{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2cd0caa5",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from IPython.display import display\n",
    "\n",
    "from epimodels.continuous.models import SISLogistic\n",
    "from epimodels.fitting import (\n",
    "    Dataset,\n",
    "    DataSeries,\n",
    "    ParameterSpec,\n",
    "    ModelFitter,\n",
    "    FittingResult,\n",
    "    fit_model,\n",
    "    SumOfSquaredErrors,\n",
    "    WeightedSSE,\n",
    "    PoissonLikelihood,\n",
    "    NegativeBinomialLikelihood,\n",
    "    NormalLikelihood,\n",
    "    HuberLoss,\n",
    "    ScipyOptimizer,\n",
    "    MultiStartOptimizer,\n",
    ")\n",
    "\n",
    "np.random.seed(42)\n",
    "plt.style.use('seaborn-v0_8-whitegrid')\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d999f4dc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True parameters:\n",
      "beta=0.5, gamma=0.2, r=0.1, k=10000\n",
      "R0 = 2.50\n"
     ]
    }
   ],
   "source": [
    "TRUE_BETA = 0.5\n",
    "TRUE_GAMMA = 0.2\n",
    "TRUE_R = 0.1\n",
    "TRUE_K = 10000\n",
    "\n",
    "INITIAL_INFECTED = 100\n",
    "INITIAL_SUSCEPTIBLE = 5000\n",
    "\n",
    "sis_model = SISLogistic()\n",
    "sis_model(\n",
    "    inits=[INITIAL_SUSCEPTIBLE, INITIAL_INFECTED],\n",
    "    trange=[0, 100],\n",
    "    totpop=INITIAL_SUSCEPTIBLE + INITIAL_INFECTED, \n",
    "    params={\"beta\": TRUE_BETA, \"gamma\": TRUE_GAMMA, \"r\": TRUE_R, \"k\": TRUE_K,\n",
    "    },\n",
    ")\n",
    "\n",
    "times = sis_model.traces[\"time\"]\n",
    "true_S = sis_model.traces[\"S\"]\n",
    "true_I = sis_model.traces[\"I\"]\n",
    "\n",
    "print(\"True parameters:\")\n",
    "print(f\"beta={TRUE_BETA}, gamma={TRUE_GAMMA}, r={TRUE_R}, k={TRUE_K}\")\n",
    "print(f\"R0 = {TRUE_BETA / TRUE_GAMMA:.2f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "2aec7cff",
   "metadata": {},
   "outputs": [],
   "source": [
    "observed_S = np.random.poisson(lam=true_S).astype(float)\n",
    "observed_I = np.random.poisson(lam=true_I).astype(float)\n",
    "\n",
    "observed_S = np.maximum(observed_S, 0)\n",
    "observed_I = np.maximum(observed_I, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d2780807",
   "metadata": {},
   "outputs": [
    {
     "data": {
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uQmCC",
      "text/plain": [
       "<Figure size 1400x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1, 2, figsize=(14, 5))\n",
    "\n",
    "axes[0].plot(times, true_S, 'g-', label='Ground Truth S', linewidth=2)\n",
    "axes[0].plot(times, observed_S, 'mo', label='Observed S', markersize=3, alpha=0.7)\n",
    "axes[0].set_xlabel('Time (days)')\n",
    "axes[0].set_ylabel('Number of Susceptible')\n",
    "axes[0].set_title('Susceptible Population')\n",
    "axes[0].legend()\n",
    "\n",
    "axes[1].plot(times, true_I, 'b-', label='Ground Truth I', linewidth=2)\n",
    "axes[1].plot(times, observed_I, 'ro', label='Observed I', markersize=3, alpha=0.7)\n",
    "axes[1].set_xlabel('Time (days)')\n",
    "axes[1].set_ylabel('Number of Infected')\n",
    "axes[1].set_title('Infectious Population')\n",
    "axes[1].legend()\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "5cacdf17",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset valid: True\n",
      "Time range: (0.0, 100.0)\n",
      "Dataset(n_series=1, variables=['I'], time_range=(0.0, 100.0))\n"
     ]
    }
   ],
   "source": [
    "model = SISLogistic()\n",
    "\n",
    "dataset = Dataset(model)\n",
    "\n",
    "dataset.register(\n",
    "    name='infected',\n",
    "    values=observed_I,\n",
