3-ML.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "2df38459-6403-4c59-8bf9-d4f00192f492",
   "metadata": {},
   "source": [
    "# Notebook 3: Classification Using Machine Learning"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9324361f-daa8-4d9e-a5b4-3ac5465c9a6b",
   "metadata": {},
   "source": [
    "In this notebook, we will use a classical machine learning method to classify the astronomical data saved in the previous notebook."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "53e4c998-a020-48c1-b7b0-1b3a755e733e",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cd7c7fbc-fa08-4312-ae32-88e30eb1d673",
   "metadata": {},
   "source": [
    "### Reading the data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a811c1b4-4512-4139-99a5-469776428dcc",
   "metadata": {},
   "source": [
    "First, we’ll load the saved image and label data from the NumPy files."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "340f6b6f-23c3-4017-b263-4d6204a44996",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Data loaded successfully from NumPy files.\n"
     ]
    }
   ],
   "source": [
    "import numpy as np  # Importing NumPy for numerical operations and array handling\n",
    "\n",
    "# Load the training images and labels back from the saved NumPy files\n",
    "train_images = np.load('data/train_images.npy')  # Load image training data\n",
    "train_labels = np.load('data/train_labels.npy')  # Load label training data\n",
    "val_images = np.load('data/val_images.npy')  # Load image training data\n",
    "val_labels = np.load('data/val_labels.npy')  # Load label training data\n",
    "\n",
    "print(\"Training Data loaded successfully from NumPy files.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5204e579-2d19-4b8f-9a9f-f4af212b3417",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4a2a94cb-00bd-4e79-ad42-ebc9652fbf18",
   "metadata": {},
   "source": [
    "## Pre-processing"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "278b7ae3-5950-44f2-80c2-749076b1ab0a",
   "metadata": {},
   "source": [
    "We will pre-process the training images to simplify the data by:\n",
    "\n",
    "1. `Grayscaling`\n",
    "2. `Normalizing` to range [0, 1]\n",
    "3. `Downscaling` to a target size of [64x64].\n",
    "4. `Flattening` the images to be ready for machine learning models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f166d93f-2b01-4c84-b52a-99290eb32b57",
   "metadata": {},
   "outputs": [],
   "source": [
    "def preprocessing_fn_ML(X): \n",
    "    from skimage.color import rgb2gray\n",
    "    from skimage.transform import resize\n",
    "    \n",
    "    # Normalize the data to [0, 1]\n",
    "    X_pre = X.astype('float32') / 255.0\n",
    "    \n",
    "    # Convert to grayscale\n",
    "    X_pre = np.array([rgb2gray(image) for image in X_pre])\n",
    "    \n",
    "    # Resize images to 64x64 pixels\n",
    "    X_pre = np.array([resize(image, (64, 64), anti_aliasing=True) for image in X_pre])\n",
    "    \n",
    "    # Flatten the images\n",
    "    num_samples = X_pre.shape[0]\n",
    "    X_pre = X_pre.reshape(num_samples, -1)\n",
    "    \n",
    "    return X_pre"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5ac38aee-a46b-487d-8b78-4583e756eb42",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_images_pre = preprocessing_fn_ML(train_images)\n",
    "val_images_pre = preprocessing_fn_ML(val_images)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0296a59a-8c8f-488c-a14a-09c569fa99ad",
   "metadata": {},
   "source": [
    "#### **⚠️ Freeing up Space**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "05cc654d-1278-4c82-9eb6-8740a52d7d81",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original images removed from memory.\n"
     ]
    }
   ],
   "source": [
    "\n",
    "import gc\n",
    "\n",
    "# Since we will no longer need the original data, we can remove it from memory\n",
    "del train_images, val_images\n",
    "\n",
    "\n",
    "# Force garbage collection to free up memory\n",
    "gc.collect()\n",
    "\n",
    "print(\"Original images removed from memory.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d642d99d-b0a8-498f-92a6-c98fc6276e5a",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9d741968-50bd-4fae-a16e-639d1774214c",
   "metadata": {},
   "source": [
    "### Visualising the data after pre-processing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "7ab2851e-5124-473b-84dc-e496ee545922",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 2000x400 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
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      "text/plain": [
       "<Figure size 2000x400 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x400 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
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    },
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      "text/plain": [
       "<Figure size 2000x400 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x400 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Define the class names\n",
    "class_names = [\"Blurry\", \"Corrupt\", \"Missing_Data\", \"Noisy\", \"Priority\"]\n",
    "\n",
    "# Get the unique labels in the training set\n",
    "unique_labels = np.unique(train_labels)\n",
    "\n",
    "# Display the first 5 images for each class after preprocessing\n",
    "for label in unique_labels:\n",
    "    # Find the indices of images belonging to the current class\n",
    "    class_indices = np.where(train_labels == label)[0]\n",
    "    \n",
    "    # Select the first 5 images of this class\n",
    "    num_images_to_display = min(5, len(class_indices))\n",
    "    selected_indices = class_indices[:num_images_to_display]\n",
    "    selected_images = train_images_pre[selected_indices]  # Already preprocessed images\n",
    "\n",
    "    # Plot the selected images\n",
    "    fig, axes = plt.subplots(1, num_images_to_display, figsize=(20, 4))\n",
    "    fig.suptitle(f'Class: {class_names[label]}', fontsize=16)\n",
    "    fig.tight_layout(rect=[0, 0.03, 1, 0.95])\n",
    "    \n",
    "    for i, ax in enumerate(axes):\n",
    "        ax.imshow(selected_images[i].reshape(64, 64), cmap='gray')  # Reshape and use grayscale colormap\n",
    "        ax.axis('off')\n",
    "    \n",
    "    plt.show()\n",
    "    print()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e247fd54-426c-41f5-ae70-104fc1cc4049",
   "metadata": {},
