3415120244026184 RMSE: 0.4522675573825836 R2: 0.40414865191013005\n",
            "\n",
            "Epoch: 309 Training MAE: 0.32431358098983765 RMSE: 0.4310012757778168 R2: 0.4459918533603826\n",
            "Epoch: 309 Validation MAE: 0.3420163094997406 RMSE: 0.45309901237487793 R2: 0.4019557049273783\n",
            "\n",
            "Epoch: 310 Training MAE: 0.3235742747783661 RMSE: 0.4310595393180847 R2: 0.44584212379669663\n",
            "Epoch: 310 Validation MAE: 0.3416157364845276 RMSE: 0.4525178074836731 R2: 0.4034890357860734\n",
            "\n",
            "Epoch: 311 Training MAE: 0.3232392966747284 RMSE: 0.4305013120174408 R2: 0.4472765243165221\n",
            "Epoch: 311 Validation MAE: 0.3413935601711273 RMSE: 0.4523111581802368 R2: 0.40403372418678574\n",
            "\n",
            "Epoch: 312 Training MAE: 0.3237278461456299 RMSE: 0.4306204617023468 R2: 0.4469704268791924\n",
            "Epoch: 312 Validation MAE: 0.34173643589019775 RMSE: 0.4526374936103821 R2: 0.4031734136856604\n",
            "\n",
            "Epoch: 313 Training MAE: 0.32354453206062317 RMSE: 0.43056803941726685 R2: 0.44710511640110084\n",
            "Epoch: 313 Validation MAE: 0.34205350279808044 RMSE: 0.4529772698879242 R2: 0.4022771010114611\n",
            "\n",
            "Epoch: 314 Training MAE: 0.32368171215057373 RMSE: 0.43039095401763916 R2: 0.44755977105717226\n",
            "Epoch: 314 Validation MAE: 0.3411157727241516 RMSE: 0.45244619250297546 R2: 0.40367782286414966\n",
            "\n",
            "Epoch: 315 Training MAE: 0.32323646545410156 RMSE: 0.43032851815223694 R2: 0.4477200732646096\n",
            "Epoch: 315 Validation MAE: 0.34103068709373474 RMSE: 0.451997309923172 R2: 0.40486042010790635\n",
            "\n",
            "Epoch: 316 Training MAE: 0.32373303174972534 RMSE: 0.4303239583969116 R2: 0.44773176988974284\n",
            "Epoch: 316 Validation MAE: 0.34171342849731445 RMSE: 0.45281878113746643 R2: 0.4026952656568633\n",
            "\n",
            "Epoch: 317 Training MAE: 0.32287684082984924 RMSE: 0.43004342913627625 R2: 0.4484515447336622\n",
            "Epoch: 317 Validation MAE: 0.3409176468849182 RMSE: 0.4519211947917938 R2: 0.4050608638002958\n",
            "\n",
            "Epoch: 318 Training MAE: 0.3248801529407501 RMSE: 0.4313192069530487 R2: 0.44517423625047725\n",
            "Epoch: 318 Validation MAE: 0.3435009717941284 RMSE: 0.45437511801719666 R2: 0.39858234500160084\n",
            "\n",
            "Epoch: 319 Training MAE: 0.324222207069397 RMSE: 0.43118715286254883 R2: 0.4455139248200821\n",
            "Epoch: 319 Validation MAE: 0.3416523039340973 RMSE: 0.4520006477832794 R2: 0.4048517033328559\n",
            "\n",
            "Epoch: 320 Training MAE: 0.3228718936443329 RMSE: 0.4302787482738495 R2: 0.4478477707793148\n",
            "Epoch: 320 Validation MAE: 0.3412156105041504 RMSE: 0.45170360803604126 R2: 0.40563356431424324\n",
            "\n",
            "Epoch: 321 Training MAE: 0.3226979076862335 RMSE: 0.42982184886932373 R2: 0.4490197990661001\n",
            "Epoch: 321 Validation MAE: 0.3406465947628021 RMSE: 0.45150771737098694 R2: 0.40614904353573567\n",
            "\n",
            "Epoch: 322 Training MAE: 0.32280007004737854 RMSE: 0.4296424090862274 R2: 0.4494797640986702\n",
            "Epoch: 322 Validation MAE: 0.3405151963233948 RMSE: 0.4512847363948822 R2: 0.40673545355677265\n",
            "\n",
            "Epoch: 323 Training MAE: 0.32284221053123474 RMSE: 0.4296448230743408 R2: 0.4494735794704129\n",
            "Epoch: 323 Validation MAE: 0.3415489196777344 RMSE: 0.4522433876991272 R2: 0.40421229364464384\n",
            "\n",
            "Epoch: 324 Training MAE: 0.32253071665763855 RMSE: 0.4296513497829437 R2: 0.4494568307033008\n",
            "Epoch: 324 Validation MAE: 0.3403259217739105 RMSE: 0.45117679238319397 R2: 0.40701924983284565\n",
            "\n",
            "Epoch: 325 Training MAE: 0.32241541147232056 RMSE: 0.4293605089187622 R2: 0.4502019172348678\n",
            "Epoch: 325 Validation MAE: 0.3410209119319916 RMSE: 0.4516824781894684 R2: 0.4056893017730978\n",
            "\n",
            "Epoch: 326 Training MAE: 0.3224544823169708 RMSE: 0.4292243421077728 R2: 0.4505506031273576\n",
            "Epoch: 326 Validation MAE: 0.34108757972717285 RMSE: 0.45182210206985474 R2: 0.40532174770727303\n",
            "\n",
            "Epoch: 327 Training MAE: 0.32240474224090576 RMSE: 0.4291899800300598 R2: 0.45063862201676275\n",
            "Epoch: 327 Validation MAE: 0.3409510552883148 RMSE: 0.4516512155532837 R2: 0.405771514739505\n",
            "\n",
            "Epoch: 328 Training MAE: 0.3223095238208771 RMSE: 0.42919185757637024 R2: 0.45063379308858353\n",
            "Epoch: 328 Validation MAE: 0.3412381112575531 RMSE: 0.45191243290901184 R2: 0.4050840102866674\n",
            "\n",
            "Epoch: 329 Training MAE: 0.32245248556137085 RMSE: 0.42916131019592285 R2: 0.45071200161416836\n",
            "Epoch: 329 Validation MAE: 0.34083130955696106 RMSE: 0.4514012038707733 R2: 0.4064292071536254\n",
            "\n",
            "Epoch: 330 Training MAE: 0.3222716748714447 RMSE: 0.42948439717292786 R2: 0.44988464083439494\n",
            "Epoch: 330 Validation MAE: 0.3415803015232086 RMSE: 0.45216450095176697 R2: 0.40442013054165704\n",
            "\n",
            "Epoch: 331 Training MAE: 0.3222401440143585 RMSE: 0.42925751209259033 R2: 0.4504656269376238\n",
            "Epoch: 331 Validation MAE: 0.3404347896575928 RMSE: 0.45105859637260437 R2: 0.4073298712036023\n",
            "\n",
            "Epoch: 332 Training MAE: 0.32256221771240234 RMSE: 0.4289279282093048 R2: 0.4513092150153575\n",
            "Epoch: 332 Validation MAE: 0.3409465551376343 RMSE: 0.45178133249282837 R2: 0.405429107064476\n",
            "\n",
            "Epoch: 333 Training MAE: 0.3222194314002991 RMSE: 0.4289657175540924 R2: 0.451212509780914\n",
            "Epoch: 333 Validation MAE: 0.34005293250083923 RMSE: 0.4507337212562561 R2: 0.4081832852395779\n",
            "\n",
            "Epoch: 334 Training MAE: 0.32237574458122253 RMSE: 0.42879483103752136 R2: 0.4516496589581821\n",
            "Epoch: 334 Validation MAE: 0.3413648009300232 RMSE: 0.4521636664867401 R2: 0.40442232682307444\n",
            "\n",
            "Epoch: 335 Training MAE: 0.3221019208431244 RMSE: 0.428508460521698 R2: 0.4523818314159763\n",
            "Epoch: 335 Validation MAE: 0.340352863073349 RMSE: 0.45127493143081665 R2: 0.4067612094093985\n",
            "\n",
            "Epoch: 336 Training MAE: 0.3221723735332489 RMSE: 0.4285195469856262 R2: 0.45235358719846097\n",
            "Epoch: 336 Validation MAE: 0.3409833312034607 RMSE: 0.45165419578552246 R2: 0.40576364918824304\n",
            "\n",
            "Epoch: 337 Training MAE: 0.32191964983940125 RMSE: 0.4288545250892639 R2: 0.45149699536888777\n",
            "Epoch: 337 Validation MAE: 0.34017258882522583 RMSE: 0.45086008310317993 R2: 0.4078514380905255\n",
            "\n",
            "Epoch: 338 Training MAE: 0.32160425186157227 RMSE: 0.4284305274486542 R2: 0.45258102527143995\n",
            "Epoch: 338 Validation MAE: 0.3403443694114685 RMSE: 0.4509422183036804 R2: 0.4076357047379293\n",
            "\n",
            "Epoch: 339 Training MAE: 0.32190656661987305 RMSE: 0.42852404713630676 R2: 0.452342046609052\n",
            "Epoch: 339 Validation MAE: 0.34086403250694275 RMSE: 0.45143571496009827 R2: 0.406338387539524\n",
            "\n",
            "Epoch: 340 Training MAE: 0.32147273421287537 RMSE: 0.42845049500465393 R2: 0.45252999556787976\n",
            "Epoch: 340 Validation MAE: 0.3403491973876953 RMSE: 0.45055729150772095 R2: 0.40864652853184247\n",
            "\n",
            "Epoch: 341 Training MAE: 0.3216300308704376 RMSE: 0.42833295464515686 R2: 0.45283032152566294\n",
            "Epoch: 341 Validation MAE: 0.33990567922592163 RMSE: 0.4503138065338135 R2: 0.40928549703819384\n",
            "\n",
            "Epoch: 342 Training MAE: 0.3214772045612335 RMSE: 0.4283880591392517 R2: 0.4526895308660106\n",
            "Epoch: 342 Validation MAE: 0.34002068638801575 RMSE: 0.45013973116874695 R2: 0.4097421108199426\n",
            "\n",
            "Epoch: 343 Training MAE: 0.3212129473686218 RMSE: 0.4277785122394562 R2: 0.4542459657747008\n",
            "Epoch: 343 Validation MAE: 0.3399818539619446 RMSE: 0.45039597153663635 R2: 0.40906986674575585\n",
            "\n",
            "Epoch: 344 Training MAE: 0.3209896981716156 RMSE: 0.4277116358280182 R2: 0.4544165734021647\n",
            "Epoch: 344 Validation MAE: 0.34042084217071533 RMSE: 0.4507465660572052 R2: 0.4081495923463898\n",
            "\n",
            "Epoch: 345 Training MAE: 0.3209299147129059 RMSE: 0.427691251039505 R2: 0.4544686321858755\n",
            "Epoch: 345 Validation MAE: 0.34000319242477417 RMSE: 0.4503493309020996 R2: 0.4091922624626382\n",
            "\n",
            "Epoch: 346 Training MAE: 0.3219904601573944 RMSE: 0.4282660186290741 R2: 0.4530013487063307\n",
            "Epoch: 346 Validation MAE: 0.34136271476745605 RMSE: 0.4517911672592163 R2: 0.4054031976192999\n",
            "\n",
            "Epoch: 347 Training MAE: 0.3209742307662964 RMSE: 0.42744529247283936 R2: 0.4550958871577956\n",
            "Epoch: 347 Validation MAE: 0.3400656282901764 RMSE: 0.45057356357574463 R2: 0.4086038242572557\n",
            "\n",
            "Epoch: 348 Training MAE: 0.32094642519950867 RMSE: 0.42747947573661804 R2: 0.4550086852880493\n",
            "Epoch: 348 Validation MAE: 0.33949798345565796 RMSE: 0.44996878504753113 R2: 0.41019044622972856\n",
            "\n",
            "Epoch: 349 Training MAE: 0.32085302472114563 RMSE: 0.4274965524673462 R2: 0.4549651834164832\n",
            "Epoch: 349 Validation MAE: 0.34057381749153137 RMSE: 0.45074114203453064 R2: 0.40816382697932974\n",
            "\n",
            "Epoch: 350 Training MAE: 0.32200193405151367 RMSE: 0.4278635084629059 R2: 0.45402907379617863\n",
            "Epoch: 350 Validation MAE: 0.3406386077404022 RMSE: 0.45061421394348145 R2: 0.40849707364009746\n",
            "\n",
            "Epoch: 351 Training MAE: 0.3219219148159027 RMSE: 0.42766422033309937 R2: 0.45453757227313174\n",
            "Epoch: 351 Validation MAE: 0.3404465317726135 RMSE: 0.45048484206199646 R2: 0.4088367004734269\n",
            "\n",
            "Epoch: 352 Training MAE: 0.3216959536075592 RMSE: 0.42867735028266907 R2: 0.45195010056667384\n",
            "Epoch: 352 Validation MAE: 0.34059032797813416 RMSE: 0.45037841796875 R2: 0.40911594127304696\n",
            "\n",
            "Epoch: 353 Training MAE: 0.32073646783828735 RMSE: 0.42760592699050903 R2: 0.45468623398501395\n",
            "Epoch: 353 Validation MAE: 0.3405292332172394 RMSE: 0.4506959617137909 R2: 0.4082824766692401\n",
            "\n",
            "Epoch: 354 Training MAE: 0.32134532928466797 RMSE: 0.4272366166114807 R2: 0.4556277518680363\n",
            "Epoch: 354 Validation MAE: 0.3400002419948578 RMSE: 0.4502602815628052 R2: 0.40942591827571295\n",
            "\n",
            "Epoch: 355 Training MAE: 0.32338935136795044 RMSE: 0.42928361892700195 R2: 0.4503987509167775\n",
            "Epoch: 355 Validation MAE: 0.34265244007110596 RMSE: 0.453003853559494 R2: 0.40220694306253046\n",
            "\n",
            "Epoch: 356 Training MAE: 0.32065701484680176 RMSE: 0.42769259214401245 R2: 0.45446516472283216\n",
            "Epoch: 356 Validation MAE: 0.3401746451854706 RMSE: 0.4504563808441162 R2: 0.4089114097175095\n",
            "\n",
            "Epoch: 357 Training MAE: 0.3207283020019531 RMSE: 0.42691347002983093 R2: 0.45645096753184855\n",
            "Epoch: 357 Validation MAE: 0.3404027819633484 RMSE: 0.45051276683807373 R2: 0.40876343519788017\n",
            "\n",
            "Epoch: 358 Training MAE: 0.32150304317474365 RMSE: 0.42709267139434814 R2: 0.45599454580619025\n",
            "Epoch: 358 Validation MAE: 0.34011369943618774 RMSE: 0.45036211609840393 R2: 0.40915877951811375\n",
            "\n",
            "Epoch: 359 Training MAE: 0.32040318846702576 RMSE: 0.42651453614234924 R2: 0.45746630763299\n",
            "Epoch: 359 Validation MAE: 0.3392258286476135 RMSE: 0.4496043026447296 R2: 0.4111454285967512\n",
            "\n",
            "Epoch: 360 Training MAE: 0.32052797079086304 RMSE: 0.42685893177986145 R2: 0.45658983789206264\n",
            "Epoch: 360 Validation MAE: 0.3394899070262909 RMSE: 0.4498225152492523 R2: 0.41057373465911384\n",
            "\n",
            "Epoch: 361 Training MAE: 0.3203350007534027 RMSE: 0.4265635311603546 R2: 0.45734165951596917\n",
            "Epoch: 361 Validation MAE: 0.33964455127716064 RMSE: 0.44983530044555664 R2: 0.41054022331558504\n",
            "\n",
            "Epoch: 362 Training MAE: 0.3209342956542969 RMSE: 0.4266866445541382 R2: 0.45702838452165817\n",
            "Epoch: 362 Validation MAE: 0.34007319808006287 RMSE: 0.4504650831222534 R2: 0.40888858483529267\n",
            "\n",
            "Epoch: 363 Training MAE: 0.3212663531303406 RMSE: 0.42711952328681946 R2: 0.45592610347142926\n",
            "Epoch: 363 Validation MAE: 0.34109407663345337 RMSE: 0.4512151777744293 R2: 0.4069183377030754\n",
            "\n",
            "Epoch: 364 Training MAE: 0.32049116492271423 RMSE: 0.4264647662639618 R2: 0.4575929696800274\n",
            "Epoch: 364 Validation MAE: 0.33962494134902954 RMSE: 0.44990649819374084 R2: 0.41035356769030185\n",
            "\n",
            "Epoch: 365 Training MAE: 0.31965991854667664 RMSE: 0.42605942487716675 R2: 0.45862349988536855\n",
            "Epoch: 365 Validation MAE: 0.33940771222114563 RMSE: 0.4494535028934479 R2: 0.41154044761036024\n",
            "\n",
            "Epoch: 366 Training MAE: 0.3196601867675781 RMSE: 0.42617863416671753 R2: 0.4583205565333013\n",
            "Epoch: 366 Validation MAE: 0.3397722542285919 RMSE: 0.44969359040260315 R2: 0.41091154220323534\n",
            "\n",
            "Epoch: 367 Training MAE: 0.3205468952655792 RMSE: 0.42644137144088745 R2: 0.45765245365263396\n",
            "Epoch: 367 Validation MAE: 0.34064218401908875 RMSE: 0.4504585266113281 R2: 0.4089057610867286\n",
            "\n",
            "Epoch: 368 Training MAE: 0.3197841942310333 RMSE: 0.42589837312698364 R2: 0.45903280279073977\n",
            "Epoch: 368 Validation MAE: 0.338451623916626 RMSE: 0.44871023297309875 R2: 0.41348508999481526\n",
            "\n",
            "Epoch: 369 Training MAE: 0.31985604763031006 RMSE: 0.4259624481201172 R2: 0.4588699071061034\n",
            "Epoch: 369 Validation MAE: 0.3402596414089203 RMSE: 0.45028772950172424 R2: 0.4093539636907134\n",
            "\n",
            "Epoch: 370 Training MAE: 0.3211359679698944 RMSE: 0.42791175842285156 R2: 0.45390596186160315\n",
            "Epoch: 370 Validation MAE: 0.3402601182460785 RMSE: 0.44990530610084534 R2: 0.41035674843316794\n",
            "\n",
            "Epoch: 371 Training MAE: 0.3196982443332672 RMSE: 0.42568495869636536 R2: 0.4595747835605609\n",
            "Epoch: 371 Validation MAE: 0.339009165763855 RMSE: 0.4488980174064636 R2: 0.41299409710152857\n",
            "\n",
            "Epoch: 372 Training MAE: 0.31951889395713806 RMSE: 0.42561137676239014 R2: 0.45976158827161095\n",
            "Epoch: 372 Validation MAE: 0.3391569256782532 RMSE: 0.44945234060287476 R2: 0.4115434655010508\n",
            "\n",
            "Epoch: 373 Training MAE: 0.3195984363555908 RMSE: 0.4255552291870117 R2: 0.459904056700458\n",
            "Epoch: 373 Validation MAE: 0.33900317549705505 RMSE: 0.4492279589176178 R2: 0.41213083003040896\n",
            "\n",
            "Epoch: 374 Training MAE: 0.31934601068496704 RMSE: 0.42572829127311707 R2: 0.45946470529996486\n",
            "Epoch: 374 Validation MAE: 0.3388129472732544 RMSE: 0.44849932193756104 R2: 0.4140362956067577\n",
            "\n",
            "Epoch: 375 Training MAE: 0.320157527923584 RMSE: 0.4256347417831421 R2: 0.45970227271428843\n",
            "Epoch: 375 Validation MAE: 0.3394012749195099 RMSE: 0.44945645332336426 R2: 0.41153271748249953\n",
            "\n",
            "Epoch: 376 Training MAE: 0.3191896677017212 RMSE: 0.42524516582489014 R2: 0.4606908614469519\n",
            "Epoch: 376 Validation MAE: 0.33892184495925903 RMSE: 0.4486308991909027 R2: 0.4136925284898382\n",
            "\n",
            "Epoch: 377 Training MAE: 0.31911611557006836 RMSE: 0.4252994656562805 R2: 0.4605531421316842\n",
            "Epoch: 377 Validation MAE: 0.3385676443576813 RMSE: 0.44833487272262573 R2: 0.41446599377355675\n",
            "\n",
            "Epoch: 378 Training MAE: 0.3194335997104645 RMSE: 0.4251806139945984 R2: 0.4608546223310451\n",
            "Epoch: 378 Validation MAE: 0.338737815618515 RMSE: 0.4486863911151886 R2: 0.4135474175522361\n",
            "\n",
            "Epoch: 379 Training MAE: 0.3200936019420624 RMSE: 0.42590004205703735 R2: 0.45902856086123867\n",
            "Epoch: 379 Validation MAE: 0.3390945494174957 RMSE: 0.44840186834335327 R2: 0.4142909725707188\n",
            "\n",
            "Epoch: 380 Training MAE: 0.3192002773284912 RMSE: 0.42507389187812805 R2: 0.4611251782908702\n",
            "Epoch: 380 Validation MAE: 0.3383913040161133 RMSE: 0.4483860731124878 R2: 0.41433227433278197\n",
            "\n",
            "Epoch: 381 Training MAE: 0.31970444321632385 RMSE: 0.426389217376709 R2: 0.45778515250385354\n",
            "Epoch: 381 Validation MAE: 0.33982011675834656 RMSE: 0.4492170214653015 R2: 0.41215944279238936\n",
            "\n",
            "Epoch: 382 Training MAE: 0.3189833462238312 RMSE: 0.42525672912597656 R2: 0.46066157045387024\n",
            "Epoch: 382 Validation MAE: 0.3393365740776062 RMSE: 0.44879409670829773 R2: 0.413265859916251\n",
            "\n",
            "Epoch: 383 Training MAE: 0.31908074021339417 RMSE: 0.4250613749027252 R2: 0.4611569445278193\n",
            "Epoch: 383 Validation MAE: 0.3392610549926758 RMSE: 0.4490564167499542 R2: 0.4125797366934938\n",
            "\n",
            "Epoch: 384 Training MAE: 0.31872427463531494 RMSE: 0.4247032403945923 R2: 0.4620644914143248\n",
            "Epoch: 384 Validation MAE: 0.33928248286247253 RMSE: 0.44896724820137024 R2: 0.41281298761068963\n",
            "\n",
            "Epoch: 385 Training MAE: 0.3194231688976288 RMSE: 0.42578640580177307 R2: 0.45931716608943896\n",
            "Epoch: 385 Validation MAE: 0.3393250107765198 RMSE: 0.448913037776947 R2: 0.4129548277285887\n",
            "\n",
            "Epoch: 386 Training MAE: 0.3186262249946594 RMSE: 0.4247300326824188 R2: 0.4619967325517045\n",
            "Epoch: 386 Validation MAE: 0.33894020318984985 RMSE: 0.4481676518917084 R2: 0.4149026746476008\n",
            "\n",
            "Epoch: 387 Training MAE: 0.3190097510814667 RMSE: 0.4246751368045807 R2: 0.4621357284849006\n",
            "Epoch: 387 Validation MAE: 0.3392167091369629 RMSE: 0.4491768479347229 R2: 0.412264667726144\n",
            "\n",
            "Epoch: 388 Training MAE: 0.31878751516342163 RMSE: 0.42451995611190796 R2: 0.4625287601522743\n",
            "Epoch: 388 Validation MAE: 0.33854660391807556 RMSE: 0.4485546052455902 R2: 0.41389189621709455\n",
            "\n",
            "Epoch: 389 Training MAE: 0.31844887137413025 RMSE: 0.4244602918624878 R2: 0.46267978710757296\n",
            "Epoch: 389 Validation MAE: 0.33823075890541077 RMSE: 0.447767436504364 R2: 0.41594719998818785\n",
            "\n",
            "Epoch: 390 Training MAE: 0.3182067573070526 RMSE: 0.4241686463356018 R2: 0.4634179912542661\n",
            "Epoch: 390 Validation MAE: 0.33865809440612793 RMSE: 0.44842344522476196 R2: 0.41423453157356593\n",
            "\n",
            "Epoch: 391 Training MAE: 0.3184451758861542 RMSE: 0.42422086000442505 R2: 0.46328582444394284\n",
            "Epoch: 391 Validation MAE: 0.33838558197021484 RMSE: 0.4478417634963989 R2: 0.4157532753363732\n",
            "\n",
            "Epoch: 392 Training MAE: 0.3182196021080017 RMSE: 0.42412036657333374 R2: 0.4635400656326243\n",
            "Epoch: 392 Validation MAE: 0.33894941210746765 RMSE: 0.44849035143852234 R2: 0.41405981059847197\n",
            "\n",
            "Epoch: 393 Training MAE: 0.31817328929901123 RMSE: 0.4240613281726837 R2: 0.4636894687869132\n",
            "Epoch: 393 Validation MAE: 0.3391713798046112 RMSE: 0.4484974443912506 R2: 0.414041211888182\n",
            "\n",
            "Epoch: 394 Training MAE: 0.3201274573802948 RMSE: 0.4264119565486908 R2: 0.4577273005135062\n",
            "Epoch: 394 Validation MAE: 0.3397192060947418 RMSE: 0.4488944709300995 R2: 0.41300332882819857\n",
            "\n",
            "Epoch: 395 Training MAE: 0.3200756013393402 RMSE: 0.42621657252311707 R2: 0.45822406869305465\n",
            "Epoch: 395 Validation MAE: 0.3392767906188965 RMSE: 0.4484696090221405 R2: 0.4141140118629897\n",
            "\n",
            "Epoch: 396 Training MAE: 0.31816306710243225 RMSE: 0.4237290918827057 R2: 0.46452944411643216\n",
            "Epoch: 396 Validation MAE: 0.33859044313430786 RMSE: 0.44801393151283264 R2: 0.41530397575041766\n",
            "\n",
            "Epoch: 397 Training MAE: 0.3182019591331482 RMSE: 0.42375630140304565 R2: 0.46446078072010655\n",
            "Epoch: 397 Validation MAE: 0.33862391114234924 RMSE: 0.448433518409729 R2: 0.4142082866571133\n",
            "\n",
            "Epoch: 398 Training MAE: 0.3193272650241852 RMSE: 0.42451736330986023 R2: 0.46253529694315076\n",
            "Epoch: 398 Validation MAE: 0.3399159610271454 RMSE: 0.44987910985946655 R2: 0.41042546393168966\n",
            "\n",
            "Epoch: 399 Training MAE: 0.3179912865161896 RMSE: 0.4238598644733429 R2: 0.46419884711038717\n",
            "Epoch: 399 Validation MAE: 0.3386074900627136 RMSE: 0.44785165786743164 R2: 0.41572746555561035\n",
            "\n",
