Neuralnet.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Missing values in each column:\n",
      "age                    0\n",
      "sex                    0\n",
      "chest pain type        0\n",
      "resting bp s           0\n",
      "cholesterol            0\n",
      "fasting blood sugar    0\n",
      "resting ecg            0\n",
      "max heart rate         0\n",
      "exercise angina        0\n",
      "oldpeak                0\n",
      "ST slope               0\n",
      "target                 0\n",
      "dtype: int64\n",
      "   age  sex  chest pain type  resting bp s  cholesterol  fasting blood sugar  \\\n",
      "0   40    1                2           140          289                    0   \n",
      "1   49    0                3           160          180                    0   \n",
      "2   37    1                2           130          283                    0   \n",
      "3   48    0                4           138          214                    0   \n",
      "4   54    1                3           150          195                    0   \n",
      "\n",
      "   resting ecg  max heart rate  exercise angina  oldpeak  ST slope  target  \n",
      "0            0             172                0      0.0         1       0  \n",
      "1            0             156                0      1.0         2       1  \n",
      "2            1              98                0      0.0         1       0  \n",
      "3            0             108                1      1.5         2       1  \n",
      "4            0             122                0      0.0         1       0  \n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x1500 with 17 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training set shape: (929, 16), Testing set shape: (233, 16)\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from scipy import stats\n",
    "\n",
    "# Load the dataset\n",
    "data = pd.read_csv('heart_statlog_cleveland_hungary_final.csv')\n",
    "\n",
    "# Checking for missing values\n",
    "print(\"Missing values in each column:\")\n",
    "print(data.isnull().sum())\n",
    "\n",
    "# Fill missing values with the mean of the column\n",
    "data.fillna(data.mean(), inplace=True)\n",
    "\n",
    "# Display the first few rows of the dataset to understand its structure\n",
    "print(data.head())\n",
    "\n",
    "# Assuming 'sex', 'chest pain type', 'fasting blood sugar', 'resting ecg', 'exercise angina', and 'ST slope' are categorical\n",
    "categorical_columns = ['sex', 'chest pain type', 'fasting blood sugar', 'resting ecg', 'exercise angina', 'ST slope']\n",
    "\n",
    "# Converting categorical columns to numerical format using one-hot encoding\n",
    "data = pd.get_dummies(data, columns=categorical_columns, drop_first=True)\n",
    "\n",
    "# Ensure all data is numeric\n",
    "data = data.apply(pd.to_numeric)\n",
    "\n",
    "# Detecting and handling outliers\n",
    "# Determine the number of rows and columns for the subplots\n",
    "num_columns = len(data.columns)\n",
    "num_rows = (num_columns + 3) // 4  # Adjust for 4 columns per row\n",
    "\n",
    "# Plotting boxplots to visualize outliers\n",
    "plt.figure(figsize=(15, num_rows * 3))\n",
    "for i, column in enumerate(data.columns, 1):\n",
    "    plt.subplot(num_rows, 4, i)\n",
    "    sns.boxplot(data[column])\n",
    "    plt.title(column)\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# Removing outliers using Z-score method\n",
    "z_scores = np.abs(stats.zscore(data.select_dtypes(include=[np.number])))\n",
    "data = data[(z_scores < 3).all(axis=1)]\n",
    "\n",
    "# Separating features and target variable\n",
    "X = data.drop('target', axis=1)\n",
    "y = data['target']\n",
    "\n",
    "# Scale the features\n",
    "scaler = StandardScaler()\n",
    "X_scaled = scaler.fit_transform(X)\n",
    "\n",
    "# Split the data into training and test sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_scaled, y, test_size=0.2, random_state=42)\n",
    "\n",
    "# Confirm the shapes of the train and test sets\n",
