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July 12, 2016 21:56
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Manatee Mortality.ipynb
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| { | |
| "cells": [ | |
| { | |
| "metadata": { | |
| "collapsed": true, | |
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| "cell_type": "code", | |
| "source": "%matplotlib inline\nimport pymc3 as pm\nimport numpy as np\nimport pandas as pd\nimport theano.tensor as tt\nfrom theano import shared, config\n\nfrom theano.tensor.shared_randomstreams import RandomStreams\nsrng = RandomStreams(seed=20090425)", | |
| "execution_count": 1, | |
| "outputs": [] | |
| }, | |
| { | |
| "metadata": { | |
| "trusted": true, | |
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| }, | |
| "cell_type": "code", | |
| "source": "config.exception_verbosity='high'", | |
| "execution_count": 23, | |
| "outputs": [] | |
| }, | |
| { | |
| "metadata": { | |
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| }, | |
| "cell_type": "code", | |
| "source": "data15 = pd.read_csv('mortalities.15km.dword.1996.2013.csv')\ndata30 = pd.read_csv('mortalities.30km.dword.1996.2013.csv')", | |
| "execution_count": 3, | |
| "outputs": [] | |
| }, | |
| { | |
| "metadata": { | |
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| "cell_type": "code", | |
| "source": "# FL regions\nregions = ['ATL', 'USJ', 'NW', 'SW']\nATL, USJ, NW, SW = range(4)\n\n# Winter habitat quality\nhabitats = ['Low', 'Medium', 'High']\nLOW, MED, HIGH = range(3)\n\n# Causes of death\ncauses = ['watercraft', 'wcs', 'debris', 'cold', 'redtide', 'other']\nlong_causes = causes + ['undetermined']\nWATERCRAFT, WCS, DEBRIS, COLD, REDTIED, OTHER, UNDETERMINED = range(7)\n\n# Age classes\nclasses = ['Calves', 'Subadults', 'Adults']\n\nSTARTYEAR, ENDYEAR = 1996, 2013\n\n# Unusual mortality event red tide years\ntide_years = (1996, 2002, 2003, 2005, 2006, 2012, 2013)\n# Moderately unusual red tide years\ntide_years0 = (2002, 2003, 2005, 2006, 2012)\n# Intense red tide years\ntide_years_intense = (1996, 2013)\n# Atlantic unusual mortality event red tide years\natl_tide_years = (2007, 2008)\n\n# Severe cold years\nsevere_years = {ATL: range(2010, 2011), USJ: range(2010, 2011), NW: (2003, 2010), SW: range(2010, 2011)} \n\n# Cold years\ncold_years = {ATL: (1996, 2001, 2003), USJ: (1996, 2001, 2009, 2011), \n NW: (1996, 1998, 2001, 2011), SW: (1996, 2001)}", | |
| "execution_count": 4, | |
| "outputs": [] | |
| }, | |
| { | |
| "metadata": {}, | |
| "cell_type": "markdown", | |
| "source": "Uninformative priors for unknown proportions" | |
| }, | |
| { | |
| "metadata": { | |
| "collapsed": true, | |
| "trusted": true | |
| }, | |
| "cell_type": "code", | |
| "source": "ncauses = len(causes), len(causes)-1, len(causes), len(causes)\nprior0 = [np.ones(k) for k in ncauses]\n", | |
| "execution_count": 5, | |
| "outputs": [] | |
| }, | |
| { | |
| "metadata": {}, | |
| "cell_type": "markdown", | |
| "source": "From Langtimm et al. 2004 (mortality ratios from USJ)" | |
| }, | |
| { | |
| "metadata": { | |
| "collapsed": true, | |
| "trusted": true | |
| }, | |
| "cell_type": "code", | |
| "source": "calf1_mort_ratio = 0.190/0.031\ncalf2_mort_ratio = 0.085/0.031", | |
| "execution_count": 6, | |
| "outputs": [] | |
| }, | |
| { | |
| "metadata": {}, | |
| "cell_type": "markdown", | |
| "source": "Adult baseline mortalities and se from Langtimm et al. unpublished (2015).\n\nChanged this to a tuple, so that you can just index with the appropriate constant" | |
| }, | |
| { | |
| "metadata": { | |
| "collapsed": false, | |
| "trusted": true | |
| }, | |
| "cell_type": "code", | |
| "source": "adult_mort_base = 0.027417, 0.020739, 0.022355, 0.023073\n\nadult_mort_base[SW]", | |
| "execution_count": 7, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": "0.023073" | |
| }, | |
| "metadata": {}, | |
| "execution_count": 7 | |
| } | |
| ] | |
| }, | |
| { | |
| "metadata": {}, | |
| "cell_type": "markdown", | |
| "source": "Generate survival rates and variances on logit scale" | |
| }, | |
| { | |
| "metadata": { | |
| "collapsed": true, | |
| "trusted": true | |
| }, | |
| "cell_type": "code", | |
| "source": "calf1_mort_base = np.array(adult_mort_base) * calf1_mort_ratio\ncalf2_mort_base = np.array(adult_mort_base) * calf2_mort_ratio\ncalf_mort_base = 1 - np.sqrt((1 - calf1_mort_base)*(1 - calf2_mort_base))\n\nwcs_protect_year = 2001", | |
| "execution_count": 8, | |
| "outputs": [] | |
| }, | |
| { | |