            "Epoch: 400 Training MAE: 0.3186971843242645 RMSE: 0.4248436689376831 R2: 0.46170873045250194\n",
            "Epoch: 400 Validation MAE: 0.33870458602905273 RMSE: 0.44812142848968506 R2: 0.41502332886992344\n",
            "\n",
            "Epoch: 401 Training MAE: 0.3187069892883301 RMSE: 0.42407843470573425 R2: 0.46364618017266723\n",
            "Epoch: 401 Validation MAE: 0.3395880162715912 RMSE: 0.4494020938873291 R2: 0.41167506678455\n",
            "\n",
            "Epoch: 402 Training MAE: 0.3178708851337433 RMSE: 0.42382124066352844 R2: 0.4642965670354007\n",
            "Epoch: 402 Validation MAE: 0.33851364254951477 RMSE: 0.44780680537223816 R2: 0.4158444851551628\n",
            "\n",
            "Epoch: 403 Training MAE: 0.31757450103759766 RMSE: 0.4232906103134155 R2: 0.46563711437902755\n",
            "Epoch: 403 Validation MAE: 0.3383513391017914 RMSE: 0.44764962792396545 R2: 0.4162545335464962\n",
            "\n",
            "Epoch: 404 Training MAE: 0.31777116656303406 RMSE: 0.42333608865737915 R2: 0.4655223227817785\n",
            "Epoch: 404 Validation MAE: 0.3382977545261383 RMSE: 0.44748419523239136 R2: 0.4166859070761334\n",
            "\n",
            "Epoch: 405 Training MAE: 0.317499041557312 RMSE: 0.4231356084346771 R2: 0.46602837678415476\n",
            "Epoch: 405 Validation MAE: 0.3380098044872284 RMSE: 0.4472256004810333 R2: 0.4173598328917727\n",
            "\n",
            "Epoch: 406 Training MAE: 0.3175821006298065 RMSE: 0.4229874312877655 R2: 0.4664022549358885\n",
            "Epoch: 406 Validation MAE: 0.3379119634628296 RMSE: 0.4473075568675995 R2: 0.41714630932230823\n",
            "\n",
            "Epoch: 407 Training MAE: 0.31970489025115967 RMSE: 0.4256437122821808 R2: 0.4596795131227287\n",
            "Epoch: 407 Validation MAE: 0.33946922421455383 RMSE: 0.44791898131370544 R2: 0.415551786568244\n",
            "\n",
            "Epoch: 408 Training MAE: 0.3173550069332123 RMSE: 0.42283064126968384 R2: 0.4667977864657171\n",
            "Epoch: 408 Validation MAE: 0.33817458152770996 RMSE: 0.447626531124115 R2: 0.41631468549800754\n",
            "\n",
            "Epoch: 409 Training MAE: 0.3182075023651123 RMSE: 0.42320752143859863 R2: 0.4658468718208474\n",
            "Epoch: 409 Validation MAE: 0.3382123112678528 RMSE: 0.4478541314601898 R2: 0.41572104062715465\n",
            "\n",
            "Epoch: 410 Training MAE: 0.3183630406856537 RMSE: 0.4233929216861725 R2: 0.4653787699974974\n",
            "Epoch: 410 Validation MAE: 0.33852410316467285 RMSE: 0.4485436677932739 R2: 0.4139204424876435\n",
            "\n",
            "Epoch: 411 Training MAE: 0.3172731101512909 RMSE: 0.4228644371032715 R2: 0.46671263640805183\n",
            "Epoch: 411 Validation MAE: 0.3384433388710022 RMSE: 0.4475407898426056 R2: 0.41653827104369356\n",
            "\n",
            "Epoch: 412 Training MAE: 0.3174266219139099 RMSE: 0.4227680265903473 R2: 0.46695576671571337\n",
            "Epoch: 412 Validation MAE: 0.3381465971469879 RMSE: 0.4474860429763794 R2: 0.41668097948161054\n",
            "\n",
            "Epoch: 413 Training MAE: 0.31728458404541016 RMSE: 0.4229850172996521 R2: 0.46640835128248104\n",
            "Epoch: 413 Validation MAE: 0.33808332681655884 RMSE: 0.44722068309783936 R2: 0.4173726589765645\n",
            "\n",
            "Epoch: 414 Training MAE: 0.3175088167190552 RMSE: 0.42257818579673767 R2: 0.46743431871342767\n",
            "Epoch: 414 Validation MAE: 0.33831316232681274 RMSE: 0.4472815692424774 R2: 0.41721399412233173\n",
            "\n",
            "Epoch: 415 Training MAE: 0.3173660337924957 RMSE: 0.4227396845817566 R2: 0.4670271493477577\n",
            "Epoch: 415 Validation MAE: 0.3386327028274536 RMSE: 0.44811001420021057 R2: 0.4150532025444169\n",
            "\n",
            "Epoch: 416 Training MAE: 0.31713196635246277 RMSE: 0.4226172864437103 R2: 0.46733574540364475\n",
            "Epoch: 416 Validation MAE: 0.3385843336582184 RMSE: 0.44768428802490234 R2: 0.41616408566273544\n",
            "\n",
            "Epoch: 417 Training MAE: 0.31676387786865234 RMSE: 0.42226845026016235 R2: 0.4682147667355603\n",
            "Epoch: 417 Validation MAE: 0.3374148905277252 RMSE: 0.4467278718948364 R2: 0.4186560485516374\n",
            "\n",
            "Epoch: 418 Training MAE: 0.31739094853401184 RMSE: 0.4226999282836914 R2: 0.4671274541153241\n",
            "Epoch: 418 Validation MAE: 0.33716222643852234 RMSE: 0.4459071457386017 R2: 0.42079010417982043\n",
            "\n",
            "Epoch: 419 Training MAE: 0.3170890510082245 RMSE: 0.422201007604599 R2: 0.46838467181926935\n",
            "Epoch: 419 Validation MAE: 0.33732181787490845 RMSE: 0.4465772211551666 R2: 0.41904799717072105\n",
            "\n",
            "Epoch: 420 Training MAE: 0.3171340525150299 RMSE: 0.42241621017456055 R2: 0.4678425098213751\n",
            "Epoch: 420 Validation MAE: 0.3371964991092682 RMSE: 0.4460783004760742 R2: 0.42034536008406387\n",
            "\n",
            "Epoch: 421 Training MAE: 0.31657305359840393 RMSE: 0.42191603779792786 R2: 0.4691019949795989\n",
            "Epoch: 421 Validation MAE: 0.3374650478363037 RMSE: 0.44675111770629883 R2: 0.41859547527126595\n",
            "\n",
            "Epoch: 422 Training MAE: 0.3162081241607666 RMSE: 0.42171186208724976 R2: 0.46961567949412875\n",
            "Epoch: 422 Validation MAE: 0.3372255861759186 RMSE: 0.44640564918518066 R2: 0.4194943716709073\n",
            "\n",
            "Epoch: 423 Training MAE: 0.31665676832199097 RMSE: 0.4224357604980469 R2: 0.4677932569406361\n",
            "Epoch: 423 Validation MAE: 0.33725878596305847 RMSE: 0.4464658200740814 R2: 0.419337836231254\n",
            "\n",
            "Epoch: 424 Training MAE: 0.31648769974708557 RMSE: 0.4218919277191162 R2: 0.46916265962469894\n",
            "Epoch: 424 Validation MAE: 0.33782878518104553 RMSE: 0.4474278390407562 R2: 0.4168327883103057\n",
            "\n",
            "Epoch: 425 Training MAE: 0.31726375222206116 RMSE: 0.4221048057079315 R2: 0.4686268435453429\n",
            "Epoch: 425 Validation MAE: 0.33757951855659485 RMSE: 0.44667312502861023 R2: 0.4187984782209949\n",
            "\n",
            "Epoch: 426 Training MAE: 0.3163496255874634 RMSE: 0.42162883281707764 R2: 0.469824590752679\n",
            "Epoch: 426 Validation MAE: 0.33610183000564575 RMSE: 0.4456203281879425 R2: 0.4215349968710008\n",
            "\n",
            "Epoch: 427 Training MAE: 0.31628236174583435 RMSE: 0.42137816548347473 R2: 0.4704547161123952\n",
            "Epoch: 427 Validation MAE: 0.336924284696579 RMSE: 0.4461127817630768 R2: 0.42025582425213137\n",
            "\n",
            "Epoch: 428 Training MAE: 0.3161904513835907 RMSE: 0.42128226161003113 R2: 0.4706957511617125\n",
            "Epoch: 428 Validation MAE: 0.337358683347702 RMSE: 0.4464972913265228 R2: 0.41925593386472326\n",
            "\n",
            "Epoch: 429 Training MAE: 0.3160220682621002 RMSE: 0.4215545952320099 R2: 0.47001121415321867\n",
            "Epoch: 429 Validation MAE: 0.3370959460735321 RMSE: 0.4458610415458679 R2: 0.4209098442512711\n",
            "\n",
            "Epoch: 430 Training MAE: 0.31610801815986633 RMSE: 0.4211452007293701 R2: 0.4710401180676136\n",
            "Epoch: 430 Validation MAE: 0.33729544281959534 RMSE: 0.44647496938705444 R2: 0.4193139859828864\n",
            "\n",
            "Epoch: 431 Training MAE: 0.3163955509662628 RMSE: 0.42153847217559814 R2: 0.47005177257417763\n",
            "Epoch: 431 Validation MAE: 0.33675965666770935 RMSE: 0.44577863812446594 R2: 0.421123873294293\n",
            "\n",
            "Epoch: 432 Training MAE: 0.31628209352493286 RMSE: 0.42143067717552185 R2: 0.47032275625070463\n",
            "Epoch: 432 Validation MAE: 0.33739978075027466 RMSE: 0.4463537037372589 R2: 0.4196293851700842\n",
            "\n",
            "Epoch: 433 Training MAE: 0.3160126507282257 RMSE: 0.4208274185657501 R2: 0.4718381276374972\n",
            "Epoch: 433 Validation MAE: 0.3365466892719269 RMSE: 0.445656955242157 R2: 0.4214399613303491\n",
            "\n",
            "Epoch: 434 Training MAE: 0.31596460938453674 RMSE: 0.4209299683570862 R2: 0.47158060416112146\n",
            "Epoch: 434 Validation MAE: 0.3375771641731262 RMSE: 0.4466661512851715 R2: 0.4188166052529564\n",
            "\n",
            "Epoch: 435 Training MAE: 0.3168971538543701 RMSE: 0.4215366840362549 R2: 0.47005623443653577\n",
            "Epoch: 435 Validation MAE: 0.3370789885520935 RMSE: 0.447131484746933 R2: 0.4176051106670162\n",
            "\n",
            "Epoch: 436 Training MAE: 0.3160572350025177 RMSE: 0.42073729634284973 R2: 0.4720642290118432\n",
            "Epoch: 436 Validation MAE: 0.3360978662967682 RMSE: 0.4454716444015503 R2: 0.42192098415631085\n",
            "\n",
            "Epoch: 437 Training MAE: 0.3159751892089844 RMSE: 0.42080962657928467 R2: 0.4718827287343569\n",
            "Epoch: 437 Validation MAE: 0.33577239513397217 RMSE: 0.4452193081378937 R2: 0.42257569132619177\n",
            "\n",
            "Epoch: 438 Training MAE: 0.3189045190811157 RMSE: 0.42467018961906433 R2: 0.46214828843709055\n",
            "Epoch: 438 Validation MAE: 0.33871209621429443 RMSE: 0.44747892022132874 R2: 0.4166995459131231\n",
            "\n",
            "Epoch: 439 Training MAE: 0.31944888830184937 RMSE: 0.4236679673194885 R2: 0.4646839569840774\n",
            "Epoch: 439 Validation MAE: 0.3395264446735382 RMSE: 0.4499880075454712 R2: 0.41013997542622904\n",
            "\n",
            "Epoch: 440 Training MAE: 0.3154711127281189 RMSE: 0.4204937219619751 R2: 0.47267542304059884\n",
            "Epoch: 440 Validation MAE: 0.3365088105201721 RMSE: 0.44553056359291077 R2: 0.42176798599814247\n",
            "\n",
            "Epoch: 441 Training MAE: 0.3167881667613983 RMSE: 0.42130357027053833 R2: 0.4706422587090213\n",
            "Epoch: 441 Validation MAE: 0.3376021087169647 RMSE: 0.4474380910396576 R2: 0.41680602172689896\n",
            "\n",
            "Epoch: 442 Training MAE: 0.3153342306613922 RMSE: 0.4202641248703003 R2: 0.4732510655510138\n",
            "Epoch: 442 Validation MAE: 0.33636483550071716 RMSE: 0.4454658329486847 R2: 0.42193596467247163\n",
            "\n",
            "Epoch: 443 Training MAE: 0.3158677816390991 RMSE: 0.4206232726573944 R2: 0.4723504202196034\n",
            "Epoch: 443 Validation MAE: 0.3371901214122772 RMSE: 0.4463949501514435 R2: 0.41952216567951894\n",
            "\n",
            "Epoch: 444 Training MAE: 0.315787136554718 RMSE: 0.42139679193496704 R2: 0.47040795039796923\n",
            "Epoch: 444 Validation MAE: 0.33661559224128723 RMSE: 0.44581007957458496 R2: 0.4210422751323821\n",
            "\n",
            "Epoch: 445 Training MAE: 0.3159187138080597 RMSE: 0.42079436779022217 R2: 0.4719210714533908\n",
            "Epoch: 445 Validation MAE: 0.33763587474823 RMSE: 0.4467737674713135 R2: 0.4185365086537659\n",
            "\n",
            "Epoch: 446 Training MAE: 0.3160589635372162 RMSE: 0.420578271150589 R2: 0.47246332530695156\n",
            "Epoch: 446 Validation MAE: 0.3380722403526306 RMSE: 0.44748494029045105 R2: 0.416683921074814\n",
            "\n",
            "Epoch: 447 Training MAE: 0.31517156958580017 RMSE: 0.4202587306499481 R2: 0.47326462022973104\n",
            "Epoch: 447 Validation MAE: 0.3358885943889618 RMSE: 0.4446268677711487 R2: 0.42411132817324004\n",
            "\n",
            "Epoch: 448 Training MAE: 0.3149867057800293 RMSE: 0.4199236035346985 R2: 0.47410439048153674\n",
            "Epoch: 448 Validation MAE: 0.33680930733680725 RMSE: 0.4460589289665222 R2: 0.42039569478458405\n",
            "\n",
            "Epoch: 449 Training MAE: 0.3150734603404999 RMSE: 0.41972044110298157 R2: 0.47461304624329503\n",
            "Epoch: 449 Validation MAE: 0.3360932469367981 RMSE: 0.4452149271965027 R2: 0.42258702931697756\n",
            "\n",
            "Epoch: 450 Training MAE: 0.3146636486053467 RMSE: 0.41959986090660095 R2: 0.47491488181563224\n",
            "Epoch: 450 Validation MAE: 0.33644431829452515 RMSE: 0.4454843997955322 R2: 0.4218878721874988\n",
            "\n",
            "Epoch: 451 Training MAE: 0.31596115231513977 RMSE: 0.42037683725357056 R2: 0.4729685282615542\n",
            "Epoch: 451 Validation MAE: 0.3370782434940338 RMSE: 0.44661444425582886 R2: 0.418951173790325\n",
            "\n",
            "Epoch: 452 Training MAE: 0.31513506174087524 RMSE: 0.42005062103271484 R2: 0.47378610786997577\n",
            "Epoch: 452 Validation MAE: 0.33584755659103394 RMSE: 0.44490915536880493 R2: 0.42337984106974547\n",
            "\n",
            "Epoch: 453 Training MAE: 0.31521421670913696 RMSE: 0.41968780755996704 R2: 0.4746948440157476\n",
            "Epoch: 453 Validation MAE: 0.335705041885376 RMSE: 0.44457122683525085 R2: 0.42425549336942836\n",
            "\n",
            "Epoch: 454 Training MAE: 0.31494754552841187 RMSE: 0.4198549687862396 R2: 0.4742761734911971\n",
            "Epoch: 454 Validation MAE: 0.336798757314682 RMSE: 0.4453122317790985 R2: 0.42233458951140246\n",
            "\n",
            "Epoch: 455 Training MAE: 0.3149733245372772 RMSE: 0.4194628596305847 R2: 0.4752577723327538\n",
            "Epoch: 455 Validation MAE: 0.33606576919555664 RMSE: 0.44478049874305725 R2: 0.4237133271434581\n",
            "\n",
            "Epoch: 456 Training MAE: 0.3147413730621338 RMSE: 0.41943275928497314 R2: 0.475333108749346\n",
            "Epoch: 456 Validation MAE: 0.33598172664642334 RMSE: 0.44490885734558105 R2: 0.4233806845154484\n",
            "\n",
            "Epoch: 457 Training MAE: 0.3145383894443512 RMSE: 0.4190689027309418 R2: 0.476242931797734\n",
            "Epoch: 457 Validation MAE: 0.3355693519115448 RMSE: 0.4448189437389374 R2: 0.42361372266857655\n",
            "\n",
            "Epoch: 458 Training MAE: 0.3150527775287628 RMSE: 0.41993248462677 R2: 0.4740820718713735\n",
            "Epoch: 458 Validation MAE: 0.33552712202072144 RMSE: 0.4446013271808624 R2: 0.4241775398687926\n",
            "\n",
            "Epoch: 459 Training MAE: 0.3145386874675751 RMSE: 0.4190601110458374 R2: 0.47626496960815223\n",
            "Epoch: 459 Validation MAE: 0.33615735173225403 RMSE: 0.4451306164264679 R2: 0.42280564466198334\n",
            "\n",
            "Epoch: 460 Training MAE: 0.3148009777069092 RMSE: 0.419143944978714 R2: 0.47605535724390935\n",
            "Epoch: 460 Validation MAE: 0.33607232570648193 RMSE: 0.4454614222049713 R2: 0.42194746053213483\n",
            "\n",
            "Epoch: 461 Training MAE: 0.3155820071697235 RMSE: 0.42121386528015137 R2: 0.4708676414975964\n",
            "Epoch: 461 Validation MAE: 0.33664175868034363 RMSE: 0.4458489716053009 R2: 0.4209412021067783\n",
            "\n",
            "Epoch: 462 Training MAE: 0.31452643871307373 RMSE: 0.4189721941947937 R2: 0.47648466448943827\n",
            "Epoch: 462 Validation MAE: 0.3362404704093933 RMSE: 0.44526010751724243 R2: 0.4224698201862702\n",
            "\n",
            "Epoch: 463 Training MAE: 0.3166638910770416 RMSE: 0.4203943908214569 R2: 0.4729244371182909\n",
            "Epoch: 463 Validation MAE: 0.33793309330940247 RMSE: 0.4467870593070984 R2: 0.41850195136648616\n",
            "\n",
            "Epoch: 464 Training MAE: 0.31424352526664734 RMSE: 0.4191003739833832 R2: 0.476164279566607\n",
            "Epoch: 464 Validation MAE: 0.3362915813922882 RMSE: 0.4451059103012085 R2: 0.42286979334989083\n",
            "\n",
            "Epoch: 465 Training MAE: 0.3176008462905884 RMSE: 0.4212111532688141 R2: 0.4708744703181834\n",
            "Epoch: 465 Validation MAE: 0.33955100178718567 RMSE: 0.44919532537460327 R2: 0.41221631829648475\n",
            "\n",
            "Epoch: 466 Training MAE: 0.31445640325546265 RMSE: 0.41875624656677246 R2: 0.4770241668987868\n",
            "Epoch: 466 Validation MAE: 0.33554336428642273 RMSE: 0.4444476068019867 R2: 0.4245756440030598\n",
            "\n",
            "Epoch: 467 Training MAE: 0.31378960609436035 RMSE: 0.4183887243270874 R2: 0.47794171341473546\n",
            "Epoch: 467 Validation MAE: 0.33526095747947693 RMSE: 0.4445722997188568 R2: 0.4242527621320896\n",
            "\n",
            "Epoch: 468 Training MAE: 0.3141059875488281 RMSE: 0.41883420944213867 R2: 0.47682944206909494\n",
            "Epoch: 468 Validation MAE: 0.33508986234664917 RMSE: 0.44414880871772766 R2: 0.42534908242120684\n",
            "\n",
            "Epoch: 469 Training MAE: 0.3141256272792816 RMSE: 0.4183706045150757 R2: 0.4779870302964664\n",
            "Epoch: 469 Validation MAE: 0.3356219232082367 RMSE: 0.4449892044067383 R2: 0.4231724134720657\n",
            "\n",
            "Epoch: 470 Training MAE: 0.31395214796066284 RMSE: 0.4185638725757599 R2: 0.4775045905733225\n",
            "Epoch: 470 Validation MAE: 0.3362181484699249 RMSE: 0.44519495964050293 R2: 0.4226388439463624\n",
            "\n",
            "Epoch: 471 Training MAE: 0.3135956823825836 RMSE: 0.41834062337875366 R2: 0.478061834343799\n",
            "Epoch: 471 Validation MAE: 0.3362574875354767 RMSE: 0.4453881084918976 R2: 0.42213768448341193\n",
            "\n",
            "Epoch: 472 Training MAE: 0.3139798939228058 RMSE: 0.4185110032558441 R2: 0.47763657570168516\n",
            "Epoch: 472 Validation MAE: 0.3356674611568451 RMSE: 0.4444718658924103 R2: 0.4245128165420292\n",
            "\n",
            "Epoch: 473 Training MAE: 0.31529122591018677 RMSE: 0.419341117143631 R2: 0.47556229293116814\n",
            "Epoch: 473 Validation MAE: 0.3384712040424347 RMSE: 0.4476575255393982 R2: 0.41623388038824227\n",
            "\n",
            "Epoch: 474 Training MAE: 0.31526637077331543 RMSE: 0.4193243980407715 R2: 0.47560411643724676\n",
            "Epoch: 474 Validation MAE: 0.33691465854644775 RMSE: 0.44610047340393066 R2: 0.42028779573379305\n",
            "\n",
            "Epoch: 475 Training MAE: 0.31401631236076355 RMSE: 0.4180821180343628 R2: 0.47870657178021037\n",
            "Epoch: 475 Validation MAE: 0.33586546778678894 RMSE: 0.4448655843734741 R2: 0.42349281614621337\n",
            "\n",
            "Epoch: 476 Training MAE: 0.3137474060058594 RMSE: 0.41795599460601807 R2: 0.47902108397645893\n",
            "Epoch: 476 Validation MAE: 0.3348383903503418 RMSE: 0.4438968300819397 R2: 0.4260009239613095\n",
            "\n",
            "Epoch: 477 Training MAE: 0.31319668889045715 RMSE: 0.4178493320941925 R2: 0.47928698623085075\n",
            "Epoch: 477 Validation MAE: 0.33542048931121826 RMSE: 0.44451016187667847 R2: 0.42441363233988594\n",
            "\n",
            "Epoch: 478 Training MAE: 0.3142126798629761 RMSE: 0.418201744556427 R2: 0.4784082740309836\n",
            "Epoch: 478 Validation MAE: 0.3366531431674957 RMSE: 0.4460722804069519 R2: 0.4203610418294923\n",
            "\n",
            "Epoch: 479 Training MAE: 0.31390905380249023 RMSE: 0.41787463426589966 R2: 0.479223954419563\n",
            "Epoch: 479 Validation MAE: 0.33555272221565247 RMSE: 0.444552481174469 R2: 0.42430401393492523\n",
            "\n",
            "Epoch: 480 Training MAE: 0.31569111347198486 RMSE: 0.41948211193084717 R2: 0.47520955600596526\n",
            "Epoch: 480 Validation MAE: 0.3379743993282318 RMSE: 0.4477062225341797 R2: 0.4161068973495826\n",
            "\n",
            "Epoch: 481 Training MAE: 0.3134708106517792 RMSE: 0.41848039627075195 R2: 0.47771292652648867\n",
            "Epoch: 481 Validation MAE: 0.3353610932826996 RMSE: 0.4444851577281952 R2: 0.42447842707812444\n",
            "\n",
            "Epoch: 482 Training MAE: 0.3128833472728729 RMSE: 0.41760826110839844 R2: 0.4798876277784567\n",
            "Epoch: 482 Validation MAE: 0.3356747031211853 RMSE: 0.4446296989917755 R2: 0.4241040360644245\n",
            "\n",
            "Epoch: 483 Training MAE: 0.3139323592185974 RMSE: 0.4177964925765991 R2: 0.479418706503292\n",
            "Epoch: 483 Validation MAE: 0.3363158404827118 RMSE: 0.44539716839790344 R2: 0.4221141895386309\n",
            "\n",
            "Epoch: 484 Training MAE: 0.3145681321620941 RMSE: 0.4186112582683563 R2: 0.47738621966740646\n",
            "Epoch: 484 Validation MAE: 0.3377928137779236 RMSE: 0.44714534282684326 R2: 0.41756888864795416\n",
            "\n",
            "Epoch: 485 Training MAE: 0.31387412548065186 RMSE: 0.41801127791404724 R2: 0.4788832663503019\n",
            "Epoch: 485 Validation MAE: 0.334982305765152 RMSE: 0.4435885548591614 R2: 0.42679786881871784\n",
            "\n",
            "Epoch: 486 Training MAE: 0.3130878508090973 RMSE: 0.4177356958389282 R2: 0.4795701530410612\n",
            "Epoch: 486 Validation MAE: 0.33546558022499084 RMSE: 0.44439566135406494 R2: 0.42471016739374357\n",
            "\n",
            "Epoch: 487 Training MAE: 0.31498074531555176 RMSE: 0.4188065528869629 R2: 0.476898496059317\n",
            "Epoch: 487 Validation MAE: 0.3368951380252838 RMSE: 0.44677042961120605 R2: 0.4185452017051803\n",
            "\n",
            "Epoch: 488 Training MAE: 0.31332361698150635 RMSE: 0.41820159554481506 R2: 0.47840864014195483\n",
            "Epoch: 488 Validation MAE: 0.33600741624832153 RMSE: 0.44497764110565186 R2: 0.4232024132249075\n",
            "\n",
            "Epoch: 489 Training MAE: 0.3133886754512787 RMSE: 0.4175092577934265 R2: 0.48013418499288385\n",
            "Epoch: 489 Validation MAE: 0.3353426158428192 RMSE: 0.44454988837242126 R2: 0.424310812493661\n",
            "\n",
            "Epoch: 490 Training MAE: 0.31276845932006836 RMSE: 0.4173901677131653 R2: 0.4804307157928235\n",
            "Epoch: 490 Validation MAE: 0.3353942632675171 RMSE: 0.44413188099861145 R2: 0.42539288862770064\n",
            "\n",
            "Epoch: 491 Training MAE: 0.313321053981781 RMSE: 0.4170401692390442 R2: 0.4813016869540433\n",
            "Epoch: 491 Validation MAE: 0.3353690803050995 RMSE: 0.44465282559394836 R2: 0.4240441220887303\n",