    "print(f\"Training set shape: {X_train.shape}, Testing set shape: {X_test.shape}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.87348402  0.88243861  0.02872154  1.03766384 -0.07846392  0.0064587\n",
      "  0.84022103  0.09591813  0.0842579   0.94243996  0.96999212  0.60259285\n",
      " -0.07846392  0.54183635  0.21580903  0.92896278  0.64792109  0.51134911\n",
      "  0.46240451 -0.00670556  1.07100101  0.33446742 -0.11574697  0.56131236\n",
      "  0.79189973  0.38941697  0.69684521  0.43369235  0.60592089  0.21629079\n",
      "  0.82713039 -0.06328751 -0.14072138  0.67286946  0.87953771  0.30529716\n",
      "  0.72598778  0.60855171  0.31636826  0.85826482  0.32452305  0.9154278\n",
      " -0.00691085  0.97016438  1.01822102  0.16015889  0.94852508  0.32971709\n",
      "  1.05096027  0.9207981   0.63338421  0.38811158  0.73379199  0.44514026\n",
      "  0.44096661  0.00570479  0.08061959  1.01493267 -0.10825013  0.63962776\n",
      "  0.96295302  0.50138788  0.12274643  0.57185696  1.02450171  0.07733502\n",
      "  0.94974129  0.85574951  0.4303509   0.25835239 -0.07037028  0.59793005\n",
      "  0.15992901  0.71421106  0.76508553  0.89165296  0.32452305  0.32650531\n",
      "  0.72885968  0.69482746  0.64451044  0.37539433  0.4244168   0.86330226\n",
      "  0.75783046  0.6793251   0.07492392  0.5679836   1.07054478  0.66013001\n",
      "  0.3748942   0.15634869  0.74274175  0.66363169  0.64321332  0.37767987\n",
      "  0.79431224  0.25461458  0.9451225   0.93448002  0.60295456  0.65719884\n",
      "  0.81460192  1.04494753  0.35615156  0.80858482  0.86238776  0.6852223\n",
      "  1.13176354  0.21140098  0.30483033  0.45178325  1.0088571   0.90224528\n",
      "  0.83745677  0.18081571  0.07921231  0.64494103  1.01789012  0.8647369\n",
      "  0.9116217   0.1049122   0.90436884  0.87001841 -0.08224872  0.19328135\n",
      "  0.61383127  0.75289057  0.32223744  0.5513352   0.80858482  0.13648194\n",
      "  0.38378197  0.56512214  0.95675239  0.87345797  0.25749288 -0.08101294\n",
      "  0.81681399  0.59911106  0.06964264  0.06248877  0.44096661  0.24498955\n",
      "  0.10380382  0.50138788  0.71109409  0.19532674  0.32650531  0.35893329\n",
      "  0.99135368  1.02579786  1.08718186  1.02371454  0.76928271  0.33749518\n",
      "  0.72624974 -0.03125478  0.91532952  0.14510071  0.54725794  0.55853065\n",
      "  0.80365149  0.45700883  1.0401913   1.01909403  0.07319025  0.65568562\n",
      "  0.05254778  0.25943236  1.10434608  0.21576689  0.74507145  0.99881537\n",
      "  0.80818447  0.21132961  0.44514026  0.32206225  0.10726485  0.93322223\n",
      "  0.16809859  0.64548484  0.09876867  1.04983876  0.28112006  0.97256134\n",
      "  0.55450335  0.74826529  0.93523647 -0.09440485  0.18876777  0.9735288\n",
      "  0.8121408   0.97184992  0.94649534  0.65222112  0.52700596  0.91616154\n",
      "  0.64548484  0.92896278  0.98053514  0.42899379  1.11874821  0.48582119\n",
      "  0.97036951  0.70945127  0.08298567  0.91270651  0.06919222  0.19240645\n",
      "  0.8135709   0.23148377  0.08892698  0.01354261 -0.1000928   0.51985296\n",
      "  0.96261343  0.47286627  0.54090395  0.0064587  -0.02990095  0.19459027\n",
      "  1.02371454  0.94277797 -0.07311872  0.22538773  0.72257374  0.58140283\n",
      "  0.07492392  0.35346775  0.82520263  0.4577586   0.78108652]\n",
      "Mean Squared Error for MLR-F: 0.1134516630335442\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "# Initialize the model\n",
    "mlr = LinearRegression()\n",
    "\n",
    "# Train the model\n",
    "mlr.fit(X_train, y_train)\n",
    "\n",
    "# Make predictions\n",
    "y_pred_mlr = mlr.predict(X_test)\n",
    "print(y_pred_mlr)\n",
    "\n",
    "# Evaluate the model\n",
    "mse_mlr = mean_squared_error(y_test, y_pred_mlr)\n",
    "print(f'Mean Squared Error for MLR-F: {mse_mlr}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 0, Train Loss: 0.25019035925059907, Validation Loss: 0.25010961163032513\n",
      "Early stopping at epoch 10\n",
      "Epoch 0, Train Loss: 0.25024705937871927, Validation Loss: 0.2500833872214125\n",
