def print_summary(self):
"""
Print summary statistics describing the fit, the coefficients, and the error bounds.
"""
# pylint: disable=unnecessary-lambda
# Print information about data first
justify = string_justify(18)
print(self)
print("{} = {}".format(justify("event col"), self.event_col))
print("{} = {}".format(justify("number of subjects"), self._n_unique))
print("{} = {}".format(justify("number of periods"), self._n_examples))
print("{} = {}".format(justify("number of events"), self.event_observed.sum()))
print("{} = {:.3f}".format(justify("log-likelihood"), self._log_likelihood))
print(
"{} = {} UTC".format(justify("time fit was run"), self._time_fit_was_called), end="\n\n"
)
print("---")
df = self.summary
# Significance codes last
df[""] = [significance_code(p) for p in df["p"]]
print(df.to_string(float_format=lambda f: "{:4.4f}".format(f)))
# Significance code explanation
print("---")
print(significance_codes_as_text(), end="\n\n")
print(
"Likelihood ratio test = {:.3f} on {} df, p={:.5f}".format(
*self._compute_likelihood_ratio_test()
)
)
def print_summary(self):
"""
Print summary statistics describing the fit, the coefficients, and the error bounds.
"""
# Print information about data first
justify = string_justify(18)
print(self)
print("{} = {}".format(justify('event col'), self.event_col))
print('{} = {}'.format(justify('number of subjects'), self._n_unique))
print('{} = {}'.format(justify('number of periods'), self._n_examples))
print('{} = {}'.format(justify('number of events'), self.event_observed.sum()))
print('{} = {:.3f}'.format(justify('log-likelihood'), self._log_likelihood))
print('{} = {} UTC'.format(justify('time fit was run'), self._time_fit_was_called), end='\n\n')
print('---')
df = self.summary
# Significance codes last
df[''] = [significance_code(p) for p in df['p']]
print(df.to_string(float_format=lambda f: '{:4.4f}'.format(f)))
# Significance code explanation
print('---')
print(significance_codes_as_text(), end='\n\n')
print("Likelihood ratio test = {:.3f} on {} df, p={:.5f}".format(*self._compute_likelihood_ratio_test()))
return
def print_summary(self, decimals=2, **kwargs):
"""
Print summary statistics describing the fit, the coefficients, and the error bounds.
Parameters
-----------
decimals: int, optional (default=2)
specify the number of decimal places to show
kwargs:
print additional metadata in the output (useful to provide model names, dataset names, etc.) when comparing
multiple outputs.
"""
justify = string_justify(18)
print(self)
print("{} = {}".format(justify("number of subjects"), self.durations.shape[0]))
print("{} = {}".format(justify("number of events"), np.where(self.event_observed)[0].shape[0]))
print("{} = {:.3f}".format(justify("log-likelihood"), self._log_likelihood))
print("{} = {}".format(justify("hypothesis"), "lambda != 1, rho != 1"))
for k, v in kwargs.items():
print("{} = {}\n".format(justify(k), v))
print(end="\n")
print("---")
df = self.summary
print(df.to_string(float_format=format_floats(decimals), formatters={"p": format_p_value(decimals)}))
def print_summary(self, decimals=2, **kwargs):
"""
Print summary statistics describing the fit, the coefficients, and the error bounds.
Parameters
-----------
decimals: int, optional (default=2)
specify the number of decimal places to show
kwargs:
print additional meta data in the output (useful to provide model names, dataset names, etc.) when comparing
multiple outputs.
"""
# Print information about data first
justify = string_justify(18)
print(self)
print("{} = '{}'".format(justify("event col"), self.event_col))
if self.weights_col:
print("{} = '{}'".format(justify("weights col"), self.weights_col))
if self.strata:
print("{} = {}".format(justify("strata"), self.strata))
if self.penalizer > 0:
print("{} = {}".format(justify("penalizer"), self.penalizer))
print("{} = {}".format(justify("number of subjects"), self._n_unique))
print("{} = {}".format(justify("number of periods"), self._n_examples))
print("{} = {}".format(justify("number of events"), self.event_observed.sum()))
print("{} = {:.{prec}f}".format(justify("log-likelihood"), self._log_likelihood, prec=decimals))
print("{} = {} UTC".format(justify("time fit was run"), self._time_fit_was_called))
for k, v in kwargs.items():
print("{} = {}\n".format(justify(k), v))
print(end="\n")
print("---")
df = self.summary
# Significance codes last
print(
df.to_string(
float_format=format_floats(decimals),
formatters={"p": format_p_value(decimals), "exp(coef)": format_exp_floats(decimals)},
)
)
# Significance code explanation
print("---")
with np.errstate(invalid="ignore", divide="ignore"):
sr = self.log_likelihood_ratio_test()
print(
"Log-likelihood ratio test = {:.{prec}f} on {} df, -log2(p)={:.{prec}f}".format(
sr.test_statistic, sr.degrees_freedom, -np.log2(sr.p_value), prec=decimals
)
)
def print_summary(self, decimals=2, **kwargs):
"""
Print summary statistics describing the fit, the coefficients, and the error bounds.
