{
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  {
   "cell_type": "markdown",
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   },
   "source": [
    "# Quantum-Classical Hybrid Neural Network Comparison\n",
    "\n",
    "This notebook compares different neural network architectures including:\n",
    "- Classical Multi-Layer Perceptrons (MLP)\n",
    "- Quantum circuits with LINEAR output mapping\n",
    "- Quantum circuits with various GROUPING strategies"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "\n",
    "## 1. Import Required Libraries\n",
    "\n",
    " First, we'll import all necessary libraries for our experiment:\n",
    " - **PyTorch**: For neural network implementation and training\n",
    " - **Perceval**: For quantum circuit simulation\n",
    " - **Matplotlib/Seaborn**: For visualization\n",
    " - **Custom modules**: For quantum layers and MLP models\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-11-10T09:09:17.656122600Z",
     "start_time": "2025-11-10T09:09:17.418122Z"
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   "outputs": [],
   "source": [
    "from collections import defaultdict\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import perceval as pcvl\n",
    "import seaborn as sns\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "from sklearn.metrics import accuracy_score, classification_report, confusion_matrix\n",
    "from tqdm import tqdm\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "from merlin import LexGrouping, ModGrouping, QuantumLayer\n",
    "from merlin.datasets import iris"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "p\n",
    " ## 2. Load and Prepare the Iris Dataset\n",
    "\n",
    " We'll use the classic Iris dataset for multi-class classification. This dataset is ideal for comparing different architectures as it provides a simple but non-trivial classification task.\n",
    "\n",
    " The dataset contains:\n",
    " - **4 features**: sepal length, sepal width, petal length, petal width\n",
    " - **3 classes**: Setosa, Versicolor, Virginica\n",
    " - **150 samples** total (split into train/test sets)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-11-10T09:09:17.672033200Z",
     "start_time": "2025-11-10T09:09:17.537523200Z"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training samples: 120\n",
      "Test samples: 30\n",
      "Features: 4\n",
      "Classes: 3\n"
     ]
    }
   ],
   "source": [
    "train_features, train_labels, train_metadata = iris.get_data_train()\n",
    "test_features, test_labels, test_metadata = iris.get_data_test()\n",
    "\n",
    "# Convert data to PyTorch tensors\n",
    "X_train = torch.FloatTensor(train_features)\n",
    "y_train = torch.LongTensor(train_labels)\n",
    "X_test = torch.FloatTensor(test_features)\n",
    "y_test = torch.LongTensor(test_labels)\n",
    "\n",
    "print(f\"Training samples: {X_train.shape[0]}\")\n",
    "print(f\"Test samples: {X_test.shape[0]}\")\n",
    "print(f\"Features: {X_train.shape[1]}\")\n",
    "print(f\"Classes: {len(torch.unique(y_train))}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "\n",
    " ## 3. Define Model Variants\n",
    "\n",
    " We'll test several model architectures to compare classical and quantum approaches:\n",
    "\n",
    " **Classical Models:**\n",
    " - `MLP`: Multi-layer perceptron with 8 hidden units, ReLU activation, and 0.1 dropout\n",
    "\n",
    " **Quantum Models:**\n",
    " - `LINEAR-7modes-nobunching`: Uses linear output mapping with 7 quantum modes and no-bunching constraint\n",
    " - `LEXGROUPING-7modes`: Uses lexicographic grouping strategy for output mapping\n",
    " - `MODGROUPING-7modes`: Uses modular grouping strategy for output mapping\n",
    "\n",
    " Each model type has a distinct color and line style for visualization.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-11-10T09:09:17.687882200Z",
     "start_time": "2025-11-10T09:09:17.607913800Z"
    },
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from dataclasses import dataclass\n",
    "from typing import Literal\n",
    "\n",
    "import torch\n",
    "\n",
    "\n",
    "@dataclass\n",
    "class MLPConfig:\n",
    "    \"\"\"Configuration for Multi-Layer Perceptron\"\"\"\n",
    "    hidden_sizes: list[int] = None\n",
    "    dropout: float = 0.0\n",
    "    activation: Literal[\"relu\", \"tanh\", \"sigmoid\", \"leaky_relu\", \"elu\", \"gelu\", \"selu\"] = \"relu\"\n",
    "    normalization: Literal[\"batch\", \"layer\"] | None = None\n",
    "\n",
    "\n",
    "class MLP(nn.Module):\n",
    "    \"\"\"\n",
    "    An enhanced Multi-Layer Perceptron (MLP) implementation with customizable options.\n",
    "\n",
    "    Features:\n",
    "    - Multiple activation functions\n",
    "    - Batch/Layer normalization options\n",
    "    - Customizable weight initialization\n",
    "\n",
    "    Args:\n",
    "        input_size (int): Dimension of the input features\n",
    "        output_size (int): Dimension of the output\n",
    "        config (MLPConfig): Configuration for the network architecture\n",
    "    \"\"\"\n",
    "\n",
    "    # Class-level mapping of available activation functions\n",
    "    ACTIVATIONS = {\n",
    "        \"relu\": nn.ReLU(),\n",
    "        \"tanh\": nn.Tanh(),\n",
    "        \"sigmoid\": nn.Sigmoid(),\n",
    "        \"leaky_relu\": nn.LeakyReLU(),\n",
    "        \"elu\": nn.ELU(),\n",
    "        \"gelu\": nn.GELU(),\n",
    "        \"selu\": nn.SELU(),\n",
    "    }\n",
    "\n",
    "    def __init__(self, input_size: int, output_size: int, config: MLPConfig):\n",
    "        super().__init__()\n",
    "        last_layer_size = input_size\n",
    "        layers = []\n",
    "\n",
    "        self.config = config\n",
    "\n",
