def test_SimpleTable_3(self):
""" Test SimpleTable.extend() as in extend down"""
desired = '''
==============================
header s1 header d1
------------------------------
stub R1 C1 10.30312 10.73999
stub R2 C1 90.30312 90.73999
header s2 header d2
------------------------------
stub R1 C2 50.95038 50.65765
stub R2 C2 40.95038 40.65765
------------------------------
'''
data1 = [[10.30312, 10.73999], [90.30312, 90.73999]]
data2 = [[50.95038, 50.65765], [40.95038, 40.65765]]
stubs1 = ['stub R1 C1', 'stub R2 C1']
stubs2 = ['stub R1 C2', 'stub R2 C2']
header1 = ['header s1', 'header d1']
header2 = ['header s2', 'header d2']
actual1 = SimpleTable(data1, header1, stubs1, txt_fmt=default_txt_fmt)
actual2 = SimpleTable(data2, header2, stubs2, txt_fmt=default_txt_fmt)
actual1.extend(actual2)
actual = '\n%s\n' % actual1.as_text()
self.assertEqual(desired, str(actual))
def summary_table(self, float_fmt="%6.3f"):
'''create a summary table with all influence and outlier measures
This does currently not distinguish between statistics that can be
calculated from the original regression results and for which a
leave-one-observation-out loop is needed
Returns
-------
res : SimpleTable instance
SimpleTable instance with the results, can be printed
Notes
-----
This also attaches table_data to the instance.
'''
#print self.dfbetas
# table_raw = [ np.arange(self.nobs),
# self.endog,
# self.fittedvalues,
# self.cooks_distance(),
# self.resid_studentized_internal,
# self.hat_matrix_diag,
# self.dffits_internal,
# self.resid_studentized_external,
# self.dffits,
# self.dfbetas
# ]
table_raw = [
('obs', np.arange(self.nobs)),
('endog', self.endog),
('fitted\nvalue', self.results.fittedvalues),
("Cook's\nd", self.cooks_distance[0]),
("student.\nresidual", self.resid_studentized_internal),
('hat diag', self.hat_matrix_diag),
('dffits \ninternal', self.dffits_internal[0]),
("ext.stud.\nresidual", self.resid_studentized_external),
('dffits', self.dffits[0]),
('dfbeta\nslope', self.dfbetas[:, 1]
) #skip needs to partially unravel
]
colnames, data = zip(*table_raw) #unzip
data = np.column_stack(data)
self.table_data = data
from gwstatsmodels.iolib.table import SimpleTable, default_html_fmt
from gwstatsmodels.iolib.tableformatting import fmt_base
from copy import deepcopy
fmt = deepcopy(fmt_base)
fmt_html = deepcopy(default_html_fmt)
fmt['data_fmts'] = ["%4d"] + [float_fmt] * (data.shape[1] - 1)
#fmt_html['data_fmts'] = fmt['data_fmts']
return SimpleTable(data,
headers=colnames,
txt_fmt=fmt,
html_fmt=fmt_html)
def test_SimpleTable_1(self):
"""Basic test, test_SimpleTable_1"""
desired = '''
=====================
header1 header2
---------------------
stub1 1.30312 2.73999
stub2 1.95038 2.65765
---------------------
'''
test1data = [[1.30312, 2.73999],[1.95038, 2.65765]]
test1stubs = ('stub1', 'stub2')
test1header = ('header1', 'header2')
actual = SimpleTable(test1data, test1header, test1stubs,
txt_fmt=default_txt_fmt)
actual = '\n%s\n' % actual.as_text()
self.assertEqual(desired, str(actual))
def summary_quantiles(self,
idx,
distppf,
frac=[0.01, 0.025, 0.05, 0.1, 0.975],
varnames=None,
title=None):
'''summary table for quantiles (critical values)
Parameters
----------
idx : None or list of integers
List of indices into the Monte Carlo results (columns) that should
be used in the calculation
distppf : callable
probability density function of reference distribution
TODO: use `crit` values instead or additional, see summary_cdf
frac : array_like, float
probabilities for which
varnames : None, or list of strings
optional list of variable names, same length as idx
Returns
-------
table : instance of SimpleTable
use `print table` to see results
'''
idx = np.atleast_1d(idx) #assure iterable, use list ?
quant, mcq = self.quantiles(idx, frac=frac)
#not sure whether this will work with single quantile
#crit = stats.chi2([2,4]).ppf(np.atleast_2d(quant).T)
crit = distppf(np.atleast_2d(quant).T)
mml = []
for i, ix in enumerate(idx): #TODO: hardcoded 2 ?
