{ "cells": [ { "cell_type": "markdown", "id": "70fa233c", "metadata": {}, "source": [ "# Inverse problem: Biology-informed assimilation of pheromone sensors data in the pheromone propagation model" ] }, { "cell_type": "code", "execution_count": 1, "id": "c52d2d6f-a976-4a5c-80bb-605c2171218f", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/tmalou/anaconda3/envs/pherosensor/lib/python3.10/site-packages/matplotlib/projections/__init__.py:63: UserWarning: Unable to import Axes3D. This may be due to multiple versions of Matplotlib being installed (e.g. as a system package and as a pip package). As a result, the 3D projection is not available.\n", " warnings.warn(\"Unable to import Axes3D. This may be due to multiple versions of \"\n" ] } ], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import matplotlib.ticker as ticker\n", "from pathlib import Path\n", "import os\n", "import time\n", "\n", "import pherosensor\n", "\n", "from pheromone_dispersion.geom import MeshRect2D\n", "from pheromone_dispersion.diffusion_tensor import DiffusionTensor\n", "from pheromone_dispersion.velocity import Velocity\n", "from pheromone_dispersion.source_term import Source\n", "from pheromone_dispersion.convection_diffusion_2D import DiffusionConvectionReaction2DEquation\n", "\n", "from source_localization.obs import Obs\n", "from source_localization.control import Control\n", "from source_localization.cost import Cost\n", "from source_localization.population_dynamique import PopulationDynamicModel\n", "from source_localization.adjoint_convection_diffusion_2D import AdjointDiffusionConvectionReaction2DEquation\n", "\n", "from utils.plot_colormap import plot_colormap\n", "from utils.plot_obs import plot_obs\n", "from utils.plot_ctrl import plot_ctrl\n", "from utils.plot_cost import plot_cost" ] }, { "cell_type": "code", "execution_count": 2, "id": "6133d41d-e3cc-41ff-bc15-bba0fa6731d6", "metadata": {}, "outputs": [], "source": [ "path = os.getcwd()\n", "path_data = path + '/data'\n", "path_output = path + '/output'\n", "if not os.path.isdir(Path(path_output)):\n", " os.makedirs(Path(path_output))\n", "path_plot = path_output + '/plot'\n", "if not os.path.isdir(Path(path_plot)):\n", " os.makedirs(Path(path_plot))" ] }, { "cell_type": "markdown", "id": "b884208f-ca7c-482a-8f52-3b3dd5ca5aea", "metadata": {}, "source": [ "This notebook aims to present the biology-informed data assimilation (BI-DA) problem solved to infer the quantity of pheromone emitted from pheromone sensors data, the solvers implemented in the module `source_localization` and the different submodules on which the solvers are based. \n", "\n", "In a first section, the data assimilation problem is presented.\n", "Then, the different submodules are presented and illustrated over a simple but representative case, in line with the case used in the notebook dedicated to [modelling pheromone propagation](https://forgemia.inra.fr/pherosensor/pherosensor-toolbox/-/blob/main/tutorials/pheromone_propagation_model.ipynb).\n", "Finally, the numerical solvers of the BI-DA problem are presented and used to infer the quantity of pheromone emitted in different scenario of biological information addition. " ] }, { "cell_type": "markdown", "id": "3f2f58d3-f3aa-44fe-99bf-9fd7103405e7", "metadata": {}, "source": [ "## Pheromone sensors data assimilation for the inference of the quantity of emitted pheromone" ] }, { "cell_type": "markdown", "id": "85da972b-e2f7-4cf7-bcb6-92438d5f38b3", "metadata": {}, "source": [ "The goal of data assimilation (DA) is to estimate the quantity of pheromone emitted by the insects, also referred to as the control variable, using time-series of pheromone sensors spatially distributed over the landscape of interest.\n", "\n", "To do so, we solve an inverse problem corresponding to the following direct problem describing pheromone propagation over the landscape (see equation (1) in the notebook [modelling pheromone propagation](https://forgemia.inra.fr/pherosensor/pherosensor-toolbox/-/blob/main/tutorials/pheromone_propagation_model.ipynb) for details)\n", "\n", "$$\n", "\\frac{\\partial c}{\\partial t}-\\nabla\\cdot(\\mathbf{K}\\nabla c)+\\nabla\\cdot(\\vec{u}c)+\\tau_{loss}c=s~\\forall (x,y,t)\\in\\Omega\\times[0;T] \\quad \\quad \\quad \\text{(1)}\n", "$$\n", "\n", "In this equation, the unknown is the pheromone density $c$, and $\\mathbf{K}$, $\\vec{u}$ and $\\tau_{loss}$ are known parameters describing respectively the diffusion tensor, the advection by the wind and a deposition parameter. The term $s$ is the emission rate of pheromone, which is the target to be inferred by the inverse problem. \n", "\n", "The method consists in finding the quantity of pheromone emitted $s$ that enables the prediction of the pheromone propagation model (1) that predicts the closest outputs to the data.\n", "\n", "Therefore, the DA problem is defined as a minimization problem over a cost function $j$:\n", "\n", "$$\n", " \\begin{cases}\n", " \\text{find }s_a(x,y,t)\\text{ such that} \\\\ \n", " s_a(x,y,t)=\\text{arg}\\min_{s(x,y,t)}j(s)\\text{ with } j(s)=j_{obs}(s)+j_{reg}(s)\n", " \\end{cases}\n", "$$\n", "\n", "The term $j_{obs}$ aims to minimize the gap between model predictions and data. It reads \n", "\n", "$$\n", "j_{obs}(s)=\\|m\\left(c(s)\\right)-m_{obs}\\|_{\\mathbf{R}^{-1}}^2\n", "$$\n", "\n", "where $m_{obs}$ are the observations, $c(s)$ is the pheromone concentration map predicted by the pheromone propagation model (1) from the given pheromone emission $s$, $m(c)$ is the observation operator that models the observed variable from a given pheromone concentration map and $\\mathbf{R}$ is the covariance operator of the observation error. The observation operator depends on the properties of the sensor. For instance, pheromone sensors commonly use a pre-concentrator that actively filters air during a time window $\\delta_t$ and releases the accumulated quantity into the sensor for the measure. In this case, the observation operator is $(m(c))_{i,k}= \\int_{t_{obs,k}-\\delta_t}^{t_{obs,k}} c(\\tau,x_{obs, i},y_{obs, i}) d\\tau$ where $(x_{obs, i},y_{obs, i})$ is the position of the $i^{th}$ sensor and $t_{obs,k}$ the $k^{th}$ time of observation.\n", "\n", "The regularization term $j_{reg}$ is commonly added in order to ensure the well-posedness of the optimization problem. In the BI-DA framework, this regularization term is based on prior biological information describing biological knowledge of the emitter insects. A short introduction of this biology-informed regularization term can be found below and a more extensive one can be found in [Malou et al (2024)](https://hal.science/hal-04572831/)." ] }, { "cell_type": "markdown", "id": "3fcf0a98-985d-447f-a927-3c8cb20da42b", "metadata": {}, "source": [ "## Biology-informed regularization terms" ] }, { "cell_type": "markdown", "id": "d7a9b1db-fbab-4a10-835d-2c40b9388d63", "metadata": {}, "source": [ "The BI-DA framework is based on regularization terms modeling biological information that enable to drive the DA method toward an optimum $s_a$ consistent with the biological knowledge introduced in $j_{reg}$. In general terms, the regularization term reads:\n", "\n", "$$\n", "j_{reg} = \\sum_{i \\in \\mathcal{L}} \\alpha_{reg,i} j_{reg,i} \\quad \\quad \\quad \\quad \\text{ (2)}\n", "$$\n", "\n", "with $\\mathcal{L}$ the list of considered BI regularization terms $j_{reg,i}$ and $\\alpha_{reg,i}$ the associated weight coefficient. This allows for composite regularization terms that take into account several biological informations.\n", "\n", "Several biology-informed regularization terms are shortly presented here. \n", "\n", "- The Tikhonov regularization $j_{reg,T}(s)=\\|s-s_b\\|^2_{\\mathbf{C}^{-1}}$ enables to take into account information on the insects life habits, such as their preferred habitat and insect life cycle, through $s_b$ a prior value of $s$ and $\\mathbf{C}$ the background error covariance operator.\n", "- The LASSO regularization $j_{reg,LASSO}(s)=\\|s\\|_{1}$ enables to select important local pheromones emissions at the beginning of the infestation, when only few insects are expected. The LASSO term enables to discard sporious emissions.\n", "- The population dynamics informed regularization $j_{reg,PD}(s)=\\|\\partial_ts-\\frac{\\partial_tq}{q}s+q\\nabla \\left(\\sum_i \\mathcal{F}_i(s)\\right)-q\\sum_i\\mathcal{R}_i(s)\\|^2_{L^2}$ (with $q$ the quantity of pheromone emitted per insect) enables to take into account the spatio-temporal evolution of the density of insects $p=s/q$ described by the flux $F_i$ and reaction $R_i$ terms that model the behavior of the insect of interest. " ] }, { "cell_type": "markdown", "id": "a893042b-d1a5-4ba9-a0c4-bfc8bf1a5291", "metadata": {}, "source": [ "## Solving the optimization problem" ] }, { "cell_type": "markdown", "id": "e6692c12-9040-4c3d-adb7-a8b98eed159e", "metadata": {}, "source": [ "To solve the minimization problem, gradient-based methods are used. Thus, at each descent step, the gradient $\\nabla_sj$ must be computed and reads:\n", "\n", "$$\n", "\\nabla_s j(s) = \\nabla_s j_{obs}(s) + \\nabla_s j_{reg}(s)\n", "$$\n", "\n", "Note that we denote $\\nabla_s$ the gradient with respect to the control $s$ to avoid confusion with $\\nabla$ the classical gradient with respect to the space variables. Since $s$ is defined in a high-dimensional space and $c(s)$ involves the computation of a PDE model, the numerical computation of its gradient is challenging. We use a variational method and compute the gradient by using the adjoint model of the CTM that reads:\n", "\n", "$$\n", "\\partial_tc^* + \\nabla\\cdot(\\mathbf{K}^T\\nabla c^*)+\\nabla(\\vec{u}c^*)-(\\nabla.\\vec{u})c^*-\\tau_{loss}c^* = \\left(\\frac{dm}{dc}(c(s))\\right)^*\\cdot2\\mathbf{R}^{-1}\\left(m(c(s))-m_{obs}\\right) \\quad \\quad \\quad \\quad \\text{ (3)}\n", "$$\n", "\n", "with $c^*$ the adjoint state.\\\n", "The adjoint model is closed by the following final and boundary conditions:\n", "\n", "- a null final condition $c^*(x,y,t=T)=0$ $\\forall (x,y)\\in\\Omega$,\n", "- a null diffusive flux $\\mathbf{K}^T\\nabla c^*\\cdot\\vec{n}=0$ $\\forall (x,y)\\in\\partial\\Omega$,\n", "- a null outgoing convective flux $\\vec{u}c^*\\cdot\\vec{n}=0$ $\\forall (x,y)\\in\\partial\\Omega\\cap \\{(x,y)|\\vec{u}(x,y,t)\\cdot\\vec{n}>0\\}~\\forall t\\in]0;T]$ with $\\vec{n}$ the outgoing normal vector.