| "metadata": { | |
| "collapsed": false, | |
| "trusted": true | |
| }, | |
| "cell_type": "code", | |
| "source": "calf_mort_base", | |
| "execution_count": 9, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": "array([ 0.12283572, 0.09266703, 0.09995114, 0.1031908 ])" | |
| }, | |
| "metadata": {}, | |
| "execution_count": 9 | |
| } | |
| ] | |
| }, | |
| { | |
| "metadata": { | |
| "collapsed": false, | |
| "trusted": true | |
| }, | |
| "cell_type": "code", | |
| "source": "analysis_subset = (data30[(data30.year >= STARTYEAR) & (data30.year <= ENDYEAR) & (data30.age == 'Calves')]\n .replace({'area': dict(zip(regions, range(4))),\n 'habitat': dict(zip(habitats, range(3)))}))", | |
| "execution_count": 10, | |
| "outputs": [] | |
| }, | |
| { | |
| "metadata": { | |
| "collapsed": false, | |
| "trusted": true | |
| }, | |
| "cell_type": "code", | |
| "source": "analysis_subset.head()", | |
| "execution_count": 11, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/html": "<div>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>year</th>\n <th>age</th>\n <th>area</th>\n <th>habitat</th>\n <th>watercraft</th>\n <th>wcs</th>\n <th>debris</th>\n <th>cold</th>\n <th>redtide</th>\n <th>other</th>\n <th>undetermined</th>\n <th>total</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>1996</td>\n <td>Calves</td>\n <td>0</td>\n <td>0</td>\n <td>4</td>\n <td>0</td>\n <td>0</td>\n <td>3</td>\n <td>0</td>\n <td>5</td>\n <td>3</td>\n <td>15</td>\n </tr>\n <tr>\n <th>1</th>\n <td>1997</td>\n <td>Calves</td>\n <td>0</td>\n <td>0</td>\n <td>6</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>3</td>\n <td>7</td>\n <td>16</td>\n </tr>\n <tr>\n <th>2</th>\n <td>1998</td>\n <td>Calves</td>\n <td>0</td>\n <td>0</td>\n <td>3</td>\n <td>0</td>\n <td>0</td>\n <td>6</td>\n <td>0</td>\n <td>5</td>\n <td>14</td>\n <td>28</td>\n </tr>\n <tr>\n <th>3</th>\n <td>1999</td>\n <td>Calves</td>\n <td>0</td>\n <td>0</td>\n <td>4</td>\n <td>1</td>\n <td>0</td>\n <td>2</td>\n <td>0</td>\n <td>2</td>\n <td>11</td>\n <td>20</td>\n </tr>\n <tr>\n <th>4</th>\n <td>2000</td>\n <td>Calves</td>\n <td>0</td>\n <td>0</td>\n <td>4</td>\n <td>0</td>\n <td>0</td>\n <td>5</td>\n <td>0</td>\n <td>7</td>\n <td>9</td>\n <td>25</td>\n </tr>\n </tbody>\n</table>\n</div>", | |
| "text/plain": " year age area habitat watercraft wcs debris cold redtide other \\\n0 1996 Calves 0 0 4 0 0 3 0 5 \n1 1997 Calves 0 0 6 0 0 0 0 3 \n2 1998 Calves 0 0 3 0 0 6 0 5 \n3 1999 Calves 0 0 4 1 0 2 0 2 \n4 2000 Calves 0 0 4 0 0 5 0 7 \n\n undetermined total \n0 3 15 \n1 7 16 \n2 14 28 \n3 11 20 \n4 9 25 " | |
| }, | |
| "metadata": {}, | |
| "execution_count": 11 | |
| } | |
| ] | |
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| { | |
| "metadata": { | |
| "collapsed": false, | |
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| "cell_type": "code", | |
| "source": "def mortality_model(data, prior=prior0, verbose=0):\n # Jeff: simplifying by getting rid of ATL red tide, I'm not really planning to use that anymore.\n with pm.Model(verbose=verbose) as model:\n \n # Red tide effect factors\n a_mort = pm.Uniform('a_mort', lower=0, upper=1-calf_mort_base[SW])\n a = a_mort / calf_mort_base[SW]\n \n b_mort = pm.Uniform('b_mort', lower=0, upper=1-calf_mort_base[SW])\n b = b_mort / calf_mort_base[SW]\n \n # Winter effect factors for each habitat type\n sev_mort = pm.Uniform('sev_mort', lower=0, upper=1-max(calf_mort_base), \n shape=(3,4))\n \n cold_mort_upper = np.resize(1-max(calf_mort_base), (3,4))\n cold_mort_upper[HIGH] = 0.0001\n cold_mort = pm.Uniform('cold_mort', lower=0, upper=cold_mort_upper, shape=(3,4))\n \n norm_mort_upper = np.resize(1-max(calf_mort_base), (3,4))\n norm_mort_upper[HIGH] = 0.0001\n norm_mort = pm.Uniform('norm_mort', lower=0, upper=norm_mort_upper, shape=(3,4))\n \n sev = sev_mort / np.resize(calf_mort_base, (3,4))\n cold = cold_mort / np.resize(calf_mort_base, (3,4))\n norm = norm_mort / np.resize(calf_mort_base, (3,4))\n \n p = [pm.Dirichlet('p_{}'.format(a), t) for t,a in zip(prior, regions)]\n \n π = [pm.Dirichlet('π_{}'.format(a), np.ones(k)) for k,a in zip(ncauses, regions)]\n \n for (area, year, habitat), data in data.groupby(['area', 'year', 'habitat']):\n \n undetermined = data.undetermined.values\n\n Z = (data[causes].drop('redtide', axis=1).values if area==USJ else data[causes].values).squeeze()\n \n # Only moderately unusual red tide (no cold effects)\n if ((year in tide_years0) and (area==SW) and (habitat==HIGH)):\n \n # theta varies from pi based on red tide