mml.extend([mcq[:, i], crit[:, i]])
#mmlar = np.column_stack(mml)
mmlar = np.column_stack([quant] + mml)
#print mmlar.shape
if title:
title = title + ' Quantiles (critical values)'
else:
title = 'Quantiles (critical values)'
#TODO use stub instead
if varnames is None:
varnames = ['var%d' % i for i in range(mmlar.shape[1] // 2)]
headers = ['\nprob'] + [
'%s\n%s' % (i, t) for i in varnames for t in ['mc', 'dist']
]
return SimpleTable(mmlar,
txt_fmt={
'data_fmts':
["%#6.3f"] + ["%#10.4f"] * (mmlar.shape[1] - 1)
},
title=title,
headers=headers)
def test_customlabel(self):
"""Limited test of custom custom labeling"""
if has_numpy:
tbl = SimpleTable(table1data, test1header, test1stubs, txt_fmt=txt_fmt1)
tbl[1][1].data = np.nan
tbl.label_cells(custom_labeller)
# print([[c.datatype for c in row] for row in tbl])
desired = """
*****************************
* * header1 * header2 *
*****************************
* stub1 * -- * 1 *
* stub2 * 2.00 * 3 *
*****************************
"""
actual = "\n%s\n" % tbl.as_text(missing="--")
# print(actual)
# print(desired)
self.assertEqual(actual, desired)
def test_SimpleTable_1(self):
"""Basic test, test_SimpleTable_1"""
desired = '''
=====================
header1 header2
---------------------
stub1 1.30312 2.73999
stub2 1.95038 2.65765
---------------------
'''
test1data = [[1.30312, 2.73999], [1.95038, 2.65765]]
test1stubs = ('stub1', 'stub2')
test1header = ('header1', 'header2')
actual = SimpleTable(test1data,
test1header,
test1stubs,
txt_fmt=default_txt_fmt)
actual = '\n%s\n' % actual.as_text()
self.assertEqual(desired, str(actual))
def test_SimpleTable_3(self):
""" Test SimpleTable.extend() as in extend down"""
desired = '''
==============================
header s1 header d1
------------------------------
stub R1 C1 10.30312 10.73999
stub R2 C1 90.30312 90.73999
header s2 header d2
------------------------------
stub R1 C2 50.95038 50.65765
stub R2 C2 40.95038 40.65765
------------------------------
'''
data1 = [[10.30312, 10.73999], [90.30312, 90.73999]]
data2 = [[50.95038, 50.65765], [40.95038, 40.65765]]
stubs1 = ['stub R1 C1', 'stub R2 C1']
stubs2 = ['stub R1 C2', 'stub R2 C2']
header1 = ['header s1', 'header d1']
header2 = ['header s2', 'header d2']
actual1 = SimpleTable(data1, header1, stubs1, txt_fmt=default_txt_fmt)
actual2 = SimpleTable(data2, header2, stubs2, txt_fmt=default_txt_fmt)
actual1.extend(actual2)
actual = '\n%s\n' % actual1.as_text()
self.assertEqual(desired, str(actual))
def summary_cdf(self, idx, frac, crit, varnames=None, title=None):
'''summary table for cumulative density function
Parameters
----------
idx : None or list of integers
List of indices into the Monte Carlo results (columns) that should
be used in the calculation
frac : array_like, float
probabilities for which
crit : array_like
values for which cdf is calculated
varnames : None, or list of strings
optional list of variable names, same length as idx
Returns
-------
table : instance of SimpleTable
use `print table` to see results
'''
idx = np.atleast_1d(idx) #assure iterable, use list ?
