def pprint_matrix(values, rlabels, clabels, col_space=None):
buf = StringIO()
T, K = len(rlabels), len(clabels)
if col_space is None:
min_space = 10
col_space = [max(len(str(c)) + 2, min_space) for c in clabels]
else:
col_space = (col_space,) * K
row_space = max([len(str(x)) for x in rlabels]) + 2
head = _pfixed('', row_space)
for j, h in enumerate(clabels):
head += _pfixed(h, col_space[j])
buf.write(head + '\n')
for i, rlab in enumerate(rlabels):
line = ('%s' % rlab).ljust(row_space)
for j in range(K):
line += _pfixed(values[i,j], col_space[j])
buf.write(line + '\n')
return buf.getvalue()
def print_ic_table(ics, selected_orders):
"""
For VAR order selection
"""
# Can factor this out into a utility method if so desired
cols = sorted(ics)
data = mat([["%#10.4g" % v for v in ics[c]] for c in cols], dtype=object).T
# start minimums
for i, col in enumerate(cols):
idx = int(selected_orders[col]), i
data[idx] = data[idx] + '*'
# data[idx] = data[idx][:-1] + '*' # super hack, ugh
fmt = dict(_default_table_fmt, data_fmts=("%s", ) * len(cols))
buf = StringIO()
table = SimpleTable(data,
cols,
lrange(len(data)),
title='VAR Order Selection',
txt_fmt=fmt)
buf.write(str(table) + '\n')
buf.write('* Minimum' + '\n')
print(buf.getvalue())
def pprint_matrix(values, rlabels, clabels, col_space=None):
buf = StringIO()
T, K = len(rlabels), len(clabels)
if col_space is None:
min_space = 10
col_space = [max(len(str(c)) + 2, min_space) for c in clabels]
else:
col_space = (col_space, ) * K
row_space = max([len(str(x)) for x in rlabels]) + 2
head = _pfixed('', row_space)
for j, h in enumerate(clabels):
head += _pfixed(h, col_space[j])
buf.write(head + '\n')
for i, rlab in enumerate(rlabels):
line = ('%s' % rlab).ljust(row_space)
for j in range(K):
line += _pfixed(values[i, j], col_space[j])
buf.write(line + '\n')
return buf.getvalue()
def print_ic_table(ics, selected_orders):
"""
For VAR order selection
"""
# Can factor this out into a utility method if so desired
cols = sorted(ics)
data = mat([["%#10.4g" % v for v in ics[c]] for c in cols],
dtype=object).T
# start minimums
for i, col in enumerate(cols):
idx = int(selected_orders[col]), i
data[idx] = data[idx] + '*'
# data[idx] = data[idx][:-1] + '*' # super hack, ugh
fmt = dict(_default_table_fmt,
data_fmts=("%s",) * len(cols))
buf = StringIO()
table = SimpleTable(data, cols, lrange(len(data)),
title='VAR Order Selection', txt_fmt=fmt)
buf.write(str(table) + '\n')
buf.write('* Minimum' + '\n')
print(buf.getvalue())
def _resid_info(self):
buf = StringIO()
names = self.model.names
buf.write("Correlation matrix of residuals" + '\n')
buf.write(pprint_matrix(self.model.resid_corr, names, names) + '\n')
return buf.getvalue()
def _resid_info(self):
buf = StringIO()
names = self.model.names
buf.write("Correlation matrix of residuals" + '\n')
buf.write(pprint_matrix(self.model.resid_corr, names, names) + '\n')
return buf.getvalue()
def summary(self):
buf = StringIO()
rng = lrange(self.periods)
for i in range(self.neqs):
ppm = output.pprint_matrix(self.decomp[i], rng, self.names)
buf.write('FEVD for %s\n' % self.names[i])
buf.write(ppm + '\n')
print(buf.getvalue())
def summary(self):
buf = StringIO()
rng = lrange(self.periods)
for i in range(self.neqs):
ppm = output.pprint_matrix(self.decomp[i], rng, self.names)
buf.write('FEVD for %s\n' % self.names[i])
buf.write(ppm + '\n')
print(buf.getvalue())
def hypothesis_test_table(results, title, null_hyp):
fmt = dict(_default_table_fmt,
data_fmts=["%#15.6F","%#15.6F","%#15.3F", "%s"])
buf = StringIO()
table = SimpleTable([[results['statistic'],
