def test_datetimes_to_durations_with_different_frequencies():
# days
start_date = ["2013-10-10 0:00:00", "2013-10-09", "2012-10-10"]
end_date = ["2013-10-13", "2013-10-10 0:00:00", "2013-10-15"]
T, C = utils.datetimes_to_durations(start_date, end_date)
npt.assert_almost_equal(T, np.array([3, 1, 5 + 365]))
npt.assert_almost_equal(C, np.array([1, 1, 1], dtype=bool))
# years
start_date = ["2013-10-10", "2013-10-09", "2012-10-10"]
end_date = ["2013-10-13", "2013-10-10", "2013-10-15"]
T, C = utils.datetimes_to_durations(start_date, end_date, freq="Y")
npt.assert_almost_equal(T, np.array([0, 0, 1]))
npt.assert_almost_equal(C, np.array([1, 1, 1], dtype=bool))
# hours
start_date = [
"2013-10-10 17:00:00", "2013-10-09 0:00:00", "2013-10-10 23:00:00"
]
end_date = [
"2013-10-10 18:00:00", "2013-10-10 0:00:00", "2013-10-11 2:00:00"
]
T, C = utils.datetimes_to_durations(start_date, end_date, freq="h")
npt.assert_almost_equal(T, np.array([1, 24, 3]))
npt.assert_almost_equal(C, np.array([1, 1, 1], dtype=bool))
def plot_lifetimes_for_diagnosis(df, diagnosis, current_time=50, subset_size=80):
df = df.loc[df['diagnosis'] == diagnosis]
# create figure and axes
fig, ax = plt.subplots()
# specify type for PyCharm help
fig: plt.Figure = fig
ax: plt.Axes = ax
if df.shape[0] >= subset_size:
df = df.sample(n=subset_size, random_state=1)
start_times, end_times = df['start_date'], df['end_date']
actual_lifetimes, death_observed = datetimes_to_durations(start_times, end_times, freq='M')
plot_lifetimes(durations=actual_lifetimes, event_observed=death_observed, ax=ax)
ax.set_title(diagnosis_list[diagnosis])
ax.set_xlabel('Čas od začátku sledování po vznik události v měsících')
ax.set_ylabel('Sledovaná osoba')
# show and save plot
fig.show()
fig.savefig(f'paper/img/image_{next(GEN)}.pdf', format='pdf')
def test_datetimes_to_durations_hours():
start_date = ['2013-10-10 17:00:00', '2013-10-09 0:00:00', '2013-10-10 23:00:00']
end_date = ['2013-10-10 18:00:00', '2013-10-10 0:00:00', '2013-10-11 2:00:00']
T, C = utils.datetimes_to_durations(start_date, end_date, freq='h')
npt.assert_almost_equal(T, np.array([1, 24, 3]))
npt.assert_almost_equal(C, np.array([1, 1, 1], dtype=bool))
return
def test_datetimes_to_durations_years():
start_date = ['2013-10-10', '2013-10-09', '2012-10-10']
end_date = ['2013-10-13', '2013-10-10', '2013-10-15']
T, C = utils.datetimes_to_durations(start_date, end_date, freq='Y')
npt.assert_almost_equal(T, np.array([0, 0, 1]))
npt.assert_almost_equal(C, np.array([1, 1, 1], dtype=bool))
return
def test_datetimes_to_durations_days():
start_date = ['2013-10-10 0:00:00', '2013-10-09', '2012-10-10']
end_date = ['2013-10-13', '2013-10-10 0:00:00', '2013-10-15']
T, C = utils.datetimes_to_durations(start_date, end_date)
npt.assert_almost_equal(T, np.array([3, 1, 5 + 365]))
npt.assert_almost_equal(C, np.array([1, 1, 1], dtype=bool))
return
def test_datetimes_to_durations_years():
start_date = ['2013-10-10', '2013-10-09', '2012-10-10']
end_date = ['2013-10-13', '2013-10-10', '2013-10-15']
T, C = utils.datetimes_to_durations(start_date, end_date, freq='Y')
