In [1]:

    

import numpy as np, matplotlib.pyplot as plt, pandas as pd
pd.set_option('display.max_rows', 10)
!date

%load_ext autoreload
%autoreload 2


    

    




Tue Jul 23 11:31:36 PDT 2019

In [2]:

    

np.random.seed(123456)


    

In [3]:

    

# if dismod_mr is not installed, it should possible to use
# !conda install --yes pymc
# !pip install dismod_mr


    

In [4]:

    

import dismod_mr


    

In [5]:

    

# you also need one more pip installable package
# !pip install vivarium_public_health


    

In [6]:

    

import vivarium_public_health


    

In [7]:

    

from vivarium_public_health.dataset_manager import Artifact
art = Artifact('/share/costeffectiveness/artifacts/obesity/obesity.hdf')


    

In [8]:

    

art.keys


    

    
Out[8]:





[EntityKey(metadata.keyspace),
 EntityKey(metadata.versions),
 EntityKey(metadata.locations),
 EntityKey(population.demographic_dimensions),
 EntityKey(population.structure),
 EntityKey(cause.all_causes.cause_specific_mortality),
 EntityKey(population.theoretical_minimum_risk_life_expectancy),
 EntityKey(cause.ischemic_heart_disease.restrictions),
 EntityKey(cause.ischemic_stroke.restrictions),
 EntityKey(cause.diabetes_mellitus_type_2.restrictions),
 EntityKey(cause.asthma.restrictions),
 EntityKey(cause.gout.restrictions),
 EntityKey(cause.osteoarthritis.restrictions),
 EntityKey(cause.chronic_kidney_disease_due_to_hypertension.restrictions),
 EntityKey(cause.chronic_kidney_disease_due_to_glomerulonephritis.restrictions),
 EntityKey(cause.chronic_kidney_disease_due_to_other_and_unspecified_causes.restrictions),
 EntityKey(cause.chronic_kidney_disease_due_to_diabetes_mellitus_type_2.restrictions),
 EntityKey(risk_factor.high_body_mass_index_in_adults.exposure),
 EntityKey(risk_factor.high_body_mass_index_in_adults.distribution),
 EntityKey(risk_factor.high_body_mass_index_in_adults.exposure_standard_deviation),
 EntityKey(risk_factor.high_body_mass_index_in_adults.exposure_distribution_weights),
 EntityKey(risk_factor.high_body_mass_index_in_adults.population_attributable_fraction),
 EntityKey(risk_factor.high_body_mass_index_in_adults.tmred),
 EntityKey(risk_factor.high_body_mass_index_in_adults.relative_risk_scalar),
 EntityKey(population.age_bins),
 EntityKey(healthcare_entity.outpatient_visits.utilization),
 EntityKey(healthcare_entity.inpatient_visits.cost),
 EntityKey(healthcare_entity.outpatient_visits.cost),
 EntityKey(cause.ischemic_heart_disease.cause_specific_mortality),
 EntityKey(cause.ischemic_stroke.cause_specific_mortality),
 EntityKey(cause.diabetes_mellitus_type_2.cause_specific_mortality),
 EntityKey(cause.asthma.cause_specific_mortality),
 EntityKey(cause.chronic_kidney_disease_due_to_hypertension.cause_specific_mortality),
 EntityKey(cause.chronic_kidney_disease_due_to_glomerulonephritis.cause_specific_mortality),
 EntityKey(cause.chronic_kidney_disease_due_to_other_and_unspecified_causes.cause_specific_mortality),
 EntityKey(cause.chronic_kidney_disease_due_to_diabetes_mellitus_type_2.cause_specific_mortality),
 EntityKey(cause.ischemic_heart_disease.prevalence),
 EntityKey(cause.ischemic_heart_disease.disability_weight),
 EntityKey(cause.ischemic_heart_disease.excess_mortality),
 EntityKey(cause.ischemic_stroke.prevalence),
 EntityKey(cause.ischemic_stroke.disability_weight),
 EntityKey(cause.ischemic_stroke.excess_mortality),
 EntityKey(cause.diabetes_mellitus_type_2.prevalence),
 EntityKey(cause.diabetes_mellitus_type_2.disability_weight),
 EntityKey(cause.diabetes_mellitus_type_2.excess_mortality),
 EntityKey(cause.asthma.prevalence),
 EntityKey(cause.asthma.disability_weight),
 EntityKey(cause.asthma.excess_mortality),
 EntityKey(cause.gout.prevalence),
 EntityKey(cause.gout.disability_weight),
 EntityKey(cause.osteoarthritis.prevalence),
 EntityKey(cause.osteoarthritis.disability_weight),
 EntityKey(cause.chronic_kidney_disease_due_to_hypertension.prevalence),
 EntityKey(cause.chronic_kidney_disease_due_to_hypertension.disability_weight),
 EntityKey(cause.chronic_kidney_disease_due_to_hypertension.excess_mortality),
 EntityKey(cause.chronic_kidney_disease_due_to_glomerulonephritis.prevalence),
 EntityKey(cause.chronic_kidney_disease_due_to_glomerulonephritis.disability_weight),
 EntityKey(cause.chronic_kidney_disease_due_to_glomerulonephritis.excess_mortality),
 EntityKey(cause.chronic_kidney_disease_due_to_other_and_unspecified_causes.prevalence),
 EntityKey(cause.chronic_kidney_disease_due_to_other_and_unspecified_causes.disability_weight),
 EntityKey(cause.chronic_kidney_disease_due_to_other_and_unspecified_causes.excess_mortality),
 EntityKey(cause.chronic_kidney_disease_due_to_diabetes_mellitus_type_2.prevalence),
 EntityKey(cause.chronic_kidney_disease_due_to_diabetes_mellitus_type_2.disability_weight),
 EntityKey(cause.chronic_kidney_disease_due_to_diabetes_mellitus_type_2.excess_mortality),
 EntityKey(cause.ischemic_heart_disease.incidence),
 EntityKey(cause.ischemic_stroke.incidence),
 EntityKey(cause.diabetes_mellitus_type_2.incidence),
 EntityKey(cause.asthma.incidence),
 EntityKey(cause.gout.incidence),
 EntityKey(cause.osteoarthritis.incidence),
 EntityKey(cause.chronic_kidney_disease_due_to_hypertension.incidence),
 EntityKey(cause.chronic_kidney_disease_due_to_glomerulonephritis.incidence),
 EntityKey(cause.chronic_kidney_disease_due_to_other_and_unspecified_causes.incidence),
 EntityKey(cause.chronic_kidney_disease_due_to_diabetes_mellitus_type_2.incidence),
 EntityKey(risk_factor.high_body_mass_index_in_adults.relative_risk)]

