def test_coxph_plotting_with_subset_of_columns(self, block):
df = load_regression_dataset()
cp = CoxPHFitter()
cp.fit(df, "T", "E")
cp.plot(columns=["var1", "var2"])
self.plt.title("test_coxph_plotting_with_subset_of_columns")
self.plt.show(block=block)
def test_coxph_plotting(self, block):
df = load_regression_dataset()
cp = CoxPHFitter()
cp.fit(df, "T", "E")
cp.plot()
self.plt.title("test_coxph_plotting")
self.plt.show(block=block)
def test_coxph_plotting_with_hazards_ratios(self, block):
df = load_regression_dataset()
cp = CoxPHFitter()
cp.fit(df, "T", "E")
cp.plot(hazard_ratios=True)
self.plt.title("test_coxph_plotting")
self.plt.show(block=block)
def fit_cox(
train_df: Union[pd.DataFrame, str],
covariates: List[str],
test_df: Union[pd.DataFrame, str] = None,
strata: List[str] = None,
plot: bool = False,
process_dir: str = None,
):
if isinstance(train_df, str):
train_df = pd.read_csv(train_df)
if isinstance(test_df, str):
test_df = pd.read_csv(test_df)
cphf = CoxPHFitter()
included_cols = ["duration", "event"] + list(covariates)
print(train_df.columns)
cphf.fit(
train_df[included_cols],
duration_col="duration",
event_col="event",
strata=strata,
)
results = {
"log_likelihood":
cphf.log_likelihood_,
"concordance_index":
cphf.concordance_index_,
"log_likelihood_ratio_test_pvalue":
cphf.log_likelihood_ratio_test().p_value,
}
if test_df is not None:
results["test_log_likelihood"] = cphf.score(
test_df[included_cols], scoring_method="log_likelihood")
results["test_concordance_index"] = cphf.score(
test_df[included_cols], scoring_method="concordance_index")
if plot and process_dir is not None:
plt.figure(figsize=(5, 10))
cphf.plot()
plt.savefig(os.path.join(process_dir, "hazard_plot.pdf"))
if process_dir is not None:
cphf.summary.to_csv(os.path.join(process_dir, "summary.csv"))
save_dict_to_json(os.path.join(process_dir, "results.json"), results)
return results, cphf
def survival_analyze(dataframe,
lifetime_col,
dead_col,
strata_cols,
covariate_col=None):
# Based on notebook here. https://github.com/CamDavidsonPilon/lifelines/tree/master/examples
import pandas as pd
from matplotlib import pyplot as plt
from lifelines import CoxPHFitter
cph = CoxPHFitter().fit(dataframe,
lifetime_col,
dead_col,
strata=strata_cols)
cph.plot(ax=ax[1])
if covariate_col:
cph.plot_covariate_groups(covariate_col, values=[0, 1])
pass
def CoxAnalysis(pd_data, pd_surval, tp):
cph = CoxPHFitter(penalizer=0.1)
if tp == 'univariate':
pd_out = ''
for i in range(pd_data.shape[1]):
df = pd_surval.T.append(pd_data.iloc[:, i].T).T
cph.fit(df, 'OS', event_col='status')
if type(pd_out) == str:
pd_out = cph.summary
else:
pd_out = pd_out.append(cph.summary)
elif tp == 'multivariable':
df = pd_data.T.append(pd_surval.T).T
df = df.dropna(axis=0, how='any')
cph.fit(df, 'OS', event_col='status', step_size=0.1)
pd_out = cph.summary
pd_out.to_csv('CoxRegress.txt', sep='\t', header=True, index=True)
plt.style.use('my-paper')
fig, axe = plt.subplots(figsize=(25, 8))
cph.plot(ax=axe)
axe.set_ylim(-0.2, 3.2)
axe.set_xlim(-2.5, 2.1)
plt.savefig('CoxRegress.pdf')
from lifelines import CoxPHFitter
import matplotlib.pyplot as plt
import json
import pandas as pd
with open("test_data_Cox.json", "r", encoding='UTF-8') as f:
temp = json.loads(f.read())
regression_dataset = pd.DataFrame(
temp, columns=['T', 'E', 'base', 'hotestVideo', 'length'])
print(regression_dataset.head())
print(regression_dataset['E'].value_counts())
cph = CoxPHFitter()
cph.fit(regression_dataset, 'T', event_col='E')
cph.print_summary()
cph.plot()
plt.savefig('Cox3.png')
cph.plot()
''')
if st.checkbox("Fit Cox model"):
with st.spinner('Construction in progress...'):
cph = CoxPHFitter()
col_list.extend(['Churn', 'Tenure'])
cph.fit(df[col_list], duration_col='Tenure', event_col='Churn')
plt.style.use('seaborn-ticks')
fig = cph.plot()
plt.tick_params(axis='both', which='major', labelsize=20)
plt.xlabel("Log Hazard Ratio", fontsize=22)
plt.tight_layout()
st.pyplot(height=400)
plt.clf()
st.markdown('''
The **hazard ratio** (HR) of each variable provides a measure of how it influences the probability of the event of interest, in
this case customer churn, happening at a given point in time.
