if __name__ == '__main__':
# Bladder Example
#raw_data = pd.read_csv('datas/bladder_recurrent.csv', sep=';')
raw_data = pd.read_csv('datas/T45.csv', sep=';')
raw_data.rename(columns={'Patient number': 'Sample', 'Event': 'Number', 'Censored': 'Event',
'Treatment group': 'Drug'}, inplace=True)
raw_data['Drug'] = raw_data['Drug'].map({1: 'Placebo', 2: 'Pyridoxine', 3: 'Thiotepa'})
raw_data['Event'] = 1 - raw_data['Event']
tb = print_data(raw_data)
print tb
df = transform_data(raw_data)
lt = mcf(df, robust=True, positive=False)
covariate = 'Drug'
cohort1, cohort2 = 'Placebo', 'Thiotepa'
group_df = df.set_index(covariate)
group_lt = df.groupby(covariate).apply(lambda sfr: mcf(sfr, robust=True, positive=False))
df1, df2 = group_df.ix[cohort1], group_df.ix[cohort2]
lt1, lt2 = group_lt.ix[cohort1], group_lt.ix[cohort2]
lt_diff = mcfdiff(lt1, lt2)
# Plot, no stratification
fig, (ax1, ax2) = plt.subplots(2, 1)
plot_data(df, ax=ax1)
plot_data, plot_datas, plot_mcf, plot_mcfs, plot_mcfdiff, plot_mcfdiffs
if __name__ == '__main__':
# Childbirths Example
raw_data = pd.read_csv('datas/childbirths_interval.csv',
sep=';') #, usecols=[0,1,2])
cohort1, cohort2 = 'Male', 'Female'
df1 = transform_population(
raw_data[raw_data['Sex'] == cohort1]).fillna(cohort1)
df2 = transform_population(
raw_data[raw_data['Sex'] == cohort2]).fillna(cohort2)
df = pd.concat((df1, df2))
lt1 = mcf(df1, robust=False, positive=False)
lt2 = mcf(df2, robust=False, positive=False)
lt_diff = mcfdiff(lt1, lt2)
# Plot, stratified
fig, (ax1, ax2, ax3) = plt.subplots(1, 3, sharey=False, sharex=True)
plot_mcf(lt1, ax=ax1, interval=True, CI=True, label=cohort1)
plot_mcfs([(cohort1, lt1), (cohort2, lt2)],
ax=ax2,
interval=True,
CI=False)
plot_mcfdiff(lt_diff, ax=ax3, label='%s vs. %s' % (cohort1, cohort2))
ax1.set_ylim([0, 2])
ax2.set_ylim([0, 2])
ax3.set_ylim([-1, 1])
plt.xlim([0, 60])
if __name__ == '__main__':
# Cook (2008, pg. 299) Example 8.1: Field Repair data
# This dataset (see Appendix D) gives simulated data on unscheduled repairs
# for a fleet of m = 134 large utility vehicles operated by a city. The data were
# collected over a three-year period on new vehicles which were purchased and
# placed in service over the first two years of the study. Time is measured in
# years from the start of the study, and costs are in hundreds of dollars.
raw_data = generate_data()
df = transform_data(raw_data)
# Table D.4.
# Marked point process (8.14) as mcf1 and cumulative cost process (8.12) as mcf2
lt1 = mcf(df, robust=True, positive=False)
mcf1 = mcfcost(df, mcf_compound=lt1, robust=False, positive=False)
mcf2 = mcfcost(df, robust=True, positive=False)
# Plot data
plot_data(df, alpha=0.5, marker='.', plot_costs=False)
# Plot MCFs
fig, (ax1, ax2) = plt.subplots(1, 2, sharex=True, sharey=False)
plot_mcf(mcf1, ax=ax1, cost=True, label='Events * Cost')
plot_mcf(mcf2, ax=ax2, cost=True, label='Cost')