            "\n",
            "Epoch: 492 Training MAE: 0.31245481967926025 RMSE: 0.4171549081802368 R2: 0.4810163343161039\n",
            "Epoch: 492 Validation MAE: 0.33423811197280884 RMSE: 0.4436846077442169 R2: 0.42654971348858284\n",
            "\n",
            "Epoch: 493 Training MAE: 0.31343507766723633 RMSE: 0.41701456904411316 R2: 0.48136536795763796\n",
            "Epoch: 493 Validation MAE: 0.3354746699333191 RMSE: 0.4445965886116028 R2: 0.4241897714515648\n",
            "\n",
            "Epoch: 494 Training MAE: 0.31386828422546387 RMSE: 0.41766881942749023 R2: 0.4797367449483616\n",
            "Epoch: 494 Validation MAE: 0.33607932925224304 RMSE: 0.44595393538475037 R2: 0.4206685652303146\n",
            "\n",
            "Epoch: 495 Training MAE: 0.3126399517059326 RMSE: 0.4166434109210968 R2: 0.48228822184292164\n",
            "Epoch: 495 Validation MAE: 0.33553293347358704 RMSE: 0.4445466697216034 R2: 0.4243191351116691\n",
            "\n",
            "Epoch: 496 Training MAE: 0.3125414252281189 RMSE: 0.4165678918361664 R2: 0.4824757752254659\n",
            "Epoch: 496 Validation MAE: 0.3348996341228485 RMSE: 0.4438689351081848 R2: 0.42607303897699034\n",
            "\n",
            "Epoch: 497 Training MAE: 0.3136875033378601 RMSE: 0.41725456714630127 R2: 0.48076827228925556\n",
            "Epoch: 497 Validation MAE: 0.3356040120124817 RMSE: 0.4448758661746979 R2: 0.42346619943438546\n",
            "\n",
            "Epoch: 498 Training MAE: 0.31554901599884033 RMSE: 0.41829991340637207 R2: 0.4781633024500648\n",
            "Epoch: 498 Validation MAE: 0.3371542990207672 RMSE: 0.44626322388648987 R2: 0.4198646749363927\n",
            "\n",
            "Epoch: 499 Training MAE: 0.3126034736633301 RMSE: 0.4163592457771301 R2: 0.48299416167847065\n",
            "Epoch: 499 Validation MAE: 0.33426401019096375 RMSE: 0.443554162979126 R2: 0.4268867886813543\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "plt.plot(list(range(ann.parameters['iterations'])), ann.rmse_train)\n",
        "plt.plot((list(range(ann.parameters['iterations']))), ann.rmse_val)\n",
        "plt.plot((list(range(ann.parameters['iterations']))), ann.mae_train)\n",
        "plt.plot((list(range(ann.parameters['iterations']))), ann.mae_val)\n",
        "plt.plot((list(range(ann.parameters['iterations']))), ann.r2_train)\n",
        "plt.plot((list(range(ann.parameters['iterations']))), ann.r2_val)\n",
        "plt.legend(['Training RMSE', 'Validation RMSE', 'Training MAE', 'Validation MAE', 'Training R2 Score', 'Validation R2 Score'])\n",
        "plt.xlabel(\"Iterations\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "GTs4Ay29fvAc",
        "outputId": "45044bed-b52a-4446-dc20-2be742d712b0"
      },
      "execution_count": 159,
      "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": [
        "ann2 = ANN(parameters = {\"encoding\": \"OneHot\", \"num_PCA_comp\": -1, \"batch_size\": 1, 'iterations': 500})\n",
        "ann2.training_validation()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "G14QAeK1fu88",
        "outputId": "5d3e5b45-c22d-4593-8535-6b94388c9b88"
      },
      "execution_count": 155,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of Highly correlated features removes:  2\n",
            "Features Removed:  {'Total Population - Female', 'IPPU'}\n",
            "\n",
            "183\n",
            "Epoch: 0 Training MAE: 0.48620814085006714 RMSE: 0.6368803381919861 R2: -0.20969162445369438\n",
            "Epoch: 0 Validation MAE: 0.4867089092731476 RMSE: 0.633669912815094 R2: -0.16969671765233807\n",
            "\n",
            "Epoch: 1 Training MAE: 0.4477211534976959 RMSE: 0.5793386697769165 R2: -0.000976789939159728\n",
            "Epoch: 1 Validation MAE: 0.45327287912368774 RMSE: 0.5831530094146729 R2: 0.009368509051396745\n",
            "\n",
            "Epoch: 2 Training MAE: 0.44480085372924805 RMSE: 0.5741172432899475 R2: 0.01698494375685833\n",
            "Epoch: 2 Validation MAE: 0.4512542486190796 RMSE: 0.5793507695198059 R2: 0.022244538722228202\n",
            "\n",
            "Epoch: 3 Training MAE: 0.4415225386619568 RMSE: 0.5695282220840454 R2: 0.03263683366595038\n",
            "Epoch: 3 Validation MAE: 0.44867271184921265 RMSE: 0.5757081508636475 R2: 0.03450103269962013\n",
            "\n",
            "Epoch: 4 Training MAE: 0.4376799762248993 RMSE: 0.5647989511489868 R2: 0.04863581152169916\n",
            "Epoch: 4 Validation MAE: 0.4454297721385956 RMSE: 0.5717191100120544 R2: 0.04783448867861051\n",
            "\n",
            "Epoch: 5 Training MAE: 0.43313688039779663 RMSE: 0.5596874356269836 R2: 0.06577782755820316\n",
            "Epoch: 5 Validation MAE: 0.4413606822490692 RMSE: 0.5671635866165161 R2: 0.06294791123422727\n",
            "\n",
            "Epoch: 6 Training MAE: 0.42812788486480713 RMSE: 0.5540522336959839 R2: 0.08449560219124741\n",
            "Epoch: 6 Validation MAE: 0.4367336332798004 RMSE: 0.5620337724685669 R2: 0.07982192229174279\n",
            "\n",
            "Epoch: 7 Training MAE: 0.4230525493621826 RMSE: 0.5475454926490784 R2: 0.10587240953109334\n",
            "Epoch: 7 Validation MAE: 0.4325467646121979 RMSE: 0.5566121935844421 R2: 0.09748900456346832\n",
            "\n",
            "Epoch: 8 Training MAE: 0.4165611267089844 RMSE: 0.5400777459144592 R2: 0.13009546639114544\n",
            "Epoch: 8 Validation MAE: 0.426663875579834 RMSE: 0.5499528646469116 R2: 0.11895510080073046\n",
            "\n",
            "Epoch: 9 Training MAE: 0.40919822454452515 RMSE: 0.531326949596405 R2: 0.15805704560016487\n",
            "Epoch: 9 Validation MAE: 0.4201031029224396 RMSE: 0.542330801486969 R2: 0.14320762562885025\n",
            "\n",
            "Epoch: 10 Training MAE: 0.4008611738681793 RMSE: 0.5210705399513245 R2: 0.19024794172093462\n",
            "Epoch: 10 Validation MAE: 0.4132550060749054 RMSE: 0.5338348150253296 R2: 0.1698418446791461\n",
            "\n",
            "Epoch: 11 Training MAE: 0.3904169201850891 RMSE: 0.509285569190979 R2: 0.2264617407310724\n",
            "Epoch: 11 Validation MAE: 0.4037039577960968 RMSE: 0.5230054259300232 R2: 0.2031814365327611\n",
            "\n",
            "Epoch: 12 Training MAE: 0.3790927827358246 RMSE: 0.49598515033721924 R2: 0.26633746366600886\n",
            "Epoch: 12 Validation MAE: 0.3940025568008423 RMSE: 0.511634111404419 R2: 0.23745390362990237\n",
            "\n",
            "Epoch: 13 Training MAE: 0.36652126908302307 RMSE: 0.4816683530807495 R2: 0.3080811106089504\n",
            "Epoch: 13 Validation MAE: 0.38298648595809937 RMSE: 0.4989573061466217 R2: 0.27477318094535375\n",
            "\n",
            "Epoch: 14 Training MAE: 0.3540540039539337 RMSE: 0.46694251894950867 R2: 0.34974175822937126\n",
            "Epoch: 14 Validation MAE: 0.37271395325660706 RMSE: 0.48723074793815613 R2: 0.3084613914274441\n",
            "\n",
            "Epoch: 15 Training MAE: 0.34123024344444275 RMSE: 0.4528380334377289 R2: 0.3884319290093984\n",
            "Epoch: 15 Validation MAE: 0.36149096488952637 RMSE: 0.4754151701927185 R2: 0.3415949643170293\n",
            "\n",
            "Epoch: 16 Training MAE: 0.3295770287513733 RMSE: 0.44016674160957336 R2: 0.4221788120785589\n",
            "Epoch: 16 Validation MAE: 0.35124364495277405 RMSE: 0.4650954008102417 R2: 0.3698685058373443\n",
            "\n",
            "Epoch: 17 Training MAE: 0.31950169801712036 RMSE: 0.42964833974838257 R2: 0.4494645080587121\n",
            "Epoch: 17 Validation MAE: 0.34124648571014404 RMSE: 0.4559255838394165 R2: 0.394470852392903\n",
            "\n",
            "Epoch: 18 Training MAE: 0.3117547929286957 RMSE: 0.4211229383945465 R2: 0.47109610030983873\n",
            "Epoch: 18 Validation MAE: 0.33442410826683044 RMSE: 0.450717031955719 R2: 0.40822714866743726\n",
            "\n",
            "Epoch: 19 Training MAE: 0.30593177676200867 RMSE: 0.4147208631038666 R2: 0.487054954156545\n",
            "Epoch: 19 Validation MAE: 0.32886749505996704 RMSE: 0.44627928733825684 R2: 0.41982289318131516\n",
            "\n",
            "Epoch: 20 Training MAE: 0.3014722168445587 RMSE: 0.40945523977279663 R2: 0.49999783108196405\n",
            "Epoch: 20 Validation MAE: 0.3231995105743408 RMSE: 0.44120657444000244 R2: 0.4329372942791977\n",
            "\n",
            "Epoch: 21 Training MAE: 0.2990998923778534 RMSE: 0.4063476622104645 R2: 0.5075585543878961\n",
            "Epoch: 21 Validation MAE: 0.3206864297389984 RMSE: 0.44017553329467773 R2: 0.43558452707560025\n",
            "\n",
            "Epoch: 22 Training MAE: 0.2963462769985199 RMSE: 0.402816504240036 R2: 0.5160800908203208\n",
            "Epoch: 22 Validation MAE: 0.31725290417671204 RMSE: 0.43591469526290894 R2: 0.4464585412128972\n",
            "\n",
            "Epoch: 23 Training MAE: 0.2950550317764282 RMSE: 0.40088799595832825 R2: 0.5207025371376033\n",
            "Epoch: 23 Validation MAE: 0.3163639307022095 RMSE: 0.43282046914100647 R2: 0.45428899413146495\n",
            "\n",
            "Epoch: 24 Training MAE: 0.29356634616851807 RMSE: 0.39884600043296814 R2: 0.5255728894851881\n",
            "Epoch: 24 Validation MAE: 0.31413179636001587 RMSE: 0.4323890805244446 R2: 0.45537628394976915\n",
            "\n",
            "Epoch: 25 Training MAE: 0.2925766408443451 RMSE: 0.3974178731441498 R2: 0.5289642932048199\n",
            "Epoch: 25 Validation MAE: 0.3132611811161041 RMSE: 0.4311259984970093 R2: 0.458553545778011\n",
            "\n",
            "Epoch: 26 Training MAE: 0.2918205261230469 RMSE: 0.39628866314888 R2: 0.5316372739445273\n",
            "Epoch: 26 Validation MAE: 0.3127617835998535 RMSE: 0.42994225025177 R2: 0.46152273059511284\n",
            "\n",
            "Epoch: 27 Training MAE: 0.2912525236606598 RMSE: 0.3953971862792969 R2: 0.5337421823949979\n",
            "Epoch: 27 Validation MAE: 0.3120186924934387 RMSE: 0.42900729179382324 R2: 0.4638621976988445\n",
            "\n",
            "Epoch: 28 Training MAE: 0.2907983958721161 RMSE: 0.39467543363571167 R2: 0.5354428318313904\n",
            "Epoch: 28 Validation MAE: 0.31159400939941406 RMSE: 0.42819690704345703 R2: 0.465885760667784\n",
            "\n",
            "Epoch: 29 Training MAE: 0.29037344455718994 RMSE: 0.39400774240493774 R2: 0.5370132975580955\n",
            "Epoch: 29 Validation MAE: 0.31113624572753906 RMSE: 0.4277211129665375 R2: 0.46707210196032534\n",
            "\n",
            "Epoch: 30 Training MAE: 0.29006481170654297 RMSE: 0.39346930384635925 R2: 0.5382777430914267\n",
            "Epoch: 30 Validation MAE: 0.31085291504859924 RMSE: 0.42711517214775085 R2: 0.468581005638544\n",
            "\n",
            "Epoch: 31 Training MAE: 0.2898768484592438 RMSE: 0.39301246404647827 R2: 0.5393493495508662\n",
            "Epoch: 31 Validation MAE: 0.3105928897857666 RMSE: 0.4277155101299286 R2: 0.46708604146901056\n",
            "\n",
            "Epoch: 32 Training MAE: 0.2899360954761505 RMSE: 0.3927948772907257 R2: 0.5398592873456378\n",
            "Epoch: 32 Validation MAE: 0.3105691075325012 RMSE: 0.42809271812438965 R2: 0.4661456644661961\n",
            "\n",
            "Epoch: 33 Training MAE: 0.2902178168296814 RMSE: 0.3932037949562073 R2: 0.538900782918526\n",
            "Epoch: 33 Validation MAE: 0.311017245054245 RMSE: 0.42598244547843933 R2: 0.47139588604011395\n",
            "\n",
            "Epoch: 34 Training MAE: 0.2896032929420471 RMSE: 0.39255067706108093 R2: 0.5404311966347328\n",
            "Epoch: 34 Validation MAE: 0.3104777932167053 RMSE: 0.425481915473938 R2: 0.4726374217749125\n",
            "\n",
            "Epoch: 35 Training MAE: 0.2889828383922577 RMSE: 0.3916284143924713 R2: 0.5425881510369532\n",
            "Epoch: 35 Validation MAE: 0.3101796507835388 RMSE: 0.4259042739868164 R2: 0.47158987070364977\n",
            "\n",
            "Epoch: 36 Training MAE: 0.28882232308387756 RMSE: 0.39142510294914246 R2: 0.5430629150174301\n",
            "Epoch: 36 Validation MAE: 0.30972349643707275 RMSE: 0.4250344932079315 R2: 0.47374584474702297\n",
            "\n",
            "Epoch: 37 Training MAE: 0.28859832882881165 RMSE: 0.3908140957355499 R2: 0.5444884446166403\n",
            "Epoch: 37 Validation MAE: 0.30993497371673584 RMSE: 0.4271182417869568 R2: 0.468573355574051\n",
            "\n",
            "Epoch: 38 Training MAE: 0.28784650564193726 RMSE: 0.3901115357875824 R2: 0.5461247001053278\n",
            "Epoch: 38 Validation MAE: 0.30933359265327454 RMSE: 0.42588385939598083 R2: 0.47164054116860876\n",
            "\n",
            "Epoch: 39 Training MAE: 0.28779035806655884 RMSE: 0.3901481628417969 R2: 0.546039371716617\n",
            "Epoch: 39 Validation MAE: 0.30927208065986633 RMSE: 0.42497196793556213 R2: 0.47390074838448226\n",
            "\n",
            "Epoch: 40 Training MAE: 0.28748831152915955 RMSE: 0.38988232612609863 R2: 0.5466579025730707\n",
            "Epoch: 40 Validation MAE: 0.30893978476524353 RMSE: 0.4245535135269165 R2: 0.4749362645397571\n",
            "\n",
            "Epoch: 41 Training MAE: 0.28698259592056274 RMSE: 0.3891831338405609 R2: 0.548282381452365\n",
            "Epoch: 41 Validation MAE: 0.3084213435649872 RMSE: 0.4247620105743408 R2: 0.47442045444685865\n",
            "\n",
            "Epoch: 42 Training MAE: 0.2867332994937897 RMSE: 0.3888523578643799 R2: 0.549049852461795\n",
            "Epoch: 42 Validation MAE: 0.30843013525009155 RMSE: 0.42483195662498474 R2: 0.47424733196528934\n",
            "\n",
            "Epoch: 43 Training MAE: 0.2865542769432068 RMSE: 0.3886914551258087 R2: 0.5494229698992693\n",
            "Epoch: 43 Validation MAE: 0.3081642985343933 RMSE: 0.4243584871292114 R2: 0.47541857648350816\n",
            "\n",
            "Epoch: 44 Training MAE: 0.2863880395889282 RMSE: 0.3884561359882355 R2: 0.5499684330252919\n",
            "Epoch: 44 Validation MAE: 0.30817389488220215 RMSE: 0.42441898584365845 R2: 0.4752689707419505\n",
            "\n",
            "Epoch: 45 Training MAE: 0.28628984093666077 RMSE: 0.38805657625198364 R2: 0.5508937437132666\n",
            "Epoch: 45 Validation MAE: 0.30853167176246643 RMSE: 0.4257655143737793 R2: 0.4719341836034666\n",
            "\n",
            "Epoch: 46 Training MAE: 0.2868374288082123 RMSE: 0.38836950063705444 R2: 0.5501691096904469\n",
            "Epoch: 46 Validation MAE: 0.30921196937561035 RMSE: 0.4271412789821625 R2: 0.4685160252164102\n",
            "\n",
            "Epoch: 47 Training MAE: 0.2857958674430847 RMSE: 0.3875269889831543 R2: 0.5521186984113019\n",
            "Epoch: 47 Validation MAE: 0.30824002623558044 RMSE: 0.42504286766052246 R2: 0.4737252133228471\n",
            "\n",
            "Epoch: 48 Training MAE: 0.2854909598827362 RMSE: 0.38728949427604675 R2: 0.5526674993108709\n",
            "Epoch: 48 Validation MAE: 0.3077383041381836 RMSE: 0.42435169219970703 R2: 0.47543539154219994\n",
            "\n",
            "Epoch: 49 Training MAE: 0.2856995165348053 RMSE: 0.387155681848526 R2: 0.5529765330091757\n",
            "Epoch: 49 Validation MAE: 0.3082635700702667 RMSE: 0.4259590804576874 R2: 0.47145392250858353\n",
            "\n",
            "Epoch: 50 Training MAE: 0.28579092025756836 RMSE: 0.3872070014476776 R2: 0.5528580574421461\n",
            "Epoch: 50 Validation MAE: 0.3084372580051422 RMSE: 0.4263167679309845 R2: 0.4705658785099598\n",
            "\n",
            "Epoch: 51 Training MAE: 0.2848060131072998 RMSE: 0.38640934228897095 R2: 0.5546983742892526\n",
            "Epoch: 51 Validation MAE: 0.30727508664131165 RMSE: 0.42434269189834595 R2: 0.475457648779932\n",
            "\n",
            "Epoch: 52 Training MAE: 0.2854674458503723 RMSE: 0.38680022954940796 R2: 0.5537970328306315\n",
            "Epoch: 52 Validation MAE: 0.3085089921951294 RMSE: 0.42663460969924927 R2: 0.4697761583400706\n",
            "\n",
            "Epoch: 53 Training MAE: 0.28439417481422424 RMSE: 0.38587918877601624 R2: 0.5559195067962517\n",
            "Epoch: 53 Validation MAE: 0.3072487711906433 RMSE: 0.4244576096534729 R2: 0.47517352386967315\n",
            "\n",
            "Epoch: 54 Training MAE: 0.2847251892089844 RMSE: 0.3864317238330841 R2: 0.554646833619703\n",
            "Epoch: 54 Validation MAE: 0.3071652054786682 RMSE: 0.4235824644565582 R2: 0.4773354411504185\n",
            "\n",
            "Epoch: 55 Training MAE: 0.28399530053138733 RMSE: 0.38547348976135254 R2: 0.5568527966865455\n",
            "Epoch: 55 Validation MAE: 0.30667343735694885 RMSE: 0.42374587059020996 R2: 0.4769321582057412\n",
            "\n",
            "Epoch: 56 Training MAE: 0.28391602635383606 RMSE: 0.3851991593837738 R2: 0.5574832801042303\n",
            "Epoch: 56 Validation MAE: 0.30704066157341003 RMSE: 0.4247985780239105 R2: 0.474330002461945\n",
            "\n",
            "Epoch: 57 Training MAE: 0.28354936838150024 RMSE: 0.3849678933620453 R2: 0.5580144475633795\n",
            "Epoch: 57 Validation MAE: 0.30655914545059204 RMSE: 0.4238754212856293 R2: 0.4766122292707664\n",
            "\n",
            "Epoch: 58 Training MAE: 0.28409910202026367 RMSE: 0.38514620065689087 R2: 0.557604945347887\n",
            "Epoch: 58 Validation MAE: 0.3077842593193054 RMSE: 0.4260435402393341 R2: 0.4712442995191083\n",
            "\n",
            "Epoch: 59 Training MAE: 0.28333842754364014 RMSE: 0.38452789187431335 R2: 0.5590242641099032\n",
            "Epoch: 59 Validation MAE: 0.30668240785598755 RMSE: 0.4244382679462433 R2: 0.475221338346305\n",
            "\n",
            "Epoch: 60 Training MAE: 0.2835403382778168 RMSE: 0.3844769597053528 R2: 0.5591410524240817\n",
            "Epoch: 60 Validation MAE: 0.3072889745235443 RMSE: 0.42543044686317444 R2: 0.47276495701962995\n",
            "\n",
            "Epoch: 61 Training MAE: 0.28352636098861694 RMSE: 0.3844539225101471 R2: 0.5591939148008321\n",
            "Epoch: 61 Validation MAE: 0.30733996629714966 RMSE: 0.425615519285202 R2: 0.4723062079273598\n",
            "\n",
            "Epoch: 62 Training MAE: 0.28302523493766785 RMSE: 0.38402336835861206 R2: 0.5601806738155245\n",
            "Epoch: 62 Validation MAE: 0.3067568838596344 RMSE: 0.4248533844947815 R2: 0.4741942807302644\n",
            "\n",
            "Epoch: 63 Training MAE: 0.2829546332359314 RMSE: 0.3838743567466736 R2: 0.5605219086273742\n",
            "Epoch: 63 Validation MAE: 0.30672144889831543 RMSE: 0.42513787746429443 R2: 0.47348993820060403\n",
            "\n",
            "Epoch: 64 Training MAE: 0.28252145648002625 RMSE: 0.3835226893424988 R2: 0.5613267659814889\n",
            "Epoch: 64 Validation MAE: 0.3060714900493622 RMSE: 0.4240220785140991 R2: 0.4762500360891948\n",
            "\n",
            "Epoch: 65 Training MAE: 0.28286445140838623 RMSE: 0.3841055929660797 R2: 0.5599923133151246\n",
            "Epoch: 65 Validation MAE: 0.3059915602207184 RMSE: 0.42309221625328064 R2: 0.4785445805057138\n",
            "\n",
            "Epoch: 66 Training MAE: 0.28219446539878845 RMSE: 0.3831797242164612 R2: 0.5621110067865978\n",
            "Epoch: 66 Validation MAE: 0.30599772930145264 RMSE: 0.4240325093269348 R2: 0.4762242429159229\n",
            "\n",
            "Epoch: 67 Training MAE: 0.2828894257545471 RMSE: 0.3835431635379791 R2: 0.5612798759991665\n",
            "Epoch: 67 Validation MAE: 0.3071616291999817 RMSE: 0.42603299021720886 R2: 0.4712704930894436\n",
            "\n",
            "Epoch: 68 Training MAE: 0.28194576501846313 RMSE: 0.382879376411438 R2: 0.5627971828363831\n",
            "Epoch: 68 Validation MAE: 0.30556604266166687 RMSE: 0.42337286472320557 R2: 0.4778525419648795\n",
            "\n",
            "Epoch: 69 Training MAE: 0.28192636370658875 RMSE: 0.382739394903183 R2: 0.5631167910826793\n",
            "Epoch: 69 Validation MAE: 0.3059750497341156 RMSE: 0.42432257533073425 R2: 0.4755073321730374\n",
            "\n",
            "Epoch: 70 Training MAE: 0.2816213369369507 RMSE: 0.38248974084854126 R2: 0.5636865394845263\n",
            "Epoch: 70 Validation MAE: 0.30557170510292053 RMSE: 0.42368561029434204 R2: 0.4770809016460713\n",
            "\n",
            "Epoch: 71 Training MAE: 0.28150245547294617 RMSE: 0.3823245167732239 R2: 0.564063373804353\n",
            "Epoch: 71 Validation MAE: 0.3058856427669525 RMSE: 0.4243216812610626 R2: 0.4755096303684878\n",
            "\n",
            "Epoch: 72 Training MAE: 0.2818439304828644 RMSE: 0.38253921270370483 R2: 0.5635736868816832\n",
            "Epoch: 72 Validation MAE: 0.3066582679748535 RMSE: 0.42563313245773315 R2: 0.47226251786218754\n",
            "\n",
            "Epoch: 73 Training MAE: 0.2813612222671509 RMSE: 0.38234370946884155 R2: 0.5640196678913532\n",
            "Epoch: 73 Validation MAE: 0.3049350678920746 RMSE: 0.4229075610637665 R2: 0.47899969289711153\n",
            "\n",
            "Epoch: 74 Training MAE: 0.281191349029541 RMSE: 0.38193148374557495 R2: 0.5649592156110427\n",
            "Epoch: 74 Validation MAE: 0.30576589703559875 RMSE: 0.4242822527885437 R2: 0.4756070654465595\n",
            "\n",
            "Epoch: 75 Training MAE: 0.28116410970687866 RMSE: 0.3819833993911743 R2: 0.5648409352760758\n",
            "Epoch: 75 Validation MAE: 0.3054591715335846 RMSE: 0.42338094115257263 R2: 0.4778325846328191\n",
            "\n",
            "Epoch: 76 Training MAE: 0.28080394864082336 RMSE: 0.3815704882144928 R2: 0.565781256143749\n",
            "Epoch: 76 Validation MAE: 0.3050552010536194 RMSE: 0.42336368560791016 R2: 0.47787522639707036\n",
            "\n",
            "Epoch: 77 Training MAE: 0.28072527050971985 RMSE: 0.38137221336364746 R2: 0.5662323592439\n",
            "Epoch: 77 Validation MAE: 0.30522212386131287 RMSE: 0.4234960377216339 R2: 0.4775487379670782\n",
            "\n",
            "Epoch: 78 Training MAE: 0.28094562888145447 RMSE: 0.3813871741294861 R2: 0.5661983899374636\n",
