def predict_cumulative_hazard(self, X, times=None):
"""
X: 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 as the training data.
times: an iterable of increasing times to predict the cumulative hazard at. Default
is the set of all durations (observed and unobserved). Uses a linear interpolation if
points in time are not in the index.
Returns the cumulative hazard of individuals.
"""
if self.strata:
cumulative_hazard_ = pd.DataFrame()
for stratum, stratified_X in X.groupby(self.strata):
try:
c_0 = self.baseline_cumulative_hazard_[[stratum]]
except KeyError:
raise StatError("""The stratum %s was not found in the original training data. For example, try
the following on the original dataset, df: `df.groupby(%s).size()`. Expected is that %s is not present in the output.
""" % (stratum, self.strata, stratum))
col = _get_index(stratified_X)
v = self.predict_partial_hazard(stratified_X)
cumulative_hazard_ = cumulative_hazard_.merge(pd.DataFrame(np.dot(c_0, v.T), index=c_0.index, columns=col), how='outer', right_index=True, left_index=True)
else:
c_0 = self.baseline_cumulative_hazard_
col = _get_index(X)
v = self.predict_partial_hazard(X)
cumulative_hazard_ = pd.DataFrame(np.dot(c_0, v.T), columns=col, index=c_0.index)
if times is not None:
# non-linear interpolations can push the survival curves above 1 and below 0.
return cumulative_hazard_.reindex(cumulative_hazard_.index.union(times)).interpolate("index").loc[times]
else:
return cumulative_hazard_
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 - \bar{x})'\beta `
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 as the training data.
Returns
-------
DataFrame
Note
-----
If X is a DataFrame, the order of the columns do not matter. But
if X is an array, then the column ordering is assumed to be the
same as the training dataset.
"""
if isinstance(X, pd.DataFrame):
order = self.hazards_.index
X = X[order]
check_for_numeric_dtypes_or_raise(X)
X = X.astype(float)
index = _get_index(X)
X = normalize(X, self._norm_mean.values, 1)
return pd.DataFrame(np.dot(X, self.hazards_), index=index)
def predict_cumulative_hazard(self, X, id_col=None):
"""
X: 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 as the training data.
Returns the hazard rates for the individuals
"""
if id_col is not None:
# see https://github.com/CamDavidsonPilon/lifelines/issues/38
raise NotImplementedError
n, d = X.shape
cols = _get_index(X)
if isinstance(X, pd.DataFrame):
order = self.cumulative_hazards_.columns
order = order.drop('baseline') if self.fit_intercept else order
X_ = X[order].values.copy()
else:
X_ = X.copy()
X_ = X_ if not self.fit_intercept else np.c_[X_, np.ones((n, 1))]
individual_cumulative_hazards_ = pd.DataFrame(np.dot(self.cumulative_hazards_, X_.T), index=self.timeline, columns=cols)
if self.nn_cumulative_hazard:
individual_cumulative_hazards_[individual_cumulative_hazards_ < 0.] = 0.
return individual_cumulative_hazards_
def predict_expectation(self, X):
"""
Compute the expected lifetime, E[T], using covarites X.
"""
index = _get_index(X)
v = self.predict_survival_function(X)[index]
return pd.DataFrame(trapz(v.values.T, v.index), index=index)
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
can be in any order. If a numpy array, columns must be in the
same order as the training data.
times: iterable, optional
an iterable of increasing times to predict the cumulative hazard at. Default
is the set of all durations (observed and unobserved). Uses a linear interpolation if
points in time are not in the index.
ancillary_X: numpy array or DataFrame, optional
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 as the training data.
