Examples/Burgers-now-20230116.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# PDE-FIND for Burger's Equation\n",
    "\n",
    "Samuel Rudy, 2016\n",
    "\n",
    "This notebook demonstrates PDE-FIND on Burger's equation with an added diffusive term.\n",
    "$$\n",
    "u_t + uu_x = 0.1u_{xx}\n",
    "$$\n",
    "The solution given is a single travelling wave, starting out as a Gaussian."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "%pylab inline\n",
    "pylab.rcParams['figure.figsize'] = (12, 8)\n",
    "import warnings; warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "import numpy as np\n",
    "from numpy.random import default_rng\n",
    "import pandas as pd\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "\n",
    "import sys; sys.path.append('../')\n",
    "from PDE_FIND import *\n",
    "\n",
    "import sys; sys.path.append('../../parametric-discovery/')\n",
    "from best_subset import *\n",
    "from frols import frols\n",
    "from p_linear_regression import PLinearRegression\n",
    "from r_pca import R_pca\n",
    "from pde_diff_new import RPCA\n",
    "from RobustPCA.rpca import RobustPCA\n",
    "\n",
    "import scipy.io as sio\n",
    "from scipy.signal import savgol_filter\n",
    "import itertools\n",
    "\n",
    "from sklearn.preprocessing import normalize\n",
    "from sklearn.linear_model import Ridge, BayesianRidge, LinearRegression as SkLinearRegression\n",
    "from sklearn.base import BaseEstimator\n",
    "from sklearn.pipeline import make_pipeline\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from abess.linear import LinearRegression\n",
    "from bess import PdasLm\n",
    "import pysindy as ps\n",
    "\n",
    "from os.path import join as join_path\n",
    "FIGURE_EXPORT_PATH = \"/Users/pongpisit/Documents/figure_export/\"\n",
    "\n",
    "# from rad import AnomalyDetection_RPCA\n",
    "\n",
    "def evaluate_coefficients(prediected_coeffs):\n",
    "    GROUND = np.array([0.1, -1])\n",
    "    errs = 100*np.abs(GROUND-np.array(prediected_coeffs).flatten())/np.abs(GROUND)\n",
    "    return errs.mean(), errs.std()\n",
    "\n",
    "import torch, sympytorch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "from sympy import symbols, simplify, lambdify\n",
    "from mathparser import math_eval\n",
    "from varname import nameof\n",
    "from misc import h5file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = sio.loadmat('../Datasets/burgers.mat')\n",
    "u = (data['usol']).real\n",
    "x = (data['x'][0]).real\n",
    "t = (data['t'][:,0]).real\n",
    "dt = t[1]-t[0]\n",
    "dx = x[2]-x[1]\n",
    "X, T = np.meshgrid(x, t)\n",
    "XT = np.asarray([X, T]).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cmap = viridis\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import cm\n",
    "cmap_name = cm.get_cmap().name\n",
    "print(\"cmap =\", cmap_name)\n",
    "\n",
    "plt.contourf(X, T, u.T, levels=10, cmap=cmap_name)\n",
    "plt.colorbar()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = figure()\n",
    "ax = fig.gca(projection='3d')\n",
    "ax.set_zlabel(\"$u$\", fontsize=20)\n",
    "ax.set_xlabel(\"$x$\", fontsize=20)\n",
    "ax.set_ylabel(\"$t$\", fontsize=20)\n",
    "surf = ax.plot_surface(X, T, u.T, \n",
    "                       rstride=1, cstride=1, cmap=cmap_name, \n",
    "                       linewidth=1, antialiased=False)\n",
    "# plt.title(\"Burgers' PDE\", fontsize=20)\n",
    "# plt.savefig(fname=join_path(FIGURE_EXPORT_PATH, \"burgers3d.png\"), dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "STLSQ model: \n",
      "(x0)' = 0.100 x0_11 + -1.001 x0x0_1\n"
     ]
    }
   ],
   "source": [
    "library_functions = [lambda x: x, lambda x: x * x]\n",
    "library_function_names = [lambda x: x, lambda x: x + x]\n",
    "pde_lib = ps.PDELibrary(\n",
    "    library_functions=library_functions,\n",
    "    function_names=library_function_names,\n",
    "    derivative_order=2,\n",
    "    spatial_grid=x,\n",
    "    is_uniform=True,\n",
    ")\n",
    "\n",
    "print('STLSQ model: ')\n",
    "optimizer = ps.STLSQ(threshold=0.1, alpha=1e-5, normalize_columns=True)\n",
    "model = ps.SINDy(feature_library=pde_lib, optimizer=optimizer)\n",
    "model.fit(np.expand_dims(u, -1), t=dt)\n",
    "model.print()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Construct $\\Theta (U)$ and compute $U_t$\n",
    "\n",
    "The function build_linear_system does this for us.  We specify <br>\n",
    "D = highest derivative to appear in $\\Theta$  <br>\n",
    "P = highest degree polynomial of $u$ to appear in $\\Theta$ (not including multiplication by a derivative.  <br>\n",
    "time_diff and space_diff taken via finite differences\n",
    "\n",
    "Printed out is a list of candidate functions for the PDE.  Each is a column of $\\Theta (U)$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "Ut, R, rhs_des = build_linear_system(u, dt, dx, D=3, P=3, time_diff = 'FD', space_diff = 'FD')\n",
    "rhs_des = rhs_des[1:]\n",
    "R = R[:, 1:].real; Ut = Ut.real"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Solve for $\\xi$\n",
    "\n",
    "TrainSTRidge splits the data up into 80% for training and 20% for validation.  It searches over various tolerances in the STRidge algorithm and finds the one with the best performance on the validation set, including an $\\ell^0$ penalty for $\\xi$ in the loss function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "l0_penalty: 1.6123253173828125\n",
      "PDE derived using STRidge\n",
      "u_t = (-1.000403 +0.000000i)uu_{x}\n",
      "    + (0.100145 +0.000000i)u_{xx}\n",
      "   \n"
     ]
    }
   ],
   "source": [
    "# Solve with STRidge\n",
    "w = TrainSTRidge(R,Ut,10**-5,0.1)\n",
    "print(\"PDE derived using STRidge\")\n",
    "print_pde(w, rhs_des)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Now identify the same dynamics but with added noise.\n",
    "\n",
    "The only difference from above is that finite differences work poorly for noisy data so here we use polynomial interpolation.  With deg_x or deg_t and width_x or width_t we specify the degree number of points used to fit the polynomials used for differentiating x or t.  Unfortunately, the result can be sensitive to these."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.00017436017271128736,\n",
       " 0.07192089134552937,\n",
       " 0.2780377297273821,\n",
       " -0.32265115805438993)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# K = 2000 experiments\n",
    "# weak lib only can tolerate up to 75 (maybe more?)\n",
    "# weak lib + KalmanDiff: 90 (maybe more?)\n",
    "# weak lib + KalmanDiff + Robust PCA: 110\n",
    "# set np.random.seed(0) to reproduce...\n",
    "np.random.seed(0)\n",
    "noise_lv = float(40)\n",
    "noise = 0.01*np.abs(noise_lv)*(u.std())*np.random.randn(u.shape[0],u.shape[1])\n",
    "un = u + noise\n",
    "np.mean(noise), np.std(noise), np.max(noise), np.min(noise)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "Utn, Rn, rhs_des = build_linear_system(un, dt, dx, D=3, P=3, time_diff = 'poly',\n",
    "                                       deg_x = 4, deg_t = 4, \n",
    "                                       width_x = 10, width_t = 10)\n",
    "rhs_des = rhs_des[1:]\n",
    "Rn = Rn[:, 1:].real; Utn = Utn.real"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "l0_penalty: 1.958869140625\n",