            "Epoch: 78 Validation MAE: 0.305988073348999 RMSE: 0.4247983694076538 R2: 0.47433050524909726\n",
            "\n",
            "Epoch: 79 Training MAE: 0.2810966670513153 RMSE: 0.38197779655456543 R2: 0.5648537876765637\n",
            "Epoch: 79 Validation MAE: 0.30510208010673523 RMSE: 0.42279237508773804 R2: 0.47928343830540054\n",
            "\n",
            "Epoch: 80 Training MAE: 0.2807023525238037 RMSE: 0.3815110921859741 R2: 0.5659164426489525\n",
            "Epoch: 80 Validation MAE: 0.3051658868789673 RMSE: 0.4231235384941101 R2: 0.4784674410516703\n",
            "\n",
            "Epoch: 81 Training MAE: 0.28061428666114807 RMSE: 0.3811013698577881 R2: 0.5668483117255847\n",
            "Epoch: 81 Validation MAE: 0.3061000406742096 RMSE: 0.42522040009498596 R2: 0.4732854791043255\n",
            "\n",
            "Epoch: 82 Training MAE: 0.2801649570465088 RMSE: 0.3807746171951294 R2: 0.5675907321403957\n",
            "Epoch: 82 Validation MAE: 0.30493560433387756 RMSE: 0.423171728849411 R2: 0.47834866309728863\n",
            "\n",
            "Epoch: 83 Training MAE: 0.28010883927345276 RMSE: 0.38058918714523315 R2: 0.5680117655911494\n",
            "Epoch: 83 Validation MAE: 0.3056910037994385 RMSE: 0.4245808720588684 R2: 0.47486866773759584\n",
            "\n",
            "Epoch: 84 Training MAE: 0.27985721826553345 RMSE: 0.38036033511161804 R2: 0.5685312003528058\n",
            "Epoch: 84 Validation MAE: 0.30501219630241394 RMSE: 0.42364275455474854 R2: 0.4771866784220652\n",
            "\n",
            "Epoch: 85 Training MAE: 0.2798323333263397 RMSE: 0.3802754580974579 R2: 0.5687236517621035\n",
            "Epoch: 85 Validation MAE: 0.30505579710006714 RMSE: 0.4240175485610962 R2: 0.4762611334836684\n",
            "\n",
            "Epoch: 86 Training MAE: 0.2795911431312561 RMSE: 0.38010621070861816 R2: 0.5691075325133137\n",
            "Epoch: 86 Validation MAE: 0.30459272861480713 RMSE: 0.4232516288757324 R2: 0.4781515317163577\n",
            "\n",
            "Epoch: 87 Training MAE: 0.2795332074165344 RMSE: 0.3800332844257355 R2: 0.5692728692821991\n",
            "Epoch: 87 Validation MAE: 0.30469778180122375 RMSE: 0.4233377277851105 R2: 0.477939287280031\n",
            "\n",
            "Epoch: 88 Training MAE: 0.27966099977493286 RMSE: 0.38013604283332825 R2: 0.5690398507304515\n",
            "Epoch: 88 Validation MAE: 0.3046979606151581 RMSE: 0.4231453835964203 R2: 0.47841357795813855\n",
            "\n",
            "Epoch: 89 Training MAE: 0.2795267105102539 RMSE: 0.37988147139549255 R2: 0.5696168550970558\n",
            "Epoch: 89 Validation MAE: 0.3052481412887573 RMSE: 0.424127459526062 R2: 0.47598964512621633\n",
            "\n",
            "Epoch: 90 Training MAE: 0.27916213870048523 RMSE: 0.3796711564064026 R2: 0.5700932392709258\n",
            "Epoch: 90 Validation MAE: 0.3041504919528961 RMSE: 0.4228541851043701 R2: 0.47913123551081316\n",
            "\n",
            "Epoch: 91 Training MAE: 0.2792964577674866 RMSE: 0.3796813488006592 R2: 0.5700702000315945\n",
            "Epoch: 91 Validation MAE: 0.304645836353302 RMSE: 0.423782080411911 R2: 0.4768427639941377\n",
            "\n",
            "Epoch: 92 Training MAE: 0.2792900502681732 RMSE: 0.3798227906227112 R2: 0.5697498271613861\n",
            "Epoch: 92 Validation MAE: 0.30407965183258057 RMSE: 0.4223243296146393 R2: 0.48043568896238664\n",
            "\n",
            "Epoch: 93 Training MAE: 0.2788781523704529 RMSE: 0.3792799711227417 R2: 0.5709787098289411\n",
            "Epoch: 93 Validation MAE: 0.3040919899940491 RMSE: 0.42274606227874756 R2: 0.4793975348499355\n",
            "\n",
            "Epoch: 94 Training MAE: 0.2788945436477661 RMSE: 0.3793775737285614 R2: 0.5707578974584915\n",
            "Epoch: 94 Validation MAE: 0.304229199886322 RMSE: 0.42327263951301575 R2: 0.4780997233872708\n",
            "\n",
            "Epoch: 95 Training MAE: 0.27928727865219116 RMSE: 0.3800133466720581 R2: 0.5693179943938691\n",
            "Epoch: 95 Validation MAE: 0.30407947301864624 RMSE: 0.42214250564575195 R2: 0.48088301310907133\n",
            "\n",
            "Epoch: 96 Training MAE: 0.2805011570453644 RMSE: 0.38144969940185547 R2: 0.5660560804355455\n",
            "Epoch: 96 Validation MAE: 0.30466023087501526 RMSE: 0.42205244302749634 R2: 0.48110452575912765\n",
            "\n",
            "Epoch: 97 Training MAE: 0.27863451838493347 RMSE: 0.3789416253566742 R2: 0.5717438173091915\n",
            "Epoch: 97 Validation MAE: 0.30386877059936523 RMSE: 0.42255350947380066 R2: 0.4798715903577293\n",
            "\n",
            "Epoch: 98 Training MAE: 0.2786932587623596 RMSE: 0.3789408206939697 R2: 0.5717456020210017\n",
            "Epoch: 98 Validation MAE: 0.3043942153453827 RMSE: 0.4236138164997101 R2: 0.4772581052507887\n",
            "\n",
            "Epoch: 99 Training MAE: 0.27847564220428467 RMSE: 0.37883567810058594 R2: 0.5719832363732402\n",
            "Epoch: 99 Validation MAE: 0.30346858501434326 RMSE: 0.42193642258644104 R2: 0.4813896539116129\n",
            "\n",
            "Epoch: 100 Training MAE: 0.2785208225250244 RMSE: 0.37897711992263794 R2: 0.5716636403522317\n",
            "Epoch: 100 Validation MAE: 0.30367669463157654 RMSE: 0.42210057377815247 R2: 0.4809860758492305\n",
            "\n",
            "Epoch: 101 Training MAE: 0.2787473499774933 RMSE: 0.3788470923900604 R2: 0.5719574788176827\n",
            "Epoch: 101 Validation MAE: 0.304973840713501 RMSE: 0.42446497082710266 R2: 0.475155355312571\n",
            "\n",
            "Epoch: 102 Training MAE: 0.2780471444129944 RMSE: 0.3783513307571411 R2: 0.5730770127025748\n",
            "Epoch: 102 Validation MAE: 0.30392101407051086 RMSE: 0.42287468910217285 R2: 0.47908059931145597\n",
            "\n",
            "Epoch: 103 Training MAE: 0.27799493074417114 RMSE: 0.37829628586769104 R2: 0.5732012292140276\n",
            "Epoch: 103 Validation MAE: 0.3039068877696991 RMSE: 0.42273199558258057 R2: 0.4794321428141841\n",
            "\n",
            "Epoch: 104 Training MAE: 0.278114914894104 RMSE: 0.37822335958480835 R2: 0.5733656936413944\n",
            "Epoch: 104 Validation MAE: 0.3045348823070526 RMSE: 0.4237721860408783 R2: 0.47686717006384693\n",
            "\n",
            "Epoch: 105 Training MAE: 0.2780591547489166 RMSE: 0.3781830966472626 R2: 0.5734565995419512\n",
            "Epoch: 105 Validation MAE: 0.3045228123664856 RMSE: 0.42386394739151 R2: 0.47664054595776073\n",
            "\n",
            "Epoch: 106 Training MAE: 0.2777140140533447 RMSE: 0.3779211640357971 R2: 0.5740472676443835\n",
            "Epoch: 106 Validation MAE: 0.3037157654762268 RMSE: 0.4226786196231842 R2: 0.47956360731500236\n",
            "\n",
            "Epoch: 107 Training MAE: 0.2775886654853821 RMSE: 0.3777942657470703 R2: 0.5743332058780102\n",
            "Epoch: 107 Validation MAE: 0.30385035276412964 RMSE: 0.4228053390979767 R2: 0.47925146018652465\n",
            "\n",
            "Epoch: 108 Training MAE: 0.2775445878505707 RMSE: 0.37771958112716675 R2: 0.5745015054189129\n",
            "Epoch: 108 Validation MAE: 0.303520530462265 RMSE: 0.4224817454814911 R2: 0.4800482995048284\n",
            "\n",
            "Epoch: 109 Training MAE: 0.2776985168457031 RMSE: 0.3779789209365845 R2: 0.5739170014917707\n",
            "Epoch: 109 Validation MAE: 0.30326011776924133 RMSE: 0.42194342613220215 R2: 0.48137247904025393\n",
            "\n",
            "Epoch: 110 Training MAE: 0.27763834595680237 RMSE: 0.37793979048728943 R2: 0.5740051910252761\n",
            "Epoch: 110 Validation MAE: 0.3030085563659668 RMSE: 0.4215928912162781 R2: 0.48223380557114903\n",
            "\n",
            "Epoch: 111 Training MAE: 0.27734076976776123 RMSE: 0.37749481201171875 R2: 0.5750078184730358\n",
            "Epoch: 111 Validation MAE: 0.3029501736164093 RMSE: 0.42186439037323 R2: 0.4815667460467077\n",
            "\n",
            "Epoch: 112 Training MAE: 0.27714797854423523 RMSE: 0.3773539960384369 R2: 0.5753247628201867\n",
            "Epoch: 112 Validation MAE: 0.3030477464199066 RMSE: 0.42199811339378357 R2: 0.48123801647690057\n",
            "\n",
            "Epoch: 113 Training MAE: 0.27713829278945923 RMSE: 0.3772258162498474 R2: 0.5756132770964932\n",
            "Epoch: 113 Validation MAE: 0.3033985197544098 RMSE: 0.42277389764785767 R2: 0.4793289574363784\n",
            "\n",
            "Epoch: 114 Training MAE: 0.27710598707199097 RMSE: 0.3771928548812866 R2: 0.5756873484994189\n",
            "Epoch: 114 Validation MAE: 0.3037737309932709 RMSE: 0.4231472909450531 R2: 0.47840881748464614\n",
            "\n",
            "Epoch: 115 Training MAE: 0.27737581729888916 RMSE: 0.37731730937957764 R2: 0.5754073815721217\n",
            "Epoch: 115 Validation MAE: 0.3043031692504883 RMSE: 0.42413651943206787 R2: 0.47596723334348623\n",
            "\n",
            "Epoch: 116 Training MAE: 0.27702414989471436 RMSE: 0.3770304024219513 R2: 0.576052841082327\n",
            "Epoch: 116 Validation MAE: 0.303692102432251 RMSE: 0.42331063747406006 R2: 0.478006066658868\n",
            "\n",
            "Epoch: 117 Training MAE: 0.27721256017684937 RMSE: 0.37722301483154297 R2: 0.5756195223479734\n",
            "Epoch: 117 Validation MAE: 0.30430176854133606 RMSE: 0.42430388927459717 R2: 0.47555356632905255\n",
            "\n",
            "Epoch: 118 Training MAE: 0.2768363058567047 RMSE: 0.37685340642929077 R2: 0.5764507761313197\n",
            "Epoch: 118 Validation MAE: 0.3036060333251953 RMSE: 0.42314738035202026 R2: 0.47840864867086375\n",
            "\n",
            "Epoch: 119 Training MAE: 0.2767041027545929 RMSE: 0.3767465353012085 R2: 0.5766909830862779\n",
            "Epoch: 119 Validation MAE: 0.3035714328289032 RMSE: 0.42323213815689087 R2: 0.4781996241348102\n",
            "\n",
            "Epoch: 120 Training MAE: 0.276971697807312 RMSE: 0.37703320384025574 R2: 0.5760464958028452\n",
            "Epoch: 120 Validation MAE: 0.30343306064605713 RMSE: 0.4232260286808014 R2: 0.47821464018207716\n",
            "\n",
            "Epoch: 121 Training MAE: 0.2772153913974762 RMSE: 0.3771291673183441 R2: 0.5758306251176185\n",
            "Epoch: 121 Validation MAE: 0.3044514060020447 RMSE: 0.42449960112571716 R2: 0.4750696800145945\n",
            "\n",
            "Epoch: 122 Training MAE: 0.2764378786087036 RMSE: 0.376484751701355 R2: 0.5772790343849528\n",
            "Epoch: 122 Validation MAE: 0.3029209077358246 RMSE: 0.42224496603012085 R2: 0.48063094391514394\n",
            "\n",
            "Epoch: 123 Training MAE: 0.2763954699039459 RMSE: 0.37658146023750305 R2: 0.5770618741524283\n",
            "Epoch: 123 Validation MAE: 0.3026336431503296 RMSE: 0.42161840200424194 R2: 0.48217121808994523\n",
            "\n",
            "Epoch: 124 Training MAE: 0.27631303668022156 RMSE: 0.37633267045021057 R2: 0.5776204395032591\n",
            "Epoch: 124 Validation MAE: 0.3030053377151489 RMSE: 0.42219364643096924 R2: 0.4807571866587276\n",
            "\n",
            "Epoch: 125 Training MAE: 0.27662384510040283 RMSE: 0.3765344023704529 R2: 0.5771675312710105\n",
            "Epoch: 125 Validation MAE: 0.3036513328552246 RMSE: 0.4232599437236786 R2: 0.4781311158454562\n",
            "\n",
            "Epoch: 126 Training MAE: 0.2763358950614929 RMSE: 0.3765927851200104 R2: 0.5770363433178449\n",
            "Epoch: 126 Validation MAE: 0.30204594135284424 RMSE: 0.42089375853538513 R2: 0.4839496983793785\n",
            "\n",
            "Epoch: 127 Training MAE: 0.27625787258148193 RMSE: 0.37613388895988464 R2: 0.5780665111596972\n",
            "Epoch: 127 Validation MAE: 0.30310550332069397 RMSE: 0.4225941002368927 R2: 0.4797717058108186\n",
            "\n",
            "Epoch: 128 Training MAE: 0.2759789228439331 RMSE: 0.37607160210609436 R2: 0.5782062670955663\n",
            "Epoch: 128 Validation MAE: 0.30210816860198975 RMSE: 0.42135587334632874 R2: 0.482815824540074\n",
            "\n",
            "Epoch: 129 Training MAE: 0.27639323472976685 RMSE: 0.3762143552303314 R2: 0.5778860122935889\n",
            "Epoch: 129 Validation MAE: 0.3034225404262543 RMSE: 0.42286407947540283 R2: 0.4791067568272842\n",
            "\n",
            "Epoch: 130 Training MAE: 0.27672627568244934 RMSE: 0.3764464259147644 R2: 0.5773650677679945\n",
            "Epoch: 130 Validation MAE: 0.3042117655277252 RMSE: 0.424296498298645 R2: 0.47557186947997876\n",
            "\n",
            "Epoch: 131 Training MAE: 0.2773270308971405 RMSE: 0.3777141571044922 R2: 0.5745137438584096\n",
            "Epoch: 131 Validation MAE: 0.30284810066223145 RMSE: 0.4212315082550049 R2: 0.4831211696277352\n",
            "\n",
            "Epoch: 132 Training MAE: 0.27580955624580383 RMSE: 0.37577924132347107 R2: 0.5788618781779485\n",
            "Epoch: 132 Validation MAE: 0.3023888170719147 RMSE: 0.42188915610313416 R2: 0.4815058965825795\n",
            "\n",
            "Epoch: 133 Training MAE: 0.27606549859046936 RMSE: 0.37592488527297974 R2: 0.5785352852084646\n",
            "Epoch: 133 Validation MAE: 0.30310389399528503 RMSE: 0.42287319898605347 R2: 0.4790843919194875\n",
            "\n",
            "Epoch: 134 Training MAE: 0.27587658166885376 RMSE: 0.37576329708099365 R2: 0.5788976009471221\n",
            "Epoch: 134 Validation MAE: 0.30276426672935486 RMSE: 0.4225277304649353 R2: 0.47993515844306045\n",
            "\n",
            "Epoch: 135 Training MAE: 0.2761372923851013 RMSE: 0.37625837326049805 R2: 0.577787262451122\n",
            "Epoch: 135 Validation MAE: 0.30221161246299744 RMSE: 0.421215683221817 R2: 0.4831599592421897\n",
            "\n",
            "Epoch: 136 Training MAE: 0.2755192220211029 RMSE: 0.37554967403411865 R2: 0.5793761650178842\n",
            "Epoch: 136 Validation MAE: 0.3018718957901001 RMSE: 0.4213481843471527 R2: 0.48283469518276445\n",
            "\n",
            "Epoch: 137 Training MAE: 0.27544838190078735 RMSE: 0.3754730820655823 R2: 0.5795478201339508\n",
            "Epoch: 137 Validation MAE: 0.30199185013771057 RMSE: 0.42181769013404846 R2: 0.4816814815586422\n",
            "\n",
            "Epoch: 138 Training MAE: 0.2755086421966553 RMSE: 0.37547972798347473 R2: 0.579532902530077\n",
            "Epoch: 138 Validation MAE: 0.30229848623275757 RMSE: 0.42191818356513977 R2: 0.48143449339087896\n",
            "\n",
            "Epoch: 139 Training MAE: 0.2753876745700836 RMSE: 0.37526124715805054 R2: 0.5800220414007696\n",
            "Epoch: 139 Validation MAE: 0.3024563491344452 RMSE: 0.42227301001548767 R2: 0.4805619574719414\n",
            "\n",
            "Epoch: 140 Training MAE: 0.27567172050476074 RMSE: 0.3754081130027771 R2: 0.5796932796893659\n",
            "Epoch: 140 Validation MAE: 0.30304068326950073 RMSE: 0.4232487976551056 R2: 0.4781585785610638\n",
            "\n",
            "Epoch: 141 Training MAE: 0.27699366211891174 RMSE: 0.3773540258407593 R2: 0.575324709230745\n",
            "Epoch: 141 Validation MAE: 0.3028130531311035 RMSE: 0.42165619134902954 R2: 0.48207830339773405\n",
            "\n",
            "Epoch: 142 Training MAE: 0.2755151689052582 RMSE: 0.3755762577056885 R2: 0.5793167006310027\n",
            "Epoch: 142 Validation MAE: 0.30184850096702576 RMSE: 0.4211393892765045 R2: 0.48334720865335457\n",
            "\n",
            "Epoch: 143 Training MAE: 0.2752183973789215 RMSE: 0.3749972879886627 R2: 0.5806127087611908\n",
            "Epoch: 143 Validation MAE: 0.30250102281570435 RMSE: 0.42213743925094604 R2: 0.4808954709606924\n",
            "\n",
            "Epoch: 144 Training MAE: 0.2751137614250183 RMSE: 0.37498101592063904 R2: 0.5806491606467872\n",
            "Epoch: 144 Validation MAE: 0.30185621976852417 RMSE: 0.421341210603714 R2: 0.482851779007897\n",
            "\n",
            "Epoch: 145 Training MAE: 0.27635136246681213 RMSE: 0.37588465213775635 R2: 0.5786255492109276\n",
            "Epoch: 145 Validation MAE: 0.3037143051624298 RMSE: 0.4239272177219391 R2: 0.47648428146921895\n",
            "\n",
            "Epoch: 146 Training MAE: 0.27506205439567566 RMSE: 0.3748569190502167 R2: 0.5809265867176374\n",
            "Epoch: 146 Validation MAE: 0.30214062333106995 RMSE: 0.4217812716960907 R2: 0.4817710594674456\n",
            "\n",
            "Epoch: 147 Training MAE: 0.27503034472465515 RMSE: 0.3747537434101105 R2: 0.5811572839084636\n",
            "Epoch: 147 Validation MAE: 0.3025733232498169 RMSE: 0.4223948121070862 R2: 0.48026233965245957\n",
            "\n",
            "Epoch: 148 Training MAE: 0.275264173746109 RMSE: 0.37483254075050354 R2: 0.5809811577467585\n",
            "Epoch: 148 Validation MAE: 0.30336257815361023 RMSE: 0.42331376671791077 R2: 0.477998352823625\n",
            "\n",
            "Epoch: 149 Training MAE: 0.2749529480934143 RMSE: 0.37493419647216797 R2: 0.580753846081942\n",
            "Epoch: 149 Validation MAE: 0.30165156722068787 RMSE: 0.42092448472976685 R2: 0.48387435742890017\n",
            "\n",
            "Epoch: 150 Training MAE: 0.27509066462516785 RMSE: 0.3750060200691223 R2: 0.5805931778529487\n",
            "Epoch: 150 Validation MAE: 0.30171146988868713 RMSE: 0.42097488045692444 R2: 0.4837507026168276\n",
            "\n",
            "Epoch: 151 Training MAE: 0.2747759222984314 RMSE: 0.3745087683200836 R2: 0.5817046479279548\n",
            "Epoch: 151 Validation MAE: 0.3022496700286865 RMSE: 0.42173507809638977 R2: 0.48188459154171603\n",
            "\n",
            "Epoch: 152 Training MAE: 0.2747010886669159 RMSE: 0.37464094161987305 R2: 0.5814094085814423\n",
            "Epoch: 152 Validation MAE: 0.3016832172870636 RMSE: 0.42107537388801575 R2: 0.48350422522159775\n",
            "\n",
            "Epoch: 153 Training MAE: 0.2755575180053711 RMSE: 0.3755550980567932 R2: 0.5793640889920062\n",
            "Epoch: 153 Validation MAE: 0.3012044131755829 RMSE: 0.42008882761001587 R2: 0.4859215962686394\n",
            "\n",
            "Epoch: 154 Training MAE: 0.27513912320137024 RMSE: 0.37458348274230957 R2: 0.5815377518691733\n",
            "Epoch: 154 Validation MAE: 0.30338147282600403 RMSE: 0.42344900965690613 R2: 0.47766476305461447\n",
            "\n",
            "Epoch: 155 Training MAE: 0.27449488639831543 RMSE: 0.37427839636802673 R2: 0.5822191536685829\n",
            "Epoch: 155 Validation MAE: 0.30165374279022217 RMSE: 0.42133083939552307 R2: 0.4828772887356887\n",
            "\n",
            "Epoch: 156 Training MAE: 0.2745150625705719 RMSE: 0.3741898536682129 R2: 0.5824168190294683\n",
            "Epoch: 156 Validation MAE: 0.30209073424339294 RMSE: 0.4220302700996399 R2: 0.48115901052876753\n",
            "\n",
            "Epoch: 157 Training MAE: 0.2747020721435547 RMSE: 0.37459108233451843 R2: 0.5815207697553071\n",
            "Epoch: 157 Validation MAE: 0.30159300565719604 RMSE: 0.4209544360637665 R2: 0.48380088786951725\n",
            "\n",
            "Epoch: 158 Training MAE: 0.27485203742980957 RMSE: 0.3743443787097931 R2: 0.5820718657827697\n",
            "Epoch: 158 Validation MAE: 0.30313774943351746 RMSE: 0.423016220331192 R2: 0.47873195640905475\n",
            "\n",
            "Epoch: 159 Training MAE: 0.2752363383769989 RMSE: 0.37464389204978943 R2: 0.5814028330706635\n",
            "Epoch: 159 Validation MAE: 0.30376607179641724 RMSE: 0.4238749146461487 R2: 0.47661353062724776\n",
            "\n",
            "Epoch: 160 Training MAE: 0.27437910437583923 RMSE: 0.3739752173423767 R2: 0.5828957286520715\n",
            "Epoch: 160 Validation MAE: 0.30257654190063477 RMSE: 0.4225766360759735 R2: 0.47981472437588324\n",
            "\n",
            "Epoch: 161 Training MAE: 0.27430713176727295 RMSE: 0.3739909827709198 R2: 0.5828605496933357\n",
            "Epoch: 161 Validation MAE: 0.3020680844783783 RMSE: 0.4220108091831207 R2: 0.48120678303480413\n",
            "\n",
            "Epoch: 162 Training MAE: 0.2743263244628906 RMSE: 0.37405234575271606 R2: 0.5827235917120253\n",
            "Epoch: 162 Validation MAE: 0.3016427159309387 RMSE: 0.42141589522361755 R2: 0.48266851069953176\n",
            "\n",
            "Epoch: 163 Training MAE: 0.27436015009880066 RMSE: 0.3740238845348358 R2: 0.5827871664066816\n",
            "Epoch: 163 Validation MAE: 0.3014545440673828 RMSE: 0.4211675822734833 R2: 0.4832779300225898\n",
            "\n",
            "Epoch: 164 Training MAE: 0.2755029797554016 RMSE: 0.37544164061546326 R2: 0.5796182378996682\n",
            "Epoch: 164 Validation MAE: 0.30187472701072693 RMSE: 0.4210970997810364 R2: 0.48345093826473107\n",
            "\n",
            "Epoch: 165 Training MAE: 0.274147629737854 RMSE: 0.3738243281841278 R2: 0.583232206199391\n",
            "Epoch: 165 Validation MAE: 0.30140289664268494 RMSE: 0.421217679977417 R2: 0.48315501483571976\n",
            "\n",
            "Epoch: 166 Training MAE: 0.27421632409095764 RMSE: 0.3736857771873474 R2: 0.5835410747048837\n",
            "Epoch: 166 Validation MAE: 0.3021993339061737 RMSE: 0.4222550392150879 R2: 0.4806062145488097\n",
            "\n",
            "Epoch: 167 Training MAE: 0.2745685577392578 RMSE: 0.37439605593681335 R2: 0.5819564271697222\n",
            "Epoch: 167 Validation MAE: 0.3015575408935547 RMSE: 0.42086949944496155 R2: 0.4840090991024357\n",
            "\n",
            "Epoch: 168 Training MAE: 0.27432629466056824 RMSE: 0.3741043508052826 R2: 0.5826076197241717\n",
            "Epoch: 168 Validation MAE: 0.30155855417251587 RMSE: 0.4211086928844452 R2: 0.48342253751975683\n",
            "\n",
            "Epoch: 169 Training MAE: 0.2739424705505371 RMSE: 0.3736734092235565 R2: 0.5835686488389894\n",
            "Epoch: 169 Validation MAE: 0.30173349380493164 RMSE: 0.4215523302555084 R2: 0.48233344907019093\n",
            "\n",
