Python Summary.add_extra_txt方法代码示例

# 需要导入模块: from statsmodels.iolib.summary import Summary [as 别名]
# 或者: from statsmodels.iolib.summary.Summary import add_extra_txt [as 别名]
    def summary(self, yname=None, xname=None, title=0, alpha=.05,
                return_fmt='text'):
        """
        This is for testing the new summary setup
        """
        from statsmodels.iolib.summary import (summary_top,
                                            summary_params, summary_return)

##        left = [(i, None) for i in (
##                        'Dependent Variable:',
##                        'Model type:',
##                        'Method:',
##			'Date:',
##                        'Time:',
##                        'Number of Obs:',
##                        'df resid',
##		        'df model',
##                         )]
        top_left = [('Dep. Variable:', None),
                    ('Model:', None),
                    ('Method:', ['IRLS']),
                    ('Norm:', [self.fit_options['norm']]),
                    ('Scale Est.:', [self.fit_options['scale_est']]),
                    ('Cov Type:', [self.fit_options['cov']]),
                    ('Date:', None),
                    ('Time:', None),
                    ('No. Iterations:', ["%d" % self.fit_history['iteration']])
                    ]
        top_right = [('No. Observations:', None),
                     ('Df Residuals:', None),
                     ('Df Model:', None)
                     ]

        if not title is None:
            title = "Robust linear Model Regression Results"

        #boiler plate
        from statsmodels.iolib.summary import Summary
        smry = Summary()
        smry.add_table_2cols(self, gleft=top_left, gright=top_right, #[],
                          yname=yname, xname=xname, title=title)
        smry.add_table_params(self, yname=yname, xname=xname, alpha=.05,
                             use_t=False)

        #diagnostic table is not used yet
#        smry.add_table_2cols(self, gleft=diagn_left, gright=diagn_right,
#                          yname=yname, xname=xname,
#                          title="")

#add warnings/notes, added to text format only
        etext =[]
        wstr = \
'''If the model instance has been used for another fit with different fit
parameters, then the fit options might not be the correct ones anymore .'''
        etext.append(wstr)

        if etext:
            smry.add_extra_txt(etext)

        return smry

# 需要导入模块: from statsmodels.iolib.summary import Summary [as 别名]
# 或者: from statsmodels.iolib.summary.Summary import add_extra_txt [as 别名]
    def summary(self, yname=None, xname=None, title=0, alpha=0.05, return_fmt="text"):
        """
        This is for testing the new summary setup
        """
        from statsmodels.iolib.summary import summary_top, summary_params, summary_return

        ##        left = [(i, None) for i in (
        ##                        'Dependent Variable:',
        ##                        'Model type:',
        ##                        'Method:',
        ##			'Date:',
        ##                        'Time:',
        ##                        'Number of Obs:',
        ##                        'df resid',
        ##		        'df model',
        ##                         )]
        top_left = [
            ("Dep. Variable:", None),
            ("Model:", None),
            ("Method:", ["IRLS"]),
            ("Norm:", [self.fit_options["norm"]]),
            ("Scale Est.:", [self.fit_options["scale_est"]]),
            ("Cov Type:", [self.fit_options["cov"]]),
            ("Date:", None),
            ("Time:", None),
            ("No. Iterations:", ["%d" % self.fit_history["iteration"]]),
        ]
        top_right = [("No. Observations:", None), ("Df Residuals:", None), ("Df Model:", None)]

        if not title is None:
            title = "Robust linear Model Regression Results"

        # boiler plate
        from statsmodels.iolib.summary import Summary

        smry = Summary()
        smry.add_table_2cols(self, gleft=top_left, gright=top_right, yname=yname, xname=xname, title=title)  # [],
        smry.add_table_params(self, yname=yname, xname=xname, alpha=alpha, use_t=self.use_t)

        # diagnostic table is not used yet
        #        smry.add_table_2cols(self, gleft=diagn_left, gright=diagn_right,
        #                          yname=yname, xname=xname,
        #                          title="")

