mgwr
mgwr copied to clipboard
Published
20 hours ago •
pysal
pysal
LinAlgError with Sel_BW
Hello guys, I'am using MGWR and Sel_BW it throws an error but when when using a sample size > 2040 but works fine for < 2040
b_y = np.log(gdf["price"].head(2050).values.reshape((-1, 1)))
b_X = gdf["surface"].head(2050).values.reshape((-1, 1))
u = gdf['x'].head(2050)
v = gdf['y'].head(2050)
b_coords = list(zip(u, v))
b_X = (b_X - b_X.mean(axis = 0)) / b_X.std(axis = 0)
b_y = (b_y - b_y.mean(axis = 0)) / b_y.std(axis = 0)
mgwr_selector = Sel_BW(b_coords, b_y, b_X, multi=True)
print(mgwr_selector.fixed)
print(mgwr_selector.kernel)
mgwr_bw = mgwr_selector.search()
print(mgwr_bw)
mgwr_results = MGWR(b_coords, b_y, b_X, mgwr_selector).fit()
/usr/local/lib/python3.7/dist-packages/mgwr/kernels.py:60: RuntimeWarning: divide by zero encountered in true_divide
self.kernel = self._kernel_funcs(self.dvec / self.bandwidth)
/usr/local/lib/python3.7/dist-packages/mgwr/kernels.py:60: RuntimeWarning: invalid value encountered in true_divide
self.kernel = self._kernel_funcs(self.dvec / self.bandwidth)
---------------------------------------------------------------------------
LinAlgError Traceback (most recent call last)
<ipython-input-208-6365a372acef> in <module>()
----> 1 mgwr_bw = mgwr_selector.search(verbose=2)
2 print(mgwr_bw)
3 mgwr_results = MGWR(b_coords, b_y, b_X, mgwr_selector).fit()
12 frames
/usr/local/lib/python3.7/dist-packages/mgwr/sel_bw.py in search(self, search_method, criterion, bw_min, bw_max, interval, tol, max_iter, init_multi, tol_multi, rss_score, max_iter_multi, multi_bw_min, multi_bw_max, bws_same_times, pool, verbose)
311
312 if self.multi:
--> 313 self._mbw()
314 self.params = self.bw[3] #params n by k
315 self.sel_hist = self.bw[-2] #bw searching history
/usr/local/lib/python3.7/dist-packages/mgwr/sel_bw.py in _mbw(self)
400 self.max_iter_multi, self.rss_score, gwr_func,
401 bw_func, sel_func, multi_bw_min, multi_bw_max,
--> 402 bws_same_times, verbose=self.verbose)
403
404 def _init_section(self, X_glob, X_loc, coords, constant):
/usr/local/lib/python3.7/dist-packages/mgwr/search.py in multi_bw(init, y, X, n, k, family, tol, max_iter, rss_score, gwr_func, bw_func, sel_func, multi_bw_min, multi_bw_max, bws_same_times, verbose)
221 bw = bws[j]
222 else:
--> 223 bw = sel_func(bw_class, multi_bw_min[j], multi_bw_max[j])
224 gwr_sel_hist.append(deepcopy(bw_class.sel_hist))
225
/usr/local/lib/python3.7/dist-packages/mgwr/sel_bw.py in sel_func(bw_func, bw_min, bw_max)
395 search_method=search_method, criterion=criterion,
396 bw_min=bw_min, bw_max=bw_max, interval=interval, tol=tol,
--> 397 max_iter=max_iter, pool=self.pool, verbose=False)
398
399 self.bw = multi_bw(self.init_multi, y, X, n, k, family, self.tol_multi,
/usr/local/lib/python3.7/dist-packages/mgwr/sel_bw.py in search(self, search_method, criterion, bw_min, bw_max, interval, tol, max_iter, init_multi, tol_multi, rss_score, max_iter_multi, multi_bw_min, multi_bw_max, bws_same_times, pool, verbose)
317 -1] #scalar, optimal bw from initial gwr model
318 else:
--> 319 self._bw()
320 self.sel_hist = self.bw[-1]
321
/usr/local/lib/python3.7/dist-packages/mgwr/sel_bw.py in _bw(self)
337 self.bw = golden_section(a, c, delta, gwr_func, self.tol,
338 self.max_iter, self.int_score,
--> 339 self.verbose)
340 elif self.search_method == 'interval':
341 self.bw = equal_interval(self.bw_min, self.bw_max, self.interval,
/usr/local/lib/python3.7/dist-packages/mgwr/search.py in golden_section(a, c, delta, function, tol, max_iter, int_score, verbose)
60 score_b = dict[b]
61 else:
---> 62 score_b = function(b)
63 dict[b] = score_b
64 if verbose:
/usr/local/lib/python3.7/dist-packages/mgwr/sel_bw.py in <lambda>(bw)
327 self.coords, self.y, self.X_loc, bw, family=self.family, kernel=
328 self.kernel, fixed=self.fixed, constant=self.constant, offset=self.
--> 329 offset, spherical=self.spherical).fit(lite=True, pool=self.pool))
330
331 self._optimized_function = gwr_func
/usr/local/lib/python3.7/dist-packages/mgwr/gwr.py in fit(self, ini_params, tol, max_iter, solve, lite, pool)
333 rslt = map(self._local_fit, range(m)) #sequential
334
--> 335 rslt_list = list(zip(*rslt))
336 influ = np.array(rslt_list[0]).reshape(-1, 1)
337 resid = np.array(rslt_list[1]).reshape(-1, 1)
/usr/local/lib/python3.7/dist-packages/mgwr/gwr.py in _local_fit(self, i)
249
250 if isinstance(self.family, Gaussian):
--> 251 betas, inv_xtx_xt = _compute_betas_gwr(self.y, self.X, wi)
252 predy = np.dot(self.X[i], betas)[0]
253 resid = self.y[i] - predy
/usr/local/lib/python3.7/dist-packages/spglm/iwls.py in _compute_betas_gwr(y, x, wi)
35 xT = (x * wi).T
36 xtx = np.dot(xT, x)
---> 37 xtx_inv_xt = linalg.solve(xtx, xT)
38 betas = np.dot(xtx_inv_xt, y)
39 return betas, xtx_inv_xt
/usr/local/lib/python3.7/dist-packages/scipy/linalg/basic.py in solve(a, b, sym_pos, lower, overwrite_a, overwrite_b, debug, check_finite, assume_a, transposed)
214 (a1, b1))
215 lu, ipvt, info = getrf(a1, overwrite_a=overwrite_a)
--> 216 _solve_check(n, info)
217 x, info = getrs(lu, ipvt, b1,
218 trans=trans, overwrite_b=overwrite_b)
/usr/local/lib/python3.7/dist-packages/scipy/linalg/basic.py in _solve_check(n, info, lamch, rcond)
29 '.'.format(-info))
30 elif 0 < info:
---> 31 raise LinAlgError('Matrix is singular.')
32
33 if lamch is None:
LinAlgError: Matrix is singular.
train_paris.csv i tried changing kernel but nothing worked, i'am using only one feature the surface to predict the price, i'am adding the file so u can reproduce the error, thanks for your help.
i just tried changing the search criterion to BIC and worked, but didn't not with the other having the same problem !
Hi @imadwalid There may be local clusters of similar values in the first 2050 records. A fix would be to set the lower bound of the golden-section search a bit higher. For example, in your dataset, setting to 50 works (default is like 40ish).
mgwr_bw = mgwr_selector.search(verbose=True,multi_bw_min=[50])
