{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "5fbc2d16-59f9-4be3-b93e-1a5440c7efd0",
   "metadata": {},
   "source": [
    "# Tutorial 4 - Classification Example"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1afe6a1e-3ab4-4f3f-ad47-f6cd66419504",
   "metadata": {},
   "source": [
    "After working with regression examples in the first tutorials, we proceed with a classification example in this one."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "0a2ef2a6-f681-427f-8252-ade2111ce0e6",
   "metadata": {},
   "outputs": [],
   "source": [
    "from jaxkan.models.KAN import KAN\n",
    "\n",
    "import jax\n",
    "import jax.numpy as jnp\n",
    "\n",
    "from sklearn.datasets import make_classification\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import f1_score\n",
    "\n",
    "from flax import nnx\n",
    "import optax\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "import os\n",
    "os.environ[\"TF_CPP_MIN_LOG_LEVEL\"] = \"2\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3ed80049-3bbd-41d9-8f52-94f8ba6544bd",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "60e70a5d-0340-4bdc-af4e-150d0098a87e",
   "metadata": {},
   "source": [
    "## Data Generation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "166ad90d-430e-45ca-86a8-e6e4dbc1c943",
   "metadata": {},
   "source": [
    "We will use the `make_classification` method of `sklearn.datasets` to generate some mock data for the classification problem."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b986e75a-6d4a-402f-bea7-36d13f4a7866",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Generate synthetic data\n",
    "seed = 42\n",
    "\n",
    "X, y = make_classification(\n",
    "    n_samples=1000,\n",
    "    n_features=2,\n",
    "    n_informative=2,\n",
    "    n_redundant=0,\n",
    "    n_clusters_per_class=1,\n",
    "    class_sep=1.5,\n",
    "    random_state=seed\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "db8bf4bc-49fe-4014-94aa-4e5dbf632369",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the generated data\n",
    "plt.figure(figsize=(7, 4))\n",
    "plt.scatter(X[y == 0][:, 0], X[y == 0][:, 1], label='Class 0', alpha=0.7, marker='x', color='#a3630f')\n",
    "plt.scatter(X[y == 1][:, 0], X[y == 1][:, 1], label='Class 1', alpha=0.7, marker='o', color='#25599c')\n",
    "plt.title(\"Synthetic Data for Binary Classification\")\n",
    "plt.xlabel(\"Feature 1\")\n",
    "plt.ylabel(\"Feature 2\")\n",
    "plt.legend()\n",
    "plt.grid(alpha=0.3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f7b5ec7c-7b0d-4fdd-aad2-f5c55f9888b5",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "485197f9-2e41-481e-a2cd-977452622ff3",
   "metadata": {},
   "source": [
    "## Preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c26fac97-6716-47ff-b8db-824df7c6aae2",
   "metadata": {},
   "source": [
    "We split the data in train/test sets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ad91a42a-ffe8-4b8c-b1a7-cc9b60dfe5c0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training set size: (800, 2)\n",
      "Test set size: (200, 2)\n"
     ]
    }
   ],
   "source": [
    "y = y.reshape(-1, 1)\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=seed)\n",
    "\n",
    "print(\"Training set size:\", X_train.shape)\n",
