{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Genetic Algorithm \n", "\n", "In this notebook, we implement a new selection operator (tournament selection) and a new cost function (for the Goodwin oscillator). At the end, there is also code to plot the parameters as they change over the generations." ] }, { "cell_type": "code", "execution_count": 79, "metadata": {}, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import os.path\n", "import scipy.linalg\n", "import scipy.integrate\n", "import scipy.signal\n", "import matplotlib.cm" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Support function for GA\n", "Copy-paste your copy of generateIndividual, generateChild, and GA below." ] }, { "cell_type": "code", "execution_count": 80, "metadata": {}, "outputs": [], "source": [ "def generateIndividual(cost_fcn, lb,ub):\n", " '''\n", " Generate a viable individual (i.e one whos cost function is finite)\n", " and return it and its cost.\n", " If 1000 tries fails to yield an individual, then the paramets and an infinite cost\n", " are returned.\n", " Each individual is generated by randomly chosing each entry params[i] \n", " so that lb[i] <= params[i] <= ub[i]\n", " Input:\n", " cost_fcn: handle to the cost function (which should take a candidate as input)\n", " lb: lower bounds for parameters (array of length NP)\n", " ub: upper bounds for parameters (array of length NP)\n", " Returns:\n", " (params, cost)\n", " where params is the same shape as the lower bounds\n", " and cost is the cost for that set of parameters\n", " '''\n", " params = np.zeros(lb.shape);\n", " cost = np.inf;\n", " for i in range( 1000 ):\n", " params = lb + np.multiply( np.random.random(lb.size), ub-lb);\n", " cost = cost_fcn(params);\n", " if np.isfinite(cost):\n", " return (params, cost);\n", " return (params, np.inf)\n", "\n", "def generateChild(cost_fcn, P, lb, ub, settings):\n", " \"\"\"\n", " Generate a viable child (i.e one whos cost function is finite)\n", " and return it and its cost function.\n", " If 1000 tries fails to yield a parent, then an infinite cost\n", " is returned.\n", " Input Parameters:\n", " cost_fcn: handle to the cost function (which should take a candidate as input)\n", " P: parents (lam x NP ndarray of parameter values)\n", " lb: lower bounds for parameters (array of length NP)\n", " ub: upper bounds for parameters (array of length NP)\n", " settings: a GASettings object\n", " Returns:\n", " (params, cost)\n", " where params is the same shape as the lower bounds\n", " and cost is the cost for that set of parameters\n", " \"\"\"\n", " params = np.zeros(lb.shape);\n", " cost = np.inf;\n", " for i in range(1000):\n", " # Choose two parenst from the breeding pool.\n", " P1idx = np.random.choice(P.shape[0]);\n", " P1 = P[P1idx,:];\n", " P2idx = np.random.choice(P.shape[0]);\n", " P2 = P[P2idx,:];\n", " for j in range(lb.size):\n", " # uniform cross-over\n", " if np.random.random() < 0.5:\n", " params[j] = P1[j];\n", " else:\n", " params[j] = P2[j];\n", " # Mutate optimization variables\n", " params = np.multiply( 1+np.random.randn(params.size)*settings.mutation, params);\n", " # Make sure the mutation hasn't moved us out of bounds\n", " params = np.maximum(params,lb);\n", " params = np.minimum(params,ub);\n", " cost = cost_fcn(params);\n", " if np.isfinite(cost):\n", " return ( params, cost );\n", " return (np.zeros((lb.size)), np.inf)\n", "\n", "def GA(cost_fcn, lb, ub, settings):\n", " \"\"\" Run the algorithm described in the notes to find a low-cost\n", " population of parameter sets.