\n", "\n", "Once the adjoint state is computed, the gradient $\\nabla_s j$ is computed using the expression:\n", "\n", "$$\n", " \\nabla_s j(s) = \\nabla_s j_{reg}(s)-c^*(s)\n", "$$\n", "\n", "The adjoint model is solved using an implicit downwind finite volume scheme on the same Cartesian grid as the direct problem. \n", "More details can be found in the notebooks located in the folder `./test_numerical_scheme/` that introduce and test the numerical scheme, including convergence, and the solvers." ] }, { "cell_type": "markdown", "id": "382aa5a2-d731-456b-ae0e-8f3b35954221", "metadata": {}, "source": [ "# Construction of the solver of the data assimilation problem" ] }, { "cell_type": "markdown", "id": "1c3d4391-e49c-4ee8-bec5-d3795918afee", "metadata": {}, "source": [ "## Specifying the geometry using the `geom` submodule " ] }, { "cell_type": "markdown", "id": "6a5c6841-70b3-4c5e-b3aa-7bdf33ffd18b", "metadata": {}, "source": [ "As mentionned in the notebook on [modelling pheromone propagation](https://forgemia.inra.fr/pherosensor/pherosensor-toolbox/-/blob/main/tutorials/pheromone_propagation_model.ipynb), for now, the numerical solver of the pheromone propagation model has been implemented for a rectangular domain $\\Omega=[0;L_x]\\times[0;L_y]$.\n", "Moreover, the mesh is supposed to be cartesian (rectangular cells) with constant space steps $\\Delta x$ and $\\Delta y$. \\\n", "Such geometry can be generated as an object of the `MeshRect2D`class of the `geom` [submodule](https://forgemia.inra.fr/pherosensor/pherosensor-toolbox/-/blob/main/src/pheromone_dispersion/geom.py)." ] }, { "cell_type": "code", "execution_count": 3, "id": "b4bd0301-fdbb-4313-aa5f-8b5eeb2b6a0f", "metadata": {}, "outputs": [], "source": [ "Lx = 30 # the length along the x-axis\n", "Ly = 40 # the length along the y-axis\n", "Delta_x = 0.5 # the space step along the x-axis\n", "Delta_y = 0.5 # the space step along the y-axis\n", "T_final = 20 # the final time of the simulation\n", "msh = MeshRect2D(Lx, Ly, Delta_x, Delta_y, T_final)\n", "msh.calc_dt_implicit_solver(.1) " ] }, { "cell_type": "markdown", "id": "4c30c5d9-0aec-4304-b573-55a5020c2d52", "metadata": {}, "source": [ "## Specifying the pheromone propagation model and its solver using the `convection_diffusion_2D` submodule" ] }, { "cell_type": "markdown", "id": "1bac8d9c-41a2-410e-9d28-fcbf6906180f", "metadata": {}, "source": [ "The first step is specifying the pheromone propagation model using the `convection_diffusion_2D` [submodule](https://forgemia.inra.fr/pherosensor/pherosensor-toolbox/-/blob/main/src/pheromone_dispersion/convection_diffusion_2D.py) of the `pheromone_propagation` module.\n", "This requires to specify the wind velocity field, the diffusion tensor, the loss coefficient and to initialize a source term (potentially by filling it with 0 or a prior estimate).\\\n", "The reader should refer to the [dedicated tutorial notebook](https://forgemia.inra.fr/pherosensor/pherosensor-toolbox/-/blob/main/tutorials/pheromone_propagation_model.ipynb) for detailed information on that topic." ] }, { "cell_type": "code", "execution_count": 4, "id": "1232cca0-2397-41ad-951a-85044b6e88e8", "metadata": {}, "outputs": [], "source": [ "U_vi = np.load(Path(path_data) / f'U_vi.npy')\n", "U_hi = np.load(Path(path_data) / f'U_hi.npy')\n", "U = Velocity(msh,U_vi, U_hi)" ] }, { "cell_type": "code", "execution_count": 5, "id": "cf62311d-c7e7-4c06-8a29-3320e73c0890", "metadata": {}, "outputs": [], "source": [ "K = DiffusionTensor(U, 10., 10.)" ] }, { "cell_type": "code", "execution_count": 6, "id": "a73c2e39-2e80-4420-8d50-992de320e257", "metadata": {}, "outputs": [], "source": [ "deposition_coeff = 0.01*np.ones((msh.y.size, msh.x.size))" ] }, { "cell_type": "code", "execution_count": 7, "id": "c0cd1f4c-eda8-46c2-8186-867328496eb6", "metadata": {}, "outputs": [], "source": [ "amp = 0.5\n", "xc = 6.\n", "yc = 6.\n", "r = 1.5\n", "S_value = np.zeros((msh.t_array.size, msh.y.size, msh.x.size))\n", "xx, yy= np.meshgrid(msh.x, msh.y)\n", "S_value[:, (xx-xc)**2 + (yy-yc)**2 < r**2] = amp\n", " \n", "S_target = Source(msh, S_value, t=msh.t_array)" ] }, { "cell_type": "code", "execution_count": 8, "id": "727aed9e-5b94-456f-8958-ef284c951860", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "=== Load of the inverse of the matrix of the implicit part of the implicit with stationnary matrix inversion scheme ===\n" ] } ], "source": [ "solver = 'implicit with stationnary matrix inversion'\n", "tol = 1e-10\n", "direct_model = DiffusionConvectionReaction2DEquation(\n", " U, \n", " K, \n", " deposition_coeff, \n", " S_target, \n", " msh, \n", " tol_inversion=tol, \n", " time_discretization=solver\n", ")\n", "fname = 'inv_matrix_implicit_solver'\n", "direct_model.init_inverse_matrix(path_to_matrix=path_data, matrix_file_name=fname)" ] }, { "cell_type": "markdown", "id": "7d5df038-1ce7-436d-ba7d-f0d41bf9a78e", "metadata": {}, "source": [ "## Specifying the pheromone sensors data and properties using the `obs` submodule" ] }, { "cell_type": "markdown", "id": "1f018db3-b87a-4130-a649-b600d2c3a8e5", "metadata": {}, "source": [ "The sensors data and the observation operator are contained in an object of the `Obs` class of the `obs` [submodule](https://forgemia.inra.fr/pherosensor/pherosensor-toolbox/-/blob/main/src/source_localization/obs.py).\\\n", "The user should provide the data array, that is of shape (nb data, 1), and arrays, resp. of shape (nb data, 1) and (nb data, 2), that contains the time and location of each data. \n", "The user can also specify the length of the time window over which the pheromone accumulates in the pre-concentration chamber of the sensor before measuring. If this time window length is not specified, it is assumed that the observation is instantaneous .\n", "\n", "The `Obs` class also contains useful attributes and methods for pheromone sensors data and properties.\n", "For instance, the attribute `c_est` contains the estimate of the concentration in pheromone at the positions and times required to compute the estimate of the observed variable that is containes in the attribute `d_est`. Initially, these attributes are filled with NaN. The attribute `c_est` can be updated using the `solver_est_at_obs_times` method of the `DiffusionConvectionReaction2DEquation` class and the attribute `d_est` can be updated from the attribute `c_est` using the method `obs_operator`.\n", "\n", "In the test case used here, the data are generated as following at random sensors location using the pheromone propagation model and the target source term introduced above." ] }, { "cell_type": "code", "execution_count": 9, "id": "16369bff-2703-493c-be7f-fa09d6c81005", "metadata": {}, "outputs": [], "source": [ "if not (Path(path_data) / 'd_obs_noised.npy').exists():\n", " from random import choices, seed\n", " print(\"--- generating synthetic data from the target source term and random sensors location---\")\n", " dt_sensor = 2.\n", " nb_sensor = 30\n", " seed(2)\n", " random_index_sensor_loc = choices(range(0, msh.x.size*msh.y.size), k=nb_sensor)\n", " X_sensor = np.zeros((nb_sensor, 2))\n", " index_sensor = 0\n", " for i, (xi,yi) in enumerate(zip(np.repeat(msh.x,msh.y.size),np.tile(msh.y,msh.x.size))):\n", " if i in random_index_sensor_loc:\n", " X_sensor[index_sensor,:]=[xi,yi]\n", " index_sensor += 1\n", " nb_obs_per_sensor = int(msh.T_final // dt_sensor)\n", " t_obs_per_sensor = np.array([dt_sensor*(i+1) for i in range(nb_obs_per_sensor)])\n", " X_obs = np.array([[x,y] for x,y in zip(np.tile(X_sensor[:,0],nb_obs_per_sensor),np.tile(X_sensor[:,1],nb_obs_per_sensor))])\n", " t_obs = np.repeat(t_obs_per_sensor, nb_sensor)\n", " d_obs = np.zeros_like(t_obs)\n", " obs_prov = Obs(t_obs, X_obs, d_obs, msh, dt_obs_operator=dt_sensor)\n", " direct_model.solver_est_at_obs_times(obs_prov)\n", " obs_prov.obs_operator() \n", " noise = np.random.normal(0., 0.01*np.max(obs_prov.d_est), size=obs_prov.d_est.size)\n", " noise_level = msh.mass_cell * msh.dt * (noise).dot(noise)\n", " np.save(Path(path_data) / 't_obs.npy', obs_prov.t_obs)\n", " np.save(Path(path_data) / 'X_obs.npy', obs_prov.X_obs)\n", " np.save(Path(path_data) / 'd_obs_no_noise.npy', obs_prov.d_est)\n", " np.save(Path(path_data) / 'd_obs_noised.npy', obs_prov.d_est+noise)\n", " np.save(Path(path_data) / 'noise_level.npy', noise_level)" ] }, { "cell_type": "code", "execution_count": 10, "id": "b50803e4-5b9e-4da6-9d0b-3a8288b7c1e9", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "||epsilon||_2^2 = 6.1122119221694315e-06\n" ] } ], "source": [ "dt_sensor = 2.\n", "t_obs = np.load(Path(path_data) / 't_obs.npy')\n", "X_obs = np.load(Path(path_data) / 'X_obs.npy')\n", "d_obs = np.load(Path(path_data) / 'd_obs_noised.npy')\n", "obs = Obs(t_obs, X_obs, d_obs, msh, dt_obs_operator=dt_sensor)\n", "\n", "noise_level = np.load(Path(path_data) / 'noise_level.npy')\n", "print(\"||epsilon||_2^2 = \", noise_level) " ] }, { "cell_type": "markdown", "id": "27bfed37-ac48-4d9a-a47e-24a6580531dc", "metadata": {}, "source": [ "Moreover, the data can be plotted at a given time using the `plot_obs` [method](https://forgemia.inra.fr/pherosensor/pherosensor-toolbox/-/blob/main/src/utils/plot_obs.py) of the `utils` submodule." ] }, { "cell_type": "code", "execution_count": 11, "id": "9f8fbe4b-250f-4bd3-88a5-42c4855b6274", "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot_obs(msh, t_obs[50], obs, figsize=(20, 15))" ] }, { "cell_type": "markdown", "id": "0dc829ec-3bbd-4e7d-8578-27c06218d95d", "metadata": {}, "source": [ "The `Obs` class also contains the method `adjoint_derivative_obs_operator` that computes the spatial map of $(d_cm)^*\\cdot\\delta c$, i.e. the adjoint of the derivative of the observation operator with respect to the state variable evaluated along $\\delta c$ at a given time. This method is needed to compute the source function of the adjoint model (see eq. (3) ).