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Binomial MGWR-attempts #70

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Merged
weikang9009 merged 2 commits into from
Jul 25, 2019
Merged

Binomial MGWR-attempts #70

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227c837
module changes tried for Binomial MGWR
mehak-sachdeva Jul 25, 2019
7ee8b63
changes to discuss but not working
mehak-sachdeva Jul 25, 2019
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56 changes: 43 additions & 13 deletions mgwr/search.py
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Original file line number Diff line number Diff line change
Expand Up @@ -187,7 +187,12 @@ def multi_bw(init, y, X, n, k, family, offset, tol, max_iter, rss_score, gwr_fun
#v = np.multiply(X, param)
#XB = 1 / (1 + np.exp(-1 * v))
#XB = v + ((1 / (mu * (1 - mu))) * (y - mu))
XB = 1/(1+np.exp(-1*np.multiply(param,X)))
m = np.multiply(param, X)
XB = 1 / ( 1 + np.exp (-1 * m))
#print("XB "+str(XB.shape))
#mu = 1 / ( 1 + np.exp (-1 * np.multiply (param, X)))
#XB = np.multiply(param, X)
#print(XB.shape)
#XB=np.log(XB/(1-XB))
else:
XB = np.multiply(param, X)
Expand All @@ -210,17 +215,30 @@ def tqdm(x, desc=''): #otherwise, just passthrough the range

for iters in tqdm(range(1, max_iter + 1), desc='Backfitting'):
new_XB = np.zeros_like(X)

params = np.zeros_like(X)

for j in range(k):
temp_y = XB[:, j].reshape((-1, 1))
temp_y = temp_y + err

if isinstance(family, Binomial):
ni_old = np.log((XB[:,j])/(1-XB[:,j]))
temp_y = (ni_old + ((optim_model.y.reshape(-1) - XB[:,j].reshape(-1))/XB[:,j].reshape(-1)*(1-XB[:,j].reshape(-1)))).reshape(-1,1)
print(temp_y.shape)
#temp_y = ((XB[:,j] + (y - mu[:,j]))/(mu[:,j] * ( 1 - mu[:,j] )))
#print(temp_y.shape)
else:
temp_y = XB[:, j].reshape((-1, 1))
#temp_y = (1 / (1+np.exp((-1*(np.multiply(temp_X ,param.reshape(-1,1)))+err))).reshape(-1))

#temp_y = y_new
#temp_y = temp_y + err
#print(min(err))
temp_X = X[:, j].reshape((-1, 1))

#if isinstance(family, Binomial):
#bw_class = bw_func_g(temp_y, temp_X)
#else:
bw_class = bw_func(temp_y, temp_X)
if isinstance(family, Binomial):
bw_class = bw_func_g(temp_y, temp_X)
else:
bw_class = bw_func(temp_y, temp_X)

if np.all(bw_stable_counter == bws_same_times):
#If in backfitting, all bws not changing in bws_same_times (default 3) iterations
Expand All @@ -232,15 +250,25 @@ def tqdm(x, desc=''): #otherwise, just passthrough the range
else:
bw_stable_counter = np.ones(k)

#if isinstance(family, Binomial):
#optim_model = gwr_func_g(temp_y, temp_X, bw)
if isinstance(family, Binomial):
optim_model = gwr_func_g(temp_y, temp_X, bw)

#else:
optim_model = gwr_func(temp_y, temp_X, bw)
else:
optim_model = gwr_func(temp_y, temp_X, bw)
#err=(temp_y - optim_model.predy).reshape(-1,1)
#print(err.shape)
err = optim_model.resid_response.reshape((-1, 1))
#print(err.shape)
param = optim_model.params.reshape((-1, ))
#new_XB[:,j] = 1/(1+np.exp(-1*np.sum(temp_X * param, axis=1).reshape(-1)))
new_XB[:, j] = optim_model.predy.reshape(-1)
#print(param.shape)
#print(temp_X.shape)
#print(err.shape)
#new_XB[:, j] = 1 / (1+np.exp(-1*np.multiply(temp_X ,param)+err)).reshape(-1)
new_XB[:, j] = 1 / (1+np.exp((-1*(np.multiply(temp_X ,param.reshape(-1,1)))))).reshape(-1)
#new_XB[:,j] = optim_model.predy.reshape(-1)
#new_XB[:,j] = np.multiply(param, temp_X).reshape(-1)
#new_XB[:, j] = optim_model.predy.reshape(-1)
#neww_XB[:, j] = (1 / (1+np.exp((-1*(np.multiply(temp_X ,param.reshape(-1,1)))+err))).reshape(-1))
#new_XB[:, j] = np.log(new_XB[:,j]/(1-new_XB[:,j]))
params[:, j] = param
bws[j] = bw
Expand All @@ -249,6 +277,8 @@ def tqdm(x, desc=''): #otherwise, just passthrough the range
den = np.sum(np.sum(new_XB, axis=1)**2)
score = (num / den)**0.5
XB = new_XB
#mu = new_mu
#XXB = neww_XB

if rss_score:
if isinstance(family, Poisson):
Expand Down
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