Returns
-------
cumulative_hazard_ : DataFrame
the cumulative hazard of individuals over the timeline
"""
import numpy as np
times = coalesce(times, self.timeline, np.unique(self.durations))
exp_mu_, sigma_ = self._prep_inputs_for_prediction_and_return_scores(X, ancillary_X)
mu_ = np.log(exp_mu_)
Z = np.subtract.outer(np.log(times), mu_) / sigma_
return pd.DataFrame(-logsf(Z), columns=_get_index(X), index=times)
def predict_median(self, X, ancillary_X=None):
"""
Returns the median lifetimes for the individuals. If the survival curve of an
individual does not cross 0.5, then the result is infinity.
http://stats.stackexchange.com/questions/102986/percentile-loss-functions
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 as the training data.
ancillary_X: numpy array or DataFrame, optional
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 as the training data.
p: float, optional (default=0.5)
the percentile, must be between 0 and 1.
Returns
-------
DataFrame
See Also
--------
predict_percentile
"""
exp_mu_, _ = self._prep_inputs_for_prediction_and_return_scores(X, ancillary_X)
return pd.DataFrame(exp_mu_, index=_get_index(X))
def predict_expectation(self, X, ancillary_X=None):
"""
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
can be in any order. If a numpy array, columns must be in the
same order as the training data.
ancillary_X: numpy array or DataFrame, optional
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 as the training data.
Returns
-------
percentiles: DataFrame
the median lifetimes for the individuals. If the survival curve of an
individual does not cross 0.5, then the result is infinity.
See Also
--------
predict_median
"""
exp_mu_, sigma_ = self._prep_inputs_for_prediction_and_return_scores(X, ancillary_X)
return pd.DataFrame(exp_mu_ * np.exp(sigma_ ** 2 / 2), index=_get_index(X))
def predict_partial_hazard(self, X):
"""
X: 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 as the training data.
If covariates were normalized during fitting, they are normalized
in the same way here.
If X is a dataframe, the order of the columns do not matter. But
if X is an array, then the column ordering is assumed to be the
same as the training dataset.
Returns the partial hazard for the individuals, partial since the
baseline hazard is not included. Equal to \exp{\beta X}
"""
index = _get_index(X)
if isinstance(X, pd.DataFrame):
order = self.hazards_.columns
X = X[order]
if self.normalize:
# Assuming correct ordering and number of columns
X = normalize(X, self._norm_mean.values, self._norm_std.values)
return pd.DataFrame(exp(np.dot(X, self.hazards_.T)), index=index)
def predict_cumulative_hazard(self, X):
"""
Returns the hazard rates for the individuals
Parameters
----------
X: 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 as the training data.
"""
n, _ = X.shape
cols = _get_index(X)
if isinstance(X, pd.DataFrame):
order = self.cumulative_hazards_.columns
order = order.drop("_intercept") if self.fit_intercept else order
X_ = X[order].values
else:
X_ = X
X_ = X_ if not self.fit_intercept else np.c_[X_, np.ones((n, 1))]
timeline = self._index
individual_cumulative_hazards_ = pd.DataFrame(
np.dot(self.cumulative_hazards_, X_.T), index=timeline, columns=cols
)
return individual_cumulative_hazards_
def predict_percentile(self, X, p=0.5):
"""
X: a (n,d) covariate matrix
Returns the median lifetimes for the individuals.
http://stats.stackexchange.com/questions/102986/percentile-loss-functions
"""
index = _get_index(X)
return qth_survival_times(p, self.predict_survival_function(X)[index])
def predict_cumulative_hazard(self, X, times=None):
"""
X: 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 as the training data.
times: an iterable of increasing times to predict the cumulative hazard at. Default
is the set of all durations (observed and unobserved). Uses a linear interpolation if
points in time are not in the index.
Returns the cumulative hazard of individuals.
"""
if self.strata:
cumulative_hazard_ = pd.DataFrame()
for stratum, stratified_X in X.groupby(self.strata):
try:
c_0 = self.baseline_cumulative_hazard_[[stratum]]
except KeyError:
raise StatError(
"""The stratum %s was not found in the original training data. For example, try
the following on the original dataset, df: `df.groupby(%s).size()`. Expected is that %s is not present in the output.