            "Epoch: 170 Training MAE: 0.274100661277771 RMSE: 0.37354275584220886 R2: 0.5838598426745093\n",
            "Epoch: 170 Validation MAE: 0.3028678894042969 RMSE: 0.423229455947876 R2: 0.4782062538436247\n",
            "\n",
            "Epoch: 171 Training MAE: 0.2737540602684021 RMSE: 0.3735111653804779 R2: 0.5839301818450247\n",
            "Epoch: 171 Validation MAE: 0.30173367261886597 RMSE: 0.4217158257961273 R2: 0.4819318550460021\n",
            "\n",
            "Epoch: 172 Training MAE: 0.2743090093135834 RMSE: 0.37404894828796387 R2: 0.5827311998785087\n",
            "Epoch: 172 Validation MAE: 0.3019246757030487 RMSE: 0.4215651750564575 R2: 0.48230192739704314\n",
            "\n",
            "Epoch: 173 Training MAE: 0.2740378975868225 RMSE: 0.3733988106250763 R2: 0.5841804904809109\n",
            "Epoch: 173 Validation MAE: 0.30303633213043213 RMSE: 0.423287034034729 R2: 0.47806422777636204\n",
            "\n",
            "Epoch: 174 Training MAE: 0.2745824158191681 RMSE: 0.37373530864715576 R2: 0.5834306847222162\n",
            "Epoch: 174 Validation MAE: 0.30378177762031555 RMSE: 0.42390382289886475 R2: 0.476542083875245\n",
            "\n",
            "Epoch: 175 Training MAE: 0.2741214334964752 RMSE: 0.3739279806613922 R2: 0.5830010560552035\n",
            "Epoch: 175 Validation MAE: 0.30128782987594604 RMSE: 0.420763224363327 R2: 0.484269696723746\n",
            "\n",
            "Epoch: 176 Training MAE: 0.27502915263175964 RMSE: 0.3742460012435913 R2: 0.5822914617675545\n",
            "Epoch: 176 Validation MAE: 0.30398061871528625 RMSE: 0.424558162689209 R2: 0.47492484680359703\n",
            "\n",
            "Epoch: 177 Training MAE: 0.27585020661354065 RMSE: 0.37491554021835327 R2: 0.580795497565415\n",
            "Epoch: 177 Validation MAE: 0.3053584098815918 RMSE: 0.42612120509147644 R2: 0.4710514949247383\n",
            "\n",
            "Epoch: 178 Training MAE: 0.27352920174598694 RMSE: 0.37316185235977173 R2: 0.584708046488438\n",
            "Epoch: 178 Validation MAE: 0.3013651967048645 RMSE: 0.42116647958755493 R2: 0.48328063765950857\n",
            "\n",
            "Epoch: 179 Training MAE: 0.2743520438671112 RMSE: 0.3735186457633972 R2: 0.5839134846613876\n",
            "Epoch: 179 Validation MAE: 0.30374661087989807 RMSE: 0.4242066740989685 R2: 0.4757938908277136\n",
            "\n",
            "Epoch: 180 Training MAE: 0.27357620000839233 RMSE: 0.37321826815605164 R2: 0.5845824907517423\n",
            "Epoch: 180 Validation MAE: 0.3017329275608063 RMSE: 0.42170771956443787 R2: 0.4819517526654943\n",
            "\n",
            "Epoch: 181 Training MAE: 0.2738247215747833 RMSE: 0.3732993006706238 R2: 0.5844020985568024\n",
            "Epoch: 181 Validation MAE: 0.3020908236503601 RMSE: 0.422486275434494 R2: 0.48003714867863545\n",
            "\n",
            "Epoch: 182 Training MAE: 0.27330633997917175 RMSE: 0.3728621304035187 R2: 0.5853749339466765\n",
            "Epoch: 182 Validation MAE: 0.3018063008785248 RMSE: 0.42185178399086 R2: 0.48159773137637596\n",
            "\n",
            "Epoch: 183 Training MAE: 0.2743658423423767 RMSE: 0.3735901117324829 R2: 0.5837542561986325\n",
            "Epoch: 183 Validation MAE: 0.30336129665374756 RMSE: 0.4241120517253876 R2: 0.47602768443760735\n",
            "\n",
            "Epoch: 184 Training MAE: 0.2733423113822937 RMSE: 0.3727791905403137 R2: 0.5855593631257672\n",
            "Epoch: 184 Validation MAE: 0.30191144347190857 RMSE: 0.42229193449020386 R2: 0.48051537264265276\n",
            "\n",
            "Epoch: 185 Training MAE: 0.2733076810836792 RMSE: 0.3727205991744995 R2: 0.5856895603552854\n",
            "Epoch: 185 Validation MAE: 0.30189523100852966 RMSE: 0.4223547577857971 R2: 0.48036088261707055\n",
            "\n",
            "Epoch: 186 Training MAE: 0.2731531262397766 RMSE: 0.37268686294555664 R2: 0.5857646094825268\n",
            "Epoch: 186 Validation MAE: 0.3017454743385315 RMSE: 0.4219967722892761 R2: 0.48124135689576575\n",
            "\n",
            "Epoch: 187 Training MAE: 0.2731240689754486 RMSE: 0.3727610111236572 R2: 0.5855998098255039\n",
            "Epoch: 187 Validation MAE: 0.30154433846473694 RMSE: 0.4215848743915558 R2: 0.48225351859714605\n",
            "\n",
            "Epoch: 188 Training MAE: 0.27310678362846375 RMSE: 0.37263163924217224 R2: 0.58588738342183\n",
            "Epoch: 188 Validation MAE: 0.3017898201942444 RMSE: 0.4215739965438843 R2: 0.4822802042781936\n",
            "\n",
            "Epoch: 189 Training MAE: 0.27345776557922363 RMSE: 0.3731963038444519 R2: 0.584631371686261\n",
            "Epoch: 189 Validation MAE: 0.3014198839664459 RMSE: 0.4212062954902649 R2: 0.48318299614055527\n",
            "\n",
            "Epoch: 190 Training MAE: 0.27303409576416016 RMSE: 0.3725091814994812 R2: 0.5861594992111115\n",
            "Epoch: 190 Validation MAE: 0.3018549978733063 RMSE: 0.4221368432044983 R2: 0.48089686734665504\n",
            "\n",
            "Epoch: 191 Training MAE: 0.27313703298568726 RMSE: 0.3724838197231293 R2: 0.5862158988066646\n",
            "Epoch: 191 Validation MAE: 0.30230820178985596 RMSE: 0.4224308133125305 R2: 0.4801736441196338\n",
            "\n",
            "Epoch: 192 Training MAE: 0.27298471331596375 RMSE: 0.3726554811000824 R2: 0.5858343323002267\n",
            "Epoch: 192 Validation MAE: 0.301189124584198 RMSE: 0.4209555685520172 R2: 0.4837981204108016\n",
            "\n",
            "Epoch: 193 Training MAE: 0.27361351251602173 RMSE: 0.37269940972328186 R2: 0.5857367239084263\n",
            "Epoch: 193 Validation MAE: 0.30331093072891235 RMSE: 0.4237099289894104 R2: 0.4770208178865416\n",
            "\n",
            "Epoch: 194 Training MAE: 0.2729590833187103 RMSE: 0.372435599565506 R2: 0.5863229984675856\n",
            "Epoch: 194 Validation MAE: 0.3015937805175781 RMSE: 0.4216521084308624 R2: 0.4820883282872933\n",
            "\n",
            "Epoch: 195 Training MAE: 0.27300116419792175 RMSE: 0.37249088287353516 R2: 0.5862001455252066\n",
            "Epoch: 195 Validation MAE: 0.3014878034591675 RMSE: 0.4215194582939148 R2: 0.4824142260853843\n",
            "\n",
            "Epoch: 196 Training MAE: 0.27319443225860596 RMSE: 0.3727857768535614 R2: 0.5855447176832306\n",
            "Epoch: 196 Validation MAE: 0.3014528453350067 RMSE: 0.42122912406921387 R2: 0.4831269464398882\n",
            "\n",
            "Epoch: 197 Training MAE: 0.2729375660419464 RMSE: 0.37249696254730225 R2: 0.5861866426728277\n",
            "Epoch: 197 Validation MAE: 0.3016557991504669 RMSE: 0.42158323526382446 R2: 0.4822575762332916\n",
            "\n",
            "Epoch: 198 Training MAE: 0.273286908864975 RMSE: 0.37292423844337463 R2: 0.5852367440022752\n",
            "Epoch: 198 Validation MAE: 0.301287978887558 RMSE: 0.4209557771682739 R2: 0.4837975541467586\n",
            "\n",
            "Epoch: 199 Training MAE: 0.2727809250354767 RMSE: 0.37232348322868347 R2: 0.5865720271839503\n",
            "Epoch: 199 Validation MAE: 0.30120715498924255 RMSE: 0.42097634077072144 R2: 0.4837470838577632\n",
            "\n",
            "Epoch: 200 Training MAE: 0.2728355824947357 RMSE: 0.37207189202308655 R2: 0.5871305816606508\n",
            "Epoch: 200 Validation MAE: 0.3020768165588379 RMSE: 0.42199891805648804 R2: 0.48123602750293537\n",
            "\n",
            "Epoch: 201 Training MAE: 0.27272188663482666 RMSE: 0.3722118139266968 R2: 0.5868199838382135\n",
            "Epoch: 201 Validation MAE: 0.3010992109775543 RMSE: 0.42104247212409973 R2: 0.48358485197528744\n",
            "\n",
            "Epoch: 202 Training MAE: 0.27432379126548767 RMSE: 0.3730570077896118 R2: 0.5849413980853263\n",
            "Epoch: 202 Validation MAE: 0.30452778935432434 RMSE: 0.42526736855506897 R2: 0.47316908582879025\n",
            "\n",
            "Epoch: 203 Training MAE: 0.2728988230228424 RMSE: 0.3722896873950958 R2: 0.5866470800606629\n",
            "Epoch: 203 Validation MAE: 0.30163708329200745 RMSE: 0.42170119285583496 R2: 0.4819677929495937\n",
            "\n",
            "Epoch: 204 Training MAE: 0.2728253901004791 RMSE: 0.3719332218170166 R2: 0.5874382320477032\n",
            "Epoch: 204 Validation MAE: 0.30246755480766296 RMSE: 0.4227536618709564 R2: 0.4793787413482531\n",
            "\n",
            "Epoch: 205 Training MAE: 0.27253690361976624 RMSE: 0.3719671368598938 R2: 0.5873630236514222\n",
            "Epoch: 205 Validation MAE: 0.301317423582077 RMSE: 0.42148667573928833 R2: 0.4824946935161394\n",
            "\n",
            "Epoch: 206 Training MAE: 0.2727831304073334 RMSE: 0.3719017803668976 R2: 0.5875079658620173\n",
            "Epoch: 206 Validation MAE: 0.3027178943157196 RMSE: 0.4232402443885803 R2: 0.4781796385475914\n",
            "\n",
            "Epoch: 207 Training MAE: 0.27251070737838745 RMSE: 0.3720097541809082 R2: 0.5872684347619863\n",
            "Epoch: 207 Validation MAE: 0.30118635296821594 RMSE: 0.42118942737579346 R2: 0.4832243539883425\n",
            "\n",
            "Epoch: 208 Training MAE: 0.27254581451416016 RMSE: 0.37175366282463074 R2: 0.5878365338119764\n",
            "Epoch: 208 Validation MAE: 0.3025847375392914 RMSE: 0.4229041635990143 R2: 0.4790080239727881\n",
            "\n",
            "Epoch: 209 Training MAE: 0.2724086046218872 RMSE: 0.37171146273612976 R2: 0.5879301051504314\n",
            "Epoch: 209 Validation MAE: 0.30163249373435974 RMSE: 0.4220837950706482 R2: 0.4810273794031197\n",
            "\n",
            "Epoch: 210 Training MAE: 0.27226510643959045 RMSE: 0.3717655539512634 R2: 0.5878101485923541\n",
            "Epoch: 210 Validation MAE: 0.3011718690395355 RMSE: 0.4215092360973358 R2: 0.4824392720359849\n",
            "\n",
            "Epoch: 211 Training MAE: 0.27232009172439575 RMSE: 0.3716995418071747 R2: 0.5879565265876829\n",
            "Epoch: 211 Validation MAE: 0.3011047840118408 RMSE: 0.42149707674980164 R2: 0.4824691802499538\n",
            "\n",
            "Epoch: 212 Training MAE: 0.27235910296440125 RMSE: 0.37160995602607727 R2: 0.588155125497104\n",
            "Epoch: 212 Validation MAE: 0.3018275797367096 RMSE: 0.422269344329834 R2: 0.4805709735140662\n",
            "\n",
            "Epoch: 213 Training MAE: 0.2724470794200897 RMSE: 0.3719480335712433 R2: 0.5874053569803901\n",
            "Epoch: 213 Validation MAE: 0.30152615904808044 RMSE: 0.42153555154800415 R2: 0.48237467554511126\n",
            "\n",
            "Epoch: 214 Training MAE: 0.2724322974681854 RMSE: 0.3714904189109802 R2: 0.5884200236854054\n",
            "Epoch: 214 Validation MAE: 0.302788108587265 RMSE: 0.4232587516307831 R2: 0.47813407645134176\n",
            "\n",
            "Epoch: 215 Training MAE: 0.27242517471313477 RMSE: 0.37185585498809814 R2: 0.5876098190622834\n",
            "Epoch: 215 Validation MAE: 0.30159991979599 RMSE: 0.4216271936893463 R2: 0.4821495755455659\n",
            "\n",
            "Epoch: 216 Training MAE: 0.2729472517967224 RMSE: 0.37180471420288086 R2: 0.5877233039551034\n",
            "Epoch: 216 Validation MAE: 0.30364516377449036 RMSE: 0.42392849922180176 R2: 0.4764811068387176\n",
            "\n",
            "Epoch: 217 Training MAE: 0.2720896005630493 RMSE: 0.3713262379169464 R2: 0.5887837192851759\n",
            "Epoch: 217 Validation MAE: 0.30192452669143677 RMSE: 0.42211881279945374 R2: 0.48094123979068537\n",
            "\n",
            "Epoch: 218 Training MAE: 0.27215227484703064 RMSE: 0.3715077042579651 R2: 0.5883817291910981\n",
            "Epoch: 218 Validation MAE: 0.30215632915496826 RMSE: 0.42240121960639954 R2: 0.48024646426599904\n",
            "\n",
            "Epoch: 219 Training MAE: 0.2721843123435974 RMSE: 0.3712690770626068 R2: 0.5889102858287774\n",
            "Epoch: 219 Validation MAE: 0.30294859409332275 RMSE: 0.4232654869556427 R2: 0.4781174370815723\n",
            "\n",
            "Epoch: 220 Training MAE: 0.2721666693687439 RMSE: 0.3713010549545288 R2: 0.5888395093336816\n",
            "Epoch: 220 Validation MAE: 0.302304208278656 RMSE: 0.42272865772247314 R2: 0.4794404345952846\n",
            "\n",
            "Epoch: 221 Training MAE: 0.27219346165657043 RMSE: 0.37124547362327576 R2: 0.5889625573041406\n",
            "Epoch: 221 Validation MAE: 0.3027710020542145 RMSE: 0.4229791462421417 R2: 0.47882326142005294\n",
            "\n",
            "Epoch: 222 Training MAE: 0.27195271849632263 RMSE: 0.3712230622768402 R2: 0.5890121986792118\n",
            "Epoch: 222 Validation MAE: 0.3019067645072937 RMSE: 0.4221818745136261 R2: 0.48078609792626414\n",
            "\n",
            "Epoch: 223 Training MAE: 0.2722429037094116 RMSE: 0.37118229269981384 R2: 0.5891024426580227\n",
            "Epoch: 223 Validation MAE: 0.30295684933662415 RMSE: 0.42330488562583923 R2: 0.47802029154821646\n",
            "\n",
            "Epoch: 224 Training MAE: 0.271945595741272 RMSE: 0.3710370361804962 R2: 0.5894240448514383\n",
            "Epoch: 224 Validation MAE: 0.3017493486404419 RMSE: 0.42219117283821106 R2: 0.4807632931875221\n",
            "\n",
            "Epoch: 225 Training MAE: 0.2719983458518982 RMSE: 0.37111803889274597 R2: 0.5892447381853136\n",
            "Epoch: 225 Validation MAE: 0.3019089102745056 RMSE: 0.42232540249824524 R2: 0.48043308470897983\n",
            "\n",
            "Epoch: 226 Training MAE: 0.27181369066238403 RMSE: 0.3709809482097626 R2: 0.5895481503763239\n",
            "Epoch: 226 Validation MAE: 0.3020598590373993 RMSE: 0.4224236011505127 R2: 0.4801914340493121\n",
            "\n",
            "Epoch: 227 Training MAE: 0.2717036008834839 RMSE: 0.3709813952445984 R2: 0.5895471619456576\n",
            "Epoch: 227 Validation MAE: 0.3020685017108917 RMSE: 0.42238518595695496 R2: 0.4802859764613364\n",
            "\n",
            "Epoch: 228 Training MAE: 0.2728673219680786 RMSE: 0.37164631485939026 R2: 0.5880744559855549\n",
            "Epoch: 228 Validation MAE: 0.30465424060821533 RMSE: 0.4251834452152252 R2: 0.4733770270400889\n",
            "\n",
            "Epoch: 229 Training MAE: 0.2717510759830475 RMSE: 0.37114110589027405 R2: 0.5891936694826101\n",
            "Epoch: 229 Validation MAE: 0.3018083870410919 RMSE: 0.42236655950546265 R2: 0.48033183201472285\n",
            "\n",
            "Epoch: 230 Training MAE: 0.2718923091888428 RMSE: 0.3714204430580139 R2: 0.588575025733036\n",
            "Epoch: 230 Validation MAE: 0.3011579215526581 RMSE: 0.421481728553772 R2: 0.48250690031185417\n",
            "\n",
            "Epoch: 231 Training MAE: 0.2715858519077301 RMSE: 0.37080755829811096 R2: 0.5899317407953228\n",
            "Epoch: 231 Validation MAE: 0.30243197083473206 RMSE: 0.42297059297561646 R2: 0.47884426153440773\n",
            "\n",
            "Epoch: 232 Training MAE: 0.27303028106689453 RMSE: 0.37182167172431946 R2: 0.5876856701666626\n",
            "Epoch: 232 Validation MAE: 0.30464500188827515 RMSE: 0.4253896474838257 R2: 0.47286610559725606\n",
            "\n",
            "Epoch: 233 Training MAE: 0.2715786099433899 RMSE: 0.37087568640708923 R2: 0.5897810621291605\n",
            "Epoch: 233 Validation MAE: 0.30113479495048523 RMSE: 0.4214676320552826 R2: 0.482541440040979\n",
            "\n",
            "Epoch: 234 Training MAE: 0.27177324891090393 RMSE: 0.3712473511695862 R2: 0.5889584444620946\n",
            "Epoch: 234 Validation MAE: 0.30090245604515076 RMSE: 0.42137736082077026 R2: 0.48276312268695964\n",
            "\n",
            "Epoch: 235 Training MAE: 0.2714611291885376 RMSE: 0.37061432003974915 R2: 0.5903589577694706\n",
            "Epoch: 235 Validation MAE: 0.30204930901527405 RMSE: 0.4224926233291626 R2: 0.48002151114599456\n",
            "\n",
            "Epoch: 236 Training MAE: 0.2721065580844879 RMSE: 0.37168219685554504 R2: 0.5879949634911683\n",
            "Epoch: 236 Validation MAE: 0.30121201276779175 RMSE: 0.4217827320098877 R2: 0.4817674661097979\n",
            "\n",
            "Epoch: 237 Training MAE: 0.2715282142162323 RMSE: 0.37078285217285156 R2: 0.589986373172668\n",
            "Epoch: 237 Validation MAE: 0.3012133836746216 RMSE: 0.4214855432510376 R2: 0.48249750768001554\n",
            "\n",
            "Epoch: 238 Training MAE: 0.27151843905448914 RMSE: 0.3705594539642334 R2: 0.5904802581447934\n",
            "Epoch: 238 Validation MAE: 0.3024024963378906 RMSE: 0.422983855009079 R2: 0.47881158937402746\n",
            "\n",
            "Epoch: 239 Training MAE: 0.2732808291912079 RMSE: 0.3718404471874237 R2: 0.5876440537807763\n",
            "Epoch: 239 Validation MAE: 0.3052957057952881 RMSE: 0.42618119716644287 R2: 0.47090252411754685\n",
            "\n",
            "Epoch: 240 Training MAE: 0.2716187536716461 RMSE: 0.37074774503707886 R2: 0.5900640489041613\n",
            "Epoch: 240 Validation MAE: 0.3019377589225769 RMSE: 0.4224901795387268 R2: 0.4800275764877665\n",
            "\n",
            "Epoch: 241 Training MAE: 0.2715045213699341 RMSE: 0.37044599652290344 R2: 0.5907310220692181\n",
            "Epoch: 241 Validation MAE: 0.3021801710128784 RMSE: 0.42281436920166016 R2: 0.47922923760598746\n",
            "\n",
            "Epoch: 242 Training MAE: 0.2713698446750641 RMSE: 0.3704911768436432 R2: 0.5906311593474227\n",
            "Epoch: 242 Validation MAE: 0.30157986283302307 RMSE: 0.4221423268318176 R2: 0.4808834357880504\n",
            "\n",
            "Epoch: 243 Training MAE: 0.27231356501579285 RMSE: 0.37096136808395386 R2: 0.5895914451119725\n",
            "Epoch: 243 Validation MAE: 0.30430278182029724 RMSE: 0.4248294234275818 R2: 0.47425356960709786\n",
            "\n",
            "Epoch: 244 Training MAE: 0.2714773714542389 RMSE: 0.3703819215297699 R2: 0.5908726298032299\n",
            "Epoch: 244 Validation MAE: 0.30274853110313416 RMSE: 0.4235130250453949 R2: 0.47750680726500894\n",
            "\n",
            "Epoch: 245 Training MAE: 0.2713162899017334 RMSE: 0.3702733516693115 R2: 0.5911123727064989\n",
            "Epoch: 245 Validation MAE: 0.3023246228694916 RMSE: 0.4228840172290802 R2: 0.4790576345849217\n",
            "\n",
            "Epoch: 246 Training MAE: 0.27133551239967346 RMSE: 0.3702446222305298 R2: 0.5911758327428658\n",
            "Epoch: 246 Validation MAE: 0.30272650718688965 RMSE: 0.4233221411705017 R2: 0.47797772352616064\n",
            "\n",
            "Epoch: 247 Training MAE: 0.27178189158439636 RMSE: 0.3705523908138275 R2: 0.590495884799941\n",
            "Epoch: 247 Validation MAE: 0.30358681082725525 RMSE: 0.42405131459236145 R2: 0.47617780381556396\n",
            "\n",
            "Epoch: 248 Training MAE: 0.27147766947746277 RMSE: 0.370233952999115 R2: 0.5911994072471807\n",
            "Epoch: 248 Validation MAE: 0.30282050371170044 RMSE: 0.423417866230011 R2: 0.4777415865143627\n",
            "\n",
            "Epoch: 249 Training MAE: 0.27128106355667114 RMSE: 0.37013494968414307 R2: 0.5914180548199357\n",
            "Epoch: 249 Validation MAE: 0.3022552728652954 RMSE: 0.42276516556739807 R2: 0.4793504161590646\n",
            "\n",
            "Epoch: 250 Training MAE: 0.2710834741592407 RMSE: 0.37013915181159973 R2: 0.5914087277871489\n",
            "Epoch: 250 Validation MAE: 0.3016356825828552 RMSE: 0.42198681831359863 R2: 0.48126581498105925\n",
            "\n",
            "Epoch: 251 Training MAE: 0.2710753381252289 RMSE: 0.3701425790786743 R2: 0.5914012007191964\n",
            "Epoch: 251 Validation MAE: 0.3019307255744934 RMSE: 0.42254379391670227 R2: 0.4798955615235154\n",
            "\n",
            "Epoch: 252 Training MAE: 0.27124014496803284 RMSE: 0.37007519602775574 R2: 0.5915499151184707\n",
            "Epoch: 252 Validation MAE: 0.30308541655540466 RMSE: 0.42425337433815 R2: 0.47567842873242483\n",
            "\n",
            "Epoch: 253 Training MAE: 0.2718115448951721 RMSE: 0.37039902806282043 R2: 0.5908348377038604\n",
            "Epoch: 253 Validation MAE: 0.3037235140800476 RMSE: 0.4245428442955017 R2: 0.47496269767260946\n",
            "\n",
            "Epoch: 254 Training MAE: 0.27114588022232056 RMSE: 0.37009215354919434 R2: 0.5915125039083967\n",
            "Epoch: 254 Validation MAE: 0.3017900884151459 RMSE: 0.4226185381412506 R2: 0.47971154506858504\n",
            "\n",
            "Epoch: 255 Training MAE: 0.27094003558158875 RMSE: 0.3699120879173279 R2: 0.5919099243265453\n",
            "Epoch: 255 Validation MAE: 0.3019627630710602 RMSE: 0.4226990044116974 R2: 0.4795133839604945\n",
            "\n",
            "Epoch: 256 Training MAE: 0.27098575234413147 RMSE: 0.370112806558609 R2: 0.5914668916897656\n",
            "Epoch: 256 Validation MAE: 0.3013991117477417 RMSE: 0.42206695675849915 R2: 0.4810687712585754\n",
            "\n",
            "Epoch: 257 Training MAE: 0.2709503769874573 RMSE: 0.3698316812515259 R2: 0.5920872733543701\n",
            "Epoch: 257 Validation MAE: 0.30198538303375244 RMSE: 0.42287561297416687 R2: 0.4790783564127048\n",
            "\n",
            "Epoch: 258 Training MAE: 0.27123787999153137 RMSE: 0.370487779378891 R2: 0.590638683870305\n",
            "Epoch: 258 Validation MAE: 0.30111995339393616 RMSE: 0.421482652425766 R2: 0.4825046009918794\n",
            "\n",
            "Epoch: 259 Training MAE: 0.2713252902030945 RMSE: 0.37002280354499817 R2: 0.5916655590096955\n",
            "Epoch: 259 Validation MAE: 0.30310341715812683 RMSE: 0.4239738881587982 R2: 0.4763690083879323\n",
            "\n",
            "Epoch: 260 Training MAE: 0.2707803249359131 RMSE: 0.3697540760040283 R2: 0.5922584513679066\n",
            "Epoch: 260 Validation MAE: 0.3017720878124237 RMSE: 0.4225136637687683 R2: 0.47996970274463824\n",
            "\n",
            "Epoch: 261 Training MAE: 0.27077582478523254 RMSE: 0.36987173557281494 R2: 0.5919989514745787\n",
            "Epoch: 261 Validation MAE: 0.3012704849243164 RMSE: 0.42207852005958557 R2: 0.4810403520445241\n",
            "\n",