      "PDE derived using STRidge\n",
      "u_t = (-0.709884 +0.000000i)uu_{x}\n",
      "   \n"
     ]
    }
   ],
   "source": [
    "# Solve with STRidge\n",
    "w = TrainSTRidge(Rn,Utn,10**-5,1)\n",
    "print(\"PDE derived using STRidge\")\n",
    "print_pde(w, rhs_des)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Goals: implement my own optimizer and differentiation_method class!\n",
    "from functools import partial\n",
    "import derivative\n",
    "from tvregdiff import TVRegDiff, tvregdiff, numdiff, pysindydiff, savgol_denoise\n",
    "# import sys; sys.path.insert(0, \"../../derivative/\"); import derivative\n",
    "class KalmanDiff(ps.BaseDifferentiation):\n",
    "    def __init__(self, alpha, poly_deg=None, rpca_lambda=None, d=1, axis=1, is_uniform=True, periodic=False):\n",
    "        super(KalmanDiff, self).__init__()\n",
    "        # Kalman diff\n",
    "        self.alpha = alpha\n",
    "        self.diff_func = derivative.Kalman(alpha=self.alpha)\n",
    "        self.d = d\n",
    "        self.diff = partial(pysindydiff, **{\"diff_method\":self.diff_func, \"order\":self.d})\n",
    "        # Savgol denoising\n",
    "        self.poly_deg = poly_deg\n",
    "        if poly_deg is not None:\n",
    "            if poly_deg%2 == 0: window_length = self.poly_deg + 1\n",
    "            else: window_length = self.poly_deg + 2\n",
    "            self.denoise = partial(savgol_denoise, **{\"window_length\":window_length, \"poly_deg\":self.poly_deg})\n",
    "        else:\n",
    "            self.denoise = lambda _: _\n",
    "        # Robust PCA\n",
    "        self.rpca_lambda = rpca_lambda\n",
    "        # Other info...\n",
    "        self.axis = axis\n",
    "        self.is_uniform = is_uniform\n",
    "        self.periodic = periodic\n",
    "        # data transformation\n",
    "        # rs = np.ones(2).astype(np.int32); rs[self.axis] = -1; rs = tuple(rs)\n",
    "        self.transform = np.vectorize(composite_function(self.diff, self.denoise, left2right=True), signature=\"(m),(m)->(m)\")\n",
    "    # _differentiate\n",
    "    def _differentiate(self, x, t):\n",
    "        in_shape = x.shape\n",
    "        if len(in_shape) == 2: x = np.expand_dims(x, -1) # x should now be 3-dimensional\n",
    "        if isinstance(t, float) and self.is_uniform: \n",
    "            t = np.linspace(0, stop=t*(x.shape[self.axis]-1), num=x.shape[self.axis])\n",
    "        out = []\n",
    "        # wrt to x var\n",
    "        if self.axis == 0:\n",
    "            for i in range(x.shape[-1]):\n",
    "                # use lambda and partial from functools to help shorten the code\n",
    "                # diff = np.hstack([self.denoise(self.diff(x[:, j:j+1, i], t)).reshape(-1, 1) \n",
    "                #                   for j in range(x.shape[1])])\n",
    "                # diff = np.hstack([self.transform(x[:, j:j+1, i], t) for j in range(x.shape[1])])\n",
    "                # diff = np.vectorize(self.transform, signature=\"(m),(m)->(m)\")(x[:,:,i].T, t).T\n",
    "                diff = self.transform(x[:,:,i].T, t).T\n",
    "                if self.rpca_lambda is not None:\n",
    "                    diff = self._get_low_rank(diff)\n",
    "                out.append(np.expand_dims(diff, axis=-1))\n",
    "        # wrt to time var\n",
    "        elif self.axis == 1:\n",
    "            for i in range(x.shape[-1]):\n",
    "                # use lambda and partial from functools to help shorten the code\n",
    "                # diff = np.vstack([self.denoise(self.diff(x[j:j+1, :, i], t)).reshape(1, -1) \n",
    "                #                   for j in range(x.shape[0])])\n",
    "                # diff = np.vstack([self.transform(x[j:j+1, :, i], t) for j in range(x.shape[0])])\n",
    "                # diff = np.vectorize(self.transform, signature=\"(m),(m)->(m)\")(x[:,:,i], t)\n",
    "                diff = self.transform(x[:,:,i], t)\n",
    "                if self.rpca_lambda is not None:\n",
    "                    diff = self._get_low_rank(diff)\n",
    "                out.append(np.expand_dims(diff, axis=-1))\n",
    "        return np.concatenate(out, axis=-1).reshape(in_shape)\n",
    "    # _get_low_rank\n",
    "    def _get_low_rank(self, x):\n",
    "        rpca = RobustPCA(lamb=self.rpca_lambda, tol=10, use_fbpca=True, max_iter=int(1e6))\n",
    "        rpca.fit(x)\n",
    "        return rpca.get_low_rank()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### RPCA before Savgol (Best with window length = 11, Seems fine)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "### also works ###\n",
    "# un, _ = RPCA(un, 6e-2)\n",
    "# un, _ = R_pca(un, lmbda=5e-2).fit(max_iter=5000, iter_print=1000)\n",
    "\n",
    "rpca_lambda = None; apply_savgol = False # Set to False for IBIS2022\n",
    "# rpca_lambda = 5e-2\n",
    "\n",
    "un_copy = un.copy()\n",
    "\n",
    "if rpca_lambda is not None:\n",
    "    rpca = RobustPCA(lamb=rpca_lambda, tol=10, use_fbpca=True, max_iter=int(1e6))\n",
    "    rpca.fit(un)\n",
    "    un = rpca.get_low_rank()\n",
    "    est_noise = un_copy-un\n",
    "    print(np.mean(est_noise), np.std(est_noise), np.max(est_noise), np.min(est_noise))\n",
    "    print(mean_squared_error(un, u))\n",
    "else:\n",
    "    # un = sio.loadmat(\"../Datasets/l1rdae_noisy_burgers.mat\")[\"rdae_denoised_un\"].real\n",
    "    # un = sio.loadmat(\"../Datasets/rdae_noisy_burgers_best.mat\")[\"rdae_denoised_un\"].real\n",
    "    \n",
    "    un = sio.loadmat(\"../RDAE_data/l21rdae_noisy40_burgers_0.5.mat\")[\"rdae_denoised_un\"].real # best\n",
    "    # un = sio.loadmat(\"../RDAE_data/l21rdae_noisy40_burgers_0.05.mat\")[\"rdae_denoised_un\"].real\n",
    "\n",
    "# divide into 2 5 10\n",
    "if apply_savgol:\n",
    "    un_savgol = np.vstack([savgol_denoise(un[i:i+1, :], 11, 2) for i in range(un.shape[0])])\n",
    "    print(mean_squared_error(u, un_savgol))\n",
    "    un = un_savgol"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Savgol before RPCA (Best with window length = 51, Seems to be tailored to perform well with highly noisy data)\n",
    "    - Need to test with other datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # divide into 2 5 10\n",
    "# apply_savgol = True\n",
    "# if apply_savgol:\n",
    "#     un_savgol = np.vstack([savgol_denoise(un[i:i+1, :], 51, 2) for i in range(un.shape[0])])\n",
    "#     print(mean_squared_error(u, un_savgol))\n",
    "#     un = un_savgol\n",
    "\n",
    "# rpca_lambda = None\n",
    "# rpca_lambda = 5e-2\n",
    "# if rpca_lambda is not None:\n",
    "#     rpca = RobustPCA(lamb=rpca_lambda, tol=10, use_fbpca=True, max_iter=int(1e6))\n",
    "#     un_copy = un.copy()\n",
    "#     rpca.fit(un)\n",
    "#     un = rpca.get_low_rank()\n",
    "#     est_noise = un_copy-un\n",
    "#     print(np.mean(est_noise), np.std(est_noise), np.max(est_noise), np.min(est_noise))\n",
    "#     print(mean_squared_error(un, u))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Just testing the code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# differentiation_method = ps.FiniteDifference\n",
    "# differentiation_kwargs = {}\n",
    "# kalpha = 1e-2; poly_deg = None; diff_order = 2 # kalpha = 1e-3 | 5e-3 | 1e-2\n",