    "    times=times,\n",
    "    state_variable='I',\n",
    "    time_unit='days',\n",
    ")\n",
    "\n",
    "validation_result = dataset.validate(total_population=INITIAL_SUSCEPTIBLE + INITIAL_INFECTED)\n",
    "print(f\"Dataset valid: {validation_result.is_valid}\")\n",
    "print(f\"Time range: {dataset.time_range}\")\n",
    "print(dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "65a1aa83",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters to fit:\n",
      "  beta: bounds=(0.1, 1.0), initial=0.7\n",
      "  gamma: bounds=(0.01, 0.5), initial=0.3\n",
      "  r: bounds=(0.01, 0.5), initial=0.4\n",
      "  k: bounds=(1000, 20000), initial=5000\n"
     ]
    }
   ],
   "source": [
    "param_specs = [\n",
    "    ParameterSpec(\n",
    "        name='beta',\n",
    "        bounds=(0.1, 1.0),\n",
    "        initial=0.7,\n",
    "    ),\n",
    "    ParameterSpec(\n",
    "        name='gamma',\n",
    "        bounds=(0.01, 0.5),\n",
    "        initial=0.3,\n",
    "    ),\n",
    "    ParameterSpec(\n",
    "        name='r',\n",
    "        bounds=(0.01, 0.5),\n",
    "        initial=0.4,\n",
    "    ),\n",
    "    ParameterSpec(\n",
    "        name='k',\n",
    "        bounds=(1000, 20000),\n",
    "        initial=5000,\n",
    "    ),\n",
    "]\n",
    "\n",
    "print(\"Parameters to fit:\")\n",
    "for spec in param_specs:\n",
    "    print(f\"  {spec.name}: bounds={spec.bounds}, initial={spec.initial}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "4e22e9b4",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\darla\\epimodels\\epimodels\\fitting\\base.py:200: UserWarning: Series 'infected': values exceed total population\n",
      "  warnings.warn(warning, UserWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "==================================================\n",
      "FITTING RESULTS\n",
      "==================================================\n",
      "Convergence: True\n",
      "Number of evaluations: 330\n",
      "Final loss: 45545.77\n",
      "\n",
      "Fitted parameters:\n",
      "  beta: 0.6176 (true: 0.5, error: 23.5%)\n",
      "  gamma: 0.2948 (true: 0.2, error: 47.4%)\n",
      "  r: 0.1048 (true: 0.1, error: 4.8%)\n",
      "  k: 11449.9407 (true: 10000, error: 14.5%)\n"
     ]
    }
   ],
   "source": [
    "fitter = ModelFitter(\n",
    "    model=model,\n",
    "    dataset=dataset,\n",
    "    parameters_to_fit=param_specs,\n",
    "    total_population=INITIAL_SUSCEPTIBLE + INITIAL_INFECTED,\n",
    "    optimizer=ScipyOptimizer(method='L-BFGS-B', max_iterations=200),\n",
    ")\n",
    "\n",
    "result = fitter.fit()\n",
    "\n",
    "print(\"\\n\" + \"=\"*50)\n",
    "print(\"FITTING RESULTS\")\n",
    "print(\"=\"*50)\n",
    "print(f\"Convergence: {result.convergence}\")\n",
    "print(f\"Number of evaluations: {result.n_evaluations}\")\n",
    "print(f\"Final loss: {result.best_loss:.2f}\")\n",
    "\n",
    "print(\"\\nFitted parameters:\")\n",
    "\n",
    "true_values = {\n",
    "    \"beta\": TRUE_BETA,\n",
    "    \"gamma\": TRUE_GAMMA,\n",
    "    \"r\": TRUE_R,\n",
    "    \"k\": TRUE_K,\n",
    "}\n",
    "\n",
    "for param, value in result.best_params.items():\n",
    "    true_val = true_values[param]\n",
    "    error = abs(value - true_val) / true_val * 100\n",
    "\n",
    "    print(f\"  {param}: {value:.4f} (true: {true_val}, error: {error:.1f}%)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "1a8320d2",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fitted_model = result.fitted_model\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(12, 6))\n",
