pytorch_Isro_space_mining.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os\n",
    "import joblib\n",
    "import matplotlib.pyplot as plt\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import confusion_matrix, classification_report, accuracy_score\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from torch.utils.data import DataLoader, TensorDataset\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def load_data(file_path):\n",
    "    df = pd.read_csv(file_path)\n",
    "    print(\"Missing values in each column:\\n\", df.isnull().sum())\n",
    "    df.hist(figsize=(15, 10))\n",
    "    plt.show()\n",
    "    return df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function to train the Random Forest model\n",
    "def train_random_forest(df):\n",
    "    le = LabelEncoder()\n",
    "    df['Celestial Body'] = le.fit_transform(df['Celestial Body'])\n",
    "    \n",
    "    # Select features and target variable\n",
    "    x = df.iloc[:, 1:-1]  # Adjust according to your dataframe\n",
    "    y = df.iloc[:, -1]\n",
    "    \n",
    "    x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=42)\n",
    "    \n",
    "    # Initialize and train the Random Forest model\n",
    "    r = RandomForestClassifier(n_estimators=100, random_state=1)\n",
    "    r.fit(x_train, y_train)\n",
    "\n",
    "    # Print feature names for debugging\n",
    "    print(\"Features used for training:\\n\", x.columns.tolist())\n",
    "    \n",
    "    y_pred = r.predict(x_test)\n",
    "    print(\"Confusion Matrix:\\n\", confusion_matrix(y_test, y_pred))\n",
    "    print(\"\\nClassification Report:\\n\", classification_report(y_test, y_pred))\n",
    "    print(\"\\nAccuracy Score:\", accuracy_score(y_test, y_pred))\n",
    "    \n",
    "    save_model(r, 'RF_mining_model.pkl')\n",
    "    return r\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function to save the trained model\n",
    "def save_model(model, model_name):\n",
    "    save_dir = \"C:\\\\imp\\\\1. ISRO\"\n",
    "    if not os.path.exists(save_dir):\n",
    "        os.makedirs(save_dir)\n",
    "    model_path = os.path.join(save_dir, model_name)\n",
    "    joblib.dump(model, model_path)\n",
    "    print(f\"Model saved at: {model_path}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function to test the Random Forest model\n",
    "def test_random_forest_model(model):\n",
    "    # Ensure that the feature names match those used during training\n",
    "    feature_columns = ['Distance from Earth (M km)', 'Iron (%)', 'Nickel (%)', \n",
    "                       'Water Ice (%)', 'Other Minerals (%)', 'Estimated Value (B USD)', \n",
    "                       'Sustainability Index', 'Efficiency Index']\n",
    "\n",
    "    print(\"Model expected feature columns:\\n\", feature_columns)\n",
    "\n",
    "    # Prepare input data for predictions\n",
    "    input_data_1 = pd.DataFrame([[413.95896411015195, 12.031864299982333, 4.860503665226579, \n",
    "                                   16.003773827097444, 67.10385820769365, 36.13170667717836, \n",
    "                                   70.128135040172, 98.46379316404845]],\n",
    "                                 columns=feature_columns)\n",
    "\n",
    "    print(\"Input Data 1:\\n\", input_data_1)  # Debugging print\n",
    "    print(\"Shape of Input Data 1:\", input_data_1.shape)  # Debugging print\n",
    "\n",
    "    try:\n",
    "        prediction_1 = model.predict(input_data_1)\n",
    "        print(\"Prediction for first input data:\", prediction_1)\n",
    "    except ValueError as e:\n",
    "        print(\"ValueError during prediction:\", e)\n",
    "\n",
    "    input_data_2 = pd.DataFrame([[262.8905503798111, 6.388814509817609, 1.3821046270023083, \n",
    "                                   11.568467371683752, 80.66061349149633, 31.054831518801926, \n",
    "                                   25.13901985443363, 89.44942857312876]],\n",
    "                                 columns=feature_columns)\n",
    "\n",