\n", " Input:\n", " cost_fcn: handle to cost function (should take a paramer set as input)\n", " lb: lower bounds for parameters (numpy array of length NP)\n", " ub: lower bounds for parameters (numpy array of length NP)\n", " settings: GASettings object\n", " Returns\n", " (params, cost, all_params, all_costs)\n", " where \n", " params is a 1xNP vector with the best parameter set found\n", " cost is a scalar with the cost of the best parameter set\n", " all_params is a list with a (settings.numChildren x NP) ndarray of parameters for each generation\n", " all_costs is a list with an NP-length array of costs for each generation\n", " \"\"\"\n", "\n", " mu = settings.numParents;\n", " lam = settings.numChildren;\n", " numGenerations = settings.numGenerations;\n", "\n", " # Create initial population\n", " G = np.zeros( (lam,lb.size) );\n", " Gcost = np.zeros( (lam,) );\n", " for i in range(lam):\n", " (G[i,:], Gcost[i]) = generateIndividual(cost_fcn, lb, ub);\n", " # sort them by cost\n", " idx = np.argsort(Gcost);\n", " Gcost = Gcost[idx]\n", " G = G[idx,:];\n", " all_params = [G]\n", " all_costs = [Gcost]\n", "\n", " for g in range(numGenerations):\n", " # Promote any elites\n", " elites = G[:settings.eliteCount,:]\n", " eliteCosts = Gcost[:settings.eliteCount]\n", " # select breeding pool\n", " (P, Pcost) = select_pool( G, Gcost, settings )\n", " # Generate the children for generation g\n", " G = np.zeros((lam, lb.size));\n", " Gcost = np.zeros((lam,));\n", " G[:settings.eliteCount,:] = elites\n", " Gcost[:settings.eliteCount] = eliteCosts\n", " for i in range(settings.eliteCount,lam):\n", " (G[i,:], Gcost[i]) = generateChild(cost_fcn, P, lb, ub, settings); \n", " # sort the children of this generation\n", " idx = np.argsort(Gcost);\n", " Gcost = Gcost[idx]\n", " G = G[idx,:];\n", " print('Best of generation ', g+1, ' has cost ', Gcost[0]);\n", " all_params.append(G)\n", " all_costs.append(Gcost)\n", "\n", " params = G[0,:];\n", " cost = Gcost[0];\n", " return (params, cost, all_params, all_costs)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Update code to include linear ranking selection\n", "\n", "Copy-paste your GASettings definition and add to it a field named linearRankingRatio. Give it a default value of 4." ] }, { "cell_type": "code", "execution_count": 103, "metadata": {}, "outputs": [], "source": [ "class GASettings:\n", " def __init__( self, numParents = 10, numChildren = 50 ):\n", " self.numParents = numParents;\n", " self.numChildren = numChildren;\n", " self.numGenerations = 5;\n", " self.selection = 'truncation'; \n", " self.mutation = 0.05; # std of normal distribution computed as mutation*value\n", " self.eliteCount = 1;\n", " self.tournamentSize = 2; \n", " self.linearRankingRatio = 4;" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Copy-paste your select_pool below. Add to it support for linear ranking selection (call it \"linearRanking\").\n", "\n", "In linear rankng selection, we compute a discrete probability distribution with the probability of each individual in the previous generation being chosen for the breeding pool. The probabilities are constructed so that the probability of choosing the lowest-cost individual is settings.linearRankingRatio times the probability of choosing the highest cost individual. For each individual we want to add to the pool we use this probability distribution by choosing a number from a uniform distribution between 0 and 1 and then determining which index in the cumulative distribution is the smallest such that the cumulative distribution is larger than the random number.\n", "\n", "The algorithm is this. If $n$ is the number of individuals in the previous generation and $r$ is the linearRankingRatio, then we construct the probability distribution according to\n", "\n", "\n", "\n", "And selection individuals in the breeding pool\n", "\n", "" ] }, { "cell_type": "code", "execution_count": 97, "metadata": {}, "outputs": [], "source": [ "def select_pool( G, Gcost, settings ):\n", " \"\"\" Select a breding pool from the previous generation.\n", " G is numChildren x numParameters\n", " Gcost is numChildren long and is sorted in ascending order.\n", " Returns (P,Pcost)\n", " where \n", " P is numParents x numParameters\n", " Pcost is numParents long.