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@fonnesbeck
Created February 12, 2014 23:03
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{
"metadata": {
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"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"%matplotlib inline\n",
"import pymc as pm\n",
"import pandas as pd\n",
"import numpy as np\n",
"import time, datetime\n",
"from matplotlib import dates\n",
"import matplotlib.pyplot as plt\n",
"\n",
"pd.set_option(\"display.max_columns\", 25) \n",
"pd.set_option(\"display.max_colwidth\", 100)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 21
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"dataset = pd.read_csv(\"data/processed_data_20140206.csv\", index_col=0, parse_dates=[-2,-1])\n",
"dataset.index.is_unique"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 2,
"text": [
"True"
]
}
],
"prompt_number": 2
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"dataset.st_time = dataset.st_time.astype(datetime.datetime)\n",
"dataset.end_time = dataset.end_time.astype(datetime.datetime)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 3
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"dataset.end_time.describe()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 4,
"text": [
"count 86293\n",
"unique 86188\n",
"first 2008-12-09 02:00:19\n",
"last 2013-07-31 18:25:13\n",
"top 2012-12-09 01:43:13\n",
"freq 3\n",
"Name: end_time, dtype: object"
]
}
],
"prompt_number": 4
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"date = datetime.date\n",
"sma_dates = {'NC_GA': [(date(y,11,1), date(y+1,4,30)) for y in range(2008,2013)],\n",
" 'NOR': [(date(y,11,1), date(y+1,4,30)) for y in range(2008,2013)],\n",
" 'SE': [(date(y,11,15), date(y+1,4,15)) for y in range(2008,2013)],\n",
" 'MOR': [(date(y,11,1), date(y+1,4,30)) for y in range(2008,2013)],\n",
" 'PHI': [(date(y,11,1), date(y+1,4,30)) for y in range(2008,2013)],\n",
" 'NY': [(date(y,11,1), date(y+1,4,30)) for y in range(2008,2013)],\n",
" 'CCB': [(date(y,1,1), date(y,5,15)) for y in range(2009,2013)],\n",
" 'ORP': [(date(y,3,1), date(y,4,15)) for y in range(2009,2013)],\n",
" 'BI': [(date(y,11,1), date(y+1,4,30)) for y in range(2008,2013)],\n",
" 'GSC': [(date(y,4,1), date(y,7,31)) for y in range(2009,2013)]\n",
" }"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 5
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def find_season(rec):\n",
" sma = rec['sma']\n",
" stime = rec['st_time']\n",
" d = stime.date()\n",
" seasons = sma_dates[sma]\n",
" for i,daterange in enumerate(seasons):\n",
" if daterange[0] <= d < daterange[1]:\n",
" return i\n",
" return np.NaN"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 6
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"dataset['season'] = dataset.apply(find_season, axis=1)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 7
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Remove non-SMA observations"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"dataset = dataset[dataset.season.notnull()]"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 8
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Filter vessel types of interest"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"valid_types = [\n",
" 'Cargo',\n",
" 'Tanker',\n",
" 'Passenger']"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 9
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"dataset = dataset[dataset.type.isin(valid_types)]"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 10
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"dataset.season = dataset.season.astype(int)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 11
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Seasonal difference model\n",
"\n",
"A straightforward way to estimate the change in PDGT10 over time is to estimate the seasonal differences in mean PDGT10 over time.\n",
"\n",
"PDGT10 is a metric that takes values between zero and one, representing the percent of total transit distance traveled at greater than 10 knots. Hence, a natural model to describe the distribution of these values in a given season is the beta distribution.\n",
"\n",
"![beta distribution](http://upload.wikimedia.org/wikipedia/commons/thumb/f/f3/Beta_distribution_pdf.svg/500px-Beta_distribution_pdf.svg.png)\n",
"\n",
"The beta is typically modeled as a function of scale ($\\alpha$) and shape ($\\beta$) parameters:\n",
"\n",
"$$f(x \\mid \\alpha, \\beta) = \\frac{\\Gamma(\\alpha + \\beta)}{\\Gamma(\\alpha) \\Gamma(\\beta)} x^{\\alpha - 1} (1 - x)^{\\beta - 1}$$\n",
"\n",