    "        # Validate activation function\n",
    "        if config.activation not in self.ACTIVATIONS:\n",
    "            raise ValueError(f\"Unsupported activation function: {config.activation}\")\n",
    "\n",
    "        # Build network architecture\n",
    "        for layer_size in config.hidden_sizes:\n",
    "            # Add linear layer\n",
    "            layers.append(nn.Linear(last_layer_size, layer_size))\n",
    "\n",
    "            # Add normalization if specified\n",
    "            if config.normalization == \"batch\":\n",
    "                layers.append(nn.BatchNorm1d(layer_size))\n",
    "            elif config.normalization == \"layer\":\n",
    "                layers.append(nn.LayerNorm(layer_size))\n",
    "\n",
    "            # Add activation function\n",
    "            layers.append(self.ACTIVATIONS[config.activation])\n",
    "\n",
    "            # Add dropout if specified\n",
    "            if config.dropout > 0:\n",
    "                layers.append(nn.Dropout(config.dropout))\n",
    "\n",
    "            last_layer_size = layer_size\n",
    "\n",
    "        # Add final output layer\n",
    "        layers.append(nn.Linear(last_layer_size, output_size))\n",
    "\n",
    "        # Create sequential model from layers\n",
    "        self.network = nn.Sequential(*layers)\n",
    "\n",
    "    def initialize_weights(self, method=\"xavier\"):\n",
    "        \"\"\"\n",
    "        Initialize network weights using the specified method.\n",
    "\n",
    "        Args:\n",
    "            method (str): Initialization method ('xavier' or 'kaiming') (default: 'xavier')\n",
    "        \"\"\"\n",
    "\n",
    "        for layer in self.network:\n",
    "            if isinstance(layer, nn.Linear):\n",
    "                if method == \"xavier\":\n",
    "                    nn.init.xavier_uniform_(layer.weight)\n",
    "                    nn.init.zeros_(layer.bias)\n",
    "                elif method == \"kaiming\":\n",
    "                    nn.init.kaiming_normal_(layer.weight, nonlinearity=\"relu\")\n",
    "                    nn.init.zeros_(layer.bias)\n",
    "                else:\n",
    "                    raise ValueError(f\"Unsupported initialization method: {method}\")\n",
    "\n",
    "    def forward(self, x):\n",
    "        \"\"\"\n",
    "        Forward pass of the network.\n",
    "\n",
    "        Args:\n",
    "            x (torch.Tensor): Input tensor with shape (batch_size, input_size)\n",
    "\n",
    "        Returns:\n",
    "            torch.Tensor: Output tensor with shape (batch_size, output_size)\n",
    "        \"\"\"\n",
    "        return self.network(x)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
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     "end_time": "2025-11-10T09:09:17.689136600Z",
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   "source": [
    "def get_model_variants():\n",
    "    \"\"\"Define different variants for each model type\"\"\"\n",
    "    # Define consistent colors for each model type\n",
    "    MODEL_COLORS = {\n",
    "        \"MLP\": \"#1f77b4\",  # Blue\n",
    "        \"LINEAR\": \"#2ca02c\",  # Green\n",
    "        \"GROUPING\": \"#ff7f0e\",  # Orange\n",
    "    }\n",
    "\n",
    "    # Define line styles for variants\n",
    "    LINE_STYLES = [\"--\", \"-\", \":\", \"-.\"]\n",
    "\n",
    "    variants = {\n",
    "        \"MLP\": [\n",
    "            {\n",
    "                \"name\": \"MLP\",\n",
    "                \"config\": MLPConfig(hidden_sizes=[8], dropout=0.1, activation=\"relu\"),\n",
    "                \"color\": MODEL_COLORS[\"MLP\"],\n",
    "                \"linestyle\": LINE_STYLES[0],\n",
    "            }\n",
    "        ],\n",
    "        \"LINEAR\": [\n",
    "            {\n",
    "                \"name\": \"LINEAR-7modes-nobunching\",\n",
    "                \"config\": {\n",
    "                    \"m\": 7,\n",
    "                    \"post_processing\": \"linear\",\n",
    "                    \"no_bunching\": True,\n",
    "                },\n",
    "                \"color\": MODEL_COLORS[\"LINEAR\"],\n",
    "                \"linestyle\": LINE_STYLES[1],\n",
    "            }\n",
    "        ],\n",
    "        \"GROUPING\": [\n",
    "            {\n",
    "                \"name\": \"LEXGROUPING-7modes\",\n",
    "                \"config\": {\n",
    "                    \"m\": 6,\n",
    "                    \"post_processing\": \"lex\",\n",
    "                },\n",
    "                \"color\": MODEL_COLORS[\"GROUPING\"],\n",
    "                \"linestyle\": LINE_STYLES[2],\n",
    "            },\n",
    "            {\n",
    "                \"name\": \"MODGROUPING-7modes\",\n",
    "                \"config\": {\n",
    "                    \"m\": 6,\n",
    "                    \"post_processing\": \"mod\",\n",
    "                },\n",
    "                \"color\": MODEL_COLORS[\"GROUPING\"],\n",
    "                \"linestyle\": LINE_STYLES[1],\n",
    "            },\n",
    "        ],\n",
    "    }\n",
    "    return variants"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    " ## 4. Quantum Circuit Architecture\n",
    "\n",
    " The quantum circuit is composed of three main sections:\n",
    "\n",
    " 1. **Left interferometer (WL)**: Performs initial quantum state transformation using beam splitters and phase shifters\n",
    " 2. **Variable phase shifters**: Encodes the 4 input features into quantum phases\n",
    " 3. **Right interferometer (WR)**: Performs final quantum state transformation\n",
    "\n",
    " The interferometers use a rectangular arrangement of optical elements, where each layer consists of beam splitters (BS) and programmable phase shifters (PS). The phase shifters contain trainable parameters that are optimized during training.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