      "Early stopping at epoch 10\n",
      "Epoch 0, Train Loss: 0.25012463341300367, Validation Loss: 0.25063603147866553\n",
      "Early stopping at epoch 0\n",
      "Epoch 0, Train Loss: 0.250306844030966, Validation Loss: 0.25010645276949567\n",
      "Early stopping at epoch 0\n",
      "Validation Error: 0.2502441135284155\n",
      "[[0.495179   0.49517869 0.49517817 0.49517944 0.49517926 0.49517798\n",
      "  0.4951784  0.49517869 0.49517933 0.49518004 0.49517851 0.49517897\n",
      "  0.49517926 0.49517814 0.49517875 0.49517976 0.49517782 0.49517857\n",
      "  0.4951792  0.49517917 0.49517982 0.49517885 0.49517863 0.49517768\n",
      "  0.49517836 0.49517911 0.4951787  0.49518072 0.49517953 0.49517956\n",
      "  0.49517921 0.49517916 0.49518028 0.49517999 0.4951791  0.49517846\n",
      "  0.49517825 0.49518016 0.49518044 0.49517976 0.49517768 0.49517844\n",
      "  0.49517891 0.49517924 0.49517936 0.4951783  0.49517849 0.49518002\n",
      "  0.4951802  0.49517985 0.49517865 0.49517895 0.49517983 0.49518002\n",
      "  0.49517864 0.49517808 0.49517984 0.49517936 0.49517911 0.49517901\n",
      "  0.49517999 0.49517888 0.49517973 0.49518001 0.49517918 0.49517877\n",
      "  0.49517861 0.49517962 0.49517855 0.49517932 0.49517839 0.49518027\n",
      "  0.49517923 0.4951789  0.49518104 0.49518024 0.49517768 0.49517881\n",
      "  0.49517984 0.49517868 0.49517859 0.49518127 0.49518123 0.49518065\n",
      "  0.49517978 0.49517985 0.49518039 0.49518062 0.4951797  0.49517965\n",
      "  0.49517905 0.49517813 0.49517933 0.49517868 0.4951798  0.49517908\n",
      "  0.49517956 0.49518003 0.49518021 0.49517855 0.49517992 0.49517897\n",
      "  0.4951792  0.49518024 0.49517983 0.49517824 0.495179   0.49518061\n",
      "  0.49517827 0.49517856 0.49517892 0.49517905 0.49517967 0.49517922\n",
      "  0.49517945 0.49517671 0.49518018 0.49517923 0.49517814 0.49518028\n",
      "  0.49517842 0.49517995 0.49517922 0.49517998 0.49517922 0.49518065\n",
      "  0.4951799  0.49518037 0.49517867 0.49517921 0.49517824 0.49517846\n",
      "  0.49517973 0.49517919 0.4951789  0.49517744 0.49517874 0.49517803\n",
      "  0.49517987 0.49517755 0.49517954 0.49518005 0.49517864 0.49517967\n",
      "  0.49517962 0.49517888 0.49517991 0.49517933 0.49517881 0.49518027\n",
      "  0.49518058 0.49517885 0.49517819 0.49517897 0.49517928 0.49517797\n",
      "  0.49517918 0.49517829 0.49517876 0.49518014 0.49517946 0.49517953\n",
      "  0.4951805  0.4951801  0.49517927 0.49517894 0.4951796  0.4951799\n",
      "  0.49517985 0.4951793  0.49517901 0.49517823 0.49517853 0.49517989\n",
      "  0.49517857 0.49517901 0.49518002 0.49517876 0.49517889 0.49517965\n",
      "  0.49517998 0.49517907 0.49517924 0.49517823 0.49517995 0.49517891\n",
      "  0.49518038 0.49517906 0.49517914 0.49517955 0.49518071 0.49517856\n",
      "  0.49517948 0.4951785  0.4951801  0.49518008 0.49517972 0.49517874\n",
      "  0.49517907 0.49517976 0.49517877 0.4951798  0.49517817 0.49518012\n",
      "  0.49517862 0.4951784  0.49517947 0.49517871 0.49517972 0.49517929\n",
      "  0.49517872 0.49517815 0.49517925 0.49517804 0.49518045 0.49517826\n",
      "  0.49517966 0.4951806  0.49517983 0.49517798 0.49517906 0.49517902\n",
      "  0.49517897 0.49517824 0.49517881 0.49517783 0.49517762 0.4951794\n",
      "  0.49518039 0.49517963 0.49517767 0.49518067 0.49517788]]\n",
      "Mean Squared Error for BP: 0.2501266696045978\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "class NeuralNet:\n",
    "    def __init__(self, layers, epochs=1000, learning_rate=0.01, momentum=0.9, activation='sigmoid', validation_split=0.2, patience=10, batch_size=32):\n",
    "        \"\"\"\n",
    "        Initialize the neural network with the given parameters.\n",
    "        \n",
    "        Parameters:\n",
    "        - layers: List of integers, the number of neurons in each layer.\n",
    "        - epochs: Integer, the number of epochs for training.\n",
    "        - learning_rate: Float, the learning rate for gradient descent.\n",