mml = []
#TODO:need broadcasting in cdf
for i in range(len(idx)):
#print i, mc1.cdf(crit[:,i], [idx[i]])[1].ravel()
mml.append(self.cdf(crit[:, i], [idx[i]])[1].ravel())
#mml = self.cdf(crit, idx)[1]
#mmlar = np.column_stack(mml)
#print mml[0].shape, np.shape(frac)
mmlar = np.column_stack([frac] + mml)
#print mmlar.shape
if title:
title = title + ' Probabilites'
else:
title = 'Probabilities'
#TODO use stub instead
#headers = ['\nprob'] + ['var%d\n%s' % (i, t) for i in range(mmlar.shape[1]-1) for t in ['mc']]
if varnames is None:
varnames = ['var%d' % i for i in range(mmlar.shape[1] - 1)]
headers = ['prob'] + varnames
return SimpleTable(mmlar,
txt_fmt={
'data_fmts': ["%#6.3f"] + ["%#10.4f"] *
(np.array(mml).shape[1] - 1)
},
title=title,
headers=headers)
def test_customlabel(self):
"""Limited test of custom custom labeling"""
if has_numpy:
tbl = SimpleTable(table1data,
test1header,
test1stubs,
txt_fmt=txt_fmt1)
tbl[1][1].data = np.nan
tbl.label_cells(custom_labeller)
#print([[c.datatype for c in row] for row in tbl])
desired = """
*****************************
* * header1 * header2 *
*****************************
* stub1 * -- * 1 *
* stub2 * 2.00 * 3 *
*****************************
"""
actual = '\n%s\n' % tbl.as_text(missing='--')
#print(actual)
#print(desired)
self.assertEqual(actual, desired)
def summary_proc(self, g):
"""
For internal use
"""
if self.exog != None:
myTitle = ('exog = ' + str(self.groups[g]) + '\n')
else:
myTitle = "Kaplan-Meier Curve"
table = np.transpose(self.results[g])
table = np.c_[np.transpose(self.ts[g]),table]
table = SimpleTable(table, headers=['Time','Survival','Std. Err'],
title = myTitle)
print(table)
def summary_find_nfact(self):
'''provides a summary for the selection of the number of factors
Returns
-------
sumstr : string
summary of the results for selecting the number of factors
'''
if not hasattr(self, 'results_find_nfact'):
self.fit_find_nfact()
results = self.results_find_nfact
sumstr = ''
sumstr += '\n' + 'Best result for k, by AIC, BIC, R2_adj, L1O'
# best = np.r_[(np.argmin(results[:,1:3],0), np.argmax(results[:,3],0),
# np.argmin(results[:,-1],0))]
sumstr += '\n' + ' '*19 + '%5d %4d %6d %5d' % tuple(self.best_nfact)
from gwstatsmodels.iolib.table import (SimpleTable, default_txt_fmt,
default_latex_fmt, default_html_fmt)
headers = 'k, AIC, BIC, R2_adj, L1O'.split(', ')
numformat = ['%6d'] + ['%10.3f']*4 #'%10.4f'
txt_fmt1 = dict(data_fmts = numformat)
tabl = SimpleTable(results, headers, None, txt_fmt=txt_fmt1)
sumstr += '\n' + "PCA regression on simulated data,"
sumstr += '\n' + "DGP: 2 factors and 4 explanatory variables"
sumstr += '\n' + tabl.__str__()
sumstr += '\n' + "Notes: k is number of components of PCA,"
sumstr += '\n' + " constant is added additionally"
sumstr += '\n' + " k=0 means regression on constant only"
sumstr += '\n' + " L1O: sum of squared prediction errors for leave-one-out"
return sumstr
def summary_find_nfact(self):
'''provides a summary for the selection of the number of factors
Returns
-------
sumstr : string
summary of the results for selecting the number of factors
'''
if not hasattr(self, 'results_find_nfact'):
self.fit_find_nfact()
results = self.results_find_nfact
sumstr = ''
sumstr += '\n' + 'Best result for k, by AIC, BIC, R2_adj, L1O'
# best = np.r_[(np.argmin(results[:,1:3],0), np.argmax(results[:,3],0),
# np.argmin(results[:,-1],0))]
sumstr += '\n' + ' ' * 19 + '%5d %4d %6d %5d' % tuple(self.best_nfact)
from gwstatsmodels.iolib.table import (SimpleTable, default_txt_fmt,
default_latex_fmt,
default_html_fmt)
headers = 'k, AIC, BIC, R2_adj, L1O'.split(', ')
numformat = ['%6d'] + ['%10.3f'] * 4 #'%10.4f'
txt_fmt1 = dict(data_fmts=numformat)
tabl = SimpleTable(results, headers, None, txt_fmt=txt_fmt1)
sumstr += '\n' + "PCA regression on simulated data,"
sumstr += '\n' + "DGP: 2 factors and 4 explanatory variables"
sumstr += '\n' + tabl.__str__()
sumstr += '\n' + "Notes: k is number of components of PCA,"
sumstr += '\n' + " constant is added additionally"
sumstr += '\n' + " k=0 means regression on constant only"
sumstr += '\n' + " L1O: sum of squared prediction errors for leave-one-out"
