def construct_net(self, df):
anxiety = make_bernoulli('anxiety', value=df.anxiety)
peer_pressure = make_bernoulli('peer_pressure', value=df.peer_pressure)
smoking = cartesian_child('smoking',
parents=[anxiety, peer_pressure],
value=df.smoking)
yellow_fingers = cartesian_child('yellow_fingers',
parents=[smoking],
value=df.yellow_fingers)
genetics = make_bernoulli('genetics', value=df.genetics)
lung_cancer = cartesian_child('lung_cancer',
parents=[smoking, genetics],
value=df.lung_cancer)
allergy = make_bernoulli('allergy', value=df.allergy)
coughing = cartesian_child('coughing',
parents=[allergy, lung_cancer],
value=df.coughing)
fatigue = cartesian_child('fatigue',
parents=[lung_cancer, coughing],
value=df.fatigue)
attention_disorder = cartesian_child('attention_disorder',
parents=[genetics],
value=df.attention_disorder)
car_accident = cartesian_child('car_accident',
parents=[fatigue, attention_disorder],
value=df.car_accident)
# sample from the prior
model = pymc.Model([
anxiety, peer_pressure, smoking, yellow_fingers, genetics,
lung_cancer, allergy, coughing, fatigue, attention_disorder,
car_accident
])
return model
def construct_net(self, df):
rain = make_bernoulli('rain', value=df.rain)
sprinkler = make_bernoulli('sprinkler', value=df.sprinkler)
sidewalk = cartesian_child('wet_sidewalk',
parents=[rain, sprinkler],
value=df.wet_sidewalk)
model = pymc.Model([rain, sprinkler, sidewalk])
return model
def get_rsw_data_random_coeffs(size):
"""generates a data from the famous 'rain - sprinkler - wet grass' bayesian network
returns as a dataframe.
"""
rain = make_bernoulli('rain', N=1)
sprinkler = cartesian_child('sprinkler', parents=[rain], levels=2, N=1)
grass_wet = cartesian_child('grass_wet',
parents=[sprinkler, rain],
levels=2,
N=1)
# sample from the prior
model = pymc.Model([rain, sprinkler, grass_wet])
coefficients = sample_coeffs(model)
# define the bayesian net
rain = make_bernoulli('rain', N=1, fixed=coefficients)
sprinkler = cartesian_child('sprinkler',
parents=[rain],
levels=2,
N=1,
fixed=coefficients)
grass_wet = cartesian_child('grass_wet',
parents=[sprinkler, rain],
levels=2,
N=1,
fixed=coefficients)
# sample from the prior
model = pymc.Model([rain, sprinkler, grass_wet])
sampler = pymc.MCMC(model)
sampler.sample(iter=size + 1000, burn=1000)
data = pd.DataFrame({
str(node): sampler.trace(str(node))[:].ravel() + 0
for node in sampler.stochastics if not str(node).startswith('CF: ')
})
return data
def get_rsw_data(size):
"""generates a data from the famous 'rain - sprinkler - wet grass' bayesian network
returns as a dataframe.
"""
coefficients = {
'CF: p(rain)': 0.2,
'CF: p(grass_wet | sprinkler=0 rain=0)': 0.,
'CF: p(grass_wet | sprinkler=0 rain=1)': 0.8,
'CF: p(grass_wet | sprinkler=1 rain=0)': 0.9,
'CF: p(grass_wet | sprinkler=1 rain=1)': 0.99,
'CF: p(sprinkler | rain=0)': 0.4,
'CF: p(sprinkler | rain=1)': 0.01
}
# define the bayesian net
rain = make_bernoulli('rain', N=1, fixed=coefficients)
sprinkler = cartesian_child('sprinkler',
parents=[rain],
levels=2,
N=1,
fixed=coefficients)
grass_wet = cartesian_child('grass_wet',
parents=[sprinkler, rain],
levels=2,
N=1,
fixed=coefficients)
# sample from the prior
model = pymc.Model([rain, sprinkler, grass_wet])
sampler = pymc.MCMC(model)
sampler.sample(iter=size, burn=0)
data = pd.DataFrame({
str(node): sampler.trace(str(node))[:].ravel() + 0
for node in sampler.stochastics
})
return data
def construct_net(self, df):