UME \n θ = tt.set_subtensor(π[area][4], π[area][4] + a) / (1+a) \n η = p[area]\n\n # Moderately unusual red tide and normal cold\n elif ((year in tide_years0) and (area==SW)):\n \n no = norm[habitat, area]\n\n # theta varies from pi based on winter severity and on red tide UME \n θ = tt.set_subtensor(π[area][4], π[area][4] + a) \n θ = tt.set_subtensor(θ[3], θ[3] + no) / (1 + a + no) \n\n # eta varies from p based on winter severity\n η = tt.set_subtensor(p[area][3], p[area][3] + no) / (1 + no)\n\n\n # Intense unusual red tide and cold year\n elif ((year in tide_years_intense) and (area==SW) and (year in cold_years[area])):\n \n co = cold[habitat, area]\n \n # theta varies from pi based on winter severity and on red tide UME \n θ = tt.set_subtensor(π[area][4], π[area][4] + b) \n θ = tt.set_subtensor(θ[3], θ[3] + co) / (1 + b + co) \n\n # eta varies from p based on winter severity\n η = tt.set_subtensor(p[area][3], p[area][3] + co) / (1 + co)\n\n\n # Intense unusual red tide and severe cold year\n elif ((year in tide_years_intense) and (area==SW) and (year in severe_years[area])):\n\n sv = sev[habitat, area]\n \n # theta varies from pi based on red tide UME \n θ = tt.set_subtensor(π[area][4], π[area][4] + b) \n θ = tt.set_subtensor(θ[3], θ[3] + sv) / (1 + b + sv) \n\n # eta varies from p based on winter severity\n η = tt.set_subtensor(p[area][3], p[area][3] + sv) / (1 + sv)\n\n\n # Intense unusual red tide only\n elif ((year in tide_years_intense) and (area==SW) and (habitat==HIGH)):\n \n # theta varies from pi based on red tide UME \n θ = tt.set_subtensor(π[area][4], π[area][4] + b) / (1+b) \n \n η = p[area]\n\n # Intense unusual red tide and normal cold\n elif (year in tide_years_intense) and (area==SW):\n \n no = norm[habitat, area]\n\n # theta varies from pi based on winter severity and on red tide UME \n θ = tt.set_subtensor(π[area][4], π[area][4] + b) \n θ = tt.set_subtensor(θ[3], θ[3] + no) / (1 + b + no) \n\n # eta varies from p based on winter severity\n η = tt.set_subtensor(p[area][3], p[area][3] + no) / (1 + no)\n \n\n # Severe cold year\n elif (year in severe_years[area]):\n \n \n sv = sev[habitat, area]\n \n # theta varies from pi based on winter severity \n θ = tt.set_subtensor(π[area][3], π[area][3] + sv) / (1 + sv)\n\n # eta varies from p based on winter severity\n η = tt.set_subtensor(p[area][3], p[area][3] + sv) / (1 + sv)\n \n\n # Cold year, low or medium habitat quality\n elif (year in cold_years[area]) and (habitat<HIGH):\n \n \n co = cold[habitat, area]\n \n # theta varies from pi based on winter severity \n θ = tt.set_subtensor(π[area][3], π[area][3] + co) / (1 + co)\n\n # eta varies from p based on winter severity\n η = tt.set_subtensor(p[area][3], p[area][3] + co) / (1 + co)\n \n\n # Normal cold year, low or medium habitat quality\n elif (habitat<HIGH):\n \n \n no = norm[habitat, area]\n\n # theta varies from pi based on winter severity \n θ = tt.set_subtensor(π[area][3], π[area][3] + no) / (1 + no) \n\n # eta varies from p based on winter severity\n η = tt.set_subtensor(p[area][3], p[area][3] + no) / (1 + no)\n \n\n # Normal year, high habitat quality\n else: \n # No adjustment to proportions\n θ = π[area]\n η = p[area]\n \n tt.printing.Print('θ')(θ)\n if undetermined:\n# U = pm.Multinomial('U_{}_{}_{}'.format(area, year, habitat), n=undetermined, p=η, shape=ncauses[area])\n U = srng.multinomial(n=undetermined, pvals=η).squeeze()\n \n deaths = Z + U\n \n else:\n \n deaths = Z\n \n if Z.sum() or undetermined:\n \n pm.Potential('X_like_{}_{}_{}'.format(area, year, habitat), \n pm.Multinomial.dist(n=deaths.sum(), p=θ).logp(deaths))\n \n \n return(model)", | |
| "execution_count": 24, | |
| "outputs": [] | |
| }, | |
| { | |
| "metadata": { | |
| "scrolled": false, | |
| "collapsed": false, | |
| "trusted": true | |
| }, | |
| "cell_type": "code", | |
| "source": "calf_model = mortality_model(data=analysis_subset, prior=prior0, verbose=1)", | |
