def _fit_varying(self, dataframe, duration_col="T", event_col="E",
id_col=None, timeline=None, show_progress=True):
from_tuples = pd.MultiIndex.from_tuples
df = dataframe.copy()
# if the regression should fit an intercept
if self.fit_intercept:
df['baseline'] = 1.
# each individual should have an ID of time of leaving study
df = df.set_index([duration_col, id_col])
# if no event_col is specified, assume all non-censorships
if event_col is None:
event_col = 'E'
df[event_col] = 1
C_panel = df[[event_col]].to_panel().transpose(2, 1, 0)
C = C_panel.minor_xs(event_col).sum().astype(bool)
T = (C_panel.minor_xs(event_col).notnull()).cumsum().idxmax()
del df[event_col]
n, d = df.shape
# so this is a problem line. bfill performs a recursion which is
# really not scalable. Plus even for modest datasets, this eats a lot of memory.
# Plus is bfill the correct thing to choose? It's forward looking...
wp = df.to_panel().bfill().fillna(0)
# initialize dataframe to store estimates
non_censorsed_times = list(T[C].iteritems())
columns = wp.items
hazards_ = pd.DataFrame(np.zeros((len(non_censorsed_times), d)),
columns=columns, index=from_tuples(non_censorsed_times))
variance_ = pd.DataFrame(np.zeros((len(non_censorsed_times), d)),
columns=columns, index=from_tuples(non_censorsed_times))
previous_hazard = np.zeros((d,))
ids = wp.minor_axis.values
progress = progress_bar(len(non_censorsed_times))
# this makes indexing times much faster
wp = wp.swapaxes(0, 1, copy=False).swapaxes(1, 2, copy=False)
for i, (id, time) in enumerate(non_censorsed_times):
relevant_individuals = (ids == id)
assert relevant_individuals.sum() == 1.
# perform linear regression step.
try:
v, V = lr(wp[time].values, relevant_individuals, c1=self.coef_penalizer, c2=self.smoothing_penalizer, offset=previous_hazard)
except LinAlgError:
print("Linear regression error. Try increasing the penalizer term.")
hazards_.ix[id, time] = v.T
variance_.ix[id, time] = V[:, relevant_individuals][:, 0] ** 2
previous_hazard = v.T
# update progress bar
if show_progress:
progress.update(i)
# print a new line so the console displays well
if show_progress:
print()
ordered_cols = df.columns # to_panel() mixes up my columns
self.hazards_ = hazards_.groupby(level=1).sum()[ordered_cols]
self.cumulative_hazards_ = self.hazards_.cumsum()[ordered_cols]
self.variance_ = variance_.groupby(level=1).sum()[ordered_cols]
if timeline is not None:
self.hazards_ = self.hazards_.reindex(timeline, method='ffill')
self.cumulative_hazards_ = self.cumulative_hazards_.reindex(timeline, method='ffill')
self.variance_ = self.variance_.reindex(timeline, method='ffill')
self.timeline = timeline
else:
self.timeline = self.hazards_.index.values.astype(float)
self.data = wp
self.durations = T
self.event_observed = C
self._compute_confidence_intervals()
return
def _fit_varying(self,
dataframe,
duration_col="T",
event_col="E",
id_col=None,
timeline=None,
show_progress=True):
from_tuples = pd.MultiIndex.from_tuples
df = dataframe.copy()
# if the regression should fit an intercept
if self.fit_intercept:
df['baseline'] = 1.
# each individual should have an ID of time of leaving study
df = df.set_index([duration_col, id_col])
# if no event_col is specified, assume all non-censorships
if event_col is None:
event_col = 'E'
df[event_col] = 1
C_panel = df[[event_col]].to_panel().transpose(2, 1, 0)
C = C_panel.minor_xs(event_col).sum().astype(bool)
T = (C_panel.minor_xs(event_col).notnull()).cumsum().idxmax()
del df[event_col]
n, d = df.shape
# so this is a problem line. bfill performs a recursion which is
# really not scalable. Plus even for modest datasets, this eats a lot of memory.
# Plus is bfill the correct thing to choose? It's forward looking...
wp = df.to_panel().bfill().fillna(0)
# initialize dataframe to store estimates
non_censorsed_times = list(T[C].iteritems())
columns = wp.items
hazards_ = pd.DataFrame(np.zeros((len(non_censorsed_times), d)),
columns=columns,
index=from_tuples(non_censorsed_times))
variance_ = pd.DataFrame(np.zeros((len(non_censorsed_times), d)),
columns=columns,
index=from_tuples(non_censorsed_times))
previous_hazard = np.zeros((d, ))
ids = wp.minor_axis.values
progress = progress_bar(len(non_censorsed_times))
# this makes indexing times much faster
wp = wp.swapaxes(0, 1, copy=False).swapaxes(1, 2, copy=False)
for i, (id, time) in enumerate(non_censorsed_times):
relevant_individuals = (ids == id)
assert relevant_individuals.sum() == 1.