\n", "\n", "It also contains the method `onesensor_adjoint_derivative_obs_operator` that is similar to the `adjoint_derivative_obs_operator` method but for the one-sensor observation operator, i.e. the observation operator obtained with only one sensor, and that can be used to compute the one-sensor-adjoint state. \n", "\n", "The `Obs` class also have the attribute `R_inv` that contains $\\mathbf{R}^{-1}$, the inverse of the observation error covariance error. In the absence of dedicated data, this attribute is set to the identity matrix. However, it will be possible in the future to specify this covariance matrix. " ] }, { "cell_type": "markdown", "id": "2d7ba14e-1cb9-4703-91f2-2097d18cc18d", "metadata": {}, "source": [ "## Specifying the control variable and some prior knowledge about it using the `control` submodule" ] }, { "cell_type": "markdown", "id": "a47e0cc0-2710-4fca-91e0-9cb5496cafcb", "metadata": {}, "source": [ "The control variable to be optimized (the quantity of emitted pheromones) and all the tools to model prior knowledge about it are contained in the `control` [submodule](https://forgemia.inra.fr/pherosensor/pherosensor-toolbox/-/blob/main/src/source_localization/control.py).\n", "\n", "The user should provide an object of the class `Source` that will be used as background/prior value of the control. When initialized, the control value is set to 0. " ] }, { "cell_type": "code", "execution_count": 12, "id": "28cfe1db-512c-4262-9734-d1218eb25049", "metadata": {}, "outputs": [], "source": [ "amp = 0.5\n", "xc = 10.\n", "yc = 10.\n", "c = 10.\n", "S_value_prior = np.zeros((msh.t_array.size, msh.y.size, msh.x.size))\n", "xx, yy= np.meshgrid(msh.x, msh.y)\n", "S_value_prior[:, np.maximum(np.abs(xx-xc), np.abs(yy-yc) ) < c] = amp\n", " \n", "S_prior = Source(msh, S_value_prior, t=msh.t_array)" ] }, { "cell_type": "code", "execution_count": 13, "id": "59acb742-69cc-4cbf-9536-aa72ce2e7770", "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "t_on = 1.\n", "S_prior.at_current_time(t_on)\n", "plot_colormap(msh,S_prior.value,'S_{prior}', 'g.m^{-2}.s^{-1}', cmap=\"jet\",title=f't = {\"{:.2f}\".format(t_on)} s')" ] }, { "cell_type": "markdown", "id": "d9a9d7a1-e09e-42af-a58b-9f0b47205187", "metadata": {}, "source": [ "The user can include more prior knowledge of the control variable such as the population dynamic model. \n", "In such case, the user should specify the matrix or linear operator (by means of its matrix-vector product and of its transpose matrix-vector product) used to compute the residual of the population dynamic model. \n", "\n", "Up to now, the [submodule](https://forgemia.inra.fr/pherosensor/pherosensor-toolbox/-/blob/main/src/source_localization/population_dynamique.py) `population_dynamic` contains a stationnary population dynamique model $\\partial_t p = 0$ and a population dynamique model with linear reaction term $\\partial_t p = \\gamma p$ modelling that a given proportion $\\gamma$ of the population stops to emit pheromone at each time step. The user can also easily specify its own population dynamic model using the different differential operators included in the toolbox (see [the different notebooks to use differential operators](https://forgemia.inra.fr/pherosensor/pherosensor-toolbox/-/blob/main/tutorials/test_numerical_scheme/)). \n", "\n", "In the following, for sake of simplicity, a population dynamic model with linear reaction term is used with a null reaction term, which is then equivalent to a stationary population dynamic." ] }, { "cell_type": "code", "execution_count": 14, "id": "f62c90fa-faea-4f53-b0ad-bac7333f0785", "metadata": {}, "outputs": [], "source": [ "death_rate = np.zeros((msh.t_array.size, msh.y.size, msh.x.size))\n", "population_dynamic_model = PopulationDynamicModel(msh, death_rate=death_rate)" ] }, { "cell_type": "code", "execution_count": 15, "id": "4d73d305-03fe-42ec-86ff-ca1b9d21369b", "metadata": {}, "outputs": [], "source": [ "ctrl = Control(S_prior, msh, population_dynamique_model=population_dynamic_model)" ] }, { "cell_type": "markdown", "id": "5cb5ccea-b6d2-474e-a88a-dd5d2c203881", "metadata": {}, "source": [ "For sake of illustration, the target source term that has been used to generate the data is stored in a dedicated object of the `Control`class." ] }, { "cell_type": "code", "execution_count": 16, "id": "677213d0-6d45-4844-9db4-d3454f1661fb", "metadata": {}, "outputs": [], "source": [ "ctrl_target = Control(S_target, msh)\n", "ctrl_target.value = ctrl_target.background_value " ] }, { "cell_type": "markdown", "id": "2ac8062d-8675-49c6-bf3e-fcfb6292facb", "metadata": {}, "source": [ "## Specifying the adjoint model and its solver using the `adjoint_convection_diffusion_2D` submodule" ] }, { "cell_type": "markdown", "id": "22a04ce2-8cd2-496b-8892-1550fbac2ca9", "metadata": {}, "source": [ "The adjoint model and its solver are here contained in the `AdjointDiffusionConvectionReaction2DEquation` class of the [submodule](https://forgemia.inra.fr/pherosensor/pherosensor-toolbox/-/blob/main/src/source_localization/adjoint_convection_diffusion_2D.py) `adjoint_convection_diffusion_2D`.\n", "\n", "Similarly to the direct model, building an object of this class requires to specify the wind velocity field, the diffusion tensor and the loss coefficient." ] }, { "cell_type": "code", "execution_count": 17, "id": "2d1b5692-504e-4941-9fb7-dc52d3e630de", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "=== Load of the inverse of the matrix of the implicit part of the implicit with stationnary matrix inversion scheme ===\n" ] } ], "source": [ "solver = 'implicit with stationnary matrix inversion'\n", "tol = 1e-10\n", "adjoint_model = AdjointDiffusionConvectionReaction2DEquation(U, K, deposition_coeff, msh, tol_inversion=tol, time_discretization=solver)\n", "fname = 'inv_matrix_implicit_solver.npy'\n", "adjoint_model.init_inverse_matrix(path_to_matrix=path_data, matrix_file_name=fname)" ] }, { "cell_type": "markdown", "id": "97cb464c-39bf-4fb4-98e2-aa1e22ca7f77", "metadata": {}, "source": [ "A special care has been put on the time discretization so that the discretized adjoint problem is effectively the adjoint (i.e. the matrix transposition) of the discretized direct problem. \n", "\n", "The reader can find more details in the notebooks located in the folder `./test_numerical_scheme/` that introduces and test the numerical schemes and their convergence. These notebooks also include test of the [adjoint operators](https://forgemia.inra.fr/pherosensor/pherosensor-toolbox/-/blob/main/tutorials/test_numerical_scheme/test_adjoint.ipynb) and of the [gradient](https://forgemia.inra.fr/pherosensor/pherosensor-toolbox/-/blob/main/tutorials/test_numerical_scheme/test_gradient.ipynb)." ] }, { "cell_type": "markdown", "id": "239cd006-3cd4-4d9f-9d54-8e45661a3a6e", "metadata": {}, "source": [ "## Specifying the cost function using the `cost` submodule" ] }, { "cell_type": "markdown", "id": "9f8fd839-b0e1-412d-ab03-fe1543fd07a7", "metadata": {}, "source": [ "The cost function, its different terms and its gradient are contained in the `Cost` class of the `cost` [submodule](https://forgemia.inra.fr/pherosensor/pherosensor-toolbox/-/blob/main/src/source_localization/cost.py). \n", "The user should provide an object of the class `Obs` that will be used to compute the observation term of the cost function, and an object of the class `Control` that will be used to compute the regularization terms based on biological prior knowledge on the control variable and updated during the optimization process" ] }, { "cell_type": "code", "execution_count": 18, "id": "15378b49-a6b7-441a-9059-bda4f2203305", "metadata": {}, "outputs": [], "source": [ "cost = Cost(msh, obs, ctrl)" ] }, { "cell_type": "markdown", "id": "a93d93f8-24bd-4f87-bd87-656f763c4471", "metadata": {}, "source": [ "The class `Cost` contains several useful attributes and methods that will be presented below. \n", "This class contains the attribute `j_obs` that is the observation term of the cost function, the attribute `j_reg` that is the dictionnary containing the different regularization terms, the attribute `alpha` that is the dictionnary of weight coefficient of the different regularization terms (see eq. (2)). \n", "The attribute `implemented_regularization_types` contains the list of keywords of the implemented regularization terms. The keywords of the attributes `j_reg` and `alpha` corresponds to keywords present in the attribute `implemented_regularization_types`. " ] }, { "cell_type": "code", "execution_count": 19, "id": "14934178-0734-40dd-a360-a34cc0e6f51e", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['Tikhonov', 'Population dynamic', 'LASSO', 'time group LASSO', 'logarithm barrier']\n" ] } ], "source": [ "print(cost.implemented_regularization_types)" ] }, { "cell_type": "markdown", "id": "b19c3863-7d60-47d2-9423-585b61ed6399", "metadata": {}, "source": [ "The list of keyword of regularization terms to be considered and the associated dictionnary of weight coefficient can be provided as input of the instanciation of the class `Cost` or can be added and modified afterwards as in the following. If no regularization terms and its weigth coefficient are specified, than the inference will be made without regularization terms by default.\n", "\n", "The class `Cost` also contains the methods `obs_cost` and `reg_cost` that computes resp. the observation and regularization terms and store the result in the associated attribute, as well as a method `cost_and_gradient` that brings together several methods and returns the evaluation of the cost function and its gradient from an array of shape (nb time point * nb cell in y axis * nb cell in x axis, 1) containing a quantity of pheromone emitted. " ] }, { "cell_type": "markdown", "id": "2bb361c5-e6d1-4fbe-ac7c-5ba424c26b1a", "metadata": {}, "source": [ "# Inference of the quantity of emitted pheromones " ] }, { "cell_type": "markdown", "id": "b679233f-0360-47d6-a01a-9110c3dc5615", "metadata": {}, "source": [ "The inference is performed using the `minimize` method of the class `Cost`. \n", "This method takes as inputs: \n", "\n", "- the direct model that has been instanciated previously,\n", "- the adjoint model that has been instanciated previously,\n", "- the keyword of the optimization algorithm (see below).\n", " \n", "The user can also provide the following optional intputs.\n", "\n", "- The input `options` is the dictionnary of optional inputs of the optimization algorithm, such as tolerance of the termination criteria, the maximal number of iteration or the step size of the gradient descent.\n", "- The input `path_save` is the string of the path to the folder in which the outputs will be saved. If not specified, the results of the optimization will not be save, just returned as outputs of the method.