# Table 8.1.
mcf1['E[C].Std'] = np.sqrt(mcf1['E[C].Var'])
mcf2['E[C].Std'] = np.sqrt(mcf2['E[C].Var'])
mcf1 = mcf1[['Time', 'E[C]', 'E[C].Std']]
'Patient number': 'Sample',
'Event': 'Number',
'Censored': 'Event',
'Treatment group': 'Drug'
},
inplace=True)
raw_data['Drug'] = raw_data['Drug'].map({
1: 'Placebo',
2: 'Pyridoxine',
3: 'Thiotepa'
})
raw_data['Event'] = 1 - raw_data['Event']
print raw_data.head()
df = transform_data(raw_data)
lt = mcf(df, robust=True, positive=False)
covariate = 'Drug'
cohort1, cohort2 = 'Placebo', 'Thiotepa'
group_df = df.set_index(covariate)
group_lt = df.groupby(covariate).apply(
lambda sfr: mcf(sfr, robust=True, positive=False))
df1, df2 = group_df.ix[cohort1], group_df.ix[cohort2]
lt1, lt2 = group_lt.ix[cohort1], group_lt.ix[cohort2]
lt_diff = mcfdiff(lt1, lt2)
# Plot, no stratification
fig, (ax1, ax2) = plt.subplots(2, 1)
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from data_operations import transform_data, mcf, mcfdiff, mcfequal,\
plot_data, plot_datas, plot_mcf, plot_mcfs, plot_mcfdiff, plot_mcfdiffs
if __name__ == '__main__':
# Transmission Example
data = pd.read_csv('datas/transmission_recurrent.csv', sep=';')
df = transform_data(data, format='recurrent')
lt = mcf(df, robust=True, positive=False)
print lt[['Time', 'Y', 'N', 'dE[N]', 'E[N]', 'E[N].lcl',
'E[N].ucl']].head()
covariate = 'Transmission'
cohort1, cohort2 = 'Automatic', 'Manual'
group_df = df.set_index(covariate)
group_lt = df.groupby(covariate).apply(
lambda sfr: mcf(sfr, robust=True, positive=True)) #, interval=True))
df1, df2 = group_df.ix[cohort1], group_df.ix[cohort2]
lt1, lt2 = group_lt.ix[cohort1], group_lt.ix[cohort2]
lt_diff = mcfdiff(lt1, lt2)
# Plot, no stratification
fig, (ax1, ax2) = plt.subplots(2, 1)
plot_data(df, ax=ax1)
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from data_operations import transform_population, mcf, mcfdiff, mcfequal,\
plot_data, plot_datas, plot_mcf, plot_mcfs, plot_mcfdiff, plot_mcfdiffs, population_reverse
if __name__ == '__main__':
# Defrost Control Example
raw_data = pd.read_csv('datas/defrost_interval.csv', sep=';')#, usecols=[0,1,2])
df = transform_population(raw_data)
lt1 = mcf(df, robust=False, positive=False, interval=True)
print df.head()
fr = population_reverse(df)
plot_data(fr, alpha=0.005)
fig, (ax1) = plt.subplots(1, 1, sharex=True, sharey=True)
plot_mcf(lt1, ax=ax1, interval=True)
plt.ylim([0.0, 0.1])
plt.show()
#lt_cost = mcf_cost(fr, mcf_compound=lt)
#print lt_cost.head()
#plot_cost(mcf_cost)
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from data_operations import transform_population, mcf, mcfcost, mcfdiff, mcfequal,\
plot_data, plot_datas, plot_mcf, plot_mcfs, plot_mcfdiff, plot_mcfdiffs, population_reverse, population_reverse_costs
if __name__ == '__main__':
# Gearboxes Example
raw_data = pd.read_csv('datas/gearbox_recurrent.csv',
sep=';') #, usecols=[0,1,2])
raw_data = raw_data[raw_data['SaleMonth'].isin([1, 2, 3, 4])]
df = transform_population(raw_data)
lt = mcf(df, robust=False, positive=False, interval=True)
