{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 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": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from sysidentpy.model_structure_selection import FROLS\n",
    "from sysidentpy.basis_function._basis_function import Polynomial\n",
    "from sysidentpy.metrics import root_relative_squared_error\n",
    "from sysidentpy.utils.generate_data import get_siso_data\n",
    "from sysidentpy.utils.display_results import results\n",
    "from sysidentpy.utils.plotting import plot_residues_correlation, plot_results\n",
    "from sysidentpy.residues.residues_correlation import compute_residues_autocorrelation, compute_cross_correlation\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=0.0001,\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": [
    "basis_function = Polynomial(degree=2)\n",
    "\n",
    "model = FROLS(\n",
    "    order_selection=True,\n",
    "    n_info_values=3,\n",
    "    extended_least_squares=False,\n",
    "    ylag=2, xlag=2,\n",
    "    info_criteria='aic',\n",
    "    estimator='least_squares',\n",
    "    basis_function=basis_function\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.\n",
    "\n",
    "Enforcing keyword-only arguments in *fit* and *predict* methods as well. This is an effort to promote clear and non-ambiguous use of the library."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sysidentpy.model_structure_selection.forward_regression_orthogonal_least_squares.FROLS at 0x29e72b1e460>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X=x_train, y=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=x_valid, y=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": [
      "0.0001786169841093124\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)  9.0001E-01  9.56443560E-01\n",
      "1         y(k-1)  1.9999E-01  4.00488853E-02\n",
      "2  x1(k-1)y(k-1)  9.9983E-02  3.50752297E-03\n"
     ]
    }
   ],
   "source": [
    "r = pd.DataFrame(\n",
    "    results(\n",
    "        model.final_model, model.theta, model.err,\n",
    "        model.n_terms, err_precision=8, dtype='sci'\n",
    "        ),\n",
    "    columns=['Regressors', 'Parameters', 'ERR'])\n",
    "print(r)"
   ]
  },
  {
   "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 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_results(y=y_valid, yhat=yhat, n=1000)\n",
    "ee = compute_residues_autocorrelation(y_valid, yhat)\n",
    "plot_residues_correlation(data=ee, title=\"Residues\", ylabel=\"$e^2$\")\n",
    "x1e = compute_cross_correlation(y_valid, yhat, x_valid)\n",
    "plot_residues_correlation(data=x1e, title=\"Residues\", ylabel=\"$x_1e$\")"
   ]
  },
  {
   "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": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "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": [
    "basis_function = Polynomial(degree=2)\n",
    "\n",
    "model = FROLS(\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",
    "    basis_function=basis_function\n",
    ")\n",
    "\n",
    "model.fit(X=x_train, y=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": [
      "0.0001786169841093124\n",
      "      Regressors  Parameters             ERR\n",
      "0        x1(k-2)  9.0001E-01  9.56443560E-01\n",
      "1         y(k-1)  1.9999E-01  4.00488853E-02\n",
      "2  x1(k-1)y(k-1)  9.9983E-02  3.50752297E-03\n"
     ]
    }
   ],
   "source": [
    "basis_function = Polynomial(degree=2)\n",
    "\n",
    "model = FROLS(\n",
    "    order_selection=False,\n",
    "    n_info_values=15,\n",
    "    n_terms=3,\n",
    "    extended_least_squares=False,\n",
    "    ylag=2, xlag=2,\n",
    "    info_criteria='aic',\n",
    "    estimator='least_squares',\n",
    "    basis_function=basis_function\n",
    ")\n",
    "model.fit(X=x_train, y=y_train)\n",
    "yhat = model.predict(X=x_valid, y=y_valid)\n",
    "rrse = root_relative_squared_error(y_valid, yhat)\n",
    "print(rrse)\n",
    "\n",
    "r = pd.DataFrame(\n",
    "    results(\n",
    "        model.final_model, model.theta, model.err,\n",
    "        model.n_terms, err_precision=8, dtype='sci'\n",
    "        ),\n",
    "    columns=['Regressors', 'Parameters', 'ERR'])\n",
    "print(r)"
   ]
  },
  {
   "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": [
      "[0.95644356 0.04004889 0.00350752 0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.        ] \n",
      "\n",
      "\n",
      "[[0.90000509]\n",
      " [0.19998932]\n",
      " [0.0999832 ]]\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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