    "print(\"Test set size:\", X_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f38f5f5b-211c-42cd-b5ca-5d2f5cd60a85",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "4e9d9bba-71e2-4d5b-95b1-36167fb70c1a",
   "metadata": {},
   "source": [
    "## KAN Model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2fd54c6c-3d28-4864-af38-3b22875d0f0a",
   "metadata": {},
   "source": [
    "We covered KAN Model selection in the first tutorial, so feel free to refer to it for more info. For this example, we will be using a Fourier KAN Layer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b350b56f-5daa-411f-9090-b544760c34ef",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[38;2;79;201;177mKAN\u001b[0m\u001b[38;2;255;213;3m(\u001b[0m\u001b[38;2;105;105;105m # Param: 1,093 (4.4 KB)\u001b[0m\n",
      "  \u001b[38;2;156;220;254mlayer_type\u001b[0m\u001b[38;2;212;212;212m=\u001b[0m\u001b[38;2;207;144;120m'fourier'\u001b[0m,\n",
      "  \u001b[38;2;156;220;254mlayers\u001b[0m\u001b[38;2;212;212;212m=\u001b[0m\u001b[38;2;79;201;177mList\u001b[0m\u001b[38;2;255;213;3m([\u001b[0m\u001b[38;2;105;105;105m\u001b[0m\n",
      "    \u001b[38;2;212;212;212m\u001b[0m\u001b[38;2;79;201;177mFourierLayer\u001b[0m\u001b[38;2;255;213;3m(\u001b[0m\u001b[38;2;105;105;105m # Param: 246 (984 B)\u001b[0m\n",
      "      \u001b[38;2;156;220;254mn_in\u001b[0m\u001b[38;2;212;212;212m=\u001b[0m\u001b[38;2;182;207;169m2\u001b[0m,\n",
      "      \u001b[38;2;156;220;254mn_out\u001b[0m\u001b[38;2;212;212;212m=\u001b[0m\u001b[38;2;182;207;169m6\u001b[0m,\n",
      "      \u001b[38;2;156;220;254mD\u001b[0m\u001b[38;2;212;212;212m=\u001b[0m\u001b[38;2;182;207;169m10\u001b[0m,\n",
      "      \u001b[38;2;156;220;254mbias\u001b[0m\u001b[38;2;212;212;212m=\u001b[0m\u001b[38;2;79;201;177mParam\u001b[0m\u001b[38;2;255;213;3m(\u001b[0m\u001b[38;2;105;105;105m # 6 (24 B)\u001b[0m\n",
      "        \u001b[38;2;156;220;254mvalue\u001b[0m\u001b[38;2;212;212;212m=\u001b[0m\u001b[38;2;79;201;177mArray\u001b[0m\u001b[38;2;255;213;3m(\u001b[0m\u001b[38;2;156;220;254mshape\u001b[0m\u001b[38;2;212;212;212m=\u001b[0m\u001b[38;2;255;213;3m(\u001b[0m\u001b[38;2;182;207;169m6\u001b[0m,\u001b[38;2;255;213;3m)\u001b[0m, \u001b[38;2;156;220;254mdtype\u001b[0m\u001b[38;2;212;212;212m=\u001b[0mdtype('float32')\u001b[38;2;255;213;3m)\u001b[0m\n",
      "      \u001b[38;2;255;213;3m)\u001b[0m,\n",
      "      \u001b[38;2;156;220;254mc_cos\u001b[0m\u001b[38;2;212;212;212m=\u001b[0m\u001b[38;2;79;201;177mParam\u001b[0m\u001b[38;2;255;213;3m(\u001b[0m\u001b[38;2;105;105;105m # 120 (480 B)\u001b[0m\n",
      "        \u001b[38;2;156;220;254mvalue\u001b[0m\u001b[38;2;212;212;212m=\u001b[0m\u001b[38;2;79;201;177mArray\u001b[0m\u001b[38;2;255;213;3m(\u001b[0m\u001b[38;2;156;220;254mshape\u001b[0m\u001b[38;2;212;212;212m=\u001b[0m\u001b[38;2;255;213;3m(\u001b[0m\u001b[38;2;182;207;169m6\u001b[0m, \u001b[38;2;182;207;169m2\u001b[0m, \u001b[38;2;182;207;169m10\u001b[0m\u001b[38;2;255;213;3m)\u001b[0m, \u001b[38;2;156;220;254mdtype\u001b[0m\u001b[38;2;212;212;212m=\u001b[0mdtype('float32')\u001b[38;2;255;213;3m)\u001b[0m\n",
      "      \u001b[38;2;255;213;3m)\u001b[0m,\n",
      "      \u001b[38;2;156;220;254mc_sin\u001b[0m\u001b[38;2;212;212;212m=\u001b[0m\u001b[38;2;79;201;177mParam\u001b[0m\u001b[38;2;255;213;3m(\u001b[0m\u001b[38;2;105;105;105m # 120 (480 B)\u001b[0m\n",