    "        - momentum: Float, the momentum factor for gradient descent.\n",
    "        - activation: String, the activation function to use ('sigmoid', 'relu', 'linear', 'tanh').\n",
    "        - validation_split: Float, the proportion of data to use for validation.\n",
    "        - patience: Integer, the number of epochs to wait for improvement before early stopping.\n",
    "        - batch_size: Integer, the number of samples per batch.\n",
    "        \"\"\"\n",
    "        self.L = len(layers)\n",
    "        self.n = layers\n",
    "        self.epochs = epochs\n",
    "        self.learning_rate = learning_rate\n",
    "        self.momentum = momentum\n",
    "        self.fact = activation\n",
    "        self.validation_split = validation_split\n",
    "        self.patience = patience\n",
    "        self.batch_size = batch_size\n",
    "\n",
    "        self.h = [np.zeros((layer, 1)) for layer in layers]\n",
    "        self.xi = [np.zeros((layer, 1)) for layer in layers]\n",
    "        self.w = [np.random.randn(layers[i], layers[i-1]) * 0.01 if i > 0 else np.zeros((1, 1)) for i in range(self.L)]\n",
    "        self.theta = [np.zeros((layer, 1)) for layer in layers]\n",
    "        self.delta = [np.zeros((layer, 1)) for layer in layers]\n",
    "        self.d_w = [np.zeros_like(w) for w in self.w]\n",
    "        self.d_theta = [np.zeros_like(theta) for theta in self.theta]\n",
    "        self.d_w_prev = [np.zeros_like(w) for w in self.w]\n",
    "        self.d_theta_prev = [np.zeros_like(theta) for theta in self.theta]\n",
    "\n",
    "        self.activation_function = self.get_activation_function(activation)\n",
    "        self.activation_derivative = self.get_activation_derivative(activation)\n",
    "\n",
    "        self.train_losses = []\n",
    "        self.val_losses = []\n",
    "\n",
    "    def get_activation_function(self, name):\n",
    "        \"\"\"Return the activation function based on the given name.\"\"\"\n",
    "        if name == 'sigmoid':\n",
    "            return lambda z: 1 / (1 + np.exp(-np.clip(z, -500, 500)))\n",
    "        elif name == 'relu':\n",
    "            return lambda z: np.maximum(0, z)\n",
    "        elif name == 'linear':\n",
    "            return lambda z: z\n",
    "        elif name == 'tanh':\n",
    "            return lambda z: np.tanh(z)\n",
    "        else:\n",
    "            raise ValueError(\"Unsupported activation function\")\n",
    "\n",
    "    def get_activation_derivative(self, name):\n",
    "        \"\"\"Return the derivative of the activation function based on the given name.\"\"\"\n",
    "        if name == 'sigmoid':\n",
    "            return lambda z: z * (1 - z)\n",
    "        elif name == 'relu':\n",
    "            return lambda z: np.where(z > 0, 1, 0)\n",
    "        elif name == 'linear':\n",
    "            return lambda z: np.ones_like(z)\n",
    "        elif name == 'tanh':\n",
    "            return lambda z: 1 - z**2\n",
    "        else:\n",
    "            raise ValueError(\"Unsupported activation function\")\n",
    "\n",
    "    def forward_propagation(self, X):\n",
    "        \"\"\"Perform forward propagation through the network.\"\"\"\n",
    "        self.xi[0] = X\n",
    "        for lay in range(1, self.L):\n",
    "            self.h[lay] = np.dot(self.w[lay], self.xi[lay - 1]) - self.theta[lay]\n",
    "            self.xi[lay] = self.activation_function(self.h[lay])\n",
    "        return self.xi[-1]\n",
    "\n",
    "    def backward_propagation(self, X, y):\n",
    "        \"\"\"Perform backward propagation through the network.\"\"\"\n",
    "        m = y.shape[1]\n",
    "        self.forward_propagation(X)\n",
    "        self.delta[-1] = (self.xi[-1] - y) * self.activation_derivative(self.h[-1])\n",
    "\n",
    "        for lay in range(self.L - 2, 0, -1):\n",
    "            self.delta[lay] = self.activation_derivative(self.h[lay]) * np.dot(self.w[lay + 1].T, self.delta[lay + 1])\n",
    "\n",
    "        for lay in range(1, self.L):\n",
    "            self.d_w[lay] = np.dot(self.delta[lay], self.xi[lay - 1].T) / m\n",
