Big code cleanup

1 files changed, 0 insertions, 117 deletions

diff --git a/simulation/bayes.py b/simulation/bayes.py
deleted file mode 100644
index bde9e94..0000000
--- a/simulation/bayes.py
+++ /dev/null
@@ -1,117 +0,0 @@
-import pymc
-import numpy as np
-
-def glm_node_setup(cascade, y_obs, prior=None, *args, **kwargs):
-    """
-    Build an IC PyMC node-level model from:
-        -observed cascades: cascade
-        -outcome vector: y_obs
-        -desired PyMC prior and parameters: prior, *args
-    Note: we use the glm formulation: y = Bernoulli[f(x.dot(theta))]
-    """
-    n_nodes = len(cascade[0])
-
-    # Container class for node's parents
-    theta = np.empty(n_nodes, dtype=object)
-    for j in xrange(n_nodes):
-        if prior is None:
-            theta[j] = pymc.Beta('theta_{}'.format(j), alpha=1, beta=1)
-        else:
-            theta[j] = prior('theta_{}'.format(j), *args, **kwargs)
-
-    # Observed container class for cascade realization
-    x = np.empty(n_nodes, dtype=object)
-    for i, val in enumerate(cascade.T):
-        x[i] = pymc.Normal('x_{}'.format(i), 0, 1, value=val, observed=True)
-
-    @pymc.deterministic
-    def glm_p(x=x, theta=theta):
-        return 1. - np.exp(-x.dot(theta))
-
-    @pymc.observed
-    def y(glm_p=glm_p, value=y_obs):
-        return pymc.bernoulli_like(value, glm_p)
-
-    return pymc.Model([y, pymc.Container(theta), pymc.Container(x)])
-
-
-def formatLabel(s, n):
-    return '0'*(len(str(n)) - len(str(s))) + str(s)
-
-
-def mc_graph_setup(infected, susceptible, prior=None, *args, **kwargs):
-    """
-    Build an IC PyMC graph-level model from:
-        -infected nodes over time: list/tuple of list/tuple of np.array
-        -susceptible nodes over time: same format as above
-    Note: we use the Markov Chain formulation: X_{t+1}|X_t,theta = f(X_t.theta)
-    """
-
-    # Container class for graph parameters
-    n_nodes = len(infected[0][0])
-    theta = np.empty((n_nodes,n_nodes), dtype=object)
-    if prior is None:
-        for i in xrange(n_nodes):
-            for j in xrange(n_nodes):
-                theta[i, j] = pymc.Beta('theta_{}{}'.format(formatLabel(i,
-                                        n_nodes-1), formatLabel(j, n_nodes-1)),
-                                        alpha=1, beta=1)
-    else:
-        theta = prior(theta=theta, *args, **kwargs)
-
-    # Container class for cascade realization
-    x = {}
-    for i, cascade in enumerate(infected):
-        for j, step in enumerate(cascade):
-            for k, node in enumerate(step):
-                if j and susceptible[i][j][k]:
-                    p = 1. - pymc.exp(-cascade[j-1].dot(theta[k]))
-                else:
-                    p = .5
-                x[i, j, k] = pymc.Bernoulli('x_{}{}{}'.format(i, j, k), p=p,
-                        value=node, observed=True)
-
-    return pymc.Model([pymc.Container(theta), pymc.Container(x)])
-
-
-if __name__=="__main__":
-    import main
-    import matplotlib.pyplot as plt
-    import seaborn
-    seaborn.set_style('whitegrid')
-    g = np.array([[0, 1, 1, 0], [1, 0, 0, 1], [1, 0, 0, 1], [0, 1, 1, 0]])
-    p = 0.5
-    g = np.log(1. / (1 - p * g))
-
-    print('running the graph-level MC set-up')
-    n_nodes = len(g)
-    cascades = main.simulate_cascades(1000, g)
-    infected, susc = main.build_cascade_list(cascades)
-    model = mc_graph_setup(infected, susc)
-    mcmc = pymc.MCMC(model)
-    mcmc.sample(10**4, 1000)
-    fig, ax = plt.subplots(len(g), len(g))
-    for i in xrange(n_nodes):
-        for j in xrange(n_nodes):
-            if n_nodes < 5:
-                ax[i,j].locator_params(nbins=3, axis='x')
-            else:
-                ax[i, j].get_xaxis().set_ticks([])
-                ax[i, j].get_yaxis().set_ticks([])
-            it, jt = formatLabel(i, n_nodes-1), formatLabel(j, n_nodes-1)
-            ax[i,j].hist(mcmc.trace('theta_{}{}'.format(it,jt))[:], normed=True)
-            ax[i, j].set_xlim([0,1])
-            ax[i, j].plot([g[i, j]]*2, [0, ax[i,j].get_ylim()[-1]], color='red')
-    plt.tight_layout(pad=0.4, w_pad=0.5, h_pad=.1)
-    plt.show()
-
-    print('running the node level set-up')
-    node = 0
-    cascades = main.simulate_cascades(100, g)
-    cascade, y_obs = main.build_matrix(cascades, node)
-    model = glm_node_setup(cascade, y_obs)
-    mcmc = pymc.MCMC(model)
-    mcmc.sample(1e5, 1e4)
-    plt.hist(mcmc.trace('theta_1')[:], bins=1e2)
-    plt.show()
-