| "execution_count": 25, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "text": "Applied interval-transform to a_mort and added transformed a_mort_interval to model.\nApplied interval-transform to b_mort and added transformed b_mort_interval to model.\nApplied interval-transform to sev_mort and added transformed sev_mort_interval to model.\nApplied interval-transform to cold_mort and added transformed cold_mort_interval to model.\nApplied interval-transform to norm_mort and added transformed norm_mort_interval to model.\nApplied stickbreaking-transform to p_ATL and added transformed p_ATL_stickbreaking to model.\nApplied stickbreaking-transform to p_USJ and added transformed p_USJ_stickbreaking to model.\nApplied stickbreaking-transform to p_NW and added transformed p_NW_stickbreaking to model.\nApplied stickbreaking-transform to p_SW and added transformed p_SW_stickbreaking to model.\nApplied stickbreaking-transform to π_ATL and added transformed π_ATL_stickbreaking to model.\nApplied stickbreaking-transform to π_USJ and added transformed π_USJ_stickbreaking to model.\nApplied stickbreaking-transform to π_NW and added transformed π_NW_stickbreaking to model.\nApplied stickbreaking-transform to π_SW and added transformed π_SW_stickbreaking to model.\n", | |
| "name": "stdout" | |
| } | |
| ] | |
| }, | |
| { | |
| "metadata": { | |
| "collapsed": false, | |
| "trusted": true | |
| }, | |
| "cell_type": "code", | |
| "source": "with calf_model:\n \n trace = pm.sample(5000, step=pm.Metropolis())\n #means, sds, elbo = pm.variational.advi(n=50000)", | |
| "execution_count": 26, | |
| "outputs": [ | |
| { | |
| "output_type": "error", | |
| "ename": "MissingInputError", | |
| "evalue": "An input of the graph, used to compute Elemwise{exp,no_inplace}(cold_mort_interval_), was not provided and not given a value.Use the Theano flag exception_verbosity='high',for more information on this error.", | |
| "traceback": [ | |
| "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", | |
| "\u001b[0;31mMissingInputError\u001b[0m Traceback (most recent call last)", | |
| "\u001b[0;32m<ipython-input-26-4cf7dc9675fe>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mcalf_model\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mtrace\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msample\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m5000\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstep\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mMetropolis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 4\u001b[0m \u001b[0;31m#means, sds, elbo = pm.variational.advi(n=50000)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", | |
| "\u001b[0;32m/Users/fonnescj/Repositories/pymc3/pymc3/step_methods/arraystep.py\u001b[0m in \u001b[0;36m__new__\u001b[0;34m(cls, *args, **kwargs)\u001b[0m\n\u001b[1;32m 56\u001b[0m \u001b[0;31m# If we don't return the instance we have to manually\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 57\u001b[0m \u001b[0;31m# call __init__\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 58\u001b[0;31m \u001b[0mstep\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mvar\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 59\u001b[0m \u001b[0;31m# Hack for creating the class correctly when unpickling.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 60\u001b[0m \u001b[0mstep\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__newargs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mvar\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", | |
| "\u001b[0;32m/Users/fonnescj/Repositories/pymc3/pymc3/step_methods/metropolis.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, vars, S, proposal_dist, scaling, tune, tune_interval, model, **kwargs)\u001b[0m\n\u001b[1;32m 99\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 100\u001b[0m \u001b[0mshared\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmake_shared_replacements\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvars\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 101\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdelta_logp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdelta_logp\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlogpt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvars\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mshared\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 102\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mMetropolis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvars\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mshared\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 103\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", | |