""" % (stratum, self.strata, stratum))
col = _get_index(stratified_X)
v = self.predict_partial_hazard(stratified_X)
cumulative_hazard_ = cumulative_hazard_.merge(pd.DataFrame(
np.dot(c_0, v.T), index=c_0.index, columns=col),
how='outer',
right_index=True,
left_index=True)
else:
c_0 = self.baseline_cumulative_hazard_
col = _get_index(X)
v = self.predict_partial_hazard(X)
cumulative_hazard_ = pd.DataFrame(np.dot(c_0, v.T),
columns=col,
index=c_0.index)
if times is not None:
# non-linear interpolations can push the survival curves above 1 and below 0.
return cumulative_hazard_.reindex(
cumulative_hazard_.index.union(times)).interpolate(
"index").loc[times]
else:
return cumulative_hazard_
def predict_percentile(self,
df,
ancillary_df=None,
p=0.5,
conditional_after=None):
"""
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: DataFrame
a (n,d) DataFrame. If a DataFrame, columns
can be in any order. If a numpy array, columns must be in the
same order as the training data.
ancillary_X: DataFrame, optional
a (n,d) DataFrame. If a DataFrame, columns
can be in any order. If a numpy array, columns must be in the
same order as the training data.
p: float, optional (default=0.5)
the percentile, must be between 0 and 1.
Returns
-------
percentiles: DataFrame
See Also
--------
predict_median
"""
alpha_, beta_ = self._prep_inputs_for_prediction_and_return_scores(
df, ancillary_df)
if conditional_after is None:
return pd.DataFrame(alpha_ * (1 / (p) - 1)**(1 / beta_),
index=_get_index(df))
else:
conditional_after = np.asarray(conditional_after)
S = 1 / (1 + (conditional_after / alpha_)**beta_)
return pd.DataFrame(alpha_ * (1 / (p * S) - 1)**(1 / beta_) -
conditional_after,
index=_get_index(df))
def predict_expectation(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 numpy array or DataFrame. If a DataFrame, columns
can be in any order. If a numpy array, columns must be in the
same order as the training data.
ancillary_X: numpy array or DataFrame, optional
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 as the training data.
Returns
-------
percentiles: DataFrame
the median lifetimes for the individuals. If the survival curve of an
individual does not cross 0.5, then the result is infinity.
See Also
--------
predict_median
"""
X = X.copy()
if ancillary_X is None:
ancillary_X = pd.DataFrame(np.ones((X.shape[0], 1)),
columns=["_intercept"])
elif isinstance(ancillary_X, pd.DataFrame):
ancillary_X = ancillary_X.copy()
if self.fit_intercept:
ancillary_X["_intercept"] = 1.0
ancillary_X = ancillary_X[self.params_.loc["rho_"].index]
else:
assert ancillary_X.shape[1] == (self.params_.loc["rho_"].shape[0] +
1) # 1 for _intercept
if isinstance(X, pd.DataFrame):
if self.fit_intercept:
X["_intercept"] = 1.0
X = X[self.params_.loc["lambda_"].index]
else:
assert X.shape[1] == (self.params_.loc["lambda_"].shape[0] + 1
) # 1 for _intercept
lambda_params = self.params_[self._LOOKUP_SLICE["lambda_"]]
lambda_ = np.exp(np.dot(X, lambda_params))
rho_params = self.params_[self._LOOKUP_SLICE["rho_"]]
rho_ = np.exp(np.dot(ancillary_X, rho_params))
subjects = _get_index(X)
return pd.DataFrame((lambda_ * gamma(1 + 1 / rho_)), index=subjects)
def predict_cumulative_hazard(self, X):
"""
X: a (n,d) covariate matrix
Returns the cumulative hazard for the individuals.