    "            self.d_theta[lay] = np.sum(self.delta[lay], axis=1, keepdims=True) / m\n",
    "\n",
    "            self.d_w_prev[lay] = self.momentum * self.d_w_prev[lay] - self.learning_rate * self.d_w[lay]\n",
    "            self.d_theta_prev[lay] = self.momentum * self.d_theta_prev[lay] - self.learning_rate * self.d_theta[lay]\n",
    "\n",
    "            self.w[lay] += self.d_w_prev[lay]\n",
    "            self.theta[lay] += self.d_theta_prev[lay]\n",
    "\n",
    "    def fit(self, X, y):\n",
    "        \"\"\"Train the neural network using the training data.\"\"\"\n",
    "        kf = KFold(n_splits=4, shuffle=True, random_state=42)\n",
    "        validation_errors = []\n",
    "\n",
    "        best_val_loss = float('inf')\n",
    "        patience_counter = 0\n",
    "\n",
    "        for train_index, val_index in kf.split(X.T):\n",
    "            X_train, X_val = X[:, train_index], X[:, val_index]\n",
    "            y_train, y_val = y[:, train_index], y[:, val_index]\n",
    "\n",
    "            for epoch in range(self.epochs):\n",
    "                for i in range(0, X_train.shape[1], self.batch_size):\n",
    "                    end = i + self.batch_size\n",
    "                    self.backward_propagation(X_train[:, i:end], y_train[:, i:end])\n",
    "                \n",
    "                if epoch % 100 == 0:\n",
    "                    train_loss = np.mean((self.forward_propagation(X_train) - y_train) ** 2)\n",
    "                    val_loss = np.mean((self.forward_propagation(X_val) - y_val) ** 2)\n",
    "                    self.train_losses.append(train_loss)\n",
    "                    self.val_losses.append(val_loss)\n",
    "                    print(f'Epoch {epoch}, Train Loss: {train_loss}, Validation Loss: {val_loss}')\n",
    "\n",
    "                # Early stopping\n",
    "                val_loss = np.mean((self.forward_propagation(X_val) - y_val) ** 2)\n",
    "                if val_loss < best_val_loss:\n",
    "                    best_val_loss = val_loss\n",
    "                    patience_counter = 0\n",
    "                else:\n",
    "                    patience_counter += 1\n",
    "\n",
    "                if patience_counter >= self.patience:\n",
    "                    print(f'Early stopping at epoch {epoch}')\n",
    "                    break\n",
    "\n",
    "            val_error = np.mean((self.forward_propagation(X_val) - y_val) ** 2)\n",
    "            validation_errors.append(val_error)\n",
    "\n",
    "        self.validation_error = np.mean(validation_errors)\n",
    "        print(f'Validation Error: {self.validation_error}')\n",
    "\n",
    "    def predict(self, X):\n",
    "        \"\"\"Make predictions using the trained neural network.\"\"\"\n",
    "        return self.forward_propagation(X)\n",
    "\n",
    "    def loss_epochs(self):\n",
    "        \"\"\"Return the training and validation losses.\"\"\"\n",
    "        return np.array(self.train_losses), np.array(self.val_losses)\n",
    "\n",
    "\n",
    "# If you have pandas DataFrame/Series, convert them to numpy arrays\n",
    "X_train_np = X_train.to_numpy().T if hasattr(X_train, 'to_numpy') else X_train.T\n",
    "y_train_np = y_train.to_numpy().reshape(1, -1) if hasattr(y_train, 'to_numpy') else y_train.reshape(1, -1)\n",
    "X_test_np = X_test.to_numpy().T if hasattr(X_test, 'to_numpy') else X_test.T\n",
    "\n",
    "layers = [X_train_np.shape[0], 9, 5, 1]\n",
    "nn = NeuralNet(layers, epochs=1000, learning_rate=0.01, activation='sigmoid', validation_split=0.2, patience=10, batch_size=32)\n",
    "\n",
    "# Train the neural network\n",
    "nn.fit(X_train_np, y_train_np)\n",
    "\n",
    "# Make predictions\n",
    "y_pred_nn = nn.predict(X_test_np)\n",
    "print(y_pred_nn)\n",
    "\n",
    "# Evaluate the model\n",
    "mse_nn = mean_squared_error(y_test, y_pred_nn.T)\n",
    "print(f'Mean Squared Error for BP: {mse_nn}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 0, Loss: 0.27338314056396484\n",
      "Epoch 100, Loss: 0.03722553327679634\n",
