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Merged
TaylorOshan merged 4 commits into from
May 22, 2018
Merged

clean up MGWR #11

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5edf480
clean up
Ziqi-Li May 3, 2018
64e5a13
test
Ziqi-Li May 3, 2018
8ea6bc0
clean-up
Ziqi-Li May 4, 2018
d69b557
rm pyc
Ziqi-Li May 4, 2018
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80 changes: 50 additions & 30 deletions gwr/gwr.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1241,7 +1241,7 @@ class MGWR(GWR):
TODO

"""
def __init__(self, coords, y, X, bws, XB, err, family=Gaussian(), offset=None,
def __init__(self, coords, y, X, selector, family=Gaussian(), offset=None,
sigma2_v1=False, kernel='bisquare', fixed=False, constant=True,
dmat=None, sorted_dmat=None, spherical=False):
"""
Expand All @@ -1250,11 +1250,9 @@ def __init__(self, coords, y, X, bws, XB, err, family=Gaussian(), offset=None,
self.coords = coords
self.y = y
self.X = X
self.XB = XB
self.err = err
self.bws = bws
self.family = family
self.offset = offset
self.selector = selector
self.sigma2_v1 = sigma2_v1
self.kernel = kernel
self.fixed = fixed
Expand All @@ -1263,7 +1261,7 @@ def __init__(self, coords, y, X, bws, XB, err, family=Gaussian(), offset=None,
self.sorted_dmat = sorted_dmat
self.spherical = spherical
if constant:
self.X = USER.check_constant(self.X)
self.X = USER.check_constant(self.X)

def fit(self, ini_params=None, tol=1.0e-5, max_iter=20, solve='iwls'):
"""
Expand All @@ -1286,24 +1284,11 @@ def fit(self, ini_params=None, tol=1.0e-5, max_iter=20, solve='iwls'):
'iwls' = iteratively (re)weighted least squares (default)

"""
params = np.zeros_like(self.X)
err = self.err
S = []
SE = []
Ws = []
for i, bw in enumerate(self.bws):
W = self._build_W(self.fixed, self.kernel, self.coords, bw)
Ws.append(W)
X = self.X[:,i].reshape((-1,1))
y = self.XB[:,i].reshape((-1,1)) + err
model = GWR(self.coords, y, X, bw, self.family, self.offset,
self.sigma2_v1, self.kernel, self.fixed, constant=False)
results = model.fit(ini_params, tol, max_iter, solve)
S.append(results.S)
params[:,i] = results.params.flatten()
SE.append(results.bse)
err = results.resid_response.reshape((-1,1))
return MGWRResults(self, params, S, SE, Ws)
S = self.selector.S
R = self.selector.R
params = self.selector.est

return MGWRResults(self, params, S, R)

class MGWRResults(object):
"""
Expand Down Expand Up @@ -1344,7 +1329,7 @@ class MGWRResults(object):
TODO

"""
def __init__(self, model, params, S, SE, Ws):
def __init__(self, model, params, S, R):
"""
Initialize class
"""
Expand All @@ -1353,19 +1338,54 @@ def __init__(self, model, params, S, SE, Ws):
self.X = model.X
self.y = model.y
self.S = S
self.SE = SE
self.Ws = Ws
self.R = R
self.n = self.y.shape[0]
self._cache = {}

@cache_readonly
def predy(self):
return np.sum(np.multiply(self.params, self.X), axis=1).reshape((-1,1))
return np.dot(self.S,self.y)

@cache_readonly
def resid_response(self):
return (self.y - self.predy).reshape((-1,1))
return (self.y - self.predy).reshape(-1)

@cache_readonly
def resid_ss(self):
u = self.resid_response.flatten()
return np.dot(u, u.T)
return np.sum(self.resid_response**2)

@cache_readonly
def tr_S(self):
return np.trace(self.S)

@cache_readonly
def sigma2(self):
return self.resid_ss / (self.n - self.tr_S)