        # add warnings/notes, added to text format only
        etext = []
        wstr = """If the model instance has been used for another fit with different fit
parameters, then the fit options might not be the correct ones anymore ."""
        etext.append(wstr)

        if etext:
            smry.add_extra_txt(etext)

        return smry

# 需要导入模块: from statsmodels.iolib.summary import Summary [as 别名]
# 或者: from statsmodels.iolib.summary.Summary import add_extra_txt [as 别名]
    def summary(self, yname=None, xname=None, title=0, alpha=.05,
                return_fmt='text'):
        """
        This is for testing the new summary setup
        """
        top_left = [('Dep. Variable:', None),
                    ('Model:', None),
                    ('Method:', ['IRLS']),
                    ('Norm:', [self.fit_options['norm']]),
                    ('Scale Est.:', [self.fit_options['scale_est']]),
                    ('Cov Type:', [self.fit_options['cov']]),
                    ('Date:', None),
                    ('Time:', None),
                    ('No. Iterations:', ["%d" % self.fit_history['iteration']])
                    ]
        top_right = [('No. Observations:', None),
                     ('Df Residuals:', None),
                     ('Df Model:', None)
                     ]

        if title is not None:
            title = "Robust linear Model Regression Results"

        # boiler plate
        from statsmodels.iolib.summary import Summary
        smry = Summary()
        smry.add_table_2cols(self, gleft=top_left, gright=top_right,
                             yname=yname, xname=xname, title=title)
        smry.add_table_params(self, yname=yname, xname=xname, alpha=alpha,
                              use_t=self.use_t)

        # add warnings/notes, added to text format only
        etext = []
        wstr = ("If the model instance has been used for another fit "
                "with different fit\n"
               "parameters, then the fit options might not be the correct "
               "ones anymore .")
        etext.append(wstr)

        if etext:
            smry.add_extra_txt(etext)

        return smry

# 需要导入模块: from statsmodels.iolib.summary import Summary [as 别名]
# 或者: from statsmodels.iolib.summary.Summary import add_extra_txt [as 别名]
    def summary(self):
        """Summary of test, containing statistic, p-value and critical values
        """
        table_data = [('Test Statistic', '{0:0.3f}'.format(self.stat)),
                      ('P-value', '{0:0.3f}'.format(self.pvalue)),
                      ('Lags', '{0:d}'.format(self.lags))]
        title = self._title

        if not title:
            title = self._test_name + " Results"
        table = SimpleTable(table_data, stubs=None, title=title, colwidths=18,
                            datatypes=[0, 1], data_aligns=("l", "r"))

        smry = Summary()
        smry.tables.append(table)

        cv_string = 'Critical Values: '
        cv = self._critical_values.keys()
        g = lambda x: float(x.split('%')[0])
        cv_numeric = array(lmap(g, cv))
        cv_numeric = sort(cv_numeric)
        for val in cv_numeric:
            p = str(int(val)) + '%'
            cv_string += '{0:0.2f}'.format(self._critical_values[p])
            cv_string += ' (' + p + ')'
            if val != cv_numeric[-1]:
                cv_string += ', '

        extra_text = ['Trend: ' + TREND_DESCRIPTION[self._trend],
                      cv_string,
                      'Null Hypothesis: ' + self.null_hypothesis,
                      'Alternative Hypothesis: ' + self.alternative_hypothesis]

        smry.add_extra_txt(extra_text)
        if self._summary_text:
            smry.add_extra_txt(self._summary_text)
        return smry

# 需要导入模块: from statsmodels.iolib.summary import Summary [as 别名]
# 或者: from statsmodels.iolib.summary.Summary import add_extra_txt [as 别名]

#.........这里部分代码省略.........
        yname : string, optional
            Default is `y`
        xname : list of strings, optional
            Default is `var_##` for ## in p the number of regressors
        title : string, optional
            Title for the top table. If not None, then this replaces the
            default title
        alpha : float
            significance level for the confidence intervals

        Returns
        -------
        smry : Summary instance
            this holds the summary tables and text, which can be printed or
            converted to various output formats.