      "Epoch 200, Loss: 0.021083321422338486\n",
      "Epoch 300, Loss: 0.011942402459681034\n",
      "Epoch 400, Loss: 0.06830113381147385\n",
      "Epoch 500, Loss: 0.22653822600841522\n",
      "Epoch 600, Loss: 0.002243427326902747\n",
      "Epoch 700, Loss: 0.0013204048154875636\n",
      "Epoch 800, Loss: 0.3856009542942047\n",
      "Epoch 900, Loss: 0.29194843769073486\n",
      "[[ 9.21560228e-01]\n",
      " [ 9.23520863e-01]\n",
      " [ 3.21213603e-02]\n",
      " [ 1.00611615e+00]\n",
      " [-1.60003901e-02]\n",
      " [-2.19106674e-04]\n",
      " [ 8.78289521e-01]\n",
      " [ 6.62925243e-02]\n",
      " [ 2.88349390e-02]\n",
      " [ 9.47542012e-01]\n",
      " [ 9.76982892e-01]\n",
      " [ 6.53286755e-01]\n",
      " [-1.60003901e-02]\n",
      " [ 6.18501961e-01]\n",
      " [ 1.76453412e-01]\n",
      " [ 9.56423342e-01]\n",
      " [ 7.53671110e-01]\n",
      " [ 5.46942294e-01]\n",
      " [ 4.80051577e-01]\n",
      " [ 5.03965616e-02]\n",
      " [ 9.95827019e-01]\n",
      " [ 2.40051150e-01]\n",
      " [-1.94397569e-02]\n",
      " [ 6.52267516e-01]\n",
      " [ 8.97889912e-01]\n",
      " [ 3.67293775e-01]\n",
      " [ 8.26220810e-01]\n",
      " [ 4.49124634e-01]\n",
      " [ 6.80723369e-01]\n",
      " [ 1.81095600e-01]\n",
      " [ 8.58659923e-01]\n",
      " [-1.62450075e-02]\n",
      " [-3.12519670e-02]\n",
      " [ 7.60089815e-01]\n",
      " [ 9.32434380e-01]\n",
      " [ 3.58182371e-01]\n",
      " [ 7.72389114e-01]\n",
      " [ 6.63493574e-01]\n",
      " [ 2.07131982e-01]\n",
      " [ 9.02771652e-01]\n",
      " [ 2.46679664e-01]\n",
      " [ 9.57621157e-01]\n",
      " [ 2.17151642e-03]\n",
      " [ 9.70218718e-01]\n",
      " [ 9.79824364e-01]\n",
      " [ 1.38212323e-01]\n",
      " [ 9.67717230e-01]\n",
      " [ 3.39412510e-01]\n",
      " [ 1.00176215e+00]\n",
      " [ 9.38364804e-01]\n",
      " [ 7.29690731e-01]\n",
      " [ 3.80863726e-01]\n",
      " [ 8.04240406e-01]\n",
      " [ 4.17526066e-01]\n",
      " [ 4.46913183e-01]\n",
      " [-4.08625603e-03]\n",
      " [ 6.72309399e-02]\n",
      " [ 1.00160694e+00]\n",
      " [-2.23539472e-02]\n",
      " [ 7.33943522e-01]\n",
      " [ 9.56432879e-01]\n",
      " [ 5.39244950e-01]\n",
      " [ 8.71377587e-02]\n",
      " [ 6.44764006e-01]\n",
      " [ 1.00302386e+00]\n",
      " [ 4.57034707e-02]\n",
      " [ 9.64303911e-01]\n",
      " [ 9.14716780e-01]\n",
      " [ 5.09520113e-01]\n",
      " [ 2.21168220e-01]\n",
      " [-3.80514264e-02]\n",
      " [ 6.66394293e-01]\n",
      " [ 1.78259671e-01]\n",
      " [ 8.13836873e-01]\n",
      " [ 8.20052087e-01]\n",
      " [ 9.06585634e-01]\n",
      " [ 2.46679664e-01]\n",
      " [ 2.72883117e-01]\n",
      " [ 8.11676681e-01]\n",
      " [ 7.21223414e-01]\n",
      " [ 7.17043102e-01]\n",
      " [ 3.71057749e-01]\n",
      " [ 4.07047212e-01]\n",
      " [ 9.03091848e-01]\n",
      " [ 8.27594817e-01]\n",
      " [ 8.29790771e-01]\n",
      " [ 7.90922046e-02]\n",
      " [ 6.40827954e-01]\n",
      " [ 9.94043171e-01]\n",
      " [ 7.39002049e-01]\n",
      " [ 3.32998812e-01]\n",
      " [ 9.67593789e-02]\n",
      " [ 8.30165088e-01]\n",
      " [ 7.67687261e-01]\n",
      " [ 7.57349193e-01]\n",
      " [ 3.44384253e-01]\n",
      " [ 8.50182354e-01]\n",
      " [ 1.63352609e-01]\n",
      " [ 9.45571005e-01]\n",
      " [ 9.47292149e-01]\n",
      " [ 6.51664436e-01]\n",
      " [ 6.45821154e-01]\n",
      " [ 8.73755753e-01]\n",
      " [ 9.91984189e-01]\n",
      " [ 2.82191634e-01]\n",
      " [ 8.88098419e-01]\n",
      " [ 9.27711070e-01]\n",
      " [ 7.24136889e-01]\n",
      " [ 1.02799201e+00]\n",
      " [ 1.21855915e-01]\n",
      " [ 2.93625057e-01]\n",
      " [ 4.65089738e-01]\n",
      " [ 9.90417302e-01]\n",
      " [ 9.36703146e-01]\n",
      " [ 9.10813034e-01]\n",
      " [ 1.32718205e-01]\n",
      " [ 6.22553229e-02]\n",
      " [ 6.54692233e-01]\n",
      " [ 9.99560416e-01]\n",
      " [ 8.84281218e-01]\n",
      " [ 9.47613060e-01]\n",
      " [ 7.18861222e-02]\n",
      " [ 9.34312522e-01]\n",
      " [ 9.07669604e-01]\n",