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   "source": [
    "def create_quantum_circuit(m):\n",
    "    \"\"\"Create quantum circuit with specified number of modes\n",
    "\n",
    "    Parameters:\n",
    "    -----------\n",
    "    m : int\n",
    "        Number of quantum modes in the circuit\n",
    "\n",
    "    Returns:\n",
    "    --------\n",
    "    pcvl.Circuit\n",
    "        Complete quantum circuit with trainable parameters\n",
    "    \"\"\"\n",
    "    # Left interferometer with trainable parameters\n",
    "    wl = pcvl.GenericInterferometer(\n",
    "        m,\n",
    "        lambda i: pcvl.BS()\n",
    "        // pcvl.PS(pcvl.P(f\"theta_li{i}\"))\n",
    "        // pcvl.BS()\n",
    "        // pcvl.PS(pcvl.P(f\"theta_lo{i}\")),\n",
    "        shape=pcvl.InterferometerShape.RECTANGLE,\n",
    "    )\n",
    "\n",
    "    # Variable phase shifters for input encoding\n",
    "    c_var = pcvl.Circuit(m)\n",
    "    for i in range(4):  # 4 input features\n",
    "        px = pcvl.P(f\"px{i + 1}\")\n",
    "        c_var.add(i + (m - 4) // 2, pcvl.PS(px))\n",
    "\n",
    "    # Right interferometer with trainable parameters\n",
    "    wr = pcvl.GenericInterferometer(\n",
    "        m,\n",
    "        lambda i: pcvl.BS()\n",
    "        // pcvl.PS(pcvl.P(f\"theta_ri{i}\"))\n",
    "        // pcvl.BS()\n",
    "        // pcvl.PS(pcvl.P(f\"theta_ro{i}\")),\n",
    "        shape=pcvl.InterferometerShape.RECTANGLE,\n",
    "    )\n",
    "\n",
    "    # Combine all components\n",
    "    c = pcvl.Circuit(m)\n",
    "    c.add(0, wl, merge=True)\n",
    "    c.add(0, c_var, merge=True)\n",
    "    c.add(0, wr, merge=True)\n",
    "\n",
    "    return c"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "\n",
    " ## 5. Model Factory Functions\n",
    "\n",
    " These utility functions handle model creation and parameter counting. The `create_model` function serves as a factory that instantiates either classical MLP models or quantum models based on the specified configuration.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
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   "source": [
    "def create_model(model_type, variant):\n",
    "    \"\"\"Create model instance based on type and variant\n",
    "\n",
    "    Parameters:\n",
    "    -----------\n",
    "    model_type : str\n",
    "        Type of model ('MLP' or quantum model types)\n",
    "    variant : dict\n",
    "        Configuration dictionary for the model variant\n",
    "\n",
    "    Returns:\n",
    "    --------\n",
    "    nn.Module\n",
    "        PyTorch model instance\n",
    "    \"\"\"\n",
    "    if model_type == \"MLP\":\n",
    "        return MLP(input_size=4, output_size=3, config=variant[\"config\"])\n",
    "    else:\n",
    "        m = variant[\"config\"][\"m\"]\n",
    "        no_bunching = variant[\"config\"].get(\"no_bunching\", False)\n",
    "        post_processing = variant[\"config\"].get(\"post_processing\")\n",
    "        c = create_quantum_circuit(m)\n",
    "        quantum_layer = QuantumLayer(\n",
    "            input_size=4,\n",
    "            circuit=c,\n",
    "            trainable_parameters=[\"theta\"],\n",
    "            input_parameters=[\"px\"],\n",
    "            input_state=[1, 0] * (m // 2) + [0] * (m % 2),\n",
    "            no_bunching=no_bunching,\n",
    "        )\n",
    "\n",
    "        if post_processing == \"linear\":\n",
    "            head = nn.Linear(quantum_layer.output_size, 3)\n",
    "            return nn.Sequential(quantum_layer, head)\n",
    "        if post_processing == \"lex\":\n",
    "            return nn.Sequential(\n",
    "                quantum_layer,\n",
    "                LexGrouping(quantum_layer.output_size, 3),\n",
    "            )\n",
    "        if post_processing == \"mod\":\n",
    "            return nn.Sequential(\n",
    "                quantum_layer,\n",
    "                ModGrouping(quantum_layer.output_size, 3),\n",
    "            )\n",
    "        return quantum_layer\n",
    "\n",
    "\n",
    "def count_parameters(model):\n",
    "    \"\"\"Count trainable parameters in a PyTorch model\"\"\"\n",
    "    return sum(p.numel() for p in model.parameters() if p.requires_grad)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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   },
   "source": [
    "\n",
    "\n",
    " ## 6. Training Function\n",
    "\n",
    " The training function implements a standard supervised learning loop with the following characteristics:\n",
    "\n",
    " - **Optimizer**: Adam with learning rate 0.02\n",
    " - **Loss function**: Cross-entropy for multi-class classification\n",
    " - **Batch size**: 32 samples\n",
    " - **Epochs**: 50 (default)\n",
    "\n",
    " The function tracks training loss, training accuracy, and test accuracy at each epoch. After training completes, it generates a detailed classification report.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
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   "source": [
    "def train_model(\n",
    "    model,\n",
    "    X_train,\n",
    "    y_train,\n",
    "    X_test,\n",
    "    y_test,\n",
    "    model_name,\n",
    "    n_epochs=50,\n",
    "    batch_size=32,\n",
    "    lr=0.02,\n",
    "):\n",
    "    \"\"\"Train a model and track metrics\n",
    "\n",
    "    Parameters:\n",
    "    -----------\n",
    "    model : nn.Module\n",
    "        Model to train\n",
    "    X_train, y_train : torch.Tensor\n",
    "        Training data and labels\n",
    "    X_test, y_test : torch.Tensor\n",