    "\n",
    "ax.plot(times, observed_I, 'ro', label='Observed I', markersize=4, alpha=0.7)\n",
    "\n",
    "ax.plot(times, true_I, 'b-', label='Ground Truth', linewidth=2, alpha=0.5)\n",
    "\n",
    "if fitted_model is not None and fitted_model.traces:\n",
    "    ax.plot(fitted_model.traces['time'], fitted_model.traces['I'], \n",
    "            'g--', label='Fitted', linewidth=2)\n",
    "\n",
    "ax.set_xlabel('Time (days)', fontsize=12)\n",
    "ax.set_ylabel('Number of Infected', fontsize=12)\n",
    "ax.set_title('Model Fitting Results', fontsize=14)\n",
    "ax.legend(fontsize=11)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "8aecb1ab",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Registered series: ['susceptible', 'infected']\n",
      "State variables mapped: ['S', 'I']\n"
     ]
    }
   ],
   "source": [
    "model2 = SISLogistic()\n",
    "dataset2 = Dataset(model2)\n",
    "\n",
    "dataset2.register(\n",
    "    name='susceptible',\n",
    "    values=observed_S,\n",
    "    times=times,\n",
    "    state_variable='S',\n",
    ").register(\n",
    "    name='infected',\n",
    "    values=observed_I,\n",
    "    times=times,\n",
    "    state_variable='I',\n",
    ")\n",
    "\n",
    "print(f\"Registered series: {list(dataset2.series.keys())}\")\n",
    "print(f\"State variables mapped: {list(set(s.state_variable for s in dataset2.series.values()))}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "e8e31d9b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Valid: False\n",
      "Errors: [\"Series 'invalid': state variable 'X' not found in model. Available: ['S', 'I']\", \"Data mapped to non-existent variables: ['X']\"]\n"
     ]
    }
   ],
   "source": [
    "from epimodels.fitting import DataValidationError\n",
    "\n",
    "model_test = SISLogistic()\n",
    "dataset_test = Dataset(model_test)\n",
    "\n",
    "dataset_test.register(\n",
    "    name='invalid',\n",
    "    values=observed_I,\n",
    "    times=times,\n",
    "    state_variable='X',\n",
    ")\n",
    "\n",
    "validation = dataset_test.validate()\n",
    "print(f\"Valid: {validation.is_valid}\")\n",
    "print(f\"Errors: {validation.errors}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "b717b1a1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset time unit: weeks\n",
      "Series time unit: weeks\n",
      "Time range: (0.0, 14.285714285714286)\n"
     ]
    }
   ],
   "source": [
    "model3 = SISLogistic()\n",
    "dataset3 = Dataset(model3)\n",
    "\n",
    "weeks = times / 7.0\n",
    "\n",
    "dataset3.register(\n",
    "    name='infected_weekly',\n",
    "    values=observed_I,\n",
    "    times=weeks,\n",
    "    state_variable='I',\n",
    "    time_unit='weeks',\n",
    ")\n",
    "\n",
    "print(f\"Dataset time unit: {dataset3.time_unit}\")\n",
    "print(f\"Series time unit: {dataset3.series['infected_weekly'].time_unit}\")\n",
    "print(f\"Time range: {dataset3.time_range}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "add6a4d9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sample data:\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>day</th>\n",
       "      <th>susceptible</th>\n",
       "      <th>infected</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>4974.0</td>\n",
       "      <td>86.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.136966</td>\n",
       "      <td>5079.0</td>\n",
       "      <td>96.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.506630</td>\n",
       "      <td>5228.0</td>\n",
       "      <td>147.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5.500993</td>\n",