      " [-1.81734562e-02]\n",
      " [ 1.36746228e-01]\n",
      " [ 6.95980728e-01]\n",
      " [ 7.72786319e-01]\n",
      " [ 2.66497314e-01]\n",
      " [ 6.43782437e-01]\n",
      " [ 8.88098419e-01]\n",
      " [ 9.58247781e-02]\n",
      " [ 3.33127439e-01]\n",
      " [ 6.31407797e-01]\n",
      " [ 9.62263286e-01]\n",
      " [ 9.52461660e-01]\n",
      " [ 1.69601262e-01]\n",
      " [-3.53808999e-02]\n",
      " [ 8.68639290e-01]\n",
      " [ 7.08414495e-01]\n",
      " [ 4.54242229e-02]\n",
      " [ 6.00729585e-02]\n",
      " [ 4.46913183e-01]\n",
      " [ 2.02647805e-01]\n",
      " [ 7.96809793e-02]\n",
      " [ 5.39244950e-01]\n",
      " [ 7.80350029e-01]\n",
      " [ 1.23142064e-01]\n",
      " [ 2.72883117e-01]\n",
      " [ 2.91567683e-01]\n",
      " [ 9.48400438e-01]\n",
      " [ 1.00625396e+00]\n",
      " [ 1.01603675e+00]\n",
      " [ 1.00467110e+00]\n",
      " [ 8.37841213e-01]\n",
      " [ 3.63823473e-01]\n",
      " [ 8.10074389e-01]\n",
      " [-2.08520889e-02]\n",
      " [ 9.46487010e-01]\n",
      " [ 8.58970881e-02]\n",
      " [ 6.32352173e-01]\n",
      " [ 5.80910504e-01]\n",
      " [ 8.62377703e-01]\n",
      " [ 4.57138360e-01]\n",
      " [ 1.01047373e+00]\n",
      " [ 9.84293282e-01]\n",
      " [ 6.16624951e-02]\n",
      " [ 7.38400161e-01]\n",
      " [ 4.40806746e-02]\n",
      " [ 2.05106020e-01]\n",
      " [ 1.00673437e+00]\n",
      " [ 1.83353603e-01]\n",
      " [ 8.37720335e-01]\n",
      " [ 9.81429636e-01]\n",
      " [ 9.00402844e-01]\n",
      " [ 1.16331995e-01]\n",
      " [ 4.17526066e-01]\n",
      " [ 3.28686357e-01]\n",
      " [ 6.62822723e-02]\n",
      " [ 9.49462235e-01]\n",
      " [ 1.12284482e-01]\n",
      " [ 6.26029074e-01]\n",
      " [ 5.27094007e-02]\n",
      " [ 1.00781655e+00]\n",
      " [ 1.90276861e-01]\n",
      " [ 9.70709026e-01]\n",
      " [ 4.81855690e-01]\n",
      " [ 8.21080744e-01]\n",
      " [ 9.53579247e-01]\n",
      " [-5.13966680e-02]\n",
      " [ 1.39902949e-01]\n",
      " [ 9.75865901e-01]\n",
      " [ 8.84154856e-01]\n",
      " [ 9.75583375e-01]\n",
      " [ 9.38863218e-01]\n",
      " [ 7.56636441e-01]\n",
      " [ 5.70055664e-01]\n",
      " [ 9.46574390e-01]\n",
      " [ 6.26029074e-01]\n",
      " [ 9.56423342e-01]\n",
      " [ 9.72340882e-01]\n",
      " [ 3.50117207e-01]\n",
      " [ 1.02267337e+00]\n",
      " [ 4.66969788e-01]\n",
      " [ 9.75489080e-01]\n",
      " [ 7.51041710e-01]\n",
      " [ 6.22457862e-02]\n",
      " [ 9.48992789e-01]\n",
      " [ 4.44089770e-02]\n",
      " [ 1.28370404e-01]\n",
      " [ 8.34195197e-01]\n",
      " [ 1.57596290e-01]\n",
      " [ 5.30425906e-02]\n",
      " [ 3.49714756e-02]\n",
      " [-4.07515168e-02]\n",
      " [ 5.79082310e-01]\n",
      " [ 9.59862292e-01]\n",
      " [ 4.62193906e-01]\n",
      " [ 5.80498159e-01]\n",
      " [-2.19106674e-04]\n",
      " [-9.12892818e-03]\n",
      " [ 1.45450115e-01]\n",
      " [ 1.00467110e+00]\n",
      " [ 9.67258036e-01]\n",
      " [-2.31715441e-02]\n",
      " [ 2.06774890e-01]\n",
      " [ 8.45593989e-01]\n",
      " [ 6.82424366e-01]\n",
      " [ 7.90922046e-02]\n",
      " [ 2.98810780e-01]\n",
      " [ 9.15616572e-01]\n",
      " [ 4.40384448e-01]\n",
      " [ 8.34309518e-01]]\n",
      "Mean Squared Error for BP-F: 0.11046543717384338\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "from torch.utils.data import DataLoader, TensorDataset\n",
    "import numpy as np\n",
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "# Ensure data is in numpy array format\n",
    "X_train = X_train.to_numpy() if hasattr(X_train, 'to_numpy') else X_train\n",
    "y_train = y_train.to_numpy() if hasattr(y_train, 'to_numpy') else y_train\n",
    "X_test = X_test.to_numpy() if hasattr(X_test, 'to_numpy') else X_test\n",
    "y_test = y_test.to_numpy() if hasattr(y_test, 'to_numpy') else y_test\n",
    "\n",
    "# Convert data to torch tensors\n",
    "X_train_tensor = torch.Tensor(X_train)\n",
    "y_train_tensor = torch.Tensor(y_train).view(-1, 1)\n",
    "X_test_tensor = torch.Tensor(X_test)\n",
    "y_test_tensor = torch.Tensor(y_test).view(-1, 1)\n",