# This is the same network as was used to generate the data, but there
# are some differences in how it is set up:
# - we don't fix the values of coefficients - the whole point is for the model to learn them!
# - we provide 'value' parameter - because these variables are observed
# - we don't need the 'N' parameter - because it is assumed that N = len(value)
rain = make_bernoulli('rain', value=df.rain)
sprinkler = make_bernoulli('sprinkler', value=df.sprinkler)
grass_wet = cartesian_child('grass_wet',
parents=[rain, sprinkler],
levels=2,
value=df.grass_wet)
model = pymc.Model([rain, sprinkler, grass_wet])
return model
def construct_net(self, df):
mileage = make_bernoulli('mileage', value=df.mileage)
age = make_bernoulli('age', value=df.age)
socio_econ = cartesian_child('socio_econ',
parents=[age],
value=df.socio_econ)
risk_aversion = cartesian_child('risk_aversion',
parents=[age, socio_econ],
value=df.risk_aversion)
senior_train = cartesian_child('senior_train',
parents=[age, risk_aversion],
value=df.senior_train)
good_student = cartesian_child('good_student',
parents=[age, socio_econ],
value=df.good_student)
extra_car = cartesian_child('extra_car',
parents=[socio_econ],
value=df.extra_car)
driving_skill = cartesian_child('driving_skill',
parents=[age, senior_train],
value=df.driving_skill)
driving_hist = cartesian_child('driving_hist',
parents=[driving_skill, risk_aversion],
value=df.driving_hist)
driving_quality = cartesian_child(
'driving_quality',
parents=[driving_skill, risk_aversion],
value=df.driving_quality)
make_model = cartesian_child('make_model',
parents=[risk_aversion, socio_econ],
value=df.make_model)
vehicle_year = cartesian_child('vehicle_year',
parents=[socio_econ],
value=df.vehicle_year)
antilock = cartesian_child('antilock',
parents=[make_model, vehicle_year],
value=df.antilock)
ruggedness = cartesian_child('ruggedness',
parents=[make_model, antilock],
value=df.ruggedness)
accident = cartesian_child(
'accident',
parents=[driving_quality, antilock, mileage],
value=df.accident)
airbag = cartesian_child('airbag',
parents=[make_model, vehicle_year],
value=df.airbag)
car_value = cartesian_child(
'car_value',
parents=[make_model, mileage, vehicle_year],
value=df.car_value)
home_base = cartesian_child('home_base',
parents=[risk_aversion, socio_econ],
value=df.home_base)
anti_theft = cartesian_child('anti_theft',
parents=[risk_aversion, socio_econ],
value=df.anti_theft)
theft = cartesian_child('theft',
parents=[car_value, home_base, anti_theft],
value=df.theft)
own_damage = cartesian_child('own_damage',
parents=[ruggedness, accident],
value=df.own_damage)
own_car_cost = cartesian_child('own_car_cost',
parents=[own_damage, car_value, theft],
value=df.own_car_cost)
cushioning = cartesian_child('cushioning',
parents=[ruggedness, accident],
value=df.cushioning)
medical_cost = cartesian_child('medical_cost',
parents=[age, cushioning, accident],
value=df.medical_cost)
liability_cost = cartesian_child('liability_cost',
parents=[accident],
value=df.liability_cost)
other_car_cost = cartesian_child('other_car_cost',
parents=[ruggedness, accident],
value=df.other_car_cost)
property_cost = cartesian_child('property_cost',
parents=[other_car_cost, own_car_cost],
value=df.property_cost)
model = pymc.Model([
mileage, age, socio_econ, good_student, extra_car, driving_skill,
risk_aversion, senior_train, driving_hist, driving_quality,
make_model, vehicle_year, antilock, ruggedness, accident, airbag,
car_value, home_base, anti_theft, theft, own_damage, own_car_cost,
cushioning, medical_cost, liability_cost, other_car_cost,
property_cost
])
return model
def get_car_data_random_coeffs(size):
mileage = make_bernoulli('mileage', N=1)
age = make_bernoulli('age', N=1)
socio_econ = cartesian_child('socio_econ', parents=[age], N=1)
risk_aversion = cartesian_child('risk_aversion',
parents=[age, socio_econ],
N=1)
senior_train = cartesian_child('senior_train',