    "# differentiation_method = KalmanDiff\n",
    "# differentiation_kwargs = {\"alpha\":kalpha, \"poly_deg\":poly_deg, \"rpca_lambda\":None}\n",
    "# weak_pde_lib = ps.WeakPDELibrary(library_functions=[lambda x: x, lambda x: x * x], \n",
    "#                                  function_names=[lambda x: x, lambda x: x + x], \n",
    "#                                  derivative_order=diff_order, p=diff_order, \n",
    "#                                  spatiotemporal_grid=XT, \n",
    "#                                  include_bias=False, is_uniform=True, K=5000, # new random K points in every calls to the ps.WeakPDELibrary\n",
    "#                                  differentiation_method=differentiation_method, \n",
    "#                                  differentiation_kwargs=differentiation_kwargs, \n",
    "#                                  cache=True\n",
    "#                                 )\n",
    "\n",
    "# to calculate u, u_1, u_11, ... up to diff_order\n",
    "# weak_pde_lib = ps.WeakPDELibrary(library_functions=[lambda x: x], \n",
    "#                                  function_names=[lambda x: x], \n",
    "#                                  derivative_order=diff_order, p=diff_order, \n",
    "#                                  include_interaction=False, \n",
    "#                                  spatiotemporal_grid=XT, \n",
    "#                                  include_bias=False, is_uniform=True, K=5000, # new random K points in every calls to the ps.WeakPDELibrary\n",
    "#                                  differentiation_method=differentiation_method, \n",
    "#                                  differentiation_kwargs=differentiation_kwargs\n",
    "#                                 )\n",
    "\n",
    "# To have that it's better to calc the coefficients using the weak lib\n",
    "# weak_pde_lib = ps.PDELibrary(library_functions=[lambda x: x, lambda x: x * x], \n",
    "#                         function_names=[lambda x: x, lambda x: x + x], \n",
    "#                         derivative_order=diff_order, spatial_grid=x, \n",
    "#                         include_bias=False, is_uniform=True, \n",
    "#                         differentiation_method=differentiation_method, \n",
    "#                         differentiation_kwargs=differentiation_kwargs\n",
    "#                        )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# uu = np.expand_dims(un,-1)\n",
    "# X_uu_pre1 = weak_pde_lib.fit_transform(uu).reshape(-1, len(weak_pde_lib.get_feature_names()))\n",
    "# y_uu_pre1 = weak_pde_lib.convert_u_dot_integral(uu)\n",
    "# # for weak_pde_lib = ps.PDELibrary(...)\n",
    "# # y_uu_pre1 = weak_pde_lib.differentiation_method(axis=1, **weak_pde_lib.differentiation_kwargs)._differentiate(uu, t).reshape(-1,1)\n",
    "# coeffs1 = np.linalg.lstsq(X_uu_pre1[:, [3, 4]], y_uu_pre1, rcond=None)[0]\n",
    "\n",
    "# kwargs = {'fit_intercept':False, 'copy_X':True, 'normalize_columns':False}\n",
    "# X_uu_pre2, y_uu_pre2, fns = ps_features(uu, t, weak_pde_lib, kwargs)\n",
    "# coeffs2 = np.linalg.lstsq(X_uu_pre2[:, [3, 4]], y_uu_pre2, rcond=None)[0]\n",
    "\n",
    "# print(np.allclose(X_uu_pre1, X_uu_pre2))\n",
    "# print(np.allclose(y_uu_pre1, y_uu_pre2))\n",
    "# print(np.allclose(coeffs1, coeffs2))\n",
    "# print(np.allclose(weak_pde_lib.cached_xp_full, X_uu_pre1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from sklearn.linear_model import BayesianRidge, LinearRegression as SkLinearRegression\n",
    "# from sklearn.base import BaseEstimator\n",
    "# from sklearn.pipeline import make_pipeline\n",
    "\n",
    "# kwargs = {'fit_intercept':False, 'copy_X':True, 'normalize_columns':False}\n",
    "# X_pre, y_pre, fns = ps_features(un, t, weak_pde_lib, kwargs)\n",
    "# print(symbols(' '.join(fns)))\n",
    "# # reg = BayesianRidge(fit_intercept=False, normalize=True, compute_score=True)\n",
    "# reg = SkLinearRegression(fit_intercept=False, normalize=True)\n",
    "# print(reg.fit(X_pre[:, [3, 4]], y_pre).coef_)\n",
    "# print(bnb(X_pre, y_pre, 2, lam=1e-3, corrected_coefficients=True))\n",
    "\n",
    "# class Normalizer(BaseEstimator):\n",
    "#     def __init__(self, order=2, axis=0):\n",
    "#         super(Normalizer, self).__init__()\n",
    "#         self.order = order\n",
    "#         self.axis = axis\n",
    "#         self.norm = None\n",
    "#     def fit(self, X, y):\n",
    "#         self.norm = np.divide(1.0, np.linalg.norm(X, ord=self.order, axis=self.axis))\n",
    "#     def transform(self, X):\n",
    "#         return np.multiply(self.norm, X)\n",
    "#     def fit_transform(self, X, y):\n",
    "#         self.fit(X, y)\n",
    "#         return self.transform(X)\n",
    "\n",
    "# # Implement something like NormalizedLinearEstimator...\n",
    "# model = make_pipeline(Normalizer(order=2, axis=0), SkLinearRegression(fit_intercept=False))\n",
    "# model.fit(X_pre[:, [3, 4]], y_pre)\n",
    "# model.steps[1][-1].coef_ = model.steps[0][-1].transform(model.steps[1][-1].coef_)\n",
    "# model = model.steps[1][-1]; model.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Avg weak coef from 10 runs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "kalpha = 1e-2; poly_deg = None; diff_order = 2\n",
    "differentiation_method = KalmanDiff\n",
    "differentiation_kwargs = {\"alpha\":kalpha, \"poly_deg\":poly_deg, \"rpca_lambda\":None}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "weak_pde_lib = ps.WeakPDELibrary(library_functions=[lambda x: x, lambda x: x * x], \n",
    "                                 function_names=[lambda x: x, lambda x: x + x], \n",
    "                                 derivative_order=diff_order, p=diff_order, \n",
    "                                 spatiotemporal_grid=XT, \n",
    "                                 include_bias=False, is_uniform=True, K=10000, # new random K points in every calls to the ps.WeakPDELibrary\n",
    "                                 differentiation_method=differentiation_method, \n",
    "                                 differentiation_kwargs=differentiation_kwargs, \n",
    "                                 cache=True\n",
    "                                )\n",
    "kwargs = {'fit_intercept':False, 'copy_X':True, 'normalize_columns':False}\n",
    "X_pre, y_pre, fns = ps_features(un, t, weak_pde_lib, kwargs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['u', 'uu', 'u_1', 'u_11', 'uu_1', 'uuu_1', 'uu_11', 'uuu_11']\n",
      "(u)' = 0.097 u_11 + -1.011 uu_1\n",
      "u_t = (0.096365 +0.000000i)u_11\n",
      "    + (-1.010189 +0.000000i)uu_1\n",
      "   \n"
     ]
    }
   ],
   "source": [
    "## give wrong answer\n",
    "# optimizer = ps.STLSQ(threshold=0.1, alpha=1e-5, normalize_columns=True)\n",
    "# optimizer = ps.SR3(threshold=0.1, thresholder='l0', tol=1e-8, normalize_columns=True, max_iter=1000)\n",
    "# optimizer = ABESS(abess_kw={'alpha':1e-2, 'support_size':2, 'cv':5}, group=None, is_normal=False, normalize_columns=True)\n",
    "# optimizer = BESS(bess_kw={'path_type':'seq', 'sequence':[2], 'is_cv':True, 'K':5}, is_normal=True, normalize_columns=False)\n",
    "\n",
    "## give correct answer (for reasonable noise levels) | L0BNB & ps.FROLS are now the best algos. \n",
    "# Fit a weak form model\n",