\n", " \"\"\"\n", " if Gcost.size != settings.numChildren:\n", " print(\"problem\")\n", " return\n", " P = np.zeros( (settings.numParents,G.shape[1]) )\n", " Pcost = np.zeros( (settings.numParents,) )\n", " Pidxs = []\n", " if settings.selection == 'truncation':\n", " for i in range(settings.numParents):\n", " idx = np.random.choice(settings.numParents)\n", " P[i,:] = G[idx,:]\n", " Pcost[i] = Gcost[idx]\n", "\n", " elif settings.selection == 'uniform':\n", " for i in range(settings.numParents):\n", " idx = np.random.choice(G.shape[0])\n", " P[i,:] = G[idx,:]\n", " Pcost[i] = Gcost[idx]\n", "\n", " elif settings.selection == 'tournament':\n", " for i in range( settings.numParents ):\n", " idxs = np.random.choice(G.shape[0], (settings.tournamentSize,) )\n", " # pick the best one, which is smaller index (since costs sorted)\n", " idx = idxs.min()\n", " P[i,:] = G[idx,:]\n", " Pcost[i] = Gcost[idx]\n", " elif settings.selection == 'linearRanking':\n", " r = settings.linearRankingRatio\n", " n = settings.numChildren\n", " pn = 2/n*(1/(1+r))\n", " p1 = r*pn\n", " ps = np.zeros((n,))\n", " for i in range(1,n+1):\n", " ps[i-1] = pn + (p1-pn)*(n-i)/(n-1)\n", " cs = ps.cumsum()\n", "\n", " for i in range( settings.numParents ):\n", " num = np.random.random() # number between 0 and 1, from uniform distribution\n", " idx = np.argmax( num < cs )\n", " P[i,:] = G[idx,:]\n", " Pcost[i] = Gcost[idx]\n", " \n", " else:\n", " print( \"Unknown selection operator: \", settings.selection )\n", " return (P, Pcost)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Test the selection operators\n", "\n", "This includes code that demonstrates the distribution of costs in the pool when selecting with linear ranking selection (ratios of 4 and 8) and tournament selection (tournament sizes of 2 and 5)." ] }, { "cell_type": "code", "execution_count": 129, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Test the selection operators\n", "# Make fake parameters, using the index as the values (so we can differentiate them)\n", "lam = 10000 # number of individuals in generation\n", "mu = 1000 # number of individuals in breeding pool\n", "NP = 5 # num of parameters per individual\n", "G = np.zeros( (lam,NP) )\n", "for i in range(lam):\n", " G[i,:] = i\n", "Gcost = np.linspace(0.1,500,lam)\n", "settings = GASettings(numParents=mu, numChildren=lam)\n", "\n", "settings.selection = 'linearRanking'\n", "settings.linearRankingRatio = 4\n", "(P, Pcost) = select_pool(G, Gcost, settings)\n", "plt.figure( figsize=(8,4) )\n", "plt.hist( Pcost );\n", "plt.title( \"Breeding Pool Chosen With Linear Ranking Selection (r=4)\")\n", "plt.xlabel( 'Cost')\n", "plt.ylabel('Frequency')\n", "\n", "settings.selection = 'linearRanking'\n", "settings.linearRankingRatio = 8\n", "(P, Pcost) = select_pool(G, Gcost, settings)\n", "plt.figure( figsize=(8,4) )\n", "plt.hist( Pcost );\n", "plt.title( \"Breeding Pool Chosen With Linear Ranking Selection (r=8)\")\n", "plt.xlabel( 'Cost')\n", "plt.ylabel('Frequency')\n", "\n", "settings.selection = 'tournament'\n", "settings.tournamentSize = 2\n", "(P, Pcost) = select_pool(G, Gcost, settings)\n", "plt.figure( figsize=(8,4) )\n", "plt.hist( Pcost );\n", "plt.title( \"Breeding Pool Chosen with Tournament Selection (k=2)\")\n", "plt.xlabel( 'Cost')\n", "plt.ylabel('Frequency')\n", "\n", "settings.selection = 'tournament'\n", "settings.tournamentSize = 5\n", "(P, Pcost) = select_pool(G, Gcost, settings)\n", "plt.figure( figsize=(8,4) )\n", "plt.hist( Pcost );\n", "plt.title( \"Breeding Pool Chosen with Tournament Selection (k=6)\")\n", "plt.xlabel( 'Cost')\n", "plt.ylabel('Frequency');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Test the GA on the pattern cost function\n", "\n", "Test it with the pattern cost function. Plot the costs for all individuals in all the generations." ] }, { "cell_type": "code", "execution_count": 109, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Best of generation 1 has cost 2.3659993945924467\n", "Best of generation 2 has cost 2.11146452432875\n", "Best of generation 3 has cost 1.9302515007434187\n", "Best of generation 4 has cost 1.9302515007434187\n", "Best of generation 5 has cost 1.9302515007434187\n" ] }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "def pattern_cost_function( params ):\n", " return scipy.linalg.norm( params-np.array([1,5,2.5,2,4,3,0]) )\n", "\n", "\n", "PN = 7\n", "lb = np.zeros( (PN,) );\n", "ub = 5 + np.zeros( (PN,) );\n", "cost_fcn = pattern_cost_function;\n", "settings = GASettings(numParents=10, numChildren=50)\n", "settings.numGenerations = 5;\n", "settings.eliteCount = 1;\n", "settings.selection = 'linearRanking';\n", "settings.linearRankingRatio = 8;\n", "\n", "(params, cost, all_params, all_costs) = GA(pattern_cost_function, lb, ub, settings);\n", "\n", "plt.figure( figsize=(8,4) )\n", "legs = []\n", "for i in range(len(all_costs)):\n", " plt.plot( np.arange(all_costs[i].size), all_costs[i], '*' )\n", " legs.append( \"Generation\" + str(i) )\n", "plt.legend( legs );" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Cost function for the Goodwin oscillator" ] }, { "cell_type": "code", "execution_count": 110, "metadata": {}, "outputs": [], "source": [ "def goodwin_oscillator( t, statevars, params ):\n", " X = statevars[0];\n", " Y = statevars[1];\n", " Z = statevars[2];\n", " alpha_1 = params[0]\n", " K = params[1]\n", " n = params[2]\n", " gamma_1 = params[3]\n", " alpha_2 = params[4]\n", " gamma_2 = params[5]\n", " alpha_3 = params[6]\n", " gamma_3 = params[7]\n", " \n", " dydt = np.zeros( (3,) )\n", " dydt[0] = alpha_1*K**n/(K**n+Z**n) - gamma_1*X\n", " dydt[1] = alpha_2*X - gamma_2*Y;\n", " dydt[2] = alpha_3*Y - gamma_3*Z;\n", "\n", " return dydt\n", "\n", "def get_period(t,x):\n", " \"\"\" Approximate the period of a 1-D x, given the time-steps t.\n", " Returns a tuple with the period and the standard deviation of the period over time.\n", " if the value of the standard deviation is not smaller than 0.1, then\n", " it means the period estimate is dodgy and you shouldn't use it. Instead,\n", " plot your simulation and figure out why it isn't periodic - maybe it just\n", " hasn't reached the limit cycle yet.\"\"\"\n", " idxs = scipy.signal.find_peaks(x)\n", " idxs = idxs[0]\n", " times = t[idxs]\n", " period = np.mean(np.diff(times));\n", " sdperiod = np.sqrt(np.var(np.diff(times)));\n", " return (period,sdperiod)\n", "\n", "def goodwin_circadian_error( params, debug=False ):\n", " mf = lambda t,y : goodwin_oscillator(t,y, params)\n", "\n", " sol1 = scipy.integrate.solve_ivp( fun=mf, t_span=(0,500), y0=(.1,.1,.1) )\n", " sol2 = scipy.integrate.solve_ivp( fun=mf, t_span=(0,100), y0=sol1.y[:,-1], \n", " t_eval=np.arange(0,100,.01) )\n", " # Compute the period of the first state. It is a scalar.\n", " (period,sdper) = get_period( sol2.t, sol2.y[0,:] )\n", " # compute the peak-to-trough amp for each state\n", " # by computing the min and max over time and subtracting.\n", " # amps will be an ndarray of length 3.\n", " amps = sol2.y.max(axis=1)-sol2.y.min(axis=1)\n", " # an amplitude larger than 0.1 is going to have low cost\n", " # scale the amplitudes and then compute the exponential for\n", " # each amp. Since log(0.001)<0, the curve will be decreasing\n", " # as the amplitude is increasing. \n", " # amp_errvals will be an ndarray of length 3.\n", " rate = np.log(0.001)/0.1;\n", " amp_errvals = np.exp(np.multiply(rate,amps));\n", " # The amplitude cost is the sum of the 3 amplitude error values.\n", " acost = amp_errvals.sum()\n", " # amplitude should not be shrinking or growing\n", " # compute the 3 amplitudes for the first half of the simulation\n", " # and then for the second\n", " half = sol2.y.shape[1]//2\n", " amps1 = sol2.y[:,:half].max(axis=1)-sol2.y[:,:half].min(axis=1)\n", " amps2 = sol2.y[:,half:].max(axis=1)-sol2.y[:,half:].min(axis=1)\n", " if debug:\n", " print( amps1 )\n", " print(amps2)\n", " # sum the amplitudes and take their difference. Then multiply\n", " # the difference by 100 (to amplify it) and square the result.\n", " bcost = (100*(amps1.sum()-amps2.sum()))**2\n", " if debug:\n", " print((period-24)**2, acost, bcost, amps1.sum(), amps2.sum() )\n", "\n", " # The total cost is the squared difference between the period and 24 hours \n", " # plue the two amplitude costs. We weight the acost with 5 because it values\n", " # tend to be small.