#ct = mcfcost(df, robust=False, positive=False, interval=True)
#lt_cost = mcf_cost(fr, mcf_compound=lt)
#print lt_cost.head()
#plot_cost(mcf_cost)
covariate = 'SaleMonth'
group_df = raw_data.groupby(covariate).apply(
lambda sfr: transform_population(sfr))
dfs = list(group_df.groupby(level=0))
lts = [(cohort, mcf(cohort_df, robust=False, positive=False))
for cohort, cohort_df in dfs]
cts = [(cohort, mcfcost(cohort_df, mcf_compound=cohort_mcf, robust=False, positive=False)) for\
(cohort, cohort_df), (cohort, cohort_mcf) in zip(dfs,lts)]
datas = [(cohort, population_reverse_costs(cohort_df))
for cohort, cohort_df in dfs]
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from data_operations import transform_data, mcf, mcfdiff, mcfequal,\
plot_data, plot_datas, plot_mcf, plot_mcfs, plot_mcfdiff, plot_mcfdiffs
if __name__ == '__main__':
# CGD example
raw_data = pd.read_csv('datas/CGD_recurrent.csv', sep=';')
df = transform_data(raw_data)
lt = mcf(df, robust=True, positive=True)
print lt.head()
covariate = 'Treatment'
cohort1, cohort2 = 'Placebo', 'Gamma Interferon'
group_df = df.set_index(covariate)
group_lt = df.groupby(covariate).apply(
lambda sfr: mcf(sfr, robust=True, positive=True))
df1, df2 = group_df.ix[cohort1], group_df.ix[cohort2]
lt1, lt2 = group_lt.ix[cohort1], group_lt.ix[cohort2]
lt_diff = mcfdiff(lt1, lt2)
# Plot, no stratification
fig, (ax1, ax2) = plt.subplots(2, 1)
plot_data(df, ax=ax1)
plot_mcf(lt, ax=ax2)
plt.tight_layout()
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from data_operations import transform_data, mcf, mcfdiff, mcfequal,\
plot_data, plot_datas, plot_mcf, plot_mcfs, plot_mcfdiff, plot_mcfdiffs
if __name__ == '__main__':
# Valve Seats (Nelson 1995) Example
data = pd.read_csv('datas/nelson1995_recurrent.csv', sep=';')
df = transform_data(data, format='recurrent')
lt = mcf(df, robust=True, positive=True)
print lt[['Time', 'Y', 'N', 'dE[N]', 'E[N]', 'E[N].lcl',
'E[N].ucl']].head()
# Plot, no stratification
fig, (ax1, ax2) = plt.subplots(2, 1)
plot_data(df, ax=ax1)
plot_mcf(lt, ax=ax2)
plt.tight_layout()
plt.show()
import matplotlib.pyplot as plt
from data_operations import transform_population, mcf, mcfdiff, mcfequal,\
plot_data, plot_datas, plot_mcf, plot_mcfs, plot_mcfdiff, plot_mcfdiffs, population_reverse
if __name__ == '__main__':
# Compressor Example
raw_data = pd.read_csv('datas/compressor_population.csv',
sep=';') #, usecols=[0,1,2])
#raw_data = raw_data.groupby('Time', as_index=False).sum()
df = transform_population(raw_data)
print population_reverse(df)
group_df = raw_data.groupby('Building').apply(
lambda sdata: population_reverse(transform_population(sdata)))
dfs = list(group_df.groupby(level=0))
lt1 = mcf(df, robust=False, positive=False)
lt2 = mcf(df, robust=False, positive=True)
plot_datas(dfs, alpha=0.1)
fig, (ax1, ax2) = plt.subplots(1, 2, sharex=True, sharey=True)
plot_mcf(lt1, ax=ax1, label='Normal limits')
plot_mcf(lt2, ax=ax2, label='Normal limits (positive)')
plt.ylim([-0.005, 0.1])
plt.show()
#lt_cost = mcf_cost(fr, mcf_compound=lt)
#print lt_cost.head()
#plot_cost(mcf_cost)