\n", "- The input `j_obs_threshold` is the value of the threshold of the observation term of the cost function under which the optimization algorithm is stopped. This is particularly useful when some properties of the observation error are known and that we want to satisfy the Morozov's principle. For instance, one do not want the observation term of the cost function to be less than the square of the norm of the noise. \n", "- The input `s_init` enables to specify the initial value of the control variable. If not, the initial value is 0.\n", "- The input `restart_flag` is a boolean flag that is False by default. It the flag is True, then the algorithm will load the outputs that has been previously saved in the folder given by the input `path_save` and start again where the algorithm stopped.\n", "\n", "When the optimization algorithm reached the termination criteria, the method `minimize` returns: \n", "\n", "- the direct model with the quantity of pheromone emitted resulting from the optimization algorithm as source term,\n", "- the array containing the observation term of the cost function at each iteration of the optimization algorithm,\n", "- the dictionnary containing the arrays containg the regularization terms of the cost function at each iteration of the optimization algorithm,\n", "- the array containg the resulting quantity of pheromone emitted.\n", "\n", "When the input `path_save` is specified, in addition to the outputs detailled above, the method saves: \n", "\n", "- the weight coefficients of each regularization terms, in order to keep track of the test case,\n", "- the array containg the resulting quantity of pheromone emitted in a file which name includes the number of the last iteration, to keep track of the different runs in case of restart,\n", "- the array containing the step size at each iteration of the optimization algorithm, to keep track of the different runs in case of restart,\n", "- the sum of the step size over the iterations of the optimization algorithm and the trajectory integrated quantity of pheromone emitted, to compute the trajectory-integrated one-sensor adjoint states. (*This feature will be improved in a future update of the module*)." ] }, { "cell_type": "markdown", "id": "69a2a617-384a-4856-b271-3e2d6a5b8726", "metadata": {}, "source": [ "## Inference without regularization" ] }, { "cell_type": "markdown", "id": "4496bf60-7d61-4b52-8770-3fcfaf78aab8", "metadata": {}, "source": [ "In a first time, the inference is illustrated without regularization. In this situation, the user should use a differentiable optimization algorithm such as the gradient descent algorithm. This algorithm has been implemented in the [submodule](https://forgemia.inra.fr/pherosensor/pherosensor-toolbox/-/blob/main/src/source_localization/gradient_descent.py) `gradient_descent`. \n", "\n", "*Warning: the values of hyperparameter used here, especially the values of the step size and the maximal number of iterations, are not ideal and more attention should be given in real cases but they allow fast computations and illustrative results in this notebook.* " ] }, { "cell_type": "code", "execution_count": 23, "id": "29f95f9a-a049-4683-ad9b-3e6d1d92e28e", "metadata": {}, "outputs": [], "source": [ "options = {\n", " \"nit_max\": 50, \n", " \"step size\": 5. * 1.0e+1\n", "} " ] }, { "cell_type": "code", "execution_count": 24, "id": "a78fc9bb-be7d-4b2d-ab77-0cc806913e17", "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "initialization of the optimization process, computing the initial cost\n", "Optimizing using the gradient descent algorithm \n", "iteration 1: j = 8.013552e-03, j_obs = 8.013552e-03, ite comp time = 1.248e+00s -\n", "iteration 2: j = 1.689399e-03, j_obs = 1.689399e-03, ite comp time = 2.435e+00s -\n", "iteration 3: j = 1.133879e-03, j_obs = 1.133879e-03, ite comp time = 2.372e+00s -\n", "iteration 4: j = 8.865612e-04, j_obs = 8.865612e-04, ite comp time = 2.434e+00s -\n", "iteration 5: j = 7.396132e-04, j_obs = 7.396132e-04, ite comp time = 2.412e+00s -\n", "iteration 6: j = 6.395291e-04, j_obs = 6.395291e-04, ite comp time = 2.431e+00s -\n", "iteration 7: j = 5.650399e-04, j_obs = 5.650399e-04, ite comp time = 2.380e+00s -\n", "iteration 8: j = 5.061600e-04, j_obs = 5.061600e-04, ite comp time = 2.364e+00s -\n", "iteration 9: j = 4.576350e-04, j_obs = 4.576350e-04, ite comp time = 2.357e+00s -\n", "iteration 10: j = 4.164616e-04, j_obs = 4.164616e-04, ite comp time = 2.349e+00s -\n", "iteration 11: j = 3.808051e-04, j_obs = 3.808051e-04, ite comp time = 2.348e+00s -\n", "iteration 12: j = 3.494758e-04, j_obs = 3.494758e-04, ite comp time = 2.615e+00s -\n", "iteration 13: j = 3.216580e-04, j_obs = 3.216580e-04, ite comp time = 2.948e+00s -\n", "iteration 14: j = 2.967633e-04, j_obs = 2.967633e-04, ite comp time = 3.254e+00s -\n", "iteration 15: j = 2.743490e-04, j_obs = 2.743490e-04, ite comp time = 2.785e+00s -\n", "iteration 16: j = 2.540692e-04, j_obs = 2.540692e-04, ite comp time = 2.928e+00s -\n", "iteration 17: j = 2.356468e-04, j_obs = 2.356468e-04, ite comp time = 3.275e+00s -\n", "iteration 18: j = 2.188544e-04, j_obs = 2.188544e-04, ite comp time = 2.990e+00s -\n", "iteration 19: j = 2.035026e-04, j_obs = 2.035026e-04, ite comp time = 2.792e+00s -\n", "iteration 20: j = 1.894317e-04, j_obs = 1.894317e-04, ite comp time = 2.761e+00s -\n", "iteration 21: j = 1.765054e-04, j_obs = 1.765054e-04, ite comp time = 3.355e+00s -\n", "iteration 22: j = 1.646066e-04, j_obs = 1.646066e-04, ite comp time = 3.209e+00s -\n", "iteration 23: j = 1.536338e-04, j_obs = 1.536338e-04, ite comp time = 2.643e+00s -\n", "iteration 24: j = 1.434988e-04, j_obs = 1.434988e-04, ite comp time = 2.728e+00s -\n", "iteration 25: j = 1.341239e-04, j_obs = 1.341239e-04, ite comp time = 3.033e+00s -\n", "iteration 26: j = 1.254410e-04, j_obs = 1.254410e-04, ite comp time = 3.325e+00s -\n", "iteration 27: j = 1.173896e-04, j_obs = 1.173896e-04, ite comp time = 3.032e+00s -\n", "iteration 28: j = 1.099160e-04, j_obs = 1.099160e-04, ite comp time = 2.602e+00s -\n", "iteration 29: j = 1.029721e-04, j_obs = 1.029721e-04, ite comp time = 2.626e+00s -\n", "iteration 30: j = 9.651484e-05, j_obs = 9.651484e-05, ite comp time = 2.628e+00s -\n", "iteration 31: j = 9.050553e-05, j_obs = 9.050553e-05, ite comp time = 2.667e+00s -\n", "iteration 32: j = 8.490912e-05, j_obs = 8.490912e-05, ite comp time = 2.613e+00s -\n", "iteration 33: j = 7.969389e-05, j_obs = 7.969389e-05, ite comp time = 2.614e+00s -\n", "iteration 34: j = 7.483102e-05, j_obs = 7.483102e-05, ite comp time = 2.633e+00s -\n", "iteration 35: j = 7.029424e-05, j_obs = 7.029424e-05, ite comp time = 2.631e+00s -\n", "iteration 36: j = 6.605958e-05, j_obs = 6.605958e-05, ite comp time = 2.590e+00s -\n", "iteration 37: j = 6.210509e-05, j_obs = 6.210509e-05, ite comp time = 2.863e+00s -\n", "iteration 38: j = 5.841063e-05, j_obs = 5.841063e-05, ite comp time = 2.590e+00s -\n", "iteration 39: j = 5.495772e-05, j_obs = 5.495772e-05, ite comp time = 2.651e+00s -\n", "iteration 40: j = 5.172934e-05, j_obs = 5.172934e-05, ite comp time = 2.709e+00s -\n", "iteration 41: j = 4.870980e-05, j_obs = 4.870980e-05, ite comp time = 2.647e+00s -\n", "iteration 42: j = 4.588462e-05, j_obs = 4.588462e-05, ite comp time = 2.621e+00s -\n", "iteration 43: j = 4.324044e-05, j_obs = 4.324044e-05, ite comp time = 3.365e+00s -\n", "iteration 44: j = 4.076488e-05, j_obs = 4.076488e-05, ite comp time = 3.452e+00s -\n", "iteration 45: j = 3.844648e-05, j_obs = 3.844648e-05, ite comp time = 2.942e+00s -\n", "iteration 46: j = 3.627464e-05, j_obs = 3.627464e-05, ite comp time = 2.770e+00s -\n", "iteration 47: j = 3.423951e-05, j_obs = 3.423951e-05, ite comp time = 2.840e+00s -\n", "iteration 48: j = 3.233196e-05, j_obs = 3.233196e-05, ite comp time = 2.798e+00s -\n", "iteration 49: j = 3.054349e-05, j_obs = 3.054349e-05, ite comp time = 2.666e+00s -\n", "iteration 50: j = 2.886622e-05, j_obs = 2.886622e-05, ite comp time = 2.490e+00s -\n", "\n", "final iteration:\n", "number of iteration = 50\n", "j = 0.003602175543057567 % of the initial cost\n", "j_obs = 0.003602175543057567 % of the initial cost\n" ] } ], "source": [ "direct_model_no_reg, j_obs_no_reg, _, norm_grad_j_no_reg, S_a_no_reg = cost.minimize(\n", " direct_model,\n", " adjoint_model,\n", " \"gradient descent\", \n", " options=options,\n", " path_save=path_output,\n", " j_obs_threshold = noise_level\n", ")" ] }, { "cell_type": "markdown", "id": "8fbb76e3-ac5f-45fa-9e05-ad1d108c83fc", "metadata": {}, "source": [ "The resulting optimal value of the quantity of emitted pheromones can be plotted at the a given time using the `plot_ctrl` [method](https://forgemia.inra.fr/pherosensor/pherosensor-toolbox/-/blob/main/src/utils/plot_ctrl.py) of the `utils` module. The cost function and its different terms can also be plotted using the `plot_cost` [method](https://forgemia.inra.fr/pherosensor/pherosensor-toolbox/-/blob/main/src/utils/plot_cost.py) of the same module (the empty inputs correspond to the regularization terms, see below)." ] }, { "cell_type": "code", "execution_count": 25, "id": "33b85251-f7c3-4f35-bedf-210af6e6e998", "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot_ctrl(\n", " msh,\n", " msh.t_array[1],\n", " cost.ctrl,\n", " ctrl_target=ctrl_target.value,\n", " cmap=\"jet\",\n", " label='s_o',\n", " obs=cost.obs,\n", " figsize=(20, 15)\n", ")\n", "plot_ctrl(\n", " msh,\n", " msh.t_array[-1],\n", " cost.ctrl,\n", " ctrl_target=ctrl_target.value,\n", " cmap=\"jet\",\n", " label='s_o',\n", " obs=cost.obs,\n", " figsize=(20, 15)\n", ")" ] }, { "cell_type": "code", "execution_count": 26, "id": "438702dc-d5d8-4579-803f-90f08d14974c", "metadata": {}, "outputs": [ { "data": { "image/png": 