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train | CoxPHFitter._compute_scaled_schoenfeld | r"""
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r"""
Let s_k be the kth schoenfeld residuals. Then E[s_k] = 0.
For tests of proportionality, we want to test if \beta_i(t) is \beta_i (constant) or not.
Let V_k be the contribution to the information matrix at time t_k.... | def _compute_scaled_schoenfeld(self, X, T, E, weights, index=None):
r"""
Let s_k be the kth schoenfeld residuals. Then E[s_k] = 0.
For tests of proportionality, we want to test if \beta_i(t) is \beta_i (constant) or not.
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train | CoxPHFitter._compute_schoenfeld_within_strata | A positive value of the residual shows an X value that is higher than expected at that death time. | lifelines/fitters/coxph_fitter.py | def _compute_schoenfeld_within_strata(self, X, T, E, weights):
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approximate change in betas as a result of excluding ith row. Good for finding outliers / specific
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train | CoxPHFitter.predict_log_partial_hazard | r"""
This is equivalent to R's linear.predictors.
Returns the log of the partial hazard for the individuals, partial since the
baseline hazard is not included. Equal to :math:`(x - mean(x_{train}))'\beta `
Parameters
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X: numpy array or DataFrame
a... | lifelines/fitters/coxph_fitter.py | def predict_log_partial_hazard(self, X):
r"""
This is equivalent to R's linear.predictors.
Returns the log of the partial hazard for the individuals, partial since the
baseline hazard is not included. Equal to :math:`(x - mean(x_{train}))'\beta `
Parameters
----------
... | def predict_log_partial_hazard(self, X):
r"""
This is equivalent to R's linear.predictors.
Returns the log of the partial hazard for the individuals, partial since the
baseline hazard is not included. Equal to :math:`(x - mean(x_{train}))'\beta `
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train | CoxPHFitter.predict_cumulative_hazard | Parameters
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a (n,d) covariate numpy array or DataFrame. If a DataFrame, columns
can be in any order. If a numpy array, columns must be in the
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times: iterable, optional
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a (n,d) covariate numpy array or DataFrame. If a DataFrame, columns
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train | CoxPHFitter.predict_survival_function | Predict the survival function for individuals, given their covariates. This assumes that the individual
just entered the study (that is, we do not condition on how long they have already lived for.)
Parameters
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X: numpy array or DataFrame
a (n,d) covariate numpy a... | lifelines/fitters/coxph_fitter.py | def predict_survival_function(self, X, times=None):
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Predict the survival function for individuals, given their covariates. This assumes that the individual
just entered the study (that is, we do not condition on how long they have already lived for.)
Parameters
----------
... | def predict_survival_function(self, X, times=None):
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Predict the survival function for individuals, given their covariates. This assumes that the individual
just entered the study (that is, we do not condition on how long they have already lived for.)
Parameters
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http://stats.stackexchange.com/questions/102986/percentile-loss-functions
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Returns the median lifetimes for the individuals, by default. If the survival curve of an
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http://stats.stackexchange.com/questions/102986/percentile-loss-functions
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... | def predict_percentile(self, X, p=0.5):
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r"""
Compute the expected lifetime, :math:`E[T]`, using covariates X. This algorithm to compute the expectation is
to use the fact that :math:`E[T] = \int_0^\inf P(T > t) dt = \int_0^\inf S(t) dt`. To compute the integral, we use the trapizoidal rule to approxim... | def predict_expectation(self, X):
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train | CoxPHFitter._compute_baseline_survival | Importantly, this agrees with what the KaplanMeierFitter produces. Ex:
Example
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>>> from lifelines.datasets import load_rossi
>>> from lifelines import CoxPHFitter, KaplanMeierFitter
>>> rossi = load_rossi()
>>> kmf = KaplanMeierFitter()
>>> kmf.fit(rossi[... | lifelines/fitters/coxph_fitter.py | def _compute_baseline_survival(self):
"""
Importantly, this agrees with what the KaplanMeierFitter produces. Ex:
Example
-------
>>> from lifelines.datasets import load_rossi
>>> from lifelines import CoxPHFitter, KaplanMeierFitter
>>> rossi = load_rossi()
... | def _compute_baseline_survival(self):
"""
Importantly, this agrees with what the KaplanMeierFitter produces. Ex:
Example
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>>> from lifelines.datasets import load_rossi
>>> from lifelines import CoxPHFitter, KaplanMeierFitter
>>> rossi = load_rossi()
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train | CoxPHFitter.plot_covariate_groups | Produces a visual representation comparing the baseline survival curve of the model versus
what happens when a covariate(s) is varied over values in a group. This is useful to compare
subjects' survival as we vary covariate(s), all else being held equal. The baseline survival
curve is equal to t... | lifelines/fitters/coxph_fitter.py | def plot_covariate_groups(self, covariates, values, plot_baseline=True, **kwargs):
"""
Produces a visual representation comparing the baseline survival curve of the model versus
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train | CoxPHFitter.check_assumptions | Use this function to test the proportional hazards assumption. See usage example at
https://lifelines.readthedocs.io/en/latest/jupyter_notebooks/Proportional%20hazard%20assumption.html
Parameters
-----------
training_df: DataFrame
the original DataFrame used in the call to... | lifelines/fitters/coxph_fitter.py | def check_assumptions(
self, training_df, advice=True, show_plots=False, p_value_threshold=0.01, plot_n_bootstraps=10
):
"""
Use this function to test the proportional hazards assumption. See usage example at
https://lifelines.readthedocs.io/en/latest/jupyter_notebooks/Proportional%2... | def check_assumptions(
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Use this function to test the proportional hazards assumption. See usage example at
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train | CoxPHFitter.score_ | The concordance score (also known as the c-index) of the fit. The c-index is a generalization of the ROC AUC
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"""
The concordance score (also known as the c-index) of the fit. The c-index is a generalization of the ROC AUC
to survival data, including censorships.