return sumstr
def print_summary(self, stats, orientation='auto'):
#TODO: need to specify a table formating for the numbers, using defualt
title = 'Summary Statistics'
header = stats
stubs = self.univariate['obs'][1]
data = [[self.univariate[astat][2][col] for astat in stats] for col in
range(len(self.univariate['obs'][2]))]
if (orientation == 'varcols') or \
(orientation == 'auto' and len(stubs) < len(header)):
#swap rows and columns
data = map(lambda *row: list(row), *data)
header, stubs = stubs, header
part_fmt = dict(data_fmts = ["%#8.4g"]*(len(header)-1))
table = SimpleTable(data,
header,
stubs,
title=title,
txt_fmt = part_fmt)
return table
title_align='r',
header_align='r',
data_aligns="r",
stubs_align="l",
fmt='txt')
cell0data = 0.0000
cell1data = 1
row0data = [cell0data, cell1data]
row1data = [2, 3.333]
table1data = [row0data, row1data]
test1stubs = ('stub1', 'stub2')
test1header = ('header1', 'header2')
#test1header = ('header1\nheader1a', 'header2\nheader2a')
tbl = SimpleTable(table1data,
test1header,
test1stubs,
txt_fmt=txt_fmt1,
ltx_fmt=ltx_fmt1,
html_fmt=html_fmt1)
def custom_labeller(cell):
if cell.data is np.nan:
return 'missing'
class test_Cell(unittest.TestCase):
def test_celldata(self):
celldata = cell0data, cell1data, row1data[0], row1data[1]
cells = [
Cell(datum, datatype=i % 2) for i, datum in enumerate(celldata)
]
def test_SimpleTable_4(self):
"""Basic test, test_SimpleTable_4
test uses custom txt_fmt"""
txt_fmt1 = dict(data_fmts=['%3.2f', '%d'],
empty_cell=' ',
colwidths=1,
colsep=' * ',
row_pre='* ',
row_post=' *',
table_dec_above='*',
table_dec_below='*',
header_dec_below='*',
header_fmt='%s',
stub_fmt='%s',
title_align='r',
header_align='r',
data_aligns="r",
stubs_align="l",
fmt='txt')
ltx_fmt1 = default_latex_fmt.copy()
html_fmt1 = default_html_fmt.copy()
cell0data = 0.0000
cell1data = 1
row0data = [cell0data, cell1data]
row1data = [2, 3.333]
table1data = [row0data, row1data]
test1stubs = ('stub1', 'stub2')
test1header = ('header1', 'header2')
tbl = SimpleTable(table1data,
test1header,
test1stubs,
txt_fmt=txt_fmt1,
ltx_fmt=ltx_fmt1,
html_fmt=html_fmt1)
def test_txt_fmt1(self):
"""Limited test of custom txt_fmt"""
desired = """
*****************************
* * header1 * header2 *
*****************************
* stub1 * 0.00 * 1 *
* stub2 * 2.00 * 3 *
*****************************
"""
actual = '\n%s\n' % tbl.as_text()
#print(actual)
#print(desired)
self.assertEqual(actual, desired)
def test_ltx_fmt1(self):
"""Limited test of custom ltx_fmt"""
desired = r"""
\begin{tabular}{lcc}
\toprule
& \textbf{header1} & \textbf{header2} \\
\midrule
\textbf{stub1} & 0.0 & 1 \\
\textbf{stub2} & 2 & 3.333 \\
\bottomrule
\end{tabular}
"""
actual = '\n%s\n' % tbl.as_latex_tabular()
#print(actual)
#print(desired)
self.assertEqual(actual, desired)
def test_html_fmt1(self):
"""Limited test of custom html_fmt"""
desired = """
<table class="simpletable">
<tr>
<td></td> <th>header1</th> <th>header2</th>
</tr>
<tr>
<th>stub1</th> <td>0.0</td> <td>1</td>
</tr>
<tr>
<th>stub2</th> <td>2</td> <td>3.333</td>
</tr>
</table>
"""
actual = '\n%s\n' % tbl.as_html()
self.assertEqual(actual, desired)
def summary_table(res, alpha=0.05):
'''generate summary table of outlier and influence similar to SAS
Parameters
----------
alpha : float
significance level for confidence interval
Returns
-------
st : SimpleTable instance
table with results that can be printed
data : ndarray
calculated measures and statistics for the table
ss2 : list of strings
column_names for table (Note: rows of table are observations)
'''
from scipy import stats
from gwstatsmodels.sandbox.regression.predstd import wls_prediction_std
infl = Influence(res)
#standard error for predicted mean
#Note: using hat_matrix only works for fitted values
predict_mean_se = np.sqrt(infl.hat_matrix_diag * res.mse_resid)
tppf = stats.t.isf(alpha / 2., res.df_resid)
predict_mean_ci = np.column_stack([
res.fittedvalues - tppf * predict_mean_se,
res.fittedvalues + tppf * predict_mean_se
])
#standard error for predicted observation
predict_se, predict_ci_low, predict_ci_upp = wls_prediction_std(res)