        See Also
        --------
        statsmodels.iolib.summary.Summary : class to hold summary
            results

        """

        # #TODO: import where we need it (for now), add as cached attributes
        # from statsmodels.stats.stattools import (jarque_bera,
        #         omni_normtest, durbin_watson)
        # jb, jbpv, skew, kurtosis = jarque_bera(self.wresid)
        # omni, omnipv = omni_normtest(self.wresid)
        #
        eigvals = self.eigenvals
        condno = self.condition_number
        #
        # self.diagn = dict(jb=jb, jbpv=jbpv, skew=skew, kurtosis=kurtosis,
        #                   omni=omni, omnipv=omnipv, condno=condno,
        #                   mineigval=eigvals[0])

        top_left = [('Dep. Variable:', None),
                    ('Model:', None),
                    ('Method:', ['Least Squares']),
                    ('Date:', None),
                    ('Time:', None)
                    ]

        top_right = [('Pseudo R-squared:', ["%#8.4g" % self.prsquared]),
                     ('Bandwidth:', ["%#8.4g" % self.bandwidth]),
                     ('Sparsity:', ["%#8.4g" % self.sparsity]),
                     ('No. Observations:', None),
                     ('Df Residuals:', None), #[self.df_resid]), #TODO: spelling
                     ('Df Model:', None) #[self.df_model])
                    ]

        # diagn_left = [('Omnibus:', ["%#6.3f" % omni]),
        #               ('Prob(Omnibus):', ["%#6.3f" % omnipv]),
        #               ('Skew:', ["%#6.3f" % skew]),
        #               ('Kurtosis:', ["%#6.3f" % kurtosis])
        #               ]
        #
        # diagn_right = [('Durbin-Watson:', ["%#8.3f" % durbin_watson(self.wresid)]),
        #                ('Jarque-Bera (JB):', ["%#8.3f" % jb]),
        #                ('Prob(JB):', ["%#8.3g" % jbpv]),
        #                ('Cond. No.', ["%#8.3g" % condno])
        #                ]


        if title is None:
            title = self.model.__class__.__name__ + ' ' + "Regression Results"

        #create summary table instance
        from statsmodels.iolib.summary import Summary
        smry = Summary()
        smry.add_table_2cols(self, gleft=top_left, gright=top_right,
                          yname=yname, xname=xname, title=title)
        smry.add_table_params(self, yname=yname, xname=xname, alpha=.05,
                             use_t=True)

#        smry.add_table_2cols(self, gleft=diagn_left, gright=diagn_right,
                          #yname=yname, xname=xname,
                          #title="")

        #add warnings/notes, added to text format only
        etext = []
        if eigvals[-1] < 1e-10:
            wstr = "The smallest eigenvalue is %6.3g. This might indicate "
            wstr += "that there are\n"
            wstr += "strong multicollinearity problems or that the design "
            wstr += "matrix is singular."
            wstr = wstr % eigvals[-1]
            etext.append(wstr)
        elif condno > 1000:  #TODO: what is recommended
            wstr = "The condition number is large, %6.3g. This might "
            wstr += "indicate that there are\n"
            wstr += "strong multicollinearity or other numerical "
            wstr += "problems."
            wstr = wstr % condno
            etext.append(wstr)

        if etext:
            smry.add_extra_txt(etext)

        return smry

# 需要导入模块: from statsmodels.iolib.summary import Summary [as 别名]
# 或者: from statsmodels.iolib.summary.Summary import add_extra_txt [as 别名]
    def summary(self, alpha=.05, start=None, model_name=None):
        """
        Summarize the Model

        Parameters
        ----------
        alpha : float, optional
            Significance level for the confidence intervals. Default is 0.05.
        start : int, optional
            Integer of the start observation. Default is 0.
        model_name : string
            The name of the model used. Default is to use model class name.

        Returns
        -------
        summary : Summary instance
            This holds the summary table and text, which can be printed or
            converted to various output formats.

        See Also
        --------
        statsmodels.iolib.summary.Summary
        """
        from statsmodels.iolib.summary import Summary
        model = self.model
        title = 'Statespace Model Results'

        if start is None:
            start = 0
        if self.data.dates is not None:
            dates = self.data.dates
            d = dates[start]
            sample = ['%02d-%02d-%02d' % (d.month, d.day, d.year)]
            d = dates[-1]
            sample += ['- ' + '%02d-%02d-%02d' % (d.month, d.day, d.year)]
        else:
            sample = [str(start), ' - ' + str(self.model.nobs)]