print(
np.mean(
k_fold_cross_validation(fitter,
times,
duration_col='time',
event_col='success')))
print("End cross-validation of " + name)
from lifelines import CoxPHFitter
cph = CoxPHFitter()
cph.fit(times, duration_col='time', event_col='success', show_progress=True)
cph.print_summary()
save('coxph', cph.plot())
#cph.check_assumptions(times, show_plots=True)
crossValidate('cox', cph)
fitters = {'cox': cph}
from lifelines import WeibullAFTFitter, LogNormalAFTFitter, LogLogisticAFTFitter
for (name, fitter) in [("weibull", WeibullAFTFitter),
("lognormal", LogNormalAFTFitter),
("loglogistic", LogLogisticAFTFitter)]:
print("BEGIN " + name)
aft = fitter()
aft.fit(times, duration_col='time', event_col='success')
aft.print_summary(3)
delimiter="\t")
regression_dataset['Class'] = regression_dataset['Class'].map({
'YES': 1,
'NO': 0
})
regression_dataset = regression_dataset.join(
pd.get_dummies(regression_dataset['clusters']))
regression_dataset = regression_dataset.drop(['id', 'clusters'], axis=1)
print(regression_dataset.head(100))
print(regression_dataset.dtypes)
kmf = KaplanMeierFitter()
kmf.fit(regression_dataset['Time'], event_observed=regression_dataset['Class'])
kmf.survival_function_
survival_function = kmf.plot_survival_function() # or just kmf.plot()
survival_function.get_figure().savefig("KaplanMeierFitter.png")
# Using Cox Proportional Hazards model
cph = CoxPHFitter(penalizer=0.1)
cph.fit(regression_dataset, 'Time', event_col='Class')
cph.print_summary()
# predict=cph.predict_survival_function(regression_dataset).plot()
# predict.get_figure().savefig("predicted.png")
ax = cph.plot()
ax.get_figure().savefig("CoxPHFitter.png")
y_te_eicu, X_te_mimic, y_te_mimic)
nzeros = neq_zero(best_cph.params_)
coefs = nzeros.index[nzeros.values].to_list()
coefs_val = best_cph.params_[nzeros.values].tolist()
summary = {
"penalizer": p,
"C_train": ctr,
"C_eicu:": ceicu,
"C_mimic": cmimic,
"nfeatures": len(coefs),
"features": coefs,
"coefs": coefs_val,
"df": pd.DataFrame({
"features": coefs,
"coefs": coefs_val
})
}
cph_results[best_ps[i]] = summary
best_cphs.append(best_cph)
plt.rcParams['figure.figsize'] = [5, 10]
fig = best_cph.plot()
figures.append(fig)
plt.close()
print(summary)
with open("{}/{}_summary.pkl".format(model_path, tag), "wb") as fout:
pickle.dump(cph_results, fout)
print(cph_results)
