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‎examples/examples-mcmc-maria.ipynb‎

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‎examples/examples-mcmc-ode.ipynb‎

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‎examples/examples-mcmc.ipynb‎

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‎examples/examples-sde-maria.ipynb‎

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‎examples/examples-sim-gLV/examples-ml-gLV.ipynb‎

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‎examples/examples-sim-gLV/examples-sim-gLV.ipynb‎

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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "f07fa1f2-187e-4ce0-af95-31d6120977fe",
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"metadata": {
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"pycharm": {
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"name": "#%%\n"
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}
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},
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"outputs": [],
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"source": [
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"import pandas as pd\n",
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"import numpy as np\n",
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"import matplotlib.pyplot as plt\n",
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"from scipy.integrate import odeint\n",
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"from numpy import linalg as la\n",
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"\n",
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"import sklearn.linear_model\n",
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"from sklearn.linear_model import LinearRegression\n",
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"from sklearn.linear_model import Ridge\n",
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"from sklearn.linear_model import Lasso\n",
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"from sklearn.linear_model import ElasticNet, ElasticNetCV\n",
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"from sklearn.model_selection import RepeatedKFold\n",
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"from sklearn.model_selection import cross_val_score\n",
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"from sklearn.model_selection import KFold\n",
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"from sklearn.model_selection import GridSearchCV\n",
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"\n",
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"import sys\n",
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"sys.path.append(\"../../\")\n",
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"sys.path.append(\"../../gMLV\")\n",
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"\n",
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"from gMLV import *\n",
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"from utilities import plot_gLV\n",
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"from utilities import set_all_seeds\n"
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]
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},
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{
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"cell_type": "markdown",
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"id": "82eb9f01",
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"metadata": {
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"pycharm": {
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"name": "#%% md\n"
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}
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},
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"source": [
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"## Simulate some time course data from the gLV\n"
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]
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},
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{
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"cell_type": "markdown",
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"id": "fbc11bbc",
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"metadata": {
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"pycharm": {
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"name": "#%% md\n"
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}
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},
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"source": [
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"### Five species, single time course"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "3e0845a5",
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"metadata": {
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"collapsed": false,
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"jupyter": {
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"outputs_hidden": false
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},
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"pycharm": {
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"name": "#%%\n"
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}
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},
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"outputs": [],
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"source": [
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"# In this example n >> p and it it is basically same as standard regression\n",
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"# We have to be careful as most of these gLV models are very weakly identifiable\n",
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"\n",
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"set_all_seeds(1234)\n",
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"\n",
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"## SETUP MODEL\n",
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"# establish size of model\n",
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"num_species = 5\n",
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"num_metabolites = 0\n",
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"\n",
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"# construct interaction matrix\n",
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"#TODO do this programmatically\n",
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"M = np.zeros((num_species, num_species))\n",
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"np.fill_diagonal(M, [-0.05, -0.1, -0.15, -0.01, -0.2])\n",
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"M[0, 2] = -0.025\n",
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"M[1, 3] = 0.05\n",
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"M[4, 0] = 0.02\n",
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"\n",
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"# construct growth rates matrix\n",
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"mu = np.random.lognormal(0.01, 0.5, num_species)\n",
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"\n",
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"# instantiate simulator\n",
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"simulator = gMLV_sim(num_species=num_species,\n",
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" num_metabolites=num_metabolites,\n",
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" M=M,\n",
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" mu=mu)\n",
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"simulator.print()\n",
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"\n",
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"## PRODUCE SIMULATED RESULTS\n",
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"# initial conditions\n",
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"init_species = 10 * np.ones(num_species)\n",
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"init_metabolites = 10 * np.ones(num_metabolites)\n",
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"\n",
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"times = np.arange(0, 5, 0.1)\n",
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"yobs, sobs, sy0, mu, M, _ = simulator.simulate(times=times, sy0=np.hstack((init_species, init_metabolites)))\n",
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"\n",
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"# add some gaussian noise\n",
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"yobs = yobs + np.random.normal(loc=0, scale=0.1, size=yobs.shape)\n",
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"sobs = sobs + np.random.normal(loc=0, scale=0.1, size=sobs.shape)\n",
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"\n",
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"# plot simulation\n",
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"plot_gLV(yobs, sobs, times)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "99ee1482",
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"metadata": {
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"pycharm": {
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"name": "#%% md\n"
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}
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},
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"source": [
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"### Five species, single time course, with a perturbation"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "cbab2390",
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"metadata": {
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"collapsed": false,
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"jupyter": {
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"outputs_hidden": false
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},
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"pycharm": {
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"name": "#%%\n"
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}
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},
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"outputs": [],
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"source": [
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"set_all_seeds(1234)\n",
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"\n",
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"## SETUP MODEL\n",
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"# establish size of model\n",
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"num_species = 5\n",
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"num_metabolites = 0\n",
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"\n",
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"# construct interaction matrix\n",
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"#TODO do this programmatically\n",
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"M = np.zeros((num_species, num_species))\n",
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"np.fill_diagonal(M, [-0.05, -0.1, -0.15, -0.01, -0.2])\n",
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"M[0, 2] = -0.025\n",
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"M[1, 3] = 0.05\n",
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"M[4, 0] = 0.02\n",
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"\n",
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"# construct growth rates matrix\n",
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"mu = np.random.lognormal(0.01, 0.5, num_species)\n",
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"\n",
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"# construct perturbation matrix\n",
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"epsilon = np.array([0, -1, 0, -1, 0])\n",
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"\n",
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"# instantiate simulator\n",
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"simulator = gMLV_sim(num_species=num_species,\n",
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" num_metabolites=num_metabolites,\n",
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" M=M,\n",
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" mu=mu,\n",
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" epsilon=epsilon)\n",
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"simulator.print()\n",
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"\n",
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"## PRODUCE SIMULATED RESULTS\n",
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"# initial conditions\n",
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"init_species = 10 * np.ones(num_species)\n",
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"init_metabolites = 10 * np.ones(num_metabolites)\n",
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"\n",
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"# perturbation\n",
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"tp = 2\n",
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"\n",
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"times = np.arange(0, 5, 0.1)\n",
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"yobs, sobs, sy0, mu, M, _ = simulator.simulate(times=times, \n",
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" sy0=np.hstack((init_species, init_metabolites)),\n",
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" tp=tp)\n",
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"\n",
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"\n",
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"# add some gaussian noise\n",
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"yobs = yobs + np.random.normal(loc=0, scale=0.1, size=yobs.shape)\n",
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"sobs = sobs + np.random.normal(loc=0, scale=0.1, size=sobs.shape)\n",
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"\n",
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"# plot simulation\n",
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"plot_gLV(yobs, sobs, times)"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.8"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}