| "\u001b[0;32m/Users/fonnescj/Repositories/pymc3/pymc3/step_methods/metropolis.py\u001b[0m in \u001b[0;36mdelta_logp\u001b[0;34m(logp, vars, shared)\u001b[0m\n\u001b[1;32m 294\u001b[0m \u001b[0mlogp1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mCallableTensor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlogp0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minarray1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 295\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 296\u001b[0;31m \u001b[0mf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtheano\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfunction\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0minarray1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minarray0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlogp1\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mlogp0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 297\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrust_input\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 298\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", | |
| "\u001b[0;32m/Users/fonnescj/anaconda3/lib/python3.5/site-packages/theano/compile/function.py\u001b[0m in \u001b[0;36mfunction\u001b[0;34m(inputs, outputs, mode, updates, givens, no_default_updates, accept_inplace, name, rebuild_strict, allow_input_downcast, profile, on_unused_input)\u001b[0m\n\u001b[1;32m 320\u001b[0m \u001b[0mon_unused_input\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mon_unused_input\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 321\u001b[0m \u001b[0mprofile\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mprofile\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 322\u001b[0;31m output_keys=output_keys)\n\u001b[0m\u001b[1;32m 323\u001b[0m \u001b[0;31m# We need to add the flag check_aliased inputs if we have any mutable or\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 324\u001b[0m \u001b[0;31m# borrowed used defined inputs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", | |
| "\u001b[0;32m/Users/fonnescj/anaconda3/lib/python3.5/site-packages/theano/compile/pfunc.py\u001b[0m in \u001b[0;36mpfunc\u001b[0;34m(params, outputs, mode, updates, givens, no_default_updates, accept_inplace, name, rebuild_strict, allow_input_downcast, profile, on_unused_input, output_keys)\u001b[0m\n\u001b[1;32m 478\u001b[0m \u001b[0maccept_inplace\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maccept_inplace\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 479\u001b[0m \u001b[0mprofile\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mprofile\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mon_unused_input\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mon_unused_input\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 480\u001b[0;31m output_keys=output_keys)\n\u001b[0m\u001b[1;32m 481\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 482\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", | |
| "\u001b[0;32m/Users/fonnescj/anaconda3/lib/python3.5/site-packages/theano/compile/function_module.py\u001b[0m in \u001b[0;36morig_function\u001b[0;34m(inputs, outputs, mode, accept_inplace, name, profile, on_unused_input, output_keys)\u001b[0m\n\u001b[1;32m 1821\u001b[0m \u001b[0mprofile\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mprofile\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1822\u001b[0m \u001b[0mon_unused_input\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mon_unused_input\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1823\u001b[0;31m \u001b[0moutput_keys\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0moutput_keys\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcreate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1824\u001b[0m defaults)\n\u001b[1;32m 1825\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", | |
| "\u001b[0;32m/Users/fonnescj/anaconda3/lib/python3.5/site-packages/theano/compile/function_module.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, inputs, outputs, mode, accept_inplace, function_builder, profile, on_unused_input, fgraph, output_keys)\u001b[0m\n\u001b[1;32m 1473\u001b[0m \u001b[0;31m# OUTPUT VARIABLES)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1474\u001b[0m fgraph, additional_outputs = std_fgraph(inputs, outputs,\n\u001b[0;32m-> 1475\u001b[0;31m accept_inplace)\n\u001b[0m\u001b[1;32m 1476\u001b[0m \u001b[0mfgraph\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprofile\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mprofile\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1477\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", | |