    "        Test data and labels\n",
    "    model_name : str\n",
    "        Name for progress bar display\n",
    "    n_epochs : int\n",
    "        Number of training epochs\n",
    "    batch_size : int\n",
    "        Batch size for mini-batch training\n",
    "    lr : float\n",
    "        Learning rate\n",
    "\n",
    "    Returns:\n",
    "    --------\n",
    "    dict\n",
    "        Training results including losses, accuracies, and final report\n",
    "    \"\"\"\n",
    "    optimizer = torch.optim.Adam(model.parameters(), lr=lr)\n",
    "    criterion = nn.CrossEntropyLoss()\n",
    "\n",
    "    losses = []\n",
    "    train_accuracies = []\n",
    "    test_accuracies = []\n",
    "\n",
    "    model.train()\n",
    "\n",
    "    pbar = tqdm(range(n_epochs), leave=False, desc=f\"Training {model_name}\")\n",
    "    for _epoch in pbar:\n",
    "        # Shuffle training data\n",
    "        permutation = torch.randperm(X_train.size()[0])\n",
    "        total_loss = 0\n",
    "\n",
    "        # Mini-batch training\n",
    "        for i in range(0, X_train.size()[0], batch_size):\n",
    "            indices = permutation[i : i + batch_size]\n",
    "            batch_x, batch_y = X_train[indices], y_train[indices]\n",
    "\n",
    "            outputs = model(batch_x)\n",
    "            loss = criterion(outputs, batch_y)\n",
    "\n",
    "            optimizer.zero_grad()\n",
    "            loss.backward()\n",
    "            optimizer.step()\n",
    "\n",
    "            total_loss += loss.item()\n",
    "\n",
    "        avg_loss = total_loss / (X_train.size()[0] // batch_size)\n",
    "        losses.append(avg_loss)\n",
    "        pbar.set_description(f\"Training {model_name} - Loss: {avg_loss:.4f}\")\n",
    "\n",
    "        # Evaluation\n",
    "        model.eval()\n",
    "        with torch.no_grad():\n",
    "            # Training accuracy\n",
    "            train_outputs = model(X_train)\n",
    "            train_preds = torch.argmax(train_outputs, dim=1).numpy()\n",
    "            train_acc = accuracy_score(y_train.numpy(), train_preds)\n",
    "            train_accuracies.append(train_acc)\n",
    "\n",
    "            # Test accuracy\n",
    "            test_outputs = model(X_test)\n",
    "            test_preds = torch.argmax(test_outputs, dim=1).numpy()\n",
    "            test_acc = accuracy_score(y_test.numpy(), test_preds)\n",
    "            test_accuracies.append(test_acc)\n",
    "\n",
    "        model.train()\n",
    "\n",
    "    # Generate final classification report\n",
    "    model.eval()\n",
    "    with torch.no_grad():\n",
    "        final_test_outputs = model(X_test)\n",
    "        final_test_preds = torch.argmax(final_test_outputs, dim=1).numpy()\n",
    "        final_report = classification_report(y_test.numpy(), final_test_preds)\n",
    "\n",
    "    return {\n",
    "        \"losses\": losses,\n",
    "        \"train_accuracies\": train_accuracies,\n",
    "        \"test_accuracies\": test_accuracies,\n",
    "        \"final_test_acc\": test_accuracies[-1],\n",
    "        \"classification_report\": final_report,\n",
    "    }"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "\n",
    " ## 7. Multi-Run Training Function\n",
    "\n",
    " To ensure robust and statistically meaningful results, we train each model variant multiple times with different random initializations. This approach helps us:\n",
    "\n",
    " - Understand the variance in model performance\n",
    " - Identify which architectures are more stable\n",
    " - Avoid drawing conclusions from lucky/unlucky single runs\n",
    " - Calculate confidence intervals for performance metrics\n",
    "\n",
    " The function tracks both individual run results and aggregate statistics across all runs.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-11-10T09:09:17.775170100Z",
     "start_time": "2025-11-10T09:09:17.756740300Z"
    },
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def train_all_variants(X_train, y_train, X_test, y_test, num_runs=5):\n",
    "    \"\"\"Train all model variants multiple times and return results\n",
    "\n",
    "    Parameters:\n",
    "    -----------\n",
    "    X_train, y_train : torch.Tensor\n",
    "        Training data and labels\n",
    "    X_test, y_test : torch.Tensor\n",
    "        Test data and labels\n",
    "    num_runs : int\n",
    "        Number of independent training runs per variant\n",
    "\n",
    "    Returns:\n",
    "    --------\n",
    "    tuple\n",
    "        (all_results, best_models) - Complete results and best performing models\n",
    "    \"\"\"\n",
    "    variants = get_model_variants()\n",
    "    all_results = defaultdict(dict)\n",
    "    best_models = {}\n",
    "\n",
    "    for model_type, model_variants in variants.items():\n",
    "        print(f\"\\n\\nTraining {model_type} variants:\")\n",
    "        best_acc = 0\n",
    "\n",
    "        for variant in model_variants:\n",
    "            print(\n",
    "                f\"\\nTraining {variant['name']}... ({count_parameters(create_model(model_type, variant))} parameters)\"\n",
    "            )\n",
    "\n",
    "            # Store results from multiple runs\n",
    "            variant_runs = []\n",
    "\n",
    "            for run in range(num_runs):\n",
    "                model = create_model(model_type, variant)\n",
    "                print(f\"  Run {run + 1}/{num_runs}...\")\n",
    "\n",
    "                results = train_model(\n",
    "                    model,\n",
    "                    X_train,\n",
    "                    y_train,\n",
    "                    X_test,\n",
    "                    y_test,\n",
    "                    f\"{variant['name']}-run{run + 1}\",\n",