        if model_name is None:
            model_name = model.__class__.__name__

        top_left = [
            ('Dep. Variable:', None),
            ('Model:', [model_name]),
            ('Date:', None),
            ('Time:', None),
            ('Sample:', [sample[0]]),
            ('', [sample[1]])
        ]

        top_right = [
            ('No. Observations:', [self.model.nobs]),
            ('Log Likelihood', ["%#5.3f" % self.llf]),
            ('AIC', ["%#5.3f" % self.aic]),
            ('BIC', ["%#5.3f" % self.bic]),
            ('HQIC', ["%#5.3f" % self.hqic])
        ]

        if hasattr(self, 'cov_type'):
            top_left.append(('Covariance Type:', [self.cov_type]))

        summary = Summary()
        summary.add_table_2cols(self, gleft=top_left, gright=top_right,
                                title=title)
        summary.add_table_params(self, alpha=alpha,
                                 xname=self.data.param_names, use_t=False)

        # Add warnings/notes, added to text format only
        etext = []
        if hasattr(self, 'cov_type'):
            etext.append(self.cov_kwds['description'])

        if etext:
            etext = ["[{0}] {1}".format(i + 1, text)
                     for i, text in enumerate(etext)]
            etext.insert(0, "Warnings:")
            summary.add_extra_txt(etext)

        return summary

# 需要导入模块: from statsmodels.iolib.summary import Summary [as 别名]
# 或者: from statsmodels.iolib.summary.Summary import add_extra_txt [as 别名]
    def summary(self, yname=None, xname=None, title=None, alpha=.05):
        """
        Summarize the Regression Results

        Parameters
        -----------
        yname : string, optional
            Default is `y`
        xname : list of strings, optional
            Default is `var_##` for ## in p the number of regressors
        title : string, optional
            Title for the top table. If not None, then this replaces the
            default title
        alpha : float
            significance level for the confidence intervals

        Returns
        -------
        smry : Summary instance
            this holds the summary tables and text, which can be printed or
            converted to various output formats.

        See Also
        --------
        statsmodels.iolib.summary.Summary : class to hold summary
            results

        """

        top_left = [('Dep. Variable:', None),
                    ('Model:', None),
                    ('Model Family:', [self.family.__class__.__name__]),
                    ('Link Function:', [self.family.link.__class__.__name__]),
                    ('Method:', ['IRLS']),
                    ('Date:', None),
                    ('Time:', None),
                    ('No. Iterations:',
                     ["%d" % self.fit_history['iteration']]),
                    ]

        top_right = [('No. Observations:', None),
                     ('Df Residuals:', None),
                     ('Df Model:', None),
                     ('Scale:', [self.scale]),
                     ('Log-Likelihood:', None),
                     ('Deviance:', ["%#8.5g" % self.deviance]),
                     ('Pearson chi2:', ["%#6.3g" % self.pearson_chi2])
                     ]

        if title is None:
            title = "Generalized Linear Model Regression Results"

        #create summary tables
        from statsmodels.iolib.summary import Summary
        smry = Summary()
        smry.add_table_2cols(self, gleft=top_left, gright=top_right,  # [],
                             yname=yname, xname=xname, title=title)
        smry.add_table_params(self, yname=yname, xname=xname, alpha=alpha,
                              use_t=self.use_t)

        if hasattr(self, 'constraints'):
            smry.add_extra_txt(['Model has been estimated subject to linear '
                          'equality constraints.'])

        #diagnostic table is not used yet:
        #smry.add_table_2cols(self, gleft=diagn_left, gright=diagn_right,
        #                  yname=yname, xname=xname,
        #                  title="")

        return smry

# 需要导入模块: from statsmodels.iolib.summary import Summary [as 别名]
# 或者: from statsmodels.iolib.summary.Summary import add_extra_txt [as 别名]

#.........这里部分代码省略.........
        Returns
        -------
        summary : Summary instance
            Object that contains tables and facilitated export to text, html or
            latex
        """
        # Summary layout
        # 1. Overall information
        # 2. Mean parameters
        # 3. Volatility parameters
        # 4. Distribution parameters
        # 5. Notes

        model = self.model
        model_name = model.name + ' - ' + model.volatility.name