Parameters
-----------
decimals: int, optional (default=2)
specify the number of decimal places to show
kwargs:
print additional meta data in the output (useful to provide model names, dataset names, etc.) when comparing
multiple outputs.
"""
# Print information about data first
justify = string_justify(18)
print(self)
print("{} = '{}'".format(justify("event col"), self.event_col))
if self.weights_col:
print("{} = '{}'".format(justify("weights col"), self.weights_col))
if self.strata:
print("{} = {}".format(justify("strata"), self.strata))
if self.penalizer > 0:
print("{} = {}".format(justify("penalizer"), self.penalizer))
print("{} = {}".format(justify("number of subjects"), self._n_unique))
print("{} = {}".format(justify("number of periods"), self._n_examples))
print("{} = {}".format(justify("number of events"), self.event_observed.sum()))
print("{} = {:.{prec}f}".format(justify("log-likelihood"), self._log_likelihood, prec=decimals))
print("{} = {} UTC".format(justify("time fit was run"), self._time_fit_was_called))
for k, v in kwargs.items():
print("{} = {}\n".format(justify(k), v))
print(end="\n")
print("---")
df = self.summary
# Significance codes last
print(
df.to_string(
float_format=format_floats(decimals),
formatters={"p": format_p_value(decimals), "exp(coef)": format_exp_floats(decimals)},
)
)
# Significance code explanation
print("---")
with np.errstate(invalid="ignore", divide="ignore"):
sr = self.log_likelihood_ratio_test()
print(
"Log-likelihood ratio test = {:.{prec}f} on {} df, -log2(p)={:.{prec}f}".format(
sr.test_statistic, sr.degrees_freedom, -np.log2(sr.p_value), prec=decimals
)
)
def _stringify_meta_data(self, dictionary):
longest_key = max([len(k) for k in dictionary])
justify = string_justify(longest_key)
s = ""
for k, v in dictionary.items():
s += "{} = {}\n".format(justify(k), v)
return s
def print_summary(self, decimals=2, **kwargs):
"""
Print summary statistics describing the fit, the coefficients, and the error bounds.
Parameters
-----------
decimals: int, optional (default=2)
specify the number of decimal places to show
alpha: float or iterable
specify confidence intervals to show
kwargs:
print additional metadata in the output (useful to provide model names, dataset names, etc.) when comparing
multiple outputs.
"""
# Print information about data first
justify = string_justify(18)
print(self)
print("{} = '{}'".format(justify("duration col"), self.duration_col))
if self.event_col:
print("{} = '{}'".format(justify("event col"), self.event_col))
if self.weights_col:
print("{} = '{}'".format(justify("weights col"), self.weights_col))
if self.penalizer > 0:
print("{} = {}".format(justify("penalizer"), self.penalizer))
if self.robust:
print("{} = {}".format(justify("robust variance"), True))
print("{} = {}".format(justify("number of subjects"), self._n_examples))
print("{} = {}".format(justify("number of events"), self.event_observed.sum()))
print("{} = {:.{prec}f}".format(justify("log-likelihood"), self._log_likelihood, prec=decimals))
print("{} = {}".format(justify("time fit was run"), self._time_fit_was_called))
for k, v in kwargs.items():
print("{} = {}\n".format(justify(k), v))
print(end="\n")
print("---")
df = self.summary
# Significance codes as last column
print(
df.to_string(
float_format=format_floats(decimals),
formatters={"p": format_p_value(decimals), "exp(coef)": format_exp_floats(decimals)},
)
)
# Significance code explanation
print("---")
print("Concordance = {:.{prec}f}".format(self.score_, prec=decimals))
print(
"Log-likelihood ratio test = {:.{prec}f} on {} df, -log2(p)={:.{prec}f}".format(
*self._compute_likelihood_ratio_test(), prec=decimals
)
)
def print_summary(self, decimals=2, style=None, columns=None, **kwargs):
"""
Print summary statistics describing the fit, the coefficients, and the error bounds.
Parameters
-----------
decimals: int, optional (default=2)
specify the number of decimal places to show
style: string
{html, ascii, latex}
columns:
only display a subset of ``summary`` columns. Default all.
kwargs:
print additional meta data in the output (useful to provide model names, dataset names, etc.) when comparing
multiple outputs.