For this purpose, the ``score_`` is a measure of the predictive accuracy of the fitted model
onto the trai... | def score_(self):
"""
The concordance score (also known as the c-index) of the fit. The c-index is a generalization of the ROC AUC
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Calculates the concordance index (C-index) between two series
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train | _concordance_summary_statistics | Find the concordance index in n * log(n) time.
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train | _valid_comparison | True if times can be compared. | lifelines/utils/concordance.py | def _valid_comparison(time_a, time_b, event_a, event_b):
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train | LogNormalAFTFitter.predict_percentile | Returns the median lifetimes for the individuals, by default. If the survival curve of an
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http://stats.stackexchange.com/questions/102986/percentile-loss-functions
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train | UnivariateFitter.subtract | Subtract the {0} of two {1} objects.
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train | ParametericUnivariateFitter._compute_confidence_bounds_of_transform | This computes the confidence intervals of a transform of the parameters. Ex: take
the fitted parameters, a function/transform and the variance matrix and give me
back confidence intervals of the transform.
Parameters
-----------
transform: function
must a function of... | lifelines/fitters/__init__.py | def _compute_confidence_bounds_of_transform(self, transform, alpha, ci_labels):
"""
This computes the confidence intervals of a transform of the parameters. Ex: take
the fitted parameters, a function/transform and the variance matrix and give me
back confidence intervals of the transform... | def _compute_confidence_bounds_of_transform(self, transform, alpha, ci_labels):
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train | ParametericUnivariateFitter.summary | Summary statistics describing the fit.
Returns
-------
df : pd.DataFrame
Contains columns coef, exp(coef), se(coef), z, p, lower, upper
See Also
--------
``print_summary`` | lifelines/fitters/__init__.py | def summary(self):
"""
Summary statistics describing the fit.
Returns
-------
df : pd.DataFrame
Contains columns coef, exp(coef), se(coef), z, p, lower, upper
See Also
--------
``print_summary``
"""
ci = 1 - self.alpha
... | def summary(self):
"""
Summary statistics describing the fit.
Returns
-------
df : pd.DataFrame
Contains columns coef, exp(coef), se(coef), z, p, lower, upper
See Also
--------
``print_summary``
"""
ci = 1 - self.alpha
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train | ParametericUnivariateFitter.print_summary | 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 n... | lifelines/fitters/__init__.py | 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:
... | 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:
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train | ParametericUnivariateFitter.fit | Parameters
----------
durations: an array, or pd.Series
length n, duration subject was observed for
event_observed: numpy array or pd.Series, optional
length n, True if the the death was observed, False if the event was lost (right-censored). Defaults all True if event_observ... | lifelines/fitters/__init__.py | def fit(
self,
durations,
event_observed=None,
timeline=None,
label=None,
alpha=None,
ci_labels=None,
show_progress=False,
entry=None,
weights=None,
left_censorship=False,
): # pylint: disable=too-many-arguments
"""
... | def fit(
self,
durations,
event_observed=None,
timeline=None,
label=None,
alpha=None,
ci_labels=None,
show_progress=False,
entry=None,
weights=None,
left_censorship=False,
): # pylint: disable=too-many-arguments
"""
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train | ParametericUnivariateFitter.fit_left_censoring | Fit the model to a left-censored dataset
Parameters
----------
durations: an array, or pd.Series
length n, duration subject was observed for
event_observed: numpy array or pd.Series, optional
length n, True if the the death was observed, False if the event was lost (... | lifelines/fitters/__init__.py | def fit_left_censoring(
self,
durations,
event_observed=None,
timeline=None,
label=None,
alpha=None,
ci_labels=None,
show_progress=False,
entry=None,
weights=None,
): # pylint: disable=too-many-arguments
"""
Fit the mod... | def fit_left_censoring(
self,
durations,
event_observed=None,
timeline=None,
label=None,
alpha=None,
ci_labels=None,
show_progress=False,
entry=None,
weights=None,
): # pylint: disable=too-many-arguments
"""
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train | ParametericUnivariateFitter.fit_interval_censoring | Fit the model to an interval censored dataset.
Parameters
----------
lower_bound: an array, or pd.Series
length n, the start of the period the subject experienced the event in.
upper_bound: an array, or pd.Series
length n, the end of the period the subject experience... | lifelines/fitters/__init__.py | def fit_interval_censoring(
self,
lower_bound,
upper_bound,
event_observed=None,
timeline=None,
label=None,
alpha=None,
ci_labels=None,
show_progress=False,
entry=None,
weights=None,
): # pylint: disable=too-many-arguments
... | def fit_interval_censoring(
self,
lower_bound,
upper_bound,
event_observed=None,
timeline=None,
label=None,
alpha=None,
ci_labels=None,
show_progress=False,
entry=None,
weights=None,
): # pylint: disable=too-many-arguments
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train | ParametericUnivariateFitter.survival_function_at_times | Return a Pandas series of the predicted survival value at specific times.
Parameters
-----------
times: iterable or float
values to return the survival function at.
label: string, optional
Rename the series returned. Useful for plotting.
Returns
----... | lifelines/fitters/__init__.py | def survival_function_at_times(self, times, label=None):
"""
Return a Pandas series of the predicted survival value at specific times.