npt.assert_almost_equal(T, np.array([0, 0, 1]))
npt.assert_almost_equal(C, np.array([1, 1, 1], dtype=bool))
return
def test_datetimes_to_durations_will_handle_dates_above_multi_fill_date():
start_date = ["2013-10-08", "2013-10-09", "2013-10-10"]
end_date = ["2013-10-10", None, "2013-10-20"]
last_observation = ["2013-10-10", "2013-10-12", "2013-10-14"]
T, E = utils.datetimes_to_durations(start_date, end_date, freq="D", fill_date=last_observation)
npt.assert_almost_equal(E, np.array([1, 0, 0], dtype=bool))
npt.assert_almost_equal(T, np.array([2, 3, 4]))
def test_datetimes_to_durations_days():
start_date = ['2013-10-10 0:00:00', '2013-10-09', '2012-10-10']
end_date = ['2013-10-13', '2013-10-10 0:00:00', '2013-10-15']
T, C = utils.datetimes_to_durations(start_date, end_date)
npt.assert_almost_equal(T, np.array([3, 1, 5 + 365]))
npt.assert_almost_equal(C, np.array([1, 1, 1], dtype=bool))
return
def test_datetimes_to_durations_will_handle_dates_above_fill_date():
start_date = ['2013-10-08', '2013-10-09', '2013-10-10']
end_date = ['2013-10-10', '2013-10-12', '2013-10-15']
T, C = utils.datetimes_to_durations(start_date,
end_date,
freq='Y',
fill_date='2013-10-12')
npt.assert_almost_equal(C, np.array([1, 1, 0], dtype=bool))
def test_datetimes_to_durations_will_handle_dates_above_fill_date():
start_date = ["2013-10-08", "2013-10-09", "2013-10-10"]
end_date = ["2013-10-10", "2013-10-12", "2013-10-15"]
T, C = utils.datetimes_to_durations(start_date,
end_date,
freq="Y",
fill_date="2013-10-12")
npt.assert_almost_equal(C, np.array([1, 1, 0], dtype=bool))
def test_datetimes_to_durations_custom_censor():
start_date = ["2013-10-10", "2013-10-09", "2012-10-10"]
end_date = ["2013-10-13", "NaT", ""]
T, C = utils.datetimes_to_durations(start_date,
end_date,
freq="Y",
na_values=["NaT", ""])
npt.assert_almost_equal(C, np.array([1, 0, 0], dtype=bool))
def test_datetimes_to_durations_custom_censor():
start_date = ['2013-10-10', '2013-10-09', '2012-10-10']
end_date = ['2013-10-13', "NaT", '']
T, C = utils.datetimes_to_durations(start_date,
end_date,
freq='Y',
na_values="NaT")
npt.assert_almost_equal(C, np.array([1, 0, 0], dtype=bool))
return
def survival_info_adding(df: pd.DataFrame):
df['Last Event'] = df['Last FU']
df['Event'] = (df['Status'] == 'Dead')
df.loc[df['Event'] , 'Last Event'] = df.loc[df['Event'], 'Date of Death']
start_date = df['RT End']
end_date = df['Last Event']
T_old, _ = datetimes_to_durations(start_date, end_date)
df['Survival Time'] = T_old /365
df['High_Risk'] = df['Survival Time']<= 4
return df
def test_datetimes_to_durations_hours():
start_date = [
'2013-10-10 17:00:00', '2013-10-09 0:00:00', '2013-10-10 23:00:00'
]
end_date = [
'2013-10-10 18:00:00', '2013-10-10 0:00:00', '2013-10-11 2:00:00'
]
T, C = utils.datetimes_to_durations(start_date, end_date, freq='h')
npt.assert_almost_equal(T, np.array([1, 24, 3]))
npt.assert_almost_equal(C, np.array([1, 1, 1], dtype=bool))
return
def test_datetimes_to_durations_with_different_frequencies():
# days
start_date = ['2013-10-10 0:00:00', '2013-10-09', '2012-10-10']
end_date = ['2013-10-13', '2013-10-10 0:00:00', '2013-10-15']