      "        \u001b[38;2;156;220;254mvalue\u001b[0m\u001b[38;2;212;212;212m=\u001b[0m\u001b[38;2;79;201;177mArray\u001b[0m\u001b[38;2;255;213;3m(\u001b[0m\u001b[38;2;156;220;254mshape\u001b[0m\u001b[38;2;212;212;212m=\u001b[0m\u001b[38;2;255;213;3m(\u001b[0m\u001b[38;2;182;207;169m6\u001b[0m, \u001b[38;2;182;207;169m2\u001b[0m, \u001b[38;2;182;207;169m10\u001b[0m\u001b[38;2;255;213;3m)\u001b[0m, \u001b[38;2;156;220;254mdtype\u001b[0m\u001b[38;2;212;212;212m=\u001b[0mdtype('float32')\u001b[38;2;255;213;3m)\u001b[0m\n",
      "      \u001b[38;2;255;213;3m)\u001b[0m\n",
      "    \u001b[38;2;255;213;3m)\u001b[0m,\n",
      "    \u001b[38;2;212;212;212m\u001b[0m\u001b[38;2;79;201;177mFourierLayer\u001b[0m\u001b[38;2;255;213;3m(\u001b[0m\u001b[38;2;105;105;105m # Param: 726 (2.9 KB)\u001b[0m\n",
      "      \u001b[38;2;156;220;254mn_in\u001b[0m\u001b[38;2;212;212;212m=\u001b[0m\u001b[38;2;182;207;169m6\u001b[0m,\n",
      "      \u001b[38;2;156;220;254mn_out\u001b[0m\u001b[38;2;212;212;212m=\u001b[0m\u001b[38;2;182;207;169m6\u001b[0m,\n",
      "      \u001b[38;2;156;220;254mD\u001b[0m\u001b[38;2;212;212;212m=\u001b[0m\u001b[38;2;182;207;169m10\u001b[0m,\n",
      "      \u001b[38;2;156;220;254mbias\u001b[0m\u001b[38;2;212;212;212m=\u001b[0m\u001b[38;2;79;201;177mParam\u001b[0m\u001b[38;2;255;213;3m(\u001b[0m\u001b[38;2;105;105;105m # 6 (24 B)\u001b[0m\n",
      "        \u001b[38;2;156;220;254mvalue\u001b[0m\u001b[38;2;212;212;212m=\u001b[0m\u001b[38;2;79;201;177mArray\u001b[0m\u001b[38;2;255;213;3m(\u001b[0m\u001b[38;2;156;220;254mshape\u001b[0m\u001b[38;2;212;212;212m=\u001b[0m\u001b[38;2;255;213;3m(\u001b[0m\u001b[38;2;182;207;169m6\u001b[0m,\u001b[38;2;255;213;3m)\u001b[0m, \u001b[38;2;156;220;254mdtype\u001b[0m\u001b[38;2;212;212;212m=\u001b[0mdtype('float32')\u001b[38;2;255;213;3m)\u001b[0m\n",
      "      \u001b[38;2;255;213;3m)\u001b[0m,\n",
      "      \u001b[38;2;156;220;254mc_cos\u001b[0m\u001b[38;2;212;212;212m=\u001b[0m\u001b[38;2;79;201;177mParam\u001b[0m\u001b[38;2;255;213;3m(\u001b[0m\u001b[38;2;105;105;105m # 360 (1.4 KB)\u001b[0m\n",
      "        \u001b[38;2;156;220;254mvalue\u001b[0m\u001b[38;2;212;212;212m=\u001b[0m\u001b[38;2;79;201;177mArray\u001b[0m\u001b[38;2;255;213;3m(\u001b[0m\u001b[38;2;156;220;254mshape\u001b[0m\u001b[38;2;212;212;212m=\u001b[0m\u001b[38;2;255;213;3m(\u001b[0m\u001b[38;2;182;207;169m6\u001b[0m, \u001b[38;2;182;207;169m6\u001b[0m, \u001b[38;2;182;207;169m10\u001b[0m\u001b[38;2;255;213;3m)\u001b[0m, \u001b[38;2;156;220;254mdtype\u001b[0m\u001b[38;2;212;212;212m=\u001b[0mdtype('float32')\u001b[38;2;255;213;3m)\u001b[0m\n",
      "      \u001b[38;2;255;213;3m)\u001b[0m,\n",
      "      \u001b[38;2;156;220;254mc_sin\u001b[0m\u001b[38;2;212;212;212m=\u001b[0m\u001b[38;2;79;201;177mParam\u001b[0m\u001b[38;2;255;213;3m(\u001b[0m\u001b[38;2;105;105;105m # 360 (1.4 KB)\u001b[0m\n",
      "        \u001b[38;2;156;220;254mvalue\u001b[0m\u001b[38;2;212;212;212m=\u001b[0m\u001b[38;2;79;201;177mArray\u001b[0m\u001b[38;2;255;213;3m(\u001b[0m\u001b[38;2;156;220;254mshape\u001b[0m\u001b[38;2;212;212;212m=\u001b[0m\u001b[38;2;255;213;3m(\u001b[0m\u001b[38;2;182;207;169m6\u001b[0m, \u001b[38;2;182;207;169m6\u001b[0m, \u001b[38;2;182;207;169m10\u001b[0m\u001b[38;2;255;213;3m)\u001b[0m, \u001b[38;2;156;220;254mdtype\u001b[0m\u001b[38;2;212;212;212m=\u001b[0mdtype('float32')\u001b[38;2;255;213;3m)\u001b[0m\n",
