def run_survival_curve(self, df):
''' used for testing only'''
aaf = AalenAdditiveFitter()
modelspec = 'YR_BRTH + AGE_DX + RADIATN + HISTREC + ERSTATUS + PRSTATUS + BEHANAL + HST_STGA + NUMPRIMS + RACE'
X = pt.dmatrix(modelspec, df, return_type='dataframe')
X = X.join(df[['SRV_TIME_MON', 'CENSORED']])
aaf.fit(X, 'SRV_TIME_MON', 'CENSORED')
# INSERT VALUES TO TEST HERE
test = np.array([[1., 1961., 52., 0, 0., 2., 1., 0., 4., 2.]])
aaf.predict_survival_function(test).plot()
plt.show()
exp = aaf.predict_expectation(test)
print(exp)
return
def run_survival_curve(self, df):
''' used for testing only'''
aaf = AalenAdditiveFitter()
modelspec = 'YR_BRTH + AGE_DX + RADIATN + HISTREC + ERSTATUS + PRSTATUS + BEHANAL + HST_STGA + NUMPRIMS + RACE'
X = pt.dmatrix(modelspec, df, return_type='dataframe')
X = X.join(df[['SRV_TIME_MON','CENSORED']])
aaf.fit(X, 'SRV_TIME_MON', 'CENSORED')
# INSERT VALUES TO TEST HERE
test = np.array([[ 1., 1961., 52., 0, 0., 2., 1., 0., 4., 2.]])
aaf.predict_survival_function(test).plot();
plt.show()
exp = aaf.predict_expectation(test)
print(exp)
return
def predict(self, R, Thetas=dict(), _type='cumulative_hazards', **kwargs):
"""
Assuming that the type to refit is the first type of
predictive_relationship
"""
if not self.regression_:
raise Exception("No regression was fitted on the traning")
X = self._modify_test_data(R, Thetas)
if _type == 'cumulative_hazards':
return AalenAdditiveFitter.predict_cumulative_hazard(
self, X, id_col=kwargs.get('id_col', None))
elif _type == 'survival_function':
return AalenAdditiveFitter.predict_survival_function(self, X)
elif _type == 'percentile':
return AalenAdditiveFitter.predict_percentile(
self, X, kwargs.get('p', 0))
elif _type == 'median':
return AalenAdditiveFitter.predict_median(self, X)
elif _type == 'expectation':
return AalenAdditiveFitter.predict_expectation(self, X)
else:
raise ValueError("Not avaialble type of prediction")