       "      <td>5939.0</td>\n",
       "      <td>468.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10.728546</td>\n",
       "      <td>5859.0</td>\n",
       "      <td>1654.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>17.141654</td>\n",
       "      <td>4371.0</td>\n",
       "      <td>3670.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>20.590730</td>\n",
       "      <td>3917.0</td>\n",
       "      <td>4286.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>24.039805</td>\n",
       "      <td>3726.0</td>\n",
       "      <td>4647.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>29.779266</td>\n",
       "      <td>3690.0</td>\n",
       "      <td>5134.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>35.469539</td>\n",
       "      <td>3672.0</td>\n",
       "      <td>5419.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         day  susceptible  infected\n",
       "0   0.000000       4974.0      86.0\n",
       "1   0.136966       5079.0      96.0\n",
       "2   1.506630       5228.0     147.0\n",
       "3   5.500993       5939.0     468.0\n",
       "4  10.728546       5859.0    1654.0\n",
       "5  17.141654       4371.0    3670.0\n",
       "6  20.590730       3917.0    4286.0\n",
       "7  24.039805       3726.0    4647.0\n",
       "8  29.779266       3690.0    5134.0\n",
       "9  35.469539       3672.0    5419.0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Registered from DataFrame: ['susceptible', 'infected']\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df = pd.DataFrame({\n",
    "    'day': times,\n",
    "    'susceptible': observed_S,\n",
    "    'infected': observed_I,\n",
    "})\n",
    "\n",
    "print(\"Sample data:\")\n",
    "display(df.head(10))\n",
    "\n",
    "model_df = SISLogistic()\n",
    "dataset_df = Dataset(model_df)\n",
    "\n",
    "dataset_df.register_from_dataframe(\n",
    "    df=df,\n",
    "    time_column='day',\n",
    "    mapping={'susceptible': 'S', 'infected': 'I'},\n",
    "    time_unit='days',\n",
    ")\n",
    "\n",
    "print(f\"\\nRegistered from DataFrame: {list(dataset_df.series.keys())}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "44565e8b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Available loss functions:\n",
      "  - SSE: SumOfSquaredErrors\n",
      "  - Poisson: PoissonLikelihood\n",
      "  - NegBinom: NegativeBinomialLikelihood\n",
      "  - Normal: NormalLikelihood\n",
      "  - Huber: HuberLoss\n"
     ]
    }
   ],
   "source": [
    "loss_functions = {\n",
    "    'SSE': SumOfSquaredErrors(),\n",
    "    'Poisson': PoissonLikelihood(),\n",
    "    'NegBinom': NegativeBinomialLikelihood(dispersion=5.0),\n",
    "    'Normal': NormalLikelihood(),\n",
    "    'Huber': HuberLoss(delta=10.0),\n",
    "}\n",
    "\n",
    "print(\"Available loss functions:\")\n",
    "for name, lf in loss_functions.items():\n",
    "    print(f\"  - {name}: {lf.__class__.__name__}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "4a181569",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting with SSE...\n",
      "Fitting with Poisson...\n",
      "Fitting with NegBinom...\n",
      "Fitting with Normal...\n",
      "Fitting with Huber...\n"
     ]
    }
   ],
   "source": [
    "results_comparison = {}\n",
    "\n",
    "for name, loss_fn in loss_functions.items():\n",
    "    print(f\"Fitting with {name}...\")\n",
    "    \n",
    "    fitter_comp = ModelFitter(\n",
    "        model=SISLogistic(),\n",
    "        dataset=dataset,\n",
    "        parameters_to_fit=param_specs,\n",
