{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from itertools import product"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('data/bel_data_test.csv')\n",
    "data = np.array(data)\n",
    "\n",
    "# Split data\n",
    "X = data[:, 1:].T\n",
    "Y = data[:, 0].astype(int)\n",
    "\n",
    "# Separate test set (first 1000 rows)\n",
    "X_test = X[:, :1000]\n",
    "Y_test = Y[:1000]\n",
    "\n",
    "# Remaining data for training and validation\n",
    "X_remain = X[:, 1000:]\n",
    "Y_remain = Y[1000:]\n",
    "\n",
    "# Split remaining data into training and validation sets\n",
    "X_train, X_val, Y_train, Y_val = train_test_split(X_remain.T, Y_remain, test_size=0.2, random_state=42)\n",
    "X_train, X_val = X_train.T, X_val.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Determine input and output layer sizes\n",
    "input_size = X_train.shape[0]\n",
    "output_size = len(np.unique(Y))-1\n",
    "\n",
    "print(f\"Input layer size: {input_size}\")\n",
    "print(f\"Output layer size: {output_size}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def init_params(layer_dims):\n",
    "    params = {}\n",
    "    L = len(layer_dims)\n",
    "    \n",
    "    for l in range(1, L):\n",
    "        params[f'W{l}'] = np.random.randn(layer_dims[l], layer_dims[l-1]) * np.sqrt(2. / layer_dims[l-1])\n",
    "        params[f'b{l}'] = np.zeros((layer_dims[l], 1))\n",
    "    \n",
    "    return params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def ReLU(Z):\n",
    "    return np.maximum(Z, 0)\n",
    "\n",
    "def softmax(Z):\n",
    "    A = np.exp(Z) / sum(np.exp(Z))\n",
    "    return A\n",
    "\n",
    "def forward_prop(X, params):\n",
    "    caches = []\n",
    "    A = X\n",
    "    L = len(params) // 2\n",
    "\n",
    "    for l in range(1, L):\n",
    "        A_prev = A\n",
    "        W = params[f'W{l}']\n",
    "        b = params[f'b{l}']\n",
    "        Z = np.dot(W, A_prev) + b\n",
    "        A = ReLU(Z)\n",
    "        caches.append((A_prev, W, b, Z))\n",
    "\n",
    "    WL = params[f'W{L}']\n",
    "    bL = params[f'b{L}']\n",
    "    ZL = np.dot(WL, A) + bL\n",
    "    AL = softmax(ZL)\n",
    "    caches.append((A, WL, bL, ZL))\n",
    "\n",
    "    return AL, caches\n",
    "\n",
    "def ReLU_deriv(Z):\n",
    "    return Z > 0\n",
    "\n",
    "def one_hot(Y):\n",
    "    one_hot_Y = np.zeros((Y.size, Y.max() + 1))\n",
    "    one_hot_Y[np.arange(Y.size), Y] = 1\n",
    "    one_hot_Y = one_hot_Y.T\n",
    "    return one_hot_Y\n",
    "\n",
    "def backward_prop(AL, Y, caches):\n",
    "    grads = {}\n",
    "    L = len(caches)\n",
    "    m = AL.shape[1]\n",
    "    Y = one_hot(Y)\n",
    "\n",
    "    dAL = AL - Y\n",
    "    current_cache = caches[L-1]\n",
    "    grads[f\"dW{L}\"] = 1 / m * np.dot(dAL, current_cache[0].T)\n",
    "    grads[f\"db{L}\"] = 1 / m * np.sum(dAL, axis=1, keepdims=True)\n",
    "    dA_prev = np.dot(current_cache[1].T, dAL)\n",
    "\n",
    "    for l in reversed(range(L-1)):\n",
    "        current_cache = caches[l]\n",
    "        dZ = dA_prev * ReLU_deriv(current_cache[3])\n",
    "        grads[f\"dW{l+1}\"] = 1 / m * np.dot(dZ, current_cache[0].T)\n",
    "        grads[f\"db{l+1}\"] = 1 / m * np.sum(dZ, axis=1, keepdims=True)\n",
    "        if l > 0:\n",
    "            dA_prev = np.dot(current_cache[1].T, dZ)\n",
    "\n",
    "    return grads\n",
    "\n",
    "def update_params(params, grads, alpha):\n",
    "    L = len(params) // 2\n",
    "\n",
    "    for l in range(1, L + 1):\n",
    "        params[f\"W{l}\"] -= alpha * grads[f\"dW{l}\"]\n",
    "        params[f\"b{l}\"] -= alpha * grads[f\"db{l}\"]\n",
    "\n",
    "    return params\n",
    "\n",
    "def get_predictions(AL):\n",
    "    return np.argmax(AL, axis=0)\n",
    "\n",
    "def get_accuracy(predictions, Y):\n",
    "    return np.sum(predictions == Y) / Y.size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def gradient_descent(X_train, Y_train, X_val, Y_val, layer_dims, alpha, iterations, accuracy_threshold=0.85):\n",
    "    params = init_params(layer_dims)\n",
    "    best_val_accuracy = 0\n",