"""
v = self.predict_partial_hazard(X)
s_0 = self.baseline_survival_
col = _get_index(X)
return pd.DataFrame(-np.dot(np.log(s_0), v.T), index=self.baseline_survival_.index, columns=col)
def predict_percentile(
self,
df: DataFrame,
*,
ancillary_df: Optional[DataFrame] = None,
p: float = 0.5,
conditional_after: Optional[ndarray] = None
) -> DataFrame:
"""
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
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 as the training data.
ancillary_X: numpy array or DataFrame, optional
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 as the training data.
p: float, optional (default=0.5)
the percentile, must be between 0 and 1.
conditional_after: iterable, optional
Must be equal is size to df.shape[0] (denoted `n` above). An iterable (array, list, series) of possibly non-zero values that represent how long the
subject has already lived for. Ex: if :math:`T` is the unknown event time, then this represents
:math:`T | T > s`. This is useful for knowing the *remaining* hazard/survival of censored subjects.
The new timeline is the remaining duration of the subject, i.e. normalized back to starting at 0.
Returns
-------
percentiles: DataFrame
See Also
--------
predict_median
"""
exp_mu_, sigma_ = self._prep_inputs_for_prediction_and_return_scores(df, ancillary_df)
if conditional_after is None:
return pd.DataFrame(exp_mu_ * np.exp(np.sqrt(2) * sigma_ * erfinv(2 * (1 - p) - 1)), index=_get_index(df))
else:
conditional_after = np.asarray(conditional_after)
Z = (np.log(conditional_after) - np.log(exp_mu_)) / sigma_
S = norm.sf(Z)
return pd.DataFrame(
exp_mu_ * np.exp(np.sqrt(2) * sigma_ * erfinv(2 * (1 - p * S) - 1)) - conditional_after,
index=_get_index(df),
)
def predict_expectation(self, X):
"""
X: 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 as the training data.
Compute the expected lifetime, E[T], using covarites X.
"""
index = _get_index(X)
v = self.predict_survival_function(X)[index]
return pd.DataFrame(trapz(v.values.T, v.index), index=index)
def predict_expectation(self, X):
"""
X: 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 as the training data.
Compute the expected lifetime, E[T], using covarites X.
"""
index = _get_index(X)
v = self.predict_survival_function(X)[index]
return pd.DataFrame(trapz(v.values.T, v.index), index=index)
def predict_percentile(self, X, p=0.5):
"""
X: 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 as the training data.
By default, returns the median lifetimes for the individuals.
http://stats.stackexchange.com/questions/102986/percentile-loss-functions
"""
index = _get_index(X)
return qth_survival_times(p, self.predict_survival_function(X)[index])
def predict_cumulative_hazard(self, X):
"""
X: 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 as the training data.
"""
if self.strata:
cumulative_hazard_ = pd.DataFrame()
for stratum, stratified_X in X.groupby(self.strata):
s_0 = self.baseline_survival_[[stratum]]
col = _get_index(stratified_X)
v = self.predict_partial_hazard(stratified_X)
cumulative_hazard_ = cumulative_hazard_.merge(pd.DataFrame(-np.dot(np.log(s_0), v.T), index=s_0.index, columns=col), how='outer', right_index=True, left_index=True)
else:
s_0 = self.baseline_survival_
col = _get_index(X)
v = self.predict_partial_hazard(X)
cumulative_hazard_ = pd.DataFrame(-np.dot(np.log(s_0), v.T), columns=col, index=s_0.index)
return cumulative_hazard_
def predict_percentile(self, X, p=0.5):
"""
X: 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 as the training data.
By default, returns the median lifetimes for the individuals.
http://stats.stackexchange.com/questions/102986/percentile-loss-functions
"""
index = _get_index(X)
return qth_survival_times(p, self.predict_survival_function(X)[index])
def predict_cumulative_hazard(self, X):
"""
X: a (n,d) covariate matrix
Returns the cumulative hazard for the individuals.
"""
v = self.predict_partial_hazard(X)
s_0 = self.baseline_survival_
col = _get_index(X)
return pd.DataFrame(-np.dot(np.log(s_0), v.T),
index=self.baseline_survival_.index,
columns=col)
def predict_cumulative_hazard(self, X):
"""
X: 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 as the training data.
