示例#1
0
def bayesian_random_effects(data, labels, group, n_samples=2000, n_burnin=500):
    import pymc as pm
    #preparing the data
    donors = data[group].unique()
    donors_lookup = dict(zip(donors, range(len(donors))))
    data['donor_code'] = data[group].replace(donors_lookup).values
    n_donors = len(data[group].unique())
    donor_idx = data['donor_code'].values
    
    #setting up the model
    with pm.Model() as hierarchical_model:
        # Hyperpriors for group nodes
        group_intercept_mean = pm.Normal('group intercept (mean)', mu=0., sd=100**2)
        group_intercept_variance = pm.Uniform('group intercept (variance)', lower=0, upper=100)
        group_slope_mean = pm.Normal('group slope (mean)', mu=0., sd=100**2)
        group_slope_variance = pm.Uniform('group slope (variance)', lower=0, upper=100)
        
        individual_intercepts = pm.Normal('individual intercepts', mu=group_intercept_mean, sd=group_intercept_variance, shape=n_donors)
        individual_slopes = pm.Normal('individual slopes', mu=group_slope_mean, sd=group_slope_variance, shape=n_donors)
        
        # Model error
        residuals = pm.Uniform('residuals', lower=0, upper=100)
        
        expression_est =  individual_slopes[donor_idx] * data[labels[0]].values + individual_intercepts[donor_idx]
        
        # Data likelihood
        expression_like = pm.Normal('expression_like', mu=expression_est, sd=residuals, observed=data[labels[1]])

        start = pm.find_MAP()
        step = pm.NUTS(scaling=start)
        hierarchical_trace = pm.sample(n_samples, step, start=start, progressbar=True)
        
    mean_slope = hierarchical_trace['group slope (mean)'][n_burnin:].mean()
    zero_percentile = percentileofscore(hierarchical_trace['group slope (mean)'][n_burnin:], 0)
    print "Mean group level slope was %g (zero was %g percentile of the posterior distribution)"%(mean_slope, zero_percentile)
    
    pm.summary(hierarchical_trace[n_burnin:], vars=['group slope (mean)'])
        
    pm.traceplot(hierarchical_trace[n_burnin:])
    
    selection = donors
    fig, axis = plt.subplots(2, 3, figsize=(12, 6), sharey=True, sharex=True)
    axis = axis.ravel()
    xvals = np.linspace(data[labels[0]].min(), data[labels[0]].max())
    for i, c in enumerate(selection):
        c_data = data.ix[data[group] == c]
        c_data = c_data.reset_index(drop = True)
        z = list(c_data['donor_code'])[0]
        for a_val, b_val in zip(hierarchical_trace['individual intercepts'][n_burnin::10][z], hierarchical_trace['individual slopes'][n_burnin::10][z]):
            axis[i].plot(xvals, a_val + b_val * xvals, 'g', alpha=.1)
        axis[i].plot(xvals, hierarchical_trace['individual intercepts'][n_burnin:][z].mean() + hierarchical_trace['individual slopes'][n_burnin:][z].mean() * xvals, 
                     'g', alpha=1, lw=2.)
        axis[i].hexbin(c_data[labels[0]], c_data[labels[1]], mincnt=1, cmap=plt.cm.YlOrRd_r)
        axis[i].set_title(c)
        axis[i].set_xlabel(labels[0])
        axis[i].set_ylabel(labels[1])
        
    plt.show()
        
    return mean_slope, zero_percentile
示例#2
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def test_summary_2d_variable_model():
    mu = -2.1
    tau = 1.3
    with Model() as model:
        x = Normal('x', mu, tau, shape=(2, 2), testval=np.tile(.1, (2, 2)))
        step = Metropolis(model.vars, np.diag([1.]), blocked=True)
        trace = pm.sample(100, step=step)
    pm.summary(trace)
示例#3
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def test_summary_1d_variable_model():
    mu = -2.1
    tau = 1.3
    with Model() as model:
        x = Normal('x', mu, tau, shape=2, testval=[.1, .1])
        step = Metropolis(model.vars, np.diag([1.]))
        trace = pm.sample(100, step=step)
    pm.summary(trace)
示例#4
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文件: test_trace.py 项目: DrDark/pymc
def test_summary_1_value_model():
    mu = -2.1
    tau = 1.3
    with Model() as model:
        x = Normal('x', mu, tau, testval=.1)
        step = Metropolis(model.vars, np.diag([1.]))
        trace = sample(100, step=step)
    pm.summary(trace)
示例#5
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def test_summary_0d_variable_model():
    mu = -2.1
    tau = 1.3
    with Model() as model:
        x = Normal('x', mu, tau, testval=.1)
        step = Metropolis(model.vars, np.diag([1.]), blocked=True)
        trace = pm.sample(100, step=step)
    pm.summary(trace)
示例#6
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def test_summary_1_value_model():
    mu = -2.1
    tau = 1.3
    with Model() as model:
        x = Normal('x', mu, tau, testval=.1)
        step = Metropolis(model.vars, np.diag([1.]))
        trace = sample(100, step=step)
    pm.summary(trace)
示例#7
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def test_summary_2d_variable_model():
    mu = -2.1
    tau = 1.3
    with Model() as model:
        x = Normal('x', mu, tau, shape=(2, 2),
                   testval=np.tile(.1, (2, 2)))
        step = Metropolis(model.vars, np.diag([1.]))
        trace = sample(100, step=step)
    pm.summary(trace)
示例#8
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def test_summary_2dim_value_model():
    mu = -2.1
    tau = 1.3
    with Model() as model:
        x = Normal('x', mu, tau, shape=(2, 2), testval=np.tile(.1, (2, 2)))
        step = Metropolis(model.vars, np.diag([1.]))
        trace = sample(100, step=step)

    with warnings.catch_warnings(record=True) as wrn:
        pm.summary(trace)
        assert len(wrn) == 1
        assert str(wrn[0].message) == 'Skipping x (above 1 dimension)'
示例#9
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文件: test_trace.py 项目: DrDark/pymc
def test_summary_2dim_value_model():
    mu = -2.1
    tau = 1.3
    with Model() as model:
        x = Normal('x', mu, tau, shape=(2, 2),
                   testval=np.tile(.1, (2, 2)))
        step = Metropolis(model.vars, np.diag([1.]))
        trace = sample(100, step=step)

    with warnings.catch_warnings(record=True) as wrn:
        pm.summary(trace)
        assert len(wrn) == 1
        assert str(wrn[0].message) == 'Skipping x (above 1 dimension)'