"""
justify = string_justify(18)
headers = []
if self.event_col:
headers.append(("event col", "'%s'" % self.event_col))
if self.weights_col:
headers.append(("weights col", "'%s'" % self.weights_col))
if isinstance(self.penalizer, np.ndarray) or self.penalizer > 0:
headers.append(("penalizer", self.penalizer))
if self.strata:
headers.append(("strata", self.strata))
headers.extend([
("number of subjects", self._n_unique),
("number of periods", self._n_examples),
("number of events", self.event_observed.sum()),
("partial log-likelihood",
"{:.{prec}f}".format(self.log_likelihood_, prec=decimals)),
("time fit was run", self._time_fit_was_called),
])
sr = self.log_likelihood_ratio_test()
footers = []
footers.extend([
("Partial AIC", "{:.{prec}f}".format(self.AIC_partial_,
prec=decimals)),
(
"log-likelihood ratio test",
"{:.{prec}f} on {} df".format(sr.test_statistic,
sr.degrees_freedom,
prec=decimals),
),
("-log2(p) of ll-ratio test",
"{:.{prec}f}".format(-utils.quiet_log2(sr.p_value),
prec=decimals)),
])
p = Printer(self, headers, footers, justify, kwargs, decimals, columns)
p.print(style=style)
def print_summary(self, decimals=2, **kwargs):
"""
Print summary statistics describing the fit, the coefficients, and the error bounds.
Parameters
-----------
decimals: int, optional (default=2)
specify the number of decimal places to show
kwargs:
print additional meta data in the output (useful to provide model names, dataset names, etc.) when comparing
multiple outputs.
"""
# Print information about data first
justify = string_justify(18)
print(self)
print("{} = '{}'".format(justify("duration col"), self.duration_col))
print("{} = '{}'".format(justify("event col"), self.event_col))
if self.weights_col:
print("{} = '{}'".format(justify("weights col"), self.weights_col))
if self.coef_penalizer > 0:
print("{} = '{}'".format(justify("coef penalizer"),
self.coef_penalizer))
if self.smoothing_penalizer > 0:
print("{} = '{}'".format(justify("smoothing penalizer"),
self.smoothing_penalizer))
print("{} = {}".format(justify("number of subjects"),
self._n_examples))
print("{} = {}".format(justify("number of events"),
self.event_observed.sum()))
print("{} = {}".format(justify("time fit was run"),
self._time_fit_was_called))
for k, v in kwargs.items():
print("{} = {}\n".format(justify(k), v))
print(end="\n")
print("---")
df = self.summary
print(
df.to_string(
float_format=format_floats(decimals),
formatters={
"p": format_p_value(decimals),
"exp(coef)": format_exp_floats(decimals)
},
))
# Significance code explanation
print("---")
print("Concordance = {:.{prec}f}".format(self.score_, prec=decimals))
def print_summary(self, decimals=2, **kwargs):
"""
Print summary statistics describing the fit, the coefficients, and the error bounds.
Parameters
-----------
decimals: int, optional (default=2)
specify the number of decimal places to show
kwargs:
print additional metadata in the output (useful to provide model names, dataset names, etc.) when comparing
multiple outputs.
"""
# Print information about data first
justify = string_justify(18)
print(self)
print("{} = '{}'".format(justify("event col"), self.event_col))
if self.weights_col:
print("{} = '{}'".format(justify("weights col"), self.weights_col))
if self.strata:
print("{} = {}".format(justify("strata"), self.strata))
print("{} = {}".format(justify("number of subjects"), self._n_unique))
print("{} = {}".format(justify("number of periods"), self._n_examples))
print("{} = {}".format(justify("number of events"),
self.event_observed.sum()))
print("{} = {:.{prec}f}".format(justify("log-likelihood"),
self._log_likelihood,
prec=decimals))
print("{} = {} UTC".format(justify("time fit was run"),
self._time_fit_was_called))
for k, v in kwargs.items():
print("{} = {}\n".format(justify(k), v))
print(end="\n")
print("---")
df = self.summary
# Significance codes last
df[""] = [significance_code(p) for p in df["p"]]
print(
df.to_string(float_format=format_floats(decimals),
formatters={"p": format_p_value(decimals)}))
# Significance code explanation
print("---")
print(significance_codes_as_text(), end="\n\n")
print(
"Likelihood ratio test = {:.{prec}f} on {} df, log(p)={:.{prec}f}".
format(*self._compute_likelihood_ratio_test(), prec=decimals))
def print_summary(self, decimals=2, **kwargs):
"""
Print summary statistics describing the fit, the coefficients, and the error bounds.
