def stats(modelgrid, inventory, dx=100., Nsamp=None, method='nearest', extent='inventory', bins=None, runtests=True, showplots=True, saveplots=False, filepath=None):
"""
Run through suite of tests for models that output probability or index that varies between 0 and 1
:param modelgrid: Grid2D object of model results
:param inventory: full file path to shapefile of inventory, must be in geographic coordinates, WGS84
:type inventory: string
:param dx: Approximate sample spacing in meters, overwritten if Nsamp is defined
:type dx: float
:param Nsamp: Total number of samples desired - will choose optimal dx to get slightly more than this number of samples delete samples outside bounds and randomly delete others until sample number is exactly Nsamp
:type Nsamp: integer
:param method: method used for interp2d when transforming sampled model values back from projected coordinates to geographic coordinates - 'nearest', 'linear', or 'cubic'
:type method: string
:param extent: extent to include in sampling - 'inventory' or 'model' or custom bounds as tuple (xmin, ymin, xmax, ymax) - in lats and lons
:param bins: bin edges to use for various binning and threshold statistical calculations. if None bins = [0, 0.2, 0.4, 0.6, 0.8, 1.]
:param runtests: if True, will run various statistical tests, if False will just output sampled values
:param showplots: if True, will disply the plots
:param saveplots: if True, will save the plots
:param filepath: Filepath for saved plots, if None, will save in current directory. Files are named with test name and time stamp
:returns yespoints: Nx2 array of geographic coordinates of positive sample locations
:returns nopoints: Nx2 array of geographic coordinates of negative sample locations
:returns modelvalyes: N model output values corresponding to yespoints
:returns modelvalno: N model output values corresponding to nopoints
:returns results: dictionary of results of statistical tests. Will be empty if runtests=False
{'Occ_nonocc': dict,
'SRC': dict,
'ROC': dict,
'AUC_ROC': float,
'Log_loss': float,
'GFC': dict,
'Pred_vs_Obs': dict,
'Brier': float,
'Brier_no': float,
'Brier_yes': float}
:rtype results: dictionary
"""
plt.close('all')
f = fiona.collection(inventory, 'r')
shapes = list(f)
bxmin, bymin, bxmax, bymax = f.bounds
gdict = modelgrid.getGeoDict()
if extent == 'model':
extent = gdict.xmin, gdict.ymin, gdict.xmax, gdict.ymax
elif extent == 'inventory':
extent = bxmin, bymin, bxmax, bymax
#yespoints, junk, nopoints, junk2, xvar, yvar, pshapes, proj = sampleFromShapes(shapes, extent, dx=dx, Nsamp=Nsamp, testPercent=100.)
yespoints, nopoints, xvar, yvar, pshapes, proj = pointsFromShapes(shapes, extent, dx=dx, Nsamp=Nsamp)
yesptx = [pt[0] for pt in yespoints]
yespty = [pt[1] for pt in yespoints]
noptx = [pt[0] for pt in nopoints]
nopty = [pt[1] for pt in nopoints]
#import pdb; pdb.set_trace()
# Get values of model at those points
lons = np.linspace(gdict.xmin, gdict.xmax, gdict.nx)
lats = np.linspace(gdict.ymax, gdict.ymin, gdict.ny)
if method.lower() == 'nearest':
modelvalyes = []
modelvalno = []
for XX, YY in zip(yesptx, yespty):
row = (np.abs(lats - YY)).argmin()
col = (np.abs(lons - XX)).argmin()
modelvalyes.append(modelgrid.getData()[row, col])
for XX, YY in zip(noptx, nopty):
row = (np.abs(lats - YY)).argmin()
col = (np.abs(lons - XX)).argmin()
modelvalno.append(modelgrid.getData()[row, col])
else:
func = interpolate.interp2d(lons, lats, modelgrid.getData(), kind=method.lower())
modelvalyes = np.array([float(func(XX, YY)) for XX, YY in zip(yesptx, yespty)])
modelvalno = np.array([float(func(XX, YY)) for XX, YY in zip(noptx, nopty)])
modelvalyes = np.nan_to_num(np.array(modelvalyes)) # replace nan with zeros
modelvalno = np.nan_to_num(np.array(modelvalno)) # replace nan with zeros
# Now run the desired tests and make the desired plots
results = {}
if runtests is True:
# Brier score
N = len(yespoints) + len(nopoints)
yessum = np.sum([(val-1)**2 for val in modelvalyes])
nosum = np.sum([(val)**2 for val in modelvalno])
results['Brier_yes'] = yessum/len(modelvalyes)
results['Brier_no'] = nosum/len(modelvalno)
results['Brier'] = (yessum + nosum)/N
print(('Brier scores: overall %0.3f\nBrier_yes score: %0.3f\nBrier_no score %0.3f' % (results['Brier'], results['Brier_yes'], results['Brier_no'])))
# Logarithmic score
tempno = np.array(modelvalno).copy()
tempyes = np.array(modelvalyes).copy()
tempno[tempno == 0] = 1.e-15
tempyes[tempyes == 0] = 1.e-15
results['Log_loss'] = -(np.sum(np.log(tempyes)) + np.sum(np.log(1.-tempno)))/N
print(('Log loss score: %0.3f' % (results['Log_loss'],)))
if bins is None:
bins = [0, 0.2, 0.4, 0.6, 0.8, 1.]