    "                )\n",
    "                results[\"model\"] = model\n",
    "                results[\"color\"] = variant[\"color\"]\n",
    "                results[\"linestyle\"] = variant[\"linestyle\"]\n",
    "                variant_runs.append(results)\n",
    "\n",
    "                # Track best model for each type\n",
    "                if results[\"final_test_acc\"] > best_acc:\n",
    "                    best_acc = results[\"final_test_acc\"]\n",
    "                    best_models[model_type] = {\n",
    "                        \"name\": variant[\"name\"],\n",
    "                        \"model\": model,\n",
    "                        \"results\": results,\n",
    "                    }\n",
    "\n",
    "            # Store all runs for this variant\n",
    "            all_results[model_type][variant[\"name\"]] = {\n",
    "                \"runs\": variant_runs,\n",
    "                \"color\": variant[\"color\"],\n",
    "                \"linestyle\": variant[\"linestyle\"],\n",
    "                \"avg_final_test_acc\": sum(run[\"final_test_acc\"] for run in variant_runs)\n",
    "                / num_runs,\n",
    "            }\n",
    "\n",
    "    return all_results, best_models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "\n",
    " ## 8. Visualization Functions\n",
    "\n",
    " ### Training Curves Visualization\n",
    "\n",
    " This function creates a comprehensive view of the training process by plotting three key metrics:\n",
    "\n",
    " 1. **Training Loss**: Shows how well the model fits the training data\n",
    " 2. **Training Accuracy**: Indicates the model's performance on training samples\n",
    " 3. **Test Accuracy**: Reveals the model's generalization capability\n",
    "\n",
    " For each model variant, we display:\n",
    " - **Solid line**: Mean performance across all runs\n",
    " - **Shaded area**: Performance envelope (min to max values)\n",
    "\n",
    " This visualization helps identify overfitting, convergence speed, and stability.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-11-10T09:09:17.782612700Z",
     "start_time": "2025-11-10T09:09:17.759907600Z"
    },
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def plot_training_curves(all_results):\n",
    "    \"\"\"Plot training curves for all model variants with average and envelope\n",
    "\n",
    "    Shows three subplots:\n",
    "    1. Training loss over epochs\n",
    "    2. Training accuracy over epochs\n",
    "    3. Test accuracy over epochs\n",
    "\n",
    "    Each line represents the mean across runs, with shaded areas showing min/max envelope.\n",
    "    \"\"\"\n",
    "    fig, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(20, 5))\n",
    "\n",
    "    # Plot each metric\n",
    "    for _model_type, variants in all_results.items():\n",
    "        for variant_name, variant_data in variants.items():\n",
    "            color = variant_data[\"color\"]\n",
    "            linestyle = variant_data[\"linestyle\"]\n",
    "            label = f\"{variant_name}\"\n",
    "\n",
    "            # Get data from all runs\n",
    "            losses_runs = [run[\"losses\"] for run in variant_data[\"runs\"]]\n",
    "            train_acc_runs = [run[\"train_accuracies\"] for run in variant_data[\"runs\"]]\n",
    "            test_acc_runs = [run[\"test_accuracies\"] for run in variant_data[\"runs\"]]\n",
    "\n",
    "            # Calculate mean values across all runs\n",
    "            epochs = len(losses_runs[0])\n",
    "            mean_losses = [\n",
    "                sum(run[i] for run in losses_runs) / len(losses_runs)\n",
    "                for i in range(epochs)\n",
    "            ]\n",
    "            mean_train_acc = [\n",
    "                sum(run[i] for run in train_acc_runs) / len(train_acc_runs)\n",
    "                for i in range(epochs)\n",
    "            ]\n",
    "            mean_test_acc = [\n",
    "                sum(run[i] for run in test_acc_runs) / len(test_acc_runs)\n",
    "                for i in range(epochs)\n",
    "            ]\n",
    "\n",
    "            # Calculate min and max values for the envelope\n",
    "            min_losses = [min(run[i] for run in losses_runs) for i in range(epochs)]\n",
    "            max_losses = [max(run[i] for run in losses_runs) for i in range(epochs)]\n",
    "\n",
    "            min_train_acc = [\n",
    "                min(run[i] for run in train_acc_runs) for i in range(epochs)\n",
    "            ]\n",
    "            max_train_acc = [\n",
    "                max(run[i] for run in train_acc_runs) for i in range(epochs)\n",
    "            ]\n",
    "\n",
    "            min_test_acc = [min(run[i] for run in test_acc_runs) for i in range(epochs)]\n",
    "            max_test_acc = [max(run[i] for run in test_acc_runs) for i in range(epochs)]\n",
    "\n",
    "            # Plot mean lines\n",
    "            ax1.plot(\n",
    "                mean_losses, label=label, color=color, linestyle=linestyle, linewidth=2\n",
    "            )\n",
    "            ax2.plot(\n",
    "                mean_train_acc,\n",
    "                label=label,\n",
    "                color=color,\n",
    "                linestyle=linestyle,\n",
    "                linewidth=2,\n",
    "            )\n",
    "            ax3.plot(\n",
    "                mean_test_acc,\n",
    "                label=label,\n",
    "                color=color,\n",
    "                linestyle=linestyle,\n",
    "                linewidth=2,\n",
    "            )\n",
    "\n",
    "            # Plot envelopes (filled area between min and max)\n",
    "            epochs_range = range(epochs)\n",
    "            ax1.fill_between(\n",