‎examples/examples-sim-gMLV/examples-ml-gMLV.ipynb‎

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‎examples/examples-sim-gMLV/examples-sim-gMLV.ipynb‎

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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "f07fa1f2-187e-4ce0-af95-31d6120977fe",
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"metadata": {
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"pycharm": {
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"name": "#%%\n"
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}
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},
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"outputs": [],
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"source": [
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"import pandas as pd\n",
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"import numpy as np\n",
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"import matplotlib.pyplot as plt\n",
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"from scipy.integrate import odeint\n",
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"from numpy import linalg as la\n",
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"\n",
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"import sklearn.linear_model\n",
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"from sklearn.linear_model import LinearRegression\n",
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"from sklearn.linear_model import Ridge\n",
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"from sklearn.linear_model import Lasso\n",
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"from sklearn.linear_model import ElasticNet, ElasticNetCV\n",
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"from sklearn.model_selection import RepeatedKFold\n",
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"from sklearn.model_selection import cross_val_score\n",
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"from sklearn.model_selection import KFold\n",
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"from sklearn.model_selection import GridSearchCV\n",
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"\n",
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"import sys\n",
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"sys.path.append(\"../../\")\n",
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"sys.path.append(\"../../gMLV\")\n",
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"\n",
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"from gMLV import *\n",
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"from utilities import plot_gMLV\n",
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"from utilities import set_all_seeds\n"
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]
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},
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{
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"cell_type": "markdown",
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"id": "82eb9f01",
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"metadata": {
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"pycharm": {
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"name": "#%% md\n"
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}
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},
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"source": [
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"## Simulate some time course data from the gMLV\n",
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"\n",
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"### Five species, six metabolites, single time course"
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]
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},
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{
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"cell_type": "markdown",
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"id": "4501a3a3",
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"metadata": {
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"pycharm": {
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"name": "#%% md\n"
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}
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},
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"source": [
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"This model assumes metabolite production is associated with abundance: dS/dt = alpha X <br>\n",
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"Note that this model needs rethinking as it cannot handle negative productivities <br>\n",
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"In this simple example we don't need to infer the time course. We just linearize and estimate the elements of alpha with Lasso<br>\n",
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"Number of metabolites is 6 here"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "9d348926",
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"metadata": {
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"collapsed": false,
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"jupyter": {
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"outputs_hidden": false
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},
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"pycharm": {
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"name": "#%%\n"
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}
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},
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"outputs": [],
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"source": [
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"# Simulate some microbiota and metabolites\n",
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"set_all_seeds(1234)\n",
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"\n",
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"# SETUP MODEL\n",
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"# establish size of model\n",
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"num_species = 5\n",
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"num_metabolites = 6\n",
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"\n",
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"# construct interaction matrix\n",
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"#TODO do this programmatically\n",
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"M = np.zeros((num_species, num_species))\n",
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"np.fill_diagonal(M, [-0.05, -0.1, -0.15, -0.01, -0.2])\n",
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"# M[0,2] = -0.025\n",
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"M[1, 3] = 0.05\n",
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"# M[4,0] = 0.02\n",
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"\n",
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"# construct growth rates matrix\n",
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"mu = np.random.lognormal(0.01, 0.5, num_species)\n",
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"\n",
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"# construct metabolite production matrix\n",
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"alpha = np.zeros((num_metabolites, num_species))\n",
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"alpha[1, 4] = 1\n",
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"alpha[4, 2] = -0.5\n",
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"\n",
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"# instantiate simulator\n",
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"simulator = gMLV_sim(num_species=num_species,\n",
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" num_metabolites=num_metabolites,\n",
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" M=M,\n",
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" mu=mu,\n",
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" beta=alpha)\n",
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"simulator.print()\n",
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"\n",
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"## PRODUCE SIMULATED RESULTS\n",
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"# initial conditions\n",
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"init_species = 10 * np.ones(num_species)\n",
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"init_metabolites = 10 * np.ones(num_metabolites)\n",
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"\n",
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"times = np.arange(0, 5, 0.1)\n",
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"yobs, sobs, sy0, _, _, _ = simulator.simulate(times=times, sy0=np.hstack((init_species, init_metabolites)))\n",
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"\n",
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"# add some gaussian noise\n",
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"yobs = yobs + np.random.normal(loc=0, scale=0.1, size=yobs.shape)\n",
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"sobs = sobs + np.random.normal(loc=0, scale=0.1, size=sobs.shape)\n",
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"\n",
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"# plot simulation\n",
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"plot_gMLV(yobs, sobs, times)\n"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.8"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}

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