parents=[age, risk_aversion],
N=1)
good_student = cartesian_child('good_student',
parents=[age, socio_econ],
N=1)
extra_car = cartesian_child('extra_car', parents=[socio_econ], N=1)
driving_skill = cartesian_child('driving_skill',
parents=[age, senior_train],
N=1)
driving_hist = cartesian_child('driving_hist',
parents=[driving_skill, risk_aversion],
N=1)
driving_quality = cartesian_child('driving_quality',
parents=[driving_skill, risk_aversion],
N=1)
make_model = cartesian_child('make_model',
parents=[risk_aversion, socio_econ],
N=1)
vehicle_year = cartesian_child('vehicle_year', parents=[socio_econ], N=1)
antilock = cartesian_child('antilock',
parents=[make_model, vehicle_year],
N=1)
ruggedness = cartesian_child('ruggedness',
parents=[make_model, antilock],
N=1)
accident = cartesian_child('accident',
parents=[driving_quality, antilock, mileage],
N=1)
airbag = cartesian_child('airbag', parents=[make_model, vehicle_year], N=1)
car_value = cartesian_child('car_value',
parents=[make_model, mileage, vehicle_year],
N=1)
home_base = cartesian_child('home_base',
parents=[risk_aversion, socio_econ],
N=1)
anti_theft = cartesian_child('anti_theft',
parents=[risk_aversion, socio_econ],
N=1)
theft = cartesian_child('theft',
parents=[car_value, home_base, anti_theft],
N=1)
own_damage = cartesian_child('own_damage',
parents=[ruggedness, accident],
N=1)
own_car_cost = cartesian_child('own_car_cost',
parents=[own_damage, car_value, theft],
N=1)
cushioning = cartesian_child('cushioning',
parents=[ruggedness, accident],
N=1)
medical_cost = cartesian_child('medical_cost',
parents=[age, cushioning, accident],
N=1)
liability_cost = cartesian_child('liability_cost', parents=[accident], N=1)
other_car_cost = cartesian_child('other_car_cost',
parents=[ruggedness, accident],
N=1)
property_cost = cartesian_child('property_cost',
parents=[other_car_cost, own_car_cost],
N=1)
model = pymc.Model([
mileage, age, socio_econ, good_student, extra_car, driving_skill,
risk_aversion, senior_train, driving_hist, driving_quality, make_model,
vehicle_year, antilock, ruggedness, accident, airbag, car_value,
home_base, anti_theft, theft, own_damage, own_car_cost, cushioning,
medical_cost, liability_cost, other_car_cost, property_cost
])
coefficients = sample_coeffs(model)
mileage = make_bernoulli('mileage', N=1, fixed=coefficients)
age = make_bernoulli('age', N=1, fixed=coefficients)
socio_econ = cartesian_child('socio_econ',
parents=[age],
N=1,
fixed=coefficients)
risk_aversion = cartesian_child('risk_aversion',
parents=[age, socio_econ],
N=1,
fixed=coefficients)
senior_train = cartesian_child('senior_train',
parents=[age, risk_aversion],
N=1,
fixed=coefficients)
good_student = cartesian_child('good_student',
parents=[age, socio_econ],
N=1,
fixed=coefficients)
extra_car = cartesian_child('extra_car',
parents=[socio_econ],
N=1,
fixed=coefficients)
driving_skill = cartesian_child('driving_skill',
parents=[age, senior_train],
N=1)
driving_hist = cartesian_child('driving_hist',
parents=[driving_skill, risk_aversion],
N=1,
fixed=coefficients)
driving_quality = cartesian_child('driving_quality',
parents=[driving_skill, risk_aversion],
N=1,
fixed=coefficients)
make_model = cartesian_child('make_model',
parents=[risk_aversion, socio_econ],
N=1,
fixed=coefficients)
vehicle_year = cartesian_child('vehicle_year',
parents=[socio_econ],
N=1,
fixed=coefficients)
antilock = cartesian_child('antilock',
parents=[make_model, vehicle_year],
N=1,
fixed=coefficients)
ruggedness = cartesian_child('ruggedness',
parents=[make_model, antilock],
N=1,
fixed=coefficients)
accident = cartesian_child('accident',
parents=[driving_quality, antilock, mileage],
N=1,
fixed=coefficients)
airbag = cartesian_child('airbag',
parents=[make_model, vehicle_year],
N=1,
fixed=coefficients)
car_value = cartesian_child('car_value',
parents=[make_model, mileage, vehicle_year],
N=1,
fixed=coefficients)