    "    acc_store = []\n",
    "    \n",
    "    for i in range(iterations):\n",
    "        AL, caches = forward_prop(X_train, params)\n",
    "        grads = backward_prop(AL, Y_train, caches)\n",
    "        params = update_params(params, grads, alpha)\n",
    "\n",
    "        if i % 100 == 0:\n",
    "            train_predictions = get_predictions(AL)\n",
    "            train_accuracy = get_accuracy(train_predictions, Y_train)\n",
    "            \n",
    "            val_AL, _ = forward_prop(X_val, params)\n",
    "            val_predictions = get_predictions(val_AL)\n",
    "            val_accuracy = get_accuracy(val_predictions, Y_val)\n",
    "            \n",
    "            print(f\"Iteration {i}: Train Accuracy: {train_accuracy:.4f}, Validation Accuracy: {val_accuracy:.4f}\")\n",
    "            acc_store.append((train_accuracy, val_accuracy))\n",
    "            \n",
    "            if val_accuracy > best_val_accuracy:\n",
    "                best_val_accuracy = val_accuracy\n",
    "                best_params = params.copy()\n",
    "            \n",
    "            # Early stopping condition based on validation accuracy threshold\n",
    "            if val_accuracy >= accuracy_threshold:\n",
    "                print(f\"Validation accuracy threshold of {accuracy_threshold:.2f} reached. Stopping training.\")\n",
    "                break\n",
    "\n",
    "    return best_params, best_val_accuracy, acc_store"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def grid_search(X_train, Y_train, X_val, Y_val, layer_configs, alpha, iterations, accuracy_threshold=0.85):\n",
    "    results = []\n",
    "    \n",
    "    for layer_config in layer_configs:\n",
    "        layer_dims = [input_size] + list(layer_config) + [output_size]\n",
    "        print(f\"Training architecture: {layer_dims}\")\n",
    "        best_params, accuracy, acc_store = gradient_descent(X_train, Y_train, X_val, Y_val, layer_dims, alpha, iterations, accuracy_threshold)\n",
    "        results.append((layer_config, accuracy, best_params, acc_store))\n",
    "        print(f\"Architecture {layer_dims}: Best Validation Accuracy: {accuracy:.4f}\\n\")\n",
    "    \n",
    "    return sorted(results, key=lambda x: x[1], reverse=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# def predict(X, parameters):\n",
    "#     AL, _ = forward_propagation(X, parameters)\n",
    "#     predictions = (AL > 0.5)  # Classify as 1 if greater than 0.5\n",
    "#     return predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "hidden_layers = [1, 2]\n",
    "neurons_per_layer = [64, 128, 256]\n",
    "layer_configs = list(product(*[neurons_per_layer] * max(hidden_layers)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Perform grid search\n",
    "print(\"Performing grid search...\")\n",
    "best_configs = grid_search(X_train, Y_train, X_val, Y_val, layer_configs, alpha=0.01, iterations=4000)\n",
    "\n",
    "print(\"\\nTop 5 Architectures:\")\n",
    "for config, accuracy, _, _ in best_configs[:5]:\n",
    "    print(f\"Hidden Layers: {config}, Validation Accuracy: {accuracy:.4f}\")\n",
    "\n",
    "# Select the best configuration\n",
    "best_config, best_accuracy, best_params, best_acc_store = best_configs[0]\n",
    "best_layer_dims = [input_size] + list(best_config) + [output_size]\n",
    "\n",
    "print(f\"\\nBest architecture: {best_layer_dims}\")\n",
    "print(f\"Best validation accuracy: {best_accuracy:.4f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save the accuracy data for the best model\n",
    "df = pd.DataFrame(best_acc_store, columns=['Train Accuracy', 'Validation Accuracy'])\n",
    "df.to_csv('results/bel_acc.csv', index=False)\n",
    "\n",
    "# Save the weights of the best model\n",
    "np.savez(\"weights/bel_weights\", **best_params)\n",
    "\n",
    "# Evaluate on test set\n",
    "test_AL, _ = forward_prop(X_test, best_params)\n",
    "test_predictions = get_predictions(test_AL)\n",
    "test_accuracy = get_accuracy(test_predictions, Y_test)\n",
    "print(f\"Test Accuracy: {test_accuracy:.4f}\")"
   ]
  }
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