"However, we are interested in modeling the mean of this distribution, which can be reparameterized in terms of a mean $\\mu$ and parameter $\\nu$, which we can interpret informally as a \"sample size\". Here we will use the distance of each segment as this sample size parameter, so that segments are weighted according to their length. This reparamterization is:\n",
"\n",
"$$\\alpha = \\mu \\nu$$\n",
"$$\\beta = (1-\\mu)\\nu$$\n",
"\n",
"We are interested in modeling means that are a function of:\n",
"\n",
"1. Vessel type\n",
"2. SMA\n",
"3. Season\n",
"\n",
"In particular, we are interested to see if the mean decreases from season to season, perhaps in response to management actions.\n",
"\n",
"Thus, we model the PDGT10 mean as:\n",
"\n",
"$$\\mu_{ijk} = \\theta_i + \\psi_j + \\phi_{ik}$$\n",
"\n",
"where $\\theta_i$ is the mean for vessel type $i$, $\\psi_j$ is a random effect corresponging to SMA $j$ and $\\phi_{ijk}$ is the effect of season $k$ on vessel type $i$. The first season in any SMA (either 2008 or 2009, depending on the location) is treated as the baseline; hence $\\theta$ can be treated as the mean in the first season, and $\\phi$ the effect of the current season, relative to the first.\n",
"\n",
"The random effect $\\psi_j$ where $j=1\\ldots,S$ is modeled as:\n",
"\n",
"$$\\psi_j \\sim N(0, \\tau_{\\psi})$$\n",
"\n",
"To account for individual variation not attributable to vessel type, season or SMA, we also model a random effect, which draws a $\\theta$ value from a normal distribution for each unique vessel (MMSI)."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from pymc import Normal, Lambda, Gamma, invlogit, Beta, HalfCauchy, Lognormal\n",
"\n",
"def generate_model(vessel, dataset=dataset, gof=False):\n",
" \n",
" # Filter vessel type\n",
" vessel_dataset = dataset[dataset.type==vessel]\n",
" \n",
" # Obtain array of unique seasons, sma and mmsi\n",
" seasons = vessel_dataset.season.unique()\n",
" mmsi = vessel_dataset.mmsi.unique()\n",
" sma = vessel_dataset.sma.unique()\n",
" \n",
" # Generate indicators for MMSI and SMA\n",
" mmsi_ind = [np.where(mmsi == s)[0][0] for s in vessel_dataset.mmsi]\n",
" sma_ind = [np.where(sma == s)[0][0] for s in vessel_dataset.sma]\n",
" \n",
" if len(seasons)<3:\n",
" return None\n",
"\n",
" # Scale PDGT10 to [0,1]\n",
" y = (vessel_dataset.pdgt10/100.).replace({0: 1e-6, 1: 1-1e-6}).values\n",
" n = vessel_dataset.seg_length.values\n",
"# mu_n = Normal('mu_n', 0, 0.001, value=0)\n",
"# sigma_n = HalfCauchy('sigma_n', 0, 25, value=1)\n",
"# tau_n = sigma_n**2\n",
"# nu = Lognormal('nu', mu_n, tau_n, value=[10]*len(y))\n",
" nu_scale = HalfCauchy('nu_scale', 0, 25)\n",
" nu = Lambda('nu', lambda s=nu_scale: n*(s**-2))\n",
" \n",
" # Vessel mean random effect\n",
" sigma_t = HalfCauchy('sigma_t', 0, 25)\n",
" tau_t = sigma_t**-2\n",
" theta = Normal('theta', 0, tau_t, value=np.zeros(len(mmsi)))\n",
" \n",
" # SMA random effect\n",
" sigma_p = HalfCauchy('sigma_p', 0, 25)\n",
" tau_p = sigma_p**-2\n",
" psi = Normal('psi', 0, tau_p, value=np.zeros(len(sma)))\n",
" \n",
" # Season covariates\n",
" phi = Normal('phi', 0, 0.001, value=[0]*(len(seasons)-1))\n",
" season_effect = Lambda('season_effect', lambda p=phi: \n",
" [(0, p[s-1])[s>0] for s in vessel_dataset.season.values])\n",
"\n",
" # Season mean\n",
" mu = Lambda('mu', lambda t=theta, p=psi, s=season_effect: invlogit(t[mmsi_ind] + p[sma_ind] + s))\n",
" \n",
" # Convert to beta parameters\n",
" alpha = Lambda('alpha', lambda mu=mu, nu=nu: mu * nu)\n",
" beta = Lambda('beta', lambda mu=mu, nu=nu: (1 - mu) * nu)\n",
" \n",
" # Likelihood\n",
" pdgt10 = Beta('pdgt10', alpha, beta, value=y, observed=True)\n",
" if gof:\n",
" pdgt10_sim = Beta('pdgt10_sim', alpha, beta, value=y) \n",
" \n",
" return locals()"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 12
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from pymc import MCMC, AdaptiveMetropolis\n",
"\n",
"M_cargo = MCMC(generate_model('Cargo', gof=False))\n",
"# M_cargo.use_step_method(AdaptiveMetropolis, M_cargo.theta)\n",
"# M_cargo.use_step_method(AdaptiveMetropolis, M_cargo.psi)\n",
"M_cargo.sample(20000, 10000)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------100%-----------------] 20001 of 20000 complete in 4253.1 sec"
]
}
],
"prompt_number": 13
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"Matplot.summary_plot([M_cargo.psi], rhat=False)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
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6x7wDeBfwCHATcJK731yzLMJCd5+TyRsQEZGWyLrGc1PNWjwjwJENyn/V3f/W3ZcC3wFO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\nSUVORK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x10b52d390>"