      "      \u001b[38;2;255;213;3m)\u001b[0m\n",
      "    \u001b[38;2;255;213;3m)\u001b[0m,\n",
      "    \u001b[38;2;212;212;212m\u001b[0m\u001b[38;2;79;201;177mFourierLayer\u001b[0m\u001b[38;2;255;213;3m(\u001b[0m\u001b[38;2;105;105;105m # Param: 121 (484 B)\u001b[0m\n",
      "      \u001b[38;2;156;220;254mn_in\u001b[0m\u001b[38;2;212;212;212m=\u001b[0m\u001b[38;2;182;207;169m6\u001b[0m,\n",
      "      \u001b[38;2;156;220;254mn_out\u001b[0m\u001b[38;2;212;212;212m=\u001b[0m\u001b[38;2;182;207;169m1\u001b[0m,\n",
      "      \u001b[38;2;156;220;254mD\u001b[0m\u001b[38;2;212;212;212m=\u001b[0m\u001b[38;2;182;207;169m10\u001b[0m,\n",
      "      \u001b[38;2;156;220;254mbias\u001b[0m\u001b[38;2;212;212;212m=\u001b[0m\u001b[38;2;79;201;177mParam\u001b[0m\u001b[38;2;255;213;3m(\u001b[0m\u001b[38;2;105;105;105m # 1 (4 B)\u001b[0m\n",
      "        \u001b[38;2;156;220;254mvalue\u001b[0m\u001b[38;2;212;212;212m=\u001b[0mArray([0.], dtype=float32)\n",
      "      \u001b[38;2;255;213;3m)\u001b[0m,\n",
      "      \u001b[38;2;156;220;254mc_cos\u001b[0m\u001b[38;2;212;212;212m=\u001b[0m\u001b[38;2;79;201;177mParam\u001b[0m\u001b[38;2;255;213;3m(\u001b[0m\u001b[38;2;105;105;105m # 60 (240 B)\u001b[0m\n",
      "        \u001b[38;2;156;220;254mvalue\u001b[0m\u001b[38;2;212;212;212m=\u001b[0m\u001b[38;2;79;201;177mArray\u001b[0m\u001b[38;2;255;213;3m(\u001b[0m\u001b[38;2;156;220;254mshape\u001b[0m\u001b[38;2;212;212;212m=\u001b[0m\u001b[38;2;255;213;3m(\u001b[0m\u001b[38;2;182;207;169m1\u001b[0m, \u001b[38;2;182;207;169m6\u001b[0m, \u001b[38;2;182;207;169m10\u001b[0m\u001b[38;2;255;213;3m)\u001b[0m, \u001b[38;2;156;220;254mdtype\u001b[0m\u001b[38;2;212;212;212m=\u001b[0mdtype('float32')\u001b[38;2;255;213;3m)\u001b[0m\n",
      "      \u001b[38;2;255;213;3m)\u001b[0m,\n",
      "      \u001b[38;2;156;220;254mc_sin\u001b[0m\u001b[38;2;212;212;212m=\u001b[0m\u001b[38;2;79;201;177mParam\u001b[0m\u001b[38;2;255;213;3m(\u001b[0m\u001b[38;2;105;105;105m # 60 (240 B)\u001b[0m\n",
      "        \u001b[38;2;156;220;254mvalue\u001b[0m\u001b[38;2;212;212;212m=\u001b[0m\u001b[38;2;79;201;177mArray\u001b[0m\u001b[38;2;255;213;3m(\u001b[0m\u001b[38;2;156;220;254mshape\u001b[0m\u001b[38;2;212;212;212m=\u001b[0m\u001b[38;2;255;213;3m(\u001b[0m\u001b[38;2;182;207;169m1\u001b[0m, \u001b[38;2;182;207;169m6\u001b[0m, \u001b[38;2;182;207;169m10\u001b[0m\u001b[38;2;255;213;3m)\u001b[0m, \u001b[38;2;156;220;254mdtype\u001b[0m\u001b[38;2;212;212;212m=\u001b[0mdtype('float32')\u001b[38;2;255;213;3m)\u001b[0m\n",
      "      \u001b[38;2;255;213;3m)\u001b[0m\n",
      "    \u001b[38;2;255;213;3m)\u001b[0m\n",
      "  \u001b[38;2;255;213;3m])\u001b[0m\n",
      "\u001b[38;2;255;213;3m)\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "# Initialize a KAN model\n",
    "n_in = X_train.shape[1]\n",
    "n_out = y_train.shape[1]\n",
    "n_hidden = 6\n",
    "\n",
    "layer_dims = [n_in, n_hidden, n_hidden, n_out]\n",
    "req_params = {'D': 10}\n",
    "\n",
    "model = KAN(layer_dims = layer_dims,\n",
    "            layer_type = 'fourier',\n",
    "            required_parameters = req_params,\n",
    "            seed = seed\n",
    "           )\n",
    "\n",
    "print(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3c938399-33e6-429f-9ad7-afc06f311346",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "f681d135-c885-4e59-87d7-1ea16a157c50",