    "# optimizer = ps.SSR(criteria='model_residual', normalize_columns=True, kappa=1e-2)\n",
    "# optimizer = ps.FROLS(normalize_columns=True, kappa=5e-4, max_iter=100, alpha=1e-1) # kappa = 1e-3 or 5e-4 gives 2 effective candidates\n",
    "optimizer = L0BNB(max_nonzeros=2, lam=1e-3, is_normal=True, normalize_columns=False) # tune lam จาก (X_pre, y_pre) | ตอนนี้ยังไม่ได้ tune\n",
    "# optimizer = BruteForceRegressor(2)\n",
    "# if feature_library=weak_pde_lib, then just differentiation_method=None is fine.\n",
    "n_ensemble_models = 50\n",
    "model = ps.SINDy(feature_library=weak_pde_lib, optimizer=optimizer, \n",
    "                 # differentiation_method=None,\n",
    "                 differentiation_method=KalmanDiff(alpha=kalpha, poly_deg=poly_deg, rpca_lambda=rpca_lambda, axis=0, is_uniform=True), \n",
    "                 cache=True,\n",
    "                 feature_names=['u'])\n",
    "model.fit(np.expand_dims(un, -1), t=dt, ensemble=True, \n",
    "          library_ensemble=True, n_candidates_to_drop=1, n_models=n_ensemble_models)\n",
    "\n",
    "# print the model selected by Pysindy package\n",
    "# (Not giving the best model if set ensemble or library_ensemble = True)\n",
    "print(model.get_feature_names())\n",
    "model.print()\n",
    "\n",
    "# Use the best-performing (on any validation set) coef in model.coef_list (Plse add this functionality to pysindy)\n",
    "# np.einsum('ij,jkl',X_pre,np.array(model.get_coef_list()).T) -> a cool-looking code\n",
    "# from opt_einsum import contract\n",
    "# contract('ij,jkl',X_pre,np.array(model.get_coef_list()).T) -> a cool-looking code\n",
    "print_pde(model.get_coef_list()[np.argmin(np.sum((np.squeeze(np.tensordot(X_pre, np.array(model.get_coef_list()).T, axes=([-1], [0])), axis=1)-y_pre)**2, axis=0))].reshape(-1,1), model.get_feature_names())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "hasattr threshold in optimizer...\n",
      "u_t = (0.097086 +0.000000i)u_11\n",
      "    + (-1.010731 +0.000000i)uu_1\n",
      "   \n",
      "['u_{xx}' 'uu_{x}']\n",
      "[ 0.0970855  -1.01073128]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(1.993811823786129, 0.9206839210979867)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# For poster: No savgol before or after RPCA\n",
    "# u_t = (0.094309 +0.000000i)u_11\n",
    "#     + (-0.851764 +0.000000i)uu_1\n",
    "# For future Savgol (win_len = 11) after RPCA (performance little bit better)\n",
    "# u_t = (0.094646 +0.000000i)u_11\n",
    "#     + (-0.859442 +0.000000i)uu_1\n",
    "# model.validate_coef_list()\n",
    "# model.validate_coef_list(X_pre, y_pre)\n",
    "ols_model, eff_rhs_indices = model.validate_coef_list()[0]\n",
    "print(np.array([convert2latex(f) for i,f in enumerate(model.get_feature_names()) if i in eff_rhs_indices]))\n",
    "print(ols_model.params)\n",
    "evaluate_coefficients(ols_model.params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "hasattr threshold in optimizer...\n",
      "u_t = (-0.947920 +0.000000i)uu_1\n",
      "   \n",
      "hasattr threshold in optimizer...\n",
      "u_t = (0.097086 +0.000000i)u_11\n",
      "    + (-1.010731 +0.000000i)uu_1\n",
      "   \n",
      "hasattr threshold in optimizer...\n",
      "u_t = (0.085327 +0.000000i)u_11\n",
      "    + (-1.026531 +0.000000i)uu_1\n",
      "    + (0.029854 +0.000000i)uu_11\n",
      "   \n",
      "hasattr threshold in optimizer...\n",
      "u_t = (0.078109 +0.000000i)u_11\n",
      "    + (-1.030419 +0.000000i)uu_1\n",
      "    + (0.098182 +0.000000i)uu_11\n",
      "    + (-0.081991 +0.000000i)uuu_11\n",
      "   \n",
      "hasattr threshold in optimizer...\n",
      "u_t = (0.078109 +0.000000i)u_11\n",
      "    + (-1.030419 +0.000000i)uu_1\n",
      "    + (0.098182 +0.000000i)uu_11\n",
      "    + (-0.081991 +0.000000i)uuu_11\n",
      "   \n",
      "hasattr threshold in optimizer...\n",
      "u_t = (0.011955 +0.000000i)u\n",
      "    + (-0.013561 +0.000000i)u_1\n",
      "    + (0.061893 +0.000000i)u_11\n",
      "    + (-1.019743 +0.000000i)uu_1\n",
      "    + (0.207788 +0.000000i)uu_11\n",
      "    + (-0.176006 +0.000000i)uuu_11\n",
      "   \n",
      "hasattr threshold in optimizer...\n",
      "u_t = (0.028067 +0.000000i)u\n",
      "    + (-0.041922 +0.000000i)uu\n",
      "    + (0.058345 +0.000000i)u_11\n",
      "    + (-1.150647 +0.000000i)uu_1\n",
      "    + (0.212135 +0.000000i)uuu_1\n",
      "    + (0.234828 +0.000000i)uu_11\n",
      "    + (-0.229233 +0.000000i)uuu_11\n",
      "   \n",
      "hasattr threshold in optimizer...\n",
      "u_t = (0.028067 +0.000000i)u\n",
      "    + (-0.041922 +0.000000i)uu\n",
      "    + (0.058345 +0.000000i)u_11\n",
      "    + (-1.150647 +0.000000i)uu_1\n",
      "    + (0.212135 +0.000000i)uuu_1\n",
      "    + (0.234828 +0.000000i)uu_11\n",
      "    + (-0.229233 +0.000000i)uuu_11\n",
      "   \n"
     ]
    }
   ],
   "source": [
    "all_ols_models = []\n",
    "all_eff_rhs = []\n",
    "for mnz in range(1, 9):\n",
    "    optimizer = L0BNB(max_nonzeros=mnz, lam=1e-3, is_normal=True, normalize_columns=False) # tune lam จาก (X_pre, y_pre) | ตอนนี้ยังไม่ได้ tune\n",
    "    n_ensemble_models = 50\n",
    "    model = ps.SINDy(feature_library=weak_pde_lib, optimizer=optimizer, \n",
    "                     differentiation_method=KalmanDiff(alpha=kalpha, poly_deg=poly_deg, rpca_lambda=rpca_lambda, axis=0, is_uniform=True), \n",
    "                     cache=True,\n",
    "                     feature_names=['u'])\n",
    "    model.fit(np.expand_dims(un, -1), t=dt, ensemble=True, \n",
    "              library_ensemble=True, n_candidates_to_drop=1, n_models=n_ensemble_models)\n",
    "    ols_model, eff_rhs_indices = model.validate_coef_list()[0]\n",
    "    all_ols_models.append(ols_model)\n",
    "    all_eff_rhs.append(eff_rhs_indices)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_eff_rhs.append(tuple(i for i in range(X_pre.shape[1])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RFE_PLinearRegression\n",
      "(3, 4)\n",
      "(4,)\n",
      "--------------------------------------------------\n",
      "(3, 4, 6)\n",
      "(3, 4)\n",
      "--------------------------------------------------\n",
      "(3, 4, 6, 7)\n",
      "(3, 4, 6)\n",
      "--------------------------------------------------\n",
      "(3, 4, 6, 7)\n",
      "--------------------------------------------------\n",
      "(0, 2, 3, 4, 6, 7)\n",
      "(0, 3, 4, 6, 7)\n",
      "(3, 4, 6, 7)\n",
      "--------------------------------------------------\n",
      "(0, 1, 3, 4, 5, 6, 7)\n",
      "(0, 3, 4, 5, 6, 7)\n",
      "(0, 3, 4, 6, 7)\n",
      "--------------------------------------------------\n",
      "(0, 1, 3, 4, 5, 6, 7)\n",
      "--------------------------------------------------\n",
      "(0, 1, 2, 3, 4, 5, 6, 7)\n",
      "(0, 1, 3, 4, 5, 6, 7)\n",
      "--------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "st = backward_refinement(all_eff_rhs, (X_pre,y_pre), mode='rfe', verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(4,)\n",
      "(3, 4)\n",
      "(3, 4, 6)\n",
      "(3, 4, 6, 7)\n",
      "(0, 3, 4, 6, 7)\n",
      "(0, 3, 4, 5, 6, 7)\n",
      "(0, 1, 3, 4, 5, 6, 7)\n",
      "(0, 1, 2, 3, 4, 5, 6, 7)\n"
     ]
    }
   ],
   "source": [
    "decreasing_aics = []\n",
    "complexities = []\n",