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e2+KYL3/5y2ltbc3LX/7yjZZAXT/rcP09N+e5557L97///STJ3/zN32w1d6lUysknn5z//M//TJI0Nzf3lJtAN+UhbK+XZh5m2bLUTe4uDR98vKPAQAAAAAAAxbj22mvT1dWVpHvW4dZmBa4vA3/1q19t9vXf/OY3+fKXv5wk+dznPrfRaxuWh9dff/0m13Z0dOTCCy9MY2NjTj/99Bx//PFJknXr1m01/8KFC5MkEydO3GSPRRjulIewvTYoD3eb0r3X4eNPdhUYCAAAAACg/9133335x3/8x57Hb3vb27Y6/vzzz0/SvczpxRdfnFWrViVJVq5cmS984Qv58z//87S1teWSSy7J6aefvtG19957b5LuJUu/9rWv5aqrrkpb20uTOx58MK9//etz3XXXZa+99sq3vvWtnuuuvPLKHHPMMfn617+eJ598suf5hoaG/Md//Efe/e53J0k+9KEPpaxMVQIbKi86AAwaG5SHe5/YlQVJlj7pHxUAAAAAYOi79tprc9VVV+W5557Lgw8+2DPrcPLkyXnPe96z1Wtf85rX5O///u/zxS9+MV/5ylfyla98JWPGjElDQ0O6urpSVlaWj3/84/nMZz6zybXrZx5+9atfzac//elccMEF+cAHPpCRI0dm9erVSZIpU6bkl7/8ZXZb/3+46S4477777tx9991JkpEjR6aqqqqnuEyS973vffmHf/iHXfp1gaFIeThMzZ49O7Nnz05ra2vRUQaP9f/wLF+el+1Xll8neWHJiK1eAgAAAAAwFFx33XWZM2fORs9NnTo11113XcaMGbPN67/whS/kqKOOyuzZszNv3ry0trZmv/32y6xZs/Le9743Rx555CbXNDU15bHHHkuSvOpVr8rtt9+eSy65JNdff31Wr16d6dOn58wzz8zFF1+curq6ja790Ic+lAMOOCA333xzHn300Tz//PNpbm7OPvvsk+OOOy7vec97MnPmzJ3/BYEhrNS1/scDGJaWLl2aKVOmZMmSJZk8eXLRcQa2VauSsWOTJNf8ckXeddr4jNhtXdqfryo2FwAAAAAMAi0tLVm0aFH23XffVFdXFx2HHXTooYfm0UcfzcSJEzN9+vS88Y1vzAUXXJCRI0cWHY2X9NZnTG+AmYewvcaMSSoqkra2HDt2VZLx6VhemYbmztSNtHwpAAAAADB0Pfjgg0VHAPqJxgO2V6nUs3TpAWUrk6qOpKuUuxeuKzgYAAAAAABA71Aewo54qTwsW7E8lXt07xd5z2NtRSYCAAAAAADoNcpD2BEvlYdZtixjJneXhg8+3llgIAAAAAAAgN6jPIQdsUF5OGnv7tLw8UVdBQYCAAAAAADoPcpD2BEblId779NdGj692McIAAAAAAAYGrQesCM2KA9fNq374/PCkhEFBgIAAAAAAOg9ykPYERuUh4fv310aNj1dWWAgAAAAAACA3qM8hB2xQXn4ygO7S8POFyqzam1HgaEAAAAAAAB6h/IQdsQG5eEBu1ckI7tLw9891lpgKAAAAAAAgN6hPIQdsUF5WFZKqvZclyT5w8L2AkMBAAAAAAD0DuUh7IiJE7uPra1JQ0PG7NVdGs5/3LKlAAAAAADA4Kc8hB0xcmRSW9t9vmxZJu3dmSR54smuAkMBAAAAAAD0DuUh7KgNli7dd9/u0vDpJ32UAAAAAAAYOmbNmpXp06dn9uzZRUehn5UXHQAGnd12S554Ilm2LC/b99AkyYtLRxQcCgAAAAAAes/NN9+cyZMnFx2DApguBTtq/czD5ctz2P7d/XvzM5UFBgIAAAAAAOgdykPYURssW3rcy7pLw84XK7NiTUeBoQAAAAAAAHad8hB21Abl4T4TypOa9iTJHY+uKzAUAAAAAADArlMewo7aoDwsKyuleo/WJMkfFrYXGAoAAAAAAGDXKQ9hR21QHiZJ/ZS2JMn8xzuLSgQAAAAAANArlIewo/6kPNx97+7S8Iknu4pKBAAAAABAP1q6dGn+7d/+LW9605syderUVFVVpba2Noccckg++MEP5oknnig6Iuy08qIDwKDzJ+Xh1H2Se5M8u1gXDwAAAAAw1C1ZsiT77LNPurr+OKGkrq4uzc3NmT9/fubPn59vfvObueaaa3LWWWcVmBR2jrYDdtT68nDFiqSjIwfvPyJJ8uISXTwAAAAAwFDX0dGRJDn99NPzox/9KCtWrMjq1avT1NSU3/72t3nFK16R5ubmnHPOObn//vsLTgs7TnkIO2r8+KRUSrq6khdeyOH7d5eGzc9UFhwMAAAAAIC+Nnbs2MybNy/XXXddzjzzzIwfPz5JUl5enle/+tW54YYbMnHixLS3t+fyyy8vNizsBOUh7Kjy8u4CMUmWLcurDuwuDbtWVeS5Ve0FBgMAAAAAoK+NGTMmhx122BZfnzRpUl7/+tcnSe65557+igW9RnkIO2ODfQ/3Hl+e0ui2JMnvHm0tMBQAAAAAAAPBhAkTkiTt7SacMPgoD2FnbFAeJkn1nt2l4byFbUUlAgAAAABggPjtb3+bJDn00EMLTgI7TnkIO+NPysP6yd0/PbLgic6iEgEAAAAAMAD89Kc/ze9///skybve9a6C08COUx7Czpg4sfv4Unm45z7dpeGiRUUFAgAAAADoX+edd15KpVJKpVLmzp3ba2N7W3/mfOqpp3LhhRcmSd70pjflz/7sz3YiMRRLeQg7409mHk6d2v3w2cU+UgAAAAAAw9GqVavyhje8IStWrMi0adPyrW99q+hIsFM0HbAz/qQ8PHjaiCTJyqXlRSUCAAAAAKAgjY2NOe200/LAAw9kzz33zJw5czJu3LiiY8FOUR4OU7Nnz8706dMzc+bMoqMMTn9SHh5xQHdp2PJ0ZVGJAAAAAAAoQFNTU04//fTccccdmThxYm688cZMmzat6Fiw05SHw9RFF12U+fPn9/va0kPGn5SHrzyguzTsWlORxS+0F5UKAAAAAIB+1NzcnDe84Q255ZZbMnbs2Nxwww05+OCDi44Fu0R5CDtjfXm4fHmSZM+x5SmNaUuS3PVYa1GpAAAAAADoJ+vWrcub3/zm3HTTTamrq8uvfvWrHH744UXHgl2mPISdsb48bGhIWlqSJCP36i4N//CYmYcAAAAAAENZW1tbzjzzzPz6179OTU1NfvGLX+SYY44pOhb0ivKiA8CgNGZMUlGRtLV1zz6cMiVjJ7enaX7y8BOdRacDAAAAABgy7rzzzlx77bX53e9+l4ULF2b16tWpqqrK+PHjM2nSpBx22GE56qijcuqpp2bq1Kl9nqejoyPnnHNOrrvuuowcOTI///nPc/zxx/f5faG/KA9hZ5RK3bMPn366e9/DKVOyx95deTrJk08WHQ4AAAAAYPB77rnn8u53vzu//OUvN3mtra0tjY2Neeqpp3LXXXflm9/8Zr72ta/l/e9/f5/nuv322/OjH/0oSdLZ2Zmzzz57q+Ofe+65Ps8EvUl5CDtrw/IwyX77Jr9P8uxTpWJzAQAAAAAMckuWLMmrXvWqPPPMMz3PjRw5Mvvuu28mTZqUhoaGLFu2LM8++2za27u3kpoxY0a/ZOvs/OPqc+vWrcvzzz/fL/eF/qI8hJ21ft/Dl8rDg6d1byG6amlFUYkAAAAAAIaEd7zjHT3F4YQJE/LZz342Z599dkaPHr3RuJaWlvzhD3/Ir3/96xx22GH9km3mzJnp6urql3tBEZSHsLP+pDycsX/3x6nl2cqiEgEAAAAADHqPPPJI5s6dmySpqqrKrbfemoMOOmizY6urq3PcccfluOOO68eEMLSVFR0ABq0/KQ+Pe1lV9+PG8ixa1lZQKAAAAACAwe2hhx7qOX/Na16zxeIQ6BvKQ9hZf1IeThg9IqWxrUmSOx5tLSoVAAAAAMCgNmrUqJ7zRx55JM3NzQWmgeHHsqWws/6kPEySUXu2Ze3Kyty7sD1nn1BQLgAAAAAYYtauXbvF10aMGJHq6urtGltWVpaRI0fu1NimpqYt7nNXKpU2Krx2ZGxzc3M6Ozu3mKOmpmazYzd8fqiZNWtW9t133yxatChPPfVUTjrppHzsYx/LkUcemUmTJqWsbHDPi7r77rvz3e9+NzfffHMWLVqUurq6HHHEEfnUpz6Vo446quh4YOYh7LTNlIfjprQnSR5+Ysv/2AMAAAAAO6a2tnaLX29961s3Grvbbrttcexpp5220dipU6duceyrX/3qjcZOnz59i2OPPvrojcYeffTRWxw7ffr0jca++tWv3uLYqVOnbjT2tNNO63ltKKuqqsqvf/3rHH/88UmSO+64I294wxuy5557ZsSIESmVShk5cmTa29sLTrpzPv/5z+faa6/Nq1/96vzrv/5rPvShD2XBggV55StfmV/84hdFxwMzD2GnbaY83HPvzixJ8uSTpWIyAQAAAAAMAVOmTMmHP/zhtLW15a677trk9UMOOSTl5YOz4vi7v/u7/OAHP0hlZWXPc+eff36mT5+ej3/843n9619fYDpQHsLOmzix+7hsWdLVlZRKmbZvcmeS5xeb1AsAAAAAvaWxsXGLr40YMWKjx8s2+GH/P/Wny10++eST2z12/vz5W12KdEN33333do+95ZZbtrps6YZ++ctfbvfYwezmm2/OhRdemMcffzxJ9+/F5MmTs+eee2bMmDFJkpNOOqnIiLvkuOOO2+S58ePHZ+bMmfnZz35WQCLYmPIQdtb68rC1NWloSMaMySH7d3+kVi310QIAAACA3rIj+/v11dgN9ynszbEb7qvYm2MHq//93//NW97ylnR0dKS8vDwXX3xx3v/+92efffYpOlqfe+aZZzJhwoSiY4A9D2GnjRqVrF9b/KWfZjripfJw3TOV6ezc/E8WAQAAAACwqdWrV+f8889PR0dHysrK8rOf/Sz/8i//MmCLw5aWlpSXl6dUKuXrX//6Lr3Xb3/729x2221529ve1kvpYOcpD2FX/Mm+h8ce8NIa1c3leXzZ4NysFwAAAACgCP/zP/+TFStWJElOP/30Ab/33wMPPJCOjo4kyYwZM3b6fZ577rmce+652WeffXLZZZf1VjzYacpD2BV/Uh7W14xI2fjWJMnvHl5XVCoAAAAAgEHnD3/4Q8/5gQceWGCS7TNv3rwk3ftuvvzlL9+p91i9enVOO+20NDY25uc//3nPno4DwaxZszJ9+vTMnj276Cj0Mxuzwa74k/IwSUbt1ZrGFypz3+MdycxiYgEAAAAADDZNTU095zfddFPa29tTXj5wa4z15eFBBx20U/tRNjU15c///M/z6KOPZs6cOTtdQPaVm2++OZMnTy46BgUw8xB2xfrycPnynqfGT+6epv7IE51FJAIAAAAAGJQOOuignvN58+blzW9+cx555JHNjl29enV+9rOf5eyzz87SpUv7K+JG7r333iQ7t2Rpa2tr3vKWt+TOO+/Mj3/84xx33HG9nA523sCt7GEw2MzMw7326cxTSZ56slRMJgAAAACAQeid73xnPve5z+XFF19Mklx33XW57rrrsvfee2fq1KmprKzM2rVrs3Tp0ixdujRdXV2pq6vLD37wg37P2tnZmfvvvz/JjpeHHR0defvb3545c+bk+9//fv7sz/6sLyLCTlMewq7YTHm4376l/F+S55eY2AsAAAAAsL0mTpyYX//61/nLv/zLPP744z3PL168OIsXL97sNYcffnhKpf6fyPHII4/0LLO6o+XhRz7ykfz4xz/OKaeckvb29nzve9/b6PU3v/nNqamp6bWssKOUh7ArNlMeTp/WXRo2LPXxAgAAAADYEUcddVQefPDB/OQnP8n//u//5r777suzzz6bxsbG1NbWZuLEidlzzz1zzDHH5MQTT8wJJ5xQSM71S5Ym3QXmjli/V+KcOXMyZ86cTV5ftGiR8pBCaTdgV2ymPDz6wIokybpnq9LZ2ZWyMsuXAgAAAABsr+rq6rz97W/P29/+9qKjbNH6AnDq1KkZO3bsDl07d+7cPkgEvce6irArNlce7l+VlLqSlhFZ8ExbQcEAAAAAAOgr68vDHZ11CIOB8hB2xfrycMWKpKMjSVI3sixlE1qTJHc91lpUMgAAAAAA+sh9992XZMf3O4TBQHkIu2L8+KRUSrq6khde6Hm6dq/uGYf3PtZRVDIAAAAAAPrA0qVLs3z58iTKQ4Ym5SHsivLy7gIx2Wjp0vFTukvDRxd1FpEKAAAAAIA+sn7J0kR5yNCkPIRdtZl9D/fap7s0XPxkqYhEAAAAAAD0kXvvvTdJMmHChEyePLnYMNAHlIewqyZO7D5uUB7uv293afj8Yh8xAAAAAIChZP3MQ7MOGarKiw4Ag95mZh4eut+IJMmapyuKSAQAAAAAQB9ZXx5Onz49jY2NWx1bW1vbH5GgVykPYVdtpjw88oDu0rD12cp0dnalrMzypQAAAADA0HLGGWdk6tSpSdJz7I2xva03c65atSpPPvlkkuSrX/1qvvrVr27xvUaNGpWGhoaMGDFiJ1JDcZSHsKs2Ux4es19lUtaVtI7I/YvX5fCpVQWFAwAAAADoG2eccUbOOOOMXh/b23oz5/r9DrfHy1/+csUhg5LyEHbVZsrDUVVlGTFxXTqer8pdj7UpDwEAAAAAhoCZM2emq6ur6BjQp8qKDgCD3mbKwySp3as1SfLA4x39nQgAAAAAAGCnKA9hV22hPJywd3dp+MgTnf2dCAAAAAAAYKcoD2FXrS8Ply/f6OnJe3dPXV/yZKm/EwEAAAAAAOwU5SHsqvXlYUND0tLS8/T++3aXhsuW2BAXAAAAAAAYHJSHsKvGjEkqKrrPN5h9+PIDukvDNUsrikgFAAAAAACww5SHsKtKpc3ue3jU/t2lYdtzlWnv6CoiGQAAAAAAwA5RHkJv2Ex5eOS+lUlZV9Jelnufai0oGAAAAAAAwPZTHg5Ts2fPzvTp0zNz5syiowwNmykPqyvLUr77uiTJXY8oDwEAAAAAgIFPeThMXXTRRZk/f37mzp1bdJShYTPlYZLU7tWWJHng8Y7+TgQAAAAAALDDlIfQG7ZQHk6c0l0aPrbInocAAAAAAMDApzyE3rCF8nDKPt2l4ZKnSv2dCAAAAAAAYIcpD6E3bKE8PGDf7tJw+ZIR/Z0IAAAAAABghykPoTdMnNh9/JPy8ND9ukvDNU9X9HciAAAAAACAHaY8hN6whZmHrzywuzRsf64yre32PQQAAAAAAAY25SH0hg3Lw64/loSHTa1KyjuTjrLc88S6gsIBAAAAAABsH+Uh9Ib1y5a2tiYNDT1PV5aXUj6pNUny+8faikgGAAAAAACw3ZSH0BtGjUpqa7vP/2Tp0rrJ3aXhA4939HcqAAAAAACAHaI8hN6yhX0PJ+7dXRo+tsiehwAAAAAAwMCmPITesoXycMo+3aXh0qdK/Z0IAAAAAABghygPobdsoTw8cFr3x2zFkhH9nQgAAAAAAGCHKA+ht6wvD5cv3+jpV+zXXRquWVrR34kAAAAAAAB2iPIQessWZh4efUB3adixrCotrZ39nQoAAAAAoE+cd955KZVKKZVKmTt3bs/z65+bOnVqYdk29N73vrcn02WXXbbJ64Pl+4D+ojyE3rKF8vAVe1cmFZ1JZym/f6K1gGAAAAAAAMPXvHnzes5nzJhRYBIYHJSH0Fu2UB6WjyilYo91SZK7Hm3r71QAAAAAAANCe3t79txzz54Zfb35VVZWlieeeGKTe3Z0dOSBBx7oeaw8hG1THkJv2UJ5mCR1e7UnSR58vKM/EwEAAAAADBjl5eV517ve1SfvfdJJJ2XatGmbPL9gwYK0tLQkSerr6y1BCttBeQi9ZSvl4W57d5eGjy/q6s9EAAAAAAADyvnnn59SqdTr73vBBRds9vlDDz00XV1d6erqysqVK3v9vjAUKQ+ht6wvD1esSDo2nmG49z7dpeHSp3r/H0UAAAAAgMFi2rRpee1rX9vz+PWvf33a2tp2+OsTn/hEz3uMHz8+b37zm4v4doa0WbNmZfr06Zk9e3bRUehn5UUHgCFj/PikVEq6upIXXvhjmZjkwGml/DrJiiU+cgAAAADA8HbBBRfkxhtvTJL8+te/zvLly7PHHnts9/WdnZ35zne+0/P4He94R6qqqno953B38803Z/LkyUXHoABmHkJvKS/vLhCTTZYuPeyAiiTJ2mcq+jsVAAAAAMCA8uY3vzkTJkxIknR0dOTb3/72Dl1/44035qmnnup5vKUlS1euXJlSqdTztWrVqp3ODMOJ8hB608SJ3cc/KQ9f+VJ52LGsMo0tnf2dCgAAAABgwKisrMw73vGOnsff+ta3duj6Dccfd9xxmT59+mbH3XvvvT3nU6dOTX19/Q7dB4Yr5SH0pvVLlf5JeTh9r4qkqiPpKuWux1sLCAYAAAAAMHBsOFvwsccey6233rpd17344ov56U9/utn3+VMbloeHH374jkaEYUt5CL1pC+VhWVkplXt0l4b3PNbW36kAAAAAAAaUgw8+OMcff3zP46uuumq7rvve976XdevWJUnq6upy1llnbXHshuXhjBkzdi4oDEPKQ+hNWygPk2TM5O7S8MGFHf2ZCAAAAABgQLrwwgt7zn/0ox9lzZo127xmwyVL3/72t6empmaLY808hJ1TXnQAGFK2Uh7uNqUzy5M8vqirfzMBAAAAwGDV1ZU0NRWdYuAaNSoplYpOsdPOOuusfPCDH8zq1avT1NSUH/7whxsVin/q97//fe67776ex1tbsrS1tTULFizoeaw8hO2nPITetJXycJ+pXXkoydOLB+8/5gAAAADQr5qaktraolMMXI2NyVZm3g10I0eOzDnnnJOvfe1rSbqXLt1aebjh0qYzZszIkUceucWxDz30UNrauleDGzduXPbee+9eSg1Dn2VLoTdtpTx82bTuj9sLS3T2AAAAAADJxkuX3nnnnXnooYc2O665uTn/+Z//2fN4a7MOE0uWwq7QYkBv2kp5eNh+I5Ika5+u6M9EAAAAADB4jRrVPbuOzRs1qugEu+zwww/PkUcemXvuuSdJ956GX/7ylzcZ9+Mf/zirV69O8scZi1vTX+Xh0qVL85Of/CS/+c1vct999+XZZ59NRUVF9tlnn5x88sn54Ac/mGnTpvXZ/aEvKA+hN60vD5cv3+SlV72sMknSuaIqDc2dqRtp4i8AAAAAbFWpNKiX5WT7XHjhhT3l4Xe/+938y7/8SyoqNp6EseGSpWeddVbGjBmz1ffsj/JwyZIl2WeffdLV1dXzXF1dXZqbmzN//vzMnz8/3/zmN3PNNdfkrLPO6pMM0Be0F9Cb1peHDQ1JS8tGLx2we0UysiNJ8rtH1/V3MgAAAACAAentb397al4qiZcvX56f//znG73++OOP57e//W3P420tWZok999/f895X5WHHR3d/997+umn50c/+lFWrFiR1atXp6mpKb/97W/zile8Is3NzTnnnHM2ygMDnfIQetOYMcn6n4j5k9mHZWWlVO3RXRr+YWFbfycDAAAAABiQRo8enbe97W09jzecZZh0L2W6fnbfQQcdlBNOOGGr77do0aKsWrUqSVJVVZWDDz64dwO/ZOzYsZk3b16uu+66nHnmmRk/fnySpLy8PK9+9atzww03ZOLEiWlvb8/ll1/eJxmgLygPoTeVSlvd93DM5PYkyUOPd/ZnKgAAAACAAW3D2YS//vWv8/TTTyfpnt337W9/e7PjtmTDJUsPPfTQlJf3zQ5uY8aMyWGHHbbF1ydNmpTXv/71SdKzLCsMBspD6G1bKQ8n7d1dGj6+qGuT1wAAAAAAhqtjjz02hx56aJKNC8MNi8TKysq84x3v2OZ79cd+h9trwoQJSZL29vZCc8COUB5Cb9tKebjP1O7jM0/56AEAAAAAbGjDWYXrlyrdcAnTM844IxMnTtzm+wyk8nD9Xo3ri1EYDDQY0Nu2Uh4ePK2UJHlx6Yj+TAQAAAAAMOD91V/9VaqqqpIkjz/+eP77v/87P//5z3te354lS5ONy8MZM2b0asYd8dO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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot_cost(\n", " j_obs_no_reg, \n", " {}, \n", " noise_level, \n", " {}, \n", " {}, \n", " grad_j = norm_grad_j_no_reg,\n", " figsize=(20, 15)\n", ")" ] }, { "cell_type": "markdown", "id": "b22d9e2f-0a88-41ea-acb5-e9e09ec26707", "metadata": {}, "source": [ "## Inference with a population dynamic-informed regularization term" ] }, { "cell_type": "markdown", "id": "bac3e9c5-df03-4c3c-b846-ce53af5766f4", "metadata": {}, "source": [ "Now, the inference is illustrated with the population dynamic-informed regularization.