@cache_readonly
def aicc(self):
n = self.n
tr_S = self.tr_S
aicc = n*np.log(self.sigma2) + n*np.log(2*np.pi) + n*(n+tr_S)/(n-tr_S-2.0)
return aicc

@cache_readonly
def ENPj(self):
k = self.params.shape[1]
ENPj = np.zeros(k)
for j in range(k):
Rj = self.R[:,:,j]
ENPj[j] = np.trace(Rj)
return ENPj

@cache_readonly
def bse(self):
n = self.n
k = self.params.shape[1]
bse = np.zeros((n,k))
for j in range(k):
Rj = self.R[:,:,j]
Cj = Rj/self.X[:,j].reshape(-1,1)
bse[:,j] = np.sqrt(np.diag(np.dot(Cj, Cj.T)*self.sigma2))

return bse
65 changes: 25 additions & 40 deletions gwr/search.py
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Original file line number Diff line number Diff line change
Expand Up @@ -152,11 +152,9 @@ def equal_interval(l_bound, u_bound, interval, function, int_score=False):

return opt_val, opt_score, output

MGWR_BW_Result = namedtuple('MGWR_BW_RESULT', ['bws_','bw_trace', 'kernel_values', 'scores',
'partial_predictions','model_residuals_',
'partial_residuals_',
'objective_functions',
'hat_matrix'])
MGWR_BW_Result = namedtuple('MGWR_BW_RESULT', ['bws_','bw_trace', 'scores',
'parameter_estimates','model_residuals',
'hat_matrix','covariate_specific_hat_matrices'])

def multi_bw(init, y, X, n, k, family, tol, max_iter, rss_score,
gwr_func, bw_func, sel_func):
Expand All @@ -170,18 +168,15 @@ def multi_bw(init, y, X, n, k, family, tol, max_iter, rss_score,
model = GLM(y, X, family=family, constant=False).fit()
err = model.resid_response.reshape((-1,1))
est = np.repeat(model.params.T, n, axis=0)

R = np.zeros((n,n,k))
hat_matrix = optim_model.S
for i in range(k):
temp_vec = np.zeros((1,k))
temp_vec[:,i] = 1.0
for j in range(n):
Wj = np.diag(optim_model.W[j])
XtW = np.dot(X.T, Wj)
XtWX_inv = np.linalg.inv(np.dot(XtW, X))
P = np.dot(XtWX_inv, XtW)
R[j,:,i] = np.dot((X[j,i] * temp_vec), P)

for j in range(k):
for i in range(n):
wi = optim_model.W[i].reshape(-1,1)
xT = (X * wi).T
P = np.dot(np.linalg.inv(np.dot(xT, X)), xT)
R[i,:,j] = X[i,j]*P[j]

XB = np.multiply(est, X)
if rss_score:
Expand All @@ -192,7 +187,6 @@ def multi_bw(init, y, X, n, k, family, tol, max_iter, rss_score,
BWs = []
VALs = []
FUNCs = []
Aj = np.zeros((n,n))

try:
from tqdm import tqdm #if they have it, let users have a progress bar
Expand All @@ -208,45 +202,36 @@ def tqdm(x): #otherwise, just passthrough the range
ests = np.zeros_like(X)
f_XB = XB.copy()
f_err = err.copy()
for i in range(k):
temp_y = XB[:,i].reshape((-1,1))

for j in range(k):
temp_y = XB[:,j].reshape((-1,1))
temp_y = temp_y + err
temp_X = X[:,i].reshape((-1,1))
temp_X = X[:,j].reshape((-1,1))
bw_class = bw_func(temp_y, temp_X)
funcs.append(bw_class._functions)
bw = sel_func(bw_class)
optim_model = gwr_func(temp_y, temp_X, bw)

for j in range(n):
Wj = np.diag(optim_model.W[j])
XtW = np.dot(temp_X.T, Wj)
XtWX_inv = np.linalg.inv(np.dot(XtW, temp_X))
P = np.dot(XtWX_inv, XtW)
Aj[j,:] = temp_X[j,:] * P

new_R = R.copy()
new_R[:,:,i] = Aj - np.dot(Aj, S) + np.dot(Aj, R[:,:,i])
new_S = S - R[:,:,i] + new_R[:,:,i]