\n", " return (period-24)**2 + 5*acost + bcost" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Run the GA for the Goodwin Oscillator\n", "\n", "Run the GA once for a given selection operator. Then plot the costs for each generation.\n", "\n", "With 50 parents and 250 kids per generation, this takes Stephanie about 1 minute to run." ] }, { "cell_type": "code", "execution_count": 111, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice.\n", " return _methods._mean(a, axis=axis, dtype=dtype,\n", "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars\n", " ret = ret.dtype.type(ret / rcount)\n", "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3621: RuntimeWarning: Degrees of freedom <= 0 for slice\n", " return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n", "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide\n", " arrmean = um.true_divide(\n", "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars\n", " ret = ret.dtype.type(ret / rcount)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Best of generation 1 has cost 6.20781966039182\n", "Best of generation 2 has cost 0.1531534671463472\n", "Best of generation 3 has cost 0.03522141805395543\n", "Best of generation 4 has cost 0.03522141805395543\n", "Best of generation 5 has cost 0.017413766926975736\n" ] }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# For reference, here is a set of working parameters.\n", "# I am going to construct the lower and upper bounds of parameter\n", "# space so that they capture these values.\n", "# alpha_1 = 1\n", "# K = 0.61\n", "# n = 11\n", "# gamma_1 = 0.15\n", "# alpha_2 = 1\n", "# gamma_2 = 0.15\n", "# alpha_3 = 1\n", "# gamma_3 = 0.25\n", "\n", "param_names = ['alpha_1','K','n','gamma_1','alpha_2','gamma_2','alpha _3','gamma_3']\n", "lb = np.array([0.001,0.001,4,0.001,0.05,0.001,0.05,0.001])\n", "ub = np.array( [2,2,15,1,2,1,2,1])\n", "\n", "# We will use more parents and children than with the toy problem.\n", "settings = GASettings(numParents=50, numChildren=250)\n", "settings.numGenerations = 5;\n", "settings.eliteCount = 1;\n", "settings.selection = 'linearRanking';\n", "settings.linearRankingRatio = 8;\n", "\n", "# Run algorithm\n", "(params, cost, all_params, all_costs) = GA(goodwin_circadian_error, lb, ub, settings);\n", "\n", "# Plot costs\n", "legs = []\n", "for i in range(len(all_costs)):\n", " plt.plot( np.arange(all_costs[i].size), all_costs[i], '*' )\n", " legs.append( \"Generation\" + str(i) )\n", "\n", "plt.legend( legs ); " ] }, { "cell_type": "code", "execution_count": 112, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Re-Plot costs using a log-scale for the y axis.\n", "# This allows us to see the differences a little bit better.\n", "legs = []\n", "for i in range(len(all_costs)):\n", " plt.semilogy( np.arange(all_costs[i].size), all_costs[i], '*' )\n", " legs.append( \"Generation\" + str(i) )\n", "plt.legend( legs ); " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Example analysis of GA with Goodwin Oscillator\n", "\n", "Run the GA with linear ranking selection with a ratio of 8. Run the algorithm 5 times, saving the results in .npz files, because this will take awhile.\n", "\n", "Subsequent cells read in the files and plot the results." ] }, { "cell_type": "code", "execution_count": 113, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice.\n", " return _methods._mean(a, axis=axis, dtype=dtype,\n", "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars\n", " ret = ret.dtype.type(ret / rcount)\n", "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3621: RuntimeWarning: Degrees of freedom <= 0 for slice\n", " return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n", "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide\n", " arrmean = um.true_divide(\n", "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars\n", " ret = ret.dtype.type(ret / rcount)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Best of generation 1 has cost 0.9731257633736008\n", "Best of generation 2 has cost 0.07748979686537318\n", "Best of generation 3 has cost 0.07748979686537318\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ ":15: RuntimeWarning: invalid value encountered in double_scalars\n", " dydt[0] = alpha_1*K**n/(K**n+Z**n) - gamma_1*X\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Best of generation 4 has cost 0.07748979686537318\n", "Best of generation 5 has cost 0.02281470778521827\n", "Best of generation 1 has cost 1.3623368307138033\n", "Best of generation 2 has cost 0.21375072797159198\n", "Best of generation 3 has cost 0.21375072797159198\n", "Best of generation 4 has cost 0.21375072797159198\n", "Best of generation 5 has cost 0.009221448866046818\n", "Best of generation 1 has cost 0.38471531154433974\n", "Best of generation 2 has cost 0.38471531154433974\n", "Best of generation 3 has cost 0.1415160963585752\n", "Best of generation 4 has cost 0.1415160963585752\n", "Best of generation 5 has cost 0.1415160963585752\n", "Best of generation 1 has cost 2.0953413800133185\n", "Best of generation 2 has cost 2.0953413800133185\n", "Best of generation 3 has cost 0.044496756982869305\n", "Best of generation 4 has cost 0.044496756982869305\n", "Best of generation 5 has cost 0.044496756982869305\n", "Best of generation 1 has cost 5.091104104289987\n", "Best of generation 2 has cost 0.31737988359372216\n", "Best of generation 3 has cost 0.11965056667389928\n", "Best of generation 4 has cost 0.11604436241947554\n", "Best of generation 5 has cost 0.045672379745803245\n" ] } ], "source": [ "# For reference, here is a set of working parameters.\n", "# I am going to construct the lower and upper bounds of parameter\n", "# space so that they capture these values.\n", "# alpha_1 = 1\n", "# K = 0.61\n", "# n = 11\n", "# gamma_1 = 0.15\n", "# alpha_2 = 1\n", "# gamma_2 = 0.15\n", "# alpha_3 = 1\n", "# gamma_3 = 0.25\n", "\n", "param_names = ['alpha_1','K','n','gamma_1','alpha_2','gamma_2','alpha _3','gamma_3']\n", "lb = np.array([0.001,0.001,4,0.001,0.05,0.001,0.05,0.001])\n", "ub = np.array( [2,2,15,1,2,1,2,1])\n", "\n", "# We will use more parents and children than with the toy problem.\n", "settings = GASettings(numParents=100, numChildren=500)\n", "settings.numGenerations = 5;\n", "settings.eliteCount = 1;\n", "settings.selection = 'linearRanking';\n", "settings.linearRankingRatio = 8;\n", "\n", "for runNum in range(1,6):\n", " fn = \"Goodwin_linearRanking%d_run%d.npz\" % (settings.linearRankingRatio,runNum)\n", " if os.path.isfile( fn ):\n", " continue\n", "\n", " (params, cost, all_params, all_costs) = GA(goodwin_circadian_error, lb, ub, settings);\n", " np.savez( fn, params=params, cost=cost, all_params=all_params, all_costs=all_costs )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Plot the costs for each generation for all 5 runs of a GA\n", "\n", "For a given tournament size, read in the files with GA results and plot the costs for each generation in a subplot." ] }, { "cell_type": "code", "execution_count": 114, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "lr_Ratio = 8\n", "\n", "plt.figure( figsize=(8,8) )\n", "for runNum in range(1,6):\n", " plt.subplot( 5, 2, (runNum-1)*2+1 )\n", " fn = \"Goodwin_linearRanking%d_run%d.npz\" % (lr_Ratio,runNum)\n", " f = np.load( fn, allow_pickle=True )\n", " all_costs = f['all_costs']\n", " all_params = f['all_params']\n", " legs = []\n", " for i in range(len(all_costs)):\n", " plt.plot( np.arange(all_costs[i].size), all_costs[i], '*' )\n", " legs.append( \"Generation\" + str(i) )\n", " plt.ylabel( \"Cost\")\n", " plt.xlabel( 'Individual')\n", "\n", " if runNum == 1:\n", " plt.legend( legs ); \n", " plt.subplot( 5, 2, (runNum-1)*2+2 )\n", " for i in range(len(all_costs)):\n", " plt.semilogy( np.arange(all_costs[i].size), all_costs[i], '*' )\n", " plt.xlabel( 'Individual')\n", "\n", "\n", "plt.tight_layout()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Plot results from a single GA run\n", "\n", "Load in the run, then