   "metadata": {},
   "source": [
    "## Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "6723a412-c313-453c-aa88-9274687ee54e",
   "metadata": {},
   "outputs": [],
   "source": [
    "opt_type = optax.adam(learning_rate=0.001)\n",
    "\n",
    "optimizer = nnx.Optimizer(model, opt_type, wrt=nnx.Param)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eaac2ddc-ad53-40db-804b-7f54f3000476",
   "metadata": {},
   "source": [
    "In this case, the loss function is modified to correspond to a cross entropy loss term."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f18ab849-3c05-418a-a30b-3928f77c332d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define train loop\n",
    "@nnx.jit\n",
    "def train_step(model, optimizer, X_train, y_train):\n",
    "\n",
    "    def loss_fn(model):\n",
    "\n",
    "        logits = model(X_train)\n",
    "        probs = nnx.sigmoid(logits)\n",
    "        loss = jnp.mean(-y_train * jnp.log(probs + 1e-8) - (1 - y_train) * jnp.log(1 - probs + 1e-8))\n",
    "\n",
    "        return loss\n",
    "    \n",
    "    loss, grads = nnx.value_and_grad(loss_fn)(model)\n",
    "    optimizer.update(model, grads)\n",
    "    \n",
    "    return loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b06282a5-8899-4801-9c9e-d8b73b55d74a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialize train_losses\n",
    "num_epochs = 2000\n",
    "train_losses = jnp.zeros((num_epochs,))\n",
    "\n",
    "for epoch in range(num_epochs):\n",
    "    # Calculate the loss\n",
    "    loss = train_step(model, optimizer, X_train, y_train)\n",
    "    \n",
    "    # Append the loss\n",
    "    train_losses = train_losses.at[epoch].set(loss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ca982f86-6d75-40dd-a492-d2b12cbb2c67",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "faab949e-dadb-4cec-bc13-63b10cb609c5",
   "metadata": {},
   "source": [
    "## Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "86630d29-9b9f-452a-b2f1-399b02768829",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(7, 4))\n",
    "\n",
    "plt.plot(np.array(train_losses), label='Train Loss', marker='o', color='#25599c', markersize=1)\n",
    "\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Loss')\n",
    "plt.title('Training Loss Over Epochs')\n",
    "plt.yscale('log')\n",
    "\n",
    "plt.legend()\n",
    "plt.grid(True, which='both', linestyle='--', linewidth=0.5) \n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5ef7d214-6efa-4124-a587-b47e98cac7d6",
   "metadata": {},
   "source": [
    "Now the model is evaluated in terms of its F1-Score."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "7798eb68-d6b5-47ec-b845-cf3d60dccf23",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The F1-Score of the fit is 93.750%\n"
     ]
    }
   ],
   "source": [
    "logits = model(X_test)\n",
    "y_pred = np.array((nnx.sigmoid(logits) > 0.5).astype(int))\n",
    "score = f1_score(y_pred, y_test)\n",
    "\n",
    "print(f\"The F1-Score of the fit is {100*score:.3f}%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "88cc341a-8869-4dd9-be77-ef9bf2d8b5c1",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.12.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}