| "\u001b[0;32m/Users/fonnescj/anaconda3/lib/python3.5/site-packages/theano/compile/function_module.py\u001b[0m in \u001b[0;36mstd_fgraph\u001b[0;34m(input_specs, output_specs, accept_inplace)\u001b[0m\n\u001b[1;32m 175\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 176\u001b[0m fgraph = gof.fg.FunctionGraph(orig_inputs, orig_outputs,\n\u001b[0;32m--> 177\u001b[0;31m update_mapping=update_mapping)\n\u001b[0m\u001b[1;32m 178\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 179\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mnode\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mfgraph\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply_nodes\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", | |
| "\u001b[0;32m/Users/fonnescj/anaconda3/lib/python3.5/site-packages/theano/gof/fg.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, inputs, outputs, features, clone, update_mapping)\u001b[0m\n\u001b[1;32m 180\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 181\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0moutput\u001b[0m \u001b[0;32min\u001b[0m \u001b[0moutputs\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 182\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__import_r__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreason\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"init\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 183\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moutput\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 184\u001b[0m \u001b[0moutput\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclients\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'output'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", | |
| "\u001b[0;32m/Users/fonnescj/anaconda3/lib/python3.5/site-packages/theano/gof/fg.py\u001b[0m in \u001b[0;36m__import_r__\u001b[0;34m(self, variable, reason)\u001b[0m\n\u001b[1;32m 369\u001b[0m \u001b[0;31m# Imports the owners of the variables\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 370\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mvariable\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mowner\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mvariable\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mowner\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply_nodes\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 371\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__import__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvariable\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mowner\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreason\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mreason\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 372\u001b[0m if (variable.owner is None and\n\u001b[1;32m 373\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvariable\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgraph\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mConstant\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", | |
| "\u001b[0;32m/Users/fonnescj/anaconda3/lib/python3.5/site-packages/theano/gof/fg.py\u001b[0m in \u001b[0;36m__import__\u001b[0;34m(self, apply_node, check, reason)\u001b[0m\n\u001b[1;32m 411\u001b[0m \u001b[0;34m\"for more information on this error.\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 412\u001b[0m % str(node)),\n\u001b[0;32m--> 413\u001b[0;31m variable=r)\n\u001b[0m\u001b[1;32m 414\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 415\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mnode\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mnew_nodes\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", | |
| "\u001b[0;31mMissingInputError\u001b[0m: An input of the graph, used to compute Elemwise{exp,no_inplace}(cold_mort_interval_), was not provided and not given a value.Use the Theano flag exception_verbosity='high',for more information on this error." | |
| ] | |
| } | |
| ] | |
| } | |
| ], | |
| "metadata": { | |
| "kernelspec": { | |
| "name": "python3", | |
| "display_name": "Python 3", | |
| "language": "python" | |
| }, | |
| "language_info": { | |
| "file_extension": ".py", | |
| "codemirror_mode": { | |
| "name": "ipython", | |
| "version": 3 | |
| }, | |
| "pygments_lexer": "ipython3", | |
| "name": "python", | |
| "mimetype": "text/x-python", | |
| "nbconvert_exporter": "python", | |
| "version": "3.5.1" | |
| }, | |
| "gist": { | |
| "id": "", | |
| "data": { | |
| "description": "Manatee Mortality.ipynb", | |
| "public": true | |
| } | |
| } | |
| }, | |
| "nbformat": 4, | |
| "nbformat_minor": 1 | |
| } |
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