plot the costs for each generation in one figure. Then, plot the values of the parameters in a jitter plot. Construct the jitter plot so that we can adjust the a threshold so that we can include parameters from all children or just those from children with costs below the threshold." ] }, { "cell_type": "code", "execution_count": 76, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "runNum = 2\n", "tournamentSize = 5\n", "fn = \"Goodwin_tournament%d_run%d.npz\" %(tournamentSize,runNum)\n", "f = np.load( fn, allow_pickle=True )\n", "all_costs = f['all_costs']\n", "all_params = f['all_params']\n", "params = f['params']\n", "cost = f['cost']\n", "\n", "plt.figure( figsize=(12,4) )\n", "plt.subplot( 1, 2, 1 )\n", "legs = []\n", "for i in range(len(all_costs)):\n", " plt.plot( np.arange(all_costs[i].size), all_costs[i], '*' )\n", " legs.append( \"Generation\" + str(i) )\n", "plt.legend( legs );\n", "plt.ylabel(\"Cost\")\n", "plt.xlabel(\"Individual\")\n", "plt.subplot( 1, 2, 2 )\n", "for i in range(len(all_costs)):\n", " plt.semilogy( np.arange(all_costs[i].size), all_costs[i], '*' )\n", "plt.xlabel(\"Individual\")\n", "\n", "plt.figure(figsize=(12,4));\n", "PN = lb.size\n", "dp = 1/(2*settings.numGenerations)\n", "cost_threshold = np.inf #5\n", "\n", "for g in range(settings.numGenerations+1):\n", " idx = all_costs[g] < cost_threshold\n", " for i in range(lb.size):\n", " plt.plot( i+g*dp + np.random.rand(np.count_nonzero(idx))*dp, all_params[g][idx,i], '.C'+ str(g))\n", "plt.xticks( ticks=np.arange(0,PN)+0.25, labels=param_names);\n", "plt.ylabel( \"Parameter Value\");" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Plot the simulation from the best individual in the GA run we have been studying." ] }, { "cell_type": "code", "execution_count": 77, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "alpha_1 : 0.8205447402924716\n", "K : 0.47903044097377767\n", "n : 14.255274697239507\n", "gamma_1 : 0.18387237605656992\n", "alpha_2 : 0.8696674455635882\n", "gamma_2 : 0.18991037604502864\n", "alpha _3 : 1.0894937827638904\n", "gamma_3 : 0.1930820228413166\n" ] }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "mf = lambda t,y : goodwin_oscillator(t,y, params)\n", "\n", "sol1 = scipy.integrate.solve_ivp( fun=mf, t_span=(0,500), y0=(.1,.1,.1) )\n", "sol2 = scipy.integrate.solve_ivp( fun=mf, t_span=(0,100), y0=sol1.y[:,-1], \n", " t_eval=np.arange(0,100,.01) )\n", "\n", "plt.figure()\n", "plt.plot( sol2.t, sol2.y.T );\n", "plt.xlabel( \"Time (h)\")\n", "plt.ylabel( \"Concentration\")\n", "plt.legend( ['X','Y','Z'])\n", "\n", "for i in range(len(params)):\n", " print(param_names[i], ':', params[i])\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Bonus Code\n", "This is the code I used to generate the figures for lecture.\n", "\n", "First, plot the probability distribution for linear ranking selection when we are selecting from a previous generation with 50 individuals." ] }, { "cell_type": "code", "execution_count": 126, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[0.032 0.0315102 0.03102041 0.03053061 0.03004082 0.02955102\n", " 0.02906122 0.02857143 0.02808163 0.02759184 0.02710204 0.02661224\n", " 0.02612245 0.02563265 0.02514286 0.02465306 0.02416327 0.02367347\n", " 0.02318367 0.02269388 0.02220408 0.02171429 0.02122449 0.02073469\n", " 0.0202449 0.0197551 0.01926531 0.01877551 0.01828571 0.01779592\n", " 0.01730612 0.01681633 0.01632653 0.01583673 0.01534694 0.01485714\n", " 0.01436735 0.01387755 0.01338776 0.01289796 0.01240816 0.01191837\n", " 0.01142857 0.01093878 0.01044898 0.00995918 0.00946939 0.00897959\n", " 0.0084898 0.008 ]\n" ] }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "lam = 50 # number of individuals in generation\n", "mu = 1000 # number of individuals in breeding pool\n", "G = np.zeros( (lam,NP) )\n", "for i in range(lam):\n", " G[i,:] = i\n", "Gcost = np.linspace(0.1,500,lam)\n", "settings = GASettings(numParents=mu, numChildren=lam)\n", "\n", "settings.selection = 'linearRanking'\n", "settings.linearRankingRatio = 4\n", "(P, Pcost) = select_pool(G, Gcost, settings)\n", "plt.figure( figsize=(8,4) )\n", "plt.subplot(2,1,1)\n", "plt.hist(Gcost)\n", "plt.ylabel('Frequency')\n", "plt.yticks([])\n", "\n", "plt.title( \"Distribution of costs of previous generation\")\n", "plt.subplot(2,1,2)\n", "plt.hist( Pcost );\n", "plt.title( \"Breeding Pool Chosen With Linear Ranking Selection (r=4)\")\n", "plt.xlabel( 'Cost')\n", "plt.ylabel('Frequency')\n", "plt.yticks([])\n", "plt.tight_layout()\n", "\n", "r = settings.linearRankingRatio\n", "n = lam\n", "pn = 2/n*(1/(1+r))\n", "p1 = r*pn\n", "\n", "ps = np.zeros((n,))\n", "for i in range(1,n+1):\n", " ps[i-1] = pn + (p1-pn)*(n-i)/(n-1)\n", " \n", "plt.figure(figsize=(12,4));\n", "plt.plot( np.arange(1,n+1), ps, 'oC0')\n", "for i in range(n):\n", " plt.plot( [i+1,i+1],[0, ps[i]],'C0')\n", "yl = plt.ylim()\n", "plt.ylim((0,yl[1]))\n", "plt.title( \"Probabilities\")\n", "plt.xlabel( \"Individual (index)\")\n", " \n", "plt.figure(figsize=(12,4));\n", "cs = ps.cumsum()\n", "plt.plot( np.arange(1,n+1), cs, 'oC0')\n", "for i in range(n):\n", " plt.plot( [i+1,i+1],[0, cs[i]],'C0')\n", "yl = plt.ylim()\n", "plt.ylim((0,yl[1]))\n", "plt.title('Cumulative Probabilities')\n", "plt.xlabel( \"Individual (index)\")\n", "\n", "plt.figure(figsize=(12,4))\n", "mat = np.zeros((100,1001))\n", "idxs = np.array(np.round(1000*cs),np.int)\n", "for i in range(len(idxs)-1):\n", " mat[:,idxs[i]:idxs[i+1]] = i/n\n", "plt.imshow( mat )\n", "print(ps)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Then plot it for just 4 so we can put the numbers in text." ] }, { "cell_type": "code", "execution_count": 76, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[0.33333333 0.27777778 0.22222222 0.16666667]\n", "[ 333 611 833 1000]\n" ] }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "r = 2\n", "n = 4\n", "pn = 2/n*(1/(1+r))\n", "p1 = r*pn\n", "\n", "ps = np.zeros((n,))\n", "for i in range(1,n+1):\n", " ps[i-1] = pn + (p1-pn)*(n-i)/(n-1)\n", " \n", "plt.figure(figsize=(5,4));\n", "plt.subplot(2,1,1)\n", "\n", "for i in range(n):\n", " plt.plot( i+1, ps[i], 'oC'+str(i))\n", " plt.plot( [i+1,i+1],[0, ps[i]],'C'+str(i))\n", "\n", "yl = plt.ylim()\n", "plt.ylim((0,yl[1]))\n", "plt.title( \"Probabilities\")\n", "plt.xlabel( \"Individual (index)\")\n", "plt.xticks(np.arange(1,n+1))\n", " \n", "plt.subplot(2,1,2)\n", "cs = ps.cumsum()\n", "for i in range(n):\n", " plt.plot( i+1, cs[i], 'oC'+str(i))\n", " plt.plot( [i+1,i+1],[0, cs[i]],'C'+str(i))\n", "yl = plt.ylim()\n", "plt.ylim((0,yl[1]))\n", "plt.title('Cumulative Probabilities')\n", "plt.xlabel( \"Individual (index)\")\n", "plt.xticks(np.arange(1,n+1))\n", "plt.tight_layout()\n", "\n", "\n", "plt.figure(figsize=(12,4))\n", "cmap = matplotlib.cm.get_cmap('tab10')\n", "mat = np.zeros((100,1001,4))\n", "idxs = np.array(np.round(1000*cs),np.int)\n", "for i in range(len(idxs)):\n", " if i == 0:\n", " mat[:,0:idxs[i],:] = cmap(i)\n", " else:\n", " mat[:,idxs[i-1]:idxs[i],:] = cmap(i)\n", "plt.imshow( mat )\n", "labs = []\n", "for i in range(n):\n", " if i == 0:\n", " x = idxs[0]/2\n", " else:\n", " x = (idxs[i-1]+idxs[i])/2\n", " plt.text( x, 35, 'Individual %d' % (i+1), horizontalalignment='center', fontsize=12)\n", " plt.text( x, 65, 'p=%0.3f' % ps[i], horizontalalignment='center', fontsize=12)\n", " labs.append('%0.3f'%cs[i])\n", "plt.xticks( idxs, labs )\n", "plt.yticks([])\n", "plt.xlabel( 'Cumulative Probability')\n", "\n", "print(ps)\n", "print(idxs)" ] }, { "cell_type": "code", "execution_count": 96, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1.3333333333333333 0.6666666666666666\n" ] }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "numTrials = 1000\n", "idxs = np.zeros((numTrials,))\n", "for i in range( numTrials ):\n", " num = np.random.random() # number between 0 and 1, from uniform distribution\n", " idxs[i] = np.argmax( num < cs )\n", "\n", "plt.hist(idxs);\n", "\n", "print(ps[0]*n,ps[-1]*n)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.5" } }, "nbformat": 4, "nbformat_minor": 4 }