def get_city_data():
"""Helper to get city data"""
data = pd.read_csv(pm.get_data_file('pymc3.examples', 'data/srrs2.dat'))
cty_data = pd.read_csv(pm.get_data_file('pymc3.examples', 'data/cty.dat'))
data = data[data.state == 'MN']
data['fips'] = data.stfips * 1000 + data.cntyfips
cty_data['fips'] = cty_data.stfips * 1000 + cty_data.ctfips
data['lradon'] = np.log(np.where(data.activity == 0, .1, data.activity))
data = data.merge(cty_data, 'inner', on='fips')
unique = data[['fips']].drop_duplicates()
unique['group'] = np.arange(len(unique))
unique.set_index('fips')
return data.merge(unique, 'inner', on='fips')
def get_city_data():
"""Helper to get city data"""
data = pd.read_csv(pm.get_data_file('pymc3.examples', 'data/srrs2.dat'))
cty_data = pd.read_csv(pm.get_data_file('pymc3.examples', 'data/cty.dat'))
data = data[data.state == 'MN']
data['fips'] = data.stfips * 1000 + data.cntyfips
cty_data['fips'] = cty_data.stfips * 1000 + cty_data.ctfips
data['lradon'] = np.log(np.where(data.activity == 0, .1, data.activity))
data = data.merge(cty_data, 'inner', on='fips')
unique = data[['fips']].drop_duplicates()
unique['group'] = np.arange(len(unique))
unique.set_index('fips')
return data.merge(unique, 'inner', on='fips')
def build_model(self):
wells = pm.get_data_file('pymc3.examples', 'data/wells.dat')
data = pd.read_csv(wells, delimiter=u' ', index_col=u'id', dtype={u'switch': np.int8})
data.dist /= 100
data.educ /= 4
col = data.columns
P = data[col[1:]]
P -= P.mean()
P['1'] = 1
with pm.Model() as model:
effects = pm.Normal('effects', mu=0, tau=100. ** -2, shape=len(P.columns))
p = pm.sigmoid(pm.dot(np.array(P), effects))
pm.Bernoulli('s', p, observed=np.array(data.switch))
return model
def build_model(self):
wells = pm.get_data_file('pymc3.examples', 'data/wells.dat')
data = pd.read_csv(wells,
delimiter=u' ',
index_col=u'id',
dtype={u'switch': np.int8})
data.dist /= 100
data.educ /= 4
col = data.columns
P = data[col[1:]]
P -= P.mean()
P['1'] = 1
with pm.Model() as model:
effects = pm.Normal('effects',
mu=0,
tau=100.**-2,
shape=len(P.columns))
p = tt.nnet.sigmoid(tt.dot(np.array(P), effects))
pm.Bernoulli('s', p, observed=np.array(data.switch))
return model
# <codecell>
import pandas as pd
from pylab import *
from pymc3 import StudentT, Model, NUTS, Normal, find_MAP, trace, get_data_file
from pymc3.distributions.timeseries import GaussianRandomWalk
# <markdowncell>
# Data
# ----
# <codecell>
data = pd.read_csv(get_data_file("pymc3.examples", "data/pancreatitis.csv"))
countries = ["CYP", "DNK", "ESP", "FIN", "GBR", "ISL"]
data = data[data.area.isin(countries)]
age = data["age"] = np.array(data.age_start + data.age_end) / 2
rate = data.value = data.value * 1000
group, countries = pd.factorize(data.area, order=countries)
ncountries = len(countries)
# <codecell>
for i, country in enumerate(countries):
subplot(2, 3, i + 1)
title(country)
import pymc3 as pm
import pandas as pd
from numpy.ma import masked_values
# Import data, filling missing values with sentinels (-999)
test_scores = pd.read_csv(pm.get_data_file(
'pymc3.examples', 'data/test_scores.csv')).fillna(-999)
# Extract variables: test score, gender, number of siblings, previous disability, age,
# mother with HS education or better, hearing loss identified by 3 months
# of age
(score, male, siblings, disability,
age, mother_hs, early_ident) = test_scores[['score', 'male', 'siblings',
'prev_disab', 'age_test',
'mother_hs', 'early_ident']].astype(float).values.T
with pm.Model() as model:
# Impute missing values
sib_mean = pm.Exponential('sib_mean', 1)
siblings_imp = pm.Poisson('siblings_imp', sib_mean,
observed=masked_values(siblings, value=-999))