        # Summary Header
        top_left = [('Dep. Variable:', self._dep_name),
                    ('Mean Model:', model.name),
                    ('Vol Model:', model.volatility.name),
                    ('Distribution:', model.distribution.name),
                    ('Method:', 'User-specified Parameters'),
                    ('', ''),
                    ('Date:', self._datetime.strftime('%a, %b %d %Y')),
                    ('Time:', self._datetime.strftime('%H:%M:%S'))]

        top_right = [('R-squared:', '--'),
                     ('Adj. R-squared:', '--'),
                     ('Log-Likelihood:', '%#10.6g' % self.loglikelihood),
                     ('AIC:', '%#10.6g' % self.aic),
                     ('BIC:', '%#10.6g' % self.bic),
                     ('No. Observations:', self._nobs),
                     ('', ''),
                     ('', ''),]

        title = model_name + ' Model Results'
        stubs = []
        vals = []
        for stub, val in top_left:
            stubs.append(stub)
            vals.append([val])
        table = SimpleTable(vals, txt_fmt=fmt_2cols, title=title, stubs=stubs)

        # create summary table instance
        smry = Summary()
        # Top Table
        # Parameter table
        fmt = fmt_2cols
        fmt['data_fmts'][1] = '%18s'

        top_right = [('%-21s' % ('  ' + k), v) for k, v in top_right]
        stubs = []
        vals = []
        for stub, val in top_right:
            stubs.append(stub)
            vals.append([val])
        table.extend_right(SimpleTable(vals, stubs=stubs))
        smry.tables.append(table)

        stubs = self._names
        header = ['coef']
        vals = (self.params,)
        formats = [(10, 4)]
        pos = 0
        param_table_data = []
        for _ in range(len(vals[0])):
            row = []
            for i, val in enumerate(vals):
                if isinstance(val[pos], np.float64):
                    converted = format_float_fixed(val[pos], *formats[i])
                else:
                    converted = val[pos]
                row.append(converted)
            pos += 1
            param_table_data.append(row)

        mc = self.model.num_params
        vc = self.model.volatility.num_params
        dc = self.model.distribution.num_params
        counts = (mc, vc, dc)
        titles = ('Mean Model', 'Volatility Model', 'Distribution')
        total = 0
        for title, count in zip(titles, counts):
            if count == 0:
                continue

            table_data = param_table_data[total:total + count]
            table_stubs = stubs[total:total + count]
            total += count
            table = SimpleTable(table_data,
                                stubs=table_stubs,
                                txt_fmt=fmt_params,
                                headers=header, title=title)
            smry.tables.append(table)

        extra_text = ('Results generated with user-specified parameters.',
                      'Since the model was not estimated, there are no std. '
                      'errors.')
        smry.add_extra_txt(extra_text)
        return smry

# 需要导入模块: from statsmodels.iolib.summary import Summary [as 别名]
# 或者: from statsmodels.iolib.summary.Summary import add_extra_txt [as 别名]
    def summary(self, yname=None, xname=None, title=None, alpha=.05):
        """Summarize the Regression Results

        Parameters
        ----------
        yname : string, optional
            Default is `y`
        xname : list of strings, optional
            Default is `var_##` for ## in p the number of regressors
        title : string, optional
            Title for the top table. If not None, then this replaces the
            default title
        alpha : float
            significance level for the confidence intervals

        Returns
        -------
        smry : Summary instance
            this holds the summary tables and text, which can be printed or
            converted to various output formats.

        See Also
        --------
        statsmodels.iolib.summary.Summary : class to hold summary results
        """
        eigvals = self.eigenvals
        condno = self.condition_number

        top_left = [('Dep. Variable:', None),
                    ('Model:', None),
                    ('Method:', ['Least Squares']),
                    ('Date:', None),
                    ('Time:', None)
                    ]

        top_right = [('Pseudo R-squared:', ["%#8.4g" % self.prsquared]),
                     ('Bandwidth:', ["%#8.4g" % self.bandwidth]),
                     ('Sparsity:', ["%#8.4g" % self.sparsity]),
                     ('No. Observations:', None),
                     ('Df Residuals:', None),
                     ('Df Model:', None)
                     ]

        if title is None:
            title = self.model.__class__.__name__ + ' ' + "Regression Results"