]
}
],
"prompt_number": 26
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"plt.figure(figsize=(10,12))\n",
"Matplot.plot(M_cargo.phi, common_scale=False)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Plotting phi_0\n",
"Plotting"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" phi_1\n",
"Plotting"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" phi_2\n",
"Plotting"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" phi_3\n"
]
},
{
"metadata": {},
"output_type": "display_data",
"text": [
"<matplotlib.figure.Figure at 0x109a02a10>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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qFNHA9mEMbK/NFLRwaY/WbJhX9bnSZLbLEQYwrFM4e88UcVG3SI+9I7bUJDS3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\nRK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x109a02b90>"
]
}
],
"prompt_number": 22
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from pymc import Matplot\n",
"\n",
"Matplot.summary_plot([M_cargo.phi], rhat=False,\n",
" custom_labels=['Active period 2', 'Active period 3', 'Active period 4', 'Active period 4'], \n",
" #xlab='Difference vs. Active period 1',\n",
" main='Cargo',\n",
" x_range=(-1.5, 0.4))"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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"text": [
"<matplotlib.figure.Figure at 0x1099e9950>"
]
}
],
"prompt_number": 16
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"M_cargo.phi.summary()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"phi:\n",
" \n",
"\tMean SD MC Error 95% HPD interval\n",
"\t------------------------------------------------------------------\n",
"\t-0.024 0.019 0.001 [-0.064 0.01 ]\n",
"\t-0.702 0.02 0.001 [-0.74 -0.664]\n",
"\t-1.204 0.019 0.001 [-1.241 -1.168]\n",
"\t-1.068 0.019 0.001 [-1.106 -1.03 ]\n",
"\t\n",
"\t\n",
"\tPosterior quantiles:\n",
"\t\n",
"\t2.5 25 50 75 97.5\n",
"\t |---------------|===============|===============|---------------|\n",
"\t-0.063 -0.037 -0.024 -0.012 0.011\n",
"\t-0.74 -0.715 -0.702 -0.688 -0.664\n",
"\t-1.243 -1.216 -1.204 -1.191 -1.168\n",
"\t-1.108 -1.079 -1.068 -1.056 -1.031\n",
"\t\n"
]
}
],
"prompt_number": 15
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"M_tanker = MCMC(generate_model('Tanker', gof=False))\n",
"M_tanker.use_step_method(AdaptiveMetropolis, M_tanker.theta)\n",
"M_tanker.use_step_method(AdaptiveMetropolis, M_tanker.psi)\n",
"M_tanker.sample(20000, 10000)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------100%-----------------] 20000 of 20000 complete in 1025.1 sec"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"/Users/fonnescj/Code/pymc2/pymc/StepMethods.py:1263: UserWarning: \n",
"Covariance was not positive definite and proposal_sd cannot be computed by \n",
"Cholesky decomposition. The next jumps will be based on the last \n",
"valid covariance matrix. This situation may have arisen because no \n",
"jumps were accepted during the last `interval`. One solution is to \n",
"increase the interval, or specify an initial covariance matrix with \n",
"a smaller variance. For this simulation, each time a similar error \n",
"occurs, proposal_sd will be reduced by a factor .9 to reduce the \n",
"jumps and increase the likelihood of accepted jumps.\n",
" warnings.warn(adjustmentwarning)\n"
]
}
],
"prompt_number": 32
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"Matplot.summary_plot([M_tanker.psi], rhat=False)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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6++Iq+5wEnA8sBl7j7hdXbHsVcFb68iPphGyY2ZeBU4C3VJYXEZHW1Ywmte0V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\nAABJRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x10b2f6150>"
]
}
],
"prompt_number": 33
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"Matplot.summary_plot([M_tanker.phi], rhat=False, \n",
" custom_labels=['Active period 2', 'Active period 3', 'Active period 4'], \n",
" #xlab='Difference vs. Active period 1',\n",
" main='Tanker',\n",
" x_range=(-1.5, 0.4))"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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"text": [
"<matplotlib.figure.Figure at 0x10b2e6890>"
]
}
],
"prompt_number": 34
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"M_tanker.phi.summary()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"phi:\n",
" \n",
"\tMean SD MC Error 95% HPD interval\n",
"\t------------------------------------------------------------------\n",
"\t0.179 0.034 0.002 [ 0.115 0.247]\n",
"\t-0.245 0.035 0.002 [-0.314 -0.178]\n",
"\t-0.478 0.034 0.002 [-0.543 -0.414]\n",
"\t-0.623 0.036 0.002 [-0.69 -0.555]\n",