    "        total_population=INITIAL_SUSCEPTIBLE + INITIAL_INFECTED,\n",
    "        loss_fn=loss_fn,\n",
    "        optimizer=ScipyOptimizer(method='L-BFGS-B', max_iterations=100),\n",
    "    )\n",
    "    \n",
    "    result = fitter_comp.fit()\n",
    "    results_comparison[name] = result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "8b15b0ec",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "==========================================================================================\n",
      "LOSS FUNCTION COMPARISON (SIS Logistic)\n",
      "==========================================================================================\n",
      "Loss             beta    gamma        r          k       R0      LossVal\n",
      "------------------------------------------------------------------------------------------\n",
      "TRUE            0.500    0.200    0.100      10000     2.50            -\n",
      "------------------------------------------------------------------------------------------\n",
      "SSE             0.618    0.295    0.105      11450     2.10     45545.77\n",
      "Poisson         0.391    0.083    0.034      10939     4.69       193.59\n",
      "NegBinom        0.434    0.111    0.406       5000     3.92       256.63\n",
      "Normal          0.336    0.010    0.010       5000    33.59       124.37\n",
      "Huber           0.311    0.010    0.010       5022    31.12     62016.12\n"
     ]
    }
   ],
   "source": [
    "print(\"\\n\" + \"=\"*90)\n",
    "print(\"LOSS FUNCTION COMPARISON (SIS Logistic)\")\n",
    "print(\"=\"*90)\n",
    "\n",
    "print(f\"{'Loss':<12} {'beta':>8} {'gamma':>8} {'r':>8} {'k':>10} {'R0':>8} {'LossVal':>12}\")\n",
    "print(\"-\"*90)\n",
    "\n",
    "print(f\"{'TRUE':<12} {TRUE_BETA:>8.3f} {TRUE_GAMMA:>8.3f} {TRUE_R:>8.3f} {TRUE_K:>10.0f} {TRUE_BETA/TRUE_GAMMA:>8.2f} {'-':>12}\")\n",
    "print(\"-\"*90)\n",
    "\n",
    "for name, result in results_comparison.items():\n",
    "    beta = result.best_params['beta']\n",
    "    gamma = result.best_params['gamma']\n",
    "    r = result.best_params['r']\n",
    "    k = result.best_params['k']\n",
    "    r0 = beta / gamma\n",
    "\n",
    "    print(f\"{name:<12} {beta:>8.3f} {gamma:>8.3f} {r:>8.3f} {k:>10.0f} {r0:>8.2f} {result.best_loss:>12.2f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "2b755f94",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing L-BFGS-B...\n",
      "Testing Nelder-Mead...\n",
      "Testing Powell...\n",
      "Testing differential_evolution...\n"
     ]
    }
   ],
   "source": [
    "scipy_methods = ['L-BFGS-B', 'Nelder-Mead', 'Powell', 'differential_evolution']\n",
    "optimizer_results = {}\n",
    "\n",
    "for method in scipy_methods:\n",
    "    print(f\"Testing {method}...\")\n",
    "    \n",
    "    optimizer = ScipyOptimizer(method=method, max_iterations=100)\n",
    "    \n",
    "    fitter_opt = ModelFitter(\n",
    "        model=SISLogistic(),\n",
    "        dataset=dataset,\n",
    "        parameters_to_fit=param_specs,\n",
    "        total_population=INITIAL_SUSCEPTIBLE + INITIAL_INFECTED,\n",
    "        optimizer=optimizer,\n",
    "    )\n",
    "    \n",
    "    result = fitter_opt.fit()\n",
    "    optimizer_results[method] = result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "789c7911",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "====================================================================================================\n",
      "OPTIMIZER COMPARISON (SIS Logistic)\n",
      "====================================================================================================\n",