        # create summary table instance
        from statsmodels.iolib.summary import Summary
        smry = Summary()
        smry.add_table_2cols(self, gleft=top_left, gright=top_right,
                             yname=yname, xname=xname, title=title)
        smry.add_table_params(self, yname=yname, xname=xname, alpha=alpha,
                              use_t=self.use_t)

        # add warnings/notes, added to text format only
        etext = []
        if eigvals[-1] < 1e-10:
            wstr = "The smallest eigenvalue is %6.3g. This might indicate "
            wstr += "that there are\n"
            wstr += "strong multicollinearity problems or that the design "
            wstr += "matrix is singular."
            wstr = wstr % eigvals[-1]
            etext.append(wstr)
        elif condno > 1000:  # TODO: what is recommended
            wstr = "The condition number is large, %6.3g. This might "
            wstr += "indicate that there are\n"
            wstr += "strong multicollinearity or other numerical "
            wstr += "problems."
            wstr = wstr % condno
            etext.append(wstr)

        if etext:
            smry.add_extra_txt(etext)

        return smry

# 需要导入模块: from statsmodels.iolib.summary import Summary [as 别名]
# 或者: from statsmodels.iolib.summary.Summary import add_extra_txt [as 别名]

#.........这里部分代码省略.........
        summary : Summary instance
            Object that contains tables and facilitated export to text, html or
            latex
        """
        # Summary layout
        # 1. Overall information
        # 2. Mean parameters
        # 3. Volatility parameters
        # 4. Distribution parameters
        # 5. Notes

        model = self.model
        model_name = model.name + " - " + model.volatility.name

        # Summary Header
        top_left = [
            ("Dep. Variable:", self._dep_name),
            ("Mean Model:", model.name),
            ("Vol Model:", model.volatility.name),
            ("Distribution:", model.distribution.name),
            ("Method:", "User-specified Parameters"),
            ("", ""),
            ("Date:", self._datetime.strftime("%a, %b %d %Y")),
            ("Time:", self._datetime.strftime("%H:%M:%S")),
        ]

        top_right = [
            ("R-squared:", "--"),
            ("Adj. R-squared:", "--"),
            ("Log-Likelihood:", "%#10.6g" % self.loglikelihood),
            ("AIC:", "%#10.6g" % self.aic),
            ("BIC:", "%#10.6g" % self.bic),
            ("No. Observations:", self._nobs),
            ("", ""),
            ("", ""),
        ]

        title = model_name + " Model Results"
        stubs = []
        vals = []
        for stub, val in top_left:
            stubs.append(stub)
            vals.append([val])
        table = SimpleTable(vals, txt_fmt=fmt_2cols, title=title, stubs=stubs)

        # create summary table instance
        smry = Summary()
        # Top Table
        # Parameter table
        fmt = fmt_2cols
        fmt["data_fmts"][1] = "%18s"

        top_right = [("%-21s" % ("  " + k), v) for k, v in top_right]
        stubs = []
        vals = []
        for stub, val in top_right:
            stubs.append(stub)
            vals.append([val])
        table.extend_right(SimpleTable(vals, stubs=stubs))
        smry.tables.append(table)

        stubs = self._names
        header = ["coef"]
        vals = (self.params,)
        formats = [(10, 4)]
        pos = 0
        param_table_data = []
        for _ in range(len(vals[0])):
            row = []
            for i, val in enumerate(vals):
                if isinstance(val[pos], np.float64):
                    converted = format_float_fixed(val[pos], *formats[i])
                else:
                    converted = val[pos]
                row.append(converted)
            pos += 1
            param_table_data.append(row)

        mc = self.model.num_params
        vc = self.model.volatility.num_params
        dc = self.model.distribution.num_params
        counts = (mc, vc, dc)
        titles = ("Mean Model", "Volatility Model", "Distribution")
        total = 0
        for title, count in zip(titles, counts):
            if count == 0:
                continue

            table_data = param_table_data[total : total + count]
            table_stubs = stubs[total : total + count]
            total += count
            table = SimpleTable(table_data, stubs=table_stubs, txt_fmt=fmt_params, headers=header, title=title)
            smry.tables.append(table)

        extra_text = (
            "Results generated with user-specified parameters.",
            "Since the model was not estimated, there are no std. " "errors.",
        )
        smry.add_extra_txt(extra_text)
        return smry