\n", "\n", "One can add a regularization term to the cost function by using the `add_reg` method of the object of the class `Cost`. It takes as input the keyword of the regularization term and the value of the weight coefficient associated. If the regularization term is already in the cost function, this method will only update the value of the weight coefficient. " ] }, { "cell_type": "code", "execution_count": 27, "id": "15844b55-bc2f-4c9f-be07-5008e6a3dede", "metadata": {}, "outputs": [], "source": [ "cost.add_reg(\"Population dynamic\", 5. * 1e-2)" ] }, { "cell_type": "markdown", "id": "e031f6d6-eb1c-40b8-a22b-34ac42eef232", "metadata": {}, "source": [ "As the population dynamic-informed regularization term is differentiable, the gradient descent algorithm is used. \n", "\n", "One may want to start from where the optimization stopped in the case without regularization (which is referred to as \"hot start\" in the litterature). To do so, the input 'restart_flag` is set to True. " ] }, { "cell_type": "code", "execution_count": 28, "id": "f274843a-aa01-4494-bfea-7e99dc42b3d5", "metadata": {}, "outputs": [], "source": [ "options = { \n", " \"nit_max\": 100, \n", " \"step size\": 5 * 1.0e-2\n", "} " ] }, { "cell_type": "code", "execution_count": 29, "id": "2349377e-8df6-4780-b2c5-32622f53dbce", "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "restarting the optimization process from the data contained in the dir /home/tmalou/Documents/pherosensor/pherosensor-toolbox/tutorials/output\n", "Warning: the regularization Population dynamic term has not been found in the given directory. It is assumed that this regularization term was not used previously and thus is set to 0.\n", "Optimizing using the gradient descent algorithm \n", "iteration 51: j = 1.135295e-02, j_obs = 2.729282e-05, ite comp time = 3.003e+00s -\n", "iteration 52: j = 6.649820e-03, j_obs = 2.751712e-05, ite comp time = 3.467e+00s -\n", "iteration 53: j = 5.462130e-03, j_obs = 2.757233e-05, ite comp time = 3.308e+00s -\n", "iteration 54: j = 4.832619e-03, j_obs = 2.766869e-05, ite comp time = 3.378e+00s -\n", "iteration 55: j = 4.412071e-03, j_obs = 2.771764e-05, ite comp time = 3.268e+00s -\n", "iteration 56: j = 4.098070e-03, j_obs = 2.778982e-05, ite comp time = 3.309e+00s -\n", "iteration 57: j = 3.848028e-03, j_obs = 2.783607e-05, ite comp time = 3.303e+00s -\n", "iteration 58: j = 3.640489e-03, j_obs = 2.789859e-05, ite comp time = 3.269e+00s -\n", "iteration 59: j = 3.463175e-03, j_obs = 2.794346e-05, ite comp time = 2.961e+00s -\n", "iteration 60: j = 3.308450e-03, j_obs = 2.800101e-05, ite comp time = 2.361e+00s -\n", "iteration 61: j = 3.171223e-03, j_obs = 2.804517e-05, ite comp time = 2.347e+00s -\n", "iteration 62: j = 3.047955e-03, j_obs = 2.809982e-05, ite comp time = 2.350e+00s -\n", "iteration 63: j = 2.936068e-03, j_obs = 2.814359e-05, ite comp time = 2.369e+00s -\n", "iteration 64: j = 2.833643e-03, j_obs = 2.819642e-05, ite comp time = 2.351e+00s -\n", "iteration 65: j = 2.739204e-03, j_obs = 2.824001e-05, ite comp time = 2.350e+00s -\n", "iteration 66: j = 2.651602e-03, j_obs = 2.829162e-05, ite comp time = 2.347e+00s -\n", "iteration 67: j = 2.569916e-03, j_obs = 2.833513e-05, ite comp time = 2.359e+00s -\n", "iteration 68: j = 2.493410e-03, j_obs = 2.838591e-05, ite comp time = 2.346e+00s -\n", "iteration 69: j = 2.421475e-03, j_obs = 2.842940e-05, ite comp time = 2.410e+00s -\n", "iteration 70: j = 2.353614e-03, j_obs = 2.847956e-05, ite comp time = 2.330e+00s -\n", "iteration 71: j = 2.289404e-03, j_obs = 2.852307e-05, ite comp time = 2.317e+00s -\n", "iteration 72: j = 2.228494e-03, j_obs = 2.857277e-05, ite comp time = 2.343e+00s -\n", "iteration 73: j = 2.170581e-03, j_obs = 2.861630e-05, ite comp time = 2.328e+00s -\n", "iteration 74: j = 2.115406e-03, j_obs = 2.866563e-05, ite comp time = 2.309e+00s -\n", "iteration 75: j = 2.062745e-03, j_obs = 2.870918e-05, ite comp time = 2.334e+00s -\n", "iteration 76: j = 2.012402e-03, j_obs = 2.875821e-05, ite comp time = 2.321e+00s -\n", "iteration 77: j = 1.964205e-03, j_obs = 2.880176e-05, ite comp time = 2.326e+00s -\n", "iteration 78: j = 1.918003e-03, j_obs = 2.885052e-05, ite comp time = 2.322e+00s -\n", "iteration 79: j = 1.873659e-03, j_obs = 2.889407e-05, ite comp time = 2.317e+00s -\n", "iteration 80: j = 1.831054e-03, j_obs = 2.894257e-05, ite comp time = 2.326e+00s -\n", "iteration 81: j = 1.790077e-03, j_obs = 2.898608e-05, ite comp time = 2.329e+00s -\n", "iteration 82: j = 1.750633e-03, j_obs = 2.903435e-05, ite comp time = 2.330e+00s -\n", "iteration 83: j = 1.712631e-03, j_obs = 2.907780e-05, ite comp time = 2.320e+00s -\n", "iteration 84: j = 1.675991e-03, j_obs = 2.912582e-05, ite comp time = 2.333e+00s -\n", "iteration 85: j = 1.640637e-03, j_obs = 2.916919e-05, ite comp time = 2.316e+00s -\n", "iteration 86: j = 1.606503e-03, j_obs = 2.921697e-05, ite comp time = 2.346e+00s -\n", "iteration 87: j = 1.573525e-03, j_obs = 2.926022e-05, ite comp time = 2.318e+00s -\n", "iteration 88: j = 1.541646e-03, j_obs = 2.930774e-05, ite comp time = 2.324e+00s -\n", "iteration 89: j = 1.510811e-03, j_obs = 2.935085e-05, ite comp time = 2.328e+00s -\n", "iteration 90: j = 1.480972e-03, j_obs = 2.939810e-05, ite comp time = 2.320e+00s -\n", "iteration 91: j = 1.452081e-03, j_obs = 2.944105e-05, ite comp time = 2.329e+00s -\n", "iteration 92: j = 1.424097e-03, j_obs = 2.948802e-05, ite comp time = 2.314e+00s -\n", "iteration 93: j = 1.396977e-03, j_obs = 2.953078e-05, ite comp time = 2.330e+00s -\n", "iteration 94: j = 1.370684e-03, j_obs = 2.957745e-05, ite comp time = 2.320e+00s -\n", "iteration 95: j = 1.345183e-03, j_obs = 2.962000e-05, ite comp time = 2.338e+00s -\n", "iteration 96: j = 1.320440e-03, j_obs = 2.966635e-05, ite comp time = 2.326e+00s -\n", "iteration 97: j = 1.296423e-03, j_obs = 2.970867e-05, ite comp time = 2.331e+00s -\n", "iteration 98: j = 1.273104e-03, j_obs = 2.975470e-05, ite comp time = 2.318e+00s -\n", "iteration 99: j = 1.250453e-03, j_obs = 2.979676e-05, ite comp time = 2.333e+00s -\n", "iteration 100: j = 1.228445e-03, j_obs = 2.984245e-05, ite comp time = 2.344e+00s -\n", "iteration 101: j = 1.207054e-03, j_obs = 2.988424e-05, ite comp time = 2.320e+00s -\n", "iteration 102: j = 1.186257e-03, j_obs = 2.992957e-05, ite comp time = 2.321e+00s -\n", "iteration 103: j = 1.166030e-03, j_obs = 2.997107e-05, ite comp time = 2.334e+00s -\n", "iteration 104: j = 1.146354e-03, j_obs = 3.001603e-05, ite comp time = 2.325e+00s -\n", "iteration 105: j = 1.127206e-03, j_obs = 3.005722e-05, ite comp time = 2.319e+00s -\n", "iteration 106: j = 1.108569e-03, j_obs = 3.010180e-05, ite comp time = 2.341e+00s -\n", "iteration 107: j = 1.090422e-03, j_obs = 3.014267e-05, ite comp time = 2.321e+00s -\n", "iteration 108: j = 1.072750e-03, j_obs = 3.018686e-05, ite comp time = 2.335e+00s -\n", "iteration 109: j = 1.055535e-03, j_obs = 3.022739e-05, ite comp time = 2.323e+00s -\n", "iteration 110: j = 1.038761e-03, j_obs = 3.027118e-05, ite comp time = 2.338e+00s -\n", "iteration 111: j = 1.022413e-03, j_obs = 3.031136e-05, ite comp time = 2.323e+00s -\n", "iteration 112: j = 1.006477e-03, j_obs = 3.035474e-05, ite comp time = 2.334e+00s -\n", "iteration 113: j = 9.909382e-04, j_obs = 3.039457e-05, ite comp time = 2.357e+00s -\n", "iteration 114: j = 9.757836e-04, j_obs = 3.043753e-05, ite comp time = 2.317e+00s -\n", "iteration 115: j = 9.610002e-04, j_obs = 3.047698e-05, ite comp time = 2.324e+00s -\n", "iteration 116: j = 9.465763e-04, j_obs = 3.051952e-05, ite comp time = 2.333e+00s -\n", "iteration 117: j = 9.324998e-04, j_obs = 3.055859e-05, ite comp time = 2.322e+00s -\n", "iteration 118: j = 9.187598e-04, j_obs = 3.060069e-05, ite comp time = 2.319e+00s -\n", "iteration 119: j = 9.053451e-04, j_obs = 3.063938e-05, ite comp time = 2.339e+00s -\n", "iteration 120: j = 8.922460e-04, j_obs = 3.068104e-05, ite comp time = 2.328e+00s -\n", "iteration 121: j = 8.794521e-04, j_obs = 3.071934e-05, ite comp time = 2.338e+00s -\n", "iteration 122: j = 8.669543e-04, j_obs = 3.076056e-05, ite comp time = 2.342e+00s -\n", "iteration 123: j = 8.547431e-04, j_obs = 3.079845e-05, ite comp time = 2.344e+00s -\n", "iteration 124: j = 8.428101e-04, j_obs = 3.083923e-05, ite comp time = 2.315e+00s -\n", "iteration 125: j = 8.311465e-04, j_obs = 3.087672e-05, ite comp time = 2.334e+00s -\n", "iteration 126: j = 8.197445e-04, j_obs = 3.091705e-05, ite comp time = 2.319e+00s -\n", "iteration 127: j = 8.085959e-04, j_obs = 3.095413e-05, ite comp time = 2.328e+00s -\n", "iteration 128: j = 7.976937e-04, j_obs = 3.099401e-05, ite comp time = 2.352e+00s -\n", "iteration 129: j = 7.870303e-04, j_obs = 3.103068e-05, ite comp time = 2.337e+00s -\n", "iteration 130: j = 7.765989e-04, j_obs = 3.107010e-05, ite comp time = 2.318e+00s -\n", "iteration 131: j = 7.663926e-04, j_obs = 3.110636e-05, ite comp time = 2.318e+00s -\n", "iteration 132: j = 7.564053e-04, j_obs = 3.114533e-05, ite comp time = 2.325e+00s -\n", "iteration 133: j = 7.466303e-04, j_obs = 3.118117e-05, ite comp time = 2.323e+00s -\n", "iteration 134: j = 7.370620e-04, j_obs = 3.121968e-05, ite comp time = 2.315e+00s -\n", "iteration 135: j = 7.276942e-04, j_obs = 3.125510e-05, ite comp time = 2.331e+00s -\n", "iteration 136: j = 7.185217e-04, j_obs = 3.129316e-05, ite comp time = 2.327e+00s -\n", "iteration 137: j = 7.095388e-04, j_obs = 3.132817e-05, ite comp time = 2.322e+00s -\n", "iteration 138: j = 7.007406e-04, j_obs = 3.136577e-05, ite comp time = 2.335e+00s -\n", "iteration 139: j = 6.921216e-04, j_obs = 3.140036e-05, ite comp time = 2.320e+00s -\n", "iteration 140: j = 6.836774e-04, j_obs = 3.143751e-05, ite comp time = 2.329e+00s -\n", "iteration 141: j = 6.754030e-04, j_obs = 3.147167e-05, ite comp time = 2.324e+00s -\n", "iteration 142: j = 6.672941e-04, j_obs = 3.150837e-05, ite comp time = 2.333e+00s -\n", "iteration 143: j = 6.593459e-04, j_obs = 3.154212e-05, ite comp time = 2.330e+00s -\n", "iteration 144: j = 6.515547e-04, j_obs = 3.157837e-05, ite comp time = 2.321e+00s -\n", "iteration 145: j = 6.439159e-04, j_obs = 3.161170e-05, ite comp time = 2.336e+00s -\n", "iteration 146: j = 6.364258e-04, j_obs = 3.164750e-05, ite comp time = 2.343e+00s -\n", "iteration 147: j = 6.290805e-04, j_obs = 3.168041e-05, ite comp time = 2.318e+00s -\n", "iteration 148: j = 6.218763e-04, j_obs = 3.171576e-05, ite comp time = 2.318e+00s -\n", "iteration 149: j = 6.148094e-04, j_obs = 3.174827e-05, ite comp time = 2.326e+00s -\n", "iteration 150: j = 6.078767e-04, j_obs = 3.178317e-05, ite comp time = 2.320e+00s -\n", "\n", "final iteration:\n", "number of iteration = 150\n", "j = 0.07585608407711002 % of the initial cost\n", "j_obs = 0.003966178017483448 % of the initial cost\n", "j_Population dynamic = 0.07188990605962658 % of the initial cost\n" ] } ], "source": [ "direct_model_pop_dyn_reg, j_obs_pop_dyn_reg, j_reg_pop_dyn_reg, norm_grad_j_pop_dyn_reg, S_a_pop_dyn_reg = cost.minimize(\n", " direct_model,\n", " adjoint_model,\n", " \"gradient