"\t\n",
"\t\n",
"\tPosterior quantiles:\n",
"\t\n",
"\t2.5 25 50 75 97.5\n",
"\t |---------------|===============|===============|---------------|\n",
"\t0.113 0.156 0.179 0.202 0.246\n",
"\t-0.313 -0.27 -0.246 -0.221 -0.175\n",
"\t-0.539 -0.503 -0.478 -0.454 -0.409\n",
"\t-0.687 -0.648 -0.625 -0.597 -0.549\n",
"\t\n"
]
}
],
"prompt_number": 37
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"M_passenger = MCMC(generate_model('Passenger', gof=True))\n",
"#M_passenger.use_step_method(AdaptiveMetropolis, M_passenger.theta)\n",
"#M_passenger.use_step_method(AdaptiveMetropolis, M_passenger.psi)\n",
"M_passenger.sample(20000, 10000)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------100%-----------------] 20000 of 20000 complete in 851.3 sec"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"/Users/fonnescj/Code/pymc2/pymc/StepMethods.py:778: UserWarning: DrawFromPrior jumped to forbidden value\n",
" warnings.warn('DrawFromPrior jumped to forbidden value')\n"
]
}
],
"prompt_number": 46
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"Matplot.plot(M_passenger.phi, common_scale=False)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Plotting phi_0\n",
"Plotting"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" phi_1\n",
"Plotting"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" phi_2\n",
"Plotting"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" phi_3\n"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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e1A8/F5f0lv7NuKV/M4fbgvx8KNc7VpIC/HxYdEsvQvytFdbKgtUvVkIDfBnQ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\noigh4v8BAdIk0HQwGw8AAAAASUVORK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x10b3676d0>"
]
}
],
"prompt_number": 39
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"Matplot.summary_plot([M_passenger.phi], rhat=False,\n",
" custom_labels=['Active period 2', 'Active period 3', 'Active period 4', 'Active period 5'], \n",
" xlab='Difference vs. Active period 1',\n",
" main='Passenger',\n",
" x_range=(-1.5, 0.4))"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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rgHMsSrd/N7M7iF3xVeo/cwe040vAxkWPNyZ+elzltNaWZrbYzDZw91fMrB/w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\n",
"text": [
"<matplotlib.figure.Figure at 0x10ab20c90>"
]
}
],
"prompt_number": 41
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"M_passenger.phi.summary()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"phi:\n",
" \n",
"\tMean SD MC Error 95% HPD interval\n",
"\t------------------------------------------------------------------\n",
"\t0.118 0.102 0.008 [-0.071 0.322]\n",
"\t0.246 0.088 0.006 [ 0.077 0.413]\n",
"\t-0.556 0.089 0.007 [-0.738 -0.39 ]\n",
"\t-0.475 0.087 0.007 [-0.646 -0.306]\n",
"\t\n",
"\t\n",
"\tPosterior quantiles:\n",
"\t\n",
"\t2.5 25 50 75 97.5\n",
"\t |---------------|===============|===============|---------------|\n",
"\t-0.069 0.049 0.115 0.184 0.326\n",
"\t0.079 0.187 0.247 0.305 0.417\n",
"\t-0.733 -0.616 -0.551 -0.497 -0.378\n",
"\t-0.644 -0.532 -0.475 -0.42 -0.3\n",
"\t\n"
]
}
],
"prompt_number": 42
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"Matplot.summary_plot([M_passenger.psi], rhat=False)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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wzGwRsBvY7e4rzKwXWA3sc/dldY45GbgYWAa82t0vr9n3SuDc9OUH0gXZMLPP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\ngg==\n",
"text": [
"<matplotlib.figure.Figure at 0x10a3ea1d0>"
]
}
],
"prompt_number": 44
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def gof_calc(simdata, trueval, alpha=0.05):\n",
" # Checks to see if the true value is within the HPD of the simulated data\n",
" \n",
" try:\n",
" if np.ndim(simdata) == 1:\n",
" simdata = simdata.trace()\n",
" except ValueError:\n",
" pass\n",
"\n",
" if np.ndim(trueval) == 1 and np.ndim(simdata == 2):\n",
" return [gof_calc(simdata[:,i], trueval[i]) for i in range(len(trueval))]\n",
"\n",
" # Generate histogram\n",
" lo, hi = pm.utils.hpd(simdata, 0.05)\n",
" \n",
" return lo <= trueval <= hi"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 45
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"gof = [gof_calc(x,y) for x,y in zip(M_passenger.pdgt10_sim.trace(), M_passenger.y)]"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 49
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"float(sum(gof))/len(gof)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 50,
"text": [
"0.9242603550295858"
]
}
],
"prompt_number": 50
}
],
"metadata": {}
}
]
}
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