R = new_R.copy()
S = new_S.copy()
Aj = optim_model.S
new_Rj = Aj - np.dot(Aj, S) + np.dot(Aj, R[:,:,j])
S = S - R[:,:,j] + new_Rj
R[:,:,j] = new_Rj

err = optim_model.resid_response.reshape((-1,1))
est = optim_model.params.reshape((-1,))

new_XB[:,i] = np.multiply(est, temp_X.reshape((-1,)))
new_XB[:,j] = optim_model.predy.reshape(-1)
bws.append(copy.deepcopy(bw))
ests[:,i] = est
ests[:,j] = est
vals.append(bw_class.bw[1])
current_partial_residuals.append(err.copy())

predy = np.sum(np.multiply(ests, X), axis=1).reshape((-1,1))
num = np.sum((new_XB - XB)**2)/n
den = np.sum(np.sum(new_XB, axis=1)**2)
score = (num/den)**0.5
XB = new_XB

if rss_score:
predy = np.sum(np.multiply(ests, X), axis=1).reshape((-1,1))
new_rss = np.sum((y - predy)**2)
score = np.abs((new_rss - rss)/new_rss)
rss = new_rss
Expand All @@ -259,6 +244,6 @@ def tqdm(x): #otherwise, just passthrough the range
break

opt_bws = BWs[-1]
return MGWR_BW_Result(opt_bws, np.array(BWs), np.array(VALs),
np.array(scores), f_XB, f_err,
current_partial_residuals, FUNCs, S)
return MGWR_BW_Result(opt_bws, np.array(BWs),
np.array(scores), ests,
err, S, R)
7 changes: 4 additions & 3 deletions gwr/sel_bw.py
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Original file line number Diff line number Diff line change
Expand Up @@ -242,9 +242,10 @@ def search(self, search_method='golden_section', criterion='AICc',

if self.multi:
self._mbw()
self.XB = self.bw[4]
self.err = self.bw[5]
self.S = self.bw[-1]
self.est = self.bw[3] #params
self.err = self.bw[4] #residual
self.S = self.bw[-2] #(n,n)
self.R = self.bw[-1] #(n,n,k)
else:
self._bw()

Expand Down
5 changes: 3 additions & 2 deletions gwr/tests/test_gwr.py
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Original file line number Diff line number Diff line change
Expand Up @@ -269,8 +269,9 @@ def test_GS_NN(self):
np.testing.assert_allclose(cooksD, rslt.cooksD, rtol=1e-00)

def test_MGWR(self):
model = MGWR(self.coords, self.y, self.X, [157.0, 65.0, 52.0],
XB=self.XB, err=self.err, constant=False)
selector = Sel_BW(self.coords, self.y, self.X,multi=True)

model = MGWR(self.coords, self.y, self.X, selector=selector, constant=False)
rslt = model.fit()

np.testing.assert_allclose(rslt.predy, self.MGWR['predy'], atol=1e-07)
Expand Down
4 changes: 2 additions & 2 deletions gwr/tests/test_sel_bw.py
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Original file line number Diff line number Diff line change
Expand Up @@ -22,9 +22,9 @@ def setUp(self):
black = np.array(data.by_col('PctBlack')).reshape((-1,1))
self.X = np.hstack([rural, pov, black])
XB_path = os.path.join(os.path.dirname(__file__),'XB.p')
self.XB = pk.load(open(XB_path, 'r'))
self.XB = pk.load(open(XB_path,'rb'))
err_path = os.path.join(os.path.dirname(__file__),'err.p')
self.err = pk.load(open(err_path, 'r'))
self.err = pk.load(open(err_path,'rb'))

def test_golden_fixed_AICc(self):
bw1 = 211020.83
Expand Down