Returns the cumulative hazard for the individuals.
"""
v = self.predict_partial_hazard(X)
s_0 = self.baseline_survival_
col = _get_index(X)
return pd.DataFrame(-np.dot(np.log(s_0), v.T), index=self.baseline_survival_.index, columns=col)
def predict_expectation(self, X):
"""
Compute the expected lifetime, E[T], using covarites X.
X: 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 as the training data.
Returns the expected lifetimes for the individuals
"""
index = _get_index(X)
t = self.cumulative_hazards_.index
return pd.DataFrame(trapz(self.predict_survival_function(X)[index].values.T, t), index=index)
def predict_expectation(self, X):
"""
Compute the expected lifetime, E[T], using covarites X.
X: 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 as the training data.
Returns the expected lifetimes for the individuals
"""
index = _get_index(X)
t = self.cumulative_hazards_.index
return pd.DataFrame(trapz(self.predict_survival_function(X)[index].values.T, t), index=index)
def predict_cumulative_hazard(self, X, times=None):
"""
X: 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 as the training data.
times: an iterable of increasing times to predict the cumulative hazard at. Default
is the set of all durations (observed and unobserved). Uses a linear interpolation if
points in time are not in the index.
Returns the cumulative hazard of individuals.
"""
if self.strata:
cumulative_hazard_ = pd.DataFrame()
for stratum, stratified_X in X.groupby(self.strata):
c_0 = self.baseline_cumulative_hazard_[[stratum]]
col = _get_index(stratified_X)
v = self.predict_partial_hazard(stratified_X)
cumulative_hazard_ = cumulative_hazard_.merge(pd.DataFrame(
np.dot(c_0, v.T), index=c_0.index, columns=col),
how='outer',
right_index=True,
left_index=True)
else:
c_0 = self.baseline_cumulative_hazard_
col = _get_index(X)
v = self.predict_partial_hazard(X)
cumulative_hazard_ = pd.DataFrame(np.dot(c_0, v.T),
columns=col,
index=c_0.index)
if times is not None:
# non-linear interpolations can push the survival curves above 1 and below 0.
return cumulative_hazard_.reindex(
cumulative_hazard_.index.union(times)).interpolate(
"index").loc[times]
else:
return cumulative_hazard_
def predict_percentile(self, X, p=0.5):
"""
X: 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 as the training data.
Returns the median lifetimes for the individuals, by default. If the survival curve of an
individual does not cross 0.5, then the result is infinity.
http://stats.stackexchange.com/questions/102986/percentile-loss-functions
"""
subjects = _get_index(X)
return qth_survival_times(
p,
self.predict_survival_function(X)[subjects]).T
def predict_percentile(
self,
df: DataFrame,
*,
ancillary_df: Optional[DataFrame] = None,
p: float = 0.5,
conditional_after: Optional[ndarray] = None) -> DataFrame:
"""
Returns the median lifetimes for the individuals, by default. If the survival curve of an
individual does not cross 0.5, then the result is infinity.
http://stats.stackexchange.com/questions/102986/percentile-loss-functions
Parameters
----------
df: DataFrame
a (n,d) DataFrame. If a DataFrame, columns
can be in any order. If a numpy array, columns must be in the
same order as the training data.
ancillary_df: DataFrame, optional
a (n,d) DataFrame. If a DataFrame, columns
can be in any order. If a numpy array, columns must be in the
same order as the training data.
p: float, optional (default=0.5)
the percentile, must be between 0 and 1.
Returns
-------
percentiles: DataFrame
See Also
--------
predict_median
"""
lambda_, rho_ = self._prep_inputs_for_prediction_and_return_scores(
df, ancillary_df)
if conditional_after is None and len(df.shape) == 2:
conditional_after = np.zeros(df.shape[0])
elif conditional_after is None and len(df.shape) == 1:
conditional_after = np.zeros(1)
return pd.DataFrame(
lambda_ *
np.power(-np.log(p) +
(conditional_after / lambda_)**rho_, 1 / rho_) -
conditional_after,
index=_get_index(df),
)
def predict_expectation(self, X):
"""
X: 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 as the training data.