    "eff_feature_names = []\n",
    "now_aic = 1e6\n",
    "for com in sorted(st.track.keys()):\n",
    "    indices, aic = st.track[com]\n",
    "    print(indices)\n",
    "    if aic < now_aic:\n",
    "        complexities.append(len(indices))\n",
    "        decreasing_aics.append(aic)\n",
    "        eff_feature_names.append([convert2latex(f) for i,f in enumerate(model.get_feature_names()) if i in indices])\n",
    "        now_aic = aic\n",
    "decreasing_aics = np.array(decreasing_aics)\n",
    "complexities = np.array(complexities)\n",
    "relative_decreasing_aics = decreasing_aics-np.min(decreasing_aics)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(8,4))\n",
    "ax.plot(complexities, relative_decreasing_aics, color='blue', linewidth=2.5, label=\"AIC-decreasing models\")\n",
    "ax.scatter(complexities, relative_decreasing_aics, marker='*', c='red', s=300)\n",
    "for com, name, rel_aic in zip(complexities, eff_feature_names, relative_decreasing_aics):\n",
    "    rotation = (90+45)/2\n",
    "    if com == 1: rotation = 0\n",
    "    ax.annotate(f\"    $({','.join(name)})$\", (com, rel_aic), rotation=rotation)\n",
    "plt.xlim(0.8,8.1)\n",
    "plt.title(r\"Model selection based on $\\frac{\\Delta AIC}{\\Delta s}$\")\n",
    "plt.ylabel(\"Relative AIC\")\n",
    "plt.xlabel(\"Number of support sizes\")\n",
    "plt.legend()\n",
    "plt.tight_layout()\n",
    "# plt.savefig(join_path(FIGURE_EXPORT_PATH, \"aic_model_selection.png\"), dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2 improves 1\n",
      "Percent improve: 0.10803697231852148\n",
      "3 does not improve 2\n",
      "Percent improve: 0.001055891859387946\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "find_transition_V5(decreasing_aics, complexities, percent_ok=0.09)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([-61942.58641101, -68634.67590443, -68707.14669999, -68723.3471394 ,\n",
       "        -68771.5720519 , -68784.91274736, -68791.13974807]),\n",
       " array([1, 2, 3, 4, 5, 6, 7]),\n",
       " [['uu_{x}'],\n",
       "  ['u_{xx}', 'uu_{x}'],\n",
       "  ['u_{xx}', 'uu_{x}', 'uu_{xx}'],\n",
       "  ['u_{xx}', 'uu_{x}', 'uu_{xx}', 'u^2u_{xx}'],\n",
       "  ['u', 'u_{xx}', 'uu_{x}', 'uu_{xx}', 'u^2u_{xx}'],\n",
       "  ['u', 'u_{xx}', 'uu_{x}', 'u^2u_{x}', 'uu_{xx}', 'u^2u_{xx}'],\n",
       "  ['u', 'u^2', 'u_{xx}', 'uu_{x}', 'u^2u_{x}', 'uu_{xx}', 'u^2u_{xx}']])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Robust PCA | 5e-2 ###\n",
    "# (array([-67232.53198227, -75588.1584368 , -76240.48984011, -76260.73668138,\n",
    "#         -76425.72108798, -76437.34969411, -76489.94051519, -76523.73245724]),\n",
    "#  array([1, 2, 3, 4, 5, 6, 7, 8]),\n",
    "#  [['u_{x}'],\n",
    "#   ['u_{xx}', 'uu_{x}'],\n",
    "#   ['u_{xx}', 'uu_{x}', 'u^2u_{x}'],\n",
    "#   ['u_{xx}', 'uu_{x}', 'u^2u_{x}', 'u^2u_{xx}'],\n",
    "#   ['u_{xx}', 'uu_{x}', 'u^2u_{x}', 'uu_{xx}', 'u^2u_{xx}'],\n",
    "#   ['u^2', 'u_{xx}', 'uu_{x}', 'u^2u_{x}', 'uu_{xx}', 'u^2u_{xx}'],\n",
    "#   ['u', 'u^2', 'u_{xx}', 'uu_{x}', 'u^2u_{x}', 'uu_{xx}', 'u^2u_{xx}'],\n",
    "#   ['u',\n",
    "#    'u^2',\n",
    "#    'u_{x}',\n",
    "#    'u_{xx}',\n",
    "#    'uu_{x}',\n",
    "#    'u^2u_{x}',\n",
    "#    'uu_{xx}',\n",
    "#    'u^2u_{xx}']])\n",
    "\n",
    "### RDAE ###\n",
    "# 5e-0\n",
    "# (array([-61457.79563302, -67768.04270879, -67892.04820035, -67912.10229129,\n",
    "#         -67929.64549213, -67937.40869858, -67945.9834522 ]),\n",
    "#  array([1, 2, 3, 4, 5, 6, 7]),\n",
    "#  [['uu_{x}'],\n",
    "#   ['u_{xx}', 'uu_{x}'],\n",
    "#   ['u_{xx}', 'uu_{x}', 'uu_{xx}'],\n",
    "#   ['u', 'u_{xx}', 'uu_{x}', 'uu_{xx}'],\n",
    "#   ['u', 'u_{xx}', 'uu_{x}', 'uu_{xx}', 'u^2u_{xx}'],\n",
    "#   ['u', 'u_{xx}', 'uu_{x}', 'u^2u_{x}', 'uu_{xx}', 'u^2u_{xx}'],\n",
    "#   ['u', 'u^2', 'u_{xx}', 'uu_{x}', 'u^2u_{x}', 'uu_{xx}', 'u^2u_{xx}']])\n",
    "\n",
    "# 1e-0\n",
    "# (array([-61470.53751784, -67403.7884947 , -67463.47315383, -67491.22503685,\n",
    "#         -67524.31114049, -67541.30459472, -67577.12280888]),\n",
    "#  array([1, 2, 3, 4, 5, 6, 7]),\n",
    "#  [['uu_{x}'],\n",
    "#   ['u_{xx}', 'uu_{x}'],\n",
    "#   ['u_{xx}', 'uu_{x}', 'uu_{xx}'],\n",
    "#   ['u_{xx}', 'uu_{x}', 'uu_{xx}', 'u^2u_{xx}'],\n",
    "#   ['u', 'u_{xx}', 'uu_{x}', 'uu_{xx}', 'u^2u_{xx}'],\n",
    "#   ['u', 'u_{xx}', 'uu_{x}', 'u^2u_{x}', 'uu_{xx}', 'u^2u_{xx}'],\n",
    "#   ['u', 'u^2', 'u_{xx}', 'uu_{x}', 'u^2u_{x}', 'uu_{xx}', 'u^2u_{xx}']])\n",
    "\n",
    "# 5e-1 (biggest drop)\n",
    "# (array([-61942.58641101, -68634.67590443, -68707.14669999, -68723.3471394 ,\n",
    "#         -68771.5720519 , -68784.91274736, -68791.13974807]),\n",
    "#  array([1, 2, 3, 4, 5, 6, 7]),\n",
    "#  [['uu_{x}'],\n",
    "#   ['u_{xx}', 'uu_{x}'],\n",
    "#   ['u_{xx}', 'uu_{x}', 'uu_{xx}'],\n",
    "#   ['u_{xx}', 'uu_{x}', 'uu_{xx}', 'u^2u_{xx}'],\n",
    "#   ['u', 'u_{xx}', 'uu_{x}', 'uu_{xx}', 'u^2u_{xx}'],\n",
    "#   ['u', 'u_{xx}', 'uu_{x}', 'u^2u_{x}', 'uu_{xx}', 'u^2u_{xx}'],\n",
    "#   ['u', 'u^2', 'u_{xx}', 'uu_{x}', 'u^2u_{x}', 'uu_{xx}', 'u^2u_{xx}']])\n",
    "\n",
    "# 5e-2\n",
    "# (array([-63319.57267493, -69422.77962975, -69521.1242639 , -69610.94610283,\n",
    "#         -69632.39125379, -69759.73072006, -69759.86095886, -69766.15596996]),\n",
    "#  array([1, 2, 3, 4, 5, 6, 7, 8]),\n",
    "#  [['uu_{x}'],\n",
    "#   ['u_{xx}', 'uu_{x}'],\n",
    "#   ['u_{xx}', 'uu_{x}', 'u^2u_{x}'],\n",
    "#   ['u_{xx}', 'uu_{x}', 'u^2u_{x}', 'uu_{xx}'],\n",
    "#   ['u_{xx}', 'uu_{x}', 'u^2u_{x}', 'uu_{xx}', 'u^2u_{xx}'],\n",
    "#   ['u', 'u^2', 'u_{xx}', 'uu_{x}', 'uu_{xx}', 'u^2u_{xx}'],\n",
    "#   ['u', 'u^2', 'u_{x}', 'u_{xx}', 'uu_{x}', 'uu_{xx}', 'u^2u_{xx}'],\n",
    "#   ['u',\n",
    "#    'u^2',\n",
    "#    'u_{x}',\n",
    "#    'u_{xx}',\n",
    "#    'uu_{x}',\n",
    "#    'u^2u_{x}',\n",
    "#    'uu_{xx}',\n",
    "#    'u^2u_{xx}']])\n",
    "\n",
    "# 5e-3\n",
    "# (array([-62616.9439043 , -64382.30756858, -65732.60951777, -67060.56858316,\n",
    "#         -67764.31672752, -67772.14160474, -67774.025401  , -67785.41418025]),\n",
    "#  array([1, 2, 3, 4, 5, 6, 7, 8]),\n",
    "#  [['uu_{x}'],\n",
    "#   ['u^2', 'uu_{x}'],\n",
    "#   ['u^2', 'uu_{x}', 'u^2u_{x}'],\n",
    "#   ['u', 'u^2', 'uu_{x}', 'u^2u_{x}'],\n",
    "#   ['u', 'u^2', 'u_{x}', 'uu_{x}', 'u^2u_{x}'],\n",
    "#   ['u', 'u^2', 'u_{x}', 'u_{xx}', 'uu_{x}', 'u^2u_{x}'],\n",
    "#   ['u', 'u^2', 'u_{x}', 'u_{xx}', 'uu_{x}', 'u^2u_{x}', 'uu_{xx}'],\n",
    "#   ['u',\n",
    "#    'u^2',\n",
    "#    'u_{x}',\n",
    "#    'u_{xx}',\n",
    "#    'uu_{x}',\n",
    "#    'u^2u_{x}',\n",
    "#    'uu_{xx}',\n",
    "#    'u^2u_{xx}']])\n",
    "\n",
    "decreasing_aics, complexities, eff_feature_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "hasattr threshold in optimizer...\n",
      "u_t = (0.095018 +0.000000i)u_11\n",
      "    + (-1.006809 +0.000000i)uu_1\n",
      "   \n",
      "['u_{xx}' 'uu_{x}']\n",
      "[ 0.09501766 -1.00680903]\n",
      "hasattr threshold in optimizer...\n",
      "u_t = (0.094381 +0.000000i)u_11\n",
      "    + (-1.008122 +0.000000i)uu_1\n",
      "   \n",
      "['u_{xx}' 'uu_{x}']\n",
      "[ 0.09438112 -1.00812189]\n",
      "hasattr threshold in optimizer...\n",