Parameters
-----------
decimals: int, optional (default=2)
specify the number of decimal places to show
kwargs:
print additional meta data in the output (useful to provide model names, dataset names, etc.) when comparing
multiple outputs.
"""
# Print information about data first
justify = string_justify(18)
print(self)
print("{} = '{}'".format(justify("duration col"), self.duration_col))
print("{} = '{}'".format(justify("event col"), self.event_col))
if self.weights_col:
print("{} = '{}'".format(justify("weights col"), self.weights_col))
if self.coef_penalizer > 0:
print("{} = '{}'".format(justify("coef penalizer"), self.coef_penalizer))
if self.smoothing_penalizer > 0:
print("{} = '{}'".format(justify("smoothing penalizer"), self.smoothing_penalizer))
print("{} = {}".format(justify("number of subjects"), self._n_examples))
print("{} = {}".format(justify("number of events"), self.event_observed.sum()))
print("{} = {}".format(justify("time fit was run"), self._time_fit_was_called))
for k, v in kwargs.items():
print("{} = {}\n".format(justify(k), v))
print(end="\n")
print("---")
df = self.summary
print(
df.to_string(
float_format=format_floats(decimals),
formatters={"p": format_p_value(decimals), "exp(coef)": format_exp_floats(decimals)},
)
)
# Significance code explanation
print("---")
print("Concordance = {:.{prec}f}".format(self.score_, prec=decimals))
def print_summary(self):
"""
Print summary statistics describing the fit.
"""
justify = string_justify(18)
print(self)
print('{} = {}'.format(justify('number of subjects'), self.durations.shape[0]))
print('{} = {}'.format(justify('number of events'), np.where(self.event_observed)[0].shape[0]))
print('{} = {:.3f}'.format(justify('log-likelihood'), self._log_likelihood), end='\n\n')
df = self.summary
df[''] = [significance_code(p) for p in df['p']]
print(df.to_string(float_format=lambda f: '{:4.4f}'.format(f)))
print('---')
print(significance_codes_as_text(), end='\n\n')
return
def print_summary(self):
"""
Print summary statistics describing the fit.
"""
# Print information about data first
justify = string_justify(18)
print(self)
print("{} = {}".format(justify('duration col'), self.duration_col))
print("{} = {}".format(justify('event col'), self.event_col))
if self.weights_col:
print("{} = {}".format(justify('weights col'), self.weights_col))
if self.cluster_col:
print("{} = {}".format(justify('cluster col'), self.cluster_col))
if self.robust or self.cluster_col:
print("{} = {}".format(justify('robust variance'), True))
if self.strata:
print('{} = {}'.format(justify('strata'), self.strata))
print('{} = {}'.format(justify('number of subjects'),
self._n_examples))
print('{} = {}'.format(justify('number of events'),
self.event_observed.sum()))
print('{} = {:.3f}'.format(justify('log-likelihood'),
self._log_likelihood))
print('{} = {}'.format(justify("time fit was run"),
self._time_fit_was_called),
end='\n\n')
print('---')
df = self.summary
# Significance codes last
df[''] = [significance_code(p) for p in df['p']]
print(df.to_string(float_format=lambda f: '{:4.4f}'.format(f)))
# Significance code explanation
print('---')
print(significance_codes_as_text(), end='\n\n')
print("Concordance = {:.3f}".format(self.score_))
print("Likelihood ratio test = {:.3f} on {} df, p={:.5f}".format(
*self._compute_likelihood_ratio_test()))
return
def print_summary(self):
"""
Print summary statistics describing the fit.
"""
# pylint: disable=unnecessary-lambda
# Print information about data first
justify = string_justify(18)
print(self)
print("{} = {}".format(justify("duration col"), self.duration_col))
print("{} = {}".format(justify("event col"), self.event_col))
if self.weights_col:
print("{} = {}".format(justify("weights col"), self.weights_col))
if self.cluster_col:
print("{} = {}".format(justify("cluster col"), self.cluster_col))
if self.robust or self.cluster_col:
print("{} = {}".format(justify("robust variance"), True))
if self.strata:
print("{} = {}".format(justify("strata"), self.strata))
print("{} = {}".format(justify("number of subjects"),
self._n_examples))
print("{} = {}".format(justify("number of events"),
self.event_observed.sum()))
print("{} = {:.3f}".format(justify("log-likelihood"),
self._log_likelihood))
print("{} = {}".format(justify("time fit was run"),
self._time_fit_was_called),
end="\n\n")
print("---")
df = self.summary
# Significance codes last
df[""] = [significance_code(p) for p in df["p"]]
print(df.to_string(float_format=lambda f: "{:4.4f}".format(f)))
# Significance code explanation
print("---")
print(significance_codes_as_text(), end="\n\n")
print("Concordance = {:.3f}".format(self.score_))
print("Likelihood ratio test = {:.3f} on {} df, p={:.5f}".format(
*self._compute_likelihood_ratio_test()))
def print_summary(self, decimals=2, style=None, columns=None, **kwargs):
"""
Print summary statistics describing the fit, the coefficients, and the error bounds.