descent\", \n", " options=options,\n", " path_save=path_output,\n", " restart_flag=True,\n", " j_obs_threshold = noise_level\n", ")" ] }, { "cell_type": "code", "execution_count": 30, "id": "9fb69db9-2395-4393-8e0b-370e573aed30", "metadata": {}, "outputs": [ { "data": { "image/png": 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H56KLLso+++yTF198Md/61rdKocnf/M3fZN999+2Q90L5tHUeLVu2LOPHj8+YMWPyhS98IYceemgGDhyYpqamLFmyJLNnz87s2bNLO2i/853v+E2kTua0007LQw89lCT57Gc/m7PPPjvPPffcVvv37NkzI0eO3Ozj1iPaYy5ZkwAA6KqqmtyVCirSsGHD8tJLL7XYb/LkybnlllvSv3//ji+KijN16tTcdtttre6/tSV/w4YN+dKXvpQf/OAHWx179tlnZ+bMmenWzS+FdTZtnUdz587N+PHjWxzXt2/fXH/99Tn33HO3u0Yq2/YepbPXXntl2bJlW3zOetS1tcdcsiYBANBV2dkPFeq2225LXV1d5s2blyVLlqS+vj6rVq1KTU1Namtrc9RRR2XKlCkZM2ZMuUulE+jWrVtuvfXWTJ48OTNnzswTTzyR+vr6DBgwIEcccUS+/OUvZ8KECeUukwr1yU9+Mj/+8Y8zb968PPnkk/nd736X+vr6NDY2Ztddd82BBx6YY445Juecc04GDhxY7nKpcNYj2sqaBABAV2VnPwAAAAAAFJzffQYAAAAAgIIT9gMAAAAAQMEJ+wEAAAAAoOCE/QAAAAAAUHDCfgAAAAAAKDhhPwAAAAAAFJywHwAAAAAACk7YDwAAAAAABSfsBwAAAACAghP2AwAAAABAwQn7AQAAAACg4IT9AAAAAABQcMJ+AAAAAAAoOGE/ANCiRx99NFVVVamqqsqVV15Z7nK26p133smgQYNSVVWVsWPHlrscAAAA+MgI+wGAbdqwYUMuuOCCJMmAAQPyV3/1V2WuaOtqamryjW98I0nyy1/+Mv/8z/9c5ooAAADgoyHsBwC26cc//nGefvrpJMmFF16Yfv36lbegFpx33nnZfffdkyQXX3xxGhsby1wRAAAAdDxhPwCwVe+//36uvvrqJMlOO+2Ur3zlK2WuqGW9e/fO1772tSTJkiVLctttt5W5IgAAAOh4wn4AYKvuuuuuLF68OEly+umnZ7fdditzRa1z3nnnpbq6Okly7bXXpqmpqcwVAQAAQMcS9gMAW3XDDTeU2meffXYZK9k+AwcOzMSJE5Mkixcvzr//+7+XuSIAAADoWMJ+AGCLnn322cyfPz9JMmLEiIwePbrMFW2f008/vdSeMWNGGSsBAACAjifsB4AK9Mgjj6Sqqqr0Z86cOS2O+eEPf7jJmJkzZ7aphp/85Cel9qRJk1o9btGiRaUaampqSkfoLF26NJdffnk+8YlPZLfddkvv3r1z4IEH5pvf/Gbee++9zV5n1apV+cd//MeMHTs2/fv3T8+ePfPxj38855xzTpYtW9ZiHRMmTEjv3r2TJA888EBef/31Vr8HAAAAKJqqJofYAkBF+tznPpf/+I//SJJ84hOfyK9//eut9n3ooYcyceLENDY2Jkkuu+yyfPOb32zT9UeOHJkXXnghSfKLX/wixxxzTKvG3XnnnTnttNOSJKNHj85//dd/5Vvf+lauuuqqrF+/fotjDj300MydOze77LJLko3h/JlnnpnXXntti/133XXXzJ07NwcffPA2a2n+Obz11ltz1llnteo9AAAAQNHY2Q8AFWr69Oml9lNPPZV77713i/2efvrpnHLKKaWg/6yzzmpz0L906dJS0N+jR4+MGTOm1WP/53/+p9Q+4IADcsYZZ+SSSy7J+vXrs+eee+bTn/50Ro0ale7du2/yHi677LIkyT/8wz/k+OOPz2uvvZaampr88R//cUaPHp2ddtqp1P/NN9/MtGnTWqxl3LhxpfaDDz7Y6vcAAAAARSPsB4AKNXr06Jx00kmlx1deeWU+/At5L7/8ciZOnJjVq1cn2Xh0TXucT19XV1dqjxo1Kn379m312KeffrrU/rd/+7fceeedOfjgg/Of//mfWb58eerq6rJw4cI88cQTqa2tLfX90Y9+lDvuuCMXXXRR+vfvnxkzZqS+vj7z58/P448/npdffjknnHBCqf9TTz21yQ8WtuSII44otefOndvq9wAAAABFI+wHgAp2zTXXpFu3jf9dP/3007nnnntKz7311ls5/vjjs2LFiiQbg+277ror1dXVbb5u8yODDjrooO0a2zzsf+ONN3Lcccdl3rx5GT9+fKqqqkrPHXbYYfnOd75TevzWW2/lzDPPzODBg/P444/n3HPPTa9evUrP77bbbpk1a9YmO/ybX2tL/uiP/qjUXrlyZV555ZXtei8AAABQFMJ+AKhgBx10UOn8++T/7+5fv359Jk2alN/85jdJkn322Sdz5szZJAhvi4ULF5baw4cPb/W4lStX5ve//33p8b777pvZs2dv9TcDPvOZz2z2sXvuuSf77rvvFvt/7GMfy4EHHlh6/NZbb22zniFDhmzyA4Pm7wsAAAA6E2E/AFS4q666Kj169EiSPPPMM5k9e3amTZtWOpZm9913zwMPPJCBAwe22zVfeumlUnvIkCGtHvfhnfbXX399ampqttr/gxvyfuDLX/5yDj/88G1eo/nrteaHG4MHDy61m78vAAAA6EyE/QBQ4fbZZ5+cddZZRJM5/QAABixJREFUpcdTpkzJHXfckSTp27dv5syZkxEjRrTrNevr60vt/v37t3pc8zP0R4wYkeOPP36b/ZcvX77J469//estXqN5YD906NAW+++6666l9muvvdZifwAAACgiYT8AFMDll1+e3r17J0nWrFmTJOnevXt+9rOfZfTo0e1+vXfffbfU7tOnT6vHNd/ZP3HixE3O6N+SZ599ttQeMWJE9ttvv232X7duXZYtW1Z63FL/ZNP6m78vAAAA6EyE/QBQAIMHD86YMWM2+dj3vve9TJw4scOv3dTU1Oq+zcP+o48+usX+CxYsKLWPOuqoFvs/99xzef/995Ns/I2Dvffeu8Ux21M/AAAAFJWwHwAKYPr06XnkkUc2+Vjzo3baW/Oz8NeuXduqMWvXrs3zzz9fenzooYe2OKb5Dwe2t/8hhxzSqro++E2IpHVn/AMAAEARCfsBoMLdfvvtueSSSzb7+HXXXZc33nijQ645YMCAUvvNN99s1ZjnnnsujY2NSZJ+/fq16j4CzXf2H3bYYS32bx72t6Z/smn9u+++e6vGAAAAQNEI+wGggj388MM555xzSo+/8Y1vpLa2NkmyatWqXHvttR1y3b322qvU/vBNdLem+c15DznkkBbP63/zzTfz8ssvlx63Zmd/82u0pn+S/O53vyu1m78vAAAA6EyE/QBQoRYsWJDJkyenoaEhSTJt2rRMnz49F198canP97///U3C7PZywAEHlNovvvhiq8Zs75E8zXf1Dxs2LP37929xzDPPPFNqt2Zn/6uvvpp169aVHh944IEtjgEAAIAiEvYDQAV66aWXMnHixKxevTpJctxxx2XmzJlJkrPPPru0Q33NmjW5+uqr2/36n/zkJ0vt5557rlVjtveIne3tv2TJkrz99ttJkp49e2bUqFEtjnn22WdL7YEDB2bo0KEtjgEAAIAiEvYDQIV544038vnPf760Y/+www7L7NmzU11dnSTp0aNHLr300lL/W265JUuXLm3XGj796U+X2osWLco777zT4pjmu+47+ua8Bx10UHr06NHimCeffLLUHjduXIv9AQAAoKiE/QBQQdauXZsTTzwxixYtSrLxjPn7778/NTU1m/SbOnVqhg8fniRpaGjIFVdc0a51DB8+vHSD3cbGxvzqV7/aZv8lS5Zk1apVSTb+MKI1x+Vs7815d+S8/rq6ulL7uOOOa9UYAAAAKCJhPwBUiA0bNuT0008vBeu77rprfv7zn2fPPffcrG91dfUmu/t/8pOfZOHChe1az8knn1xqP/jgg9vs2zyIHzVqVHr16rXN/uvWrSv9QCPZ/p39rfnhwLvvvlv6XFZXV+fEE09scQwAAAAUlbAfACrEBRdckH/9139NkvTq1Sv33nvvNs+lP/PMM0u77zds2LBJ+N8eTj/99FL7nnvu2Wbf7Q3in3322TQ2NiZJPvaxj6W2trbFMdt77M/Pf/7zrF27NsnGXf277757i2MAAACgqIT9AFABvv3tb+f73/9+kqSqqiq33357xo4du80x3bt3z+WXX156fPfdd+eJJ55ot5oOPvjgHHnkkUmSpUuXZv78+Vvtu71H7GxvcP/WW2/l5ZdfTrLx83PIIYe0OOaOO+4otc8999wW+wMAAECRVTU1NTWVuwgAoDLdeeedOe2005Ik55xzTv7pn/6pzBW1zmuvvZYhQ4akoaEhw4cPzwsvvJBu3exxAAAAoPPyXS8AsFWnnnpq6aign/70p3njjTfKXFHrzJgxIw0NDUmSiy66SNAPAABAp+c7XwBgq7p3757LLrssycYb3t50001lrqhla9euzY033pgkGTZsWKZNm1bmigAAAKDjCfsBgG0644wzSmfkf/e7383q1avLXNG2zZgxIytXrkySTJ8+PT169ChzRQAAANDxhP0AwDZ169YtN9xwQ5Kkvr4+1113XZkr2rp3330306dPT5IcddRRpfsNAAAAQGfnBr0AAAAAAFBwdvYDAAAAAEDBCfsBAAAAAKDghP0AAAAAAFBwwn4AAAAAACg4YT8AAAAAABScsB8AAAAAAApO2A8AAAAAAAUn7AcAAAAAgIIT9gMAAAAAQMEJ+wEAAAAAoOD+H5bz7HZ2VcpLAAAAAElFTkSuQmCC", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ctrl_pop_dyn_reg = Control(direct_model_pop_dyn_reg.S, msh)\n", "ctrl_pop_dyn_reg.value = ctrl_pop_dyn_reg.background_value\n", "\n", "plot_ctrl(\n", " msh,\n", " msh.t_array[1],\n", " ctrl_pop_dyn_reg,\n", " ctrl_target=ctrl_target.value,\n", " cmap=\"jet\",\n", " label='s_o',\n", " obs=cost.obs,\n", " figsize=(20, 15)\n", ")\n", "plot_ctrl(\n", " msh,\n", " msh.t_array[-1],\n", " ctrl_pop_dyn_reg,\n", " ctrl_target=ctrl_target.value,\n", " cmap=\"jet\",\n", " label='s_o',\n", " obs=cost.obs,\n", " figsize=(20, 15)\n", ")" ] }, { "cell_type": "code", "execution_count": 32, "id": "578c61d9-6628-4593-999b-74cdfb502bef", "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot_cost(\n", " j_obs_pop_dyn_reg, \n", " j_reg_pop_dyn_reg, \n", " noise_level, \n", " {'Population dynamic': 'PD'}, \n", " {'Population dynamic': 'g'}, \n", " grad_j = norm_grad_j_pop_dyn_reg,\n", " figsize=(20, 15)\n", ")" ] }, { "cell_type": "markdown", "id": "63ed80b9-7fd2-4e20-83c5-d17ba863eaec", "metadata": {}, "source": [ "Let us note that if the optimization is started again and no regularization terms are found in the provided folder, the regularization terms are set to 0." ] }, { "cell_type": "markdown", "id": "8c8ffdcd-bcfc-4120-b31f-9149cdbd6d99", "metadata": {}, "source": [ "## Inference with the LASSO regularization term" ] }, { "cell_type": "markdown", "id": "4fadbdea-0492-452a-886d-007cd38fc5ed", "metadata": {}, "source": [ "Now, the inference is illustrated with both the population dynamic-informed and the LASSO regularizations." ] }, { "cell_type": "code", "execution_count": 33, "id": "de8702e7-4a05-4c0f-9c7d-f024f8548af5", "metadata": {}, "outputs": [], "source": [ "cost.add_reg('LASSO', 10 * 1e-4)" ] }, { "cell_type": "markdown", "id": "34582663-13ce-4c2f-b400-d4fd6d5b057d", "metadata": {}, "source": [ "In this case, the weight coefficient of the population dynamic-informed regularization term is modified using the `modify_weight_reg` method of the object of the class `Cost`.\n", "This method takes as input the keyword of the regularization term and the new value of the weight coefficient.\n", "If the regularization term was not already considered, then this method acts as the `add_reg` method. If the new weight coefficient is 0, then the regularization term will be removed." ] }, { "cell_type": "code", "execution_count": 34, "id": "c036e9a7-a457-4d8c-a1e5-9de85857c79e", "metadata": {}, "outputs": [], "source": [ "cost.modify_weight_reg(\"Population dynamic\", 2. * 1e-4)" ] }, { "cell_type": "markdown", "id": "d3f9832b-497c-446e-af24-f02a0fca570a", "metadata": {}, "source": [ "As the cost function is no longer differentiable due to the LASSO regularization term, the user should use a convexe non-differentiable optimization algorithm such as the proximal gradient algorithm. \n", "This algorithm has been implemented in the [submodule](https://forgemia.inra.fr/pherosensor/pherosensor-toolbox/-/blob/main/src/source_localization/proximal_gradient.py) `proximal_gradient`. This type of algorithm should also be used when the group-LASSO regularization term is used. " ] }, { "cell_type": "code", "execution_count": 35, "id": "65b17f3e-7919-4a47-96b0-63a79e84c011", "metadata": {}, "outputs": [], "source": [ "options = {\n", " \"nit_max\": 40, \n", " \"step size\": 10 * 1.0e-1\n", "} " ] }, { "cell_type": "code", "execution_count": 36, "id": "39e9a404-5486-4f9e-8e0d-a53d834b2006", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "restarting the optimization process from the data contained in the dir /home/tmalou/Documents/pherosensor/pherosensor-toolbox/tutorials/output\n", "Warning: the regularization LASSO term has not been found in the given directory. It is assumed that this regularization term was not used previously and thus is set to 0.\n", "Optimizing using the proximal gradient algorithm \n", "iteration 151: j = 9.338490e-02, j_obs = 3.181527e-05, ite comp time = 7.046e+00s -\n", "iteration 152: j = 9.281711e-02, j_obs = 3.199154e-05, ite comp time = 7.203e+00s -\n", "iteration 153: j = 9.229196e-02, j_obs = 3.219392e-05, ite comp time = 7.923e+00s -\n", "iteration 154: j = 9.180241e-02, j_obs = 3.242089e-05, ite comp time = 7.389e+00s -\n", "iteration 155: j = 9.134358e-02, j_obs = 3.267094e-05, ite comp time = 7.035e+00s -\n", "iteration 156: j = 9.091152e-02, j_obs = 3.294278e-05, ite comp time = 6.990e+00s -\n", "iteration 157: j = 9.050352e-02, j_obs = 3.323519e-05, ite comp time = 7.067e+00s -\n", "iteration 158: j = 9.011692e-02, j_obs = 3.354718e-05, ite comp time = 6.964e+00s -\n", "iteration 159: j = 8.974959e-02, j_obs = 3.387795e-05, ite comp time = 7.167e+00s -\n", "iteration 160: j = 8.939993e-02, j_obs = 3.422682e-05, ite comp time = 7.165e+00s -\n", "iteration 161: j = 8.906679e-02, j_obs = 3.459316e-05, ite comp time = 7.367e+00s -\n", "iteration 162: j = 8.874883e-02, j_obs = 3.497645e-05, ite comp time = 7.011e+00s -\n", "iteration 163: j = 8.844463e-02, j_obs = 3.537603e-05, ite comp time = 7.005e+00s -\n", "iteration 164: j = 8.815306e-02, j_obs = 3.579135e-05, ite comp time = 7.002e+00s -\n", "iteration 165: j = 8.787304e-02, j_obs = 3.622216e-05, ite comp time = 6.989e+00s -\n", "iteration 166: j = 8.760356e-02, j_obs = 3.666805e-05, ite comp time = 7.059e+00s -\n", "iteration 167: j = 8.734380e-02, j_obs = 3.712871e-05, ite comp time = 7.121e+00s -\n", "iteration 168: j = 8.709325e-02, j_obs = 3.760369e-05, ite comp time = 7.170e+00s -\n", "iteration 169: j = 8.685117e-02, j_obs = 3.809257e-05, ite comp time = 7.101e+00s -\n", "iteration 170: j = 8.661680e-02, j_obs = 3.859487e-05, ite comp time = 7.148e+00s -\n", "iteration 171: j = 8.638964e-02, j_obs = 3.911003e-05, ite comp time = 7.819e+00s -\n", "iteration 172: j = 8.616915e-02, j_obs = 3.963757e-05, ite comp time = 8.777e+00s -\n", "iteration 173: j = 8.595489e-02, j_obs = 4.017700e-05, ite comp time = 1.020e+01s -\n", "iteration 174: j = 8.574641e-02, j_obs = 4.072784e-05, ite comp time = 9.483e+00s -\n", "iteration 175: j = 8.554331e-02, j_obs = 4.128968e-05, ite comp time = 9.633e+00s -\n", "iteration 176: j = 8.534528e-02, j_obs = 4.186206e-05, ite comp time = 9.109e+00s -\n", "iteration 177: j = 8.515213e-02, j_obs = 4.244450e-05, ite comp time = 7.221e+00s -\n", "iteration 178: j = 8.496366e-02, j_obs = 4.303658e-05, ite comp time = 7.459e+00s -\n", "iteration 179: j = 8.477969e-02, j_obs = 4.363782e-05, ite comp time = 8.826e+00s -\n", "iteration 180: j = 8.459997e-02, j_obs = 4.424775e-05, ite comp time = 8.358e+00s -\n", "iteration 181: j = 8.442435e-02, j_obs = 4.486591e-05, ite comp time = 8.338e+00s -\n", "iteration 182: j = 8.425261e-02, j_obs = 4.549205e-05, ite comp time = 8.713e+00s -\n", "iteration 183: j = 8.408463e-02, j_obs = 4.612574e-05, ite comp time = 9.442e+00s -\n", "iteration 184: j = 8.392020e-02, j_obs = 4.676671e-05, ite comp time = 9.597e+00s -\n", "iteration 185: j = 8.375918e-02, j_obs = 4.741443e-05, ite comp time = 9.417e+00s -\n", "iteration 186: j = 8.360145e-02, j_obs = 4.806860e-05, ite comp time = 1.017e+01s -\n", "iteration 187: j = 8.344694e-02, j_obs = 4.872889e-05, ite comp time = 8.591e+00s -\n", "iteration 188: j = 8.329551e-02, j_obs = 4.939483e-05, ite comp time = 8.345e+00s -\n", "iteration 189: j = 8.314700e-02, j_obs = 5.006609e-05, ite comp time = 8.459e+00s -\n", "iteration 190: j = 8.300133e-02, j_obs = 5.074247e-05, ite comp time = 8.224e+00s -\n", "\n", "final iteration:\n", "number of iteration = 190\n", "j = 10.357621264114805 % of the initial cost\n", "j_obs = 0.006332082061661504 % of the initial cost\n", "j_Population dynamic = 0.00025602780786269463 % of the initial cost\n", "j_LASSO = 10.351033154245279 % of the initial cost\n" ] } ], "source": [ "direct_model_LASSO_reg, j_obs_LASSO_reg, j_reg_LASSO_reg, norm_grad_j_LASSO_reg, S_a_LASSO_reg = cost.minimize(\n", " direct_model,\n", " adjoint_model,\n", " \"proximal gradient\", \n", " options=options,\n", " path_save=path_output,\n", " restart_flag=True,\n", " j_obs_threshold = noise_level\n", ")" ] }, { "cell_type": "code", "execution_count": 37, "id": "62daaeb5-214b-402e-8263-6448c60d8590", "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ctrl_LASSO = Control(direct_model_LASSO_reg.S, msh)\n", "ctrl_LASSO.value = ctrl_LASSO.background_value\n", "plot_ctrl(\n", " msh,\n", " msh.t_array[1],\n", " ctrl_LASSO,\n", " ctrl_target=ctrl_target.value,\n", " cmap=\"jet\",\n", " label='s_o',\n", " obs=cost.obs,\n", " figsize=(20, 15)\n", ")\n", "plot_ctrl(\n", " msh,\n", " msh.t_array[-1],\n", " ctrl_LASSO,\n", " ctrl_target=ctrl_target.value,\n", " cmap=\"jet\",\n", " label='s_o',\n", " obs=cost.obs,\n", " figsize=(20, 15)\n", ")" ] }, { "cell_type": "markdown", "id": "03240969-036d-4343-87aa-f8238e7a246e", "metadata": {}, "source": [ "## Inference with the Tikhonov regularization term" ] }, { "cell_type": "markdown", "id": "c9644060-585c-4f68-815d-0da927c10193", "metadata": {}, "source": [ "Now, the inference is illustrated with both the population dynamic-informed and the Tikhonov regularizations." ] }, { "cell_type": "code", "execution_count": 38, "id": "044f71d6-bc53-4054-a627-dd56a096e98b", "metadata": {}, "outputs": [], "source": [ "cost.add_reg('Tikhonov',5 * 1e-5)" ] }, { "cell_type": "markdown", "id": "a9c27879-1e04-4b60-b53c-5173ccfcd295", "metadata": {}, "source": [ "One can remove a regularization term from the cost function using the `remove_reg` method of the object of the class `Cost`. This method just take the keyword of the regularization term to remove. " ] }, { "cell_type": "code", "execution_count": 39, "id": "45f2b255-bce1-483d-96cf-0df96f99bac2", "metadata": {}, "outputs": [], "source": [ "cost.remove_reg(\"LASSO\")" ] }, { "cell_type": "markdown", "id": "f56e15de-015f-41ad-8bd0-8e50750ac33c", "metadata": {}, "source": [ "As the LASSO regularization was removed and as the Tikhonov regularization term is differentiable, the cost function is differentiable and thus, the gradient descent algorithm will be used. " ] }, { "cell_type": "code", "execution_count": 40, "id": "801c2032-9668-4027-88df-b75dda52d67a", "metadata": {}, "outputs": [], "source": [ "options = {\n", " \"nit_max\": 40, \n", " \"step size\": 15 * 1.0e-1\n", "} " ] }, { "cell_type": "code", "execution_count": 41, "id": "a49208f7-012a-4007-9427-6431d45790f2", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "restarting the optimization process from the data contained in the dir /home/tmalou/Documents/pherosensor/pherosensor-toolbox/tutorials/output\n", "Warning: the regularization Tikhonov term has not been found in the given directory. It is assumed that this regularization term was not used previously and thus is set to 0.\n", "Optimizing using the gradient descent algorithm \n", "iteration 191: j = 9.693198e-02, j_obs = 5.142371e-05, ite comp time = 3.082e+00s -\n", "iteration 192: j = 9.689534e-02, j_obs = 4.746530e-05, ite comp time = 3.272e+00s -\n", "iteration 193: j = 9.686015e-02, j_obs = 4.431922e-05, ite comp time = 2.871e+00s -\n", "iteration 194: j = 9.682635e-02, j_obs = 4.192340e-05, ite comp time = 3.203e+00s -\n", "iteration 195: j = 9.679384e-02, j_obs = 4.021999e-05, ite comp time = 2.930e+00s -\n", "iteration 196: j = 9.676256e-02, j_obs = 3.915510e-05, ite comp time = 3.221e+00s -\n", "iteration 197: j = 9.673244e-02, j_obs = 3.867849e-05, ite comp time = 3.358e+00s -\n", "iteration 198: j = 9.670342e-02, j_obs = 3.874339e-05, ite comp time = 3.486e+00s -\n", "iteration 199: j = 9.667544e-02, j_obs = 3.930624e-05, ite comp time = 3.502e+00s -\n", "iteration 200: j = 9.664845e-02, j_obs = 4.032645e-05, ite comp time = 3.509e+00s -\n", "iteration 201: j = 9.662239e-02, j_obs = 4.176627e-05, ite comp time = 3.106e+00s -\n", "iteration 202: j = 9.659721e-02, j_obs = 4.359055e-05, ite comp time = 3.394e+00s -\n", "iteration 203: j = 9.657287e-02, j_obs = 4.576657e-05, ite comp time = 3.582e+00s -\n", "iteration 204: j = 9.654932e-02, j_obs = 4.826393e-05, ite comp time = 2.817e+00s -\n", "iteration 205: j = 9.652653e-02, j_obs = 5.105431e-05, ite comp time = 3.177e+00s -\n", "iteration 206: j = 9.650445e-02, j_obs = 5.411142e-05, ite comp time = 3.025e+00s -\n", "iteration 207: j = 9.648305e-02, j_obs = 5.741080e-05, ite comp time = 2.978e+00s -\n", "iteration 208: j = 9.646229e-02, j_obs = 6.092973e-05, ite comp time = 3.511e+00s -\n", "iteration 209: j = 9.644213e-02, j_obs = 6.464712e-05, ite comp time = 2.830e+00s -\n", "iteration 210: j = 9.642256e-02, j_obs = 6.854337e-05, ite comp time = 2.795e+00s -\n", "iteration 211: j = 9.640354e-02, j_obs = 7.260029e-05, ite comp time = 3.345e+00s -\n", "iteration 212: j = 9.638505e-02, j_obs = 7.680102e-05, ite comp time = 2.946e+00s -\n", "iteration 213: j = 9.636705e-02, j_obs = 8.112990e-05, ite comp time = 3.347e+00s -\n", "iteration 214: j = 9.634953e-02, j_obs = 8.557243e-05, ite comp time = 3.341e+00s -\n", "iteration 215: j = 9.633245e-02, j_obs = 9.011517e-05, ite comp time = 3.561e+00s -\n", "iteration 216: j = 9.631581e-02, j_obs = 9.474569e-05, ite comp time = 3.533e+00s -\n", "iteration 217: j = 9.629957e-02, j_obs = 9.945245e-05, ite comp time = 2.967e+00s -\n", "iteration 218: j = 9.628372e-02, j_obs = 1.042248e-04, ite comp time = 3.443e+00s -\n", "iteration 219: j = 9.626825e-02, j_obs = 1.090529e-04, ite comp time = 3.522e+00s -\n", "iteration 220: j = 9.625313e-02, j_obs = 1.139277e-04, ite comp time = 3.069e+00s -\n", "iteration 221: j = 9.623834e-02, j_obs = 1.188407e-04, ite comp time = 3.313e+00s -\n", "iteration 222: j = 9.622388e-02, j_obs = 1.237842e-04, ite comp time = 2.828e+00s -\n", "iteration 223: j = 9.620973e-02, j_obs = 1.287511e-04, ite comp time = 3.166e+00s -\n", "iteration 224: j = 9.619587e-02, j_obs = 1.337348e-04, ite comp time = 3.335e+00s -\n", "iteration 225: j = 9.618229e-02, j_obs = 1.387293e-04, ite comp time = 3.179e+00s -\n", "iteration 226: j = 9.616898e-02, j_obs = 1.437289e-04, ite comp time = 3.334e+00s -\n", "iteration 227: j = 9.615593e-02, j_obs = 1.487286e-04, ite comp time = 3.501e+00s -\n", "iteration 228: j = 9.614312e-02, j_obs = 1.537237e-04, ite comp time = 3.492e+00s -\n", "iteration 229: j = 9.613055e-02, j_obs = 1.587099e-04, ite comp time = 3.551e+00s -\n", "iteration 230: j = 9.611820e-02, j_obs = 1.636834e-04, ite comp time = 3.234e+00s -\n", "\n", "final iteration:\n", "number of iteration = 230\n", "j = 11.994456343087876 % of the initial cost\n", "j_obs = 0.020425818900109183 % of the initial cost\n", "j_Population dynamic = 0.00024127529208929966 % of the initial cost\n", "j_Tikhonov = 11.973789248895677 % of the initial cost\n" ] } ], "source": [ "direct_model_Tikhonov_reg, j_obs_Tikhonov_reg, j_reg_Tikhonov_reg, norm_grad_j_Tikhonov_reg, S_a_Tikhonov_reg = cost.minimize(\n", " direct_model,\n", " adjoint_model,\n", " \"gradient descent\", \n", " options=options,\n", " path_save=path_output,\n", " restart_flag=True,\n", " j_obs_threshold = noise_level\n", ")\n" ] }, { "cell_type": "code", "execution_count": 42, "id": "d1880613-693a-4bd9-a26f-55602e1ece91", "metadata": {}, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ctrl_Tikhonov = Control(direct_model_Tikhonov_reg.S, msh)\n", "ctrl_Tikhonov.value = ctrl_Tikhonov.background_value\n", "plot_ctrl(\n", " msh,\n", " msh.t_array[1],\n", " ctrl_Tikhonov,\n", " ctrl_target=ctrl_target.value,\n", " cmap=\"jet\",\n", " label='s_o',\n", " obs=cost.obs,\n", " figsize=(20, 15)\n", ")\n", "plot_ctrl(\n", " msh,\n", " msh.t_array[-1],\n", " ctrl_Tikhonov,\n", " ctrl_target=ctrl_target.value,\n", " cmap=\"jet\",\n", " label='s_o',\n", " obs=cost.obs,\n", " figsize=(20, 15)\n", ")" ] } ], "metadata": { "kernelspec": { "display_name": "pherosensor", "language": "python", "name": "pherosensor" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.15" } }, "nbformat": 4, "nbformat_minor": 5 }