      "u_t = (0.095071 +0.000000i)u_11\n",
      "    + (-1.001951 +0.000000i)uu_1\n",
      "   \n",
      "['u_{xx}' 'uu_{x}']\n",
      "[ 0.09507057 -1.00195133]\n",
      "hasattr threshold in optimizer...\n",
      "u_t = (0.096951 +0.000000i)u_11\n",
      "    + (-1.011645 +0.000000i)uu_1\n",
      "   \n",
      "['u_{xx}' 'uu_{x}']\n",
      "[ 0.09695065 -1.01164504]\n",
      "hasattr threshold in optimizer...\n",
      "u_t = (0.094945 +0.000000i)u_11\n",
      "    + (-1.008106 +0.000000i)uu_1\n",
      "   \n",
      "['u_{xx}' 'uu_{x}']\n",
      "[ 0.09494541 -1.00810561]\n",
      "hasattr threshold in optimizer...\n",
      "u_t = (0.095566 +0.000000i)u_11\n",
      "    + (-1.003437 +0.000000i)uu_1\n",
      "   \n",
      "['u_{xx}' 'uu_{x}']\n",
      "[ 0.09556625 -1.00343747]\n",
      "hasattr threshold in optimizer...\n",
      "u_t = (0.096531 +0.000000i)u_11\n",
      "    + (-1.015528 +0.000000i)uu_1\n",
      "   \n",
      "['u_{xx}' 'uu_{x}']\n",
      "[ 0.09653069 -1.01552786]\n",
      "hasattr threshold in optimizer...\n",
      "u_t = (0.096375 +0.000000i)u_11\n",
      "    + (-1.013274 +0.000000i)uu_1\n",
      "   \n",
      "['u_{xx}' 'uu_{x}']\n",
      "[ 0.09637497 -1.01327402]\n",
      "hasattr threshold in optimizer...\n",
      "u_t = (0.095140 +0.000000i)u_11\n",
      "    + (-1.000776 +0.000000i)uu_1\n",
      "   \n",
      "['u_{xx}' 'uu_{x}']\n",
      "[ 0.09513957 -1.00077563]\n",
      "hasattr threshold in optimizer...\n",
      "u_t = (0.098165 +0.000000i)u_11\n",
      "    + (-1.005608 +0.000000i)uu_1\n",
      "   \n",
      "['u_{xx}' 'uu_{x}']\n",
      "[ 0.09816495 -1.00560754]\n"
     ]
    }
   ],
   "source": [
    "X_pre = np.zeros(X_pre.shape)\n",
    "y_pre = np.zeros(y_pre.shape)\n",
    "n_times = 10\n",
    "weak_coeffs = []\n",
    "for _ in range(n_times):\n",
    "    weak_pde_lib = ps.WeakPDELibrary(library_functions=[lambda x: x, lambda x: x * x], \n",
    "                                     function_names=[lambda x: x, lambda x: x + x], \n",
    "                                     derivative_order=diff_order, p=diff_order, \n",
    "                                     spatiotemporal_grid=XT, \n",
    "                                     include_bias=False, is_uniform=True, K=10000, \n",
    "                                     differentiation_method=differentiation_method, \n",
    "                                     differentiation_kwargs=differentiation_kwargs, \n",
    "                                     cache=True\n",
    "                                    )\n",
    "    kwargs = {'fit_intercept':False, 'copy_X':True, 'normalize_columns':False}\n",
    "    # Apply moving horizon to un is an interesting idea.\n",
    "    X_pre_sub, y_pre_sub, fns = ps_features(un, t, weak_pde_lib, kwargs)\n",
    "    X_pre = X_pre + X_pre_sub\n",
    "    y_pre = y_pre + y_pre_sub\n",
    "    optimizer = L0BNB(max_nonzeros=2, lam=1e-3, is_normal=True, normalize_columns=False)\n",
    "    n_ensemble_models = 50\n",
    "    model = ps.SINDy(feature_library=weak_pde_lib, optimizer=optimizer, \n",
    "                     differentiation_method=KalmanDiff(alpha=kalpha, poly_deg=poly_deg, rpca_lambda=rpca_lambda, axis=0, is_uniform=True), \n",
    "                     cache=True,\n",
    "                     feature_names=['u'])\n",
    "    model.fit(np.expand_dims(un, -1), t=dt, ensemble=True, \n",
    "              library_ensemble=True, n_candidates_to_drop=1, n_models=n_ensemble_models)\n",
    "    ols_model, eff_rhs_indices = model.validate_coef_list()[0]\n",
    "    print(np.array([convert2latex(f) for i,f in enumerate(model.get_feature_names()) if i in eff_rhs_indices]))\n",
    "    print(ols_model.params)\n",
    "    # percent_coeff = evaluate_coefficients(ols_model.params)\n",
    "    # print(percent_coeff)\n",
    "    # if percent_coeff[0] > 100: break\n",
    "    weak_coeffs.append(ols_model.params)\n",
    "weak_coeffs = np.array(weak_coeffs)\n",
    "avg_weak_coeff = np.mean(weak_coeffs, axis=0)\n",
    "X_pre = X_pre/n_times\n",
    "y_pre = y_pre/n_times"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.003467511174737858, 0.008040505161991068)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# RDAE 5e-0: cv = 0.005062929348375081, 0.011023507990422338\n",
    "# RDAE 1e-0: cv = 0.004408008789094885, 0.00910193468516509\n",
    "# RDAE 5e-1: cv = 0.003467511174737858, 0.008040505161991068\n",
    "# RDAE 5e-2: cv = 0.004305311364345706, 0.007809099375818377\n",
    "\n",
    "cv_abs = (weak_coeffs.std(axis=0)/np.abs(weak_coeffs.mean(axis=0))).mean()\n",
    "cv = (weak_coeffs.std(axis=0)/weak_coeffs.mean(axis=0)).mean()\n",
    "cv, cv_abs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2.469185388081714, 1.7166311808760781)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 50 percent noise\n",
    "# Robust PCA: (14.745720655392669, 6.347415766367691)\n",
    "# RDAE failed...\n",
    "\n",
    "# 40 percent noise\n",
    "# Robust PCA: (14.818002368919029, 7.899799685479938)\n",
    "# RDAE 5e-0: (2.9505659028666087, 1.8024006266427972)\n",
    "# RDAE 1e-0: (3.2008235132427463, 2.56690175990938)\n",
    "# RDAE 5e-1: (2.469185388081714, 1.7166311808760781)\n",
    "# RDAE 5e-2: (6.015975755333656, 5.859365864415776)\n",
    "# RDAE 5e-3: Failed\n",
    "\n",
    "evaluate_coefficients(avg_weak_coeff)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.09581418 -1.00752554]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(2.469185388081714, 1.7166311808760781)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def percent_coeff(pred, ground):\n",
    "    return 100*np.abs(pred-ground)/np.abs(ground)\n",
    "print(avg_weak_coeff)\n",
    "errs = percent_coeff(avg_weak_coeff, np.array([0.1, -1]))\n",
    "errs.mean(), errs.std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# best_subsets = [(4,), (3, 4), (3, 4, 6), (0, 3, 4, 6), (0, 3, 4, 6, 7), (0, 1, 3, 4, 6, 7), (0, 1, 2, 3, 4, 6, 7), (0, 1, 2, 3, 4, 5, 6, 7)]\n",
    "# tmp = np.zeros((len(best_subsets), X_pre.shape[-1]))\n",
    "# for i in range(len(best_subsets)):\n",
    "#     tmp[i:i+1, :][0, best_subsets[i]] = 1.0\n",
    "# best_subsets = tmp.astype(np.float32)\n",
    "# del tmp\n",
    "\n",
    "# pms_data = {nameof(X_pre): X_pre.astype(np.float32), \n",
    "#             nameof(y_pre): y_pre.astype(np.float32), \n",
    "#             nameof(best_subsets): best_subsets.astype(np.float32)}\n",
    "# h5file(file_path=\"../PMS_data/burgers_pms.h5\", cdict=pms_data, mode='w')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['X_pre', 'best_subsets', 'y_pre']\n"
     ]
    }
   ],
   "source": [
    "X_pre, best_subsets, y_pre = h5file(file_path=\"../PMS_data/burgers_pms.h5\", mode='r', return_dict=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['u', 'u*u', 'u_1', 'u_11', 'u*u_1', 'u*u*u_1', 'u*u_11',\n",
       "       'u*u*u_11'], dtype='<U8')"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "basic_vars = {'u'}\n",
    "encoded_feature_names = []\n",
    "for feature in model.get_feature_names():\n",
    "    out_feature = []\n",
    "    last_index = -1\n",
    "    for i, c in enumerate(feature):\n",
    "        if i+1 < len(feature) and feature[i+1] in basic_vars:\n",
    "            last_index = i\n",
    "            out_feature.append(c+'*')\n",