            "Epoch: 262 Training MAE: 0.2707919478416443 RMSE: 0.3697223961353302 R2: 0.5923283298347459\n",
            "Epoch: 262 Validation MAE: 0.30174940824508667 RMSE: 0.42260926961898804 R2: 0.47973443072860855\n",
            "\n",
            "Epoch: 263 Training MAE: 0.27138903737068176 RMSE: 0.3707084357738495 R2: 0.5901508978178651\n",
            "Epoch: 263 Validation MAE: 0.3010527491569519 RMSE: 0.4216487407684326 R2: 0.4820966508771535\n",
            "\n",
            "Epoch: 264 Training MAE: 0.27168363332748413 RMSE: 0.3701957166194916 R2: 0.5912838497989406\n",
            "Epoch: 264 Validation MAE: 0.3038126826286316 RMSE: 0.42536404728889465 R2: 0.47292961080002627\n",
            "\n",
            "Epoch: 265 Training MAE: 0.27082234621047974 RMSE: 0.37011587619781494 R2: 0.5914601218596249\n",
            "Epoch: 265 Validation MAE: 0.3010965883731842 RMSE: 0.42202767729759216 R2: 0.4811653590960553\n",
            "\n",
            "Epoch: 266 Training MAE: 0.27065375447273254 RMSE: 0.3695254325866699 R2: 0.5927625632660769\n",
            "Epoch: 266 Validation MAE: 0.3019876480102539 RMSE: 0.4227604269981384 R2: 0.4793621292746235\n",
            "\n",
            "Epoch: 267 Training MAE: 0.2706007957458496 RMSE: 0.3695790469646454 R2: 0.5926443833426768\n",
            "Epoch: 267 Validation MAE: 0.3010573983192444 RMSE: 0.42180848121643066 R2: 0.48170416512763947\n",
            "\n",
            "Epoch: 268 Training MAE: 0.27084237337112427 RMSE: 0.36963316798210144 R2: 0.592525083132076\n",
            "Epoch: 268 Validation MAE: 0.30240103602409363 RMSE: 0.42319416999816895 R2: 0.4782933018834351\n",
            "\n",
            "Epoch: 269 Training MAE: 0.2709002196788788 RMSE: 0.3700154721736908 R2: 0.591681727543196\n",
            "Epoch: 269 Validation MAE: 0.3016456663608551 RMSE: 0.42243707180023193 R2: 0.4801582858159589\n",
            "\n",
            "Epoch: 270 Training MAE: 0.2706573009490967 RMSE: 0.36980944871902466 R2: 0.5921363282938291\n",
            "Epoch: 270 Validation MAE: 0.3011077642440796 RMSE: 0.4221664369106293 R2: 0.48082416406332684\n",
            "\n",
            "Epoch: 271 Training MAE: 0.27049973607063293 RMSE: 0.3693940341472626 R2: 0.5930521746664134\n",
            "Epoch: 271 Validation MAE: 0.3016621768474579 RMSE: 0.4226764440536499 R2: 0.4795689771847451\n",
            "\n",
            "Epoch: 272 Training MAE: 0.27086976170539856 RMSE: 0.3695811331272125 R2: 0.592639795683116\n",
            "Epoch: 272 Validation MAE: 0.30262652039527893 RMSE: 0.42403069138526917 R2: 0.4762287741206209\n",
            "\n",
            "Epoch: 273 Training MAE: 0.2708645761013031 RMSE: 0.3695164620876312 R2: 0.5927823594948682\n",
            "Epoch: 273 Validation MAE: 0.30226171016693115 RMSE: 0.4233928918838501 R2: 0.4778031946887309\n",
            "\n",
            "Epoch: 274 Training MAE: 0.2706499397754669 RMSE: 0.3694549798965454 R2: 0.5929178485490414\n",
            "Epoch: 274 Validation MAE: 0.30225834250450134 RMSE: 0.4231172204017639 R2: 0.4784829755333675\n",
            "\n",
            "Epoch: 275 Training MAE: 0.27100569009780884 RMSE: 0.36953890323638916 R2: 0.5927328467541728\n",
            "Epoch: 275 Validation MAE: 0.30290547013282776 RMSE: 0.42425966262817383 R2: 0.47566291063087285\n",
            "\n",
            "Epoch: 276 Training MAE: 0.2705467641353607 RMSE: 0.36927029490470886 R2: 0.593324793752215\n",
            "Epoch: 276 Validation MAE: 0.3026842176914215 RMSE: 0.4237213432788849 R2: 0.47699259008443173\n",
            "\n",
            "Epoch: 277 Training MAE: 0.27100610733032227 RMSE: 0.3696362376213074 R2: 0.5925183424112752\n",
            "Epoch: 277 Validation MAE: 0.30340126156806946 RMSE: 0.4246874451637268 R2: 0.47460496255052476\n",
            "\n",
            "Epoch: 278 Training MAE: 0.2705592215061188 RMSE: 0.36926203966140747 R2: 0.5933429212455484\n",
            "Epoch: 278 Validation MAE: 0.3026486039161682 RMSE: 0.42369982600212097 R2: 0.47704577472000087\n",
            "\n",
            "Epoch: 279 Training MAE: 0.2706042230129242 RMSE: 0.36922797560691833 R2: 0.593417958677172\n",
            "Epoch: 279 Validation MAE: 0.302501916885376 RMSE: 0.42331862449645996 R2: 0.47798634466521095\n",
            "\n",
            "Epoch: 280 Training MAE: 0.2704869210720062 RMSE: 0.3691413104534149 R2: 0.5936087534272465\n",
            "Epoch: 280 Validation MAE: 0.3019609749317169 RMSE: 0.42287975549697876 R2: 0.4790681507147869\n",
            "\n",
            "Epoch: 281 Training MAE: 0.2712629735469818 RMSE: 0.37034308910369873 R2: 0.5909583943718866\n",
            "Epoch: 281 Validation MAE: 0.3013058602809906 RMSE: 0.4221354126930237 R2: 0.48090043034947705\n",
            "\n",
            "Epoch: 282 Training MAE: 0.27032649517059326 RMSE: 0.3691478967666626 R2: 0.5935942208321121\n",
            "Epoch: 282 Validation MAE: 0.3016594648361206 RMSE: 0.42259636521339417 R2: 0.47976615463695216\n",
            "\n",
            "Epoch: 283 Training MAE: 0.2704000174999237 RMSE: 0.36903733015060425 R2: 0.593837679823441\n",
            "Epoch: 283 Validation MAE: 0.30251410603523254 RMSE: 0.42349258065223694 R2: 0.4775572201521985\n",
            "\n",
            "Epoch: 284 Training MAE: 0.2709605097770691 RMSE: 0.369449257850647 R2: 0.5929304817426692\n",
            "Epoch: 284 Validation MAE: 0.30343660712242126 RMSE: 0.4245130121707916 R2: 0.47503647799838356\n",
            "\n",
            "Epoch: 285 Training MAE: 0.27006077766418457 RMSE: 0.3691433072090149 R2: 0.5936044075333698\n",
            "Epoch: 285 Validation MAE: 0.30117958784103394 RMSE: 0.4226219654083252 R2: 0.4797031583042213\n",
            "\n",
            "Epoch: 286 Training MAE: 0.2701348066329956 RMSE: 0.3693062365055084 R2: 0.5932455408184552\n",
            "Epoch: 286 Validation MAE: 0.30119892954826355 RMSE: 0.4225407540798187 R2: 0.47990306968219487\n",
            "\n",
            "Epoch: 287 Training MAE: 0.27038124203681946 RMSE: 0.36942222714424133 R2: 0.5929899729825523\n",
            "Epoch: 287 Validation MAE: 0.3017958998680115 RMSE: 0.4227273166179657 R2: 0.4794436673460484\n",
            "\n",
            "Epoch: 288 Training MAE: 0.27001556754112244 RMSE: 0.3688545525074005 R2: 0.5942399335033859\n",
            "Epoch: 288 Validation MAE: 0.30148208141326904 RMSE: 0.4226057827472687 R2: 0.4797429786419223\n",
            "\n",
            "Epoch: 289 Training MAE: 0.2706092298030853 RMSE: 0.36968880891799927 R2: 0.5924023671919557\n",
            "Epoch: 289 Validation MAE: 0.30123916268348694 RMSE: 0.42219939827919006 R2: 0.48074301004651687\n",
            "\n",
            "Epoch: 290 Training MAE: 0.27021870017051697 RMSE: 0.36885252594947815 R2: 0.594244337419563\n",
            "Epoch: 290 Validation MAE: 0.30291250348091125 RMSE: 0.4242106080055237 R2: 0.47578417818801266\n",
            "\n",
            "Epoch: 291 Training MAE: 0.2700631618499756 RMSE: 0.36912381649017334 R2: 0.5936473364039431\n",
            "Epoch: 291 Validation MAE: 0.30081862211227417 RMSE: 0.42210307717323303 R2: 0.48097995776058666\n",
            "\n",
            "Epoch: 292 Training MAE: 0.26993152499198914 RMSE: 0.36872440576553345 R2: 0.5945262198097102\n",
            "Epoch: 292 Validation MAE: 0.3019733726978302 RMSE: 0.4232414960861206 R2: 0.4781765850547811\n",
            "\n",
            "Epoch: 293 Training MAE: 0.2703455090522766 RMSE: 0.36939936876296997 R2: 0.5930403880279342\n",
            "Epoch: 293 Validation MAE: 0.3012089431285858 RMSE: 0.42243438959121704 R2: 0.4801648440160383\n",
            "\n",
            "Epoch: 294 Training MAE: 0.2701602578163147 RMSE: 0.36871787905693054 R2: 0.5945405820507368\n",
            "Epoch: 294 Validation MAE: 0.3026978075504303 RMSE: 0.4239332377910614 R2: 0.47646942107153945\n",
            "\n",
            "Epoch: 295 Training MAE: 0.270162969827652 RMSE: 0.3689267635345459 R2: 0.5940810245335304\n",
            "Epoch: 295 Validation MAE: 0.30193576216697693 RMSE: 0.4228815734386444 R2: 0.4790637152898881\n",
            "\n",
            "Epoch: 296 Training MAE: 0.27035102248191833 RMSE: 0.36883610486984253 R2: 0.5942804853932866\n",
            "Epoch: 296 Validation MAE: 0.30295324325561523 RMSE: 0.42421987652778625 R2: 0.4757612534560772\n",
            "\n",
            "Epoch: 297 Training MAE: 0.2702065110206604 RMSE: 0.36873576045036316 R2: 0.5945012127957153\n",
            "Epoch: 297 Validation MAE: 0.3028671145439148 RMSE: 0.42374178767204285 R2: 0.4769421182918816\n",
            "\n",
            "Epoch: 298 Training MAE: 0.2699386179447174 RMSE: 0.368782103061676 R2: 0.594399335251959\n",
            "Epoch: 298 Validation MAE: 0.3010408878326416 RMSE: 0.4221764802932739 R2: 0.4807994166024847\n",
            "\n",
            "Epoch: 299 Training MAE: 0.27010151743888855 RMSE: 0.36864084005355835 R2: 0.5947099814888801\n",
            "Epoch: 299 Validation MAE: 0.3029181659221649 RMSE: 0.4238590598106384 R2: 0.47665260914557095\n",
            "\n",
            "Epoch: 300 Training MAE: 0.27006521821022034 RMSE: 0.36858072876930237 R2: 0.5948421588243769\n",
            "Epoch: 300 Validation MAE: 0.30266907811164856 RMSE: 0.42376846075057983 R2: 0.4768763234120842\n",
            "\n",
            "Epoch: 301 Training MAE: 0.27030596137046814 RMSE: 0.3687371611595154 R2: 0.5944981745802247\n",
            "Epoch: 301 Validation MAE: 0.3035139739513397 RMSE: 0.42487603425979614 R2: 0.47413821514971677\n",
            "\n",
            "Epoch: 302 Training MAE: 0.27034416794776917 RMSE: 0.3694523274898529 R2: 0.5929236818078278\n",
            "Epoch: 302 Validation MAE: 0.3011988401412964 RMSE: 0.42199036478996277 R2: 0.4812570920041527\n",
            "\n",
            "Epoch: 303 Training MAE: 0.2700726389884949 RMSE: 0.36916252970695496 R2: 0.5935620636637264\n",
            "Epoch: 303 Validation MAE: 0.30124029517173767 RMSE: 0.4220737814903259 R2: 0.48105199266615695\n",
            "\n",
            "Epoch: 304 Training MAE: 0.27250152826309204 RMSE: 0.37052616477012634 R2: 0.5905538659089339\n",
            "Epoch: 304 Validation MAE: 0.30576661229133606 RMSE: 0.4272169768810272 R2: 0.4683276180923358\n",
            "\n",
            "Epoch: 305 Training MAE: 0.270428866147995 RMSE: 0.36875900626182556 R2: 0.5944501201454433\n",
            "Epoch: 305 Validation MAE: 0.30376768112182617 RMSE: 0.42503994703292847 R2: 0.4737324098385345\n",
            "\n",
            "Epoch: 306 Training MAE: 0.2696446180343628 RMSE: 0.3685164153575897 R2: 0.5949834952480805\n",
            "Epoch: 306 Validation MAE: 0.30135518312454224 RMSE: 0.42239850759506226 R2: 0.48025318295551045\n",
            "\n",
            "Epoch: 307 Training MAE: 0.26972079277038574 RMSE: 0.36856719851493835 R2: 0.5948719067481422\n",
            "Epoch: 307 Validation MAE: 0.3013206124305725 RMSE: 0.4222278296947479 R2: 0.4806731309886928\n",
            "\n",
            "Epoch: 308 Training MAE: 0.26959413290023804 RMSE: 0.3684387505054474 R2: 0.5951541953143838\n",
            "Epoch: 308 Validation MAE: 0.3015153408050537 RMSE: 0.42288175225257874 R2: 0.47906325297120755\n",
            "\n",
            "Epoch: 309 Training MAE: 0.2704300582408905 RMSE: 0.3687945306301117 R2: 0.5943720171013736\n",
            "Epoch: 309 Validation MAE: 0.3036462366580963 RMSE: 0.42553266882896423 R2: 0.4725115999459625\n",
            "\n",
            "Epoch: 310 Training MAE: 0.26953256130218506 RMSE: 0.36822569370269775 R2: 0.5956222830100337\n",
            "Epoch: 310 Validation MAE: 0.3020579516887665 RMSE: 0.423555463552475 R2: 0.47740201778478564\n",
            "\n",
            "Epoch: 311 Training MAE: 0.2696312665939331 RMSE: 0.3685776889324188 R2: 0.5948488305089847\n",
            "Epoch: 311 Validation MAE: 0.30153149366378784 RMSE: 0.423038512468338 R2: 0.4786769469368335\n",
            "\n",
            "Epoch: 312 Training MAE: 0.2696869969367981 RMSE: 0.36820167303085327 R2: 0.5956750774230244\n",
            "Epoch: 312 Validation MAE: 0.3024880290031433 RMSE: 0.4242028594017029 R2: 0.47580333517828977\n",
            "\n",
            "Epoch: 313 Training MAE: 0.2706720530986786 RMSE: 0.3690491318702698 R2: 0.5938117107007012\n",
            "Epoch: 313 Validation MAE: 0.3038521111011505 RMSE: 0.42563286423683167 R2: 0.47226318561947966\n",
            "\n",
            "Epoch: 314 Training MAE: 0.26945561170578003 RMSE: 0.36824360489845276 R2: 0.5955829726807655\n",
            "Epoch: 314 Validation MAE: 0.3017022907733917 RMSE: 0.4227874279022217 R2: 0.4792956313010388\n",
            "\n",
            "Epoch: 315 Training MAE: 0.2696314752101898 RMSE: 0.36845704913139343 R2: 0.5951139819829752\n",
            "Epoch: 315 Validation MAE: 0.3015243709087372 RMSE: 0.4229581952095032 R2: 0.47887493168024764\n",
            "\n",
            "Epoch: 316 Training MAE: 0.27189719676971436 RMSE: 0.3698161244392395 R2: 0.5921216599223149\n",
            "Epoch: 316 Validation MAE: 0.3064956068992615 RMSE: 0.4281226396560669 R2: 0.4660710030902574\n",
            "\n",
            "Epoch: 317 Training MAE: 0.26965591311454773 RMSE: 0.3686780035495758 R2: 0.594628292778081\n",
            "Epoch: 317 Validation MAE: 0.3008561432361603 RMSE: 0.42197543382644653 R2: 0.4812938521546207\n",
            "\n",
            "Epoch: 318 Training MAE: 0.2694043219089508 RMSE: 0.3680449426174164 R2: 0.5960191989629233\n",
            "Epoch: 318 Validation MAE: 0.30204635858535767 RMSE: 0.4229872524738312 R2: 0.478803255717585\n",
            "\n",
            "Epoch: 319 Training MAE: 0.2695516049861908 RMSE: 0.3680562973022461 R2: 0.5959942485966101\n",
            "Epoch: 319 Validation MAE: 0.3027927279472351 RMSE: 0.4238579273223877 R2: 0.4766553479389779\n",
            "\n",
            "Epoch: 320 Training MAE: 0.26923269033432007 RMSE: 0.3679444193840027 R2: 0.596239845964169\n",
            "Epoch: 320 Validation MAE: 0.3020625114440918 RMSE: 0.4233364164829254 R2: 0.4779424587179417\n",
            "\n",
            "Epoch: 321 Training MAE: 0.26992863416671753 RMSE: 0.368226557970047 R2: 0.5956204298459293\n",
            "Epoch: 321 Validation MAE: 0.30316847562789917 RMSE: 0.424712598323822 R2: 0.47454274050949563\n",
            "\n",
            "Epoch: 322 Training MAE: 0.2694624364376068 RMSE: 0.3682854175567627 R2: 0.5954910814653546\n",
            "Epoch: 322 Validation MAE: 0.3011183440685272 RMSE: 0.4222494959831238 R2: 0.480619817168065\n",
            "\n",
            "Epoch: 323 Training MAE: 0.27038368582725525 RMSE: 0.36860787868499756 R2: 0.5947824759477504\n",
            "Epoch: 323 Validation MAE: 0.3033130466938019 RMSE: 0.4243643879890442 R2: 0.4754040722704457\n",
            "\n",
            "Epoch: 324 Training MAE: 0.26937106251716614 RMSE: 0.36833468079566956 R2: 0.5953828999604956\n",
            "Epoch: 324 Validation MAE: 0.3008350431919098 RMSE: 0.42208218574523926 R2: 0.48103134417331717\n",
            "\n",
            "Epoch: 325 Training MAE: 0.2693975865840912 RMSE: 0.3678394854068756 R2: 0.5964701662827236\n",
            "Epoch: 325 Validation MAE: 0.30250033736228943 RMSE: 0.42396408319473267 R2: 0.47639327678619503\n",
            "\n",
            "Epoch: 326 Training MAE: 0.26918065547943115 RMSE: 0.367820143699646 R2: 0.5965125764430605\n",
            "Epoch: 326 Validation MAE: 0.30168265104293823 RMSE: 0.4235555827617645 R2: 0.4774018298592234\n",
            "\n",
            "Epoch: 327 Training MAE: 0.2692221701145172 RMSE: 0.36773136258125305 R2: 0.5967072699319923\n",
            "Epoch: 327 Validation MAE: 0.30191919207572937 RMSE: 0.4233536124229431 R2: 0.4779000397135206\n",
            "\n",
            "Epoch: 328 Training MAE: 0.26956963539123535 RMSE: 0.3683594763278961 R2: 0.5953283827322442\n",
            "Epoch: 328 Validation MAE: 0.30148133635520935 RMSE: 0.42258375883102417 R2: 0.4797972048071991\n",
            "\n",
            "Epoch: 329 Training MAE: 0.2692543864250183 RMSE: 0.36775949597358704 R2: 0.596645564249997\n",
            "Epoch: 329 Validation MAE: 0.30171748995780945 RMSE: 0.42297691106796265 R2: 0.47882875640587774\n",
            "\n",
            "Epoch: 330 Training MAE: 0.2693111002445221 RMSE: 0.3676743507385254 R2: 0.5968323462983283\n",
            "Epoch: 330 Validation MAE: 0.30205896496772766 RMSE: 0.4234335422515869 R2: 0.4777028939780492\n",
            "\n",
            "Epoch: 331 Training MAE: 0.2703815698623657 RMSE: 0.36951079964637756 R2: 0.5927948441565792\n",
            "Epoch: 331 Validation MAE: 0.30109572410583496 RMSE: 0.4220530688762665 R2: 0.48110293235051405\n",
            "\n",
            "Epoch: 332 Training MAE: 0.26920250058174133 RMSE: 0.3679848313331604 R2: 0.5961511452438437\n",
            "Epoch: 332 Validation MAE: 0.30136728286743164 RMSE: 0.4224795699119568 R2: 0.48005372040659755\n",
            "\n",
            "Epoch: 333 Training MAE: 0.26921260356903076 RMSE: 0.36768266558647156 R2: 0.5968140973167313\n",
            "Epoch: 333 Validation MAE: 0.30147111415863037 RMSE: 0.4226644039154053 R2: 0.4795986134385051\n",
            "\n",
            "Epoch: 334 Training MAE: 0.2693667709827423 RMSE: 0.36819952726364136 R2: 0.5956797758081855\n",
            "Epoch: 334 Validation MAE: 0.30107757449150085 RMSE: 0.42230889201164246 R2: 0.48047372156188206\n",
            "\n",
            "Epoch: 335 Training MAE: 0.26903873682022095 RMSE: 0.3675325810909271 R2: 0.597143249664813\n",
            "Epoch: 335 Validation MAE: 0.3016950786113739 RMSE: 0.4232080280780792 R2: 0.47825906711683164\n",
            "\n",
            "Epoch: 336 Training MAE: 0.26947569847106934 RMSE: 0.36839786171913147 R2: 0.5952440568772719\n",
            "Epoch: 336 Validation MAE: 0.30098333954811096 RMSE: 0.422031968832016 R2: 0.4811548387114224\n",
            "\n",
            "Epoch: 337 Training MAE: 0.2691188454627991 RMSE: 0.36760595440864563 R2: 0.5969823211479235\n",
            "Epoch: 337 Validation MAE: 0.3024943172931671 RMSE: 0.42392709851264954 R2: 0.4764845244157443\n",
            "\n",
            "Epoch: 338 Training MAE: 0.2690804600715637 RMSE: 0.3676168918609619 R2: 0.5969583252664259\n",
            "Epoch: 338 Validation MAE: 0.30263498425483704 RMSE: 0.42405134439468384 R2: 0.4761776686629673\n",
            "\n",
            "Epoch: 339 Training MAE: 0.2688760757446289 RMSE: 0.36746320128440857 R2: 0.5972953010484585\n",
            "Epoch: 339 Validation MAE: 0.30171331763267517 RMSE: 0.42325514554977417 R2: 0.4781429090498692\n",
            "\n",
            "Epoch: 340 Training MAE: 0.2690260708332062 RMSE: 0.36772456765174866 R2: 0.5967222883177207\n",
            "Epoch: 340 Validation MAE: 0.30167102813720703 RMSE: 0.4230262041091919 R2: 0.4787073699692226\n",
            "\n",
            "Epoch: 341 Training MAE: 0.26924851536750793 RMSE: 0.367578387260437 R2: 0.5970427930926309\n",
            "Epoch: 341 Validation MAE: 0.3030813932418823 RMSE: 0.42450976371765137 R2: 0.4750444954327441\n",
            "\n",
            "Epoch: 342 Training MAE: 0.2689157724380493 RMSE: 0.3673706650733948 R2: 0.5974980868415825\n",
            "Epoch: 342 Validation MAE: 0.3025893568992615 RMSE: 0.423904687166214 R2: 0.47654002317902167\n",
            "\n",
            "Epoch: 343 Training MAE: 0.2690396010875702 RMSE: 0.3679210841655731 R2: 0.5962910805148746\n",
            "Epoch: 343 Validation MAE: 0.30127376317977905 RMSE: 0.42280757427215576 R2: 0.47924601804946243\n",
            "\n",
            "Epoch: 344 Training MAE: 0.26912420988082886 RMSE: 0.3674778938293457 R2: 0.5972630701804534\n",
            "Epoch: 344 Validation MAE: 0.3033889830112457 RMSE: 0.42544806003570557 R2: 0.4727212769270619\n",
            "\n",
            "Epoch: 345 Training MAE: 0.2691257894039154 RMSE: 0.36745455861091614 R2: 0.5973142554452884\n",
            "Epoch: 345 Validation MAE: 0.3039168417453766 RMSE: 0.426078200340271 R2: 0.47115827525306797\n",
            "\n",
            "Epoch: 346 Training MAE: 0.26939108967781067 RMSE: 0.36761292815208435 R2: 0.5969670173006516\n",
            "Epoch: 346 Validation MAE: 0.3041256368160248 RMSE: 0.42627280950546265 R2: 0.4706750989902577\n",
            "\n",
            "Epoch: 347 Training MAE: 0.26876214146614075 RMSE: 0.36726856231689453 R2: 0.5977218267086906\n",
            "Epoch: 347 Validation MAE: 0.3026285469532013 RMSE: 0.4242779612541199 R2: 0.4756176635547579\n",
            "\n",
            "Epoch: 348 Training MAE: 0.26880472898483276 RMSE: 0.3672329783439636 R2: 0.5977997787452849\n",
            "Epoch: 348 Validation MAE: 0.30226948857307434 RMSE: 0.4237775206565857 R2: 0.4768539024851389\n",
            "\n",
            "Epoch: 349 Training MAE: 0.26865240931510925 RMSE: 0.36717352271080017 R2: 0.597929944342768\n",
            "Epoch: 349 Validation MAE: 0.3021741211414337 RMSE: 0.42364227771759033 R2: 0.4771878571113972\n",
            "\n",
            "Epoch: 350 Training MAE: 0.2687223553657532 RMSE: 0.36715584993362427 R2: 0.597968671227465\n",
            "Epoch: 350 Validation MAE: 0.3023030459880829 RMSE: 0.4239928126335144 R2: 0.47632231234777167\n",
            "\n",
            "Epoch: 351 Training MAE: 0.26888424158096313 RMSE: 0.3678115904331207 R2: 0.5965313052322408\n",
            "Epoch: 351 Validation MAE: 0.3012424409389496 RMSE: 0.4225790500640869 R2: 0.47980881260006414\n",
            "\n",
            "Epoch: 352 Training MAE: 0.26865649223327637 RMSE: 0.3673236072063446 R2: 0.5976011719036629\n",
            "Epoch: 352 Validation MAE: 0.30157771706581116 RMSE: 0.4229769706726074 R2: 0.47882865099760474\n",
            "\n",
            "Epoch: 353 Training MAE: 0.26864519715309143 RMSE: 0.36734211444854736 R2: 0.5975606328985006\n",
            "Epoch: 353 Validation MAE: 0.3013727068901062 RMSE: 0.4228091537952423 R2: 0.47924211626113766\n",