Parameters
-----------
times: iterable or float
values to return the survival function at.
label: string, optional
R... | def survival_function_at_times(self, times, label=None):
"""
Return a Pandas series of the predicted survival value at specific times.
Parameters
-----------
times: iterable or float
values to return the survival function at.
label: string, optional
R... | [
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train | ParametericUnivariateFitter.cumulative_density_at_times | Return a Pandas series of the predicted cumulative density function (1-survival function) at specific times.
Parameters
-----------
times: iterable or float
values to return the survival function at.
label: string, optional
Rename the series returned. Useful for plot... | lifelines/fitters/__init__.py | def cumulative_density_at_times(self, times, label=None):
"""
Return a Pandas series of the predicted cumulative density function (1-survival function) at specific times.
Parameters
-----------
times: iterable or float
values to return the survival function at.
... | def cumulative_density_at_times(self, times, label=None):
"""
Return a Pandas series of the predicted cumulative density function (1-survival function) at specific times.
Parameters
-----------
times: iterable or float
values to return the survival function at.
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train | ParametericUnivariateFitter.cumulative_hazard_at_times | Return a Pandas series of the predicted cumulative hazard value at specific times.
Parameters
-----------
times: iterable or float
values to return the cumulative hazard at.
label: string, optional
Rename the series returned. Useful for plotting.
Returns
... | lifelines/fitters/__init__.py | def cumulative_hazard_at_times(self, times, label=None):
"""
Return a Pandas series of the predicted cumulative hazard value at specific times.
Parameters
-----------
times: iterable or float
values to return the cumulative hazard at.
label: string, optional
... | def cumulative_hazard_at_times(self, times, label=None):
"""
Return a Pandas series of the predicted cumulative hazard value at specific times.
Parameters
-----------
times: iterable or float
values to return the cumulative hazard at.
label: string, optional
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train | ParametericUnivariateFitter.hazard_at_times | Return a Pandas series of the predicted hazard at specific times.
Parameters
-----------
times: iterable or float
values to return the hazard at.
label: string, optional
Rename the series returned. Useful for plotting.
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--------
pd.Ser... | lifelines/fitters/__init__.py | def hazard_at_times(self, times, label=None):
"""
Return a Pandas series of the predicted hazard at specific times.
Parameters
-----------
times: iterable or float
values to return the hazard at.
label: string, optional
Rename the series returned. Use... | def hazard_at_times(self, times, label=None):
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Return a Pandas series of the predicted hazard at specific times.
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-----------
times: iterable or float
values to return the hazard at.
label: string, optional
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train | ParametericUnivariateFitter.confidence_interval_hazard_ | The confidence interval of the hazard. | lifelines/fitters/__init__.py | def confidence_interval_hazard_(self):
"""
The confidence interval of the hazard.
"""
return self._compute_confidence_bounds_of_transform(self._hazard, self.alpha, self._ci_labels) | def confidence_interval_hazard_(self):
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The confidence interval of the hazard.
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train | ParametericUnivariateFitter.confidence_interval_survival_function_ | The confidence interval of the survival function. | lifelines/fitters/__init__.py | def confidence_interval_survival_function_(self):
"""
The confidence interval of the survival function.
"""
return self._compute_confidence_bounds_of_transform(self._survival_function, self.alpha, self._ci_labels) | def confidence_interval_survival_function_(self):
"""
The confidence interval of the survival function.
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train | ParametericUnivariateFitter.confidence_interval_cumulative_density_ | The confidence interval of the survival function. | lifelines/fitters/__init__.py | def confidence_interval_cumulative_density_(self):
"""
The confidence interval of the survival function.
"""
return self._compute_confidence_bounds_of_transform(self._cumulative_density, self.alpha, self._ci_labels) | def confidence_interval_cumulative_density_(self):
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train | ParametericUnivariateFitter.plot | Produce a pretty-plot of the estimate. | lifelines/fitters/__init__.py | def plot(self, **kwargs):
"""
Produce a pretty-plot of the estimate.
"""
set_kwargs_drawstyle(kwargs, "default")
return _plot_estimate(
self, estimate=getattr(self, self._estimate_name), confidence_intervals=self.confidence_interval_, **kwargs
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"""
Produce a pretty-plot of the estimate.
"""
set_kwargs_drawstyle(kwargs, "default")
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... | bdf6be6f1d10eea4c46365ee0ee6a47d8c30edf8 |
train | ParametericAFTRegressionFitter.fit | Fit the accelerated failure time model to a right-censored dataset.
Parameters
----------
df: DataFrame
a Pandas DataFrame with necessary columns `duration_col` and
`event_col` (see below), covariates columns, and special columns (weights).
`duration_col` ref... | lifelines/fitters/__init__.py | def fit(
self,
df,
duration_col,
event_col=None,
ancillary_df=None,
show_progress=False,
timeline=None,
weights_col=None,
robust=False,
initial_point=None,
entry_col=None,
):
"""
Fit the accelerated failure time ... | def fit(
self,
df,
duration_col,
event_col=None,
ancillary_df=None,
show_progress=False,
timeline=None,
weights_col=None,
robust=False,
initial_point=None,
entry_col=None,
):
"""
Fit the accelerated failure time ... | [
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train | ParametericAFTRegressionFitter.fit_interval_censoring | Fit the accelerated failure time model to a left-censored dataset.