    "    out_feature.append(feature[last_index+1:])\n",
    "    encoded_feature_names.append(''.join(out_feature))\n",
    "encoded_feature_names = np.array(encoded_feature_names)\n",
    "encoded_feature_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'u_1+u_11'"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'+'.join(np.array(encoded_feature_names)[[2,3]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "u*u_1\n",
      "----------\n",
      "u_11+u*u_1\n",
      "----------\n",
      "u_11+u*u_1+u*u_11\n",
      "----------\n",
      "u_11+u*u_1+u*u_11+u*u*u_11\n",
      "----------\n",
      "u+u_11+u*u_1+u*u_11+u*u*u_11\n",
      "----------\n",
      "u+u_11+u*u_1+u*u*u_1+u*u_11+u*u*u_11\n",
      "----------\n",
      "u+u*u+u_11+u*u_1+u*u*u_1+u*u_11+u*u*u_11\n",
      "----------\n",
      "u+u*u+u_1+u_11+u*u_1+u*u*u_1+u*u_11+u*u*u_11\n",
      "----------\n"
     ]
    }
   ],
   "source": [
    "for com in sorted(st.track.keys()):\n",
    "    indices, _ = st.track[com]\n",
    "    expr = '+'.join(encoded_feature_names[list(indices)])\n",
    "    print(expr)\n",
    "    print('----------')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<function _lambdifygenerated(u, u_1, u_11)>, ['u', 'u_1', 'u_11'])"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "indices, _ = st.track[2]\n",
    "sympy_expr, basic_vars = math_eval('+'.join(encoded_feature_names[list(indices)]))\n",
    "sympy_expr, basic_vars"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'u': array([0.04970421, 0.05950946, 0.03799988, ..., 0.04010152, 0.06877201,\n",
      "       0.04658481], dtype=float32), 'u_1': array([-0.02309883,  0.02021311,  0.0148508 , ..., -0.00020373,\n",
      "        0.01919393, -0.01195044], dtype=float32), 'u_11': array([-0.01942476, -0.00494556,  0.00399714, ...,  0.04948348,\n",
      "       -0.01323579,  0.06216776], dtype=float32)}\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([-0.02057287, -0.00374269,  0.00456147, ...,  0.04947531,\n",
       "       -0.01191578,  0.06161105], dtype=float32)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# tmp = X_pre[:, [model.get_feature_names().index(e) for e in basic_vars]]\n",
    "# tmp = [aa[:, i:i+1].flatten() for i in range(aa.shape[-1])]\n",
    "# tmp\n",
    "sympy_expr_kwargs = dict(zip(basic_vars, \n",
    "                             list(X_pre[:, [model.get_feature_names().index(e) for e in basic_vars]].T)))\n",
    "print(sympy_expr_kwargs)\n",
    "sympy_expr(**sympy_expr_kwargs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "mod, basic_vars = math_eval('+'.join(encoded_feature_names[list(indices)]), return_torch=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([0.0497, 0.0595, 0.0380,  ..., 0.0401, 0.0688, 0.0466])"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "torch.tensor(sympy_expr_kwargs['u']).float()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[-0.0206],\n",
       "        [-0.0037],\n",
       "        [ 0.0046],\n",
       "        ...,\n",
       "        [ 0.0495],\n",
       "        [-0.0119],\n",
       "        [ 0.0616]])"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# sympytorch.SymPyModule -> implement แยกตามเครื่องหมาย +\n",
    "torch_vars = [symbols(var) for var in basic_vars]\n",
    "out = mod(u=torch.tensor(sympy_expr_kwargs['u']).float(), \n",
    "          u_1=torch.tensor(sympy_expr_kwargs['u_1']).float(), \n",
    "         u_11=torch.tensor(sympy_expr_kwargs['u_11']).float())  # out has shape (3, 2)\n",
    "# out[:,0]\n",
    "out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.allclose(sympy_expr(**sympy_expr_kwargs), out[:,0].detach().numpy())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[3.4913e-01, 1.2189e-01],\n",
       "        [2.0430e-02, 4.1740e-04],\n",
       "        [9.3016e-01, 8.6519e-01]])"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mod, basic_vars = math_eval(\"u_11+u*u_1\", \n",
    "                            return_torch=True, split_by_addition=True)\n",
    "x_ = torch.rand(3)\n",
    "mod(u_11=x_, u=x_, u_1=x_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('x_',\n",
       " tensor([0.3491, 0.0204, 0.9302]),\n",
       " tensor([1.2189e-01, 4.1740e-04, 8.6519e-01]))"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nameof(x_), x_, x_**2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "class PhysicalConstraintCalculator(torch.nn.Module):\n",
    "    def __init__(self, symbolic_module, basic_vars, init_coefficients=None, learnable_coefficients=False):\n",
    "        super(PhysicalConstraintCalculator, self).__init__()\n",
    "        self.symbolic_module = symbolic_module\n",
    "        self.basic_vars = basic_vars\n",
    "        \n",
    "        self.coefficients = init_coefficients\n",
    "        self.learnable_coefficients = learnable_coefficients\n",
    "\n",
    "        if self.coefficients is None:\n",
    "            self.coefficients = torch.ones(len(symbolic_module.sympy())).float().requires_grad_(self.learnable_coefficients)\n",
    "        else:\n",
    "            self.coefficients = torch.tensor(data=self.coefficients).float().requires_grad_(self.learnable_coefficients)\n",
    "            \n",
    "        if self.learnable_coefficients: print(\"Learnable coefficients:\", self.coefficients)\n",
    "        else: print(\"NOT learnable coefficients:\", self.coefficients)\n",
    "        print(symbolic_module.sympy())\n",
    "        print(\"Basic variables:\", self.basic_vars)\n",
    "            \n",
    "    def forward(self, input_dict):\n",
    "        return self.symbolic_module(**input_dict)*self.coefficients"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "class TorchMLP(nn.Module):\n",
    "    def __init__(self, dimensions, bias=True, activation_function=nn.Tanh(), bn=None, dropout=None):\n",
    "        super(TorchMLP, self).__init__()\n",
    "        # setup ModuleList\n",
    "        self.model  = nn.ModuleList()\n",
    "        for i in range(len(dimensions)-1):\n",
    "            self.model.append(nn.Linear(dimensions[i], dimensions[i+1], bias=bias))\n",
    "            if bn is not None and i!=len(dimensions)-2:\n",
    "                self.model.append(bn(dimensions[i+1]))\n",
    "                if dropout is not None:\n",
    "                    self.model.append(dropout)\n",
    "            if i==len(dimensions)-2: break\n",
    "            self.model.append(activation_function)\n",
    "        # weight init\n",
    "        self.model.apply(self.xavier_init)\n",
    "\n",
    "    def xavier_init(self, m):\n",
    "        if type(m) == nn.Linear:\n",
    "            torch.nn.init.xavier_uniform_(m.weight)\n",
    "            m.bias.data.fill_(0.0)\n",
    "\n",
    "    def forward(self, x):\n",
    "        for i, l in enumerate(self.model): \n",
    "            x = l(x)\n",
    "        return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "rng = default_rng()\n",
    "sampled_indices_x = rng.choice(len(x), size=int(len(x)//(5)), replace=False)\n",
    "sampled_indices_t = rng.choice(len(t), size=int(len(t)//(5)), replace=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = np.hstack((X[sampled_indices_t, :][:, sampled_indices_x].flatten()[:,None], \n",
    "                     T[sampled_indices_t, :][:, sampled_indices_x].flatten()[:,None]))\n",