T, C = utils.datetimes_to_durations(start_date, end_date)
npt.assert_almost_equal(T, np.array([3, 1, 5 + 365]))
npt.assert_almost_equal(C, np.array([1, 1, 1], dtype=bool))
# years
start_date = ['2013-10-10', '2013-10-09', '2012-10-10']
end_date = ['2013-10-13', '2013-10-10', '2013-10-15']
T, C = utils.datetimes_to_durations(start_date, end_date, freq='Y')
npt.assert_almost_equal(T, np.array([0, 0, 1]))
npt.assert_almost_equal(C, np.array([1, 1, 1], dtype=bool))
# hours
start_date = ['2013-10-10 17:00:00', '2013-10-09 0:00:00', '2013-10-10 23:00:00']
end_date = ['2013-10-10 18:00:00', '2013-10-10 0:00:00', '2013-10-11 2:00:00']
T, C = utils.datetimes_to_durations(start_date, end_date, freq='h')
npt.assert_almost_equal(T, np.array([1, 24, 3]))
npt.assert_almost_equal(C, np.array([1, 1, 1], dtype=bool))
def kaplan_meier_analysis(df, rows=2, columns=3):
"""
Kaplan-Meier estimates for individual diagnoses
:param df: Pandas dataframe with data
:param rows: number of rows for subplots
:param columns: number of columns for subplots
:return: Plots the data
"""
fig, axes = plt.subplots(rows, columns, figsize=[10, 6])
for pos, diagnosis in enumerate(diagnosis_list):
timeline = np.linspace(0, 12)
# initialize start and end times
start_times, end_times = start_end_for_diagnosis(df, diagnosis)
# get data in the right format - t is time_span, e is event (1 is death)
t, e = datetimes_to_durations(start_times, end_times, freq='M') # M - months, D - days
# initialize Kaplan-Meier fitters
kmf = KaplanMeierFitter()
# fit the data
kmf.fit(t, event_observed=e, timeline=timeline)
# get the plot position
ax: plt.Axes = axes[pos % rows, pos % columns]
# plot Kaplan-Meier
kmf.plot(ax=ax)
# create legend
legend_elements = [Line2D([0], [0], color='b', lw=1, label=f'n = {len(start_times)}')]
# format plot
ax.set_ylim(0, 1)
ax.set_xlabel('Time in months')
ax.set_title('\n'.join(wrap(diagnosis_list[diagnosis], 30)))
ax.legend(handles=legend_elements, loc='lower right')
ax.margins(y=50)
# show plot
fig.show()
fig.savefig(f'paper/img/image_{next(GEN)}.pdf', format='pdf')
def plot_kp_time_to_next_treatment(lot, line, metric, cohort_enhanced, censoring_date=None,
displayed_regimen=None, timeline='M'):
"""
Inputs:
- metric: 'time_to_next_treatment', 'time_to_last_activity'
"""
kmf = KaplanMeierFitter()
figsize(10, 8)
ax = plt.subplot(111)
if timeline == 'M':
x_label = 'Duration in months'
elif timeline == 'D':
x_label = 'Duration in days'
elif timeline == 'Y':
x_label = 'Duration in years'
if metric == 'time_to_last_activity':
lot = lot.merge(cohort_enhanced, how='left', on='person_id')
title = "Time to last activity for different regimens"
end_date = 'last_activity_date'
elif metric == 'time_to_next_treatment':
title = "Time to next treatment for different regimens"
end_date = 'end_date'
df = lot[lot['line_number']==line]
groups = df['regimen_name']
if displayed_regimen is None:
labels = groups.unique().tolist()
else:
labels = displayed_regimen
for a, label in enumerate(labels):
i = (groups == label)
start_dates = df[i]['start_date']
end_dates = df[i][end_date]