# perform linear regression step.
try:
v, V = lr(wp[time].values,
relevant_individuals,
c1=self.coef_penalizer,
c2=self.smoothing_penalizer,
offset=previous_hazard)
except LinAlgError:
print(
"Linear regression error. Try increasing the penalizer term."
)
hazards_.ix[id, time] = v.T
variance_.ix[id, time] = V[:, relevant_individuals][:, 0]**2
previous_hazard = v.T
# update progress bar
if show_progress:
progress.update(i)
# print a new line so the console displays well
if show_progress:
print()
ordered_cols = df.columns # to_panel() mixes up my columns
self.hazards_ = hazards_.groupby(level=1).sum()[ordered_cols]
self.cumulative_hazards_ = self.hazards_.cumsum()[ordered_cols]
self.variance_ = variance_.groupby(level=1).sum()[ordered_cols]
if timeline is not None:
self.hazards_ = self.hazards_.reindex(timeline, method='ffill')
self.cumulative_hazards_ = self.cumulative_hazards_.reindex(
timeline, method='ffill')
self.variance_ = self.variance_.reindex(timeline, method='ffill')
self.timeline = timeline
else:
self.timeline = self.hazards_.index.values.astype(float)
self.data = wp
self.durations = T
self.event_observed = C
self._compute_confidence_intervals()
return
def _fit_static(self, dataframe, duration_col, event_col=None,
timeline=None, show_progress=True):
"""
Perform inference on the coefficients of the Aalen additive model.
Parameters:
dataframe: a pandas dataframe, with covariates and a duration_col and a event_col.
one row per individual. duration_col refers to how long the individual was
observed for. event_col is a boolean: 1 if individual 'died', 0 else. id_col
should be left as None.
duration_col: specify what the duration column is called in the dataframe
event_col: specify what the event occurred column is called in the dataframe
timeline: reformat the estimates index to a new timeline.
progress_bar: include a fancy progress bar!
Returns:
self, with new methods like plot, smoothed_hazards_ and properties like cumulative_hazards_
"""
from_tuples = pd.MultiIndex.from_tuples
df = dataframe.copy()
# set unique ids for individuals
id_col = 'id'
ids = np.arange(df.shape[0])
df[id_col] = ids
# if the regression should fit an intercept
if self.fit_intercept:
df['baseline'] = 1.
# if no event_col is specified, assume all non-censorships
if event_col:
c = df[event_col].values
del df[event_col]
else:
c = np.ones_like(ids)
# each individual should have an ID of time of leaving study
C = pd.Series(c, dtype=bool, index=ids)
T = pd.Series(df[duration_col].values, index=ids)
df = df.set_index(id_col)
ix = T.argsort()
T, C = T.iloc[ix], C.iloc[ix]
del df[duration_col]
n, d = df.shape
columns = df.columns
# initialize dataframe to store estimates
non_censorsed_times = list(T[C].iteritems())
n_deaths = len(non_censorsed_times)
hazards_ = pd.DataFrame(np.zeros((n_deaths, d)), columns=columns,
index=from_tuples(non_censorsed_times)).swaplevel(1, 0)
variance_ = pd.DataFrame(np.zeros((n_deaths, d)), columns=columns,
index=from_tuples(non_censorsed_times)).swaplevel(1, 0)
# initialize loop variables.
previous_hazard = np.zeros((d,))
progress = progress_bar(n_deaths)
to_remove = []
t = T.iloc[0]
i = 0
for id, time in T.iteritems(): # should be sorted.
if t != time:
assert t < time
# remove the individuals from the previous loop.
df.iloc[to_remove] = 0.
to_remove = []
t = time
to_remove.append(id)
if C[id] == 0:
continue
relevant_individuals = (ids == id)
assert relevant_individuals.sum() == 1.