    "\n",
    "train_dataset = TensorDataset(X_train_tensor, y_train_tensor)\n",
    "train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True)\n",
    "\n",
    "# Define the neural network\n",
    "class SimpleNet(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(SimpleNet, self).__init__()\n",
    "        self.fc1 = nn.Linear(X_train.shape[1], 9)\n",
    "        self.fc2 = nn.Linear(9, 5)\n",
    "        self.fc3 = nn.Linear(5, 1)\n",
    "    \n",
    "    def forward(self, x):\n",
    "        x = torch.sigmoid(self.fc1(x))\n",
    "        x = torch.sigmoid(self.fc2(x))\n",
    "        x = self.fc3(x)\n",
    "        return x\n",
    "\n",
    "# Initialize the model, loss function, and optimizer\n",
    "model = SimpleNet()\n",
    "criterion = nn.MSELoss()\n",
    "optimizer = optim.SGD(model.parameters(), lr=0.01)\n",
    "\n",
    "# Train the model\n",
    "epochs = 1000\n",
    "for epoch in range(epochs):\n",
    "    for X_batch, y_batch in train_loader:\n",
    "        optimizer.zero_grad()\n",
    "        outputs = model(X_batch)\n",
    "        loss = criterion(outputs, y_batch)\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "    \n",
    "    if epoch % 100 == 0:\n",
    "        print(f'Epoch {epoch}, Loss: {loss.item()}')\n",
    "\n",
    "# Make predictions\n",
    "y_pred_bp_f = model(X_test_tensor).detach().numpy()\n",
    "print(y_pred_bp_f)\n",
    "\n",
    "# Evaluate the model\n",
    "mse_bp_f = mean_squared_error(y_test, y_pred_bp_f)\n",
    "print(f'Mean Squared Error for BP-F: {mse_bp_f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error for BP: 0.5001034296557859\n",
      "Mean Absolute Percentage Error for BP: 1091116429384702.6\n",
      "Mean Absolute Error for BP-F: 0.22911150753498077\n",
      "Mean Absolute Percentage Error for BP-F: 625576415068160.0\n",
      "Mean Absolute Error for MLR-F: 0.25678392272131745\n",
      "Mean Absolute Percentage Error for MLR-F: 659701156098309.2\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Required imports\n",
    "from sklearn.metrics import mean_absolute_error, mean_absolute_percentage_error\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Calculate additional evaluation metrics for each model\n",
    "\n",
    "# BP (Backpropagation)\n",
    "mae_bp = mean_absolute_error(y_test, y_pred_nn.T)\n",
    "mape_bp = mean_absolute_percentage_error(y_test, y_pred_nn.T)\n",
    "print(f'Mean Absolute Error for BP: {mae_bp}')\n",
    "print(f'Mean Absolute Percentage Error for BP: {mape_bp}')\n",
    "\n",
    "# BP-F (Backpropagation with Framework)\n",
    "mae_bp_f = mean_absolute_error(y_test, y_pred_bp_f)\n",
    "mape_bp_f = mean_absolute_percentage_error(y_test, y_pred_bp_f)\n",
    "print(f'Mean Absolute Error for BP-F: {mae_bp_f}')\n",
    "print(f'Mean Absolute Percentage Error for BP-F: {mape_bp_f}')\n",
    "\n",
    "# MLR-F (Multiple Linear Regression with Framework)\n",
    "mae_mlr = mean_absolute_error(y_test, y_pred_mlr)\n",
    "mape_mlr = mean_absolute_percentage_error(y_test, y_pred_mlr)\n",
    "print(f'Mean Absolute Error for MLR-F: {mae_mlr}')\n",
    "print(f'Mean Absolute Percentage Error for MLR-F: {mape_mlr}')\n",
    "\n",
    "# Visualizations - Scatter plots comparing predictions with actual values\n",
    "\n",
    "# Function to plot scatter plot for a model\n",
    "def plot_scatter(y_true, y_pred, title):\n",
    "    plt.figure(figsize=(6, 6))\n",
    "    plt.scatter(y_true, y_pred, alpha=0.5)\n",
    "    plt.plot([y_true.min(), y_true.max()], [y_true.min(), y_true.max()], 'r--')\n",
    "    plt.xlabel('Actual Values')\n",
    "    plt.ylabel('Predicted Values')\n",
    "    plt.title(title)\n",
    "    plt.show()\n",
    "\n",
    "# Scatter plots for each model\n",
    "plot_scatter(y_test, y_pred_nn.T, 'BP Model Predictions vs Actual Values')\n",
    "plot_scatter(y_test, y_pred_bp_f, 'BP-F Model Predictions vs Actual Values')\n",
    "plot_scatter(y_test, y_pred_mlr, 'MLR-F Model Predictions vs Actual Values')"
   ]
  }
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