{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# V0.1.6 - Presenting main functionality\n",
    "\n",
    "Example created by Wilson Rocha Lacerda Junior"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we import the NARMAX model, the metric for model evaluation and the methods to generate sample data for tests. Also, we import pandas for specific usage."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pip install sysidentpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from sysidentpy.polynomial_basis import PolynomialNarmax\n",
    "from sysidentpy.metrics import root_relative_squared_error\n",
    "from sysidentpy.utils.generate_data import get_miso_data, get_siso_data\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generating 1 input 1 output sample data  \n",
    "\n",
    "The data is generated by simulating the following model:\n",
    "\n",
    "$y_k = 0.2y_{k-1} + 0.1y_{k-1}x_{k-1} + 0.9x_{k-1} + e_{k}$\n",
    "\n",
    "If *colored_noise* is set to True:\n",
    "\n",
    "$e_{k} = 0.8\\nu_{k-1} + \\nu_{k}$\n",
    "\n",
    "where $x$ is a uniformly distributed random variable and $\\nu$ is a gaussian distributed variable with $\\mu=0$ and $\\sigma=0.1$\n",
    "\n",
    "In the next example we will generate a data with 1000 samples with white noise and selecting 90% of the data to train the model. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train, x_valid, y_train, y_valid = get_siso_data(n=1000,\n",
    "                                                   colored_noise=False,\n",
    "                                                   sigma=0.001,\n",
    "                                                   train_percentage=90)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To obtain a NARMAX model we have to choose some values, *e.g*, the nonlinearity degree (*non_degree*), the maximum lag for the inputs and output (*xlag* and *ylag*). \n",
    "\n",
    "In addition, you can select the information criteria to be used with the Error Reduction Ratio to select the model order and the method to estimate the model paramaters:\n",
    "\n",
    "- Information Criteria: aic, bic, lilc, fpe\n",
    "- Parameter Estimation: least_squares, total_least_squares, recursive_least_squares, least_mean_squres and many other (see the docs)\n",
    "\n",
    "The *n_terms* values is optional. It refer to the number of terms to inclued in the final model. You can set this value based on the information criteria (see below) or based on priori information about the model struture. The default value is *n_terms=None*, so the algorithm will choose the minimum value reached by the information criteria.\n",
    "\n",
    "To use information criteria you have to set *order_selection=True*. You can also select *n_info_values* (default = 15)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "model = PolynomialNarmax(non_degree=2,\n",
    "                         order_selection=True,\n",
    "                         n_info_values=10,\n",
    "                         extended_least_squares=False,\n",
    "                         ylag=2, xlag=2,\n",
    "                         info_criteria='aic',\n",
    "                         estimator='least_squares',\n",
    "                         )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Structure Selection\n",
    "\n",
    "The *fit* method executes the Error Reduction Ratio algorithm using Househoulder reflection to select the model structure. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sysidentpy.polynomial_basis.narmax.PolynomialNarmax at 0x1fd33cab3d0>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Free run simulation\n",
    "\n",
    "The *predict* method is use to generate the predictions. For now we only support *free run simulation* (also known as *infinity steps ahead*). Soon will let the user define a *one-step ahead* or *k-step ahead* prediction."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "yhat = model.predict(x_valid, y_valid)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluate the model\n",
    "\n",
    "In this example we use the *root_relative_squared_error* metric because it is often used in System Idenfication. More metrics and information about it can be found on documentation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0018401326800931033\n"
     ]
    }
   ],
   "source": [
    "rrse = root_relative_squared_error(y_valid, yhat)\n",
    "print(rrse)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*model_object.results* return the selected model regressors, the estimated parameters and the ERR values. As shown below, the algorithm detect the exact model that was used for simulate the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      Regressors Parameters         ERR\n",
      "0        x1(k-2)     0.8999  0.95739001\n",
      "1         y(k-1)     0.2000  0.03917632\n",
      "2  x1(k-1)y(k-1)     0.0999  0.00343057\n",
      "3      x1(k-2)^2    -0.0002  0.00000002\n",
      "4        x1(k-1)     0.0001  0.00000001\n",
      "5  x1(k-1)y(k-2)     0.0002  0.00000001\n"
     ]
    }
   ],
   "source": [
    "results = pd.DataFrame(model.results(err_precision=8,\n",
    "                                     dtype='dec'),\n",
    "                       columns=['Regressors', 'Parameters', 'ERR'])\n",
    "\n",
    "print(results)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In addition, you can access the *residuals* and *plot_result* methods to take a look at the prediction and two residual analysis. The *extras* and *lam* values below contain another residues analysis so you can plot it mannualy. This method will be improved soon. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 720x576 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ee, ex, extras, lam = model.residuals(x_valid, y_valid, yhat)\n",
    "model.plot_result(y_valid, yhat, ee, ex)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setting the *n_terms* parameter\n",
    "\n",
    "In the example above we let the number of terms to compose the final model to be defined as the minimum value of the information criteria. Once you ran the algorithm and choose the best number of parameters, you can turn *order_selection* to *False* and set the *n_terms* value (3 in this example). Here we have a small dataset, but in bigger data this can be critical because running information criteria algorithm is more computational expensive. Since we already know the best number of regressor, we set *n_terms* and we get the same result.\n",
    "\n",