binvec = []
observed = []
percyes = []
percno = []
overall_tot = len(modelvalyes) + len(modelvalno)
for i in range(len(bins[:-1])):
binvec.append(bins[i]+(bins[i+1]-bins[i])/2)
yestot = np.sum([(modelvalyes > bins[i]) & (modelvalyes < bins[i+1])])
notot = np.sum([(modelvalno > bins[i]) & (modelvalno < bins[i+1])])
if notot+yestot != 0:
observed.append(float(yestot)/(yestot+notot))
else:
observed.append('nan')
percyes.append((yestot/float(overall_tot))*100.)
percno.append((notot/float(overall_tot))*100.)
plt.ioff()
# Predicted vs. Observed ratios
fig = plt.figure()
ax = fig.add_subplot(111)
ax.plot(binvec, observed, '-o')
ax.plot([0]+binvec, [0]+binvec, '--', color='gray')
ax.set_xlabel('Expected ratio')
ax.set_ylabel('Observed ratio')
ax.set_xlim([bins[0], bins[-1]])
ax.set_title('Predicted vs. Observed')
results['Pred_vs_Obs'] = {'binvec': binvec, 'observed': observed}
# Ground failure occurrence/nonoccurrence
fig1 = plt.figure()
ax1 = fig1.add_subplot(111)
wid = (bins[1]-bins[0])/2.5
rects1 = ax1.bar(np.array(bins[:-1]), percyes, width=wid)
rects2 = ax1.bar(np.array(bins[:-1])+wid, percno, width=wid, color='r')
ax1.set_xlabel('Predicted susceptibility range')
ax1.set_ylabel('% of samples')
ax1.legend((rects1[0], rects2[0]), ('Occurrence', 'Nonoccurrence'))
ax1.set_title('Occurrence vs. Nonoccurrence')
results['Occ_nonocc'] = {'bins': bins, 'percyes': percyes, 'percno': percno}
# Ground failure capture for various thresholds
gfc = []
for val in bins:
gfc.append(np.sum([modelvalyes > val])/float(len(yespoints)))
fig2 = plt.figure()
ax2 = fig2.add_subplot(111)
ax2.plot(bins, gfc, 'o-')
ax2.set_xlabel('Threshold')
ax2.set_ylabel(r'%GFC')
ax2.set_title('Ground Failure Capture')
results['GFC'] = {'thresholds': bins, 'gfc': gfc}
# ROC curves
fpr, tpr, thresholds = roc_curve(np.concatenate((np.ones(len(yespoints)), np.zeros(len(nopoints)))), np.concatenate((modelvalyes, modelvalno)))
fig3 = plt.figure()
ax3 = fig3.add_subplot(111)
ax3.plot(fpr, tpr)
ax3.set_xlabel('False positive rate')
ax3.set_ylabel('True positive rate')
ax3.set_xlim([0, 1.])
ax3.set_ylim([0, 1.])
ax3.plot(fpr, fpr, '--', color='gray')
ax3.set_title('ROC curve')
results['ROC'] = {'thresholds': bins, 'gfc': gfc}
results['AUC_ROC'] = roc_auc_score(np.concatenate((np.ones(len(yespoints)), np.zeros(len(nopoints)))), np.concatenate((modelvalyes, modelvalno)))
print(('AUC_ROC: %0.3f' % (results['AUC_ROC'],)))
ax3.text(0.8, 0.2, 'AUC: %0.3f' % results['AUC_ROC'])
# Success rate curves
sucbin = np.linspace(0, 1., 100)
prop = []
realvals = np.concatenate((np.ones(len(yespoints)), np.zeros(len(nopoints))))
predvals = np.concatenate((modelvalyes, modelvalno))
indx = np.argsort(predvals)
predvals = predvals[indx]
realvals = realvals[indx]
for val in sucbin:
prop.append(np.sum(realvals[predvals < val])/len(yespoints))
fig4 = plt.figure()
ax4 = fig4.add_subplot(111)
ax4.plot(sucbin, prop)
ax4.set_xlabel('Success Rate Curve')
ax4.set_ylabel('Proportion of actual occurrences')
ax4.set_title('Proportion of Study Area')
AUC = auc(sucbin, prop)
print(('AUC_SRC: %0.3f' % AUC))
ax4.text(0.8, 0.2, 'AUC: %0.3f' % AUC)
ax4.set_xlim([0, 1.])