    "                epochs_range, min_losses, max_losses, color=color, alpha=0.2\n",
    "            )\n",
    "            ax2.fill_between(\n",
    "                epochs_range, min_train_acc, max_train_acc, color=color, alpha=0.2\n",
    "            )\n",
    "            ax3.fill_between(\n",
    "                epochs_range, min_test_acc, max_test_acc, color=color, alpha=0.2\n",
    "            )\n",
    "\n",
    "    # Customize plots\n",
    "    for ax, title in zip(\n",
    "        [ax1, ax2, ax3],\n",
    "        [\"Training Loss\", \"Training Accuracy\", \"Test Accuracy\"],\n",
    "        strict=False,\n",
    "    ):\n",
    "        ax.set_title(title, fontsize=12, pad=10)\n",
    "        ax.set_xlabel(\"Epoch\", fontsize=10)\n",
    "        ax.set_ylabel(title.split()[-1], fontsize=10)\n",
    "        ax.legend(fontsize=8, bbox_to_anchor=(1.05, 1), loc=\"upper left\")\n",
    "        ax.grid(True, linestyle=\"--\", alpha=0.7)\n",
    "        ax.spines[\"top\"].set_visible(False)\n",
    "        ax.spines[\"right\"].set_visible(False)\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "\n",
    " ### Confusion Matrix Visualization\n",
    "\n",
    " Confusion matrices provide detailed insights into classification performance by showing:\n",
    " - Which classes are correctly classified (diagonal elements)\n",
    " - Which classes are confused with each other (off-diagonal elements)\n",
    " - Overall performance patterns for each model architecture\n",
    "\n",
    " We display the confusion matrix for the best-performing model of each type.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-11-10T09:09:17.798960200Z",
     "start_time": "2025-11-10T09:09:17.793299500Z"
    },
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def plot_best_confusion_matrices(best_models, X_test, y_test):\n",
    "    \"\"\"Plot confusion matrices for the best model of each type\"\"\"\n",
    "    fig, axes = plt.subplots(1, 3, figsize=(20, 5))\n",
    "    class_names = [\"setosa\", \"versicolor\", \"virginica\"]\n",
    "\n",
    "    for idx, (model_type, best) in enumerate(best_models.items()):\n",
    "        model = best[\"model\"]\n",
    "        model.eval()\n",
    "        with torch.no_grad():\n",
    "            outputs = model(X_test)\n",
    "            predictions = torch.argmax(outputs, dim=1).numpy()\n",
    "\n",
    "        cm = confusion_matrix(y_test.numpy(), predictions)\n",
    "        sns.heatmap(\n",
    "            cm,\n",
    "            annot=True,\n",
    "            fmt=\"d\",\n",
    "            cmap=\"Blues\",\n",
    "            xticklabels=class_names,\n",
    "            yticklabels=class_names,\n",
    "            ax=axes[idx],\n",
    "        )\n",
    "        axes[idx].set_title(f\"Best {model_type}\\n({best['name']})\")\n",
    "        axes[idx].set_xlabel(\"Predicted\")\n",
    "        axes[idx].set_ylabel(\"True\")\n",
    "        plt.setp(axes[idx].get_xticklabels(), rotation=45)\n",
    "        plt.setp(axes[idx].get_yticklabels(), rotation=45)\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "\n",
    " ## 9. Results Summary Function\n",
    "\n",
    " This function provides a comprehensive statistical analysis of the experimental results, including:\n",
    "\n",
    " - **Parameter efficiency**: Number of trainable parameters for each model\n",
    " - **Performance statistics**: Mean accuracy, standard deviation, min/max values\n",
    " - **Detailed classification reports**: Precision, recall, and F1-score for each class\n",
    "\n",
    " These metrics help in making informed decisions about model selection based on the specific requirements of your application.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-11-10T09:09:17.846457400Z",
     "start_time": "2025-11-10T09:09:17.819929900Z"
    },
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def print_comparison_results(all_results, best_models):\n",
    "    \"\"\"Print detailed comparison of all models and variants with multi-run statistics\"\"\"\n",
    "    print(\"\\n----- Model Comparison Results -----\")\n",
    "\n",
    "    # Print results for all variants\n",
    "    print(\"\\nAll Model Variants Results (averaged over multiple runs):\")\n",
    "    for model_type, variants in all_results.items():\n",
    "        print(f\"\\n{model_type} Variants:\")\n",
    "        for variant_name, variant_data in variants.items():\n",
    "            print(f\"\\n{variant_name}:\")\n",
    "            print(f\"Parameters: {count_parameters(variant_data['runs'][0]['model'])}\")\n",
    "\n",
    "            # Calculate statistics across runs\n",
    "            final_accs = [run[\"final_test_acc\"] for run in variant_data[\"runs\"]]\n",
    "            avg_acc = sum(final_accs) / len(final_accs)\n",
    "            min_acc = min(final_accs)\n",
    "            max_acc = max(final_accs)\n",
    "            std_acc = (\n",
    "                sum((acc - avg_acc) ** 2 for acc in final_accs) / len(final_accs)\n",
    "            ) ** 0.5\n",
    "\n",
    "            print(\n",
    "                f\"Final Test Accuracy: {avg_acc:.4f} ± {std_acc:.4f} (min: {min_acc:.4f}, max: {max_acc:.4f})\"\n",
    "            )\n",
    "\n",
    "    # Print best model results\n",
    "    print(\"\\nBest Models:\")\n",
    "    for model_type, best in best_models.items():\n",
    "        print(f\"\\nBest {model_type} Model: {best['name']}\")\n",
    "        print(f\"Final Test Accuracy: {best['results']['final_test_acc']:.4f}\")\n",
    "        print(f\"Classification Report:\\n{best['results']['classification_report']}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "\n",
    " ## 10. Run the Complete Experiment\n",
    "\n",
    " Now we'll execute the full experimental pipeline. The experiment consists of:\n",