Compute the expected lifetime, E[T], using covarites X. This algorithm to compute the expection is
to use the fact that E[T] = int_0^inf P(T > t) dt = int_0^inf S(t) dt
To compute the integal, we use the trapizoidal rule to approximate the integral. However, if the
survival function, S(t), doesn't converge to 0, the the expectation is really infinity.
"""
subjects = _get_index(X)
v = self.predict_survival_function(X)[subjects]
return pd.DataFrame(trapz(v.values.T, v.index), index=subjects)
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:`\beta (X - mean(X_{train}))`
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 as the training data.
Returns
-------
log_partial_hazard: DataFrame
Notes
-----
If X is a dataframe, the order of the columns do not matter. But
if X is an array, then the column ordering is assumed to be the
same as the training dataset.
"""
hazard_names = self.hazards_.columns
if isinstance(X, pd.DataFrame):
order = hazard_names
X = X[order]
pass_for_numeric_dtypes_or_raise(X)
elif isinstance(
X, pd.Series) and ((X.shape[0] == len(hazard_names) + 2) or
(X.shape[0] == len(hazard_names))):
X = X.to_frame().T
order = hazard_names
X = X[order]
pass_for_numeric_dtypes_or_raise(X)
elif isinstance(X, pd.Series):
assert len(hazard_names) == 1, "Series not the correct arugment"
X = pd.DataFrame(X)
pass_for_numeric_dtypes_or_raise(X)
X = X.astype(float)
index = _get_index(X)
X = normalize(X, self._norm_mean.values, 1)
return pd.DataFrame(np.dot(X, self.hazards_.T), index=index)
def predict_cumulative_hazard(self,
df,
times=None,
conditional_after=None) -> pd.DataFrame:
"""
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 as the training data.
times: iterable, optional
an iterable of increasing times to predict the cumulative hazard at. Default
is the set of all durations (observed and unobserved). Uses a linear interpolation if
points in time are not in the index.
Returns
-------
cumulative_hazard_ : DataFrame
the cumulative hazard of individuals over the timeline
"""
if isinstance(df, pd.Series):
return self.predict_cumulative_hazard(df.to_frame().T)
if conditional_after is not None:
raise NotImplementedError()
times = np.atleast_1d(
coalesce(times, self.timeline,
np.unique(self.durations))).astype(float)
n = times.shape[0]
times = times.reshape((n, 1))
lambdas_ = self._prep_inputs_for_prediction_and_return_parameters(df)
bp = np.append(self.breakpoints, [np.inf])
M = np.minimum(np.tile(bp, (n, 1)), times)
M = np.hstack([M[:, tuple([0])], np.diff(M, axis=1)])
return pd.DataFrame(np.dot(M, (1 / lambdas_)),
columns=_get_index(df),
index=times[:, 0])
def predict_log_partial_hazard(self, X):
"""
X: 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 as the training data.
If X is a dataframe, the order of the columns do not matter. But
if X is an array, then the column ordering is assumed to be the
same as the training dataset.
Returns the log of the partial hazard for the individuals, partial since the
baseline hazard is not included. Equal to \beta X
"""
if isinstance(X, pd.DataFrame):
order = self.hazards_.columns
X = X[order]
index = _get_index(X)
X = normalize(X, self._norm_mean.values, 1)
return pd.DataFrame(np.dot(X, self.hazards_.T), index=index)
def predict_log_partial_hazard(self, X):
"""
X: 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 as the training data.
If X is a dataframe, the order of the columns do not matter. But
if X is an array, then the column ordering is assumed to be the
same as the training dataset.