T, E = datetimes_to_durations(start_dates, end_dates, fill_date = censoring_date, freq=timeline)
kmf.fit(T, event_observed=E, label=label)
kmf.plot(ax=ax)
plt.ylim(0, 1)
plt.xlim(0, 20)
plt.xlabel(x_label)
plt.title(title)
def run_two_churn_defs(df):
for i, f in enumerate([get_death_time_v1, get_death_time_v2]):
# Calculate the death date over all rows
df['death_date'] = df[cols_payments].apply(lambda x: f(x), axis=1)
# Create duration and churn status
start_times = df['incorporation_date']
end_times = df['death_date']
obs_time = datetime.datetime(2015, 1, 1)
T, E = datetimes_to_durations(start_times,
end_times,
freq='M',
fill_date=obs_time)
df['T'] = T # duration (in months)
df['E'] = E # churn status
kmf = KaplanMeierFitter()
kmf.fit(T, event_observed=E) # or, more succiently, kmf.fit(T, E)
if i == 0:
ax = kmf.plot()
else:
kmf.plot(ax=ax)
# plt.title('KM Survival Function using v1 churn def')
pass
data['quit_date'] = data.quit_date.fillna('2015-12-13')
data['quit_date'] = pd.to_datetime(data['quit_date'])
data['join_date'] = pd.to_datetime(data['join_date'])
# data['duration'] = data['quit_date'] - data['join_date']
# Turn this into an integer so we can work with it
# data['duration'] = data['duration'].dt.days
# Actually, there is a built in function to get DURATION appropriate for SA
from lifelines.utils import datetimes_to_durations
start_date = data['join_date']
end_date = data['quit_date']
T, E = datetimes_to_durations(start_date, end_date)
print('T (durations): ', T)
print('E (event_observed): ', E)
data2 = data
data2['duration'], data2['observed'] = datetimes_to_durations(
start_date, end_date)
from lifelines import KaplanMeierFitter
kmf = KaplanMeierFitter()
# Made T and E up there ^
kmf.fit(T, event_observed=E)
kmf.survival_function_.plot()
plt.title('Survival function of employee churn')
def test_datetimes_to_durations_censor():
start_date = ["2013-10-10", "2013-10-09", "2012-10-10"]
end_date = ["2013-10-13", None, ""]
T, C = utils.datetimes_to_durations(start_date, end_date, freq="Y")
npt.assert_almost_equal(C, np.array([1, 0, 0], dtype=bool))
import pandas as pd
from lifelines.utils import datetimes_to_durations
from lifelines import KaplanMeierFitter
df = pd.read_csv('data/parl_data.csv')
df['start_date'] = pd.to_datetime(df['start_date'])
df['end_date'] = pd.to_datetime(df['end_date'])
df['decade'] = df['start_date'].map( lambda d: str(d.year)[:3])
T, C = datetimes_to_durations(df['start_date'], df['end_date'])
df['T'] = T
df['C'] = C
kmf = KaplanMeierFitter()
ax = subplot(111)
for decade in df['decade'].unique():
ix = df['decade'] == decade
kmf.fit(df.ix[ix]['T'], df.ix[ix]['C'], label=decade)
if decade not in ('200', '199'):
kmf.plot(ax=ax, c='#777777', ci_show=False, alpha = 0.5)
else:
kmf.plot(ax=ax, lw=4)