Parameters
-----------
decimals: int, optional (default=2)
specify the number of decimal places to show
style: string
{html, ascii, latex}
columns:
only display a subset of ``summary`` columns. Default all.
kwargs:
print additional meta data in the output (useful to provide model names, dataset names, etc.) when comparing
multiple outputs.
"""
justify = string_justify(25)
headers = []
headers.append(("duration col", "'%s'" % self.duration_col))
if self.event_col:
headers.append(("event col", "'%s'" % self.event_col))
if self.weights_col:
headers.append(("weights col", "'%s'" % self.weights_col))
if self.coef_penalizer > 0:
headers.append(("coef penalizer", self.coef_penalizer))
if self.smoothing_penalizer > 0:
headers.append(("smoothing penalizer", self.smoothing_penalizer))
headers.extend(
[
("number of subjects", self._n_examples),
("number of events observed", self.event_observed.sum()),
("time fit was run", self._time_fit_was_called),
]
)
footers = [("Concordance", "{:.{prec}f}".format(self.concordance_index_, prec=decimals))]
p = Printer(self, headers, footers, justify, kwargs, decimals, columns)
p.print(style=style)
def print_summary(self):
"""
Print summary statistics describing the fit.
"""
# pylint: disable=unnecessary-lambda
justify = string_justify(18)
print(self)
print("{} = {}".format(justify("number of subjects"),
self.durations.shape[0]))
print("{} = {}".format(justify("number of events"),
np.where(self.event_observed)[0].shape[0]))
print("{} = {:.3f}".format(justify("log-likelihood"),
self._log_likelihood),
end="\n\n")
df = self.summary
df[""] = [significance_code(p) for p in df["p"]]
print(df.to_string(float_format=lambda f: "{:4.4f}".format(f)))
print("---")
print(significance_codes_as_text(), end="\n\n")
def print_summary(self, decimals=2, style=None, **kwargs):
"""
Print summary statistics describing the fit, the coefficients, and the error bounds.
Parameters
-----------
decimals: int, optional (default=2)
specify the number of decimal places to show
style: string
{html, ascii, latex}
kwargs:
print additional meta data in the output (useful to provide model names, dataset names, etc.) when comparing
multiple outputs.
"""
justify = string_justify(18)
headers = []
if self.event_col:
headers.append(("event col", "'%s'" % self.event_col))
if self.weights_col:
headers.append(("weights col", "'%s'" % self.weights_col))
if self.penalizer > 0:
headers.append(("penalizer", self.penalizer))
if self.strata:
headers.append(("strata", self.strata))
headers.extend([
("number of subjects", self._n_unique),
("number of periods", self._n_examples),
("number of events", self.event_observed.sum()),
("partial log-likelihood",
"{:.{prec}f}".format(self.log_likelihood_, prec=decimals)),
("time fit was run", self._time_fit_was_called),
])
p = Printer(headers, self, justify, decimals, kwargs)
p.print(style=style)
def print_summary(self, decimals=2, **kwargs):
"""
Print summary statistics describing the fit, the coefficients, and the error bounds.
Parameters
-----------
decimals: int, optional (default=2)
specify the number of decimal places to show
kwargs:
print additional meta data in the output (useful to provide model names, dataset names, etc.) when comparing
multiple outputs.
"""
justify = string_justify(25)
headers = []
headers.append(("duration col", "'%s'" % self.duration_col))
if self.event_col:
headers.append(("event col", "'%s'" % self.event_col))
if self.weights_col:
headers.append(("weights col", "'%s'" % self.weights_col))
if self.cluster_col:
headers.append(("cluster col", "'%s'" % self.cluster_col))
if self.coef_penalizer > 0:
headers.append(("coef penalizer", self.coef_penalizer))
if self.smoothing_penalizer > 0:
headers.append(("smoothing penalizer", self.smoothing_penalizer))
headers.extend([
("number of subjects", self._n_examples),
("number of events observed", self.event_observed.sum()),
("time fit was run", self._time_fit_was_called),
])
p = Printer(headers, self, justify, decimals, kwargs)
p.print()