    "\n",
    " 1. **Multiple training runs**: Each model variant is trained 5 times with different random seeds\n",
    " 2. **Performance tracking**: All metrics are recorded for statistical analysis\n",
    " 3. **Visualization**: Training curves and confusion matrices are generated\n",
    " 4. **Statistical summary**: Detailed performance statistics are computed\n",
    "\n",
    " This comprehensive approach ensures reliable and reproducible results.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-11-10T09:10:55.159331300Z",
     "start_time": "2025-11-10T09:09:17.830714100Z"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting training process...\n",
      "\n",
      "\n",
      "Training MLP variants:\n",
      "\n",
      "Training MLP... (67 parameters)\n",
      "  Run 1/5...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                 \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Run 2/5...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                 \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Run 3/5...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                 \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Run 4/5...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                  \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Run 5/5...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                 \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Training LINEAR variants:\n",
      "\n",
      "Training LINEAR-7modes-nobunching... (192 parameters)\n",
      "  Run 1/5...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                                      \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Run 2/5...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                                      \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Run 3/5...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                                      \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Run 4/5...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                                      \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Run 5/5...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                                      \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Training GROUPING variants:\n",
      "\n",
      "Training LEXGROUPING-7modes... (60 parameters)\n",
      "  Run 1/5...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                                \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Run 2/5...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                                \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Run 3/5...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\BenjaminStott\\Dev\\merlin\\venv2\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1731: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", result.shape[0])\n",
      "C:\\Users\\BenjaminStott\\Dev\\merlin\\venv2\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1731: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", result.shape[0])\n",
      "C:\\Users\\BenjaminStott\\Dev\\merlin\\venv2\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1731: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", result.shape[0])\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Run 4/5...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                                \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Run 5/5...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\BenjaminStott\\Dev\\merlin\\venv2\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1731: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", result.shape[0])\n",
      "C:\\Users\\BenjaminStott\\Dev\\merlin\\venv2\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1731: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", result.shape[0])\n",
      "C:\\Users\\BenjaminStott\\Dev\\merlin\\venv2\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1731: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", result.shape[0])\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Training MODGROUPING-7modes... (60 parameters)\n",
      "  Run 1/5...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                                \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Run 2/5...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                                \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Run 3/5...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                                \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Run 4/5...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                                \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Run 5/5...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\BenjaminStott\\Dev\\merlin\\venv2\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1731: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", result.shape[0])\n",
      "C:\\Users\\BenjaminStott\\Dev\\merlin\\venv2\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1731: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", result.shape[0])\n",
      "C:\\Users\\BenjaminStott\\Dev\\merlin\\venv2\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1731: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", result.shape[0])\n"
     ]
    }
   ],
   "source": [
    "# Set number of independent runs per model variant\n",
    "num_runs = 5\n",
    "\n",
    "# Train all variants\n",
    "print(\"Starting training process...\")\n",
    "all_results, best_models = train_all_variants(\n",