predict_ci = np.column_stack((predict_ci_low, predict_ci_upp))
#standard deviation of residual
resid_se = np.sqrt(res.mse_resid * (1 - infl.hat_matrix_diag))
table_sm = np.column_stack([
np.arange(res.nobs) + 1, res.model.endog, res.fittedvalues,
predict_mean_se, predict_mean_ci[:, 0], predict_mean_ci[:, 1],
predict_ci[:, 0], predict_ci[:, 1], res.resid, resid_se,
infl.resid_studentized_internal, infl.cooks_distance[0]
])
#colnames, data = zip(*table_raw) #unzip
data = table_sm
ss2 = [
'Obs', 'Dep Var\nPopulation', 'Predicted\nValue',
'Std Error\nMean Predict', 'Mean ci\n95% low', 'Mean ci\n95% upp',
'Predict ci\n95% low', 'Predict ci\n95% upp', 'Residual',
'Std Error\nResidual', 'Student\nResidual', "Cook's\nD"
]
colnames = ss2
#self.table_data = data
#data = np.column_stack(data)
from gwstatsmodels.iolib.table import SimpleTable, default_html_fmt
from gwstatsmodels.iolib.tableformatting import fmt_base
from copy import deepcopy
fmt = deepcopy(fmt_base)
fmt_html = deepcopy(default_html_fmt)
fmt['data_fmts'] = ["%4d"] + ["%6.3f"] * (data.shape[1] - 1)
#fmt_html['data_fmts'] = fmt['data_fmts']
st = SimpleTable(data, headers=colnames, txt_fmt=fmt, html_fmt=fmt_html)
return st, data, ss2
def summary_params_2d(result, extras=None, endog_names=None, exog_names=None,
title=None):
'''create summary table of regression parameters with several equations
This allows interleaving of parameters with bse and/or tvalues
Parameter
---------
result : result instance
the result instance with params and attributes in extras
extras : list of strings
additional attributes to add below a parameter row, e.g. bse or tvalues
endog_names : None or list of strings
names for rows of the parameter array (multivariate endog)
exog_names : None or list of strings
names for columns of the parameter array (exog)
alpha : float
level for confidence intervals, default 0.95
title : None or string
Returns
-------
tables : list of SimpleTable
this contains a list of all seperate Subtables
table_all : SimpleTable
the merged table with results concatenated for each row of the parameter
array
'''
if endog_names is None:
#TODO: note the [1:] is specific to current MNLogit
endog_names = ['endog_%d' % i for i in
np.unique(result.model.endog)[1:]]
if exog_names is None:
exog_names = ['var%d' %i for i in range(len(result.params))]
#TODO: check formatting options with different values
#res_params = [['%10.4f'%item for item in row] for row in result.params]
res_params = [[forg(item, prec=4) for item in row] for row in result.params]
if extras: #not None or non-empty
#maybe this should be a simple triple loop instead of list comprehension?
#below_list = [[['%10s' % ('('+('%10.3f'%v).strip()+')')
extras_list = [[['%10s' % ('(' + forg(v, prec=3).strip() + ')')
for v in col]
for col in getattr(result, what)]
for what in extras
]
data = zip(res_params, *extras_list)
data = [i for j in data for i in j] #flatten
stubs = zip(endog_names, *[['']*len(endog_names)]*len(extras))
stubs = [i for j in stubs for i in j] #flatten
#return SimpleTable(data, headers=exog_names, stubs=stubs)
else:
data = res_params
stubs = endog_names
# return SimpleTable(data, headers=exog_names, stubs=stubs,
# data_fmts=['%10.4f'])
import copy
txt_fmt = copy.deepcopy(fmt_params)
txt_fmt.update(dict(data_fmts = ["%s"]*result.params.shape[1]))
return SimpleTable(data, headers=exog_names,
stubs=stubs,
title=title,
# data_fmts = ["%s"]),
txt_fmt = txt_fmt)
def summary_params(results, yname=None, xname=None, alpha=.05, use_t=True,
skip_header=False):
'''create a summary table for the parameters
Parameters
----------
res : results instance
some required information is directly taken from the result
instance
yname : string or None
optional name for the endogenous variable, default is "y"
xname : list of strings or None
optional names for the exogenous variables, default is "var_xx"
alpha : float
significance level for the confidence intervals
use_t : bool
indicator whether the p-values are based on the Student-t
distribution (if True) or on the normal distribution (if False)
skip_headers : bool
If false (default), then the header row is added. If true, then no
header row is added.