Parameters
----------
df: DataFrame
a Pandas DataFrame with necessary columns ``lower_bound_col``, ``upper_bound_col`` (see below),
and any other covariates or weights.
lower_bound_col: string
... | lifelines/fitters/__init__.py | def fit_interval_censoring(
self,
df,
lower_bound_col,
upper_bound_col,
event_col=None,
ancillary_df=None,
show_progress=False,
timeline=None,
weights_col=None,
robust=False,
initial_point=None,
entry_col=None,
):
... | def fit_interval_censoring(
self,
df,
lower_bound_col,
upper_bound_col,
event_col=None,
ancillary_df=None,
show_progress=False,
timeline=None,
weights_col=None,
robust=False,
initial_point=None,
entry_col=None,
):
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] | CamDavidsonPilon/lifelines | python | https://github.com/CamDavidsonPilon/lifelines/blob/bdf6be6f1d10eea4c46365ee0ee6a47d8c30edf8/lifelines/fitters/__init__.py#L1327-L1455 | [
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train | ParametericAFTRegressionFitter.fit_left_censoring | Fit the accelerated failure time model to a left-censored dataset.
Parameters
----------
df: DataFrame
a Pandas DataFrame with necessary columns `duration_col` and
`event_col` (see below), covariates columns, and special columns (weights).
`duration_col` refe... | lifelines/fitters/__init__.py | def fit_left_censoring(
self,
df,
duration_col=None,
event_col=None,
ancillary_df=None,
show_progress=False,
timeline=None,
weights_col=None,
robust=False,
initial_point=None,
entry_col=None,
):
"""
Fit the accel... | def fit_left_censoring(
self,
df,
duration_col=None,
event_col=None,
ancillary_df=None,
show_progress=False,
timeline=None,
weights_col=None,
robust=False,
initial_point=None,
entry_col=None,
):
"""
Fit the accel... | [
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train | ParametericAFTRegressionFitter._create_initial_point | See https://github.com/CamDavidsonPilon/lifelines/issues/664 | lifelines/fitters/__init__.py | def _create_initial_point(self, Ts, E, entries, weights, Xs):
"""
See https://github.com/CamDavidsonPilon/lifelines/issues/664
"""
import lifelines # kinda hacky but lol
def transform_ith_param(model, i):
param = model._fitted_parameters_[i]
if param <= ... | def _create_initial_point(self, Ts, E, entries, weights, Xs):
"""
See https://github.com/CamDavidsonPilon/lifelines/issues/664
"""
import lifelines # kinda hacky but lol
def transform_ith_param(model, i):
param = model._fitted_parameters_[i]
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train | ParametericAFTRegressionFitter.predict_median | Predict the median lifetimes for the individuals. If the survival curve of an
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Parameters
----------
X: numpy array or DataFrame
a (n,d) covariate numpy array or DataFrame. If a DataFrame, columns
can be ... | lifelines/fitters/__init__.py | def predict_median(self, X, ancillary_X=None):
"""
Predict the median lifetimes for the individuals. If the survival curve of an
individual does not cross 0.5, then the result is infinity.
Parameters
----------
X: numpy array or DataFrame
a (n,d) covariate nu... | def predict_median(self, X, ancillary_X=None):
"""
Predict the median lifetimes for the individuals. If the survival curve of an
individual does not cross 0.5, then the result is infinity.
Parameters
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X: numpy array or DataFrame
a (n,d) covariate nu... | [
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train | ParametericAFTRegressionFitter.score_ | The concordance score (also known as the c-index) of the fit. The c-index is a generalization of the ROC AUC
to survival data, including censorships.
For this purpose, the ``score_`` is a measure of the predictive accuracy of the fitted model
onto the training dataset. | lifelines/fitters/__init__.py | def score_(self):
"""
The concordance score (also known as the c-index) of the fit. The c-index is a generalization of the ROC AUC
to survival data, including censorships.
For this purpose, the ``score_`` is a measure of the predictive accuracy of the fitted model
onto the trai... | def score_(self):
"""
The concordance score (also known as the c-index) of the fit. The c-index is a generalization of the ROC AUC
to survival data, including censorships.
For this purpose, the ``score_`` is a measure of the predictive accuracy of the fitted model
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train | ParametericAFTRegressionFitter.plot | Produces a visual representation of the coefficients, including their standard errors and magnitudes.
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----------
columns : list, optional
specify a subset of the columns to plot
errorbar_kwargs:
pass in additional plotting commands to matplotlib errorb... | lifelines/fitters/__init__.py | def plot(self, columns=None, parameter=None, **errorbar_kwargs):
"""
Produces a visual representation of the coefficients, including their standard errors and magnitudes.
Parameters
----------
columns : list, optional
specify a subset of the columns to plot
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Produces a visual representation of the coefficients, including their standard errors and magnitudes.
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columns : list, optional
specify a subset of the columns to plot
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train | ParametericAFTRegressionFitter.plot_covariate_groups | Produces a visual representation comparing the baseline survival curve of the model versus
what happens when a covariate(s) is varied over values in a group. This is useful to compare
subjects' survival as we vary covariate(s), all else being held equal. The baseline survival
curve is equal to t... | lifelines/fitters/__init__.py | def plot_covariate_groups(self, covariates, values, plot_baseline=True, **kwargs):
"""
Produces a visual representation comparing the baseline survival curve of the model versus
what happens when a covariate(s) is varied over values in a group. This is useful to compare
subjects' surviva... | def plot_covariate_groups(self, covariates, values, plot_baseline=True, **kwargs):
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Produces a visual representation comparing the baseline survival curve of the model versus
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train | LogLogisticAFTFitter.predict_percentile | Returns the median lifetimes for the individuals, by default. If the survival curve of an
individual does not cross ``p``, then the result is infinity.