results['crit_value'],
results['pvalue'],
str(results['df'])]],
['Test statistic', 'Critical Value', 'p-value',
'df'], [''], title=None, txt_fmt=fmt)
buf.write(title + '\n')
buf.write(str(table) + '\n')
buf.write(null_hyp + '\n')
buf.write("Conclusion: %s H_0" % results['conclusion'])
buf.write(" at %.2f%% significance level" % (results['signif'] * 100))
return buf.getvalue()
def hypothesis_test_table(results, title, null_hyp):
fmt = dict(_default_table_fmt,
data_fmts=["%#15.6F", "%#15.6F", "%#15.3F", "%s"])
buf = StringIO()
table = SimpleTable([[
results['statistic'], results['crit_value'], results['pvalue'],
str(results['df'])
]], ['Test statistic', 'Critical Value', 'p-value', 'df'], [''],
title=None,
txt_fmt=fmt)
buf.write(title + '\n')
buf.write(str(table) + '\n')
buf.write(null_hyp + '\n')
buf.write("Conclusion: %s H_0" % results['conclusion'])
buf.write(" at %.2f%% significance level" % (results['signif'] * 100))
return buf.getvalue()
def _coef_table(self):
model = self.model
k = model.neqs
Xnames = self.model.exog_names
data = lzip(model.params.T.ravel(),
model.stderr.T.ravel(),
model.tvalues.T.ravel(),
model.pvalues.T.ravel())
header = ('coefficient','std. error','t-stat','prob')
buf = StringIO()
dim = k * model.k_ar + model.k_trend
for i in range(k):
section = "Results for equation %s" % model.names[i]
buf.write(section + '\n')
#print >> buf, section
table = SimpleTable(data[dim * i : dim * (i + 1)], header,
Xnames, title=None, txt_fmt = self.default_fmt)
buf.write(str(table) + '\n')
if i < k - 1:
buf.write('\n')
return buf.getvalue()
def _coef_table(self):
model = self.model
k = model.neqs
Xnames = self.model.exog_names
data = lzip(model.params.T.ravel(), model.stderr.T.ravel(),
model.tvalues.T.ravel(), model.pvalues.T.ravel())
header = ('coefficient', 'std. error', 't-stat', 'prob')
buf = StringIO()
dim = k * model.k_ar + model.k_trend
for i in range(k):
section = "Results for equation %s" % model.names[i]
buf.write(section + '\n')
#print >> buf, section
table = SimpleTable(data[dim * i:dim * (i + 1)],
header,
Xnames,
title=None,
txt_fmt=self.default_fmt)
buf.write(str(table) + '\n')
if i < k - 1:
buf.write('\n')
return buf.getvalue()
def get_ic_table(ics, selected_orders):
'''
该方法将滞后阶数结果转换为表格化的分析结果
:param ics: 滞后阶数结果
:param selected_orders: 最大滞后阶数
:return: 返回表格化的滞后阶数分析结果
'''
_default_table_fmt = dict(empty_cell='',
colsep=' ',
row_pre='',
row_post='',
table_dec_above='=',
table_dec_below='=',
header_dec_below='-',
header_fmt='%s',
stub_fmt='%s',
title_align='c',
header_align='r',
data_aligns='r',
stubs_align='l',
fmt='txt')
cols = sorted(ics)
data = np.array([["%#10.4g" % v for v in ics[c]] for c in cols],
dtype=object).T
for i, col in enumerate(cols):
idx = int(selected_orders[col]), i
data[idx] = data[idx] + '*'
fmt = dict(_default_table_fmt, data_fmts=("%s", ) * len(cols))
buf = StringIO()
table = SimpleTable(data,
cols,
lrange(len(data)),
title='VAR Order Selection',
txt_fmt=fmt)
buf.write(str(table) + '\n')
buf.write('* Minimum' + '\n')
return buf.getvalue()
def make(self, endog_names=None, exog_names=None):
"""
Summary of VAR model
"""
buf = StringIO()
buf.write(self._header_table() + '\n')
buf.write(self._stats_table() + '\n')
buf.write(self._coef_table() + '\n')
buf.write(self._resid_info() + '\n')
return buf.getvalue()
def make(self, endog_names=None, exog_names=None):
"""
Summary of VAR model
"""
buf = StringIO()
buf.write(self._header_table() + '\n')
buf.write(self._stats_table() + '\n')
buf.write(self._coef_table() + '\n')
buf.write(self._resid_info() + '\n')
return buf.getvalue()