In [9]:

    

def format_for_dismod(df, data_type):
    df = df.query('draw==0 and sex=="Female" and year_start==2017').copy()
    df['data_type'] = data_type
    df['area'] = 'all'
    df['standard_error'] = 0.001
    df['upper_ci'] = np.nan
    df['lower_ci'] = np.nan
    df['effective_sample_size'] = 10_000

    df['sex'] = 'total'
    df = df.rename({'age_group_start': 'age_start',
                                     'age_group_end': 'age_end',}, axis=1)
    return df

p = format_for_dismod(art.load('cause.ischemic_heart_disease.prevalence'), 'p')


    

In [10]:

    

i = format_for_dismod(art.load('cause.ischemic_heart_disease.incidence'), 'i')


    

In [11]:

    

f = format_for_dismod(art.load('cause.ischemic_heart_disease.excess_mortality'), 'f')


    

In [12]:

    

m_all = format_for_dismod(art.load('cause.all_causes.cause_specific_mortality'), 'm_all')


    

In [13]:

    

csmr = format_for_dismod(art.load('cause.ischemic_heart_disease.cause_specific_mortality'), 'csmr') # could also try 'pf'


    

In [14]:

    

dm = dismod_mr.data.ModelData()
dm.input_data = pd.concat([p, i, f, m_all,
                          csmr
                          ], ignore_index=True)
for rate_type in 'ifr':
    dm.set_knots(rate_type, [0,40,60,80,90,100])

dm.set_level_value('i', age_before=30, age_after=101, value=0)
dm.set_increasing('i', age_start=50, age_end=100)

dm.set_level_value('p', value=0, age_before=30, age_after=101)

dm.set_level_value('r', value=0, age_before=101, age_after=101)


    

In [15]:

    

dm.input_data.data_type.value_counts()


    

    
Out[15]:





m_all    23
f        23
p        23
csmr     23
i        23
Name: data_type, dtype: int64

In [16]:

    

dm.setup_model(rate_model='normal', include_covariates=False)


    

    




/ihme/homes/abie/.local/lib/python3.6/site-packages/pandas/core/indexing.py:480: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  self.obj[item] = s
/ihme/homes/abie/.local/lib/python3.6/site-packages/pandas/core/indexing.py:480: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  self.obj[item] = s

In [17]:

    

import pymc as pm
m = pm.MAP(dm.vars)


    

In [18]:

    

%%time
m.fit(verbose=1)


    

    




Current log-probability : 1347.572455
Current log-probability : 1377.997951
Current log-probability : 1396.019187
Current log-probability : 1400.324203
Current log-probability : 1400.952332
Current log-probability : 1401.056619
Optimization terminated successfully.
         Current function value: -1401.056619
         Iterations: 6
         Function evaluations: 7548
CPU times: user 26.7 s, sys: 43.6 ms, total: 26.7 s
Wall time: 26.7 s

In [19]:

    

from IPython.core.pylabtools import figsize
figsize(11, 5.5)


    

In [20]:

    

dm.plot()


    

In [21]:

    

!date


    

    




Tue Jul 23 11:33:23 PDT 2019