      "Method                        beta    gamma        r          k       R0    Evals     Conv\n",
      "----------------------------------------------------------------------------------------------------\n",
      "L-BFGS-B                     0.618    0.295    0.105      11450     2.10      330     True\n",
      "Nelder-Mead                  0.618    0.295    0.105      11450     2.10      330     True\n",
      "Powell                       0.618    0.295    0.105      11450     2.10      330     True\n",
      "differential_evolution       0.608    0.286    0.104      11315     2.13     6105    False\n"
     ]
    }
   ],
   "source": [
    "print(\"\\n\" + \"=\"*100)\n",
    "print(\"OPTIMIZER COMPARISON (SIS Logistic)\")\n",
    "print(\"=\"*100)\n",
    "\n",
    "print(f\"{'Method':<25} {'beta':>8} {'gamma':>8} {'r':>8} {'k':>10} {'R0':>8} {'Evals':>8} {'Conv':>8}\")\n",
    "print(\"-\"*100)\n",
    "\n",
    "for method, result in optimizer_results.items():\n",
    "    beta = result.best_params['beta']\n",
    "    gamma = result.best_params['gamma']\n",
    "    r = result.best_params['r']\n",
    "    k = result.best_params['k']\n",
    "    r0 = beta / gamma\n",
    "\n",
    "    print(f\"{method:<25} {beta:>8.3f} {gamma:>8.3f} {r:>8.3f} {k:>10.0f} {r0:>8.2f} {result.n_evaluations:>8} {str(result.convergence):>8}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "8f2204a0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Multi-start optimization results:\n",
      "  beta: 0.6176\n",
      "  gamma: 0.2948\n",
      "  r: 0.1048\n",
      "  k: 11450\n",
      "  Total evaluations: 1440\n",
      "  Converged: True\n"
     ]
    }
   ],
   "source": [
    "base_optimizer = ScipyOptimizer(method='L-BFGS-B', max_iterations=50)\n",
    "\n",
    "multi_start = MultiStartOptimizer(\n",
    "    base_optimizer=base_optimizer,\n",
    "    n_starts=5,\n",
    "    sampling_method='latin_hypercube',\n",
    "    seed=42,\n",
    ")\n",
    "\n",
    "fitter_ms = ModelFitter(\n",
    "    model=SISLogistic(),\n",
    "    dataset=dataset,\n",
    "    parameters_to_fit=param_specs,\n",
    "    total_population=INITIAL_SUSCEPTIBLE + INITIAL_INFECTED,\n",
    "    optimizer=multi_start,\n",
    ")\n",
    "\n",
    "result_ms = fitter_ms.fit()\n",
    "\n",
    "print(f\"Multi-start optimization results:\")\n",
    "\n",
    "print(f\"  beta: {result_ms.best_params['beta']:.4f}\")\n",
    "print(f\"  gamma: {result_ms.best_params['gamma']:.4f}\")\n",
    "print(f\"  r: {result_ms.best_params['r']:.4f}\")\n",
    "print(f\"  k: {result_ms.best_params['k']:.0f}\")\n",
    "\n",
    "print(f\"  Total evaluations: {result_ms.n_evaluations}\")\n",
    "print(f\"  Converged: {result_ms.convergence}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "143ca7bb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting with fixed gamma:\n",
      "  Fitted beta: 0.5215 (true: 0.5)\n",
      "  Fitted gamma: 0.2090 (true: 0.2)\n",
      "  Fixed r: 0.1\n",
      "  Fixed k: 10000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\darla\\epimodels\\epimodels\\fitting\\base.py:200: UserWarning: Series 'infected': values exceed total population\n",
      "  warnings.warn(warning, UserWarning)\n"
     ]
    }
   ],
   "source": [
    "fitter_fixed = ModelFitter(\n",
    "    model=SISLogistic(),\n",
    "    dataset=dataset,\n",
    "    parameters_to_fit=[\n",
    "        ParameterSpec(name='beta', bounds=(0.1, 1.0), initial=0.7),\n",
    "        ParameterSpec(name='gamma',bounds=(0.01, 0.5), initial=0.3)\n",