p_disab = pm.Beta('p_disab', 1, 1)
disability_imp = pm.Bernoulli(
'disability_imp', p_disab, observed=masked_values(disability, value=-999))
p_mother = pm.Beta('p_mother', 1, 1)
mother_imp = pm.Bernoulli('mother_imp', p_mother,
observed=masked_values(mother_hs, value=-999))
# <codecell>
import pandas as pd
from pylab import *
from pymc3 import StudentT, Model, NUTS, Normal, find_MAP, trace, get_data_file
from pymc3.distributions.timeseries import GaussianRandomWalk
# <markdowncell>
# Data
# ----
# <codecell>
data = pd.read_csv(get_data_file('pymc3.examples', 'data/pancreatitis.csv'))
countries = ['CYP', 'DNK', 'ESP', 'FIN', 'GBR', 'ISL']
data = data[data.area.isin(countries)]
age = data['age'] = np.array(data.age_start + data.age_end) / 2
rate = data.value = data.value * 1000
group, countries = pd.factorize(data.area, order=countries)
ncountries = len(countries)
# <codecell>
for i, country in enumerate(countries):
subplot(2, 3, i + 1)
title(country)
#https://github.com/pymc-devs/pymc3/blob/master/pymc3/examples/stochastic_volatility.py
import numpy as np
import matplotlib.pyplot as plt
import pymc3
import pymc3.distributions.timeseries as ts
import pandas as pd
import scipy
#fname = 'https://github.com/pymc-devs/pymc3/blob/master/pymc3/examples/data/SP500.csv'
#returns = pd.read_csv(fname)
#returns = pd.read_csv('SP500.csv')
#print(len(returns))
n = 400
returns = np.genfromtxt(pymc3.get_data_file('pymc3.examples',
"data/SP500.csv"))[-n:]
returns[:5]
plt.plot(returns)
plt.ylabel('daily returns in %')
with pymc3.Model() as sp500_model:
nu = pymc3.Exponential('nu', 1. / 10, testval=5.)
sigma = pymc3.Exponential('sigma', 1. / .02, testval=.1)
s = ts.GaussianRandomWalk('s', sigma**-2, shape=len(returns))
volatility_process = pymc3.Deterministic('volatility_process',
pymc3.exp(-2 * s))
r = pymc3.StudentT('r', nu, lam=1 / volatility_process, observed=returns)
with sp500_model:
print('optimizing...')
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from pymc3 import HalfCauchy, Model, Normal, get_data_file, sample
from pymc3.distributions.timeseries import GaussianRandomWalk
data = pd.read_csv(get_data_file('pymc3.examples', 'data/pancreatitis.csv'))
countries = ['CYP', 'DNK', 'ESP', 'FIN', 'GBR', 'ISL']
data = data[data.area.isin(countries)]
age = data['age'] = np.array(data.age_start + data.age_end) / 2
rate = data.value = data.value * 1000
group, countries = pd.factorize(data.area, order=countries)
ncountries = len(countries)
for i, country in enumerate(countries):
plt.subplot(2, 3, i + 1)
plt.title(country)
d = data[data.area == country]
plt.plot(d.age, d.value, '.')
plt.ylim(0, rate.max())
nknots = 10
knots = np.linspace(data.age_start.min(), data.age_end.max(), nknots)
#https://github.com/pymc-devs/pymc3/blob/master/pymc3/examples/stochastic_volatility.py
import numpy as np
import matplotlib.pyplot as plt
import pymc3
import pymc3.distributions.timeseries as ts
import pandas as pd
import scipy
#fname = 'https://github.com/pymc-devs/pymc3/blob/master/pymc3/examples/data/SP500.csv'
#returns = pd.read_csv(fname)
#returns = pd.read_csv('SP500.csv')
#print(len(returns))
n = 400
returns = np.genfromtxt(pymc3.get_data_file('pymc3.examples', "data/SP500.csv"))[-n:]
returns[:5]
plt.plot(returns)
plt.ylabel('daily returns in %');
with pymc3.Model() as sp500_model:
nu = pymc3.Exponential('nu', 1./10, testval=5.)
sigma = pymc3.Exponential('sigma', 1./.02, testval=.1)
s = ts.GaussianRandomWalk('s', sigma**-2, shape=len(returns))
volatility_process = pymc3.Deterministic('volatility_process', pymc3.exp(-2*s))
r = pymc3.StudentT('r', nu, lam=1/volatility_process, observed=returns)
with sp500_model:
print 'optimizing...'