   "source": [
    "After reviewing the pre-processed images, it appears that reducing the size from 512x512 to 64x64 may not have been the best choice. Visually, it has become more challenging to distinguish between the **Priority**, **Noisy**, and **Blurry** categories. Now, let’s apply a machine learning method to classify these images."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fc3950ac-b778-440e-9d1c-b2acdcb42f95",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4920f428-c5a6-4f7b-a07a-52392896719e",
   "metadata": {},
   "source": [
    "## ML Classification"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "77290125-274d-4084-a0e9-1ebca479a52e",
   "metadata": {},
   "source": [
    "### Train the Stochastic Gradient Descent (SGD) Model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a1105699-f55f-4208-bc3f-1797f82a3e9d",
   "metadata": {},
   "source": [
    "The Stochastic Gradient Descent (SGD) model uses the SGD optimization method to train machine learning models. The SGDClassifier is effective for efficiently training linear models, particularly on large datasets. However, it may not perform well on complex, non-linear data such as images."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ccf5efcd-59d1-411f-b666-52bac92e0507",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import SGDClassifier\n",
    "import pickle\n",
    "\n",
    "# Create the stochastic gradient descent model\n",
    "\n",
    "sgd_model = SGDClassifier( loss='log_loss', max_iter=10000, n_jobs=4, random_state=42)\n",
    "\n",
    "# Fit the model on the training data\n",
    "sgd_model.fit(train_images_pre, train_labels)\n",
    "\n",
    "# Save the model to a file\n",
    "with open('sgd_model.pkl', 'wb') as file:\n",
    "    pickle.dump(sgd_model, file)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "898b0dee-2d68-465e-a907-b5c30dd037b8",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e31153bc-9f63-4281-a048-7eedb0f3742b",
   "metadata": {},
   "source": [
    "### ML: Validation set results"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd65a826-eeec-4c03-bcd5-d4b595cbce08",
   "metadata": {},
   "source": [
    "`Validation sets` are commonly used to evaluate the accuracy of machine learning models and guide improvements in their performance. Participants can modify the model `parameters` or adjust the `pre-processing` steps, then retrain the model. The results on the `validation set` help determine if these changes have improved the model’s performance. This iterative testing process should be performed exclusively on the `validation set`, not the `testing set`, to prevent overfitting and ensure the model generalizes well to unseen data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "fe81936d-3432-476c-9fc1-90b0bfc1d7a4",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('sgd_model.pkl', 'rb') as file:\n",
    "    sgd_loaded_model = pickle.load(file)\n",
    "    \n",
    "val_predictions = sgd_loaded_model.predict(val_images_pre)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "107a1f94-21fd-4221-8f68-605133971680",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Classification Report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.25      0.09      0.14       727\n",
      "           1       0.98      0.97      0.98       222\n",
      "           2       0.44      0.20      0.27       421\n",
      "           3       0.26      0.10      0.15       721\n",
      "           4       0.41      0.82      0.55      1146\n",
      "\n",
      "    accuracy                           0.43      3237\n",
      "   macro avg       0.47      0.44      0.42      3237\n",
      "weighted avg       0.39      0.43      0.36      3237\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report, confusion_matrix\n",
    "\n",
    "\n",
    "# Detailed classification report\n",
    "print(\"\\nClassification Report:\")\n",
    "print(classification_report(val_labels, val_predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "d883f78c-26f6-432b-ad7c-b24d3d83bb0e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay\n",
    "\n",
    "# Define class names\n",
    "class_names = [\"Blurry\", \"Corrupt\", \"Missing_Data\", \"Noisy\", \"Priority\"]\n",
    "\n",
    "# Compute the confusion matrix\n",
    "cm = confusion_matrix(val_labels, val_predictions)\n",
    "\n",
    "# Plot the confusion matrix with class names\n",
    "disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=class_names)\n",
    "\n",
    "# Customize and display the plot\n",
    "fig, ax = plt.subplots(figsize=(8, 8))  # Set the figure size\n",
    "disp.plot(ax=ax, cmap='Blues', xticks_rotation='vertical')  # Use a blue colormap\n",
    "plt.title(\"Confusion Matrix with Class Names\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "69a3a4e3-6d3e-4046-9fd6-05da86a90f3e",
   "metadata": {},
   "source": [
    "The machine learning model is performing poorly. The confusion matrix reveals significant misclassification, particularly between the `Priority` class and the `Noisy` and `Blurry` classes. This observation aligns with our analysis when we visualized the pre-processed images."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b8545012-9a47-4340-a4ca-6fc3bf204502",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "41027dd0-ec8f-4a99-97ab-c4420cb2d6e0",
   "metadata": {},
   "source": [
    "#### **⚠️ Freeing up Space**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "efecfdf4-4359-4aa9-b16e-11f5d5393e80",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data removed from memory.\n"
     ]
    }
   ],
   "source": [
    "import gc\n",
    "\n",
    "# Remove the data from memory\n",
    "del train_images_pre, train_labels, val_images_pre, val_labels\n",
    "\n",
    "# Force garbage collection to free up memory\n",
    "gc.collect()\n",
    "\n",
    "# Clear the input/output cache\n",
    "print(\"Data removed from memory.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3b841767-8e4a-4289-b106-7e10a7ca4f8b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import sys\n",
    "\n",
    "# Restart the kernel programmatically to free up space\n",
    "os.execv(sys.executable, ['python'] + sys.argv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "88c814b6-d072-4e99-9be1-41c9425133f0",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "CubeHackKer",
   "language": "python",
   "name": "cubehackker"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}