            "\n",
            "Epoch: 354 Training MAE: 0.2688538730144501 RMSE: 0.36724644899368286 R2: 0.5977701809735505\n",
            "Epoch: 354 Validation MAE: 0.3020627498626709 RMSE: 0.4236621856689453 R2: 0.47713875165326725\n",
            "\n",
            "Epoch: 355 Training MAE: 0.26860347390174866 RMSE: 0.36713406443595886 R2: 0.5980163503154399\n",
            "Epoch: 355 Validation MAE: 0.30172958970069885 RMSE: 0.423140287399292 R2: 0.4784260523249907\n",
            "\n",
            "Epoch: 356 Training MAE: 0.26886308193206787 RMSE: 0.36760684847831726 R2: 0.5969804104856312\n",
            "Epoch: 356 Validation MAE: 0.30129721760749817 RMSE: 0.42256730794906616 R2: 0.47983775916655025\n",
            "\n",
            "Epoch: 357 Training MAE: 0.2685914635658264 RMSE: 0.36704686284065247 R2: 0.5982072647584284\n",
            "Epoch: 357 Validation MAE: 0.3024267256259918 RMSE: 0.42382174730300903 R2: 0.47674470845253747\n",
            "\n",
            "Epoch: 358 Training MAE: 0.2684989273548126 RMSE: 0.3671266734600067 R2: 0.5980325877516566\n",
            "Epoch: 358 Validation MAE: 0.30189210176467896 RMSE: 0.42342409491539 R2: 0.477726206114759\n",
            "\n",
            "Epoch: 359 Training MAE: 0.26846763491630554 RMSE: 0.36708614230155945 R2: 0.5981212955829291\n",
            "Epoch: 359 Validation MAE: 0.30132827162742615 RMSE: 0.4228478968143463 R2: 0.4791466384035086\n",
            "\n",
            "Epoch: 360 Training MAE: 0.2684957981109619 RMSE: 0.3673095703125 R2: 0.5976319540187007\n",
            "Epoch: 360 Validation MAE: 0.3012400269508362 RMSE: 0.4227234721183777 R2: 0.47945311769210064\n",
            "\n",
            "Epoch: 361 Training MAE: 0.26826730370521545 RMSE: 0.36689630150794983 R2: 0.5985368416993069\n",
            "Epoch: 361 Validation MAE: 0.30202266573905945 RMSE: 0.423844575881958 R2: 0.4766884283363313\n",
            "\n",
            "Epoch: 362 Training MAE: 0.26897209882736206 RMSE: 0.3672085106372833 R2: 0.5978533420989114\n",
            "Epoch: 362 Validation MAE: 0.303570955991745 RMSE: 0.42520034313201904 R2: 0.47333514558585965\n",
            "\n",
            "Epoch: 363 Training MAE: 0.2687956988811493 RMSE: 0.3670176565647125 R2: 0.5982712738482856\n",
            "Epoch: 363 Validation MAE: 0.3031993806362152 RMSE: 0.4248046875 R2: 0.47431486917526977\n",
            "\n",
            "Epoch: 364 Training MAE: 0.26841187477111816 RMSE: 0.366874098777771 R2: 0.5985854561946826\n",
            "Epoch: 364 Validation MAE: 0.30240127444267273 RMSE: 0.42397475242614746 R2: 0.47636689557525647\n",
            "\n",
            "Epoch: 365 Training MAE: 0.26867276430130005 RMSE: 0.36692944169044495 R2: 0.5984642959174719\n",
            "Epoch: 365 Validation MAE: 0.30368953943252563 RMSE: 0.4254326820373535 R2: 0.4727593791831326\n",
            "\n",
            "Epoch: 366 Training MAE: 0.2684633135795593 RMSE: 0.36717721819877625 R2: 0.5979218169005238\n",
            "Epoch: 366 Validation MAE: 0.3019910156726837 RMSE: 0.4236064851284027 R2: 0.4772761638344388\n",
            "\n",
            "Epoch: 367 Training MAE: 0.26843616366386414 RMSE: 0.3668370544910431 R2: 0.5986664930029052\n",
            "Epoch: 367 Validation MAE: 0.30269667506217957 RMSE: 0.42442581057548523 R2: 0.4752521755889165\n",
            "\n",
            "Epoch: 368 Training MAE: 0.2686546742916107 RMSE: 0.3669233024120331 R2: 0.5984777889199858\n",
            "Epoch: 368 Validation MAE: 0.3030490279197693 RMSE: 0.4254392981529236 R2: 0.4727430670779792\n",
            "\n",
            "Epoch: 369 Training MAE: 0.26878005266189575 RMSE: 0.36749401688575745 R2: 0.597227756147993\n",
            "Epoch: 369 Validation MAE: 0.3012661635875702 RMSE: 0.42305412888526917 R2: 0.4786384270938322\n",
            "\n",
            "Epoch: 370 Training MAE: 0.26868101954460144 RMSE: 0.36683395504951477 R2: 0.5986732501537564\n",
            "Epoch: 370 Validation MAE: 0.30367812514305115 RMSE: 0.4255884885787964 R2: 0.4723732082326785\n",
            "\n",
            "Epoch: 371 Training MAE: 0.26867279410362244 RMSE: 0.3668420910835266 R2: 0.5986555171446619\n",
            "Epoch: 371 Validation MAE: 0.30384087562561035 RMSE: 0.42581668496131897 R2: 0.47180720079202365\n",
            "\n",
            "Epoch: 372 Training MAE: 0.26822343468666077 RMSE: 0.36661094427108765 R2: 0.5991610904310443\n",
            "Epoch: 372 Validation MAE: 0.30255383253097534 RMSE: 0.4242516756057739 R2: 0.4756826117978471\n",
            "\n",
            "Epoch: 373 Training MAE: 0.2682565152645111 RMSE: 0.36657360196113586 R2: 0.5992427178851865\n",
            "Epoch: 373 Validation MAE: 0.3024565577507019 RMSE: 0.42422690987586975 R2: 0.47574386763131293\n",
            "\n",
            "Epoch: 374 Training MAE: 0.2684655487537384 RMSE: 0.3669949173927307 R2: 0.5983209903334367\n",
            "Epoch: 374 Validation MAE: 0.3019200563430786 RMSE: 0.4240455627441406 R2: 0.4761919758127251\n",
            "\n",
            "Epoch: 375 Training MAE: 0.26883262395858765 RMSE: 0.36694905161857605 R2: 0.598421374208151\n",
            "Epoch: 375 Validation MAE: 0.3040262460708618 RMSE: 0.4263904392719269 R2: 0.4703828447925109\n",
            "\n",
            "Epoch: 376 Training MAE: 0.26820820569992065 RMSE: 0.3664834499359131 R2: 0.5994398915879141\n",
            "Epoch: 376 Validation MAE: 0.3022196888923645 RMSE: 0.42430099844932556 R2: 0.47556073196858883\n",
            "\n",
            "Epoch: 377 Training MAE: 0.26819753646850586 RMSE: 0.36662063002586365 R2: 0.5991398986390678\n",
            "Epoch: 377 Validation MAE: 0.3018262982368469 RMSE: 0.4239617884159088 R2: 0.47639891323403505\n",
            "\n",
            "Epoch: 378 Training MAE: 0.26848268508911133 RMSE: 0.3670205771923065 R2: 0.59826479466629\n",
            "Epoch: 378 Validation MAE: 0.301751971244812 RMSE: 0.42333799600601196 R2: 0.4779385643854904\n",
            "\n",
            "Epoch: 379 Training MAE: 0.2681584656238556 RMSE: 0.36645570397377014 R2: 0.5995004742003418\n",
            "Epoch: 379 Validation MAE: 0.30228766798973083 RMSE: 0.42423418164253235 R2: 0.4757258972023498\n",
            "\n",
            "Epoch: 380 Training MAE: 0.2683373689651489 RMSE: 0.3664928376674652 R2: 0.5994193455095129\n",
            "Epoch: 380 Validation MAE: 0.30316054821014404 RMSE: 0.4250252842903137 R2: 0.4737687447916539\n",
            "\n",
            "Epoch: 381 Training MAE: 0.2681954801082611 RMSE: 0.36645403504371643 R2: 0.5995041202214104\n",
            "Epoch: 381 Validation MAE: 0.302959144115448 RMSE: 0.4246675670146942 R2: 0.4746540945732203\n",
            "\n",
            "Epoch: 382 Training MAE: 0.2682550847530365 RMSE: 0.36695435643196106 R2: 0.5984097988660785\n",
            "Epoch: 382 Validation MAE: 0.3015543222427368 RMSE: 0.4231356084346771 R2: 0.47843762578473703\n",
            "\n",
            "Epoch: 383 Training MAE: 0.2681072950363159 RMSE: 0.36632606387138367 R2: 0.5997838615996394\n",
            "Epoch: 383 Validation MAE: 0.3027760684490204 RMSE: 0.42477184534072876 R2: 0.4743961108678614\n",
            "\n",
            "Epoch: 384 Training MAE: 0.2681325078010559 RMSE: 0.36649903655052185 R2: 0.5994058062619536\n",
            "Epoch: 384 Validation MAE: 0.3019794523715973 RMSE: 0.4241220951080322 R2: 0.4760028771464614\n",
            "\n",
            "Epoch: 385 Training MAE: 0.2686278820037842 RMSE: 0.36734089255332947 R2: 0.597563311199621\n",
            "Epoch: 385 Validation MAE: 0.3015749454498291 RMSE: 0.4235987663269043 R2: 0.4772952587455407\n",
            "\n",
            "Epoch: 386 Training MAE: 0.26848676800727844 RMSE: 0.3665415346622467 R2: 0.5993128512521977\n",
            "Epoch: 386 Validation MAE: 0.30410629510879517 RMSE: 0.42588841915130615 R2: 0.4716292132888763\n",
            "\n",
            "Epoch: 387 Training MAE: 0.26789942383766174 RMSE: 0.3664536774158478 R2: 0.5995049346591288\n",
            "Epoch: 387 Validation MAE: 0.3018322288990021 RMSE: 0.42378970980644226 R2: 0.47682390891255555\n",
            "\n",
            "Epoch: 388 Training MAE: 0.2684681713581085 RMSE: 0.3666072189807892 R2: 0.5991692571383396\n",
            "Epoch: 388 Validation MAE: 0.3036065101623535 RMSE: 0.42556145787239075 R2: 0.4724402323341754\n",
            "\n",
            "Epoch: 389 Training MAE: 0.2680947780609131 RMSE: 0.3666028678417206 R2: 0.5991787460078157\n",
            "Epoch: 389 Validation MAE: 0.30159497261047363 RMSE: 0.4238142669200897 R2: 0.47676326175351846\n",
            "\n",
            "Epoch: 390 Training MAE: 0.2679511308670044 RMSE: 0.3662865459918976 R2: 0.5998701364023723\n",
            "Epoch: 390 Validation MAE: 0.30216777324676514 RMSE: 0.424299418926239 R2: 0.4755645703732433\n",
            "\n",
            "Epoch: 391 Training MAE: 0.26806527376174927 RMSE: 0.3662438690662384 R2: 0.5999634307867974\n",
            "Epoch: 391 Validation MAE: 0.3036515712738037 RMSE: 0.42518332600593567 R2: 0.4733773643731548\n",
            "\n",
            "Epoch: 392 Training MAE: 0.26818472146987915 RMSE: 0.3667358160018921 R2: 0.5988879576060702\n",
            "Epoch: 392 Validation MAE: 0.3021605312824249 RMSE: 0.42401957511901855 R2: 0.4762561959843632\n",
            "\n",
            "Epoch: 393 Training MAE: 0.26810747385025024 RMSE: 0.3662029504776001 R2: 0.6000527454039922\n",
            "Epoch: 393 Validation MAE: 0.3033890128135681 RMSE: 0.4250320494174957 R2: 0.47375205132059206\n",
            "\n",
            "Epoch: 394 Training MAE: 0.2684171199798584 RMSE: 0.3664218485355377 R2: 0.5995745346497093\n",
            "Epoch: 394 Validation MAE: 0.3039703667163849 RMSE: 0.4258088767528534 R2: 0.4718266770477496\n",
            "\n",
            "Epoch: 395 Training MAE: 0.26793190836906433 RMSE: 0.36627981066703796 R2: 0.5998848782082249\n",
            "Epoch: 395 Validation MAE: 0.3023764193058014 RMSE: 0.4244147539138794 R2: 0.47527949336419817\n",
            "\n",
            "Epoch: 396 Training MAE: 0.26787805557250977 RMSE: 0.3662477135658264 R2: 0.5999549762992479\n",
            "Epoch: 396 Validation MAE: 0.3013841509819031 RMSE: 0.42332005500793457 R2: 0.47798284637852895\n",
            "\n",
            "Epoch: 397 Training MAE: 0.2677631080150604 RMSE: 0.3660542368888855 R2: 0.6003774730735765\n",
            "Epoch: 397 Validation MAE: 0.3026141822338104 RMSE: 0.4244619905948639 R2: 0.47516267318533234\n",
            "\n",
            "Epoch: 398 Training MAE: 0.2680169939994812 RMSE: 0.3663969039916992 R2: 0.5996290252870975\n",
            "Epoch: 398 Validation MAE: 0.3015851676464081 RMSE: 0.4235442578792572 R2: 0.47742969671735414\n",
            "\n",
            "Epoch: 399 Training MAE: 0.26781901717185974 RMSE: 0.36598721146583557 R2: 0.600523861801888\n",
            "Epoch: 399 Validation MAE: 0.3022376000881195 RMSE: 0.4243090748786926 R2: 0.47554074838034543\n",
            "\n",
            "Epoch: 400 Training MAE: 0.2678520679473877 RMSE: 0.3661193549633026 R2: 0.6002353905213407\n",
            "Epoch: 400 Validation MAE: 0.3025541603565216 RMSE: 0.4245648682117462 R2: 0.4749082164308267\n",
            "\n",
            "Epoch: 401 Training MAE: 0.26761510968208313 RMSE: 0.36601322889328003 R2: 0.6004670491848325\n",
            "Epoch: 401 Validation MAE: 0.30234622955322266 RMSE: 0.4243161082267761 R2: 0.47552341230112305\n",
            "\n",
            "Epoch: 402 Training MAE: 0.26761594414711 RMSE: 0.36606565117836 R2: 0.600352617580695\n",
            "Epoch: 402 Validation MAE: 0.302188903093338 RMSE: 0.424188494682312 R2: 0.47583885466289666\n",
            "\n",
            "Epoch: 403 Training MAE: 0.26796379685401917 RMSE: 0.3664368689060211 R2: 0.5995416856055855\n",
            "Epoch: 403 Validation MAE: 0.3009956181049347 RMSE: 0.4228873550891876 R2: 0.47904946900042567\n",
            "\n",
            "Epoch: 404 Training MAE: 0.26837217807769775 RMSE: 0.36677879095077515 R2: 0.5987939843321561\n",
            "Epoch: 404 Validation MAE: 0.3017691373825073 RMSE: 0.4235377013683319 R2: 0.47744595379820653\n",
            "\n",
            "Epoch: 405 Training MAE: 0.2675988972187042 RMSE: 0.3658248782157898 R2: 0.6008781193826565\n",
            "Epoch: 405 Validation MAE: 0.30242323875427246 RMSE: 0.42481324076652527 R2: 0.4742936851100722\n",
            "\n",
            "Epoch: 406 Training MAE: 0.2677740752696991 RMSE: 0.366266667842865 R2: 0.5999136220904219\n",
            "Epoch: 406 Validation MAE: 0.3019390404224396 RMSE: 0.4241526126861572 R2: 0.47592751747706563\n",
            "\n",
            "Epoch: 407 Training MAE: 0.26763537526130676 RMSE: 0.3659277856349945 R2: 0.600653585688768\n",
            "Epoch: 407 Validation MAE: 0.3030409514904022 RMSE: 0.42551708221435547 R2: 0.47255028356484985\n",
            "\n",
            "Epoch: 408 Training MAE: 0.2677135765552521 RMSE: 0.36579519510269165 R2: 0.6009429487802134\n",
            "Epoch: 408 Validation MAE: 0.3028235137462616 RMSE: 0.4251648485660553 R2: 0.4734231198686317\n",
            "\n",
            "Epoch: 409 Training MAE: 0.2675526738166809 RMSE: 0.36573338508605957 R2: 0.6010777602122508\n",
            "Epoch: 409 Validation MAE: 0.30244824290275574 RMSE: 0.42447492480278015 R2: 0.4751306700643477\n",
            "\n",
            "Epoch: 410 Training MAE: 0.26752427220344543 RMSE: 0.3658597767353058 R2: 0.6008020451990864\n",
            "Epoch: 410 Validation MAE: 0.30185648798942566 RMSE: 0.4237883687019348 R2: 0.4768272203281867\n",
            "\n",
            "Epoch: 411 Training MAE: 0.26845863461494446 RMSE: 0.36623191833496094 R2: 0.5999894606403985\n",
            "Epoch: 411 Validation MAE: 0.30370140075683594 RMSE: 0.42612895369529724 R2: 0.4710322014469165\n",
            "\n",
            "Epoch: 412 Training MAE: 0.26777124404907227 RMSE: 0.3657865822315216 R2: 0.6009617317391962\n",
            "Epoch: 412 Validation MAE: 0.302673876285553 RMSE: 0.42557209730148315 R2: 0.47241392151137884\n",
            "\n",
            "Epoch: 413 Training MAE: 0.2675246596336365 RMSE: 0.36561429500579834 R2: 0.6013375578347357\n",
            "Epoch: 413 Validation MAE: 0.30300164222717285 RMSE: 0.42525312304496765 R2: 0.4732044385252172\n",
            "\n",
            "Epoch: 414 Training MAE: 0.2675299346446991 RMSE: 0.3657526969909668 R2: 0.6010356892351421\n",
            "Epoch: 414 Validation MAE: 0.3022572696208954 RMSE: 0.42458638548851013 R2: 0.4748549505413896\n",
            "\n",
            "Epoch: 415 Training MAE: 0.26767295598983765 RMSE: 0.3660207688808441 R2: 0.6004506216234574\n",
            "Epoch: 415 Validation MAE: 0.30134686827659607 RMSE: 0.4235486686229706 R2: 0.4774188625329353\n",
            "\n",
            "Epoch: 416 Training MAE: 0.2674884796142578 RMSE: 0.36553964018821716 R2: 0.6015003382725852\n",
            "Epoch: 416 Validation MAE: 0.30316147208213806 RMSE: 0.4251621961593628 R2: 0.47342967121765644\n",
            "\n",
            "Epoch: 417 Training MAE: 0.2672501802444458 RMSE: 0.36565572023391724 R2: 0.6012472333431798\n",
            "Epoch: 417 Validation MAE: 0.30201277136802673 RMSE: 0.4241954982280731 R2: 0.4758214216994432\n",
            "\n",
            "Epoch: 418 Training MAE: 0.2672746479511261 RMSE: 0.36561697721481323 R2: 0.6013316861880355\n",
            "Epoch: 418 Validation MAE: 0.30227404832839966 RMSE: 0.4243406653404236 R2: 0.47546269041942546\n",
            "\n",
            "Epoch: 419 Training MAE: 0.2675980031490326 RMSE: 0.36556071043014526 R2: 0.6014544235967428\n",
            "Epoch: 419 Validation MAE: 0.3036399781703949 RMSE: 0.42603787779808044 R2: 0.47125835671483973\n",
            "\n",
            "Epoch: 420 Training MAE: 0.26730671525001526 RMSE: 0.3654468059539795 R2: 0.6017027237040347\n",
            "Epoch: 420 Validation MAE: 0.30272170901298523 RMSE: 0.425208181142807 R2: 0.47331574486162464\n",
            "\n",
            "Epoch: 421 Training MAE: 0.26810628175735474 RMSE: 0.3658800721168518 R2: 0.6007577342340404\n",
            "Epoch: 421 Validation MAE: 0.3044760525226593 RMSE: 0.4265729486942291 R2: 0.469929374877202\n",
            "\n",
            "Epoch: 422 Training MAE: 0.2674050033092499 RMSE: 0.36547765135765076 R2: 0.6016355037515435\n",
            "Epoch: 422 Validation MAE: 0.3038085699081421 RMSE: 0.4258541464805603 R2: 0.47171426926581606\n",
            "\n",
            "Epoch: 423 Training MAE: 0.2675519287586212 RMSE: 0.36555996537208557 R2: 0.6014560017997355\n",
            "Epoch: 423 Validation MAE: 0.303672730922699 RMSE: 0.42555904388427734 R2: 0.4724461826782981\n",
            "\n",
            "Epoch: 424 Training MAE: 0.2672550082206726 RMSE: 0.3655281960964203 R2: 0.6015253123311621\n",
            "Epoch: 424 Validation MAE: 0.30236151814460754 RMSE: 0.4246581196784973 R2: 0.4746775772915087\n",
            "\n",
            "Epoch: 425 Training MAE: 0.2675131559371948 RMSE: 0.3655921518802643 R2: 0.6013858137591885\n",
            "Epoch: 425 Validation MAE: 0.3038380742073059 RMSE: 0.42578914761543274 R2: 0.4718755688317232\n",
            "\n",
            "Epoch: 426 Training MAE: 0.26708415150642395 RMSE: 0.36533477902412415 R2: 0.6019468551439278\n",
            "Epoch: 426 Validation MAE: 0.30207324028015137 RMSE: 0.4246571660041809 R2: 0.47467987642549414\n",
            "\n",
            "Epoch: 427 Training MAE: 0.2673596739768982 RMSE: 0.36567723751068115 R2: 0.6012003019721612\n",
            "Epoch: 427 Validation MAE: 0.30138903856277466 RMSE: 0.4237234890460968 R2: 0.4769873546149428\n",
            "\n",
            "Epoch: 428 Training MAE: 0.26724565029144287 RMSE: 0.36526647210121155 R2: 0.6020956802440787\n",
            "Epoch: 428 Validation MAE: 0.3029158115386963 RMSE: 0.4252455234527588 R2: 0.4732232447167086\n",
            "\n",
            "Epoch: 429 Training MAE: 0.2673897445201874 RMSE: 0.36544936895370483 R2: 0.6016971080178728\n",
            "Epoch: 429 Validation MAE: 0.30150267481803894 RMSE: 0.4238842725753784 R2: 0.4765903097027816\n",
            "\n",
            "Epoch: 430 Training MAE: 0.26864975690841675 RMSE: 0.3662623465061188 R2: 0.5999230401683651\n",
            "Epoch: 430 Validation MAE: 0.30477964878082275 RMSE: 0.42724916338920593 R2: 0.46824747828070656\n",
            "\n",
            "Epoch: 431 Training MAE: 0.26742205023765564 RMSE: 0.3653043806552887 R2: 0.6020131130835512\n",
            "Epoch: 431 Validation MAE: 0.30289602279663086 RMSE: 0.4255613684654236 R2: 0.4724404397792179\n",
            "\n",
            "Epoch: 432 Training MAE: 0.26711907982826233 RMSE: 0.3652448058128357 R2: 0.602142837582687\n",
            "Epoch: 432 Validation MAE: 0.3028964400291443 RMSE: 0.4252074360847473 R2: 0.47331761000578143\n",
            "\n",
            "Epoch: 433 Training MAE: 0.2690216600894928 RMSE: 0.36660975217819214 R2: 0.5991637182206547\n",
            "Epoch: 433 Validation MAE: 0.30604228377342224 RMSE: 0.4285889267921448 R2: 0.46490733939370543\n",
            "\n",
            "Epoch: 434 Training MAE: 0.2673596441745758 RMSE: 0.36531132459640503 R2: 0.601997992977543\n",
            "Epoch: 434 Validation MAE: 0.3038843274116516 RMSE: 0.4260777235031128 R2: 0.47115943729586696\n",
            "\n",
            "Epoch: 435 Training MAE: 0.26709240674972534 RMSE: 0.3651959002017975 R2: 0.6022494268468456\n",
            "Epoch: 435 Validation MAE: 0.30389389395713806 RMSE: 0.4261733591556549 R2: 0.4709219795595355\n",
            "\n",
            "Epoch: 436 Training MAE: 0.2674087584018707 RMSE: 0.36547037959098816 R2: 0.601651313396429\n",
            "Epoch: 436 Validation MAE: 0.3040674328804016 RMSE: 0.42669644951820374 R2: 0.4696224171730542\n",
            "\n",
            "Epoch: 437 Training MAE: 0.2670958638191223 RMSE: 0.36518803238868713 R2: 0.6022666164533956\n",
            "Epoch: 437 Validation MAE: 0.30340009927749634 RMSE: 0.4254900813102722 R2: 0.472617128546058\n",
            "\n",
            "Epoch: 438 Training MAE: 0.26698246598243713 RMSE: 0.3651253581047058 R2: 0.6024031337208088\n",
            "Epoch: 438 Validation MAE: 0.30224961042404175 RMSE: 0.4246037006378174 R2: 0.4748121753304123\n",
            "\n",
            "Epoch: 439 Training MAE: 0.26699715852737427 RMSE: 0.36498531699180603 R2: 0.6027079988636128\n",
            "Epoch: 439 Validation MAE: 0.3031645715236664 RMSE: 0.4254290461540222 R2: 0.47276850532072456\n",
            "\n",
            "Epoch: 440 Training MAE: 0.2669873535633087 RMSE: 0.365065336227417 R2: 0.6025337441331369\n",
            "Epoch: 440 Validation MAE: 0.30237138271331787 RMSE: 0.4248321056365967 R2: 0.47424698880037175\n",
            "\n",
            "Epoch: 441 Training MAE: 0.266830712556839 RMSE: 0.3651980757713318 R2: 0.6022447136029061\n",
            "Epoch: 441 Validation MAE: 0.30274349451065063 RMSE: 0.4250504672527313 R2: 0.4737064611349424\n",
            "\n",
            "Epoch: 442 Training MAE: 0.2668537497520447 RMSE: 0.36496034264564514 R2: 0.6027624363811168\n",
            "Epoch: 442 Validation MAE: 0.3028257191181183 RMSE: 0.4248403310775757 R2: 0.4742266425875309\n",
            "\n",
            "Epoch: 443 Training MAE: 0.26746538281440735 RMSE: 0.3659174144268036 R2: 0.6006762406458366\n",
            "Epoch: 443 Validation MAE: 0.30228981375694275 RMSE: 0.4245617985725403 R2: 0.4749158883006025\n",
            "\n",
            "Epoch: 444 Training MAE: 0.26693621277809143 RMSE: 0.36516502499580383 R2: 0.6023166849526731\n",
            "Epoch: 444 Validation MAE: 0.3022060990333557 RMSE: 0.4246254861354828 R2: 0.47475830107758665\n",
            "\n",
            "Epoch: 445 Training MAE: 0.2668161690235138 RMSE: 0.3650560677051544 R2: 0.6025539351462732\n",