http://stats.stackexchange.com/questions/102986/percentile-loss-functions
Parameters
----------
X: numpy array or DataFrame
... | lifelines/fitters/log_logistic_aft_fitter.py | def predict_percentile(self, X, ancillary_X=None, p=0.5):
"""
Returns the median lifetimes for the individuals, by default. If the survival curve of an
individual does not cross ``p``, then the result is infinity.
http://stats.stackexchange.com/questions/102986/percentile-loss-functions
... | def predict_percentile(self, X, ancillary_X=None, p=0.5):
"""
Returns the median lifetimes for the individuals, by default. If the survival curve of an
individual does not cross ``p``, then the result is infinity.
http://stats.stackexchange.com/questions/102986/percentile-loss-functions
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train | LogLogisticAFTFitter.predict_expectation | Predict the expectation of lifetimes, :math:`E[T | x]`.
Parameters
----------
X: numpy array or DataFrame
a (n,d) covariate numpy array or DataFrame. If a DataFrame, columns
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Predict the expectation of lifetimes, :math:`E[T | x]`.
Parameters
----------
X: numpy array or DataFrame
a (n,d) covariate numpy array or DataFrame. If a DataFrame, columns
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Predict the expectation of lifetimes, :math:`E[T | x]`.
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X: numpy array or DataFrame
a (n,d) covariate numpy array or DataFrame. If a DataFrame, columns
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train | LogLogisticAFTFitter.predict_cumulative_hazard | Return the cumulative hazard rate of subjects in X at time points.
Parameters
----------
X: numpy array or DataFrame
a (n,d) covariate numpy array or DataFrame. If a DataFrame, columns
can be in any order. If a numpy array, columns must be in the
same order a... | lifelines/fitters/log_logistic_aft_fitter.py | def predict_cumulative_hazard(self, X, times=None, ancillary_X=None):
"""
Return the cumulative hazard rate of subjects in X at time points.
Parameters
----------
X: numpy array or DataFrame
a (n,d) covariate numpy array or DataFrame. If a DataFrame, columns
... | def predict_cumulative_hazard(self, X, times=None, ancillary_X=None):
"""
Return the cumulative hazard rate of subjects in X at time points.
Parameters
----------
X: numpy array or DataFrame
a (n,d) covariate numpy array or DataFrame. If a DataFrame, columns
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train | NelsonAalenFitter.fit | Parameters
-----------
durations: an array, or pd.Series, of length n
duration subject was observed for
timeline: iterable
return the best estimate at the values in timelines (positively increasing)
event_observed: an array, or pd.Series, of length n
Tru... | lifelines/fitters/nelson_aalen_fitter.py | def fit(
self,
durations,
event_observed=None,
timeline=None,
entry=None,
label="NA_estimate",
alpha=None,
ci_labels=None,
weights=None,
): # pylint: disable=too-many-arguments
"""
Parameters
-----------
duratio... | def fit(
self,
durations,
event_observed=None,
timeline=None,
entry=None,
label="NA_estimate",
alpha=None,
ci_labels=None,
weights=None,
): # pylint: disable=too-many-arguments
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train | NelsonAalenFitter.smoothed_hazard_ | Parameters
-----------
bandwidth: float
the bandwith used in the Epanechnikov kernel.
Returns
-------
DataFrame:
a DataFrame of the smoothed hazard | lifelines/fitters/nelson_aalen_fitter.py | def smoothed_hazard_(self, bandwidth):
"""
Parameters
-----------
bandwidth: float
the bandwith used in the Epanechnikov kernel.
Returns
-------
DataFrame:
a DataFrame of the smoothed hazard
"""
timeline = self.timeline
... | def smoothed_hazard_(self, bandwidth):
"""
Parameters
-----------
bandwidth: float
the bandwith used in the Epanechnikov kernel.
Returns
-------
DataFrame:
a DataFrame of the smoothed hazard
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timeline = self.timeline
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train | NelsonAalenFitter.smoothed_hazard_confidence_intervals_ | Parameters
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bandwidth: float
the bandwidth to use in the Epanechnikov kernel. > 0
hazard_: numpy array
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"""
Parameters
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bandwidth: float
the bandwidth to use in the Epanechnikov kernel. > 0
hazard_: numpy array
a computed (n,) numpy array of estimated hazard rates. If non... | def smoothed_hazard_confidence_intervals_(self, bandwidth, hazard_=None):
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train | _BTree._treeify | Convert the np.ndarray `values` into a complete balanced tree.
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if len(values) == 1: # this case causes problems later
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train | _BTree.insert | Insert an occurrence of `value` into the btree. | lifelines/utils/btree.py | def insert(self, value):
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train | _BTree.rank | Returns the rank and count of the value in the btree. | lifelines/utils/btree.py | def rank(self, value):
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rank = 0
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train | qq_plot | Produces a quantile-quantile plot of the empirical CDF against
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can invalidate a model (though we expect some natural deviance in the tails).
Parameters
-----------
model: obj
A fitted lifelines univariate parametric model, li... | lifelines/plotting.py | def qq_plot(model, **plot_kwargs):
"""
Produces a quantile-quantile plot of the empirical CDF against
the fitted parametric CDF. Large deviances away from the line y=x
can invalidate a model (though we expect some natural deviance in the tails).
Parameters
-----------
model: obj
A f... | def qq_plot(model, **plot_kwargs):
"""
Produces a quantile-quantile plot of the empirical CDF against
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can invalidate a model (though we expect some natural deviance in the tails).