Returns
-------
params_table : SimpleTable instance
'''
#Parameters part of the summary table
#------------------------------------
#Note: this is not necessary since we standardized names, only t versus normal
if isinstance(results, tuple):
#for multivariate endog
#TODO: check whether I don't want to refactor this
#we need to give parameter alpha to conf_int
results, params, std_err, tvalues, pvalues, conf_int = results
else:
params = results.params
std_err = results.bse
tvalues = results.tvalues #is this sometimes called zvalues
pvalues = results.pvalues
conf_int = results.conf_int(alpha)
#Dictionary to store the header names for the parameter part of the
#summary table. look up by modeltype
alp = str((1-alpha)*100)+'%'
if use_t:
param_header = ['coef', 'std err', 't', 'P>|t|',
'[' + alp + ' Conf. Int.]']
else:
param_header = ['coef', 'std err', 'z', 'P>|z|',
'[' + alp + ' Conf. Int.]']
if skip_header:
param_header = None
_, xname = _getnames(results, yname=yname, xname=xname)
params_stubs = xname
exog_idx = xrange(len(xname))
#center confidence intervals if they are unequal lengths
# confint = ["(%#6.3g, %#6.3g)" % tuple(conf_int[i]) for i in \
# exog_idx]
confint = ["%s %s" % tuple(map(forg, conf_int[i])) for i in \
exog_idx]
len_ci = map(len, confint)
max_ci = max(len_ci)
min_ci = min(len_ci)
if min_ci < max_ci:
confint = [ci.center(max_ci) for ci in confint]
#explicit f/g formatting, now uses forg, f or g depending on values
# params_data = zip(["%#6.4g" % (params[i]) for i in exog_idx],
# ["%#6.4f" % (std_err[i]) for i in exog_idx],
# ["%#6.3f" % (tvalues[i]) for i in exog_idx],
# ["%#6.3f" % (pvalues[i]) for i in exog_idx],
# confint
## ["(%#6.3g, %#6.3g)" % tuple(conf_int[i]) for i in \
## exog_idx]
# )
params_data = zip([forg(params[i], prec=4) for i in exog_idx],
[forg(std_err[i]) for i in exog_idx],
[forg(tvalues[i]) for i in exog_idx],
["%#6.3f" % (pvalues[i]) for i in exog_idx],
confint
# ["(%#6.3g, %#6.3g)" % tuple(conf_int[i]) for i in \
# exog_idx]
)
parameter_table = SimpleTable(params_data,
param_header,
params_stubs,
title = None,
txt_fmt = fmt_params #gen_fmt #fmt_2, #gen_fmt,
)
return parameter_table
def summary_top(results, title=None, gleft=None, gright=None, yname=None, xname=None):
'''generate top table(s)
TODO: this still uses predefined model_methods
? allow gleft, gright to be 1 element tuples instead of filling with None?
'''
#change of names ?
gen_left, gen_right = gleft, gright
#time and names are always included
import time
time_now = time.localtime()
time_of_day = [time.strftime("%H:%M:%S", time_now)]
date = time.strftime("%a, %d %b %Y", time_now)
yname, xname = _getnames(results, yname=yname, xname=xname)
#create dictionary with default
#use lambdas because some values raise exception if they are not available
#alternate spellings are commented out to force unique labels
default_items = dict([
('Dependent Variable:', lambda: [yname]),
('Dep. Variable:', lambda: [yname]),
('Model:', lambda: [results.model.__class__.__name__]),
#('Model type:', lambda: [results.model.__class__.__name__]),
('Date:', lambda: [date]),
('Time:', lambda: time_of_day),
('Number of Obs:', lambda: [results.nobs]),
#('No. of Observations:', lambda: ["%#6d" % results.nobs]),
('No. Observations:', lambda: ["%#6d" % results.nobs]),
#('Df model:', lambda: [results.df_model]),
('Df Model:', lambda: ["%#6d" % results.df_model]),
#TODO: check when we have non-integer df
('Df Residuals:', lambda: ["%#6d" % results.df_resid]),
#('Df resid:', lambda: [results.df_resid]),
#('df resid:', lambda: [results.df_resid]), #check capitalization
('Log-Likelihood:', lambda: ["%#8.5g" % results.llf]) #doesn't exist for RLM - exception
#('Method:', lambda: [???]), #no default for this
])
if title is None:
title = results.model.__class__.__name__ + 'Regression Results'