df[fecha] = pd.to_datetime(df[fecha]) # Remove rows with ilogical dates: df.drop(df[df.fecha_egreso < df.fecha_admision].index, inplace=True) # df[df['fecha_admision'] > df['fecha_egreso']] # check # Check too large waiting times df['t_admin_egreso'] = df['fecha_egreso'] - df['fecha_admision'] df.sort_values(by='t_admin_egreso', ascending=False) df = df[df['t_admin_egreso'].dt.days < 1] df #%% Lifelines analysis from lifelines.utils import datetimes_to_durations T, E = datetimes_to_durations(start_times=df.fecha_admision, end_times=df.fecha_egreso, freq='h') from lifelines import KaplanMeierFitter kmf = KaplanMeierFitter() kmf.fit(T, event_observed=E) # or, more succiently, kmf.fit(T, E) kmf.survival_function_ kmf.median_ kmf.plot() plt.show() #%% Lifelines analysis from lifelines.utils import datetimes_to_durations T, E = datetimes_to_durations(start_times=df.fecha_admision, end_times=df.fecha_egreso, freq='h') from lifelines import KaplanMeierFitter kmf = KaplanMeierFitter() kmf.fit(T, event_observed=E) # or, more succiently, kmf.fit(T, E) kmf.survival_function_
def test_datetimes_to_durations_censor():
start_date = ['2013-10-10', '2013-10-09', '2012-10-10']
end_date = ['2013-10-13', None, '']
T, C = utils.datetimes_to_durations(start_date, end_date, freq='Y')
npt.assert_almost_equal(C, np.array([1, 0, 0], dtype=bool))
def test_datetimes_to_durations_will_handle_dates_above_fill_date():
start_date = ["2013-10-08", "2013-10-09", "2013-10-10"]
end_date = ["2013-10-10", "2013-10-12", "2013-10-15"]
T, C = utils.datetimes_to_durations(start_date, end_date, freq="Y", fill_date="2013-10-12")
npt.assert_almost_equal(C, np.array([1, 1, 0], dtype=bool))
def test_datetimes_to_durations_custom_censor():
start_date = ['2013-10-10', '2013-10-09', '2012-10-10']
end_date = ['2013-10-13', "NaT", '']
T, C = utils.datetimes_to_durations(start_date, end_date, freq='Y', na_values=["NaT", ""])
npt.assert_almost_equal(C, np.array([1, 0, 0], dtype=bool))
def test_datetimes_to_durations_censor():
start_date = ['2013-10-10', '2013-10-09', '2012-10-10']
end_date = ['2013-10-13', None, '']
T, C = utils.datetimes_to_durations(start_date, end_date, freq='Y')
npt.assert_almost_equal(C, np.array([1, 0, 0], dtype=bool))
def test_datetimes_to_durations_will_handle_dates_above_fill_date():
start_date = ['2013-10-08', '2013-10-09', '2013-10-10']
end_date = ['2013-10-10', '2013-10-12', '2013-10-15']
T, C = utils.datetimes_to_durations(start_date, end_date, freq='Y', fill_date='2013-10-12')
npt.assert_almost_equal(C, np.array([1, 1, 0], dtype=bool))
# Reorder and select columns
cols_chars = ['company_id', 'vertical', 'incorporation_date']
cols_payments = [x for x in df if 'payments' in x]
df = df[cols_chars + cols_payments]
# Calculate the death date over all rows
df['death_date'] = df[cols_payments].apply(lambda x: get_death_time_v2(x),
axis=1)
# Create duration and churn status
start_times = df['incorporation_date']
end_times = df['death_date']
obs_time = datetime.datetime(2015, 1, 1)
T, E = datetimes_to_durations(start_times,
end_times,
freq='M',
fill_date=obs_time)
df['T'] = T # duration (in months)
df['E'] = E # churn status
kmf = KaplanMeierFitter()
# vertical_type = 'gym/fitness'
vertical_types = np.unique(df['vertical'])
for i, _type in enumerate(vertical_types):
ix = (df['vertical'] == _type)
kmf.fit(T[ix], E[ix], label=_type)
if i == 0:
ax = kmf.plot()
else:
kmf.plot(ax=ax)