            "Epoch: 445 Validation MAE: 0.30251049995422363 RMSE: 0.42505913972854614 R2: 0.4736849460128971\n",
            "\n",
            "Epoch: 446 Training MAE: 0.2671848237514496 RMSE: 0.36571359634399414 R2: 0.6011209822135737\n",
            "Epoch: 446 Validation MAE: 0.30163365602493286 RMSE: 0.42402246594429016 R2: 0.4762490359250563\n",
            "\n",
            "Epoch: 447 Training MAE: 0.26675552129745483 RMSE: 0.3651549816131592 R2: 0.6023385965310128\n",
            "Epoch: 447 Validation MAE: 0.3019936978816986 RMSE: 0.42459824681282043 R2: 0.4748256713505502\n",
            "\n",
            "Epoch: 448 Training MAE: 0.26736873388290405 RMSE: 0.36556190252304077 R2: 0.6014517851050523\n",
            "Epoch: 448 Validation MAE: 0.30276691913604736 RMSE: 0.4252428710460663 R2: 0.4732297824875187\n",
            "\n",
            "Epoch: 449 Training MAE: 0.26676276326179504 RMSE: 0.3648226261138916 R2: 0.6030620972254245\n",
            "Epoch: 449 Validation MAE: 0.30284175276756287 RMSE: 0.42525339126586914 R2: 0.47320376482596693\n",
            "\n",
            "Epoch: 450 Training MAE: 0.27135878801345825 RMSE: 0.36861902475357056 R2: 0.5947579387696975\n",
            "Epoch: 450 Validation MAE: 0.3087039589881897 RMSE: 0.43148893117904663 R2: 0.4576415466815421\n",
            "\n",
            "Epoch: 451 Training MAE: 0.26660943031311035 RMSE: 0.36486974358558655 R2: 0.6029595888561037\n",
            "Epoch: 451 Validation MAE: 0.30242007970809937 RMSE: 0.42453527450561523 R2: 0.4749814539161279\n",
            "\n",
            "Epoch: 452 Training MAE: 0.2666412889957428 RMSE: 0.3647027909755707 R2: 0.6033228386773923\n",
            "Epoch: 452 Validation MAE: 0.3031141459941864 RMSE: 0.4252742528915405 R2: 0.4731520792387227\n",
            "\n",
            "Epoch: 453 Training MAE: 0.26670968532562256 RMSE: 0.36474910378456116 R2: 0.6032220524521847\n",
            "Epoch: 453 Validation MAE: 0.30321258306503296 RMSE: 0.4255252778530121 R2: 0.4725299264184405\n",
            "\n",
            "Epoch: 454 Training MAE: 0.2665300667285919 RMSE: 0.36468130350112915 R2: 0.603369548935211\n",
            "Epoch: 454 Validation MAE: 0.3028634190559387 RMSE: 0.4249892830848694 R2: 0.47385789305441317\n",
            "\n",
            "Epoch: 455 Training MAE: 0.266817569732666 RMSE: 0.36480391025543213 R2: 0.6031028119000954\n",
            "Epoch: 455 Validation MAE: 0.3040555715560913 RMSE: 0.42553845047950745 R2: 0.47249732780753007\n",
            "\n",
            "Epoch: 456 Training MAE: 0.26706668734550476 RMSE: 0.3653774559497833 R2: 0.6018538349570213\n",
            "Epoch: 456 Validation MAE: 0.30178433656692505 RMSE: 0.4235898554325104 R2: 0.4773171775585463\n",
            "\n",
            "Epoch: 457 Training MAE: 0.26673266291618347 RMSE: 0.36461371183395386 R2: 0.603516581728726\n",
            "Epoch: 457 Validation MAE: 0.3027227818965912 RMSE: 0.4245262145996094 R2: 0.4750038836258861\n",
            "\n",
            "Epoch: 458 Training MAE: 0.2670784592628479 RMSE: 0.364787757396698 R2: 0.6031379783761848\n",
            "Epoch: 458 Validation MAE: 0.30363544821739197 RMSE: 0.4261665344238281 R2: 0.4709389364624076\n",
            "\n",
            "Epoch: 459 Training MAE: 0.2667321562767029 RMSE: 0.3647635281085968 R2: 0.6031906779762495\n",
            "Epoch: 459 Validation MAE: 0.3022926449775696 RMSE: 0.4244318902492523 R2: 0.4752371698859025\n",
            "\n",
            "Epoch: 460 Training MAE: 0.2673240900039673 RMSE: 0.3649947941303253 R2: 0.6026873551317979\n",
            "Epoch: 460 Validation MAE: 0.3046337068080902 RMSE: 0.4272308647632599 R2: 0.46829308120711366\n",
            "\n",
            "Epoch: 461 Training MAE: 0.2668190896511078 RMSE: 0.3645746409893036 R2: 0.6036015496524597\n",
            "Epoch: 461 Validation MAE: 0.3034924566745758 RMSE: 0.42593204975128174 R2: 0.47152100959464627\n",
            "\n",
            "Epoch: 462 Training MAE: 0.2708146870136261 RMSE: 0.36804330348968506 R2: 0.5960227558382071\n",
            "Epoch: 462 Validation MAE: 0.30802565813064575 RMSE: 0.4308953285217285 R2: 0.4591327780558502\n",
            "\n",
            "Epoch: 463 Training MAE: 0.2667866051197052 RMSE: 0.3646356165409088 R2: 0.6034689306240979\n",
            "Epoch: 463 Validation MAE: 0.3036140501499176 RMSE: 0.42558807134628296 R2: 0.4723742957117554\n",
            "\n",
            "Epoch: 464 Training MAE: 0.2665262222290039 RMSE: 0.36456403136253357 R2: 0.6036246024770098\n",
            "Epoch: 464 Validation MAE: 0.302295982837677 RMSE: 0.42433688044548035 R2: 0.47547201497814284\n",
            "\n",
            "Epoch: 465 Training MAE: 0.26769426465034485 RMSE: 0.3652831017971039 R2: 0.6020594368531997\n",
            "Epoch: 465 Validation MAE: 0.3048877418041229 RMSE: 0.42718127369880676 R2: 0.46841642849216425\n",
            "\n",
            "Epoch: 466 Training MAE: 0.2675708532333374 RMSE: 0.36518043279647827 R2: 0.6022831017273494\n",
            "Epoch: 466 Validation MAE: 0.30541735887527466 RMSE: 0.42798757553100586 R2: 0.46640782092496236\n",
            "\n",
            "Epoch: 467 Training MAE: 0.26668766140937805 RMSE: 0.3648565411567688 R2: 0.6029882968504527\n",
            "Epoch: 467 Validation MAE: 0.3023621141910553 RMSE: 0.42483633756637573 R2: 0.47423653480148475\n",
            "\n",
            "Epoch: 468 Training MAE: 0.26652655005455017 RMSE: 0.36478859186172485 R2: 0.6031361824136549\n",
            "Epoch: 468 Validation MAE: 0.3019229471683502 RMSE: 0.42408692836761475 R2: 0.47608974427654305\n",
            "\n",
            "Epoch: 469 Training MAE: 0.2669118642807007 RMSE: 0.36463692784309387 R2: 0.6034661324485937\n",
            "Epoch: 469 Validation MAE: 0.3038003444671631 RMSE: 0.4260849952697754 R2: 0.4711413367835027\n",
            "\n",
            "Epoch: 470 Training MAE: 0.2669980525970459 RMSE: 0.3646668791770935 R2: 0.6034009649055705\n",
            "Epoch: 470 Validation MAE: 0.30499082803726196 RMSE: 0.4269678294658661 R2: 0.4689475547185784\n",
            "\n",
            "Epoch: 471 Training MAE: 0.2665432095527649 RMSE: 0.3644011914730072 R2: 0.6039786554468074\n",
            "Epoch: 471 Validation MAE: 0.3025245666503906 RMSE: 0.42497244477272034 R2: 0.4738996177796124\n",
            "\n",
            "Epoch: 472 Training MAE: 0.2664816081523895 RMSE: 0.36443352699279785 R2: 0.6039083603163069\n",
            "Epoch: 472 Validation MAE: 0.3026648759841919 RMSE: 0.4251919388771057 R2: 0.4733559425631354\n",
            "\n",
            "Epoch: 473 Training MAE: 0.266679972410202 RMSE: 0.36489105224609375 R2: 0.6029132039695186\n",
            "Epoch: 473 Validation MAE: 0.3022211194038391 RMSE: 0.4245091676712036 R2: 0.4750459381326687\n",
            "\n",
            "Epoch: 474 Training MAE: 0.2662954330444336 RMSE: 0.36439546942710876 R2: 0.6039910822793862\n",
            "Epoch: 474 Validation MAE: 0.30257296562194824 RMSE: 0.42528775334358215 R2: 0.4731186524549612\n",
            "\n",
            "Epoch: 475 Training MAE: 0.266206830739975 RMSE: 0.3642038106918335 R2: 0.6044075815584891\n",
            "Epoch: 475 Validation MAE: 0.30356720089912415 RMSE: 0.4261946976184845 R2: 0.4708690199998353\n",
            "\n",
            "Epoch: 476 Training MAE: 0.26638367772102356 RMSE: 0.36423254013061523 R2: 0.6043451682879826\n",
            "Epoch: 476 Validation MAE: 0.303805410861969 RMSE: 0.42628636956214905 R2: 0.47064136835125436\n",
            "\n",
            "Epoch: 477 Training MAE: 0.26639050245285034 RMSE: 0.36457425355911255 R2: 0.603602392297645\n",
            "Epoch: 477 Validation MAE: 0.30255141854286194 RMSE: 0.4249270260334015 R2: 0.4740119985697321\n",
            "\n",
            "Epoch: 478 Training MAE: 0.26640817523002625 RMSE: 0.3641855716705322 R2: 0.6044471779763294\n",
            "Epoch: 478 Validation MAE: 0.30390670895576477 RMSE: 0.42641764879226685 R2: 0.4703152954204953\n",
            "\n",
            "Epoch: 479 Training MAE: 0.2665432095527649 RMSE: 0.3642781674861908 R2: 0.604245986125852\n",
            "Epoch: 479 Validation MAE: 0.3036044239997864 RMSE: 0.42671945691108704 R2: 0.46956522108363963\n",
            "\n",
            "Epoch: 480 Training MAE: 0.2668217420578003 RMSE: 0.36499714851379395 R2: 0.6026822513016996\n",
            "Epoch: 480 Validation MAE: 0.30239182710647583 RMSE: 0.42507174611091614 R2: 0.4736537216241339\n",
            "\n",
            "Epoch: 481 Training MAE: 0.26631006598472595 RMSE: 0.3641262948513031 R2: 0.604575944328863\n",
            "Epoch: 481 Validation MAE: 0.30349698662757874 RMSE: 0.426052063703537 R2: 0.47122315710643947\n",
            "\n",
            "Epoch: 482 Training MAE: 0.26813703775405884 RMSE: 0.36552274227142334 R2: 0.6015371783409001\n",
            "Epoch: 482 Validation MAE: 0.3060078024864197 RMSE: 0.428705632686615 R2: 0.4646158981250178\n",
            "\n",
            "Epoch: 483 Training MAE: 0.2664094567298889 RMSE: 0.3644847571849823 R2: 0.6037970100717689\n",
            "Epoch: 483 Validation MAE: 0.3023095428943634 RMSE: 0.4246331453323364 R2: 0.47473933547325287\n",
            "\n",
            "Epoch: 484 Training MAE: 0.2662167549133301 RMSE: 0.36425507068634033 R2: 0.6042962179681945\n",
            "Epoch: 484 Validation MAE: 0.3023294508457184 RMSE: 0.4246005713939667 R2: 0.47481993457685256\n",
            "\n",
            "Epoch: 485 Training MAE: 0.26667821407318115 RMSE: 0.3643551170825958 R2: 0.6040787641326593\n",
            "Epoch: 485 Validation MAE: 0.3052619695663452 RMSE: 0.42761674523353577 R2: 0.4673321140967207\n",
            "\n",
            "Epoch: 486 Training MAE: 0.2661716938018799 RMSE: 0.3641694486141205 R2: 0.604482213414627\n",
            "Epoch: 486 Validation MAE: 0.30269506573677063 RMSE: 0.42497143149375916 R2: 0.4739020811852386\n",
            "\n",
            "Epoch: 487 Training MAE: 0.2664535343647003 RMSE: 0.364236444234848 R2: 0.6043366948493045\n",
            "Epoch: 487 Validation MAE: 0.3048054575920105 RMSE: 0.42775464057922363 R2: 0.4669885395698826\n",
            "\n",
            "Epoch: 488 Training MAE: 0.26706773042678833 RMSE: 0.3646209239959717 R2: 0.6035009199208561\n",
            "Epoch: 488 Validation MAE: 0.30424410104751587 RMSE: 0.4273076057434082 R2: 0.4681020429862264\n",
            "\n",
            "Epoch: 489 Training MAE: 0.26611971855163574 RMSE: 0.36396512389183044 R2: 0.6049259222344853\n",
            "Epoch: 489 Validation MAE: 0.3039250373840332 RMSE: 0.4263567328453064 R2: 0.4704665662885612\n",
            "\n",
            "Epoch: 490 Training MAE: 0.26780539751052856 RMSE: 0.3652501404285431 R2: 0.6021312583535563\n",
            "Epoch: 490 Validation MAE: 0.30607104301452637 RMSE: 0.42866742610931396 R2: 0.4647113234254837\n",
            "\n",
            "Epoch: 491 Training MAE: 0.26600292325019836 RMSE: 0.36408674716949463 R2: 0.6046618625590566\n",
            "Epoch: 491 Validation MAE: 0.30242568254470825 RMSE: 0.4247519075870514 R2: 0.4744454514277816\n",
            "\n",
            "Epoch: 492 Training MAE: 0.2663428485393524 RMSE: 0.36408883333206177 R2: 0.6046573260886448\n",
            "Epoch: 492 Validation MAE: 0.3046397566795349 RMSE: 0.4273169934749603 R2: 0.4680786296659015\n",
            "\n",
            "Epoch: 493 Training MAE: 0.2672782838344574 RMSE: 0.3656303882598877 R2: 0.6013024487496375\n",
            "Epoch: 493 Validation MAE: 0.302989661693573 RMSE: 0.4256485104560852 R2: 0.47222436453721717\n",
            "\n",
            "Epoch: 494 Training MAE: 0.26587849855422974 RMSE: 0.3639542758464813 R2: 0.6049494181502635\n",
            "Epoch: 494 Validation MAE: 0.30278587341308594 RMSE: 0.4257141053676605 R2: 0.47206172114362277\n",
            "\n",
            "Epoch: 495 Training MAE: 0.2667678892612457 RMSE: 0.3644549548625946 R2: 0.6038617593971475\n",
            "Epoch: 495 Validation MAE: 0.3054888844490051 RMSE: 0.42850592732429504 R2: 0.4651145595165641\n",
            "\n",
            "Epoch: 496 Training MAE: 0.26616358757019043 RMSE: 0.36443614959716797 R2: 0.6039026919261538\n",
            "Epoch: 496 Validation MAE: 0.3022201657295227 RMSE: 0.42501378059387207 R2: 0.47379730859186253\n",
            "\n",
            "Epoch: 497 Training MAE: 0.26579993963241577 RMSE: 0.36389607191085815 R2: 0.605075765126428\n",
            "Epoch: 497 Validation MAE: 0.3027830719947815 RMSE: 0.4258444011211395 R2: 0.47173843795439874\n",
            "\n",
            "Epoch: 498 Training MAE: 0.2662762701511383 RMSE: 0.36408254504203796 R2: 0.6046709970734367\n",
            "Epoch: 498 Validation MAE: 0.30431121587753296 RMSE: 0.42725056409835815 R2: 0.46824397380572336\n",
            "\n",
            "Epoch: 499 Training MAE: 0.26598209142684937 RMSE: 0.36388397216796875 R2: 0.605102066983201\n",
            "Epoch: 499 Validation MAE: 0.30388256907463074 RMSE: 0.4267350733280182 R2: 0.46952640318559713\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "plt.plot(list(range(ann2.parameters['iterations'])), ann2.rmse_train)\n",
        "plt.plot((list(range(ann2.parameters['iterations']))), ann2.rmse_val)\n",
        "plt.plot((list(range(ann2.parameters['iterations']))), ann2.mae_train)\n",
        "plt.plot((list(range(ann2.parameters['iterations']))), ann2.mae_val)\n",
        "plt.plot((list(range(ann2.parameters['iterations']))), ann2.r2_train)\n",
        "plt.plot((list(range(ann2.parameters['iterations']))), ann2.r2_val)\n",
        "plt.legend(['Training RMSE', 'Validation RMSE', 'Training MAE', 'Validation MAE', 'Training R2 Score', 'Validation R2 Score'])\n",
        "plt.xlabel(\"Iterations\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "HGTfJQ5hfu7J",
        "outputId": "926c5ccf-7d2f-4e27-e1e7-360c581efa54"
      },
      "execution_count": 160,
      "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": [],
      "metadata": {
        "id": "6eH6Fowofu3V"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "IVoRXW4wfu1U"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "---\n",
        "\n"
      ],
      "metadata": {
        "id": "aTuVlDcUeCdu"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Random Forest Regressor, Label Encoding, No PCA, Isolation Forest"
      ],
      "metadata": {
        "id": "UOJ5r233-O8h"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = Model(model = RandomForestRegressor(), parameters = {\"encoding\": \"Label\", \"num_PCA_comp\": -1, \"outlier_detector\": IsolationForest()}, kf = kf)\n",
        "model.training_validation()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f1VbHjkovlfY",
        "outputId": "0e8295f8-9d47-4448-865c-005a4cab2c6c"
      },
      "execution_count": 165,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of Highly correlated features removes:  2\n",
            "Features Removed:  {'Total Population - Female', 'IPPU'}\n",
            "\n",
            "Number of outliers in the training set: 250\n",
            "Number of outliers in the training set: 251\n",
            "Number of outliers in the training set: 268\n",
            "Number of outliers in the training set: 235\n",
            "Number of outliers in the training set: 260\n",
            "Train mean absolute error:  0.10430410728385955\n",
            "Train root mean squared error:  0.14230615593062781\n",
            "Train r2-score: 0.9405134276021835\n",
            "\n",
            "Validation mean absolute error:  0.28350653835950923\n",
            "Validation root mean squared error:  0.38458564816040514\n",
            "Validation r2-score: 0.5600295725873755\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model = Model(model = RandomForestRegressor(), parameters = {\"encoding\": \"OneHot\", \"num_PCA_comp\": -1, \"outlier_detector\": IsolationForest()}, kf = kf)\n",
        "model.training_validation()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BRG1F5_dILNx",
        "outputId": "590595f1-2c11-4257-c6a7-d1fef8969b85"
      },
      "execution_count": 168,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of Highly correlated features removes:  2\n",
            "Features Removed:  {'Total Population - Female', 'IPPU'}\n",
            "\n",
            "Number of outliers in the training set: 19\n",
            "Number of outliers in the training set: 25\n",
            "Number of outliers in the training set: 2\n",
            "Number of outliers in the training set: 8\n",
            "Number of outliers in the training set: 4\n",
            "Train mean absolute error:  0.10285356628492755\n",
            "Train root mean squared error:  0.1409752797539307\n",
            "Train r2-score: 0.941105192433518\n",
            "\n",
            "Validation mean absolute error:  0.2777999951363599\n",
            "Validation root mean squared error:  0.37578737577329446\n",
            "Validation r2-score: 0.5801578654076945\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "XGB Regressor, Label Encoding, No PCA, Isolation Forest"
      ],
      "metadata": {
        "id": "yFGwi9zHySY1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = Model(model = XGBRegressor(), parameters = {\"encoding\": \"Label\", \"num_PCA_comp\": -1, \"outlier_detector\": IsolationForest()}, kf = kf)\n",
        "model.training_validation()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1dIMmJd4xYV-",
        "outputId": "ac97c36c-ea2c-4a76-81d8-cef01e8285c9"
      },
      "execution_count": 167,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of Highly correlated features removes:  2\n",
            "Features Removed:  {'Total Population - Female', 'IPPU'}\n",
            "\n",
            "Number of outliers in the training set: 240\n",
            "Number of outliers in the training set: 258\n",
            "Number of outliers in the training set: 212\n",
            "Number of outliers in the training set: 194\n",
            "Number of outliers in the training set: 208\n",
            "Train mean absolute error:  0.08834686956746673\n",
            "Train root mean squared error:  0.11745224343482734\n",
            "Train r2-score: 0.9594489616356432\n",
            "\n",
            "Validation mean absolute error:  0.28756775403460727\n",
            "Validation root mean squared error:  0.3884257351620856\n",
            "Validation r2-score: 0.5511715521628616\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model = Model(model = XGBRegressor(), parameters = {\"encoding\": \"OneHot\", \"num_PCA_comp\": -1, \"outlier_detector\": IsolationForest()}, kf = kf)\n",
        "model.training_validation()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aAAlc-gTJLp0",
        "outputId": "e7aa2f68-077c-4c07-da9e-3ca044ec5fdd"
      },
      "execution_count": 169,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of Highly correlated features removes:  2\n",
            "Features Removed:  {'Total Population - Female', 'IPPU'}\n",
            "\n",
            "Number of outliers in the training set: 21\n",
            "Number of outliers in the training set: 20\n",
            "Number of outliers in the training set: 8\n",
            "Number of outliers in the training set: 15\n",
            "Number of outliers in the training set: 21\n",
            "Train mean absolute error:  0.10726342847396372\n",
            "Train root mean squared error:  0.1422805398742716\n",
            "Train r2-score: 0.9400274999817902\n",
            "\n",
            "Validation mean absolute error:  0.27840617189844025\n",
            "Validation root mean squared error:  0.37712425093175234\n",
            "Validation r2-score: 0.5767057422437742\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Adding Additional Feature (Cluster ID) - Kmeans"
      ],
      "metadata": {
        "id": "hvwJjlmDd2DF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = Model(model = RandomForestRegressor(), parameters = {\"encoding\": \"Label\", \"num_PCA_comp\": -1, \"additional_feature\": True, \"outlier_detector\": None}, kf = kf)\n",
        "model.training_validation()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rkFFM-S1d897",
        "outputId": "68262236-42f1-489e-a62e-e719d64263d9"
      },
      "execution_count": 185,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of Highly correlated features removes:  2\n",
            "Features Removed:  {'Total Population - Female', 'IPPU'}\n",
            "\n",
            "Train mean absolute error:  0.1029289541742213\n",
            "Train root mean squared error:  0.14086403975727774\n",
            "Train r2-score: 0.9411050042506843\n",
            "\n",
            "Validation mean absolute error:  0.27724322856589906\n",
            "Validation root mean squared error:  0.37507966205463283\n",
            "Validation r2-score: 0.5817092668853507\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model = Model(model = XGBRegressor(), parameters = {\"encoding\": \"Label\", \"num_PCA_comp\": -1, \"additional_feature\": True, \"outlier_detector\": None}, kf = kf)\n",
        "model.training_validation()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a6OW6oiBhY8V",
        "outputId": "8b98c50f-23f4-4b2d-cb05-28f18d1e1608"
      },
      "execution_count": 186,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of Highly correlated features removes:  2\n",
            "Features Removed:  {'Total Population - Female', 'IPPU'}\n",
            "\n",
            "Train mean absolute error:  0.0884727107669114\n",
            "Train root mean squared error:  0.11699745907209773\n",
            "Train r2-score: 0.959330646723729\n",
            "\n",
            "Validation mean absolute error:  0.2776655601822541\n",
            "Validation root mean squared error:  0.3735054388924413\n",
            "Validation r2-score: 0.5851113323643502\n",
            "\n"
          ]
        }
      ]
    }
  ]
}