Returns the log of the partial hazard for the individuals, partial since the
baseline hazard is not included. Equal to \beta X
"""
if isinstance(X, pd.DataFrame):
order = self.hazards_.columns
X = X[order]
index = _get_index(X)
X = normalize(X, self._norm_mean.values, 1)
return pd.DataFrame(np.dot(X, self.hazards_.T), index=index)
def predict_cumulative_hazard(self, X, times=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
can be in any order. If a numpy array, columns must be in the
same order as the training data.
times: iterable, optional
an iterable of increasing times to predict the cumulative hazard at. Default
is the set of all durations (observed and unobserved). Uses a linear interpolation if
points in time are not in the index.
ancillary_X: numpy array or DataFrame, optional
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 as the training data.
Returns
-------
cumulative_hazard_ : DataFrame
the cumulative hazard of individuals over the timeline
"""
times = np.asarray(
coalesce(times, self.timeline, np.unique(self.durations)))
n = times.shape[0]
times = times.reshape((n, 1))
lambdas_ = self._prep_inputs_for_prediction_and_return_parameters(X)
bp = self.breakpoints
M = np.minimum(np.tile(bp, (n, 1)), times)
M = np.hstack([M[:, tuple([0])], np.diff(M, axis=1)])
return pd.DataFrame(np.dot(M, (1 / lambdas_)),
columns=_get_index(X),
index=times[:, 0])
def predict_cumulative_hazard(self, X, id_col=None):
"""
X: 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 as the training data.
Returns the hazard rates for the individuals
"""
if id_col is not None:
# see https://github.com/CamDavidsonPilon/lifelines/issues/38
raise NotImplementedError
n, d = X.shape
cols = _get_index(X)
if isinstance(X, pd.DataFrame):
order = self.cumulative_hazards_.columns
order = order.drop('baseline') if self.fit_intercept else order
X_ = X[order].values.copy()
else:
X_ = X.copy()
X_ = X_ if not self.fit_intercept else np.c_[X_, np.ones((n, 1))]
return pd.DataFrame(np.dot(self.cumulative_hazards_, X_.T), index=self.timeline, columns=cols)
def predict_expectation(self, X):
r"""
Compute the expected lifetime, :math:`E[T]`, using covarites X. This algorithm to compute the expection 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 integal, we use the trapizoidal rule to approximate the integral.
Caution
--------
However, if the survival function doesn't converge to 0, the the expectation is really infinity and the returned
values are meaningless/too large. In that case, using ``predict_median`` or ``predict_percentile`` would be better.
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 as the training data.
Returns
-------
expectations : DataFrame
Notes
-----
If X is a dataframe, the order of the columns do not matter. But
if X is an array, then the column ordering is assumed to be the
same as the training dataset.
See Also
--------
predict_median
predict_percentile
"""
subjects = _get_index(X)
v = self.predict_survival_function(X)[subjects]
return pd.DataFrame(trapz(v.values.T, v.index), index=subjects)
def predict_cumulative_hazard(self, X):
"""
Returns the hazard rates for the individuals
Parameters
----------
X: 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 as the training data.
"""
cols = _get_index(X)
if isinstance(X, pd.DataFrame):
X = self.regressors.transform_df(X)["beta_"]
elif isinstance(X, pd.Series):
return self.predict_cumulative_hazard(X.to_frame().T.infer_objects())
X = X.astype(float)
timeline = self._index
individual_cumulative_hazards_ = pd.DataFrame(np.dot(self.cumulative_hazards_, X.T), index=timeline, columns=cols)
return individual_cumulative_hazards_
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
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 as the training data.
ancillary_X: numpy array or DataFrame, optional
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 as the training data.
p: float, optional (default=0.5)
the percentile, must be between 0 and 1.
Returns
-------
percentiles: DataFrame
See Also
--------
predict_median
"""
exp_mu_, sigma_ = self._prep_inputs_for_prediction_and_return_scores(X, ancillary_X)
return pd.DataFrame(exp_mu_ * np.exp(np.sqrt(2) * sigma_ * erfinv(2 * p - 1)), index=_get_index(X))