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model: obj
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train | remove_spines | Remove spines of axis.
Parameters:
ax: axes to operate on
sides: list of sides: top, left, bottom, right
Examples:
removespines(ax, ['top'])
removespines(ax, ['top', 'bottom', 'right', 'left']) | lifelines/plotting.py | def remove_spines(ax, sides):
"""
Remove spines of axis.
Parameters:
ax: axes to operate on
sides: list of sides: top, left, bottom, right
Examples:
removespines(ax, ['top'])
removespines(ax, ['top', 'bottom', 'right', 'left'])
"""
for side in sides:
ax.spines[side]... | def remove_spines(ax, sides):
"""
Remove spines of axis.
Parameters:
ax: axes to operate on
sides: list of sides: top, left, bottom, right
Examples:
removespines(ax, ['top'])
removespines(ax, ['top', 'bottom', 'right', 'left'])
"""
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train | move_spines | Move the entire spine relative to the figure.
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ax: axes to operate on
sides: list of sides to move. Sides: top, left, bottom, right
dists: list of float distances to move. Should match sides in length.
Example:
move_spines(ax, sides=['left', 'bottom'], dists=[-0.02, 0.1]) | lifelines/plotting.py | def move_spines(ax, sides, dists):
"""
Move the entire spine relative to the figure.
Parameters:
ax: axes to operate on
sides: list of sides to move. Sides: top, left, bottom, right
dists: list of float distances to move. Should match sides in length.
Example:
move_spines(ax, sid... | def move_spines(ax, sides, dists):
"""
Move the entire spine relative to the figure.
Parameters:
ax: axes to operate on
sides: list of sides to move. Sides: top, left, bottom, right
dists: list of float distances to move. Should match sides in length.
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train | remove_ticks | Remove ticks from axis.
Parameters:
ax: axes to work on
x: if True, remove xticks. Default False.
y: if True, remove yticks. Default False.
Examples:
removeticks(ax, x=True)
removeticks(ax, x=True, y=True) | lifelines/plotting.py | def remove_ticks(ax, x=False, y=False):
"""
Remove ticks from axis.
Parameters:
ax: axes to work on
x: if True, remove xticks. Default False.
y: if True, remove yticks. Default False.
Examples:
removeticks(ax, x=True)
removeticks(ax, x=True, y=True)
"""
if x:
... | def remove_ticks(ax, x=False, y=False):
"""
Remove ticks from axis.
Parameters:
ax: axes to work on
x: if True, remove xticks. Default False.
y: if True, remove yticks. Default False.
Examples:
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removeticks(ax, x=True, y=True)
"""
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train | add_at_risk_counts | Add counts showing how many individuals were at risk at each time point in
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Parameters
----------
fitters:
One or several fitters, for example KaplanMeierFitter,
NelsonAalenFitter, etc...
Returns
--------
ax: The axes which was used.
Examples
-... | lifelines/plotting.py | def add_at_risk_counts(*fitters, **kwargs):
"""
Add counts showing how many individuals were at risk at each time point in
survival/hazard plots.
Parameters
----------
fitters:
One or several fitters, for example KaplanMeierFitter,
NelsonAalenFitter, etc...
Returns
-------... | def add_at_risk_counts(*fitters, **kwargs):
"""
Add counts showing how many individuals were at risk at each time point in
survival/hazard plots.
Parameters
----------
fitters:
One or several fitters, for example KaplanMeierFitter,
NelsonAalenFitter, etc...
Returns
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train | plot_lifetimes | Returns a lifetime plot, see examples: https://lifelines.readthedocs.io/en/latest/Survival%20Analysis%20intro.html#Censoring
Parameters
-----------
durations: (n,) numpy array or pd.Series
duration subject was observed for.
event_observed: (n,) numpy array or pd.Series
array of booleans: Tr... | lifelines/plotting.py | def plot_lifetimes(
durations,
event_observed=None,
entry=None,
left_truncated=False,
sort_by_duration=True,
event_observed_color="#A60628",
event_censored_color="#348ABD",
**kwargs
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"""
Returns a lifetime plot, see examples: https://lifelines.readthedocs.io/en/latest/Survival%... | def plot_lifetimes(
durations,
event_observed=None,
entry=None,
left_truncated=False,
sort_by_duration=True,
event_observed_color="#A60628",
event_censored_color="#348ABD",
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train | plot_loglogs | Specifies a plot of the log(-log(SV)) versus log(time) where SV is the estimated survival function. | lifelines/plotting.py | def plot_loglogs(cls, loc=None, iloc=None, show_censors=False, censor_styles=None, **kwargs):
"""
Specifies a plot of the log(-log(SV)) versus log(time) where SV is the estimated survival function.
"""
def loglog(s):
return np.log(-np.log(s))
if (loc is not None) and (iloc is not None):
... | def plot_loglogs(cls, loc=None, iloc=None, show_censors=False, censor_styles=None, **kwargs):
"""
Specifies a plot of the log(-log(SV)) versus log(time) where SV is the estimated survival function.
"""
def loglog(s):
return np.log(-np.log(s))
if (loc is not None) and (iloc is not None):
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train | _plot_estimate | Plots a pretty figure of {0}.{1}
Matplotlib plot arguments can be passed in inside the kwargs, plus
Parameters
-----------
show_censors: bool
place markers at censorship events. Default: False
censor_styles: bool
If show_censors, this dictionary will be passed into the plot call.