ax4.set_ylim([0, 1.])
results['SRC'] = {'xvals': sucbin, 'proportion': prop, 'auc': AUC}
if showplots is True:
plt.show()
if saveplots is True:
if filepath is None:
filepath = os.getcwd()
import datetime
time1 = datetime.datetime.utcnow().strftime('%d%b%Y_%H%M')
fig.savefig(os.path.join(filepath, 'Pred_vs_obs_%s.pdf' % (time1,)))
fig1.savefig(os.path.join(filepath, 'Occ_nonocc_%s.pdf' % (time1,)))
fig2.savefig(os.path.join(filepath, 'GFC_%s.pdf' % (time1,)))
fig3.savefig(os.path.join(filepath, 'ROC_%s.pdf' % (time1,)))
fig4.savefig(os.path.join(filepath, 'SRC_%s.pdf' % (time1,)))
return yespoints, nopoints, modelvalyes, modelvalno, results
def computeCoverage_accurate(gdict, inventory, numdiv=10.):
"""
VERY SLOW!!
Slow but more accurate method to produce grid of area actually affected by landsliding in each cell defined by geodict
:param gdict: geodict, likely taken from model to compare inventory against
:param inventory: full file path to shapefile of inventory, must be in geographic coordinates, WGS84
:type inventory: string
:param numdiv: Approximate amount to subdivide each cell of geodict by to compute areas (higher number slower but more accurate)
:return inventorygrid: Grid2D object reporting areal coverage of landsliding inside each cell defined by geodict
"""
f = fiona.collection(inventory, 'r')
shapes = list(f)
bxmin, bymin, bxmax, bymax = f.bounds
lons = np.linspace(gdict.xmin, gdict.xmax, gdict.nx)
lats = np.linspace(gdict.ymax, gdict.ymin, gdict.ny)
llons, llats = np.meshgrid(lons, lats)
spacing = np.round(np.abs(((lats[1]-lats[0])*111.12*1000.)/numdiv)) # in meters
yespoints, nopoints, xvar, yvar, pshapes, proj = pointsFromShapes(shapes, bounds=(gdict.xmin, gdict.ymin, gdict.xmax, gdict.ymax), dx=spacing)
# Loop over lat lon pairs that are within boundaries of yes and no points
ptlonmax = (np.max((yespoints[:, 0].max(), nopoints[:, 0].max())))
ptlonmin = (np.max((yespoints[:, 0].min(), nopoints[:, 0].min())))
ptlatmax = (np.max((yespoints[:, 1].max(), nopoints[:, 1].max())))
ptlatmin = (np.max((yespoints[:, 1].min(), nopoints[:, 1].min())))
subllons = llons[(llons >= ptlonmin) & (llons <= ptlonmax) & (llats >= ptlatmin) & (llats <= ptlatmax)]
subllats = llats[(llons >= ptlonmin) & (llons <= ptlonmax) & (llats >= ptlatmin) & (llats <= ptlatmax)]
import time
# Contains points method
t1 = time.clock()
dx = gdict.dx
area = np.zeros(np.shape(llons))
numpts = area.copy()
numyes = area.copy()
for lat1, lon1 in zip(subllats, subllons):
# Find ratio of yes points to no points
bbPath = mplPath.Path(np.array([[lon1-0.5*dx, lat1-0.5*dx], [lon1-0.5*dx, lat1+0.5*dx], [lon1+0.5*dx, lat1+0.5*dx], [lon1+0.5*dx, lat1-0.5*dx]]))
yesin = sum(bbPath.contains_points(yespoints)) # sum([(yes0 > lon1-0.5*dx) & (yes0 <= lon1+0.5*dx) & (yes1 > lat1-0.5*dx) & (yes1 <= lat1+0.5*dx)])
noin = sum(bbPath.contains_points(nopoints)) # sum([(no0 > lon1-0.5*dx) & (no0 <= lon1+0.5*dx) & (no1 > lat1-0.5*dx) & (no1 <= lat1+0.5*dx)])
total = yesin + noin
if total == 0.:
continue
# get indices
row = np.where(lats == lat1)
col = np.where(lons == lon1)
# Store total number of points in matrix
numpts[row, col] = total
# Store area
numyes[row, col] = yesin
t2 = time.clock()
print(('Time elapsed %0.2f seconds' % (t2-t1)))
# Correct for incompletely sampled squared (all unsampled points would be no points)
numpts[numpts < (numpts[numpts != 0].mean() - numpts[numpts != 0].std())] = np.median(numpts[numpts != 0]) # Will change zeros to nonzeros, but yeses will be 0 in those cells so it doesn't matter
area = numyes/numpts
inventorygrid = GDALGrid(area, gdict)
return inventorygrid