if gen_left is None:
#default: General part of the summary table, Applicable to all? models
gen_left = [('Dep. Variable:', None),
('Model type:', None),
('Date:', None),
('No. Observations:', None)
('Df model:', None),
('Df resid:', None)]
try:
llf = results.llf
gen_left.append(('Log-Likelihood', None))
except: #AttributeError, NotImplementedError
pass
gen_right = []
gen_title = title
gen_header = None
#needed_values = [k for k,v in gleft + gright if v is None] #not used anymore
#replace missing (None) values with default values
gen_left_ = []
for item, value in gen_left:
if value is None:
value = default_items[item]() #let KeyErrors raise exception
gen_left_.append((item, value))
gen_left = gen_left_
if gen_right:
gen_right_ = []
for item, value in gen_right:
if value is None:
value = default_items[item]() #let KeyErrors raise exception
gen_right_.append((item, value))
gen_right = gen_right_
#check
missing_values = [k for k,v in gen_left + gen_right if v is None]
assert missing_values == [], missing_values
#pad both tables to equal number of rows
if gen_right:
if len(gen_right) < len(gen_left):
#fill up with blank lines to same length
gen_right += [(' ', ' ')] * (len(gen_left) - len(gen_right))
elif len(gen_right) > len(gen_left):
#fill up with blank lines to same length, just to keep it symmetric
gen_left += [(' ', ' ')] * (len(gen_right) - len(gen_left))
#padding in SimpleTable doesn't work like I want
#force extra spacing and exact string length in right table
gen_right = [('%-21s' % (' '+k), v) for k,v in gen_right]
gen_stubs_right, gen_data_right = zip_longest(*gen_right) #transpose row col
gen_table_right = SimpleTable(gen_data_right,
gen_header,
gen_stubs_right,
title = gen_title,
txt_fmt = fmt_2cols #gen_fmt
)
else:
gen_table_right = [] #because .extend_right seems works with []
#moved below so that we can pad if needed to match length of gen_right
#transpose rows and columns, `unzip`
gen_stubs_left, gen_data_left = zip_longest(*gen_left) #transpose row col
gen_table_left = SimpleTable(gen_data_left,
gen_header,
gen_stubs_left,
title = gen_title,
txt_fmt = fmt_2cols
)
gen_table_left.extend_right(gen_table_right)
general_table = gen_table_left
return general_table #, gen_table_left, gen_table_right
def summary(self, yname=None, xname=None, title=0, alpha=.05,
returns='text', model_info=None):
"""
Parameters
-----------
yname : string
optional, Default is `Y`
xname : list of strings
optional, Default is `X.#` for # in p the number of regressors
Confidance interval : (0,1) not implimented
title : string
optional, Defualt is 'Generalized linear model'
returns : string
'text', 'table', 'csv', 'latex', 'html'
Returns
-------
Defualt :
returns='print'
Prints the summarirized results
Option :
returns='text'
Prints the summarirized results
Option :
returns='table'
SimpleTable instance : summarizing the fit of a linear model.
Option :
returns='csv'
returns a string of csv of the results, to import into a spreadsheet
Option :
returns='latex'
Not implimented yet
Option :
returns='HTML'
Not implimented yet
Examples (needs updating)
--------
>>> import gwstatsmodels as sm
>>> data = sm.datasets.longley.load()
>>> data.exog = sm.add_constant(data.exog)
>>> ols_results = sm.OLS(data.endog, data.exog).results
>>> print ols_results.summary()
...
Notes
-----
conf_int calculated from normal dist.
"""
import time as time
#TODO Make sure all self.model.__class__.__name__ are listed
model_types = {'OLS' : 'Ordinary least squares',
'GLS' : 'Generalized least squares',
'GLSAR' : 'Generalized least squares with AR(p)',
'WLS' : 'Weigthed least squares',
'RLM' : 'Robust linear model',
'GLM' : 'Generalized linear model'
}
model_methods = {'OLS' : 'Least Squares',
'GLS' : 'Least Squares',
'GLSAR' : 'Least Squares',
'WLS' : 'Least Squares',
'RLM' : '?',
'GLM' : '?'