... | lifelines/plotting.py | def _plot_estimate(
cls,
estimate=None,
confidence_intervals=None,
loc=None,
iloc=None,
show_censors=False,
censor_styles=None,
ci_legend=False,
ci_force_lines=False,
ci_alpha=0.25,
ci_show=True,
at_risk_counts=False,
**kwargs
):
"""
Plots a pretty figure of ... | def _plot_estimate(
cls,
estimate=None,
confidence_intervals=None,
loc=None,
iloc=None,
show_censors=False,
censor_styles=None,
ci_legend=False,
ci_force_lines=False,
ci_alpha=0.25,
ci_show=True,
at_risk_counts=False,
**kwargs
):
"""
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train | translate_doc | translate a word document type of file and save the result as document and keep the exactly same file format.
:param filename: word doc file
:param destination='zh-CN':
:param mix=True: if True, will have original language and target language into the same doc. paragraphs by paragraphs. | example/translate_word_doc.py | def translate_doc(filename, destination='zh-CN', mix=True):
"""
translate a word document type of file and save the result as document and keep the exactly same file format.
:param filename: word doc file
:param destination='zh-CN':
:param mix=True: if True, will have original languag... | def translate_doc(filename, destination='zh-CN', mix=True):
"""
translate a word document type of file and save the result as document and keep the exactly same file format.
:param filename: word doc file
:param destination='zh-CN':
:param mix=True: if True, will have original languag... | [
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train | Embedding.most_frequent | Only most frequent k words to be included in the embeddings. | polyglot/mapping/embeddings.py | def most_frequent(self, k, inplace=False):
"""Only most frequent k words to be included in the embeddings."""
vocabulary = self.vocabulary.most_frequent(k)
vectors = np.asarray([self[w] for w in vocabulary])
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train | Embedding.nearest_neighbors | Return the nearest k words to the given `word`.
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word (string): single word.
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word (string): single word.
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word (string): single word.
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train | Embedding.distances | Calculate eucledean pairwise distances between `word` and `words`.
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word (string): single word.
words (list): list of strings.
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word (string): single word.
words (list): list of strings.
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numpy array of the distances.
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word (string): single word.
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train | Embedding.load | Load an embedding dump generated by `save` | polyglot/mapping/embeddings.py | def load(fname):
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content = _open(fname).read()
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train | Embedding.save | Save a pickled version of the embedding into `fname`. | polyglot/mapping/embeddings.py | def save(self, fname):
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train | unzip | Extract the contents of the zip file ``filename`` into the
directory ``root``. | polyglot/downloader.py | def unzip(filename, root, verbose=True):
"""
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"""
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Create a new data.xml index file, by combining the xml description
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train | _indent_xml | Helper for ``build_index()``: Given an XML ``ElementTree``, modify it
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"""
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Helper for ``build_index()``: Given an XML ``ElementTree``, modify it
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train | _check_package | Helper for ``build_index()``: Perform some checks to make sure that
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"""
Helper for ``build_index()``: Perform some checks to make sure that
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"""
# The filename must patch the id given in the XML file.
uid = os.path.splitext(os.path.split(zipfilename)[1])[0]
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Helper for ``build_index()``: Perform some checks to make sure that
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train | _svn_revision | Helper for ``build_index()``: Calculate the subversion revision
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"""
Helper for ``build_index()``: Calculate the subversion revision
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"""
p = subprocess.Popen(['svn', 'status', '-v', filename],
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"""
Helper for ``build_index()``: Calculate the subversion revision
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train | _find_collections | Helper for ``build_index()``: Yield a list of ElementTree.Element
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Helper for ``build_index()``: Yield a list of ElementTree.Element
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train | Downloader.status | Return a constant describing the status of the given package
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Return a constant describing the status of the given package
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train | Downloader.update | Re-download any packages whose status is STALE. | polyglot/downloader.py | def update(self, quiet=False, prefix='[polyglot_data] '):
"""
Re-download any packages whose status is STALE.
"""
self.clear_status_cache()
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train | Downloader._update_index | A helper function that ensures that self._index is
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"""A helper function that ensures that self._index is
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# Check if the index is aleady up-to-date. If so, do nothing.
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train | Downloader.get_collection | Return the collection that represents a specific language or task.
Args:
lang (string): Language code.
task (string): Task name. | polyglot/downloader.py | def get_collection(self, lang=None, task=None):
""" Return the collection that represents a specific language or task.
Args:
lang (string): Language code.
task (string): Task name.
"""
if lang: id = "{}{}".format(Downloader.LANG_PREFIX, lang)
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lang (string): Language code.
task (string): Task name.
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train | Downloader.supported_language | Return True if polyglot supports the language.
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lang (string): Language code.
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train | Downloader.supported_languages | Languages that are covered by a specific task.
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"""Languages that are covered by a specific task.
Args:
task (string): Task name.
"""
if task:
collection = self.get_collection(task=task)
return [isoLangs[x.id.split('.')[1]]["name"]
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task (string): Task name.
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train | Downloader.supported_tasks | Languages that are covered by a specific task.
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"""Languages that are covered by a specific task.
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train | Downloader.xmlinfo | Return the XML info record for the given item | polyglot/downloader.py | def xmlinfo(self, id):
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self._update_index()
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if package.get('id') == id:
return package
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train | Downloader._set_url | Set a new URL for the data server. If we're unable to contact
the given url, then the original url is kept. | polyglot/downloader.py | def _set_url(self, url):
"""
Set a new URL for the data server. If we're unable to contact
the given url, then the original url is kept.
"""
original_url = self._url
try:
self._update_index(url)
except:
self._url = original_url
raise | def _set_url(self, url):
"""
Set a new URL for the data server. If we're unable to contact
the given url, then the original url is kept.