home_base = cartesian_child('home_base',
parents=[risk_aversion, socio_econ],
N=1,
fixed=coefficients)
anti_theft = cartesian_child('anti_theft',
parents=[risk_aversion, socio_econ],
N=1,
fixed=coefficients)
theft = cartesian_child('theft',
parents=[car_value, home_base, anti_theft],
N=1,
fixed=coefficients)
own_damage = cartesian_child('own_damage',
parents=[ruggedness, accident],
N=1,
fixed=coefficients)
own_car_cost = cartesian_child('own_car_cost',
parents=[own_damage, car_value, theft],
N=1,
fixed=coefficients)
cushioning = cartesian_child('cushioning',
parents=[ruggedness, accident],
N=1,
fixed=coefficients)
medical_cost = cartesian_child('medical_cost',
parents=[age, cushioning, accident],
N=1,
fixed=coefficients)
liability_cost = cartesian_child('liability_cost',
parents=[accident],
N=1,
fixed=coefficients)
other_car_cost = cartesian_child('other_car_cost',
parents=[ruggedness, accident],
N=1,
fixed=coefficients)
property_cost = cartesian_child('property_cost',
parents=[other_car_cost, own_car_cost],
N=1,
fixed=coefficients)
model = pymc.Model([
mileage, age, socio_econ, good_student, extra_car, driving_skill,
risk_aversion, senior_train, driving_hist, driving_quality, make_model,
vehicle_year, antilock, ruggedness, accident, airbag, car_value,
home_base, anti_theft, theft, own_damage, own_car_cost, cushioning,
medical_cost, liability_cost, other_car_cost, property_cost
])
sampler = pymc.MCMC(model)
sampler.sample(iter=size, burn=0)
data = pd.DataFrame({
str(node): sampler.trace(str(node))[:].ravel() + 0
for node in sampler.stochastics if not str(node).startswith('CF: ')
})
return data
def get_cancer_data_random_coeffs(size):
anxiety = make_bernoulli('anxiety', N=1)
peer_pressure = make_bernoulli('peer_pressure', N=1)
smoking = cartesian_child('smoking', parents=[anxiety, peer_pressure], N=1)
yellow_fingers = cartesian_child('yellow_fingers', parents=[smoking], N=1)
genetics = make_bernoulli('genetics', N=1)
lung_cancer = cartesian_child('lung_cancer',
parents=[smoking, genetics],
N=1)
allergy = make_bernoulli('allergy', N=1)
coughing = cartesian_child('coughing', parents=[allergy, lung_cancer], N=1)
fatigue = cartesian_child('fatigue', parents=[lung_cancer, coughing], N=1)
attention_disorder = cartesian_child('attention_disorder',
parents=[genetics],
N=1)
car_accident = cartesian_child('car_accident',
parents=[fatigue, attention_disorder],
N=1)
model = pymc.Model([
anxiety, peer_pressure, smoking, yellow_fingers, genetics, lung_cancer,
allergy, coughing, fatigue, attention_disorder, car_accident
])
coefficients = sample_coeffs(model)
anxiety = make_bernoulli('anxiety', N=1, fixed=coefficients)
peer_pressure = make_bernoulli('peer_pressure', N=1, fixed=coefficients)
smoking = cartesian_child('smoking',
parents=[anxiety, peer_pressure],
N=1,
fixed=coefficients)
yellow_fingers = cartesian_child('yellow_fingers',
parents=[smoking],
N=1,
fixed=coefficients)
genetics = make_bernoulli('genetics', N=1, fixed=coefficients)
lung_cancer = cartesian_child('lung_cancer',
parents=[smoking, genetics],
N=1,
fixed=coefficients)
allergy = make_bernoulli('allergy', N=1, fixed=coefficients)
coughing = cartesian_child('coughing',
parents=[allergy, lung_cancer],
N=1,
fixed=coefficients)
fatigue = cartesian_child('fatigue',
parents=[lung_cancer, coughing],
N=1,
fixed=coefficients)
attention_disorder = cartesian_child('attention_disorder',
parents=[genetics],
N=1,
fixed=coefficients)
car_accident = cartesian_child('car_accident',
parents=[fatigue, attention_disorder],
N=1,
fixed=coefficients)
# sample from the prior
model = pymc.Model([
anxiety, peer_pressure, smoking, yellow_fingers, genetics, lung_cancer,
allergy, coughing, fatigue, attention_disorder, car_accident
])
sampler = pymc.MCMC(model)
sampler.sample(iter=size, burn=0)
data = pd.DataFrame({
str(node): sampler.trace(str(node))[:].ravel() + 0
for node in sampler.stochastics
})
return data