    "    print(\"Input Data 2:\\n\", input_data_2)  # Debugging print\n",
    "    print(\"Shape of Input Data 2:\", input_data_2.shape)  # Debugging print\n",
    "\n",
    "    try:\n",
    "        prediction_2 = model.predict(input_data_2)\n",
    "        print(\"Prediction for second input data:\", prediction_2)\n",
    "    except ValueError as e:\n",
    "        print(\"ValueError during prediction:\", e)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Simple Neural Network Model Class\n",
    "class SimpleNN(nn.Module):\n",
    "    def __init__(self, input_size, hidden_size, output_size):\n",
    "        super(SimpleNN, self).__init__()\n",
    "        self.fc1 = nn.Linear(input_size, hidden_size)\n",
    "        self.relu = nn.ReLU()\n",
    "        self.fc2 = nn.Linear(hidden_size, output_size)\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = self.fc1(x)\n",
    "        x = self.relu(x)\n",
    "        x = self.fc2(x)\n",
    "        return x\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function to train the PyTorch model\n",
    "def train_pytorch_model(df):\n",
    "    le = LabelEncoder()\n",
    "    df['Celestial Body'] = le.fit_transform(df['Celestial Body'])\n",
    "\n",
    "    # Select features and target variable\n",
    "    x = df.iloc[:, 1:-1].values.astype(np.float32)  # Convert to float32\n",
    "    y = df.iloc[:, -1].values.astype(np.int64)       # Convert to int64\n",
    "\n",
    "    x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=42)\n",
    "\n",
    "    # Convert to PyTorch tensors\n",
    "    x_train_tensor = torch.tensor(x_train)\n",
    "    y_train_tensor = torch.tensor(y_train)\n",
    "    x_test_tensor = torch.tensor(x_test)\n",
    "    y_test_tensor = torch.tensor(y_test)\n",
    "\n",
    "    # Create DataLoader\n",
    "    train_dataset = TensorDataset(x_train_tensor, y_train_tensor)\n",
    "    train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True)\n",
    "\n",
    "    # Define model, loss function, and optimizer\n",
    "    input_size = x.shape[1]\n",
    "    hidden_size = 64\n",
    "    output_size = len(np.unique(y))  # Number of unique classes\n",
    "\n",
    "    model = SimpleNN(input_size, hidden_size, output_size)\n",
    "    criterion = nn.CrossEntropyLoss()\n",
    "    optimizer = optim.Adam(model.parameters(), lr=0.001)\n",
    "\n",
    "    # Training loop\n",
    "    num_epochs = 100\n",
    "    for epoch in range(num_epochs):  \n",
    "        for batch_x, batch_y in train_loader:\n",
    "            optimizer.zero_grad()\n",
    "            outputs = model(batch_x)\n",
    "            loss = criterion(outputs, batch_y)\n",
    "            loss.backward()\n",
    "            optimizer.step()\n",
    "    \n",
    "    print(\"Training complete.\")\n",
    "    \n",
    "    # Test the model and print accuracy\n",
    "    with torch.no_grad():\n",
    "        model.eval()  # Set model to evaluation mode\n",
    "        test_outputs = model(x_test_tensor)\n",
    "        _, predicted = torch.max(test_outputs, 1)\n",
    "        accuracy = (predicted.numpy() == y_test).mean()\n",
    "        print(f\"Test Accuracy: {accuracy * 100:.2f}%\")\n",
    "\n",
    "    # Save the model\n",
    "    save_pytorch_model(model, 'pytorch_model.pth')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function to save the trained PyTorch model\n",
    "def save_pytorch_model(model, model_name):\n",
    "    save_dir = \"C:\\\\imp\\\\1. ISRO\"\n",
    "    if not os.path.exists(save_dir):\n",
    "        os.makedirs(save_dir)\n",
    "    model_path = os.path.join(save_dir, model_name)\n",
    "    torch.save(model.state_dict(), model_path)\n",
    "    print(f\"PyTorch model saved at: {model_path}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Missing values in each column:\n",
      " Celestial Body             0\n",
      "distance_from_earth        0\n",
      "iron                       0\n",
      "nickel                     0\n",
      "water_ice                  0\n",