}
if title==0:
title = model_types[self.model.__class__.__name__]
if yname is None:
try:
yname = self.model.endog_names
except AttributeError:
yname = 'y'
if xname is None:
try:
xname = self.model.exog_names
except AttributeError:
xname = ['var_%d' % i for i in range(len(self.params))]
time_now = time.localtime()
time_of_day = [time.strftime("%H:%M:%S", time_now)]
date = time.strftime("%a, %d %b %Y", time_now)
modeltype = self.model.__class__.__name__
#dist_family = self.model.family.__class__.__name__
nobs = self.nobs
df_model = self.df_model
df_resid = self.df_resid
#General part of the summary table, Applicable to all? models
#------------------------------------------------------------
#TODO: define this generically, overwrite in model classes
#replace definition of stubs data by single list
#e.g.
gen_left = [('Model type:', [modeltype]),
('Date:', [date]),
('Dependent Variable:', yname), #What happens with multiple names?
('df model', [df_model])
]
gen_stubs_left, gen_data_left = zip_longest(*gen_left) #transpose row col
gen_title = title
gen_header = None
## gen_stubs_left = ('Model type:',
## 'Date:',
## 'Dependent Variable:',
## 'df model'
## )
## gen_data_left = [[modeltype],
## [date],
## yname, #What happens with multiple names?
## [df_model]
## ]
gen_table_left = SimpleTable(gen_data_left,
gen_header,
gen_stubs_left,
title = gen_title,
txt_fmt = gen_fmt
)
gen_stubs_right = ('Method:',
'Time:',
'Number of Obs:',
'df resid'
)
gen_data_right = ([modeltype], #was dist family need to look at more
time_of_day,
[nobs],
[df_resid]
)
gen_table_right = SimpleTable(gen_data_right,
gen_header,
gen_stubs_right,
title = gen_title,
txt_fmt = gen_fmt
)
gen_table_left.extend_right(gen_table_right)
general_table = gen_table_left
#Parameters part of the summary table
#------------------------------------
#Note: this is not necessary since we standardized names, only t versus normal
tstats = {'OLS' : self.t(),
'GLS' : self.t(),
'GLSAR' : self.t(),
'WLS' : self.t(),
'RLM' : self.t(),
'GLM' : self.t()
}
prob_stats = {'OLS' : self.pvalues,
'GLS' : self.pvalues,
'GLSAR' : self.pvalues,
'WLS' : self.pvalues,
'RLM' : self.pvalues,
'GLM' : self.pvalues
}
#Dictionary to store the header names for the parameter part of the
#summary table. look up by modeltype
alp = str((1-alpha)*100)+'%'
param_header = {
'OLS' : ['coef', 'std err', 't', 'P>|t|', alp + ' Conf. Interval'],
'GLS' : ['coef', 'std err', 't', 'P>|t|', alp + ' Conf. Interval'],
'GLSAR' : ['coef', 'std err', 't', 'P>|t|', alp + ' Conf. Interval'],
'WLS' : ['coef', 'std err', 't', 'P>|t|', alp + ' Conf. Interval'],
'GLM' : ['coef', 'std err', 't', 'P>|t|', alp + ' Conf. Interval'], #glm uses t-distribution
'RLM' : ['coef', 'std err', 'z', 'P>|z|', alp + ' Conf. Interval'] #checke z
}
params_stubs = xname
params = self.params
conf_int = self.conf_int(alpha)
std_err = self.bse
exog_len = xrange(len(xname))
tstat = tstats[modeltype]
prob_stat = prob_stats[modeltype]
# Simpletable should be able to handle the formating
params_data = zip(["%#6.4g" % (params[i]) for i in exog_len],
["%#6.4f" % (std_err[i]) for i in exog_len],
["%#6.4f" % (tstat[i]) for i in exog_len],
["%#6.4f" % (prob_stat[i]) for i in exog_len],
["(%#5g, %#5g)" % tuple(conf_int[i]) for i in \
exog_len]
)
parameter_table = SimpleTable(params_data,
param_header[modeltype],
params_stubs,
title = None,
txt_fmt = fmt_2, #gen_fmt,
)
#special table
#-------------
#TODO: exists in linear_model, what about other models
#residual diagnostics
#output options
#--------------
#TODO: JP the rest needs to be fixed, similar to summary in linear_model
def ols_printer():
"""
print summary table for ols models
"""
table = str(general_table)+'\n'+str(parameter_table)
return table
def ols_to_csv():
"""
exports ols summary data to csv
"""
pass
def glm_printer():
table = str(general_table)+'\n'+str(parameter_table)
return table
pass
printers = {'OLS': ols_printer,
'GLM' : glm_printer
}
if returns=='print':
try:
return printers[modeltype]()
except KeyError:
return printers['OLS']()