    "However, this is not only critical because computational eficiency. In many situation, the minimum value of the information criteria can lead to overfiting. In some cases, the diference between choosing a model with 30 regressors or 10 is minimal, so you can take the model with 10 terms without loosing accuracy.\n",
    "\n",
    "In the following we use *info_values* to plot the information criteria values. As you can see, the minimum value relies where $xaxis = 5$ "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Information Criteria')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
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     "metadata": {},
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    }
   ],
   "source": [
    "xaxis = np.arange(1, model.n_info_values + 1)\n",
    "plt.plot(xaxis, model.info_values)\n",
    "plt.xlabel('n_terms')\n",
    "plt.ylabel('Information Criteria')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```{note}\n",
    " Here we are creating random samples with white noise and letting the algorithm choose\n",
    " the number of terms based on the minimum value of information criteria. \n",
    " This is not the best approach in System Identification, but serves as a simple example. \n",
    " The information criteria must be used as an __auxiliary tool__ to select *n_terms*. \n",
    " Plot the information values to help you on that!\n",
    "\n",
    " If you run the example above several times you might find some cases where the\n",
    " algorithm choose only the first two regressors, or four (depending on the information\n",
    " criteria method selected). This is because the minimum value of information criteria\n",
    " depends on residual variance (affected by noise) and have some limitations in nonlinear\n",
    " scenarios. However, if you check the ERR values (robust to noise) you will see that the\n",
    " ERR is ordering the regressors in the correct way!\n",
    "\n",
    " We have some examples on *information_criteria* notebook!\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```{note}\n",
    "This documentation and the examples below are written with MyST Markdown, a form\n",
    "of markdown that works with Sphinx. For more information about MyST markdown, and\n",
    "to use MyST markdown with your Sphinx website,\n",
    "see [the MyST-parser documentation](https://myst-parser.readthedocs.io/)\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The *n_info_values* limits the number of regressors to apply the information criteria. We choose $n_y = n_x = \\ell = 2$, so the candidate regressor is a list of 15 regressors. We can set *n_info_values = 15* and see the information values for all regressors. This option can save some amount of computational resources when dealing with multiples inputs and large datasets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Information Criteria')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model = PolynomialNarmax(non_degree=2,\n",
    "                         order_selection=True,\n",
    "                         n_info_values=15,\n",
    "                         extended_least_squares=False,\n",
    "                         ylag=2, xlag=2,\n",
    "                         info_criteria='aic',\n",
    "                         estimator='least_squares',\n",
    "                         )\n",
    "\n",
    "model.fit(x_train, y_train)\n",
    "\n",
    "xaxis = np.arange(1, model.n_info_values + 1)\n",
    "plt.plot(xaxis, model.info_values)\n",
    "plt.xlabel('n_terms')\n",
    "plt.ylabel('Information Criteria')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now running without executing information criteria methods (setting the *n_terms*) because we already know the optimal number of regressors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "rrse:  0.0018136024659265537\n",
      "\n",
      "       Regressors Parameters         ERR\n",
      "0        x1(k-2)     0.9000  0.95739001\n",
      "1         y(k-1)     0.2000  0.03917632\n",
      "2  x1(k-1)y(k-1)     0.0999  0.00343057\n"
     ]
    }
   ],
   "source": [
    "model = PolynomialNarmax(non_degree=2,\n",
    "                         # order_selection=True,\n",
    "                         n_terms = 3,\n",
    "                         # n_info_values=15,\n",
    "                         extended_least_squares=False,\n",
    "                         ylag=2, xlag=2,\n",
    "                         info_criteria='aic',\n",
    "                         estimator='least_squares',\n",
    "                         )\n",
    "\n",
    "model.fit(x_train, y_train)\n",
    "yhat = model.predict(x_valid, y_valid)\n",
    "rrse = root_relative_squared_error(y_valid, yhat)\n",
    "print('rrse: ', rrse)\n",
    "\n",
    "results = pd.DataFrame(model.results(err_precision=8,\n",
    "                                     dtype='dec'),\n",
    "                       columns=['Regressors', 'Parameters', 'ERR'])\n",
    "\n",
    "print('\\n', results)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Extra information\n",
    "\n",
    "You can acess some extra information like the list of all candidate regressors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[   0,    0],\n",
       "       [1001,    0],\n",
       "       [1002,    0],\n",
       "       [2001,    0],\n",
       "       [2002,    0],\n",
       "       [1001, 1001],\n",
       "       [1002, 1001],\n",
       "       [2001, 1001],\n",
       "       [2002, 1001],\n",
       "       [1002, 1002],\n",
       "       [2001, 1002],\n",
       "       [2002, 1002],\n",
       "       [2001, 2001],\n",
       "       [2002, 2001],\n",
       "       [2002, 2002]])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# for now the list is returned as a codification. Here, $0$ is the constant term, $[1001]=y{k-1}, [100n]=y_{k-n}, [200n] = x1_{k-n}, [300n]=x2_{k-n}$ and so on\n",
    "model.regressor_code  # list of all possible regressors given non_degree, n_y and n_x values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.95739001 0.03917632 0.00343057 0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.        ] \n",
      "\n",
      "\n",
      "[[0.89995854]\n",
      " [0.20004441]\n",
      " [0.09991068]]\n"
     ]
    }
   ],
   "source": [
    "print(model.err, '\\n\\n')  # err values for the selected terms\n",
    "print(model.theta)  # estimated parameters for the final model structure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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