      "other_minerals             0\n",
      "Estimated Value (B USD)    0\n",
      "sustainability_index       0\n",
      "efficiency_index           0\n",
      "Potential Mining Site      0\n",
      "dtype: int64\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 1500x1000 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Features used for training:\n",
      " ['distance_from_earth', 'iron', 'nickel', 'water_ice', 'other_minerals', 'Estimated Value (B USD)', 'sustainability_index', 'efficiency_index']\n",
      "Confusion Matrix:\n",
      " [[136   0]\n",
      " [  0  64]]\n",
      "\n",
      "Classification Report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "       False       1.00      1.00      1.00       136\n",
      "        True       1.00      1.00      1.00        64\n",
      "\n",
      "    accuracy                           1.00       200\n",
      "   macro avg       1.00      1.00      1.00       200\n",
      "weighted avg       1.00      1.00      1.00       200\n",
      "\n",
      "\n",
      "Accuracy Score: 1.0\n",
      "Model saved at: C:\\imp\\1. ISRO\\RF_mining_model.pkl\n",
      "Model expected feature columns:\n",
      " ['Distance from Earth (M km)', 'Iron (%)', 'Nickel (%)', 'Water Ice (%)', 'Other Minerals (%)', 'Estimated Value (B USD)', 'Sustainability Index', 'Efficiency Index']\n",
      "Input Data 1:\n",
      "    Distance from Earth (M km)   Iron (%)  Nickel (%)  Water Ice (%)  \\\n",
      "0                  413.958964  12.031864    4.860504      16.003774   \n",
      "\n",
      "   Other Minerals (%)  Estimated Value (B USD)  Sustainability Index  \\\n",
      "0           67.103858                36.131707             70.128135   \n",
      "\n",
      "   Efficiency Index  \n",
      "0         98.463793  \n",
      "Shape of Input Data 1: (1, 8)\n",
      "ValueError during prediction: The feature names should match those that were passed during fit.\n",
      "Feature names unseen at fit time:\n",
      "- Distance from Earth (M km)\n",
      "- Efficiency Index\n",
      "- Iron (%)\n",
      "- Nickel (%)\n",
      "- Other Minerals (%)\n",
      "- ...\n",
      "Feature names seen at fit time, yet now missing:\n",
      "- distance_from_earth\n",
      "- efficiency_index\n",
      "- iron\n",
      "- nickel\n",
      "- other_minerals\n",
      "- ...\n",
      "\n",
      "Input Data 2:\n",
      "    Distance from Earth (M km)  Iron (%)  Nickel (%)  Water Ice (%)  \\\n",
      "0                   262.89055  6.388815    1.382105      11.568467   \n",
      "\n",
      "   Other Minerals (%)  Estimated Value (B USD)  Sustainability Index  \\\n",
      "0           80.660613                31.054832              25.13902   \n",
      "\n",
      "   Efficiency Index  \n",
      "0         89.449429  \n",
      "Shape of Input Data 2: (1, 8)\n",
      "ValueError during prediction: The feature names should match those that were passed during fit.\n",
      "Feature names unseen at fit time:\n",
      "- Distance from Earth (M km)\n",
      "- Efficiency Index\n",
      "- Iron (%)\n",
      "- Nickel (%)\n",
      "- Other Minerals (%)\n",
      "- ...\n",
      "Feature names seen at fit time, yet now missing:\n",
      "- distance_from_earth\n",
      "- efficiency_index\n",
      "- iron\n",
      "- nickel\n",
      "- other_minerals\n",
      "- ...\n",
      "\n",
      "Training complete.\n",
      "Test Accuracy: 98.50%\n",
      "PyTorch model saved at: C:\\imp\\1. ISRO\\pytorch_model.pth\n"
     ]
    }
   ],
   "source": [
    "def main():\n",
    "    df = load_data('space_mining_dataset.csv')\n",
    "    rf_model = train_random_forest(df)\n",
    "    test_random_forest_model(rf_model)\n",
    "    train_pytorch_model(df)  # Train the PyTorch model\n",
    "\n",
    "if __name__ == \"__main__\":\n",
    "    main()\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": ".venv",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.5"
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