# perform linear regression step.
try:
v, V = lr(df.values, relevant_individuals, c1=self.coef_penalizer, c2=self.smoothing_penalizer, offset=previous_hazard)
except LinAlgError:
print("Linear regression error. Try increasing the penalizer term.")
hazards_.ix[time, id] = v.T
variance_.ix[time, id] = V[:, relevant_individuals][:, 0] ** 2
previous_hazard = v.T
# update progress bar
if show_progress:
i += 1
progress.update(i)
# print a new line so the console displays well
if show_progress:
print()
# not sure this is the correct thing to do.
self.hazards_ = hazards_.groupby(level=0).sum()
self.cumulative_hazards_ = self.hazards_.cumsum()
self.variance_ = variance_.groupby(level=0).sum()
if timeline is not None:
self.hazards_ = self.hazards_.reindex(timeline, method='ffill')
self.cumulative_hazards_ = self.cumulative_hazards_.reindex(timeline, method='ffill')
self.variance_ = self.variance_.reindex(timeline, method='ffill')
self.timeline = timeline
else:
self.timeline = self.hazards_.index.values.astype(float)
self.data = dataframe
self.durations = T
self.event_observed = C
self._compute_confidence_intervals()
return
def _fit_static(self,
dataframe,
duration_col,
event_col=None,
timeline=None,
show_progress=True):
"""
Perform inference on the coefficients of the Aalen additive model.
Parameters:
dataframe: a pandas dataframe, with covariates and a duration_col and a event_col.
one row per individual. duration_col refers to how long the individual was
observed for. event_col is a boolean: 1 if individual 'died', 0 else. id_col
should be left as None.
duration_col: specify what the duration column is called in the dataframe
event_col: specify what the event occurred column is called in the dataframe
timeline: reformat the estimates index to a new timeline.
progress_bar: include a fancy progress bar!
Returns:
self, with new methods like plot, smoothed_hazards_ and properties like cumulative_hazards_
"""
from_tuples = pd.MultiIndex.from_tuples
df = dataframe.copy()
# set unique ids for individuals
id_col = 'id'
ids = np.arange(df.shape[0])
df[id_col] = ids
# if the regression should fit an intercept
if self.fit_intercept:
df['baseline'] = 1.
# if no event_col is specified, assume all non-censorships
if event_col:
c = df[event_col].values
del df[event_col]
else:
c = np.ones_like(ids)
# each individual should have an ID of time of leaving study
C = pd.Series(c, dtype=bool, index=ids)
T = pd.Series(df[duration_col].values, index=ids)
df = df.set_index(id_col)
ix = T.argsort()
T, C = T.iloc[ix], C.iloc[ix]
del df[duration_col]
n, d = df.shape
columns = df.columns
# initialize dataframe to store estimates
non_censorsed_times = list(T[C].iteritems())
n_deaths = len(non_censorsed_times)
hazards_ = pd.DataFrame(
np.zeros((n_deaths, d)),
columns=columns,
index=from_tuples(non_censorsed_times)).swaplevel(1, 0)
variance_ = pd.DataFrame(
np.zeros((n_deaths, d)),
columns=columns,
index=from_tuples(non_censorsed_times)).swaplevel(1, 0)
# initialize loop variables.
previous_hazard = np.zeros((d, ))
progress = progress_bar(n_deaths)
to_remove = []
t = T.iloc[0]
i = 0
for id, time in T.iteritems(): # should be sorted.
if t != time:
assert t < time
# remove the individuals from the previous loop.
df.iloc[to_remove] = 0.
to_remove = []
t = time
to_remove.append(id)
if C[id] == 0:
continue
relevant_individuals = (ids == id)
assert relevant_individuals.sum() == 1.
# perform linear regression step.
try:
v, V = lr(df.values,
relevant_individuals,
c1=self.coef_penalizer,
c2=self.smoothing_penalizer,
offset=previous_hazard)
except LinAlgError:
print(
"Linear regression error. Try increasing the penalizer term."
)
hazards_.ix[time, id] = v.T
variance_.ix[time, id] = V[:, relevant_individuals][:, 0]**2
previous_hazard = v.T
# update progress bar
if show_progress:
i += 1
progress.update(i)
# print a new line so the console displays well
if show_progress:
print()
# not sure this is the correct thing to do.
self.hazards_ = hazards_.groupby(level=0).sum()
self.cumulative_hazards_ = self.hazards_.cumsum()
self.variance_ = variance_.groupby(level=0).sum()
if timeline is not None:
self.hazards_ = self.hazards_.reindex(timeline, method='ffill')
self.cumulative_hazards_ = self.cumulative_hazards_.reindex(
timeline, method='ffill')
self.variance_ = self.variance_.reindex(timeline, method='ffill')
self.timeline = timeline
else:
self.timeline = self.hazards_.index.values.astype(float)
self.data = dataframe
self.durations = T
self.event_observed = C
self._compute_confidence_intervals()
return