    "    X_train, y_train, X_test, y_test, num_runs=num_runs\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    " ### Visualize Training Progress\n",
    "\n",
    " The following plots show how each model's performance evolves during training. Pay attention to:\n",
    " - **Convergence speed**: How quickly each model reaches its best performance\n",
    " - **Stability**: Width of the shaded area indicates variance across runs\n",
    " - **Final performance**: Where each model plateaus"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-11-10T09:10:56.751500900Z",
     "start_time": "2025-11-10T09:10:55.159331300Z"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": "<Figure size 2000x500 with 3 Axes>"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_training_curves(all_results)"
   ]
  },
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    "\n",
    " ### Confusion Matrices for Best Models\n",
    "\n",
    " These confusion matrices reveal the classification patterns of the best-performing model from each architecture type. Look for:\n",
    " - **Perfect classification**: Bright blue diagonal with zeros elsewhere\n",
    " - **Common confusions**: Off-diagonal elements show which classes are hard to distinguish\n",
    "\n"
   ]
  },
  {
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    {
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     "text": [
      "\n",
      "----- Model Comparison Results -----\n",
      "\n",
      "All Model Variants Results (averaged over multiple runs):\n",
      "\n",
      "MLP Variants:\n",
      "\n",
      "MLP:\n",
      "Parameters: 67\n",
      "Final Test Accuracy: 0.9133 ± 0.0163 (min: 0.9000, max: 0.9333)\n",
      "\n",
      "LINEAR Variants:\n",
      "\n",
      "LINEAR-7modes-nobunching:\n",
      "Parameters: 192\n",
      "Final Test Accuracy: 0.9333 ± 0.0298 (min: 0.9000, max: 0.9667)\n",
      "\n",
      "GROUPING Variants:\n",
      "\n",
      "LEXGROUPING-7modes:\n",
      "Parameters: 60\n",
      "Final Test Accuracy: 0.7467 ± 0.0718 (min: 0.6333, max: 0.8333)\n",
      "\n",
      "MODGROUPING-7modes:\n",
      "Parameters: 60\n",
      "Final Test Accuracy: 0.7333 ± 0.0699 (min: 0.6333, max: 0.8000)\n",
      "\n",
      "Best Models:\n",
      "\n",
      "Best MLP Model: MLP\n",
      "Final Test Accuracy: 0.9333\n",
      "Classification Report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00        13\n",
      "           1       0.83      0.83      0.83         6\n",
      "           2       0.91      0.91      0.91        11\n",
      "\n",
      "    accuracy                           0.93        30\n",
      "   macro avg       0.91      0.91      0.91        30\n",
      "weighted avg       0.93      0.93      0.93        30\n",
      "\n",
      "\n",
      "Best LINEAR Model: LINEAR-7modes-nobunching\n",
      "Final Test Accuracy: 0.9667\n",
      "Classification Report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00        13\n",
      "           1       0.86      1.00      0.92         6\n",
      "           2       1.00      0.91      0.95        11\n",
      "\n",
      "    accuracy                           0.97        30\n",
      "   macro avg       0.95      0.97      0.96        30\n",
      "weighted avg       0.97      0.97      0.97        30\n",
      "\n",
      "\n",
      "Best GROUPING Model: LEXGROUPING-7modes\n",
      "Final Test Accuracy: 0.8333\n",
      "Classification Report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00        13\n",
      "           1       0.60      0.50      0.55         6\n",
      "           2       0.75      0.82      0.78        11\n",
      "\n",
      "    accuracy                           0.83        30\n",
      "   macro avg       0.78      0.77      0.78        30\n",
      "weighted avg       0.83      0.83      0.83        30\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print_comparison_results(all_results, best_models)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    " ## 11. Key Findings and Conclusions\n",
    "\n",
    " Based on the experimental results, we can draw several important conclusions:\n",
    "\n",
    " **Model Complexity vs Performance:**\n",
    " - Compare the number of parameters required by each architecture\n",
    " - Assess whether quantum models achieve similar performance with fewer parameters\n",
    "\n",
    " **Training Stability:**\n",
    " - The variance across multiple runs indicates how sensitive each model is to initialization\n",
    " - Lower variance suggests more reliable training\n",
    "\n",
    " **Generalization Capability:**\n",
    " - The gap between training and test accuracy reveals overfitting tendencies\n",
    " - Smaller gaps indicate better generalization\n",
    "\n",
    " **Classification Patterns:**\n",
    " - Confusion matrices show which flower species are most difficult to distinguish\n",
    " - This can guide feature engineering or model selection\n",
    "\n",
    " **Practical Considerations:**\n",
    "\n",
    " For deployment, consider:\n",
    " - **Classical MLPs**: Well-understood, fast inference, established deployment pipelines\n",
    " - **Quantum models**: Potentially more parameter-efficient, may offer advantages on quantum hardware\n",
    "\n",
    " **Future Research Directions:**\n",
    "\n",
    " 1. **Scaling to larger datasets**: Test on more complex classification tasks\n",
    " 2. **Noise modeling**: Investigate performance under realistic quantum noise conditions\n",
    " 3. **Hybrid architectures**: Combine classical and quantum layers\n",
    " 4. **Hardware implementation**: Evaluate on actual quantum photonic devices\n",
    " 5. **Feature encoding strategies**: Explore different ways to encode classical data into quantum states"
   ]
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