Merge pull request #129 from jGaboardi/fix_import_bug

spreg/diagnostics.py

@@ -6,9 +6,14 @@
  "Luc Anselin [email protected], Nicholas Malizia [email protected] "
 )
 
-from libpysal.common import *
-import scipy.sparse as SP
 from math import sqrt, pi
+
+from libpysal.common import MISSINGVALUE
+import numpy as np
+import numpy.linalg as la
+import scipy.sparse as SP
+from scipy import stats
+
 from .utils import spmultiply, sphstack, spmin, spmax
 
 
@@ -160,12 +165,9 @@ def t_stat(reg, z_stat=False):
  vm = reg.vm # (array) coefficients of variance matrix (k x k)
  betas = reg.betas # (array) coefficients of the regressors (1 x k)
  variance = vm.diagonal()
- tStat = (
- betas[list(range(0, len(vm)))].reshape(
- len(vm),
- )
- / np.sqrt(variance)
- )
+ tStat = betas[list(range(0, len(vm)))].reshape(
+ len(vm),
+ ) / np.sqrt(variance)
  ts_result = []
  for t in tStat:
  if z_stat:
@@ -678,15 +680,15 @@ def jarque_bera(reg):
  """
  n = reg.n # (scalar) number of observations
  u = reg.u # (array) residuals from regression
- u2 = u ** 2
- u3 = u ** 3
- u4 = u ** 4
+ u2 = u**2
+ u3 = u**3
+ u4 = u**4
  mu2 = np.mean(u2)
  mu3 = np.mean(u3)
  mu4 = np.mean(u4)
  S = mu3 / (mu2 ** (1.5)) # skewness measure
- K = mu4 / (mu2 ** 2) # kurtosis measure
- jb = n * (((S ** 2) / 6) + ((K - 3) ** 2) / 24)
+ K = mu4 / (mu2**2) # kurtosis measure
+ jb = n * (((S**2) / 6) + ((K - 3) ** 2) / 24)
  pvalue = stats.chisqprob(jb, 2)
  jb_result = {"df": 2, "jb": jb, "pvalue": pvalue}
  return jb_result
@@ -776,7 +778,7 @@ def breusch_pagan(reg, z=None):
  0.0193
 
  """
- e2 = reg.u ** 2
+ e2 = reg.u**2
  e = reg.u
  n = reg.n
  k = reg.k
@@ -919,7 +921,7 @@ def white(reg):
  0.0013
 
  """
- e = reg.u ** 2
+ e = reg.u**2
  k = int(reg.k)
  n = int(reg.n)
  y = reg.y
@@ -1084,7 +1086,7 @@ def koenker_bassett(reg, z=None):
 
  """
  # The notation here matches that of Greene (2003).
- u = reg.u ** 2
+ u = reg.u**2
  e = reg.u
  n = reg.n
  k = reg.k

spreg/diagnostics_tsls.py

@@ -7,7 +7,8 @@
  "Luc Anselin [email protected], Nicholas Malizia [email protected] "
 )
 
-from libpysal.common import *
+import numpy as np
+from scipy import stats
 from scipy.stats import pearsonr
 
 __all__ = ["t_stat", "pr2_aspatial", "pr2_spatial"]
@@ -118,12 +119,9 @@ def t_stat(reg, z_stat=False):
  vm = reg.vm # (array) coefficients of variance matrix (k x k)
  betas = reg.betas # (array) coefficients of the regressors (1 x k)
  variance = vm.diagonal()
- tStat = (
- betas.reshape(
- len(betas),
- )
- / np.sqrt(variance)
- )
+ tStat = betas.reshape(
+ len(betas),
+ ) / np.sqrt(variance)
  ts_result = []
  for t in tStat:
  if z_stat:
@@ -221,7 +219,7 @@ def pr2_aspatial(tslsreg):
  y = tslsreg.y
  predy = tslsreg.predy
  pr = pearsonr(y.flatten(), predy.flatten())[0]
- pr2_result = float(pr ** 2)
+ pr2_result = float(pr**2)
  return pr2_result
 
 
@@ -329,7 +327,7 @@ def pr2_spatial(tslsreg):
  y = tslsreg.y
  predy_e = tslsreg.predy_e
  pr = pearsonr(y.flatten(), predy_e.flatten())[0]
- pr2_result = float(pr ** 2)
+ pr2_result = float(pr**2)
  return pr2_result