"""
original_url = self._url
try:
self._update_index(url)
except:
self._url = original_url
raise | [
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train | Transliterator._decoder | Transliterate a string from English to the target language. | polyglot/transliteration/base.py | def _decoder(self):
"""Transliterate a string from English to the target language."""
if self.target_lang == 'en':
return Transliterator._dummy_coder
else:
weights = load_transliteration_table(self.target_lang)
decoder_weights = weights["decoder"]
return Transliterator._transliterate... | def _decoder(self):
"""Transliterate a string from English to the target language."""
if self.target_lang == 'en':
return Transliterator._dummy_coder
else:
weights = load_transliteration_table(self.target_lang)
decoder_weights = weights["decoder"]
return Transliterator._transliterate... | [
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train | Transliterator._encoder | Transliterate a string from the input language to English. | polyglot/transliteration/base.py | def _encoder(self):
"""Transliterate a string from the input language to English."""
if self.source_lang == 'en':
return Transliterator._dummy_coder
else:
weights = load_transliteration_table(self.source_lang)
encoder_weights = weights["encoder"]
return Transliterator._transliterate_... | def _encoder(self):
"""Transliterate a string from the input language to English."""
if self.source_lang == 'en':
return Transliterator._dummy_coder
else:
weights = load_transliteration_table(self.source_lang)
encoder_weights = weights["encoder"]
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train | Transliterator.transliterate | Transliterate the word from its source language to the target one.
The method works by encoding the word into English then decoding the new
Enlgish word to the target language. | polyglot/transliteration/base.py | def transliterate(self, word):
"""Transliterate the word from its source language to the target one.
The method works by encoding the word into English then decoding the new
Enlgish word to the target language.
"""
encoded_word = self.encoder(word)
decoded_word = self.decoder(encoded_word)
... | def transliterate(self, word):
"""Transliterate the word from its source language to the target one.
The method works by encoding the word into English then decoding the new
Enlgish word to the target language.
"""
encoded_word = self.encoder(word)
decoded_word = self.decoder(encoded_word)
... | [
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] | aboSamoor/polyglot | python | https://github.com/aboSamoor/polyglot/blob/d0d2aa8d06cec4e03bd96618ae960030f7069a17/polyglot/transliteration/base.py#L56-L64 | [
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] | d0d2aa8d06cec4e03bd96618ae960030f7069a17 |
train | BaseBlob.tokens | Return a list of tokens, using this blob's tokenizer object
(defaults to :class:`WordTokenizer <textblob.tokenizers.WordTokenizer>`). | polyglot/text.py | def tokens(self):
"""Return a list of tokens, using this blob's tokenizer object
(defaults to :class:`WordTokenizer <textblob.tokenizers.WordTokenizer>`).
"""
seq = self.word_tokenizer.transform(Sequence(self.raw))
return WordList(seq.tokens(), parent=self, language=self.language.code) | def tokens(self):
"""Return a list of tokens, using this blob's tokenizer object
(defaults to :class:`WordTokenizer <textblob.tokenizers.WordTokenizer>`).
"""
seq = self.word_tokenizer.transform(Sequence(self.raw))
return WordList(seq.tokens(), parent=self, language=self.language.code) | [
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train | BaseBlob.polarity | Return the polarity score as a float within the range [-1.0, 1.0] | polyglot/text.py | def polarity(self):
"""Return the polarity score as a float within the range [-1.0, 1.0]
"""
scores = [w.polarity for w in self.words if w.polarity != 0]
if not scores:
return 0.0
return sum(scores) / float(len(scores)) | def polarity(self):
"""Return the polarity score as a float within the range [-1.0, 1.0]
"""
scores = [w.polarity for w in self.words if w.polarity != 0]
if not scores:
return 0.0
return sum(scores) / float(len(scores)) | [
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train | BaseBlob.transliterate | Transliterate the string to the target language. | polyglot/text.py | def transliterate(self, target_language="en"):
"""Transliterate the string to the target language."""
return WordList([w.transliterate(target_language) for w in self.words],
language=target_language, parent=self) | def transliterate(self, target_language="en"):
"""Transliterate the string to the target language."""
return WordList([w.transliterate(target_language) for w in self.words],
language=target_language, parent=self) | [
"Transliterate",
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] | aboSamoor/polyglot | python | https://github.com/aboSamoor/polyglot/blob/d0d2aa8d06cec4e03bd96618ae960030f7069a17/polyglot/text.py#L117-L120 | [
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train | BaseBlob.entities | Returns a list of entities for this blob. | polyglot/text.py | def entities(self):
"""Returns a list of entities for this blob."""
start = 0
end = 0
prev_tag = u'O'
chunks = []
for i, (w, tag) in enumerate(self.ne_chunker.annotate(self.words)):
if tag != prev_tag:
if prev_tag == u'O':
start = i
else:
chunks.append(C... | def entities(self):
"""Returns a list of entities for this blob."""
start = 0
end = 0
prev_tag = u'O'
chunks = []
for i, (w, tag) in enumerate(self.ne_chunker.annotate(self.words)):
if tag != prev_tag:
if prev_tag == u'O':
start = i
else:
chunks.append(C... | [
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] | aboSamoor/polyglot | python | https://github.com/aboSamoor/polyglot/blob/d0d2aa8d06cec4e03bd96618ae960030f7069a17/polyglot/text.py#L129-L146 | [
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... | d0d2aa8d06cec4e03bd96618ae960030f7069a17 |
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