    "y_train = un.T[sampled_indices_t, :][:, sampled_indices_x].flatten()[:,None]\n",
    "lb = torch.tensor(X_train.min(axis=0)).float().requires_grad_(False)\n",
    "ub = torch.tensor(X_train.max(axis=0)).float().requires_grad_(False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(torch.Size([1020, 2]), torch.Size([1020, 1]))"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Converting to tensors\n",
    "X_train = torch.tensor(X_train).float().requires_grad_(True)\n",
    "y_train = torch.tensor(y_train).float().requires_grad_(False)\n",
    "X_train.shape, y_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.09517879, -1.0106909 ], dtype=float32)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "com = 2; com = max(com, 1)\n",
    "init_coefficients = np.linalg.lstsq(X_pre[:, np.where(best_subsets[com-1]>0)[0]], \n",
    "                                    y_pre)[0].flatten()\n",
    "init_coefficients"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NOT learnable coefficients: tensor([ 0.0952, -1.0107])\n",
      "[u_11, u*u_1]\n",
      "Basic variables: ['u', 'u_1', 'u_11']\n"
     ]
    }
   ],
   "source": [
    "physics_calculator = PhysicalConstraintCalculator(symbolic_module=mod, \n",
    "                                                  basic_vars=basic_vars, \n",
    "                                                  init_coefficients=init_coefficients)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "from torch.autograd import grad\n",
    "\n",
    "class PINN(nn.Module):\n",
    "    def __init__(self, solver, physics_calculator, lb, ub):\n",
    "        super(PINN, self).__init__()\n",
    "        self.solver = solver\n",
    "        self.physics_calculator = physics_calculator\n",
    "        self.lb = lb\n",
    "        self.ub = ub\n",
    "        \n",
    "    def forward(self, x, t):\n",
    "        return self.solver(self.input_normalize(torch.cat([x, t],  dim=-1)))\n",
    "    \n",
    "    def loss(self, x, t, y_input, print_losses=False):\n",
    "        u = self.forward(x, t)\n",
    "        \n",
    "        # PDE Loss calculation\n",
    "        u_t = self.gradients(u, t)[0]\n",
    "        u_1 = self.gradients(u, x)[0]\n",
    "        u_11 = self.gradients(u_1, x)[0]\n",
    "        physics = self.physics_calculator({nameof(u):u, \n",
    "                                        nameof(u_1):u_1, \n",
    "                                        nameof(u_11):u_11}).sum(axis=-1)\n",
    "        \n",
    "        l_eq = F.mse_loss(u_t, physics, reduction='mean')\n",
    "        mse = F.mse_loss(u, y_input, reduction='mean')\n",
    "        \n",
    "        return l_eq+mse\n",
    "    \n",
    "    def gradients(self, func, x):\n",
    "        return grad(func, x, create_graph=True, retain_graph=True, grad_outputs=torch.ones(func.shape))\n",
    "\n",
    "    def input_normalize(self, inp):\n",
    "        return -1.0+2.0*(inp-self.lb)/(self.ub-self.lb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "solver = TorchMLP([2,50,50,50,50,1], bn=None, activation_function=nn.Tanh())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor(0.0849, grad_fn=<AddBackward0>)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pinn = PINN(solver, physics_calculator, lb, ub)\n",
    "pinn.loss(X_train[:,0:1][:10, :], X_train[:,1:2][:10, :], y_train[:10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### For training DeepONets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "# data['un'] = un_copy+0.0j\n",
    "# data['denoised_un'] = un+0.0j\n",
    "# data['discovered_coefficients'] = ols_model.params\n",
    "# data['feature_names'] = np.array([convert2latex(f) for i,f in enumerate(model.get_feature_names()) if i in eff_rhs_indices])\n",
    "# sio.savemat(\"../Datasets/noisy_burgers.mat\", data)\n",
    "\n",
    "# data['un'] = un_copy+0.0j\n",
    "# sio.savemat(\"../RDAE_data/noisy40_burgers.mat\", data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Physics-informed model selection.... (2 options)\n",
    "    - Fixed coefficients + learned diffivative values (by autodiff)\n",
    "    - Fixed polynomial candidate values (following feature_names: ['u', 'uu', 'u_1', 'u_11', 'uu_1', 'uuu_1', 'uu_11', 'uuu_11']) + learned coefficients"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ic1 = np.array([-61457.79563302, -67768.04270879, -67892.04820035, -67912.10229129, -67929.64549213, -67937.40869858, -67945.9834522 ])\n",
    "coms1 = np.array([1, 2, 3, 4, 5, 6, 7])\n",
    "ic2 = np.array([-61942.58641101, -68634.67590443, -68707.14669999, -68723.3471394 , -68771.5720519 , -68784.91274736, -68791.13974807])\n",
    "coms2 = np.array([1, 2, 3, 4, 5, 6, 7])\n",
    "ic3 = np.array([-61470.53751784, -67403.7884947 , -67463.47315383, -67491.22503685, -67524.31114049, -67541.30459472, -67577.12280888])\n",
    "coms3 = np.array([1, 2, 3, 4, 5, 6, 7])\n",
    "ic4 = np.array([-63319.57267493, -69422.77962975, -69521.1242639 , -69610.94610283, -69632.39125379, -69759.73072006, -69759.86095886, -69766.15596996])\n",
    "coms4 = np.array([1, 2, 3, 4, 5, 6, 7, 8])\n",
    "\n",
    "plt.plot(coms1, ic1, '-o', label='$\\lambda_{1}=5$', color='blue')\n",
    "plt.plot(coms3, ic3, '-o', label='$\\lambda_{1}=1.0$', color='red')\n",
    "plt.plot(coms2, ic2, '-o', label='$\\lambda_{1}=0.5$', color='darkgreen')\n",
    "plt.plot(coms4, ic4, '-o', label='$\\lambda_{1}=0.05$', color='black')\n",
    "plt.ylabel(\"(Decreasing) AIC\")\n",
    "plt.xlabel(\"#parameters\")\n",
    "plt.legend()\n",
    "plt.tight_layout()\n",
    "# plt.savefig('ffff1.pdf')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "xs = ['$5$', '$1.0$', '$0.5$', '$0.05$']\n",
    "ys = [ic1[0]-ic1[1], ic3[0]-ic3[1], ic2[0]-ic2[1], ic4[0]-ic4[1]]\n",
    "colors = ['blue', 'red', 'darkgreen', 'black']\n",
    "for i in range(len(xs)):\n",
    "    plt.bar(xs[i], ys[i], color=colors[i])\n",
    "plt.ylabel('Maximum AIC decrease')\n",
    "plt.xlabel('$\\lambda_{1}$')\n",
    "plt.tight_layout()\n",
    "# plt.savefig('ffff2.pdf')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "# RDAE 5e-0: (2.9505659028666087, 1.8024006266427972) -> 1\n",
    "# RDAE 1e-1: (3.2008235132427463, 2.56690175990938) -> 2\n",
    "# RDAE 5e-1: (2.469185388081714, 1.7166311808760781) -> 0\n",
    "# RDAE 5e-2: (6.015975755333656, 5.859365864415776) -> 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "# cv_abs/(max_diff_bic) -> Experiments on more PDEs are required."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "ics = np.array([ic1[0]-ic1[1], ic3[0]-ic3[1], ic2[0]-ic2[1], ic4[0]-ic4[1]])\n",
    "cvs = np.array([0.005062929348375081, 0.004408008789094885, 0.003467511174737858, 0.004305311364345706])\n",
    "cvs_abs = np.array([0.011023507990422338, 0.00910193468516509, 0.008040505161991068, 0.007809099375818377])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([8.02334566e-07, 7.42933142e-07, 5.18150748e-07, 7.05417889e-07])"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cvs/ics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.74692177e-06, 1.53405523e-06, 1.20149397e-06, 1.27950755e-06])"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cvs_abs/ics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [conda env:py3.7] *",
   "language": "python",
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