    "    ],\n",
    "    total_population=INITIAL_SUSCEPTIBLE + INITIAL_INFECTED,\n",
    "    fixed_params={'r': TRUE_R, 'k': TRUE_K},  \n",
    ")\n",
    "\n",
    "result_fixed = fitter_fixed.fit()\n",
    "\n",
    "print(\"Fitting with fixed gamma:\")\n",
    "print(f\"  Fitted beta: {result_fixed.best_params['beta']:.4f} (true: {TRUE_BETA})\")\n",
    "print(f\"  Fitted gamma: {result_fixed.best_params['gamma']:.4f} (true: {TRUE_GAMMA})\")\n",
    "print(f\"  Fixed r: {TRUE_R}\")\n",
    "print(f\"  Fixed k: {TRUE_K}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "57dc09ed",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Log-scale parameter specs:\n",
      "  beta: bounds=(0.01, 1.0), log_scale=True\n",
      "  gamma: bounds=(0.01, 0.5), log_scale=True\n",
      "  r: bounds=(0.01, 0.5), log_scale=True\n",
      "  k: bounds=(1000, 20000), log_scale=True\n"
     ]
    }
   ],
   "source": [
    "param_specs_log = [\n",
    "    ParameterSpec(\n",
    "        name='beta',\n",
    "        bounds=(0.01, 1.0),\n",
    "        initial=0.3,\n",
    "        log_scale=True,\n",
    "    ),\n",
    "    ParameterSpec(\n",
    "        name='gamma',\n",
    "        bounds=(0.01, 0.5),\n",
    "        initial=0.1,\n",
    "        log_scale=True,\n",
    "    ),\n",
    "    ParameterSpec(\n",
    "        name='r',\n",
    "        bounds=(0.01, 0.5),\n",
    "        initial=0.05,\n",
    "        log_scale=True,\n",
    "    ),\n",
    "    ParameterSpec(\n",
    "        name='k',\n",
    "        bounds=(1000, 20000),\n",
    "        initial=8000,\n",
    "        log_scale=True,\n",
    "    ),\n",
    "]\n",
    "\n",
    "print(\"Log-scale parameter specs:\")\n",
    "for spec in param_specs_log:\n",
    "    print(f\"  {spec.name}: bounds={spec.bounds}, log_scale={spec.log_scale}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "033a92cb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Profile likelihood analysis for gamma:\n",
      "  Minimum loss: 45528.81\n",
      "  95% CI threshold: 45530.73\n",
      "  Confidence interval: (np.float64(0.29368421052631577), np.float64(0.29368421052631577))\n"
     ]
    }
   ],
   "source": [
    "profile_result = fitter.profile_likelihood(\n",
    "    param_name='gamma',\n",
    "    n_points=20,\n",
    "    threshold=3.84,\n",
    ")\n",
    "\n",
    "print(\"Profile likelihood analysis for gamma:\")\n",
    "print(f\"  Minimum loss: {profile_result['min_loss']:.2f}\")\n",
    "print(f\"  95% CI threshold: {profile_result['threshold_loss']:.2f}\")\n",
    "print(f\"  Confidence interval: {profile_result['confidence_interval']}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "0a6f5bb0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(10, 6))\n",
    "\n",
    "ax.plot(profile_result['values'], profile_result['losses'], 'b-', linewidth=2)\n",
    "ax.axhline(y=profile_result['threshold_loss'], color='r', linestyle='--', \n",
    "           label=f\"95% CI threshold\")\n",
    "ax.axvline(x=TRUE_GAMMA, color='g', linestyle=':', label=f\"True gamma = {TRUE_GAMMA}\")\n",
    "\n",
    "ci = profile_result['confidence_interval']\n",
    "if ci[0] is not None and ci[1] is not None:\n",
    "    ax.axvspan(ci[0], ci[1], alpha=0.2, color='blue', label=f\"95% CI: [{ci[0]:.3f}, {ci[1]:.3f}]\")\n",
    "\n",
    "ax.set_xlabel('gamma', fontsize=12)\n",
    "ax.set_ylabel('Loss', fontsize=12)\n",
    "ax.set_title('Profile Likelihood for gamma', fontsize=14)\n",
    "ax.legend(fontsize=10)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.12.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}