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TaylorOshan/FBGWR_validation.ipynb

Created October 21, 2016 15:32
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{
"cells": [
{
"cell_type": "code",
"execution_count": 125,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import sys\n",
"sys.path.append('/Users/toshan/Dropbox/GWR/PyGWRJing/PyGWR/')\n",
"from M_FBGWR_May2016 import FBGWR as FBGWR_Wei\n",
"sys.path.append('/Users/toshan/Dropbox/GWR/PyGWRJing/PyGWR/')\n",
"sys.path.append('/Users/toshan/dev/pysal/pysal/contrib/gwr/')\n",
"from pysal.contrib.gwr.gwr import FBGWR\n",
"from pysal.contrib.gwr.sel_bw import Sel_BW\n",
"import pysal as ps\n",
"import numpy as np\n",
"import geopandas as gp\n",
"import pandas as pd"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"shp = gp.read_file('/Users/toshan/dev/pysal/pysal/examples/georgia/G_utm.shp')"
]
},
{
"cell_type": "code",
"execution_count": 155,
"metadata": {
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"source": [
"# Prep data into design matrix and coordinates\n",
"\n",
"#Dependent variable\n",
"y = shp.PctBach.reshape((-1,1))\n",
"\n",
"#Design matrix - covariates - intercept added automatically\n",
"pov = shp.PctPov.reshape((-1,1))\n",
"rural = shp.PctRural.reshape((-1,1))\n",
"blk = shp.PctBlack.reshape((-1,1))\n",
"X = np.hstack([pov, rural, blk])\n",
"\n",
"#Coordinates for calibration points\n",
"u = shp.X\n",
"v = shp.Y\n",
"coords = zip(u,v)\n",
"\n",
"coords_dict = {}\n",
"for i, x in enumerate(coords):\n",
" coords_dict[i] = x"
]
},
{
"cell_type": "code",
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{
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"64.0\n",
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" [ 66. 171. 47.]]\n",
"CPU times: user 33.1 s, sys: 139 ms, total: 33.2 s\n",
"Wall time: 33 s\n"
]
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"source": [
"%%time \n",
"bw = Sel_BW(coords, y, X, fb=True, constant=False)\n",
"bws = bw.search(tol_fb=1e-03, rss_score=True)\n",
"XB = bw.XB\n",
"err = bw.err \n",
"results = FBGWR(coords, y, X, bws, XB, err, constant=False).fit()"
]
},
{
"cell_type": "code",
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"metadata": {
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{
"name": "stdout",
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"0.104281828852\n",
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"CPU times: user 55.6 s, sys: 243 ms, total: 55.9 s\n",
"Wall time: 55.7 s\n"
]
}
],
"source": [
"%%time \n",
"results_Wei = FBGWR_Wei(y, X, coords_dict, tolFB=1e-03, socType=1)"
]
},
{
"cell_type": "code",
"execution_count": 109,
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{
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"execution_count": 109,
"metadata": {},
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"source": [
"results.params[0]"
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{
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{
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{
"data": {
"text/plain": [
"True"
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{
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{
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{
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"execution_count": 113,
"metadata": {},
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"source": [
"results_Wei.band_all"
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{
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{
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"source": [
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{
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{
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"source": [
"results_Wei.y_pred[0:5]"
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{
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{
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"bw.bw[3]"
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{
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{
"data": {
"text/plain": [
"array([ 0.10428183, 0.03426215, 0.01302839, 0.00957037, 0.00674095,\n",
" 0.00332274, 0.01112391, 0.00096075])"
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"execution_count": 118,
"metadata": {},
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"source": [
"results_Wei.SOC_all"
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{
"cell_type": "code",
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{
"data": {
"text/plain": [
"True"
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"execution_count": 122,
"metadata": {},
"output_type": "execute_result"
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"source": [
"np.allclose(bw.bw[3], results_Wei.SOC_all)"
]
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{
"cell_type": "code",
"execution_count": 126,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"sim = pd.read_csv('/Users/toshan/Dropbox/Articles/MC_Sims/data/variables/0_0.csv')"
]
},
{
"cell_type": "code",
"execution_count": 158,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#Dependent variable\n",
"y = sim.Y.reshape((-1,1))\n",
"\n",
"#Design matrix - covariates - intercept added automatically\n",
"X1 = sim.X1.reshape((-1,1))\n",
"X2 = sim.X2.reshape((-1,1))\n",
"X = np.hstack([X1, X2])\n",
"\n",
"#Coordinates for calibration points\n",
"u = sim.Xcoord\n",
"v = sim.Ycoord\n",
"coords = zip(u,v)\n",
"\n",
"coords_dict = {}\n",
"for i, x in enumerate(coords):\n",
" coords_dict[i] = x"
]
},
{
"cell_type": "code",
"execution_count": 137,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"146.0\n",
"0.000692626583633\n",
"0.0131164032183\n",
"0.00310245920562\n",
"0.000257446065538\n",
"0.000488325613785\n",
"4.20055728058e-05\n",
"2.75235389631e-06\n",
"[[ 171. 428. 102.]\n",
" [ 296. 442. 102.]\n",
" [ 359. 442. 102.]\n",
" [ 359. 442. 102.]\n",
" [ 375. 442. 102.]\n",
" [ 375. 442. 102.]\n",
" [ 375. 442. 102.]]\n",
"CPU times: user 1h 59min 25s, sys: 4min 30s, total: 2h 3min 55s\n",
"Wall time: 59min 1s\n"
]
}
],
"source": [
"%%time \n",
"bw = Sel_BW(coords, y, X, fb=True, constant=True)\n",
"bws = bw.search(tol_fb=1e-05, rss_score=True)\n",
"XB = bw.XB\n",
"err = bw.err \n",
"results = FBGWR(coords, y, X, bws, XB, err, constant=True).fit()"
]
},
{
"cell_type": "code",
"execution_count": 159,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.000692631587086\n",
"0.0131164052867\n",
"0.00310245935604\n",
"0.000257446162065\n",
"0.00048832561998\n",
"4.20055892427e-05\n",
"2.75235641788e-06\n",
"CPU times: user 9h 34min 26s, sys: 8min 37s, total: 9h 43min 4s\n",
"Wall time: 5h 22min 17s\n"
]
}
],
"source": [
"%%time \n",
"X = np.concatenate([np.ones((2500)).reshape((-1,1)), X], axis=1)\n",
"results_Wei = FBGWR_Wei(y, X, coords_dict, tolFB=1e-05, socType=1, )"
]
},
{
"cell_type": "code",
"execution_count": 160,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([ 3.31194789, 0.19643385, 0.60366654])"
]
},
"execution_count": 160,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"results.params[0]"
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},
{
"cell_type": "code",
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"metadata": {
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{
"data": {
"text/plain": [
"array([ 3.31194747, 0.19643384, 0.60366654])"
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"execution_count": 161,
"metadata": {},
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],
"source": [
"results_Wei.Betas[0]"
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{
"cell_type": "code",
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{
"data": {
"text/plain": [
"True"
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},
"execution_count": 162,
"metadata": {},
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],
"source": [
"np.allclose(results.params, results_Wei.Betas)"
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{
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{
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"execution_count": 164,
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{
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